How complex should models be? Comparing correlative and mechanistic range dynamics models

Abstract

Criticism has been levelled at climate-change-induced forecasts of species range shifts that do not account explicitly for complex population dynamics. The relative importance of such dynamics under climate change is, however, undetermined because direct tests comparing the performance of demographic models vs. simpler ecological niche models are still lacking owing to difficulties in evaluating forecasts using real-world data. We provide the first comparison of the skill of coupled ecological-niche-population models and ecological niche models in predicting documented shifts in the ranges of 20 British breeding bird species across a 40-year period. Forecasts from models calibrated with data centred on 1970 were evaluated using data centred on 2010. We found that more complex coupled ecological-niche-population models (that account for dispersal and metapopulation dynamics) tend to have higher predictive accuracy in forecasting species range shifts than structurally simpler models that only account for variation in climate. However, these better forecasts are achieved only if ecological responses to climate change are simulated without static snapshots of historic land use, taken at a single point in time. In contrast, including both static land use and dynamic climate variables in simpler ecological niche models improve forecasts of observed range shifts. Despite being less skilful at predicting range changes at the grid-cell level, ecological niche models do as well, or better, than more complex models at predicting the magnitude of relative change in range size. Therefore, ecological niche models can provide a reasonable first approximation of the magnitude of species' potential range shifts, especially when more detailed data are lacking on dispersal dynamics, demographic processes underpinning population performance, and change in land cover.

Forecasting species range dynamics with process‐explicit models: matching methods to applications

“…Similarly, the detailed knowledge and data required to select appropriate physiological thresholds, reliably model microclimates or identify key processes, can make it difficult for eco‐physiological models to achieve good predictive performance (Buckley et al ). Two of the key studies that evaluate process‐explicit SDMs have shown that more complex models that explicitly capture additional processes do not always provide more reliable predictions (Zurell et al ; Fordham et al ). Investment in the collection and collation of species and environmental data required for these methods is needed (Urban et al ), but these efforts should be strategic and informed by explorations of how data availability and quality affect model performance for different taxonomic groups or applications (Buckley ; Pagel & Schurr ; Rossman et al ).…”

How will climate novelty influence ecological forecasts? Using the Quaternary to assess future reliability

“…Another solution is to rely more on mechanism and less on correlation for forecasting future ecological responses, whenever possible. However, all mechanistic models rely to some degree on parameterization against observational data, so they do not entirely avoid the novelty challenge, and their predictions are not necessarily superior to empirical models (Fordham et al., ; Kearney, Wintle, & Porter, ; Shabani, Kumar, & Ahmadi, ). A third solution is to refine correlative models by selecting predictor variables and ecologically realistic response curves based on biological knowledge (Guevara, Gerstner, Kass, & Anderson, ) and by including abundance or population dynamics not just presence–absence, which may provide richer and better‐constrained estimates of species distributions and their governing processes (Howard, Stephens, Pearce‐Higgins, Gregory, & Willis, ).…”

Major challenges for correlational ecological niche model projections to future climate conditions

“…Rather, correlational ecological niche models have considerable promise to inform policy and science about future distributions of species as climate change proceeds. One important step will be to develop and explore new algorithms that fit models with responses that approximate shapes that indeed resemble fundamental ecological niches (see problem #1, above)—first explorations of such ideas have been intriguing, yet further exploration is merited, fitting increasingly general shapes to the data and incorporating other important processes such as dispersal . We anticipate that correlational ecological niche models that incorporate these crucial factors, and that are appropriately controlled for significance, performance, and complexity (problem #2), will prove increasingly useful and informative in this field.…”