Post doctoral fellow University of Oklahoma Norman, Oklahoma, United States
Global change affects the timing of life history events such as aquatic insect emergence. Climate warming occurs differently in differently affects aerial and aquatic temperature, and may result in variation in phenological cues across taxa that interact at the aquatic-aerial interface.
In this study, we build a general model of insect emergence phenology based on detailed environmental data and information on species-specific growing degree days. We use data on aerial and aquatic temperature collected at NEON (National Ecological Observatory Network) sites, as well as species lists generated by aquatic macroinvertebrate sampling, and developmental data available in the literature. These data streams are integrated in a single site description of community emergence, which can serve as the basis for predictive models in other locations.
We validate this model using field data on insect emergence from NEON sites at Andrews Experimental Forest (OR). Detailed information on temperature is available for these sites, allowing a comparison between predicted and observed phenology. Species and locations for which the model is a poor fit show the importance of incorporating complex life histories and local differences.
This general model of insect emergence timing can predict emergence across locations where detailed environmental data is collected and community composition is known- such as other NEON sites. A continental scale predictive model of community emergence will provide insight in global change effects on emergence phenology, and on the broader community and ecosystem.
