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Vertebrate herbivores require symbiotic gastrointestinal (GI) microbes to extract energy and nutrients from fibrous and sometimes toxic plant diets. Because GI microbes vary in their relative abundance, function, and degree of specialization, the microbial community depends on both the characteristics of plants consumed and the anatomical, physiological, and behavioral characteristics of the herbivore host. To tease apart the relative contribution of diet and herbivore phylogeny to the microbiome, we leveraged a unique study system in which mule deer (
INTRODUCTION
The evolution and radiation of large vertebrate herbivores was catalyzed by the symbiotic relationship with the anaerobic microbes that colonized their gastrointestinal (GI) tract (Hammer & Bowers, 2015). GI microbial communities consist of diverse species of bacteria, archaea, and fungi that allow herbivores to exploit plants as food more efficiently. They do so by fermenting otherwise ubiquitous, yet indigestible plant fibers for energy, nutrient synthesis, and detoxifying toxic plant secondary metabolites (PSMs; Dearing & Weinstein, 2022; Janis, 1976; Troyer, 1984). For example, ~80% of the energy derived by ruminants is produced from the rumen microbiota, without which they would be unable to meet their energy and nutrient requirements (Van Soest, 1994). Because GI microbes vary in their relative abundance, function, and degree of specialization (Chow et al., 2014; Solden et al., 2018), the composition, structure, and diversity of the GI microbiome depend on both the characteristics of plants consumed (Ishaq et al., 2015; Ishaq & Wright, 2014; Solden et al., 2017) and the anatomical, physiological, and behavioral characteristics of the herbivore host (Kohl et al., 2018; Kohl, Miller, et al., 2014; Kohl, Weiss, et al., 2014).
The process of fermenting complex plant fibers and detoxifying PSMs requires specialized adaptations; therefore, the GI microbial community is generally characterized by a core community with extensive functional redundancy, where many different microbes may perform the same role (Solden et al., 2018; Söllinger et al., 2018; Weimer, 2015). This redundancy creates a more resilient community that can retain function after disturbances (e.g., disease, rapid diet change) and enhances the performance of other services to the host, such as detoxification (Ribas et al., 2023; Weimer, 2015). A study of over 30 ruminant species determined that the microbial communities found in the rumen are composed of seven major bacterial groups, including Prevotella, Butyrivibrio, Ruminococcus, unclassified Lachnospiraceae, Ruminococcaceae, Bacteroidales, and Clostridiales (Henderson et al., 2015; Solden et al., 2017). However, microbial subcommunities are spatially separated, have short generation times, and can horizontally transfer genes, which allows the rumen microbial community to quickly adapt to rapid changes in food availability and nutritional quality of an animal's diet by reseeding from these subcommunities (Giraud et al., 2001; Hammer & Bowers, 2015). For example, as plants senesce during autumn and winter, herbivores consume plants that are higher in fiber and PSMs and lower in nitrogen (e.g., woody twigs, evergreens, dry grass), which promotes the growth of microbes that are able to break down cellulose and other complex carbohydrates (García et al., 2000; Solden et al., 2017). During spring, the GI microbial community shifts to faster-growing microbes that specialize in soluble sugars and starches that can quickly take advantage of emerging forbs, grass, and shrub leaves that are lower in fiber and higher in available nitrogen (Jung & Deetz, 1993; Kartzinel et al., 2015; Solden et al., 2018). However, when diets shift too drastically or too quickly (e.g., when animals are brought into captivity or relocated to new areas), the microbiome may not be able to adapt fast enough, causing illness from acidosis and omasal compaction (Butler et al., 2008; Gattiker et al., 2014).
Not only does the microbiome respond to the herbivore host's diet, but it also varies with characteristics of the host's evolutionary history. The most basic anatomical differences among herbivores are the location and morphology of their fermentation chambers, especially in relation to the gastric stomach. Herbivores with a foregut fermentation chamber (e.g., ruminants, macropod marsupials; Alexander, 1993; Godoy-Vitorino et al., 2012; Kohl, Miller, et al., 2014) often have different microbial community composition than species that rely solely on hindgut fermentation (i.e., large perissodactyl colon fermenters, small rodent or lagomorph cecal fermenters), presumably because food that enters the foregut fermentation chamber is in a greater state of digestion (De La Fuente et al., 2019; Godoy-Vitorino et al., 2012; Kohl, Miller, et al., 2014; Neumann et al., 2017). Even within a host's digestive tract, microbial communities may differ. For example, the foregut, cecum, and colon communities play different roles in cellulose degradation and PSM detoxification in reindeer (Rangifer tarandus), bison (Bison bison), and woodrats (Neotoma spp.; Bergmann, 2017; Kohl et al., 2018; Salgado-Flores et al., 2016). Further divergence in microbial community composition can be found within a single fermentation structure, such as the rumen (Russell, 2002). Microbes along the rumen wall attach to the papillae and primarily digest dietary and animal proteins, whereas microbes that attach to the fibrous digesta are primarily cellulolytic (Dehority & Orpin, 1997; Russell, 2002). Therefore, differences in GI anatomy might cause a divergence in microbiomes among herbivore species, regardless of plant diets consumed.
