Bik, E. M. et al. Marine mammals harbor unique microbiotas shaped by and yet distinct from the sea. Nat. Commun. 7, 10516 (2016).
Apprill, A. et al. Extensive core microbiome in drone-captured whale blow supports a framework for health monitoring. MSystems 2, e00119-e117 (2017).
Pirotta, V. et al. An economical custom-built drone for assessing whale health. Front. Mar. Sci. 4, 425 (2017).
Raverty, S. A. et al. Respiratory microbiome of endangered southern resident killer whales and microbiota of surrounding sea surface microlayer in the Eastern North Pacific. Sci. Rep. 7, 394 (2017).
Lima, N., Rogers, T., Acevedo-Whitehouse, K. & Brown, M. V. Temporal stability and species specificity in bacteria associated with the bottlenose dolphins respiratory system. Environ. Microbiol. Rep. 4, 89–96 (2012).
Johnson, W. R. et al. Novel diversity of bacterial communities associated with bottlenose dolphin upper respiratory tracts. Environ. Microbiol. Rep. 1, 555–562 (2009).
Acevedo-Whitehouse, K., Rocha-Gosselin, A. & Gendron, D. A novel non-invasive tool for disease surveillance of free-ranging whales and its relevance to conservation programs. Anim. Conserv. 13, 217–225 (2010).
Bassis, C. M., Tang, A. L., Young, V. B. & Pynnonen, M. A. The nasal cavity microbiota of healthy adults. Microbiome 2, 27 (2014).
Dickson, R. P. et al. Bacterial topography of the healthy human lower respiratory tract. MBio 8, e02287-e2216 (2017).
Bond, S. L., Timsit, E., Workentine, M., Alexander, T. & Léguillette, R. Upper and lower respiratory tract microbiota in horses: bacterial communities associated with health and mild asthma (inflammatory airway disease) and effects of dexamethasone. BMC Microbiol. 17, 184 (2017).
Ericsson, A. C., Personett, A. R., Grobman, M. E., Rindt, H. & Reinero, C. R. Composition and predicted metabolic capacity of upper and lower airway microbiota of healthy dogs in relation to the fecal microbiota. PLoS ONE 11, e0154646 (2016).
Vientos-Plotts, A. I. et al. Dynamic changes of the respiratory microbiota and its relationship to fecal and blood microbiota in healthy young cats. PLoS ONE 12, e0173818 (2017).
Dickson, R. P. et al. The lung microbiota of healthy mice are highly variable, cluster by environment, and reflect variation in baseline lung innate immunity. Am. J. Respir. Crit. Care Med. 198, 497–508 (2018).
Segal, L. N. et al. Enrichment of the lung microbiome with oral taxa is associated with lung inflammation of a th17 phenotype. Nat. Microbiol. 1, 16031 (2016).
Shenoy, M. K. et al. Immune response and mortality risk relate to distinct lung microbiomes in patients with HIV and pneumonia. Am. J. Respir. Crit. Care Med. 195, 104–114 (2017).
Biswas, K., Hoggard, M., Jain, R., Taylor, M. W. & Douglas, R. G. The nasal microbiota in health and disease: variation within and between subjects. Front. Microbiol. 6, 134 (2015).
Dickson, R. P., Erb-Downward, J. R., Martinez, F. J. & Huffnagle, G. B. The microbiome and the respiratory tract. Annu. Rev. Physiol. 78, 481–504 (2016).
Garcia-Nuñez, M. et al. Severity-related changes of bronchial microbiome in chronic obstructive pulmonary disease. J. Clin. Microbiol. 52, 4217–4223 (2014).
Dickson, R. P. et al. Cell-associated bacteria in the human lung microbiome. Microbiome 2, 28 (2014).
Huang, Y. J. et al. Airway microbiota and bronchial hyperresponsiveness in patients with suboptimally controlled asthma. J. Allergy Clin. Immunol. 127, 372–381 (2011).
Dickson, R. P., Martinez, F. J. & Huffnagle, G. B. The role of the microbiome in exacerbations of chronic lung diseases. Lancet 384, 691–702 (2014).
Cardona, C. et al. Environmental sources of bacteria differentially influence host-associated microbial dynamics. MSystems 3, e00052-00018 (2018).
Hunt, K. E. et al. Overcoming the challenges of studying conservation physiology in large whales: a review of available methods. Conserv. Physiol. 1, 1–24 (2013).
International Whaling Commission. Annexe. Report of the sub-committee on other great whales. J. Cetacean Res. Manag. 1, 117–155 (1999).
Rasmussen, K. et al. Southern hemisphere humpback whales wintering off Central America: insights from water temperature into the longest mammalian migration. Biol. Lett. 3, 302–305 (2007).
