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

spatial dataset. The tool also provides users with an interface and attractive visual outputs to facilitate the inclusion of national responsibility and conservation priority assessments in national and regional biodiversity reports. It is also possible to use it in combination with soft- ware tools for conservation prioritization assessments based on different algorithmic founda- tions like MARXAN with Zones [17] and ZONATION [16, 15], or multivariate statistical analyses [33]. These avenues of integration ensure that our tool can provide important assistance in making informed policy decisions [1]. Our results show that the national responsibility assessment is sensitive to the size of focal areas and focal species ranges when area and biophysical regions, such as the GenZ, are used to classify focal species distributions. Both global and regional assessment scales [36] are strongly dependent on spatial distributions, the extent of overlap between focal species distri- bution and focal area, and the number of biogeographical units in the focal area. A more lim- ited global species distribution is likely to result in a smaller proportion of overlap between biogeographical units and the reference area. At the same time, the proportion of overlap between the focal species distribution and the focal area will probably be high. Accordingly, regional responsibility is a biogeographic metric that is related to the distribution range [69]. If a species is widespread outside of a region of interest, then, for a given reference area, the

regional responsibility score for the species in the focal area will be low [69]. Maps of national responsibility and conservation priority obtained from our data using the Polygon Count-Approach and the Polygon Area-Approach showed that countries with at least “medium” responsibility for a high number of species fall into conservation priority Class 2. The Russian Federation did not have a high responsibility score for any species in the regional assessment (see Fig 4). This is because Russia shares many widespread species with Europe, has few endemic species relative to its size, and covers fewer Global Environmental Zones compared to other large counties, such as China, for example. We found that countries previously identified as Asian biodiversity hotspots [70, 71] had higher responsibility scores for a larger number of species compared with countries outside designated hotspot regions. According to Pimm et al. [70], these hotspots overlapped by 68% with BirdLife International’s Endemic Bird Areas, 82% with the areas designated as Interna- tional Centres of Plant Diversity and Endemism by IUCN/WWF, and 92% with the most Table 3. Summary statistics for the number of species per country and the ranking of countries based on the two approaches, the Polygon Count-Approach and the Polygon Area-Approach, and stored in the results tables created by the ArcGIS NRA-Tool (see S1 and S2 Data). Number of Species Mini- mum 25% Quartile 50% Quartile (Median) 75% Quartile Maxi- mum Arithmetic Mean SD Species per Country PC-A 1 10.75 21.00 38.25 150 36.17 38.12

PA-A 1 10.50 20.50 39.75 152 36.31 38.73 Thresholds (rounded) 1 11 21 40 152 PA-A—PC-A -6.00 -0.25 0.00 1.00 4.00 0.13 1.65 Range 0–25% 25– 50% 50– 75% 75– 100% Overall Country Rank PC-A = PA-A (true) 3 4 5 11 23 PC-A <> PA-A (false) 10 10 7 2 29 Sum (true+false) 13 14 12 13 52 Percentage (true) 23.08% 28.57% 41.67% 84.62% 44.23% Percentage (false) 76.92% 71.43% 58.33% 15.38% 55.77% https://doi.org/10.1371/journal.pone.0243135.t003 PLOS ONE GIS-based policy support tool to determine national responsibilities for biodiversity conservation PLOS ONE | https://doi.org/10.1371/journal.pone.0243135 December 3, 2020 17 / 23