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A major challenge in biology is to understand how phylogeny, diet, and environment shape the mammalian gut microbiome. Yet most studies of nonhuman microbiomes have relied on relatively coarse dietary categorizations and have focused either on individual wild populations or on captive animals that are sheltered from environmental pressures, which may obscure the effects of dietary and environmental variation on microbiome composition in diverse natural communities. We analyzed plant and bacterial DNA in fecal samples from an assemblage of 33 sympatric large-herbivore species (27 native, 6 domesticated) in a semiarid East African savanna, which enabled high-resolution assessment of seasonal variation in both diet and microbiome composition. Phylogenetic relatedness strongly predicted microbiome composition ( r = 0.91) and was weakly but significantly correlated with diet composition ( r = 0.20). Dietary diversity did not significantly predict microbiome diversity across species or within any species except kudu; however, diet composition was significantly correlated with microbiome composition both across and within most species. We found a spectrum of seasonal sensitivity at the diet−microbiome nexus: Seasonal changes in diet composition explained 25% of seasonal variation in microbiome composition across species. Species’ positions on (and deviations from) this spectrum were not obviously driven by phylogeny, body size, digestive strategy, or diet composition; however, domesticated species tended to exhibit greater diet−microbiome turnover than wildlife. Our results reveal marked differences in the influence of environment on the degree of diet−microbiome covariation in free-ranging African megafauna, and this variation is not well explained by canonical predictors of nutritional ecology.
Phylogenetic variation in diet and gut microbiome composition. (A) The phylogeny of 33 sympatric mammalian herbivores in central Kenya, grouped by family and order (identified here by the first 3 letters of families and orders; see also Dataset S1). Species names are in gray for ruminants (toward the top), orange for pseudoruminants (hippo and camel), and black for nonruminants. Sample sizes for each species are listed parenthetically (diet, microbiome). (B) The mean RRA of the 9 most eaten plant families and the 45 other plant families, expressed as percentages. There was modest phylogenetic signal in grass (Poaceae) RRA (Pagel's λ = 0.55, P = 0.03), but no phylogenetic signal in the RRA of other abundant plant families (Fabaceae: λ = 0.20, P > 0.05; Malvaceae: λ = 0.62, P > 0.05). (C) Mean dietary richness (±1 SE) did not exhibit significant phylogenetic signal (λ < 0.01, P ≈ 1.0; diversity yielded similar results: λ < 0.01, P ≈ 1.0; Dataset S1). (D) Mean RRA of the 9 most prevalent clades of gut bacteria (identified to family when possible and listed by phylum), along with the remaining ≥238 other clades (gray). There was significant phylogenetic signal in mean RRA for 2 of the 3 predominant bacterial families that were identified (Ruminococcaceae: λ ≈ 1.0, P < 0.001; Bacteroidaceae: λ = 0.93, P < 0.001; not Lachnospiraceae: λ = 0.98, P > 0.05). (E) Mean microbial richness (±SE) exhibited significant phylogenetic signal (λ ≈ 1.0, P < 0.001; diversity yielded similar results: λ ≈ 1.0, P < 0.001; Dataset S1).
… 
Dietary richness did not predict microbiome richness across species, but diet composition did predict microbiome composition. (A) We found no relationship between mean dietary and microbiome richness (±1 SE) across species (ordinary least squares, OLS: F 1,15 < 0.01, R 2 < 0.001, P = 0.96; phylogenetic generalized least squares, PGLS: F 1,15 < 0.01, R 2 < 0.001, P = 0.95). Point colors correspond to the ordering of species from top (oryx) to bottom (elephant) of the phylogeny in Fig. 1 (squares, nonruminants; circles, ruminants/camels). Intraspecific correlations between dietary and microbiome richness within these 17 species are shown in SI Appendix, Fig. S3. (B) Microbiome dissimilarity within and between pairs of species increased with dietary dissimilarity. Intraspecific comparisons were the most similar (gray crosses), followed by interspecific comparisons between species with similar digestive systems (black crosses). Comparisons between species with dissimilar digestive systems (i.e., ruminants/camels vs. nonruminants, red crosses) had almost entirely distinct microbiomes (all dissimilarities > 0.99), irrespective of dietary overlap (dissimilarities ranging 0.67 to 0.97). Shading represents 95% confidence ellipses. Interspecific comparisons (black and red) revealed a significant increase in microbiome dissimilarity with diet dissimilarity, after accounting for the phylogenetic relatedness of species (partial Mantel: r = 0.28, P = 0.005). The correlation between intraspecific diet−microbiome dissimilarities across species (gray) was not statistically significant, but the trend was positive (OLS: F 1,15 = 0.93, R 2 = 0.06, P = 0.35; PGLS: F 1,15 = 0.24, R 2 = 0.02, P = 0.62). Analagous diet−microbiome comparisons among samples within each species were generally strong and positive (SI Appendix, Fig. S4).
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Covariation of diet and gut microbiome in
African megafauna
Tyler R. Kartzinel
a,1
, Julianna C. Hsing
b
, Paul M. Musili
c
, Bianca R. P. Brown
a
, and Robert M. Pringle
b
a
Department of Ecology & Evolutionary Biology, Brown University, Providence, RI 02912;
b
Department of Ecology & Evolutionary Biology, Princeton
University, Princeton, NJ 08544; and
c
Botany Department, National Museums of Kenya, Nairobi, Kenya 00100
Edited by John Terborgh, University of Florida, Cedar Key, FL, and approved October 10, 2019 (received for review April 2, 2019)
A major challenge in biology is to understand how phylogeny,
diet, and environment shape the mammalian gut microbiome. Yet
most studies of nonhuman microbiomes have relied on relatively
coarse dietary categorizations and have focused either on individ-
ual wild populations or on captive animals that are sheltered from
environmental pressures, which may obscure the effects of dietary
and environmental variation on microbiome composition in diverse
natural communities. We analyzed plant and bacterial DNA in fecal
samples from an assemblage of 33 sympatric large-herbivore species
(27 native, 6 domesticated) in a semiarid East African savanna,
which enabled high-resolution assessment of seasonal variation in
both diet and microbiome composition. Phylogenetic relatedness
strongly predicted microbiome composition (r=0.91) and was
weakly but significantly correlated with diet composition (r=
0.20). Dietary diversity did not significantly predict microbiome di-
versity across species or within any species except kudu; however,
diet composition was significantly correlated with microbiome com-
position both across and within most species. We found a spectrum
of seasonal sensitivity at the dietmicrobiome nexus: Seasonal
changes in diet composition explained 25% of seasonal variation in
microbiome composition across species. Speciespositions on
(and deviations from) this spectrum were not obviously driven by
phylogeny, body size, digestive strategy, or diet composition; how-
ever, domesticated species tended to exhibit greater dietmicrobiome
turnover than wildlife. Our results reveal marked differences in the
influence of environment on the degree of dietmicrobiome covari-
ation in free-ranging African megafauna, and this variation is not
well explained by canonical predictors of nutritional ecology.
