Estimating conservation area coverage 195
TABLE 1 Details of how we defined the features used in the analysis and their data sources. Factor
Features Biomes Elevation 16 biomes
Five features: 0 , 300 m, 300 , 800 m, 800 , 1400 m, 1400, 2000 m, $ 2000 m
Government effectiveness Four features: 0, 25%, 25 , 50%, 50 ,75%, 75, 100% Income (per capita)
Islands & continents Land cover
Latitude
Population density Realms
Sub-regions
Four features: low-income, lower-middle-income, upper-middle-income, high-income countries
Five categories: , 1,000 km2, 1,000 ,10,000 km2, 10,000, 100,000 km2, 100,000, 1,000,000 km2, ‘Continent’ ($ 1,000,000 km2) 12 land-cover types
Seven features: five 20° bands; two 40° bands at the poles to avoid over-representation of these smaller regions
Five features using a logarithmic scale: 0 , 1, 1, 10, 10, 100, 100 , 1000, $ 1000 people per km2 Eight realms 22 sub-regions
and producing a near-optimal portfolio each time. Marxan then produces two key outputs: the ‘best’ output, which is the portfolio from the run with the lowest cost, and the ‘selection frequency’ output, which counts the number of times each sampling unit appears in each of the portfolios. Sampling units with high selection scores are always needed to meet the targets; lower-scoring sampling units can be swapped with similar sampling units without affecting target attainment (Ball et al., 2009). For Stage 1 we derived the sampling units from the Data-
base of GlobalAdministrativeAreas (GADM, 2018)thatcom- prised countries or nations with an area ,1,000,000 km2 or the highest sub-national administrative-level polygons for larger countries (e.g. states, provinces, etc., which are classified as L1 in the database and referred to as ‘sub- national sampling units’ hereafter). We took this approach because larger nations tend to have sub-national conserva- tion agencies and legislation, so we wanted to minimize the number of these sub-national administrative units
UNPD (2013)
Olson et al. (2001) UNSD (2019)
selected to avoid having to collate data from a large number of expert groups. We followed established practice for reporting terrestrial coverage statistics by excluding Antarc- tica from our analyses (Butchart et al., 2015). We based the Stage 2 sampling units on a global set of 100 × 100 km grid squares created in QGIS 3 (QGIS, 2019). We then clipped this global grid layer with the national and sub-national sampling units used in Stage 1 to produce the final sampling unit layer. We used the CLUZ plugin (Smith, 2019) for QGIS to im-
port the feature raster layers, calculate the area of each fea- ture in each sampling unit and run Marxan. To ensure the sampling units selected in Stages 1 and 2 were representative of the terrestrial realm, we used Marxan to identify sam- pling units that, when combined, met the same per cent of total extent target for every feature.We carried out a sensitiv- ity analysis to select this target based on identifying a good compromise between sampling a sufficient proportion of the planet to produce a robust estimate of conservation area
TABLE 2 Details of the factors used in the analysis that are likely to shape total conservation area network extent and patterns of global biodiversity, the extent of the feature with the smallest and largest area for each factor in the terrestrial realm and the per factor mean per cent coverage of each feature identified in the Stage 1 and Stage 2 best portfolios.
Factor Biomes
Elevation
Islands & continents Land cover Latitude
Population density Realms
Sub-regions
Number of features
16 5
Government effectiveness 4 Income
4 5
12 7 5 9
22
Global area of feature with smallest extent (%)
0.24 5.38
17.34 10.66 0.36 0.01 0.16 0.79
, 0.01 , 0.01
Global area of feature with largest extent (%)
20.67 41.24 35.99 44.91 94.23 19.41 23.74 39.82 38.95 15.95
Stage 1 mean of % of each feature in the selected sample
15.23 12.94 15.28 15.45 14.95 17.26 17.79 13.76 21.79 22.18
Stage 2 mean of % of each feature in the selected sample
10.92 10.73 11.01 10.60 11.61 11.04 13.87 10.59 14.82 13.87
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
Data source Olson et al. (2001)
Shuttle Radar Topography Mission
World Bank (2019b) World Bank (2019a)
GADM (2018) ESA GlobCover Project (2009)
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