Community assembly history alters relationships between biodiversity and ecosystem functions during restoration
Bibliographic record
Abstract
Relationships between biodiversity and ecosystem functioning depend on the processes structuring community assembly. However, predicting biodiversity-ecosystem functioning (BEF) relationships based on community assembly remains challenging because assembly outcomes are often contingent on history and the consequences of history for ecosystem functions are poorly understood. In a grassland restoration experiment, we isolated the role of history for the relationships between plant biodiversity and multiple ecosystem functions by initiating assembly in three different years, while controlling for all other aspects of community assembly. We found that two aspects of assembly history-establishment year and succession-altered species and trait community trajectories, which in turn altered net primary productivity, decomposition rates, and floral resources. Moreover, history altered BEF relationships (which ranged from positive to negative), both within and across functions, by modifying the causal pathways linking species identity, traits, diversity, and ecosystem functions. Our results show that the interplay of deterministic succession and environmental stochasticity during establishment mediate historical contingencies that cause variation in biodiversity and ecosystem functions, even under otherwise identical assembly conditions. An explicit attention to history is needed to understand why biodiversity-ecosystem function relationships vary in natural ecosystems: a critical question at the intersection of fundamental theory and applications to environmental change biology and ecosystem restoration.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".