Fluctuating temperatures alter environmental pathogen transmission in a Daphnia-pathogen system

Environmental conditions are rarely constant, but instead vary spatially and temporally. This variation influences ecological interactions and epidemiological dynamics, yet most experimental studies examine interactions under constant conditions. We examined the effects of variability in temperature on the host–pathogen relationship between an aquatic zooplankton host (Daphnia laevis) and an environmentally transmitted fungal pathogen (Metschnikowia bicuspidata). We manipulated temperature variability by exposing all populations to mean temperatures of 20°C for the length of the experiments, but introducing periods of 1, 2, and 4 hr each day where the populations were exposed to 28°C followed by periods of the same length (1, 2, and 4 hr, respectively) where the populations were exposed to 12°C. Three experiments were performed to assess the role of thermal variability on Daphnia–pathogen interactions, specifically with respect to: (1) host infection prevalence and intensity; (2) free‐living pathogen survival; and (3) host foraging ecology. We found that temperature variability affected host filtering rate, which is closely related to pathogen transmission in this system. Further, infection prevalence was reduced as a function of temperature variability, while infection intensity was not influenced, suggesting that pathogen transmission was influenced by temperature variability, but the growth of pathogen within infected hosts was not. Host survival was reduced by temperature variability, but environmental pathogen survival was unaffected, suggesting that zooplankton hosts were more sensitive than the fungal pathogen to variable temperatures. Together, these experiments suggest that temperature variability may influence host demography and host–pathogen interactions, providing a link between host foraging ecology and pathogen transmission.

Waiting time to infectious disease emergence

Emerging diseases must make a transition from stuttering chains of transmission to sustained chains of transmission, but this critical transition need not coincide with the system becoming supercritical. That is, the introduction of infection to a supercritical system results in a significant fraction of the population becoming infected only with a certain probability. Understanding the waiting time to the first major outbreak of an emerging disease is then more complicated than determining when the system becomes supercritical. We treat emergence as a dynamic bifurcation, and use the concept of bifurcation delay to understand the time to emergence after a system becomes supercritical. Specifically, we consider an SIR model with a time-varying transmission term and random infections originating from outside the population. We derive an analytic density function for the delay times and find it to be, in general, in agreement with stochastic simulations. We find the key parameters to be the rate of introduction of infection and the rate of change of the basic reproductive ratio. These findings aid our understanding of real emergence events, and can be incorporated into early-warning systems aimed at forecasting disease risk.

The contribution of marine aggregate-associated bacteria to the accumulation of pathogenic bacteria in oysters: an agent-based model

Bivalves process large volumes of water, leading to their accumulation of bacteria, including potential human pathogens (e.g., vibrios). These bacteria are captured at low efficiencies when freely suspended in the water column, but they also attach to marine aggregates, which are captured with near 100% efficiency. For this reason, and because they are often enriched with heterotrophic bacteria, marine aggregates have been hypothesized to function as important transporters of bacteria into bivalves. The relative contribution of aggregates and unattached bacteria to the accumulation of these cells, however, is unknown. We developed an agent‐based model to simulate accumulation of vibrio‐type bacteria in oysters. Simulations were conducted over a realistic range of concentrations of bacteria and aggregates and incorporated the dependence of pseudofeces production on particulate matter. The model shows that the contribution of aggregate‐attached bacteria depends strongly on the unattached bacteria, which form the colonization pool for aggregates and are directly captured by the simulated oysters. The concentration of aggregates is also important, but its effect depends on the concentration of unattached bacteria. At high bacterial concentrations, aggregates contribute the majority of bacteria in the oysters. At low concentrations of unattached bacteria, aggregates have a neutral or even a slightly negative effect on bacterial accumulation. These results provide the first evidence suggesting that the concentration of aggregates could influence uptake of pathogenic bacteria in bivalves and show that the tendency of a bacterial species to remain attached to aggregates is a key factor for understanding species‐specific accumulation.

Spatial spread of the West Africa Ebola epidemic

Controlling Ebola outbreaks and planning an effective response to future emerging diseases are enhanced by understanding the role of geography in transmission. Here we show how epidemic expansion may be predicted by evaluating the relative probability of alternative epidemic paths. We compared multiple candidate models to characterize the spatial network over which the 2013-2015 West Africa epidemic of Ebola virus spread and estimate the effects of geographical covariates on transmission during peak spread. The best model was a generalized gravity model where the probability of transmission between locations depended on distance, population density and international border closures between Guinea, Liberia and Sierra Leone and neighbouring countries. This model out-performed alternative models based on diffusive spread, the force of infection, mobility estimated from cell phone records and other hypothesized patterns of spread. These findings highlight the importance of integrated geography to epidemic expansion and may contribute to identifying both the most vulnerable unaffected areas and locations of maximum intervention value.

