Global conservation area coverage substantially exceeds current estimates

Conservation areas, geographical spaces where human use and access are often limited¹, are widely adopted to reduce biodiversity loss, with 18.5% of the terrestrial realm now formally recognized for conservation². Most of this conserved land is designated in protected areas (PAs), such as national parks and nature reserves². More recently, governments have begun to formally recognize other effective area-based conservation measures (OECMs), a conservation area type that does not need to have conservation as its primary objective⁹. It is expected that many OECMs will be governed by private actors, Indigenous Peoples or local communities. In 2022, the 195 government signatories to the Convention on Biological Diversity agreed to increase PA and OECM global coverage to at least 30% by 2030 (ref. ³) (known as 30 × 30). This commitment is part of Target 3 of the Kunming-Montreal Global Biodiversity Framework (GBF), which also states these sites should be effectively managed, equitably governed, ecologically representative and well-connected³.

The coverage component of GBF Target 3 is considered by some as controversial⁴. Proponents argue that at least 30% of the planet needs to be conserved to prevent major biodiversity loss^10,11. Others highlight potential social, equity and food security impacts^12,13 and in some places the possible entrenching of top-down approaches led by governments and international donors^14,15,16. There are also concerns that over-focusing on percentage coverage risks spreading conservation funds more thinly, perpetuating present under-resourcing of PAs¹⁷ and undermining implementation and management effectiveness^4,18. Conservation scientists are also concerned about repeating past mistakes, as present PA systems are generally fragmented and under-represent ecosystems with high economic value^19,20,21. Target 3 and the broader GBF take these issues into account, but concerns remain about whether they will be given appropriate attention during implementation.

Present official coverage statistics indicate that meeting Target 3 will require conserving an extra 11.5% (15.5 million km²) of the terrestrial realm². Coverage statistics are derived from the World Database on Protected and Conserved Areas (WDPCA), available through the Protected Planet website². This database tracks the implementation of Target 3, providing the headline indicator for the target. However, these official statistics chiefly comprise state-governed PAs because most nations only recently began to recognize, map and report on other conservation area types^9,22. Yet, studies based on nationally available data indicate that substantially more land is managed for conservation than appears in official global statistics^5,7,23, much of which has the potential to be recognized as PAs or OECMs¹⁶. Moreover, these community-, Indigenous- or privately-governed lands can have better conservation outcomes than some state-governed PAs²⁴ and could improve ecological representativeness and connectivity of conservation area networks^5,6,25. We therefore need a complementary approach to estimate global conservation area trends that can incorporate national-level datasets from a broader range of sources⁸. Collecting such data from every country would be extremely time-consuming, so a more rapid method is needed to complement official reporting.

Here we use an approach based on a sample that covers 10% of the terrestrial realm⁸, designed to equally represent factors known to influence conservation area extent and global biodiversity patterns in the minimum number of countries and territories (Fig. 1a, Extended Data Figs. 1 and 2, Supplementary Fig. 1 and Supplementary Tables 1 and 2). We collected all the available conservation area data for this sample, finding two global datasets and 65 national and subnational datasets (Extended Data Tables 1 and 2). We then identified the conservation areas that fell in one of three categories (Box 1): (1) globally recognized PAs from the WDPCA in May 2022 (the dataset used to inform the GBF discussions), (2) nationally recognized PAs and (3) possible OECMs. For OECMs, we measured possible (not actual) coverage because in most countries the processes for formally recognizing them have only just begun. Instead, we identified existing conservation areas under long-term governance and management that could possibly be recognized as OECMs in future⁷, if requested by the people governing these sites and approved by the relevant national authority²⁶. We analysed the data on these three conservation area types, compared this with the corresponding WDPCA data, conducted sensitivity analyses (Supplementary Figs. 2 and 3) and extrapolated likely global conservation area coverage. We present our results at the global level to avoid contradicting official country-level reports and to maintain the confidentiality of unofficial data sources.

