Colonization history of the Swiss Rhine basin by the bullhead (<i>Cottus gobio</i>): inference under a Bayesian spatially explicit framework

Neuenschwander, Samuel; Largiadèr, Carlo R.; Ray, Nicolas; Currat, Mathias; Vonlanthen, Pascal; Excoffier, Laurent

doi:10.1111/j.1365-294x.2007.03621.x

Cited by 74 publications

(107 citation statements)

References 66 publications

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“…ABC techniques in demo-genetics studies (see, e.g., Tallmon et al 2004;Excoffier et al 2005a;Hamilton et al 2005;Chan et al 2006;Hickerson et al 2006;Shriner et al 2006;Pascual et al 2007;Legras et al 2007;Rosenblum et al 2007;Cornuet et al 2008;Neuenschwander et al 2008). Indeed, setting up simulation files for an arbitrarily complex model can be done in a few hours using existing simulation programs (e.g., Hudson 2002;Laval and Excoffier 2004;Cornuet et al 2008), allowing one to focus on realistic evolutionary models rather than restricting oneself only to models for which specific programs have been developed.…”

Section: Resultsmentioning

confidence: 99%

Efficient Approximate Bayesian Computation Coupled With Markov Chain Monte Carlo Without Likelihood

2009

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Approximate Bayesian computation (ABC) techniques permit inferences in complex demographic models, but are computationally inefficient. A Markov chain Monte Carlo (MCMC) approach has been proposed (Marjoram et al. 2003), but it suffers from computational problems and poor mixing. We propose several methodological developments to overcome the shortcomings of this MCMC approach and hence realize substantial computational advances over standard ABC. The principal idea is to relax the tolerance within MCMC to permit good mixing, but retain a good approximation to the posterior by a combination of subsampling the output and regression adjustment. We also propose to use a partial leastsquares (PLS) transformation to choose informative statistics. The accuracy of our approach is examined in the case of the divergence of two populations with and without migration. In that case, our ABC-MCMC approach needs considerably lower computation time to reach the same accuracy than conventional ABC. We then apply our method to a more complex case with the estimation of divergence times and migration rates between three African populations.W ITH the advent of large-scale genotyping techniques (e.g., Green et al. 2006;Levy et al. 2007), genetic data can be produced at an unprecedented scale (Altshuler et al. 2005;Bustamante et al. 2005), and the genetic variability of individuals and populations can now routinely be examined at hundreds of loci across the genome (Rosenberg et al. 2002;Williamson et al. 2005;Becquet and Przeworski 2007;Frazer et al. 2007). These large data sets offer the hope to better understand the evolutionary forces that have shaped the diversity of many species, including humans, and to identify genome regions involved in past selective events (Anisimova and Liberles 2007;Nielsen et al. 2007). However, the demographic history of the populations needs to be accounted for to disentangle its effects from those of selection (Haddrill et al. 2005;Nielsen et al. 2005;Biswas and Akey 2006). It therefore seems important to be able to properly estimate this past demography from neutral genetic data or to estimate demography and selection simultaneously (e.g., Williamson et al. 2005). The statistical estimation of mutation and demographic parameters has drastically improved in the last 10 years, particularly with the use of Bayesian and full-likelihood approaches (Beaumont et al. 2002;Marjoram and Tavare 2006). However, these methods are still restricted to relatively simple models whose likelihood can be computed or to small data sets that can be analyzed in a reasonable amount of time. The handling of large data sets and the estimation of demographic parameters under realistic models remain problematic, and goodness-of-fit methods have been often used in those cases (see, e.g., Marth et al. 2004;Schaffner et al. 2005;Plagnol and Wall 2006).The approximate Bayesian computation (ABC) framework (Tavare et al. 1997;Pritchard et al. 1999;Beaumont et al. 2002), which is based on a simple rejection algorithm, has bee...

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Section: Resultsmentioning

confidence: 99%

Efficient Approximate Bayesian Computation Coupled With Markov Chain Monte Carlo Without Likelihood

2009

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“…This ABC technique has been applied essentially to relatively complex scenarios of colonization (Pascual et al 2007), spatial expansion (Estoup et al 2004;François et al 2008;Hamilton et al 2005a;Neuenschwander et al 2008), pest introduction (Miller et al 2005), pathogen transmission (Tanaka et al 2006), population bottleneck (Chan et al 2006), complex phylogeography (Hickerson et al 2006), and admixture ).…”

