Short-term response of macroalgal communities to ocean warming in the Southern Bay of Biscay

Marine Environmental Research 190 (2023) 106098 Available online 8 July 2023 0141-1136/© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Short-term response of macroalgal communities to ocean warming in the Southern Bay of Biscay O. Arriaga a , * , P. Wawrzynkowski b , 1 , H. Ib´ a˜ nez a , N. Muguerza a , I. Díez a , I. P´ erez-Ruzafa c , J.M. Gorostiaga a , E. Quintano a , M.A. Becerro b a Laboratory of Botany, Department of Plant Biology and Ecology, Fac. of Science and Technology & Research Centre for Experimental Marine Biology and Biotechnology PIE-UPV/EHU, University of the Basque Country (UPV/EHU), PO Box 644, 48080, Bilbao, Spain b The BITES Lab, Center for Advanced Studies of Blanes (CEAB-CSIC), Access Cala S Francesc 14, 17300, Blanes, Girona, Spain c Department of Biodiversity, Ecology and Evolution, Complutense University of Madrid (UCM), C/Jos´ e Antonio Novais, 12, 28040, Madrid, Spain ARTICLE INFO Keywords: Climate change Temperature Macroalgae Community temperature index (CTI) Canopy Subtidal Iberian Peninsula Monitoring ABSTRACT Climate change is causing significant shifts in biological communities worldwide, including the degradation of marine communities. Previous research has predicted that southern Bay of Biscay canopy-forming subtidal macroalgal communities will shift into turf-forming Mediterranean-like communities by the end of the century. These predictions were based on a community-environment relationship model that used macroalgal abundance data and IPCC environmental projections. We have tested the short-term accuracy of that model by resampling the same communities and locations four years later and found the short-term predictions to be consistent with the observed communities. Changes in sea surface temperature were positively correlated with changes in the Community Temperature Index, suggesting that macroalgal communities had responded quickly to global warming. The changes over four years were significant, but canopy-forming macroalgae were more resilient in local sites with favourable temperature conditions. Our study demonstrated that updating predictive models with new data has the potential to yield reliable predictions and inform effective conservation strategies. 1. Introduction Anthropogenic activities, especially the combustion of fossil fuels, have led to a concerning increase in greenhouse gas emissions and a significant warming of the Earth (Al-Ghussain, 2019). Since 1880, Earth’s temperature has risen by 0.08 ◦C per decade, but that figure has more than doubled since 1981 to a current rate of 0.18 ◦C per decade (Lindsey and Dahlman, 2022). The average temperature of the planet’s land and ocean surface in 2021 was 1.06 ◦C warmer than in pre-industrial times (1880–1900; NOAA, 2023). The ocean has absorbed more than 90% of the excess heat, which has led to an increase in the Ocean Heat Content (OHC) in the upper 2000 m layer of the world’s oceans (Cheng et al., 2021). Due to the expected increase in OHC, Earth will likely reach 1.5 ◦C above pre-industrial levels in the near term (2021–2040) unless we take significant measures to reduce carbon emissions (IPCC, 2023). Temperature plays a crucial role in shaping species distributions (Chen et al., 2011; Tittensor et al., 2010). Global warming is causing many species worldwide to shift poleward, both on land (Chen et al., 2011; Devictor et al., 2012) and in the oceans (Kortsch et al., 2015; Kumagai et al., 2018; Pinsky et al., 2020). Marine species are shifting poleward at an average rate of 72 km per decade, a full order of magnitude faster than the rate for terrestrial species (Poloczanska et al., 2013). This shift means that the “warming” or “thermophilisation” of biological communities is one of the main consequences of global change (Bowler and B¨ ohning-Gaese, 2017; Parmesan and Yohe, 2003). In many temperate areas, cold-adapted species are declining while warm-adapted species are increasing in relative abundance (Gottfried et al., 2012). Macroalgae are important primary producers and ecosystem engineers that modify their environment and offer shelter and food for associated organisms (Harley et al., 2012). They also provide many * Corresponding author. E-mail addresses: [email protected] (O. Arriaga), [email protected] (H. Ib´ a˜ nez), [email protected] (N. Muguerza), [email protected] (I. Díez), [email protected] (I. P´ erez-Ruzafa), [email protected] (J.M. Gorostiaga), [email protected] (E. Quintano), [email protected] (M.A. Becerro). 1 University of Girona, Institute of Aquatic Ecology, C/Maria Aur` elia Capmany 69, Girona, E-17003, Catalonia, Spain. [email protected] Contents lists available at ScienceDirect Marine Environmental Research journal homepage: www.elsevier.com/locate/marenvrev https://doi.org/10.1016/j.marenvres.2023.106098 Received 8 March 2023; Received in revised form 4 July 2023; Accepted 7 July 2023 Marine Environmental Research 190 (2023) 106098 2 ecosystem goods and services since they operate as carbon sinks and help prevent environmental disturbances such as storms or floodings (R¨ onnb¨ ack et al., 2007). There is ample evidence of macroalgal community degradation including a decrease in canopy-forming species that results in turf-dominated communities (Filbee-Dexter and Wernberg, 2018; Orlando-Bonaca et al., 2021), increased overgrazing leading to barren ground formation (Kumagai et al., 2018; Verg´ es et al., 2014), and the contraction and expansion of cold-affinity and warm-affinity species (Burrows et al., 2019; Smale and Wernberg, 2013; Wernberg et al., 2016). Macroalgal community degradation is a complex process driven by numerous intertwined factors. Temperature is often reported as the main driver of change and is the focus of our study. Yet, heat waves, storms, wave exposure, overgrowth by epiphytes, pollution, eutrophication, species invasion, or harvesting may also play a role and covary with warming (Borja et al., 2013, 2018; Fern´ andez, 2011; Filbee-Dexter and Wernberg, 2018; Muguerza et al., 2017). The Southern Bay of Biscay offers an excellent opportunity to investigate changes in macroalgal community structure resulting from rising temperatures. First, the region has a strong thermal gradient, with a colder western part influenced by upwelling conditions during the summer and a warmer eastern part (Lavín et al., 2006). Second, the Cantabrian Sea that borders the northern coast of the Iberian Peninsula has warmed by 0.22 ◦C per decade since the 1970s (Chust et al., 2022; deCastro et al., 2009), exceeding the global trend of 0.15 ◦C per decade (Rhein et al., 2013). This warming trend is most prominent during spring and summer (Díez et al., 2012; G´ omez-Gesteira et al., 2008, 2011; Izquierdo et al., 2022). Third, several studies report a marked decrease in canopy-forming subtidal macroalgae such as kelp species, the warm-temperate red algae Gelidium corneum, and the fucoid Gongolaria baccata, which have started to retract from the eastern end of the Cantabrian Sea towards the west (Borja et al., 2013; Casado-Amezúa et al., 2019; Díez et al., 2012; Fern´ andez, 2011; Muguerza et al., 2017). At the westernmost region of the study area (Coast of Galicia), these changes are buffered by the effect of cold water upwellings in summer (Casado-Amezúa et al., 2019). However, less pronounced declines for several cold-temperate canopy-forming species have been recently reported off the Galician and northern Portuguese Coasts (Monteiro et al., 2022).” A practical approach to shed light on the effect of temperature on macroalgal communities is to use thermal metrics that can target temperature effects alone. The Community Temperature Index (CTI) is the abundance-weighted mean of the species’ optimal temperature. CTI helps understand the thermal preferences of birds (Bowler and B¨ ohning-Gaese, 2017; Devictor et al., 2012), fish (Bowler and B¨ ohning-Gaese, 2017; Burrows et al., 2019; Day et al., 2018), and invertebrates (Flanagan et al., 2019; Stuart-Smith et al., 2015). What little information is available on the thermal preferences of macroalgal communities stems primarily from experimental approaches for a few ecologically-relevant macroalgal