metadata: authors: 0: author: Dasgupta,Susmita
1: author: Blankespoor,Brian
2: author: Wheeler,David J.
count: Brazil,Costa Rica,Ecuador,Cameroon,India,South Africa,Madagascar,Papua New Guinea,Philippines,China,World
docna: 0: docna: Implementing 30x30 : Lessons from Country
            Case Studies
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 publication of nearly 600,000 new
            species occurrence maps using Global Biodiversity
            Information Facility data provides an opportunity to
            reassess international species protection with broader
            representation for plants, invertebrates, and other species.
            This development aligns with the global 30x30 initiative,
            where 188 governments have committed to expanding
            terrestrial and marine protection to cover 30 percent of the
            planet by 2030. This study leverages Global Biodiversity
            Information Facility occurrence maps to identify new
            opportunities for species protection in 10 countries in
            Latin America (Brazil, Costa Rica, and Ecuador), Africa
            (Cameroon, South Africa, and Madagascar), and the
            Asia-Pacific region (Papua New Guinea, the Philippines,
            India, and China). By focusing on individual countries, the
            paper emphasizes the importance of local conservation
            stewardship. Both terrestrial and marine cases are analyzed,
            with particular attention to endemic species. Unlike
            previous efforts, this approach assigns equal weight to all
            vertebrates, invertebrates, plants, and other species mapped
            in the database. A spatially efficient algorithm identifies
            priority localities for establishing new protected areas to
            safeguard unprotected species. The findings reveal that
            initial conditions, such as existing protection levels and
            the spatial clustering of unprotected species, greatly
            influence outcomes. Unprotected species are shown to be
            spatially clustered in some countries but not in others, and
            the representation of different taxa among unprotected
            species is found to vary significantly across countries.
            Some countries can achieve full protection within the 30
            percent territorial limit, while others may need to exceed
            it. However, in all cases, spatial clustering enables
            significant protection gains through modest expansions of
            protected areas, demonstrating a path forward for enhancing
            biodiversity conservation within global commitments.
display_title: Implementing 30x30 : Lessons from
            Country Case Studies
listing_relative_url: /research/2025/01/34449488/implementing-30x30-lessons-country-case-studies
topicv3: Environmental Sustainability m1325984 1786,Protected Areas Systems m1325987 1833,Terrestrial Biodiversity m1584052 4722
publishtoextweb_dt: 2025-01-24T00:00:00Z
dois: 10.1596/1813-9450-11045
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 51
author_keys: 0: author_key: 95558
1: author_key: 957135
2: author_key: 1048754
model_extractions: Policy Research Working Paper 11045 Implementing 30x30 Lessons from Country Case Studies _Susmita Dasgupta Brian Blankespoor David Wheeler_ Development Research Group Development Data Group & Environment Global Department January 2025
Policy Research Working Paper 11045

# **Abstract**

The publication of nearly 600,000 new species occurrence maps using Global Biodiversity Information Facility data provides an opportunity to reassess international species protection with broader representation for plants, invertebrates, and other species. This development aligns with the global 30x30 initiative, where 188 governments have committed to expanding terrestrial and marine protection to cover 30 percent of the planet by 2030. This study leverages Global Biodiversity Information Facility occurrence maps to identify new opportunities for species protection in 10 countries in Latin America (Brazil, Costa Rica, and Ecuador), Africa (Cameroon, South Africa, and Madagascar), and the Asia-Pacific region (Papua New Guinea, the Philippines, India, and China). By focusing on individual countries, the paper emphasizes the importance of local conservation stewardship. Both terrestrial and marine cases are analyzed, with particular attention to endemic species.

Unlike previous efforts, this approach assigns equal weight to all vertebrates, invertebrates, plants, and other species mapped in the database. A spatially efficient algorithm identifies priority localities for establishing new protected areas to safeguard unprotected species. The findings reveal that initial conditions, such as existing protection levels and the spatial clustering of unprotected species, greatly influence outcomes. Unprotected species are shown to be spatially clustered in some countries but not in others, and the representation of different taxa among unprotected species is found to vary significantly across countries. Some countries can achieve full protection within the 30 percent territorial limit, while others may need to exceed it. However, in all cases, spatial clustering enables significant protection gains through modest expansions of protected areas, demonstrating a path forward for enhancing biodiversity conservation within global commitments.

This paper is a product of the Development Research Group and the Development Data Group, Development Economics and the Environment Global Department. 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 sdasgupta@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
# **Implementing 30x30:**

# **Lessons from Country Case Studies**

## **Susmita Dasgupta Brian Blankespoor David Wheeler**

_Funding_ : This research was funded by the Global Environment Facility.

Acknowledgements: We acknowledge the use of georeferenced species occurrence data provided by GBIF, which was downloaded following their citation guidelines

- (https://www.gbif.org/citation <u>guidelines#derivedDatasets). The GBIF occurrence data was</u> accessed via Google BigQuery on February 17, 2024.

We extend our gratitude to Pritthijit (Raja) Kundu for his valuable assistance with the graphics.

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.

_Keywords:_ Biodiversity Conservation, Protected Areas, Endemic S pecies, Kunming-Montreal Global Biodiversity Framework _JEL classification: Q57_
# **1. Introduction**

The world is rapidly losing biodiversity. Pimm et al. (2014)<sup>1</sup> found that the current rate of species extinction is at least 1,000 times the background rate. Supporting evidence from the Living Planet Index (LPI), which tracks population trends for vertebrate species in terrestrial, freshwater, and marine habitats, shows a 69% decline since 1970.

The LPI informs the Convention on Biological Diversity (CBD) and its Conference of Parties (COP). In response to indicators of rapid decline, 188 governments ratified the Kunming-Montreal Global Biodiversity Framework (GBF) at COP 15 in December 2022. Among other measures, the Framework commits participants to protecting 30% of the planet by 2030. This has been labeled the Global 30x30 Initiative and commonly abbreviated as “30x30”.

Effective implementation of 30x30 requires addressing two key questions: (1) What is the spatial distribution of global biodiversity that should be protected? (2) How can protecting 30% of the planet best conserve this biodiversity?

In a previous paper (Dasgupta et al. 2024), we addressed the first question using the Global Biodiversity Information Facility (GBIF), which has expanded to include occurrences for over 2 million species. In the past two years, the GBIF has added about 1.3 million occurrence records daily. Most records include locational coordinates, enabling new estimates of spatial distributions for previously unmapped species and improved estimates for species with existing maps. Using machine-based pattern recognition, we estimated spatial occurrence maps for over 600,000 species. These maps complement previous work by greatly expanding representation for plants, invertebrates and other non-vertebrate species. Our algorithm allows rapid updates and new maps as the GBIF data increase.

In this paper, we draw on the previous work to explore some implications for 30x30 in a sample of 10 countries in Latin America, Africa and the Asia-Pacific region. We focus on individual countries to highlight the role of conservation stewardship in local settings. Our country analyses begin with current protected areas, which exhibit great cross-country variation in territorial coverage and representation of species taxa. We consider both terrestrial and marine cases, focusing on species that are endemic to each country. Drawing on our GBIF spatial database for nearly 600,000 species, we overlay species occurrence maps on the most recent protected-area maps from the World Database of Protected Areas. This enables identification of endemic species whose coverage by existing protected areas is either non-existent or extremely sparse.

