Southwest Fisheries Science Center Research Articles

Biologically Important Areas II for cetaceans within U.S. and adjacent waters - West Coast Region

Here we update U.S. West Coast Biologically Important Areas (BIAs) that were published in 2015 using new data and approaches. Additionally, BIAs were delineated for two species that were not delineated in the 2015 BIAs: fin whales and Southern Resident killer whales (SRKW). While harbor porpoise BIAs remained the same, substantial changes were made for other species including identifying both larger overall areas (parent BIAs) and smaller core areas (child BIAs). For blue, fin, and humpback whales we identified, delineated, and scored BIAs using the overlap between the distribution and relative density from three data sources, leveraging the strengths and weaknesses of these approaches: 1) habitat density models based on Southwest Fisheries Science Center (SWFSC) line-transect data from systematic ship surveys conducted through 2018, 2) satellite tag data from deployments conducted by three research groups, and 3) sightings of feeding behavior from non-systematic effort mostly associated with small-boat surveys for photo-identification conducted by Cascadia Research Collective. While the previous BIAs were based solely on a more subjective assignment from only the small boat sightings, here we incorporate the other two data sources and use a more rigorous, quantitative approach to identify higher density areas and integrate the data types. This resulted in larger, better-supported, objective BIAs compared to the previous effort. Our methods are also more consistent with the delineation of BIAs in other regions. For SRKWs, the parent BIA was based on a modification of the Critical Habitat boundaries defined by the National Oceanographic and Atmospheric Administration (NOAA) and the Department of Fisheries and Oceans (DFO) Canada; a core BIA highlighting areas of intensified use was identified using both NOAA’s Critical Habitat and kernel density analyses of satellite tag data. Gray whale BIAs were re-evaluated for the migratory corridor of Eastern North Pacific gray whales, for Pacific Coast Feeding Group feeding areas, and for gray whales that feed regularly in Puget Sound.

A multi-predator trophic database for the California Current Large Marine Ecosystem

The California Current Trophic Database (CCTD) was developed at NOAA Southwest Fisheries Science Center in collaboration with numerous diet data contributors. We compiled the CCTD from twenty-four data sets, representing both systematic collections and directed trophic studies. Diet composition data, including stomach and scat samples, were obtained from 105,694 individual predators among 143 taxa collected throughout the California Current Large Marine Ecosystem (CCLME) from 1967–2019. Predator taxa consist of squids (n = 5), elasmobranchs (n = 13), bony fishes (n = 118), and marine mammals (n = 7). Extensive time series are available for some predators (e.g., California Sea Lion, Pacific Hake, Chinook Salmon). The CCTD represents the largest compilation of raw trophic data within the CCLME, allowing for more refined analyses and modeling studies within this region. Our intention is to further augment and periodically update the dataset as additional historical or contemporary data become available to increase its utility and impact.

Predicting Cuvier’s (Ziphius cavirostris) and Mesoplodon beaked whale population density from habitat characteristics in the eastern tropical Pacific Ocean

Temporally dynamic environmental variables and fixed geographic variables were used to construct generalised additive models to predict Cuvier’s (Ziphius cavirostris) and Mesoplodon beaked whale encounter rates (number of groups per unit survey effort) and group sizes in the eastern tropical Pacific Ocean. The beaked whale sightings and environmental data were collected simultaneously during the Southwest Fisheries Science Center’s cetacean line-transect surveys conducted during the summers and autumns of 1986-90 and 1993. Predictions from the encounter rate and group size models were combined with previously published estimates of line-transect sighting parameters to describe patterns in beaked whale population density (number of individuals per unit area) throughout the study area. Results provide evidence that the previously proposed definition of beaked whale habitat may be too narrow and that beaked whales may be found from the continental slope to the abyssal plain, in waters ranging from well-mixed to highly stratified. Areas with the highest predicted population densities were the Gulf of California, the equatorial cold tongue and coastal waters, including the west coast of the Baja Peninsula and the Costa Rica Dome. Offshore waters in the northern and southern subtropical gyres had the lowest predicted Mesoplodon densities, but density predictions were high for Cuvier’s beaked whales in the waters southeast of the Hawaiian Islands. For both encounter rate and group size models, there was no geographic pattern evident in the residuals as measured by the ratio of pooled predicted to pooled observed values within geographic strata.

