Predicting population dynamics of weed biological control agents: science or gazing into crystal balls?

Abstract

Various factors can influence the population dynamics of phytophages post introduction, of which climate is fundamental. Here we present an approach, using a mechanistic modelling package (CLIMEX), that at least enables one to make predictions of likely dynamics based on climate alone. As biological control programs will have minimal funding for basic work (particularly on population dynamics), we show how predictions can be made using a species geographical distribution, relative abundance across its range, seasonal phenology and laboratory rearing data. Many of these data sets are more likely to be available than long-term population data, and some can be incorporated into the exploratory phase of a biocontrol program. Although models are likely to be more robust the more information is available, useful models can be developed using information on species distribution alone. The fitted model estimates a species average response to climate, and can be used to predict likely geographical distribution if introduced, where the agent is likely to be more abundant (i.e. good locations) and more importantly for interpretation of release success, the likely variation in abundance over time due to intra-and inter-year climate variability. The latter will be useful in predicting both the seasonal and long-term impacts of the potential biocontrol agent on the target weed. We believe this tool may not only aid in the agent selection process, but also in the design of release strategies, and for interpretation of post-introduction dynamics and impacts. More importantly we are making testable predictions. If biological control is to become more of a science making and testing such hypothesis will be a key component.

Assessing the biosecurity risk from pathogens and herbivores to indigenous plants: lessons from weed biological control

“…While detailed modelling is not usually feasible, it is possible to use a mechanistic climate-modelling approach where it is assumed that climate is the main limiting factor (Zalucki and van Klinken 2006). This kind of modelling can be assisted and improved by collecting appropriate data (e.g.…”

Classical biological control: exploiting enemy escape to manage plant invasions

“…The prerequisite of successful biological control is that the biological control agent reaches high population densities in the introduced range (Gassmann 1996). Hence, predicting the likelihood of success of a biological control programme largely depends on improving our understanding of the effects of biotic (e.g., host-plant attributes, mortality due to parasitism or predation) and abiotic factors (climate) on the survival, development rate and fecundity of biological control agents (Gassmann 1996;Zalucki and van Kinken 2006). To date, only a few attempts have been made to model the population dynamics of biological control agents (e.g., Buckley et al 2005;Zalucki and van Kinken 2006).…”

Long term climate effects are confounded with the biological control programme against the invasive weed Baccharis halimifolia in Australia

“…In recent years, bioclimatic models have been used to predict which agents are more likely to succeed based on climatic tolerances (e.g. van Klinken et al 2003;Robertson et al 2008) and then often used to predict the 'best' release site for the introduced agent (Adair and Scott 1991;Palmer et al 2007) and even their potential population dynamics (Zalucki and van Klinken 2006). Climate models have also been used to predict the potential distribution of an invader (Kriticos et al 2003(Kriticos et al , 2006Dunlop et al 2006).…”