A synergistic future for AI and ecology.

Research in both ecology and AI strives for predictive understanding of complex systems, where nonlinearities arise from multidimensional interactions and feedbacks across multiple scales. After a century of independent, asynchronous advances in computational and ecological research, we foresee a critical need for intentional synergy to meet current societal challenges against the backdrop of global change. These challenges include understanding the unpredictability of systems-level phenomena and resilience dynamics on a rapidly changing planet. Here, we spotlight both the promise and the urgency of a convergence research paradigm between ecology and AI. Ecological systems are a challenge to fully and holistically model, even using the most prominent AI technique today: deep neural networks. Moreover, ecological systems have emergent and resilient behaviors that may inspire new, robust AI architectures and methodologies. We share examples of how challenges in ecological systems modeling would benefit from advances in AI techniques that are themselves inspired by the systems they seek to model. Both fields have inspired each other, albeit indirectly, in an evolution toward this convergence. We emphasize the need for more purposeful synergy to accelerate the understanding of ecological resilience whilst building the resilience currently lacking in modern AI systems, which have been shown to fail at times because of poor generalization in different contexts. Persistent epistemic barriers would benefit from attention in both disciplines. The implications of a successful convergence go beyond advancing ecological disciplines or achieving an artificial general intelligence-they are critical for both persisting and thriving in an uncertain future.

A review of emerging health threats from zoonotic New World mammarenaviruses.

Despite repeated spillover transmission and their potential to cause significant morbidity and mortality in human hosts, the New World mammarenaviruses remain largely understudied. These viruses are endemic to South America, with animal reservoir hosts covering large geographic areas and whose transmission ecology and spillover potential are driven in part by land use change and agriculture that put humans in regular contact with zoonotic hosts.We compiled published studies about Guanarito virus, Junin virus, Machupo virus, Chapare virus, Sabia virus, and Lymphocytic Choriomeningitis virus to review the state of knowledge about the viral hemorrhagic fevers caused by New World mammarenaviruses. We summarize what is known about rodent reservoirs, the conditions of spillover transmission for each of these pathogens, and the characteristics of human populations at greatest risk for hemorrhagic fever diseases. We also review the implications of repeated outbreaks and biosecurity concerns where these diseases are endemic, and steps that countries can take to strengthen surveillance and increase capacity of local healthcare systems. While there are unique risks posed by each of these six viruses, their ecological and epidemiological similarities suggest common steps to mitigate spillover transmission and better contain future outbreaks.

Dilution of Epidemic Potential of Environmentally Transmitted Infectious Diseases for Species with Partially Overlapping Habitats.

AbstractSpecies diversity may play an important role in the modulation of pathogen transmission through the dilution effect. Infectious disease models can help elucidate mechanisms that may underlie this effect. While many modeling studies have assumed direct host-to-host transmission, many pathogens are transmitted through the environment. We present a mathematical modeling analysis exploring conditions under which we observe the dilution effect in systems with environmental transmission where host species interact through fully or partially overlapping habitats. We measure the strength of the dilution effect by the relative decrease in the basic reproduction number of two-species assemblages compared with that of a focal host species. We find that a dilution effect is most likely when the pathogen is environmentally persistent (frequency-dependent-like transmission). The magnitude of this effect is strongest when the species with the greater epidemic potential is relatively slow to pick up pathogens in the environment (density-dependent transmission) and the species with the lesser epidemic potential is efficient at picking up pathogens (frequency-dependent transmission). These findings suggest that measurable factors, including pathogen persistence and the host's relative efficiency of pathogen pickup, can guide predictions of when biodiversity might lead to a dilution effect and may thus give concrete direction to future ecological work.

Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2.

Back and forth transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between humans and animals will establish wild reservoirs of virus that endanger long-term efforts to control COVID-19 in people and to protect vulnerable animal populations. Better targeting surveillance and laboratory experiments to validate zoonotic potential requires predicting high-risk host species. A major bottleneck to this effort is the few species with available sequences for angiotensin-converting enzyme 2 receptor, a key receptor required for viral cell entry. We overcome this bottleneck by combining species' ecological and biological traits with three-dimensional modelling of host-virus protein-protein interactions using machine learning. This approach enables predictions about the zoonotic capacity of SARS-CoV-2 for greater than 5000 mammals-an order of magnitude more species than previously possible. Our predictions are strongly corroborated by in vivo studies. The predicted zoonotic capacity and proximity to humans suggest enhanced transmission risk from several common mammals, and priority areas of geographic overlap between these species and global COVID-19 hotspots. With molecular data available for only a small fraction of potential animal hosts, linking data across biological scales offers a conceptual advance that may expand our predictive modelling capacity for zoonotic viruses with similarly unknown host ranges.

