Supplementary Information
2
Table S1. Results of quasibinomial GLMs testing for microbial OTU relationships with
host diet. This table is provided in a separate .xls file, due to its size. The table lists OTU
4
taxonomic identity, to the highest available resolution, OTU mean relative abundance, OTU
incidence (proportion of hosts carrying the OTU), and P-values for monotonic
6
quasibinomial general linear models testing for diet effects on OTU relative abundance
(diet effects including carbon and nitrogen signatures converted into proportion littoral
8
carbon and trophic position, respectively). Separate models were run for perch, and for
stickleback. No model results are provided for OTUs that are absent in a given host species.
10
Results are sorted by mean abundance. Here we focus only on OTUs exhibiting at least
0.01% mean relative abundance and found in multiple hosts.
12
1
14
Table S2. Results of a linear model in which stickleback phylodiversity (PD) depends
16
on linear and quadratic effects of diet as measured by the proportion littoral carbon
α, trophic position (tpos), and sex and size. This is a reduced version of a model that
18
originally included effects of sex, length, linear and quadratic diet effects, and pairwise and
3-way interactions. AIC model selection criteria were used to identify a simpler model that
20
contains essential effects but removes effects that contribute little explanatory power. The
overall model is significant (p=0.014).
22
Effect
Estimate
Std. Error
t
P
Intercept
183.8046
56.6672
Sex
20.8342
9.4092
2.2143
0.0281
Length
-4.1454
1.2036
-3.4442
0.0007
α2
55.9113
24.0147
2.3282
0.0211
tpos2
14.4968
4.8526
2.9874
0.0032
Sex * Length
-0.4613
0.2034
-2.2683
0.0246
α2 * tpos2
-4.6964
2.0372
-2.3054
0.0224
0.3573
0.1031
3.4643
0.0007
Length * tpos2
2
24
Table S3. Results of a linear model in which perch phylodiversity (PD) depends on
linear and quadratic effects of diet as measured by the proportion littoral carbon α,
26
trophic position (tpos), and sex and size. This is a reduced version of a model that
originally included effects of sex, length, linear and quadratic diet effects, and pairwise and
28
3-way interactions. AIC model selection criteria were used to identify a simpler model that
contains essential effects but removes effects that contribute little explanatory power. The
30
overall model is significant (p=0.0268).
Effect
Estimate
Std. Error
t
P
Intercept
-130.0632
129.1471
Sex
-545.5272
224.0753
-2.4346
0.0160
1.4479
0.3671
3.9444
0.0001
α
483.5506
166.0533
2.9120
0.0041
tpos
-20.5669
64.5291
-0.3187
0.7503
α2
481.8605
311.3499
1.5476
0.1236
13.9263
9.2240
1.5098
0.1330
0.0782
0.0390
2.0026
0.0469
-126.7562
49.0069
-2.5865
0.0106
Sex * α
-18.3193
37.3461
-0.4905
0.6244
Sex * tpos
302.9908
128.1194
2.3649
0.0192
71.9325
72.0933
0.9978
0.3199
Sex * tpos2
-42.7618
18.1530
-2.3556
0.0197
Length * α
-3.9310
1.3345
-2.9456
0.0037
Length * α2
-3.8125
2.3211
-1.6426
0.1024
Length * tpos2
-0.0882
0.0216
-4.0884
0.0001
1.0175
0.3688
2.7588
0.0065
Length
tpos2
Sex * Length
α2 * tpos2
Sex * α2
Length * α2 * tpos2
32
3
Table S4. Results of quadratic quasibinomial GLMs testing for microbial OTU
34
relationships with host diet in the captive stickleback diet-manipulation experiment.
This table is provided in a separate .xls file, due to its size. The table provides taxonomic
36
information for the common OTUs in the laboratory feeding experiment with males and
females fed Daphnia (0% littoral prey), mixed diet (50%), or chironomids (100%), mean
38
relative abundance of each OTU, and the results of the quadratic quasibinomial GLM
including slope, standard error of slope, and P-value of the linear and quadratic effects of
40
the proportion littoral carbon. Entries are sorted by a t-statistic measure of effect size and
direction for the quadratic effect. Negative values denote OTUs that are less common in
42
mixed-diet fish, positive values denote OTUs that are more common in mixed-diet fish.
