Seabird Group Seabird Group

First description of Manx Shearwater Puffinus puffinus diet using DNA metabarcoding

Katrina Siddiqi-Davies1*ORCID logo, Lewis Fisher-Reeves1ORCID logo, Joe Morford1ORCID logo, Gemma ClucasORCID logo and Tim Guilford

https://doi.org/10.61350/sbj.37.2

1 Department of Biology, University of Oxford, Mansfield Road, Oxford, OX1 3SZ, United Kingdom.

2 Cornell Lab of Ornithology 159 Sapsucker Woods Road, Ithaca, NY 14850.

§ These authors contributed equally to the work as co-senior authors.

Full paper

Abstract

Obtaining quantitative information on fundamental aspects of ecology such as diet can be challenging for pelagic seabird species. DNA metabarcoding of faecal samples is a non-invasive method of dietary analysis, with potential to identify prey at high taxonomic resolution. We apply this method to understand the diet of the Manx Shearwater Puffinus puffinus, a highly pelagic species where little quantitative information on diet is available. Using DNA metabarcoding analysis on faecal samples for this species, we identified six species of fishes: European Sprat Sprattus sprattus, Blue Whiting Micromesistius poutassou, Atlantic Herring Clupea harengus, Northern Rockling Ciliata septentrionalis, sandeel Ammodytes sp and a species of Cottoidei (sculpins and allies). Of 153 dietary samples, only 19 produced sufficient DNA to identify prey species. We speculate that our low success rate may be the result of long foraging trips early in the breeding season, where excretion prior to sampling may reduce the amount of prey matter in faecal samples and prey DNA degradation during prolonged residence in the gastrointestinal tract may reduce the detectability of prey matter. We discuss improvements in sample collection for future dietary studies of this and other highly mobile species with similar traits.

Introduction

Many seabird species face declines due to fish stocks being depleted via over-fishing, pollution, and climate change (Kowalczyk et al. 2014; Wanless et al. 2018). With recent reports suggesting almost half of UK seabird species have declined over the last 20 years (Burnell et al. 2023), it is becoming increasingly important to study diet—an often-overlooked but fundamental aspect of a species’ ecology. This is especially true of wide-ranging pelagic seabirds, where direct observation of at-sea foraging behaviour is challenging, and the identification of prey in regurgitated food brought to offspring is often difficult (Weimerskirch et al. 2005; Kane et al. 2020). As seabirds return to land to breed, they are more accessible as study organisms than most pelagic foragers. Changes in seabird foraging behaviour can therefore provide insights into the productivity of remote marine ecosystems (Thompson et al. 1998; Piatt et al. 2007; Grémillet & Boulinier 2009).

Innovations in biotelemetry have allowed the remote observation of foraging behaviour (Dean et al. 2015; Whitford & Klimley 2019; Bolton 2021); however, until recently, only traditional methods of understanding diet itself were available (Weiser & Powell 2011; Nielsen et al. 2018; Whitford & Klimley 2019). For species that carry whole prey to their offspring, some prey identification can occur through field observation (Baillie & Jones 2003). In cases where feeding occurs through regurgitation, this requires forcing regurgitation (Thompson 1987; Weiser & Powell 2011), a method that is both invasive and does not allow for clear identification of prey species (Nielsen et al. 2018). Stable isotope analysis is a less invasive method for investigating diet, but the results of which can only indicate the trophic position of prey, and approximate foraging locations (Navarro et al. 2007; Meier et al. 2017; Austin et al. 2019). Here we employ an alternative approach for sampling seabird prey, using DNA metabarcoding of faecal samples. This method has potential to provide both quantitative and qualitative information and can distinguish between morphologically similar prey types (Deagle et al. 2007, 2019). DNA metabarcoding has been applied to the faecal samples of multiple seabird species to understand diet and assess conservation risks (McInnes et al. 2017; Komura et al. 2018; Marcuk et al. 2024). For example, in Brown Boobies Sula leucogaster and Cape Verde Shearwaters Calonectris edwardsii, DNA metabarcoding was used to identify prey overlap with commercially targeted fish to assess bycatch risk (Carreiro et al. 2023). In Atlantic Puffins Fratercula arctica, DNA metabarcoding was applied to investigate how adult and chick diets differ and how foraging behaviour links to diet across colonies of differing productivity (Fayet et al. 2021; Kennerley et al. 2024).

