Identifying leatherback turtle foraging behaviour from satellite telemetry using a switching state-space model
Bibliographic record
Abstract
Identifying the foraging habitat of marine predators is vital to understanding the ecology of these species and for their management and conservation. Foraging habitat for many marine predators is dynamic, and this poses a serious challenge for understanding how oceanographic features may shape the ecology of these animals. To help resolve this issue, we present a switching state-space model (SSSM) for discerning different movement behaviours hidden within error-prone satellite telemetry data. Along with modelling the movement dynamics, the SSSM estimates the probability that an animal is in a particular discrete behavioural mode, such as transiting or foraging. Using Argos satellite telemetry for leatherback sea turtles, we show that the SSSM readily identifies distinct classes of movement behaviour from the noisy data. Moreover, patterns in simultaneously collected diving data, to which the model is blind, match well with behavioural mode estimates. By combining behavioural mode estimates from the model with the diving data, we show that while transiting, leatherbacks make longer, deeper dives; and while foraging, they encounter cooler waters that range from 13 to 22C. These differences are consistent among the turtles studied and within the same turtle in different years. This modelling approach can enhance standard kernel density estimators for identifying habitat use by incorporating behavioural information into the estimation procedure. Ultimately, we can build predictive models of habitat use by incorporating environmental data and diving behaviour directly into the SSSM framework.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".