Potential consequences of regional species loss for global species

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Characterization of Natural and Affected Environments

Potential consequences of regional species loss for global species richness – a quantitative approach for estimating global extinction probabilities Koen Jacobus Josefus Kuipers, Stefanie Hellweg, and Francesca Verones Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b06173 • Publication Date (Web): 17 Apr 2019 Downloaded from http://pubs.acs.org on April 18, 2019

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Environmental Science & Technology

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Potential consequences of regional species loss for global species richness—a

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quantitative approach for estimating global extinction probabilities

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Koen J.J. Kuipers*†, Stefanie Hellweg‡, Francesca Verones†

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†

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Trondheim, Norway

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‡

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* corresponding author: [email protected]

Industrial Ecology Programme, Department of Energy and Process Engineering, Norwegian University of Science & Technology (NTNU), NO-7491

Ecological Systems Design, Institute of Environmental Engineering (IfU), ETH Zürich, CH-8093 Zürich, Switzerland

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Abstract. Because the biosphere is highly heterogeneous, species diversity impacts are

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typically assessed at local or regional scales. Since regional species richness impact metrics

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refer to different species compositions, they cannot be easily compared or aggregated to global

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impacts. Translating regional species richness impacts into global impacts allows for

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comparisons between impacts and facilitates the estimation of global species extinctions. This

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requires a conversion (or weighting) factor that takes into account the characteristics of

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regionally-specific species compositions. We developed a methodology to derive such

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conversion factors based on species’ habitat ranges, IUCN threat levels, and species richness.

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We call these conversion factors global extinction probabilities (GEPs) of the reference location

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or region. The proposed methodology allows for the calculation of GEPs for any spatial unit and 1 ACS Paragon Plus Environment


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species group for which data on spatial distribution is available, and can be implemented in

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methodologies like life cycle impact assessment. Furthermore, the GEPs can be used for the

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identification of conservation hotspots. The results of the proposed GEPs (for various taxonomic

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groups) show that the risk that regional species loss may result in global species extinctions

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significantly differs per region, and informs where irreversible biodiversity impacts are more

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likely to occur.

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Keywords. Species extinctions; species loss; biodiversity impacts; species richness;

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ecoregions; global extinction probability (GEP); species distribution; IUCN red list; threat level;

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life cycle impact assessment (LCIA)

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TOC Art

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1.

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Human activities progressively affect ecosystems, causing biodiversity loss and alterations,

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ultimately threatening the biosphere’s integrity globally.1–4 These ecosystem changes are

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severe, and comparing current species extinction rates to those during and between the

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previous five mass extinction events indicates that we have entered a sixth mass extinction.5–11

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Furthermore, human influence is considered to be currently the dominating factor driving

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environmental change and some argue that this pushed us into a new geological epoch, the

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Anthropocene.8,12–15

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Humans have the capacity to influence trends regarding ecosystem change, indicating that

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conservation efforts may significantly reduce biodiversity loss.3,4,16,17 The biosphere is a highly

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heterogeneous system18 and in order to organise international conservation efforts effectively,

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biodiversity hotspots are identified for the establishment of priority areas for the protection of

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ecosystems.19 The identification of these hotspots is based on a number of characteristics

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including species range sizes, species vulnerabilities, and species richness.20,21 Furthermore,

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since anthropogenic stressors are not uniformly distributed, international conservation efforts

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require environmental assessments to inform where impacts are happening and how these

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could be mitigated.22 Approaches that help to assess these impacts need to be spatially explicit,

Introduction

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since nature changes widely in space and differences in environmental conditions and species

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richness and composition need to be reflected. In addition, most anthropogenic impacts take

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place along global value chains, substantiating the need for global, spatially explicit

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assessments in order to trace these impacts along complete value chains.

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Life cycle assessment (LCA) is such a method that quantifies potential impacts of product

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systems along global value chains.23 Spatially explicit life cycle impact assessments (LCIAs)

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estimate, inter alia, regionally specific species loss. Local or regional species richness impacts

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(e.g., species loss) cannot be easily aggregated or compared for several reasons. First, many

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species occur across various regions. Hence, regional species loss does not necessarily lead to

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global species loss.24 Therefore, aggregating regional species loss across regions may lead to

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double counting of species. Second, some regions host more threatened species than others.25

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It is more likely that regional losses in those regions lead to global extinctions than in regions

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associated with fewer threatened species. Third, statistically, there are more threatened species

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present in locations characterised by high species density compared to locations characterised

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by low species density.2 Consequently, impacts occurring in high-diversity locations are more

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likely to lead to global extinctions than impacts occurring in low-density locations.


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Hence, to assess or compare the environmental footprints of cross-regional value chains,

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regional species richness impacts should be converted into globally comparable and summable

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impacts.26 A first attempt in doing so was carried out by Verones et al.27 who created factors (so-

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called vulnerability scores) to convert regional species richness impacts into potentially global

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species extinctions at the ecoregion scale. However, they only created conversion factors for

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four terrestrial taxonomic groups (amphibians, birds, mammals, and reptiles), thereby omitting

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marine and freshwater species. Moreover, the sum of their regionally specified (i.e., ecoregion)

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conversion factors exceeds the value of 1, meaning that the application of these vulnerability

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scores allows for a prediction of more than 100% species loss if this is not corrected for.

