Find research datasets worth reusing

opencc-by-4.0Dec 2023View details →

Fig. 7 in An assessment of new character in hoverfly species delimitation using linear and geometric morphometrics - genus Merodon Meigen, 1803 (Diptera: Syrphidae) as a case study

Fig. 7. Results of the geometric morphometric wing shape analysis of males of the Merodon clavipes and pruni groups. A–B. Scatter plot of individual scores showing the R4+5 vein shape variability. C–D. Scatter plot of individual scores showing the wing shape variability from Vujić et al. (in prep.). E–F. Scatter plot of individual scores showing the semilandmark R4+5 vein shape and landmark wing shape variability. G–H. Superimposed outline drawings showing the R4+5 vein shape differences between the males of the investigated species.

opencc-by-4.0Dec 2023View details →

Fig. 6 in An assessment of new character in hoverfly species delimitation using linear and geometric morphometrics - genus Merodon Meigen, 1803 (Diptera: Syrphidae) as a case study

Fig. 6. Results of the geometric morphometric wing shape analysis of females of the Merodon natans group. A. Scatter plot of individual scores showing the R4+5 vein shape variability. B. Scatter plot of individual scores showing wing the shape variability from Vujić et al. (2021c). C. Scatter plot of individual scores showing the semilandmark R4+5 vein shape and landmark wing shape variability D. Superimposed outline drawings showing the R4+5 vein shape differences among females of the investigated species.

dryad40/100

Metazoa-level USCOs as markers in species delimitation and classification

Metazoa-level Universal Single-Copy Orthologs (USCOs) are universally applicable markers for DNA taxonomy in animals which can replace or supplement single-gene barcoding. While Metazoa-level USCOs from target enrichment data were shown to reliably distinguish species, it remains to be tested whether USCOs are an evenly distributed, representative sample of a given metazoan genome, and hence can facilitate detection of past hybridization events. Besides, unlinked loci are a principal assumption in coalescent-based species delimitation approaches. 239 chromosome-level genomes were analyzed to show that Metazoa-level USCOs are a representative sample of a genome: in terms of distances to each other on a chromosome, but also over the chromosomes, they are almost as evenly distributed as protein-coding genes in general are. We tested the suitability of Metazoa-level USCOs extracted from genomes for species delimitation and phylogeny in four case studies: Anopheles mosquitos, Drosophila fruit flies, Heliconius butterflies, and Darwin's finches. In almost all instances USCOs allowed delineating species and yielded phylogenies that correspond to those generated from whole genome data. Our results show that USCO genes can be considered as genetically unlinked for practical purposes and representative for an entire metazoan genome. Our phylogenetic analyses demonstrate that USCOs may complement single-gene barcoding and provide more accurate taxonomic inferences. Combining USCOs from sources that used different versions of ortholog reference libraries to infer marker orthology may be challenging and at times impact taxonomic conclusions. However, we expect this problem to become less severe as the size of genome reference libraries and their sampling of organismic lineages is rapidly increasing.

opencc-zeroDec 2023View details →

dryad40/100

ClimateAnalyzer: set of scripts to delimit regions based on bioclimatic variables

Habitat stability is important for maintaining biodiversity by preventing species extinction, but this stability is being challenged by climate change. The tropical alpine ecosystem is currently one of the ecosystems most threatened by global warming, and the flora close to the permanent snow line is at high risk of extinction. The tropical alpine ecosystem, found in South and Central America, Malesia and Papuasia, Africa, and Hawaii, is of relatively young evolutionary age, and it has been exposed to changing climates since its origin, particularly during the Pleistocene. Estimating habitat loss and gain between the Last Glacial Maximum (LGM) and the present allows us to relate current biodiversity to past changes in climate and habitat stability. In order to do so, 1) we developed a unifying climate-based delimitation of tropical alpine regions across continents, and 2) we used this delimitation to assess the degree of habitat stability, i.e. the overlap of suitable areas between the LGM and the present, in different tropical alpine regions. Finally, we discuss the link between habitat stability and tropical alpine plant diversity. Our climate-based delimitation approach can be easily applied to other ecosystems using our developed code, facilitating macro-comparative studies of habitat dynamics through time.

opencc-zeroJan 2024View details →

dryad40/100

Data for: Morphological species delimitation in the Western Pond Turtle (Actinemys): Can machine learning methods aid in cryptic species identification?

