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Published (Version of record)CC BY V4.0, Open Access
Published (Version of record)CC BY V4.0, Open
Journal article
Combining Spatial, Genetic, and Environmental Risk Data to Define and Prioritize In Situ Conservation Units
Ecology and evolution, Vol.15(5), pp.1-17
26/05/2025
PMID: 40421059
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Abstract
In situ management aims to preserve species and their genetic integrity within their natural habitat. To achieve this, conservation strategies must strike a balance between safeguarding genetic diversity, mitigating environmental risks, and addressing practical management constraints. Here, we present a clear and reproducible framework that addresses these goals.
We applied this framework to the Nightcap reserves in the Gondwanan Rainforests of Australia, a UNESCO World Heritage site impacted by the 2019/20 Black Summer fires. We analyzed the genetic diversity of 12 rainforest tree species, including three endangered species—Eidothea hardeniana, Uromyrtus australis, and Elaeocarpus sedentarius —and examined how fire risk, influenced by the presence of fire‐dependent species such as eucalypts, impacts genetic diversity.
To guide specific in situ management for the endangered species, we developed a flexible framework that uses clustering algorithms (DBSCAN and k‐means) to define spatial management units while considering resource limitations (e.g., maximum perimeter or area). Our framework also incorporates a composite genetic value metric (combining Essential Biodiversity Variables heterozygosity, allelic richness, and genetic differentiation) and evaluates future fire risk based on vegetation flammability. This approach allowed us to identify priority management areas while adhering to resource constraints.
We provide some reproducible examples of how the proposed framework can be applied, either partially or fully, to optimize in situ conservation efforts. Its flexibility allows for adjustments to fit different habitat types, species, and environmental threats, making it a valuable tool for enhancing conservation management across diverse conservation contexts.
This study develops a flexible framework for in situ conservation using clustering algorithms (DBSCAN and k‐means) to define management units while considering resource constraints. It integrates genetic metrics (heterozygosity, allelic richness, differentiation) and fire risk assessments based on vegetation flammability to prioritize conservation areas. The approach is demonstrated with reproducible examples for optimizing conservation efforts.
Details
- Title
- Combining Spatial, Genetic, and Environmental Risk Data to Define and Prioritize In Situ Conservation Units
- Creators
- Eilish S. McMaster - University of SydneyRichard J. Dimon - Botanic Gardens of Sydney (Australia, Sydney)Andrew G. Baker - Southern Cross UniversityCraig Harre - North Coast Aerial MappingJustin Mallee - NSWNPWSAleks Maric - NSWNPWSPeter Richards - North Coast Aerial MappingMatthew Wiseman - NSWNPWSSimon Y. W. Ho - University of SydneyMaurizio Rossetto - Botanic Gardens of Sydney (Australia, Sydney)
- Publication Details
- Ecology and evolution, Vol.15(5), pp.1-17
- Publisher
- British Ecological Society and John Wiley & Sons Ltd.; HOBOKEN
- Number of pages
- 17
- Grant note
- This work was supported by Big Scrub Rainforest Conservancy, NSW Department of Climate Change, Energy, the Environment and Water, NSW Department of Planning and Environment
- Identifiers
- 991013286148902368
- Copyright
- © 2025 The Author(s)
- Academic Unit
- Faculty of Science and Engineering
- Language
- English
- Resource Type
- Journal article