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BMC Ecology

Open Access

Vertebrate bacterial gut diversity: size also matters

  • Jean-Jacques Godon1Email author,
  • Pugazhendi Arulazhagan1, 2,
  • Jean-Philippe Steyer1 and
  • Jérôme Hamelin1
BMC EcologyBMC series – open, inclusive and trusted201616:12

https://doi.org/10.1186/s12898-016-0071-2

Received: 18 May 2015

Accepted: 10 March 2016

Published: 23 March 2016

Abstract

Background

One of the central issues in microbial ecology is to understand the parameters that drive diversity. Among these parameters, size has often been considered to be the main driver in many different ecosystems. Surprisingly, the influence of size on gut microbial diversity has not yet been investigated, and so far in studies reported in the literature only the influences of age, diet, phylogeny and digestive tract structures have been considered. This study explicitly challenges the underexplored relationship connecting gut volume and bacterial diversity.

Results

The bacterial diversity of 189 faeces produced by 71 vertebrate species covering a body mass range of 5.6 log. The animals comprised mammals, birds and reptiles. The diversity was evaluated based on the Simpson Diversity Index extracted from 16S rDNA gene fingerprinting patterns. Diversity presented an increase along with animal body mass following a power law with a slope z of 0.338 ± 0.027, whatever the age, phylogeny, diet or digestive tract structure.

Conclusions

The results presented here suggest that gut volume cannot be neglected as a major driver of gut microbial diversity. The characteristics of the gut microbiota follow general principles of biogeography that arise in many ecological systems.

Keywords

BiodiversityBiogeographyGutFingerprintSpecies-area relationship

Background

Among a number of parameters, the ‘size’ of an ecosystem is often assumed to have a key impact on the management of diversity. In fact, the species-area relationship is central to the ecological theory [1] and was first described for macro-organisms [2]. For bacteria, the species-area relationship is generally expressed in terms of habitat volume (i.e., volume-area relationship) and has been illustrated in liquid sump tanks of metal-cutting machines [3], membrane bioreactors [4] and tree holes (i.e., rainwater accumulated in holes at the base of large trees) [5]. However, until present, the microbial species-volume relationship has never yet been studied for gut or body size, even though vertebrate gut size covers a wide range of magnitudes. There is a 106 body mass factor between a tiny bird or a shrew and an elephant.

The vertebrate gut hosts a microbial community that fulfils many vital functions for the host: it enhances resistance to infection, stimulates mucosal immune defences, synthesizes essential vitamins and promotes caloric uptake by hydrolysing complex carbohydrates. The bacterial populations inhabiting the gut are complex, varying considerably from individual to individual and from species to species. However, gut microbial ecosystems are not a random association of microbes but are shaped by the host. A transfer occurs vertically from mothers to offspring or horizontally between individuals within a specific group. Such transfers have given rise to the long-standing co-evolution of microbiota and their hosts [6].

The benefit of bacterial diversity in the human gut has often been highlighted [7] and driving factors such as age [8], diverse lifestyles [9] and diet variations [10] have already been explored. Despite such an interest, the relationship between body mass and gut microbiota has never been explored whereas, in contrast, the positive links between the abundance of parasitic organisms or protozoal faunas and animal body size have been thoroughly referenced [11] [12]. The aim of the present study is to analyse a large bacterial dataset, comprising faeces collected from 71 different vertebrate species, in order to examine the effect of the volume-microbial diversity relationship in animal digestive tracts.

Methods

Sampling

All the animal samples were obtained from domesticated or captive populations in France (zoo, farm, aquarium, recreative farm or individual keeper). There is non-experimental research dedicated for this study, faeces samples were collected on ground with the animal keeper or animal owner without stresses for the animals. We obtained permissions from Lunaret zoo, Montpellier; Océanopolis, Brest; Réserve Africaine, Sigean; Mini Ferme Zoo, Cessenon sur Orb and consent from the animal owners (Jean-Philippe Steyer, Anais Bonnafous, Jean-Jacques Godon). Animal were living alone or in small groups (1 to 5). Furthermore, their food (meat, seeds, fruits or hay) were more standardized in comparison to wild diets.

