Evidence-based guidelines for developing automated conservation assessment methods [PREPRINT].

Assessing species’ extinction risk is vital to setting conservation priorities. However, assessment endeavours like the IUCN Red List of Threatened Species have significant gaps in coverage of some taxonomic groups. Automated assessment (AA) methods are gaining popularity to fill these gaps, leveraging improvements in computing and digitally-available information. Choices made in developing and reporting AA methods could hinder successful adoption or lead to poor allocation of conservation resources.We explored how choice of data-cleaning, taxonomic group, training sample, and automation method affected performance of threat status predictions. We used occurrence records from GBIF to generate assessments for three taxonomic groups using four different AA methods. We measured each method’s performance and coverage after applying increasingly stringent cleaning to occurrence data.Automatically cleaned data from GBIF yielded comparable performance to occurrence records cleaned manually by experts. However, all types of data-cleaning removed species and limited the coverage of automated assessments. Overall, machine learning-based methods performed well on all taxonomic groups, even with minimal data-cleaning.Results suggest using a machine learning-based method on minimally cleaned data offers the best compromise between performance and species coverage. However, optimal data-cleaning, training sample, and automation methods depend on the study group, intended applications and expertise. We recommend evaluating new AA methods across multiple groups and providing additional context with extinction risk predictions so users can make informed decisions