Finally, microbial communities can differ among herbivore species and even individuals based on the original source of microbes that colonized the GI tract as a neonate. Establishment of the GI microbial community depends on the mother's microbiome and behavior, the environment in which the animal is born, and the presence of conspecifics (Ge et al., 2021; Jin et al., 2023; Troyer, 1984). During the first few weeks of life when a neonate ruminant consumes primarily milk, the rumen is not yet functional. Milk, which does not need fermentation, bypasses the rumen into the abomasum through the esophageal groove (Robbins, 1994; Van Soest, 1994). The rumen becomes anaerobic and supports the colonization of microbes as the individual ingests water, plants, and soil and interacts with its environment, its mother, and conspecifics. The introduction of forage into the diet further enhances the microbial community, especially those that digest cell solubles, as ruminants are typically born in spring when the plants have higher concentrations of cell solubles (Leffler et al., 2022). This colonization of microbes is stimulated by substrates which result in the production of short-chain fatty acids (SCFAs) that stimulate papillae growth for increased absorption and ruminal contractions needed for mixing and absorption (Robbins, 1994; Russell, 2002). As the animal grows and its ruminal folds and papillae develop, spatially explicit microbial communities form in the rumen (Distel & Villalba, 2018; Russell, 2002). The GI microbial community directly determines the digestive efficiency of ruminants (Paz et al., 2018; Wang et al., 2019). Moreover, the formation of the microbial community can determine lifelong success of the animal, its offspring (Wang et al., 2019), and even its social group (Kho & Lal, 2018).
Although the importance and general functionality of the GI microbial community has been well established, teasing apart the relative contribution of diet and herbivore phylogeny to the microbiome has remained challenging, especially for free-ranging ungulates. Here, we took advantage of a unique study system in which two species of congeneric deer, mule deer (Odocoileus hemionus) and white-tailed deer (Odocoileus virginianus), had been hand-raised from just a few days after birth to adulthood in identical conditions on a pelleted ration, then transitioned over 2 weeks in the spring onto a natural plant diet as they foraged together in the same natural habitats across summer (i.e., common garden experiment), then transitioned back onto the pelleted ration in late summer. Because the animals were hand-raised, we could control the effects of early life experiences such as maternal and social learning, exposure to microbes, and diets. This design allowed us to explore the relative effects of diet (captive pellets vs. natural plant diets, diet nutritional quality) and species (different evolutionary behavior and GI structure and function between deer species) on GI microbial diversity and taxonomy as the fecal microbiome.
Our main objective was the changes in microbial community composition and diversity as hand-raised deer transitioned from pelleted rations to a natural forage diet. We examined three food-related hypotheses. First, we hypothesized that the microbial community composition and community diversity would be similar at the beginning and end of the experiment when deer were consuming a low-fiber, high-protein/high-energy pelleted ration (Figure 1a–c). We also hypothesized microbial diversity to be highest during the period in which the deer were consuming a wide variety of natural forages (conifers, ferns, graminoids, forbs, shrubs) that ranged greatly in nutritional and chemical composition, and lowest when consuming the pelleted ration (Figure 1a–c; Beck & Gregorini, 2020). Finally, we hypothesized the relative abundance of prominent bacterial families in the GI would change as the deer transitioned from pellets to natural forages and back again based on the transition from quickly fermentable high-protein, starchy pellets to high cellulose, lignin, and PSMs that ferment more slowly in natural plant diets (Figure 1a–c).
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We also examined three alternative species-related hypotheses. The Same Diet-Same Microbes Hypothesis is based on the shared experiences of mule and white-tailed deer. These two species share physical (e.g., both ruminants, similar body size) and ecological attributes (e.g., habitat generalists, similar home range sizes), and co-occur in a geographic area generally following the Rocky Mountains (Geist, 1998; Staudenmaier, 2021). The study animals shared similar early life experiences because they were brought to the captive facility at approximately the same age and were raised on the same diet regime (Parker & Wong, 1987). Therefore, we expected their microbiome to be similar when consuming the same pelleted ration and when foraging within the same 0.5-ha sites within a native forest (Figure 1a). However, the Different Diets-Different Microbes Hypothesis is supported by modest variation in the plant composition, diversity, and nutritional quality of the diets between the deer species even when foraging in the same forest stands (Berry et al., 2019, Table 1). Therefore, we expected that white-tailed deer may have higher microbial diversity corresponding to greater dietary diversity and mule deer might have a greater proportion of taxa that function to degrade tannins and lignin because of their observed preference for deciduous shrubs (Figure 1b). Finally, the Different Species-Different Microbes Hypothesis is based on evidence suggesting that mule deer and white-tailed deer have subtle differences in GI anatomy that could affect microbial communities regardless of diet (Kuzyk & Hudson, 2008; Zimmerman et al., 2006). They also have modest differences in their requirements for digestible energy (DE, kilojoules per gram) and digestible protein (DP, grams of protein per 100 grams of forage), cost of detoxifying PSMs, and ability to digest fiber (Staudenmaier et al., 2022). Therefore, we expected that these deer species would have different microbial communities based on their evolutionary history (Figure 1c).
