Global trends in aquatic animal satellite telemetry studies
Bibliographic record
Abstract
Satellite telemetry has revolutionized the study of aquatic animal movement by enabling high-resolution tracking across vast spatial and temporal scales. Here we undertake a global systematic review of studies since 1982 to summarise state of knowledge by taxonomic group, sample size, life history stage studied, and tracking mode (i.e., archival vs. near real-time). We then classify studies according to defined research and management themes, highlight geographic trends aligned with FAO major fishing areas, and examine how these themes are distributed globally. Of a total of 1137 studies, encompassing over 30 000 tagged individuals across diverse aquatic taxa, mammals, fish, and reptiles were the most studied. Research has largely focused on marine systems, particularly in the northern Atlantic and Pacific, but freshwater ecosystems remain underrepresented. Most studies explored general movement patterns, with fewer addressing applied conservation topics such as movement barriers or protected area effectiveness. Overall, integration with complementary methods (e.g., genetic or physiological sampling) was limited. Addressing identified gaps in underrepresented taxa (e.g., invertebrates), regions (e.g., the Indian Ocean), and emerging topics (e.g., climate change responses) will be critical to fully realize the potential of satellite telemetry for conservation and management of aquatic biodiversity.
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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 | 0.020 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".