Invasive pests threaten global food security and economies, making timely and accurate forecasts critical for effective management. Predicting the spread of generalist invaders is particularly challenging – these pests move rapidly across varied hosts and environments, demanding models that capture both broad-scale patterns and fine-scale processes driving pest spread. Meeting these dual demands is computationally expensive, particularly for spatiotemporal analyses. One common way to mitigate these costs is to coarsen the spatial resolution, but this can compromise data quality, introduce errors, and affect utility for management.

Using the Pest or Pathogen Spread (PoPS) Forecasting System and Spotted Lanternfly as a case study, we generated hindcasts at five resolutions (100, 300, 500, 700, and 900 m) and compared performance, predicted infested area, and computational tradeoffs. We further examined how results varied spatially across invasion zones (core, front, edge) that reflect operationally distinct management contexts.

Performance changed modestly across resolutions. Intermediate-to-coarse hindcasts performed better, specifically in the invasion core. In the front and edge regions, finer-to-intermediate hindcasts performed similarly or better than coarser hindcasts, and substantially reduced the predicted infested area, creating opportunities for more targeted interventions. However, finer-grained forecasts require significantly more time and memory, potentially limiting their value during the early stages of an invasion when rapid action is paramount. Ultimately, choosing the right spatial resolution is not merely a technical detail—it shapes the real-world utility of forecasts. Choosing the right spatial resolution to align with management goals is vital for delivering forecasts that better inform on-the-ground actions.

Keywords: Invasive Species, Modeling, Scaling