Representative range of acoustic point source measurements in the Chukchi Sea

Abstract

The use of stationary, active acoustics provides an effective approach to characterize and monitor temporal variability in the abundance and behavior of pelagic organisms, especially in seasonally ice-covered waters of high latitude marine ecosystems. However, point measurements from stationary echosounders are limited in their spatial coverage. A quantification of the spatial area represented by point measurements (i.e., representative range) is required to ensure effective biological characterization and monitoring. Here, concurrent mobile and stationary active acoustic data collected during summers of 2015 and 2017 are used to assess the representative range of fish and zooplankton density measurements from the Chukchi Ecosystem Observatory located at Hanna Shoal, Northeast Chukchi Sea. Six methods used to calculate representative ranges of backscatter means and variances resulted in representative ranges between approximately 0.3 and 86 km, depending on the year and calculation method. Such relatively large representative ranges reflect the tight bio-physical associations and large characteristic environmental length scales of the NE Chukchi Sea. Between years, up to 10-fold variations in representative ranges were attributed to interannual changes in water mass characteristics and associated species assemblages. Differences of 1–2 orders of magnitude in our calculated ranges among methods are attributed to differences in the rationale and associated assumptions of each approach. The choice of method and resulting representative range depends on monitoring goals: detection of change, mapping of spatial distributions, characterization of spatial variance, or interpolation of temporal variability over space. Our comparison of stationary acoustic to mobile surveys extends the understanding of spatiotemporal variability of marine organism distributions in the NE Chukchi Sea and informs cost-effective design of observing systems to monitor and predict impacts of environmental change.