Application of a biological trait‐based framework for plant species conservation assessments in ecological restoration.

White, Kaitalin; Cornwell‐Davison, Freya; Cockel, Christopher; Chapman, Ted; Mattana, Efisio; Viruel, Juan

Application of a biological trait‐based framework for plant species conservation assessments in ecological restoration.

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Restoration_Ecology_-_2025_-_White_-_Application_of_a_biological_trait_based_framework_for_plant_species_conservation__1_.pdf

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360.61 KB

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Resource type

Journal article

Creator (person)

White, Kaitalin

ORCID

Cornwell‐Davison, Freya

Cockel, Christopher

Chapman, Ted

Mattana, Efisio

Viruel, Juan

Date published

February 21, 2025

Abstract

The United Kingdom is one of the most nature‐depleted countries in the world, with only 50% of its native biodiversity remaining, leading to an overall decline in U.K. ecosystems and genetic diversity. Genetic diversity is essential for evolutionary processes, including genetic drift, gene flow, selection, and mutation, as it provides the raw material for populations to respond adaptively to changing environments. Although recognized as a form of biodiversity, genetic diversity is often overlooked in conservation programs due to a lack of genetic data and difficulties in generating it. We have devised a framework that utilizes life‐history data as an alternative to genetic data to infer population genetics parameters that are used to inform conservation recommendations. Our framework combines approaches incorporating risk assessments predicting species population genetics parameters based on a combination of specific biological trait values and provides management recommendations to ensure conservation of genetic diversity. Here, we apply our framework to the assessment of 52 U.K. native or archaeophyte plant species of conservation and ecological restoration interest. We found that species assessed as least likely to require conservation management were in line with the Red List assessment of Least Concern species. While our framework aligns with Red List categories, it extends beyond threat classification by providing targeted management recommendations. In the absence of genetic data, this framework proved to be very informative for conservation practitioners. However, further species‐specific genetic analysis is still needed to confirm the results of this study and provide robust conservation management recommendations.