Classification of Animal Movement Behavior through Residence in Space and Time

Abstract

Identification and classification of behavior states in animal movement data can be complex, temporally biased, time-intensive, scale-dependent, and unstandardized across studies and taxa. Large movement datasets are increasingly common and there is a need for efficient methods of data exploration that adjust to the individual variability of each track. We present the Residence in Space and Time (RST) method to classify behavior patterns in movement data based on the concept that behavior states can be partitioned by the amount of space and time occupied in an area of constant scale. Using normalized values of Residence Time and Residence Distance within a constant search radius, RST is able to differentiate behavior patterns that are time-intensive (e.g., rest), time & distance-intensive (e.g., area restricted search), and transit (short time and distance). We use grey-headed albatross (Thalassarche chrysostoma) GPS tracks to demonstrate RST’s ability to classify behavior patterns and adjust to the inherent scale and individuality of each track. Next, we evaluate RST’s ability to discriminate between behavior states relative to other classical movement metrics. We then temporally sub-sample albatross track data to illustrate RST’s response to less resolved data. Finally, we evaluate RST’s performance using datasets from four taxa with diverse ecology, functional scales, ecosystems, and data-types. We conclude that RST is a robust, rapid, and flexible method for detailed exploratory analysis and meta-analyses of behavioral states in animal movement data based on its ability to integrate distance and time measurements into one descriptive metric of behavior groupings. Given the increasing amount of animal movement data collected, it is timely and useful to implement a consistent metric of behavior classification to enable efficient and comparative analyses. Overall, the application of RST to objectively explore and compare behavior patterns in movement data can enhance our fine- and broad- scale understanding of animal movement ecology.

Assessment of vessel disturbance to gray whales to inform sustainable ecotourism

“…However, it can be argued that time of season and prey availability are unlikely to influence behavior patterns between the 2 sites because of shared habitat characteristics, and because whales maintained similar activity budgets at each site. Animal behavior classification can be subjective and scale‐dependent (Ogburn et al ), yet the application of RST (Torres et al ) here allowed us to assess each track at an appropriate scale and assign behavior states objectively.…”

Short‐term prey field lability constrains individual specialisation in resource selection and foraging site fidelity in a marine predator

“…The radius r was determined with an automated dynamic scaling process for each foraging trip (Torres et al . ). Its average value was 0.52 ± 0.14 km (± SD).…”

“…We considered a foraging trip as a round trip between colony/ colony or colony/raft because sometimes shearwaters stopped at rafts localised within 4 km of the colony before starting a new trip. We identified the foraging locations of each trip using the residence in space and time method (Torres et al 2017). This method discriminates between behavioural states by calculating the amount of space and time occupied by an individual in an area of constant surface (a circle of radius r): Movement across long distances and protracted residency time within an area reflect foraging behaviour; short movement distances and long residency time reflect resting, and short distance and time correspond to travelling.…”

“…Foraging locations were determined using an automatic process discriminating between different behavioural modes (travelling, resting and foraging) along foraging trips (Torres et al . ).…”

Addressing Challenges in the Application of Animal Movement Ecology to Aquatic Conservation and Management

“…These efforts can address pressing questions such as how movement patterns scale with body size in different ecosystems. The recently developed method, Residence in Space and Time (RST; Torres et al, 2017), shows promise for such meta-analyses.…”