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

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.

Using machine learning to predict habitat suitability of sloth bears at multiple spatial scales

Background:Habitat resources occur across the range of spatial scales in the environment.The environmental resources are characterized by upper and lower limits,which define organisms’distribution in their communities.Animals respond to these resources at the optimal spatial scale.Therefore,multi-scale assessments are critical to identifying the correct spatial scale at which habitat resources are most influential in determining the specieshabitat relationships.This study used a machine learning algorithm random forest(RF),to evaluate the scaledependent habitat selection of sloth bears(Melursus ursinus)in and around Bandhavgarh Tiger Reserve,Madhya Pradesh,India.Results:We used 155 spatially rarified occurrences out of 248 occurrence records of sloth bears obtained from camera trap captures(n=36)and scats located(n=212)in the field.We calculated focal statistics for 13 habitat variables across ten spatial scales surrounding each presence-absence record of sloth bears.Large(>5000 m)and small(1000–2000 m)spatial scales were the most dominant scales at which sloth bears perceived the habitat features.Among the habitat covariates,farmlands and degraded forests were the essential patches associated with sloth bear occurrences,followed by sal and dry deciduous forests.The final habitat suitability model was highly accurate and had a very low out-of-bag(OOB)error rate.The high accuracy rate was also obtained using alternate validation matrices.Conclusions:Human-dominated landscapes are characterized by expanding human populations,changing landuse patterns,and increasing habitat fragmentation.Farmland and degraded habitats constitute~40%of the landform in the buffer zone of the reserve.One of the management implications may be identifying the highly suitable bear habitats in human-modified landscapes and integrating them with the existing conservation landscapes.