Abstract

BackgroundThe 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.ResultsWe 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.ConclusionsOur 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.

Highlights

  • The processes that determine the distribution of the species occur at multiple spatial scales (Wiens 1989; Cunningham and Johnson 2006; Thogmartin and Knutson 2007), e.g., the occurrence of a carnivore species in a habitat patch can depend on the factors that influence the prey densities in their home ranges

  • Future climatic data We modeled the potential distribution of all four species under two representative concentration pathway scenarios (RCP 2.6 and Representative concentration pathway scenario (RCP) 8.5) developed by Model for Interdisciplinary Research on Climate change (MIROC5) for the two timelines (Watanabe et al 2010)

  • Univariate scaling A total of ten spatial scales (500–5000 m) for each predictor variable except road and river density were chosen for univariate random forest modeling

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Summary

Introduction

The processes that determine the distribution of the species occur at multiple spatial scales (Wiens 1989; Cunningham and Johnson 2006; Thogmartin and Knutson 2007), e.g., the occurrence of a carnivore species in a habitat patch can depend on the factors that influence the prey densities in their home ranges (thirdand fourth-order selection; Johnson 1980). Multi-scale species distribution models can improve the predictive ability compared to single-scale approaches (Cunningham and Johnson 2006). Multi-scale species distribution models (SDMs) can 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

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