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Conservation as vaccination : Integrated approaches to public health and environmental protection could prevent future disease outbreaks
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Publication date (Electronic):
29 January 2016
Journal:
EMBO Reports
Publisher:
John Wiley and Sons Inc.
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Abstract
The main lesson from the 2015 Ebola outbreak is that we need to increase global preparedness to better deal with zoonotic disease outbreaks. Yet, it might be a more efficient strategy to prevent such zoonotic spillover events in the first place through conservation measures to protect biodiversity and wildlife.
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How Many Species Are There on Earth and in the Ocean?
Camilo Mora, Derek P. Tittensor, Sina M Adl … (2011)
Introduction Robert May [1] recently noted that if aliens visited our planet, one of their first questions would be, “How many distinct life forms—species—does your planet have?” He also pointed out that we would be “embarrassed” by the uncertainty in our answer. This narrative illustrates the fundamental nature of knowing how many species there are on Earth, and our limited progress with this research topic thus far [1]–[4]. Unfortunately, limited sampling of the world's biodiversity to date has prevented a direct quantification of the number of species on Earth, while indirect estimates remain uncertain due to the use of controversial approaches (see detailed review of available methods, estimates, and limitations in Table 1). Globally, our best approximation to the total number of species is based on the opinion of taxonomic experts, whose estimates range between 3 and 100 million species [1]; although these estimations likely represent the outer bounds of the total number of species, expert-opinion approaches have been questioned due to their limited empirical basis [5] and subjectivity [5]–[6] (Table 1). Other studies have used macroecological patterns and biodiversity ratios in novel ways to improve estimates of the total number of species (Table 1), but several of the underlying assumptions in these approaches have been the topic of sometimes heated controversy ([3]–[17], Table 1); moreover their overall predictions concern only specific groups, such as insects [9],[18]–[19], deep sea invertebrates [13], large organisms [6]–[7],[10], animals [7], fungi [20], or plants [21]. With the exception of a few extensively studied taxa (e.g., birds [22], fishes [23]), we are still remarkably uncertain as to how many species exist, highlighting a significant gap in our basic knowledge of life on Earth. Here we present a quantitative method to estimate the global number of species in all domains of life. We report that the number of higher taxa, which is much more completely known than the total number of species [24], is strongly correlated to taxonomic rank [25] and that such a pattern allows the extrapolation of the global number of species for any kingdom of life (Figures 1 and 2). 10.1371/journal.pbio.1001127.g001 Figure 1 Predicting the global number of species in Animalia from their higher taxonomy. (A–F) The temporal accumulation of taxa (black lines) and the frequency of the multimodel fits to all starting years selected (graded colors). The horizontal dashed lines indicate the consensus asymptotic number of taxa, and the horizontal grey area its consensus standard error. (G) Relationship between the consensus asymptotic number of higher taxa and the numerical hierarchy of each taxonomic rank. Black circles represent the consensus asymptotes, green circles the catalogued number of taxa, and the box at the species level indicates the 95% confidence interval around the predicted number of species (see Materials and Methods). 10.1371/journal.pbio.1001127.g002 Figure 2 Validating the higher taxon approach. We compared the number of species estimated from the higher taxon approach implemented here to the known number of species in relatively well-studied taxonomic groups as derived from published sources [37]. We also used estimations from multimodel averaging from species accumulation curves for taxa with near-complete inventories. Vertical lines indicate the range of variation in the number of species from different sources. The dotted line indicates the 1∶1 ratio. Note that published species numbers (y-axis values) are mostly derived from expert approximations for well-known groups; hence there is a possibility that those estimates are subject to biases arising from synonyms. 10.1371/journal.pbio.1001127.t001 Table 1 Available methods for estimating the global number of species and their limitations. Case Study Limitations Macroecological patterns Body size frequency distributions. By extrapolation from the frequency of large to small species, May [7] estimated 10 to 50 million species of animals. May [7] suggested that there was no reason to expect a simple scaling law from large to small species. Further studies confirmed different modes of evolution among small species [4] and inconsistent body size frequency distributions among taxa [4]. Latitudinal gradients in species. By extrapolation from the better sampled temperate regions to the tropics, Raven [10] estimated 3 to 5 million species of large organisms. May [2] questioned the assumption that temperate regions were better sampled than tropical ones; the approach also assumed consistent diversity gradients across taxa which is not factual [4]. Species-area relationships. By extrapolation from the number of species in deep-sea samples, Grassle & Maciolek [13] estimated that the world's deep seafloor could contain up to 10 million species. Lambshead & Bouchet [12] questioned this estimation by showing that high local diversity in the deep sea does not necessarily reflect high global biodiversity given low species turnover. Diversity ratios Ratios between taxa. By assuming a global 6∶1 ratio of fungi to vascular plants and that there are ∼270,000 species of vascular plants, Hawksworth [20] estimated 1.6 million fungi species. Ratio-like approaches have been heavily critiqued because, given known patterns of species turnover, locally estimated ratios between taxa may or may not be consistent at the global scale [3],[12] and because at least one group of organisms should be well known at the global scale, which may not always be true [15]. Bouchet [6] elegantly demonstrated the shortcomings of ratio-based approaches by showing how even for a well-inventoried marine region, the ratio of fishes to total multicellular organisms would yield ∼0.5 million global marine species whereas the ratio of Brachyura to total multicellular