Spatial coherence between predators and prey has rarely been observed in pelagic marine ecosystems. We used measures of the environment, prey abundance, prey quality, and prey distribution to explain the observed distributions of three co-occurring predator species breeding on islands in the southeastern Bering Sea: black-legged kittiwakes (Rissa tridactyla), thick-billed murres (Uria lomvia), and northern fur seals (Callorhinus ursinus). Predictions of statistical models were tested using movement patterns obtained from satellite-tracked individual animals. With the most commonly used measures to quantify prey distributions - areal biomass, density, and numerical abundance - we were unable to find a spatial relationship between predators and their prey. We instead found that habitat use by all three predators was predicted most strongly by prey patch characteristics such as depth and local density within spatial aggregations. Additional prey patch characteristics and physical habitat also contributed significantly to characterizing predator patterns. Our results indicate that the small-scale prey patch characteristics are critical to how predators perceive the quality of their food supply and the mechanisms they use to exploit it, regardless of time of day, sampling year, or source colony. The three focal predator species had different constraints and employed different foraging strategies – a shallow diver that makes trips of moderate distance (kittiwakes), a deep diver that makes trip of short distances (murres), and a deep diver that makes extensive trips (fur seals). However, all three were similarly linked by patchiness of prey rather than by the distribution of overall biomass. This supports the hypothesis that patchiness may be critical for understanding predator-prey relationships in pelagic marine systems more generally.

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.

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BackgroundWe sought to quantitatively describe the fine-scale foraging behavior of northern resident killer whales (Orcinus orca), a population of fish-eating killer whales that feeds almost exclusively on Pacific salmon (Oncorhynchus spp.). To reconstruct the underwater movements of these specialist predators, we deployed 34 biologging Dtags on 32 individuals and collected high-resolution, three-dimensional accelerometry and acoustic data. We used the resulting dive paths to compare killer whale foraging behavior to the distributions of different salmonid prey species. Understanding the foraging movements of these threatened predators is important from a conservation standpoint, since prey availability has been identified as a limiting factor in their population dynamics and recovery.ResultsThree-dimensional dive tracks indicated that foraging (N = 701) and non-foraging dives (N = 10,618) were kinematically distinct (Wilks’ lambda: λ 16 = 0.321, P < 0.001). While foraging, killer whales dove deeper, remained submerged longer, swam faster, increased their dive path tortuosity, and rolled their bodies to a greater extent than during other activities. Maximum foraging dive depths reflected the deeper vertical distribution of Chinook (compared to other salmonids) and the tendency of Pacific salmon to evade predators by diving steeply. Kinematic characteristics of prey pursuit by resident killer whales also revealed several other escape strategies employed by salmon attempting to avoid predation, including increased swimming speeds and evasive maneuvering.ConclusionsHigh-resolution dive tracks reconstructed using data collected by multi-sensor accelerometer tags found that movements by resident killer whales relate significantly to the vertical distributions and escape responses of their primary prey, Pacific salmon.Electronic supplementary materialThe online version of this article (doi:10.1186/s40462-017-0094-0) contains supplementary material, which is available to authorized users.

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Background: Biologgers incorporating triaxial magnetometers and accelerometers can record animal movements at infra-second frequencies. Such data allow the Dead-Reckoned (DR) path of an animal to be reconstructed at high resolution. However, poor measures of speed, undocumented movements caused by ocean currents, confounding between movement and gravitational acceleration and measurement error in the sensors, limits the accuracy and precision of DR paths. The conventional method for calculating DR paths attempts to reduce random errors and systematic biases using GPS observations without rigorous statistical justification or quantification of uncertainty in the derived swimming paths. Methods:We developed a Bayesian Melding (BM) approach to characterize uncertainty and correct for bias of DR paths. Our method used a Brownian Bridge process to combine the fine-resolution (but seriously biased) DR path and the sparse (but precise and accurate) GPS measurements in a statistically rigorous way. We also exploited the properties of underlying processes and some approximations to the likelihood to dramatically reduce the computational burden of handling large, high-resolution data sets. We implemented this approach in an R package "BayesianAnimalTracker", and applied it to bio-logging data obtained from northern fur seals (Callorhinus ursinus) foraging in the Bering Sea. We also tested the accuracy of our method using cross-validation analysis and compared it to the conventional bias correction of DR and linear interpolation between GPS observations (connecting two consecutive GPS observations by a straight line).Results: Our BM approach yielded accurate, high-resolution estimated paths with uncertainty quantified as credible intervals. Cross-validation analysis demonstrated the greater prediction accuracy of the BM method to reconstruct movements versus the conventional and linear interpolation methods. Moreover, the credible intervals covered the true path points albeit with probabilities somewhat higher than 95 %. The GPS corrected high-resolution path also revealed that the total distance traveled by the northern fur seals we tracked was 40-50 % further than that calculated by linear interpolation of the GPS observations.