A novel index to aid in prioritizing habitats for site-based conservation
Basooma, Anthony et al. (2022), A novel index to aid in prioritizing habitats for site-based conservation, Dryad, Dataset, https://doi.org/10.5061/dryad.4b8gthtcx
Funding biodiversity conservation strategies are usually minimal, thus prioritizing habitats at high risk should be conducted. We developed and tested a conservation priority index (CPI) that ranks habitats to aid in prioritizing them for conservation. We tested the index using 1897 fish species from 273 African inland lakes and 34 countries. In the index, lake surface area, rarity, and their International Union for Conservation of Nature (IUCN) Red List status were incorporated. We retrieved data from the Global Biodiversity Information Facility (GBIF), and IUCN data repositories. Lake Nyasa had the highest species richness (424), Tanganyika (391), Nokoué (246), Victoria (216), and Ahémé (216). However, lakes Otjikoto and Giunas had the highest CPI of 137.2 and 52.1, respectively. Lakes were grouped into high priority (CPI > 0.5; n=56) and low priority (CPI <0.5; n=217). The median surface area between priority classes was significantly different (W = 11768, p<0.05, effect size = 0.65). Prediction accuracy of Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) for priority classes were 0.912 and 0.954 respectively. Both models exhibited lake surface area as the variable with the highest importance. CPI generally increased with a decrease in lake surface area. This was attributed to less ecological substitutability and higher exposure levels of anthropogenic stressors such as pollution to a species in smaller lakes. Also, the highest species richness per unit area was recorded for high-priority lakes. Thus, smaller habitats or lakes may be prioritized for conservation although larger water bodies or habitats should not be ignored. The index can be customized to local, regional, and international scales as well as marine and terrestrial habitats.
We considered fish species records from all the lakes in Africa found in the Global Biodiversity Information Facility (GBIF, 2020). We retrieved the records with the occ_download_get function in rgbif package (Chamberlain et al. 2020), and genera names were changed in conformity with FishBase nomenclature (Froese & Pauly 2021), which is based on Oijen (1996).
Only species data used was retained in the analysis dataset