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We develop a methodology to derive scores—based on species range sizes, species

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vulnerabilities, and species richness—indicating to what extent regional species loss in the

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respective area may contribute to global species loss. We call these scores global extinction

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probabilities (GEPs) and generate them for marine, terrestrial, and freshwater species groups

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on the local (i.e., 0.05° × 0.05° grid) and ecoregion scale. As the sum of all area scores adds up

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to 1, the methodology predicts 100% global species extinction if regional species loss is 100%

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in all regions. In effect, these area scores can be used to translate fractions of species lost per

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region into potential global species extinctions in ecosystem assessments and tools like LCA. 5 ACS Paragon Plus Environment


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Furthermore, these GEPs can assist in the identification of priority conservation areas.

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Effectively, GEPs can be used as weighting factors to weight regional species diversity impacts

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(in terms of regional fraction of species lost) to assess its global implications.

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It is important to note that the GEP should be interpreted as a proxy of global species

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extinctions if regional extinctions occur. That is, a GEP of 0.1 would mean that if all species

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would disappear from a region, global species diversity would be reduced by 10%.

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2.

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The presented methodology builds upon the proposed vulnerability scores from Verones et

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al.27—which have been applied in Verones et al.28–30 and Chaudhary et al.31–34 —and

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incorporates concepts like the extent of occurrence35, endemic richness36–38, range rarity,20,37,39

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and the IUCN Red List of threatened species.40 The identification and quantification of priority

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conservation areas and GEPs requires spatial data on global species distributions and species

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threat levels, and the identification of administrative spatial units (e.g., countries or ecoregions).

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2.1.

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Since species respond heterogeneously to anthropogenic pressures, species richness impact

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assessments are often tailored to taxonomic groups. For this reason, we generate GEPs for

Materials and Methods

Quantifying the global extinction probabilities (GEPs)


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various taxonomic groups in marine, terrestrial, and freshwater environments. Apart from

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individual taxonomic groups, the GEPs can be calculated for any other specific species groups.

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When using the GEPs to translate regional species loss into potential global species extinctions,

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it is important that the GEP refers to the same species group(s) and spatial unit to which the

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regional species loss refers to. For instance, translating mammal extinctions in Madagascar into

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global mammal extinctions requires a GEP referring to mammals in Madagascar—using a GEP

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that refers to vertebrate species in East Africa is not appropriate.

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Calculating GEPs requires data on species ranges, threat levels, and distributions. The first step

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in the calculation process is to rasterise each species range individually, basing the cell values

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on the occurrence of the species in that location (0.0-1.0, Table 1) and a value of 0 in the cells

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where the species is not present (gridding based on overlap with the centre of the cell). The

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occurrence value of the species in the cell is 1 if the species is extant, and progressively lower

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as the uncertainty about its presence increases (Section 2.2). We restricted the occurrence of

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marine species to marine ecoregions, terrestrial species to terrestrial ecoregions, and

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freshwater species to freshwater bodies. We did not refine the geographical range based on

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elevation and habitat preferences because of limited data availability regarding these

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preferences for all species. Second, range rarity is calculated by dividing all species’ cell values 7 ACS Paragon Plus Environment


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by the global sum of the cell values of its distribution, resulting in cell values that represent a

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fraction of the total species’ occurrence. Hence, individual cell values of species with small

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ranges are relatively high compared to species with widespread ranges. Third, all species

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rasters are aggregated, weighting each species raster based on the species’ threat level (Table

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2, Sections 2.3 and 2.5). Cells that contain a relatively high number of threatened species with

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small ranges will have relatively high values. Fourth, all cell values in the aggregated raster are

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divided by the sum of all species’ threat levels, resulting in cell values that individually represent

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a fraction of the total—i.e., the sum of all cell values of the species group is 1. These cell values

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represent the GEP per grid cell (in arc degrees).

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This process is illustrated in equation 1, where o is the occurrence value of species s in cell p,

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and TL is the threat level of species s.

∑

𝑜𝑠,𝑝 ∙ 𝑇𝐿𝑠 𝑠 ∑𝑝𝑜𝑠,𝑝

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𝐺𝐸𝑃𝑝 =

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The result is an aggregated cell-level map for each species group. The cell values can be

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interpreted as proxies for the contribution of regional species extirpations in the cell location

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lead to global species extinctions. Hence, cells with high values should receive high priority for

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the conservation of global species richness. Thereby, GEPs provide an important component for

(1)

∑ 𝑇𝐿𝑠 𝑠


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the quantification and ranking of areas of importance for the conservation of global species

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richness.

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Upscaling the cell-level map to the desired spatial units involves the aggregation of all cells in

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the spatial unit. That is, the value of the spatial unit is the sum of all the cells it contains, as

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shown in equation 2 where r denotes the spatial unit (e.g., ecoregion). The global sum of the

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values (GEPs) of all spatial units is 1. These values can be interpreted analogously to the cell-

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level GEPs, but instead of referring to a single cell, the GEPs refer to a bigger spatial unit. Since

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the ecoregion-level GEPs are aggregated cell-level GEPs, large ecoregions will generally have

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higher GEPs. Conceptually, this makes sense as the GEP quantifies the contribution of species

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extirpations in a region to global species extinctions.

