Species‐pair associations, null models, and tests of mechanisms structuring ecological communities
Bibliographic record
Abstract
Abstract Biotic interactions and niche processes are fundamental determinants of community structure and species co‐occurrence. Most studies of species co‐occurrence have focused on negative association patterns (segregation presumed to arise from competition), often ignoring positive aggregations, although positive and negative associations may arise from multiple mechanisms. We used a pairwise approach to identify co‐occurrence patterns of 76 fish species across almost 9500 lakes, followed by a meta‐analytic approach to compare the co‐occurrence pattern of each species pair across watersheds and determine their cumulative species associations. Biological information relating to species’ phylogeny, habitat preferences, and diet was used to group species into relevant subsets in order to test community assembly processes of competition, predation, and habitat filtering. We found consistent non‐random patterns of co‐occurrence in nearly half the species pairs and more extremely aggregated than segregated species pairs. Observed co‐occurrence patterns indicated the importance of shared habitat requirements and predation, rather than competition, driving positively aggregated and negatively segregated species associations, respectively. Our meta‐analytic approach incorporating important biological attributes permitted the testing of specific mechanisms of community assembly, providing novel insights into the major determinants of fish community structure and their generality across a vast set of lakes.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".