Philippa Kaur

Extent and location of critical natural assetsCritical natural assets providing the 12 local NCP (Fig. 1a) occupy only 30% (41 million km2) of total land area (excluding Antarctica) and 24% (34 million km2) of marine Exclusive Economic Zones (EEZs), reflecting the steep slope of the aggregate NCP accumulation curve (Fig. 1b). Despite this modest proportion of global land area, the shares of countries’ land areas that are designated as critical can vary substantially. The 20 largest countries require only 24% of their land area, on average, to maintain 90% of current levels of NCP, while smaller countries (10,000 to 1.5 million km2) require on average 40% of their land area (Supplementary Data 1). This high variability in the NCP–area relationship is primarily driven by the proportion of countries’ land areas made up by natural assets (that is, excluding barren, ice and snow, and developed lands), but even when this is accounted for, there are outliers (Extended Data Fig. 2). Outliers may be due to spatial patterns in human population density (for example, countries with dense population centres and vast expanses with few people, such as Canada and Russia, require far less area to achieve NCP targets) or large ecosystem heterogeneity (if greater ecosystem diversity yields higher levels of diverse NCP in a smaller proportion of area, which may explain patterns in Chile and Australia).The highest-value critical natural assets (the locations delivering the highest magnitudes of NCP in the smallest area, denoted by the darkest blue or green shades in Fig. 1c) often coincide with diverse, relatively intact natural areas near or upstream from large numbers of people. Many of these high-value areas coincide with areas of greatest spatial congruence among multiple NCP (Extended Data Fig. 3). Spatially correlated pairs of local NCP (Supplementary Table 4) include those related to water (flood risk reduction with nitrogen retention and nitrogen with sediment retention); forest products (timber and fuelwood); and those occurring closer to human-modified habitats (pollination with nature access and with nitrogen retention). Coastal risk reduction, forage production for grazing, and riverine fish harvest are the most spatially distinct from other local NCP. In the marine realm, there is substantial overlap of fisheries with coastal risk reduction and reef tourism (though not between the latter two, which each have much smaller critical areas than exist for fisheries).Number of people benefitting from critical natural assetsWe estimate that ~87% of the world’s current population, 6.4 billion people, benefit directly from at least one of the 12 local NCP provided by critical natural assets, while only 16% live on the lands providing these benefits (and they may also benefit; Fig. 2a). To quantify the number of beneficiaries of critical natural assets, we spatially delineate their benefitting areas (which varies on the basis of NCP: for example, areas downstream, within the floodplain, in low-lying areas near the coast, or accessible by a short travel). While our optimization selects for the provision of 90% of the current value of each NCP, it is not guaranteed that 90% of the world’s population would benefit (since it does not include considerations for redundancy in adjacent pixels and therefore many of the areas selected benefit the same populations), so it is notable that an estimated 87% do. This estimate of ‘local’ beneficiaries probably underestimates the total number of people benefitting because it includes only NCP for which beneficiaries can be spatially delineated to avoid double-counting, yet it is striking that the vast majority, 6.1 billion people, live within 1 h travel (by road, rail, boat or foot, taking the fastest path17) of critical natural assets, and more than half of the world’s population lives downstream of these areas (Fig. 2b). Material NCP are often delivered locally, but many also enter global supply chains, making it difficult to delineate beneficiaries spatially for these NCP. However, past studies have calculated that globally more than 54 million people benefit directly from the timber industry18, 157 million from riverine fisheries19, 565 million from marine fisheries20 and 1.3 billion from livestock grazing21, and across the tropics alone 2.7 billion are estimated to be dependent on nature for one or more basic needs22.Fig. 2: People benefitting from and living on