Community structure and diversity changes for fish in a temperate tidal lagoon, as a response to changes in water temperature and depth

Community structure and diversity changes for fish in a temperate tidal lagoon, as a response to changes in water temperature and depth | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Community structure and diversity changes for fish in a temperate tidal lagoon, as a response to changes in water temperature and depth Victor Odongo, Harald Asmus, Maarten Boersma, Katja Heubel, Lasse Sander, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4583467/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Coastal marine ecosystems are characterized by high productivity and biodiversity supporting coastal fisheries for centuries. These ecosystems have undergone changes over the last few decades, experiencing shifts in seasonal patterns due to climate change. As a case study for responses of cold temperate coastal ecosystems to climate change, changes in fish diversity in the Sylt-Rømø Bight (SRB), northern Wadden Sea; 54°58’40”N, 8°29’45”E, were analyzed using data from the monthly monitoring from 2007 to 2019. Results showed that the diversity changes correlated to seasonal changes in water temperature. The spatial distribution of fish to intertidal areas for feeding and refuge was correlated to changes in water depth. Rank abundance curves (RACs) showed that a few species dominated the fish community and this changed per season and habitat type. General Additive Models (GAMs) showed higher species richness ( S ) at 5°C and 15°C, which are seasonal transition phases for winter/spring and summer/autumn, respectively. Evenness (J) and Shannon-Wiener Index (H) decreased with increasing water temperatures in the benthic and pelagic habitats while dominance (D) increased. Generalized linear mixed-effects models (GLMMs) showed that S decreased while J increased with water depth in benthic habitats. Similar patterns were observed in the nearshore pelagic habitats contrary to the deep tidal channels. There were no significant effects of water depth on H. The diversity changes reveal the sensitivity of fish to seasonal changes in oceanographic processes and the use of intertidal habitats. Thus, the significance of shallow coastal habitats for fish needs implementation in conservation and management measures. Cold temperate coastal ecosystems Sylt-Rømø Bight seasonal changes ecotones spatial distribution habitat utilization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Coastal marine ecosystems are characterized by high productivity and biodiversity that have supported valuable coastal fisheries for centuries (Barbier et al. 2011 ). These ecosystems have undergone tremendous changes over the last decades due to climate change (Dulvy et al. 2008 ) and multiple anthropogenic stressors (Holm et al. 2024 ). Global warming, for example, has affected marine ecosystems and caused changes in phenology (van Walraven et al. 2017 ), and poleward movement of distributions in many species (Montero-Serra et al. 2015 ). Changes in community structure (Clark et al. 2020 ; Rutterford et al. 2023 ), predator-prey interactions (Durant et al. 2007 ), and shifts of bottom-dwelling species to deeper areas (Dulvy et al. 2008 ) are related to global warming. These changes have great implications for many species; here we focus on fish because of their high sensitivity to climate-driven environmental changes (Peck et al. 2013 ). For example, demersal fish species with limited movement capabilities must acclimatize or adapt to warming when they are no longer in their optimal ranges, otherwise, consequences such as population declines are inevitable (Rutterford et al. 2023 ). Such a scenario was reported for eelpout ( Zoarces viviparus ) whose growth and abundance decreased in the Wadden Sea because of rising water temperatures (Pörtner and Knust 2007 ). The Wadden Sea is an important nursery ground providing rich feeding and refuge areas for many North Sea and resident fish species (Tulp et al. 2017 ; van der Veer et al. 2022 ). Global warming and the changes in seasonal environmental patterns can cause substantial changes in the nursery function by affecting species recruitment and diversity. For example, early timing of spawning, shorter egg and larval development times, and early immigration (van der Veer et al. 2022 ). Besides, temperature affects interspecific interactions such as predation rates between fish and key prey items (Edwards and Richardson 2004 ) which has implications on the total food web (Baird et al. 2019 ). The Sylt-Rømø Bight (SRB) is one of the largest tidal catchments of the Wadden Sea and is located between the islands of Sylt (Germany), Rømø (Denmark), and the mainland coasts. Two causeways delimit the area to the north and south preventing water exchange with the adjacent tidal environments (Reise et al. 1998 ). The SRB comprises diverse subtidal and intertidal habitats such as mussel beds, seagrass meadows, and substrates of different grain sizes such as mudflats and sandflats (Baird et al. 2007 ). These offer feeding and refuge grounds for an array of different fish species (Kellnreitner et al. 2012 ). As a transient area between rivers Vidå and Bredeå and the North Sea, many individuals of anadromous species such as houting ( Coregonus oxyrinchus ) and catadromous species such as European eel ( Anguilla anguilla ) pass through during their migration (Odongo et al. 2024 ). Furthermore, during summers, individuals of the warm-adapted fish species of Lusitanian and Atlantic biogeographic origins migrate northwards in the North Sea and into the Wadden Sea, while boreal species migrate into the deeper North Sea and southwards in cold winters (Tulp et al. 2008 ). Records of water temperature in the SRB indicate an increase in annual means over the last decades and changes on seasonal scales with high inter-annual variabilities (Rick et al. 2023 ). There are warmer days in summer and fewer cold days in winter in comparison to the 1962 to 1990 period (de Amorim et al. 2023 ) as well as higher autumn temperatures (Rick et al. 2023 ). The temperature changes could influence the distributions and abundance of various fish species (Clark et al. 2020 ). The magnitude, duration, frequency, and temporal scales at which temperature changes are likely to occur, together influence fish community structure and diversity (Holbrook et al. 1994 ; Clark et al. 2020 ). Thus, diversity changes may either be a reflection of decadal fluctuations or directional changes in hydro-meteorological forcing (Beaugrand 2004 ) or short-term ones based on seasonal changes in water temperature (Corten and van de Kamp 1996 ). Additionally, the local habitat characteristics such as changes in water depth with tides influence the spatial distribution of fish through the movement of individuals between habitats in shallow coastal ecosystems (Couperus et al. 2016 ). In the SRB, water depth changes per tidal cycle and varies between different areas (Fofonova et al. 2019 ). Thus, the fish diversity in a specific location may vary based on differences in tidal velocity or changes on short-term scales with tides. The question arises whether there are potential effects of changes in water temperature on the community structure and diversity of fish in the SRB. As a tidal lagoon, there is a much shorter time effect on the diversity in different locations as fish may migrate to submerged intertidal areas for feeding and refuge during high tide and retreat with ebbing waters. Some fish species on the other hand may persist in different areas that differ in water depth within a tidal system. Therefore, we assessed the diversity of fish in the SRB using data from the monthly fish monitoring between the years 2007 and 2019 (Asmus et al. 2020 ). From the same dataset, changes in the fish community composition in comparison to previous investigations that took place from 1989 to 1995 (Herrmann, J.P., Jansen, S., Temming 1998; Vorberg and Breckling 1999 ) were determined. Additionally, trends and common patterns of the 22 most abundant species and the potential effects of water temperature, salinity, and chlorophyll a were assessed (Odongo et al. 2024 ). Here we focussed on the whole fish community and investigated how the fish community structure and diversity changes as a response to the seasonal changes in water temperature. The spatial distribution and utilization of intertidal habitats during high tide were investigated to show the significance of shallow coastal ecosystems as important feeding, refuge, and camouflage areas. Materials and Methods Study area The Sylt-Rømø Bight (SRB) is a semi-enclosed tidal lagoon located between the islands of Sylt (Germany) and Rømø (Denmark) (Fig. 1 ) (central coordinates: 54°58’40N, 8°29’45E). The area is protected as a national park under both German and Danish legislation and is part of the internationally recognized Wadden Sea World Heritage Site (UNESCO 2009 ). Commercially, only local (without crossing the international borders) mussels and shrimp fisheries are allowed in the area. The SRB is connected to the open North Sea through the Lister Deep, a 2.8 km wide tidal inlet, where 8 to 12% of the bight’s water is exchanged per tidal cycle and the water residence time ranges from 19 to 29 days. The bight covers a total aerial surface of 404 km 2 , which includes 135 km 2 of intertidal area (Reise et al. 1998 ). The area is characterized by low salinities in winter and spring, and highs in summer and autumn with an overall range between 24 and 33 (Rick et al. 2023 ). Fish monitoring and data collection of environmental parameters The details of sampling methods are described in Odongo et al. ( 2024 ) as we used the same dataset in Asmus et al. ( 2020 ). Fish monitoring took place monthly at seven stations inside the SRB and at two complementary stations, one outside the bight (Sylt_8) and one close to the Danish border (Sylt_9), which are sampled four times per year (Fig. 1 ). Water flow within the bight follows four major tidal channels and sampling mainly targeted the Lister Tief, Lister Ley, and Pander Tief that are all on the German side of the bight (Fig. 1 ). Due to international maritime boundaries and legislations, the Danish side of the bight was not sampled. All sampling stations were permanently submerged and the sampling gear is efficient with the capability of cruising at minimum water depths of 1.4 m. Sampling stations were chosen in such a way that they, together, are representative of the differences in the magnitude of tidally induced water currents, water depth, substrate type, and macrophyte systems. Sylt_1, Sylt_2, Sylt_4, and Sylt_8 are located in the deep tidal channels and differ in terms of water depth and tidal velocities from the other stations that are adjacent to the tidal channels (Fig. 1 ) (Fofonova et al. 2019 ). Fish were sampled using research vessels MYA I (January 2007 to April 2013) and MYA II (from June 2013 onwards). In the early stages of MYA II operation, technical issues limited fish sampling and caused data gaps until April 2014. At each station, fish were sampled using a 17 m long mini bottom trawl with a mouth measuring 7 m width, 3 m height, and mesh sizes of 32 mm in the wings,16 mm in the mid part, and 6 mm in the cod end. Sampling per haul take 15 minutes at a speed of approximately 2 knots (Asmus et al. 2020 ). Both benthic and pelagic hauls were taken at the sampling stations that are located in the deep tidal channels while only benthic hauls were taken in the shallow stations where the vertical opening of the bottom trawl is quite similar to the total water depth (Fig. 1 ). Benthic hauls were always carried out directly above the sediments and targeted near-bottom communities. Pelagic hauls were always carried out in the middle of the water column, thus, the position of the sampling net changed relative to the total water depth and targeted the mid-water communities. In this paper, we refer to benthic hauls as benthic habitats while pelagic hauls are considered representatives of pelagic habitats. Sampled fish were sorted and identified to species level using the identification keys in Miller and Loates ( 1997 ) and Muus and Dahlstrøm ( 1978 ), then counted to determine species abundances. Respective sensors mounted on the onboard ferry box (Petersen 2014 ) measured various parameters such as water temperature and salinity while the ship's navigational echo sounder measured water depth at the start and the end of each haul. Due to strong tidal mixing in a shallow coastal system, sea surface temperature (SST) and water temperature are the same, thus, we use both terms in our analyses. The datasets are contained in the PANGEA repository (Felden et al. 2023 ) and the dataset links in this paper in Asmus et al. ( 2020 ) and Rick et al. ( 2023 ). Data analysis Non-parametric Kruskal-Wallis test was used to test depth differences between sampling stations. Similarly, the same test was used to determine the differences in water temperature between seasons and between years. Then Dunn’s test with Bonferroni corrections was performed for post hoc analysis to find significant differences between pairs. Fish abundances in the benthic and pelagic hauls were standardized per sampled area (Individuals/10000 m 2 ). Rank Abundance Curves (RACs) were used to assess the seasonal species abundance distributions, the dynamics of co-occurring species, and the seasonal changes in community structures (Izsák and Pavoine 2012 ). This was done to elucidate the changes in species` use of the SRB at different times of the year. For this analysis, the monthly fish abundance data were aggregated into four seasons, winter (December, January, and February), spring (March, April, and May), summer (June, July, and August), and autumn (September, October, and November). The seasonal community dynamics in terms of changes in the relative abundances and the number of co-occurring species (Avolio et al. 2019 ) for benthic and pelagic habitats were assessed independently. Additionally, Jaccard's coefficient, analysis of similarity (ANOSIM), and similarity percentage (SIMPER) were used to further assess the differences in the seasonal community structures. Jaccard's coefficient assesses the similarity between seasons in terms of species presence/absence. ANOSIM compares ranked differences within- and between-groups that produce an R-value that approaches one when between-group differences are greater than within-group differences and vice-versa. R-values close to one signify higher dissimilarity between groups (James et al., 2023). SIMPER assesses the major taxa that are responsible for the observed differences between pair combinations (Clarke, 1993 ). SIMPER and ANOSIM analyses were based on the Bray-Curtis similarity coefficient at 999 permutations (Clarke 1993 ) on square-root transformed seasonal mean fish abundances. Fish community diversity within the SRB for each month in each station was determined by computing diversity indices comprising species richness ( S ), evenness (J), dominance (D), and Shannon-Wiener Index (H). Species richness is a measure of the number of taxa present and was used to assess whether the numbers changed with time or changed per season. Evenness represents species abundance distribution in a community and ranges from 0 to 1. Low values indicate that one or few species dominate the community while high values indicate that relatively equal numbers of individuals belong to each taxon (Morris et al. 2014 ). The opposite is true for D, which also ranges from 0 to 1. Dominance was used to assess the abundance fluctuations of dominant species with changes in water temperature. Shannon-Wiener Index quantifies the uncertainty of randomly selecting a taxon based on the relative abundance of each species and was used to assess the distribution of both rare and abundant species. It ranges from 1.5 to 3.5 with high values indicating high diversity/ecological status (Morris et al. 2014 ). General additive models (GAMs) in the mgcv package in R were used to assess the relationships between the diversity indices and the nominal variable (sampling station), ordinal variables (year and season), and continuous variable (water temperature). The GAMs were formulated as: D i ~ f (water temperature) + year + season + sampling station + ε i ), where D i = diversity index, f () specifies the smoothing term, and ε i is the error term that follows a normal distribution with zero mean (ε i ∼N(0,σ)). The smoothing functions use a back-fitting algorithm to estimate the intercept and the smoothing curve and estimate one smoother at a time (Zuur et al. 2007 , 2009b ). The smoothing curves are estimated by cubic regression splines where the explanatory variables are divided into several intervals. In each interval, a cubic polynomial is fitted, and the fitted values per segment are connected using conditions that involve first- and second-order derivatives to form a smoothing curve (Zuur et al. 2009b ). The smoothing curves were used to assess the non-linear relationships between water temperature and the diversity indices. The ordinal variables year and season were included in the GAMs to assess the inter- and intra-annual changes in fish diversity while sampling stations were to assess the spatial distribution patterns as sampling was concentrated in or adjacent to the main tidal channels (Fig. 1 ). Several combinations of explanatory variables were tested in GAMs and Akaike Information Criterion (AIC) was used to select the best model. The Sylt-Rømø Bight is a tidal environment, water depth varies over each tidal cycle, and the bedform morphology differs within each sampling station. Generalized linear mixed-effects models (GLMMs) with identity link functions were used to model species diversity ( S , J, and H) as linear functions of water depth where the intercept and slope changed per sampling station. This was to investigate the spatial distributions of fish with varying water depths at each sampling station. The mean water depth in meters (m), which was obtained by averaging depth at the start and the end of each haul, was used in GLMMs. The effect of the sampling station was included in the model as a random component where changes in water depth per sampling station had a different effect on the diversity. The variation around the intercept for each sampling station was assumed to be normally distributed with a certain variance. A small variance represented a small difference between sampling stations and vice versa. Further, a linear regression model with sampling station and water depth and the interaction between them, in other words, analysis of covariance (ANCOVA) was used to find their relationship with fish diversity. The GLMMs were formulated as D ij ~depth i +(depth i |Station j )+ ε i ), where D ij is the diversity index at depth i in station j , depth i is the combined effect of water depth on a diversity index, (depth i |Station j ) is the interaction between water depth and sampling station, and ε i is the error term (ε i ∼N(0,σ)). For more details on model specifications and formulation for the GAMs and GLMMs, refer to Zuur et al. ( 2007 , 2009a , b ). Statistical significance was assumed for all tests if the p-value was below 0.05. The Software R version 4.3.1 (R Development Core Team, 2023) was used to perform all statistical analyses. Results Temporal changes in water temperature Water temperature ranged from − 1.5 to 22.6°C during the survey period. Seasonal water temperature cycles that are typical in the Wadden Sea were observed as all seasons were significantly different from each other. No significant within-season mean temperature differences were observed except for the warm winter of 2006/2007 compared to the cold winters of 2009/2010, 2010/2011, and 2012/2013 (Fig. 2 , d). For more information on changes in water temperature and significant differences, see Supplementary Information 1. Spatial changes in water depth Sampling of fish took place during daylight and more than four hours to sample half the number of stations. Thus, sampling at each of the stations generally occurred at different moments of the tidal cycle and under different meteorological conditions. Besides the influence of tides on water depth, transects differed within sampling stations as it was difficult to always maintain one transect due to strong tidal currents and the influence of wind velocity and direction. Therefore, towing directions and transects within the sampling areas differed and covered the edges, slopes, or centers of the tidal channels. Consequently, there were changes in water depth within each sampling station (Fig. 3 ). For more information on the significant differences in water depth between stations, see Supplementary Information 1. Species frequency of occurrence and relative abundances In total, 55 fish species were found during the survey period and showed strong inter- and intra-annual abundance fluctuations. Some species were present all year round, others occurred only occasionally or were season-specific, while others were rare and present only in some years. Thus, only 22 species accounted for more than 95% of the total abundance for the entire survey. Table 1 and Table 2 show the details of the seasonal frequency of occurrence and the fluctuating abundances in both habitats. Herring ( Clupea harengus Linnaeus 1758 ) was the dominant species while the second and subsequent rankings slightly differed between the habitats. Small sand eel ( Ammodytes tobianus Linnaeus, 1758 ) was ranked second in the benthic habitats with 70% occurrence in summer while in the pelagic habitats, it was third-ranked with high occurrences in spring (Tables 1 and 2 ). Sprat ( Sprattus sprattus Linnaeus, 1758 ) was ranked third in the benthic habitats with high occurrence in spring followed by summer (Table 1 ). It was ranked second in the pelagic habitats with high occurrence in spring followed by winter (Table 2 ). The sequence of species organization in terms of highly ranked to the least of the 22 abundant species differed per season. Clupea harengus was dominant in both habitats in all seasons. In the benthic habitats, A. tobianus was ranked second in spring and summer, sand goby ( Pomatoschistus minutus Pallas 1770 ) in winter, and whiting ( Merlangius merlangus Linnaeus, 1758 ) in autumn (Fig. 4 ). In the pelagic habitats, S. sprattus was ranked second in winter, summer and autumn, and A. tobianus in spring. Third and subsequent ranks differed per season (Fig. 4 and Fig. 5 ). The Relative Abundance Curves (RACs) show seasonal changes in species distributions and evenness. The steep gradients between first ranked to the second-ranked species in all habitats in spring and summer indicate uneven communities (Figs. 4 and 5 ). In the pelagic habitats, the relatively low steepness in winter and spring indicates a moderately even community as species abundances were almost in a similar range (Fig. 5 ). Additional analyses of the seasonal changes in community structure showed that all winters compared to all summers had the highest percentage dissimilarity; ANOSIM, R = 0.63, p = 0.0001, SIMPER = 67.55%. The two seasons were 55% similar in terms of species presence and absence (Jaccard’s coefficient = 0.55). The dissimilarities between other seasonal comparisons were lower, SIMPER ranged from 55–58.49%, ANOSIM, R < 0.3, p < 0.001, and Jaccard’s coefficient was relatively higher and ranged from 0.62–0.79. In all seasonal community structure comparisons, six dominant species, C. harengus, A. tobianus, M. merlangus, S. sprattus, P. minutus , and Nilsson's pipefish ( Syngnathus rostellatu s Nilsson 1855 ) contributed the highest percentage dissimilarities. See Table S1 for the details of the percentage contributions of different taxa to the dissimilarities between seasons. Table 1 The total abundance, the percentage of each species to the total abundance, seasonal mean abundances, and