44
46
Table S5. Results of a general linear model testing whether microbial diversity (PD)
depends on diet generalization (G) in perch.
48
Effect
t
P
12.67
-2.58
0.0104
-151.94
51.05
-2.97
0.0033
-0.27
0.079
-3.50
0.0005
140.47
67.04
2.09
0.0375
Sex * Length
0.25
0.106
2.42
0.0162
G * Length
1.23
0.40
3.04
0.0027
Sex * Length * G
1.11
0.55
-2.02
0.0449
Intercept
Sex
Length
G
Sex * G
Estimate
Std. Error
48.06
9.93
-32.82
4
50
52
Table S6. Results of quadratic quasibinomial GLMs testing for non-linear microbial
54
class relationships with host diet in the two wild species (separate statistical
models). This table is provided in a separate .xls file, due to its size. We present taxonomic
56
details for each common OTU, relative abundance, and incidence (as in Table S1), sorted by
incidence. This table differs from Table S1 in that we present quadratic GLMs testing for
58
non-linear diet effects, including linear and quadratic effects of the proportion littoral
carbon and trophic position, an interaction between littoral carbon and trophic position,
60
and an interaction between their squared effects.
5
2
Bloodworms
Mixed
Daphnia
0
-2
-1
LD2
1
MANOVA p = 0.036
-2
-1
0
1
2
3
4
LD1
62
Figure S1. Effect of experimental diet manipulation on gut microbial composition,
64
measured here by linear discriminant function axes 1 and 2 (LD1 and LD2). Weighted
PCoA axes 1-10 were used to generate LD axes. Individual fish are plotted as small circles color-
66
coded by diet treatment. Larger triangles indicate treatment centroids, with 95% (2 standard
error) confidence intervals plotted around each centroid. A MANOVA confirmed that the
68
between-treatment differences are significant. Similar results are obtained for weighted and
unweighted PCoA scores, as well as combinations of additional higher-order PCoA axes, eg.
70
PCoA 2 versus 3.. This figure presents results from diet manipulation of pre-reproductive male
and female stickleback; similar results are obtained for reproductive males only.
6
60
40
Microbial diversity, PD
50
B
10
20
30
60
40
30
10
20
Microbial diversity, PD
50
A
0.2
0.4
0.6
0.8
Proportion littoral carbon
1.0
3.2
3.4
3.6
3.8
Trophic position, tpos
72
74
Figure S2. Null versus observed values of microbial diversity (PD) in stickleback.
We evaluated the extent to which individuals’ observed microbial diversity differs from null
76
expectations under two null models of community assembly: (M1) random colonization by a
well mixed metacommunity of microbes to which all individuals are exposed, or (M2) random
78
colonization by a diet-dependent metacommunity of microbes. If individuals exhibit less
phylogenetic diversity than null expectations, it suggests a tendency for phylogenetically related
80
microbes to coexist within an individual more frequently than expected by chance. In this legend,
we first present the methods used, then explain the results plotted in Figure S2.
82
Methods For model M1, we determined the mean relative abundance of each microbe
OTU across all wild-caught stickleback to obtain an average metacommunity composition, after
84
rarefying to 10,000 sequence reads per individual. We drew randomly from this multinomial
probability distribution to resample 10,000 reads per individual, and recalculate PD (and OTU
7
86
richness) for each individual. This was done 1,000 times (Figure S2 shows one typical realization
of this resampling) to generate a mean population-wide null PD that ignores individual diet (null
88
PDpop). For model M2, we examined each individual stickleback in turn and found the 20 closest
neighboring fish in bivariate isotopic space. These neighboring fish approximate that focal
90
individuals’ diet, and so can be used to represent the microbial community expected for the focal
individuals’ diet. We calculated the mean relative abundance of each microbe OTU across those
92
20 neighbors (excluding the focal fish). This represents a null meta-community given the
individual’s diet. We then randomly sampled 10,000 OTUs from this null meta-community to
94
generate a null community for the individual (repeated 1,000 times). For each null community
for the individual, we obtained PD (and OTU richness), yielding a diet-based null diversity (null
96
PDdiet). Whereas null community diversity under model M1 is shared by all fish, the null
community diversity under M2 is unique for each individual, being defined by the microbes of
98
its dietary neighbors. We then used t-tests to check for differences between the observed PD,
null PDdiet, and null PDpop to determine whether individuals’ microbiota are more
100
phylogenetically similar and less diverse than expected if individuals sampled from a shared
microbial metacommunity, either population-wide or diet-based (M1 and M2, respectively).