As the discovery of cryptic species has increased in frequency, there has been interest in whether geometric morphometric data can detect fine-scale patterns of variation that can be used to morphologically diagnose such species. We used a combination of geometric morphometric data and an ensemble of five supervised machine learning methods to investigate whether plastron shape can differentiate two putative cryptic turtle species, Actinemys marmorata and Actinemys pallida. Actinemys has been the focus of considerable research due to its biogeographic distribution and conservation status. Despite this work, reliable morphological diagnoses for its two species are still lacking. We validated our approach on two datasets, one consisting of eight morphologically disparate emydid species, and the other consisting of two subspecies of Trachemys (T. scripta scripta, T. scripta elegans). The validation tests returned near-perfect classification rates, demonstrating that plastron shape is an effective means for distinguishing taxonomic groups of emydids via machine learning methods. By contrast, the same methods did not return high classification rates for a set of alternative phylogeographic and morphological binning schemes in Actinemys. All classification hypotheses performed poorly relative to the validation datasets and no single hypothesis was unequivocally supported for Actinemys. Two hypotheses had machine learning performance that was marginally better than our remaining hypotheses. In both cases, those hypotheses favored a two-species split between A. marmorata and A. pallida specimens, lending tentative morphological support to the hypothesis of two Actinemys species. However, the machine learning results also underscore that Actinemys as a whole have lower levels of plastral variation than other turtles within Emydidae, but the reason for this morphological conservatism is unclear.

opencc-zeroMar 2024View details →

F I G U R E 6 A in Assessing the diversity of Australian tarantulas (Araneae: Theraphosidae) using DNA barcoding and iterative species delimitation

F I G U R E 6 A midpoint rooted, maximum likelihood phylogeny using IQ-TREE of the 20 biological species found in this study. The phylogeny was estimated using 1000 ultrafast bootstraps (ufBS) from the three-gene concatenated sequence alignment. Coloured boxes and borders correspond to the colours used under the 5% cut-off (Figure S1). Only ufBS of 90 or greater are shown. Species sharing the same colour were found to share at least one nuclear allele.

F I G U R E 3 A 16S in Assessing the diversity of Australian tarantulas (Araneae: Theraphosidae) using DNA barcoding and iterative species delimitation

F I G U R E 3 A 16S neighbour-joining tree constructed under an HKY substitution model used for identifying putative species.

F I G U R E 5 in Assessing the diversity of Australian tarantulas (Araneae: Theraphosidae) using DNA barcoding and iterative species delimitation

F I G U R E 5 TCS haplotype network coloured by putative species identified at the 4% cut-off. (a) EF1γ. (b) 28S.

F I G U R E 1 in Assessing the diversity of Australian tarantulas (Araneae: Theraphosidae) using DNA barcoding and iterative species delimitation

F I G U R E 1 Map of sampling localities across Australia identified by putative species under the 4% threshold.

F I G U R E 4 in Assessing the diversity of Australian tarantulas (Araneae: Theraphosidae) using DNA barcoding and iterative species delimitation

F I G U R E 4 Species delimitation summary. Boxes in white represent which initial putative species specimens are assigned to as estimated under different barcode gap cut-offs. Letters within boxes represent different putative species hypotheses. Boxes in green are where the nuclear loci are consistent with being a distinct biological species, with no evidence of allele sharing. Boxes in grey are where putative species are found to share alleles of the nuclear loci. Boxes in orange are where putative species share internal haplotypes but also have alleles unique to each putative species (neotypy). Boxes in black represent the putative species found to be supported under different initial putative species thresholds.

Figure 1 in Why we should develop guidelines and quantitative standards for using genetic data to delimit subspecies for data-poor organisms like cetaceans

Figure 1. Depiction of the divergence of lineages with four times (T1–T4) chosen to illustrate different levels of biological organization. At T1 the yellow lineage is found across the distribution and although there are likely Demographically Independent Populations (DIPs) that differ in frequencies of the blue, yellow, and red lineages, there are no discontinuities. At T2 some lineages may be diagnosable but likely do not yet appear to be separate lineages. At T3 three groups (the blue/green, yellow, and orange/red lineages) meet the subspecies definition (they are diagnosable and appear to be diverging separately). The divergence level is not sufficient that reconvergence can be ruled out. Between T3 and T4, barriers to gene flow change such that the yellow lineage comes into contact with the blue/green and red-dominated lineages. Blue has diverged in a manner by which gene flow does not resume and the green/yellow lineage dies out. The yellow lineage reconverges and persists alongside the red lineage with a small level of gene flow (orange). At T4 the blue lineage is a species evolving separately from the yellow/red species. The yellow/red species has two subspecies that are both diagnosable and partially diverged.

