Riverscape genetic variation, migration patterns, and morphological variation of the threatened Round Rocksnail, Leptoxis ampla

As populations become increasingly fragmented, managers are often faced with the dilemma that intentional hybridization might save a population from inbreeding depression but it might also induce outbreeding depression. While empirical evidence for inbreeding depression is vastly greater than that for outbreeding depression, the available data suggest that risks of outbreeding, particularly in the second generation, are on par with the risks of inbreeding. Predicting the relative risks in any particular situation is complicated by variation among taxa, characters being measured, level of divergence between hybridizing populations, mating history, environmental conditions and the potential for inbreeding and outbreeding effects to be occurring simultaneously. Further work on consequences of interpopulation hybridization is sorely needed with particular emphasis on the taxonomic scope, the duration of fitness problems and the joint effects of inbreeding and outbreeding. Meanwhile, managers can minimize the risks of both inbreeding and outbreeding by using intentional hybridization only for populations clearly suffering from inbreeding depression, maximizing the genetic and adaptive similarity between populations, and testing the effects of hybridization for at least two generations whenever possible.

For many biological investigations, groups of individuals are genetically sampled from several geographic locations. These sampling locations often do not reflect the genetic population structure. We describe a framework using marginal likelihoods to compare and order structured population models, such as testing whether the sampling locations belong to the same randomly mating population or comparing unidirectional and multidirectional gene flow models. In the context of inferences employing Markov chain Monte Carlo methods, the accuracy of the marginal likelihoods depends heavily on the approximation method used to calculate the marginal likelihood. Two methods, modified thermodynamic integration and a stabilized harmonic mean estimator, are compared. With finite Markov chain Monte Carlo run lengths, the harmonic mean estimator may not be consistent. Thermodynamic integration, in contrast, delivers considerably better estimates of the marginal likelihood. The choice of prior distributions does not influence the order and choice of the better models when the marginal likelihood is estimated using thermodynamic integration, whereas with the harmonic mean estimator the influence of the prior is pronounced and the order of the models changes. The approximation of marginal likelihood using thermodynamic integration in MIGRATE allows the evaluation of complex population genetic models, not only of whether sampling locations belong to a single panmictic population, but also of competing complex structured population models.