Utility of time-lapse photography in studies of seabird ecology.

Marine ecosystems are heavily influenced by a wide range of human-related impacts, and thus monitoring is essential to preserve and manage these sensitive habitats. Seabirds are considered important bioindicators of the oceans, but accessing breeding populations can be difficult, expensive and time consuming. New technologies have been employed to facilitate data collection on seabirds that can reduce costs and minimize disturbance. Among these, the use of time-lapse photography is a potentially effective way to reduce researcher effort, while collecting valuable information on key ecological parameters. However, the feasibility of this approach remains uncertain. Here, we assessed the use of time-lapse photography as a tool for estimating foraging behaviour from breeding seabirds, and evaluate ways forward for this method. We deployed cameras in front of active nests at a colony of black-legged kittiwakes (Rissa tridactyla) during two breeding seasons, 5 nests in 2013 and 5 in 2014, taking pictures every 4 minutes. A subsample of monitored individuals were also equipped with accelerometers. Approximately 100,000 frames, covering incubation and chick-rearing periods, were analysed. Estimates of foraging trip duration from images were positively correlated with accelerometry estimates (R2 = 0.967). Equal partitioning of effort between pairs, predation events, nest attendance patterns and variation in trip metrics with breeding stage were also identified. Our results suggest that time-lapse photography is potentially a useful tool for assessing foraging trip duration and other fine-scale nesting ecology parameters as well as for assessing the effect of bio-logging devices on seabird foraging behaviour. Nevertheless, the time investment to manually extract data from images was high, and the process to set up cameras was not straightforward. To encourage wide use of time-lapse photography in seabird ecology, we thus provide guidelines for camera deployment and we suggest a need for further development of automated approaches to allow data extraction.

Interpreting behaviors from accelerometry: a method combining simplicity and objectivity.

Quantifying the behavior of motile, free-ranging animals is difficult. The accelerometry technique offers a method for recording behaviors but interpretation of the data is not straightforward. To date, analysis of such data has either involved subjective, study-specific assignments of behavior to acceleration data or the use of complex analyses based on machine learning. Here, we present a method for automatically classifying acceleration data to represent discrete, coarse-scale behaviors. The method centers on examining the shape of histograms of basic metrics readily derived from acceleration data to objectively determine threshold values by which to separate behaviors. Through application of this method to data collected on two distinct species with greatly differing behavioral repertoires, kittiwakes, and humans, the accuracy of this approach is demonstrated to be very high, comparable to that reported for other automated approaches already published. The method presented offers an alternative to existing methods as it uses biologically grounded arguments to distinguish behaviors, it is objective in determining values by which to separate these behaviors, and it is simple to implement, thus making it potentially widely applicable. The R script coding the method is provided.