The Saccharomyces cerevisiae Yvh1, a dual-specificity protein phosphatase involved in glycogen accumulation and sporulation, is required for normal vegetative growth. To further elucidate the role of Yvh1, we generated dominant mutants suppressing the slow growth caused by YVH1 disruption. One of the mutant alleles, designated as SVH1-1 (suppressor of Δyvh1 deletion), was identical to MRT4 (mRNA turnover) that contained a single-base substitution causing an amino acid change from Gly(68) to Asp. Mrt4(G68D) restored the deficiencies in growth and rRNA biogenesis that occurs in absence of Yvh1. Here, we report that the interaction between Mrt4 and Yvh1 is also essential for normal glycogen accumulation and mRNA decay as well as the induction of sporulation genes IME2, SPO13 and HOP1. The Mrt4(G68D) could restore the plethora of phenotypes we observed in absence of Yvh1. We found that Yvh1 is not essential for wild-type induction of the transcriptional regulator of these genes, IME1, suggesting that either translation or post-translational modification to activate Ime1 has been compromised. Since a defect in ribosome biogenesis in general can be related to other various defects, the ribosome biogenesis defect caused by absence of Yvh1 might be an indirect cause of observed phenotypes.

The AN/SSQ-53 Directional Frequency Analysis and Recording (DIFAR) sonobuoy is an expendable device that can derive acoustic particle velocity along two orthogonal horizontal axes, along with acoustic pressure. This information enables computation of azimuths of low-frequency acoustic sources from a single compact sensor. The standard approach for estimating azimuth from these sensors is by conventional beamforming (i.e., adding weighted time series), but the resulting “cardioid” beampattern is imprecise, computationally expensive, and vulnerable to directional noise contamination for weak signals. Demonstrated here is an alternative multiplicative processing scheme that computes the “active intensity” of an acoustic signal to obtain the dominant directionality of a noise field as a function of time and frequency. This information is conveniently displayed as an “azigram,” which is analogous to a spectrogram, but uses color to indicate azimuth instead of intensity. Data from several locations demonstrate this approach, which can be computed without demultiplexing the raw signal. Azigrams have been used to help diagnose sonobuoy issues, improve detectability, and estimate bearings of low signal-to-noise ratio signals. Azigrams may also enhance the detection and potential classification of signals embedded in directional noise fields.

Use of underwater passive acoustic datasets for species-specific inference requires robust classification systems to identify encounters to species from characteristics of detected sounds. A suite of routines designed to efficiently detect cetacean sounds, extract features, and classify the detection to species is described using ship-based, visually verified detections of false killer whales (Pseudorca crassidens). The best-performing model included features from clicks, whistles, and burst pulses, which correctly classified 99.6% of events. This case study illustrates use of these tools to build classifiers for any group of cetacean species and assess classification confidence when visual confirmation is not available.

Using passive acoustic methods for biodiversity conservation and effective ecosystem monitoring is hindered by laborious, human-mediated processes of accurately identifying biologic and anthropogenic sounds within large datasets. Soundscape ecology provides a potential means of addressing this need through the use of automated acoustic-based biodiversity indices, which show promise in representing biodiversity in terrestrial environments. However, the direct relationship between specific underwater sounds and acoustic index measurements are largely unexplored. Using passive acoustic data collected from three broadband hydrophones within the Ocean Observatories Initiative’s cabled arrays in the Pacific northwest, we identified periods of vocalizing marine mammals and sources of anthropogenic noise. Automated calculations of seven acoustic indices were compared were compared across biologic and anthropogenic sound type and call parameters. Although several index measurements did not vary significantly, the Acoustic Complexity Index (ACI) measurements increased in response to echolocation clicks from sperm whales (Physeter macrocephalus) and burst pulses originating from unidentified delphinid species. Measurements of the Bioacoustic Index (BI) decreased dramatically in responses sperm whale echolocation clicks, a more obvious trend when loud clicks were parsed from moderate and quite clicks. Correlations coefficient and confidence interval values between ACI and BI measurements and call characteristics from sperm whales indicate a moderate to strong relationship, which was not found in correlations with delphnid calls. A generalized linear mixed-effect model indicated multiple species and sound types contribute significantly to the variation of several index measurements. Noise generated by passing ships consistently resulted in decreased values for the Normalized Difference Soundscape Index (NDSI) and Total Entropy (H) as compared to quiet periods and periods with vocalizing marine mammals. These findings provide information on the relationship between several acoustic indices and specific underwater sounds produced by marine mammals and anthropogenic sources. This ground-truthing endeavor expands the understanding of acoustic indices and their potential use as a tool for conservation and ecosystem health management purposes.

Sperm whales are an ideal species to study using passive acoustic technology because they spend the majority of their time underwater and produce echolocation clicks almost continuously while foraging. Passive acoustic line transect data collected between June and August 2016 were used to estimate a depth-corrected acoustic abundance and study the dive behaviour of foraging sperm whales in the western North Atlantic Ocean. 2D localizations (n = 699) were truncated at a slant range of 6500 m and combined with the multipath arrivals of surface reflected echoes to calculate 3D localizations (n = 274). Distance sampling using depth-corrected perpendicular distances resulted in a 10.5% change in the acoustic abundance estimate (2199 whales, CV = 14.6%) compared to uncorrected slant ranges (1969 whales, CV = 14.1%), and a detection function that was a better fit for the data. Sperm whales exhibited multiple foraging strategies, with bottom phases occurring at depths of 400–800, 800–1200, or > 1200 m, accounting for an average 39.2, 49.5, or 44.9% of the total recorded dive time, respectively. These results suggest that estimating 3D localizations using acoustic line transect data improves acoustic abundance estimation and can be used to answer population level questions about foraging ecology.