species, but there is less information available at the community level of organisation (Anderson, 2006; Graba-Landry et al., 2020; Orfanidis, 1991). Understanding how macroalgal communities will respond to global warming in the future is crucial for the study of the impact of climate change on benthic communities and for making predictions about future scenarios. Muguerza et al. (2022a) developed a community-environment relationship model for the northern Iberian Peninsula to forecast changes in subtidal macroalgal communities under multiple climate change scenarios. They found that water temperature is the primary factor influencing the distribution of macroalgal communities, while nutrient availability plays a secondary role. They suggested that northern Iberian communities could undergo a meridionalisation trend by the end of the century. This meridionalisation will occur from east to west (Muguerza et al., 2022a). However, to test the model’s predictive capacity it is essential to gather updated biological and environmental data to see whether macroalgal communities are shifting and if so whether they follow the trend predicted by their model. Here, we combined global species occurrence data with gridded temperature datasets to calculate the Species Temperature Index (STI) of over 200 macroalgal species and quantified the impact of temperature increase on macroalgal communities. After four years, we resampled the locations investigated in 2015 by Muguerza et al. (2022a) in their study. First, we quantified the actual short-term community shifts and determined their evolution. Second, we tested the model’s short-term predictive capacity. To do so, we compared the model’s predicted communities with the observed natural communities in 2019. Third, to isolate the role of temperature in the observed macroalgal shifts, we used species’ thermal traits to calculate a Community Temperature Index (CTI). Even in the short term, we anticipated that some signs of the east-west meridionalisation forecasted by their model may become visible (H 0 : no short-term community change). We expected a good correlation between modelled and observed communities (H 0 : no predictive capacity of the model). Considering that Muguerza et al. (2022a) identified water temperature as the primary factor influencing the distribution of the species, and in the face of the ongoing global warming, we also hypothesized that SST could partially explain the observed community shifts (H 0 : no correlation between changes in SST and changes in CTI). 2. Methods 2.1. Study area In the summers of 2019–2020 (from July to September), we quantified species composition and abundance in the exact same locations sampled in 2015 by Muguerza et al. (2022a). We studied eight locations in the southern Bay of Biscay in 2019 (L1: Malpica; L2: Cedeira; L3: Luarca; L4: Lastres; L5: San Vicente; L6: Somocuevas; L7: Kobaron; and L8: Ea), and two reference locations in the Mediterranean Sea in 2020: one in the Alboran Sea (L9: La Herradura) and the other in the Western Mediterranean Sea (L10: Cabo Palos) (Fig. 1, Appendix a). Our study area has notable differences in environmental conditions. We focus here on sea surface temperature (SST) and nitrate concentration (NIT) as the primary drivers of community change (Muguerza et al., 2022a). The southern Bay of Biscay has a distinct west-to-east thermal gradient, with a summer upwelling system that cools SST to 16 ◦C during upwelling conditions in the western area (G´ omez-Gesteira et al., 2008). Here, SST ranges from 14 ◦C in winter to 19.5 ◦C during summer downwelling conditions (G´ omez-Gesteira et al., 2008; Ospina-Alvarez et al., 2010). The influence of this upwelling weakens towards the east, mainly because Cape Pe˜ nas acts as a boundary for the upwelling system’s area of influence (Lavín et al., 2006; Valencia et al., 2004). Consequently, the range of annual variation in SST in the easternmost area is higher than in the western area, ranging from 12 ◦C in winter up to 22 ◦C in summer (Borja et al., 2000). The locations on the Mediterranean Sea are warmer on average, with SST in the Alboran Sea ranging from 15.5 ◦C in winter to 22.8 ◦C in summer (Shaltout and Omstedt, 2014). Proximity to the Atlantic Ocean and the influence of an upwelling system that mainly occurs during spring and summer cool the Alboran Sea SST to 17 ◦C during these months. The Western Mediterranean Sea is warmer than the Alboran Sea, with SST ranging between 15 ◦C in winter and 25 ◦C in summer (Shaltout and Omstedt, 2014). Nitrate concentration in surface waters is lower in summer due to the stratification of the water column, and higher in winter due to the mixing induced by waves and wind (Serret et al., 1999). In the western area of the Southern Bay of Biscay, nitrate concentration ranges from 0.3 mmol m −3 in summer to around 5 mmol m −3 in winter (Gonz´ alez-Gil et al., 2018), whereas in the eastern area these values are around 1 mmol m −3 in summer to 4–5 mmol m −3 in winter (Ba˜ na et al., 2020; Mu˜ niz et al., 2018). In the Alboran Sea, nitrate concentration ranges from 5 * 10 −4 mmol m −3 in summer to 1.2 * 10 −3 mmol m −3 in winter (Lazzari et al., 2016). However, the summer upwelling systems in the southwest Bay of Biscay and in northern Alboran Sea bring up deep, O. Arriaga et al. Marine Environmental Research 190 (2023) 106098 3 nutrient-rich, cool waters. In south-eastern Bay of Biscay, this system has little or no influence, as evidenced by the increase in temperature and the stratification of the water column, so the nutrient input comes mainly from rivers (Fern´ andez-Salas et al., 2015). The Western Mediterranean Sea is oligotrophic, since the influence of the Atlantic decreases towards the east, and the main input is riverine but it has a low annual precipitation rate (Valdes-Abellan et al., 2017). Here the nitrate concentration ranges from 7.4 * 10 −4 mmol m −3 in summer to 1.3 * 10 −3 mmol m −3 in winter (Lazzari et al., 2016). 2.2. Data collection 2.2.1. Field data collection Following the same methodology as Muguerza et al. (2022a), we conducted sampling at 5 m below chart datum. Two sites were selected per location, at least 15 m apart. We randomly placed eight 50 ×50 cm surfaces within each site at least 1 m apart, on rocky platforms or stable rocks, flattened or gently sloping, and with low sedimentation levels, always avoiding special habitats such as crevices or boulders. We accessed the locations by boat and used SCUBA to quantify each surface’s macroalgae composition and abundance in situ. To estimate abundance, we used a 5% interval-scale cover range (from 5 to 100%). For macroalgal cover below 5% we used a 1% interval-scale (1, 2, 3, 4, 5%). For macroalgal cover below 1% we assigned a value of 0.5% to the taxa. All vertical strata of the community were sampled (crustose, basal, canopy, and epiphytic strata) and thus, the total cover of the vegetation could exceed 100%. We followed the taxonomic nomenclature used in AlgaeBase (Guiry and Guiry, 2023). We identified taxa at the species level in situ whenever possible. Samples for taxa that could not be determined in situ (turf species) were collected and transported to the laboratory for further identification. Samples were transported in moistened cotton bags and frozen within a few hours. We used binocular and optical microscopes for reliable taxonomical identification, carried out by the same researchers in both periods (2015 and 2019–2020). 2.2.2. Environmental data collection The environmental data included in this study was downloaded from E.U. Copernicus Marine Service Information (Merchant et al., 2019). SST was obtained from the Multi Observation Global Ocean 3D Temperature Salinity Height Geostrophic Current and MLD dataset (https://doi.org/ 10.48670/moi-00052) and NIT was obtained from the Global Ocean Biogeochemistry hindcast dataset (https://doi.org/10.48670/ moi-00019), both with a spatial resolution of 0.25 ×0.25◦(about 27.75 ×27.75 km) and a monthly temporal resolution. 