Communities contain closely related species during ecosystem disturbance
Bibliographic record
Abstract
Predicting community and species responses to disturbance is complicated by incomplete knowledge about species traits. A phylogenetic framework should partially solve this problem, as trait similarity is generally correlated with species relatedness, closely related species should have similar sensitivities to disturbance. Disturbance should thus result in community assemblages of closely related species. We tested this hypothesis with 18 disturbed and 16 reference whole-lake, long-term zooplankton data sets. Regardless of disturbance type, communities generally contained more closely related species when disturbed. This effect was independent of species richness, evenness, and abundance. Communities already under stress (i.e., those in acidic lakes) changed most when disturbed. Species sensitivities to specific disturbances were phylogenetically conserved, were independent of body size, and could be predicted by the sensitivities of close relatives within the same community. Phylogenetic relatedness can effectively act as a proxy for missing trait information when predicting community and species responses to disturbance.
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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 | 0.001 | 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) | 0.004 | 0.001 |
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; both teacher heads agree on what is shown here.
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".