Phylogenetic diversity promotes ecosystem stability
Bibliographic record
Abstract
Ecosystem stability in variable environments depends on the diversity of form and function of the constituent species. Species phenotypes and ecologies are the product of evolution, and the evolutionary history represented by co‐occurring species has been shown to be an important predictor of ecosystem function. If phylogenetic distance is a surrogate for ecological differences, then greater evolutionary diversity should buffer ecosystems against environmental variation and result in greater ecosystem stability. We calculated both abundance‐weighted and unweighted phylogenetic measures of plant community diversity for a long‐term biodiversity–ecosystem function experiment at Cedar Creek, Minnesota, USA. We calculated a detrended measure of stability in aboveground biomass production in experimental plots and showed that phylogenetic relatedness explained variation in stability. Our results indicate that communities where species are evenly and distantly related to one another are more stable compared to communities where phylogenetic relationships are more clumped. This result could be explained by a phylogenetic sampling effect, where some lineages show greater stability in productivity compared to other lineages, and greater evolutionary distances reduce the chance of sampling only unstable groups. However, we failed to find evidence for similar stabilities among closely related species. Alternatively, we found evidence that plot biomass variance declined with increasing phylogenetic distances, and greater evolutionary distances may represent species that are ecologically different (phylogenetic complementarity). Accounting for evolutionary relationships can reveal how diversity in form and function may affect stability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".