Respiratory microbiota of humpback whales may be reduced in diversity and richness the longer they fast


Humpback whales endure several months of fasting while undertaking one of the longest annual migrations of any mammal, which depletes the whales’ energy stores and likely compromises their physiological state. Airway microbiota are linked to respiratory health in mammals. To illuminate the dynamics of airway microbiota in a physiologically challenged mammal, we investigated the bacterial communities in the blow of East Australian humpback whales at two stages of their migration: at the beginning (n = 20) and several months into their migration (n = 20), using barcoded tag sequencing of the bacterial 16S rRNA gene. We show that early in the fasting the whale blow samples had a higher diversity and richness combined with a larger number of core taxa and a different bacterial composition than later in the fasting. This study provides some evidence that the rich blow microbiota at the beginning of their fasting might reflect the whales’ uncompromised physiology and that changes in the microbiota occur during the whales’ migration.


The airways of cetaceans1,2,3,4,5,6,7 and other mammals (humans8,9, horses10, dogs11, cats12, mice13 harbour a large diversity of bacteria. Studies focusing on airway microbiota of humans14,15,16,17 and equines10 have identified a close relationship between the composition of microbial communities and the airways’ physiological state. A compromised respiratory system typically correlates with an altered microbiota17, as it changes the rate of immigration and elimination of bacteria as well as the growth conditions within the airways. The cause that compromises the airways determines the type of change taking place in the microbial communities. In humans, certain conditions like advanced chronic obstructive pulmonary disease (COPD)18, chronic rhinosinusitis (CRS) and pneumonia result in a decrease of community richness and in the case of pneumonia also in the dominance of few or even a single opportunistic pathogen, like Staphylococcus aureus or Pseudomonas aeruginosa19. In contrast, other conditions like asthma typically cause an increase in bacterial diversity20. Dickson et al.17 summarised these points by adapting a famous Tolstoy quote: ‘All healthy lungs are alike,every unhealthy lung is unhealthy in its own way’. Dickson et al.21 and Dickson et al.17 have suggested a model, characterizing the interplay between respiratory microbiota and their host not only as dynamic and continuous, but also as bidirectional. In other words, any changes taking place within the respiratory system are capable of activating the so called dysbiosis-inflammation cycle leading to shifts of both immune response and microbiota.

Several studies investigating the airway microbiota of whales2,3,7 and dolphins1,4,5,6,22 analysed their exhaled breath condensate or ‘blow’. Unlike the studies on humans, the cetacean-focused studies exclusively characterized the blow microbiota of the studied specimens and did not investigate a potential correlation of blow microbiota and overall health. While this would be possible for captive cetaceans, it is often difficult to gain reliable health parameters on whales and dolphins in their natural habitat2,23. To gain insight into the dynamics of cetacean airway microbiota in response to physiological challenges, we focused here on East Australian humpback whales (HW), Megaptera novaeangliae, (breeding population E1 as defined by the International Whaling Commission24 during their annual migration.

With a journey that can exceed 8000 km, HW undertake the longest seasonal migration of all mammals25,26,27,28. The East Australian HW leave their feeding grounds in Antarctica at the end of the austral summer to travel north along the East Australian Coast, until they reach their low latitude breeding and mating areas on the Great Barrier Reef29 (Fig. 1). In late winter, they return south to feed in the Southern Ocean. Chittleborough et al.29 have reported that the whales’ food intake is marginal during the breeding season, thus resulting in a period of fasting of at least 4 months per year. In addition to the energy requirements for the migration, female HW also spend massive resources on gestation and lactation30, whereas males invest large amounts of energy in competing for females31.

Figure 1

Map of the sampling sites off the coast of Sydney and Hervey Bay, Australia. The green arrows represent the northern leg and the red arrows the southern leg of the annual migration of the East Australian humpback whales. We drew this figure according to the information provided in Franklin32.

We hypothesise that an extended period of fasting compromises the whales’ physiological state, which results in a change of the microbial community composition of the airways. We therefore compared the blow microbiota of HW at the beginning of their northern migration along the East Australian Coast (HumpbackNM), where they were at the beginning of their fasting, to those after approximately 3–4 months of fasting during the southern leg of their migration (HumpbackSM).


Dataset overview

We collected 20 blow samples from HW, seven seawater (SeawaterSM, environmental controls) and six air (technical controls) samples in Hervey Bay, Southern Queensland, Australia, during August 2017. The HW enter Hervey Bay on the southern leg of their migration a few weeks after leaving their breeding grounds at the Great Barrier Reef and 3–4 months after leaving33,34 their feeding grounds in Antarctica. We compared their blow microbiota based on 16S rRNA gene sequencing with the blow microbiota from 20 different individuals in the same population of HW along with six air samples collected by Pirotta et al.3off the coast of Sydney, New South Wales, Australia, during May and June 2017, about 1 month after the whales left their feeding grounds in Antarctica, and 26 seawater samples (SeawaterNM) obtained from the same area as Pirotta’s HumpbackNM samples35.

