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First Large-Scale DNA Barcoding Assessment of Reptiles in the Biodiversity Hotspot of Madagascar, Based on Newly Designed COI Primers
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Abstract
Background
DNA barcoding of non-avian reptiles based on the cytochrome oxidase subunit I (COI) gene is still in a very early stage, mainly due to technical problems. Using a newly developed set of reptile-specific primers for COI we present the first comprehensive study targeting the entire reptile fauna of the fourth-largest island in the world, the biodiversity hotspot of Madagascar.
Methodology/Principal Findings
Representatives of the majority of Madagascan non-avian reptile species (including Squamata and Testudines) were sampled and successfully DNA barcoded. The new primer pair achieved a constantly high success rate (72.7–100%) for most squamates. More than 250 species of reptiles (out of the 393 described ones; representing around 64% of the known diversity of species) were barcoded. The average interspecific genetic distance within families ranged from a low of 13.4% in the Boidae to a high of 29.8% in the Gekkonidae. Using the average genetic divergence between sister species as a threshold, 41–48 new candidate (undescribed) species were identified. Simulations were used to evaluate the performance of DNA barcoding as a function of completeness of taxon sampling and fragment length. Compared with available multi-gene phylogenies, DNA barcoding correctly assigned most samples to species, genus and family with high confidence and the analysis of fewer taxa resulted in an increased number of well supported lineages. Shorter marker-lengths generally decreased the number of well supported nodes, but even mini-barcodes of 100 bp correctly assigned many samples to genus and family.
Conclusions/Significance
The new protocols might help to promote DNA barcoding of reptiles and the established library of reference DNA barcodes will facilitate the molecular identification of Madagascan reptiles. Our results might be useful to easily recognize undescribed diversity (i.e. novel taxa), to resolve taxonomic problems, and to monitor the international pet trade without specialized expert knowledge.
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Most cited references73
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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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Identification of Birds through DNA Barcodes
Paul Hebert, Mark Y. Stoeckle, Tyler Zemlak … (2004)
Introduction The use of nucleotide sequence differences in a single gene to investigate evolutionary relationships was first widely applied by Carl Woese (Woese and Fox 1977). He recognized that sequence differences in a conserved gene, ribosomal RNA, could be used to infer phylogenetic relationships. Sequence comparisons of rRNA from many different organisms led initially to recognition of the Archaea, and subsequently to a redrawing of the tree of life. More recently, the polymerase chain reaction has allowed sequence diversity in any gene to be examined. Genes that evolve slowly, like rRNA, often do not differ among closely related organisms, but they are indispensable in recovering ancient relationships, providing insights as far back as the origin of cellular life (Woese 2000). On the other hand, genes that evolve rapidly may overwrite the traces of ancient affinities, but regularly reveal divergences between closely related species. Mitochondrial DNA (mtDNA) has been widely employed in phylogenetic studies of animals because it evolves much more rapidly than nuclear DNA, resulting in the accumulation of differences between closely related species (Brown et al. 1979; Moore 1995; Mindell et al. 1997). In fact, the rapid pace of sequence change in mtDNA results in differences between populations that have only been separated for brief periods of time. John Avise was the first to recognize that sequence divergences in mtDNA provide a record of evolutionary history within species, thereby linking population genetics and systematics and establishing the field of phylogeography (Avise et al. 1987). Avise and others also found that sister species usually show pronounced mtDNA divergences, and more generally that “biotic entities registered in mtDNA genealogies…and traditional taxonomic assignments tend to converge” (Avise and Walker 1999). Although many species show phylogeographic subdivisions, these usually coalesce into single lineages “at distances much shorter than the internodal branch lengths of the species tree” (Moore 1995). In other words, sequence divergences are much larger among species than within species, and thus mtDNA genealogies generally capture the biological discontinuities recognized by taxonomists as species. Taking advantage of this fact, taxonomic revisions at the species level now regularly include analysis of mtDNA divergences. For example, many newly