2.3. Data treatment and analyses 2.3.1. Macroalgal community structure In this study, we used square-root transformed species cover data based on Bray-Curtis distance to analyse the similarity between pairs of averaged site-samples. Square-root transformation was applied to reduce the strong influence of dominant taxa in community analyses and allow for a broader number of species to play a part (Clarke and Warwick, 2001). We used the cluster classification technique to explore potential macroalgal community groups and represented the results in a dendrogram. To determine what species contributed most to the similarity within groups, a similarity percentage analysis (SIMPER) was conducted. We compared the classification obtained in 2015 with that obtained in 2019–2020. We also examined the species richness for each year and location and assessed for any significant differences between years and locations. These analyses were performed using PERMANOVA +for PRIMER software (Anderson et al., 2008). 2.3.2. Predictive capacity of the model Using distance-based linear models, Muguerza et al. (2022a) showed that spring SST and, to a lesser degree, winter NIT were the main environmental variables responsible for the variation in the spatial pattern of macroalgae. Using these predictor variables, they run three regressions to calculate “modelled ecological distances” for each pair of locations. To assess the predictive capacity of their model, they also calculated the “observed ecological distances”, which are the distance between the centroids of locations detected in the nMDS (based on the matrix of similarities between macroalgal communities). They then plotted the “observed ecological distances” with the “modelled ecological distances”. Here, to assess the predictive capacity of their model in the short term, we compiled monthly SST (◦C) and NIT (mmol*m −3 ) Fig. 1. Map of the study area showing the ten sampling locations (L1: Malpica; L2: Cedeira; L3: Luarca; L4: Lastres; L5: San Vicente; L6: Somocuevas; L7: Kobaron; L8: Ea; L9: La Herradura; and L10: Cabo Palos) and the mean sea surface temperature distribution for 2019, from Copernicus database. Source: own elaboration generated by ArcGIS Pro 2.2.0 software (https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview)". O. Arriaga et al. Marine Environmental Research 190 (2023) 106098 4 data from the cell grid at each sampling location for the year 2019 for the southern Bay of Biscay and 2020 for the Mediterranean locations. We determined a mean spring SST by averaging the SSTs for April, May, and June and a mean winter value by averaging the NITs for January, February, and March. We then used the updated environmental data to run the same model as Muguerza et al. (2022a) and obtained “modelled ecological distances” between all pairs of locations for 2019–2020. We also constructed a nMDS (based on the matrix of similarities between macroalgal communities) with the community data gathered in 2019–2020 to quantify the “observed ecological distances” four (and five for Mediterranean locations) years after the initial sampling by Muguerza et al. (2022a), using PERMANOVA +for PRIMER software (Anderson et al., 2008). We then correlated the modelled and observed ecological distances for 2019–2020 to test the model’s ability to predict short-term macroalgal community changes. The Pearson correlation coefficient provided a quantitative estimate of the model’s accuracy. We turned the modelled ecological distances between locations into similarity values (by subtracting this distance from 100) to construct a nMDS based on the model and compare it with the nMDS performed with the observed data. 2.3.3. Temperature and CTI correlation The similarity between sites within each location was high (over 80% on average), so we conducted the following analyses at the location level. The model developed by Muguerza et al. (2022a) identified temperature as the primary environmental variable influencing community structure. Given that warming in the area was mainly observed during spring and summer (Díez et al., 2012; G´ omez-Gesteira et al., 2008, 2011; Izquierdo et al., 2022), we used Distance-based linear model analysis (DISTLM) to examine the impact of mean annual SST, spring SST (April, May, June), and summer SST (July, August, September) on the macroalgal community structure. We plotted distance-based redundancy analyses (dbRDA) to visualise the relationship and determined which temperature-related variables best explained the observed variation in macroalgal community structure, using PERMANOVA (Anderson et al., 2008). We then correlated this environmental variable with the Community Temperature Index (CTI). For each recorded species, the Species Temperature Index (STI) was calculated as the average temperature of the species occurrence range, considered a proxy for the species’ dependence on temperature (Devictor et al., 2012). We obtained the global distribution data for each species from the open-access databases “Ocean Biodiversity Information System” (OBIS) (https://www.obis.org/) and “Global Biodiversity Information Facility” (GBIF) (https://www.gbif.org/), by using the special packages robis (Provoost and Bosch, 2019) and rgbif (Chamberlain et al., 2021) from the open-source software R (R Development Core Team, 2023; RStudio Team, 2016). The time-frame considered for the distribution data was 1993–2020. We downloaded monthly SST data for the sites of occurrence of every species from the Copernicus database to calculate annual means. We calculated the CTI for each sampling unit to measure the average thermal affinity of the macroalgal communities. The CTI is the abundance-weighted STI average of all species in a sampling unit (Devictor et al., 2008, 2012). We then calculated the average CTI for each location and year. Differences in CTI were tested with a univariate PERMANOVA analysis based on Euclidean distance with time and location as fixed and random factors, respectively. For post-hoc pairwise comparisons Gosset t-statistic was used (Anderson et al., 2008). Macroalgal species have varying lifespans and many have a life cycle that exceeds one year (i.e. Gelidium corneum), so current and past environmental conditions may determine the current state of the community (Chust et al., 2022; Ramos et al., 2020). To test the degree of association between temperature and CTI, we analysed the correlation between the change in temperature and the change in CTI. SST changes were calculated using the mean of the sampling year and the two previous years and compared 2017–2019 (2018–2020 for the Mediterranean) to 2013–2015 to accommodate the multiannual SST variance that multiple macroalgal species may experience (Appendix b). For the CTI change, we calculated the difference between CTI values in 2019 (2020 for Mediterranean locations) and 2015. 3. Results 3.1. Changes in the community structure Based on the 43% similarity level used by Muguerza et al. (2022a), we found locations clustered in four groups with distinct species compositions. We found three geographic areas in the Southern Bay of Biscay macroalgal communities (western, central, and eastern sections) and another in the Mediterranean (Fig. 2b). The kelp Saccorhiza polyschides and the geniculate calcareous macroalga Corallina officinalis dominated in the westernmost Bay of Biscay locations (Group A: L1, L2, and L3). Gelidium corneum and the encrusting Corallinales Mesophyllum expansum dominated in the central Bay of Biscay locations (Group B: L4 and L6). Mesophyllum expansum and the ephemeral filamentous Ceramiales Aphanocladia stichidiosa dominated in the easternmost Bay of Biscay locations (Group C: L5, L7, and L8). The Corallinales Lithophyllum incrustans and Jania pedunculata var. adhaerens dominated the Mediterranean locations (Group D: L9 and L10). See Appendix c for a visual representation of these species in each location. The three sections in the southern Bay of Biscay remained distinct in 2019 and 2015, although Group A was slightly more similar to Groups B and C in 2019 (33.15%) than in 2015 (29.75%). Location composition in the central section differed from 2015 to 2019 (Appendix d). L5 shifted from the central area in 2015 to the eastern area in 2019. In four years, the percent cover of Gelidium corneum in this location decreased from 59.1% to 23.4%, while the percent cover of Aphanocladia stichidiosa increased from 0% to 7.2% (Appendix e). We also found the two Mediterranean macroalgal communities to be more similar in 2020 (54.13%) than in 2015 (38.63%). Lithophyllum incrustans and Jania pedunculata var. adhaerens replaced previously dominant species Halopteris scoparia and Padina pavonica in the Mediterranean locations (Appendix d). Undetected in 2015, Jania pedunculata var. adhaerens showed a percent cover of 15.63 in the Alboran Sea and 50.63 in the western Mediterranean in 2020 (Appendix e). The species richness also differed from one location and year to another, except in Ea (L8) (Fig. 3, Appendix f). The lowest value corresponded to Somocuevas (L6) in both surveys (a mean of 12 taxa in 2015 and a mean of 8 taxa in 2019). The highest values were found in the two Mediterranean locations and in Luarca (L3) (around 27 taxa in 2015 and 34 in 2019), followed by north-eastern locations. In four years, the number of taxa decreased in the westernmost location (L1, from 19 to 14) and in the central group (L4, from 27 to 19 and L6 from 12 to 8), but in the other locations an average increase of 7 taxa was observed (Fig. 3). 