We detected a total of 11,573,157 raw 16S rRNA gene sequences, which were clustered into 8838 zero-distance operational taxonomic units (zOTUs). We removed 336 zOTUs as they had an overall relative abundance of less than 0.0001% and deleted 465 zOTUs from the dataset, which were present and highly abundant in most air samples (technical controls). The resulting dataset of whale and seawater samples contained 8,037 zOTUs with a mean of 78,827 reads per sample (sd = 116,046). The rarefaction curves (Supplementary Fig S1) and Good’s coverage (HW–NM: 99.9398, sd = 0.0365; SeawaterNM: 98.7193, sd = 0.5321; HW–SM: 99.9346, sd = 0.0348; SeawaterSM: 99.7689, sd = 0.02747) after filtering showed that the majority of samples was sequenced to near-saturation. The 8,037 zOTUs covered 681 genera, 295 families, 162 orders, 63 classes and 30 phyla.

Whales at the beginning of their fasting had a higher alpha diversity than the whales at a later stage

We calculated richness, Shannon–Wiener diversity index, Chao136,37 and ACE38,39 species estimator for whale and seawater samples using rarefied counts (lowest number of reads: 3432) to account for the difference in sampling depth (Table 1). SeawaterNM and SeawaterSM did not significantly differ in their richness (Kruskal–Wallis test with Holm’s adjustment for multiple comparisons40,41: Z = 1.9394, p value = 0.0524), Shannon–Wiener diversity (Z = 1.5897, p value = 0.1119), Chao1 (Z = 1.7819, p value = 0.0748) and ACE species estimator (Z = 1.8287, p value = 0.0674). The seawater samples had a significantly higher richness, Chao1 and ACE than their corresponding whale samples (richness: SeawaterNM–HumpbackNM: Z = − 3.7093, p value = 0.0005; SeawaterSM–HumpbackSM: Z = − 3.1001, p value = 0.0029; Chao1: SeawaterNM–HumpbackNM: Z = − 4.5099, p value < 0.0000; SeawaterSM–HumpbackSM: Z = − 3.3819, p value = 0.0014; ACE: SeawaterNM–HumpbackNM: Z = − 4.2640, p value < 0.0000; SeawaterSM–HumpbackSM: Z = − 3.2611, p value = 0.0022). Interestingly, the HumpbackSM samples showed a lower richness, Chao1 and ACE than HumpbackNM samples (richness: Z = 3.428, p value = 0.0012; Chao1: Z = 2.8542, p value = 0.0065; ACE: Z = 2.9809, p value = 0.0043). Also, the Shannon Wiener diversity of HumpbackSM samples was significantly lower than HumpbackNM samples (Z = 5.6562, p value < 0.0000) (Table 1). The entire results of the Kruskal–Wallis tests of alpha diversity parameters are displayed in Table 2 and Supplementary Table S1. We also calculated and compared the same alpha diversity values with unrarefied counts (Supplementary Table S2) and received very similar results (Supplementary Table S1).

Table 1 Number of reads and alpha diversity parameters (samples size, richness, Shannon index, Chao1 and ACE) of rarefied data (rarefied number of reads = 3432) of samples of 20 humpback whale (HumpbackNM) and 26 seawater (SeawaterNM) samples at the beginning of their fasting and of 20 humpback whale (HumpbackSM) and 7 seawater (SeawaterSM) samples at a later stage. Numbers are after deleting putative technical contaminant zOTUs from the dataset.
Table 2 Z and p values of Kruskal–Wallis test of richness, Shannon–Wiener diversity, Chao1 and ACE species estimator of rarefied counts. The upper value is the Z, the lower the p value in each cell. Those p values with an asterisk are statistically significant.

The beta diversity analysis based on the Bray–Curtis dissimilarity coefficient of unrarefied relative abundances and ordinated in a non-metric multidimensional scaling (NMDS) plot (Fig. 2) showed group-specific and relatively tight clustering of the HumpbackSM, SeawaterSM and SeawaterNM replicates, whereas the replicates of HumpbackNM samples did not form a distinct cluster. An overlap between whale blow and seawater samples, especially in the case of HumpbackNM and SeawaterNM was observed. The PCoA plot of the weighted and unweighted generalized UNIFRAC distances (Supplementary Figures S2 and S3) displayed a tighter clustering of the HumpbackNM samples, whereas the HumpbackSM samples were more spread out. To determine if the composition of bacterial communities was significantly different between the four groups (SeawaterNM, HumpbackNM, SeawaterSM, HumpbackSM), we fitted negative binomial models to each zOTU. We detected a significant difference between the bacterial communities of HumpbackNM and HumpbackSM (sum-of-LR = 16,626, p = 0.001), of HumpbackSM and SeawaterSM (sum-of-LR = 32,792, p = 0.001), of HumpbackNM and SeawaterNM (sum-of-LR = 86,687, p = 0.001) and of SeawaterNM and SeawaterSM (sum-of-LR = 35,528, p = 0.001). The negative binomial models also identified those zOTUs that accounted for the majority of differences in the microbiota across the samples of HumpbackSM, HumpbackNM, SeawaterSM and SeawaterNM. The heatmap in Fig. 3 shows the prevalence of the most abundant 50 of those zOTUs. When applying the negative binomial models to the zOTUs of the blow samples of HumpbackSM and HumpbackNM only, we identified 311 zOTUs as significantly different between the two whale groups (Fig. 4). These zOTUs accounted for 15% (HumpbackSM) and 33% (HumpbackNM) of total relative sequence abundance and mostly belonged to unclassified genera. The first and second most abundant genera that could be classified were Corynebacterium (HumpbackSM: 1%; HumpbackNM: 2%) and Helcococcus (HumpbackSM: 1%; HumpbackNM: 2%).

