Linking frass and insect phenology to optimize annual forest defoliation estimation.

It is often logistically impractical to measure forest defoliation events in the field due to seasonal variability in larval feeding phenology (e.g., start, peak, and end) in any given year. As such, field data collections are either incomplete or at coarse temporal resolutions, both of which result in inaccurate estimation of annual defoliation (frass or foliage loss). Using F. and L., we present a novel approach that leverages a weather-driven insect simulation model (BioSIM) and defoliation field data. Our approach includes optimization of a weighting parameter (w) for each instar and imputation of defoliation. Results show a negative skew in this weighting parameter, where the second to last instar in a season exhibits the maximum consumption and provides better estimates of annual frass and foliage biomass loss where sampling data gaps exist. Respective cross-validation RMSE (and normalized RMSE) results for and are 77.53 kg·ha (0.16) and 38.24 kg·ha (0.02) for frass and 74.85 kg·ha (0.10) and 47.77 kg·ha (0.02) for foliage biomass loss imputation. Our method provides better estimates for ecosystem studies that leverage remote sensing data to scale defoliation rates from the field to broader landscapes and regions.•Utilize fine temporal resolution insect life cycle data derived from weather-driven insect simulation model (BioSIM) to bridge critical gaps in coarse temporal resolution defoliation field data.•Fitting distributions to optimize the instar weighting parameter (w) and impute frass and foliage biomass loss based on a cumulative density function (CDF).•Enables accurate estimation of annual defoliation impacts on ecosystems across multiple insect taxa that exhibit distinct but seasonally variable feeding phenology.