Globally recognized PA coverage in our sample was 16.15% (s.d. = 0.275), very similar to the official 2022 WDPCA measure of 15.79% coverage²⁷. Assuming the same accuracy for the other conservation area types, for which equivalent datasets are not available, we multiplied all estimates by the ratio of official to sampled globally recognized PA coverage (mean ratio = 0.978, s.d. = 0.017).

We estimate that 26.81% (s.d. = 0.239%) of the terrestrial realm was in some type of conservation area in 2022 (Fig. 1b and Extended Data Table 3): 15.79% in globally recognized PAs, plus our estimate of 3.64% (s.d. = 0.132%) in nationally recognized PAs and 7.38% (s.d. = 0.150%) in possible OECMs. As most nationally recognized PAs are likely to qualify for globally recognized PA or OECM status²⁶, our data indicate that we may only be 3.2% short of achieving 30% coverage. Although our sample only covered 10% of the terrestrial realm, these results are consistent with evidence from national studies^5,6,7 and strongly indicate that present debates and analyses underestimate the global area of land already under conservation.

We investigated the ecological representativeness of the different conservation area networks by first measuring coverage of the six terrestrial functional biomes³⁰ found in our sample (Fig. 2a). The polar or alpine biome had by far the highest conservation area coverage, which has been widely reported elsewhere and explained in terms of the low economic opportunity costs of protecting such land^10,31,32. There was substantial variation in the percentage of the different conservation area types covering each biome, with conservation areas in the possible OECM category having a particularly important role for tropical or subtropical forests, as well as savannas and grassland⁵.

We also measured ecological representativeness at the ecoregion scale³³, determining the percentage of the area of each ecoregion in our sample found in the three conservation area types. This probably under- or over-estimated global coverage for some ecoregions but is indicative of wider trends. We calculated the percentage coverage of each ecoregion for three conservation area networks: (1) globally recognized PAs, (2) all PAs, by adding nationally recognized PAs, and (3) all conservation area types. We then calculated their protection equality values (Methods, Supplementary Methods and Extended Data Table 4), which range between 0 and 1, scoring 1 for conservation area networks containing an equal percentage of each ecoregion³⁴. The globally recognized PAs had a protection equality score of 0.405 (s.d. = 0.0059), showing their low representativeness. Adding the other conservation area types into the network only increased this to 0.505 (s.d. = 0.0053), leaving it still relatively unequal.

To understand these protection equality patterns, we grouped ecoregion percentage coverage data into categories that reflect past, present and proposed international targets (Fig. 2b). We found that of the median 173 ecoregions in each sample, 80 had less than 10% coverage in globally recognized PAs. This decreases to 64 ecoregions when including all PAs and 51 when including all conservation area types. In terms of those ecoregions having at least 30% coverage, 43 would meet this target based on globally recognized PA data, 52 based on all PAs and 80 based on all conservation areas. This explains why the protection equality scores remain low when including the other conservation area types.

The low representativeness result for the globally recognized PAs and all PAs combined was expected (Fig. 2b), as previous research has shown that these sites are often located on economically marginal lands in remote and/or upland areas^31,32 and expanding coverage often leads to more of the same ecoregions being protected^19,35. Adding possible OECMs did little to improve this representativeness, as they are largely found in the same ecoregions and possibly influenced by similar economic factors³⁶. Some of these areas have high species richness and endemism^5,10, so conserving them will be important for meeting several GBF targets³. However, our results indicate that recognizing them as OECMs may do relatively little to achieve the ecological representation component of Target 3.

GBF Target 3 was intended as an ambitious response to the continuing biodiversity crisis³⁷. However, our findings indicate that we could be closer to meeting the 30% coverage component on land than previously thought, with our estimate showing 19.43% of the terrestrial realm in some type of PA and a further 7.38% with the possibility of being recognized as OECMs. Moreover, this is probably an underestimate, as some nations lack the capacity to map their conservation areas²². Despite this conservation area coverage being much higher than past estimates, alarming amounts of biodiversity loss continue³⁸. One likely reason is that the extra conserved land we identify often covers ecoregions that are already well represented in globally recognized PAs¹⁰. This leaves many other ecoregions under-represented (Fig. 2). Our results also illustrate the disconnect between conservation area coverage and nature recovery, emphasizing the need to increase PA and OECM management effectiveness by developing, funding, implementing and monitoring improved strategies and actions that account for diverse socio-ecological contexts^39,40.

