A model to illustrate the potential pairing of animal biotelemetry with individual-based modeling
Bibliographic record
Abstract
Abstract Background Animal biotelemetry and individual-based modeling (IBM) are natural complements, but there are few published examples where they are applied together to address fundamental or applied ecological questions. Existing studies are often found in the modeling literature and frequently re-use small datasets collected for purposes other than the model application. Animal biotelemetry can provide the robust measurements that capture relevant ecological patterns needed to parameterize, calibrate, and assess hypotheses in IBMs; together they could help meet demand for predictive modeling and decision-support in the face of environmental change. Results We used an simple exemplar IBM that uses spatio-temporal movement patterns of 103 acoustic-tagged juvenile yearling Chinook salmon ( Oncorhynchus tshawytscha ), termed ‘smolts’, to quantitatively assess plausibility of two migratory strategies that smolts are hypothesized to use while migrating north through the plume of the Columbia River (United States of America). We find that model smolts that seek to maximize growth demonstrate movement patterns consistent with those of tagged smolts. Model smolts that seek to move quickly out of the plume region by seeking favorable currents do not reproduce the same patterns. Conclusions Animal biotelemetry and individual-based modeling are maturing fields of inquiry. Our hope is that this model description and the basic analytical techniques will effectively illustrate individual-based models for the biotelemetry community, and perhaps inspire new collaborations between biotelemetry researchers and individual-based modelers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 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".