TABLE 1 Fecal samples collected from hand-raised mule deer (MD;
| Diet phase | Dates on diet | Fecal sample collection dates | No. animals | No. fecal samples |
| Start Diet | 22 May | 22 May | 10 (6 MD, 4 WTD) | 10 |
| Pre-Transition Diet | 01–05 June | 02 or 03 June | 8 (4 MD, 4 WTD) | 8 |
| Natural Diet | 15 June–05 August | 17 June, 23 June, 06 July, 19 July, 02 August | 11 (5MD, 6 WTD) | 41 |
| Post-Transition Diet | 05–06 August | 05 or 06 August | 8 (4 MD, 4 WTD) | 15 |
| End Diet | 12–17 August | 12 or 17 August | 9 (5 MD, 4 WTD) | 16 |
METHODS
Field sampling
To compare the GI microbiome among diets and deer species, we used a common garden experimental design where individuals within two congeneric deer species fed together in five dietary phases as they moved among enclosures across summer. These phases included the Start Diet, Pre-Transition Diet, Natural Diet, Post-Transition Diet, and End Diet, and varied in the amount of pelleted ration and natural forages available. Twelve deer fawns—six mule deer and six white-tailed deer—were bottle-raised under identical environmental conditions at the Wild Ungulate Facility (WUF) located at Washington State University in Pullman, WA, to ensure similar exposure to microbes, diets, and learning experiences. Fawns were acquired from wildlife rehabilitators in 2014 and 2015 in eastern Washington when they were less than 3 days old, ensuring they received colostrum from their mother but were also young enough to bond with technicians at the facility. Fawns were bottle-fed with deer milk replacer (Fox Valley DayOne 30/40, Animal Nutrition, Lake Zurich, IL, USA) following established feeding schedules that mimic natural feeding and growth patterns of dam-raised fawns (Parker & Wong, 1987). Fawns were also offered the same pelleted ration, pasture grass, alfalfa, and soil within the first weeks of life to help stimulate growth and development of the rumen and GI tract. Fawns were then trained to load into a stock trailer so they could be safely transported to the field sites for the common garden experiments when they were older. When they were 4 months old, all deer were continuously fed a maintenance diet composed of a completely balanced grain-alfalfa herbivore pellet, alfalfa hay, and a small amount of pasture grass and supplemental browse.
Our common garden experiment began on 20 May 2016 when the hand-raised deer were 1–2 years old and consuming their pelleted ration at WUF (i.e., Start Diet, 80% pelleted ration). Between 25 May and 2 June, deer were provided with increasing amounts of supplemental browse, including serviceberry (Amelanchier alnifolia), ocean spray (Holodiscus discolor), clover (Trifolium spp.), and fireweed (Chamerion angustifolium), and decreasing amounts of pellets. On 2 June 2016, the animals were transported to a 0.5-ha enclosure within the ponderosa pine (Pinus ponderosa) and Douglas fir (Pseudotsuga menzeseii) forested study area located in the Colville National Forest in northeastern Washington (Table 1, Berry et al., 2019). During the Pre-Transition Diet phase, they received decreasing amounts of their pelleted ration for at least 7 days while they were feeding on plants within the forest understory. Next, from 7 June through 2 August, animals were moved together approximately every 5 days between five temporary enclosures, hereafter called Natural Diets, where they were co-housed and foraged only on natural browse with no access to pelleted ration (Figure 2). On 5 August 2016, the deer were transported back to WUF where they received the Post-Transition Diet comprised of ad libitum pelleted ration and supplemental natural forages for 7 days (Table 1). By 12 August, animals were in the End Diet phase where they were offered only their pelleted ration, alfalfa hay, and pasture grass for 6 days.
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During the Natural Diet phase, the composition, biomass, and nutritional quality of forage and diet composition, diet quality, harvest rate, and daily foraging time were measured for each deer each day (for more details, see Berry et al., 2019). While deer were foraging in the experimental enclosures during the Natural Diet phase, trained observers would follow an individual deer, recording the number of bites of each plant species and visually estimating the size of each bite. At the end of each day, bite mass (in grams) was estimated by collecting 10 representative bites and drying them at 100°C for 24 h when back in the lab. Observers then collected simulated diets based on the composition and bite size of plants they observed deer consuming each day for each deer. These were placed on ice in a cooler until they could be moved to a −20°C freezer.
Fecal collection and microbiome analysis
We used the fecal microbiome as a surrogate for the microbial communities in the deer's GI tracts because it is noninvasive, can be easily collected in the field during feeding trials, and the animal, location, diet, and time of defecation are known (Combrink et al., 2023). To sample fecal microbiome, deer were followed during each diet phase until they defecated and collected fresh fecal pellets from each deer in each pen with a sterile glove. Fresh fecal samples were placed in a sterile plastic bag and frozen along with the plant samples. In total, 90 samples were collected and analyzed across diet phases (Table 1). We analyzed each fecal sample for microbial community composition using 16S rRNA gene amplicon sequencing targeting the V4 region to analyze alpha and beta diversity. This sequencing targets the 16S rRNA gene, a highly conserved region of the transcriptional machinery found in all DNA-based life forms, making it an ideal target for identifying mixed microbial communities in environmental samples (Combrink et al., 2023).
DNA was extracted and library preparation was done by the Knight Lab in the Center for Microbiome Innovation at the University of California San Diego (UCSD). The UCSD Institute for Genomic Medicine Facility sequenced the samples using established methods described in Caporaso et al. (2018). DNA was extracted using the Qiagen MagAttract PowerSoil DNA King Fisher kit following manufacturer protocols. Samples were amplified in triplicate with 25-μL polymerase chain reaction cycles. Pooled samples were run on an agarose gel to check for quality. Amplicons were quantified using a Quant-iT PicoGreen dsDNA Assay Kit following manufacturer instructions and cleaned using MoBio UltraClean PCR Clean-Up Kit following manufacturer instructions (Caporaso et al., 2018). The laboratory facility included eight extraction blanks as negative controls to ensure there was no contamination of samples. 16S rRNA gene libraries were sequenced on one lane of an Illumina MiSeq using 2 × 150 bp paired-end sequencing. Sequence data were stored under study number 124030 on the Qiita platform (Gonzalez et al., 2018).
