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1. Variability in environmental persistence but not per capita transmission rates of the amphibian chytrid fungus leads to differences in host infection prevalence

   https://doi.org/10.1111/1365-2656.13612  

   Rumschlag, Samantha L.; Roth, Sadie A.; McMahon, Taegan A.; Rohr, Jason R.; Civitello, David J. (November 2021, Journal of Animal Ecology)

   Abstract Heterogeneities in infections among host populations may arise through differences in environmental conditions through two mechanisms. First, environmental conditions may alter host exposure to pathogens via effects on survival. Second, environmental conditions may alter host susceptibility, making infection more or less likely if contact between a host and pathogen occurs. Further, host susceptibility might be altered through acquired resistance, which hosts can develop, in some systems, through exposure to dead or decaying pathogens and their metabolites. Environmental conditions may alter the rates of pathogen decomposition, influencing the likelihood of hosts developing acquired resistance.The present study primarily tests how environmental context influences the relative contributions of pathogen survival and per capita transmission on host infection prevalence using the amphibian chytrid fungus (Batrachochytrium dendrobatidis; Bd) as a model system. Secondarily, we evaluate how environmental context influences the decomposition of Bd because previous studies have shown that dead Bd and its metabolites can illicit acquired resistance in hosts. We conducted Bd survival and infection experiments and then fit models to discern how Bd mortality, decomposition and per capita transmission rates vary among water sources [e.g. artificial spring water (ASW) or water from three ponds].We found that infection prevalence differed among water sources, which was driven by differences in mortality rates of Bd, rather than differences in per capita transmission rates. Bd mortality rates varied among pond water treatments and were lower in ASW compared to pond water.These results suggest that variation in Bd infection dynamics could be a function of environmental factors in waterbodies that result in differences in exposure of hosts to live Bd. In contrast to the persistence of live Bd, we found that the rates of decomposition of dead Bd did not vary among water sources, which may suggest that exposure of hosts to dead Bd or its metabolites might not commonly vary among nearby sites. Ultimately, a mechanistic understanding of the environmental dependence of free‐living pathogens could lead to a deeper understanding of the patterns of outbreak heterogeneity, which could inform surveillance and management strategies. 

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2. Counterintuitive scaling between population abundance and local density: Implications for modelling transmission of infectious diseases in bat populations

   https://doi.org/10.1111/1365-2656.13634  

   Lunn, Tamika J.; Peel, Alison J.; Eby, Peggy; Brooks, Remy; Plowright, Raina K.; Kessler, Maureen K.; McCallum, Hamish (November 2021, Journal of Animal Ecology)

   Abstract Models of host–pathogen interactions help to explain infection dynamics in wildlife populations and to predict and mitigate the risk of zoonotic spillover. Insights from models inherently depend on the way contacts between hosts are modelled, and crucially, how transmission scales with animal density.Bats are important reservoirs of zoonotic disease and are among the most gregarious of all mammals. Their population structures can be highly heterogeneous, underpinned by ecological processes across different scales, complicating assumptions regarding the nature of contacts and transmission. Although models commonly parameterise transmission using metrics of total abundance, whether this is an ecologically representative approximation of host–pathogen interactions is not routinely evaluated.We collected a 13‐month dataset of tree‐roostingPteropusspp. from 2,522 spatially referenced trees across eight roosts to empirically evaluate the relationship between total roost abundance and tree‐level measures of abundance and density—the scale most likely to be relevant for virus transmission. We also evaluate whether roost features at different scales (roost level, subplot level, tree level) are predictive of these local density dynamics.Roost‐level features were not representative of tree‐level abundance (bats per tree) or tree‐level density (bats per m²or m³), with roost‐level models explaining minimal variation in tree‐level measures. Total roost abundance itself was either not a significant predictor (tree‐level 3D density) or only weakly predictive (tree‐level abundance).This indicates that basic measures, such as total abundance of bats in a roost, may not provide adequate approximations for population dynamics at scales relevant for transmission, and that alternative measures are needed to compare transmission potential between roosts. From the best candidate models, the strongest predictor of local population structure was tree density within roosts, where roosts with low tree density had a higher abundance but lower density of bats (more spacing between bats) per tree.Together, these data highlight unpredictable and counterintuitive relationships between total abundance and local density. More nuanced modelling of transmission, spread and spillover from bats likely requires alternative approaches to integrating contact structure in host–pathogen models, rather than simply modifying the transmission function. 

