metadata: authors: 0: author: Dasgupta,Susmita
1: author: Blankespoor,Brian
2: author: Wheeler,David J.
count: World
docna: 0: docna: Revisiting Global Biodiversity : A Spatial
            Analysis of Species Occurrence Data from the Global
            Biodiversity Information Facility
docty: Policy Research Working Paper
theme: Renewable Natural Resources Asset Management,Mitigation,Data Development and Capacity Building,Environment and Natural Resource Management,Public Sector Management,Biodiversity,Oceans,Climate change,Adaptation,Data production, accessibility and use
abstracts: cdata!: This paper builds on recent advances in
            machine-based pattern recognition to estimate species
            occurrence maps, using georeferenced open-source data from
            the Global Biodiversity Information Facility. With currently
            available data, the estimation algorithm has produced maps
            for more than 600,000 vertebrates, invertebrates, other
            animals, vascular plants, and fungi. The algorithm is
            designed for rapid map updates and estimation of new maps
            with continued increases in Global Biodiversity Information
            Facility occurrence reports. The paper compares the
            algorithm-produced maps with species-matched sets of expert
            maps for mammals, ants, and vascular plants. Using
            comparative species density counts in a high-resolution
            grid, it finds close similarity in global distribution
            patterns. It also traces regional differences to technical
            differences in estimation methods or cases where the
            boundaries of existing expert maps could be revised to
            reflect species-level patterns in Global Biodiversity
            Information Facility reports. The paper uses the estimated
            Global Biodiversity Information Facility maps to explore the
            global distributions of endemic species and species whose
            small occurrence regions increase their extinction risks. It
            finds a high overall incidence of endemism, with significant
            variations across major species groups. It also identifies
            about 118,000 species whose small occurrence regions create
            significant extinction risks. For both endemic and
            small-occurrence region species, the paper finds patterns of
            spatial clustering that identify candidate areas for
            cost-effective protection.
display_title: Revisiting Global Biodiversity : A
            Spatial Analysis of Species Occurrence Data from the Global
            Biodiversity Information Facility
listing_relative_url: /research/2024/06/34348490/revisiting-global-biodiversity-spatial-analysis-species-occurrence-data-global-biodiversity-information-facility
topicv3: Conservation Finance m1326426 588,Marine Protected Areas m1327608 1048
publishtoextweb_dt: 2024-06-25T00:00:00Z
dois: 10.1596/1813-9450-10821
no_of_pages: 30
author_keys: 0: author_key: 95558
1: author_key: 957135
2: author_key: 1048754
model_extractions: Policy Research Working Paper 10821

# Revisiting Global Biodiversity

A Spatial Analysis of Species Occurrence Data from the Global Biodiversity Information Facility _Susmita Dasgupta Brian Blankespoor David Wheeler_ Development Research Group Development Data Group & Environment, Natural Resources and Blue Economy Global Practice June 2024
Policy Research Working Paper 10821

# **Abstract**

This paper builds on recent advances in machine-based pattern recognition to estimate species occurrence maps, using georeferenced open-source data from the Global Biodiversity Information Facility. With currently available data, the estimation algorithm has produced maps for more than 600,000 vertebrates, invertebrates, other animals, vascular plants, and fungi. The algorithm is designed for rapid map updates and estimation of new maps with continued increases in Global Biodiversity Information Facility occurrence reports. The paper compares the algorithm-produced maps with species-matched sets of expert maps for mammals, ants, and vascular plants. Using comparative species density counts in a high-resolution grid, it finds close similarity in global distribution patterns. It also traces regional differences to technical differences in estimation methods or cases where the boundaries of existing expert maps could be revised to reflect species-level patterns in Global Biodiversity Information Facility reports. The paper uses the estimated Global Biodiversity Information Facility maps to explore the global distributions of endemic species and species whose small occurrence regions increase their extinction risks. It finds a high overall incidence of endemism, with significant variations across major species groups. It also identifies about 118,000 species whose small occurrence regions create significant extinction risks. For both endemic and small-occurrence region species, the paper finds patterns of spatial clustering that identify candidate areas for cost-effective protection.

