A sense of scale : Foraging cetaceans’ use of scale-dependent multimodal sensory systems

Research on cetacean foraging ecology is central to our understanding of their spatial and behavioral ecology. Yet, functional mechanisms by which cetaceans detect prey across different scales remain unclear. Here, I postulate that cetaceans utilize a scale-dependent, multimodal sensory system to assess and increase prey encounters. I review the literature on cetacean sensory systems related to foraging ecology, and hypothesize the effective scales of each sensory modality to inform foraging opportunities. Next, I build two “scale-of-senses” schematics for the general groups of dolphins and baleen whales. These schematics illustrate the hypothetical interchange of sensory modalities used to locate and discriminate prey at spatial scales ranging from 0 m to 1,000 km: (1) vision, (2) audition (sound production and sound reception), (3) chemoreception, (4) magnetoreception, and somatosensory perception of (5) prey, or (6) oceanographic stimuli. The schematics illustrate how a cetacean may integrate sensory modalities to form an adaptive foraging landscape as a function of distance to prey. The scale-of-senses schematic is flexible, allowing for case-specific application and enhancement with improved cetacean sensory data. The framework serves to improve our understanding of functional cetacean foraging ecology, and to develop new hypotheses, methods, and results regarding how cetaceans forage at multiple scales.Key words: acoustics, baleen whale, cetacean, distance, dolphin, foraging, olfaction, scale, sensory system, vision

Obstacles and Opportunities of Using a Mobile App for Marine Mammal Research

This study investigates the use of a mobile application, Whale mAPP, as a citizen science tool for collecting marine mammal sighting data. In just over three months, 1261 marine mammal sightings were observed and recorded by 39 citizen scientists in Southeast Alaska. The resulting data, along with a preliminary and post-Whale mAPP questionnaires, were used to evaluate the tool’s scientific, educational, and engagement feasibility. A comparison of Whale mAPP Steller sea lion distribution data to a scientific dataset were comparable (91% overlap) given a high enough sample size (n = 73) and dense spatial coverage. In addition, after using Whale mAPP for two weeks, citizen scientists improved their marine mammal identification skills and self-initiated further learning, representing preliminary steps in developing an engaging citizen science project. While the app experienced high initial enthusiasm, maintaining prolonged commitment represents one of the fundamental challenges for this project. Increasing participation with targeted recruitment and sustained communication will help combat the limitations of sample size and spatial coverage. Overall, this study emphasizes the importance of early evaluation of the educational and scientific outcomes of a citizen science project, so that limitations are recognized and reduced

Drone Up! Quantifying Whale Behavior From a New Perspective Improves Observational Capacity

During traditional boat-based surveys of marine megafauna, behavioral observations are typically limited to records of animal surfacings obtained from a horizontal perspective. Achieving an aerial perspective has been restricted to brief helicopter or airplane based observations that are costly, noisy, and risky. The emergence of commercial small unmanned aerial systems (UAS) has significantly reduced these constraints to provide a stable, relatively quiet, and inexpensive platform that enables replicate observations for prolonged periods with minimal disturbance. The potential of UAS for behavioral observation appears immense, yet quantitative proof of utility as an observational tool is required. We use UAS footage of gray whales foraging in the coastal waters of Oregon, United States to develop video behavior analysis methods, determine the change in observation time enabled by UAS, and describe unique behaviors observed via UAS. Boat-based behavioral observations from 53 gray whale sightings between May and October 2016 were compared to behavioral data extracted from video analysis of UAS flights during those sightings. We used a DJI Phantom 3 Pro or 4 Advanced, recorded video from an altitude ≥25 m, and detected no behavioral response by whales to the UAS. Two experienced whale ethologists conducted UAS video behavioral analysis, including tabulation of whale behavior states and events, and whale surface time and whale visible time (total time the whale was visible including underwater). UAS provided three times more observational capacity than boat-based observations alone (300 vs. 103 min). When observation time is accounted for, UAS data provided more and longer observations of all primary behavior states (travel, forage, social, and rest) relative to boat-based data, especially foraging. Furthermore, UAS enable documentation of multiple novel gray whale foraging tactics (e.g., headstands: n = 58; side-swimming: n = 17; jaw snapping and flexing: n = 10) and 33 social events (nursing and pair coordinated surfacings) not identified from boat-based observation. This study demonstrates the significant added value of UAS to marine megafauna behavior and ecological studies. With technological advances, robust study designs, and effective analytical tools, we foresee increased UAS applications to marine megafauna studies to elucidate foraging strategies, habitat associations, social patterns, and response to human disturbance