Our approach departs from many previous exercises by giving equal weight to all vertebrates, invertebrates, plants, and other species whose occurrence regions are mapped in our database. We use a spatially-efficient algorithm to identify a hierarchy of priorities for localities where new

> 1 Pimm, S. L., Jenkins, C. N., Abell, R., Brooks, T. M., Gittleman, J. L., Joppa, L. N., ... & Sexton, J. O. 2014. The biodiversity of species and their rates of extinction, distribution, and protection. Science, 344(6187), 1246752.

2
results to explore the spatial implications for countries’ 30x30 commitments.

Rather than presenting a detailed description of our methodology at the outset, we use our country cases to introduce its features sequentially. We begin with an econometric analysis that identifies the appropriate scale for envisioned protected areas in each country.

# **2.  Country Size and Protected Area Scale**

In each country, our sequential algorithm begins by overlaying the occurrence maps of all unprotected species on a spatial grid. It counts resident species for each grid cell and identifies the highest-count cell (P1) as the priority 1 candidate for protection coverage. In step 2, it sets aside the species resident in P1 and repeats the grid cell count for the remaining species. It identifies the highest-count cell as P2. The algorithm can repeat this exercise until all species have coverage in at least one grid cell.<sup>2</sup> A critical feature of our approach is the selection of grid scale for each country. We use a spatial grid whose cell area is the same as the average protected-area size, controlling for country size. Figure 1 displays the relationship (in logarithms) between country area and mean protected area (PA) size in sq km for 212 countries and political units.<sup>3</sup> The regression results in Table 1 confirm the strong visual relationship: Mean PA size increases by 0.614% with each 1% increase in country area, with very high significance (regression t-statistic of 15.47<sup>4</sup> ). Using this result, we can predict the mean PA size for any area and translate it to an appropriate grid cell resolution.<sup>5</sup>

> 2 When maximum resident populations are the same in more than one cell, the algorithm makes a random selection.

> 3 We compute average protected area size from WDPA (2024) and country area from the World Bank’s country shapefile (World Bank, 2024).

> 4 The t-statistic is the estimated regression coefficient divided by its standard error.  Classical significance with 95% confidence generally requires a t-statistic above 2.0.

> 5 We could compute mean PA size directly for individual countries, but across the 212 countries and political units we observe great variation in both the numbers and sizes of PAs.  We believe that our logarithmic regression provides more robust and stable results for the computation of appropriate grid scales.

3
<!-- Start of picture text --> Se<br>_<br>e<br>onl®<br>e . % «42,<br>**e<br>6 ad ai,_% % e<br>% e<br>© .e.0.* as wo e<br>o e * e<br>e® e ee 4%<br>o ] 2 o %s<br>e By e<br>. e<br>e<br>ee<br>e<br>me<br>0 5 10 15 20<br>Log Country Area (sq.km.)<br><!-- End of picture text -->
# **3.  Priority Area Selection:  Brazil and Cameroon**

We introduce our methodology with applications for Brazil and Cameroon, whose size difference dictates grid cell resolutions of 0.332 and 0.136 decimal degrees (dd) respectively. With our algorithm, each step identifies a new PA in a grid cell with side dimensions of 0.332 dd (36.9 km) in Brazil, and 0.136 dd (15.1 km) in Cameroon.

# **Brazil**

Figure 3 displays Brazil’s terrestrial protected areas in dark green. Its existing protected areas cover about 2.6 million sq km, or 30.6% of the national land territory (8.5 million sq km). Brazil has 20,245 endemic species in our database, and its current protection system is impressive by international standards. Brazil’s 5,499 protected areas incorporate 30.6% of its land area and provide significant coverage for 93% of its endemic species in our database. At the same time, 1,412 endemic species have occurrence areas that either lie totally outside existing protected areas or have such sparse coverage that they are essentially unprotected.<sup>6</sup> To illustrate, Figure 2 displays images of some unprotected endemic species and Figure 3 displays occurrence maps and images of some unprotected species.<sup>7</sup> The figure includes vertebrates (Figure 3a), plants (3b) and arthropods (3c).

The public-domain requirement for our images has produced a quasi-random selection of species for Figure 2, although their small number cannot ensure that they are representative of Brazil’s 1,412 unprotected species. However, two striking features are evident: Many species are in eastern and southeastern Brazil, and the majority have relatively small ranges. These features are logical from a spatial perspective. First, protected areas are generally much larger in the north and northwest, so more local species are protected there. Second, protected areas are sufficiently pervasive in Brazil that species with large occurrence areas will often have significant protection in parts of their ranges. It is also worth noting that many unprotected species in our database have been mapped for the first time by Dasgupta et al. (2024), and many are in groups (e.g. arthropods) that have not received primary attention in protected area selection. Overall, Figure 3 suggests that these species have a significant presence in eastern and southeastern regions where protected areas are substantially smaller than in the north and northwest.

> 6 We define sparse coverage as protected-area coverage which is less than 5% of a species’ occurrence area and less than 25 sq km.

> 7 This paper contains many high-resolution species images because we believe that understanding of the protection problem can be enhanced by making unprotected species more visible.  All images are in the public domain, and we have devoted considerable effort to verifying that they correctly represent individual species.  However, we have no field expertise in this area, and we recognize that some expert readers may well recognize discrepancies.  We welcome suggestions for improvement of our image presentation, with the proviso that all images must be in the public domain.

