Comparison of the red fox gut microbiota among different habitat types in southern Anatolia

doi:10.21203/rs.3.rs-4707128/v1

Download PDF

Research Article

Comparison of the red fox gut microbiota among different habitat types in southern Anatolia

https://doi.org/10.21203/rs.3.rs-4707128/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Environmental conditions, especially diet, affect the diversity of gut microbiota (GM). This diversity within and between populations may influence the host’s health and fitness, therefore plays important roles in adaptation. Regarding this, we collected fecal samples from natural, rural, suburban, and urban habitats to reveal the interaction between diet and compositional and functional diversity of GM of a generalist carnivore, the red fox. The prokaryotic diversity of fecal microbiota was investigated by sequencing the 16S rRNA gene V3-V4 regions. 46 archaeal and bacterial phyla were identified, and Firmicutes was the most common phylum in most samples. The dominant genera in the GM of the red fox were Collinsella, Fusobacterium, Faecalibacterium, Escherichia-Shigella, and Blautia. Fusobacterium was significantly more abundant in suburban (16.0%), natural (11.0%), and rural habitats (10.8%) than in urban habitats (2.0%) indicating dietary differences of the red foxes that feed close to human settlements. However, PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) showed that the host’s habitat did not significantly affect the functional diversity. Our study determined the compositional changes of the GM of a wild animal for the first time in the Anatolian peninsula and revealed the effects of dietary changes, especially urbanization, on the diversity of GM of red foxes.

Vulpes vulpes

metabarcoding

16S rRNA

fecal microbiota

diet

The red fox (Vulpes vulpes L. 1758) is a medium-sized canid, found throughout the entire northern hemisphere and has the widest range of distribution among all members of the order Carnivora (Hoffmann and Sillero-Zubiri 2021). Red foxes are often characterized as generalist predators and scavengers that exploit the most available food sources (Lloyd 1980; Larivière and Pasitschniak-Arts 1996), and can therefore live in many different habitat types, including urban areas (Roemer et al. 2009). These species expand their distribution over time to habitats where larger carnivores, which have relatively small population sizes and are habitat-specialized depending on their specific food preferences, have disappeared due to anthropogenic influences (Shao et al. 2021). In many ecosystems, generalist carnivores rise to the top of the food chain and act as apex predators (Prugh et al. 2009), so their feeding habits and niche width provide critical information for understanding trophic interactions in an ecosystem and better insights into predator-prey relationships and the food web hierarchy (Lanszki et al. 2019).

Since animals gradually lost their ability to digest many essential nutrients during evolution, they developed a symbiotic relationship with microbes (Dale and Moran 2006; Ley et al. 2008) to uptake nutrients, regulate metabolism (Turnbaugh et al. 2006; Greenblum et al. 2012), and improve the role in host immune function (Ganal et al. 2012; Markle et al. 2013). Furthermore, these microbes play a significant role in environmental adaptation of the host species (Petersen et al. 2023). Recently, the adaptation and convergence of gut microbiota (GM) to diet have been widely studied across mammals. These works showed that the composition of the GM of mammals is very complex and species-specific, influenced by such variables as the anatomy and diet of the species (Rinninella et al. 2019; de Jonge et al. 2022). Maintaining microbial diversity and functional redundancy in wildlife populations is crucial for ecosystem resilience and species adaptability in response to environmental changes. Understanding the complex interactions between habitat, diet, and microbial communities can inform conservation strategies aimed at preserving biodiversity and mitigating the impacts of human activities on wildlife. In addition, studies focusing on the microbiome may improve our understanding of how GM affects host health and co-evolution.

Considering its highly diverse dietary habits, the red fox is a model organism to examine the relationship between diet and GM. In this work, we addressed this question by evaluating the compositional and functional diversity of the GM in red foxes, using bacterial 16S rRNA sequences from feces sampled from different habitat types (natural, rural, suburban, and urban). Our work will help us to assess the functional consequences of the microbes to their hosts.

