Applied Ecosystem Services, LLC

To make informed regulatory decisions it is necessary to understand differences between ecological and environmental data. Analyses of environmental data historically use models adapted from engineering models by numerical ecologists for ecological data collected by academic and research agency scientists. These numeric and statistic models require well-structured data collected to fit assumptions and requirements of the models. This works for researchers who identify a question to be answered and work forward from that to determine when, where, and how much data need collecting to answer that question. The research approach of fitting data to models has leaked into the analyses of environmental data gathered in response to statutory and regulatory requirements. Most often, the results are mis-leading or incorrect. Regulatory decisions based on these results are ineffective at best or economically and socially harmful at worst.

Environmental data are messy and unstructured, collected to support environmental permit applications and monitor compliance with permit conditions. Locations change over time, data collection frequency is irregular, and chemical or biological data elements can cease being collected and re-instated at a future time. Such data cannot be fit to research models such as species diversity, indices of biotic integrity (IBI) or community indices (CI), predictive models based on expected taxa (RIVPACS), hydroelectric fish passage models (CRiSP), or pit lake water quality (PITLAKQ). For real-world environmental regulatory decision-making it is necessary to fit the model to the data by applying the appropriate statistical models.