Methanogenic patterns in the gut microbiome are associated with survival in a population of feral horses

Domain: earth_science
Published: 2024-07-22
Editorial outcome: accept

Abstract
Gut microbiomes are widely hypothesised to influence host fitness and have been experimentally shown to affect host health and phenotypes under laboratory conditions. However, the extent to which they do so in free-living animal populations and the proximate mechanisms involved remain open questions. In this study, using long-term, individual-based life history and shallow shotgun metagenomic sequencing data (2394 fecal samples from 794 individuals collected between 2013–2019), we quantify relationships between gut microbiome variation and survival in a feral population of horses under natural food limitation (Sable Island, Canada), and test metagenome-derived predictions using short-chain fatty acid data. We report detailed evidence that variation in the gut microbiome is associated with a host fitness proxy in nature and outline hypotheses of pathogenesis and methanogenesis as key causal mechanisms which may underlie such patterns in feral horses, and perhaps, wild herbivores more generally.

Introduction
Gut microbiomes can influence animal health and the expression of host phenotypes1. Microbial residents of the intestinal tract are now recognized as integral to the adaptive programming of host physiology or capable of performing functions beyond the capacity of the host genome2,3. Conversely, common constituents of the gut microbiome can cause infectious disease, while disruption of the microbiome deprives animals of critical metabolic functions4. Growing recognition of the microbiome’s integral importance underlies hypotheses that host-associated microbial communities can facilitate adaptive phenotypic change in animal populations3,5,6. For adaptive evolution in the host-microbiome relationship to occur, variation in the microbiome must be both causally connected to host fitness and intergenerationally transmissible. While heritability of the microbiome has been demonstrated7—including in the wild8—we still lack robust demonstrations that selection acts on naturally occurring microbiome variation within free-living animal populations.

Mounting experimental research conducted in laboratory environments provides strong evidence that host-associated microbial communities may partly underlie fitness variation within wild animal populations9–11. Evidence linking gut microbiome and host fitness proxies has similarly begun to emerge in wild populations, including the documentation of microbiome associations with host survival in wild Seychelles warblers (Acrocephalus sechellensis; 148 individuals across 4 years of study)12 and meerkats (Suricata suricatta; 235 individuals across 23 years of study)13. However, precisely estimating selection gradients in the wild requires large sample sizes and can be confounded by environmental heterogeneity within and among years. Furthermore, previous reliance on 16S rRNA gene amplicon sequencing methods has limited the characterization of non-bacterial constituents of the microbiome and made it challenging to identify the microbial mechanisms that proximately link the microbiome to host fitness. Using detailed life history data and shallow shotgun metagenomic sequencing of individual-linked fecal samples, we quantified the strength of selection acting on metagenomic variation in a free-living animal population (Sable Island feral horses; Equus ferus caballus). Using these results and analyses of short-chain fatty acid and dietary metabarcoding data, we identified likely causal mechanisms which appear to connect the gut microbiome to a host fitness proxy (overwinter survival).

Horses were first introduced to Sable Island (Nova Scotia, Canada; Fig. 1) in the 18th century and have since remained free-living. From 2007, the ~500-horse population has been the focus of a long-term, individual-based study (Supplementary Note 1). Every year, the entirety of the population is surveyed repeatedly on foot from July–September, and all horses are identified against a comprehensive photographic database using unique color markings and morphological features. These surveys provide individual-based location, behavioral, reproductive and survival data (100% resighting probability since 2011).

Microbiome variation may not be consequential for all animals14, but we predicted a strong relationship between the hindgut microbiome and survival in Sable Island horses. As large-bodied herbivores, horses are obligately reliant on their intestinal microbiota to digest their fibrous plant-based diet. Since Sable Island horses lack predators and competitors, host mortality is expected to be primarily attributable to pathogens and nutrient limitation in the winter. The microbiome likely mediates host responses to both these selective pressures. Nearly all mortalities in the Sable Island horse population occur during a discrete window of selection in late winter and early spring, when the abundance and quality of above-ground plant biomass is low. Survival in Sable Island horses is therefore dependent on energy reserves amassed during the preceding summer when forage is abundant. The latter coincides with our annual population surveys, during which fresh individual-linked fecal samples can be collected and immediately preserved. Notably, as hindgut fermenters, the microbiota observed in horse feces are representative of the microbial communities on which horses rely to extract nutrients from their diet15. Using a shallow shotgun sequencing method validated for this system16, we jointly characterized the taxonomic and microbial gene contents of 2394 fecal samples collected from 794 horses between 2013 and 2019. Individuals in our dataset were represented by 1 random sample per year for up to 6 years (median = 3) and were at least 1 year of age at the time of sampling, since the equid microbiome stabilizes within the first year of life17.

We tested for associations between the gut microbiome and horse survival using GLMMs (generalized linear mixed models) in which overwinter survival to the following year was modeled as a function of composite microbiome measures (alpha diversity, principal components, deviation from the average microbiome) or measures of microbe or gene family abundances in the microbiome (CLR-transformed read counts) while accounting for age, sex, sex-specific parental status, and mean longitude of horse sightings, which describes major environmental variation on Sable Island (Fig. 1). In this study, we provide strong evidence that variation in the gut microbiome is associated with host survival under free-living conditions and outline hypotheses of pathogenesis and methanogenesis as key causal mechanisms which may underlie such patterns.

Results
Composite measures of microbiome diversity
Highly diverse microbiomes are frequently hypothesized to be beneficial to hosts, but relationships between animal health and microbiome richness depend on the host species, environment, and microbiome type (gut, skin, reproductive, oral, reproductive, etc.)18,19. We observed no relationship between taxon richness and horse survival (β = 0.19 ± 0.27 SE, z = 0.688, p = 0.49) and a negative relationship between horse survival and estimates of Shannon evenness (β = −0.16 ± 0.07 SE, z = −2.391, p = 0.02). We similarly observed negative relationships between horse survival and microbial gene family richness (β = −0.45 ± 0.15 SE, z = −3.058, p = 2.2e−03) and Shannon evenness (β = −0.16 ± 0.06 SE, z = −2.506, p = 0.01). Microbial richness in the gut microbiome may thus be less important for host fitness than underlying patterns of community composition and metabolic function in this population18. This interpretation is supported by significant associations between horse survival and Aitchison distance principal components of microbiome community and gene family composition (Fig. 2; Supplementary Table 1, 2).

Because host-microbiome relationships may tend to exist near an adaptive peak, it has been hypothesized that deviation of microbiomes from the population average could signal dysbiosis4. This hypothesis was partly supported by a negative association between horse survival and the Aitchison distance separating an individual’s microbiome from the population’s within-year average community composition (β = −0.15 ± 0.06 SE, z = −2.537, p = 0.01); however, a comparable relationship was not observed with respect to gene family composition (β = −0.002 ± 0.06 SE, z = −0.03, p = 0.98). These results indicate that horse mortality may be linked to specific microbial gene content in the microbiome, but that mortality-associated patterns in gene family contents may derive from different microbiota or alternative community states.

To evaluate whether microbiome data were informative of horse survival to the following year, while simultaneously evaluating the comparative explanatory power of taxonomic versus gene family measures, we used AIC model selection to compare: (1) a base model containing only non-microbiome terms; (2) base models plus principal components from either microbial community or gene family ordinations which significantly predicted horse survival; and (3) base models plus microbiome distances from the population’s within-year centroid from ordinations of either microbial community or gene family composition. The model including gene family principal components was the most parsimonious (AICw = 0.88; Supplementary Table 3), followed by the model which included principal components from community ordinations (ΔAIC = 3.89, AICw = 0.12). Distance-from-centroid models (community: ΔAIC = 18.18; gene family: ΔAIC = 24.58) and the base non-microbiome model (ΔAIC = 22.58) performed comparatively poorly. Collectively, these results suggest that microbiome data can provide an informative bioindicator of animal health in the wild and that measures of microbial gene contents may be marginally more informative than measures of community composition.

Metagenomic predictors of horse survival
Associations between composite microbiome measures and horse survival support a widely hypothesised link between host fitness and the microbiome in the wild but provide limited insight as to which microbes in the horse gut (Fig. 3a) underlie these associations, or the proximate mechanisms involved. By individually testing relationships between horse survival and microbial taxa and gene families, we identified 155 of 1574 taxa (51 positive, 104 negative associations, Supplementary Data 1) and 27 of 1416 gene families (23 positive, 4 negative associations, Supplementary Data 2) significantly associated with horse survival, after false discovery rate correction (FDR, Benjamini and Hochberg)20.

These results provided evidence for at least 2 major mechanisms which we hypothesize underlie microbiome-survival associations in this system. First, survival of Sable Island horses was negatively associated with a collection of putative opportunistic pathogens (e.g., Clostridium spp., Erysipelothrix rhusiopathiae, Eggerthella lenta, Hathewaya proteolytica, Streptococcus equi) and the only gene families that were negatively associated with horse survival—sortase B, acid phosphatase, and nitrite reductase genes—are all recognized as virulence factors in bacterial infection21–23. Second, we observed an array of taxonomic and functional patterns that suggest horse survival may be associated with methanogenesis in the hindgut. Specifically, taxa associated with methane production generally decreased the probability of horse survival, while microbiota and gene families involved in methanotrophy or the competitive inhibition of methanogenesis had the opposite effect. Methanogenesis can prevent H2 from accumulating in the gut, where at high partial pressures it inhibits critical metabolic reactions; but it does so at a significant energetic cost. For example, among herbivorous mammals, methane emissions account for a loss of 2%–15% of gross energy intake24–26. We posit that comparable methane-related energy deficits would be consequential in Sable Island horses for which a depletion of energy reserves in the winter is the most common cause of mortality27.

A relationship between methanogenesis and host performance was most apparent from the strong, negative associations between horse survival and taxa known to produce methane or promote its synthesis, including archaea of the methanogenic genus Methanobrevibacter28 and bacteria associated with high methane emissions in domestic animals (e.g., Mogibacterium, Pyramidobacter, Christensenella, Anaerostipes, Ruminococcus, Aminipila; Supplementary Data 1; Fig. 3b)28–32. As hydrogenotrophic methanogens, Methanobrevibacter produce methane from CO2 and H2, while the aforementioned bacteria can increase methanogenesis by providing Methanobrevibacter with rate limiting H2—a byproduct of plant fiber degradation and common fermentation pathways that end in the short-chain fatty acids (SCFAs) acetate and butyrate28,33. Furthermore, some acetate and butyrate producing bacteria (e.g., mortality-associated Christensenella minuta; β = −0.18 ± 0.06 SE, z = −2.99, q = 0.04) directly support methanogenesis by actively cross-feeding Methanobrevibacter with rate-limiting H231. Partly owing to methanogenic energy losses, the methanogenic syntrophy between Christensenella and Methanobrevibacter has been identified as among the strongest microbiome-based predictors of a lean body mass index in humans31. A similar microbiome-derived metabolic phenotype is likely to be maladaptive in nutritionally stressed wildlife like Sable Island horses.

Metagenomic patterns linked to the competitive interference of methanogenesis in the microbiome were positively associated with horse survival. This includes positive associations between horse survival and bacteria capable of sulfate or nitrate reduction (e.g., Actinoplanes, Desulfobacteraceae, Nitrospirae, Paracoccus, Sulfuricella; Supplementary Data 1; Fig. 3b)34–38, as well as the normalized abundance of reads mapped to hydrogen dehydrogenase genes (β = 0.26 ± 0.06 SE, z = 4.066, q = 0.03), a key enzyme used by bacteria to reduce sulfate in a reaction that consumes H239. Sulfate and nitrate reduction operate as non-methane hydrogen sinks28, and therefore, nitrate and sulfate reducing bacteria can inhibit methanogenesis by competing with methanogens for H2.

The ability for sulfate and nitrate reduction to reduce methanogenic energy loss may also partly explain why some of the strongest positive associations with survival we observed were in response to genera of Bacteroidota (formerly Bacteroidetes), which contains many intestinal mucus foragers, including Bacteroides, Muribaculum, and Alistipes (Fig. 3b)40. In the mouse gut, Bacteroides can cross-feed sulfate reducing bacteria with rate-limiting sulfate salvaged from mucin glycans38, and in cultures of human fecal bacteria, the addition and subsequent degradation of mucin glycans by Bacteroidota is necessary for facilitating the competitive inhibition of methanogenesis by sulfate reducing bacteria37.

The general trend whereby Bacteroidota were positively associated with horse survival may also be partly attributable to the prevalence of metabolic pathways for propionate formation among this phylum41,42. Unlike hexose fermentation to acetate or butyrate, propionate formation results in a net sequestration of hydrogen and can therefore competitively reduce methanogenesis28,32,33. This might explain the divergent associations with survival between two of the most abundant bacterial phyla in the horse hindgut (Bacteroidota and Bacillota; Fig. 3a), since Bacillota (formerly Firmicutes) rarely produce propionate as a major SCFA42. Propionate producing Negativicutes are a well-known exception to this general pattern41,42, but were also notable in our analyses as the only Bacillota which had significant positive associations with horse survival (β = 0.21 ± 0.07 SE, z = 3.266, q = 0.02; Fig. 3b).

The strongest positive associations with overwinter survival were observed in relation to Fibrobacter succinogenes (Fig. 3c), an abundant plant-fiber degrading bacteria which produce acetate and succinate as its primary fermentation end-products43. Like sulfate and nitrate reducers, succinate producers compete with methanogens for electron donors (H2 and formate), while reducing oxaloacetate to malate and fumarate to succinate, which is rapidly converted to propionate in the gut (predominately by Bacteroidota and Negativicutes). F. succinogenes demonstrate the ability to competitively interfere with methanogenesis in vitro44 and in vivo45–47, and in doing so, redirect energy away from methane emissions and towards SCFA synthesis. Competitive interference with methanogenesis may therefore contribute to the association we observed between F. succinogenes and horse survival. This interpretation is supported by a positive association between horse survival and the abundance of reads which mapped to phosphoenolpyruvate carboxykinase genes (β = 0.24 ± 0.07 SE, z = 3.443, q = 0.04), since this enzyme catalyzes the interconversion of phosphoenolpyruvate to oxaloacetate in the first step towards succinate synthesis. We predict that a greater abundance of phosphoenolpyruvate carboxykinase gene content signals more succinate (and therefore propionate) versus acetate and butyrate formation in the horse gut.

