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. 2024 Aug 7;12(1):coae052. doi: 10.1093/conphys/coae052

The impact of management on the fecal microbiome of endangered greater sage-grouse (Centrocercus urophasianus) in a zoo-based conservation program

Emma Vaasjo 1,2,✉, Mason R Stothart 3, Sandra R Black 4, Jocelyn Poissant 5, Douglas P Whiteside 6,7
Editor: Jennifer Provencher
PMCID: PMC11304599  PMID: 39113731

This study characterizes the bacterial fecal microbiome of a managed flock of endangered greater sage-grouse, an avian dietary specialist. The effects of sex, age, antibiotic-use, and incubation strategies were considered. Incubation and hand-rearing was shown to have a significant impact on delaying the development of the microbiome compared to hen-rearing.

Keywords: Centrocercus urophasianus, conservation, conservation breeding, gastrointestinal, greater sage-grouse, managed care, microbiome

Abstract

Greater sage-grouse (Centrocercus urophasianus) are a critically endangered species in Canada with fewer than 140 individuals remaining on native habitats in southern Alberta and Saskatchewan. In 2014, the Wilder Institute/Calgary Zoo initiated North America’s only zoo-based conservation breeding program for this species to bolster declining wild populations through conservation reintroductions. Within the managed population of sage-grouse, morbidity and mortality have primarily been associated with intestinal bacterial infections. As a preliminary study to assess the gastrointestinal health of this species in managed care, the fecal bacterial microbiome of adult and juvenile captive sage-grouse was characterized with 16S rRNA sequencing. The composition of the microbiome at the phylum level in greater sage-grouse is consistent with previous studies of the avian microbiome, with Bacillota as the most abundant phyla, and Actinomycetota, Bacteroidota and Pseudomonadota also being highly abundant. Antibiotic use and sex did not have a significant impact on the diversity or composition of the microbiome, but the management of juvenile sage-grouse did influence the development of the microbiome. Juveniles that were raised outdoors under maternal care developed a microbiome much more similar to adults when compared to chicks that were incubated and hand-raised. The local environment and parental care appear to be important factors influencing the diversity and composition of the gastrointestinal microbiome in this species.

Introduction

The effects of managed care on the gastrointestinal microbiome have been studied in various species, but avian studies are relatively sparse (Sun et al., 2022). Across terrestrial vertebrate taxa, studies describe a decrease in diversity and/or a significant difference in the composition of microbial communities in managed individuals compared to their wild counterparts (Eigeland et al., 2012; Clayton et al., 2016; Delport et al., 2016; Metcalf et al., 2017; Jia et al., 2018; Guo et al., 2019; Hernández-Gómez et al., 2019). In occasional cases, an increase in microbial diversity in managed animals has been observed, while studies on both cutaneous and intestinal microbiota in several species show no difference between wild and managed individuals (Alfano et al., 2015; Flechas et al., 2017; McKenzie et al., 2017). These findings highlight the need for individual studies on species under human care, as each species appears to differ in their ability to develop or conserve their wild microbiome in a managed environment.

Antibiotics are commonly used to treat bacterial infections causing disease in managed populations; though potentially lifesaving, their use can lead to dysbiosis, other disease states, or the extirpation of beneficial microbiota (Petersen and Round, 2014; West et al., 2019). While there are many studies on disruptions to the microbiome after antibiotic use in humans, similar studies on threatened and endangered species are limited (Vlčková et al., 2016; Dahlhausen et al., 2018; West et al., 2019). The consequences of variation in microbial communities on host health may not be apparent until post-release if microbial species critical to survival in the wild have been lost in captivity (West et al., 2019).

The greater sage-grouse (Centrocercus urophasianus, hereafter sage-grouse), the largest grouse species in North America and a flagship species for sagebrush habitat, is critically endangered in Canada. Listed on the Species at Risk Act since 1998, sage-grouse have been lost from 90% of their historic Canadian range, with small, isolated populations remaining in southern Alberta and Saskatchewan. Extant populations have suffered significant population declines in the last three decades (Connelly et al., 2000; Aldridge and Brigham, 2003; Schroeder et al., 2004). In winter, sage-grouse are specialists, consuming a largely monospecific diet of chemically toxin-rich sagebrush. Wild sage-grouse have enlarged ceca with important microbial populations that aid in the digestion and detoxification of sage (Wienemann et al., 2011; Kohl et al., 2016).

In 2014, the Wilder Institute/Calgary Zoo initiated North America’s only zoo-based conservation breeding program for this species to bolster declining wild populations through conservation reintroductions. The first releases of young of the year birds to wild habitats occurred in the autumn of 2018 and have continued annually. Unfortunately, survival rates in these juveniles post-release have been extremely low at less than 2% (Black et al., 2019). Within the captive population, the largest proportion of morbidity and mortality (approximately 60–80% per year) have been associated with diseases arising from intestinal bacterial infections, seen especially in the neonatal and juvenile population (Black et al., 2019; Pastor et al., 2021). A lack of diversity and/or richness of a more wild-type microbial community may be negatively affecting the health of sage-grouse under human care and may also impact their ability to survive once released back into native habitats.

The purpose of this research was to characterize the microbiome of sage-grouse under managed care in a zoo-based conservation program and describe community changes of the microbiome in relation to sex, growth, neonatal management strategies and antibiotic use. Findings will aid in the management of sage-grouse under human care and help to determine if altered microbiome diversity possibly contributes to low survival post-release.