organisms in the same sampled region would yield ∼1.5 million species. Host-specificity and spatial ratios. Given 50,000 known species of tropical trees and assuming a 5∶1 ratio of host beetles to trees, that beetles represent 40% of the canopy arthropods, and that the canopy has twice the species of the ground, Erwin [9] estimated 30 million species of arthropods in the tropics. Known to unknown ratios . Hodkinson & Casson [18] estimated that 62.5% of the bug (Hemiptera) species in a sampled location were unknown; by assuming that 7.5%–10% of the global diversity of insects is bugs, they estimated between 1.84 and 2.57 million species of insects globally. Taxonomic patterns Time-species accumulation curves. By extrapolation from the discovery record it was estimated that there are ∼19,800 species of marine fishes [23] and ∼11,997 birds [22]. This approach is not widely applicable because it requires species accumulation curves to approach asymptotic levels, which is only true for a small number of well-described taxa [22]–[23]. Authors-species accumulation curves. Modeling the number of authors describing species over time allowed researchers to estimate that the proportion of flowering plants yet to be discovered is 13% to 18% [21]. This is a very recent method and the effect of a number of assumptions remains to be evaluated. One is the extent to which the description of new species is shifting from using taxonomic expertise alone to relying on molecular methods (particularly among small organisms [26]) and the other that not all authors listed on a manuscript are taxonomic experts, particularly in recent times when the number of coauthors per taxa described is increasing [21],[38], which could be due to more collaborative research [38] and the acknowledgment of technicians, field assistants, specimen collectors, and so on as coauthors (Philippe Bouchet, personal communication). Analysis of expert estimations. Estimates of ∼5 million species of insects [15] and ∼200,000 marine species [14] were arrived at by compiling opinion-based estimates from taxonomic experts. Robustness in the estimations is assumed from the consistency of responses among different experts. Erwin [5] labeled this approach as “non-scientific” due to a lack of verification. Estimates can vary widely, even those of a single expert [5],[6]. Bouchet [6] argues that expert estimations are often passed on from one expert to another and therefore a robust estimation could be the “same guess copied again and again”. Higher taxonomy data have been previously used to quantify species richness within specific areas by relating the number of species to the number of genera or families at well-sampled locations, and then using the resulting regression model to estimate the number of species at other locations for which the number of families or genera are better known than species richness (reviewed by Gaston & Williams [24]). This method, however, relies on extrapolation of patterns from relatively small areas to estimate the number of species in other locations (i.e., alpha diversity). Matching the spatial scale of this method to quantify the Earth's total number of species would require knowing the richness of replicated planets; not an option as far as we know, although May's aliens may disagree. Here we analyze higher taxonomic data using a different approach by assessing patterns across all taxonomic levels of major taxonomic groups. The existence of predictable patterns in the higher taxonomic classification of species allows prediction of the total number of species within taxonomic groups and may help to better constrain our estimates of global species richness. Results We compiled the full taxonomic classifications of ∼1.2 million currently valid species from several publicly accessible sources (see Materials and Methods). Among eukaryote “kingdoms,” assessment of the temporal accumulation curves of higher taxa (i.e., the cumulative number of species, genera, orders, classes, and phyla described over time) indicated that higher taxonomic ranks are much more completely described than lower levels, as shown by strongly asymptoting trajectories over time ([24], Figure 1A–1F, Figure S1). However, this is not the case for prokaryotes, where there is little indication of reaching an asymptote at any taxonomic level (Figure S1). For most eukaryotes, in contrast, the rate of discovery of new taxa has slowed along the taxonomic hierarchy, with clear signs of asymptotes for phyla (or “divisions” in botanical nomenclature) on one hand and a steady increase in the number of species on the other (Figure 1A–1F, Figure S1). This prevents direct extrapolation of the number of species from species-accumulation curves [22],[23] and highlights our current uncertainty regarding estimates of total species richness (Figure 1F). However, the increasing completeness of higher taxonomic ranks could facilitate the estimation of the total number of species, if the former predicts the latter. We evaluated this hypothesis for all kingdoms of life on Earth. First, we accounted for undiscovered higher taxa by fitting, for each taxonomic level from phylum to genus, asymptotic regression models to the temporal accumulation curves of higher taxa (Figure 1A–1E) and using a formal multimodel averaging framework based on Akaike's Information Criterion [23] to predict the asymptotic number of taxa of each taxonomic level (dotted horizontal line in Figure 1A–11E; see Materials and Methods for details). Secondly, the predicted number of taxa at each taxonomic rank down to genus was regressed against the numerical rank, and the fitted models used to predict the number of species (Figure 1G, Materials and Methods). We applied this approach to 18 taxonomic groups for which the total numbers of species are thought to be relatively well known. We found that this approach yields predictions of species numbers that are consistent with inventory totals for these groups (Figure 2). When applied to all eukaryote kingdoms, our approach predicted ∼7.77 million species of animals, ∼298,000 species of plants, ∼611,000 species of fungi, ∼36,400 species of protozoa, and ∼27,500 species of chromists; in total the approach predicted that ∼8.74 million species of eukaryotes exist on Earth (Table 2). Restricting this approach to marine taxa resulted in a prediction of 2.21 million eukaryote species in the world's oceans (Table 2). We also applied the approach to prokaryotes; unfortunately, the steady pace of description of taxa at all taxonomic ranks precluded the calculation of asymptotes for higher taxa (Figure S1). Thus, we used raw numbers of higher taxa (rather than asymptotic estimates) for prokaryotes, and as such our estimates represent only lower bounds on the diversity in this group. Our approach predicted a lower bound of ∼10,100 species of prokaryotes, of which ∼1,320 are marine. It is important to note that for prokaryotes, the species concept tolerates a much higher degree of genetic dissimilarity than in most eukaryotes [26],[27]; additionally, due to horizontal gene transfers among phylogenetic clades, species take longer to isolate in prokaryotes than in eukaryotes, and thus the former species are much older than the latter [26],[27]; as a result the number of described species of prokaryotes is small (only ∼10,000 species are currently accepted). 