As well as maintaining and improving existing sites⁴¹, designating more conservation areas will be necessary to achieve the ecological representativeness components of Target 3. This will have to be done as efficiently as possible, given it is likely that more than a quarter of the terrestrial realm is already conserved. These extra PAs and OECMs will need to account for how the land is used presently and its cultural significance for local peoples, be based on diverse and equitable approaches to conservation^42,43 and seek to minimize their effect on food security and sustainable trade²⁰. This expansion should be informed by systematic conservation planning⁴⁴, which can account for trade-offs between conservation and other societal goals, and should underpin both national- and local-level spatial planning⁴⁵, targeting resources to establish and support PAs and OECMs in priority areas^18,46.

The requirement for equitable conservation means that OECMs and more diversified PA governance arrangements could play a major role in meeting Target 3, as these frameworks could provide recognition to groups, including Indigenous Peoples and local communities, who govern substantial portions of the world but are under-represented in formally recognized PA networks¹³. According to our estimates, 7.38% of the terrestrial realm has the possibility to be recognized as OECMs, although it is important to stress that these areas are not at present included in official global coverage statistics. The process of formal OECM recognition is at an early stage, and its success depends on governments developing effective identification, implementation and funding approaches²⁶. Crucially, the recognition of OECMs will depend on the free, prior and informed consent of those governing and stewarding each area, some of whom may be sceptical about the benefits and wary of potential restrictions, resulting from this type of official recognition⁴⁷. Overcoming such obstacles will be important, as many countries contain large areas governed by Indigenous Peoples and local communities in ways that benefit biodiversity^15,48, so there are clear advantages to governments creating a range of mechanisms that empower and formally recognize these broader forms of stewardship⁴⁹. Continued donor investment will be essential, helping governments to implement these mechanisms and directly supporting the custodians of these important lands.

Large parts of the world are probably already managed in line with PA and OECM definitions but are not reported at the international level. Measuring progress towards GBF Target 3, and conservation area coverage more broadly, depends on collecting and using accurate and relevant data on the locations, effectiveness and governance of all conservation area types^8,21,50. The official globally recognized datasets will continue to play a vital, central role in informing and monitoring progress²². Nonetheless, our study shows the benefits of a complementary approach that incorporates more national-level datasets to estimate the effect of officially recognizing further conservation areas. This needs to be based on a diverse set of data sources for the terrestrial and marine realms, with strengthened global capacity to ensure robust reporting from government and non-state actors^7,16,50. These data will help to ensure that the international community and national groups can empower the myriad of people who steward their lands, producing the effective and equitable network of conservation areas on which nature depends.

Selection of sample area

Our analysis built upon a previous study⁸ that identified 10% of the terrestrial realm for use in measuring conservation area coverage and ecological representativeness, while minimizing the number of countries selected to reduce subsequent data collection effort (Extended Data Fig. 1a and Supplementary Discussion). This first involved identifying ten factors that drive conservation area extent and global biodiversity patterns. These were (1) biomes, (2) elevation, (3) government effectiveness, (4) income per capita, (5) islands and continents, (6) landcover, (7) latitude, (8) population density, (9) realms and (10) subregions (Supplementary Methods). Each factor was then subdivided into several ‘features’ (Supplementary Table 1). For factors based on categorical data, each category was used as a different feature, for example, income per capita was divided into the World Bank categories: (1) low income, (2) lower middle income, (3) upper middle income and (4) high income⁸. For factors based on continuous data, each category was divided on the basis of appropriate break points in the data, for example, government effectiveness was based on dividing the World Bank government effectiveness scores into four features using the following values⁸: (1) 0 < 25, (2) 25 < 50, (3) 50 < 75 and (4) 75 < 100. This produced 89 features, which were mapped from the best available datasets⁸.