Beaked whale dive cycle estimation based on acoustic data from drifting recording systems

Marine Mammal ScienceEarly View NOTE Beaked whale dive cycle estimation based on acoustic data from drifting recording systems Jay Barlow, Corresponding Author Jay Barlow jaybarlow33@yahoo.com orcid.org/0000-0001-7862-855X National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Southwest Fisheries Science Center, California (retired) Correspondence Jay Barlow, 5072 Gasconade Avenue, San Diego, CA 92110. Email: jaybarlow33@yahoo.com Contribution: Conceptualization, Data curation, Formal analysis, Funding acquisition, ​Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - original draft, Writing - review & editingSearch for more papers by this authorJennifer L. K. McCullough, Jennifer L. K. McCullough orcid.org/0000-0003-4212-7846 National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Pacific Islands Fisheries Science Center, Honolulu, Hawaii Contribution: Conceptualization, Data curation, Resources, Supervision, Validation, Visualization, Writing - review & editingSearch for more papers by this author Jay Barlow, Corresponding Author Jay Barlow jaybarlow33@yahoo.com orcid.org/0000-0001-7862-855X National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Southwest Fisheries Science Center, California (retired) Correspondence Jay Barlow, 5072 Gasconade Avenue, San Diego, CA 92110. Email: jaybarlow33@yahoo.com Contribution: Conceptualization, Data curation, Formal analysis, Funding acquisition, ​Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - original draft, Writing - review & editingSearch for more papers by this authorJennifer L. K. McCullough, Jennifer L. K. McCullough orcid.org/0000-0003-4212-7846 National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Pacific Islands Fisheries Science Center, Honolulu, Hawaii Contribution: Conceptualization, Data curation, Resources, Supervision, Validation, Visualization, Writing - review & editingSearch for more papers by this author First published: 09 November 2022 https://doi.org/10.1111/mms.12987 Funding information: Bureau of Ocean Energy Management, Grant/Award Number: M17PG00024; NOAA Pacific Islands Fisheries Science Center; NOAA Southwest Fisheries Science Center; U.S. Navy, Chief of Naval Operations, Grant/Award Number: N0002518GTC032-06; U.S. Navy, Pacific Fleet, Grant/Award Number: N0007018MP4C587-01 Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Early ViewOnline Version of Record before inclusion in an issue RelatedInformation

Recent cetacean taxonomic changes and conservation action

Aquatic Conservation: Marine and Freshwater EcosystemsVolume 31, Issue 10 p. 3011-3012 CORRESPONDENCE Recent cetacean taxonomic changes and conservation action Gill T. Braulik, Corresponding Author Gill T. Braulik gtb7@st-andrews.ac.uk orcid.org/0000-0001-8919-4187 Sea Mammal Research Unit, University of St Andrews, St Andrews, UK Correspondence Gill. T. Braulik, Sea Mammal Research Unit, University of St Andrews, East Sands, St Andrews, KY16 9AJ, UK. Email: gtb7@st-andrews.ac.ukSearch for more papers by this authorRandall R. Reeves, Randall R. Reeves Okapi Research Associates, Hudson, Quebec, CanadaSearch for more papers by this authorBarbara L. Taylor, Barbara L. Taylor Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration (NOAA), San Diego, CA, USASearch for more papers by this author Gill T. Braulik, Corresponding Author Gill T. Braulik gtb7@st-andrews.ac.uk orcid.org/0000-0001-8919-4187 Sea Mammal Research Unit, University of St Andrews, St Andrews, UK Correspondence Gill. T. Braulik, Sea Mammal Research Unit, University of St Andrews, East Sands, St Andrews, KY16 9AJ, UK. Email: gtb7@st-andrews.ac.ukSearch for more papers by this authorRandall R. Reeves, Randall R. Reeves Okapi Research Associates, Hudson, Quebec, CanadaSearch for more papers by this authorBarbara L. Taylor, Barbara L. Taylor Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration (NOAA), San Diego, CA, USASearch for more papers by this author First published: 07 September 2021 https://doi.org/10.1002/aqc.3715Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume31, Issue10October 2021Pages 3011-3012 RelatedInformation

