Big data science is at the forefront of many fields. However, long-term (50+ years) spatio-ecological data sets are still rare. Newer studies, though likely well-structured around particular ecological questions, models, and analysis, often lack temporal or spatial depth necessary to ask deeper questions about long-term, wide-scale population dynamics. Conversely, data from alternate sources, such as government pest management programs, can prove rich and underutilized sources of spatial data, but present analytical challenges. In this study, we utilized a data set compiled by Dr. James Carey (U.C. Davis) from the California Department of Food & Agriculture’s continued monitoring and management of invasive tephritid fruit flies across California. Though this data set is spatially and temporally rich – 60+ years of fine-scale data across the entire state – the underlying ecological dynamics of these species are enigmatic and the data itself precludes standard analytical methods. Additionally, since singular detections are relatively spatio-temporally rare, distinguishing signals from solitary individuals or small-scale outbreaks from subtler from longer-term establishment from year-to-year has proven difficult.
Results/Conclusions
To infer deeper dynamics of introduction, persistence, and spread of tephritid fruit flies in California, we analyzed how classic distance-based spatial statistics, such as the G-function, change as a function of time, spatial scale, spatial extent, and invasion phase. By comparing observed patterns to simulated or randomized detection data, we were able to identify previously obscured geographic areas and temporal pockets of tephritid persistence within the state. By refining this process and including bioclimatic and points-of-entry data, we hope to develop a method to dynamically predict areas of future invasion risk in real-time at a micro-scale across the state.