This paper is a product of the Development Research Group, the Development Data Group, Development Economics and the Environment, Natural Resources and Blue Economy Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at bblankespoor@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Revisiting Global Biodiversity: A Spatial Analysis of Species Occurrence Data from the Global Biodiversity Information Facility ~~JEL Classification:~~ Q57, C8 Conservation planning, global biodiversity, species’ occurrence region, endemic and smallBank program managed by the authors and Dr. Nagaraja Rao Harshadeep, Global Lead, Disruptive Technology. We are also thankful to the participants of the webinar conducted during Biodiversity Week, including our colleagues from the World Bank Sustainable Development Global Practice and the Environment, Natural Resources and Blue Economy Global Practice.
1. Introduction

The world is rapidly losing biodiversity. Pimm et al. (2014) find that the current rate of species extinction is at least 1,000 times the background rate. Corroborating evidence is provided by the Living Planet Index (LPI), an indicator of global biodiversity based on population trends for vertebrate species in terrestrial, The LPI informs the Convention on Biological Diversity (CBD) and its Conference of the Parties (COP). In response to such indicators of rapid decline, 188 governments in the COP ratified the Kunming-Montreal Global Biodiversity Framework (GBF) at the COP 15 meeting in December 2022. Among other measures, the Framework commits participants to protecting 30 percent of the planet by 2030.

Effective implementation of the Framework will require addressing two difficult questions: (1) What is the spatial distribution of global biodiversity that should be protected? (2) How can protecting 30% of the This paper attempts to contribute by revisiting the spatial distribution of global biodiversity with species occurrence records from the Global Biodiversity Information Facility (GBIF). During the past 15 years, the GBIF’s global reporting network has expanded to include occurrences for over 2 million species. During of these records include locational coordinates, the GBIF data can enable new estimates of spatial distributions for previously-unmapped species, as well as improved estimates for species with existing Species maps underpin spatial analyses of global biodiversity. If all species are treated equally, then a species; (2) Overlay all chosen maps on a high-resolution global grid; (3) Count the total species incidence in each grid cell. In practice, global biodiversity analyses can modify species counting in three dimensions. life” that describes overall genetic variation in the global biome. Second, species may have different conservation priority weights because their vulnerability to human encroachment differs greatly. Third, species’ distributions may be far from uniform in the spaces enclosed by their maps. If a species’ spatial distribution density is known, its counting weight for each cell can be made proportional to its likelihood The scientific literature features plentiful examples of weighted species counting (e.g., Guo et al. 2022; genetic and environmental data, as well as expert analysis of their roles in the assignment of counting weights. These are inevitably time-consuming and intensive in technical resources that remain in short to 30% global protection by 2030. Global biodiversity could be assessed with expert-mapped, wellresearched species only, but this would ignore the huge population of other species with recorded GBIF occurrences. Unfortunately, policy makers cannot automatically assume that well-documented species adequately represent the larger population.

For more detailed information, see ~~hitps://www.livingplanetindex.org/latest_results~~ https://www.gbif.org/analytics/global
This paper addresses the information gap by developing and applying an algorithm that generates species maps directly from GBIF occurrence data. The algorithm can automatically update maps as new occurrence data become available. We generate maps for all species whose data currently satisfy our computational criteria (over 600,000). Overlaying these maps with a high-resolution grid provides a view of global biodiversity that broadens the traditional focus on vertebrate animals to include more representation for invertebrates, other animals, plants, fungi and other non-plant and non-animal species.

We believe that the new maps are complements rather than substitutes for in-depth biodiversity assessments. Where more information is available, the policy process will undoubtedly benefit from incorporating it. At the same time, we believe that our approach can contribute in three ways. First, for currently-mapped species, fast updates using our algorithm can help identify cases where newly-reported occurrences suggest alteration of map boundaries. Second, for currently-unmapped species, our approach can provide useful new information for global biodiversity assessments. Third, our mapping exercise can yield new insights about the global distribution of endemic species and species that are particularly vulnerable to human encroachment.

The remainder ofthe paper<sup>isorganizedasfollows.Section2describestheGBIFoccurrencedata</sup> and<sup>the</sup> application of our mapping algorithm. In Section 3, we explore overall results for the 610,694 species maps generated by our exercise. Section 4 extends the results to a new view of global endemism, while Section 5 explores the distributions of small-occurrence region species that are particularly vulnerable to human encroachment. Section 6 summarizes and concludes the paper.

The Global Biodiversity Information Facility provides a constantly-updated open-source repository of geolocated, date-stamped reports of species occurrences from many public institutions and NGOs. These In addition, the GBIF identified reporting locations.

