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The temporal spectrum of adult mosquito population fluctuations: conceptual and modeling implications.

Jian Y, Silvestri S, Brown J, Hickman R, Marani M - PLoS ONE (2014)

Bottom Line: We further suggest that the range of time scales over which adult mosquito population variability takes place can be divided into three main parts.At longer scales (annual to multi-annual) mosquito populations follow seasonal and inter-annual environmental changes.We conclude that observations of adult mosquito populations should be based on a sub-weekly sampling frequency and that predictive models of mosquito abundance must include behavioral dynamics to separate the effects of a varying mosquito activity from actual changes in the abundance of the underlying population.

View Article: PubMed Central - PubMed

Affiliation: Nicholas School of the Environment, Duke University, Durham, North Carolina, 27708, United States of America.

ABSTRACT
An improved understanding of mosquito population dynamics under natural environmental forcing requires adequate field observations spanning the full range of temporal scales over which mosquito abundance fluctuates in natural conditions. Here we analyze a 9-year daily time series of uninterrupted observations of adult mosquito abundance for multiple mosquito species in North Carolina to identify characteristic scales of temporal variability, the processes generating them, and the representativeness of observations at different sampling resolutions. We focus in particular on Aedes vexans and Culiseta melanura and, using a combination of spectral analysis and modeling, we find significant population fluctuations with characteristic periodicity between 2 days and several years. Population dynamical modelling suggests that the observed fast fluctuations scales (2 days-weeks) are importantly affected by a varying mosquito activity in response to rapid changes in meteorological conditions, a process neglected in most representations of mosquito population dynamics. We further suggest that the range of time scales over which adult mosquito population variability takes place can be divided into three main parts. At small time scales (indicatively 2 days-1 month) observed population fluctuations are mainly driven by behavioral responses to rapid changes in weather conditions. At intermediate scales (1 to several month) environmentally-forced fluctuations in generation times, mortality rates, and density dependence determine the population characteristic response times. At longer scales (annual to multi-annual) mosquito populations follow seasonal and inter-annual environmental changes. We conclude that observations of adult mosquito populations should be based on a sub-weekly sampling frequency and that predictive models of mosquito abundance must include behavioral dynamics to separate the effects of a varying mosquito activity from actual changes in the abundance of the underlying population.

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PACF of observed abundances for Ae.vexans and Cs.melanura, ((a) and (f)), for IBS model realizations including activity ((b) and (g)), IBS model realizations without activity ((c) and (h)); Gompertz model realizations with density dependence at lag = 0 days ((d) and (i)), and Ricker model realizations with density dependence at lag = 0 days [58] and (j)).
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pone-0114301-g007: PACF of observed abundances for Ae.vexans and Cs.melanura, ((a) and (f)), for IBS model realizations including activity ((b) and (g)), IBS model realizations without activity ((c) and (h)); Gompertz model realizations with density dependence at lag = 0 days ((d) and (i)), and Ricker model realizations with density dependence at lag = 0 days [58] and (j)).

Mentions: We also compared the PACFs of the observations, of the IBS simulations, and of the output of canonical population models. The PACFs for the daily observations are positive for both species up to about one week, while the PACFs generated by the Ricker and Gompertz models with density dependence dictated by the current abundance (lag 0) are negative at the same temporal scale (Figure 7 (d), (e), (i), (j)). Models which embed density dependence at the 1 day or 5-day lag exhibit similar results (Figure S4 in File S1). Moreover, adding multiple lags (lags 0–5) to the density dependence representation does not produce a better match of the observed correlations, as it increases the short term correlations but model outputs are less correlated in time than measured abundance (Figure S4 in File S1).


The temporal spectrum of adult mosquito population fluctuations: conceptual and modeling implications.

Jian Y, Silvestri S, Brown J, Hickman R, Marani M - PLoS ONE (2014)

PACF of observed abundances for Ae.vexans and Cs.melanura, ((a) and (f)), for IBS model realizations including activity ((b) and (g)), IBS model realizations without activity ((c) and (h)); Gompertz model realizations with density dependence at lag = 0 days ((d) and (i)), and Ricker model realizations with density dependence at lag = 0 days [58] and (j)).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4257610&req=5

pone-0114301-g007: PACF of observed abundances for Ae.vexans and Cs.melanura, ((a) and (f)), for IBS model realizations including activity ((b) and (g)), IBS model realizations without activity ((c) and (h)); Gompertz model realizations with density dependence at lag = 0 days ((d) and (i)), and Ricker model realizations with density dependence at lag = 0 days [58] and (j)).
Mentions: We also compared the PACFs of the observations, of the IBS simulations, and of the output of canonical population models. The PACFs for the daily observations are positive for both species up to about one week, while the PACFs generated by the Ricker and Gompertz models with density dependence dictated by the current abundance (lag 0) are negative at the same temporal scale (Figure 7 (d), (e), (i), (j)). Models which embed density dependence at the 1 day or 5-day lag exhibit similar results (Figure S4 in File S1). Moreover, adding multiple lags (lags 0–5) to the density dependence representation does not produce a better match of the observed correlations, as it increases the short term correlations but model outputs are less correlated in time than measured abundance (Figure S4 in File S1).

Bottom Line: We further suggest that the range of time scales over which adult mosquito population variability takes place can be divided into three main parts.At longer scales (annual to multi-annual) mosquito populations follow seasonal and inter-annual environmental changes.We conclude that observations of adult mosquito populations should be based on a sub-weekly sampling frequency and that predictive models of mosquito abundance must include behavioral dynamics to separate the effects of a varying mosquito activity from actual changes in the abundance of the underlying population.

View Article: PubMed Central - PubMed

Affiliation: Nicholas School of the Environment, Duke University, Durham, North Carolina, 27708, United States of America.

ABSTRACT
An improved understanding of mosquito population dynamics under natural environmental forcing requires adequate field observations spanning the full range of temporal scales over which mosquito abundance fluctuates in natural conditions. Here we analyze a 9-year daily time series of uninterrupted observations of adult mosquito abundance for multiple mosquito species in North Carolina to identify characteristic scales of temporal variability, the processes generating them, and the representativeness of observations at different sampling resolutions. We focus in particular on Aedes vexans and Culiseta melanura and, using a combination of spectral analysis and modeling, we find significant population fluctuations with characteristic periodicity between 2 days and several years. Population dynamical modelling suggests that the observed fast fluctuations scales (2 days-weeks) are importantly affected by a varying mosquito activity in response to rapid changes in meteorological conditions, a process neglected in most representations of mosquito population dynamics. We further suggest that the range of time scales over which adult mosquito population variability takes place can be divided into three main parts. At small time scales (indicatively 2 days-1 month) observed population fluctuations are mainly driven by behavioral responses to rapid changes in weather conditions. At intermediate scales (1 to several month) environmentally-forced fluctuations in generation times, mortality rates, and density dependence determine the population characteristic response times. At longer scales (annual to multi-annual) mosquito populations follow seasonal and inter-annual environmental changes. We conclude that observations of adult mosquito populations should be based on a sub-weekly sampling frequency and that predictive models of mosquito abundance must include behavioral dynamics to separate the effects of a varying mosquito activity from actual changes in the abundance of the underlying population.

Show MeSH
Related in: MedlinePlus