Green landscape and macrophyte cover influence macroinvertebrate taxonomic and functional composition in urban waterbodies at multiple spatial scales

Green landscape and macrophyte cover influence macroinvertebrate taxonomic and functional composition in urban waterbodies at multiple spatial scales | 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 Green landscape and macrophyte cover influence macroinvertebrate taxonomic and functional composition in urban waterbodies at multiple spatial scales Audrey Robert, Bernadette Pinel-Alloul, Zofia E. Taranu, Eric Harvey This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3891411/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 Urban waterbodies provide important services to humans and play a considerable role in biodiversity conservation. Yet, we still know very little about how urban pond ecosystems may respond to ongoing and future stresses operating at multiple spatial scales. Here we examined the littoral macroinvertebrates in 20 urban waterbodies as an indicator community to assess how local waterbody condition and urban land use affected their taxonomic and functional composition. Although macroinvertebrates were diverse (total richness of 60 taxa ranging from 10 to 41), they were dominated by two major taxonomic groups, the Diptera Chironomidae (36%) and the Annelida Oligochaeta (22%), which largely represented the dominant functional group of the Collectors-Gatherers (63%). Fuzzy clustering identified four different types of communities based on taxonomic and functional groups. These reflected inversed gradients in the dominance of Collectors-Gatherers versus ponds with higher abundances of Herbivores (Gastropoda Pulmonata, Hemiptera, Trichoptera), Collectors-Filterers (Gastropoda Prosobranchia, Crustacea Ostracoda), Predators (Odonata), and Parasites (Nematoda, Hydracarina). Distance-based redundancy analysis identified macrophyte cover and green landscape (parks and buildings with yards) within a 100 m radius as the best drivers of macroinvertebrate taxonomic and functional composition. We also noted a comparable variance explained by models that included parks within a 500 m radius or buildings with yards within a 2000 m radius. Our results have implications for urban landscape management as it suggests that human alteration in the urban landscape can be transmitted at least up to 2000 m from ponds. Urban ecology green-blue space integration Macroinvertebrate communities land-use change landscape management functional biodiversity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The planet is urbanizing at a fast pace (Seto et al. 2011 , 2012 ), with projected increases in urban areas from 55% in 2018 to 68% by 2050 (United Nations 2019), which poses a serious threat to biodiversity and ecosystem health (Grimm et al. 2008 ; Dudgeon et al. 2006 ; Marzluff et al. 2008 ; McDonnell and Hahs 2013 ). Increasing human density comes with the challenge of maintaining the function of urban ecosystems while mitigating the loss in ecosystem services. In the context of global change, where extreme events such as storms, heatwaves, and droughts are expected to increase, it is critical to understand the impact of the ongoing expansion of urban areas. Increasing evidence suggests that the proper management of green and blue space configurations in urban landscapes can reduce heat island effects (Gunawardena et al. 2017 ), increase mental (White et al. 2010 ; Smith et al. 2021 ) and physical (Völker and Kistemann 2011 , 2013 ; Brückner et al. 2022 ) human health, while their mismanagement can have the opposite effect (Gunawardena et al. 2017 ; Solcerova et al. 2019 ). Therefore, the integration of green and blue spaces into urbanized landscapes is now at the forefront of new city management policies as a means to simultaneously support environmental, economic, and human development goals (Pickett et al. 1997 ; Niemelä 1999 ; Anquez and Herlem 2011 ; McPhearson et al. 2016 ). Blue spaces, notably urban ponds, lakes and marshes, represent a set of complex habitat mosaics that have important ecological function, as well as social and economic uses (Werner 2011 ; Hassall 2014 ; Clifford and Hefferman 2018). However, urban aquatic habitats may suffer greater biodiversity threats than terrestrial habitats due to catchment disturbances (residential and industrial land use and mismanagement), increased nutrient loading, contamination, and species invasion (Brönmark and Hansson 2002 ; Hassall 2014 ; Moggridge et al. 2014 ). Despite their importance within the urban landscape, we still know little about urban waterbodies, their role as a refuge for biodiversity (Werner 2011 ; Chester and Robson 2013 ), how to maximize conservation management (Hassall et al. 2016 ), and how they respond to stressors operating at multiple scales (at the local scale of waterbodies and at the landscape scales) (Blicharska et al. 2017 ; Thornhill et al. 2017 ; Oertli and Parris 2019 ). The latter is especially important in the context of managing a suite of urban land use types while minimizing the loss of ecosystem function and services in blue spaces. Many studies have already highlighted the importance of a wide range of pond types to provide heterogeneous habitats both in urban (Hassall et al. 2016 ; Pinel-Alloul et al. 2022 ) and rural (Biggs et al. 2005 réghino et al. 2008a-c) regions. Despite a generally lower local diversity than their rural counterparts (Savić et al. 2022 ; Hassall and Anderson, 2015 ), urban waterbodies are also often more heterogeneous in abiotic conditions at the landscape scale, which could potentially lead to similar gamma diversity (Hill et al. 2016 ). In that context, urban and rural ponds are considered as a model system in conservation, ecology and evolutionary biology (De Meester et al. 2005 ; Parris 2018 ). Approaches using both taxonomic and functional composition have been instrumental for identifying groups of taxa with uniform responses to environmental gradients, as well as keystone traits whose loss may lead to a rapid collapse of the ecosystem (McGill et al. 2006 ; Baird et al. 2008 ). Given the tight association between trait representation in a community and habitat conditions, macroinvertebrate functional traits have been used to predict restoration management in shallow freshwater lakes (Van Kleef et al. 2006 ), and to evaluate anthropogenic impacts on European ponds (Céréghino et al. 2012 ) and large rivers (Desrosiers et al. 2019 ). However, despite the important effort devoted to integrating a functional approach into freshwater biomonitoring and ecological risk assessment (Menezes et al. 2010 ; Van den Brink et al. 2011 ), further research is needed to improve the application of functional traits to assess health and integrity of aquatic ecosystems facing human disturbances such as urban ponds and lakes (Marzluff et al. 2008 ; Purcell et al. 2009 ). In this study, we aim to understand how factors related to local waterbody environment and regional land use could influence the taxonomic and functional composition of aquatic communities in urban ponds, using macroinvertebrates as model organisms. Building from previous studies (Castillo et al. 2018 ; Pinel-Alloul et al. 2022 ; Talaga et al. 2017 ; Thornhill et al. 2017 ), we measured environmental variables known to be significantly related to macroinvertebrate communities at local and landscape scales, namely, local waterbody properties and urban land use at different spatial scales. Our objective was to disentangle the relative importance of different types of cross-scale drivers that are generally studied in isolation. To do so, we tested the relative importance (and interaction) of (i) abiotic factors (water transparency, morphometry, connectivity and density of waterbodies), (ii) water quality (nutrients and contaminants), (iii) macrophyte cover, (iv) urban land use (at 100–2000 m radius from each waterbody), and (v) management practices (winter drainage). The selected abiotic factors are expected to influence macroinvertebrate communities via direct physiological impacts, and thus, mainly reflect population-level adaptative tolerance to environmental change (Heino 2000 ). The water-quality variables are expected to influence community-level processes via different taxa tolerances to organic matter and contaminants (Hildrew and Townsend 1987 ). Macrophyte cover has been shown to be an important driver of macroinvertebrate communities in periurban lakes and urban ponds by providing a habitat for feeding (periphyton) and refuge from predators (Declerck et al. 2005 réghino et al. 2008c; De Sousa et al. 2008 ; Pinel-Alloul et al. 2022 ). Catchment land use has a direct influence on water quality (loading of nutrients and contaminants) (Carignan and Steedman 2000 ; Burcher et al. 2007 ), and thus can indirectly affect macroinvertebrate communities. Finally, management practices such as winter drainage can also affect aquatic communities of urban ponds (Pinel-Alloul et al. 2022 ). Previous studies integrating landscape-scale drivers vary greatly in the scale considered, ranging from 50 m to 10 km from the waterbodies (Blicharska et al. 2017 ; Thornhill et al. 2017 ; Oertli and Parris 2019 ). With that in mind, one of our key interests was to determine the scale at which the additive effect of waterbody condition and land use on macroinvertebrate communities was most prominent. Disentangling this cross-scale interaction in urban waterbodies is an essential step to identify where and to what extent management must take place, as well as key targets for the maintenance or restoration of urban blue spaces (Grimm et al. 2000 l et al. 2019 ). Methods Study sites and urban landscape To carry out this study, we used a dataset collected in the summer of 2011 (Robert 2016 ) in 20 urban waterbodies (Fig. 1 ) across the island of Montréal (Québec, Canada) (45.46–45.69ºN; 73.50–73.90ºW) (see Table S1 , Supplemental material, for details on coordinates and main characteristics of each waterbody). Located in the middle of two important watercourses, the St-Lawrence River along the south shore and the Rivière des Prairies on the north shore, the Island of Montréal is the most populated city in the province of Québec, with 1 784 000 inhabitants (City of Montréal, 2022: http://ville.montreal.qc.ca ) and the second-most populous city in Canada. With a total area of 432.8 km 2 , the city is dominated by residential and industrial zones (Fig. 1 ). The waterbodies studied herein cover a large range of environmental features and site conditions and represent the normal typology of freshwater systems found in North America’s large cities (Clifford and Hefferman 2018). Most of the waterbodies (17 of 20) were artificial ponds or reservoir constructed during the last century in municipal parks and residential zones for water retention and recreation. The remaining tree were marshes formed naturally by the hydrological network in large recreational parks. Waterbody local environments Water quality data for all 20 waterbodies were obtained from the monitoring program of the city of Montréal (RMSA, Réseau de suivi du milieu aquatique: http://ville.montreal.qc.ca/ ). For the abiotic variables, we selected nutrients (ammonium, total phosphorus, total organic carbon) and contaminants (copper, sodium) as water quality variables. We also estimated morphometric (depth, surface area), and physical (Secchi water transparency) variables. For the biotic variables, we visually assessed the cover of macrophytes in the littoral zone of each waterbody using five cover classes based on the absence of macrophytes (0% = 0) to increasing percentages of water surface cover ( 80% = 5). Six waterbodies did not have any detectable macrophytes, while the remaining 14 were covered with emergent, submerged, and floating plants at different extent. Finally, we also considered management practices in 7 temporary waterbodies where water was drained in the aqueduct system before winter (see Table S1 , Supplemental material). Landscape variables and land use types at different scales We regrouped seven of the 13 major land use categories defined by the city of Montréal into three categories: buildings with yards (residential or institutional), buildings surrounded by concrete (commercial, office, parking or industry), and park/green space (Fig. 1 ). Using QGIS, an open-source geospatial analysis software, we separated the 20 waterbodies from the hydrographic layer. Then, using a proximity function, we created four different buffer zones around each waterbody: 100 m, 500 m, 1000 m and 2000 m. Using an extraction function, the proportion of each land use type was calculated for each buffer zone. QGIS was also used to estimate the surface area, connectivity (distance to closest waterbody) and density (number of ponds in the surrounding) of waterbodies within each buffer zone. Macroinvertebrates sampling and analysis To cover intra-site variations, macroinvertebrates were sampled at three different months (June, July, and August) during summer 2011 in each of the 20 waterbodies. We sampled macroinvertebrates in the littoral zone because it provided more microhabitats (rocks, plants, woods and sediments) than the profundal zone, leading to higher biodiversity (De Sousa et al. 2008 ). Three replicates were collected in each waterbody, resulting in a total of nine samples per site for the summer season. Macroinvertebrates were collected by the same person using a kick net (46 x 23cm opening and 500 µm mesh size) dragged over 1.5 m. They were then screened on site and dispatched into 1 mm and 500 µm metal sieves, pooled into a large bucket and preserved in 75% ethanol solution. Before sorting, macroinvertebrates were stained with Rose Bengal and identified and counted in the laboratory using a stereomicroscope Leica WILD M3B at 64X, 160X, or 400X. As family-level identification is recommended for bioassessment surveys to evaluate macroinvertebrate responses to environmental changes (Reynoldson et al. 2001 ; Chessman et al. 2007 ), insects and mollusks were identified to the family level according to Merritt and Cummins ( 2008 ) and Clarke ( 1981 ). Other groups such as Oligochaeta, Nematoda, Hydracarina, Nemertea, Planaria, Hydra, and Hirudinea were identified to the order level according to Moisan and Pelletier ( 2008 ). Each taxon was then classified within a functional trophic group according to Merritt and Cummins ( 2008 ). This led to seven different functional groups: Collectors-gatherers, Collectors-filterers, Herbivores, Predators, Scavengers, Parasites, and Shredders (Table 1 ). These same functional groups were previously used to determine the structure of macroinvertebrates in temporary ponds of Central Italy (Bazzanti et al. 2010 ) and in bromeliads tanks along an urban-rural gradient in French Guiana (Talaga et al. 2017 ). They were also shown to be good indicators of the typology of European ponds (Céréghino et al. 2012 ), and they are more easily used for management purposes by municipal agencies than more defined functional traits (Menezes et al. 2010 ; Desrosiers et al. 2020 ). Table 1 General composition of the macroinvertebrates in the 20 waterbodies: average and relative abundances (%) at the functional (feeding trait), taxonomic (order) and taxa (family) level, where the most abundant taxonomic groups (orders and families) within each functional group are highlighted in bold within the table. Functional groups (Feeding trait) Taxonomic groups (Order) Taxa (Family) Abundance % Collectors-gatherers 23243 63.0 Diptera Chironomidae 13122 35.6 Diptera Stratiomyidae 24 0.1 Coleoptera Elmidae 1 0.1 Trichoptera Hydropsychidae 4 0.1 Ephemeroptera Baetidae 452 1.2 Ephemeroptera Caenidae 1485 4.0 Isopoda Asellidae 10 0.1 Annelidae Oligochaeta 8126 22.0 Collembola Poduridae 8 0.1 Collembola Sminthuridae 11 0.1 Collectors-filterers 1824 4.9 Diptera Ephyridae 10 0.1 Gastropoda Bithynidae 498 1.3 Bivalvia Sphaeridae 171 0.5 Crustacea Ostracoda 1145 3.1 Herbivores 3982 10.8 Diptera Tipulidae 3 0.1 Coleoptera Chrysomelidae 5 0.1 Coleoptera Curculionidae 4 0.1 Coleoptera Haliplidae 188 0.5 Hemiptera Corixidae 475 1.3 Trichoptera Hydroptilidae 230 0.6 Lepidoptera Pyralidae 96 0.3 Gastropoda Ancylidae 88 0.2 Gastropoda Lymnaeidae 708 1.9 Gastropoda Physidae 406 1.1 Gastropoda Planorbidae 1751 4.7 Gastropoda Valvatidae 18 0.1 Gastropoda