Materials and methods

Sample population

The flock at the Wilder Institute/Calgary Zoo was founded by the collection of eggs from wild sage-grouse hens in Montana, Saskatchewan and Alberta in 2014 (Heinrichs et al., 2019). At the time of sample collection, the number of breeding adults was maintained between 50 and 60 individuals, with the goal of hatching 200–250 chicks annually. All birds were offered the same diet, a commercial formulated dry pellet; game-bird starter as chicks (Mazuri) or an in-house developed pheasant breeder pellet as adults (Country Junction Feeds), with the addition of lettuce, chopped greens, cucumber, peas, edamame, mealworms and crickets. Dried sage was also offered in bundles and sage plants were used when available. From 2018 to 2021, a diet transition over a period of 4–5 weeks was used for juveniles leading up to their release to native wild habitats. During this transition, pelleted food was slowly decreased with a corresponding increase in vegetables and legumes and the introduction of fresh sage.

Sample collection

This research was approved by the Calgary Zoo Welfare, Ethics and Research Committee (CZWERC 2020-02) and by the University of Calgary Veterinary Sciences Animal Care Committee (AC20-0037).

The study group consisted of 10 healthy adult females and 10 healthy adult males, with a representative number of adults from each age group (age ranged from 1 to 5 years). Males were chosen randomly, while females housed alone during chick rearing were preferentially selected to decrease disturbance to nesting and brood rearing. If adults died during the course of the study, they were replaced with birds of the same sex and a similar age to maintain a consistent sample size throughout the sampling period.

Once hatched, one cohort of three to five incubated chicks was recruited into the study each week for 4 weeks. Six broods of hen-raised chicks were also selected and followed from hatch. Following mortalities in the incubated group and random selection from the hen-raised group, there were 15 incubated and hand-reared and 15 hen-raised chicks that were followed for the length of the study, with additional samples from other chicks collected opportunistically.

Sample collection commenced in June 2020. At least one fresh sample was collected monthly from adults, apart from September and October 2020 while the flock was undergoing intensive treatment and testing due to an avian malaria outbreak. If animal care staff observed an individual defecate, or the adult was placed in a crate for any reason (weight checks, transport between pens, or veterinary exam), a sample was collected. Occasionally, adults in group housing were administered a food safe non-toxic glitter in superworms or peas and a fecal sample was able to be identified from that specific individual 3–4 hours later.

Weekly samples were collected from both hen-raised (group samples or individual samples while being weighed) and artificially incubated chicks until they were moved into the larger flight pen. Three groups of juveniles were moved to soft release pens on wild native habitat during the fall of 2020 (two in Alberta and one in Saskatchewan) and multiple group samples were collected from these soft release pens at least one week apart.

Information on date, time and the substrate from which the sample was collected, and antibiotic exposure were recorded for each sample. Juveniles were treated as antibiotic-exposed if they had any history of antibiotic administration. As every adult bird within the breeding flock had previously been treated with antibiotics sometime during their time in managed care, antibiotic exposure for each individual sample was defined based on administration within the last 6 months, and the number of days since their last antibiotic administration was also recorded. Juvenile samples were also placed into a binary variable for management type (hen-raised versus incubated and hand-raised) and grouped into age cohorts to facilitate some analyses (see Table S1 for collection data for all samples used for analysis). Samples were immediately placed in a −20°C freezer and later moved to a −80°C freezer until analysis.

DNA extraction, PCR amplification and sequencing

A total of 311 samples (0.25 g each, or entire sample if < 0.25 g) were placed in Powerbead Pro Plates (Qiagen Sciences) with one empty well in each plate (negative control) and shipped overnight on dry ice to Genome Quebec (Montreal, Canada).

Plates were thawed for 1–2 hours, then total DNA was extracted with the standard protocol for DNeasy 96 PowerSoil Pro QIAcube HT kit (Qiagen Sciences) using the QIAcube HT extraction robot. TissueLyserII (Qiagen Sciences) was used for bead beating. One plate was run per day. Final DNA extracts were eluted in 100 μl of TopElute buffer and stored at −20°C until library preparation.

The fourth variable region (V4) of the 16S rRNA gene (252–254 bp) was amplified using 5 μl of DNA extract and primers 515FB and 806RB as outlined by the Earth Microbiome Project (Caporaso et al., 2012; Thompson et al., 2017; Drovetski et al., 2019). Only 35% of samples were successfully amplified based on DNA quantification and gel electrophoresis, likely due to low initial DNA concentrations after extraction (<1 ng/μl, quantified with Quant-iT PicoGreen dsDNA Assay Kit, Life Technologies). Sequencing libraries were prepared by pooling a standardized amount of PCR products from all samples (including those that were not successfully amplified). SparQ PureMag Beads (Quantabio) were used to clean libraries and fragment sizes were verified using a LabChip GX (PerkinElmer) instrument prior to sequencing on an Illumina MiSeq platform (v2 chemistry, 250-bp paired-end reads, 500 cycles) at Genome Quebec.

