Climatic variability alters the outcome of long‐term community assembly
Bibliographic record
Abstract
Summary We used seed additions to test experimentally whether long‐term community assembly unfolds consistently (deterministic model) or whether different outcomes are possible depending on variations in climate and the presence of exotic dominants (stochastic model). The experiment was conducted in homogeneous semi‐arid grassland on the northern Great Plains of North America. Native grass seed additions in each of 3 years (1994–96) were combined factorially with repeated selective herbicide applications to reduce the cover of the extant dominant, a Eurasian C 3 grass ( Agropyron cristatum ). We assessed whether composition converged or diverged among treatments between 1996 and 2004. The outcome of long‐term community assembly varied by establishment year, in association with significant variations in monthly growing‐season precipitation from 68‐year averages during the years when seed was added. In 2004, the added native C 4 grass Bouteloua gracilis dominated plots sprayed and seeded in 1994 and 1995, resisting re‐invasion and having significantly higher plot diversity. In contrast, plots sown in 1996 – one of the driest years on record – reverted to the exotic C 3 grass. Seeded but unsprayed plots maintained their pre‐treatment dominance by exotic grass. Rainfall variability was also associated with the extent of exotic grass cover and bare soil in unsprayed plots, and natural recruitment by native species from nearby prairie. Establishment success varied significantly among the added species. All seeded grasses, other than B. gracilis , were mostly absent by 2004. The failure of these native C 3 species to establish appears consistent with deterministic assembly models where local abiotic conditions filter out unsuitable species. Synthesis . Our results indicate that stochastic and deterministic processes operate simultaneously to influence community assembly, depending on interactions among climate, seed availability, species identity and disturbance during the initial stages of establishment. Multiple assembly trajectories developed, but the assembled communities did not include all the added species. Conversion of degraded grassland back to native‐dominated grassland was possible, but only with the removal of the exotic dominant and seed additions during years that were suitably wet for establishment. Once formed, however, the assembled native community resisted re‐invasion. This suggests that native grassland restoration will depend on establishment measures as intense as those used during initial cultivation almost a century earlier.
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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 | 0.006 | 0.001 |
| 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) | 0.000 | 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".