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bioRxiv2026

Multi-cohort analysis of 37,739 oral microbiomes reveals ecologically influential health-associated microbial sub-communities across major oral subsites

The oral cavity contains multiple microbial sub-niches, but which taxa consistently play an ecologically important, health-associated role within each niche, and how conserved they are across populations, remains poorly understood, partly due to the lack of a standardised identification framework. We developed a multi-cohort framework integrating 37,739 oral microbiome profiles (16S rRNA and shotgun sequencing) from 142 cohorts (41 countries) ranking 542 taxa across four oral habitats, supragingival, subgingival, tongue-tonsil, and buccal-palate-mucosa, via a new Health-Associated-Core (HAC) score capturing consistent prevalence, ecological influence, and health-association. For saliva, with available longitudinal sampling, we extended this into a salivary-Health-Associated-Core-Keystone (sHACK) score additionally capturing stability-association, ranking 499 taxa. Using two complementary approaches for identifying ecological modules, high-sHACK salivary taxa concentrated within a single, connected sub-community of 28 members, consistently linked to prevalence, ecological influence, stability, and health. This sub-communitys abundance alone outperformed conventional dysbiosis indices in distinguishing healthy from diseased individuals and tracked stability in an independent cohort of 4,621 microbiomes. Comparable sub-communities emerged across three other subsites, with compositional differences mirroring physicochemical variation between sites. Machine learning linked taxa-specific-genome-encoded functions to their corresponding subsite-specific HAC/sHACK scores, offering a unified framework for prioritizing oral microbes diagnostically and therapeutically.

microbiology↗

bioRxiv2026

Mosaic foreleg convergence disentangles phylogeny from ecology in Cretaceous amber crickets

Mosaic evolution assembles organisms from ancestral and derived parts, and traits shaped by convergent selection can mislead phylogenetic reconstruction while recording ecology. Disentangling these two signals within the same anatomy is a challenge for placing fossils and reconstructing evolution in deep time. We present a character-partitioning framework that fixes a molecular backbone of living species, projects fossils onto it with partitioned morphological matrices, and quantifies each anatomical module's contribution to phylogenetic placement and ecological prediction. In mid-Cretaceous Myanmar amber crickets, which combine a cricket-like body with mole-cricket-like digging forelegs, the foreleg module drove most phylogenetic distortion and carried most ecological information: removing it restored a convergent living control species to its family and collapsed habitat-prediction accuracy from 77.8% to below the 44.4% baseline. Partitioning convergent modules thus separates phylogenetic signal from ecological information, turning mosaicism from a confound of fossil interpretation into a quantitative record of history and niche.

evolutionary biology↗

bioRxiv2026

Multilayer interaction networks reveal multidimensional ecological strategies and context-dependent competition in soil actinobacteria

Microbial communities are structured by interactions spanning antagonism, inducible responses, metabolic modulation, and morphogenetic effects, yet these processes are often reduced to a single antagonistic dimension. Here, we characterized pairwise interactions among 60 soil-derived actinobacterial strains, predominantly Streptomyces, across 12 phenotypes represented as a binary, directed multilayer network. The layers were neither reducible to a single ecological axis nor globally independent. Degree-preserving null models identified significant excess overlap in 8 of 65 layer pairs. Although antimicrobial release and growth inhibition formed the principal connectivity backbone, strongly connected component sizes were consistent with degree-based null expectations. Strain-level strategies formed a heterogeneous multidimensional space. Two principal components exceeded the 95th percentile of Horn parallel-analysis null, explaining 49.8% of variance; a third axis exceeded only the weaker mean-eigenvalue criterion and is reported as marginal. Gaussian mixture modelling gave no stable component count across covariance parameterizations, leaving the number of ecological groups unresolved rather than excluded. Dominance was phenotype dependent, with weak cross-layer correlations of David scores and no universal sender. Interaction responses also showed phenotype-specific spatial dependence. Ecological strategy was unrelated to 16S rRNA phylogeny. Pagel {lambda}showed no significant signal across 21 traits (all q [≥] 0.39), and multilayer ecological distance was unrelated to 16S rRNA distance ({rho}=-0.04, P=0.59), although inference was constrained by marker resolution. These results reveal a heterogeneous, multidimensional interaction landscape structured by partially coupled functional phenotypes rather than a single antagonistic hierarchy, potentially reflecting semi-independent evolution of secondary metabolism, development, sensing and stress-response pathways.

