Professor Texas A&M University College Station, Texas
Behavior affects insect phenology, and accounting for behavior in insect phenology models can involve mechanistic and statistical interpretations. We show that a mechanistically implicit interpretation of behavior that assumes insects nonrandomly experience their environment improves statistically-derived phenology predictions. This interpretation relies on realistic description of environments in which insects behave, and enables error-in-the-data approaches to estimating uncertainty in phenological predictions. We demonstrate methods for modeling insect environments as functions of available data, and how ensemble forecasts can be generated using environmental model output, all within the established degree-day framework of phenology prediction. Consequences of nonrandom environmental experience in a changing or variably dynamic environment are able to be predicted through this approach. Species observed and modeled in this approach include blow flies of forensic relevance and pests/vectors of field crops.