3.2. Predictive capacity of the model We found a positive correlation (R =0.740, p <0.0001) between the observed and modelled spatial distribution in 2019–2020 (Fig. 4). Overall, locations from the southern Bay of Biscay (L1 to L8) were closer to each other than forecasted, particularly Cedeira (L2), i.e, they were more similar in species composition and abundance. Contrary to model’s predictions, the locations here did not follow a succession along the coast, but instead, they became more similar, they homogenized. Somocuevas (L6) remained more distinct from the other northern locations, and San Vicente (L5) shifted closer to the eastern communities of Kobaron (L7) and Ea (L8) in the observed pattern. The Mediterranean locations of La Herradura (L9) and Cabo de Palos (L10) remained distant from the Bay of Biscay locations but were more similar than the model predicted (Fig. 5). O. Arriaga et al. Marine Environmental Research 190 (2023) 106098 5 3.3. The effect of temperature on spatial distribution Annual SST and summer SST (SSTsu) were the best fit-explanatory variables, explaining 42.4% of the total variation (Distance-Based Linear Model, distLM). Annual SST discriminated between the Southern Bay of Biscay and Mediterranean communities, and SSTsu discriminated within the Southern Bay of Biscay communities, following an east-west gradient (Fig. 6, Appendix g). Annual SST also distinguished between the two periods for the Mediterranean locations and for locations L4 to L8, whereas for locations L1 to L3 the difference between the two periods was established by summer SST. We also found significant differences in the Community Temperature Index (CTI) across locations and years (Fig. 7, Appendix h). Mediterranean locations (L9 and L10) showed the highest CTI values both in 2015 and 2020, with average values ranging from 14.7 ◦C to 16.34 ◦C in L9, and 16.5 ◦C to 17.3 ◦C in L10. The westernmost locations of the Bay of Biscay had the lowest CTI values, with average values of 13.5 ◦C in 2015, and 13.3 ◦C in 2019 (Fig. 7). CTI also varied from year to year in six out of the ten locations (Fig. 7). On average, CTI decreased by 2.38% at L1, L2, L3 (Group A), and L8, but increased by 4.88% at the remaining locations. We found a significant positive correlation between the changes in SSTsu and in CTI (R =0.835, p =0.003, Fig. 8). 4. Discussion Accurately predicting the impact of global change on marine benthic communities is crucial to help preserve our ecosystems and the services they provide. While models can help us simulate potential effects, inform preventive measures, and aid in adaptation strategies, validating their accuracy using real-world data is crucial. Muguerza et al. (2022a) used spatial data to forecast that macroalgal communities in the Cantabrian Sea would resemble those in the Mediterranean region more closely than those along the Galician coast by the end of the century, which would follow an east-to-west gradient. Our study focused on testing the short-term predictions of this model using new observational data gathered four years after the initial assessments. Despite relying on spatial data for long-term projections, we found that the short-term predictions aligned with actual observations of the same communities, indicating the model’s robustness (Muguerza et al., 2022a). Short-term temperature changes were positively correlated with changes in the Community Temperature Index (CTI), suggesting that macroalgal communities quickly adapt to temperature changes, potentially triggering the observed phase shift in the region. We also detected discrepancies between the model’s projections and the observed communities, providing additional insights into climate-induced changes in community composition. In our study, locations west of Cape Pe˜ nas (L1, L2, and L3) formed a Fig. 2. Hierarchical clustering dendrogram of sampling sites, based on Bray-Curtis similarities calculated on square-root transformed abundance data. Dashed lines indicate the 43% similarity level. “L” refers to location (L1: Malpica; L2: Cedeira; L3: Luarca; L4: Lastres; L5: San Vicente; L6: Somocuevas; L7: Kobaron; L8: Ea; L9: La Herradura; and L10: Cabo Palos). Each location had two replicated sites, labelled as “0.1” and “0.2”. a) 2015, b) 2019–2020. Fig. 3. Average number of taxa per surface for each location in 2015 and 2019–2020. “L” refers to location (L1: Malpica; L2: Cedeira; L3: Luarca; L4: Lastres; L5: San Vicente; L6: Somocuevas; L7: Kobaron; L8: Ea; L9: La Herradura; and L10: Cabo Palos). Asterisk indicate significant differences between years within each location: *p <0.05, **p <0.01, ***p <0.001. O. Arriaga et al. Marine Environmental Research 190 (2023) 106098 6 consistent geographic section characterized by the warm-temperate kelp Saccorhiza polyschides, with a thermal optimum of 10.3 ◦C (Casado-Amezúa et al., 2019; Fern´ andez, 2016; Muguerza et al., 2017). East of Cape Pe˜ nas, a central region (L4, L6) was dominated by the canopy-forming Gelidium corneum, which has a thermal optimum of 12.5 ◦C, and the easternmost area (L5, L7, and L8) was dominated by the morphologically simple Aphanocladia stichidiosa, with a thermal optimum of 17.2 ◦C (Casado-Amezúa et al., 2019; Muguerza et al., 2022a). These distribution patterns align with previous studies describing the retreat of S. polyschides and G. corneum toward the west (Assis et al., 2017; Borja et al., 2018; Gorostiaga 1995). Muguerza et al. (2022a) already reported this same distribution of the dominant species in 2015, showing a thallus size decrease with increasing temperature, which leads to a structural simplification of the communities. Our analyses revealed changes in the distribution of dominant canopy-forming species from 2015 to 2019, supporting the predicted east-west meridionalisation of the southern Bay of Biscay (Muguerza et al., 2022a). Specifically, the macroalgal communities of San Vicente (L5) shifted from the central section to join the eastern area, accompanied by a decrease in the cover of G. corneum and an increase in the number of taxa (Díez et al., 2012; Muguerza et al., 2017). This transition resulted in a community dominated by morphologically simple forms (Aphanocladia stichidiosa and Ceramium spp.) and coralline species (Jania squamata, Ellisolandia elongata, and Corallina officinalis), which is consistent with the phase shifts from canopy-dominated macroalgal communities to turf-forming communities observed worldwide (Benedetti-Cecchi et al., 2001; Díez et al., 2012; Perkol-Finkel and Airoldi, 2010; Smale and Wernberg, 2013; Voerman et al., 2013; Wernberg et al., 2016). Notably, we also found that the Mediterranean communities have undergone more significant changes and have become more Fig. 4. Association (linear regression) between the observed and modelled ecological distances for 2019–2020. Ecological distance is the distance between centroids as detected in the non-metric multidimensional scaling (nMDS). Fig. 5. Non-metric multidimensional scaling (nMDS) diagrams (Anderson et al., 2008) of the 10 locations (L1: Malpica; L2: Cedeira; L3: Luarca; L4: Lastres; L5: San Vicente; L6: Somocuevas; L7: Kobaron; L8: Ea; L9: La Herradura; and L10: Cabo Palos) based on square-root