Following extraction, samples were processed using QIIME2 v 2021.4 (Bolyen et al., 2019). We demultiplexed FASTQ files, yielding 94 samples (including the eight blanks) and a total of 433,125 reads. We then denoised and dereplicated samples using the Divisive Amplicon Denoising Algorithm (DADA2) module within QIIME2 and removed sequences that had less than 300 bp. Unique amplicon sequence variants (ASVs) were generated and assigned a taxonomy using the SILVA 138 ribosomal RNA database (Quast et al., 2012). ASVs that could not be assigned to phylum were removed. Downstream analysis was conducted in R v 4.0.735. We imported QIIME2 readable files (*.qza) into R using the package “qiime2R” v0.99 (Bisanz, 2024). We used the “phyloseq” package (McMurdie & Holmes, 2013) to remove nonbacterial, mitochondrial, and chloroplast ASVs. We included a decontamination step, thus removing the negative controls.
Diet composition and nutritional analysis
To examine the effect of diet composition, diversity, and nutritional content on the deer's microbiome, we first assumed that the deer were eating ≥90% pellets in the Start and End Diet phases and ≥50% pellets in the Pre and Post-Transition Diet phases. We calculated several dietary metrics from natural forages and pellets they consumed (Berry et al., 2019). These included diet composition by dry mass for each deer at the level of plant species and functional group (i.e., forbs, graminoids, deciduous shrubs, evergreen shrubs), plant species richness, and Shannon-Weiner Diversity Index (hereafter Shannon Index). For nutritional analysis, simulated diets were freeze-dried and ground to be able to pass through a 1-mm screen. We quantified the neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), and acid insoluble ash of each simulated diet using sequential detergent analysis, including sodium sulfite and alpha-amylase (Berry et al., 2019; Goering & Van Soest, 1970; Table 2). Nitrogen content (in percentage) was determined using a Carbon-Nitrogen TruSpec Analyzer (Leco, St. Joseph, Michigan, USA), and crude protein content (in percentage) by multiplying the nitrogen content by 6.25 (Robbins, 1994). We also determined the gross energy content of each simulated diet and forage species using a bomb calorimeter (C5000, IKA Works, Inc., Wilmington, NC, USA). We calculated dietary Digestible Energy (DE, kilojoules per gram) for all samples from gross energy content × dry matter digestibility (DMD; Table 2). Additionally, the protein-binding activity of tannins found in the samples was measured using tannin-specific protein-binding capacity (in milligrams of bovine serum albumin precipitate per milligram of forage, hereafter tannin content) using the Martin and Martin assay (Martin & Martin, 1982; Table 2). We estimated DMD (in percentage) and DP (grams of protein per 100 grams of forage) from summative equations developed and tested in mule, black-tailed, and white-tailed deer (Hanley et al., 1992; Parker et al., 1999; Robbins, Hanley, et al., 1987; Robbins, Mole, et al., 1987).
TABLE 2 Average plant species richness and diversity of natural plant diets and nutritional quality of pelleted ration and natural plant diets consumed by hand-raised mule deer (
| Nutritional components | Pellets | Mule deer on natural diet | White-tailed deer on natural diet |
| Dietary richness (no. species) | Not applicable | 25.2 (2.1) | 30.4 (2.5) |
| Dietary Shannon Index | Not applicable | 2.04 (0.09) | 2.25 (0.10) |
| Neutral detergent fiber (%) | 35.59 (0) | 33.89 (4.89) | 32.35 (1.93) |
| Acid detergent lignin (%) | 5.97 (0) | 6.62 (0.71) | 6.58 (0.74) |
| Digestible energy (kJ/g) | 11.39 (0) | 11.58 (0.10) | 11.76 (0.09) |
| Digestible protein (g/100 g) | 12.96 (0) | 5.52 (0.24) | 5.98 (0.18) |
| Diet tannin content (mg BSA precipitate/mg forage) | 0 | 0.053 (0.005) | 0.041 (0.004) |
Statistical analysis
To determine how differences in diet and phylogeny influenced the deer's GI microbial community, we first calculated the relative abundance of unique microbial sequences. We then calculated microbial alpha diversity, which quantifies diversity within individual samples and can be compared across sample groups, using the “phyloseq” package (McMurdie & Holmes, 2013). We calculated microbial richness as the number of microbial sequences per fecal sample, and microbial Shannon Index as the number of unique reads and their proportional abundance in the sequence. We then examined the effects of diet phase, deer species, and diet phase × species interactions on relative abundance of the five most abundant microbial taxa, species richness, and Shannon Index of the deer's GI microbiome using general linear models (GLMs). For models with significant interactions, we ran the model separately by diet phase and then by deer species, followed by a Tukey mean separation test. In separate models, we examined how species richness and Shannon Index responded to two additional characteristics of the deer's diets, plant functional groups (i.e., forbs, graminoids, deciduous shrubs, and evergreen shrubs) and dietary nutritional components (i.e., NDF, ADL, DE, DP, and tannin content, Table 2) and their interaction with deer species.
