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

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

Refining the process of agent selection through understanding plant demography and plant response to herbivory

Understanding plant demography and plant response to herbivory is critical to the selection of effective weed biological control agents. We adopt the metaphor of 'filters' to suggest how agent prioritisation may be improved to narrow our choices down to those likely to be most effective in achieving the desired weed management outcome. Models can serve to capture our level of knowledge (or ignorance) about our study system and we illustrate how one type of modelling approach (matrix models) may be useful in identifying the weak link in a plant life cycle by using a hypothetical and an actual weed example (Parkinsonia aculeata). Once the vulnerable stage has been identified we propose that studying plant response to herbivory (simulated and/or actual) can help identify the guilds of herbivores to which a plant is most likely to succumb. Taking only potentially effective agents through the filter of host specificity may improve the chances of releasing safe and effective agents. The methods we outline may not always lead us definitively to the successful agent(s), but such an empirical, data-driven approach will make the basis for agent selection explicit and serve as testable hypotheses once agents are released

Pest species distribution modelling: origins and lessons from history

Pest species distribution modelling was designed to extrapolate risks in the biosecurity sector in order to protect agricultural crops against the spread of both endemic and introduced pest species. The need to identify sources of biological control agents for importation added to this demand. Independently, biogeographers mapped species distributions to interpolate their niche requirements. Recently the threat of climate change caused an explosion in demand for guidance on likely shifts in potential distributions of species. The different technology platforms in the two sectors resulted in divergence in their approaches to mapping actual and potential species distributions under rapidly changing environmental scenarios. Much of the contemporary discussion of species mapping ignores the lessons from the history of pest species distribution modelling. This has major implications for modelling of the non-equilibrium distributions of all species that occur with rapid climate change. The current review is intended to remind researchers of historical findings and their significance for current mapping of all species. I argue that the dream of automating species mapping for multiple species is an illusion. More modest goals and use of other approaches are necessary to protect biodiversity under current and future climates. Pest risk mapping tools have greater prospects of success because they are generic in nature and so able to be used both to interpolate and to extrapolate from field observations of any species based on climatic variables. In addition invasive species are less numerous and usually better understood, while the risk assessments are applied on regional scales in which climate is the dominant variable

Population dynamics of Thaumastocoris peregrinus in Eucalyptus plantations of South Africa

Thaumastocoris peregrinus is a sap-sucking insect that infests non-native Eucalyptus plantations in Africa, New Zealand, South America and parts of Southern Europe, in addition to street trees in parts of its native range of Australia. In South Africa, pronounced fluctuations in the population densities have been observed. To characterise spatiotemporal variability in T. peregrinus abundance and the factors that might influence it, we monitored adult population densities at six sites in the main eucalypt growing regions of South Africa. At each site, twenty yellow sticky traps were monitored weekly for 30 months, together with climatic data. We also characterised the influence of temperature on growth and survival experimentally and used this to model how temperature may influence population dynamics. T. peregrinus was present throughout the year at all sites, with annual site-specific peaks in abundance. Peaks occurred during autumn (February–April) for the Pretoria site, summer (November–January) for the Zululand site and spring (August–October) for the Tzaneen, Sabie and Piet Retief monitoring sites. Temperature (both experimental and field-collected), humidity and rainfall were mostly weakly, or not at all, associated with population fluctuations. It is clear that a complex interaction of these and other factors (e.g. host quality) influence population fluctuations in an annual, site specific cycle. The results obtained not only provide insights into the biology of T. peregrinus, but will also be important for future planning of monitoring and control programs using semiochemicals, chemical insecticides or biological control agents