metadata: authors: 0: author: Dasgupta,Susmita
1: author: Blankespoor,Brian
2: author: Wheeler,David
count: World
docna: 0: docna: Estimating Extinction Threats with 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!: The world is experiencing a severe loss
            of biodiversity, highlighting the need for a strong global
            conservation strategy. Effective conservation depends on
            accurate information about where endangered species live and
            the local threats they face. Using data from the Global
            Biodiversity Information Facility, this paper creates threat
            and protection indicators for more than 600,000 species,
            including animals, plants, and fungi. The indicators include
            habitat size, level of protection, nearby population
            density, and specific threats like population encroachment
            for land species and fishing activity for marine species.
            The paper then uses an ordered logit model to analyze the
            relationship between these indicators and the extinction
            risk categories assigned by the International Union for
            Conservation of Nature. The model is based on 87,731 species
            in the Global Biodiversity Information Facility database
            that have been assessed by the International Union for
            Conservation of Nature. The results are used to predict
            threat levels for 512,675 species without International
            Union for Conservation of Nature ratings, revealing many
            more potentially threatened species and changing the maps of
            &#x201c;conservation hotspots.&#x201d; The paper
            concludes by noting that its methods can support rapid
            updates of species maps and threat indicators as the Global
            Biodiversity Information Facility database continues to grow.
display_title: Estimating Extinction Threats with
            Species Occurrence Data from the Global Biodiversity
            Information Facility
listing_relative_url: /research/2024/06/34349032/estimating-extinction-threats-species-occurrence-data-global-biodiversity-information-facility
topicv3: Integrated Coastal Zone Management
            m1325969 1958,Environmental Sustainability m1325984 1786,Conservation Finance m1326426 588,Marine Protected Areas m1327608 1048,Terrestrial Biodiversity m1584052 4722
publishtoextweb_dt: 2024-06-25T00:00:00Z
dois: 10.1596/1813-9450-10822
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 32
author_keys: 0: author_key: 95558
1: author_key: 957135
2: author_key: NA
model_extractions: Policy Research Working Paper 10822 Estimating Extinction Threats with 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 10822

# **Abstract**

The world is experiencing a severe loss of biodiversity, highlighting the need for a strong global conservation strategy. Effective conservation depends on accurate information about where endangered species live and the local threats they face. Using data from the Global Biodiversity Information Facility, this paper creates threat and protection indicators for more than 600,000 species, including animals, plants, and fungi. The indicators include habitat size, level of protection, nearby population density, and specific threats like population encroachment for land species and fishing activity for marine species. The paper then uses an ordered logit model to analyze the relationship between these indicators and the extinction risk categories assigned by the International Union for Conservation of Nature. The model is based on 87,731 species in the Global Biodiversity Information Facility database that have been assessed by the International Union for Conservation of Nature. The results are used to predict threat levels for 512,675 species without International Union for Conservation of Nature ratings, revealing many more potentially threatened species and changing the maps of “conservation hotspots.” The paper concludes by noting that its methods can support rapid updates of species maps and threat indicators as the Global Biodiversity Information Facility database continues to grow.

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
Estimating Extinction Threats with Species Occurrence Data from the Global Biodiversity Information Facility Conservation planning, global biodiversity, species’ occurrence region, Species’ Extinction Bank program managed by the authors, with Dr. Nagaraja Rao Harshadeep, Global Lead for Disruptive Technology. We are also thankful to the participants of the webinar on this subject conducted during the Practice and the Environment, Natural Resources, and Blue Economy Global Practice. 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
<!-- Start of picture text -->
1. Introduction<br><!-- End of picture text -->

- The remainder of the paper is organized as follows. We develop the extinction risk indicators in Section 2, while Section 3 explores their combination into composite indicators and the implications. Section 4 summarizes and concludes the paper. 2. Extinction Risk Indicators Among our extinction risk indicators, some apply to all species while others are confined to terrestrial or marine habitats. This section begins with two indicators for all species: habitat size and degree of formal protection. For each terrestrial species, we add human population density in its habitat, along with a species-specific measure of “human settlement sensitivity” computed a measure of pressure from proximate coastal populations, and the degree of coverage by
maps on the spatial distributions of risk determinants.