To quantify the beta diversity of the fecal microbiome, which quantifies the differences in the overall taxonomic composition between two samples, we first filtered and clustered sequences into ASVs that were then used to reconstruct phylogenies (Combrink et al., 2023). We transformed the read counts of our non-rarefied data to calculate relative abundance by dividing the number of reads for each taxon within a sample by the total number of reads for that sample. We modeled the relative abundances of the identified ASVs using similar techniques to how we analyzed alpha diversity. We performed a permutational multivariate analysis of variance (PERMANOVA) to compare Bray–Curtis distances among diet phase, deer species, diet phase × species interactions, using the “adonis2” function in the vegan package v2.6-4 (Oksanen et al., 2025). To visualize differences among independent variables, we used a Principal Coordinates Analysis (PCoA) with the Bray–Curtis distance that considers presence/absence of different ASVs and their abundance and an Analysis of Similarity (ANOSIM). We compared all statistical results to an alpha value of 0.05 to determine significance.
RESULTS
Because some deer were removed early from the feeding trial, we removed four of the 90 samples collected, analyzing 86 fresh fecal samples from eight deer (Table 1). Alpha diversity metrics of the fecal microbial community of the two deer species were influenced by diet phase, plant functional groups in the diet, nutritional components of the diet, and the deer species, as shown in the GLMs. Microbial richness responded to deer species (t = 7.99, p = 0.006), diet phase (t = 3.05, p = 0.022), and the interaction between the two (t = 5.57, p = 0.0005; Figure 3a). The microbial richness of mule deer was higher in the Pre-Transition Diet (mean = 350.25, SE = 36.55) than in the End Diet (mean = 206.86, SE = 66.75, p = 0.006) and the Natural Diet (mean = 212.95, SE = 66.99, p = 0.003). Conversely, the microbial richness of the white-tailed deer was lower in the Pre-Transition Diet (mean = 138.0, SE = 75.61) than in the End Diet (mean = 259.6, SE = 68.0, p = 0.039) and lower in the Natural Diet (mean = 172.90, SE = 62.8) than the End Diet (p = 0.03; Figure 3). On the other hand, the Shannon Index responded to the interaction between deer species and diet phase (t = 2.98, p = 0.008) but was not influenced by the main effects of diet phase (t = 1.72, p = 0.15) or deer species (t = 2.98, p = 0.09; Figure 3b). Tukey tests did not indicate differences among diet phases for white-tailed deer but did for mule deer. The Shannon index was higher in the Pre-Transition Diet (mean = 5.02, SE = 0.11) than the End Diet (mean = 4.27, SE = 0.55, p = 0.017) and the Natural Diet (mean = 4.42, SE = 0.34, p = 0.036; Figure 3b).
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The diversity of the diets consumed by the deer also influenced the diversity and richness of their fecal microbial community (Different Diets-Different Microbes Hypothesis, Figure 1c). Fecal microbial richness was influenced by the interaction between deer species and dietary richness (t = −3.39, p = 0.001). Specifically, the fecal microbial richness of white-tailed deer responded to dietary richness (t = −3.32, p = 0.002; Table 2). We also found that the Shannon index of fecal microbial communities was influenced by the deer species (t = 2.81, p = 0.006) and the interaction between species and dietary Shannon Index (t = −3.44, p = 0.0009). When the GLM was run separately by deer species, we found no effect of dietary Shannon Index on the fecal microbial Shannon Index for mule deer (p = 0.21), whereas the fecal microbial Shannon Index of white-tailed deer decreased with increasing dietary Shannon Index (t = −3.55, p = 0.0009).
Fecal microbial diversity was also influenced by the relative contribution of pelleted rations in the Start, End, and Transition Diets, and the different plant functional groups in the Natural Diet phase. Increasing dietary contribution of pelleted ration increased fecal microbial richness (t = 3.9, p = 0.0003) and Shannon Index (t = 3.13, p = 0.003) for white-tailed deer but did not affect the fecal microbiome of mule deer (richness p = 0.78, Shannon Index p = 0.48) on the Start, Transition, and End Diets. The proportion of forbs in the diet decreased fecal microbial richness for white-tailed deer (t = −2.97, p = 0.005) but increased the fecal Shannon Index for mule deer (t = 2.23, p = 0.03). Lastly, the proportion of evergreen shrubs decreased fecal microbial richness (t = −2.91, p = 0.006) and Shannon Index (t = −4.07, p = 0.0002) for white-tailed deer but did not affect mule deer (richness p = 0.87, Shannon Index p = 0.95). Other plant functional groups (i.e., graminoids and deciduous shrubs) did not influence fecal microbial Shannon Index or richness in either deer species (all p values > 0.05).
Nutritional composition of the diet influenced the microbial communities of the two deer species to different degrees. NDF did not affect mule deer fecal microbiome but increased fecal microbial richness for white-tailed deer (t = 2.34, p = 0.02), whereas ADL did not affect white-tailed deer but increased fecal microbial richness for mule deer (t = 2.48, p = 0.018). Digestible protein did not affect mule deer (richness p = 0.75, Shannon Index p = 0.9), but increased fecal microbial richness (t = 3.5, p = 0.001) and Shannon Index (t = 2.38, p = 0.02) in white-tailed deer. Lastly, tannins decreased the fecal richness (t = −2.52, p = 0.02) and Shannon Index (t = −2.23, p = 0.02) for white-tailed deer but had no effect on the mule deer's fecal microbiome.
