Endogenous and Exogenous Factors Controlling Temporal Abundance Patterns of Tropical Mosquitoes

Abstract

Abstract. The growing demand for efficient and effective mosquito control requires a better understanding of vector population dynamics and how these are modified by endogenous and exogenous factors. A long-term (11-year) monitoring data set describing the relative abundance of the saltmarsh mosquito (Aedes vigilax) in the greater Darwin region, northern Australia, was examined in a suite of Gompertz-logistic (GL) models with and without hypothesized environmental correlates (high tide frequency, rainfall, and relative humidity). High tide frequency and humidity were hypothesized to influence saltmarsh mosquito abundance positively, and rainfall was hypothesized to correlate negatively by reducing the availability of suitable habitats (moist substrata) required by ovipositing adult female mosquitoes. We also examined whether environmental correlates explained the variance in seasonal carrying capacity (K ) because environmental stochasticity is hypothesized to modify population growth rate (r), carrying capacity, or both. Current and lagged-time effects were tested by comparing alternative population dynamics models using three different information criteria (Akaike's Information Criterion [corrected; AIC c ], Bayesian Information Criterion [BIC], and cross-validation [C -V]). The GL model with a two-month lag without environmental effects explained 31% of the deviance in population growth rate. This increased to .70% under various model combinations of high tide frequency, rainfall, and relative humidity, of which, high tide frequency and rainfall had the highest contributions. Temporal variation in K was explained weakly by high tide frequency, and there was some evidence that the filling of depressions to reduce standing water availability has reduced Aedes vigilax carrying capacity over the study period. This study underscores the need to consider simultaneously both types of drivers (endogenous and exogenous) when predicting mosquito abundance and population growth patterns. This work also indicates that climate change, via continued increases in rainfall and higher expected frequencies and intensities of high tide events with sea level rise, will alter mosquito abundance trends in northern Australia.

Climatic variability and landscape heterogeneity impact urban mosquito diversity and vector abundance and infection

“…Beyond the major role of climatic variability in determining the patterns of abundance of all mosquito species, and especially Cx pipiens, a pattern described in other settings (Yasuoka and Levins 2007, Yang et al 2008, Barker et al 2010, our results show that increases in biodiversity reduce the abundance of mosquitoes. For all mosquitoes the linear regression showed a convex relationship with species richness, meaning that the most species rich communities harbor fewer mosquitoes.…”

Mapping and predicting malaria transmission in the People’s Republic of China, using integrated biology-driven and statistical models

“…The distribution of the rainy season can impact the fluctuation of malaria transmission in a year (Briët et al, 2008). For instance, the transmission peak of malaria in low-lying land of the temperate zone occurs after the rainy season, while it always takes place during the rainy season in the tropical forest (Craig et al, 1999;Yang et al, 2008). Once the basic knowledge of the ecology of vectors is grasped and the weak point in the life cycle identified, a strategy to control the mosquito (leading to reduction or even interruption of malaria transmission) can be formulated.…”

“…Typical examples are the minimum and maximum thermal thresholds for the survival of infectious agents developing in a vector or an intermediate host (Malone, 2005;Peterson, 2006). The mathematical statistical approach, on the other hand, requires the development of a new, weatherdependent mathematical dynamic model, which takes into account known risk factors quantitatively (Yang et al, 2005(Yang et al, , 2008Galardo et al, 2009). The various statistical models rely on various mathematical approaches to quantitatively investigate the linkage between risk factors and disease prevalence or incidence.…”

Temporal Variation in the Carrying Capacity of a Perennial Grass Population

“…Variation in K may provide insights into mechanisms underlying population regulation, especially if variation in K accounts for a substantial portion of the variation in N, as it did here (j K was at least three times the variation in N from all other sources). Previous models that have implicitly incorporated variation in K have done so by making resource levels a function of one or more climatic variables, often rainfall, usually with good success (e.g., Illius and O'Connor 2000;Davis et al 2002;Owen-Smith 2002;Georgiadis et al 2003;Lima et al 2006Lima et al , 2008Hone and Clutton-Brock 2007;Chamaillé-Jammes et al 2008;Yang et al 2008). Bouteloua rigidiseta readily adds tillers in response to rain and dies back in response to drought (N. L. Fowler, personal observation).…”

“…In most cases, the inclusion of rainfall-dependent variation in resource levels has improved the prediction of herbivore population sizes. Similar models have been constructed for rodents (Lima et al 2006(Lima et al , 2008, for mosquitoes (Yang et al 2008), and for large herbivores in a wet climate (Coulson et al 2001;Hone and Clutton-Brock 2007). Stochastic variation in K is also implicit in some plant population studies that have estimated related population parameters (e.g., Freckleton and Watkinson 1998;Freckleton et al 2000;Turnbull et al 2004Turnbull et al , 2007Adams et al 2005).…”