Detecting Invasive Round Goby in Wadeable Streams: A Comparison of Gear Types
Bibliographic record
Abstract
Abstract The invasion of round goby Neogobius melanostomus into tributaries of the Laurentian Great Lakes poses concerns for the conservation of native fish communities and the functioning of stream ecosystems. In streams, where standard methods for detection are lacking, development of sensitive sampling methods to identify round goby-invaded sites is a prerequisite for the implementation of effective management strategies. We sampled 34 stream sites that spanned a range of goby densities with two active gears (electrofishing and seining) and one passive gear (minnow traps) to compare gear efficiency at detecting invasive round goby. Naive occupancy, defined as the number of sites where goby were detected with each gear divided by the total number of sites, was 0.79 (27/34 sites) for seining, 0.68 (23/34) for electrofishing, and 0.50 (17/34) for traps. The probability of detecting a single round goby in a single pass (determined with single-season, constant-probability models) was 0.75 ± 0.065 (mean ± SE) for minnow traps, 0.69 ± 0.056 for seining, and 0.47 ± 0.075 for electrofishing. Mean ± SE catch per unit effort (CPUE; round goby/min) was 0.716 + 0.158 for seining, 0.137 ± 0.043 for electrofishing, and 0.078 ± 0.022 for minnow traps, seining being significantly more efficient than minnow traps and electrofishing. The CPUE did not differ between electrofishing and traps. Mean goby size did not differ among gear types, but a size bias was detected when data from a related study were included in the analysis, seining capturing smaller round goby. Based on the sensitivity and efficiency of seining, we conclude that when stream conditions allow, this gear is a practical means of detecting round goby and determining their abundance in streams. Received August 11, 2011; accepted January 4, 2012
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".