10.1371/journal.pbio.1001127.t002 Table 2 Currently catalogued and predicted total number of species on Earth and in the ocean. Species Earth Ocean Catalogued Predicted ±SE Catalogued Predicted ±SE Eukaryotes Animalia 953,434 7,770,000 958,000 171,082 2,150,000 145,000 Chromista 13,033 27,500 30,500 4,859 7,400 9,640 Fungi 43,271 611,000 297,000 1,097 5,320 11,100 Plantae 215,644 298,000 8,200 8,600 16,600 9,130 Protozoa 8,118 36,400 6,690 8,118 36,400 6,690 Total 1,233,500 8,740,000 1,300,000 193,756 2,210,000 182,000 Prokaryotes Archaea 502 455 160 1 1 0 Bacteria 10,358 9,680 3,470 652 1,320 436 Total 10,860 10,100 3,630 653 1,320 436 Grand Total 1,244,360 8,750,000 1,300,000 194,409 2,210,000 182,000 Predictions for prokaryotes represent a lower bound because they do not consider undescribed higher taxa. For protozoa, the ocean database was substantially more complete than the database for the entire Earth so we only used the former to estimate the total number of species in this taxon. All predictions were rounded to three significant digits. Assessment of Possible Limitations We recognize a number of factors that can influence the interpretation and robustness of the estimates derived from the method described here. These are analyzed below. Species definitions An important caveat to the interpretation of our results concerns the definition of species. Different taxonomic communities (e.g., zoologists, botanists, and bacteriologists) use different levels of differentiation to define a species. This implies that the numbers of species for taxa classified according to different conventions are not directly comparable. For example, that prokaryotes add only 0.1% to the total number of known species is not so much a statement about the diversity of prokaryotes as it is a statement about what a species means in this group. Thus, although estimates of the number of species are internally consistent for kingdoms classified under the same conventions, our aggregated predictions for eukaryotes and prokaryotes should be interpreted with that caution in mind. Changes in higher taxonomy Increases or decreases in the number of higher taxa will affect the raw data used in our method and thus its estimates of the total number of species. The number of higher taxa can change for several reasons including new discoveries, the lumping or splitting of taxa due to improved phylogenies and switching from phenetic to phylogenetic classifications, and the detection of synonyms. A survey of 2,938 taxonomists with expertise across all major domains of life (response rate 19%, see Materials and Methods) revealed that synonyms are a major problem at the species level, but much less so at higher taxonomic levels. The percentage of taxa names currently believed to be synonyms ranged from 17.9 (±28.7 SD) for species, to 7.38 (±15.8 SD) for genera, to 5.5 (±34.0 SD) for families, to 3.72 (±45.2 SD) for orders, to 1.15 (±8.37 SD) for classes, to 0.99 (±7.74 SD) for phyla. These results suggest that by not using the species-level data, our higher-taxon approach is less sensitive to the problem of synonyms. Nevertheless, to assess the extent to which any changes in higher taxonomy will influence our current estimates, we carried out a sensitivity analysis in which the number of species was calculated in response to variations in the number of higher taxa (Figure 3A–3E, Figure S2). This analysis indicates that our current estimates are remarkably robust to changes in higher taxonomy. 10.1371/journal.pbio.1001127.g003 Figure 3 Assessment of factors affecting the higher taxon approach. (A–E) To test the effects of changes in higher taxonomy, we performed a sensitivity analysis in which the number of species was calculated after altering the number of higher taxa. We used Animalia as a test case. For each taxonomic level, we added or removed a random proportion of taxa from 10% to 100% of the current number of taxa and recalculated the number of species using our method. The test was repeated 1,000 times and the average and 95% confidence limits of the simulations are shown as points and dark areas, respectively. Light gray lines and boxes indicate the currently estimated number of species and its 95% prediction interval, respectively. Our current estimation of the number of species appear robust to changes in higher taxonomy as in most cases changes in higher taxonomy led to estimations that remained within the current estimated number of species. The results for changes in all possible combinations of taxonomic levels are shown in Figure S2. (F–J) The yearly ratio of new higher taxa in Animalia (black points and red line) and the yearly number of new species (grey line); this reflects the fraction of newly described species that also represent new higher taxa. The contrasting patterns in the description of new species and new higher taxa suggest that taxonomic effort is probably not driving observed flattening of accumulation curves in higher taxonomic levels as there is at least sufficient effort to maintain a constant description of new species. (K–O) Sensitivity analysis on the completeness of taxonomic inventories. To assess the extent to which incomplete inventories affect the predicted consensus asymptotic values obtained from the temporal accumulation of taxa, we performed a sensitivity analysis in which the consensus asymptotic number of taxa was calculated from curves at different levels of completeness. We used the accumulation curves at the genus level for major groups of vertebrates, given the relative completeness of these data (i.e., reaching an asymptote). Vertical lines indicate the consensus standard error. (P–T) Frequency distribution of the number of subordinate taxa at different taxonomic levels. For display purposes we present only the data for Animalia; lines and test statistics are from a regression model fitted with a power function. Changes in taxonomic effort Taxonomic effort can be a strong determinant of species discovery rates [21]. Hence the estimated asymptotes from the temporal accumulation curves of