Edited by: Hugh Possingham, The University of Queensland, Australia
Reviewed by: Joseph Michael Kiesecker, The Nature Conservancy, United States
David Lindenmayer, Australian National University, Australia
*Correspondence: Eric Dinerstein,
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Ambitious biodiversity goals to protect 30% or more of the Earth’s surface by 2030 (30x30) require strategic near-term targets. To define areas that must be protected to prevent the most likely and imminent extinctions, we propose Conservation Imperatives—16,825 unprotected sites spanning ~164 Mha of the terrestrial realm that harbor rare and threatened species. We estimate that protecting the Conservation Imperatives would cost approximately US$169 billion (90% probability: US$146—US$228 billion). Globally, 38% of the 16,825 sites are either adjacent to or within 2.5 km of an existing protected area, potentially reducing land acquisition and management costs. These sites should be prioritized for conservation action over the next 5 years as part of a broader strategy to expand the global protected area network. The expansion of global protected areas between 2018 and 2023 incorporated only 7% of sites harboring range-limited and threatened species, highlighting a renewed urgency to conserve these habitats. Permanently protecting only 0.74% of land found in the tropics, where Conservation Imperatives are concentrated, could prevent the majority of predicted near-term extinctions once adequately resourced. We estimate this cost to be from US$29 billion to US$46 billion per year over the next 5 years. Multiple approaches will be required to meet long-term protection goals: providing rights and titles to Indigenous Peoples and Local Communities (IPLCs) conserving traditional lands, government designation of new protected areas on federal and state lands, and land purchase or long-term leasing of privately held lands.
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There is an urgent need to prioritize the conservation of habitats of rare and threatened species as part of a larger global biodiversity strategy.
Conservation Imperatives offer a solution to conserving the last unprotected sites harboring rare, range-restricted, and threatened species and should be a central component of the ambitious goals to protect at least 30% of the Earth’s surface by 2030.
The Conservation Imperatives identified in this study are highly concentrated, requiring only ~164 Mha globally to avoid extinctions; this equates to only 1.22% of the Earth’s entire terrestrial surface and 0.74% of land in the tropics.
Targeted investments to prevent extinctions in parallel with the conservation of carbon-rich regions are necessary as the world sets about expanding the protected area network from 15.7% today to 30% by 2030.
Conserving Conservation Imperatives is achievable and affordable, especially in the tropics, as the purchase of the tropical subset of Conservation Imperatives costs about US$169 billion (90% probability: US$146–US$228 billion), or US$34 billion (90% probability: US$29.2–US$45.6 billion) per year over 5 years.
As Conservation Imperatives represent the most biologically important and threatened places to protect, they can be thought of as “anchor points” to design regional-scale conservation planning efforts under 30×30.
In late December 2022, at the United Nations Convention on Biological Diversity’s 15th Conference of Parties (COP15), more than 190 parties adopted the 30×30 target—to protect at least 30% of the world’s lands, oceans, and inland waters by 2030 (
The purpose of this paper is to offer a science-based strategy to secure and protect the remaining homes of rare and endangered species through timely, affordable investments in land acquisition and habitat conservation. To this end, we introduce the term Conservation Imperatives, defined as currently unprotected sites that contain rare, threatened, and narrow-range endemic species. Specifically, our approach is to map unprotected sites harboring rare species while accounting for converted habitats and estimating costs to put these lands under conservation stewardship. We also seek to determine progress in the protection of global sites of rarity as determined from 2018 to 2023. Finally, we outline new efforts to leverage Conservation Imperatives to finance protection where immediate focus is needed and create anchor points for wider conservation planning under a global 5-year strategy.