5
# Examples

<!-- Start of picture text --> . * = ‘7 —; ; WE ial . F 4<br>ict Loe = 4 : ~—- a” -s ys<br>:ESO TS a neee<br>a: SSere ¢ Be een 4<br>Sa hEBrachycephalusen”alipioiSO toe<br>Hemigrammus ataktos<br>&f<br>4<br>%<br><!-- End of picture text --> Trimezia nana <!-- Start of picture text --> = 1] ba a ¥<br>-— J Al SH il hi ‘ ;<br>Nn es Pe ed NG See, :<br>SSEare Ly,<br>EG OT TNT) See<br>Papa Nid aise Mp \ 3 Dee<br>ol Sy mI me WBONLIEN. +7<br>A] (eae = Rie ‘<br>Scherya bahiensis<br><!-- End of picture text --> <!-- Start of picture text --> ——_<br>_ ae<br>r < yd sh eae Zlm<br>RE a<br>Hyphessobrycon togoi:<br><!-- End of picture text --> Hypostomus nigrolineatus <!-- Start of picture text --> sw<br>.<br>fs<br>Anathallis brevipes<br>pectin y a<br>GE SN<br>. _—ph<br>;<br>Cattleya munchowiana<br><!-- End of picture text -->
<!-- Start of picture text --> b<br>=e<br>% . * Sneed sa” ~@ ;<br>. : Py<br>9 ; soe@ ow aioong a 4<br>; * oOe<br>oe ;<br>- . ©<br>i tA hee See? . 4 Qocurrence Regions of<br>Protected Area<br>se re i ip k pelected Unprotected<br>. N ¥y . Peet. aw + . a<br>~7<br>i<br><!-- End of picture text --> <!-- Start of picture text --> b<br>: ,<br>t a esa. : a.a °%oeTs Q<br>of<br>‘ : s ay . a<br>“We 3 » . . ~<br>a ;<br>ems7] 2n . yd© =TfieieA eee» : © "!.Aesaref 3 curl .<br>\ /(oe.a A en aE mic Specie<br>* i ok; 4. ieoot Seeos ‘ seof [| Selecta Unprotected<br>» rm . os af - { BBR Protected Area °<br>of Stage<br>~7f<br>ae<br><!-- End of picture text -->
<!-- Start of picture text --> b<br>ier Se or ae<br>eet 2 ae ; mm -. 8<br>ia: * . pee3<br>3 * ' ce<br>3 5<br>ae i. WF. o-a :oAst. 4 [ | Occurrence RegionsSelected Unprotected of<br>s jm tA a 8. . tec, yj r Endemic Species<br>. are . oer. . . ; - | Protected Area<br>“+s<br>a<br><!-- End of picture text -->
<!-- Start of picture text --> Priority 1 Priority 2<br>Ss } aly * ;<br>; i y ; aeDe7 =. a» Bag mysolarhoSs ay ireeson 4id0 54- ‘>oeESSaSas* Prwalter aeeeeeeee __8 | | withcountProvince maximum<br>casasa eae Smit -o aD di, aedsee meae Pe+ Species. counts<br>ee re eT * ms ee ee oe (decile)<br>a ae<br>Priority 3 Priority 4 5<br>:$6<br>s4<br>7<br>5 , - ‘, ¢ gtEneas * 4 - ‘4 © gheepais 10<br>ayOr. Oe+ TM.. 1 ; iyoe” Yer f. Ce+ sae TM.. k ;<br>“ee ‘Lartie?Z,ie ey tied,ie NA<br>; is ah all pe r fy até velit ye Fs<br>i Ree oe J i Pi ais ee. Bas<br>~ iy os yee ~ Dye “<br>vk a || Protected Area<br>ay ‘an<br><!-- End of picture text -->
Figure 4b reports the counting results when these 98 species are withdrawn from consideration for the second round. Although high counts in the east and southeast are again evident, the maximum cell count in this round is in the northeast part of Amazonas state. This identifies the second-priority area, which provides some coverage for 43 unprotected species. The next iteration identifies the third priority area in the southeast, in eastern Paraná state, which offers some coverage for 32 species. The fourth iteration also identifies the highest-count cell in Paraná, in its northern area, offering coverage for 30 species. Together, these iteration steps identify four 37 km x 37 km areas that provide coverage for 203 (14.4%) of Brazil’s 1,412 unprotected endemic species.

# **Cameroon**

Figure 6 displays Cameroon’s protected areas in dark green. Its protection intensity is substantially less than Brazil’s, with 55,600 sq km (12%) of Cameroon’s 464,319 sq km in protected areas. In our database Cameroon has 645 endemic species, of which 189 (29.3%) are effectively unprotected. Figure 5 displays images of some unprotected species and Figure 6 displays the ranges and public-domain images of some vertebrates (Figure 6a), plants (6b) and arthropods (6c) from the unprotected group. Their geography has a pattern that resembles Brazil’s: They lie outside existing protected areas (by construction), and their occurrence areas are generally small because larger occurrence areas will often overlap with protected areas. In Cameroon, the displayed species are predominantly located in the country’s west and southwest.

10
# Examples

Hyperolius dintelmanni Lagonosticta umbrinodorsalis <!-- Start of picture text --> +40 ee osaae<br>a ea<br>Pelvicachromis kribensis<br>Acraea wigginsi<br>ey 4<br>Calyptrochilum aurantiacum<br><!-- End of picture text --> <!-- Start of picture text --> za, ts<br><!-- End of picture text --> Campylomormyrus phantasticus Caelorrhina superba <!-- Start of picture text --> ee Ne<br>Glenea fasciata<br><!-- End of picture text -->
<!-- Start of picture text --> a<br>e>*<br>P - [| OccurrenceSelected UnprotectedEndemic SpeciesRegions of<br><— | | Protected Area<br>2g.<br>At ; &<br>ay” e ¥<br>es JS .<br>y LD<br><!-- End of picture text -->

<!-- Start of picture text --> a<br>P , [| OccurrenceSelectedEndemic UnprotectedSpeciesRegions of<br>x a Protected Area<br>a.Wi, & :<br>“43 e<br>os ,<br>e<br>4<br><!-- End of picture text -->
<!-- Start of picture text --> Fd<br>P ww | OccurrenceSelected UnprotectedEndemic SpeciesRegions of<br><— | Protected Area<br>a.<br>’* & :<br>¥, t<br>e<br>5 ~| ><br>; 7<br>Fi WA<br><!-- End of picture text -->
<!-- Start of picture text --> Priority 1 Priority 2<br>Ad 4 ‘ e > ‘ Province<br>F a a = i u<br>& $ oh. + ‘deal) counts<br>Me es| :1<br>F a ae. 3<br>"Fa F ra:<br>Priority 3 Priority 4 5<br>6<br>7<br>f f 8<br>)<br>‘ ‘<br>al ul 10<br>e>* e>* NA<br>P “ P “<br>a rf | * i | | Protected Area<br>i‘ a<br>4 ed _@¢ 3<br>“a yal,<br>Fn Foe WA<br>a<br><!-- End of picture text -->
In summary, our introductory case analyses for Brazil and Cameroon have illustrated our basic methodology for identifying unprotected endemic species, establishing grid scales, and selecting high-priority areas for protection. The results suggest that relatively few new protected areas can provide coverage for a substantial portion of unprotected endemic species. However, an important caveat has to be addressed: If the species occurrence maps are accurate, the grid squares identified by our exercise will provide some coverage for all overlapping species. However, our methodology does not guarantee that each species within a grid cell will be protected by our definition. This can be remedied by expanding the protected area until the coverage achieved removes all species from unprotected status as defined by our original criterion (protection for less than 5% of a species’ occurrence map and less than 25 sq km).

For Brazil, we compute the needed expansion for the 98 species identified in the first iteration of our area selection methodology. We generate a sequence of circles from the centroid of the identified grid cell, starting with the circle that circumscribes the cell.<sup>8</sup> In each iteration, we expand the radius by 10%. We find that the first circle removes 92 of 98 species from unprotected status. Expanding the radius by 10% removes 3 additional species, and the remaining 3 species are removed from unprotected status by respective expansions (from the original radius) of 20%, 40% and 50%.

For Cameroon, the first iteration identifies a grid cell that removes 24 species from unprotected status. With the same protection test, the initial circle protects 9 species and successive increases of 20% to 50% of the original radius add protection for 4, 3, 1 and 2 species, respectively. The remaining 5 species require radial expansions of 70%, 80%, 120%, 150% and 160%, respectively.

Figure 8 displays the circular areas that provide protection coverage for all species in the priority 1 areas of Brazil and Cameroon. In both cases, these areas fall well within the size range of typical protected areas in the two countries.

> 8 The radius of the circle is one-half the cell side length.