102
Results. In Figure S2, individuals’ observed PD values are plotted as solid dots, and
connected by vertical light grey lines to their corresponding average null PDdiet that is based on
104
fish with similar diets. The population-wide null PD (ignoring individual diet) is indicated by a
thick dotted horizontal line. Three main trends emerge from this analysis.
106
First, individuals’ observed PD (solid dot) is always less than PD expected under random
community assembly (less than the dotted horizontal line), and less than PD expected under diet-
108
dependent random community assembly (open circles). The same is true for microbial species
8
richness (number of OTUs, not plotted). The null expectation, assuming random sampling and
110
10,000 reads per host, is that individuals should have PDpop = 61.97, with an average species
richness of 903 microbial OTUs observed per host. Instead, we observed an average PD = 16.65
112
(sd = 6.01) and 166.3 microbial OTUs per host (sd= 78.08), significantly lower than null
expectations (p<0.0001; this figure). This implies that individuals’ microbiota are a non-random
114
sample of the microbiota available to colonize, even when accounting for diet’s likely influence
microbial colonization. This inference suggests a substantial role either of stochastic community
116
assembly (e.g., priority effects, or multiple stable community states), or unaccounted-for sources
of among-individual variation (e.g., immune genotype).
118
Second, the null PD based on individuals with similar diets is less than the null PD based
on the whole metacommunity of microbes (open circles are below the horizontal dotted lines).
120
The resulting null diversity, PDdiet = 50.02, is 19.3% smaller than the population-wide null PDpop
(p<0.0001), suggesting that diet contributes to but does not fully explain the observed low
122
within-individual microbial diversity, which is 73% smaller than population-wide null. This
implies that diet does explain some of the reduced diversity within individuals, but much of the
124
low individual microbial diversity is due to factors other than diet.
Finally, the quadratic effect of diet on microbial diversity can be seen for null PDdiet as a
126
function of proportion littoral carbon (panel A), and observed PD as a function of trophic
position, tpos (panel B). Focusing on the diet-based null PD, expected diversity is lower for
128
generalists (intermediate diets) than either specialist (this figure), as indicated by significant diet
effects on PDdiet (α p<0.0001, α2 p<0.0001, tpos p=0.0261, tpos2 p=0.0310). In contrast, only
130
tpos has a significant quadratic effect on the observed PD. Note that because observed PD
depends on a quadratic interaction between  and tpos, the trend plotted in Fig. S2B (which
9
132
focuses only on tpos) is not as strong as the trend in Fig. 2. Based on this result, we infer that
among-individual variation in the microbiota adds noise that partially obscures an otherwise
134
strong tendency (more clearly revealed by calculating PDdiet) for intermediate diets to have lower
PD.