Advancing Conservation Imperatives is a global prioritization scheme in the sense that preventing extinctions is proposed as an immediate conservation target. We strive for maximum buy-in by all nations, Indigenous groups, and local communities who have jurisdiction over such lands to preserve opportunities for expanding protection to Conservation Imperatives. We intentionally avoid prioritizing sites on a global scale. The maps and data we present here should be used as a starting point for subsequent ecoregion-based or regional prioritizations within each realm. A rich literature on systematic conservation planning and reserve design can inform methodologies for evaluating and delineating proposed sites at the regional level (
We combined six widely used data layers employed in published global biodiversity assessments to identify sites supporting rare, narrow-range endemic, and endangered species (
Schematic illustrating the construction of Conservation Imperatives and adjacency analysis. 1) Six layers of rare species data were overlaid together with the World Database on Protected Areas (
For freshwater species, which are on average more endangered than terrestrial species, we relied on the same data layers for the following reasons: i) the life histories of some of the most endangered vertebrates in the IUCN Red List of Endangered Species (Layer 3, see
We introduced a fractional land cover analysis to derive a more accurate estimate of the true Area of Habitat (hereafter “AoH”) for rare and threatened species because published range data contain varying amounts of agricultural, pastoral, and urban lands. The uneven resolution of the most widely used global biodiversity layers, coupled with rapid land-use change from conversion to agriculture and urbanization, results in many species rarity sites now containing areas of non-habitat. To identify and remove non-habitat, we used Copernicus Global Land Cover Layers CGLS-LC100 Collection 3 at 100 m resolution (
We used seven classes to create the fractional layer: Forest, Shrub, Grass, Crop, Urban, Bare Ground, and Permanent Water (inland water bodies). We defined Forest using the percent tree cover in the Copernicus data that varied by biome and set cutoff levels based on expert knowledge in each biome and their distinguishing ecological characteristics. Forest is defined as pixels with a tree cover fraction > 80% for the tropical forest biome, > 50% for the temperate forest biome, and > 30% for the boreal forest and mangrove biomes. To differentiate desert habitat from bare ground in the desert and xeric shrub biome, desert is defined as > 70% bare soil and bare ground as 50–69% bare soil in this biome. For all other cover types, we did not differentiate the percent cover among biomes. Shrub cover is defined as pixels with a shrub cover fraction ≥ 30%; Grass as a grass cover fraction ≥ 50%; Bare Ground as a bare cover fraction ≥ 50%; Urban as an urban cover fraction ≥ 10%; Permanent Water (inland) as a permanent water cover fraction ≥ 30%; and Crop as a cropland cover fraction >1% (to avoid any potential cultivated areas).
The species rarity layer and the fractional land cover map were overlaid to calculate the contribution of different cover types to unprotected polygons (
To determine the adjacency of Conservation Imperatives to existing reserves mapped in the World Database on Protected Areas (WDPA) layer (
Establishing accurate spatial delineation of Conservation Imperatives sets the stage for estimating the expected costs of protected area designation. Previous assessments of costs for conservation at the global scale have relied on extrapolation of land values based on agricultural and pastoral potential (
To estimate the cost of securing Conservation Imperative sites in the tropical belt, we collected empirical data from land protection projects occurring between 2008 and 2022, fit generalized linear regression models, and applied a simulation approach. Our dataset consisted of 1,016 projects compiled from IUCN Netherlands, the Quick Response Fund for Nature, and World Land Trust (
We next fit linear regression models to the empirical cost per hectare of land protection projects. We used a log transformation on cost-per-hectare values to reduce skew and create an approximate normal distribution. We hypothesized that land value could be influenced by the biogeographical realm, region, ecoregion, area of land being secured, type of land acquisition, and country-level economic factors (
To calculate the price to place Conservation Imperatives under conservation stewardship, we used Monte Carlo simulations (
To determine if the increases in the global protected area estate over the last 5 years have effectively addressed rare and endemic species exposed to the greatest risks of extinction, we intersected the Conservation Imperatives polygons with the most recent map of the WDPA using protected area categories 1–7 (April 2023) (
We identified 16,825 sites harboring rare and threatened species, covering ~164 Mha or 1.22% of the Earth’s land surface (
Map of global unprotected species rarity site. Global distribution of the unprotected species rarity sites (magenta area) across predominantly forested habitat (green) and non-forested habitat (yellow), with non-habitat areas (grey) removed from previously designated species rarity sites, covering 1.22%. Non-habitat areas include land classified as urban, agricultural, and degraded.
Reduction in total AoH harboring unprotected rarity differed by latitude and by biome. In the four major tropical realms, we found a 45% reduction in total land area. In the non-tropical realms, we estimated a 49% reduction in area (
Extent of habitat by biogeographic realm after applying fractional land cover to species rarity sites and removing non-habitat area.
Biogeographic realm | Forested habitat (km2) | Non-forested habitat (km2) | Total habitat (km2) | Habitat reduction (%)* |
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Afrotropic | 65,301 | 350,050 | 415,351 | 32 |
Australasia | 180,550 | 37,066 | 217,616 | 36 |
Indomalayan | 150,262 | 4,662 | 154,924 | 56 |
Nearctic | 17,512 | 23,501 | 41,012 | 49 |
Neotropic | 174,945 | 137,045 | 311,990 | 54 |
Oceania | 1,766 | 241 | 2,007 | 84 |
Palearctic | 73,220 | 423,791 | 497,010 | 49 |
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*Approximate reduction of unprotected rare and threatened species areas from 2019 levels versus total area extent from newly compiled data sets.
Effect of fractional analysis when identifying and removing non-habitat (Other) areas from species rarity polygons in several regions with high species rarity. Forested and non-forested habitats are retained.
Extent of habitat by biome after applying fractional land cover to species rarity sites and removing non-habitat area.
