Simulation and empirical analysis of novel sibship-based genetic determination of fish passage

Whiteley, Andrew R.; Coombs, Jason A.; Letcher, Benjamin H.; Nislow, Keith H.

doi:10.1139/cjfas-2014-0137

Cited by 15 publications

(34 citation statements)

References 44 publications

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“…No other sites had more than two significant LD tests. The deviations from HW expectations and significant LD tests observed may have been due to the inclusion of sibling pairs in the datasets, as the eight microsatellites used in this study do not typically exhibit such patterns when full siblings are removed (Whiteley, Coombs, Letcher, & Nislow, ; Whiteley et al., ). Based on the observations made in this study, we retained all loci and populations in subsequent analyses.…”

Section: Resultsmentioning

confidence: 94%

Watershed‐level brook trout genetic structuring: Evaluation and application of riverscape genetics models

2018

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Stream connectivity promotes resilience and population viability of aquatic organisms. Landscape genetic approaches, traditionally applied to terrestrial systems, may reveal important watershed‐level dynamics that influence connectivity. Additional validation would improve understanding of how the models perform when gene flow is constrained to dendritic networks. The objectives of this study were to use simulations to assess the utility of landscape genetics analyses in dendritic stream networks and investigate riverscape variables influencing gene flow among brook trout Salvelinus fontinalis populations in headwater streams. We used an individual‐based simulation program to simulate different dispersal scenarios and used combinations of landscape genetic models, model selection methods, and genetic metrics to determine the best combination for riverscape systems. We also sampled brook trout from 76 headwater streams in two watersheds (c. 1,000 km2) in Connecticut, U.S.A. to assess connectivity and riverscape influences on genetic structuring. Gravity models with Bayesian information criterion (BIC) selection were the most accurate (>85%) landscape genetic models that consistently identified the correct simulated gene flow barrier. However, all models performed poorly when unidirectional barriers were simulated without a distance‐based dispersal limitation. Excluding this scenario, model accuracy for the gravity models using BIC selection was >90% across multiple genetic metrics, validating the application of landscape genetic models to riverscape systems. We found highly variable levels of brook trout genetic connectivity (FST range 0.01–0.19) at the watershed level (5–15 river km). Gravity models identified increases in upstream impervious surfaces and decreases in riparian tree canopy cover as riverscape variables associated with increases in genetic differentiation in one watershed, while the other watershed was consistent with an isolation by distance pattern. In this study we used demogenetic (i.e. combined demographic and genetic) simulations to demonstrate the utility of landscape genetics techniques in dendritic river networks. Our empirical genetic study documented gene flow among headwater populations of brook trout at the watershed level and also suggested connectivity can be limited by watershed development. Incorporating the heterogeneity of riverscapes into connectivity‐focused conservation planning is essential to the development of effective restoration actions, and landscape genetics approaches can be useful tools to identify watershed‐level connectivity in stream systems.

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Section: Resultsmentioning

confidence: 94%

Watershed‐level brook trout genetic structuring: Evaluation and application of riverscape genetics models

2018

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“…4), thus reducing the costs of genetic analyses. Minimally sufficient sampling schemes will obviously depend on the distribution of YOY in a given study area, but our results (see also Whiteley et al 2014, this issue) suggest a pilot study or a staged approach to genotyping (i.e., collecting more broadly above and below a barrier but running initial analyses only on a subset of individuals) may help avoid unnecessary genotyping and reduce costs. Finally, where simply detecting movement is the goal, an advantage of sib-split is that it should still be effective even where hybrids occur.…”

Section: Discussionmentioning

confidence: 86%

“…Finally, other authors have proposed a "majority rule" to determine the directionality of movement from sib-split, whereby movement is inferred in the direction with the fewest siblings under the assumption that the majority of siblings would still be located closest to the natal redd (Hudy et al 2010;Whiteley et al 2014, this issue). We did not feel comfortable quantifying the actual number of movers based on inferring directionality from sibling distributions in our study.…”

Section: Discussionmentioning

confidence: 99%

“…Here, we contrasted a suite of genetic techniques, including traditional population-level metrics of genetic variability, individual clustering and assignment, and a novel "sib-split" method (Whiteley et al 2014, this issue) based on pedigree reconstruction of young-of-year (YOY) to evaluate movement of westslope cutthroat trout (WCT, Oncorhynchus clarkii lewisi) through remediated culverts in Montana and Idaho. We focused on these complementary techniques because each relies on different assumptions, provides different types of information, is relevant at different time scales (Manel and Holderegger 2013), and focuses on different age classes within the target populations.…”

Section: Introductionmentioning

confidence: 99%

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Genetic monitoring of trout movement after culvert remediation: family matters

Neville

Peterson

2014

Can. J. Fish. Aquat. Sci.

