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Bacillus anthracis is a spore-forming, Gram-positive bacterium responsible for anthrax, an acute infection that most significantly affects grazing livestock and wild ungulates, but also poses a threat to human health. The geographic extent of B. anthracis is poorly understood, despite multi-decade research on anthrax epizootic and epidemic dynamics; many countries have limited or inadequate surveillance systems, even within known endemic regions. Here, we compile a global occurrence dataset of human, livestock and wildlife anthrax outbreaks. With these records, we use boosted regression trees to produce a map of the global distribution of B. anthracis as a proxy for anthrax risk. We estimate that 1.83 billion people (95% credible interval (CI): 0.59-4.16 billion) live within regions of anthrax risk, but most of that population faces little occupational exposure. More informatively, a global total of 63.8 million poor livestock keepers (95% CI: 17.5-168.6 million) and 1.1 billion livestock (95% CI: 0.4-2.3 billion) live within vulnerable regions. Human and livestock vulnerability are both concentrated in rural rainfed systems throughout arid and temperate land across Eurasia, Africa and North America. We conclude by mapping where anthrax risk could disrupt sensitive conservation efforts for wild ungulates that coincide with anthrax-prone landscapes.

Environmentally transmitted diseases are comparatively poorly understood and managed, and their ecology is particularly understudied. Here we identify challenges of studying environmental transmission and persistence with a six-sided interdisciplinary review of the biology of anthrax (Bacillus anthracis). Anthrax is a zoonotic disease capable of maintaining infectious spore banks in soil for decades (or even potentially centuries), and the mechanisms of its environmental persistence have been the topic of significant research and controversy. Where anthrax is endemic, it plays an important ecological role, shaping the dynamics of entire herbivore communities. The complex eco-epidemiology of anthrax, and the mysterious biology of Bacillus anthracis during its environmental stage, have necessitated an interdisciplinary approach to pathogen research. Here, we illustrate different disciplinary perspectives through key advances made by researchers working in Etosha National Park, a long-term ecological research site in Namibia that has exemplified the complexities of the enzootic process of anthrax over decades of surveillance. In Etosha, the role of scavengers and alternative routes (waterborne transmission and flies) has proved unimportant relative to the long-term persistence of anthrax spores in soil and their infection of herbivore hosts. Carcass deposition facilitates green-ups of vegetation to attract herbivores, potentially facilitated by the role of anthrax spores in the rhizosphere. The underlying seasonal pattern of vegetation, and herbivores' immune and behavioural responses to anthrax risk, interact to produce regular 'anthrax seasons' that appear to be a stable feature of the Etosha ecosystem. Through the lens of microbiologists, geneticists, immunologists, ecologists, epidemiologists, and clinicians, we discuss how anthrax dynamics are shaped at the smallest scale by population genetics and interactions within the bacterial communities up to the broadest scales of ecosystem structure. We illustrate the benefits and challenges of this interdisciplinary approach to disease ecology, and suggest ways anthrax might offer insights into the biology of other important pathogens. Bacillus anthracis, and the more recently emerged Bacillus cereus biovar anthracis, share key features with other environmentally transmitted pathogens, including several zoonoses and panzootics of special interest for global health and conservation efforts. Understanding the dynamics of anthrax, and developing interdisciplinary research programs that explore environmental persistence, is a critical step forward for understanding these emerging threats.

Though epidemiology dates back to the 1700s, most mathematical representations of epidemics still use transmission rates averaged at the population scale, especially for wildlife diseases. In simplifying the contact process, we ignore the heterogeneities in host movements that complicate the real world, and overlook their impact on spatiotemporal patterns of disease burden. Movement ecology offers a set of tools that help unpack the transmission process, letting researchers more accurately model how animals within a population interact and spread pathogens. Analytical techniques from this growing field can also help expose the reverse process: how infection impacts movement behaviours, and therefore other ecological processes like feeding, reproduction, and dispersal. Here, we synthesise the contributions of movement ecology in disease research, with a particular focus on studies that have successfully used movement-based methods to quantify individual heterogeneity in exposure and transmission risk. Throughout, we highlight the rapid growth of both disease and movement ecology and comment on promising but unexplored avenues for research at their overlap. Ultimately, we suggest, including movement empowers ecologists to pose new questions, expanding our understanding of host-pathogen dynamics and improving our predictive capacity for wildlife and even human diseases.

