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Article

Bacterial Diversity and Composition in the Internal Organs of Taiga Bean Goose, Greater White-Fronted Goose and Willow Ptarmigan as a New Tools in the Arctic Biomonitoring System

by
Evdokia Durnova
,
Elena Karmanova
,
Tatiana Sorokina
,
Ksenia Mayorova
and
Andrey Aksenov
*
Arctic Biomonitoring Laboratory, Northern (Arctic) Federal University, Northern Dvina Embankment 17, 163000 Arkhangelsk, Russia
*
Author to whom correspondence should be addressed.
Diversity 2025, 17(2), 101; https://doi.org/10.3390/d17020101
Submission received: 2 December 2024 / Revised: 12 January 2025 / Accepted: 21 January 2025 / Published: 29 January 2025
(This article belongs to the Special Issue Avian Ecology and Diversity, Population Monitoring and Conservation II: Recent Advances and New Challenges)
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Abstract

:
Birds, fish, and marine mammals consumed by indigenous people are included in Arctic biomonitoring. However, there are still many gaps in the data on the microbiota associated with these animals. In the current study, we used high-throughput 16S rRNA gene sequencing to explore the bacterial diversity and composition in the intestines of willow ptarmigans, greater white-fronted geese, and taiga bean geese, which are widely consumed by indigenous people in the Arctic. For the first time, meta-taxonomic data have been obtained on the lungs of wild resident and migratory birds of the Russian North. The potentially pathogenic bacterial genera Helicobacter and Olsenella were found in the intestinal microbiomes of three bird species and in the lungs of willow ptarmigan. Bacteria of the genus Staphylococcus were individually identified in the intestines of willow ptarmigan, Campylobacter sp. in the intestines of taiga bean goose, and Sutterella sp. in the intestines of greater white-fronted goose as potential pathogens. The primary findings will be used to propose a next-generation sequencing scheme for monitoring both chemical and biological contaminants in the Arctic in line with One Health approach.

1. Introduction

Threats to health security such as zoonotic diseases, antimicrobial resistance, and food contamination can only be overcome through global collaboration within the One Health approach [1]. Advancing the One Health approach requires both an increase in the number of new regions implementing the programme and the use of modern analytical methods [2]. Each of the elements of this triad—human health, animal health, and environmental health—cannot currently be considered without the use of new genomics-informed technologies for microbiota identification [3]. In some regions, such research is already underway, and this approach is referred to as next-generation biomonitoring [4]; however, it is severely limited in the Arctic [5,6]. Meanwhile, the health relevance of indigenous peoples living in sparsely populated areas and following traditional diets has been recognised [7]. It is, therefore, necessary to assess not only the risks to human health from chemical compounds, but also from microbiological contaminants, the sources of which continue to be migratory birds, fish, and wildlife [8].
The respiratory and gastrointestinal tracts are areas of direct contact of animals with the environment on the one hand, and with the whole organism through the circulatory system on the other. Information about physiologically normal and pathogenic bacterial microbiota makes it possible to diagnose diseases within the biocenosis found in the habitat and in the hunting of the wildlife, as well as to prevent infection of humans who are consumers of meat from these wild animals [9,10].
The study of the avian microbiome is limited by the lack of selective methods to identify the required taxonomic unit unambiguously. The best solution is a comprehensive analysis of the microbial community composition using genomic and metagenomic tools. High-throughput nucleic acid sequencing technologies, combined with bioinformatic analysis of phylogenetic relationships, allow the detection and classification of culturable and, in particular, non-culturable species of microorganisms, thus providing data on the microbial communities inhabiting the organ cavities of the digestive and respiratory systems of animals and birds. Metagenomic profiling makes it possible to study the biochemical properties of the associated microbiota in sufficient detail and to characterise the enzymatic potential of the microbiota from the nucleotide sequences of the genes. In the case of birds habiting in Arctic and sub-Arctic regions, high-throughput next-generation sequencing and shotgun metagenomics have been used to profile the microorganisms associated with the intestinal tract of the host [11,12,13,14]. These methods allow the prediction of degradation pathways of polysaccharides and other polymers in food composition under the action of enzymes produced by microbiota [15], which are tools for the adaptation of animals and birds to the harsh climate of the Arctic [16]. Metagenomic profiles of microbiota can be used to identify probiotic and potential-pathogenic strains [17,18,19], as well as to identify species, determine the degree of relatedness, and assess the presence of divergence and mutations in host birds [20].
The Arkhangelsk region is the habitat of many species of migratory birds, including commercial species of geese and grouse, which are part of the diet of the indigenous people. The study of the associated microbiota of harvested birds of the Arkhangelsk region will allow a more constructive assessment of the physiological state of birds and identification of potential health risks for humans in active contact with these birds. The present study is dedicated to the bacterial communities of the lungs and intestines of geese and ptarmigan consumed by indigenous people of the Arkhangelsk region. We try to fill the gaps in scientific knowledge about the state of intra-organism microbial associations in this Arctic territory, as well as investigate the assessment of the risks of the introduction of pathogens through the food chains in the Russian North.

