Microbiota of little penguins and short-tailed shearwaters during development

Meagan L. Dewar; John P. Y. Arnould; Theo R. Allnutt; Tamsyn Crowley; Lutz Krause; John Reynolds; Peter Dann; Stuart C. Smith

doi:10.1371/journal.pone.0183117

Abstract

The establishment and early colonisation of the gastrointestinal (GI) tract has been recognised as a crucial stage in chick development, with pioneering microbial species responsible for influencing the development of the GI tract and influencing host health, fitness and disease status throughout life. Development of the microbiota in long lived seabirds is poorly understood. This study characterised the microbial composition of little penguin and short-tailed shearwater chicks throughout development, using Quantitative Real Time PCR (qPCR) and 16S rRNA sequencing. The results indicated that microbial development differed between the two seabird species with the short-tailed shearwater microbiota being relatively stable throughout development whilst significant fluctuations in the microbial composition and an upward trend in the abundance of Firmicutes and Bacteroidetes were observed in the little penguin. When the microbial composition of adults and chicks was compared, both species showed low similarity in microbial composition, indicating that the adult microbiota may have a negligible influence over the chick’s microbiota.

Citation: Dewar ML, Arnould JPY, Allnutt TR, Crowley T, Krause L, Reynolds J, et al. (2017) Microbiota of little penguins and short-tailed shearwaters during development. PLoS ONE 12(8): e0183117. https://doi.org/10.1371/journal.pone.0183117

Editor: Dee A. Carter, University of Sydney, AUSTRALIA

Received: May 4, 2017; Accepted: July 28, 2017; Published: August 14, 2017

Copyright: © 2017 Dewar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Submitted to the NCBI SRA archive (Bioproject PRJNA347517).

Funding: This work was supported by funding from Holsworth Wildlife Research Endowment, Centre for Molecular and Medical Research (Deakin University), Life and Environmental Sciences (Deakin University) and Phillip Island Nature Parks. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors do not have any competing interests.

Introduction

At birth, the gastrointestinal (GI) tract is considered to be devoid of any microbiota [1]. In mammals, the microbial communities are inherited from the mother through contact with their faecal and vaginal microbes and from breast milk [1]. In birds, however, newly hatched chicks acquire their microbiota from multiple sources including, the surface of the egg, the surrounding environment (i.e. nest) and their first meal [2–9]. In seabirds, vertical transmission of microbes via regurgitation of an undigested or partially digested meal is also said to influence microbial colonisation [10]. The GI tract is rapidly colonised by aerobic and facultative anaerobic bacteria such as Escherichia coli and Streptococci spp. immediately after birth [6, 11–14]. After initial colonisation, aerobic bacteria modify the GI tract environment, by reducing the oxygen level through oxidation/reduction. This process creates an environment that is highly favourable for facultative and obligate anaerobes [13, 15], such as Bacteroides, Clostridia, and Bifidobacterium [13, 16] to flourish before stabilising as an anaerobic environment that is representative of the adult GI tract.

This study examined the GI microbiome of the short-tailed shearwater (STS) (Ardenna tenuirostris) and the little penguin (LP) (Eudyptula minor). STS breed between September and April with egg laying occurring in the last week of November [17]. STS nest in burrows, lined with vegetation and lay one egg, which is incubated for a period of 50–55 days [18, 19]. In contrast to other seabirds, Procellariiformes are able to regulate body temperature much earlier, reducing their guard stage (where parents protect the chick from the cold) from 2–3 weeks to just to 2–3 days. This ability to regulate body temperature is in part due to the subcutaneous fat deposits laid down during the first few days of life [19]. Like all Procellariiformes, STS use a twofold foraging strategy during the breeding season, with adults alternating between two short foraging trips of 1–4 days, with one long foraging trip of approximately 8–19 days [20, 21]. Previous studies have documented that the diet of short-tailed shearwaters is dominated by Australian krill, Nyctiphanes australis [18, 21, 22]. However, in contrast to previous studies, Weimerskirch & Cherel [20], discovered that during long foraging trips, Antarctic krill and Myctophid fish also dominated the diet of STS found breeding in Australia. In Victoria, STS feed in local waters predominantly eating crustaceans, Nyctiphanes australis and Paraprone clausi, [22, 23]. Although chicks do not fledge until around 97 days old, adults begin their migration north when their chicks are around 85 days old. For the final 2–3 weeks prior to fledging chicks must survive off their endogenous fat stores [24].

At Phillip Island, Australia, LP breed between August to February [25], with peak egg laying occurring between September and October [26]. Similarly to STS, LPs nest in burrows, but will also nest under rocks, in crevices and artificial nest boxes lined with vegetation. LPs produce two eggs which are incubated for 35 days [26, 27]. Unlike STS, the guard state in LP lasts around 2–3 weeks as chicks are unable to regulate their body temperature during the early stages of development [28, 29], During the guard stage, chicks are fed on average every 1–2 days [30], whereas during the post-guard stage adults alternate between short and long foraging trips [31], with fledging occurring between 60–70 days (9–10 weeks) of age [28].

The aim of this study was to characterise the microbial community of these two seabird species during development from newly hatched chicks to fully fledged juveniles. Quantitative Real Time PCR and pyrosequencing of the 16S rRNA gene were used to identify the abundance of major taxa and microbial composition at different stages during development. To our knowledge, this is the first study to investigate the microbial composition of seabirds throughout development.

Experimental design

Study design and sample collection

All ethics approval was received by Phillip Island Nature Parks and Deakin University Animal Ethics Committees. Permits were received from the Department of Sustainability and Environment. Cloacal swabs were collected from little penguin (n = 9) and short-tailed shearwater chicks (n = 10) during development at the Phillip Island Nature Parks (38.4833°S, 145.2333°E). Little penguin samples were collected within the Summerland Peninsula, a previous housing estate, which had been reclaimed for penguin habitat. Due to severe compaction of the ground, burrows in the area consist of artificial wooden burrows. Burrows are lined with vegetation and excrement from previous breeding seasons. At the beginning of the breeding season, 10 nest boxes with eggs present were selected, with each nest containing two eggs. Of the 20 eggs, 16 successfully hatched, with nine chicks successfully fledging at the end of the breeding season. Short-tailed shearwater samples were collected from shearwaters nesting in natural burrows among the sand dunes of the Phillip Island Nature Parks, Penguin Parade. At the beginning of the breeding season, 20 burrows with eggs were randomly selected. Of the 20 eggs, 17 successfully hatched, with 10 chicks successfully fledging at the end of the breeding season.

Following hatching, cloacal swabs were collected from each chick directly from the cloacae using Eswabs (Copan, Italy). On the same day, each week (LPs)/fortnight (STS), chicks would be removed from their nest, weighed, inked (red or green food dye) for identification purposes (little penguin chicks), swabbed and then placed back into the nest within 5–10 minutes to avoid stress. Both male and female adults were captured at night when returning to their nests. Due to their feeding strategies, adult shearwaters and little penguin adults were not always present in the colony at times of sampling. Samples were collected via direct cloacal swab. Samples were placed into an amine solution for preservation of the DNA and stored in liquid nitrogen (-196°C) for shipment and then stored at -80°C until DNA extraction.

DNA extraction and real time PCR

DNA was extracted from the swabs using the Qiagen^™ QIAamp DNA Stool Mini Kit (Hilden, Germany) following the manufacturer’s instructions. The major phyla selected for analysis in this study were selected on the basis of previous studies that had examined the predominant gastrointestinal microbiota of vertebrates [22–27], which included Firmicutes, Bacteroidetes, Actinobacteria and Proteobacteria [28]. The primer sequences and annealing temperatures for the chosen bacterial groups can be found in Dewar et al [32]. The quantitative real time PCR was performed on a Stratagene MX3000P. Each PCR reaction mixture comprised of 5 μl of Brilliant II SYBR green (Stratagene^™), 20 pmol/μL of forward and reverse primer, 2 ng of template DNA and made up to a final volume of 20 μl with nuclease free water. The cycling conditions were 95°C for 2 mins, followed by 40 cycles of 95°C for 5 secs, followed by annealing temperature [32] for 30 secs with all samples run in triplicate. Bacterial concentration was determined by comparing the threshold value (Ct. Values) with a standard curve. The standard curve was created by using a serial 10-fold dilution from DNA extracted from a pure culture of E. coli ranging from 10²–10¹⁰ CFU/g.