No. | Biome name | Forested habitat (km2) | Non-forested habitat (km2) | Total habitat (km2) | Habitat reduction (%)* |
---|---|---|---|---|---|
1 | Tropical/subtropical moist broadleaf forests | 536,606 | 55,436 | 592,043 | 49 |
2 | Tropical/subtropical dry broadleaf forests | 7,903 | 13,248 | 21,152 | 77 |
3 | Tropical/subtropical coniferous forests | 13,152 | 3,073 | 16,225 | 58 |
4 | Temperate broadleaf/mixed forests | 28,563 | 25,156 | 53,719 | 68 |
5 | Temperate conifer forests | 19,777 | 8,481 | 28,257 | 33 |
6 | Boreal forests/taiga | 51,147 | 35,018 | 86,165 | 22 |
7 | Tropical/subtropical grasslands, savannas, shrublands | 17 | 370,057 | 370,075 | 14 |
8 | Temperate grasslands, savannas, shrublands | 5 | 82,146 | 82,151 | 53 |
9 | Flooded grasslands, savannas | 2 | 8,794 | 8,796 | 65 |
10 | Montane grasslands, shrublands | 41 | 32,775 | 32,816 | 62 |
11 | Tundra | 1 | 45,632 | 45,633 | 35 |
12 | Mediterranean forests, woodlands, scrub | 5 | 36,162 | 36,167 | 78 |
13 | Deserts, xeric shrublands | 7 | 259,015 | 259,022 | 46 |
14 | Mangroves | 6,329 | 1,361 | 7,690 | 44 |
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*Approximate reduction of unprotected rare and threatened species areas from 2019 levels versus total area extent from newly compiled data sets.
Conservation Imperatives are highly concentrated. We found a distinct skew in the distribution of the 16,825 sites harboring unprotected rarity across biogeographic realms and biomes (
Distribution of Conservation Imperative sites (2023) by realm.
Biogeographic realm | Forest (km2) | Grass (km2) | Shrub (km2) | Desert (km2) | Total (km2) | Number of sites | Total |
---|---|---|---|---|---|---|---|
Afrotropic | 65,301 | 124,904 | 224,425 | 722 | 415,351 | 1,870 | 11.1 |
Australasia | 180,550 | 30,538 | 6,210 | 318 | 217,616 | 2,526 | 15.0 |
Indomalayan | 150,262 | 2,681 | 1,963 | 18 | 154,924 | 4,569 | 27.2 |
Nearctic | 17,512 | 11,355 | 11,914 | 233 | 41,012 | 184 | 1.1 |
Neotropic | 174,945 | 89,346 | 47,455 | 244 | 311,990 | 5,972 | 35.5 |
Oceania | 1,766 | 149 | 92 | - | 2,007 | 52 | 0.3 |
Palearctic | 73,220 | 262,573 | 20,868 | 140,349 | 497,010 | 1,652 | 9.8 |
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The four tropical realms account for 89% of all sites globally.
Distribution of Conservation Imperative sites in each biome (2023).
No. | Biome name | Forest (km2) | Grass (km2) | Shrub (km2) | Desert (km2) | Total (km2) | Number of sites | Total sites (%) |
---|---|---|---|---|---|---|---|---|
1 | Tropical/subtropical moist broadleaf forests | 536,606 | 27,081 | 28,355 | − | 592,043 | 12,580 | 74.8 |
2 | Tropical/subtropical dry broadleaf forests | 7,903 | 5,925 | 7,323 | − | 21,152 | 554 | 3.3 |
3 | Tropical/subtropical coniferous forests | 13,152 | 552 | 2,521 | − | 16,225 | 170 | 1.0 |
4 | Temperate broadleaf, mixed forests | 28,563 | 24,055 | 1,101 | − | 53,719 | 503 | 3.0 |
5 | Temperate conifer forests | 19,777 | 7,860 | 620 | − | 28,257 | 125 | 0.7 |
6 | Boreal forests, taiga | 51,147 | 25,828 | 9,191 | − | 86,165 | 88 | 0.5 |
7 | Tropical/subtropical grasslands, savannas, shrublands | 17 | 165,980 | 204,077 | − | 370,075 | 562 | 3.3 |
8 | Temperate grasslands, savannas, shrublands | 5 | 63,503 | 18,643 | − | 82,151 | 439 | 2.6 |
9 | Flooded grasslands, savannas | 2 | 8,435 | 358 | − | 8,796 | 57 | 0.3 |
10 | Montane grasslands, shrublands | 41 | 29,993 | 2,782 | − | 32,816 | 428 | 2.5 |
11 | Tundra | 1 | 43,136 | 2,497 | − | 45,633 | 37 | 0.2 |
12 | Mediterranean forests, Woodlands, scrub | 5 | 21,619 | 14,543 | − | 36,167 | 436 | 2.6 |
13 | Deserts, xeric shrublands | 7 | 96,743 | 20,389 | 141,883 | 259,022 | 619 | 3.7 |
14 | Mangroves | 6,329 | 835 | 526 | − | 7,690 | 227 | 1.3 |
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The tropical and subtropical moist broadleaf forests biome alone accounts for three-quarters of all sites globally.
