Reducing Context-Dependency in Ecology: Environmental Variation Leads to Predictable Patterns of Species Associations Across Local Communities
Bibliographic record
Abstract
Predicting how communities change in space and time requires an understanding of how mechanisms such as environmental filtering and biotic interactions shape distributions and abundances. However, ecological communities are often context dependent, so mechanisms that are important in certain environments may not be in others. To understand how context dependency affects the prediction of community structure, we asked: Can the environment predict the outcomes of community assembly? Using fish association networks estimated with Markov random fields for over 700 lakes in Ontario, Canada, we tested if species association patterns, representing potential community assembly mechanisms, varied as a function of the environment. We examined the effect of the environment at two scales: pairwise and community level, summarizing potential mechanisms between species and across whole communities. The environment was a strong predictor of community level species association patterns but not of pairwise patterns, suggesting that the cumulative outcome of mechanisms structuring communities can be explained by the environment. We then tested if community level patterns were associated with the uniqueness of a lake’s species composition. We found that as species association patterns became stronger, they lead to lakes with more common species compositions. Taken together, our results show that variation in the outcome of community assembly can be explained by the environment using community level patterns, offering a way for community ecologists to study context-dependency in community structure across differing environmental gradients and species compositions.
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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 | 0.001 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".