SCFA tests of metagenomic predictions
To test predictions derived from our shotgun metagenomic dataset related to changes in acetate, butyrate, and propionate concentrations, we generated a short-chain fatty acid dataset from a subset of 40 fecal samples. All samples were collected from adult males in 2019, spanned extremes in Fibrobacterota (formerly Fibrobacteres) relative abundance and included samples from both horses that lived or died during the following winter.

First, we sought to test whether the abundance of Fibrobacterota and Bacteroidota (primary succinate and propionate producers in the gut, respectively) relative to Bacillota (primary butyrate producers in the gut) was associated with shifts in the balance of SCFAs, as we predicted from metagenomic patterns. The ratio of Fibrobacterota and Bacteroidota versus Bacillota ([Fibrobacterota + Bacteroidota]:Bacillota) was strongly negatively associated with % butyric acid of total SCFA content as predicted (β = −0.66% ± 0.10% SE, t = −6.462, p < 0.001; Fig. 4a), but contrary to our predictions, not % acetic acid (p = 0.34; Fig. 4b) or % propionic acid (p = 0.26; Fig. 4c). This could be because low partial pressures of H2 are thermodynamically favorable for acetate formation but rate-limiting for propionate synthesis which require hydrogen incorporating steps31,48. Low H2 availability can likewise slow rates of methanogenesis31,48, perhaps explaining the negative association between log-transformed Methanobrevibacter relative abundance and % acetic acid (β = −1.75% ± 0.58% SE, t = −3.00, p = 0.005), but a positive association with % butyric acid (β = 0.31% ± 0.14% SE, t = −2.215, p = 0.03) and a non-significant positive trend with % propionic acid (β = 1.04% ± 0.60% SE, t = 1.721, p = 0.09). Metabolic transitions by Fibrobacterota and Bacteroidota from acetate to succinate or propionate formation, as well as Methanobrevibacter growth, may both depend on H2 availability in the gut48. We found no evidence that % butyric acid was affected by an interaction between log-transformed Methanobrevibacter relative abundance and [Fibrobacterota + Bacteroidota]:Bacillota ratio (p = 0.50; Fig. 4d). However, interactions were observed whereby, at higher Methanobrevibacter abundance, [Fibrobacterota + Bacteroidota]:Bacillota was more strongly negatively and positively associated with % acetic acid (β = −1.9% ± 0.57% SE, t = −3.374, p = 0.002; Fig. 4e) and % propionic acid (β = 1.98% ± 0.53% SE, t = 3.748, p < 0.001), respectively. After accounting for these interactions, % propionate (β = 2.48% ± 0.60% SE, t = 4.134, p < 0.001), but not % acetate (p = 0.09), was associated with [Fibrobacterota + Bacteroidota]:Bacillota.

The ratio of [Fibrobacterota + Bacteroidota]:Bacillota was not associated with total SCFA concentration in feces (p = 0.45), but a negative relationship was observed in response to Methanobrevibacter log-transformed relative abundance (β = −6.053 ± 2.872 mmol/kg SE, t = −2.108, p = 0.04). As above, an interaction existed whereby higher values of [Fibrobacterota + Bacteroidota]:Bacillota weakened the negative association between Methanobrevibacter and total SCFA content (β = 4.062 ± 1.66 mmol/kg SE, t = 2.441, p = 0.02), possibly as a result of shifting SCFA production from acetate to hydrogen-sequestering succinate and propionate formation, as suggested by our analysis of % SCFA, and predicted by kinetic models of SCFA formation48. These results support our interpretation that propionate production from succinate may act as a hydrogen sink capable of mitigating methanogenic energy loss among Sable Island horses.

As predicted, similar patterns in SCFA profiles were observed in response to survival-associated phosphoenolpyruvate carboxykinase gene hit abundance (Supplementary Fig. 1). However, phosphoenolpyruvate carboxykinase gene abundance was not associated with total SCFA concentration (p = 0.35), and a significant interaction with Methanobrevibacter was not observed (p = 0.09). Therefore, phosphoenolpyruvate carboxykinase alone does not account for the maintenance of SCFA production under conditions of high Methanobrevibacter abundance.

Evidence for strain-specific effects
All F. succinogenes degrade plant fibers and produce succinate43, but only reads which mapped to strains belonging to a monophyletic clade of F. succinogenes were significantly associated with survival (Fig. 3b, c). Strains within this ‘clade C’ group of F. succinogenes have a reduced capacity to degrade plant fibers when compared to conspecifics but possess urease genes and can grow on urea as a substrate43. Relationships between F. succinogenes strain abundances and cellulase and urease gene hit abundances in our dataset are aligned with these previously described niche differences between strains (Fig. 5a, b)43. Like cellulase, but unlike urease, phosphoenolpyruvate carboxykinase gene hits were positively associated with both ureolytic and non-ureolytic F. succinogenes strain abundance (Fig. 5c), suggesting that both ureolytic and non-ureolytic F. succinogenes use this enzyme to produce succinate. Therefore, differences in phosphoenolpyruvate carboxykinase abundance may not be responsible for strain-specific associations with horse survival. Ureolytic gut microbiota improve host nitrogen retention and facilitate hibernation in mammals49. We therefore hypothesized that strain-specific associations of F. succinogenes with horse survival might be attributed to the combined abilities of ureolytic F. succinogenes to simultaneously degrade plant fibers and recycle nitrogen; however, we observed little support for this hypothesis, since neither cellulase (β = 0.19 ± 0.07 SE, z = 2.849, q = 0.11) nor urease (β = 0.20 ± 0.06 SE, z = 3.057, q = 0.09) gene hits were significantly associated with horse survival.

Horse survival was instead strongly positively associated with phosphate acetyltransferase gene abundance (β = 0.27 ± 0.07 SE, z = 4.031, q = 0.02), and phosphate acetyltransferase gene hits were positively associated with ureolytic (but not non-ureolytic) F. succinogenes strain abundance (Fig. 5d). Phosphate acetyltransferases catalyze the formation of acetylphosphate, which can be used as a substrate for acetate formation, or for the post-translational modification of proteins via lysine acetylation. Accordingly, phosphate acetyltransferase abundance is strongly positively correlated with the abundance of lysine acetylated proteins in the microbiome50. Phosphoenolpyruvate carboxykinase is the most abundant protein in the mammalian microbiome to undergo lysine acetylation50, and the acetylation of phosphoenolpyruvate carboxykinase causes it to strongly favor the oxaloacetate (and therefore succinate) forming direction of the reversible reaction that this enzyme catalyzes51. Accordingly, phosphoenolpyruvate carboxykinase acetylation is hypothesized to shift the balance of SCFA synthesis in the gut microbiome away from acetate and butyrate, and towards succinate and propionate50. We hypothesize that phosphoenolpyruvate carboxykinase may more frequently be acetylated in ureolytic strains of F. succinogenes. If so, they may favor succinate over acetate as a fermentation end-product, placing them in more direct competition with methanogens for H2, when compared to non-ureolytic strains.

Interestingly, methane monooxygenase genes had the strongest positive association with horse survival (β = 0.30 ± 0.06 SE, z = 4.772, q = 0.001) of all identifiable gene families. This enzyme is used by methanotrophs to catalyze the oxidation of methane to methanol52. Like urease and phosphate acetyltransferase, methane monooxygenase gene hits were positively associated with the abundance of survival-associated ureolytic (but not non-ureolytic) F. succinogenes strains (Fig. 5e). Therefore, in addition to perhaps competitively inhibiting methanogenesis, ureolytic F. succinogenes might be capable of directly recapturing energy otherwise lost to methanogenesis. Laboratory experimentation and in-depth ‘omics analyses will be required to determine if ureolytic F. succinogenes can occupy a facultatively methanotrophic niche or facilitate methanotrophy in the horse hindgut.

Like [Fibrobacterota + Bacteroidota]:Bacillota, total SCFA concentration was shaped by an interaction between Methanobrevibacter and ureolytic F. succinogenes abundance (β = 3.78 ± 1.80 mmol/kg SE, t = 2.102, p = 0.04), but a comparable interaction was not observed with respect to non-ureolytic F. succinogenes strains (p = 0.92). The proportion of total Fibrobacter reads belonging to ureolytic F. succinogenes strains were also strongly positively associated with absolute concentrations of total SCFAs (β = 5.86 ± 1.35 mmol/kg SE, t = 4.350, p = 0.001), propionic acid (β = 1.57 ± 0.38 mmol/kg SE, t = 4.138, p < 0.001), and acetic acid (β = 3.96 ± 0.96 mmol/kg SE, t = 4.146, p < 0.001), but not butyric acid (p = 0.08). These results support the hypothesis that survival-associated F. succinogenes may more efficiently competitively interfere with methanogenesis within the horse gut than non-ureolytic strains.

A significant interaction was also observed, whereby phosphate acetyltransferase gene abundance modulated the relationship between Methanobrevibacter and total SCFA concentration (β = 4.96 ± 1.88 mmol/kg SE, t = 2.624, p = 0.01; Fig. 6). This interactive effect was observed with respect to concentrations of acetic acid (β = 2.635 ± 1.252 mmol/kg SE, t = 2.104, p = 0.04) and propionic acid (β = 1.80 ± 0.47 mmol/kg SE, t = 3.821, p < 0.001) but not butyric acid (p = 0.1). The relative abundance of phosphate acetyltransferase gene hits was also positively associated overall with total SCFA (β = 4.18 ± 1.81 mmol/kg SE, t = 2.094, p = 0.04) and propionic acid (β = 1.88 ± 0.45 mmol/kg SE, t = 3.801, p < 0.001) concentrations, but not acetic (p = 0.13) or butyric (p = 0.76) acid. These results support predictions from our metagenomic dataset that phosphate acetyltransferases may partly mediate F. succinogenes competition with Methanobrevibacter for H2, by facilitating succinate, and indirectly propionate, production.

Change in the microbiome preceding host death
While microbiome variation predicts the overwinter survival of horses, it is unclear from our preceding analyses whether mortality-associated features are repeatable within individuals, change only in the year immediately preceding death, or emerge over longer timescales; for example, if immunosenescence contributes to dysregulation in the microbiome53. In instances where the microbiome changes prior to mortality events, it is further unclear whether beneficial and deleterious features shift together or asynchronously. Additionally, our initial analytical approach would not have detected survival-associated features that reach their maximum or minimum multiple years prior to host death. To address these gaps, we (i) used restricted maximum likelihood models to estimate the repeatability of CLR-transformed abundances of microbes or gene families within the microbiome, and (ii) compared competing models which used either overwinter survival (yes/no; abrupt change model) or the number of years before death (multi-year change model; maximum 5 years prior to death) as fixed effects for explaining variation in microbiome feature abundance.

Horse identity accounted for 10% ± 6.0% SD of variance in microbe abundance (max = 38%) and 5% ± 4.5% SD of variance in gene family abundance (max = 28%) on average. However, the relationships between microbiome features and horse survival were not independent of repeatability estimates. The microbes most strongly associated with horse survival tended to be more repeatable within individuals across years (linear term: p = 0.10; quadratic term: β = 0.68 ± 0.06 SE, t = 11.954, p < 0.001; Fig. 7a), as were gene families (Supplementary Fig. 2). Although microbiomes can be dynamic, these results suggest that the features most strongly connected to horse survival may be relatively stable.

Overall, we observed stronger evidence for abrupt rather than multi-year changes preceding death for most taxonomic (mean ΔAIC = −2.38 ± 1.51 SD, median ΔAIC = −2.47; Supplementary Data 3; Supplementary Fig. 3) and gene family (mean ΔAIC = −2.89 ± 1.89 SD, median ΔAIC = −2.75; Supplementary Data 4) features. This could indicate that when change occurs in survival-associated microbiome features, it tends to do so only in the year immediately preceding death, although selective disappearance may also partly underlie these patterns (i.e., individuals with maladaptive microbiomes may be less likely to be sampled multiple years prior to death). Nonetheless, outliers from these trends suggested that some features in the hindgut linked to methanogenesis may instead increase across lengthier multi-year timescales.

Of the 25 gene families significantly associated with years before death (after FDR adjustment) and which showed more support for multi-year than abrupt change (ΔAIC > 0; Supplementary Data 4), 24 increased in abundance preceding death. One of the largest multi-year increases preceding death was with respect to pyruvate synthase gene hit abundance (β = 0.06 ± 0.02 SE, t = 3.22, q = 0.03). Pyruvate synthase catalyzes the interconversion of pyruvate to acetyl-CoA in the first step of the most common pathways of acetate and butyrate synthesis54, and in doing so, releases H2. Much as phosphoenolpyruvate carboxykinase gene abundance could signal succinate synthesis in the microbiome, increases in pyruvate synthase might indicate a shift away from succinate and propionate (net hydrogen sinks), towards acetate and butyrate (net hydrogen sources)28. We find support for this interpretation among our SCFA data, in which pyruvate synthase gene abundance is positively associated with ratios of butyric:propionic (β = 0.031 ± 0.009 SE, t = 3.518, p = 0.001) but not acetic:propionic (p = 0.54).

Significant increases in Vinylacetyl-CoA Delta-isomerase (β = 0.06 ± 0.02 SE, t = 3.024, q = 0.03) and 4-hydroxybutanoyl-CoA dehydratase (β = 0.05 ± 0.02 SE, t = 2.87, q = 0.04) gene hit abundance in the years preceding host death provided further evidence that Sable Island horse mortality could be associated with a subversion of succinate (and therefore propionate) as a non-methane hydrogen sink. Vinylacetyl-CoA Delta-isomerase and 4-hydroxybutanoyl-CoA dehydratase are key participants in the fermentation of succinate to butyrate. In support of this interpretation, we find that butyric:propionic acid is positively associated with both Vinylacetyl-CoA Delta-isomerase (β = 0.027 ± 0.009 SE, t = 2.953, p = 0.005) and 4-hydroxybutanoyl-CoA dehydratase (β = 0.031 ± 0.009 SE, t = 3.597, p < 0.001) abundance. The fermentation of succinate to butyrate is relatively uncommon and is perhaps best characterized among Bacillota belonging to the genus Clostridium55. A total of 69 of 1574 taxa showed greater evidence for multi-year rather than abrupt change (ΔAIC > 0; 39 taxa increased and 30 taxa decreased), including many Clostridium and Clostridium-associated co-abundant gene groups (Supplementary Fig. 3) which collectively increased in abundance in the years preceding horse death (β = 0.04 ± 0.02 SE, t = 2.408, p = 0.02; Fig. 7b).

