The biodiversity revolution Ecologists are increasingly looking at traits — rather than species — to measure the health of ecosystems. BY RACHEL CERNANSKY E mmett Duffy was about 5 metres under water off the coast of Panama, when a giant, tan-and-white porcupinefish caught his eye. The slow-moving creature would have been a prime target for predators if not for the large, treelike branches of elkhorn coral (Acropora palmata) it was sheltering under. The sighting was a light-bulb moment for Duffy, a marine biologist. He’d been to places in the Caribbean where corals were more abundant and more diverse, but smaller; the fish there were always small, too. Here, in the Bocas Del Toro archipelago, he was seeing a variety of big fish among the elkhorns. “The reason these large fish were able to thrive,” he says, “was that there were places for them to hide and places for them to live.” For Duffy, that encounter with the porcupinefish (Diodon hystrix) brought to life a concept that had long been simmering in the back of his head: that the health of an ecosystem may depend not . d e v r e s e r s t h g i r l l A . e r u t a N r e g n i r p S f o t r a p , d e t i m i L s r e h s i l b u P n a l l i m c a M 7 1 0 2 © 2 2 | NAT U R E | VO L 5 4 6 | 1 J U N E 2 0 1 7 only on the number of species present, but also on the diversity of their traits. This idea, which goes by the name of functional-trait ecology, had been part of his lab work for years but had always felt academic and abstract, says Duffy, now director of the Smithsonian Institution’s Tennenbaum Marine Observatories Network in Washington DC. It’s an idea that’s increasingly in vogue for ecologists. Biodiversity, it states, doesn’t have to be just about the number of a species in an ecosystem. Equally important to keeping an ecosystem healthy and resilient are the species’ different characteristics and the things they can do — measured in terms of specific traits such as body size or branch length. That shift in thinking could have big implications for ecology. It may be necessary for understanding and forecasting how plants and animals cope with a changing climate. And functional diversity has started to influence how ecologists Common species such as this guineafowl pufferfish (Arothron meleagris) may have important functions in their ecosystems. FEATURE NEWS think about conservation; some governments have even started to incorporate traits into their management policies. Belize, for example, moved several years ago to protect parrotfish species from overfishing — not necessarily because their numbers are dwindling, but because the fish clean algae from coral and are crucial to reef survival. “Just going for species numbers basically doesn’t allow us to harness all this incredibly rich information we have of how the real world operates,” says Sandra Díaz, an ecologist with Argentina’s National Scientific and Technical Research Council (CONICET) and the University of Córdoba. Still, some experts are concerned. How traits are defined remains a source of debate, and without robust data on trait and species diversity in settings around the world, any choices directed by the approach could turn out to be short-sighted. “I’m really excited, but I worry,” says Walter Jetz, an ecologist and evolutionary biologist at Yale University in New Haven, Connecticut. “We as a community need to be really careful in appreciating the data limitations that exist.” JEFF ROTMAN/GETTY QUALITY VERSUS QUANTITY For decades, the study of biodiversity was essentially a numbers game: the more species an ecosystem had, the more stable and resilient to change it was thought to be. That mindset made sense because there was so little information available about the structures of an ecosystem and the functions of species within it. The technology didn’t exist to measure many traits or to process the large amount of data that would have resulted if they could have been measured. Various developments have changed that. Advances in molecular biology have enabled the study of microbes en masse. Satellites can assess traits such as tree-canopy height and marine plankton productivity. And leaps in statistical tools and computing power have helped to make use of all the data that are now being generated. Some trace the new way of thinking about ecosystems — at least in formal research — to ecologist David Tilman at the University of Minnesota in St Paul. In 1994, he published a landmark paper1 that tracked species diversity in Minnesota grasslands through a major drought in the 1980s. Species-rich areas tended to weather the drought much better than those with few species, supporting the link between diversity and stability. But the relationship wasn’t linear. Only a few drought-resistant grasses were needed to greatly enhance a plot’s ability to rebound. Three years later, Tilman and his collaborators published findings2 from 289 grassland plots they had planted with varying numbers of species and levels of functional diversity. Here, the presence of certain traits, such as the C4 photosynthesis pathway or nitrogen fixation, made a bigger difference to the plots’ overall health than the number of species. Around the same time, Shahid Naeem, director of Columbia University’s Earth Institute Center for Environmental Sustainability in New York City, was also looking beyond species numbers to study ecosystem function, zeroing in on the diversity of species at different levels of the food web. Looking at species number alone, he says, is like listing the parts of a car without saying what they do. That provides no guidance for when things start to break down, he says. “We just sort of stand there scratching our heads like primitive people who’ve never seen a car before, saying, ‘The car’s not working now, I wonder what’s wrong with it’.” From the mid-1990s, studies of functional diversity started to take root. Work on plants and forests led the charge because it is relatively easy to manipulate such systems. But the approach gradually expanded to include birds, sea life and soils. Diana Wall, a soil ecologist at Colorado State University in Fort Collins, says that she and her colleagues have focused on functional traits and diversity for years, in part because the activities of soil microorgan- “Just going for species numbers doesn’t allow us to harness all this incredibly rich information of how the real world operates.” isms are often easier to identify than the species themselves. She is excited that researchers are developing a firmer grasp of traits and species above and below ground. “New knowledge on both fronts brings us understanding of the dependence on species and functions,” she says. GET YOUR PRIORITIES STRAIGHT Conservation biologists are excited about functional traits because they could influence decisions about what to protect. Researchers and environmentalists have typically focused on regions brimming with species, such as the Amazon rainforest and Australia’s Great Barrier Reef. But Rick Stuart-Smith, an ecologist at the University of Tasmania in Taroona, Australia, has suggested reframing the definition of a biodiversity hotspot. Integrating functional traits could point to the importance of previously understudied areas. For Stuart-Smith, it’s too early to identify specific places that would qualify — more in-depth research is needed. But, he says, functional-trait ecology should ultimately extend to conservation strategies and how governments choose which areas to protect. And the new way of thinking about diversity could reveal vulnerabilities that weren’t recognized before. Species-rich areas may seem to have a sort of insurance against loss of traits because the functions the traits provide are assumed to be found in many species, says David Mouillot, a marine ecologist at the University of Montpellier in France. But some functions are provided by only one species, or a few. He and his colleagues are racing to locate these rare functions. The lens of functional diversity helps to create a more nuanced picture of ecosystems. Greg Asner, an ecologist with the Carnegie Institution for Science’s Department of Global Ecology at Stanford University in California, has used a unique spectral imager to map 15 traits for forests across Peru. Conventional studies recognized three types of forest in