Can fish species co-occurrence patterns be predicted by their trait dissimilarities?
Bibliographic record
Abstract
Trait-based analyses have been successful in determining and predicting species association outcomes in diverse communities. Most studies have limited the scope of this approach to the biotic responses of a small number of species or geographical regions. We focused on determining whether three biologically relevant traits (body size, temperature preference and trophic level) influence the patterns of co-occurrence between multiple species. We used fish species presence/absence from 9204 lakes in Ontario, Canada, to obtain effect sizes of 2001 species-pair co-occurrence values, using a null model approach. Euclidean distances between each species-pair were calculated for each of the three traits selected. Multiple regression models and randomization tests were used to determine the direction and significance of the relationship of each trait with the observed co-occurrence values. The results show that species temperature preference was significantly related to co-occurrence patterns, indicating the effect of environmental filtering. Trophic level was significantly related to co-occurrence values for both linear and quadratic terms, suggesting that segregation between species is driven by large differences in this trait (predation effects). Unexpectedly, body size was not significantly related to the observed co-occurrence patterns. We provide a new approach to test relationships between species assemblages and trait conditions.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".