Distinctions in sound patterns of calls by killer whales (Orcinus Orca) from analysis of computer sound features

Calls of killer whales, Orcinus orca, were analyzed using computed sound features to classify sound patterns and identify call similarties. Calls were classified and separated according to the podfamily group within clans identified previously by John Ford (U. BC) in the Vancouver whale populations. Acoustic characteristics of the same call type from different individuals were extremely similar, so that discriminating these different sounds was the goal. The WHOI AcouStat program and associated database systems were used to define numerical statistics for each call, and then, these were compared to sort and classify the sounds. The results were in agreement with Ford's descriptions of the calls derived from visual inspection of sound spectrograms of calls. The classification analyses demonstrated that although specific shared calls from different killer whales were much alike, they could be sorted by the pod/subpod of the whales producing the calls. A typical analysis, for example, of the N4 call from Clan A (Vancouver, BC), classified 97% of the calls correctly according to the pod/family of the whales producing the calls. Remaining calls were variant, and likely a result of individual differences in call sounds. Similar classification analysis were tested on unsorted, unalyzed recordings from different populations of whales, and these too could be distinguished, with 98.5% correct separation of the calls

Whale call data for the North Pacific : November 1995 through July 1999 occurrence of calling whales and source locations from SOSUS and other acoustic systems

May also be cited as: WHOI-00-02Calls of blue whales (Balaenoptera musculus), fin whales (Balaenoptera physalus), and humpback whales (Megaptera novaeangliae) were identified in the data from U.S. Navy Sound Surveilance System (SOSUS) and other hydrophone arrays. These data on calling whales from November 1995 through July 1999 have been listed here for four offshore, deep-water Regions along continental margins of the North and Northeast Pacific. The occurrence of calling whales was monitored during two-day periods each week. Call data recorded from each array identified species, call occurrence, variation, received beam, and relative numbers of calling whales. This allowed assessment of seasonal distribution of calls for the different species, and provided locations for sources received at multiple arrays. Blue whale tonal sounds were distributed widely, received most in the NW Region, with a peak in occurrence in the fall. Fin whale "20-Hz" repetitive pulse sequences were received from whales grouped in local areas in all Regions, with a peak in occurrence in midwinter. Humpback songs were received from December through May particularly in the SE Region. The offshore listening systems allowed basin-wide monitoring of the seasonal distribution of these callng whales.Funding was provided by the Office Naval Research under Grant No. N00014-96-1-1130, SERDP and CNO N45

Spatially explicit capture–recapture methods to estimate minke whale density from data collected at bottom-mounted hydrophones

Estimation of cetacean abundance or density using visual methods can be cost-ineffective under many scenarios. Methods based on acoustic data have recently been proposed as an alternative, and could potentially be more effective for visually elusive species that produce loud sounds. Motivated by a dataset of minke whale (Balaenoptera acutorostrata) “boing” sounds detected at multiple hydrophones at the U.S. Navy’s Pacific Missile Range Facility (PMRF), we present an approach to estimate density or abundance based on spatially explicit capture–recapture (SECR) methods. We implement the proposed methods in both a likelihood and a Bayesian framework. The point estimates for abundance and detection parameters from both implementation methods are very similar and agree well with current knowledge about the species. The two implementation approaches are compared in a small simulation study. While the Bayesian approach might be easier to generalize, the likelihood approach is faster to implement (at least in simple cases like the one presented here) and more readily amenable to model selection. SECR methods seem to be a strong candidate for estimating density from acoustic data where recaptures of sound at multiple acoustic sensors are available, and we anticipate further development of related methodologies.This is the publisher’s final pdf. The published article is copyrighted by Springer and can be found at: http://www.springer.com/life+sciences/animal+sciences/journal/10336Keywords: Spatially explicit capture recapture (SECR), Proximity detector, Passive acoustic monitoring, Minke whale, OpenBUG

Using individual-based bioenergetic models to predict the aggregate effects of disturbance on populations : a case study with beaked whales and Navy sonar