∑

𝑜𝑠,𝑝,𝑟 ∙ 𝑇𝐿𝑠 𝑠 ∑𝑝,𝑟𝑜𝑠,𝑝,𝑟

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𝐺𝐸𝑃𝑟 = ∑𝑝

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2.2. Species distribution data

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Spatially explicit data of species ranges is extracted from the International Union for

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Conservation of Nature’s (IUCN) Red List spatial data41 and BirdLife’s species distribution

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data.42

(2)

∑ 𝑇𝐿𝑠 𝑠



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For the marine environment, we selected all marine areas on continental shelves, as specified

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by the World Wildlife Fund (WWF) marine ecoregions of the world.43 We assessed five

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taxonomic groups individually for which species-specific spatial data was available:

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chondrichthyes (1,088 species), corals (1,476 species), marine fish (2,562 species), marine

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mammals (125 species), and seagrasses (6,365 species). For the terrestrial environment, we

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selected all terrestrial areas, as specified by the WWF terrestrial ecoregions of the world.44 We

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assessed four taxonomic groups individually: amphibians (6,490 species), birds (11,120

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species), mammals (5,303 species), and reptiles (4,923 species). All bird species—i.e., marine,

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terrestrial, and freshwater birds—are assessed together, as all bird species need terrestrial

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habitat for breeding. For the freshwater environment, we selected all freshwater areas, as

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specified by the WWF freshwater ecoregions of the world.45 We assessed seven taxonomic

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groups combined: crabs (1,257 species), crayfish (498 species), freshwater fish (6,410 species),

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molluscs (1,406 species), freshwater plants (1,323 species), odonata (1,476 species), and

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freshwater shrimps (699 species). Note that any environment and any (combination of) species

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group(s) could be chosen with the proposed methodology, as long as spatial data and species

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threat levels are available.


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The IUCN41 and BirdLife42 data distinguish six categories of species occurrences in the species’

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spatial data: (1) extant, (2) probably extant, (3) possibly extant, (4) possibly extinct, (5) extinct,

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and (6) presence uncertain. We use these categories as a weighting factor for the importance of

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each local occurrence for the total species distribution. The weighting scheme is based on the

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weighting scheme used by Pouzols et al.46 for their global conservation priority maps (Table 1).

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We use weighting to exclude locations where species are currently not present anymore, and to

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attribute lower importance to locations where species presence is uncertain. Disregarding this

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type of weighting may skew species distributions to regions where the species are not present

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anymore.

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Table 1. Weighting scheme for species occurrence in total distribution.

Presence

Cell Value

Extant

1.0

Probably extant

0.5

Possibly extant

0.5

Possibly extinct

0.1

Extinct

0.0

Presence uncertain

0.0



172 173

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2.3.

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The IUCN Red List of Threatened Species41 is used for determining the threat level of the

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species. The IUCN Red List distinguishes ten threat level categories: (i) extinct, (ii) extinct in the

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wild, (iii) regionally extinct, (iv) critically endangered, (v) endangered, (vi) vulnerable, (vii) lower

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risk, (viii) near threatened, (ix) data deficient, and (x) least concern. These categories are used

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as a weighting factor for the importance of each species relative to other species in the same

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species group. Furthermore, it is used to exclude species distributions in locations where these

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species have become extinct (categories i-iii). The default weighting scheme is based on the

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weighting scheme used by Pouzols et al.46 (Table 2, linear scheme column, values from 0-8).

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2.4.

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In order to be able to do calculations with the species distribution maps from IUCN and BirdLife,

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the shapefiles of all species’ distributions (polygons) are rasterised. The size of the raster cells

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can be chosen arbitrarily. Small raster cells increase the level of detail, but involve longer

Species threat level data

Spatial resolution and scale


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computation times. Here, a grid size of 0.05° × 0.05° (approximately 5.5 latitude × 5.6 longitude

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km near the equator) was chosen, following Verones et al.28

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For practical reasons for those wanting to apply the GEPs, the cell-level maps are also

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aggregated to administrative or ecologically homogeneous spatial units (e.g., countries or

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ecoregions). These can be chosen arbitrarily, as long as the spatial units are bigger than the

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grid cells. Here, the WWF ecoregions of the world were chosen as the spatial units. These

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comprise 232 distinct marine,43 827 terrestrial,44 and 449 freshwater ecoregions.45 These

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ecoregions reflect biogeographic classifications of the complex distribution of the Earth’s natural

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communities, and are therefore, from an ecological perspective, more appropriate to use for

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priority conservation areas and GEPs than administrative units like countries.

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2.5.

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In the aggregation process, the weighting of the species based on the IUCN Red List threat

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level classification25 requires conversions from the qualitative classification into an arbitrary

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quantitative classification. We adopted Pouzols et al.’s46 non-linear quantification scheme for the

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species threat levels, ranging from 0 to 8. However, as this quantification is arbitrary, other

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quantification schemes could be chosen. Verones et al.27,28,30 and de Baan et al.24 used for

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example a linear approach for their quantification schemes of the IUCN Red List threat levels.

Sensitivity of the threat level parametrisation



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To estimate the sensitivity of these arbitrary parameter choices, we performed the above-

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mentioned analysis with three distinct IUCN Red List threat level quantification (parametrisation)

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schemes: (1) a linear approach based on Verones et al.27,28,30 and de Baan et al.24, (2) a

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categorical approach based on Pouzols et al.,46 as introduced earlier, and (3) a logarithmic

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approach based on Butchart et al.47 to include a case where particular attention is paid to

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species that are relatively close to extinction (Table 2).