Sampling

This study was carried out in four different sampling areas representing four different habitat types located in Adana province in southern Anatolia, Turkiye. The first location was a natural area away from human disturbance in Yumurtalık district (midpoint coordinates: 36° 49.376'N − 35° 42.556'E); the second was near and around agricultural areas (rural) known for pesticide use (37° 2.010'N − 35° 22.811'E); the third was a suburban area in the university campus near Seyhan Dam Lake (37° 2.900'N − 35° 21.102'E); and the fourth was an urban area close to the city center (37° 1.184'N − 35° 19.737'E). Sampling areas were chosen to prevent foxes from migrating between them due to human settlement and/or distance barriers.

Since the fecal microbial flora of canids reflects the microbial structure of the distal GM, fecal samples were collected to determine the GM of the red foxes as conducted in previous studies (Zhang and Chen 2010; Zhang et al. 2012; Peng et al. 2018; Nardi et al. 2022; Wang et al. 2022). The day before each sampling day, all feces in the area were cleaned at sunset, and fresh feces to be analyzed were collected at sunrise the next day to prevent degradation and ensure the quality of feces for further analysis. An expert researcher collected the fecal samples thought to belong to the red foxes from these areas in close periods between June and September 2021 to prevent seasonal bias. During the fieldwork, more than 20 samples were collected from each sampling area as environmental factors may affect the samples. However, a total of 24 samples were included in molecular analyses based on habitat type and sample quality. Samples were placed in sterile plastic sampling bottles and stored at + 4°C until transferred to the laboratory. All feces were frozen at − 80°C in the laboratory until molecular analyses.

DNA Extraction, Library Preparation and Sequencing

DNA was extracted using ZymoBIOMICS DNA MiniPrep Kit (Zymo Research) according to the manufacturer's protocol, and all DNA samples were stored at -20°C. The 314F (CCTAYGGGRBGCASCAG) and 860R (GGACTACNNGGGTATCTAAT) primers that targeted the 16S gene were used to profile microbiota composition (Klindworth et al. 2013). The libraries were prepared with the KaPa HiFi master mix (Roche) and Nextera XT indexes (Illumina). Pooled libraries were cleaned up with specific size selection were applied following the manufacturer's protocol using the AMPure XP beads (Beckman Coulter). The libraries were sequenced with Illumina NovaSeq 6000 system using 2 × 250 read length (Diagen Biotechnological Systems, Turkiye), producing a minimum of 100,000 reads per sample. Raw sequence reads have been deposited at the NCBI SRA under Project number PRJNA1132502.

Bioinformatics

The raw reads were trimmed using cutadapt (Martin 2011). Bioinformatics analyses were conducted using the DADA2 v1.16 (Callahan et al. 2016a), following the suggested workflow (Callahan et al. 2016b). Briefly, quality-filtered reads were merged and aligned to the SILVA reference database of 16S rRNA sequences (SSU Ref NR 99 release 138.1) (Quast et al. 2013). Further analyses were performed using R v4.3.0 and Bioconductor packages “TreeSummarizedExperiment v2.10.0” (Huang et al. 2021), “vegan v2.6” (Oksanen et al. 2013), “phyloseq v1.46.0” (McMurdie and Holmes 2013), and “mia v1.10.0” (Ernst et al. 2024), then “ggplot2 v3.5.0” (R Development Core Team, 2015; Wickham 2016) used for visualization. Alpha and beta diversity were calculated using vegan and mia packages (Oksanen et al. 2013; Ernst et al. 2024). PICRUSt pipeline was used to examine the functional diversity (Langille et al. 2013). Kyoto Encyclopedia of Genes and Genomes (KEGG) ortholog groups (KOs) predicted from the 16S rRNA gene sequences were assigned to broad functional categories based on the BRITE hierarchy. PICRUSt data were visualized using R package ggpicrust2 v1.7.3 (Yang et. 2023).

