Abstract
A growing number of studies are using accelerometers to examine activity level patterns in aquatic animals. However, given the amount of data generated from accelerometers, most of these studies use loggers that archive acceleration data, thus requiring physical recovery of the loggers or acoustic transmission from within a receiver array to obtain the data. These limitations have restricted the duration of tracking (ranging from hours to days) and/or type of species studied (e.g., relatively sessile species or those returning to predictable areas). To address these logistical challenges, we present and test a satellite-transmitted metric for the remote monitoring of changes in activity, measured via a pop-off satellite archival tag (PSAT) with an integrated accelerometer. Along with depth, temperature, and irradiance for geolocation, the PSAT transmits activity data as a time-series (ATS) with a user-programmable resolution. ATS is a count of high-activity events, relative to overall activity/mobility during a summary period. An algorithm is used to identify the high-activity events from accelerometer data and reports the data as a count per time-series interval. Summary statistics describing the data used to identify high-activity events accompany the activity time-series. In this study, we first tested the ATS activity metric through simulating PSAT output from accelerometer data logger archives, comparing ATS to vectorial dynamic body acceleration. Next, we deployed PSATs with ATS under captive conditions with cobia (Rachycentron canadum). Lastly, we deployed seven pop-off satellite archival tags (PSATs) able to collect and transmit ATS in the wild on adult sandbar sharks (Carcharhinus plumbeus). In the captive trials, we identified both resting and non-resting behavior for species and used logistic regression to compare ATS values with observed activity levels. In captive cobia, ATS was a significant predictor of observed activity levels. For 30-day wild deployments on sandbar sharks, satellites received 57.4–73.2% of the transmitted activity data. Of these ATS datapoints, between 21.9 and 41.2% of records had a concurrent set of temperature, depth, and light measurements. These results suggest that ATS is a practical metric for remotely monitoring and transmitting relative high-activity data in large-bodied aquatic species with variable activity levels, under changing environmental conditions, and across broad spatiotemporal scales.
Highlights
Interpretation of animal movement patterns has been a central focus of ecological studies and is a critical component of modern conservation research [1, 2]
Captive deployments pop-off satellite tag (PSAT) deployments Of the four PSATs deployed on cobia, three tags dislodged prematurely from the fish after 1, 1.5, and 4.5 days, and one tag remained attached for the full 5 days
57.4–73.2% of Discussion The simulated activity metric compared with VeDBA As we anticipated, our simulation of activity time-series (ATS) from accelerometer data loggers reflected the timing of switches to relatively high values in the VeDBA time-series (Fig. 2)
Summary
Interpretation of animal movement patterns has been a central focus of ecological studies and is a critical component of modern conservation research [1, 2]. Given the challenges of directly observing the movements and associated behaviors of marine and freshwater animals under natural conditions, researchers have used biologging and Skubel et al Anim Biotelemetry (2020) 8:34 biotelemetry tools to monitor activity remotely. These methods provide a glimpse into the animals’ behavior in wild environments, without the burden of human presence for observation [3]. Similar combinations have been used to study where and when certain behaviors occur, such as mating or feeding [6,7,8]; to investigate biological drivers of movement patterns, such as circadian rhythms or behavioral thermoregulation [9, 10]; to identify impacts of human activity, such as post-release fishing mortality [11, 12] or provisioning for dive tourism [13]; and to measure field metabolic rates, infer thermal performance, and measure activity levels and their responses to environmental settings [14, 15]
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