Despite being a well-studied bird, very little is known about the diet of the Manx Shearwater Puffinus puffinus, an apex predator that is believed to specialise in the active subsurface pursuit of small fish and some cephalopods (Brooke 1990). Most of the global population of Manx Shearwaters breeds on islands around the UK, with foraging efforts centred in the Irish and Celtic seas, both of which are sites for significant offshore wind development in the next decade (Guilford et al. 2008; Crown Estate 2024). It is unclear how large-scale modifications to foraging areas may impact seabirds, and it is therefore important to understand and quantify any dietary specificities for this species (Masden et al. 2010; Warwick-Evans et al. 2018).

The last investigation of Manx Shearwater diet was conducted in the 1980s on a small number of samples, the results of which suggested pre-laying diet might be different to other breeding season stages (Thompson 1987). However, as diet was investigated through forced regurgitation, most fish could not be identified to a species level. Other dietary studies in seabirds suggest that prior to laying, females may require specific nutrients for egg synthesis (Boersma et al. 2004; Sorensen et al. 2009; Phillips et al. 2011). Here, we aim to provide novel dietary information; establishing the prey species present in the diet of Manx Shearwater and comparing diet between pre- and post-laying periods to understand how diet may vary with breeding stage. To understand how diet links to foraging sites, we additionally compared pre- and post-laying foraging areas using geolocator (GLS) devices. We also aimed to investigate sex differences in diet between males and females.

Acknowledgements

We would like to thank the Wildlife Trust of South and West Wales, in particular Leighton Newman and Ceris Aston. Also, we would like to thank Alice Skehel for her assistance in sample collection. We would like to additionally acknowledge the funders of this project, a Merton College D.Phil. studentship (KSD, TG), the Mary Griffiths award (TG), the Leverhulme Trust (LFR) and the Elizabeth Hannah Jenkinson Fund (KSD). We would also like to thank the Brettschneider Fund (KSD) for enabling the exchange between Oxford and Cornell University.

References

Austin, R. E., Wynn, R. B., Votier, S. C., Trueman, C., McMinn, M., Rodríguez, A., Suberg, L., Maurice, L., Newton, J., Genovart, M., Péron, C., Grémillet, D. & Guilford, T. 2019. Patterns of at-sea behaviour at a hybrid zone between two threatened seabirds. Scientific Reports, 9(1), 1–13. https://doi.org/10.1038/s41598-019-51188-8

Baillie, S. M., & Jones, I. L. 2003. Atlantic Puffin (Fratercula arctica) chick diet and reproductive performance at colonies with high and low capelin (Mallotus villosus) abundance. Canadian Journal of Zoology, 81(9), 1598–1607. https://doi.org/10.1139/z03-145

Boersma, P. D., Rebstock, G. A., & Stokes, D. L. 2004. Why penguin eggshells are thick. Auk, 121(1), 148–155. https://doi.org/10.2307/4090063

Bokulich, N. A., Kaehler, B. D., Rideout, J. R., Dillon, M., Bolyen, E., Knight, R., Huttley, G. A. & Gregory Caporaso, J. 2018. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome, 6(1), 1–17. https://doi.org/10.1186/s40168-018-0470-z

Bolton, M. 2021. GPS tracking reveals highly consistent use of restricted foraging areas by European Storm-petrels Hydrobates pelagicus breeding at the largest UK colony: implications for conservation management. Bird Conservation International, 31(1), 35–52. https://doi.org/10.1017/S0959270920000374

