[MARMAM] Review paper on genetic and genomic monitoring with minimally invasive sampling methods

Emma Carroll elc6 at st-andrews.ac.uk
Mon Mar 26 07:08:23 PDT 2018

Hi All,

We are pleased to announce that our new review paper "Genetic and genomic
monitoring with minimally invasive sampling methods" has recently been
released on the *Evolutionary Applications* Early View website as part of
the Special Issue: Next generation genetic monitoring. An open access
version of the full paper is available at:


kind regards
Emma Carroll

Genetic and genomic monitoring with minimally invasive sampling methods

Emma L. Carroll, Mike W. Bruford, J. Andrew DeWoody, Gregoire Leroy, Alan
Strand,  Lisette Waits and Jinliang Wang

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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