Efficient statistical inference of turning points in animal movement data

Recent years have seen a proliferation of high‐frequency animal movement data, often at greater than 1 Hz, allowing us to gain much greater insight into behaviour than with lower frequency data. In particular, it is becoming possible to detect the precise points at which animals are making decisions to turn, thus placing the idea that the animals move in ‘steps and turns’ onto rigorous grounding. Despite this, current efforts to ascertain the points at which animals turn tend to rely on the user making pre‐determined choices of certain model parameter values. Furthermore, whilst they may give good results, there is often no theory explaining why the inferred turning points are most likely to be correct, for example by maximising a likelihood function. Here, we propose a theoretically grounded statistical technique to find turning points in high‐frequency movement data that does not require any a priori choices of parameter values. By testing our algorithm on simulated data, we show that our technique is both fast (e.g. 3 s to parse 106 data points) and accurate. For example, when the standard deviation of the noise is less than around π12 radians then our algorithm correctly identifies nearly 100% of the turning points, providing the noise is not heavily autocorrelated. Additionally, we demonstrate the effectiveness of our technique on magnetometer data from free‐ranging Arabian oryx (Oryx leucoryx). Overall, our work gives a fast, accurate and statistically grounded algorithm for turning point detection in high‐frequency data. The resulting model of straight‐line steps and turns provides a biologically meaningful summary of the animal's movement behaviour, which has potential to be used as an input to the wide range of step‐and‐turn techniques used in movement ecology, such as step selection analysis and hidden Markov models of behavioural states.

• Publication year

  2025

• Venue

  Methods in Ecology and Evolution

• Publication date

  2025-12-12

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1111/2041-210x.70221
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Bayesian inference for velocity-jump models for movement

  2025cited by this paper
• FInCH: Fast statistical inference for continuous‐time animal movement

  2025cited by this paper
• Flexible hidden Markov models for behaviour-dependent habitat selection

  2022cited by this paper
• Big-data approaches lead to an increased understanding of the ecology of animal movement

  2022cited by this paper
• Why did the animal turn? Time-varying step selection analysis for inference between observed turning points in high frequency data

  2020cited by this paper
• Evaluating Contributions of Recent Tracking-Based Animal Movement Ecology to Conservation Management

  2020cited by this paper
• Optimising the use of bio-loggers for movement ecology research.

  2019cited by this paper
• Finding turning‐points in ultra‐high‐resolution animal movement data

  2018influential reference
• Fast nonconvex deconvolution of calcium imaging data.

  2018cited by this paper
• Wildlife biology, big data, and reproducible research

  2018cited by this paper
• EXACT SPIKE TRAIN INFERENCE VIA ℓ0 OPTIMIZATION.

  2017cited by this paper
• Most Recent Changepoint Detection in Panel Data

  2016cited by this paper
• Migration, Prospecting, Dispersal? What Host Movement Matters for Infectious Agent Circulation?

  2016cited by this paper
• Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges

  2016cited by this paper
• moveHMM: an R package for the statistical modelling of animal movement data using hidden Markov models

  2016cited by this paper
• Changepoint Detection in the Presence of Outliers

  2016cited by this paper
• Integrated step selection analysis: bridging the gap between resource selection and animal movement

  2015cited by this paper
• Step by step: reconstruction of terrestrial animal movement paths by dead-reckoning

  2015cited by this paper
• On optimal multiple changepoint algorithms for large data

  2014cited by this paper
• A robust approach for estimating change-points in the mean of an AR(1) process

  2014cited by this paper
• Wild binary segmentation for multiple change-point detection

  2014cited by this paper
• Segmentor3IsBack: an R package for the fast and exact segmentation of Seq-data

  2012cited by this paper
• Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions.

  2012cited by this paper
• Efficient change point detection for genomic sequences of continuous measurements

  2011cited by this paper
• Turn designation, sampling rate and the misidentification of power laws in movement path data using maximum likelihood estimates

  2011cited by this paper
• Optimal detection of changepoints with a linear computational cost

  2011cited by this paper
• A movement ecology paradigm for unifying organismal movement research

  2008cited by this paper
• Hidden Markov model segmentation of hydrological and enviromental time series

  2002cited by this paper
• Least‐squares Estimation of an Unknown Number of Shifts in a Time Series

  2000cited by this paper
• Quantifying insect movement in the field

  1991cited by this paper

• No citing papers are available for this paper.