recognized species of birds have been defined, in part, on the basis of divergences in their mtDNA (e.g., Avise and Zink 1988; Gill and Slikas 1992; Murray et al. 1994; AOU 1998; Banks et al. 2000, 2002, 2003). The general concordance of mtDNA trees with species trees implies that, rather than analyzing DNA from morphologically identified specimens, it could be used the other way around, namely to identify specimens by analyzing their DNA. Past applications of DNA-based species identification range from reconstructing food webs by identifying fragments in stomachs (Symondson 2002) to recognizing products prepared from protected species (Palumbi and Cipriano 1998) and resolving complexes of mosquitoes that transmit malaria and dengue fever (Phuc et al. 2003). Despite such demonstrations, the lack of a lingua franca has limited the use of DNA as a general tool for species identifications. If a short region of mtDNA that consistently differentiated species could be found and accepted as a standard, a library of sequences linked to vouchered specimens would make this sequence an identifier for species, a “DNA barcode” (Hebert et al. 2003a). Recent work suggests that a 648-bp region of the mitochondrial gene, cytochrome c oxidase I (COI), might serve as a DNA barcode for the identification of animal species. This gene region is easily recovered and it provides good resolution, as evidenced by the fact that deep sequence divergences were the rule between 13,000 closely related pairs of animal species (Hebert et al. 2003b). The present study extends these earlier investigations by testing the correspondence between species boundaries signaled by COI barcodes and those established by prior taxonomic work. Such tests require the analysis of groups that have been studied intensively enough to create a firm system of binomials; birds satisfy this requirement. Although GenBank holds many bird sequences, these derive from varied gene regions while a test of species identification requires comparisons of sequences from a standard gene region across species. Accordingly, the barcode region of COI was sequenced in 260 of the 667 bird species that breed in North America (AOU 1998). Results All 260 bird species had a different COI sequence(s); none was shared between species. COI sequences in the 130 species represented by two or more individuals were either identical or most similar to other sequences of the same species. Furthermore, with a few interesting exceptions discussed below, COI sequence differences between closely related species were far higher than differences within species (18-fold higher; average Kimura-2-parameter [K2P] differences between and within species, 7.93% and 0.43%, respectively) (Figure 1). In most cases the neighbor-joining (NJ) tree showed shallow intraspecific and deep interspecific divergences (Figure 2). However, in four exceptional cases, there were deep divergences within a species (Tringa solitaria, Solitary Sandpiper; Sturnella magna, Eastern Meadowlark; Cisthorus palustris, Marsh Wren; and Vireo gilvus, Warbling Vireo). COI sequences in each of these polytypic species separated into pairs of divergent clusters in the NJ tree. The intraspecific K2P distances in these exceptional species were 3.7%–7.2%, 9- to 17-fold higher than the average distance (Figures 2, 3, and S1). Setting aside these polytypic species, the average intraspecific distance was very low, 0.27%, and the maximum average intraspecific difference was only 1.24%. Most congeneric species pairs showed divergences well above this value, but 13 species in four genera had interspecific distances that were below 1.25%. They included Larus argentatus, L. canus, L. delawarensis, L. glaucoides, L. hyperboreus, L. marinus, and L. thayeri (Herring Gull, Mew Gull, Ring-billed Gull, Iceland Gull, Glaucous Gull, Great Black-Backed Gull, and Thayer's Gull); Haematopus bachmani and H. palliatus (Black Oystercatcher and American Oystercatcher); Corvus brachyrhynchos and C. caurinus (American Crow and Northwestern Crow); and Anas platyrhynchos and A. rubripes (Mallard and American Black Duck) (Figure S1). Although species were the focus of this study, we noted that the NJ tree of COI sequences generally matched avian classifications at higher levels, with most genera, families, and orders appearing as nested monophyletic lineages concordant with current taxonomy (Figures 3 and S1). Discussion The simplest test of species identification by DNA barcode is whether any sequences are found in two species; none was in this study. Although sequences were not shared by species, sequence variation did occur in some species. Thus the second test is whether the differences within species are much less than those among species. In this study we found that COI differences among most of the 260 North American bird species far exceeded those within species. In order to conservatively test the effectiveness of COI barcodes as an identification tool, our sample must not have underestimated