# Data for: Recent changes in thermal niche position and breadth of bird assemblages in Spain in relation to increasing temperatures Name: David Ramón-Martínez ORCID:0000-0001-7537-6254 Institution: Doñana Biological Station (EBD-CSIC) Address: Amrico Vespucio 26, Sevilla 41092, Spain Email: Name: Javier Seoane ORCID:0000-0001-9975-4846 Institution: Centro de Investigacion en Biodiversidad y Cambio Global, Universidad Autonoma de Madrid (CIBC-UAM); Terrestrial Ecology Group, Department of Ecology, Universidad Autonoma de Madrid(TEG-UAM). Address: Darwin, 2. Madrid 28049, Spain Email: **Aim:** Animal communities around the world are responding to climate change by altering their taxonomic composition, mainly through an increase in the colonisation rate of warm-dwelling species and the local extinction of cold-dwelling ones. We assessed whether the taxonomic composition of bird assemblages in peninsular Spain has changed in accordance with the recent increase in temperature. We also evaluated the role of species' thermal affinities and population dynamics on these changes. **Location:** Peninsular Spain. **Taxon:** Birds. **Methods:** We compared assemblages reported in the last Spanish breeding bird atlases (1998-2002 vs 2014-2019) in 10x10 km squares. We described species’ thermal niches by overlaying global species breeding distributions and world temperature metrics (based on mean, minimum, maximum and range), and then aggregated them to obtain a set of community thermal indices for each assemblage (CTIs, and CTR for ranges). Long-term average temperatures and local current temperatures were related to changes in CTIs using spatial GLMMs, which considered habitat change. We identified the species most responsible for variation in assemblages and regressed species’ influence on thermal affinities and population dynamics. **Results:** CTIs increased with temperature and warm-dwelling species became more prevalent to the detriment of cold-dwelling ones. However, we found a counteracting effect of temperature and habitat. Cold-dwelling forest species were among the most influential species, mainly through colonisation, while warm-dwelling farmland species contributed through local extinctions (both attenuated local increases in CTI). The mean thermal breadth of assemblages (CTR) decreased with temperatures. **Main conclusions:** The taxonomic composition of bird assemblages shifted in line with the main expectations due to global change (thermophilisation), mainly due to local colonisation of warm-dwelling species, although it did not show the pattern of thermal homogenization suggested elsewhere. Our results add further evidence of the interplay between climate warming and land-use change in the ongoing adjustment of animal communities. ## Description of the Data and file structure The dataset is a dataframe that comprises the Community Thermal Indices (response variable) and the standardized and unstandardized environmental and geographic variables employed as predictors of the spatial GLMM. This model related the temperatures to the changes in CTI, considering the habitat (forest) change. The Community Thermal Indices were computed from the Species Thermal Indices (Devictor et al., 2008). We obtained four thermal indices for each species (Species Thermal Index STI) by combining the global breeding species distribution and the climate information. The STI1 (i) shows the mean temperature of the breeding season (April-July) throughout the species breeding distribution range. Similarly, the STI2 (ii) is the average of the maximum temperatures above the percentile 95 in July and the STI3 (iii) is the average minimum temperature below the percentile 05 in April in the species’ breeding distribution range. These three indices represent a species' thermal affinity. On the other hand, the fourth index (iv) (Species Thermal Range - STR) represents the average thermal range (April-July) throughout the breeding distribution area and can be understood as species thermal breadth. We calculated a set of community thermal indices (CTI) for the assemblage of bird species in each of the 10x10km UTM grid squares of each of the breeding bird atlases. We obtained four different CTIs: CTI1, CTI2, CTI3, and CTR. The first three were calculated as the average of the STI1, STI2, and STI3 of the species present in the assemblage, respectively. The CTR (Community Thermal Range) is based on the average temperature range of the species (STR) that make up the assemblage and thus informs on the average niche breadth (Gaget et al., 2020). We calculated CTIs for each of the four-year periods covered by the atlases. The dataset also includes the standardized and unstandardized local temperature and forest cover for each grid square and for each breeding bird atlas. It also includes the standardized and unstandardized coordinates of each grid square. Local temperatures were obtained from Chelsa (v.2.1., Karger et al., 2017), averaging data for each five-year sampling period in each square. We used the CORINE Land Cover Accounting Layers built for the years 2000 and 2018, to link forest cover with the community indices for the first and second sampling periods, respectively. The variables