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A machine learning conservation apPROach to evaluaTE extinCTion risk in freshwater biodiversity

CORDIS · observation · Publication date unknown

A machine learning conservation apPROach to evaluaTE extinCTion risk in freshwater biodiversity "Accurate assessments of species’ contemporaneous extinction risk (CER) are vital to quantifying the current biodiversity crisis and prioritising conservation efforts. However, the most comprehensive global dataset of CER - the IUCN Red List of Threatened Species - is taxonomically biased due to the lengthy assessment process, leaving understudied taxa, such as those in freshwaters, under no formal PROTECTion. Prediction-based models based on novel machine learning methods enable large-scale automated assessments of CER, reducing data deficits rapidly. The main goal of this project is to identify predictors of CER in freshwater habitats, focusing on the largest family of freshwater gastropods, the Hydrobiidae. First, we will use a deep-learning approach to automatically predict the Red List stat

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recordType

award

status

TERMINATED

region

EU

value

181152.96

unit

EUR