Modelling the population dynamics of the Queensland fruit fly, Bactrocera (Dacus) tryoni: a cohort-based approach incorporating the effects of weather

Queensland fruit fly, Bactrocera (Dacus) tryoni (QFF) is arguably the most costly horticultural insect pest in Australia. Despite this, no model is available to describe its population dynamics and aid in its management. This paper describes a cohort-based model of the population dynamics of the Queensland fruit fly. The model is primarily driven by weather variables, and so can be used at any location where appropriate meteorological data are available. In the model, the life cycle is divided into a number of discreet stages to allow physiological processes to be defined as accurately as possible. Eggs develop and hatch into larvae, which develop into pupae, which emerge as either teneral females or males. Both females and males can enter reproductive and over-wintering life stages, and there is a trapped male life stage to allow model predictions to be compared with trap catch data. All development rates are temperature-dependent. Daily mortality rates are temperature-dependent, but may also be influenced by moisture, density of larvae in fruit, fruit suitability, and age. Eggs, larvae and pupae all have constant establishment mortalities, causing a defined proportion of individuals to die upon entering that life stage. Transfer from one immature stage to the next is based on physiological age. In the adult life stages, transfer between stages may require additional and/or alternative functions. Maximum fecundity is 1400 eggs per female per day, and maximum daily oviposition rate is 80 eggs/female per day. The actual number of eggs laid by a female on any given day is restricted by temperature, density of larva in fruit, suitability of fruit for oviposition, and female activity. Activity of reproductive females and males, which affects reproduction and trapping, decreases with rainfall. Trapping of reproductive males is determined by activity, temperature and the proportion of males in the active population. Limitations of the model are discussed. Despite these, the model provides a useful agreement with trap catch data, and allows key areas for future research to be identified. These critical gaps in the current state of knowledge exist despite over 50 years of research on this key pest. By explicitly attempting to model the population dynamics of this pest we have clearly identified the research areas that must be addressed before progress can be made in developing the model into an operational tool for the management of Queensland fruit fly. (C) 2003 Published by Elsevier B.V.

Factors supporting the non-persistence of fruit fly populations in South Australia

For purposes of interstate and international fruit trade, it is necessary to demonstrate that in areas in which fruit fly species have not previously established permanent populations, but which are subject to introductions of fruit flies from outside the area, the introduced population once detected, has not become established. In this paper, we apply methodology suggested mainly by Carey (1991, 1995) to introductions of Mediterranean fruit fly (Medfly), Ceratitis capitata Weid., and Queensland fruit fly (QFF) Bactrocera tryoni Froggatt (Diptera: Tephritidae) to South Australia, a state in which these species do not occur naturally and in which introductions, once detected, are actively treated. By analysing historical data associated with fruit fly outbreaks in South Australia, we demonstrate that: (i) fruit flies occur seasonally, as would occur in established populations, except there is no evidence of the critical spring generation of either species; (ii) there is no evidence of increasing frequency of outbreaks, trapped flies or larval occurrences over 29 years; (iii) there is no evidence of decreasing time between catches of adult flies as the years progress; (iv) there is no decrease in the mean number of years between outbreaks in the same locations; (v) there is no statistically significant recurrence of outbreaks in the same locations in successive years; (vi) there is no evidence of spread of outbreaks outwards from a central location; (vii) the likelihood of outbreaks in a city or town is related to the size of the human population; (viii) introduction pathways by road from Western Australia (for Medfly) and eastern Australia (for QFF) are shown to exist and to illegally or accidentally carry considerable amounts of fruit into South Australia; and (ix) there was no association between the numbers of either Queensland fruit fly or Medfly and the spatial pattern of either loquat or cumquat trees as sources of larval food in spring. This analysis supports the hypothesis that most fruit fly outbreaks in South Australia have been the result of separate introductions of infested fruit by vehicular traffic and that most of the resultant fly outbreaks were detected and died out within a few weeks of the application of eradication procedures. An alternative hypothesis, that populations of fruit flies are established in South Australia at below detectable levels, is impossible to disprove with conventional technology, but the likelihood of it being true is minimised by our analysis. Both hypotheses could be tested soon with newly developed genetic techniques.

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.