Ocean-wide genomic variation in Gray's beaked whales, Mesoplodon grayi

The deep oceans of the Southern Hemisphere are home to several elusive and poorly studied marine megafauna. In the absence of robust observational data for these species, genetic data can aid inferences on population connectivity, demography and ecology. A previous investigation of genetic diversity and population structure in Gray's beaked whale (Mesoplodon grayi) from Western Australia and New Zealand found high levels of mtDNA diversity, no geographic structure and stable demographic history. To further investigate phylogeographic and demographic patterns across their range, we generated complete mitochondrial and partial nuclear genomes of 16 of the individuals previously analysed and included additional samples from South Africa (n = 2) and South Australia (n = 4), greatly expanding the spatial range of genomic data for the species. Gray's beaked whales are highly elusive and rarely observed, and our data represents a unique and geographically broad dataset. We find relatively high levels of diversity in the mitochondrial genome, despite an absence of population structure at the mitochondrial and nuclear level. Demographic analyses suggest these whales existed at stable levels over at least the past 1.1 million years, with an approximately twofold increase in female effective population size approximately 250 thousand years ago, coinciding with a period of increased Southern Ocean productivity, sea surface temperature and a potential expansion of suitable habitat. Our results suggest that Gray's beaked whales are likely to be resilient to near-future ecosystem changes, facilitating their conservation. Our study demonstrates the utility of low-effort shotgun sequencing in providing ecological information on highly elusive species.

Nuclear analyses: How was linkage taken into account? For example, Foote et al 2019 restricted the analyses to sites greater than ≥20 kb apart to avoid linkage when doing PCA. Perhaps I missed something as I did not read the sup mat closely but linked loci are not independent and so should not be treated as separate markers for PCA or genetic clustering algorithms.
Genomic markers: How genotyping was conducted and how many markers were used in subsequent analyses needs to be clearer in the main body of the manuscript. Presumably some threshold/confidence on genotype likelihood was used? How many loci were used for each of the specific analyses? What was the average depth for loci, and number of individuals typed at each loci, and did this vary between analyses?
Overall, the analyses were standard but not specifically tailored to detecting low levels of divergence, if any existed.
-Was FST calculated between sample sites, e.g., NZ, SA ? -Was DAPC considered? It is more sensitive at lower levels of divergence than PCA -Was outlier analysis considered? Loci under selection are often more sensitive to population structure than neutral markers -How did patterns of relatedness vary across sites? Given the jump from 12 to some unspecified by presumably larger number of nuclear markers will give a much better impression of relatedness.
Given the large number of genetic markers available, it seems that more analyses could be done to answer this specific question -one that was already obvious from the results of Thompson et al 2016.
D-statistic analyses Line 168: I've not seen this approach before, a reference to where it's been done before would be useful. What was the outgroup used in this analysis? How many loci were used? Given the small sample sizes, is this analysis really contributing much. Also South Australia is part of Australasia (line 253) so there are only 2 samples from outside the distribution previously analysed (ie, 2 from South Africa).

Sex determination:
Not sure what this section contributes to the paper? It's not mentioned in the Methods or Discussion. The NZ and WA samples were sexed in Thompson et al 2016 so it seems that the other samples might have been similarly sex determined by PCR as well? As sex is not used in any analyses that I can see think this could go in the supplementary material.

Do you have any ethical concerns with this paper? No
Have you any concerns about statistical analyses in this paper? Yes

Recommendation?
Accept with minor revision (please list in comments) Comments to the Author(s) Westbury et al. have sequenced mitogenomes and partial nuclear genomes to elucidate the phylogeographic and demographic history of Gray's beaked whale. This is a valuable contribution to the literature that backs up previous hypotheses based off partial mitochondrial control region and microsatellite data. I have only minor comments: In the methods can you please bring in some of the supplementary information to justify decisions and make results clear, namely why the killer whale genome was used as a scaffold, and not say sperm whale; and brief methodology on DNA sexing, as it jumps out in the results.
In the DNA sexing section of the results can you please state that you are sexing Gray's beaked whale as it is not clear.
For the demographic reconstructions using whole mitogenomes, can you please repeat your analyses with a constant population size prior, and compare this to the Bayesian Skyline prior (shown in the paper) using model comparison stats (e.g. Bayes Factor), to determine whether a constant population size or dynamic population size is supported.

Decision letter (RSOS-201788.R0)
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Reviewer comments to Author: Reviewer: 1 Comments to the Author(s) This paper revisits Thompson et al 2016's examination of the population structure of grey's beaked whale using fewer samples but more genomic markers and whole mitochondrial genomes. The paper is reasonably well-written, but needs to be clearer about the novelty of the findings presented vs what was previously published. I also have concerns and suggestions about the analyses.
The previous study found no detectable genetic population structure using 94 samples, 530 bp of mtDNA control region and 12 microsats. This work builds on that paper with 2 samples from South African and 4 from South Australia. The natural extension of this work would be to see if the lack of population structure holds with more markers, as simulation work suggests that more markers rather than more samples are better at detecting subtle population structure. Indeed, while many marine mammals do show population structure, often linked to behavioural or social factors, many marine species have high effective population sizes and low levels of divergence, including in sharks and fish. It would be good to consider these in the context of the work, as methods that test for subtle population structure are probably of more relevance to many readers.

