Estimating conservation area coverage 193


the accuracy of data from state protected areas and to collect information on non-state protected areas and other effective area-based conservation measures (UNEP-WCMC, 2019). Such work is important, but is a resource-intensive, long-term process (Juffe-Bignoli et al., 2016), so comple- mentary, more rapid approaches could provide additional insights. Such approaches are particularly needed to account for


the new international focus on other effective area-based conservation measures (Maxwell et al., 2020; Gurney et al., 2021). However, we lack global data on these other types of conservation area. Thus, although there is a wealth of im- portant literature on the effectiveness of state-governed pro- tected areas (Venter et al., 2014; Geldmann et al., 2019; Maxwell et al., 2020), we cannot estimate current levels of conservation area coverage or accurately measure progress towards international area-based conservation targets. This also makes it difficult to measure how well the global network represents biodiversity, especially as recent work suggests that non-state conservation areas can play an im- portant role in representing ecosystems that are missing from state protected areas (Garnett et al., 2018; Palfrey et al., 2022). In addition, more accurate data would help the international community better estimate funding re- quirements to improve management effectiveness (Geld- mann et al., 2019) and inform ongoing debates regarding the social impacts of meeting Target 3 (Sandbrook et al., 2023). Fortunately, the relevant conservation area data that we


need are often collected at the nation-state level, so one complementary approach would be to base global analyses on information from a subset of countries. Collecting and analysing data from a smaller number of nations would have obvious benefits in terms of time and resources. Just as importantly, producing such estimates would not in- volve reporting results per country, so analyses could use the latest and most accurate national conservation area datasets without contradicting official data reported by governments. Using such an approach would provide additional insights on trends in global protected area and other effective area-based conservation measure networks alongside the existing official datasets countries maintain as part of their requirements as signatories to the Con- vention on Biological Diversity (UNEP-WCMC, 2019). However, the sample of countries needs to be selected carefully, minimizing the number of countries to allow rapid data collection whilst also ensuring they reflect the underlying global variation. Here we present the first step in producing such a sampled approach for future estimates of global conservation area coverage and representative- ness, developing a framework to identify a representative set of countries and a set of sampling units within them. Identifying a representative sample of countries so that conservation area data from this subset can be used to


estimate the extent to which the existing global protected area and other effective area-based conservation measure networks meet area and biodiversity targets involves con- sidering two sets of factors: drivers of conservation area establishment and drivers of biodiversity patterns. Estab- lishment of conservation areas is influenced by a range of economic, political and social factors. For example, it is well known that conservation area coverage is higher on land of lower commercial value for agriculture or resource extraction (Loucks et al., 2008; Joppa & Pfaff, 2009). Drivers of biodiversity patterns include latitude and ele- vation, as species and ecosystems show strong variation across these gradients (Gaston & Spicer, 2004). Selecting a set of countries that best mirror these patterns is mathe- matically defined by the minimum set problem, so our framework is based on algorithms typically designed to solve these problems. This involves selecting and mapping the features that influence conservation area extent and/or biodiversity pattern features, setting targets for how much of each feature should be included in the sample and using complementarity-based algorithms to choose the best sets of countries that contain the specified amounts of these features (Kukkala & Moilanen, 2013). Using this approach also involves choosing a cost metric,


so that the prioritization process minimizes the cost whilst achieving the feature representation goals (Naidoo et al., 2006). In our case this metric needs to reflect the substantial time and effort involved in collecting the conservation area data. Protected area and other effective area-based conser- vation measure datasets are generally collected and collated at the national level (Bingham et al., 2019), so each new country added to our sample would add an extra cost in terms of effort required. Thus, we define our cost metric as the number of countries in which our sample areas are found. Such a metric is a simplification, as the effort required will vary between countries based on their capacity to collect and provide relevant data and the number of conservation agencies that are responsible for national or sub-national data collection. We partially account for this in our study by dividing larger countries into their highest administrative units below the level of national government, such as states or provinces, to better match the devolved nature of conservation management and data collection in these countries. Collecting data at the national level has one main disad-


vantage, as these large sampling units are likely to contain some land that is not needed to meet the targets, producing a less balanced sample because larger countries will be over- represented (Nhancale & Smith, 2011). However, this can be overcome by repeating the spatial prioritization using smal- ler sampling units within the subset of selected countries. Here we describe a sampling approach using this two-stage process to identify a representative set of countries and grid squares designed to inform future efforts to collect, collate


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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