The 10 ecoregions in each realm containing the highest number of Conservation Imperatives.
The top 10 ecoregions in each realm with the highest number of Conservation Imperative sites (2023) and the total remaining natural habitat and estimated cost to place under conservation stewardship.
ID | Ecoregion name | Total habitat area (km2) | Number of sites | % of sites in realm | Estimated cost (million US$) | ||
---|---|---|---|---|---|---|---|
Mean | Lower 90% CI | Upper 90% CI | |||||
Afrotropic | |||||||
17 | Madagascar humid forests | 4,295 | 614 | 32 | 337 | 190 | 539 |
18 | Madagascar subhumid forests | 3,836 | 250 | 13 | 302 | 164 | 477 |
32 | Madagascar dry deciduous forests | 3,025 | 59 | 3 | 241 | 120 | 398 |
79 | Ethiopian montane grasslands and woodlands | 725 | 49 | 3 | 56 | 24 | 103 |
25 | Northern Swahili coastal forests | 16,190 | 48 | 3 | 1,201 | 447 | 2,259 |
1 | Albertine Rift montane forests | 5,200 | 43 | 2 | 352 | 111 | 713 |
108 | Southwest Arabian Escarpment shrublands and woodlands | 2,407 | 38 | 2 | 272 | 133 | 462 |
42 | Dry miombo woodlands | 376 | 35 | 2 | 26 | 10 | 50 |
51 | Northern Acacia-Commiphora bushlands and thickets | 10,976 | 32 | 2 | 710 | 179 | 1,545 |
89 | Fynbos shrubland | 2,049 | 29 | 2 | 221 | 64 | 472 |
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3,717 | ||||||
Australasia | |||||||
156 | Sulawesi lowland rain forests | 25,417 | 1,090 | 45 | 197 | 136 | 276 |
157 | Sulawesi montane rain forests | 36,785 | 421 | 18 | 270 | 152 | 428 |
139 | Central Range Papuan montane rain forests | 39,150 | 379 | 16 | 231 | 83 | 441 |
153 | Southeast Papuan rain forests | 15,727 | 46 | 2 | 98 | 37 | 184 |
163 | Lesser Sundas deciduous forests | 1,916 | 41 | 2 | 15 | 9 | 22 |
168 | Eastern Australian temperate forests | 2,192 | 39 | 2 | 31 | 19 | 45 |
140 | Halmahera rain forests | 3,147 | 32 | 1 | 24 | 16 | 35 |
152 | Solomon Islands rain forests | 10,456 | 25 | 1 | 69 | 46 | 97 |
148 | Northern New Guinea lowland rain and freshwater swamp forests | 6,101 | 22 | 1 | 39 | 18 | 69 |
159 | Vanuatu rain forests | 992 | 18 | 1 | 7 | 5 | 10 |
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980 | ||||||
Indomalayan | |||||||
247 | Mindanao-Eastern Visayas rain forests | 22,648 | 1,561 | 36 | 14,948 | 9,354 | 22,070 |
241 | Luzon rain forests | 15,139 | 1,123 | 26 | 9,912 | 6,336 | 14,223 |
231 | Greater Negros-Panay rain forests | 1,813 | 190 | 4 | 1,184 | 672 | 1,819 |
248 | Mindoro rain forests | 1,663 | 178 | 4 | 971 | 501 | 1,664 |
246 | Mindanao montane rain forests | 7,517 | 139 | 3 | 4,880 | 2,411 | 8,015 |
288 | Western Java montane rain forests | 709 | 100 | 2 | 467 | 239 | 765 |
240 | Luzon montane rain forests | 2,644 | 57 | 1 | 1,732 | 752 | 2,975 |
249 | Mizoram-Manipur-Kachin rain forests | 5,395 | 52 | 1 | 3,037 | 1,796 | 4,651 |
256 | Northern Indochina subtropical forests | 3,171 | 44 | 1 | 2,097 | 1,174 | 3,205 |
