Climate and urbanization drive changes in the habitat suitability of Schistosoma mansoni competent snails in Brazil.

Schistosomiasis is a neglected tropical disease caused by Schistosoma parasites. Schistosoma are obligate parasites of freshwater Biomphalaria snails, so controlling snail populations is critical to reducing transmission risk. As snails are sensitive to environmental conditions, we expect their distribution is significantly impacted by global change. Here, we leveraged machine learning, remote sensing, and 30 years of snail occurrence records to map the historical and current distribution of competent Biomphalaria throughout Brazil. We identified key features influencing the distribution of suitable habitat and determined how Biomphalaria habitat has changed with climate and urbanization over the last three decades. Our models show that climate change has driven broad shifts in snail host range, whereas expansion of urban and peri-urban areas has driven localized increases in habitat suitability. Elucidating change in Biomphalaria distribution - while accounting for non-linearities that are difficult to detect from local case studies - can help inform schistosomiasis control strategies.

Temperature dependence of mosquitoes: comparing mechanistic and machine learning approaches.

Mosquito vectors of pathogens (e.g., Aedes , Anopheles , and Culex spp. which transmit dengue, Zika, chikungunya, West Nile, malaria, and others) are of increasing concern for global public health. These vectors are geographically shifting under climate and other anthropogenic changes. As small-bodied ectotherms, mosquitoes are strongly affected by temperature, which causes unimodal responses in mosquito life history traits (e.g., biting rate, adult mortality rate, mosquito development rate, and probability of egg-to-adult survival) that exhibit upper and lower thermal limits and intermediate thermal optima in laboratory studies. However, it remains unknown how mosquito thermal responses measured in laboratory experiments relate to the realized thermal responses of mosquitoes in the field. To address this gap, we leverage thousands of global mosquito occurrences and geospatial satellite data at high spatial resolution to construct machine-learning based species distribution models, from which vector thermal responses are estimated. We apply methods to restrict models to the relevant mosquito activity season and to conduct ecologically-plausible spatial background sampling centered around ecoregions for comparison to mosquito occurrence records. We found that thermal minima estimated from laboratory studies were highly correlated with those from the species distributions (r = 0.90). The thermal optima were less strongly correlated (r = 0.69). For most species, we did not detect thermal maxima from their observed distributions so were unable to compare to laboratory-based estimates. The results suggest that laboratory studies have the potential to be highly transportable to predicting lower thermal limits and thermal optima of mosquitoes in the field. At the same time, lab-based models likely capture physiological limits on mosquito persistence at high temperatures that are not apparent from field-based observational studies but may critically determine mosquito responses to climate warming.

The heterogeneous herd: Drivers of close-contact variation in African buffalo and implications for pathogen invasion.

Many infectious pathogens are shared through social interactions, and examining host connectivity has offered valuable insights for understanding patterns of pathogen transmission across wildlife species. African buffalo are social ungulates and important reservoirs of directly-transmitted pathogens that impact numerous wildlife and livestock species. Here, we analyzed African buffalo social networks to quantify variation in close contacts, examined drivers of contact heterogeneity, and investigated how the observed contact patterns affect pathogen invasion likelihoods for a wild social ungulate. We collected continuous association data using proximity collars and sampled host traits approximately every 2 months during a 15-month study period in Kruger National Park, South Africa. Although the observed herd was well connected, with most individuals contacting each other during each bimonthly interval, our analyses revealed striking heterogeneity in close-contact associations among herd members. Network analysis showed that individual connectivity was stable over time and that individual age, sex, reproductive status, and pairwise genetic relatedness were important predictors of buffalo connectivity. Calves were the most connected members of the herd, and adult males were the least connected. These findings highlight the role susceptible calves may play in the transmission of pathogens within the herd. We also demonstrate that, at time scales relevant to infectious pathogens found in nature, the observed level of connectivity affects pathogen invasion likelihoods for a wide range of infectious periods and transmissibilities. Ultimately, our study identifies key predictors of social connectivity in a social ungulate and illustrates how contact heterogeneity, even within a highly connected herd, can shape pathogen invasion likelihoods.

Human footprint is associated with shifts in the assemblages of major vector-borne diseases.

