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      A review of marine phylogeography in southern Africa

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          Abstract

          The southern African marine realm is located at the transition zone between the Atlantic and Indo-Pacific biomes. Its biodiversity is particularly rich and comprises faunal and floral elements from the two major oceanic regions, as well as a large number of endemics. Within this realm, strikingly different biota occur in close geographic proximity to each other, and many of the species with distributions spanning two or more of the region's marine biogeographic provinces are divided into evolutionary units that can often only be distinguished on the basis of genetic data. In this review, we describe the state of marine phylogeography in southern Africa, that is, the study of evolutionary relationships at the species level, or amongst closely related species, in relation to the region's marine environment. We focus particularly on coastal phylogeography, where much progress has recently been made in identifying phylogeographic breaks and explaining how they originated and are maintained. We also highlight numerous shortcomings that should be addressed in the near future. These include: the limited data available for commercially important organisms, particularly offshore species; the paucity of oceanographic data for nearshore areas; a dearth of studies based on multilocus data; and the fact that studying the role of diversifying selection in speciation has been limited to physiological approaches to the exclusion of genetics. It is becoming apparent that the southern African marine realm is one of the world's most interesting environments in which to study the evolutionary processes that shape not only regional, but also global patterns of marine biodiversity.

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          Most cited references154

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          Unified framework to evaluate panmixia and migration direction among multiple sampling locations.

          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.
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            Gene Flow in Natural Populations

            M Slarkin (1985)
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              Statistical phylogeography.

              While studies of phylogeography and speciation in the past have largely focused on the documentation or detection of significant patterns of population genetic structure, the emerging field of statistical phylogeography aims to infer the history and processes underlying that structure, and to provide objective, rather than ad hoc explanations. Methods for parameter estimation are now commonly used to make inferences about demographic past. Although these approaches are well developed statistically, they typically pay little attention to geographical history. In contrast, methods that seek to reconstruct phylogeographic history are able to consider many alternative geographical scenarios, but are primarily nonstatistical, making inferences about particular biological processes without explicit reference to stochastically derived expectations. We advocate the merging of these two traditions so that statistical phylogeographic methods can provide an accurate representation of the past, consider a diverse array of processes, and yet yield a statistical estimate of that history. We discuss various conceptual issues associated with statistical phylogeographic inferences, considering especially the stochasticity of population genetic processes and assessing the confidence of phylogeographic conclusions. To this end, we present some empirical examples that utilize a statistical phylogeographic approach, and then by contrasting results from a coalescent-based approach to those from Templeton's nested cladistic analysis (NCA), we illustrate the importance of assessing error. Because NCA does not assess error in its inferences about historical processes or contemporary gene flow, we performed a small-scale study using simulated data to examine how our conclusions might be affected by such unconsidered errors. NCA did not identify the processes used to simulate the data, confusing among deterministic processes and the stochastic sorting of gene lineages. There is as yet insufficient justification of NCA's ability to accurately infer or distinguish among alternative processes. We close with a discussion of some unresolved problems of current statistical phylogeographic methods to propose areas in need of future development.
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                Author and article information

                Contributors
                Role: ND
                Role: ND
                Role: ND
                Role: ND
                Journal
                sajs
                South African Journal of Science
                S. Afr. j. sci.
                Academy of Science of South Africa (Pretoria )
                1996-7489
                June 2011
                : 107
                : 5-6
                : 43-53
                Affiliations
                [1 ] Rhodes University South Africa
                [2 ] Universiteit Stellenbosch South Africa
                [3 ] Rhodes University South Africa
                Article
                S0038-23532011000300011
                10.4102/sajs.v107i5/6.514
                1224c924-54f1-4214-b7af-2623987b0a65

                http://creativecommons.org/licenses/by/4.0/

                History
                Product

                SciELO South Africa

                Self URI (journal page): http://www.scielo.org.za/scielo.php?script=sci_serial&pid=0038-2353&lng=en
                Categories
                Biology
                Humanities, Multidisciplinary

                General life sciences
                General life sciences

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