Bolyen, E., Rideout, J. R., Dillon, M.R., Bokulich, N. A., Abnet, C., Al-Ghalith, G. A., Alexander, H., Alm, E. J., Arumugam, M., Asnicar, F., Bai, Y., Bisanz, J. E., Bittinger, K., Brejnrod, A., Brislawn, C. J., Brown, C. T., Callahan, B. J., Caraballo-Rodríguez, A. M., Chase, J., Cope, E., Da Silva, R., Dorrestein, P. C., Douglas, G. M., Durall, D. M., Duvallet, C., Edwardson, C. F., Ernst, M., Estaki, M., Fouquier, J., Gauglitz, J. M., Gibson, D. L., Gonzalez, A., Gorlick, K., Guo, J., Hillmann, B., Holmes, S., Holste, H., Huttenhower, C., Huttley, G., Janssen, S., Jarmusch, A. K., Jiang, L., Kaehler, B., Kang, K. B., Keefe, C. R., Keim, P., Kelley, S. T., Knights, D., Koester, I., Kosciolek, T., Kreps, J., Langille, M.G., Lee, J., Ley, R., Liu, Y., Loftfield, E., Lozupone, C., Maher, M., Marotz, C., Martin, B. D., McDonald, D., McIver, L. J., Melnik, A. V., Metcalf, J. L., Morgan, S. C., Morton, J., Naimey, A. T., Navas-Molina, J. A., Nothias, L. F., Orchanian, S. B., Pearson, T., Peoples, S. L., Petras, D., Preuss, M. L., Pruesse, E., Rasmussen, L. B., Rivers, A., Robeson, II. M. S., Rosenthal, P., Segata, N., Shaffer, M., Shiffer, A., Sinha, R., Song, S. J., Spear, J. R., Swafford, A. D., Thompson, L. R., Torres, P. J., Trinh, P., Tripathi, A., Turnbaugh, P. J., Ul-Hasan, S., van der Hooft, J. J., Vargas, F., Vázquez-Baeza, Y., Vogtmann, E., von Hippel, M., Walters, W., Wan, Y., Wang, M., Warren, J., Weber, K. C., Williamson, C. H., Willis, A. D., Xu, Z. Z., Zaneveld, J. R., Zhang, Y., Zhu, Q., Knight, R. & Caporaso, J. G. 2019. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology, 37(8), 852–857. https://doi.org/10.1038/s41587-019-0209-9

Bråthen, V. S., Moe, B., Amélineau, F., Ekker, M., Fauchald, P., Helgason, H. H., Johansen, M. K., Merkel, B., Tarroux, A., Åström, J. & Strøm, H. 2021. An automated procedure (v2.0) to obtain positions from light-level geolocators in large-scale tracking of seabirds. A method description for the SEATRACK project. NINA Report 1893 Norwegian Institute for Nature Research, Trondheim (Issue April; ISSN: 1504-3312).

Brooke, M. 1990. The Manx Shearwater. Poyser, London. https://doi.org/10.2307/1521283

Burnell, D., Perkins, A. J., Newton, S., Bolton, M., Tierney, T. D., Dunn, T. E. & Vaughan, R. 2023. Seabird Counts: A Census of Breeding Seabirds in Britain and Ireland (2015–2021). Lynx Edicions, Barcelona.

Calenge, C. 2006. The package ‘adehabitat’ for the R software: A tool for the analysis of space and habitat use by animals. Ecological Modelling, 197(3–4), 516–519. https://doi.org/10.1016/j.ecolmodel.2006.03.017

Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A. & Holmes, S. P. 2016. DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13(7), 581–583. https://doi.org/10.1038/nmeth.3869

Carreiro, A. R., Paiva, V. H., Medeiros, R., Franklin, K. A., Oliveira, N., Fagundes, A. I. & Ramos, J. A. 2020. Metabarcoding, stables isotopes, and tracking: unravelling the trophic ecology of a winter-breeding storm petrel (Hydrobates castro) with a multimethod approach. Marine Biology, 167(2), 1–13. https://doi.org/10.1007/s00227-019-3626-x

Carreiro, A. R., Ramos, J. A., Mata, V. A., Almeida, N. M., Rodrigues, I., dos Santos, I., Matos, D. M., Araújo, P. M., Militão, T., González-Sólis, J., Paiva, V. H. & Lopes, R. J. 2023. DNA metabarcoding to assess prey overlap between tuna and seabirds in the Eastern tropical Atlantic: Implications for an ecosystem-based management. Marine Environmental Research, 187, 1–12. https://doi.org/10.1016/j.marenvres.2023.105955

Crown Estate. 2024. UK Offshore Wind Report 2023. 1–47. https://www.thecrownestate.co.uk/our-business/marine/offshore-wind-report-2023