A machine-learning approach to assign species to ‘unidentified’ entangled whales

ESR Endangered Species Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials ESR 36:89-98 (2018) - DOI: https://doi.org/10.3354/esr00894 ESR Special: Marine pollution and endangered species A machine-learning approach to assign species to ‘unidentified’ entangled whales James V. Carretta* Southwest Fisheries Science Center, National Oceanic and Atmospheric Administration, 8901 La Jolla Shores Drive, La Jolla, CA 92037, USA *Corresponding author: jim.carretta@noaa.gov ABSTRACT: Whale entanglements in US west coast fishing gear are largely represented by opportunistic sightings, and some reports lack species identifications due to rough seas, distance from whales, or a lack of cetacean identification expertise. Unidentified entanglements are often ignored in species risk assessments and thus, entanglement risk is underestimated. To address this negative bias, a species identification model was built from random forest (RF) classification trees using 199 identified entanglements (‘model data’). Humpback Megaptera novaeangliae and gray whales Eschrichtius robustus represented 92% of identified entanglements; the remaining 8% were minke whales Balaenoptera acutorostrata, fin whales B. physalus, blue whales B. musculus, and sperm whales Physeter macrocephalus. Predictor variables included year, gear type, location, season, sea surface temperature, water depth, and a multivariate El Niño index. Cross-validated species classifications were correct in 78% (155/199) of cases, significantly higher (p < 0.001, permutation test) than the 49% correct classification rate expected by chance. The RF model correctly classified 91% of humpback whale cases, 64% of gray whale cases, and 100% of sperm whale cases, but misclassified all minke, blue, and fin whale cases. The cross-validated RF classification-tree species model was used to classify 35 entanglements without species identifications (‘novel data’) and each case was assigned a probability of belonging to each of 6 model data species. This approach eliminates the negative bias associated with ignoring unidentified entanglements in species risk assessments. Applications to other wildlife studies where some detections are unidentified include fisheries bycatch, line-transect surveys, and large-whale vessel strikes. KEY WORDS: Fishery entanglement · Humpback whale · Gray whale · Species assignment · Random forest Full text in pdf format PreviousNextCite this article as: Carretta JV (2018) A machine-learning approach to assign species to ‘unidentified’ entangled whales. Endang Species Res 36:89-98. https://doi.org/10.3354/esr00894 Export citation Mail this link - Contents Mailing Lists - RSS Facebook - Tweet - linkedIn Cited by Published in ESR Vol. 36. Online publication date: June 13, 2018 Print ISSN: 1863-5407; Online ISSN: 1613-4796 Copyright © 2018 Inter-Research.

The correlation between the local environment and gray whale behavior as tracked using a sparse hydrophone array in Monterey Bay National Marine Sanctuary

Gray whales (Eschrichtius robustus) make one of the longest annual migrations of any marine mammal. While the seasonal periodicity of the migration and related travel corridors have been well-studied, little is known about how these whales use acoustics while migrating. Acoustic array processing is a powerful tool to localize and track vocalizing whales without disrupting their behavior. Concurrent with the NOAA Southwest Fisheries Science Center visual and infrared gray whale census, four hydrophone-recorder packages with sites separated by approximately 2 km in 58 to 110 m water in Monterey Bay National Marine Sanctuary offshore of Granite Canyon, California, recorded from November 2014 to June 2015. An automatic call detector identified gray whale M3 calls in the dataset. Spectrogram cross-correlation was used to measure the time-difference-of-arrival of these calls and estimate the location of the calling animal. These localizations provide gray whale swimming and calling behavior and indicate changes on diel and seasonal time scales. Results from 15 high-quality tracks show calling gray whales swimming at a mean speed of 1.97 m/s with a range of 1.18 to 2.54 m/s. Acoustic detections and tracks are compared with environmental parameters to investigate how gray whale behavior relates to their physical environment. These results are necessary for understanding the cues gray whales use to navigate from their feeding grounds offshore of Alaska to their breeding grounds in Baja California.