critical natural assets (CNA).a,b, ‘Local’ beneficiaries were calculated through the intersection of areas benefitting from different NCP, to avoid double-counting people in areas of overlap; only those NCP for which beneficiaries could be spatially delineated were included (that is, not material NCP that enter global supply chains: fisheries, timber, livestock or crop pollination). Bars show percentages of total population globally and for large and small countries (a) or the percentage of relevant population globally (b). Numbers inset in bars show millions of people making up that percentage. Numbers to the right of bars in b show total relevant population (in millions of people, equivalent to total global population from Landscan 2017 for population within 1 h travel or downstream, but limited to the total population living within 10 km of floodplains or along coastlines 80%) of their populations benefitting from critical natural assets, but small countries have much larger proportions of their populations living within the footprint of critical natural assets than do large countries (Fig. 2a and Supplementary Data 2). When people live in these areas, and especially when current levels of use of natural assets are not sustainable, regulations or incentives may be needed to maintain the benefits these assets provide. While protected areas are an important conservation strategy, they represent only 15% of the critical natural assets for local NCP (Supplementary Table 5); additional areas should not necessarily be protected using designations that restrict human access and use, or they could cease to provide some of the diverse values that make them so critical23. Other area-based conservation measures, such as those based on Indigenous and local communities’ governance systems, Payments for Ecosystem Services programmes, and sustainable use of land- and seascapes, can all contribute to maintaining critical flows of NCP in natural and semi-natural ecosystems24.Overlaps between local and global prioritiesUnlike the 12 local NCP prioritized here at the national scale, certain benefits of natural assets accrue continentally or even globally. We therefore optimize two additional NCP at a global scale: vulnerable terrestrial ecosystem carbon storage (that is, the amount of total ecosystem carbon lost in a typical disturbance event25, hereafter ‘ecosystem carbon’) and vegetation-regulated atmospheric moisture recycling (the supply of atmospheric moisture and precipitation sustained by plant life26, hereafter ‘moisture recycling’). Over 80% of the natural asset locations identified as critical for the 12 local NCP are also critical for the two global NCP (Fig. 3). The spatial overlap between critical natural assets for local and global NCP accounts for 24% of land area, with an additional 14% of land area critical for global NCP that is not considered critical for local NCP (Extended Data Fig. 4). Together, critical natural assets for securing both local and global NCP require 44% of total global land area. When each NCP is optimized individually (carbon and moisture NCP at the global scale; the other 12 at the country scale), the overlap between carbon or moisture NCP and the other NCP exceeds 50% for all terrestrial (and freshwater) NCP except coastal risk reduction (which overlaps only 36% with ecosystem carbon, 5% with moisture recycling; Supplementary Table 4).Fig. 3: Spatial overlaps between critical natural assets for local and global NCP.Red and teal denote where critical natural assets for global NCP (providing 90% of ecosystem carbon and moisture recycling globally) or for local NCP (providing 90% of the 12 NCP listed in Fig. 1), respectively, but not both, occur; gold shows areas where the two overlap (24% of the total area). Together, local and global critical natural assets account for 44% of total global land area (excluding Antarctica). Grey areas show natural assets not defined as ‘critical’ by this analysis, though still providing some values to certain populations. White areas were excluded from the optimization.Full size imageSynergies can also be found between NCP and biodiversity and cultural diversity. Critical natural assets for local NCP at national levels overlap with part or all of the area of habitat (AOH, mapped on the basis of species range maps, habitat preferences and elevation27) for 60% of 28,177 terrestrial vertebrates (Supplementary Data 3). Birds (73%) and mammals (66%) are