Insight into the kinematics of blue whale surface foraging through drone observations and prey data

To understand how predators optimize foraging strategies, extensive knowledge of predator behavior and prey distribution is needed. Blue whales employ an energetically demanding lunge feeding method that requires the whales to selectively feed where energetic gain exceeds energetic loss, while also balancing oxygen consumption, breath holding capacity, and surface recuperation time. Hence, blue whale foraging behavior is primarily driven by krill patch density and depth, but many studies have not fully considered surface feeding as a significant foraging strategy in energetic models. We collected predator and prey data on a blue whale (Balaenoptera musculus brevicauda) foraging ground in New Zealand in February 2017 to assess the distributional and behavioral response of blue whales to the distribution and density of krill prey aggregations. Krill density across the study region was greater toward the surface (upper 20 m), and blue whales were encountered where prey was relatively shallow and more dense. This relationship was particularly evident where foraging and surface lunge feeding were observed. Furthermore, New Zealand blue whales also had relatively short dive times (2.83 ± 0.27 SE min) as compared to other blue whale populations, which became even shorter at foraging sightings and where surface lunge feeding was observed. Using an unmanned aerial system (UAS; drone) we also captured unique video of a New Zealand blue whale’s surface feeding behavior on well-illuminated krill patches. Video analysis illustrates the whale’s potential use of vision to target prey, make foraging decisions, and orient body mechanics relative to prey patch characteristics. Kinematic analysis of a surface lunge feeding event revealed biomechanical coordination through speed, acceleration, head inclination, roll, and distance from krill patch to maximize prey engulfment. We compared these lunge kinematics to data previously reported from tagged blue whale lunges at depth to demonstrate strong similarities, and provide rare measurements of gape size, and krill response distance and time. These findings elucidate the predator-prey relationship between blue whales and krill, and provide support for the hypothesis that surface feeding by New Zealand blue whales is an important component to their foraging ecology used to optimize their energetic efficiency. Understanding how blue whales make foraging decisions presents logistical challenges, which may cause incomplete sampling and biased ecological knowledge if portions of their foraging behavior are undocumented. We conclude that surface foraging could be an important strategy for blue whales, and integration of UAS with tag-based studies may expand our understanding of their foraging ecology by examining surface feeding events in conjunction with behaviors at depth

Acoustic monitoring reveals the times and tides of harbor porpoise (Phocoena phocoena) distribution off central Oregon, U.S.A.

Harbor porpoises (Phocoena phocoena) are commonly observed in Oregon's nearshore marine environment yet knowledge of their ecosystem use and behavior remains limited, generating concerns for potential impacts on this species from future coastal development. Passive acoustic monitoring was used to investigate spatial and temporal variations in the presence and foraging activity of harbor porpoises off the Oregon coast from May through October 2014. Digital monitoring devices (DMONs) were deployed to record acoustic data (320 kHz sample rate) in two neighboring but bathymetrically different locations off the Oregon coast: (1) a site on the 30 m isobath in close proximity (<50 m) to a rocky reef, and (2) a site on the 60 m isobath in an open sandy environment. Data were analyzed with respect to two dynamic cyclic variables: diel and tidal phase. Porpoise presence at the rocky reef site was aligned with the ebb phase of the tidal forcing, while, harbor porpoise presence and foraging at the offshore, sandy bottom site was associated with night‐time foraging. The spatial and temporal patterns identified in this study suggest harbor porpoise habitat use is modulated by specific environmental conditions particular to each site that maximize foraging efficiency

Drone Up! Quantifying Whale Behavior From a New Perspective Improves Observational Capacity

