00870nas a2200157 4500008004100000245006000041210005900101520037400160100001700534700001600551700002900567700002400596700002300620700002300643856004600666 2019 eng d00aUsing Soundscapes to Assess Deep-Sea Benthic Ecosystems0 aUsing Soundscapes to Assess DeepSea Benthic Ecosystems3 a

Targets of deep-sea mining commonly coincide with biodiversity hotspots, such as hydrothermal vents. The resilience of these ecosystems relies on larval dispersal, which may be directed by habitat-specific soundscapes. We urge for a global effort to implement soundscape as a conservation tool to assess anthropogenic disruption to deep-sea benthic ecosystems.

1 aLin, Tzu-Hao1 aChen, Chong1 aWatanabe, Hiromi, Kayama1 aKawagucci, Shinsuke1 aYamamoto, Hiroyuki1 aAkamatsu, Tomonari uhttps://bio.acousti.ca/zh-hans/node/5788502024nas a2200181 4500008004100000022001400041245013600055210006900191260001600260300001600276490000800292520140700300100002201707700002001729700001901749700002301768856005101791 2018 eng d a0001-496600aAutomatic detection of fish sounds based on multi-stage classification including logistic regression via adaptive feature weighting0 aAutomatic detection of fish sounds based on multistage classific cJan-11-2018 a2709 - 27180 v1443 a

This paper presents a method for automatic detection of fish sounds in an underwater environment. There exist two difficulties: (i) features and classifiers that provide good detection results differ depending on the underwater environment and (ii) there are cases where a large amount of training data that is necessary for supervised machine learning cannot be prepared. A method presented in this paper (the proposed hybrid method) overcomes these difficulties as follows. First, novel logistic regression (NLR) is derived via adaptive feature weighting by focusing on the accuracy of classification results by multiple classifiers, support vector machine (SVM), and k-nearest neighbors (k-NN). Although there are cases where SVM or k-NN cannot work well due to divergence of useful features, NLR can produce complementary results. Second, the proposed hybrid method performs multi-stage classification with consideration of the accuracy of SVM, k-NN, and NLR. The multi-stage acquisition of reliable results works adaptively according to the underwater environment to reduce performance degradation due to diversity of useful classifiers even if abundant training data cannot be prepared. Experiments on underwater recordings including sounds of Sciaenidae such as silver croakers (Pennahia argentata) and blue drums (Nibea mitsukurii) show the effectiveness of the proposed hybrid method.

1 aHarakawa, Ryosuke1 aOgawa, Takahiro1 aHaseyama, Miki1 aAkamatsu, Tomonari uhttp://asa.scitation.org/doi/10.1121/1.506737302880nas a2200313 4500008004100000022001300041245008700054210006900141260001600210300001400226490000800240520197300248653001002221653001802231653001702249653001502266653001702281653002002298653002402318653002302342653000902365100002302374700002302397700002302420700002102443700001602464700002002480856006602500 2017 eng d a0003347200aSilent porpoise: potential sleeping behaviour identified in wild harbour porpoises0 aSilent porpoise potential sleeping behaviour identified in wild  cJan-11-2017 a211 - 2220 v1333 a

All animals sleep and it is essential for maintaining optimal brain function. However, cetaceans engage in the unusual practice of unihemispherical sleep, where only half of their brain sleeps at a time, due to their constant need for movement and breathing. Most studies of sleep in cetaceans have occurred in captivity. However, tagging devices have now developed to the point where the data collected from wild animals can be assessed against published criteria for defining sleep behaviourally. Seven acoustic and behavioural data loggers were deployed on harbour porpoises, Phocoena phocoena, in Danish waters between May 2010 and August 2011 and stayed on the animals between 53 and 72 h, recording 1884 to 2755 valid dives per animal. Parabolic dives with significantly reduced bioacoustic activity and a ste- reotyped behavioural pattern were identified as potential sleeping periods. The recordings for nearly half of the parabolic dives were found to contain no vocalization (echolocation clicks), significantly more than other dive types. Of the remaining parabolic dives, the click rate was, when normalized to their indi- vidual means, also significantly lower than detected in other dive types. Additionally, parabolic dives were shallow compared to all other dive types, and found to have a stereotypic low-energy profile. They were also found to contain fewer rolls and incorporate a lower vertical descent rate than most other dive types. If the data are representative, harbour porpoises spend a small, but meaningful amount of their diving time engaged in parabolic dives and thus potentially sleeping. All animals have a fundamental need for undisturbed sleep. These quiet periods thus need to be considered in studies of anthropogenic effects, but also those employing passive acoustic monitoring techniques, as well as in efforts to reduce incidental bycatch in fisheries, given the associated periods of reduced environmental awareness.

10aA-tag10aacoustic tags10adata storage10ae Phocoena10aecholocation10aharbour porpois10asatellite telemetry10asleeping behaviour10atags1 aWright, Andrew, J.1 aAkamatsu, Tomonari1 aMouritsen, Kim, N.1 aSveegaard, Signe1 aDietz, Rune1 aTeilmann, Jonas uhttp://linkinghub.elsevier.com/retrieve/pii/S0003347217303020