The gut microbiota compensates for seasonal diet variation in the wild black howler
monkey (Alouatta pigra)
Katherine R. Amato1,2*, Steven R. Leigh2, Angela Kent3, Roderick I. Mackie4,5, Carl J.
Yeoman6, Rebecca M. Stumpf5,7, Brenda A. Wilson5,8, Karen E. Nelson9, Bryan A. White4,5,
Paul A. Garber7
1
Program in Ecology, Evolution, and Conservation Biology, University of Illinois, Urbana, IL,
61801; 2Department of Anthropology, University of Colorado, Boulder, CO 80309; 3Department
of Natural Resources and Environmental Sciences, University of Illinois, Urbana, IL, 61801;
4
Department of Animal Sciences, University of Illinois, Urbana, IL, 61801; 5Institute for
Genomic Biology, University of Illinois, Urbana, IL, 61801; f6Department of Animal and Range
Sciences, Montana State University, Bozeman, MT, 59717; 7Department of Anthropology,
University of Illinois, Urbana, IL, 80301; 8Department of Microbiology, University of Illinois,
Urbana, IL, 61801; 9The J. Craig Venter Institute, Rockville, MD, 20850
*
Author for correspondence: katherine.amato@colorado.edu, (773)952-0098, fax: (303)492-8425
Microbial Ecology
Supporting Information
Methods
Diet data analysis: For behavioral data collection, the focal individual was chosen pseudorandomly (no individual was sampled twice consecutively and priority was given to individuals
that had been undersampled on previous days). During each twenty minute behavioral sampling
period, the focal individual’s activity was recorded every two minutes. Five activities were
recorded: feeding (ingestion of food items), foraging (movement within a feeding tree), resting
(inactivity), traveling (movement between trees), and social activity (howling, play, sexual
interaction, aggression, etc.). During feeding bouts, the type of food resource (e.g. ripe fruit,
unripe fruit, mature leaves, young leaves, flowers, and stems) was recorded as well as the plant
species. The number of food items consumed per minute was quantified when possible to
provide an estimate of intake rate.
1
We measured the average wet and dry mass of the top ten food items consumed during
each sampling period (based on the percentage of feeding time) for each howler group and used
these data together with intake rates to estimate macronutrient intake based on published values
for specific plant species as well as average values for Neotropical fruits, leaves, and flowers
(Amato and Garber 2014). We also preserved samples of the top ten food items in 70% methanol
for metabolite profiling. Metabolite data were generated using gas chromatography separation
and mass spectrometry analysis, as previously described (Poroyko et al 2011). 1.25mM of
saturated C31 fatty acid (hentriacontanoic acid) was used as an internal standard in each sample.
We reported metabolites in terms of relative concentration (compared to this internal standard)
per gram of wet weight and categorized them into amino acids, sugars, and lipids when possible.
Due to the method by which the metabolite concentrations were standardized, these data do not
provide accurate estimates of the actual amount of each metabolite in a food item, nor can their
amounts be accurately compared across categories (i.e. amino acid, sugar, lipid) within a sample
(Poroyko et al 2011). However, relative concentrations of the same metabolite across food items
and howler diets can be compared to determine their impact on howler nutrition and the gut
microbiota. We standardized all diet data by metabolic body weight to control for age and sex
differences in the composition of the howler groups.
Fecal sample analysis: We stored fecal samples in 96% ethanol for microbial community
composition analyses, 1M NaOH for VFA analyses, and 1M HCl for ammonia analyses. After
collection, we shipped the samples to the University of Illinois where they were kept at -80C
until processing. Permits to collect and export fecal and plant samples were obtained through the
Secretaria del Medio Ambiente y Recursos Naturales (SEMARNAT), the Comision Nacional de
2
Areas Naturales Protegidas (CONANP), and the Secretaria de Agricultura, Ganaderia, Desarollo
Rural, Pesca y Alimentacion (SAGARPA) in Mexico. Permits to import samples to the United
States were obtained through the Center for Disease Control (CDC) and the Animal and Plant
Health Inspection Service (APHIS). Permission to conduct this research was approved through
the IACUC, University of Illinois (#12083).
We amplified the intergenic spacer region of the 16S rRNA gene in all samples using
polymerase chain reaction and used automated ribosomal intergenic spacer analysis (ARISA) to
create a microbial “fingerprint” for each sample (Kent et al 2007). ARISA PCR products were
visualized by denaturing capillary electrophoresis using an ABI 3730xl Genetic Analyzer
(Applied Biosystems, Foster City, CA) at the UIUC Keck Center for Comparative and
Functional Genomics, as described previously (Kent and Bayne 2010). Size-calling and ARISA
profile alignment were carried out using GeneMarker version 1.95 (SoftGenetics, State College,
PA). We used a signal detection threshold of 500 fluorescence units to exclude background
fluorescence and normalized the signal strength (i.e., peak area) of each peak to account for runto-run variations in signal detection by dividing the area of individual peaks by the total
fluorescence (area) detected in each profile (Yannarell and Triplett 2005).
We used 454 FLX-Titanium technology at the J. Craig Venter Institute (Rockville, MD)
for pyrosequencing of the V1-V3 region of the 16S ribosomal RNA gene to generate taxonomic
data for the microbiota in a subset of samples (N=8 individuals at 15 time points)(Amato et al
2013). We successfully sequenced 119 samples. After we removed sequences shorter than 250nt,
with homopolymers longer than 6 nucleotides, containing ambiguous base calls or incorrect
primer sequences, there were 3,242.8 ± 1,610.5 reads per sample on average. We aligned
sequences against the silva database (Pruesse et al 2007) and pre-clustered using mothur (Schloss
3
et al 2009). We detected and removed potentially chimeric sequences using mothur’s
implementation of uchime (Schloss et al 2009) and assembled sequences using mothur’s
average-neighbor algorithm. We defined OTUs as sharing > 97 % sequence identity. We
produced rarefaction data, Shannon-Weaver and Chao1 indices using mothur (Schloss et al
2009) after rarefying data to 1,600 reads. We generated taxonomic profiles using the RDP
Classifier (Wang et al 2007).
Statistical analysis: For all analyses, data were pooled by individual for each sampling block,
and only individuals from which data were collected during every sampling block were included
in analyses (N=13). Because we detected differences across groups in most measured variables,
we stratified PERMANOVA models by group, allowing us to test for differences across
sampling blocks while controlling for differences among groups. We used Type III sums of
squares to determine the significance of each factor in the model and ran all models for 5000
permutations. To ensure that the method by which we were dividing the data into three time
periods was not creating artificial patterns, we also ran analyses on data for which samples from
each individual were randomly pooled into three groups.