15
<!-- Start of picture text --> A. Brazil, Espirito Santo B. Cameroon, Centre<br>-<br>-<br>-<br>f i<br>“<br>zw >* é &<br>‘ ' <<br>i a<br>. J in *7"<br>|<br>=<br>| ati<br>fm ene | :<br>al F]<br>4 Ate Ged<br><!-- End of picture text -->
# Examples

<!-- Start of picture text --> ~ i x : t: P “at : ee % eae “ 25 os i |<br>Thamnaconus arenaceus Diodora crucifera<br>a _ | rk P —- * * » - “4 b Se \ ss a<br>Ss Zi i = = Ke eae . CaN<br>Arctotheca marginata Babiana papyracea<br>[> | 4“ aR /<br>iy Oe ak<br>ONG ey pr<br>Callioratis abraxas Moraea franrnas Lachenalia alba<br>ee I na ig em ha” eee RS et eee Cig acme RCO A Be =<br>De START<br>SE RIR eee wae” a ee ee WeBe ee,<br>ON Grane SaZeid<br>Omphalucha albosignata Bullia mozambicensis<br><!-- End of picture text -->
<!-- Start of picture text --> Priority 1 Priority 2<br>0d ete, a 0d ete, 3 with maximum<br>\ afees~ as weeae a countProvince<br>a2 ROS ey a2 Reo NS:<br>tay Se BON<br>< ge ead i Eg?et<br>Mag este & pir Stee te Species counts<br>a 4<br>Priority 3 5<br>Po”) we<br>a iy ». Mi<br>a ee 9<br>o ae *s CUT ca oH<br>«i a) .ee)See TS “aeON 10<br>2 ee a NA<br>Vite i + . /<br>ae Se<br>5 a ——. || Protected Area<br><!-- End of picture text -->
# **Table 2:  South Africa – Species in top 3 protected areas**

## **2a. Group 1**

|**Vertebrates**|**Plants**|**Arthropods**|**Other Species**|
|---|---|---|---|
|_Anchichoerops natalensis_|_Crassulafloribunda_|_Aedes demeilloni_|_Achnanthes rupestris_|
|_Bathygobius niger_|_Eriosema superpositum_|_Aedes hansfordi_|_Bullia mozambicensis_|
|_Canthigasterpunctata_|_Gerrardanthus_<br>_tomentosus_|_Anoba disjuncta_|_Chalcocystis burnupi_|
|_Halichoeres zulu_|_Gymnosporia woodii_|_Bandusia rubicunda_|_Diodora crucifera_|
|_Pempheris eatoni_|_Phymaspermum_<br>_pinnatifidum_|_Brachyplatys_<br>_testudonigra_|_Echinolittorina natalensis_|
|_Thamnaconus arenaceus_|_Senecio exuberans_|_Cabomina hilariformis_|_Gomphonema entolejum_|
||_Syncolostemon latidens_|_Callioratis abraxas_|_Halgerda dichromis_|
|||_Callyna decora_|_Kerkophorus corneus_|
|||_Carbula recurva_|_Kerkophorus melvilli_|
|||_Cassidaguttipennis_|_Medusafissurella dubia_|
|||_Catamonus melancholicus_||
|||_Catephia amplificans_||
|||_Chiasmia natalensis_||
|||_Cicynethus decoratus_||
|||_Drosophila vulcana_||
|||_Glenea arida_||
|||_Helcita wahlbergi_||
|||_Hortipes mesembrinus_||
|||_Hyblaea occidentalium_||
|||_Hypenapalpitralis_||
|||_Leptopholcusgracilis_||
|||_Monoleptajacksoni_||
|||_Negera natalensis_||
|||_Phonoctonusfasciatus_||
|||_Plecopterapoderis_||
|||_Plusiopalpa dichora_||
|||_Pseudandriasa mutata_||
|||_Pseudochromatosia_<br>_nigrapex_||
|||_Raceloma natalensis_||
|||_Rhynocoris venustus_||
|||_Stagira microcephala_||
|||_Syllepte orbiferalis_||
|||_Synema diana_||
|||_Syngamiafervidalis_||
|||_Thysanoplusia spoliata_||
|||_Trichoplusia roseofasciata_||
|||_Veniliodes inflammata_||
|||_Xosopharaguttata_||
|||_Zerenopsisgeometrina_||

19
**2b. Group 2 and Group 3**

|**Gro**|**up2**||**Group 3**|
|---|---|---|---|
|**Plants**|**Arthropods**|**Plants**|**Arthropods**|
|_Albucapentheri_|_Clania macgregori_|_Pearsonia bracteata_|_Ableptina nubifera_|
|_Antimimagracillima_|_Colletes zygophyllum_||_Acollesisfraudulenta_|
|_Arctotheca marginata_|_Cryptolarynx estriatus_||_Acontia tanzaniae_|
|_Babianapapyracea_|_Idopompilus braunsi_||_Acontia tinctilis_|
|_Chamarea snijmaniae_|_Merostenus longistylus_||_Acontia torrefacta_|
|_Chrysocoma hantamensis_|_Moluris semiscabra_||_Afrobirthama hobohmi_|
|_Cyanella aquatica_|_Nesomyrmex nanniae_||_Aphilopota iphia_|
|_Cygnicollum immersum_|_Nixonia mcgregori_||_Aphilopota subalbata_|
|_Daubenya capensis_|_Opistophthalmus laticauda_||_Aspilatopsisgloriola_|
|_Daubenya stylosa_|_Patellapis bifurcata_||_Athetis xanthopis_|
|_Diascia insignis_|_Patellapis doleritica_||_Audeafumata_|
|_Diascia lewisiae_|_Patellapis minima_||_Chiasmia kirbyi_|
|_Euryops mirus_|_Patellapis tenuihirta_||_Clostera lentisignata_|
|_Geissorhiza divaricata_|_Patellapis timpageleri_||_Concavia transvaali_|
|_Geissorhiza subrigida_|_Prosoeca marinusi_||_Conchylia sesquifascia_|
|_Hesperantha rivulicola_|_Scrapter mellonholgeri_||_Cropera sericea_|
|_Hesperantha vaginata_|_Scrapter oxaloides_||_Desmeocraera vernalis_|
|_Ixia brunneobractea_|||_Drepanogynisglaucichorda_|
|_Lachenalia alba_|||_Epilepia melanobrunnea_|
|_Massoniapseudoechinata_|||_Epiplema inconspicua_|
|_Mesembryanthemum_<br>_tenuiflorum_|||_Eublemma albivena_|
|_Moraea aspera_|||_Eublemmafulvitermina_|
|_Moraeafragrans_|||_Eutrichafulvida_|
|_Moraea hesperantha_|||_Euxoa anarmodia_|
|_Moraeapseudospicata_|||_Gibbalaria sistrata_|
|_Oxalis argillacea_|||_Halseyia rufilinea_|
|_Oxalis callosa_|||_Idaea associata_|
|_Oxalisfilifoliolata_|||_Laelia extatura_|
|_Pelargonium aristatum_|||_Lamoria exiguata_|
|_Pelargoniumpachypodium_|||_Leucania cupreata_|
|_Pentameris dentata_|||_Lithacodia normalis_|
|_Romulea discifera_|||_Lymantriades xanthosoma_|
|_Romulea sabulosa_|||_Mauna ava_|
|_Selago spectabilis_|||_Metasia eremialis_|
|_Strumariapicta_|||_Metopteryx rattus_|
|_Trachyandraprolifera_|||_Mimasura innotata_|
|_Xiphotheca canescens_|||_Mimasuraquadripuncta_|
|_Zaluzianskya regalis_|||_Mythimna atrinota_|
||||_Naarda melanomma_|
||||_Nycteola malachitis_|
||||_Odontopera homales_|
||||_Omphalucha albosignata_|
||||_Panagropsis muricolor_|
||||_Pseudomallada sjostedti_|
||||_Scopula deserta_|
||||_Scotopteryx cryptocycla_|

