Generating degree day models using metabolic rates: comparing brown marmorated stink bug model predictions across the USA.

Degree day (DD) models are an important tool for predicting insect development and geographic distribution, aiding in pest mitigation. This study seeks to integrate new data on insect thermal physiology into DD modeling, focusing on the invasive pest, the brown marmorated stink bug (BMSB) and testing the new models across diverse climatic regions within the United States. Incorporating metabolic rates may increase the precision and speed of generation of DD models, potentially allowing for more accurate predictions of BMSB life stages and presence. We evaluate the performance of the metabolic DD models against conventionally calculated ones at predicting a range of geographically separated insect occurrences. Our findings highlight the notable improvements in accuracy achieved by considering metabolic rates, with predictions exhibiting reduced discrepancies between observed and projected developmental milestones of BMSB populations. As the climate changes, understanding the relationship between insect physiology and temperature becomes increasingly important for effective pest mitigation. This study advances entomological modeling based on physiology, saving time, effort, and money while improving accuracy.