A GIS-based policy support tool to determine national responsibilities and priorities for biodiversity conservation

Abstract

Efficient biodiversity conservation requires that limited resources be allocated in accordance with national responsibilities and priorities. Without appropriate computational tools, the process of determining these national responsibilities and conservation priorities is time intensive when considering many species across geographic scales. Here, we have developed a computational tool as a module for the ArcGIS geographic information system. The ArcGIS National Responsibility Assessment Tool (NRA-Tool) can be used to create hierarchical lists of national responsibilities and priorities for global species conservation. Our tool will allow conservationists to prioritize conservation efforts and to focus limited resources on relevant species and regions. We showcase our tool with data on 258 bird species and various biophysical regions, including Environmental Zones in 58 Asian countries and regions. Our tool provides a decision support system for conservation policy with attractive and easily interpretable visual outputs illustrating national responsibilities and priorities for species conservation. The graphical output allows for smooth integration into assessment reports, such as the European Article 17 report, the Living Planet Index report, or similar regional and global reports.

On the Edge: Identifying priority areas for conservation of Fishing Cat, a threatened wetland felid, amidst rapidly altering freshwater landscapes

“…The tools and techniques offered by Geographical Information Systems (GIS) have immense potential in combining large scale ground information with remotely sensed satellite data and modeled spatial data layers to demarcate species’ geographical ranges, identify its movement corridors (Jalkanen et al, 2020; Xiao et al, 2020), and pinch points of connectivity (Yu et al, 2021), leading to prioritization of regions for their conservation (Lin et al, 2020; Shrestha et al, 2021). Species distribution modeling (SDM) approach has demonstrated the applicability of machine learning in defining species-environment relationship and the geographic extent of species distribution (Ahmed et al, 2021; De Simone et al, 2021; Lozano, 2021).…”