Understanding observable historical pest dynamics through a data rescue approach

Abstract Long-term historical data on the presence of invasive pests can inform current pest management. By examining correlations between shifts in pest occurrences and various potential drivers, we can better inform decision-making and management strategies. However, the availability of such long-term data is often limited. We apply a data rescue protocol to recover difficult-to-access pest information from periodical annual forestry reports on the island of Ireland from 1970 to 2020, resulting in an open-access dataset of pest dynamics and their management for the island. We combined the pest dataset with auxiliary weather data to estimate the effects of surveying effort, control measures and weather upon observed pest outbreak dynamics. A first-order auto-logistic regression model was used to model rates of transition between observed presences and absences of non-native insects, fungi, chromista, and bacteria. The results provide evidence that multi-year systematic surveillance efforts have improved the detection of pest species before they have arrived and help towards preventing false absences of invaded species being recorded. We provide reporting recommendations for invasive species which would improve the usability of reported data to better understand observed pest dynamics going forward. Our methodology for data collection and analysis serves as a blueprint for other regions of the world and other invasive species assemblages where data is physically available but not ready for analysis.