the percentage seasonal frequency of occurrence of the 22 species in the benthic habitats for the entire survey. Spp_code show the species names in other analyses Species Total abundance % of the total abundance Seasonal mean abundance % Seasonal frequency of occurrence Common names Scientific names Spp_code Spring Summer Autumn Winter Spring Summer Autumn Winter Herring Clupea harengus C_har 818841 67.6 1091.2 1989.7 368.1 137.6 30 56 10 4 Small sand eel Ammodytes tobianus A_tob 176934 14.6 180.8 540.8 47.6 2.7 23 70 6 0 Sprat Sprattus sprattus S_spr 69791 5.8 160.4 75.1 50.7 16.2 53 25 17 5 Whiting Merlangius merlangus M_mer 48971 4.0 10.6 137.0 71.0 0.7 5 63 32 0 Sand goby Pomatoschistus minutus P_min 35714 3.0 58.7 15.0 51.3 39.3 36 9 31 24 Nilsson's pipefish Syngnathus rostellatus S_ros 11717 1.0 15.4 17.5 16.6 2.4 30 34 32 5 Plaice Pleuronectes platessa P_pla 7346 0.6 5.4 13.4 7.3 8.0 16 39 21 23 Great sand eel Hyperoplus lanceolatus H_lan 5304 0.4 14.6 7.4 0.4 0.0 65 33 2 0 Bull-rout Myoxocephalus scorpius M_sco 4764 0.4 2.7 1.4 2.5 18.3 11 6 10 74 Common goby Pomatoschistus microps P_mic 4602 0.4 4.1 0.2 4.6 14.4 17 1 20 62 Three-spined stickleback Gasterosteus aculeatus G_acu 3934 0.3 4.6 4.5 1.0 8.9 24 24 5 47 Hooknose Agonus cataphractus A_cat 3715 0.3 3.5 2.0 5.9 6.4 20 11 33 36 Dab Limanda limanda L_lim 3331 0.3 4.5 0.8 6.4 3.8 29 5 41 25 Cod Gadus morhua G_mor 2923 0.2 0.4 7.0 4.5 1.6 3 52 33 12 Smelt Osmerus eperlanus O_epe 2667 0.2 3.0 1.2 1.7 7.3 23 9 13 56 Eelpout Zoarces viviparus Z_viv 2642 0.2 2.8 3.2 0.8 6.1 22 25 6 48 European anchovy Engraulis encrasicolus E_enc 1768 0.1 0.2 0.5 7.4 0.0 3 7 91 0 Fluonder Platichthys flesus P_fle 1278 0.1 2.1 0.4 0.6 3.0 35 7 11 45 Horse mackerel Trachurus trachurus T_tra 885 0.1 0.0 3.8 0.1 0.0 0 96 4 0 Gunnel Pholis gunnellus P_gun 814 0.1 1.6 0.7 0.5 1.0 43 19 12 26 Striped seasnail Liparis liparis L_lip 639 0.1 1.6 0.8 0.2 0.2 59 30 6 6 Scaldfish Arnoglossus laterna A_lat 261 0.0 0.1 0.0 1.1 0.0 7 3 89 1 Table 2 The total abundance, the percentage of each species to the total abundance, overall seasonal mean abundances, and the percentage seasonal frequency of occurrence of the 19 abundant species in the pelagic habitats for the entire survey period Species Total abundance % of the total abundance Seasonal mean abundances % seasonal frequency of occurrence Common names Scientific names Spring Summer Autumn Winter Spring Summer Autumn Winter Herring Clupea harengus 300829 87.9 10087 12621 1423 401 29 22 25 23 Sprat Sprattus sprattus 18472 5.4 331 518 685 111 36 14 22 28 Small sand eel Ammodytes tobianus 10867 3.2 634 45 206 2 57 23 12 7 Great sand eel Hyperoplus lanceolatus 3282 1.0 245 5 5 0 54 23 22 1 Nilsson's pipefish Syngnathus rostellatus 2436 0.7 33 50 131 9 30 28 33 9 Three-spined stickleback Gasterosteus aculeatus 1764 0.5 45 21 22 71 35 9 21 35 Horse mackerel Trachurus trachurus 1752 0.5 0 143 3 0 0 82 18 0 Sand goby Pomatoschistus minutus 716 0.2 32 8 6 14 25 7 26 43 Smelt Osmerus eperlanus 496 0.1 17 2 6 19 20 11 16 53 Whiting Merlangius merlangus 451 0.1 0 23 18 0 4 65 31 0 Plaice Pleuronectes platessa 197 0.1 9 4 1 2 27 30 16 27 Common goby Pomatoschistus microps 193 0.1 1 0 2 16 11 0 26 63 Fluonder Platichthys flesus 159 0.0 9 1 0 3 49 8 8 36 Bull-rout Myoxocephalus scorpius 92 0.0 3 0 1 3 30 11 19 41 Hooknose Agonus cataphractus 82 0.0 3 1 2 2 22 22 19 38 Garfish Belone belone 65 0.0 4 1 0 0 41 47 12 0 Lumpfish Cyclopterus lumpus 63 0.0 1 0 4 2 16 3 63 19 Dab Limanda limanda 62 0.0 1 0 5 0 40 13 40 7 Eelpout Zoarces viviparus 49 0.0 2 0 1 1 46 8 15 31 Temporal changes in fish diversity The diversity indices, species richness ( S ), evenness (J), dominance (D), and Shannon-Wiener Index (H) varied both spatially and temporally over the study period. In general, there were higher taxa numbers in the benthic habitats compared to pelagic habitats (Fig. S1 ). Species richness (S) in benthic habitats was the highest in spring and summer. In contrast, S in pelagic habitats was low in summer (Fig. S1 ). Inter-annual differences in S were observed with high variabilities in both benthic and pelagic habitats (numerous outliers in Fig. S1 ). The General Additive Model (GAM) comprising smoothing terms of water temperature, sampling station and the factors year and season explained 15% and 24% of the variations in S in the benthic and pelagic habitats, respectively. Water temperature was significant and explained 4% and 11% variations in S in the benthic and pelagic habitats, respectively (Table 3 and Table 4 ). Smoothing curves show higher S in temperatures between 4°C to 7°C and 13°C to 16°C in both habitats and remain stable when temperatures are above 19°C (Fig. 6 ). However, S ranged from 2 to 15 species (mean of 7.6±3.1 (SD), n = 100) between 19°C and 22.6°C in the benthic habitat and from 1 to 7 species (mean of 2.6±1.5 (SD), n = 42) in the pelagic habitats (Fig. S2 ). Thus, the wide 95% confidence bands indicate high uncertainty in S prediction when water temperature is > 19°C. Numerical output of different GAMs for both habitats are summarized in Table 3 and Table 4 , best models are shown in bold. The tables show various information on the performance of various models in explaining the variations in diversity indices. For instance, deviance explained which is equivalent to R 2 in linear regressions and the Akaike Information Criterion (AIC). The GAMs show that the factor year explained higher variability in S at 7% (benthic) and 10% (pelagic) habitats compared to the seasonal effect that explained only 2% and 3%. Table 3 Estimated parameters, standard errors, t-values, p-values, deviance explained, scale estimates, and Akaike Information Criterion (AIC) of the parametric components of the general additive models of the explanatory variables to the diversity indices in benthic habitats. Best models are shown in bold, s() represents the smoothing terms of water temperature Diversity index Explanatory variables Estimate/ Intercept SE t-value p -value Deviance explained Scale est. AIC Species richness ( S) s(Temperature) + Year + Season + Station 7.47 0.51 14.57 *** 15% 10.8 4385.1 s(Temperature) + Year + Season 8.11 0.43 18.95 *** 13% 10.9 4390.8 s(Temperature) + Year 9.16 0.36 25.46 *** 10% 11.2 4405.6 s(Temperature) + Season 6.88 0.26 26.70 *** 6% 11.6 4429.3 s(Temperature) 7.84 0.12 65.84 *** 4% 11.8 4441.4 Year + Season 5.99 0.41 21.01 *** 8% 11.4 4419.1 Year 9.40 0.36 26.12 *** 7% 11.5 4426.8 Season 7.13 0.24 29.69 *** 2% 12.1 4458.2 Station 7.28 0.32 22.60 *** 2% 12.1 4465.6 Evenness (J) s(Temperature) + Year + Season + Station 0.56 0.03 16.69 *** 17% 0.0 -167.2 s(Temperature) + Year + Season 0.53 0.03 18.87 *** 15% 0.0 -169.2 s(Temperature) + Year 0.47 0.02 19.98 *** 13% 0.0 -156.3 s(Temperature) + Season 0.49 0.02 29.97 *** 12% 0.0 -169.2 s(Temperature) 0.44 0.01 58.08 *** 11% 0.0 -157.5 Year + Season 0.49 0.03 18.03 *** 11% 0.0 -139.5 Year 0.46 0.02 19.18 *** 3% 0.1 -79.7 Season 0.46 0.02 30.22 *** 8% 0.0 -134.2 Station 0.47 0.02 22.20 *** 1% 0.1 69.9 Dominance (D) s(Temperature) + Year + Season + Station 0.43 0.04 11.39 *** 20% 0.1 25.2 s(Temperature) + Year + Season 0.42 0.03 13.47 *** 18% 0.1 29.5 s(Temperature) + Year 0.45 0.03 17.43 *** 17% 0.1 27.3 s(Temperature) + Season 0.51 0.02 26.83 *** 11% 0.1 76.2 s(Temperature) 0.54 0.01 62.30 *** 10% 0.1 74.2 Year + Season 0.43 0.03 14.22 *** 15% 0.1 55.1 Year 0.45 0.03 16.46 *** 9% 0.1 103.0 Season 0.53 0.02 29.93 *** 7% 0.1 103.1 Station 0.55 0.02 22.42 *** 2% 0.1 156.4 Shannon (H) s(Temperature) + Year + Season + Station 1.22 0.08 14.54 *** 22% 0.3 1361.3 s(Temperature) + Year + Season 1.25 0.07 17.75 *** 19% 0.3 1374.3 s(Temperature) + Year 1.22 0.06 20.90 *** 19% 0.3 1369.8 s(Temperature) + Season 1.01 0.04 23.54 *** 12% 0.3 1425.5 s(Temperature) 0.98 0.02 49.87 *** 11% 0.3 1424.0 Year + Season 1.23 0.07 18.06 *** 15% 0.3 1406.4 Year 1.24 0.06 20.45 *** 9% 0.3 1454.5 Season 0.97 0.04 24.27 *** 7% 0.3 1457.7 Station 0.96 0.05 17.43 *** 2% 0.4 1508.9 Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Table 4 Estimated parameters, standard errors, t-values, p-values, deviance explained, scale estimates, and Akaike Information Criterion (AIC) of the parametric components of the general additive models of the explanatory variables to the diversity indices in the pelagic habitats. Best models are shown in bold, s() represents the smoothing terms of water temperature Diversity index Explanatory variables Estimate/ Intercept SE t-value p -value Deviance explained Scale est. AIC Species richness ( S) s(Temperature) + Year + Season + Station 4.65 0.34 13.65 *** 24% 3.74 1831.91 s(Temperature) + Year + Season 5.08 0.29 17.32 *** 19% 3.90 1845.48 s(Temperature) + Year 5.08 0.29 17.38 *** 19% 3.90 1842.11 s(Temperature) + Season 3.86 0.10 38.91 *** 11% 4.18 1863.83 s(Temperature) 3.87 0.10 39.45 *** 11% 4.18 1861.08 Year + Season 5.31 0.35 15.11 *** 14% 4.12 1863.96 Year 5.30 0.30 17.58 *** 10% 4.27 1876.53 Season 3.89 0.21 18.17 *** 3% 4.49 1889.15 Station 3.55 0.22 15.96 *** 4% 4.50 1892.58 Evenness s(Temperature) + Year + Season + Station 0.71 0.04 16.21 *** 17% 0.06 48.27 (J) s(Temperature) + Year + Season 0.65 0.04 17.46 *** 13% 0.06 58.18 s(Temperature) + Year 0.65 0.04 17.11 *** 9% 0.07 70.51 s(Temperature) + Season 0.76 0.03 26.78 *** 9% 0.06 51.46 s(Temperature) 0.66 0.01 53.84 ** 6% 0.07 60.62 Year + Season 0.69 0.05 15.23 *** 5% 0.07 81.18 Year 0.64 0.04 16.65 *** 3% 0.07 86.05 Season 0.70 0.03 26.67 *** 2% 0.07 73.54 Station 0.70 0.03 25.82 *** 3% 0.07 70.78 Dominance s(Temperature) + Year + Season + Station 0.54 0.05 11.15 *** 15% 0.08 147.67 (D) s(Temperature) + Year + Season 0.51 0.05 10.08 *** 12% 0.08 150.36 s(Temperature) + Year 0.56 0.04 13.61 *** 11% 0.08 150.84 s(Temperature) + Season 0.61 0.01 44.49 *** 8% 0.08 148.81 s(Temperature) 0.61 0.01 44.84 *** 6% 0.08 149.15 Year + Season 0.52 0.05 10.47 *** 8% 0.08 163.78 Year 0.56 0.04 13.24 *** 6% 0.08 166.45 Season 0.59 0.03 19.91 *** 2% 0.08 166.53 Station 0.59 0.03 19.33 *** 2% 0.09 170.97 Shannon s(Temperature) + Year + Season + Station 0.86 0.08 10.21 *** 20% 0.23 627.33 (H) s(Temperature) + Year + Season 0.94 0.09 10.63 *** 18% 0.24 627.85 s(Temperature) + Year 0.87 0.07 12.08 *** 17% 0.24 626.28 s(Temperature) + Season 0.71 0.02 29.29 *** 12% 0.25 633.68 s(Temperature) 0.70 0.02 29.50 *** 11% 0.25 633.15 Year + Season 0.95 0.09 10.95 *** 12% 0.25 649.46 Year 0.89 0.07 11.96 *** 8% 0.26 661.48 Season 0.75 0.05 14.33 *** 4% 0.27 662.26 Station 0.72 0.06 13.02 *** 1% 0.28 680.01 Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Evenness (J) varied both annually and seasonally in both habitats. Evenness was higher in winter and spring than in summer in most of the years. Evenness was higher in the pelagic habitats than in the benthic habitats (Fig. S3 ). Water temperature explained 11% and 6% variations in J in the benthic and pelagic habitats, respectively (Table 3 and Table 4 ). In the benthic habitats, the smoothing terms of water temperature show that J decreases with increasing temperature up to 15°C followed by a minor increase. This is followed by a decline at temperatures above 18°C (Fig. 7 , a). In the pelagic habitats, J declines with increasing temperatures up to 8°C then remains constant up to 11°C. This is followed by a further decrease to 15°C then an increase up to 19°C after which it remains constant (Fig. 7 , b). The factor season explained a higher variability in J (8%) than the factor year (4%) in the benthic habitats (Table 3 ). In the pelagic habitat, both factors year and season explained low variability in J at 3% and 2%, respectively (Table 4 ). Dominance (D) varied both annually and seasonally in both habitats. The highest values were found in most summers. However, in 2015 spring and autumn had higher values than summer in the benthic habitat while in the pelagic habitat, springs of 2009 and 2017 showed relatively higher values (Fig. S3 ). In the benthic habitats, smoothing terms showed that D is unstable at temperatures below 5°C then gradually increases up to 14°C after which it remains stable to 18°C. This is followed by an increase at temperatures above 19°C (Fig. 8 , a). A similar pattern occurred in the pelagic habitat. However, the wide 95% confidence interval indicates the uncertainties in D at temperatures above 16°C (Fig. 8 , b). Water temperature explained 10% and 6% variations in D in the benthic and pelagic habitats, respectively. The factor year explained 9% and 6% of the variability in D in benthic and pelagic habitats, respectively. The factor season on the other hand explained 7% variability in benthic and only 2% in the pelagic habitats (Tables 3 and 4 ). Shannon-Wiener Index (H) varied annually and across seasons in both habitats. Generally, H was high in winter and low in summer. However, in 2017 spring showed the highest H values in the benthic habitat, and springs of 2012, 2016, and 2017 had the highest values in the pelagic habitats (Fig. S4). Smoothing terms of water temperature showed a gradual increase in H from 1°C obtaining a maximum value at 5°C in both benthic and pelagic habitats (Fig. 9 ), which represents an equal proportion of all species at this temperature. As temperatures increase beyond 5°C, H gradually declines in all the habitats. However, in the benthic habitat, H was stable between 14°C and 18°C after which further decline occurs at higher temperatures. Water temperature explained 11% variability in H in both habitats. The factor year explained 9% and 8% variations in H while season explained 7% and 4% of the same in the benthic and pelagic habitats, respectively (Table 3 and Table 4 ). Spatial distribution of fish with changes in water depth The tidal effects and changes in water depth within sampling stations on the distribution of fish were analyzed using generalized linear mixed-effects models (GLMMs). The different slopes and intercepts showed the distribution patterns in different sampling stations with changes in water depth. Generally, S decreased with increasing water depth in all sampling stations in the benthic habitats even though sampling was always slightly above the sediments irrespective of the water depth. The intercepts and slopes show minimal variations in S (variance (σ 2 ) = 0.59; sampling station, and σ 2 = 0.004; depth) between sampling stations in the benthic habitats regardless of whether the station was located in or adjacent to the deep tidal channels (Fig. 10 , a). In the pelagic habitats, variations in S with water depth between sampling stations (σ 2 = 1.14 (sampling station) and σ 2 = 0.01 (depth) were observed. For instance, S decreased with water depth in Sylt_6 while Sylt_1 and Sylt_9 showed positive relationships between S and water depth (Fig. 10 , b). Evenness (J) increased with water depth in all sampling stations in the benthic habitats with no variations between stations (same intercept and slope, Fig. 11 , a). In the pelagic habitats, J varied between sampling stations. For instance, Sylt_2 and Sylt_6 showed positive relationships between J and water depth while no depth effects were observed in Sylt_8. In the stations adjacent to deep tidal channels (i.e. Sylt_1, Sylt_4, and Sylt_9), J decreased with an increase in water depth (Fig. 11 , b). Dominance on the other hand showed a slight decrease with an increase in water depth in all sampling stations in all habitats (Fig. S5). Individuals of various species utilized the submerged areas at high water depths resulting in equal abundance proportions in the sampled areas. There were minor variations in D with water depth between sampling stations. For instance, in the benthic habitats, Sylt_2 and Sylt_4 showed relatively steeper slopes than the other stations. Only Sylt_2 in the pelagic habitat showed such steepness (Fig. S5). There were no significant effects of water depth on H. Discussion Species' relative abundances and seasonal distributions This study investigated the potential effects of the seasonal changes in water temperature on the community structure and diversity of fish in the Sylt-Rømø Bight (SRB). In addition, the utilization of intertidal habitats during high tide and spatial distribution within and adjacent to the tidal channels were investigated. The species' frequency of occurrence showed the overall seasonal distribution patterns in the benthic and pelagic habitats and the transitions of some species between these habitats. For example, A. tobianus had the highest frequency of occurrence in spring in its pelagic phase (Table 2 ). Thus, its highest frequency of occurrence in summer in the benthic habitats (Table 1 ) shows its transition from pelagic to benthopelagic behavior that occurs in the middle of their juvenile stages (Laugier et al. 2015 ). For the pelagic species such as C. harengus , the highest occurrence in spring in the pelagic habitats (Table 2 ) in comparison to the highest occurrence in summer in the benthic habitats (Table 1 ) may also indicate differences in size distributions. Sprattus sprattus on the other hand was abundant in spring in both habitats (Table 1 and Table 2 ). Its high occurrence in winter in the pelagic habitats (28%) compared to benthic habitats (5%) (Table 1 and Table 2 ), may indicate differences in size distributions between the habitats. However, the area is relatively shallow (Fig. 3 ), therefore, the size distributions of different species between the habitats need further investigations Community ecology aims to understand how communities are organized by identifying, describing, and explaining the patterns that underlie the structure and diversity across space and time (Verberk 2011 ), and the associated environmental drivers (Heaven and Scrosati 2008 ). In every community, a few species are always present in the highest numbers suggesting that there are general macro-ecological rules or processes such as migration patterns underlying the species distribution and abundances (Verberk 2011 ; Murphy and Smith 2021 ). These patterns portray how such communities function, the types of ecological interactions, the relationships of co-occurring species, and the manner they respond to environmental changes (Verberk 2011 ). However, such predictions must be able to take into account the multiple interacting processes operating across spatio-temporal scales that dictate where and when species occur (Murphy and Smith 2021 ). This brings the question as to whether the observed seasonal abundance rank patterns (Figs. 4 and 5 ) are related to temperature changes (Fig. 2 ), and/or ecological interactions such as competition and predation that can lead to the exclusion of other species or whether the other species simply prefer different habitats at different life stages. Furthermore, in a resource-limited environment, dietary competition occurs among individuals of the same species (Borcherding et al. 2019 ) posing the complexities of ecosystem functioning. Temperature is a critical environmental parameter structuring the fish diversity patterns through physiological tolerances (Selleslagh and Amara 2008 ) that leads to seasonal changes through community reorganizations shown by the Rank abundance curves (Fig. 4 and Fig. 5 ). Accordingly, temperature influences reproduction, recruitment, migration patterns, and ecological relationships (Selleslagh and Amara 2008 ; Clark et al. 2020 ). Clupea harengus dominated all seasons because it is the most abundant species in the North Sea (Corten 1986 ), has bi-annual (spring and autumn) spawning (Bierman et al. 2010 ), continuous inflow of juveniles from the adjacent North Sea (Maathuis et al. 2023 ), and high consumption of prey items (Utne et al. 2012 ). Thus, it outcompetes species of similar prey items but can co-occur with species with diverse prey choices and similar temperature preferences such as A. tobianus (Kellnreitner et al. 2012 ), which also spawns twice per year (Laugier et al. 2015 ). Merlangius merlangus has diverse prey choices (Kellnreitner et al. 2012 ; Ross et al. 2016 ) but overlapping temperature preferences to C. harengus and A. tobianus (Table 1 ). Therefore, M. merlangus co-occurs with both C. harengus and A. tobianus in late spring/early summer attaining high relative abundances in autumn (Fig. 4 ). This co-occurrence pattern changes depending on the temperature conditions, for instance, after the prolonged cold winters of 2009/2010 and 2010/2011, low recruitment of M. merlangus in the SRB were observed while there were strong recruitments of C. harengus and A. tobianus (Odongo et al. 2024 ). Such abundance fluctuations explain why C. harengus was highly ranked even in autumn since we used seasonal mean abundances in the analysis. Pomatoschistus minutus feeds on diverse prey items (Kellnreitner et al. 2012 ) but its high ranking in winter in the benthic habitats (Fig. 4 ) is related to its local migration to deeper areas to avoid the unstable winter temperature conditions in the shallow intertidal zones. The second-ranking of S. sprattus in the pelagic habitats in most of the seasons (Fig. 5 ) is related to its preferred habitat. Other species had low relative abundance distributions but their rankings fluctuated with seasons and habitats (Figs. 4 and 5 ). These observations support the temperature role in the species’ co-occurrence and/or exclusions in habitat use. Potential effects of temperature changes on fish diversity The Wadden Sea is characterized by seasonal changes in water temperature (Fig. 2 ) (Rick et al. 2023 ). Rutterford et al., ( 2023 ) predicted that global warming could cause shifts in fish species composition across the Northeast Atlantic continental shelf with greater implications at higher latitudes. Our monthly monitoring of 13 years, which might be considered short in determining the effects of climate change on community structures revealed a strong influence of seasonal changes in water temperature on the phenology of various fish species. For instance, the effects of anomalous events such as the prolonged cold winters of 2009/2010 (Fig. 2 ), also described by Osborn ( 2011 ) and 2010/2011 resulted in low taxa numbers compared to the relatively warm winters of 2006/2007, 2007/2008, and 2008/2009 (Fig. S1 ). Such events also influenced the diversity in the seasons that followed. For example, the higher number of taxa in the summer of 2010 (Fig. S1 ) may have resulted from the delayed emigration of boreal species that coincided with the immigration of Lusitanian species. Similarly, the high dominance in the 2015 autumn (Fig. S3 ) which was contrary to other years is attributed to the delayed emigration of C. harengus and A. tobianus after the cold summer of 2015 (Fig. 2 ). In the Dutch Wadden Sea, van Walraven et al. ( 2017 ) reported delayed immigration and emigration of fish because of an increase in offshore temperature based on a 53-year survey. However, changes in the fish community assemblages depend on the capacity for and the rate of distributional changes between species under different climate change scenarios (Rutterford et al. 2023 ). This is why we observed varying diversity patterns resulting from either delayed or advanced migration related to inter-annual variations in water temperature. Other factors besides temperature though not investigated in this study, may additionally play significant roles. For example, there was high species richness in the spring of 2016 (Fig. S1 ) despite similar spring temperature ranges from 2014 to 2017 (Fig. 2 ). Diversity patterns reflect the underlying processes that shape ecological communities (Vasconcelos et al. 2015 ). Species richness has been used as the simplest metric to represent community diversity. Although limited in measuring ecological dynamics, it provides information on co-occurring species under different environmental conditions (Avolio et al. 2019 ). Furthermore, S is a better measure when the primary objective is to detect the effects of environmental parameters on community diversity (Magurran and Dornelas 2010). For example, the low number of taxa at lower and higher temperatures (Fig. 6 ). Higher species richness occurred at temperatures around 5°C and 15°C (Fig. 6 ), which are typical temperatures for seasonal transition phases for winter/spring and summer/autumn, respectively (Fig. 2 ). This is because immigration and emigration occur simultaneously in the SRB resulting in higher species numbers at the seasonal transition phases. This is further supported by Jaccard’s coefficients, which showed high similarity in terms of species present between adjacent seasons (Table S1 ) and explain why the factor season explained low variations in S in both habitats (Table 2 and Table 3 ). Furthermore, the migration patterns explain why S was not subjected to the bi-modal nature of the number of temperature observations (highs around 5°C and 15°C) which are typical in the Wadden Sea (de Amorim et al. 2023 ) or the North Sea (Boersma et al. 2016 ) (see Fig. S2 ). At 10°C and 18°C, which are the typical mean spring and summer temperatures, respectively (Fig. 2 ), similar species richness (Fig. 6 ) and evenness (Fig. 7 , a) were observed. The species numbers were lower than those of the winter/spring and summer/autumn transition phases because only species that are adapted to specific temperatures use the habitats at specific times otherwise migrate to more favorable environments. The S patterns (Fig. 6 ), show that the emigration of C. harengus and A. tobianus and the immigration of M. merlangus and T. trachurus (Table 1 ) starts at 15°C achieving a relatively even community at 18°C. Beyond 18°C, M. merlangus becomes the dominant species as indicated by the decrease in evenness (Fig. 7 , a) and an increase in dominance (Fig. 8 , a). Compound indices such as the Shannon-Wiener Index (H) and evenness (J) provide more information on community dynamics (Morris et al. 2014 ). Nearby communities or seasonal assemblages with similar taxa numbers can have different community structures (Heaven and Scrosati 2008 ). For instance, 10°C (spring) and 18°C (summer) had similar species richness (Fig. 7 ) but different H (Fig. 9 ). The decrease in H with increasing temperatures indicates that the proportion of less abundant and/or rare species gradually declined with temperature increase and a few species dominated the community except between 15°C and 18°C because of the community transitions or ecotones (Fig. 9 ). Similarly, in the tidal habitats of the Irish Sea, seasonality played a bigger role in structuring the fish community (Jovanovic et al. 2007 ) as well as the English Channel where higher diversities were observed in winter and low in summer (Selleslagh and Amara 2008 ). The changes in H (Fig. S4) and the variations explained by year (Tables 2 and 3 ) indicate that the inter-annual variations of biotic and abiotic factors that cause abundance fluctuations influence diversity as well (Morris et al. 2014 ). Spatial distribution of fish with changes in water depth A greater range of local habitat types and environmental conditions support higher diversity (Morris et al. 2014 ). Species-area relationships assume that the availability of important features such as habitat size influences species numbers and abundances (Tittensor et al. 2010 ). Local habitats may contain various microhabitat types that comprise different growth forms such as seagrass meadows, seaweed ( Sargassum muticum ), or bivalve beds (Fig. 1 ) that contain a high diversity of benthic and epibenthic communities (Armonies et al. 2018 ). The intertidal habitats when submerged during high tide offer additional ecological functions such as more prey, camouflage, and refuge spaces that promote higher taxa numbers (Gratwicke and Speight 2005 ). The negative correlation between S and water depth (Fig. 10 ) shows that during high tide, fish migrate to the intertidal habitats and recede with ebbing currents. The migration decreases the total fish density per area as the total aerial coverage increases during high tide. Thus, a positive correlation exists between water depth and evenness (Fig. 11 ). On the other hand, a negative correlation exists with dominance (Fig. S5). Tidally induced current velocities vary within the tidal cycle and differ in various sub-areas of the SRB. Maximum velocity, which is approximately 1.8 m s − 1 occurs at the opening of Lister Tief (Fig. 1 ) and decreases towards the intertidal flats (Fofonova et al. 2019 ). Besides, the seabed morphology differs within sampling stations. Thus, the positive correlation between water depth and J indicates that fish concentrated in smaller areas during low tide and/or simply avoided the deeper parts within the tidal system irrespective of the tidal condition. The negative correlations in stations adjacent to subtidal channels (i.e. Sylt_1, Sylt_4, and Sylt_9) in the pelagic habitats (Fig. 11 , b), show that a few species dominated these areas during high tide. These differences are attributed to species habitat preferences as Kellnreitner et al. ( 2012 ) observed that benthivorous fish species mainly dominated the shallower areas while planktivorous fish were mostly abundant in the deep areas. Similarly, in the Irish Sea, flatfishes were more abundant in shallow areas or near the receding water edges as they fed on macroinvertebrates (Jovanovic et al. 2007 ). In the Dutch Wadden Sea, higher densities of S. sprattus, C. harengus, E. encrasicolus , and pilchard ( Sardina pilchardus ) occurred during high tide and dominated the top 10 m of the water column (Couperus et al. 2016 ). These observations show the significance of tidal dynamics on habitat utilization and distribution of common and rare species. Different aspects of the composition, structure, and functioning of natural communities vary independently so a suite of metrics is needed to cover all types of changes (Greenstreet et al. 2012 ). For example, current velocity and sediment characteristics are important structural factors that influence the spatial distribution and composition of macrofaunal species (Schückel et al. 2015 ) that offer diverse food sources to fish (Kellnreitner et al. 2012 ). Incorporating these different aspects of habitat structure and composition (Gratwicke and Speight 2005 ) could offer more information on habitat utilization. This could better explain the changes in diversity between sites than generalizing only on sampling stations, which was significant but explained low percentage variations of the diversity (Tables 2 and 3 ). Nevertheless, incorporating and disentangling singular or cumulative effects of parameters such as habitat dependencies and shared food resources for different fish species is complex as it varies across key life stages and space (Rutterford et al. 2023 ). Therefore, exploring the behavioral responses, distribution patterns, and abundances of singular species with abiotic responses and biotic interactions could determine further habitat preferences and provide more information on spatial distribution patterns. Conclusion The fish community structure and diversity patterns demonstrated high seasonal dynamics that were potentially driven by the changes in water temperature in a cold temperate coastal ecosystem. The seasonal transition phases of winter/spring and summer/autumn recorded higher species richness ( S ) because immigration and emigration occur simultaneously. The community was less diverse at very low temperatures because of the low S and abundances and constrained biological interactions whereas the low diversity at higher temperatures was related to the dominance of a few species and physical tolerance. The higher percentage variations of S explained by temperature in the pelagic habitats compared to the benthic habitats showed the high sensitivity of species’ pelagic phases to temperature changes. Since temperature effects cut across food webs, incorporating its effects on all trophic levels could provide more information on the fish community dynamics as we targeted only one group or compartment of the food web. Additionally, an analysis of the size distributions of various species between benthic and pelagic habitats could provide more information on the use of these habitats at different times of the year. The GLMMs showed the spatial distribution with changes in water depth per tidal cycle and within and between sampling stations. Thus, the significant roles of the shallow and intertidal areas as important feeding and refuge grounds. The effect of water depth on the diversity patterns was less pronounced because of the low depth changes in the SRB in comparison to other North Sea areas. Nevertheless, the GLMMs showed the species distribution patterns in different habitats, which calls for further investigations on the role of habitat complexity on species richness, abundance, and the distribution of common and rare fish species. The spatial distribution patterns provide baseline information on the significance of shallow coastal systems for fish. This is useful not only in the Wadden Sea but can be used as a guideline for management and conservation measures for the maintenance of biodiversity and valuable coastal and offshore ecosystems. Declarations Supplementary Information The online version contains supplementary information including descriptions of temperature and depth changes as well as additional figures of diversity indices. Data/code availability Data and code will be made available upon request. Conflict of interest The authors declare that there are no competing financial interests or personal relationships, which could influence the scientific work presented in this paper. Ethical approval All applicable national and international regulations on scientific monitoring in protected areas were followed during the sampling and handling of fish specimens. The National Park “Schleswig- Holsteinisches Wattenmeer” issued the permit and authorization to work in the Sylt-Rømø Bight. Funding Open Access funding will be provided by the library of Alfred-Wegener-Institute for Polar and Marine Research. Victor Odongo is grateful to the German Academic Exchange Service (DAAD) for funding his doctoral studies (Funding program number: 57507871) at the University of Bremen and Alfred-Wegener-Institut, Helmholtz Zentrum für Polar- und Meeresforschung. Author contributions Victor Odongo conceptualized the research questions, and data analysis methods, wrote the first draft of the manuscript, and led the writing process. Harald Asmus conceptualized the fish monitoring ideas and designed the sampling methodology and gear selection. Maarten Boersma, Sabine Horn, and Katja Heubel guided the manuscript structure and clarity, review, and editing. Lasse Sander provided the map of the study area and editing. Sara Rubinetti and Vera Sidorenko review and editing. All the authors contributed to the drafts and gave final approval for publication. Acknowledgments We are grateful to the authorities of the National Park “Schleswig- Holsteinisches Wattenmeer” (Landesbetrieb für Küstenschutz, Nationalpark und Meeresschutz) for the possibilities of scientific research in the area. Moreover, we wish to thank Petra Kadel, Birgit Hussel, and Timm Kress for planning, leading the fish monitoring, and collating the data. We are indebted to everyone including all the students who helped and participated in the fish monitoring over the years. We are grateful for the contributions of Merten Saathoff and Harald Ahnelt to the manuscript. Appreciation to the crew of the RV MYA I and II for operating and making sure the fish monitoring is a success. References Armonies W, Asmus H, Buschbaum C, Lackschewitz D, Reise K, Rick J (2018) Microscopic species make the diversity: A checklist of marine flora and fauna around the Island of Sylt in the North Sea. Helgol Mar Res doi. 10.1186/s10152-018-0512-8 Asmus H, Hussel B, Petra K, Asmus R, Rick JJ, Wiltshire KH (2020) Fish monitoring in the Sylt Rømø bight (2007 et seq). In: Alfred Wegener Institute - Wadden Sea Station Sylt, PANGEA. https://doi.pangaea.de/10.1594/PANGAEA.911261 . Accessed 15 Dec 2021 Avolio ML, Carroll IT, Collins SL, Houseman GR, Hallett LM, Isbell F, Koerner SE, Komatsu KJ, Smith MD, Wilcox KR (2019) A comprehensive approach to analyzing community dynamics using rank abundance curves. Ecosphere. 10.1002/ecs2.2881 Baird D, Asmus H, Asmus R (2007) Trophic dynamics of eight intertidal communities of the Sylt-Rømø Bight ecosystem, northern Wadden Sea. Mar Ecol Prog Ser 351:25–41. 10.3354/meps07137 Baird D, Asmus H, Asmus R, Horn S, de la Vega C (2019) Ecosystem response to increasing ambient water temperatures due to climate warming in the Sylt- Rømø Bight, northern Wadden Sea, Germany. Estuar Coast Shelf Sci. 10.1016/j.ecss.2019.106322 Barbier EB, Hacker SD, Kennedy C, Koch EW, Stier AC, Silliman BR (2011) The value of estuarine and coastal ecosystem services. Ecol Monogr 81:169–193. 10.1890/10-1510.1 Beaugrand G (2004) The North Sea regime shift: Evidence, causes, mechanisms and consequences. Prog Oceanogr 60:245–262. 10.1016/j.pocean.2004.02.018 Bierman S, Dickey-Collas M, van Damme G, van Overzee CJ, Pennock-Vos H, Tribuhl M, Clausen S L (2010) Between-year variability in the mixing of North Sea herring spawning components leads to pronounced variation in the composition of the catch. ICES J Mar Sci 67:885–896 Boersma M, Grüner N, Signorelli NT, Montoro González PE, Peck MA, Wiltshire KH (2016) Projecting effects of climate change on marine systems: Is the mean all that matters? Proceedings of the Royal Society B: Biological Sciences. 10.1098/rspb.2015.2274 Borcherding J, Heubel K, Storm S (2019) Competition fluctuates across years and seasons in a 6-species-fish community: empirical evidence from the field. Rev Fish Biol Fish 29:589–604 BSH (2023) Meeresboden DGM https:// . Accessed 23 Oct 2023 Clarke KR (1993) Non-parametric multivariate analyses of changes in community structure. Aust J Ecol 18:117–143. 10.1111/j.1442-9993.1993.tb00438.x Clark NJ, Kerry JT, Fraser CI (2020) Rapid winter warming could disrupt coastal marine fish community structure. Nat Clim Chang 10:862–867. 10.1038/s41558-020-0838-5 Corten A (1986) On the causes of the recruitment failure of herring in the central and northern North Sea in the years 1972–1978. J Cons int Explor Mer 42:281–294 Corten A, van de Kamp G (1996) Variation in the abundance of southern fish species in the southern North Sea in relation to hydrography and wind. ICES J Mar Sci 53:1113–1119 Couperus B, Gastauer S, Fässler SMM, Tulp I, van der Veer HW, Poos JJ (2016) Abundance and tidal behaviour of pelagic fish in the gateway to the Wadden Sea. J Sea Res 109:42–51. 10.1016/j.seares.2016.01.007 de Amorim F, Wiltshire HK, Lemke P, Carstens K, Peters S, Rick J, Gimenez L, Scharfe M (2023) Investigation of Marine Temperature Changes across Temporal and Spatial Gradients: Providing a Fundament for Studies on the Effects of Warming on Marine Ecosystem Function and Biodiversity. Prog Oceanogr 103080. 10.1016/j.pocean.2023.103080 Dulvy NK, Rogers SI, Jennings S, Stelzenmüller V, Dye SR, Skjoldal HR (2008) Climate change and deepening of the North Sea fish assemblage: A biotic indicator of warming seas. J Appl Ecol 45:1029–1039. 10.1111/j.1365-2664.2008.01488.x Durant JM, Hjermann DØ, Ottersen G, Stenseth NC (2007) Climate and the match or mismatch between predator requirement and resource availability. Clim Res 33:271–283 Edwards M, Richardson AJ (2004) Impact of climate change on marine pelagic phenology and trophic mismatch. Nature 430:881–884. 10.1038/nature02808 Felden J, Möller L, Schindler U, Huber R, Schumacher S, Koppe R, Diepenbroek M, Glöckner FO (2023) PANGAEA - Data Publisher for Earth & Environmental Science. Sci Data. 10.1038/s41597-023-02269-x Fofonova V, Androsov A, Sander L, Kuznetsov I, Amorim F, Hass CH, Wiltshire KH (2019) Non-linear aspects of the tidal dynamics in the Sylt-Rømø Bight, south-eastern North Sea. Ocean Sci 15:1761–1782. 10.5194/os-15-1761-2019 Gratwicke B, Speight MR (2005) The relationship between fish species richness, abundance and habitat complexity in a range of shallow tropical marine habitats. J Fish Biol 66:650–667. 10.1111/j.1095-8649.2005.00629.x Greenstreet SPR, Fraser HM, Rogers SI, Trenkel VM, Simpson SD, Pinnegar JK (2012) Redundancy in metrics describing the composition, structure, and functioning of the North Sea demersal fish community. ICES J Mar Sci 69:8–22. 10.1093/icesjms/fsr188 Heaven CS, Scrosati RA (2008) Benthic community composition across gradients of intertidal elevation, wave exposure, and ice scour in Atlantic Canada. Mar Ecol Prog Ser 369:13–23. 10.3354/meps07655 Herrmann JP, Jansen S, Temming A (1998) Consumption of fish and decapod crustaceans and their role in the trophic relations of the Sylt-Rømø Bight. In: Gätje C, Reise K (eds) Ökosystem Wattenmeer-Austausch-Transport- Und Stoffumwandlungsprozesse, 1st edn. Springer, Berlin-Heidelberg, pp 437–462 Holbrook SJ, Kingsford MJ, Schmitt RJ, Stephens NS (1994) Spatial and Temporal Patterns in Assemblages of Temperate Reef Fish. Am Zool 34:463–475 Holm P, Hayes P, Nicholls J (2024) Historical marine footprint for Atlantic Europe, 1500–2019. Ambio. 10.1007/s13280-023-01939-9 Izsák J, Pavoine S (2012) Links between the species abundance distribution and the shape of the corresponding rank abundance curve. Ecol Indic 14:1–6. 10.1016/j.ecolind.2011.06.030 Jovanovic B, Longmore C, O’Leary Á, Mariani S (2007) Fish community structure and distribution in a macro-tidal inshore habitat in the Irish Sea. Estuar Coast Shelf Sci 75:135–142. 10.1016/j.ecss.2007.02.023 Kellnreitner F, Pockberger M, Asmus H (2012) Seasonal variation of assemblage and feeding guild structure of fish species in a boreal tidal basin. Estuar Coast Shelf Sci 108:97–108. 10.1016/j.ecss.2011.02.020 Laugier F, Feunteun E, Pecheyran C, Carpentier A (2015) Life history of the Small Sandeel, Ammodytes tobianus, inferred from otolith microchemistry. A methodological approach. Estuar Coast Shelf Sci 165:237–246. 10.1016/j.ecss.2015.05.022 Linnaeus C (1758) Systema Naturae per regna tria naturae, secundum classes, ordines, genera, species, cum characteribus, differentiis, synonymis, locis, 10th revised edition. Laurentius Salvius, Holmiae Maathuis M, Couperus B, van der Molen J, Poos J, Tulp I, Sakinan S (2023) Resolving the variability in habitat use of juvenile small pelagic fish in a major tidal system by continuous echosounder measurements. Mar Ecol Prog Ser. 10.3354/meps14368 Miller PJ, Loates MJ (1997) Fish of Britain & Europe. HarperCollins, London Montero-Serra I, Edwards M, Genner MJ (2015) Warming shelf seas drive the subtropicalization of European pelagic fish communities. Glob Chang Biol 21:144–153. 10.1111/GCB.12747 Morris EK, Caruso T, Buscot F, Fischer M, Hancock C et al (2014) Choosing and using diversity indices: Insights for ecological applications from the German Biodiversity Exploratories. Ecol Evol 4:3514–3524. 10.1002/ece3.1155 Murphy SJ, Smith AB (2021) What can community ecologists learn from species distribution models? Ecosphere. 10.1002/ecs2.3864 Muus BJ, Dahlstrøm P (1978) Meeresfische der Ostsee, der Nordsee, des Atlantik Biologie, Fang, wirtschaftliche Bedeutung, 1st edn. BLV Nilsson S (1855) Skandinavisk fauna. Fjerde Delen: Fiskarna. Första Häftet Lund i-xxxiv +:1–768 Odongo V, Asmus H, Ahnelt H, Boersma M, Rick J, Wiltshire KH, Horn S (2024) Seasonal variations of a coastal fish community in relation to environmental parameters - A case study of the Sylt-Rømø Bight, southeastern North Sea. Estuar Coast Shelf Sci 300:108723. 10.1016/j.ecss.2024.108723 Osborn TJ (2011) Winter 2009/2010 temperatures and a record-breaking North Atlantic Oscillation index. Weather 66:16–21. 10.1002/wea.666 Pallas PS (1770) Spicilegia Zoologica quibus novae imprimis et obscurae animalium species iconibus, descriptionibus atque commentariis illustrantur. Berolini, Gottl August Lange v. 1 (fasc. 8): 1–56:1 Peck MA, Reglero P, Takahashi M, Catalán IA (2013) Life cycle ecophysiology of small pelagic fish and climate-driven changes in populations. Prog Oceanogr 116:220–245. 10.1016/j.pocean.2013.05.012 Petersen W (2014) FerryBox systems: State-of-the-art in Europe and future development. J Mar Syst 140:4–12. 10.1016/j.jmarsys.2014.07.003 Pörtner HO, Knust R (2007) Climate change affects marine fishes through the oxygen limitation of thermal tolerance. Science (1979) 315:92–97. 10.1126/science.1135013 Reise K, Köste R, Müller A, Armonies W, Asmus H, Asmus R, Hickel W, Riethmüller R (1998) Exchange Processes in the Sylt-Rømø Wadden Sea: A Summary and Implications. In: Gätje C (ed) Ökosystem Wattenmeer / The Wadden Sea Ecosystem. Springer, Berlin, Heidelberg, pp 529–558 Rick J, Scharfe M, Romanova T, van Beusekom J, Asmus R, Asmus H, Mielck F, Kamp A, Sieger R, Wiltshire KH (2023) An evaluation of long-term physical and hydrochemical measurements at the Sylt Roads Marine Observatory (1973–2019), Wadden Sea, North Sea. Earth Syst Sci Data 15:1037–1057. 10.5194/essd-15-1037-2023 Ross SD, Gislason H, Andersen NG, Lewy P, Nielsen JR (2016) The diet of whiting Merlangius merlangus in the western Baltic Sea. J Fish Biol 88:1965–1988. 10.1111/jfb.12959 Rutterford LA, Simpson SD, Bogstad B, Devine JA, Genner MJ (2023) Sea temperature is the primary driver of recent and predicted fish community structure across Northeast Atlantic shelf seas. Glob Chang Biol. 10.1111/gcb.16633 Schückel U, Beck M, Kröncke I (2015) Macrofauna communities of tidal channels in Jade Bay (German Wadden Sea): spatial patterns, relationships with environmental characteristics, and comparative aspects. Marine Biodivers 45:841–855. 10.1007/s12526-014-0308-2 Selleslagh J, Amara R (2008) Inter-season and interannual variations in fish and macrocrustacean community structure on a eastern English Channel sandy beach: Influence of environmental factors. Estuar Coast Shelf Sci 77:721–730. 10.1016/j.ecss.2007.11.004 Tittensor DP, Mora C, Jetz W, Lotze HK, Ricard D, Berghe E, Vanden, Worm B (2010) Global patterns and predictors of marine biodiversity across taxa. Nature 466:1098–1101. 10.1038/nature09329 Tulp I, Bolle LJ, Rijnsdorp AD (2008) Signals from the shallows: In search of common patterns in long-term trends in Dutch estuarine and coastal fish. J Sea Res 60:54–73. 10.1016/j.seares.2008.04.004 Tulp I, van der Veer HW, Walker P, van Walraven L, Bolle LJ (2017) Can guild- or site-specific contrasts in trends or phenology explain the changed role of the Dutch Wadden Sea for fish? J Sea Res 127:150–163. 10.1016/j.seares.2016.10.001 UNESCO (2009) Wadden Sea - UNESCO World Heritage Centre. In: World Heritage Convention. https://whc.unesco.org/en/list/1314/ . Accessed 31 Jan 2024 Utne KR, Hjøllo SS, Huse G, Skogen M (2012) Estimating the consumption of Calanus finmarchicus by planktivorous fish in the Norwegian Sea using a fully coupled 3D model system. Mar Biol Res 8:527–547. 10.1080/17451000.2011.642804 van der Veer H, Tulp I, Witte JIJ, Poiesz SSH, Bolle LJ (2022) Changes in functioning of the largest coastal North Sea flatfish nursery, the Wadden Sea, over the past half century. Mar Ecol Prog Ser 693:183–201. 10.3354/meps14082 van Walraven L, Dapper R, Nauw JJ, Tulp I, Witte JI, van der Veer HW (2017) Long-term patterns in fish phenology in the western Dutch Wadden Sea in relation to climate change. J Sea Res 127:173–181. 10.1016/j.seares.2017.04.001 Vasconcelos RP, Henriques S, França S, Pasquaud S, Cardoso I, Laborde M, Cabral HN (2015) Global patterns and predictors of fish species richness in estuaries. J Anim Ecol 84:1331–1341. 10.1111/1365-2656.12372 Verberk W (2011) Explaining General Patterns in Species Abundance and Distributions. Nat Educ Knowl 3(10):1–11 Vorberg R, Breckling P (1999) Atlas der Fische im Schleswig-Holsteinischen Wattenmeer. Nationalpark Schleswig-Holsteinisches Wattenmeer, Tönning Zuur AF, Ieno EN, Smith GM (2007) Additive and generalised additive modelling. Analysing Ecological Data. Statistics for Biology and Health. Springer, New York, NY, pp 97–124 Zuur AF, Ieno EN, Walker NJ, Saveliev AA, Smith GM (2009a) Mixed Effects Modelling for Nested Data. Mixed Effects Models and Extensions in Ecology with R. Statistics for Biology and Health. Springer, New York, NY, pp 101–142 Zuur AF, Ieno EN, Walker NJ, Saveliev AA, Smith GM (2009b) Things are not Always Linear; Additive Modelling. Mixed effects models and extensions in ecology with R. Statistics for Biology and Health. Springer, New York, NY, pp 35–69 Supplementary Files SupplementaryInformation1.pdf SupplementaryInformation2.pdf SupplementaryTable1.