Functional and taxonomic diversity of intertidal macroalgae communities from a climate refugia hotspot

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Tropicalization events are reshaping communities, with declines in species sensitive to local climate variability and increases in climate-tolerant and invasive species. Understanding taxonomic and functional biodiversity patterns over space and time is critical to evaluate whether certain regions may act as climate refugia. We investigate the spatial and temporal patterns of intertidal macroalgae community diversity, taxonomic and functional (α-diversity and β-diversity), along the northern Portuguese coast. Data was collected over an 18-year interval from five distinct locations (spanning from 41°42'41.4"N 8°51'43.4"W to 41°03'06.8"N 8°39'28.6"W). The objective of this work was to characterize the spatial and temporal patterns of intertidal macroalgae communities, along with their inherent changes. Our key findings include (1) coastal sea surface temperatures were approximately 2°C cooler than offshore waters, suggesting the area may function as a climate refugia; (2) both taxonomic and functional space contracted over time, indicating losses of species and functions; (3) for both space and time, turnover (β-replacement) was the main driver of taxonomic changes, whereas nestedness (β-richness) primarily drove functional changes. These spatial and temporal shifts in community composition are likely to have significant functional impacts, such as reduced habitat availability and lower productivity rates, with important implications for ecosystem services like blue carbon storage and habitat provision. This knowledge is crucial for mitigating the effects of climate change and best implementing effective conservation management strategies. functional diversity beta-diversity macroalgae intertidal Portugal climate refugia Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Coastal areas are among the most productive ecosystems worldwide (Barbier et al. 2011 ). Macroalgae are dominant primary producers within these systems, providing several ecosystem functions and services for human wellbeing (Krause-Jensen and Duarte 2016 ; Piñeiro-Corbeira et al. 2018 ; Eger et al. 2023 ), such as shelter and nursery habitats (Wernberg et al. 2010 ; Smale et al. 2013 ), coastline protection (Barbier et al. 2011 ; Smale and Vance 2016 ; Wernberg et al. 2019 ) and blue carbon potential (Krause-Jensen and Duarte 2016 ; Piñeiro-Corbeira et al. 2018 ; Fredriksen et al. 2020 ; Tanaka et al. 2023 ). Despite the inherent adaptability of coastal ecosystems and their communities to considerable environmental variability, climate change is altering such communities, leading to shifts in species composition and distribution (Lima et al. 2007 ; Hoegh-Guldberg and Bruno 2010 ; Pessarrodona et al. 2019 ). However, there is still great uncertainty about the real consequences of climate change in marine coastal ecosystems, as responses are extremely context-dependent. For instance, the tropicalization phenomenon is increasingly being documented (Vergés et al. 2016 ; Harvey et al. 2021 ; de Azevedo et al. 2023 ), leading to species loss or replacement due to local climate variability. Understanding the key processes driving such biodiversity changes, the associated functional impacts, and whether certain regions or species can act as refugia is critical to mitigate the effects of climate change and implement effective conservation and management strategies. Biodiversity research has usually focused on taxonomic entities, specifically on species richness, their abundance, or biomass, to elucidate their distribution patterns (Dencker et al. 2017 ; Pessarrodona et al. 2019 ). However, the addition of functional information is now acknowledged as a crucial cornerstone of contemporary ecology, as species’ traits directly or indirectly influence the processes that regulate their impact on ecosystem functioning and their responses to disturbance (Dolbeth et al. 2013 ; Malaterre et al. 2019 ; de Bello et al. 2021b ; Ricotta and Pavoine 2024 ). It also allows us to understand the importance of, for example, functional redundancy or uniqueness (Pimiento et al. 2023 ) and to elucidate drivers of species coexistence when integrated with taxonomic approaches (e.g. Dolbeth et al., 2013 ). Thus, studies incorporating functional diversity have substantially grown, with thousands of publications emerging in recent years for different taxa (Malaterre et al. 2019 ; de Bello et al. 2021b ; Morim et al. 2023 ). However, while studies on functional diversity in marine systems emerged in the early 2000’s (Bremner et al., 2006 ; Beukhof et al., 2019 ; Cappelatti et al., 2020 , 2019 ; Dencker et al., 2017 ; Dolbeth et al., 2013 ; Litchman and Klausmeier, 2008 ; Mauffrey et al., 2020 ; Vale et al., 2021 ) they still lag behind the level of exploration dedicated to understanding functional diversity in terrestrial systems. Also, due to its recent history and increasing use, the investigation of functional diversity is continuously expanding with the constant development and application of approaches and methods (Laliberté and Legendre 2010 ; Laliberté et al. 2014 ; Cardoso et al. 2015 ; Malaterre et al. 2019 ; Mammola and Cardoso 2020 ; Pavoine 2020 ; Mammola et al. 2021 ; Magneville et al. 2022 ; Blonder 2023 ). An important aspect of biodiversity is how species are distributed within and between ecosystems. This is often described using two key measures: alpha (α) diversity, which refers to the diversity within a single habitat or site, and beta (β) diversity, which describes the differences in species composition between sites or habitats (Whittaker, 1972 in Anderson et al., 2011 ). β-diversity can be further broken down into two main processes: turnover (i.e. β replacement) and nestedness (i.e. β richness) (Mammola and Cardoso 2020 ). Turnover occurs when some species are replaced by others from site to site or over time, that is, the identities of species change but the total number may stay the same. In contrast, nestedness results from differences in species richness caused by species being lost or gained (for example, when one site has only a subset of the species found in another) (Baselga 2010 ; Carvalho et al. 2012 ; Socolar et al. 2016 ; Mammola and Cardoso 2020 ). Understanding beta-diversity patterns and their underlying processes is crucial to comprehend how populations and communities are shaped by spatial, environmental and temporal changes (Carvalho et al. 2012 ). Furthermore, analysing functional β-diversity provides a deeper understanding of how community composition evolves over time and space, driven by species fitness and the processes that govern it (Petchey and Gaston 2006 ; Lourenço et al. 2016 ; Beukhof et al. 2019 ). This is particularly relevant in the context of climate change, where rapid shifts in species distributions can lead to cascading effects on biodiversity, ecosystem resilience, and the ability of ecosystems to support human wellbeing. As β-diversity is sensitive to changes in species distributions and extinctions based on their traits, it serves as a valuable metric for monitoring these shifts (Magurran et al. 2015 ; Vale et al. 2021 ). Therefore, gaining a comprehensive understanding of beta-diversity patterns is key to understand the structural and functional impacts of new environmental scenarios in highly dynamic ecosystems, such as coastal areas (Vale et al. 2021 ; Silva et al. 2024 ). Within coastal areas, upwelling regions can serve as climate refugia (Morelli et al. 2016 ), potentially mitigating sea temperature rise and helping to counteract species range contractions caused by climate warming (Lima et al. 2007 ; Lourenço et al. 2016 ). The Portuguese coast, where summer upwelling occurs, is recognized as a transition zone between cold- and warm-water fauna and flora (Boaventura et al. 2002 ; Lima et al. 2007 ; Alvarez et al. 2011 ; Monteiro et al. 2022 ). The northern coast, in particular, is considered a climate refugia for distinct macroalgae cold-water boreal species which reach their southernmost limit there (i.e., Himanthalia elongata , Ascophyllum nodosum , and Fucus serratus [Ardré, 1971 ; Boaventura et al., 2002 ; Lima et al., 2007 ] in the intertidal and Laminaria hyperborea in the subtidal [de Azevedo et al., 2023 ]). The summer upwelling is key in maintaining these populations (de Azevedo et al. 2023 ). However, recent data show signs of tropicalization in this region, threatening these populations and altering community composition in both intertidal (Monteiro et al. 2022 ) and subtidal (de Azevedo et al. 2023 ) areas. Additionally, and unlike other upwelling areas, the Iberian coast is expected to experience weakened upwelling (Sydeman et al. 2014 ; Sousa et al. 2020 ), which may multiply overall ocean warming affecting species composition due to changes in temperature and nutrient input (de Azevedo et al., 2023 ; Monteiro et al., 2022 ) and impairing its role as climate refugia. In this study, we examined intertidal macroalgae communities using data collected over seven years spread over an 18-year period, at five locations along the northern Portuguese coast to investigate how this climate refugia region has responded to environmental changes over time and space. Despite the limited spatial extent between sites, this region still harbours the characteristic cold-water species and is highly dynamic, justifying a finer scale to depict changes in these communities (Monteiro et al. 2022 ). To this end, we investigate how α and β diversity patterns, both taxonomic and functional, are changing at the lower intertidal level and identify the processes and functional impacts behind these changes. Material and Methods Data collection Environmental data Sea surface temperature data were obtained from the Copernicus Marine Service, from which a high-resolution dataset was statistically downscaled to the Iberian Peninsula region (Kristiansen et al. 2024 ). Upwelling data were sourced from the National Oceanic and Atmospheric Administration (NOAA), and an upwelling index was calculated following the methodology described in Alvarez et al., ( 2017 ), where positive values indicate the occurrence of upwelling events. These data were analysed to assess seasonal and temporal trends, and sea surface temperature was further examined spatially and mapped with QGIS to identify regional differences in the northern area, considering a 1990–2020 annual average. Biological data The temporal and spatial variation of low intertidal macroalgae communities was assessed for 5 distinct locations along the north Portuguese coast (from the north to the south): Praia da Areosa (41°42'41.4"N 8°51'43.4"W), Praia Norte (41°41'49.8"N 8°51'05.5"W), Praia da Amorosa (41°39'22.7"N 8°49'35.1"W), Praia de Mindelo (41°18'25.1"N 8°44'39.9"W), and Praia da Aguda (41°03'06.8"N 8°39'28.6"W) (Supplementary Fig. 1). Monitoring campaigns were conducted between March and April in the years 2006, 2007, 2008, 2021, 2022, 2023 and 2024. During these campaigns, the percentage cover of low intertidal macroalgae communities was recorded in situ using 50x50 cm quadrats and through photographic documentation (total of 15 replicates), with specimens identified to the lowest possible taxonomic level. Quadrats were randomly placed, with distances between them ranging from approximately 10 to 20 meters, depending on the availability and topography of the site. Functional traits All species were characterized by a comprehensive set of seven functional traits representing their morphology, life cycle, growth and reproduction (see Supplementary Table 1). Functional traits were chosen based on their ecological relevance (Cappelatti et al. 2020 ; Mauffrey et al. 2020 ) and limited by the accessibility of data (Table 1 ). Data was obtained from various sources, including online databases such as AlgaeTraits ( https://algaetraits.org/ ) , The Seaweed Site( https://www.seaweed.ie/ ) , MarLIN – The Marine Life Information Network ( https://www.marlin.ac.uk/ ) , and BIOTIC – Biological Traits Information Catalogue ( https://www.marlin.ac.uk/biotic/ ) , as well as relevant literature. To assess temporal and spatial variation of community structure we removed taxa with incomplete trait information, which only represented < 1% of total percentage cover, as no missing values are allowed in our methodological approach. Table 1 Description of functional traits selected, with indication of their ecological relevance based on Cappelatti et al., 2020 and Mauffrey et al., 2021, the type of measurement, categories inside each trait and examples of species for the qualitative traits. Category Traits Ecological relevance Measurement Category/Description Morphology Maximum thallus height productivity and blue carbon potential Quantitative Maximum thallus height the species can reach (worldwide) Holdfast habitat/nursery function attachment/fixation capacity Qualitative Disc-like (e.g., Ahnfeltiopsis devoniensis, Ophidocladus simpliciusculus ) Rhizoid-like (incl. bulbous) (e.g., Bifurcaria bifurcata , Ophidocladus simpliciusculus ) Crustose (e.g., Litophylum incrustans , Petrocellis cruenta ) Body