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Fig. 4 in A new cryptic species of Pithecopus (Anura, Phyllomedusidae) in north-eastern Brazil
Fig. 4. Scatterplot of the discriminant analyses of principal components (DAPC) on the morphometric and acoustic datasets of Pithecopus araguaius Haga et al., 2017, P. azureus (Cope, 1862), P. gonzagai sp. nov., P. hypochondrialis (Daudin, 1800) and P. nordestinus (Caramaschi, 2006). A. The two first axes on the morphometric data (11 first PCs, 95% retained variance). Variance explained by the axes: LD1 = 58% (F-statistic = 131.7) and LD2 = 25% (F-statistic = 57.3). B. The two first axes on the acoustic data (5 first PCs, 96% retained variance). LD1 = 91% (F-statistic = 182.0) and LD2 = 7% (F-statistic = 13.5).
FIGURE 30 in Morphological and molecular data reveal the cryptic diversity among populations of Aegla paulensis (Decapoda, Anomura, Aeglidae), with descriptions of four new species and comments on dispersal routes and conservation status
FIGURE 30. Bayesian tree (TPM 2 uf + G) for Aegla species based on partial fragment of 16 S. Node numbers represent posterior probabilities (values <50 % are not shown), and divergence time in millions of years (my); * indicates the calibration points to molecular clock. The clade C proposed by Pérez-Losada et al. (2004) is highlighted in grey. The basin and sub-basin origin of the discussed species in this study are shown after the specific names.
FIGURE 24. A – L in Morphological and molecular data reveal the cryptic diversity among populations of Aegla paulensis (Decapoda, Anomura, Aeglidae), with descriptions of four new species and comments on dispersal routes and conservation status
FIGURE 24. A – L, proximal portion of fifth pereiopod showing coxa and sexual tube of long and narrow type. A – B, Aegla paulensis Schmitt, 1942 s. str., male topotype (MZUSP 34368). C – D, Aegla rosanae Campos Jr., 1998, male topotype (MZUSP 34369). E – F, Aegla vanini n. sp., male paratype (MZUSP 34372). G – H, Aegla japi n. sp., male paratype (MZUSP 34375). I – J, Aegla jaragua n. sp. male paratype (MZUSP 34378). K-L, Aegla jundiai n. sp., male paratype (MZUSP 13490). Bars: A – D, F – H, J = 200 µm; K, L = 100 µm; E, I = 500 µm.
FIGURE 8 in Morphological and molecular data reveal the cryptic diversity among populations of Aegla paulensis (Decapoda, Anomura, Aeglidae), with descriptions of four new species and comments on dispersal routes and conservation status
FIGURE 8. Types of Aegla Leach, 1820 male sexual tubes. A, long and narrow (A. lancinhas Bond-Buckup & Buckup in Santos et al., 2015, MZUSP 34403). B, short and wide (A. leptochela Bond-Buckup & Buckup, 1994, MZUSP 34491).
FIGURE 1 in Morphological and molecular data reveal the cryptic diversity among populations of Aegla paulensis (Decapoda, Anomura, Aeglidae), with descriptions of four new species and comments on dispersal routes and conservation status
FIGURE 1. Distribution of the species of Aegla in four main hydrographic basins of southern Brazil: Rio Grande, Rio Tietê (Upper Paraná system), Rio Paraíba do Sul and Ribeira de Iguape. Indications L 1 through L 7 refer to the locations mentioned under “ sampling area ” in the Material & Methods section.