Species are traditionally identified as endemic if they are 100% resident in one country. We also explore the implications of lowering the endemism condition to 95% and 90%. ~~a a,~~ respectively. Our mapping algorithm requires at least this many observations to work reliably. This drastically reduces the size of the download dataset, since some species have millions of occurrence records. Detailed descriptions of the protocols and database elements are provided by the GBIF at
<!-- Start of picture text --> We begin with an illustration of the mapping algorithm that we have used for this exercise. Figure 1<br>range includes areas in Argentina, Chile, Bolivia and Peru. Figure 1(a) presents an expert range map for<br>Lagidium viscacia developed by Burgin et al. (2020). Figure 1(b) shows the locations of the GBIF occurrence<br>reports used for our mapping exercise. The two figures show that reported sightings of this species include<br>the Burgin map but extends its northern and southern boundary areas to incorporate these sightings.<br>The mapping algorithm is a byproduct of recent research on machine-based pattern recognition, cluster<br>analysis and image processing. Formally, it addresses a problem in computational geometry: efficient<br>bounding of a spatial set, given a subset of points that are actually observed. Sets with irregular shapes<br>are poorly represented by traditional algorithms that draw simple convex hulls.<br>continuous non-convex boundaries for efficient representation. This powerful property has motivated<br>rapid adoption of alphahull for species range analysis in the recent professional literature (Kass et al. 2022;<br>Of the 610,694 species in our database, alphahull successfully estimates occurrence maps for 92.9%<br>occurrence reports into spatial clusters and draw a convex hull around each cluster. Overall, our algorithms<br>estimate occurrence maps for more than 560,000 terrestrial species and more than 41,000 marine species.<br>To our knowledge, this is the largest set of species maps that have been estimated from open-source data.<br>Our estimation algorithms are designed to support expansion of GBIF species maps with the continuing<br>As an illustration, Figure 1(c) displays the algorithm’s application to GBIF occurrence reports for Lagidium<br>viscacia. The alphahull boundary follows the curvilinear north-south orientation ofthe point set, widening<br>Burgin et al., while extending it to incorporate new occurrence information.<br><!-- End of picture text --> <!-- Start of picture text --> This algorithm is a function in the R programming language.<br>Two other powerful features of alphahull are not illustrated here, but play an important role in the production of<br>The second is production of separate bounded areas for point clusters that are distant from one another.<br><!-- End of picture text -->
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52,433 vertebrates (amphibians, birds, fish, mammals, reptiles); 213,268 arthropods; 32,355 molluscs; Table 1 provides a more detailed accounting. We limit the exercise to GBIF species in the kingdoms two methods to remove spurious observations from locales such as zoos and botanical gardens. The first is exclusion of isolated outlier occurrences before map estimation, which happens automatically in alphahull. The second occurs after map estimation, when we exclude alphahull-bounded point sets with fewer than three observations. Many species maps have multiple bounded areas; in those maps, our approach imposes a conservative interpretation of the evidence. We drop species maps with single bounded areas when they contain fewer than three observations.

3. Global Species Distributions 3.1 Spatial Selection Bias GBIF occurrence reports are often produced by voluntary exercises that do not employ scientific sampling methods. In consequence, spatial point densities in species occurrence reports are positively related to physical accessibility, population density and income (Garcia-Rosello et al. 2023; Borgelt et al. 2022; Isaac and Silman 2011; Reddy and Davalos 2003). Other things equal, more species sightings occur near transport arteries; in areas where there are more inhabitants to identify species; and in areas where more inhabitants have disposable income sufficient to support species search and the cost of reporting. These factors complicate attempts to map species population densities from occurrence reports (e.g., Kass et al. 2022). Alphahull estimation of boundaries is different, because it focuses on exterior points in spatial sets. However, accurate representation still requires a critical minimum number of sightings in areas that are not advantaged by transport access, population density or disposable income. As Feeley and Smith (2011) note, the accuracy of boundary estimation improves as more sightings occur in non-advantaged million new reports per day. The following section tests alphahull boundary mapping from the current provided by existing expert maps.