Viviparidae 10 0.1 Predators 3258 8.8 Diptera Ceratopogonidae 1158 3.1 Diptera Chaoboridae 10 0.1 Diptera Culicidae 3 0.1 Diptera Empididae 12 0.1 Diptera Tabanidae 8 0.1 Coleoptera Dysticidae 72 0.1 Coleoptera Gyridinae 17 0.1 Coleoptera Hydrophilidae 8 0.1 Coleoptera Noteridae 17 0.1 Hemiptera Belostomidae 8 0.1 Hemiptera Gerridae 28 0.1 Hemiptera Hebridae 1 0.1 Hemiptera Macrovellidae 1 0.1 Hemiptera Mesoveliidae 1 0.1 Hemiptera Notonectidae 218 0.6 Hemiptera Pleidae 574 1.6 Hemiptera Veliidae 162 0.4 Trichoptera Polycentropidae 1 0.1 Odonata Aeshnidae 20 0.1 Odonata Coenagrionidae 765 2.1 Odonata Cordullidae 28 0.1 Odonata Lestidae 8 0.1 Odonata Libellulidae 117 0.3 Hydrozoa Hydridae 21 0.1 Scavengers 962 2.6 Amphipoda Gammaridae 98 0.3 Amphipoda Talitridae 837 2.3 Crustacea Cambaridae 27 0.1 Parasites 3401 9.2 Hydrachnidia Hydracarina 936 2.5 Annelida Hirudinae 117 0.3 Nematoda Nemata 2348 6.4 Shredders 219 0.6 Trichoptera Lepidostomidae 1 0.1 Trichoptera Leptoceridae 210 0.6 Trichoptera Phryganidae 8 0.1 Total 36889 100.0 Data analyses Prior analysis to test space-time variation (Legendre et al. 2012) in macroinvertebrate taxonomic composition showed that spatial variation among waterbodies was much more important (R 2 = 0.655) than temporal variation during summer (R 2 = 0.047), and as such, the space-time interaction was not significant ( P = 0.42 after 999 permutations) (Robert, 2016 ). We thus opted to average summer values of the abundances of taxonomic and functional groups for all other statistical analyses. To first describe spatial variation in macroinvertebrate diversity, we calculated the taxa richness within each water body (α diversity) and in the urban region (γ diversity). To then explore whether the macroinvertebrate communities of certain waterbodies were more similar, we used a fuzzy clustering analysis based on the mean summer abundance of i) the taxonomic groups and ii) the functional groups. Lastly, to determine whether variation in taxonomic and functional composition among the 20 waterbodies was due to environmental filtering, we applied a constrained ordination (a distance-based redundancy analyses, db-RDA) separating all explanatory variables into seven sets, each representing different land use and local waterbody condition, as well as waterbody connectivity and density, and management. The first set consisted of the land use variables (buildings with yards, buildings surrounded by concrete, and park/green space) in the four buffer zones surrounding each pond. The remaining six were sets of local waterbody condition: 1) nutrients (ammonium, total phosphorous and total organic carbon), 2) contaminants (copper and sodium), 3) physical environment (depth, surface area, water transparency), 4) macrophyte cover class, 5) pond connectivity and density (distance to closest water body and number of ponds in the surrounding) and 6) management (winter drainage). The explanatory power of each set of variables was tested in isolation (i.e., running six separate db-RDAs). We then evaluated combinations of the six different sets, iteratively adding and removing sets, to find the combination that led to the highest adjusted R 2 . Akin to AIC, the adjusted R 2 imposes a penalty for the number of parameters in the model, making it a good candidate for multivariate model comparisons. Finally, to determine the most parsimonious model and to identify specific key drivers of the variation in macroinvertebrate taxonomic and functional composition among waterbodies, we ran a stepwise selection process on a db-RDA with all explanatory variables (full model). This approach helped determine the interplay among variables measured at different spatial scale. Prior to analysis, environmental data were transformed to reduce skewness and standardized to mean 0 and variance units (Borcard et al. 2011 ). As abiotic data were monitored by the city throughout the summer period, while macroinvertebrates were sampled at three specific dates, we used the mean across the summer for both environmental and macroinvertebrate variables in our models. A square-root transformed Bray-Curtis dissimilarity was applied to the taxonomic and functional group abundance matrix to dissociate samples from each other and visualize the differences in the community. Analyses were performed using the {tidyverse} (for data manipulation), {cluster} (for fuzzy clustering), and {vegan} (for data transformation, clustering, and constrained ordination) packages in R (R Core Team 2021 ). In particular, for the fuzzy clustering we first used the vegdist() function to calculate the Bray-Curtis (percentage difference) distance (with method = “bray”), followed by a K-means analysis using the cascadeKM() function (with criterion = “ssi” for a simple structure index) to identify the optimal number of clusters, to then finally realize the fuzzy clustering using the fanny() function. For the db-RDA, we used the capscale() function with dist = “bray” and sqrt.dit = TRUE to run the models, anova() with by = “term” to assess the significance of each term sequentially from first to last, anova() with by = “NULL” to assess the overall model significance, RsquareAdj() to calculate the R 2 and R 2 -adj, and ordiR2step() with Pin = 0.05 to perform a forward selection of variables with a limit p-value < 0.05. Results Macroinvertebrate richness, taxonomic and functional composition A total of 60 macroinvertebrate families (taxa level) represented by 17 orders (taxonomic groups) were recorded in the 20 waterbodies during summer 2011 (Table 1 ). At the taxa (family) and waterbody levels, we found that mean local richness was 26 taxa but varied greatly from 10 to 41 taxa across waterbodies (Table S2, supplemental material). Nine waterbodies (Bizzard, JBN, JBA, Heritage, Angrignon, Marais des castors, Montigny, Lac des castors, Lacoursière) showed high taxa-level richness (> 25 taxa). Seven waterbodies (Prairies, Battures, Jarry, Liesse, Lafontaine, Brunante, Cygnes, Pratt2) had medium taxa richness (15–25 taxa) whereas the three others (Beaubien, Centenaire, Pratt1) showed low taxa richness (< 15 taxa). Macroinvertebrate abundances were also highly variable at the taxa level, ranging from 88 to 7516 individuals (Table S2, supplemental material). Five waterbodies (Lac des castors, Bizard, JBN, JBA, Marais des castors) showed the highest taxa densities (> 3000); seven waterbodies (Heritage. Lacoursière, Pratt1, Brunante, Angrignon, Lafontaine, Montigny) showed moderate densities (1000–3000) while the others (Prairies, Battures, Jarry, Cygnes, Pratt2, Liesse, Beaubien, Centenaire) showed low densities (< 1000). When examining communities at the level of functional trait, we found globally that Collectors-gatherers were the dominant functional group (63%), followed by the Herbivores (11%), and the Predators and Parasites (9% each) (Table 1 , Fig. 2 a). The Collectors-gatherers (largely composed of the Diptera Chironomidae and Annelida Oligochaeta taxonomic families; Table 1 , Fig. 2 b) were highly dominant in Pratt1, Lac des castors, Brunante, Liesse, JBN, Pratt2 and Centenaire. The Herbivores (largely composed of the Gastropoda Pulmonata: Planorbidae, Lymnaeidae and Physidae families; Table 1 ) were dominant in Prairies, Lacoursière and Lafontaine (Fig. 2 a, b). The Collectors-filterers (Gastropoda Prosobranchia: Bithynidae and Crustacea Ostracoda families; Table 1 ) were the most frequent in Beaubien and Lafontaine (Fig. 2 a, b). Parasites (Nematoda, Hydracarina and Hirudinea families) were more abundant in Cygnes, Heritage and JBA, and the Predators (Diptera Ceratopogonidae, Odonata Coenagrionidae, Hemiptera Pleidae families) in Bizard, Heritage, and Marais des castors (see also Table S2, supplemental material). In parallel, two major taxonomic groups, the Diptera (Chironomidae, Ceratopogonidae) and the Annelida (Oligochaeta) accounted respectively for 39% and 22% of total abundance (Table 1 ; Fig. 2 b). We also noted that the dominance pattern indicated a strong inverse gradient across waterbodies in both the taxonomic (order level) and functional (trait) groups (Fig. 2 ). The dominance of the Collectors-gatherers (Diptera, Annelida, Ephemeroptera) in the less diverse communities was inversely related to a dominance of the Herbivores (Gastropoda Pulmonata, Hemiptera, Trichoptera), Collectors-filterers (Gastropoda Bithynidae, Crustacea Ostracoda), Predators (Odonata) and Parasites (Nematoda, Hydracarina) in the most diverse communities. This pattern did not hold, however, when viewing the finer taxa (family) level, where for instance groups dominated by Collectors-gatherers ranked relatively high or low in taxa level richness (Table 2 ). Table 2 Typology of macroinvertebrate communities based on functional (trait) and taxonomic (order) groups (mean ± sd). See Fig. 3 for the waterbodies included in each group: Group 1 (blue), Group 2 (green), Group 3 (dark yellow), Group 4 (pink). Group1 Group 2 Group 3 Group 4 Functional (trait) groups Collectors-gatherers Herbivores Parasites Predators Collectors-filterers Scavengers Shredders 70.8 \(\pm\) 20.6 6.3 ± 6.2 9.4 ± 8.5 8.9 ± 7.9 2.8 ± 2.0 1.3 ± 2.2 0.5 ± 0.6 65.8 ± 11.9 11.8 ± 7.6 7.9 ± 10.3 4.8 ± 4.3 7. 1 ± 14.8 2.2 ± 3.7 0.4 ± 0.7 39.8 ± 7.4 10.4 ± 0.9 14.3 ± 2.7 15.5 ± 1.0 1.5 ± 0.7 16.7 ± 1.5 1.8 ± 0.6 16.5 ± 14.1 46.8 ± 20.5 6.1 ± 9.0 8.2 ± 7.9 19.5 ± 17.2 2.1 ± 1.8 0.7 ± 1.2 Taxonomic (order) groups Diptera Annelida Gastropoda Nematoda Ephemeroptera Hemiptera Crustacea Odonata Hydracarina Amphipoda Trichoptera Others 52.7 ± 21.4 18.5 ± 12.0 4.7 ± 5.2 5.7 ± 6.5 5.3 ± 3.0 4.0 ± 4.5 1.9 ± 2.2 1.7 ± 2.7 1.4 ± 1.2 1.3 ± 2.3 1.0 ± 1.1 1.9 ± 2.2 40.0 ± 11.7 21.1 ± 14.5 4.4 ± 4.6 2.9 ± 3.1 7.1 ± 9.5 6.6 ± 6.6 7.2 ± 14.7 2.3 ± 3.0 3.3 ± 7.1 1.4 ± 2.3 2.1 ± 2.8 1.5 ± 1.3 17.9 ± 3.2 21.8 ± 10.5 15.0 ± 18.1 9.7 ± 5.9 5.6 ± 2.8 3.5 ± 2.4 2.2 ± 1.9 5.6 ± 3.5 7.5 ± 1.7 8.8 ± 9.0 1.6 ± 1.3 0.7 ± 0.3 9.3 ± 4.9 1.7 ± 0.9 53.6 ± 18.5 0.3 ± 0.3 0.7 ± 0.8 8.6 ± 4.0 15.8 ± 19.0 0.5 ± 0.7 0.1 ± 0.02 2.4 ± 2.4 3.2 ± 4.5 3.8 ± 0.6 Total abundance 3768 ± 1668 377 ± 149 1862 ± 703 973 ± 336 Taxa richness 34 ± 7 18 ± 5 35 ± 3 22 ± 4 Pond typology Fuzzy clustering based on the abundances of functional and taxonomic groups identified four types of macroinvertebrate pond communities (Fig. 3 ). Although very similar in clustering, waterbodies included in each cluster were slightly different depending on whether the clustering used the taxonomic order or functional trait groupings. Considering the functional composition (Fig. 3 a, Table 2 ), Group 1 (blue) consisted of four ponds in municipal (Pratt1) and recreational (Lac des castors, JBN, JBA) parks, two lakes in a residential zone (Heritage, Brunante), and two marshes (Marais des Castors, Bizzard). Group 1 waterbodies largely supported communities that while dominated by one major functional group (Collectors-gatherers at 71%), were highly diverse (average richness of 34 taxa) and abundant (3768 ind.,) at the taxa level (Table 2 ). In these small and shallow waterbodies, nutrient concentrations were moderate, and the macrophyte cover was important (Table S3, Supplemental material). Group 2 (green) comprised four ponds located in municipal (Pratt2, Beaubien) and recreational (Jarry, Liesse) parks, and three lakes in residential zones (Centenaire, Battures, Cygnes). Group 2 waterbodies were also dominated by the Collectors-gatherers (66%, mostly Diptera, Annelida, and Ephemeroptera), but taxa-level abundance and richness was lower than in Group 1 (377 ind., 18 taxa; Table 2 ). In these waterbodies, both nutrient concentrations and macrophyte cover were lower (Table S3, Supplemental material). Group 3 (dark yellow) was comprised of one lake (Angrignon) in a recreational park, and one reservoir (Montigny). This small group had a more even distribution of functional groups with lower dominance of the Collectors-gatherers (40%) but higher importance of the Herbivores (10%), Parasites (14%), Predators (15%) and Scavengers (17%), while at the taxa level the ponds were more diverse and moderately abundant communities (1862 ind., 35 taxa; Table 2 ). In these large and deep waterbodies, nutrient concentrations and macrophyte cover were highly variable (Table S3, Supplemental material). Finally, Group 4 (pink) included one pond (Lafontaine) in a municipal park, one lake (Lacoursière) in a residential zone and one marsh (Prairies) dominated by Herbivores (47%) and Collectors-filterers (19%), richness and abundance were at the lower range as seen in Group 2. These waterbodies were very different from each other: one managed pond with low nutrients and without macrophytes (Lafontaine), and one lake (Lacoursière) and one marsh (Prairies) that were nutrient enriched and highly covered by macrophytes (Table S3, Supplemental material). Considering the order-level taxonomic composition, clustering showed high similarity with the functional (trait) composition but some lakes as Heritage and Lacoursière were associated with Group 3, instead of Group 1 and Group 4. In general, waterbody clustering was more clearly defined when using functional groups. Indeed, more waterbodies shared a multi-cluster position based on taxonomic (order) composition (Beaubien, Jarry, Battures, Centenaire, Heritage, Lacoursière, Lac des castors, Marais des castors) compared to functional composition (Centenaire, Beaubien, Lacoursière) (Fig. 3 ). Influence of local waterbody condition and urban land use drivers Overall, the db-RDA identified key groups of correlated variables that explained variation of macroinvertebrate functional and taxonomic composition among waterbodies (Fig. 4 a, Table 3 ) (see also, Fig. S1 a, supplemental material). The greatest variation explained for functional group composition was noted when all variable sets were included in the model with land use at 100 m (R 2 -adj = 0.21), slightly outperforming the model with land use 500 m (R 2 -adj = 0.17). The two other runner up models included macrophyte cover class with land use factors within a 100 m (R 2 -adj = 0.10) or 500 m (R 2 -adj = 0.12) buffer (Table 3 ; Fig. 4 , S1). Using taxonomic (order) groups, the best two models included all variables with land-use factors within a 100 m (R 2 -adj = 14%) or 2000 m (R 2 -adj = 13%) buffer. As for functional groups, the best runner up combination of predictors sets included macrophyte cover in conjunction with land use within a 100 m (R 2 -adj = 0.08; Fig. 4 b) or 2000 m (R 2 -adj = 0.06; Fig. S1 b) radius. Table 3 Contributions of set of variables to variations in functional (trait) and taxonomic (order) groups for the models at each land-use scale (100, 500, 1000, and 2000 m radius). An asterisk next to a variable indicates the variable was significant, while an asterisk under the p-value column indicates that the overall model was significant, where “***” p < 0.001. “**” p < 0.01. “*” p < 0.05 and “.” < 0.1. Variables significant for both the functional and taxonomic groups are indicated by an ampersand (example “* & *” indicates that the variable was significant at the alpha level of 0.05 for both functional and taxonomic groups). Functional groups Taxonomic groups Set Variables within set Adjusted-R 2 p-value Adjusted-R 2 p-value Contaminants Copper 0.00 0.911 0.00 0.909 Sodium Nutrients Ammonium 0.038 0.16 0.020 0.236 Phosphorus Total Organic Carbon * & * Physical variables Water transparency 0.00 0.889 0.00 0.755 Morphometry Depth 0.00 0.785 0.00 0.701 Surface area Connectivity Distance to nearest waterbody 0.00 0.816 0.00 0.859 Number of waterbodies in the surrounding Macrophytes Macrophyte cover class * & * 0.049 0.046 * 0.037 0.037 Management Winter drainage 0.026 0.123 0.019 0.124 All variables Copper 0.027 0.429 0.00 0.536 Sodium Ammonium Phosphorus Total Organic Carbon *** & ** Water transparency Depth Surface area Distance to nearest waterbody Number of waterbodies in the surrounding Macrophyte cover class Land use 100m Parks 0.016 0.308 0.015 0.284 Buildings with yards Buildings surrounded by concrete Land use 500m Parks 0.002 0.453 0.00 0.509 Buildings with yards Buildings surrounded by concrete Land use 1000m Parks 0.00 0.535 0.00 0.539 Buildings with yards Buildings surrounded by concrete Land use 2000m Parks 0.00 0.482 0.01 0.387 Buildings with yards Buildings surrounded by concrete Nutrients + Macrophytes Ammonium 0.037 0.21 0.020 0.272 Phosphorus Total Organic Carbon Macrophyte