Methods for sampling sequential annual bone growth layers for stable isotope analysis

Summary Stable carbon (δ13C) and nitrogen (δ15N) isotope analysis (SIA) has proven useful in addressing fundamental questions in ecology such as reconstructing trophic interactions, habitat connections and climate regime shifts. The temporal scales over which SIA can be used to address ecological problems vary depending on the protein turnover times of the analysed tissue. Hard, inert tissues, such as teeth, bones and mollusc shells, grow in regular intervals (i.e. daily or annually), and sequential sampling of these growth layers provides a time series of isotopic patterns. As a result, SIA on these tissues is useful for elucidating behaviour and ecology of animals over time, especially those with cryptic life‐history stages, such as marine turtles that retain growth layers in their humerus bones. To date, there exists no standard protocol for the sequential sampling of cortical bone samples taken from fresh, modern samples for SIA. We tested two different methods, micromilling untreated bone cross sections and biopsy coring bone cross sections processed for skeletochronology, for sequentially sampling individual growth layers from marine turtle humerus bones. We present a standard protocol for sequential bone growth layer sampling for SIA, facilitating direct comparison of future studies. We recommend using the micromilling sampling technique on untreated bone cross sections, as it facilitated higher precision sampling of growth layers that were not affected by chemical processing, and minimized sample handling, thereby reducing chances for contamination. This is the first study to present a standardized method to sequentially sample annual bone growth layers for stable isotope analysis and facilitates direct comparison among future studies.

Two-Million-Liter Tank Expands the Boundaries of Marine Technology Innovation: National Resource Available for Advancing Marine Science

AbstractWith one small move across a street, NOAA Fisheries, our nation's steward of marine ecosystems, made a giant leap into the future of ocean science and technology development. The new Southwest Fisheries Science Center facility in La Jolla, California, houses dozens of interdisciplinary research and engineering groups and features a world-class Ocean Technology Development Tank that expands the possibilities for advancing marine industry and science. This 20 × 10 × 10-meter, two-million-liter, freshwater or seawater “Tech Tank” is clear, quiet, and large enough to calibrate and test a wide variety of sensitive instruments with minimal boundary effects. The tank's temperature, salinity, and circulation can be made to mimic the broad range of water conditions encountered in the field, its water conditioning system greatly mitigates microbubbles and turbidity, and it can accommodate live marine animals. This unique combination of features opens doors to efficient engineering and scientific experimentation. The Tech Tank, supported by scientists and engineers and co-located with other state-of-the-art laboratory facilities, is a unique national resource for marine research and development and a catalyst for government, academic, and industry partnerships. The broad range of new possibilities is exemplified by multiple recent collaborative developments of acoustic and optical sensors and sensor platforms that effectively expand the boundaries of oceanic sampling, particularly near the sea surface, seabed, and seashore, to more efficiently and accurately monitor large marine ecosystems.

Declining abundance of beaked whales (family Ziphiidae) in the California Current large marine ecosystem.

Beaked whales are among the most diverse yet least understood groups of marine mammals. A diverse set of mostly anthropogenic threats necessitates improvement in our ability to assess population status for this cryptic group. The Southwest Fisheries Science Center (NOAA) conducted six ship line-transect cetacean abundance surveys in the California Current off the contiguous western United States between 1991 and 2008. We used a Bayesian hidden-process modeling approach to estimate abundance and population trends of beaked whales using sightings data from these surveys. We also compiled records of beaked whale stranding events (3 genera, at least 8 species) on adjacent beaches from 1900 to 2012, to help assess population status of beaked whales in the northern part of the California Current. Bayesian posterior summaries for trend parameters provide strong evidence of declining beaked whale abundance in the study area. The probability of negative trend for Cuvier's beaked whale (Ziphius cavirostris) during 1991–2008 was 0.84, with 1991 and 2008 estimates of 10771 (CV = 0.51) and ≈7550 (CV = 0.55), respectively. The probability of decline for Mesoplodon spp. (pooled across species) was 0.96, with 1991 and 2008 estimates of 2206 (CV = 0.46) and 811 (CV = 0.65). The mean posterior estimates for average rate of decline were 2.9% and 7.0% per year. There was no evidence of abundance trend for Baird's beaked whale (Berardius bairdii), for which annual abundance estimates in the survey area ranged from ≈900 to 1300 (CV≈1.3). Stranding data were consistent with the survey results. Causes of apparent declines are unknown. Direct impacts of fisheries (bycatch) can be ruled out, but impacts of anthropogenic sound (e.g., naval active sonar) and ecosystem change are plausible hypotheses that merit investigation.

