Forecasting the risk of Phytophthora cinnamomi related-decline in Mediterranean forest ecosystems under climate change scenarios

P. cinnamomi is an invasive pathogen which threatens the evergreen oak and sweet chestnut ecosystems in the Mediterranean Basin. Understanding the distribution of this forest pathogen remains uncertain due to the challenges in accurately assessing their presence until symptoms become apparent, making it challenging to anticipate its occurrence. In this study, we investigated the distribution and suitability of P. cinnamomi in France, Italy, Portugal, and Spain implementing a hybrid model (i.e. correlative and process-based) with the validation of a total of 527 recorded occurrences. We used a correlative model incorporating two categories of abiotic environmental variables: edaphic and topographic. Additionally, we utilized three process-based models accounting for key climate factors and considering earth observation data with high temporal resolution. Specifically, we estimated survival under extreme minimum and maximum temperatures, as well as growth risk during the growing season as a proxy of the severity of the pathogen. The combination of these four models yielded a more reliable estimation of the pathogen's distribution. Our findings revealed that higher probability of P. cinnamomi presence currently stem from acidic and less nutrient rich soils. Among the process-based models, the spring growth risk model displayed the most significant variation across the study area, with an expected increase over time. Nevertheless, the survival of P. cinnamomi during summer is predicted to limit its presence in certain areas of the Iberian Peninsula in the long term, particularly under higher emissions scenarios. Interestingly, the results also indicate a potential enhancement in the growth of P. cinnamomi in some regions, while simultaneously noting a decrease in summer survival in those same areas. These observations underscore the complexity and dynamic nature of pathogen distribution and emphasize the importance of considering multiple factors to gain a comprehensive understanding of its potential impact.