PCR amplification and 16S rRNA pyrosequencing

From the original chick samples, one sample per fortnight from four Individuals for each species was amplified using universal primers Roche adapter A (5’GCC TCC CTC GCG CCA TCA GT-3’) and reverse 338R (5’-CAT GCT GCC TCC CGT AGG AGT-3’) to amplify the V2-V3 region as per Dewar et al [32]. Due to irregular attendance by adults during the breeding season, samples during the guard stage (1^st week post hatching) were used for analysis. 16S rDNA from faecal samples from four individuals per species, was amplified as above. Following amplification samples were pooled with the attachment of MID tag barcodes to ensure that bacterial DNA from all members of each species was equally represented (i.e. Barcode 338R_BC0500 “CAGCTCAACTA” was attached to all week 1 samples). Samples were then sequenced on the Roche/454 FLX Genome Sequencer by Engencore (USA) according to Fierer et al [33].

Data processing and analysis

Unprocessed reads were submitted to the NCBI SRA archive (Bioproject PRJNA347517). The UPARSE 454 pipeline was used for 16S amplicon analysis [34] with a 3% cluster radius. Reads were filtered, prior to UPARSE, to a minimum length of 300 bp. The RDP 16S database v16 (http://drive5.com/utax/data/rdp_v16.tar.gz) was used to taxonomically classify OTUs in UPARSE with a 90% confidence threshold. UPARSE detected 498 OTUs. OTUs with abundance less than 0.1% of the total number of reads or occurrence in less than two samples across all samples were removed from analysis, 62 OTUs remained. QIIME 1.8 [35] was used to calculate diversity metrics and statistically compare diversity and OTU abundance between species. OTU abundance was CSS normalised prior to statistical analysis [36]. Pearson correlations were calculated to examine changes in OTU abundance and diversity over time. All commands and associated python scripts in the analysis are provided in the GitHub repository: https://github.com/bioinformatics-deakin/Dewar_and_Allnutt_et.al_2016. Principal coordinates analysis (PCoA) was performed on weighted UniFrac distances [37] calculated from a maximum likelihood tree (FastTree v2.3.1, [38]) of the filtered OTUs. Beta diversity was analysed by AMOVA [39].

Prediction of the functions of the microbial communities

PICRUSt v1.0 [40] was used to predict functional pathways of the GI communities from the 16S rDNA data using the KEGG pathway database. Differences in abundance of pathways were tested using ANOVA in QIIME. All scripts and commands used in bioinformatic analyses are detailed in: https://github.com/bioinformatics-deakin/Dewar_and_Allnutt_et.al_2016.

qPCR statistical analysis

In order to explore the variation in bacterial counts from the qPCR analysis, linear mixed models with individual birds and samples from birds, as random effects and the common sampling times (weeks) as a fixed effect were fitted to the log (base 10) transformed bacterial counts–this variance-stabilizing transformation was deemed to be necessary after inspection of diagnostic plots of Studentized residuals versus predicted values [41] for analyses based on the raw counts.

Six types of linear mixed models were investigated (three autocorrelation models for the repeated measurements by two models for the effect of time):

Repeated Measurements:

No autocorrelation in the repeated counts from the penguin chicks and homogeneous variances at each assessment (scaled identity model).
No autocorrelation in the repeated counts from the penguin chicks and heterogeneous variances at each assessment (diagonal model).
First order autoregressive process for the co-variation in the repeated counts (AR (1) model) and homogeneous variances.

Time effect:

Week as a categorical factor (i.e. no systematic trend)
Week as a linear trend.

For each abundance measure (i.e. log-transformed count), the fitted model with the smallest value of the Akaike Information Criterion (AIC) [42] was selected and the significance of the association of the common assessment times with variation in abundance was assessed using the F-test. If the selected model included week as a categorical factor, weekly means and their standard errors, as calculated from the fitted fixed model, were reported otherwise, if the selected model included week as a linear trend, the estimated intercept and slope coefficients, and their standard errors, were reported. All analyses used the mixed model procedures in the IBM SPSS Statistics package (Version 20) and unless otherwise stated, all abundance measures are reported on the log (base 10) scale.

Results

Quantitative real time PCR

The abundance of four major phyla; Firmicutes, Bacteroidetes, Actinobacteria and Proteobacteria was analysed using quantitative real time PCR. In LPs, Bacteroidetes and Firmicutes appeared to dominate the microbial composition throughout development, followed by Proteobacteria. Actinobacteria had the lowest abundance. During weeks 1–3, 5, 7–9 Firmicutes appeared to dominate the microbial composition, whilst during weeks 4, 6 and 10 Bacteroidetes were prevalent (Fig 1a). When analysing the data using a linear regression model with time as a covariate, a significant increase was observed for both Firmicutes and Bacteroidetes from hatching to fledging. For STS chicks, Firmicutes appeared to dominate the microbial composition throughout the entire breeding season. When analysing the data using a linear regression model with time as a covariate, no significant trends in the abundance of the major phyla were detected in the STS chicks (Fig 1b).

Download:

Fig 1.

a. Mean abundance of the major phyla; Actinobacteria (a), Bacteroidetes (b), Firmicutes (c) and Proteobacteria (d) during development in little penguin chicks b. Mean abundance of the major phyla; Actinobacteria (a), Bacteroidetes (b), Firmicutes (c) and Proteobacteria (d) during development in short-tailed shearwater chicks.

https://doi.org/10.1371/journal.pone.0183117.g001

When comparing the microbial composition of the two seabird species during development, significant species effects for Actinobacteria (P <0.001), Firmicutes (P <0.005) and Proteobacteria (P <0.001) were observed with LP log(Actino) values being, on average, -3.602 (± 0.384) below STS values, LP log(Firm) values being, on average, -0.686 (± 0.277) below STS values and LP log(Proteo) values being, on average, -1.154 (± 0.206) below STS values. No significant species effect for Bacteroidetes (P = 0.485) was noted with LP log(Bacter) values being, on average, 0.193 (± 0.248) above STS value. With time fitted as a continuous covariate there was a significant linear trend in Actinobacteria (P <0.005) common to both species with log(Actino) values increasing 0.0585 (±0.0194) per week. However, the species by time interaction was not significant (P = 0.207). A significant trend, common to both species, for time fitted as a continuous covariate was also significant for Firmicutes (P<0.001), with log(Firm) values increasing 0.0360 (±0.0211) per week. The species by time interaction was significant (P = 0.033) with an additional linear increase of 0.0729 (±0.0338) per week in the LP species. Whilst for Bacteroidetes (P = 0.557) and Proteobacteria (P = 0.302) there was no linear trend for time. However, the species by time interaction was significant for Bacteroidetes (P <0.001) with a linear increase of 0.0987 (±0.0217) per week in the LP species.

PCoA and beta diversity

UPARSE identified 498 OTUs at the 97% identify level. After filtering to 0.1% abundance and occurrence in at least two samples, 63 OTUs remained. Fig 2 shows a heat map of OTU abundance for the 20 most abundant OTUs. STS and LP species form distinct clusters with significantly different abundances of taxa: at the class level, Flavobacteria, Betaproteobacteria, and Bacilli were more abundant in STS than LP (FDR = 0.002, 0.004, and 0.002 respectively); and Clostridia and Fusobacteria were more common in LP than STS (FDR = 0.01, 0.05 respectively). At the order level, Flavobacteriales and Burkholderiales were more abundant in STS than LP (FDR = 0.005, 1.24 x 10⁻¹¹ respectively); and Clostridiales and Fusobacteriales were more common in LP than STS (FDR = 0.01, 0.02 respectively). At the family level, Corynebacteriaceae, Veillonellaceae, Flavobacteriaceae, Burkholderiaceae, Planococcaceae, Streptococcaceae, Leuconostocaceae were more abundant in STS than LP (FDR = 0.03, 0.03, 0.0003, 9.4242 x10^-10, 0.007, 0.0001, 0.0003 respectively); and Eubacteriaceae and Fusobacteriaceae were more common in LP than STS (FDR = 0.07, 0.03 respectively). At the genus level, Chryseobacterium, Burkholderia, Streptococcus, Acinetobacter, Coenonia, Lactococcus, and Leuconostoc were more abundant in STS than LP (FDR = 0.002, 1.5 x 10⁻⁵, 0.003, 0.05, 0.006, 7.8 x 10⁻⁶, 0.0001 respectively); Psychrobacter and Clostridium_XlVb were more common in LP than STS (FDR = 0.05, 0.04 respectively).

Download:

Fig 2. Heat map of 20 most abundant OTUs in STS and LP chicks and adults.

Assigned taxonomy (UPARSE, 90% confidence) is shown for each OTU.

https://doi.org/10.1371/journal.pone.0183117.g002

Fig 3 shows a PCoA of UniFrac distances of all STS and LP samples. Microbial samples clearly cluster by host species, indicating that STS and LP have very different GI bacterial populations. STS also forms a tighter cluster than LP, indicating a lower diversity of OTUs. There is no evidence of clustering by age. AMOVA identified a significant proportion of variation in UniFrac distance between species, 43%, P = 0.001.

Download:

Fig 3. PCoA of weighted UniFrac distances among STS and LP samples.

