Multi‐Scale Habitat Drivers of Hainan Gibbon Distribution: Integrating Macro‐Environmental and Fine‐Scale Ecological Factors for Targeted Conservation of Threatened Species

ABSTRACT

As habitat fragmentation intensifies due to Anthropocene impacts, ecological variation across spatial scales may cause conservation strategies to misalign with species' actual needs. Understanding how cross‐scale spatial heterogeneity in ecological variables shapes species distributions is therefore critical to optimize conservation prioritization. The Critically Endangered Hainan gibbon is restricted to highly degraded forests, yet the interplay of ecological drivers shaping its current distribution, from macro‐environmental constraints to fine‐scale habitat selection, remains poorly quantified. This study integrates two‐year monitoring data from all seven known Hainan gibbon groups (GA‐GG, representing the global population) using a multi‐scale sampling design. We employed 395 population‐level and 135 group‐level plots to evaluate 23 ecological variables spanning macro‐ (e.g., climate) to micro‐scales (e.g., food plant diversity), identifying drivers of gibbon distribution. We found that population‐scale distribution is driven by climatic and topographic factors (e.g., precipitation, elevation), whereas group‐scale distribution responded to fine‐scale habitat features (e.g., food plant diversity, terrain ruggedness, crown area). GC distribution reflects optimal foraging (preference for food plant diversity), whereas GE adopts an energy‐conserving strategy (selecting complex terrain with high food plant abundance). Habitat suitability followed a J‐shaped threshold response at the population scale, and divergent responses at the group level (rapid in GE, stable in GC). These findings underscore the need for scale‐ and habitat‐specific conservation: expanding climatically and vegetatively suitable areas at the population level, restoring food availability in GE habitats, and maintaining stability in GC habitats. Our multi‐scale mechanistic framework provides actionable conservation insights, with applications for other range‐restricted species in fragmented landscapes.