Figure 2

nMDS of microbiota, based on Bray–Curtis dissimilarity and unrarefied data, found in the blow of 20 humpback whale (HumpbackNM) and 26 seawater (SeawaterNM) samples at the beginning of the whales’ fasting and of 20 humpback whale (HumpbackSM) and 7 seawater (SeawaterSM) samples at a later state after deleting putative technical contaminant zOTUs from the dataset. The nMDS plot shows group-specific clustering of HumpbackSM, SeawaterSM and SeawaterNM, whereas the HumpbackNM samples did not form a distinct cluster. An overlap between whale and seawater samples, especially in the case of HumpbackNM and HumpbackNM is observed.

Figure 3

Heatmap of fourth-root transformed relative abundance of the 50 most abundant zOTUs that were identified as significantly different when comparing the microbiota in the blow of humpback whales at the beginning of their fasting (HumpbackNM) and humpback whales at a later stage of fasting (HumpbackSM) and their corresponding seawater samples (SeawaterNM, SeawaterSM).

Figure 4

Heatmap of fourth-root transformed relative abundance of the 50 most abundant (out of 311) zOTUs that were identified as significantly different when comparing the microbiota in the blow of humpback whales at the beginning of their fasting (HumpbackNM) and humpback whales at a later stages of fasting (HumpbackSM).

Whales at the beginning of their fasting had a diverse and abundant core

Another useful tool to determine a change in microbial community composition across hosts or time is the identification of core taxa42,43,44,45,46. We used an 80% threshold (taxa present in 80% of all sampled individuals) to determine the core microbiota within the blow of HumpbackNM and HumpbackSM samples at two taxonomic levels (zOTU and genus) (Tables 3, 4). To apply a conservative estimate of core we excluded those zOTUs that were commonly found in seawater, as we were unable to tell if these were contaminants of the blow samples or true residents of the airways. The HumpbackNM samples contained 55 core zOTUs that accounted for a total relative read abundance of 14%. 13 zOTUs were disregarded as core due to their common association with seawater. In contrast, the HumpbackSM samples did not share a single core zOTU that was not also present in seawater. The large majority of core zOTUs (42 out of 55) of HumpbackNM were most similar to 16S rRNA gene sequences detected in the mouth of bottlenose dolphins (e.g. GenBank accession: KC258936.1)1 (Table 1). Out of these zOTUs, 27 belonged to the class Gammaproteobacteria and more precise classification was not possible for most of these sequences. Another 13 core zOTUs of HumpbackNM were similar to sequences found on skin and nares (KF104811.1) and in the mouth of humans and other mammals (JN713454.1). The 42 dolphin-related zOTUs accounted for a total relative abundance of 12%. The HumpbackNM samples had 20 core genera with a total relative abundance of 18%, whereas HumpbackSM had only one belonging to the genus Geobacillus, accounting for 5% (Table 4). The most abundant core genera of HumpbackNM with relative read abundances of more than 1.5% each were Arcobacter, Corynebacterium, Enhydrobacter, Helcococcus and Tenacibaculum. The two groups of HW did not share any core genera. The findings that HumpbackNM had a large number of core zOTUs and genera and therefore overall more diverse blow microbiota than HumpbackSM was also observed at class level (Supplementary Fig S4).

Table 3 Taxonomy, relative abundance and details of environment of core zOTUs in the blow of 20 humpback whales at the beginning of their fasting (HW–NM) and 20 humpback whales at a later stage (HW–SM), listed in descending order of the zOTUs’ relative abundance. An 80% threshold (core taxa present in 80 of individuals in each group) was used. Whereas HumpbackNM samples showed a large number of core zOTUs, HumpbackSM had none that were not associated with seawater.
Table 4 Taxonomy and relative abundance of core genera of the blow of 20 humpback whales at the beginning of their fasting (HW–NM) and 20 humpback whales at a later stage (HW–SM) in descending order of the genera’s relative abundance. An 80% threshold (core taxa present in 80 of individuals in each group) was used. Whereas the HumpbackNM samples showed a large number of core genera, the HumpbackSM samples had only one.

In addition, we determined those zOTUs and genera as ‘overall’ core that were shared across at least 80% of all 40 whale blow samples (HumpbackNM and HumpbackSM). The only zOTU matching these criteria was seawater-associated and therefore disregarded. Hence, the 40 whale blow samples did not share any overall core zOTUs. The same applied to the genera. Whereas Pseudomonas and Corynebacterium were shared by 29 (73%) and 28 (70%) whale blow samples, respectively, none of the genera were harboured by 80%.