Stevick, P. T. et al. A quarter of a world away: female humpback whale moves 10 000 km between breeding areas. Biol. Lett. 7, 299–302 (2010).
Riekkola, L. et al. Application of a multi-disciplinary approach to reveal population structure and Southern Ocean feeding grounds of humpback whales. Ecol. Indic. 89, 455–465 (2018).
Zerbini, A. N. et al. Satellite-monitored movements of humpback whales Megaptera novaeangliae in the southwest Atlantic Ocean. Mar. Ecol. Prog. Ser. 313, 295–304 (2006).
Chittleborough, R. G. Dynamics of two populations of the humpback whale, Megaptera novaeangliae (borowski). Mar. Freshw. Res. 16, 33–128 (1965).
Chittleborough, R. G. The breeding cycle of the female humpback whale, Megaptera nodosa (bonnaterre). Mar. Freshw. Res. 9, 1–18 (1958).
Clapham, P. J. The humpback whale: seasonal feeding and breeding in a baleen whale. In Cetacean societies: field studies of dolphins and whales (eds Mann, J. et al.) 173–196 (University of Chicago Press, Chicago, 2000).
Franklin, T. The social and ecological significance of Hervey bay Queensland for Eastern Australian humpback whales (Megaptera novaeangliae). Ph.D. thesis, Southern Cross University, Lismore, NSW (2012).
Franklin, T. et al. Seasonal changes in pod characteristics of Eastern Australian humpback whales (Megaptera novaeangliae). Mar. Mammal Sci. 27, E134–E152 (2011).
Dawbin, W. H. The seasonal migratory cycle of humpback whales. In Whales, dolphins and porpoises (ed. Norris, K. S.) (University of California Press, Berkeley, 1996).
Seymour, J. R. et al. Contrasting microbial assemblages in adjacent water masses associated with the East Australian current. Environ. Microbiol. 4, 548–555 (2012).
Chao, A. Non-parametric estimation of the number of classes in a population. Scand. J. Stat. 11, 265–270 (1984).
Chao, A. Estimating the population size for capture-recapture data with unequal catchability. Biometrics 43, 783–791 (1987).
Chao, A. & Yang, M. C. Stopping rules and estimation for recapture debugging with unequal failure rates. Biometrika 80, 193–201 (1993).
Chao, A., Hwang, W. H., Chen, Y. C. & Kuo, C. Y. Estimating the number of shared species in two communities. Stat. Sin. 10, 227–246 (2000).
Dunn, O. J. Multiple comparisons using rank sums. Technometrics 6, 241–252 (1964).
Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979).
Shade, A. & Handelsman, J. Beyond the Venn diagram: the hunt for a core microbiome. Environ. Microbiol. 14, 4–12 (2012).
Magurran, A. E. & Henderson, P. A. Explaining the excess of rare species in natural species abundance distributions. Nature 422, 714 (2003).
Hernandez-Agreda, A., Gates, R. D. & Ainsworth, T. D. Defining the core microbiome in corals’ microbial soup. Trends Microbiol. 25, 125–140 (2017).
Astudillo-García, C. et al. Evaluating the core microbiota in complex communities: a systematic investigation. Environ. Microbiol. 19, 1450–1462 (2017).
Einarsson, G. G. et al. Community dynamics and the lower airway microbiota in stable chronic obstructive pulmonary disease, smokers and healthy non-smokers. Thorax 79, 795–803 (2016).
Venn-Watson, S., Daniels, R. & Smith, C. Thirty year retrospective evaluation of pneumonia in a bottlenose dolphin Tursiops truncatus population. Dis. Aquat. Organ. 99, 237–242 (2012).
Venn-Watson, S., Smith, C. R. & Jensen, E. D. Primary bacterial pathogens in bottlenose dolphins Tursiops truncatus: needles in haystacks of commensal and environmental microbes. Dis. Aquat. Organ. 79, 87–93 (2008).
Waltzek, T., Cortés-Hinojosa, G., Wellehan, J. Jr. & Gray, G. C. Marine mammal zoonoses: a review of disease manifestations. Zoonoses Public Health 59, 521–535 (2012).
Cusick, P. & Bullock, B. Ulcerative dermatitis and pneumonia associated with Aeromonas hydrophila infection in the bottle-nosed dolphin. J. Am. Vet. Med. Assoc. 163, 578–579 (1973).
Owen, K. et al. Effect of prey type on the fine-scale feeding behaviour of migrating East Australian humpback whales. Mar. Ecol. Prog. Ser. 541, 231–244 (2015).
Lockyer, C. Body weights of some species of large whales. ICES J. Mar. Sci. 36, 259–273 (1976).