Points are labelled with the chick age (weeks). LP = little penguin, STS = short tailed shearwater. PCO 1 and 2 shown, described 49% and 23% of total variation respectively.

https://doi.org/10.1371/journal.pone.0183117.g003

Alpha diversity

Alpha diversity measures, Shannon's, Simpson's and observed OTUs, calculated for STS and LP species, are shown in Table 1. Rarefaction depth was set to the minimum for the filtered OTU set (2104). The number of observed OTUs was significantly different between species (LP mean = 25; STS mean = 32; P = 0.025). Shannon's and Simpson's diversity measures were not significantly different between species. Increase in alpha diversity was not evident during the development period studied, with no significant correlation for all measures (Table 2).

Download:

Table 1. Alpha diversity measures based on a rarefaction depth of 2104.

https://doi.org/10.1371/journal.pone.0183117.t001

Download:

Table 2. Alpha diversity correlated with development age of chicks.

https://doi.org/10.1371/journal.pone.0183117.t002

Taxonomic composition

Bar charts of taxonomic composition are shown in Fig 4. The distribution of major phyla varied between STS and LP. Firmicutes were significantly more abundant in STS than LP (ANOVA, P = 0.023). Fusobacteria appeared more common in LP, but were not statistically significant—reflecting the large variability in this Phylum in LP, where it was absent from the youngest LP chick sample and the LP adult. At the class level, only Bacilli were significant, being more common in STS (P = 0.007). At the level of order, Lactobacillales, Burkholderiales, and Selenomonadales were more prevalent in STS than LP (P = 0.044, 0.044 and 0.049 respectively). There were no significant differences at family level. At genus level five taxa were significantly more abundant in STS than LP: Leuconostoc, Chryseobacterium, Streptococcus, Burholderia, and Lactococus; P = 0.004, 0.014, 0.033, and 0.042 respectively. Taxon abundance was not correlated (Pearson correlation, t-test P > 0.05) to chick age for any taxa.

Download:

Fig 4. Bar charts showing the taxonomic diversity of LP and STS samples (proportion of total reads assigned to each taxon).

Chick age is shown in weeks.

https://doi.org/10.1371/journal.pone.0183117.g004

Inferred functional diversity

PICRUSt identified 219 biological processes in both little penguins and short-tailed shearwaters including pathways in cellular processes, Environmental Information Processing, Genetic information processing and metabolism (S1 Table). None of the identified processes differed significantly between species and none correlated significantly with chick age.

Discussion

Establishment and early colonisation of the GI tract has been recognised as a crucial stage in chick development with pioneering microbial species responsible for influencing the GI tract pH, oxygen levels, mucosal structure and immunity, and therefore, these pioneering species influence a host’s overall health and disease status throughout life [43–45]. Immediately after birth/hatching, the GI microbiota is rapidly colonised and undergoes successional changes until a dense and stable community is achieved. In most vertebrates, this process can take anywhere from 40 days [6, 12, 46–48] to 2 years [13, 49–51]. In black legged kittiwakes (Rissa tridactyla) chicks are colonised by many transient species, gradually transitioning into a more stable microbiota that is representative of the adult microbiota [52]. To date there are limited data available on the GI microbiota of long lived seabirds, and the microbial succession that occurs during development. Accordingly, this study aimed to examine the establishment and microbial succession that occurs in little penguin and short-tailed shearwater chicks throughout development.

Little penguins

As with other young vertebrates, the little penguin microbiota is dominated by aerobic and facultative anaerobic bacteria, with the total population, estimated at over 10⁷ CFU/ml by 7 days, with a significant upward trend observed throughout development in Firmicutes and Bacteroidetes populations [134,166]. During the early stages of development, the microbiota is dominated by members of the family Enterobacteriaceae which have been identified as pioneering populations associated with gut maturation and development, absorption of dietary fats and lipids, and soluble vitamins, enhance the immune system, alteration of lipoprotein profiles, modulation of proinflammatory and anti-inflammatory gene expression. However, these microbes have also been implicated in GI pathologies, brain-gut axis, and neurological conditions [53–60]. While Psychrobacter, isn’t a normal component of the infant microbiota, it has been shown to have probiotic properties boosting the immune system and weight gain of Groupers [61]. These pioneering microbes are also said to alter the GI environment in favour of the growth of anaerobic bacteria. Similar to other studies, the microbiota of the little penguin began to transition from an aerobic/facultative anaerobic microbial population, to a strict anaerobic population by week 3, and continued throughout development, with the anaerobic population being dominated by butyrate producing microbes by week 5. In poultry, butyrate has been shown to not only boost the immune system and protect against colonisation by pathogenic bacteria, but has also been shown to influence host adiposity [62]. In little penguins, there is a strong link between host body mass and fledging survival. Therefore, the high abundance of butyrate producing microbes and presence of Psychrobacter in little penguin chicks could influence chick body mass positively and therefore fledging survival. However, this would require further analysis.

Short-tailed shearwater

Throughout the development of short-tailed shearwater chicks, the microbiota is dominated by members of the phylum Firmicutes. In humans and mice, the ratio of Firmicutes to Bacteroidetes has been shown to influence a host’s adiposity level by enhancing energy extraction and modulating genes that regulate fat storage [63–67] and is currently considered to play a crucial role in host adiposity and obesity. In obese individuals, the ratio of Firmicutes to Bacteroidetes is 100:1, whereas in their lean counterparts the ratio is 10:1 [63, 64, 67]. Similarly to adult short-tailed shearwaters, the ratio of Firmicutes to Bacteroidetes in short-tailed shearwater chicks ranged from 73:1 to 85:1 and are similar to that of obese humans, mice and Australian fur seals, except for week 5, where the ratio was 62:18 [63, 64, 67, 68]. Following hatching, the body mass of short-tailed shearwater chicks rapidly increased and by week 7, the body mass of chicks exceeded that of the adult shearwater by 15–50% [69]. While many factors, such as a high lipid diet and gut physiology, may influence body mass, the high abundance of Firmicutes of the short-tailed shearwater chick may confer a predisposition towards developing body fat stores. A chick’s ability to rapidly accumulate large fat reserves enables chicks to survive the long periods between feeds and is essential for survival post fledging [70]. Unlike penguins, the microbiota of short-tailed shearwaters showed little variation in composition and diversity throughout development; even during the final stages of development when adults abandon their chicks to return to the northern hemisphere, leaving chicks to survive off their endogenous fat reserves [24]. However due to the absence of data available on the role of microbes in seabirds, the importance of the microbiota for the survival of little penguins and short-tailed shearwaters requires more study.

Species differences

Establishment and early colonisation of the GI tract has been recognised as a crucial stage in infant development with pioneering species responsible for influencing the GI tract pH, oxygen levels, mucosal structure and immunity [43–45]. Immediately after hatching/birth, the GI microbiota is rapidly colonised and undergoes successional changes until a dense and stable community is achieved. In most vertebrates, this process can take anywhere from 40 days [6, 12, 46–48] to 2 years [13, 49–51]. The results from this study identified that the development of the seabird microbiota differs greatly between species. In the little penguin, there were significant differences throughout development and significant upward trends in the abundance of Firmicutes and Bacteroidetes, whereas, in the short-tailed shearwater there were no significant differences in the microbial composition throughout development and appeared to be relatively stable throughout development with a high level of similarity between each age class.

The microbial composition of the little penguin is dominated by microbes that are responsible for the absorption of dietary fats, lipids, and soluble vitamins, they maintain intestinal barrier function, regulate triglycerides, impact intestinal permeability, activate intestine-brain-liver neural axis, enhance the immune system, alter lipoprotein profiles, protect against stress-induced lesions, modulate proinflammatory and anti-inflammatory gene expression, butyrate production [71] and strengthen epithelial cell barrier properties. However, these microbes have also been implicated in GI pathologies, brain-gut axis, and a few neurological conditions [53–59]. In the short-tailed shearwater chick, however, the GI microbiota appears to be associated with soil and lactic acid bacteria that are not commonly associated with the GI tract.

In seabirds, pre-fledging body mass is associated with juvenile survival and therefore, the accumulation of fat reserves is important for post fledging survival. In little penguin chicks the relatively high abundance of butyrate-producing microbes could influence body mass, as previously observed in poultry [62]. Although the ratio of Firmicutes to Bacteroidetes strongly resembles that of obese humans, mice and Australian pinnipeds, the microbes within the phylum Firmicutes (Lysinibacillus, Leuconostoc and Lactococcus) have previously not been associated with energy extraction, lipid accumulation or obesity.

