Assessing species’ risk of extinction is a vital first step in setting conservation priorities. However, assessment endeavours like the IUCN Red List of Threatened Species still have significant gaps in their coverage of some taxonomic groups. Automated assessment (AA) methods are gaining popularity to rapidly fill these gaps, taking advantage of improvements in computing and digitally available information. However, implicit choices made when developing and reporting automated assessment methods could prevent their successful adoption or, even worse, their predictions could lead to poor allocation of conservation resources.We systematically explored how the choice of data cleaning, taxonomic group, training sample, and automation method affected predicted threat status. We used occurrence records from GBIF to generate assessments for three distinct taxonomic groups using four different automated assessment methods. We measured each method’s performance and coverage after applying increasingly stringent cleaning to the input occurrence data. We used these results to build evidence-based guidelines for developing and reporting automated assessments.Automatically cleaned data from GBIF resulted in comparable performance to occurrence records cleaned manually by an expert. However, all types of data cleaning removed species and limited the coverage of automated assessments. This limitation was more severe for some groups of species than others. Overall, machine learning-based methods performed well on all taxonomic groups, even with minimal data cleaning.We recommend using a machine learning-based method on minimally cleaned data to get the best compromise between performance and species coverage. However, our results demonstrate that the optimal data cleaning, training sample, and automation method depends on the focal group of species. Therefore, we recommend evaluating new AA methods across multiple groups and providing additional context with extinction risk predictions for users to make informed decisions.
Automated assessment of joint synovitis activity from medical ultrasound and power doppler examinations using image processing and machine learning methods
