Cetaceans are protected worldwide but vulnerable to incidental harm from an expanding array of human activities at sea. Managing potential hazards to these highly-mobile populations increasingly requires a detailed understanding of their seasonal distributions and habitats. Pursuant to the urgent need for this knowledge for the U.S. Atlantic and Gulf of Mexico, we integrated 23 years of aerial and shipboard cetacean surveys, linked them to environmental covariates obtained from remote sensing and ocean models, and built habitat-based density models for 26 species and 3 multi-species guilds using distance sampling methodology. In the Atlantic, for 11 well-known species, model predictions resembled seasonal movement patterns previously suggested in the literature. For these we produced monthly mean density maps. For lesser-known taxa, and in the Gulf of Mexico, where seasonal movements were less well described, we produced year-round mean density maps. The results revealed high regional differences in small delphinoid densities, confirmed the importance of the continental slope to large delphinoids and of canyons and seamounts to beaked and sperm whales, and quantified seasonal shifts in the densities of migratory baleen whales. The density maps, freely available online, are the first for these regions to be published in the peer-reviewed literature.

The goal of this research was to determine when harbor seal pup vocalizations become sufficiently distinctive to allow individual recognition. A total of 4593 calls were analyzed from 15 captive pups. Nineteen were harsh, broadband, staccato calls used in an aggressive context. The rest were tonal "mother attraction calls," having an inverted "v"- or "u"-shaped spectrogram with harmonics and a fundamental frequency around 200-600 Hz. Calls were individually distinctive even in pups less than 2 weeks old, suggesting that mothers may be able to recognize pup vocalizations at this early age. Classification rates from discriminant function analysis were generally comparable to those of other phocids and less than in otariids, supporting the theory that recognition is more highly developed in otariids. Significant differences were found between male and female pup calls, and there were significant interactions between pup sex and age. The results of this study should be interpreted with caution until the findings are verified in wild harbor seal pups.

Photo identification is an important tool for estimating abundance and monitoring population trends over time. However, manually matching photographs to known individuals is time‐consuming. Motivated by recent developments in image recognition, we hosted a data science challenge on the crowdsourcing platform Kaggle to automate the identification of endangered North Atlantic right whales (Eubalaena glacialis). The winning solution automatically identified individual whales with 87% accuracy with a series of convolutional neural networks to identify the region of interest on an image, rotate, crop, and create standardized photographs of uniform size and orientation and then identify the correct individual whale from these passport‐like photographs. Recent advances in deep learning coupled with this fully automated workflow have yielded impressive results and have the potential to revolutionize traditional methods for the collection of data on the abundance and distribution of wild populations. Presenting these results to a broad audience should further bridge the gap between the data science and conservation science communities.