Sequencing data from MiSeq platform was de-multiplexed and converted to Fastq format using Illumina’s bcl2fastq software. Cutadapt v1.16 was used to remove primers and for initial quality trimming with minimum quality score of 20 (Martin, 2011). Trimmed reads were then processed by using the DADA2 (v 1.22.0) pipeline to clean, deduplicate and merge forward and reverse reads to generate an amplicon sequence variant (ASV) table (Callahan et al., 2016). Taxonomy was assigned to the representative sequences using the RDP classifier (Wang et al., 2007; Lan et al., 2012) and the SILVA v138.1 database (Quast et al., 2013; Yilmaz et al., 2014) using an 80% confidence threshold. Only ASVs identified as bacterial were retained. Phylogenetic distances among ASVs were estimated using a relaxed neighbour-joining method via mothur implementation of clearcut (Sheneman et al., 2006; Kozich et al., 2013). An unexpectantly large number of paired reads were obtained from two out of four negative controls (10 135 and 14 345), which may have been caused by spillover from neighbouring wells during the DNA extraction process.

Although over 300 samples were extracted and sequenced, 220 samples (including samples from both chicks and adults) had less than 8000 reads and were excluded from analyses. Rarefaction curves demonstrated that a sampling depth of 8000 reads was sufficient to estimate diversity (Fig. S1). Most samples were unusable likely due to issues with low yields from DNA extraction, a known issue for avian feces when using commercially available kits (Eriksson et al., 2017; Edwards et al., 2023). The final sample size used for analysis was n = 35 from 20 adults and n = 56 from juveniles (44 samples from 23 chicks, 6 group samples from 4 hen-raised broods and 6 group samples from soft release pens). After the quality control methods described above were completed, the number of sequences obtained from single samples used for analysis ranged from 8112 to 165 628 with a median of 41 239. Unless otherwise specified, samples were normalized by rarefying read counts to the lowest observed sequencing depth (8112 reads) prior to analysis.

Statistical analyses

Statistical analysis was performed using R v4.2.1 (R Core Team, 2021) and various packages including phyloseq v1.40.0 (McMurdie and Holmes, 2013) for data handling, vegan v2.6-4 (Dixon, 2003) and lmerTest v.3.1–3 (Kuznetsova et al., 2017) for statistical analysis and ggplot2 v3.3.6 (Wickham et al., 2016) for visualization. The R script used for analyses can be found in the supplementary material.

Alpha diversity was measured by calculating the observed number of ASVs per sample as well as Simpson and Shannon indexes (Simpson, 1949; Spellerberg and Fedor, 2003). Linear mixed effects models were used to model alpha diversity estimates as a response to fixed effects for sex, antibiotic use and sample collection substrate/location among adult samples, or management type (hen- vs. hand-raised), age (as a continuous variable) and antibiotic use among juvenile sample tests. Additional models were used to test for differences in alpha diversity between adult and juvenile samples that included age-class (adult vs juvenile) or age cohort as a covariate. Age cohorts separated juvenile samples into five groups: 1–2 weeks, 3–4 weeks, 5–6 weeks, 7–12 weeks and > 12 weeks. Individual bird identity was included as a random intercept in all models to account for multiple samples collected from the same individual. Samples from the oldest juveniles in soft release pens were excluded from alpha diversity assessments based on management and antibiotic use as only group samples could be collected. Group samples consisted of a mixture of several different fresh fecal samples collected from the environment, without confirmation on which birds within a specific pen produced the collected fecal material.

Aitchison distances from non-rarefied count tables were used as one measure of beta diversity, alongside estimates of weighted and unweighted UniFrac distances calculated from rarefied datasets. Permutational multivariate analysis of variance (PERMANOVA; 999 permutations, Anderson, 2017, Kelly et al., 2015) were used to determine the marginal effects of age class (adult or juvenile), antibiotic use (both as a binary variable and days since last antibiotic dose as a continuous variable for adults), age, sex, management style (hen vs. hand raised) and sample collection location on phylogeny-weighted/unweighted measures of microbiome beta-diversity. Additional tests for multivariate homogeneity of groups dispersions using the function betadisper() from the R package ‘vegan’ were applied to fixed effects found in PERMANOVA analyses to be significantly associated with measures of beta diversity. Post hoc Tukey honest significant difference tests were then performed to determine the direction of differences in beta dispersion among pairwise comparisons.

To more directly compare adult microbiomes to juvenile microbiomes, juvenile samples were categorized into five age cohorts as described above. Cohort 5 chicks (>12 weeks) had been moved to soft release pens closer to release sites and had undergone a diet transition to reduce pelleted feed. As group samples were included in these comparisons, individual-linked variables on management and antibiotic use could not be included.

When needed, the R package pairwiseAdonis v.0.4 was used to make pairwise comparisons (Martinez Arbizu, 2017) and P-values were corrected for multiple comparisons using the Holm–Bonferroni method (Holm, 1979).

Differential abundance analysis between adults based on antibiotic use, juveniles based on management type and juveniles and adults were assessed at the family level using a non-rarefied count table and the function ANCOMBC from the eponymous package, v.1.6.3 (Lin and Peddada, 2020; Nearing et al., 2022). As above, Holm–Bonferroni corrections were applied to account for multiple comparisons (Holm, 1979).

