Extending spatial modelling of climate change responses beyond the realized niche: Estimating, and accommodating, physiological limits and adaptive evolution

Aim: Spatial models of the impacts of climate change generally assume that species are restricted to their realized niche, and will persist only if that niche remains accessible through time. However, species often have physiological limits beyond the range of environmental conditions experienced in their present realized niche, and these limits may also be extended further through evolution in response to the selection pressure provided by climate change. Our aim was to develop a general framework for incorporating key parameters relating to physiological limits and adaptive evolution into models of the impact of climate change. Location: Global. Methods: Four types of parameter are defined in our framework: the realized limit, the current physiological limit, the evolutionary physiological limit and the rate of adaptive evolution. These parameters can be estimated or predicted using a variety of information sources, and can be applied to a diverse range of modelling approaches. Results: We illustrate the utility of this approach by describing how parameters can be measured directly for model species, and by exploring how minimal information on phylogeny and distribution might enable parameter estimation for less well-studied species. We outline a general strategy for deriving these parameters from ongoing research, involving a cascading hierarchy of information ranging from direct observations of traits closely linked to the parameters of interest (e.g. from physiological or evolutionary experimentation) through to more distal indicators (e.g. ecological traits such as niche position or range size). Main conclusions: The incorporation of adaptive capacity into spatial modelling of biological responses to climate change is now eminently achievable. Significant sources of data are available that can be used as predictors or indicators of physiological limits and the capacity for adaptive evolution in non-model organisms. These data offer a common currency for addressing one of the most important limitations of current efforts to model the impacts of climate change on biological distributions.