On the importance and practical conservation of nongame fishes
Bibliographic record
Abstract
ABSTRACT Fisheries management has historically focused conservation efforts on game or sport species. However, most species are nongame—those not traditionally captured for sport or harvest in countries where recreational fisheries predominate. Greater conservation of nongame species could help ensure that population declines do not go unnoticed. Unfortunately, fisheries managers already manage complex ecosystems with limited resources, and they frequently are directed to focus on game fishes. However, game fish populations can also be tightly coupled to nongame fishes, so nongame management can sometimes also benefit game species. We reviewed functional roles of freshwater nongame fishes and suggest categories that may be especially important for conservation. Of note, nongame fishes are more imperiled than game fishes and fill largely distinct functional roles. These roles include food-web impacts, ecosystem engineering, and mussel hosting. Management priorities could include nongame piscivores and species with high biomass, especially herbivores, nest builders, and imperiled mussel hosts. We provide practical options for including nongame fishes in current management, many of which require little additional funding. These include recognizing when sport fish funding and conservation can also benefit nongame species, whole-community sampling at some monitoring locations, collecting catch data for select species observed during game fish surveys, embracing environmental DNA sampling, and making presence–absence record keeping the default option.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".