2. Materials and Methods

2.1. Sampling Strategy

The field sampling of birds was initiated in the north-western territories of the Russian Arctic, namely in the zone of residence of the local population of the Arkhangelsk region, which actively consumes the meat of wild birds of the three studied species. The most commonly consumed bird species by the population in the Arctic territories of Arkhangelsk region were identified using a food frequency questionnaire. A food frequency questionnaire was conducted among the local population of the Arctic territories of the Arkhangelsk region, according to [7,21], within the project “Arctic Biomonitoring Laboratory” [22] and during the Arctic Floating University expedition. The aim of the questionnaire was to determine wild bird species, which are the most commonly consumed by indigenous population. Based on collected questionnaire data, willow ptarmigan, taiga bean goose, and greater white-fronted goose were selected for metagenomic analysis as a the most consuming bird species. Wild willow ptarmigan Lagopus lagopus, greater white-fronted geese Anser albifrons, and taiga bean geese Anser fabalis were shot in 2023 by local hunters in an area north of the Arkhangelsk region frontier, near the village of Nes (66.87° N, 44.66° E and 66.36° N, 44.40° E) and were handed over for metagenomic studies in accordance with an agreement between Northern (Arctic) federal university and the regional administration. The locations of the sampling sites are indicated in Figure 1.
Species identification of the analyzed samples was based on morphology. The main metric indicators of birds were total body length and weight, length and wingspan, tail length, beak, and tarsometatarsus length (Table 1).
Additional registration of external characteristics of birds as objects of selection of biological materials allows tracing of the general state of health of birds by the presence of abnormalities—muscle dystrophy, emaciation, deformation of limbs due to natural causes.

2.2. DNA Extraction and PCR Amplification

Samples of lungs and intestines of birds after collection were placed in sterile containers, frozen at −20 °C, and transported to the laboratory with preservation of the temperature regime. Further manipulations on the extraction of hereditary material (DNA) and its analysis were carried out using the equipment of the resource center “Genomic Technologies, Proteomics, and Cell Biology” of the All-Russia Research Institute for Agricultural microbiology (ARRIAM) (Saint-Petersburg, Russia), using methodological approaches from the article [23]. DNA extraction from intestinal and lung samples was performed using a modified CTAB method. Disruption of bacterial cells was performed by double homogenisation (speed 6000 shaking/min for 30 s). On the basis of the obtained DNA preparations, libraries of 16S rRNA marker gene fragments (V4 variable region) were created by amplification using primers: F515 (5′-3′) GTGCCAGCMGCCGCGGCGGTAA and R806 (5′-3′) GGACTACVSGGGTATCTAAT. The PCR working mixture contained Q5® High-Fidelity DNA Polymerase (New England BioLabs (NEB), Ipswich, MA, USA), forward and reverse primers, matrix-DNA, and each dNTP (LifeTechnologies, Carlsbad, CA, USA). The amplification parameters were as follows: denaturing at 94 °C, 1 min, 25 cycles with modes: 94 °C—30 s, 55 °C—30 s, 72 °C—1 min, and final elongation at 72 °C 3 min. PCR products were purified using magnetic particles AMPureXP (BeckmanCoulter, Brea, CA, USA). Raw sequencing data were submitted as an NCBI BioProject (PRJNA1192528).

2.3. Sequencing and Data Processing

Sequencing was carried out on Illumina MiSeq (Illumina, San Diego, CA, USA) using the commercial MiSeq® ReagentKit v3 with two-way reading (2 × 300 nt). The Illumina software was used, as well as the dada2, phyloseq, and DECIPHER v2.0 software packages and the R environment. The results of taxonomic analysis of the obtained phylotypes (Amplicon sequence variant, ASV) were represented using QIIME software.

