What is the animal doing? Tools for exploring behavioural structure in animal movements

Abstract

Movement data provide a window - often our only window - into the cognitive, social and biological processes that underlie the behavioural ecology of animals in the wild. Robust methods for identifying and interpreting distinct modes of movement behaviour are of great importance, but complicated by the fact that movement data are complex, multivariate and dependent. Many different approaches to exploratory analysis of movement have been developed to answer similar questions, and practitioners are often at a loss for how to choose an appropriate tool for a specific question. We apply and compare four methodological approaches: first passage time (FPT), Bayesian partitioning of Markov models (BPMM), behavioural change point analysis (BCPA) and a fitted multistate random walk (MRW) to three simulated tracks and two animal trajectories - a sea lamprey (Petromyzon marinus) tracked for 12 h and a wolf (Canis lupus) tracked for 1 year. The simulations - in which, respectively, velocity, tortuosity and spatial bias change - highlight the sensitivity of all methods to model misspecification. Methods that do not account for autocorrelation in the movement variables lead to spurious change points, while methods that do not account for spatial bias completely miss changes in orientation. When applied to the animal data, the methods broadly agree on the structure of the movement behaviours. Important discrepancies, however, reflect differences in the assumptions and nature of the outputs. Important trade-offs are between the strength of the a priori assumptions (low in BCPA, high in MRW), complexity of output (high in the BCPA, low in the BPMM and MRW) and explanatory potential (highest in the MRW). The animal track analysis suggests some general principles for the exploratory analysis of movement data, including ways to exploit the strengths of the various methods. We argue for close and detailed exploratory analysis of movement before fitting complex movement models.

Remote monitoring of vigilance behavior in large herbivores using acceleration data

“…Not only the proportion but also the temporal sequence of specific structural behaviors promises to hold important information for discriminating these more complex states. The wide array of techniques being developed for state detection in movement ecology [4] might provide helpful tools to process this extended set of parameters.…”

“…So far, internal states were always deduced from movement pattern [2][3][4], physiological measurements [32] or activity measurements [11,33,34]. Combining these with behavioral parameters could substantially improve state detection, because fine-resolution behavioral data facilitate plausible interpretation and, thereby, detailed discrimination of states.…”

“…Starting in the 1990s, the use of GPS telemetry stimulated substantial progress in animal ecology [1]. By analyzing animal trajectories, researchers also started to gain information about animal behavior that was driving the observed movement pattern [2][3][4].…”

On-Board Video Recording Unravels Bird Behavior and Mortality Produced by High-Speed Trains

“…Added to these resources, would be preparation time for field material and datasets (total of around 24 person-days). In short, on-board recording systems can enable much more efficient data collection than traditional methods based on direct personal observation (83 vs. 216 person-days in this example), underscoring the current trend of increasing reliance on information technology for ecological studies (Tomkiewicz et al, 2010;Galliard et al, 2012;Gurarie et al, 2016;Price and Schmitz, 2016). In our case, automation not only saved labor costs but also enabled us to broaden the study beyond what is possible with direct fieldwork to the more than 300 km that comprise the entire rail line.…”

Modelling Animal Activity as Curves: An Approach Using Wavelet-Based Functional Data Analysis

“…Temporal activity of animals is the result of behavioral responses to external (environmental) factors, such as availability of food resources and predation risk, and the internal states of individuals, such as nutritional condition, aversion to risk and reproductive drive [1] [2] [3] [4]. The constraints imposed by external factors and internal states dictate that animal temporal activity pattern in terrestrial, aquatic, and aerial environments is interrupted by pauses, angle turnings, and changes in speed [5] [6] [7] [8] [9].…”