Passive acoustic monitoring of marine mammal calls is an increasingly important method for assessing population numbers, distribution, and behavior. A common mistake in the analysis of marine mammal acoustic data is formulating conclusions about these animals without first understanding how environmental properties such as bathymetry, sediment properties, water column sound speed, and ocean acoustic noise influence the detection and character of vocalizations in the acoustic data. The approach in this paper is to use Monte Carlo simulations with a full wave field acoustic propagation model to characterize the site specific probability of detection of six types of humpback whale calls at three passive acoustic monitoring locations off the California coast. Results show that the probability of detection can vary by factors greater than ten when comparing detections across locations, or comparing detections at the same location over time, due to environmental effects. Effects of uncertainties in the inputs to the propagation model are also quantified, and the model accuracy is assessed by comparing calling statistics amassed from 24,690 humpback units recorded in the month of October 2008. Under certain conditions, the probability of detection can be estimated with uncertainties sufficiently small to allow for accurate density estimates.

Offshore activities elevate ambient sound levels at sea, which may affect marine fauna. We reviewed the literature about impact of airgun acoustic exposure on fish in terms of damage, disturbance and detection and explored the nature of impact assessment at population level. We provided a conceptual framework for how to address this interdisciplinary challenge, and we listed potential tools for investigation. We focused on limitations in data currently available, and we stressed the potential benefits from cross‐species comparisons. Well‐replicated and controlled studies do not exist for hearing thresholds and dose–response curves for airgun acoustic exposure. We especially lack insight into behavioural changes for free‐ranging fish to actual seismic surveys and on lasting effects of behavioural changes in terms of time and energy budgets, missed feeding or mating opportunities, decreased performance in predator‐prey interactions, and chronic stress effects on growth, development and reproduction. We also lack insight into whether any of these effects could have population‐level consequences. General “population consequences of acoustic disturbance” (PCAD) models have been developed for marine mammals, but there has been little progress so far in other taxa. The acoustic world of fishes is quite different from human perception and imagination as fish perceive particle motion and sound pressure. Progress is therefore also required in understanding the nature and extent to which fishes extract acoustic information from their environment. We addressed the challenges and opportunities for upscaling individual impact to the population, community and ecosystem level and provided a guide to critical gaps in our knowledge.

The problem of how to optimally deploy a suite of sensors to estimate the oceanographic environment is addressed. An optimal way to estimate ͑nowcast͒ and predict ͑forecast͒ the ocean environment is to assimilate measurements from dynamic and uncertain regions into a dynamical ocean model. In order to determine the sensor deployment strategy that optimally samples the regions of uncertainty, a Genetic Algorithm ͑GA͒ approach is presented. The scalar cost function is defined as a weighted combination of a sensor suite's sampling of the ocean variability, ocean dynamics, transmission loss sensitivity, modeled temperature uncertainty ͑and others͒. The benefit of the GA approach is that the user can determine "optimal" via a weighting of constituent cost functions, which can include ocean dynamics, acoustics, cost, time, etc. A numerical example with three gliders, two powered AUVs, and three moorings is presented to illustrate the optimization approach in the complex shelfbreak region south of New England.

A 1960 experiment is examined in which sound from three underwater explosions near Perth, Australia, was detected near Bermuda. A recent attempt [Munk et al., J. Phys. Ocean. 18, 1876 (1988) ] to calculate propagation paths for this event included rotational flattening of the Earth and horizontal refraction determined from the vertical sound speed minimum. That calculation left Bermuda in a shadow zone. The current work invokes adiabatic mode theory to include refraction due to horizontal variations in the vertical mode structure. These results include separate horizontal rays for each of the first few vertical modes, using an archival data set of 230 ocean sound profiles to generate the modes numerically. Where appropriate, interaction with bathymetry is included. This solution possesses two eigenray groups' Group A passes just south of the Cape of Good Hope, at which point group B is almost 1000 km to the south. Intermediate rays are blocked by islands. Group A proceeds unimpeded to Bermuda for a total time of flight of 13 354 q-5 s, while group B interacts slightly with bathymetry off Brazil, arriving at 13 403 q-9 s, and suffering roughly 7-12 dB more bottom attenuation. The spread in these arrivals overlaps satisfactorily with experimental data (main arrival at 13 364 q-5 s; pulse train half-width 15 s; second arrival 30 q-5 s later roughly 10 dB below the first arrival).

Acoustic signals transmitted from a 75-Hz broadband source near Kauai as part of the North Pacific Acoustic Laboratory (NPAL) experiment were recorded on an array of receivers near California at a range of 3890 km, and on a vertical line array at a range of 3336 km in the Gulf of Alaska. Because the source is approximately 2 m above the seafloor, and the bottom depth at the receivers near California is approximately 1800 m, acoustic interaction with the bathymetry complicates the identification of the recorded arrivals with those present in numerical simulations of the experiment. Ray methods were used to categorize acoustic energy according to interactions with the sea bottom and surface and to examine the significance of seafloor geometry. A modal decomposition was also used to examine the role of range-dependent bathymetry and to associate the effects on the acoustic field with seafloor features at specific ranges. Parabolic-equation simulations were performed in order to investigate the sensitivity of the received signal to geoacoustic parameters; shear excitations within the seafloor were modeled using a complex-density, equivalent-fluid technique. Incorporation of bottom interaction into models of the propagation enables an identification between experimental and simulated arrivals.