Thermal specialization across large geographical scales predicts the resilience of mangrove crab populations to global warming
Mcquaid, Christopher D.
KAUST DepartmentBiological and Environmental Sciences and Engineering (BESE) Division
Extreme Systems Microbiology Lab
Permanent link to this recordhttp://hdl.handle.net/10754/563862
MetadataShow full item record
AbstractThe broad prediction that ectotherms will be more vulnerable to climate change in the tropics than in temperate regions includes assumptions about centre/edge population effects that can only be tested by within-species comparisons across wide latitudinal gradients. Here, we investigated the thermal vulnerability of two mangrove crab species, comparing populations at the centre (Kenya) and edge (South Africa) of their distributions. At the same time, we investigated the role of respiratory mode (water- versus air-breathing) in determining the thermal tolerance in amphibious organisms. To do this, we compared the vulnerability to acute temperature fluctuations of two sympatric species with two different lifestyle adaptations: the free living Perisesarma guttatum and the burrowing Uca urvillei, both pivotal to the ecosystem functioning of mangroves. The results revealed the air-breathing U. urvillei to be a thermal generalist with much higher thermal tolerances than P. guttatum. Importantly, however, we found that, while U. urvillei showed little difference between edge and centre populations, P. guttatum showed adaptation to local conditions. Equatorial populations had elevated tolerances to acute heat stress and mechanisms of partial thermoregulation, which make them less vulnerable to global warming than temperate conspecifics. The results reveal both the importance of respiratory mode to thermal tolerance and the unexpected potential for low latitude populations/species to endure a warming climate. The results also contribute to a conceptual model on the latitudinal thermal tolerance of these key species. This highlights the need for an integrated population-level approach to predict the consequences of climate change. © 2014 The Authors.