16S rRNA
|
DNA metabarcoding
|
megaherbivores
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phylosymbiosis
Links between diet and the gut microbiome are important for
health, nutrition, and ecology in mammals. Gut bacteria
modify immune responses to maintain health (1), and pertur-
bations in the gut microbiome can cause disease (2). Mammalian
herbivores associate with particular gut microbial taxa, in part
because they rely on these bacteria to extract energy and nutri-
ents from food, synthesize vitamins, and detoxify plant defense
compounds (3). The many services that gut microbes provide to
their hosts almost certainly influence not just individual fitness
but also the dynamics of the populations, communities, and food
webs in which individuals are embedded (4).
The mammalian gut flora has traditionally been studied in the
context of 3 major drivers of variation: phylogeny, diet type, and
environment. The phylogeny of mammals reflects the evolution
of diverse diets and digestive morphophysiologies, as well as
coevolution with symbiotic gut bacteria (5). Closely related
mammalian species tend to share similar body plans, craniofacial
anatomies, and gut architectures. Similarities between species
evolutionary histories and their characteristic gut microbiomes
suggest that phylosymbiosisconcordant evolutionary divergence
between species and their degree of microbiome dissimilarity
(6)is a globally common phenomenon. Many mammalian
lineages have evolved disparate diet types that are associated
with different microbiome compositions, such as herbivory and
carnivory (5). Moreover, subgroups of species with similar diet
microbiome associations can be distinguished within broad
trophic categories; for example, carbon stable isotope ratios that
discriminate between grazing and browsing herbivores are asso-
ciated with differences in gut microbiome compositions (5). At
finer grains, studies that experimentally impose dietary treat-
ments (7) or rely on self-reporting of diets by humans (8) have
shown that the microbiome is dynamic and responsive to subtle
dietary changes within individuals. As a result, microbiome
variation among individuals from the same population, among
populations of the same species in different environments, and
among populations of closely related species is often interpreted
in light of dietary variation (3, 9, 10).
Dietmicrobiome covariation is predicted to be common, on
the grounds that diet composition is a principal determinant of
microbial niche diversity in the gut (11). For example, diets
containing more high-fiber and plant-based foods should gen-
erate a greater diversity of microbial niches than those that do
not (5). Likewise, temporal variation in diet could increase the
heterogeneity of microbial niches and thereby increase micro-
biome diversity. However, data deficiencies currently limit
even basic comparisons of dietary and microbiome diversity for
most mammalian species, which inhibits theoretical development
and impedes efforts to explain the causes and consequences of
Significance
Diet and gut microbiome composition are important for health
and nutrition in mammals, but how they covary in response to
environmental change remains poorly understoodboth be-
cause diet composition is rarely quantified precisely, and be-
cause studies of dietmicrobiome linkages in captive animals
may not accurately reflect the dynamics of natural communities.
By analyzing dietmicrobiome linkages in an assemblage of
large mammalian herbivores in Kenya, we found that seasonal
changes in diet and microbiome composition were strongly
correlated within some populations, whereas other populations
exhibited little temporal turnover in either diet or microbiome.
Identifying mechanisms that generate species-specific variation
in the sensitivity of the dietmicrobiome nexus to environ-
mental changes could help to explain differential population
performance and food-web structure within ecological
communities.
Author contributions: T.R.K. and R.M.P. designed research; T.R.K., J.C.H., P.M.M., B.R.P.B.,
and R.M.P. perfor med research; T.R .K. and R.M.P. cont ributed new reag ents/analytic
tools; T.R.K. and B.R.P.B. analyzed data; and T.R.K. and R.M.P. wrote the paper.
The authors declare no competing interest.
This article is a PNAS Direct Submission.
This open access article is distributed under Creative Commons Attribution-NonCommercial-
NoDeriv atives L icense 4. 0 (CC BY-N C-ND).
Data deposition: Illumina data and unrarefied sequence count tables are available at
Dryad (https://doi.org/10.5061/dryad.c119gm5); mitochondrial DNA sequences are avail-
able at GenBank (accession nos. MN262920MN262991 and MN262700MN262919).
1
To whom correspondence may be addressed. Email: tyler_kartzinel@brown.edu.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1905666116/-/DCSupplemental.
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microbiome variation (11). Exploring the strength, extent, and
generality of dietmicrobiome covariation within and between
species could help to contextualize the patterns observed across
studies and to guide further investigation into the functional sig-
nificance of variation in gut microbiome composition.
Understanding species-specific differences in dietmicrobiome
linkages may require studying them in a community context. Many
prior studies have investigated species in isolation by drawing
samples from geographically isolated populations of different wild
species, or from animals kept in controlled settings such as labo-
ratories or zoos (5, 12). The strength of this approach is that it
allows researchers to sample species from clades that span the
mammalian tree of life and host disparate microbiota; the draw-
back is that it often leads to comparisons of animals that have
evolved and naturally occur within disparate biomes, which might
confound the influences of phylogeny, functional morphology, and
ecology (13). In addition, animals in controlled settings are rarely
able to choose their own diets and are typically not subjected to
competition, predation risk, or seasonal variation in the avail-
ability of food and water. Studies of populations of the same
species from seasonally or geographically heterogeneous envi-
ronments have often found that dietary changes are associated
with microbiome changes (14, 15). These studies provide deeper
understanding of intraspecific dietmicrobiome covariation in
animals that occupy similar habitats and are subjected to natural
environmental pressures, but typically include only one or a few
species from the entire community. Comparative studies of species
sampled at different times and places are inevitably limited in their
ability to capture the effects of species interactionsyet we know
that animalsinteractions with each other, their foods, and their
abiotic environments have profound effects on fitness and eco-
logical networks (1618).
