Automated methods to accelerate fungal red listing: a case study for North American macrofungi

This is the authors' abstract. We don't add key points for this paper.

Extinction risk assessment of the world's funga lags far behind that of flora and fauna, impeding inclusion in conservation policy and action. Prediction and automation have helped accelerate Red Listing of plants, facilitating prioritization of taxa for assessment and reducing the manual input required. Similar approaches for fungi could help address their extreme underrepresentation on the Red List. We used Bayesian logistic regression modelling and an automated assessment pipeline to identify and rapidly assess 715 species of North American macrofungi confidently predicted to be Least Concern. However, fundamental data are lacking for risk prediction for many fungi: only one in twenty species analysed could be classified as confidently Least Concern. Our workflow could facilitate increased coverage of fungi on the Red List. Nonetheless, continued investment in assessing potentially threatened fungi is essential for improving the accuracy of future predictions and, more importantly, for guiding conservation action for species at risk.