Human stool specimens used in the present study were from infant and adult subjects included in international multicentric studies. Samples were collected between 2001 to 2005 and used on previous published studies. Infants samples were collected in the frame of the European project INFABIO (http://www.gla.ac.uk/departments/infabio/), ethical permission was obtained from Yorkhill Research Ethics Committee P16/03 and parents gave written informed consent [13]. Adults samples were collected in the frame of the European project Crownalife, the studies were approved by the Ethics Committee of Versailles Hospital Centre and written informed consent was obtained from all participants [14]. Approval for Institut National de la Recherche Agronomique to manage human-derived biological samples in accordance with Articles L.1243-3, R.1243-49 of “Code de la Santé Publique” was granted by the Ministry of Research and Education under number DC-2012-1728.

Faeces from 189 individuals belonging to 71 vertebrate species (31 mammals, 37 birds and 3 reptiles) were collected (Table 1). They were sub-divided into 80 categories according to species or to body mass (i.e., age (young–adult), sex (female–male), breed size (small–big–domesticated–wild), see Table 1). Body masses were provided by the breeder for large animals or obtained from literature for small animals. Body masses, along with diversity, were displayed with a logarithmic scale in order to highlight the linear shape of the power-law relationship. Except for the distinct dimorphism of male and female turkey samples, an average value of male and female body mass values was used. Dwarf or young individuals from the same species were also classified in specific body mass categories. For example, human samples were divided into two body mass categories: babies between 1 and 10 months old (mean of 5.8 kg) and adults between 29 and 61 years old (set at 70 kg). Composite faeces samples were avoided except for those that could not provide enough material for DNA extraction (less than 0.5 g).
Table 1

Animal data ranked by body mass

Name (common name)

Phylogeny

Body mass (kg)

Feeding type

Type of digestive tract

Size of animal husbandry group

Diversity

SD

Number of samples

Taeniopygia guttata (zebra finch)

Aves, Passeriformes

0.012

Granivorous

Hindgut colon

Large

1.2

0.1

3

Serinus canaria (canary)

Aves, Passeriformes

0.024

Granivorous

Hindgut colon

Large

1.6

0.5

2

Ramphocelus bresilius (brazilian tanager)

Aves, Passeriformes

0.035

Frugivorous

Hindgut colon

Small

3.4

0.4

4

Melopsittacus undulatus (budgerigar)

Aves, Psittaciformes

0.04

Granivorous

Hindgut colon

Large

1.9

3.0

3

Ploceus cucullatus (village weaver)

Aves, Passeriformes

0.04

Granivorous

Hindgut colon

Small

2.3

0.0

2

Agapornis fischeri (Fischer’s lovebird)

Aves, Psittaciformes

0.05

Granivorous

Hindgut colon

Large

1.5

 

1

Agapornis roseicollis (rosy-faced lovebird)

Aves, Psittaciformes

0.05

Granivorous

Hindgut colon

Large

2.1

0.0

2

Amblyramphus holosericeus (scarlet-headed blackbird)

Aves, Passeriformes

0.08

Carnivorous

Hindgut colon

Small

3.4

0.2

2

Nymphicus hollandicus (cockatiel)

Aves, Psittaciformes

0.08

Granivorous

Hindgut colon

Small

1.4

0.3

2

Guira guira (guira cuckoo)

Aves, Cuculiformes

0.14

Carnivorous

Hindgut colon

Small

2.9

0.5

4

Poicephalus senegalus (senegal parrot)

Aves, Psittaciformes

0.14

Granivorous

Hindgut colon

Small

2.6

 

1

Streptopelia decaocto (eurasian collard dove)

Aves, Columbidae

0.19

Granivorous

Hindgut colon

Large

2.4

0.5

3

Corvus monedula (eurasian jackdaw)

Aves, Passeriformes

0.22

Omnivorous

Hindgut colon

Small

1.8

 

1

Psarocolius decumanus (crested oropendola)

Aves, Passeriformes

0.3

Omnivorous

Hindgut colon

Small

3.8

0.3

3

Columba livia (pigeon)

Aves, Columbidae

0.3

Granivorous

Hindgut colon

Large

1.9

 

1

Gallus gallus (dwarf chicken)a

Aves, Galliformes

0.3

Granivorous

Hindgut caecum

Large

2.1

0.5

2

Tauraco erythrolophus (red-crested turaco)

Aves, Cuculiformes

0.35

Frugivorous

Hindgut colon

Small

3.8

 

1

Agamia agami (agami heron)