cover class * & Nutrients + Land use 100m Ammonium 0.051 0.191 0.032 0.26 Phosphorus Total Organic Carbon * & * Parks Buildings with yards Buildings surrounded by concrete Macrophytes + Land use 100m Macrophyte cover class * & * 0.10 0.041* 0.079 0.031 * Parks Buildings with yards & Buildings surrounded by concrete Macrophytes + Nutrients + Land use 100m Ammonium 0.074 0.149 0.061 0.148 Phosphorus Total Organic Carbon Macrophyte cover class * & * Parks Buildings with yards & Buildings surrounded by concrete Using a stepwise selection on the full models with all variables included to identify specific target variables, we identified macrophyte cover and land use (presence of parks at 100 m and at 500 m or buildings with yards within a 2000 m radius) as the most important variables influencing variations in taxonomic and functional composition (red arrows in Fig. 5 and Fig. S2, supplemental material). Despite some heterogeneity, these factors separated the groups determined by fuzzy clustering. In particular, on the one hand, marshes with higher macrophyte cover surrounded by buildings with yards (Marais des castors, Bizzard, Heritage) and characterized by a dominance of Collector-gatherers at the functional level, but highly diverse and abundant communities at the taxa (family) level (Group 1, blue); on the other hand, the presence of parks around temporary ponds and lakes without or less macrophyte cover characterized by a slight increase in Herbivores and Collectors-filterers functional groups, but less diverse and abundant communities at the taxa level (Group 2, green). Variables not selected by forward selection, but important in the variable set models (Fig. 4 ) were management practices, where winter drainage was associated with lower taxa-level diversity in temporary ponds (Group 2, green, dominated by Diptera and Annelida), and nutrients (TP, TOC), associated with the Groups 3 (yellow) and 4 (pink) waterbodies that had a more diverse composition of functional groups (Table 2 ). Discussion First, our study indicated that Montréal’s blue spaces support high diversity of macroinvertebrates at the regional scale (up to 60 families detected across 20 ponds), equivalent to macroinvertebrate richness in farm ponds of agricultural zones in Spain (68 families: Fuentes-Rodriguez et al. 2013 ) and France (52 family-genus taxa: Céréghino et al. 2008b ). We acknowledge that our estimate of the regional pool may be underestimated when using family and order level groupings. If taxa were identified at the genus-species level, regional taxa pool in urban waterbodies of Montréal might have reached one hundred taxa, as found in field ponds in England (Hill and Wood 2014 ). Mean macroinvertebrate richness (26 families) and range of variation (10–41 families) were comparable to taxa richness and variation estimated in ephemeral waterbodies in agricultural karstic regions of Ireland (mean: 20 taxa; range: 7–26 taxa: Porst and Irvine 2009 ) but was higher than in urban ponds of poor ecological quality in northern England (range: 4–13 taxa: Noble and Hassall 2015 ) and in pristine alpine ponds in Switzerland (mean 11 taxa; range: 6–24: Oertli et al. 2008 ). Secondly, our study emphasizes previous statements on the importance of preserving different types of waterbodies in urban region to maximize diverse macroinvertebrate communities (Hassall et al. 2016 ; Pinel-Alloul et al. 2022 ). It highlights the convergence of typology with both functional (feeding trait) and coarse taxonomic (order level) approaches that determined relatively similar types of waterbodies (Groups 1–4). However, waterbody clustering was more clearly defined when using functional groups, offering a more efficient approach for biomonitoring. The waterbodies had an inverse gradient in major functional (trait) and taxonomic (order) groups, with waterbodies from Groups 1 and 2 being dominated by Collectors-gatherers, while waterbodies from Groups 3 and 4 had a greater representation of Herbivores, in association with Collectors-filterers, Parasites, Predators and Scavengers. This difference in dominance patterns could be indicative of the ecological integrity of urban aquatic habitats. The high occurrence of Collector-gatherers across many of our study ponds can be due in part to the ability of Diptera Chironomidae and Annelida Oligochaeta taxa to thrive in many different habitats (Hill et al., 2016 ; Wood et al., 2001 ) and tolerate pressures such as pond management (Hilsenhoff 1988 ; Desrosiers et al. 2020 ). Chironomids and oligochaetes, which can feed on organic matter that accumulates in sediments (Solimini et al. 2005 , 2008 ), were especially dominant in temporary ponds (Pratt1, Lac des castors, Liesse, Pratt2) and in permanent lakes of residential zones (Brunante, Centenaire), and thus able to resist relatively lower nutrient concentrations (TOC and/or TP). In contrast, the diverse composition of Herbivores, Collectors-filterers, Parasites, Predators and Scavengers in Groups 3 and 4 can be indicative of more nutrient enriched systems (i.e., higher TOC, TP and a healthy cover of macrophytes or periphyton). The Herbivores (especially Gastropoda Planorbidae and Physidae) were especially dominant in a marsh (Prairie) and a eutrophic lake (Lacoursière) covered by submerged macrophytes because these pulmonated gastropods can breathe at the water surface and feed on periphyton. They were also predominantly found in a transparent artificial pond without fish (Lafontaine), colonizing the concrete walls covered with periphyton in association with Collectors-filterers such as branchial gastropods (Bithynidae) and crustacean ostracods. These sensitive taxa with branchial respiration were also encountered in higher abundance in a municipal pond with floating and submerged plants covered with periphyton (Beaubien). The Parasites (Hydracarina, Nematoda) and Predators (Diptera Ceratopogonidae, Odonata) were more frequent in residential lakes (Cygnes, Heritage) and a marsh (Marais des castors) covered by macrophytes. Finally, the Scavengers (Amphipoda) were more frequent in a municipal lake (Angrignon) and a reservoir (Montigny) where macrophyte cover or high turbidity offered a refuge against fish predation. Third, our study showed that macroinvertebrate communities responded to a combination of abiotic and biotic factors related to waterbody condition and urban land use in the surrounding 100 m up to 2000 m radius. First, variation in functional and taxonomic composition was related to the heterogeneity in waterbody condition, notably to macrophyte cover, and at lesser extent to nutrients (total organic carbon, total phosphorus). Overall, aquatic vegetation (emergent and submerged macrophytes) was a key biotic variable structuring macroinvertebrate functional and taxonomic (order) composition in our urban systems, which is concordant with a recent study concerning the entire aquatic foodweb (Pinel-Alloul et al. 2022 ) and other studies carried out in temporary ponds (Hassall et al. 2011 ; Florencio et al. 2014 ), and river wetlands (Tessier et al. 2008 ; Schad et al. 2020 ). Macrophytes provide a wide range of functional niches for macroinvertebrates, good oxygenation, food resources from algal periphyton, and a refuge against predators (Bazzanti et al. 2010 ). In addition, the structural complexity (not measured in this study) provided by macrophytes may increase macroinvertebrate abundance and functional diversity (Walker et al. 2013 ). Nutrient (TP) and organic enrichment (TOC) also influenced macroinvertebrate composition shifting up the importance of the Herbivores (Gastropoda Planorbidae) especially in waterbodies well covered by macrophytes. In urban ponds, excess of nutrients might come from fertilizers applied on the catchment (lawn in parks and residential zones) and animal waste (ducks). In our study, we did not detect a significant effect of contaminants (Cu, Na) used for water treatment and road de-icing, although their concentrations were higher in waterbodies of Groups 3 and 4. As reported in other studies (Blicharska et al. 2017 ; Thornhill et al. 2017 ), our study emphasizes the effect of land use within a 100 m to 2000 m radius on macroinvertebrate communities, with important implications for the management of urban ponds. Thornhill et al. ( 2017 ) also found that pond assemblages were influenced by the degree of urbanization and the presence of naturalized land within 100 m of the pond’s edge. Others found that aquatic insect diversity in urban ponds was influenced by urbanisation within a 500 m buffer zone (Blicharska et al. 2017 ). Patenaude et al. ( 2015 ) indicated that a radius between 800 m and 1.8 km had the greatest influence on benthic macroinvertebrates in wetlands. The threshold buffer zone size may vary according to the taxa investigated or the type of urban matrix the pond is embedded in (Patenaude et al. 2015 ). The presence of green landscape such as parks and buildings with yards within a radius ranging from 100 m to 2000 m of waterbodies were retained as land use factors related to waterbody clustering in terms of functional and taxonomic composition. On the one hand, the presence of buildings with yards within a 100 m to 2000 m radius, in conjunction with a good cover of macrophytes, was associated to waterbodies of Group 1, the most diverse and abundant communities at the taxa (family) level, with the highest dominance of the Collector-gatherers (mainly Diptera chironomids, Annelida oligochaetes). The importance of parks in reducing nutrient runoff to aquatic ecosystems has indeed been demonstrated (Setälä 2017). Our results additionally emphasize the benefits of buildings with green backyards. This is interesting because it shows that green yards are sufficient to reduce nutrient runoff and support diverse aquatic communities at the family level. On the other hand, the presence of parks within a 100 m to 500 m radius, in conjunction with management (winter drainage), characterized waterbodies of Group 2; though less diverse and abundant communities at the taxa (family) level, these ponds were dominated by the ubiquitous Collector-gatherers at the functional level, followed by Herbivores. The separation between ponds surrounded by parks to those by building with yards in ordination space suggests that the type of green space is also impactful, as is their interaction with management practices and macrophyte cover. In the present study, the taxa poor communities observed in ponds of municipal parks (Group 2) result mainly from management practices as winter drainage that only enables the colonisation of ponds by resistant taxa such as Diptera Chironomidae and Annelida. Ultimately, our results emphasize a synergy between local waterbody condition, management, and urban landscape cover. Indeed, the coefficient of determination of models run on macrophyte cover, nutrients, and land use variables alone and combined were not additive, reinstating the importance of considering land use management as a key vector of nutrients to waterbodies, as well as the filtering/stabilizing effect of macrophytes within waterbodies. We observed that the average concentration of phosphorus was positively correlated with building surrounded by concrete (impervious surface) and therefore negatively correlated with building with yards. It is thus likely that concrete surfaces like asphalt allow nutrients to accumulate more easily in nearby waterbodies while yards with grass have a higher holding capacity. To conclude, urban ponds are greatly important to sustain biodiversity in cities such as Montréal (this study, Pinel-Alloul et al. 2022 ) and Stockholm (Blicharska et al. 2017 ). To some extent, those small ecosystems could contribute to global biodiversity (Parris 2006 and 2016 ). Our study suggests that approaches based on coarse functional and taxonomic (family to order level) components would be very useful for biomonitoring of urban ponds, without a fine determination at the genus or species level. Management of urban ponds and lakes should favor green landscape such as parks and yards in the near (100 m) and regional (2000 m) vicinity while minimizing drastic management practices such as winter drainage to sustain urban aquatic biodiversity. Declarations Ackowledgements We thank a group of graduate students (El Amine Mimouni, Adrien André, Joseph Nziéleu Tchapgnouo) who assisted during field sampling. We are particularly indebted to Ginette Méthot (research assistant) and Louise Cloutier (manager of the Ouellet Robert insect collection) for helping in macroinvertebrate taxonomic analysis. We are also thankful for the field support and water quality data provided by the Direction of Environment of the City of Montréal. This article is a contribution of the GRIL (Groupe de Recherche Interuniversitaire en Limnologie). Author contributions Eric Harvey (EH) and Bernadette Pinel-Alloul (BPA) conceptualized the research. Macrobenthos taxonomic data come from the master thesis of Maryse Robert (laboratory of BPA). Audrey Robert (AR, Project Honor, undergraduate student, laboratory EH) analyzed the macrobenthos data, determined the functional groups, carried out the QGIS and statistical analyses and drafted the manuscript and produced some figures. Zofia E Taranu (ZET) supervised the statistical analysis, carried out the fuzzy clustering, and created additional figures. All authors participated to the final redaction of the manuscript. Funding This research was partially supported by grants from the Natural Science and Engineering Research Council and the Fonds Québécois de la Recherche sur la Nature et les Technologies to BPA and EH. 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Smith N, Georgiou M, King AC, Tieges Z, Webb S, Chastin S (2021) Urban Blue Spaces and Human Health: A Systematic Review and Meta-Analysis of Quantitative Studies. Cities 119: 103413. https://doi.org/10.1016/j.cities.2021.103413 Solcerova A, van de Ven F, van de Giesen N (2019) Nighttime Cooling of an Urban Pond. Front. Earth Sci. 7: 156. https://doi:10.3389/feart.2019.00156 Solimini AG, Della Bella V, Bazzanti M (2005) Macroinvertebrate size spectra of Mediterranean ponds with different hydroperiod length. Aquat. Conser: Mar. Fresh. Ecost. 15: 601-611. Solimini AG, Bazzanti M, Ruggiero A, Carchini G (2008) Developing a multimetric index of ecological integrity based on macroinvertebrates of mountain ponds in central Italy. Hydrobiologia 597: 109-123. Talaga S, Dézerald O, Carteron A, Lery C, Carrias J-F, Céréghino R, Dejean A (2017) Urbanization impacts the taxonomic and functional structure of aquatic macroinvertebrate communities in a small Neotropical city. Urban Ecosyst. 20: 1001-1009. Doi: 10.1007/s11252-017-0653-6 Tessier C, Cattaneo A, Pinel-Alloul B, Hudon C, Borcard D (2008) Invertebrates communities associated with metaphyton and emergent and submerged macrophytes in a large river. Aquat. Sci. 70: 10-20. Thornhill I, Batty L, Death RG, Friberg NR, Ledger ME (2017) Local and Landscape Scale Determinants of Macroinvertebrate Assemblages and Their Conservation Value in Ponds across an Urban Land-Use Gradient. Biodivers. Conserv. 26(5): 1065‑1086. https://doi.org/10.1007/s10531-016-1286-4 United Nations, Department of Economic and Social Affairs, Population Division (2019) World Urbanization Prospects 2018: Highlights (ST/ESA/SER.A/421). Van den Brink PJ, Alexander AC, Desrosiers M, Goedkoop W, Goethals PLM, Liess M, Dyer SD (2011) Traits-based approaches in bioassessment and ecological risk assessment: Strengths, weaknesses, opportunities and threats. Integr Environ Assess Manag. 7 : 198-208. Van Kleef H, Verberk W, Leuven R., Esselink H, van der Velde G, van Duinen GH (2006) Biological traits successfully predict the effects of restoration management on macroinvertebrates in shallow softwater lakes. Hydrobiologia 565: 201-216. Völker S, Kistemann T (2011) The impact of blue space on human health and well-being - Salutogenetic health effects of inland surface waters: a review. Int. J. Hyg. Environ. 214: 449–460. Völker S, Kistemann T (2013) I’m always entirely happy when I’m here! Urban blue enhancing human health and well-being in Cologne and Düsseldorf, Germany. Soc. sci. med. 78: 113–124. Walker PD, Wijnhovenb S, van der Veldea G (2013) Macrophyte presence and growth form influence macroinvertebrate community structure. Aquat. Bot. 104: 80–87. Werner P (2011) The Ecology of urban areas and their functions for species diversity. Landsc. Ecol. Eng. 7: 231-240. White M, Smith A, Humphryes K, Pahl S, Snelling D, Depledge M (2010) Blue space: the importance of water for preference, affect, and restorativeness ratings of natural and built scenes. J. Environ. Psychol. 