Undiscovered Bat Hosts of Filoviruses

Ebola and other filoviruses pose significant public health and conservation threats by causing high mortality in primates, including humans. Preventing future outbreaks of ebolavirus depends on identifying wildlife reservoirs, but extraordinarily high biodiversity of potential hosts in temporally dynamic environments of equatorial Africa contributes to sporadic, unpredictable outbreaks that have hampered efforts to identify wild reservoirs for nearly 40 years. Using a machine learning algorithm, generalized boosted regression, we characterize potential filovirus-positive bat species with estimated 87% accuracy. Our model produces two specific outputs with immediate utility for guiding filovirus surveillance in the wild. First, we report a profile of intrinsic traits that discriminates hosts from non-hosts, providing a biological caricature of a filovirus-positive bat species. This profile emphasizes traits describing adult and neonate body sizes and rates of reproductive fitness, as well as species’ geographic range overlap with regions of high mammalian diversity. Second, we identify several bat species ranked most likely to be filovirus-positive on the basis of intrinsic trait similarity with known filovirus-positive bats. New bat species predicted to be positive for filoviruses are widely distributed outside of equatorial Africa, with a majority of species overlapping in Southeast Asia. Taken together, these results spotlight several potential host species and geographical regions as high-probability targets for future filovirus surveillance.

Preventing future outbreaks of ebolaviruses in humans and other vulnerable animal populations will require identifying the natural reservoirs of filoviruses. Accumulating indirect evidence points to certain bat species as prime suspects. To guide the search for natural filovirus reservoirs, we mined intrinsic biological data on the world’s bat species to determine what features best predict filovirus hosts compared to bats at large. We report a suite of traits that distinguishes seropositive bat species from all others with an estimated 87% accuracy. We also identify several bat species not currently known to be filovirus hosts whose trait profiles indicate should be surveillance targets. Geographic regions where numerous potential filovirus hosts co-occur (potential filovirus hotspots) suggest that filovirus distribution and diversity may be greater than previously thought.

The macroecology of infectious diseases: a new perspective on global-scale drivers of pathogen distributions and impacts

Identifying drivers of infectious disease patterns and impacts at the broadest scales of organisation is one of the most crucial challenges for modern science, yet answers to many fundamental questions remain elusive. These include what factors commonly facilitate transmission of pathogens to novel host species, what drives variation in immune investment among host species, and more generally what drives global patterns of parasite diversity and distribution? Here we consider how the perspectives and tools of macroecology, a field that investigates patterns and processes at broad spatial, temporal and taxonomic scales, are expanding scientific understanding of global infectious disease ecology. In particular, emerging approaches are providing new insights about scaling properties across all living taxa, and new strategies for mapping pathogen biodiversity and infection risk. Ultimately, macroecology is establishing a framework to more accurately predict global patterns of infectious disease distribution and emergence.

Global Patterns of Zoonotic Disease in Mammals

As the frequency and prevalence of zoonotic diseases increase worldwide, investigating how mammal host distributions determine patterns of human disease and predicting which regions are at greatest risk for future zoonotic disease emergence are two goals which both require better understanding of the current distributions of zoonotic hosts and pathogens. We review here the existing data about mammalian host species, comparing and contrasting these patterns against global maps of zoonotic hosts from all 27 orders of terrestrial mammals. We discuss the zoonotic potential of host species from the top six most species-rich mammal groups, and review the literature to identify analytical and conceptual gaps that must be addressed to improve our ability to generate testable predictions about zoonotic diseases originating from wild mammals.

Predicting zoonotic disease events remains a prominent scientific challenge.

In response to increasing frequency of emerging infectious disease events caused by animal-borne (zoonotic) pathogens, recent advances assess the biogeographic patterns of human infectious diseases.

A disproportionate representation of mammal-borne zoonoses among emerging human disease has sparked research emphasis on mammal reservoirs because improved understanding of mammal host distributions may lead to improved predictions of future hotspots for zoonotic disease emergence.