higher taxa (dotted horizontal line in Figure 1A–1E) might be driven by a decline in taxonomic effort. We presume, however, that this is not a major factor: while the discovery rate of higher taxa is declining (black dots and red lines in Figure 3F–3J), the rate of description of new species remains relatively constant (grey lines in Figure 3F–3J). This suggests that the asymptotic trends among higher taxonomic levels do not result from a lack of taxonomic effort as there has been at least sufficient effort to describe new species at a constant rate. Secondly, although a majority (79.4%) of experts that we polled in our taxonomic survey felt that the number of taxonomic experts is decreasing, it was pointed out that other factors are counteracting this trend. These included, among others, more amateur taxonomists and phylogeneticists, new sampling methods and molecular identification tools, increased international collaboration, better access to information, and access to new areas of exploration. Taken together these factors have resulted in a constant rate of description of new species, as evident in our Figure 1, Figure 3F–3J, and Figure S1 and suggest that the observed flattening of the discovery curves of higher taxa is unlikely to be driven by a lack of taxonomic effort. Completeness of taxonomic inventories To account for yet-to-be-discovered higher taxa, our approach fitted asymptotic regression models to the temporal accumulation curve of higher taxa. A critical question is how the completeness of such curves will affect the asymptotic prediction. To address this, we performed a sensitivity analysis in which the asymptotic number of taxa was calculated for accumulation curves with different levels of completeness. The results of this test indicated that the asymptotic regression models used here would underestimate the number of predicted taxa when very incomplete inventories are used (Figure 3K–3O). This underestimation in the number of higher taxa would lower our prediction of the number of species through our higher taxon approach, which suggests that our species estimates are conservative, particularly for poorly sampled taxa. We reason that underestimation due to this effect is severe for prokaryotes due to the ongoing discovery of higher taxa (Figure S1) but is likely to be modest in most eukaryote groups because the rate of discovery of higher taxa is rapidly declining (Figure 1A– 3E, Figure S1, Figure 3F–3J). Since higher taxonomic levels are described more completely (Figure 1A–1E), the resulting error from incomplete inventories should decrease while rising in the taxonomic hierarchy. Recalculating the number of species while omitting all data from genera yielded new estimates that were mostly within the intervals of our original estimates (Figure S3). However, Chromista (on Earth and in the ocean) and Fungi (in the ocean) were exceptions, having inflated predictions without the genera data (Figure S3). This inflation in the predicted number of species without genera data highlights the high incompleteness of at least the genera data in those three cases. In fact, Adl et al.'s [28] survey of expert opinions reported that the number of described species of chromists could be in the order of 140,000, which is nearly 10 times the number of species currently catalogued in the databases used here (Table 1). These results suggest that our estimates for Chromista and Fungi (in the ocean) need to be considered with caution due to the incomplete nature of their data. Subjectivity in the Linnaean system of classification Different ideas about the correct classification of species into a taxonomic hierarchy may distort the shape of the relationships we describe here. However, an assessment of the taxonomic hierarchy shows a consistent pattern; we found that at any taxonomic rank, the diversity of subordinate taxa is concentrated within a few groups with a long tail of low-diversity groups (Figure 3P–3T). Although we cannot refute the possibility of arbitrary decisions in the classification of some taxa, the consistent patterns in Figure 3P–3T imply that these decisions do not obscure the robust underlying relationship between taxonomic levels. The mechanism for the exponential relationships between nested taxonomic levels is uncertain, but in the case of taxa classified phylogenetically, it may reflect patterns of diversification likely characterized by radiations within a few clades and little cladogenesis in most others [29]. We would like to caution that the database we used here for protistan eukaryotes (mostly in Protozoa and Chromista in this work) combines elements of various classification schemes from different ages—in fact the very division of these organisms into “Protozoa” and “Chromista” kingdoms is non-phylogenetic and not widely followed among protistologists [28]. It would be valuable to revisit the species estimates for protistan eukaryotes once their global catalogue can be organized into a valid and stable higher taxonomy (and their catalogue of described species is more complete—see above). Discussion Knowing the total number of species has been a question of great interest motivated in part by our collective curiosity about the diversity of life on Earth and in part by the need to provide a reference point for current and future losses of biodiversity. Unfortunately, incomplete sampling of the world's biodiversity combined with a lack of robust extrapolation approaches has yielded highly uncertain and controversial estimates of how many species there are on Earth. In this paper, we describe a new approach whose validation against existing inventories and explicit statistical nature adds greater robustness to the estimation of the number of species of given taxa. In general, the approach was reasonably robust to various caveats, and we hope that future improvements in data quality will further diminish problems with synonyms and incompleteness of data, and lead to even better (and likely higher) estimates of global species richness. Our current estimate of ∼8.7 million species narrows the range of 3 to 100 million species suggested by taxonomic experts [1] and it suggests that after 250 years of taxonomic classification only a small fraction of species on Earth (∼14%) and in the ocean (∼9%) have been indexed in a central database (Table 2). Closing this knowledge gap may still take a lot longer. Considering current rates of description of eukaryote species in the last 20 years (i.e., 6,200 species per year; ±811 SD; Figure 3F–3J), the average number of new species described per taxonomist's career (i.e., 24.8 species, [30]) and the estimated average cost to describe animal species (i.e., US$48,500 per species [30]) and