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14 results for “genetic population assignment”
Genetic assignments for Spring Evolutionary Significant Unit reanalysis, Central Valley Chinook Salmon populations, CA, 2011-2024
Central Valley Chinook Salmon populations differ in their Endangered Species Act listing status. It is difficult to visually distinguish individuals from the different Evolutionarily Significant Units (ESU). As such, many of the salmon monitoring and evaluation efforts in the Central Valley and San Francisco Bay-Delta are hampered by uncertainty about population (stock) identification and proportional effects of management actions (Dekar et al. 2013; IEP 2019). Studies have identified that the current identification method (length-at-date models) of juvenile Chinook salmon (Fisher 1992) captured in the watershed vary in their accuracy, particularly for spring-run (NMFS 2013; Harvey et al. 2014; Merz et al. 2014). The inaccuracy of the size-based methods is likely due to differences in fish distribution during early rearing, habitat-specific growth rates, and inter-annual variability in temperatures and food availability that lead to overlap in size ranges among stocks. The primary objective of this project was the genetic classification (to genetic lineage; Evolutionary Significant Unit) of Chinook Salmon captured from State Water Project and Central Valley Project fish protection facilities and Interagency Ecological Program compliance monitoring programs. The genetic lineage was determined for sampled fish by comparing their genotypes to reference genetic baselines. Genetic methods, having less statistical uncertainty that size-based models for population identification, were intended to directly target (and reduce) one source of uncertainty in the estimation of loss (take) from water diversions (operations) and develop the information necessary for understanding stock-specific distribution, habitat utilization, abundance, and life history variation. This project supports recommendations from the Interagency Ecological Program’s Salmon and Sturgeon Assessment of Indicators by Life Stage and Interagency Ecological Program Science Agenda efforts to improve Central V
Genetic assignment of individuals to source populations using network estimation tools
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Data from: SNPs reveal a genetic cline across the northeast Atlantic and enable powerful population assignment in the European lobster
Resolving stock structure is crucial for fisheries conservation to ensure that the spatial implementation of management is commensurate with that of biological population units. To address this in the economically important European lobster (Homarus gammarus), genetic structure was explored across the species' range using a small panel of single nucleotide polymorphisms (SNPs) previously isolated from restriction-site associated DNA sequencing; these SNPs were selected to maximise differentiation at a range of both broad- and fine-scales. After quality control and filtering, 1,278 lobsters from 38 sampling sites were genotyped at 79 SNPs. The results revealed a pronounced phylogeographic break between the Atlantic and Mediterranean basins, while structure within the Mediterranean was also apparent, partitioned between lobsters from the central Mediterranean and the Aegean Sea. In addition, a genetic cline across the northeast Atlantic was revealed using both putatively neutral and outlier SNPs, but the precise driver(s) of this clinal pattern –isolation-by-distance, secondary contact, selection across an environmental gradient, or a combination of these factors– remains undetermined. Putatively neutral markers differentiated lobsters from Oosterschelde, an estuary on the Dutch coast, a finding likely explained by past bottlenecks and limited gene flow with adjacent North Sea populations. Building on the findings of our spatial genetic analysis, we were able to test the accuracy of assigning lobsters at various spatial scales, including to basin of origin (Atlantic or Mediterranean), region of origin and sampling location. The predictive model assembled using 79 SNPs correctly assigned 99.7 % of lobsters not used to build the model to their basin of origin, but accuracy decreased to region of origin and again to sampling location. These results are of direct relevance to managers of lobster fisheries and hatcheries, and provide the basis for a genetic tool for tracing the origin of European lobsters in the food supply chain.
Data from: Applications of random forest feature selection for fine-scale genetic population assignment
Genetic population assignment used to inform wildlife management and conservation efforts requires panels of highly informative genetic markers and sensitive assignment tests. We explored the utility of machine-learning algorithms (random forest, regularized random forest, and guided regularized random forest) compared with FST ranking for selection of single nucleotide polymorphisms (SNP) for fine-scale population assignment. We applied these methods to an unpublished SNP dataset for Atlantic salmon (Salmo salar) and a published SNP data set for Alaskan Chinook salmon (Oncorhynchus tshawytscha). In each species, we identified the minimum panel size required to obtain a self-assignment accuracy of at least 90% using each method to create panels of 50-700 markers Panels of SNPs identified using random forest-based methods performed up to 7.8 and 11.2 percentage points better than FST-selected panels of similar size for the Atlantic salmon and Chinook salmon data, respectively. Self-assignment accuracy ≥90% was obtained with panels of 670 and 384 SNPs for each dataset, respectively, a level of accuracy never reached for these species using FST-selected panels. Our results demonstrate a role for machine-learning approaches in marker selection across large genomic datasets to improve assignment for management and conservation of exploited populations.
FIGURE 3 in Genetic and morphological variability among the populations assigned to the genus Tropiocolotes Peters, 1880 (Squamata: Gekkonidae) in south Iran
FIGURE 3. Bayesian inference phylogenetic tree of Tropiocolotes populations in southern Iran using two mtDNA genes (COI and 16S). Tropiocolotes steudneri sensu stricto from Egypt was used as the outgroup. Numbers next to the nodes are the MP and ML bootstrap values and BI posterior probabilities (MP/ML/BI).
