Limited genetic differentiation among chinstrap penguin (Pygoscelis antarctica) colonies in the Scotia Arc and Western Antarctic Peninsula

Freer, Jennifer; Mable, Barbara K.; Clucas, Gemma V.; Rogers, Alex D.; Polito, Michael J.; Dünn, Michael J.; Naveen, Ron; Levy, Hila; Hart, Tom

doi:10.1007/s00300-015-1711-2

Cited by 20 publications

(24 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, the PGN dataset was relatively uniform and exhibited low levels of polymorphism. This corroborates with limited genetic variation and high gene flow that has been shown to exist in penguin colonies (Roeder et al 2001;Nims et al 2008;Freer et al 2015). The bimodal distribution for the number of loci observed in PGN corroborates well with the known stochastic demographic history of the species (Cristofari et al 2016a).…”

Section: Discussionsupporting

confidence: 75%

“…; Freer et al . ). The bimodal distribution for the number of loci observed in PGN corroborates well with the known stochastic demographic history of the species (Cristofari et al .…”

Section: Discussionmentioning

confidence: 97%

“…) and between (Freer et al . ) colonies, and it has recently been suggested that Antarctic penguins should be considered single panmictic populations due to extreme levels of genetic homogeneity and low F ST values between populations (Cristofari et al . ).…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Lost in parameter space: a road map for stacks

2017

View full text Add to dashboard Cite

Summary Restriction site‐Associated DNA sequencing (RAD‐seq) has become a widely adopted method for genotyping populations of model and non‐model organisms. Generating a reliable set of loci for downstream analysis requires appropriate use of bioinformatics software, such as the program stacks. Using three empirical RAD‐seq datasets, we demonstrate a method for optimising a de novo assembly of loci using stacks. By iterating values of the program's main parameters and plotting resultant core metrics for visualisation, researchers can gain a much better understanding of their dataset and select an optimal set of parameters; we present the 80% rule as a generally effective method to select the core parameters for stacks. Visualisation of the metrics plotted for the three RAD‐seq datasets shows that they differ in the optimal parameters that should be used to maximise the amount of available biological information. We also demonstrate that building loci de novo and then integrating alignment positions is more effective than aligning raw reads directly to a reference genome. Our methods will help the community in honing the analytical skills necessary to accurately assemble a RAD‐seq dataset.

show abstract

Section: Discussionsupporting

confidence: 75%

“…; Freer et al . ). The bimodal distribution for the number of loci observed in PGN corroborates well with the known stochastic demographic history of the species (Cristofari et al .…”

Section: Discussionmentioning

confidence: 97%

See 1 more Smart Citation

Lost in parameter space: a road map for stacks

2017

View full text Add to dashboard Cite

show abstract

“…This indicates that SNPs are superior to mtDNA for detecting fine‐scale genetic differentiation among colonies, as expected (Baird et al., ). Analyses of genetic connectivity within other penguin species, including Adélies (Clucas et al., ; Ritchie et al., ; Roeder et al., ; Younger, Emmerson, Southwell, Lelliott, & Miller, ) and chinstraps (Clucas et al., ; Freer et al., ) using either mtDNA or microsatellites, resolved similar levels of genetic differentiation as found in the mtDNA analysis of emperor penguins (Younger, Clucas, et al., ). It is therefore possible that other penguin species have higher levels of genetic differentiation than reported to date, which could be detected using genomewide SNPs.…”

Section: Discussionmentioning

confidence: 79%

The challenges of detecting subtle population structure and its importance for the conservation of emperor penguins

et al. 2017

Self Cite

View full text Add to dashboard Cite

Understanding the boundaries of breeding populations is of great importance for conservation efforts and estimates of extinction risk for threatened species. However, determining these boundaries can be difficult when population structure is subtle. Emperor penguins are highly reliant on sea ice, and some populations may be in jeopardy as climate change alters sea-ice extent and quality. An understanding of emperor penguin population structure is therefore urgently needed. Two previous studies have differed in their conclusions, particularly whether the Ross Sea, a major stronghold for the species, is isolated or not. We assessed emperor penguin population structure using 4,596 genome-wide single nucleotide polymorphisms (SNPs), characterized in 110 individuals (10-16 per colony) from eight colonies around Antarctica. In contrast to a previous conclusion that emperor penguins are panmictic around the entire continent, we find that emperor penguins comprise at least four metapopulations, and that the Ross Sea is clearly a distinct metapopulation. Using larger sample sizes and a thorough assessment of the limitations of different analytical methods, we have shown that population structure within emperor penguins does exist and argue that its recognition is vital for the effective conservation of the species. We discuss the many difficulties that molecular ecologists and managers face in the detection and interpretation of subtle population structure using large SNP data sets, and argue that subtle structure should be taken into account when determining management strategies for threatened species, until accurate estimates of demographic connectivity among populations can be made.

show abstract

“…Banding studies initially suggested a high degree of philopatry in many species (Weimerskirch, Jouventin, Mougin, Stahl, & Van, ), and, until recently (Jenouvrier, Garnier, Patout, & Desvillettes, ), forecasts of extinction risk had not considered the potential buffering effect of dispersal (Cimino, Lynch, Saba, & Oliver, ; Jenouvrier et al., ). Genetic analyses (Clucas, Younger et al., ; Freer et al., ; Roeder et al., ; Younger, Clucas, et al., , ), observations of colony movements (LaRue, Kooyman, Lynch, & Fretwell, ) and fluctuations in colony size (Kooyman & Ponganis, ) indicate that dispersal may be common. However, hydrographic features are thought to act as barriers to dispersal in a handful of sub‐Antarctic and temperate penguin species (see Munro & Burg, , for a review).…”

Section: Introductionmentioning

confidence: 99%

Comparative population genomics reveals key barriers to dispersal in Southern Ocean penguins

et al. 2018

Self Cite

View full text Add to dashboard Cite

The mechanisms that determine patterns of species dispersal are important factors in the production and maintenance of biodiversity. Understanding these mechanisms helps to forecast the responses of species to environmental change. Here, we used a comparative framework and genomewide data obtained through RAD‐Seq to compare the patterns of connectivity among breeding colonies for five penguin species with shared ancestry, overlapping distributions and differing ecological niches, allowing an examination of the intrinsic and extrinsic barriers governing dispersal patterns. Our findings show that at‐sea range and oceanography underlie patterns of dispersal in these penguins. The pelagic niche of emperor (Aptenodytes forsteri), king (A. patagonicus), Adélie (Pygoscelis adeliae) and chinstrap (P. antarctica) penguins facilitates gene flow over thousands of kilometres. In contrast, the coastal niche of gentoo penguins (P. papua) limits dispersal, resulting in population divergences. Oceanographic fronts also act as dispersal barriers to some extent. We recommend that forecasts of extinction risk incorporate dispersal and that management units are defined by at‐sea range and oceanography in species lacking genetic data.

show abstract

Limited genetic differentiation among chinstrap penguin (Pygoscelis antarctica) colonies in the Scotia Arc and Western Antarctic Peninsula

Cited by 20 publications

References 59 publications

Lost in parameter space: a road map for stacks

Lost in parameter space: a road map for stacks

The challenges of detecting subtle population structure and its importance for the conservation of emperor penguins

Comparative population genomics reveals key barriers to dispersal in Southern Ocean penguins

Contact Info

Product

Resources

About