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<p>This study presents habitat suitability modelling of critically endangered bird species - Jerdon’s Courser, <i>Rhinoptilus bitorquatus</i>. Species distribution analysis was conducted using bioclimatic, soil, vegetation, biophysical, topographic, hydrological, and habitat-structure variables covering the Sri Lankamalleswara wildlife sanctuary and the surrounding Lankamalla landscape in Eastern Ghats of Andhra Pradesh. Across the study area, dry deciduous forest is the most widespread vegetation type, followed by scrub. Multiple species distribution models were used to delineate areas of high habitat suitability. MaxEnt offers ecologically interpretable outputs, for species distribution modelling compared with general profile, statistical and machine-learning algorithms. The predictors that contributed most to the MaxEnt model performance were vegetation height, elevation, slope and habitat structure. By using high-resolution environmental variables, the species distribution model shifts from representing the broad climatic niche to approximating the realized niche. The ecological niche map identified highly suitable habitats within the scrub, including those located outside the protected area. This methodology offers a spatial basis for identifying critical habitats, and developing conservation strategies for elusive and threatened avian species.</p>

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In search of survival grounds—Habitat preference analysis and species distribution modelling of critically endangered Jerdon’s Courser, Rhinoptilus bitorquatus

  • 期刊论文

  • 发表日期：

    2026年3月9日

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  • C. Sudhakar Reddy,
  • G. Pranathi

摘要

This study presents habitat suitability modelling of critically endangered bird species - Jerdon’s Courser, Rhinoptilus bitorquatus. Species distribution analysis was conducted using bioclimatic, soil, vegetation, biophysical, topographic, hydrological, and habitat-structure variables covering the Sri Lankamalleswara wildlife sanctuary and the surrounding Lankamalla landscape in Eastern Ghats of Andhra Pradesh. Across the study area, dry deciduous forest is the most widespread vegetation type, followed by scrub. Multiple species distribution models were used to delineate areas of high habitat suitability. MaxEnt offers ecologically interpretable outputs, for species distribution modelling compared with general profile, statistical and machine-learning algorithms. The predictors that contributed most to the MaxEnt model performance were vegetation height, elevation, slope and habitat structure. By using high-resolution environmental variables, the species distribution model shifts from representing the broad climatic niche to approximating the realized niche. The ecological niche map identified highly suitable habitats within the scrub, including those located outside the protected area. This methodology offers a spatial basis for identifying critical habitats, and developing conservation strategies for elusive and threatened avian species.