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

Radhika Dhingra⁴  Violeta Jimenez⁴  Howard H. Chang¹  Manoj Gambhir²  Joshua S. Fu³  Yang Liu⁴ 

[1] Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Rd. NE, Atlanta, GA 30322, USA; E-Mail:;MRC Centre for Outbreak Analysis and Modeling, Department of Infectious Disease Epidemiology, Imperial College London, London, SW7 2AZ, UK; E-Mail:;Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, 62 Perkins Hall, Knoxville, TN 37996, USA; E-Mail:;Department of Environmental Health, Rollins School of Public Health, Emory University, 1518 Clifton Rd. NE, Atlanta, GA 30322, USA; E-Mails:

关键词: vector-borne disease;    spatially-explicit;    dynamic;    population model;    Ixodes scapularis;    climate change;    temperature;    population response;    deer ticks;   

DOI  :  10.3390/ijgi2030645

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.