Like numerous Clostridium, the majority of Methanobrevibacter displayed slightly stronger evidence for multi-year rather than abrupt increases in abundance preceding death (median ΔAIC = 0.66; Supplementary Fig. 3). Increases in abundance were statistically significant among 5 Methanobrevibacter (Supplementary Data 3) and Methanobrevibacter overall (β = 0.05 ± 0.02 SE, t = 2.921, p = 0.004; Fig. 7b). However, the strongest evidence for multi-year rather than abrupt change preceding death was observed with respect to Candidatus Methanomethylophilus alvus (ΔAIC = 5.36; β = 0.05 ± 0.02 SE, t = 2.730, q = 0.02; Fig. 7b). Unlike hydrogenotrophic Methanobrevibacter, archaea of the genus Methanomethylophilus produce methane from methylated compounds, including methanol56. Since methanol is a byproduct of methane oxidation by methane monooxygenase, Candidatus M. alvus may undermine methanotrophy as an effective means of preventing energy loss to methane emissions from the gut.

Reads which mapped to ciliate reference genomes nearly universally increased in abundance preceding horse death (β = 0.06 ± 0.02 SE, t = 3.144, p = 0.002; Fig. 4B) and showed stronger evidence for multi-year rather than abrupt changes (median ΔAIC = 1.29; Supplementary Fig. 3). Ciliates are associated with methane emissions in ruminants and can facilitate methanogenesis by: (1) cross-feeding methanogens with hydrogen produced by specialized organelles (hydrogenosomes); (2) disrupting non-methane hydrogen sinks by preying on bacteria; or (3) harboring endosymbiotic methanogens57,58. Our findings hint that ciliates could similarly promote methanogenesis in the horse hindgut.

A multi-year perspective provides evidence that some mortality-associated changes in the gut microbiome might compound across longer timescales. Features with predicted connections to methanogenesis showed the strongest evidence for multi-year increases in abundance preceding horse death, with some features peaking in abundance years prior to mortality events. But critically, most other features showed stronger evidence for change in abundance only in the year immediately preceding horse death (Supplementary Fig. 3). Mismatches in the timing of features which positively versus negatively predicted horse survival lead us to hypothesize that in some cases, functional changes in the microbiome (e.g. increasing methanogenic energy deficits) might trigger a positive feedback that interferes with a host’s ability to regulate their microbiome, thereby causing further deterioration of microbiome function, additional energy loss, and eventually, host death. However, microbiome features strongly associated with horse survival also tended to be more repeatable across years (Fig. 7a), and so could provide a target for selection.

Methods
Animal ethics approval
Sample collection and laboratory analyses were performed under Parks Canada Agency Research and Collections Permit SINP-2013-2014, University of Saskatchewan Animal Care Protocol 20090032, and University of Calgary Animal Care Protocol AC18-0078.

Demographic data collection
The core of the Sable Island Horse long-term individual-based study is comprised of annual population surveys completed during the late summer. All surveys are conducted on-foot by researchers and span a continuous period of 6–7 weeks. In the 2013–2019 timespan represented in the present study, field seasons occurred between July 10th and September 10th. To complete accurate population surveys, Sable Island is subdivided into 7 segments of approximately equal length along its longitudinal axis. These segments correspond approximately to the distance that can be walked by researchers while conducting population surveys within a day. Only a single section is typically surveyed per day, and adjacent sections are not surveyed on consecutive days. Each section is surveyed 6–7 times on average during a single field season.

The location of every horse encountered during population surveys is recorded using handheld GPS devices. Given a history of domestication, and an absence of predators, Sable Island horses are tolerant to the presence of researchers, and most individuals have flight initiation distances of less than 5 meters69. Sable Island horse tolerance towards researchers allows us to profile every horse encountered in detail and from multiple angles with Canon Powershot SX530/SX540 cameras. Attention is given to photographing distinguishing physiological features (e.g., dominant coat color, color markings, nasal wrinkles, scarring patterns, chestnut size/shape etc.). Individual horses can be identified across years using these detailed photographs and linked to written observations and location data. Patterns in horse co-occurrence are used to define social band structure, since horses within the same social bands occupy discrete social units on the landscape which are clearly distinguishable during surveys.

Individual-based data allow us to follow every horse in this closed population from birth to death. Using these data, we can estimate horse age to a resolution of one year, since foals and horses ~1 year of age (yearlings) can be easily distinguished from adults. Horses for which a birth year is unknown—i.e., individuals who were older than 1 year of age in the first year of the long-term study (2007)—were assigned an estimated birth date of 2005 (137 of 794 individuals).

Fecal sample collection
Fecal samples have been collected during targeted fecal sample collection efforts and opportunistically during population surveys, since 2013. Samples are linked to individuals via detailed photographs and data recorded at sampling. All fecal samples are retrieved within 5 min of defecation, and while collecting fecal samples, care is given to avoid portions of feces contaminated with substrate. All samples were collected using clean nitrile gloves, which are turned inside out, sealed, and kept on ice in the field. After a maximum of ~6 h on ice in the field, samples are subset into microcentrifuge tubes (1–2 g of feces per tube), and stored at −20°C on Sable Island for up to 2 months, before being transferred to long-term storage at −80°C on the mainland; however, methodological validations in this system show that no difference in microbiome structure can be observed between technical replicates of samples stored at −20°C versus −80°C for multiple years70.

The dataset used in the present study is comprised of 2394 fecal samples collected between 2013 and 2019 from 794 individuals aged 1–14 years. A median of 3 samples were sequenced per individual (min = 1, max = 6), and every individual was represented by a maximum of 1 sample within a given survey year. An additional 73 samples (3% of total sample set) were sequenced but failed to meet minimum sequencing depth thresholds after quality filtering.

DNA extraction and sequencing
Feces from microcentrifuge tubes (stored at −80°C) were subsampled into Qiagen 0.7-mm dry garnet bead beating tubes using bleach sterilized metal spatulas (~0.16 g of feces per tube). All samples were kept on ice during and after subsampling to prevent asynchronous thaw prior to DNA extraction. Lysis buffer (680 μl) from the Qiagen QIAamp 96 PowerFecal QIAcube HT kit was added to each tube, and tubes were bead beat in sets of 24 samples for 10 min on a Vortex-Genie 2 using Qiagen’s Vortex Adapter (cat. No 13000-V1-24). All subsequent steps conformed to default recommendations from the Qiagen QIAamp 96 PowerFecal QIAcube HT kit single tube start protocol. Final DNA concentrates were eluted in 100 μl of molecular grade water. DNA from 2820 fecal samples across thirty 96-well extraction plates was extracted using this protocol. Each plate comprised of 94 samples, a negative control, and a positive control. Samples were randomized across plates, and randomly distributed within plates. Negative control tubes contained a sliver of clean nitrile glove to mimic possible contamination associated with our feces collection protocol (see above). Positive control tubes contained 75 μl of ZymoBIOMICS Microbial Community Standard II (Log Distribution). Negative and positive controls were processed identically to fecal samples during the DNA extraction protocol. DNA from only a single set of 94 samples was extracted per day, and each extraction set was performed on the same day that samples were weighed to prevent freeze/thaw degradation of samples. DNA elutes were stored at −80°C prior to quantification, library preparation, and sequencing.

DNA concentration of elutes were quantified using Quant-iT dsDNA Broad Range Assay kits. Samples were concentrated using a centrifugal vacuum concentrator in instances where estimated yields were below 12.5 ng/μl. Notably, all positive and negative controls had values below this detection threshold before and after centrifugal concentration, suggesting nominal kit and glove contamination. DNA extracts were sent to the University of Calgary Center for Health Genomics and Informatics, where shotgun metagenomic libraries were created using a 1:1 mixture of low and high GC primers from the iGenomx Riptide preparation kits (as per manufacturer specifications). Despite falling below input DNA thresholds, positive and negative controls were processed identically to fecal samples. A pilot shotgun metagenomic library of 188 samples (alongside 2 positive controls and 2 negative controls) was sequenced across 2 lanes of an Illumina NovaSeq 6000 (300 cycle SP sequencing kit v1.5) to a target depth of 4.1 million 150-bp read pairs per sample. These pilot data were used to validate the use of shallow shotgun metagenomic characterization of the Sable Island horse microbiome16. The remaining 2632 samples (alongside 58 positive and 2 negative controls) were sequenced across 4 lanes of an Illumina NovaSeq 6000 (300 cycle S4 sequencing kit v1.5) to a target depth 3.8 million 150-bp read pairs per sample (658 samples per lane).

Bioinformatics
Demultiplexed reads were quality filtered, trimmed, and mapped against the EquCab3 horse reference genome71 to remove host DNA (<2% in 95% of samples) using the KNEADDATA implementation of BOWTIE2 and TRIMMOMATIC72–74. To estimate the taxonomic origins of read pairs, quality filtered reads were translated into all 6 reading frames and mapped to the NCBI BLAST non-redundant protein database using default paired-end implementation of KAIJU at the species/strain level75. HUMANN373, which uses an implementation of DIAMOND76, was used to profile the gene family contents of samples against the UniProt50 protein database. In implementing HUMANN3, we bypassed METAPHLAN estimates of taxonomy due to poor representation of Sable Island horse specific strains in available reference databases. As per DIAMOND default settings, we set subject coverage thresholds to 0 to prevent overly conservative filtering of a shallow short read dataset. Summed alignment scores were normalized by gene length and regrouped to MetaCyc reaction gene family groups using HUMANN377.

The observation of low DNA extraction yields and sequencing depths from positive and negative controls (when compared to horse fecal samples) suggested negligible kit contamination. Nonetheless, to account for putative contaminant microbiota, we identified and removed taxa which were apparent outliers from abundance-prevalence curves and showed evidence of heterogenous distribution across iGenomx Riptide library preparation plates (Supplementary Fig. 6a-b). Reads which could not be classified to strain or species were binned to the lowest classifiable level, and discarded if they could not be assigned a phylum. Since KAIJU is known to result in a high volume of low abundance false positives78, taxa not observed at a relative abundance of at least 0.0005 in at least 1 sample were removed (1598 taxa retained). Similarly, gene families not observed at a relative abundance of at least 0.0001 in at least 1 sample were likewise removed (1808 gene families retained). These filtering thresholds also correspond to the abundance at which a shotgun metagenomic dataset of similar depth might be expected to accurately estimate feature abundance79. Based on previous comparisons of varied shotgun metagenomic sequencing depths in this system16, samples which contained fewer than 400,000 read pairs with a taxonomic assignment after filtering were removed prior to analysis. This resulted in the retention of 2394 samples from horses aged 1–14 years.

Short-chain fatty acid analysis
Forty frozen horse feces stored in 2-ml cryotubes were sent to Microbiome Insights (Richmond, BC, Canada) on dry ice for short-chain fatty acid using gas chromatography-flame ionization detection (GC-FID)80. Briefly, 1 gram of feces were resuspended in MilliQ-grade H2O and homogenized using MP Biomedicals FastPrep lysis system for 1 min at 4.0 m/s. Fecal suspensions were acidified using the addition of 5 M HCl to a pH of 2.0, and after 10 min of incubation at room temperature, centrifuged at 12,298 × g to separate the supernatant. Fecal supernatants were spiked with 2-Ethylbutyric acid for a final concentration of 1 mM. SCFAs (acetic, propionic, butyric, isobutyric, isovaleric, valeric, and hexanoic acids) were detected using gas chromatography (Thermo Trace 1310), coupled to a flame ionization detector (Thermo), and a Thermo TG-WAXMS A GC Column, 30 m, 0.32 mm, 0.25 μm. See Zhao, Nyman, and Jönsson for further details80. Succinate was not measured as it is not thought to accumulate in the gut, since it is rapidly converted to propionate54.

To mitigate the potential confounding effects of host or environmental factors, we constrained our sample selection to samples collected in 2019, from males at least 4 years of age. Samples were selected evenly from horses that lived and those that died during the following winter, and spanned extremes in Fibrobacterota relative abundance. Absolute and % SCFA contents, with a focus on acetic, butyric, and propionic acids were modeled as a response to metagenomic feature relative abundance measures (centered and variance standardized), using general liner models. To more directly test our hypothesis that methanogenic energy loss is mediated by microbial competition or facilitation, we also tested for interactive effects between Methanobrevibacter (log-transformed relative abundance) and focal metagenomic features in shaping SCFA measures.

A single anomalous sample was excluded from analyses, as it was a statistically significant outlier, and an apparent biological outlier with no detectable butyric acid and 1/5 the acetic acid content of the average sample. Acetic acid was the dominant SCFA (70% ± 4% SD), followed by propionic acid (22% ± 4% SD) and butyric acid (4% ± 1% SD).

Statistical methods
All analyses were completed in R (v. 4.2.1) primarily using the packages phyloseq81 and lme482. To estimate the effect of microbiome features on horse over winter survival, we identified environmental and life history variables known to predict Sable Island horse survival, including: sex, age (linear and quadratic terms fitted), sex-specific parental status (sex: offspring presence in the year of sampling), and mean horse longitude on the island (linear and quadratic terms fitted)83–85. The relationships between each composite measure of the microbiome (richness, Shannon diversity, principal components [PC], beta-dispersion) or the abundance of each microbiome feature (centered and variance standardized centered log ratio [CLR] transformed values) on horse overwinter survival were estimated in succession alongside aforementioned covariates within binomial GLMMs (generalized linear mixed models; logit link). Models with principal components included the first 6 PCs simultaneously.

Abundance filtering of taxonomic and gene family estimates was necessary, given the tendency of KAIJU and HUMANN3 to return a high number of low-abundance false positive assignments73,78. However, filtering also resulted in severely under-dispersed taxon and gene family richness estimates. Accordingly, taxon and gene richness estimates were obtained from unfiltered and unnormalized data, and GLMMs which modeled horse survival as a response to richness included sequencing depth and log-sequencing depth as covariates to account for unequal sampling effort between samples. Terms for sequencing depth and log-transformed sequencing depth precisely described relationship between sequencing depth and microbiota richness (R2 = 0.964, Supplementary Fig. 7a) and residuals from this sequencing depth-richness relationship are positively correlated with ASV (r_pearson = 0.55, t = 5.959, p < 0.01) and genus (r_pearson = 0.49, t = 5.142, p < 0.01) richness estimates obtained from paired 16S rRNA gene amplicon data from biological replicates (Supplementary Fig. 7b).