Like alpha diversity, beta diversity of the fecal microbiome responded to both deer species and diet. Diet phase (F = 4.39, df = 4, p = 0.001), deer species (F = 2.44, df = 1, p = 0.002), and the interaction between deer species and diet phase (F = 1.37, df = 4, p = 0.003) influenced beta diversity of the fecal microbial community (Figure 4). Beta diversity of white-tailed deer did not respond to diet phase (all pairwise p-values > 0.08), but microbial communities diverged among diet phases for mule deer. Contrary to our first food hypothesis (Same Diet–Same Microbes), the fecal beta diversity of deer on the End Diet differed from the Start Diet (p = 0.015), from the Pre-Transition Diet (p = 0.004), and from the Post-Transition Diet (p = 0.002). Fecal beta diversity of deer on the Natural Diet also differed from the Pre-Transition (p = 0.025) and Post-Transition (p = 0.013) Diets. The first two axes of the PCoA using the Bray–Curtis distance for ordinations accounted for 20% of the variation in fecal beta diversity for both mule deer and white-tailed deer (Figure 4). We found distinct clustering based on diet phase, especially for the mule deer (Figure 4). The ANOSIM showed that each group was significantly different from each other (p < 0.05).
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Overall, we only detected modest differences in the composition of fecal microbial taxa between white-tailed deer and mule deer and across diet phases. Of the top 10 phyla identified as the individuals transitioned from a pelleted captive diet to a natural browse diet and back, the top three phyla were shared between the two deer species, Firmicutes, Bacteroidota, and Verrucomicrobiota. The Firmicutes phyla contributed approximately 70% of the relative abundance across deer species and diet phase. Bacteroidota then contributed nearly the rest, approximately 15% (Figure 5). Furthermore, at the family level, white-tailed deer and mule deer shared the top three microbial families identified as measured by their relative abundances (Figure 5). However, the order of relative abundance of the next 17 of the top 20 microbial families differed to some degree. One family (Erysipelatoclostridaceae) was only found in the top 20 taxa for white-tailed deer, and three families (Spirochaetaceae, Clostridia_vadinBB60_group, and Butyricicoccaceae) were only found in the top 20 taxa for mule deer (Figure 6). In general, the top three ranked microbial families (Oscillospiraceae, Lachnospiraceae, and Christensenellaceae) were more abundant when deer were in the natural pens than when deer consumed portions of a pelleted ration (i.e., Start, Pre-Transition, Post-Transition, and End Diets). The next two families (Bacteroidaceae, Prevotellaceae) were lower on the Natural Diet than one or more of the other diet phases (all p values <0.05; Figure 6, Table 3). Relative abundance only differed between mule and white-tailed deer for Oscillospiraceae, and only on the Post-Transition and End Diets, where mule deer had a greater relative abundance of each microbial family.
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TABLE 3 A comparison of the relative abundance of five of the top 20 microbial families identified in the feces of mule deer (
| Family | Diet phase | White-tailed deer | Mule deer |
| Oscillospiraceae | Start | 19.87 (2.67) B | 24.22 (2.67) B |
| Pre-Transition | 26.49 (5.67) AB | 23.55 (1.77) B | |
| Natural | 35.18 (2.32) A | 40.49 (2.15) A | |
| Post-Transition | 20.92 (3.41) B* | 35.48 (2.57) AB | |
| End | 20.65 (2.94) B* | 33.05 (3.81) AB | |
| Lachnospiraceae | Start | 8.33 (0.76) A | 9.14 (0.71) BC |
| Pre-Transition | 18.26 (5.01) A | 10.19 (2.26) BC | |
| Natural | 17.46 (0.98) A | 15.66 (0.62) A | |
| Post-Transition | 10.43 (2.08) A | 13.43 (1.32) AB | |
| End | 9.85 (0.76) A | 8.83 (0.77) C | |
| Christensenellaceae | Start | 8.89 (1.09) B | 6.22 (1.06) A |
| Pre-Transition | 4.16 (1.99) B | 7.59 (1.71) A | |
| Natural | 9.13 (0.80) AB | 8.92 (0.55) A | |
| Post–transition | 11.95 (1.53) A | 9.35 (1.19) A | |
| End | 5.29 (0.27) B | 7.30 (0.92) A | |
| Bacteroidaceae | Start | 7.52 (1.0) AB | 9.82 (1.19) A |
| Pre-Transition | 11.40 (3.85) A | 8.83 (1.92) A | |
| Natural | 2.71 (0.41) C | 2.52 (0.25) B | |
| Post-Transition | 4.60 (0.92) BC | 5.47 (0.93) AB | |
| End | 7.81 (1.16) B | 6.95 (1.58) A | |
| Prevotellaceae | Start | 7.81 (1.22) AB | 12.08 (1.64) A |
| Pre-Transition | 6.49 (3.65) AB | 12.26 (1.75) AB | |
| Natural | 3.02 (0.64) B | 3.66 (0.68) C | |
| Post-Transition | 4.04 (0.84) AB | 3.92 (0.66) BC | |
| End | 8.00 (1.05) A | 6.10 (1.79) BC |
DISCUSSION
Our experiments demonstrated that the GI microbial community of congeneric mule and white-tailed deer responded to both diet characteristics and differences in species. As expected, alpha and beta microbial diversity and microbial composition differed when deer consumed the pelleted ration versus natural browse and varied with other dietary characteristics including plant diversity, the composition of certain plant functional groups (e.g., evergreen shrubs and forbs), and nutritional constituents (e.g., NDF, ADL, DP, and tannin concentration). The microbial communities of the two deer species responded differently to dietary changes, but most strongly when selecting modestly different natural plant diets; therefore, our results conform best to the Different Diets-Different Microbes hypothesis (Figure 1b). However, we could not rule out the Different Species-Different Microbes hypothesis (Figure 1c) because of potential interactions between species and diet on the microbiome that were mediated by innate behavior.