The differences between little penguins and short-tailed shearwaters could be in part due to differences in diet composition, digestive physiology, nesting environment and host phylogeny, all of which have been previously shown to strongly influence the microbial composition of vertebrates [7, 72–74]. According to the PICRUSt predictions, the microbiome may be involved in a number of metabolic, cellular, environmental and genetic information processing. No significant differences between functional pathways and species or age could be found. It must be noted that PICRUSt is a computation model that predicts the functional composition of the microbiome, by using marker gene data and a database of reference genomes [40], and are therefore a prediction of the potential functional role of microbes in seabird chicks. Therefore, further metagenomic and functional studies are required to identify the role these specific microbes play in seabird chicks.

Conclusion

The results from this study identified that the development of the seabird microbiota differs greatly between LP and STS. In the little penguin, there were significant differences in changes throughout development, with a significant upward trend in the abundance of Firmicutes and Bacteroidetes. Whereas, in the short-tailed shearwater there were no significant differences throughout development with a high level of similarity between each age class. However, there is low similarity in the microbial composition between adults and chicks for both the STS and LP, indicating that the adult’s microbiota may have a negligible influence over the chick’s microbiota. The differences between little penguins and short-tailed shearwaters could be in part due to differences in diet composition, digestive physiology, nesting environment, host phylogeny and reproductive strategy, all of which have been previously shown to strongly influence the microbial composition of vertebrates [7, 72–74].

In seabirds, fledging body mass is associated with juvenile survival [75–78] and therefore, the accumulation of fat reserves is important for post fledging survival. In little penguin chicks the relatively high abundance of butyrate-producing microbes could influence body mass, as previously observed in poultry [62]. Although the ratio of Firmicutes to Bacteroidetes in short-tailed shearwaters strongly resembles that of obese humans, mice and Australian Sea Lions (Neophoca cinerea), the microbes within the phylum Firmicutes (Lysinibacillus, Leuconostoc and Lactococcus) have not previously been associated with energy extraction, lipid accumulation or obesity. Due to the absence of information on the functional role of the dominant microbiota, we are unable to elucidate the role of the GI microbiota in fledging survival of little penguins and short-tailed shearwaters.

Supporting information

S1 Table. Functional pathways predicted by PICRUSt and statistical analysis.

https://doi.org/10.1371/journal.pone.0183117.s001

(XLSX)

Acknowledgments

The authors would like to thank; David, Kaye and Belinda Dewar, Joel Maloney, Dr Louise Kelly and Dr Tiana Preston for assistance in the field and Dr Martha Zakrzewski for her bioinformatics assistance. The authors would also like to acknowledge Engencore and Ribocon for their assistance. All ethics approval was received by Phillip Island Nature Parks and Deakin University Animal Ethics Committees.