After sequencing, a total of 4.23 million raw reads were obtained, with an average of 176,210 reads per sample. 82% of the total reads passed quality filtering. After denoising and merging, 6.5% of the reads were removed as chimeric, and accounted for less than 5% of all reads. The remaining 2.85 million reads had an average of 118,723 reads per sample (min = 87,314, max = 151,918). A total of 10,358 OTUs were detected and assigned by comparison with the SILVA v138.1 database (Quast et al. 2013) applying a threshold of 97% sequence identity.

Our data included 46 archaeal and bacterial phyla, with five dominant phyla (Firmicutes, Proteobacteria, Actinobacteria, Fusobacteridota, and Bacteroidota) (Table 1 and Fig. 1). Firmicutes and Bacteroidota were significantly more abundant in urban and suburban samples (over 70%), compared to rural samples, where they represented only 20% of the total community. Regarding the habitat, we observed phylum-level differences in the composition of the GM between samples. While Firmicutes was the most common phylum in most samples (13 out of 24), its proportion was different between the samples that were collected from different habitats (37.6% in natural, 17.2% in rural, 58.4% in suburban, and 66.1% in urban). The Fusobacteridota phylum was observed in all samples but was significantly less abundant in urban samples (2.0%) than in other samples (13.7% in suburban, 9.8% in rural, and 10.9% in natural). The abundance of Bacteroidota was relatively lower than Proteobacteria and Fusobacteridota in the total community (6.4% vs. 23.4% and 9.7%, respectively).

At the genus-level, Collinsella, Fusobacterium, Faecaelibacterium, Escherichia-Shigella, and Blautia were significantly more abundant in all samples (Table 1 and Fig. 2). Faecalibacterium was more abundant in urban samples (19.5%) than in other samples (4.3% in suburban, 1.6% in rural, and 8.9% in natural), whereas Fusobacterium was significantly more abundant in samples collected from natural (11%), rural (10.8%), and suburban habitats (16.0%) compared to urban samples (2.0%). The genera Catenibacterium and Peptoclostridium from the Firmicutes phylum were more abundant in suburban, and urban samples than in natural and rural samples.

We then estimated α-diversity indices to test the effect of different habitats on the fecal microbiota of red foxes. Observed species, richness (Shannon index), and equitability (Simpson evenness) at the phylum and genus levels were examined, but no statistically significant differences were found (Suppl. Figure 1). Likewise, fecal prokaryotic communities of red foxes from natural, rural, suburban, and urban habitats did not cluster separately in a PCoA plot of Bray-Curtis dissimilarity metrics analysis (Suppl. Figure 2). In addition, the PICRUSt pipeline was used to examine whether the habitat differences in the prokaryotic composition are linked to changes in functional diversity. However, PCoA based on KEGG orthologous groups predicted by PICRUSt showed that the host habitat did not significantly affect the functional diversity in this study (Suppl. Figure 3).

It has been known that diet and environmental conditions are the main factors affecting GM (Rinninella et al. 2019). In many studies, the efficacy of differences in the GM of species Canidae – that adapted to different ecological niches and habitats – was evaluated, and the relationship between GM composition and environmental conditions was examined (Peng et al. 2018; DeCandia et al. 2021; Wang et al. 2022, Nardi et al. 2022). In this work, we investigated the relationship between diet and the compositional and functional diversity of the GM in red fox, chosen as a model organism that can inhabit many different habitats, including human settlements (Hoffmann and Sillero-Zubiri 2021). To address this question, fecal samples of red foxes were collected from four different habitat types (natural, rural, suburban, and urban) in Adana province, Turkiye, and applied 16S rRNA amplicon-based next-generation sequencing.