GBIF species and the smaller inventory of expert range maps, with two primary objectives. The first is incorporating more species into an expanded view of global biodiversity. The second is assessing the relationship between GBIF occurrence maps and expert range maps that may “undershoot” if their This final condition may seem redundant, since the initial condition is exclusion of species with fewer than five unique occurrences since 1970. However, a species can pass the initial condition but fail the final one because the alphahull algorithm may exclude an outlier point or two from its computation of the bounded area.
**Table 1:  GBIF species occurrence maps**

||Group|Count|
|---|---|---|
||Amphibians|5,055|
||Birds|11,064|
|Vertebrates [Class]|Mammals|4,881|
||Reptiles|7,644|
||Fish|23,789|
||Subtotal|52,433|
||Araneae|10,438|
||Coleoptera|44,152|
||Diptera|23,567|
|Arthropods [Order]|Hemiptera|13,272|
||Hymenoptera|26,159|
||Lepidoptera|50,675|
||Other|45,005|
||Subtotal|213,268|
|Molluscs||32,355|
|Other Animals||24,109|
||Asparagales|22,978|
||Asterales|17,439|
||Caryophyllales|9,554|
||Ericales|8,574|
||Fabales|16,277|
|Vascular Plants [Order]|Gentianales|13,811|
||Lamiales|16,545|
||Malpighiales|12,790|
||Myrtales|10,336|
||Poales|16,845|
||Other|87,544|
||Subtotal|232,693|
|Fungi||38,122|
|||17,714|
|Other species|||
||Total|610,694|

7
In this section, we pursue our two objectives with three cases that compare our GBIF species maps with expert maps from recently-published research. Our comparisons identify thousands of species with GBIF The first case retains the traditional focus on vertebrates in a comparison with mammal range maps estimated by Marsh et al. (2022). The second extends the comparison to invertebrates, whose huge numbers are greatly underrepresented by existing expert maps (Kass et al. 2022). As Table 1 shows, our exercise provides more ample representation by estimating maps for 213,268 Arthropods. In this section, vascular plants, for which we have estimated 232,693 maps. Our comparison focuses on the more limited 3.3 The Global Distribution of Mammals Marsh et al. (2022 — henceforth Marsh) map the native ranges of global mammals using the authoritative In our GBIF occurrence maps database, we identify 3,530 mammals that are also mapped by Marsh. We rasterize both sets of maps using a global grid with 0.05 degree (5 km) resolution. densities by cellwise addition across 3,530 rasters for each set. Figure 2 provides comparisons of cell Where the two patterns diverge, the technical explanation seems clear. The Marsh maps estimate Marsh ranks have many species with reported occurrences beyond their estimated native ranges; regions with lower GBIF ranks have occurrence reports clustered in subareas within native ranges. This difference could reflect underreporting for GBIF species in lower-ranked areas, although many higher-ranked areas seem similarly-disadvantaged for species observation. The alternative, and in our view more plausible, explanation is that lower-ranked regions are populated by many species whose ranges have contracted over time. Continuing accumulation of GBIF occurrence reports should help resolve this question.

