home

Latest Literature

  1. Persistence of Influenza H5N1 and H1N1 Viruses in Unpasteurized Milk on Milking Unit Surfaces

    V. Le Sage et al.

    (wwwnc.cdc.gov)
  2. The devil is in the detail: Environmental variables frequently used for habitat suitability modeling lack information for forest‐dwelling bats in Germany

    Traditional habitat suitability models, relying on variables with coarse level of detail, may inadequately represent the complexities of specialized species. This study shows that habitat suitability modeling for forest specialist bat species can be improved by integrating more targeted data, such as tree species classification map and LiDAR-derived vertical structure information. Furthermore, the results show a high difference in resulting distribution maps when using different levels of detail in environmental predictors, which underscores the importance of exercising caution when using habitat models as a tool to inform conservation strategies and spatial planning efforts. Abstract In response to the pressing challenges of the ongoing biodiversity crisis, the protection of endangered species and their habitats, as well as the monitoring of invasive species are crucial. Habitat suitability modeling (HSM) is often treated as the silver bullet to address these challenges, commonly relying on generic variables sourced from widely available datasets. However, for species with high habitat requirements, or for modeling the suitability of habitats within the geographic range of a species, variables at a coarse level of detail may fall short. Consequently, there is potential value in considering the incorporation of more targeted data, which may extend beyond readily available land cover and climate datasets. In this study, we investigate the impact of incorporating targeted land cover variables (specifically tree species composition) and vertical structure information (derived from LiDAR data) on HSM outcomes for three forest specialist bat species (Barbastella barbastellus, Myotis bechsteinii, and Plecotus auritus) in Rhineland-Palatinate, Germany, compared to commonly utilized environmental variables, such as generic land-cover classifications (e.g., Corine Land Cover) and climate variables (e.g., Bioclim). The integration of targeted variables enhanced the performance of habitat suitability models for all three bat species. Furthermore, our results showed a high difference in the distribution maps that resulted from using different levels of detail in environmental variables. This underscores the importance of making the effort to generate the appropriate variables, rather than simply relying on commonly used ones, and the necessity of exercising caution when using habitat models as a tool to inform conservation strategies and spatial planning efforts.

    (onlinelibrary.wiley.com)
  3. Estimating pathogen‐spillover risk using host–ectoparasite interactions

    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 use parasite ecology, phylogenetics, and geography to predict known and unknown hosts of hantavirus. This improves our knowledge of how host interaction influences disease transmission and can be used to inform targeted surveillance programs. 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.

    (onlinelibrary.wiley.com)
  4. Using an adaptive modeling framework to identify avian influenza spillover risk at the wild-domestic interface

    (www.nature.com)