20
21
**Table 3:  South Africa – Distributions of new priority areas and species**

|**Priority**|**Species**|**% Total**<br>**Species**|**Plants**|**Vertebrates**|**Arthropods**|**Other**<br>**Species**|**%**<br>**Plants**|**%Vertebrates**|**%**<br>**Arthropods**|**% Other**<br>**Species**|
|---|---|---|---|---|---|---|---|---|---|---|
|1|62|5.2|7|6|39|10|11.3|9.7|62.9|16.1|
|2|55|4.6|38|0|17|0|69.1|0|30.9|0|
|3|47|3.9|1|0|46|0|2.1|0|97.9|0|
|4|40|3.4|38|0|2|0|95|0|5|0|
|5|39|3.3|20|2|0|17|51.3|5.1|0|43.6|
|6|31|2.6|30|0|1|0|96.8|0|3.2|0|
|7|30|2.5|27|1|1|1|90|3.3|3.3|3.3|
|8|27|2.3|18|5|4|0|66.7|18.5|14.8|0|
|9|26|2.2|24|1|1|0|92.3|3.8|3.8|0|
|10|25|2.1|3|1|19|2|12|4|76|8|

# **Costa Rica**

Arthropods figure prominently in the Costa Rican case, and Figure 11 presents our identification of the top 3 priority areas for new protection. These display more geographic concentration than their South African counterparts, with the top two areas (Figures 11a and 11b) in contiguous subregions of western Alajuela province and the third priority region (Figure 11c) in the central coastal region in Puntarenas Province. In contrast to the South African case, we observe little dispersion of high grid cell counts beyond the priority areas.

22
<!-- Start of picture text --> Priority 1 Priority 2<br>a<br>, Province<br>»- > 8 - : 8 with maximum<br>w . e\—~ wn count<br>@ " 7 al ~ _ ‘ @ " s Ae Cy<br>ler “os & igre “NS ¢ |<br>Qs eo: ~ QW a TM Species counts<br>(decile)<br>‘ ‘<br>~e ~ '<br>ee2<br>'; 3<br>4<br>Priority 3 5<br>S 6<br>| . : 8<br>evr.”- oN= S 9<br>a NA<br>at<br>~?<br>C “ed<br>N eS | Protected Area<br>x<br><!-- End of picture text -->
Table 4 provides a detailed accounting for the unprotected species that would be protected by the three priority areas in Costa Rica.

# **Table 4:   Costa Rica:  Species in Top 3 New Protected Areas**

**Group 1                                                                            Group 2                                    Group 3 Arthropods                                                 Arthropods                                Arthropods                           Other Species** _Aerophilus sandraberriosae Houghia destituta Apanteles leonelgarayi Alabagrus jennyphillipsae Houghia ochrofemur Euplectrus gerarddelvarei Chaetozone nicoyana Alabagrus scottmilleri Iconella andydeansi Euplectrus mikegatesi Alabagrus tanyadapkeyae Janhalacaste guanacastensis Exasticolus sigifredomarini Apanteles deifiliadavilae Leurus iangauldi Hormius eddysanchezi Oxydromus minutus Apanteles eulogiosequeirai Macrocentrus harisridhari Lytopylus johanvalerioi Apanteles guadaluperodriguezae Megalota crassana Macrocentrus iangauldi Apanteles hectorsolisi Megalota ochreoapex Snellenius isidrochaconi Apanteles keineraragoni Microplitis francopupulini Apanteles luisbrizuelai Neurophyseta completalis Apanteles marisolarroyoae Niconiades gladys Apanteles monicachavarriae Ophionellus albofasciatus Apanteles ronaldquirosi Orgilus ebbenielsoni Bracon andrewwalshi Orgilus elizabethpennisiae Cardiochiles alejandrosolorzanoi Orgilus johnpipolyi Cardiochiles rogerblancoi Parapilocrocis albomarginalis Catharylla myrabonillae Plesiocoelus vanachterbergi Chelonus hartmanguidoi Potamanaxas effusa Chelonus jimlewisi Prasmodon johnbrowni Chelonus nataliaivanovae Prasmodon scottmilleri Chelonus normwoodleyi Pseudapanteles carlosespinachi Chelonus robertoespinozai Sparganocosma docsturnerorum Chelonus scottmilleri Spathidexia aurantiaca Chlamydastis irenecanasae Stantonia henrikekmani Cordyligaster fuscipennis Stenoma leucana Cosmorrhyncha albistrigulana Telothyria eldaararyae Cubus montywoodi Telothyria incisa Cystomastax angelagonzalezae Telothyria manuelpereirai Dolichogenidea josealfredohernandezi Telothyria ricardocalerloi Dynamine gillotti Trichomma batistai Enosis pruinosa Trigonospila uniformis Eriglenum tristum Wilkinsonellus alexsmithi Euplectrus eowilsoni Xiphosomella belinda Euplectrus victoriapookae Yelicones dirksteinkei Gonioterma ignobilis Gonodonta pulverea Himmacia inflammata Hormius jorgecarvajali Hormius manuelzumbadoi_

_Aricidea petacalcoensis Chaetozone nicoyana Glyphohesione nicoyensis Leitoscoloplos multipapillatus Oxydromus minutus_ 24
Here the pattern of geographic clustering is joined to a pattern of extreme clustering by species group. The species in priority group 1 are dominant numerically, and all are arthropods. All species in group 2 are arthropods as well. Group exclusivity persists in group 3, although it is populated entirely by other species.

Table 5 provides an expanded view for the top 10 priority areas, and the count pattern across priority groups provides an extreme contrast to the South African case. In Costa Rica, the area 1 group of 73 species declines sharply to 8 species in group 2, 5 in group 3, and small numbers for groups 4 – 10. Arthropods are overwhelmingly dominant in the top groups, while the smaller groups have varying patterns of representation for plants, vertebrates, arthropods and other species.

**Table 5:  Costa Rica – Distributions of new priority areas and species**

|**Priority**|**Species**|**% Total**<br>**Species**|**Plants**|**Vertebrates**|**Arthropods**|**Other**<br>**Species**|**%**<br>**Plants**|**%Vertebrates**|**%**<br>**Arthropods**|**% Other**<br>**Species**|
|---|---|---|---|---|---|---|---|---|---|---|
|1|73|58.4|0|0|73|0|0|0|100|0|
|2|8|6.4|0|0|8|0|0|0|100|0|
|3|5|4|0|0|0|5|0|0|0|100|
|4|4|3.2|0|0|4|0|0|0|100|0|
|5|3|2.4|2|0|0|1|66.7|0|0|33.3|
|6|3|2.4|0|1|1|1|0|33.3|33.3|33.3|
|7|3|2.4|0|0|3|0|0|0|100|0|
|8|2|1.6|1|0|0|1|50|0|0|50|
|9|2|1.6|0|0|0|2|0|0|0|100|
|10|2|1.6|0|0|2|0|0|0|100|0|

In summary, the South African and Costa Rican cases illustrate the potential diversity of outcomes when our methodology for identifying priority areas is applied to countries with very different patterns of geographic dispersion and taxonomic representation for unprotected species. Historically, vertebrates have commanded much of the attention when protected areas have been delineated. However, these two country cases suggest much more important roles for other taxonomic groups in the identification of priority areas for future protection. In addition, the contrasting rates of decline in priority area species counts suggest significant variations in the land opportunity costs of movement toward greater species protection. Opportunity costs may be significantly lower in countries like Costa Rica, where the remaining unprotected species are highly clustered in a few areas. In countries like South Africa, on the other hand, widely-dispersed clusters of unprotected species will require more extensive land acquisition to meet ambitious conservation goals.

