Enhancing catch‐and‐release science with biotelemetry
Bibliographic record
Abstract
Abstract Catch‐and‐release (C&R) angling is widely practised by anglers and is a common fisheries management strategy or is a by‐product of harvest regulations. Accordingly, there is a growing body of research that examines not only the mortality associated with C&R, but also the sublethal physiological and behavioural consequences. Biotelemetry offers a powerful means of remotely monitoring the behaviour, physiology and mortality of fish caught and released in their natural environment, but we contend that its usefulness is still underappreciated by scholars and managers. In this study, we review the applications of biotelemetry in C&R science, identify novel research directions, opportunities and challenges. There are now about 250 C&R studies but only one quarter of these utilize biotelemetry. In fact, almost all of the C&R studies that have used biotelemetry have been conducted within the last decade. We found that the majority of C&R telemetry studies used either radio or acoustic telemetry, while comparatively few studies have used satellite technologies. Most C&R biotelemetry studies have been used to assess mortality rates, behavioural impairments or to evaluate the effects of displacement on fish. A small fraction of studies (<8%) have used physiological sensors despite the fact that these tools are highly applicable to understanding the multiple sublethal consequences of C&R and are useful for providing mechanistic insights into endpoints such as death. We conclude that C&R science has the potential to benefit greatly from biotelemetry technology, particularly with respect to providing more robust short‐term and delayed mortality estimates and adopting a more integrative and comparative approach to understanding the lethal and sublethal impacts of C&R. However, there are still a number of challenges including (i) the need for appropriate controls and methodological approaches, (ii) the need for accounting for tagging and handling stress and mortality, and (iii) the need for certainty in assessing mortality. However, the benefits associated with C&R biotelemetry outweigh its disadvantages and limitations and thereby offer C&R researchers a suite of new tools to enhance fisheries management and conservation.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".