assuming that these values remain constant and are general among taxonomic groups, describing Earth's remaining species may take as long as 1,200 years and would require 303,000 taxonomists at an approximated cost of US$364 billion. With extinction rates now exceeding natural background rates by a factor of 100 to 1,000 [31], our results also suggest that this slow advance in the description of species will lead to species becoming extinct before we know they even existed. High rates of biodiversity loss provide an urgent incentive to increase our knowledge of Earth's remaining species. Previous studies have indicated that current catalogues of species are biased towards conspicuous species with large geographical ranges, body sizes, and abundances [4],[32]. This suggests that the bulk of species that remain to be discovered are likely to be small-ranged and perhaps concentrated in hotspots and less explored areas such as the deep sea and soil; although their small body-size and cryptic nature suggest that many could be found literally in our own “backyards” (after Hawksworth and Rossman [33]). Though remarkable efforts and progress have been made, a further closing of this knowledge gap will require a renewed interest in exploration and taxonomy by both researchers and funding agencies, and a continuing effort to catalogue existing biodiversity data in publicly available databases. Materials and Methods Databases Calculations of the number of species on Earth were based on the classification of currently valid species from the Catalogue of Life (www.sp2000.org, [34]) and the estimations for species in the ocean were based on The World's Register of Marine Species (www.marinespecies.org, [35]). The latter database is largely contained within the former. These databases were screened for inconsistencies in the higher taxonomy including homonyms and the classification of taxa into multiple clades (e.g., ensuring that all diatom taxa were assigned to “Chromista” and not to “plants”). The Earth's prokaryotes were analyzed independently using the most recent classification available in the List of Prokaryotic Names with Standing in Nomenclature database (http://www.bacterio.cict.fr). Additional information on the year of description of taxa was obtained from the Global Names Index database (http://www.globalnames.org). We only used data to 2006 to prevent artificial flattening of accumulation curves due to recent discoveries and descriptions not yet being entered into databases. Statistical Analysis To account for higher taxa yet to be discovered, we used the following approach. First, for each taxonomic rank from phylum to genus, we fitted six asymptotic parametric regression models (i.e., negative exponential, asymptotic, Michaelis-Menten, rational, Chapman-Richards, and modified Weibull [23]) to the temporal accumulation curve of higher taxa (Figure 1A–1E) and used multimodel averaging based on the small-sample size corrected version of Akaike's Information Criteria (AICc) to predict the asymptotic number of taxa (dotted horizontal line in Figure 1A–1E) [23]. Ideally data should be modeled using only the decelerating part of the accumulation curve [22]–[23], however, frequently there was no obvious breakpoint at which accumulation curves switched from an increasing to a decelerating rate of discovery (Figure 1A–1E). Therefore, we fitted models to data starting at all possible years from 1758 onwards (data before 1758 were added as an intercept to prevent a spike due to Linnaeus) and selected the model predictions if at least 10 years of data were available and if five of the six asymptotic models converged to the subset data. Then, the estimated multimodel asymptotes and standard errors for each selected year were used to estimate a consensus asymptote and its standard error. In this approach, the multimodel asymptotes for all cut-off years selected and their standard errors are weighted proportionally to their standard error, thus ensuring that the uncertainty both within and among predictions were incorporated [36]. To estimate the number of species in a taxonomic group from its higher taxonomy, we used Least Squares Regression models to relate the consensus asymptotic number of higher taxa against their numerical rank, and then used the resulting regression model to extrapolate to the species level (Figure 1G). Since data are not strictly independent across hierarchically organized taxa, we also used models based on Generalized Least Squares assuming autocorrelated regression errors. Both types of models were run with and without the inverse of the consensus estimate variances as weights to account for differences in certainty in the asymptotic number of higher taxa. We evaluated the fit of exponential, power, and hyperexponential functions to the data and obtained a prediction of the number of species by multimodel averaging based on AICc of the best type of function. The hyperexponential function was chosen for kingdoms whereas the exponential function for the smaller groups was used in the validation analysis (see comparison of fits in Figure S4). Survey of Taxonomists We contacted 4,771 taxonomy experts with electronic mail addresses as listed in the World Taxonomist Database (www.eti.uva.nl/tools/wtd.php); 1,833 were faulty e-mails, hence about 2,938 experts received our request, of which 548 responded to our survey (response rate of 18.7%). Respondents were asked to identify their taxon of expertise, and to comment on what percentage of currently valid names could be synonyms at taxonomic levels from species to kingdom. We also polled taxonomists about whether the taxonomic effort (measured as numbers of professional taxonomists) in their area of expertise in recent times was increasing, decreasing, or stable. Supporting Information Figure S1 Completeness of the higher taxonomy of kingdoms of life on Earth. (DOC) Click here for additional data file. Figure S2 Sensitivity analysis due to changes in higher taxonomy. (DOC) Click here for additional data file. Figure S3 Assessing the effects of data incompleteness. (DOC) Click here for additional data file. Figure S4 Comparison of the fits of the hyperexponential, exponential, and power functions to the relationship between the number of higher taxa and their numerical rank. (DOC) Click here for additional data file.
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Biodiversity conservation and the eradication of poverty.
William M Adams, Ros Aveling, Dan Brockington … (2004)
