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Understanding species distribution and behavior is essential for conservation programs of migratory species with recovering populations. The critically endangered Antarctic blue whale ( Balaenoptera musculus intermedia) was heavily exploited during the whaling era. Because of their low numbers, highly migratory behavior, and occurrence in remote areas, their distribution and range are not fully understood, particularly in the southwest Pacific Ocean. This is the first Antarctic blue whale study covering the southwest Pacific Ocean region from temperate to tropical waters (32°S to 15°S). Passive acoustic data were recorded between 2010 and 2011 across the southwest Pacific (SWPO) and southeast Indian (SEIO) oceans. We detected Antarctic blue whale calls in previously undocumented SWPO locations off eastern Australia (32°S, 152°E) and within the Lau Basin (20°S, 176°W and 15°S, 173°W), and SEIO off northwest Australia (19°S, 115°E).In temperate waters, adjacent ocean basins had similar seasonal occurrence, in that calling Antarctic blue whales were present for long periods, almost year round in some areas. In northern tropical waters, calling whales were mostly present during the austral winter. Clarifying the occurrence and distribution of critically endangered species is fundamental for monitoring population recovery, marine protected area planning, and in mitigating anthropogenic threats. [ABSTRACT FROM AUTHOR]

Abstract: Identifying the properties of on-going events by the sound they produce is crucial for our interaction with the environment when visual information is not available. Here, we investigated the ability of listeners to estimate the size of an object (a ball) dropped on a plate with ecological listening conditions (balls were dropped in real time) and response methods (listeners estimate ball-size by drawing a disk). Previous studies had shown that listeners can veridically estimate the size of objects by the sound they produce, but it is yet unclear which acoustical index listeners use to produce their estimates. In particular, it is unclear whether listeners listen to amplitude (related to loudness) or frequency (related to the sound's brightness) domain cue to produce their estimates. In the current study, in order to understand which cue is used by the listener to recover the size of the object, we manipulated the sound source event in such a way that frequency and amplitude cues provided contrasting size-information (balls were dropped from various heights). Results showed that listeners' estimations were accurate regardless of the experimental manipulations performed in the experiments. In addition, results suggest that listeners were likely integrating frequency and amplitude acoustical cues in order to produce their estimate and although these cues were often providing contrasting size-information. [Copyright &y& Elsevier]

• Machine learning models using acoustic indices can distinguish time of day in audio. • Such models may misclassify samples from dawn and day. • Models may need to be trained (calibrated) for each observation site. • Signal-to-noise ratio, spectral entropy and spectral cover appear most informative. Environmental sounds, such as bird calls, insects, animal and human activities, support monitoring the health of an area being listened to. Historically these observations needed to be made in the field, but in recent decades non-intrusive acoustic recorders can be deployed for long periods instead, reducing time spent in the field and increasing the volume of raw data collected. The volume of data that can be collected makes human-based processing impractical, so automated analysis approaches are required. Based on the observation that different times of the day exhibit characteristically different soundscapes, this paper investigates predictive (i.e., machine learning) models that use acoustic indices (a calculated representation of some aspect of the recording) to learn and later identify the gross time of day (dawn, day, evening or night). The analysis was based on recordings from north-west Tasmania, Australia, captured in Spring 2017, with 1-min segments of audio sampled at regular intervals across each day. No attempt was made to eliminate unwanted noise (such as wind and rain) before the audio was processed. While audio recordings will typically have accompanying time stamps, this study can be used as the basis for future work in environmental acoustics on: the preferred machine learning classifier; portability of models across sites; the quantity of data required for training; and feature selection. [ABSTRACT FROM AUTHOR]