Year of sample collection was included as a random effect in all models. Inclusion of horse ID to control for repeated measures across years resulted in a singular fit error, since every individual is only represented by a single sample within a given year. Therefore, every individual only has a maximum of one sample collected in the year preceding death. Furthermore, some individuals only have a sample collected in the year immediately preceding their death, while others were not sampled in the year preceding their death. However, analysis of a subset of data which included only a single randomly selected sample per individual yielded the same results (Supplementary Fig. 8).

To mitigate sparsity-related inflation of false positive discovery rates in analyses of feature abundance86, we constrained our analyses to taxon or gene family features which were present in at least 75% of samples (1574 of 1598 taxa, 1416 of 1808 gene families), and samples containing zero reads for a given features were omitted from analyses of that feature. A Benjamini and Hochberg false-discovery rate adjustment was applied to p-values of the same feature type for all models which contained taxon or gene family CLR-transformed read abundances as explanatory variables20 using the function p.adjust().

To obtain estimates of repeatability for CLR-transformed microbe and gene family abundance estimates, we used restricted maximum likelihood models in ASReml-R87. CLR-transformed values were modeled as a response to intrinsic or environmental factors (age as a second order polynomial, longitude as a second order polynomial, collection date, and sex), and microbiome feature repeatability was quantified as the proportion of remaining variance attributable to a random effect of horse ID. In comparing the support for abrupt versus gradual change in the microbiome preceding horse death, we retained only samples collected from individuals who died prior to 2022 (1127 samples, 418 individuals). The CLR-transformed abundances (centered and variance standardized) of each microbiome feature (taxon or gene family) were treated as response variables within GLMMs. We included life history covariates that predicted horse survival (sex, sex: parental status, and linear and quadratic terms for age) and variables predicted to influence microbiome feature abundance (day of year and linear and quadratic terms for longitude of sample collection)88 as fixed effects within GLMMs. Year and horse ID were included in all models as random effects. For each microbiome feature, we created a pair of models which included the same covariates, alongside either (a) survival to the following summer as a binary factor (yes or no), or (b) years before death as a continuous numeric variable. To balance sample sizes across years before death estimates, and to prevent disproportionate leverage by samples collected many years preceding horse death, any samples collected more than 5 years preceding death were binned to a ≥5 grouping. Both model variants contained the same degrees of freedom, therefore we compared ΔAIC values to infer the relative support for abrupt versus multi-year change in microbiome features preceding horse death. We calculated ΔAIC values as AIC_abrupt − AIC_multi-year, such that negative values indicated greater relative support for abrupt change versus multi-year change, and vice versa. As above, a Benjamini and Hochberg false-discovery rate adjustment was applied to the p-values of features with ΔAIC estimates greater than zero. Features identified to show stronger evidence for multi-year change were those which had: (a) a ΔAIC greater than zero, and (b) a statistically significant association with years before death, after Benjamini and Hochberg false-discovery rate adjustment.

Dietary metabarcoding analysis
To characterize variation in Sable Island horse diets, we obtained dietary metabarcoding data from a subset of 85 fecal samples. All samples were collected from female horses in 2014 and were paired with shotgun metagenomic sequencing data derived from the same fecal DNA extracts. Briefly, we PCR amplified the trnL-UAA P6 loop chloroplast gene region using g region 5′-GGGCAATCCTGAGCCAA-3′ and h region 5′-CCATTGAGTCTCTGCACCTATC-3′ primers89. Both primers consisted of a pool of 7 salt-free primers mixed in equal amounts containing between one and seven random bases (N) at the 5’ end to increase diversity of amplicons and an adapter sequence to allow incorporation of barcodes in a second PCR.

Prior to conducting PCR amplification on the entire set of samples, we first identified suitable PCR conditions by comparing amplification using the conditions detailed below but using either 40 ng or 80 ng of template, 25 or 30 amplification cycles, and an annealing temperature of 60°C or 62°C. Based on visualization on 2% agarose gels, we determined that using 40 ng of template, 30 amplification cycles, and an annealing temperature of 62°C performed best among those conditions. Co-loading of a 25-766 bp ladder (Quick-Load Low Molecular Weight DNA Ladder, New England Biolabs) was also used to determine that most amplicons (including primer sequences) were approximately 200 bp long. A set of 95 samples (which included 85 samples with paired metagenomic data) and 1 negative control (molecular grade water) were then amplified using PCR with the following reagents: 5 μL 5X KAPA HiFi HotStart Fidelity Buffer (KAPA Biosystems, USA), 0.75 μL g primer (10 μM), 0.75 μL h primer (10 μM), 0.75 μL dNTPs (10 mM), 0.5 μL KAPA HiFi HotStart Polymerase (0.5 U), 9.25 μL ddH2O, and 8 μL template DNA (standardized to 10 ng/ul). Thermocycling conditions were 95°C for 2 min, followed by 30 cycles of 98°C for 20 s, 62°C for 15 s, 72°C for 15 s, followed by a 2-min final extension at 72°C.

After confirming successful PCR amplification by visualizing 5 μL of product on a 2% agarose gel, the remainder was purified using AMPure XP Magnetic Beads following manufacturer protocols with a 1.8X bead to template ratio (Beckman Coulter, Inc.). Ninety-six pairs of Unique Dual Indices (UDI, IDT for Illumina Set A) and P5/P7 sequencing tags were then added to the amplicons in a second PCR amplification. Reagents for this second PCR consisted of: 5 μL 5X KAPA HiFi HotStart Fidelity Buffer, 1.25 μL Forward Primer (10 μM), 1.25 μL Reverse Primer (10 μM), 0.75 μL dNTPs (10 mM), 0.5 μL KAPA HiFi Polymerase (0.5 U), 10.25 μL H2O, and 6 μL of purified first round PCR product. Thermocycling conditions were 98°C for 45 s, followed by seven cycles of 98°C for 20 s, 63°C for 20 s, and 72°C for two minutes. Products were again purified using AMPure XP magnetic beads (1.8X), visualized on a 2% agarose gel, and quantified using the Quant-it BR kit (2 μL of template).

Samples were then pooled in equal amounts (100 ng/sample) into a single library, except for the negative control for which 5.2 μl was added (which corresponded to the average volume added per sample). This library was quantified using absorbance with a Take3 Trio microplate adapter (averaged from 8 2 μl replicates), diluted to ~5 ng/μl using molecular grade water, and re-quantified using a Quant-it HS kit (10 μL of template, 8 replicates). The library concentration in nM was then estimated assuming an average library size of 242 bp (inferred from the dominant peak on an Agilent TapeStation D1000 ScreenTape) and diluted to 1 nM using 10 mM Tris-HCl (pH 8.5). This 1 nM library was further diluted to 50 pM using Tris-HCl and combined 20:1 with 50 pM PhiX Control v3 (Illumina, San Diego, USA). The library (20 μL input) was sequenced on a local Illumina iSeq 100 with a v1 300-cycle Reagent Kit following the iSeq 100 Sequencing Guide, and demultiplexed FASTQ files created using the Local Run Manager Generate FASTQ Analysis Module V2.0.1.

Raw reads were trimmed, quality controlled and merged using DADA2 in R90. We used a workflow developed for amplicon sequencing of the ITS2 gene region modified as per Poissant et al.91. A custom trnL reference database was constructed using sequences obtained from the NCBI SRA. This database contained reference sequences from 153 plant species (or close relatives) identified during detailed plant surveys conducted in 2019 and 2021. Reference sequences obtained from closely related plant species were used in instances where an exact species match was not available. Amplicon sequence variants (ASVs) were assigned to the finest taxonomic scale possible using the assignTaxonomy() function and an 80% confidence threshold cutoff92. We analyzed dietary metabarcoding profiles at the levels of genus and order, to allow for the possibility that plant nutritional profiles could be either deeply or shallowly phylogenetically conserved. ASVs which could not be assigned to a genus or order were instead binned to their finest classifiable grouping.

No evidence for a relationship between gut microbiome and diet dissimilarity (Aitchison distance) was observed among mantel tests, regardless of whether we grouped diet derived ASVs to the level of genus (p = 0.55; Supplementary Fig. 5a) or order (p = 0.28; Supplementary Fig. 5b). The absence of a strong relationship between gut microbiome variation and the Sable Island horse diet is consistent with previous phylogenetic null modeling results from this system, which more strongly indicate microbial dispersal limitation (rather than heterogenous selection due to diet) as a key determinant of microbiome variation in this population88.

At both the level of genus and order, the first principal component of Aitchison distances in diet dissimilarity was strongly quadratically associated with longitude, whereby by fecal samples collected from the eastern and western extremes of Sable Island tended to differ from samples collected towards the island midpoint (genus: β_linear = 4.72, t_linear = 7.553, p < 0.01; β_quadratic = −5.445, t_quadratic = −8.709, p < 0.01; order: β_linear = 5.30, t_linear = 8.856, p < 0.01; β_quadratic = −5.16, t_quadratic = −8.618, p < 0.01; Supplementary Fig. 5c, d). Fitting longitude as a second-order polynomial within survival models is therefore expected to control for the effects of major dietary variation on horse survival.