Although the GI microbiome responded to the diets the deer consumed, our results varied to some degree from our food-related hypotheses (Figure 1a–c). As expected, fecal microbial Shannon Index, alpha richness, and beta diversity were similar on the Start and End Diets for both species, as was the relative proportion of the top five microbial families. However, we did not observe expected increases followed by decreases in microbial diversity as the deer moved from rationed pellet ration on the Start Diet to more diverse plants on the Natural Diet and back to pellets on the End Diet. Instead, we found the greatest difference in the fecal Shannon Index and richness in the Pre-Transition Diet, where mule deer had their highest microbial diversity and white-tailed deer their lowest, after which microbial diversity in mule deer decreased and remained low, while white-tailed deer increased and remained high. Fecal beta diversity did not differ among diets for white-tailed deer, but the Pre-Transition Diet differed from the Start and End Diets for mule deer, and the Natural Diet showed a distinct PCoA cluster for mule deer and a less distinct clustering for white-tailed deer. Rapid transitions to more fibrous diets can cause the accumulation of unfermented fiber in the omasum, especially in small browsing ruminants. This can be lethal when large particles of slowly fermenting forages cannot pass through the digestive system (Baker & Hobbs, 1985; Clauss & Dierenfeld, 2008). On the other hand, if ruminants move abruptly to a concentrated pelleted diet high in digestible and fermentable substrates such as starch and sugars, they can suffer from ruminal acidosis, which can also be lethal (Clauss & Dierenfeld, 2008).
Although our Different Diets-Different Microbes Hypothesis was based on the assumption that a greater variety of food items on Natural Diet phases would result in a more diverse microbiome (Beck & Gregorini, 2020), microbial diversity may have instead been responding to the greater digestible energy and protein in the pelleted diet compared to natural forage (Kand et al., 2018). For example, previous studies on domestic lambs showed that diets with higher nutritional quality, especially digestible protein and energy (Cui et al., 2019), have greater microbial species richness, which will then increase digestive efficiency for the animal (Frame et al., 2020; Zhang, 2022). The role of nutrients in microbial diversity is also supported by our observations that microbial diversity increased in white-tailed deer when they ate more nutritious foods, including pellets and plants with higher DP and lower PSMs (i.e. forbs, Table 2). Increased dietary protein enhances microbial diversity and increases the relative abundances of the microbes involved in amino acid metabolism (Cui et al., 2019; Zhao et al., 2018). The inverse relationship between diet diversity and microbial diversity in white-tailed deer also might reflect this response (Kohl, Weiss, et al., 2014). Because of the availability of highly digestible forage during summer, fast-growing microbes can take advantage of cell solubles, starches, and proteins (Jung & Deetz 1993; Kartzinel et al., 2015; Solden et al., 2018). The observation of lower microbial diversity as dietary diversity increased may be explained by shifts of microbial communities to higher proportions of these fast-growing microbes. However, previous studies quantifying how specific diet variables influence microbial community diversity are based on controlled studies that did not include the effects of selective foraging (Bergmann, 2017; Ishaq et al., 2015; Li et al., 2015; Sun et al., 2020). If greater microbial diversity improves the resilience of the microbial community from deleterious dietary changes through high functional redundancy (Pannoni et al., 2022; Ribas et al., 2023; Söllinger et al., 2018; Weimer, 2015), then management actions that promote high-quality forages for wild ruminants may enhance host health beyond simple energetics.
Although microbial diversity did not follow predicted patterns, the microbial communities of deer feeding on Natural Diets either had the highest or lowest relative abundance of each of the five most abundant families of bacteria, indicating that the microbial composition changed from pelleted ration to natural plant diets and back again. The relative abundance of Oscillospriaceae, Lachnospiraceae, and Christenellaceae—all within the Firmicutes phylum of bacteria—was highest when deer were consuming Natural Diets. These findings are consistent with Firmicutes having the broadest capacity for polysaccharide degradation and being more abundant in livestock species (e.g., domestic cattle) that were consuming forage-based diets compared to standard grain and hay diets (Clemmons et al., 2019; Söllinger et al., 2018). Oscillospiraceae have been positively correlated with the production of propionate and butyrate, which are both associated with stimulating mitosis and growth and were found attached to the rumen epithelium, which may allow for better absorption of short-chain fatty acids, such as butyrate (Chen et al., 2024; Kiela & Ghishan, 2016). In addition, Lachnospiraceae and Christenellaceae have both been found in animals with a high-fiber diet or a diet with a high percentage of shrub species, allowing for more efficient degradation of cellulose compared to animals with low-fiber diets (Deusch et al., 2017; Söllinger et al., 2018; Yang et al., 2020). In contrast to the Firmicutes phylum, the Bacteroidetes phyla, including Prevotellaceae and Bacteriodaceae, were lowest when deer were consuming Natural Diets. The lower abundance of Bacteroidetes and higher abundance of Firmicutes on the Natural Diet is supported by Sun et al. (2020) who found that wild populations of cervids have a higher abundance of Firmicutes than did captive populations. Captive populations that consumed grain-based diets with lower fiber, higher fat, and simple carbohydrates had more Bacteroidota (Sun et al., 2020) than wild populations. However, the lower abundance of Prevotellaceae on Natural Diets is surprising because it has been associated with tannin degradation in a Sika deer (Cervus nippon; Li et al., 2015). In our study, condensed tannins were absent from the pelleted ration but were relatively high in the deer's selected diets in the Natural Diet phase (Table 2), which included mostly forbs and shrubs (Li et al., 2015; Staudenmaier et al., 2022).