During traditional boat-based surveys of marine megafauna, behavioral observations are typically limited to records of animal surfacings obtained from a horizontal perspective. Achieving an aerial perspective has been restricted to brief helicopter or airplane based observations that are costly, noisy, and risky. The emergence of commercial small unmanned aerial systems (UAS) has significantly reduced these constraints to provide a stable, relatively quiet, and inexpensive platform that enables replicate observations for prolonged periods with minimal disturbance. The potential of UAS for behavioral observation appears immense, yet quantitative proof of utility as an observational tool is required. We use UAS footage of gray whales foraging in the coastal waters of Oregon, United States to develop video behavior analysis methods, determine the change in observation time enabled by UAS, and describe unique behaviors observed via UAS. Boat-based behavioral observations from 53 gray whale sightings between May and October 2016 were compared to behavioral data extracted from video analysis of UAS flights during those sightings. We used a DJI Phantom 3 Pro or 4 Advanced, recorded video from an altitude >= 25 m, and detected no behavioral response by whales to the UAS. Two experienced whale ethologists conducted UAS video behavioral analysis, including tabulation of whale behavior states and events, and whale surface time and whale visible time (total time the whale was visible including underwater). UAS provided three times more observational capacity than boat-based observations alone (300 vs. 103 min). When observation time is accounted for, UAS data provided more and longer observations of all primary behavior states (travel, forage, social, and rest) relative to boat-based data, especially foraging. Furthermore, UAS enable documentation of multiple novel gray whale foraging tactics (e.g., headstands: n = 58; side-swimming: n = 17; jaw snapping and flexing: n = 10) and 33 social events (nursing and pair coordinated surfacings) not identified from boat-based observation. This study demonstrates the significant added value of UAS to marine megafauna behavior and ecological studies. With technological advances, robust study designs, and effective analytical tools, we foresee increased UAS applications to marine megafauna studies to elucidate foraging strategies, habitat associations, social patterns, and response to human disturbance

Drone Up! Quantifying Whale Behavior From a New Perspective Improves Observational Capacity

During traditional boat-based surveys of marine megafauna, behavioral observations are typically limited to records of animal surfacings obtained from a horizontal perspective. Achieving an aerial perspective has been restricted to brief helicopter or airplane based observations that are costly, noisy, and risky. The emergence of commercial small unmanned aerial systems (UAS) has significantly reduced these constraints to provide a stable, relatively quiet, and inexpensive platform that enables replicate observations for prolonged periods with minimal disturbance. The potential of UAS for behavioral observation appears immense, yet quantitative proof of utility as an observational tool is required. We use UAS footage of gray whales foraging in the coastal waters of Oregon, United States to develop video behavior analysis methods, determine the change in observation time enabled by UAS, and describe unique behaviors observed via UAS. Boat-based behavioral observations from 53 gray whale sightings between May and October 2016 were compared to behavioral data extracted from video analysis of UAS flights during those sightings. We used a DJI Phantom 3 Pro or 4 Advanced, recorded video from an altitude ≥25 m, and detected no behavioral response by whales to the UAS. Two experienced whale ethologists conducted UAS video behavioral analysis, including tabulation of whale behavior states and events, and whale surface time and whale visible time (total time the whale was visible including underwater). UAS provided three times more observational capacity than boat-based observations alone (300 vs. 103 min). When observation time is accounted for, UAS data provided more and longer observations of all primary behavior states (travel, forage, social, and rest) relative to boat-based data, especially foraging. Furthermore, UAS enable documentation of multiple novel gray whale foraging tactics (e.g., headstands: n = 58; side-swimming: n = 17; jaw snapping and flexing: n = 10) and 33 social events (nursing and pair coordinated surfacings) not identified from boat-based observation. This study demonstrates the significant added value of UAS to marine megafauna behavior and ecological studies. With technological advances, robust study designs, and effective analytical tools, we foresee increased UAS applications to marine megafauna studies to elucidate foraging strategies, habitat associations, social patterns, and response to human disturbance