Longitudinal differences in horse diet are likely strongly attributable to sea sandwort (Honckenya peploides) at the eastern and western tips of Sable Island (Supplementary Note 2)16,88. Sea sandwort is lower in fiber (49% neutral detergent fiber) than marram grass (Calamagrostis breviligulata), which comprises a large part of the Sable Island horse diet (63% neutral detergent fiber). However, the exclusion of samples collected from horses with access to sea sandwort from survival analyses (exclusion of 325 samples with a longitude less than −60.08°; retention of 2069 samples from 747 individuals) had no effect on the estimated relationships between microbiota and horse survival (Supplementary Fig. 9). The absence of strong relationships between diet and microbiome dissimilarity, the robustness of our findings to the exclusion of fecal samples likely to represent the most divergent diets, and evidence for inter-annual repeatability of the gut microbiome within individuals (Fig. 7a) do not support the interpretation that the associations we observed between the gut microbiome and horse survival are caused by diet.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Dear editors and authors,\nThank you for the opportunity to review this interesting manuscript. This is undoubtedly the\nlargest study in this field using almost 2400 fecal samples from wild horses analyzed by shotgun\nsequencing. Samples were collected over a 6 years period constituting an unique model of an\nenvironment with high selection pressure.\nThe manuscript is very well written, the analyzes are sound and conclusions supported by their\nresults. I have only minor suggestions for the authors consideration.\nTitle: why to use “a wild herbivore” if you are evaluating horses? I would specify that in the title.\nIt would be helpful maybe having a graphical summary of the rationale on how methane\nconsumption by the microbiota can predict survival. You could include other main results as the\nincrease/decrease of certain microorganisms predicting mortality.\nThe discussion about causality is mentioned in the conclusion session, but I believe this needs to\nbe further expanded. Although authors used their words carefully, the manuscript gives the\nimpression that increased methane production is responsible for death. Other unaccounted\nvariable should be mentioned. It is possible that the changes in the microbiome of non-survivors\nwere actually caused by undelaying disease, inflammation or other factors. For instance, dental\nproblems could difficult mastication that could change digestibility and therefore microbiome\ncomposition, leading to increased features linked to methanogenesis.\nOther limitations to be discussed: lack of metabolites and gene expression analysis.\nLine 36: can 6 years be considered “long term” for a selection study?\nLine 122-123: I found this statement not very appropriate. The common hypothesis is not that\nhigh diversity is associated with survival or longevity, so your results are not contradictory to the\ncurrent hypothesis. You should consider to discuss that low diversity is a common feature of food\nanimals treated with low doses antibiotics, which is also associated with weight gain through\nmicrobiome selection: Cho et al., Nature. 2012;488(7413):621-6. doi: 10.1038/nature11400.\nLine 167: I don’t agree that Clostridia sp. are listed as opportunistic pathogens. Only a minority of\nthe hundreds of species in that genus are actually pathogens, but the vast majority are\ncommensal. In fact, those commensals are present in high abundances in healthy horses.\nLine 262: it is not clear why you started the sentence with “Rather than cellulase or urease,”.\nLine 280: why did you keep the strongest association to the end? It would make sense to start\nwith your major findings, unless they are less important in your data interpretation?\nLine 354-358: this is a strong statement. Could you elaborate a little more and maybe use data\nfrom your results to better support this hypothesis?\nLine 366-367: how do you intend to do that? Can you give some examples? I would say that the\nnext step would be to test your hypothesis in an experimental model.\nIt is very difficult to read the legends in pretty much all the figures.\nFor figure 2, you should write Fibrobacter succinogenes the first time it appears in the text.", "reviewer_2": "Overall, the manuscript is well written and very clearly presented. It was easy to follow the\ndifferent storylines, which can be tricky to accomplish when presenting so much data. The figures\nmade the results easy to understand, and I appreciated the historical information provided in the\nsupplemental material to understand the life history of these horses and how their\nmicrobiomes were affected by their ecosystem.\nI couldn't find anything to suggest adding or fixing - well done!", "reviewer_3": "The current paper represents a longitudinal study of the gut microbiota in horses aiming to link\nchanges in the gut microbiota with the overwinter survival in wild horses. This experiment implies\na substantial amount of field work to collect the biological samples and environmental information,\nas well as for the sample analysis and interpretation.\nHowever, the experimental design may be insufficient to test the hypothesis as the gut samples\nwere collected nearly a year before the potential death of the horses, therefore the data\ninterpretation may be biased as there are a large number of environmental, health or behavioural\nfactors across horses that can bias the results. Moreover, correlation does not imply causality.\nTitle should say “horse” instead of “herbivores” as these findings may not apply to other animal\nspecies. Moreover, “predict” should be turned down to “may be linked”.\nL510 Explain why Principal Component (PC) Analysis was performed using the first 6 PC instead of\nusing other plotting analysis which includes the entire variability (CCA, PCoA, etc.)\nL514 Differences in sequencing depth lead to large bias in diversity index. Thus, it is necessary to\nexplain why diversity was calculated before normalization. Using sequencing depth as covariant\nmay not be sufficient to address this issue and needs further justification.\n-Gut microbiota can change substantially from one year to another mostly depending on the diet.\nTherefore, analysis of the gut microbiota one year before death (or even more) cannot be\nconsidered as representative description of the gut microbial ecosystem to stablish correlation or\ncausality of the horse fitness.\nL530 “In relation to potential abrupt vs gradual changes in the microbiome preceding horse death”\nIt seems unlike to make associations between changes in the gut microbiota and horse death\nconsidering that many samples were collected months or even years before the horse death.\nSimilarly, some horses could have been sampled when the horse was suffering a disease process\nbut which did not end up in the horse death. All these situations indicate the presence of many\nexperimental bias which can limit the possibility of drawing robust conclusion.\nIt should be identified the reason of each horse deaths in order to avoid potential bias. For\nexample, a lack of gut microbial disbiosis would be expected if a horse die due to an accident.\nL124 All herbivores prefer eating highly digestible forage instead of highly fibrous forage if they\nhave the chance, as this imply a lower digestive effort. In ruminants a decrease in gut diversity is\nobserved when animals consume highly digestible diets. Therefore, this may indicate that horses\nwith lower diversity may eat the best grass (more digestible) and ultimately have a higher\noverwinter survival. On the contrary, the weak horses may have problems to reach the best grass\n(due to walking or group hierarchy limitations). This observation would explain why horses with a\nhigher survival rate have a lower diversity and abundance of methanogens and ciliates (which are\nassociated to de digestion of fibrous feeds), whereas horses with a lower survival rate seems to be\nmore adapted to eat fibrous diet (possibly because they are unable to reach the best grass). If this\nis true, the differences in the microbiota must be considered as a consequence of the diet but not\nthe reason to explain the horse survival.\nL132 A deviation of an individual´s microbiome composition from the populaton´s average does\nnot necessarily imply a dysbiosis. It may indicate an adaptation to a specific environment or diet.\nL137-140 It is well known that the diet is the main driver that determines de gut microbiota being\nmuch more relevant that other minor factors such as the geographical location, host genetics or\neven the animal species (a horse and a zebra eating the same diet tend to have a similar\nmicrobiota). Thus, these associations do not imply causality as they may be mediated by the type\nof diet consumed by the horse.\nL148 Can the AIC model be overfitted? It is well known that prediction models with a reasonable\naccuracy can be developed when using an internal validation process which implies the use of the\nsame dataset to develop the model and to test its performance. On the contrary, an external\nvalidation, which implies the use of a large proportion of samples for model development (e.g.\n90%) and the remaining samples for model validation, would imply a more robust determination of\nthe model performance and it would prevent model overfitting (Fig S1). The inclusion of indicators\nof the model performance such as the determination coefficients of calibration R2C, cross\nvalidation R2CV, prediction R2P and their respective root mean square erros (RMSEC, RMSECV,\nRMSEP), the performance to prediction ratio (RPD) or the range in reference ratio (RER) would be\nappreciated.\nL171-179 It is mentioned that food limitation is the most common cause of mortality in Sable\nIsland horses. Thus, changes in the gut microbiota are most likely associated to insufficient feed\nintake or to the intake of low quality diet (fibrous diet) which is often associated to high abundance\nof methanogens and methanogenesis. Thus, changes in the gut microbiota cannot be considered\nas the cause of higher or lower survival as they are likely an indirect effect derived from other\naspects such as physical, health or social horse limitations which can limit food intake or lead to\nchanges in the diet (e.g. more fibrous diet). A chemical analysis of the horse faeces (e.g. fiber and\nprotein content) and/or in vitro quantification of the CH4 production after incubation of horse\nfaeces with the same feed substrate across animals could help to proof this hypothesis.\nAlternatively, a quantification of the fermentation products (e.g. volatile fatty acids) from the horse\nfaeces could provide useful information to link gut microbiota and function.\nL198-208 When analysing metagenomics data, it is very common to find associations between gut\nmicrobes and genes as huge number of combinations are tested. However, those associations\ndo not imply causality. This is particularly critical when assuming microbial effects on gut fermentation\nor CH4 production without direct measurements of those parameters. Moreover, most of the\nconcepts described here (H2 sinks, sulphate or nitrate reduction) are derived from ruminant\nstudies which have a different digestive physiology as they are foregut fermenters while horses are\nhindgut fermenters.\nL212-226. Again, CH4, propionate, lactate and fermentation products were not measured in this\nstudy. As a result, this paragraph is highly speculative.\nL228 Fibrobacter succinogenes produces acetate and succinate as main fermentation products. As\nacetate production releases the highest amount of H2 which is transformed into CH4 by\nmethanogens, it may imply an overall increase in CH4 production. As indicated before, it has been\nobserved that F. succinogenes is one of the most fibrolytic microbes in the gut and its abundance is\nlink to the fibre intake. This again, highlights that the diet composition may be the key drier in the\noverwinter horse survival, while the effect in the gut microbiota may be indirect.\nL274-277 This sentence is highly speculative as fermentation products were not measured in this\nstudy.\nTables S4 to S7 are not provided.\nL342 The increase of protozoa during the year before death may indicate food scarcity which\nforced that horse to eat a high-fibrous diet as protozoa are fibrolytic microbes. This highlights that\nthe type of diet consumed by the horse is linked to the survival rate, possibly as less fitted horses\nare pushed into the worst pastures with highly fibrous grasses.\nL361 Is should be defined what do you mean by “natural occurring variations in the hindgut\nmicrobiota”. For example, a lame or a nearly-blind or shy horse can have a different rumen gut\nmicrobiota in comparison with the population average, but this is driven by the diet as they are\nless fit and likely pushed to areas with the low-quality grass.\nL365 “causal dependence of survival on methanogenic pattern” can be a speculation as the CH4\nproduction was not measured and there are potential bias data interpretation.\nL374-376 Most of the CH4 production from livestock in coming from ruminants, therefore the\nhorse, as a hindgut fermenter, cannot be used an accurate model for ruminants."}, "author_response": "RESPONSE TO REVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nDear editors and authors,\nThank you for the opportunity to review this interesting manuscript. This is\nundoubtedly the largest study in this field using almost 2400 fecal samples from\nwild horses analyzed by shotgun sequencing. Samples were collected over a 6\nyears period constituting an unique model of an environment with high selection\npressure.\nThe manuscript is very well written, the analyzes are sound and conclusions\nsupported by their results. I have only minor suggestions for the authors\nconsideration.\n>>We thank the reviewer for their positive feedback, and their suggestions which have\nhelped to improve our manuscript.\n• Title: why to use “a wild herbivore” if you are evaluating horses? I would\nspecify that in the title.\n>>As the reviewer suggests, we now specify ‘feral population of horses” in the title. We\ninitially used the term ‘wild herbivore’, as we wanted to keep the title as broad as\npossible to ensure it finds its target audience (wildlife ecology/evolution researchers),\nwho might otherwise overlook a paper on horses, if they think it pertains to domestic\npopulations. We feel the specification of ‘feral’ will help to accomplish this objective.\n• It would be helpful maybe having a graphical summary of the rationale on\nhow methane consumption by the microbiota can predict survival. You\ncould include other main results as the increase/decrease of certain\nmicroorganisms predicting mortality.\n>>As suggested by the reviewer, we now include a graphical summary in the\nsupplemental materials which outlines our hypotheses of how 19 different survival-\nassociated metagenomic features discussed in the main text are connected to\nmethanogenesis. (Supplementary Figure 4).\n• The discussion about causality is mentioned in the conclusion session, but\nI believe this needs to be further expanded. Although authors used their\nwords carefully, the manuscript gives the impression that increased\nmethane production is responsible for death. Other unaccounted variable\nshould be mentioned. It is possible that the changes in the microbiome of\nnon-survivors were actually caused by undelaying disease, inflammation or\nother factors. For instance, dental problems could difficult mastication that\ncould change digestibility and therefore microbiome composition, leading\nto increased features linked to methanogenesis.\n>>As suggested by the reviewer, we have expanded upon our discussion of causality in\nthe patterns we observed (L516, L522, L562). The reviewer’s point about inflammation\nor dental problems causing microbiome disruption is a good one, however, we would\nargue that if inflammation or other physiological problems indeed contribute to horse\ndeath, this would not mean that microbiome disruptions aren’t a proximate cause of\nmortality, as now acknowledged on L536. We now also supplement our previous\nanalyses with dietary metabarcoding data which do not support the existence of a\nstrong relationship between gut microbiome and diet variation (L526), and report that\nsurvival-associated microbiome features are repeatable within individuals across years\n(L529)\n• Other limitations to be discussed: lack of metabolites and gene expression\nanalysis.\n>>As suggested by the reviewer, we generated a complementary short-chain fatty acid\ndataset from a subset of samples, which we use in our revised manuscript to test the\npredictions derived from our metagenomics dataset. Analyses of these data support our\npredictions (L279, L381, L457, L466), and so we thank the reviewer for their\nrecommendation which we feel has improved the quality of our manuscript.\nWe agree with the reviewer that gene expression data would also be useful for testing\nthese predictions. However, we were unable to also generate a transcriptomic dataset,\ngiven finite time and financial resources, as well as the likely unsuitability of archived\nfecal samples for quality RNA recovery. We agree that this would be an interesting next\nstep and test of our predictions, and future samples will be collected with these\nanalyses in mind. We now highlight on lines L377 & L560 that more comprehensive\n‘omics data would be useful for testing the hypotheses we outline.\n• Line 36: can 6 years be considered “long term” for a selection study?\n>> Our fecal sample collections spanned 7 years of collection, which is relatively long-\nterm compared to most other studies of wildlife microbiomes. However, here our use of\n‘long-term’ was in more direct reference to the broader Sable Island Horse data, which\nbegan collections in 2007. Data collected since 2007 have been critical to our analyses\n(for example, providing us measurements of horse age and an understanding of the\necology of this population, which has helped us identify potential confounds). We have\namended wording to make this clearer (L97).\n• Line 122-123: I found this statement not very appropriate. The common\nhypothesis is not that high diversity is associated with survival or\nlongevity, so your results are not contradictory to the current hypothesis.\nYou should consider to discuss that low diversity is a common feature of\nfood animals treated with low doses antibiotics, which is also associated\nwith weight gain through microbiome selection: Cho et al., Nature.