Bottom-Up Forces Drive Increases in the Abundance of Large Daphnids in Four Small Lakes Stocked with Rainbow Trout (Oncorhynchus mykiss), Interior British Columbia, Canada
Bibliographic record
Abstract
Abstract The introduction of salmonids into lakes of western North America for sport fishing is a widespread phenomenon. While numerous investigations have documented cascading trophic interactions upon the introduction of fish into naturally fishless systems, little research has been done to investigate the importance of natural fish status (fishless vs. fish bearing) in modulating historical food web response to dual forcing by bottom-up (resource regulation from nutrients) and top-down (planktivory from stocked fish) processes. We used the paleolimnological record to reconstruct food web changes in four lakes in interior British Columbia that have been stocked with rainbow trout since the early to mid-1900s. Analysis of pigments, diatoms, and Cladocera was undertaken in cores from all lakes. We predicted that if fish were important in structuring cladoceran abundance and composition, we would document a decline in the abundance of large daphnids post-stocking in our two naturally fishless lakes, and little change in the two fish-bearing lakes. Instead, we documented increased abundance of large daphnids after stocking in all lakes in the early to mid-1900s, a finding inconsistent with size-selective predation from planktivorous fish. Further, our data suggest that deep, low-oxygen refugia may be important in sustaining populations of large Daphnia , a process which was enhanced by increased nutrients and lake production according to sub-fossil diatom and pigment analyses. This study shows that fish stocking does not invariably result in a decrease in large-bodied Cladocera and that nutrients and lake type can modulate the response of invertebrate planktivores.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".