microbiology↗

bioRxiv2026

Ecological stability masks rapid, convergent evolution within the honey bee worker microbiome

While ecological dynamics within host-associated microbiomes are well-documented, how evolutionary processes unfold within them remains poorly understood. Using longitudinal metagenomics, we quantified ecological and evolutionary dynamics across over 1,000 bacterial genomes from honey bee worker microbiomes sampled across 12 colonies. We show that microbial community composition is strongly tied to colony identity and diverges continuously over time, rather than through discrete seasonal shifts. Despite this ecological turnover, most bacterial populations are stable, harboring extensive genetic variation shaped by strong purifying selection. Against this constrained backdrop, we identify thousands of rapid shifts in allele frequency that cannot be explained by strain replacement, population abundance, or genetic drift. Nonsynonymous variants exhibit localized signatures of selection at neighboring synonymous sites and independently recur within homologous proteins shared by phylogentically distant taxa found across independent colonies . Altogether, our findings demonstrate that ecological persistence can mask rapid evolutionary dynamics, revealing recurrent genetic signatures consistent with adaptive evolution across diverse bacteria.

evolutionary biology↗

bioRxiv2026

Global distributions and plant associations of the ecologically diverse ascomycete order Helotiales

Background Fungi in plant root systems are key to understanding the dynamics and functioning of terrestrial ecosystems. Whereas our knowledge of below-ground plant-fungus symbioses has derived mainly from studies of mycorrhizal and pathogenic fungi, recent studies have uncovered an unexpected diversity of ecological niches occupied by root-associated fungi. The fungal order Helotiales is conspicuous in this respect because this predominantly root-associated taxon encompasses a broad ecological spectrum, ranging from saprotrophic and pathogenic fungi to endophytic and mycorrhizal fungi with plant growth-promoting functions. In this study, we integrated a worldwide dataset of 1,996 Helotiales Species Hypotheses (i.e., species-level taxa tentatively defined based on DNA sequences) detected in roots of 362 plant genera representing 117 families and 47 orders, and systematically quantified the specificity of Helotiales-plant associations. Results The analysis of 37,688 unique occurrence records of Helotiales fungi across 1,940 global sampling locations revealed highly structured associations between Helotiales genera and plant lineages, exemplified by the strong specificity of Hyaloscypha, Oidiodendron, and Phialocephala for Pinales and that of Glutinomyces for Fagales, even after statistically controlling background biogeographic patterns. At the Species-Hypothesis-level analysis, major clades within the Helotiales phylogeny consistently exhibited high specificity for Pinaceae, whereas highly specific associations with other ectomycorrhizal, arbuscular mycorrhizal, and nonmycorrhizal plants were restricted to small terminal clades of the fungal phylogeny. Conclusions The integration of ecological specificity and phylogenetic analyses suggests the possibility that Helotiales fungi established close associations with Pinaceae early in their evolutionary history and subsequently underwent host shifts to diverse angiosperm lineages. Given the emerging evidence that Helotiales fungi play pivotal ecological and physiological roles in plant root systems, these statistical insights into Helotiales-plant associations provide a fundamental basis for understanding the evolutionary history and functioning of terrestrial ecosystems.

microbiology↗

bioRxiv2014

Emergence of structural and dynamical properties of ecological mutualistic networks

