The problem of scale in the prediction and management of pathogen spillover

Disease emergence events, epidemics and pandemics all underscore the need to predict zoonotic pathogen spillover. Because cross-species transmission is inherently hierarchical, involving processes that occur at varying levels of biological organization, such predictive efforts can be complicated by the many scales and vastness of data potentially required for forecasting. A wide range of approaches are currently used to forecast spillover risk (e.g. macroecology, pathogen discovery, surveillance of human populations, among others), each of which is bound within particular phylogenetic, spatial and temporal scales of prediction. Here, we contextualize these diverse approaches within their forecasting goals and resulting scales of prediction to illustrate critical areas of conceptual and pragmatic overlap. Specifically, we focus on an ecological perspective to envision a research pipeline that connects these different scales of data and predictions from the aims of discovery to intervention. Pathogen discovery and predictions focused at the phylogenetic scale can first provide coarse and pattern-based guidance for which reservoirs, vectors and pathogens are likely to be involved in spillover, thereby narrowing surveillance targets and where such efforts should be conducted. Next, these predictions can be followed with ecologically driven spatio-temporal studies of reservoirs and vectors to quantify spatio-temporal fluctuations in infection and to mechanistically understand how pathogens circulate and are transmitted to humans. This approach can also help identify general regions and periods for which spillover is most likely. We illustrate this point by highlighting several case studies where long-term, ecologically focused studies (e.g. Lyme disease in the northeast USA, Hendra virus in eastern Australia, Plasmodium knowlesi in Southeast Asia) have facilitated predicting spillover in space and time and facilitated the design of possible intervention strategies. Such studies can in turn help narrow human surveillance efforts and help refine and improve future large-scale, phylogenetic predictions. We conclude by discussing how greater integration and exchange between data and predictions generated across these varying scales could ultimately help generate more actionable forecasts and interventions

The Microbiome Stress Project: Toward a Global Meta-Analysis of Environmental Stressors and Their Effects on Microbial Communities

Microbial community structure is highly sensitive to natural (e.g., drought, temperature, fire) and anthropogenic (e.g., heavy metal exposure, land-use change) stressors. However, despite an immense amount of data generated, systematic, cross-environment analyses of microbiome responses to multiple disturbances are lacking. Here, we present the Microbiome Stress Project, an open-access database of environmental and host-associated 16S rRNA amplicon sequencing studies collected to facilitate cross-study analyses of microbiome responses to stressors. This database will comprise published and unpublished datasets re-processed from the raw sequences into exact sequence variants using our standardized computational pipeline. Our database will provide insight into general response patterns of microbiome diversity, structure, and stability to environmental stressors. It will also enable the identification of cross-study associations between single or multiple stressors and specific microbial clades. Here, we present a proof-of-concept meta-analysis of 606 microbiomes (from nine studies) to assess microbial community responses to: (1) one stressor in one environment: soil warming across a variety of soil types, (2) a range of stressors in one environment: soil microbiome responses to a comprehensive set of stressors (incl. temperature, diesel, antibiotics, land use change, drought, and heavy metals), (3) one stressor across a range of environments: copper exposure effects on soil, sediment, activated-sludge reactors, and gut environments, and (4) the general trends of microbiome stressor responses. Overall, we found that stressor exposure significantly decreases microbiome alpha diversity and increases beta diversity (community dispersion) across a range of environments and stressor types. We observed a hump-shaped relationship between microbial community resistance to stressors (i.e., the average pairwise similarity score between the control and stressed communities) and alpha diversity. We used Phylofactor to identify microbial clades and individual taxa as potential bioindicators of copper contamination across different environments. Using standardized computational and statistical methods, the Microbiome Stress Project will leverage thousands of existing datasets to build a general framework for how microbial communities respond to environmental stress

