Body size, not other morphological traits, characterizes cascading effects in fish assemblage composition following commercial netting
Bibliographic record
Abstract
Traits used to group species and generalize predatorprey interactions can aid in constructing models to assess human impacts on food webs, especially in complex, species-rich systems. Commercial netting has reduced populations of large-bodied piscivores in some lagoons of a Venezuelan floodplain river, and cascading effects result in distinct prey fish communities in netted and unnetted lagoons. In 2002 and 2003, we sampled assemblages of prey fishes in netted and unnetted lagoons and tested whether fish size and (or) other morphological characteristics were associated with differences in assemblage composition. In both years, prey fish assemblages in netted lagoons were dominated numerically by larger species. We used geometric morphometric methods to test for a relationship between species morphological characteristics and found that neither overall morphological ordination nor specific morphological traits could be used to distinguish among assemblages. Thus, size was the only variable that was useful in explaining differences in assemblage composition. Even in this species-rich river with a complex food web, size-structured predatorprey interactions apparently influence community-level patterns and can be used to characterize human impacts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".