25
# **5.  Spatial Implications of Full Species Protection:  Ecuador and Papua New Guinea**

In this section, we explore the implications of movement toward full protection of endemic species with illustrations from Ecuador and Papua New Guinea. The two countries are in the same size class, so they have similar grid scales for priority identification: 0.113 dd (12.5 km) for Ecuador and 0.136 dd (15.1 km) for Papua New Guinea. Ecuador has 3,477 endemic species in our database, with 698 species that are unprotected by our criteria. Ecuador’s protected areas occupy about 23% of the national territory, offering protection for about 80% of its endemic species. In contrast, Papua New Guinea’s protected areas occupy about 4% of the national territory, affording protection to 61% of its 3,266 endemic species in our database. In Papua New Guinea, 1,270 endemic species remain unprotected by our criteria.

For this exercise, we run our algorithm for priority area identification until all unprotected species have coverage in new protected areas. We divide priority areas into decile groups based on cumulative species protected. Figure 12 displays the geographic implications for Ecuador. The priority areas are grid cells with scales identified above, but we size the colored symbols for both visibility and reflection of relative importance. As the first panel shows, 20% of unprotected species are afforded coverage by two new priority areas (purple) at opposite ends of the country, in Pichincha and Loja Provinces. The next 10% are covered by two areas in the south, in Azuay and Zamora Chinchipe Provinces. In a similar vein, relatively few priority areas cover the unprotected species in the third, fourth and fifth groups. After that, the number of species covered by new areas begins to decline steadily and the number of areas grows proportionately. A very large number of new areas are required for the final species decile. They are in almost every province and particularly numerous in the axis from Esmeraldas and Carchi Provinces in the north to Loja and Zamora Chinchipe Provinces in the south.

Figure 13 displays analogous results for Papua New Guinea, and a similar pattern emerges as we move through the deciles. In the top four priority classes, the number of identified areas grows steadily: 1 (Madang Province); 2 (Madang, Oro); 3 (Western Highlands, Morobe, Milne Bay); 4 (Sandaun, Gulf, Milne Bay, New Ireland (2 areas); 5 (Western, Chimbu, Morobe, Central, Milne Bay, Manus). Then the required number of areas begins expanding as the number of covered species per area declines. In the lowest-priority panel, the country is covered with a dense pattern of dots that are present in every province.

26
<!-- Start of picture text --> 10-20 20-30 30-40<br>pian . r rr ‘| ’ *7* ‘ ’<br>« ad « ad « ad<br>Ip» >, >,<br>gam gam gan<br>: : :<br>I a: I ; I 7<br>& & &<br>40-50 50-60 60-70<br>re yy r ‘= he ve,<br>. a . a ° ace<br>© ® ©<br>omx omx r%<br>7) eB 7) te 3 7) % 3<br>Fey | roy a<br>F € ,<br>70-80 80-90 90-100<br>‘10 r) »6 eov5e<br>he fi . i*f® ° . £ ae,<br>o °®@ iad & e eM . Say”<br>+a . re ( r) . iS e@ e<br>fe): 2) 2)<br>C/ # 7/ #e§ 7/ tfP%e,©<br>5c 6 e-<br>rote Feet Fas<br>e © he<br>é é e”<br><!-- End of picture text -->
<!-- Start of picture text --> 10-20 20-30 30-40<br>. : :<br>e, = e, = e, -<br>40-50 50-60 60-70<br>‘ ‘ ;<br>é, = é, = 6, a<br>@-. @-. @-.,<br>70-80 80-90 90-100<br>. : ;<br>@, - @. = % =<br>RSs x Ss Cs.<br><!-- End of picture text -->
<!-- Start of picture text --> =]<br>uw<br>Sw<br>=<br>2<br>i<br>S$<br>i]<br>=<br>is}<br>Fo<br>to ae]<br>2<br><<br>=|<br>&<br>oS<br>fe<br>a.<br>Ww<br>a|<br>0 20 40 60 80 100<br>% of Target Species Protected<br><!-- End of picture text -->
<!-- Start of picture text --> uy<br>a|<br>fag<br>2<br>Eo<br>an<br>a<br>=<br>2<br>ro<br>=n<br>sr<br>#<br>ui)<br>2<br><<<br>32<br>@<br>fe<br>o<br>uw<br>20 40 60 80 100<br>% of Target Species Protected<br><!-- End of picture text -->
unprotected if its overlap with protected areas is less than 25 sq km and less than 5% of its occurrence area. We change our method for setting grid cell resolutions because, in contrast with the terrestrial case, marine protected area sizes vary enormously across countries whose EEZ areas are comparable. For this exercise, we set each country’s grid scale to create 2,500 grid cells within its EEZ. For small-EEZ cases, we generate fewer than 2,500 grid cells by setting a lower bound of 1 km for cell side length.

In this section, we explore marine protection for Philippines and Madagascar. Our measure of Philippines’ EEZ area is 1,964,764 sq. km., of which 32,053 sq km (1.6%) are within 103 marine protected areas. Philippines has 82 endemic marine species in our database, of which 55 (67%) are protected by our criteria. Madagascar’s EEZ area is 1,196,285 sq km, of which 14,457 (1.2%) lie within 79 protected areas. Madagascar has 67 marine endemic species, of which 43 (64.2%) are protected.

# **Philippines**

Philippines’ grid resolution is 0.253 dd (28.1 km). We replicate our terrestrial methodology for the marine case, and Figure 16 identifies the four highest-priority sites for marine protection. The highest-priority area, covering 8 unprotected species, lies off of the northwest coast directly west of Batangas Province. The second-priority area, covering 7 species, lies off the northern coast of Palawan Province. Area 3, covering 3 species, lies off the northeast coast directly offshore from Aurora Province, and area 4, covering 2 species, lies off the northern coast of the main island of Palawan Province.

When compared with the terrestrial cases, our results suggest that moving to full species protection in Philippines would impose very modest requirements on its EEZ. As Figure 17 shows, coverage of all unprotected species would move the current protected marine area from 1.7% to 2.3%.