extinction risk and, thus, the first metric for conservation priority”. This factor has been studied Jenkins et al. 2015; Manne and Pimm 2001; Manne, Brooks and Pimm 1999). It has particular significance because it is a widely-recognized indicator of extinction risk that can be computed for 2.1.2 Formal Protection identifies all areas defined as protected under UNEP-WCMC standards. To compute the formal protection index, we have transformed the shapefile (which includes 283,568 polygons) into a global raster with a resolution of .05 decimal degrees (about 5 km). Each raster cell has value 1 if percent coverage by formal protection.

2.2.1 Population Density Other things equal, we would expect species’ extinction risk to increase with population density. We measure density with a spatial raster at 2.5 min resolution (.042 decimal degrees) from the Gridded Population of the World (GPW), v4 (SEDAC/CIESIN, 2023). We overlay the population raster with species occurrence maps and compute the mean cell populations of occurrence areas 2.2.2 Human Settlement Sensitivity Species differ greatly in their sensitivity to human incursion. In North America, for example, bears commonly observed in settled areas. If species occurrence reports provided unbiased information about species populations, a “settlement sensitivity” metric could be derived from statistical analysis of the relationship between the spatial distributions of human and species populations. The metric would capture the degree to which a species’ population density decreases, increases or remains constant as human spatial density increases. Other things equal, a species whose Raster cell populations are equivalent to population densities in this case because all cells have the same
population declines sharply with human population density would be expected to face greater extinction risk if the human population surged in its habitat.

Unfortunately, computing settlement sensitivity is complicated by well-known sampling biases in GBIF occurrence reporting, which is often a voluntary exercise that does not employ scientific sampling. In practice, reporting is more prevalent in accessible areas with higher incomes and larger populations (Garcia-Rosello et al. 2023; Borgelt et al. 2022; Isaac and Pocock 2015; Reddy sensitive, so that unbiased reporting would reveal a modestly-sloped negative relationship between its spatial density and human population density. However, population-biased reporting entails a countervailing effect which may producea statistical relationship that is positively sloped. While this complication is unavoidable, our methodology can still derive meaningful inferences from the data. Using the previously-described population raster, we assign each terrestrial raster cell to one of 15 scaled population groups. We overlay the population raster with species occurrence maps and compute total occurrences by population group for all terrestrial species. Total reported occurrences for species with extreme settlement sensitivity will decline continuously across population groups because the sensitivity effect dominates populationrelated reporting bias. Conversely, reporting bias will ensure that total occurrences increase continuously across population groups for species with modest or zero settlement sensitivity, as by settlement sensitivities that increase with human population density. Species with increasing sensitivity may exhibit trend reversal across population groups, with initial dominance for reporting bias ultimately superseded by dominance for settlement sensitivity.

For each species, we incorporate these factors by identifying the population group with the most reported occurrences. Relative settlement sensitivity is highest for group 6. For 432,278 terrestrial species, Figure 1 displays the distribution across population groups. The highest-sensitivity group (6) accounts for 123,875 species; counts increase in the first few groups and are relatively lower between population groups 9-15. This distribution illustrates a varied response where some high human settlement areas (population group 9-15).

We assign cell populations to 15 groups as follows (by interval upper limit):
Figure 1: Terrestrial species with maximum occurrence reports by population group <!-- Start of picture text --> @<br>120,000-<br>@<br>80,000<br>2 -<br>Oo<br>£<br>&<br>(s) e@<br>a @<br>®<br>40,000-<br>@ e v<br>@<br>O- @ iy eee?<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 «15<br>Population Group<br><!-- End of picture text --> economic incentives to maintain sustainable fishing stocks and prevent unlicensed incursions.

also provide secondary protection from uncontrolled bottom trawling and other practices that boundary shapefile from the Maritime Regions Geodatabase maintained by the Flanders Marine Institute (2019). We intersect this shapefile with occurrence maps for the 145,092 marine species Other things equal, the intensity of fishing activity within a species’ occurrence area may have a
GFW’s most comprehensive activity is near-real-time recording of fishing effort by vessels which broadcast their locations using the global automatic identification system (AIS). GFW stores daily estimates of vessel-specific fishing hours for the period 2012-2020 in georeferenced daily files at a resolution of 0.1 decimal degrees.