better represented than reptiles and amphibians (44%). However, these critical natural assets represent only 34% of the area for endemic vertebrate species (with 100% of their AOH located within a given country; Supplementary Data 3) and 16% of the area for all vertebrates if using a more conservative representation target framework based on the IUCN Red List criteria (though, notably, achieving Red List representation targets is impossible for 24% of species without restoration or other expansion of existing AOH; Supplementary Data 4). Cultural diversity (proxied by linguistic diversity) has far higher overlaps with critical natural assets than does biodiversity; these areas intersect 96% of global Indigenous and non-migrant languages28 (Supplementary Data 5). The degree to which languages are represented in association with critical natural assets is consistent across most countries, even at the high end of language diversity (countries containing >100 Indigenous and non-migrant languages, such as Indonesia, Nigeria and India). This high correspondence provides further support for the importance of safeguarding rights to access critical natural assets, especially for Indigenous cultures that benefit from and help maintain them. Despite the larger land area required for maintaining the global NCP compared with local NCP, global NCP priority areas overlap with slightly fewer languages (92%) and with only 2% more species (60% of species AOH), although a substantially greater overlap is seen with global NCP if Red List criteria are considered (36% compared with 16% for local NCP; Supplementary Data 4). These results provide different insights than previous efforts at smaller scales, particularly a similar exercise in Europe that found less overlap with priority areas for biodiversity and NCP29. However, the 40% of all vertebrate species whose habitats did not overlap with critical natural assets could drive very different patterns if biodiversity were included in the optimization.Although these 14 NCP are not comprehensive of the myriad ways that nature benefits and is valued by people23, they capture, spatially and thematically, many elements explicitly mentioned in the First Draft of the CBD’s post-2020 Global Biodiversity Framework13: food security, water security, protection from hazards and extreme events, livelihoods and access to green and blue spaces. Our emphasis here is to highlight the contributions of natural and semi-natural ecosystems to human wellbeing, specifically contributions that are often overlooked in mainstream conservation and development policies around the world. For example, considerations for global food security often include only crop production rather than nature’s contributions to it via pollination or vegetation-mediated precipitation, or livestock production without partitioning out the contribution of grasslands from more intensified feed production.Gaps and next stepsOur synthesis of these 14 NCP represents a substantial advance beyond other global prioritizations that include NCP limited to ecosystem carbon stocks, fresh water and marine fisheries30,31,32, though still falls short of including all important contributions of nature such as its relational values33. Despite the omission of many NCP that were not able to be mapped, further analyses indicate that results are fairly robust to inclusion of additional NCP. Dropping one of the 12 local NCP at a time results in More

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Genetic variationThe best fitting substitution models for COI codons 1–3 were identified as TN + F + G4, F81 + F + I, and TN + F, respectively. The maximum likelihood (ML) and Bayesian inference (BI) trees showed similar topologies of the main nodes, although the BI tree displayed greater resolution of the ingroup branches (Fig. 1). Furthermore, the BI tree revealed three monophyletic clades, while two of those clades were merged in the ML tree. Two of the three molecular species delimitation methods (ASAP and TCS) recovered three groups in the BI tree as distinct taxa (Fig. 1), corresponding to the three previously described ESUs27,28. The third method (bPTP) recovered between 8 and 43 groups (mean = 28.03) suggesting that there is evidence of additional genetic differentiation within the three groups identified by ASAP and TCS. The outputs of the three methods are provided in the Supplementary information. The molecular diagnosis uncovered