Deagle, B. E., Gales, N. J., Evans, K., Jarman, S. N., Robinson, S., Trebilco, R. & Hindell, M. A. 2007. Studying seabird diet through genetic analysis of faeces: A case study on macaroni penguins (Eudyptes chrysolophus). PLoS ONE, 2(9). https://doi.org/10.1371/journal.pone.0000831

Deagle, B. E., Thomas, A. C., McInnes, J. C., Clarke, L. J., Vesterinen, E. J., Clare, E. L., Kartzinel, T. R. & Eveson, J. P. 2019. Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data? Molecular Ecology, 28(2), 391–406. https://doi.org/10.1111/mec.14734

Dean, B., Freeman, R., Kirk, H., Leonard, K., Phillips, R. A., Perrins, C. M. & Guilford, T. 2013. Behavioural mapping of a pelagic seabird: Combining multiple sensors and a hidden Markov model reveals the distribution of at-sea behaviour. Journal of the Royal Society Interface, 10(78), 1–12. https://doi.org/10.1098/rsif.2012.0570

Dean, B., Kirk, H., Fayet, A., Shoji, A., Freeman, R., Leonard, K., Perrins, C. M. & Guilford, T. 2015. Simultaneous multi-colony tracking of a pelagic seabird reveals cross-colony utilization of a shared foraging area. Marine Ecology Progress Series, 538, 239–248. https://doi.org/10.3354/meps11443

Fayet, A. L., Clucas, G. V., Anker-Nilssen, T., Syposz, M. & Hansen, E. S. 2021. Local prey shortages drive foraging costs and breeding success in a declining seabird, the Atlantic puffin. Journal of Animal Ecology, 90(5), 1152–1164. https://doi.org/10.1111/1365-2656.13442

Furness, R. W., Edwards, A. E. & Oro, D. 2007. Influence of management practices and of scavenging seabirds on availability of fisheries discards to benthic scavengers. Marine Ecology Progress Series, 350, 235–244. https://doi.org/10.3354/meps07191

Grémillet, D. & Boulinier, T. 2009. Spatial ecology and conservation of seabirds facing global climate change: A review. Marine Ecology Progress Series, 391(2), 121–137. https://doi.org/10.3354/meps08212

Guilford, T. C., Meade, J., Freeman, R., Biro, D., Evans, T., Bonadonna, F., Boyle, D., Roberts, S. & Perrins, C. M. (2008). GPS tracking of the foraging movements of Manx Shearwaters Puffinus puffinus breeding on Skomer Island, Wales. Ibis, 150(3), 462–473. https://doi.org/10.1111/j.1474-919X.2008.00805.x

Guilford, T., Meade, J., Willis, J., Phillips, R. A., Boyle, D., Roberts, S., Collett, M., Freeman, R. & Perrins, C. M. 2009. Migration and stopover in a small pelagic seabird, the Manx shearwater Puffinus puffinus: Insights from machine learning. Proceedings of the Royal Society B: Biological Sciences, 276(1660), 1215–1223. https://doi.org/10.1098/rspb.2008.1577

Hilton, G. M., Furness, R. W. & Houston, D. C. 2000. A comparative study of digestion in North Atlantic seabirds. Journal of Avian Biology, 31(1), 36–46. https://doi.org/10.1034/j.1600-048X.2000.310106.x

Jouanin, C., Roux, F., Mougin, J. L. & Stahl, J. C. 2001. Prelaying exodus of Cory’s Shearwaters (Calonectris diomedes borealis) on Selvagem Grande. Journal Fur Ornithologie, 142(2), 212–217. https://doi.org/10.1046/j.1439-0361.2001.00073.x

Kane, A., Pirotta, E., Wischnewski, S., Critchley, E. J., Bennison, A., Jessopp, M. & Quinn, J. L. 2020. Spatio-Temporal patterns of foraging behaviour in a wide-ranging seabird reveal the role of primary productivity in locating prey. Marine Ecology Progress Series, 646, 175–188. https://doi.org/10.3354/meps13386

Kennerley, W. L., Clucas, G. V. & Lyons, D. E. 2024. Multiple methods of diet assessment reveal differences in Atlantic puffin diet between ages, breeding stages, and years. Frontiers in Marine Science, 11(June), 1–15. https://doi.org/10.3389/fmars.2024.1410805