219 | Borneo lowland rain forests | 13,993 | 43 | 1 | 8,399 | 2,961 | 16,403 |
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47,628 | ||||||
Nearctic | |||||||
327 | Sierra Madre Oriental pine-oak forests | 1,828 | 16 | 9 | 76 | 47 | 112 |
399 | Southeast US conifer savannas | 1,149 | 15 | 8 | 66 | 35 | 107 |
386 | Canadian Aspen forests and parklands | 121 | 9 | 5 | 7 | 4 | 12 |
396 | Northern Shortgrass prairie | 672 | 9 | 5 | 40 | 21 | 64 |
427 | Central Mexican matorral | 603 | 8 | 4 | 21 | 7 | 41 |
432 | Meseta Central matorral | 819 | 8 | 4 | 31 | 15 | 55 |
342 | Southern Great Lakes forests | 222 | 7 | 4 | 11 | 4 | 22 |
428 | Chihuahuan desert | 3,490 | 7 | 4 | 131 | 55 | 241 |
382 | Southern Hudson Bay taiga | 1,782 | 6 | 3 | 99 | 42 | 177 |
376 | Mid-Canada Boreal Plains forests | 561 | 5 | 3 | 30 | 12 | 55 |
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513 | ||||||
Neotropic | |||||||
442 | Bahia coastal forests | 3,563 | 1,635 | 27 | 410 | 307 | 543 |
443 | Bahia interior forests | 1,161 | 579 | 10 | 138 | 107 | 174 |
500 | Serra do Mar coastal forests | 3,134 | 434 | 7 | 372 | 277 | 481 |
460 | Eastern Cordillera Real montane forests | 18,176 | 279 | 5 | 1,796 | 1,201 | 2,541 |
439 | Alto Paraná Atlantic forests | 2,177 | 192 | 3 | 241 | 162 | 338 |
486 | Northwest Andean montane forests | 18,454 | 192 | 3 | 1,888 | 1,169 | 2,775 |
477 | Magdalena Valley montane forests | 9,685 | 156 | 3 | 927 | 516 | 1,511 |
491 | Pernambuco coastal forests | 160 | 150 | 2 | 19 | 13 | 26 |
493 | Peruvian Yungas | 11,658 | 142 | 2 | 1,191 | 852 | 1,600 |
593 | Northern Andean páramo | 892 | 121 | 2 | 92 | 66 | 125 |
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7,075 | ||||||
Palearctic | |||||||
791 | Eastern Mediterranean conifer-broadleaf forests | 6,900 | 114 | 7 | 1,092 | 634 | 1,681 |
735 | Pontic steppe | 9,506 | 101 | 6 | 1,675 | 1,017 | 2,497 |
804 | Southern Anatolian montane conifer and deciduous forests | 12,680 | 70 | 4 | 2,255 | 1,241 | 3,512 |
727 | Eastern Anatolian montane steppe | 9,761 | 57 | 3 | 1,501 | 757 | 2,492 |
732 | Kazakh steppe | 9,220 | 53 | 3 | 1,504 | 845 | 2,375 |
785 | Aegean and Western Turkey sclerophyllous and mixed forests | 1,577 | 43 | 2 | 270 | 143 | 437 |
798 | Mediterranean woodlands and forests | 2,221 | 40 | 2 | 295 | 137 | 511 |
661 | East European forest steppe | 2,191 | 39 | 2 | 382 | 210 | 608 |
819 | Central Asian southern desert | 3,436 | 37 | 2 | 486 | 269 | 780 |
650 | Caucasus mixed forests | 5,851 | 36 | 2 | 901 | 488 | 1,431 |
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10,361 |
This includes tropical and non-tropical ecoregions.
Top 30 countries with the highest number of Conservation Imperative sites, their percentage total, median and total area of sites (km2), and the number and percentage of sites within each country that are adjacent to existing protected areas (i.e., within 2.5 km of boundary).
