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Fig. 2. Physatocheila spp. A, C, E in Descriptions of three new species and one subspecies of Physatocheila (Heteroptera: Tingidae) from China and the Russian Far East, with an identification key to the species of the Russian fauna

Fig. 2. Physatocheila spp. A, C, E, Ph. potanini sp. nov., holotype, female; B, D, F, G, Ph. miyatakei latiuscula subsp. nov., holotype, male. General appearance, dorsal view (A, B); head and pronotum, dorsolateral view (C, D); labels of holotype (E, F) and paratype (G). Paranotum shown by arrow. Scale bars: 1 mm.

Fig. 3. Physatocheila spp. A, C in Descriptions of three new species and one subspecies of Physatocheila (Heteroptera: Tingidae) from China and the Russian Far East, with an identification key to the species of the Russian fauna

Fig. 3. Physatocheila spp. A, C, Ph. distinguenda (Jakovlev, 1880), female, southern Primorskiy Territory, Russia; B, D, Ph. costata (Fabricius, 1794), male, environs of St Petersburg, Russia. General appearance, dorsal view (A, B); head and pronotum, dorsolateral view (C, D). Paranotum shown by arrow. Scale bars: 1 mm.

Fig. 1. Physatocheila spp. A, C, E, F in Descriptions of three new species and one subspecies of Physatocheila (Heteroptera: Tingidae) from China and the Russian Far East, with an identification key to the species of the Russian fauna

Fig. 1. Physatocheila spp. A, C, E, F, Ph. explanata sp. nov., holotype, male; B, D, Ph. angusta sp. nov. (B, G, holotype, male; D, paratype, female). General appearance, dorsal view (A, B); head and pronotum, dorsolateral view (C, D); labels of holotype (E, G) and paratype (F). Paranotum shown by arrow. Scale bars: 1 mm.

Fig. 4. Physatocheila spp. A, C in Descriptions of three new species and one subspecies of Physatocheila (Heteroptera: Tingidae) from China and the Russian Far East, with an identification key to the species of the Russian fauna

Fig. 4. Physatocheila spp. A, C, Ph. putshkovi Golub, 1976, paratype, female, foothills of Saur Ridge, Kazakhstan; B, D, Ph. marginulata Golub, 1976, female, holotype, southern Primorskiy Territory, Russia. General appearance, dorsal view (A, B); head and pronotum, dorsolateral view (C, D). Paranotum shown by arrow. Scale bars: 1 mm.

opencc-by-4.0Jan 2023View details →

Figure 3 in Ecological niche differentiation among Aztec fruit-eating bat subspecies (Chiroptera: Phyllostomidae) in Mesoamerica

Figure 3. Niche overlap values for Schoener's D and Hellinger's I compared to a null distribution: (a) Artibeus a. aztecus (yellow) vs. A. a. minor (blue), (b) A. a. aztecus vs.A. A. major (red), (c) A. a. minor vs. A. a. major.

opencc-by-4.0Jan 2023View details →

Figure 2 in Ecological niche differentiation among Aztec fruit-eating bat subspecies (Chiroptera: Phyllostomidae) in Mesoamerica

Figure 2. Maxent predicted potential distribution for (a) Artibeus a. aztecus, (b) A. a. minor, and (c) A. a. major.

opencc-by-4.0Mar 2024View details →

Aerial photographs of Atlantic walruses subspecies Odobenus rosmarus rosmarus on Matveev Island

The dataset represents aerial photographs taken from a UAV on Matveev Island, Russian Federation. There are a total of 197 images with a minimum resolution of 1632x1088 and a maximum of 5472x3648 (WxH). The main directory "walruses" contains three directories ("images", "markup", "masks"). The data is marked for instance segmentation in the form of json files. The minimum number of objects per image is 22, and the maximum is 948.

opencc-by-4.0Jan 2022View details →

FIG. 1 in A new subspecies of Ruthenica filograna (Pulmonata: Clausiliidae) from Croatia

FIG. 1. Schematic map of distribution of Ruthenica filograna pocaterrae ssp. nov. (red circle) and Ruthenica filograna s.l. (black circles) in the South-western part of the area. РИС. 1. Карта-схема распространениЯ Ruthenica filograna pocaterrae ssp. nov. (красный круг) и Ruthenica filograna s.l. (черные круги) в Юго-Западной части ареала.

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.

Figure 2. A in Guidelines and quantitative standards to improve consistency in cetacean subspecies and species delimitation relying on molecular genetic data

Figure 2. A comparison of the pairs of populations (red triangles), subspecies (green squares) and species (blue circles) estimated by Rosel et al. (2017a). Net nucleotide divergence (dA) is shown on a natural log scale to better illustrate differences between the pairwise comparisons at low levels of divergence. Bars show the central 95th-pecentile of the estimate distributions. The solid vertical line at dA = 0.020 delimits all but one species and correctly excludes all subspecies pairs. The vertical dashed line at dA = 0.004 delimits all populations from the higher taxonomic levels and correctly delimits seven of eleven subspecies. The horizontal dashed lines are two potential thresholds for percent diagnosable (80% and 95%) that are discussed in the text.

Figure 4 in Diagnosability of mtDNA with Random Forests: Using sequence data to delimit subspecies

Figure 4. GAM fit of number of migrants (Nem) from Model 2 parameters. Solid line shows median value of predicted percent correctly classified, and shaded area shows 95% CI. The switch from bimodal distribution to a normal distribution occurs at Nem = 1 (log10Nem = 0).