Individual variability in sub-Arctic krill material properties, lipid composition, and other scattering model inputs affect acoustic estimates of their population

Abstract

Target strength model inputs including morphometry, material properties, lipid composition, and in situ orientations were measured for sub-Arctic krill (Euphausia pacifica, Thysanoessa spinifera, T. inermis, and T. raschii) in the eastern Bering Sea (EBS, 2016) and Gulf of Alaska (GOA, 2017). Inter-species and -regional animal lengths were significantly different (F1,680 = 114.10, p < 0.01), while animal shape was consistent for all species measured. The polar lipid phosphatidycholine was the dominant lipid, comprising 86 ± 16% (mean ± SD) and 56 ± 22% of total lipid mass in GOA and EBS krill, respectively. Krill density contrasts varied by species and region rather than with morphometry, lipid composition, or local chla fluorescence. Mean in situ krill orientation was 1 ± 31°, with 25% of observed krill within ±5° of broadside incidence. Modelled target strength sensitivity was frequency independent for variations in material properties but was primarily sensitive to morphometry and orientation at lower (38 kHz) and higher (200 kHz) frequencies, respectively. Measured variability in material properties corresponded to an order of magnitude difference in acoustic estimates of biomass at 120 kHz. These results provide important inputs and constraints for acoustic scattering models of ecologically important sub-Arctic krill species.

Environmental variability and krill abundance in the central California current: Implications for ecosystem monitoring

“…Input parameters used in the target strength model were derived from measurements of krill morphology, density, and sound speed contrast from 160 individual krill (110 E. pacifica and 50 T. spinifera) collected and measured during the 2019 RREAS (Warren and Lucca, 2020). Morphology and density contrast were measured for individuals (i.e., speciesspecific results) whereas sound speed contrast measurements were made on groups of animals (same value for both species) due to methodological limitations (Becker and Warren, 2014;Lucca et al, 2021). The measured parameter values (Table 1) were similar in range to measurements from previous studies of euphausiids in the Northeast Pacific (Becker and Warren, 2014) and Bering Sea (Smith et al, 2010).…”

“…For each animal length, 1000 iterations of the model were run using normal distributions of density and sound speed contrast. Other parameters that were varied for each model iteration included animal shape (i.e., fatness as a function of animal length) based on species-specific shape measurements made during the cruise, and animal orientation with data from a previous study (Lucca et al, 2021). The modeled target strength values were then averaged (in the linear domain) to produce a mean backscattering crosssection value for each species and length increment (s BS l ) that was then used for the biomass calculations.…”

“…This grid cell size was chosen to reduce autocorrelation between cells (Santora et al, 2011a), and to reflect a reasonable trade-off between sampling density and spatial resolution (Santora et al, 2011b). In this paper, we et al, 2006), and shape (Lucca et al, 2021) were varied for each model iteration. Shape, size, and density contrast data are from 110 E. pacifica and 50 T. spinifera.…”