30: 482–493. Wood PJ, Greenwood MT, Barker SA, Gunn J (2001) The Effects of Amenity Management for Angling on the Conservation Value of Aquatic Invertebrate Communities in Old Industrial Ponds. Biol. Conserv. 102(1): 17‑29. https://doi.org/10.1016/S0006-3207(01)00087-8 Additional Declarations No competing interests reported. Supplementary Files SupplementalmaterialRobertalFinal.docx 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. 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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-3891411","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268978033,"identity":"b8594aa5-49ab-40a2-86d1-22591ec7170b","order_by":0,"name":"Audrey Robert","email":"","orcid":"","institution":"Université du Québec à Trois-Rivières","correspondingAuthor":false,"prefix":"","firstName":"Audrey","middleName":"","lastName":"Robert","suffix":""},{"id":268978034,"identity":"621be0b7-5d1e-47c0-bda1-e5b581120747","order_by":1,"name":"Bernadette Pinel-Alloul","email":"","orcid":"","institution":"Université de Montréal","correspondingAuthor":false,"prefix":"","firstName":"Bernadette","middleName":"","lastName":"Pinel-Alloul","suffix":""},{"id":268978035,"identity":"f45e2cee-aec4-4423-9d2a-fb287b9632fe","order_by":2,"name":"Zofia E. Taranu","email":"","orcid":"","institution":"Environment and Climate Change Canada, ECCC","correspondingAuthor":false,"prefix":"","firstName":"Zofia","middleName":"E.","lastName":"Taranu","suffix":""},{"id":268978036,"identity":"bb158d39-c337-4ecd-9cf1-85d1e074c821","order_by":3,"name":"Eric Harvey","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYFACxgYQArMekKyF2YCBwYB4XSDAJkGUFn7pw40fGHfY5Rsc7zGr5in7k8/Afhi/AyX7EpslGM8kW244c8bsNs85A8sGnjT8VhmcYWxjYGxjNpCckWN2m7fNwIBBgoDr7CFa6g0k578xK4ZoYf+A3xYesJbDBvwSPGbMEC08+G2ROMPYLJHYdtyAnyetWHLOOWMDNp6cArxa+HvYH3742FZtwMZ+eOOHN2VyBvzsxzfg1QIGCWCSA+geNjAiGrA/IE39KBgFo2AUjBgAAI3NOhirP2t1AAAAAElFTkSuQmCC","orcid":"","institution":"Université du Québec à Trois-Rivières","correspondingAuthor":true,"prefix":"","firstName":"Eric","middleName":"","lastName":"Harvey","suffix":""}],"badges":[],"createdAt":"2024-01-23 15:05:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3891411/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3891411/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50148979,"identity":"b566cf5e-7f1d-443f-941f-ce59f9dfc3fa","added_by":"auto","created_at":"2024-01-25 09:26:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1619963,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the 20 waterbodies under study and land use throughout the island of Montréal (Québec. Canada).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3891411/v1/48dc19ed14ffac890cb8c11f.png"},{"id":50148977,"identity":"d2c38a95-ea81-4ba3-aa5e-60f92a84a7cb","added_by":"auto","created_at":"2024-01-25 09:26:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":262063,"visible":true,"origin":"","legend":"\u003cp\u003eMacroinvertebrate community structure in the 20 waterbodies during summer 2011 based on the a) functional (feeding trait) and b) taxonomic (order) groups, where ponds are ordered in decreasing relative abundance of Collector-gatherers in both panels but colour-coded by respectively fuzzy clustering membership (i.e., functional vs taxonomic shown in Figure 3)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3891411/v1/266f058392770507579eb722.png"},{"id":50149480,"identity":"f0b4bdd4-4d1e-4e0f-b372-3595fdc67458","added_by":"auto","created_at":"2024-01-25 09:34:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":202052,"visible":true,"origin":"","legend":"\u003cp\u003eFuzzy clustering. a) Clusters based on the functional (trait) and b) taxonomic (order) groups. The different clusters are represented by different colors. Group 1: blue, Group 2: green, Group 3: dark yellow, Group 4: pink.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3891411/v1/231691ea40059a40b63b3282.png"},{"id":50149776,"identity":"2514dd40-3ac8-4f6a-96f4-0077a0e5b919","added_by":"auto","created_at":"2024-01-25 09:42:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":40474,"visible":true,"origin":"","legend":"\u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e of candidate models with a) functional (trait) and b) taxonomic (order) groups with 100 m land use buffers. Colors represent a gradient of performance from worst (red) to best (green).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3891411/v1/19bd8bffeec362b2b4ee78cf.png"},{"id":50149478,"identity":"f86a2034-4750-42f5-ba6b-14cdc4d26e02","added_by":"auto","created_at":"2024-01-25 09:34:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":360599,"visible":true,"origin":"","legend":"\u003cp\u003eDistance based RDA combining all variables using\u0026nbsp; land use with a buffer of 100 m and all other predictors for a) the functional (trait) (R\u003csup\u003e2\u003c/sup\u003e-adj = 0.21) and b) taxonomic (order) (R\u003csup\u003e2\u003c/sup\u003e-adj = 0.19) groups. The red arrows indicate the variables selected by the stepwise approach.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3891411/v1/be0dfd8374531d2b33b24b4e.png"},{"id":54197876,"identity":"c8770c2b-d4bc-44f6-aa83-0bd6f5a18870","added_by":"auto","created_at":"2024-04-06 02:37:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2602162,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3891411/v1/638bd0db-c9a0-4218-9ce5-04da2ec17f89.pdf"},{"id":50148980,"identity":"280c0d4c-ab26-4273-a55f-55536448f811","added_by":"auto","created_at":"2024-01-25 09:26:05","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":582259,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalmaterialRobertalFinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-3891411/v1/bee012b9cab8192fccb9a71d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Green landscape and macrophyte cover influence macroinvertebrate taxonomic and functional composition in urban waterbodies at multiple spatial scales","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe planet is urbanizing at a fast pace (Seto et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), with projected increases in urban areas from 55% in 2018 to 68% by 2050 (United Nations 2019), which poses a serious threat to biodiversity and ecosystem health (Grimm et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Dudgeon et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Marzluff et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; McDonnell and Hahs \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Increasing human density comes with the challenge of maintaining the function of urban ecosystems while mitigating the loss in ecosystem services. In the context of global change, where extreme events such as storms, heatwaves, and droughts are expected to increase, it is critical to understand the impact of the ongoing expansion of urban areas. Increasing evidence suggests that the proper management of green and blue space configurations in urban landscapes can reduce heat island effects (Gunawardena et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), increase mental (White et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Smith et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and physical (V\u0026ouml;lker and Kistemann \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Br\u0026uuml;ckner et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) human health, while their mismanagement can have the opposite effect (Gunawardena et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Solcerova et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, the integration of green and blue spaces into urbanized landscapes is now at the forefront of new city management policies as a means to simultaneously support environmental, economic, and human development goals (Pickett et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Niemel\u0026auml; \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Anquez and Herlem \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; McPhearson et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBlue spaces, notably urban ponds, lakes and marshes, represent a set of complex habitat mosaics that have important ecological function, as well as social and economic uses (Werner \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hassall \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Clifford and Hefferman 2018). However, urban aquatic habitats may suffer greater biodiversity threats than terrestrial habitats due to catchment disturbances (residential and industrial land use and mismanagement), increased nutrient loading, contamination, and species invasion (Br\u0026ouml;nmark and Hansson \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Hassall \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Moggridge et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Despite their importance within the urban landscape, we still know little about urban waterbodies, their role as a refuge for biodiversity (Werner \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Chester and Robson \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), how to maximize conservation management (Hassall et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and how they respond to stressors operating at multiple scales (at the local scale of waterbodies and at the landscape scales) (Blicharska et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Thornhill et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Oertli and Parris \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The latter is especially important in the context of managing a suite of urban land use types while minimizing the loss of ecosystem function and services in blue spaces. Many studies have already highlighted the importance of a wide range of pond types to provide heterogeneous habitats both in urban (Hassall et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pinel-Alloul et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and rural (Biggs et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2005\u003c/span\u003er\u0026eacute;ghino et al. 2008a-c) regions. Despite a generally lower local diversity than their rural counterparts (Savić et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hassall and Anderson, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), urban waterbodies are also often more heterogeneous in abiotic conditions at the landscape scale, which could potentially lead to similar gamma diversity (Hill et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In that context, urban and rural ponds are considered as a model system in conservation, ecology and evolutionary biology (De Meester et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Parris \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eApproaches using both taxonomic and functional composition have been instrumental for identifying groups of taxa with uniform responses to environmental gradients, as well as keystone traits whose loss may lead to a rapid collapse of the ecosystem (McGill et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Baird et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Given the tight association between trait representation in a community and habitat conditions, macroinvertebrate functional traits have been used to predict restoration management in shallow freshwater lakes (Van Kleef et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and to evaluate anthropogenic impacts on European ponds (C\u0026eacute;r\u0026eacute;ghino et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and large rivers (Desrosiers et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, despite the important effort devoted to integrating a functional approach into freshwater biomonitoring and ecological risk assessment (Menezes et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Van den Brink et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), further research is needed to improve the application of functional traits to assess health and integrity of aquatic ecosystems facing human disturbances such as urban ponds and lakes (Marzluff et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Purcell et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we aim to understand how factors related to local waterbody environment and regional land use could influence the taxonomic and functional composition of aquatic communities in urban ponds, using macroinvertebrates as model organisms. Building from previous studies (Castillo et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pinel-Alloul et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Talaga et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Thornhill et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), we measured environmental variables known to be significantly related to macroinvertebrate communities at local and landscape scales, namely, local waterbody properties and urban land use at different spatial scales. Our objective was to disentangle the relative importance of different types of cross-scale drivers that are generally studied in isolation. To do so, we tested the relative importance (and interaction) of (i) abiotic factors (water transparency, morphometry, connectivity and density of waterbodies), (ii) water quality (nutrients and contaminants), (iii) macrophyte cover, (iv) urban land use (at 100\u0026ndash;2000 m radius from each waterbody), and (v) management practices (winter drainage). The selected abiotic factors are expected to influence macroinvertebrate communities via direct physiological impacts, and thus, mainly reflect population-level adaptative tolerance to environmental change (Heino \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The water-quality variables are expected to influence community-level processes via different taxa tolerances to organic matter and contaminants (Hildrew and Townsend \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). Macrophyte cover has been shown to be an important driver of macroinvertebrate communities in periurban lakes and urban ponds by providing a habitat for feeding (periphyton) and refuge from predators (Declerck et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003er\u0026eacute;ghino et al. 2008c; De Sousa et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Pinel-Alloul et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Catchment land use has a direct influence on water quality (loading of nutrients and contaminants) (Carignan and Steedman \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Burcher et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), and thus can indirectly affect macroinvertebrate communities. Finally, management practices such as winter drainage can also affect aquatic communities of urban ponds (Pinel-Alloul et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Previous studies integrating landscape-scale drivers vary greatly in the scale considered, ranging from 50 m to 10 km from the waterbodies (Blicharska et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Thornhill et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Oertli and Parris \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). With that in mind, one of our key interests was to determine the scale at which the additive effect of waterbody condition and land use on macroinvertebrate communities was most prominent. Disentangling this cross-scale interaction in urban waterbodies is an essential step to identify where and to what extent management must take place, as well as key targets for the maintenance or restoration of urban blue spaces (Grimm et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2000\u003c/span\u003el et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy sites and urban landscape\u003c/h2\u003e \u003cp\u003eTo carry out this study, we used a dataset collected in the summer of 2011 (Robert \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) in 20 urban waterbodies (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) across the island of Montr\u0026eacute;al (Qu\u0026eacute;bec, Canada) (45.46\u0026ndash;45.69\u0026ordm;N; 73.50\u0026ndash;73.90\u0026ordm;W) (see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Supplemental material, for details on coordinates and main characteristics of each waterbody). Located in the middle of two important watercourses, the St-Lawrence River along the south shore and the Rivi\u0026egrave;re des Prairies on the north shore, the Island of Montr\u0026eacute;al is the most populated city in the province of Qu\u0026eacute;bec, with 1 784 000 inhabitants (City of Montr\u0026eacute;al, 2022: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ville.montreal.qc.ca\u003c/span\u003e\u003cspan address=\"http://ville.montreal.qc.ca\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the second-most populous city in Canada. With a total area of 432.8 km\u003csup\u003e2\u003c/sup\u003e, the city is dominated by residential and industrial zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The waterbodies studied herein cover a large range of environmental features and site conditions and represent the normal typology of freshwater systems found in North America\u0026rsquo;s large cities (Clifford and Hefferman 2018). Most of the waterbodies (17 of 20) were artificial ponds or reservoir constructed during the last century in municipal parks and residential zones for water retention and recreation. The remaining tree were marshes formed naturally by the hydrological network in large recreational parks.