Mutualistic networks are formed when the interactions between two classes of species are mutually beneficial. They are important examples of cooperation shaped by evolution. Mutualism between animals and plants plays a key role in the organization of ecological communities1-3. Such networks in ecology have generically evolved a nested architecture4,5 independent of species composition and latitude6,7 - specialists interact with proper subsets of the nodes with whom generalists interact1. Despite sustained efforts5,8,9,10 to explain observed network structure on the basis of community-level stability or persistence, such correlative studies have reached minimal consensus11,12,13. Here we demonstrate that nested interaction networks could emerge as a consequence of an optimization principle aimed at maximizing the species abundance in mutualistic communities. Using analytical and numerical approaches, we show that because of the mutualistic interactions, an increase in abundance of a given species results in a corresponding increase in the total number of individuals in the community, as also the nestedness of the interaction matrix. Indeed, the species abundances and the nestedness of the interaction matrix are correlated by an amount that depends on the strength of the mutualistic interactions. Nestedness and the observed spontaneous emergence of generalist and specialist species occur for several dynamical implementations of the variational principle under stationary conditions. Optimized networks, while remaining stable, tend to be less resilient than their counterparts with randomly assigned interactions. In particular, we analytically show that the abundance of the rarest species is directly linked to the resilience of the community. Our work provides a unifying framework for studying the emergent structural and dynamical properties of ecological mutualistic networks2,5,10,14.

Ecology↗

bioRxiv2015

Automated discovery of relationships, models and principles in ecology

O_LIEcological systems are the quintessential complex systems, involving numerous high-order interactions and non-linear relationships. The most commonly used statistical modelling techniques can hardly reflect the complexity of ecological patterns and processes. Finding hidden relationships in complex data is now possible through the use of massive computational power, particularly by means of Artificial Intelligence methods, such as evolutionary computation.\nC_LIO_LIHere we use symbolic regression (SR), which searches for both the formal structure of equations and the fitting parameters simultaneously, hence providing the required flexibility to characterize complex ecological systems.\nC_LIO_LIFirst, we demonstrate how SR can deal with complex datasets for: 1) modelling species richness; and 2) modelling species spatial distributions. Second, we illustrate how SR can be used to find general models in ecology, by using it to: 3) develop species richness estimators; and 4) develop the species-area relationship and the general dynamic model of oceanic island biogeography.\nC_LIO_LIAll the examples suggest that evolving free-form equations purely from data, often without prior human inference or hypotheses, may represent a very powerful tool for ecologists and biogeographers to become aware of hidden relationships and suggest general theoretical models and principles.\nC_LI

Ecology↗

bioRxiv2016

An ecological assessment of the pandemic threat of Zika virus

The current outbreak of Zika virus poses a threat of unknown magnitude to human health1. While the range of the virus has been cataloged growing slowly over the last 50 years, the recent explosive expansion in the Americas indicates that the full potential distribution of Zika remains uncertain2-4. Moreover, most current epidemiology relies on its similarities to dengue fever, a phylogenetically closely related disease of unknown similarity in spatial range or ecological niche5,6. Here we compile the first spatially explicit global occurrence dataset from Zika viral surveillance and serological surveys, and construct ecological niche models to test basic hypotheses about its spread and potential establishment. The hypothesis that the outbreak of cases in Mexico and North America are anomalous and outside the ecological niche of the disease, and may be linked to El Nino or similar climatic events, remains plausible at this time7. Comparison of the Zika niche against the known distribution of dengue fever suggests that Zika is more constrained by the seasonality of precipitation and diurnal temperature fluctuations, likely confining the disease to the tropics outside of pandemic scenarios. Projecting the range of the diseases in conjunction with vector species (Aedes africanus, Ae. aegypti, and Ae. albopictus) that transmit the pathogens, under climate change, suggests that Zika has potential for northward expansion; but, based on current knowledge, Zika is unlikely to fill the full range its vectors occupy. With recent sexual transmission of the virus known to have occurred in the United States, we caution that our results only apply to the vector-borne aspect of the disease, and while the threat of a mosquito-carried Zika pandemic may be overstated in the media, other transmission modes of the virus may emerge and facilitate naturalization worldwide.