shape (described as the dominant form) productivity and blue carbon potential resilience habitat/nursey function Qualitative Crustose (e.g., Litophylum incrustans, Petrocellis cruenta ) Foliose (e.g., Laminaria spp, Ulva spp) Filamentous (e.g., Rhodotameliaela floridula, Himanthalia elongata ) Branched_filamentous (e.g. Chondrocantus spp , Ceramium spp ) Spherical_liked (e.g., Colpomenia pererina, Gastroclonium ovatum) Pigment photosynthetic capacity productivity and blue carbon potential Qualitative Red (e.g., Anhfeltiopsis devoniensis , Ophidocladus simplicicusculus ) Brown (e.g., Dictyota dichotoma , Laminaria spp) Green (e.g., Ulva spp, Codium tomentosum ) Life cycle and growth Life span resilience productivity and blue carbon potential Qualitative Perennial (e.g., Laminaria spp , Ahnfeltiopsis devoniensis ) Annual (e.g., Osmundea pinnatifida , Saccorhiza polyschides ) Asexual reproduction possibility dispersal resilience Binary (0 and 1) 0 = No ( Laminaria ochroleuca , Codium tomentosum ) Reproduction 1 = Yes (e.g., Ahnfeltiopsis devoniensis , Jania rubens ) Spawning dispersal persistence vs dispersal (r-K strategy) Qualitative Water column (e.g., Bifurcaria bifurcata , Himanthalia elongata ) Female gametophyte (e.g., Gongolaria baccata , Sargassum muticum ) ● Data analysis Taxonomic and functional spaces were visualized using principal coordinate analysis (PCoA), performed with the function pco() from the R package labdsv (v. 2.1-0) (Roberts 2023 ), accounting for both identity and respective density within the community. Taxonomic and functional diversity indices were calculated, focusing on both their α (diversity within communities) and β (dissimilarity between communities) components (de Bello et al. 2021b ). The following metrics were computed for all sites and years, to assess both the spatial and temporal changes in diversity patterns. Taxonomic diversity metrics (e.g. species richness, Simpson index, evenness) were calculated with diversity() function from the R package vegan (Oksanen et al., 2022) while β-diversity was calculated with the function beta() from the R package BAT (v. 2.9.3) (Cardoso et al. 2023 ). Functional diversity metrics (e.g. functional richness, functional dispersion, functional evenness) were calculated through kernel density hypervolumes (Mammola and Cardoso 2020 ) using the function kernel.beta() from the R package BAT (v. 2.9.3) (Cardoso et al. 2023 ). Although the functional diversity indices were calculated using kernel density hypervolumes, they were visually represented using convex hulls. The latter is a widely used and computationally efficient method, but it has limitations, such as the assumption that there is no empty space within extreme values (Blonder 2016 ). To address this, probabilistic hypervolumes have been developed, with high-dimensional kernel density estimations being the most popular. This method delineates the shape and volume of the multidimensional space. Finally, each taxonomic and functional diversity metric was tested with a PERMANOVA, with a crossed design including the factors Year (fixed) and Beach (random), to detect potential differences across sites and time (Anderson et al. 2008 ). Data were first converted into a similarity matrix, using the Euclidean distance, for each diversity index. These analyses were done with the PRIMER – PERMANOVA software (Anderson et al. 2008 ). ANOVA was used to test the components of β diversity, except for β richness, which did not meet the necessary assumptions. Instead, PERMANOVA was applied for the analysis of this component. Both analyses were done for factor Year and Beach separately, as β diversity refers to a spatial or to a temporal gradient, and were done with R Statistical Software ( v. 4.1.2; R Core Team, 2021.). The assumptions of ANOVA were verified graphically following Zuur et al., ( 2010 ). All plots were performed with the R package ggplot2 (v.3.4.2) (Wickham 2016 )from R Statistical Software ( v. 4.1.2; R Core Team, 2021.). β-diversity followed a similar trend to that observed at the spatial scale, with β-replacement contributing more to the taxonomic dimension and β-richness contributing more to the functional dimension (Fig. 7 ). Both taxonomic and functional β-diversity components differed significantly for most of the years with a few exceptions in the latest years (Table 2 and Supplementary Table 7). Over time, certain species, especially in the Phaeophyceae group, have notably declined, with some nearly disappearing or vanishing completely in recent years, such as Laminaria ochroleuca and Sacchorhiza polyschides and the others increasing, as Xiphosiphonia spp., firstly only common at the southern region. Results 3.1. Temperature and upwelling trends In the northern Portuguese region, sea surface temperatures near the coast were on average 2°C lower compared to offshore and southern areas (Fig. 1 ). Temperatures on the coast reached as low as 13.5°C for the annual average between 1990–2020. Looking at the temporal trends since 2001, maximum sea surface temperatures picked at highest values in 2003 and 2008 and in latest years, especially in 2023-24 (mode close to 18ºC and highest SST reaching 21ºC, Fig. 1 ). The minimum values ranged mostly within 12–17ºC across all years, with 2 modes around 13 and 15ºC. There were clear seasonal changes in sea surface temperatures with a temperature increase in spring and summer. Taking into account the study years and an average value for an 0.5° × 0.5° latitude-longitude grid, i.e. nearshore and offshore areas (Kristiansen et al. 2024 ), the average summer temperature ranged around 15–16ºC, but in the last years 2023-24 the average temperature was almost 1ºC higher (17ºC). The upwelling index showed clear seasonal patterns, with positive values typically occurring from late spring to summer (mid-March to mid-September) across nearly all years of the study, with the exception of the first years, 2006 and 2007, with mild positive values only in June-July (Fig. 2 ). In contrast, 2024 showed a shift in timing, with the stronger upwelling occurring later in the year, from July onwards. Over time, the fluctuation range between positive and negative values has become less pronounced, indicating a weakening of the seasonal extremes. Nevertheless, in latest years, we still have positive upwelling (Fig. 2 ). 3.2. Spatial patterns of taxonomic and functional composition and β-diversity Figure 3 shows the taxonomic and functional convex hulls for the five sites examined in our study, considering the full sampling period. The first two axes of the taxonomic PCoA accounted for 36.9% of the variability in species composition, while the first two axes of the functional PCoA explained 97.4% of the variability in trait composition. The ordination plots reveal clear spatial variation, with the high variance explained, particularly in the functional space, highlighting the reliability of the observed differences. Amorosa had the smallest convex hull (0.27) followed by Praia Norte (0.28), Areosa (0.34), Aguda (0.42) and Mindelo (0.47). In addition, Aguda had the smallest functional convex hull (0.06) followed by Amorosa (0.06), Praia Norte (0.08), Areosa (0.11) and Mindelo (0.13). When analysing taxonomic and functional diversity indices, the general tendency is for indices to remain relatively stable (Fig. 4 ). Still, the statistical differences between the sites depended on the year, as confirmed by the significant interaction (Table 2 ). In earlier years, we mainly found significant differences between Areosa, the site with the highest latitude, and the remaining sites, while in recent years, statistical differences were also found within the closest sites (Supplementary Tables 2 and 3), with a few exceptions. For instance, in 2024 almost no statistical differences were observed between sites across all taxonomic indices (Supplementary Table 2), while for 2022 functional richness was statistically different between almost all sites (Supplementary Table 3). Notably, functional indices were generally more statistically different than taxonomic indices. For β-diversity, analyses were done considering the spatial or the temporal gradient alone. Regarding the taxonomic dimension along the spatial scale, β-replacement was the primary contributor to the total β-diversity. On the other hand, for the functional dimension, the main contributor to total β-diversity was β-richness (Fig. 5 ). As a general trend, taxonomic β-total and β-replacement were different for all sites, but not for β-richness and for the functional metrics (Figs. 4 and 5 , Supplementary Table 4). Taxonomic β-richness was statistically different for Praia Norte compared to all other and some specific site pairs, while functional β-richness was statistically different for Mindelo alone compared to all other sites (Fig. 5 , Supplementary Table 4). Table 2 Statistical results (ANOVA and Permanova) for taxonomic and functional diversity metrics, and β-diversity components. Due to its nature β-diversity analysis had to be separated by site and time. Model Significant terms Df Pseudo-F / F P-perm /p-value Unique perms Richness Permanova Year * Beach 24 6,1935 0.001 998 Simpson Permanova Year * Beach 24 3.2992 0.001 998 Evenness Permanova Year * Beach 24 2.2651 0.002 999 Taxonomic β-total ANOVA Beach Year 4 6 1108 77.33 < 0.001 < 0.001 Taxonomic β-replacement ANOVA Beach Year 4 6 460.5 70.12 < 0.001 < 0.001 Taxonomic β-richness Permanova Beach Year 4 6 27.19 34.246 < 0.001 < 0.001 999 Functional richness Permanova Year * Beach 24 4.6426 0.001 998 Functional dispersion Permanova Year * Beach 24 5.2595 0.001 999 Functional evenness Permanova Year * Beach 24 4.0446 0.001 998 Functional β-total ANOVA Beach Year 4 6 354.1 150.7 < 0.001 < 0.001 Functional β-replacement ANOVA Beach Year 4 6 56.04 192 < 0.001 < 0.001 Functional β-richness Permanova Beach Year 4 6 21.144 34.246 < 0.001 < 0.001 999 Regarding species identity, we found that species from the Phaeophyceae group (brown algae) were more abundant in northern locations, namely Laminaria ochroleuca and Gongolaria baccata (see Supplementary Table 8). Nonetheless, Rhodophyta species (red ones) were dominant overall (e.g. Chondracanthus acicularis ), but especially in southern areas, where Osmundea pinnatifida, Calliblepharis jubata, Pterosiphonia complanata and Grateloupia turuturu were more abundant compared to other regions (Supplementary Table 8). In this southern region, Phaeophyceae and Chlorophyta were less common. 3.3. Temporal patterns of taxonomic and functional composition and β-diversity The first two axes of the taxonomic PCoA for the temporal analyses captured 36.9% of the variability in species composition, while the first two axes of the functional PCoA accounted for 97.4% of the variability in trait composition (Fig. 6 ). As what happened for the spatial analysis, the ordination plots reveal clear temporal variation, with the high variance explained, particularly in the functional space, highlighting the reliability of the observed differences. The most recent years demonstrated on average smaller taxonomic convex hulls (0.31 ± 0.07), compared to the earlier period (0.36 ± 0.07). Likewise, functional convex hulls also declined in recent years (0.06 ± 0.02) relative to the earlier years (0.09 ± 0.01). In 2024, however, taxonomic space was not as low as 2021–2023 (0.37 compared to 0.35, 0.30 and 0.22), but the functional one was, along with 2021, the lowest of the whole study period (0.044 for 2021 and 0.049 for 2024, Fig. 6 ). When analysing taxonomic and functional diversity indices, again, statistical differences between years were depended on the sites (significant interaction, Table 2 ), as not all years were different among sites (Supplementary Tables 5 and 6). The overall tendency was for, taxonomic diversity to decrease over time, with a slight increase in 2024 as an exception (Fig. 4 ). However, this pattern was not consistent and statistically different for all sites and indices. For instance, for Amorosa and Mindelo, 2006 was generally distinct than all other years in richness and Simpson, while for Aguda only 2022 was statistically different than the other years (Fig. 4 , Supplementary Table 5). For evenness, patterns were even less consistent per site (Fig. 4 ). For instance, for Areosa, evenness patterns changed when comparing earlier and most recent years, while for Amorosa they almost did not change (Fig. 4 , Supplementary Table 5). For functional diversity, we found a similar trend of variation over time, highly dependent on the site (Table 2 ). The decreasing trend of functional diversity over time was even more pronounced, but only for the northernmost sites, Areosa and Praia Norte (Fig. 4 ). In Aguda, for instance, most of the changes in functional richness and dispersion occurred in latest years being significant for most of the year comparisons (with a few exceptions), but not for functional evenness (Supplementary Table 6). As a general trend, 2024, had the lowest functional diversity of all the years observed in all sites, especially for richness and dispersion (Fig. 4 ). Discussion Our findings underscore the importance of local oceanographic conditions in shaping marine community