included in the dataset are the following: * **UTM10**: The identity of each 10x10 km square grid from the Spanish Breeding Bird Atlases. * **fperiod**: Each of the sampling periods considered (1998-2002; 2014-2019). * **longitude**: Longitude of the grid square centroid (CRS: WGS84; EPSG=4326 ). * **latitude**: Latitude of the grid square centroid (CRS: WGS84; EPSG=4326). * **sd_longitude**: Standardized longitude of the grid square centroid. * **sd_latitude**: Standardized latitude of the grid square centroid. * **forest_cover**: Forest landcover (ha) in each square in 2000 and 2018 CORINE LandCover Accounting Layers versions. The forest landcover in 2018 is assigned to the second period observations (2014-2019), whereas the forest landcover in 2000 is assigned to the first period observations (1998-2002). We considered as forest landcover the CORINE/Landcover categories 311 “Broad leaf forest”; 312 “Coniferous Forest” and 313 “Mixed forest”. * **sd_forest_cover**: The standardized forest landcover in each square in 2000 and 2018 CORINE LandCover Accounting Layers versions. The forest landcover in 2018 is assigned to the second period observations (2014-2019), whereas the forest landcover in 2000 is assigned to the first period observations (1998-2002). We considered as forest landcover the CORINE/Landcover categories 311 “Broad leaf forest”; 312 “Coniferous Forest” and 313 “Mixed forest”. * **temperature**: The mean annual temperature (ºC) of each square grid in each period obtained from Chelsa v.2.1 (Karger et al., 2017). This dataset is based on downscaled air temperature two meters above the ground modelized from the data collected from many sources (mainly weather stations, weather balloons, aircraft, ships and satellites). The mean annual temperature of the period 1998-2002 is assigned to the observations from the first period (1998-2002). The mean annual temperature of the period 2014-2018 is assigned to the observations from the second period (2014-2019). Temperature was downloaded in Kelvin*10, and then converted to ºC previous to the analysis. * **sd_temperature:** The standardized mean annual temperature of each square grid in each period obtained from Chelsa v.2.1 (Karger et al., 2017). This dataset is based on downscaled air temperature two meters above the ground modelized from the data collected from many sources (mainly weather stations, weather balloons, aircraft, ships and satellites). The mean annual temperature of the period 1998-2002 is assigned to the observations from the first period (1998-2002). The mean annual temperature of the period 2014-2018 is assigned to the observations from the second period (2014-2019). * **CTI1**: Community Thermal Index 1. Average of the STI1 (thermal optimum) of the species present in a square grid. The STI1 is computed as the mean temperature (ºC) of the breeding season (April-July) along the global distribution range of a species during the breeding season. Wordclim monthly average temperatures for 1970-2000 (Worldclim 2.0: (Fick & Hijmans, 2017)) were used for this purpose. * **CTI2**: Community Thermal Index 2. Average of the STI2 (thermal maximum) of the species present in a square grid. The STI2 is computed as the average of the maximum temperatures (ºC) above the percentile 95 in July along the global distribution range of a species during the breeding season. Wordclim maximum temperatures of July for 1970-2000 (Worldclim 2.0: (Fick & Hijmans, 2017)) were used for this purpose. * **CTI3**: Community Thermal Index 3. Average of the STI3 (thermal minimum) of the species present in a square grid. The STI3 is computed as the average minimum temperature (ºC) below the percentile 05 in April along the global distribution range of a species during the breeding season. Wordclim minimum temperatures of April for 1970-2000 (Worldclim 2.0: (Fick & Hijmans, 2017)) were used for this purpose. * **CTR**: Community Thermal Range. Average of the STR (thermal range) of the species present in a square grid. The STR is computed as the difference between STI3 and STI2. ## Sharing/access Information Temperature for obtaining STI and CTI was obtained from Wordclim 2.0 (Fick & Hijmans, 2017). Local temperatures of square grids were computed from Chelsa v.2.1. (Karger et al., 2017) Grid square forest cover was obtained from CORINE LandCover Accounting Layers (EEA, 2019). Species global distribution maps were facilitated by Birdlife-International () REFERENCES 1. Devictor, V., Julliard, R., Couvet, D., & Jiguet, F. (2008). Birds are tracking climate warming, but not fast enough. Proceedings of the Royal Society B: Biological Sciences, 275(1652), 27432748. 2. EEA. (2019). Corine Land Cover Accounting Layers. 3. Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 43024315. 4. Gaget, E., Galewski, T., Jiguet, F., Guelmami, A., Perennou, C., Beltrame, C., & Le Viol, I. (2020). Antagonistic effect of natural habitat conversion on community adjustment to climate warming in nonbreeding waterbirds. Conservation Biology, 34(4), 966976. 5. Karger, D. N., Conrad, O., Bhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017). Climatologies at high resolution for the earths land surface areas. Scientific Data, 4(1), 120.