3. Results and Discussion

3.1. Bacterial Taxa Dominance in Wild Resident and Migratory Birds’ Microbiomes: Similarities and Differences

A total of 9 lung samples of willow ptarmigan Lagopus lagopus and 23 intestine samples of 3 bird species were collected—11 intestine samples of willow ptarmigan Lagopus lagopus, 7 intestine samples of greater white-fronted goose Anser albifrons, and 5 intestine samples of taiga bean goose Anser fabalis. DNA extraction, 16S rRNA amplification, and amplicon sequencing were successfully performed for all 32 samples. A database of 176,132 read (max: 28,040 per sample) sequences were obtained. The overwhelming number of sequences belong to the Bacteria type (55.7%), but a significant presence of Archaea was observed in the greater white-fronted goose and taiga bean goose lung samples analysed in a single sample. Thus, Archaea of the phylum Thermoplasmatota were detected in greater white-fronted goose lungs with a relative abundance of up to 81% at the phylum level. The reads were distributed into 11 phyla, the relative abundance of representatives of dominant phylum is presented in Table 2.
In the composition of the intestine and lung microbiome of birds of the three species studied, the predominating phylum in relative abundance was Bacillota phylum. The intestinal microbiome of the greater white-fronted geese and taiga bean geese was characterised by bacteria belonging to the phylum Bacteroidota, Campilobacterota, and Fusobacteriota. The intestinal and lung microbiota of the willow ptarmigans differed from that of geese in the predominance of bacteria belonging to the phyla Pseudomonadota and Actinomycetota in addition to Bacillota. The relative abundances obtained are comparable to data obtained for other migratory geese and ptarmigans [24,25]. In the compositions of the faecal and gut microbiome of the taiga bean goose, which has one of the points of seasonal migration to Shengjin and Caizi lakes [26], the faecal microbiota of the hooded cranes Grus monacha and greater white-fronted geese on the same lakes [27], and in the gut of the greylag geese Anser anser [24], the dominance of the phylum Bacillota was observed. As noted [26,28], the dominance of Bacillota phylum bacteria in vertebrate biota has a significant effect on the regulation of metabolism and energy in the organism. Also, Wang et al. associated the dominance of Bacillota phylum bacteria with weight changes in greylag geese and accepted as a characteristic feature of birds migrating long distances [24].
Based on the relative abundance of bacterial families present in the intestinal and lung microbiota communities of birds, a heat map was made (Figure 2).
In the microbial communities associated with the digestive and respiratory tracts of willow ptarmigans, the dominant phylum Bacillota was primarily represented by the families Lachnospiraceae, Erysipelolotrichaceae, Oscillospiraceae, and Veillonellaceae. The lung microbiota of the willow ptarmigans contained bacteria of the family Selenomonadaceae in addition to the families listed above for the intestine. A distinctive feature of the composition of the intestinal microbiota of the greater white-fronted geese was the predominance among the bacteria of the phylum Bacillota of the family Ruminococcaceae. According to Cho et al., the Ruminococcaceae family is among the 20 families with the highest relative abundance in the faecal microbiome of the snow bunting [12]. The families Butyricoccaceae, Lactobacillaceae and Clostridiaceae, characteristic of the intestine of the greater white-fronted geese, do not occur in the intestines of ptarmigans, but are present in small numbers in the lungs of this bird. The composition of the intestinal microbiota of taiga bean geese differed from that of the above-mentioned birds, and the differences were manifested in the predominance in abundance of the family Selenomonadaceae, as well as the presence of other representatives of the phylum Bacillota—bacteria of the families Lactobacillaceae, Clostridiaceae, Ruminococcaceae, and Lachnospiraceae.
Further distribution by phylum has a divergent pattern, but Pseudomonadota has the second highest abundance in the host bird [24,26,27]. In the present work, the difference in the relative abundance of bacteria of the phylum Actinomycetota, Bacteroidota, Campilobacterota, Fusobacteriota, and Pseudomonadota for different bird species was observed. Most bacteria of these phyla are normal components of the intestinal microbiome of shore and terrestrial birds and ensure maintenance of homeostasis [14].
Bacteria of the phylum Pseudomonadota were typical only for the willow ptarmigans and are represented by the families Comamonadaceae, Sphingomonadaceae, and Xanthobacteraceae, as well as the family Beijerinckiaceae, which was found only in the lung tracts of birds. The dominant representatives of the phylum Bacteroidota in the intestinal microbiota of geese belonged to the families Bacteroidaceae and Prevotellaceae. It has been noted that bacteria of the phylum Bacteroidota in the intestine perform the functions of depolymerisation of carbohydrates and proteins in plant cell walls [24]. The presence of bacteria of these families is also characteristic of faecal microbiota samples of the pink-footed goose and sanderling [12]. In comparison with geese, the intestinal microbiome of ptarmigans contained predominantly bacteria of the phylum Actinomycetota—Propionibacteriaceae and Atopobiaceae. Wang et al. found that bacteria of the phylum Actinobacteria are characterised by mutualistic behaviour, which improves the digestion of dietary fibres in the gut of the greylag geese [24].

3.2. Benefit or Threat: Highly Abundant Bacterial Genera in the Intestinal and Lung Microbiome of Arctic Human-Consumed Ptarmigan and Geese

