How does knowledge of intraspecific variation improve our ability to predict geographic distributions A case study of the eastern fence lizard, Sceloporus undulatus


Meeting Abstract

P3.124  Saturday, Jan. 5  How does knowledge of intraspecific variation improve our ability to predict geographic distributions? A case study of the eastern fence lizard, Sceloporus undulatus EHRENBERGER, J. C,*; BUCKLEY, L. B.; ANGILLETTA, M. J.; Indiana State Univ, Terre Haute; Santa Fe Institute, Santa Fe; Indiana State Univ, Terre Haute jehrenberge@indstate.edu

Biologists have long recognized that the geographic range of a species expands and contracts in response to environmental change, but predicting such responses remains an elusive goal. Most efforts to predict shifts in species� ranges rely on a correlational approach that assumes environmental tolerances do not vary over space or time. In this approach, species are assumed to track specific environmental conditions across space during climate change. However, geographic variation in phenotypes should influence how subpopulations respond to climate change, leading to predictions that differ from those of correlational models. We adopt a mechanistic approach that accounts for physiological variation among subpopulations of a species. This approach will be exemplified using data from a widespread species, the eastern fence lizard (Sceloporus undulatus). We investigate how physiological traits vary across this species’ range and ask how this influences the distributional response to climate change. Phylogenetic analyses have defined four major clades in S. undulatus, each spanning a latitudinal cline. We use preferred body temperature, critical thermal limits and standard metabolic rates of lizards from four populations representing two clades (eastern and western). These data parameterize a population-specific model of the geographic range. The population-specific model differs from the more common model, in which one assumes all populations possess the same physiological traits. Developing mechanistic models of species’ ranges that incorporate geographic variation should improve our ability to predict responses to climate change.

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