dynamics. Coastal sea surface temperatures (SSTs) were, on average, 2°C cooler than offshore waters, demonstrating the potential of this region to act as a climate refugia for boreal species. These cooler conditions, maintained by coastal upwelling during summer, play a key role in supporting cold water affinity species (de Azevedo et al. 2023 ). Indeed, cold-water species such as Laminaria hyperborea in the subtidal zone (de Azevedo et al. 2023 ), Ascophyllum nodosum and Fucus serratus , occurring at tidal levels not covered in this study, currently still persist in the region (pers. obs.), further supporting its role as a climate refugia. However, our results also reveal concerning trends: while minimum temperatures remain relatively stable, maximum SSTs have increased, and upwelling intensity appears to be weakening, as suggested by previous authors (Sydeman et al. 2014 ; Sousa et al. 2020 ). The alignment of these two patterns may pose significant risks to the persistence of boreal species in this coastal zone. These risks have already been documented on macroalgae communities, both in intertidal (Monteiro et al. 2022 ) as the subtidal (de Azevedo et al. 2023 ) habitats along this shore. Overall, our results reveal a contraction in both the taxonomic and functional space occupied by lower intertidal macroalgae communities over time, indicating a loss of species and/or their relative abundance as well as the ecological functions they perform. Remarkably, the last year of the study, 2024, deviated from this trend: while taxonomic space remained within the range observed in earlier years, functional space reached its lowest level over the entire study period. This indicates a pronounced erosion of functional diversity, even when taxonomic diversity appeared relatively stable. The processes underlying the observed declines differed depending on whether taxonomic or functional diversity was considered, but remained consistent across spatial and temporal scales, with β-replacement driving taxonomic change and β-richness driving functional change. Hence, we discuss the results in terms of these scales together and respective diversity dimensions. From a taxonomic standpoint, the primary driver of change was turnover (β-replacement), indicating that species were replaced by others through space and time. More specifically, brown seaweeds tend to be replaced by red macroalgae as latitude increases. For instance, species like Gongolaria baccata and Laminaria ochroleuca show a decline in percentage cover across latitudes, eventually disappearing from the southernmost locations. In contrast, there is an increase in red seaweeds, such as Osmundea pinnatifida and the invasive Grateloupia turuturu . Previous studies, including part of this Portuguese stretch, have also noted this spatial variability in diversity, which could be related to local differences in microclimate due to topography, wave action, hydrodynamics and solar exposition, including thermal refugia possibilities (Lourenço et al., 2016 ). Furthermore, we found that brown seaweeds like Laminaria ochroleuca and Sacchorhiza polyschides have been progressively replaced by green and red algae, including Codium tomentosum and Xiphosiphonia spp over time. The species replacement process across spatial scales has been recognized elsewhere (e.g. Red Sea (Issa et al. 2014 ), Galicia (Vale et al. 2021 )) as a key driver of shifts in intertidal macroalgal communities. A meta-analysis further supports this, showing that most spatial changes within marine primary producers are driven by β-replacement (turnover) (Soininen et al. 2018 ). However, beta-diversity patterns do not consistently hold over time (Vale et al. 2021 ). Our findings align with previous studies on macroalgae communities in the Red Sea (Issa et al. 2014 ), and with time series analyses across different organisms and biomes (Dornelas et al. 2014 ), where species replacement is also regarded the main driver of change. This is additionally supported by the observed patterns of diversity indices, where abundance-weighted metrics remain relatively stable across space and time, despite the decrease trends in richness over time (yet not consistent for all sites). Overall, this represents a spatial homogenization of the community. On the other hand, in the nearby Galicia region (which borders Northern Portugal), changes in macroalgae communities over time were driven either by species loss (e.g. cold-water affinity species) or by newly introduced species (e.g. invasive species) (Vale et al. 2021 ) and not from species replacement. Remarkably, our results for functional diversity contrast with those for taxonomic diversity. For functional diversity, nestedness (β-richness) was the dominant process, indicating loss or gain of traits and, consequently, potential ecosystem functions. Both spatial and temporal patterns suggest that communities tend to be nested subsets of one another: spatially, communities at lower latitudes appear to be subsets of those at higher latitudes; while temporally, recent communities appear to be subsets of past communities. Adding to this, a consistent decrease in functional richness across latitude and time was found with different metrics, showing that assemblages have been losing traits and associated functions, particularly over recent periods. Thus, while the communities along the northern Portuguese coast have managed to maintain their taxonomic diversity levels across both space and time, they are failing to preserve their functional potential. This loss of traits and reduction of functional space might suggest trait convergence as a mechanism for structuring these communities. Trait convergence has been linked to environmental filtering, in which species are selected based on their ecological tolerances towards the environment, being less functionally different than expected by chance (de Bello et al. 2012 ; Valdivia et al. 2017 ). Conversely, trait divergence might also emerge at different scales (Dolbeth et al. 2016 ; de Bello et al. 2021a ), typically reflecting niche differentiation driven by biotic interactions such as competition (Leibold et al. 2004 ; Mayfield and Levine 2010 ; de Bello et al. 2012 ; Valdivia et al. 2017 ). However, as recently debated, such explanations may oversimplify the dynamics at play, as competition can also generate community patterns similar to environmental filtering (Cadotte and Tucker 2017 ). To further understand these dynamics, complementary statistical approaches would be needed (e.g., null models and traits correlation with the environment) (Mayfield and Levine 2010 ). Still, our results confirmed that species previously absent from our communities, particularly invasive species associated with warmer waters, such as Asparagopsis armata and Grateloupia turuturu , have begun to appear and increase in abundance in recent years. The arrival of these particular new species may come with associated functional impacts, such as reduced community complexity, lower carbon sequestration potential and diminished nursery/habitat function, as they tend to be simpler and have limited capacity for carbon (Janiak and Whitlatch 2012 ; Silva et al. 2021 ). Despite the narrow spatial extent of our study area, our analyses reveal clear spatiotemporal variation in both taxonomic and functional diversity over the 18-year study period. The observed shifts highlight that macroalgae communities are not only changing in species composition but are also losing functional traits (i.e., contraction of the functional space over time), what can lead to potential ecosystem functions reconfigurations. Our findings further highlight that climate refugia are neither static or immune to change, especially facing contemporary climate change (Morelli et al. 2016 ). Although we found signs of environmental change (i.e. increased maximum temperatures and upwelling weakening trends), the presence of boreal macroalgae at their southern distribution limits at other tidal levels indicate that the region may still offer buffered conditions suitable for their persistence. However, the community-level changes observed at the lower intertidal, marked by declines in taxonomic and functional diversity and the loss or displacement of certain species and functions suggest that this refugial capacity may be weakening. Although we studied different communities than those where the above-mentioned cold-water species occur, our results serve as a warning that even these iconic boreal species could be at risk. This illustrates that climate refugia may erode or shift under sustained environmental pressures, particularly those driven by ongoing climate change. Finally, our understanding of the functional dynamics of macroalgae communities and their drivers remains surprisingly limited. While only a handful of studies have explored patterns of β-diversity in marine macroalgae communities (Mazariegos-Villarreal et al. 2012 ; Issa et al. 2014 ; Vale et al. 2021 ), and some have addressed functional β-diversity across spatial gradients (Cappelatti et al. 2020 ), studies explicitly considering functional β-diversity across time are still lacking. Hence, our research provides new and needed insight into the ecological processes underlying community assembly by simultaneously investigating both taxonomic and functional α and β-diversity, as well as their underlying components (i.e., turnover and nestedness). Conclusions Our results have important implications for conservation strategies. When communities are driven by turnover (i.e. replacement processes), conservation efforts should target multiple sites, whereas when communities are driven by nestedness (i.e. richness variations), conservation should focus on the richest sites (Socolar et al. 2016 ). Given that ecosystem services are tightly linked to the functional traits (Tilman 2001 ; Petchey and Gaston 2006 ), conservation policies along the northern Portuguese coast should prioritize the highest functional diversity and thus potential for providing those services (i.e. Mindelo, Areosa and Praia Norte). Our findings also contributed to better understanding the region's current role as a climate refugia. While cold-affinity species still persist in the region, the changes in lower intertidal communities suggest that this refugial capacity may be weakening. Overall, this research is a valuable reference for future work in this area and as a case study within macroalgae communities facing rapid changes worldwide. It also emphasizes the need for ongoing monitoring and adaptive management, both to maintain diversity and safeguard the region role as a climate refugia under accelerating environmental change. Declarations Competing Interests: The authors have no relevant financial or non-financial interests to disclose. Author contributions Marta Martins, Martin Lindegren, Francisco Arenas and Marina Dolbeth conceived the ideas. Marta Martins, Martin Lindegren, Antoni Vivó-Pons, Francisco Arenas and Marina Dolbeth designed the methodology and formal analysis. Marta Martins, Hugo Sainz Meyer, Oscar Babe, Harold Casalis, Francisco Arenas and Marina Dolbeth collected the data. Marta Martins led the writing of the manuscript. Marta Martins, Martin Lindegren, Francisco Arenas and Marina Dolbeth contributed to the writing (review and editing). All authors contributed critically to the drafts and gave the final approval for publication. Funding: Marta Martins was supported by the Portuguese Foundation for Science and Technology (FCT) under the grant Ref: UI/BD/150934/2021. Marina Dolbeth was supported by the CEEC-INST contract, with ref CEECINST/00027/2021/CP2789/CT0001 and DOI 10.54499/CEECINST/00027/2021/CP2789/CT0001 . This work was also funded by FCT/MCTES through the financial support to CIIMAR (UIDB/04423/2020 and UIDP/04423/2020), FutureMares (European Union’s Horizon Europe Research and Innovation Programme grant nº 869300) and ACTNOW (European Union’s Horizon Europe Research and Innovation Programme grant nº 101060072). Data Availability: The data supporting the current study are not publicly available as they will be used in further ongoing and future publications by the authors. Data may be made available from the corresponding author on reasonable request, subject to restrictions related to further analyses and planned publications. 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Methods Ecol Evol 1:3–14. 