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FIGURE 6 in On the morphological, biological and genetic heterogeneity of the genus Orchis (Orchidaceae, Orchidinae)
FIGURE 6. Comparison of total numbers of fungal OTUs (operational taxonomic units) and average number of OTUs per species between representatives of subgenera Orchis and Masculae (data from Jacquemyn et al. 2011).
FIGURE 3 in On the morphological, biological and genetic heterogeneity of the genus Orchis (Orchidaceae, Orchidinae)
FIGURE 3. Phenogram obtained with the Ward clustering procedure using Mahalanobis distances between samples of populations. Length of horizontal segments corresponds to distances.
Data from: Scale-dependent effects of terrestrial habitat on genetic variation in the great crested newt (Triturus cristatus)
<p><b>Context</b></p> <p class="CxSpMiddle">Terrestrial landscapes surrounding aquatic habitat influence the persistence of amphibian spatially structured populations (SSPs) via their crucial role in providing estivation and overwintering sites, facilitating or hampering dispersal and colonisation, and consequently the maintenance or loss of genetic diversity.</p> <p><b>Objectives</b></p> <p class="CxSpMiddle">To highlight the landscape drivers of genetic variation, we investigated the relationship between the level of genetic variation measured within ponds of the great crested newt (<i>Triturus cristatus</i>), and the composition of the surrounding landscape at various spatial scales.</p> <p><b>Methods</b></p> <p class="CxSpMiddle">Based on the sampling of 40 ponds in thirteen SSPs, the influence of landscape features on several estimators of genetic variation was investigated via linear mixed models, with effects within and between SSPs incorporated.</p> <p><b>Results</b></p> <p class="CxSpMiddle">The best models depended on the spatial scale, with more significant associations within radii of 50 and 100 m of core ponds, particularly for allelic richness. Responses within and between SSPs were mostly similar. The availability of aquatic habitat in the landscape had a positive effect, while woodland, arable land and pasture had different effects depending on scale and response variable. Total length of roads within a 250 m radius influenced effective population size negatively.</p> <p><b>Conclusions</b></p> <p class="CxSpMiddle">Our results stress the need to investigate the influence of environmental predictors at multiple spatial scales for an adequate understanding of ongoing processes. Generally, the landscape affected genetic variation similarly within and between SSPs. This allowed us to provide general guidelines for the persistence of great crested newt populations, with an emphasis on the importance of the aquatic habitat.</p>
FIGURE 2 in Plastid DNA fingerprinting of the rare Fritillaria moggridgei (Liliaceae) reveals population differentiation and genetic isolation within the Fritillaria tubiformis complex
FIGURE 2. Strict consensus tree of more than 2600 most parsimonious trees from analysis of the combined plastid matK and rpl16 intron sequences. Tree length = 451 steps, CI = 0.89 and RI = 0.85. Bootstrap percentages (> 50%) are indicated above branches. Cardiocrinum giganteum and Notholirion thomsonianum are the outgroups. See also Table 2.
FIGURE 4 in Plastid DNA fingerprinting of the rare Fritillaria moggridgei (Liliaceae) reveals population differentiation and genetic isolation within the Fritillaria tubiformis complex
FIGURE 4. Principal coordinate analysis (PCO) of eleven populations of Fritillaria tubiformis s.l. analyzed for ten microsatellite loci. The first (PCO1) and the second (PCO2) axes explain 50.4% and 20.9% of total variation, respectively. Acronyms correspond to populations (see Table 1). The two genetic groups corresponding to the two subspecific taxa are circled.
FIGURE 1 in Plastid DNA fingerprinting of the rare Fritillaria moggridgei (Liliaceae) reveals population differentiation and genetic isolation within the Fritillaria tubiformis complex
FIGURE 1. Map of the Italian populations of F. tubiformis s.l. Populations belonging to var. burnatii are labelled with a star and those belonging to subsp. moggridgei with a solid circle. Most sampled populations are located at the boundary between Piedmont (P) and Liguria (L) (Italy). Insets show, top left, sampling sites numbered as for populations (see Table 1) and, top right, the position of the sampled area within the Alps.
