Common to rare transfer learning (CORAL) enables inference and prediction for a quarter million rare Malagasy arthropods

DNA-based biodiversity surveys result in massive-scale data, including up to millions of species-of which, most are rare. Making the most of such data for inference and prediction requires modeling approaches that can relate species occurrences to environmental and spatial predictors, while incorporating information about their taxonomic or phylogenetic placement. Even if the scalability of joint species distribution models to large communities has greatly advanced, incorporating hundreds of thousands of species has not been feasible to date, leading to compromised analyses. Here we present a 'common to rare transfer learning' (CORAL) approach, based on borrowing information from the common species to enable statistically and computationally efficient modeling of both common and rare species. We illustrate that CORAL leads to much improved prediction and inference in the context of DNA metabarcoding data from Madagascar, comprising 255,188 arthropod species detected in 2,874 samples.

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Publication Details

Journal
Nature Methods
Published
2025-09-23
DOI
https://doi.org/10.1038/s41592-025-02823-y
Citations
1
Primary Topic
Environmental DNA in Biodiversity Studies
Type
article
Field-Weighted Citation Impact
1.72
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article

Common to rare transfer learning (CORAL) enables inference and prediction for a quarter million rare Malagasy arthropods

Steven L. Winter, Panu Somervuo, Eric Tsiriniaina Rajoelison, Otso Ovaskainen et al.
1 citations
Nature Methods
Environmental DNA in Biodiversity Studies
1.72
article

Common to rare transfer learning (CORAL) enables inference and prediction for a quarter million rare Malagasy arthropods

Steven L. Winter, Panu Somervuo, Eric Tsiriniaina Rajoelison, Otso Ovaskainen, Tomas V. Roslin, Nerea Abrego, Brian Lee Fisher, Deirdre Kerdraon, Evgeny V. Zakharov, Bess Hardwick, Gleb Tikhonov, Brendan Furneaux, Sujeevan Ratnasingham, Mikko Pentinsaari, Paul D. N. Hebert, Jeremy R deWaard, Sten Anslan, Dimby Raharinjanahary, David B. Dunson, Stephanie deWaard, Jayme E Sones
article en
1 citations

Abstract

DNA-based biodiversity surveys result in massive-scale data, including up to millions of species-of which, most are rare. Making the most of such data for inference and prediction requires modeling approaches that can relate species occurrences to environmental and spatial predictors, while incorporating information about their taxonomic or phylogenetic placement. Even if the scalability of joint species distribution models to large communities has greatly advanced, incorporating hundreds of thousands of species has not been feasible to date, leading to compromised analyses. Here we present a 'common to rare transfer learning' (CORAL) approach, based on borrowing information from the common species to enable statistically and computationally efficient modeling of both common and rare species. We illustrate that CORAL leads to much improved prediction and inference in the context of DNA metabarcoding data from Madagascar, comprising 255,188 arthropod species detected in 2,874 samples.

Nature MethodsVol. 22(10)
Agriculture and Agri-Food Canada (CA), University of Helsinki (FI), National Museum of Natural History (US), Duke University (US), Tsimbazaza Zoo (MG), California Academy of Sciences (US), Swedish University of Agricultural Sciences (SE), University of Guelph (CA), University of Jyväskylä (FI)
Openalex Percentile: Top 14%
Environmental DNA in Biodiversity Studies
1.72
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