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

Authors

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

Published in

Scientific reports. Jul 19, 2026. Epub Jul 19, 2026.

Abstract

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 [Formula: see text]), with road density showing the highest value ([Formula: see text]), followed by population density ([Formula: see text]), slope ([Formula: see text]), nighttime light intensity ([Formula: see text]), 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.

PMID:
42472913
Bibliographic data and abstract were imported from PubMed on 20 Jul 2026.

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