Dear Colleagues, My coauthors and I are excited to share our new paper in Animal Biotelemetry describing an analytical method to accurately detect feeding using head-mounted accelerometry in California sea lions. The article is open access and can be found here: https://rdcu.be/czXAW Cole, M.R., Zeligs, J.A., Skrovan, S., and McDonald, B.I. Head-mounted accelerometry accurately detects prey capture in California sea lions. Anim Biotelemetry 9, 44 (2021). https://doi.org/10.1186/s40317-021-00267-7 Abstract: Detecting when and where animals feed is key to understanding their ecophysiology, but our ability to collect these data in marine mammals remains limited. Here, we test a tag-based accelerometry method to detect prey capture in California sea lions. From synchronized underwater video and acceleration data of two trained sea lions, we isolated a combined acceleration and Jerk pattern that reliably indicated prey capture in training datasets. We observed a stereotyped feeding motion in underwater video that included (1) mouth opening while approaching prey; (2) head deceleration to allow initial suction or prey engulfment, and (3) jaw closure. This motion (1–3) was repeated if a prey item was not initially engulfed. This stereotyped feeding motion informed a signal pattern phrase that accurately detected feeding in a training dataset. This phrase required (1) an initial heave-axis Jerk signal surpassing a threshold based on sampling rate; (2) an estimated dynamic surge-axis deceleration signal surpassing −0.7 g beginning within 0.2 s of the initial Jerk signal; and (3) an estimated dynamic surge-axis acceleration signal surpassing 1.0 g within 0.5 s of the beginning of the prior deceleration signal. We built an automated detector in MATLAB to identify and quantify these patterns. Blind tests of this detector on non-training datasets found high true-positive detection rates (91%– 100%) with acceleration sampled at 50–333 Hz and low false-positive detection rates (0%–4.8%) at all sampling rates (16–333 Hz). At 32 Hz and below, true-positive detection rates decreased due to attenuation of signal detail. A detector optimized for an adult female was also accurate at 32–100 Hz when tested on an adult male’s data, suggesting the potential future use of a generalized detector in wild subjects. When tested on the same data, a published triaxial Jerk method produced high true-positive detection rates (91–100%) and low-to-moderate false-positive detection rates (15–43%) at≥32 Hz. Using our detector, larger prey elicited longer prey capture duration in both animals at almost all sampling rates 32 Hz or faster. We conclude that this method can accurately detect feeding and estimate relative prey length in California sea lions. Please feel free to contact me at masonrcole@gmail.com if you have any questions. Cheers, Mason Cole -- Mason Cole, MSc Vertebrate Ecology Lab Moss Landing Marine Laboratories mcole@mlml.calstate.edu