Aves, Ciconiiformes

0.46

Piscivorous

Hindgut colon

Small

3.6

 

1

Coracopsis vasa (vasa parrot)

Aves, Psittaciformes

0.5

Frugivorous

Hindgut colon

Small

2.4

 

1

Chinchilla laniger xChinchilla brevicaudata (chinchilla)

Mammalia, Rodentia

0.6

Herbivorous

Hindgut caecum

Small

4.2

0.1

2

Ramphastos tucanus (white-throated toucan)

Aves, Piciformes

0.675

Frugivorous

Hindgut colon

Small

3.6

0.4

3

Chrysolophus pictus (golden pheasant)

Aves, Galliformes

0.700

Granivorous

Hindgut caecum

Small

3.4

 

1

Cavia porcellus (domestic guinea pig)

Mammalia, Rodentia

0.8

Herbivorous

Hindgut caecum

Large

5.0

0.2

3

Anas acuta (northern pintail)

Aves, Anatidae

0.9

Granivorous

Hindgut caecum

Small

3.6

 

1

Elaphe guttata (corn snake)

Sauropsida, Serpentes

0.9

Carnivorous

Hindgut colon

Small

4.1

 

1

Lampropeltis getula (common kingsnake)

Sauropsida, Serpentes

1

Carnivorous

Hindgut colon

Small

3.3

 

1

Ara ararauna (blue-and-yellow macaw)

Aves, Psittaciformes

1

Granivorous

Hindgut colon

Small

2.8

0.1

2

Anas platyrhynchos (wild type duck)a

Aves, Anatidae

1.1

Granivorous

Hindgut caecum

Large

3.1

 

1

Neochen jubata (orinoco goose)

Aves, Anatidae

1.25

Granivorous

Hindgut caecum

Small

3.5

 

1

Gallus gallus (chicken)a

Aves, Galliformes

1.5

Granivorous

Hindgut caecum

Large

1.7

0.5

6

Numida meleagris (guinea-fowl)

Aves, Galliformes

2

Granivorous

Hindgut caecum

Large

3.0

0.7

3

Branta sandvicensis (nene)

Aves, Anatidae

2

Granivorous

Hindgut caecum

Small

2.6

 

1

Oryctolagus cuniculus (domestic rabbit)

Mammalia, Lagomorpha

2.2

Herbivorous

Hindgut caecum

Large

4.5

0.7

3

Anas platyrhynchos (domestic duck)a

Aves, Anatidae

2.3

Granivorous

Hindgut colon

Small

2.3

1.4

3

Eudyptes chrysocome (western rockhopper penguin)

Aves, Sphenisciformes

2.6

Piscivorous

Hindgut colon

Large

1.6

0.6

3

Testudo hermanni boettgeri (Hermann’s tortoise)

Sauropsida, Testudines

3

Herbivorous

Hindgut colon

Large

5.6

 

1

Meleagris gallopavo (turkey female)a

Aves, Galliformes

3

Granivorous

Hindgut caecum

Small

2.2

 

1

Thylogale sp. (pademelon)

Mammalia, Marsupials

3.5

Herbivorous

Hindgut colon

Small

4.3

 

1

Cairina moschata (muscovy duck)

Aves, Anatidae

4

Granivorous

Hindgut colon

Large

3.3

 

2

Chauna torquata (southern screamer)

Aves, Anseriformes

4

Herbivorous

Hindgut caecum

Small

3.4

 

1

Canis lupus familiaris (puppy)a

Mammalia, Carnivora

4

Carnivorous

Hindgut colon

Small

2.6

 

1

Pavo cristatus (blue peafowl)

Aves, Galliformes

5

Granivorous

Hindgut caecum

Small

3.7

0.1

2

Anser anser domesticus (domestic goose)

Aves, Anatidae

5

Granivorous

Hindgut caecum

Small

3.5

0.1

2

Homo sapiens (baby human caucasian)a

Mammalia, Primates

6

Omnivorous

Hindgut colon

Small

3.2

0.7

15

Meleagris gallopavo (turkey male)a

Aves, Galliformes

8

Granivorous

Hindgut caecum

Small

3.8

 

1

Wallabia bicolor (black wallaby)

Mammalia, Marsupials

9

Herbivorous

Hindgut colon

Small

4.8

 