It is widely accepted that biodiversity loss and poverty are linked problems and that conservation and poverty reduction should be tackled together. However, success with integrated strategies is elusive. There is sharp debate about the social impacts of conservation programs and the success of community-based approaches to conservation. Clear conceptual frameworks are needed if policies in these two areas are to be combined. We review the links between poverty alleviation and biodiversity conservation and present a conceptual typology of these relationships.
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A Strategy To Estimate Unknown Viral Diversity in Mammals
Introduction The majority of emerging infectious diseases (EIDs) of humans are zoonoses, and the majority of these originate in wildlife (1–3). These diseases are largely viral (e.g., severe acute respiratory syndrome [SARS] and Nipah virus) and represent a significant global health threat. Analyses of trends in EIDs suggest that the rate of infectious disease emergence is increasing (3) and that the emergence of new viruses is not yet constrained by the richness (number of viruses) or diversity (genetic variability) of unknown viruses in wildlife, which is thought to be high. Systematically measuring viral richness, abundance, and diversity (here termed “virodiversity”) in wildlife is hindered by the large number of host species (e.g., around 5,500 mammals), their global distribution and often remote habitats (4), and the expense of collection, sampling, and viral identification or discovery (5), and it has not yet been achieved for even a single host species. In this study, we repeatedly sampled a mammalian host known to harbor emerging zoonotic pathogens (the Indian Flying Fox, Pteropus giganteus) and used PCR with degenerate primers targeting nine viral families to discover a large number and diversity of viruses. We then adapted the techniques normally used to estimate biodiversity in vertebrates and plants to estimate the total viral richness within these nine families in P. giganteus. Our analyses demonstrate proof-of-concept and provide the first statistically supported estimates of the unknown viral richness of a mammalian host and the sampling effort required to achieve it. RESULTS Viral discovery. A total of 12,793 consensus PCR assays were performed for the detection of viruses from nine different families/genera, including coronaviruses (CoVs; n = 1,631), paramyxoviruses (PMVs; n = 1,108), hantaviruses (HTVs; n = 1,108), astroviruses (AstVs; n = 1,348), influenza A viruses (IFAVs; n = 1,108), bocaviruses (BoVs; n = 1,739), adenoviruses (AdVs; n = 1,902), herpesviruses (HVs; n = 1,741), and polyomaviruses (PyVs; n = 1,108) (Table 1). None of the samples were positive for IFAVs or HTVs, despite previous studies documenting their presence in other bat species (6–8); however, a total of 985 viral sequences representing the other seven viral families were detected in these bats. These sequences were segregated into 55 discrete viruses based on distinct monophyletic clustering (see Materials and Methods) (Table 1), and a virus was considered novel if the sequence identity to its closest relative was less than or equal to the identity between the two closest species for a given viral family (9). TABLE 1 Summary of viral discovery performed on P. giganteus Virus No. of samples PCR positive/no. tested a Urine Throat Feces Roost urine Total Herpesvirus PgHV-1 9/926 29/711 0/78 0/26 38/1,741 PgHV-2 4/926 9/711 0/78 0/26 13/1,741 PgHV-3 1/926 0/711 0/78 0/26 1/1,741 PgHV-4 9/926 21/711 0/78 0/26 30/1,741 PgHV-5 1/926 0/711 0/78 1/26 2/1,741 PgHV-6 1/926 0/711 0/78 0/26 1/1,741 PgHV-7 2/926 8/711 0/78 0/26 10/1,741 PgHV-8 23/926 157/711 0/78 0/26 180/1,741 PgHV-9 0/926 3/711 0/78 0/26 3/1,741 PgHV-10 15/926 68/711 0/78 0/26 83/1,741 PgHV-11 0/926 4/711 0/78 0/26 4/1,741 PgHV-12 10/926 99/711 0/78 0/26 109/1,741 PgHV-13 6/926 159/711 0/78 0/26 165/1,741 Total 81/926 557/711 0/78 1/26 Paramyxovirus PgPMV-1 1/598 0/510 NT b NT 1/1,108 PgPMV-2 2/598 0/510 NT NT 2/1,108 PgPMV-3 0/598 2/510 NT NT 2/1,108 PgPMV-4 0/598 1/510 NT NT 1/1,108 PgPMV-5 0/598 3/510 NT NT 3/1,108 PgPMV-6 1/598 7/510 NT NT 8/1,108 PgPMV-7 0/598 2/510 NT NT 2/1,108 PgPMV-8 1/598 0/510 NT NT 1/1,108 PgPMV-9 2/598 0/510 NT NT 2/1,108 PgPMV-10 1/598 1/510 NT NT 2/1,108 PgPMV-11 (NiV) 1/598 2/510 NT NT 3/1,108 Total 9/598 18/510 Polyomavirus PgPyV-1 1/598 0/510 NT NT 1/1,108 PgPyV-2 0/598 3/510 NT NT 3/1,108 PgPyV-3 3/598 1/510 NT NT 4/1,108 Total 4/598 4/510 Coronavirus PgCoV-1 8/816 1/745 NT 5/70 14/1,631 PgCoV-2 33/816 10/745 NT 17/70 60/1,631 PgCoV-3 (bovine/human-like) 1/816 0/745 NT 0/70 1/1,631 PgCoV-4 (avian IBV-like) 0/816 1/745 NT 0/70 1/1,631 Total 42/816 12/745 22/70 Adenovirus PgAdV-1 1/931 0/806 0/78 0/87 1/1,902 PgAdV-2 (avian AdV) 1/931 0/806 0/78 0/87 1/1,902 PgAdV-3 4/931 4/806 0/78 0/87 8/1,902 PgAdV-4 0/931 1/806 0/78 0/87 1/1,902 PgAdV-5 34/931 16/806 0/78 3/87 53/1,902 PgAdV-6 0/931 2/806 0/78 0/87 2/1,902 PgAdV-7 11/931 1/806 0/78 1/87 13/1,902 PgAdV-8 17/931 2/806 0/78 0/87 19/1,902 PgAdV-9 5/931 1/806 0/78 2/87 8/1,902 PgAdV-10 1/931 0/806 0/78 0/87 1/1,902 PgAdV-11 4/931 0/806 0/78 0/87 4/1,902 PgAdV-12 1/931 0/806 0/78 0/87 1/1,902 PgAdV-13 22/931 3/806 0/78 6/87 31/1,902 PgAdV-14 38/931 11/806 0/78 5/87 54/1,902 Total 139/931 41/806 0/78 17/87 Astrovirus PgAstV-1 0/696 1/585 NT 1/67 2/1,348 PgAstV-2 1/696 0/585 NT 0/67 1/1,348 PgAstV-3 3/696 0/585 NT 8/67 11/1,348 PgAstV-4 0/696 0/585 NT 2/67 2/1,348 PgAstV-5 0/696 0/585 NT 3/67 3/1,348 PgAstV-6 0/696 0/585 NT 1/67 1/1,348 PgAstV-7 1/696 0/585 NT 0/67 1/1,348 PgAstV-8 0/696 0/585 NT 15/67 15/1,348 Total 5/696 1/585 30/67 Bocavirus PgBoV-1 (human BoV) 1/925 0/710 0/78 0/26 1/1,739 PgBoV-2 (human BoV) 0/925 1/710 0/78 0/26 1/1,739 Total 1/925 1/710 0/78 0/26 a A total of 55 viruses from seven viral families were discovered. The discovery effort (number of samples tested) and prevalence of each virus is presented. b NT, not tested. Eleven PMVs were detected, including 10 novel viruses (PMV-1 from P. giganteus [PgPMV-1] to PgPMV-10) and Nipah virus (PgPMV-11). These PMVs exhibited high sequence variation and clustered phylogenetically with either the rubulaviruses or an unassigned group related to the henipaviruses (Fig. 1). Within the AdV family, 14 viruses were discovered (PgAdV-1 to -14). Thirteen were novel mastadenoviruses, while one virus (PgAdV-2) had 98% nucleotide identity to the aviadenovirus Fowl adenovirus E (Fig. 2). Eight different AstVs were found (PgAstV-1 to -8), all of which were novel and clustered within the genus Mamastrovirus (Fig. 3). Within the CoV family, four distinct viruses were discovered. The first two were closely related betacoronaviruses (PgCoV-1 and -2). The third was also a betacoronavirus (PgCoV-3) but was more distantly related and showed 97% nucleotide identity to bovine and human coronaviruses (human strains 