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4583467","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":324392749,"identity":"739826a8-0ccf-4a59-984f-21088fb4cb94","order_by":0,"name":"Victor Odongo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYDACZhjjAIioYGBgY2AwIKSFsQGh5QwxWhiQtTC2gZn4tRgcZ37+4OMeBjm+472HX3ycZ5fYx8C88QFeLYfZDBtnPGMwljxzLs1y5rbkxDYGtmK81kg28zA28xxgSNxwI8fMmHcbc24bA4+ZBAla5tSDtJj/wKeFnxmhxfgxb8NhsC34dAC1sBnOnHFAAuiXM2aMM44dr29jZivG6zA2/sMPPnw4YAMMsR7jDx9qqo3l25s3fsBrDQSAjWWDGM6MVyUqYCbG8FEwCkbBKBiBAABilEYtYXGOGwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-9760-1643","institution":"Alfred Wegener Institute Helmholtz Center for Polar and Marine Research Sylt: Alfred-Wegener-Institut Helmholtz-Zentrum fur Polar- und Meeresforschung Sylt","correspondingAuthor":true,"prefix":"","firstName":"Victor","middleName":"","lastName":"Odongo","suffix":""},{"id":324392750,"identity":"100f2b9e-5605-4f2c-a2f5-91c3a163f8ee","order_by":1,"name":"Harald Asmus","email":"","orcid":"","institution":"Alfred-Wegener-Institut Helmholtz-Zentrum fur Polar- und Meeresforschung Sylt","correspondingAuthor":false,"prefix":"","firstName":"Harald","middleName":"","lastName":"Asmus","suffix":""},{"id":324392751,"identity":"ce7eb292-a14c-42a2-887c-ade359eda22f","order_by":2,"name":"Maarten Boersma","email":"","orcid":"","institution":"Alfred-Wegener-Institut fur Polar- und Meeresforschung Biologische Anstalt Helgoland","correspondingAuthor":false,"prefix":"","firstName":"Maarten","middleName":"","lastName":"Boersma","suffix":""},{"id":324392752,"identity":"5dcf45fe-7a1f-44e5-b910-f2685eab2f0d","order_by":3,"name":"Katja Heubel","email":"","orcid":"","institution":"Christian-Albrechts-Universität Kiel, Forschungs- und Technologiezentrum Westküste","correspondingAuthor":false,"prefix":"","firstName":"Katja","middleName":"","lastName":"Heubel","suffix":""},{"id":324392753,"identity":"99c0fa5f-8cc2-4d5e-b378-cff0a879091a","order_by":4,"name":"Lasse Sander","email":"","orcid":"","institution":"Alfred Wegener Institute Helmholtz Center for Polar and Marine Research Sylt: Alfred-Wegener-Institut Helmholtz-Zentrum fur Polar- und Meeresforschung Sylt","correspondingAuthor":false,"prefix":"","firstName":"Lasse","middleName":"","lastName":"Sander","suffix":""},{"id":324392754,"identity":"e0f8d687-2ab4-402d-baed-19f05961e07e","order_by":5,"name":"Sara Rubinetti","email":"","orcid":"","institution":"Alfred Wegener Institute Helmholtz Center for Polar and Marine Research Sylt: Alfred-Wegener-Institut Helmholtz-Zentrum fur Polar- und Meeresforschung Sylt","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Rubinetti","suffix":""},{"id":324392755,"identity":"e1d28e54-3be0-40ee-893a-e2a69323b828","order_by":6,"name":"Vera Sidorenko","email":"","orcid":"","institution":"Alfred Wegener Institute Helmholtz Center for Polar and Marine Research Sylt: Alfred-Wegener-Institut Helmholtz-Zentrum fur Polar- und Meeresforschung Sylt","correspondingAuthor":false,"prefix":"","firstName":"Vera","middleName":"","lastName":"Sidorenko","suffix":""},{"id":324392756,"identity":"5d7525e6-7704-4acb-9e60-e3dfe7afce5b","order_by":7,"name":"Sabine Horn","email":"","orcid":"","institution":"Alfred Wegener Institute Helmholtz Center for Polar and Marine Research Sylt: Alfred-Wegener-Institut Helmholtz-Zentrum fur Polar- und Meeresforschung Sylt","correspondingAuthor":false,"prefix":"","firstName":"Sabine","middleName":"","lastName":"Horn","suffix":""}],"badges":[],"createdAt":"2024-06-14 17:18:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4583467/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4583467/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62152990,"identity":"e5aa135b-4d7c-4126-9963-a639e815ccdb","added_by":"auto","created_at":"2024-08-09 20:52:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":477037,"visible":true,"origin":"","legend":"\u003cp\u003eOverview maps: Location of the Sylt-Rømø Bight on the eastern seaboard of the North Sea (top left) and the northern Wadden Sea (bottom left). Main map: Intertidal flats, bivalve beds, seagrass meadows, and tidal channels with varying water depths. Red dots, yellow dots, and red squares with labels Sylt_1 to Sylt_9 represent the positions of sampling areas and the sampling frequency (see legend). Map modified from Odongo et al. (2024), layout by authors based on own data and additional data from BSH (2023)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4583467/v1/4ab403b1a4345a32e72d32f6.png"},{"id":62152988,"identity":"d1b03f98-a52b-4ac6-8a8d-1ee6f20f0f52","added_by":"auto","created_at":"2024-08-09 20:52:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":160381,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots of the distribution of water temperature per season in the SRB. Each box represents the monthly measurements made in the seven stations (Sylt_1 to Sylt_7). The black dot indicates the seasonal mean, the horizontal line is the median, and the black line is the LOESS smoother of trends, see the different temperature ranges in the y-axis. Summer (b) and winter (d) temperature figures are also described in Odongo et al. (2024)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4583467/v1/527602bc08b04f32c71c64fd.png"},{"id":62154788,"identity":"8131a45e-1863-4086-8f7f-2c63c8240eda","added_by":"auto","created_at":"2024-08-09 21:00:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":159523,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots of depth variations per sampling station arranged as deep stations versus shallow stations. Each box and black dots represent all the measurements made per sampling station during the survey period. The horizontal line represents the median, and the Y-axis is reversed. Note the different scales in the Y-axis (see Fig. 1 for the location of sampling stations)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4583467/v1/4dfb99da3186f25f2b9fb7e8.png"},{"id":62156307,"identity":"5e04dd83-f92b-4fb3-8864-ddf7df97d814","added_by":"auto","created_at":"2024-08-09 21:08:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":53103,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal Rank abundance curves (RACs) of the 22 dominant fish species in the benthic habitat. The x-axis shows species ranked from the most abundant to the least, and the y-axis is the relative abundance (see the column Spp_code in Table 1 for species full names)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4583467/v1/320e001110278833540406ed.png"},{"id":62154787,"identity":"42a968a6-95ec-4ff6-ae82-4e1ede48291d","added_by":"auto","created_at":"2024-08-09 21:00:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":48081,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal Rank abundance curves (RACs) of the dominant fish species in the pelagic habitat. The x-axis shows species ranked from the most abundant to the least, and the y-axis is the relative abundance (see the column Spp_code in Table 1 for species full names)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4583467/v1/91d9b46fb90d5019adb51a32.png"},{"id":62152991,"identity":"7607139a-b2b8-418b-86c7-b06a28726e58","added_by":"auto","created_at":"2024-08-09 20:52:23","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":97491,"visible":true,"origin":"","legend":"\u003cp\u003eSmoothing functions of water temperature (°C) for the additive models applied to species richness (S). The x-axis shows temperature values and the y-axis is the contribution of the smoother to the fitted S values for benthic and pelagic habitats. The solid line is the estimated smoother and the dotted line represents 95% confidence bands. The thin vertical lines along the x-axis indicate the temperature values of the observations\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4583467/v1/3bb5ffad3ff4756d88873c06.png"},{"id":62152993,"identity":"41c8588c-28ac-4f84-a66a-26867c2d5888","added_by":"auto","created_at":"2024-08-09 20:52:23","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":100318,"visible":true,"origin":"","legend":"\u003cp\u003eSmoothing functions of water temperature (°C) for the additive models applied to evenness (J). The x-axis shows temperature values and the y-axis is the contribution of the smoother to the fitted J values for benthic and pelagic habitats. The solid line is the estimated smoother and the dotted line represents 95% confidence bands\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4583467/v1/d01717c155ca8680f8a22079.png"},{"id":62153004,"identity":"e7655bb2-3e59-46d5-84da-5a3eaa303b5c","added_by":"auto","created_at":"2024-08-09 20:52:24","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":90089,"visible":true,"origin":"","legend":"\u003cp\u003eSmoothing functions of water temperature for the additive models applied on dominance (D). The x-axis shows the temperature values and the y-axis the value of the smoother to the fitted D values in benthic and pelagic habitats. The solid line is the estimated smoother and the dotted line represents 95% confidence bands\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4583467/v1/8ca3e6175a99e4929f91f51b.png"},{"id":62156308,"identity":"fda4b5e9-5e9f-4578-b0de-76820efc7145","added_by":"auto","created_at":"2024-08-09 21:08:24","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":93824,"visible":true,"origin":"","legend":"\u003cp\u003eSmoothing functions of water temperature for the additive models applied on the Shannon-Wiener Index (H). The x-axis shows the temperature values and the y-axis the value of the smoother to the fitted H values in the benthic and pelagic habitats. The solid line is the estimated smoother and the dotted line represents 95% confidence bands\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4583467/v1/35b8a2ce5ba3c2ea09228206.png"},{"id":62152996,"identity":"a04674e6-cd75-4956-9798-da14f1d09471","added_by":"auto","created_at":"2024-08-09 20:52:24","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":229103,"visible":true,"origin":"","legend":"\u003cp\u003eRandom slope and intercept models (GLMMs) showing the variations of species richness (S) (y-axis) with water depth (m) (x-axis) at every sampling station in the benthic and pelagic habitats. Different intercepts show S variations per sampling station, the dark line represents the linear regression of combined depth effects on S\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4583467/v1/4a1bdd11821fccc9822f335e.png"},{"id":62152999,"identity":"f65bee75-1433-4608-9854-5446727f6832","added_by":"auto","created_at":"2024-08-09 20:52:24","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":241335,"visible":true,"origin":"","legend":"\u003cp\u003eRandom slope and intercept models (GLMMs) showing variations of evenness (J) (y-axis) with water depth (x-axis) at each sampling station in the benthic (a) and pelagic (b) habitats. Different intercepts show J variations per sampling station in the pelagic habitats. The dark line represents the linear regression of combined depth effects on J\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-4583467/v1/e4a9d314dc71ffb26637df80.png"},{"id":64221470,"identity":"fc8c99f9-726b-4280-b012-a738d36427ff","added_by":"auto","created_at":"2024-09-10 11:54:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3140660,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4583467/v1/38e44ed7-42ef-4524-bc54-2a7980aadd21.pdf"},{"id":62152995,"identity":"1f8ecfd4-5c16-4e62-9eb1-9f6bbd1f0346","added_by":"auto","created_at":"2024-08-09 20:52:24","extension":"pdf","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":311992,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4583467/v1/8eb0a6d955c547be3cd09421.pdf"},{"id":62153002,"identity":"71e7bdce-25aa-43eb-8009-e76978b0f66f","added_by":"auto","created_at":"2024-08-09 20:52:24","extension":"pdf","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":332118,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4583467/v1/d9902d64fd6243aab5e6d6c0.pdf"},{"id":62154791,"identity":"6b8a2b87-0147-4503-89b1-edd284bd7655","added_by":"auto","created_at":"2024-08-09 21:00:24","extension":"pdf","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":345049,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4583467/v1/56396756fbaf58ae46102048.pdf"}],"financialInterests":"","formattedTitle":"Community structure and diversity changes for fish in a temperate tidal lagoon, as a response to changes in water temperature and depth","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoastal marine ecosystems are characterized by high productivity and biodiversity that have supported valuable coastal fisheries for centuries (Barbier et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). These ecosystems have undergone tremendous changes over the last decades due to climate change (Dulvy et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and multiple anthropogenic stressors (Holm et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Global warming, for example, has affected marine ecosystems and caused changes in phenology (van Walraven et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and poleward movement of distributions in many species (Montero-Serra et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Changes in community structure (Clark et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rutterford et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), predator-prey interactions (Durant et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), and shifts of bottom-dwelling species to deeper areas (Dulvy et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) are related to global warming. These changes have great implications for many species; here we focus on fish because of their high sensitivity to climate-driven environmental changes (Peck et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). For example, demersal fish species with limited movement capabilities must acclimatize or adapt to warming when they are no longer in their optimal ranges, otherwise, consequences such as population declines are inevitable (Rutterford et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Such a scenario was reported for eelpout (\u003cem\u003eZoarces viviparus\u003c/em\u003e) whose growth and abundance decreased in the Wadden Sea because of rising water temperatures (P\u0026ouml;rtner and Knust \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Wadden Sea is an important nursery ground providing rich feeding and refuge areas for many North Sea and resident fish species (Tulp et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; van der Veer et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Global warming and the changes in seasonal environmental patterns can cause substantial changes in the nursery function by affecting species recruitment and diversity. For example, early timing of spawning, shorter egg and larval development times, and early immigration (van der Veer et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Besides, temperature affects interspecific interactions such as predation rates between fish and key prey items (Edwards and Richardson \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) which has implications on the total food web (Baird et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Sylt-R\u0026oslash;m\u0026oslash; Bight (SRB) is one of the largest tidal catchments of the Wadden Sea and is located between the islands of Sylt (Germany), R\u0026oslash;m\u0026oslash; (Denmark), and the mainland coasts. Two causeways delimit the area to the north and south preventing water exchange with the adjacent tidal environments (Reise et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The SRB comprises diverse subtidal and intertidal habitats such as mussel beds, seagrass meadows, and substrates of different grain sizes such as mudflats and sandflats (Baird et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). These offer feeding and refuge grounds for an array of different fish species (Kellnreitner et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). As a transient area between rivers Vid\u0026aring; and Brede\u0026aring; and the North Sea, many individuals of anadromous species such as houting (\u003cem\u003eCoregonus oxyrinchus\u003c/em\u003e) and catadromous species such as European eel (\u003cem\u003eAnguilla anguilla\u003c/em\u003e) pass through during their migration (Odongo et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, during summers, individuals of the warm-adapted fish species of Lusitanian and Atlantic biogeographic origins migrate northwards in the North Sea and into the Wadden Sea, while boreal species migrate into the deeper North Sea and southwards in cold winters (Tulp et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecords of water temperature in the SRB indicate an increase in annual means over the last decades and changes on seasonal scales with high inter-annual variabilities (Rick et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). There are warmer days in summer and fewer cold days in winter in comparison to the 1962 to 1990 period (de Amorim et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) as well as higher autumn temperatures (Rick et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The temperature changes could influence the distributions and abundance of various fish species (Clark et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The magnitude, duration, frequency, and temporal scales at which temperature changes are likely to occur, together influence fish community structure and diversity (Holbrook et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Clark et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thus, diversity changes may either be a reflection of decadal fluctuations or directional changes in hydro-meteorological forcing (Beaugrand \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) or short-term ones based on seasonal changes in water temperature (Corten and van de Kamp \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Additionally, the local habitat characteristics such as changes in water depth with tides influence the spatial distribution of fish through the movement of individuals between habitats in shallow coastal ecosystems (Couperus et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In the SRB, water depth changes per tidal cycle and varies between different areas (Fofonova et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Thus, the fish diversity in a specific location may vary based on differences in tidal velocity or changes on short-term scales with tides.\u003c/p\u003e \u003cp\u003eThe question arises whether there are potential effects of changes in water temperature on the community structure and diversity of fish in the SRB. As a tidal lagoon, there is a much shorter time effect on the diversity in different locations as fish may migrate to submerged intertidal areas for feeding and refuge during high tide and retreat with ebbing waters. Some fish species on the other hand may persist in different areas that differ in water depth within a tidal system. Therefore, we assessed the diversity of fish in the SRB using data from the monthly fish monitoring between the years 2007 and 2019 (Asmus et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). From the same dataset, changes in the fish community composition in comparison to previous investigations that took place from 1989 to 1995 (Herrmann, J.P., Jansen, S., Temming 1998; Vorberg and Breckling \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) were determined. Additionally, trends and common patterns of the 22 most abundant species and the potential effects of water temperature, salinity, and chlorophyll \u003cem\u003ea\u003c/em\u003e were assessed (Odongo et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Here we focussed on the whole fish community and investigated how the fish community structure and diversity changes as a response to the seasonal changes in water temperature. The spatial distribution and utilization of intertidal habitats during high tide were investigated to show the significance of shallow coastal ecosystems as important feeding, refuge, and camouflage areas.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThe Sylt-R\u0026oslash;m\u0026oslash; Bight (SRB) is a semi-enclosed tidal lagoon located between the islands of Sylt (Germany) and R\u0026oslash;m\u0026oslash; (Denmark) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (central coordinates: 54\u0026deg;58\u0026rsquo;40N, 8\u0026deg;29\u0026rsquo;45E). The area is protected as a national park under both German and Danish legislation and is part of the internationally recognized Wadden Sea World Heritage Site (UNESCO \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Commercially, only local (without crossing the international borders) mussels and shrimp fisheries are allowed in the area. The SRB is connected to the open North Sea through the Lister Deep, a 2.8 km wide tidal inlet, where 8 to 12% of the bight\u0026rsquo;s water is exchanged per tidal cycle and the water residence time ranges from 19 to 29 days. The bight covers a total aerial surface of 404 km\u003csup\u003e2\u003c/sup\u003e, which includes 135 km\u003csup\u003e2\u003c/sup\u003e of intertidal area (Reise et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The area is characterized by low salinities in winter and spring, and highs in summer and autumn with an overall range between 24 and 33 (Rick et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eFish monitoring and data collection of environmental parameters\u003c/h2\u003e \u003cp\u003eThe details of sampling methods are described in Odongo et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) as we used the same dataset in Asmus et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Fish monitoring took place monthly at seven stations inside the SRB and at two complementary stations, one outside the bight (Sylt_8) and one close to the Danish border (Sylt_9), which are sampled four times per year (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Water flow within the bight follows four major tidal channels and sampling mainly targeted the Lister Tief, Lister Ley, and Pander Tief that are all on the German side of the bight (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Due to international maritime boundaries and legislations, the Danish side of the bight was not sampled. All sampling stations were permanently submerged and the sampling gear is efficient with the capability of cruising at minimum water depths of 1.4 m. Sampling stations were chosen in such a way that they, together, are representative of the differences in the magnitude of tidally induced water currents, water depth, substrate type, and macrophyte systems. Sylt_1, Sylt_2, Sylt_4, and Sylt_8 are located in the deep tidal channels and differ in terms of water depth and tidal velocities from the other stations that are adjacent to the tidal channels (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (Fofonova et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFish were sampled using research vessels MYA I (January 2007 to April 2013) and MYA II (from June 2013 onwards). In the early stages of MYA II operation, technical issues limited fish sampling and caused data gaps until April 2014. At each station, fish were sampled using a 17 m long mini bottom trawl with a mouth measuring 7 m width, 3 m height, and mesh sizes of 32 mm in the wings,16 mm in the mid part, and 6 mm in the cod end. Sampling per haul take 15 minutes at a speed of approximately 2 knots (Asmus et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Both benthic and pelagic hauls were taken at the sampling stations that are located in the deep tidal channels while only benthic hauls were taken in the shallow stations where the vertical opening of the bottom trawl is quite similar to the total water depth (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Benthic hauls were always carried out directly above the sediments and targeted near-bottom communities. Pelagic hauls were always carried out in the middle of the water column, thus, the position of the sampling net changed relative to the total water depth and targeted the mid-water communities. In this paper, we refer to benthic hauls as benthic habitats while pelagic hauls are considered representatives of pelagic habitats. Sampled fish were sorted and identified to species level using the identification keys in Miller and Loates (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) and Muus and Dahlstr\u0026oslash;m (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1978\u003c/span\u003e), then counted to determine species abundances. Respective sensors mounted on the onboard ferry box (Petersen \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) measured various parameters such as water temperature and salinity while the ship's navigational echo sounder measured water depth at the start and the end of each haul. Due to strong tidal mixing in a shallow coastal system, sea surface temperature (SST) and water temperature are the same, thus, we use both terms in our analyses. The datasets are contained in the PANGEA repository (Felden et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and the dataset links in this paper in Asmus et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Rick et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eNon-parametric Kruskal-Wallis test was used to test depth differences between sampling stations. Similarly, the same test was used to determine the differences in water temperature between seasons and between years. Then Dunn\u0026rsquo;s test with Bonferroni corrections was performed for post hoc analysis to find significant differences between pairs.