The previous study used the Marxan software package to identify a sample that covers 10% of the global extent of each of the 89 features⁸, so the influence of the different factors was in proportion to that experienced at the global scale. The 10% coverage target was chosen on the basis of a sensitivity analysis to find a balance between the area selected and number of countries and territories included in the sample⁸. Marxan uses a simulated annealing algorithm, which involves running the software several times and producing a near-optimal output from each run. The Marxan analysis was done in two stages (Supplementary Methods) and both stages involved 1,000 runs. Stage 1 treated every country/territory as an analysis unit, apart from countries with a land area >1,000,000 km², which were divided into units based on their highest subnational administrative level (for example, states and provinces). This stage identified the best of the 1,000 outputs, that is, the one that contained the fewest countries and territories needed to meet all the 10% global coverage targets (Fig. 1a). Stage 2 was designed to remove superfluous land from the stage 1 countries and territories, thus reducing coverage of each feature to as close to 10% as possible to balance out their influence. This involved dividing the stage 1 countries and territories into a series of 100 km × 100 km sampling units and using Marxan to identify near-optimal sets of sampling units that met all the 10% global coverage targets while minimizing the area selected. For our work, we used the 1,000 outputs from the stage 2 analysis (Extended Data Fig. 2), so that we could calculate the conservation area coverage for each different set of sampling units with associated uncertainty estimates.

Conservation areas dataset

We collected the available conservation area data for our focal sample of 25 countries and territories from several sources. The WDPCA is the official dataset for measuring global PA coverage² and was launched in 2025, on the basis of combining the pre-existing World Database on Protected Areas (WDPA) and World Database on OECMs (WD-OECM). We downloaded PA boundary data for each country and territory in our sample area in May 2022 (there were no OECMs listed in our sample area by May 2022), so our data were extracted from the WDPA. We followed the recommended method for calculating PA coverage⁵¹; we removed United Nations Educational, Scientific and Cultural Organization (UNESCO) Man and Biosphere Reserves (MAB) sites, as well as areas with the status of ‘proposed’ and ‘not reported’. The PA spatial data consisted of 36,835 boundary polygons, 138 point locations with an areal extent and 1,077 point locations without an areal extent. For point data with an areal extent, a circular buffer was drawn around the point, equating to the reported area; point locations without area data were removed. We then combined the buffered point data and polygon data into a single spatial data layer for each country and territory.

As mentioned, MAB sites are removed from WDPA coverage calculations because the data do not distinguish between core, buffer and transition zones⁵¹. To include MABs in our analyses, we mapped sites using available information from the UNESCO website⁵², which lists point coordinates and the size of core, buffer and transition zones. For each of the MABs where suitable data exist, we produced a circular buffer around the point coordinates to produce a core zone of the listed area. We excluded data on buffer and transition zones based on expert feedback that these zones do not meet the criteria for the conservation area categories defined earlier (Box 1).

Additionally, we downloaded publicly available national/regional conservation area spatial data for each country in the sample (Extended Data Tables 1 and 2). These included national PA databases, community conservation areas and any other area type that may be managed in a way that conserves biodiversity. We also asked conservation scientists and practitioners with expertise on conservation area types and designations in our sample countries to send us any other relevant spatial datasets. We wanted our analysis to be based on as complete a set of conservation area data as possible. However, some of this information is politically sensitive, especially data on conservation area boundaries, so these datasets were shared on the understanding that we would not publish the raw spatial data, summarize results at the national level or make these datasets publicly available. We also ensured that expert input at the later stage of our analysis was anonymized. Ethics approval for these processes was granted by the University of Kent Faculty of Social Sciences Research Ethics Advisory Group for Human Participants (ID 20221648467987119). In total, our analysis was based on 2 publicly available global datasets, 14 publicly available national datasets from 5 countries and 51 national datasets from 16 countries and territories that are not publicly available (Extended Data Tables 1 and 2).