with 0.25 degree resolution for faster computation. We have constructed a fishing intensity index by overlaying this raster with the occurrence map for each marine species and computing mean AlS-based estimates are incomplete because many fishing vessels do not use AIS consistently. In response, GFW has recently collaborated with several research institutions to derive more accurate estimates from high-resolution satellite imagery (Paolo et al. 2024). The authors note industrial fishing activity is concentrated. We have downloaded the summary raster at 0.10 degree resolution and resampled to 0.25 degrees for faster computation. Then we have constructed our species and computing the mean value for raster cells within the map boundary.

2.3.3 Coastal Population Influence Human settlements in coastal areas influence offshore species via recreation, tourism, small-scale fishing, and exploitation of marine resources in other ways. Although quantification of specific have constructed a general index of population influence with a spatial kriging algorithm that replaces offshore missing values in our global terrestrial population raster (Section 2.2.1) with projected values from proximate onshore populations.

https://figshare.com/articles/journal_contribution/Satellite_mapping reveals_extensive_industrial_activi We use the inverse distance weighting (idw) function in R, with a maximum projection distance of 5 decimal degrees. We spread the effects of highly-concentrated urban populations by including 100 neighboring points in the projections and setting the distance power parameter (the exponent of distance
The indicators developed in the previous section can all be associated with extinction risk. However, their relative importance remains a subject for debate because rigorous empirical analysis is difficult. In practice, the conservation community has relied on risk assessments by experts who draw on unpublished species-level information as well as publicly-available data. These assessments typically group species into categories based on their estimated likelihood of future extinction, given current conditions. The resource intensity of species assessments has created several problems for conservation planning, including relatively long intervals between updates; an often-sizable gap between currently-available information on species sightings and The latter gap has motivated the development of methods that can translate generally-observable risk factors into extinction risk estimates for non-assessed species. These methods frequently use other organizations. Such models attempt to capture the collective judgment of thousands of independent experts. They are fitted to data on estimated extinction risks, not observed extinctions, and they unavoidably incorporate assessors’ assumptions about risk determinants section to estimating and applying a predictive model for non-assessed GBIF species that is based on IUCN assessments. In later sections we will also develop and illustrate some alternative approaches to risk index construction.

3.1 Estimating Extinction Risks from IUCN Data Numerous publications cite IUCN as the authoritative source of species risk assessments, while noting the large gap between species documented by organizations like the GBIF and species that have been assessed using IUCN’s time- and resource-intensive evaluation processes. Researchers have responded to this gap with machine-based learning algorithms that predict extinction risks from readily-observable characteristics of species and their environments. These exercises have 2022 [reptiles], Wieringa 2022 [bats], Cazalis et al. 2023 [mammals, reptiles, amphibians, fish], plants (Brazil)], Levin et al. 2022 (North American plants); and species across multiple taxa classed scant attention to other animals (most notably invertebrates) and other major phyla. Second, these pilot modeling exercises are idiosyncratic, with no convergence to a standard approach as difficult to explain their predictions to non-technical people who are stakeholders in conservation
produce extinction risk ratings for GBIF species as soon as their georeferenced occurrence data are it provides a transparent view ofthe relationship between risk ratings and the predictive indicators that we have developed in Section 2.

3.1.1 Model Estimation We focus on species in five IUCN Red List categories: Least Critical (LC), Near Threatened (NT), Vulnerable (VU), Endangered (EN) and Critically Endangered (CR) (IUCN 2022). We incorporate the IUCN categories into a model that predicts each species’ extinction threat status from the values vulnerable). Ordered probability models can employ probit or logit estimators, depending upon the distribution of model errors (normal for probit; logistic for logit). We chose ordered logit estimation because it provides a somewhat better fit to the data.