Predicting how increasing intensity of human-environment interactions affects pathogen transmission is essential to anticipate changing disease risks and identify appropriate mitigation strategies. Vector-borne diseases (VBDs) are highly responsive to environmental changes, but such responses are notoriously difficult to isolate because pathogen transmission depends on a suite of ecological and social responses in vectors and hosts that may differ across species. Here we use the emerging tools of cumulative pressure mapping and machine learning to better understand how the occurrence of six medically important VBDs, differing in ecology from sylvatic to urban, respond to multidimensional effects of human pressure. We find that not only is human footprint-an index of human pressure, incorporating built environments, energy and transportation infrastructure, agricultural lands and human population density-an important predictor of VBD occurrence, but there are clear thresholds governing the occurrence of different VBDs. Across a spectrum of human pressure, diseases associated with lower human pressure, including malaria, cutaneous leishmaniasis and visceral leishmaniasis, give way to diseases associated with high human pressure, such as dengue, chikungunya and Zika. These heterogeneous responses of VBDs to human pressure highlight thresholds of land-use transitions that may lead to abrupt shifts in infectious disease burdens and public health needs.

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.

Distinct life history strategies underpin clear patterns of succession in microparasite communities infecting a wild mammalian host.

Individual animals in natural populations tend to host diverse parasite species concurrently over their lifetimes. In free-living ecological communities, organismal life histories shape interactions with their environment, which ultimately forms the basis of ecological succession. However, the structure and dynamics of mammalian parasite communities have not been contextualized in terms of primary ecological succession, in part because few datasets track occupancy and abundance of multiple parasites in wild hosts starting at birth. Here, we studied community dynamics of 12 subtypes of protozoan microparasites (Theileria spp.) in a herd of African buffalo. We show that Theileria communities followed predictable patterns of succession underpinned by four different parasite life history strategies. However, in contrast to many free-living communities, network complexity decreased with host age. Examining parasite communities through the lens of succession may better inform the effect of complex within host eco-evolutionary dynamics on infection outcomes, including parasite co-existence through the lifetime of the host.

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.

Strategies for managing marine disease.

The incidence of emerging infectious diseases (EIDs) has increased in wildlife populations in recent years and is expected to continue to increase with global environmental change. Marine diseases are relatively understudied compared with terrestrial diseases but warrant parallel attention as they can disrupt ecosystems, cause economic loss, and threaten human livelihoods. Although there are many existing tools to combat the direct and indirect consequences of EIDs, these management strategies are often insufficient or ineffective in marine habitats compared with their terrestrial counterparts, often due to fundamental differences between marine and terrestrial systems. Here, we first illustrate how the marine environment and marine organism life histories present challenges and opportunities for wildlife disease management. We then assess the application of common disease management strategies to marine versus terrestrial systems to identify those that may be most effective for marine disease outbreak prevention, response, and recovery. Finally, we recommend multiple actions that will enable more successful management of marine wildlife disease emergencies in the future. These include prioritizing marine disease research and understanding its links to climate change, improving marine ecosystem health, forming better monitoring and response networks, developing marine veterinary medicine programs, and enacting policy that addresses marine and other wildlife diseases. Overall, we encourage a more proactive rather than reactive approach to marine wildlife disease management and emphasize that multidisciplinary collaborations are crucial to managing marine wildlife health.

Corrigendum: Global Patterns of the Fungal Pathogen Batrachochytrium dendrobatidis Support Conservation Urgency.

[This corrects the article DOI: 10.3389/fvets.2021.685877.].

Human-mediated impacts on biodiversity and the consequences for zoonotic disease spillover.

