Accuracy of calculated speeds from vessel monitoring system data: Considerations for fisheries management
Bibliographic record
Abstract
ABSTRACT Objective Vessel monitoring systems (VMSs) transmit positional information on vessels via satellite, and these data are increasingly used by researchers to study and assess fishing activity, where vessel speeds are typically used to identify and categorize fishing from nonfishing activity. In Atlantic Canada, vessel speeds are typically not transmitted with VMS data, requiring speeds to be calculated; this involves assuming a straight-line displacement between successive vessel positions and then dividing this distance by time. Problematically, the calculated speed will underestimate the true speed, with the extent of this bias depending on the time between successive records (i.e., the polling interval) and the true underlying movement path. We aim to demonstrate the impact that the VMS polling interval can have on the accuracy of calculated vessel speeds and fishing activity metrics for a fishery with complex movement patterns: the fishery for sea scallop Placopecten magellanicus in the Bay of Fundy, Canada. Our results are discussed in the context of VMS-related considerations for fisheries managers. Methods Through engagement with commercial fishers, we obtained a 1-min GPS tracking data set that describes fine-scale vessel movement of scallop fishing in the Bay of Fundy. Using this 1-min GPS data set, we assessed how changes in the polling interval can influence the performance of speed ranges used to classify fishing activity and quantify the effect of the polling interval on calculated speeds and fishing activity metrics of trip distance, swept area, and the fishing spatial footprint. Results Relatively small increases in the polling interval resulted in marked declines in the accuracy of calculated vessel speeds and derived fishing activity metrics. At the current VMS polling interval of 1 h mandated for scallop fishing in the Bay of Fundy, estimates of trip distance, swept area, and the fishing spatial footprint of individual trips significantly underestimated their true values (27, 25, and 43% of the true values, respectively). However, shorter polling intervals only improved accuracy when the polling interval was less than 35 min. The substantial tortuosity in a scallop vessels path reflects high-frequency movements that are difficult to capture and accurately reconstruct without similarly high-frequency sampling, which in the case of scallop fishing would be polling intervals of approximately 10 min or less. Conclusions The VMS polling interval relative to the frequency of vessel movement affects the accuracy of calculated speeds and metrics of fishing activity. The current 1-h VMS polling interval appears inadequate for resolving the true movement of sea scallop fishing vessels in the Bay of Fundy. However, increased polling does not always improve the accuracy of calculated speeds or fishing activity metrics; therefore, more data is not necessarily better. Although shorter polling intervals may improve the accuracy of vessel tracks and calculated speeds, this would come at an increased cost for fishers since the cost model for VMS is borne by fishers and they pay as a function of the polling frequency. Policy intervention to require instantaneous speed transmission with VMS could benefit future analyses.
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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 | 0.012 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".