Understanding the relationship between the Bering Sea Cold Pool and vocal presence of odontocetes in the context of climate changea).

The Cold Pool is a subsurface layer with water temperatures below 2 °C that is formed in the eastern Bering Sea. This oceanographic feature of relatively cooler bottom temperature impacts zooplankton and forage fish dynamics, driving different energetic pathways dependent upon Bering Sea climatic regime. Odontocetes echolocate to find prey, so tracking foraging vocalizations acoustically provides information to understand the implications of climate change on Cold Pool variability influencing regional food web processes. Vocal foraging dynamics of ice-associated and seasonally migrant marine mammal species suggest that sperm whales spend more time searching for prey in warm years when the Cold Pool is reduced but are more successful at capturing prey during cold years when the Cold Pool is stronger. Beluga whale foraging vocal activity was relatively consistent across climate regimes but peaked during the warm regime. Killer whale foraging vocal activity peaked in both warm and cold regimes with indicators of different ecotypes exploiting changing prey conditions across climate regimes. Foraging activity of odontocete apex predators may serve as a sentinel indicator of future ecosystem change related to prey availability that is linked to a diminishing Cold Pool as water temperatures rise and seasonal sea ice decreases due to climate change.

Modeled underwater sound levels in the Pan-Arctic due to increased shipping: Analysis from 2013 to 2019.

The loss of Arctic sea ice is one of the most visible signs of global climate change. As Arctic sea ice has retreated, Arctic marine shipping has increased. The Pan-Arctic's unique underwater acoustic properties mean that even small increases in ship traffic can have a significant effect on the ambient soundscape. This study presents the first long-term, basin-scale model of shipping noise in the Pan-Arctic with a focus on a few select sub-regions. The Arctic Ship Traffic Database from the Protection of the Arctic Marine Environment is used in this study to model the locations and source levels from ships operating in the Pan-Arctic between 2013 and 2019. The acoustic footprint of these ships is explored temporally for the entire basin as well as for the select large maritime ecosystems of the Barents Sea, the Northern Bering-Chukchi Sea, and Baffin Bay. From 2013 to 2019, modeled shipping noise propagating underwater broadly increased between 5-20 dB across the Pan-Arctic, but more specific results in sub-regions are presented and discussed.

Decadal community structure shifts with cold pool variability in the eastern Bering Sea shelf.

A characteristic feature of the eastern Bering Sea (EBS) is a subsurface layer linked to seasonal sea ice (SSI) and defined by bottom temperatures less than 2 °C, which is termed the cold pool. Cold pool variability is directly tied to regional zooplankton and fish dynamics. Multifrequency (200 and 460 kHz) acoustic backscatter data were collected remotely using upward looking echosounders along the EBS shelf from 2008 and 2018 and used as a proxy of biological abundance. Acoustic data were coupled with bottom temperature and regional SSI data from the cold (2006-2013) and warm (2014-2018) regimes to assess the relationship between biological scattering communities and cold pool variation. Acoustic backscatter was 2 orders of magnitude greater during the cold regime than during the warm regime, with multifrequency analysis indicating a shift in the warm regime frequency-dependent scattering communities. Cold pool proxy SSI was a stronger predictor for biological scattering than bottom temperature in the cold regime, while warm regime bottom temperature and SSI were equal in predictive power and resulted in improved predictive model performance. Results suggest coupled cold pool and frequency-dependent scattering dynamics are a potential regime shift indicator and may be useful for management practices in surrounding Arctic ecosystems.

Caller ID for Risso's and Pacific White-sided dolphins.

Tracking species with expanding ranges is crucial to conservation efforts and some typically temperate marine species are spreading northward into the Arctic Ocean. Risso's (Gg) and Pacific white-sided (Lo) dolphins have been documented spreading poleward. Further, they make very similar sounds, so it is difficult for both human analysts and classification algorithms to tell them apart. Using automatic detectors and classifiers on large acoustic datasets would improve the efficiency of monitoring these species. variational mode decomposition (VMD) provides both an easier visualization tool for human analysts and exhibited robustness to background noise while extracting features in pulsed signals with very similar spectral properties. The goal of this work was to develop a new visualization tool using VMD and a statistics-based classification algorithm to differentiate similar pulsed signals. The proposed VMD method achieved 81% accuracy, even when using audio files with low SNR that did not have concurrent visual survey data. While many dolphins whistle, pulsed signals are one of the more useful vocalizations to use in detection and classification because of their species-specific acoustic features. Automating the VMD method and expanding it to other dolphin species that have very similar pulsed signals would complement current detection and classification methods and lead to a more complete understanding of ecosystem dynamics under a changing climate.

An Empirical Mode Decomposition-based detection and classification approach for marine mammal vocal signals.