\u003c/p\u003e \u003cp\u003eFish abundances in the benthic and pelagic hauls were standardized per sampled area (Individuals/10000 m\u003csup\u003e2\u003c/sup\u003e). Rank Abundance Curves (RACs) were used to assess the seasonal species abundance distributions, the dynamics of co-occurring species, and the seasonal changes in community structures (Izs\u0026aacute;k and Pavoine \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This was done to elucidate the changes in species` use of the SRB at different times of the year. For this analysis, the monthly fish abundance data were aggregated into four seasons, winter (December, January, and February), spring (March, April, and May), summer (June, July, and August), and autumn (September, October, and November). The seasonal community dynamics in terms of changes in the relative abundances and the number of co-occurring species (Avolio et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) for benthic and pelagic habitats were assessed independently. Additionally, Jaccard's coefficient, analysis of similarity (ANOSIM), and similarity percentage (SIMPER) were used to further assess the differences in the seasonal community structures. Jaccard's coefficient assesses the similarity between seasons in terms of species presence/absence. ANOSIM compares ranked differences within- and between-groups that produce an R-value that approaches one when between-group differences are greater than within-group differences and vice-versa. R-values close to one signify higher dissimilarity between groups (James et al., 2023). SIMPER assesses the major taxa that are responsible for the observed differences between pair combinations (Clarke, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). SIMPER and ANOSIM analyses were based on the Bray-Curtis similarity coefficient at 999 permutations (Clarke \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) on square-root transformed seasonal mean fish abundances.\u003c/p\u003e \u003cp\u003eFish community diversity within the SRB for each month in each station was determined by computing diversity indices comprising species richness (\u003cem\u003eS\u003c/em\u003e), evenness (J), dominance (D), and Shannon-Wiener Index (H). Species richness is a measure of the number of taxa present and was used to assess whether the numbers changed with time or changed per season. Evenness represents species abundance distribution in a community and ranges from 0 to 1. Low values indicate that one or few species dominate the community while high values indicate that relatively equal numbers of individuals belong to each taxon (Morris et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The opposite is true for D, which also ranges from 0 to 1. Dominance was used to assess the abundance fluctuations of dominant species with changes in water temperature. Shannon-Wiener Index quantifies the uncertainty of randomly selecting a taxon based on the relative abundance of each species and was used to assess the distribution of both rare and abundant species. It ranges from 1.5 to 3.5 with high values indicating high diversity/ecological status (Morris et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGeneral additive models (GAMs) in the \u003cem\u003emgcv\u003c/em\u003e package in R were used to assess the relationships between the diversity indices and the nominal variable (sampling station), ordinal variables (year and season), and continuous variable (water temperature). The GAMs were formulated as: D\u003csub\u003ei\u003c/sub\u003e ~\u003cem\u003ef\u003c/em\u003e(water temperature)\u0026thinsp;+\u0026thinsp;year\u0026thinsp;+\u0026thinsp;season\u0026thinsp;+\u0026thinsp;sampling station\u0026thinsp;+\u0026thinsp;ε\u003csub\u003ei\u003c/sub\u003e), where D\u003csub\u003ei\u003c/sub\u003e = diversity index, \u003cem\u003ef\u003c/em\u003e() specifies the smoothing term, and ε\u003csub\u003ei\u003c/sub\u003e is the error term that follows a normal distribution with zero mean (ε\u003csub\u003ei\u003c/sub\u003e\u0026sim;N(0,σ)). The smoothing functions use a back-fitting algorithm to estimate the intercept and the smoothing curve and estimate one smoother at a time (Zuur et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2009b\u003c/span\u003e). The smoothing curves are estimated by cubic regression splines where the explanatory variables are divided into several intervals. In each interval, a cubic polynomial is fitted, and the fitted values per segment are connected using conditions that involve first- and second-order derivatives to form a smoothing curve (Zuur et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2009b\u003c/span\u003e). The smoothing curves were used to assess the non-linear relationships between water temperature and the diversity indices. The ordinal variables year and season were included in the GAMs to assess the inter- and intra-annual changes in fish diversity while sampling stations were to assess the spatial distribution patterns as sampling was concentrated in or adjacent to the main tidal channels (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Several combinations of explanatory variables were tested in GAMs and Akaike Information Criterion (AIC) was used to select the best model.\u003c/p\u003e \u003cp\u003eThe Sylt-R\u0026oslash;m\u0026oslash; Bight is a tidal environment, water depth varies over each tidal cycle, and the bedform morphology differs within each sampling station. Generalized linear mixed-effects models (GLMMs) with identity link functions were used to model species diversity (\u003cem\u003eS\u003c/em\u003e, J, and H) as linear functions of water depth where the intercept and slope changed per sampling station. This was to investigate the spatial distributions of fish with varying water depths at each sampling station. The mean water depth in meters (m), which was obtained by averaging depth at the start and the end of each haul, was used in GLMMs. The effect of the sampling station was included in the model as a random component where changes in water depth per sampling station had a different effect on the diversity. The variation around the intercept for each sampling station was assumed to be normally distributed with a certain variance. A small variance represented a small difference between sampling stations and vice versa. Further, a linear regression model with sampling station and water depth and the interaction between them, in other words, analysis of covariance (ANCOVA) was used to find their relationship with fish diversity. The GLMMs were formulated as D\u003csub\u003eij\u003c/sub\u003e~depth\u003csub\u003ei\u003c/sub\u003e+(depth\u003csub\u003ei\u003c/sub\u003e|Station\u003csub\u003ej\u003c/sub\u003e)+ ε\u003csub\u003ei\u003c/sub\u003e), where D\u003csub\u003eij\u003c/sub\u003e is the diversity index at depth\u003csub\u003ei\u003c/sub\u003e in station\u003csub\u003ej\u003c/sub\u003e, depth\u003csub\u003ei\u003c/sub\u003e is the combined effect of water depth on a diversity index, (depth\u003csub\u003ei\u003c/sub\u003e|Station\u003csub\u003ej\u003c/sub\u003e) is the interaction between water depth and sampling station, and ε\u003csub\u003ei\u003c/sub\u003e is the error term (ε\u003csub\u003ei\u003c/sub\u003e\u0026sim;N(0,σ)). For more details on model specifications and formulation for the GAMs and GLMMs, refer to Zuur et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2009a\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003eb\u003c/span\u003e). Statistical significance was assumed for all tests if the p-value was below 0.05. The Software R version 4.3.1 (R Development Core Team, 2023) was used to perform all statistical analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eTemporal changes in water temperature\u003c/h2\u003e \u003cp\u003eWater temperature ranged from \u0026minus;\u0026thinsp;1.5 to 22.6\u0026deg;C during the survey period. Seasonal water temperature cycles that are typical in the Wadden Sea were observed as all seasons were significantly different from each other. No significant within-season mean temperature differences were observed except for the warm winter of 2006/2007 compared to the cold winters of 2009/2010, 2010/2011, and 2012/2013 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, d). For more information on changes in water temperature and significant differences, see Supplementary Information 1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSpatial changes in water depth\u003c/h2\u003e \u003cp\u003eSampling of fish took place during daylight and more than four hours to sample half the number of stations. Thus, sampling at each of the stations generally occurred at different moments of the tidal cycle and under different meteorological conditions. Besides the influence of tides on water depth, transects differed within sampling stations as it was difficult to always maintain one transect due to strong tidal currents and the influence of wind velocity and direction. Therefore, towing directions and transects within the sampling areas differed and covered the edges, slopes, or centers of the tidal channels. Consequently, there were changes in water depth within each sampling station (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For more information on the significant differences in water depth between stations, see Supplementary Information 1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSpecies frequency of occurrence and relative abundances\u003c/h2\u003e \u003cp\u003eIn total, 55 fish species were found during the survey period and showed strong inter- and intra-annual abundance fluctuations. Some species were present all year round, others occurred only occasionally or were season-specific, while others were rare and present only in some years. Thus, only 22 species accounted for more than 95% of the total abundance for the entire survey. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e show the details of the seasonal frequency of occurrence and the fluctuating abundances in both habitats. Herring (\u003cem\u003eClupea harengus\u003c/em\u003e Linnaeus \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1758\u003c/span\u003e) was the dominant species while the second and subsequent rankings slightly differed between the habitats. Small sand eel (\u003cem\u003eAmmodytes tobianus\u003c/em\u003e Linnaeus, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1758\u003c/span\u003e) was ranked second in the benthic habitats with 70% occurrence in summer while in the pelagic habitats, it was third-ranked with high occurrences in spring (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Sprat (\u003cem\u003eSprattus sprattus\u003c/em\u003e Linnaeus, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1758\u003c/span\u003e) was ranked third in the benthic habitats with high occurrence in spring followed by summer (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It was ranked second in the pelagic habitats with high occurrence in spring followed by winter (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe sequence of species organization in terms of highly ranked to the least of the 22 abundant species differed per season. \u003cem\u003eClupea harengus\u003c/em\u003e was dominant in both habitats in all seasons. In the benthic habitats, \u003cem\u003eA. tobianus\u003c/em\u003e was ranked second in spring and summer, sand goby (\u003cem\u003ePomatoschistus minutus\u003c/em\u003e Pallas \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1770\u003c/span\u003e) in winter, and whiting (\u003cem\u003eMerlangius merlangus\u003c/em\u003e Linnaeus, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1758\u003c/span\u003e) in autumn (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the pelagic habitats, \u003cem\u003eS. sprattus\u003c/em\u003e was ranked second in winter, summer and autumn, and \u003cem\u003eA. tobianus\u003c/em\u003e in spring. Third and subsequent ranks differed per season (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The Relative Abundance Curves (RACs) show seasonal changes in species distributions and evenness. The steep gradients between first ranked to the second-ranked species in all habitats in spring and summer indicate uneven communities (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In the pelagic habitats, the relatively low steepness in winter and spring indicates a moderately even community as species abundances were almost in a similar range (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditional analyses of the seasonal changes in community structure showed that all winters compared to all summers had the highest percentage dissimilarity; ANOSIM, R\u0026thinsp;=\u0026thinsp;0.63, \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.0001, SIMPER\u0026thinsp;=\u0026thinsp;67.55%. The two seasons were 55% similar in terms of species presence and absence (Jaccard\u0026rsquo;s coefficient\u0026thinsp;=\u0026thinsp;0.55). The dissimilarities between other seasonal comparisons were lower, SIMPER ranged from 55\u0026ndash;58.49%, ANOSIM, R\u0026thinsp;\u0026lt;\u0026thinsp;0.3, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and Jaccard\u0026rsquo;s coefficient was relatively higher and ranged from 0.62\u0026ndash;0.79. In all seasonal community structure comparisons, six dominant species, \u003cem\u003eC. harengus, A. tobianus, M. merlangus, S. sprattus, P. minutus\u003c/em\u003e, and Nilsson's pipefish (\u003cem\u003eSyngnathus rostellatu\u003c/em\u003es Nilsson \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1855\u003c/span\u003e) contributed the highest percentage dissimilarities. See Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e for the details of the percentage contributions of different taxa to the dissimilarities between seasons.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe total abundance, the percentage of each species to the total abundance, seasonal mean abundances, and the percentage seasonal frequency of occurrence of the 22 species in the benthic habitats for the entire survey. Spp_code show the species names in other analyses\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal abundance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e% of the total abundance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eSeasonal mean abundance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003e% Seasonal frequency of occurrence\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommon names\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScientific names\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpp_code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAutumn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eAutumn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHerring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eClupea harengus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC_har\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e818841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1091.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1989.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e368.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e137.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall sand eel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAmmodytes tobianus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA_tob\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e176934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e180.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e540.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e47.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSprat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSprattus sprattus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS_spr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e160.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e75.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e50.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMerlangius merlangus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM_mer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e137.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e71.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSand goby\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePomatoschistus minutus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP_min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e58.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e51.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e39.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNilsson's pipefish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSyngnathus rostellatus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS_ros\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlaice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePleuronectes platessa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP_pla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreat sand eel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHyperoplus lanceolatus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH_lan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBull-rout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMyoxocephalus scorpius\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM_sco\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommon goby\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePomatoschistus microps\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP_mic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThree-spined stickleback\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGasterosteus aculeatus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG_acu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHooknose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAgonus cataphractus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA_cat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLimanda limanda\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL_lim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGadus morhua\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG_mor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmelt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOsmerus eperlanus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eO_epe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEelpout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eZoarces viviparus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZ_viv\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEuropean anchovy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEngraulis encrasicolus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE_enc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFluonder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePlatichthys flesus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP_fle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHorse mackerel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTrachurus trachurus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT_tra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGunnel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePholis gunnellus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP_gun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStriped seasnail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLiparis liparis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL_lip\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScaldfish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eArnoglossus laterna\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA_lat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe total abundance, the percentage of each species to the total abundance, overall seasonal mean abundances, and the percentage seasonal frequency of occurrence of the 19 abundant species in the pelagic habitats for the entire survey period\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal abundance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e% of the total abundance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eSeasonal mean abundances\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e \u003cp\u003e% seasonal frequency of occurrence\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommon names\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScientific names\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAutumn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eAutumn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHerring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eClupea harengus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e300829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSprat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSprattus sprattus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall sand eel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAmmodytes tobianus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreat sand eel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHyperoplus lanceolatus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNilsson's pipefish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSyngnathus rostellatus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThree-spined stickleback\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGasterosteus aculeatus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHorse mackerel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTrachurus trachurus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSand goby\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePomatoschistus