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The effect of seasonality, density and climate on the population dynamics of Montana deer mice, important reservoir hosts for Sin Nombre hantavirus
Author(s) -
Luis Angela D.,
Douglass Richard J.,
Mills James N.,
Bjørnstad Ottar N.
Publication year - 2010
Publication title -
journal of animal ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.134
H-Index - 157
eISSN - 1365-2656
pISSN - 0021-8790
DOI - 10.1111/j.1365-2656.2009.01646.x
Subject(s) - peromyscus , hantavirus , seasonality , biology , population , deer mouse , density dependence , hantavirus pulmonary syndrome , rodent , ecology , population density , zoology , demography , virus , immunology , sociology
Summary 1. Since Sin Nombre virus was discovered in the U.S. in 1993, longitudinal studies of the rodent reservoir host, the deer mouse ( Peromyscus maniculatus ) have demonstrated a qualitative correlation among mouse population dynamics and risk of hantavirus pulmonary syndrome (HPS) in humans, indicating the importance of understanding deer mouse population dynamics for evaluating risk of HPS. 2. Using capture–mark–recapture statistical methods on a 15‐year data set from Montana, we estimated deer mouse survival, maturation and recruitment rates and tested the relative importance of seasonality, population density and local climate in explaining temporal variation in deer mouse demography. 3. From these estimates, we designed a population model to simulate deer mouse population dynamics given climatic variables and compared the model to observed patterns. 4. Month, precipitation 5 months previously, temperature 5 months previously and to a lesser extent precipitation and temperature in the current month, were important in determining deer mouse survival. Month, the sum of precipitation over the last 4 months, and the sum of the temperature over the last 4 months were important in determining recruitment rates. Survival was more important in determining the growth rate of the population than recruitment. 5. While climatic drivers appear to have a complex influence on dynamics, our forecasts were good. Our quantitative model may allow public health officials to better predict increased human risk from basic climatic data.