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      Guidelines for planning genomic assessment and monitoring of locally adaptive variation to inform species conservation

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          Abstract

          Identifying and monitoring locally adaptive genetic variation can have direct utility for conserving species at risk, especially when management may include actions such as translocations for restoration, genetic rescue, or assisted gene flow. However, genomic studies of local adaptation require careful planning to be successful, and in some cases may not be a worthwhile use of resources. Here, we offer an adaptive management framework to help conservation biologists and managers decide when genomics is likely to be effective in detecting local adaptation, and how to plan assessment and monitoring of adaptive variation to address conservation objectives. Studies of adaptive variation using genomic tools will inform conservation actions in many cases, including applications such as assisted gene flow and identifying conservation units. In others, assessing genetic diversity, inbreeding, and demographics using selectively neutral genetic markers may be most useful. And in some cases, local adaptation may be assessed more efficiently using alternative approaches such as common garden experiments. Here, we identify key considerations of genomics studies of locally adaptive variation, provide a road map for successful collaborations with genomics experts including key issues for study design and data analysis, and offer guidelines for interpreting and using results from genomic assessments to inform monitoring programs and conservation actions.

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          A quantitative survey of local adaptation and fitness trade-offs.

          The long history of reciprocal transplant studies testing the hypothesis of local adaptation has shown that populations are often adapted to their local environments. Yet many studies have not demonstrated local adaptation, suggesting that sometimes native populations are no better adapted than are genotypes from foreign environments. Local adaptation may also lead to trade-offs, in which adaptation to one environment comes at a cost of adaptation to another environment. I conducted a survey of published studies of local adaptation to quantify its frequency and magnitude and the costs associated with local adaptation. I also quantified the relationship between local adaptation and environmental differences and the relationship between local adaptation and phenotypic divergence. The overall frequency of local adaptation was 0.71, and the magnitude of the native population advantage in relative fitness was 45%. Divergence between home site environments was positively associated with the magnitude of local adaptation, but phenotypic divergence was not. I found a small negative correlation between a population's relative fitness in its native environment and its fitness in a foreign environment, indicating weak trade-offs associated with local adaptation. These results suggest that populations are often locally adapted but stochastic processes such as genetic drift may limit the efficacy of divergent selection.
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            Genetic rescue to the rescue.

            Genetic rescue can increase the fitness of small, imperiled populations via immigration. A suite of studies from the past decade highlights the value of genetic rescue in increasing population fitness. Nonetheless, genetic rescue has not been widely applied to conserve many of the threatened populations that it could benefit. In this review, we highlight recent studies of genetic rescue and place it in the larger context of theoretical and empirical developments in evolutionary and conservation biology. We also propose directions to help shape future research on genetic rescue. Genetic rescue is a tool that can stem biodiversity loss more than has been appreciated, provides population resilience, and will become increasingly useful if integrated with molecular advances in population genomics.
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              Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data

              Many dynamical systems, including lakes, organisms, ocean circulation patterns, or financial markets, are now thought to have tipping points where critical transitions to a contrasting state can happen. Because critical transitions can occur unexpectedly and are difficult to manage, there is a need for methods that can be used to identify when a critical transition is approaching. Recent theory shows that we can identify the proximity of a system to a critical transition using a variety of so-called ‘early warning signals’, and successful empirical examples suggest a potential for practical applicability. However, while the range of proposed methods for predicting critical transitions is rapidly expanding, opinions on their practical use differ widely, and there is no comparative study that tests the limitations of the different methods to identify approaching critical transitions using time-series data. Here, we summarize a range of currently available early warning methods and apply them to two simulated time series that are typical of systems undergoing a critical transition. In addition to a methodological guide, our work offers a practical toolbox that may be used in a wide range of fields to help detect early warning signals of critical transitions in time series data.
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                Author and article information

                Contributors
                spflanagan.phd@gmail.com
                Journal
                Evol Appl
                Evol Appl
                10.1111/(ISSN)1752-4571
                EVA
                Evolutionary Applications
                John Wiley and Sons Inc. (Hoboken )
                1752-4571
                02 December 2017
                August 2018
                : 11
                : 7 , Next generation conservation genetics and biodiversity monitoring ( doiID: 10.1111/eva.2018.11.issue-7 )
                : 1035-1052
                Affiliations
                [ 1 ] National Institute for Mathematical and Biological Synthesis University of Tennessee Knoxville TN USA
                [ 2 ] Duke University, Nicholas School of the Environment Durham NC USA
                [ 3 ] Department of Biological Sciences University of Wisconsin‐Milwaukee Milwaukee WI USA
                [ 4 ] Faculty of Forestry University of British Columbia Vancouver BC Canada
                [ 5 ] The Morton Arboretum Lisle IL USA
                [ 6 ]Present address: Department of Biology Colorado State University Fort Collins CO USA
                Author notes
                [*] [* ] Correspondence

                Sarah P. Flanagan, National Institute for Mathematical and Biological Synthesis, University of Tennessee, Knoxville, TN, USA.

                Email: spflanagan.phd@ 123456gmail.com

                [†]

                These authors contributed equally to this work.

                Author information
                http://orcid.org/0000-0002-2226-4213
                http://orcid.org/0000-0002-1608-1904
                http://orcid.org/0000-0002-9892-1056
                Article
                EVA12569
                10.1111/eva.12569
                6050180
                30026796
                a4b7cd61-0ba4-4a3c-838a-6ce1c3492e60
                © 2017 The Authors. Evolutionary Applications published by John Wiley & Sons Ltd

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 11 August 2017
                : 20 October 2017
                Page count
                Figures: 2, Tables: 0, Pages: 18, Words: 16809
                Funding
                Funded by: Next Generation Genetic Monitoring Investigative Workshop at the National Institute for Mathematical and Biological Synthesis, sponsored by the National Science Foundation through
                Award ID: #DBI‐1300426
                Funded by: The University of Tennessee, Knoxville
                Funded by: P.E.O. Scholars Award
                Funded by: National Science Foundation
                Funded by: Duke University Interdisciplinary Studies
                Categories
                Review and Syntheses
                Review and Syntheses
                Custom metadata
                2.0
                eva12569
                August 2018
                Converter:WILEY_ML3GV2_TO_NLMPMC version:version=5.4.3 mode:remove_FC converted:17.07.2018

                Evolutionary Biology
                adaptive management,conservation genetics,conservation planning,local adaptation,natural selection,next‐generation sequencing,outlier detection

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