References

1. Palmer C, Bik EM, DiGiulio DB, Relman DA, Brown PO. Development of the human infant intestinal microbiota. PLoS Biology. 2007;5(7):e117. pmid:17594176
- View Article
- PubMed/NCBI
- Google Scholar
2. Apajalahti J, Kettunen A. Microbes of the chicken gastrointestinal tract. In: Perry GC, editor. Avian Function in Health and Disease. 28. Bristol: CAB International; 2005. p. 124–7.
3. Gabriel I, Lessire M, Mallet S, Guillot JF. Microflora of the digestive tract: critical factors and consequences for poultry. World's Poultry Science Journal. 2006;62(03):499–511.
- View Article
- Google Scholar
4. Lu J, Idris U, Harmon B, Hofacre C, Maurer JJ, Lee MD. Diversity and succession of the intestinal bacterial community of the maturing broiler chicken. Applied and Environmental Microbiology. 2003;69(11):6816–24. pmid:14602645
- View Article
- PubMed/NCBI
- Google Scholar
5. Lucas FS, Heeb P. Environmental factors shape cloacal bacterial assemblages in great tit Parus major and blue tit P. caeruleus nestlings. Journal of Avian Biology. 2005;36(6):510–6.
- View Article
- Google Scholar
6. Rehman HU, Vahjen W, Awad WA, Zentek J. Indigenous bacteria and bacterial metabolic products in the gastrointestinal tract of broiler chickens. Archives of Animal Nutrition. 2007;61(5):319–35. pmid:18030916
- View Article
- PubMed/NCBI
- Google Scholar
7. Torok VA, Ophel-Keller K, Hughes RJ, Forder R, Ali M, MacAlpine R. Environment and age: impact on poultry gut microflora. Australian Poultry Science Symposium 2007. p. 149–52
- View Article
- Google Scholar
8. Yi Yin, F Lei, L Zhu, S Li, Z Wu, R Zhang, et al. Exposure of different bacterial inocula to newborn chicken affects gut microbiota development and ileum gene expression. International Society for Microbial Ecology Journal. 2009;4(3):367–76. http://www.nature.com/ismej/journal/v4/n3/suppinfo/ismej2009128s1.html.
- View Article
- Google Scholar
9. Fuller R. Microbial activity in the alimentary tract of birds. Proceedings of the Nutrition Society. 1984;43(01):55–61.
- View Article
- Google Scholar
10. Potti J, Moreno J, Yorio P, Briones V, Garcia-Borboroglu P, Villar S, et al. Bacteria divert resources from growth for magellanic penguin chicks. Ecology Letters. 2002;5(6):709–14.
- View Article
- Google Scholar
11. Amit-Romach E, Sklan D, Uni Z. Microflora ecology of the chicken intestine using 16S ribosomal DNA primers. Poultry Science. 2004;83(7):1093–8. pmid:15285498
- View Article
- PubMed/NCBI
- Google Scholar
12. Coloe PJ, Bagust TJ, Ireland L. Development of the normal gastrointestinal microflora of specific pathogen-free chickens. The Journal of Hygiene. 1984;92(1):79–87. pmid:6229572
- View Article
- PubMed/NCBI
- Google Scholar
13. Mackie RI, Sghir A, Gaskins HR. Developmental microbial ecology of the neonatal gastrointestinal tract. The American Journal of Clinical Nutrition. 1999;69(5):1035S–45S. pmid:10232646
- View Article
- PubMed/NCBI
- Google Scholar
14. Vaishampayan PA, Kuehl JV, Froula JL, Morgan JL, Ochman H, Francino MP. Comparative metagenomics and population dynamics of the gut microbiota in mother and infant. Genome Biology and Evolution. 2010;2:53–66. pmid:20333224
- View Article
- PubMed/NCBI
- Google Scholar
15. Penders J, Thijs C, Vink C, Stelma FF, Snijders B, Kummeling I, et al. Factors influencing the composition of the intestinal microbiota in early infancy. Pediatrics. 2010;118(2):511–21.
- View Article
- Google Scholar
16. Stark PL, Lee A. The microbial ecology of the large bowel of breast-fed and formula-fed infants during the first year of life. Journal of Medical Microbiology. 1982;15:189–203. pmid:7143428
- View Article
- PubMed/NCBI
- Google Scholar
17. Seventry DL, Seventry V, Warham J. The handbook of Australian seabirds. Sydney: A.H. & A.W. Reed Ltd.; 1971.
18. Carey M. Effects of investigator disturbance on the reporductive success of Short-tailed Shearwater [Honours]. Bundoora: La Trobe University; 2011.
19. Warham J. The petrel: their ecology and breeding systems. London: Academic Press; 1990.
20. Weimerskirch H, Cherel Y. Feeding ecology of short-tailed shearwaters: breeding in Tasmania and foraging in Antarctic? Marine Ecology Progress Series. 1998;167:261–74.
- View Article
- Google Scholar
21. Klomp NI, Schultz MA. Short-tailed shearwaters breeding in Australia forage in Antarctic waters. Marine Ecology Progress Series. 2000;194:307–10.
- View Article
- Google Scholar
22. Montague TL, Cullen JM, Fitzherbert K. The diet of the short-tailed shearwater Puffinus tenuirostris during its breeding season. Emu. 1986;86:207–13.
- View Article
- Google Scholar
23. Schumann N. Trophic relationships of Bass Strait seabirds. Melbourne: Deakin University; 2012.
24. Oka N. Differential assimilation efficiency of prey and growth of short-tailed shearwater chicks. Ornithological Science. 2011;10(1):39–50.
- View Article
- Google Scholar
25. Merchant S, Higgins PJ. Handbook of Australian, New Zealand and Antarctic birds. Melbourne: Oxford University Press; 1990.
26. Reilly PN, Cullen JM. The little penguin Eudyptula minor in Victoria. II: breeding. Emu. 1981;81:1–19.
- View Article
- Google Scholar
27. Chiaradia A, Kerry KR. Daily nest attendence and breeding performance in the little penguin Eudyptula minor at Phillip Island, Australia. Marine Ornithology. 1999;27:13–20.
- View Article
- Google Scholar
28. Reilly P. Penguins of the world. New York: Oxford University Press; 1994.
29. Stahel C, Gales R. Little penguin: fairy penguins in Australia. Taxonomy: The naming of names. Kensington: New South Wales University Press; 1991.
30. Chiaradia A, Nisbet ICT. Plasticity in parental provisioning and chick growth in little penguins in years of high and low breeding success. Ardea 2006;94:257–70.
- View Article
- Google Scholar
31. Saraux C, Robinson-Laverick SM, Le Maho Y, Ropert-Coudert Y, Chiaradia A. Plasticity in foraging strategies of inshore birds: how little penguins maintain body reserves while feeding offspring. Ecology. 2011;92(10):1909–16. pmid:22073782
- View Article
- PubMed/NCBI
- Google Scholar
32. Dewar ML, Arnould JPY, Dann P, Groscolas R, Trathan P, Smith SC. Inter-specific variations in the gastrointestinal microbiota of penguins. MicrobiologyOpen. 2013;2(1):195–204. pmid:23349094
- View Article
- PubMed/NCBI
- Google Scholar
33. Fierer N, Hamady M, Lauber CL, Knight R. The influence of sex, handedness, and washing on the diversity of hand surface bacteria. Proceedings of the National Academy of Sciences. 2008. pmid:19004758
- View Article
- PubMed/NCBI
- Google Scholar
34. Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods. 2013;10(10):996–8. pmid:23955772
- View Article
- PubMed/NCBI
- Google Scholar
35. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nature Methods. 2010;7(5):335–6. pmid:20383131
- View Article
- PubMed/NCBI
- Google Scholar
36. Paulson JN, Stine OS, Bravo HC, Pop M. Differential abundance analysis for microbial marker-gene surveys. Nature Methods. 2013;10:1200–2. pmid:24076764
- View Article
- PubMed/NCBI
- Google Scholar
37. Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Applied and Environmental Microbiology. 2005;71(12):8228–35. pmid:16332807
- View Article
- PubMed/NCBI
- Google Scholar
38. Price MN, Dehal PS, Arkin AP. FastTree 2—Approximately maximum-likelihood trees for large alignments. PLoS One. 2010;5(3):e9490. pmid:20224823
- View Article
- PubMed/NCBI
- Google Scholar
39. Excoffier L, Smouse PE, Quattro JM. Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics. 1992;131(2):479–91. pmid:1644282
- View Article
- PubMed/NCBI
- Google Scholar
40. Langille MGI, Zaneveld J, Caporaso J, G., McDonald D, Knights D, et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nature Biotechnology. 2013;31(9):814–21. pmid:23975157
- View Article
- PubMed/NCBI
- Google Scholar
41. Snedecor GW, Cochran WG. Statistical methods: John Wiley & Sons; 1991.
42. Burnham KP, Anderson DR. Model selection and multimodel interference: A practical information-theoretic approach. 2nd ed: Spinger-Verlag; 2002.
43. Jost T, Lacroix C, Braegger CP, Chassard C. New insights in gut microbiota establishment in healthy breast fed neonates. PLoS ONE. 2012;7(8):e44595. pmid:22957008
- View Article
- PubMed/NCBI
- Google Scholar
44. Penders J, Thijs C, van den Brandt PA, Kummeling I, Snijders B, Stelma FF. Gut microbiota composition and development of atopic manifestations in infancy: the KOALA Birth Cohort Study. Gut. 2007;56:661–7. pmid:17047098
- View Article
- PubMed/NCBI
- Google Scholar
45. Kelly D, King T, Aminov RI. Importance of microbial colonisation of the gut in early life to the development of immunity. Mutation Research. 2007;622:58–69. pmid:17612575
- View Article
- PubMed/NCBI
- Google Scholar
46. Dicksved J. Exploring the human intestinal microbiome in health and disease. Uppsala: Swedish University of Agricultural Sciences; 2008.
47. Barnes EM. The avian intestinal flora with particular reference to the possible ecological significance of the cecal anaerobic bacteria. American Journal of Clinical Nutrition. 1972;25(12):1475–9. pmid:4346128
- View Article
- PubMed/NCBI
- Google Scholar
48. Barnes EM, Mead GC, Barnuml DA, Harry EG. The intestinal flora of the chicken in the period 2 to 6 weeks of age, with particular reference to the anaerobic bacteria. British Poultry Science. 1972;13(3):311–26. pmid:4555258
- View Article
- PubMed/NCBI
- Google Scholar
49. Mackie RI. Mutualistic fermentative digestion in the gastrointestinal tract: diversity and evolution. Integrative and Comparative Biology. 2002;42:319–26. pmid:21708724
- View Article
- PubMed/NCBI
- Google Scholar
50. Soledad Castillo Gomes M. Development of gut microbiota in the pig: modulation of bacterial communities by different feeding strategies. Barcelona: Universitat Autonoma De Barcelona; 2006.
51. Lumpkins BS, Batal AB, Lee MD. Evaluation of the bacterial community and intestinal development of different genetic lines of chickens. Poultry Science. 2010;89(8):1614–21. pmid:20634515
- View Article
- PubMed/NCBI
- Google Scholar
52. van Dongen WFD, White J, Brandl HB, Moodley Y, Merkling T, Leclaire S, et al. Age-related differences in the cloacal microbiota of a wild bird species. BMC Ecology. 2013;13(11).
- View Article
- Google Scholar
53. Cani PD, Amar J, Iglesias MA, Poggi M, Knauf C, Bastelica D, et al. Metabolic endotoxemia initiates obesity and insulin resistance. Diabetes. 2007;56:1761–72. pmid:17456850
- View Article
- PubMed/NCBI
- Google Scholar
54. Groh H, Schade K, Hörhold-Schubert C. Steroid metabolism with intestinal microorganisms. Journal of Basic Microbiology. 1993;33(1):59–72. pmid:8478793
- View Article
- PubMed/NCBI
- Google Scholar
55. Keszthelyi D, Troost FJ, Masclee AAM. Understanding the role of tryptophan and serotonin metabolism in gastrointestinal function. Neurogastroenterology and Motility. 2009;21(12):1239–49. pmid:19650771
- View Article
- PubMed/NCBI
- Google Scholar
56. Ridlon JM, Kang DJ, Hylemon PB. Bile salt biotransformations by human intestinal bacteria. Journal of Lipid Research. 2006;47(2): 241–59. pmid:16299351
- View Article
- PubMed/NCBI
- Google Scholar
57. Serino M, Luche E, Gres S, Baylac A, Bergé M, Cenac C, et al. Metabolic adaptation to a high fat diet is associated with a change in the gut microbiota. Gut. 2012;61:543–53. pmid:22110050
- View Article
- PubMed/NCBI
- Google Scholar
58. Swann JR, Want EJ, Geier FM, Spagou K, Wilson ID, Sidaway JE, et al. Systemic gut microbial modulation of bile acid metabolism in host tissue compartments PNAS. 2011;108(Supplement 1):4523–30.
- View Article
- Google Scholar
59. Yokoyama MT, Carlson JR. Microbial metabolites of trytophan in the intestinal tract with special reference to skatole. American Journal of Clinical Nutrition. 1979;32:173–8. pmid:367144
- View Article
- PubMed/NCBI
- Google Scholar
60. Bercik P, Denou E, Collins J, Jackson W, Lu J, Jury J, et al. The intestinal microbiota affect central levels of brain-derived neurotropic factor and behavior in mice. Gastroenterology. 2011;141(2):599–609.e3. pmid:21683077
- View Article
- PubMed/NCBI
- Google Scholar
61. Sun YZ, Yang HL, Ma RL, Zhang CX, Lin WY. Effect of dietary administration of Psychrobacter sp. on the growth, feed utilization, digestive enzymes and immune responses of grouper Epinephelus coioides. Aquaculture Nutrition. 2011;17(3):e733–e40.
- View Article
- Google Scholar
62. Panda AK, Rama Rao SV, Raju MVLN, Sunder GS. Effect of butyric acid in performance, gastrointestinal tract health and carcass characteristics in broiler chickens. Asian-Australian Journal of Animal Science. 2009;22(7):1026–31.
- View Article
- Google Scholar
63. Ley RE, Backhead F, Turnbaugh PJ, Lozupone CA, Knight RD, Gordon JI. Obesity alters gut microbial ecology. Proceedings of National Academy of Sciences. 2005;102(31):11070–5.
- View Article
- Google Scholar
64. Ley RE, Turnbaugh P, Klein S, Gordon JI. Microbial ecology: human gut microbes associated with obesity. Nature. 2006;444:1022–3. pmid:17183309
- View Article
- PubMed/NCBI
- Google Scholar
65. Tremaroli V, Kovatcheva-Datchary P, Bäckhed F. A role for the gut microbiota in energy harvesting? Gut. 2010. pmid:20940281
- View Article
- PubMed/NCBI
- Google Scholar
66. Turnbaugh PJ, Bäckhed F, Fulton L, Gordon JI. Diet-induced obesity is linked to marked but reversible alterations in the mouse distal gut microbiome. Cell Host & Microbe. 2008;3(4):213–23.
- View Article
- Google Scholar
67. Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut microbiome with increased energy harvest Nature. 2006;444(7122):1027–31.
- View Article
- Google Scholar
68. Smith SC, Chalker A, Dewar ML, Arnould JPY. Age related differences revealed in Australian fur seal Arctocephalus pusillus doriferus gut microbiota. FEMS Microbiology Ecology. 2013;86(2):246–55. pmid:23746080
- View Article
- PubMed/NCBI
- Google Scholar
69. Oka N. Chick growth and development of short-tailed shearwater Puffinus tenuiostris in Tasmania. Journal of Yamashina Institute of Ornithology. 1989;21:193–207.
- View Article
- Google Scholar
70. Perrins CM, Harris MP, Britton CK. Survival of manx shearwaters Puffinus puffinus. Ibis. 1973;115:535–48.
- View Article
- Google Scholar
71. Potrykus J, White RL, Bearne SL. Proteomic investigation of amino acid catabolism in the indigenous gut anaerobe Fusobacterium varium. Proteomics. 2008;8(13):2691–703. pmid:18546150
- View Article
- PubMed/NCBI
- Google Scholar
72. Banks JC, Craig S, Cary I, Hogg D. The phylogeography of adelie penguin faecal flora. Environmental Microbiology. 2009;11(3):577–88. pmid:19040454
- View Article
- PubMed/NCBI
- Google Scholar
73. Ley RE, Lozupone CA, Hamady M, Knight R, Gordon JI. Worlds within worlds: evolution of the vertebrate gut microbiota. Nature Reviews Microbiology. 2008;6(10):776–88. pmid:18794915
- View Article
- PubMed/NCBI
- Google Scholar
74. Stevens EC, Hume ID. Contributions of microbes in vertebrate gastrointestinal tract to production and conservation of nutrients. Physiological Reviews. 1998;78(2):393–427. pmid:9562034
- View Article
- PubMed/NCBI
- Google Scholar
75. Chapman EW, Hofmann EE, Patterson DL, Fraser WR. The effects of variability in Antarctic krill (Euphausia superba) spawning behavior and sex/maturity stage distribution on Adélie penguin (Pygoscelis adeliae) chick growth: a modeling study. Deep-Sea Research Part II. 2010;57:543–58.
- View Article
- Google Scholar
76. Cimino MA, Fraser WR, Patterson-Fraser DL, Saba VS, Oliver MJ. Large-scale climate and local weather drive interannual variability in Adélie penguin chick fledging mass. Marine Ecology Progress Series. 2014;513:253–68.
- View Article
- Google Scholar
77. Naef-Daenzer B, Widmer F, Nuber M. Differential post-fledging survival of great and coal tits in relation to their condition and fledging date. J Animal Ecology. 2001;70:730–8.
- View Article
- Google Scholar
78. Saraux C, Viblanc VA, Hanuise N, Le Maho Y, Le Bohec C. Effects of individual pre-fledging traits and environmental conditions on return patterns in juvenile king penguins. PLoS One. 2011;6:e20407. pmid:21687715
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Palmer C, Bik EM, DiGiulio DB, Relman DA, Brown PO. Development of the human infant intestinal microbiota. PLoS Biology. 2007;5(7):e117. pmid:17594176
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Apajalahti J, Kettunen A. Microbes of the chicken gastrointestinal tract. In: Perry GC, editor. Avian Function in Health and Disease. 28. Bristol: CAB International; 2005. p. 124–7.

