Estimating conservation area coverage 197
FIG. 2 (a) Sample of countries (national sampling units) and administrative units (sub-national sampling units) that meet 10% of targets selected based on 1,000 Marxan runs and selecting the result with the smallest number of sampling units, most even spread across the continents and with sampling units with the highest mean selection frequency. (b) Selection frequency scores from Marxan showing the number of times each sampling unit was selected across the 1,000 runs used to identify the sample.
continents. All of the Stage 1 and Stage 2 Marxan outputs met all of the 89 feature coverage targets (Table 1, Supple- mentary Table 4), whereas the Stage 1 random sets failed to meet a mean of 15.7 ± SD 4.0 targets and the Stage 2 random sets failed to meet a mean of 16.5 ± SD 3.6 targets. Using the best Marxan output would require collecting
protected area data from 25 countries and across 42 Stage 1 (national and sub-national) sampling units. In compar- ison, the Stage 1 random sets of sampling units covered a mean of 64.3 ± SD 7.4 countries (Supplementary Fig. 1) and 152.9 ± SD 20.6 national and sub-national sampling units. The Stage 2 random sets of sampling units covered a mean of 162.1 ± SD 4.9 countries (Supplementary Fig. 2) and 514.1 ± SD 10.3 national and sub-national sampling units.
The publicly available World Database of Protected
Areas data showed that 15.3% of the terrestrial realm is under protection compared to a mean of 15.3 ± SD 2.2% for the Stage 1 Marxan outputs and a mean of 16.0 ± SD 0.3% for the Stage 2 Marxan outputs. This compares to a mean area under protection for the Stage 1 random sets of sampling units of 15.2 ± SD 2.5% and for the Stage 2 ran- dom sets of sampling units of 15.2 ± SD 0.572%.
Discussion Choosing the factors and features
In this study we outline a framework for producing more accurate estimates of progress towards global conservation area targets, identifying a sample of countries and grid squares that are representative of the factors that shape total conservation area network extent and patterns of glo- bal biodiversity (Fig. 1). There is an established literature on the factors that shape global biodiversity patterns, so we can be confident that our final sample is representative at this global scale (Gaston & Spicer, 2004). The literature on con- servation area establishment factors is less well established, although we know that demographic, economic and govern- ance factors are important (Mascia et al., 2014; Kroner et al., 2019), so differing social and socio-economic conditions will result in conservation area networks with differing extents (Bohn & Deacon, 2000). More specifically, previous studies have shown conservation area coverage is influenced by human population density and proxies of agricultural op- portunity cost such as elevation and land cover (Loucks et al., 2008; Joppa & Pfaff, 2009) and the link between
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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