Quantitative genetics of extreme insular dwarfing: the case of red deer (Cervus elaphus) on Jersey
<p><b>Aim: </b>The Island Rule – i.e. the tendency for body size to decrease in large mammals and increase in small mammals on islands has been commonly evaluated through macroecological or macroevolutionary, pattern-orientated approaches, which generally fail to model the microevolutionary processes driving either dwarfing or gigantism. Here, we seek to identify which microevolutionary process could have driven extreme insular dwarfism in the extinct dwarf red deer population on the island of Jersey.</p> <p><b>Location:</b> Jersey, UK (Channel Islands).</p> <p><b>Taxon:</b> Red deer ( <i>Cervus elaphus</i>)</p> <p><b>Methods:</b> We applied an individual-based quantitative genetics model parameterized with red deer life-history data to study the evolution of dwarfism in Jersey's deer, considering variations in island area and isolation through time due to sea level changes.</p> <p><b>Results:</b> The body size of red deer on Jersey decreased fast early on, due to phenotypic plasticity, then kept decreasing almost linearly over time down to the actual body size of the Jersey deer (36 kg on average). Only 1% out of 10,000 replicates failed to reach that size in our simulations. The distribution of time to adaptation in these simulations was right-skewed, with a median of 395 generations (equivalent to roughly 4 ky years), with complete dwarfism effectively occurring in less than 6 ky 84.6% of times. About 72% of the variation in the time to adaptation between simulations was collectively explained by higher mutational variance, the number of immigrants from the continent after isolation, available genetic variance, heritability, and phenotypic plasticity.</p> <p><b>Main Conclusions:</b> The extreme dwarfing of red deer on Jersey is an expected outcome of high mutational variance, high immigration rate, a wide adaptive landscape, low levels of inbreeding, and high phenotypic plasticity (in the early phase of dwarfing), all occurring within a time window of around 6 ky. Our model reveals how extreme dwarfism is a plausible outcome of common, well-known evolutionary processes.</p>
Isolation by geographical distance after release from Pleistocene refugia explains genetic and phenotypic variation in Xylotrupes siamensis (Coleoptera: Scarabaeidae)
<p class="BodyA">Consistent and objective species delimitation is crucial to biodiversity studies, but challenges remain when conflicting taxonomic decisions have been made because different data sets and analytical methods were used to delineate species. In the rhinoceros beetle, <em>Xylotrupes siamensis</em>, the use of different morphological characters has resulted in taxonomic disagreement between studies. We used three molecular loci (mitochondrial <em>CO1</em> and nuclear ITS2 and <em>H3</em>) to investigate the genetic divergence between populations exhibiting different male horn phenotypes. We also applied an approximate Bayesian computation approach to test alternative historical hypotheses that might explain the present genetic diversity among geographical populations. Furthermore, we used species distribution models to estimate the temporal variation in the geographical distribution of suitable habitats. The results show that the two phenotypic taxa within <em>X. siamensis</em> are not genetically structured and that their genetic structure can be explained using isolation by geographical distance. The emergence of the two phenotypic taxa might have been associated with historical isolation in separate refugia. However, spatial expansion and genetic interchange between populations might have gradually eroded the spatial genetic structure. We demonstrate that understanding the historical processes responsible for phenotypic divergence and genetic diversity among current populations could help with making evolutionarily coherent taxonomic decisions.</p>
Data from: The genetic legacy of 50 years of desert bighorn sheep translocations
Conservation biologists have increasingly used translocations to mitigate population declines and restore locally extirpated populations. Genetic data can guide the selection of source populations for translocations and help evaluate restoration success. Bighorn sheep (Ovis canadensis) are a managed big game species that suffered widespread population extirpations across western North America throughout the early 1900's. Subsequent translocation programs have successfully re-established many formally extirpated bighorn herds, but most of these programs pre-date genetically-informed management practices. The state of Nevada presents a particularly well-documented case of decline followed by restoration of extirpated herds. Desert bighorn sheep (O. c. nelsoni) populations declined to less than 3,000 individuals restricted to remnant herds in the Mojave Desert and a few locations in the Great Basin Desert. Beginning in 1968, the Nevada Department of Wildlife translocated ~2,000 individuals from remnant populations to restore previously extirpated areas, possibly establishing herds with mixed ancestries. Here we examined genetic diversity and structure among remnant herds and the genetic consequences of translocation from these herds using a genotyping-by-sequencing approach to genotype 17,095 loci in 303 desert bighorn sheep. We found a signal of population genetic structure among remnant Mojave Desert populations, even across geographically proximate mountain ranges. Further, we found evidence of a genetically distinct, potential relict herd from a previously hypothesized Great Basin lineage of desert bighorn sheep. The genetic structure of source herds was clearly reflected in translocated populations. In most cases, herds retained genetic evidence of multiple translocation events and subsequent admixture when founded from multiple remnant source herds. Our results add to a growing literature on how population genomic data can be used to guide and monitor restoration programs.