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eWaterbody local environments\u003c/h2\u003e \u003cp\u003eWater quality data for all 20 waterbodies were obtained from the monitoring program of the city of Montr\u0026eacute;al (RMSA, R\u0026eacute;seau de suivi du milieu aquatique: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ville.montreal.qc.ca/\u003c/span\u003e\u003cspan address=\"http://ville.montreal.qc.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). For the abiotic variables, we selected nutrients (ammonium, total phosphorus, total organic carbon) and contaminants (copper, sodium) as water quality variables. We also estimated morphometric (depth, surface area), and physical (Secchi water transparency) variables. For the biotic variables, we visually assessed the cover of macrophytes in the littoral zone of each waterbody using five cover classes based on the absence of macrophytes (0% = 0) to increasing percentages of water surface cover (\u0026lt;\u0026thinsp;20% = 1, 20\u0026ndash;40% = 2, 40\u0026ndash;60% = 3, 60\u0026ndash;80% = 4 and \u0026gt;\u0026thinsp;80% = 5). Six waterbodies did not have any detectable macrophytes, while the remaining 14 were covered with emergent, submerged, and floating plants at different extent. Finally, we also considered management practices in 7 temporary waterbodies where water was drained in the aqueduct system before winter (see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Supplemental material).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eLandscape variables and land use types at different scales\u003c/h2\u003e \u003cp\u003eWe regrouped seven of the 13 major land use categories defined by the city of Montr\u0026eacute;al into three categories: buildings with yards (residential or institutional), buildings surrounded by concrete (commercial, office, parking or industry), and park/green space (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Using QGIS, an open-source geospatial analysis software, we separated the 20 waterbodies from the hydrographic layer. Then, using a proximity function, we created four different buffer zones around each waterbody: 100 m, 500 m, 1000 m and 2000 m. Using an extraction function, the proportion of each land use type was calculated for each buffer zone. QGIS was also used to estimate the surface area, connectivity (distance to closest waterbody) and density (number of ponds in the surrounding) of waterbodies within each buffer zone.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMacroinvertebrates sampling and analysis\u003c/h2\u003e \u003cp\u003eTo cover intra-site variations, macroinvertebrates were sampled at three different months (June, July, and August) during summer 2011 in each of the 20 waterbodies. We sampled macroinvertebrates in the littoral zone because it provided more microhabitats (rocks, plants, woods and sediments) than the profundal zone, leading to higher biodiversity (De Sousa et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Three replicates were collected in each waterbody, resulting in a total of nine samples per site for the summer season. Macroinvertebrates were collected by the same person using a kick net (46 x 23cm opening and 500 \u0026micro;m mesh size) dragged over 1.5 m. They were then screened on site and dispatched into 1 mm and 500 \u0026micro;m metal sieves, pooled into a large bucket and preserved in 75% ethanol solution. Before sorting, macroinvertebrates were stained with Rose Bengal and identified and counted in the laboratory using a stereomicroscope Leica WILD M3B at 64X, 160X, or 400X. As family-level identification is recommended for bioassessment surveys to evaluate macroinvertebrate responses to environmental changes (Reynoldson et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Chessman et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), insects and mollusks were identified to the family level according to Merritt and Cummins (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and Clarke (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). Other groups such as Oligochaeta, Nematoda, Hydracarina, Nemertea, Planaria, Hydra, and Hirudinea were identified to the order level according to Moisan and Pelletier (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Each taxon was then classified within a functional trophic group according to Merritt and Cummins (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This led to seven different functional groups: Collectors-gatherers, Collectors-filterers, Herbivores, Predators, Scavengers, Parasites, and Shredders (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These same functional groups were previously used to determine the structure of macroinvertebrates in temporary ponds of Central Italy (Bazzanti et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and in bromeliads tanks along an urban-rural gradient in French Guiana (Talaga et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). They were also shown to be good indicators of the typology of European ponds (C\u0026eacute;r\u0026eacute;ghino et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and they are more easily used for management purposes by municipal agencies than more defined functional traits (Menezes et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Desrosiers et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\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\u003eGeneral composition of the macroinvertebrates in the 20 waterbodies: average and relative abundances (%) at the functional (feeding trait), taxonomic (order) and taxa (family) level, where the most abundant taxonomic groups (orders and families) within each functional group are highlighted in bold within the table.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunctional groups\u003c/p\u003e \u003cp\u003e(Feeding trait)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTaxonomic groups (Order)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTaxa\u003c/p\u003e \u003cp\u003e(Family)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbundance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollectors-gatherers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23243\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.0\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDiptera\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eChironomidae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e13122\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.6\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\u003eDiptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStratiomyidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\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\u003eColeoptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElmidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\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\u003eTrichoptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHydropsychidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\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\u003e\u003cb\u003eEphemeroptera\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBaetidae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e452\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.2\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\u003e\u003cb\u003eEphemeroptera\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eCaenidae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1485\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.0\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\u003eIsopoda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAsellidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\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\u003e\u003cb\u003eAnnelidae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eOligochaeta\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e8126\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.0\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\u003eCollembola\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoduridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\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\u003eCollembola\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSminthuridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCollectors-filterers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1824\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4.9\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\u003eDiptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEphyridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\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\u003e\u003cb\u003eGastropoda\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBithynidae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e498\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.3\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\u003eBivalvia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSphaeridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\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\u003e\u003cb\u003eCrustacea\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eOstracoda\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1145\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHerbivores\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3982\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e10.8\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\u003eDiptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTipulidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\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\u003eColeoptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChrysomelidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\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\u003eColeoptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCurculionidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\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\u003eColeoptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHaliplidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\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\u003e\u003cb\u003eHemiptera\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eCorixidae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e475\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.3\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\u003e\u003cb\u003eTrichoptera\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eHydroptilidae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e230\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6\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\u003eLepidoptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePyralidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\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\u003eGastropoda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAncylidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2\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\u003e\u003cb\u003eGastropoda\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eLymnaeidae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e708\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.9\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\u003eGastropoda\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePhysidae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e406\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.1\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\u003e\u003cb\u003eGastropoda\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePlanorbidae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1751\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.7\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\u003eGastropoda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValvatidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\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\u003eGastropoda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eViviparidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePredators\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3258\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e8.8\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\u003e\u003cb\u003eDiptera\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eCeratopogonidae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1158\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.1\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\u003eDiptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChaoboridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\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\u003eDiptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCulicidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\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\u003eDiptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmpididae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\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\u003eDiptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTabanidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\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\u003eColeoptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDysticidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\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\u003eColeoptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGyridinae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\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\u003eColeoptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHydrophilidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\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\u003eColeoptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNoteridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\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\u003eHemiptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBelostomidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\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\u003eHemiptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGerridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\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\u003eHemiptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHebridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\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\u003eHemiptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMacrovellidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\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\u003eHemiptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMesoveliidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\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\u003eHemiptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNotonectidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6\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\u003e\u003cb\u003eHemiptera\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePleidae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e574\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.6\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\u003eHemiptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVeliidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4\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\u003eTrichoptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePolycentropidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\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\u003eOdonata\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAeshnidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\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\u003e\u003cb\u003eOdonata\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eCoenagrionidae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e765\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.1\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\u003eOdonata\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCordullidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\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\u003eOdonata\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLestidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\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\u003eOdonata\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLibellulidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\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\u003eHydrozoa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHydridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eScavengers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e962\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.6\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\u003eAmphipoda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGammaridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\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\u003e\u003cb\u003eAmphipoda\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eTalitridae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e837\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.3\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\u003eCrustacea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCambaridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParasites\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3401\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e9.2\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\u003e\u003cb\u003eHydrachnidia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eHydracarina\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e936\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.5\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\u003eAnnelida\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHirudinae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\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\u003e\u003cb\u003eNematoda\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNemata\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2348\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eShredders\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e219\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.6\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\u003eTrichoptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLepidostomidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\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\u003e\u003cb\u003eTrichoptera\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eLeptoceridae\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e210\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6\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\u003eTrichoptera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhryganidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e36889\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData analyses\u003c/h2\u003e \u003cp\u003ePrior analysis to test space-time variation (Legendre et al. 2012) in macroinvertebrate taxonomic composition showed that spatial variation among waterbodies was much more important (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.655) than temporal variation during summer (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.047), and as such, the space-time interaction was not significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.42 after 999 permutations) (Robert, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). We thus opted to average summer values of the abundances of taxonomic and functional groups for all other statistical analyses.\u003c/p\u003e \u003cp\u003eTo first describe spatial variation in macroinvertebrate diversity, we calculated the taxa richness within each water body (α diversity) and in the urban region (γ diversity). To then explore whether the macroinvertebrate communities of certain waterbodies were more similar, we used a fuzzy clustering analysis based on the mean summer abundance of i) the taxonomic groups and ii) the functional groups. Lastly, to determine whether variation in taxonomic and functional composition among the 20 waterbodies was due to environmental filtering, we applied a constrained ordination (a distance-based redundancy analyses, db-RDA) separating all explanatory variables into seven sets, each representing different land use and local waterbody condition, as well as waterbody connectivity and density, and management. The first set consisted of the land use variables (buildings with yards, buildings surrounded by concrete, and park/green space) in the four buffer zones surrounding each pond. The remaining six were sets of local waterbody condition: 1) nutrients (ammonium, total phosphorous and total organic carbon), 2) contaminants (copper and sodium), 3) physical environment (depth, surface area, water transparency), 4) macrophyte cover class, 5) pond connectivity and density (distance to closest water body and number of ponds in the surrounding) and 6) management (winter drainage). The explanatory power of each set of variables was tested in isolation (i.e., running six separate db-RDAs). We then evaluated combinations of the six different sets, iteratively adding and removing sets, to find the combination that led to the highest adjusted R\u003csup\u003e2\u003c/sup\u003e. Akin to AIC, the adjusted R\u003csup\u003e2\u003c/sup\u003e imposes a penalty for the number of parameters in the model, making it a good candidate for multivariate model comparisons. Finally, to determine the most parsimonious model and to identify specific key drivers of the variation in macroinvertebrate taxonomic and functional composition among waterbodies, we ran a stepwise selection process on a db-RDA with all explanatory variables (full model). This approach helped determine the interplay among variables measured at different spatial scale.\u003c/p\u003e \u003cp\u003ePrior to analysis, environmental data were transformed to reduce skewness and standardized to mean 0 and variance units (Borcard et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). As abiotic data were monitored by the city throughout the summer period, while macroinvertebrates were sampled at three specific dates, we used the mean across the summer for both environmental and macroinvertebrate variables in our models. A square-root transformed Bray-Curtis dissimilarity was applied to the taxonomic and functional group abundance matrix to dissociate samples from each other and visualize the differences in the community. Analyses were performed using the {tidyverse} (for data manipulation), {cluster} (for fuzzy clustering), and {vegan} (for data transformation, clustering, and constrained ordination) packages in R (R Core Team \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In particular, for the fuzzy clustering we first used the vegdist() function to calculate the Bray-Curtis (percentage difference) distance (with method = \u0026ldquo;bray\u0026rdquo;), followed by a K-means analysis using the cascadeKM() function (with criterion = \u0026ldquo;ssi\u0026rdquo; for a simple structure index) to identify the optimal number of clusters, to then finally realize the fuzzy clustering using the fanny() function. For the db-RDA, we used the capscale() function with dist = \u0026ldquo;bray\u0026rdquo; and sqrt.dit\u0026thinsp;=\u0026thinsp;TRUE to run the models, anova() with by = \u0026ldquo;term\u0026rdquo; to assess the significance of each term sequentially from first to last, anova() with by = \u0026ldquo;NULL\u0026rdquo; to assess the overall model significance, RsquareAdj() to calculate the R\u003csup\u003e2\u003c/sup\u003e and R\u003csup\u003e2\u003c/sup\u003e-adj, and ordiR2step() with Pin\u0026thinsp;=\u0026thinsp;0.05 to perform a forward selection of variables with a limit p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMacroinvertebrate richness, taxonomic and functional composition\u003c/h2\u003e \u003cp\u003eA total of 60 macroinvertebrate families (taxa level) represented by 17 orders (taxonomic groups) were recorded in the 20 waterbodies during summer 2011 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). At the taxa (family) and waterbody levels, we found that mean local richness was 26 taxa but varied greatly from 10 to 41 taxa across waterbodies (Table S2, supplemental material). Nine waterbodies (Bizzard, JBN, JBA, Heritage, Angrignon, Marais des castors, Montigny, Lac des castors, Lacoursi\u0026egrave;re) showed high taxa-level richness (\u0026gt;\u0026thinsp;25 taxa). Seven waterbodies (Prairies, Battures, Jarry, Liesse, Lafontaine, Brunante, Cygnes, Pratt2) had medium taxa richness (15\u0026ndash;25 taxa) whereas the three others (Beaubien, Centenaire, Pratt1) showed low taxa richness (\u0026lt;\u0026thinsp;15 taxa). Macroinvertebrate abundances were also highly variable at the taxa level, ranging from 88 to 7516 individuals (Table S2, supplemental material). Five waterbodies (Lac des castors, Bizard, JBN, JBA, Marais des castors) showed the highest taxa densities (\u0026gt;\u0026thinsp;3000); seven waterbodies (Heritage. Lacoursi\u0026egrave;re, Pratt1, Brunante, Angrignon, Lafontaine, Montigny) showed moderate densities (1000\u0026ndash;3000) while the others (Prairies, Battures, Jarry, Cygnes, Pratt2, Liesse, Beaubien, Centenaire) showed low densities (\u0026lt;\u0026thinsp;1000).\u003c/p\u003e \u003cp\u003eWhen examining communities at the level of functional trait, we found globally that Collectors-gatherers were the dominant functional group (63%), followed by the Herbivores (11%), and the Predators and Parasites (9% each) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The Collectors-gatherers (largely composed of the Diptera Chironomidae and Annelida Oligochaeta taxonomic families; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) were highly dominant in Pratt1, Lac des castors, Brunante, Liesse, JBN, Pratt2 and Centenaire. The Herbivores (largely composed of the Gastropoda Pulmonata: Planorbidae, Lymnaeidae and Physidae families; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were dominant in Prairies, Lacoursi\u0026egrave;re and Lafontaine (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, b). The Collectors-filterers (Gastropoda Prosobranchia: Bithynidae and Crustacea Ostracoda families; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were the most frequent in Beaubien and Lafontaine (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, b). Parasites (Nematoda, Hydracarina and Hirudinea families) were more abundant in Cygnes, Heritage and JBA, and the Predators (Diptera Ceratopogonidae, Odonata Coenagrionidae, Hemiptera Pleidae families) in Bizard, Heritage, and Marais des castors (see also Table S2, supplemental material). In parallel, two major taxonomic groups, the Diptera (Chironomidae, Ceratopogonidae) and the Annelida (Oligochaeta) accounted respectively for 39% and 22% of total abundance (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). We also noted that the dominance pattern indicated a strong inverse gradient across waterbodies in both the taxonomic (order level) and functional (trait) groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The dominance of the Collectors-gatherers (Diptera, Annelida, Ephemeroptera) in the less diverse communities was inversely related to a dominance of the Herbivores (Gastropoda Pulmonata, Hemiptera, Trichoptera), Collectors-filterers (Gastropoda Bithynidae, Crustacea Ostracoda), Predators (Odonata) and Parasites (Nematoda, Hydracarina) in the most diverse communities. This pattern did not hold, however, when viewing the finer taxa (family) level, where for instance groups dominated by Collectors-gatherers ranked relatively high or low in taxa level richness (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\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\u003eTypology of macroinvertebrate communities based on functional (trait) and taxonomic (order) groups (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd). See Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for the waterbodies included in each group: Group 1 (blue), Group 2 (green), Group 3 (dark yellow), Group 4 (pink).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGroup 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunctional (trait) groups\u003c/p\u003e \u003cp\u003eCollectors-gatherers\u003c/p\u003e \u003cp\u003eHerbivores\u003c/p\u003e \u003cp\u003eParasites\u003c/p\u003e \u003cp\u003ePredators\u003c/p\u003e \u003cp\u003eCollectors-filterers\u003c/p\u003e \u003cp\u003eScavengers\u003c/p\u003e \u003cp\u003eShredders\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.8 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e 20.6\u003c/p\u003e \u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003cp\u003e9.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e \u003cp\u003e8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9\u003c/p\u003e \u003cp\u003e11.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003c/p\u003e \u003cp\u003e7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e \u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e \u003cp\u003e7. 1\u0026thinsp;\u0026plusmn;\u0026thinsp;14.8\u003c/p\u003e \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e \u003cp\u003e0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e \u003cp\u003e10.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003cp\u003e14.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003cp\u003e15.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003cp\u003e16.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.5\u0026thinsp;\u0026plusmn;\u0026thinsp;14.1\u003c/p\u003e \u003cp\u003e46.8\u0026thinsp;\u0026plusmn;\u0026thinsp;20.5\u003c/p\u003e \u003cp\u003e6.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e \u003cp\u003e8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e \u003cp\u003e19.5\u0026thinsp;\u0026plusmn;\u0026thinsp;17.2\u003c/p\u003e \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaxonomic (order) groups\u003c/p\u003e \u003cp\u003eDiptera\u003c/p\u003e \u003cp\u003eAnnelida\u003c/p\u003e \u003cp\u003eGastropoda\u003c/p\u003e \u003cp\u003eNematoda\u003c/p\u003e \u003cp\u003eEphemeroptera\u003c/p\u003e \u003cp\u003eHemiptera\u003c/p\u003e \u003cp\u003eCrustacea\u003c/p\u003e \u003cp\u003eOdonata\u003c/p\u003e \u003cp\u003eHydracarina\u003c/p\u003e \u003cp\u003eAmphipoda\u003c/p\u003e \u003cp\u003eTrichoptera\u003c/p\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.7\u0026thinsp;\u0026plusmn;\u0026thinsp;21.4\u003c/p\u003e \u003cp\u003e18.