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Accepted VersionCC BY-NC V4.0, Open Access
Ecological Applications - 2023 - Buenafe - A metric‐based framework for climate‐smart conservation planning7.65 MB
Published VersionCC BY-NC V4.0, Open Access
Journal article
A metric-based framework for climate-smart conservation planning
Ecological Applications, Vol.33(4), pp.1-29
2023
PMID: 36946332
Abstract
Climate change is already having profound effects on biodiversity, but climate change adaptation has yet to be fully incorporated into area based management tools used to conserve biodiversity, such as protected areas. One main obstacle is the lack of consensus regarding how impacts of climate change can be included in spatial conservation plans. We propose a climate smart framework that prioritizes the protection of climate refugia areas of low climate exposure and high biodiversity retention using climate metrics. We explore four aspects of climate smart conservation planning: i) climate model ensembles; ii) multiple emission scenarios; iii) climate metrics; and iv) approaches to identifying climate refugia. We illustrate this framework in the Western Pacific Ocean, but it is equally applicable to terrestrial systems. We found that all aspects of climate smart conservation planning considered affected the configuration of spatial plans. The choice of climate metrics and approaches to identifying refugia have large effects in the resulting climate smart spatial plans, whereas the choice of climate models and emission scenarios have smaller effects. As the configuration of spatial plans depended on climate metrics used, a spatial plan based on a single measure of climate change (e.g., warming) will not necessarily be robust against other measures of climate change (e.g., ocean acidification). We therefore recommend using climate metrics most relevant for the biodiversity and region considered based on a single or multiple climate drivers. To include the uncertainty associated with different climate futures, we recommend using multiple climate models (i.e., an ensemble) and emission scenarios. Finally, we show that the approaches we used to identify climate refugia feature trade offs between: i) the degree to which they are climate-smart; and ii) their efficiency in meeting conservation targets. Hence, the choice of approach will depend on the relative value that stakeholders place on climate adaptation. By using this framework, protected areas can be designed with improved longevity and thus safeguard biodiversity against current and future climate change. We hope that the proposed climate smart framework helps transition conservation planning towards climate smart approaches.
Details
- Title
- A metric-based framework for climate-smart conservation planning
- Authors
- Kristine Camille V Buenafe (Corresponding Author) - The University of QueenslandDaniel C Dunn (Author) - The University of QueenslandJason D Everett (Author) - UNSW SydneyIsaac Brito-Morales (Author) - University of California, Santa BarbaraDavid Schoeman (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringJeffrey O Hanson (Author) - Carleton UniversityAlvise Dabalà (Author) - Ecology and Biodiversity, Laboratory of Plant Biology and Nature Management, Biology Department, Vrije Universiteit Brussel - VUB, Pleinlaan 2, VUB-APNA-WE, 1050, Brussels, BelgiumSandra Neubert (Author) - Leipzig UniversityStefano Cannicci (Author) - University of Hong KongKristin Kaschner (Author) - University of FreiburgAnthony J Richardson (Author) - The University of Queensland
- Publication details
- Ecological Applications, Vol.33(4), pp.1-29
- Publisher
- John Wiley & Sons, Inc.
- Date published
- 2023
- DOI
- 10.1002/eap.2852
- ISSN
- 1939-5582; 1051-0761
- PMID
- 36946332
- Grant note
- European Commission (EC) Environment and Climate Change Canada (ECCC) Nature Conservancy of Canada (NCC) 2029710/ National Science Foundation (NSF)
- Organisation Unit
- University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 99714998702621
- Output Type
- Journal article
Metrics
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