1

Hylobates lar (gibbon)

Mammalia, Primates

10

Frugivorous

Hindgut colon

Small

5.5

 

1

Aptenodytes patagonicus (king penguin)

Aves, Sphenisciformes

13

Piscivorous

Hindgut colon

Small

2.8

1.1

4

Capra hircus (dwarf goat)

Mammalia, Ruminantia

15

Herbivorous

Ruminants foregut

Small

5.2

1.1

2

Canis lupus familiaris (medium size dog)a

Mammalia, Carnivora

20

Carnivorous

Hindgut colon

Small

3.2

1.1

2

Ovis aries (dwarf sheep)a

Mammalia, Ruminantia

20

Herbivorous

Ruminants foregut

Small

5.7

0.7

2

Hippotragus equinus (roan antelope)

Mammalia, Ruminantia

20

Herbivorous

Ruminants foregut

Small

5.7

 

1

Tragelaphus streps (greater kudu)

Mammalia, Ruminantia

20

Herbivorous

Ruminants foregut

Small

5.5

 

1

Hystrix cristata (crested porcupine)

Mammalia, Rodentia

25

Herbivorous

Hindgut caecum

Small

5.7

 

1

Rhea americana (greater rhea)

Aves, Rheiformes

31

Granivorous

Hindgut caecum

Large

4.0

0.5

4

Ovis aries (sheep)a

Mammalia, Ruminantia

40

Herbivorous

Ruminants foregut

Small

5.0

 

1

Canis lupus familiaris (big size dog)a

Mammalia, Carnivora

40

Carnivorous

Hindgut colon

Small

3.1

 

1

Pan troglodytes (chimpanzee)

Mammalia, Primates

40

Omnivorous

Hindgut colon

Small

5.3

 

1

Dromaius novaehollandiae (emu)

Aves, Casuariiformes

40

Granivorous

Hindgut colon

Small

3.9

 

1

Capra hircus (goat)

Mammalia, Ruminantia

50

Herbivorous

Ruminants foregut

Small

7.0

 

1

Sus scrofa (dwarf pig)a

Mammalia, Suina

55

Omnivorous

Hindgut colon

Small

5.4

0.4

2

Lama glama (llama)

Mammalia, Tylopoda

55

Herbivorous

Ruminants foregut

Small

5.4

 

1

Homo sapiens (adult human caucasian)a

Mammalia, Primates

70

Omnivorous

Hindgut colon

Large

4.4

0.8

34

Sus scrofa (pig)a

Mammalia, Suina

100

Omnivorous

Hindgut colon

Small

5.8

1.1

4

Tragelaphus spekei (sitatunga)

Mammalia, Ruminantia

100

Herbivorous

Ruminants foregut

Small

7.5

 

1

Struthio camelus (ostrich)

Aves, Struthioniformes

120

Herbivorous

Hindgut colon

Small

4.4

0.2

3

Equus asinus (donkey)

Mammalia, Equidae

150

Herbivorous

Hindgut caecum

Small

5.3

0.4

2

Ammotragus lervia (aoudad)

Mammalia, Ruminantia

150

Herbivorous

Ruminants foregut

Small

6.1

 

1

Equus caballus (pony)

Mammalia, Equidae

160

Herbivorous

Hindgut caecum

Small

5.6

0.1

2

Panthera leo (african lion)

Mammalia, Carnivora

160

Carnivorous

Hindgut colon

Small

4.4

 

1

Equus zebra hartmannae (mountain zebra)

Mammalia, Equidae

350

Herbivorous

Hindgut caecum

Small

5.4

 

1

Syncerus caffer nanus (forest buffalo)

Mammalia, Ruminantia

450

Herbivorous

Ruminants foregut

Small

2.9

 

1

Camelus dromedarius (arabian Camel)

Mammalia, Tylopoda

500

Herbivorous

Ruminants foregut

Small

3.2

 

1

Bos grunniens (yak)

Mammalia, Ruminantia

600

Herbivorous

Ruminants foregut

Small

5.3

 

1

Tragelaphus oryx (eland antelope)

Mammalia, Ruminantia

600

Herbivorous

Ruminants foregut

Small

6.2

 

1

Bos taurus (cow)

Mammalia, Ruminantia

750

Herbivorous

Ruminants foregut

Large

6.2

0.7

4

Giraffa camelopardalis reticulata (somali giraffe)

