Climate change is expected to alter the geographic distribution and abundance of many species. Here we examine the potential effects of climate warming on olive (Olea europaea) and olive fly (Bactrocera oleae) across the ecological zones of Arizona-California (AZ-CA) and Italy. A weather-driven physiologically-based demographic model was developed from the extensive literature and used to simulate the phenology, growth and population dynamics of both species. Observed weather for several years from 151 sites in AZ-CA and 84 sites in Italy were used in the study. Three climate-warming scenarios were developed by increasing observed average daily temperature 1 • , 2 • and 3 • C. Predictions of bloom dates, yield, total fly pupae and percent infestation were mapped using GRASS GIS. Linear multiple-regression was used to estimate the effects of weather on yield and fly abundance. Olive has a much wider temperature range of favorability than olive fly. The model predicted the present distributions of both species and gave important insights on the potential effects of climate warming on them. In AZ-CA, climate warming is expected to 196 Climatic Change (2009) 95:195-217 contract the range of olive in southern desert areas, and expand it northward and along coastal areas. Olive fly is currently limited by high temperature in the southern part of its range and by cold weather in northern areas. Climate warming is expected to increase the range of olive fly northward and in coastal areas, but decrease it in southern areas. In Italy, the range of olive is expected to increase into currently unfavorable cold areas in higher elevations in the Apennine Mountains in central Italy, and in the Po Valley in the north. Climate warming is expected to increase the range of olive fly northward throughout most of Italy.

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Abstract. In Sardinia (Italy), the highest frequency of extreme events is recorded in the Central-East area (3-4 events per year). The presence of high and steep mountains near the sea on the central and south-eastern coast, causes an EastWest precipitation gradient in autumn especially, due to hot and moist currents coming from Africa. Soil structure and utilization make this area highly vulnerable to flash flooding and landslides. The specific purpose of this work is to provide a description of the heavy rainfall phenomenon on a statistical basis. The analysis mainly focuses on i) the existence of trends in heavy rainfall and ii) the characterization of the distribution of extreme events. First, to study possible trends in extreme events a few indices have been analyzed by the linear regression test. The analysis has been carried out at annual and seasonal scales. Then, extreme values analysis has been carried out by fitting a Generalized Pareto Distribution (GPD) to the data. As far as trends are concerned, different results are obtained at the two temporal scales: significant trends are obtained at the seasonal scale which are masked at the annual scale. By combining trend analysis and GPD analysis, the vulnerability of the study area to the occurrence of heavy rainfall has been characterized. Therefore, this work might support the improvement of land use planning and the application of suitable prevention systems. Future work will consider the extension of the analysis to all Sardinia and the application of statistical methods taking into account the spatial correlation of extreme events.

Global warming will affect all species but in largely unknown ways, with certain regions such as the Mediterranean Basin and its major islands including Sardinia being particularly vulnerable to desertification. Olive (Olea europaea) is of eco-social importance in the Mediterranean where it was domesticated. This drought-resistant crop and its major pest, the olive fly (Bactrocera oleae), have tight biological links that make them a suitable model system for climate change studies in the Mediterranean. Here a physiologically based weather-driven demographic model of olive and olive fly is used to analyze in detail this plant-pest system in Sardinia under observed weather (10 years of daily data from 48 locations), three climate warming scenarios (increases of 1, 2 and 3 1C in average daily temperature), and a 105-year climate model scenario for the Alghero location (e.g. 1951-2055). GRASS GIS is used to map model predictions of olive bloom dates and yield, total season-long olive fly pupae, and percent fruit attacked by the fly. Island wide simulation data are summarized using multivariate regression. Model calibration with field bloom date data were performed to increase simulation accuracy of olive flowering predictions under climate change. As climate warms, the range of olive is predicted to expand to higher altitudes and consolidate elsewhere, especially in coastal areas. The range of olive fly will extend into previously unfavorable cold areas, but will contract in warm inland lowlands where temperatures approach its upper thermal limits. Consequently, many areas of current high risk are predicted to have decreased risk of fly damage with climate warming. Simulation using a 105-year climate model scenario for Alghero, Sardinia predicts changes in the olive-olive fly system expected to occur if climate continued to warm at the low rate observed during in the past half century.