Freshwater fish sanctuaries provide benefits for riparian wildlife
Bibliographic record
Abstract
Abstract Benefits to wildlife communities stemming from the protection of a single species have been documented in terrestrial and marine systems but remain understudied within the context of freshwater‐protected areas (FPAs). We used five long‐standing (>80 years) FPAs in three lakes in eastern Ontario, Canada, which were initially established to protect native black bass (Micropterus spp.) from angling exploitation, to assess whether this protection affected wildlife communities found in the riparian areas of these FPAs. From May to July 2021, we used baited remote camera traps and visual surveys to assess species diversity within and outside of FPAs. We recorded 61 species spanning mammalian, avian and herpetofauna taxa, with the two assessment methods identifying unique sets of species (23% overlap). Camera traps showed that animals were more active in riparian areas during the day (62% of detections) than at night. FPAs had a variable but overall positive influence on riparian wildlife biodiversity, hosting more bird, mammal, amphibian and reptile species than non‐protected areas and having higher species richness. FPAs differed from other sites in the lakes by having higher habitat complexity, less human infrastructure and less human use, which potentially contributed to these differences. This study raises awareness that even small FPAs can have legacy, umbrella‐type benefits that extend beyond fishes to the wildlife that use the adjacent riparian areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".