Depending on the distribution of data, Kruskal-Wallis tests or analysis of variance
(ANOVA) were used to test for seasonal patterns in total grams of food consumed, total fecal
VFA content, and total fecal ammonia content. We also employed a series of Kruskal-Wallis
tests to test for temporal patterns in intake of individual diet components, concentration of
individual VFAs, and relative abundance of bacterial taxa. For correlations between plant
metabolites and microbial OTUs, we tested for correlations using only those metabolites and
OTUs that exhibited significant differences in relative consumption or relative abundance across
4
sampling periods. We adjusted p-values for repeated tests using a sequential Bonferroni
correction with the initial p = 0.05 (Holm 1979, Rice 1989). However, to detect bacterial taxa
that differed in abundance across sampling blocks, we used a p-value of 0.05 since sequencing
restrictions reduced the number of individuals for which data were generated, and therefore
reduced statistical power. We also used indicator species analysis (R Software, labdsv package)
to detect microbial genera (pyrosequencing) characterizing each sampling block based on both
abundance and frequency of occurrence (De Caceres and Legendre 2009). Taxa with a
significant (p < 0.05) indicator value higher than 0.5 were considered characteristic of each
sampling block
5
RESULTS
Tables
Table S1. Average nutrient and energy intake for each howler group across sampling periods
based on literature estimates of food resource nutritional content, and average metabolite intake.
Energy and nutrient intake values are expressed in grams per metabolic body weight, energy per
metabolic body weight, and percent dry weight ingested. Metabolite values are expressed in
relative concentration (compared to a standard) per metabolic body weight. WFD = Wet-Fruit
Dominated Period, DLD = Dry-Leaf Dominated Period, DFD = Dry-Fruit Dominated Period
WFD
Motiepa
Food consumed
Available Protein
(g/MBW)
% Available Protein
Total Non-Structural
Carbohydrates
(g/MBW)
% Total Non-Structural
Carbohydrates
DLD
Balam
Motiepa
DFD
Balam
Motiepa
Balam
Average
416.5
340.4
228.8
349.8
115.8
126.3
SD
101.2
68.6
85.8
72.1
36.0
54.0
Average
9.4
11.9
7.7
10.0
4.7
5.3
SD
3.3
3.2
2.6
3.0
1.0
1.8
Average
10.2%
11.3%
12.8%
11.5%
9.4%
9.4%
SD
1.1%
0.9%
1.6%
0.6%
0.8%
0.8%
Average
29.0
29.7
12.4
16.2
16.3
17.0
SD
12.5
12.0
3.5
3.7
3.4
5.0
Average
30.0%
27.2%
21.2%
18.9%
32.9%
32.6%
SD
4.3%
6.6%
3.1%
3.2%
3.6%
10.4%
Lipids
Average
4.2
3.2
1.2
3.2
1.8
2.1
(g/MBW)
SD
1.6
0.9
0.3
1.5
0.5
0.6
% Lipids
Average
4.5%
3.1%
2.1%
3.5%
3.5%
3.7%
SD
0.5%
0.3%
0.3%
0.5%
0.4%
0.1%
Neutral Detergent Fiber
Average
42.3
50.3
27.2
39.9
21.9
24.7
(g/MBW)
SD
15.9
14.5
8.8
14.0
4.9
8.0
Total Energy (Kcal/MBW)
Average
182.9
177.4
105.5
172.4
106.6
114.5
SD
Amino Acid Metabolites
Average
Range
Sugar Metabolites
Average
Range
Lipid Metabolites
Average
Range
64.9
56.1
30.0
56.9
26.7
30.6
3,874,740
150,05413,677,598
7,655,871
192,07930,206,249
4,356,907
138,72215,353,681
4,971,969
505,80114,321,945
944,692
46,9685,857,965
1,952,207
18,47710,598,416
179,809,901
17,355,828646,243,338
106,698,939
5,413,802782,947,762
46,591,468
50,833,383160,876,872
44,597,474
30,250,67550,793,104
50,043,025
6,687,14629,075,974
96,710,217
3,539,931631,756,346
6,521,496
959,43613,868,615
4,173,832
800,97928,930,967
3,079,078
507,9877,645,966
2,637,125
416,3534,813,883
2,367,494
251,1755,618,803
1,534,664
102,872317,739
6
Table S2. Top ten plant species and parts consumed by each howler group during each sampling
block reported in terms of percent of total grams of food ingested. Shannon diversity of plant
species and parts consumed by each howler group during each sampling block is also included.
WFD = Wet-Fruit Dominated Period, DLD = Dry-Leaf Dominated Period, DFD = Dry-Fruit
Dominated Period
Motiepa WFD
Balam WFD
Plant Species
Plant Part
Plant Species
Plant Part
Dendropanax arboreus
Ripe Fruit
12.3
11.2
Brosimum alicastrum
Ripe Fruit
24.2
15.9
Guatteria anomala
Ficus americana
Ripe Fruit
Ripe/Unripe
Fruit
9.6
10.1
Schizolobium parahyba
Stem
14.7
10.6
9.1
4.1
Dendropanax arboreus
Ripe Fruit
8.7
8.6
Ficus americana
Ripe Fruit
8.3
5.4
Ficus yoponensis
Ripe Fruit
8.0
4.0
Poulsenia armata
Young Leaf
7.9
5.6
Guatteria anomala
Ripe Fruit
6.0
7.5
Schizolobium parahyba
Stem
4.4
6.0
Cojoba arborea
Young Leaf
5.6
1.5
Ficus yoponensis
Unripe Fruit
4.2
5.5
Brosimum alicastrum
Unripe Fruit
3.9
2.9
Ficus insipida
Ripe Fruit
3.5
3.9
Cojoba arborea
Stem
3.7
3.5
Ficus aurea
Ripe Fruit
2.5
3.0
Ficus yoponensis
Young Leaf
2.7
1.1
Ficus pertusa
Ripe Fruit
2.3
2.4
Ficus yoponensis
Unripe Fruit
2.1
2.0
2.5
0.1
2.3
0.2
Shannon Diversity
Avg %
SD
Motiepa DLD
Plant Species
Poulsenia armata
Avg %
SD
Balam DLD
Plant Part
Young Leaf
Avg %
19.5
SD
Plant Species
10.6
Ficus yoponensis
Plant Part
Ripe Fruit
Avg %
SD
36.4
11.1
Schizolobium parahyba
Stem
11.2
8.8
Schizolobium parahyba
Stem
9.1
8.2
Brosimum alicastrum
Unripe Fruit
10.9
5.7
Brosimum alicastrum
Unripe Fruit
6.2
3.7
Ficus insipida
Ripe Fruit
7.5
6.0
Poulsenia armata
Young Leaf
6.1
3.7
Platymiscium dimorphandrum
Young Leaf
4.2
4.6
Cojoba arborea
Young Leaf
5.9
5.2
Brosimum alicastrum
Ripe Fruit
3.4
9.0
Dialium guianense
Flower
5.2
5.0
Poulsenia armata
Unripe Fruit
3.3
4.2
Fabaceae sp.
Young Leaf
4.2
4.6
Brosimum alicastrum
Young Leaf
2.6
3.2
Platymiscium dimorphandrum
Young Leaf
4.0
2.6
Fabaceae sp.
Young Leaf
2.5
5.1
Ficus yoponensis
Unripe Fruit
3.5
2.3
Ficus yoponensis
Young Leaf
1.7
1.8
Brosimum alicastrum
Young Leaf
2.2
2.5
2.0
0.2
2.2
0.3
Shannon Diversity
Motiepa DFD
Balam DFD
Plant Species
Plant Part
Avg %
Poulsenia armata
Ripe Fruit
24.5
Ficus americana
Ripe Fruit
Ficus aurea
Ampelocera hottlei
SD
Plant Species
Plant Part
Avg %
SD
8.5
Poulsenia armata
Ripe Fruit
23.2
17.3
15.1
5.2
Ficus yoponensis
Ripe Fruit
14.1
8.5
Ripe Fruit
10.4
4.1
Ficus aurea
Ripe Fruit
12.3
3.2
Ripe Fruit
8.2
6.6
Compsoneura sp.
