Guidelines for the Use of Mathematics in Operational Area-Wide Integrated Pest Management Programmes Using the Sterile Insect Technique with a Special Focus on Tephritid Fruit Flies

This guideline attempts to assist managers in the use of mathematics in area-wide Integrated Pest Management (AW-IPM) programmes using the Sterile Insect Technique (SIT). It describes mathematical tools that can be used at different stages of suppression/eradication programmes. For instance, it provides simple methods for calculating the various quantities of sterile insects required in the intervention area so that more realistic sterile: fertile rates to suppress pest populations can be achieved. The calculations, for the most part, only involve high school mathematics and can be done easily with small portable computers or calculators. The guideline is intended to be a reference book, to be consulted when necessary. As such, any particular AW-IPM programme using the SIT will probably only need certain sections, and much of the book can be ignored if that is the case. For example, if the intervention area is relatively small and well isolated, then the section on dispersal can safely be ignored, as the boundedness of the area means that dispersal should not be a problem, and so the section on diffusion equations can be ignored. An overview is given in each chapter to try to let the programme manager make a decision about where to put the programme efforts. On the other hand, most SIT programmes have an information system (many of them based on GIS) that produces reliable profiles of historic information. Based on the results of past activities they describe what has happened in the last days or weeks but usually do not explain, or barely explain, what is expected in the following days or weeks. Current AW-IPM progammes using the SIT have produced over many years a vast amount of every-day data from the field operations and from the mass rearing facility and packing and sterile insect releasing centres. With the help of this guideline, that information can be used to develop predictive models for their particular conditions to better plan control measures.