Interspecific competition between rainbow trout (Salmo gairdneri Richardson) and redside shiners (Richardsonius balteatus (Richardson)) in two British Columbia lakes
Bibliographic record
Abstract
Competition is defined as the demand of two or more organisms for the same extrinsic resources in excess of supply. The distribution, movements, behaviour and food of trout and shiners in Paul and Pinantan lakes were studied in order to determine the item's and mechanisms of interspecific competition between them. Data from recent years were compared with data for years when trout alone inhabited the lake. No interspecific aggression was observed. The possibility that the two species were competing for space was discounted. Stomach contents of shiners in Pinantan Lake revealed a marked qualitative diurnal food cycle. In Paul Lake, shiners have drastically reduced the Gammarus population relative to its pre-shiner abundance. This overgrazing was caused by the concentration of large numbers of shiners over the shoals where Gammarus are also present in their highest concentrations and the ability of shiners to pursue food deeper into the weeds and to graze an area more thoroughly than trout. In Pinantan Lake shiners have apparently-reduced the density of Daphnia to a point where trout are unable to feed on them as rapidly as in pre-shiner years. The ability of both species to utilize many types of food tends to reduce the intensity of competition. The study demonstrates how false implications may arise from a delayed appraisal of competition. If observations had not been made on Paul Lake until after competition had been observed the importance of Gammarus as an item of competition would have probably been overlooked and the whole competitive relationship misconstrued. Included among the basic mechanisms of competition is the consumption by one or more organisms of something in short supply before it reaches a potential habitat where it would become available to another organism or group. Environmental factors and behaviour were shown to be important influences in the dynamics of competition. The physical and biological environment and the distribution and behaviour of competitors may be in states of continual flux. Hence natural competitive relationships can be considerably more complicated and variable than situations described by the most elastic of theoretical models.
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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".