Priority maps showing the grid cells with the highest complements of species were generated by iteratively ranking of grid cells from lowest to highest priority for conservation. Together with ranking maps, we produced performance curves that describe the extent to which each species is retained in any given high- or low-priority fraction of the landscape and seascape. We implemented complementarity-based, balanced, priority ranking with the Zonation software for spatial conservation planning (13, 14), which produces the ranking maps and performance curves as main outputs. Specifically, we used the Zonation software v.4 (13, 14).

Zonation produces a ranking that is balanced in the sense that, irrespective of landscape fraction chosen, the areas are complementary and jointly achieve a well-balanced representation across all species. We used the additive benefit function analysis variant of Zonation (13, 14), which can be interpreted as minimization of aggregate extinction rates via feature-specific species-area curves. This method can produce a high return on investment in terms of average coverage of species per grid cell and does not require targets or thresholds that are necessarily arbitrary to a degree (41). Species were weighted according to their IUCN Red List category of extinction risk, with highest weights assigned to Critically Endangered species (Near Threatened: 2, Vulnerable: 4, Endangered: 6, and Critically Endangered: 8) (26, 42). This weighting scheme induces a relatively higher coverage of more threatened species, while the prioritization method maintains an overall balanced representation across different species and groups of species (26, 42).

We carried out two separate assessments, one for terrestrial and freshwater species only and one for marine species only. For each assessment, we developed three different prioritization setups: (i) Baseline, based on species range maps only to understand where the unconstrained priorities for the conservation of the species threatened by unsustainable harvesting are; (ii) lower pressure, based on species range maps and the threat maps, mentioned above, included as cost layers; and (iii) more stable, based on species range maps and the governance maps, mentioned above, included as a cost layers. In the second and third prioritizations, threat maps and the governance maps were negatively weighted, and the aggregate weight of each layer was equal to the aggregate weight of all species together. We did so to produce a spatial priority ranking that reduced the overlap between priority regions for the species and regions either under high human pressure (lower pressure) or with lower rule of law (more stable).

For the baseline prioritization, we carried out four analyses at different spatial resolutions (0.1°, 0.2°, 0.5°, and 1°) to assess the sensitivity of our results to different spatial resolutions. We analyzed the overlap of the different resolution rankings by comparing 20% priority areas identified by different analyses using the Jaccard similarity index. Specifically, the coarser-resolution priority ranking maps were compared with upscaled versions of the reference priority ranking maps (0.1°), generated by calculating the median values of the blocks of cells. We also carried out separate assessments for each taxonomic group included in the analysis and for each type of threat (hunting and collecting terrestrial animals, gathering terrestrial plants, logging and wood harvesting, and fishing and harvesting aquatic resources).

For each setup, we produced performance curves for all species. In addition, we further included 579 Data Deficient species, which are coded as threatened by unsustainable harvesting in the IUCN Red List, to generate a measure of uncertainty for the average range coverage of data sufficient species by assuming that unsustainably harvested Data Deficient species are threatened in the same proportion as are unsustainably harvested threatened and Near Threatened Data Sufficient species. We sampled this proportion at random 1000 times from among unsustainably threatened Data Deficient species to generate confidence intervals for the curves in R (39).

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