Classification of Animal Movement Behavior through Residence in Space and Time

Identification and classification of behavior states in animal movement data can be complex, temporally biased, time-intensive, scale-dependent, and unstandardized across studies and taxa. Large movement datasets are increasingly common and there is a need for efficient methods of data exploration that adjust to the individual variability of each track. We present the Residence in Space and Time (RST) method to classify behavior patterns in movement data based on the concept that behavior states can be partitioned by the amount of space and time occupied in an area of constant scale. Using normalized values of Residence Time and Residence Distance within a constant search radius, RST is able to differentiate behavior patterns that are time-intensive (e.g., rest), time & distance-intensive (e.g., area restricted search), and transit (short time and distance). We use grey-headed albatross (Thalassarche chrysostoma) GPS tracks to demonstrate RST’s ability to classify behavior patterns and adjust to the inherent scale and individuality of each track. Next, we evaluate RST’s ability to discriminate between behavior states relative to other classical movement metrics. We then temporally sub-sample albatross track data to illustrate RST’s response to less resolved data. Finally, we evaluate RST’s performance using datasets from four taxa with diverse ecology, functional scales, ecosystems, and data-types. We conclude that RST is a robust, rapid, and flexible method for detailed exploratory analysis and meta-analyses of behavioral states in animal movement data based on its ability to integrate distance and time measurements into one descriptive metric of behavior groupings. Given the increasing amount of animal movement data collected, it is timely and useful to implement a consistent metric of behavior classification to enable efficient and comparative analyses. Overall, the application of RST to objectively explore and compare behavior patterns in movement data can enhance our fine- and broad- scale understanding of animal movement ecology

Foraging in marine habitats increases mercury concentrations in a generalist seabird

Methylmercury concentrations vary widely across geographic space and among habitat types, with marine and aquatic-feeding organisms typically exhibiting higher mercury concentrations than terrestrial-feeding organisms. However, there are few model organisms to directly compare mercury concentrations as a result of foraging in marine, estuarine, or terrestrial food webs. The ecological impacts of differential foraging may be especially important for generalist species that exhibit high plasticity in foraging habitats, locations, or diet. Here, we investigate whether foraging habitat, sex, or fidelity to a foraging area impact blood mercury concentrations in western gulls (Larus occidentalis) from three colonies on the US west coast. Cluster analyses showed that nearly 70% of western gulls foraged primarily in ocean or coastal habitats, whereas the remaining gulls foraged in terrestrial and freshwater habitats. Gulls that foraged in ocean or coastal habitats for half or more of their foraging locations had 55% higher mercury concentrations than gulls that forage in freshwater and terrestrial habitats. Ocean-foraging gulls also had lower fidelity to a specific foraging area than freshwater and terrestrial-foraging gulls, but fidelity and sex were unrelated to gull blood mercury concentrations in all models. These findings support existing research that has described elevated mercury levels in species using aquatic habitats. Our analyses also demonstrate that gulls can be used to detect differences in contaminant exposure over broad geographic scales and across coarse habitat types, a factor that may influence gull health and persistence of other populations that forage across the land-sea gradient

Assessment of a non-invasive approach to pregnancy diagnosis in gray whales through drone-based photogrammetry and faecal hormone analysis

This project was supported by the NOAA National Marine Fisheries Service Office of Science and Technology, the Office of Naval Research Marine Mammals and Biology Program (no. N00014-20-1-2760), the Oregon State University Marine Mammal Institute and Oregon Sea Grant.Knowledge of baleen whales’ reproductive physiology is limited and requires long-term individual-based studies and innovative tools. We used 6 years of individual-level data on the Pacific Coast Feeding Group gray whales to evaluate the utility of faecal progesterone immunoassays and drone-based photogrammetry for pregnancy diagnosis. We explored the variability in faecal progesterone metabolites and body morphology relative to observed reproductive status and estimated the pregnancy probability for mature females of unknown reproductive status using normal mixture models. Individual females had higher faecal progesterone concentrations when pregnant than when presumed nonpregnant. Yet, at the population level, high overlap and variability in progesterone metabolite concentrations occurred between pregnant and non-pregnant groups, limiting this metric for accurate pregnancy diagnosis in gray whales. Alternatively, body width at 50% of the total body length (W50) correctly discriminated pregnant from non-pregnant females at individual and population levels, with high accuracy. Application of the model using W50 metric to mature females of unknown pregnancy status identified eight additional pregnancies with high confidence. Our findings highlight the utility of drone-based photogrammetry to non-invasively diagnose pregnancy in this group of gray whales, and the potential for improved data on reproductive rates for population management of baleen whales generally.Publisher PDFPeer reviewe