Is the Climate Right for Pleistocene Rewilding? Using Species Distribution Models to Extrapolate Climatic Suitability for Mammals across Continents

Abstract

Species distribution models (SDMs) are increasingly used for extrapolation, or predicting suitable regions for species under new geographic or temporal scenarios. However, SDM predictions may be prone to errors if species are not at equilibrium with climatic conditions in the current range and if training samples are not representative. Here the controversial “Pleistocene rewilding” proposal was used as a novel example to address some of the challenges of extrapolating modeled species-climate relationships outside of current ranges. Climatic suitability for three proposed proxy species (Asian elephant, African cheetah and African lion) was extrapolated to the American southwest and Great Plains using Maxent, a machine-learning species distribution model. Similar models were fit for Oryx gazella, a species native to Africa that has naturalized in North America, to test model predictions. To overcome biases introduced by contracted modern ranges and limited occurrence data, random pseudo-presence points generated from modern and historical ranges were used for model training. For all species except the oryx, models of climatic suitability fit to training data from historical ranges produced larger areas of predicted suitability in North America than models fit to training data from modern ranges. Four naturalized oryx populations in the American southwest were correctly predicted with a generous model threshold, but none of these locations were predicted with a more stringent threshold. In general, the northern Great Plains had low climatic suitability for all focal species and scenarios considered, while portions of the southern Great Plains and American southwest had low to intermediate suitability for some species in some scenarios. The results suggest that the use of historical, in addition to modern, range information and randomly sampled pseudo-presence points may improve model accuracy. This has implications for modeling range shifts of organisms in response to climate change.

Alternative biological assumptions strongly influence models of climate change effects on mountain gorillas

“…While we do not have reliable historical range maps, it is clear that mountain gorilla range is presently restricted by non-climatic factors, particularly expanding human populations. Range reductions resulting from interactions with humans probably play an important role in modeling many other species (Richmond et al 2010), and can limit the utility of species distribution models. Other reasons why standard species distribution models may not reflect the entire ecological niche of a species include; that climatically suitable areas identified by standard SDMs cannot be reached by the species because of dispersal barriers, that the range has been reduced because of the influence of other species, or that the relevant predictor variables are not known or available (Davis et al 1998, Pearson and Dawson 2003, Guisan and Thuiller 2005, Jackson et al 2009, Sinclair et al 2010, Beale and Lennon 2012.…”

“…This should provide a sufficiently representative sample of the environment in which the species occurs (cf. Richmond et al 2010).…”

Geographical distribution, climatic variability and thermo‐tolerance of Chagas disease vectors

“…In order to be more conservative, we did not take pseudo‐absences due to the potential errors that can be introduced (Mackenzie and Royle ). A similar approach was used in Richmond et al (). Using random pseudo‐presence data has the advantage that there is no bias problem with sampling, although it may lead to over‐predictions in characterizing climatic suitability, mainly because large‐scale ‘extent‐of‐occurrence’ in geographical ranges may include some unsuitable areas, and thus tend to exaggerate actual occurrences (Graham and Hijmans , Hurlbert and Jetz , Jetz et al ).…”

Incorporating movement in species distribution models

“…SDM studies addressing movement did so for a number of different organisms, ranging from amoebas (Aguilar and Lado 2012;Aguilar et al 2014) to elephants (Richmond et al 2010;Pittiglio et al 2012). However, the majority of studies which addressed movement did so for plant taxa (Figure 1.3).…”