One long-standing obstacle to studying community-wide pat-
terns of dietmicrobiome covariation has been the difficulty of
precisely identifying foods eaten by free-ranging animals. DNA-
based analysis of fecal samples now enables concurrent charac-
terization of both diet and microbiome, creating the opportunity
to test for fine-grained variation at the dietmicrobiome nexus
(19, 20). We performed such analyses on a community of 33
large mammalian herbivore species in a semiarid Kenyan savanna
(Fig. 1A). The functionally diverse members of this community rep-
resent 7 mammalian orders, span sizes from 2to4,000 kg, have
various gut anatomies (ruminants, pseudoruminants, hindgut
fermenters), include both wild (n=27) and free-ranging do-
mesticated (n=6) species (as well as 8 globally threatened and
near-threatened species), and occupy a range of herbivore feeding
guilds (grazers, browsers, and mixed feeders).
We used these data to evaluate 4 hypotheses about the
mammalian gut microbiome that have not previously been tested
in a community context. First, we hypothesized that phylogeny is
a stronger predictor of microbiome richness and composition
(reflecting phylosymbiosis) than it is of dietary richness and
compositionin part because the competitive pressures of
sympatric coexistence should promote dietary niche differentia-
tion among otherwise similar species but permit resource overlap
among otherwise dissimilar species (21, 22). Second, we hy-
pothesized that the individuals and species with the most diverse
diets also have the most diverse microbiomes, on the grounds
that more diverse diets should be associated with a greater di-
versity of microbial niches (11). Third, we hypothesized that
0 100 0600 100 0 600
Hystrix cristata (2, 1)
Lepus sp. B (7, 7)
Lepus sp. A (11, 11)
Papio anubis (7, 9)
Oryx gazella (22, 20)
Alcelaphus buselaphus (27, 13)
Ovis aries (48, 24)
Capra hircus (19, 19)
Aepyceros melampus (129, 25)
Nanger granti (46, 17)
Litocranius walleri (10, 10)
Sylvicapra grimmia (2, 2)
Kobus ellipsiprymnus (6, 6)
Madoqua guentheri (120, 21)
Oreotragus oreotragus (13, 12)
Bos taurus (163, 27)
Syncerus caffer (92, 20)
Taurotragus oryx (54, 27)
Tragelaphus strepsiceros (28, 15)
Tragelaphus scriptus (12, 7)
Giraffa camelopardalis (58, 29)
Hippopotamus amphibius (23, 18)
Phacochoerus africanus (30, 22)
Camelus dromedarius (39, 18)
Ceratotherium simum (13, 12)
Diceros bicornis (18, 18)
Equus asinus (29, 20)
Equus burchellii (106, 19)
Equus grevyi (91, 21)
Equus caballus (6, 6)
Heterohyrax brucei (9, 9)
Procavia capensis (2, 2)
Loxodonta africana (80, 22)
Cam
Art
Hyr
Lag
Per
Pri
Pro
Rod
Bov
Cer
Ele
Equ
Gir
Hip
Hys
Lep
Pro
Rhi
Sui
Poaceae
Fabaceae
Malvaceae
Phyllanthaceae
Solanaceae
Rubiaceae
Acanthaceae
Ebenaceae
Convolvulaceae
Other
Plant families
Firmicutes:Ruminococcaceae
Bacteroidetes:unclassified
Firmicutes:Lachnospiraceae
Firmicutes:unclassified
Bacteroidetes:Bacteroidaceae
Verrucomicrobia:RFP12
Spirochaetes:Spirochaetaceae
Bacteroidetes:Rikenellaceae
Cyanobacteria:unclassified
Other
Bacterial families
20 million
years
ABEDC
RRA(%) RRA (%) RichnessRichness
Fig. 1. Phylogenetic variation in diet and gut microbiome composition. (A) The phylogeny of 33 sympatric mammalian herbivores in central Kenya, grouped
by family and order (identified here by the first 3 letters of families and orders; see also Dataset S1). Species names are in gray for ruminants (toward the top),
orange for pseudoruminants (hippo and camel), and black for nonruminants. Sample sizes for each species are listed parenthetically (diet, microbiome). (B)
The mean RRA of the 9 most eaten plant families and the 45 other plant families, expressed as percentages. There was modest phylogenetic signal in grass
(Poaceae) RRA (Pagelsλ=0.55, P=0.03), but no phylogenetic signal in the RRA of other abundant plant families (Fabaceae: λ=0.20, P>0.05; Malvaceae: λ=
0.62, P>0.05). (C) Mean dietary richness (±1 SE) did not exhibit significant phylogenetic signal (λ<0.01, P1.0; diversity yielded similar results: λ<0.01, P
1.0; Dataset S1). (D) Mean RRA of the 9 most prevalent clades of gut bacteria (identified to family when possible and listed by phylum), along with the
remaining 238 other clades (gray). There was significant phylogenetic signal in mean RRA for 2 of the 3 predominant bacterial families that were identified
(Ruminococcaceae: λ1.0, P<0.001; Bacteroidaceae: λ=0.93, P<0.001; not Lachnospiraceae: λ=0.98, P>0.05). (E) Mean microbial richness (±SE) exhibited
significant phylogenetic signal (λ1.0, P<0.001; diversity yielded similar results: λ1.0, P<0.001; Dataset S1).