Ecology↗

bioRxiv2016

The power and pitfalls of Dirichlet-multinomial mixture models for ecological count data

The Dirichlet-multinomial mixture model (DMM) and its extensions provide powerful new tools for interpreting the ecological dynamics underlying taxon abundance data. However, like many complex models, how effectively they capture the many features of empirical data is not well understood. In this work, we expand the DMM to an infinite mixture model (iDMM) and use posterior predictive distributions (PPDs) to explore the performance in three case studies, including two amplicon metagenomic time series. We avoid concentrating on fluctuations within individual taxa and instead focus on consortial-level dynamics, using straight-forward methods for visualizing this perspective. In each study, the iDMM appears to perform well in organizing the data as a framework for biological interpretation. Using the PPDs, we also observe several exceptions where the data appear to significantly depart from the model in ways that give useful ecological insight. We summarize the conclusions as a set of considerations for field researchers: problems with samples and taxa; relevant scales of ecological fluctuation; additional niches as outgroups; and possible violations of niche neutrality.

Ecology↗

bioRxiv2016

A phenomenological spatial model for macro-ecological patterns in species-rich ecosystems

Over the last few decades, ecologists have come to appreciate that key ecological patterns, which describe ecological communities at relatively large spatial scales, are not only scale dependent, but also intimately intertwined. The relative abundance of species - which informs us about the commonness and rarity of species - changes its shape from small to large spatial scales. The average number of species as a function of area has a steep initial increase, followed by decreasing slopes at large scales. Finally, if we find a species in a given location, it is more likely we find an individual of the same species close-by, rather than farther apart. Such spatial turnover depends on the geographical distribution of species, which often are spatially aggregated. This reverberates on the abundances as well as the richness of species within a region, but so far it has been difficult to quantify such relationships.\n\nWithin a neutral framework - which considers all individuals competitively equivalent - we introduce a spatial stochastic model, which phenomenologically accounts for birth, death, immigration and local dispersal of individuals. We calculate the pair correlation function - which encapsulates spatial turnover - and the conditional probability to find a species with a certain population within a given circular area. Also, we calculate the macro-ecological patterns, which we have referred to above, and compare the analytical formulae with the numerical integration of the model. Finally, we contrast the model predictions with the empirical data for two lowland tropical forest inventories, showing always a good agreement.

Ecology↗

bioRxiv2016

ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling

Species distribution modeling is a valuable tool with many applications across ecology and evolutionary biology. The selection of biologically meaningful environmental variables that determine relative habitat suitability is a crucial aspect of the modeling pipeline. The 19 bioclimatic variables from WorldClim are frequently employed, primarily because they are easily accessible and available globally for past, present and future climate scenarios. Yet, the availability of relatively few other comparable environmental datasets potentially limits our ability to select appropriate variables that will most successfully characterize a species distribution. We identified a set of 16 climatic and two topographic variables in the literature, which we call the O_SCPLOWENVIREMC_SCPLOW dataset, many of which are likely to have direct relevance to ecological or physiological processes determining species distributions. We generated this set of variables at the same resolutions as WorldClim, for the present, mid-Holocene, and Last Glacial Maximum (LGM). For 20 North American vertebrate species, we then assessed whether including the O_SCPLOWENVIREMC_SCPLOW variables led to improved species distribution models compared to models using only the existing WorldClim variables. We found that including the ENVIREM dataset in the pool of variables to select from led to substantial improvements in niche modeling performance in 17 out of 20 species. We also show that, when comparing models constructed with different environmental variables, differences in projected distributions were often greater in the LGM than in the present. These variables are worth consideration in species distribution modeling applications, especially as many of the variables have direct links to processes important for species ecology. We provide these variables for download at multiple resolutions and for several time periods at envirem.github.io. Furthermore, we have written the envirem R package to facilitate the generation of these variables from other input datasets.