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Table 2. IUCN Red List threat level quantification schemes

IUCN Red List threat level

Linear scheme

Categorical scheme

Logarithmic scheme

Extinct

0.0

0.0

0.0

Extinct in the wild

0.0

0.0

0.0

Regionally extinct

0.0

0.0

0.0

Critically endangered

1.0

8.0

1.0

Endangered

0.8

6.0

1.0E-1

Vulnerable

0.6

4.0

1.0E-2

Lower risk

0.4

2.0

1.0E-3

Near threatened

0.4

2.0

1.0E-3

Data deficient

0.2

2.0

1.0E-4


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Least concern

0.2

1.0

1.0E-4

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The sensitivity of the choice of quantification scheme is analysed by assessing the magnitude of

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the differences between the ecoregion GEP results when adopting the different threat level

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quantification schemes. Maps are produced to qualitatively assess the differences between the

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different parameter schemes and boxplots are used to show the distribution of the GEP values

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for each quantification scheme.

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2.6.

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The GEP merges range rarity, species threat levels, and species richness. The IUCN Red List

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threat levels are determined based on whether the species meets certain criteria. Most of these

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criteria refer to the total number of individuals, size and number of subpopulations, and

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fluctuations therein.48 In addition, criterion B of the IUCN Red List classification scheme48 refers

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to combinations of size, fragmentation, and decline of, and fluctuation in the extent of

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occurrence and the total number of individuals and number and size of subpopulations.

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Consequently, information about the size of the species’ geographical range may enter the GEP

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twice.

Analysing spatial patterns of range rarity and threat levels individually



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In order to check whether combining the two indicators leads to double counting of range size,

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we calculate mean range rarity20,37,39 and mean threat level scores for terrestrial mammals and

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derive Spearman correlation coefficients—as the data is not normally distributed (figure S9)—for

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range rarity and mean threat level scores with the linear, categorical, and logarithmic

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parametrisation schemes.

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Mean range rarity (MRR) is the average range rarity in the grid cell across all species in the

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species class, as illustrated in equation 3, where n is the number of species present in grid cell

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p. Similar to the GEP, the range rarity is based on presence in a cell (Table 1).

𝑜𝑠,𝑝

∑𝑛

𝑠 = 1∑𝑝𝑜𝑠,𝑝

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𝑀𝑅𝑅𝑝 =

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Similarly, the mean threat level (MTL) is the average threat level in the grid cell across all

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species in the species class, as illustrated in equation 4.

(3)

𝑛𝑝

∑𝑛

𝑇𝐿𝑠,𝑝

𝑠=1

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𝑀𝑇𝐿𝑝 =

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For the correlation coefficients, the cell-level mean range rarity and mean threat levels are

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aggregated to the ecoregion-level by calculating the average MRR and MTL of the cells

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contained in the ecoregion. There are a few outliers (always islands, which typically host

(4)

𝑛𝑝


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endemic species) in both the ecoregion-level mean range rarity and mean threat level of

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terrestrial mammals. We tested the correlation for all ecoregions, and with the most extreme

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outlier (Ogasawara subtropical moist forests) omitted.

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2.7.

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To demonstrate the application and effect of the GEP, the terrestrial mammal GEPs (using the

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categorical parametrisation scheme for TL values) have been used in combination with spatially

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explicit (ecoregions) characterisation factors, developed by Chaudhary and Brooks,49 indicating

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the predicted number of mammal species going extinct per m2 land used (occupied) for

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agriculture. we converted their characterisation factors for regional mammal extinctions into

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fractions of regionally disappeared mammal species by dividing the species loss data through

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regional species richness in each ecoregion (table S4). The converted characterization factors

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in terms of regional fractions of mammal extinctions per m2 were then multiplied with the

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corresponding GEPs (categorical parametrisation scheme) to calculate potential global

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fractions of mammal extinctions per m2.

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Subsequently, we ranked the ecoregion-specific characterisation factors based on predicted

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fractions of regional extinctions and predicted fractions of global extinctions, and compared the

Case study



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rankings to illustrate how including the GEP can shift the severity of the impacts spatially when

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translating regional species diversity impacts into global species diversity impacts.

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3.

Results and discussion

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3.1.

Cell-level GEPs

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The cell-level GEPs of marine fish, terrestrial mammals, and freshwater groups are shown in

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Figure 1—maps of the other species groups can be found in the Supporting Information 1 (SI-1,

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Figures S1-3). All species groups show similar patterns: the differences between maximum and

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minimum cell values are high (4-9 orders of magnitude); cell values can differ substantially,

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even on local scales; and cells near the equator have generally higher values compared to

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moderate latitudes because species density and the number of endemic species is generally

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high near the tropics.

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Furthermore, marine fish have high GEPs close to coastal areas relative to marine areas further

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away from the coasts. This also holds for most other marine groups (SI-1, Figure S1). As most

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fisheries occur close to coastal areas, this indicates that there is an increased risk of marine

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species extinctions. The distribution of marine mammal GEPs shows a different pattern

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compared to the GEP distributions of most other species. Instead of having high cell values 18 ACS Paragon Plus Environment

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near the equator, marine mammal GEPs are generally higher at moderate latitudes. For some

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marine groups, the cell-level GEPs appear absent in some regions (e.g., subantarctic regions

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for marine fish). This can either mean that there are no species of that species group in those

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regions, or that there is no data on species of that species group in those regions.