In addition to spatial distributions of animal hosts and human disease, the concept of ‘disease risk’ is a topic of intense analysis, and has been quantified on the basis of hindsight where regions undergoing frequent or intense human disease events are categorized as possessing numerous factors that interact to increase disease risk.

The potential for sexual transmission to compromise control of Ebola virus outbreaks

Recent evidence suggests that sexual contact may give rise to transmission of Ebola virus long after infection has been cleared from blood. We develop a simple mathematical model that incorporates contact transmission and sexual transmission parametrized from data relating to the 2013-2015 West African Ebola epidemic. The model explores scenarios where contact transmission is reduced following infection events, capturing behaviour change, and quantifies how these actions reducing transmission may be compromised by sexual transmission in terms of increasing likelihood, size and duration of outbreaks. We characterize the extent to which sexual transmission operates in terms of the probability of initial infection resolving to sexual infectiousness and the sexual transmission rate, and relate these parameters to the overall case burden. We find that sexual transmission can have large effects on epidemic dynamics (increasing attack ratios from 25% in scenarios without sexual transmission but with contact-transmission-reducing behaviour, up to 80% in equivalent scenarios with sexual transmission).

Modelling the multidimensional niche by linking functional traits to competitive performance

Linking competitive outcomes to environmental conditions is necessary for understanding species' distributions and responses to environmental change. Despite this importance, generalizable approaches for predicting competitive outcomes across abiotic gradients are lacking, driven largely by the highly complex and context-dependent nature of biotic interactions. Here, we present and empirically test a novel niche model that uses functional traits to model the niche space of organisms and predict competitive outcomes of co-occurring populations across multiple resource gradients. The model makes no assumptions about the underlying mode of competition and instead applies to those settings where relative competitive ability across environments correlates with a quantifiable performance metric. To test the model, a series of controlled microcosm experiments were conducted using genetically related strains of a widespread microbe. The model identified trait microevolution and performance differences among strains, with the predicted competitive ability of each organism mapped across a two-dimensional carbon and nitrogen resource space. Areas of coexistence and competitive dominance between strains were identified,and the predicted competitive outcomes were validated in approximately 95% of the pairings. By linking trait variation to competitive ability, our work demonstrates a generalizable approach for predicting and modelling competitive outcomes across changing environmental contexts.

Transmission Models of Historical Ebola Outbreaks

To guide the collection of data under emergent epidemic conditions, we reviewed compartmental models of historical Ebola outbreaks to determine their implications and limitations. We identified future modeling directions and propose that the minimal epidemiologic dataset for Ebola model construction comprises duration of incubation period and symptomatic period, distribution of secondary cases by infection setting, and compliance with intervention recommendations.

Competition-mediated feedbacks in experimental multispecies epizootics

Competition structures ecological communities and alters host-pathogen interactions. In environmentally transmitted pathogens, an infection-resistant competitor may influence infection dynamics in a susceptible species through the negative impacts of competition (e.g., by reducing host density or causing nutritional stress that increases susceptibility to infection) and/or the positive impacts of reducing transmission efficiency (e.g., by removing environmental pathogen stages). Thus, a non-susceptible competitor may enhance, reduce, or have no net effect on susceptible host density and infection prevalence. Here, we couple an epidemiological model with experimental epidemics to test how resource competition with a non-susceptible competitor (Daphnia pulicaria) influences fungal microparasite (Metschnikowia bicuspidata) infection dynamics in a susceptible host species (D. dentifera). Our model and experiments suggest that competitor density can mediate the direction and magnitude of the effect of competition on infection dynamics, with a peak in infection prevalence occurring at intermediate competitor densities. At low densities, the non-susceptible competitor D. pulicaria may reduce infection prevalence in the susceptible host by removing fungal spores from the environment through feeding. However, when competitor density is increased and resources become limiting, D. pulicaria negatively impacts the susceptible host by increasing susceptible host feeding rates, and therefore fungal spore intake, and further by reducing susceptible host population size as it is driven toward competitive exclusion. In conclusion, these results show that a tradeoff between the competitor as a consumer of pathogen, which serves to reduce epidemic size, and as a modifier of susceptible host foraging ecology, which influences infection rates, may alternately enhance or dampen the magnitude of local epidemics.