transformed abundances and Bray-Curtis similarities. a) observed pattern. B) modelled pattern. Colours are based on 43% similarity clustering (Fig. 2b). O. Arriaga et al. Marine Environmental Research 190 (2023) 106098 7 homogeneous, with a shift towards coralline-dominated communities. Increased temperature, water transparency, and solar radiation may have stimulated coralline calcification and growth rates (Díez et al., 2012; Jian-Zhang et al., 2010; Steller et al., 2007). However, it is uncertain whether these findings apply to the broader Mediterranean region, as our study primarily focused on the southern Bay of Biscay and included only two Mediterranean locations as references (Muguerza et al., 2022a; Ramos et al., 2020). Discrepancies between the model projections and the observed communities also provided insights into the complex dynamics of climate-driven community changes. For example, the model predicted reduced distances between Somocuevas (L6) and the eastern locations, but actual observational data did not support these predictions. The unique characteristics of Somocuevas (L6), situated near the mouth of the River Pas and influenced by its freshwater input, may explain the disparity between the model and reality. The river’s current freshwater inflow likely cools the water and provides additional nutrients, supporting the thriving Gelidium corneum communities in this area (Gorostiaga, 1995). Somocuevas (L6) exhibited the best-preserved G. corneum-dominated community in our study, characterized by low species richness and floristic homogeneity, consistent with other G. corneum-dominated communities such as Lastres (L4, see Appendix d). Our study revealed an increase in G. corneum and associated epiphytic species (Dictyota dichotoma and Plocamium cartilagineum) in Lastres (L4). Conversely, we observed a decrease in coralline (Ellisolandia elongata, Corallina officinalis, and Jania rubens) and Ceramiales (Gayliella flaccida, Microcladia glandulosa, Vertebrata fruticulosa) species (see Appendix d). The G. corneum canopy expansion led to a significant decrease in species richness. In the summer of 2014, before our initial sampling, Lastres (L4) experienced the highest summer temperature of the entire decade (2010–2020), causing an imbalance in the G. corneum community, manifested by stress symptoms such as frond yellowing and an increase in epiphytic ceramiaceous species. Similar stress symptoms occurred in San Vicente (L5) during the same year. However, unlike San Vicente (L5), which continued to deteriorate progressively in 2019, macroalgal communities in Lastres (L4) displayed greater resilience, with an increased percent cover of G. corneum. The cooler water temperature and potentially more nutrient-rich conditions in Lastres (L4) compared to San Vicente (L5) may account for the success of G. corneum in Lastres, as observed in Somocuevas (L6), where G. corneum stands have thrived over time. This phenomenon may be analogous to the effects of upwellings in other coastal areas (Lourenço et al., 2016). The presence of refuge locations with favourable local environmental conditions, such as Somocuevas (L6), is ecologically and biologically significant as they can safeguard the genetic heritage of threatened foundational species in the face of climate change effects (Assis et al., 2017; García et al., 2021). These findings underscore the importance of combining model predictions with empirical data to understand ecosystem responses to global change comprehensively. Models are crucial to understand community shifts under global change, offering valuable insights into future scenarios and identifying Fig. 6. Distance-based redundancy analysis (dbRDA) ordination plots showing the relationship between the temperature-related environmental variables that best explain the variation of macroalgal communities for 2015 and 2019–2020. L1: Malpica; L2: Cedeira; L3: Luarca; L4: Lastres; L5: San Vicente; L6: Somocuevas; L7: Kobaron; L8: Ea; L9: La Herradura; and L10: Cabo Palos. SST (annual sea surface temperature). SSTsu (summer sea surface temperature). The colour gradient represents the SSTsu gradient along the study area. Fig. 7. Community Temperature Index (CTI) for each location in 2015 and 2019–2020. L1: Malpica; L2: Cedeira; L3: Luarca; L4: Lastres; L5: San Vicente; L6: Somocuevas; L7: Kobaron; L8: Ea; L9: La Herradura; and L10: Cabo Palos. Asterisk indicates significant differences from one year to another within each location: *p <0.05, **p <0.01, ***p <0.001. O. Arriaga et al. Marine Environmental Research 190 (2023) 106098 8 key drivers of ecological transformations. Here, we validated Muguerza et al.’s (2022a) model using real-world data and showed evidence of the model’s consistency and accuracy in the short term, reinforcing the model’s robustness. Yet, the model under discussion has some limitations. Firstly, there is a critical oversight in the model as it fails to consider biotic interactions, such as herbivory or competition for resources, which can strongly impact the organization and functioning of marine ecosystems (Falkenberg et al., 2013; Steneck et al., 2002). Secondly, collecting on-site environmental data, including temperature and nitrate concentration, would help obtain more precise estimates of local conditions. The model’s current resolution (0.25◦×0.25◦) covers over 750 km 2 , which may not fully represent the local conditions of our sampling sites. Recording on-site environmental data could improve estimates of local conditions and the model’s accuracy. The model would also benefit from accounting for the impact of riverine inputs or small upwelling systems, as these factors can significantly influence ecosystem dynamics. Finally, improving the model’s accuracy could also involve increased replication and a broader spatial scale, including the western Iberian Peninsula and additional Mediterranean sites. However, given our goal of comparing Muguerza et al. (2022a) predictions to actual communities four years later, we could not modify the model. There is increasing evidence worldwide that canopy-dominated macroalgal communities are shifting to structurally simpler alternate states (Benedetti-Cecchi et al., 2001; Fern´ andez, 2016; Orlando-Bonaca et al., 2021; Perkol-Finkel and Airoldi, 2010; Wernberg et al., 2016). The factors behind phase shifts are numerous and intertwined, but temperature is an important parameter affecting species distribution (Husa et al., 2008; Müller et al., 2009; Sjøtun et al., 2015; Tittensor et al., 2010; Wernberg et al., 2016). Our data provided evidence to support the contention that the southern Bay of Biscay phase shift from three-dimensional macroalgal communities to turf-dominated communities may be temperature driven. Our study found a strong positive correlation between changes in water temperature and changes in the Community Temperature Index (CTI), suggesting that macroalgal communities responded quickly to ocean temperature change. The increasing temperature may favour species with warmer affinities and add stress to those with colder affinities, which may strongly influence species abundance and distribution. We used the Species Temperature Index (STI) as a proxy for thermal affinity. Given that the CTI is the abundance-weighted average of the STI, shifts in CTI represent changes in the thermal affinity of the system. A higher CTI represented a greater dominance of warmer species in the community, driven either by an increase in their percent cover or a decrease in the percent cover of colder affinity species (McLean et al., 2021). The southern Bay of Biscay has been warming since the early 80s (Anad´ on et al., 2009; Chust et al., 2022; Costoya et al., 2015; Fern´ andez, 2011; Voerman et al., 2013). Colder affinity species, such as Gelidium corneum may be subjected to more significant stress, unlike warmer affinity species, such as ceramiaceous or coralline species, which may trigger the phase shift. We found one noteworthy exception to this pattern in Ea (L8), in the easternmost area of the southern Bay of Biscay. Contrary to expectations, the warm affinity species Aphanocladia stichidiosa and Mesophyllum expansum declined in this location. The reason behind this decline is unclear, given the rising