Country | Number of Conservation Imperative sites | % of total sites | Median area of sites (km2) | Total area of sites (km2) | Number of sites adjacent to an existing protected area (within 2.5 km of boundary) | % of sites adjacent to an existing protected area in country |
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Philippines | 3,355 | 19.5 | 0.46 | 53,816 | 833 | 25 |
Brazil | 3,342 | 19.4 | 0.31 | 35,632 | 781 | 23 |
Indonesia | 1,893 | 11.0 | 0.50 | 116,773 | 387 | 20 |
Madagascar | 968 | 5.6 | 0.37 | 14,585 | 183 | 19 |
Colombia | 761 | 4.4 | 0.93 | 39,827 | 423 | 56 |
Ecuador | 653 | 3.8 | 0.38 | 35,026 | 157 | 24 |
Papua New Guinea | 527 | 3.1 | 0.36 | 81,800 | 26 | 5 |
India | 437 | 2.5 | 5.23 | 20,861 | 65 | 15 |
Peru | 342 | 2.0 | 13.42 | 43,590 | 101 | 30 |
Turkey | 304 | 1.8 | 28.53 | 50,166 | 2 | 1 |
Russia | 291 | 1.7 | 54.48 | 138,436 | 89 | 31 |
China | 276 | 1.6 | 22.68 | 41,276 | 47 | 17 |
Mexico | 230 | 1.3 | 17.22 | 33,441 | 63 | 27 |
Argentina | 187 | 1.1 | 40.87 | 61,285 | 38 | 20 |
Australia | 137 | 0.8 | 2.31 | 35,705 | 54 | 39 |
United Republic of Tanzania | 127 | 0.7 | 0.24 | 1,041 | 52 | 41 |
South Africa | 116 | 0.7 | 9.74 | 40,648 | 52 | 45 |
Myanmar | 114 | 0.7 | 16.78 | 22,883 | 16 | 14 |
Ethiopia | 109 | 0.6 | 0.86 | 40,513 | 6 | 6 |
Kazakhstan | 104 | 0.6 | 85.39 | 58,230 | 19 | 18 |
United States of America | 102 | 0.6 | 17.78 | 10,636 | 51 | 50 |
Venezuela | 93 | 0.5 | 1.77 | 2,793 | 50 | 54 |
Kenya | 92 | 0.5 | 0.69 | 16,297 | 22 | 24 |
Vietnam | 85 | 0.5 | 5.47 | 3,274 | 42 | 49 |
Bolivia | 81 | 0.5 | 16.31 | 8,612 | 27 | 33 |
Yemen | 78 | 0.5 | 27.00 | 6,111 | 1 | 1 |
Malaysia | 76 | 0.4 | 7.88 | 9,141 | 25 | 33 |
Democratic Republic of the Congo | 73 | 0.4 | 13.46 | 49,350 | 23 | 32 |
Syria | 70 | 0.4 | 5.16 | 2,360 | 1 | 1 |
Chile | 66 | 0.4 | 3.49 | 2,652 | 22 | 33 |
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We predicted that >50% of new protected areas designated between 2018 and 2023 would overlap with unprotected species rarity sites. We estimated that 1.2 million km2 was added to the global protected area estate over this 5-year time period (
Expansion of protection in species rarity sites in World Database on Protected Areas (WDPA) between 2018 and 2023 after overlaying the fractional land cover. Green polygons show unprotected species rarity sites that have gained protection between 2018 and 2023, representing only 7% of the global increase in protection coverage. Magenta polygons represent sites that remain unprotected in 2023.
The model of land acquisition costs per hectare that included realm, purchase type, purchase size, per capita GDP, and population size performed best and had an R2 value of 0.76 (
In Monte Carlo simulations of the land cost for Conservation Imperatives, we found that the total cost of the Conservation Imperatives in the tropics is US$169 billion, with a 90% probability between US$146 and US$228 billion (
Predicted cost per km2 and total purchase cost for securing Conservation Imperatives (2023) within tropical latitudes by realm.
Realm | Mean cost/km2 (US$) | Mean acquisition size (km2) | Mean total cost (billions US$) | 90% probability (billions US$) |
---|---|---|---|---|
Afrotropic | 32,548 | 21,811 | 38.53 | 24.39–59.70 |
Australasia | 5,800 | 131,750 | 1.59 | 1.19–2.11 |
Indomalayan | 361,840 | 1,840 | 90.39 | 72.36–112.49 |
Nearctic | 29,545 | 14,911 | 0.14 | 0.08–0.22 |
Neotropic | 75,010 | 11,025 | 28.39 | 23.84–34.02 |
Palearctic | 61,082 | 7,441 | 9.50 | 3.58–19.70 |
All costs are in 2023 US$. The mean total cost and 90% probability intervals are reported in billions of dollars.
Adjacency analysis of Conservation Imperative sites relative to existing protected areas revealed that 38% (SD = 36.01) of the 16,825 sites either bordered or were within 2.5 km of a nearby existing protected area (
Five key insights emerging from this study highlight the need to prioritize the conservation of rare and threatened species and their habitats as an urgent near-term target within a larger global biodiversity strategy: i) Conservation Imperatives identified in this study represent a mere 1.2% of the Earth’s terrestrial surface (0.74% in the tropical belt); ii) Conservation Imperatives were underrepresented in the creation of new protected areas over the last 5 years, indicating that a focus on species rarity is necessary; iii) if new protected areas created from 2018 to 2023 had been more strategically located to cover polygons identified as Conservation Imperatives, 73% of them could have been protected; iv) the bulk of the world’s rare and endangered species could be represented in protected areas for approximately US$25 billion/year for 5 years, and for only US$5 billion/year for 5 years in the Neotropics, where ecoregions contain the largest number of Conservation Imperatives; and v) the proximity of 38% of the 16,825 Conservation Imperatives to existing protected areas could greatly reduce barriers to protection and the costs of subsequent management of these areas while enhancing connectivity and augmenting climate adaptation strategies.