The study of the spread of ornithosis in wild birds is of particular importance for the health of indigenous people who are in contact with birds and who consume wild harvested birds on a regular basis. In addition to bacterial taxa that are neutral and safe for bird host and human health, the avian microbiome may contain respiratory and gastrointestinal pathogens. These may act as putative indicators of the presence of infections in the wild bird habitat. Metagenomic profiling of microbial communities associated with the lungs and gastrointestinal tract of Arctic human-consumed ptarmigan and geese revealed the most abundant bacterial genera (Table 3).
The bacterial genera with the highest relative abundance were clearly distinguished into groups inhabiting the intestinal and lung microbiome of ptarmigans (Ensifer sp., Staphylococcus sp., Streptococcus sp., Cutibacterium sp., etc.), and the intestinal microbiome of geese (Megamonas sp., Bacteroides sp., Subdoligranulum sp., Lactobacillus sp., etc.). Among the above genera of bacteria, there are useful representatives and active participants in metabolism. For example, bacteria of the genus Bacteroides can secrete enzymes that ensure the hydrolytic digestion of xyloglucans in plant foods and produce short-chain fatty acids with anti-inflammatory action. By producing folate, a precursor of nucleic acids, Faecalibacterium prausnitzii and Bifidobacterium sp. in humans help to regulate the immune response in the intestinal mucosa. Lactobacillus reuteri and Bifidobacterium sp. produce vitamin B12 and are capable of activating molecular mechanisms on several epithelial barrier cell types [29]. Therefore, the presence of significant numbers of bacteria of the genera Faecalibacterium, Bacteroides, and Lactobacillus could have a potential anti-inflammatory role in the intestinal tract of geese consumed by indigenous populations of Arctic regions.
Common pathogens of bacterial diseases in wild resident and migratory birds, as well as in captive ones, are members of the Salmonella sp., Streptococcus sp., Staphylococcus sp., Yersiniae sp., Listeria sp., Pseudomonas sp., Pasteurella sp., Clostridium sp., and Helicobacter sp. [27,30]. In the case of avian health, they are causative agents of diseases of the cardiovascular and immune systems, such as arthritis, and septicemia due to staphylococcosis [31,32]. Judging by the relative abundance of Staphylococcus bacteria in the intestinal microbiome of ptarmigan, these birds are at risk of staphylococcosis. For human health, Clostridium sp. bacteria are commonly found on poultry faeces and cause acute gastrointestinal illness. The spread of Clostridia perfringens increases the risk of human infection through the use of infected birds as food sources [27]. Consumption of inadequately heat-treated wild bird meat poses a risk of bacterial enteritis, which causes diarrhoea and vomiting in humans. It is caused by infection of humans with bacteria of the genus Campylobacter [33,34,35], which was identified at a relative abundance of 1% in only 20% of taiga bean goose intestines. In this study, genus Helicobacter (phylum Campilobacteriota) as well as the genus Fusobacterium (phylum Fusobacteriota) are significantly present in the intestines of geese and ptarmigan. They are common inhabitants of the intestinal tract of birds, but pose a potential threat to vertebrates and humans, as increased levels of metabolites modulate the effects of the inflammatory process and crayfish tumorigenesis [29].

3.3. Next-Generation Biomonitoring as a Tool for Biological Risks Minimisation in the Arctic

An Arctic biomonitoring system is in place and has some benefits [21,36]. We are beginning to see the One Health concept implemented at high latitudes [37]. However, the focus of most efforts has been on the reduction of chemical risks to indigenous people, and biological contaminants have still not been given enough attention. However, timely action to reduce risks to human health can be taken through systematic early detection of pathogens in Arctic ecosystems.
Using NGS technologies to identify several hundred representatives of microbial communities could play an important role in developing new human biomonitoring systems in the Arctic. Genetic profiling of prokaryotes associated with wild birds regularly consumed by people living in the Arkhangelsk region indicates the likely presence of potential pathogenic groups of bacteria. This has a direct impact on the immunity and health of the birds themselves, both migratory (greater white-fronted goose and taiga bean goose) and resident (willow ptarmigan), along with the levels of beneficial bacteria. But the presence of potential pathogens in birds increases the risk of infection for hunters and their families when cutting and preparing food. However, reliable microbiological hazard information cannot be derived from the method used alone. Therefore, as shown in Figure 3, a multidisciplinary approach is required.
A new generation of Arctic biomonitoring could be developed, building on successful large-scale projects [21,36]: logistics, population questionnaires, databases on chemical contamination of traditional foods and indigenous people’s products, sample storage, and processing of experimental results. The proposed approach should be based on meta-taxonomy, which is the profiling of microbial communities by means of profile genes, similar to its use in food technologies [38]. This screening can become an essential tool for identifying potential biological risks. Receiving statistically significant data on bacterial profiles in various organs of resident and migratory birds will help to determine the presence or absence of specific pathogenic microorganisms in certain Arctic regions. Classical culture methods should be used when statistically significant amounts of undesirable microbiota are detected. In addition, a more robust metagenetic apparatus can be used to confirm microbial pathogenicity. If a pathogenicity factor is confirmed, protocols will be developed for faster identification methods such as qPCR, digital droplet PCR, or alternative, cheaper techniques [39]. Thus, Arctic monitoring will provide the basis for protecting indigenous peoples not only from chemical, but also from biological threats.

4. Conclusions

Bacterial communities associated with animals’ internal organs play an important role in immunity and general biota health. The identification of individual genera and species of microorganisms is facilitated by the use of high-throughput methods based on biomarkers. On the basis of the NGS results, we found significant differences in taxonomic diversity in the bacteriome of ptarmigan and geese consumed by people in the Arkhangelsk region. Increasing the number of such studies and quality assurance could be the basis for the screening of zoonotic infections in the Arctic. Using alternative approaches, which involve culturing identified bacteria and studying their genomes and properties, a new next-generation biomonitoring platform is emerging at high latitudes. Subsequent development of rapid tests or techniques will help to improve the timeliness of dealing with biological contaminants in the Arctic.

Author Contributions

Conceptualization, A.A. and T.S.; methodology, E.D., A.A. and E.K.; resources, E.D.; data curation, E.K. and K.M.; writing—original draft preparation, K.M. and A.A.; writing—review and editing, A.A.; visualization, A.A. and E.D.; project administration, A.A. and T.S. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the Russian Science Foundation (project no. 22-15-20076).

