The olive fruit fly Bactrocera oleae is one of the key insect pests infesting olive orchards in Mediterranean areas. Its coevolution with the olive tree, Olea europaea, made this pest highly specialized for this crop, being responsible for several yield reductions in terms of olive fruits and olive oil organoleptic properties. Monitoring is, to date, the main tool to assess the entity of infestations, but the increasing availability of biological information is making possible a quantitative interpretation of B. oleae’s biological traits in mathematical language. In this study, we aim to synthesize this plethora of information by applying a general physiologically based model theory of recent introduction. As a result, we obtained a parameterized model capable of describing B. oleae populations and with a high potential for implementation in Decision Support System programs. Besides the parameterization, model validation has been carried out in a three-year survey conducted in two representative productive areas of Sabina (Lazio, Central Italy). The model showed overall reliability in describing the field data trend, and it is a good starting point to be further improved.

Life tables are one of the most common tools to describe the biology of insect species and their response to environmental conditions. Although the benefits of life tables are beyond question, we raise some doubts about the completeness of the information reported in life tables. To substantiate these doubts, we consider a case study (Corcyra cephalonica) for which the raw dataset is available. The data suggest that the Gaussian approximation of the development times which is implied by the average and standard error usually reported in life tables does not describe reliably the actual distribution of the data which can be misleading and hide interesting biological aspects. Furthermore, it can be risky when life table data are used to build models to predict the demographic changes of the population. The present study highlights this aspect by comparing the impulse response generated by the raw data and by its Gaussian approximation based on the mean and the standard error. The conclusions of this paper highlight: i) the importance of adding more information to life tables and, ii) the role of raw data to ensure the completeness of this kind of studies. Given the importance of raw data, we also point out the need for further developments of a standard in the community for sharing and analysing data of life tables experiments.