|Document Type:||Journal Article|
|Title:||Evaluating Models that Characterize Baseline Conditions: Ecological Monitoring at Marine Renewable Energy Sites|
|Author:||Hannah Linder, John K. Horne, E. J. Ward|
Ecological indicators are often collected to detect and monitor environmental change. Statistical models are used to estimate natural variability, pre-existing trends, and environmental predictors of baseline indicator conditions. Establishing standard models for baseline characterization is critical to the effective design and implementation of environmental monitoring programs. An anthropogenic activity that requires monitoring is the development of Marine Renewable Energy (MRE) sites. Currently, there are no standards for the analysis of MRE environmental monitoring data. MRE monitoring data are used as a case study to develop and apply a model evaluation to establish best practices for characterizing baseline ecological indicator data. We examined a range of models, including six generalized regression models, four time series models, and three nonparametric models. Because monitoring data are not always normally distributed, we evaluated model ability to characterize normal and non-normal data using hydroacoustic metrics that serve as proxies for ecological indicator data. The nonparametric support vector regression (SVR), random forest models, and parametric state-space time series models generally were the most accurate in interpolating the normal metric data. SVR and state-space models best interpolated the non-normally distributed data. If parametric results are preferred, then state-space models are the most robust for baseline characterization. Evaluation of a wide range of models provides a comprehensive characterization of the case study data, and highlights advantages of models rarely used in MRE environmental monitoring. Our model findings are relevant for any ecological indicator data with similar properties, and the evaluation approach is applicable to any monitoring program.
|Theme:||Habitats to Support Sustainable Fisheries and Recovered Populations|
Characterize the interaction of human use and habitat distribution, quantity and quality.