Physiologically grounded metrics of model skill: a case study estimating heat stress in intertidal populations.

Models of ecological responses to climate change fundamentally assume that predictor variables, which are often measured at large scales, are to some degree diagnostic of the smaller-scale biological processes that ultimately drive patterns of abundance and distribution. Given that organisms respond physiologically to stressors, such as temperature, in highly non-linear ways, small modelling errors in predictor variables can potentially result in failures to predict mortality or severe stress, especially if an organism exists near its physiological limits. As a result, a central challenge facing ecologists, particularly those attempting to forecast future responses to environmental change, is how to develop metrics of forecast model skill (the ability of a model to predict defined events) that are biologically meaningful and reflective of underlying processes. We quantified the skill of four simple models of body temperature (a primary determinant of physiological stress) of an intertidal mussel, , using common metrics of model performance, such as root mean square error, as well as forecast verification skill scores developed by the meteorological community. We used a physiologically grounded framework to assess each model's ability to predict optimal, sub-optimal, sub-lethal and lethal physiological responses. Models diverged in their ability to predict different levels of physiological stress when evaluated using skill scores, even though common metrics, such as root mean square error, indicated similar accuracy overall. Results from this study emphasize the importance of grounding assessments of model skill in the context of an organism's physiology and, especially, of considering the implications of false-positive and false-negative errors when forecasting the ecological effects of environmental change.