Institutional Review Board Statement

The animal study protocol was prepared by the researchers, approved by the First Vice-Rector for Strategic Development and Science of Northern Arctic federal university and by the Ethics Committee of the Northern State Medical university, Arkhangelsk (protocol no. 06/09-22 of 28 September 2022).

Data Availability Statement

Data available in a publicly accessible repository.

Acknowledgments

The authors are grateful to the researchers of the N. Laverov Federal Center for Integrated Arctic Research of the Ural Branch of the Russian Academy of Sciences (Arkhangelsk)—Popov S., Korobitsyna R., Kotsur D., researchers of Northern (Arctic) Federal University, (Arkhangelsk)—Lekareva E. for assistance in primary sample preparation and Varakina Yu. for assistance in design of the figures. Also, the authors are grateful to the world-class research and education center “Russian Arctic: New Materials, Technologies, and Research Methods” for the support it has given in carrying out this research.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Sinclair, J.R. Importance of a One Health approach in advancing global health security and the Sustainable Development Goals. Rev. Sci. Tech. 2019, 38, 145–154. [Google Scholar] [CrossRef] [PubMed]
  2. Ruscio, B.A.; Brubaker, M.; Glasser, J.; Hueston, W.; Hennessy, T.W. One Health–a strategy for resilience in a changing arctic. Int. J. Circumpol. Health 2015, 74, 27913. [Google Scholar] [CrossRef] [PubMed]
  3. Gardy, J.L.; Loman, N.J. Towards a genomics-informed, real-time, global pathogen surveillance system. Nat. Rev. Genet. 2018, 19, 9–20. [Google Scholar] [CrossRef] [PubMed]
  4. Makiola, A.; Compson, Z.G.; Baird, D.J.; Barnes, M.A.; Boerlijst, S.P.; Bouchez, A.; Brennan, G.; Bush, A.; Canard, E.; Cordier, T.; et al. Key questions for next-generation biomonitoring. Front. Environ. Sci. 2020, 7, 197. [Google Scholar] [CrossRef]
  5. Michaux, J.; Dyck, M.; Boag, P.; Lougheed, S.; Van Coeverden de Groot, P. New insights on polar bear (Ursus maritimus) diet from faeces based on next-generation sequencing technologies. Arctic 2021, 74, 87–99. [Google Scholar] [CrossRef]
  6. Chen, Y.; Bell, T.H.; Gourlie, S.; Lei, Y.D.; Wania, F. Contaminant biomagnification in polar bears: Interindividual Differences, Dietary Intake Rate, and the Gut Microbiome. Environ. Sci. Technol. 2024, 58, 10504–10514. [Google Scholar] [CrossRef] [PubMed]
  7. Sobolev, N.; Aksenov, A.; Sorokina, T.; Chashchin, V.; Ellingsen, D.G.; Nieboer, E.; Varakina, Y.; Veselkina, E.; Kotsur, D.; Thomassen, Y. Essential and non-essential trace elements in fish consumed by indigenous peoples of the European Russian Arctic. Environ. Pollut. 2019, 253, 966–973. [Google Scholar] [CrossRef] [PubMed]
  8. Waits, A.; Emelyanova, A.; Oksanen, A.; Abass, K.; Rautio, A. Human infectious diseases and the changing climate in the Arctic. Environ. Int. 2018, 121, 703–713. [Google Scholar] [CrossRef]
  9. Andersen-Ranberg, E.; Nymo, I.H.; Jokelainen, P.; Emelyanova, A.; Jore, S.; Laird, B.; Davidson, R.K.; Ostertag, S.; Bouchard, E.; Fagerholm, F.; et al. Environmental stressors and zoonoses in the Arctic: Learning from the past to prepare for the future. Sci. Total Environ. 2024, 957, 176869. [Google Scholar] [CrossRef]
  10. Stimmelmayr, R.; Sheffield, G. Traditional conservation methods and food habits in the Arctic. In Arctic One Health: Challenges for Northern Animals and People; Tryland, M., Ed.; Springer International Publishing: Cham, Switzerland, 2022; pp. 469–501. [Google Scholar] [CrossRef]
  11. 10R Salgado-Flores, A.; Tveit, A.T.; Wright, A.D.; Pope, P.B.; Sundset, M.A. Characterization of the cecum microbiome from wild and captive rock ptarmigans indigenous to Arctic Norway. PLoS ONE 2019, 14, e0213503. [Google Scholar] [CrossRef] [PubMed]
  12. Cho, H.; Lee, W.Y. Interspecific comparison of the fecal microbiota structure in three Arctic migratory bird species. Ecol. Evol. 2020, 10, 5582–5594. [Google Scholar] [CrossRef] [PubMed]