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individuals and species with the most dissimilar diet composi-
tions also have the most dissimilar microbiome compositions,
given that variation in the traits, behaviors, and environmental
influences that promote differences in diet should also promote
differences in microbiome (5, 23). Fourth, we hypothesized that
species with the most seasonally variable diets also have sea-
sonally variable gut microbiomes, as expected if microbiome
composition is tightly linked to diet composition (14). Based
on this hypothesis, we predicted that mixed-feeding herbivores
which frequently switch between grass- and browse-dominated
diets in response to seasonal variation in grass availability
(higher in wet seasons)would exhibit the strongest seasonal
turnover in their gut microbiomes, for both wild and domesti-
cated species and regardless of gut anatomy.
Results
Diet and Microbiome Composition. We collected fresh fecal sam-
ples from 33 mammal species in Laikipia, Kenya, during 5
sampling periods that spanned both wet and dry seasons over 4 y
(SI Appendix, Texts S1 and S2). We used DNA metabarcoding to
characterize diets and microbiomes, sequencing the plant trnL-P6
marker and the bacterial 16S-V4 ribosomal RNA marker (refs. 24
and 25 and SI Appendix,TextS1). We identified DNA sequences
by comparison to a local plant reference database and the
GreenGenes bacterial database (refs. 26 and 27 and SI Appendix,
Texts S3S5). In total, we analyzed plant DNA from 1,322 fecal
samples (median, 23 samples per species; range, 2 to 163) and
bacterial DNA from 509 fecal samples (median, 18 samples per
species; range, 1 to 29; Dataset S1).
Diets and microbiomes differed widely across herbivore spe-
cies (Fig. 1). We identified 213 unique food-plant sequences
from 54 plant families, out of at least 460 plant species from 66
families that are known to occur at this site (27). These food
plants were utilized differently by herbivore species (Dataset S2).
Dietary richness averaged 19 to 57 taxa per sample (Fig. 1). The
3 plant families with the highest overall mean relative read
abundance (RRA) were Poaceae (grasses; range of mean RRA
across species, 0 to 97%; median, 23%), Fabaceae (legumes;
range, 2 to 83% RRA; median, 30%), and Malvaceae (mallows;
range, 0 to 18% RRA; median, 4%; Fig. 1). The microbiome
data included 29,308 bacterial amplicon sequence variants from
at least 35 phyla (range, 177 to 494 variants per sample; Dataset
S3). The most abundant bacterial families were Ruminococca-
ceae (range of mean RRA across species, 1 to 55%; median,
37%), Lachnospiraceae (range, 2 to 13% RRA; median, 5%),
and Bacteroidaceae (range, 0 to 13% RRA; median, 8%; Fig. 1).
Phylogenetic Signals. Herbivore species differed in the richness
and composition of their diets and microbiomes in ways that
supported our first hypothesis. Consistent with the conventional
categorization of these species into feeding guilds, grass RRA
was a key axis of dietary differentiation (Fig. 1 and SI Appendix,
Fig. S1). There was no statistically significant phylogenetic signal
among herbivore species in the mean richness or diversity of
plant taxa eaten, and pairs of closest relatives often differed
starkly in their relative consumption of plants from different
families (Fig. 1). There was a modest but statistically significant
phylogenetic signal in grass consumption, but we found no sig-
nificant phylogenetic signal in the RRA of other major food
plant families (Fig. 1). Congeneric zebras, horses, and donkeys
(Equidae, Equus spp.) all consumed predominantly grass-based
diets (range of mean grass RRA, 73 to 97%), whereas other sets
of confamilial herbivore taxa diverged sharply in grass RRA:
bovids (Bovidae), 0 to 89%; hyraxes (Procaviidae), 59% vs. 1%;
rhinos (Rhinocerotidae), 74% vs. 7%; and hares (Leporidae),
42% vs. 29%. In contrast to diet, there was strong phylogenetic
signal in the mean richness and diversity of the microbiome.
There was also strong phylogenetic signal in the RRA of 2 major
bacterial families (Ruminococcaceae and Bacteroidaceae; Fig. 1).
Including all plant and bacterial taxa in the analysis, we found
significant positive correlations between the phylogenetic distance
Dietary richness
Microbiome richness
A
0204060
0 250 500
1
2
34
5
6
7
8
9
10
11
12
13
14
1516
17
1 Oryx
2 Sheep
3 Goat
4 Impala
5 Grant's gazelle
6 Dik−Dik
7 Cattle
8 Buffalo
9 Eland
10 Greater kudu
11 Reticulated giraffe
12 Warthog
13 Camel
14 Donkey
15 Plains zebra
16 Grevy's zebra
17 Elephant
Mean Bray-Curtis dietary dissimilarity
Mean Bray-Curtis microbiome dissimilarity
B
0.00 0.25 0.50 0.75 1.00
0.00 0.25 0.50 0.75 1.00
Intraspecific
Interspecific: similar digestive systems
Interspecific: dissimilar digestive systems
Fig. 2. Dietary richness did not predict microbiome richness across species,
but diet composition did predict microbiome composition. (A) We found
no relationship between mean dietary and microbiome richness (±1SE)
across species (ordinary least squares, OLS: F
1,15
<0.01, R
2
<0.001, P=
0.96; phylogenetic generalized least squares, PGLS: F
1,15
<0.01, R
2
<0.001,
P=0.95). Point colors correspond to the ordering of species from top (oryx) to
bottom (elephant) of the phylogeny in Fig. 1 (squares, nonruminants; circles,
ruminants/camels). Intraspecific correlations between dietary and microbiome
richness within these 17 species are shown in SI Appendix, Fig. S3.(B) Micro-
biome dissimilarity within and between pairs of species increased with dietary
dissimilarity. Intraspecific comparisons were the most similar (gray crosses),
followed by interspecific comparisons between species with similar digestive
systems (black crosses). Comparisons between species with dissimilar digestive
systems (i.e., ruminants/camels vs. nonruminants, red crosses) had almost en-
tirely distinct microbiomes (all dissimilarities >0.99), irrespective of dietary
overlap (dissimilarities ranging 0.67 to 0.97). Shading represents 95% confi-
dence ellipses. Interspecific comparisons (black and red) revealed a significant
increase in microbiome dissimilaritywith diet dissimilarity, after accounting for
the phylogenetic relatedness of species (partial Mantel: r=0.28, P=0.005).