Results

Microbiome composition

A total of 11 bacterial phyla were present between all sequenced samples (Fig. 1). Bacillota was the most abundant phyla in both adults (64.6% ± 5% SE) and juveniles (69.9% ± 3.1% SE). Actinomycetota was the second most abundant phylum in adults (18.1% ± 3.9% SE) but was less abundant in juveniles (8% ± 1.5% SE). The second most abundant phylum in juvenile samples was Bacteroidota (16.2% ± 2.7% SE), which was found in lower abundance in adult samples (1.5% ± 0.9% SE). Pseudomonadota was also abundant in both sample types (11.2% ± 3.9% SE in adults and 4.1% ± 1.5% SE in juveniles).

Figure 1.

Figure 1

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Bar plot of the relative abundance of the seven most abundant bacterial phyla sequenced from the sage-grouse fecal microbiome. Phyla whose median relative abundance were less than five percent are combined and represented in grey. Each column represents an individual sample, separated by sample type.

Characterization of the adult microbiome

No significant differences in alpha diversity were noted based on sex, antibiotic use or sample location when considering Observed, Simpson or Shannon measures of alpha diversity (Table S2). Similarly, no significant differences in beta diversity were noted on the basis of sex (n = 15 males, n = 20 females), or location of sample collection (Table S3). Beta diversity was, however, found to vary marginally based on the binary variable for antibiotic use in the past six months for both Aitchison (P = 0.04) and unweighted UniFrac distances (P = 0.04, Table S3), as well as the continuous variable for number of days since the last antibiotic treatment for both Aitchison (P = 0.03) and weighted UniFrac distances (P = 0.04, Table S3). Despite these measured differences, there were no bacterial families that significantly differed in abundance based on the binary variable for antibiotic use.

Characterization of the juvenile microbiome

No significant differences in alpha diversity were observed with respect to antibiotic use or management condition (Table S4). Age (measured in days) was positively associated with ASV richness (β = 0.69 ± 0.32 SE, t = 2.17, P = 0.04, Fig. 2), but was not a significant variable when assessing Shannon evenness or Simpson diversity (Table S4).

Figure 2.

Figure 2

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Changes in Observed bacterial alpha diversity in relation to increasing age of juvenile sage-grouse (measured in days). The solid line represents the linear regression line, shaded area represents the 95% confidence interval.

Antibiotic use in juveniles (n = 26 not treated, n = 24 treated) did not significantly affect microbiome beta diversity (Table S5), but chick age and sample location were significantly associated with Aitchison distance (P = 0.02, P < 0.01) and unweighted UniFrac distance (P = 0.03, P < 0.01) measures of beta diversity, but not weighted UniFrac distances (Table S5). However, among pairwise comparisons of Aitchison and unweighted UniFrac distances between sample locations, no pairs of sample locations showed statistically significant differences in beta diversity after Holm–Bonferroni adjustment. Chick management style (n = 12 for hen-raised, n = 38 for incubated and hand-raised) had a significant effect on all measures of beta diversity (Aitchison, P < 0.01; unweighted UniFrac, P < 0.01; weighted UniFrac, P = 0.03; Fig. 3, Table S5). Post hoc tests of differences in beta dispersion between management styles indicated that hen-raised chicks had more variable microbiomes in Aitchison (difference = 0.225, lower estimate = 0.080, upper estimate = 0.37, P = 0.003), but not weighted UniFrac (P = 0.50) or unweighted UniFrac (P = 0.12) space. Differential abundance analysis revealed 14 bacterial families that were significantly more abundant in hen-raised chicks, and no families that were significantly more abundant in hand-raised chicks (Table S6, Fig. 4).

Figure 3.

Figure 3

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PCoA plot of bacterial community composition based on weighted UniFrac metric of juvenile fecal samples, coloured based on management type. Numbers in parenthesis refer to the variance explained by the ordination axes. Ellipses denote the 95% confidence interval.

Figure 4.

Figure 4

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Plot of bacterial families with significantly different abundance in the fecal microbiome of juveniles based on management type. All families were more abundant in samples from hen-raised chicks. LFC = log-fold change in abundance of taxon between groups.

Developmental changes in the microbiome of greater sage-grouse

There were no significant differences in alpha diversity between juvenile and adult samples (Observed P = 0.545, Shannon P = 0.312, Simpson P = 0.259). However, microbiome composition differed between juvenile and adult age classes, based on analyses of unweighted UniFrac, weighted UniFrac and Aitchison distance beta diversity measures (Table 1, Fig. 5a). No difference in Aitchison distance (P = 0.39) or weighted UniFrac (P = 0.49) beta dispersion were observed between adults and juveniles. However, adults exhibited more variable microbiomes among unweighted UniFrac measures of beta diversity (difference = 0.050, lower estimate = 0.019, upper estimate = 0.073, P = 0.001).

Table 1.

Summary statistics of PERMANOVA comparisons of bacterial beta diversity measures between juvenile sage-grouse fecal samples to adults. Juvenile samples were then separated into management type and compared again to adult samples. F = test statistic from PERMANOVA, Df = degrees of freedom, R2 = explanatory power and P = p value. Bolded p values indicate a statistically significant difference (α < 0.05)

Aitchison Unweighted UniFrac Weighted UniFrac
F Df R2 P F Df R2 P F Df R2 P
Juveniles vs. adults 10.45 1,89 0.105 0.0001 12.21 1,89 0.121 0.0001 8.16 1,89 0.084 0.0001
Hand-raised vs. adults 12.07 1,71 0.145 0.0001 14.08 1,71 0.166 0.0001 8.88 1,71 0.111 0.0001
Hen-raised vs. adults 2.37 1,45 0.050 0.016 2.30 1,45 0.049 0.024 1.53 1,45 0.033 0.151
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Figure 5.