Funding: This research was supported by the Office of Naval Research (https://www.onr.navy.mil/) grant N0001419WX00431 and N000142012045: “Integrating information on displacement caused by mid-frequency active sonar and measurements of prey field into a population consequences of disturbance model for beaked whales” awarded to Dave Moretti, ND, SW, JH, LT, KB-B, AdR & VH. Funding support for tagging was provided by the US Navy's Office of Naval Research and Living Marine Resources program, the Chief of Naval Operations' Energy and Environmental Readiness Division and the NOAA Fisheries Ocean Acoustics Program.Anthropogenic activities can lead to changes in animal behavior. Predicting population consequences of these behavioral changes requires integrating short-term individual responses into models that forecast population dynamics across multiple generations. This is especially challenging for long-lived animals, because of the different time scales involved. Beaked whales are a group of deep-diving odontocete whales that respond behaviorally when exposed to military mid-frequency active sonar (MFAS), but the effect of these nonlethal responses on beaked whale populations is unknown. Population consequences of aggregate exposure to MFAS was assessed for two beaked whale populations that are regularly present on U.S. Navy training ranges where MFAS is frequently used. Our approach integrates a wide range of data sources, including telemetry data, information on spatial variation in habitat quality, passive acoustic data on the temporal pattern of sonar use and its relationship to beaked whale foraging activity, into an individual-based model with a dynamic bioenergetic module that governs individual life history. The predicted effect of disturbance from MFAS on population abundance ranged between population extinction to a slight increase in population abundance. These effects were driven by the interaction between the temporal pattern of MFAS use, baseline movement patterns, the spatial distribution of prey, the nature of beaked whale behavioral response to MFAS and the top-down impact of whale foraging on prey abundance. Based on these findings, we provide recommendations for monitoring of marine mammal populations and highlight key uncertainties to help guide future directions for assessing population impacts of nonlethal disturbance for these and other long-lived animals.Publisher PDFPeer reviewe

Beaked whales respond to simulated and actual navy sonar

This article is distributed under the terms of the Creative Commons Public Domain declaration. The definitive version was published in PLoS One 6 (2011): e17009, doi:10.1371/journal.pone.0017009.Beaked whales have mass stranded during some naval sonar exercises, but the cause is unknown. They are difficult to sight but can reliably be detected by listening for echolocation clicks produced during deep foraging dives. Listening for these clicks, we documented Blainville's beaked whales, Mesoplodon densirostris, in a naval underwater range where sonars are in regular use near Andros Island, Bahamas. An array of bottom-mounted hydrophones can detect beaked whales when they click anywhere within the range. We used two complementary methods to investigate behavioral responses of beaked whales to sonar: an opportunistic approach that monitored whale responses to multi-day naval exercises involving tactical mid-frequency sonars, and an experimental approach using playbacks of simulated sonar and control sounds to whales tagged with a device that records sound, movement, and orientation. Here we show that in both exposure conditions beaked whales stopped echolocating during deep foraging dives and moved away. During actual sonar exercises, beaked whales were primarily detected near the periphery of the range, on average 16 km away from the sonar transmissions. Once the exercise stopped, beaked whales gradually filled in the center of the range over 2–3 days. A satellite tagged whale moved outside the range during an exercise, returning over 2–3 days post-exercise. The experimental approach used tags to measure acoustic exposure and behavioral reactions of beaked whales to one controlled exposure each of simulated military sonar, killer whale calls, and band-limited noise. The beaked whales reacted to these three sound playbacks at sound pressure levels below 142 dB re 1 µPa by stopping echolocation followed by unusually long and slow ascents from their foraging dives. The combined results indicate similar disruption of foraging behavior and avoidance by beaked whales in the two different contexts, at exposures well below those used by regulators to define disturbance.The research reported here was financially supported by the United States (U.S.) Office of Naval Research (www.onr.navy.mil) Grants N00014-07-10988, N00014-07-11023, N00014-08-10990; the U.S. Strategic Environmental Research and Development Program (www.serdp.org) Grant SI-1539, the Environmental Readiness Division of the U.S. Navy (http://www.navy.mil/local/n45/), the U.S. Chief of Naval Operations Submarine Warfare Division (Undersea Surveillance), the U.S. National Oceanic and Atmospheric Administration (National Marine Fisheries Service, Office of Science and Technology) (http://www.st.nmfs.noaa.gov/), U.S. National Oceanic and Atmospheric Administration Ocean Acoustics Program (http://www.nmfs.noaa.gov/pr/acoustics/), and the Joint Industry Program on Sound and Marine Life of the International Association of Oil and Gas Producers (www.soundandmarinelife.org)

