Evidence map›Paper›PMID 25553062›Full record

ArticleEvolutionary applications2014

Comparative evaluation of potential indicators and temporal sampling protocols for monitoring genetic erosion.

Sean Hoban, Jan A Arntzen, Michael W Bruford, José A Godoy, A Rus Hoelzel, Gernot Segelbacher, Carles Vilà, Giorgio Bertorelle

Abstract read
In one paragraph

Article in Evolutionary applications, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

37 citing papers in PubMed.

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  20. Intraspecific genetic variation of aEcology and evolution · 2020
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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

8 authors.

Sean HobanNational Institute for Mathematical and Biological Synthesis (NIMBioS), University of Tennessee Knoxville, TN, USA ; Department of Life Science, Università di Ferrara Ferrara, Italy.
Jan A ArntzenNaturalis Biodiversity Center Leiden, the Netherlands.
Michael W BrufordSchool of Biosciences, Cardiff University Cardiff, Wales, UK.
José A GodoyEstación Biológica de Doñana (EBD-CSIC) Seville, Spain.
A Rus HoelzelDurham University Durham, UK.
Gernot SegelbacherUniversity Freiburg Freiburg, Germany.
Carles VilàEstación Biológica de Doñana (EBD-CSIC) Seville, Spain.
Giorgio BertorelleDepartment of Life Science, Università di Ferrara Ferrara, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genetic biodiversity contributes to individual fitness, species' evolutionary potential, and ecosystem stability. Temporal monitoring of the genetic status and trends of wild populations' genetic diversity can provide vital data to inform policy decisions and management actions. However, there is a lack of knowledge regarding which genetic metrics, temporal sampling protocols, and genetic markers are sufficiently sensitive and robust, on conservation-relevant timescales. Here, we tested six genetic metrics and various sampling protocols (number and arrangement of temporal samples) for monitoring genetic erosion following demographic decline. To do so, we utilized individual-based simulations featuring an array of different initial population sizes, types and severity of demographic decline, and DNA markers [single nucleotide polymorphisms (SNPs) and microsatellites] as well as decline followed by recovery. Number of alleles markedly outperformed other indicators across all situations. The type and severity of demographic decline strongly affected power, while the number and arrangement of temporal samples had small effect. Sampling 50 individuals at as few as two time points with 20 microsatellites performed well (good power), and could detect genetic erosion while 80-90% of diversity remained. This sampling and genotyping effort should often be affordable. Power increased substantially with more samples or markers, and we observe that power of 2500 SNPs was nearly equivalent to 250 microsatellites, a result of theoretical and practical interest. Our results suggest high potential for using historic collections in monitoring programs, and demonstrate the need to monitor genetic as well as other levels of biodiversity.

Indexed as

alpha diversityconservation biologyconvention on biological diversitymicrosatellitespopulation geneticsimulationsingle nucleotide polymorphismtemporal sampling

Identifiers

PMID25553062
PMCPMC4231590

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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.