Optimal methods for sampling Tench (Tinca tinca) in its introduced range
Bibliographic record
Abstract
Monitoring the occurrence and abundance of aquatic invasive species is a significant challenge for managing freshwater ecosystems, given limited resources.Efficient sampling strategies are essential for early detection and accurate population assessment.Here, we sought to identify the most effective methods and site-selection strategies for sampling an introduced riverine population of Tench (Tinca tinca), a globally invasive fish.A comparative analysis of four fishing methods and deployment conditions revealed fyke nets and gill nets as the most effective gears for capturing numbers of Tench, particularly in areas with a speciose fish assemblage.Gill nets and electrofishing were the most effective for Tench detection.To reduce gear-related mortality of non-target fishes during capture efforts, fyke nets should be prioritized in areas with at-risk species or where high levels of bycatch are expected.Although Tench is considered to be most active at night, time of day had no effect on gear capture effectiveness.The presence of vegetation was slightly correlated with the presence of Tench, while substrate type was not, suggesting habitat preferences in Tench may be more flexible than previously thought.Positive associations between Tench and native fishes across heterogeneous sites corroborate a preference by Tench for low-flow, vegetated areas.These associations have the potential to inform site selection for sampling at the invasion front.Our study revealed gaps in knowledge of the ecological relationships between Tench and environmental variables associated with its capture.Nonetheless, our results point to methods for maximizing Tench detection and capture efficiency and for targeting habitats or sites where the species is likely to occur.This information is necessary for informing mitigation plans in established areas and supporting early detection and removal of Tench along the invasion front, with the ultimate goals of slowing its secondary spread.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".