Comparing California Current cetacean–habitat models developed using in situ and remotely sensed sea surface temperature data

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 413:163-183 (2010) - DOI: https://doi.org/10.3354/meps08696 Comparing California Current cetacean–habitat models developed using in situ and remotely sensed sea surface temperature data E. A. Becker1,*, K. A. Forney1, M. C. Ferguson2, D. G. Foley3,4, R. C. Smith5, J. Barlow2, J. V. Redfern2 1NOAA, Southwest Fisheries Science Center, 110 Shaffer Rd, Santa Cruz, California 95060, USA 2NOAA, Southwest Fisheries Science Center, 3333 N. Torrey Pines Ct., La Jolla, California 92037, USA 3Joint Institute for Marine and Atmospheric Research, University of Hawaii, 1000 Pope Rd, Honolulu, Hawaii 96822, USA 4NOAA, Southwest Fisheries Science Center, 1352 Lighthouse Ave, Pacific Grove, California 93950, USA 5Institute for Computational Earth System Science, UCSB, Santa Barbara, California 93106, USA *Email: ebecker77@cox.net ABSTRACT: Generalized linear and generalized additive habitat models were used to predict cetacean densities for 10 species in an 818000 km2 area off California. The performance of models built with remotely sensed oceanic data was compared to that of models built with in situ measurements. Cetacean sighting data were collected by the Southwest Fisheries Science Center on 4 systematic line-transect surveys during the summer and fall of 1991, 1993, 1996, and 2001. Predictor variables included temporally dynamic, remotely sensed environmental variables (sea surface temperature and measures of its variance) and more static geographical variables (water depth, bathymetric slope, and a categorical variable representing oceanic zone). The explanatory and predictive power of different spatial and temporal resolutions of satellite data were examined and included in the models for each of the 10 species. Alternative models were built using in situ analogs for sea surface temperature and its variance. The remotely sensed and in situ models with the highest predictive ability were selected based on a pseudo-jackknife cross validation procedure. Environmental predictors included in the final models varied by species, but, for each species, overall explanatory power was similar between the remotely sensed and in situ models. Cetacean–habitat models developed using satellite data at 8 d temporal resolution and from 5 to 35 km spatial resolution were shown to have predictive ability that generally met or exceeded models developed with analogous in situ data. This suggests that the former could be an effective tool for resource managers to develop near real-time predictions of cetacean density. KEY WORDS: Cetacean density · Habitat modeling · GAM · GLM · California Current · Remote sensing · Whale · Dolphin · Porpoise Full text in pdf format PreviousNextCite this article as: Becker EA, Forney KA, Ferguson MC, Foley DG, Smith RC, Barlow J, Redfern JV (2010) Comparing California Current cetacean–habitat models developed using in situ and remotely sensed sea surface temperature data. Mar Ecol Prog Ser 413:163-183. https://doi.org/10.3354/meps08696 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 413. Online publication date: August 26, 2010 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2010 Inter-Research.

Using barnacle and pigmentation characteristics to identify gray whale calves on their feeding grounds

Marine Mammal ScienceVolume 27, Issue 3 p. 644-651 Notes Using barnacle and pigmentation characteristics to identify gray whale calves on their feeding grounds Amanda L. Bradford, Amanda L. Bradford School of Aquatic and Fishery Sciences,University of Washington,P. O. Box 355020,Seattle, Washington 98195-5020, U.S.A.E-mail: [email protected]Search for more papers by this authorDavid W. Weller, David W. Weller Southwest Fisheries Science Center, NMFS, NOAA,3333 North Torrey Pines Court,La Jolla, California 92037-1022, U.S.A.Search for more papers by this authorAlexander M. Burdin, Alexander M. Burdin Kamchatka Branch of Pacific Institute of Geography,Far East Branch of the Russian Academy of Sciences,Pr. Rybakov, 19-a,Petropavlovsk-Kamchatsky, 683024, RussiaandAlaska SeaLife Center,301 Railway Avenue,Seward, Alaska 99664, U.S.A.Search for more papers by this authorRobert L. Brownell, Jr., Robert L. Brownell, Jr. Southwest Fisheries Science Center, NMFS, NOAA,1352 Lighthouse Avenue,Pacific Grove, California 93950, U.S.A.Search for more papers by this author Amanda L. Bradford, Amanda L. Bradford School of Aquatic and Fishery Sciences,University of Washington,P. O. Box 355020,Seattle, Washington 98195-5020, U.S.A.E-mail: [email protected]Search for more papers by this authorDavid W. Weller, David W. Weller Southwest Fisheries Science Center, NMFS, NOAA,3333 North Torrey Pines Court,La Jolla, California 92037-1022, U.S.A.Search for more papers by this authorAlexander M. Burdin, Alexander M. Burdin Kamchatka Branch of Pacific Institute of Geography,Far East Branch of the Russian Academy of Sciences,Pr. Rybakov, 19-a,Petropavlovsk-Kamchatsky, 683024, RussiaandAlaska SeaLife Center,301 Railway Avenue,Seward, Alaska 99664, U.S.A.Search for more papers by this authorRobert L. Brownell, Jr., Robert L. Brownell, Jr. Southwest Fisheries Science Center, NMFS, NOAA,1352 Lighthouse Avenue,Pacific Grove, California 93950, U.S.A.Search for more papers by this author First published: 26 August 2010 https://doi.org/10.1111/j.1748-7692.2010.00413.xCitations: 4Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume27, Issue3July 2011Pages 644-651 RelatedInformation

