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182 results for “genetic fish”
Knights Landing, California Department of Fish and Wildlife, Genetic Determination of Population of Origin 2017 through 2019
Central Valley Chinook Salmon populations differ in their Endangered Species Act listing status. It is often difficult to distinguish individuals from the different Evolutionarily Significant Units. 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 race; Evolutionary Significant Unit) of Chinook Salmon captured from State Water Project and Central Valley Project fish protection facilities and Interagency Ecological Program monitoring programs. The population-of-origin 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 Valley salmonid monitoring
Sacramento trawl, Delta Juvenile Fish Monitoring Program, Genetic Determination of Population of Origin 2017-2021
Central Valley Chinook Salmon populations differ in their Endangered Species Act listing status. It is often difficult to distinguish individuals from the different Evolutionarily Significant Units. 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 race; Evolutionary Significant Unit) of Chinook Salmon captured from State Water Project and Central Valley Project fish protection facilities and Interagency Ecological Program monitoring programs. The population-of-origin 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 Valley salmonid monitoring
Chipps Island trawl, Delta Juvenile Fish Monitoring Program, Genetic Determination of Population of Origin 2017-2021
Central Valley Chinook Salmon populations differ in their Endangered Species Act listing status. It is often difficult to distinguish individuals from the different Evolutionarily Significant Units. 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 race; Evolutionary Significant Unit) of Chinook Salmon captured from State Water Project and Central Valley Project fish protection facilities and Interagency Ecological Program monitoring programs. The population-of-origin 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 Valley salmonid monitoring
Fig. 3 in Genetic diversity and population structure of Brycon nattereri (Characiformes: Bryconidae): a Neotropical fish under threat of extinction
Fig. 3. Haplotype network based on partial sequencing of the D-loop region (mtDNA) of 92 individuals of Brycon nattereri from the Laranjinha River. Circle sizes are pro- portional to haplotype frequency.
Global patterns of nuclear and mitochondrial genetic diversity in marine fishes
<p>Genetic diversity is a fundamental component of biodiversity. Examination of global patterns of genetic diversity can help highlight mechanisms underlying species diversity, though a recurring challenge has been that patterns may vary by molecular marker. Here, we compiled 6862 observations of genetic diversity from 492 species of marine fish and tested among hypotheses for diversity gradients: the founder effect hypothesis, the kinetic energy hypothesis, and the productivity-diversity hypothesis. We fit generalized linear mixed effect models (GLMMs) and explored the extent to which various macroecological drivers (latitude, longitude, temperature (SST), and chlorophyll-a concentration) explained variation in genetic diversity. We found that mitochondrial genetic diversity followed geographic gradients similar to those of species diversity, being highest near the Equator, particularly in the Coral Triangle, while nuclear genetic diversity did not follow clear geographic patterns. Despite these differences, all genetic diversity metrics were correlated with chlorophyll-a concentration, while mitochondrial diversity was also positively associated with SST. Our results provide support for the kinetic energy hypothesis, which predicts that elevated mutation rates at higher temperatures increase mitochondrial but not necessarily nuclear diversity, and the productivity-diversity hypothesis, which posits that resource-rich regions support larger populations with greater genetic diversity. Overall, these findings reveal how environmental variables can influence mutation rates and genetic drift in the ocean, caution against using mitochondrial macro-genetic patterns as proxies for whole-genome diversity, and aid in defining global gradients of genetic diversity.</p>
Fig. 3 in Distribution Patterns And Genetic Variability Of Three Stream-Dwelling Fish Species
Fig. 3. Principal Coordinates Analysis (PCoA) of the genetic and hydrographic distances of the sampled stocks (a, c, e: genetic distances; b, d, f: hydrographic distances)
Fig. 2 in Distribution Patterns And Genetic Variability Of Three Stream-Dwelling Fish Species
Fig. 2. Canonical Correspondence Analysis (CCA) ordination diagram showing the effect of environmental factors on the relative abundances of studied species. The first axis explains 29.93% and
Fig. 1 in Distribution Patterns And Genetic Variability Of Three Stream-Dwelling Fish Species
Fig. 1. Map of sampling sites. (bold = sampling sites of genetic surveys) (drawn with grey: lowland sections of streams) Latitudes and longitudes: top (of box): 48°05'N; bottom: 47°21'N; left: 20°16'E;
Fig. 3 in The roles of marginal lagoons in the maintenance of genetic diversity in the Brazilian migratory fishes Prochilodus argenteus and P. costatus
Fig. 3. Dendrogram representing the chord genetic distance among sampling groups of Prochilodus costatus. ABAr = rio Abaeté at rainy season; PAR = rio Paracatu lagoons; SFR = rio São Francisco lagoons; TMDd = Três Marias Dam at dry season; TMDr = Três Marias Dam at rainy season.
Fig. 2 in The roles of marginal lagoons in the maintenance of genetic diversity in the Brazilian migratory fishes Prochilodus argenteus and P. costatus
Fig. 2. Dendrogram representing the chord genetic distance among sampling groups of Prochilodus argenteus. ABAr = rio Abaeté at rainy season; CAR = rio Carinhanha lagoons; JEQ = rio Jequitaí lagoons; PAR = rio Paracatu lagoons; SFR = rio São Francisco lagoons; URU = rio Urucuia lagoons; VEL = rio das Velhas lagoons; TMDd = Três Marias Dam at dry season; TMDr = Três Marias Dam at rainy season.
