Leveraging spatial scale and temporal variation to optimize estimates of invasive spread rates

Estimating the extent and speed of an invasive species spread is crucial for optimizing surveys and management, but this estimation is challenging due to the scale-dependent nature of spread. For instance, when discretizing occurrence data to grids to estimate the extent of invaded ranges, larger cells can decrease boundary precision, while smaller cells can increase the risk of missing invaded areas. Moreover, on shorter timescales such as year-to-year comparisons, spread rates can exhibit lags, accelerations, and slowdowns. We present a multiscale spread optimization methodology developed for the spotted lanternfly (Lycorma delicatula), a pest impacting grapes that is spreading westward across the continental United States. We analyzed a dataset of >900,000 occurrence records covering spread from this pests initial detection in eastern Pennsylvania in 2014 to its invasion front in Chicago, IL, in 2023. First, we delineated the annual U.S. invasion front using square grids with varying cell sizes and -convex hulls with different boundary resolutions. For each method and scale, we regressed invaded range area against year using both simple linear and logistic nonlinear models. Coarser spatial scales yielded faster estimated spread rates, and logistic models outperformed linear regressions, indicating a lag, acceleration, and slowdown in annual spread rate. Next, to determine the spatial scale that best captured the invasion front boundary, we used cross-validation, optimizing the F{beta} metric, which balances recall and precision. Emphasizing recall generally favored coarser spatial scales, and - convex hulls outperformed grid-based methods in delineating invasion fronts. Finally, L. delicatula has dispersed long distances from its primary epicenter, establishing satellite populations. We delineated each satellite population each year since its inception using optimized -hull values and measured their year-to-year boundary expansion. Like the primary epicenter, these satellite populations showed accelerating expansion, at least until merging with the primary invasion. Overall, the expansion rate of the primary invasion has slowed since 2017, decreasing from a maximum of 21 km/year, possibly due in part to control efforts, although the exact cause remains unresolved. Considering spatial scale and temporal variation can optimize the analysis of invasive spread and motivate early interventions to manage nascent epicenters before their spread accelerates.