4408 and OC43). The fourth CoV was a gammacoronavirus (PgCoV-4) with 91% nucleotide identity (97% at the amino acid level) to the avian Infectious bronchitis virus (Fig. 4). Three novel PyVs were identified (PgPyV-1 to -3), all of which clustered with viruses in the genus Orthopolyomavirus (Fig. 5). A total of 639 HV sequences were detected, which segregated into 13 distinct clades (PgHV 1 to 13) using hierarchical clustering (see Materials and Methods). None could be reliably classified within any existing genus, and they likely represent new groups within the Betaherpesvirinae and Gammaherpesvirinae subfamilies (Fig. 6). One virus, PgHV-11, appears to be a recombinant between PgHV-10 and PgHV-13, with a breakpoint evident at approximately nucleotide 90. Upstream from this breakpoint, the sequences for PgHV-11 are related to PgHV-10, while downstream from the breakpoint, they are related to PgHV-13. Finally, two different BoVs were discovered (PgBoV-1 and -2), both of which showed >98% nucleotide identity to known human BoVs (Fig. 7). FIG 1 Phylogenetic tree (ML) of PMV large gene (RdRp). Alignment length, 534 bp of nucleotide sequence. PgPMV-1 to -10 were discovered in this study. PgPMV-11 is Nipah virus. The number of samples that tested positive for each respective virus in urine (U) and throat (T) is indicated in parentheses. *, published bat PMV sequences. Novel viruses detected in this study are identified with the prefix Pg (Pteropus giganteus) and were assigned accession numbers KC692403 to KC692412 FIG 2 Phylogenetic tree (ML) of AdV polymerase. Alignment length, 301 bp of nucleotide sequence. PgAdV-1 to -14 were discovered in this study. The number of samples that tested positive for each respective virus in urine (U), throat (T), feces (F), and roost urine (RU) is indicated in parentheses. *, published bat AdV sequences. Viruses detected in this study are identified with the prefix Pg (Pteropus giganteus) and were assigned accession numbers KC692417 to KC692430 FIG 3 Phylogenetic tree (ML) of AstV RdRp. Alignment length, 320 bp of nucleotide sequence. PgAstV-1 to -8 were discovered in this study. The number of samples that tested positive for each respective virus in urine (U), throat (T), and roost urine (RU) is indicated in parentheses. *, published bat AstV sequences. Viruses detected in this study are identified with the prefix Pg (Pteropus giganteus) and were assigned accession numbers KC692431 to KC692437 FIG 4 Phylogenetic tree (ML) of CoV RdRp. Alignment length, 310 bp of nucleotide sequence. PgCoV-1 to -4 were discovered in this study. The number of samples that tested positive for each respective virus in urine (U), throat (T), and roost urine (RU) is indicated in parentheses. *, published bat CoV sequences. Bat coronaviruses cluster based on the host family (indicated). ~, HKU2 seems anomalously positioned as it was detected in Rhinolophus sinicus, which is unrelated to bats from the families Vespertilionidae or Molossidae. The reason for this is unknown. Viruses detected in this study are identified with the prefix Pg (Pteropus giganteus), and were assigned accession numbers KC692413 to KC692416. IBV, infectious bronchitis virus; MHV, mouse hepatitis virus; PHEV, porcine hemagglutinating encephalomyelitis virus; HCoV, human CoV; BtCoV, bat CoV; FIPV, feline infectious peritonitis virus; TGEV, transmissible gastroenteritis coronavirus; PEDV, porcine epidemic diarrhea virus. FIG 5 Phylogenetic tree (ML) of PyV VP1 (major capsid protein). Alignment length, 320 bp of nucleotide sequence. PgPyV-1 to -3 were discovered in this study. The number of samples that tested positive for each respective virus in urine (U) and throat (T) is indicated in parentheses. *, published bat PyV sequences. Viruses detected in this study are identified with the prefix Pg (Pteropus giganteus) and were assigned accession numbers KC692400 to KC692402. FIG 6 Phylogenetic tree (ML) of HV polymerase. Alignment length, 211 bp of nucleotide sequence. PgHV-1 to -13 were discovered in this study. The number of samples that tested positive for each respective virus in urine (U), throat (T), feces (F), and roost urine (RU) is indicated in parentheses. *, published bat HV sequences. Viruses detected in this study are identified with the prefix Pg (Pteropus giganteus) and were assigned accession numbers KC692438 to KC692450. FIG 7 Phylogenetic tree (ML) of BoV NS1. Alignment length, 287 bp of nucleotide sequence. PgBoV-1 and -2 were detected in this study. The number of samples that tested positive for each respective virus in urine (U), throat (T), feces (F), and roost urine (RU) is indicated in parentheses. Viruses detected in this study are identified with the prefix Pg (Pteropus giganteus) and were assigned accession numbers KC692451 to KC692452. Viral discovery curves and estimates of viral richness. Asymptotic viral richness was estimated from observed detections using three statistical models, Chao2, ICE, and Jackknife (10). To ensure internal consistency, only those samples screened for the full complement of nine viral families/genera were included (n = 1,092), which accounted for 44/55 viruses identified in this study. The relative frequency of these viruses is presented in Fig. S1 in the supplemental material. Of the 1,092 samples included, 766 were negative for all viruses. There were 595 viral detections from 326 positive samples, with 167 samples containing >1 virus. When all 44 viruses were considered, the accumulative discovery curve began to show signs of saturation (Fig. 8). The Chao2 estimator demonstrated asymptotic behavior as early as 500 samples ( 1 virus, including urine (n = 56), throat swabs (n = 199), and roost urine (n = 56). Between 2 and 5 viruses were found to coexist, and both intrafamilial (n = 223/276) and interfamilial (n = 93/276) viral family cooccurrences were observed (Fig. 9). Intraspecific codetections were limited to the families Herpesviridae and Adenoviridae (Fig. 9 and see Table S1 in the supplemental material). The patterns of HV cooccurrence were significantly nonrandom (P = 7% nucleotide difference (Hamming distance) was used to define HV clusters. PgHV sequences were then segregated using hierarchical clustering, as implemented in the SciPy package (59) using average linkage clustering. Virus richness and sample estimation. We implemented models from the biodiversity literature that utilize incidence distributions to estimate virus richness (number of unique viruses) and, hence, to estimate the number of undetected viruses in the assemblage (60, 61). Incidence data result where each virus detected in the assemblage is noted in each sample as either present (verified detection) or absent (not detected, which