These insights raise a strategic question: Why have sites harboring rarity and impending global extinction been overlooked? Numerous studies have shown that stabilizing the Earth’s climate and reversing biodiversity loss are interdependent goals (
Our results corroborate observations that conservation efforts are failing to target regions rich in rare species (
Of most concern is that only 2.4% of newly created protected areas added to the WDPA were in the tropical and subtropical moist forest biome, which contains by far the highest numbers of Conservation Imperatives. In contrast, 69% of protection occurred in the temperate broadleaf and mixed forests biome, 14% in the boreal forest/taiga biome, and 6% in the temperate conifer forest biome—none of which contain high numbers of Conservation Imperatives. As a result, a targeted effort is now required to secure the remaining fraction of rare unprotected species sites before more land conversion occurs and without leaving to chance the selection of new protected areas. Our results yield a surprisingly low number of Conservation Imperatives in the five ecoregion complexes that make up the endemism-rich Mediterranean scrub biome. This finding may be because this biome is one of the most heavily converted among the 14 terrestrial biomes and much of what remains is either protected or so degraded that the fractional land cover analysis inadvertently removed areas that are still viable.
Using the Conservation Imperatives identified in this analysis, a starting strategy that targets the 10 ecoregions within each of the four tropical realms containing the highest number of sites could put 63% of all identified sites under conservation stewardship and represent 12 different biomes. With the geographic concentration of Conservation Imperative sites, this approach will retain representation across distinct biomes and realms (
While land purchase or leasing values provide a starting point for costs, a diversity of approaches will be needed to secure the protection of Conservation Imperatives. Whereas traditional land trust models focus on the purchase of land for private management, options such as community reserves, government re-designations, private sector commitments, and other effective area-based conservation measures (OECMs) may be more effective, less costly, and more sustainable. Where national governments incorporate the creation of new protected areas into their sovereign biodiversity strategies as a unique contribution, the global cost of the initial protection of Conservation Imperatives will drop dramatically.
Conservation Imperatives that are adjacent to or within 2.5 km of an existing reserve could be much cheaper to manage than isolated Conservation Imperatives. This would especially be true where entities or agencies responsible for protecting nearby reserves could extend management protocols to the adjacent Conservation Imperatives. Alternatively, where these adjacent lands constitute buffer areas or corridors, they could be managed as community reserves. Promoting this landscape approach to reserve management will help ensure these protected areas remain home to the rare and endangered species they protect, even in a rapidly changing world.
As the best conservation strategy will depend on site conditions and land tenure, much of the work to secure Conservation Imperatives will depend on close collaboration with local groups, communities, and governments. For example, 17% of Conservation Imperatives are located within current and historical Indigenous lands (
Conservation Imperatives can serve as a starting point to guide biodiversity protection commitments from the public and private sectors. Efforts are now underway to finance Conservation Imperatives in 5 of the top 10 countries (
Conservation Imperatives can also act as “anchor points” or connectivity nodes in comprehensive conservation planning efforts. Multicriteria analysis and decision-making platforms can utilize Conservation Imperatives to optimize broader strategies for designing compact and connected protected area networks at the national, ecoregional, or subnational levels (
One of the most critical aspects of these fine-scale assessments is determining the viability of sites. A number of Conservation Imperatives that are not adjacent to existing protected areas are small fragments. The long-term viability of these sites and the endangered populations they contain must be subjected to feasibility analyses, such as those conducted recently for a subset of mammal species (
Efforts to reach the 30×30 goal will incur long-term costs for protection and restoration. As assessments of Conservation Imperatives move to the country, ecoregional, or landscape scales, the work of local teams of scientists and planners to identify critical areas for restoration and tap into these resources could help safeguard many threatened Conservation Imperatives. Such funding is typically earmarked for restoring lands by allowing for natural regeneration or targeted re-planting (preferably with native species) and is not applicable to land purchase. However, time frames for restoration of degraded habitats can be on the order of 5–20 years or more. A central point of our paper is that the Conservation Imperatives require protection within the next 5 years. This urgency is underlined by two levels of extinction crisis documented by conservation biologists: the accelerated rates of species extinction compared to the historical background rate (
The largest gap in our approach occurs where adding new parcels alone will not achieve the desired outcome of avoiding extinctions. The best examples of this problem are where exotic invasive species are introduced into tropical archipelagos and where poaching of endangered species, particularly keystone species, remains unchecked. In the first instance, simply setting aside land will not guarantee a future for island endemics that have evolved in the absence of exotic invasive herbivores, omnivores, and carnivores, invasive plants, or new diseases. Even those archipelagos that contain formally protected areas are subjected to these threats. Here, targeted eradication and control campaigns are the primary approaches to prevent extinctions, and funding is desperately needed to conserve the large number of tropical flora and fauna on remote islands facing these threats. In the second instance, excessive hunting and poaching of large mammal species could remove critical species whose presence or abundance is essential to maintain critical ecological function. New technologies are emerging to assist those charged with protecting endangered populations and should be part of this global funding effort to avoid extinctions (
Conservation Imperatives can contribute to a science-based priority-setting strategy for expanding the global protected area network to at least 30% by 2030, which is in line with ambitious targets outlined in the Kunming-Montreal Global Biodiversity Framework. Area-based conservation targets have moved to the forefront of conservation, and we welcome this approach. Embedded in the area-based approach, however, should be the commitment to protecting irreplaceable sites harboring rare and endangered biodiversity as we strive towards 30×30. Conservation Imperatives occupy only a small portion of the emerging global conservation portfolio but offer high-quality opportunities to protect the diversity of life on Earth.
