Modeling Ganges river dolphin distribution and prioritizing areas for efficient conservation planning- a range-wide assessment

栖息地 濒危物种 航程(航空) 生态学 自然2000 物种分布 保护状况 地理 环境科学 环境资源管理 生物 材料科学 复合材料
作者
Anu Rai,Tawqir Bashir,Elio Guarionex Lagunes – Díaz,Bibek Man Shrestha
出处
期刊:Ecological Modelling [Elsevier]
卷期号:481: 110362-110362 被引量:1
标识
DOI:10.1016/j.ecolmodel.2023.110362
摘要

The Endangered Ganges river dolphin is a flagship species that inhabits the Ganges-Brahmaputra-Meghna and Karnaphuli-Sangu river systems. Exposed to a multitude of environmental stresses and anthropogenic perturbations, the species has experienced severe population decline and shrinking of potential habitat in much of its distribution range. A range-wide mapping of its potential habitat and assessment of factors associated with species occurrence therefore bears immense relevance for conservation planning. We used an ensemble of species distribution models (SDM) using occurrence data to investigate dolphin distribution in relation to hydrological, climatic, physiographic, land cover and anthropogenic variables across the species entire range. We followed a systematic prioritization approach to identify highly suitable habitat patches (potential hotspots) for dolphins that require immediate protection. We identified nearly 7945 sq. km of river area as highly suitable for dolphins as habitat refugia for the species, more than 93% of which are outside the protected area regime, thus demanding immediate conservation attention. This study is a step towards delivering evidence required for spatial risk assessments and conservation planning wherein the habitat suitability maps will help in developing better management plans and strategies, and the predicted three tier classification of suitable habitats will facilitate decision making and prioritization of river segments for inclusion in the protected area network. We recommend that the inferences drawn from our study must be implemented on priority to ensure species conservation in the riverscape. Besides, our study provides huge scope for fine scale assessment and validation of the predicted suitable areas for model improvement.
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