DOI: 10.5061/dryad.h70rxwdnt
title: Data for: Recent changes in thermal niche position and breadth of bird assemblages in Spain in relation to increasing temperatures

description: 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.
description: 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.<br> 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 
description: # 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.
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 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.