Comparison of beaked whale detection algorithms

Due to recent advances in passive acoustic monitoring techniques, beaked whales are now more effectively detected acoustically than visually during vessel-based (e.g. line-transect) surveys. Beaked whales signals can be discriminated from those of other cetaceans by the unique characteristics of their echolocation clicks (e.g. duration >175 μs, center frequencies between 30 and 40 kHz, inter-click intervals between 0.2 and 0.4 s and frequency upsweeps). Furthermore, these same characteristics make these signals ideal candidates for testing automated detection and classification algorithms. There are several different beaked whale automated detectors currently available for use. However, no comparative analysis of detectors exists. Therefore, comparison between studies and datasets is difficult. The purpose of this study was to test, validate, and compare algorithms for detection of beaked whales in acoustic line-transect survey data. Six different detection algorithms (XBAT, Ishmael, PAMGUARD, ERMA, GMM and FMCD) were evaluated and compared. Detection trials were run on three sample days of towed-hydrophone array recordings collected by NOAA Southwest Fisheries Science Center (SWFSC) during which were confirmed visual sightings of beaked whales ( Ziphius cavirostris and Mesoplodon peruvianus). Detections also were compared to human verified acoustic detections for a subset of these data. In order to measure the probabilities of false detection, each detector was also run on three sample recordings containing clicks from another species: Risso’s dolphin ( Grampus griseus). Qualitative and quantitative comparisons and the detection performance of the different algorithms are discussed.

Marine ecosystems, climate and phenology: introduction

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 393:185-188 (2009) - DOI: https://doi.org/10.3354/meps08382 Marine ecosystems, climate and phenology: introduction William J. Sydeman1,*, Steven J. Bograd2 1Farallon Institute for Advanced Ecosystem Research, PO Box 750756, Petaluma, California 94975, USA 2NOAA/NMFS, Southwest Fisheries Science Center, Environmental Research Division, 1352 Lighthouse Avenue, Pacific Grove, California 93950-2097, USA *Email: wsydeman@comcast.net ABSTRACT: Mid to high-latitude marine ecosystems are characterized by strong intra-seasonal variability in productivity across multiple trophic levels. It is understood that climate variability, as well as projected climate change, is likely to cause substantial changes in the timing of key seasonal events, such as the spring phytoplankton bloom, dates of diapause for zooplankton, or nesting dates in seabirds. However, it is not well known how changes in timing across multiple trophic levels will impact predator-prey relationships. Several mechanistic hypotheses have been put forth to explain changes in fish production in relation to phenological variability of prey, including Cushing’s (1990; Adv Mar Biol 26:249–293) ‘match-mismatch’ hypothesis, yet there have been few tests of these ideas relative to ongoing oceanographic change. In this Theme Section, we present 9 papers that relate ocean climate variability and climate change to timing of key events for zooplankton, fish, and seabirds from northern hemisphere marine ecosystems. They cover phenological variability and consequences of timing changes for species of the California Current, Gulf of Alaska, NE Atlantic Ocean, Arctic Ocean and northern Japan Sea, all cold-water ecosystems, and highlight the importance of phenology as a key response variable, as well as the complexity of ecological relationships to be impacted by marine climate change. Multi-trophic level changes in phenology of species abundance and productivity are likely to have important consequences to marine ecosystem structure and function. KEY WORDS:Abundance · Match-mismatch · Ocean warming · Plankton · Prey · Seabirds · Timing Full text in pdf format Complete Theme Section in pdf format PreviousNextCite this article as: Sydeman WJ, Bograd SJ (2009) Marine ecosystems, climate and phenology: introduction. Mar Ecol Prog Ser 393:185-188. https://doi.org/10.3354/meps08382 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 393. Online publication date: October 30, 2009 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2009 Inter-Research.

