The Relationship Between Landscape Metrics and Human Interventions on the Floristic Structure and Composition of the Community Reserves of Meghalaya, India

Abstract

Community Reserves (CRs) are a unique category of protected areas managed by local communities, playing a crucial role in biodiversity conservation. However, the impact of landscape matrices and human intervention on vegetation in these reserves remains less explored. This study examines the relationship between landscape structure, human interventions, and vegetation in 30 CRs across Meghalaya, India, each associated with local villages and clans. Vegetation data were collected using stratified random sampling techniques, and indices such as Shannon, Simpson, and Evenness were calculated. For spatial analysis, Sentinel-2 imagery was utilized, with land use and land cover classification to gather landscape information by using QGIS tool. The classified data were further analysed using landscape metrics in FRAGSTAT software. The data on vegetation composition, landscape metrics, and the human disturbance index were then analysed for correlations. Canonical Correspondence Analysis (CCA) was performed using R software to explore these relationships. Plant diversity varied significantly, with Shannon indices ranging from 2.147 (Mikadogre CR) to 3.845 (Kur Pyrtuh CR). Simpson indices were generally high (>0.8), indicating low species dominance. Evenness ranged from 0.153 (Sakalgre CR) to 0.557 (Kur Pyrtuh CR). Human intervention index varied from 0 (e.g., Chyrmang CR) to 6 (e.g., Jirang CR), with most reserves showing low to moderate levels of disturbance. CCA analysis revealed that 69.08% of the variation in vegetation indices is strongly associated with landscape metrics (Total Area, Number of Patches, Patch Density, Least Patch Index, Total Edge, Edge Density, Least Shape Index, Area Mean, Area Range, Landscape Division Index, Effective Mesh Size, Splitting Index, Patch Richness) and human intervention. The results suggest that CRs with more complex patch shapes tend to support higher plant species diversity. These findings have significant implications for CR management.