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To What Extent Can Life History Strategies Inform Reptile Conservation Planning?

Lookup NU author(s): Emily Stevenson, Dr Sol Lucas, Professor Philip McGowanORCiD, Dr Isabel SmallegangeORCiD, Dr Louise MairORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2025 The Author(s). Ecology and Evolution published by British Ecological Society and John Wiley & Sons Ltd. Global policy aims to prevent species extinctions; to support these aims, conservation planners must effectively target interventions to reduce the extinction risk of species. However, there is often a lack of knowledge on the magnitude and direction of species responses to interventions and, in turn, the extent to which a species extinction risk is reduced. If we can use a species' life history strategies to predict their responses to interventions, this offers a promising approach to better understand species extinction risks and conservation potential. Here we apply Dynamic Energy Budget Integral Projection Models to 23 reptile species to investigate whether their derived life history traits can be summarised into a life history strategy framework using principal component analysis, and whether species' positions along these axes predict their population growth rate, demographic resilience, sensitivity to perturbations and extinction risk. We found that species' positions on reproductive and pace of life axes predicted reptile population growth rate and demographic resilience but not sensitivity to perturbations or extinction risk. Our findings show that reptile life history strategies can inform our understanding of reptile species conservation potential and could be applied to influence management decisions such as establishing monitoring timelines.


Publication metadata

Author(s): Stevenson EA, Lucas S, McGowan PJK, Smallegange IM, Mair L

Publication type: Article

Publication status: Published

Journal: Ecology and Evolution

Year: 2025

Volume: 15

Issue: 6

Print publication date: 01/06/2025

Online publication date: 29/05/2025

Acceptance date: 13/05/2025

Date deposited: 16/06/2025

ISSN (electronic): 2045-7758

Publisher: John Wiley and Sons Ltd

URL: https://doi.org/10.1002/ece3.71488

DOI: 10.1002/ece3.71488

Data Access Statement: The data and MatLab code supporting this study were published by Smallegange and Lucas (2024) and are available on FigShare (https://doi.org/10.6084/m9.figshare.13241972.v18).


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Funding

Funder referenceFunder name
DS-2017-015Leverhulme Trust, The
Newcastle University

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