[ref3] 3. Gabriel I, Lessire M, Mallet S, Guillot JF. Microflora of the digestive tract: critical factors and consequences for poultry. World's Poultry Science Journal. 2006;62(03):499–511.
View Article
Google Scholar

[7] View Article

[8] Google Scholar

[ref4] 4. Lu J, Idris U, Harmon B, Hofacre C, Maurer JJ, Lee MD. Diversity and succession of the intestinal bacterial community of the maturing broiler chicken. Applied and Environmental Microbiology. 2003;69(11):6816–24. pmid:14602645
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref5] 5. Lucas FS, Heeb P. Environmental factors shape cloacal bacterial assemblages in great tit Parus major and blue tit P. caeruleus nestlings. Journal of Avian Biology. 2005;36(6):510–6.
View Article
Google Scholar

[14] View Article

[15] Google Scholar

[ref6] 6. Rehman HU, Vahjen W, Awad WA, Zentek J. Indigenous bacteria and bacterial metabolic products in the gastrointestinal tract of broiler chickens. Archives of Animal Nutrition. 2007;61(5):319–35. pmid:18030916
View Article
PubMed/NCBI
Google Scholar

[17] View Article

[18] PubMed/NCBI

[19] Google Scholar

[ref7] 7. Torok VA, Ophel-Keller K, Hughes RJ, Forder R, Ali M, MacAlpine R. Environment and age: impact on poultry gut microflora. Australian Poultry Science Symposium 2007. p. 149–52
View Article
Google Scholar

[21] View Article

[22] Google Scholar

[ref8] 8. Yi Yin, F Lei, L Zhu, S Li, Z Wu, R Zhang, et al. Exposure of different bacterial inocula to newborn chicken affects gut microbiota development and ileum gene expression. International Society for Microbial Ecology Journal. 2009;4(3):367–76. http://www.nature.com/ismej/journal/v4/n3/suppinfo/ismej2009128s1.html.
View Article
Google Scholar

[24] View Article

[25] Google Scholar

[ref9] 9. Fuller R. Microbial activity in the alimentary tract of birds. Proceedings of the Nutrition Society. 1984;43(01):55–61.
View Article
Google Scholar

[27] View Article

[28] Google Scholar

[ref10] 10. Potti J, Moreno J, Yorio P, Briones V, Garcia-Borboroglu P, Villar S, et al. Bacteria divert resources from growth for magellanic penguin chicks. Ecology Letters. 2002;5(6):709–14.
View Article
Google Scholar

[30] View Article

[31] Google Scholar

[ref11] 11. Amit-Romach E, Sklan D, Uni Z. Microflora ecology of the chicken intestine using 16S ribosomal DNA primers. Poultry Science. 2004;83(7):1093–8. pmid:15285498
View Article
PubMed/NCBI
Google Scholar

[33] View Article

[34] PubMed/NCBI

[35] Google Scholar

[ref12] 12. Coloe PJ, Bagust TJ, Ireland L. Development of the normal gastrointestinal microflora of specific pathogen-free chickens. The Journal of Hygiene. 1984;92(1):79–87. pmid:6229572
View Article
PubMed/NCBI
Google Scholar

[37] View Article

[38] PubMed/NCBI

[39] Google Scholar

[ref13] 13. Mackie RI, Sghir A, Gaskins HR. Developmental microbial ecology of the neonatal gastrointestinal tract. The American Journal of Clinical Nutrition. 1999;69(5):1035S–45S. pmid:10232646
View Article
PubMed/NCBI
Google Scholar

[41] View Article

[42] PubMed/NCBI

[43] Google Scholar

[ref14] 14. Vaishampayan PA, Kuehl JV, Froula JL, Morgan JL, Ochman H, Francino MP. Comparative metagenomics and population dynamics of the gut microbiota in mother and infant. Genome Biology and Evolution. 2010;2:53–66. pmid:20333224
View Article
PubMed/NCBI
Google Scholar

[45] View Article

[46] PubMed/NCBI

[47] Google Scholar

[ref15] 15. Penders J, Thijs C, Vink C, Stelma FF, Snijders B, Kummeling I, et al. Factors influencing the composition of the intestinal microbiota in early infancy. Pediatrics. 2010;118(2):511–21.
View Article
Google Scholar

[49] View Article

[50] Google Scholar

[ref16] 16. Stark PL, Lee A. The microbial ecology of the large bowel of breast-fed and formula-fed infants during the first year of life. Journal of Medical Microbiology. 1982;15:189–203. pmid:7143428
View Article
PubMed/NCBI
Google Scholar

[52] View Article

[53] PubMed/NCBI

[54] Google Scholar

[ref17] 17. Seventry DL, Seventry V, Warham J. The handbook of Australian seabirds. Sydney: A.H. & A.W. Reed Ltd.; 1971.

[ref18] 18. Carey M. Effects of investigator disturbance on the reporductive success of Short-tailed Shearwater [Honours]. Bundoora: La Trobe University; 2011.

[ref19] 19. Warham J. The petrel: their ecology and breeding systems. London: Academic Press; 1990.