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For terrestrial areas, only the birds group has species occurring everywhere, while mammal

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species span across all terrestrial areas with the exception of Antarctica. Islands, especially

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those near the equator, have distinctively high GEPs. This is mainly due to relatively high levels

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of species richness near the equator and because islands generally host high number of

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endemic species.38 In addition, this may be due to the relatively high number and share of

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endangered species in the tropics (Figure S7).25 A combination of both high number of endemic

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and endangered species is also possible. Differences in GEP cell values are more pronounced

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for amphibians and reptiles compared to birds and mammals. This can be explained by

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amphibians and reptiles having generally smaller ranges being more confined to specific

286

habitats, whereas birds and mammals have generally more widespread distributions across a

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great variety of habitats.25

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The distribution of GEPs of freshwater species is similar to the distribution of terrestrial species

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(i.e., generally higher values near the tropics and lower values in arctic regions). Nevertheless, 19 ACS Paragon Plus Environment


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in contrast to the GEPs of terrestrial species, the eastern part of the USA is highlighted by high

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GEP values for the freshwater groups.

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Figure 1. Cell-level GEPs of a) marine fish, b) terrestrial mammals, and c) freshwater groups (legend

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scaled logarithmically). The maps use the marine,43 terrestrial,44 and freshwater45 ecoregions of the world,

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and the Global Lakes and Wetlands Database50 to delineate borders.

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3.2.

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As ecoregion-level GEPs are aggregations of the cell-level GEPs, the ecoregion-level GEP

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maps convey similar patterns (Figure 2): high GEP values near the equator. Differences

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between the maximum and minimum values of ecoregion GEPs are not as extreme as the

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differences between the cell-level GEPs. Maps of the other species groups can be found in SI-1

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Figures S4-6, and a complete list of all ecoregion GEPs can be found in SI-2 Tables S2-4.

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Relatively big ecoregions contain a high number of cells. Consequently, the GEP of a relatively

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big ecoregion can be high in comparison to a smaller ecoregion, even though the average

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individual cell values might be relatively low. Conceptually this makes sense: big ecoregions are

305

generally more likely to contain more species in total than small ecoregions; therefore, if all

306

species would be lost in a big ecoregion, this is likely to have relatively big consequences for

307

global biodiversity loss. Nevertheless, these results show that ecoregion size is not the

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dominating factor, as the ecoregions with the highest species density, smallest species ranges,

Ecoregion-level GEPs



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and highest number of threatened species stand out—often located near the equator—, no

310

matter the size of the ecoregion.

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However, although big ecoregions may have relatively high GEPs, the actual number of species

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lost in big ecoregions is often relatively low because chances are smaller that a species gets

313

extinct across a large region compared to a small region. In the species-area curve, relating the

314

size of natural habitat to species richness,51,52 this is represented by the flattening of the curve

315

with increasing area. Hence, when estimating potential global extinctions in large ecoregions,

316

the relatively low number of species lost regionally may be combined with a high GEP indicating

317

that if these species become extinct in this large region, it is relatively likely that they become

318

extinct globally.


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319 320

Figure 2. Ecoregion-level GEPs of a) marine fish, b) terrestrial mammals, and c) freshwater groups

321

(legend scaled linearly). The maps use the marine,43 terrestrial,44 and freshwater45 ecoregions of the

322

world to delineate borders.



323

3.3.

324

The results shown above should be interpreted carefully. GEPs can be interpreted as

325

approximations of the percentage of species gone extinct globally if all species in a specific

326

location or region will be lost. Important to note is that the GEP does not estimate actual species

327

extinctions, but that it approximates the consequences of fractions of species disappeared per

328

region for global species diversity. That is, the GEP can be used as a standalone hotspot

329

indicator when used at homogeneous spatial resolution (e.g. cell level), but to become an

330

appropriate impact indicator for global extinction risks at ecoregion level, it needs to be

331

combined with fractions of regionally lost species. Essentially, the GEP informs about the

332

potential irreversibility of impacts if the local or regional ecosystems are exposed to pressures

333

and consequently lose species richness.

334

Hence, GEPs can serve several purposes: they can be used as indicators of potentially

335

vulnerable areas in terms of global species loss; and they can be used as conversion factors to

336

translate fractions of regional species lost into potential global species extinctions in

337

combination with impact assessments to estimate global biodiversity impacts due to

338

anthropogenic pressures. If the GEPs are used as conversion factors to translate regional

339

species loss (e.g., potentially disappeared fraction of species within a region) into potential

Application


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340

global species extinctions, it is important that the GEPs refer to the same spatial unit as to which

341

the regional species loss refers. Potential global species extinctions or global potentially

342

disappeared fractions are calculated by multiplying the regional potentially disappeared fractions

343

with the GEP corresponding to the same taxonomic group and spatial unit, as shown in the case

344

study. When used in this context, the GEP indicates the extent to which regional fractions of

345

species lost contribute to global species extinctions.

346

GEPs can also be used in contexts different from translating regional species loss into

347

potentially global species extinctions—for example, to identify priority conservation areas. The

348

WWF terrestrial ecoregions are attributed with three conservation status categories: (1) critical

349

or endangered (54% of the terrestrial ecoregions), (2) vulnerable (27% of the terrestrial

350

ecoregions), and (3) relatively stable or intact (19% of the terrestrial ecoregions).44,53 The

351

ecoregion conservation status is related to anthropogenic pressures (e.g., habitat loss, the

352

degree of fragmentation, and estimates of future threat and degree of protection), whereas the

353

GEPs are related to species compositions (species richness, geographical distributions, and

354

threat levels) isolated from any pressures. Combining the ecoregion conservation status index

355

(anthropogenic threats) with ecoregion GEPs (potential irreversibility of prospective impacts)



356

may enhance the understanding of anthropogenic impacts on ecosystems and aid the

357

identification of priority areas for global biodiversity conservation.

