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Open AccessJournal ArticleDOI

Harnessing genomics for delineating conservation units

TLDR
A new framework to integrate data on neutral and adaptive markers to protect biodiversity is provided and it is proposed that the use of neutral versus adaptive markers should not be viewed as alternatives.
Abstract
Genomic data have the potential to revolutionize the delineation of conservation units (CUs) by allowing the detection of adaptive genetic variation, which is otherwise difficult for rare, endangered species. In contrast to previous recommendations, we propose that the use of neutral versus adaptive markers should not be viewed as alternatives. Rather, neutral and adaptive markers provide different types of information that should be combined to make optimal management decisions. Genetic patterns at neutral markers reflect the interaction of gene flow and genetic drift that affects genome-wide variation within and among populations. This population genetic structure is what natural selection operates on to cause adaptive divergence. Here, we provide a new framework to integrate data on neutral and adaptive markers to protect biodiversity.

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Journal ArticleDOI

Finding the Genomic Basis of Local Adaptation: Pitfalls, Practical Solutions, and Future Directions.

TL;DR: The promises and challenges of these genome scan methods are reviewed, including correcting for the confounding influence of a species’ demographic history, biases caused by missing aspects of the genome, matching scales of environmental data with population structure, and other statistical considerations.
Journal ArticleDOI

Genetic rescue to the rescue

TL;DR: 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.
Journal ArticleDOI

Genomics and the challenging translation into conservation practice

Aaron B. A. Shafer, +44 more
TL;DR: Before the real-world conservation potential of genomic research can be realized, current infrastructures need to be modified, methods must mature, analytical pipelines need to been developed, and successful case studies must be disseminated to practitioners.
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The K = 2 conundrum.

TL;DR: This review suggests that many studies may have been over‐ or underestimating population genetic structure; both scenarios have serious consequences, particularly with respect to conservation and management.
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Genetics and the conservation of natural populations: allozymes to genomes.

TL;DR: I consider how the study of genetic variation has influenced efforts to conserve natural populations over the last 50 years and believes current training in conservation genetics focuses too much on the latest techniques and too little on understanding the conceptual basis which is needed to interpret data and ask good questions.
References
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Journal ArticleDOI

Controlling the false discovery rate: a practical and powerful approach to multiple testing

TL;DR: In this paper, a different approach to problems of multiple significance testing is presented, which calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate, which is equivalent to the FWER when all hypotheses are true but is smaller otherwise.
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Inference of population structure using multilocus genotype data

TL;DR: Pritch et al. as discussed by the authors proposed a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations, which can be applied to most of the commonly used genetic markers, provided that they are not closely linked.
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BEAST: Bayesian evolutionary analysis by sampling trees

TL;DR: BEAST is a fast, flexible software architecture for Bayesian analysis of molecular sequences related by an evolutionary tree that provides models for DNA and protein sequence evolution, highly parametric coalescent analysis, relaxed clock phylogenetics, non-contemporaneous sequence data, statistical alignment and a wide range of options for prior distributions.
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Evolution in Mendelian Populations.

TL;DR: Page 108, last line of text, for "P/P″" read "P′/ P″."
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Evolution in Mendelian populations

TL;DR: The frequency of a given gene in a population may be modified by a number of conditions including recurrent mutation to and from it, migration, selection of various sorts and, far from least in importance, were chance variation.
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