Major comments
Abstract: needs to highlight more clearly what samples are new and what have been analysed previously, likewise findings. For example, high levels of mtDNA diversity, no population structure and stable demographic history was found in Thompson et al 2016.
Nuclear analyses: How was linkage taken into account? For example, Foote et al 2019 restricted the analyses to sites greater than ≥20 kb apart to avoid linkage when doing PCA. Perhaps I missed something as I did not read the sup mat closely but linked loci are not independent and so should not be treated as separate markers for PCA or genetic clustering algorithms.
Genomic markers: How genotyping was conducted and how many markers were used in subsequent analyses needs to be clearer in the main body of the manuscript. Presumably some threshold/confidence on genotype likelihood was used? How many loci were used for each of the specific analyses? What was the average depth for loci, and number of individuals typed at each loci, and did this vary between analyses?
Overall, the analyses were standard but not specifically tailored to detecting low levels of divergence, if any existed.
-Was FST calculated between sample sites, e.g., NZ, SA ? -Was DAPC considered? It is more sensitive at lower levels of divergence than PCA -Was outlier analysis considered? Loci under selection are often more sensitive to population structure than neutral markers -How did patterns of relatedness vary across sites? Given the jump from 12 to some unspecified by presumably larger number of nuclear markers will give a much better impression of relatedness.
Given the large number of genetic markers available, it seems that more analyses could be done to answer this specific question -one that was already obvious from the results of Thompson et al 2016.
D-statistic analyses Line 168: I've not seen this approach before, a reference to where it's been done before would be useful. What was the outgroup used in this analysis? How many loci were used? Given the small sample sizes, is this analysis really contributing much. Also South Australia is part of Australasia (line 253) so there are only 2 samples from outside the distribution previously analysed (ie, 2 from South Africa).  Reviewer: 2 Comments to the Author(s) Westbury et al. have sequenced mitogenomes and partial nuclear genomes to elucidate the phylogeographic and demographic history of Gray's beaked whale. This is a valuable contribution to the literature that backs up previous hypotheses based off partial mitochondrial control region and microsatellite data. I have only minor comments: In the methods can you please bring in some of the supplementary information to justify decisions and make results clear, namely why the killer whale genome was used as a scaffold, and not say sperm whale; and brief methodology on DNA sexing, as it jumps out in the results.
In the DNA sexing section of the results can you please state that you are sexing Gray's beaked whale as it is not clear.
For the demographic reconstructions using whole mitogenomes, can you please repeat your analyses with a constant population size prior, and compare this to the Bayesian Skyline prior (shown in the paper) using model comparison stats (e.g. Bayes Factor), to determine whether a constant population size or dynamic population size is supported.

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Author's Response to Decision Letter for (RSOS-201788.R0)
See Appendix A.

Recommendation?
Accept as is

Comments to the Author(s)
Thank you for addressing the reviewer's concerns and congratulations on an impressive swathe of genomic analyses.

Review form: Reviewer 2
Is the manuscript scientifically sound in its present form? Yes

Recommendation? Accept as is
Comments to the Author(s) Westbury et al. have done a thorough and excellent job in revising their manuscript. I am satisfied with the changes and new analyses that have been incorporated. I have no further comments.

Decision letter (RSOS-201788.R1)
We hope you are keeping well at this difficult and unusual time. We continue to value your support of the journal in these challenging circumstances. If Royal Society Open Science can assist you at all, please don't hesitate to let us know at the email address below.

Dear Ms Thompson,
It is a pleasure to accept your manuscript entitled "Ocean-wide genomic variation in Gray's beaked whales, Mesoplodon grayi" in its current form for publication in Royal Society Open Science. The comments of the reviewers who reviewed your manuscript are included at the foot of this letter.
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Thank you for your fine contribution. On behalf of the Editors of Royal Society Open Science, we look forward to your continued contributions to the Journal. Please accept our apologies for the delay in reviewing -one of the reviewers was unexpectedly delayed by an injury (and the editors are very grateful to them for nevertheless completing the report with this additional difficulty). In any case, the general view is that your revisions have resolved the concerns the referees had. Please ensure that you make your data publicly accessible on receipt of this message and contact the editorial office to confirm. The previous study found no detectable genetic population structure using 94 samples, 530 bp of mtDNA control region and 12 microsats. This work builds on that paper with 2 samples from South African and 4 from South Australia. The natural extension of this work would be to see if the lack of population structure holds with more markers, as simulation work suggests that more markers rather than more samples are better at detecting subtle population structure. Indeed, while many marine mammals do show population structure, often linked to behavioural or social factors, many marine species have high effective population sizes and low levels of divergence, including in sharks and fish. It would be good to consider these in the context of the work, as methods that test for subtle population structure are probably of more relevance to many readers. Answer: Many thanks for this comment, please find changes throughout the text shown in red to highlight the additional samples used in the current study.

Q2. Nuclear analyses:
How was linkage taken into account? For example, Foote et al 2019 restricted the analyses to sites greater than ≥20 kb apart to avoid linkage when doing PCA. Perhaps I missed something as I did not read the sup mat closely but linked loci are not independent and so should not be treated as separate markers for PCA or genetic clustering algorithms. Answer: We have now also filtered the dataset for sites under linkage disequilibrium using the software ngsLD. We have rerun the PCA and admixture proportion analyses with this