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The ocean continues to be a sink for microparticle (MP) pollution, which includes microplastics and other anthropogenic debris. While documentation of MP in marine systems is now common, we lack information on rates of MP ingestion by baleen whales and their prey. We collected and assessed MP loads in zooplankton prey and fecal samples of gray whales ( Eschrichtius robustus ) feeding in coastal Oregon, USA and produced the first estimates of baleen whale MP consumption rates from empirical data of zooplankton MP loads (i.e., not modeled). All zooplankton species examined were documented gray whale prey items ( Atylus tridens, Holmesimysis sculpta, Neomysis rayii ) and contained an average of 4 MP per gram of tissue, mostly of the microfiber morphotype. We extrapolated MP loads in zooplankton prey to estimate the daily MP consumption rates of pregnant and lactating gray whales, which ranged between 6.5 and 21 million MP/day. However, these estimates do not account for MP ingested from ambient water or benthic sediments, which may be high for gray whales given their benthic foraging strategy. We also assessed MP loads in fecal samples from gray whales feeding in the same spatio-temporal area and detected MP in all samples examined, which included microfibers and significantly larger morphotypes than in the zooplankton. We theorize that gray whales ingest MP via both indirect trophic transfer from their zooplankton prey and directly through indiscriminate consumption of ambient MPs when foraging benthically where they consume larger MP morphotypes that have sunk and accumulated on the seafloor. Hence, our estimated daily MP consumption rates for gray whales are likely conservative because they are only based on indirect MP ingestion via prey. Our results improve the understanding of MP loads in marine ecosystems and highlight the need to assess the health impacts of MP consumption on zooplankton and baleen whales, particularly due to the predominance of microfibers in samples, which may be more toxic and difficult to excrete than other MP types. Furthermore, the high estimated rates of MP consumption by gray whales highlights the need to assess health consequences to individuals and subsequent scaled-up effects on population vital rates. 

Quantifying how animals respond to disturbance events bears relevance for understanding consequences to population health. We investigate whether blue whales respond acoustically to naturally occurring episodic noise by examining calling before and after earthquakes (27 040 calls, 32 earthquakes; 27 January–29 June 2016). Two vocalization types were evaluated: New Zealand blue whale song and downswept vocalizations ('D calls'). Blue whales did not alter the number of D calls, D call received level or song intensity following earthquakes (paired t -tests, p > 0.7 for all). Linear models accounting for earthquake strength and proximity revealed significant relationships between change in calling activity surrounding earthquakes and prior calling activity (D calls: R 2 = 0.277, p < 0.0001; song: R 2 = 0.080, p = 0.028); however, these same relationships were true for ‘null’ periods without earthquakes (D calls: R 2 = 0.262, p < 0.0001; song: R 2 = 0.149, p = 0.0002), indicating that the pattern is driven by blue whale calling context regardless of earthquake presence. Our findings that blue whales do not respond to episodic natural noise provide context for interpreting documented acoustic responses to anthropogenic noise sources, including shipping traffic and petroleum development, indicating that they potentially evolved tolerance for natural noise sources but not novel noise from anthropogenic origins. 

Body condition is a crucial and indicative measure of an animal’s fitness, reflecting overall foraging success, habitat quality, and balance between energy intake and energetic investment toward growth, maintenance, and reproduction. Recently, drone-based photogrammetry has provided new opportunities to obtain body condition estimates of baleen whales in one, two or three dimensions (1D, 2D, and 3D, respectively) – a single width, a projected dorsal surface area, or a body volume measure, respectively. However, no study to date has yet compared variation among these methods and described how measurement uncertainty scales across these dimensions. This associated uncertainty may affect inference derived from these measurements, which can lead to misinterpretation of data, and lack of comparison across body condition measurements restricts comparison of results between studies. Here we develop a Bayesian statistical model using known-sized calibration objects to predict the length and width measurements of unknown-sized objects (e.g., a whale). We use the fitted model to predict and compare uncertainty associated with 1D, 2D, and 3D photogrammetry-based body condition measurements of blue, humpback, and Antarctic minke whales – three species of baleen whales with a range of body sizes. The model outputs a posterior predictive distribution of body condition measurements and allows for the construction of highest posterior density intervals to define measurement uncertainty. We find that uncertainty does not scale linearly across multi-dimensional measurements, with 2D and 3D uncertainty increasing by a factor of 1.45 and 1.76 compared to 1D, respectively. Each standardized body condition measurement is highly correlated with one another, yet 2D body area index (BAI) accounts for potential variation along the body for each species and was the most precise body condition metric. We hope this study will serve as a guide to help researchers select the most appropriate body condition measurement for their purposes and allow them to incorporate photogrammetric uncertainty associated with these measurements which, in turn, will facilitate comparison of results across studies. 

Researchers can investigate many aspects of animal ecology through noninvasive photo–identification. Photo–identification is becoming more efficient as matching individuals between photos is increasingly automated. However, the convolutional neural network models that have facilitated this change need many training images to generalize well. As a result, they have often been developed for individual species that meet this threshold. These single‐species methods might underperform, as they ignore potential similarities in identifying characteristics and the photo–identification process among species.

In this paper, we introduce a multi‐species photo–identification model based on a state‐of‐the‐art method in human facial recognition, the ArcFace classification head. Our model uses two such heads to jointly classify species and identities, allowing species to share information and parameters within the network. As a demonstration, we trained this model with 50,796 images from 39 catalogues of 24 cetacean species, evaluating its predictive performance on 21,192 test images from the same catalogues. We further evaluated its predictive performance with two external catalogues entirely composed of identities that the model did not see during training.

The model achieved a mean average precision (MAP) of 0.869 on the test set. Of these, 10 catalogues representing seven species achieved a MAP score over 0.95. For some species, there was notable variation in performance among catalogues, largely explained by variation in photo quality. Finally, the model appeared to generalize well, with the two external catalogues scoring similarly to their species' counterparts in the larger test set.

From our cetacean application, we provide a list of recommendations for potential users of this model, focusing on those with cetacean photo–identification catalogues. For example, users with high quality images of animals identified by dorsal nicks and notches should expect near optimal performance. Users can expect decreasing performance for catalogues with higher proportions of indistinct individuals or poor quality photos. Finally, we note that this model is currently freely available as code in a GitHub repository and as a graphical user interface, with additional functionality for collaborative data management, via Happywhale.com.