DOI: 10.5061/dryad.9ghx3ffn7
title: Data for: Allometry reveals trade-offs between Bergmann’s and Allen’s rules, and different avian adaptive strategies for thermoregulation

description: Animals tend to decrease in body size (Bergmann’s rule) and elongate
 appendages (Allen’s rule) in warm climates. However, it is unknown whether
 these patterns depend on each other or constitute independent responses to
 thermal environment. Here, based on a global phylogenetic comparative
 analysis across 99.7% of the world’s bird species, we show that the way in
 which the relative length of unfeathered appendages co-varies with
 temperature depends on body size and vice versa. First, the larger the
 body, the greater the increase in beak length with temperature. Second,
 the temperature-based increase in tarsus length is apparent only in larger
 birds, whereas in smaller birds, tarsus length decreases with temperature.
 Third, body size and the length of beak and tarsus interact each other to
 predict the species’ temperature preferences. These findings suggest that
 the animals’ body size and shape are products of an evolutionary
 compromise that reflects distinct alternative thermoregulatory
 adaptations.
description: BirdTherm.xlsx The data was obtained by using
 <em>sf (version 1.0-8)</em><sup>1</sup> and
 <em>raster (version 3.5-15)</em><sup>2</sup> R
 packages. As input, we used global raster layers of temperatures
 <em>World Clim (version 2.1)</em><sup>3</sup>.
 These rasters had a resolution of 30” and were consist of monthly averages
 from a period of 58 years (1960-2018). The temperature metrics have been
 calculated within polygons of species ranges available in form of
 multi-polygon vector layers extracted from the <em>BirdLife
 International database (version
 2020.1)</em><sup>4</sup>. First, we
 excluded polygons identified as uncertain species presence, uncertain
 season of presence, non-native presence or species extinct in a region,
 leaving us with 9,962 species (out of 9,993 species) with complete
 geographic data. Second, having polygons with only a
 certain, native and extant species presence we grouped them by the species
 (according to the phylogenetic taxonomy<sup>5</sup>) and the
 season of presence (either breeding season, winter or year-round presence)
 and then we aggregated them to obtain single polygons specific to species
 and season. Third, using breeding and year-round
 species ranges, where species live at hotter period of the year, we
 calculated their zonal means of monthly temperature maximums
 (Tmax<sup>3</sup>) and took the largest monthly value for each
 species (here, <strong>maximum temperature of all months,
 maxTmax</strong>). We also calculated their zonal means of monthly
 temperature averages (Tavg<sup>3</sup>) and took the largest
 monthly value for each species (here, <strong>average temperature of
 hottest month, maxTavg</strong>). Fourth, we
 analogously used winter and year-round species ranges, where species live
 at colder period of the year. Then, we calculated their zonal means of
 monthly temperature minimum (Tmin<sup>3</sup>) and took the
 lowest monthly value for each species (here, <strong>minimum
 temperature of all months, minTmin</strong>). We also calculated
 their zonal means of monthly temperature averages
 (Tavg<sup>3</sup>) and took the lowest monthly value for each
 species (here, <strong>average temperature of coldest month,
 minTavg</strong>). Fifth, we took all species
 ranges (breeding, winter and year-round, summarized to single polygon per
 species) and we calculated their zonal means of monthly temperature
 averages (Tavg<sup>3</sup>) and averaged all monthly values to
 obtain <strong>average temperature of all months
 (avgTavg)</strong> for each species. Note that this measure was
 accurate to describe conditions for resident species (which comprise of
 79.9% of all species), but less for migrants, especially where their
 breeding and wintering ranges are on opposite hemispheres.
 Sixth, the obtained temperature measures: minimum tmperature of
 all months (minTmin), average temperature of coldest month (minTavg),
 average temperature of all months (avgTavg), average temperature of
 hottest month (maxTavg) and maximum temperature of all months (maxTmax)
 were transformed with two different formulas to normalize left-shewed
 distribution: <em>tr2minTmin =
 -log10(max(x+1)-minTmin)</em>
 <em>tr2minTavg =
 -log10(max(x+1)-minTavg)</em>
 <em>tr2avgTavg =
 -log10(max(x+1)-avgTavg)</em>
 <em>tr1maxTavg =
 -log10(max(x+1)-maxTavg)</em>
 <em>tr1maxTmax =
 -log10(max(x+1)-maxTmax)</em> Seventh, we also assessed geographic range
 size by aggregating all seasonal ranges of a species to a single polygon
 and calculated the area (in km<sup>2</sup>) on an equal-area
 cylindrical map projection (Eckert IV), ensuring comparable measurements
 from poles to equator. Eights, we have also extracted
 latitudinal and longitudinal data of the centroids of avian geographic
 ranges for visualization purposes. The temperature measures reflected the
 full range of global thermal environments occupied by birds. For example,
 the maximum temperature of all months ranged from -3.8 °C (in the
 emperor penguin
 <em>Aptenodytes forsteri</em>), through 29.9 °C
 (median, in the Minas gerais tyrannulet <em>Phylloscartes
 roquettei</em>) to 43.8 °C (in the Basra reed-warbler
 <em>Acrocephalus griseldis</em>). In contrast, the minimum
 temperature of all months ranged from -35.3 °C (in black-billed
 capercaillie <em>Tetrao urogalloides</em>), through 14.1 °C
 (median, e.g. in Yellow-breasted apalis <em>Apalis
 flavida</em>) to 24.8 °C (in the Seychelles warbler
 <em>Acrocephalus sechellensis</em>). Notably, our multiple
 measures of temperature indicated distinct aspects of seasonality in
 thermal conditions that may require different phenotypic adaptations
 across avian lineages. References 1.
 Pebesma, E. Simple Features for R: Standardized Support for Spatial Vector
 Data. R J. 10, 439–446 (2018). 2. Hijmans, R. J.
 raster: Geographic Data Analysis and Modeling. R package version 3. 5–15
 (2022). 3. Fick, S. E. & Hijmans, R. J.
 WorldClim 2: new 1-km spatial resolution climate surfaces for global land
 areas. Int. J. Climatol. 37, 4302–4315 (2017). 4.
 BirdLife International. Data Zone. Data Zone (2020). Available at:
 http://datazone.birdlife.org/, accessed in March 2021.
 5. Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. &
 Mooers, A. O. The global diversity of birds in space and time. Nature 491,
 444–448 (2012).
description: This data is available through excel and R (or R Studio).