[ref20] 20. Weimerskirch H, Cherel Y. Feeding ecology of short-tailed shearwaters: breeding in Tasmania and foraging in Antarctic? Marine Ecology Progress Series. 1998;167:261–74.
View Article
Google Scholar

[59] View Article

[60] Google Scholar

[ref21] 21. Klomp NI, Schultz MA. Short-tailed shearwaters breeding in Australia forage in Antarctic waters. Marine Ecology Progress Series. 2000;194:307–10.
View Article
Google Scholar

[62] View Article

[63] Google Scholar

[ref22] 22. Montague TL, Cullen JM, Fitzherbert K. The diet of the short-tailed shearwater Puffinus tenuirostris during its breeding season. Emu. 1986;86:207–13.
View Article
Google Scholar

[65] View Article

[66] Google Scholar

[ref23] 23. Schumann N. Trophic relationships of Bass Strait seabirds. Melbourne: Deakin University; 2012.

[ref24] 24. Oka N. Differential assimilation efficiency of prey and growth of short-tailed shearwater chicks. Ornithological Science. 2011;10(1):39–50.
View Article
Google Scholar

[69] View Article

[70] Google Scholar

[ref25] 25. Merchant S, Higgins PJ. Handbook of Australian, New Zealand and Antarctic birds. Melbourne: Oxford University Press; 1990.

[ref26] 26. Reilly PN, Cullen JM. The little penguin Eudyptula minor in Victoria. II: breeding. Emu. 1981;81:1–19.
View Article
Google Scholar

[73] View Article

[74] Google Scholar

[ref27] 27. Chiaradia A, Kerry KR. Daily nest attendence and breeding performance in the little penguin Eudyptula minor at Phillip Island, Australia. Marine Ornithology. 1999;27:13–20.
View Article
Google Scholar

[76] View Article

[77] Google Scholar

[ref28] 28. Reilly P. Penguins of the world. New York: Oxford University Press; 1994.

[ref29] 29. Stahel C, Gales R. Little penguin: fairy penguins in Australia. Taxonomy: The naming of names. Kensington: New South Wales University Press; 1991.

[ref30] 30. Chiaradia A, Nisbet ICT. Plasticity in parental provisioning and chick growth in little penguins in years of high and low breeding success. Ardea 2006;94:257–70.
View Article
Google Scholar

[81] View Article

[82] Google Scholar

[ref31] 31. Saraux C, Robinson-Laverick SM, Le Maho Y, Ropert-Coudert Y, Chiaradia A. Plasticity in foraging strategies of inshore birds: how little penguins maintain body reserves while feeding offspring. Ecology. 2011;92(10):1909–16. pmid:22073782
View Article
PubMed/NCBI
Google Scholar

[84] View Article

[85] PubMed/NCBI

[86] Google Scholar

[ref32] 32. Dewar ML, Arnould JPY, Dann P, Groscolas R, Trathan P, Smith SC. Inter-specific variations in the gastrointestinal microbiota of penguins. MicrobiologyOpen. 2013;2(1):195–204. pmid:23349094
View Article
PubMed/NCBI
Google Scholar

[88] View Article

[89] PubMed/NCBI

[90] Google Scholar

[ref33] 33. Fierer N, Hamady M, Lauber CL, Knight R. The influence of sex, handedness, and washing on the diversity of hand surface bacteria. Proceedings of the National Academy of Sciences. 2008. pmid:19004758
View Article
PubMed/NCBI
Google Scholar

[92] View Article

[93] PubMed/NCBI

[94] Google Scholar

[ref34] 34. Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods. 2013;10(10):996–8. pmid:23955772
View Article
PubMed/NCBI
Google Scholar

[96] View Article

[97] PubMed/NCBI

[98] Google Scholar

[ref35] 35. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nature Methods. 2010;7(5):335–6. pmid:20383131
View Article
PubMed/NCBI
Google Scholar

[100] View Article

[101] PubMed/NCBI

[102] Google Scholar

[ref36] 36. Paulson JN, Stine OS, Bravo HC, Pop M. Differential abundance analysis for microbial marker-gene surveys. Nature Methods. 2013;10:1200–2. pmid:24076764
View Article
PubMed/NCBI
Google Scholar

[104] View Article

[105] PubMed/NCBI

[106] Google Scholar

[ref37] 37. Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Applied and Environmental Microbiology. 2005;71(12):8228–35. pmid:16332807
View Article
PubMed/NCBI
Google Scholar

[108] View Article

[109] PubMed/NCBI

[110] Google Scholar

[ref38] 38. Price MN, Dehal PS, Arkin AP. FastTree 2—Approximately maximum-likelihood trees for large alignments. PLoS One. 2010;5(3):e9490. pmid:20224823
View Article
PubMed/NCBI
Google Scholar

[112] View Article

[113] PubMed/NCBI

[114] Google Scholar

[ref39] 39. Excoffier L, Smouse PE, Quattro JM. Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics. 1992;131(2):479–91. pmid:1644282
View Article
PubMed/NCBI
Google Scholar

[116] View Article

[117] PubMed/NCBI

[118] Google Scholar

[ref40] 40. Langille MGI, Zaneveld J, Caporaso J, G., McDonald D, Knights D, et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nature Biotechnology. 2013;31(9):814–21. pmid:23975157
View Article
PubMed/NCBI
Google Scholar

[120] View Article

[121] PubMed/NCBI

[122] Google Scholar

[ref41] 41. Snedecor GW, Cochran WG. Statistical methods: John Wiley & Sons; 1991.

[ref42] 42. Burnham KP, Anderson DR. Model selection and multimodel interference: A practical information-theoretic approach. 2nd ed: Spinger-Verlag; 2002.

[ref43] 43. Jost T, Lacroix C, Braegger CP, Chassard C. New insights in gut microbiota establishment in healthy breast fed neonates. PLoS ONE. 2012;7(8):e44595. pmid:22957008
View Article
PubMed/NCBI
Google Scholar

[126] View Article

[127] PubMed/NCBI

[128] Google Scholar

[ref44] 44. Penders J, Thijs C, van den Brandt PA, Kummeling I, Snijders B, Stelma FF. Gut microbiota composition and development of atopic manifestations in infancy: the KOALA Birth Cohort Study. Gut. 2007;56:661–7. pmid:17047098
View Article
PubMed/NCBI
Google Scholar

[130] View Article

[131] PubMed/NCBI

[132] Google Scholar

[ref45] 45. Kelly D, King T, Aminov RI. Importance of microbial colonisation of the gut in early life to the development of immunity. Mutation Research. 2007;622:58–69. pmid:17612575
View Article
PubMed/NCBI
Google Scholar

[134] View Article

[135] PubMed/NCBI

[136] Google Scholar

[ref46] 46. Dicksved J. Exploring the human intestinal microbiome in health and disease. Uppsala: Swedish University of Agricultural Sciences; 2008.

[ref47] 47. Barnes EM. The avian intestinal flora with particular reference to the possible ecological significance of the cecal anaerobic bacteria. American Journal of Clinical Nutrition. 1972;25(12):1475–9. pmid:4346128
View Article
PubMed/NCBI
Google Scholar

[139] View Article

[140] PubMed/NCBI

[141] Google Scholar

[ref48] 48. Barnes EM, Mead GC, Barnuml DA, Harry EG. The intestinal flora of the chicken in the period 2 to 6 weeks of age, with particular reference to the anaerobic bacteria. British Poultry Science. 1972;13(3):311–26. pmid:4555258
View Article
PubMed/NCBI
Google Scholar

[143] View Article

[144] PubMed/NCBI

[145] Google Scholar

[ref49] 49. Mackie RI. Mutualistic fermentative digestion in the gastrointestinal tract: diversity and evolution. Integrative and Comparative Biology. 2002;42:319–26. pmid:21708724
View Article
PubMed/NCBI
Google Scholar

[147] View Article

[148] PubMed/NCBI

[149] Google Scholar

[ref50] 50. Soledad Castillo Gomes M. Development of gut microbiota in the pig: modulation of bacterial communities by different feeding strategies. Barcelona: Universitat Autonoma De Barcelona; 2006.

