Impact of abiotic factor changes in blowfly, Achoetandrus rufifacies (Diptera: Calliphoridae), in northern Thailand - Tunwadee Klong-Klaew (2018)
Understanding how medically important flies respond to abiotic factor changes is necessary for predicting their population dynamics. In this study, we investigated the geographical distribution of the medically important blowfly, Achoetandrus rufifacies (Macquart) (Diptera: Calliphoridae), and ascertained the response to climatic and physio-environmental factors in Chiang Mai, northern Thailand. Adult fly surveys were carried out every 2 weeks from May 2009 to May 2010 at 18 systematically randomized study sites in three districts of Chiang Mai province (Mueang Chiang Mai, Mae Rim, and Hang Dong), using reconstructable funnel traps with 1-day tainted beef offal as bait. During the study period, 8,861 adult A. rufifacies were captured, with peak densities being observed at the end of winter (i.e., late February) and throughout most of the summer (May to March). Population density had a weak but significant (α=0.05) positive correlation with temperature (r=0.329) and light intensity (r=0.231), and a weak but significant (α=0.05) negative correlation with relative humidity (r=-0.236). From the six ecological land use types (disturbed mixed deciduous forest, mixed deciduous forest, mixed orchard, lowland village, city town, and paddy field), greater fly densities were observed generally in the disturbed mixed deciduous forest and lowland village, but not in the paddy fields. In conclusion, A. rufifacies are abundant from the end of winter and throughout most of the summer in northern Thailand, with population density being weakly positively correlated with temperature and light intensity, but weakly negatively correlated with relative humidity. The greatest densities of this fly species were collected in disturbed mixed deciduous forest and lowland village land uses. The prediction of annual and season specific distributions of A. rufifacies were provided in each season and all-year patterns using a co-kriging approach (ArcGIS9.2). © 2014 Springer-Verlag.