Abstract
Baseline characterization of fish and macrozooplankton is required for marine renewable energy (MRE) site developments such as offshore wind, surface wave, and tidal power. Baseline measurements typically cover a small proportion of the total project area and need to be scaled to develop monitoring programs at pilot and commercial sites after installation. Spatial representativeness, the range to which observations from a point source can be interpolated, can be used to calculate the density of point source monitoring instruments. We demonstrate a framework for calculating the spatial representativeness of stationary splitbeam echosounders used to monitor pelagic fish and macrozooplankton at MRE sites by comparing observed variability between mobile and stationary acoustic surveys at a proposed MRE tidal site in Puget Sound, WA. Three approaches were used to test the consistency of spatial representativeness estimates. First, stationary observations of nekton variability were compared to mobile observations at different spatial scales to identify the scale at which similar patterns and variability were measured. Second, correlation coefficient models generated from spatial and temporal variograms and autocorrelation were used to describe the representativeness of point sources as a function of range. Spatial autocorrelation was used to show that nekton abundance measurements became independent at 300m, while temporal measurements became independent within 24 seconds. Third, an equation translating power law slopes of the spatial and temporal global wavelet spectrums, analogous to spectral densities, was used to translate variability between spatial and temporal measurement scales. Preliminary results indicate that spatial (Log10(Wavelet Power) = 1.24 + 0.223log10(meters)) and temporal (Log10(Wavelet Power) = 0.74 + 0.405log10(hours) power laws are equivalent at scales of approximately one month and 1 km. Subtracting these equations gives a scalar equation to translate between spatial and temporal variability across measurement scales. A standardized spatial representativeness calculation provides an objective technique to determine minimum monitoring effort, maximizes the cost effectiveness of monitoring for developers, and ensures adequate monitoring resolution for environmental regulators.