the country using the species-richness concept, says Asner — dryland, floodplain and swamp forest. But Asner and his team looked at which traits could help to distinguish new functional groups, and found that seven were key. They then classified the forests based on those traits, and came up with 36 classes representing different combinations of the seven traits3. The researchers used their findings to help Peru rebalance its conservation portfolio. Asner says he’s also been asked to identify a 400,000-hectare area in northern Borneo to set aside for protection on the basis of traits. “They want to know, where is the million acres where you can get the most variation in traits?” he says. “Where can you put a fence around the most functional variation?” That level of interest is encouraging to him and other researchers because ecosystems are so complex that once certain species, functions or ecosystem processes are lost, there’s no getting them back — at least not using current techniques or knowledge. “We don’t have the science or technology on Earth to engineer a forest from scratch the way that nature and evolution have,” says Asner. Some experts, however, advise against making decisions based on functional traits until more complete data are available. “As soon as you’re missing a single species in your data matrix, you may be missing a key function that is only represented by that species,” says Jetz, who has studied functional traits in plants and vertebrate animals, particularly birds. He warns not only about gaps in data, but also about biases — such as where researchers choose to sample, which can skew a data set towards or away from certain regions or types of environment. Naeem, too, would like to see a concerted global effort to create a more complete and comprehensive database of traits for the natural world. “When we get really excited about . d e v r e s e r s t h g i r l l A . e r u t a N r e g n i r p S f o t r a p , d e t i m i L s r e h s i l b u P n a l l i m c a M 7 1 0 2 © 1 J U N E 2 0 1 7 | VO L 5 4 6 | NAT U R E | 2 3 DAVID DOUBILET/NGC/ALAMY The branches of elkhorn coral (Acropora palmata) provide shelter for large fish, but researchers disagree on whether this is a functional trait or an interaction. a field, one of the big, major investments and efforts that everybody has to get behind is getting the data that we need,” he says. Some work is afoot to build such databases for both terrestrial and aquatic environments. TRY, hosted at the Max Planck Institute for Biogeochemistry in Jena, Germany, is an international network of plant scientists who have been building a publicly accessible database of traits and functions since 2007. It now contains records for 100,000 plant species. There’s also the ReeFish database, now led by Mouillot, which aims to provide trait and geographic information for all tropical reef fish. And the Reef Life Survey, begun in Tasmania by Stuart-Smith and marine ecologist Graham Edgar in 2007, has trait records for more than 5,000 species from all ocean basins. Duffy, meanwhile, is spearheading the Smithsonian’s Marine Global Earth Observatory programme, which he says is a “major opportunity to map out the links between diversity and functioning of marine eco­ systems on a global scale”. There are currently ten sites in the network, which aims to establish a global, pole-to-pole presence. These are all works in progress, and despite wide agreement on the importance of focusing on functional traits across ecosystems, there doesn’t yet seem to be a clear definition of what a trait is. Agreeing on one that spans the plant and animal kingdoms will be difficult. How detailed should one get? Is it appropriate to stop at observable traits, such as leaf size, or to dig into individual gene sequences? Diet seems to be a grey area. Some TRAIT TALKING Interactions between species open up another area of debate. Some might interpret a porcupinefish taking shelter among corals, as Duffy observed in Panama, as an interaction between species — and not count it as a trait. For Duffy, however, traits can influence, and be a reflection of, how species interact with each other. The traits of the coral — its branch structure and size — are what enabled the fish to thrive. Whether or not to rank the importance of traits to an ecosystem is another area of contention. Some researchers are working to identify the most valuable traits, whereas others, such as Mouillot, take a more agnostic approach. “We do not rank them. We do not say two or three traits are the most important and the other ones are marginal,” he says. And for all the focus on functional diversity, it is probably just one step towards finding a truly comprehensive view of biodiversity — the ultimate goal for ecologists and conservationists. Simultaneous work is being done on the evolutionary histories of species in an ecosystem in an attempt to understand and mitigate the effects of biodiversity loss. Some view this ‘phylogenetic diversity’ as the third leg of the stool with functional and species diversity. And researchers around the world are working to fill in other gaps, too. A large German consortium has been studying how land-use intensification affects functional diversity, and more work needs to be done on the role of spatial data and interactions at the landscape level, rather than in microcosms or individual study sites. For now, however, researchers are embracing functional traits for the sophistication they have already added to understanding of ecosystems. That includes Jetz — despite his warnings against making decisions based on functional diversity too soon. The data may be incomplete, but functional traits could potentially convey the importance of ecosystems to people outside the scientific community, including policy­makers and economists, in a more tangible way than species richness ever has. “If you lose a species or two, it’s hard to interpret what that means,” Jetz says. But being able to show explicitly how the loss of a function could decimate an ecosystem might have a bigger impact. “It’s something that more people are able to relate to.” ■ SEE EDITORIAL P.7 Rachel Cernansky is a freelance writer in Denver, Colorado. 1. Tilman, D. & Downing, J. A. Nature 367, 363–365 (1994). 2. Tilman, D. et al. Science 277, 1300–1302 (1997). 3. Asner, G. P. et al. Science 355, 385–389 (2017). . d e v r e s e r s t h g i r l l A . e r u t a N r e g n i r p S f o t r a p , d e t i m i L s r e h s i l b u P n a l l i m c a M 7 1 0 2 © 2 4 | NAT U R E | VO L 5 4 6 | 1 J U N E 2 0 1 7 researchers include dietary patterns when they evaluate an organism’s functional traits, for example, by looking at whether it can eat a variety of organisms or is specialized to feed on a single flower species. Others scoff at including diet. “If it’s not on a genome, it’s not a trait,” says Naeem, who points out that foxes may have certain dietary preferences, but will still eat packaged dog food, given the chance. He says that traits linked to genes — tooth size in a predator, for example — will influence diet and can be used to infer feeding patterns. ARTICLE Received 26 Feb 2015 | Accepted 29 Jul 2015 | Published 8 Sep 2015 DOI: 10.1038/ncomms9221 OPEN Global priorities for an effective information basis of biodiversity distributions Carsten Meyer1, Holger Kreft1, Robert Guralnick2 & Walter Jetz3,4 Gaps in digital accessible information (DAI) on species distributions hamper prospects of safeguarding biodiversity and ecosystem services, and addressing central ecological and evolutionary questions. Achieving international targets on biodiversity knowledge requires that information gaps be identified and actions prioritized. Integrating 157 million point records and distribution maps for 21,170 terrestrial vertebrate species, we find that outside a few well-sampled regions, DAI on point occurrences provides very limited and spatially biased inventories of species. Surprisingly, many large, emerging economies are even more under-represented in global DAI than species-rich, developing countries in the tropics. Multi-model inference reveals that completeness is mainly limited by distance to researchers, locally available research funding and participation in data-sharing networks, rather than transportation infrastructure, or size and funding of Western data contributors as often assumed. Our results highlight the urgent need for integrating non-Western data sources and intensifying cooperation to more effectively address societal biodiversity information needs. 