A review of acoustic telemetry in Europe and the need for a regional aquatic telemetry network
Bibliographic record
Abstract
Globally, there are a large and growing number of researchers using biotelemetry as a tool to study aquatic animals. In Europe, this community lacks a formal network structure. The aim of this study is to review the use of acoustic telemetry in Europe and document the contribution of cross-boundary studies and inter-research group collaborations. Based on this, we explore the potential benefits and challenges of a network approach to identify future priorities and best practices for aquatic biotelemetry research in Europe. Over the past decade, there was an approximately sevenfold increase in the number of acoustic telemetry studies published on marine and diadromous species in Europe compared to a sixfold increase globally. Over 90% of these studies were conducted on fishes and undertaken in coastal areas, estuaries, or rivers. 75% of these studies were conducted by researchers based in one of five nations (Norway, UK, France, Portugal, and Spain) and, even though 34% were based on collaborations between scientists from several countries, there was only one study with an acoustic receiver array that extended beyond the borders of a single country. In recent years, acoustic telemetry in European waters has evolved from studying behavioural aspects of animals (82.2%), into more holistic approaches addressing management-related issues (10%), tagging methods and effects (5%), and technology and data analysis development (2.8%). Despite the increasing number of publications and species tracked, there is a prominent lack of planned and structured acoustic telemetry collaborations in Europe. A formal pan-European network structure would promote the development of (1) a research platform that could benefit the acoustic telemetry community through capacity building, (2) a centralized database, and (3) key deployment sites and studies on priority species requiring research in Europe. A network may increase efficiency, expand the scope of research that can be undertaken, promote European science integration, enhance the opportunities and success of acquiring research funding and, ultimately, foster regional and transatlantic collaborations. It may also help address research priorities such as the large-scale societal challenges arising from climate change impacts and assist the EU’s Marine Strategy Framework Directive via identification of good environmental status of endangered or commercially important 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".