There is growing evidence of worldwide decline of populations of top predators, including marine ones (Heithaus et al, 2008, Mc Cauley et al., 2015), with cascading effects expected at the ecosystem level, due to global change and human activities, including habitat loss or fragmentation, the collapse or the range shifts of their preys. On a global scale, seabirds are among the most threatened group of birds, about one-third of them being considered as threatened or endangered (Votier& Sherley, 2017). The large consequences of the decrease of the populations of preys they feed on (Cury et al, 2011) points diet flexibility as one important element to understand for effective management (McInnes et al, 2017). Nevertheless, morphological inventory of preys requires intrusive protocols, and the differential digestion rate of distinct taxa may lead to a large bias in morphological-based diet assessments. The use of DNA metabarcoding on feces (or diet DNA, dDNA) now allows non-invasive approaches facilitating the recollection of samples and the detection of multiple preys independently of their digestion rates (Deagle et al., 2019). Although no gold standard exists yet to avoid bias associated with metabarcoding (primer bias, gaps in reference databases, inability to differentiate primary from secondary predation…), the use of these recent techniques has already improved the knowledge of the foraging behaviour and diet of many animals (Ando et al., 2020).
Both promise and shortcomings of this approach are illustrated in the article “Metabarcoding faecal samples to investigate spatiotemporal variation in the diet of the endangered Westland petrel (Procellaria westlandica)” by Quereteja et al. (2021). In this work, the authors assessed the nature and spatio-temporal flexibility of the foraging behaviour and consequent diet of the endangered petrel Procellaria westlandica from New-Zealand through metabarcoding of faeces samples.
The results of this dDNA, non-invasive approach, identify some expected and also unexpected prey items, some of which require further investigation likely due to large gaps in the reference databases. They also reveal the temporal (before and after hatching) and spatial (across colonies only 1.5km apart) flexibility of the foraging behaviour, additionally suggesting a possible influence of fisheries activities in the surroundings of the colonies. This study thus both underlines the power of the non-invasive metabarcoding approach on faeces, and the important results such analysis can deliver for conservation, pointing a potential for diet flexibility that may be essential for the resilience of this iconic yet endangered species.
Ando H, Mukai H, Komura T, Dewi T, Ando M, Isagi Y (2020) Methodological trends and perspectives of animal dietary studies by noninvasive fecal DNA metabarcoding. Environmental DNA, 2, 391–406. https://doi.org/10.1002/edn3.117
Cury PM, Boyd IL, Bonhommeau S, Anker-Nilssen T, Crawford RJM, Furness RW, Mills JA, Murphy EJ, Österblom H, Paleczny M, Piatt JF, Roux J-P, Shannon L, Sydeman WJ (2011) Global Seabird Response to Forage Fish Depletion—One-Third for the Birds. Science, 334, 1703–1706. https://doi.org/10.1126/science.1212928
Deagle BE, Thomas AC, McInnes JC, Clarke LJ, Vesterinen EJ, Clare EL, Kartzinel TR, Eveson JP (2019) Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data? Molecular Ecology, 28, 391–406. https://doi.org/10.1111/mec.14734
Heithaus MR, Frid A, Wirsing AJ, Worm B (2008) Predicting ecological consequences of marine top predator declines. Trends in Ecology & Evolution, 23, 202–210. https://doi.org/10.1016/j.tree.2008.01.003
McCauley DJ, Pinsky ML, Palumbi SR, Estes JA, Joyce FH, Warner RR (2015) Marine defaunation: Animal loss in the global ocean. Science, 347, 1255641. https://doi.org/10.1126/science.1255641
McInnes JC, Jarman SN, Lea M-A, Raymond B, Deagle BE, Phillips RA, Catry P, Stanworth A, Weimerskirch H, Kusch A, Gras M, Cherel Y, Maschette D, Alderman R (2017) DNA Metabarcoding as a Marine Conservation and Management Tool: A Circumpolar Examination of Fishery Discards in the Diet of Threatened Albatrosses. Frontiers in Marine Science, 4, 277. https://doi.org/10.3389/fmars.2017.00277
Querejeta M, Lefort M-C, Bretagnolle V, Boyer S (2021) Metabarcoding faecal samples to investigate spatiotemporal variation in the diet of the endangered Westland petrel (Procellaria westlandica). bioRxiv, 2020.10.30.360289, ver. 4 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2020.10.30.360289
Votier SC, Sherley RB (2017) Seabirds. Current Biology, 27, R448–R450. https://doi.org/10.1016/j.cub.2017.01.042
DOI or URL of the preprint: https://doi.org/10.1101/2020.10.30.360289
Version of the preprint: 2
Dear Marina Querejeta and colleagues,
Your article has now been reviewed for the second time by two referees and myself. All acknowledge the large improvements made on the MS, including a spectacular improvement in data analysis and results thanks to the referee’s advise. Nevertheless, both referee still have a couple of concern I believe deserve to be seriously accounted for.
