Performance of a fine-scale acoustic positioning system for monitoring temperate fish behavior in relation to offshore marine developments
Bibliographic record
Abstract
Rapid global expansion of offshore wind farms, tidal, and wave technologies signifies a new era of renewable energy development. While a promising means to combat the impacts of climate change, such developments necessitate fine-scale monitoring of biological communities to determine impacts associated with construction, operation, and eventual decommission. Here, we evaluate the performance of a gridded, Innovasea Systems, Inc. fine-scale acoustic telemetry positioning system (FSPS, n = 20 acoustic receivers) for tracking behaviors of diverse, temperate fish assemblages in relation to a subsea cable route supporting the Ørsted offshore wind development in coastal New York. We examined array performance through positioning error derived from receiver reference transmitters and tracked animals ( n = 260) comprising 17 species of teleost and elasmobranch. We evaluated the effects of environmental variables (temperature, tilt, noise, and depth), transmitter power, individual movement rates, and receiver loss on horizontal positioning error (HPE) and route mean squared error (RMSE). Across a 16-month deployment period, many positions were derived for Atlantic sturgeon ( n = 2,612), black sea bass ( n = 9,175), clearnose skate ( n = 10,306), summer flounder ( n = 13,304), and little skate ( n = 15,186), suggesting that these species may serve as sentinel candidates for assessing behavioral changes following construction, operation, and decommission. We found that receivers placed at the boundary of the grid exhibited higher HPE and RMSE, however these errors did not significantly change despite large receiver losses (25%). Generalized Linear Models revealed that temperature, noise, tilt, and depth were often significant predictors of HPE and RMSE, however, a substantial amount of variance was not explained by the models (~ 70%). Average movement rates ranged from 1.1 m s −1 (common thresher shark) to 0.03 m s −1 (little skate and summer flounder) but had minimal effects on positioning error. Finally, we observed that higher transmitter powers (158 dB) may lead to higher and more variable HPE values. Overall, these findings provide new insight into the drivers of FSPS array performance and illustrate their broad utility for monitoring fish behavior associated with offshore marine developments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".