Predicting aquatic animal movements and behavioural states from acoustic telemetry arrays
Bibliographic record
Abstract
Abstract Fine‐scale tracking with passive acoustic telemetry can yield great insights into the movement ecology of aquatic animals. To predict fine‐scale positions of tagged animals in continuous space from spatially‐discrete detection data, state‐space modelling through the R package YAPS provides a promising alternative to frequently used positioning algorithms. However, YAPS cannot currently classify multiple kinds of movement that may be used as proxies for individual behaviours of study animals (behavioural states), an endeavour that is of increasing interest to movement ecologists. We advance YAPS by incorporating the functionality to predict behavioural states by using an iterative maximization framework. Our model, which we call YAMS, occurs in continuous time and therefore we adapt current hidden Markov model (HMM) machinery to accommodate this while remaining within a likelihood framework that provides rapid fitting. We test our model using simulations and approximately 6 days’ worth of Northern pike data from Hald Lake, Denmark. YAMS is shown to produce accurate parameter estimates and random effect predictions when model results were compared to simulated data, with behavioural state accuracies of 0.94 and 0.79 for two‐ and three‐state models, respectively, and location state root mean squared errors of 1.8 m for both models. In addition, the behavioural states are shown to reflect varying speeds of the pike, yielding a highly interpretable classification. This research has the potential to be broadly applicable to both ecologists interested in identifying fine‐scale space use and behavioural states from acoustic telemetry data, as well as to statisticians who may wish to use standard HMM machinery to fit continuous‐time HMMs to animal movement data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".