  13. Zielińska, S.; Kidawa, D.; Stempniewicz, L.; Łoś, M.; Łoś, J.M. Environmental DNA as a valuable and unique source of information about ecological networks in Arctic terrestrial ecosystems. Environ. Rev. 2017, 25, 282–291. [Google Scholar] [CrossRef]
  14. Grond, K.; Santo Domingo, J.W.; Lanctot, R.B.; Jumpponen, A.; Bentzen, R.L.; Boldenow, M.L.; Brown, S.C.; Casler, B.; Cunningham, J.A.; Doll, A.C.; et al. Composition and drivers of gut microbial communities in arctic-breeding shorebirds. Front. Microbiol. 2019, 10, 2258. [Google Scholar] [CrossRef] [PubMed]
  15. Bender, K.M.; Svenning, M.M.; Hu, Y.; Richter, A.; Schückel, J.; Jørgensen, B.; Liebner, S.; Tveit, A.T. Microbial responses to herbivory-induced vegetation changes in a high-Arctic peatland. Polar Biol. 2021, 44, 899–911. [Google Scholar] [CrossRef]
  16. Prewer, E.; Vilaça, S.T.; Bird, S.; Kutz, S.; Leclerc, L.M.; Kyle, C.J. Metabarcoding of fecal pellets in wild muskox populations reveals negative relationships between microbiome and diet alpha diversity. Ecol. Evol. 2023, 13, e10192. [Google Scholar] [CrossRef]
  17. Hansen, C.M.; Himschoot, E.A.; Hare, R.F.; Meixell, B.W.; Hemert, C.V.; Hueffer, K. Neisseria arctica sp. nov., isolated from nonviable eggs of greater white-fronted geese (Anser albifrons) in Arctic Alaska. Int. J. Syst. Evol. Micr. 2017, 67, 1115–1119. [Google Scholar] [CrossRef] [PubMed]
  18. Andersen-Ranberg, E.U.; Barnes, C.J.; Rasmussen, L.; Salgado-Flores, A.; Grøndahl, C.; Mosbacher, J.B.; Hansen, A.J.; Sundset, M.A.; Schmidt, N.M.; Sonne, C. A Comparative study on the faecal bacterial community and potential zoonotic bacteria of muskoxen (Ovibos moschatus) in Northeast Greenland, Northwest Greenland and Norway. Microorganisms 2018, 6, 76. [Google Scholar] [CrossRef]
  19. Rimoldi, S.G.; Tamoni, A.; Rizzo, A.; Longobardi, C.; Pagani, C.; Salari, F.; Matinato, C.; Vismara, C.; Gagliardi, G.; Cutrera, M.; et al. Evaluation of 16S-based metagenomic NGS as diagnostic tool in different types of culture-negative infections. Pathogens 2024, 13, 743. [Google Scholar] [CrossRef]
  20. Squires, T.E.; Rödin-Mörch, P.; Formenti, G.; Tracey, A.; Abueg, L.; Brajuka, N.; Jarvis, E.; Halapi, E.C.; Melsted, P.; Höglund, J.; et al. A chromosome-level genome assembly for the Rock Ptarmigan (Lagopus muta). G3 Genes Genomes Genet. 2023, 13, jkad099. [Google Scholar] [CrossRef] [PubMed]
  21. Sobolev, N.; Nieboer, E.; Aksenov, A.; Sorokina, T.; Chashchin, V.; Ellingsen, D.G.; Varakina, Y.; Plakhina, E.; Kotsur, D.; Kosheleva, A.; et al. Concentration dataset for 4 essential and 5 non-essential elements in fish collected in Arctic and sub-Arctic territories of the Nenets Autonomous and Arkhangelsk regions of Russia. Data Brief. 2019, 27, 104631. [Google Scholar] [CrossRef]
  22. Sorokina, T.Y.; Zarubina, L.; Gutenev, M.; Kudryashova, E. International interdisciplinary Arctic research: Case study of the Russian Arctic biomonitoring mega-grant project. Polar Rec. 2023, 59, e28. [Google Scholar] [CrossRef]
  23. Aksenov, A.S.; Kisil, O.Y.; Chervochkina, A.S.; Khrebtova, I.S.; Mantsurova, K.S.; Bespalaya, Y.V.; Aksenova, O.V. Bacterial communities within the freshwater lymnaeid snail Kamtschaticana kamtschatica (Middendorff, 1850) in Northeastern Siberia. Microbiology 2024, 93, 180–183. [Google Scholar] [CrossRef]
  24. Wang, W.; Liu, Y.; Yang, Y.; Wang, A.; Sharshov, K.; Li, Y.; Cao, M.; Mao, P.; Li, L. Comparative analyses of the gut microbiota among three different wild geese species in the genus Anser. J. Basic Microb. 2018, 58, 543–553. [Google Scholar] [CrossRef] [PubMed]
  25. Yin, J.; Yuan, D.; Xu, Z.; Wu, Y.; Chen, Z.; Xiang, X. Significant Differences in Intestinal Bacterial Communities of Sympatric Bean Goose, Hooded Crane, and Domestic Goose. Animals 2024, 14, 1688. [Google Scholar] [CrossRef] [PubMed]
  26. Zhao, K.; Zhou, D.; Ge, M.; Zhang, Y.; Li, W.; Han, Y.; He, G.; Shi, S. Intestinal Microbiota of Anser fabalis Wintering in Two Lakes in the Middle and Lower Yangtze River Floodplain. Animals 2023, 13, 707. [Google Scholar] [CrossRef] [PubMed]