Figure 5

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PCoA plot of bacterial community composition based on weighted UniFrac metric of juvenile and adult fecal samples, coloured based on sample type with juveniles combined (A) and coloured based on management type (B). Numbers in parenthesis refer to the variance explained by the ordination axes. Ellipses denote the 95% confidence interval. Violin plot of pairwise weighted UniFrac distance between juvenile and adult fecal samples separated and coloured by age cohort (C). Lower values of distance indicate a more similar bacterial community. Cohort 4 is the only cohort not significantly different from adults.

There was a significant difference in all measures of beta diversity based on age cohort, including adults grouped into one cohort (P < 0.01, Table 1). Pairwise comparisons between juvenile cohorts revealed significant differences in beta diversity, consistent among all measures, only between cohorts 2 and 5 and between cohorts 3 and 5 (Table S7). Among post hoc tests of multivariate homogeneity of groups dispersions in Weighted UniFrac space, juveniles of cohort five were found to have less variable microbiomes than adults (P (adj.) < 0.01), or juveniles from cohorts one (P (adj.) < 0.01), two (P (adj.) < 0.01) and three (P (adj.) < 0.01). Similar patterns were observed with respect to between-cohort dispersion in Aitchison and unweighted UniFrac space (Table S8).

Seven families were differentially abundant between cohorts 2 and 5 (Table S9, Fig. S2), four of which were more abundant in younger chicks (Erysipelatoclostridiaceae, Monoglobaceae, Streptococcaceae and Bifidobacteriaceae) and three which were more abundant in older chicks (Geodermatophilacae, an unclassified family of order Victivallales and an unclassified family from order Chloroflexales). Though significant differences in beta diversity were also noted between cohorts 3 and 5, no families were statistically differentially abundant between these age groups.

There was a significant difference between juvenile cohorts 1, 2 and 3 when compared to adults for all three measures of beta diversity (Table S7, Fig. 5c). In comparing juvenile cohort 4 to adults (when sample collection would have occurred in older birds, but before the diet transition or relocation to soft release pens) a significant difference was noted for Aitchison distance (q = 0.03), but not unweighted (q = 0.06) or weighted UniFrac measures (q = 0.17, Fig. 5c). After diet transition and relocation to soft release pens, there was again a significant difference between cohort 5 and adults for all measures of beta diversity (Aitchison: q < 0.01, unweighted UniFrac: q < 0.01, weighted UniFrac: q = 0.01). Differential abundance testing revealed 16 families that were in higher abundance in cohort 1 samples compared to adults (Table S10a), and 13 of those families were also differentially abundant in cohort 5 samples compared to adults (Table S10b).

Adult samples were also compared to juveniles based on management type. When adults were compared to only incubated and hand-raised chicks, assessment of the differences in Aitchison, unweighted and weighted UniFrac distances all increased (Table 1). When adult samples were compared to only hen-raised chicks, Aitchison and unweighted UniFrac distances were still significantly different, but only by a much smaller margin and weighted UniFrac distance was not significantly different (Table 1, Fig. 5b).

Differential abundance analysis revealed 29 bacterial families that had statistically higher abundance in juvenile samples compared to adults (Table S11a, Fig. S3a). When the same comparison was made between only hand-raised chicks (n = 38) and adults, the number of families statistically higher in abundance in juvenile samples increased to 33 (Table S11b, Fig. S3b), and when adults were compared to only hen-raised juveniles (n = 12), only one family was differentially abundant, Lactobacillaceae, which remained more abundant in juveniles than adults (Table S11c).

Discussion

Microbiome composition

The composition of the gastrointestinal bacterial microbiome at the phylum level in greater sage-grouse is consistent with previous studies of the avian microbiome in both free-ranging and managed settings, with Bacillota as the most abundant phyla, and Actinomycetota, Bacteroidota and Pseudomonadota also being highly abundant (Kohl, 2012; Waite and Taylor, 2014; Hird et al., 2015; Colston and Jackson, 2016; Grond et al., 2018; Liu et al., 2019). Variations in sample collection methodology, DNA extraction techniques, PCR primer selection, sequencing platforms and taxonomic database selection make further assessment between study results challenging.

Characterization of the microbiome of adult greater sage-grouse

No significant differences were noted in microbiome diversity measurements between male and female adults, likely indicating that sex does not have a significant influence on the microbiome of managed sage-grouse. This is in contrast to findings in other wild avian species, where differences in community composition based on sex were observed in thick billed murres (Uria lomvia), as well as three migratory passerine species (Gongora et al., 2021; Yan et al., 2022). Differences in diet and feeding strategies of males and females during either the reproductive season or migration may have a significant impact on the microbiome, as well as sex. The microbiome of wild great bustards (Otis tarda) is known to be influenced by both diet and sex, based on sampling of two flocks dependent on different farmed crops over the winter. Great bustards are known as one of the most sexually dimorphic avian species in terms of size difference, which likely leads to differences in food requirements and alterations in the microbiome (Liu et al., 2020). Sage-grouse are sexually dimorphic, with varied investments related to reproduction know to impact survival in the wild; lekking behaviour in males and incubation and chick rearing in females (Boyko et al., 2004; Blomberg et al., 2012). Adult sage-grouse in this study were fed a consistent diet, with no difference in access between males and females. The consistency in both diet and environment appears to be a more important contributor to the composition of the microbiome than differences based on sex in this species. Further studies focused on increased sampling during the breeding and nesting period, and from wild animals with access to varied diets could better determine the impact of reproductive behaviours and circulating sex hormones on the microbiome.