Komura, T., Ando, H., Horikoshi, K., Suzuki, H. & Isagi, Y. 2018. DNA barcoding reveals seasonal shifts in diet and consumption of deep-sea fishes in wedge-tailed shearwaters. PLoS ONE, 13(4), 1–18. https://doi.org/10.1371/journal.pone.0195385

Kowalczyk, N. D., Chiaradia, A., Preston, T. J. & Reina, R. D. 2014. Linking dietary shifts and reproductive failure in seabirds: A stable isotope approach. Functional Ecology, 28(3), 755–765. https://doi.org/10.1111/1365-2435.12216

Livoski, S. & Hahn, S. 2012. GeoLight-processing and analysing light-based geolocator data in R. Methods in Ecology and Evolution, 3(6), 1055-1059. https://doi.org/10.1111/j.2041-210X.2012.00248.x

Marcuk, V., Piña-Ortiz, A., Castillo-Guerrero, J. A., Masello, J. F., Bustamante, P., Griep, S. & Quillfeldt, P. 2024. Trophic plasticity of a tropical seabird revealed through DNA metabarcoding and stable isotope analyses. Marine Environmental Research, 199(May). https://doi.org/10.1016/j.marenvres.2024.106627

Martin, M. 2011. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal, 17 (1), 10-12. https://doi.org/10.14806/ej.17.1.200.

Masden, E. A., Haydon, D. T., Fox, A. D. & Furness, R. W. 2010. Barriers to movement: Modelling energetic costs of avoiding marine wind farms amongst breeding seabirds. Marine Pollution Bulletin, 60(7), 1085–1091. https://doi.org/10.1016/j.marpolbul.2010.01.016

McInnes, J. 2017. The development and application of DNA metabarcoding to non-invasively assess seabird diets, using albatross as a model.

######

McInnes, J. C., Alderman, R., Deagle, B. E., Lea, M. A., Raymond, B. & Jarman, S. N. 2017. Optimised scat collection protocols for dietary DNA metabarcoding in vertebrates. Methods in Ecology and Evolution, 8(2), 192–202. https://doi.org/10.1111/2041-210X.12677

Meier, R. E., Votier, S. C., Wynn, R. B., Guilford, T., McMinn Grivé, M., Rodríguez, A., Newton, J., Maurice, L., Chouvelon, T., Dessier, A. & Trueman, C. N. 2017. Tracking, feather moult and stable isotopes reveal foraging behaviour of a critically endangered seabird during the non-breeding season. Diversity and Distributions, 23(2), 130–145. https://doi.org/10.1111/ddi.12509

Miya, M., Sato, Y., Fukunaga, T., Sado, T., Poulsen, J. Y., Sato, K., Minamoto, T., Yamamoto, S., Yamanaka, H., Araki, H., Kondoh, M. & IWasaki, W. 2015. MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: Detection of more than 230 subtropical marine species. Royal Society Open Science, 2(7). https://doi.org/10.1098/rsos.150088

Navarro, J., González-Solís, J. & Viscor, G. 2007. Nutritional and feeding ecology in Cory’s shearwater Calonectris diomedea during breeding. Marine Ecology Progress Series, 351, 261–271. https://doi.org/10.3354/meps07115

Nielsen, J. M., Clare, E. L., Hayden, B., Brett, M. T. & Kratina, P. 2018. Diet tracing in ecology: Method comparison and selection. Methods in Ecology and Evolution, 9(2), 278–291. https://doi.org/10.1111/2041-210X.12869

Oehm, J., Juen, A., Nagiller, K., Neuhauser, S. & Traugott, M. 2011. Molecular scatology: How to improve prey DNA detection success in avian faeces? Molecular Ecology Resources, 11(4), 620–628. https://doi.org/10.1111/j.1755-0998.2011.03001.x

Pante, E. & Simon-Bouhet, B. 2013. marmap: A Package for Importing, Plotting and Analyzing Bathymetric and Topographic Data in R. PLoS ONE, 8(9), 6–9. https://doi.org/10.1371/journal.pone.0073051

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. & Duchesnay, E. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830.