Categorizing the conservation area dataset

Our analysis focused on PAs and OECMs and developed three conservation area categories to describe them (Box 1), based on the International Union for Conservation of Nature (IUCN) and Convention on Biological Diversity (CBD) definitions that underpin work to measure progress towards achieving GBF Target 3 (Supplementary Methods) and the metadata of the spatial datasets we collected. We then classified each conservation area in our dataset into one of the three categories or, if an area did not meet the criteria of any of the definitions, we removed it from the dataset. We did this by first producing a provisional classification for each conservation area based on its metadata. We then put these provisional assessments for each sample country in an online form and asked local experts to check, correct and comment on the classifications. Where there was disagreement among the experts about how conservation areas should be categorized, the majority response was used. If the responses were tied, a working group of six of the co-authors made the final decision (as described in the Supplementary Methods, we did a sensitivity analysis to test the effect of this decision-making process and found that the results were robust to change). In general, all the conservation areas with the same national designation were classified into the same conservation area category. However, the experts identified some exceptions and used the available data to identify which sites should be assigned to a different category (Supplementary Methods). The specific details we accounted for are described in more detail in the following sections.

Category 1 are globally recognized PAs

Category 1 consists of all conservation areas included in the WDPCA with a status of inscribed, adopted, designated or established², that is, those PAs that meet the criteria specified in the recommended method for calculating PA coverage⁵¹.

Category 2 are nationally recognized PAs

These are sites that are not in the publicly available WDPCA but are included in national databases and meet the IUCN definition of a PA, as assessed by relevant experts using the available metadata, peer-reviewed evidence and local knowledge. The absence of these sites from the WDPCA can be due to: (1) delays in national agencies sending updates about new sites⁵³; (2) countries placing restrictions on the public availability of their data²; or (3) when the national legislation of a particular country does not recognize specific PA governance types⁷, for example, sites that are privately owned and governed²².

Category 3 are possible OECMs

For this category we needed to make clear from the definition that we were identifying areas where there is the possibility of a site being counted as an OECM in the future. The process of designating an area as an OECM and listing it on the WDPCA takes time as it entails an in-depth assessment of the management, governance and biodiversity values of each individual site and crucially requires the group governing the site to consent to the area being listed²⁶. Our possible OECMs definition was based on translating the IUCN site-level tool for identifying OECMs⁵⁴ into six components that could be applied by national experts. The six components and how we assessed them are listed below:

(1) Not a PA. We did not include sites that could be classified as globally recognized PAs or nationally recognized PAs.

(2) A geographically defined area with agreed and demarcated boundaries. We only used spatial datasets with demarcated boundaries produced by relevant government agencies or groups responsible for governing, managing and/or supporting the sites.

(3) Governed by a specified entity or group. This was assessed by relevant experts on the basis of the available metadata, peer-reviewed evidence and local knowledge.

(4) Governance is likely to be sustained into the indefinite future. This was assessed by relevant experts on the basis of the available metadata, peer-reviewed evidence and local knowledge.

(5) Under a management regime that contributes to in situ conservation, regardless of intention. This was assessed by relevant experts on the basis of the available metadata, peer-reviewed evidence and local knowledge.

(6) Contains important biodiversity values. This was assessed by relevant experts on the basis of the available metadata, peer-reviewed evidence and local knowledge. We used a broad interpretation of ‘important biodiversity values’ because although this wording is used in the IUCN site-level identification tool⁵⁴, the OECM definition only specifies that sites should have ‘long-term outcomes for the in situ conservation of biodiversity’. Thus, we retained all sites that included natural greenspace, even if the site was designated for another reason, for example, protecting built heritage.