The correlation between intraspecific dietmicrobiome dissimilarities across
species (gray) was not statistically significant, but the trend was positive (OLS:
F
1,15
=0.93, R
2
=0.06, P=0.35; PGLS: F
1,15
=0.24, R
2
=0.02, P=0.62). Ana-
lagous dietmicrobiome comparisons among samples within each species
were generally strong and positive (SI Appendix,Fig.S4).
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separating herbivore species and the dissimilarity of both their
diets and their microbiomes (SI Appendix,Fig.S2). The stronger
association between phylogeny and microbiome composition (r=
0.91) compared to diet composition (r=0.20) indicates that
shared evolutionary history more strongly constrains closely re-
lated species to associate with similar gut bacteria than to share
food plants (SI Appendix, Fig. S2).
DietMicrobiome Covariation within and among Species. Microbiome
richness was not consistently correlated with dietary richness.
Contrary to our second hypothesis that species and individuals
with more species-rich diets also have more diverse microbiomes,
we found no significant correlation between mean dietary and
microbiome richness across species (Fig. 2A). Likewise, dietary
richness did not strongly predict microbiome richness within spe-
cies: Only one species (kudu) exhibited a significant correlation
(positive) between diet and microbiome richness (SI Appendix,Fig.
S3 and Table S1).
Consistent with our third hypothesis, there were generally strong
correlations between the compositional dissimilarities of diets and
microbiomes. Microbiome dissimilarity increased with dietary
dissimilarity after accounting for phylogenetic distance between
species (Fig. 2B). This interspecific correlation in dietmicro-
biome dissimilarity was heavily influenced by differences be-
tween ruminants and nonruminants, which had almost entirely
nonoverlapping microbiomes (BrayCurtis dissimilarity of 1)
even when their diets were no more dissimilar than pairs of species
with similar digestive systems. Within species, we found a wide
range of mean BrayCurtis dissimilarities among individuals for
both diet (0.41 to 0.82) and microbiome (0.54 to 0.78), and these
ranges were nearly as broad as the corresponding ranges between
species (diet, 0.53 to 0.98; microbiome, 0.65 to 1.0; Dataset S4).
Across samples within species, microbiome dissimilarity increased
significantly with diet dissimilarity in all but 3 cases (Grevyszebra,
elephant, and dik-dik; SI Appendix, Fig. S4 and Table S2).
Seasonal Variation and Covariation in Diet and Microbiome. We
evaluated seasonal dietmicrobiome linkages within and among
species. For this, we used a subset of the data from the 17 best-
sampled species, comprising 365 samples that were analyzed for
both diet and microbiome (15 total samples per species across
the 3 largest sampling bouts, and 3 samples per species within
each bout; range 3 to 17, median 6 samples per species per
season). Consistent with our fourth hypothesis, we found a sig-
nificant positive correlation between the degree of seasonal
turnover in diet and microbiome composition across species (Fig.
3A). Overall, diet turnover explained 25% of the variance in
microbiome turnover; however, several species exhibited con-
siderably higher or lower microbial turnover than predicted by
regressions based on diet turnover alone (Fig. 3A). In contrast to
our prediction that the strongest seasonal turnover would occur in
mixed feeders (e.g., elephant, impala), species across the grazer
browser continuum occupied various positions along the seasonal
sensitivity spectrum (Fig. 3). The low end of this spectrum in-
cluded species such as elephant, which exhibited almost as much
variation within seasons as between seasons (Fig. 3D); other
species from multiple feeding guilds exhibited differences be-
tween at least the wettest and driest seasons (e.g., Fig. 3 Eand F);
and camels (browsers) exhibited uniquely high seasonal turnover
in both diet and microbiome (Fig. 3G). In general, domesticated
species (which occupied disparate positions across the grazer
browser continuum and the phylogeny) exhibited greater turn-
over in both diet and microbiome than did wild species (Fig. 3 B
and C).
All species differed significantly in diet and microbiome
composition across seasons, with the lone exception of the ele-
phant microbiome (SI Appendix, Tables S3 and S4). However,
microbiome variation among samples mapped significantly onto
diet composition (using Procrustes analysis) for only 8 of 17
species, which included ruminants and hindgut fermenters,
grazers and browsers, and wild and domesticated species (SI
Appendix, Fig. S5). These 8 species included all 5 of the do-
mesticated species, but not their closest wild relatives (e.g., cattle
but not buffalo; donkey but not zebras). The proportion of var-
iation explained by season was much broader for diet (range, 12
to 73%; median, 27%; SI Appendix, Table S3) than for micro-
biome, which was always low (range, 13 to 28%; median, 20%; SI
Seasonal diet turnover
Seasonal microbiome turnover
0.00 0.25 0.5
0.00 0.05 0.10 0.15
Buffalo
Cattle
DikDik
Donkey
Eland
Elephant
Goat
Grant’s gazelle
Kudu
Grevy’s
zebra
Impala
Oryx
Plains
zebra
Giraffe
Sheep
Warthog
Regressions
OLS
PGLS
1.4 1.5
Camel
Axis 1
Axis 2
SS = 0.84
P = 0.176
0.3 0.0 0.3
0.1 0.1 0.3
Impala
Axis 1
Axis 2
SS = 0.56
P = 0.003
0.3 0.0 0.2
0.2 0.0 0.2
Cattle
Axis 1
Axis 2
SS = 0.69
P = 0.003
0.1 0.1
0.2 0.0 0.2
Camel
Axis 1
Axis 2
SS = 0.37
P = 0.001
0.3 0.0 0.3
0.1 0.0 0.2
Wild
Domesticated
Wild
Domesticated
B
ED
C
A
FG
Elephant
DiettoMicrobiome
vectors
Diet
(open hulls)
Microbiome
(full hulls)
019 mm rain
224 mm rain
Fig. 3. A spectrum of seasonal sensitivity in dietmicrobiome covariation.