variability within species or have overestimated it among species. Our measures of intraspecific variation could be underestimates if members of a species show sequence divergence across their distribution that our study failed to adequately register. The two to three representatives of the 130 species used to examine this issue were collected from sites that were, on average, approximately 1,080 km apart, suggesting adequate representation of genetic diversity across their ranges. However, to further investigate this issue, we compared sequence differences within species to geographic distances between the collection points for their specimens and found these were unrelated (Figure 4). Based on these results, high levels of intraspecific divergence in COI in North American birds appear uncommon, given that we analyzed 130 different species in a variety of orders. Our findings are supported by a review of 34 mostly North American birds which showed a similarly low average maximum intraspecific K2P divergence of mtDNA of 0.7% (Moore 1995). Similarly, Weibel and Moore (2002) reported an average intraspecific divergence of 0.24% in their study of COI variation in woodpeckers. We conclude that our investigation has not underestimated intraspecific variation in any systematic fashion. On the other hand, our discovery of four polytypic species within a sample of 130 makes it likely there are other North American birds with divergent populations that may represent hidden species. Recent studies have identified marked mtDNA divergences within North American populations of Common Ravens (Omland et al. 2000), Fox Sparrows (Zink and Weckstein 2003), and Curve-billed Thrashers (Zink and Blackwell-Rago 2000), leading to proposals to split each into two or more species. Species with Holarctic distributions are particularly good candidates for unrecognized species, and recent DNA and morphological investigations have led taxonomists to split several such species into two, including Wilson's and Common Snipes, American and Eurasian Three-toed Woodpeckers, and American and Water Pipits (Zink et al. 1995, 2002; Miller 1996; AOU 1998; Banks et al. 2000, 2002, 2003). Widespread application of COI barcodes across the global ranges of birds will undoubtedly lead to the recognition of further hidden species. Any critical test of the effectiveness of barcodes must also consider the possibility that our study has overestimated variability among species. We therefore looked at species individually, comparing their minimum distance to a congener with the maximum divergence within each species. This analysis included a number of well-recognized sibling species, including Calidris mauri and C. pusilla, Fraternicula arctica and F. corniculata, and Empidonax traillii and E. virescens. There were sufficient data to perform this analysis on three of the four polytypic species and on 70 of the 126 remaining species (Figure 5). The average maximum K2P divergence within these 70 species was 0.29%, while the average minimum distance to a congener was 7.05% (24-fold higher), values comparable to those for the entire data set. Prior studies that looked exclusively at sister species of birds found an average K2P mtDNA distance of 5.1% in 35 pairs (Klicka and Zink 1997) and 3.5% in 47 pairs (Johns and Avise 1998). More generally, 98% of sister species pairs of vertebrates were observed to have K2P mtDNA divergences greater than 2% (Johns and Avise 1998). Thus it appears that a COI barcode will enable the separation of most sister species of birds. There is a possibility that the North American bird fauna is not representative of the global situation. The recent and extensive glaciations in North America may have decreased within-species variability by inducing bottlenecks in population size or may have increased variation between species by pruning many sister taxa (Avise and Walker 1998; Mila et al. 2000). This issue can only be resolved by evaluating the efficacy of barcodes in tropical and southern temperate faunas to ascertain if our results are general. We note that recent mtDNA studies in these settings have found both multiple sibling species in what were thought to be single species (Ryan and Bloomer 1999) and geographically structured variation suggesting the presence of cryptic species (Hackett and Rosenberg 1990; Bates et al. 1999). The diagnosis of species is particularly difficult when they are young. Moreover, hybridization is often common when the ranges of recently arisen species overlap, further complicating identifications. Such newly emerged species are sometimes referred to as superspecies (Mayr and Short 1970), or species complexes, to indicate their close genetic similarity. For example, the white-headed gulls are thought to have diverged very recently, some less than 10,000 years ago (Crochet et al. 2002, 2003), and hybridization is common among many of them. It is thus not surprising that their COI barcodes and other gene loci are very similar. DNA