Spatial analyses

Preparation for analysis

All analyses and data processing were conducted in R v.4.2.1⁵⁵ and QGIS v.3.16.3⁵⁶. All datasets were reprojected to Mollweide equal area projection, with the analyses conducted in this projection unless otherwise stated. To prevent double counting, we used the st_difference function in the sf package⁵⁷ in R to remove duplicates and overlaps across the different datasets. As part of this, if a polygon was represented in several datasets or if there were overlapping polygons that belonged to more than one conservation area type, we assigned that area to the lowest numbered type (Box 1), that is, if a site was a globally recognized PA but overlapped with a site identified as a possible OECM, then the area of overlap would be categorized as a globally recognized PA. This ordering reflects level of recognition and permanence.

Conservation area coverage statistics

Using the combined conservation area dataset, we assessed the extent of each conservation area type in each of the 100 km × 100 km sampling units. We then used the results to determine the percentage coverage of each of these conservation area types for each of the 1,000 sets of sampling units⁸ (Extended Data Fig. 2). The mean globally recognized PA coverage for these sets was 16.15%, slightly higher than the 15.79% global coverage from the WDPCA in May 2022 (ref. ²⁷). Given the contentious nature of debates around 30 × 30, we decided to counterbalance the impact of the slightly higher globally recognized PA coverage in our sample. We did this by adjusting each of the 1,000 sets by dividing the 15.79% WDPCA value by the globally recognized PA percentage coverage value of the set. For each set, we then multiplied the conservation area extent result by the adjustment value, so that the percentage coverage of globally recognized PAs equalled that of the reported global coverage from the WDPCA and the other conservation area types had the same proportional adjustment (Extended Data Table 3).

Finally, we calculated the adjusted mean percentage coverage and s.d. for each of the three conservation area types in each of the 1,000 sets of sampling units (Extended Data Fig. 1b and Extended Data Table 3). We then assumed that these percentages calculated from our sample equalled those of the whole terrestrial realm. We made this direct extrapolation because each set was designed using the Marxan software to be representative of the terrestrial realm (Extended Data Fig. 2, Supplementary Fig. 1 and Supplementary Tables 1 and 2), based on containing 10% of the total extent of each of the spatial features that are known to influence conservation area coverage (Extended Data Fig. 1b). It should be noted that because these 1,000 sets were selected to be representative, the sampling units they contain are more similar than if chosen at random (Extended Data Fig. 2), so the s.d. values calculated from these sets are relatively low.

Ecosystem integrity

To evaluate the present status of the ecosystems in the different conservation area types, we used the Ecosystem Integrity Index (EII) dataset²⁸. The EII is a metric that accounts for the structural, functional and compositional integrity of ecosystems, with each 1 km × 1 km pixel given a value of between 0 and 1 according to the lowest integrity score for each of the three components. We categorized the EII into quartiles (low, 0 < 0.25; moderate, 0.25 < 0.5; high, 0.5 < 0.75; and intact, 0.75 < 1) and for each of the 1,000 sets of sampling units intersected it with the conservation area dataset. From this, we determined the mean percentage of each conservation area type covered by each of the EII quartiles across all sets.

Agricultural suitability

To investigate the level of pressure that areas may come under for land conversion, and potential food security impacts, we also calculated the agricultural suitability of land in the different conservation area types in the 1,000 sets of sampling units. We based this on the Global Agricultural Land Resources dataset, covering 1980 to 2009²⁹ and grouped the values into four categories (unsuitable, 0; marginally suitable, 1–32; moderately suitable, 33–74; and highly suitable, 75–100) based on the classification system developed by ref. ⁵⁸. As with the ecological integrity analyses, for each of the 1,000 sets of sampling units we ascertained the percentage of each agricultural suitability category in each conservation area type and then calculated the mean across all sets.

Ecological representativeness

We measured the ecological representativeness of the different conservation area types in each of the 1,000 sets of sampling units based on their coverage of different biomes and ecoregions. GBF Target 3 does not specify how much of each biome or ecoregion should be conserved to achieve ecological representativeness, partly because their biodiversity values vary widely⁵⁹. Nevertheless, these statistics are widely reported because they give an indication of the distribution of coverage, helping to identify biomes and ecoregions with high or low representation⁶⁰. We did not measure the extent to which the different conservation areas overlapped with Key Biodiversity Areas, even though GBF Target 3 refers to conserving ‘areas of particular importance for biodiversity’^3,61. This is because Key Biodiversity Areas are generally assessed at the national level and some countries have yet to assess their sites for a range of taxonomic groups using the revised Global Standard⁶². Thus, although our sample of countries and territories was selected to represent broad biogeographic patterns, it may not be representative of Key Biodiversity Area assessment patterns.