minutus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmelt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOsmerus eperlanus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMerlangius merlangus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlaice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePleuronectes platessa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommon goby\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePomatoschistus microps\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFluonder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePlatichthys flesus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBull-rout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMyoxocephalus scorpius\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHooknose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAgonus cataphractus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGarfish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBelone belone\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLumpfish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCyclopterus\u0026nbsp;lumpus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLimanda limanda\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEelpout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eZoarces viviparus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eTemporal changes in fish diversity\u003c/h2\u003e \u003cp\u003eThe diversity indices, species richness (\u003cem\u003eS\u003c/em\u003e), evenness (J), dominance (D), and Shannon-Wiener Index (H) varied both spatially and temporally over the study period. In general, there were higher taxa numbers in the benthic habitats compared to pelagic habitats (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Species richness \u003cem\u003e(S)\u003c/em\u003e in benthic habitats was the highest in spring and summer. In contrast, \u003cem\u003eS\u003c/em\u003e in pelagic habitats was low in summer (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Inter-annual differences in \u003cem\u003eS\u003c/em\u003e were observed with high variabilities in both benthic and pelagic habitats (numerous outliers in Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe General Additive Model (GAM) comprising smoothing terms of water temperature, sampling station and the factors year and season explained 15% and 24% of the variations in \u003cem\u003eS\u003c/em\u003e in the benthic and pelagic habitats, respectively. Water temperature was significant and explained 4% and 11% variations in \u003cem\u003eS\u003c/em\u003e in the benthic and pelagic habitats, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Smoothing curves show higher \u003cem\u003eS\u003c/em\u003e in temperatures between 4\u0026deg;C to 7\u0026deg;C and 13\u0026deg;C to 16\u0026deg;C in both habitats and remain stable when temperatures are above 19\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). However, \u003cem\u003eS\u003c/em\u003e ranged from 2 to 15 species (mean of 7.6\u0026plusmn;3.1 (SD), n\u0026thinsp;=\u0026thinsp;100) between 19\u0026deg;C and 22.6\u0026deg;C in the benthic habitat and from 1 to 7 species (mean of 2.6\u0026plusmn;1.5 (SD), n\u0026thinsp;=\u0026thinsp;42) in the pelagic habitats (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Thus, the wide 95% confidence bands indicate high uncertainty in \u003cem\u003eS\u003c/em\u003e prediction when water temperature is \u0026gt;\u0026thinsp;19\u0026deg;C. Numerical output of different GAMs for both habitats are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, best models are shown in bold. The tables show various information on the performance of various models in explaining the variations in diversity indices. For instance, deviance explained which is equivalent to R\u003csup\u003e2\u003c/sup\u003e in linear regressions and the Akaike Information Criterion (AIC). The GAMs show that the factor year explained higher variability in \u003cem\u003eS\u003c/em\u003e at 7% (benthic) and 10% (pelagic) habitats compared to the seasonal effect that explained only 2% and 3%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated parameters, standard errors, t-values, p-values, deviance explained, scale estimates, and Akaike Information Criterion (AIC) of the parametric components of the general additive models of the explanatory variables to the diversity indices in benthic habitats. Best models are shown in bold, s() represents the smoothing terms of water temperature\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiversity index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExplanatory variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate/\u003c/p\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDeviance explained\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eScale est.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpecies richness (\u003cem\u003eS)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Year\u0026thinsp;+\u0026thinsp;Season\u0026thinsp;+\u0026thinsp;Station\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e7.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e14.57\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e15%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e10.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e4385.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Year\u0026thinsp;+\u0026thinsp;Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4390.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4405.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4429.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4441.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u0026thinsp;+\u0026thinsp;Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4419.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4426.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4458.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4465.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvenness (J)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Year\u0026thinsp;+\u0026thinsp;Season\u0026thinsp;+\u0026thinsp;Station\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e16.69\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e17%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-167.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Year\u0026thinsp;+\u0026thinsp;Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-169.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-156.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-169.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-157.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u0026thinsp;+\u0026thinsp;Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-139.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-79.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-134.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e69.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDominance (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Year\u0026thinsp;+\u0026thinsp;Season\u0026thinsp;+\u0026thinsp;Station\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.43\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e11.39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e20%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e25.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Year\u0026thinsp;+\u0026thinsp;Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e29.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e76.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e62.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e74.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u0026thinsp;+\u0026thinsp;Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e55.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e103.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e103.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e156.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShannon (H)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Year\u0026thinsp;+\u0026thinsp;Season\u0026thinsp;+\u0026thinsp;Station\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.08\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e14.54\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e22%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1361.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Year\u0026thinsp;+\u0026thinsp;Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1374.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1369.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1425.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1424.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u0026thinsp;+\u0026thinsp;Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1406.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1454.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1457.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1508.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eSignif. codes: 0 \u0026lsquo;***\u0026rsquo; 0.001 \u0026lsquo;**\u0026rsquo; 0.01 \u0026lsquo;*\u0026rsquo; 0.05 \u0026lsquo;.\u0026rsquo; 0.1 \u0026lsquo; \u0026rsquo; 1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated parameters, standard errors, t-values, p-values, deviance explained, scale estimates, and Akaike Information Criterion (AIC) of the parametric components of the general additive models of the explanatory variables to the diversity indices in the pelagic habitats. Best models are shown in bold, s() represents the smoothing terms of water temperature\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiversity index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExplanatory variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate/\u003c/p\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDeviance explained\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eScale est.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpecies richness (\u003cem\u003eS)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Year\u0026thinsp;+\u0026thinsp;Season\u0026thinsp;+\u0026thinsp;Station\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e4.65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e13.65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e24%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e3.74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1831.91\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Year\u0026thinsp;+\u0026thinsp;Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1845.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1842.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1863.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1861.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u0026thinsp;+\u0026thinsp;Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1863.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1876.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1889.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1892.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvenness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Year\u0026thinsp;+\u0026thinsp;Season\u0026thinsp;+\u0026thinsp;Station\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.71\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e16.21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e17%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e48.27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(J)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Year\u0026thinsp;+\u0026thinsp;Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e58.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e70.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e51.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e60.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u0026thinsp;+\u0026thinsp;Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e81.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e86.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e73.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e70.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDominance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Year\u0026thinsp;+\u0026thinsp;Season\u0026thinsp;+\u0026thinsp;Station\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.54\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e11.15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e15%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.08\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e147.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Year\u0026thinsp;+\u0026thinsp;Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e150.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e150.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e148.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e149.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u0026thinsp;+\u0026thinsp;Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e163.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e166.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e166.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e170.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShannon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Year\u0026thinsp;+\u0026thinsp;Season\u0026thinsp;+\u0026thinsp;Station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e627.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(H)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Year\u0026thinsp;+\u0026thinsp;Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e627.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Year\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e12.08\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e17%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e626.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u0026thinsp;+\u0026thinsp;Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e633.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003es(Temperature)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e633.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u0026thinsp;+\u0026thinsp;Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e649.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e661.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e662.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e680.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eSignif. codes: 0 \u0026lsquo;***\u0026rsquo; 0.001 \u0026lsquo;**\u0026rsquo; 0.01 \u0026lsquo;*\u0026rsquo; 0.05 \u0026lsquo;.\u0026rsquo; 0.1 \u0026lsquo; \u0026rsquo; 1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEvenness (J) varied both annually and seasonally in both habitats. Evenness was higher in winter and spring than in summer in most of the years. Evenness was higher in the pelagic habitats than in the benthic habitats (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Water temperature explained 11% and 6% variations in J in the benthic and pelagic habitats, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the benthic habitats, the smoothing terms of water temperature show that J decreases with increasing temperature up to 15\u0026deg;C followed by a minor increase. This is followed by a decline at temperatures above 18\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, a). In the pelagic habitats, J declines with increasing temperatures up to 8\u0026deg;C then remains constant up to 11\u0026deg;C. This is followed by a further decrease to 15\u0026deg;C then an increase up to 19\u0026deg;C after which it remains constant (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, b). The factor season explained a higher variability in J (8%) than the factor year (4%) in the benthic habitats (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the pelagic habitat, both factors year and season explained low variability in J at 3% and 2%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDominance (D) varied both annually and seasonally in both habitats. The highest values were found in most summers. However, in 2015 spring and autumn had higher values than summer in the benthic habitat while in the pelagic habitat, springs of 2009 and 2017 showed relatively higher values (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). In the benthic habitats, smoothing terms showed that D is unstable at temperatures below 5\u0026deg;C then gradually increases up to 14\u0026deg;C after which it remains stable to 18\u0026deg;C. This is followed by an increase at temperatures above 19\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, a). A similar pattern occurred in the pelagic habitat. However, the wide 95% confidence interval indicates the uncertainties in D at temperatures above 16\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, b). Water temperature explained 10% and 6% variations in D in the benthic and pelagic habitats, respectively. The factor year explained 9% and 6% of the variability in D in benthic and pelagic habitats, respectively. The factor season on the other hand explained 7% variability in benthic and only 2% in the pelagic habitats (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eShannon-Wiener Index (H) varied annually and across seasons in both habitats. Generally, H was high in winter and low in summer. However, in 2017 spring showed the highest H values in the benthic habitat, and springs of 2012, 2016, and 2017 had the highest values in the pelagic habitats (Fig. S4). Smoothing terms of water temperature showed a gradual increase in H from 1\u0026deg;C obtaining a maximum value at 5\u0026deg;C in both benthic and pelagic habitats (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e), which represents an equal proportion of all species at this temperature. As temperatures increase beyond 5\u0026deg;C, H gradually declines in all the habitats. However, in the benthic habitat, H was stable between 14\u0026deg;C and 18\u0026deg;C after which further decline occurs at higher temperatures. Water temperature explained 11% variability in H in both habitats. The factor year explained 9% and 8% variations in H while season explained 7% and 4% of the same in the benthic and pelagic habitats, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSpatial distribution of fish with changes in water depth\u003c/h2\u003e \u003cp\u003eThe tidal effects and changes in water depth within sampling stations on the distribution of fish were analyzed using generalized linear mixed-effects models (GLMMs). The different slopes and intercepts showed the distribution patterns in different sampling stations with changes in water depth. Generally, \u003cem\u003eS\u003c/em\u003e decreased with increasing water depth in all sampling stations in the benthic habitats even though sampling was always slightly above the sediments irrespective of the water depth. The intercepts and slopes show minimal variations in \u003cem\u003eS\u003c/em\u003e (variance (σ\u003csup\u003e2\u003c/sup\u003e)\u0026thinsp;=\u0026thinsp;0.59; sampling station, and σ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.004; depth) between sampling stations in the benthic habitats regardless of whether the station was located in or adjacent to the deep tidal channels (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, a). In the pelagic habitats, variations in \u003cem\u003eS\u003c/em\u003e with water depth between sampling stations (σ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.14 (sampling station) and σ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.01 (depth) were observed. For instance, \u003cem\u003eS\u003c/em\u003e decreased with water depth in Sylt_6 while Sylt_1 and Sylt_9 showed positive relationships between \u003cem\u003eS\u003c/em\u003e and water depth (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, b).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEvenness (J) increased with water depth in all sampling stations in the benthic habitats with no variations between stations (same intercept and slope, Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e, a). In the pelagic habitats, J varied between sampling stations. For instance, Sylt_2 and Sylt_6 showed positive relationships between J and water depth while no depth effects were observed in Sylt_8. In the stations adjacent to deep tidal channels (i.e. Sylt_1, Sylt_4, and Sylt_9), J decreased with an increase in water depth (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e, b). Dominance on the other hand showed a slight decrease with an increase in water depth in all sampling stations in all habitats (Fig. S5). Individuals of various species utilized the submerged areas at high water depths resulting in equal abundance proportions in the sampled areas. There were minor variations in D with water depth between sampling stations. For instance, in the benthic habitats, Sylt_2 and Sylt_4 showed relatively steeper slopes than the other stations. Only Sylt_2 in the pelagic habitat showed such steepness (Fig. S5). There were no significant effects of water depth on H.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSpecies' relative abundances and seasonal distributions\u003c/h2\u003e \u003cp\u003eThis study investigated the potential effects of the seasonal changes in water temperature on the community structure and diversity of fish in the Sylt-R\u0026oslash;m\u0026oslash; Bight (SRB). In addition, the utilization of intertidal habitats during high tide and spatial distribution within and adjacent to the tidal channels were investigated. The species' frequency of occurrence showed the overall seasonal distribution patterns in the benthic and pelagic habitats and the transitions of some species between these habitats. For example, \u003cem\u003eA. tobianus\u003c/em\u003e had the highest frequency of occurrence in spring in its pelagic phase (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Thus, its highest frequency of occurrence in summer in the benthic habitats (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) shows its transition from pelagic to benthopelagic behavior that occurs in the middle of their juvenile stages (Laugier et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). For the pelagic species such as \u003cem\u003eC. harengus\u003c/em\u003e, the highest occurrence in spring in the pelagic habitats (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) in comparison to the highest occurrence in summer in the benthic habitats (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) may also indicate differences in size distributions. \u003cem\u003eSprattus sprattus\u003c/em\u003e on the other hand was abundant in spring in both habitats (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Its high occurrence in winter in the pelagic habitats (28%) compared to benthic habitats (5%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), may indicate differences in size distributions between the habitats. However, the area is relatively shallow (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), therefore, the size distributions of different species between the habitats need further investigations\u003c/p\u003e \u003cp\u003eCommunity ecology aims to understand how communities are organized by identifying, describing, and explaining the patterns that underlie the structure and diversity across space and time (Verberk \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and the associated environmental drivers (Heaven and Scrosati \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In every community, a few species are always present in the highest numbers suggesting that there are general macro-ecological rules or processes such as migration patterns underlying the species distribution and abundances (Verberk \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Murphy and Smith \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These patterns portray how such communities function, the types of ecological interactions, the relationships of co-occurring species, and the manner they respond to environmental changes (Verberk \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, such predictions must be able to take into account the multiple interacting processes operating across spatio-temporal scales that dictate where and when species occur (Murphy and Smith \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This brings the question as to whether the observed seasonal abundance rank patterns (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) are related to temperature changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and/or ecological interactions such as competition and predation that can lead to the exclusion of other species or whether the other species simply prefer different habitats at different life stages. Furthermore, in a resource-limited environment, dietary competition occurs among individuals of the same species (Borcherding et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) posing the complexities of ecosystem functioning.