Fig. 1 in The roles of marginal lagoons in the maintenance of genetic diversity in the Brazilian migratory fishes Prochilodus argenteus and P. costatus
Fig. 1. Map showing the central portion of the rio São Francisco basin and the distribution of the samples of Prochilodus argenteus (yellow) and of P. costatus (black). The circles represent marginal lagoons from tributaries, squares represent marginal lagoons from the rio São Francisco, and triangles represent places in the mainstream rio São Francisco in the Três Marias region. ABA = rio Abaeté; CAR = rio Carinhanha lagoons; JEQ = rio Jequitaí lagoons; PAR = rio Paracatu lagoons; SFR = rio São Francisco lagoons; URU = rio Urucuia lagoons; VEL = rio das Velhas lagoons; TMD = Três Marias Dam.
FIGURE 5 in Genetic differentiation through dispersal and isolation in two freshwater fish species from coastal basins of Northeastern Brazil
FIGURE 5 | Time-calibrated phylogeny for samples of Prochilodus lacustris from Maranhão coastal basins and Tocantins basin. Time is in thousands of years. The color bars in the tree correspond to the populations presented in Fig. 3A.
FIGURE 4 in Genetic differentiation through dispersal and isolation in two freshwater fish species from coastal basins of Northeastern Brazil
FIGURE 4 | Time-calibrated phylogeny for samples of Schizodon dissimilis from coastal basins of northeastern Brazil. Time is in thousands of years. The color bars in the tree correspond to the populations presented in Fig. 2A.
FIGURE 3 in Genetic differentiation through dispersal and isolation in two freshwater fish species from coastal basins of Northeastern Brazil
FIGURE 3 | Distribution and haplotype structure of Prochilodus lacustris. A. Paleodrainage reconstruction of coastal basins from northeastern Brazil and geographic distribution of groups defined by SAMOVA. B. Estimate of the probable groups of populations produced by the BAPS. C. Haplotypes networks of mtDNA control region. All analysis recovered a total of six groups in this area, but the SAMOVA do not identify the same groups that other analysis. The colors used to highlight areas in the network correspond to populations in map.
Fig. 1. Circular Bayesian tree inferred from mtDNA cox-2 in Temporal stability of parasite distribution and genetic variability values of Contracaecum osculatum sp. D and C. osculatum sp. E (Nematoda: Anisakidae) from fish of the Ross Sea (Antarctica)
Fig. 1. Circular Bayesian tree inferred from mtDNA cox-2 sequences obtained from specimens of C. osculatum sp. D and C. osculatum sp. E analysed in the present study, based on Bayesian Inference (BI) method using MrBayes v3.2.2 (Ronquist et al., 2012). Evolutionary distance was estimated using the TrN + G (G = 0.60) substitution model as implemented in jModeltest (Posada, 2008), with the AIC approach (Posada and Buckley, 2004). Posterior probability values are the result of 1.000000 of runs and are reported at the nodes. The coloured icons correspond to the two species considered in this study (red = C. osculatum sp. D and blue = C. osculatum sp. E).
Fig. 2 in Temporal stability of parasite distribution and genetic variability values of Contracaecum osculatum sp. D and C. osculatum sp. E (Nematoda: Anisakidae) from fish of the Ross Sea (Antarctica)
Fig. 2. Schematic distribution of the fish species examined in the present study for larval of C. osculatum sp. D and C. osculatum sp. E, along the continental shelf of the Ross Sea coastal ecosystem. Arrows indicating preferred preys and the diet preference for each fish species are reported according to the literature (La Mesa et al., 2004). The represented pelagic organisms comprise species of euphausiids and fish juveniles, benthic and epibenthic organisms are polychaetes, amphipods, decapods and gastropods. A pie chart with the relative proportions of C. osculatum sp. D and C. osculatum sp. E is given for each fish species. Squares and circles represent the hypothetical distribution of C. osculatum sp. D and C. osculatum sp. E larvae in their intermediate hosts.
Fig. 3 in Temporal stability of parasite distribution and genetic variability values of Contracaecum osculatum sp. D and C. osculatum sp. E (Nematoda: Anisakidae) from fish of the Ross Sea (Antarctica)
Fig. 3. Schematic representation of the hypothetic life-cycle of C. osculatum sp. D (a) and C. osculatum sp. E (b) in the Ross Sea.
Fig. 1 in Fish passage ladders from Canoas Complex - Paranapanema River: evaluation of genetic structure maintenance of Salminus brasiliensis (Teleostei: Characiformes)
Fig. 1. Partial view of the Paranapanema River and its principal affluents (Tibagi and Cinzas Rivers). Featured are the two collection sites: HEP Canoas I and HEP Canoas II.
Species ecology explains the various spatial components of genetic diversity in tropical reef fishes
<p>Generating genomic data for 19 tropical reef fish species of the Western Indian Ocean, we investigate how species ecology influences genetic diver- sity patterns from local to regional scales. We distinguish between the α, β and γ components of genetic diversity, which we subsequently link to six ecological traits. We find that the α and γ components of genetic diversity are strongly correlated so that species with a high total regional genetic diversity display systematically high local diversity. The α and γ diversity components are negatively associated with species abundance recorded using underwater visual surveys and positively with body size. Pelagic larval duration is found to be negatively related to genetic β diversity supporting its role as a dispersal trait in marine fishes. Deviation from the neutral theory of molecular evolution motivates further effort to understand the processes shaping genetic diversity and ultimately the diversification of the exceptional diversity of tropical reef fishes.</p>
Spacial and temporal genetic pattern of Semaprochilodus insignis (Prochilodontidae), the most popular fish from the Amazon basin
<p>Dataset of 8 genotyped microsatellite loci for 180 individuals of Semaprochilodus insignis from 11 locations in the Amazon basin.</p>
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