1 Biodiversity, Macroecology and Conservation Biogeography Group, Faculty of Forest Sciences, University of Göttingen, Büsgenweg 1, 37077 Göttingen, Germany. 2 University of Florida Museum of Natural History, University of Florida at Gainesville, 358 Dickinson Hall, Gainesville, Florida 32611-2710, USA. 3 Department of Ecology and Evolutionary Biology, Yale University, 165 Prospect Street, New Haven, Connecticut 06520, USA. 4 Department of Life Sciences, Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot, Berks SL5 7PY, UK. Correspondence and requests for materials should be addressed to C.M. (email: cmeyer2@uni-goettingen.de) or to H.K. (email: hkreft@uni-goettingen.de). NATURE COMMUNICATIONS | 6:8221 | DOI: 10.1038/ncomms9221 | www.nature.com/naturecommunications & 2015 Macmillan Publishers Limited. All rights reserved. 1 ARTICLE T NATURE COMMUNICATIONS | DOI: 10.1038/ncomms9221 he parties to the Convention on Biological Diversity (CBD) have agreed on 20 targets to improve the state of biodiversity by 2020 (https://www.cbd.int/sp/targets/). Aichi Target 19 specifically mandates the development of an advanced and shared biodiversity knowledge base. Information on species distributions in space is a central aspect of biodiversity knowledge that can enable the effective management of biodiversity and associated ecosystem services in a rapidly changing world1–5. Species distributions are critical for informing actions towards multiple Aichi targets, associated environmental indicators6 and the recently launched assessment work of the Intergovernmental science-policy Platform on Biodiversity and Ecosystem Services7. International efforts to mobilize and aggregate distribution data, most notably through the Global Biodiversity Information Facility (GBIF), have facilitated access to large quantities of digital species occurrence records from a variety of data sources, especially museum specimens and field observations8,9. Such records provide vital, fine-scale information about where and when species occur and are widely used in ecology, evolution and conservation research. In contrast to expert knowledge or data sets that are either non-digital or not openly shared, and thus effectively inaccessible to most users, such mobilized records form the bulk of de facto ‘digital accessible information’ (DAI, originally referred to as DAK in ref. 10). Although in a recent study11 the authors saw evidence for progress towards Aichi Target 19 in increasing volumes of GBIF-facilitated DAI, they had to acknowledge the critical caveat of unclear ‘taxonomic coverage (e.g., number of species), record completeness or geographic biases’. Severe gaps and biases usually exist in DAI10,12–14 and these require careful consideration in ecological modelling15–17 and conservation research3. These data limitations may result from the way data are collected in the field, digitized in museums or mobilized and aggregated as digital species records into global biodiversity data-sharing networks. Different socio-economic and geographic drivers of data limitations have been hypothesized, including inadequate financial and institutional resources18–20, poor international scientific cooperation20, lack of access or regional safety concerns20–23, or a focus on regions with certain appeal like endemism-, species-rich or protected areas12,21,24. The amount of data required to completely inventory species assemblages is a function of their richness and the spatial grain13,14,25. To be relevant for conservation applications, distribution data sets must inform about species occurrences at fine spatial grains26, either directly or by facilitating derived, finegrain models5,13. Such fine-grain models are integral to conservation research, but can also directly influence conservation decision-making. For instance, occurrence records have facilitated the identification of ‘priority areas’27 in Madagascar, where following a legal decree, no mining and forestry activities can be permitted (Arrêté Interministériel n18633/2008/MEFT/MEM, renewed in 2014; further examples in ref. 5). Identifying information gaps and factors limiting the dissemination of biodiversity information are recognized as priorities both at the political28 and scientific29 levels of the CBD. To date, magnitude and exact location of gaps in global DAI as well as the generality and relative importance of underlying causes remain unclear, hampering prioritization of future data mobilization efforts30. International efforts to mobilize biodiversity records remain un-assessed for their success and effectiveness in addressing targets to improve and share biodiversity knowledge. Here we perform this assessment for 21,170 species of birds, mammals and amphibians, and c. 157 million geographically and 2 taxonomically validated point records that were provided to GBIF by 160 data publishers, including small institutions with a distinct taxonomic and geographic focus, large internationally active research museums and citizen science programmes. We determine the factors currently limiting biodiversity inventory completeness in global DAI and identify priority regions and activities to advance it. We find that most gaps in inventories exist in large emerging economies and DAI is mainly limited by distance to data contributors, locally available research funding and political commitment to data sharing. To advance global DAI effectively, efforts to foster participation in data-sharing networks and mobilize non-Western data sources should be prioritized. Results and Discussion Patterns in global DAI on species distributions. At a grain size of 110 km grid cells, the density of terrestrial vertebrate records varies by five orders of magnitude (Fig. 1a), peaking in parts of Europe, North and Central America and Australia. Conversely, 48% of Asian, 35% of African and 21% of South American cells have no records mobilized into DAI. At this spatial grain, the finest ensuring sufficient accuracy of species expert-range maps31,32, species richness derived from point records shows little concordance with expected richness (Fig. 1b,c). Although spatial patterns between the two data sources show at least weak associations (rs ¼ 0.28–0.39, see Supplementary Table 1a), only 4.2% of all 12,029 cells reach Z80% completeness (Fig. 1d). Completeness, defined as percentage of expected richness documented with point records, is moderately to strongly predicted by record density (binomial generalized linear model (GLM), d2 ¼ 0.59–0.90, Supplementary Fig. 1, Supplementary Table 1b and see Supplementary Notes 1–3 for details). Whereas high record density results in high levels of completeness in much of the Nearctic and Australasia, this is less the case for the more species-rich Neo- and Afrotropics (Fig. 1a,b,d,e and Supplementary Fig. 1D). The Eastern Palaearctic and Indomalayan realms are characterized by particularly low levels of completeness. Average completeness also varies greatly among the world’s major biomes and biomes within biogeographical realms (Fig. 1e and Supplementary Table 2a–c). Specifically, tropical and subtropical forests, grasslands and savannas, but also boreal forests and tundra biomes remain vastly underinventoried. Surprisingly, we cannot confirm a pronounced ‘tropical data gap’33 (max-t test, PDut ¼ 0.27, N ¼ 4,717/7,286; tropics versus non-tropics). Instead, a severe gap emerges across most of Asia (including temperate regions), non-Southern Africa and Brazil (max-t test, PDuto0.01, N ¼ 6,089/5,914; when comparing mean completeness in these areas to all others; see also Supplementary Tables 2 and 3). Although these strong geographic differences in completeness are broadly repeated among the three vertebrate groups (Fig. 2a), completeness patterns among the three taxa only show moderately strong positive associations (rs ¼ 0.65–0.74 depending on taxon and grain, max-t tests, all PDuto0.001, N ¼ 323–11,522). This suggests that the completeness pattern of a single-taxon is a poor predictor for un-assessed taxa and highlights the need to identify taxon-specific information gaps34. As expected from substantially fewer records for mammals and amphibians compared with birds (B3 and B1 M compared with B150 M, see Supplementary Table 4), their overall level of completeness is significantly lower (Tukey’s test, all PDuto0.001, N ¼ 280–11,757, depending on spatial grain, when comparing mammal/amphibian completeness with bird completeness). Completeness levels of Z80% over large extents, even at a relatively coarse grain of 110 km, are only achieved in birds and only in North America, Europe and Australia (Fig. 2a). NATURE COMMUNICATIONS | 6:8221 | DOI: 10.1038/ncomms9221 | www.nature.com/naturecommunications & 2015 Macmillan Publishers Limited. All rights reserved. ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms9221 a N records / 104 km2 No data 0.8 19.4 1 155 7 220.2 2,722 1.4×10 b N species No data 262 1,424 434 but necessarily leads to inferior opportunities for inference and application. Such coarse grains are not adequate for most questions in ecology35 and, with land-use and conservation actions typically set at the kilometer scale or finer, are unsuited for effective resource management. Most species distribution models (SDMs) connecting records with finegrained environmental data for extrapolation17 are unable to provide a general remedy here, owing to their known sensitivity to environmental bias14,36. This pervasive lack of DAI over vast extents (for example, only o20% completeness at 880 km grain over much of Asia, Fig. 2a) demonstrates that for many regions with large conservation opportunities37 there are not sufficient mobilized occurrence data to facilitate even the most sophisticated modelling approaches. Global numbers of sampling locations for the majority of species are far below the 50–100 typically recommmended3,38,39 as minimum SDM requirements (54.9% of all bird species have o50 records, median ¼ 37; mammals: 79.2%, median ¼ 6; amphibians: 91.3%, median ¼ 2) (compare refs 14,40). c N species No data 1 155 >0 20 1,424 434 262 d Completeness (%) e 40 60 80 100 Re Ne alm a Ne rctic o Afr tropi ot ca Pa ropic l le a Ind arcti l om c Au ala ya s Oc tralas n ea n ain Wo ain rld No data Mean completeness (%) 1 10 25 50 75 100 Biome Trop/subtrop moist broadleaf Trop/subtrop dry broadleaf Trop/subtrop coniferous Flooded grasslands/savannas Trop/subtrop grass/savan/shrub Deserts/xeric shrublands Mediterranean Temperate broadleaf/mixed Temperate coniferous Temp grass/savannas/shrublands Montane grasslands/shrublands Boreal forests/taiga Tundra Total realm Figure 1 | Global unevenness and gaps in the DAI on distributions of 21,170 species of terrestrial vertebrates (birds, mammals and amphibians). (a) Density of point records, (b) species richness from point records, (c) species richness from expert opinion and (d) inventory completeness (percentage of expected richness documented by records). Grey areas do not have any mobilized records. (e) Mean inventory completeness in biome-realm combinations. Size of black circles is proportional to mean inventory completeness and grey areas show s.d. All assessed over a 110-km equal-area grid. Coarsening grains even further to 440 or 880 km substantially increases completeness in all groups (Kruskal–Wallis test, all Po0.001, N ¼ 280–11,757, Fig. 2a,b and Supplementary Fig. 2), Addressing information gaps effectively. Such glaring data gaps highlight the need to identify and, where possible, address the root causes of low inventory completeness. Understanding of the key driving factors of bias is important to prioritize activities in data mobilization. Further, drivers of bias can be explicitly incorporated into biodiversity models41,42. To this end, we tested 12 hypotheses falling into 5 broad categories: appeal, accessibility, security, international scientific integration, and financial and institutional resources (details in Fig. 3 and Supplementary Notes 2 and 3, Supplementary Figs 3–6 and Supplementary Table 5). Most hypotheses receive at least some support in our multi-model inference framework, highlighting the complex interplay of geographic and socio-economic factors as drivers of inventory completeness (Fig. 3; for record density and bivariate model results, see Supplementary Fig. 5; detailed results in Supplementary Tables 6–8). Depending on taxon and grain, minimum adequate models of inventory completeness explain 60%–78% of the deviance (Supplementary Table 6) and the relative importance of factors varies more strongly among taxonomic groups than among grain sizes (depending on the predictor, percentages of sums of squares explained in an analysis of variance are 16.5%–72.5% higher for factor ‘taxon’ compared with factor ‘spatial grain’). A strong role for data collection has been attributed to region or species ‘appeal’, for example, researchers’ preference for reserves, mountains or other areas of high total, rare and range-restricted species richness21,24,43. We find this supported in birds and mammals by strong positive effects on inventory completeness of endemism richness and weaker effects of protected area coverage. Surprisingly, we find relatively low importance of on-ground accessibility from cities and proximity to airports (Fig. 3), which have previously been suggested to strongly constrain field collections21,23. In contrast, spatial distance to data-contributing institutions (Supplementary Table 9) consistently emerges as a key predictor of inventory completeness and record density (Fig. 3 and Supplementary Fig. 5). This highlights the imprint that long-term logistics of maintaining field sampling and specimen transport leave on global biodiversity information (compare refs 22,24). Insecure conditions may discourage field sampling20,44, but we find little evidence that security aspects are important in limiting completeness or record density (Fig. 3, Supplementary Fig. 5 and Supplementary Note 2). We expected our index of integration into scientific activities, that is, country’s H-index in ecology multiplied by level of international collaboration, to be strongly NATURE COMMUNICATIONS | 6:8221 | DOI: 10.1038/ncomms9221 | www.nature.com/naturecommunications & 2015 Macmillan Publishers Limited. All rights reserved. 3 ARTICLE Birds Mammals Amphibians 880 km 110 km a NATURE COMMUNICATIONS | DOI: 10.1038/ncomms9221 Completeness (%) No data >0 20 40 60 80 100 b Grain for 80% completeness Not reached 880 km 440 km 220 km 110 km Figure 2 | Spatial variation in point record-based inventory completeness for three vertebrate taxa at different spatial grains. (a) Inventory completeness at the 110- and 880-km grain. (b) Minimum grain size to reach 80% inventory completeness, mapped at 110 km. Grey grid cells (a) show areas within the taxon’s global range without mobilized records and (b) areas that do not reach 80% completeness at 880 km. Appeal GLM β GLM % SS 110 220 440 880 Grain (km) 110 220 440 880 GLM % SS 110 220 440 880 110 220 440 880 GLM β Grain (km) International scientific integration Endemism richness: collectors prefer to work in areas with many or rare species. Protected areas: collectors prefer to work in and around protected areas. Scientific activities: countries in which ecologists engage in peer-reviewed publication and international collaborations are more likely to mobilize biodiversity data. GBIF participation: national commitment to data sharing and mobilization is a limiting factor for data availabilty. Mountains: collectors prefer to work in mountainous areas. Financial & institutional resources Accessibility National research funding: national research funding limits local scientific activities and local data availability. On-ground accessibility: collectors frequent areas that are easy to reach from major cities via roads, rivers, etc. Proximity to airports: collectors frequent areas that are easily accessible via the global network of airports. Publisher size: large institutions have specimens of more and rarer species. Areas in the focus of larger institutions are better sampled and inventoried. + Proximity to research institutions: collectors restrict most of their sampling activities to areas close to their home institutions. Research funding of institutions: funding potentially available to data publishers limits data availability in their focal areas. % SS Security Secure conditions: collectors restrict most of their sampling to areas that are perceived as secure due to political stability, high levels of public safety and lack of armed conflicts. GLM β 75 50 0.75 0.50 25 10 0.25 0.10 – 1 0.01 Figure 3 | Determinants of inventory completeness in DAI on species distributions. Effects were tested in multiple generalized linear regression models with a binomial distribution and a logit link (GLM b and GLM % SS). All possible model subsets were ranked based on AIC scores and subsets with DAICo10 re-run as spatial models to account for spatial autocorrelation in model residuals. Bubble size represents the relative strength of predictor– response relationships. Vertebrate groups are represented by different colours, with shading denoting the direction of the relationship. We show the relative importance of predictors using two different metrics: (i) the standardized coefficients of the reduced spatial multiple regression models with the lowest AIC score (blank cells indicate variables that were not included in these models) (GLM b), and (ii) the percentage each predictor has in the total sum of squares (GLM % SS) of a type III analysis of variance. 