Ripe Fruit
9.8
7.4
Ficus yoponensis
Ripe Fruit
7.2
6.6
Cojoba arborea
Young Leaf
4.9
3.5
Poulsenia armata
Young Leaf
6.7
3.3
Brosimum alicastrum
Ripe Fruit
3.8
7.0
Brosimum alicastrum
Unripe Fruit
1.6
2.1
Cojoba arborea
Stem
2.9
2.8
Brosimum alicastrum
Young Leaf
1.1
1.3
Poulsenia armata
Young Leaf
2.7
1.9
Cojoba arborea
Young Leaf
1.1
1.3
Ficus maxima
Ripe Fruit
1.3
2.1
7
Ficus yoponensis
Shannon Diversity
Stem
0.9
1.6
2.2
0.2
Ficus aurea
Unripe Fruit
1.2
1.6
2.3
0.4
8
Table S3. Relative abundances of bacterial families for each howler group across sampling
periods. Values are expressed in percent of total sequences. Taxa highlighted in bold showed
significant changes across sampling periods (p < 0.05). Analyses were performed on combined
data for both howler groups. WFD = Wet-Fruit Dominated Period, DLD = Dry-Leaf Dominated
Period, DFD = Dry-Fruit Dominated Period
WFD
DLD
Balam
Motiepa
DFD
Balam
Motiepa
AVG
Balam
SD
AVG
SD
Motiepa
Family
AVG
SD
AVG
SD
AVG
SD
AVG
SD
Lachnospiraceae
32.577%
4.108%
39.526%
5.567%
30.807%
2.424%
33.991%
5.748%
22.295%
8.150%
27.831%
2.368%
Ruminococcaceae
15.152%
0.719%
14.223%
1.450%
20.910%
5.691%
16.987%
6.127%
29.755%
9.678%
22.338%
4.414%
Prevotellaceae
8.287%
3.450%
8.278%
4.841%
10.453%
7.860%
12.539%
5.686%
7.513%
3.277%
7.102%
3.397%
Veillonellaceae
2.464%
3.061%
0.647%
0.423%
0.979%
0.649%
1.189%
0.488%
0.883%
0.434%
1.492%
0.748%
Coriobacteriaceae
1.893%
0.753%
0.633%
0.261%
0.955%
0.384%
0.729%
0.350%
0.752%
0.204%
0.646%
0.190%
Erysipelotrichaceae
1.743%
0.739%
1.747%
0.325%
0.435%
0.222%
0.362%
0.107%
0.774%
0.485%
1.642%
1.154%
Verrucomicrobiaceae
1.434%
1.655%
0.192%
0.232%
0.146%
0.278%
0.064%
0.040%
0.215%
0.251%
0.255%
0.203%
Incertae Sedis XIII
0.603%
0.443%
0.160%
0.098%
0.157%
0.048%
0.318%
0.245%
0.098%
0.044%
0.104%
0.155%
Synergistaceae
0.477%
0.406%
0.331%
0.403%
0.109%
0.091%
0.119%
0.160%
0.099%
0.052%
0.165%
0.103%
Incertae Sedis XIV
0.343%
0.155%
0.426%
0.165%
0.378%
0.197%
0.361%
0.045%
0.355%
0.197%
0.584%
0.195%
Desulfovibrionaceae
0.196%
0.218%
0.161%
0.176%
0.122%
0.029%
0.190%
0.178%
0.095%
0.110%
0.208%
0.171%
Porphyromonadaceae
0.174%
0.143%
0.147%
0.156%
0.210%
0.126%
0.362%
0.453%
0.211%
0.217%
0.669%
1.034%
Chloroplast
0.158%
0.199%
0.055%
0.049%
0.070%
0.031%
0.127%
0.113%
0.036%
0.052%
0.027%
0.013%
Streptococcaceae
0.067%
0.029%
0.156%
0.112%
0.022%
0.018%
0.043%
0.034%
0.011%
0.009%
0.015%
0.010%
Spirochaetaceae
0.060%
0.120%
0.000%
0.000%
0.020%
0.039%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Sphingomonadaceae
0.046%
0.088%
0.004%
0.005%
0.003%
0.004%
0.010%
0.009%
0.002%
0.004%
0.004%
0.006%
Brachyspiraceae
0.035%
0.012%
0.022%
0.008%
0.020%
0.006%
0.019%
0.009%
0.023%
0.003%
0.029%
0.010%
Oxalobacteraceae
0.022%
0.020%
0.000%
0.000%
0.019%
0.021%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Anaeroplasmataceae
0.022%
0.022%
0.091%
0.058%
0.009%
0.014%
0.011%
0.013%
0.028%
0.015%
0.037%
0.031%
Enterococcaceae
0.012%
0.017%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.009%
0.010%
0.000%
0.000%
Hyphomicrobiaceae
0.012%
0.012%
0.021%
0.032%
0.004%
0.005%
0.006%
0.009%
0.000%
0.000%
0.004%
0.005%
Planctomycetaceae
0.009%
0.018%
0.007%
0.009%
0.006%
0.002%
0.002%
0.004%
0.004%
0.004%
0.001%
0.003%
Clostridiaceae
0.008%
0.016%
0.059%
0.068%
0.054%
0.061%
0.037%
0.053%
0.031%
0.053%
0.027%
0.013%
Opitutaceae
0.008%
0.006%
0.054%
0.045%
0.015%
0.026%
0.050%
0.054%
0.016%
0.011%
0.203%
0.169%
Neisseriaceae
0.008%
0.015%
0.039%
0.061%
0.000%
0.000%
0.023%
0.023%
0.003%
0.007%
0.016%
0.015%
Enterobacteriaceae
0.006%
0.012%
0.060%
0.025%
0.035%
0.049%
0.110%
0.070%
0.001%
0.002%
0.059%
0.025%
Nocardioidaceae
0.006%
0.011%
0.000%
0.000%
0.000%
0.000%
0.002%
0.003%
0.000%
0.000%
0.003%
0.003%
Bradyrhizobiaceae
0.005%
0.006%
0.003%
0.005%
0.006%
0.008%
0.003%
0.004%
0.000%
0.000%
0.006%
0.011%
Phyllobacteriaceae
0.004%
0.005%
0.011%
0.013%
0.004%
0.008%
0.000%
0.000%
0.000%
0.000%
0.006%
0.012%
Flavobacteriaceae
0.004%
0.005%
0.000%
0.000%
0.004%
0.009%
0.004%
0.005%
0.001%
0.002%
0.000%
0.000%
Mycobacteriaceae
0.004%
0.007%
0.004%
0.008%
0.002%
0.004%
0.004%
0.005%
0.000%
0.000%
0.001%
0.002%
Alcaligenaceae
0.003%
0.006%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Pseudomonadaceae
0.002%
0.005%
0.006%
0.007%
0.000%
0.000%
0.002%
0.004%
0.000%
0.000%
0.000%
0.000%
Comamonadaceae
0.002%
0.004%
0.002%
0.003%
0.000%
0.000%
0.001%
0.002%
0.000%
0.000%
0.001%
0.002%
9
Helicobacteraceae
0.002%
0.004%
0.004%
0.008%
0.002%
0.004%
0.005%
0.006%
0.005%
0.007%
0.004%
0.003%
Pseudonocardiaceae
0.002%
0.004%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Sphingobacteriaceae
0.002%
0.004%
0.000%
0.000%
0.000%
0.000%
0.002%
0.004%
0.000%
0.000%
0.000%
0.000%
Thiotrichaceae
0.002%
0.004%
0.000%
0.000%
0.000%
0.000%
0.003%
0.004%
0.000%
0.000%
0.000%
0.000%
Bacteroidaceae
0.002%
0.003%
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
0.001%
0.002%
0.006%
0.012%
Leuconostocaceae
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.006%
0.011%
0.000%
0.000%
Bacillaceae
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.002%
0.004%
0.000%
0.000%
Actinomycetaceae
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.001%
0.003%
0.003%
0.005%
Intrasporangiaceae
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
0.000%
0.000%
Micromonosporaceae
0.000%
0.000%
0.000%
0.000%
0.002%
0.004%
0.004%
0.004%
0.001%
0.002%
0.000%
0.000%
Nocardiaceae
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
0.000%
0.000%
Acidimicrobidae
0.000%
0.000%
0.002%
0.004%
0.000%
0.000%
0.002%
0.004%
0.000%
0.000%
0.002%
0.004%
Anaerolineaceae
0.000%
0.000%
0.000%
0.000%
0.001%
0.003%
0.002%
0.004%
0.000%
0.000%