For each species map, rasterization assigns a value of 1 to grid cells that overlap with the map and 0 to the other
|densely-populated Indonesia. In ourview, the more likely explanation isthatthe areas populated by many<br>3.4 The Global Distribution of Ants<br>In 2002, Clarkand May identified a severetaxonomic bias in conservation research. Vertebrates accounted<br>and techniques (including alphahull, the algorithm we employ) to estimate range maps.<br>In our GBIF<br>occurrence maps database, we identify 5,445 ant species that are also mapped by Kass. We rasterize both<br>sets of maps using a global grid with 0.05 degree (5 km) resolution and compute species densities by<br>cellwise addition across 5,445 rasters for each set.<br>are notable. Both maps identify a large high-ranking region in the Western Hemisphere, but it is further<br>north for GBIF than for Kass. Both maps identify a band of relatively high ranks across West and northern<br>drawn from the Global Ant Biodiversity Informatics (GABI) database in July 2020.<br>In contrast, our records<br>estimated map regions are significantly more conservative.<br>For example, our maps exclude bounded<br>in methodology.<br>Kass joins us in making plentiful use of alphahull estimation, but our approach is more<br>conservative. We exclude species with fewer than 3 unique occurrences, but Kass includes 5,168 ant<br>species with fewer than 3 unique occurrences.<br>Since alphahulls cannot be estimated for these species,<br>Kass estimates their ranges by drawing 30 km buffer zones around the occurrence locations.<br>Given this<br>difference,comparingfulldatabaseresultsforGBIFandKasswouldeffectivelybecomparingapplesand|
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<!-- Start of picture text --> 3.5 The Global Distribution of Vascular Plants<br>Borgelt et al. (2022 — henceforth Borgelt) have recently developed spatial density maps for vascular plants<br>they identify “native regions” from a web-scraping exercise using the Plants of the World Online (POWO)<br>database, with regional identification standardized from the World Geographical Scheme for Recording<br>Plant Distributions (WGSRPD). Typically, the resulting “native regions” are the boundaries of small<br>countries or provinces (GADM level-1 administrative units) in large countries.<br>Borgelt estimates the models using GBIF occurrence data with restrictive prior conditions. The data are<br>confined to the period 2000-2020 to preserve compatibility with the environmental variables used for<br>Maxent estimation. For each species, georeferenced occurrence reports are pruned to exclude all<br>observations outside of pre-identified “native regions”. Maxent-estimated species distributions are also<br>confined to native regions. This approach has the advantage of guaranteeing the exclusion of spurious<br>observations from entities like botanical gardens and private collections in other regions. However, it also<br>incurs the cost of excluding potentially-large numbers of occurrence observations that lie outside pre-<br>identified native regions that are arbitrarily defined by national or provincial boundaries.<br>Our exercise draws on a longer time period (1970-2023) because we are not constrained by the need for<br>compatibility with environmental modeling variables. In addition, we impose no prior geographic<br>restrictions on the data. As we explained previously, our methodologies estimate occurrence map<br>boundaries after removing spurious single outliers and small isolated occurrence clusters.<br>We have identified 32,339 vascular plant species that are in both databases. As before, we rasterize our<br>occurrence maps and compute cell counts at 5 km resolution. Borgelt provides species maps in a raster<br>stack with much coarser resolution (50 km). We extract raster layers for the 32,339 common species and<br>add across layers to obtain relative incidence scores for the 50 km grid cells. Then we use mean smoothing<br>to approximate the effect of higher resolution.<br>Figure 5 provides the comparative results, displayed as ranks in 10 groups. Inspection of the two maps<br>reveals essentially the same density pattern, with the exception of somewhat more extensive high-ranking<br>In this section, we have compared our estimated GBIF occurrence maps with species-matched sets of<br>expert maps from recent publications for mammals, ants and vascular plants. In all three cases, the<br>comparative global patterns of species density are quite similar. Where the patterns diverge, the<br>maps and expert “native range” maps are attributable to “undershooting”, where the expert map<br>boundaries exclude many GBIF occurrences, or “overshooting”, where GBIF occurrences are persistently<br>absent in parts of the native range maps. For ants, where the research also employs aphahull estimation,<br>the differences are attributable to differences in the source databases and the relative conservatism of our<br>approach to map estimation. For plants, where the research employs GBIF occurrences and the global<br>pattern similarity is most striking, the few discrepancies are attributable to temporal and spatial<br>restrictions imposed by the expert research team.<br><!-- End of picture text -->
4. The Global Distribution of Endemism responsibilities for species that are heavily concentrated within their borders. Endemic status is traditionally assigned to a species if its range lies entirely within one country. However, a country’s stewardship could remain critical for a species that resides almost entirely within its borders. To explore the implications, we overlay the World Bank’s country map with our GBIF species occurrence maps and classed as endemic if the conventional 100% criterion is applied. When the criterion is changed to 95% and 90%, species classified as endemic increase to 317,815 (52%) and 339,656 (55.6%), respectively. As and the maximum to 62.1%. In this paper, we will employ the traditional 100% criterion for subsequent analyses. However, the results in Table 2 are cautionary because they show that countries assume stewardship responsibilities for about 339,656 species if the criterion is reduced to 90%.

major role in raw counts, so we standardize by total country species to highlight the relative importance New Zealand, Madagascar, Japan and Costa Rica. Even for the top 30 among 297 entities tabulated,
have extremely high endemicity percents (89.6%, 88.1% and 84.4%, respectively). The maxima are even respectively. Molluscs, Fungi and other species more closely resemble Arthropods and Plants in the relative compactness of their occurrence regions

