Wetland fishes avoid a carbon dioxide deterrent deployed in the field
Bibliographic record
Abstract
Abstract Biological invasions are poorly controlled and contribute to the loss of ecosystem services and function. Altered watershed connectivity contributes to aquatic invasions, but such hydrologic connections have become important for human transport. Carbon dioxide (CO2) deterrents have been proposed to control the range expansion of invasive fishes, particularly through altered hydrologic connections, without impeding human transport. However, the effectiveness of CO2 deterrents needs to be further evaluated in the field, where fishes are situated in their natural environment and logistical challenges are present. We deployed a proof-of-concept CO2 deterrent within a trap-and-sort fishway in Cootes Paradise, Ontario, Canada, to determine the avoidance responses of fishes attempting to disperse into a wetland. We aimed to describe deterrent efficiency for our target species, common carp, and for native fishes dispersing into the wetland. Our inexpensive inline CO2 deterrent was deployed quickly and rapidly produced a CO2 plume of 60 mg/l. Over 2000 fishes, representing 13 species, were captured between 23 May and 8 July 2019. A generalized linear model determined that the catch rates of our target species, common carp (n = 1662), decreased significantly during deterrent activation, with catch rates falling from 2.56 to 0.26 individuals per hour. Aggregated catch rates for low-abundance species (n < 150 individuals per species) also decreased, while catch rates for non-target brown bullhead (n = 294) increased. Species did not express a phylogenetic signal in avoidance responses. These results indicate that CO2 deterrents produce a robust common carp avoidance response in the field. This pilot study deployed an inexpensive and rapidly operating deterrent, but to be a reliable management tool, permanent deterrents would need to produce a more concentrated CO2 plume with greater infrastructural support.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".