Acceleration-triggered animal-borne videos show a dominance of fish in the diet of female northern elephant seals

Abstract

Knowledge of the diet of marine mammals is fundamental to understanding their role in marine ecosystems and response to environmental change. Recently, animal-borne video cameras have revealed the diet of marine mammals that make short foraging trips. However, novel approaches that allocate video time to target prey capture events is required to obtain diet information for species that make long foraging trips over great distances. We combined satellite telemetry and depth recorders with newly developed date/time-, depth-, and acceleration-triggered animal-borne video cameras to examine the diet of female northern elephant seals during their foraging migrations across the eastern North Pacific. We obtained 48.2 hours of underwater video, from cameras mounted on the head (n=12) and jaw (n=3) of seals. Fish dominated the diet (78% of 697 prey items recorded) across all foraging locations (range: 37–55°N, 122–150°W), diving depths (range: 238–1167 m) and water temperatures (range: 3.2–7.4 °C), while squid comprised only 7% of the diet. Identified prey included fishes such as myctophids, Merluccius sp., and Icosteus aenigmaticus, and squids such as Histioteuthis sp., Octopoteuthis sp., and Taningia danae. Our results corroborate fatty acid analysis, which also found that fish are more important in the diet and contrasts to stomach content analyses that found cephalopods to be the most important component of the diet. Our work shows that in-situ video observation is a useful method for studying the at-sea diet of long-ranging marine predators.

Lightscapes of fear: How mesopredators balance starvation and predation in the open ocean

“…As compared to their predators, elephant seals are highly proficient at nighttime foraging due to their highly innervated vibrissal sensory systems that can be used to track prey (46)(47)(48). In addition, the visual systems of elephant seals are well adapted to dim light conditions (49), and elephant seals have been shown to capture bioluminescent prey (32,(50)(51)(52), which helps explain why feeding success is not compromised at night. Thus, we did not expect elephant seal foraging success to vary widely by time of day.…”

“…Lipid mass estimates resampled from the posterior distribution N = 100 times did not significantly affect results. When including model uncertainty for lipid (by resampling 100 values and fitting the best-fitting model to these new datasets), average model fits (SD) for the best-fitting model are as follows: DIC = 102,069 (34), DIC alt = 102,219 (32), WAIC = 102,129 (11), WAIC 2 = 102,130 (11).…”

Forced into an ecological corner: Round-the-clock deep foraging on small prey by elephant seals

“…Elephant seals are extreme in nearly every aspect of their foraging ecology and are particularly noteworthy for their outstanding physiological diving capacities ( 18 ), which allow them to continuously dive, for long periods of time (20 min on average, >100 min at maximum) and deep (500 m on average, >1500 m at maximum) with only a few minutes breathing at the surface ( 19 ). We measured several complementary foraging variables using data loggers attached to seals: (i) seal locations using the Argos satellite tracking system ( 19 ), (ii) feeding events from smart mandible accelerometers that implement onboard data processing to record the number of jaw motion events every 5 s ( 7 ), (iii) prey size/type (e.g., fish or squid) from a newly developed smart video system that ensures efficient memory allocation by an onboard program with three implemented triggers (day/time, depth, and timing by first feeding-related acceleration signals in each dive) ( 4 ), and (iv) at-sea buoyancy change to infer body lipid store gains each day, which is a key measure of energetic balance in individual animals ( 19 ). Months-long monitoring of at-sea body condition changes is only possible in elephant seals that conduct the key drift dive behavior [rest motionless at depth ( 20 )], which provides a unique opportunity to study marine ecosystem dynamics from the perspective of energetic balance in top predators ( 19 , 21 – 25 ).…”

Machine learning enables improved runtime and precision for bio-loggers on seabirds

“…In addition, several previous studies involving bio-loggers have introduced trigger mechanisms that can be used to control when high-cost sensors are activated based on coarse-level characterisations of behaviour, e.g., underwater vs. surface activity, with many of these studies focusing on controlling animal-borne cameras such as the one used in this study 10 , 11 , 13 , 27 – 32 . In contrast, our method can be used to distinguish between complex behaviours at a finer scale, allowing us to target a specific behaviour; thereby greatly increasing the likelihood that interesting behaviours will be captured.…”

“…The bio-loggers are controlled by an ATmega328 MCU (32 KB programme memory, 2 KB RAM) and have an integrated video camera (640 × 480, 15 FPS) that can be controlled by the MCU, with the video data streamed directly to its own dedicated storage. Note that digital cameras such as the one used in this bio-logger have a delay of several seconds from powering on to when they can begin recording, which in the case of our bio-logger resulted in a 2- to 3-s delay between when the MCU signals the start of recording and the actual start of recording when attempting to save energy by powering off the camera when not in use (see also Yoshino et al 13 for another example of this camera delay). Our bio-loggers also include several low-cost sensors that are controlled by the MCU (see Supplementary Fig.…”