Ecologists are increasingly involved in the pandemic prediction process. In the course of the Zika outbreak in the Americas, several ecological models were developed to forecast the potential global distribution of the disease. Conflicting results produced by alternative methods are unresolved, hindering the development of appropriate public health forecasts. We compare ecological niche models and experimentally-driven mechanistic forecasts for Zika transmission in the continental United States. We use generic and uninformed stochastic county-level simulations to demonstrate the downstream epidemiological consequences of conflict among ecological models, and show how assumptions and parameterization in the ecological and epidemiological models propagate uncertainty and produce downstream model conflict. We conclude by proposing a basic consensus method that could resolve conflicting models of potential outbreak geography and seasonality. Our results illustrate the usually-undocumented margin of uncertainty that could emerge from using any one of these predictions without reservation or qualification. In the short term, ecologists face the task of developing better post hoc consensus that accurately forecasts spatial patterns of Zika virus outbreaks. Ultimately, methods are needed that bridge the gap between ecological and epidemiological approaches to predicting transmission and realistically capture both outbreak size and geography.

Critical evaluation of the adequacy of ecological models is urgently needed to enhance their utility in developing theory and enabling environmental managers and policymakers to make informed decisions. Poorly supported management can have detrimental, costly or irreversible impacts on the environment and society. Here, we examine common issues in ecological modelling and suggest criteria for improving modelling frameworks. An appropriate level of process description is crucial to constructing the best possible model, given the available data and understanding of ecological structures. Model details unsupported by data typically lead to over parameterisation and poor model performance. Conversely, a lack of mechanistic details may limit a model's ability to predict ecological systems' responses to management. Ecological studies that employ models should follow a set of model adequacy assessment protocols that include: asking a series of critical questions regarding state and control variable selection, the determinacy of data, and the sensitivity and validity of analyses. We also need to improve model elaboration, refinement and coarse graining procedures to better understand the relevancy and adequacy of our models and the role they play in advancing theory, improving hind and forecasting, and enabling problem solving and management.

BackgroundContinued exploration of the performance of the recently proposed cross-validation-based approach for delimiting home ranges using the Time Local Convex Hull (T-LoCoH) method has revealed a number of issues with the original formulation.Main textHere we replace the ad hoc cross-validation score with a new formulation based on the total log probability of out-of-sample predictions. To obtain these probabilities, we interpret the normalized LoCoH hulls as a probability density. The application of the approach described here results in optimal parameter sets that differ dramatically from those selected using the original formulation. The derived metrics of home range size, mean revisitation rate, and mean duration of visit are also altered using the corrected formulation.ConclusionDespite these differences, we encourage the use of the cross-validation-based approach, as it provides a unifying framework governed by the statistical properties of the home ranges rather than subjective selections by the user.

[This corrects the article DOI: 10.1186/s40462-017-0110-4.].

BackgroundWith decreasing costs of GPS telemetry devices, data repositories of animal movement paths are increasing almost exponentially in size. A series of complex statistical tools have been developed in conjunction with this increase in data. Each of these methods offers certain improvements over previously proposed methods, but each has certain assumptions or shortcomings that make its general application difficult. In the case of the recently developed Time Local Convex Hull (T-LoCoH) method, the subjectivity in parameter selection serves as one of the primary impediments to its more widespread use. While there are certain advantages to the flexibility it offers for question-driven research, the lack of an objective approach for parameter selection may prevent some users from exploring the benefits of the method.MethodsHere we present a cross-validation-based approach for selecting parameter values to optimize the T-LoCoH algorithm. We demonstrate the utility of the approach using a case study from the Etosha National Park anthrax system.ResultsUtilizing the proposed algorithm, rather than the guidelines in the T-LoCoH documentation, results in significantly different values for derived site fidelity metrics.ConclusionsDue to its basis in principles of cross-validation, the application of this method offers a more objective approach than the relatively subjective guidelines set forth in the T-LoCoH documentation and enables a more accurate basis for the comparison of home ranges among individuals and species, as well as among studies.

Climate change is a well-documented driver of both wildlife extinction and disease emergence, but the negative impacts of climate change on parasite diversity are undocumented. We compiled the most comprehensive spatially explicit data set available for parasites, projected range shifts in a changing climate, and estimated extinction rates for eight major parasite clades. On the basis of 53,133 occurrences capturing the geographic ranges of 457 parasite species, conservative model projections suggest that 5 to 10% of these species are committed to extinction by 2070 from climate-driven habitat loss alone. We find no evidence that parasites with zoonotic potential have a significantly higher potential to gain range in a changing climate, but we do find that ectoparasites (especially ticks) fare disproportionately worse than endoparasites. Accounting for host-driven coextinctions, models predict that up to 30% of parasitic worms are committed to extinction, driven by a combination of direct and indirect pressures. Despite high local extinction rates, parasite richness could still increase by an order of magnitude in some places, because species successfully tracking climate change invade temperate ecosystems and replace native species with unpredictable ecological consequences.