Ecology↗

bioRxiv2016

Ecological Interactions and the Netflix Problem

0Species interactions are a key component of ecosystems but we generally have an incomplete picture of who-eats-who in a given community. Different techniques have been devised to predict species interactions using theoretical models or abundances. Here, we explore the K nearest neighbour approach, with a special emphasis on recommendation, along with other machine learning techniques. Recommenders are algorithms developed for companies like Netflix to predict if a customer would like a product given the preferences of similar customers. These machine learning techniques are well-suited to study binary ecological interactions since they focus on positive-only data. We also explore how the K nearest neighbour approach can be used with both positive and negative information, in which case the goal of the algorithm is to fill missing entries from a matrix (imputation). By removing a prey from a predator, we find that recommenders can guess the missing prey around 50% of the times on the first try, with up to 881 possibilities. Traits do not improve significantly the results for the K nearest neighbour, although a simple test with a supervised learning approach (random forests) show we can predict interactions with high accuracy using only three traits per species. This result shows that binary interactions can be predicted without regard to the ecological community given only three variables: body mass and two variables for the species phylogeny. These techniques are complementary, as recommenders can predict interactions in the absence of traits, using only information about other species interactions, while supervised learning algorithms such as random forests base their predictions on traits only but do not exploit other species interactions. Further work should focus on developing custom similarity measures specialized to ecology to improve the KNN algorithms and using richer data to capture indirect relationships between species.

ecology↗

bioRxiv2017

Unifying Population and Landscape Ecology with Spatial Capture-recapture

Spatial heterogeneity in the environment induces variation in population demographic rates and dispersal patterns, which result in spatio-temporal variation in density and gene flow. Unfortunately, applying theory to learn about the role of spatial structure on populations has been hindered by the lack of mechanistic spatial models and inability to make precise observations of population structure. Spatial capture-recapture (SCR) represents an individual-based analytic framework for overcoming this fundamental obstacle that has limited the utility of ecological theory. SCR methods make explicit use of spatial encounter information on individuals in order to model density and other spatial aspects of animal population structure, and have been widely adopted in the last decade. We review the historical context and emerging developments in SCR models that enable the integration of explicit ecological hypotheses about landscape connectivity, movement, resource selection, and spatial variation in density, directly with individual encounter history data obtained by new technologies (e.g., camera trapping, non-invasive DNA sampling). We describe ways in which SCR methods stand to revolutionize the study of animal population ecology.

ecology↗

bioRxiv2017

Proper experimental design requires randomization/balancing of molecular ecology experiments

Properly designed (randomized and/or balanced) experiments are standard in ecological research. Molecular methods are increasingly used in ecology, but studies generally do not report the detailed design of sample processing in the laboratory. This may strongly influence the interpretability of results if the laboratory procedures do not account for the confounding effects of unexpected laboratory events. We demonstrate this with a simple experiment where unexpected differences in laboratory processing of samples would have biased results if randomization in DNA extraction and PCR steps do not provide safeguards. We emphasize the need for proper experimental design and reporting of the laboratory phase of molecular ecology research to ensure the reliability and interpretability of results.

ecology↗

bioRxiv2017

Elucidating dispersal ecology of reclusive species through genetic analyses of parentage and relatedness: the island night lizard (Xantusia riversiana) as a case study.