Potential marine mammal-specific pathogens in HW blow

In the blow of the HW at the late stage of their migration, we found eight out of a total of 146 genera that were previously identified as pathogens in marine mammals. These eight genera accounted for a total relative abundance of 7%. The HW early in their migration harboured ten potential marine mammal pathogens that included those found in their conspecifics later in their migration. These ten genera out of a total of 294 accounted for a relative abundance of 6%. The potentially pathogenic genera shared by both whale groups included Corynebacterium, Pseudomonas, Staphylococcus, Fusobacterium, Mycoplasma, Streptococcus, Acinetobacter, Stenotrophomonas47,48,49. Table 5 lists the medical conditions caused by these genera and the affected species.

Table 5 Potential marine mammal-specific pathogenic genera found in the blow of both groups of humpback whales [20 humpback whales at the beginning of their fasting (HW–NM) and 20 humpback whales at a later stage (HW–SM)]. The table provides information on the medical conditions and affected species reported in the literature.


We showed that the microbiota of whale blow samples were significantly different to those of their surrounding seawater (Fig. 2, Table 2). While some similarities between communities exist, these findings confirm previous reports by Bik et al.1, Apprill et al.2 and Raverty et al.4, who also found the bacterial community composition of cetacean blow to be different to seawater. Moreover, we conclude that the bacterial communities of blow samples of HW at a later stage of their fasting were significantly altered compared with those at the beginning of their migration (Figs. 2, 3, 4). Different techniques in sample collection, DNA extraction, PCR cycling as well as two separate sequencing runs of the two groups of samples might have contributed to the differences seen in the bacterial composition of the blow. We applied appropriate statistical techniques (e.g. subsampling, PIT-trap resampling) to minimise the impact of these factors on the alpha- and beta-diversity measurement determined here. In addition, the bacterial sequences of the seawater samples taken from the two sampling sites, but processed in the different manners outlined above, were similar in their alpha diversity indicating that sample processing and sequencing runs might overall have a negligible impact for our study. We therefore provide an indication that the HW at the beginning of their fasting exhibited a significantly higher richness and diversity in their blow microbiota (Table 1) than those from a later stage of fasting.

We postulate that the relatively rich and diverse whale blow microbiota at the beginning of their fasting reflected an uncompromised, and therefore stable physiological state of the airways. The whales left their feeding grounds in Antarctica only a few weeks before the sample collection of Pirotta et al.3 and Owen et al.51 presented evidence of feeding activity of HW off the south coast of New South Wales. Consequently, the whales had likely just entered a fasting state and hence were in their “nutritional prime”. In contrast, the comparably low bacterial diversity, richness and number of core taxa of whales later in their migration is correlated to their prolonged fasting. As HW lose 25–50% of their body weight during their annual migration34,52,53, and are therefore susceptible to exhaustion of their energy stores before they resume feeding54, the whales’ physiological state and hence their immune system may be compromised. This state may cause a change in the composition of their airway microbiota. Studies on mice and humans showed that the airways did not need to be acutely infected to show changes in their microbial composition13,14. Even in healthy or subclinically infected individuals, a shift in immunological parameters resulted in a change of the diversity of microbiota.

The loss of energy stores, metabolic demands on the host and impacts on physiology and immune status due to prolonged fasting may have become increasingly relevant in recent decades. While the population of East Australian HW is growing by almost 11% per year and supposedly approaching their carrying capacity55, the population density of the whales’ main prey, Antarctic krill, are decreasing. Atkinson et al.56 estimated a 70% decrease in krill numbers since the 1970s. Growing whale numbers together with shrinking prey density could potentially exacerbate the whales’ efforts to renew their energy stores in the Antarctic feeding grounds, leaving them more vulnerable to the consequences of fasting-related exhaustion in the future.

The number and abundance of core zOTUs and genera was higher in the blow of whales at the beginning compared to the later stage of their fasting (Tables 3, 4). This observation is correlated to the reduced diversity and richness of the whales in the late fasting stage. Apprill et al.2 did an analysis of core taxa in the blow of Northern hemisphere HW and found 25 core maximum entropy distribution (MED) sequences in 100% of samples. While they did not look at 80% core microbiota thresholds and their MED sequences are not directly comparable to the zOTUs produced in this study, Apprill et al.2 likely sampled non-fasting individuals. They performed sample collection in the North Atlantic north of Cape Cod, Massachusetts, USA, in July, and in the North Pacific near Vancouver Island, British Columbia, Canada, in August and off the coast of Washington State, USA, in September. These regions are part of the high-latitude summer feeding areas of the HW that reach up to the Arctic57. In autumn, the whales start making their way to tropical waters near the Caribbean57. Consequently, sample collection by Apprill et al.2 took place in the feeding grounds of the whales right before the beginning of the migration to their breeding grounds and when energy stores were expected to have filled up.

We found 13 zOTUs in the blow of at least 80% of whales at the beginning of their fasting that were associated with seawater. A similar finding was described in Apprill et al.2 where three out of 25 MED sequences were related to seawater. It is possible that these zOTUs were mixed in with the blow when the whale exhaled and thus were contaminants. However, according to Apprill et al.2 seawater regularly enters the airways of whales between breaths and thus may represent ‘seawater lavages of the upper respiratory tract seeded with condensed exhalation‘. Therefore, these seawater-related zOTUs may also be true inhabitants of the airways, either of transient or more permanent nature. However, as we were not able to distinguish between contamination and ‘seawater lavage’ at this point, we decided not to include these 13 zOTUs into our conservative estimate of the core.

