2018
DOI: 10.1111/1755-0998.12920
|View full text |Cite
|
Sign up to set email alerts
|

Reliably discriminating stock structure with genetic markers: Mixture models with robust and fast computation

Abstract: Delineating naturally occurring and self-sustaining subpopulations (stocks) of a species is an important task, especially for species harvested from the wild. Despite its central importance to natural resource management, analytical methods used to delineate stocks are often, and increasingly, borrowed from superficially similar analytical tasks in human genetics even though models specifically for stock identification have been previously developed. Unfortunately, the analytical tasks in resource management a… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
15
0

Year Published

2020
2020
2024
2024

Publication Types

Select...
6

Relationship

4
2

Authors

Journals

citations
Cited by 11 publications
(15 citation statements)
references
References 63 publications
(138 reference statements)
0
15
0
Order By: Relevance
“…The graph "All individuals" represents the proportion of significant results obtained when performing an AMOVA using all the haplotypes generated in each population used to analyze eDNA data so that this tool can be rigorously use for population genetics. Here, our study focused on a hypothesis-testing method, but a similar approach could be used to assess the power of hypothesis-generating algorithms such as Bayesian clustering analysis of population structure (Corander & Tang, 2007), AMOVA-based Kmeans clustering (Meirmans, 2012) or the power of models for stock identification (Foster et al, 2018) with eDNA data.…”
Section: Discussionmentioning
confidence: 99%
“…The graph "All individuals" represents the proportion of significant results obtained when performing an AMOVA using all the haplotypes generated in each population used to analyze eDNA data so that this tool can be rigorously use for population genetics. Here, our study focused on a hypothesis-testing method, but a similar approach could be used to assess the power of hypothesis-generating algorithms such as Bayesian clustering analysis of population structure (Corander & Tang, 2007), AMOVA-based Kmeans clustering (Meirmans, 2012) or the power of models for stock identification (Foster et al, 2018) with eDNA data.…”
Section: Discussionmentioning
confidence: 99%
“…Moreover, there are no spatial replicates from exactly same sampling sites even though sites at different times may be located close to each other. Further complexity stems from the fact that a mixture model's likelihood and posterior densities are known to be "bumpy" with many local maxima (Foster et al, 2018;McLachlan & Peel, 2000). In order to make analyses feasible, we propose to use approximate Bayesian inference methods in three increasing levels of accuracy and computation time.…”
Section: Inferential Methodsmentioning
confidence: 99%
“…Finally, the R packages Radiator (Gosselin, Lamothe, Devloo‐Delva, & Grewe, 2020) and OutFLANK (Whitlock & Lotterhos, 2015) were used to detect and remove sex‐linked and outlier loci respectively. Since unequal sample sizes could introduce bias in clustering algorithms (Foster et al., 2018), filtering was applied to the full data set and a subsampled data set including only 30 randomly chosen sharks from Van Diemen Gulf (VDG) was selected so that putative populations would roughly be of equal size (Supporting Information S1, section 6).…”
Section: Methodsmentioning
confidence: 99%