Effect of antibiotic use on the microbiome

Alpha diversity did not vary according to antibiotic use in either adult or juvenile samples in this study. This contrasts with commonly observed reductions in alpha diversity following antibiotic use in other animal species (Dallas and Warne, 2022), including well beyond withdrawal times for the treatments used (Cuccato et al., 2021). The comparisons differed between groups as assessments were made between juvenile samples that had either been treated at any point with antibiotics or had never been treated (though all were hatched from adults with antibiotic exposure), and adults assessed based on treatment in the previous 6 months. Small effects of antibiotic exposure on beta diversity were noted within adult samples only; however, no bacterial families were differentially abundant based on recent antibiotic use. This may indicate that either zoo-housed sage-grouse return to their established microbiome after a disruption caused by antibiotic use, or the effects of any antibiotic use or other associated alterations related to managed care, are long-lasting and generational. Based on the limited availability of untreated samples and the sampling method of this study (fecal samples vs. intestinal contents), no significant conclusions can be made on the impact of antibiotic use on the microbiome in this population, and further study is recommended.

Characterization of the microbiome of juvenile greater sage-grouse

There were no significant differences in measures of alpha diversity between juveniles based on management strategy (hen-raised versus hand-raised), but management strategy did contribute significantly to bacterial community membership and structure. These differences suggest that incubation or hand-raising of chicks can significantly impact both relative abundance of microbial species and colonization by more distantly related bacteria. This finding also supports conclusions from research in passerines that suggest the avian microbiome is significantly impacted by environmental factors. A study of the avian brood parasite, the brown-headed cowbird (Molothrus ater) and other passerine species discovered that environmental variables including physical location and diet had the strongest influence on the gut microbiota (Hird et al., 2014). A cross-foster study of zebra finches (Taeniopygia guttata) to society finches (Lonchura striata domestica) in a controlled environment also hypothesized that acquisition of bacterial symbionts primarily occurs through indirect maternal transmission in the nest environment. Researchers noted that chicks had microbial communities that converged with the species that raised them, concluding that paternal care is an important factor in the development of the microbiome (Chen et al., 2020). Hand-raised finches also had an overall lower microbial diversity, again emphasizing the contribution of parental care and the nest environment to the establishment of the microbiome (Chen et al., 2020). Differential abundance analysis between the two management groups of sage-grouse revealed 14 bacterial families that were more abundant in hen-raised chicks. These differentially abundant bacterial families include Micrococcaceae, which contains the genus Arthrobacter, hypothesized to contribute significantly to detoxification of sage in sage-grouse by degrading phenols and catechols to pyruvate (Kohl et al., 2016). These findings support the previous conclusions that the nest environment, location and parental care all contribute significantly to microbiome development.

Developmental changes in the microbiome of greater sage-grouse

Among juvenile sage-grouse samples, measures of alpha diversity changed over time with increased numbers of ASVs with increasing age (Fig. 2); however, when compared overall there were no significant differences in alpha diversity between juvenile and adult samples. This increase in ASV richness with age is consistent with other avian research, including a study of domestic chickens that also used fecal samples for overall microbiome assessment (Jurburg et al., 2019), a study of house sparrows that assessed both luminal and mucosal intestinal samples (Kohl et al., 2019) and research in arctic shorebirds that included the assessment of the embryonic intestinal microbiome and fecal microbiome of chicks (Grond et al., 2017). The lack of significant differences in measures of alpha diversity overall between juvenile and adult sage-grouse is comparable to studies of the crop microbiome in hoatzins (Opisthocomus hoazin) (Godoy-Vitorino et al., 2010) and the fecal microbiome of kākāpō (Strigops habroptilus) (Waite et al., 2014). However, several studies, for example in the cloacal microbiome in black-legged kittiwakes (Rissa tridactyla) (van Dongen et al., 2013) and the fecal microbiome of barn swallows (Hirundo rustica) (Kreisinger et al., 2017) and Eurasian kestrels (Falco tinnunculus) (Zhou et al., 2020) note an overall difference in alpha diversity between juveniles and adults. The differences in alpha diversity noted in these studies may be related to diet, life-history traits or the variety of ages included in differing studies. Studies in which a significant difference in alpha diversity was observed between adults and juveniles invariably included chicks less than four weeks of age, whereas this study, and the study of hoatzin and kākāpō, included older animals that have begun to converge on a more stable, adult microbiome (Godoy-Vitorino et al., 2010; Waite et al., 2014). Younger chicks have been shown to have significant changes in the microbiome, likely related to cessation of yolk absorption, increased diet diversity and transient colonization of environmental microbes (Kohl, 2012; Teyssier et al., 2018; Videvall et al., 2019; Chen et al., 2020). By also including older chicks with further intestinal and immune system development, a difference in alpha diversity between juvenile and adult sage-grouse may have been obscured.

