Significance: Divergent lineages can respond to common environmental factors through convergent processes involving shared genomic components or pathways, but the molecular mechanisms are poorly understood. Here, we provide genomic resources and insights into the evolution of mammalian lineages adapting to aquatic life. Our data suggest convergent evolution, for example, in association with thermoregulation through genes associated with a surface heat barrier (NFIA) and internal heat exchange (SEMA3E). Combined with the support of previous reports showing that the UCP1 locus has been lost in many marine mammals independently, our results suggest that the thermostatic strategy of marine mammals shifted from enhancing heat production to limiting heat loss.

Aim: The urgency for remote, reliable and scalable biodiversity monitoring amidst mounting human pressures on ecosystems has sparked worldwide interest in Passive Acoustic Monitoring (PAM), which can track life underwater and on land. However, we lack a unified methodology to report this sampling effort and a comprehensive overview of PAM coverage to gauge its potential as a global research and monitoring tool. To address this gap, we created the Worldwide Soundscapes project, a collaborative network and growing database comprising metadata from 416 datasets across all realms (terrestrial, marine, freshwater and subterranean).Location: Worldwide, 12,343 sites, all ecosystem types.Time Period: 1991 to present.Major Taxa Studied: All soniferous taxa.Methods: We synthesise sampling coverage across spatial, temporal and ecological scales using metadata describing sampling locations, deployment schedules, focal taxa and audio recording parameters. We explore global trends in biological, anthropogenic and geophysical sounds based on 168 selected recordings from 12 ecosystems across all realms.Results: Terrestrial sampling is spatially denser (46 sites per million square kilometre—Mkm²) than aquatic sampling (0.3 and 1.8 sites/Mkm² in oceans and fresh water) with only two subterranean datasets. Although diel and lunar cycles are well sampled across realms, only marine datasets (55%) comprehensively sample all seasons. Across the 12 ecosystems selected for exploring global acoustic trends, biological sounds showed contrasting diel patterns across ecosystems, declined with distance from the Equator, and were negatively correlated with anthropogenic sounds.Main Conclusions: PAM can inform macroecological studies as well as global conservation and phenology syntheses, but representation can be improved by expanding terrestrial taxonomic scope, sampling coverage in the high seas and subterranean ecosystems, and spatio‐temporal replication in freshwater habitats. Overall, this worldwide PAM network holds promise to support cross‐realm biodiversity research and monitoring efforts.

This paper describes the high-frequency echolocation signals from free-ranging Yangtze finless porpoise in the Tian-e-zhou Baiji National Natural Reserve in Hubei Province, China. Signal analysis showed that the Yangtze finless porpoise clicks are typical high-frequency narrow-band (relative width of the frequency spectrum $\text{Q=6.6±1.56,}$ $\text{N=548)}$ ultrasonic pulses. The peak frequencies of the typical clicks range from 87 to 145 kHz with an average of $\text{125±6.92\hspace{0.17em}}\mathrm{kHz}.$ The durations range from 30 to 122 μs with an average of $\text{68±14.12\hspace{0.17em}μ}\mathrm{s}.$ The characteristics of the signals are similar to those of other members of the Phocoenidae as well as the distantly related delphinids, Cephalorhynchus spp. Comparison of these signals to those of the baiji (Lipotes vexillifer), who occupies habitat similar to that of the Yangtze finless porpoise, showed that the peak frequencies of clicks produced by the Yangtze finless porpoise are remarkably higher than those produced by the baiji. Difference in peak frequency between the two species is probably linked to the different size of preferred prey fish. Clear double-pulse and multi-pulse reverberation structures of clicks are noticed, and there is no indication of any low-frequency $\text{(<70\hspace{0.17em}}\mathrm{kHz})$ components during the recording period.

Passive acoustic monitoring (PAM) is increasingly being adopted as a non-invasive method for the assessment of ocean ecological dynamics. PAM is an important sampling approach for acquiring critical information about marine mammals, especially in areas where data are lacking and where evaluations of threats for vulnerable populations are required. The Indo-Pacific humpback dolphin (IPHD, Sousa chinensis) is a coastal species which inhabits tropical and warm-temperate waters from the eastern Indian Ocean throughout Southeast Asia to central China. A new population of this species was recently discovered in waters southwest of Hainan Island, China. An array of passive acoustic platforms was deployed at depths of 10–20 m (the preferred habitat of humpback dolphins), across sites covering more than 100 km of coastline. In this study, we explored whether the acoustic data recorded by the array could be used to classify IPHD echolocation clicks, with the aim of investigating the spatiotemporal patterns of distribution and acoustic behavior of this species. A number of supervised machine learning algorithms were trained to automatically classify echolocation clicks from the different types of short-broadband pulses recorded. The best performance was reported by a cubic support vector machine (Cubic SVM), which was applied to 19,215 5-min recordings (∼4.2 TB), collected over a period of 75 days at six locations. Subsequently, using spectrogram visualization and audio listening, human operators confirmed the presence of clicks within the selected files. Additionally, other dolphin vocalizations (including whistles, buzzes, and burst pulses) and different sound sources (soniferous fishes, snapping shrimps, human activities) were also reported. The detection range of IPHD clicks was estimated using a transmission loss (TL) model and the performance of the trained classifier was compared with data synchronously collected by an acoustic data logger (A-tag). This study demonstrates that the distribution and habitat use of a coastal and resident dolphin species can be monitored over a large spatiotemporal scale, using an array of passive acoustic platforms and a data analysis protocol that includes both machine learning techniques and spectrogram inspection.