Density Dependence, Landscape, and Weather Impacts on Aquatic Aedes japonicus japonicus (Diptera: Culicidae) Abundance Along an Urban Altitudinal Gradient

All climate change scenarios predict an increase in both global temperature means and the magnitude of seasonal and diel temperature variation. The nonlinear relationship between temperature and biological processes means that fluctuating temperatures lead to physiological, life history, and ecological consequences for ectothermic insects that diverge from those predicted from constant temperatures. Fluctuating temperatures that remain within permissive temperature ranges generally improve performance. By contrast, those which extend to stressful temperatures may have either positive impacts, allowing repair of damage accrued during exposure to thermal extremes, or negative impacts from cumulative damage during successive exposures. We discuss the mechanisms underlying these differing effects. Fluctuating temperatures could be used to enhance or weaken insects in applied rearing programs, and any prediction of insect performance in the field-including models of climate change or population performance-must account for the effect of fluctuating temperatures.

Extinction risk in natural populations depends on stochastic factors that affect individuals, and is estimated by incorporating such factors into stochastic models. Stochasticity can be divided into four categories, which include the probabilistic nature of birth and death at the level of individuals (demographic stochasticity), variation in population-level birth and death rates among times or locations (environmental stochasticity), the sex of individuals and variation in vital rates among individuals within a population (demographic heterogeneity). Mechanistic stochastic models that include all of these factors have not previously been developed to examine their combined effects on extinction risk. Here we derive a family of stochastic Ricker models using different combinations of all these stochastic factors, and show that extinction risk depends strongly on the combination of factors that contribute to stochasticity. Furthermore, we show that only with the full stochastic model can the relative importance of environmental and demographic variability, and therefore extinction risk, be correctly determined. Using the full model, we find that demographic sources of stochasticity are the prominent cause of variability in a laboratory population of Tribolium castaneum (red flour beetle), whereas using only the standard simpler models would lead to the erroneous conclusion that environmental variability dominates. Our results demonstrate that current estimates of extinction risk for natural populations could be greatly underestimated because variability has been mistakenly attributed to the environment rather than the demographic factors described here that entail much higher extinction risk for the same variability level.