4 NATURE COMMUNICATIONS | 6:8221 | DOI: 10.1038/ncomms9221 | www.nature.com/naturecommunications & 2015 Macmillan Publishers Limited. All rights reserved. ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms9221 4×105 N spp-cell combinations correlated with inventory completeness, as it should reflect the routine of making research results public20,33. However, it is not an important factor for explaining completeness or record density (Fig. 3 and Supplementary Fig. 5). Conversely, GBIF participation emerges as a consistently strong factor determining completeness in DAI. Supporting previous suggestions19,45, national research funding (gross expenditure on research and development) is strongly positively correlated with completeness (Fig. 3). Surprisingly, however, research funding of countries where data-publishing institutions are situated does not affect inventory completeness in the regions of their sampling activity (Supplementary Note 2). Finally, publisher size, estimated from contributed data volume, only weakly predicts inventory completeness for mammals and amphibians, but it has much stronger effects for birds, where the largest data contributors are not museums but aggregators of citizen-science observations (Supplementary Table 9), pointing to the potential of alternative, non-institution-based ways of producing DAI for certain taxa (see discussions in refs 13,46,47). Most of the strongest limiting factors of completeness affect digitization and mobilization of existing data rather than the actual collection of new records in the field. Although adequate national research funding is vital for producing DAI on local biodiversity, our results suggest that funding for university research usually leading to peer-reviewed publications is not improving our ability to close information gaps as greatly as direct support for data mobilization programmes (Fig. 3: ‘Scientific activities’ versus ‘GBIF participation’). A likely reason is that current data-archiving policies48 and academic reward systems49 do not favour data-sharing activities. They further suggest that the largest or best-funded museums alone are unable to guarantee high inventory completeness in distant regions, unless their efforts are backed by supportive local conditions, such as locally available research funding, mobilization efforts in local research institutions and national commitment to data sharing. The most effective strategy for closing gaps in DAI may therefore lie in supporting mobilization efforts in institutions nearby identified data gaps and supporting participation in international data-sharing programmes. Dedicated funds and specialized personnel for data mobilization in developed, often low-diversity countries may be better applied to support efforts in countries that lag behind, due to lack of expertise or cyber infrastructure50, for example, through direct partnerships or capacity building assistance. The need to mobilize more data to increase completeness is obvious: 69%–95% of the deviance in completeness explained by our minimum adequate models can also be explained by differences in record density (Supplementary Table 7a). However, we find that there is much room for improving the effectiveness of such mobilization: representing each known species of the three vertebrate groups once in every 110 km cell within its range, and thus achieving 100% inventory completeness globally at that spatial grain, would require c. 3.7 M ideally sampled records. Currently, about 42 times that many (157 M) validated records represent only 21.6% (0.8 M) of these 3.7 M unique species-grid cell combinations, demonstrating a huge level of informational redundancy concentrated in a few places (Fig. 4, compare ref. 47). Such intensive but localized sampling and data mobilization may benefit local conservation efforts as well as many purely scientific endeavors, but surely trades off against global-scale data needs, such that gaps in DAI are particularly severe in regions where higher-resolution data sets are most needed to support costeffective progress towards multiple Aichi Targets37,51. Strategic mobilization of data sources that likely contain many missing species-grid cell combinations could prove effective in quickly closing gaps and reducing geographical bias in global DAI. 3×105 2×105 105 0 >0 10 102 103 104 Records per spp-cell combination 105 Figure 4 | Redundancy of information in 157 M globally mobilized point records that constitute DAI on species distributions. The histogram shows the frequency of different degrees of information duplication (duplicated species-grid cell combinations) at the 110-km grain. Theoretically, and under ideal sampling, representing each of 3.7 M species-grid cell combinations by one record would achieve 100% inventory completeness at that spatial grain. This in turn would facilitate robust, fine-grain distribution models from SDM or downscaling approaches52 for a greater and geographically more representative sample of species than previously possible3, and could immediately support various Aichi Targets6. Examples include land-use planning to minimize biodiversity loss (Target 7), creating species lists for protected areas and improving global reserve networks (Target 11), safeguarding threatened species (Target 12) and mapping and securing associated ecosystem services (Target 14). Targeting sufficiently recent data sources would furthermore create strong synergies with keeping conservation assessments up-to-date53. As a concrete example of potential conservation impacts, GBIFfacilitated records were recently used in the legal listing of five species of sawfish (Pristidae) under the US Endangered Species Act54. Increased access to occurrence information alone cannot ensure sound application nor conservation outcomes, but it can facilitate sound, data-driven decision-making5, which in many parts of the world is currently impossible. We therefore argue that data mobilization efforts should be coordinated and strive to maximize return-on-investment for global conservation applicability. Immediate opportunities for addressing gaps in DAI are most apparent at the national level: we find that even after controlling for all investigated factors (which explain 92.1%–97.2% of cross-national variation), country identity still explains a significant portion of inventory completeness (2.4%–7.1% of D2; Supplementary Table 7b), pointing to an important role of country-specific political, legal, historical, linguistic or cultural factors (Supplementary Note 4). If countries were equally committed to providing access to their biodiversity information, as agreed upon by CBD signatories, completeness should be mainly limited by available financial resources. However, there is only a moderate relationship between country-level completeness and per capita gross domestic product (r2 ¼ 0.34, Po0.001; Fig. 5a,b) or total conservation spending55 (r2 ¼ 0.16, Po0.001). Notably, several large emerging economies including Brazil, China, India, Indonesia, Russia or Turkey lag behind (Fig. 5b,c and Supplementary Table 3), which is worrying given increasing pressure on their biodiversity from rising global and domestic consumption56,57. Success in building an adequate information basis for global biodiversity conservation and thus globally informed policies for environmental sustainability will depend on their support and may be determined by political rather than economic factors. For example, despite the large mobilization needs owing to its megadiverse biota, Mexico has a leading role NATURE COMMUNICATIONS | 6:8221 | DOI: 10.1038/ncomms9221 | www.nature.com/naturecommunications & 2015 Macmillan Publishers Limited. All rights reserved. 