Abstract
Vessel-based sonar systems that focus on the water column provide valuable information on the distribution of underwater marine organisms, but such data are expensive to collect and limited in their spatiotemporal coverage. Satellite data, however, are widely available across large regions and provide information on surface ocean conditions. If satellite data can be linked to subsurface sonar measurements, it may be possible to predict marine life over broader spatial regions with higher frequency using satellite observations. Here, we use random forest models to evaluate the potential for predicting a sonar-derived proxy for subsurface biomass as a function of satellite imagery in the California Current Ecosystem. We find that satellite data may be useful for prediction under some circumstances, but across a range of sonar frequencies and depths, overall model performance was low. Performance in spatial interpolation tasks exceeded performance in spatial and temporal extrapolation, suggesting that this approach is not yet reliable for forecasting or spatial extrapolation. We conclude with some potential limitations and extensions of this work.
Highlights
Sonars that focus on the water column, the volume of ocean from the near surface to near the seabed, are used widely in fisheries science and management
The present study focuses on the Coastal Ecosystem (CCE) as a test region, motivated by several reasons
A Simrad EK60 split-beam echosounder with 18 kHz, 38 kHz, 70 kHz, 120 kHz, and 200 kHz transducers was employed on Northwest Fisheries Science Center (NWFSC) cruises conducted in 2011, 2012, 2013 and 2015 [18,19,20,21]
Summary
Sonars that focus on the water column, the volume of ocean from the near surface to near the seabed, are used widely in fisheries science and management. The backscatter (or acoustic reflectance) from these sonars help characterize the distribution of marine life beneath the surface, from zooplankton to large predatory fish, by estimating biomass [1], trophic- and species-level identification [2, 3], and measuring school size and behavior [4,5,6]. This information can be used to understand ecosystem dynamics and inform stock assessments.
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