In the blow of HW at the early and late stage of their migration, we detected several genera that include members previously reported as marine mammal pathogens (Table 5). Yet, the significance of these findings is unclear. Most bacterial genera commonly contain non-pathogenic ‘species’ alongside pathogenic ones. As 16S rRNA sequencing as used in this study rarely allows the assignment of ‘species’ identity, we are unable to estimate the true pathogenic capacity of the detected zOTUs. In addition, both groups of whales harboured a large percentage of zOTUs that belonged to unclassified genera (74% in the whales at the beginning of their migration and 55% in the whales later on). The pathogenic potential of these unclassified genera is completely unknown. Therefore, we cannot determine if one group of whales carried a larger ratio of potentially pathogenic bacteria than the other.

In conclusion our study provides some evidence that migration-associated fasting and physiological stress might correlate with a general shift and loss of diversity and richness in the airway microbiota of HW. Such a change in the bacterial community could be the direct result of a weakened immune system, which in turn may influence the rate of elimination of bacteria as well as the growth conditions within the airways enabling certain bacterial groups to proliferate. The analysis of the diversity and composition of HW blow might therefore represent a viable approach to assess the physiological state of cetaceans. In the future, the use of additional techniques to determine body condition including photogrammetry58,59,60, measuring cortisol levels61,62 from the whales’ blow and blubber and determining adipocyte size and number in external blubber63 will contribute to even better understand the correlation between blow microbiota and the physiological state of the whale.


Sample collection

We collected 20 blow samples of humpback whales (HumpbackSM), in Hervey Bay, Queensland, Australia, in August 2017. Sample collection and experimental procedures were approved by the University of New South Wales (UNSW Sydney) animal care and ethics committee (ACEC) (permit no. 16/81A), the Department of National Parks, Sport and Racing (under the Marine Parks Act 2004, permit no. QS2016/GS066) and the Department of Environment and Heritage Protection of Queensland (permit no. 20194/16). The experimental protocol was performed in compliance with the guidelines of the above-mentioned permits.

We collected the samples from the vessel of a commercial whale watching operator at Hervey Bay (25.2882° S, 152.7677° E), Queensland, Australia, in August 2017 (n = 20). The sampled HW’s were of unknown sex and age. Images were taken of each sampled whale to compare significant features between sampled individuals (e.g. wounds, scars and pigmentation) to ensure that each whale was only sampled once. The blow collection device followed the design of Hogg et al.64 and Acevedo-Whitehouse et al.7. For each sampling effort, six blank sterile petri dishes (14 mm diameter) were attached to the Perspex plate and a long pole. When a whale surfaced within the range of the pole near the boat, the Perspex plate was positioned about 50 cm above the blowhole of the exhaling whale to collect the droplets in the blow. Seven seawater samples were obtained by filtering 500 ml sea water collected at the blow sampling sites through a Sterivex filter unit (0.22 µm, EMD Millipore Corporation, Billerica, USA). To obtain air samples, the sampling device was exposed to air for 30 min in the absence of whale blow. The petri dishes were subsequently swabbed and then processed in the same way as the blow samples. The air samples acted as technical controls during the DNA extraction process. After sample collection the petri dishes were removed from the Perspex plate and immediately swabbed with sterile cotton swabs (Interpath Services PTY LTD, Heidelberg, Baden-Württemberg, Germany). Each cotton swab had an absorption capacity of about 70 μl of sample material and per sample one cotton swab was used in the downstream process. Face masks were worn at all times while handling the samples to avoid contaminations. The cotton swabs were stored in sterile tubes and chilled on ice until the return of the boat to the harbour. In the field lab, the shaft of the cotton swabs was trimmed and the tip transferred into a 2 ml cryovial. About 300 µl of TE-buffer (10 mM Tris–Cl pH 7.5; 1 mM EDTA) were added to each cryovial to stabilize bacterial DNA. The samples were then stored at—8 °C during transport and then at − 20 °C until processing.

DNA extraction and 16S rRNA gene sequencing

Nucleic acids from blow samples were extracted from cotton swabs and in the case of seawater from filtering units using the FastDNA Spin Kit for Soil following the manufacturer’s protocol (MP Biomedicals, Santa Ana, California, USA). The V1-V3 region of the bacterial 16S rRNA gene was amplified using barcoded primers 27F (5′-AGAGTTTGATCMTGGCTCAG-3′) and 519R (5′-GWATTACCGCGGCKGCTG-3′)65,66. Each sample was amplified in duplicate and then pooled in the data analysis. The final PCR reaction volume was 25 µl consisting of 0.125 µl of each primer (40 µM, Integrated DNA Technologies, Coralville, IA, USA), 5 µl of 5X Green GoTaq Flexi Buffer (Promega, Madison, WI, USA), 0.3125 µl of Ex Taq HotStart Version (5 Units per µl, TaKaRa, Bio Inc., Shiga, Japan), 2 µl of dNTP-mix (10 mM, TaKaRa, Bio Inc., Shiga, Japan), 5 µl of MgCl2 (25 mM; Promega, Madison, WI, USA) and 4 µl of DNA. Negative (no DNA template) and positive controls (Escherichia coli genomic DNA) were included. Reactions were performed using a CFX96 TouchTM Real-Time PCR Detection System (Bio-Rad Laboratories Inc, Hercules, CA, USA) under the following program conditions: Initial denaturation at 98 °C for 2 min; then 35 cycles of denaturation at 98 °C for 10 s, annealing at 50 °C for 30 s and elongation at 72 °C for 60 s; and a final elongation at 72 °C for 5 min. PCR products were submitted to the Ramaciotti Centre for Genomics (Sydney, Australia) for purification, library preparation and paired-end amplicon sequencing (2 × 300 bp) on the Illumina MiSeq platform.