31
<!-- Start of picture text --> ook AS po Pixel with<br>aps , TM,pee Species counts<br>|| Protected Area<br>he a B ae 4 y B<br><!-- End of picture text -->
<!-- Start of picture text --> a<br>|<br>rang<br>£<br>‘=<br>Boi<br>Ts]<br>=<br>&<br>o<br>=<br>om<br>=<br>oo<br>2<br><<<br>a<br>Lo<br>ae<br>2<br>a<br>©<br>20 40 60 80 100<br>% of Target Species Protected<br><!-- End of picture text -->
<!-- Start of picture text --> maximum<br>| | countPixel with<br>Species counts<br>(decile)<br>1<br>2<br>3<br>4<br>5<br>6<br>7<br>8<br>9<br>10<br>NA<br>|| Protected Area<br><!-- End of picture text -->
<!-- Start of picture text --> reg<br>Ss<br>Eo<br>= =<br>co]<br>=<br>2<br>o<br>Zz<br>57<br>=<br>i)<br>2<br>st<br>a —_<br>o<br>2<br>a.<br>7<br>70 80 90 100<br>% of Target Species Protected<br><!-- End of picture text -->
not publicly reported.<sup>10</sup> By implication, our estimates of 1,211 unprotected species for India and 2,156 for China must be significantly higher than the actual numbers. Nevertheless, we believe that analyses for the two countries are worthwhile because our methodology can provide comparative templates that may be useful for colleagues in India and China whose information is more complete than ours. In particular, the inclusion of many new species in our GBIF database may provide new insights about priority areas for protection in the two countries.

# **India**

Our Indian case methodology goes through 175 iterations to identify candidate areas for protecting all of the 1,211 species that are not covered by publicly-reported protected areas. Table 6 displays results for the first 40 iterations, which account for 958 (79.1%) of the unprotected species. The distribution is very skewed, with the first 3 areas accounting for nearly 25% of unprotected species and 11 areas accounting for 50%. Within the new protected areas, the diversity of species representation is striking. Representation varies from 0 to 87.5% for plants, 0 to 82.9% for vertebrates, 0 to 100% for arthropods and 0 to 100% for other species.

> 10 Citation:  Protected Planet (2024). “China chooses to restrict some data on its protected areas. As a result, data on 2,960 protected areas is not publicly-available and cannot be viewed or downloaded on this page. While these sites are included in the coverage statistics presented here, all other statistics (e.g. number of protected areas; breakdown by governance type etc.) on this page are based upon the publiclyavailable data only.” <u>htps://www.protectedplanet.net/country/CHN</u>

36
**Table 6:  India – Distributions of new priority areas and species**

|Priority|Species|Species %|Cum %|% Plants|%Vertebrates|% Arthropods|% Other<br>Species|
|---|---|---|---|---|---|---|---|
|1|117|9.7|9.7|24.8|10.3|62.4|2.6|
|2|93|7.7|17.3|8.6|3.2|28.0|60.2|
|3|86|7.1|24.4|61.6|11.6|24.4|2.3|
|4|46|3.8|28.2|4.3|10.9|52.2|32.6|
|5|41|3.4|31.6|68.3|7.3|14.6|9.8|
|6|41|3.4|35.0|4.9|4.9|90.2|0.0|
|7|41|3.4|38.4|7.3|82.9|7.3|2.4|
|8|37|3.1|41.5|0.0|0.0|100.0|0.0|
|9|36|3.0|44.4|41.7|16.7|36.1|5.6|
|10|35|2.9|47.3|28.6|0.0|40.0|31.4|
|11|31|2.6|49.9|29.0|45.2|19.4|6.5|
|12|24|2.0|51.9|29.2|16.7|45.8|8.3|
|13|23|1.9|53.8|56.5|8.7|17.4|17.4|
|14|20|1.7|55.4|40.0|20.0|35.0|5.0|
|15|19|1.6|57.0|31.6|21.1|31.6|15.8|
|16|19|1.6|58.5|10.5|36.8|42.1|10.5|
|17|17|1.4|60.0|11.8|0.0|76.5|11.8|
|18|16|1.3|61.3|0.0|25.0|75.0|0.0|
|19|16|1.3|62.6|0.0|75.0|25.0|0.0|
|20|16|1.3|63.9|31.2|25.0|31.2|12.5|
|21|15|1.2|65.2|0.0|0.0|0.0|100.0|
|22|14|1.2|66.3|50.0|7.1|14.3|28.6|
|23|12|1.0|67.3|0.0|8.3|83.3|8.3|
|24|12|1.0|68.3|41.7|33.3|16.7|8.3|
|25|12|1.0|69.3|16.7|25.0|41.7|16.7|
|26|12|1.0|70.3|33.3|16.7|41.7|8.3|
|27|11|0.9|71.2|63.6|27.3|9.1|0.0|
|28|9|0.7|71.9|77.8|0.0|11.1|11.1|
|29|9|0.7|72.7|0.0|22.2|11.1|66.7|
|30|9|0.7|73.4|77.8|0.0|0.0|22.2|
|31|8|0.7|74.1|37.5|0.0|62.5|0.0|
|32|8|0.7|74.7|87.5|0.0|12.5|0.0|
|33|8|0.7|75.4|50.0|25.0|25.0|0.0|
|34|8|0.7|76.1|0.0|25.0|50.0|25.0|
|35|7|0.6|76.6|57.1|14.3|14.3|14.3|
|36|7|0.6|77.2|28.6|0.0|71.4|0.0|
|37|7|0.6|77.8|14.3|0.0|71.4|14.3|
|38|6|0.5|78.3|16.7|0.0|83.3|0.0|
|39|5|0.4|78.7|20.0|0.0|80.0|0.0|
|Figure XX: I<br>40|ndia: Spec<br>5|ies Protecto<br>0.4|n vs. Land<br>79.1|in Protected<br>0.0|Areas<br>40.0|40.0|20.0|

37
We present the complete species allocation results for 175 iterations in Figure 20. New protected areas are in grid cells that surround the centroids of circles that are sized and color-coded by number of new species protected (from purple through red to orange and yellow). The three largest (purple) circles identify areas in Maharashtra, Telangana and Karnataka that provide coverage for about 25% of all unprotected species. The next-priority (dark red) circles in Gujarat, Tamil Nadu, Uttarakhand and Andaman and Nicobar account for another 14%, bringing cumulative coverage to 38%. The next-priority (red) circles in Kerala, southern and western Maharashtra and Sikkim add 12% and bring cumulative coverage to 50%. The remaining 50% are protected in areas that are scattered across the country, with particularly large concentrations in the northern mountain region and the Western Ghats.

As Figure 21 shows, India’s spatial requirements for complete coverage of the unprotected species are relatively modest by the 30x30 standard. These calculations begin with India’s publicly-reported protected areas, which occupy less than 1% of the national territory. As the figure shows, covering 50% of the unprotected species in our analysis requires only 1.3% of India’s territory. This rises to 2.6% for 80% coverage, 5.4% for 95% coverage, and 7.7% for 100% coverage.

