Ecological niche models reveal divergent habitat use of Pallas's cat in the Eurasian cold steppes

Abstract

Identifying the association between the patterns of niche occupation and phylogenetic relationships among sister clades and assisting conservation planning implications are of the most important applications of species distribution models (SDMs). However, most studies have been carried out regardless of within taxon genetic differentiation and the potential of local adaptation occurring within the species level. The Pallas's cat ( Otocolobus manul ) is a less‐studied species with unknown biogeography and phylogenetic structure across a widespread yet isolated range from the Caucasus to eastern China. In the current study, by considering a previously proposed genetic structure and based on a cluster analysis on climatic variables, we supposed three clades for this species, including O. m. manul , O. m. ferrugineus , and O. m. nigripectus . We developed SDM for each clade separately and compared it with a general distribution model of the species to determine whether the hypothesized taxonomic resolution affects the predicted ecological niche of the within‐species structures. We assessed the effect of climate change on the future distribution of the species to detect the most sensitive clades to global warming scenarios. Our results showed that for all clades' models, the AUC and TSS were greater than the general model. Access to the preferred prey of the Pallas's cat, that is, pika, had a significant effect on the distribution of O. m. manul and O. m. ferrugineus , whereas the most influential variable affecting O. m. nigripectus habitat suitability was terrain slope. Based on our future projections, we found that future climate change likely threatens the clades O. m. ferrugineus and O. m. nigripectus more than O. m. manul , findings that were hidden in the general model. Our results highlight the proficiency of SDMs in recognizing within‐taxon habitat use of widespread species and the necessity of this procedure for implementing effective conservation planning of these species.

Protecting alpine biodiversity in the Middle East from climate change: Implications for high‐elevation birds

“…Moreover, in SDM approaches, it would be naïve to consider a distance between presence points to which the spatial autocorrelation (SAC) is zero, as many species, particularly range‐restricted alpine ones, exhibit clustered distribution patterns. To balance the requirement of having an adequate number of points for modelling and reducing the spatial autocorrelation (SAC), we carefully selected the minimum distance based on similar studies on alpine species (Lorestani et al., 2022; Malakoutikhah et al., 2018). For each species, we also downloaded shapefile of its range map from the Birdlife International.…”

“…Using different GCMs allows researchers to account for the inherent uncertainties and limitations in climate modelling, providing a more comprehensive and robust assessment of potential future climatic conditions suitable for a species. While there is no direct comparison of the performance of these GCMs specifically in the study area, they have been widely used in previous studies on climate change projections for Iran and the Eastern Mediterranean region (Logothetis et al., 2023; Lorestani et al., 2022; Shahsavarzadeh et al., 2023). For each species, the final ensemble of habitat suitability was calculated based on averaging GCMs' climatic suitability over two SSP scenarios.…”

“…We obtained 19 bioclimatic data layers from WorldClim (version 2.1), at a spatial resolution of 2.5 min, for both the baseline of the near-current period, as well as for two future time periods: 2040-2060 (representing 2050) and 2060-2080 (representing 2070) (Fick & Hijmans, 2017). For the future, we used two shared (Logothetis et al, 2023;Lorestani et al, 2022;Shahsavarzadeh et al, 2023). For each species, the final ensemble of habitat suitability was calculated based on averaging GCMs' climatic suitability over two SSP scenarios.…”

MaxEnt brings comparable results when the input data are being completed; Model parameterization of four species distribution models

“…A wealth of literature exists on the utility of SDMs all aiming at explaining, predicting, and projecting species distribution (Araújo et al, 2019 ). In particular identifying geographic distribution and most effective variables in different geographic scales (Brito et al, 2009 ; Hemami et al, 2018 ; Salinas‐Ramos et al, 2021 ), assessing conservation coverage and efficiency of protected areas (Farhadinia et al, 2015 ; Lentini & Wintle, 2015 ; Zupan et al, 2014 ), predicting the biological invasion of alien species (Bosso et al, 2022 ; Thuiller et al, 2005 ; Tingley et al, 2014 ), climate change‐induced range shifts (Lorestani et al, 2022 ; Thuiller et al, 2011 ; Yousefi et al, 2015 ), and combining its results with phylogenetic analyses to explore species evolutionary history (Ahmadi et al, 2018 ; Ahmadzadeh et al, 2016 ; Boucher et al, 2015 ; Saladin et al, 2019 ) are among the most widely‐used aspects of SDMs.…”

“…For example, in the first step, improving the sampling design can reduce bias and inaccuracy in the geographical distribution of the collected data (Araújo & Guisan, 2006 ; Chauvier et al, 2021 ). At this level, ensuring that the collected data correctly represent the actual distribution of the species (Guillera‐Arroita et al, 2015 ; Tessarolo et al, 2014 ) and that the scale of modeling and independent variables are consistent with sampling precision (Chauvier et al, 2022 ; Guisan et al, 2007 ; Wiens et al, 2009 ), and reducing unbiased recognition of the taxonomy of the species (Lorestani et al, 2022 ; Rocchini et al, 2011 ) improve results of an SDM analysis.…”