Temperature Influences on Functional Response of Coenosia attenuata (Diptera: Muscidae) Individuals

Abstract

The functional response of single Coenosia attenuta Stein (Diptera: Muscidae) adult females to Drosophila melanogaster (Meigen) adults has been investigated at different but constant temperatures and prey densities. The experiments were conducted in cages over a wide range of temperatures between 12 and 42 degrees C. At each experimental temperature, the number of attacks increases with increasing prey density up to a maximum attack rate referred to as demand rate. A ratio-dependent and demand-driven functional response model was appropriate to describe the attack rate at different temperatures. Both the demand and the search rate linearly increased with increasing temperatures throughout the range. Based on these results, a temperature- and prey density-dependent bidimensional attack rate model was developed and parameterized. The resulting response surface shows that C. attenuata is active over a wide range of temperatures (from approximately 12 to 36 degrees C) and attacks occur up to 42 degrees C. Thus, C. attenuata may be a promising biological control agent in Mediterranean greenhouses. To support this conclusion, further studies including the evaluation of spatial scale effects on the search rate and the consideration of the economically relevant whitefly prey are recommended.

A simulation-based method to compare the pest suppression potential of predators: A case study with spiders

“…high temperature usually hinders the natural enemies action in greenhouses (Gilioli et al, 2005;Vacante, 2000), we applied our method to experimental data obtained in the laboratory at different temperatures (21, 24 and 27°C) on two widespread species of generalist predators, the orb-weaver spider A. cucurbitina and the ambusher spider S. globosum against the pest C. capitata.…”

“…Dinis et al, 2016;Gilioli et al, 2005;Pasandideh et al, 2015;Tello Mercado et al, 2017). However, variation within these two parameters can be difficult to interpret in terms of bioecology since they are estimated non-independently of each other.…”

Bayesian Inference for Functional Response in a Stochastic Predator–Prey System

“…These approaches have been criticized for the oversimplification of the environment in which predator behavior occurs (Kareiva, 1990). More complex experimental setups can be considered by manipulating environmental variables (Gilioli et al, 2005) and spatial organization of the two species, as well as elements of experimental environment (e.g. spatial arrangement of the plants that represent the resource for the prey) (Kareiva, 1982).…”

Temperature dependent functional response of Diaeretiella rapae (Hymenoptera: Aphidiidae) to the cabbage aphid, Brevicoryne brassicae (Hemiptera: Aphididae)

“…The response of a natural enemy to increasing host density (functional response) is important for the outcome of parasitoidbased biocontrol programs (Berryman, 2004) and together with the numerical response an indispensable element in their biological characterization (Cedola et al, 2001, Gilioli et al, 2005 and an important determinant of their effectiveness as biological control agents (Houck & Strauss, 1985;Jost & Ellner, 2000). Different factors may influence their functional responses, including host plant surface traits (Coll & Ridgway, 1995;Messina & Hanks, 1998;Madadi et al, 2007), host species (Mansfield & Mills, 2004), natural enemy life stages (Seko & Miura, 2008), host age (King, 1994;Ode & Strand, 1995) and spatial complexity (Madadi et al, 2011).…”