Figure 2 in Examining metrics and magnitudes of molecular genetic differentiation used to delimit cetacean subspecies based on mitochondrial DNA control region sequences

Figure 2. Relationship between ΦST and Nei's estimate of net divergence (dA) among cetacean population, subspecies, and species pairs estimated using mitochondrial DNA control region sequence data. Specific values mentioned in the text are numbered: 1 = Neophocaena species; 2 = killer whale populations. The three green squares in the left-hand side of the figure (ΦST <0.07) represent, from bottom to top, the subspecies comparisons for S. attenuata, S. longirostris, and L. obscurus, respectively.

Figure 1 in Examining metrics and magnitudes of molecular genetic differentiation used to delimit cetacean subspecies based on mitochondrial DNA control region sequences

Figure 1. Box and whisker plots showing median and 1st and 3rd quartiles, and minimum and maximum values for six metrics of genetic divergence among cetacean population, subspecies, and species pairs estimated using mitochondrial DNA control region sequence data.

Figure 3 in A review of molecular genetic markers and analytical approaches that have been used for delimiting marine mammal subspecies and species

Figure 3. Published values of percent divergence between cetacean subspecies (black bars), species (white bars), and taxa of uncertain taxonomic status (gray bars). Values are based on mtDNA control region sequence data. Not all values represent net sequence divergence. See Table 1 for list of papers corresponding to each value. Since completing this work, Sousa species have been supported ((Mendez et al. 2013) and Inia subspecies changed.

Figure 1 in A review of molecular genetic markers and analytical approaches that have been used for delimiting marine mammal subspecies and species

Figure 1. Sample sizes used in publications of molecular genetic studies of marine mammals at different taxonomic levels. Graphs present the proportion of studies at each taxonomic level that fall into each sample size category. (A) minimum total sample size per focal taxon; (B) maximum sample size per single sampling locality. Papers were categorized as examining taxonomic questions at: species = subspecies/species boundary; subspecies = population/subspecies boundary; uncertain = taxonomic boundary uncertain (see text).

Figure 2 in A review of molecular genetic markers and analytical approaches that have been used for delimiting marine mammal subspecies and species

Figure 2. Types of molecular genetic data used in published studies examining questions at the species-level, subspecies-level, or undefined taxonomic level for marine mammals. Note that studies may have used more than one data type. Mitochondrial DNA sequence data (MtDNASeq), nuclear DNA sequence data (NuSeq), microsatellites (Msats), morphological data (Morph).

Figure 3 in Guidelines and quantitative standards to improve consistency in cetacean subspecies and species delimitation relying on molecular genetic data

Figure 3. Flow diagram for subspecies delineation using combined quantitative and qualitative standards. The threshold values assume the user is evaluating a case relying on mtDNA control region data. Percent Diagnosable (PD) is the smallest strata-specific correct classification score in a given comparison (e.g., PD50 in two-strata comparisons in Archer et al. 2017). The second box in the second row (other evidence to meet subspecies definition) allows for subspecies delineation when both conditions are not met using mtDNA. This box could be used either for the case when one condition is met and one unmet or when both just barely miss meeting the standards. For example, consider the case with PD <95% and dA> 0.004. Diagnosability could be achieved with morphological data or nuclear data that are sufficient for subspecies but not for full species.

Figure 7 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies

Figure 7. Summary of Random Forests classifications for each empirical comparison. Each row shows results from the stratum with the smallest fraction of individuals correctly classified, with comparisons labeled by their taxonomic codes as listed in Table 2. Colors identify comparison type as species (blue), subspecies (green), and populations (red). Points show the fraction of individuals correctly classified with probabilities> 50% (PD50, circles), and> 95% (PD95, triangles). Thin colored lines show 95% confidence intervals (CI) around PD50 estimates. Gray bars show range of a priori random classification rates based on individual size (left) to maximum possible classification rates based on shared haplotypes (right).

Figure 6 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies

Figure 6. Frequency distributions of the change in observed diagnosability (x-axis) in the simulated data for increasing levels of the probability of misstratification (vertical panels). Figures on the left and right columns are censored by data sets for original diagnosability ≤50% and>50%, respectively.

Figure 5 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies

Figure 5. Two-dimensional GAM fits of theta (Ɵ), number of migrants (Nem), and divergence time in generations (T) from Model 2 simulated data. From left to right, columns show results from models without migration (m = 0), with migration and Nem <1, and Nem ≥ 1. Colors indicate model prediction of percent correctly classified.