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Abstract Understanding animal movement often relies upon telemetry and biologging devices. These data are frequently used to estimate latent behavioural states to help understand why animals move across the landscape. While there are a variety of methods that make behavioural inferences from biotelemetry data, some features of these methods (e.g. analysis of a single data stream, use of parametric distributions) may limit their generality to reliably discriminate among behavioural states.To address some of the limitations of existing behavioural state estimation models, we introduce a nonparametric Bayesian framework called the mixed‐membership method for movement (M4), which is available within the open‐sourcebayesmoveR package. This framework can analyse multiple data streams (e.g. step length, turning angle, acceleration) without relying on parametric distributions, which may capture complex behaviours more successfully than current methods. We tested our Bayesian framework using simulated trajectories and compared model performance against two segmentation methods (behavioural change point analysis (BCPA) and segclust2d), one machine learning method [expectation‐maximization binary clustering (EMbC)] and one type of state‐space model [hidden Markov model (HMM)]. We also illustrated this Bayesian framework using movements of juvenile snail kitesRostrhamus sociabilisin Florida, USA.The Bayesian framework estimated breakpoints more accurately than the other segmentation methods for tracks of different lengths. Likewise, the Bayesian framework provided more accurate estimates of behaviour than the other state estimation methods when simulations were generated from less frequently considered distributions (e.g. truncated normal, beta, uniform). Three behavioural states were estimated from snail kite movements, which were labelled as ‘encamped’, ‘area‐restricted search’ and ‘transit’. Changes in these behaviours over time were associated with known dispersal events from the nest site, as well as movements to and from possible breeding locations.Our nonparametric Bayesian framework estimated behavioural states with comparable or superior accuracy compared to the other methods when step lengths and turning angles of simulations were generated from less frequently considered distributions. Since the most appropriate parametric distributions may not be obvious a priori, methods (such as M4) that are agnostic to the underlying distributions can provide powerful alternatives to address questions in movement ecology.
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This content will become publicly available on November 14, 2026
Method selection and temporal scale greatly influence ecological inferences on estimated animal behavioral states
Abstract BackgroundAnimal movement data are increasingly used to make ecological inferences, as well as to inform conservation and management actions. While advanced statistical methods to estimate behavioral states from these datasets have become widely available, the large number to choose from may make it difficult for practitioners to decide which method best addresses their needs. To guide decisions, we compared the behavioral state estimates and inferences from three methods (movement persistence models [MPM], hidden Markov models [HMM], and mixed-membership method for movement [M4]) when analyzing animal telemetry data. Tracks of post-breeding adult male green sea turtles (Chelonia mydas) were treated as an empirical example for this method comparison. The effect of temporal scale on behavioral state estimates was also investigated (at 1, 4, and 8 h time steps). ResultsThe HMM and M4 models produced relatively similar behavioral state estimates (compared to the MPM) and estimated anywhere from three to five states depending on the time interval of the tracks and the method used. Likewise, for all three methods, sampling movement at coarser time scales smoothed estimates of behavioral transitions. Additionally, the selection of movement metrics for analysis by the HMM and M4 also appeared to be a critical decision regarding state estimation and interpretation. At the longest time step (8 h), all three models were able to distinguish area-restricted search (ARS) behavior from migratory behavior, with greater nuance estimated by the HMM and M4 methods. By comparison, the MPM was the only model that was able to identify fine-scale behavioral patterns when analyzing the shortest time step (1 h). Moreover, the analysis of tracks with short time steps via MPM identified likely periods of resting during long-distance migration, which had only previously been hypothesized in green turtles. ConclusionsWhile there is no single best method to estimate behavioral states, our findings demonstrate that results can vary widely among different statistical methods and that model assumptions should be thoroughly checked during the model fitting process to reduce any potential biases. Thus, practitioners should carefully consider which methods best address their needs while also accounting for the inherent properties of their telemetry dataset.
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- Award ID(s):
- 2126583
- PAR ID:
- 10648562
- Publisher / Repository:
- Springer Nature
- Date Published:
- Journal Name:
- Animal Biotelemetry
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 2050-3385
- Page Range / eLocation ID:
- 39
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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