10.1111/J.2041-210X.2009.00001.X Supplementary Files SupplementaryFigure1.pdf Supplementarytables.pdf Cite Share Download PDF Status: Published Journal Publication published 13 Jan, 2026 Read the published version in Marine Biology → Version 1 posted Reviewers agreed at journal 11 Aug, 2025 Reviewers invited by journal 07 Aug, 2025 Editor assigned by journal 05 Aug, 2025 First submitted to journal 30 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7251423","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":496943289,"identity":"a1f4b50d-2f0d-41f2-b35a-680828bc6164","order_by":0,"name":"Marta Martins","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYNCDBAYGOQMwy8ACnzrGBjgDqMXYgIEZpEWCSC1AInEDWAsDbi267WefP/gBYzzcUZe+nb3/6IYfBRIM/O3dCdi0mJ1JN2zsgTIaEs8czt3Zc5jtZg/QYRJnzm7AquVAGmMDD4yR2HYgd8ONZLYbPEAtBhK52LWcf8bY+AfKAGqpSzcAarn5B5+WG2mMzTxQBlALcwJIy228ttx4xjhbBhg+IMaMxLbDhhvOHDa7LWMgwYPTL+fTGD6+YbCBMH621ckbHG98dvPNHxs5/vZerFrAgPGfRH0DuiAPTuWjYBSMglEwCggCAFMhYUdjQLRKAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-3440-0876","institution":"UP CIIMAR: Universidade do Porto Centro Interdisciplinar de Investigacao Marinha e Ambiental","correspondingAuthor":true,"prefix":"","firstName":"Marta","middleName":"","lastName":"Martins","suffix":""},{"id":496943290,"identity":"cc9bfd2c-26f8-423d-a4ea-98a3c516fa19","order_by":1,"name":"Martin Lindegren","email":"","orcid":"","institution":"DTU Aqua: Danmarks Tekniske Universitet Institut for Akvatiske Ressourcer","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Lindegren","suffix":""},{"id":496943291,"identity":"78630014-e588-4eca-8d48-a4e4b1bc4afe","order_by":2,"name":"Hugo Sainz Meyer","email":"","orcid":"","institution":"UP CIIMAR: Universidade do Porto Centro Interdisciplinar de Investigacao Marinha e Ambiental","correspondingAuthor":false,"prefix":"","firstName":"Hugo","middleName":"Sainz","lastName":"Meyer","suffix":""},{"id":496943292,"identity":"3f33cf0b-184e-4a5b-b2e5-5e8a87c5b0b6","order_by":3,"name":"Oscar Babe","email":"","orcid":"","institution":"UP CIIMAR: Universidade do Porto Centro Interdisciplinar de Investigacao Marinha e Ambiental","correspondingAuthor":false,"prefix":"","firstName":"Oscar","middleName":"","lastName":"Babe","suffix":""},{"id":496943293,"identity":"507c456d-3ec3-41e7-a327-dca17cbcc422","order_by":4,"name":"Harold Casalis","email":"","orcid":"","institution":"UP CIIMAR: Universidade do Porto Centro Interdisciplinar de Investigacao Marinha e Ambiental","correspondingAuthor":false,"prefix":"","firstName":"Harold","middleName":"","lastName":"Casalis","suffix":""},{"id":496943294,"identity":"54a684ac-23d1-4fa0-9c17-545b204da7d4","order_by":5,"name":"Antoni Vivó-Pons","email":"","orcid":"","institution":"CEAB: Centre d'Estudis Avancats de Blanes","correspondingAuthor":false,"prefix":"","firstName":"Antoni","middleName":"","lastName":"Vivó-Pons","suffix":""},{"id":496943295,"identity":"d94ea349-ec11-484d-9b83-49acf27270a9","order_by":6,"name":"Francisco Arenas","email":"","orcid":"","institution":"UP CIIMAR: Universidade do Porto Centro Interdisciplinar de Investigacao Marinha e Ambiental","correspondingAuthor":false,"prefix":"","firstName":"Francisco","middleName":"","lastName":"Arenas","suffix":""},{"id":496943296,"identity":"efa2a7ea-ede2-4ce5-869c-852403a653a5","order_by":7,"name":"Marina Dolbeth","email":"","orcid":"","institution":"UP CIIMAR: Universidade do Porto Centro Interdisciplinar de Investigacao Marinha e Ambiental","correspondingAuthor":false,"prefix":"","firstName":"Marina","middleName":"","lastName":"Dolbeth","suffix":""}],"badges":[],"createdAt":"2025-07-30 09:57:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7251423/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7251423/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00227-025-04786-2","type":"published","date":"2026-01-13T16:29:15+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88905638,"identity":"ebb37d87-2005-40c1-adf5-b3eab2410af1","added_by":"auto","created_at":"2025-08-12 14:24:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2611153,"visible":true,"origin":"","legend":"\u003cp\u003eMean (1990-2020) sea surface temperature (SST) map along the Portuguese coast and maximum and minimum temporal trends (2001-2024).\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-7251423/v1/741fc7a77d37fe232cfb45de.png"},{"id":88902959,"identity":"dcb21b51-d028-4eec-b901-3e41f3385941","added_by":"auto","created_at":"2025-08-12 13:59:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":860016,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly upwelling index. The top panel shows annual trends (2001-2024) in upwelling intensity, where positive values indicate conditions favourable for upwelling and negative values indicate conditions unfavourable for upwelling. The bottom panel presents the monthly distribution of the upwelling index across the sampled years.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-7251423/v1/8f0d22dae36ccb508eb058ed.png"},{"id":88903340,"identity":"c4d958a1-9340-4e50-965f-7a1d70d282c5","added_by":"auto","created_at":"2025-08-12 14:07:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1931458,"visible":true,"origin":"","legend":"\u003cp\u003eTaxonomic (top panel) and functional (bottom panel) composition of communities across sites represented as the convex of hull.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-7251423/v1/9fcbcfa244852250f8d20bbf.png"},{"id":88903337,"identity":"5596ab9b-a81b-48d0-9668-5dbadc19f2fe","added_by":"auto","created_at":"2025-08-12 14:07:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3121077,"visible":true,"origin":"","legend":"\u003cp\u003eTaxonomic (richness, Simpson and evenness) and functional (richness, dispersion and evenness) diversity indices considering the temporal variation at each site.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-7251423/v1/081d0fa01bd53ae5aa3f71a6.png"},{"id":88902979,"identity":"a18165a8-11ee-480d-9ad4-a98bfbf18ebe","added_by":"auto","created_at":"2025-08-12 13:59:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1473377,"visible":true,"origin":"","legend":"\u003cp\u003eDensity graphs of total β-diversity and its components (β-replacement and β-richness) of macroalgae intertidal communities across latitudes.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-7251423/v1/e88cafb8d9aab7055fc1cc49.png"},{"id":88902963,"identity":"39e07fd6-7591-4b5b-9e2e-a270756c2f5f","added_by":"auto","created_at":"2025-08-12 13:59:49","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2074406,"visible":true,"origin":"","legend":"\u003cp\u003eTaxonomic (top panel) and functional (bottom panel) composition of communities across time represented as the convex of hull.\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-7251423/v1/b3e51d6ab80c262de9700679.png"},{"id":88903341,"identity":"e4054c4a-4a32-4da6-afdf-cc14c135d2f8","added_by":"auto","created_at":"2025-08-12 14:07:50","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1544775,"visible":true,"origin":"","legend":"\u003cp\u003eDensity graphs of total β-diversity and its components (β-replacement and β-richness) of macroalgae intertidal communities across time.\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-7251423/v1/75f0886924576b9da0b17682.png"},{"id":100614488,"identity":"3ebf5463-e674-4966-886f-113e82405bea","added_by":"auto","created_at":"2026-01-19 17:20:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12105199,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7251423/v1/20b6f11c-46ac-4263-9c50-70b696db4306.pdf"},{"id":88903344,"identity":"b834d33d-3fb3-4332-bbe9-9ddf110541ba","added_by":"auto","created_at":"2025-08-12 14:07:50","extension":"pdf","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":228174,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7251423/v1/89a911857827f7d92b4224be.pdf"},{"id":88902980,"identity":"51f5a6c8-bda2-40c2-9dca-18a1f0680656","added_by":"auto","created_at":"2025-08-12 13:59:50","extension":"pdf","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":707074,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7251423/v1/0f1f91ad44e4cb2ff63b3f26.pdf"}],"financialInterests":"","formattedTitle":"Functional and taxonomic diversity of intertidal macroalgae communities from a climate refugia hotspot","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoastal areas are among the most productive ecosystems worldwide (Barbier et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Macroalgae are dominant primary producers within these systems, providing several ecosystem functions and services for human wellbeing (Krause-Jensen and Duarte \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pi\u0026ntilde;eiro-Corbeira et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Eger et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), such as shelter and nursery habitats (Wernberg et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Smale et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), coastline protection (Barbier et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Smale and Vance \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wernberg et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and blue carbon potential (Krause-Jensen and Duarte \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pi\u0026ntilde;eiro-Corbeira et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Fredriksen et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tanaka et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite the inherent adaptability of coastal ecosystems and their communities to considerable environmental variability, climate change is altering such communities, leading to shifts in species composition and distribution (Lima et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Hoegh-Guldberg and Bruno \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Pessarrodona et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, there is still great uncertainty about the real consequences of climate change in marine coastal ecosystems, as responses are extremely context-dependent. For instance, the tropicalization phenomenon is increasingly being documented (Verg\u0026eacute;s et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Harvey et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; de Azevedo et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), leading to species loss or replacement due to local climate variability. Understanding the key processes driving such biodiversity changes, the associated functional impacts, and whether certain regions or species can act as refugia is critical to mitigate the effects of climate change and implement effective conservation and management strategies.\u003c/p\u003e\u003cp\u003eBiodiversity research has usually focused on taxonomic entities, specifically on species richness, their abundance, or biomass, to elucidate their distribution patterns (Dencker et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pessarrodona et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, the addition of functional information is now acknowledged as a crucial cornerstone of contemporary ecology, as species\u0026rsquo; traits directly or indirectly influence the processes that regulate their impact on ecosystem functioning and their responses to disturbance (Dolbeth et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Malaterre et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; de Bello et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Ricotta and Pavoine \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It also allows us to understand the importance of, for example, functional redundancy or uniqueness (Pimiento et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and to elucidate drivers of species coexistence when integrated with taxonomic approaches (e.g. Dolbeth et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Thus, studies incorporating functional diversity have substantially grown, with thousands of publications emerging in recent years for different taxa (Malaterre et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; de Bello et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Morim et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, while studies on functional diversity in marine systems emerged in the early 2000\u0026rsquo;s (Bremner et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Beukhof et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Cappelatti et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dencker et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dolbeth et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Litchman and Klausmeier, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Mauffrey et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Vale et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) they still lag behind the level of exploration dedicated to understanding functional diversity in terrestrial systems. Also, due to its recent history and increasing use, the investigation of functional diversity is continuously expanding with the constant development and application of approaches and methods (Lalibert\u0026eacute; and Legendre \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Lalibert\u0026eacute; et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Cardoso et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Malaterre et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mammola and Cardoso \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pavoine \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mammola et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Magneville et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Blonder \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAn important aspect of biodiversity is how species are distributed within and between ecosystems. This is often described using two key measures: alpha (α) diversity, which refers to the diversity within a single habitat or site, and