Projecting the influence of climate change and daily temperature range on insect phenology and risk period of vector borne diseases

Open Access
Chen, Shi
Graduate Program:
Doctor of Philosophy
Document Type:
Date of Defense:
June 02, 2011
Committee Members:
  • Shelby Jay Fleischer, Dissertation Advisor
  • Shelby Jay Fleischer, Committee Chair
  • Michael Craig Saunders, Committee Chair
  • Paul Heinz Heinemann, Committee Member
  • Matthew Brian Thomas, Committee Member
  • John Fricks, Committee Member
  • vector borne diseases
  • insect phenology
  • daily temperature range
  • climate change
  • risk period
Understanding how climate change would influence the life history and population dynamics of insects is a key component needed to evaluate the impact of climate change on ecological and epidemiological systems, since insects are relatively sensitive to their surrounding environment, especially ambient temperature. Previous research has demonstrated that rising mean temperatures can influence voltinism, geographic distribution, and vectoring capacity. However, the distributional change among life stages for specific climate scenarios, and the importance of the daily temperature range (DTR), or daily temperature fluctuations, has not been emphasized. In this thesis we employed an agroecosystem and an epidemiological system and quantitatively study how changes in daily temperature range, as well as in mean temperature, would shift pest/vector insect phenology. In the first chapter (chapter II) following the introduction chapter (chapter I), I developed an individual based Monte Carlo simulation method to project grape berry moth (Paralobesia viteana) life history (e.g. number of generations per year, mean and distribution of emergence time for each generation) under two different climate change scenarios, A1fi and B1, which represent upper and lower global warming conditions, respectively. The results showed that under A1fi condition the number of generations per year would increase nonlinearly after an initial lag of approximately 35 years (from 2009), and by the end of this century we might expect almost one more generations per year on average and a 5th generation would be more common. I then further investigated how changes in the daily temperature range would influence degree day accumulation and consequently P. viteana phenology in chapter III. I discovered that mean temperature alone could not sufficiently and accurately compute degree day accumulation, and various daily temperature range conditions substantially influences degree day accumulation. Large changes in daily temperature fluctuations around current climatic conditions were sufficient to allow the voltinism of P. viteana to approach that simulated to occur in at the end of this century. The simulations based on different daily temperature ranges and mean temperature conditions revealed that, even when mean temperature decreased, larger daily temperature ranges could compensate the effect of decreasing mean temperature, resulting in a similar number of generations per year. These results confirmed that daily temperature range should be an important factor in modeling insect phenology and population dynamics. In chapter IV I considered an epidemiological system and investigated if degree day models could predict West Nile virus (WNV) incidence dates in Pennsylvania. The original degree days model did not consider a temperature-dependent extrinsic incubation period or longevity changes in vector mosquitoes (Culex spp.), and did not capture virus transmission period well in four locations (Harrisburg, Philadelphia, Pittsburgh, and Williamsport) in Pennsylvania from 2002 to 2008. By treating decreasing the extrinsic incubation period (EIP) at the beginning of the season, and incorporating adult mosquito longevity a variable as well, model performance was increased significantly. I also demonstrated that mosquito species composition should be an important factor for predicting WNV transmission period. The calibrated models performed well to predict WNV emergence period in Pennsylvania, with a success rate of more than 70% compared to less than 30% in the uncalibrated model. In the successive research in chapter V I demonstrated that mean temperature alone did not provide enough information to compute degree day accumulation and predict disease transmission period well. I quantified WNV transmission period by comprehensively exploring combinations of changing daily temperature range and mean temperature in four locations: Harrisburg, Philadelphia, Pittsburgh, and Williamsport in Pennsylvania. I investigated vector mosquito life history change according to these different climate change conditions. The results again showed that even when the mean temperatures remained the same, a large daily temperature variation results in more degree day accumulation and consequently a longer transmission period. Increasing mean temperature with increasing or decreasing the daily temperature range would yield earlier first incidences and later last incidences. I also demonstrated that increasing daily temperature range would impact disease transmission more dramatically in relatively cooler areas (Williamsport, Harrisburg, and Pittsburgh) than relatively warm areas (Philadelphia). These results showed how daily temperature range alters WNV epidemics quantitatively and confirmed that I need to consider daily temperature range in our research in addition to mean temperature.