0.000%
0.000%
Caulobacteraceae
0.000%
0.000%
0.002%
0.004%
0.002%
0.004%
0.004%
0.004%
0.000%
0.000%
0.007%
0.014%
Chitinophagaceae
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
0.000%
0.000%
0.001%
0.003%
Conexibacteraceae
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
0.000%
0.000%
0.000%
0.000%
Corynebacteriaceae
0.000%
0.000%
0.002%
0.004%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Cystobacteraceae
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.002%
0.004%
Erythrobacteraceae
0.000%
0.000%
0.000%
0.000%
0.002%
0.004%
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
Gemmatimonadaceae
0.000%
0.000%
0.002%
0.004%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Incertae Sedis XI
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.020%
0.039%
0.000%
0.000%
0.001%
0.003%
Kineosporiaceae
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
0.000%
0.000%
0.000%
0.000%
Kofleriaceae
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Microbacteriaceae
0.000%
0.000%
0.004%
0.005%
0.000%
0.000%
0.004%
0.007%
0.000%
0.000%
0.002%
0.002%
Nitrospiraceae
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
0.000%
0.000%
0.000%
0.000%
0.002%
0.004%
Propionibacteriaceae
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.002%
0.004%
Puniceicoccaceae
0.000%
0.000%
0.000%
0.000%
0.001%
0.001%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Rhizobiaceae
0.000%
0.000%
0.002%
0.005%
0.006%
0.007%
0.001%
0.002%
0.000%
0.000%
0.008%
0.009%
Rhodobacteraceae
0.000%
0.000%
0.005%
0.006%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Rubrobacteraceae
0.000%
0.000%
0.003%
0.005%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Sinobacteraceae
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
0.000%
0.000%
0.005%
0.010%
Streptomycetaceae
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Streptosporangiaceae
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.016%
0.026%
Syntrophomonadaceae
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.001%
0.003%
Victivallaceae
0.000%
0.000%
0.016%
0.021%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Xanthomonadaceae
0.000%
0.000%
0.002%
0.004%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
10
Table S4. Relative abundances of bacterial genera for each howler group across sampling
periods. Values are expressed in percent of total sequences. Taxa highlighted in bold showed
significant changes across sampling periods (p < 0.05). Analyses were performed on combined
data for both howler groups. **again it might be helpful to the reader to define WFD, DLD, and
DFD in the table**
WFD
Balam
DLD
Motiepa
Balam
DFD
Motiepa
Balam
Motiepa
Genus
AVG
SD
AVG
SD
AVG
SD
AVG
SD
AVG
SD
AVG
SD
Oscillibacter
2.847%
2.154%
1.427%
0.272%
2.282%
1.829%
0.838%
0.226%
0.634%
0.356%
0.827%
0.283%
Succiniclasticum
1.703%
3.047%
0.242%
0.459%
0.183%
0.148%
0.334%
0.482%
0.042%
0.029%
0.100%
0.180%
Prevotella
1.608%
1.458%
1.680%
1.184%
4.605%
5.733%
2.682%
3.331%
2.098%
2.672%
1.928%
1.480%
Akkermansia
1.434%
1.655%
0.192%
0.232%
0.146%
0.278%
0.064%
0.040%
0.229%
0.264%
0.252%
0.201%
Hallella
1.356%
1.189%
1.741%
0.641%
0.743%
0.513%
6.294%
5.168%
1.352%
1.257%
2.589%
1.640%
Coprobacillus
1.058%
0.720%
1.085%
0.324%
0.192%
0.106%
0.124%
0.081%
0.421%
0.247%
1.168%
0.996%
Subdoligranulum
0.874%
0.372%
0.529%
0.202%
0.439%
0.198%
0.552%
0.133%
0.734%
0.349%
0.813%
0.394%
Faecalibacterium
0.808%
0.739%
1.247%
0.430%
0.318%
0.155%
1.543%
1.728%
1.119%
1.231%
1.837%
1.222%
Xylanibacter
0.734%
0.732%
0.272%
0.256%
0.327%
0.294%
0.189%
0.243%
0.206%
0.204%
0.134%
0.184%
Marvinbryantia
0.582%
0.271%
0.725%
0.232%
0.668%
0.079%
0.903%
0.447%
0.457%
0.182%
0.711%
0.257%
TM7
0.392%
0.500%
0.015%
0.019%
0.181%
0.215%
0.070%
0.058%
0.440%
0.414%
0.238%
0.150%
Roseburia
0.371%
0.141%
0.998%
0.821%
0.751%
0.361%
0.584%
0.500%
0.386%
0.355%
0.415%
0.182%
Mogibacterium
0.364%
0.251%
0.110%
0.109%
0.105%
0.048%
0.140%
0.194%
0.053%
0.024%
0.076%
0.125%
Blautia
0.320%
0.165%
0.416%
0.174%
0.347%
0.194%
0.456%
0.290%
0.330%
0.196%
0.573%
0.193%
Butyricicoccus
0.295%
0.122%
0.254%
0.103%
0.447%
0.172%
0.395%
0.049%
0.252%
0.075%
0.207%
0.085%
Ruminococcus
0.263%
0.099%
0.117%
0.074%
0.090%
0.033%
0.085%
0.042%
0.357%
0.468%
0.091%
0.033%
Coprococcus
0.222%
0.089%
0.718%
0.436%
0.522%
0.127%
0.332%
0.259%
0.452%
0.388%
0.942%
0.657%
Papillibacter
0.173%
0.079%
0.259%
0.169%
0.446%
0.112%
0.549%
0.584%
0.859%
0.106%
0.608%
0.094%
Atopobium
0.162%
0.042%
0.091%
0.017%
0.078%
0.070%
0.095%
0.055%
0.138%
0.067%
0.139%
0.077%
Streptophyta
0.158%
0.199%
0.055%
0.049%
0.070%
0.031%
0.125%
0.114%
0.036%
0.052%
0.027%
0.013%
Gordonibacter
0.151%
0.133%
0.028%
0.008%
0.067%
0.045%
0.027%
0.018%
0.023%
0.024%
0.032%
0.018%
Desulfovibrio
0.128%
0.189%
0.074%
0.130%
0.029%
0.020%
0.121%
0.175%
0.066%
0.120%
0.166%
0.161%
Anaerovorax
0.117%
0.084%
0.034%
0.031%
0.029%
0.012%
0.059%
0.032%
0.022%
0.021%
0.016%
0.008%
Treponema
0.105%
0.210%
0.000%
0.000%
0.011%
0.022%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Solobacterium
0.066%
0.053%
0.125%
0.090%
0.030%
0.013%
0.073%
0.042%
0.184%
0.135%
0.105%
0.025%
Streptococcus
0.065%
0.032%
0.156%
0.112%
0.017%
0.016%
0.043%
0.034%
0.007%
0.006%
0.015%
0.010%
Collinsella
0.055%
0.026%
0.027%
0.014%
0.031%
0.023%
0.048%
0.043%
0.039%
0.014%
0.023%
0.016%
Moryella
0.053%
0.017%
0.147%
0.051%
0.107%
0.064%
0.042%
0.016%
0.022%
0.018%
0.062%
0.020%
Helicobacter
0.044%
0.088%
0.004%
0.008%
0.001%
0.002%
0.005%
0.006%
0.005%
0.007%
0.124%
0.237%
Anaerotruncus
0.044%
0.047%
0.008%
0.009%
0.085%
0.134%
0.050%
0.024%
0.039%
0.022%
0.016%
0.008%
Brachyspira
0.033%
0.012%
0.022%
0.008%
0.020%
0.006%
0.019%
