New Framework Reveals Whether Hosts or Environments Shape Wildlife Microbiomes

Every animal carries an invisible ecosystem. Bacteria, fungi and other microorganisms live on the skin, in the mouth, along the respiratory tract and throughout the gut, forming what scientists call the microbiome. In humans, decades of research have revealed that these microbial communities are not passive passengers but active partners in health, influencing digestion, immunity and even mood. Now, a team of researchers led by Vanessa Morris of Macquarie University argues that wildlife conservation is dangerously overlooking this hidden dimension of animal biology, and they have proposed a practical framework to fix the problem. Writing in the journal Discover Conservation, the authors present a comparative approach designed to answer a deceptively simple question: when a wild population’s microbiome changes, is the cause inside the animal or outside it?

The stakes are far higher than the question might suggest. The authors compile a sobering catalogue of cases where disrupted microbiomes have translated into real consequences for wild species. In coral reefs, extremely polluted environments alter the bacterial communities of corals in ways that culminate in bleaching, the loss of the symbiotic microbes and algae on which the animals depend. Honey bees exposed to in-hive pesticides show significant alterations to their gut bacterial communities, undermining their ability to metabolise essential nutrients. The critically endangered Egyptian vulture, which forages on livestock carcasses contaminated with antibiotics, suffers life-threatening oral fungal infections that restrict population growth. Perhaps most strikingly, rising temperatures linked to climate change have shifted the faecal microbiota of wild meerkats, leading to reduced body condition and greater exposure to disease.

These examples illustrate a central point of the new perspective: microbiome disruption may be the easily overlooked mechanism that tips a struggling population toward collapse. The authors argue that while microbial adaptation can sometimes buffer hosts against environmental change, rapid or extreme perturbation can overwhelm that capacity. In a world of accelerating habitat loss, contamination, emerging disease and climate disruption, they contend that conservation biology cannot afford to treat microbes as an afterthought. Ignoring them, they warn, may impose a hidden toll on conservation efforts, quietly undermining interventions that appear sound on every other measure.

To bring microbiome thinking into mainstream conservation practice, the researchers first clarify what shapes microbial communities in the first place. They define drivers as influences that cause changes in the state of a microbiome, either by introducing new microbes or by exerting selective pressures that filter the microbes already present. Drivers fall into two broad categories. Endogenous drivers originate within the host and include genetics, relatedness, physiology, anatomy, age and reproductive stage. Exogenous drivers come from outside the host and include habitat type, season, diet, social interactions and anthropogenic impacts such as pollution. The diversity and magnitude of these drivers vary enormously across individuals, populations, species and environments, which is precisely what makes them so difficult to disentangle.

The problem, as the authors see it, is that many microbiome studies in wild animals attempt to identify a single driver without accounting for the fact that drivers are intertwined, correlated, confounded or simply not sampled. They point to the example of beluga whales, whose skin microbiomes were found to differ between two geographical areas. That observation alone, they note, does not reveal why the difference exists. Without evaluating host population data, it remains uncertain whether the divergence reflects population genetics, an endogenous driver, or environmental conditions, an exogenous one. Simply documenting that two populations host different microbes, they argue, does not inform conservation. What managers need to know is which factor dominates, because that determines what kind of action might help.

The framework the team proposes is deliberately simple, designed to be applied by ecologists across any taxon and any body site, not just the gut. It works by comparing the microbiomes of two or more populations that differ in their host characteristics, their environmental characteristics, or both. Host similarity is confirmed using molecular tools such as genomic distance analysis, while environmental similarity can be measured through geographical distance, diet type or other relevant variables. Microbial similarity is quantified using standard dissimilarity and distance indices that compare community composition. The comparisons are organised into four quadrants: similar hosts in similar environments, similar hosts in different environments, different hosts in similar environments, and different hosts in different environments. Depending on whether the microbiomes of the compared populations turn out to be similar or different, the framework points to either an endogenous or an exogenous key driver, or indicates that a third population must be sampled to resolve the ambiguity.

