Selecting biological control agents
Predicting direct non-target impacts
Modelling non-target impacts
Detailed population models
A deliberate introduction for the purpose of classical biological control typically involves a programme of a priori research to estimate the risks and benefits of the introduction, and this presents an opportunity to collect the data needed to parameterize more detailed models for the impacts on target and nontarget species. For example, Raghu et al. (2007) used pre-introduction native range and quarantine experiments to parameterise a model for the likely impacts of a herbivorous beetle on target and nontarget plants in Australia.
Cleopus japonicus on Buddleja davidii.
© Copyright Scion, used with permission.
A similar modelling approach was used to predict the impacts of the herbivorous weevil Cleopus japonicus on Buddleja davidii and nontarget plants in New Zealand. A detailed model was developed, using climate variables to drive weevil development and plant growth rates, and to predict when food shortages would drive C. japonicus to spill over to nontarget plants. The model suggested that emigration to nontarget host plants was likely to occur in late summer or autumn, with the magnitude of the nontarget threat being greatest at relatively warm sites. These predictions were borne out by the field trials at four sites (Watson 2007), in which spill-over damage to Scrophularia auriculata occurred in March when defoliation of B. davidii was greatest. Significantly, S. auriculata is an annual plant which sets its seed in summer, so defoliation in March is unlikely to have any impact on its population density. This work showed that detailed population models can successfully predict the timing and impact of nontarget attack from introduced species.
Raghu S., Dhileepan K. and Scanlan J.C. (2007). Predicting risk and benefit a priori in biological control of invasive plant species: A systems modelling approach. Ecological Modelling 208: 247-262
Watson M.C. (2007). Buddleia weevil blooms. What's New in Biological Control of Weeds 41: 11.
What can be predicted?