temperatures and abundance trends of these species in the area over the last few decades (Muguerza et al., 2017, 2022b). This location had a bloom of Ostreopsis siamensis covering an estimated 25% of the substratum (author’s observation). This epiphytic dinoflagellate is on the increase in the south-eastern Bay of Biscay (Drouet et al., 2021), and it contains several toxic chemicals that cause mortality in marine organisms, including macroalgae (Accoroni et al., 2015). The canopy that dominated the area was almost non-existent, and the community had shifted to small, fast-growing, opportunistic turf species. Utilizing species’ thermal traits to predict species distribution and abundance provides valuable insights into the impacts of climate change on marine communities. Our study showed that the Species Temperature Index (STI) and Community Temperature Index (CTI), in combination with SST predictions, enhance our understanding of how temperature influences community changes. The STI offers a proxy to evaluate species’ responses to temperature variations by assessing thermal affinity. The CTI, as an abundance-weighted average of the STI, enables us to quantify changes in the thermal affinity of the entire community. Integrating these indices with SST projections would allow for more accurate predictions of future marine community shifts associated with climate change, empowering effective conservation and management strategies. Global open-access databases are not perfect. The lack of abundance Fig. 8. Association (linear regression) between changes in summer SST (x axis) and changes in CTI (y axis). L1: Malpica; L2: Cedeira; L3: Luarca; L4: Lastres; L5: San Vicente; L6: Somocuevas; L7: Kobaron; L8: Ea; L9: La Herradura; and L10: Cabo Palos. O. Arriaga et al. Marine Environmental Research 190 (2023) 106098 9 data makes it difficult to accurately calculate species’ thermal optimum. However, Webb et al. (2020) already showed a strong correlation between experimentally derived thermal maxima and the thermal affinity derived from matching global sea temperature databases with occurrence records. Our study provided further support for the usefulness of global datasets in calculating species’ thermal traits. Given the speed and scale at which communities are shifting, the use of global open-access databases should not be overlooked in this context of rapid climate change. The information in those global databases may help us assess the vulnerability of thousands of species to global warming, find possible refugees for cold-affinity species, or protect those species at greater risk. In our study area, we found a location in the central area of the southern Bay of Biscay that operated as a refuge for cold-affinity species that were disappearing at adjacent sites. This is particularly significant since many studies provide evidence for a weakening intensity of the south-western Bay of Biscay upwelling system associated with changes in wind regimes (G´ omez-Gesteira et al., 2011; Lemos and Pires, 2004; P´ erez et al., 2010). The loss of the influence of upwelling in the southern Bay of Biscay could limit the distribution of kelp species to locally colder and nutrient-richer habitats. In summary, accurate prediction of global change impacts on marine benthic communities is crucial for ecosystem preservation. Our study demonstrated that short-term predictions based on Muguerza et al.’s (2022a) model aligned with real-world observations, building confidence in predictive models. We observed deviations from the model, shedding light on climate-driven community changes. Temperature emerged as the primary driver, with macroalgal communities showing rapid responses. Given the widespread degradation of macroalgal communities worldwide, climate-driven macroalgal phase shifts may be a current reality rather than a long-term prediction. Testing ecological models may be crucial for predicting the impacts of global change on our natural communities. Credit author statement Olatz Arriaga: Conceptualization, Investigation, Methodology, Writing-Original Draft, Visualization. Paul Wawrzynkowski: Software, Data Curation, Formal analysis. Hector Iba˜ nez: Investigation, Resources. Nahiara Muguerza: Investigation, Writing - Review & Editing. Isabel Díez: Investigation, Writing - Review & Editing. Isabel PerezRuzafa: Investigation, Resources. Jose María Gorostiaga: Conceptualization, Investigation, Writing - Review & Editing, Supervision, Funding Acquisition, Project administration. Endika Quintano: Investigation, Writing - Review & Editing, Supervision. Mikel Becerro: Conceptualization, Methodology, Writing - Review & Editing, Supervision, Funding Acquisition. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability Data will be made available on request. Acknowledgements This work was supported by the DIVERSAT project (Spanish Ministry of Science, Innovation and Universities RTI2018-098970-B-I00) and the Catalan Government BEG 2017SGR-378. The main author is financially supported by a Basque Government predoctoral grant (Reference N◦: PRE_2022_2_0127) APPENDICES. Appendix a. GPS positioning of the sampling locations Location Latitude Longitude L1: Malpica 43◦21 ′ 07.4 ″ N 08◦49 ′ 52.2 ″ W L2: Cedeira 43◦39 ′ 48.8 ″ N 08◦05 ′ 34.8 ″ W L3: Luarca 43◦33 ′ 24.4 ″ N 06◦32 ′ 50.4 ″ W L4: Lastres 43◦31 ′ 09.4 ″ N 05◦15 ′ 59.3 ″ W L5: San Vicente de la Barquera 43◦24 ′ 06.0 ″ N 04◦23 ′ 59.4 ″ W L6: Somocuevas 43◦28 ′ 15.1 ″ N 03◦56 ′ 28.1 ″ W L7: Kobaron 43◦21 ′ 11.2 ″ N 03◦08 ′ 56.3 ″ W L8: Ea 43◦23 ′ 18.3 ″ N 02◦34 ′ 57.7 ″ W L9: La Herradura 36◦44 ′ 07.4 ″ N 03◦45 ′ 36.1 ″ W L10: Cabo Palos 37◦38 ′ 08.3 ″ N 00◦41 ′ 17.5 ″ W Appendix b. Summer Sea Surface Temperature (SST) values for the sampling years and the two previous years for each location Summer SST (◦C) Locations 2013 2014 2015 2017 2018 2019 2020 L1: Malpica 16.492 17.410 16.650 17.272 16.308 16.525 – L2: Cedeira 17.120 17.686 17.046 17.504 15.889 16.980 – L3: Luarca 18.528 19.130 18.872 19.110 18.190 18.370 – L4: Lastres 19.827 20.490 19.862 19.919 20.109 20.058 – L5: San Vicente 20.498 20.846 20.323 20.429 21.036 20.741 – L6: Somocuevas 20.698 20.978 20.526 20.430 21.142 20.603 – L7: Kobaron 21.160 21.287 20.777 20.946 21.764 20.997 – L8: Ea 21.582 21.869 21.400 21.229 22.217 21.590 – L9: La Herradura 22.999 21.716 22.400 – 23.279 22.738 23.303 L10: Cabo Palos 24.926 25.683 25.952 – 26.144 25.703 25.716 O. Arriaga et al. Marine Environmental Research 190 (2023) 106098 16 Costoya, X., deCastro, M., G´ omez-Gesteira, M., Santos, F., 2015. Changes in sea surface temperature seasonality in the Bay of Biscay over the last decades (1982–2014). J. Mar. Syst. 150, 91–101. https://doi.org/10.1016/j.jmarsys.2015.06.002. Day, P.B., Stuart-Smith, R.D., Edgar, G.J., Bates, A.E., 2018. Species’ thermal ranges predict changes in reef fish community structure during 8 years of extreme temperature variation. Divers. Distrib. 24, 1036–1046. https://doi.org/10.1111/ ddi.12753. deCastro, M., G´ omez-Gesteira, M., Alvarez, I., Gesteira, J.L.G., 2009. Present warming within the context of cooling–warming cycles observed since 1854 in the Bay of Biscay. Continent. Shelf Res. 29 (8), 1053–1059. https://doi.org/10.1016/j. csr.2008.11.016. Devictor, V., Julliard, R., Couvet, D., Jiguet, F., 2008. Birds are tracking climate warming, but not fast enough. Proceedings of the Royal Society B 275, 2743–2748. https://doi.org/10.1098/rspb.2008.0878. Devictor, V., van Swaay, C., Brereton, T., Brotons, L., Chamberlain, D., Heli¨ ol¨ a, J., Herrando, S., Julliard, R., Kuussaari, M., Lindstr¨ om, Å., Reif, J., Roy, D.B., Schweiger, O., Settele, J., Stefanescu, C., Van Strien, A., Van Turnhout, C., Vermouzek, Z., WallisDeVries, M., Wynhoff, I., Jiguet, F., 2012. Differences in the climatic debts of birds and butterflies at a continental scale. Nat. Clim. Change 2, 121–124. https://doi.org/10.1038/nclimate1347. Díez, I., Muguerza, N., Santolaria, A., Ganzedo, U., Gorostiaga, J.M., 2012. Seaweed assemblage changes in the eastern Cantabrian Sea and their potential relationship to climate change. Estuar. Coast Shelf Sci. 99, 108–120. https://doi.org/10.1016/j. ecss.2011.12.027. Drouet, K., Jauzein, C., Herviot-Heath, D., Hariri, S., Laza-Martinez, A., Lecadet, C., Plus, M., Seoane, S., Sourisseau, M., Lem´ ee, R., Siano, R., 2021. Current distribution and potential expansion of the harmful benthic dinoflagellate Ostreopsis cf. siamensis towards the warming waters of the Bay of Biscay, North-East Atlantic. Environ. Microbiol. 