(A) Seasonal turnover in diet composition was positively correlated with
seasonal turnover in microbiome composition (squares, nonruminants; cir-
cles, ruminants/camels). Trend lines were fit using OLS (F
1,15
=5.0, R
2
=0.25,
P=0.041) and PGLS (F
1,15
=5.5, R
2
=0.27, P=0.034). Excluding camels, an
extreme outlier in diet turnover, would yield similar results, although OLS
regression would only be marginally significant (OLS: F
1,14
=3.3, R
2
=0.19,
P=0.090; PGLS: F
1,14
=7.5, R
2
=0.35, P=0.016). (Band C) There was greater
turnover in domesticated vs. wild species in (B) microbiome (phylogenetic
ANOVA: F
1,15
=19.2, P<0.001) and (C)diet(F
1,15
=4.95, P=0.042). Boxplots
show ranges (whiskers), interquartile ranges (boxes), and medians (central
lines). (DG) Using examples of 4 species at increasing distances from the
origin in A, we performed Procrustes analyses to visualize how seasonal diet
and microbiome compositions mapped onto each other. Procrustes rotates
the results of separate principal coordinates analyses of diet composition
(open symbols) and microbiome composition (closed symbols) so that results
can be compared. For illustration, we show results from the driest and
wettest sampling periods, with color-coded minimum convex hulls drawn
around the set of points representing diet (open hulls) and microbiome
(shaded hulls) compositions in each season. Vectors (arrows) show corre-
spondence between diet and microbiome for each fecal sample; shorter
vectors indicate closer compositional correspondence. Below each panel is
the Procrustes sum of squares and Pvalue testing whether there is signifi-
cant dissimilarity in configuration between the diet and microbiome ordi-
nations. Procrustes analyses for all 17 species and all 3 seasons are provided in
SI Appendix,Fig.S5.
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Appendix, Table S4). The relative abundance of the predominant
bacterial families was similar across seasons within species, but
the overall richness of microbial taxa differed seasonally for 9 of
the 17 species (SI Appendix, Table S4 and Fig. S6). Across spe-
cies, diets were often dominated by relatively few plant taxa in
the wet season and were more diverse in the dry season (SI
Appendix, Fig. S6). For example, camelsthe extreme outlier in
Fig. 3specialized on a single shrub species in the wet season
(Acacia brevispica, mean RRA of 87 to 92% vs. 18% in the dry
season) but consumed a more even variety of plants in the dry
season (including other Acacia species and the shrub Euclea
divinorum).
Discussion
By focusing on an entire sympatric large-herbivore community
and using fine-grained dietary data, we sought to illuminate how
host phylogeny, diet, and environmental variation influence di-
etmicrobiome linkages in the presence of natural species in-
teractions and in the absence of confounding geographic
variation. We found strong support for hypotheses about diet
microbiome linkages within and across species (Figs. 1 and 2),
but less support for hypotheses related to diversity or predictions
about the seasonal drivers of dietmicrobiome covariation (Figs.
2 and 3). Our results build on previous research into the dietary
ecology of African herbivores (21, 28) and the composition of gut
microbiomes across the mammalian phylogeny (5, 12), while also
revealing marked variation in the degree of dietmicrobiome
covariation within and among cooccurring species. In particular,
we reveal a previously undocumented spectrum in the sensitivity
of dietmicrobiome covariation to environmental fluctuations
(Fig. 3). This seasonal sensitivity spectrumwhich could only be
detected in light of dietary and microbial data that are highly
resolved both temporally and taxonomicallydefies explana-
tion by canonical predictors of nutritional ecology (phylogeny,
gut morphology, diet type) but does appear to be influenced by
specieshistory of domestication.
Consistent with our first hypothesis, both diet and microbiome
were constrained by phylogeny, but the constraint on diet was
weaker than that on the microbiome. The weak but significant
effect of phylogeny on diet composition accords with competi-
tion and niche theory, as ecological constraints on the degree
of resource overlap between closely related species should run
counter to any phylogenetic constraints on diet composition; in
this community, neither constraint has completely obscured the
other. Our results affirmed well-known differences in the bac-
terial lineages that occupy and facilitate digestion in mammals
(5), but also revealed unexpected variation among species within
these groups. Specifically, and contrary to our second hypoth-
esis, species and individuals with the most diverse diets gener-
ally did not have the most diverse microbiomes (Fig. 2). These
results are consistent with those previously reported for 2
sympatric equid species (19), but encompass a much larger
and more functionally diverse set of species. Furthermore,
even though we found that animals with more dissimilar diets
also had more dissimilar microbiomes (per our third hypoth-
esis; Fig. 2), and that species with a high degree of seasonal
diet turnover also had extensive microbiome turnover (per our
fourth hypothesis; Fig. 3), we did not find support for a spe-
cific prediction of our fourth hypothesisnamely that mixed-
feeding herbivores exhibit the most pronounced temporal
turnover in diet and microbiome (22).
Our DNA-based measure of seasonal dietary turnover within
each species explained only 25% of the variation in the degree of
microbiome turnover (Fig. 3). The unexplained variation is in-
teresting because this community included sets of closely related
species and species with similar diets, digestive morphologies
(ruminant vs. nonruminant), and body sizes (and hence gut ca-
pacities). These phylogenetic and functional characteristics are
all known to contribute to microbiome differentiation between
species (5, 12), and we therefore expected sets of similar species
to respond to seasonal variation in similar ways. The data did not
support this expectation. We found, instead, that livestock ten-
ded to have stronger dietmicrobiome linkages and greater
temporal turnover than did wildlife (Fig. 3 and SI Appendix, Figs.
S4 and S5), even when controlling as much as possible for phy-
logenetic and functional differences. For example, giraffes and
camels occupied similar dietary niches (SI Appendix, Fig. S1), but
camels had considerably greater microbiome diversity (30%
greater richness) and were an outlier in temporal dietmicro-
biome turnover (Fig. 3). Similarly, donkeys and zebras are all
members of the same genus, all consumed overwhelmingly grass-
based diets, and all had similar microbiome diversity (Fig. 1), yet
donkeys had much greater dietmicrobiome turnover than did
either zebra species (Fig. 3). These results contrast with a prior
study showing that anthropogenic influences reduced micro-
biome diversity in a domesticated species relative to a cooccur-
ring wild relative (both equids; ref. 19) and suggest potent
anthropogenic effects on both the diets and microbiomes of
free-ranging livestock (9).