The Supplementary Material for this article can be found online at:
This paper is dedicated to a visionary in biodiversity conservation, Dr. Thomas Lovejoy: a friend of wild nature, colleague, mentor, and inspiration to us all. We would like to thank R. Naidoo, S. Butchart, and S. Pimm for their helpful comments on the manuscript. We thank N. D. Burgess and A. Arnell at UNEP-WCMC for providing support and verification of the use of the World Database on Protected Area data in the analysis and for valuable editorial comments. A pre-print version of this manuscript was submitted to Preprints.org, and the most recent version is available online at
ED: Writing – original draft, Writing – review & editing, Conceptualization, Investigation, Methodology, Project administration, Supervision, Validation. AJ: Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing, Conceptualization, Data curation, Formal Analysis. NH: Writing – original draft, Writing – review & editing, Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Resources, Software, Validation, Visualization. AL: Writing – original draft, Writing – review & editing, Conceptualization, Formal Analysis, Investigation, Project administration, Validation, Visualization. CVy: Writing – original draft, Writing – review & editing, Conceptualization, Methodology, Supervision, Validation. KB: Writing – original draft, Writing – review & editing, Conceptualization, Funding acquisition, Supervision, Validation. GA: Writing – original draft, Writing – review & editing, Methodology, Software. CB: Writing – original draft, Writing – review & editing, Resources. GC: Writing – original draft, Writing – review & editing. RC: Writing – original draft, Writing – review & editing, Resources. RD: Writing – original draft, Writing – review & editing. OF: Writing – original draft, Writing – review & editing. SH: Writing – original draft, Writing – review & editing, Funding acquisition. BL: Writing – original draft, Writing – review & editing. HM: Writing – original draft, Writing – review & editing. FP: Writing – original draft, Writing – review & editing, Visualization. DO: Writing – original draft, Writing – review & editing. BP: Writing – original draft, Writing – review & editing. CP: Writing – original draft, Writing – review & editing. RP: Writing – original draft, Writing – review & editing. AR: Writing – original draft, Writing – review & editing. CVe: Writing – original draft, Writing – review & editing, Resources. EW: Writing – original draft, Writing – review & editing. AZ: Writing – original draft, Writing – review & editing.
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/
The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by a grant from the non‐profit organization One Earth (
Authors AR and AZ are employed by the company Planet Labs PBC. The company was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.
This study received funding from the non‐profit organization One Earth (
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The handling editor HP declared a past collaboration with the author KB. The reviewer JK declared a shared research project Global Renewables Watch with the author AZ to the handling editor, and declared a collaboration with the author KB which started after peer review of the present manuscript.
The author BL declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
The findings and perspectives in this paper do not necessarily reflect the position of Planet Labs PBC.
See Supplementary Material (
SM Table 1. The six biodiversity datasets comprising the species rarity layer and their 923 terrestrial areas.
SM Table 2. Distribution of Conservation Imperative sites by ecoregion. (See Supplementary Spreadsheet.)
SM Table 3. Distribution of Conservation Imperative sites by administration. (See Supplementary Spreadsheet.)
SM Table 4. Model selection table with AIC and R2 values.
SM Table 5. Model estimates for the top candidate model using realm purchase size, purchase type per capita GDP, and population size.
SM Figure 1. Locations of project cost data.
SM Figure 2. Probability distributions for the predicted mean cost per hectare and total land costs.
SM Figure 3. Maps of Conservation Imperatives in A) the Philippines, B) Brazil, C) Indonesia, D) Madagascar, and E) Colombia.
SM File. R Code for land cost model fitting and simulation.