Our model estimation dataset comprises the 87,731 mapped GBIF species that have been assigned depart from machine learning convention by using an explicit model, but we allow for highly nonlinear relations among model variables with a translog specification.

translog estimation terms (the squared logs and log-interactions of risk indicators). Table 1 presents pairwise correlations for the variables in the five estimating equations. The correlations are almost all very small, indicating that collinearity of model variables is not a problem in this difficult to interpret. Table 2 provides a useful partial view by reporting results for a basic model that is restricted to the logs of the relevant risk indicator variables. In this model, the t-statistic for the estimated coefficient of each variable provides an approximate measure of its relative variable across the five estimates, with the appropriate sign and very high significance in all cases. Formal protection has the appropriate sign and consistently high significance in all three terrestrial The translog specification incorporates the logs and squared logs of all model variables, as well as all pairwise interactions among them. It is a second-order approximation to an unknown set of nonlinear relationships.
cases. The marine cases offer a different view of protection, with weak results for formal protection but very strong results for species’ habitats that lie within Exclusive Economic Zones. Population The effect of commercial pressure is evident for marine fish, with positive and highly-significant is insignificant. For terrestrial species, the settlement sensitivity measure yields mixed results. Settlement sensitivity is measured from 1 to 15, indicating the population group that includes the greatest number of species observations. A negative result indicates that (other things equal), the risk is greatest and least, respectively, for species with the most observations in the smallest and largest population groups. However, these basic model results suggest that a different relationship 3.1.3 Implications for Prediction Our database includes risk indicator values for 512,675 species that lack IUCN ratings, and we can use our model estimation results for the 87,731 Red List species to predict extinction threats for probabilities for each IUCN category. We form composite probabilities by adding the assignment probabilities for NT, VU, EN and CR. We express the probabilities as percents, round to the nearest level of aggregation, model predictions track IUCN assignments very closely. In the case of land (percent) probabilities between 0 and 10% of assignment to above-LC status. For this group, IUCN down the rows; in the final row (model-predicted probabilities between 90% and 100%), IUCN with last-row IUCN above-LC assignment probabilities of 92.7% (land plants), 90.3% (other land
Table 1: Correlations between predictive model variables <!-- Start of picture text --> Settlement Sensitivity<br>Population Density<br>Formal Protection<br>Settlement Sensitivity<br>Population Density<br>Formal Protection<br>Settlement Sensitivity<br>Population Density<br>Formal Protection<br>Coastal Population<br>Formal Protection<br><!-- End of picture text -->
# **Table 2:  Ordered logit results for IUCN Red List categories**

##

||Verteb<br>|rates<br>|Pla<br>|nts<br>|Ot<br>|her<br>|
|---|---|---|---|---|---|---|
|Log Variable|Coefficient|t-Statistic|Coefficient|t-Statistic|Coefficient|t-Statistic|
|Species Occurrence|||||||
|Region Size|-0.183|55.25**|-0.319|93.16**|-0.138|23.17**|
|Settlement Sensitivity|-0.050|2.77**|0.079|4.24**|-0.126|4.06**|
|Population Density|0.054|11.02**|0.022|4.50**|-0.031|3.43**|
|Formal Protection|-0.038|5.93**|-0.035|5.53**|-0.076|6.84**|
|N|24,943||32,667||6,681||
|R2|0.12||0.22||0.11||
|R2(Full)<sup>a</sup>|0.22||0.29||0.26||

##

||Fis<br>|h<br>|Other S<br>|pecies<br>|
|---|---|---|---|---|
|Log Variable|Coefficient|t-Statistic|Coefficient|t-Statistic|
|Species Occurrence Region|||||
|Size|-0.034|7.80**|-0.227|34.79**|
|Exclusive Economic Zone|-0.256|24.22**|-0.075|10.34**|
|Fishing Intensity (1)|-0.01|1.75|||
|Fishing Intensity (2)|0.047|6.11**|||
|Coastal Population Influence|||0.034|5.67**|
|Formal Protection|||-0.013|1.69|
|N|8,530||14,897||
|R2|0.09||0.14||
|R2 (Full)<sup>a</sup>|0.27||0.22||

a Including translog terms.