Integrating data mining and transmission theory in the ecology of infectious diseases.

Our understanding of ecological processes is built on patterns inferred from data. Applying modern analytical tools such as machine learning to increasingly high dimensional data offers the potential to expand our perspectives on these processes, shedding new light on complex ecological phenomena such as pathogen transmission in wild populations. Here, we propose a novel approach that combines data mining with theoretical models of disease dynamics. Using rodents as an example, we incorporate statistical differences in the life history features of zoonotic reservoir hosts into pathogen transmission models, enabling us to bound the range of dynamical phenomena associated with hosts, based on their traits. We then test for associations between equilibrium prevalence, a key epidemiological metric and data on human outbreaks of rodent-borne zoonoses, identifying matches between empirical evidence and theoretical predictions of transmission dynamics. We show how this framework can be generalized to other systems through a rubric of disease models and parameters that can be derived from empirical data. By linking life history components directly to their effects on disease dynamics, our mining-modelling approach integrates machine learning and theoretical models to explore mechanisms in the macroecology of pathogen transmission and their consequences for spillover infection to humans.

High mortality in aquatic predators of mosquito larvae caused by exposure to insect repellent.

In the face of mosquito-borne disease outbreaks, effective mosquito control is a primary goal for public health. Insect repellents, containing active compounds such as DEET and picaridin, are a first defence against biting insects. Owing to widespread use and incomplete sewage treatment, these compounds are frequently detected in surface waters, but their effects on aquatic taxa such as mosquito larvae or their naturally occurring aquatic predators are poorly understood. We investigated the effects of environmentally realistic concentrations of commercial products containing DEET and picaridin on survivorship of mosquito larvae, and their potential indirect effects on survival of larval salamanders, a major predator of mosquito larvae. Larval mosquitos were not affected by exposure to repellents containing DEET or picaridin. We found no larval salamander mortality in control and DEET treatments, but mortality rates in picaridin treatments ranged from 45 to 65% after 25 days of exposure. Salamander larvae exposed to repellents containing picaridin began to display tail deformities and impaired development four days after the experiment began. Our findings suggest the possibility that environmentally realistic concentrations of picaridin-containing repellents in surface waters may increase the abundance of adult mosquitos owing to decreased predation pressure.

Data-driven predictions and novel hypotheses about zoonotic tick vectors from the genus Ixodes.

BACKGROUND: With the resurgence of tick-borne diseases such as Lyme disease and the emergence of new tick-borne pathogens such as Powassan virus, understanding what distinguishes vectors from non-vectors, and predicting undiscovered tick vectors is a crucial step towards mitigating disease risk in humans. We aimed to identify intrinsic traits that predict which Ixodes tick species are confirmed or strongly suspected to be vectors of zoonotic pathogens. METHODS: We focused on the well-studied tick genus Ixodes from which many species are known to transmit zoonotic diseases to humans. We apply generalized boosted regression to interrogate over 90 features for over 240 species of Ixodes ticks to learn what intrinsic features distinguish zoonotic vectors from non-vector species. In addition to better understanding the biological underpinnings of tick vectorial capacity, the model generates a per species probability of being a zoonotic vector on the basis of intrinsic biological similarity with known Ixodes vector species. RESULTS: Our model predicted vector status with over 91% accuracy, and identified 14 Ixodes species with high probabilities (80%) of transmitting infections from animal hosts to humans on the basis of their traits. Distinguishing characteristics of zoonotic tick vectors of Ixodes tick species include several anatomical structures that influence host seeking behavior and blood-feeding efficiency from a greater diversity of host species compared to non-vectors. CONCLUSIONS: Overall, these results suggest that zoonotic tick vectors are most likely to be those species where adult females hold a fecundity advantage by producing more eggs per clutch, which develop into larvae that feed on a greater diversity of host species compared to non-vector species. These larvae develop into nymphs whose anatomy are well suited for more efficient and longer feeding times on soft-bodied hosts compared to non-vectors, leading to larger adult females with greater fecundity. In addition to identifying novel, testable hypotheses about intrinsic features driving vectorial capacity across Ixodes tick species, our model identifies particular Ixodes species with the highest probability of carrying zoonotic diseases, offering specific targets for increased zoonotic investigation and surveillance.