Our analyses were based on the Terrestrial Ecoregions of the World dataset³³. For each of the 1,000 sets we assessed the total area of each ecoregion in our sampling units and the percentage of this in each of the conservation area types. These percentage values were adjusted using the same approach used to produce the conservation area coverage estimates described earlier, that is, for each set the ecoregion area per conservation area type was divided by the globally recognized PA percentage coverage of the set and multiplied by the 15.79% WDPCA value. We removed the lake, rock and ice, and mangrove ecoregions to account for spatial mismatches between country and ecoregion boundaries (following ref. ³⁴).

We used this adjusted ecoregion dataset to calculate the total area of each biome in our sampling units and the total area in each conservation area type. To do this, we assigned each of the ecoregions found in the 1,000 sets of sampling units to its most relevant terrestrial functional biome (Supplementary Table 5). We based this on the Global Ecosystem Typology functional biome classification³⁰, as this has been designed to measure progress towards GBF targets. We used this to calculate the percentage coverage of each conservation area type in each biome.

We also used the adjusted ecoregion dataset to analyse ecoregion protection equality and coverage in relation to past, present and potential international coverage targets. As a first step, we removed ecoregions with less than 10% of their extent found in our sample area, leaving us with a median 173 ecoregions in each set of sampling units (Supplementary Table 6). We did this on the basis of a sensitivity analysis that investigated the extent to which small ecoregion patches that barely overlap with the sampling units would bias the results (Supplementary Methods). Such bias is possible because these small patches are more likely to be completely outside or completely in the conservation area network, thus appearing to have very low or very high levels of percentage conservation area coverage.

Protection equality is a metric based on the Gini coefficient that has been widely used to measure the evenness of protection across ecoregions^34,63. We calculated the level of proportional protection equality across the different terrestrial ecoregions of the world³³, based on the mean values for each of the 1,000 sets of sampling units, using the ProtectEqual package in R⁶⁴. These calculations were conducted iteratively (Extended Data Table 4), beginning with only the globally recognized PAs, then all PAs (globally recognized PAs + nationally recognized PAs) and finally all conservation areas (globally recognized PAs + nationally recognized PAs + possible OECMs).

To measure ecoregion coverage in relation to international commitments, we produced six percentage coverage groups (0% to represent no coverage; 10% based on the 2010 CBD Biodiversity Target goal 1; ref. ⁶⁰; 17% based on the 2020 CBD target⁶⁵; 30% based on the 2030 CBD target³; 50% based on the recommendations of the Half-Earth project⁶⁶; and 100% to represent complete coverage). We then counted the number of ecoregions belonging to each group on the basis of adding the three conservation area types incrementally to the global conservation area network. It is important to note that these calculations were based on the adjusted areas of each ecoregion in our sample, meaning that some estimates of conservation area coverage may under- or over-represent global levels. However, as our sample included 173 ecoregions, the results we present are probably indicative of global patterns.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

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Authors and Affiliations

1. Durrell Institute of Conservation and Ecology, University of Kent, Canterbury, UK

   Helen M. K. O’Neill, Nuwanthika Dharmaratne, John MacKinnon, Zoe G. Davies & Robert J. Smith