several fixed nucleotide differences COI characters for each taxon (Table 1: “W. carteri” I = 10; “W. carteri” II = 3; “W. carteri” III = 5). There were also 13 fixed nucleotide differences in W. carteri for the 16S gene. The remaining two taxa had no fixed nucleotide differences for the 16S gene.Figure 1Phylogenetic trees obtained by maximum likelihood (left) and Bayesian inference (right) analysis of “Westralunio carteri” mtDNA COI sequences, including support values for the major genetic clades [ultrafast bootstrap values (left) and Bayesian posterior probabilities (right)]. Colour coded bars show support for the three major clades by the species delimitation methods (ASAP = dark shade; TCS = lighter shade). Green = WcI = “W. carteri” I; blue = WcIII = “W. carteri” III; red = WcII = “W. carteri” II. Results of bPTP analysis not shown (see supplementary data). Haplotype names correspond to Benson et al.28. Outgroup taxa are Velesunio ambiguus (Philippi, 1847) (Hyriidae: Velesunioninae) and Cucumerunio novaehollandiae (Gray, 1834) (Hyriidae: Hyriinae: Hyridellini).Full size imageTable 1 Molecular diagnoses of “Westralunio carteri” Evolutionarily Significant Units (ESUs) from southwestern Australia (after Bolotov et al.122 with reanalysis of data from Klunzinger et al.27 and Benson et al.28).Full size tableVariation in shell morphologyBased on results from analyses of variances (ANOVAs), shells of “W. carteri” I were significantly larger (for size metrics total length (TL), maximum height (MH), beak height (BH) and beak length (BL)) and more elongated (i.e., had a lower maximum height index (MHI)) than shells of “W. carteri” II and “W. carteri” II + III combined (Table 2). However, there was no difference in size or shape metrics between “W. carteri” I and “W. carteri” III (Table 2). The lack of significant differences in beak height index (BHI) and beak length index (BLI) among any of the taxa (Table 2) indicates that wing and anterior shell development was not discernibly different between any of the ESUs.Table 2 Shell size metrics [mm], shape indices [%] and scores for the first two principal components (PC) obtained by Principal Component Analysis of shape indices and 18 Fourier coefficients generated by Fourier Shape Analysis for each “Westralunio carteri” species and subspecies-level Evolutionarily Significant Units (ESUs): n, number of specimens measured; minimum (min) to maximum (max) and mean (± standard error (SE)).Full size tableThis pattern was partly confirmed in the principal component analysis (PCA) of these three shell shape indices, where PC1, largely explained by variation in BLI (Fig. 2A), did not differ between the two species (i.e., “W. carteri” I vs. “W. carteri” II + III) or among the three taxa (Table 2). The PC2, largely explained by variation in MHI and BHI (Fig. 2A), differed significantly between “W. carteri” I and “W. carteri” II (Table 2). Accordingly, 70% (70% jack-knifed) of specimens were assigned to the correct species in the corresponding discriminant analysis (DA), whilst this was true for only 55% (54%) at the MOTU-level.Figure 2Scatterplots of the first two PC axes obtained by PCA on (A) calculated shape indices based on shell measurements, and (B) 18 Fourier coefficients for “Westralunio carteri” I, “W. carteri” II and “W. carteri” III. 95% Confidence Intervals are displayed at the species level, i.e., for “W. carteri” I (full line) and “W. carteri” II + III (dashed line). Extreme shell outlines in (B) are depicted to visualise trends in sagittal shell shape, along PC axes.Full size imageThe difference in shell elongation between “W. carteri” I and “W. carteri” II was confirmed by Fourier shape analysis. As visualised by synthetic outlines in Fig. 2B, shell elongation is expressed along the PC1 (explaining 15% of total variation in Fourier coefficients). The PC1 as well as PC2 scores differed significantly between the two species (i.e., “W. carteri” I vs. “W. carteri” II + III) as well as between “W. carteri” I and “W. carteri” II, respectively (Table 2). Combined with synthetic outlines, this indicated a tendency towards a more elongated, somewhat wedge-shaped shell in “W. carteri” I, whilst “W. carteri” II shells tended to be relatively high with a stout anterior margin (Fig. 2B). An analysis of similarities (ANOSIM) analysis on all Fourier coefficients revealed no significant difference between the two species (i.e., “W. carteri” I vs. “W. carteri” II + III; ANOSIM: R = − 0.018, p = 0.097), but did indicate a significant difference between the three ESUs (ANOSIM: R = 0.0625, p = 0.0051). Specifically, “W. carteri” I differed significantly from “W. carteri” II (Bonferroni-corrected p = 0.0009). Only 66% and 65% (62% and 62% jack-knifed) of specimens were assigned to the correct species and taxon in DAs on that dataset, respectively.Taxonomic accountsClass: Bivalvia Linnaeus, 175831.Subclass: Autobranchia Grobben, 189432.Infraclass: Heteroconchia Gray, 185433.Cohort: Palaeoheterodonta Newell, 196534.Order: Unionida Gray, 185433 in Bouchet & Rocroi, 201035.Superfamily: Unionoidea Rafinesque, 182036.Family: Hyriidae Parodiz & Bonetto 196337.Genus: Westralunio Iredale, 19349.Type species: Westralunio ambiguus carteri Iredale, 19349 (by original designation).Redescription: Westralunio carteri (Iredale, 1934)SynonymyUnio australis Lamarck38: Menke39, p. 38, specimen 219. (Non Unio australis Lamarck, 181938).Unio moretonicus Reeve40: Smith41, p. 3, pl. iv, Fig. 2. (misidentified reference to Unio moretonicus Reeve, 186540).Hyridella australis (Lam.38): Cotton & Gabriel42 (in part), p. 156. (misidentified reference to Unio australis Lamarck, 181938).Hyridella ambigua (Philippi26): Cotton & Gabriel42 (in part), p. 157. (misidentified reference to Unio ambiguus Philippi, 184726).Westralunio ambiguus carteri: Iredale, 19349, p. 62.Westralunio ambiguus (Philippi26): Iredale9, p. 62, pl. iii, Fig. 8, pl. iv, Fig. 8. (Non Unio ambiguus Phil. 184726), Iredale43, p. 190.Centralhyria angasi subjecta Iredale, 19349, p. 67 (in part), Iredale43, p. 190.Westralunio carteri Iredale9: McMichael & Hiscock10pl. viii, Figs. 1, 2, 3, 4, 5, 6 and 7, pl. xvii, Figs. 4, 5.Type materialLectotype: AMS C.61724 (Fig. 3A) Westralunio ambiguus carteri Iredale, 19349.Figure 3(A) Westralunio ambiguus carteri Iredale, 1934, Lectotype: Victoria Reservoir, Darling Range, 12 mi E of Perth, AMS C.061724. Detail of fusion in anterior muscle scars from either valve represented by dashed lines and black polygons. Bottom image showing detail of hinge teeth. Photos provided with permission by Dr Mandy Reid, AMS. (B) Valves and detail of sculptured umbo of a juvenile W. carteri from Yule Brook, Western Australia, UMZC 2013.2.9. Photo by Dr Michael W. Klunzinger. (C) Glochidia of W. carteri from Canning River, Western Australia. Photo by Dr Michael W. Klunzinger.Full size imageParalectotypes: AMS C.170635 Westralunio ambiguus carteri Iredale, 19349 (n = 4).Type locality: Victoria Reservoir, Darling Range, 12 miles east of Perth, Western Australia (Fig. 4A).Figure 4(A) Victoria Reservoir, Canning River, near Perth, Western Australia, type locality for W. carteri. Photo by Corey Whisson. (B) Goodga River, Western Australia, type locality for W. inbisi inbisi, at vertical slot fishway where holotype of W. inbisi inbisi was collected from. Photo provided with permission by Dr Stephen J. Beatty. (C) Margaret River, Western Australia, type locality for W. inbisi meridiemus, at Canebreak Pool. Photo by Dr Michael W. Klunzinger.Full size imageLectotype: BMNH 1840–10-21–29 Centralhyria angasi subjecta Iredale (selected by McMichael & Hiscock10).Type locality: Avon River, Western Australia.Material examined for redescription: For W. carteri (= “W. carteri” I), molecular data examined included 52 and 61 individual COI mtDNA and 16S rDNA sequences, respectively, for species delimitation. Additionally, Fourier shell shape outline analysis and traditional shell morphometric measurements were examined from 238 and 290 individuals, respectively. Complete details on all specimens examined are provided in Supplementary Table S1.ZooBank registration: urn:lsid:zoobank.org:act:6B740F4D-40C3-4D6A-8938-B0FD7FD1F6D7.Etymology: The species name carteri is most likely named after the surname of the collector who provided original type specimens to the Australian Museum, although Iredale9 did not specify this as the case. We have applied ICZN Articles 46.1 and 47.144, designating W. carteri as the nominotypical species.Revised diagnosis: Specimens of W. carteri are distinguished from other Australian Westralunio taxa by having shell series that are significantly larger and more elongated than Westralunio inbisi inbisi subsp. nov., but not different from Westralunio inbisi meridiemus subsp. nov. The species has 10 diagnostic nucleotides at COI (57 G, 117 T, 210 G, 249 T, 255 C, 345 G, 423 T, 447 T, 465 A, 499 T) and 13 at 16S (137 T, 