Detecting marine mammal vocalizations in underwater acoustic environments and classifying them to species level is typically an arduous manual analysis task for skilled bioacousticians. In recent years, machine learning and other automated algorithms have been explored for quickly detecting and classifying all sound sources in an ambient acoustic environment, but many of these still require a large training dataset compiled through time-intensive manual pre-processing. Here, an application of the signal decomposition technique Empirical Mode Decomposition (EMD) is presented, which does not require a priori knowledge and quickly detects all sound sources in a given recording. The EMD detection process extracts the possible signals in a dataset for minimal quality control post-processing before moving onto the second phase: the EMD classification process. The EMD classification process uniquely identifies and labels most sound sources in a given environment. Thirty-five recordings containing different marine mammal species and mooring hardware noises were tested with the new EMD detection and classification processes. Ultimately, these processes can be applied to acoustic index development and refinement.

Predicting coastal algal blooms in southern California.

The irregular appearance of planktonic algae blooms off the coast of southern California has been a source of wonder for over a century. Although large algal blooms can have significant negative impacts on ecosystems and human health, a predictive understanding of these events has eluded science, and many have come to regard them as ultimately random phenomena. However, the highly nonlinear nature of ecological dynamics can give the appearance of randomness and stress traditional methods-such as model fitting or analysis of variance-to the point of breaking. The intractability of this problem from a classical linear standpoint can thus give the impression that algal blooms are fundamentally unpredictable. Here, we use an exceptional time series study of coastal phytoplankton dynamics at La Jolla, CA, with an equation-free modeling approach, to show that these phenomena are not random, but can be understood as nonlinear population dynamics forced by external stochastic drivers (so-called "stochastic chaos"). The combination of this modeling approach with an extensive dataset allows us to not only describe historical behavior and clarify existing hypotheses about the mechanisms, but also make out-of-sample predictions of recent algal blooms at La Jolla that were not included in the model development.

Humpback whale-generated ambient noise levels provide insight into singers' spatial densities.

Baleen whale vocal activity can be the dominant underwater ambient noise source for certain locations and seasons. Previous wind-driven ambient-noise formulations have been adjusted to model ambient noise levels generated by random distributions of singing humpback whales in ocean waveguides and have been combined to a single model. This theoretical model predicts that changes in ambient noise levels with respect to fractional changes in singer population (defined as the noise "sensitivity") are relatively unaffected by the source level distributions and song spectra of individual humpback whales (Megaptera novaeangliae). However, the noise "sensitivity" does depend on frequency and on how the singers' spatial density changes with population size. The theoretical model was tested by comparing visual line transect surveys with bottom-mounted passive acoustic data collected during the 2013 and 2014 humpback whale breeding seasons off Los Cabos, Mexico. A generalized linear model (GLM) estimated the noise "sensitivity" across multiple frequency bands. Comparing the GLM estimates with the theoretical predictions suggests that humpback whales tend to maintain relatively constant spacing between one another while singing, but that individual singers either slightly increase their source levels or song duration, or cluster more tightly as the singing population increases.

Source level and calling depth distributions of migrating bowhead whale calls in the shallow Beaufort Sea.

Automated and manual acoustic localizations of migrating bowhead whales were used to estimate source level and calling depth distributions of their frequency-modulated-modulated calls over seven years between 2008 and 2014. Whale positions were initially triangulated using directional autonomous seafloor acoustic recorders, deployed between 25 and 55 m water depth near Kaktovik, Alaska, during the fall westward migration. Calling depths were estimated by minimizing the "discrepancy" between source level estimates from at least three recorders detecting the same call. Applying a detailed waveguide propagation model to the data yielded broadband source levels of 161 ± 9 dB re 1 µPa2 s at 1 m (SEL) for calls received between 20 and 170 Hz. Applying a simpler 15 log10(R) power-law propagation model yielded SEL source levels of 158 ± 10 dB. The most probable calling depths lay between 22 and 30 m: optimal depths for long-range acoustic signal transmission in this particular environment.

The ambient acoustic environment in Laguna San Ignacio, Baja California Sur, Mexico.

Each winter gray whales (Eschrichtius robustus) breed and calve in Laguna San Ignacio, Mexico, where a robust, yet regulated, whale-watching industry exists. Baseline acoustic environments in LSI's three zones were monitored between 2008 and 2013, in anticipation of a new road being paved that will potentially increase tourist activity to this relatively isolated location. These zones differ in levels of both gray whale usage and tourist activity. Ambient sound level distributions were computed in terms of percentiles of power spectral densities. While these distributions are consistent across years within each zone, inter-zone differences are substantial. The acoustic environment in the upper zone is dominated by snapping shrimp that display a crepuscular cycle. Snapping shrimp also affect the middle zone, but tourist boat transits contribute to noise distributions during daylight hours. The lower zone has three source contributors to its acoustic environment: snapping shrimp, boats, and croaker fish. As suggested from earlier studies, a 300 Hz noise minimum exists in both the middle and lower zones of the lagoon, but not in the upper zone.