Toward ecologically realistic predictions of species distributions: A cross‐time example from tropical montane cloud forests

Abstract

There is an urgent need for more ecologically realistic models for better predicting the effects of climate change on species' potential geographic distributions. Here we build ecological niche models using MAXENT and test whether selecting predictor variables based on biological knowledge and selecting ecologically realistic response curves can improve cross-time distributional predictions. We also evaluate how the method chosen for extrapolation into nonanalog conditions affects the prediction. We do so by estimating the potential distribution of a montane shrew (Mammalia, Soricidae, Cryptotis mexicanus) at present and the Last Glacial Maximum (LGM). Because it is tightly associated with cloud forests (with climatically determined upper and lower limits) whose distributional shifts are well characterized, this species provides clear expectations of plausible vs. implausible results. Response curves for the MAXENT model made using variables selected via biological justification were ecologically more realistic compared with those of the model made using many potential predictors. This strategy also led to much more plausible geographic predictions for upper and lower elevational limits of the species both for the present and during the LGM. By inspecting the modeled response curves, we also determined the most appropriate way to extrapolate into nonanalog environments, a previously overlooked factor in studies involving model transfer. This study provides intuitive context for recommendations that should promote more realistic ecological niche models for transfer across space and time.

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

“…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, ). A final solution is to rely on models such as CLMs or joint species distribution models (Clark, Gelfand, Woodall, & Zhu, ) that better incorporate information about species co‐occurrences and implicitly include the processes that drive these patterns.…”

Spatial variability in species' potential distributions during the Last Glacial Maximum under different Global Circulation Models: Relevance in evolutionary biology

“…Clearly, validating these hypotheses of past potential distributions using several GCMs is complicated. Nevertheless, considering paleoecological knowledge can be very useful to make realistic expectations for these distributional predictions (Davis, McGuire, & Orcutt, ; Gavin et al., ; Guevara, Gerstner, et al., ). Unfortunately, paleoecological evidence has been typically overlooked to validate the outcomes of ENM.…”

“…For example, Guevara, Gerstner, et al. () analyzed the LGM potential distribution of a small‐eared shrew, Cryptotis mexicanus , a high mountain species from Mexico, using only the CCSM scenario. They found that C. mexicanus showed expansion into middle to lowlands on the eastern slope of a mountain range during the LGM, which could be reflected in a postglacial demographic expansion.…”

“…Ecological niche models (ENM) are increasingly used to estimate climate‐driven range shifts across the Quaternary (Araújo & Peterson, ; Elith & Leathwick, ; Martínez‐Meyer, Peterson, & Hargrove, ). These geographic predictions may help assess distributional shifts that likely occurred during glacial/interglacial cycles, which are useful for testing evolutionary hypotheses about the processes generating actual distributional patterns (Cabanne et al., ; Carnaval, Hickerson, Haddad, Rodrigues, & Moritz, ; Collevatti et al., ; Guevara, Gerstner, Kass, & Anderson, ). More commonly, ENM are used to reconstruct geographic distributions during the Last Glacial Maximum (LGM, ~ 21 kyr bp ), one of the episodes that have had a major impact on the evolutionary history of extant species (Hewitt, ), allowing to speculate whether the climate during the LGM have led to fragmentation, connectivity, extinction, or population expansion (Alvarado‐Serrano & Knowles, ; Peterson & Lieberman, ).…”

Improving species distribution models for invasive non‐native species with biologically informed pseudo‐absence selection

“…First, it “anchored” the curves by constraining the models to fit near‐zero suitability where the climate variables exceeded the thresholds of the species, providing a more pronounced delineation of suitability gradients. Second, the response curves spanned a much wider range of environmental conditions than were found in the accessible background, which has previously been shown to be important for accurate spatial and temporal transfer of SDMs (Guevara, Gerstner, Kass, & Anderson, ). Sampling unsuitable conditions only from within the accessible part of the species range would therefore require a greater amount of model extrapolation than our strategy does.…”