Forecasting trajectories of Southern Resident killer whales with stochastic movement models incorporating direction modification
Bibliographic record
Abstract
Animal movement forecasting is a novel area in ecological modelling that can significantly aid marine coastal management and facilitate timely conservation actions, particularly for endangered species. Animal movement modelling research has focused largely on statistical inference for long-term spatial distributions of animal movement and changes in behavioural states, with relatively little attention being given to short-term animal movement forecasting. We propose a straightforward forecasting framework that employs a continuous-time Ornstein–Uhlenbeck (O–U) velocity process as the foundation for a movement forecast system. Specifically, we incorporate a direction modification method to ensure directional persistence and to guide the movement towards preferred historical locations and pathways. We demonstrate our forecasting methods using movement data from 11 years of Southern Resident killer whale (SRKW) movement data. We evaluate its forecasting performance on a historical trajectory of the SRKW. The resultant ensemble forecast outcome defines a 90% probability region indicating the most likely region where animals may be found for a specific forecast horizon. Our stochastic dynamic framework successfully predicts an SRKW trajectory up to three hours ahead with incoming observations covered by our 90% probability regions. This shows the approach is suitable for our conservation objectives of using short-term SRKW forecasts to aid in dynamic management of marine traffic and to reduce whale-vessel interactions. Importantly, our forecasting framework is versatile and can be readily applied to a wide range of animal species, provided there is a historical trajectory database available. It can be initiated with observed locations and conducted in real time for ecological management plans, and it can also be integrated into data-assimilative forecasting.
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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 | 0.000 | 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".