Mammalia, Ruminantia

1100

Herbivorous

Ruminants foregut

Small

6.4

 

1

Giraffa camelopardalis peralta (nigerian giraffe)

Mammalia, Ruminantia

1100

Herbivorous

Ruminants foregut

Small

6.6

 

1

Ceratotherium simum (white rhinoceros)

Mammalia, Rhinocerotidae

2500

Herbivorous

Hindgut colon

Small

5.6

 

1

Elephas maximus (asian elephant)

Mammalia, Proboscidea

3500

Herbivorous

Hindgut colon

Small

4.9

 

1

SD standard deviation

a species with different sizes (young-adult, female-male, small-big or domesticated-wild)

DNA extraction, PCR amplification and Capillary Electrophoresis Single Strand Conformation Polymorphism (CE-SSCP) fingerprinting

Genomic DNAs were extracted from 0.5 g of raw material using the procedure described by Godon et al. [15]. The V3 region of the 16S rRNA gene was amplified with Bacteria-specific primers and PCR products were analysed by CE-SSCP analysis using an ABI3130 Genetic Analyzer (Applied Biosystems, Foster City, CA, USA) in accordance with a previously described method [16]. All raw CE-SSCP data are available on Additional file 4.

Calculation of diversity and statistical computing

Diversity was estimated by the Simpson Diversity Index from CE-SSCP fingerprinting patterns. The Simpson Diversity Index was expressed as \(D = {1 \mathord{\left/ {\vphantom {1 {\sum {_{i = 1}^{p} a_{i}^{2} } }}} \right. \kern-0pt} {\sum {_{i = 1}^{p} a_{i}^{2} } }}\) where \(a_{i}\) is the relative abundance of each CE-SSCP peak p. This index was directly calculated from each CE-SSCP fingerprint [17] using the R StatFingerprints library [18].

Preference was given to the Simpson Diversity Index from CE-SSCP fingerprinting rather than the Richness estimation because: (1) neither fingerprinting nor sequencing data can provide a robust estimation of richness [19]; (2) the Simpson Diversity Index can be estimated accurately with CE-SSCP fingerprinting [17, 20].

A generalized linear model was applied to fit the relationship between body mass and diversity. ANOVA followed by Tukey post hoc tests were used for determining the statistical difference between (sub-) categories and body mass or diversity, both expressed in a logarithmic scale. All statistics were performed under R software (version 3.1.2) [21]. The calculation of the slope z was based on the exponent of the power-law relationship as follows: diversity = c weight z.

Results and discussion

The bacterial diversity of faeces from 189 vertebrates belonging to 71 species (31 mammals, 37 birds and 3 reptiles) was analysed (Table 1; Fig. 1). Analysis was only focused on diversity (Simpson Diversity Index), which can be accurately measured according to CE-SSCP fingerprinting patterns [15] (see the “Methods” section and Additional file 1). Apart from their phylogenetic position, animals can also be classified according to: (1) their diet (herbivorous, granivorous, omnivorous, carnivorous, piscivorous and frugivorous); (2) their metabolic body mass (from 12 g (zebra finch) to 3500 kg (Asian elephant)); (3) the structure of their digestive tracts; (4) and the size of the animal husbandry group (small and large). The present study focused on bacterial diversity, although changes within the structure of the bacterial communities were not taken into account. This study is also based on two assumptions: (1) the gut size should be proportional to the animal body mass, as has been demonstrated for herbivores [22] and birds [23]; and (2) the microbial diversity of faeces should be similar to that in the gut [24].
Fig. 1

Relationship between the animal body mass and the Simpson Diversity Index for gut microbiota. Diamonds, circles and triangles correspond to birds, mammals and reptiles, respectively. Small, medium and large sizes correspond to 1, 2–5, >10 individuals, respectively. Green, brown, grey, red, blue and yellow colors correspond to herbivorous, granivorous, omnivorous, carnivorous, piscivorous and frugivorous diets, respectively. Bold fonts make reference to the animals mentioned in the text

Results point to a correlation between animal body mass and microbial diversity (linear regression with a slope z of 0.338 ± 0.027; p value <2.2 × 10−16), irrespective of the diet, phylogeny or structure of the digestive tracts (Fig. 1). Consequently, the use of a greater amount of samples over a wider size range confirms previous works on unrelated bacterial communities that have suggested the existence of a link between volume and diversity in tree holes [5], membrane bioreactors [4] and metal-cutting fluid sump tanks [3]. In the present results, the Simpson Diversity Index ranges between 3.3 and 1789.5, thus corresponding to a 5.6 log body mass range (Fig. 1).