FIGURE 4 in Molecular genetics and phylogeny of Ephippigera species group of genus Saga Charpentier, 1825 (Orthoptera: Ensifera: Saginae) in Anatolia
FIGURE 4. Cladogenetic dating among the Saga genus Ephippigera species group inferred by Bayesian analysis using a relaxed molecular clock.
FIGURE 1 in Molecular genetics and phylogeny of Ephippigera species group of genus Saga Charpentier, 1825 (Orthoptera: Ensifera: Saginae) in Anatolia
FIGURE 1. The geographic position of the Saga genus Ephippigera species group populations included in this study. The light blue shaded area represent the location of S. ephippigera species from literatures, filled shapes represent the location of studied samples from this study, circles represent the records of S. hakkarica; triangle represent the record of S. ephippigera; quadrangle represent the records of S. syriaca.
FIGURE 3 in Molecular genetics and phylogeny of Ephippigera species group of genus Saga Charpentier, 1825 (Orthoptera: Ensifera: Saginae) in Anatolia
FIGURE 3. Phylogenetic relationships among the Saga genus Ephippigera species group inferred by Maximum Likelihood analysis for 16S rDNA gene. Scale refers to the number of substitution per site. Posterior probability (PP) values>0.95 are reported on each node.
FIGURE 2 in Molecular genetics and phylogeny of Ephippigera species group of genus Saga Charpentier, 1825 (Orthoptera: Ensifera: Saginae) in Anatolia
FIGURE 2. Phylogenetic relationships among the Saga genus Ephippigera species group inferred by Bayesian analysis for 16S rDNA gene. Scale refers to the number of substitution per site. Posterior probability (PP) values>0.95 are reported on each node.
FIGURE 1. Ribosomal DNA ITS2 in Molecular confirmation of Anopheles (Anopheles) lesteri from the Republic of South Korea and its genetic identity with An. (Ano.) anthropophagus from China (Diptera: Culicidae)
FIGURE 1. Ribosomal DNA ITS2 sequence for potential malaria vectors belonging to Anopheles (Anopheles) Hyrcanus Group from Korea, China, Japan and the Philippines. See Table 2 and text for sequence summaries and discussion. The following GenBank accession numbers correspond to the label numbers at the 5' end of the sequence: 1) AY375464; 2) AY375465; 3) AJ004942; 4) AY375466; 5) AF384172, AJ004941 and AF543860; 6) AY375467; 7) AY187728; 8) AY375468; 9) AY375469; 10) AY375470; 11) AY375471. The number of individuals sequenced, of those presented here for the first time, appears in parentheses
FIGURE 1 in Genetic structure of Ipomoea imperati (Convolvulaceae) in the Mediterranean region and implications for its conservation
FIGURE 1. Geographical distribution of chloroplast haplotypes. Further locality details are provided in Table 1.
