Assessing conservation risks to populations of an anadromous Arctic salmonid, the northern Dolly Varden (Salvelinus malma malma), via estimates of effective and census population sizes and approximate Bayesian computation

2016 ◽  
Vol 18 (2) ◽  
pp. 393-410 ◽  
Author(s):  
Les N. Harris ◽  
Friso P. Palstra ◽  
Robert Bajno ◽  
Colin P. Gallagher ◽  
Kimberly L. Howland ◽  
...  
Keyword(s):  
Approximate Bayesian Computation ◽  
Bayesian Computation ◽  
Dolly Varden ◽  
Salvelinus Malma ◽  
Dolly Varden Salvelinus Malma ◽  
Northern Dolly Varden ◽  
Population Sizes ◽  
Approximate Bayesian

Life-history characteristics and landscape attributes as drivers of genetic variation, gene flow, and fine-scale population structure in northern Dolly Varden (Salvelinus malma malma) in Canada

2015 ◽  
Vol 72 (10) ◽  
pp. 1477-1493 ◽  
Author(s):  
Les N. Harris ◽  
Robert Bajno ◽  
Colin P. Gallagher ◽  
Itsuro Koizumi ◽  
Lucy K. Johnson ◽  
...  
Keyword(s):  
Population Structure ◽  
Genetic Variation ◽  
Life History ◽  
Gene Flow ◽  
Isolation By Distance ◽  
Dolly Varden ◽  
Salvelinus Malma ◽  
Dolly Varden Salvelinus Malma ◽  
Northern Dolly Varden ◽  
Population Demographic

The northern Dolly Varden (Salvelinus malma malma) displays variable life-history types and occupies freshwater habitats with varying levels of connectivity. Here, we assayed microsatellite DNA variation in northern Dolly Varden from the western Canadian Arctic to resolve landscape and life-history variables driving variation in genetic diversity and population structure. Overall, genetic variation was highest in anadromous populations and lowest in those isolated above waterfalls, with stream-resident forms intermediate between the two. Anadromous and isolated populations were genetically divergent from each other, while no genetic differentiation was detectable between sympatric anadromous and stream-resident forms. Population structure was stable over 25 years, hierarchically organized, and conformed to an isolation-by-distance pattern, but stream-isolated forms often deviated from these patterns. Gene flow occurred primarily among Yukon North Slope populations and between sympatric anadromous and resident forms. These results were sex-dependent to some extent, but were influenced more by reproductive status and life history. Our study provides novel insights into the life history, population demographic, and habitat variables that shape the distribution of genetic variation and population structure in Arctic fluvial habitats while providing a spatial context for management and conservation.

Summer growth patterns of northern Dolly Varden (Salvelinus malma) smolts from the Prudhoe Bay region of Alaska

1997 ◽  
Vol 54 (5) ◽  
pp. 1103-1110 ◽  
Author(s):  
R G Fechhelm ◽  
J D Bryan ◽  
W B Griffiths ◽  
L R Martin
Keyword(s):  
Prey Availability ◽  
Late Summer ◽  
Early Summer ◽  
Growth Patterns ◽  
Dolly Varden ◽  
Salvelinus Malma ◽  
Dolly Varden Salvelinus Malma ◽  
Northern Dolly Varden ◽  
Stock Mixing ◽  
Rates Of Growth

Summer length growth patterns of northern Dolly Varden (Salvelinus malma) smolts from the Sagavanirktok River, northern Alaska, were analyzed for the years 1985-1994 and found to be sigmoidal, indicating slow rates of growth in early and late summer with the most rapid growth occurring in midseason. Nonlinear logistic regression functions of mean cohort length against date provided a reasonable fit of the data for all years except for 1991, accounting for more than 94% (r2 values ranged from 0.95 to 0.99) of the variation in mean daily length in any given year. Slow growth in early summer is in direct contrast with the growth patterns reported for juvenile broad whitefish (Coregonus nasus) and Arctic cisco (C. autumnalis) which inhabit the Sagavanirktok River and estuary. Some possible explanations for the observed Dolly Varden growth patterns include migration, dispersal, prey availability, water quality, and stock mixing.

scholarly journals Demographic inference through approximate-Bayesian-computation skyline plots

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3530 ◽  
Author(s):  
Miguel Navascués ◽  
Raphaël Leblois ◽  
Concetta Burgarella
Keyword(s):  
Approximate Bayesian Computation ◽  
Graphical Representation ◽  
A Priori ◽  
Natural Populations ◽  
Composite Likelihood ◽  
Bayesian Computation ◽  
Mathematical Function ◽  
Effective Population ◽  
Population Sizes ◽  
Approximate Bayesian

The skyline plot is a graphical representation of historical effective population sizes as a function of time. Past population sizes for these plots are estimated from genetic data, without a priori assumptions on the mathematical function defining the shape of the demographic trajectory. Because of this flexibility in shape, skyline plots can, in principle, provide realistic descriptions of the complex demographic scenarios that occur in natural populations. Currently, demographic estimates needed for skyline plots are estimated using coalescent samplers or a composite likelihood approach. Here, we provide a way to estimate historical effective population sizes using an Approximate Bayesian Computation (ABC) framework. We assess its performance using simulated and actual microsatellite datasets. Our method correctly retrieves the signal of contracting, constant and expanding populations, although the graphical shape of the plot is not always an accurate representation of the true demographic trajectory, particularly for recent changes in size and contracting populations. Because of the flexibility of ABC, similar approaches can be extended to other types of data, to multiple populations, or to other parameters that can change through time, such as the migration rate.

