Evidence map›Paper›PMID 30026800›Full record

ArticleEvolutionary applications2018

Genetic and genomic monitoring with minimally invasive sampling methods.

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

Abstract read
In one paragraph

Article in Evolutionary applications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.

0numbers the graph read from it
0cells of the map it votes in
68citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

68 citing papers in PubMed.

  1. Article
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  8. Ecology and evolution · 2026
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8 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Emma L CarrollScottish Oceans Institute and Sea Mammal Research Unit University of St Andrews St Andrews UK.ORCID 0000-0003-3193-7288
Mike W BrufordCardiff School of Biosciences and Sustainable Places Research Institute Cardiff University Cardiff, Wales UK.
J Andrew DeWoodyDepartment of Forestry and Natural Resources and Department of Biological Sciences Purdue University West Lafayette IN USA.ORCID 0000-0002-7315-5631
Gregoire LeroyAnimal Production and Health Division Food and Agriculture Organization of the United Nations Rome Italy.ORCID 0000-0003-2588-4431
Alan StrandGrice Marine Laboratory Department of Biology College of Charleston Charleston SC USA.
Lisette WaitsDepartment of Fish and Wildlife Sciences University of Idaho Moscow ID USA.
Jinliang WangInstitute of Zoology Zoological Society of London London UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

conservation geneticsDNA fingerprintingindividual identificationnoninvasive genetic samplingpopulation demographywildlife forensicswildlife management

Identifiers

PMID30026800
PMCPMC6050181

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.