Density estimation implications of increasing ambient noise on beaked whale click detection and classification

Acoustic based density estimates are being increasingly used. Usually density estimation methods require one to evaluate the effective survey area of the acoustic sensors, or equivalently estimate the mean detection probability of detecting the animals or cues of interest. This is often done based on an estimated detection function, the probability of detecting an object of interest as a function of covariates, usually distance and additional covariates. If the actual survey data and the data used to estimate a detection function are not collected simultaneously, as in Marques et al. (2009), the estimated detection function might not correspond to the detection process that generated the survey data. This would lead to biaseddensity estimates. Here we evaluate the influence of ambient noise in the detection and classification of beaked whale clicks at the Atlantic Undersea Test and Evaluation Center (AUTEC) hydrophones, to assess if the density estimates reported in Marques et al. (2009) might have been biased. To do so we contaminated a data set with increasing levels of ambient noise, and then estimated the detection function accounting for the noise level as an additional covariate. The results obtained suggest that for the particular results obtained at AUTEC’s deep water hydrophones the influence of ambient noise on the beaked whale’s click detection probability might have been minor, and hence unlikely to have had an impact on density estimates. However, we do not exclude the possibility that the results could be different under other scenarios

Estimating minke whale (Balaenoptera acutorostrata) boing sound density using passive acoustic sensors

Density estimation for marine mammal species is performed primarily usingvisual distance sampling or capture-recapture. Minke whales in Hawaiian watersare very difficult to sight; however, they produce a distinctive “boing” call, making them ideal candidates for passive acoustic density estimation. We used an array of 14 bottom-mounted hydrophones, distributed over a 60 × 30 km area off Kauai, Hawaii, to estimate density during 12 d of recordings in early 2006.We converted the number of acoustic cues (i.e., boings) detected using signal processing software into a cue density by accounting for the false positive rate and probability of detection. The former was estimated by manual validation, the latter by applying spatially explicit capture-recapture (SECR) methods to a subset of data where we had determined which hydrophones detected each call. Estimated boing density was 130 boings per hour per 10,000 km2 (95% CI 104–163). Little is known about the population’s acoustic behavior, so conversion from boing to animal density is difficult. As a demonstration of the method, we used a tentative boing rate of 6.04boings per hour, from a single animal tracked in 2009, to give an estimate of 21.5 boing-calling minke whales per 10,000 km2

Spatially explicit capture-recapture methods to estimate minke whale density from data collected at bottom-mounted hydrophones

International audienceEstimation of cetacean abundance or density using visual methods can be cost-ineffective under many scenarios. Methods based on acoustic data have recently been proposed as an alternative, and could potentially be more effective for visually elusive species that produce loud sounds. Motivated by a dataset of minke whale () "boing" sounds detected at multiple hydrophones at the U.S. Navy's Pacific Missile Range Facility (PMRF), we present an approach to estimate density or abundance based on spatially explicit capture-recapture (SECR) methods. We implement the proposed methods in both a likelihood and a Bayesian framework. The point estimates for abundance and detection parameters from both implementation methods are very similar and agree well with current knowledge about the species. The two implementation approaches are compared in a small simulation study. While the Bayesian approach might be easier to generalize, the likelihood approach is faster to implement (at least in simple cases like the one presented here) and more readily amenable to model selection. SECR methods seem to be a strong candidate for estimating density from acoustic data where recaptures of sound at multiple acoustic sensors are available, and we anticipate further development of related methodologies