Characterizing Population Dynamics of <i>Aedes sollicitans</i> (Diptera: Culicidae) Using Meteorological Data

Shone, Scott M.; Curriero, Frank C.; Lesser, Cyrus R.; Glass, Gregory E.

doi:10.1093/jmedent/43.2.393

Cited by 28 publications

(7 citation statements)

References 19 publications

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“…The duration of the aquatic stages also varies with temperature, and weather conditions from about 8 to 20 days prior to capture presumably affect the aquatic stage [7]; weather conditions from about 20 to 28 days affect the adult stage of the parent generation. Previous studies with CCMs considered a time period of about 4 weeks, which approximately represents the lifetime of a mosquito, inclusive all aquatic stages [5,18,19]. The results of this study however show that environmental factors have to be considered further back in time.…”

Section: Discussionmentioning

confidence: 86%

Predicting Culex pipiens/restuans population dynamics by interval lagged weather data

2013

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BackgroundCulex pipiens/restuans mosquitoes are important vectors for a variety of arthropod borne viral infections. In this study, the associations between 20 years of mosquito capture data and the time lagged environmental quantities daytime length, temperature, precipitation, relative humidity and wind speed were used to generate a predictive model for the population dynamics of this vector species.MethodsMosquito population in the study area was represented by averaged time series of mosquitos counts captured at 6 sites in Cook County (Illinois, USA). Cross-correlation maps (CCMs) were compiled to investigate the association between mosquito abundances and environmental quantities. The results obtained from the CCMs were incorporated into a Poisson regression to generate a predictive model. To optimize the predictive model the time lags obtained from the CCMs were adjusted using a genetic algorithm.ResultsCCMs for weekly data showed a highly positive correlation of mosquito abundances with daytime length 4 to 5 weeks prior to capture (quantified by a Spearman rank order correlation of rS = 0.898) and with temperature during 2 weeks prior to capture (rS = 0.870). Maximal negative correlations were found for wind speed averaged over 3 week prior to capture (rS = −0.621). Cx. pipiens/restuans population dynamics was predicted by integrating the CCM results in Poisson regression models. They were used to simulate the average seasonal cycle of the mosquito abundance. Verification with observations resulted in a correlation of rS = 0.899 for daily and rS = 0.917 for weekly data. Applying the optimized models to the entire 20-years time series also resulted in a suitable fit with rS = 0.876 for daily and rS = 0.899 for weekly data.ConclusionsThe study demonstrates the application of interval lagged weather data to predict mosquito abundances with a feasible accuracy, especially when related to weekly Cx. pipiens/restuans populations.

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Section: Discussionmentioning

confidence: 86%

Predicting Culex pipiens/restuans population dynamics by interval lagged weather data

2013

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“…CCMs were adopted for example by [27] to investigate the relationship between Ochlerotatus sollicitans abundance and environmental parameters. Further applications comprise studies on Aedes sollicitans [28] as well as Aedes sollicitans and Culex salinarius [29].…”

Section: Methodsmentioning

confidence: 99%

Bluetongue Disease Risk Assessment Based on Observed and Projected Culicoides obsoletus spp. Vector Densities

Brugger

Rubel

2013

PLoS ONE

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Bluetongue is an arboviral disease of ruminants causing significant economic losses. Our risk assessment is based on the epidemiological key parameter, the basic reproduction number. It is defined as the number of secondary cases caused by one primary case in a fully susceptible host population, in which values greater than one indicate the possibility, i.e., the risk, for a major disease outbreak. In the course of the Bluetongue virus serotype 8 (BTV-8) outbreak in Europe in 2006 we developed such a risk assessment for the University of Veterinary Medicine Vienna, Austria. Basic reproduction numbers were calculated using a well-known formula for vector-borne diseases considering the population densities of hosts (cattle and small ruminants) and vectors (biting midges of the Culicoides obsoletus spp.) as well as temperature dependent rates. The latter comprise the biting and mortality rate of midges as well as the reciprocal of the extrinsic incubation period. Most important, but generally unknown, is the spatio-temporal distribution of the vector density. Therefore, we established a continuously operating daily monitoring to quantify the seasonal cycle of the vector population by a statistical model. We used cross-correlation maps and Poisson regression to describe vector densities by environmental temperature and precipitation. Our results comprise time series of observed and simulated Culicoides obsoletus spp. counts as well as basic reproduction numbers for the period 2009–2011. For a spatio-temporal risk assessment we projected our results from the location of Vienna to the entire region of Austria. We compiled both daily maps of vector densities and the basic reproduction numbers, respectively. Basic reproduction numbers above one were generally found between June and August except in the mountainous regions of the Alps. The highest values coincide with the locations of confirmed BTV cases.

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“…Epidemiologists have long been concerned with aspects of disease spread in spatially distributed host populations (Ch. 7 of Keeling and Rohhani, 2007 and references therein), and with temporal variation in pathogen transmission (e.g., seasonal outbreaks in a locality; Shone et al 2006; Moore et al 2014). However, the impact of spatiotemporal variation along the line of our inquiry has been largely neglected.…”

Section: Introductionmentioning

confidence: 99%

Metapopulations, the inflationary effect, and consequences for public health

Kortessis,

Glass,

Gonzalez

et al. 2023

Preprint

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The metapopulation perspective is an important conceptual framework in ecology and evolutionary ecology. Metapopulations are spatially distributed populations linked by dispersal. Both metapopulation models and their community and ecosystem level analogues, metacommunity and meta-ecosystem models, tend to be more stable regionally than locally and display enhanced abundance because of the interplay of spatiotemporal heterogeneity and dispersal (an effect that has been called the “inflationary effect”). We highlight the essential role of spatiotemporal heterogeneity in metapopulation biology, sketch empirical demonstrations of the inflationary effect, and provide a mechanistic interpretation of how the inflationary effect arises and impacts population growth and abundance. We illustrate the effect with examples from the spread of infectious disease. Namely, failure to recognize the full possible effects of spatiotemporal heterogeneity likely enhanced the spread of COVID-19, a failure based on lack of understanding of emergent population processes at large scales which may hamper control and eradication of other infectious diseases. We finish by noting how the effects of spatiotemporal heterogeneity have implicitly played roles in the history of ecology, ranging across subdisciplines as diverse as natural enemy-victim dynamics, species coexistence, and conservation biology. Seriously confronting the complexity of spatiotemporal heterogeneity could push many of these subdisciplines forward.

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Characterizing Population Dynamics of Aedes sollicitans (Diptera: Culicidae) Using Meteorological Data

Cited by 28 publications

References 19 publications

Predicting Culex pipiens/restuans population dynamics by interval lagged weather data

Predicting Culex pipiens/restuans population dynamics by interval lagged weather data

Bluetongue Disease Risk Assessment Based on Observed and Projected Culicoides obsoletus spp. Vector Densities

Metapopulations, the inflationary effect, and consequences for public health

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