  27. Xiang, X.; Zhang, F.; Fu, R.; Yan, S.; Zhou, L. Significant differences in bacterial and potentially pathogenic communities between sympatric hooded crane and greater white-fronted goose. Front. Microbiol. 2019, 10, 163. [Google Scholar] [CrossRef]
  28. Zhao, M.; Cong, P.; Barter, M.; Fox, A.D.; Cao, L.E.I. The changing abundance and distribution of Greater White-fronted Geese Anser albifrons in the Yangtze River floodplain: Impacts of recent hydrological changes. Bird Conserv. Int. 2012, 22, 135–143. [Google Scholar] [CrossRef]
  29. Carvalho, R.; Carmo, F.; Heloisa, S.; Cordeiro, B.; Vaz, A.; Gimenez, E.; Goulart, L.; Góes-Neto, A.; Loir, Y.L.; Azevedo, V. Metagenomic approaches for investigating the role of the microbiome in gut health and inflammatory diseases. Metagenomics for Gut Microbes. In Metagenomics for Gut Microbes; Kumavath, R.N., Ed.; IntechOpen: London, UK, 2017; p. 55. [Google Scholar] [CrossRef]
  30. Jennings, A.R. Diseases in wild birds. Bird Study 1955, 2, 69–72. [Google Scholar] [CrossRef]
  31. Chege, S.M. Opportunistic Infections of Avians. In The Rasputin Effect: When Commensals and Symbionts Become Parasitic: Advances in Environmental Microbiology; Hurst, C., Ed.; Springer International Publishing: Cham, Switzerland, 2022; Volume 3, pp. 221–260. [Google Scholar] [CrossRef]
  32. Benskin, C.M.H.; Wilson, K.; Jones, K.; Hartley, I.R. Bacterial pathogens in wild birds: A review of the frequency and effects of infection. Biol. Rev. 2009, 84, 349–373. [Google Scholar] [CrossRef] [PubMed]
  33. Acheson, D.; Allos, B.M. Campylobacter jejuni infections: Update on emerging issues and trends. Clin. Infect. Dis. 2001, 32, 1201–1206. [Google Scholar] [CrossRef]
  34. Craven, S.E.; Stern, N.J.; Line, E.; Bailey, J.S.; Cox, N.A.; Fedorka-Cray, P. Determination of the incidence of Salmonella spp., Campylobacter jejuni, and Clostridium perfringens in wild birds near broiler chicken houses by sampling intestinal droppings. Avian Dis. 2000, 44, 715–720. [Google Scholar] [CrossRef] [PubMed]
  35. Gillespie, I.A.; O’Brien, S.J.; Frost, J.A.; Adak, G.K.; Horby, P.; Swan, A.V.; Painter, M.J.; Neal, K.R.; Campylobacter Sentinel Surveillance System Collaborators. A case-case comparison of Campylobacter coli and Campylobacter jejuni infection: A tool for generating hypotheses. Emerg. Infect. Dis. 2002, 8, 937. [Google Scholar] [CrossRef]
  36. Lakhmanov, D.; Varakina, Y.; Aksenov, A.; Sorokina, T.; Sobolev, N.; Kotsur, D.; Plakhina, E.; Chashchin, V.; Thomassen, Y. Persistent Organic Pollutants (POPs) in Fish Consumed by the Indigenous Peoples from Nenets Autonomous Okrug. Environments 2020, 7, 3. [Google Scholar] [CrossRef]
  37. Tryland, M. Arctic One Health: Challenges for Northern Animals and People, 1st ed.; Springer International Publishing: Cham, Switzerland, 2022; 573p. [Google Scholar] [CrossRef]
  38. Mataragas, M.; Alessandria, V.; Ferrocino, I.; Rantsiou, K.; Cocolin, L. A bioinformatics pipeline integrating predictive metagenomics profiling for the analysis of 16S rDNA/rRNA sequencing data originated from foods. Food Microbiol. 2018, 76, 279–286. [Google Scholar] [CrossRef]
  39. Freen-van Heeren, J.J. Flow-FISH as a tool for studying bacteria, fungi and viruses. BioTech 2021, 10, 21. [Google Scholar] [CrossRef]
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Figure 1. Locating sampling site on a general map of the Arctic. The images of the birds are based on photographs taken by Oleg Kulyabin (Naryan-Mar, Russian Federation) and Natalia Bogorodskaya (Vytegra, Russian Federation).
Figure 1. Locating sampling site on a general map of the Arctic. The images of the birds are based on photographs taken by Oleg Kulyabin (Naryan-Mar, Russian Federation) and Natalia Bogorodskaya (Vytegra, Russian Federation).
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Figure 2. Heat map of the distribution of bacterium families associated with the intestines and lungs of geese and ptarmigan consumed by people in the Arctic.
Figure 2. Heat map of the distribution of bacterium families associated with the intestines and lungs of geese and ptarmigan consumed by people in the Arctic.
