Summary of useable dives.

<p>Total useable dives (n = 193) with overlapping depth, video, and 3-axis accelerometer data per Australian fur seal. For cross-validation, each dive was randomly assigned to the training or testing subset (approximately 50% each). Dives with prey visible in video were classified as “prey present”, and dives with no prey visible on video were classified as “prey absent”. Prey chases without capture attempts on video were classified as “prey absent”.</p><p>Summary of useable dives.</p

Diagram of accelerometer data processing method.

<p>Example of accelerometer identification of attempted prey captures (APC) with variance of acceleration on the surge axis. Dive depth (A) was synchronized with raw acceleration (g, B) as evident by the bottom phase of the dive matching on A and B. Head movements were isolated from body movements with a 3 Hz high-pass filter (C) and variance of acceleration (g²) was calculated for each individual dive (excluding surface ≥ 2 m). Peaks in variance of acceleration above a variance threshold (——) and within a minimum time interval (i) were used to estimate APC (APC, D). Consecutive peaks greater than the minimum interval apart were counted as separate APC, and events less than the interval were counted as single APC. Duration of individual APC (indicated by brackets), integral area under the peak of variance (g², inset, cumulative integral of individual peaks in APC group), time of the first peak in each APC (<b>−</b>), and number of peaks per APC were also calculated.</p

Videos to BEH 3835

Video 1: Breaching behaviour by Mobula alfredi equipped with a Crittercam in Raa Atoll, Maldives.Video 2: Surface feeding behaviour by Mobula alfredi equipped with a Crittercam in Raa Atoll, Maldives.Video 3: Courtship behaviour as recorded Mobula alfredi equipped with a Crittercam in Raa Atoll, Maldives.Video 4: A reef manta ray (Mobula alfredi) equipped with a Crittercam approaches a cleaning station. Species of cleaner wrasse (Labroides dimidiatus, Thalassoma amblycephalum, Thalassoma Lunare) can be observed cleaning the mantas.Video 5: Cleaning behaviour by Clarion Angelfish (Holacanthus clarionensis) as recorded by Mobula birostris equipped with a Crittercam in Revillagigedo Archipelago, Mexico.Video 6: Social cruising behaviour as recorded by Mobula alfredi in Raa Atoll, Maldives.Video 7: Solitary cruising along the seabed by Mobula birostris in Revillagigedo Archipelago, Mexico.Video 8: Crittercam recording of spinetail devil rays (Mobula mobular) interacting with Mobula alfredi in Raa Atoll, Maldives.Video 9: A crittercam recording of trevally (Caranx ignobilis) interacting with Mobula alfredi in Raa Atoll, Maldives, possibly as an abrasive surface to scratch themselves to remove parasites.</p

Categorization of attempted prey captures (APC).

<p>Each APC was classified as true positive (TP), true negative (TN), false positive (FP), and false negative (FN) relative to the actual values on the animal-borne video.</p><p>Categorization of attempted prey captures (APC).</p

Summary of accelerometer error metrics relative to video data calculated on Australian fur seals.

<p>Error metrics included detection, false positive rate (FP rate), and precision as defined in the abbreviations. Animal-specific parameters were the parameters of minimum interval and variance threshold that yielded the greatest detection per animal per acceleration axis (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0128789#pone.0128789.g004" target="_blank">Fig 4</a>). Generic parameters (same for all animal) were set at 0.1variance threshold with 5 second minimum interval for all animals and acceleration axis. Data are from the Random Testing subset (n = 97 dives).</p><p>Summary of accelerometer error metrics relative to video data calculated on Australian fur seals.</p

Analysis of successful prey captures by accelerometers.

<p>The accelerometer was not able to distinguish between successful and unsuccessful attempted prey captures (APC) on any axis of acceleration. Successful events included video where prey was captured, and unsuccessful events included events where the fur seal chased and missed an attempted capture attempt of the prey on video. Neither mean event duration (A, D, G), or integral area under the APC (B, E, H) significantly varied between successful or unsuccessful APC. The number of peaks in surge marginally differed between types of APC (C), but not for sway (F) or heave (I). Results are from Function 1 optimized with generic parameters (0.1 variance and 5 s). Each data point represents an individual APC.</p

Selection of model parameters on training subset.

<p>Detection rate of Function 1 used to identify attempted prey captures (APC) during optimization on the training subset of data. Parameters with the greatest detection rate (*) on the training subset were used to test Function 1 on the other approximately 50% of the dives in the testing subset (termed animal-specific parameters). Each data point represents the mean detection over all APC per animal within the training subset for each parameter combination. Variance thresholds tested included 0.1 (open diamond), 0.2 (black circle), 0.4 (grey square), 0.8 (grey triangle; sway and heave only). Variance thresholds tested for the heave axis were lower than the surge and sway because heave acceleration had lower amplitude peaks.</p

Analysis of prey types by accelerometers.

<p>The accelerometer was not able to distinguish among prey types. Neither mean APC duration (A, D, G), integral area under the APC (B, E, G), or number of variance peaks per APC (C, F, I) varied among prey types on any axis of acceleration. Results are from Function 1 optimized with generic parameters (all same per animal). Each data point represents an individual APC.</p

Animal-Borne Imaging Reveals Novel Insights into the Foraging Behaviors and Diel Activity of a Large-Bodied Apex Predator, the American Alligator (<i>Alligator mississippiensis</i>)

<div><p>Large-bodied, top- and apex predators (e.g., crocodilians, sharks, wolves, killer whales) can exert strong top-down effects within ecological communities through their interactions with prey. Due to inherent difficulties while studying the behavior of these often dangerous predatory species, relatively little is known regarding their feeding behaviors and activity patterns, information that is essential to understanding their role in regulating food web dynamics and ecological processes. Here we use animal-borne imaging systems (Crittercam) to study the foraging behavior and activity patterns of a cryptic, large-bodied predator, the American alligator (<i>Alligator mississippiensis</i>) in two estuaries of coastal Florida, USA. Using retrieved video data we examine the variation in foraging behaviors and activity patterns due to abiotic factors. We found the frequency of prey-attacks (mean = 0.49 prey attacks/hour) as well as the probability of prey-capture success (mean = 0.52 per attack) were significantly affected by time of day. Alligators attempted to capture prey most frequently during the night. Probability of prey-capture success per attack was highest during morning hours and sequentially lower during day, night, and sunset, respectively. Position in the water column also significantly affected prey-capture success, as individuals’ experienced two-fold greater success when attacking prey while submerged. These estimates are the first for wild adult American alligators and one of the few examples for any crocodilian species worldwide. More broadly, these results reveal that our understandings of crocodilian foraging behaviors are biased due to previous studies containing limited observations of cryptic and nocturnal foraging interactions. Our results can be used to inform greater understanding regarding the top-down effects of American alligators in estuarine food webs. Additionally, our results highlight the importance and power of using animal-borne imaging when studying the behavior of elusive large-bodied, apex predators, as it provides critical insights into their trophic and behavioral interactions.</p></div

AIC values for Poisson GLMs, predicting prey-capture attempt frequency.

<p>AIC values for Poisson GLMs, predicting prey-capture attempt frequency.</p