Dynamic and integrative approaches to understanding pathogen spillover

No abstract available

Taxonomic patterns in the zoonotic potential of mammalian viruses

Predicting and simplifying which pathogens may spill over from animals to humans is a major priority in infectious disease biology. Many efforts to determine which viruses are at risk of spillover use a subset of viral traits to find trait-based associations with spillover. We adapt a new method—phylofactorization—to identify not traits but lineages of viruses at risk of spilling over. Phylofactorization is used to partition the International Committee on Taxonomy of Viruses viral taxonomy based on non-human host range of viruses and whether there exists evidence the viruses have infected humans. We identify clades on a range of taxonomic levels with high or low propensities to spillover, thereby simplifying the classification of zoonotic potential of mammalian viruses. Phylofactorization by whether a virus is zoonotic yields many disjoint clades of viruses containing few to no representatives that have spilled over to humans. Phylofactorization by non-human host breadth yields several clades with significantly higher host breadth. We connect the phylogenetic factors above with life-histories of clades, revisit trait-based analyses, and illustrate how cladistic coarse-graining of zoonotic potential can refine trait-based analyses by illuminating clade-specific determinants of spillover risk

Ecological and evolutionary drivers of hemoplasma infection and bacterial genotype sharing in a Neotropical bat community

Most emerging pathogens can infect multiple species, underlining the importance of understanding the ecological and evolutionary factors that allow some hosts to harbour greater infection prevalence and share pathogens with other species. However, our understanding of pathogen jumps is based primarily around viruses, despite bacteria accounting for the greatest proportion of zoonoses. Because bacterial pathogens in bats (order Chiroptera) can have conservation and human health consequences, studies that examine the ecological and evolutionary drivers of bacterial prevalence and barriers to pathogen sharing are crucially needed. Here were studied haemotropic Mycoplasma spp. (i.e., haemoplasmas) across a speciesâ€rich bat community in Belize over two years. Across 469 bats spanning 33 species, half of individuals and twoâ€thirds of species were haemoplasma positive. Infection prevalence was higher for males and for species with larger body mass and colony sizes. Haemoplasmas displayed high genetic diversity (21 novel genotypes) and strong host specificity. Evolutionary patterns supported codivergence of bats and bacterial genotypes alongside phylogenetically constrained host shifts. Bat species centrality to the network of shared haemoplasma genotypes was phylogenetically clustered and unrelated to prevalence, further suggesting rare—but detectable—bacterial sharing between species. Our study highlights the importance of using fine phylogenetic scales when assessing host specificity and suggests phylogenetic similarity may play a key role in host shifts not only for viruses but also for bacteria. Such work more broadly contributes to increasing efforts to understand crossâ€species transmission and the epidemiological consequences of bacterial pathogens

Homogeneous selection promotes microdiversity in the glacier-fed stream microbiome

Microdiversity, the organization of microorganisms into groups with closely related but ecologically different sub-types, is widespread and represents an important linchpin between microbial ecology and evolution. However, the drivers of microdiversification remain largely unknown. Here we show that selection promotes microdiversity in the microbiome associated with sediments in glacier-fed streams (GFS). Applying a novel phylogenetic framework, we identify several clades that are under homogeneous selection and that contain genera with higher levels of microdiversity than the rest of the genera. Overall these clades constituted ∼44% and ∼64% of community α-diversity and abundance, and both percentages increased further in GFS that were largely devoid of primary producers. Our findings show that strong homogeneous selection drives the microdiversification of specialized microbial groups putatively underlying their success in the extreme environment of GFS. This microdiversity could be threatened as glaciers shrink, with unknown consequences for microbial diversity and functionality in these ecosystems

Novel Covariance-Based Neutrality Test of Time-Series Data Reveals Asymmetries in Ecological and Economic Systems.

