Putting the health in hidden Markov models: incorporating allostatic load indices into movement ecology analyses
Bibliographic record
Abstract
Individual animal health assessments are a key consideration for conservation initiatives. Environmental shifts associated with climate change, such as documented rises in pathogen emergence, predation pressures and human activities, create an increasingly stressful world for many species and have been linked with marked changes in movement behaviour. Even in healthy individuals, variations in allostatic load, the cumulative effects of long-term stress, may alter behavioural priorities over time. Here, we aimed to build links between animal health assessment information and movement ecology, using narwhals in the Canadian Arctic as a case study. A composite stress index was developed to incorporate multiple available health (e.g. health assessments), stress (e.g. hormones) and body condition metrics from clinically healthy individuals, and applied within the framework of widely used hidden Markov modelling of animal movement data. Individuals with a higher composite stress index tended to prioritize behaviours indicative of a stress response, including increasing the probability of transitioning to transiting behaviour as compared to those with a lower stress index. By incorporating a composite stress index that synthesizes multiple health indices in a flexible framework, we highlight that including information indicative of allostatic load may be important in explaining variation in behaviour, even for seemingly healthy animals. The modelling framework presented here highlights a flexible approach to incorporate health assessment information and provides an approach that is widely applicable to existing and future work on a range of species.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".