The dataset is a dataframe that comprises the Community Thermal Indices (response variable) and the environmental and geographic variables employed as predictors of the spatial GLMM. This model related the temperatures to the changes of CTI, considering the habitat (forest) change. The Community Thermal Indices were computed from the Species Thermal Indices. We obtained four thermal indices for each species (Species Thermal Index – STI) by combining the global species’ distribution and the climate information. The STI1 (i) shows the mean temperature of the breeding season (April-July) throughout the species’ distribution range. Similarly, the STI2 (ii) is the average of the maximum temperatures above the percentile 95 in July, and the STI3 (iii) is the average minimum temperature below the percentile 05 in April in the species’ breeding distribution range. These three indices represent a species’ thermal affinity. On the other hand, the fourth index (iv) (Species Thermal Range - STR) represents the average thermal range (April-July) throughout the distribution area and can be understood as species thermal breadth. It is computed as STI3-STI2. We calculated a set of community thermal indices (CTI) for the assemblage of bird species in each of the 10x10km UTM grid squares of each of the breeding bird atlases. We obtained four different CTIs: CTI1, CTI2, CTI3, and CTR. The first three were calculated as the average of the STI, STI2, and STI3 of the species present in the assemblage, respectively. The CTR (Community Thermal Range) is based on the average temperature range of the species (STR) that make up the assemblage and thus informs on the average niche breadth (Gaget et al., 2020). We calculated CTIs for each of the four-year periods covered by the atlases. The dataset also includes the standardized and unstandardized local temperature (ºC) and forest cover (ha) for each grid square and for each breeding bird atlas. It also includes the standardized and unstandardized coordinates of each grid square in decimal degrees (WGS84). Local temperatures were obtained from Chelsa (v.2.1., Karger et al., 2017), averaging data for each five-year sampling period in each square. We used the CORINE Land Cover Accounting Layers built for the years 2000 and 2018, to link forest cover with the community indices for the first and second sampling periods, respectively 

Aim: Animal communities around the world are responding to climate change by altering their taxonomic composition, mainly through an increase in the colonisation rate of warm-dwelling species and the local extinction of cold-dwelling ones. We assessed whether the taxonomic composition of bird assemblages in peninsular Spain has changed in accordance with the recent increase in temperature. We also evaluated the role of species' thermal affinities and population dynamics in these changes. Location: Peninsular Spain. Taxon: Birds. Methods: We compared assemblages reported in the last Spanish breeding bird atlases (1998–2002 vs 2014–2019) in 10x10 km squares. We described species’ thermal niches by overlaying global species breeding distributions and world temperature metrics (based on mean, minimum, maximum and range), and then aggregated them to obtain a set of community thermal indices for each assemblage (CTIs, and CTR for ranges). Long-term average temperatures and local current temperatures were related to changes in CTIs using spatial GLMMs, which considered habitat change. We identified the species most responsible for variation in assemblages and regressed species’ influence on thermal affinities and population dynamics. Results: CTIs increased with temperature and warm-dwelling species became more prevalent to the detriment of cold-dwelling ones. However, we found a counteracting effect of temperature and habitat. Cold-dwelling forest species were among the most influential species, mainly through colonisation, while warm-dwelling farmland species contributed through local extinctions (both attenuated local increases in CTI). The mean thermal breadth of assemblages (CTR) decreased with temperatures. Main conclusions: The taxonomic composition of bird assemblages shifted in line with the main expectations due to global change (thermophilisation), mainly due to local colonisation of warm-dwelling species, although it did not show the pattern of thermal homogenization suggested elsewhere. Our results add further evidence of the interplay between climate warming and land-use change in the ongoing adjustment of animal communities.