Data used in the article "Cryptic disease-induced mortality may cause host extinction in an apparently-stable host-parasite system"
<p>These data files include all the capture-history matrices that were used in the article, including two matrices with the age of captured individuals (adults or juveniles) according to the definition presented in the main text. Infection intensity (zoospore equivalents per swab) is provided in separate files for all <em>Rhinoderma darwini</em>i and<em> Eupsophus contulmoensis</em> individuals that tested positive for <em>Batrachochytrium dendrobatidis</em> infection. Also, the R code used for the fully paramaterized matrix population model 1 (including figures) is provided. Other codes used to analyze our data, especifically capture-recapture models, were obtained from Kéry and Schaub 2012 (<em>Bayesian population analysis using WinBUGS. A hierarchical perspective</em>. Waltham, USA: Academic Press.)</p>
Figure 2 in Cicada minimum age tree: Cryptic speciation and exponentially increasing base substitution rates in recent geologic time
Figure 2. Cicada timetree built by BEAST v1.X, applying 1,534 bp COI sequence. OUTs with isolate number: our own analyzed specimens shown in Table 1, and others: from GenBank/DDJB. In outgroup Hemiptera; #: analyzed family by Johnson et al. (2018); % analyzed family by Misof et al. (2014). Inserted figure: Base substitution rate (= rate median shown at each node; substitutions per site per million year; s/s/myr) vs age (= posterior age shown at each node) diagram. Red approximate curve with its formula was drawn by Excel function, with the intersection for the curve = 0.0128 s/s/myr, the rate median shown on Tracer.
Figure 4 in Cicada minimum age tree: Cryptic speciation and exponentially increasing base substitution rates in recent geologic time
Figure 4. Number of base changes of transition and tansversion vs corrected pairwide distance diagram for whole mitochondrial gene.
Figure 1 in Cicada minimum age tree: Cryptic speciation and exponentially increasing base substitution rates in recent geologic time
Figure 1. Simplified cicada timetree built by BEAST v1.X, applying a 1,534 bp in maximum COI sequence. Inserted figure: Base substitution rate (= ratemedian shown at each node; substitutions per siteper millionyear; s/s/ myr) vsage (= posterior age shown at each node) diagram. Red approximate curve with its formula was drawn by an Excel function, with the intersection for the curve = 0.0128 s/s/myr, the rate median shown on Tracer.
Figure 3 in Cicada minimum age tree: Cryptic speciation and exponentially increasing base substitution rates in recent geologic time
Figure 3. Cicada timetree built by BEAST v1.X, applying 1,534 bp COI and 874 bp 18S rRNA sequences. OUTswith isolate number: our own analyzed specimens shown in Table 1, and others: from GenBank/DDJB. In outgroup Hemiptera; #: analyzed family by Johnson et al. (2018); % analyzed family by Misof et al. (2014). Inserted figure: Base substitution rate (= rate median shown at each node; substitutions per site per million year; s/s/myr) vs age (= posterior age shown at each node) diagram. Red approximatecurve with its formulawas drawn by Excel function, with the intersection for the curve = 0.0114 s/s/myr, the rate median shown on Tracer. Note that this rate is a little slower than thatsolely of COI in Figures 1 and 2, reflecting slowerrate of 18S rRNAthan COI (see Osozawa et al. 2017a).
FIGURE 5 in Cryptic diversity and gene introgression of Moinidae (Crustacea: Cladocera) in Nigeria
FIGURE 5 Haplotype network of Moinidae lineages within species, based on the mitochondrial COI gene (478 bp). Each circle represents a unique haplotype and its size reflects the number of sequences. Segment sizes within circles indicate the distribution of haplotypes among different regions (color key to regions is on the left side of the figure). The lineage ID s are shown in columns relating to the species-delimitation methods, and those newly detected from Nigeria are indicated in colored squares. The number of marks on connecting lines shows the number of mutations separating haplotypes.
FIGURE 4 in Cryptic diversity and gene introgression of Moinidae (Crustacea: Cladocera) in Nigeria
FIGURE 4 Bayesian phylogenetic tree of the (a) ITS-1 region (677 bp) and (b) ITS-2 region (955 bp) of Moinidae lineages from Nigeria. Only posterior probabilities> 0.70 are shown. The lineage ID s are shown in columns relating to the species-delimitation methods, and those newly detected from Nigeria are indicated in colored squares. The mismatch assignments by COI and ITS-1 are in bold and highlighted with an asterisk. For abbreviations of country names refer to Fig. 3.