Section: Approximate Bayesian Computationmentioning

confidence: 99%

Inferring Past Demography Using Spatially Explicit Population Genetic Models

Ray

Excoffier

2009

Human Biology

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Inferring the past demography of human populations has been classically approached through data from the archaeological record but more recently by the use of genetic data from contemporary samples. Building realistic demographic models at the continental scale is a necessary step toward the improvement of current genomic methods aimed at fi nding genes under selection. In light of recent advances in Bayesian statistical inference, we discuss here the importance of considering spatially explicit approaches for modeling population expansion and dispersal. Neutral processes, such as the surfi ng phenomenon, that occur at the front of a range expansion may indeed mimic selection, and they may have played a signifi cant role in spreading particular alleles over large geographic areas. Finally, we discuss a few important issues that require further investigation, notably the use of archaeological data to inform population genetic models, the simulation of range contraction and reexpansion, and the importance of long-distance dispersal.Keywords range expansion, human evolution, introgression, surfing phenomena, Bayesian inference, paleodemography, genetic diversity, population genetic models, SPLATCHE software. Cover Page FootnoteThis work was supported by the Swiss National Science Foundation through grant 3100A0-112072 awarded to L. Excoffier. We thank two anonymous reviewers for their comments on an earlier draft.This open access article is available in Human Biology: http://digitalcommons.wayne.edu/humbiol/vol81/iss2/3 Inferring Past Demography Using Spatially Explicit Population Genetic ModelsNicolas Ray 1,2 and Laurent Excoffier 1 Abstract Inferring the past demography of human populations has been classically approached through data from the archaeological record but more recently by the use of genetic data from contemporary samples. Building realistic demographic models at the continental scale is a necessary step toward the improvement of current genomic methods aimed at fi nding genes under selection. In light of recent advances in Bayesian statistical inference, we discuss here the importance of considering spatially explicit approaches for modeling population expansion and dispersal. Neutral processes, such as the surfi ng phenomenon, that occur at the front of a range expansion may indeed mimic selection, and they may have played a signifi cant role in spreading particular alleles over large geographic areas. Finally, we discuss a few important issues that require further investigation, notably the use of archaeological data to inform population genetic models, the simulation of range contraction and reexpansion, and the importance of long-distance dispersal.

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“…They assume that individuals can only disperse through stream corridors and no longer through a two-dimensional landscape (e.g., BAR-RIER: Manni et al 2004;STREAM TREES: Kalinowski et al 2008). Another example is the extension of the spatially explicit coalescent simulation program SPLATCHE to linear habitats (AQUASPLATCHE: Neuenschwander 2006), which was used to study the colonisation history of the Swiss Rhine basin by bullhead (Cottus gobio) (Neuenschwander et al 2008b). Other riverscape genetics studies investigated the influence of the spatial structure of river networks on population connectivity (e.g., Labonne et al 2008;Morrissey & de Kerckhove 2009), and identified barriers to gene flow for fish (see Storfer et al 2010).…”

Section: Individual-based Simulation Modelsmentioning

confidence: 99%

A review of ecological models for brown trout: towards a new demogenetic model

Frank

Piccolo

Baret

2011

Ecology of Freshwater Fish

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-Ecological models for stream fish range in scale from individual fish to entire populations. They have been used to assess habitat quality and to predict the demographic and genetic responses to management or disturbance. In this paper, we conduct the first comprehensive review and synthesis of the vast body of modelling literature on the brown trout, Salmo trutta L., with the aim of developing the framework for a demogenetic model, i.e., a model integrating both population dynamics and genetics. We use a bibliometric literature review to identify two main categories of models: population ecology (including population dynamics and population genetics) and population distribution (including habitat-hydraulic and spatial distribution). We assess how these models have previously been applied to stream fish, particularly brown trout, and how recent models have begun to integrate them to address two key management and conservation questions: (i) How can we predict fish population responses to management intervention? and (ii) How is the genetic structure of fish populations influenced by landscape characteristics? Because salmonid populations tend to show watershed scale variation in both demographic and genetic traits, we propose that models combining demographic, genetic and spatial data are promising tools for improving their management and conservation. We conclude with a framework for an individual-based, spatially explicit demogenetic model that we will apply to stream-dwelling brown trout populations in the near future.

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Colonization history of the Swiss Rhine basin by the bullhead (Cottus gobio): inference under a Bayesian spatially explicit framework

Cited by 74 publications

References 66 publications

Efficient Approximate Bayesian Computation Coupled With Markov Chain Monte Carlo Without Likelihood

Efficient Approximate Bayesian Computation Coupled With Markov Chain Monte Carlo Without Likelihood

Inferring Past Demography Using Spatially Explicit Population Genetic Models

A review of ecological models for brown trout: towards a new demogenetic model

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