A wide variability in the diversity between individuals for a given species was observed. However the average diversity value for species that were represented by several individuals was close to the regression line (Fig. 1). For example, the average diversity value for adult human microbiota (34 samples) was 80.8 with a standard deviation of 294.2, and 23.7 ± 20.3 for the 15 baby human microbiota. As a matter of comparison, Trosvik et al. [25] observed a similar range of diversity (over 2 log-units of Shannon index) when analysing a time-series of 332 sequencings over 443 days, on a single male adult individual.

Animal gut microbiota covered a broad range of diversity ranging from 2.2 to 1808.0. This was comparable to the values found in various types of environment, like drinking water, raw milk, plant roots, activated sludge in wastewater treatment plants, compost or soil (Additional file 2). On one hand, the lowest diversity in gut microbiota varied around 2, similarly to those found in drinking water. On the other hand, the highest diversity in gut microbiota reaching about 1808 resembled the values found in soils (Additional file 2).

This vast range of variations in gut diversity is often associated with factors that are different to the body mass: diet [10], phylogeny [26], digestive tract structure [27], age [8] [28], way of life [29], ethnic origin [30], state of health (immune system, pregnancy, obesity) [31] [32], or genetic background [32]. Among these parameters, age has been well documented as the major one to explain these variations and the diversity or richness between human baby microbiota and those of adults [33] [34]. However the size of the gut also varies during infant growth. In this case, a difference in the microbial diversity between infant (29.9 ± 20.3) and adults (106.6 ± 76.0) was observed, concomitantly with changes in body mass when comparing human babies (6.5 ± 1.9) and adults (70 kg). The same observation was made for young and adult dog samples (Table 1). Furthermore, when comparing two penguin species that only differ in their body mass (only adult specimens, with the same diet and living in the same location), the relationship between microbial diversity and body mass still remain valid.

The correlation between body mass and diversity has been assessed for homogenous sub-categories (Table 2 and Additional file 3), thus excluding the potential effects of the different parameters. Indeed, the 189 samples could also be analysed according to phylogeny (reptile, bird, and mammal), diet (carnivorous, herbivorous, granivorous, omnivorous and piscivorous), gut structure (hindgut caecum, hindgut colon and foregut ruminant), age (baby and adult), and size of the animal husbandry group (small and large). Except for the latter category, all of them depended on the body mass (e.g. body mass was related to phylogeny, related to age or to ruminants). Significantly positive body mass/diversity correlations were observed for each sub-category, provided that a sufficient amount of data was available (over 50 samples minimum per sub-category) (Table 2; Additional file 3). The significant slopes z of the mass-diversity relationships generally ranged from 0.202 ± 0.043 to 0.380 ± 0.039. As the herbivorous group only contained 44 samples, the interestingly weak body mass diversity correlation with a z value of 0.137 could not be correctly interpreted.
Table 2

Bacterial diversity and animal weight within sub-categories, correlation between diversity and weight, and slope of the relationship of the diversity versus log-weight

Category 

Number of samples

Simpson diversity mean (SD)

Weight in kg mean (SD)

Pearson correlation between diversity and weight

Power law relationship diversity = c weight z

cor

p value

Slope z

Confidence interval

Sub-categories

 Diet

  Carnivorous

13

32.0 (23.4)

19.0 (44.1)

0.277

0.359 (NS)

0.075 (NS)

  Frugivorous

10

55.2 (68.5)

1.3 (3.1)

0.533

0.113 (NS)

0.234 (NS)

  Granivorous

54

20.4 (21.7)

4.4 (9.4)

0.667

3.7e−08 (***)

0.298 (***)

0.205–0.391

  Herbivorous

44

301.6 (342.3)

354.5 (668.1)

0.338

0.025 (*)

0.137 (*)

0.018–0.256

  Omnivorous

60

111.3 (149.0)

50.5 (32.4)

0.542

7.7e−06 (***)

0.361 (***)

0.214– 0.508

  Piscivorous

8

20.4 (20.8)