Obvious potential differences between livestock and wildlife
are unlikely to explain our result: These species occupy the same
areas within the same landscape, and livestock are herded but
choose foods freely without supplemental forage or routine an-
tibiotic treatment (see Methods). It is therefore necessary to
consider more nuanced hypotheses. Relative to domesticated
species, wildlife might have 1) greater variation in traits that
underpin plantherbivore or hostmicrobiome interactions,
2) more social and demographic heterogeneity, and/or 3) less
ability to forage optimally. These nonmutually exclusive possi-
bilities assume different mechanisms underpinning dietmicro-
biome linkages. The cost-of-domestication hypothesis posits that
domestication reduces genetic variation through inbreeding and
selective breeding (29), which could homogenize diets and
microbiomes within populations (23). The social-network hy-
pothesis posits that physical proximity and demographic simi-
larity constrain foraging opportunities and enhance microorganism
transmission, thereby homogenizing diets and microbiomes (15, 30).
These homogenizing influences may act strongly on livestock pop-
ulations, which tend to have biased sex ratios, even age distributions,
and relatively dense feeding and sleeping aggregations (31). Finally,
optimal foraging theory posits that animals should minimize ener-
getic costs while maximizing food intake (32). There is disagreement
about whether domestication is likely to relax selection on opti-
malforaging behavior (33) or to intensify it (34). Theoretically,
energetically costly strategies that involve frequent movements be-
tween food patches might be necessary for wildlife to reduce the risk
of predation, but might be unnecessary for domesticated species
that are protected by humans (31, 35); experimentally, at least some
domesticated ungulates are genetically more predisposed to energy-
saving, infrequent movements than are wildlife (34). Understanding
whether these mechanisms contribute to the seasonal sensitivity
spectrum could help determine the extent to which such sensitivity
represents a cost or a benefit of domestication under different
environmental conditions.
Challenges inherent to sampling an entire community of free-
ranging animals and caveats inherent to correlative datasets limit
our ability to understand sources of variation in the strength of
dietmicrobiome linkages among species. Limitations inherent
to DNA metabarcoding may also obscure variation in animal
nutrition, as animals that eat different parts of the same food
plant may diverge in nutritional status without diverging in DNA-
based dietary profiles. Longer community-level time series of
change in both diet and microbiome could be generated using the
methods employed here (8, 20), but would be subject to similar
limitations. Perhaps more usefully, resources could be manipulated
Kartzinel et al. PNAS Latest Articles
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experimentally to strengthen inferences about causality in diet
microbiome relationships.
Two complementary bodies of theory suggest that understand-
ing dietmicrobiome linkages can improve our understanding of
how species interactions shape communities by differentially af-
fecting fitness and population growth. Coexistence theory em-
phasizes resource competition and niche differentiation within
guilds (36), and food-web theory emphasizes trophic regulation of
populations (37). Both frameworks can account for species-
specific differences in traits such as body size, social structure,
and diet selectivity that affect how individuals balance the need for
resources against the risk of predation or infection (22, 28).
Whereas these characteristics can clearly affect fitness, the health
and fitness consequences of microbiome compositions are only
well established in biomedical studies on humans and model or-
ganisms (1, 3). If dietmicrobiome linkages affect demographically
vital processes such as health, stamina, and predator evasion, then
they could shape the fitness, trophic networks, and coexistence of
free-ranging animals.
Methods
We collected samples from adjoining wildlife conservancies with active
ranching operations (Mpala and Ol Jogi; Dataset S1 and SI Appendix, Text S1
and Fig. S7). Livestock in these conservancies are occasionally treated with
antibiotics during disease outbreaks, but no such outbreaks were reported
during our study, and, to our knowledge, none of the animals we sampled
were treated. Livestock forage and drink during the day following routes
that are determined by herders, and are protected from predators at night
within corrals (31). We used mammalian mitochondrial DNA markers to
confirm the source of a subset of sa mples, including 2 species of hare that
are visually indistinguishable in the field and require further taxonomic
investigation (here designated Lepus A and B; SI Appendix, Text S2). We
used Illumina sequencing to generate diet and microbiome data. We
identified these sequences, and rarefied the resulting data to enable
comparisons of plant and bacterial taxa as percentages of the total rarefied
sequences within samples (i.e., RRA; SI Appendix,TextsS3S5). Prior use of
this protocol indicated that RRA is likely to be a reliable proxy for con-
sumption of plants at our study site, based on a strong correlation between
proportional grass consumption measured using RRA and stable-isotope
ratios (δ
13
C) from feces (38). Although RRA may not always accurately re-
flect quantitative consumption (39), its use for diet analysis with trnL-P6 has
been validated using isotopic analyses and feeding trials (40, 41). Together
with a published mammalian megaphylogeny (42), we evaluated support
for our 4 hypotheses about dietary and microbiome richness, diversity, and
dissimilarity (SI Appendix,TextS6).
ACKNOWLEDGMENTS. We thank the Government of Kenya, National
Museums of Kenya, Mpala Research Centre, and Ol Jogi Conservancy for
permission to conduct this research; Sam Kurukura and Ali Hassan for field
assistance; Patricia Chen and Tina Hansen for sample preparation; Law-
rence David and 2 anonymous reviewers for comments; and funders including
The Institute at Brown for Environment and Society, The Nature Conservancys
NatureNet Fellowship, The Princeton Environmental Institute, The Fund
for New Ideas in the Natural Sciences from the Office of the Dean of
Research at Princeton University, The Cameron Schrier Foundation, and
NSF Grants DEB- 1355122, DEB-1457697, and IOS-1656527 and Graduate Re-
search Fellowship Program.
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... Furthermore, a large proportion of the microbiota found in the baboons was divergent between wild and captive specimens, suggesting altered microbiota. This is in agreement with recent studies where a strong correlation was found between gut microbiota and food availability, habitat and dietary composition in primates and elephants (Nakamura et al., 2011;Clayton et al., 2016;McKenzie et al., 2017;Kartzinel et al., 2019;Zhang et al., 2019). Among the most differentially abundant organisms were the genera Collinsella (abundant in the wild specimens) and Lactobacillus (abundant in captive individuals). ...
