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HEIndividual fellowship2024–2026

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

Horizon Europe — Marie Skłodowska-Curie Actions

Duration
2024-09-01 → 2026-08-31
EU contribution
€181,153
Participants
1
Scheme
HORIZON-TMA-MSCA-PF-EF

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Results in brief

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

Biodiversity loss is accelerating worldwide, yet the true magnitude of this crisis remains uncertain, as many species have not yet been assessed and therefore remain unprotected. Accurate assessments of extinction risk are essential to guide conservation priorities, but the current IUCN Red List of Threatened Species is taxonomically biased towards well-studied groups. Freshwater invertebrates, despite their crucial ecological roles, are among the most neglected. The PROTECT project aimed to address this gap by developing a predictive framework to approximate extinction risk assessments for freshwater biodiversity using machine learning tools. Focusing on the family Hydrobiidae—one of the most diverse and threatened groups of freshwater snails—the project sought to identify ecological, evolutionary, and genetic predictors of species vulnerability. By integrating molecular, morphological, and ecological data, PROTECT provided complementary insights into extinction risk patterns in poorly known taxa and contributed to advancing large-scale conservation research. The results demonstrated the feasibility of using machine learning approaches (IUCNN) to predict extinction risk in freshwater gastropods. Among the non-evaluated Hydrobiidae species, a large proportion were classified into one of the threatened categories. Habitat type, taxonomic placement and extent of occupancy were identified as the most influential factors shaping these classifications. Range reconstructions based on historical records and field surveys conducted during the project revealed that the distribution of several Iberian species is shrinking, likely due to harsh climatic conditions, water pollution and the presence of the invasive species Potamopyrgus antipodarum. Furthermore, the project highlighted the value of museum collections as genomic resources for conservation research.

Data: CORDIS, © European Union

Project 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.""

Original text from CORDIS.

Participants

  • AGENCIA ESTATAL CONSEJO SUPERIOR DE INVESTIGACIONES CIENTIFICAS · MadridCoordinatorSpain

Links

Data: CORDIS, © European Union