Sequence data processing

We compared the sequences of 20 blow samples collected from HumpbackNM3 in May to June, 2017, with our 20 samples of HumpbackSM blow samples collected in August of the same year. The HumpbackNM sequences of Pirotta et al.3, including controls (air), were accessed from the European Nucleotide Archive under project PRJEB23634. The seawater controls Pirotta et al.3 used were accessed from the NCBI Sequence Read Archive under PRJNA38573635.

All sequences (the HumpbackNM blow and air samples collected by Pirotta et al.3, the SeawaterNM samples Pirotta et al.3 used and the HumpbackSM blow, air and seawater samples collected in this study) were analysed together. An initial quality check was performed with FastQC67. Paired-end reads were processed with USEARCH (version 10.240)68. The reads were merged and low-quality sequences (maximum number of expected errors > 2 and more than 1 ambiguous base) and those shorter than 440 bp were removed. Primers were also removed. Processed sequences of all samples were dereplicated and unique sequences were denoised and de novo clustered into zero-radius operational taxonomic units (zOTUs) with 100% similarity using the unoise3 algorithm69. This is the same clustering approach utilized for amplicon sequence variants (ASV)70. The difference between zOTUs and ASVs does not lie in the applied similarity-threshold across sequences but in the bioinformatics program. While Callahan et al.70 used the program DADA2 for sequence processing and ‘species’ assemblage, we worked with USEARCH68.

A de novo chimera removal was included in the unoise step. Afterwards, remaining chimeric sequences were removed using the uchime2 algorithm71 in high confidence mode with the SILVA database (version 132) as reference dataset72. Subsequently, processed sequences were mapped onto zOTU sequences to calculate the presence and relative abundance of each zOTU in every sample using the otutab command with maxrejects and maxaccepts options disabled. Representative zOTU sequences were assigned a taxonomy using the SILVA rRNA sequence database, release 132 ( and the ribosomal database project (rdp), release 11 ( The taxonomical information derived from the SILVA rRNA sequence database was exclusively used to identify and delete zOTU sequences derived from mitochondria and chloroplasts.

Data analysis

zOTUs that were identified as Archaea, chloroplasts or mitochondria were deleted from the dataset. We identified 457 zOTUs that were present in at least two air samples. As the air samples served as technical control, we considered those zOTUs as technical contaminants and deleted them from the dataset. We used the package phyloseq (v1.24.2) to perform a rarefaction analysis to test if a complete representation of the blow and seawater microbiota was achieved given the observed sequence sampling depths. Bacterial alpha and beta diversity of seawater and whale samples were assessed using the package vegan (v2.5-5) for community ecology analysis74. To determine alpha diversity, we calculated richness, Shannon–Wiener diversity index, Chao1 and ACE species estimator for whale and seawater samples using rarefied counts (lowest number of reads: 3432) to account for the difference in sampling depth (Table 1) and unrarefied counts (Supplementary Table S2). To visualize beta diversity, we used the Bray–Curtis dissimilarity coefficient and weighted and unweighted generalized UNIFRAC distances75 of unrarefied relative abundances. To determine if the composition of microbial communities of the two groups of whales and their corresponding seawater samples were significantly different from each other, a typical approach is to use PERMANOVA, the Permutational Multivariate Analysis of Variance76. However, the implicit assumptions made by distance-based analyses such as PERMANOVA about the mean–variance relationship of the outcome are often unrealistic in community composition data. As demonstrated by Warton et al.77, this misspecification can lead to the confounding of location and dispersion effects, and therefore incorrect conclusions. For this reason, we instead followed the approach recommended by Warton et al.77 and used mvabund (v4.0.1)78 to fit negative binomial regression models to each zOTU, with the log of total sequence counts per sample included as an offset. The sum of likelihood ratio statistics was used as a community-level statistic to compare models with ‘migration’ and ‘whale blow/seawater’ as an explanatory factor to an intercept-only model. Using mvabund, the sum of likelihood ratio statistics and statistical significance was evaluated with anova.manyglm using pit-trap resampling79. Further details are provided in the supplementary materials (S3, S4). Lokmer et al.80 applied a similar approach for the analysis of bacterial community composition in oysters. Statistical analysis of microbial community results was performed using R statistical software (v 3.5.1) (

Data availability

Sequence data of the blow (HumpbackSM), seawater (SeawaterSM) and the according air samples are available in the NCBI Sequence Read Archive under BioProject accession no. PRJNA521078.