\n2012;488(7413):621-6. doi: 10.1038/nature11400.\n>> We wholeheartedly agree with the reviewer that high diversity is not always\nassociated with survival, longevity, or other host performance metrics. Unfortunately—\nespecially within the wildlife microbiome literature—high diversity is commonly\npresumed to be beneficial, and so we felt it important to flag this common (if misguided)\nhypothesis for our target audience (which may include wildlife researchers new to the\nworld of microbiome research).\nIn response to the reviewer’s concern, we have amended our wording to make it clear\nwe do not ourselves hypothesize that microbiome richness is beneficial to the host\n(L137). We now also cite two perspectives/review articles on this topic, which we feel\nprovide a good overview of how nuance is required when interpreting patterns of alpha\ndiversity. The reviewer suggested citation is indeed exemplary of the fact that alpha\ndiversity is not always best, but since we are at our citation limit, we feel the two reviews\nnow cited provide a more fulsome overview of this topic.\nLine 167: I don’t agree that Clostridia sp. are listed as opportunistic pathogens.\nOnly a minority of the hundreds of species in that genus are actually pathogens,\nbut the vast majority are commensal. In fact, those commensals are present in\nhigh abundances in healthy horses.\n>>We thank the reviewer for this comment. We meant ‘Clostridium’ rather than\n‘Clostridia’; this was a typo which we have now corrected. We further recognize that not\nall Clostridium are necessarily pathogens or might not be pathogenic in all contexts.\nHowever, Clostridium are frequently associated with disease states in horses, and so\nwe feel it important to highlight this genus. In interpreting our results, we have tried to\nstrike a balance between acknowledging the potential for pathogenesis, versus making\nbroad strokes claims about pathogenicity of various microbes, which is often highly\ncontext dependent.\n• Line 262: it is not clear why you started the sentence with “Rather than\ncellulase or urease,”.\n>>As suggested by the reviewer, we have removed this phrasing which was meant as a\nbridge to the end of the preceding paragraph. We have reworded for clarity (L347).\n• Line 280: why did you keep the strongest association to the end? It would\nmake sense to start with your major findings, unless they are less\nimportant in your data interpretation?\n>>The reviewer’s suggestion is a valid alternative narrative framing. Ultimately, this\nresult was placed at the end of this section, so that we could unpack results in a more\norganized manner, without overwhelming the reader with too much information from the\nbeginning. For example, placing the result pertaining to methane monooxygenase at the\nstart of the results section would require simultaneously presenting gene family and\ntaxonomic results, discussing strain-specific effects, while also explaining the biological\nsignificance of methanogenesis without providing the reader the context necessary to\nunderstand the broader picture. We hope that in its current iteration, we are better able\nto describe how our results fit together to provide the basis for a key methane-related\nhypothesis.\n• Line 354-358: this is a strong statement. Could you elaborate a little more\nand maybe use data from your results to better support this hypothesis?\n>>L359: As suggested by the reviewer, we have clarified this statement, specifying that\nmost features did not show strong evidence of multi-year change (including those\npositively associated with survival, as well as some features negatively associated with\nsurvival; L504). We have now also expanded this section to include analyses of\nmicrobiome repeatability which indicate that features strongly connected to horse\nsurvival also tended to be more repeatable within individuals across years (L429).\n• Line 366-367: how do you intend to do that? Can you give some examples?\nI would say that the next step would be to test your hypothesis in an\nexperimental model.\n>>As suggested by the reviewer, we now include examples of next steps which could\nbe used to test the hypotheses we propose (L562).\n• It is very difficult to read the legends in pretty much all the figures.\nFor figure 2, you should write Fibrobacter succinogenes the first time it\nappears in the text.\n>>As suggested by the reviewer, we have provided a new set of figures with larger and\nclearer text.\nReviewer #2 (Remarks to the Author):\nOverall, the manuscript is well written and very clearly presented. It was easy\nto follow the different storylines, which can be tricky to accomplish when\npresenting so much data. The figures made the results easy to understand,\nand I appreciated the historical information provided in the supplemental\nmaterial to understand the life history of these horses and how their\nmicrobiomes were affected by their ecosystem.\nI couldn't find anything to suggest adding or fixing - well done!\n>>We thank the reviewer for their time and positive feedback. We also appreciate\nthe confirmation about the clarity of our interpretations.\nReviewer #3 (Remarks to the Author):\nThe current paper represents a longitudinal study of the gut microbiota in\nhorses aiming to link changes in the gut microbiota with the overwinter\nsurvival in wild horses. This experiment implies a substantial amount of\nfield work to collect the biological samples and environmental information,\nas well as for the sample analysis and interpretation.\n>>We thank the reviewer for their time and thoughtful feedback which we feel have\nhelped to strengthen our manuscript. We also thank the reviewer for their\nacknowledgement of the substantive time investment that contributed to the\nconstruction of the expansive dataset used in this research.\n• However, the experimental design may be insufficient to test the\nhypothesis as the gut samples were collected nearly a year before the\npotential death of the horses, therefore the data interpretation may be\nbiased as there are a large number of environmental, health or behavioural\nfactors across horses that can bias the results. Moreover, correlation does\nnot imply causality.\n>>We agree with the reviewer that correlation does not imply causation but argue that\ncorrelational observations can nonetheless be used as the basis to form testable\nhypotheses. Observation is a critical first step in the scientific method.\nWe also note that all long-term individual-based ecological study are complicated\nby environmental, health, and behavioural factors. This is in fact integral to the entire\npremise of long-term ecological research, which seek to parse biological patterns in\ncomplex real-world ecological systems, and in doing so, garner a greater fundamental\nunderstanding of nature. Despite their inherent complexity, long-term individual-based\nstudy systems have disproportionately contributed to our understanding of ecology and\nevolutionary biology (Clutton-Brock and Sheldon 2010, Science; Clutton-Brock and\nSheldon 2010, Trends in Ecology and Evolution). While valuable, designing an artificial\nsystem which controls for these factors would defeat the purpose of our research, which\nwas to determine whether we could detect associations between the microbiome and a\nhost fitness proxy in the wild.\nThere is in fact an abundance of laboratory experiments which demonstrate the\nimportance of microbiome variation under highly controlled conditions—whether these\nsignals are detectable in under real-world conditions is less clear and this is precisely\nthe knowledge gap we sought to fill. Nonetheless, we note that otherwise confounding\nenvironmental gradients on Sable Island can be effectively controlled for by\nmeasurements of longitude, given Sable Island’s simple linear geomorphology (1.5 km x\n45 km; Figure 1). This is a major advantage to our study system, compared to nearly all\nother wild animal study systems.\nAlthough samples were collected months rather than a year prior to horse death,\nas now clarified on L108, we hypothesize that it is gut microbiome variation during the\nsummer that is critical for overwinter horse survival, since Sable Island horses are\ndependent upon energy reserves amassed during the summer to live through the winter\n(when there is little quality plant biomass available). It is therefore digestive efficiency\nduring these narrow windows of quality forage availability that are critical for horses.\nThis hypothesis appears to be supported by our results.\nWith respect to the reviewer’s concerns that microbiomes sampled months prior to\nhorse death are irrelevant to survival, recent research suggests that, despite the\necological complexities of studying microbiome variation in wild animal populations, gut\nmicrobiome variation is highly repeatable within individuals across years of sampling\n(Björk et al. 2022, Nature Ecology & Evolution). To address this point, we now provide\nestimates of repeatability across the 7 years of sampling (L429). As in other recent\nstudies, we report that microbiome features are indeed repeatable across years, but\nalso that features most strongly connected to horse survival are those which are most\nrepeatable.\n• Title should say “horse” instead of “herbivores” as these findings may not\napply to other animal species. Moreover, “predict” should be turned down\nto “may be linked”.\n>> As suggested by the reviewer, we have changed our title to “associated with”. We\nhad not intended to imply causation with the word ‘predict’ but understand the reviewers\nconcerns that this might be misinterpreted by readers. Likewise, we have changed\n“herbivores” to “feral horse population”. We initially sought to keep our title broad, to\nensure this manuscript finds its target audience (wildlife ecology and evolutionary\nbiology researchers) and to not mislead researchers who study domestic horses. We\nfeel the specification of ‘feral’ will help to accomplish these objectives.\n• L510 Explain why Principal Component (PC) Analysis was performed using\nthe first 6 PC instead of using other plotting analysis which includes the\nentire variability (CCA, PCoA, etc.)\n>> We broadly sought to test whether microbiome data was informative of horse\nsurvival, above and beyond environmental variables or host-level factors. A\nPERMANOVA analysis, which uses beta diversity matrices as a response variable, does\nnot accomplish this objective. In parameterizing generalized linear mixed effects\nmodels, it was necessary to decompose multi-dimensional microbe community and\ngene family composition data into univariate measures (PCs) that could be fitted as\nfixed effects within our model competition framework. Of course, the Principal\nComponent Analysis we performed partitions complex community data across n-1\ndimensions (where n = the # of taxa / gene families). It was therefore also necessary to\nuse only a subset of PCs, rather than attempting to fit thousands of PCs within a single\nmodel.\nThe plotting analyses the reviewer mentioned can be a useful visualization tool, but\nwe argue it is not overly informative nor appropriate for our research question, in which\nhorse survival (rather than microbiome composition) is the focal response variable. We\ndo indeed find among PERMANOVA analyses that microbiome community/gene family\ncomposition significantly differs between horses that survived versus died (even after\ncontrolling for environmental and life history factors). However, this does not provide\ninformation about what dimensions of the microbiome differ between groups, nor does it\ntell us whether a null ‘non-microbiome’ model would be more parsimonious. This is why\nwe made the decision to adopt an AIC model competition framework using a subset of\nthe top PCs.\n• L514 Differences in sequencing depth lead to large bias in diversity index.\nThus, it is necessary to explain why diversity was calculated before\nnormalization. Using sequencing depth as covariant may not be sufficient\nto address this issue and needs further justification.\n>> As suggested by the reviewer, we now detail on L792 and Supplementary Figure 7\nthat ‘depth’ & ‘log(depth)’ fit as covariates very precisely describes the relationship\nbetween sequencing depth and richness (Supplementary Figure 7; R2 = 0.964). In our\nsurvival analyses, we are thus testing whether microbiomes being more or less diverse\nthan expected given their sequencing depth is predictive of horse survival. We further\ndemonstrate these estimates of richness are positively associated with richness\nestimated from more conventional rarefied 16S rRNA data, and thus provide biologically\nrelevant estimates of alpha diversity. As explained in the Methods section (L789) we\nopted for this alternative method of richness estimation because conventional\nrarefaction methods resulted in estimates that were severely under-dispersed, which is\nlikely a result of the economical shallow shotgun metagenomic sequencing method\nused in our study.\n• Gut microbiota can change substantially from one year to another mostly\ndepending on the diet. Therefore, analysis of the gut microbiota one year\nbefore death (or even more) cannot be considered as representative\ndescription of the gut microbial ecosystem to stablish correlation or\ncausality of the horse fitness.\n>> To address the reviewer’s valid concern about microbiome repeatability, we now\ninclude analyses demonstrating that microbiome features are repeatable within\nindividuals across the 7 years of sampling, and that features which are the most\nrepeatable are those most strongly associated with horse survival (L429). These new\nresults do not support the interpretation that the survival-associations we observed are\ntransient and primarily attributable to diet.\nThe microbiome repeatability we report are aligned with the latest research,\nwhich demonstrates that, while microbiomes can be dynamic, they are also idiosyncratic\nand highly repeatable within individuals across years (Björk et al. 2022, Nature Ecology\n& Evolution; Valles-Colomer et al. 2023, Nature; Zhou et al. 2024, Cell Host & Microbe).\nSimilarly, research in human, livestock, and wildlife populations also find evidence for\nheritability (Goodrich et al., 2016, Science; Grieneisen et al., 2021, Science; Ryu &\nDavenport, 2022, Annual Review of Animal Biosciences; Wallace et al., 2019, Science\nAdvances). One of the largest microbiome studies to date (>8000 human subjects and\n100s of covariates) finds that diet accounts for ~1% of total variation in the microbiome,\nwhich is ~<1/20th the effect size of cohabitation and additive genetic effects estimated\nwithin the same study (Gacesa et al. 2022, Nature).\nNonetheless, as the reviewer notes, we agree that diet can contribute to variation\nin the microbiome. To more directly investigate the concerns raised by the reviewer, we\nthought it would be informative to test for connections between the microbiome and\nhorse diet. To that end, thanks to the reviewer’s feedback, we now provide dietary\nmetabarcoding data. Analysis of these data find no correlations between microbiome\nand diet dissimilarity among Sable Island horses (L526; Supplementary Materials;\nSupplementary Figure 5a,b). Furthermore, we find that dietary variation can be largely\naccounted for by fitting longitude as a covariate within survival models (Supplementary\nFigure 5c,d). These results are consistent with our previous work, wherein using\nphylogenetic null models we find that dietary factors do not appear to be the main\ncontributor to between-individual microbiome differences (Stothart et al. 2021; Molecular\nEcology). To further test the robustness of our findings, we also performed an additional\nset of selection analyses, after excluding horses from the western tip of Sable Island\nwhich have the most strongly differentiated diet profiles (attributable primarily to access\nto a low-fibre forage, sea sandwort; Supplementary Materials). Exclusion of these\nputative dietary outliers had negligeable effects on the estimated associations between\nmicrobiome features and horse survival (Supplementary Figure 9). We further now\nclarify in the main text:\n1. If the associations we observed between the microbiome and horse survival were\nproximately attributable to diet, then we would have expected microbiota and\nmicrobial gene contents related to the degradation of plant fibre to be strongly\nrelated to survival. Instead, cellulase gene abundance was not significantly\nassociated with survival and major fibrolytic microbiota (Ruminococcaceae and\nFibrobacteraceae) showed divergent associations with horse survival. Similarly,\nmany asaccharolytic protein-degrading microbiota exhibited some of the\nstrongest negative associations with horse survival. (L533).\n2. Moreso than the plant species composition of horse diets (as measured with\nmetabarcoding data), forage fibre content is strongly expected to increase across\nthe growing season. However, horses which survived versus died were randomly\nsampled between July to September. If dietary fibre was the causal mechanism\nunderlying survival, and the microbiome was only correlatively but non-causally\nlinked to survival, we would expect seasonal decreases in forage quality to have\nobscured our ability to estimate selection acting on the microbiome (rather than\ncause spurious associations as the reviewer suggests).\n3. Even if diet variation partly underlies variation in methane emissions,\nmethanogenic energy loss is still a very real energetic consequence that\nproximately derives from the microbiome. Similarly, if malnutrition leaves animals\nvulnerable to infection or dysbiosis in the gut, the microbiome still has a causal\nconnection to host health. This then becomes a discussion about whether diet is\nan ultimate versus proximate mechanism, and the relative importance of other\ncontributions to microbiome variation. (L536).\n• L530 “In relation to potential abrupt vs gradual changes in the microbiome\npreceding horse death” It seems unlike to make associations between\nchanges in the gut microbiota and horse death considering that many\nsamples were collected months or even years before the horse death.\nSimilarly, some horses could have been sampled when the horse was\nsuffering a disease process but which did not end up in the horse death. All\nthese situations indicate the presence of many experimental bias which\ncan limit the possibility of drawing robust conclusion.