Using Surrogate Taxa to Inform Response Methods for Invasive Grass Carp in the Laurentian Great Lakes
Bibliographic record
Abstract
Abstract Sampling method decisions are critical for the effective monitoring and management of fisheries. Deploying the most effective sampling methodologies is particularly important when responding to new invasive species, where early response efforts have the best chances for eradication. In the Laurentian Great Lakes, the invasive Grass Carp Ctenopharyngodon idella is sampled using boat electrofishing and the combination method of boat electrofishing within and around a trammel net enclosure. We conducted a field study to compare the effectiveness of the two methods. We used capture data for surrogate taxa (i.e., Common Carp Cyprinus carpio and buffalo Ictiobus spp.) to compare the two methods because few Grass Carp were collected during the study. The sampling methods were compared within an occupancy modeling framework using an information-criteria model selection approach to evaluate seven alternative models. The base model included sampling method, year, water temperature, and sampling effort as covariates in the detection submodel and assumed that occupancy probability was constant across sites. The other six models built on the base model by including site, water body type (i.e., lentic vs. lotic), and interaction covariates in the detection submodel. The top-performing model, built on the base model, accounted for the influence of water body type and assumed the exchangeability of site effects in the detection submodel. The results indicated that the detection probabilities for both taxa were higher for the combination method than for boat electrofishing, with a median estimated difference in detection probability between the two methods of 0.11 (95% CI: 0.04–0.22) for Common Carp and 0.18 (95% CI: 0.08–0.28) for buffalo. Given that the combination method was more effective for detecting the surrogate taxa, we expect the combination method may be preferable to only boat electrofishing for Grass Carp removal.
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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 | 0.022 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".