0.009%
0.023%
0.003%
0.029%
0.010%
Butyrivibrio
0.031%
0.022%
0.028%
0.010%
0.022%
0.018%
0.024%
0.014%
0.018%
0.004%
0.018%
0.013%
Barnesiella
0.023%
0.020%
0.020%
0.039%
0.017%
0.022%
0.059%
0.118%
0.012%
0.012%
0.035%
0.061%
11
Howardella
0.023%
0.017%
0.009%
0.013%
0.031%
0.009%
0.025%
0.019%
0.025%
0.005%
0.011%
0.005%
Oxalobacter
0.022%
0.020%
0.000%
0.000%
0.019%
0.021%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Anaerostipes
0.021%
0.013%
0.025%
0.015%
0.049%
0.066%
0.022%
0.034%
0.026%
0.012%
0.013%
0.013%
Syntrophococcus
0.019%
0.011%
0.011%
0.009%
0.017%
0.017%
0.007%
0.012%
0.007%
0.003%
0.007%
0.006%
Oribacterium
0.019%
0.013%
0.007%
0.005%
0.004%
0.005%
0.004%
0.009%
0.002%
0.004%
0.001%
0.003%
Dorea
0.018%
0.023%
0.033%
0.016%
0.009%
0.014%
0.018%
0.013%
0.007%
0.005%
0.011%
0.015%
Anaeroplasma
0.018%
0.019%
0.058%
0.020%
0.022%
0.029%
0.008%
0.012%
0.025%
0.014%
0.033%
0.028%
Acetivibrio
0.016%
0.015%
0.010%
0.010%
0.011%
0.005%
0.002%
0.003%
0.001%
0.003%
0.000%
0.000%
Parabacteroides
0.013%
0.010%
0.000%
0.000%
0.029%
0.008%
0.004%
0.004%
0.016%
0.014%
0.011%
0.009%
Enterococcus
0.012%
0.017%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.009%
0.010%
0.000%
0.000%
Cryptobacterium
0.012%
0.013%
0.005%
0.006%
0.024%
0.005%
0.014%
0.009%
0.018%
0.013%
0.011%
0.013%
Eggerthella
0.012%
0.008%
0.007%
0.005%
0.012%
0.006%
0.009%
0.011%
0.012%
0.008%
0.004%
0.005%
Dialister
0.008%
0.010%
0.004%
0.005%
0.001%
0.003%
0.011%
0.004%
0.007%
0.003%
0.313%
0.593%
Sarcina
0.008%
0.016%
0.059%
0.068%
0.053%
0.040%
0.036%
0.051%
0.029%
0.049%
0.025%
0.009%
Robinsoniella
0.005%
0.006%
0.007%
0.014%
0.002%
0.003%
0.004%
0.003%
0.003%
0.004%
0.003%
0.003%
Gp6
0.005%
0.006%
0.002%
0.004%
0.007%
0.003%
0.004%
0.004%
0.002%
0.004%
0.004%
0.008%
Brevundimonas
0.005%
0.010%
0.002%
0.004%
0.002%
0.004%
0.002%
0.004%
0.000%
0.000%
0.005%
0.010%
Devosia
0.004%
0.005%
0.002%
0.004%
0.000%
0.000%
0.005%
0.009%
0.000%
0.000%
0.000%
0.000%
Cloacibacterium
0.004%
0.005%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Asteroleplasma
0.004%
0.008%
0.032%
0.044%
0.002%
0.004%
0.003%
0.004%
0.004%
0.004%
0.004%
0.003%
Mycobacterium
0.004%
0.007%
0.004%
0.008%
0.002%
0.004%
0.004%
0.005%
0.000%
0.000%
0.001%
0.002%
Spartobacteria
0.004%
0.007%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Schlesneria
0.003%
0.006%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Veillonella
0.003%
0.006%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Marmoricola
0.003%
0.006%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Escherichia/Shigella
0.003%
0.006%
0.055%
0.022%
0.022%
0.040%
0.108%
0.070%
0.000%
0.000%
0.057%
0.026%
Aeromicrobium
0.002%
0.005%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Bradyrhizobium
0.002%
0.005%
0.000%
0.000%
0.006%
0.008%
0.001%
0.002%
0.000%
0.000%
0.003%
0.005%
Acetitomaculum
0.002%
0.005%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Pseudomonas
0.002%
0.005%
0.004%
0.004%
0.000%
0.000%
0.002%
0.004%
0.000%
0.000%
0.000%
0.000%
Paraprevotella
0.002%
0.005%
0.003%
0.006%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Olsenella
0.002%
0.005%
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
0.002%
0.004%
0.000%
0.000%
Hespellia
0.002%
0.004%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Opitutus
0.002%
0.004%
0.002%
0.004%
0.030%
0.060%
0.007%
0.011%
0.002%
0.002%
0.020%
0.011%
Pirellula
0.002%
0.004%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.002%
0.004%
0.000%
0.000%
Aminobacter
0.002%
0.004%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Beggiatoa
0.002%
0.004%
0.000%
0.000%
0.000%
0.000%
0.003%
0.004%
0.000%
0.000%
0.000%
0.000%
Blastochloris
0.002%
0.004%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Hyphomicrobium
0.002%
0.004%
0.000%
0.000%
0.001%
0.002%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Pedomicrobium
0.002%
0.004%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Pseudonocardia
0.002%
0.004%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Sphingobacterium
0.002%
0.004%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Bacteroides
0.002%
0.003%
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
0.001%
0.002%
0.006%
0.012%
12
Acetanaerobacterium
0.002%
0.003%
0.000%
0.000%
0.005%
0.006%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Catonella
0.002%
0.003%
0.000%
0.000%
0.002%
0.003%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Gp3
0.002%
0.003%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Lactococcus
0.002%
0.003%
0.000%
0.000%
0.001%
0.002%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Sandaracinobacter
0.002%
0.003%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Weissella
0.000%
0.000%
0.003%
0.006%
0.000%
0.000%
0.000%
0.000%
0.006%
0.011%
0.000%
0.000%
Corynebacterium
0.000%
0.000%
0.002%
0.004%
0.000%
0.000%
0.000%
0.000%
0.004%
0.009%
0.000%
0.000%
Synergistes
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.004%
0.007%
0.000%
0.000%
Slackia
0.000%
0.000%
0.003%
0.006%
0.002%
0.004%
0.002%
0.004%
0.002%
0.004%
0.001%
0.002%
Bacillus
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.002%
0.004%
0.000%
0.000%
Pseudobutyrivibrio
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.002%
0.004%
0.000%
0.000%
Mobiluncus
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.001%
0.003%
0.000%
0.000%
Flavobacterium
0.000%
0.000%
0.000%
0.000%
0.004%
0.009%
0.004%
0.004%
0.001%
0.002%
0.000%
0.000%
Gordonia
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.009%
0.014%
0.001%
0.002%
0.000%
0.000%
Anaerosporobacter
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
0.002%
0.004%
Acidovorax
0.000%
0.000%
0.002%
0.003%
0.000%