could result due to the virus being absent or being present but not detected by the test, i.e., false absence). From our samples, we first constructed virus accumulation and rarefaction curves for visualization. The asymptote of the rarefaction curve provides the estimate of the number of viruses that characterizes the assemblage. However, sampling to reach this asymptote is impractical, as the number of samples required may be prohibitively large (61). We thus used statistical methods to estimate the asymptote from the data at hand. We used the nonparametric asymptotic estimator, Chao2 (15, 10), and also calculated ICE and Jackknife statistics for comparison. Unlike conventional curve-fitting procedures, the nonparametric estimators make no assumptions of an underlying abundance distribution, do not require ad hoc or a priori model fitting, are relatively robust to spatial autocorrelation and scale, and frequently outperform other methods of richness estimation (61). They rely on the principle that the frequencies of the rarest species in a set of samples can be used to estimate the frequencies of undetected species and provide a minimum richness estimate. All analyses were conducted with the fossil package (62) implemented in R (63). We followed Chao et al. (10) to calculate how many additional samples would be required to detect any proportion (including 100%) of the asymptotic virus richness. All statistics were incorporated into a single plot. Cooccurrence. Patterns of association/disassociation were explored with the Fortran software program PAIRS (11), utilizing the C score statistic as our measure of species cooccurrence. PAIRS implements a Bayesian approach (Bayes M criterion) to detect nonrandom associations between pairs of species (12). Assumptions and caveats We considered the detection and discovery of viruses akin to the problem of detection and discovery of biodiversity, as is frequently the goal of ecological studies. The basic mechanism of species detection occurs from drawing samples by collection from some larger assemblage (61). In this context, our samples are as described above, urine, throat, fecal, or roost urine taken from an individual bat or bat roost, which represent the biomes for our assemblage of interest. These methods require the assemblage of viruses under sampling to be closed for valid inference, that is, that the assemblage size and composition remained stable throughout the course of the study, an assumption we felt was justified. Although each of these sample types targets a unique biome of potential viral habitat from the host species, each with potentially differing efficacy for detecting any given virus, for the purposes of our analyses, we considered each sample a random and equivalent draw from the assemblage of viruses associated with this host species. We also assumed sample independence, even though multiple samples (e.g., urine and throat) were often drawn from the same individual host and sampled bat populations are likely to be geographically nonrandom. The consequence of this sampling strategy is that our analysis is blind to this additional source of geographical variation and occasional pseudoreplication, which means our virus accumulation results are specific to our sampling methodology and our extrapolations assume ongoing sampling with a similar average composition of samples. The results of additional analyses in which we isolated sample types and individuals and considered geographic variation are not presented herein. Nucleotide sequence accession numbers. The GenBank accession numbers for viruses discovered in this study are KC692400 to KC692452. SUPPLEMENTAL MATERIAL Text S1 Supplemental discussion. Download Text S1, DOCX file, 0.1 MB Figure S1 Relative distribution of viruses included in discovery curve analyses (see also Fig. 8). A subset of samples (n = 1,092) was used for discovery curve analysis. Only those samples that were screened for all nine viral families were included, ensuring internal consistency. Eleven of the 55 identified viruses had zero abundance in this subset and were therefore not considered in the analysis. The 11 omitted viruses were PgHV-2, -5, -6, and -9, PgAdV-1 and -10, PgAstV-4, -5, -6, and -8, and PgBoV-1. Forty-four viruses were therefore retained in our estimates, and the relative frequency of each is presented here. Download Figure S1, PDF file, 0.1 MB Figure S2 Viral discovery curves are presented for (i) all viruses (see also Fig. 8), (ii) PMV, (iii) AstV, (iv) HV, and (v) AdV. Discovery effort (number of samples tested) is indicated by the horizontal dotted line. Red line, collector curve showing accumulation of novel viruses over samples tested; blue line, Chao2 estimator at every sample point, with arrow indicating 95% confidence intervals; gray lines, ICE and Jackknife estimators at every sample point; Φ, estimated total diversity; dashed horizontal lines, required sampling effort to discover an arbitrary proportion of the total diversity; Ω, effort required to discover 100% of the estimated diversity. Download Figure S2, PDF file, 0.3 MB Table S1 Summary of intraspecific coinfections. The total number of samples testing positive for each family is presented, together with the number of the samples containing >1 virus of the same family. Table S1, PDF file, 0.6 MB.
Author and article information
Contributors
David TS Hayman: d.t.s.hayman@massey.ac.nz
Journal
Journal ID (nlm-ta): EMBO Rep
Journal ID (iso-abbrev): EMBO Rep
Journal ID (doi): 10.1002/(ISSN)1469-3178
Journal ID (publisher-id): EMBR
Journal ID (hwp): embor
Title:
EMBO Reports
Publisher:
John Wiley and Sons Inc.
(Hoboken
)
ISSN
(Print):
1469-221X
ISSN
(Electronic):
1469-3178
Publication date
(Electronic):
29
January
2016
Publication date
(Print):
March
2016
Volume: 17
Issue: 3
(
doiID:
10.1002/embr.v17.3
)
Pages: 286-291
Affiliations
Article
Publisher ID: EMBR201541675DOI: 10.15252/embr.201541675
PMC ID: 4772977
PubMed ID: 26825428
SO-VID: ebe92988-2cf5-4d51-8a3c-f5b9ef4336fe
Copyright © © 2016 The Author
History
Page count
Pages: 6
Funding
Funded by: GRC
Funded by: Science and Technology Directorate
Funded by: US Department of Homeland Security
Funded by: Fogarty International Center
Funded by: US National Institutes of Health (NIH)
Categories
Subject:
Science & Society
Subject:
Science & Society
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source-schema-version-number 2.0
component-id embr201541675
cover-date March 2016
details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_NLMPMC version:4.7.6 mode:remove_FC converted:01.03.2016
ScienceOpen disciplines: Molecular biology
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ScienceOpen disciplines: Molecular biology