Pertoldi, C., Schmidt, J. B., Thomsen, P. M., Nielsen, L. B., de Jonge, N., Iacolina, L., Muro, F., Nielsen, K. T., Pagh, S., Lauridsen, T. L., Andersen, L. H., Yashiro, E., Lukassen, M. B., Nielsen, J. L., Elmeros, M. & Bruhn, D. 2021. Comparing DNA metabarcoding with faecal analysis for diet determination of the Eurasian otter (Lutra lutra) in Vejlerne, Denmark. Mammal Research, 66(1), 115–122. https://doi.org/10.1007/s13364-020-00552-5

Phillips, R. A., McGill, R. A. R., Dawson, D. A. & Bearhop, S. 2011. Sexual segregation in distribution, diet and trophic level of seabirds: Insights from stable isotope analysis. Marine Biology, 158(10), 2199–2208. https://doi.org/10.1007/s00227-011-1725-4

Phillips, R. A., Silk, J. R. D., Croxall, J. P., Afanasyev, V. & Briggs, D. R. 2004. Accuracy of geolocation estimates for flying seabirds. Marine Ecology Progress Series, 266, 265–272. https://doi.org/10.3354/meps266265

Piatt, J. F., Harding, A. M. A., Shultz, M., Speckman, S. G., Van Pelt, T. I., Drew, G. S. & Kettle, A. B. 2007. Seabirds as indicators of marine food supplies: Cairns revisited. Marine Ecology Progress Series, 352(1988), 221–234. https://doi.org/10.3354/meps07078

R Core Team. 2021. R: A language and environment for statistical computing. R Foundation for Statistical Computing. R Core Team. https://www.r-project.org/

Robeson, M. S., O’Rourke, D. R., Kaehler, B. D., Ziemski, M., Dillon, M. R., Foster, J. T. & Bokulich, N. A. 2021. RESCRIPt: Reproducible sequence taxonomy reference database management. PLoS Computational Biology, 17 (11). https://doi.org/10.1371/journal.pcbi.1009581

Sorensen, M. C., Hipfner, J. M., Kyser, T. K. & Norris, D. R. 2009. Carry-over effects in a Pacific seabird: Stable isotope evidence that pre-breeding diet quality influences reproductive success. Journal of Animal Ecology, 78(2), 460–467. https://doi.org/10.1111/j.1365-2656.2008.01492.x

Thompson, D. R., Furness, R. W. & Monteiro, L. R. 1998. Seabirds as biomonitors of mercury inputs to epipelagic and mesopelagic marine food chains. Science of the Total Environment, 213(1–3), 299–305. https://doi.org/10.1016/S0048-9697(98)00103-X

Thompson, K. R. 1987. The ecology of the Manx shearwater Puffinus Puffinus on Rhum, West Scotland. University of Glasgow.

Wanless, S., Harris, M. P., Newell, M. A., Speakman, J. R. & Daunt, F. 2018. Community-wide decline in the occurrence of lesser sandeels Ammodytes marinus in seabird chick diets at a North Sea colony. Marine Ecology Progress Series, 600, 193–206. https://doi.org/10.3354/meps12679

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Warwick-Evans, V., Atkinson, P. W., Walkington, I. & Green, J. A. 2018. Predicting the impacts of wind farms on seabirds: An individual-based model. Journal of Applied Ecology, 55(2), 503–515. https://doi.org/10.1111/1365-2664.12996

Weimerskirch, H., Gault, A. & Cherel, Y. 2005. Prey distribution and patchiness: Factors in foraging success and efficiency of Wandering Albatrosses. Ecology, 86(10), 2611–2622. https://doi.org/10.1890/04-1866

Weiser, E. L. & Powell, A. N. 2011. Evaluating gull diets: A comparison of conventional methods and stable isotope analysis. Journal of Field Ornithology, 82(3), 297–310. https://doi.org/10.1111/j.1557-9263.2011.00333.x

Whitford, M. & Klimley, A. P. 2019. An overview of behavioral, physiological, and environmental sensors used in animal biotelemetry and biologging studies. Animal Biotelemetry, 7(1), 1–24. https://doi.org/10.1186/s40317-019-0189-z