11
**Table 3: Model-predicted risk assignments vs. IUCN assignments**

|Model<br>Predictions:|Verteb|rates|Pla|nts|Other|Species|
|---|---|---|---|---|---|---|
|Probability||Above||Above||Above|
|Group (%)|LC|LC|LC|LC|LC|LC|
|0|94.8|5.22|94.8|5.25|94.9|5.09|
|10|86.5|13.5|85.9|14.1|85.5|14.5|
|20|74.6|25.4|76.7|23.3|75.7|24.3|
|30|65.7|34.3|65.9|34.1|65.1|34.9|
|40|53.5|46.5|56.6|43.4|54.7|45.3|
|50|45.9|54.1|45.9|54.1|42.0|58.0|
|60|35.7|64.3|30.4|69.6|33.5|66.5|
|70|23.6|76.4|21.7|78.3|21.1|78.9|
|80|13.2|86.8|14.5|85.5|17.7|82.3|
|90|4.9|95.1|7.3|92.7|9.68|90.3|

#

**(1b:  Marine)**

|Model<br>Predictions:|Fi|sh|Other Sp|ecies|
|---|---|---|---|---|
|Probability||Above||Above|
|Group (%)|LC|LC|LC|LC|
|0|95.9|4.05|91.4|8.59|
|10|87.5|12.5|80.4|19.6|
|20|73.7|26.3|74.5|25.5|
|30|60.7|39.3|67.8|32.2|
|40|50.4|49.6|58.1|41.9|
|50|48.6|51.4|46.3|53.7|
|60|32.6|67.4|32.9|67.1|
|70|23.4|76.6|23.4|76.6|
|80|22.6|77.4|14.2|85.8|
|90|33.3|66.7|10.8|89.2|

