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      Combined analyses of kinship and F ST suggest potential drivers of chaotic genetic patchiness in high gene-flow populations

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          Abstract

          We combine kinship estimates with traditional F-statistics to explain contemporary drivers of population genetic differentiation despite high gene flow. We investigate range-wide population genetic structure of the California spiny (or red rock) lobster ( Panulirus interruptus) and find slight, but significant global population differentiation in mtDNA (Φ ST = 0.006, P = 0.001; D est_Chao = 0.025) and seven nuclear microsatellites ( F ST = 0.004, P < 0.001; D est_Chao = 0.03), despite the species’ 240- to 330-day pelagic larval duration. Significant population structure does not correlate with distance between sampling locations, and pairwise F ST between adjacent sites often exceeds that among geographically distant locations. This result would typically be interpreted as unexplainable, chaotic genetic patchiness. However, kinship levels differ significantly among sites (pseudo- F 16,988 = 1.39, P = 0.001), and ten of 17 sample sites have significantly greater numbers of kin than expected by chance ( P < 0.05). Moreover, a higher proportion of kin within sites strongly correlates with greater genetic differentiation among sites ( D est_Chao, R 2 = 0.66, P < 0.005). Sites with elevated mean kinship were geographically proximate to regions of high upwelling intensity ( R 2 = 0.41, P = 0.0009). These results indicate that P. interruptus does not maintain a single homogenous population, despite extreme dispersal potential. Instead, these lobsters appear to either have substantial localized recruitment or maintain planktonic larval cohesiveness whereby siblings more likely settle together than disperse across sites. More broadly, our results contribute to a growing number of studies showing that low F ST and high family structure across populations can coexist, illuminating the foundations of cryptic genetic patterns and the nature of marine dispersal.

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          DnaSP, DNA polymorphism analyses by the coalescent and other methods.

          DnaSP is a software package for the analysis of DNA polymorphism data. Present version introduces several new modules and features which, among other options allow: (1) handling big data sets (approximately 5 Mb per sequence); (2) conducting a large number of coalescent-based tests by Monte Carlo computer simulations; (3) extensive analyses of the genetic differentiation and gene flow among populations; (4) analysing the evolutionary pattern of preferred and unpreferred codons; (5) generating graphical outputs for an easy visualization of results. The software package, including complete documentation and examples, is freely available to academic users from: http://www.ub.es/dnasp
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            What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity.

            We review commonly used population definitions under both the ecological paradigm (which emphasizes demographic cohesion) and the evolutionary paradigm (which emphasizes reproductive cohesion) and find that none are truly operational. We suggest several quantitative criteria that might be used to determine when groups of individuals are different enough to be considered 'populations'. Units for these criteria are migration rate (m) for the ecological paradigm and migrants per generation (Nm) for the evolutionary paradigm. These criteria are then evaluated by applying analytical methods to simulated genetic data for a finite island model. Under the standard parameter set that includes L = 20 High mutation (microsatellite-like) loci and samples of S = 50 individuals from each of n = 4 subpopulations, power to detect departures from panmixia was very high ( approximately 100%; P < 0.001) even with high gene flow (Nm = 25). A new method, comparing the number of correct population assignments with the random expectation, performed as well as a multilocus contingency test and warrants further consideration. Use of Low mutation (allozyme-like) markers reduced power more than did halving S or L. Under the standard parameter set, power to detect restricted gene flow below a certain level X (H(0): Nm < X) can also be high, provided that true Nm < or = 0.5X. Developing the appropriate test criterion, however, requires assumptions about several key parameters that are difficult to estimate in most natural populations. Methods that cluster individuals without using a priori sampling information detected the true number of populations only under conditions of moderate or low gene flow (Nm < or = 5), and power dropped sharply with smaller samples of loci and individuals. A simple algorithm based on a multilocus contingency test of allele frequencies in pairs of samples has high power to detect the true number of populations even with Nm = 25 but requires more rigorous statistical evaluation. The ecological paradigm remains challenging for evaluations using genetic markers, because the transition from demographic dependence to independence occurs in a region of high migration where genetic methods have relatively little power. Some recent theoretical developments and continued advances in computational power provide hope that this situation may change in the future.
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              What can genetics tell us about population connectivity?

              Genetic data are often used to assess 'population connectivity' because it is difficult to measure dispersal directly at large spatial scales. Genetic connectivity, however, depends primarily on the absolute number of dispersers among populations, whereas demographic connectivity depends on the relative contributions to population growth rates of dispersal vs. local recruitment (i.e. survival and reproduction of residents). Although many questions are best answered with data on genetic connectivity, genetic data alone provide little information on demographic connectivity. The importance of demographic connectivity is clear when the elimination of immigration results in a shift from stable or positive population growth to negative population growth. Otherwise, the amount of dispersal required for demographic connectivity depends on the context (e.g. conservation or harvest management), and even high dispersal rates may not indicate demographic interdependence. Therefore, it is risky to infer the importance of demographic connectivity without information on local demographic rates and how those rates vary over time. Genetic methods can provide insight on demographic connectivity when combined with these local demographic rates, data on movement behaviour, or estimates of reproductive success of immigrants and residents. We also consider the strengths and limitations of genetic measures of connectivity and discuss three concepts of genetic connectivity that depend upon the evolutionary criteria of interest: inbreeding connectivity, drift connectivity, and adaptive connectivity. To conclude, we describe alternative approaches for assessing population connectivity, highlighting the value of combining genetic data with capture-mark-recapture methods or other direct measures of movement to elucidate the complex role of dispersal in natural populations.
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                Author and article information

                Journal
                Mol Ecol
                Mol. Ecol
                mec
                Molecular Ecology
                Blackwell Publishing Ltd
                0962-1083
                1365-294X
                July 2013
                26 June 2013
                : 22
                : 13
                : 3476-3494
                Affiliations
                [* ]Hawai'i Institute of Marine Biology, School of Ocean and Earth Science and Technology, University of Hawai'i at Mānoa Kāne'ohe, HI, 96744, USA
                []Department of Biology, University of Hawai'i at Mānoa Honolulu, HI, 96822, USA
                []Bren School of Environmental Science and Management, University of California, Santa Barbara Santa Barbara, CA, 93106, USA
                [§ ]National Center for Ecological Analysis and Synthesis Santa Barbara, CA, 93101, USA
                []Department of Life Sciences, Texas A&M University – Corpus Christi Corpus Christi, TX, 78412, USA
                [** ]Instituto Politécnico Nacional, Centro Interdisciplinario de Ciencias Marinas, Colección Ictiológica La Paz, Baja California Sur, 23096, México
                Author notes
                Matthew Iacchei, Fax: 808-236-7443; E-mail: Iacchei@ 123456hawaii.edu

                M.I., T.B., K.A.H. and R.J.T. conceived and designed the experiments. M.I., T.B., K.A.H. and F.J.G. collected and contributed tissue samples. M.I. and T.B. sequenced and genotyped all samples, respectively. M.I., T.B., K.A.H, C.E.B. and R.J.T. contributed to data analysis and synthesis. M.I. wrote the manuscript with contributions from all authors.

                Article
                10.1111/mec.12341
                3749441
                23802550
                57597237-5f22-4a45-82c4-7c2cf5c115e9
                Copyright © 2013 John Wiley & Sons Ltd

                Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation.

                History
                : 01 January 2013
                : 10 March 2013
                : 12 March 2013
                Categories
                Original Articles

                Ecology
                larval behaviour,marine connectivity,pelagic larval dispersal,phylogeography,population genetics,southern california bight,spiny lobster

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