
AbstractWe use data collected on 18, 1‐ha live trapping grids monitored from 1994 through 2005 and on five of those grids through 2013 in the mesic northwestern United States to illustrate the complexity of the deer mouse (Peromyscus maniculatus)/Sin Nombre virus (SNV) host‐pathogen system. Important factors necessary to understand zoonotic disease ecology include those associated with distribution and population dynamics of reservoir species as well as infection dynamics. Results are based on more than 851,000 trap nights, 16,608 individual deer mice and 10,572 collected blood samples. Deer mice were distributed throughout every habitat we sampled and were present during every sampling period in all habitats except high altitude habitats over 1900 m. Abundance varied greatly among locations with peak numbers occurring mostly during fall. However, peak rodent abundance occurred during fall, winter and spring during various years on three grids trapped 12 months/yr. Prevalence of antibodies to SNV averaged 3.9% to 22.1% but no grids had mice with antibodies during every month. The maximum period without antibody‐positive mice ranged from 1 to 52 months, or even more at high altitude grids where deer mice were not always present. Months without antibody‐positive mice were more prevalent during fall than spring. Population fluctuations were not synchronous over broad geographic areas and antibody prevalences were not well spatially consistent, differing greatly over short distances. We observed an apparently negative, but nonstatistically significant relationship between average antibody prevalence and average deer mouse population abundance and a statistically significant positive relationship between the average number of antibody positive mice and average population abundance. We present data from which potential researchers can estimate the effort required to adequately describe the ecology of a rodent‐borne viral system. We address different factors affecting population dynamics and hantavirus antibody prevalence and discuss the path to understanding a complex rodent‐borne disease system as well as the obstacles in that path.
emerging and infectious disease, Sin Nombre virus, Ecosystem Resilience, SIN NOMBRE VIRUS, FOS: Health sciences, hantavirus, Impact of Climate Change on Forest Wildfires, Agricultural and Biological Sciences, antibody prevalence, https://purl.org/becyt/ford/1.6, Peromyscus maniculatus, Health Sciences, https://purl.org/becyt/ford/1, Biology, QH540-549.5, Ecology, Evolution, Behavior and Systematics, Global and Planetary Change, Rodent, Montana, Ecology, Life Sciences, EMERGING AND INFECTIOUS DISEASE, ANTIBODY PREVALENCE, PEROMYSCUS MANICULATUS, Bluetongue Virus and Culicoides-Borne Diseases in Europe, MONTANA, Infectious Diseases, FOS: Biological sciences, Environmental Science, Physical Sciences, HANTAVIRUS, Medicine, Viral Hemorrhagic Fevers and Zoonotic Infections
emerging and infectious disease, Sin Nombre virus, Ecosystem Resilience, SIN NOMBRE VIRUS, FOS: Health sciences, hantavirus, Impact of Climate Change on Forest Wildfires, Agricultural and Biological Sciences, antibody prevalence, https://purl.org/becyt/ford/1.6, Peromyscus maniculatus, Health Sciences, https://purl.org/becyt/ford/1, Biology, QH540-549.5, Ecology, Evolution, Behavior and Systematics, Global and Planetary Change, Rodent, Montana, Ecology, Life Sciences, EMERGING AND INFECTIOUS DISEASE, ANTIBODY PREVALENCE, PEROMYSCUS MANICULATUS, Bluetongue Virus and Culicoides-Borne Diseases in Europe, MONTANA, Infectious Diseases, FOS: Biological sciences, Environmental Science, Physical Sciences, HANTAVIRUS, Medicine, Viral Hemorrhagic Fevers and Zoonotic Infections
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 9 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