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0\u003c/p\u003e \u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e \u003cp\u003e5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e \u003cp\u003e5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003cp\u003e1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003cp\u003e1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7\u003c/p\u003e \u003cp\u003e21.1\u0026thinsp;\u0026plusmn;\u0026thinsp;14.5\u003c/p\u003e \u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e \u003cp\u003e2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003cp\u003e7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e \u003cp\u003e6.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e \u003cp\u003e7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.7\u003c/p\u003e \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003cp\u003e21.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003cp\u003e15.0\u0026thinsp;\u0026plusmn;\u0026thinsp;18.1\u003c/p\u003e \u003cp\u003e9.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003cp\u003e7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003cp\u003e8.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003cp\u003e53.6\u0026thinsp;\u0026plusmn;\u0026thinsp;18.5\u003c/p\u003e \u003cp\u003e0.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003cp\u003e8.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003cp\u003e15.8\u0026thinsp;\u0026plusmn;\u0026thinsp;19.0\u003c/p\u003e \u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal abundance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3768\u0026thinsp;\u0026plusmn;\u0026thinsp;1668\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e377\u0026thinsp;\u0026plusmn;\u0026thinsp;149\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1862\u0026thinsp;\u0026plusmn;\u0026thinsp;703\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e973\u0026thinsp;\u0026plusmn;\u0026thinsp;336\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTaxa richness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e34\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e18\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e35\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e22\u0026thinsp;\u0026plusmn;\u0026thinsp;4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePond typology\u003c/h2\u003e \u003cp\u003eFuzzy clustering based on the abundances of functional and taxonomic groups identified four types of macroinvertebrate pond communities (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Although very similar in clustering, waterbodies included in each cluster were slightly different depending on whether the clustering used the taxonomic order or functional trait groupings. Considering the functional composition (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), Group 1 (blue) consisted of four ponds in municipal (Pratt1) and recreational (Lac des castors, JBN, JBA) parks, two lakes in a residential zone (Heritage, Brunante), and two marshes (Marais des Castors, Bizzard). Group 1 waterbodies largely supported communities that while dominated by one major functional group (Collectors-gatherers at 71%), were highly diverse (average richness of 34 taxa) and abundant (3768 ind.,) at the taxa level (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In these small and shallow waterbodies, nutrient concentrations were moderate, and the macrophyte cover was important (Table S3, Supplemental material). Group 2 (green) comprised four ponds located in municipal (Pratt2, Beaubien) and recreational (Jarry, Liesse) parks, and three lakes in residential zones (Centenaire, Battures, Cygnes). Group 2 waterbodies were also dominated by the Collectors-gatherers (66%, mostly Diptera, Annelida, and Ephemeroptera), but taxa-level abundance and richness was lower than in Group 1 (377 ind., 18 taxa; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In these waterbodies, both nutrient concentrations and macrophyte cover were lower (Table S3, Supplemental material). Group 3 (dark yellow) was comprised of one lake (Angrignon) in a recreational park, and one reservoir (Montigny). This small group had a more even distribution of functional groups with lower dominance of the Collectors-gatherers (40%) but higher importance of the Herbivores (10%), Parasites (14%), Predators (15%) and Scavengers (17%), while at the taxa level the ponds were more diverse and moderately abundant communities (1862 ind., 35 taxa; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In these large and deep waterbodies, nutrient concentrations and macrophyte cover were highly variable (Table S3, Supplemental material). Finally, Group 4 (pink) included one pond (Lafontaine) in a municipal park, one lake (Lacoursi\u0026egrave;re) in a residential zone and one marsh (Prairies) dominated by Herbivores (47%) and Collectors-filterers (19%), richness and abundance were at the lower range as seen in Group 2. These waterbodies were very different from each other: one managed pond with low nutrients and without macrophytes (Lafontaine), and one lake (Lacoursi\u0026egrave;re) and one marsh (Prairies) that were nutrient enriched and highly covered by macrophytes (Table S3, Supplemental material). Considering the order-level taxonomic composition, clustering showed high similarity with the functional (trait) composition but some lakes as Heritage and Lacoursi\u0026egrave;re were associated with Group 3, instead of Group 1 and Group 4. In general, waterbody clustering was more clearly defined when using functional groups. Indeed, more waterbodies shared a multi-cluster position based on taxonomic (order) composition (Beaubien, Jarry, Battures, Centenaire, Heritage, Lacoursi\u0026egrave;re, Lac des castors, Marais des castors) compared to functional composition (Centenaire, Beaubien, Lacoursi\u0026egrave;re) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eInfluence of local waterbody condition and urban land use drivers\u003c/h2\u003e \u003cp\u003eOverall, the db-RDA identified key groups of correlated variables that explained variation of macroinvertebrate functional and taxonomic composition among waterbodies (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) (see also, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea, supplemental material). The greatest variation explained for functional group composition was noted when all variable sets were included in the model with land use at 100 m (R\u003csup\u003e2\u003c/sup\u003e-adj\u0026thinsp;=\u0026thinsp;0.21), slightly outperforming the model with land use 500 m (R\u003csup\u003e2\u003c/sup\u003e-adj\u0026thinsp;=\u0026thinsp;0.17). The two other runner up models included macrophyte cover class with land use factors within a 100 m (R\u003csup\u003e2\u003c/sup\u003e-adj\u0026thinsp;=\u0026thinsp;0.10) or 500 m (R\u003csup\u003e2\u003c/sup\u003e-adj\u0026thinsp;=\u0026thinsp;0.12) buffer (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, S1). Using taxonomic (order) groups, the best two models included all variables with land-use factors within a 100 m (R\u003csup\u003e2\u003c/sup\u003e-adj\u0026thinsp;=\u0026thinsp;14%) or 2000 m (R\u003csup\u003e2\u003c/sup\u003e-adj\u0026thinsp;=\u0026thinsp;13%) buffer. As for functional groups, the best runner up combination of predictors sets included macrophyte cover in conjunction with land use within a 100 m (R\u003csup\u003e2\u003c/sup\u003e-adj\u0026thinsp;=\u0026thinsp;0.08; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb) or 2000 m (R\u003csup\u003e2\u003c/sup\u003e-adj\u0026thinsp;=\u0026thinsp;0.06; Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb) radius.\u003c/p\u003e \u003cp\u003e \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\u003eContributions of set of variables to variations in functional (trait) and taxonomic (order) groups for the models at each land-use scale (100, 500, 1000, and 2000 m radius). An asterisk next to a variable indicates the variable was significant, while an asterisk under the p-value column indicates that the overall model was significant, where \u0026ldquo;***\u0026rdquo; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. \u0026ldquo;**\u0026rdquo; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01. \u0026ldquo;*\u0026rdquo; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and \u0026ldquo;.\u0026rdquo; \u0026lt; 0.1. Variables significant for both the functional and taxonomic groups are indicated by an ampersand (example \u0026ldquo;* \u0026amp; *\u0026rdquo; indicates that the variable was significant at the alpha level of 0.05 for both functional and taxonomic groups).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eFunctional groups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eTaxonomic groups\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSet\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariables within set\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted-R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted-R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\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\u003e\u003cb\u003eContaminants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCopper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSodium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eNutrients\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmmonium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhosphorus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Organic Carbon * \u0026amp; *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater transparency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMorphometry\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.701\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\u003eSurface area\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConnectivity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance to nearest waterbody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.859\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\u003eNumber of waterbodies in the surrounding\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMacrophytes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMacrophyte cover class * \u0026amp; *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.046 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eManagement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWinter drainage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAll variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCopper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003e0.536\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\u003eSodium\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\u003eAmmonium\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\u003ePhosphorus\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\u003eTotal Organic Carbon *** \u0026amp; **\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\u003eWater transparency\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\u003eDepth\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\u003eSurface area\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\u003eDistance to nearest waterbody\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\u003eNumber of waterbodies in the surrounding\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\u003eMacrophyte cover class\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eLand use 100m\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuildings with yards\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuildings surrounded by concrete\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eLand use 500m\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuildings with yards\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuildings surrounded by concrete\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eLand use 1000m\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuildings with yards\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuildings surrounded by concrete\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eLand use 2000m\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuildings with yards\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuildings surrounded by concrete\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eNutrients\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eMacrophytes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmmonium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhosphorus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Organic Carbon\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMacrophyte cover class * \u0026amp;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eNutrients\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eLand use 100m\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmmonium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhosphorus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Organic Carbon * \u0026amp; *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParks\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuildings with yards\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuildings surrounded by concrete\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eMacrophytes\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eLand use 100m\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMacrophyte cover class * \u0026amp; *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.041*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.031 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParks\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuildings with yards \u0026amp;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuildings surrounded by concrete\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003eMacrophytes\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eNutrients\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eLand use 100m\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmmonium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhosphorus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Organic Carbon\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMacrophyte cover class * \u0026amp; *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParks\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuildings with yards \u0026amp;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuildings surrounded by concrete\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\u003eUsing a stepwise selection on the full models with all variables included to identify specific target variables, we identified macrophyte cover and land use (presence of parks at 100 m and at 500 m or buildings with yards within a 2000 m radius) as the most important variables influencing variations in taxonomic and functional composition (red arrows in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig. S2, supplemental material). Despite some heterogeneity, these factors separated the groups determined by fuzzy clustering. In particular, on the one hand, marshes with higher macrophyte cover surrounded by buildings with yards (Marais des castors, Bizzard, Heritage) and characterized by a dominance of Collector-gatherers at the functional level, but highly diverse and abundant communities at the taxa (family) level (Group 1, blue); on the other hand, the presence of parks around temporary ponds and lakes without or less macrophyte cover characterized by a slight increase in Herbivores and Collectors-filterers functional groups, but less diverse and abundant communities at the taxa level (Group 2, green). Variables not selected by forward selection, but important in the variable set models (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) were management practices, where winter drainage was associated with lower taxa-level diversity in temporary ponds (Group 2, green, dominated by Diptera and Annelida), and nutrients (TP, TOC), associated with the Groups 3 (yellow) and 4 (pink) waterbodies that had a more diverse composition of functional groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eFirst, our study indicated that Montr\u0026eacute;al\u0026rsquo;s blue spaces support high diversity of macroinvertebrates at the regional scale (up to 60 families detected across 20 ponds), equivalent to macroinvertebrate richness in farm ponds of agricultural zones in Spain (68 families: Fuentes-Rodriguez et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and France (52 family-genus taxa: C\u0026eacute;r\u0026eacute;ghino