Subsampling reveals that unbalanced sampling affects Structure results in a multi-species dataset

Meirmans, Patrick G.

doi:10.1038/s41437-018-0124-8

Cited by 43 publications

(38 citation statements)

References 31 publications

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“…The objective of this work was not to comment on all of the potential caveats to consider using structure and the Δ K method, as these have been reviewed elsewhere (Gilbert et al, ; Kalinowski, ; Meirmans, ; Puechmaille, ; Waples & Gaggiotti, ). We also were not aiming to answer the question of what parameters define a population as a separate unit (Waples & Gaggiotti, ).…”

Section: Discussionmentioning

confidence: 99%

Confidently identifying the correct K value using the ΔK method: When does K = 2?

et al. 2020

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Populations delineated based on genetic data are commonly used for wildlife conservation and management. Many studies use the program structure combined with the ΔK method to identify the most probable number of populations (K). We recently found K = 2 was identified more often when studies used ΔK compared to studies that did not. We suggested two reasons for this: hierarchical population structure leads to underestimation, or the ΔK method does not evaluate K = 1 causing an overestimation. The present contribution aims to develop a better understanding of the limits of the method using one, two and three population simulations across migration scenarios. From these simulations we identified the “best K” using model likelihood and ΔK. Our findings show that mean probability plots and ΔK are unable to resolve the correct number of populations once migration rate exceeds 0.005. We also found a strong bias towards selecting K = 2 using the ΔK method. We used these data to identify the range of values where the ΔK statistic identifies a value of K that is not well supported. Finally, using the simulations and a review of empirical data, we found that the magnitude of ΔK corresponds to the level of divergence between populations. Based on our findings, we suggest researchers should use the ΔK method cautiously; they need to report all relevant data, including the magnitude of ΔK, and an estimate of connectivity for the research community to assess whether meaningful genetic structure exists within the context of management and conservation.

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Section: Discussionmentioning

confidence: 99%

Confidently identifying the correct K value using the ΔK method: When does K = 2?

et al. 2020

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“…2017) was used to automate running the clustering analyses and for parsing the output produced by the different programs. For determining the fit of the clustering results to either the populations (correct) or the ploidy levels (bias), we used the approach of Meirmans (2019). For this, we calculated a test statistic β , which is the absolute value of the variable coefficient (“slope”) of an Analysis of Variance with either population ( β pop ) or ploidy level ( β ploidy ) as explanatory variable and the clustering results at k = 2 as response variable.…”

Section: Methodsmentioning

confidence: 99%

Structure is more robust than other clustering methods in simulated mixed-ploidy populations

2019

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Analysis of population genetic structure has become a standard approach in population genetics. In polyploid complexes, clustering analyses can elucidate the origin of polyploid populations and patterns of admixture between different cytotypes. However, combining diploid and polyploid data can theoretically lead to biased inference with (artefactual) clustering by ploidy. We used simulated mixed-ploidy (diploid-autotetraploid) data to systematically compare the performance of k-means clustering and the model-based clustering methods implemented in Structure, Admixture, FastStructure and InStruct under different scenarios of differentiation and with different marker types. Under scenarios of strong population differentiation, the tested applications performed equally well. However, when population differentiation was weak, Structure was the only method that allowed unbiased inference with markers with limited genotypic information (co-dominant markers with unknown dosage or dominant markers). Still, since Structure was comparatively slow, the much faster but less powerful FastStructure provides a reasonable alternative for large datasets. Finally, although bias makes k-means clustering unsuitable for markers with incomplete genotype information, for large numbers of loci (>1000) with known dosage k-means clustering was superior to FastStructure in terms of power and speed. We conclude that Structure is the most robust method for the analysis of genetic structure in mixed-ploidy populations, although alternative methods should be considered under some specific conditions.

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“…In the merged dataset, there were large differences in sample sizes between populations, which could bias the inference of population genetic structure 60 . As recommended by Meirmans 61 , we reduced the sample sizes of larger populations by removing individuals with more than 20% of missing data and then randomly removing excess individuals in order to obtain comparable sample sizes of different wolf and dog populations, as well as similar total number of wolves and dogs. The final dataset included 697 individuals: 83 Asian, 108 European and 119 North American wolves, 160 free-ranging dogs, 180 pure-bred dogs, the Neolithic Irish dog, three Czechoslovakian wolfdogs, two Saarloos wolfdogs, 29 coyotes, eight golden jackals and three black backed jackals.…”

Section: Methodsmentioning

confidence: 99%

Global Phylogeographic and Admixture Patterns in Grey Wolves and Genetic Legacy of An Ancient Siberian Lineage

Pilot

Moura

Охлопков

et al. 2019

Sci Rep

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The evolutionary relationships between extinct and extant lineages provide important insight into species’ response to environmental change. The grey wolf is among the few Holarctic large carnivores that survived the Late Pleistocene megafaunal extinctions, responding to that period’s profound environmental changes with loss of distinct lineages and phylogeographic shifts, and undergoing domestication. We reconstructed global genome-wide phylogeographic patterns in modern wolves, including previously underrepresented Siberian wolves, and assessed their evolutionary relationships with a previously genotyped wolf from Taimyr, Siberia, dated at 35 Kya. The inferred phylogeographic structure was affected by admixture with dogs, coyotes and golden jackals, stressing the importance of accounting for this process in phylogeographic studies. The Taimyr lineage was distinct from modern Siberian wolves and constituted a sister lineage of modern Eurasian wolves and domestic dogs, with an ambiguous position relative to North American wolves. We detected gene flow from the Taimyr lineage to Arctic dog breeds, but population clustering methods indicated closer similarity of the Taimyr wolf to modern wolves than dogs, implying complex post-divergence relationships among these lineages. Our study shows that introgression from ecologically diverse con-specific and con-generic populations was common in wolves’ evolutionary history, and could have facilitated their adaptation to environmental change.

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Subsampling reveals that unbalanced sampling affects Structure results in a multi-species dataset

Cited by 43 publications

References 31 publications

Confidently identifying the correct K value using the ΔK method: When does K = 2?

Confidently identifying the correct K value using the ΔK method: When does K = 2?

Structure is more robust than other clustering methods in simulated mixed-ploidy populations

Global Phylogeographic and Admixture Patterns in Grey Wolves and Genetic Legacy of An Ancient Siberian Lineage

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