12
|3.1.4 Spatial Implications<br>In this subsection, we assess the model by comparing global maps for its predictions and IUCN<br>occurrence region [0-100] to facilitate comparison.<br>Figure 2 displays the global distribution of<br>comparison with Figure 2, each cell value in Figure 3 is the sum of total predicted probabilities for<br>all overlayed species (normalized to [0 — 100]).<br>Spatial patterns in the two figures are strikingly<br>Figure 3 displays total model-predicted cell probabilities for GBIF plants and vertebrates that are<br>rated in the IUCN database. We extend the exercise by using the estimated ordered logit model<br>vertebrates) that are not rated by IUCN. Addition of these species more than triples the size of<br>the application database (from 77,000 to 281,000). Figure 4 replicates the methodology of Figure<br>displaying the cellwise changes from Figure 5 to Figure 6 (in normalized values).<br>Incorporating<br>coastal North America, the Northern part ofSouth America and Atlantic Brazil, coastal West Africa,<br>emphasize that while normalization to the species occurrence region<br>[0 — 100]<br>is useful for<br>comparisons, it should not mask the fact that tripling the species population has the effect of<br>raising absolute values across the board.<br>A balanced interpretation of Figures<br>2 — 5 should<br>therefore note that many more potentially-threatened species have been identified across the<br>globebythisexercise,whilethespatialdistributionofthreatenedspecieshasalsobeenaltered.|
|---|
<!-- Start of picture text --> Biodiversity Index Value<br>1st dec. 3rd dec. 5th dec. [| 7th dec. [| 9th dec.<br>2nd dec. Ath dec. 7 6th dec. |_| 8th dec. [| 10th dec.<br><!-- End of picture text -->
Figure 3: Plants and vertebrates: global cell totals for model-predicted threat probabilities, IUCN species match <!-- Start of picture text --> Biodiversity Index Value<br>1st dec. 3rd dec. 5th dec. [| 7th dec. | | 9th dec.<br>2nd dec. Ath dec. J 6th dec. [| 8th dec. [| 10th dec.<br><!-- End of picture text -->
Figure 4: Plants and vertebrates: global cell totals for model-predicted threat probabilities, all GBIF Species <!-- Start of picture text --> t — )0=—tiéd an<br>% EN , w Pa y<br>Biodiversity Index Value<br>1st dec. 3rd dec. 5th dec. [| 7th dec. [| 9th dec.<br>2nd dec. Ath dec. a 6th dec. [| 8th dec. [| 10th dec.<br><!-- End of picture text -->
Figure 5: Plants and vertebrates: difference in model-predicted threat probabilities, IUCN match vs all GBIF species <!-- Start of picture text --> Le 5 ‘ ae # ee ia ye ” Sa ; Ne .<br>ae. a.<br>Biodiversity Index value Difference<br>[| [-28,-2.54] J (-0.284,-0.0748] (0.001,0.32] | (0.973,2.11]<br>| | (-2.54,-0.82] (-0.0748,-0.001] J (0.32,0.577] : | (2.11,37.7]<br>|| (-0.82,-0.284] (-0.001,0.001] [| (0.577,0.973] NA<br><!-- End of picture text -->
3.2 Alternative Composite Indicators While species occurrence region size is commonly cited as the best predictor of extinction risk comparative exercise includes 87,718 IUCN-assessed terrestrial and marine species that we have mapped with GBIF data. Among these, many species with tiny species occurrence regions (grid ) are rated Least Critical (LC) by IUCN. However, it would be reasonable to assert that species with such tiny species occurrence regions are in constant jeopardy, because spatial economics evolve unpredictably and modern systems can clear large areas in time periods At the same time, the econometric results in Table 2 show that habitat size is the most significant determining factor for IUCN threat ratings in all five estimation groups. Figure 6 reveals the source of this apparent contradiction. To create the figure, we have computed the percent of LC species <!-- Start of picture text --> @<br>60- ee”<br>= e* e<br>Oo r) e<br>oD e<br>& 50- oe<br>aa e@<br>3 ooo°<br>w eo”<br>7 ee<br>@ 40- @ ee<br>vann e®<br>o@<br>°ry<br>oO e<br>oa 30- @<br>Con”<br>0 50 400 150 200<br>Species Occurrence Map Scale (km)<br><!-- End of picture text --> In this subsection, we explore the implications of assigning greater priority to small- species
1 in the top 77,290 positions. In the model-predicted threat probability index, the scale composition of the top 77,290 positions is quite different. Among these, for example, only 3.2.2 Spatial Implications To compare spatial distributions before and after the change, we divide the original (Figure 4) after stacking, we construct variables in which species are assigned values equal to their maximum group percentiles [5, 10, ..., 95, 100]. Then we overlay all GBIF species maps for vertebrates and plants on our 0.25 degree grid, compute grid cell sums for the before and after northwest South America. At this scale, small- species occurrence region prioritization via stacking clearly makes a difference; areas with relatively high threat ratings expand eastward and grow markedly in size. By implication, species with very small habitats are more widely In contrast, modest relative increases are observable in many of the areas with highest threat status in Figure 7 (Mexico, the western Andean region, Atlantic Brazil, coastal West and Central
Figure 7: Plants and vertebrates: Global cell totals for model predictions by threat group ae a j —= I —° - ~~" Biodiversity Index Value 1st dec. 3rd dec. | 5th dec. [| 7th dec. [| 9th dec. 2nd dec. Ath dec. [| 6th dec. [| 8th dec. [| 10th dec.
Figure 8: Plants and vertebrates: Global cell totals for model-predictions after priority reassignment for small species occurrence <!-- Start of picture text --> i 4 © 4 : : F<br>Biodiversity Index Value<br>1st dec. 3rd dec. [J 5th dec. |_| 7th dec. [| 9th dec.<br>2nd dec. Ath dec. [| 6th dec. [| 8th dec. [| 10th dec.<br><!-- End of picture text -->
<!-- Start of picture text --> Panel a: Model-based prediction<br><!-- End of picture text --> <!-- Start of picture text --> Panel b: After priority reassignment for small range species<br><!-- End of picture text --> Biodiversity Index Value 1st dec. 3rd dec. 5th dec. [| 7th dec. || 9th dec. 2nd dec. ath dec. J) 6th dec. J sth dec. I t0th dec.
Figure 10: Plants and vertebrates: differences in global cell totals for model-predictions after priority reassignment for small species <!-- Start of picture text --> Se ee 4<br><!-- End of picture text --> Biodiversity Index value Difference [| [-30.1,-0.0903] a (-0.0467,-0.0235] (0.001,0.24] [| (2.49,8.36] [| (-0.0903,-0.0668] (-0.0235,-0.001] (0.24,0.685] [| (8.36,44.3] [| (-0.0668,-0.0467] (-0.001,0.001] ] (0.685,2.49] NA
In our econometric analysis, estimation of the ordered logit model for each of five groups identified four risk indicators that play significant roles in determining extinction threat probabilities. Our database identifies these as Factors 1 to 4, with accompanying definitions that are included in Table 1. We have devoted considerable attention to prediction of species-level threat probabilities from econometric estimation of the relationships between these factors and the factors themselves. In this section, we develop and explore the implications of three such be interpreted as the most conservative available indicator, because each species’ threat measure dominated by one outlier among the four factors. The standard alternative is the mean value, which incorporates all available information at the cost of some outlier risk.