Costs of resistance and infection by a generalist pathogen

Pathogen infection is typically costly to hosts, resulting in reduced fitness. However, pathogen exposure may also come at a cost even if the host does not become infected. These fitness reductions, referred to as “resistance costs”, are inducible physiological costs expressed as a result of a trade‐off between resistance to a pathogen and aspects of host fitness (e.g., reproduction). Here, we examine resistance and infection costs of a generalist fungal pathogen (Metschnikowia bicuspidata) capable of infecting a number of host species. Costs were quantified as reductions in host lifespan, total reproduction, and mean clutch size as a function of pathogen exposure (resistance cost) or infection (infection cost). We provide empirical support for infection costs and modest support for resistance costs for five Daphnia host species. Specifically, only one host species examined incurred a significant cost of resistance. This species was the least susceptible to infection, suggesting the possibility that host susceptibility to infection is associated with the detectability and size of resistance cost. Host age at the time of pathogen exposure did not influence the magnitude of resistance or infection cost. Lastly, resistant hosts had fitness values intermediate between unexposed control hosts and infected hosts. Although not statistically significant, this could suggest that pathogen exposure does come at some marginal cost. Taken together, our findings suggest that infection is costly, resistance costs may simply be difficult to detect, and the magnitude of resistance cost may vary among host species as a result of host life history or susceptibility.

Leading indicators of mosquito-borne disease elimination

Mosquito-borne diseases contribute significantly to the global disease burden. High-profile elimination campaigns are currently underway for many parasites, e.g., Plasmodium spp., the causal agent of malaria. Sustaining momentum near the end of elimination programs is often difficult to achieve and consequently quantitative tools that enable monitoring the effectiveness of elimination activities after the initial reduction of cases has occurred are needed. Documenting progress in vector-borne disease elimination is a potentially important application for the theory of critical transitions. Non-parametric approaches that are independent of model-fitting would advance infectious disease forecasting significantly. In this paper, we consider compartmental Ross-McDonald models that are slowly forced through a critical transition through gradually deployed control measures. We derive expressions for the behavior of candidate indicators, including the autocorrelation coefficient, variance, and coefficient of variation in the number of human cases during the approach to elimination. We conducted a simulation study to test the performance of each summary statistic as an early warning system of mosquito-borne disease elimination. Variance and coefficient of variation were highly predictive of elimination but autocorrelation performed poorly as an indicator in some control contexts. Our results suggest that tipping points (bifurcations) in mosquito-borne infectious disease systems may be foreshadowed by characteristic temporal patterns of disease prevalence.

The effect of hydroperiod and predation on the diversity of temporary pond zooplankton communities

In temporary pond ecosystems, it is hypothesized that the two dominant structuring forces on zooplankton communities are predation and demographic constraints due to wetland drying. Both of these forces are deterministic processes that act most strongly at opposing ends of a hydroperiod gradient. Our objective was to test how these two processes affect α- and β-diversity of zooplankton communities derived from a diverse temporary pond system. We hypothesized that decreased hydroperiod length and the presence of salamander larvae as predators would decrease β-diversity and that intermediate hydroperiod communities would have the greatest species richness. Our 1-year mesocosm experiment (n = 36) consisted of two predation treatments (present/absent) and three hydroperiod treatments (short/medium/long) fully crossed, seeded from the resting egg bank of multiple temporary ponds. In total, we collected 37 species of microcrustacean zooplankton from our mesocosms. A reduction in hydroperiod length resulted in lower α-diversity, with short-hydroperiod treatments affected most strongly. Endpoint community dissimilarity (β-diversity) was greatest in the medium-hydroperiod treatment with regard to species presence/absence, but was greatest in the long-hydroperiod treatment when abundances were included. Predation by salamander larvae led to reduced β-diversity with respect to species presence/absence, but not among abundant species, and had no effect on α-diversity. Our results suggest that environmental changes that reduce hydroperiod length would result in reduced α-diversity; however, intermediate hydroperiod length appear to enhance β-diversity within a group of wetlands.

