Estimating animal behavior and residency from movement data

Abstract

We present a process‐based approach to estimate residency and behavior from uncertain and temporally correlated movement data collected with electronic tags. The estimation problem is formulated as a hidden Markov model (HMM) on a spatial grid in continuous time, which allows straightforward implementation of barriers to movement. Using the grid to explicitly resolve space, location estimation can be supplemented by or based entirely on environmental data (e.g. temperature, daylight). The HMM method can therefore analyze any type of electronic tag data. The HMM computes the joint posterior probability distribution of location and behavior at each point in time. With this, the behavioral state of the animal can be associated to regions in space, thus revealing migration corridors and residence areas. We demonstrate the inferential potential of the method by analyzing satellite‐linked archival tag data from a southern bluefin tuna Thunnus maccoyii where longitudinal coordinates inferred from daylight are supplemented by latitudinal information in recorded sea surface temperatures.

Revealing the full ocean migration of individual Atlantic salmon

“…Individual spatial distributions were quantified for the first summer (time of ocean entry-August 6), autumn (August 7-November 6), winter (November 7-February 2), spring (February 3-May 4), and second summer (May 5-recapture date), by calculating the residency distributions (RD) for each season. RDs are the cumulative probability distribution across the spatial domain, thus providing an estimate of the spatial distribution including the uncertainty of the data [28]. Seasons were defined based on the annual solstice and equinoxes by dividing the time series depending on the cross-quarter moments (i.e.…”

Behavioural mapping of a pelagic seabird: combining multiple sensors and a hidden Markov model reveals the distribution of at-sea behaviour

“…For some behaviours, interpolation may not represent a realistic model of complex fine-scale movement between sampled locations. An alternative approach would have been to estimate location and behavioural state simultaneously within an integrated state-space framework [14,19], incorporating models of individual movement such as random walks, correlated random walks or Lévy walks. While such approaches may offer some advantages, they can be computationally more complex, and their performance may depend on the choice of appropriate movement models and their parameters.…”

“…However, behaviour may be inferred by characterizing patterns of movement based solely on the geometry or complexity of an animal's path using techniques such as tortuosity [8,9], positional entropy [10,11] or first-passage time [12]. In addition, modelling approaches such as Gaussian mixtures have been used to classify animal tracking data into discrete modes of movement [2,13], while state-space models including hidden Markov models (HMMs) [10,[14][15][16][17][18][19] have been used to identify different modes of movement and the dynamics of switching between them.…”

Behavioral attributes of turbine entrainment risk for adult resident fish revealed by acoustic telemetry and state-space modeling

“…The resulting estimates were used to investigate the relationship between adult bull trout behavior in the forebay of Kinbasket Reservoir and putative factors influencing their behavior. Behavior was characterized in terms of bull trout residence time and spatial distribution in the forebay, distance to the intakes and two behavioral states based on movement patterns: transiting (fast, directed movement) and exploratory (slow, undirected movement) [24,25]. Specifically, our objectives were to investigate (1) temporal (diel and seasonal) patterns in bull trout behavior; (2) the association between bull trout behavior and physical (that is, turbine operations, reservoir water elevation) and biological (that is, body temperature) factors; and (3) the behavior of fish preceding their entrainment (should entrainment be observed).…”