Human-mediated changes to natural ecosystems have consequences for both ecosystem and human health. Historically, efforts to preserve or restore 'biodiversity' can seem to be in opposition to human interests. However, the integration of biodiversity conservation and public health has gained significant traction in recent years, and new efforts to identify solutions that benefit both environmental and human health are ongoing. At the forefront of these efforts is an attempt to clarify ways in which biodiversity conservation can help reduce the risk of zoonotic spillover of pathogens from wild animals, sparking epidemics and pandemics in humans and livestock. However, our understanding of the mechanisms by which biodiversity change influences the spillover process is incomplete, limiting the application of integrated strategies aimed at achieving positive outcomes for both conservation and disease management. Here, we review the literature, considering a broad scope of biodiversity dimensions, to identify cases where zoonotic pathogen spillover is mechanistically linked to changes in biodiversity. By reframing the discussion around biodiversity and disease using mechanistic evidence - while encompassing multiple aspects of biodiversity including functional diversity, landscape diversity, phenological diversity, and interaction diversity - we work toward general principles that can guide future research and more effectively integrate the related goals of biodiversity conservation and spillover prevention. We conclude by summarizing how these principles could be used to integrate the goal of spillover prevention into ongoing biodiversity conservation initiatives.

Global Patterns of the Fungal Pathogen Batrachochytrium dendrobatidis Support Conservation Urgency.

The amphibian chytrid fungus Batrachochytrium dendrobatidis (Bd) is a skin pathogen that can cause the emerging infectious disease chytridiomycosis in susceptible species. It has been considered one of the most severe threats to amphibian biodiversity. We aimed to provide an updated compilation of global Bd occurrences by host taxon and geography, and with the larger global Bd dataset we reanalyzed Bd associations with environmental metrics at the world and regional scales. We also compared our Bd data compilation with a recent independent assessment to provide a more comprehensive count of species and countries with Bd occurrences. Bd has been detected in 1,375 of 2,525 (55%) species sampled, more than doubling known species infections since 2013. Bd occurrence is known from 93 of 134 (69%) countries at this writing; this compares to known occurrences in 56 of 82 (68%) countries in 2013. Climate-niche space is highly associated with Bd detection, with different climate metrics emerging as key predictors of Bd occurrence at regional scales; this warrants further assessment relative to climate-change projections. The accretion of Bd occurrence reports points to the common aims of worldwide investigators to understand the conservation concerns for amphibian biodiversity in the face of potential disease threat. Renewed calls for better mitigation of amphibian disease threats resonate across continents with amphibians, especially outside Asia. As Bd appears to be able to infect about half of amphibian taxa and sites, there is considerable room for biosecurity actions to forestall its spread using both bottom-up community-run efforts and top-down national-to-international policies. Conservation safeguards for sensitive species and biodiversity refugia are continuing priorities.

Co-infection best predicts respiratory viral infection in a wild host.

The dynamics of directly transmitted pathogens in natural populations are likely to result from the combined effects of host traits, pathogen biology, and interactions among pathogens within a host. Discovering how these factors work in concert to shape variation in pathogen dynamics in natural host-multi-pathogen systems is fundamental to understanding population health. Here, we describe temporal variation in incidence and then elucidate the effect of hosts trait, season and pathogen co-occurrence on host infection risk using one of the most comprehensive studies of co-infection in a wild population: a suite of seven directly transmitted viral and bacterial respiratory infections from a 4-year study of 200 free-ranging African buffalo Syncerus caffer. Incidence of upper respiratory infections was common throughout the study-five out of the seven pathogens appeared to be consistently circulating throughout our study population. One pathogen exhibited clear outbreak dynamics in our final study year and another was rarely detected. Co-infection was also common in this system: The strongest indicator of pathogen occurrence for respiratory viruses was in fact the presence of other viral respiratory infections. Host traits had minimal effects on odds of pathogen occurrence but did modify pathogen-pathogen associations. In contrast, only season predicted bacterial pathogen occurrence. Though a combination of environmental, behavioural, and physiological factors work together to shape disease dynamics, we found pathogen associations best determined infection risk. Our study demonstrates that, in the absence of very fine-scale data, the intricate changes among these factors are best represented by co-infection.

Evolutionary consequences of feedbacks between within-host competition and disease control.

Lay Summary: Competition often occurs among diverse parasites within a single host, but control efforts could change its strength. We examined how the interplay between competition and control could shape the evolution of parasite traits like drug resistance and disease severity.

Age of first infection across a range of parasite taxa in a wild mammalian population.