\n>> As noted earlier, we now include analyses demonstrating that the microbiome\nfeatures most strongly associated with horse survival are also the features that are most\nrepeatable within individuals across 7 years of sampling (L429). We further clarify on\nL108 that although samples were collected months preceding horse death, they were\ncollected during a critical window of energy accumulation when we hypothesize that the\nmicrobiome is especially important.\nWe agree with the reviewer that the unknown of whether microbiome variation in the\nyears prior to horse death can be predictive of mortality is an interesting question—this\nis precisely the prediction we sought to test in the analyses referenced by the reviewer.\nIndeed, we find that, to the extent that the microbiome does change prior to horse\ndeath, most features do so only in the year immediately preceding death (L437).\nOverall, this would seem to support the hypothesis proposed by the reviewer. However,\nthere were also notable outliers from this pattern (L446), which speaks to the value of\nconsidering both acute and long-term perspectives of microbiome change.\nThe study of senescent patterns (distinct from aging) in wild animal populations\nrepresents a rather large field of study, and senescent patterns in the human\nmicrobiome have been well characterized. The likelihood that senescent patterns exist\nin the microbiome of horses therefore seems high, since immunosenescence across\nmultiple years is one commonly observed pattern in long-lived mammalian wildlife (Froy\net al. 2019, Science). Given that the gut microbiome is strongly regulated by host\nimmunity, immunosenescence is one example of a mechanism that could cause\ndetectable multi-year change in the microbiome preceding host death.\nAs noted in our response to a previous comment, scenarios like what the reviewer\nsuggests (where horses were suffering from a disease process or had high abundance\nof mortality-associated microbiome features but did not die) would add noise to our\nanalyses, weakening rather than statistically biasing our ability to detect associations\nbetween the microbiome and horse survival. As we note above, the purpose of studying\nhost-microbiome associations in a long-term individual-based study is to determine\nwhether microbiome-fitness associations can be detected despite this real-world noise\nand complexity. The fact that we are able to quantify such associations, despite real-\nworld noise and complexity, speaks to the strength of the host-microbiome relationship,\nand the impact of our research findings.\n• It should be identified the reason of each horse deaths in order to avoid\npotential bias. For example, a lack of gut microbial disbiosis would be\nexpected if a horse die due to an accident.\n>> This is not possible for any long-term individual based ecological study, nor is it\nnecessary for the research question we pursued. Despite the inability to track the\nprecise cause of mortality for every study individual, decades of long-term studies have\nnonetheless been able to directly estimate the strength of natural selection acting on\ntrait variation within wild populations. These studies provide the foundation for, and have\ndisproportionately contributed to, our understanding the evolutionary biology of plant\nand animal populations (Clutton-Brock & Sheldon, Trends in Ecology and Evolution,\n2010). This high bar set by the reviewer would exclude nearly all empirical studies of\nevolution by natural selection in the wild.\nWe further note that, while we cannot know the exact cause of death for every\nhorse, we can be confident of every horse’s death given this is a closed population\nwhich we exhaustively survey. This is a notable advantage when compared to the vast\nmajority of long-term individual-based study systems, making Sable Island perhaps one\nof the best study systems in which to study relationships between the microbiome and\nhost fitness.\nFinally, we note that if ‘accidents’ unrelated to the microbiome are a primary cause of\nhorse mortality, then this would have masked our ability to detect associations between\nthe gut microbiome and horse survival (i.e., the microbiome would not be under\nselection)—this was not the case. Stated another way, mortality events that are\nunrelated to microbiome-linked starvation or nutritional deficits will have contributed\nnoise to our dataset, rather than caused the patterns we observed. We are uncertain\nwhat ‘accidents’ could mean in the context of Sable Island, but note that, the rate of\nsuch events does not appear ecologically relevant, as indicated by the strength of the\nsurvival associations we were able to measure. Additionally, among periodic necropsy\nsurveys conduct in early spring, we indeed find that horse mortality is predominately\nrelated to energy deficits.\n• L124 All herbivores prefer eating highly digestible forage instead of highly\nfibrous forage if they have the chance, as this imply a lower digestive\neffort. In ruminants a decrease in gut diversity is observed when animals\nconsume highly digestible diets. Therefore, this may indicate that horses\nwith lower diversity may eat the best grass (more digestible) and ultimately\nhave a higher overwinter survival. On the contrary, the weak horses may\nhave problems to reach the best grass (due to walking or group hierarchy\nlimitations). This observation would explain why horses with a higher\nsurvival rate have a lower diversity and abundance of methanogens and\nciliates (which are associated to de digestion of fibrous feeds), whereas\nhorses with a lower survival rate seems to be more adapted to eat fibrous\ndiet (possibly because they are unable to reach the best grass). If this\nis true, the differences in the microbiota must be considered as a\nconsequence of the diet but not the reason to explain the horse survival.\n>> As noted earlier, we now include analyses demonstrating that the microbiome\nfeatures most strongly associated with horse survival are also the features that are most\nrepeatable within individuals across 7 years of sampling (L429). Our results therefore do\nnot support the interpretation that the survival-associations we observed are transient\nand primarily attributable to diet.\nAs noted earlier, we now provide analyses of dietary metabarcoding data, in\nwhich we find no evidence for a relationship between microbiome and diet dissimilarity\namong Sable Island horses (L526; Supplementary Materials; Supplementary Figure\n5a,b). See earlier comment for a fuller discussion of this topic.\nDecades of research has demonstrated a causal connection between methane\nemissions and energetic loss. It is extremely unlikely that variation in methane\nemissions between individuals are wholly attributable to diet variation. In fact,\nsubstantive variation in methane emissions exist among livestock fed identical diets.\nMethane-associated microbiota have also consistently emerged as among the most\nhighly heritable microbiome features across human, livestock, and wild animal\npopulations, and methane emissions are themselves heritable (L540). This provides the\nbasis for animal breeding programs around the world which are now selecting for low\nmethane emitting genotypes. Recent experimental transplants of bacteria and\nmethanogens into gnotobiotic sheep fed identical diets further demonstrate that\nmethanogenic outcomes strongly derive from priority effects and competitive\ninteractions between gut microbiota (Yeoman et al. 2021, mBio). We now discuss this\non L547.\nThe reviewer’s concerns about horses being unable to reach quality forage, is not\nreflective of the ecology of this population, in which individuals of poor and good\ncondition co-graze. Horses on Sable Island segregate into social groups/bands, and\nprevious research has shown that group identity does not explain any variance in body\ncondition scores after having taken age, sex, and longitude into account (Table 2 in\nDebeffe et al. 2016, Parasitology). Across decades of research on this population, we\nhave yet to find evidence that weak horses are pushed away from the best grasses at a\nfine spatial scale. While we do observe some heterogeneity in plant communities and\nbiomass across the Island (Supplementary Material), this environmental gradient can be\naccounted for by longitude within our models (Supplementary Figure 5c,d).\nFurthermore, associations between the microbiome and horse survival appears similar\nacross the island. We now also provide panoramic photographs from across the island\n(Fig. 1) to provide the reader with context as to the plant biomass available to the\nhorses.\n• L132 A deviation of an individual´s microbiome composition from the\npopulaton´s average does not necessarily imply a dysbiosis. It may\nindicate an adaptation to a specific environment or diet.\n>> We agree with the reviewer that deviation of microbiomes from the population\ncentroid does not guarantee dysbiosis. As we now clarify on L150, we were testing a\nhypothesis proposed by Zaneveld et al. (2017), in which they speculate that, in some\ncases it can. Given that we have generated the largest shotgun metagenomic dataset\nderived from a wild population, we feel it is valuable to test major hypotheses pertaining\nto the host-microbiome relationship that pervade the literature, but for which (until now)\nwe’ve lacked sufficient empirical data to evaluate. In this instance, analysis of our\ndataset would appear to support the reviewer’s hypothesis in the context of gene family\nprofiles, but not community composition, as we discuss on L154.\n• L137-140 It is well known that the diet is the main driver that determines de\ngut microbiota being much more relevant that other minor factors such as\nthe geographical location, host genetics or even the animal species (a\nhorse and a zebra eating the same diet tend to have a similar microbiota).\nThus, these associations do not imply causality as they may be mediated\nby the type of diet consumed by the horse.\n>> As noted earlier, we now provide analyses of dietary metabarcoding data which do\nnot support the prediction that the patterns we observed are caused by dietary variation.\nOur findings are well-aligned with the vast majority of microbiome research which\nfinds that, while diet can contribute to microbiome variation, its influence is often vastly\nover-estimated. The widespread mirroring of microbiome and host phylogenetic\nrelationship (phylosymbiosis) is also a well characterized phenomenon. We therefore\nrespectfully disagree with the reviewer’s assertion that host genetics and differences\nbetween animal species are subordinate to diet effects. In recognition of the reviewer’s\nconcern that diet is the main driver of microbiome variation, we felt it could be beneficial\nto provide a brief cross-sectional overview of diet and genetic effects across human, lab\nanimal, and wild animal systems, as well as directly test some of the points raised by\nthe reviewer.\nIn a large study of 33 sympatric large-bodied mammalian herbivores in the East\nAfrican savannah ecosystem, Kartzinel et al. (2019, PNAS) found that microbiome\nvariation is strongly correlated with host phylogenetic relatedness (r = 0.91) and only\nweakly correlated with diet relatedness (r = 0.20). Furthermore, dramatic dietary\nturnover in species between wet and dry seasons accounted for only ¼ of seasonal\nturnover in the microbiome over the same period. Our own sampling took place within a\nvery narrow seasonal context (little seasonal turnover in diet) and seasonal dietary\nvariation would not have systematically biased our results, as horses which survived\nversus died were randomly sampled.\nKnowles et al. (2019, Ecology Letters) likewise found that among mice, voles,\nand shrews sampled across disparate environments, host species family explained 35%\nof variation in the microbiome (Bray-Curtis dissimilarity) and trapping location upwards\nof 13%. In contrast, diet analysis based on gut contents found that diet similarity was\nnot, or only extremely weakly, correlated with diet similarity among mice (r = 0.07, p =\n0.074), shews (r = 0.10, p = 0.07), and voles (r = 0.22, 0.002).\nTo the reviewer’s own example of horses versus zebra, cursory analysis of\npublicly available equid microbiome data prepared and sequenced on the same\nsequencing runs (Edwards et al. 2020, Microbiome) reveals that—contrary to the\nreviewer’s concern—clear differences in the microbiome exist between horses and\ncaptive zebra fed similar diets (Reviewer Response Figure 1; diet data available in\npaper supplementals). Samples from this study were sourced from horses and zebra\nspread across different locations, indicating that these species-effects are genuine, and\nnot ‘location’ or ‘technical’ effects.\nReviewer Response Figure 1: A) An Aitchison distance principal component analysis\nordination of 16S rRNA gene amplicon fecal microbiome data in domestic horses and\nzebra, and B) a plot of pairwise Aitchison distances across host-species contrasts.\nIn one of the largest human microbiome datasets assembled (samples from\n8,208 individuals), Gacesa et al. (2022; Nature) find that, using 241 measurements of\ntechnical factors, anthropometrics, early-life and current exposome, self-reported\ndiseases, use of medication, and diet, they are able to explain 12.9% of variation in the\nmicrobiome, and only 1% of total variation is attributable to diet. In contrast, additive\ngenetics and co-housing each account for ~10% of variance in microbiome features, on\naverage (~20% cumulatively). Therefore, in this study, host additive genetics and social\ncontext (co-housing) had ~20x the effect of diet.\nDiet does not appear to be the ‘main driver’ of microbiome variation among\ninbred lab mice kept under highly controlled laboratory conditions and exposed to\nextreme (non-ecologically realistic) diet manipulations, as one large meta-analysis of 27\ndiet-manipulation studies found that prevalent control versus high-fat/low-carbohydrate\ndiet treatments accounted for between 3.5% to 11.6% of variation in microbiome\ndissimilarity, depending on the beta diversity measure considered (Bisanz et al., 2019,\nCell Host & Microbe). Although notably, even these effects are likely over-estimations,\nsince the authors only considered phylogeny-weighted beta diversity measures, and so\nlikely underestimate heterogeneity and location-specific variation in ASV/OTU\ndistribution.\nMoeller et al. (2018; Science) demonstrate that clear location-specific signatures\nin the gut microbiome can be stably maintained across 11 generations of wild-derived\nmice bred in captivity and fed identical diets (highlighting the importance of host genetic\neffects and/or vertical microbial transmission).\nUsing embryo transplants of inbred mice into wild-derived mice, Rosshart et al.\n(2019; Science) experimentally demonstrate that lab-genotype mice born to wild mice\nacquire a ‘wild’ microbiome, and that the ‘wild’ microbiome acquired early in life is\nunaffected by diet manipulations (showing the importance of priority effects and early-\nlife programming).\nThese examples are a small cross-section of the multitude of observational and\nexperimental works which demonstrates that diet is often overemphasised as a\ndeterminant of microbiome structure. Host genetics can also be important (Ryu and\nDavenport, 2022, Annual Review of Animal Biosciences), and microbiome\nmetacommunities and social transmission are increasingly being recognized as\nimportant drivers of microbiome variation (Raulo et al. 2020, ISME; Gacesa et al. 2022,\nNature; Valles-Colomer et al. 2023, Nature; Sarkar et al. 2020, Nature Ecology &\nEvolution). Microbiomes are also now known to be highly idiosyncratic and repeatable\nwithin individuals through time (Björk et al. 2022, Nature Ecology & Evolution; Zhou et\nal. 2024, Cell Host & Microbe), partly due to genetics, but also priority effects and\nstabilizing interactions between microbiota themselves (Roche et al. 2023, eLife).\nOur own current and previous work is consistent with other findings in the\nliterature. For example, we (i) find spatial separation of individuals to be a key\ndeterminant of microbiome structure, (ii) do not find a strong effect of horse access to\ndifferent plant forage species, and (iii) observe phylogenetic null-modelling evidence\nthat microbial dispersal limitation (rather than heterogenous selective pressures due to\ndiet) is the more likely mechanism underlying spatial turnover in the microbiome\n(Stothart et al. 2020, Molecular Ecology). Like Knowles et al. (2019, Ecology Letters) we\ndo not find strong evidence for diet and microbiome dissimilarity in Sable Island horses\n(Supplementary Materials; Supplementary Figure 5, L526). Like Björk et al. (2022, Nature Ecology & Evolution), we also find that components of the microbiome are\nrepeatable within individuals across years (L429). Interestingly, our results also suggest\nthat those features which are more consequential for horse survival also tend to be\nmore repeatable (Figure 7a).\nIn summary, while diet can have statistically significant effects on the\nmicrobiome, we respectfully disagree that it is the ‘main’ driver of microbiome variation,\nor that it supersedes effects of ‘geographical location, host genetics or even the animal\nspecies’. We hope that our brief overview of a broad cross-section of literature helps to\nclarify our perspective. We now include an abridged version of this discussion on L524.\n• L148 Can the AIC model be overfitted? It is well known that prediction\nmodels with a reasonable accuracy can be developed when using an\ninternal validation process which implies the use of the same dataset to\ndevelop the model and to test its performance. On the contrary, an external\nvalidation, which implies the use of a large proportion of samples for model\ndevelopment (e.g. 90%) and the remaining samples for model validation,\nwould imply a more robust determination of the model performance and it\nwould prevent model overfitting (Fig S1). The inclusion of indicators of the\nmodel performance such as the determination coefficients of calibration\nR2C, cross validation R2CV, prediction R2P and their respective root mean\nsquare erros (RMSEC, RMSECV, RMSEP), the performance to prediction\nratio (RPD) or the range in reference ratio (RER) would be appreciated.