FIGURE 1 in Genetic and morphological variability among the populations assigned to the genus Tropiocolotes Peters, 1880 (Squamata: Gekkonidae) in south Iran
FIGURE 1. Map of southern Iran showing sampling localities for the populations of Tropicolates. Blue circles denote T. naybandensis and red circles denote Tropiocolotes cf. steudneri. The type locality of T. naybandensis is marked with a star.
FIGURE 2 in Genetic and morphological variability among the populations assigned to the genus Tropiocolotes Peters, 1880 (Squamata: Gekkonidae) in south Iran
FIGURE 2. Ordination of principal component 1 (PC1) against principal component 2 (PC2) for differentiated characters of the genus Tropiocolotes in southern Iran.
Data from: Genetic sex assignment in wild populations using GBS data: a statistical threshold approach
Establishing the sex of individuals in wild systems can be challenging and often requires genetic testing. Genotyping-by-sequencing (GBS) and other reduced representation DNA sequencing (RRS) protocols (e.g., RADseq, ddRAD) have enabled the analysis of genetic data on an unprecedented scale. Here, we present a novel approach for the discovery and statistical validation of sex-specific loci in GBS datasets. We used GBS to genotype 166 New Zealand fur seals (NZFS, Arctocephalus forsteri) of known sex. We retained monomorphic loci as potential sex-specific markers in the locus discovery phase. We then used (i) a sex-specific locus threshold (SSLT) to identify significantly male-specific loci within our dataset and (ii) a significant sex-assignment threshold (SSAT) to confidently assign sex in silico the presence or absence of significantly male-specific loci to individuals in our dataset treated as unknowns (98.9% accuracy for females; 95.8% for males, estimated via cross-validation). Furthermore, we assigned sex to 86 individuals of true unknown sex using our SSAT, and assessed the effect of SSLT adjustments on these assignments. From 90 verified sex-specific loci, we developed a panel of three sex-specific PCR primers that we used to ascertain sex independently of our GBS data, which we show amplify reliably in at least three other pinniped species. Using monomorphic loci normally discarded from large SNP datasets is an effective way to identify robust sex-linked markers for non-model species. Our novel pipeline can be used to identify and statistically validate monomorphic and polymorphic sex-specific markers across a range of species and RRS datasets.
Data from: SNPs reveal a genetic cline across the northeast Atlantic and enable powerful population assignment in the European lobster
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Data from: Genetic sex assignment in wild populations using GBS data: a statistical threshold approach
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Data from: Applications of random forest feature selection for fine-scale genetic population assignment
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Data from: Sequencing improves our ability to study threatened migratory species: genetic population assignment in California's Central Valley Chinook salmon
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Data from: RAD genotyping reveals fine-scale genetic structuring and provides powerful population assignment in a widely distributed marine species, the American lobster (Homarus americanus).
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Subspecies and Distribution. R. c.c clivosus Cretzschmar, 1828 - SWJordan, W & C Saudi Arabia, Yemen, and SW Oman. R c. acrotis Heuglin, 1862 — Eritrea, Ethiopia, Djibouti, and N Somalia. R c. augur ÌL. Andersen, 1904 — N & C South Africa. R c. brachygnathus K. Andersen, 1905 - Israel, Egypt, and N Sudan. R c. geoffmyii A. Smith, 1829 — SW South Africa. R c. keniensis Hollister, 1916 - SE Sudan, South Sudan, Uganda, NE DR Congo, Rwanda, Burundi, Kenya, and N Tanzania; other records throughout C Africa need further investigation. R c. schwarzi Heim de Balsac, 1934 - SE Algeria and W Libya. 7t c., socotranus Benda, Reiter & Vallo, 2017 - Socotra I, Yemen. AE c. zambesiensis K. Andersen, 1904 - S Tanzania, Malawi, Zambia, and SE DR Congo S to NE South Africa. R c. zuluensis K. Andersen, 1904 - E & S South Africa, Swaziland, and Lesotho. There is also a record from W DR Congo with no subspecific affinity and populations from Namibia and SW Angola are not currently assigned to any subspecies but might be associated with subspecies augur or geoffroyii following further morphological and genetic tests. in Rhinolophidae
Subspecies and Distribution. R. c.c clivosus Cretzschmar, 1828 - SWJordan, W & C Saudi Arabia, Yemen, and SW Oman. R c. acrotis Heuglin, 1862 — Eritrea, Ethiopia, Djibouti, and N Somalia. R c. augur ÌL. Andersen, 1904 — N & C South Africa. R c. brachygnathus K. Andersen, 1905 - Israel, Egypt, and N Sudan. R c. geoffmyii A. Smith, 1829 — SW South Africa. R c. keniensis Hollister, 1916 - SE Sudan, South Sudan, Uganda, NE DR Congo, Rwanda, Burundi, Kenya, and N Tanzania; other records throughout C Africa need further investigation. R c. schwarzi Heim de Balsac, 1934 - SE Algeria and W Libya. 7t c., socotranus Benda, Reiter & Vallo, 2017 - Socotra I, Yemen. AE c. zambesiensis K. Andersen, 1904 - S Tanzania, Malawi, Zambia, and SE DR Congo S to NE South Africa. R c. zuluensis K. Andersen, 1904 - E & S South Africa, Swaziland, and Lesotho. There is also a record from W DR Congo with no subspecific affinity and populations from Namibia and SW Angola are not currently assigned to any subspecies but might be associated with subspecies augur or geoffroyii following further morphological and genetic tests.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.