Effectiveness of stream sampling methods in capturing non-native Rusty Crayfish (<em>Orconectes rusticus</em>) in Ontario
Bibliographic record
Abstract
Habitat alteration and species introductions have contributed to the decline of native crayfish in Ontario. although lake populations of crayfish in Ontario are monitored, there is no corresponding program for streams. We used removal-based sampling to evaluate the efficacy of three sampling techniques (backpack electrofishing, hand capture, and seining) to characterize native and non-native crayfish populations in six streams in the Kawartha lakes region and five streams in the Muskoka/Haliburton lakes region. all types were effective at detecting non-native Rusty Crayfish (Orconectes rusticus). Rusty Crayfish were collected from 65% of samples, constituted 90% of the total catch, and were the only species present in 30% of streams. Compared with other methods, electrofishing was more likely to capture additional crayfish species. Removal-based sampling was not a reliable approach for estimating capture probability and population size. Failure of the removal model was due to increases in the number of crayfish captured after the first pass or too few individuals collected. Mean capture probabilities for electrofishing (0.30) and hand capture (0.31) did not result in reliable population estimates. Compared with seining, electrofishing and hand capture resulted in more sexually active males (fewer sexually inactive males) and more small (< 25 mm carapace length) individuals. For each method, there were differences in capture probability among length classes. a combination of electrofishing and seining (with multiple passes) would maximize species detection, permit sampling of a range of habitat types, and be easily integrated into existing stream fish surveys.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".