\n>> We now clarify that our purpose was not to develop a predictive tool, but rather, infer\nwhether the gut microbiome is associated with horse survival in the wild by comparing\ncompeting hypotheses of microbiome form, function, or beta diversity dispersion against\na null non-microbiome hypothesis (L159), and explore potential causal mechanisms\nwhich can be tested in future work (L176). The AIC approach we used penalizes each\nadditional terms, and in doing so, helps to prevent over-fitting/parameterization. The\ncross-validation suggested by the reviewer would be most appropriate if prediction was\nour objective. For a more detailed overview of our reasoning and the philosophical\ndifferences between exploratory, inferential, and predictive studies, and for choosing the\nmost appropriate statistical approach (including discussions of AIC and cross-\nvalidation), we refer to Tredennick et al. (2021), Ecology.\nWe are uncertain why Fig S1 is referenced as an example of model overfitting, as this\nwas not a plot of actual versus predicted values. This was intended as a demonstration\nof how, using an even smaller subset of data (1 sample per individual) we observe the\nsame general patterns of associations between the microbiome and horse survival.\n• L171-179 It is mentioned that food limitation is the most common cause of\nmortality in Sable Island horses. Thus, changes in the gut microbiota are\nmost likely associated to insufficient feed intake or to the intake of low\nquality diet (fibrous diet) which is often associated to high abundance of\nmethanogens and methanogenesis. Thus, changes in the gut microbiota\ncannot be considered as the cause of higher or lower survival as they are\nlikely an indirect effect derived from other aspects such as physical, health\nor social horse limitations which can limit food intake or lead to changes in\nthe diet (e.g. more fibrous diet). A chemical analysis of the horse faeces\n(e.g. fiber and protein content) and/or in vitro quantification of the CH4\nproduction after incubation of horse faeces with the same feed substrate\nacross animals could help to proof this hypothesis. Alternatively, a\nquantification of the fermentation products (e.g. volatile fatty acids) from\nthe horse faeces could provide useful information to link gut microbiota and function.\n>> We now clarify that the food limitation we refer to occurs seasonally (i.e. little\nbiomass during the winter) but is not limiting during the summer when horse\nmicrobiomes were sampled in the summer (L108). To help illustrate this, we now include\npanoramic photographs of the island landscape in Fig. 1, which are illustrative of the\ndense plant biomass available to horses over the summer. Sable Island horses are\nlargely reliant on energy reserves amassed during the summer to survive the\nsubsequent winter. We therefore predicted that it is this ‘summer’ microbiome that is\ncritical for horse survival (L111), since inefficiencies or gut disruptions during this time\nwould prevent individuals from accumulating the necessary fat/energy reserves. As\npreviously mentioned in an earlier comment, we now include analysis of dietary\nmetabarcoding data which do not support the prediction that variation in the Sable\nIsland horse microbiome is primarily attributable to diet.\nAs suggested, we now provide analysis of short-chain fatty acid metabolomics\ndata (L279, L381, L457). Analysis of these data strongly support the predictions made\nfrom our metagenomic dataset. We thank the reviewer for their suggestion to include\nthese data.\nAs we are working with historically archived samples from a long-term study, it is\nnot possible to retrospectively conduct the chemical and incubation experiments\nsuggested. We agree this would be an exciting next step to further test our hypotheses\nin future years of data collection.\n• L198-208 When analysing metagenomics data, it is very common to find\nassociations between gut microbes and genes as huge number of\ncombinations are tested. However, those associations do not imply\ncausality. This is particularly critical when assuming microbial effects on\ngut fermentation or CH4 production without direct measurements of those\nparameters.\n>> We agree that performing a large number of tests can result in false positive\nfindings, and this is why we applied a bias correction to account for multiple\ncomparisons (L183). Of course, this does not guarantee the elimination of all false\npositives and may even result in a greater number of false negatives. However, given\nthe diversity and complexity of the microbiome, there is little viable alternative for an\nexploratory study of this nature. This approach is therefore extremely common across\nnearly all microbiome studies.\nBecause of the potential for false positive results, we deliberately refrained from\nfocusing solely on associations between horse survival and any single microbiome\nfeature. Instead, we opted to comprehensively consider the features that show the\nstrongest associations with horse survival. In doing so, we find independent lines of\nevidence (different microbiota and different gene functions) that all suggest a connection\nbetween the microbiome the production of methane and horse survival. As suggested\nby reviewer 1, we now detail these 19 metagenomic associations in Supplementary\nFigure 4. We agree with the reviewer that these associations do not guarantee causality,\nhowever, we explain on L516, L522, L198 why a causal connection is likely. Publishing\nour results alongside our proposed mechanism lays the foundation for us, and other\nresearch groups, to begin more directly experimentally testing our hypothesis that\nmethanogenesis may be a microbiome feature under selection among wild mammalian\nherbivores (L562).\n• Moreover, most of the concepts described here (H2 sinks, sulphate or\nnitrate reduction) are derived from ruminant studies which have a different\ndigestive physiology as they are foregut fermenters while horses are\nhindgut fermenters.\n>> The concepts we discuss and cite are derived from a broad cross-section of studies\non ruminants, humans, hindgut fermenters, in vitro experimental studies, free-living\nenvironments, and lab mice. In fact, it is this recapitulation of the same metabolic\npatterns and methanogenic mechanisms across microbial communities that serves as\nthe basis for our hypothesis.\nWe respectfully disagree with the reviewer that the metabolic pathways we\ndiscuss pertain only to ruminants. For example, (i) Hydrogenotrophic methanogens\nproduce methane from H2 and CO2, whether they are in the rumen, intestines, or free-\nliving environment, (ii) non-rumen sulfate and nitrate reducers will use H while reducing\n2\nsulfate/nitrate, and (iii) propionate/succinate is a hydrogen sink when compared to the\nmost common pathways of acetate and butyrate synthesis etc. The metabolic pathways\nwe discuss with respect to hydrogenotrophy are not specific to the rumen and have\nbeen well studied across a range of host, environmental, and artificial laboratory\nconditions. Similarly, the energetic cost of enteric methane emissions have been\nquantified across a wide array of mammalian herbivores, both wild and domestic (for\nexample Clauss et al. 2020, Animal).\n• L212-226. Again, CH4, propionate, lactate and fermentation products were\nnot measured in this study. As a result, this paragraph is highly\nspeculative.\n>> As suggested by the reviewer, we now provide analysis of short-chain fatty acid\nmetabolomics data (L279, L381, L457, L465). Analysis of these data support the\npredictions made from our metagenomic dataset. We thank the reviewer for their\nsuggestion to include these data.\n• L228 Fibrobacter succinogenes produces acetate and succinate as main\nfermentation products. As acetate production releases the highest amount\nof H2 which is transformed into CH4 by methanogens, it may imply an\noverall increase in CH4 production. As indicated before, it has been\nobserved that F. succinogenes is one of the most fibrolytic microbes in the\ngut and its abundance is link to the fibre intake. This again, highlights that\nthe diet composition may be the key drier in the overwinter horse survival,\nwhile the effect in the gut microbiota may be indirect.\n>> As the reviewer suggests, Fibrobacter succinogenes produce acetate and succinate\nwhich we now clarify on L259 & L. However, succinate is known to be the major\nfermentation product, accounting for 70%-83% of the fermentation end-products\nproduced by F. succinogenes (Gokarn et al. 1997, Applied Biochemistry and\nBiotechnology), and this pattern is consistent across growth substrates (Neumann,\nWeimer, and Suen 2018, Biotechnology for Biofuels). Furthermore, as now discussed\non L290, the formation of acetate is thermodynamically unfavourable at high partial\npressures of H , conditions where the risk of methanogenic energy loss are greatest.\nmethanogenesis, despite being capable of acetate production, F. succinogenes have (i)\nbeen experimentally shown to competitively inhibit methanogenesis in vitro by\ncompeting for hydrogen (Asanuma et al. 1999, Journal of Dairy Science), (ii) been\nobservationally linked to lower methane emissions among sheep (Stewart et al. 2019,\nNature Biotechnology), and (iii) experimentally connected to lower methane emissions\namong gnotobiotic lambs (Chaucheyras-Durand et al. 2010, Applied and Environmental\nMicrobiology). Metabolic models based on species-specific K estimates for dihydrogen\nm\nfurther identify F. succinogenes as a key species capable of reducing H flow to\n2\nmethanogenesis (Ungerfeld 2020, Frontiers in Microbiology). In short, F. succinogenes\nis a well-established model bacterial species for the competitive inhibition of\nmethanogenesis in the mammalian gut.\nAs suggested by the reviewer, we now also provide analysis of short-chain fatty acid\nmetabolomics data (L279, L381, L457, L465). Analysis of these data strongly support\nthe predictions made from our metagenomic dataset. Namely, Fibrobacterota are\nnegatively associated with butyrate concentrations, and associated with increases in\npropionate concentrations (L279), as we predicted. We thank the reviewer for their\nsuggestion to include these data.\nFinally, we would kindly request clarification, since—assuming we have properly\nunderstood the reviewer—they appear to have raised contradictory concerns about the\npossible confounding effects of diet. In an earlier comment it was suggested that a high\nfibre diet will support greater methanogen abundance, thereby driving a negative\nassociation between horse survival and Methanobrevibacter—and that it is the high\nfibre content (rather than methanogens per se) that is proximately related to horse\nsurvival. If we understand properly, the reviewer is here suggesting that a high fibre diet\nis also positively related to Fibrobacter succinogenes abundance, and responsible for\nthe positive association between horse survival and Fibrobacter succinogenes\nabundance. We are perhaps misunderstanding, in which case we apologize, but these\nscenarios seem to be mutually exclusive to us. Specifically, we are now unsure as to\nwhether the reviewer is suggesting high fibre diets are positively or negatively\nassociated with horse survival.\nNonetheless, as we now clarify on L533, if dietary fibre content was the proximate\ndriver of horse survival, we would have expected gene contents related to fibre\ndegradation (e.g., cellulase gene abundance) to be associated with survival (it was not).\nSimilarly, we would have predicted fibre-degrading microbiota to show consistent, rather\nthan divergent, associations with horse survival. As noted earlier, analysis of dietary\nmetabarcoding data also do not support the interpretation that the patterns we observed\nare being chiefly driven by diet (L526).\n• L274-277 This sentence is highly speculative as fermentation products\nwere not measured in this study.\n>> As suggested by the reviewer, we now also provide analysis of short-chain fatty acid\nmetabolomics data (L279, L381, L457, L465). Analysis of these data strongly support\nthe predictions made from our metagenomic dataset. We thank the reviewer for their\nsuggestion to include these data.\n• Tables S4 to S7 are not provided.\n>> We thank the reviewer for raising concerns about this missing file. Tables S4 – S7\nwere uploaded in a separate appendix from the Supplementary Materials, since they\ncontain a large volume of statistical outputs. We have ensured they have been\nuploaded with this resubmission. If they are again absent, we ask that the reviewer\nplease contact the editor.\n• L342 The increase of protozoa during the year before death may indicate\nfood scarcity which forced that horse to eat a high-fibrous diet as protozoa\nare fibrolytic microbes. This highlights that the type of diet consumed by\nthe horse is linked to the survival rate, possibly as less fitted horses are\npushed into the worst pastures with highly fibrous grasses.\n>> As suggested, we now clarify that the food limitation we refer to occurs seasonally\n(i.e. little biomass during the winter) but is not limiting during the summer when horse\nmicrobiomes were sampled (L108). To help illustrate this, we now include panoramic\nphotographs in Figure 1, which are illustrative of the dense plant biomass available to\nhorses over the summer. Sable Island horses are largely reliant on energy reserves\namassed during the summer to survive the subsequent winter. We therefore predicted\nthat it is this ‘summer’ microbiome that is critical for horse survival (L111), since\ninefficiencies or gut disruptions during this time would prevent individuals from\naccumulating the necessary fat/energy reserves. Therefore, insufficient food intake\nwould not therefore explain the patterns we observed.\nWe now provide analyses of dietary metabarcoding data, in which we find no\nevidence for a relationship between microbiome and diet dissimilarity among Sable\nIsland horses (L526; Supplementary Materials; Supplementary Figure 5a,b). Therefore,\nthe patterns in the horse microbiome do not appear proximately attributable to\ndifferences in horse diet. Furthermore, what major dietary variation exists, can be\nlargely accounted for by fitting longitude as a covariate within survival models\n(Supplementary Figure 5c,d). These results are consistent with our previous work,\nwherein we find using phylogenetic null models that dietary factors do not appear to be\nthe main contributor to between-individual microbiome differences (Stothart et al. 2021;\nMolecular Ecology). To further test the robustness of our findings, we performed an\nadditional set of selection analyses, after excluding horses from the western tip of Sable\nIsland which have the most strongly differentiated diet profiles (attributable primarily to\naccess to a low-fibre forage, sea sandwort; Supplementary Materials). Exclusion of\nthese putative dietary outliers had superficial effects on the estimated associations\nbetween microbiome features and horse survival (Supplementary Figure 9).\nWe respectfully disagree with the reviewer’s characterization of gut ciliates as\nfibrolytic specialists. As we understand the literature, ciliates are now known to be\nkeystone predators within the gastrointestinal environment. For example, a large-scale\nmeta-analysis found that the most repeatable effect of ciliate defaunation in the rumen\nenvironment across studies is to decrease ammonia concentrations, due to the removal\nof ciliate-based degradation of bacterial proteins (Newbold et al. 2015, Frontiers in\nMicrobiology). In contrast, defaunation had nominal effects on fibre digestibility.\nBreakthroughs in techniques for cultivating rumen ciliates in vitro now reveal that\ndiffering competitive and predation pressures from ciliates shape prokaryotic community\nstructure, metabolomic profiles, and methane emissions across cultures otherwise\nincubated on identical substrates (Solomon et al. 2022, ISME).\n• L361 Is should be defined what do you mean by “natural occurring\nvariations in the hindgut microbiota”. For example, a lame or a nearly-blind\nor shy horse can have a different rumen gut microbiota in comparison with\nthe population average, but this is driven by the diet as they are less fit and\nlikely pushed to areas with the low-quality grass.\n>> As requested by the reviewer, we now clarify that ‘naturally occurring’ refers to\nmicrobiome variation within an unmanipulated free-living animal population in the wild\n(L132). This is in contrast to the vast majority of animal microbiome work, which is\nconducted in lab environments, domestic livestock, or human populations. The purpose\nof our work was to determine whether this variation was associated with horse survival\noutcomes, as predicted by hypotheses pertaining to the eco-evolutionary significance of\nhost-associated microbiomes in nature. These hypotheses are widespread in the\nliterature but have not been robustly empirically tested. This is the knowledge gap our\nwork seeks to fill. See above for our use of dietary metabarcoding data which does not\nsupport the existence of a strong relationship between the gut microbiome and horse\ndiet.\nAs detailed above and in our supplementary methods, the reviewer’s hypothesis of\nless fit horses being pushed to poorer pastures is not reflective of the ecology of this\nsystem, where large numbers of horses of varying condition are known to co-graze.\nWhat variation in ‘pasture quality’ or forage availability that exists on the island varies\nspatially from east to west and can be controlled for by fitting longitude as a covariate\nwithin our models. We further now demonstrate in the supplementary materials that diet\ncomposition is not strongly correlated with gut metagenomic variation.\n• L365 “causal dependence of survival on methanogenic pattern” can be a\nspeculation as the CH4 production was not measured and there are\npotential bias data interpretation.\n>> As suggested by the reviewer, we now clarify that we are proposing a hypothesis\nbased upon observation of the microbiome features most strongly connected with horse\nsurvival (L186, L520, L562), and by drawing on decades of experimental evidence\ndetailing the microbiome mechanisms underlying methanogenic energy loss. See earlier\nresponses for descriptions of repeatability and dietary metabarcoding analyses which\naddress the reviewer’s concerns about biased data interpretation.\n• L374-376 Most of the CH4 production from livestock in coming from\nruminants, therefore the horse, as a hindgut fermenter, cannot be used an\naccurate model for ruminants.\n>> As suggested by the reviewer, we have removed this sentence as we agree it\ndetracts from the main focus of our conclusions.\nREVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nThank you for addressing my comments.\nReviewer #3 (Remarks to the Author):\nAuthors have successfully answered all my questions and addressed my main concerns. I thank\nthem for including additional information to clarify the main findings. Therefore, I do not have\nadditional comments."}]

Decision letter