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Biogeography of bird and mammal trophic structures

Citation

Mendoza, Manuel; Araujo, Miguel B. (2022), Biogeography of bird and mammal trophic structures, Dryad, Dataset, https://doi.org/10.5061/dryad.nk98sf7st

Abstract

Does climate determine the trophic organization of communities around the world? A recent study showed that a limited number of community trophic structures emerge when co-occurrence of trophic guilds among large mammals is examined globally. We ask whether the pattern is general across a all terrestrial mammals (n=5272) and birds (n=9993). We found that the six community-trophic structures previously identified with large mammals are largely maintained when all mammals and birds are examined, both together and separately, and that bioclimatic variables, including net primary productivity (NPP), are strongly related to variation in the geographical boundaries of community trophic structures. We argue that results are consistent with the view that trophic communities are self-organized structures optimizing energy flows, and that climate likely acts as the main control parameter by modulating the amount of solar energy available for conversion by plants and percolated through food webs across trophic communities. Gradual changes in climate parameters would thus be expected to trigger abrupt changes in energy flows resulting from phase transitions (tipping points) between different dynamical stable states. We expect future research to examine if our results are general across organisms, ecosystems, scales, and methodologies, and whether inferences rooted in complex systems theory are supported. The emergence of general patterns in the functional properties of animal communities at broad scales supports the emergence of food-web biogeography as a sub-discipline of biogeography focused on the analysis of the geographical distributions of trophic relationships among organisms.

Methods

Methods: How was this dataset collected? How has it been processed?

Three sources of geographical data were extracted and plotted in a world terrestrial 1º×1º grid system: (1) global distributional ranges of non-marine mammal and bird species; (2) bioclimatic variables; and (3) net primary productivity. The global species distributions were derived from IUCN Global Assessment distributional data for native ranges (IUCN 2014). Specific occurrences in grid cells were used to produce a presence/absence matrix with names of 9993 non-marine birds and 5272 terrestrial mammals (15265 species) as columns and 15370 1º × 1º grid cells as rows.

In the global species-level compilation published by Wilman et al. (2014), trophic resources are classified into 10 categories. The trophic profile of each species (Data_I) is obtained from the estimated percentage of each type of resource in their diet. The result is a matrix with the 10 trophic resources categories as columns, 15265 species of birds and mammals as rows, and values representing the estimated percentage of each type of resource. The eleventh column in Data_I is the trophic guild (TG) in which these species were classified. These trophic guilds were obtained using c-means clustering, on the basis of the Euclidean distance between the 15265 species in the 10-dimensional ‘species-level trophic space’ defined by the estimated percentage of each type of resource in their diet.

In Data_II, 15370 1º × 1º terrestrial grid cells are identified by their coordinates (latitude and longitude). To obtain their trophic profile, we assigned species to their correspondent guild (Data_I) and then counted the number of species of each guild within the cell. The result is a matrix with the 9 trophic guilds as columns, 15370 communities as rows, and values representing numbers of species. The trophic profile of every community is thus a point in a 9-dimensional ‘trophic space’ defined by the number of species from each trophic guild (a vector of dimension 9). The tenth column in Data_II is the type of trophic structure found in the cell. These trophic structures (TS1 to TS6) correspond to well-defined clusters across this 9-dimensional ‘community-level trophic space’ identified with the help of the AMD index.

 Bioclimatic data for the terrestrial surface of the Earth (Data_III) were obtained from WorldClim - Global Climate Data (Hijmans et al. 2005).

Funding

Spanish Ministry of Science, Innovation, and Universities