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Published on: February 27, 2021
Mechanisms of thermal adaptation and evolutionary potential of conspecific populations to changing environments
Zhongqi Chen1, Anthony P Farrell1,2, Amanda Matala3
1Department of Zoology, The University of British Columbia, Vancouver, BC, Canada.
Abstract:
Heterogeneous and ever-changing thermal environments drive the evolution of populations and species, especially when extreme conditions increase selection pressure for traits influencing fitness. However, projections of biological diversity under scenarios of climate change rarely consider evolutionary adaptive potential of natural species. In this study, we tested for mechanistic evidence of evolutionary thermal adaptation among ecologically divergent redband trout populations (Oncorhynchus mykiss gairdneri) in cardiorespiratory function, cellular response and genomic variation. In a common garden environment, fish from an extreme desert climate had significantly higher critical thermal maximum (p < .05) and broader optimum thermal window for aerobic scope (>3°C) than fish from cooler montane climate. In addition, the desert population had the highest maximum heart rate during warming (20% greater than montane populations), indicating improved capacity to deliver oxygen to internal tissues. In response to acute heat stress, distinct sets of cardiac genes were induced among ecotypes, which helps to explain the differences in cardiorespiratory function. Candidate genomic markers and genes underlying these physiological adaptations were also pinpointed, such as genes involved in stress response and metabolic activity (hsp40, ldh-b and camkk2). These markers were developed into a multivariate model that not only accurately predicted critical thermal maxima, but also evolutionary limit of thermal adaptation in these specific redband trout populations relative to the expected limit for the species. This study demonstrates mechanisms and limitations of an aquatic species to evolve under changing environments that can be incorporated into advanced models to predict ecological consequences of climate change for natural organisms.
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