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... Tragelaphine antelopes, including Cape bushbuck and greater kudu, are broadly distributed across sub-Saharan Africa, often in forest or thicket habitats where they browse on woody plants (Khademi, 2017). MRC is a ~ 20,000 ha ranch and wildlife conservancy comprising semi-arid thorn-scrub savanna that hosts a great diversity of avian and ungulate wildlife along with domestic cattle, camel, sheep, goat, and donkey (Kartzinel et al., 2019;Young et al., 1997). Both wild and domestic ungulates frequent the field station at the southern end of the property, which is fenced to exclude elephants and accordingly supports dense woody vegetation. ...
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Advances in DNA sequencing technology have revolutionised the field of molecular analysis of trophic interactions and it is now possible to recover counts of food DNA sequences from a wide range of dietary samples. But what do these counts mean? To obtain an accurate estimate of a consumer's diet should we work strictly with datasets summarising frequency of occurrence of different food taxa, or is it possible to use relative number of sequences? Both approaches are applied to obtain semi‐quantitative diet summaries, but occurrence data is often promoted as a more conservative and reliable option due to taxa‐specific biases in recovery of sequences. We explore representative dietary metabarcoding datasets and point out that diet summaries based on occurrence data often overestimate the importance of food consumed in small quantities (potentially including low‐level contaminants) and are sensitive to the count threshold used to define an occurrence. Our simulations indicate that using relative read abundance (RRA) information often provide a more accurate view of population‐level diet even with moderate recovery biases incorporated; however, RRA summaries are sensitive to recovery biases impacting common diet taxa. Both approaches are more accurate when the mean number of food taxa in samples is small. The ideas presented here highlight the need to consider all sources of bias and to justify the methods used to interpret count data in dietary metabarcoding studies. We encourage researchers to continue addressing methodological challenges, and acknowledge unanswered questions to help spur future investigations in this rapidly developing area of research. This article is protected by copyright. All rights reserved.
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African savannas support an iconic fauna, but they are undergoing large-scale population declines and extinctions of large (>5 kg) mammals. Long-term, controlled, replicated experiments that explore the consequences of this defaunation (and its replacement with livestock) are rare. The Mpala Research Centre in Laikipia County, Kenya, hosts three such experiments, spanning two adjacent ecosystems and environmental gradients within them: the Kenya Long-Term Exclosure Experiment (KLEE; since 1995), the Glade Legacies and Defaunation Experiment (GLADE; since 1999), and the Ungulate Herbivory Under Rainfall Uncertainty experiment (UHURU; since 2008). Common themes unifying these experiments are (1) evidence of profound effects of large mammalian herbivores on herbaceous and woody plant communities; (2) competition and compensation across herbivore guilds, including rodents; and (3) trophic cascades and other indirect effects. We synthesize findings from the past two decades to highlight generalities and idiosyncrasies among these experiments, and highlight six lessons that we believe are pertinent for conservation. The removal of large mammalian herbivores has dramatic effects on the ecology of these ecosystems; their ability to rebound from these changes (after possible refaunation) remains unexplored.
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The mammal gut microbiome, which includes host microbes and their respective genes, is now recognized as an essential second genome that provides critical functions to the host. In humans, studies have revealed that lifestyle strongly influences the composition and diversity of the gastrointestinal microbiome. We hypothesized that these trends in humans may be paralleled in mammals subjected to anthropogenic forces such as domestication and captivity, in which diets and natural life histories are often greatly modified. We investigated fecal microbiomes of Przewalski’s horse (PH; Equus ferus przewalskii ), the only horses alive today not successfully domesticated by humans, and herded, domestic horse ( E. f. caballus ) living in adjacent natural grasslands. We discovered PH fecal microbiomes hosted a distinct and more diverse community of bacteria compared to domestic horses, which is likely partly explained by different plant diets as revealed by trnL maker data. Within the PH population, four individuals were born in captivity in European zoos and hosted a strikingly low diversity of fecal microbiota compared to individuals born in natural reserves in France and Mongolia. These results suggest that anthropogenic forces can dramatically reshape equid gastrointestinal microbiomes, which has broader implications for the conservation management of endangered mammals.
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Over the past decade several studies have reported that the gut microbiomes of mammals with similar dietary niches exhibit similar compositional and functional traits. However, these studies rely heavily on samples from captive individuals and often confound host phylogeny, gut morphology, and diet. To more explicitly test the influence of host dietary niche on the mammalian gut microbiome we use 16S rRNA gene amplicon sequencing and shotgun metagenomics to compare the gut microbiota of 18 species of wild non-human primates classified as either folivores or closely related non-folivores, evenly distributed throughout the primate order and representing a range of gut morphological specializations. While folivory results in some convergent microbial traits, collectively we show that the influence of host phylogeny on both gut microbial composition and function is much stronger than that of host dietary niche. This pattern does not result from differences in host geographic location or actual dietary intake at the time of sampling, but instead appears to result from of differences in host physiology. These findings indicate that mammalian gut microbiome plasticity in response to dietary shifts over both the lifespan of an individual host and the evolutionary history of a given host species is constrained by host physiological evolution. Therefore, the gut microbiome cannot be considered separately from host physiology when describing host nutritional strategies and the emergence of host dietary niches.
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Microbiologists often evaluate microbial community dynamics by formulating functional hypotheses based on ecological processes. Indeed, many of the methods and terms currently used to describe animal microbiomes derive from ecology and evolutionary biology. As our understanding of the composition and functional dynamics of “the microbiome” grows, we increasingly refer to the host as an ecosystem within which microbial processes play out. Even so, an ecosystem services framework that extends to the context of the host has thus far been lacking. Here, we argue that ecosystem services are a useful framework with which to consider the value of microbes to their hosts. We discuss those “microbiome services” in the specific context of the mammalian gut, providing a context from which to develop new hypotheses and to evaluate microbial functions in future studies and novel systems.