358

3.4.

359

The different parametrisation schemes for parametrising the IUCN Red List threat levels of

360

species give different weights to endangered species. In the linear parametrisation scheme, the

361

differences between the lowest (least concern = 0.2) and highest (critically endangered = 1.0)

362

threat level values is smallest (factor 5 difference). The differences are biggest in the logarithmic

363

parametrisation scheme, where the least concern category is parametrised as 1.0E-04 and the

364

critically endangered category as 1.0 (factor 1.0E+04 difference). This means that cells

365

containing endangered species get higher importance compared to cells not containing

366

endangered species with the logarithmic parametrisation scheme.

367

Calculating the GEPs with the different parametrisation schemes does not alter the pattern. The

368

main difference is that the regional differences are more pronounced with the logarithmic

369

parametrisation scheme (both on the cell and ecoregion-level). The linear and categorical

370

categorisation schemes result in very similar GEP distributions. This is illustrated in Figure 3,

371

where the cell- and ecoregion-level GEPs of terrestrial mammals are shown under the three

372

different parametrisation schemes. The logarithmic parametrisation scheme puts an even

Sensitivity of the threat level parametrisation


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373

stronger focus on areas near the equator. A complete list of all ecoregion GEPs calculated with

374

the three distinct parametrisation schemes can be found in SI-2 Tables S2-4.

375



376

Figure 3. GEPs on cell and terrestrial ecoregion levels of terrestrial mammals calculated with different

377

parameter schemes (legend scaled logarithmically). The maps use the terrestrial44 ecoregions of the

378

world to delineate borders.

379

Figure 4 displays boxplots of the ecoregion GEP distributions. This shows that varying the

380

parametrisation scheme has no significant effect for the distributions of GEPs for corals, marine

381

mammals, seagrasses, and freshwater groups. For the other groups, the ecoregion GEP value

382

can differ more substantially when adopting the different parametrisation schemes.

383

Furthermore, the Figure illustrates that the GEP distribution is very similar under the categorical

384

and linear scheme, but that the GEP values are more distinct when the logarithmic

385

parametrisation scheme is used.

386

Figures 3 and 4 indicate that the pattern of the GEP distribution becomes more pronounced

387

when using a logarithmic parametrisation scheme. The general direction of GEPs calculated

388

according to the three schemes remains the same. It should be stressed that these

389

parametrisation schemes have been chosen arbitrarily. We cannot conclude which

390

parametrisation scheme would be more appropriate to use, but we can conclude that the GEPs

391

show the same pattern, no matter the parametrisation scheme used. This underlines that the

392

spatial pattern of GEPs is fairly robust, but that the individual GEP values are approximations. 28 ACS Paragon Plus Environment

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393

394

Figure 4. Boxplots of ecoregion GEP distributions under the various parametrisation schemes.

395

3.5.

396

In order to assess whether combining range rarity and threat levels leads to double counting, we

397

calculate mean range rarity and mean threat level scores for terrestrial mammals, and analysed

398

the degree of similarity between the two indicators.

399

The mean range rarity and mean threat level maps for terrestrial mammals can be found in the

400

supporting information 1 (SI-1, figure S7). In the mean range rarity, mostly islands stand out

401

(e.g., Madagascar, Caribbean Islands, Azores, Solomon Islands, and Papua New Guinea),

402

whereas arctic and subarctic regions tend to have relatively low values. Furthermore, the mean

403

range rarity map shows a smooth gradient between slightly higher values around tropical and

Assessing spatial patterns of range rarity and threat levels individually



404

subtropical regions (with the exception of the Sahara), and lower values in the Northern

405

Hemisphere.

406

The mean threat level is equally distributed with some location-specific extremes. Arctic and

407

some coastal regions tend to stand out in terms of high values. In contrast to the mean range

408

rarity, islands tend to have relatively low values and the Sahara is characterised by higher mean

409

threat levels compared to its surrounding regions. The mean threat level with logarithmic

410

parametrisation primarily shows the distribution of critically endangered and endangered

411

species, as the other species become almost irrelevant for the mean threat level score. Hence,

412

this parametrisation shows more pronounced regional differences compared to the distribution

413

of the mean threat levels related to the linear and categorical parametrisations.

414

Table 3 shows Spearman (ρ) correlation coefficients for the mean range rarity and mean threat

415

level averaged to the ecoregion-level. One outlier (Ogasawara subtropical moist forest) affects

416

the correlation for mean range rarity and mean threat level (logarithmic), because of the low

417

total number of species on this island most are endemic and threatened (leading to very high

418

mean range rarity and high mean threat level scores, especially under the logarithmic

419

parametrisation scheme). The scatterplot in figure S8 shows how extreme the outlier of the

420

Ogasawara subtropical moist forest ecoregion is. Nevertheless, the results presented in table 3 30 ACS Paragon Plus Environment

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421

suggest that there is weak to moderate correlation54 between mean range rarity and mean

422

threat level (for all parametrisation schemes). Considering figures S7-8 and the correlation

423

coefficients in table 3, we can conclude that range rarity and threat levels capture distinct

424

aspects related to extinction probabilities.

425

Table 3. Spearman correlation coefficients for mean range rarity and mean threat levels of terrestrial

426

mammals at the ecoregion-level.