barcodes can help to define the limits of such recently emerged species, but more gene loci need to be surveyed and more work is required to determine which analytical methods can best deduce species boundaries in such cases. The NJ method used here has the advantage of speed, and performs strongly when sequence divergences are low, so it is generally appropriate for recovering intra- and interspecies phylogeny. However, a library of COI barcodes linked to named specimens will provide the large data sets needed to test the efficacy of varied tree-building methods (for review, see Holder and Lewis 2003). Even between species that diverged long ago, hybridization will lead to shared or very similar sequences at COI and other gene loci. Because mitochondrial DNA is maternally inherited, a COI barcode will assign F1 hybrids to the species of their female parent. Hybridization leading to the transfer of mtDNA from one species to another can result in a mtDNA tree that is incongruent with the species tree, but it will not necessarily prevent species from being distinguished, unless the mitochondrial transfer is so recent that their sequences have not diverged (Moore 1995). However, recent hybridization will lead species to share COI barcodes, and we expect that more intensive study will reveal such shared sequences in species that are known to hybridize, such as the white-headed gulls (Crochet et al. 2003) and Mallard/Black Ducks (Ankney et al. 1986; Avise et al. 1990). In other cases, a lack of COI divergence may indicate that populations are part of a single species, helping to sort out misleading morphological classifications. For example, the blue and white morphs of Chen caerulescens, Snow Goose, were thought to be different species until recently (Cooke et al. 1995). The close COI similarity of American and Black Oystercatchers revealed in this study is consistent with suggestions that these are allopatrically distributed color morphs of a single species (Jehl 1985). Low COI divergences between American and Northwestern Crows similarly support earlier suggestions that these taxa are conspecific (Sibley and Monroe 1990; Madge and Burn 1994). Just as COI similarities among species already questioned by taxonomists may reinforce these queries, deep COI divergences within species may reinforce suspicions of hidden diversity. For example, three of the four polytypic species in this study (Eastern Meadowlark, Marsh Wren, and Warbling Vireo) are split into two by some taxonomists (Wells 1998), and the fourth, Solitary Sandpiper, contains two allopatric subspecies with morphological differences (Godfrey 1976). In these cases, suspicions in the minds of taxonomists are reinforced by large COI divergences. If these species had not been the subject of prior scrutiny, COI barcoding would have flagged them as deserving of such attention. The importance of sampling multiple individuals within each species is highlighted by a recent review which found evidence of species-level paraphyly or polyphyly in 23% of 2,319 animal species, including 16.7% of 331 bird species (Funk and Omland 2003). This review provides a clear discussion of possible causes (imperfect taxonomy, hybridization, incomplete lineage sorting) and indicates the need for the careful reexamination of current taxonomy and for the collection of genetic data across both geographic ranges and morphological variants. Barcoding, together with related developments in sequencing technology, is likely to provide an efficient approach to the assembly of such genetic data. We expect that the assembly of a comprehensive barcode library will help to initiate taxonomic investigations that will ultimately lead to the recognition of many new avian species. This process will begin with the discovery of novel COI barcodes. Some of these cases will simply represent the first barcode records for described but previously unanalyzed species, but taxonomic study will confirm that others derive from new species. We propose that specimens with barcodes diverging deeply from known taxa should be known by a “provisional species” designation that links them to the nearest established taxon. For example, the divergent clusters of Solitary Sandpiper specimens might be called T. solitaria PS-1 and T. solitaria PS-2, highlighting a need for further taxonomic study. What threshold might be appropriate for flagging genetically divergent specimens as provisional species? This threshold should certainly be high enough to separate only specimens that very likely belong to different species. Because patterns of intraspecific and interspecific variation in COI appear similar in various animal groups (Grant and Bowen 1998 [sardines]; Hebert et al. 2003a [moths]; Hogg and Hebert 2004 [springtails]), we propose a standard sequence threshold: 10× the mean intraspecific variation for the group under study. If applied to the birds examined in this study (0.27% average intraspecific variation; 2.7% threshold), a 10× threshold