Worked examples drawn from the published literature show the framework in action. In central Ghana, social grouping in the white-thighed black-and-white colobus monkey explained differences in gut microbial composition even though the groups shared the same semi-deciduous forest habitat, with age, collection site, reproductive status and relatedness all ruled out as predictors. Identifying the driver as exogenous underscores the importance of protecting habitat and maintaining connectivity for this critically endangered primate. In Lithuania, by contrast, the gut microbial composition of voles, shrews and mice was more similar within species than between them, regardless of habitat, pointing to host identity as the dominant force. Olympia oysters from the same parental family showed gut bacteria that varied by field site, apparently in response to differences in temperature and dissolved oxygen, an exogenous signal with direct implications for restoration management.

Further examples reinforce the framework’s versatility. In a Malagasy reserve, gut microbiome structure across six mammal species was primarily driven by species type, with genetic distance a significant predictor even after controlling for diet and geography, though species sharing terrestrial habitat also showed similar microbial structure, hinting at bacterial transmission between ground-dwelling animals and possible shared vulnerability to disease. Among Darwin’s finches, eleven of twelve species sampled across nine islands had similar gut microbiomes, suggesting remarkable conservation of these communities over short evolutionary timescales. And in Oregon, genetic divergence, rather than environment or location, best predicted gut microbiome composition in wild threespine stickleback, a conclusion made possible only because the underlying study sampled multiple populations across estuarine and freshwater environments at both coastal and inland sites.

The conservation payoffs flow in two directions. Where an exogenous driver dominates, managers can act on the environment itself. Marine sponges offer a pertinent case: these animals depend on symbiotic bacteria for nutrients, and when water temperatures exceed suitable conditions they undergo a bleaching process akin to that of corals, losing their microbial communities and dying. Because tropical and subtropical environments, rather than host phylogeny, correlate with sponge microbiomes worldwide, populations in rapidly warming waters can be prioritised for protection, for example through marine protected areas that reduce compounding threats like overexploitation. Where an endogenous driver dominates, microbiome data can instead serve as a window into population connectivity, health status and disease risk. Host genetic diversity, for instance, plays a key role in structuring the skin microbiomes of amphibians, and understanding population genomics can identify which populations are most susceptible to the devastating chytrid fungus Batrachochytrium dendrobatidis, informing both monitoring and direct interventions such as probiotic strategies.

The authors are candid about the framework’s limits. Drivers may not operate in isolation, historic exposures can shape present-day susceptibility, and when populations share similar environments or diets the key driver may be genuinely ambiguous. Temporal scale matters too, since the dominant driver can change over an individual’s lifespan or across seasons. Composition, moreover, is not the same as function: microbial communities can differ in their constituent species yet perform identical roles through functional redundancy, or lose critical functions despite superficially similar composition, as illustrated by Père David’s deer, where wild and captive populations showed functional differences only at finer taxonomic resolution. Even so, the researchers argue that measuring composition is cheaper, faster and more accessible than measuring function, making it a practical first step. Their broader message is historical as much as technical: conservation has progressed from a focus on individuals in the 1960s, to genetic diversity in the 1980s, to behaviour and genetic structure in later decades, and the next frontier is the microbial communities that wildlife hosts. Without ensuring microbiome continuance, they warn, there may be a collapse of the entire edifice, and taking a precautionary approach means protecting microbiomes before it is too late to understand what their changes mean.

Subject of Research: Disentangling endogenous and exogenous drivers of wildlife microbiome composition for conservation

Article Title: A framework for disentangling drivers of microbiome composition for wildlife conservation

Article References: Morris, V., Pitcher, B. J., Harcourt, R., Charrier, I., & Chariton, A. (2026). A framework for disentangling drivers of microbiome composition for wildlife conservation. Discover Conservation, 3(1), Article 18. https://doi.org/10.1007/s44353-026-00088-w

Image Credits: AI Generated

DOI: 10.1007/s44353-026-00088-w

Keywords: microbiome, wildlife conservation, endogenous drivers, exogenous drivers, host genetics, microbial ecology, population comparisons, habitat loss, climate change, disease susceptibility, probiotics, biodiversity