38
<!-- Start of picture text --> New Species<br>Protected<br>[0,2]<br>(2,6]<br>(6,10)<br>(10,13]<br>® (13,18)<br>@ 18,21]<br>@ (21,25)<br>@ 25,38]<br>@ 6347<br>O (47,117]<br>a | | Protected area<br><!-- End of picture text -->
<!-- Start of picture text --> New Species<br>Protected<br>New Species Protected [0,2]<br>2 and Protected areas (2, 6]<br>; e ~ _ (6,10]<br>eee e. 10,13]<br>° aC ) © (13,18)<br>~ @ (18,21]<br>@ ee<br>ee @ (21,25)<br>e e 8° e<br>° *e ya oo @ (25,38)<br>e — @ (38,47)<br>e<br>¢ 47,117<br>A<br>Py % ‘<br>| | Protected area<br><!-- End of picture text -->
<!-- Start of picture text --> "<br>Ye @<br>e *e New Species<br>Protected<br>.<br>°. . e [0,2]<br>(2,6]<br>| C) ee (6,10]<br>Ca) (10,13]<br>@<br>e © (13,18)<br>@<br>; ._ e e : . e :“« @® (18,21]21,25)<br>ca . @ (25,38)<br>e<br>: e ° @ «347<br>7 ee e e<br>. °° O (47,117]<br>; . @ : ee@<br>-<br>; eco e@<br>e<br>@<br>) @<br>,e @ e . » [| Protected area<br><!-- End of picture text -->
<!-- Start of picture text --> oo<br>eo<br>&<br>cE<br>2 itm)<br>ro]<br>c<br>2<br>o<br>=<br>‘ot<br>#<br>oo<br>2<br>:-<br>a<br>o<br>S On<br>2<br>o<br>ao<br>0 20 40 60 80 100<br>% of Target Species Protected<br><!-- End of picture text -->
**Table 7:  China – Distributions of new priority areas and species**

|Priority|Species|Species %|Cum %|% Plants|%Vertebrates|% Arthropods|% Other<br>Species|
|---|---|---|---|---|---|---|---|
|1|103|4.8|4.8|24.3|15.5|54.4|5.8|
|2|101|4.7|9.5|9.9|1.0|81.2|7.9|
|3|96|4.5|13.9|30.2|3.1|61.5|5.2|
|4|73|3.4|17.3|75.3|1.4|12.3|11.0|
|5|71|3.3|20.6|38.0|18.3|32.4|11.3|
|6|62|2.9|23.5|93.5|0.0|6.5|0.0|
|7|59|2.7|26.2|49.2|25.4|25.4|0.0|
|8|58|2.7|28.9|55.2|0.0|43.1|1.7|
|9|52|2.4|31.3|96.2|0.0|3.8|0.0|
|10|47|2.2|33.5|78.7|2.1|19.1|0.0|
|11|42|1.9|35.4|40.5|16.7|40.5|2.4|
|12|37|1.7|37.2|13.5|0.0|78.4|8.1|
|13|33|1.5|38.7|90.9|6.1|3.0|0.0|
|14|33|1.5|40.2|78.8|6.1|12.1|3.0|
|15|32|1.5|41.7|37.5|6.2|46.9|9.4|
|16|31|1.4|43.1|90.3|3.2|6.5|0.0|
|17|10|0.5|43.6|70.0|10.0|10.0|10.0|
|18|25|1.2|44.8|92.0|4.0|4.0|0.0|
|19|24|1.1|45.9|83.3|0.0|12.5|4.2|
|20|23|1.1|46.9|87.0|4.3|0.0|8.7|
|21|21|1.0|47.9|19.0|9.5|71.4|0.0|
|22|20|0.9|48.8|100.0|0.0|0.0|0.0|
|23|20|0.9|49.8|95.0|0.0|5.0|0.0|
|24|19|0.9|50.6|36.8|10.5|47.4|5.3|
|25|19|0.9|51.5|68.4|10.5|21.1|0.0|
|26|18|0.8|52.4|50.0|0.0|44.4|5.6|
|27|18|0.8|53.2|83.3|0.0|11.1|5.6|
|28|17|0.8|54.0|94.1|0.0|5.9|0.0|
|29|16|0.7|54.7|62.5|6.2|31.2|0.0|
|30|16|0.7|55.5|100.0|0.0|0.0|0.0|
|31|15|0.7|56.2|100.0|0.0|0.0|0.0|
|32|15|0.7|56.9|93.3|6.7|0.0|0.0|
|33|14|0.6|57.5|42.9|0.0|50.0|7.1|
|34|14|0.6|58.2|100.0|0.0|0.0|0.0|
|35|13|0.6|58.8|76.9|7.7|7.7|7.7|
|36|13|0.6|59.4|53.8|7.7|38.5|0.0|
|37|13|0.6|60.0|92.3|0.0|7.7|0.0|
|38|13|0.6|60.6|69.2|15.4|15.4|0.0|
|39|12|0.6|61.1|8.3|0.0|8.3|83.3|
|40|12|0.6|61.7|83.3|8.3|8.3|0.0|

43
We present the complete species allocation results for 303 iterations in Figure 22, with circles again sized and color-coded by number of new species protected. The three largest (purple) circles identify areas in Hainan, Jiangsu and Beijing that provide coverage for 14% of all unprotected species. The next-priority (dark red) circles in Yunnan, Sichuan, Shaanxi and Fujian account for another 15%, bringing cumulative coverage to 29%. The next-priority (red) circles in Xizang, Sichuan and Guangdong bring cumulative coverage to 35%. The remaining 65% are protected in areas that are scattered across the country, with particularly large concentrations in southwest China.

44
<!-- Start of picture text --> e<br>e<br>J<br>e<br>= | Protected area<br>e<br>]<br>© A) ° : .<br>o Ve . % ; New Species<br>e = °* «6 Protected<br>~<br>®’e<br>°<br>° aw + [0,2]<br>= :<br>ese ce °° (2.6)<br>1 e ~me ee..,* oe© °@8° “e (6,10](10,15]<br>°° ee 3° 8 ha SS © (15,21]<br>a ee ..* %e0 , @ (21,28<br>e° $e Sx o rh Pe<br>wi eee e °% e° Cau @ (28,37)<br>! See ee e f : @ 37,52]<br>1<br>New Species Protected . é ry) a,&° . } ¢« ¥ @ 62.73)<br>and Protected areas bd ee a 2 e @ one _" . ,<br>oe ; < ° ew C) (73,104<br><!-- End of picture text -->
<!-- Start of picture text --> =<br>rang<br>£<br>=o<br>2<br>o<br>=<br>2<br>os<br>=<br>5 oo<br>=<br>i]<br>2<br><<br>a=<br>ot<br>o<br>©<br>a.<br>NM<br>0 20 40 60 80 100<br>% of Target Species Protected<br><!-- End of picture text -->
New Guinea, Philippines, India and China). We focus on individual countries to highlight the role of conservation stewardship in local settings. We consider both terrestrial and marine cases, focusing on species that are endemic to each country. Our approach departs from many previous exercises by giving equal weight to all vertebrates, invertebrates, plants, and other species whose occurrence regions are mapped in our database. We use a spatially-efficient algorithm to identify a hierarchy of priorities for localities where new protected areas would offer coverage to unprotected species.

Our 10 country cases draw on our results to explore the spatial implications for countries’ 30x30 commitments. We find that initial conditions matter greatly, because some countries already protect large parts of their territories while others do not; unprotected species are spatially clustered in some countries but not others; and the representation of different taxa among unprotected species varies greatly across countries. In consequence, full protection for newlymapped species can be achieved inside the 30% territorial standard for some countries, while others would have to go well beyond that limit to achieve the same result. In all cases, however, our results show that spatial clustering is sufficient to yield very significant protection gains for relatively modest expansion of protected areas.

to a much larger group of countries. We have treated all taxa as equal for these cases, but we recognize that not all countries may choose this approach. Similarly, our unique focus on endemics and our criteria for identifying unprotected species may not suit the preferences of environmental policy makers in all countries. At the same time, we believe that our iterative approach to the identification of high-priority areas for new protection can be useful, whatever species weighting criteria are employed.

47