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Integrating multiple technologies to understand the foraging behaviour of Hawaiian monk seals

View Article: PubMed Central - PubMed

ABSTRACT

The objective of this research was to investigate and describe the foraging behaviour of monk seals in the main Hawaiian Islands. Specifically, our goal was to identify a metric to classify foraging behaviour from telemetry instruments. We deployed accelerometers, seal-mounted cameras and GPS tags on six monk seals during 2012–2014 on the islands of Molokai, Kauai and Oahu. We used pitch, calculated from the accelerometer, to identify search events and thus classify foraging dives. A search event and consequent ‘foraging dive’ occurred when the pitch was greater than or equal to 70° at a depth less than or equal to −3 m. By integrating data from the accelerometers with video and GPS, we were able to ground-truth this classification method and identify environmental variables associated with each foraging dive. We used Bayesian logistic regression to identify the variables that influenced search events. Dive depth, body motion (mean overall dynamic body acceleration during the dive) and proximity to the sea floor were the best predictors of search events for these seals. Search events typically occurred on long, deep dives, with more time spent at the bottom (more than 50% bottom time). We can now identify where monk seals are foraging in the main Hawaiian Islands (MHI) and what covariates influence foraging behaviour in this region. This increased understanding will inform management strategies and supplement outreach and recovery efforts.

No MeSH data available.


Related in: MedlinePlus

Screen shot of the animated pitch metric (electronic supplementary material, S1). This shows the movement axes from the Open Tag with concurrent video footage highlighting a peak in pitch as the animal searches for prey. The top graph shows Depth versus Time over the course of a dive and the bottom graph shows Pitch versus Time. All peaks in the pitch axis that occurred deeper than −3 m were recorded as search events. See electronic supplementary material, S1 for the full animation of this dive showing depth, pitch and concurrent video footage.
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RSOS160703F3: Screen shot of the animated pitch metric (electronic supplementary material, S1). This shows the movement axes from the Open Tag with concurrent video footage highlighting a peak in pitch as the animal searches for prey. The top graph shows Depth versus Time over the course of a dive and the bottom graph shows Pitch versus Time. All peaks in the pitch axis that occurred deeper than −3 m were recorded as search events. See electronic supplementary material, S1 for the full animation of this dive showing depth, pitch and concurrent video footage.

Mentions: We identified 3138 dives on the Open Tag for the six seals. Five of these seals had concurrent video footage, but the time stamps for one Crittercam malfunctioned and hence video matching was possible only for four seals. Ninety-three dives (3%) were matched to Crittercam footage to validate the pitch metric (figure 3; electronic supplementary material, S1). Two seals had one false-positive prediction each; the other two seals had a number of false negatives, but no false positives. Overall, the combination of pitch and depth was 78% successful at predicting dives that contained search events for monk seals (table 3). According to this metric, search events occurred on most dives, but some seals did spend more time foraging in particular areas. For example, figure 4 shows that although most dives for this animal contained search events (size of the circles), it spent more time actively foraging on dives that occurred on the edge of Penguin Bank.Figure 3.


Integrating multiple technologies to understand the foraging behaviour of Hawaiian monk seals
Screen shot of the animated pitch metric (electronic supplementary material, S1). This shows the movement axes from the Open Tag with concurrent video footage highlighting a peak in pitch as the animal searches for prey. The top graph shows Depth versus Time over the course of a dive and the bottom graph shows Pitch versus Time. All peaks in the pitch axis that occurred deeper than −3 m were recorded as search events. See electronic supplementary material, S1 for the full animation of this dive showing depth, pitch and concurrent video footage.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5383815&req=5

RSOS160703F3: Screen shot of the animated pitch metric (electronic supplementary material, S1). This shows the movement axes from the Open Tag with concurrent video footage highlighting a peak in pitch as the animal searches for prey. The top graph shows Depth versus Time over the course of a dive and the bottom graph shows Pitch versus Time. All peaks in the pitch axis that occurred deeper than −3 m were recorded as search events. See electronic supplementary material, S1 for the full animation of this dive showing depth, pitch and concurrent video footage.
Mentions: We identified 3138 dives on the Open Tag for the six seals. Five of these seals had concurrent video footage, but the time stamps for one Crittercam malfunctioned and hence video matching was possible only for four seals. Ninety-three dives (3%) were matched to Crittercam footage to validate the pitch metric (figure 3; electronic supplementary material, S1). Two seals had one false-positive prediction each; the other two seals had a number of false negatives, but no false positives. Overall, the combination of pitch and depth was 78% successful at predicting dives that contained search events for monk seals (table 3). According to this metric, search events occurred on most dives, but some seals did spend more time foraging in particular areas. For example, figure 4 shows that although most dives for this animal contained search events (size of the circles), it spent more time actively foraging on dives that occurred on the edge of Penguin Bank.Figure 3.

View Article: PubMed Central - PubMed

ABSTRACT

The objective of this research was to investigate and describe the foraging behaviour of monk seals in the main Hawaiian Islands. Specifically, our goal was to identify a metric to classify foraging behaviour from telemetry instruments. We deployed accelerometers, seal-mounted cameras and GPS tags on six monk seals during 2012–2014 on the islands of Molokai, Kauai and Oahu. We used pitch, calculated from the accelerometer, to identify search events and thus classify foraging dives. A search event and consequent ‘foraging dive’ occurred when the pitch was greater than or equal to 70° at a depth less than or equal to −3 m. By integrating data from the accelerometers with video and GPS, we were able to ground-truth this classification method and identify environmental variables associated with each foraging dive. We used Bayesian logistic regression to identify the variables that influenced search events. Dive depth, body motion (mean overall dynamic body acceleration during the dive) and proximity to the sea floor were the best predictors of search events for these seals. Search events typically occurred on long, deep dives, with more time spent at the bottom (more than 50% bottom time). We can now identify where monk seals are foraging in the main Hawaiian Islands (MHI) and what covariates influence foraging behaviour in this region. This increased understanding will inform management strategies and supplement outreach and recovery efforts.

No MeSH data available.


Related in: MedlinePlus