Seagrass canopies and the performance of acoustic telemetry: implications for the interpretation of fish movements
Bibliographic record
Abstract
Abstract Background Acoustic telemetry has been used with great success to quantify the movements of marine fishes in open habitats, however research has begun to focus on patterns of movement and habitat usage within more structurally complex habitats. To date, there has been no detailed assessment of the performance of acoustic telemetry within seagrass, which forms a crucial nursery and foraging habitat for many fish species globally. Information on the detection range of acoustic receivers within seagrass is essential to guide receiver array design, particularly positioning systems. Here, we compare detection ranges for transmitters (Vemco V7) within and above the seagrass to determine impacts on the performance of a Vemco Positioning System (VPS). We also investigate the influence of environmental conditions (i.e. wind, time of day, background noise, atmospheric pressure and depth) on detection probability. Results The performance of the VPS declined dramatically when the transmitters were positioned within the seagrass (positional accuracy = 2.69 m, precision = 0.9 m, system efficiency (i.e. the proportion of successful positions) = 5.9%) compared to above the canopy (positional accuracy = 2.21 m, precision = 0.45 m, system efficiency = 30.9%). The reduction in VPS efficiency when transmitters were within seagrass was caused by a decline in the detection range of receivers (range of 50% detections) from 85 to 40 m, as this limited the ability of the three receivers to simultaneously detect transmissions. Additionally, no detections were recorded for the transmitters within seagrass at a distance greater than 150 m from the receiver. Increasing wind speed from 0 to 50 km h −1 correlated with a 15% reduction in detections while detection probability decreased from 0.8 during the day to 0.55 at night, due to higher in-band noise (69 kHz). Conclusions Our findings demonstrate that tagged fish ensconced within seagrass are unlikely to be detected by receivers or positioned by a VPS. Further, we demonstrate that wind conditions and the time of day create temporal variation in detection probability. These findings highlight the need for telemetry studies to perform in situ range testing and consider how fish use vegetated habitats such as seagrasses when positioning receivers and interpreting data.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".