  1. 1.

    Bik, E. M. et al. Marine mammals harbor unique microbiotas shaped by and yet distinct from the sea. Nat. Commun. 7, 10516 (2016).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Apprill, A. et al. Extensive core microbiome in drone-captured whale blow supports a framework for health monitoring. MSystems 2, e00119-e117 (2017).

    Google Scholar 

  3. 3.

    Pirotta, V. et al. An economical custom-built drone for assessing whale health. Front. Mar. Sci. 4, 425 (2017).

    Google Scholar 

  4. 4.

    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).

    ADS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    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).

    PubMed  Google Scholar 

  6. 6.

    Johnson, W. R. et al. Novel diversity of bacterial communities associated with bottlenose dolphin upper respiratory tracts. Environ. Microbiol. Rep. 1, 555–562 (2009).

    CAS  PubMed  Google Scholar 

  7. 7.

    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).

    Google Scholar 

  8. 8.

    Bassis, C. M., Tang, A. L., Young, V. B. & Pynnonen, M. A. The nasal cavity microbiota of healthy adults. Microbiome 2, 27 (2014).

    PubMed  PubMed Central  Google Scholar 

  9. 9.

    Dickson, R. P. et al. Bacterial topography of the healthy human lower respiratory tract. MBio 8, e02287-e2216 (2017).

    Google Scholar 

  10. 10.

    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).

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    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).

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    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).

    PubMed  PubMed Central  Google Scholar 

  13. 13.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    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).

    PubMed Central  Google Scholar 

  17. 17.

    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).

    CAS  PubMed  Google Scholar 

  18. 18.

    Garcia-Nuñez, M. et al. Severity-related changes of bronchial microbiome in chronic obstructive pulmonary disease. J. Clin. Microbiol. 52, 4217–4223 (2014).

    PubMed  PubMed Central  Google Scholar 

  19. 19.

    Dickson, R. P. et al. Cell-associated bacteria in the human lung microbiome. Microbiome 2, 28 (2014).

    PubMed  PubMed Central  Google Scholar 

  20. 20.

    Huang, Y. J. et al. Airway microbiota and bronchial hyperresponsiveness in patients with suboptimally controlled asthma. J. Allergy Clin. Immunol. 127, 372–381 (2011).

    PubMed  Google Scholar 

  21. 21.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Cardona, C. et al. Environmental sources of bacteria differentially influence host-associated microbial dynamics. MSystems 3, e00052-00018 (2018).

    Google Scholar 

  23. 23.

    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).

    Google Scholar 

  24. 24.

    International Whaling Commission. Annexe. Report of the sub-committee on other great whales. J. Cetacean Res. Manag. 1, 117–155 (1999).

    Google Scholar 

  25. 25.

    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).

    PubMed  PubMed Central  Google Scholar 

  26. 26.

    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).

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    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).

    Google Scholar 

  28. 28.

    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).

    ADS  Google Scholar 

  29. 29.

    Chittleborough, R. G. Dynamics of two populations of the humpback whale, Megaptera novaeangliae (borowski). Mar. Freshw. Res. 16, 33–128 (1965).

    Google Scholar 

  30. 30.

    Chittleborough, R. G. The breeding cycle of the female humpback whale, Megaptera nodosa (bonnaterre). Mar. Freshw. Res. 9, 1–18 (1958).

    Google Scholar 

  31. 31.

    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).

    Google Scholar 

  32. 32.

    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).

  33. 33.

    Franklin, T. et al. Seasonal changes in pod characteristics of Eastern Australian humpback whales (Megaptera novaeangliae). Mar. Mammal Sci. 27, E134–E152 (2011).

    Google Scholar 

  34. 34.

    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).

    Google Scholar 

  35. 35.

    Seymour, J. R. et al. Contrasting microbial assemblages in adjacent water masses associated with the East Australian current. Environ. Microbiol. 4, 548–555 (2012).

    CAS  Google Scholar 

  36. 36.

    Chao, A. Non-parametric estimation of the number of classes in a population. Scand. J. Stat. 11, 265–270 (1984).

    Google Scholar 

  37. 37.

    Chao, A. Estimating the population size for capture-recapture data with unequal catchability. Biometrics 43, 783–791 (1987).

    MathSciNet  CAS  PubMed  MATH  Google Scholar 

  38. 38.

    Chao, A. & Yang, M. C. Stopping rules and estimation for recapture debugging with unequal failure rates. Biometrika 80, 193–201 (1993).

    MathSciNet  MATH  Google Scholar 

  39. 39.

    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).

    MathSciNet  MATH  Google Scholar 

  40. 40.

    Dunn, O. J. Multiple comparisons using rank sums. Technometrics 6, 241–252 (1964).

    Google Scholar 

  41. 41.

    Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979).

    MathSciNet  MATH  Google Scholar 

  42. 42.

    Shade, A. & Handelsman, J. Beyond the Venn diagram: the hunt for a core microbiome. Environ. Microbiol. 14, 4–12 (2012).

    CAS  PubMed  Google Scholar 

  43. 43.

    Magurran, A. E. & Henderson, P. A. Explaining the excess of rare species in natural species abundance distributions. Nature 422, 714 (2003).