136
138
10
140
A
25
20
15
10
Microbial phylogenetic diversity (PD)
30
p = 0.0271
0.00
0.05
0.10
0.15
0.20
0.25
Diet diversity
142
Figure S3. Microbial diversity (PD) is negatively correlated with a metric of diet diversity
(G) in male stickleback. Dashed lines represent the 95% confidence interval around the
144
regression line. To calculate a measure of diet generalization, we acquired individual scores
along the first principal component in bivariate isotope space (α and tpos), rescaled these to
146
range from b=0 to b=1 (min to max PC score). We measure diet generalization as G = b (1- b),
which is maximized when b = 0.5 (generalists). We then used linear regression to test whether
148
PD varies as a function of G. An important caveat is that our measure of diet diversity reflects
even use of littoral vs pelagic prey, but is not synonymous with prey species richness. An
11
150
individual forager might use exclusive pelagic prey but still consume diverse species of
cladocera. However, because littoral prey are predominantly insect larvae while pelagic
152
zooplankton are predominantly crustaceans, littoral/pelagic generalists use a more diverse
combination of prey at a deep taxonomic level (different ratios of Subphyla). In stickleback,
154
regression of PD on G confirms that microbial diversity declines with diet diversity (Fig. 2C; t =
-2.29, df = 175, p = 0.0271). Note that although the effects of diet or G are statistically
156
significant, there remains substantial unexplained residual variation in PD: the correlation
between G and PD is only -0.166. Adding in effects of sex and standard length and their
158
interactions explains some of the residual variation. In perch, regression of PD on G also
confirmed trends found with quadratic diet effects (Table S5). There was an overall negative
160
effect of diet generalization G on microbial diversity (p=0.0033), as in stickleback, that is
revealed after accounting for significant effects of size, sex, and all interactions (all p<0.05;
162
Table S5). The interaction effects are consistent with quadratic regression results: the negative
correlation between G and PD is more negative in females than males (sex*G p=0.016), in
164
smaller individuals (size*G p=0.0027). Note that the range of PD plotted here differs from Fig.
2, because in the latter figure we plot quadratic regression estimates of the surface of PD, rather
166
than individual data point values.
12
0.6
0.8
0.4
Y
0.6
0.2
0.4
0.0
0.2
0.0
-1
0
1
-3
-2
D. Clostridia
-1
0
1
xdata
0.4
0.2
0.4
Y
0.6
0.6
0.8
0.8
1.0
-2
C. Gammaproteobacteria
xdata
0.0
0.0
0.2
Microbial
Y
class relative abundance
Y
-3
-3
168
B. Bacilli
0.8
1.0
1.0
A. Gammaproteobacteria
-2
-1
0
1
2
-3
-2
-1
0
1
2
Isotopic
xdata
principal component axis
xdata
1
Figure S4. Non-monotonic relationships between diet and the relative abundance of some
170
common microbial classes. Curves and shaded 95% confidence intervals are calculated from
quadratic quasibinomial general linear models whose predictions and confidence intervals are
172
back-calculated into response variable scales. Significance for quadratic effects are (A) p = 0.007
for Gammaproteobacteria in stickleback; (B) p = 0.040 for Bacilli in stickleback ; (C) p = 0.046
174
for Gammaproteobacteria in perch, and (D) p = 0.010 for Clostridia in perch. Although these
significance levels are not extremely low, there are too many such quadratic effects (>19% of
176
classes) to be explained by false positives alone (5% expected). We focus here not on the most
significant models, but on the microbial classes that are found in most individuals and show a
178
wide range of relative abundances. The full set of quadratic quasibinomial GLM results are
provided in Table S6.
13
50% (mixed)
100% (Chironomids)
Percent littoral diet
60
40
50
C
10
20
30
Frequency
0.008
6.1%
11.4%
0
0.000
0% (Daphnia)
182
B
0.004
OTU relative abundance
0.015
0.010
0.005
0.000
OTU relative abundance
A
0.012
70
180
0% (Daphnia)
50% (mixed)
100% (Chironomids)
Percent littoral diet
-4
-2
0
2
4
Quadratic effect of diet (t)
Figure S5. Non-monotonic relationships between experimental diet and the relative
abundance of some common microbial OTUs. We used quadratic quasibinomial general linear
184
models to test whether the relative abundance of gut microbial OTUs exhibit non-linear effects
of diet mixing. We focus on OTUs here merely to illustrate the point that the quadratic effects in
186
Fig. S4 are not unique to any single taxonomic rank; analogous results are obtained at other
taxonomic ranks. (A) The relative abundance of Pseudomonas sp (Gammaproteobacteria) is
188
significantly lower in mixed-diet than in single-diet fish (quadratic P = 0.0002), yielding a
positive quadratic effect. Slight jitter is added to the horizontal axis values to distinguish
190
overlapping points. The line and shaded confidence interval are from GLM predictions; because
these are back-calculated into proportions for graphical purposes, the estimated curve may not
192
match curves fit by eye. (B) The relative abundance of an OTU in the Rhodobacteraceae
(Alphaproteobacteria) is more common in mixed-diet fish than in either extreme diet (quadratic
194
P < 0.0001) yielding a negative quadratic. (C) the distribution of quadratic effects from 263
OTUs. 11.4% of OTUs exhibited significant positive quadratic effects (as in A), and 6.1%
196
exhibited significant negative effects (as in B). The bias towards positive quadratic results is
itself significant (proportion test, P = 0.038) and may help explain the lower microbial diversity
198
in intermediate diets.