The study of the ecology and natural history of species has traditionally ceased when a species goes extinct, despite the benefit to current and future generations of potential findings. We used the extinct Carolina parakeet as a case study to develop a framework investigating the distributional limits, subspecific variation, and migratory habits of this species as a means to recover important information about recently extinct species. We united historical accounts with museum collections to develop an exhaustive, comprehensive database of every known occurrence of this once iconic species. With these data, we combined species distribution models and ordinal niche comparisons to confront multiple conjectured hypotheses about the parakeet's ecology with empirical data on where and when this species occurred. Our results demonstrate that the Carolina parakeet's range was likely much smaller than previously believed, that the eastern and western subspecies occupied different climatic niches with broad geographical separation, and that the western subspecies was likely a seasonal migrant while the eastern subspecies was not. This study highlights the novelty and importance of collecting occurrence data from published observations on extinct species, providing a starting point for future investigations of the factors that drove the Carolina parakeet to extinction. Moreover, the recovery of lost autecological knowledge could benefit the conservation of other parrot species currently in decline and would be crucial to the success of potential de-extinction efforts for the Carolina parakeet.

Population viability analysis (PVA) is used to assess the probability that a biological population will persist for a specified period of time. Such models are typically cast as Markov processes that may include age, stage, sex and metapopulation structures, density-dependence and ecological interaction processes. They may also include harvesting, stocking, and thresholds that trigger interventions. Here we present a PVA web app that includes extensible user-selected options. Specifically, this PVA web app allows for the specification of one to ten age classes, one or two sexes, single population or metapopulation configurations with 2 or 3 subpopulations, as well as density-dependent settings for inducing region-specific carrying capacities. Movement among subpopulations can be influenced by age, metapopulation connectivity, and attractivity of regions based on the relative fitness of the youngest age classes in each region. Simulations can be carried out deterministically or stochastically, with a user-specified combination of demographic and environmental processes. This PVA web app is freely available at http://www.numerusinc.com/webapps/pva for running directly on any browser and device. It is easily modified by users familiar with the NovaModeler Software Platform.

Environmentally Transmitted Diseases Are Comparatively Poorly Understood And Managed, And Their Ecology Is Particularly Understudied. Here We Identify Challenges Of Studying Environmental Transmission And Persistence With A Six-Sided Interdisciplinary Review Of The Biology Of Anthrax ( Bacillus Anthracis ). Anthrax Is A Zoonotic Disease Capable Of Maintaining Infectious Spore Banks In Soil For Decades (Or Even Potentially Centuries), And The Mechanisms Of Its Environmental Persistence Have Been The Topic Of Significant Research And Controversy. Where Anthrax Is Endemic, It Plays An Important Ecological Role, Shaping The Dynamics Of Entire Herbivore Communities. The Complex Eco-Epidemiology Of Anthrax, And The Mysterious Biology Of Bacillus Anthracis During Its Environmental Stage, Have Necessitated An Interdisciplinary Approach To Pathogen Research. Here, We Illustrate Different Disciplinary Perspectives Through Key Advances Made By Researchers Working In Etosha National Park, A Long-Term Ecological Research Site In Namibia That Has Exemplified The Complexities Of Anthrax’S Enzootic Process Over Decades Of Surveillance. In Etosha, The Role Of Scavengers And Alternate Routes (Waterborne Transmission And Flies) Has Proved Unimportant, Relative To The Long-Term Persistence Of Anthrax Spores In Soil And Their Infection Of Herbivore Hosts. Carcass Deposition Facilitates Green-Ups Of Vegetation To Attract Herbivores, Potentially Facilitated By Anthrax Spores’ Role In The Rhizosphere. The Underlying Seasonal Pattern Of Vegetation, And Herbivores’ Immune And Behavioral Responses To Anthrax Risk, Interact To Produce Regular “Anthrax Seasons” That Appear To Be A Stable Feature Of The Etosha Ecosystem. Through The Lens Of Microbiologists, Geneticists, Immunologists, Ecologists, Epidemiologists, And Clinicians, We Discuss How Anthrax Dynamics Are Shaped At The Smallest Scale By Population Genetics And Interactions Within The Bacterial Communities Up To The Broadest Scales Of Ecosystem Structure. We Illustrate The Benefits And Challenges Of This Interdisciplinary Approach To Disease Ecology, And Suggest Ways Anthrax Might Offer Insights Into The Biology Of Other Important Pathogens. Bacillus Anthracis, And The More Recently Emerged Bacillus Cereus Biovar Anthracis , Share Key Features With Other Environmentally-Transmitted Pathogens, Including Several Zoonoses And Panzootics Of Special Interest For Global Health And Conservation Efforts. Understanding The Dynamics Of Anthrax, And Developing Interdisciplinary Research Programs That Explore Environmental Persistence, Is A Critical Step Forward For Understanding These Emerging Threats.

