Characterizing change points and continuous transitions in movement behaviours using wavelet decomposition
Bibliographic record
Abstract
Summary Individual behaviour, that is, the reaction of an organism to internal state, conspecifics and individuals of other species as well as the environment, is a crucial building block of their ecology. Modern tracking techniques produce high‐frequency observations of spatial positions of animals and accompanying speed and tortuosity measurements. However, inferring behavioural modes from movement trajectories remains a challenge. Changes in behavioural modes occur at different temporal and spatial scales and may take two forms: abrupt, representing distinct change points; or continuous, representing smooth transitions between movement modes. The multi‐scale nature of these behavioural changes necessitates development of methods that can pinpoint behavioural states across spatial and temporal scales. We propose a novel segmentation method based on the discrete wavelet transform (DWT), where the movement signal is decomposed into low‐frequency approximation and high‐frequency detail sub‐bands to screen for behavioural changes at multiple scales. Approximation sub‐bands characterizes broad changes by taking the continuous variations between behavioural modes into account, whereas detail sub‐bands are employed to detect abrupt, finer scale change points. We tested the ability of our method to identify behavioural modes in simulated trajectories by comparing it to three state‐of‐the‐art methods from the literature. We further validated the method using an annotated dataset of turkey vultures ( Cathartes aura ) relating extracted segments to the expert knowledge of migratory vs. non‐migratory patterns. Our results show that the proposed DWT segmentation is more versatile than other segmentation methods, as it can be applied to different movement parameters, performs better or equally well on the simulated data, and correctly identifies behavioural modes identified by the experts. It is hence a valuable addition to the toolbox of land managers and conservation practitioners to understand the behavioural patterns expressed by animals in natural and human‐dominated landscapes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".