Our results showed that a diverse microbial community with Firmicutes, Proteobacteria, Actinobacteria, Fusobacteridota, and Bacteroidota were the most abundant phyla across all samples. This observation was consistent with previous studies about red foxes (Peng et al. 2018; Wang et al. 2022), and also the core microbiota determined in this study has been found similar to the dominant members in the GM of other canids such as wolves (Zhang & Chen 2010; Zhang et al. 2012; Wu et al. 2017; Lyu et al. 2018), coyotes (Sugden et al. 2021), raccoon dogs (An et al. 2017), and domestic dogs (Pinna and Biagi 2014). Notably, Firmicutes and Bacteroidetes were significantly more abundant in urban and suburban habitats, comprising over 70% of the microbial community, whereas the rural habitat exhibited a more balanced distribution among major phyla. It is known that urban areas significantly affect animal foraging behavior and diet due to increased access to human-associated food impacting animal GM by altering factors such as microbial diversity and composition (Anders et al. 2022). The higher prevalence of Firmicutes and Bacteroidetes in urban habitats reflected potential dietary adaptations rich in anthropogenic food sources, contrasting with the broader dietary diversity observed in less urbanized habitats. These results were consistent with previous fecal microbiota studies in animals with different diets (O'Donnell et al. 2017; Youngblut et al. 2019). On the contrary, natural and rural habitats showed higher proportions of Fusobacteridota, potentially indicative of a diet based on wild or agricultural food sources. Since this phylum plays a role in the digestion of amino acids (Vázquez-Baeza et al. 2016; Pilla and Suchodolski 2020), it is known to be increased in protein-based diets (Bermingham et al. 2017; Sugden et al. 2021).

Collinsella, Fusobacterium, Faecalibacterium, Escherichia-Shigella, and Blautia were the most abundant genera in the GM of red foxes in this study, and significant differences were observed at the genus level in different habitat types. Faecalibacterium was more prevalent in urban samples, which may indicate the adaptations to urban diets rich in lipids and processed foods (Louis et al. 2010; Peng et al. 2018). Conversely, Fusobacterium showed higher abundance in natural and rural habitats, which may reflect a diet influenced by habitat-specific food availability. It suggests that the relatively higher abundance of Fusobacterium in natural and rural habitats might be due to the higher protein intake from prey. On the other hand, the significant decrease in the relative abundance of Fusobacterium in the GM of urban red foxes may be related to a diet based on anthropogenic food sources rich in carbohydrates and fats (Vital et al. 2015; Pilla and Suchodolski 2020).

Red foxes have an opportunistic feeding behavior that allows them to survive in various environments, including highly modified urban areas, where they consume anthropogenic foods (Bateman and Fleming 2012). Available food sources can turn their protein-based diets, mainly consist of rodents, birds, lizards, and invertebrates in natural areas, into carbohydrate and fat-based diets, including garbage in urban areas (Castañeda et al. 2022). Our results showed that the diet of urban foxes was based on less protein and - high fat, and consistently found a higher abundance of Catenibacterium and Peptoclostridium in the samples from suburban and urban habitats (Phungviwatnikul et al. 2021; Xu et al. 2021).

Functional predictions based on PICRUSt revealed no significant differences in the functional diversity of microbial communities in different habitats, which may suggest the functional capabilities of the microbial communities are often conserved (Louca et al. 2016). Similarly, alpha and beta diversity indices did not significantly vary among habitat types in our work, which may indicate a relatively consistent microbial community structure regardless of environmental variabilities, possibly due to shared functional roles within the GM.

In conclusion, this study is the first report that determined the compositional changes of the GM of a wild animal in the Anatolian peninsula. In addition, four distinct habitat types sampled in this work may improve our understanding of how urbanization and human activities influence the GM of red foxes. Our results showed that the GM of red foxes varies significantly across different habitats, which suggests that it may play a crucial role in red foxes’ adaptation to diverse environments. Future research could incorporate longitudinal sampling to assess temporal dynamics and explore additional environmental factors impacting GM, such as dietary preferences, habitat fragmentation, and pollution levels. Revealing the microbiome of generalist carnivores can provide deeper insights into the ecological strategies of these species and improve conservation and wildlife management practices.

ACKNOWLEDGMENTS

In this study, some of the suburban and rural samples were collected from Çukurova University Faculty of Agriculture Research and Application Farm. We would like to thank the university administration for making this possible. We also would like to thank Zuhal Sultan Akbaba for her assistance in the field studies. Legal permissions for studies carried out in natural areas were obtained from The Ministry of Agriculture and Forestry, General Directorate of Nature Protection and National Parks (Turkiye).

