The potential role of models in biological control is far more extensive than is commonly practiced (Table 1). Common rejoinders to suggestions that ecological studies of the weed should be undertaken prior to the introduction of agents are that it costs too much, and that it takes away from the main objective of the exercise which is to import agents. Analyses such as Myers (1985) and Denoth et al. (2002) underline the fact that most agents that have been imported have been redundant. The costs of importing each agent is considerable (3 scientist-years, according to McFadyen, 1998) and processes which can reduce the redundancy should be attractive. Whilst it may be difficult to predict successful agents using ecological models and giving due
consideration to both plant and insect ecologies and the effects of other disturbances, it is hardly surprising that results have not been better when models have not been used, and when the ecology of the plant has received such scant attention. The costs of processing even one agent should be sufficient to underwrite the ecological studies and model development.
At present, classical weed biological control practice is primarily conducted as an applied entomology or applied pathology project. Primary responsibility for prioritising agents for import and testing is left with the scientist who conducted the field search for agents. In order to now make significant advances, classical biological control must evolve into an inter-disciplinary activity involving inter alia plant ecologists and ecological modellers throughout the project lifecycle. The sooner the factors regulating the weed population are
understood, the better placed we will be to make better judgements as to what characteristics a successful agent should have, and the better placed we will be to prioritise agents for processing.
It is perhaps axiomatic that biological control scientists have tended to look for evidence of agent impact by focussing their attention on the agent they have introduced. The collection of evidence of impact starts logically enough with agent establishment and persistence. More detailed investigations include attack rates and other proximal effects of the agents at the individual plant level. In very few cases are rigorous experiments to quantify agent impact undertaken (McClay, 1995), and fewer still scale this up to the plant population level (McFadyen, 1998) c.f. (Hoffmann, 1990; Lonsdale et al. 1995; Rees & Paynter, 1997; Shea & Kelly, 1998).
References
Blossey, B. (1995) A Comparison of Various Approaches for Evaluating Potential Biological Control Agents using Insects on Lythrum salicaria. Biological Control
5, 113-122.
Crawley, M.J. (1986) The population biology of invaders. Philosophical Transactions of the Royal Society of London Series B 314, 711-731.
Crawley, M.J. (1989) The successes and failures of weed biocontrol using insects. Biocontrol News and Information 10, 213-223.
Denoth, M., Frid, L. & Myers, J. (2002) Multiple agents in biological control: Improving the odds? Biological Control 24, 20-30.
Godfray, H.C.J. & Waage, J.K. (1991) Predictive modelling in biological control: The mango mealy bug
(Rastrococcus invadens) and its parasitoids. Journal of Applied Ecology 28, 434-453. Goeden, R.D. (1983) Critique and revision of Harris
scoring system for selection of insect agents for biological control of weeds. Protection Ecology
The r
oles of ecological models in evaluating weed biological contr
ol agents and pr
ojects
Harris, P. (1973) The selection of effective agents for the biological control of weeds. Canadian Entomologist
105, 1495-1503.
Hoffmann, J.H. (1990) Interactions between three weevil species in the biocontrol of Sesbania punicea (Fabaceae): the role of simulation models in
evaluation. Agriculture, Ecosystems and Environment
32, 77-87.
Hoffmann, J.H. & Moran, V.C. (1991) Biocontrol of a perennial legume, Sesbania punicia, using a florivorous weevil, Trichapion lativentre: weed population dynamics with a scarcity of seeds. Oecologia 88, 574-574. Isaacson, D.L., Sharratt, D.B. & Coombs, E.M. (1996)
Biological control in the management and spread of invasive weed species. Proceedings of the IX International symposium on Biological Control of Weeds (eds V.C. Moran & J.H. Hoffmann), pp. 19-26. University of Cape Town.
Julien, M.H., Skarratt, B. & Maywald, G.F. (1995) Potential geographical distribution of Alligator Weed and its biological control by Agasicles hygrophila. Journal of Aquatic Plant Management 33, 55-60 .
Kriticos, D.J., Brown, J.R., Radford, I.D. & Nicholas, M. (1999) Plant population ecology and biological control: Acacia nilotica as a case study. Biological Control 16, 230-239.
Lonsdale, W.M., Farrell, G. & Wilson, C.G. (1995) Biological control of a tropical weed: a population model and experiment for Sida acuta. Journal of Applied Ecology
32, 391-399.
Louda, S.M., Kendall, D., Connor, J. & Simberloff, D. (1997) Ecological effects of an insect introduced for the biological control of weeds. Science 277, 1088-1090. Mann, J. (1970) Cacti naturalised in Australia and their
control. Department of Lands, Brisbane. McClay, A.S. (1995) Beyond “Before and After:”
Experimental Design and Evaluation in classical weed biological control. Proceedings of the Eighth
International Symposium on Biological Control of Weeds (eds E.S. Delfosse & R.R. Scott), pp. 213-219. DSIR/CSIRO, Melbourne.
McEvoy, P.B. & Coombs, E.M. (1999) Biological control of plant invaders: Regional patterns, field experiments, 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.
Myers, J.H. (1985) How many insects are necessary for successful biocontrol of weeds? Proceedings of the sixth international symposium on biological control of weeds (ed E.S. Delfosse), pp. 77-82. Agriculture Canada, Ottawa, Ontario.
Nordblom, T., Smyth, M., Swirepik, A., Sheppard, A. & Briese, D. (2001) Benefit-cost analysis for biological control of Echium weed species (Paterson's curse / Salvation Jane). The CRC for Weed Management Systems: An impact assessment (ed Centre for International Economics) CRC Weed Management Systems, Waite Campus, University of Adelaide. Pantone, D.J., Williams, W.A. & Maggenti, A.R. (1989)
An alternative approach for evaluating the efficacy of potential biocontrol agents of weeds. 1 Inverse Linear Model. Weed Science 37, 771-777.
Rees, M. & Paynter, Q. (1997) Biological control of Scotch Broom: modelling the determinants of abundance and the potential impact of introduced insect herbivores. Journal of Applied Ecology 34, 1203-1221. Shea, K. & Kelly, D. (1998) Estimating biocontrol agent
impact with matrix models: Carduus nutans in New Zealand. Ecological Applications 8, 824-832. Sheppard, A.W. (1996) The interaction between natural
enemies and interspecific plant competition in the control of invasive pasture weeds. Proceedings of the IX International symposium on Biological Control of Weeds (eds V.C. Moran & J.H. Hoffmann), pp. 47-53. University of Cape Town.
Simberloff, D. & Stiling, P. (1996) How Risky is Biological Control? Ecology 77, 1965-1974.
Sutherst, R.W. & Maywald, G.F. (1985) A computerised system for matching climates in ecology. Agriculture, Ecosystems and Environment 13, 281-299.
Sutherst, R.W., Maywald, G.F., Yonow, T. & Stevens, P.M. (1999) CLIMEX: Predicting the effects of climate on plants and animals. User Guide. CSIRO Publishing, Melbourne.
Van, T.K., Wheeler, G.S. & Center, T.D. (1998) Competitive interactions between hydrilla (Hydrilla verticillata) and vallisneria (Vallisneria americana) as influenced by insect herbivory. Biological Control 11, 185-192. Wapshere, A.J. (1985) Effectiveness of biological control
agents for weeds: Present quandaries. Agriculture, Ecosystems and Environment 13, 261-280. Withers, T.M., Barton Browne, L. & Stanley, J. (2000)
Host-Specificity Testing in Australasia: Towards Improved Assays for Biological Control. Scientific Publishing, Indooroopilly.
Putting biological r
eality into economic assessments of biocontr
ol