Sounds of Captive Rockfishes

Abstract Sound production by many fish species has been studied extensively, but little is known about sound production by rockfishes (genus Sebastes), and only a few species have been reported to be soniferous. To determine if additional rockfish species produce sounds, passive acoustic recordings were made during 2007/08 at Hubbs-SeaWorld Research Institute and Southwest Fisheries Science Center in tanks containing Bocaccio (S. paucispinis), Cowcod (S. levis), Starry Rockfish (S. constellatus), and Sunset Rockfish (S. crocotulus). Data were collected using pre-amplified hydrophones (HTI-94-SSQ) and digitized at sample rates of 44,100 or 8,000 Hz (using an Edirol R-09 recorder or Edirol UA-5 sound card and Ishmael software, respectively). Three distinct sounds were recorded in tanks containing only S. paucispinis and two of those sounds occurred at different rates during light and dark conditions. Their common characteristics were low frequency (below 800 Hz), short duration (<4 s), and low source levels...

Multiple paternity increases effective population size

*NOAA Southwest Fisheries Science Center, National MarineFisheries Service, 110 Shaffer Road, Santa Cruz, CA 95060,USA, †Department of Ecology and Evolutionary Biology,University of California, Santa Cruz, CA, USA, ‡Department ofApplied Math and Statistics, University of California, SantaCruz, CA, USAReceived 11 March 2009; revision received 30 April 2009;accepted 5 May 2009The effective size, N

Integration of automated detection methods into NOAA Southwest Fisheries Science Center (SWFSC) acoustic marine mammal monitoring protocol.

Southwest Fisheries Science Center (SWFSC) has used combined visual and acoustic techniques to monitor marine mammal populations for the past 8 yrs. Currently, SWFSC passive acoustic surveys of cetaceans require specially trained personnel to monitor hydrophone signals in real-time. While effective, this method is time-consuming and costly. Automated detection of cetacean vocalizations would be a valuable tool during SWFSC surveys, allowing for detection when experienced technicians are unavailable. This technique is advantageous because it significantly reduces effort and removes sources of human error and bias in detection ability. PAMGUARD 1.0 CORE software was evaluated for use in automated detection of cetacean acoustic signals. Three different detector configurations of PAMGUARD were evaluated. This work shows that the majority of whistle and click events can be detected using PAMGUARD software. All of the PAMGUARD trials were capable of detecting whistles and clicks of cetacean species with varying success. These techniques were field-tested at sea during a recent SWFSC marine mammal survey. Automated detection of beaked whales and Dall’s porpoise during this survey will also be discussed. It is our goal to integrate automated detection methods into SWFSC’s acoustic marine mammal monitoring protocol and this work is an important step in doing so.

Bocaccio sounds in the Southern California Bight.

Sound production by many fish species has been studied extensively, but little is known about sound production by rockfishes (genus Sebastes), and only a few species have been reported to be soniferous. Passive acoustic recordings were made during 2007 and 2008 at Hubbs‐SeaWorld Research Institute and Southwest Fisheries Science Center tanks containing Bocaccio (S. paucispinis). Sounds in tanks were recorded using preamplified hydrophones (HTI‐94‐SSQ) and were digitized at sample rates of 44,100 or 8000 Hz. Recordings were collected at 14 locations in the Southern California Bight (SCB) using passive acoustic moorings, AURALs, sampling at 8000 Hz from August through October 2007. Three low frequency (<900 Hz), short duration (<4 s) sounds were recorded in tanks containing only Bocaccio multiple times. One of these sounds was also commonly recorded in the SCB and is the same sound that was recorded first by Thompson off San Clemente Island in the 1960s. This sound exhibited the same diel pattern as noted by Thompson, with maximum calling at night, peaking at dusk and dawn. This long‐term persistence of the same sound indicates that passive acoustic tools may be used for efficient monitoring of changes in rockfish populations over long time periods.