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Figure 3. Proposed Arctic biomonitoring enhancement approach implementation scheme.
Figure 3. Proposed Arctic biomonitoring enhancement approach implementation scheme.
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Table 1. Summary of characteristics from each bird sample.
Table 1. Summary of characteristics from each bird sample.
MetricsWillow
Ptarmigan
(n 1 = 11)		Greater White-Fronted Goose
(n = 7)		Taiga Bean Goose
(n = 5)
Body length, sm37.3 ± 10.8 266.3 ± 3.371.4 ± 3.2
Wing length, sm20.5 ± 0.739.6 ± 2.042.7 ± 2.6
Wingspan, sm66.8 ± 2.6130.6 ± 6.7135.2 ± 3.9
Tail length, sm9.4 ± 5.612.3 ± 0.913.9 ± 0.9
Beak length, sm1.6 ± 0.43.1 ± 0.34.1 ± 0.8
Tarsometatarsus length, sm4.1 ± 0.37.1 ± 0.47.0 ± 0.3
Weight, kg0.7 ± 0.12.0 ± 0.22.9 ± 0.4
1 Sample size. 2 Standard deviation.
Table 2. Profiling bacterial microbiota by phylum.
Table 2. Profiling bacterial microbiota by phylum.
Bacterial
Phylum		Relative Abundance, %
Willow
Ptarmigan,
Intestine
(n 1 = 11)		Greater White-Fronted Goose,
Intestine
(n = 7)		Taiga Bean Goose, Intestine
(n = 5)		Willow
Ptarmigan, Lungs
(n = 9)
Bacillota30.7 ± 25.9 236.2 ± 24.123.3 ± 16.828.0 ± 23.5
Pseudomonadota24.4 ± 30.40.5 ± 0.41.0 ± 1.215.9 ± 13.1
Actinomycetota9.1 ± 6.51.1 ± 1.20.6 ± 0.714.6 ± 8.8
Campylobacterota3.4 ± 8.110.8 ± 14.15.6 ± 7.13.0 ± 6.7
Bacteroidota0.8 ± 0.914.8 ± 13.98.3 ± 11.51.7 ± 4.3
Fusobacteriota0.1 ± 0.33.3 ± 6.71.0 ± 1.5<0.1
Deferribacterota<0.10.6 ± 1.30.1 ± 0.2<0.1
Desulfobacterota<0.1<0.10.1 ± 0.1<0.1
1 Sample size. 2 Standard deviation.
Table 3. Relative abundance (RA) and detection frequency (DF) of prevalent bacterial genera in the intestine and lung microbiota of ptarmigans and geese consumed by the people in the Arctic.
Table 3. Relative abundance (RA) and detection frequency (DF) of prevalent bacterial genera in the intestine and lung microbiota of ptarmigans and geese consumed by the people in the Arctic.
GenusWillow
Ptarmigan,
Intestine
(n 1 = 11)		Greater White-Fronted Goose, Intestine
(n = 7)		Taiga Bean Goose, Intestine
(n = 5)		Willow
Ptarmigan, Lungs
(n = 9)
RA, %DF, %RA, %DF, %RA, %DF, %RA, %DF, %
Streptococcus1927 2----5622
Staphylococcus1718------
Ensifer3245------
Rikenellaceae
RC9 gut group		154----822
Cutibacterium545----978
Methylobacterium-
Methylorubrum		------544
Sphingomonas------644
Corynebacterium------422
Actinomyces254------
Helicobacter132713865100933
Olsenella336157020933
Subdoligranulum--9100260--
Campylobacter----120--
Faecalibacterium--286160--
Megamonas--7861560--
Sutterella--186----
Bacteroides--1486880--
Fusobacterium--843140--
Lactobacillus--4866100--
Clostridium
sensu stricto 2		----360--
1 Sample size. 2 Minimum detection frequency cut-off: ≥20%.
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Durnova, E.; Karmanova, E.; Sorokina, T.; Mayorova, K.; Aksenov, A. Bacterial Diversity and Composition in the Internal Organs of Taiga Bean Goose, Greater White-Fronted Goose and Willow Ptarmigan as a New Tools in the Arctic Biomonitoring System. Diversity 2025, 17, 101. https://doi.org/10.3390/d17020101

AMA Style

Durnova E, Karmanova E, Sorokina T, Mayorova K, Aksenov A. Bacterial Diversity and Composition in the Internal Organs of Taiga Bean Goose, Greater White-Fronted Goose and Willow Ptarmigan as a New Tools in the Arctic Biomonitoring System. Diversity. 2025; 17(2):101. https://doi.org/10.3390/d17020101

Chicago/Turabian Style

Durnova, Evdokia, Elena Karmanova, Tatiana Sorokina, Ksenia Mayorova, and Andrey Aksenov. 2025. "Bacterial Diversity and Composition in the Internal Organs of Taiga Bean Goose, Greater White-Fronted Goose and Willow Ptarmigan as a New Tools in the Arctic Biomonitoring System" Diversity 17, no. 2: 101. https://doi.org/10.3390/d17020101

APA Style

Durnova, E., Karmanova, E., Sorokina, T., Mayorova, K., & Aksenov, A. (2025). Bacterial Diversity and Composition in the Internal Organs of Taiga Bean Goose, Greater White-Fronted Goose and Willow Ptarmigan as a New Tools in the Arctic Biomonitoring System. Diversity, 17(2), 101. https://doi.org/10.3390/d17020101

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