AUTHOR CONTRIBUTIONS

BA collected fecal samples; SK and HU conducted genetic and bioinformatic analyses. All authors contribute in writing the manuscript. All authors have read and agreed to the published version of the manuscript.

FUNDING

This study was funded by Hacettepe University Scientific Research Unit (Project number: FHD-2021-19590).

DATA AVAILABILITY

The datasets used and/or analyzed during the current study are available from the corresponding author.

Compliance with ethical standards

Not applicable.

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

An C, Okamoto Y, Xu S, Eo KY, Kimura J, Yamamoto N (2017) Comparison of fecal microbiota of three captive carnivore species inhabiting Korea. J. Vet. Med. 79(3) 542-546.
Bateman PW, Fleming PA (2012) Big city life: carnivores in urban environments. J. Zool 287(1) 1-23.
Bermingham EN, Maclean P, Thomas DG, Cave NJ, Young W (2017) Key bacterial families (Clostridiaceae, Erysipelotrichaceae and Bacteroidaceae) are related to the digestion of protein and energy in dogs. PeerJ 5 e3019.
Castañeda I, Doherty TS, Fleming PA, Stobo‐Wilson AM, Woinarski JC, Newsome TM (2022) Variation in red fox Vulpes vulpes diet in five continents. Mammal Review 52(3) 328-342.
Dale C, Moran NA (2006) Molecular interactions between bacterial symbionts and their hosts. Cell 126(3) 453-465.
DeCandia AL, Cassidy KA, Stahler DR, Stahler EA, VonHoldt BM (2021) Social environment and genetics underlie body site‐specific microbiomes of Yellowstone National Park gray wolves (Canis lupus). Ecol. Evol. 11(14) 9472-9488.
de Jonge N, Carlsen B, Christensen MH, Pertoldi C, Nielsen JL (2022) The gut microbiome of 54 mammalian species. Front. microbiol. 13 886252.
Ernst F, Shetty S, Borman T, Lahti L (2024) mia: Microbiome analysis. R package version 1.12.0
Ganal SC, Sanos SL, Kallfass C, Oberle K, Johner C, Kirschning C, Lienenklaus S, Weiss S, Staeheli P, Aichele P, Diefenbach A (2012) Priming of natural killer cells by nonmucosal mononuclear phagocytes requires instructive signals from commensal microbiota. Immunity 37(1) 171-186.
Greenblum S, Turnbaugh PJ, Borenstein E (2012) Metagenomic systems biology of the human gut microbiome reveals topological shifts associated with obesity and inflammatory bowel disease. PNAS 109(2) 594-599.
Hoffmann M, Sillero-Zubiri C (2021) Vulpes vulpes (amended version of 2016 assessment). The IUCN Red List of Threatened Species 2021: e.T23062A193903628. https://dx.doi.org/10.2305/IUCN.UK.2021-1.RLTS.T23062A193903628.en. Accessed on 01 July 2024.
Huang R, Soneson C, Ernst FG, Rue-Albrecht KC, Yu G, Hicks SC, Robinson MD (2021) Tree Summarized Experiment: a S4 class for data with hierarchical structure. F1000Research 9 1246.
Langille MGI, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, Clemente JC, Burkepile DE, Vega Thurber RL, Knight R, Beiko RG, Huttenhower C (2013) Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences.Nat. Biotechnol 1-10.
Lanszki J, Heltai M, Kövér G, Zalewski A (2019) Non-linear relationship between body size of terrestrial carnivores and their trophic niche breadth and overlap. Basic Appl. Ecol 38 36-46.
Larivière S, Pasitschniak-Arts M (1996) Vulpes vulpes. Mammalian Species (537) 1-11.
Ley RE, Hamady M, Lozupone C, Turnbaugh PJ, Ramey RR, Bircher JS, Schlegel ML, Tucker TA, Schrenzel MD, Knight R, Gordon JI (2008) Evolution of mammals and their gut microbes. Science 320(5883) 1647-1651.
Lloyd HG (1980) The Red Fox. Batsford Ltd., London, England.
Louca S, Parfrey LW, Doebeli M (2016) Decoupling function and taxonomy in the global ocean microbiome. Science 353(6305) 1272-1277.
Louis P, Young P, Holtrop G, Flint HJ (2010) Diversity of human colonic butyrate‐producing bacteria revealed by analysis of the butyryl‐CoA: acetate CoA‐transferase gene. Environ. Microbiol. 12(2) 304-314.
Lyu T, Liu G, Zhang H, Wang L, Zhou S, Dou H, Pang B, Sha W, Zhang H (2018) Changes in feeding habits promoted the differentiation of the composition and function of gut microbiotas between domestic dogs (Canis lupus familiaris) and gray wolves (Canis lupus). Amb Express 8 1-12.
Markle JG, Frank DN, Mortin-Toth S, Robertson CE, Feazel LM, Rolle-Kampczyk U, vonBergen M, McCoy KD, Macpherson AJ, Danska JS (2013) Sex differences in the gut microbiome drive hormone-dependent regulation of autoimmunity. Science 339(6123) 1084-1088.
McMurdie PJ, Holmes S (2013) phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLOS ONE 8(4) e61217.
Nardi F, Lazzeri L, Iannotti N, Donini V, Cucini C, Belardi I, Frati F, Carepelli A, Ferretti F (2022) Analysis of scat for gut microbiome identification in wolves from a Mediterranean and an Alpine area. Diversity 15(1) 37.