23 (9), 4956–4979. https://doi.org/10.1111/1462-2920.15406. Falkenberg, L.J., Russell, B.D., Connell, S.D., 2013. Contrasting resource limitations of marine primary producers: implications for competitive interactions under enriched CO2 and nutrient regimes. Oecologia 172, 575–583. https://doi.org/10.1007/ s00442-012-2507-5. Fern´ andez, C., 2016. Current status and multidecadal biogeographical changes in rocky intertidal algal assemblages: the northern Spanish coast. Estuarine. Coastal Shelf Sci. 171, 35–40. https://doi.org/10.1016/j.ecss.2016.01.026. Fern´ andez, C., 2011. The retreat of large brown seaweeds on the north coast of Spain: the case of Saccorhiza polyschides. Eur. J. Phycol. 46, 352–360. https://doi.org/10.1080/ 09670262.2011.617840. Fern´ andez-Salas, L.M., Dur´ an, R., Mendes, I., Galparsoro, I., Lobo, F.J., B´ arcenas, P., Rosa, F., Rib´ o, M., García-Gil, S., Ferrín, A., Carrara, G., Roque, C., Canals, M., 2015. Shelves of the iberian Peninsula and the balearic islands (I): morphology and sediment types. Bol. Geol. Min. 126, 327–376. Filbee-Dexter, K., Wernberg, T., 2018. Rise of turfs: a new battlefront for globally declining kelp forests. Bioscience 68 (2), 64–76. https://doi.org/10.1093/biosci/ bix147. Flanagan, P.H., Jensen, O.P., Morley, J.W., Pinsky, M.L., 2019. Response of marine communities to local temperature changes. Ecography 42, 214–224. https://doi.org/ 10.1111/ecog.03961. García, A.G., Olabarria, C., Alvarez-Losadaa, O., Viejo, R.M., 2021. Differential responses of trailing-edge populations of a foundation alga to thermal stres. Eur. J. Phycol. 56 (4), 373–388. https://doi.org/10.1080/09670262.2020.1842909. GBIF.org, 2022. GBIF home page. https://www.gbif.org. G´ omez-Gesteira, M., deCastro, M., Alvarez, I., G´ omez-Gesteira, J.L., 2008. Coastal sea surface temperature warming trend along the continental part of the Atlantic Arc (1985–2005). J. Geophys. Res. 113, C04010 https://doi.org/10.1029/ 2007JC004315. G´ omez-Gesteira, M., Gimeno, L., deCastro, M., Lorenzo, M., Alvarez, I., Nieto, R., Taboada, J., Crespo, A., Ramos, A., Iglesias, I., G´ omez-Gesteira, J., Santo, F., Barriopedro, D., Trigo, I., 2011. The state of climate in NW Iberia. Clim. Res. 48, 109–144. https://doi.org/10.3354/cr00967. Gonz´ alez-Gil, R., Gonz´ alez Taboada, F., C´ aceres, C., Largier, J.L., Anad´ on, R., 2018. Winter-mixing preconditioning of the spring phytoplankton bloom in the Bay of Biscay. Limnol. Oceanogr. 63, 1264–1282. https://doi.org/10.1002/lno.10769. Gorostiaga, J.M., 1995. Sublittoral seaweed vegetation of a very exposed shore on the Basque Coast (N. Spain). Bot. Mar. 38 https://doi.org/10.1515/botm.1995.38.1-6.9. Gottfried, M., Pauli, H., Futschik, A., Akhalkatsi, M., Baranˇ cok, P., Benito Alonso, J.L., Coldea, G., Dick, J., Erschbamer, B., Fern´ andez Calzado, M.R., Kazakis, G., Krajˇ ci, J., Larsson, P., Mallaun, M., Michelsen, O., Moiseev, D., Moiseev, P., Molau, U., Merzouki, A., Nagy, L., Nakhutsrishvili, G., Pedersen, B., Pelino, G., Puscas, M., Rossi, G., Stanisci, A., Theurillat, J.-P., Tomaselli, M., Villar, L., Vittoz, P., Vogiatzakis, I., Grabherr, G., 2012. Continent-wide response of mountain vegetation to climate change. Nat. Clim. Change 2, 111–115. https://doi.org/10.1038/ nclimate1329. Graba-Landry, A.C., Loffler, Z., McClure, E.C., Pratchett, M.S., Hoey, A.S., 2020. Impaired growth and survival of tropical macroalgae (Sargassum spp.) at elevated temperatures. Coral Reefs 39, 475–486. https://doi.org/10.1007/s00338-02001909-7. Guiry, M.D., Guiry, G.M., 2023. AlgaeBase. Worldwide Electronic Publication. National University of Ireland, Galway. https://www.algaebase.org. searched on 12 January 2023. Harley, C.D.G., Anderson, K.M., Demes, K.W., Jorve, J.P., Kordas, R.L., Coyle, T.A., Graham, M.H., 2012. Effects of climate change on global seaweed communities. J. Phycol. 48, 1064–1078. https://doi.org/10.1111/j.1529-8817.2012.01224.x. Husa, V., Sjøtun, K., Brattenborg, N., Lein, T.E., 2008. Changes of macroalgal diversity in sublittoral sites in southwest Norway: impact of an introduced species or higher temperature? Mar. Biol. Res. 4, 414–428. https://doi.org/10.1080/ 17451000802232874. IPCC, 2023. AR6 Synthesis Report: Climate Change 2023. Synthesis Report of the IPCC Sixth Assessment Report (AR6): Summary for Policymakers. Izquierdo, P., Rico, J.M., Taboada, F.G., Gonz´ alez-Gil, R., Arrontes, J., 2022. Characterization of marine heatwaves in the Cantabrian Sea, SW bay of Biscay. Estuarine. Coastal Shelf Sci. 274, 107923 https://doi.org/10.1016/j. ecss.2022.107923. Jian-Zhang, S., Xiu-Ren, N., Feng-Feng, L., Wan-Dong, C., Ding-Gen, Z., 2010. Long term changes of biodiversity of benthic macroalgae in the intertidal zone of the Nanji Islands. Acta Ecol. Sin. 30, 106–112. https://doi.org/10.1016/j.chnaes.2010.03.010. Kortsch, S., Primicerio, R., Fossheim, M., Dolgov, A.V., Aschan, M., 2015. Climate change alters the structure of arctic marine food webs due to poleward shifts of boreal generalists. Proceedings of the Royal Society B 282, 20151546. https://doi.org/ 10.1098/rspb.2015.1546. Kumagai, N.H., García Molinos, J., Yamano, H., Takao, S., Fujii, M., Yamanaka, Y., 2018. Ocean currents and herbivory drive macroalgae-to-coral community shift under climate warming. Proc. Nat. Acad. Sci. U.S.A. 115, 8990–8995. https://doi.org/ 10.1073/pnas.1716826115. Lavín, A., Vald´ es, L., S´ anchez, F., Abaunza, P., Forest, A., Boucher, J., Lazure, P., Anne Jegou, A.M., 2006. The Bay of Biscay: the encountering of the ocean and the shelf. In: Robinson, A.R., Brink, K.H. (Eds.), The Global Coastal Ocean: Interdisciplinary Regional Studies and Syntheses. Harvard Press, pp. 933–999. Lazzari, P., Solidoro, C., Salon, S., Bolzon, G., 2016. Spatial variability of phosphate and nitrate in the Mediterranean Sea: a modeling approach. Deep-Sea Res. Part I Oceanogr. Res. Pap. 108, 39–52. https://doi.org/10.1016/j.dsr.2015.12.006. Lemos, R.T., Pires, H.O., 2004. The upwelling regime off the West Portuguese Coast, 1941–2000. Int. J. Climatol. 24, 511–524. https://doi.org/10.1002/joc.1009. Lindsey, R., Dahlman, L., 2022. National center for atmospheric research. Clim. Change: Global Temperature. Lourenço, C.R., Zardi, G.I., McQuaid, C.D., Serr˜ ao, E.A., Pearson, G.A., Jacinto, R., Nicastro, K.R., 2016. Upwelling areas as climate change refugia for the distribution and genetic diversity of a marine macroalga. J. Biogeogr. 43, 1595–1607. https:// doi.org/10.1111/jbi.12744. McLean, M., Mouillot, D., Maureaud, A.A., Hattab, T., MacNeil, M.A., Goberville, E., Lindegren, M., Engelhard, G., Pinsky, M., Auber, A., 2021. Disentangling tropicalization and deborealization in marine ecosystems under climate change. Curr. Biol. 31, 4817–4823.e4815. https://doi.org/10.1016/j.cub.2021.08.034. Merchant, C.J., Embury, O., Bulgin, C.E., Block, T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C., 2019. Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Sci. Data 6, 223. https://doi.org/ 10.1038/s41597-019-0236-x. Monteiro, C., Pereira, J., Seabra, R., Lima, F.P., 2022. Fine-scale survey of intertidal macroalgae reveals recent changes in a cold-water biogeographic stronghold. Front. Mar. Sci. 9, 880074 https://doi.org/10.3389/fmars.2022.880074. Muguerza, N., Arriaga, O., Díez, I., Becerro, M.A., Quintano, E., Gorostiaga, J.M., 2022a. A spatially-modelled snapshot of future marine macroalgal assemblages in southern Europe: towards a broader Mediterranean region? Mar. Environ. Res. 176, 105592 https://doi.org/10.1016/j.marenvres.2022.105592. Muguerza, N., Díez, I., Quintano, E., Bustamante, M., Gorostiaga, J.M., 2017. Structural impoverishment of the subtidal vegetation of southeastern Bay of Biscay from 1991 to 2013 in the context of climate change. J. Sea Res. 130, 166–179. https://doi.org/ 10.1016/j.seares.2017.06.006. Muguerza, N., Díez, I., Quintano, E., Gorostiaga, J.M., 2022b. Decades of biomass loss in the shallow rocky subtidal vegetation of the southeastern Bay of Biscay. Mar. Biodivers. 