Bengtson Nash, S. M., Waugh, C. A. & Schlabach, M. Metabolic concentration of lipid soluble organochlorine burdens in the blubber of Southern Hemisphere humpback whales through migration and fasting. Environ. Sci. Technol. 47, 9004–9413 (2013).
Johnson, K. L. Capital and income breeding as alternative tactics of resource use in reproduction. Oikos 78, 57–66 (1997).
Noad, M. J., Dunlop, R. A., Paton, D. & Cato, D. H. Absolute and relative abundance estimates of Australian east coast humpback whales (Megaptera novaeangliae). J. Cetacean Res. Manag. 243, 252 (2011).
Atkinson, A. et al. Krill (Euphausia superba) distribution contracts southward during rapid regional warming. Nat. Clim. Change 9, 142 (2019).
Katona, S. K. & Beard, J. A. Population size, migrations and feeding aggregations of the humpback whale (Megaptera novaeangliae) in the western North Atlantic Ocean. Rep. Int. Whal. Comm. Spec. Issue 12, 295–306 (1990).
Christiansen, F. et al. Maternal body size and condition determine calf growth rates in southern right whales. Mar. Ecol. Prog. Ser. 592, 267–281 (2018).
Durban, J. W., Fearnbach, H., Barrett-Lennard, L., Perryman, W. & Leroi, D. Photogrammetry of killer whales using a small hexacopter launched at sea. J. Unmanned Veh. Syst. 3, 131–135 (2015).
Christiansen, F., Dujon, A. M., Sprogis, K. R., Arnould, J. P. & Bejder, L. Noninvasive unmanned aerial vehicle provides estimates of the energetic cost of reproduction in humpback whales. Ecosphere 7, e01468 (2016).
Mingramm, F., Dunlop, R., Blyde, D., Whitworth, D. & Keeley, T. Evaluation of respiratory vapour and blubber samples for use in endocrine assessments of bottlenose dolphins (Tursiops spp.). Gen. Comp. Endocrinol. 274, 37–49 (2019).
Mingramm, F. M., Keeley, T., Whitworth, D. J. & Dunlop, R. A. Relationships between blubber and respiratory vapour steroid hormone concentrations in humpback whales (Megaptera novaeangliae). Aquat. Mamm. 45, 465–477 (2019).
Castrillon, J., Huston, W. & Bengtson Nash, S. The blubber adipocyte index: a nondestructive biomarker of adiposity in humpback whales (Megaptera novaeangliae). Ecol. Evol. 7, 5131–5139 (2017).
Hogg, C. J. et al. Determination of steroid hormones in whale blow: it is possible. Mar. Mammal Sci. 25, 605–618 (2009).
Lane, D. J. et al. Rapid determination of 16s ribosomal RNA sequences for phylogenetic analyses. Proc. Natl. Acad. Sci. 82, 6955–6959 (1985).
Lane, D. J. 16s/23s rRNA sequencing. In Nucleic acid techniques in bacterial systematics (eds Stackebrandt, E. & Goodfellow, M.) (Wiley, New York, 1991).
Bioinformatics.babraham.ac.uk (2019) Babraham bioinformatics—fastqc a quality control tool for high throughput sequence data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc. 20 Jan 2019.
Edgar, R. C. Uparse: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996 (2013).
Edgar, R. C. Unoise2: improved error-correction for illumina 16s and its amplicon sequencing. BioRxiv 081257 (2016).
Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639 (2017).
Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. Uchime improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).
Quast, C. et al. The silva ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2012).
Cole, J. R. et al. Ribosomal database project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 42, D633–D642 (2013).
Oksanen, J., Blanchet, F. G., Kindt, R., Legendre, P., O’hara, R. B., Simpson, G. L., Solymos, P., Stevens, M. H. H. & Wagner, H. (2010) Vegan: community ecology package. R package version 1.17-4. https://cran.r-project.org. 30 Jan 2019.
Lozupone, C. & Knight, R. Unifrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).
Anderson, M. J. Permutational multivariate analysis of variance (PERMANOVA). Wiley statsref: statistics reference online, 1–15 (2014).
Warton, D. I., Wright, S. T. & Wang, Y. Distance-based multivariate analyses confound location and dispersion effects. Methods Ecol. Evol. 3, 89–101 (2012).
Wang, Y. I., Naumann, U., Wright, S. T. & Warton, D. I. Mvabund—an r package for model-based analysis of multivariate abundance data. Methods Ecol. Evol. 3, 471–474 (2012).
Warton, D. I., Thibaut, L. & Wang, Y. A. The pit-trap—a “model-free” bootstrap procedure for inference about regression models with discrete, multivariate responses. PLoS ONE 12, e0181790 (2017).
Lokmer, A. et al. Spatial and temporal dynamics of Pacific oyster hemolymph microbiota across multiple scales. Front. Microbiol. 7, 1367 (2016).