5 ARTICLE a NATURE COMMUNICATIONS | DOI: 10.1038/ncomms9221 c Country-level inventory completeness Share of missing spp-cell combinations % of total missing Completeness (%) 0 b 20 40 60 80 100 0 0.5 1.4 3.1 6.8 15.4 100 IRL GBR NOR BEL DNK SWE FIN FRA CRI 80 ISR ESP ISL BLZ USA AUS NLD SLV CHE DEU SVK Completeness (%) DOM NZL DMA EST SWZ WSM GTM 60 JAM VCT ARE MUS MEX ZAF POL AUT PRT ATG LCA GRD BRB LSO STP HND HTI RWA TTO KOR CYP CHLHUNCZE KNA NIC 40 CAN BTN QAT FJI CPV BDI 20 ECUPAN LBR KWT BHS JOR LKA GRC FSM MAR PER LVA UGA PNG VUT BOL KEN PHL BWA MYS NAM BGR COL MWI MNE MKD TUN URY GUYPRY BIH ARG HRV MDG ALB SUR NPL VEN LTU SEN EGYTHAROU GHA MDA SLETZA TUR SLB ITA JPN SYC MLT SVN COM KHM OMN VNM ARM CMR BGD ZMB GIN LAO SYR BEN GAB GNQ GEO UKR IRQ GNB CIV BFA TGO ERIMOZ EAFG TH MNG IRN DZA PAK AGO KAZ COG BLR AZE NGA KGZ TJK YEM SDN UZB NER TKM LBYSAU TCD MRT DJI CAF MLI IND IDN CHNBRA TON COD MDV KIR RUS 0 3×102 103 3×103 104 3×104 105 Per capita GDP (PPP $) Figure 5 | Gaps in DAI on species distributions at the country level. (a) Country-level inventory completeness, measured as the percentage of the total unique species-grid cell combinations in each country that are covered by GBIF records. (b) Country-level inventory completeness in relation to per capita gross domestic product (in purchase power parity dollars, PPP $); r2 ¼ 0.34, Po0.001. Font size of country ISO codes is proportional to the total number of unique species-grid cell combinations that need to be recorded in each country to reach 100% inventory completeness at the 110-km grain. Font colour is for geographical reference (compare inset map). Countries mentioned in the main text: BRA, Brazil; CHN, China; IDN, Indonesia; IND, India; MEX, Mexico; RUS, Russia; TUR, Turkey. (c) Share that each country has in the unique species-grid cell combinations that are missing globally from a complete inventory at the 110-km grain. in biodiversity informatics due to early political support for establishment of a national biodiversity programme58. Data-rich institutions in economically powerful countries such as Brazil, China and Russia12,14,24, which together account for 31% of missing species-grid cell combinations (Fig. 5c and Supplementary Table 3), seem particularly well-poised to contribute significantly to globally accessible species distribution information. As countries such as Brazil recently announced intentions to relax biodiversity research restrictions59, as well as to improve and unlock their data store, existing national programmes (for example, speciesLink; http://splink.cria.org.br) will increasingly be integrated into global DAI, and information gaps and priorities may rapidly shift. More than current snapshots, tools for ongoing re-evaluation (see http://patterns.mol.org/completeness) may aid researchers to assess or account for data bias60 as well as monitor progress in data mobilization11. This global cross-taxon assessment represents a first in a number of steps required for more effective understanding and confrontation of information gaps on species distributions. Although terrestrial vertebrates represent only c. 1.6% of 6 described species61, addressing the factors that emerged as important across vertebrate taxa may hold the greatest promise for closing gaps for biodiversity in general. Vitally, and confirmed by the strong taxon dependence of our results, assessments of distribution information need to be extended to more species-rich groups such as fishes, plants and invertebrates (for example, see refs 10,23,25 for regional assessments). Comparing ratios between mobilized record volumes and described species numbers suggests that gaps in DAI may be one to three orders of magnitude more severe in those groups (average records per species: tetrapods (31,032 spp.): 6,909; fishes (31,658 spp.): 347; vascular plants (283,701 spp.): 317; invertebrates (1.38M spp.): 31; numbers of geo-referenced records from GBIF website, June 2014, species numbers from ref. 61). Such profound data limitations call for more holistic solutions. Our assessment highlights potential ways for making institutionbased data mobilization more effective, but also the limitations of such efforts. Point records from biocollections only represent one of a variety of data sources13 and their targeted mobilization should be complemented by other ways to address biodiversity information needs. Thorough biodiversity assessments led by trained field biologists will continue to play an important role in the creation of primary information for unsurveyed, biodiverse areas. In addition, novel approaches such as citizen science projects are already providing increasingly valuable records for certain taxa at comparatively low cost46. Improved reward systems49 and new data publishing mechanisms and journal requirements48 can incentivize both individual scientists and larger project teams to openly share biodiversity records. Much information held by conservation non-governmental or governmental organizations may be unlocked through supportive mechanisms, such as stronger evaluation and attribution of progress towards declared national commitments (for example, Aichi Target 19) and more widely adopted strategies to address sensitive information, for example, on threatened species62. Further opportunities for improvements lie in better use of available information. Novel Bayesian modelling approaches can address some of the typical limitations of classical SDMs, for example, by connecting different data types across spatial scales52 or by explicitly modelling bias-causing processes41,42,63. Geographically or thematically focused data platforms such as eBird46 or Atlas of Living Australia62 have already highlighted the opportunities of using enriched information together with models. Novel biodiversity informatics infrastructure such as Map of Life13 has the potential to provide an integration of disparate information sources, and to link these with environmental information through best-suited modelling tools to address species distributions and their changes globally. Rapid biodiversity loss, limited funding and potential trade-offs with direct conservation investments64 require priorities for future collection and mobilization of biodiversity records into DAI. Targeted integration of available information and assessments of gaps, along with continued evaluation of effectiveness of DAI for conservation needs, are as vital as increased commitment to biodiversity data sharing by political stakeholders, institutions and individual scientists. With time running out to meet CBD targets on biodiversity knowledge, more effective data use and mobilization, and a cultural shift about data sharing are urgently needed. Methods Species distribution data. We overlaid expert-based extent-of-occurrence range maps for terrestrial birds (excluding pelagic feeders; N ¼ 9,712), terrestrial mammals (N ¼ 5,270) and amphibians (N ¼ 6,188) with four nested equal-area grids (grain sizes: 110, 220, 440 and 880 km) to infer coarse-resolution species richness patterns. As a representation of international efforts to collect, digitize and NATURE COMMUNICATIONS | 6:8221 | DOI: 10.1038/ncomms9221 | www.nature.com/naturecommunications & 2015 Macmillan Publishers Limited. All rights reserved. ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms9221 share biodiversity records, we compiled a database of nearly 200 M records for the three groups, aggregated by GBIF (see Supplementary Tables 4 and 9, and Supplementary Note 1). We focus on GBIF given that it is by far the largest such effort in geographic and taxonomic scope8,9 and has an intergovernmental mandate to openly make accessible data from a worldwide base of data publishers. Data from GBIF represent the greatest body of existing DAI on species occurrences, based on centuries’ worth of museum specimens, citizen science observations, surveys, literature and other sources. GBIF also has a vital role in sharing skills, software, tools and best practices for biodiversity data mobilization. Thus, GBIF-facilitated DAI is currently the best available indicator of ‘shared biodiversity knowledge, science base and technologies’ as referred to by Aichi Target 19 (ref. 11). To link GBIF-facilitated records with range maps, extensive taxonomic standardization was necessary (our approach as well as various filtering and validation steps are explained in the Supplementary Note 1). We defined inventory completeness as the percentage of expert-opinion species richness documented by mobilized records. We note that other DAI sources play vital and often complementary roles in progressing towards Aichi Targets (Supplementary Note 4). Yet, other data sets may not be shared but nevertheless influence regional research and conservation. Thus, results here should not be interpreted as definite maps of knowledge gaps, but the analyses of drivers are likely indicative of factors limiting biodiversity information in other data sources. Geographic and socio-economic drivers of gaps in DAI. We analysed relationships of 12 geographic and socio-economic factors with record density and inventory completeness. We used three variables to describe the appeal of areas to attract collectors: (i) endemism richness65, that is, the sum of inverse range sizes of all species present in a grid cell, was calculated from the number of 110 km cells. (ii) To model effects of mountains on record collection, we calculated the topographic range in each cell based on a digital elevation model. (iii) We modelled the effects of protected areas using proportions of land area in grid cells that fall within protected areas of International Union for Conservation of Nature categories I–IV. We investigated three aspects of accessibility: (i) to test for effects of on-ground accessibility, we used a data set on the time needed to travel to cities with a population 450,000 (ref. 66). (ii) To model effects of the proximity to airports, we created an index based on the locations of 49,300 airports and airfields67. (iii) ‘Proximity to institutions’ was expressed as weighted geographic proximity of a grid cell to those data publishers that contributed records for the area surrounding the cell. Index values are high if the majority of records are contributed by geographically close data publishers. We modelled effects of secure conditions using the Global Peace Index68, which aggregates information on political stability, armed conflicts and levels of public safety. We investigated two aspects of international scientific integration: (i) to quantify integration into ‘scientific activities’, we extracted the H-index for every country based on peer-reviewed papers published in the field ‘Ecology, Evolution, Behavior and Systematics’ from Elsevier’s Scopus database (covering the years 1996–2011), and multiplied it with the proportion of papers resulting from international collaborations (see Supplementary Note 2). (ii) We tested for effects of political commitment to data sharing using the proportion of the land area within each grid cell that falls within GBIF-participating countries. We used three measures of financial and institutional resources: we estimated financial resources that are potentially available for biodiversity research from per capita gross domestic expenditure on research and development (i) within grid cell-overlaying countries (‘National research funding’) as well as (ii) in countries where the publishers of records for a particular cell are situated (‘Research funding of institutions’). (iii) We used record volumes contributed to GBIF by different data publishers to estimate institution size. Details on calculation and transformation of predictor variables, along with detailed information on the respective hypotheses and the limitations of our data sources are in Supplementary Notes 2 and 4. Statistical methods. We investigated effects of predictor variables on inventory completeness separately for amphibians, birds and mammals at each of the four spatial grains with simple and multiple regressions. Specifically, we used non-spatial and spatial generalized linear models with a binomial distribution, where completeness enters as a composite variable (‘species covered by records’, ‘species not covered but presumed present’) and where differences in species richness are automatically accounted for. Spatial models account for residual spatial autocorrelation by including a ‘residuals autocovariate’ built from residuals of the non-spatial model and an optimized spatial neighbourhood structure69. Because of long computation times for spatial models, we ran all possible non-spatial models and re-ran those model subsets that would likely be among the minimum adequate spatial models (with DAIC o10 to the lowest Akaike Information Criterion score) as spatial models. We assessed model fits of minimum adequate spatial models as the % deviance explained (D2) (Supplementary Table 6). We investigated interactions among variables as well as nonlinear effects, but— although many were significant—accounting for them did not greatly alter model fit or parameter estimates of main effects in preliminary analyses. To maintain as much simplicity as possible given 12 predictor variables and 12 separate sets of models (3 taxa  4 spatial grains), we decided to focus on the main effects. We used standardized coefficients (b) of minimum adequate spatial models (with the lowest AIC scores) to measure the relative importance of predictor variables. As an alternative measure, we used percentages of the sums of squares attributable to each factor, based on analyses of variance with a response variable consisting of the AIC scores of all possible models and predictor variables coding the presence/ absence of each predictor in the respective model. 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We thank Jeremy Malczyk, Javier Otegui, Tim Robertson, Gaurav Vaidya and Patrick Weigelt for help with data assembly and handlings, and Carsten Dormann for advice on statistical methods. C.M. acknowledges funding from the Deutsche Bundesstiftung Umwelt (DBU), German Academic Exchange Service (DAAD) and Universitätsbund Göttingen. H.K. acknowledges funding by the German Research Council (DFG) in the framework of the German Excellence Initiative within the Free Floater Program at the University of Göttingen. W.J. and R.P.G. acknowledge support from NSF (DBI 0960550, DEB 1026764 and DBI-1262600), NASA (NNX11AP72G) and the Yale Program in Spatial Biodiversity Science and Conservation. We acknowledge support by the Open Access Publication Fund of the University of Göttingen. Author contributions H.K. and W.J. led this study. All authors designed this study. C.M. performed the analyses and led the writing with major contribution from H.K., R.G. and W.J. Additional information Supplementary Information accompanies this paper at http://www.nature.com/ naturecommunications Competing financial interests: The authors declare no competing financial interests. Reprints and permission information is available online at http://npg.nature.com/ reprintsandpermissions/ How to cite this article: Meyer, C. et al. Global priorities for an effective information basis of biodiversity distributions. Nat. Commun. 