52, 28. https://doi.org/10.1007/s12526-022-01268-2. Müller, R., Laepple, T., Bartsch, I., Wiencke, C., 2009. Impact of oceanic warming on the distribution of seaweeds in polar and cold-temperate waters. Bot. Mar. 52, 617–638. https://doi.org/10.1515/BOT.2009.080. Mu˜ niz, O., Rodríguez, J.G., Revilla, M., Laza-Martínez, A., Seoane, S., Franco, J., 2018. Seasonal variations of phytoplankton community in relation to environmental factors in an oligotrophic area of the European Atlantic coast (southeastern Bay of Biscay). Regional Studies in Marine Science 17, 59–72. https://doi.org/10.1016/j. rsma.2017.11.011. NOAA National Centers for Environmental Information, 2022. Monthly Global Climate Report for Annual published online January 2023. https://www.ncei.noaa.gov/ access/monitoring/monthly-report/global/202213. (Accessed 11 April 2023). OBIS, 2022. Ocean Biodiversity Information System. Intergovernmental Oceanographic Commission of UNESCO. www.obis.org. Orfanidis, S., 1991. Temperature responses and distribution of macroalgae belonging to the warm-temperate Mediterranean-Atlantic distribution group. Bot. Mar. 34, 12. https://doi.org/10.1515/botm.1991.34.6.541. Orlando-Bonaca, M., Pitacco, V., Lipej, L., 2021. Loss of canopy-forming algal richness and coverage in the northern Adriatic Sea. Ecol. Indicat. 125, 107501 https://doi. org/10.1016/j.ecolind.2021.107501. Ospina-Alvarez, N., Prego, R., ´ Alvarez, I., deCastro, M., ´ Alvarez-Ossorio, M.T., Pazos, Y., Campos, M.J., Bern´ ardez, P., Garcia-Soto, C., G´ omez-Gesteira, M., Varela, M., 2010. Oceanographical patterns during a summer upwelling-downwelling event in the northern Galician rias: comparison with the whole ria system (NW of Iberian Peninsula). Continent. Shelf Res. 30, 1362–1372. https://doi.org/10.1016/j. csr.2010.04.018. Parmesan, C., Yohe, G., 2003. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42. https://doi.org/10.1038/nature01286. P´ erez, F.F., Padín, X.A., Pazos, Y., Gilcoto, M., Cabanas, M., Pardo, P.C., Doval, M.D., Farina-Busto, L., 2010. Plankton response to weakening of the Iberian coastal O. Arriaga et al. Marine Environmental Research 190 (2023) 106098 17 upwelling. Global Change Biol. 16, 1258–1267. https://doi.org/10.1111/j.13652486.2009.02125.x. Perkol-Finkel, S., Airoldi, L., 2010. Loss and recovery potential of marine habitats: an experimental study of factors maintaining resilience in subtidal algal forests at the Adriatic Sea. PLoS One 5, e10791. https://doi.org/10.1371/journal.pone.0010791. Pinsky, M.L., Selden, R.L., Kitchel, Z.J., 2020. Climate-driven shifts in marine species ranges: scaling from organisms to communities. Ann. Rev. Mar. Sci 12, 153–179. https://doi.org/10.1146/annurev-marine-010419-010916. Poloczanska, E.S., Brown, C.J., Sydeman, W.J., Kiessling, W., Schoeman, D.S., Moore, P. J., Brander, K., Bruno, J.F., Buckley, L.B., Burrows, M.T., Duarte, C.M., Halpern, B.S., Holding, J., Kappel, C.V., O’Connor, M.I., Pandolfi, J.M., Parmesan, C., Schwing, F., Thompson, S.A., Richardson, A.J., 2013. Global imprint of climate change on marine life. Nat. Clim. Change 3, 919–925. https://doi.org/10.1038/nclimate1958. Provoost, P., Bosch, S., 2019. "robis: R Client to Access Data from the OBIS API." Ocean Biogeographic Information System. Intergovernmental Oceanographic Commission of UNESCO. R package version 2.1.8. https://cran.r-project.org/package=robis. R Development Core Team, 2023. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. URL, Available at: https://www.R-project.org/.). Ramos, E., Guinda, X., Puente, A., de la Hoz, C.F., Juanes, J.A., 2020. Changes in the distribution of intertidal macroalgae along a longitudinal gradient in the northern coast of Spain. Mar. Environ. Res. 157, 104930 https://doi.org/10.1016/j. marenvres.2020.104930. Rhein, M., Rintoul, S.R., Aoki, S., Campos, E., Chambers, D., Feely, R.A., Gulev, S., Johnson, G.C., Josey, S.A., Kostianoy, A., Mauritzen, C., Roemmich, D., Talley, L.D., Wang, F., 2013. Observations: ocean. In: Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P.M. (Eds.), Climate Change 2013: the Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. R¨ onnb¨ ack, P., Kautsky, N., Pihl, L., Troell, M., S¨ oderqvist, T., Wennhage, H., 2007. Ecosystem goods and services from Swedish coastal habitats: identification, valuation, and implications of ecosystem shifts. AMBIO A J. Hum. Environ. 36, 534–544. https://doi.org/10.1579/0044-7447(2007)36[534:EGASFS]2.0.CO;2. RStudio Team, 2016. RStudio. Integrated development for R. RStudio, Inc., Boston, MA. http://www.rstudio.com/. Serret, P., Fern´ andez, E., Sostres, J.A., Anad´ on, R., 1999. Seasonal compensation of microbial production and respiration in a temperate sea. Mar. Ecol. Prog. Ser. 187, 43–57. https://doi.org/10.3354/MEPS187043. Shaltout, M., Omstedt, A., 2014. Recent sea surface temperature trends and future scenarios for the Mediterranean Sea. Oceanologia 56, 411–443. https://doi.org/ 10.5697/oc.56-3.411. Sjøtun, K., Husa, V., Asplin, L., Sandvik, A., 2015. Climatic and environmental factors influencing occurrence and distribution of macroalgae - a fjord gradient revisited. Mar. Ecol. Prog. Ser. 532, 73–88. https://doi.org/10.3354/meps11341. Smale, D.A., Wernberg, T., 2013. Extreme climatic event drives range contraction of a habitat-forming species. Proceedings of the Royal Society B 280, 20122829. https:// doi.org/10.1098/rspb.2012.2829. Steller, D., Hernandez-Ay´ on, J., Riosmena-Rodríguez, R., Cabello-Pasini, A., 2007. Effect of temperature on photosynthesis, growth and calcification rates of the free-living coralline alga Lithophyllum margaritae. Cienc. Mar. 33, 441–456. https://doi.org/ 10.7773/cm.v33i4.1255. Steneck, R.S., Graham, M.H., Bourque, B.J., Corbett, D., Erlandson, J.M., Estes, J.A., Tegner, M.J., 2002. Kelp forest ecosystems: biodiversity, stability, resilience and future. Environ. Conserv. 29, 436–459. https://doi.org/10.1017/ S0376892902000322. Stuart-Smith, R.D., Edgar, G.J., Barrett, N.S., Kininmonth, S.J., Bates, A.E., 2015. Thermal biases and vulnerability to warming in the world’s marine fauna. Nature 528, 88–92. https://doi.org/10.1038/nature16144. Tittensor, D.P., Mora, C., Jetz, W., Lotze, H.K., Ricard, D., Berghe, E.V., Worm, B., 2010. Global patterns and predictors of marine biodiversity across taxa. Nature 466, 1098–1101. https://doi.org/10.1038/nature09329. Valdes-Abellan, J., Pardo, M.A., Tenza-Abril, A.J., 2017. Observed precipitation trend changes in the western Mediterranean region: observed precipitation trends in western Mediterranean. Int. J. Climatol. 37, 1285–1296. https://doi.org/10.1002/ joc.4984. Valencia, V., Franco, J., Borja, A., Font´ an, A., 2004. Hydrography of the south-eastern bay of Biscay. In: Borja, A., Collins, M. (Eds.), Oceanography and Marine Environment of the Basque Country, vol. 70. Elsevier Oceanography Series, pp. 159–187. https://doi.org/10.1016/S0422-9894(04)80045-X. Verg´ es, A., Steinberg, P.D., Hay, M.E., Poore, A.G.B., Campbell, A.H., Ballesteros, E., Heck, K.L., Booth, D.J., Coleman, M.A., Feary, D.A., Figueira, W., Langlois, T., Marzinelli, E.M., Mizerek, T., Mumby, P.J., Nakamura, Y., Roughan, M., van Sebille, E., Gupta, A.S., Smale, D.A., Tomas, F., Wernberg, T., Wilson, S.K., 2014. The tropicalization of temperate marine ecosystems: climate-mediated changes in herbivory and community phase shifts. Proceedings of the Royal Society B 281, 20140846. https://doi.org/10.1098/rspb.2014.0846. Voerman, S.E., Llera, E., Rico, J.M., 2013. Climate driven changes in subtidal kelp forest communities in NW Spain. Mar. Environ. Res. 90, 119–127. https://doi.org/ 10.1016/j.marenvres.2013.06.006. Webb, T.J., Lines, A., Howarth, L.M., 2020. Occupancy-derived thermal affinities reflect known physiological thermal limits of marine species. Ecol. Evol. 10, 7050–7061. https://doi.org/10.1002/ece3.6407. Wernberg, T., Bennett, S., Babcock, R.C., de Bettignies, T., Cure, K., Depczynski, M., Dufois, F., Fromont, J., Fulton, C.J., Hovey, R.K., Harvey, E.S., Holmes, T.H., Kendrick, G.A., Radford, B., Santana-Garcon, J., Saunders, B.J., Smale, D.A., Thomsen, M.S., Tuckett, C.A., Tuya, F., Vanderklift, M.A., Wilson, S., 2016. Climatedriven regime shift of a temperate marine ecosystem. Science 353, 169–172. https:// doi.org/10.1126/science.aad8745. O. Arriaga et al.