Global Ecology and Conservation (Dec 2022)
Food resources and competition rather than eco-geographic rules explain trait variations in two contrasting rat species: Implications for future climate change
Abstract
Understanding how morphological traits are influenced by temperature, resource availability and competition is critical for predicting future ecosystem resilience to climate change. Here we tested the effects of biotic and abiotic factors, on the morphology of two contrasting Muridae species (Chinese white-bellied rats Niviventer confucianus [n = 565; 57.1 g] and Edwards's long-tailed giant rats Leopoldamys edwardsi [n = 45; 459.5 g]) in relation to Bergmann’s, Allen’s, Hesse’s and Geist’s eco-geographical rules along a 2900-m elevational gradient. Specifically, we tested the “body size threshold” theory, which posits that animals heavier than 0.16 kg are more likely to follow Bergmann’s rule. We measured body length and mass, appendage lengths and heart mass for 610 adult specimens from 115 localities, and used principal components analysis to quantify food resource availability. Linear mixed models were employed to test these eco-geographical rules, along with the effects of associated biotic and abiotic factors. We found that for N. confucianus ( 0.16 kg) did not conform with any eco-geographic rules, and so we found no support for the “body size threshold” theory. Our study proposes that food resource abundance and inter- and intraspecific competition mediated variations in morphological traits in these rodent species to a greater extent than any reduction in temperature alone, giving little support for ecogeographical rule effects. We interpret that where food supply can meet energetic demands, even under colder conditions, then any selective pressure for thermal/energy efficiency through larger body size and shorter appendages will be alleviated. Considering the full range of inter-related factors that are affected by temperature, rather than just the bio-physics of heat loss, is important to better predict future climate change responses.