FIGURE 3 in Genetic structure of Ipomoea imperati (Convolvulaceae) in the Mediterranean region and implications for its conservation
FIGURE 3. Single most parsimonious (MP) tree /Maximum Clade Credibility (MCC) tree for ITS sequences of the organisms in study. Topology of the MP tree is identical to that of MCC tree from the Bayesian analyses (see text for details). Numbers below branches are, left to right, bootstrap percentages (Felsenstein 1985) out of 1000 replicates, the clade decay index (Bremer 1994) computed out of trees up to 10 steps longer and the posterior probability of the maximum clade credibility tree (branches with p.p. <0.5 were collapsed, with the exception of the Israel/Honduras/Valencia clade, which was present in the MP tree). Estimated divergence times are provided above nodes. Nodes numbers refer to Table 3.
FIGURE 2 in Genetic structure of Ipomoea imperati (Convolvulaceae) in the Mediterranean region and implications for its conservation
FIGURE 2. Median-joining network for plastid DNA haplotypes in Mediterranean, Mexican and Honduran samples of Ipomoea imperati. Haplotypes are represented by the circles, the size of each being proportional to the observed frequency of each haplotype. Median vectors are labelled mv1 and mv2; these represent possible unsampled or extinct haplotypes. Information on the geographic locations where each haplotype is found is provided in Table 1.
Data from: Genetic basis of adult migration timing in anadromous steelhead discovered through multivariate association testing
Migration traits are presumed to be complex and to involve interaction among multiple genes, thus we employed both univariate analyses and multivariate Random Forest (RF) machine learning algorithm to conduct association mapping of 15,239 single nucleotide polymorphisms (SNPs) for adult migration-timing phenotype in steelhead (Oncorhynchus mykiss). Our study focused on a model natural population of steelhead that exhibits two distinct migration-timing life histories with high levels of admixture in nature. Neutral divergence was limited between fish exhibiting summer- and winter-run migration owing to high levels of interbreeding, but a univariate mixed linear model found three SNPs from a major effect gene to be significantly associated with migration-timing (p < 0.000005) that explained 46% of trait variation. Alignment to the annotated S. salar genome provided evidence that all three SNPs localize within a 46 kb region overlapping GREB1-like (an estrogen target gene) on chromosome Ssa03. Additionally, multivariate analyses with RF identified that these 3 SNPs plus 15 additional SNPs explained up to 60% of trait variation. These candidate SNPs may provide the ability to predict adult migration-timing of steelhead to facilitate conservation management of this species and this study demonstrates the benefit of multivariate analyses for association studies.
FIGURE 6 in Biogeography of Glandulocaudinae (Teleostei: Characiformes: Characidae) revisited: phylogenetic patterns, historical geology and genetic connectivity
FIGURE 6. Spermann correlation between average genetic distances (GTR) and geographic distances showing no statistical support among variables.
FIGURE 5 in Biogeography of Glandulocaudinae (Teleostei: Characiformes: Characidae) revisited: phylogenetic patterns, historical geology and genetic connectivity
FIGURE 5. Bayesian cladogram showing the phylogentic relationships among populations of Mimagoniates microlepis from SE and S Brazil. Orange branches represent samples from rio Iguaçu basin. Blue branches represent streams from the coastal area. Numbers in branches indicates the posterior probabilities of the clusterings for GTR distances, obtained by Monte Carlo Markov Chains. Sampled localities are the follow: PIR= rio Piraquara, Piraquara, state of Paraná; CAE= rio Caerana, Piraquara, state of Paraná; PEQ= rio Pequeno, São José dos Pinhais, state of Paraná; VAR= rio da Várzea, Lapa, state of Paraná; AGU= rio Água Verde, Canoinhas, state of Santa Catarina; MER= rio Mergulhão, Antonina, state of Paraná; RIB= rio Ribeirão, Paranaguá, state of Paraná; MON= rio Mongaguá, Mongaguá, state of São Paulo; ITA= rio Caixa d'Água, Itariri, state of São Paulo; ACA= tributary of rio Acaraí, São Francisco do Sul, state of Santa Catarina. For each locality, 10 specimens were sampled and are indicated by numbers 0-9.
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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.