bioRxivEcologyPreprint
Mechanistic macroecology: exploring the drivers of latitudinal variation in terrestrial body size in a General Ecosystem Model
M. B. J. Harfoot, A. Abraham, D. P. Tittensor, G. C. Costa, S. Faurby, A. Feldman, Y. Itescu, S. Meiri, I. Morales-Castilla, B. F. Oliveira, D. Purves
Abstract
Many mechanisms have been hypothesized to explain Bergmanns rule - the correlation of body size with latitude. However, it is not feasible to assess the contribution of hypothesised mechanisms by experimental manipulation or statistical correlation. Here, we evaluate two of the principal hypothesised mechanisms, related to thermoregulation and resource availability, using structured experiments in a mechanistic global ecosystem model. We simulated the broad structure of assemblages and ecosystems using the Madingley model, a mechanistic General Ecosystem Model (GEM). We compared emergent modelled biogeographic patterns in body mass to empirical patterns for mammals and birds. We then explored the relative contribution of thermoregulation and resource availability to body mass clines by manipulating the models environmental gradients. Madingley produces body size gradients that are in broad agreement with empirical estimates. Thermoregulation and resource availability were both important controls on body mass for endotherms, but only temperature for ectotherms. Our results suggest that seasonality explains animal body mass patterns through a complex set of mechanisms. Process-based GEMs generate broadly realistic biogeographic body mass patterns. Ecologists can use them in novel ways: to explore causality, or for generating and testing hypotheses for large-scale, emergent ecological patterns. At the same time, macroecological patterns are useful for evaluating mechanistic models. Iteratively developing GEMs, and evaluating them against macroecological patterns, could generate new insights into the complex causes of such patterns.
- organism: multiple species
- research focus: ecology & biodiversity
- research focus: benchmarking
- approach: mechanistic / dynamical model
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