Comparing predictions of IUCN Red List categories from machine learning and other methods for bats

Abstract

AbstractConsisting of over 1,400 species, bats are the second most diverse group of mammals. Many species are currently threatened, while another ~244 species are currently listed as Data Deficient by International Union for Conservation Science (IUCN) Global Red List. IUCN assessments can be vital for early conservation intervention and would be aided by a rapid preliminary tool to determine priority for further research and full IUCN Red List assessments. While some tools currently exist to generate extinction risk predictions for bats, they generally require difficult to obtain data, such as phylogenies and trait data. New approaches exist that utilize machine learning algorithms, such as random forest and neural networks, and can accomplish the same task using easier to obtain occurrence-derived data. Here, I fit models that can predict a species’ potential IUCN Red List category using prior assignments (critically endangered, endangered, vulnerable, near threatened, and least concern) grouped into binary categories of “Not Threatened” and “Threatened” as training data and applied the best approach to some Data Deficient bat species. These classifications can be used to prioritize investments in conservation for these species. Methods used included index-based approaches (ConR and rCat) and machine learning (IUC-NN and random forest). The best performing model used a random forest algorithm and could accurately predict IUCN binary categories (“Threatened” and “Not Threatened”) 86.9% of the time. While the overall accuracy is similar to the other approaches used here, it vastly outperforms when looking at other metrics like false-negative rate (incorrectly listing a “Threatened” species as “Not threatened”). As a result, this approach could be used as a first step to predict possible IUCN categories for bats that could be used to prioritize conservation research and is not a replacement for full IUCN Global Red List assessment into extinction risk categories.