Parametrisation scheme

Spearman’s ρ

Mean threat level (linear)

0.370

Mean threat level (categorical)

0.364

Mean threat level (logarithmic)

0.435

427

428

Essentially, the IUCN Red List classification indicates how close a species is to extinction and

429

criterion B specifically describes how critical intact habitat is for the species’ persistence. As the

430

threat levels are determined based on whether species meet any in the set of criteria, criterion B

431

is not necessarily relevant for all species. Range rarity alone fails to capture the likelihood that

432

endangered species with large ranges are more likely to become extinct than non-threatened

433

species with large ranges as species with big ranges are not necessarily abundant throughout 31 ACS Paragon Plus Environment


434

the whole range. For example, the endangered Giant Otter’s (Pteronura brasiliensis) extent of

435

occurrence stretches over about a third of South America, but the small number of total

436

individuals and subpopulations increases the risk that local extirpation leads to global extinction.

437

On the other hand, the threat level alone fails to capture the likelihood that non-threatened

438

species with small ranges are more likely to become extinct than non-threatened species with

439

large ranges if these species become extinct locally. For example, the Green Mango

440

(Anthracothorax viridis) is endemic to Puerto Rico so local extinction in Puerto Rico is likely to

441

result in global extinction, even though the Green Mango is currently listed as “least concern”.

442

Combining both range rarity and threat level in the GEP attributes lowest risk to locations

443

hosting non-threatened species with large geographical ranges, medium risks to locations

444

hosting non-threatened species with small geographical ranges and endangered species with

445

large geographical ranges, and highest risks to locations hosting endangered species with small

446

geographical ranges.

447

Considering both range rarity and threat levels also conforms with other methods identifying

448

locations that are critical for global biodiversity such as Key Biodiversity Areas (KBAs),55

449

Important Bird and Biodiversity Areas (IBAs), and Alliance for Zero Extinction sizes (AZEs).56


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450

3.6.

451

The GEPs can be used with any set of data for translating fractions of regional species

452

extinctions into global species extinctions, as long as the spatial resolution and the taxonomic

453

group referring to the extinctions correspond with the same spatial resolution and taxonomic

454

group of the GEPs. One example is to use the GEPs in combination with predicted net changes

455

in local species richness.10,17,57–61 Another example is to use the GEPs in combination with

456

characterisation factors indicating regionally disappeared fractions of species in life cycle impact

457

assessments.

458

Combining the ecoregion-level GEPs for terrestrial mammals with the ecoregion-specific

459

characterisation factors for agricultural land use impacts per m2 on regional mammal species

460

diversity, developed by Chaudhary and Brooks,49 results in characterisation factors for global

461

mammal species diversity impacts (table S5). The ranking of these regional and global

462

ecoregion characterisation factors differ substantially, indicating that regional species diversity

463

impacts do not reflect global species diversity impacts. These rankings, presented in table S1

464

andS5 in the supplementary information, show that e.g. the Enriquillo wetlands ecoregion ranks

465

sixth in terms of regional mammal species diversity impacts per m2 of land used for agriculture.

466

However, for the global impacts, this ecoregion ranks place 373, meaning that these relatively

Case study



467

high regional extinctions are not strongly contributing to global species extinctions. This may be

468

explained by intensive agricultural land use, but low numbers of endemic and threatened

469

species in this region.

470

Conversely, the Mentawai Islands rain forests ecoregion is characterised by relatively low

471

regional mammal diversity impacts (ranked 72nd) compared to global diversity impacts (ranked

472

6th) due to agricultural land use. This can be explained by to high numbers of mammal species

473

that are endemic to these islands and relatively high numbers of threatened species in this

474

region, whereas the land use related pressures exerted to these local populations might not be

475

as severe.

476


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477

Figure 5. Ranking of ecoregion characterisation factors [PDF mammal species/m2 agricultural land use]

478

for a) regional diversity impacts and b) global diversity impacts. Yellow colour indicates high rank, blue

479

colour indicates low rank, and grey indicates regions for which no data was available. The maps use the

480

terrestrial44 ecoregions of the world to delineate borders.

481

Figure 5 shows the ranking on a map, where the colours indicate the rank of the ecoregion

482

regarding the regional mammal species diversity impacts per m2 agricultural land use (figure 5a)

483

and the global mammal species diversity impacts (figure 5b). These maps show that agricultural

484

land use impacts in the tropics cause the highest impacts in terms of global species extinctions,

485

whereas the impacts on regional species extinctions are more scattered across specific

486

ecoregions.

487

Chaudhary and Brooks49 also attempted to capture global species diversity impacts by

488

considering species endemic to the region only. In another study,32 they considered all species

489

and used a factor (vulnerability score31) to flag regions hosting high numbers of threatened and

490

endemic species. Using the vulnerability score is appropriate for use in combination with

491

absolute regional species extinctions, whereas the GEP is designed for use in combination with

492

fractions of regionally disappeared species.



493

This case study on agricultural land use impacts on regional and global mammal species

494

diversity shows how regional species diversity and global diversity impacts can differ spatially,

495

and how the methods using the GEP can differ from other methods trying to capture global

496

species diversity impacts.

497

3.7.

498

The GEPs proposed here have been limited to species groups for which IUCN species range

499

maps are available. The included species are by no means exhaustive. First of all, spatial data

500

is not available for all known species within the included species groups. Second, there is

501

consensus that there are probably many species that are unknown.6 Third, the IUCN does not

502

have species range maps available for all species groups (e.g., bacteria, fungi, and insects).

