This study investigated prey captures in free-ranging adult female Australian fur seals (Arctocephalus pusillus doriferus) using head-mounted 3-axis accelerometers and animal-borne video cameras. Acceleration data was used to identify individual attempted prey captures (APC), and video data were used to independently verify APC and prey types. Results demonstrated that head-mounted accelerometers could detect individual APC but were unable to distinguish among prey types (fish, cephalopod, stingray) or between successful captures and unsuccessful capture attempts. Mean detection rate (true positive rate) on individual animals in the testing subset ranged from 67-100%, and mean detection on the testing subset averaged across 4 animals ranged from 82-97%. Mean False positive (FP) rate ranged from 15-67% individually in the testing subset, and 26-59% averaged across 4 animals. Surge and sway had significantly greater detection rates, but also conversely greater FP rates compared to heave. Video data also indicated that some head movements recorded by the accelerometers were unrelated to APC and that a peak in acceleration variance did not always equate to an individual prey item. The results of the present study indicate that head-mounted accelerometers provide a complementary tool for investigating foraging behaviour in pinnipeds, but that detection and FP correction factors need to be applied for reliable field application.
We tested the ability of overall dynamic body acceleration (ODBA) to predict the rate of oxygen consumption ([Formula: see text]) in freely diving Steller sea lions (Eumetopias jubatus) while resting at the surface and diving. The trained sea lions executed three dive types-single dives, bouts of multiple long dives with 4-6 dives per bout, or bouts of multiple short dives with 10-12 dives per bout-to depths of 40 m, resulting in a range of activity and oxygen consumption levels. Average metabolic rate (AMR) over the dive cycle or dive bout calculated was calculated from [Formula: see text]. We found that ODBA could statistically predict AMR when data from all dive types were combined, but that dive type was a significant model factor. However, there were no significant linear relationships between AMR and ODBA when data for each dive type were analyzed separately. The potential relationships between AMR and ODBA were not improved by including dive duration, food consumed, proportion of dive cycle spent submerged, or number of dives per bout. It is not clear whether the lack of predictive power within dive type was due to low statistical power, or whether it reflected a true absence of a relationship between ODBA and AMR. The average percent error for predicting AMR from ODBA was 7-11 %, and standard error of the estimated AMR was 5-32 %. Overall, the extensive range of dive behaviors and physiological conditions we tested indicated that ODBA was not suitable for estimating AMR in the field due to considerable error and the inconclusive effects of dive type.
1Marine mammals are characterised as having physiological specializations that 2 maximize use of oxygen stores to prolong time spent under water. However, it has 3 been difficult to undertake controlled studies to determine the physiological 4 limitations and trade-offs that marine mammals face while diving in the wild under 5 varying environmental and nutritional conditions. For the past decade, Steller sea 6 lions (Eumetopias jubatus) trained to swim and dive in the open ocean away from 7 the physical confines of pools participated in studies that investigated the 8 interactions between diving behaviour, energetic costs, physiological constraints, 9and prey availability. Many of these studies measured the costs of diving to 10 understand how they vary with behaviour and environmental and physiological 11 conditions. Collectively, these studies show that the type of diving (dive bouts or 12 single dives), the level of underwater activity, the depth and duration of dives, and 13 the nutritional status and physical condition of the animal affect the cost of diving 14 and foraging. They show that dive depth, dive and surface duration, and the type of 15 dive result in physiological adjustments (heart rate, gas exchange) that may be 16 independent of energy expenditure. They also demonstrate that changes in prey 17 abundance and nutritional status causes sea lions to alter the balance between time 18 spent at the surface acquiring oxygen (and offloading CO2 and other metabolic by-19 products) and time spent at depth acquiring prey. These new insights into the 20 physiological basis of diving behaviour furthers understanding of the potential 21 scope for behavioural responses of marine mammals to environmental changes, the 22 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Diving metabolism of Steller sea lions 3 energetic consequences of these adjustments, and the consequences of approaching 23 physiological limits. 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Diving metabolism of Steller sea lions 4The need to study diving metabolism 25Marine mammals are well known for being able to remain submerged for extended 26 durations. Early studies of marine mammals (mainly phocid or "true" seals) 27 investigated the anatomical features by which they managed to do so. These include 28 adaptations for withstanding the intense pressures experienced at depth and 29 greater relative on-board oxygen stores than their terrestrial counterparts, which 30 allows them to remain active during submergence breath-holding. For example, 31 elevated oxygen storage is present in both circulating haemoglobin and the 32 myoglo...
Dive characteristics and dive shape are often used to infer foraging success in pinnipeds. However, these inferences have not been directly validated in the field with video, and it remains unclear if this method can be applied to benthic foraging animals. This study assessed the ability of dive characteristics from time-depth recorders (TDR) to predict attempted prey capture events (APC) that were directly observed on animal-borne video in Australian fur seals (Arctocephalus pusillus doriferus, n=11). The most parsimonious model predicting the probability of a dive with ≥1 APC on video included only descent rate as a predictor variable. The majority (94%) of the 389 total APC were successful, and the majority of the dives (68%) contained at least one successful APC. The best model predicting these successful dives included descent rate as a predictor. Comparisons of the TDR model predictions to video yielded a maximum accuracy of 77.5% in classifying dives as either APC or non-APC or 77.1% in classifying dives as successful verses unsuccessful. Foraging intensity, measured as either total APC per dive or total successful APC per dive, was best predicted by bottom duration and ascent rate. The accuracy in predicting total APC per dive varied based on the number of APC per dive with maximum accuracy occurring at 1 APC for both total (54%) and only successful APC (52%). Results from this study linking verified foraging dives to dive characteristics potentially opens the door to decades of historical TDR datasets across several otariid species.
Previous research has presented contradictory evidence on the ability of overall dynamic body acceleration (ODBA) to predict mass-corrected oxygen consumption (sV O 2) in airbreathing diving vertebrates. We investigated a potential source of these discrepancies by partitioning the ODBA−sV O 2 relationship over 3 phases of the dive cycle (transiting to and from depth, bottom time, and post-dive surface interval). Trained Steller sea lions Eumetopias jubatus executed 4 types of dives to 40 m (single dives, long-duration dive bouts of 4−6 dives, short-duration dive bouts of 10 or 12 dives, and transit dives with minimal bottom duration). Partitioning single dives by dive phase showed differing patterns in the ODBA−sV O 2 relationship among dive phases, but no significant linear relationships were observed. The proportion of the dive cycle spent tran siting to and from the surface was a significant predictive factor in the ODBA−sV O 2 relationship, while bottom duration or post-dive surface interval had no effect. ODBA only predicted sV O 2 for dives when the proportion of time spent transiting was small. The apparent inability of ODBA to reliably predict sV O 2 reflects differences in the inherent relationships between ODBA and sV O 2 during different phases of the dive. These results support the growing body of evidence that ODBA on its own is not a reliable field predictor of energy expenditure at the level of the single dive or dive bout in air-breathing diving vertebrates likely because ODBA (a physical measure) cannot account for physiological changes in sV O 2 that occur during the different phases of a dive cycle.
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