155 C, 228 C, 229 T, 260 G, 290 A, 305 G, 307 T, 310 A, 311 C, 321 T, 330 A, 460 A), which differentiate it from its sister taxa, W. inbisi inbisi and W. inbisi meridiemus (each described below) using ASAP and TCS species delimitation models.RedescriptionThis species is of the ESU “W. carteri” I27,28.Shell morphology: Shells of relatively small to medium size, generally less than 70 mm in length, but to a maximum length of approximately 100 mm10,45, MHI 46–89%; anterior portion of shell with moderate development, BLI 22–49%; larger shells with abraded umbos scarcely winged; wing development variable, generally decreasing with size, BHI 76–104% (Table 2). Shell outline oblong-ovate to rounded; posterior end obliquely to squarely truncate, anterior end round; ventral edge slightly curved, nearly straight in larger specimens; hinge line curved, hinge strong. Umbos usually abraded in specimens  > 20 mm in length; unabraded umbos with distinctive v- or w-shaped plicated sculpturing (Fig. 3B and Zieritz et al.46). Shell substance typically thick; shells of medium width with pronounced posterior ridge; periostracum smooth, dark brown to reddish, with fine growth lines. Pallial line less developed in smaller specimens and prominent only in large specimens (e.g.,  > 60 mm TL). Lateral teeth longer and blade-like, slightly serrated to smooth and singular in left valve, fitting into deep groove in right valve; pseudocardinal tooth in right valve coarsely serrated, thick, and erect, fitting into deeply grooved socket in left valve. Anterior muscle scars well impressed and anchored deeply in larger specimens; anterior retractor pedis and protractor pedis scars both small and fused with adductor muscle scar; posterior muscle scars lightly impressed; dorsal muscle scars usually with two or three deep pits anchored to internal umbo region.Anatomy: Supra-anal opening absent, siphons of moderate size, not prominent but protrude beyond shell margin in actively filtering live specimens, pigmented dark brown with mottled lighter brown to orange splotches; inhalant siphon aperture about 1.5 times size of exhalant and bearing 2–4 rows of internal papillae (Fig. 5A); ctenidial diaphragm relatively long and perforated. Outer lamellae of outer ctenidia completely fused to mantle, inner lamellae of inner ctenidia fused to visceral mass then united to form diaphragm; palps relatively small, usually semilunar in shape; marsupium well developed as a distinctive swollen interlamellar space in the middle third of the inner ctenidium of females. Outer ctenidia in both sexes thin, with numerous, short intrafilamentary junctions and few, irregular interlamellar junctions; in females similar, but marsupium has numerous, tightly packed, well-developed interlamellar junctions. Thus, brooding in females is endobranchous.Figure 5Live specimens of actively filtering freshwater mussels in the burrowed position. (A) Westralunio carteri (Iredale, 1934), Canning River at Kelmscott, Western Australia, inhalant siphon with 2–4 rows of papillae oriented toward substrate. Photo by Dr Michael W. Klunzinger. (B) Westralunio inbisi meridiemus subsp. nov., Canebreak Pool, Margaret River, Western Australia; inhalant siphon edges lined with protruding papillae facing towards water surface, away from substrate. Photo by Dr Michael W. Klunzinger.Full size imageLife history: Sexes are separate in W. carteri, and hermaphroditism appears to be rare47,48,49. Males and females both produce gametes year-round but brooding of glochidia appears to be seasonal and ‘tachyticitc’ (i.e., as defined by Bauer & Wächtler19, fertilisation and embryonic development occurring in late winter/early spring and glochidia release in early summer)50. Glochidia are released within vitelline membranes, embedded in mucus which extrude from exhalant siphons of females (i.e., ‘amorphous mucus conglutinates’) during spring/summer. Glochidia attach to host fishes and live parasitically on fins, gills or body surfaces for 3–4 weeks while undergoing metamorphosis to the juvenile stage. Host fishes which have been shown to support glochidia metamorphosis to the juvenile stage in the laboratory include Afurcagobius suppositus (Sauvage, 188051), Gambusia holbrooki (Girard, 185952), Nannoperca vitttata (Castelnau, 187353), Pseudogobius olorum (Sauvage, 188051) and Tandanus bostocki Whitley, 194454 but not Carassisus auratus Linnaeus, 175831 or Geophagus brasiliensis (Quoy & Gaimard, 1824 More