503

Thus, the GEPs should be updated as more data becomes available.

504

Furthermore, the accuracy of the species distribution data differs substantially per species. The

505

species distribution data may indicate the extent of occurrence or the area of occupancy,

506

depending on the species. For example, the range of the giant otter (Pteronura brasiliensis)

507

stretches over about a third of South America in one continuous patch. This range, indicating

508

the extent of occurrence, also covers areas currently unsuitable as habitat for the giant otter. In

509

contrast, the range of the Asian elephant (Elephas maximus) stretches over about a third of

Critical appraisal of the method and data


Page 36 of 44

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510

Southeast Asia in numerous discontinuous patches, indicating area of occupancy of the Asian

511

elephant in these locations. Following our modelling approach, the average cell value will be

512

lower for the giant otter compared to the Asian elephant because its total range is bigger. This

513

discrepancy in accurateness of species distribution data is due to the data availability and

514

cannot be solved easily.

515

The proposed method for estimating range rarity is based on the presence or absence in a cell

516

(in arc degrees). This means that the range rarity is not based on the actual size of the total

517

species range, as area sizes per cell vary over different latitudes. Hence, range rarity is not

518

included as a fraction of the total range size area, but as a fraction of the total locations (i.e.,

519

cells) in which the species is present.

520

The threat level parameter values have been set arbitrarily, as there is no method to quantify

521

how more likely it is that endangered species become extinct compared to non-threatened

522

species. This arbitrary parametrisation introduces uncertainty into the model. Although choosing

523

different parametrisation schemes is an attempt to capture part of the uncertainty, an

524

uncertainty range cannot be quantified because these parameter values have been set

525

arbitrarily. Similarly, due to limited data availability, uncertainty regarding the species distribution

526

data can also not be quantified. 37 ACS Paragon Plus Environment


527

Nevertheless, the GEP can be useful for assessments in species diversity trends and ecological

528

impact assessments at large scales. The magnitude of human activities has led to impacts on

529

global scales, However, as specific anthropogenic and environmental pressures and species

530

assemblages differ substantially regionally, the ecological responses to these pressures differ

531

as well. Assessing the global implications of spatially differentiated trends in and impacts on

532

biodiversity requires a conversion factor like the GEP.

533

The main spatial patterns of GEPs conform with other species distribution maps indicating

534

potential vulnerability to extinction6,21,36,38 and maps identifying priority conservation

535

areas,6,16,19,21,46 All maps show relatively high values around the tropics, and especially high

536

values on tropical islands. Other areas that stand out in most maps are northwest South

537

America, Madagascar, the Himalayas, and Southeast Asia. This correspondence with existing

538

maps supports that the GEP captures extinction risks into a factor that can used to translate

539

regional species diversity loss into global species diversity loss.

540

The simple methodology, based on quantitative data and categories such as the distribution of

541

species, their geographical ranges, and their threat levels results in location-specific GEPs that

542

sum up to 1. This means that if all species are lost regionally, all species will be globally extinct


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543

as well. This theoretical logic is important when applying a conversion or weighting factor like

544

the GEP at global scales.

545

The GEP is easy to use in species diversity assessments as it is used as a multiplication factor

546

with regional species diversity impacts (as done in the case study). The GEP is available

547

globally at a fine resolution (0.05° × 0.05°) that can be aggregated to any bigger resolution. The

548

GEPs cover marine, terrestrial, and freshwater realms and various taxonomic groups. That is,

549

the GEP is additive, scalable, and comparable globally across various realms and taxonomic

550

groups. This means that the GEPs can be used for many different studies focusing on different

551

realms, species groups, and locations. Having one factor that can be used across various

552

impact assessments can foster the coherence between these assessments.

553

554

Associated content

555

Supporting Information:

556

SI-1 includes the cell-level (Figures S1-3) and ecoregion-level (Figures S4-6) GEPs of all

557

taxonomic groups considered in this study, including chondrichthyes, corals, marine fish, marine

558

mammals, seagrasses, amphibians, birds, terrestrial mammals, reptiles, and freshwater groups. 39 ACS Paragon Plus Environment


559

Furthermore, it includes mean range rarity and mean threat level maps of terrestrial mammals

560

(Figure S7), scatterplots of the relation between mean range rarity and mean threat level scores

561

per ecoregion (Figure S8), and density figures of the distribution of the MRR and MTL data.

562

Lastly, it includes a ranking of the GEP, regional, and global characterisation factors per

563

ecoregion (Table S1).

564

SI-2 includes the marine, terrestrial, and freshwater GEP tables (Tables S2-4, respectively),

565

containing GEP values for each taxonomic group (i.e., all groups mentioned above) and

566

parametrisation scheme (i.e., linear, categorical, and logarithmic schemes). Furthermore, it

567

includes the agricultural land use impacts on regional and global mammal species extinctions

568

(Table S5).

569

570

Author information

571

Corresponding author: [email protected].

572

Funding sources: This research was funded by NTNU Sustainability

573

(https://www.ntnu.edu/sustainability).

574

Notes: The authors declare no competing financial interest. 40 ACS Paragon Plus Environment

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575

576

Acknowledgements

577

This research was funded by NTNU Sustainability (https://www.ntnu.edu/sustainability). The

578

authors would like to thank Christopher Oberschelp, John Woods, and Martin Dorber for their

579

support in both early and later developmental stages of this research.

580

581

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Potential consequences of regional species loss for global species

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