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In this section, we increase spatial resolution to assess the distribution of endemic percentages using a global grid with 0.25 degree (25 km) resolution. We perform cellwise addition across all rasters in apparent. First, all three groups exhibit huge global variation, from 0% to 60% endemic for terrestrial variation within their borders (e.g., Brazil, United States, South Africa, India, China) while others do not very high endemic intensities over their entire areas. In Brazil, by contrast, all three groups exhibit progressively-declining endemic intensities from the Atlantic coast to the western frontier region. Similarly-broad patterns of variation across regions are visible in the United States, Mexico, Colombia, Argentina, South Africa, Morocco, Ethiopia, India, China, the Russian Federation and Indonesia. Countries have added protection responsibilities for endemic species, so large spatial variations in endemic intensity have significant implications for protected-area policies. Scale economies are important divert fewer hectares from economic use. For both reasons, spatial clusters with high endemic intensity offer the potential for cost-effective area protection. Figures 6 and 7 identify candidate clusters in many 5. The Global Distribution of Small-Occurrence Region Species Jenkins et al. (2015) note that “small range size is the best predictor of extinction risk and, thus, the first metric for conservation priority”. This factor has been studied extensively in the empirical literature (Kraus Brooks and Pimm 1999). It has particular significance because it is a widely-recognized indicator of extinction risk that is computable for any species that can be mapped. In contrast, expert assessments of extinction risks for individual species are limited by their resource intensity. The result is a gap between In this section, we explore the size and global distribution of small-occurrence region species in our GBIF maps database. Then we consider the implications for conservation policymakers, who must balance the of that territory. At the outset we should note that small-range status is not determinate; no single critical minimum habitat size exists, given the myriad interactions between species and habitat characteristics that affect extinction risks. Instead, we begin by exploring the implications of changing the species map These factors are considered as general propositions, . Other factors may intervene in particular
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Table 4 displays the cumulative global count for species groups and all species as occurrence region 57,765 species are identified. This increases to 85,310 at 25 km x 25 km or less and 117,946 at 50 km x 50 km or less. Differences across species groups reflect their varying representation in the database and We believe that the 50 km x 50 km upper bound on critical scale in Table 4 is conservative; many experts count to 170,081 at 100 km x 100 km and 246,172 at 200 km x 200 km. From a policy perspective, we are aware that the feasibility and sustainability of species protection tend to decline with increases in the regions for understanding the regional distribution of the species with small occurrence regions. Overall, Latin America & Caribbean (26.9%); two regions are mid-range: East Europe & Central Asia (17.3%), and Our maximum grid scale of 50 km is conservative and designed to focus attention on species with highlyrange sizes. If we adopted the same criterion, the maximum grid scale would be 517 km, 10 times our maximum protection schemes to include.
Sub-Saharan Africa, 11.7%; and two regions have small representation (South Asia, 1.9% and Middle East encouraging for conservation, since 85.1% of recorded small-occurrence region species are in High Income and Upper Middle Income countries that have significant resources for conservation.

The total species count in Table 5 (117,934) is more than the total in Table 6 (114,842) because some political entities with small-occurrence region species are not included in the World Bank’s income group assignments.
We find a highly-skewed international distribution when we also count small-occurrence region species ee eee ee eee ee eee ee eee ee eee ee eee ee eee eeeee eeee eeeee eeee eeeee eeee eeeee eeee eeeee ee Federation [| Russian | ee| eeee e **ee** eeee ee ee **eee** eeeee eeee eeeee eeee eeeee eeee eeee
administrative areas. The skewed distribution principally reflects factors other than country size, since the rank correlation of country area and small-occurrence region species count is only 0.40.

5.4 Global Variations in Territorial Clustering Resources for habitat protection are scarce, so protection is most cost-effective in areas inhabited by occurrence region species on the World Bank country map that includes 38,229 level-2 administrative assign each species to the subnational administrative level 2 (henceforth WB2) unit that has the largest share of its habitat. After assigning all species, we compute WB2 species counts and order each country’s WB2s in descending order by count. We compute the cumulative count and identify the WB2s that collectively account for 90% of the country’s small-occurrence region species. We divide the number of identified WB2s by the total number of WB2s in the country to obtain a concentration measure for smallrespectively. In each table, the rows divide countries into four groups by WB2 concentration.

