ISPRS Int. J. Geo-Inf. 2013, 2(3), 645-664; doi:10.3390/ijgi2030645
Article

Spatially-Explicit Simulation Modeling of Ecological Response to Climate Change: Methodological Considerations in Predicting Shifting Population Dynamics of Infectious Disease Vectors

1 Department of Environmental Health, Rollins School of Public Health, Emory University, 1518 Clifton Rd. NE, Atlanta, GA 30322, USA 2 Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Rd. NE, Atlanta, GA 30322, USA 3 MRC Centre for Outbreak Analysis and Modeling, Department of Infectious Disease Epidemiology, Imperial College London, London, SW7 2AZ, UK 4 Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, 62 Perkins Hall, Knoxville, TN 37996, USA 5 Program in Population Biology, Ecology and Evolution, Graduate Division of Biological and Biomedical Sciences, Emory University, 1510 Clifton Rd., Atlanta, GA 30322, USA
* Author to whom correspondence should be addressed.
Received: 18 May 2013; in revised form: 17 June 2013 / Accepted: 27 June 2013 / Published: 22 July 2013
(This article belongs to the Special Issue Geovisualization and Analysis of Dynamic Phenomena)
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Abstract: Poikilothermic disease vectors can respond to altered climates through spatial changes in both population size and phenology. Quantitative descriptors to characterize, analyze and visualize these dynamic responses are lacking, particularly across large spatial domains. In order to demonstrate the value of a spatially explicit, dynamic modeling approach, we assessed spatial changes in the population dynamics of Ixodes scapularis, the Lyme disease vector, using a temperature-forced population model simulated across a grid of 4 × 4 km cells covering the eastern United States, using both modeled (Weather Research and Forecasting (WRF) 3.2.1) baseline/current (2001–2004) and projected (Representative Concentration Pathway (RCP) 4.5 and RCP 8.5; 2057–2059) climate data. Ten dynamic population features (DPFs) were derived from simulated populations and analyzed spatially to characterize the regional population response to current and future climate across the domain. Each DPF under the current climate was assessed for its ability to discriminate observed Lyme disease risk and known vector presence/absence, using data from the US Centers for Disease Control and Prevention. Peak vector population and month of peak vector population were the DPFs that performed best as predictors of current Lyme disease risk. When examined under baseline and projected climate scenarios, the spatial and temporal distributions of DPFs shift and the seasonal cycle of key questing life stages is compressed under some scenarios. Our results demonstrate the utility of spatial characterization, analysis and visualization of dynamic population responses—including altered phenology—of disease vectors to altered climate.
Keywords: vector-borne disease; spatially-explicit; dynamic; population model; Ixodes scapularis; climate change; temperature; population response; deer ticks

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MDPI and ACS Style

Dhingra, R.; Jimenez, V.; Chang, H.H.; Gambhir, M.; Fu, J.S.; Liu, Y.; Remais, J.V. Spatially-Explicit Simulation Modeling of Ecological Response to Climate Change: Methodological Considerations in Predicting Shifting Population Dynamics of Infectious Disease Vectors. ISPRS Int. J. Geo-Inf. 2013, 2, 645-664.

AMA Style

Dhingra R, Jimenez V, Chang HH, Gambhir M, Fu JS, Liu Y, Remais JV. Spatially-Explicit Simulation Modeling of Ecological Response to Climate Change: Methodological Considerations in Predicting Shifting Population Dynamics of Infectious Disease Vectors. ISPRS International Journal of Geo-Information. 2013; 2(3):645-664.

Chicago/Turabian Style

Dhingra, Radhika; Jimenez, Violeta; Chang, Howard H.; Gambhir, Manoj; Fu, Joshua S.; Liu, Yang; Remais, Justin V. 2013. "Spatially-Explicit Simulation Modeling of Ecological Response to Climate Change: Methodological Considerations in Predicting Shifting Population Dynamics of Infectious Disease Vectors." ISPRS Int. J. Geo-Inf. 2, no. 3: 645-664.

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