0.000%
0.007%
0.013%
0.000%
0.000%
0.000%
0.000%
Actinomyces
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.003%
0.005%
Allobaculum
0.000%
0.000%
0.005%
0.010%
0.000%
0.000%
0.016%
0.032%
0.000%
0.000%
0.031%
0.063%
Arthrobacter
0.000%
0.000%
0.002%
0.004%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Asaccharobacter
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
0.000%
0.000%
0.000%
0.000%
Asticcacaulis
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.002%
0.004%
Balneimonas
0.000%
0.000%
0.003%
0.005%
0.000%
0.000%
0.002%
0.004%
0.000%
0.000%
0.000%
0.000%
Chryseobacterium
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
0.000%
0.000%
0.000%
0.000%
Conexibacter
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
0.000%
0.000%
0.000%
0.000%
Enterobacter
0.000%
0.000%
0.000%
0.000%
0.002%
0.004%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Erwinia
0.000%
0.000%
0.000%
0.000%
0.001%
0.001%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Filomicrobium
0.000%
0.000%
0.003%
0.005%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Gemmatimonas
0.000%
0.000%
0.002%
0.004%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Gp1
0.000%
0.000%
0.004%
0.009%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Gp4
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Ilumatobacter
0.000%
0.000%
0.002%
0.004%
0.000%
0.000%
0.002%
0.004%
0.000%
0.000%
0.002%
0.004%
Kofleria
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Leucobacter
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
Luteolibacter
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.003%
0.004%
Mesorhizobium
0.000%
0.000%
0.003%
0.005%
0.001%
0.002%
0.000%
0.000%
0.000%
0.000%
0.002%
0.004%
Microbacterium
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.004%
0.007%
0.000%
0.000%
0.000%
0.000%
Nitrospira
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
0.000%
0.000%
0.000%
0.000%
0.002%
0.004%
Nocardioides
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
0.000%
0.000%
0.003%
0.003%
Novosphingobium
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
0.000%
0.000%
0.003%
0.004%
Parasporobacterium
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
Pelospora
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.001%
0.003%
Phyllobacterium
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Porphyrobacter
0.000%
0.000%
0.000%
0.000%
0.002%
0.004%
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
13
Prosthecomicrobium
0.000%
0.000%
0.003%
0.005%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Rhizobium
0.000%
0.000%
0.002%
0.005%
0.028%
0.049%
0.000%
0.000%
0.000%
0.000%
0.008%
0.009%
Rhodoplanes
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
Rubrobacter
0.000%
0.000%
0.003%
0.005%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Serratia
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.002%
0.004%
Shuttleworthia
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.002%
0.004%
Sphingomonas
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
Steroidobacter
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
0.000%
0.000%
0.005%
0.010%
Streptomyces
0.000%
0.000%
0.000%
0.000%
0.001%
0.002%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Subdivision 3
0.000%
0.000%
0.000%
0.000%
0.001%
0.001%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Tannerella
0.000%
0.000%
0.000%
0.000%
0.003%
0.005%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Terrimonas
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.001%
0.003%
Victivallis
0.000%
0.000%
0.013%
0.016%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
Xanthomonas
0.000%
0.000%
0.002%
0.004%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
0.000%
14
Table S5. Spearman’s ρ values for significant correlations (p < 0.05) between microbial taxa and
amounts of different diet components consumed. NDF = Neutral Detergent Fiber, TNC = Total
Non-structural Carbohydrates
Taxon
Acetivibrio
Kcal
0.70
Protein
Lipid
0.75
0.69
NDF
0.75
Butyricicoccus
TNC
Young
Leaf
0.61
Unripe
Fruit
Stem
0.58
0.54
Coprobacillus
0.57
0.53
Dialister
-0.53
Hallella
0.57
-0.60
-0.55
Mogibacterium
Incertae Sedis XIII
Mature
Leaf
0.55
0.64
Oscillibacter
-0.60
0.72
0.56
Streptococcus
Streptophyta
0.59
0.68
15
0.99
1-Methyl-beta-D-galactopyranoside
0.68
1-Octedecenoylglycerol
0.68
0.76
0.72
0.78
0.66
0.71
0.78
0.8
0.74
0.75
0.67
0.67
24-Methylenecycloartanol
2-Methylcitric acid
0.75
0.74
0.7
0.73
2-Methylglutamic acid
0.69
0.72
0.7
0.68
2-Methylmalic acid
0.76
0.72
0.73
0.75
0.71
0.74
2-methylsuccinic acid
0.73
0.65
0.66
0.68
0.79
0.69
0.68
0.67
2-Piperidinecarboxylic acid
3,4-Dihydroxybutanoic acid
0.71
0.84
0.8
3,4-Dimethoxycinnamic acid
0.71
0.7
0.7
3-Deoxy-arabino-hexaric acid
0.72
0.82
0.79
3-methyl-2-hydroxypentanoic acid
0.69
0.71
0.74
-0.69
0.73
0.69
-0.68
0.67
3,4,5-Trihydroxypentanoic acid
0.68
4,5-dimethyl-2,6-dihydroxypyrimidine
0.69
0.69
0.68
0.68
0.67
0.7
0.67
-0.68
0.66
0.69
0.67
4-Hydroxy-3-methoxyphenethyleneglycol
0.7
0.69
4-Hydroxyproline
0.66
0.68
Adenine
0.68
0.68
0.7
0.66
0.68
0.66
0.74
C16:1
Eicosanol
Xylanibacter
0.67
2,3-dihydroxysuccinic acid
Diethyleneglycol
TM7
0.66
2-hydroxyglutaric acid
Aspartic acid
Streptophyta
0.99
1,6-Anhydroglucose
4-Caffeoylquinic acid
Solobacterium
0.88
1-Methyl-alpha-D-glucopyranoside
2,3-Dihydroxybutanedioic acid
Prevotella
Papillibacter
0.89
Oscillibacter
Dialister
0.66
Oribacterium
Coprobacillus
0.68
Hallella
Butyricicoccus
0.95
Gordonibacter
Akkermanisia
1-hexadecanoylglycerol
Acetivibrio
Metabolite
Ruminococcaeeae
Table S6. R values for significant correlations (p < 0.05) between microbial taxa and relative concentration of diet metabolites.