6:8221 doi: 10.1038/ncomms9221 (2015). This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ NATURE COMMUNICATIONS | 6:8221 | DOI: 10.1038/ncomms9221 | www.nature.com/naturecommunications & 2015 Macmillan Publishers Limited. All rights reserved. REVIEW doi:10.1038/nature22900 Future threats to biodiversity and pathways to their prevention David Tilman1,2, Michael Clark3, David R. Williams2, Kaitlin Kimmel1, Stephen Polasky1,4 & Craig Packer1,5,6 Tens of thousands of species are threatened with extinction as a result of human activities. Here we explore how the extinction risks of terrestrial mammals and birds might change in the next 50 years. Future population growth and economic development are forecasted to impose unprecedented levels of extinction risk on many more species worldwide, especially the large mammals of tropical Africa, Asia and South America. Yet these threats are not inevitable. Proactive international efforts to increase crop yields, minimize land clearing and habitat fragmentation, and protect natural lands could increase food security in developing nations and preserve much of Earth’s remaining biodiversity. H uman impacts on the environment are imperilling the species and ecosystems of Earth at ever-increasing rates1–3. Land-use change and habitat fragmentation4, overhunting, invasive species and pollution5 already threaten 25% of all mammal species and 13% of all bird species, as well as more than 21,000 other species of plants and other animals, with extinction6. In one of the few remaining centres of terrestrial large mammal diversity worldwide, an area that comprises southeast Asia, India and China (referred to as SAIC in this Review), rapid increases in wealth, land clearing and population density in the last 50 years have resulted in almost two-thirds of mammals that weigh 10 kg or more being threatened with extinction6. Another such centre — sub-Saharan Africa — is likely to be swept by a similar wave of human impacts in the coming decades. Indeed, various analyses suggest that Earth’s most biodiverse regions will experience elevated extinction risks in the near future if human impacts continue along current trajectories7–10. To prevent and reduce threats to global biodiversity, more substantial conservation efforts will be needed and proactive policies, such as shifts in agricultural practices, increased agricultural trade and improved land-use planning, will also be essential10. Human-influenced extinctions began when modern humans moved out of Africa. Successive waves of extinctions in Australia (50,000 years (50 kyr) ago), North America and South America (10–11 kyr ago) and Europe (3–12 kyr ago) were driven largely by a combination of hunting by humans and natural climate change. By 3 kyr ago, Earth had lost half of all terrestrial mammalian megafauna species (with a mass of more than 44 kg) and 15% of all bird species11–14. Since 1500 ad, the impacts of humans have accelerated6. Extinction rates for birds, mammals and amphibians15–17 are similar at present to those of the five global mass-extinction events of the past 500 million years (500 Myr) that probably resulted from meteorite impacts, massive volcanism and other cataclysmic forces13. With the human population worldwide now 25 times greater than 3 kyr ago and projected to increase by about 4 billion people by the end of the twenty-first century18, extinction rates will accelerate in the absence of large-scale conservation actions. Here, we explore current patterns of extinction risks and their drivers, and discuss where and how these risks may change in the coming 50 years, which species groups are most likely to be jeopardized and how future risks might be minimized or prevented. Human-driven extinction risks In this Review, we focus on terrestrial mammals and birds because of the comprehensive assessments of the threats to and stresses on these two groups conducted by the International Union for Conservation of Nature (IUCN). We expect that human-driven changes in the environment will increasingly threaten these and other groups of terrestrial, marine and freshwater species19. The IUCN has assessed the risk of extinction for 61,000 animal species, including essentially all known species of mammals and birds, against its Red List categories and criteria6,20 and classified the status of each as one of the following: ‘least concern’ (extinction risk (v) of 0); ‘near threatened’ (v = 1); ‘vulnerable’ (v = 2); ‘endangered’ (v = 3); ‘critically endangered’ (v = 4); and ‘extinct’ or ‘extinct in the wild’ (v = 5). We have adopted the IUCN terminology, in which a species is considered to be threatened if it is listed as vulnerable, endangered or critically endangered. We also used two metrics of the extinction risks faced by a particular country’s mammal and bird species: the percentage of all species that are threatened with extinction; and the mean extinction risk value for all of the species in a country. In these calculations, we excluded the few ‘data deficient’ and ‘not evaluated’ species (see Supplementary Methods). Land-use change is associated with declining biodiversity worldwide4. Habitat loss and degradation pose the most frequent direct threats to terrestrial mammals and birds19 (Fig. 1a) by decreasing the size of the area that a species can occupy, and therefore its abundance21, and by fragmenting populations and species ranges into small, isolated patches. About 80% of all threatened terrestrial bird and mammal species are imperilled by agriculturally driven habitat loss (Fig. 1a). Other considerable drivers of habitat destruction include logging, urbanization, mining and the establishment of transport corridors. Hunting by humans and other forms of direct mortality imperil 40–50% of all threatened bird and mammal species (Fig. 1a) and an even greater proportion of large herbivores22. Hunting for valued body parts (such as rhinoceros horn and elephant ivory) is also a serious threat 1 Department of Ecology, Evolution, and Behavior, University of Minnesota, St Paul, Minnesota 55108, USA. 2Bren School of Environmental Science and Management, University of California, Santa Barbara, California 93106, USA. 3Natural Resources Science and Management, University of Minnesota, St Paul, Minnesota 55108, USA. 4Department of Applied Economics, University of Minnesota, St Paul, Minnesota 55108, USA. 5Wildlife Conservation Research Unit, Department of Zoology, University of Oxford, Recanati-Kaplan Centre, Tubney, Oxfordshire OX13 5QL, UK. 6School of Life Sciences, University of KwaZulu-Natal, Pietermaritzburg Campus, Scottsville 3209, South Africa. . d e v r e s e r s t h g i r l l A . e r u t a N r e g n i r p S f o t r a p , d e t i m i L s r e h s i l b u P n a l l i m c a M 7 1 0 2 © 1 J U N E 2 0 1 7 | VO L 5 4 6 | NAT U R E | 7 3 INSIGHT REVIEW a 80 Mammals Threatened species affected (%) Birds 60 40 20 0 Agriculture Logging Development Habitat loss Direct mortality b Mammals Body mass Large 60 Species threatened with extinction (%) Hunting Invasive species Medium Small 40 The geography of current endangerment 20 0 SAIC Sub-Saharan Africa c Birds Tropical South America Body mass 60 Large Medium Species threatened with extinction (%) But even in large protected areas, unlawful hunting and grazing can reduce species populations to well below their carrying capacities31. Because of the threats that they pose to humans32 and livestock, large carnivores may also experience high mortality as a result of problem animal control strategies33,34. Relatively few terrestrial mammals are threatened by invasive species (Fig. 1a) but introduced predators threaten, and have even extirpated, many species of island bird35. Invasive and other problematic species threaten 21% of terrestrial birds (Fig. 1a), a figure that rises to 26% if seabirds are included. Water pollution, often from agriculture, is a modest threat to mammals and birds yet a considerable threat to amphibians6. Exotic diseases can be a major threat to amphibians and to some bird, mammal and plant species. Anthropogenic climate change does not yet represent a considerable threat to biodiversity6 but, as we will discuss, will probably pose challenges in the future36–38. Large-bodied species are especially vulnerable to human-driven impacts22,39. For both mammals (Fig. 1b) and birds (Fig. 1c), such species are about three times more likely to be threatened than smallbodied species. In the SAIC region, tropical South America and sub-Saharan Africa, all of which have high numbers of mammal or bird species, 30–60% of large mammals (weighing more than 10 kg) and 25–40% of large birds (weighing more than 2 kg) are classified as threatened with extinction. Small 40 20 0 SAIC Sub-Saharan Africa Tropical South America Figure 1 | Anthropogenic threats to mammals and birds and the role of body mass. a, Major threats for terrestrial mammals and birds, separated by the mechanism of the threat (habitat loss or direct mortality). Categories are aggregations of various stresses and threats, as defined by the IUCN (see Supplementary Methods). b, Percentage of large (mass ≥10 kg), medium (
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