Systems as diverse as the interacting species in a community, alleles at a genetic locus, and companies in a market are characterized by competition (over resources, space, capital, etc) and adaptation. Neutral theory, built around the hypothesis that individual performance is independent of group membership, has found utility across the disciplines of ecology, population genetics, and economics, both because of the success of the neutral hypothesis in predicting system properties and because deviations from these predictions provide information about the underlying dynamics. However, most tests of neutrality are weak, based on static system properties such as species-abundance distributions or the number of singletons in a sample. Time-series data provide a window onto a system's dynamics, and should furnish tests of the neutral hypothesis that are more powerful to detect deviations from neutrality and more informative about to the type of competitive asymmetry that drives the deviation. Here, we present a neutrality test for time-series data. We apply this test to several microbial time-series and financial time-series and find that most of these systems are not neutral. Our test isolates the covariance structure of neutral competition, thus facilitating further exploration of the nature of asymmetry in the covariance structure of competitive systems. Much like neutrality tests from population genetics that use relative abundance distributions have enabled researchers to scan entire genomes for genes under selection, we anticipate our time-series test will be useful for quick significance tests of neutrality across a range of ecological, economic, and sociological systems for which time-series data are available. Future work can use our test to categorize and compare the dynamic fingerprints of particular competitive asymmetries (frequency dependence, volatility smiles, etc) to improve forecasting and management of complex adaptive systems

Conceptual map of covariance-testing.

<p><b>(A)</b> The dynamics of a 15-species neutral community of 10,000 individuals and migration probability <i>m</i> = 0.0002 (shown here) can be approximated by a WFP with <i>λ</i> = 20 (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005124#pcbi.1005124.e002" target="_blank">eq 1</a>). If the community is neutral, then the CVTs should yield homoskedastic plots of <i>ν</i>_<i>t</i> versus <i>f</i>_<i>t</i>. We test neutrality by randomly drawing from the 2<i>ⁿ</i> possible CVTs, performing homoskedasticity tests on <i>ν</i>_<i>t</i> versus <i>f</i>_<i>t</i>, and then testing the uniformity of the resulting P-value distribution using a modified KS-test (see details in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005124#pcbi.1005124.s001" target="_blank">S1 Text</a>, part 3). <b>(B)</b> The relative abundances of 15 independent, mean-reverting geometric brownian motions, <i>d</i> log <i>X</i>_<i>t</i> = <i>μ</i>(<i>b</i> − log <i>X</i>_<i>t</i>)<i>dt</i> + <i>σdW</i>_<i>t</i> with <i>μ</i> = 15, <i>σ</i> = 30, <i>b</i> = 10. Neutrality is rejected by the highly non-uniform distribution of P-values. The left-skewed P-value distribution indicates many CVTs had volatilities that depended on the state variable, <i>f</i>_<i>t</i>.</p

Testing a volatility-stabilized market model (VSM).

<p>Pal (2011) proved that the relative abundances / market shares in the VSM follow a WFP, i.e. are neutral. <b>(A)</b> Community dynamics under a volatility-stabilized market model in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005124#pcbi.1005124.e009" target="_blank">eq (7)</a> over the time window <i>t</i> ∈ [0, 1], with <i>n</i> = 50 species and <i>δ</i> = 0.02. <b>(B)</b> The scaling of the variance of population sizes, Var[<i>N</i>_<i>t</i>], against their mean, E[<i>N</i>_<i>t</i>], over the time window. A common assumption is that neutrality can only yield a linear relationship bewteen the mean and variance, yet the VSM provides a clear counter-example. <b>(C)</b> The scaling of <i>D</i>_Δ<i>t</i> vs. Δ<i>t</i> for different initial population sizes in the VSM. A common assumption is that the dependence of the slope of Var[<i>D</i>_Δ<i>t</i>] vs Δ<i>t</i> on the initial population size rejects neutrality, yet the VSM provides a clear counter-example. <b>(D)</b> While the existing methods suggest the VSM is not neutral, despite proven otherwise, applying our test to the VSM correctly reveals that the VSM simulated here is very close to neutrality.</p