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3. Spatial dynamics of pathogen transmission in communally roosting species: Impacts of changing habitats on bat‐virus dynamics

   https://doi.org/10.1111/1365-2656.13566  

   Lunn, Tamika J.; Peel, Alison J.; McCallum, Hamish; Eby, Peggy; Kessler, Maureen K.; Plowright, Raina K.; Restif, Olivier (August 2021, Journal of Animal Ecology)

   Abstract The spatial organization of populations determines their pathogen dynamics. This is particularly important for communally roosting species, whose aggregations are often driven by the spatial structure of their environment.We develop a spatially explicit model for virus transmission within roosts of Australian tree‐dwelling bats (Pteropusspp.), parameterized to reflect Hendra virus. The spatial structure of roosts mirrors three study sites, and viral transmission between groups of bats in trees was modelled as a function of distance between roost trees. Using three levels of tree density to reflect anthropogenic changes in bat habitats, we investigate the potential effects of recent ecological shifts in Australia on the dynamics of zoonotic viruses in reservoir hosts.We show that simulated infection dynamics in spatially structured roosts differ from that of mean‐field models for equivalently sized populations, highlighting the importance of spatial structure in disease models of gregarious taxa. Under contrasting scenarios of flying‐fox roosting structures, sparse stand structures (with fewer trees but more bats per tree) generate higher probabilities of successful outbreaks, larger and faster epidemics, and shorter virus extinction times, compared to intermediate and dense stand structures with more trees but fewer bats per tree. These observations are consistent with the greater force of infection generated by structured populations with less numerous but larger infected groups, and may flag an increased risk of pathogen spillover from these increasingly abundant roost types.Outputs from our models contribute insights into the spread of viruses in structured animal populations, like communally roosting species, as well as specific insights into Hendra virus infection dynamics and spillover risk in a situation of changing host ecology. These insights will be relevant for modelling other zoonotic viruses in wildlife reservoir hosts in response to habitat modification and changing populations, including coronaviruses like SARS‐CoV‐2. 

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4. Estimating pathogen‐spillover risk using host–ectoparasite interactions

   https://doi.org/10.1002/ece3.11509  

   Brennan, Reilly_N; Paulson, Sally_L; Escobar, Luis_E (June 2024, Ecology and Evolution)

   Abstract Pathogen spillover corresponds to the transmission of a pathogen or parasite from an original host species to a novel host species, preluding disease emergence. Understanding the interacting factors that lead to pathogen transmission in a zoonotic cycle could help identify novel hosts of pathogens and the patterns that lead to disease emergence. We hypothesize that ecological and biogeographic factors drive host encounters, infection susceptibility, and cross‐species spillover transmission. Using a rodent–ectoparasite system in the Neotropics, with shared ectoparasite associations as a proxy for ecological interaction between rodent species, we assessed relationships between rodents using geographic range, phylogenetic relatedness, and ectoparasite associations to determine the roles of generalist and specialist hosts in the transmission cycle of hantavirus. A total of 50 rodent species were ranked on their centrality in a network model based on ectoparasites sharing. Geographic proximity and phylogenetic relatedness were predictors for rodents to share ectoparasite species and were associated with shorter network path distance between rodents through shared ectoparasites. The rodent–ectoparasite network model successfully predicted independent data of seven known hantavirus hosts. The model predicted five novel rodent species as potential, unrecognized hantavirus hosts in South America. Findings suggest that ectoparasite data, geographic range, and phylogenetic relatedness of wildlife species could help predict novel hosts susceptible to infection and possible transmission of zoonotic pathogens. Hantavirus is a high‐consequence zoonotic pathogen with documented animal‐to‐animal, animal‐to‐human, and human‐to‐human transmission. Predictions of new rodent hosts can guide active epidemiological surveillance in specific areas and wildlife species to mitigate hantavirus spillover transmission risk from rodents to humans. This study supports the idea that ectoparasite relationships among rodents are a proxy of host species interactions and can inform transmission cycles of diverse pathogens circulating in wildlife disease systems, including wildlife viruses with epidemic potential, such as hantavirus. 

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5. Environmental reservoir dynamics predict global infection patterns and population impacts for the fungal disease white-nose syndrome

   https://doi.org/10.1073/pnas.1914794117  

   Hoyt, Joseph R.; Langwig, Kate E.; Sun, Keping; Parise, Katy L.; Li, Aoqiang; Wang, Yujuan; Huang, Xiaobin; Worledge, Lisa; Miller, Helen; White, J. Paul; et al (March 2020, Proceedings of the National Academy of Sciences)

   Disease outbreaks and pathogen introductions can have significant effects on host populations, and the ability of pathogens to persist in the environment can exacerbate disease impacts by fueling sustained transmission, seasonal epidemics, and repeated spillover events. While theory suggests that the presence of an environmental reservoir increases the risk of host declines and threat of extinction, the influence of reservoir dynamics on transmission and population impacts remains poorly described. Here we show that the extent of the environmental reservoir explains broad patterns of host infection and the severity of disease impacts of a virulent pathogen. We examined reservoir and host infection dynamics and the resulting impacts of Pseudogymnoascus destructans , the fungal pathogen that causes white-nose syndrome, in 39 species of bats at 101 sites across the globe. Lower levels of pathogen in the environment consistently corresponded to delayed infection of hosts, fewer and less severe infections, and reduced population impacts. In contrast, an extensive and persistent environmental reservoir led to early and widespread infections and severe population declines. These results suggest that continental differences in the persistence or decay of P. destructans in the environment altered infection patterns in bats and influenced whether host populations were stable or experienced severe declines from this disease. Quantifying the impact of the environmental reservoir on disease dynamics can provide specific targets for reducing pathogen levels in the environment to prevent or control future epidemics. 

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