Developing a framework to improve global estimates of conservation area coverage RAC H E L E. S YK ES 1,HEL EN M.K. O’NEILL 1 ,DIEGO J UFFE -B IGNO LI 1
KRISTIAN METC A LFE 2 , P.J. STEPHENSON3 ,MATTHEW J. S TRUEBIG 1 P IE R O VI S CONTI 4 ,NEI L D. BURGE SS 5 , 6 ,NAOMI KIN GST O N 5 ZOE G. DAV I E S 1 and ROB ERT J. S MI TH * 1
Abstract Area-based conservation is a widely used ap- proach for maintaining biodiversity, and there are ongoing discussions over what is an appropriate global conservation area coverage target. To inform such debates, it is necessary to know the extent and ecological representativeness of the current conservation area network, but this is hampered by gaps in existing global datasets. In particular, although data on privately and community-governed protected areas and other effective area-based conservation measures are often available at the national level, it can take many years to incorporate these into official datasets. This suggests a com- plementary approach is needed based on selecting a sample of countries and using their national-scale datasets to produce more accurate metrics. However, every country added to the sample increases the costs of data collection, collation and analysis. To address this, here we present a data collection framework underpinned by a spatial priori- tization algorithm, which identifies a minimum set of coun- tries that are also representative of 10 factors that influence conservation area establishment and biodiversity patterns. We then illustrate this approach by identifying a represen- tative set of sampling units that cover 10% of the terrestrial realm, which included areas in only 25 countries. In contrast, selecting 10% of the terrestrial realm at random included areas across a mean of 162 countries. These sampling units could be the focus of future data collation on different types of conservation area. Analysing these data could pro- duce more rapid and accurate estimates of global conserva- tion area coverage and ecological representativeness, comple- menting existing international reporting systems.
*Corresponding author,
r.j.smith@
kent.ac.uk 1Durrell Institute of Conservation and Ecology, School of Anthropology and
Conservation, University of Kent, Canterbury, UK 2Centre for Ecology and Conservation, Faculty of Environment, Science and
Economy, University of Exeter, Penryn, UK 3IUCN Species Survival Commission Species Monitoring Specialist Group, Laboratory for Conservation Biology, Department of Ecology and Evolution,
University of Lausanne, Lausanne, Switzerland 4Biodiversity, Ecology and Conservation Group, Biodiversity and Natural Resources Management Programme, International Institute for Applied
Systems Analysis, Laxenburg, Austria 5UN Environment Programme World Conservation Monitoring Centre,
Cambridge, UK 6Centre for Macroecology, Evolution and Climate, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
Received 23 October 2022. Revision requested 15 March 2023. Accepted 5 May 2023. First published online 7 November 2023.
Keywords Conservation areas, conservation targets, Global Biodiversity Framework Target 3, OECM, other effective area-based conservation measures, protected areas
The supplementary material for this article is available at
doi.org/10.1017/S0030605323000625
Introduction C
onservation areas are an essential component of global efforts to prevent biodiversity loss (Watson et al., 2014).
To this end, the 196 signatories to the Convention on Biological Diversity (2022) recently committed through the Kunming–Montreal Global Biodiversity Framework Target 3 to conserve at least 30% of the planet by 2030 through systems of protected areas and other effective area-based conservation measures. Progress towards this Target will be assessed using data from the World Database of Protected Areas and World Database of Other Effective Area-Based Conservation Measures. These data- bases are compiled and maintained by the UN Environ- ment Programme World Conservation Monitoring Centre based on conservation area data approved by each national government or following an expert review and validation process (Bingham et al., 2019; Lewis et al., 2019; UNEP- WCMC & IUCN, 2021). These two databases therefore need long-term, sustained resourcing to maintain their accuracy (Juffe-Bignoli et al., 2016). However, there are data limitations (Visconti et al., 2013),
as some countries lack the capacity to provide up-to-date and accurate information, so it can take time for newer protected areas to be included in these databases (UNEP- WCMC, 2019). More generally, non-state protected areas and other effective area-based conservation measures are under-represented in the databases (Bingham et al., 2017; Corrigan et al., 2018), partly because governments only re- cently started collecting data on conservation areas not governed by the state. Additionally, some custodians of non-state conservation areas lack the capacity or are wary of providing information to governments about their land (Clements et al., 2018). Investing in improving the quality of global conservation area datasets will address this; there is an ongoing process working with countries to increase
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (
http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. Oryx, 2024, 58(2), 192–201 © The Author(s), 2023. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605323000625
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