Bayesian inference for acoustic monitoring of above-ground biomass in a seagrass meadow over two annual cycles

Studies of acoustic propagation in seagrass meadow environments have demonstrated a high degree of sensitivity of passive and active acoustics to diurnal photosynthetic cycles and seasonal growth patterns. While empirical metrics based on these measurements in combination with traditional ecological monitoring methods provide insight into trends in primary productivity, estimates of seagrass biomass and other ecological condition indicators have not yet been calculated directly from acoustic measurements. Towards this goal, an acoustic inference was developed utilizing a Bayesian framework to infer estimates of seagrass biomass. This work was based on active acoustic measurements collected over the course of a 25-month remote monitoring experiment conducted in a seagrass meadow in a shallow-water sub-tropical estuary. A ray-based acoustic propagation model was developed to incorporate effects of the seagrass leaf canopy and model parameter estimates related to the height and leaf density of the canopy are used to predict above-ground seagrass biomass over the course of the experiment. The study found biomass to be consistent with seasonal trends found in previous measurements. Comparison of acoustic estimates with an 11-month record of direct measurements of biomass shows good agreement, demonstrating the potential of acoustic inversion to facilitate acoustic-based ecosystem monitoring in seagrass meadow environments.