et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008b\u003c/span\u003e). We acknowledge that our estimate of the regional pool may be underestimated when using family and order level groupings. If taxa were identified at the genus-species level, regional taxa pool in urban waterbodies of Montr\u0026eacute;al might have reached one hundred taxa, as found in field ponds in England (Hill and Wood \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Mean macroinvertebrate richness (26 families) and range of variation (10\u0026ndash;41 families) were comparable to taxa richness and variation estimated in ephemeral waterbodies in agricultural karstic regions of Ireland (mean: 20 taxa; range: 7\u0026ndash;26 taxa: Porst and Irvine \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) but was higher than in urban ponds of poor ecological quality in northern England (range: 4\u0026ndash;13 taxa: Noble and Hassall \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and in pristine alpine ponds in Switzerland (mean 11 taxa; range: 6\u0026ndash;24: Oertli et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSecondly, our study emphasizes previous statements on the importance of preserving different types of waterbodies in urban region to maximize diverse macroinvertebrate communities (Hassall et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pinel-Alloul et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It highlights the convergence of typology with both functional (feeding trait) and coarse taxonomic (order level) approaches that determined relatively similar types of waterbodies (Groups 1\u0026ndash;4). However, waterbody clustering was more clearly defined when using functional groups, offering a more efficient approach for biomonitoring. The waterbodies had an inverse gradient in major functional (trait) and taxonomic (order) groups, with waterbodies from Groups 1 and 2 being dominated by Collectors-gatherers, while waterbodies from Groups 3 and 4 had a greater representation of Herbivores, in association with Collectors-filterers, Parasites, Predators and Scavengers. This difference in dominance patterns could be indicative of the ecological integrity of urban aquatic habitats. The high occurrence of Collector-gatherers across many of our study ponds can be due in part to the ability of Diptera Chironomidae and Annelida Oligochaeta taxa to thrive in many different habitats (Hill et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wood et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and tolerate pressures such as pond management (Hilsenhoff \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Desrosiers et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Chironomids and oligochaetes, which can feed on organic matter that accumulates in sediments (Solimini et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), were especially dominant in temporary ponds (Pratt1, Lac des castors, Liesse, Pratt2) and in permanent lakes of residential zones (Brunante, Centenaire), and thus able to resist relatively lower nutrient concentrations (TOC and/or TP). In contrast, the diverse composition of Herbivores, Collectors-filterers, Parasites, Predators and Scavengers in Groups 3 and 4 can be indicative of more nutrient enriched systems (i.e., higher TOC, TP and a healthy cover of macrophytes or periphyton). The Herbivores (especially Gastropoda Planorbidae and Physidae) were especially dominant in a marsh (Prairie) and a eutrophic lake (Lacoursi\u0026egrave;re) covered by submerged macrophytes because these pulmonated gastropods can breathe at the water surface and feed on periphyton. They were also predominantly found in a transparent artificial pond without fish (Lafontaine), colonizing the concrete walls covered with periphyton in association with Collectors-filterers such as branchial gastropods (Bithynidae) and crustacean ostracods. These sensitive taxa with branchial respiration were also encountered in higher abundance in a municipal pond with floating and submerged plants covered with periphyton (Beaubien). The Parasites (Hydracarina, Nematoda) and Predators (Diptera Ceratopogonidae, Odonata) were more frequent in residential lakes (Cygnes, Heritage) and a marsh (Marais des castors) covered by macrophytes. Finally, the Scavengers (Amphipoda) were more frequent in a municipal lake (Angrignon) and a reservoir (Montigny) where macrophyte cover or high turbidity offered a refuge against fish predation.\u003c/p\u003e \u003cp\u003eThird, our study showed that macroinvertebrate communities responded to a combination of abiotic and biotic factors related to waterbody condition and urban land use in the surrounding 100 m up to 2000 m radius. First, variation in functional and taxonomic composition was related to the heterogeneity in waterbody condition, notably to macrophyte cover, and at lesser extent to nutrients (total organic carbon, total phosphorus). Overall, aquatic vegetation (emergent and submerged macrophytes) was a key biotic variable structuring macroinvertebrate functional and taxonomic (order) composition in our urban systems, which is concordant with a recent study concerning the entire aquatic foodweb (Pinel-Alloul et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and other studies carried out in temporary ponds (Hassall et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Florencio et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and river wetlands (Tessier et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Schad et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Macrophytes provide a wide range of functional niches for macroinvertebrates, good oxygenation, food resources from algal periphyton, and a refuge against predators (Bazzanti et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In addition, the structural complexity (not measured in this study) provided by macrophytes may increase macroinvertebrate abundance and functional diversity (Walker et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Nutrient (TP) and organic enrichment (TOC) also influenced macroinvertebrate composition shifting up the importance of the Herbivores (Gastropoda Planorbidae) especially in waterbodies well covered by macrophytes. In urban ponds, excess of nutrients might come from fertilizers applied on the catchment (lawn in parks and residential zones) and animal waste (ducks). In our study, we did not detect a significant effect of contaminants (Cu, Na) used for water treatment and road de-icing, although their concentrations were higher in waterbodies of Groups 3 and 4.\u003c/p\u003e \u003cp\u003eAs reported in other studies (Blicharska et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Thornhill et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), our study emphasizes the effect of land use within a 100 m to 2000 m radius on macroinvertebrate communities, with important implications for the management of urban ponds. Thornhill et al. (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) also found that pond assemblages were influenced by the degree of urbanization and the presence of naturalized land within 100 m of the pond\u0026rsquo;s edge. Others found that aquatic insect diversity in urban ponds was influenced by urbanisation within a 500 m buffer zone (Blicharska et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Patenaude et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) indicated that a radius between 800 m and 1.8 km had the greatest influence on benthic macroinvertebrates in wetlands. The threshold buffer zone size may vary according to the taxa investigated or the type of urban matrix the pond is embedded in (Patenaude et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe presence of green landscape such as parks and buildings with yards within a radius ranging from 100 m to 2000 m of waterbodies were retained as land use factors related to waterbody clustering in terms of functional and taxonomic composition. On the one hand, the presence of buildings with yards within a 100 m to 2000 m radius, in conjunction with a good cover of macrophytes, was associated to waterbodies of Group 1, the most diverse and abundant communities at the taxa (family) level, with the highest dominance of the Collector-gatherers (mainly Diptera chironomids, Annelida oligochaetes). The importance of parks in reducing nutrient runoff to aquatic ecosystems has indeed been demonstrated (Set\u0026auml;l\u0026auml; 2017). Our results additionally emphasize the benefits of buildings with green backyards. This is interesting because it shows that green yards are sufficient to reduce nutrient runoff and support diverse aquatic communities at the family level. On the other hand, the presence of parks within a 100 m to 500 m radius, in conjunction with management (winter drainage), characterized waterbodies of Group 2; though less diverse and abundant communities at the taxa (family) level, these ponds were dominated by the ubiquitous Collector-gatherers at the functional level, followed by Herbivores. The separation between ponds surrounded by parks to those by building with yards in ordination space suggests that the type of green space is also impactful, as is their interaction with management practices and macrophyte cover. In the present study, the taxa poor communities observed in ponds of municipal parks (Group 2) result mainly from management practices as winter drainage that only enables the colonisation of ponds by resistant taxa such as Diptera Chironomidae and Annelida.\u003c/p\u003e \u003cp\u003eUltimately, our results emphasize a synergy between local waterbody condition, management, and urban landscape cover. Indeed, the coefficient of determination of models run on macrophyte cover, nutrients, and land use variables alone and combined were not additive, reinstating the importance of considering land use management as a key vector of nutrients to waterbodies, as well as the filtering/stabilizing effect of macrophytes within waterbodies. We observed that the average concentration of phosphorus was positively correlated with building surrounded by concrete (impervious surface) and therefore negatively correlated with building with yards. It is thus likely that concrete surfaces like asphalt allow nutrients to accumulate more easily in nearby waterbodies while yards with grass have a higher holding capacity.\u003c/p\u003e \u003cp\u003eTo conclude, urban ponds are greatly important to sustain biodiversity in cities such as Montr\u0026eacute;al (this study, Pinel-Alloul et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Stockholm (Blicharska et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). To some extent, those small ecosystems could contribute to global biodiversity (Parris \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2006\u003c/span\u003e and \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Our study suggests that approaches based on coarse functional and taxonomic (family to order level) components would be very useful for biomonitoring of urban ponds, without a fine determination at the genus or species level. Management of urban ponds and lakes should favor green landscape such as parks and yards in the near (100 m) and regional (2000 m) vicinity while minimizing drastic management practices such as winter drainage to sustain urban aquatic biodiversity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAckowledgements\u0026nbsp;\u003c/strong\u003eWe thank a group of graduate students (El Amine Mimouni, Adrien Andr\u0026eacute;, Joseph\u0026nbsp;Nzi\u0026eacute;leu Tchapgnouo) who assisted during field sampling.\u0026nbsp;We are particularly indebted to Ginette M\u0026eacute;thot (research assistant) and Louise Cloutier (manager of the Ouellet Robert insect collection) for helping in macroinvertebrate taxonomic analysis.\u0026nbsp;We are also thankful for the field support and water quality data provided by the Direction of Environment of the City of Montr\u0026eacute;al.\u0026nbsp;This article is a contribution of the GRIL (Groupe de Recherche Interuniversitaire en Limnologie).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eEric Harvey (EH) and Bernadette Pinel-Alloul (BPA) conceptualized the research. Macrobenthos taxonomic data come from the master thesis of Maryse Robert (laboratory of BPA). Audrey Robert (AR, Project Honor, undergraduate student, laboratory EH) analyzed the macrobenthos data, determined the functional groups, carried out the QGIS and statistical analyses and drafted the manuscript and produced some figures. Zofia E Taranu (ZET) supervised the statistical analysis, carried out the fuzzy clustering, and created additional figures. All authors participated to the final redaction of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis research was partially supported by grants from the Natural Science and Engineering Research Council and the Fonds Qu\u0026eacute;b\u0026eacute;cois de la Recherche sur la Nature et les Technologies to BPA and EH.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAnquez P, Herlem A (2011) Les \u0026icirc;lots de chaleur dans la r\u0026eacute;gion m\u0026eacute;tropolitaine de Montr\u0026eacute;al: causes, impacts et solutions. Chaire de responsabilit\u0026eacute; sociale et de d\u0026eacute;veloppement durable, UQAM. \u003c/li\u003e\n\u003cli\u003eBaird DJ, Rubach MN, Van Den Brink PJ (2008) Trait-based ecological risk assessment (TERA): the new frontier? Integr. Environ. 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Conserv. 102(1): 17‑29. https://doi.org/10.1016/S0006-3207(01)00087-8\u003c/li\u003e\n\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":"Urban ecology, green-blue space integration, Macroinvertebrate communities, land-use change, landscape management, functional biodiversity","lastPublishedDoi":"10.21203/rs.3.rs-3891411/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3891411/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrban waterbodies provide important services to humans and play a considerable role in biodiversity conservation. Yet, we still know very little about how urban pond ecosystems may respond to ongoing and future stresses operating at multiple spatial scales. Here we examined the littoral macroinvertebrates in 20 urban waterbodies as an indicator community to assess how local waterbody condition and urban land use affected their taxonomic and functional composition. Although macroinvertebrates were diverse (total richness of 60 taxa ranging from 10 to 41), they were dominated by two major taxonomic groups, the Diptera Chironomidae (36%) and the Annelida Oligochaeta (22%), which largely represented the dominant functional group of the Collectors-Gatherers (63%). Fuzzy clustering identified four different types of communities based on taxonomic and functional groups. These reflected inversed gradients in the dominance of Collectors-Gatherers versus ponds with higher abundances of Herbivores (Gastropoda Pulmonata, Hemiptera, Trichoptera), Collectors-Filterers (Gastropoda Prosobranchia, Crustacea Ostracoda), Predators (Odonata), and Parasites (Nematoda, Hydracarina). Distance-based redundancy analysis identified macrophyte cover and green landscape (parks and buildings with yards) within a 100 m radius as the best drivers of macroinvertebrate taxonomic and functional composition. We also noted a comparable variance explained by models that included parks within a 500 m radius or buildings with yards within a 2000 m radius. Our results have implications for urban landscape management as it suggests that human alteration in the urban landscape can be transmitted at least up to 2000 m from ponds.\u003c/p\u003e","manuscriptTitle":"Green landscape and macrophyte cover influence macroinvertebrate taxonomic and functional composition in urban waterbodies at multiple spatial scales","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-25 09:26:00","doi":"10.21203/rs.3.rs-3891411/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":"1b32d725-75ed-4aa7-9295-3d975412e260","owner":[],"postedDate":"January 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-10T16:33:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-25 09:26:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3891411","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3891411","identity":"rs-3891411","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}