FIGURE 3 in Cryptic diversity and gene introgression of Moinidae (Crustacea: Cladocera) in Nigeria
FIGURE 3 Bayesian phylogenetic tree and species- delimitation of Moinidae from Southeast Nigeria, based on the mitochondrial COI gene (478 bp). A single representative of each haplotype (for reference sequences see Supplementary Table S1) is included in the tree. Codes of Moinidae haplotypes from Nigeria are provided in Table 1. Only posterior probabilities> 0.70 are shown. The numbers in the bands relating to the bPTP method indicate the statistical support (PP) for lineage membership. The lineage ID s are shown in columns relating to the species-delimitation methods, and the newly detected lineages from Nigeria are indicated in colored squares. Abbreviations of country names in which each haplotype was detected are, BO: Bolivia, CA: Canada, CN: China, CZ: Czech Republic, HU: Hungary, IN: India, JP: Japan, KZ: Kazakhstan, KR: Korea, MX: Mexico, MN: Mongolia, NG: Nigeria, RU: Russia, TH: Thailand, UA: Ukraine, US: U.S.A. Downloaded from Brill.com 12/12/2023 04:27:13PM via Open Access. This is an open access article distributed under the terms of the CC BY 4.0 license. https://creativecommons.org/licenses/by/4.0/
FIGURE 2 in Cryptic diversity and gene introgression of Moinidae (Crustacea: Cladocera) in Nigeria
FIGURE 2 Morphology of Moinidae from Southeast Nigeria. Monia cf. micrura from the Nome Pool 2, Amaho: lateral view of (a) parthenogenetic female, (b) male and (c) ephippial female; (d) antenna II, (e) postabdomen (f) valve and (g) postero-ventral margin of valve of the parthenogenetic female. Monia cf. macrocopa, parthenogenetic female from Nome Pool 1: (h) lateral view, (i) antenna II, (j) limb I, (k) postabdomen and (l) valve. Moinodaphnia macleayi, parthenogenetic female from Adanni Opanda Rd Pool 1: (m) lateral view, (n) antenna II, (o) postabdomen and (p) valve. Scale bars 0.1 mm.
FIGURE 1 in Cryptic diversity and gene introgression of Moinidae (Crustacea: Cladocera) in Nigeria
FIGURE 1 Geographic locations of sampling for Moinidae in Southeast Nigeria. Solid black circles indicate locations where moinids were present, empty circles indicate locations where no moinids were detected. Large colored circles near solid black circles represent the distribution of COI lineages. For abbreviations of location names, refer to Table 1.
Fig. 9 in Cryptic Speciation And Characteristics Of The Transition Bias Following An Example Of The Cytb Gene In Palearctic Mammals
Fig. 9. Variation of summarized tv/ts-index in micro- (1) and macromammals (2) depending on nucleotide substitution level. Thick lines illustrate exponential approximation.
Fig. 8 in Cryptic Speciation And Characteristics Of The Transition Bias Following An Example Of The Cytb Gene In Palearctic Mammals
Fig. 8. Variation of transition (upper lines) and transversion (lower lines) frequencies accordingly to substitution frequencies level. Think lines are empirical data for each subfamily/family, thick ones — polynomial approximations of averaged data.
Fig. 2 in Cryptic Speciation And Characteristics Of The Transition Bias Following An Example Of The Cytb Gene In Palearctic Mammals
Fig. 2. Average frequencies of nucleotide substitutions frequencies (sub) and its standard errors of the three taxonomical levels in micromammals (black) and macromammals (gray).
T a b l e 1 in Cryptic Speciation And Characteristics Of The Transition Bias Following An Example Of The Cytb Gene In Palearctic Mammals
T a b l e 1. Average (M), sample deviations (SD) of nucleotide substitution, ts/tv and F indexes of different taxonomical levels within 15 Palearctic mammal families/subfamilies
Data for: Morphological species delimitation in the Western Pond Turtle (Actinemys): Can machine learning methods aid in cryptic species identification?
<p>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, <em>Actinemys marmorata</em> and <em>Actinemys pallida</em>. <em>Actinemys</em> 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 <em>Trachemys</em> (<em>T. scripta scripta</em>, <em>T. scripta elegans</em>). 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 <em>Actinemys</em>. All classification hypotheses performed poorly relative to the validation datasets and no single hypothesis was unequivocally supported for <em>Actinemys</em>. 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 <em>A. marmorata</em> and <em>A. pallida</em> specimens, lending tentative morphological support to the hypothesis of two <em>Actinemys</em> species. However, the machine learning results also underscore that <em>Actinemys</em> as a whole have lower levels of plastral variation than other turtles within Emydidae, but the reason for this morphological conservatism is unclear.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.