Range bagging: a new method for ecological niche modelling from presence-only data

The ecological niche is the set of environments in which a population of a species can persist without introduction of individuals from other locations. A good mathematical or computational representation of the niche is a prerequisite to addressing many questions in ecology, biogeography, evolutionary biology and conservation. A particularly challenging question for ecological niche modelling is the problem of presence-only modelling. That is, can an ecological niche be identified from records drawn only from the set of niche environments without records from non-niche environments for comparison? Here, I introduce a new method for ecological niche modelling from presence-only data called range bagging. Range bagging draws on the concept of a species' environmental range, but was inspired by the empirical performance of ensemble learning algorithms in other areas of ecological research. This paper extends the concept of environmental range to multiple dimensions and shows that range bagging is computationally feasible even when the number of environmental dimensions is large. The target of the range bagging base learner is an environmental tolerance of the species in a projection of its niche and is therefore an ecologically interpretable property of a species' biological requirements. The computational complexity of range bagging is linear in the number of examples, which compares favourably with the main alternative, Qhull. In conclusion, range bagging appears to be a reasonable choice for niche modelling in applications in which a presence-only method is desired and may provide a solution to problems in other disciplines where one-class classification is required, such as outlier detection and concept learning.

What factors might have led to the emergence of Ebola in West Africa?

An Ebola outbreak of unprecedented scope emerged in West Africa in December 2013 and presently continues unabated in the countries of Guinea, Sierra Leone, and Liberia. Ebola is not new to Africa, and outbreaks have been confirmed as far back as 1976. The current West African Ebola outbreak is the largest ever recorded and differs dramatically from prior outbreaks in its duration, number of people affected, and geographic extent. The emergence of this deadly disease in West Africa invites many questions, foremost among these: why now, and why in West Africa? Here, we review the sociological, ecological, and environmental drivers that might have influenced the emergence of Ebola in this region of Africa and its spread throughout the region. Containment of the West African Ebola outbreak is the most pressing, immediate need. A comprehensive assessment of the drivers of Ebola emergence and sustained human-to-human transmission is also needed in order to prepare other countries for importation or emergence of this disease. Such assessment includes identification of country-level protocols and interagency policies for outbreak detection and rapid response, increased understanding of cultural and traditional risk factors within and between nations, delivery of culturally embedded public health education, and regional coordination and collaboration, particularly with governments and health ministries throughout Africa. Public health education is also urgently needed in countries outside of Africa in order to ensure that risk is properly understood and public concerns do not escalate unnecessarily. To prevent future outbreaks, coordinated, multiscale, early warning systems should be developed that make full use of these integrated assessments, partner with local communities in high-risk areas, and provide clearly defined response recommendations specific to the needs of each community.

Factors influencing the detectability of early warning signals of population collapse

The recent description of potentially generic early warning signals is a promising development that may help conservationists to anticipate a population's collapse prior to its occurrence. So far, the majority of such warning signals documented have been in highly controlled laboratory systems or in theoretical models. Data from wild populations, however, are typically restricted both temporally and spatially due to limited monitoring resources and intrinsic ecological heterogeneity-limitations that may affect the detectability of generic early warning signals, as they add additional stochasticity to population abundance estimates. Consequently, spatial and temporal subsampling may serve to either muffle or magnify early warning signals. Using a combination of theoretical models and analysis of experimental data, we evaluate the extent to which statistical warning signs are robust to data corruption.

Multi-scale model of epidemic fade-out: Will local extirpation events inhibit the spread of white-nose syndrome?

White-nose syndrome (WNS) is an emerging infectious disease that has resulted in severe declines of its hibernating bat hosts in North America. The ongoing epidemic of white-nose syndrome is a multi-scale phenomenon becau.se it causes hibernaculum-level extirpations, while simultaneously spreading over larger spatial scales. We investigate a neglected topic in ecological epidemiology: how local pathogen-driven extirpations impact large-scale pathogen spread. Previous studies have identified risk factors for propagation of WNS over hibernaculum and landscape scales but none of these have tested the hypothesis that separation of spatial scales and disease-induced mortality at the hibernaculum level might slow or halt its spread. To test this hypothesis, we developed a mechanistic multi-scale model parameterized using white-nose syndrome.county and site incidence data that connects hibernaculum-level susceptible-infectious-removed (SIR) epidemiology to the county-scale contagion process. Our key result is that hibernaculum-level extirpations will not inhibit county-scale spread of WNS. We show that over 80% of counties of the contiguous USA are likely to become infected before the current epidemic is over and that geometry of habitat connectivity is such that host refuges are exceedingly rare. The macroscale spatiotemporal infection pattern that emerges from local SIR epidemiological processes falls within a narrow spectrum of possible outcomes, suggesting that recolonization, rescue effects, and multi-host complexities at local scales are not important to forward propagation of WNS at large spatial scales. If effective control measures are not implemented, precipitous declines in bat populations are likely, particularly in cave-dense regions that constitute the main geographic corridors of the USA, a serious concern for bat conservation.