-0.77
0.72
0.74
0.66
0.75
-0.77
-0.75
0.76
0.67
16
Ethanol, (2-(3,4-dihydroxyphenyl)-
0.66
Erythritol
0.65
Ferulic acid
0.69
0.79
0.74
0.67
Gallic acid
0.78
0.67
0.76
0.74
0.68
Glucaric acid
0.73
0.68
0.66
0.74
0.7
0.65
0.65
0.69
Glycolic acid
0.67
0.68
0.66
Guanine
0.73
0.77
0.74
0.72
Hentriacontanol
0.81
0.67
0.65
0.75
0.7
0.74
Heptacosanol
0.75
0.71
0.67
0.71
0.68
0.7
0.75
0.76
0.77
0.74
Glucuronic acid
0.66
0.7
-0.72
0.72
-0.67
0.7
Hexacosanoic acid
0.73
Isoleucine
0.74
Kaempferol
Lactic Acid
0.73
0.75
0.75
0.73
N-Acetyl glucosamine
0.7
0.75
0.68
0.69
N-Acetylglucosylamine
0.72
0.7
0.69
-0.67
0.75
0.66
-0.67
0.71
0.72
0.78
Octacosanol
0.67
0.66
-0.7
0.7
Quinic acid
0.65
0.67
0.67
0.71
0.73
0.71
0.67
0.73
Shikimic acid
0.67
Sorbitol
Tocopherol-a
0.71
0.7
Nicotinic acid
Ribose
0.72
0.68
Monomethylphosphate
Protocatechuic acid
0.73
-0.67
Leucine
p-hydroxyCoumaric acid
-0.69
0.72
0.75
0.67
0.7
0.67
0.67
0.74
0.81
0.8
0.7
0.71
0.7
0.68
Uridine
Xylitol
0.69
-0.67
17
Table S7. Fecal concentrations (mM) of volatile fatty acids and ammonia for each howler group across sampling periods. *Indicates
significant changes across time (p < 0.05). VFA = Volatile Fatty Acid
Sampling
Block
WFD
DLD
DFD
Group
Acetic*
Propanoic
Butanoic*
Pentanoic
Isobutanoic
Isopentanoic*
Total VFAs*
Ammonia*
Average
SD
Average
SD
Average
SD
Average
SD
Average
SD
Average
SD
Average
SD
Average
SD
Balam
43.0
8.2
3.4
1.1
1.9
1.0
0.3
0.1
0.2
0.1
0.2
0.1
49.0
10.3
9.6
4.0
Motiepa
36.9
4.8
3.0
0.5
1.8
0.5
0.3
0.1
0.2
0.1
0.2
0.1
42.4
4.9
11.0
7.8
Balam
33.0
2.1
3.6
0.4
2.4
0.4
0.4
0.2
0.1
0.0
0.1
0.0
39.7
2.4
3.9
0.8
Motiepa
36.1
2.8
3.7
0.3
2.8
0.6
0.4
0.1
0.1
0.0
0.1
0.1
43.2
3.2
4.2
1.0
Balam
46.5
3.0
3.7
0.5
1.7
0.2
0.3
0.1
0.1
0.0
0.2
0.1
52.5
3.1
4.2
0.9
Motiepa
48.4
4.5
4.0
0.7
2.1
0.3
0.4
0.1
0.1
0.0
0.2
0.0
55.2
5.4
4.0
0.9
18
Table S8. Spearman’s ρ values for significant correlations (p < 0.05) between individual diet
components and fecal VFA and ammonia concentration. NDF = Neutral Detergent Fiber, TNC =
Total Non-structural Carbohydrates
Microbial Product
Acetic Acid
Butanoic Acid
Isobutanoic Acid
Isopentanoic Acid
Ammonia
Unripe Fruit
-0.58
Young Leaves
-0.65
0.43
Mature Leaves
0.50
Protein
-0.51
TNC
NDF
0.46
-0.42
0.43
0.51
0.50
19
Table S9. Spearman’s ρ values for significant correlations (p < 0.05) between the relative
concentration of ingested metabolites and fecal volatile fatty acid and ammonia concentration.
Metabolite
Acetic
Butanoic
1,2,3-trihydroxybenzene
1,2,3-trihydroxybutane
0.79
-0.21
1,6-Anhydroglucose
1-Methyl-beta-D-galactopyranoside
0.7
-0.71
1-Octedecenoylglycerol
2,3-Dihydroxybutanedioic acid
Ammonia
0.65
-0.67
2,3-dihydroxysuccinic acid
0.66
2,4,5-Trihydroxypentanoic acid
0.55
2,5-dihydroxybenzoic acid
0.56
2,4-Methylenecycloartanol
0.57
2-Methylcitric acid
0.56
2-Methylmalic acid
0.76
2-Oxoisocaproic acid
0.61
2-Piperidinecarboxylic acid
-0.71
3,4,5-Trihydroxypentanoic acid
0.73
3,4-Dimethoxycinnamic acid
0.63
3-Deoxy-arabino-hexaric acid
0.63
3-hydroxybenzoic acid
0.57
3-methyl-2-hydroxypentanoic acid
0.63
3-methyl-2-oxobutanoic acid
0.65
4-Caffeoylquinic acid
-0.7
4-Hydroxy-3-methoxyphenethyleneglycol
0.65
4-Hydroxyproline
-0.55
Allantoin
-0.62
0.55
beta-Amyrin
0.62
C16:1
0.62
C18:1
0.69
chlorogenic acid
Citric acid
-0.71
0.61
Diethyleneglycol
0.53
Erythronic acid
0.63
Ethanol, (2-(3,4-dihydroxyphenyl)galacturonic acid
0.6
-0.63
gallic acid
0.59
Glucaric acid
0.64
glucuronic acid
0.62
Hentriacontanol
0.71
Inositol, myo-
0.56
Itaconic acid
0.64
20
Kaempferol
0.62
Mannitol
N-Acetylglucosylamine
0.55
-0.36
N-Acetyl glucosamine
neochlorogenic acid
0.55
-0.73
Nonacosanol
0.58
Octacosanol
0.71
p-hydroxybenzaldehyde
0.59
Protocatechuic acid
0.63
Shikimic acid
Sorbitol
0.59
-0.63
Thymine
0.58
Vanillic acid
0.67
Xylopyranoside
0.56
Xylose
0.57
21
Table S10. Spearman’s ρ values for significant correlations (p < 0.05) between microbial taxa
and fecal volatile fatty acid and ammonia concentration.