An important task for this exercise is to determine whether the spatial distributions produced by these alternative indicators are similar or quite different. Our core comparison includes four values from our 4 factor variables. In addition, we acknowledge the econometric dominance of Figure 11 displays all five indicators with identical color scales for ease of comparison. Visual inspection indicates that the Max, Median and Mean indexes have only second-order differences. Some differences are apparent on close scrutiny, but the overall global pattern is basically the same. When Factor 1 Species Occurrence Region Scale) alone is employed, the identified threat
<!-- Start of picture text --> Panel a: Model-Predicted Panel b: Range scale<br>eS _ = ee —<br>= a<br>Panel c: Max Index Panel d: Median Index<br>a b C : y —_ . is a , = Df<br>Panel e: Mean Index<br>Biodiversity Index Value<br>1st dec. arddec. |) sthdec. [MM 7th dec. [I oth dec.<br>2nd dec. Ath dec. || 6th dec. | 8th dec. || 10th dec.<br><!-- End of picture text -->
species from georeferenced data provided by the Global Biodiversity Information Facility (GBIF). In this paper, we have combined those maps with extinction threat factors to produce composite global indicators of extinction threat that provide new insights into the spatial domain for conservation planning. The paper develops threat factors for terrestrial species that include their species occurrence region sizes, their sensitivity to human encroachment, the density of neighboring populations, and the degree of formal protection. We also develop threat factors for marine species that include species occurrence region size, degree of formal protection, degree of coverage by EEZs (Exclusive Economic Zones), the impact of coastal population, and two measures of commercial fishing activity. The paper explores three approaches to composite threat indicator construction. The first comes from econometric estimates of the relationship between our threat indicators and expert-based Red List ratings from IUCN. Our approach extends numerous pilot exercises in the published database. The model uses our threat indicators to estimate the probability that a species is assigned a threatened status (NT, VU, EN, CR) by IUCN. We find a close statistical association between the threat probabilities predicted by our model and IUCN threat status assignments. We also find a close spatial association between the two variables. We use our econometric results to predict threat probabilities for mapped GBIF species that have not been rated by IUCN. We add these to the comparative mapping exercise and find that radical expansion of the species We construct a second composite indicator that assigns higher conservation priority to species based threat indicator, but modifies it to accommodate grouping by habitat scale. A spatial are more widely distributed than threatened species generally. By implication, assignment of warrant formal protection.

|medians and means of the four factors that have significant effects on IUCN threat ratings for<br>second-order differences at global scale.<br>We compare their spatial pattern with the pattern<br>concentration of high-threat areas in the latter case, although the overall spatial pattern of<br>indicators.<br>Thistrendcontinuesforthemodel-based<br>indicator,whichshowsevenmore|
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al. 2023) by showing that the GBIF’s radical expansion of georeferenced species information has significant implications for global conservation planning. From a static perspective, spatial analysis suggests that broadening species representation also broadens the critical domain for biodiversity conservation. The concurrent availability of new risk indicators also broadens the scope for stakeholder participation because, as our results show, identification of high-priority by adding a dynamic perspective as well. Continued rapid growth of the GBIF database will both expand the number of mapped species and alter the estimated boundaries of existing maps. As this process continues, the global stakeholder community will be best served by a georeferenced database that supports continually updated estimation of species occurrence maps and the associated extinction threat indicators.