Though epidemiology dates back to the 1700s, most mathematical representations of epidemics still use transmission rates averaged at the population scale, especially for wildlife diseases. In simplifying the contact process, we ignore the heterogeneities in host movements that complicate the real world, and overlook their impact on spatiotemporal patterns of disease burden. Movement ecology offers a set of tools that help unpack the transmission process, letting researchers more accurately model how animals within a population interact and spread pathogens. Analytical techniques from this growing field can also help expose the reverse process: how infection impacts movement behaviors, and therefore other ecological processes like feeding, reproduction, and dispersal. Here, we synthesize the contributions of movement ecology in disease research, with a particular focus on studies that have successfully used movement-based methods to quantify individual heterogeneity in exposure and transmission risk. Throughout, we highlight the rapid growth of both disease and movement ecology, and comment on promising but unexplored avenues for research at their overlap. Ultimately, we suggest, including movement empowers ecologists to pose new questions expanding our understanding of host-pathogen dynamics, and improving our predictive capacity for wildlife and even human diseases.

ABSTRACT Ecologists are increasingly involved in the pandemic prediction process. In the course of the Zika outbreak in the Americas, several ecological models were developed to forecast the potential global distribution of the disease. Conflicting results produced by alternative methods are unresolved, hindering the development of appropriate public health forecasts. We compare ecological niche models and experimentally-driven mechanistic forecasts for Zika transmission in the continental United States, a region of high model conflict. We use generic and uninformed stochastic county-level simulations to demonstrate the downstream epidemiological consequences of conflict among ecological models, and show how assumptions and parameterization in the ecological and epidemiological models propagate uncertainty and produce downstream model conflict. We conclude by proposing a basic consensus method that could resolve conflicting models of potential outbreak geography and seasonality. Our results illustrate the unacceptable and often undocumented margin of uncertainty that could emerge from using any one of these predictions without reservation or qualification. In the short term, ecologists face the task of developing better post hoc consensus that accurately forecasts spatial patterns of Zika virus outbreaks. Ultimately, methods are needed that bridge the gap between ecological and epidemiological approaches to predicting transmission and realistically capture both outbreak size and geography.

Quantifying how global change impacts wild populations remains challenging, especially for species poorly represented by systematic datasets. Here, we infer climate change effects on masting by Joshua trees (Yucca brevifolia and Y. jaegeriana), keystone perennials of the Mojave Desert, from 15 years of crowdsourced observations. We annotated phenophase in 10,212 geo‐referenced images of Joshua trees on the iNaturalist crowdsourcing platform, and used them to train machine learning models predicting flowering from annual weather records. Hindcasting to 1900 with a trained model successfully recovers flowering events in independent historical records and reveals a slightly rising frequency of conditions supporting flowering since the early 20th Century. This reflects increased variation in annual precipitation, which drives masting events in wet years—but also increasing temperatures and drought stress, which may have net negative impacts on recruitment. Our findings reaffirm the value of crowdsourcing for understanding climate change impacts on biodiversity. [ABSTRACT FROM AUTHOR]

The current outbreak of Zika virus poses a severe threat to human health. 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 uncertain. Moreover, many studies rely on its similarity to dengue fever, a phylogenetically closely related disease of unknown ecological comparability. Here we compile a comprehensive spatially-explicit occurrence dataset from Zika viral surveillance and serological surveys based in its native range, 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 native ecological niche of the disease, and may be linked to either genetic shifts between strains, or El Nino or similar climatic events, remains plausible at this time. 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 autochthonous non-sexual transmission to the tropics without significant evolutionary change. Projecting the range of the diseases in conjunction with three major 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, our models indicate Zika is unlikely to fill the full range its vectors occupy, and public fear of a vector-borne Zika epidemic in the mainland United States is potentially informed by biased or limited scientific knowledge. With recent sexual transmission of the virus globally, we caution that our results only apply to the vector-borne transmission route of the pathogen, 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.

Population viability analysis (PVA) is used to assess the probability that a biological population will persist for a specified period of time. Such models are typically cast as Markov processes that may include age, stage, sex and metapopulation structures, density-dependence and ecological interaction processes. They may also include harvesting, stocking, and thresholds that trigger interventions. Here we present Numerus PVA, which is a web app that includes extensible user-selected options. Specifically, Numerus PVA allows for the specification of one to ten age classes, one or two sexes, single population or metapopulation configurations with 2 or 3 subpopulations, as well as density-dependent settings for inducing region-specific carrying capacities. Movement among subpopulations can be influenced by age, metapopulation connectivity, and attractivity of regions based on the relative fitness of the youngest age classes in each region. Simulations can be carried out deterministically or stochastically, with a user-specified combination of demographic and environmental processes. Numerus PVA is freely available at http://www.numerusinc.com/webapps/pva for running directly on any browser and device. Numerus PVA is easily modified by users familiar with the NovaModeler Software Platform.