Newborn mammals have an immature immune system that cannot sufficiently protect them against infectious diseases. However, variation in the effectiveness of maternal immunity against different parasites may couple with temporal trends in parasite exposure to influence disparities in the timing of infection risk. Determining the relationship between age and infection risk is critical in identifying the portion of a host population that contributes to parasite dynamics, as well as the parasites that regulate host recruitment. However, there are no data directly identifying timing of first infection among parasites in wildlife. Here, we took advantage of a longitudinal dataset, tracking infection status by viruses, bacteria, protists and gastro-intestinal worms in a herd of African buffalo (Syncerus caffer) to ask: how does age of first infection differ among parasite taxa? We found distinct differences in the age of first infection among parasites that aligned with the mode of transmission and parasite taxonomy. Specifically, we found that tick-borne and environmentally transmitted protists were acquired earlier than directly transmitted bacteria and viruses. These results emphasize the importance of understanding infection risk in juveniles, especially in host species where juveniles are purported to sustain parasite persistence and/or where mortality rates of juveniles influence population dynamics.

Elucidating cryptic dynamics of Theileria communities in African buffalo using a high-throughput sequencing informatics approach.

Increasing access to next-generation sequencing (NGS) technologies is revolutionizing the life sciences. In disease ecology, NGS-based methods have the potential to provide higher-resolution data on communities of parasites found in individual hosts as well as host populations.Here, we demonstrate how a novel analytical method, utilizing high-throughput sequencing of PCR amplicons, can be used to explore variation in blood-borne parasite (Theileria-Apicomplexa: Piroplasmida) communities of African buffalo at higher resolutions than has been obtained with conventional molecular tools.Results reveal temporal patterns of synchronized and opposite fluctuations of prevalence and relative abundance of Theileria spp. within the host population, suggesting heterogeneous transmission across taxa. Furthermore, we show that the community composition of Theileria spp. and their subtypes varies considerably between buffalo, with differences in composition reflected in mean and variance of overall parasitemia, thereby showing potential to elucidate previously unexplained contrasts in infection outcomes for host individuals.Importantly, our methods are generalizable as they can be utilized to describe blood-borne parasite communities in any host species. Furthermore, our methodological framework can be adapted to any parasite system given the appropriate genetic marker.The findings of this study demonstrate how a novel NGS-based analytical approach can provide fine-scale, quantitative data, unlocking opportunities for discovery in disease ecology.

Detection of Pathogen Exposure in African Buffalo Using Non-Specific Markers of Inflammation.

Detecting exposure to new or emerging pathogens is a critical challenge to protecting human, domestic animal, and wildlife health. Yet, current techniques to detect infections typically target known pathogens of humans or economically important animals. In the face of the current surge in infectious disease emergence, non-specific disease surveillance tools are urgently needed. Tracking common host immune responses indicative of recent infection may have potential as a non-specific diagnostic approach for disease surveillance. The challenge to immunologists is to identify the most promising markers, which ideally should be highly conserved across pathogens and host species, become upregulated rapidly and consistently in response to pathogen invasion, and remain elevated beyond clearance of infection. This study combined an infection experiment and a longitudinal observational study to evaluate the utility of non-specific markers of inflammation [NSMI; two acute phase proteins (haptoglobin and serum amyloid A), two pro-inflammatory cytokines (IFNγ and TNF-α)] as indicators of pathogen exposure in a wild mammalian species, African buffalo (Syncerus caffer). Specifically, in the experimental study, we asked (1) How quickly do buffalo mount NSMI responses upon challenge with an endemic pathogen, foot-and-mouth disease virus; (2) for how long do NSMI remain elevated after viral clearance and; (3) how pronounced is the difference between peak NSMI concentration and baseline NSMI concentration? In the longitudinal study, we asked (4) Are elevated NSMI associated with recent exposure to a suite of bacterial and viral respiratory pathogens in a wild population? Among the four NSMI that we tested, haptoglobin showed the strongest potential as a surveillance marker in African buffalo: concentrations quickly and consistently reached high levels in response to experimental infection, remaining elevated for almost a month. Moreover, elevated haptoglobin was indicative of recent exposure to two respiratory pathogens assessed in the longitudinal study. We hope this work motivates studies investigating suites of NSMI as indicators for pathogen exposure in a broader range of both pathogen and host species, potentially transforming how we track disease burden in natural populations.