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.

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.

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.

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.

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 = 0.87). 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. Our results indicate that lab-based and field-based studies are highly complementary; performing the analyses in concert can help to more comprehensively understand vector response to climate change.

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 and Bulinus snails, thus 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 used machine learning, remote sensing, and 30 years of snail occurrence records to map the historical and current distribution of forward-transmitting Biomphalaria hosts 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.

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.

Species distribution modeling for disease ecology: A multi-scale case study for schistosomiasis host snails in Brazil.

Species distribution models (SDMs) are increasingly popular tools for profiling disease risk in ecology, particularly for infectious diseases of public health importance that include an obligate non-human host in their transmission cycle. SDMs can create high-resolution maps of host distribution across geographical scales, reflecting baseline risk of disease. However, as SDM computational methods have rapidly expanded, there are many outstanding methodological questions. Here we address key questions about SDM application, using schistosomiasis risk in Brazil as a case study. Schistosomiasis is transmitted to humans through contact with the free-living infectious stage of Schistosoma spp. parasites released from freshwater snails, the parasite's obligate intermediate hosts. In this study, we compared snail SDM performance across machine learning (ML) approaches (MaxEnt, Random Forest, and Boosted Regression Trees), geographic extents (national, regional, and state), types of presence data (expert-collected and publicly-available), and snail species (Biomphalaria glabrata, B. straminea, and B. tenagophila). We used high-resolution (1km) climate, hydrology, land-use/land-cover (LULC), and soil property data to describe the snails' ecological niche and evaluated models on multiple criteria. Although all ML approaches produced comparable spatially cross-validated performance metrics, their suitability maps showed major qualitative differences that required validation based on local expert knowledge. Additionally, our findings revealed varying importance of LULC and bioclimatic variables for different snail species at different spatial scales. Finally, we found that models using publicly-available data predicted snail distribution with comparable AUC values to models using expert-collected data. This work serves as an instructional guide to SDM methods that can be applied to a range of vector-borne and zoonotic diseases. In addition, it advances our understanding of the relevant environment and bioclimatic determinants of schistosomiasis risk in Brazil.

Multi-parametric atlas of the pre-metastatic liver for prediction of metastatic outcome in early-stage pancreatic cancer.

Metastasis occurs frequently after resection of pancreatic cancer (PaC). In this study, we hypothesized that multi-parametric analysis of pre-metastatic liver biopsies would classify patients according to their metastatic risk, timing and organ site. Liver biopsies obtained during pancreatectomy from 49 patients with localized PaC and 19 control patients with non-cancerous pancreatic lesions were analyzed, combining metabolomic, tissue and single-cell transcriptomics and multiplex imaging approaches. Patients were followed prospectively (median 3 years) and classified into four recurrence groups; early (<6 months after resection) or late (>6 months after resection) liver metastasis (LiM); extrahepatic metastasis (EHM); and disease-free survivors (no evidence of disease (NED)). Overall, PaC livers exhibited signs of augmented inflammation compared to controls. Enrichment of neutrophil extracellular traps (NETs), Ki-67 upregulation and decreased liver creatine significantly distinguished those with future metastasis from NED. Patients with future LiM were characterized by scant T cell lobular infiltration, less steatosis and higher levels of citrullinated H3 compared to patients who developed EHM, who had overexpression of interferon target genes (MX1 and NR1D1) and an increase of CD11B+ natural killer (NK) cells. Upregulation of sortilin-1 and prominent NETs, together with the lack of T cells and a reduction in CD11B+ NK cells, differentiated patients with early-onset LiM from those with late-onset LiM. Liver profiles of NED closely resembled those of controls. Using the above parameters, a machine-learning-based model was developed that successfully predicted the metastatic outcome at the time of surgery with 78% accuracy. Therefore, multi-parametric profiling of liver biopsies at the time of PaC diagnosis may determine metastatic risk and organotropism and guide clinical stratification for optimal treatment selection.

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.

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.

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.

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.

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.

EXPLORING THE USE OF THE ERYTHROCYTE SEDIMENTATION RATE AS AN INFLAMMATORY MARKER FOR FREE-RANGING WILDLIFE: A CASE STUDY IN AFRICAN BUFFALO (SYNCERUS CAFFER).

Measuring inflammatory markers is critical to evaluating both recent infection status and overall human and animal health; however, there are relatively few techniques that do not require specialized equipment or personnel for detecting inflammation among wildlife. Such techniques are useful in that they help determine individual and population-level inflammatory status without the infrastructure and reagents that many more-specific assays require. One such technique, known as the erythrocyte sedimentation rate (ESR), is a measure of how quickly erythrocytes (red blood cells) settle in serum, with a faster rate indicating a general, underlying inflammatory process is occurring. The technique is simple, inexpensive, and can be performed in the field without specialized equipment. We took advantage of a population of African buffalo (Syncerus caffer), well studied from June 2014 to May 2017, to understand the utility of ESR in an important wildlife species. When ESR was compared with other markers of immunity in African buffalo, it correlated to known measures of inflammation. We found that a faster ESR was significantly positively correlated with increased total globulin levels and significantly negatively correlated with increased red blood cell count and albumin levels. We then evaluated if ESR correlated to the incidence of five respiratory pathogens and infection with two tick-borne pathogens in African buffalo. Our results suggest that elevated ESR is associated with the incidence of bovine viral diarrhea virus infection, parainfluenza virus, and Mannheimia haemolytica infections as well as concurrent Anaplasma marginale and Anaplasma centrale coinfection. These findings suggest that ESR is a useful field test as an inflammatory marker in individuals and herds, helping us better monitor overall health status in wild populations.

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

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

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.

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.

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.

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.