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Assessing and mapping habitat quantity and quality in High Nature Value (HNV) agricultural landscapes

栖息地 农业 地理 价值(数学) 质量(理念) 生态学 环境资源管理 环境科学 生物 数学 统计 考古 哲学 认识论
作者
Alessio Volpato,Cathal Buckley,James Moran
出处
期刊:Journal for Nature Conservation [Elsevier BV]
卷期号:78: 126568-126568
标识
DOI:10.1016/j.jnc.2024.126568
摘要

Low-intensity agricultural activities have found to greatly promote biodiversity in agricultural landscapes. High nature value (HNV) agricultural landscapes are generally characterised by low-intensity agricultural systems and high cover of semi-natural habitats, providing valuable areas for biodiversity conservation. Considering the importance of HNV landscapes in terms of biodiversity conservation, it is important to develop adequate tools for assessing and mapping biodiversity at landscape level. In this study, we developed a mixed methodological framework to assess and map biodiversity in the Burren HNV agricultural landscape using habitat quality as a proxy. This methodology involved the use of data collected in the field using Rapid Assessment Cards and data obtained from an expert knowledge-based model. Overall, the main habitat categories in the region in terms of their cover and their influence on habitat quality were improved grassland and calcareous grassland and limestone heath mosaic group. Improved grassland had the highest cover in the study area but low habitat quality score (score=2.4), representing areas of concern for biodiversity. Calcareous grassland and limestone heath mosaic group had the second highest cover and the highest habitat quality (score=8.0), representing valuable biodiversity hotspots. Adequate management strategies should therefore mainly target these two habitat categories to efficiently conserve and promote biodiversity in the Burren case study region. This study demonstrates that the methodological framework applied may represent an effective tool to assess and map biodiversity at landscape level, enabling the mapping of the distribution and extent of biodiversity hotspots and areas of conservation concern more widely. It may also provide key spatially-explicit information for optimal targeting of land-use management for biodiversity.
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