Machine-learning models trained on genomic summary statistics assess extinction risk and recovery potential of bottlenecked populations

Winder, Johanna C., Marsters, Jack N., Birley, Thomas, Gooding, Jasmyn, Wu, Taoyang, Groombridge, Jim J., Morales, Hernán, van Oosterhout, Cock (2026) Machine-learning models trained on genomic summary statistics assess extinction risk and recovery potential of bottlenecked populations. Evolutionary Applications, . ISSN 1752-4563. (In press) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:116332)

PDF Author's Accepted Manuscript Language: English Restricted to Repository staff only This work is licensed under a Creative Commons Attribution 4.0 International License.
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Item Type:	Article
Uncontrolled keywords:	SLiM simulations, Machine Learning, biodiversity conservation, extinction, population recovery, genomic data
Subjects:	Q Science > QH Natural history > QH75 Conservation (Biology)
Institutional Unit:	Institutes > Durrell Institute of Conservation and Ecology
Former Institutional Unit:	There are no former institutional units.
Funders:	Biotechnology and Biological Sciences Research Council (https://ror.org/00cwqg982) Conseil européen de la recherche (https://ror.org/0472cxd90) Research England (https://ror.org/02wxr8x18)
Depositing User:	Jim Groombridge
Date Deposited:	23 Sep 2026 11:14 UTC
Last Modified:	24 Sep 2026 03:24 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/116332 (The current URI for this page, for reference purposes)

University of Kent Author Information

Winder, Johanna C..

Creator's ORCID:	https://orcid.org/0000-0002-7509-772X
CReDIT Contributor Roles:	Methodology, Writing - review and editing, Software, Formal analysis, Writing - original draft, Conceptualisation, Visualisation

Groombridge, Jim J..

Creator's ORCID:	https://orcid.org/0000-0002-6941-8187
CReDIT Contributor Roles:	Writing - review and editing

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