Because we used fecal samples as a surrogate for the entire GI tract, our results might miss key families for fermentation that were digested in the abomasum and small intestine before they reached the colon and formed fecal samples (Hagey et al., 2022; Mott et al., 2022). For this reason, and the sheer number of bacteria taxa in GI microbial communities (i.e., >200 species; Matthews et al., 2018), a deeper understanding of the compositional changes in GI microbes in response to diet requires (1) sampling the entire GI tract, including the liquid, solid, and epithelial communities; (2) targeted whole genome metagenomic studies investigating bacterial function; and (3) community analyses of interactions among GI and other microbes (i.e., fungi, protozoa).
We also posed three alternative hypotheses about how the microbial community of two related deer species foraging together in the same environment would differ depending on the degree of dietary and phylogenetic (e.g., innate genetically controlled behavior, anatomy, and physiology due to shared common ancestry) differences. We predicted that the primary differences in microbial communities would reflect dietary differences rather than phylogenetic differences between the species; therefore, we expected dietary differences, even on the Natural Diets, to have a minimal effect on the microbiome (Same Diet—Same Microbes hypothesis). On the Natural Diet, about half of the diet of both species was composed of deciduous shrubs and another quarter of the diet was forbs (Berry et al., 2019). Evergreen shrubs, graminoids, conifers, ferns, lichens, and mushrooms were also consumed, but each of these plant functional groups accounted for <10% of their overall diets (Berry et al., 2019). Therefore, our results conformed more to our second alternative hypothesis (Different Diet-Different Microbes). Since the diversity and composition of the microbial community did not differ between the deer species when they were on the Start and End Diets, in which deer consumed the pelleted ration on which they had been raised, differences in the microbial community detected in the Pre-Transition, Natural Diet, and/or Post-Transition were likely driven by subtle differences in diet choices when deer were able to select specific plant species and plant parts. Even though all of the deer lived and foraged together within the same plant community in 0.5-ha pens for at least 2 days each, the composition of plant species in their diets diverged with 38% of plant species differing between deer species (Berry et al., 2019). Mule deer were more likely to eat shrubs that were higher in tannins and lower in DMD, and white-tailed deer consumed a more diverse diet containing 25% more plant species higher in digestible protein (Berry et al., 2019). These dietary differences could result in different substrates available for the microbial communities, resulting in differences in community structure.
The influence of unique phylogenetic characteristics of the deer species versus subtle dietary differences on the microbial community is likely confounded. Diet selection is a behavior that can be acquired through social learning from the mother or social group (e.g., nurture; Provenza & Balph, 1987; Thornton & Clutton-Brock, 2011) and is a genetically controlled behavior that can be retained despite early life experience (e.g., nature; Spalinger et al., 1997). We controlled learned behavior within our common garden experiment, so differences in diet choices observed in our study likely reflect innate behavior that we could not control for in our study design. Our results suggest that even if phylogenetic differences between the deer species do not directly control characteristics of the digestion system that directly affect microbes, phylogeny may still influence the microbial community indirectly through diet choice. For example, when compared to white-tailed deer, mule deer had higher fiber, energy, and DMD, produced glucuronic acid (a byproduct of PSM detoxification) at a slower rate when consuming the monoterpene -pinene, and required 54% less digestible protein and 21% less digestible energy intake per day to maintain body mass and nitrogen balance (Staudenmaier et al., 2022). Our observation that mule deer had a relatively greater abundance of microbial groups, such as RF39 that are associated with increased accumulation or decreased utilization of body protein stores through increased digestive efficiency (Eddington et al., 2021), might play some role in the lower nitrogen balance and higher NDF digestibility in mule deer than white-tailed deer (Staudenmaier et al., 2022). In addition, mule deer have a larger rumen than white-tailed deer and a higher papillae density than white-tailed deer, which may encourage the growth of microbes that attach to the epithelial layers of the rumen (Kuzyk & Hudson, 2008; Zimmerman et al., 2006). Diets chosen by herbivores may reflect phylogenetically distinct capacities and limitations of their digestive systems (Codron & Clauss, 2010; Karasov et al., 2011), therefore, genetically controlled behavior should maximize efficiency relative to those digestive abilities and nutritional requirements. When deer consumed a completely balanced, high-quality diet on the Start and End Diet phases, the influence of phylogeny may have been less evident than when their digestive systems were challenged by more difficult forages when they were on the Natural Diets.
Changes in microbial diversity when transitioning from a pelleted and a natural plant diet emphasize the potential microbial disruption of drastic dietary changes in ruminants, such as in wildlife rehabilitation, translocations, rapid revegetation after major disturbances, emergency winter feeding (Baker & Hobbs, 1985), or providing pelleted diets to control herbivore damage to vulnerable native vegetation or crops (Milner et al., 2014; Sullivan & Sullivan, 2008). A better understanding of how these two deer species, and other co-occurring ruminant species such as moose (Alces alces) and caribou (Rangifer tarandus; Christopherson et al., 2019; Jung et al., 2015) take advantage of the same available resources is crucial for predicting the consequences of increasing overlap in wildlife distributions, as both the nutritional content of available food (e.g., less digestible invasive grasses; Ferdinands et al., 2005; Dukes et al., 2011) and the chemical defenses of plants (e.g., PSM; Verma & Shukla, 2015) are expected to increase with climate change and human disturbances (Hiltunen et al., 2022).
CONFLICT OF INTEREST STATEMENT
The authors declare no conflicts of interest.
DATA AVAILABILITY STATEMENT
Data (Anderson, 2025) are available from Zenodo: .
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