beta (β) diversity, which describes the differences in species composition between sites or habitats (Whittaker, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e1972\u003c/span\u003e in Anderson et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). β-diversity can be further broken down into two main processes: turnover (i.e. β replacement) and nestedness (i.e. β richness) (Mammola and Cardoso \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Turnover occurs when some species are replaced by others from site to site or over time, that is, the identities of species change but the total number may stay the same. In contrast, nestedness results from differences in species richness caused by species being lost or gained (for example, when one site has only a subset of the species found in another) (Baselga \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Carvalho et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Socolar et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mammola and Cardoso \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Understanding beta-diversity patterns and their underlying processes is crucial to comprehend how populations and communities are shaped by spatial, environmental and temporal changes (Carvalho et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Furthermore, analysing functional β-diversity provides a deeper understanding of how community composition evolves over time and space, driven by species fitness and the processes that govern it (Petchey and Gaston \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Louren\u0026ccedil;o et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Beukhof et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This is particularly relevant in the context of climate change, where rapid shifts in species distributions can lead to cascading effects on biodiversity, ecosystem resilience, and the ability of ecosystems to support human wellbeing. As β-diversity is sensitive to changes in species distributions and extinctions based on their traits, it serves as a valuable metric for monitoring these shifts (Magurran et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Vale et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, gaining a comprehensive understanding of beta-diversity patterns is key to understand the structural and functional impacts of new environmental scenarios in highly dynamic ecosystems, such as coastal areas (Vale et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Silva et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWithin coastal areas, upwelling regions can serve as climate refugia (Morelli et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), potentially mitigating sea temperature rise and helping to counteract species range contractions caused by climate warming (Lima et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Louren\u0026ccedil;o et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The Portuguese coast, where summer upwelling occurs, is recognized as a transition zone between cold- and warm-water fauna and flora (Boaventura et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Lima et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Alvarez et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Monteiro et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The northern coast, in particular, is considered a climate refugia for distinct macroalgae cold-water boreal species which reach their southernmost limit there (i.e., \u003cem\u003eHimanthalia elongata\u003c/em\u003e, \u003cem\u003eAscophyllum nodosum\u003c/em\u003e, and \u003cem\u003eFucus serratus\u003c/em\u003e [Ardr\u0026eacute;, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1971\u003c/span\u003e; Boaventura et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Lima et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2007\u003c/span\u003e] in the intertidal and \u003cem\u003eLaminaria hyperborea\u003c/em\u003e in the subtidal [de Azevedo et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e]). The summer upwelling is key in maintaining these populations (de Azevedo et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, recent data show signs of tropicalization in this region, threatening these populations and altering community composition in both intertidal (Monteiro et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and subtidal (de Azevedo et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) areas. Additionally, and unlike other upwelling areas, the Iberian coast is expected to experience weakened upwelling (Sydeman et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sousa et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which may multiply overall ocean warming affecting species composition due to changes in temperature and nutrient input (de Azevedo et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Monteiro et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and impairing its role as climate refugia.\u003c/p\u003e\u003cp\u003eIn this study, we examined intertidal macroalgae communities using data collected over seven years spread over an 18-year period, at five locations along the northern Portuguese coast to investigate how this climate refugia region has responded to environmental changes over time and space. Despite the limited spatial extent between sites, this region still harbours the characteristic cold-water species and is highly dynamic, justifying a finer scale to depict changes in these communities (Monteiro et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To this end, we investigate how α and β diversity patterns, both taxonomic and functional, are changing at the lower intertidal level and identify the processes and functional impacts behind these changes.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eData collection\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eEnvironmental data\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSea surface temperature data were obtained from the Copernicus Marine Service, from which a high-resolution dataset was statistically downscaled to the Iberian Peninsula region (Kristiansen et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Upwelling data were sourced from the National Oceanic and Atmospheric Administration (NOAA), and an upwelling index was calculated following the methodology described in Alvarez et al., (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), where positive values indicate the occurrence of upwelling events. These data were analysed to assess seasonal and temporal trends, and sea surface temperature was further examined spatially and mapped with QGIS to identify regional differences in the northern area, considering a 1990–2020 annual average.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eBiological data\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe temporal and spatial variation of low intertidal macroalgae communities was assessed for 5 distinct locations along the north Portuguese coast (from the north to the south): Praia da Areosa (41°42'41.4\"N 8°51'43.4\"W), Praia Norte (41°41'49.8\"N 8°51'05.5\"W), Praia da Amorosa (41°39'22.7\"N 8°49'35.1\"W), Praia de Mindelo (41°18'25.1\"N 8°44'39.9\"W), and Praia da Aguda (41°03'06.8\"N 8°39'28.6\"W) (Supplementary Fig.\u0026nbsp;1). Monitoring campaigns were conducted between March and April in the years 2006, 2007, 2008, 2021, 2022, 2023 and 2024. During these campaigns, the percentage cover of low intertidal macroalgae communities was recorded \u003cem\u003ein situ\u003c/em\u003e using 50x50 cm quadrats and through photographic documentation (total of 15 replicates), with specimens identified to the lowest possible taxonomic level. Quadrats were randomly placed, with distances between them ranging from approximately 10 to 20 meters, depending on the availability and topography of the site.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eFunctional traits\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAll species were characterized by a comprehensive set of seven functional traits representing their morphology, life cycle, growth and reproduction (see Supplementary Table\u0026nbsp;1). Functional traits were chosen based on their ecological relevance (Cappelatti et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mauffrey et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and limited by the accessibility of data (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Data was obtained from various sources, including online databases such as AlgaeTraits (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://algaetraits.org/\u003c/span\u003e\u003cspan address=\"https://algaetraits.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, The Seaweed Site(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.seaweed.ie/\u003c/span\u003e\u003cspan address=\"https://www.seaweed.ie/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, MarLIN – The Marine Life Information Network (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.marlin.ac.uk/\u003c/span\u003e\u003cspan address=\"https://www.marlin.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, and BIOTIC – Biological Traits Information Catalogue (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.marlin.ac.uk/biotic/\u003c/span\u003e\u003cspan address=\"https://www.marlin.ac.uk/biotic/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, as well as relevant literature. To assess temporal and spatial variation of community structure we removed taxa with incomplete trait information, which only represented \u0026lt; 1% of total percentage cover, as no missing values are allowed in our methodological approach.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\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\u003eDescription of functional traits selected, with indication of their ecological relevance based on Cappelatti et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e and Mauffrey et al., 2021, the type of measurement, categories inside each trait and examples of species for the qualitative traits.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTraits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEcological relevance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMeasurement\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCategory/Description\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e\u003cp\u003eMorphology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMaximum thallus height\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eproductivity and blue carbon potential\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQuantitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMaximum thallus height the species can reach (worldwide)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eHoldfast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ehabitat/nursery function attachment/fixation capacity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDisc-like (e.g., \u003cem\u003eAhnfeltiopsis devoniensis, Ophidocladus simpliciusculus\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRhizoid-like (incl. bulbous) (e.g., \u003cem\u003eBifurcaria bifurcata\u003c/em\u003e, \u003cem\u003eOphidocladus simpliciusculus\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCrustose (e.g., \u003cem\u003eLitophylum incrustans\u003c/em\u003e, \u003cem\u003ePetrocellis cruenta\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eBody shape (described as the dominant form)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eproductivity and blue carbon potential resilience\u003c/p\u003e\u003cp\u003ehabitat/nursey function\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCrustose (e.g., \u003cem\u003eLitophylum incrustans, Petrocellis cruenta\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFoliose (e.g., \u003cem\u003eLaminaria\u003c/em\u003e spp, \u003cem\u003eUlva\u003c/em\u003e spp)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFilamentous (e.g., \u003cem\u003eRhodotameliaela floridula, Himanthalia elongata\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBranched_filamentous (e.g. \u003cem\u003eChondrocantus spp\u003c/em\u003e, \u003cem\u003eCeramium spp\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSpherical_liked (e.g., \u003cem\u003eColpomenia pererina, Gastroclonium ovatum)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ePigment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ephotosynthetic capacity productivity and blue carbon potential\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRed (e.g., \u003cem\u003eAnhfeltiopsis devoniensis\u003c/em\u003e, \u003cem\u003eOphidocladus simplicicusculus\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBrown (e.g., \u003cem\u003eDictyota dichotoma\u003c/em\u003e, \u003cem\u003eLaminaria\u003c/em\u003e spp)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGreen (e.g., \u003cem\u003eUlva\u003c/em\u003e spp, \u003cem\u003eCodium tomentosum\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLife cycle and growth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLife span\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eresilience\u003c/p\u003e\u003cp\u003eproductivity and blue carbon potential\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePerennial (e.g., \u003cem\u003eLaminaria spp\u003c/em\u003e, \u003cem\u003eAhnfeltiopsis devoniensis\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAnnual (e.g., \u003cem\u003eOsmundea pinnatifida\u003c/em\u003e, \u003cem\u003eSaccorhiza polyschides\u003c/em\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\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAsexual reproduction possibility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003edispersal resilience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBinary (0 and 1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 = No (\u003cem\u003eLaminaria ochroleuca\u003c/em\u003e, \u003cem\u003eCodium tomentosum\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eReproduction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 = Yes (e.g., \u003cem\u003eAhnfeltiopsis devoniensis\u003c/em\u003e, \u003cem\u003eJania rubens\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSpawning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003edispersal persistence vs dispersal (r-K strategy)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWater column (e.g., \u003cem\u003eBifurcaria bifurcata\u003c/em\u003e, \u003cem\u003eHimanthalia elongata\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFemale gametophyte (e.g., \u003cem\u003eGongolaria baccata\u003c/em\u003e, \u003cem\u003eSargassum muticum\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e● Data analysis\u003c/h2\u003e\u003cp\u003eTaxonomic and functional spaces were visualized using principal coordinate analysis (PCoA), performed with the function pco() from the R package \u003cem\u003elabdsv\u003c/em\u003e (v. 2.1-0) (Roberts \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), accounting for both identity and respective density within the community.