O’Donnell MM, Harris HM, Ross RP, O'Toole PW (2017) Core fecal microbiota of domesticated herbivorous ruminant, hindgut fermenters, and monogastric animals. Microbiologyopen 6(5)e00509.
Oksanen J et al. (2013) vegan: Community ecology package. R package version 2.0–10.
Peng Y, Zhang Z, Li H, Li S, Shi Q, Zhang J (2018) Assessment of fecal microbiota in farmed silver fox (Vulpes vulpes fulva) and raccoon dog (Nyctereutes procyonoides). Acta Agriculturae Scandinavica, Section A-Animal Science 68(3) 142-151.
Petersen C, Hamerich IK, Adair KL, Griem-Krey H, Torres Oliva M, Hoeppner MP, Bohannam BJM, Schulenburg, H (2023) Host and microbiome jointly contribute to environmental adaptation. The ISME Journal 17(11) 1953-1965.
Phungviwatnikul T, Alexander C, Do S, He F, Suchodolski JS, De Godoy MR, Swanson KS (2021) Effects of dietary macronutrient profile on apparent total tract macronutrient digestibility and fecal microbiota, fermentative metabolites, and bile acids of female dogs after spay surgery. Journal of Animal Science 99(9) skab225.
Pilla R, Suchodolski JS (2020) The role of the canine gut microbiome and metabolome in health and gastrointestinal disease Front. vet. sci. 6 498.
Pinna C, Biagi G (2014) The utilisation of prebiotics and synbiotics in dogs. Ital. J. Anim. Sci, 13(1) 3107.
Prugh LR, Stoner CJ, Epps CW, Bean WT, Ripple WJ, Laliberte AS, Brashares JS (2009) The rise of the mesopredator. Bioscience 59(9) 779-791.
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO (2013) The SILVA ribosomal RNA gene database project: improved data processing and web tools. Nucleic Acids Research 42 (D1): D590-D596.
Rinninella E, Raoul P, Cintoni M, Franceschi F, Miggiano GAD, Gasbarrini A, Mele MC (2019) What is the healthy gut microbiota composition? A changing ecosystem across age, environment, diet, and diseases. Microorganisms 7(1) 14.
Roemer GW, Gompper ME,Van Valkenburgh B (2009) The ecological role of the mammalian mesocarnivore. BioScience 59(2) 165-173.
R Development Core Team (2015) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
Shao X, Lu Q, Liu M, Xiong M, Bu H, Wang D, Liu S, Zhao JD, Pirrone A, Yao M. (2021). Generalist carnivores can be effective biodiversity samplers of terrestrial vertebrates. Frontiers in Ecology and the Environment 19(10) 557-563.
Sugden S, St. Clair CC, Stein LY (2021) Individual and site-specific variation in a biogeographical profile of the coyote gastrointestinal microbiota. Microbial Ecology 81(1) 240-252.
Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI (2006) An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444(7122)1027-1031.
Vázquez-Baeza Y, Hyde ER, Suchodolski JS, Knight R (2016) Dog and human inflammatory bowel disease rely on overlapping yet distinct dysbiosis networks. Nature Microbiology 1(12) 1-5.
Vital M, Gao J, Rizzo M, Harrison T, Tiedje JM (2015) Diet is a major factor governing the fecal butyrate-producing community structure across Mammalia, Aves and Reptilia. The ISME journal 9(4) 832-843.
Wang X, Shang Y, Wei Q, Wu X, Dou H, Zhang H, Zhou S, Sha W, Sun G, Ma S, Zhang H (2022) Comparative analyses of the gut microbiome of two fox species, the red fox (Vulpes vulpes) and Corsac fox (Vulpes corsac), that occupy different ecological niches. Microbial Ecology 1-13.
Wickham H (2016) ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.
Wu X, Zhang H, Chen J, Shang S, Yan J, Chen Y, Tang X, Zhang, H (2017) Analysis and comparison of the wolf microbiome under different environmental factors using three different data of Next Generation Sequencing. Scientific Reports 7(1) 11332.
Xu J, Becker AA, Luo Y, Zhang W, Ge B, Leng C, Wang G, Ding L, Wang J, Fu X, Janssens GP (2021) The fecal microbiota of dogs switching to a raw diet only partially converges to that of wolves Front. microbiol 12 701439.
Yang C, Burberry A, Cao X, Mai J, Zhang L (2023). ggpicrust2: an R package for PICRUSt2 predicted functional profile analysis and visualization. arXiv preprint arXiv:2303.10388.
Youngblut ND, Reischer GH, Walters W, Schuster N, Walzer C, Stalder G, Ley RE, Farnleitner AH (2019) Host diet and evolutionary history explain different aspects of gut microbiome diversity among vertebrate clades. Nat. Commun. 10(1) 1-15.
Zhang H, Chen L (2010) Phylogenetic analysis of 16S rRNA gene sequences reveals distal gut bacterial diversity in wild wolves (Canis lupus). Mol. Biol. Rep. 37 4013-4022.
Zhang H, Cao X, Chen L (2012) Composition and diversity of Canidae fecal flora. Acta Ecologica Sinica 32(5) 253-257.

