Abiotic factors influence species co‐occurrence patterns of lake fishes
Bibliographic record
Abstract
Abiotic factors are recognized for their strong influence on community structure. Habitat diversity is related to resource availability that influences species richness and abundance. In lakes, surface area and depth have been used as measures of the size and diversity of habitat, and have strong effects on the structure of entire communities. We tested whether abiotic variables, related to habitat size, influence co-occurrence patterns of species pairs of fishes by analysing groups of lakes within a specific area and depth categories in two regions in Ontario, Canada. We used null models to obtain co-occurrence patterns and standard effect sizes for each species pair within each area and depth category. We estimated standard effect sizes relative to lake area or depth and determined whether species co-occurrence patterns change systematically as these measures of habitat increase. We evaluated groups of species where factors such as predation and habitat filtering have been shown to structure those assemblages, and we tested whether area and depth alter the species associations and our interpretation of these relationships. We found significant differences between the observed and expected distributions of regression slopes relating co-occurrences to area and depth in both regions across all species, which indicated the strong influence of both variables on the overall co-occurrence patterns. We observed a significant negative trend of the co-occurrence patterns across lake area categories for the predator-prey species, indicating that the effect of predation was stronger in smaller lakes, but it was reduced in larger lakes, possibly due to increased habitat and resource availability. We show that pooling results as done in standard community null models can lead to Type II errors due to the 'cancellation' of opposing ecological signals. Our results demonstrate the effect of environmental variables on species co-occurrence patterns, but the divergent results obtained between geographical regions suggest that such patterns are context-dependent. This study emphasizes the importance of considering abiotic factors in null models of species co-occurrence to obtain reliable and detailed information about the association patterns between species.
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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 | 0.002 |
| 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".