Rodent reservoirs of future zoonotic diseases.

The increasing frequency of zoonotic disease events underscores a need to develop forecasting tools toward a more preemptive approach to outbreak investigation. We apply machine learning to data describing the traits and zoonotic pathogen diversity of the most speciose group of mammals, the rodents, which also comprise a disproportionate number of zoonotic disease reservoirs. Our models predict reservoir status in this group with over 90% accuracy, identifying species with high probabilities of harboring undiscovered zoonotic pathogens based on trait profiles that may serve as rules of thumb to distinguish reservoirs from nonreservoir species. Key predictors of zoonotic reservoirs include biogeographical properties, such as range size, as well as intrinsic host traits associated with lifetime reproductive output. Predicted hotspots of novel rodent reservoir diversity occur in the Middle East and Central Asia and the Midwestern United States.

Spatiotemporal Fluctuations and Triggers of Ebola Virus Spillover.

Because the natural reservoir of Ebola virus remains unclear and disease outbreaks in humans have occurred only sporadically over a large region, forecasting when and where Ebola spillovers are most likely to occur constitutes a continuing and urgent public health challenge. We developed a statistical modeling approach that associates 37 human or great ape Ebola spillovers since 1982 with spatiotemporally dynamic covariates including vegetative cover, human population size, and absolute and relative rainfall over 3 decades across sub-Saharan Africa. Our model (area under the curve 0.80 on test data) shows that spillover intensity is highest during transitions between wet and dry seasons; overall, high seasonal intensity occurs over much of tropical Africa; and spillover intensity is greatest at high (>1,000/km2) and very low (<100/km2) human population densities compared with intermediate levels. These results suggest strong seasonality in Ebola spillover from wild reservoirs and indicate particular times and regions for targeted surveillance.

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.

Infectious disease transmission and behavioural allometry in wild mammals.

Animals' social and movement behaviours can impact the transmission dynamics of infectious diseases, especially for pathogens transmitted through close contact between hosts or through contact with infectious stages in the environment. Estimating pathogen transmission rates and R0 from natural systems can be challenging. Because host behavioural traits that underlie the transmission process vary predictably with body size, one of the best-studied traits among animals, body size might therefore also predict variation in parasite transmission dynamics. Here, we examine how two host behaviours, social group living and the intensity of habitat use, scale allometrically using comparative data from wild primate, carnivore and ungulate species. We use these empirical relationships to parameterize classical compartment models for infectious micro- and macroparasitic diseases, and examine how the risk of pathogen invasion changes as a function of host behaviour and body size. We then test model predictions using comparative data on parasite prevalence and richness from wild mammals. We report a general pattern suggesting that smaller-bodied mammal species utilizing home ranges more intensively experience greater risk for invasion by environmentally transmitted macroparasites. Conversely, larger-bodied hosts exhibiting a high degree of social group living could be more readily invaded by directly transmitted microparasites. These trends were supported through comparison of micro- and macroparasite species richness across a large number of carnivore, primate and ungulate species, but empirical data on carnivore macroparasite prevalence showed mixed results. Collectively, our study demonstrates that combining host behavioural traits with dynamical models of infectious disease scaled against host body size can generate testable predictions for variation in parasite risk across species; a similar approach might be useful in future work focused on predicting parasite distributions in local host communities.

Understanding transmission risk and predicting environmental suitability for Mayaro Virus in Central and South America.