would recognize over 90% of the 260 known species, as well as the four probable new species. As this result demonstrates, a threshold approach will overlook species with short evolutionary histories and those exposed to recent hybridization, but it will be a useful screening tool, especially for groups that have not received intensive taxonomic analysis. For 260 of the 667 bird species breeding in North America, our evidence shows that COI barcodes separate individuals into the categories that taxonomists call species. This adds to the evidence already in hand for insects and other arthropods that barcodes can be an efficient tool for species identification. Should future studies broaden this evidence, a comprehensive library of barcodes will make it easier to probe varied areas of avian biology. A DNA barcode will help, for example, when morphological diagnoses are difficult, as when identifying remnants (including eggs, nestlings, and adults) in the stomachs of predators. A DNA barcode could similarly identify fragments of birds that strike aircraft (Dove 2000) and recognize carcasses of protected or regulated species (Guglich et al. 1994). DNA barcodes could also reveal the species of avian blood in mosquitoes carrying West Nile virus (Michael et al. 2001; Lee et al. 2002), help experts distinguish morphologically similar juveniles or nonbreeding adults in banding work, and allow expanded nonlethal study of endangered or threatened populations. The two essential components for an effective DNA barcode system (and thus a new master key to the encyclopedia of life [Wilson 2003]) are standardization on a uniform barcode sequence, such as COI, and a library of sequences linked to named voucher specimens. The present study provides an initial set of COI barcodes for about 40% of North American birds. More detailed sampling of COI sequences is needed for these species, and barcodes need to be gathered for the remaining North American birds and for those in other geographic regions. This work could represent a first step toward a DNA barcode system for all animal and plant life, an initiative with potentially widespread scientific and practical benefits (Stoeckle 2003; Wilson 2003; Blaxter 2004; Janzen 2004). Materials and Methods Existing data can only yield limited new insights into the effectiveness of a DNA-based identification system for birds. Two mitochondrial genes, cyt b and COI, are rivals for the largest number of animal sequence records greater than 600 bp in GenBank (4,791 and 3,009 species, respectively). However, COI coverage for birds is modest; 173 species share COI sequences with 600-bp overlap. As these records derive from a global avifauna of 10,000 species, they provide a limited basis to evaluate the utility of a COI-based identification system for any continental fauna, impelling us to gather new sequences. We employed a stratified sampling design to gain an overview of the patterns of COI sequence divergence among North American birds. The initial level of sampling examined a single individual from each of 260 species to ascertain COI divergences among species. These species were selected on the basis of accessibility without regard to known taxonomic issues. The second level of sampling examined one to three additional individuals from 130 of these species to provide a general sense of intraspecific sequence divergences, as well as a preliminary indication of variation in each species. When possible, these individuals were obtained from widely separated localities in North America. The third level of our analysis involved sequencing four to eight more individuals for the few species where the second level detected more than 2% sequence divergence among individuals. Our studies examined specimens collected over the last 20 years; 98% were obtained from the tissue bank at the Royal Ontario Museum, Toronto, Canada. Collection localities and other specimen information are available in the “Birds of North America” file in the Completed Projects section of the Barcode of Life website (http://www.barcodinglife.com). Taxonomic assignments follow the latest North American checklist (AOU 1998) and its recent supplements (Banks et al. 2000, 2002, 2003). Mitochondrial pseudogenes can complicate PCR-based studies of mitochondrial gene diversity (Bensasson et al. 2001; Thalmann et al. 2004). We used protocols to reduce pseudogene impacts that included extracting DNA from tissues rich in mitochondria (Sorenson and Quinn 1998), employing primers with high universality (Sorenson and Quinn 1998), and amplifying a relatively long PCR product because most pseudogenes are short (Pereira and Baker 2004). DNA extracts were prepared from small samples of muscle using the GeneElute DNA miniprep Kit (Sigma, St. Louis, Missouri, United States), following the manufacturer's protocols. DNA extracts were resuspended in 10 μl of H2O, and a 749-bp region near the 5′ terminus of the COI gene was amplified using primers (BirdF1-TTCTCCAACCACAAAGACATTGGCAC