scholarly journals Demographic inference through approximate-Bayesian-computation skyline plots

2017 ◽  
Author(s):  
Miguel Navascués ◽  
Raphaël Leblois ◽  
Concetta Burgarella
Keyword(s):  
Approximate Bayesian Computation ◽  
Graphical Representation ◽  
Natural Populations ◽  
Composite Likelihood ◽  
Bayesian Computation ◽  
Mathematical Function ◽  
Effective Population ◽  
Population Sizes ◽  
Demographic Inference ◽  
Approximate Bayesian

AbstractThe skyline plot is a graphical representation of historical effective population sizes as a function of time. Past population sizes for these plots are estimated from genetic data, without a priori assumptions on the mathematical function defining the shape of the demographic trajectory. Because of this flexibility in shape, skyline plots can, in principle, provide realistic descriptions of the complex demographic scenarios that occur in natural populations. Currently, demographic estimates needed for skyline plots are estimated using coalescent samplers or a composite likelihood approach. Here, we provide a way to estimate historical effective population sizes using an Approximate Bayesian Computation (ABC) framework. We assess its performance using simulated and actual microsatellite datasets. Our method correctly retrieves the signal of contracting, constant and expanding populations, although the graphical shape of the plot is not always an accurate representation of the true demographic trajectory, particularly for recent changes in size and contracting populations. Because of the flexibility of ABC, similar approaches can be extended to other types of data, to multiple populations, or to other parameters that can change through time, such as the migration rate.

Genetic analysis of Dolly Varden (Salvelinus malma) across its North American range: evidence for a contact zone in southcentral Alaska

2015 ◽  
Vol 72 (7) ◽  
pp. 1048-1057 ◽  
Author(s):  
Eric B. Taylor ◽  
Shannan L. May-McNally
Keyword(s):  
Contact Zone ◽  
North American ◽  
Salvelinus Alpinus ◽  
Glacial Refugia ◽  
Bull Trout ◽  
Dolly Varden ◽  
Salvelinus Malma ◽  
Genetic Lineages ◽  
Dolly Varden Salvelinus Malma ◽  
Northern Dolly Varden

Contact zones between divergent lineages of aquatic taxa have been described from the northeastern Pacific Ocean. We surveyed samples of Dolly Varden (Salvelinus malma) from their North American range for variation at 14 microsatellite DNA loci. After accounting for hybridization between Dolly Varden and co-occurring bull trout (Salvelinus confluentus) and Arctic char (Salvelinus alpinus), we found evidence for two genetic lineages of Dolly Varden consistent with the previously recognized subspecies, northern Dolly Varden (S. m. malma) and southern Dolly Varden (S. m. lordii). We documented a contact zone between the two subspecies from the eastern Alaska Peninsula to Cook Inlet, Alaska, where admixture values (i.e., the proportion of the genome estimated to be composed of northern Dolly Varden, QNDV) ranged between QNDV = 0.245 and 0.754 across about 700 ocean kilometres. Populations of Dolly Varden showing low admixture (i.e., less than 5%) were located a minimum of 346 km to the west to 1200 km to the southeast, respectively, from the contact zone. The two lineages of Dolly Varden probably stem from isolation and subsequent divergence in, and dispersal from, distinct northern and southern Pleistocene glacial refugia and substantiate the treatment of S. malma as two subspecies and as at least two designatable units under Canada’s Species at Risk Act.

scholarly journals Weighted approximate Bayesian computation via Sanov’s theorem

2021 ◽  
Author(s):  
Cecilia Viscardi ◽  
Michele Boreale ◽  
Fabio Corradi
Keyword(s):  
Large Deviations ◽  
Posterior Distribution ◽  
Approximate Bayesian Computation ◽  
Bayesian Computation ◽  
Information Theoretic ◽  
Discrete Random Variables ◽  
Positive Weights ◽  
Approximate Bayesian ◽  
Information Theoretic Method ◽  
Computational Resources

AbstractWe consider the problem of sample degeneracy in Approximate Bayesian Computation. It arises when proposed values of the parameters, once given as input to the generative model, rarely lead to simulations resembling the observed data and are hence discarded. Such “poor” parameter proposals do not contribute at all to the representation of the parameter’s posterior distribution. This leads to a very large number of required simulations and/or a waste of computational resources, as well as to distortions in the computed posterior distribution. To mitigate this problem, we propose an algorithm, referred to as the Large Deviations Weighted Approximate Bayesian Computation algorithm, where, via Sanov’s Theorem, strictly positive weights are computed for all proposed parameters, thus avoiding the rejection step altogether. In order to derive a computable asymptotic approximation from Sanov’s result, we adopt the information theoretic “method of types” formulation of the method of Large Deviations, thus restricting our attention to models for i.i.d. discrete random variables. Finally, we experimentally evaluate our method through a proof-of-concept implementation.

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