Table 1 is available in the Supplementary Files section.

SupplFig1.pdf
Supplementary Figure 1. The plots show the alpha diversity estimates of the samples collected from different habitats. Observed taxa (A), Shannon diversity index (B), Simpson diversity (evenness) index (C), and Relative index (D) of samples from different habitats.
SupplFig2.pdf
Supplementary Figure 2. Beta diversity plot. Bray-Curtis Principal Component Analysis (PCA) of bacterial 16S sequencing data grouped by sampling site.
SupplFig3.pdf
Supplementary Figure 3. Principal coordinates analysis (PCoA) on functional pathway di abundance data predicted by PICRUSt.
Table1.pdf
Table 1. The relative abundance (%) of the most abundant bacterial taxa in the samples. Numbers in parentheses indicate sample size.

Download PDF

Reviewers agreed at journal
26 Jul, 2024
Reviewers invited by journal
23 Jul, 2024
Editor assigned by journal
15 Jul, 2024
First submitted to journal
10 Jul, 2024

You are reading this latest preprint version

Comparison of the red fox gut microbiota among different habitat types in southern Anatolia

Status:

Version 1

Abstract

Figures

INTRODUCTION

MATERIALS AND METHODS

Sampling

DNA Extraction, Library Preparation and Sequencing

Bioinformatics

RESULTS

DISCUSSION

Declarations

References

Table 1

Supplementary Files

Status:

Version 1