\u003c/p\u003e\u003cp\u003eTaxonomic and functional diversity indices were calculated, focusing on both their α (diversity within communities) and β (dissimilarity between communities) components (de Bello et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). The following metrics were computed for all sites and years, to assess both the spatial and temporal changes in diversity patterns. Taxonomic diversity metrics (e.g. species richness, Simpson index, evenness) were calculated with diversity() function from the R package \u003cem\u003evegan\u003c/em\u003e (Oksanen et al., 2022) while β-diversity was calculated with the function beta() from the R package \u003cem\u003eBAT\u003c/em\u003e (v. 2.9.3) (Cardoso et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFunctional diversity metrics (e.g. functional richness, functional dispersion, functional evenness) were calculated through kernel density hypervolumes (Mammola and Cardoso \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) using the function kernel.beta() from the R package \u003cem\u003eBAT\u003c/em\u003e (v. 2.9.3) (Cardoso et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although the functional diversity indices were calculated using kernel density hypervolumes, they were visually represented using convex hulls. The latter is a widely used and computationally efficient method, but it has limitations, such as the assumption that there is no empty space within extreme values (Blonder \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). To address this, probabilistic hypervolumes have been developed, with high-dimensional kernel density estimations being the most popular. This method delineates the shape and volume of the multidimensional space.\u003c/p\u003e\u003cp\u003eFinally, each taxonomic and functional diversity metric was tested with a PERMANOVA, with a crossed design including the factors Year (fixed) and Beach (random), to detect potential differences across sites and time (Anderson et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Data were first converted into a similarity matrix, using the Euclidean distance, for each diversity index. These analyses were done with the PRIMER – PERMANOVA software (Anderson et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eANOVA was used to test the components of β diversity, except for β richness, which did not meet the necessary assumptions. Instead, PERMANOVA was applied for the analysis of this component. Both analyses were done for factor Year and Beach separately, as β diversity refers to a spatial or to a temporal gradient, and were done with R Statistical Software ( v. 4.1.2; R Core Team, 2021.). The assumptions of ANOVA were verified graphically following Zuur et al., (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAll plots were performed with the R package \u003cem\u003eggplot2\u003c/em\u003e (v.3.4.2) (Wickham \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)from R Statistical Software ( v. 4.1.2; R Core Team, 2021.).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eβ-diversity followed a similar trend to that observed at the spatial scale, with β-replacement contributing more to the taxonomic dimension and β-richness contributing more to the functional dimension (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Both taxonomic and functional β-diversity components differed significantly for most of the years with a few exceptions in the latest years (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementary Table\u0026nbsp;7).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOver time, certain species, especially in the Phaeophyceae group, have notably declined, with some nearly disappearing or vanishing completely in recent years, such as \u003cem\u003eLaminaria ochroleuca\u003c/em\u003e and \u003cem\u003eSacchorhiza polyschides\u003c/em\u003e and the others increasing, as \u003cem\u003eXiphosiphonia\u003c/em\u003e spp., firstly only common at the southern region.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003ch2\u003e3.1. Temperature and upwelling trends\u003c/h2\u003e\n\u003cp\u003eIn the northern Portuguese region, sea surface temperatures near the coast were on average 2\u0026deg;C lower compared to offshore and southern areas (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Temperatures on the coast reached as low as 13.5\u0026deg;C for the annual average between 1990\u0026ndash;2020. Looking at the temporal trends since 2001, maximum sea surface temperatures picked at highest values in 2003 and 2008 and in latest years, especially in 2023-24 (mode close to 18\u0026ordm;C and highest SST reaching 21\u0026ordm;C, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The minimum values ranged mostly within 12\u0026ndash;17\u0026ordm;C across all years, with 2 modes around 13 and 15\u0026ordm;C. There were clear seasonal changes in sea surface temperatures with a temperature increase in spring and summer. Taking into account the study years and an average value for an 0.5\u0026deg; \u0026times; 0.5\u0026deg; latitude-longitude grid, i.e. nearshore and offshore areas (Kristiansen et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), the average summer temperature ranged around 15\u0026ndash;16\u0026ordm;C, but in the last years 2023-24 the average temperature was almost 1\u0026ordm;C higher (17\u0026ordm;C).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe upwelling index showed clear seasonal patterns, with positive values typically occurring from late spring to summer (mid-March to mid-September) across nearly all years of the study, with the exception of the first years, 2006 and 2007, with mild positive values only in June-July (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast, 2024 showed a shift in timing, with the stronger upwelling occurring later in the year, from July onwards. Over time, the fluctuation range between positive and negative values has become less pronounced, indicating a weakening of the seasonal extremes. Nevertheless, in latest years, we still have positive upwelling (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003ch2\u003e3.2. Spatial patterns of taxonomic and functional composition and \u0026beta;-diversity\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the taxonomic and functional convex hulls for the five sites examined in our study, considering the full sampling period. The first two axes of the taxonomic PCoA accounted for 36.9% of the variability in species composition, while the first two axes of the functional PCoA explained 97.4% of the variability in trait composition. The ordination plots reveal clear spatial variation, with the high variance explained, particularly in the functional space, highlighting the reliability of the observed differences. Amorosa had the smallest convex hull (0.27) followed by Praia Norte (0.28), Areosa (0.34), Aguda (0.42) and Mindelo (0.47). In addition, Aguda had the smallest functional convex hull (0.06) followed by Amorosa (0.06), Praia Norte (0.08), Areosa (0.11) and Mindelo (0.13).\u003c/p\u003e\n\u003cp\u003eWhen analysing taxonomic and functional diversity indices, the general tendency is for indices to remain relatively stable (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Still, the statistical differences between the sites depended on the year, as confirmed by the significant interaction (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). In earlier years, we mainly found significant differences between Areosa, the site with the highest latitude, and the remaining sites, while in recent years, statistical differences were also found within the closest sites (Supplementary Tables\u0026nbsp;2 and 3), with a few exceptions. For instance, in 2024 almost no statistical differences were observed between sites across all taxonomic indices (Supplementary Table\u0026nbsp;2), while for 2022 functional richness was statistically different between almost all sites (Supplementary Table\u0026nbsp;3). Notably, functional indices were generally more statistically different than taxonomic indices.\u003c/p\u003e\n\u003cp\u003eFor \u0026beta;-diversity, analyses were done considering the spatial or the temporal gradient alone. Regarding the taxonomic dimension along the spatial scale, \u0026beta;-replacement was the primary contributor to the total \u0026beta;-diversity. On the other hand, for the functional dimension, the main contributor to total \u0026beta;-diversity was \u0026beta;-richness (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). As a general trend, taxonomic \u0026beta;-total and \u0026beta;-replacement were different for all sites, but not for \u0026beta;-richness and for the functional metrics (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, Supplementary Table\u0026nbsp;4). Taxonomic \u0026beta;-richness was statistically different for Praia Norte compared to all other and some specific site pairs, while functional \u0026beta;-richness was statistically different for Mindelo alone compared to all other sites (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, Supplementary Table\u0026nbsp;4).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eStatistical results (ANOVA and Permanova) for taxonomic and functional diversity metrics, and \u0026beta;-diversity components. Due to its nature \u0026beta;-diversity analysis had to be separated by site and time.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\u003ccolgroup\u003e\u003c/colgroup\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eModel\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSignificant terms\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDf\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePseudo-F / F\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP-perm /p-value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eUnique perms\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRichness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePermanova\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYear * Beach\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6,1935\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e998\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSimpson\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePermanova\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYear * Beach\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.2992\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e998\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEvenness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePermanova\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYear * Beach\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.2651\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e999\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTaxonomic \u0026beta;-total\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eANOVA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBeach\u003c/p\u003e\n\u003cp\u003eYear\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1108\u003c/p\u003e\n\u003cp\u003e77.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTaxonomic \u0026beta;-replacement\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eANOVA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBeach\u003c/p\u003e\n\u003cp\u003eYear\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e460.5\u003c/p\u003e\n\u003cp\u003e70.