    ADS  CAS  PubMed  Google Scholar 

  44. 44.

    Hernandez-Agreda, A., Gates, R. D. & Ainsworth, T. D. Defining the core microbiome in corals’ microbial soup. Trends Microbiol. 25, 125–140 (2017).

    CAS  PubMed  Google Scholar 

  45. 45.

    Astudillo-García, C. et al. Evaluating the core microbiota in complex communities: a systematic investigation. Environ. Microbiol. 19, 1450–1462 (2017).

    PubMed  Google Scholar 

  46. 46.

    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).

    Google Scholar 

  47. 47.

    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).

    PubMed  Google Scholar 

  48. 48.

    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).

    PubMed  Google Scholar 

  49. 49.

    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).

    CAS  PubMed  Google Scholar 

  50. 50.

    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).

    CAS  PubMed  Google Scholar 

  51. 51.

    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).

    ADS  Google Scholar 

  52. 52.

    Lockyer, C. Body weights of some species of large whales. ICES J. Mar. Sci. 36, 259–273 (1976).

    Google Scholar 

  53. 53.

    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).

    Google Scholar 

  54. 54.

    Johnson, K. L. Capital and income breeding as alternative tactics of resource use in reproduction. Oikos 78, 57–66 (1997).

    Google Scholar 

  55. 55.

    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).

    Google Scholar 

  56. 56.

    Atkinson, A. et al. Krill (Euphausia superba) distribution contracts southward during rapid regional warming. Nat. Clim. Change 9, 142 (2019).

    ADS  Google Scholar 

  57. 57.

    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).

    Google Scholar 

  58. 58.

    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).

    ADS  Google Scholar 

  59. 59.

    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).

    Google Scholar 

  60. 60.

    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).

    Google Scholar 

  61. 61.

    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).

    CAS  PubMed  Google Scholar 

  62. 62.

    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).

    Google Scholar 

  63. 63.

    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).

    PubMed  PubMed Central  Google Scholar 

  64. 64.

    Hogg, C. J. et al. Determination of steroid hormones in whale blow: it is possible. Mar. Mammal Sci. 25, 605–618 (2009).

    CAS  Google Scholar 

  65. 65.

    Lane, D. J. et al. Rapid determination of 16s ribosomal RNA sequences for phylogenetic analyses. Proc. Natl. Acad. Sci. 82, 6955–6959 (1985).

    ADS  CAS  PubMed  Google Scholar 

  66. 66.

    Lane, D. J. 16s/23s rRNA sequencing. In Nucleic acid techniques in bacterial systematics (eds Stackebrandt, E. & Goodfellow, M.) (Wiley, New York, 1991).

    Google Scholar 

  67. 67. (2019) Babraham bioinformatics—fastqc a quality control tool for high throughput sequence data. 20 Jan 2019.

  68. 68.

    Edgar, R. C. Uparse: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Edgar, R. C. Unoise2: improved error-correction for illumina 16s and its amplicon sequencing. BioRxiv 081257 (2016).

  70. 70.

    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).

    PubMed  PubMed Central  Google Scholar 

  71. 71.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    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).

    PubMed  PubMed Central  Google Scholar 

  73. 73.

    Cole, J. R. et al. Ribosomal database project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 42, D633–D642 (2013).

    PubMed  PubMed Central  Google Scholar 

  74. 74.

    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. 30 Jan 2019.

  75. 75.

    Lozupone, C. & Knight, R. Unifrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Anderson, M. J. Permutational multivariate analysis of variance (PERMANOVA). Wiley statsref: statistics reference online, 1–15 (2014).

  77. 77.

    Warton, D. I., Wright, S. T. & Wang, Y. Distance-based multivariate analyses confound location and dispersion effects. Methods Ecol. Evol. 3, 89–101 (2012).

    Google Scholar 

  78. 78.

    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).

    Google Scholar 

  79. 79.

    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).

    PubMed  PubMed Central  Google Scholar 

  80. 80.

    Lokmer, A. et al. Spatial and temporal dynamics of Pacific oyster hemolymph microbiota across multiple scales. Front. Microbiol. 7, 1367 (2016).

    PubMed  PubMed Central  Google Scholar 

Download references


We thank Adelaide Dedden and Nahal Nejadi for field assistance and Brian and Jill Perry and their Crew of the QuickCat II for having us on board for blow sample collection. We also thank all lab members of the Ferrari lab for their support and advice. This project was funded by the Scott Foundation. Torsten Thomas and Belinda Ferrari were supported by the Australian Research Council.

Author information




T.N., T.T., T.R., B.F. and C.V. contributed to the design of the study. C.V. performed the collection of the data. E.S., B.W. and C.V. performed the analysis of the data. T.N., B.F., T.T., T.R. and C.V. contributed to the interpretation of the results. T.N., T.T., T.R. and C.V. contributed to the writing of the manuscript. All authors reviewed and approved of the final version of the manuscript.

Corresponding author

Correspondence to Catharina Vendl.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Vendl, C., Slavich, E., Wemheuer, B. et al. Respiratory microbiota of humpback whales may be reduced in diversity and richness the longer they fast. Sci Rep 10, 12645 (2020).

Download citation


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.