Selecting biological control agents
Selecting effective agents
Selecting more effective agents
The approach for weed biocontrol
Cullen (1995) and others have suggested a more systematic approach to predicting the effectiveness of weed control agents, based for the first time not only on specificity of the agent but also on its ability to damage the weed, the environmental factors that might limit agent population density, and the level of damage required to control the weed. More recently there have been calls for population dynamics to be the driving force in agent selection. McEvoy and Coombs (1999) robustly questioned what has been called the 'lottery model' for biological control of weeds - introduction of a number of agents in the expectation that one will work. They considered that introducing multiple agents increased the risk of non-target impacts in North America (however low that might be) without increasing the likelihood of control by any one agent. Instead they also suggested the more parsimonious approach of using modelling approaches to help design biological control systems that targeted the life cycle stages that are both critical to population growth in the target environment and amenable to manipulation.
Sheppard (2003) has made a case for selecting agents in the full knowledge of:
- studies of the ecology and population dynamics of the weeds in the target country, to estimate the key population parameters and life stage transitions as the first step to understanding which factors are most likely to suppress weed populations;
- comparison with the demography of the weed in its native range to help identify the drivers for successful invasion, and the most susceptible life stages of the weed;
- quantitative surveys in the native range that assess the abundance of agents and variation in field impact that can support prioritisation of agents;
- experiments in the native range that manipulate both target and agents to isolate the effects on the population dynamics of the target weed.
Sheppard (2003) points out that advances in control agent selection will only occur when the success of agent selection strategies can be formally assessed retrospectively. This requires setting formal a priori strategies that can be tested over time. This approach is rare (Sheppard 2003), but Briese (2006) provides one such example.
A workshop conducted in Australia in 2004 explored how the effectiveness of biological control of weeds could be improved by the selection of better control agents (Raghu and Van Klinken 2006, van Klinken and Raghu 2006). Using the rangeland weed Parkinsonia aculeata as an example, Raghu et al. (2006) suggested using matrix models in the early stages of programme development to identify the transitions between life stages that were most critical to population growth rate. Having identified the susceptible stages, then modelling or experimentation were used to explore how much change was required in each to achieve a specified population growth level. With these thresholds in mind, the final stage would assess whether each prospective agent was capable of achieving this aim, and (ideally) eliminating from consideration those that were not. The focus of this approach was not minimising the environmental or economic effects of the weed, but on deciding what needed to be done to manage the weed population. However, van Klinken (2006) complemented this study by defining a range of ecological and economic performance criteria (such as reduced patch size, density, and control costs) that would constitute successful management of the weed in pastures or native habitats, and that could be modelled. This approach is closer to the model required by the HSNO Act.
As with the examples provided here, the weed-herbivore models reviewed by Barlow (1999) posed the question 'what degree of control is required to obtain the suppression required' but none explicitly addressed the question of whether agent populations could provide a sustained degree of control.
Morin et al. (2006) proposed a framework for selecting well-adapted pathogens for biocontrol of weeds, critically examined each step, and provided a comprehensive bibliography on pathogen selection. They compared and contrasted their framework with that for selection of herbivorous insects for the same purpose. In the same volume Morin and Edwards (2006) applied this approach to the selection of the most appropriate pathogens for the control of bridal creeper, Asparagus asparagoides.
Testing the host range of an agent is the most costly part of any weed biological control project, and knowing which of the available control agents will be the most effective is likely to save resources and be safer in the long run. Sheppard (2003) concluded that sufficient ecological tools now exist to allow weed researchers to move on from the simple experienced-based value judgements of the past. There is little doubt that ecological studies can assist in better standards for selection of agents, but whether such approaches can be undertaken depends largely on what resources can be accessed for a project, not least the availability of skilled staff in the country of origin of the pest. He acknowledged that even if an ecologically-based approach to selecting agents was preferable, the success and safety rate (Fowler et al. 2000) of what he disparagingly called the 'grab-and-run' approach to biological control of weeds was still high and possibly cost-effective. In fact, most biological control programmes for weeds tend to find a middle course between these extremes. McFadyen (1998) reviews the mixed success of predicting which biological control agents for weeds are best.
Barlow N.D. (1999). Models in biological control: a field guide Pp. 43-70 In: Theoretical approaches to biological control, B.A. Hawkins and H.V. Cornell (Ed.) Cambridge University Press UK.
Briese D.T. (2006). Can an a priori strategy be developed for biological control? The case for Onopordum spp. thistles in Australia. Australian Journal of Entomology 45: 317-323
Cullen J.M. (1995). Predicting effectiveness: fact or fantasy? Pp. 103-109 In: Proceedings of the VIII International Symposium on Biological Control of Weeds, E. S. Delfosse and R. R. Scott (Ed.) Lincoln University, New Zealand, DSIR/CSIRO, Melbourne, Australia.
Fowler S.V., Syrett P. and Hill R.L. (2000). Success and safety in the biological control of environmental weeds in New Zealand. Austral Ecology 25: 553-562.
McEvoy P.B. and Coombs E.M. (1999). Biological control of plant invaders: regional patterns, field experiment and structured population models. Ecological Applications 9: 387-401
McFadyen R.E.C. (1998). Biological control of weeds. Annual Review of Entomology 43: 369-393.
Morin L. and Edwards P.B. (2006). Selection of biological control agents for bridal creeper: a retrospective review. Australian Journal of Entomology 45: 287-291
Morin L., Evans K.J. and Sheppard A.W. (2006). Selection of pathogen agents in weed biological control: critical issues and peculiarities in relation to arthropod agents. Australian Journal of Entomology 45: 349-365
Raghu S. and Van Klinken R.D. (2006). Refining the ecological basis for agent selection in weed biological control. Australian Journal of Entomology 45: 251-252
Raghu S., Wilson J.R. and Dhileepan K. (2006). Refining the process of agent selection through understanding plant demography and plant response to herbivory. Australian Journal of Entomology 45: 308-316
Sheppard A.W. (2003). Prioritising agents based on predicted efficacy: beyond the lottery approach. Pp. 11-21 In: Improving the selection, testing and evaluation of weed biocontrol agents, H. Spafford-Jacobs and D. T. Briese (Ed.) Cooperative Research Centre for Australian Weed Management, Adelaide, Australia
van Klinken R.D. (2006). Biological control of Parkinsonia aculeata: what are we trying to achieve? Australian Journal of Entomology 45: 268-271
van Klinken R.D. and Raghu S. (2006). A scientific approach to agent selection. Australian Journal of Entomology 45: 253-258
What is success in biocontrol?
For insect biocontrol