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTaxonomic \u0026beta;-richness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePermanova\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBeach\u003c/p\u003e\n\u003cp\u003eYear\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.19\u003c/p\u003e\n\u003cp\u003e34.246\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e999\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFunctional richness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePermanova\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYear * Beach\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.6426\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e998\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFunctional dispersion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePermanova\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYear * Beach\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.2595\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e999\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFunctional evenness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePermanova\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYear * Beach\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.0446\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e998\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFunctional \u0026beta;-total\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eANOVA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBeach\u003c/p\u003e\n\u003cp\u003eYear\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e354.1\u003c/p\u003e\n\u003cp\u003e150.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFunctional \u0026beta;-replacement\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eANOVA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBeach\u003c/p\u003e\n\u003cp\u003eYear\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56.04\u003c/p\u003e\n\u003cp\u003e192\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFunctional \u0026beta;-richness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePermanova\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBeach\u003c/p\u003e\n\u003cp\u003eYear\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.144\u003c/p\u003e\n\u003cp\u003e34.246\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e999\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding species identity, we found that species from the Phaeophyceae group (brown algae) were more abundant in northern locations, namely \u003cem\u003eLaminaria ochroleuca and Gongolaria baccata\u003c/em\u003e (see Supplementary Table\u0026nbsp;8). Nonetheless, Rhodophyta species (red ones) were dominant overall (e.g. \u003cem\u003eChondracanthus acicularis\u003c/em\u003e), but especially in southern areas, where \u003cem\u003eOsmundea pinnatifida, Calliblepharis jubata, Pterosiphonia complanata\u003c/em\u003e and \u003cem\u003eGrateloupia turuturu\u003c/em\u003e were more abundant compared to other regions (Supplementary Table\u0026nbsp;8). In this southern region, Phaeophyceae and Chlorophyta were less common.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. Temporal patterns of taxonomic and functional composition and \u0026beta;-diversity\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe first two axes of the taxonomic PCoA for the temporal analyses captured 36.9% of the variability in species composition, while the first two axes of the functional PCoA accounted for 97.4% of the variability in trait composition (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). As what happened for the spatial analysis, the ordination plots reveal clear temporal variation, with the high variance explained, particularly in the functional space, highlighting the reliability of the observed differences. The most recent years demonstrated on average smaller taxonomic convex hulls (0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07), compared to the earlier period (0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07). Likewise, functional convex hulls also declined in recent years (0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02) relative to the earlier years (0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01). In 2024, however, taxonomic space was not as low as 2021\u0026ndash;2023 (0.37 compared to 0.35, 0.30 and 0.22), but the functional one was, along with 2021, the lowest of the whole study period (0.044 for 2021 and 0.049 for 2024, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eWhen analysing taxonomic and functional diversity indices, again, statistical differences between years were depended on the sites (significant interaction, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), as not all years were different among sites (Supplementary Tables\u0026nbsp;5 and 6). The overall tendency was for, taxonomic diversity to decrease over time, with a slight increase in 2024 as an exception (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). However, this pattern was not consistent and statistically different for all sites and indices. For instance, for Amorosa and Mindelo, 2006 was generally distinct than all other years in richness and Simpson, while for Aguda only 2022 was statistically different than the other years (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, Supplementary Table\u0026nbsp;5). For evenness, patterns were even less consistent per site (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). For instance, for Areosa, evenness patterns changed when comparing earlier and most recent years, while for Amorosa they almost did not change (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, Supplementary Table\u0026nbsp;5). For functional diversity, we found a similar trend of variation over time, highly dependent on the site (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The decreasing trend of functional diversity over time was even more pronounced, but only for the northernmost sites, Areosa and Praia Norte (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). In Aguda, for instance, most of the changes in functional richness and dispersion occurred in latest years being significant for most of the year comparisons (with a few exceptions), but not for functional evenness (Supplementary Table\u0026nbsp;6). As a general trend, 2024, had the lowest functional diversity of all the years observed in all sites, especially for richness and dispersion (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings underscore the importance of local oceanographic conditions in shaping marine community dynamics. Coastal sea surface temperatures (SSTs) were, on average, 2\u0026deg;C cooler than offshore waters, demonstrating the potential of this region to act as a climate refugia for boreal species. These cooler conditions, maintained by coastal upwelling during summer, play a key role in supporting cold water affinity species (de Azevedo et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Indeed, cold-water species such as \u003cem\u003eLaminaria hyperborea\u003c/em\u003e in the subtidal zone (de Azevedo et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), \u003cem\u003eAscophyllum nodosum\u003c/em\u003e and \u003cem\u003eFucus serratus\u003c/em\u003e, occurring at tidal levels not covered in this study, currently still persist in the region (pers. obs.), further supporting its role as a climate refugia. However, our results also reveal concerning trends: while minimum temperatures remain relatively stable, maximum SSTs have increased, and upwelling intensity appears to be weakening, as suggested by previous authors (Sydeman et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sousa et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The alignment of these two patterns may pose significant risks to the persistence of boreal species in this coastal zone. These risks have already been documented on macroalgae communities, both in intertidal (Monteiro et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) as the subtidal (de Azevedo et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) habitats along this shore.\u003c/p\u003e\u003cp\u003eOverall, our results reveal a contraction in both the taxonomic and functional space occupied by lower intertidal macroalgae communities over time, indicating a loss of species and/or their relative abundance as well as the ecological functions they perform. Remarkably, the last year of the study, 2024, deviated from this trend: while taxonomic space remained within the range observed in earlier years, functional space reached its lowest level over the entire study period. This indicates a pronounced erosion of functional diversity, even when taxonomic diversity appeared relatively stable. The processes underlying the observed declines differed depending on whether taxonomic or functional diversity was considered, but remained consistent across spatial and temporal scales, with β-replacement driving taxonomic change and β-richness driving functional change. Hence, we discuss the results in terms of these scales together and respective diversity dimensions.\u003c/p\u003e\u003cp\u003eFrom a taxonomic standpoint, the primary driver of change was turnover (β-replacement), indicating that species were replaced by others through space and time. More specifically, brown seaweeds tend to be replaced by red macroalgae as latitude increases. For instance, species like \u003cem\u003eGongolaria baccata\u003c/em\u003e and \u003cem\u003eLaminaria ochroleuca\u003c/em\u003e show a decline in percentage cover across latitudes, eventually disappearing from the southernmost locations. In contrast, there is an increase in red seaweeds, such as \u003cem\u003eOsmundea pinnatifida\u003c/em\u003e and the invasive \u003cem\u003eGrateloupia turuturu\u003c/em\u003e. Previous studies, including part of this Portuguese stretch, have also noted this spatial variability in diversity, which could be related to local differences in microclimate due to topography, wave action, hydrodynamics and solar exposition, including thermal refugia possibilities (Louren\u0026ccedil;o et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Furthermore, we found that brown seaweeds like \u003cem\u003eLaminaria ochroleuca\u003c/em\u003e and \u003cem\u003eSacchorhiza polyschides\u003c/em\u003e have been progressively replaced by green and red algae, including \u003cem\u003eCodium tomentosum\u003c/em\u003e and \u003cem\u003eXiphosiphonia spp\u003c/em\u003e over time. The species replacement process across spatial scales has been recognized elsewhere (e.g. Red Sea (Issa et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), Galicia (Vale et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)) as a key driver of shifts in intertidal macroalgal communities. A meta-analysis further supports this, showing that most spatial changes within marine primary producers are driven by β-replacement (turnover) (Soininen et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, beta-diversity patterns do not consistently hold over time (Vale et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our findings align with previous studies on macroalgae communities in the Red Sea (Issa et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and with time series analyses across different organisms and biomes (Dornelas et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), where species replacement is also regarded the main driver of change. This is additionally supported by the observed patterns of diversity indices, where abundance-weighted metrics remain relatively stable across space and time, despite the decrease trends in richness over time (yet not consistent for all sites). Overall, this represents a spatial homogenization of the community. On the other hand, in the nearby Galicia region (which borders Northern Portugal), changes in macroalgae communities over time were driven either by species loss (e.g. cold-water affinity species) or by newly introduced species (e.g. invasive species) (Vale et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and not from species replacement.