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      Navigating the currents of seascape genomics: how spatial analyses can augment population genomic studies

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          Abstract

          Population genomic approaches are making rapid inroads in the study of non-model organisms, including marine taxa. To date, these marine studies have predominantly focused on rudimentary metrics describing the spatial and environmental context of their study region (e.g., geographical distance, average sea surface temperature, average salinity). We contend that a more nuanced and considered approach to quantifying seascape dynamics and patterns can strengthen population genomic investigations and help identify spatial, temporal, and environmental factors associated with differing selective regimes or demographic histories. Nevertheless, approaches for quantifying marine landscapes are complicated. Characteristic features of the marine environment, including pelagic living in flowing water (experienced by most marine taxa at some point in their life cycle), require a well-designed spatial-temporal sampling strategy and analysis. Many genetic summary statistics used to describe populations may be inappropriate for marine species with large population sizes, large species ranges, stochastic recruitment, and asymmetrical gene flow. Finally, statistical approaches for testing associations between seascapes and population genomic patterns are still maturing with no single approach able to capture all relevant considerations. None of these issues are completely unique to marine systems and therefore similar issues and solutions will be shared for many organisms regardless of habitat. Here, we outline goals and spatial approaches for landscape genomics with an emphasis on marine systems and review the growing empirical literature on seascape genomics. We review established tools and approaches and highlight promising new strategies to overcome select issues including a strategy to spatially optimize sampling. Despite the many challenges, we argue that marine systems may be especially well suited for identifying candidate genomic regions under environmentally mediated selection and that seascape genomic approaches are especially useful for identifying robust locus-by-environment associations.

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          The Analysis of Spatial Association by Use of Distance Statistics

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            Improving the accuracy of demographic and molecular clock model comparison while accommodating phylogenetic uncertainty.

            Recent developments in marginal likelihood estimation for model selection in the field of Bayesian phylogenetics and molecular evolution have emphasized the poor performance of the harmonic mean estimator (HME). Although these studies have shown the merits of new approaches applied to standard normally distributed examples and small real-world data sets, not much is currently known concerning the performance and computational issues of these methods when fitting complex evolutionary and population genetic models to empirical real-world data sets. Further, these approaches have not yet seen widespread application in the field due to the lack of implementations of these computationally demanding techniques in commonly used phylogenetic packages. We here investigate the performance of some of these new marginal likelihood estimators, specifically, path sampling (PS) and stepping-stone (SS) sampling for comparing models of demographic change and relaxed molecular clocks, using synthetic data and real-world examples for which unexpected inferences were made using the HME. Given the drastically increased computational demands of PS and SS sampling, we also investigate a posterior simulation-based analogue of Akaike's information criterion (AIC) through Markov chain Monte Carlo (MCMC), a model comparison approach that shares with the HME the appealing feature of having a low computational overhead over the original MCMC analysis. We confirm that the HME systematically overestimates the marginal likelihood and fails to yield reliable model classification and show that the AICM performs better and may be a useful initial evaluation of model choice but that it is also, to a lesser degree, unreliable. We show that PS and SS sampling substantially outperform these estimators and adjust the conclusions made concerning previous analyses for the three real-world data sets that we reanalyzed. The methods used in this article are now available in BEAST, a powerful user-friendly software package to perform Bayesian evolutionary analyses.
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              Maps of Pleistocene sea levels in Southeast Asia: shorelines, river systems and time durations

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                Author and article information

                Journal
                Curr Zool
                Curr Zool
                czoolo
                Current Zoology
                Oxford University Press
                1674-5507
                December 2016
                06 July 2016
                06 July 2016
                : 62
                : 6
                : 581-601
                Affiliations
                [a ]School of Biological Sciences, The University of Queensland, St Lucia, QLD 4072, Australia
                [b ]Division of Science and Environmental Policy, California State University, Seaside, CA 93955, USA
                [c ]Institute of Natural and Mathematical Sciences, Massey University, Auckland 0745, New Zealand
                [d ]Global Change Institute, The University of Queensland, QLD 4072, St Lucia, Australia,
                [e ]School of BioSciences, The University of Melbourne, VIC, 3010, Australia
                Author notes
                [* ]Address correspondence to Cynthia Riginos. E-mail: c.riginos@ 123456uq.edu.au .
                Article
                zow067
                10.1093/cz/zow067
                5804261
                29491947
                651164b8-b09b-4a28-90c6-334d83b928b2
                © The Author (2016). Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 13 February 2016
                : 25 May 2016
                Page count
                Pages: 22
                Categories
                Articles

                adaptation,genetic–environment association,landscape,oceanography,population genomics,remote sensing,seascape genetics.

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