Fish migrate underground: the example ofDelminichthys adspersus(Cyprinidae)

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Abstract

Complex aquatic systems of karst harbour a rich but little-investigated biodiversity. In Croatia and Bosnia-Herzegovina karst, temporal springs are inhabited by a group of minnow-like fishes that retreat to the associated ground water during dry seasons and spend several months underground. The most abundant species in this group is Delminichthys adspersus (Heckel 1843), which also has the most fragmented distribution range. To determine the population composition and dispersal patterns, and to detect potential underground migration, a large genetic data set comprising 544 specimens of D. adspersus covering most of its distribution area was analysed. Analysis of mitochondrial cytochrome b sequences (∼1000 bp) and eight microsatellite loci showed that D. adspersus comprises at least three subpopulations with gene flow occurring among them. Coalescent-based analysis revealed a complex migration pattern, with several unidirectional dispersal paths, including between temporal springs that share no surface connection. The results of this study suggest the existence of recurrent underground migration of fish in a karst environment and demonstrate the complexity of its hydrological network. The findings are relevant to conservation strategies for endemic karst organisms and karst ecosystems as a whole.

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Genetic assignment methods for the direct, real-time estimation of migration rate: a simulation-based exploration of accuracy and power.

David Paetkau, Robert W. Slade, Michael Burden … (2004)

Genetic assignment methods use genotype likelihoods to draw inference about where individuals were or were not born, potentially allowing direct, real-time estimates of dispersal. We used simulated data sets to test the power and accuracy of Monte Carlo resampling methods in generating statistical thresholds for identifying F0 immigrants in populations with ongoing gene flow, and hence for providing direct, real-time estimates of migration rates. The identification of accurate critical values required that resampling methods preserved the linkage disequilibrium deriving from recent generations of immigrants and reflected the sampling variance present in the data set being analysed. A novel Monte Carlo resampling method taking into account these aspects was proposed and its efficiency was evaluated. Power and error were relatively insensitive to the frequency assumed for missing alleles. Power to identify F0 immigrants was improved by using large sample size (up to about 50 individuals) and by sampling all populations from which migrants may have originated. A combination of plotting genotype likelihoods and calculating mean genotype likelihood ratios (DLR) appeared to be an effective way to predict whether F0 immigrants could be identified for a particular pair of populations using a given set of markers.

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Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations

Jukka Corander, Pekka Marttinen, Jukka Sirén … (2008)

Background During the most recent decade many Bayesian statistical models and software for answering questions related to the genetic structure underlying population samples have appeared in the scientific literature. Most of these methods utilize molecular markers for the inferences, while some are also capable of handling DNA sequence data. In a number of earlier works, we have introduced an array of statistical methods for population genetic inference that are implemented in the software BAPS. However, the complexity of biological problems related to genetic structure analysis keeps increasing such that in many cases the current methods may provide either inappropriate or insufficient solutions. Results We discuss the necessity of enhancing the statistical approaches to face the challenges posed by the ever-increasing amounts of molecular data generated by scientists over a wide range of research areas and introduce an array of new statistical tools implemented in the most recent version of BAPS. With these methods it is possible, e.g., to fit genetic mixture models using user-specified numbers of clusters and to estimate levels of admixture under a genetic linkage model. Also, alleles representing a different ancestry compared to the average observed genomic positions can be tracked for the sampled individuals, and a priori specified hypotheses about genetic population structure can be directly compared using Bayes' theorem. In general, we have improved further the computational characteristics of the algorithms behind the methods implemented in BAPS facilitating the analyses of large and complex datasets. In particular, analysis of a single dataset can now be spread over multiple computers using a script interface to the software. Conclusion The Bayesian modelling methods introduced in this article represent an array of enhanced tools for learning the genetic structure of populations. Their implementations in the BAPS software are designed to meet the increasing need for analyzing large-scale population genetics data. The software is freely downloadable for Windows, Linux and Mac OS X systems at .

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Bayesian identification of admixture events using multilocus molecular markers.

Jukka Corander, Pekka Marttinen (2006)

Bayesian statistical methods for the estimation of hidden genetic structure of populations have gained considerable popularity in the recent years. Utilizing molecular marker data, Bayesian mixture models attempt to identify a hidden population structure by clustering individuals into genetically divergent groups, whereas admixture models target at separating the ancestral sources of the alleles observed in different individuals. We discuss the difficulties involved in the simultaneous estimation of the number of ancestral populations and the levels of admixture in studied individuals' genomes. To resolve this issue, we introduce a computationally efficient method for the identification of admixture events in the population history. Our approach is illustrated by analyses of several challenging real and simulated data sets. The software (baps), implementing the methods introduced here, is freely available at http://www.rni.helsinki.fi/~jic/bapspage.html.