Taxon
Anaeroplasmataceae
Acetic Acid
Isobutanoic Acid
Isopentanoic
Acid
Ammonia
0.55
Erysipeltotrichaceae
0.53
Streptococcaceae
0.61
Acetivibrio
0.56
Butyricicoccus
-0.61
Coprobacillus
0.54
0.54
Oscillibacter
0.53
Streptococcus
0.63
Streptophyta
Xylanibacter
-0.61
0.53
22
Table S11. Spearman’s ρ values for significant correlations (p < 0.05) between howler activity
and diet components and between howler activity and average daily maximum temperature over
the entire study period. NDF = Neutral Detergent Fiber, TNC = Total Non-structural
Carbohydrates
Energy
(kcal/MBW)
Protein
(g/MBW)
NDF
(g/MBW)
Resting
-0.5
-0.67
-0.68
Feeding
0.76
0.71
0.87
TNC
(g/MBW)
Lipids
(g/MBW)
Max Temp
(°C)
0.87
0.54
0.64
-0.89
23
Table S12. Volatile fatty acid concentrations and profiles in nonhuman primates and mammals.
Table modified from Lambert (2012).
WILD
Total VFA
Concentration (mM)
% Acetate
% Butyrate
%Propionate
Source
Procolobus verus
230
Ohwaki et al. 1974
Colobus guereza
107–434
Ohwaki et al. 1974
Cercopithecus aethiops
190–229
Brourton et al. 1991
Cercopithecus mitis
122–199
Brourton et al. 1991
138–180
Clemens and Phillips 1980
Papio cynocephalus
95–170
Pan troglodytes
44
Alouatta palliata
Alouatta pigra
40-55
Homo sapiens
100
Clemens and Phillips 1980
31
10
3
Ushida et al. 2006
94
6
0.4
Milton and McBee 1983
83-89
3-7
7-9
this study
57
22
21
Cummings 1987
CAPTIVE
Trachypithecus cristatus
95–133
47–56
24–26
10–18
Bauchop and Martucci 1968
Semnopithecus entellus
89–233
46–50
22–23
14–23
Bauchop and Martucci 1968
Colobus guereza
53–65
Colobus guereza
79
61
23
10
Lambert and Fellner 2012
Cercopithecus neglectus
65
47
21
26
Lambert and Fellner 2012
Papio hamadryas
87
48
36
11
Lambert and Fellner 2012
Pan troglodytes
81
55
25
12
Lambert and Fellner 2012
Gorilla gorilla
89
58
25
11
Lambert and Fellner 2012
Pongo abelii
62-67
22-27
10-14
Bos taurus
96–210
48–74
14–28
7–18
van Soest 1994
Ovis aries
70–140
40–66
19–40
9–15
Blaxter et al. 1956
Kay et al. 1976
Schmidt et al. 2005
24
Figures
Figure S1. Partial correspondence analysis illustrating patterns in the grams of plant parts
consumed by the howlers each season with the effect of individual removed. Each point
represents the average diet of one individual during each sampling period. PERMANOVA
results indicate that 54% of the variation is explained by sampling period. WFD = Wet-Fruit
Dominated Period, DLD = Dry-Leaf Dominated Period, DFD = Dry-Fruit Dominated Period
25
Figure S2. Partial correspondence analysis illustrating patterns in the grams of plant parts from
each plant species consumed by the howlers each season with the effect of individual removed.
Each point represents the average diet of one individual during a sampling period.
PERMANOVA results indicate that 38% of the variance is explained by sampling period. WFD
= Wet-Fruit Dominated Period, DLD = Dry-Leaf Dominated Period, DFD = Dry-Fruit
Dominated Period
WFD= wet fruit dominated season, DLD=
, DFD = ------
26
Figure S3. Partial correspondence analysis illustrating patterns in the plant metabolites consumed
by the howlers each season with the effect of individual removed. Each point represents the
average diet of one individual during a sampling period. PERMANOVA results indicate that
36% of the variance is explained by sampling period. WFD = Wet-Fruit Dominated Period, DLD
= Dry-Leaf Dominated Period, DFD = Dry-Fruit Dominated Period
27
Figure S4. Partial correspondence analysis illustrating patterns in gut microbial community
composition across sampling periods at the (A) Family and (B) genus level with the effect of
individual removed. Each point represents the average gut microbiota of one individual during a
sampling period. PERMANOVA results indicate that 29% of the variance is explained by
sampling block at the Family level and 13% at the genus level. WFD = Wet-Fruit Dominated
Period, DLD = Dry-Leaf Dominated Period, DFD = Dry-Fruit Dominated Period
28
Figure S5. Network diagram illustrating significant correlations (p < 0.05) between quantity of
diet components ingested (green nodes: plant metabolites, plant parts, macronutrients), relative
abundances of bacterial taxa (teal nodes), and fecal concentrations of microbial products
(magenta nodes: volatile fatty acids, ammonia). Edge lengths are proportional to the magnitude
of each correlation (Spearman’s ρ).
29
Figure S6. Partial correspondence analysis illustrating patterns in the millimoles of volatile fatty
acids excreted by the howlers each season with the effect of individual removed. Each point
represents the average VFA profile of one individual during a sampling block. PERMANOVA
results indicate that 36% of the variance is explained by sampling block.
30
REFERENCES
Amato KR, Yeoman CJ, Kent A, Carbonero F, Righini N, Estrada AE et al (2013). Habitat degradation
impacts primate gastrointestinal microbiomes. ISME J 7: 1344-1353.
Amato KR, Garber PA (2014). Nutrition and foraging strategies of the black howler monkey (Alouatta
pigra) in Palenque National Park, Mexico. Am J Primatol 76.
De Caceres M, Legendre P (2009). Associations between species and groups of sites: Indices and stastical
inference. Ecol 90: 3566-3574.
Holm S (1979). A simple sequentially rejective multiple test procedure. Scand J Stat 6: 65-70.
Kent AD, Yannarell AC, Rusak JA, Triplett EW, McMahon KD (2007). Synchrony in aquatic microbial
community dynamics. ISME J 1: 38-47.
Kent AD, Bayne ZL (2010). Degraded water quality influences microbial community composition and
perception of health risks in the Chattooga River. DNA Cell Biol 29: 509-517.
Lambert JE, Fellner V (2012). In vitro fermentation of dietary carbohydrate consumed by african apes
and monkeys: preliminary results for interpreting microbial and digestive strategy. Int J Primatol 33:
263-281.
Poroyko V, Morowitz M, Bell T, Ulanov A, Wang M, Donovan S et al (2011). Diet creates metabolic niches
in the "inmature gut" that shape microbial communities. Nutricion Hospitalaria 26: 1283-1295.
Pruesse EC, Quast C, Knittel K, Fuchs BM, Ludwig W, Peplies J et al (2007). SILVA: A comprehensive
online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB.
Nucleic Acids Res 35: 7188-7196.
Rice WR (1989). Analyzing tables of statistical tests. Evol 43: 223-225.
Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB et al (2009). Introducing mothur:
Open-source, platform-independent, community-supported software for describing and comparing
microbial communities. Appl Environ Microbiol 75: 7537-7541.
Wang Q, Garrity GM, Tiedje JM, Cole JR (2007). Naive Bayesian Classifier for rapid assignment of rRNA
sequences into the new bacterial taxonomy. Appl Environ Microbiol 73: 5261-5267.
Yannarell AC, Triplett EW (2005). Geographic and environmental sources of variation in lake bacterial
community composition. Appl Environ Microbiol 71: 227-239.
31