Mayaro virus (MAYV) is a mosquito-borne Alphavirus that is widespread in South America. MAYV infection often presents with non-specific febrile symptoms but may progress to debilitating chronic arthritis or arthralgia. Despite the pandemic threat of MAYV, its true distribution remains unknown. The objective of this study was to clarify the geographic distribution of MAYV using an established risk mapping framework. This consisted of generating evidence consensus scores for MAYV presence, modeling the potential distribution of MAYV in select countries across Central and South America, and estimating the population residing in areas suitable for MAYV transmission. We compiled a georeferenced compendium of MAYV occurrence in humans, animals, and arthropods. Based on an established evidence consensus framework, we integrated multiple information sources to assess the total evidence supporting ongoing transmission of MAYV within each country in our study region. We then developed high resolution maps of the disease's estimated distribution using a boosted regression tree approach. Models were developed using nine climatic and environmental covariates that are related to the MAYV transmission cycle. Using the output of our boosted regression tree models, we estimated the total population living in regions suitable for MAYV transmission. The evidence consensus scores revealed high or very high evidence of MAYV transmission in several countries including Brazil (especially the states of Mato Grosso and Goiás), Venezuela, Peru, Trinidad and Tobago, and French Guiana. According to the boosted regression tree models, a substantial region of South America is suitable for MAYV transmission, including north and central Brazil, French Guiana, and Suriname. Some regions (e.g., Guyana) with only moderate evidence of known transmission were identified as highly suitable for MAYV. We estimate that approximately 58.9 million people (95% CI: 21.4-100.4) in Central and South America live in areas that may be suitable for MAYV transmission, including 46.2 million people (95% CI: 17.6-68.9) in Brazil. Our results may assist in prioritizing high-risk areas for vector control, human disease surveillance and ecological studies.

Data-driven predictions of potential Leishmania vectors in the Americas.

The incidence of vector-borne diseases is rising as deforestation, climate change, and globalization bring humans in contact with arthropods that can transmit pathogens. In particular, incidence of American Cutaneous Leishmaniasis (ACL), a disease caused by parasites transmitted by sandflies, is increasing as previously intact habitats are cleared for agriculture and urban areas, potentially bringing people into contact with vectors and reservoir hosts. Previous evidence has identified dozens of sandfly species that have been infected with and/or transmit Leishmania parasites. However, there is an incomplete understanding of which sandfly species transmit the parasite, complicating efforts to limit disease spread. Here, we apply machine learning models (boosted regression trees) to leverage biological and geographical traits of known sandfly vectors to predict potential vectors. Additionally, we generate trait profiles of confirmed vectors and identify important factors in transmission. Our model performed well with an average out of sample accuracy of 86%. The models predict that synanthropic sandflies living in areas with greater canopy height, less human modification, and within an optimal range of rainfall are more likely to be Leishmania vectors. We also observed that generalist sandflies that are able to inhabit many different ecoregions are more likely to transmit the parasites. Our results suggest that Psychodopygus amazonensis and Nyssomia antunesi are unidentified potential vectors, and should be the focus of sampling and research efforts. Overall, we found that our machine learning approach provides valuable information for Leishmania surveillance and management in an otherwise complex and data sparse system.

Correction: Prioritizing surveillance of Nipah virus in India.

[This corrects the article DOI: 10.1371/journal.pntd.0007393.].

Phylogenetic and biogeographical traits predict unrecognized hosts of zoonotic leishmaniasis.

The spatio-temporal distribution of leishmaniasis, a parasitic vector-borne zoonotic disease, is significantly impacted by land-use change and climate warming in the Americas. However, predicting and containing outbreaks is challenging as the zoonotic Leishmania system is highly complex: leishmaniasis (visceral, cutaneous and muco-cutaneous) in humans is caused by up to 14 different Leishmania species, and the parasite is transmitted by dozens of sandfly species and is known to infect almost twice as many wildlife species. Despite the already broad known host range, new hosts are discovered almost annually and Leishmania transmission to humans occurs in absence of a known host. As such, the full range of Leishmania hosts is undetermined, inhibiting the use of ecological interventions to limit pathogen spread and the ability to accurately predict the impact of global change on disease risk. Here, we employed a machine learning approach to generate trait profiles of known zoonotic Leishmania wildlife hosts (mammals that are naturally exposed and susceptible to infection) and used trait-profiles of known hosts to identify potentially unrecognized hosts. We found that biogeography, phylogenetic distance, and study effort best predicted Leishmania host status. Traits associated with global change, such as agricultural land-cover, urban land-cover, and climate, were among the top predictors of host status. Most notably, our analysis suggested that zoonotic Leishmania hosts are significantly undersampled, as our model predicted just as many unrecognized hosts as unknown hosts. Overall, our analysis facilitates targeted surveillance strategies and improved understanding of the impact of environmental change on local transmission cycles.

A geopositioned and evidence-graded pan-species compendium of Mayaro virus occurrence.

