Dataset Information

Weather influences M. arvalis reproduction but not population dynamics in a 17-year time series.

ABSTRACT: Rodent outbreaks have plagued European agriculture for centuries, but continue to elude comprehensive explanation. Modelling and empirical work in some cyclic rodent systems suggests that changes in reproductive parameters are partly responsible for observed population dynamics. Using a 17-year time series of Microtus arvalis population abundance and demographic data, we explored the relationship between meteorological conditions (temperature and rainfall), female reproductive activity, and population growth rates in a non-cyclic population of this grassland vole species. We found strong but complex relationships between female reproduction and climate variables, with spring female reproduction depressed after cold winters. Population growth rates were, however, uncorrelated with either weather conditions (current and up to three months prior) or with female reproduction (number of foetuses per female and/or proportion of females reproductively active in the population). These results, coupled with age-structure data, suggest that mortality, via predation, disease, or a combination of the two, are responsible for the large multi-annual but non-cyclic population dynamics observed in this population of the common vole.

SUBMITTER: Giraudoux P

PROVIDER: S-EPMC6763496 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Weather influences M. arvalis reproduction but not population dynamics in a 17-year time series.

Giraudoux Patrick P Villette Petra P Quéré Jean-Pierre JP Damange Jean-Pierre JP Delattre Pierre P

Scientific reports 20190926 1

Rodent outbreaks have plagued European agriculture for centuries, but continue to elude comprehensive explanation. Modelling and empirical work in some cyclic rodent systems suggests that changes in reproductive parameters are partly responsible for observed population dynamics. Using a 17-year time series of Microtus arvalis population abundance and demographic data, we explored the relationship between meteorological conditions (temperature and rainfall), female reproductive activity, and popu ...[more]

PMID: 31558762

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Project description:BackgroundThe past decade witnessed an increment in the incidence of hand foot mouth disease (HFMD) in the Pacific Asian region; specifically, in Guangzhou China. This emphasized the requirement of an early warning system designed to allow the medical community to better prepare for outbreaks and thus minimize the number of fatalities.MethodsSamples from 1,556 inpatients (hospitalized) and 11,004 outpatients (non-admitted) diagnosed with HFMD were collected in this study from January 2009 to October 2013. Seasonal Autoregressive Integrated Moving Average (SARIMA) model was applied to establish high predictive model for inpatients and outpatient as well as three viral serotypes (EV71, Pan-EV and CA16). To integrate climate variables in the data analyses, data from eight climate variables were simultaneously obtained during this period. Significant climate variable identified by correlation analyses was executed to improve time series modeling as external repressors.ResultsAmong inpatients with HFMD, 248 (15.9%) were affected by EV71, 137 (8.8%) were affected by Pan-EV+, and 436 (28.0%) were affected by CA16. Optimal Univariate SARIMA model was identified: (2,0,3)(1,0,0)52 for inpatients, (0,1,0)(0,0,2)52 for outpatients as well as three serotypes (EV71, (1,0,1)(0,0,1)52; CA16, (1,0,1)(0,0,0)52; Pan-EV, (1,0,1)(0,0,0)52). Using climate as our independent variable, precipitation (PP) was first identified to be associated with inpatients (r = 0.211, P = 0.001), CA16-serotype (r = 0.171, P = 0.007) and outpatients (r = 0.214, P = 0.01) in partial correlation analyses, and was then shown a significant lag in cross-autocorrelation analyses. However, inclusion of PP [lag -3 week] as external repressor showed a moderate impact on the predictive performance of the SARIMA model described here-in.ConclusionClimate patterns and HFMD incidences have been shown to be strongly correlated. The SARIMA model developed here can be a helpful tool in developing an early warning system for HFMD.

Dataset Information

Weather influences M. arvalis reproduction but not population dynamics in a 17-year time series.

Publications

Weather influences M. arvalis reproduction but not population dynamics in a 17-year time series.

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