[ref51] 51. Lumpkins BS, Batal AB, Lee MD. Evaluation of the bacterial community and intestinal development of different genetic lines of chickens. Poultry Science. 2010;89(8):1614–21. pmid:20634515
View Article
PubMed/NCBI
Google Scholar

[152] View Article

[153] PubMed/NCBI

[154] Google Scholar

[ref52] 52. van Dongen WFD, White J, Brandl HB, Moodley Y, Merkling T, Leclaire S, et al. Age-related differences in the cloacal microbiota of a wild bird species. BMC Ecology. 2013;13(11).
View Article
Google Scholar

[156] View Article

[157] Google Scholar

[ref53] 53. Cani PD, Amar J, Iglesias MA, Poggi M, Knauf C, Bastelica D, et al. Metabolic endotoxemia initiates obesity and insulin resistance. Diabetes. 2007;56:1761–72. pmid:17456850
View Article
PubMed/NCBI
Google Scholar

[159] View Article

[160] PubMed/NCBI

[161] Google Scholar

[ref54] 54. Groh H, Schade K, Hörhold-Schubert C. Steroid metabolism with intestinal microorganisms. Journal of Basic Microbiology. 1993;33(1):59–72. pmid:8478793
View Article
PubMed/NCBI
Google Scholar

[163] View Article

[164] PubMed/NCBI

[165] Google Scholar

[ref55] 55. Keszthelyi D, Troost FJ, Masclee AAM. Understanding the role of tryptophan and serotonin metabolism in gastrointestinal function. Neurogastroenterology and Motility. 2009;21(12):1239–49. pmid:19650771
View Article
PubMed/NCBI
Google Scholar

[167] View Article

[168] PubMed/NCBI

[169] Google Scholar

[ref56] 56. Ridlon JM, Kang DJ, Hylemon PB. Bile salt biotransformations by human intestinal bacteria. Journal of Lipid Research. 2006;47(2): 241–59. pmid:16299351
View Article
PubMed/NCBI
Google Scholar

[171] View Article

[172] PubMed/NCBI

[173] Google Scholar

[ref57] 57. Serino M, Luche E, Gres S, Baylac A, Bergé M, Cenac C, et al. Metabolic adaptation to a high fat diet is associated with a change in the gut microbiota. Gut. 2012;61:543–53. pmid:22110050
View Article
PubMed/NCBI
Google Scholar

[175] View Article

[176] PubMed/NCBI

[177] Google Scholar

[ref58] 58. Swann JR, Want EJ, Geier FM, Spagou K, Wilson ID, Sidaway JE, et al. Systemic gut microbial modulation of bile acid metabolism in host tissue compartments PNAS. 2011;108(Supplement 1):4523–30.
View Article
Google Scholar

[179] View Article

[180] Google Scholar

[ref59] 59. Yokoyama MT, Carlson JR. Microbial metabolites of trytophan in the intestinal tract with special reference to skatole. American Journal of Clinical Nutrition. 1979;32:173–8. pmid:367144
View Article
PubMed/NCBI
Google Scholar

[182] View Article

[183] PubMed/NCBI

[184] Google Scholar

[ref60] 60. Bercik P, Denou E, Collins J, Jackson W, Lu J, Jury J, et al. The intestinal microbiota affect central levels of brain-derived neurotropic factor and behavior in mice. Gastroenterology. 2011;141(2):599–609.e3. pmid:21683077
View Article
PubMed/NCBI
Google Scholar

[186] View Article

[187] PubMed/NCBI

[188] Google Scholar

[ref61] 61. Sun YZ, Yang HL, Ma RL, Zhang CX, Lin WY. Effect of dietary administration of Psychrobacter sp. on the growth, feed utilization, digestive enzymes and immune responses of grouper Epinephelus coioides. Aquaculture Nutrition. 2011;17(3):e733–e40.
View Article
Google Scholar

[190] View Article

[191] Google Scholar

[ref62] 62. Panda AK, Rama Rao SV, Raju MVLN, Sunder GS. Effect of butyric acid in performance, gastrointestinal tract health and carcass characteristics in broiler chickens. Asian-Australian Journal of Animal Science. 2009;22(7):1026–31.
View Article
Google Scholar

[193] View Article

[194] Google Scholar

[ref63] 63. Ley RE, Backhead F, Turnbaugh PJ, Lozupone CA, Knight RD, Gordon JI. Obesity alters gut microbial ecology. Proceedings of National Academy of Sciences. 2005;102(31):11070–5.
View Article
Google Scholar

[196] View Article

[197] Google Scholar

[ref64] 64. Ley RE, Turnbaugh P, Klein S, Gordon JI. Microbial ecology: human gut microbes associated with obesity. Nature. 2006;444:1022–3. pmid:17183309
View Article
PubMed/NCBI
Google Scholar

[199] View Article

[200] PubMed/NCBI

[201] Google Scholar

[ref65] 65. Tremaroli V, Kovatcheva-Datchary P, Bäckhed F. A role for the gut microbiota in energy harvesting? Gut. 2010. pmid:20940281
View Article
PubMed/NCBI
Google Scholar

[203] View Article

[204] PubMed/NCBI

[205] Google Scholar

[ref66] 66. Turnbaugh PJ, Bäckhed F, Fulton L, Gordon JI. Diet-induced obesity is linked to marked but reversible alterations in the mouse distal gut microbiome. Cell Host & Microbe. 2008;3(4):213–23.
View Article
Google Scholar

[207] View Article

[208] Google Scholar

[ref67] 67. Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut microbiome with increased energy harvest Nature. 2006;444(7122):1027–31.
View Article
Google Scholar

[210] View Article

[211] Google Scholar

[ref68] 68. Smith SC, Chalker A, Dewar ML, Arnould JPY. Age related differences revealed in Australian fur seal Arctocephalus pusillus doriferus gut microbiota. FEMS Microbiology Ecology. 2013;86(2):246–55. pmid:23746080
View Article
PubMed/NCBI
Google Scholar

[213] View Article

[214] PubMed/NCBI

[215] Google Scholar

[ref69] 69. Oka N. Chick growth and development of short-tailed shearwater Puffinus tenuiostris in Tasmania. Journal of Yamashina Institute of Ornithology. 1989;21:193–207.
View Article
Google Scholar

[217] View Article

[218] Google Scholar

[ref70] 70. Perrins CM, Harris MP, Britton CK. Survival of manx shearwaters Puffinus puffinus. Ibis. 1973;115:535–48.
View Article
Google Scholar

[220] View Article

[221] Google Scholar

[ref71] 71. Potrykus J, White RL, Bearne SL. Proteomic investigation of amino acid catabolism in the indigenous gut anaerobe Fusobacterium varium. Proteomics. 2008;8(13):2691–703. pmid:18546150
View Article
PubMed/NCBI
Google Scholar

[223] View Article

[224] PubMed/NCBI

[225] Google Scholar

[ref72] 72. Banks JC, Craig S, Cary I, Hogg D. The phylogeography of adelie penguin faecal flora. Environmental Microbiology. 2009;11(3):577–88. pmid:19040454
View Article
PubMed/NCBI
Google Scholar

[227] View Article

[228] PubMed/NCBI

[229] Google Scholar

[ref73] 73. Ley RE, Lozupone CA, Hamady M, Knight R, Gordon JI. Worlds within worlds: evolution of the vertebrate gut microbiota. Nature Reviews Microbiology. 2008;6(10):776–88. pmid:18794915
View Article
PubMed/NCBI
Google Scholar

[231] View Article

[232] PubMed/NCBI

[233] Google Scholar

[ref74] 74. Stevens EC, Hume ID. Contributions of microbes in vertebrate gastrointestinal tract to production and conservation of nutrients. Physiological Reviews. 1998;78(2):393–427. pmid:9562034
View Article
PubMed/NCBI
Google Scholar

[235] View Article

[236] PubMed/NCBI

[237] Google Scholar

[ref75] 75. Chapman EW, Hofmann EE, Patterson DL, Fraser WR. The effects of variability in Antarctic krill (Euphausia superba) spawning behavior and sex/maturity stage distribution on Adélie penguin (Pygoscelis adeliae) chick growth: a modeling study. Deep-Sea Research Part II. 2010;57:543–58.
View Article
Google Scholar

[239] View Article

[240] Google Scholar

[ref76] 76. Cimino MA, Fraser WR, Patterson-Fraser DL, Saba VS, Oliver MJ. Large-scale climate and local weather drive interannual variability in Adélie penguin chick fledging mass. Marine Ecology Progress Series. 2014;513:253–68.
View Article
Google Scholar

[242] View Article

[243] Google Scholar

[ref77] 77. Naef-Daenzer B, Widmer F, Nuber M. Differential post-fledging survival of great and coal tits in relation to their condition and fledging date. J Animal Ecology. 2001;70:730–8.
View Article
Google Scholar

[245] View Article

[246] Google Scholar

[ref78] 78. Saraux C, Viblanc VA, Hanuise N, Le Maho Y, Le Bohec C. Effects of individual pre-fledging traits and environmental conditions on return patterns in juvenile king penguins. PLoS One. 2011;6:e20407. pmid:21687715
View Article
PubMed/NCBI
Google Scholar

[248] View Article

[249] PubMed/NCBI

[250] Google Scholar

Figures

Abstract

Introduction

Experimental design

Study design and sample collection

DNA extraction and real time PCR

PCR amplification and 16S rRNA pyrosequencing

Data processing and analysis

Prediction of the functions of the microbial communities

qPCR statistical analysis

Results

Quantitative real time PCR

PCoA and beta diversity

Alpha diversity

Taxonomic composition

Inferred functional diversity

Discussion

Little penguins

Short-tailed shearwater

Species differences

Conclusion

Supporting information

S1 Table. Functional pathways predicted by PICRUSt and statistical analysis.

Acknowledgments

References