\u003c/p\u003e\u003cp\u003eRemarkably, our results for functional diversity contrast with those for taxonomic diversity. For functional diversity, nestedness (β-richness) was the dominant process, indicating loss or gain of traits and, consequently, potential ecosystem functions. Both spatial and temporal patterns suggest that communities tend to be nested subsets of one another: spatially, communities at lower latitudes appear to be subsets of those at higher latitudes; while temporally, recent communities appear to be subsets of past communities. Adding to this, a consistent decrease in functional richness across latitude and time was found with different metrics, showing that assemblages have been losing traits and associated functions, particularly over recent periods. Thus, while the communities along the northern Portuguese coast have managed to maintain their taxonomic diversity levels across both space and time, they are failing to preserve their functional potential. This loss of traits and reduction of functional space might suggest trait convergence as a mechanism for structuring these communities. Trait convergence has been linked to environmental filtering, in which species are selected based on their ecological tolerances towards the environment, being less functionally different than expected by chance (de Bello et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Valdivia et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Conversely, trait divergence might also emerge at different scales (Dolbeth et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; de Bello et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e), typically reflecting niche differentiation driven by biotic interactions such as competition (Leibold et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Mayfield and Levine \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; de Bello et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Valdivia et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, as recently debated, such explanations may oversimplify the dynamics at play, as competition can also generate community patterns similar to environmental filtering (Cadotte and Tucker \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). To further understand these dynamics, complementary statistical approaches would be needed (e.g., null models and traits correlation with the environment) (Mayfield and Levine \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Still, our results confirmed that species previously absent from our communities, particularly invasive species associated with warmer waters, such as \u003cem\u003eAsparagopsis armata\u003c/em\u003e and \u003cem\u003eGrateloupia turuturu\u003c/em\u003e, have begun to appear and increase in abundance in recent years. The arrival of these particular new species may come with associated functional impacts, such as reduced community complexity, lower carbon sequestration potential and diminished nursery/habitat function, as they tend to be simpler and have limited capacity for carbon (Janiak and Whitlatch \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Silva et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite the narrow spatial extent of our study area, our analyses reveal clear spatiotemporal variation in both taxonomic and functional diversity over the 18-year study period. The observed shifts highlight that macroalgae communities are not only changing in species composition but are also losing functional traits (i.e., contraction of the functional space over time), what can lead to potential ecosystem functions reconfigurations.\u003c/p\u003e\u003cp\u003eOur findings further highlight that climate refugia are neither static or immune to change, especially facing contemporary climate change (Morelli et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Although we found signs of environmental change (i.e. increased maximum temperatures and upwelling weakening trends), the presence of boreal macroalgae at their southern distribution limits at other tidal levels indicate that the region may still offer buffered conditions suitable for their persistence. However, the community-level changes observed at the lower intertidal, marked by declines in taxonomic and functional diversity and the loss or displacement of certain species and functions suggest that this refugial capacity may be weakening. Although we studied different communities than those where the above-mentioned cold-water species occur, our results serve as a warning that even these iconic boreal species could be at risk. This illustrates that climate refugia may erode or shift under sustained environmental pressures, particularly those driven by ongoing climate change.\u003c/p\u003e\u003cp\u003eFinally, our understanding of the functional dynamics of macroalgae communities and their drivers remains surprisingly limited. While only a handful of studies have explored patterns of β-diversity in marine macroalgae communities (Mazariegos-Villarreal et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Issa et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Vale et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and some have addressed functional β-diversity across spatial gradients (Cappelatti et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), studies explicitly considering functional β-diversity across time are still lacking. Hence, our research provides new and needed insight into the ecological processes underlying community assembly by simultaneously investigating both taxonomic and functional α and β-diversity, as well as their underlying components (i.e., turnover and nestedness).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur results have important implications for conservation strategies. When communities are driven by turnover (i.e. replacement processes), conservation efforts should target multiple sites, whereas when communities are driven by nestedness (i.e. richness variations), conservation should focus on the richest sites (Socolar et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Given that ecosystem services are tightly linked to the functional traits (Tilman \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Petchey and Gaston \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), conservation policies along the northern Portuguese coast should prioritize the highest functional diversity and thus potential for providing those services (i.e. Mindelo, Areosa and Praia Norte). Our findings also contributed to better understanding the region's current role as a climate refugia. While cold-affinity species still persist in the region, the changes in lower intertidal communities suggest that this refugial capacity may be weakening. Overall, this research is a valuable reference for future work in this area and as a case study within macroalgae communities facing rapid changes worldwide. It also emphasizes the need for ongoing monitoring and adaptive management, both to maintain diversity and safeguard the region role as a climate refugia under accelerating environmental change.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests:\u003c/h2\u003e\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003e\u003cb\u003eAuthor contributions\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eMarta Martins, Martin Lindegren, Francisco Arenas and Marina Dolbeth conceived the ideas. Marta Martins, Martin Lindegren, Antoni Viv\u0026oacute;-Pons, Francisco Arenas and Marina Dolbeth designed the methodology and formal analysis. Marta Martins, Hugo Sainz Meyer, Oscar Babe, Harold Casalis, Francisco Arenas and Marina Dolbeth collected the data. Marta Martins led the writing of the manuscript. Marta Martins, Martin Lindegren, Francisco Arenas and Marina Dolbeth contributed to the writing (review and editing). All authors contributed critically to the drafts and gave the final approval for publication.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eMarta Martins was supported by the Portuguese Foundation for Science and Technology (FCT) under the grant Ref: UI/BD/150934/2021. Marina Dolbeth was supported by the CEEC-INST contract, with ref CEECINST/00027/2021/CP2789/CT0001 and DOI \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.54499/CEECINST/00027/2021/CP2789/CT0001\u003c/span\u003e\u003cspan address=\"10.54499/CEECINST/00027/2021/CP2789/CT0001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. This work was also funded by FCT/MCTES through the financial support to CIIMAR (UIDB/04423/2020 and UIDP/04423/2020), FutureMares (European Union\u0026rsquo;s Horizon Europe Research and Innovation Programme grant n\u0026ordm; 869300) and ACTNOW (European Union\u0026rsquo;s Horizon Europe Research and Innovation Programme grant n\u0026ordm; 101060072).\u003c/p\u003e\u003ch2\u003eData Availability:\u003c/h2\u003e\u003cp\u003eThe data supporting the current study are not publicly available as they will be used in further ongoing and future publications by the authors. Data may be made available from the corresponding author on reasonable request, subject to restrictions related to further analyses and planned publications.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlvarez I, Gomez-gesteira M, Lorenzo MN, Crespo AJC, Dias JM (2011) Comparative analysis of upwelling influence between the western and northern coast of the Iberian Peninsula. Cont Shelf Res 31:388\u0026ndash;399. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.csr.2010.07.009\u003c/span\u003e\u003cspan address=\"10.1016/j.csr.2010.07.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlvarez I, Lorenzo MN, DeCastro M, Gomez-Gesteira M (2017) Coastal upwelling trends under future warming scenarios from the CORDEX project along the Galician coast (NW Iberian Peninsula). 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Methods Ecol Evol 1:3\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/J.2041-210X.2009.00001.X\u003c/span\u003e\u003cspan address=\"10.1111/J.2041-210X.2009.00001.X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"marine-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mabi","sideBox":"Learn more about [Marine Biology](https://www.springer.com/journal/227)","snPcode":"227","submissionUrl":"https://submission.nature.com/new-submission/227/3","title":"Marine Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"functional diversity, beta-diversity, macroalgae, intertidal, Portugal; climate refugia","lastPublishedDoi":"10.21203/rs.3.rs-7251423/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7251423/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAssessing the consequences of climate change in coastal ecosystems is challenging, largely due to their natural variability and the context-dependent responses of organisms. Tropicalization events are reshaping communities, with declines in species sensitive to local climate variability and increases in climate-tolerant and invasive species. Understanding taxonomic and functional biodiversity patterns over space and time is critical to evaluate whether certain regions may act as climate refugia. We investigate the spatial and temporal patterns of intertidal macroalgae community diversity, taxonomic and functional (α-diversity and β-diversity), along the northern Portuguese coast. Data was collected over an 18-year interval from five distinct locations (spanning from 41\u0026deg;42'41.4\"N 8\u0026deg;51'43.4\"W to 41\u0026deg;03'06.8\"N 8\u0026deg;39'28.6\"W). The objective of this work was to characterize the spatial and temporal patterns of intertidal macroalgae communities, along with their inherent changes. Our key findings include (1) coastal sea surface temperatures were approximately 2\u0026deg;C cooler than offshore waters, suggesting the area may function as a climate refugia; (2) both taxonomic and functional space contracted over time, indicating losses of species and functions; (3) for both space and time, turnover (β-replacement) was the main driver of taxonomic changes, whereas nestedness (β-richness) primarily drove functional changes. These spatial and temporal shifts in community composition are likely to have significant functional impacts, such as reduced habitat availability and lower productivity rates, with important implications for ecosystem services like blue carbon storage and habitat provision. This knowledge is crucial for mitigating the effects of climate change and best implementing effective conservation management strategies.\u003c/p\u003e","manuscriptTitle":"Functional and taxonomic diversity of intertidal macroalgae communities from a climate refugia hotspot","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-12 13:59:45","doi":"10.21203/rs.3.rs-7251423/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-08-11T14:41:52+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-07T06:01:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-05T11:31:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Marine Biology","date":"2025-07-30T05:55:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"marine-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mabi","sideBox":"Learn more about [Marine Biology](https://www.springer.com/journal/227)","snPcode":"227","submissionUrl":"https://submission.nature.com/new-submission/227/3","title":"Marine Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"acceee29-d40b-4f04-af8a-cfb6e33283b7","owner":[],"postedDate":"August 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T16:45:54+00:00","versionOfRecord":{"articleIdentity":"rs-7251423","link":"https://doi.org/10.1007/s00227-025-04786-2","journal":{"identity":"marine-biology","isVorOnly":false,"title":"Marine Biology"},"publishedOn":"2026-01-13 16:29:15","publishedOnDateReadable":"January 13th, 2026"},"versionCreatedAt":"2025-08-12 13:59:45","video":"","vorDoi":"10.1007/s00227-025-04786-2","vorDoiUrl":"https://doi.org/10.1007/s00227-025-04786-2","workflowStages":[]},"version":"v1","identity":"rs-7251423","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7251423","identity":"rs-7251423","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}