The effects of two fish predators on Wood Frog (<i>Lithobates sylvaticus</i>) tadpoles in a subarctic wetland: Hudson Bay Lowlands, Canada
Bibliographic record
Abstract
Fish can have strong predatory impacts on aquatic food webs. Indeed, fish are known to have strong effects on amphibians, with some species being excluded from communities where fish are present. Most research with amphibians and fish has focused on lower latitudes and very little is known of amphibian–fish interactions at higher latitudes. Therefore, we conducted an enclosure experiment in a subarctic natural wetland to examine the predatory effects of two species of fish, brook sticklebacks (Culaea inconstans (Cuvier, 1829)) and ninespine sticklebacks (Pungitius pungitius (L., 1758)), on the survival and growth of Wood Frogs (Lithobates sylvaticus (LeConte, 1825)). We found no significant difference in survival and size at metamorphosis among the two fish species treatments and fish-free treatments. We found that individuals from fish-free treatments metamorphosed earlier than those from either fish species present treatment. Our work suggests that stickleback fish predation may not have a major impact on Wood Frog tadpole survival and growth in a subarctic wetland. Sticklebacks may still have an impact on earlier developmental stages of Wood Frogs. This work begins to fill an important gap in potential factors that may impact larval amphibian survival and growth at higher latitudes.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".