Characterizing dispersal and movement patterns are vital to understanding the evolutionary ecology of species. For many reclusive species, such as reptiles, the observation of direct dispersal may be difficult or intractable. However, dispersal distances and patterns may be characterized through indirect genetic methods. We used genetic and capture data from the island night lizard (Xantusia riversiana) to estimate natal dispersal distances through indirect genetic methods, characterize movement and space use patterns, and compare these distances to previous estimates made from more traditional ecological approaches. We found that indirect estimates of natal dispersal were greater than previous field-based estimates of individual displacement of 3-5 m. Parent-offspring differences had a mean of approximately 14 m on Santa Barbara Island (SBI) and 41 m on San Clemente Island (SCI) whereas Wrights {sigma} was estimated at 16 m on SBI and 20 m for SCI. Spatial autocorrelation with correlograms of Morans I revealed large differences in the scale of autocorrelation between islands (SBI=375 m, SCI=1,813 m). Interpretation of these distances as average per generation distance of gene flow was incongruent with parentage analyses and {sigma}. We also used variograms to evaluate the range of spatial autocorrelation among two inter-individual genetic differences. The range of spatial autocorrelation again identified different scales on the two islands (102 - 169 m on SBI and 955 - 1,424 m on SCI). No evidence of sex-biased dispersal was found on either island. However, a permutation logistic regression revealed that related individuals >0.8 years old were more likely to be captured together on both islands. Overall, our findings suggest that field-based estimates of individual displacement within this species may underestimate genetic dispersal. We suggest indirect inferences of natal dispersal distances should focus on parentage analyses and Wrights {sigma} for parameter estimation of individual movement, whereas the ranges identified by spatial autocorrelation and variograms are likely to be relevant at the metapopulation or patch scales. Furthermore, characterization of capture patterns and relatedness revealed kin-affiliative behavior in X. riversiana, which may be indicative of delayed dispersal and cryptic sociality. These results highlight the power of parentage- and relatedness-based analyses for characterizing aspects of the movement ecology of reclusive species that may be difficult to observe directly. These data can then be leveraged to support future conservation and population modeling efforts and assess extinction risks and management strategies.

ecology↗

bioRxiv2017

Food Webs Over Time: Evaluating The Variability Of Degree Distribution On Ecological Networks

Although networks analysis has moved from static to dynamic, ecological networks are still analyzed as time-aggregated units where time-specific interactions are aggregated into one single network. As a result, several questions arise such as what is the functional form of and how variable is the topology of time-specific versus time-aggregated ecological networks? Furthermore, it is yet unknown to what extent the structure of time-aggregated networks is representative of the dynamics of the community. Here, we compared the topology of time-specific and time-aggregated networks by analyzing a set of intertidal networks containing more than 1,000 interactions, and assessed the spatiotemporal dynamics of their degree distributions. By fitting different distribution models, we found that the out-degree distributions of seasonal and time-aggregated networks were best described by an exponential model while the in-degree distributions were best described by a discrete generalized beta model. The degree distributions of the seasonal networks were highly temporally variable and are significantly different from those of time-aggregated networks. We observed that seasonal degree distributions converged toward time-aggregated network distributions after 1.5 years of sampling. Our results highlight the importance of understanding the dynamics of ecological networks, which can show topological characteristics significantly different from those of time-aggregated networks.

ecology↗

bioRxiv2017

The cavity method for community ecology

This article is addressed to researchers and students in theoretical ecology, as an introduction to \"disordered systems\" approaches from statistical physics, and how they can help understand large ecological communities. We discuss the relevance of these approaches, and how they fit within the broader landscape of models in community ecology. We focus on a remarkably simple technique, the cavity method, which allows to derive the equilibrium properties of Lotka-Volterra systems. We present its predictions, the new intuitions it suggests, and its technical underpinnings. We also discuss a number of new results concerning possible extensions, including different functional responses and community structures.

ecology↗

bioRxiv2017

Habitat diversification promotes environmental selection in planktonic prokaryotes and ecological drift in microbial eukaryotes

Whether or not communities of microbial eukaryotes are structured in the same way as prokaryotes is a basic and poorly explored question in ecology. Here we investigated this question in a set of planktonic lake microbiotas in Eastern Antarctica that represent a natural community ecology experiment. Most of the analysed lakes emerged from the sea during the last 6,000 years, giving rise to waterbodies that originally contained marine microbiotas and that subsequently evolved into habitats ranging from freshwater to hypersaline. We show that habitat diversification has promoted environmental selection driven by a salinity gradient in prokaryotes and ecological drift in microeukaryotes. Nevertheless, we detected also a number of microeukaryotes with specific responses to salinity, indicating that albeit minor, environmental selection has had a role in the assembly of their communities. Altogether, we conclude that habitat diversification can promote contrasting responses in planktonic microeukaryotes and prokaryotes belonging to the same communities.

ecology↗