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.

BirdTherm.xlsx

The data was obtained by using sf (version 1.0-8)¹ and raster (version 3.5-15)² R packages. As input, we used global raster layers of temperatures World Clim (version 2.1)³. 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 BirdLife International database (version 2020.1)⁴.

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 taxonomy⁵) 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 (Tmax³) and took the largest monthly value for each species (here, maximum temperature of all months, maxTmax). We also calculated their zonal means of monthly temperature averages (Tavg³) and took the largest monthly value for each species (here, average temperature of hottest month, maxTavg).

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 (Tmin³) and took the lowest monthly value for each species (here, minimum temperature of all months, minTmin). We also calculated their zonal means of monthly temperature averages (Tavg³) and took the lowest monthly value for each species (here, average temperature of coldest month, minTavg).

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 (Tavg³) and averaged all monthly values to obtain average temperature of all months (avgTavg) 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:

tr2minTmin = -log10(max(x+1)-minTmin)

tr2minTavg = -log10(max(x+1)-minTavg)

tr2avgTavg = -log10(max(x+1)-avgTavg)

tr1maxTavg = -log10(max(x+1)-maxTavg)

tr1maxTmax = -log10(max(x+1)-maxTmax)

Seventh, we also assessed geographic range size by aggregating all seasonal ranges of a species to a single polygon and calculated the area (in km²) 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 Aptenodytes forsteri), through 29.9 °C (median, in the Minas gerais tyrannulet Phylloscartes roquettei) to 43.8 °C (in the Basra reed-warbler Acrocephalus griseldis). In contrast, the minimum temperature of all months ranged from -35.3 °C (in black-billed capercaillie Tetrao urogalloides), through 14.1 °C (median, e.g. in Yellow-breasted apalis Apalis flavida) to 24.8 °C (in the Seychelles warbler Acrocephalus sechellensis). 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).

This data is available through excel and R (or R Studio).