Data from: Do evolutionary constraints on thermal performance manifest at different organizational scales?


The two foremost hypotheses on the evolutionary constraints on an organism's thermal sensitivity – the hotter-is-better expectation, and the specialist–generalist trade-off – have received mixed support from empirical studies testing for their existence. Could these conflicting results reflect confusion regarding the organizational level (i.e. species > population > individual) at which these constraints should manifest? We propose that these evolutionary constraints should manifest at different organizational levels because of differences in their underlying causes and requirements. The hotter-is-better expectation should only manifest across separate evolutionary units (e.g. species, populations), and not within populations. The specialist–generalist trade-off, by contrast, should manifest within as well as between separate evolutionary units. We measured the thermal sensitivity of sprint performance for 440 rainforest sun skinks (Lampropholis coggeri) representing 10 populations, and used the resulting performance curves to test for evidence for the hypothesized constraints at two organizational levels: (i) across populations and (ii) within populations. As predicted, the hotter-is-better expectation was evident only at the across-population level, whereas the specialist–generalist trade-off was evident within, as well as across, populations. Our results suggest that, depending on the processes that drive them, evolutionary constraints can manifest at different organizational levels. Consideration of these underlying processes, and the organizational level at which a constraint should manifest, may help resolve conflicting empirical results.

Metadata Access
Creator Phillips, Ben L.; Llewelyn, John; Hatcher, Amberlee; Macdonald, Stewart; Moritz, Craig
Publisher Data Archiving and Networked Services (DANS)
Publication Year 2014
Rights info:eu-repo/semantics/openAccess; License:
OpenAccess true
Resource Type Dataset
Discipline Life Sciences;Medicine