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      Genetic and genomic monitoring with minimally invasive sampling methods

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          Abstract

          The decreasing cost and increasing scope and power of emerging genomic technologies are reshaping the field of molecular ecology. However, many modern genomic approaches (e.g., RAD‐seq) require large amounts of high‐quality template DNA. This poses a problem for an active branch of conservation biology: genetic monitoring using minimally invasive sampling ( MIS) methods. Without handling or even observing an animal, MIS methods (e.g., collection of hair, skin, faeces) can provide genetic information on individuals or populations. Such samples typically yield low‐quality and/or quantities of DNA, restricting the type of molecular methods that can be used. Despite this limitation, genetic monitoring using MIS is an effective tool for estimating population demographic parameters and monitoring genetic diversity in natural populations. Genetic monitoring is likely to become more important in the future as many natural populations are undergoing anthropogenically driven declines, which are unlikely to abate without intensive adaptive management efforts that often include MIS approaches. Here, we profile the expanding suite of genomic methods and platforms compatible with producing genotypes from MIS, considering factors such as development costs and error rates. We evaluate how powerful new approaches will enhance our ability to investigate questions typically answered using genetic monitoring, such as estimating abundance, genetic structure and relatedness. As the field is in a period of unusually rapid transition, we also highlight the importance of legacy data sets and recommend how to address the challenges of moving between traditional and next‐generation genetic monitoring platforms. Finally, we consider how genetic monitoring could move beyond genotypes in the future. For example, assessing microbiomes or epigenetic markers could provide a greater understanding of the relationship between individuals and their environment.

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          Most cited references222

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          Maximum likelihood estimation of a migration matrix and effective population sizes in n subpopulations by using a coalescent approach.

          A maximum likelihood estimator based on the coalescent for unequal migration rates and different subpopulation sizes is developed. The method uses a Markov chain Monte Carlo approach to investigate possible genealogies with branch lengths and with migration events. Properties of the new method are shown by using simulated data from a four-population n-island model and a source-sink population model. Our estimation method as coded in migrate is tested against genetree; both programs deliver a very similar likelihood surface. The algorithm converges to the estimates fairly quickly, even when the Markov chain is started from unfavorable parameters. The method was used to estimate gene flow in the Nile valley by using mtDNA data from three human populations.
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            How to track and assess genotyping errors in population genetics studies.

            Genotyping errors occur when the genotype determined after molecular analysis does not correspond to the real genotype of the individual under consideration. Virtually every genetic data set includes some erroneous genotypes, but genotyping errors remain a taboo subject in population genetics, even though they might greatly bias the final conclusions, especially for studies based on individual identification. Here, we consider four case studies representing a large variety of population genetics investigations differing in their sampling strategies (noninvasive or traditional), in the type of organism studied (plant or animal) and the molecular markers used [microsatellites or amplified fragment length polymorphisms (AFLPs)]. In these data sets, the estimated genotyping error rate ranges from 0.8% for microsatellite loci from bear tissues to 2.6% for AFLP loci from dwarf birch leaves. Main sources of errors were allelic dropouts for microsatellites and differences in peak intensities for AFLPs, but in both cases human factors were non-negligible error generators. Therefore, tracking genotyping errors and identifying their causes are necessary to clean up the data sets and validate the final results according to the precision required. In addition, we propose the outline of a protocol designed to limit and quantify genotyping errors at each step of the genotyping process. In particular, we recommend (i) several efficient precautions to prevent contaminations and technical artefacts; (ii) systematic use of blind samples and automation; (iii) experience and rigor for laboratory work and scoring; and (iv) systematic reporting of the error rate in population genetics studies.
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              Genotyping errors: causes, consequences and solutions.

              Although genotyping errors affect most data and can markedly influence the biological conclusions of a study, they are too often neglected. Errors have various causes, but their occurrence and effect can be limited by considering these causes in the production and analysis of the data. Procedures that have been developed for dealing with errors in linkage studies, forensic analyses and non-invasive genotyping should be applied more broadly to any genetic study. We propose a protocol for estimating error rates and recommend that these measures be systemically reported to attest the reliability of published genotyping studies.
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                Author and article information

                Contributors
                carrollemz@gmail.com
                Journal
                Evol Appl
                Evol Appl
                10.1111/(ISSN)1752-4571
                EVA
                Evolutionary Applications
                John Wiley and Sons Inc. (Hoboken )
                1752-4571
                24 March 2018
                August 2018
                : 11
                : 7 , Next generation conservation genetics and biodiversity monitoring ( doiID: 10.1111/eva.2018.11.issue-7 )
                : 1094-1119
                Affiliations
                [ 1 ] Scottish Oceans Institute and Sea Mammal Research Unit University of St Andrews St Andrews UK
                [ 2 ] Cardiff School of Biosciences and Sustainable Places Research Institute Cardiff University Cardiff, Wales UK
                [ 3 ] Department of Forestry and Natural Resources and Department of Biological Sciences Purdue University West Lafayette IN USA
                [ 4 ] Animal Production and Health Division Food and Agriculture Organization of the United Nations Rome Italy
                [ 5 ] Grice Marine Laboratory Department of Biology College of Charleston Charleston SC USA
                [ 6 ] Department of Fish and Wildlife Sciences University of Idaho Moscow ID USA
                [ 7 ] Institute of Zoology Zoological Society of London London UK
                Author notes
                [*] [* ] Correspondence

                Emma L. Carroll, Scottish Oceans Institute and Sea Mammal Research Unit, University of St Andrews, St Andrews, UK.

                Email: carrollemz@ 123456gmail.com

                Author information
                http://orcid.org/0000-0003-3193-7288
                http://orcid.org/0000-0002-7315-5631
                http://orcid.org/0000-0003-2588-4431
                Article
                EVA12600
                10.1111/eva.12600
                6050181
                30026800
                b0134dcb-be88-4d47-a4c4-1acd485418af
                © 2018 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
                : 19 July 2017
                : 02 January 2018
                Page count
                Figures: 0, Tables: 3, Pages: 26, Words: 22242
                Funding
                Funded by: EU Horizon 2020 Programme
                Funded by: Royal Society Wolfson
                Funded by: University of Idaho
                Funded by: NSF
                Award ID: 1355106
                Award ID: 1357386
                Categories
                Review and Syntheses
                Review and Syntheses
                Custom metadata
                2.0
                eva12600
                August 2018
                Converter:WILEY_ML3GV2_TO_NLMPMC version:version=5.4.3 mode:remove_FC converted:17.07.2018

                Evolutionary Biology
                conservation genetics,dna fingerprinting,individual identification,noninvasive genetic sampling,population demography,wildlife forensics,wildlife management

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