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We contrasted various genetic analyses to evaluate their utility and constraints for detecting movement of cutthroat trout (Oncorhynchus clarkii) through restored culverts in different field settings: population-level metrics of genetic variability (heterozygosity and allelic richness); Bayesian clustering and assignment of individual genotypes from age 1+ fish; and a novel "sib-split" approach, where movement patterns are extracted from the spatial distribution of young-of-year (YOY) full-sibling groups inferred via pedigree reconstruction. Family structure greatly influenced population-level and individual clustering results in our small headwater populations, even though field sampling was implemented to avoid siblings. Sib-split, which uses family structure to detect movement, uncovered passage of YOY just weeks after emergence. When retrospectively applied to older individuals, it proved essential in interpreting clustering patterns and captured passage in several families of 1-and 2-year-olds. Where family structuring may negatively affect genetic analyses or, alternatively, be prominent enough to allow application of sib-split is difficult to predict a priori; we discuss benefits and limitations of all approaches under different ecological, spatial, and management scenarios.Résumé : Nous avons comparé différentes méthodes d'analyse génétique afin d'évaluer leur utilité et leurs limites pour ce qui est de détecter les déplacements de la truite fardée (Oncorhynchus clarkii) par des ponceaux remis en état dans différents contextes de terrain. Ces analyses comprennent des mesures de la variabilité génétique (hétérozygotie et richesse allélique) au niveau de la population, le regroupement bayésien et l'affectation de génotypes individuels de poissons de plus de 1 an, ainsi qu'une nouvelle approche dite de « sib-split », dans laquelle les motifs de déplacement sont extraits de la répartition spatiale de groupes de fratrie de jeunes de l'année (YOY) inférés à la lumière de la reconstitution de l'ascendance. La structure familiale influençait considérablement les résultats au niveau de la population et ceux du regroupement des individus dans nos petites populations d'amont, même si l'échantillonnage sur le terrain était conçu pour éviter la fratrie. La méthode sib-split, qui repose sur la structure familiale pour détecter les déplacements, a détecté le passage de YOY quelques semaines seulement après l'émergence. Appliquée de manière rétrospective à des individus plus âgés, elle s'est avérée essentielle dans l'interprétation des motifs de regroupement et a relevé les passages dans plusieurs familles d'individus de 1 an et de 2 ans. Il est difficile de prévoir les situations où la structure de la famille pourrait avoir un effet négatif sur les analyses génétiques ou être suffisamment forte pour permettre l'application de l'approche sib-split. Nous abordons les avantages et limites de toutes les approches dans différents scénarios écologiques, spatiaux et de gestion. [Traduit par la Rédaction]

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“…These traditional tagging studies may also potentially fail to detect movement because fish movement is highly episodic and not every individual will move during the designated time period (Natsumeda ; Whiteley et al. ). Traditional population genetic approaches—for example, genetic assignment tests (Manel and Holderegger ) and population metrics like within‐population genetic diversity (heterozygosity and allelic diversity) and among‐population divergence (often measured by F ST or related statistics; Jost ; Meirmans and Hedrick )—have limitations as well.…”

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confidence: 99%

Evaluating the Effects of Barriers on Slimy Sculpin Movement and Population Connectivity Using Novel Sibship‐based and Traditional Genetic Metrics

et al. 2019

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Population genetics‐based approaches can provide robust and cost‐effective ways to assess the effects of potential barriers, including dams and road‐stream crossings, on the passage and population connectivity of aquatic organisms. Determining the best way to apply and modify genetic tools for different species and situations is essential for making these genetics‐based approaches broadly applicable to fisheries and aquatic habitat management. Here, we used multiple genetic approaches to assess the movement and population structure of Slimy Sculpin Cottus cognatus at two road‐stream crossings in Michigan and one dam in Massachusetts, USA. We captured and genotyped individual sculpin and assessed movement and population connectivity by using (1) a sibship‐based approach, where the presence and proportional distribution of siblings on either side of a barrier indicates population connectivity and the possible direction of movement (i.e., presumed movement from higher to lower proportions), and (2) two Bayesian genetic assignment approaches (STRUCTURE and BayesAss) to identify migrants across potential barriers based on individual population assignment probabilities. We also used traditional genetic metrics to assess within‐population genetic variation and among‐population genetic divergence. At all three locations, we found evidence for sculpin movement across the potential barrier based on sibship reconstruction, but small family sizes limited the ability of this approach to provide robust estimates of the rate and direction of movement. At two sites, a lack of genetic differentiation between above‐ and below‐barrier populations limited the effectiveness of the genetic assignment methods for identifying possible migrants. At the third site, reduced upstream allelic diversity and effective number of breeders resulted in high genetic differentiation (FST) between above‐ and below‐barrier populations, and both sibship and genetic assignment methods provided strong evidence of limited connectivity and bias against upstream movement. Overall, combining approaches and metrics may help overcome the limitations of any one method and maximize the value of datasets for genetics‐based monitoring and assessment.

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Simulation and empirical analysis of novel sibship-based genetic determination of fish passage

Cited by 15 publications

References 44 publications

Watershed‐level brook trout genetic structuring: Evaluation and application of riverscape genetics models

Watershed‐level brook trout genetic structuring: Evaluation and application of riverscape genetics models

Genetic monitoring of trout movement after culvert remediation: family matters

Evaluating the Effects of Barriers on Slimy Sculpin Movement and Population Connectivity Using Novel Sibship‐based and Traditional Genetic Metrics

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