Mayaro Virus (MAYV) is an emerging health threat in the Americas that can cause febrile illness as well as debilitating arthralgia or arthritis. To better understand the geographic distribution of MAYV risk, we developed a georeferenced database of MAYV occurrence based on peer-reviewed literature and unpublished reports. Here we present this compendium, which includes both point and polygon locations linked to occurrence data documented from its discovery in 1954 until 2022. We describe all methods used to develop the database including data collection, georeferencing, management and quality-control. We also describe a customized grading system used to assess the quality of each study included in our review. The result is a comprehensive, evidence-graded database of confirmed MAYV occurrence in humans, non-human animals, and arthropods to-date, containing 262 geo-positioned occurrences in total. This database - which can be updated over time - may be useful for local spill-over risk assessment, epidemiological modelling to understand key transmission dynamics and drivers of MAYV spread, as well as identification of major surveillance gaps.

Ecological Predictors of Zoonotic Vector Status Among Dermacentor Ticks (Acari: Ixodidae): A Trait-Based Approach.

Increasing incidence of tick-borne human diseases and geographic range expansion of tick vectors elevates the importance of research on characteristics of tick species that transmit pathogens. Despite their global distribution and role as vectors of pathogens such as Rickettsia spp., ticks in the genus Dermacentor Koch, 1844 (Acari: Ixodidae) have recently received less attention than ticks in the genus Ixodes Latreille, 1795 (Acari: Ixodidae). To address this knowledge gap, we compiled an extensive database of Dermacentor tick traits, including morphological characteristics, host range, and geographic distribution. Zoonotic vector status was determined by compiling information about zoonotic pathogens found in Dermacentor species derived from primary literature and data repositories. We trained a machine learning algorithm on this data set to assess which traits were the most important predictors of zoonotic vector status. Our model successfully classified vector species with ~84% accuracy (mean AUC) and identified two additional Dermacentor species as potential zoonotic vectors. Our results suggest that Dermacentor species that are most likely to be zoonotic vectors are broad ranging, both in terms of the range of hosts they infest and the range of ecoregions across which they are found, and also tend to have large hypostomes and be small-bodied as immature ticks. Beyond the patterns we observed, high spatial and species-level resolution of this new, synthetic dataset has the potential to support future analyses of public health relevance, including species distribution modeling and predictive analytics, to draw attention to emerging or newly identified Dermacentor species that warrant closer monitoring for zoonotic pathogens.

Traits, phylogeny and host cell receptors predict Ebolavirus host status among African mammals.

We explore how animal host traits, phylogenetic identity and cell receptor sequences relate to infection status and mortality from ebolaviruses. We gathered exhaustive databases of mortality from Ebolavirus after exposure and infection status based on PCR and antibody tests. We performed ridge regressions predicting mortality and infection as a function of traits, phylogenetic eigenvectors and separately host receptor sequences. We found that mortality from Ebolavirus had a strong association to life history characteristics and phylogeny. In contrast, infection status related not just to life history and phylogeny, but also to fruit consumption which suggests that geographic overlap of frugivorous mammals can lead to spread of virus in the wild. Niemann Pick C1 (NPC1) receptor sequences predicted infection statuses of bats included in our study with very high accuracy, suggesting that characterizing NPC1 in additional species is a promising avenue for future work. We combine the predictions from our mortality and infection status models to differentiate between species that are infected and also die from Ebolavirus versus species that are infected but tolerate the virus (possible reservoirs of Ebolavirus). We therefore present the first comprehensive estimates of Ebolavirus reservoir statuses for all known terrestrial mammals in Africa.

Population fluctuations and synanthropy explain transmission risk in rodent-borne zoonoses.

Population fluctuations are widespread across the animal kingdom, especially in the order Rodentia, which includes many globally important reservoir species for zoonotic pathogens. The implications of these fluctuations for zoonotic spillover remain poorly understood. Here, we report a global empirical analysis of data describing the linkages between habitat use, population fluctuations and zoonotic reservoir status in rodents. Our quantitative synthesis is based on data collated from papers and databases. We show that the magnitude of population fluctuations combined with species' synanthropy and degree of human exploitation together distinguish most rodent reservoirs at a global scale, a result that was consistent across all pathogen types and pathogen transmission modes. Our spatial analyses identified hotspots of high transmission risk, including regions where reservoir species dominate the rodent community. Beyond rodents, these generalities inform our understanding of how natural and anthropogenic factors interact to increase the risk of zoonotic spillover in a rapidly changing world.