7.5 (5.9)

0.030

0.944 (NS)

0.029 (NS)

 Phylogeny

  Bird

85

25.1 (23.6)

7.8 (23.0)

0.456

1.1e−05 (***)

0.202 (***)

0.116–0.288

  Mammal

101

194.9 (268.8)

183.3 (464.0)

0.415

1.6e−05 (***)

0.272 (***)

0.153–0.391

  Reptile

3

119.3 (131.9)

1.6 (1.2)

0.964

0.172 (NS)

1.686 (NS)

 Gut structure

  Caecum

46

70.4 (80.6)

26.1 (65.3)

0.528

1.7e−04 (***)

0.397 (***)

0.203–0.591

  Colon

124

78.5 (122.5)

79.0 (382.7)

0.678

<2.2e−16 (***)

0.293 (***)

0.236–0.350

  Rumen

19

484.9 (449.7)

411.8 (384.5)

−0.036

0.883 (NS)

−0.031 (NS)

 Group size

  Large

85

93.1 (151.0)

65.8 (156.4)

0.734

1.3e−15 (***)

0.380 (***)

0.303–0.457

  Small

104

137.2 (253.7)

130.6 (449.1)

0.632

6.6e−13 (***)

0.300 (***)

0.227–0.372

 Age

  Adult

173

125.6 (222.2)

110.3 (364.6)

0.687

<2.2e−16 (***)

0.337 (***)

0.283–0.391

   Baby

16

28.9 (20.1)

6.3 (2.0)

0.205

0.446 (NS)

0.427 (NS)

 All

189

117.4 (214.3)

101.5 (350.0)

0.675

<2.2e−16 (***)

0.338 (***)

0.284–0.391

NS not significant

* low significance, *** high significance

The observed slope z was similar to that reported for ‘island’ patterns of bacterial diversity such as metal-cutting fluid sump tanks (z = 0.245–0.295) [3] and tree holes (z = 0.26) [5] and varied within a similar range to that reported for plants and animals from discrete islands (z = 0.25–0.35). The slope z-values reported for continuous patterns (such as marsh sediment [35] with z-values between 0.02 and 0.04) are generally much lower than those reported for discrete habitats.

According to these results, which confirm the assumption that species and volume are related, guts can compared to an archipelago, where microbes originating from feed tend to colonise the available niches provided by the gut. This is also in line with the MacArthur and Wilson biogeography theory [1]. Size, similarly to island environments appears to reflect the heterogeneity of the environment. Hence, a large gut size should provide more space, enabling a large microbial diversity to settle in [36].

Conclusions

The aim of this study was not to explain the genesis of bacterial diversity in vertebrate guts but was rather focused on producing evidence on the role of gut size in the maintenance of a level of microbial diversity. This work highlights the hitherto unexplored relationship between volume and diversity in the case of gut microbiota. Gut volume should henceforth be taken into account along with other parameters to explain the level of diversity. Finally, this work confirms the relevance of the microbial world when addressing ecological issues such as the relationship between species diversity and the size of the habitat [37].

Availability of supporting data

Our data are provided in the electronic supplementary materials (Additional file 4).

Abbreviations

CE-SSCP: 

capillary electrophoresis single strand conformation polymorphism

PCR: 

polymerase chain reaction

16S rRNA: 

16S ribosomal RNA

ANOVA: 

analysis of variance

Declarations

Authors’ contributions

JJG designed the experiment, JJG and JH collected the data, PA performed the experiments and contributed to the analysis of the data. Analysis and interpretation were carried out by JH and JJG. JH and JJG contributed to the first draft, which was completed by JPS. All authors read and approved the final manuscript.

Acknowledgements

We thank the following people and organizations who generously provided samples: Joël Doré; Thierry Gidenne; ‘Reserve africaine’, Sigean, France; ‘Mini-ferme zoo’, Cessenon/Orb, France; ‘Oceanopolis’, Brest, France; and Lunaret Zoo, Montpellier, France. We thank Biswarup Sen, Anais Bonnafous and Valérie Bru-Adan for technical assistance. INRA funded this research.

Competing interests

The authors declare that they have no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
UR0050, Laboratoire de Biotechnologie de l’Environnement, INRA
(2)
Centre of Excellence in Environmental Studies, King Abdulaziz University

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Copyright

© Godon et al. 2016

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