Multi-scale habitat selection and impacts of climate change on the distribution of four sympatric meso-carnivores using random forest algorithm

Abstract

Background The habitat resources are structured across different spatial scales in the environment, and thus animals perceive and select habitat resources at different spatial scales. Failure to adopt the scale-dependent framework in species habitat relationships may lead to biased inferences. Multi-scale species distribution models (SDMs) can thus improve the predictive ability as compared to single-scale approaches. This study outlines the importance of multi-scale modeling in assessing the species habitat relationships and may provide a methodological framework using a robust algorithm to model and predict habitat suitability maps (HSMs) for similar multi-species and multi-scale studies. Results We used a supervised machine learning algorithm, random forest (RF), to assess the habitat relationships of Asiatic wildcat (Felis lybica ornata), jungle cat (Felis chaus), Indian fox (Vulpes bengalensis), and golden-jackal (Canis aureus) at ten spatial scales (500–5000 m) in human-dominated landscapes. We calculated out-of-bag (OOB) error rates of each predictor variable across ten scales to select the most influential spatial scale variables. The scale optimization (OOB rates) indicated that model performance was associated with variables at multiple spatial scales. The species occurrence tended to be related strongest to predictor variables at broader scales (5000 m). Multivariate RF models indicated landscape composition to be strong predictors of the Asiatic wildcat, jungle cat, and Indian fox occurrences. At the same time, topographic and climatic variables were the most important predictors determining the golden jackal distribution. Our models predicted range expansion in all four species under future climatic scenarios. Conclusions Our results highlight the importance of using multiscale distribution models when predicting the distribution and species habitat relationships. The wide adaptability of meso-carnivores allows them to persist in human-dominated regions and may even thrive in disturbed habitats. These meso-carnivores are among the few species that may benefit from climate change.

Stay home, stay safe? High habitat suitability and environmental connectivity increases road mortality in a colonizing mesocarnivore

“…Furthermore, it was not surprising to discover that golden jackal presence was linked to heterogeneous human cultivations (i.e., intermixed with natural elements), as it can take advantage of anthropogenic resources because of its opportunistic dietary habits (Lanszki et al 2015) and can find protection against human pressure (Giannatos 2004; Šálek et al 2013). Furthermore, our results match those obtained from previous research in which a negative correlation of both elevation (Giannatos 2004;Ranc et al 2018b;Spassov and Acosta-Pankov 2019) and slope (Spassov and Acosta-Pankov 2019;Rather et al 2020) with habitat suitability was shown. Indeed, higher values of these covariates were associated with lower habitat suitability values and vice versa.…”

Estimating potential range shift of some wild bees in response to climate change scenarios in northwestern regions of Iran

“…The number of trees and the number of predictor variables are the key parameters of the random forest model (Jafarian et al 2019). The high efficiency of the random forest method has been confirmed by many studies (Bradter et al 2013;Cushman and Wasserman 2018;Rather et al 2020), especially when the presence points are limited (Strobl et al 2007).…”

Roads to the Hills: Potential Space Use Patterns of Sloth Bears and Leopards in Semiarid Landscape of Western India

“…We selected eight predictor variables covering the major topographic and anthropogenic factors that could affect large carnivores' habitat use in a semiarid landscape (Rather et al 2020). We acquired these variables using remotely-sensed sources (Supplementary Table S1).…”