Following referee 1 advise, please pay attention to the use of terms such as read abundance and biomass, and necessary details in the description of what has been done: for example manual curation of data requires detailed information on the strategy adopted, the rationale behind it, and the steps leading from raw to exploited data (and associated information on taxa discarded). Discarding arbitrarily OTUs that are considered as contaminant because they are not potential prey, to the best of your knowledge, is not acceptable. You may want to use a specific package such as decontam to make an objective work and discuss uncertainties about remaining OTUs. Referee 1 also ask a set of relevant technical questions in need for an answer and more detailed & clearer explanation of the steps followed, keeping in mind that anybody with the article in hands would be able to repeat exactly the work starting with raw data and the material and method section.
Referee 2 still has two very important comments, beyond other relevant minor comments: the first one is the uncertainty around the assignment to Talitrid, the second is the interpretation (would the assignment be ascertained) in terms of dependence to fisheries and cascading impacts of the change in diet for petrels. First of all, I suggest the authors to carefully revise the manuscript replacing the taxa name ‘xxx’ by ‘assigned to xxx’, particularly when the homology is low and the assignment to the genera or family level, which is clearly the case for Talitrids. This phrasing helps to keep in mind the uncertainties we are dealing with. Second, the authors may think through the hypothesis and suggestions made by referee2, to improve data analysis and revise part of the discussion. The authors may for example consider extracting the fasta sequences of these ASVs and align them against a homemade database for amphipods, in order to ascertain manually the level of homology and the closest possible relative. Such homemade database may as much as possible strongly favour sequences of high-level confidence taxonomic inference, such as holotypes, in order to avoid badly assigned sequences in the public databases. Given the 78-86% homology that is low, it is in fact impossible to ascertain these OTUs really belong to Talidrid rather than any other closely related family absent from 16S reference databases. Would the uncertainty remain the same, a leveraged and more cautious discussion (with a lighter mention to the possibility of Talidrid dominance in the diet) would be advisable. Would the uncertainty be much lower, and the Talidrid assignment be confirmed with a much higher level of certainty, the discussion may include the path suggested by referee 2.
I will finish with a first general advice, following a first submission of the wrong file and a second submission with a track changes files that contains only a subset of the changes made since the primary submission, and personal comments exchanged among coauthors. This reflects badly on the carefulness of the prime author and on the attention dedicated to the review process.
I thus urge you to carefully account for the comments of referee, and to prepare a carefully checked last version with very clear track changes compared to this one. A simple option is to compare the very last version to the very first one with Word, allowing a clean and complete file with track changes without omitting important ones and including personal comments.
DOI or URL of the preprint: https://doi.org/10.1101/2020.10.30.360289
Version of the preprint: 1
Dear Marina Querejeta and colleagues, Your article has now been reviewed by two referees and myself. Both referee have found your approach valuable and results of interest and they both have serious and complementary recommendations to ascertain or improve data and interpretations. Based on their reviews and my lecture, I suggest this preprint deserves a revision. I hope comments below and referee’s detailed suggestions will help. I suggest you follow those very detailed suggestions. Particularly, referee 1 suggest detailed paths to re-analyze data and make sure a large set of sequences could not have been lost in the first bioinformatic steps, introducing a bias in the prey recognized with the sequences left. Reading the MS, I found no information on the way primers were treated and understand this wonder particularly considering the 300bp limit and the 2M merged reads for nearly 10M raw ones. This raises the point of access to data and bioinformatic scripts, that will eventually be required and would ease the review process. Would referee1 be right on his guess, results may come strongly different in terms or RAA and dominant preys, thus I suggest to follow his recommendation and make data and script available for the next round of submission. Referee 1 also made a number of suggestions to improve the MS. Among other the use of eDNA is confusing when it comes to diet, for stomach content or poo are not exactly environmental. The use of dDNA was recently coined by Sousa et al (2019, see below) for dietary DNA, and may be a nice option? Referee 2 is more worried by the dominance of Talitridae in the inferred diet. First of all, i) looking at Table 1, these assignments seem to suffer from a small % of identity (around 80% , which means it may be a different family?) and ii) they were performed on NCBI database that is not curated and contains sometimes sequences associated to highly misleading identifications. Considering the dominance of the sequence of these OTUs in the dataset, despite the acknowledgement for possible secondary predation in the discussion (that would, I agree with referee 2, still sound even more awkward from cephalopods and fishes) , it may be advisable to also blast the results on curated or at least reduced databases such as Silva or Midori, in order to check these assignments, or to blast them individually and check the status/level of confidence of the blasted sequences (for example are there holotypes among those?). Would such taxa remain as dominant in the revised results, discussion should emphasize this level of uncertainty to clarify the assignment does not necessarily imply the taxa at the origin of the sequence is a sandhopper or a grandhopper. Referee 2 also made suggestion to improve the indirect evidence offered by metabarcoding by producing predictions for prey size, that may be interesting to consider. Finally, it seems this family of amphipod has exhibited range shifts associated to climate change and/or human activities in the Northern hemisphere (the baltic sea). I wondered if such report would exist in the Southern hemisphere Sousa LL, Silva SM, Xavier R. 2019. DNA metabarcoding in diet studies: Unveiling ecological aspects in aquatic and terrestrial ecosystems. Environmental DNA 1:199-214.