Previous avian studies have consistently noted changes in community composition over chick development and/or between chicks and adults (Godoy-Vitorino et al., 2010; van Dongen et al., 2013; Kreisinger et al., 2017; Teyssier et al., 2018; Jurburg et al., 2019; Kohl et al., 2019; Videvall et al., 2019; Zhou et al., 2020). In this study, measures of beta diversity were significantly different overall between juveniles and adults. Pairwise comparisons also found significant differences in all measures of beta diversity between juvenile cohort 2 (3–4 weeks) and 5 (>12 weeks) and cohort 3 (5–6 weeks) and 5, as well as significant differences between most juvenile cohorts (1, 2, 3 and 5) and adults. Although there was a significant difference in Aitchison distance between cohort 4 and adults, methods of beta diversity that weigh the phylogenetic relatedness of ASVs (unweighted and weighted UniFrac distances) were not significantly different. This finding suggests that the gastrointestinal microbiome of juveniles between 7 and 12 weeks of age are starting to converge with adults also housed in managed care. This age range coincides with when hand-raised and hen-raised chicks start to live together in larger enclosures, which may also influence this convergence. Differential abundance analysis identified a number of bacterial families that were more abundant in juvenile samples compared to adult samples, which may indicate transient colonization of microbes prior to the development of a stable adult microbiome.

Large compositional changes in the microbiome within the first 2 weeks of development have been discovered in previous studies in a variety of avian species (Grond et al., 2017; Teyssier et al., 2018; Jurburg et al., 2019; Kohl et al., 2019; Videvall et al., 2019). Cohort 1 in this study spans this entire age range and had a small sample size (n = 10) with variable richness (range of 10–122 ASVs per sample). Future studies should focus on increased sampling within this age range to better determine microbial community shifts as they relate to development and better inform potential strategies for early microbiome interventions (Ballou et al., 2016; Jurburg et al., 2019).

Assessments of beta dispersion between age cohorts revealed that juveniles in cohort 5 have less variable microbiomes compared younger chicks, likely due to transient colonization of microbial species in younger animals. Interestingly, these older juveniles sampled after both a diet transition to reduce reliance on pelleted feed and moving to soft release pens on native habitats also had less variable microbiomes compared to managed adult sage-grouse. Some researchers have hypothesized that the microbiomes of healthy individuals will have less variation compared to dysbiotic individuals, which would suggest that these management changes support a healthier microbial community composition (Zaneveld et al., 2017).

Chick management had a significant impact on the microbiome of juveniles and was also found to be significant in the comparison of juvenile samples to adults. When incubated and hand-raised chicks were removed from comparisons, there was no longer a significant difference in community structure of juveniles and adults. Differential abundance testing was also assessed and the number of differentially abundant families between groups decreased from 29 to only one, Lactobacillaceae. Increased Lactobacillaceae in gastrointestinal flora have shown to experimentally increase weight gain in domestic chickens and ducks (Anas platyrhynchos domestica) (Angelakis and Raoult, 2010), and increases in abundance of this family in chicks have been noted in other studies of avian development, suggesting a relationship with periods of rapid growth (Waite et al., 2014; Teyssier et al., 2018). Differential abundance testing comparing adults to incubated and hand-raised juveniles noted many bacterial families that were found in higher abundance in chicks. As has been noted in previous studies, the early-life microbiome of chicks appears to be significantly associated with parental care and environmental acquisition of microbiota (Hird et al., 2014; Teyssier et al., 2018; Chen et al., 2020). These differentially abundant families are likely related to the difference in environment and access to adults experienced by incubated and hand-raised juveniles. Increased abundance of some bacterial families may also be related to closer proximity and increased interactions with human caretakers in hand-raised chicks. Humanization of the microbiome is often discussed as a contribution towards microbiome alterations in mammals under human care, related to both the ingestion of more highly cultivated foods, and increased proximity and interactions with humans, but has not yet been assessed in wild avian studies (Clayton et al., 2016; Trevelline and Moeller, 2022).

Further research is needed to assess if the current diet transition and the length of time grouse spend in pens within their native range prior to release is sufficient to overcome any potential long-term impacts of hand-raising and successive generations hatched under human care on the microbiome of managed birds. If managed care has significantly altered the composition of the sage-grouse microbiome, this may correlate with low survival post-release due to a decreased abundance or absence of bacterial species that aid in the digestion and detoxification of their specialist winter diet, sage brush. Studies of another grouse species with a specialist diet, capercaillie (Tetrao urogallus), have hypothesized that the significant difference seen between the cecal microbiome of captive and wild birds may be responsible for high post-release mortality (Wienemann et al., 2011), and noted significantly longer small intestines and ceca in wild animals compared to hand-raised birds (Liukkonen-Anttila et al., 2000). These studies indicate the potential impact of management not only to the microbiome itself, but on the surface area and anatomy of the gastrointestinal tract. Many of the grouse included in the current study are the result of several years of breeding under human care; studies using rodent models show that a diet change results in continued alteration of the microbiome over generations and concluded that after several generations a reintroduction of a traditional diet alone was not sufficient for the reestablishment of microbial diversity (Sonnenburg et al., 2016).