14
Figure S6.
200
15
202
Figure S6. Overlap in the presence of microbial OTUs among food sources (Daphnia or
chironomids) and stickleback fed either (A) Daphnia, (B) a mixed diet, or (C) chironomids.
204
The numbers in each area represent the number of microbial OTUs that are observed in one
sample alone (non-overlapping regions) or in each of two or more samples (overlapping regions).
206
Microbial OTUs found in only one individual in the entire study were excluded. Significance of
overlap between fish and diets was evaluated by a χ2 test contrasting the number of OTUs that a
208
fish sample uniquely shares with each food source, relative to expectations derived from the
numbers of OTUs exclusive to each food (discounting fish).
210
212
16
Figure S7.
214
17
216
Figure S7. Effects of microbial diversity on male body condition. Host body condition was
218
calculated as the residuals of log body mass regressed on log standard length, separately for
males from the lab and wild stickleback, and perch. We used path analysis to estimate the partial
220
correlations between diet (α and tpos), diet disparity (squared distance of individuals’ α or tpos
values from the mean α or tpos), PD, and condition. These analyses focused exclusively on male
222
fish, as their body condition is insensitive to reproductive condition whereas female condition is
highly dependent on egg mass. The resulting partial correlations are presented for (A) male
224
stickleback from Cedar Lake and (B) male perch from Lake Erken. Significant positive partial
correlations are represented as blue arrows, negative correlations are red arrows, and non-
226
significant partial correlations tested in the model are grey arrows. Partial correlations are
provided alongside significant effects. Asterisks indicate statistical significance: * p<0.05; **
228
p<0.01, *** p<0.001. In wild stickleback (A) body condition was positively correlated with both
the proportional littoral carbon, α, and α2, consistent with previous evidence that individuals with
230
specialized diets experience less resource competition and so have higher fitness. We also found
that individuals with far-from-average tpos had higher PD, consistent with the quadratic effects
232
of diet on PD. However, we found no direct effect of PD on host condition after controlling for
their joint association with diet. In perch (B) we found significant direct positive effects of a
234
littoral diet on body condition but no direct effects of diet disparity on body condition (all
p>0.1). However, diet disparity had indirect effects on body condition via microbial diversity.
236
Male perch with extreme α values tend to have higher microbial diversity, whereas individuals
with extreme tpos tend to have lower microbial diversity, again consistent with our quadratic
238
regression analyses. The higher microbial diversity (PD) in specialist fish, in turn, was associated
18
with lower body condition (p=0.0049). Thus, controlling for diet we find that individuals with
240
higher microbial diversity tend to have lower body mass for a given size. The opposite trend was
found in laboratory-reared male stickleback (C) fed either littoral or pelagic prey. We carried out
242
an additional laboratory diet manipulation using only reproductive male stickleback (N = 50)
equally split into chironomid- and Daphnia-fed treatments. The focus on males only was
244
intended to mitigate body condition variation due to reproductive status in females, whose shortterm changes in ovary mass generate substantial residual variation in condition. Each treatment
246
was applied to 4 aquaria (no significant effect) for two months, and males microbiota were
identified as explained in the Methods. Green and blue points indicate chironomid (littoral) and
248
Daphnia (pelagic) diets, respectively. The black regression line represents the overall effect of
PD on condition, green and blue lines for each diet treatment separately (dashed line indicates a
250
non-significant effect). We found higher body condition in chironomid-fed (littoral) males than
in Daphnia -fed (pelagic) males. Chironomid-fed males also exhibited higher microbial diversity
252
(PD) than Daphnia -fed males (p=0.0001). Focusing on PD variation within diet treatments we
found a significant positive association between microbial diversity and host condition
254
(ANCOVA, PD effect p=0.0019; no diet*PD interaction p=0.488).
19