2. Institute of Zoology, Zoological Society of London, London, UK

   Helen M. K. O’Neill

3. CABI, Wallingford, UK

   Joe Beeken

4. UN Environment Programme World Conservation Monitoring Centre, Cambridge, UK

   Heather C. Bingham, Jessica L. Stewart & Samantha Hill

5. Department of Environmental Science and Policy, George Mason University, Fairfax, VA, USA

   Rachel E. Golden Kroner & Ryan Richards

6. Walsh School of Foreign Service, Georgetown University, Washington, DC, USA

   Yifan He

7. Sudanese Environment Conservation Society, Khartoum, Sudan

   Adil Mohamed Ali

8. The Nature Conservancy, Buenos Aires, Argentina

   Javier Beltran

9. Department of Biology, University of Oxford, Oxford, UK

   Joseph W. Bull

10. Wildlife Conservation Research Unit, Department of Biology, University of Oxford, Oxford, UK

    Susan Canney

11. WILD Foundation, Boulder, CO, USA

    Susan Canney

12. Biome Conservation, Chester, Nova Scotia, Canada

    Susan Canney

13. Working Group ICCAs Indonesia, Kota Bogor, Indonesia

    Cristina Eghenter, Cindy Julianty, Dewi Puspitasari Sutejo & Kasmita Widodo

14. The Nature Conservancy, Carlton, Victoria, Australia

    James A. Fitzsimons

15. School of Life and Environmental Sciences, Deakin University, Burwood, Victoria, Australia

    James A. Fitzsimons

16. School of Law, University of Tasmania, Hobart, Tasmania, Australia

    James A. Fitzsimons

17. Department of Earth and Environment & Kimberly Green Latin American and Caribbean Center, Florida International University, Miami, FL, USA

    Clinton N. Jenkins

18. Grupo Jaragua, Santo Domingo, Dominican Republic

    Yolanda M. Leon

19. The Nature Conservancy, Arusha, Tanzania

    Alphonce B. Mallya

20. IUCN Eastern and Southern Africa Regional Office, Pretoria, South Africa

    Daniel Marnewick

21. BirdLife Australia, Collingwood, Victoria, Australia

    Golo Maurer

22. College of Marine & Environmental Sciences and Australian Tropical Herbarium, James Cook University, Smithfield, Queensland, Australia

    Golo Maurer

23. Independent Researcher, Buenos Aires, Argentina

    Mayra Milkovic

24. Wildlife Research Center, Khartoum, Sudan

    Ameer Awad Mohammed

25. Mpingo Conservation and Development Initiative, Kilwa Masoko, Tanzania

    Yuvenal Pantaleo

26. Greening Australia, Perth, Western Australia, Australia

    Blair Parsons

27. Department of Protected Areas, Ministry of Environment and Climate Change, Brasilia, Brazil

    Fábio Ricarte

28. Independent Researcher, Washington, DC, USA

    Sushma Shrestha Sangat

29. Independent Researcher (IUCN-World Heritage), New Delhi, India

    Mridhu Tandon

30. Federparchi—Europarc Italia, Rome, Italy

    Corrado Teofili

31. Michael Succow Foundation, Greifswald, Germany

    Jens Wunderlich

32. Association for the Conservation of Biodiversity of Kazakhstan, Astana, Kazakhstan

    Steffen Zuther

33. Frankfurt Zoological Society, Frankfurt, Germany

    Steffen Zuther

Contributions

R.J.S. and Z.G.D. conceived the idea for the project and developed the conceptual framework. J. Beeken and H.M.K.O. collated data. H.M.K.O. and R.J.S. developed the analytical framework. H.M.K.O. conducted all the analyses. H.C.B., Z.G.D., R.E.G.K., Y.H., H.M.K.O., R.J.S. and J.L.S. developed the conservation area type definitions, interpreted the results and led on writing the paper. A.M.A., J. Beeken, J. Beltran, J.W.B., S.C., N.D., C.E., J.A.F., S.H., C.N.J., C.J., Y.M.L., A.B.M., D.M., G.M., J.M., M.M., A.A.M., Y.P., B.P., F.R., R.R., S.S.S., D.P.S., M.T., C.T., K.W., J.W. and S.Z. provided data, helped to interpret the results and gave feedback on drafts of the paper. All authors commented on and agreed with the final version of the paper.

Corresponding author

Correspondence to Robert J. Smith.

Competing interests

The authors declare no competing interests.

Peer review information

Nature thanks James Watson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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