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

Project description

Novel machine learning solution for extinction risk evaluation

The rapidly increasing threat to global biodiversity caused by human-induced climate change has prompted urgent calls for the implementation of new policies and actions to enhance and broaden conservation efforts. Assessing the contemporaneous extinction risk (CER) of species is crucial for quantifying the biodiversity crisis and guiding conservation measures. However, due to taxonomic bias, many understudied taxa, particularly those inhabiting freshwater environments, remain poorly assessed and receive minimal protection. The MSCA-funded PROTECT project will use innovative technologies such as machine learning to identify predictors of CER in freshwater habitats, with a specific focus on hydrobiidae species. The resulting efforts will enhance the understanding of CER predictors, improve the integration of frameworks for conservation, and enhance categorisation methods.

Objective

"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 status of hundreds of hydrobiid species from multiple regions and ecosystems that have not been evaluated yet, basing the predictions on ecological and macroevolutionary data. Second, high-throughput sequencing methods will be conducted for the first time in this taxon to compare microevolutionary diversity with population trends derived from long-term field surveys. Last, by establishing a multifactorial prediction-based method, the project will identify which features (ecological, macro-, microevolutionary or all) are meaningful to inferring CER in freshwater organisms. The implications of this proposal are threefold and relevant to scientific, technological and societal concerns. Our findings may provide a basis for comparing predictors of CER across taxa. They will also open up a more integrative framework for conservation actions, moving beyond species-by-species categorisation. Focussing on the ""Natural Resources, Agriculture & Environment"" area from HORIZON 2021-2027, this project addresses knowledge gaps in species threats and safeguards freshwater resources, illustrating this with understudied taxa."

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

(opens in new window) HORIZON-MSCA-2023-PF-01

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Coordinator

AGENCIA ESTATAL CONSEJO SUPERIOR DE INVESTIGACIONES CIENTIFICAS
Net EU contribution
€ 181 152,96
Address
CALLE SERRANO 117
28006 Madrid
Spain
Region
Comunidad de Madrid Comunidad de Madrid Madrid
Activity type
Research Organisations
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Total cost
No data