Anthropogenic Noise as a Stressor in Animals: A Multidisciplinary Perspective

This paper could not have been written without the financial and organizational support from Dieter Paulmann and Jo Hastie respectively. Thanks are also due to 2 anonymous reviewers, whose comments on an earlier version of the manuscript greatly improved the paper. The views expressed in this paper are those of the authors alone and do not represent those of Stellwagen Bank National Marine Sanctuary, the U.S. National Marine Sanctuary Program, Southwest Fisheries Science Center, the U.S. National Marine Fisheries Service, NOAA and/or any other institution or agency. Correspondence concerning this article should be addressed to Andrew Wright, Leviathan Sciences, U.S.A. (marinebrit@gmail.com). Consequences of extreme noise exposure are obvious and usually taken into some consideration in the management of many human activities that affect either human or animal populations. However, the more subtle effects such as masking, annoyance and changes in behavior are often overlooked, especially in animals, because these subtleties can be very difficult to detect. To better understand the possible consequences of exposure to noise, this review draws from the available information on human and animal physiology and psychology, and addresses the importance of context (including physiological and psychological state resulting from any previous stressor exposure) in assessing the true meaning of behavioral responses. The current consensus is that the physiological responses to stressors of various natures are fairly stereotyped across the range of species studied. It is thus expected that exposure to noise can also lead to a physiological stress response in other species either directly or indirectly through annoyance, a secondary stressor. In fact many consequences of exposure to noise can result in a cascade of secondary stressors such as increasing the ambiguity in received signals or causing animals to leave a resourceful area, all with potential negative if not disastrous consequences. The context in which stressors are presented was found to be important not only in affecting behavioral responses, but also in affecting the physiological and psychological responses. Young animals may be particularly sensitive to stressors for a number of reasons including the sensitivity of their still-developing brains. Additionally, short exposure to stressors may result in long-term consequences. Furthermore, physiological acclimation to noise exposure cannot be determined from apparent behavioral reactions alone due to contextual influence, and negative impacts may persist or increase as a consequence of such behavioral changes. Despite the lack of information available to managers, uncertainty analysis and modeling tools can be coupled with adaptive management strategies to support decision making and continuous improvements to managing the impacts of noise on free-ranging animals.

Do Marine Mammals Experience Stress Related to Anthropogenic Noise?

This paper could not have been written without the financial and organizational support from Dieter Paulmann and Jo Hastie respectively. Thanks are also due to two anonymous reviewers, whose comments on an earlier version of the manuscript greatly improved the paper. The views expressed in this paper are those of the authors alone and do not represent those of Stellwagen Bank National Marine Sanctuary, the U.S. National Marine Sanctuary Program, Southwest Fisheries Science Center, the U.S. National Marine Fisheries Service, NOAA and/or any other institution or agency. Correspondence concerning this article should be addressed to Andrew Wright, Leviathan Sciences, U.S.A. (marinebrit@gmail.com). Sound travels much further than light in the marine environment. As a result, marine mammals, especially cetaceans, rely heavily on sound for many important life functions, including breeding and foraging. This reliance on sound means it is quite likely that exposure to noise will have some detrimental effects on these life functions. However, there has been little application to marine mammals of the knowledge available in other species of stress responses to noise and other stressors. In this paper we begin to integrate what is known about marine mammals with the current knowledge gained in terrestrial mammals about stress physiology, specifically considering physiological and psychological context and thus also cumulative and synergistic impacts. We determined that it is reasonable to extrapolate information regarding stress responses in other species to marine mammals, because these responses are highly conserved among all species in which they have been examined to date. As a result, we determined that noise acts as a stressor to marine mammals. Furthermore, given that marine mammals will likely respond in a manner consistent with other species studied, repeated and prolonged exposures to stressors (including or induced by noise) will be problematic for marine mammals of all ages. A range of issues may arise from the extended stress response including, but not limited to, suppression of reproduction (physiologically and behaviorally), accelerated aging and sickness-like symptoms. We also determined that interpretation of a reduction in behavioral responses to noise as acclimation will be a mistake in many situations, as alternative reasons for the observed results are much more likely. We recommend that research be conducted on both stress responses and life-history consequences of noise exposure in marine mammals, while emphasizing that very careful study designs will be required. We also recommend that managers incorporate the findings presented here in decisions regarding activities that expose marine mammals to noise. In particular, the effects of cumulative and synergistic responses to stressors can be very important and should not be dismissed lightly.