and BirdR1-ACGTGGGAGATAATTCCAAATCCTG). In cases where this primer pair failed, an alternate reverse primer (BirdR2-ACTACATGTGAGATGATTCCGAATCCAG) was generally combined with BirdF1 to generate a 751-bp product, but a third reverse primer (BirdR3-AGGAGTTTGCTAGTACGATGCC) was used for two species of Falco. The 50-μl PCR reaction mixes included 40 μl of ultrapure water, 1.0 U of Taq polymerase, 2.5 μl of MgCl2, 4.5 μl of 10× PCR buffer, 0.5 μl of each primer (0.1 mM), 0.25 μl of each dNTP (0.05 mM), and 0.5–3.0 μl of DNA. The amplification regime consisted of 1 min at 94 °C followed by 5 cycles of 1 min at 94 °C, 1.5 min at 45 °C, and 1.5 min at 72 °C, followed in turn by 30 cycles of 1 min at 4 °C, 1.5 min at 51 °C, and 1.5 min at 72 °C, and a final 5 min at 72 °C. PCR products were visualized in a 1.2% agarose gel. All PCR reactions that generated a single, circa 750-bp, product were then cycle sequenced, while gel purification was used to recover the target gene product in cases where more than one band was present. Sequencing reactions, carried out using Big Dye v3.1 and the BirdF1 primer, were analyzed on an ABI 377 sequencer. The electropherogram and sequence for each specimen are in the “Birds of North America” file, but all sequences have also been deposited in GenBank (see Supporting Information). COI sequences were recovered from all 260 bird species and did not contain insertions, deletions, nonsense, or stop codons, supporting the absence of nuclear pseudogene amplification (Pereira and Baker 2004). In addition to 429 newly collected sequences, nine GenBank sequences from five species were included (these were the only full-length COI sequences corresponding to species in this study). Sequence divergences were calculated using the K2P distance model (Kimura 1980). A NJ tree of K2P distances was created to provide a graphic representation of the patterning of divergences among species (Saitou and Nei 1987). Supporting Information Figure S1 Birds Appendix Complete NJ tree based on K2P distances at COI for 437 sequences from 260 species of North American birds. Entries marked with an asterisk represent COI sequences from GenBank. (100 KB PDF). Click here for additional data file. Accession Numbers Sequences described in Materials and Methods have been deposited in GenBank under accession numbers AY666171 to AY666596.
- Record: found
- Abstract: found
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DNA barcodes distinguish species of tropical Lepidoptera.
Mehrdad Hajibabaei, Daniel Janzen, John M Burns … (2006)
Although central to much biological research, the identification of species is often difficult. The use of DNA barcodes, short DNA sequences from a standardized region of the genome, has recently been proposed as a tool to facilitate species identification and discovery. However, the effectiveness of DNA barcoding for identifying specimens in species-rich tropical biotas is unknown. Here we show that cytochrome c oxidase I DNA barcodes effectively discriminate among species in three Lepidoptera families from Area de Conservación Guanacaste in northwestern Costa Rica. We found that 97.9% of the 521 species recognized by prior taxonomic work possess distinctive cytochrome c oxidase I barcodes and that the few instances of interspecific sequence overlap involve very similar species. We also found two or more barcode clusters within each of 13 supposedly single species. Covariation between these clusters and morphological and/or ecological traits indicates overlooked species complexes. If these results are general, DNA barcoding will significantly aid species identification and discovery in tropical settings.
Author and article information
Contributors
: Role: Editor
Journal
Journal ID (nlm-ta): PLoS One
Journal ID (iso-abbrev): PLoS ONE
Journal ID (publisher-id): plos
Journal ID (pmc): plosone
Title:
PLoS ONE
Publisher:
Public Library of Science
(San Francisco, USA
)
ISSN
(Electronic):
1932-6203
Publication date Collection: 2012
Publication date
(Electronic):
30
March
2012
Volume: 7
Issue: 3
Electronic Location Identifier: e34506
Affiliations
Author notes
* E-mail:
lustimaci@
123456yahoo.com
Article
Publisher ID: PONE-D-11-20137DOI: 10.1371/journal.pone.0034506
PMC ID: 3316696
PubMed ID: 22479636
SO-VID: 8f57ff12-4c08-451b-90dc-bac30d45a8a6
Copyright © Nagy et al. This is an open-access article distributed under the terms of the Creative
Commons Attribution License, which permits unrestricted use, distribution, and reproduction
in any medium, provided the original author and source are credited.
History
Date
received
: 11
October
2011
Date
accepted
: 2
March
2012
Page count
Pages: 11
Categories
Subject:
Research Article
Subject:
Biology
Subject:
Computational Biology
Subject:
Ecology
Subject:
Evolutionary Biology
Subject:
Evolutionary Systematics
Subject:
Taxonomy
Subject:
Organismal Evolution
Subject:
Genomics
Subject:
Zoology
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ScienceOpen disciplines: Uncategorized