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<p>Understanding the spatiotemporal heterogeneity of habitat quality (HQ) and its associated drivers is important for ecological assessment in fragile karst mountain regions. In this study, we combined the InVEST Habitat Quality model and Geodetector to quantify HQ patterns and explain their spatial heterogeneity in Anshun City, China, using five periods of data (2000, 2005, 2010, 2015, and 2020). Multi-source land-use/land-cover, normalized difference vegetation index, nighttime light intensity, population density, road density, topographic, climatic, and soil variables were spatially standardized and analyzed on an optimized 3&#xa0;km grid. The results showed that mean HQ remained generally stable during 2000–2020, with values of 0.5583, 0.5587, 0.5628, 0.5634, and 0.5581, respectively, with a slight increase in spatial heterogeneity as indicated by the standard deviation. Higher HQ values were mainly distributed in mountainous and forested areas, whereas lower values were concentrated in built-up and cultivated zones. In the pooled Geodetector analysis, the explanatory power of single variables was generally low (all q values <InlineEquation ID="IEq1"><EquationSource Format="TEX">\(&lt; 0.10\)</EquationSource></InlineEquation>), with road density showing the highest value (<InlineEquation ID="IEq2"><EquationSource Format="TEX">\(q = 0.0862\)</EquationSource></InlineEquation>), followed by population density (<InlineEquation ID="IEq3"><EquationSource Format="TEX">\(q = 0.0651\)</EquationSource></InlineEquation>), slope (<InlineEquation ID="IEq4"><EquationSource Format="TEX">\(q = 0.0515\)</EquationSource></InlineEquation>), nighttime light intensity (<InlineEquation ID="IEq5"><EquationSource Format="TEX">\(q = 0.0513\)</EquationSource></InlineEquation>), and several precipitation-related variables. Interaction detection showed that most paired factors exhibited bi-factor or nonlinear enhancement, and the strongest interactions were mainly associated with road density, especially in combination with precipitation and slope. These results suggest that the modeled HQ heterogeneity in Anshun City was associated not with a single dominant factor but with the coupled influence of anthropogenic disturbance, topographic constraints, hydro-climatic conditions, and selected soil properties. The study provides a transparent multi-period framework for landscape-level habitat-quality assessment and driver analysis in karst regions.</p>

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Spatiotemporal patterns of habitat quality and associated drivers in Anshun City, China, based on InVEST and Geodetector

  • 期刊论文

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  • 发表日期：

    2026年7月19日

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  • Hongyi Yang,
  • Jiegang Liu,
  • Shuang Li,
  • Shixue Mei,
  • Hongkang Deng,
  • Fang Cheng,
  • Ke Jiang,
  • Taoze Liu,
  • Zhanghong Wang

摘要

Understanding the spatiotemporal heterogeneity of habitat quality (HQ) and its associated drivers is important for ecological assessment in fragile karst mountain regions. In this study, we combined the InVEST Habitat Quality model and Geodetector to quantify HQ patterns and explain their spatial heterogeneity in Anshun City, China, using five periods of data (2000, 2005, 2010, 2015, and 2020). Multi-source land-use/land-cover, normalized difference vegetation index, nighttime light intensity, population density, road density, topographic, climatic, and soil variables were spatially standardized and analyzed on an optimized 3 km grid. The results showed that mean HQ remained generally stable during 2000–2020, with values of 0.5583, 0.5587, 0.5628, 0.5634, and 0.5581, respectively, with a slight increase in spatial heterogeneity as indicated by the standard deviation. Higher HQ values were mainly distributed in mountainous and forested areas, whereas lower values were concentrated in built-up and cultivated zones. In the pooled Geodetector analysis, the explanatory power of single variables was generally low (all q values \(< 0.10\)), with road density showing the highest value (\(q = 0.0862\)), followed by population density (\(q = 0.0651\)), slope (\(q = 0.0515\)), nighttime light intensity (\(q = 0.0513\)), and several precipitation-related variables. Interaction detection showed that most paired factors exhibited bi-factor or nonlinear enhancement, and the strongest interactions were mainly associated with road density, especially in combination with precipitation and slope. These results suggest that the modeled HQ heterogeneity in Anshun City was associated not with a single dominant factor but with the coupled influence of anthropogenic disturbance, topographic constraints, hydro-climatic conditions, and selected soil properties. The study provides a transparent multi-period framework for landscape-level habitat-quality assessment and driver analysis in karst regions.