198 R. E. Sykes et al.


FIG. 3 (a) Sample of 100 × 100 km grid squares found in the focal countries (national sampling units) and administrative units (sub-national sampling units) selected by Marxan that best meets 10% of targets for biogeographical and conservation area extent factors whilst minimizing sample area. (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 best sample.


government effectiveness and wealth in determining con- servation outcomes (Waldron et al., 2017). Some factors that our expert group identified as poten-


tially important could not be included because they have not been mapped at the global scale (Supplementary Table 1). Political and public support for conservation in each country, for example, could have an effect on conser- vation area establishment but global datasets focused on this factor were not available. This could be resolved in future through using polling data and citizen science initiatives (McKinley et al., 2017). Collecting data on national land tenure systems could also be important, as these are likely to have a large impact on the extent of privately and community-governed protected areas and other effective area-based conservation measures in each country (Bingham et al., 2017). However, we did broadly account for this, as well as other potential factors, by using the geo- graphical sub-regions dataset, ensuring representation of countries with shared legal, cultural and historical back- grounds. Another issue is that although some of our datasets represent snapshots of the current situation, conservation area coverage reflects both past and current circumstances, although governments often add or remove conservation


areas in response to current conditions (Mascia & Pailler, 2011; Radeloff et al., 2013).


Defining the sampling units and selecting the sample


The second key aim of our study was to ensure that the sam- pling approach represented a feasible basis for future data collection and study. Such data collection is resource inten- sive (Juffe-Bignoli et al., 2016), so we needed to balance between selecting a sample that was large enough to be suf- ficiently representative but not so large as to make collecting data for every area in the sample unrealistic. Webased Stage 1 of our framework on identifying countries and large within-country sub-regions to be included in our sample. This is because the nation state is the functional unit in con- servation area data collection and reporting (Dallimer & Strange, 2015), but large countries often have sub-national conservation agencies. Thus, by minimizing the number of countries in our sample we also minimized the number of agencies and organizations involved in data collection. For the largest countries we also assumed their conservation authorities would have a devolved structure involving na- tional and sub-national agencies, hence our use of sub-


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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