2014
DOI: 10.1098/rspb.2014.0528
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King penguin demography since the last glaciation inferred from genome-wide data

Abstract: How natural climate cycles, such as past glacial/interglacial patterns, have shaped species distributions at the high-latitude regions of the Southern Hemisphere is still largely unclear. Here, we show how the post-glacial warming following the Last Glacial Maximum (ca 18 000 years ago), allowed the (re)colonization of the fragmented sub-Antarctic habitat by an upperlevel marine predator, the king penguin Aptenodytes patagonicus. Using restriction site-associated DNA sequencing and standard mitochondrial data,… Show more

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Cited by 60 publications
(92 citation statements)
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“…). Thus, contrary to a hypothesis that emperor penguins would benefit from glaciation as a result of reduced competition with other predators (Thatje et al ., ), it seems that they, like other Antarctic and sub‐Antarctic penguin species (Ritchie et al ., ; Clucas et al ., ; Trucchi et al ., ), were adversely affected by the LGM.…”
Section: Discussionmentioning
confidence: 99%
“…). Thus, contrary to a hypothesis that emperor penguins would benefit from glaciation as a result of reduced competition with other predators (Thatje et al ., ), it seems that they, like other Antarctic and sub‐Antarctic penguin species (Ritchie et al ., ; Clucas et al ., ; Trucchi et al ., ), were adversely affected by the LGM.…”
Section: Discussionmentioning
confidence: 99%
“…We followed the protocol of Trucchi et al. () to avoid overparameterization when analyzing RADseq datasets. For each population, this analysis was performed on three independent subsamples of 50 loci with four SNPs in the forward read (using loci defined by Stacks).…”
Section: Methodsmentioning
confidence: 99%
“…We followed the protocol of Trucchi et al (2014) (Figure 1). In the absence of any rapidly evolving markers with a known mutation rate to calibrate this analysis, it was not possible to date demographic events.…”
Section: Demographymentioning
confidence: 99%
“…Contemporary effective population size for each population was estimated by Neestimator version 2.0 (Do et al, 2014) with LD model based on SNPs and microsatellites, respectively. Extended Bayesian Skyline Plot (EBSP) (Heled & Drummond, 2008; Trucchi et al, 2014) analysis was performed with BEAST2, which investigated effective population size changes through time using SNP data sets. Bottlenecks for each population were tested using bottleneck version 1.2.02 (Piry, Luikart, & Cornuet, 1999) based on microsatellites with a two‐phase model (TPM) and a stepwise mutation model (SMM).…”
Section: Methodsmentioning
confidence: 99%