Limitations and suggestions for further research

Although originally 311 samples were collected for DNA extraction and sequencing, only 91 samples were included in the final analyses. Most samples were unusable due to issues with low yields from DNA sequencing. Low yields following DNA extraction specifically from avian feces using commercially available kits are a known issue for microbiome researchers (Eriksson et al., 2017; Edwards et al., 2023). Future studies should consider collecting a larger number of samples than needed for analysis with the expectation that a significant proportion may be lost due to low DNA yields and inadequate sampling depth.

A study published in 2019 measured seasonal change and regional variation in distinct gut regions (crop, ventriculus, duodenum, cecum and colon) of wild sage-grouse specimens collected in Wyoming and concluded that the crop and cecum had distinct microbiomes with limited overlap with samples from the colon (Drovetski et al., 2019). Fecal collection is a well-documented non-invasive approach for assessment of gut microbiome (Grond et al., 2018) and is therefore a useful approach when working with endangered species like sage-grouse. Gallinaceous birds do pass cecum feces, a dark liquid containing cecum content separate from other digesta, approximately once a day (Fenna and Boag, 1974). These samples have been used in previous studies as a proxy for cecal content without invasive sampling (Wienemann et al., 2011) and would likely be beneficial in studies of the sage-grouse microbiome. Due to requirements for limited disturbance to the managed population, fecal samples were used in this study. Though more accurate than cloacal swabbing (Videvall et al., 2018), samples from feces are likely not thoroughly representative of the relative diversity and richness of the bacterial community throughout length of the gastrointestinal tract.

The 16S rRNA gene amplicon data collected in this research was used only for taxonomic identification of ASVs, future research should focus on sampling from wild individuals and functional metagenomic sequencing to better understand the consequences of an altered microbiome composition under managed care. A previous study utilized metagenomic sequencing on samples from three sage-grouse from Idaho and hypothesized bacteria in the genus Arthrobacter are the main source of genes responsible for degrading toxic phenols and catechols from sage brush in the cecum (Kohl et al., 2016). However, this genus was not sequenced in the cecal samples from Wyoming sage-grouse (Drovetski et al., 2019), suggesting there may be other species involved in the detoxification of sage brush, these species may vary geographically over the native range of sage-grouse, or genus-level identification of these species is limited using 16S data and the SILVA database. If the bacterial species involved in sage detoxification is identified within the wild Canadian population, this finding could help guide treatment and manipulation of the microbiome in managed care with the goal of increasing sage-grouse survival post-release.

Supplementary Material

Web_Material_coae052
web_material_coae052.pdf (1MB, pdf)

Acknowledgements

This work would not have been possible without support from the Wilder Institute/Calgary Zoo and the assistance from animal care staff in sample collection. Specific thanks to Marc Imfeld, Michelle Benzen, Kristina Stephens and Matt Pelehos. Thanks to the other members of the Animal Health team, Dr Adriana Pastor, Lori Rogers, Lindsay Palmer and Carmen Peccie for their assistance with this project. Thanks also to members of the Conservation and Science Department, Ilsa Gribel, Millie Coleing and Don McKinnon.

Contributor Information

Emma Vaasjo, Faculty of Veterinary Medicine, University of Calgary, 3280 Hospital Dr NW, Calgary, AB T2N 4Z6, Canada; Animal Health Department, Wilder Institute/Calgary Zoo, 1300 Zoo Rd NE, Calgary, AB T2E 7V6, Canada.

Mason R Stothart, Faculty of Veterinary Medicine, University of Calgary, 3280 Hospital Dr NW, Calgary, AB T2N 4Z6, Canada.

Sandra R Black, Animal Health Department, Wilder Institute/Calgary Zoo, 1300 Zoo Rd NE, Calgary, AB T2E 7V6, Canada.

Jocelyn Poissant, Faculty of Veterinary Medicine, University of Calgary, 3280 Hospital Dr NW, Calgary, AB T2N 4Z6, Canada.

Douglas P Whiteside, Faculty of Veterinary Medicine, University of Calgary, 3280 Hospital Dr NW, Calgary, AB T2N 4Z6, Canada; Animal Health Department, Wilder Institute/Calgary Zoo, 1300 Zoo Rd NE, Calgary, AB T2E 7V6, Canada.

Author contributions

Emma Vaasjo and Douglas Whiteside developed the research questions and study design. Emma Vaasjo completed sample collection. Mason Stothart and Jocelyn Poissant aided with initial sample analysis and troubleshooting. Data analysis was completed by Emma Vaasjo and Mason Stothart. Douglas Whiteside, Sandra Black and Jocelyn Poissant supervised the project. Emma Vaasjo wrote the manuscript and all authors provided critical feedback and contributed to the final submission.

Conflict of interest

The authors have no conflicts of interest to declare.

Funding

We gratefully acknowledge the ACA Grants in Biodiversity (supported by the Alberta Conservation Association), the University of Calgary and the Wilder Institute/Calgary Zoo for the financial support of this project.

Data availability

Raw sequence reads used in analysis are publicly available in the Sequence Read Archive of the National Center for Biotechnology Information, under BioProject ID PRJNA1129547.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Web_Material_coae052
web_material_coae052.pdf (1MB, pdf)

Data Availability Statement

Raw sequence reads used in analysis are publicly available in the Sequence Read Archive of the National Center for Biotechnology Information, under BioProject ID PRJNA1129547.

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