Table 8: Small-occurrence region species spatial clustering by size class, These are administrative entities that have level-2 units mapped by the World Bank. Lower income countries concentrations by World Bank income classifications
Table 9: Small-occurrence region species spatial clustering by size class, Group 1 countries have the highest spatial concentrations of small-occurrence region species and the greatest potential for cost-effective protection, other things equal. In this group, 5% or less of WB2s account for 90% of small-occurrence region species. The remaining groups are partitioned as follows: countries have strong spatial clustering of small-occurrence region species. In an additional 49 countries, To summarize, our results identify a relatively small subset of countries where clustering of smalloccurrence region species offers exceptional opportunities for cost-effective protection. In many cases, we believe that such an assessment will be a useful future application of our GBIF species maps database. However, we perform an illustrative exercise for Brazil, which has 6,058 small occurrence region species and high spatial clustering (6.5% of municipios account for 90% of small-occurrence region species). Highlighting the degree of clustering, Table 10 shows that only 5 of Brazil’s 5,510 municipios account for <!-- Start of picture text --> —fDiamanting |<br><!-- End of picture text -->
<!-- Start of picture text --> »<br>0°- \ > ;<br>K View:<br>4 riety<br>10°S- Pret of Small occurrence<br>e ts, = ih mind S/ region<br>a’ a " x Top 5 count<br>ee es Q f Other count<br>aoe by Maca Ly . NA<br>~<br>70°W 6o°w 50°w 40°W 30°W<br><!-- End of picture text --> To summarize, the Brazil case illustrates the potential of the GBIF database for identifying protection opportunities offered by spatial clusters of small-occurrence region species, as well as the extent of existing protection for these species. From a global perspective, our overall results identify many countries with high spatial clustering where cost-effective small-occurrence region species protection In December 2022, 188 governments ratified the Kunming-Montreal Global Biodiversity Framework that commits participants to protecting 30 percent of the planet by 2030. Implementation with the limited resources available will require accurate identification of areas that are both critical for global biodiversity and suitable for cost-effective protection. This paper attempts to inform the identification process with new species habitat information from the Global Biodiversity Information Facility (GBIF). The GBIF’s previously-unmapped species, as well as improved estimates for species with existing maps. In the paper, we use recent advances in machine-based pattern recognition to estimate occurrence maps
|that have been estimated from open-source data.<br>Our estimation algorithm<br>is designed to support<br>expansion of GBIF species maps with the continuing increase in occurrence reports. We test the maps<br>sets to compute species density counts for a high-resolution global grid and compare the resulting global<br>distributions. We find close similarity in the global comparisons for mammals, ants and vascular plants.<br>Where there are regional differences, we trace them to technical differences in estimation methods or<br>cases where the boundaries of existing expert maps could be revised to reflect species-level patterns in<br>We use our occurrence maps database to explore two important issues for priority-setting in the Global<br>Biodiversity Framework.<br>First, we estimate the incidence of endemism across broad species groups<br>because it identifies countries’ stewardship responsibilities for species that reside entirely or almost<br>entirely within their boundaries. We find a heavy incidence of endemism for 610,694 species in our<br>(55.6%) for90% habitat. We also find thatendemism varies widely by species group. In a high-resolution<br>spatial analysis, we identify candidate “hotspot” areas for protection within countries where endemic<br>Our second exploration focuses on species whose small habitat areas increase their extinction risk. Our<br>algorithm produces area estimates for all mapped species, offering a low-cost complement to traditional<br>risk indicators whose species coverage<br>is limited by their resource intensity.<br>Using a conservative<br>standard for minimum critical habitat size, we find 117,934 vulnerable species. Our global assessment<br>finds a highly-skewed spatial distribution that isonly modestly correlated with country size. We find many<br>skewed distributions within countries as well, with 90% ofsmall-occurrence region species concentrated<br>in less than 10% of WB level-2 administrative units for 50 of 167 entities analyzed. This creates a<br>significant opportunity for species protection that incorporates both extinction risk and cost-effective<br>administration within limited areas. We illustrate the potential with a Brazilian case that identifies 5 of<br>of species maps estimated from the GBIF’s open-source occurrence records database.<br>Our estimation<br>algorithm is designed for rapid production of new maps as more records become available. We find that<br>existing expert mapping exercises, as well as highlighting cases where existing expert maps could be<br>revised to incorporate new GBIF information.<br>In pilot applications, we use our newly-estimated maps to<br>shed new light on the global distributions ofendemicand small-occurrence region species. We hopethat<br>manymoreapplicationswillaccompanythecontinuedrapidexpansionofopen-sourceGBIFoccurrence|
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