\u003c/p\u003e \u003cp\u003eTemperature is a critical environmental parameter structuring the fish diversity patterns through physiological tolerances (Selleslagh and Amara \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) that leads to seasonal changes through community reorganizations shown by the Rank abundance curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Accordingly, temperature influences reproduction, recruitment, migration patterns, and ecological relationships (Selleslagh and Amara \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Clark et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). \u003cem\u003eClupea harengus\u003c/em\u003e dominated all seasons because it is the most abundant species in the North Sea (Corten \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1986\u003c/span\u003e), has bi-annual (spring and autumn) spawning (Bierman et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), continuous inflow of juveniles from the adjacent North Sea (Maathuis et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and high consumption of prey items (Utne et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Thus, it outcompetes species of similar prey items but can co-occur with species with diverse prey choices and similar temperature preferences such as \u003cem\u003eA. tobianus\u003c/em\u003e (Kellnreitner et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), which also spawns twice per year (Laugier et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eMerlangius merlangus\u003c/em\u003e has diverse prey choices (Kellnreitner et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ross et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) but overlapping temperature preferences to \u003cem\u003eC. harengus\u003c/em\u003e and \u003cem\u003eA. tobianus\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Therefore, \u003cem\u003eM. merlangus\u003c/em\u003e co-occurs with both \u003cem\u003eC. harengus\u003c/em\u003e and \u003cem\u003eA. tobianus\u003c/em\u003e in late spring/early summer attaining high relative abundances in autumn (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This co-occurrence pattern changes depending on the temperature conditions, for instance, after the prolonged cold winters of 2009/2010 and 2010/2011, low recruitment of \u003cem\u003eM. merlangus\u003c/em\u003e in the SRB were observed while there were strong recruitments of \u003cem\u003eC. harengus\u003c/em\u003e and \u003cem\u003eA. tobianus\u003c/em\u003e (Odongo et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Such abundance fluctuations explain why \u003cem\u003eC. harengus\u003c/em\u003e was highly ranked even in autumn since we used seasonal mean abundances in the analysis. \u003cem\u003ePomatoschistus minutus\u003c/em\u003e feeds on diverse prey items (Kellnreitner et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) but its high ranking in winter in the benthic habitats (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) is related to its local migration to deeper areas to avoid the unstable winter temperature conditions in the shallow intertidal zones. The second-ranking of \u003cem\u003eS. sprattus\u003c/em\u003e in the pelagic habitats in most of the seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) is related to its preferred habitat. Other species had low relative abundance distributions but their rankings fluctuated with seasons and habitats (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These observations support the temperature role in the species\u0026rsquo; co-occurrence and/or exclusions in habitat use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePotential effects of temperature changes on fish diversity\u003c/h2\u003e \u003cp\u003eThe Wadden Sea is characterized by seasonal changes in water temperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) (Rick et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Rutterford et al., (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) predicted that global warming could cause shifts in fish species composition across the Northeast Atlantic continental shelf with greater implications at higher latitudes. Our monthly monitoring of 13 years, which might be considered short in determining the effects of climate change on community structures revealed a strong influence of seasonal changes in water temperature on the phenology of various fish species. For instance, the effects of anomalous events such as the prolonged cold winters of 2009/2010 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), also described by Osborn (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and 2010/2011 resulted in low taxa numbers compared to the relatively warm winters of 2006/2007, 2007/2008, and 2008/2009 (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Such events also influenced the diversity in the seasons that followed. For example, the higher number of taxa in the summer of 2010 (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) may have resulted from the delayed emigration of boreal species that coincided with the immigration of Lusitanian species. Similarly, the high dominance in the 2015 autumn (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e) which was contrary to other years is attributed to the delayed emigration of \u003cem\u003eC. harengus\u003c/em\u003e and \u003cem\u003eA. tobianus\u003c/em\u003e after the cold summer of 2015 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the Dutch Wadden Sea, van Walraven et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) reported delayed immigration and emigration of fish because of an increase in offshore temperature based on a 53-year survey. However, changes in the fish community assemblages depend on the capacity for and the rate of distributional changes between species under different climate change scenarios (Rutterford et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This is why we observed varying diversity patterns resulting from either delayed or advanced migration related to inter-annual variations in water temperature. Other factors besides temperature though not investigated in this study, may additionally play significant roles. For example, there was high species richness in the spring of 2016 (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) despite similar spring temperature ranges from 2014 to 2017 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDiversity patterns reflect the underlying processes that shape ecological communities (Vasconcelos et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Species richness has been used as the simplest metric to represent community diversity. Although limited in measuring ecological dynamics, it provides information on co-occurring species under different environmental conditions (Avolio et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, \u003cem\u003eS\u003c/em\u003e is a better measure when the primary objective is to detect the effects of environmental parameters on community diversity (Magurran and Dornelas 2010). For example, the low number of taxa at lower and higher temperatures (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Higher species richness occurred at temperatures around 5\u0026deg;C and 15\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), which are typical temperatures for seasonal transition phases for winter/spring and summer/autumn, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This is because immigration and emigration occur simultaneously in the SRB resulting in higher species numbers at the seasonal transition phases. This is further supported by Jaccard\u0026rsquo;s coefficients, which showed high similarity in terms of species present between adjacent seasons (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) and explain why the factor season explained low variations in \u003cem\u003eS\u003c/em\u003e in both habitats (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Furthermore, the migration patterns explain why \u003cem\u003eS\u003c/em\u003e was not subjected to the bi-modal nature of the number of temperature observations (highs around 5\u0026deg;C and 15\u0026deg;C) which are typical in the Wadden Sea (de Amorim et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) or the North Sea (Boersma et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) (see Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt 10\u0026deg;C and 18\u0026deg;C, which are the typical mean spring and summer temperatures, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), similar species richness (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) and evenness (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, a) were observed. The species numbers were lower than those of the winter/spring and summer/autumn transition phases because only species that are adapted to specific temperatures use the habitats at specific times otherwise migrate to more favorable environments. The \u003cem\u003eS\u003c/em\u003e patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), show that the emigration of \u003cem\u003eC. harengus\u003c/em\u003e and \u003cem\u003eA. tobianus\u003c/em\u003e and the immigration of \u003cem\u003eM. merlangus\u003c/em\u003e and \u003cem\u003eT. trachurus\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) starts at 15\u0026deg;C achieving a relatively even community at 18\u0026deg;C. Beyond 18\u0026deg;C, \u003cem\u003eM. merlangus\u003c/em\u003e becomes the dominant species as indicated by the decrease in evenness (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, a) and an increase in dominance (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, a).\u003c/p\u003e \u003cp\u003eCompound indices such as the Shannon-Wiener Index (H) and evenness (J) provide more information on community dynamics (Morris et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Nearby communities or seasonal assemblages with similar taxa numbers can have different community structures (Heaven and Scrosati \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). For instance, 10\u0026deg;C (spring) and 18\u0026deg;C (summer) had similar species richness (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) but different H (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The decrease in H with increasing temperatures indicates that the proportion of less abundant and/or rare species gradually declined with temperature increase and a few species dominated the community except between 15\u0026deg;C and 18\u0026deg;C because of the community transitions or ecotones (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Similarly, in the tidal habitats of the Irish Sea, seasonality played a bigger role in structuring the fish community (Jovanovic et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) as well as the English Channel where higher diversities were observed in winter and low in summer (Selleslagh and Amara \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The changes in H (Fig. S4) and the variations explained by year (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) indicate that the inter-annual variations of biotic and abiotic factors that cause abundance fluctuations influence diversity as well (Morris et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSpatial distribution of fish with changes in water depth\u003c/h2\u003e \u003cp\u003eA greater range of local habitat types and environmental conditions support higher diversity (Morris et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Species-area relationships assume that the availability of important features such as habitat size influences species numbers and abundances (Tittensor et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Local habitats may contain various microhabitat types that comprise different growth forms such as seagrass meadows, seaweed (\u003cem\u003eSargassum muticum\u003c/em\u003e), or bivalve beds (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) that contain a high diversity of benthic and epibenthic communities (Armonies et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The intertidal habitats when submerged during high tide offer additional ecological functions such as more prey, camouflage, and refuge spaces that promote higher taxa numbers (Gratwicke and Speight \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The negative correlation between \u003cem\u003eS\u003c/em\u003e and water depth (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e) shows that during high tide, fish migrate to the intertidal habitats and recede with ebbing currents. The migration decreases the total fish density per area as the total aerial coverage increases during high tide. Thus, a positive correlation exists between water depth and evenness (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). On the other hand, a negative correlation exists with dominance (Fig. S5).\u003c/p\u003e \u003cp\u003eTidally induced current velocities vary within the tidal cycle and differ in various sub-areas of the SRB. Maximum velocity, which is approximately 1.8 m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e occurs at the opening of Lister Tief (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and decreases towards the intertidal flats (Fofonova et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Besides, the seabed morphology differs within sampling stations. Thus, the positive correlation between water depth and J indicates that fish concentrated in smaller areas during low tide and/or simply avoided the deeper parts within the tidal system irrespective of the tidal condition. The negative correlations in stations adjacent to subtidal channels (i.e. Sylt_1, Sylt_4, and Sylt_9) in the pelagic habitats (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e, b), show that a few species dominated these areas during high tide. These differences are attributed to species habitat preferences as Kellnreitner et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) observed that benthivorous fish species mainly dominated the shallower areas while planktivorous fish were mostly abundant in the deep areas. Similarly, in the Irish Sea, flatfishes were more abundant in shallow areas or near the receding water edges as they fed on macroinvertebrates (Jovanovic et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In the Dutch Wadden Sea, higher densities of \u003cem\u003eS. sprattus, C. harengus, E. encrasicolus\u003c/em\u003e, and pilchard (\u003cem\u003eSardina pilchardus\u003c/em\u003e) occurred during high tide and dominated the top 10 m of the water column (Couperus et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These observations show the significance of tidal dynamics on habitat utilization and distribution of common and rare species.\u003c/p\u003e \u003cp\u003eDifferent aspects of the composition, structure, and functioning of natural communities vary independently so a suite of metrics is needed to cover all types of changes (Greenstreet et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). For example, current velocity and sediment characteristics are important structural factors that influence the spatial distribution and composition of macrofaunal species (Sch\u0026uuml;ckel et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) that offer diverse food sources to fish (Kellnreitner et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Incorporating these different aspects of habitat structure and composition (Gratwicke and Speight \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) could offer more information on habitat utilization. This could better explain the changes in diversity between sites than generalizing only on sampling stations, which was significant but explained low percentage variations of the diversity (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Nevertheless, incorporating and disentangling singular or cumulative effects of parameters such as habitat dependencies and shared food resources for different fish species is complex as it varies across key life stages and space (Rutterford et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, exploring the behavioral responses, distribution patterns, and abundances of singular species with abiotic responses and biotic interactions could determine further habitat preferences and provide more information on spatial distribution patterns.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe fish community structure and diversity patterns demonstrated high seasonal dynamics that were potentially driven by the changes in water temperature in a cold temperate coastal ecosystem. The seasonal transition phases of winter/spring and summer/autumn recorded higher species richness (\u003cem\u003eS\u003c/em\u003e) because immigration and emigration occur simultaneously. The community was less diverse at very low temperatures because of the low \u003cem\u003eS\u003c/em\u003e and abundances and constrained biological interactions whereas the low diversity at higher temperatures was related to the dominance of a few species and physical tolerance. The higher percentage variations of \u003cem\u003eS\u003c/em\u003e explained by temperature in the pelagic habitats compared to the benthic habitats showed the high sensitivity of species\u0026rsquo; pelagic phases to temperature changes. Since temperature effects cut across food webs, incorporating its effects on all trophic levels could provide more information on the fish community dynamics as we targeted only one group or compartment of the food web. Additionally, an analysis of the size distributions of various species between benthic and pelagic habitats could provide more information on the use of these habitats at different times of the year. The GLMMs showed the spatial distribution with changes in water depth per tidal cycle and within and between sampling stations. Thus, the significant roles of the shallow and intertidal areas as important feeding and refuge grounds. The effect of water depth on the diversity patterns was less pronounced because of the low depth changes in the SRB in comparison to other North Sea areas. Nevertheless, the GLMMs showed the species distribution patterns in different habitats, which calls for further investigations on the role of habitat complexity on species richness, abundance, and the distribution of common and rare fish species. The spatial distribution patterns provide baseline information on the significance of shallow coastal systems for fish. This is useful not only in the Wadden Sea but can be used as a guideline for management and conservation measures for the maintenance of biodiversity and valuable coastal and offshore ecosystems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eSupplementary Information\u003c/h2\u003e \u003cp\u003eThe online version contains supplementary information including descriptions of temperature and depth changes as well as additional figures of diversity indices.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eData/code availability\u003c/h2\u003e \u003cp\u003eData and code will be made available upon request.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eThe authors declare that there are no competing financial interests or personal relationships, which could influence the scientific work presented in this paper.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003eAll applicable national and international regulations on scientific monitoring in protected areas were followed during the sampling and handling of fish specimens. The National Park \u0026ldquo;Schleswig- Holsteinisches Wattenmeer\u0026rdquo; issued the permit and authorization to work in the Sylt-R\u0026oslash;m\u0026oslash; Bight.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eOpen Access funding will be provided by the library of Alfred-Wegener-Institute for Polar and Marine Research. Victor Odongo is grateful to the German Academic Exchange Service (DAAD) for funding his doctoral studies (Funding program number: 57507871) at the University of Bremen and Alfred-Wegener-Institut, Helmholtz Zentrum f\u0026uuml;r Polar- und Meeresforschung.\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eVictor Odongo conceptualized the research questions, and data analysis methods, wrote the first draft of the manuscript, and led the writing process. Harald Asmus conceptualized the fish monitoring ideas and designed the sampling methodology and gear selection. Maarten Boersma, Sabine Horn, and Katja Heubel guided the manuscript structure and clarity, review, and editing. Lasse Sander provided the map of the study area and editing. Sara Rubinetti and Vera Sidorenko review and editing. All the authors contributed to the drafts and gave final approval for publication.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eWe are grateful to the authorities of the National Park \u0026ldquo;Schleswig- Holsteinisches Wattenmeer\u0026rdquo; (Landesbetrieb f\u0026uuml;r K\u0026uuml;stenschutz, Nationalpark und Meeresschutz) for the possibilities of scientific research in the area. Moreover, we wish to thank Petra Kadel, Birgit Hussel, and Timm Kress for planning, leading the fish monitoring, and collating the data. We are indebted to everyone including all the students who helped and participated in the fish monitoring over the years. We are grateful for the contributions of Merten Saathoff and Harald Ahnelt to the manuscript. Appreciation to the crew of the RV MYA I and II for operating and making sure the fish monitoring is a success.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArmonies W, Asmus H, Buschbaum C, Lackschewitz D, Reise K, Rick J (2018) Microscopic species make the diversity: A checklist of marine flora and fauna around the Island of Sylt in the North Sea. Helgol Mar Res doi. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s10152-018-0512-8\u003c/span\u003e\u003cspan address=\"10.1186/s10152-018-0512-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsmus H, Hussel B, Petra K, Asmus R, Rick JJ, Wiltshire KH (2020) Fish monitoring in the Sylt R\u0026oslash;m\u0026oslash; bight (2007 et seq). In: Alfred Wegener Institute - Wadden Sea Station Sylt, PANGEA. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.pangaea.de/10.1594/PANGAEA.911261\u003c/span\u003e\u003cspan address=\"https://doi.pangaea.de/10.1594/PANGAEA.911261\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 15 Dec 2021\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAvolio ML, Carroll IT, Collins SL, Houseman GR, Hallett LM, Isbell F, Koerner SE, Komatsu KJ, Smith MD, Wilcox KR (2019) A comprehensive approach to analyzing community dynamics using rank abundance curves. Ecosphere. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ecs2.2881\u003c/span\u003e\u003cspan address=\"10.1002/ecs2.2881\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaird D, Asmus H, Asmus R (2007) Trophic dynamics of eight intertidal communities of the Sylt-R\u0026oslash;m\u0026oslash; Bight ecosystem, northern Wadden Sea. Mar Ecol Prog Ser 351:25\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3354/meps07137\u003c/span\u003e\u003cspan address=\"10.3354/meps07137\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaird D, Asmus H, Asmus R, Horn S, de la Vega C (2019) Ecosystem response to increasing ambient water temperatures due to climate warming in the Sylt- R\u0026oslash;m\u0026oslash; Bight, northern Wadden Sea, Germany. Estuar Coast Shelf Sci. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ecss.2019.106322\u003c/span\u003e\u003cspan address=\"10.1016/j.ecss.2019.106322\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarbier EB, Hacker SD, Kennedy C, Koch EW, Stier AC, Silliman BR (2011) The value of estuarine and coastal ecosystem services. Ecol Monogr 81:169\u0026ndash;193. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1890/10-1510.1\u003c/span\u003e\u003cspan address=\"10.1890/10-1510.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeaugrand G (2004) The North Sea regime shift: Evidence, causes, mechanisms and consequences. Prog Oceanogr 60:245\u0026ndash;262. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.pocean.2004.02.018\u003c/span\u003e\u003cspan address=\"10.1016/j.pocean.2004.02.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBierman S, Dickey-Collas M, van Damme G, van Overzee CJ, Pennock-Vos H, Tribuhl M, Clausen S L (2010) Between-year variability in the mixing of North Sea herring spawning components leads to pronounced variation in the composition of the catch. ICES J Mar Sci 67:885\u0026ndash;896\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoersma M, Gr\u0026uuml;ner N, Signorelli NT, Montoro Gonz\u0026aacute;lez PE, Peck MA, Wiltshire KH (2016) Projecting effects of climate change on marine systems: Is the mean all that matters? Proceedings of the Royal Society B: Biological Sciences. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1098/rspb.2015.2274\u003c/span\u003e\u003cspan address=\"10.1098/rspb.2015.2274\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorcherding J, Heubel K, Storm S (2019) Competition fluctuates across years and seasons in a 6-species-fish community: empirical evidence from the field. Rev Fish Biol Fish 29:589\u0026ndash;604\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBSH (2023) Meeresboden DGM\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ehttps://\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003c/span\u003e\u003cspan address=\"http://www.geoseaportal.de/mapapps/resources/apps/bathymetrie/index.html?lang=de\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 23 Oct 2023\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClarke KR (1993) Non-parametric multivariate analyses of changes in community structure. Aust J Ecol 18:117\u0026ndash;143. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1442-9993.1993.tb00438.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1442-9993.1993.tb00438.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClark NJ, Kerry JT, Fraser CI (2020) Rapid winter warming could disrupt coastal marine fish community structure. Nat Clim Chang 10:862\u0026ndash;867. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41558-020-0838-5\u003c/span\u003e\u003cspan address=\"10.1038/s41558-020-0838-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorten A (1986) On the causes of the recruitment failure of herring in the central and northern North Sea in the years 1972\u0026ndash;1978. J Cons int Explor Mer 42:281\u0026ndash;294\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorten A, van de Kamp G (1996) Variation in the abundance of southern fish species in the southern North Sea in relation to hydrography and wind. ICES J Mar Sci 53:1113\u0026ndash;1119\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCouperus B, Gastauer S, F\u0026auml;ssler SMM, Tulp I, van der Veer HW, Poos JJ (2016) Abundance and tidal behaviour of pelagic fish in the gateway to the Wadden Sea. J Sea Res 109:42\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.seares.2016.01.007\u003c/span\u003e\u003cspan address=\"10.1016/j.seares.2016.01.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Amorim F, Wiltshire HK, Lemke P, Carstens K, Peters S, Rick J, Gimenez L, Scharfe M (2023) Investigation of Marine Temperature Changes across Temporal and Spatial Gradients: Providing a Fundament for Studies on the Effects of Warming on Marine Ecosystem Function and Biodiversity. Prog Oceanogr 103080. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.pocean.2023.103080\u003c/span\u003e\u003cspan address=\"10.1016/j.pocean.2023.103080\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDulvy NK, Rogers SI, Jennings S, Stelzenm\u0026uuml;ller V, Dye SR, Skjoldal HR (2008) Climate change and deepening of the North Sea fish assemblage: A biotic indicator of warming seas. J Appl Ecol 45:1029\u0026ndash;1039. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1365-2664.2008.01488.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2664.2008.01488.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDurant JM, Hjermann D\u0026Oslash;, Ottersen G, Stenseth NC (2007) Climate and the match or mismatch between predator requirement and resource availability. Clim Res 33:271\u0026ndash;283\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdwards M, Richardson AJ (2004) Impact of climate change on marine pelagic phenology and trophic mismatch. Nature 430:881\u0026ndash;884. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nature02808\u003c/span\u003e\u003cspan address=\"10.1038/nature02808\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFelden J, M\u0026ouml;ller L, Schindler U, Huber R, Schumacher S, Koppe R, Diepenbroek M, Gl\u0026ouml;ckner FO (2023) PANGAEA - Data Publisher for Earth \u0026amp; Environmental Science. Sci Data. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41597-023-02269-x\u003c/span\u003e\u003cspan address=\"10.1038/s41597-023-02269-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFofonova V, Androsov A, Sander L, Kuznetsov I, Amorim F, Hass CH, Wiltshire KH (2019) Non-linear aspects of the tidal dynamics in the Sylt-R\u0026oslash;m\u0026oslash; Bight, south-eastern North Sea. Ocean Sci 15:1761\u0026ndash;1782. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5194/os-15-1761-2019\u003c/span\u003e\u003cspan address=\"10.5194/os-15-1761-2019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGratwicke B, Speight MR (2005) The relationship between fish species richness, abundance and habitat complexity in a range of shallow tropical marine habitats. J Fish Biol 66:650\u0026ndash;667. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1095-8649.2005.00629.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1095-8649.2005.00629.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreenstreet SPR, Fraser HM, Rogers SI, Trenkel VM, Simpson SD, Pinnegar JK (2012) Redundancy in metrics describing the composition, structure, and functioning of the North Sea demersal fish community. ICES J Mar Sci 69:8\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/icesjms/fsr188\u003c/span\u003e\u003cspan address=\"10.1093/icesjms/fsr188\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeaven CS, Scrosati RA (2008) Benthic community composition across gradients of intertidal elevation, wave exposure, and ice scour in Atlantic Canada. Mar Ecol Prog Ser 369:13\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3354/meps07655\u003c/span\u003e\u003cspan address=\"10.3354/meps07655\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerrmann JP, Jansen S, Temming A (1998) Consumption of fish and decapod crustaceans and their role in the trophic relations of the Sylt-R\u0026oslash;m\u0026oslash; Bight. In: G\u0026auml;tje C, Reise K (eds) \u0026Ouml;kosystem Wattenmeer-Austausch-Transport- Und Stoffumwandlungsprozesse, 1st edn. Springer, Berlin-Heidelberg, pp 437\u0026ndash;462\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolbrook SJ, Kingsford MJ, Schmitt RJ, Stephens NS (1994) Spatial and Temporal Patterns in Assemblages of Temperate Reef Fish. Am Zool 34:463\u0026ndash;475\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolm P, Hayes P, Nicholls J (2024) Historical marine footprint for Atlantic Europe, 1500\u0026ndash;2019. Ambio. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s13280-023-01939-9\u003c/span\u003e\u003cspan address=\"10.1007/s13280-023-01939-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIzs\u0026aacute;k J, Pavoine S (2012) Links between the species abundance distribution and the shape of the corresponding rank abundance curve. Ecol Indic 14:1\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ecolind.2011.06.030\u003c/span\u003e\u003cspan address=\"10.1016/j.ecolind.2011.06.030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJovanovic B, Longmore C, O\u0026rsquo;Leary \u0026Aacute;, Mariani S (2007) Fish community structure and distribution in a macro-tidal inshore habitat in the Irish Sea. Estuar Coast Shelf Sci 75:135\u0026ndash;142. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ecss.2007.02.023\u003c/span\u003e\u003cspan address=\"10.1016/j.ecss.2007.02.023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKellnreitner F, Pockberger M, Asmus H (2012) Seasonal variation of assemblage and feeding guild structure of fish species in a boreal tidal basin. Estuar Coast Shelf Sci 108:97\u0026ndash;108. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ecss.2011.02.020\u003c/span\u003e\u003cspan address=\"10.1016/j.ecss.2011.02.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaugier F, Feunteun E, Pecheyran C, Carpentier A (2015) Life history of the Small Sandeel, Ammodytes tobianus, inferred from otolith microchemistry. A methodological approach. Estuar Coast Shelf Sci 165:237\u0026ndash;246. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ecss.2015.05.022\u003c/span\u003e\u003cspan address=\"10.1016/j.ecss.2015.05.022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLinnaeus C (1758) Systema Naturae per regna tria naturae, secundum classes, ordines, genera, species, cum characteribus, differentiis, synonymis, locis, 10th revised edition. Laurentius Salvius, Holmiae\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaathuis M, Couperus B, van der Molen J, Poos J, Tulp I, Sakinan S (2023) Resolving the variability in habitat use of juvenile small pelagic fish in a major tidal system by continuous echosounder measurements. Mar Ecol Prog Ser. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3354/meps14368\u003c/span\u003e\u003cspan address=\"10.3354/meps14368\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiller PJ, Loates MJ (1997) Fish of Britain \u0026amp; Europe. HarperCollins, London\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontero-Serra I, Edwards M, Genner MJ (2015) Warming shelf seas drive the subtropicalization of European pelagic fish communities. Glob Chang Biol 21:144\u0026ndash;153. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/GCB.12747\u003c/span\u003e\u003cspan address=\"10.1111/GCB.12747\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorris EK, Caruso T, Buscot F, Fischer M, Hancock C et al (2014) Choosing and using diversity indices: Insights for ecological applications from the German Biodiversity Exploratories. Ecol Evol 4:3514\u0026ndash;3524. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ece3.1155\u003c/span\u003e\u003cspan address=\"10.1002/ece3.1155\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurphy SJ, Smith AB (2021) What can community ecologists learn from species distribution models? Ecosphere. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ecs2.3864\u003c/span\u003e\u003cspan address=\"10.1002/ecs2.3864\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuus BJ, Dahlstr\u0026oslash;m P (1978) Meeresfische der Ostsee, der Nordsee, des Atlantik Biologie, Fang, wirtschaftliche Bedeutung, 1st edn. BLV\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNilsson S (1855) Skandinavisk fauna. Fjerde Delen: Fiskarna. F\u0026ouml;rsta H\u0026auml;ftet Lund i-xxxiv +:1\u0026ndash;768\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOdongo V, Asmus H, Ahnelt H, Boersma M, Rick J, Wiltshire KH, Horn S (2024) Seasonal variations of a coastal fish community in relation to environmental parameters - A case study of the Sylt-R\u0026oslash;m\u0026oslash; Bight, southeastern North Sea. Estuar Coast Shelf Sci 300:108723. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ecss.2024.108723\u003c/span\u003e\u003cspan address=\"10.1016/j.ecss.2024.108723\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsborn TJ (2011) Winter 2009/2010 temperatures and a record-breaking North Atlantic Oscillation index. Weather 66:16\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/wea.666\u003c/span\u003e\u003cspan address=\"10.1002/wea.666\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePallas PS (1770) Spicilegia Zoologica quibus novae imprimis et obscurae animalium species iconibus, descriptionibus atque commentariis illustrantur. Berolini, Gottl August Lange v. 1 (fasc. 8): 1\u0026ndash;56:1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeck MA, Reglero P, Takahashi M, Catal\u0026aacute;n IA (2013) Life cycle ecophysiology of small pelagic fish and climate-driven changes in populations. Prog Oceanogr 116:220\u0026ndash;245. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.pocean.2013.05.012\u003c/span\u003e\u003cspan address=\"10.1016/j.pocean.2013.05.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetersen W (2014) FerryBox systems: State-of-the-art in Europe and future development. J Mar Syst 140:4\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jmarsys.2014.07.003\u003c/span\u003e\u003cspan address=\"10.1016/j.jmarsys.2014.07.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP\u0026ouml;rtner HO, Knust R (2007) Climate change affects marine fishes through the oxygen limitation of thermal tolerance. Science (1979) 315:92\u0026ndash;97. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/science.1135013\u003c/span\u003e\u003cspan address=\"10.1126/science.1135013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReise K, K\u0026ouml;ste R, M\u0026uuml;ller A, Armonies W, Asmus H, Asmus R, Hickel W, Riethm\u0026uuml;ller R (1998) Exchange Processes in the Sylt-R\u0026oslash;m\u0026oslash; Wadden Sea: A Summary and Implications. In: G\u0026auml;tje C (ed) \u0026Ouml;kosystem Wattenmeer / The Wadden Sea Ecosystem. Springer, Berlin, Heidelberg, pp 529\u0026ndash;558\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRick J, Scharfe M, Romanova T, van Beusekom J, Asmus R, Asmus H, Mielck F, Kamp A, Sieger R, Wiltshire KH (2023) An evaluation of long-term physical and hydrochemical measurements at the Sylt Roads Marine Observatory (1973\u0026ndash;2019), Wadden Sea, North Sea. Earth Syst Sci Data 15:1037\u0026ndash;1057. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5194/essd-15-1037-2023\u003c/span\u003e\u003cspan address=\"10.5194/essd-15-1037-2023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoss SD, Gislason H, Andersen NG, Lewy P, Nielsen JR (2016) The diet of whiting Merlangius merlangus in the western Baltic Sea. J Fish Biol 88:1965\u0026ndash;1988. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jfb.12959\u003c/span\u003e\u003cspan address=\"10.1111/jfb.12959\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRutterford LA, Simpson SD, Bogstad B, Devine JA, Genner MJ (2023) Sea temperature is the primary driver of recent and predicted fish community structure across Northeast Atlantic shelf seas. Glob Chang Biol. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/gcb.16633\u003c/span\u003e\u003cspan address=\"10.1111/gcb.16633\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSch\u0026uuml;ckel U, Beck M, Kr\u0026ouml;ncke I (2015) Macrofauna communities of tidal channels in Jade Bay (German Wadden Sea): spatial patterns, relationships with environmental characteristics, and comparative aspects. Marine Biodivers 45:841\u0026ndash;855. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12526-014-0308-2\u003c/span\u003e\u003cspan address=\"10.1007/s12526-014-0308-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSelleslagh J, Amara R (2008) Inter-season and interannual variations in fish and macrocrustacean community structure on a eastern English Channel sandy beach: Influence of environmental factors. Estuar Coast Shelf Sci 77:721\u0026ndash;730. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ecss.2007.11.004\u003c/span\u003e\u003cspan address=\"10.1016/j.ecss.2007.11.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTittensor DP, Mora C, Jetz W, Lotze HK, Ricard D, Berghe E, Vanden, Worm B (2010) Global patterns and predictors of marine biodiversity across taxa. Nature 466:1098\u0026ndash;1101. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nature09329\u003c/span\u003e\u003cspan address=\"10.1038/nature09329\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTulp I, Bolle LJ, Rijnsdorp AD (2008) Signals from the shallows: In search of common patterns in long-term trends in Dutch estuarine and coastal fish. J Sea Res 60:54\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.seares.2008.04.004\u003c/span\u003e\u003cspan address=\"10.1016/j.seares.2008.04.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTulp I, van der Veer HW, Walker P, van Walraven L, Bolle LJ (2017) Can guild- or site-specific contrasts in trends or phenology explain the changed role of the Dutch Wadden Sea for fish? J Sea Res 127:150\u0026ndash;163. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.seares.2016.10.001\u003c/span\u003e\u003cspan address=\"10.1016/j.seares.2016.10.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUNESCO (2009) Wadden Sea - UNESCO World Heritage Centre. In: World Heritage Convention. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://whc.unesco.org/en/list/1314/\u003c/span\u003e\u003cspan address=\"https://whc.unesco.org/en/list/1314/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 31 Jan 2024\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUtne KR, Hj\u0026oslash;llo SS, Huse G, Skogen M (2012) Estimating the consumption of Calanus finmarchicus by planktivorous fish in the Norwegian Sea using a fully coupled 3D model system. Mar Biol Res 8:527\u0026ndash;547. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/17451000.2011.642804\u003c/span\u003e\u003cspan address=\"10.1080/17451000.2011.642804\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan der Veer H, Tulp I, Witte JIJ, Poiesz SSH, Bolle LJ (2022) Changes in functioning of the largest coastal North Sea flatfish nursery, the Wadden Sea, over the past half century. Mar Ecol Prog Ser 693:183\u0026ndash;201. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3354/meps14082\u003c/span\u003e\u003cspan address=\"10.3354/meps14082\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Walraven L, Dapper R, Nauw JJ, Tulp I, Witte JI, van der Veer HW (2017) Long-term patterns in fish phenology in the western Dutch Wadden Sea in relation to climate change. J Sea Res 127:173\u0026ndash;181. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.seares.2017.04.001\u003c/span\u003e\u003cspan address=\"10.1016/j.seares.2017.04.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVasconcelos RP, Henriques S, Fran\u0026ccedil;a S, Pasquaud S, Cardoso I, Laborde M, Cabral HN (2015) Global patterns and predictors of fish species richness in estuaries. J Anim Ecol 84:1331\u0026ndash;1341. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/1365-2656.12372\u003c/span\u003e\u003cspan address=\"10.1111/1365-2656.12372\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerberk W (2011) Explaining General Patterns in Species Abundance and Distributions. Nat Educ Knowl 3(10):1\u0026ndash;11\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVorberg R, Breckling P (1999) Atlas der Fische im Schleswig-Holsteinischen Wattenmeer. Nationalpark Schleswig-Holsteinisches Wattenmeer, T\u0026ouml;nning\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuur AF, Ieno EN, Smith GM (2007) Additive and generalised additive modelling. Analysing Ecological Data. Statistics for Biology and Health. Springer, New York, NY, pp 97\u0026ndash;124\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuur AF, Ieno EN, Walker NJ, Saveliev AA, Smith GM (2009a) Mixed Effects Modelling for Nested Data. Mixed Effects Models and Extensions in Ecology with R. Statistics for Biology and Health. Springer, New York, NY, pp 101\u0026ndash;142\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuur AF, Ieno EN, Walker NJ, Saveliev AA, Smith GM (2009b) Things are not Always Linear; Additive Modelling. Mixed effects models and extensions in ecology with R. Statistics for Biology and Health. Springer, New York, NY, pp 35\u0026ndash;69\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cold temperate coastal ecosystems, Sylt-Rømø Bight, seasonal changes, ecotones, spatial distribution, habitat utilization","lastPublishedDoi":"10.21203/rs.3.rs-4583467/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4583467/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCoastal marine ecosystems are characterized by high productivity and biodiversity supporting coastal fisheries for centuries. These ecosystems have undergone changes over the last few decades, experiencing shifts in seasonal patterns due to climate change. As a case study for responses of cold temperate coastal ecosystems to climate change, changes in fish diversity in the Sylt-R\u0026oslash;m\u0026oslash; Bight (SRB), northern Wadden Sea; 54\u0026deg;58\u0026rsquo;40\u0026rdquo;N, 8\u0026deg;29\u0026rsquo;45\u0026rdquo;E, were analyzed using data from the monthly monitoring from 2007 to 2019. Results showed that the diversity changes correlated to seasonal changes in water temperature. The spatial distribution of fish to intertidal areas for feeding and refuge was correlated to changes in water depth. Rank abundance curves (RACs) showed that a few species dominated the fish community and this changed per season and habitat type. General Additive Models (GAMs) showed higher species richness (\u003cem\u003eS\u003c/em\u003e) at 5\u0026deg;C and 15\u0026deg;C, which are seasonal transition phases for winter/spring and summer/autumn, respectively. Evenness (J) and Shannon-Wiener Index (H) decreased with increasing water temperatures in the benthic and pelagic habitats while dominance (D) increased. Generalized linear mixed-effects models (GLMMs) showed that \u003cem\u003eS\u003c/em\u003e decreased while J increased with water depth in benthic habitats. Similar patterns were observed in the nearshore pelagic habitats contrary to the deep tidal channels. There were no significant effects of water depth on H. The diversity changes reveal the sensitivity of fish to seasonal changes in oceanographic processes and the use of intertidal habitats. Thus, the significance of shallow coastal habitats for fish needs implementation in conservation and management measures.\u003c/p\u003e","manuscriptTitle":"Community structure and diversity changes for fish in a temperate tidal lagoon, as a response to changes in water temperature and depth","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-09 20:52:18","doi":"10.21203/rs.3.rs-4583467/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1902598c-d55b-4769-b11e-f5806fa84fd6","owner":[],"postedDate":"August 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-10T11:46:43+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-09 20:52:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4583467","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4583467","identity":"rs-4583467","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}