Electronic tagging and tracking aquatic animals to understand a world increasingly shaped by a changing climate and extreme weather events
Bibliographic record
Abstract
Despite great promise for understanding the impacts and extent of climate change and extreme weather events on aquatic animals, their species, and ecological communities, it is surprising that electronic tagging and tracking tools, like biotelemetry and biologging, have not been extensively used to understand climate change or develop and evaluate potential interventions that may help adapt to its impacts. In this review, we provide an overview of methodologies and study designs that leverage available electronic tracking tools to investigate aspects of climate change and extreme weather events in aquatic ecosystems. Key interventions to protect aquatic life from the impacts of climate change, including habitat restoration, protected areas, conservation translocations, mitigations against interactive effects of climate change, and simulation of future scenarios, can all be greatly facilitated by using electronic tagging and tracking. We anticipate that adopting animal tracking to identify phenotypes, species, or ecosystems that are vulnerable or resilient to climate change will help in applying management interventions such as fisheries management, habitat restoration, invasive species control, or enhancement measures that prevent extinction and strengthen the resilience of communities against the most damaging effects of climate change. Given the scalability and increasing accessibility of animal tracking tools for researchers, tracking individual organisms will hopefully also facilitate research into effective solutions and interventions against the most extreme and acute impacts on species, populations, and ecosystems.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".