Redhorse suckers (Moxostoma) in the Grand River, Ontario: how do six ecologically similar species coexist?
Bibliographic record
Abstract
The syntopic existence of five or six species of redhorse suckers in small sections of some Ontario rivers leads us to question how fishes with very similar ecology and life history are able to coexist. My thesis adopts a resource partitioning hypothesis to examine this phenomenon. I investigated body shape variation among all six redhorse species found in Ontario with the hypothesis that the different redhorse species are adapted to varying degrees to living in fast currents. I found significant differences among the six species of redhorse found in Ontario. I also compared the utility of traditional morphometric techniques with newer geometric morphometric methods (Thin Plate Spline Analysis--TPS) for the purpose of size removal in morphometric data. I found TPS results to be easier to interpret due to the generation of visual deformation grids and more consistent in identifying the specific location of shape variation. I also examined home range and spatial distribution patterns in three of the six species, to test the prediction that different redhorse species prefer different habitats. I did not find any significant differences in home range size among species however, I did find significant differences in current velocity and depth of fish locations among species. In addition, I found low spatial overlap between the three redhorse species examined. Based on the findings of these two chapters, I have concluded that resource partitioning is occurring among redhorse suckers in the Grand River and that it may be an important mechanism in facilitating their coexistence.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".