Non‐interacting impacts of fertilization and habitat area on plant diversity via contrasting assembly mechanisms
Bibliographic record
Abstract
Abstract Aim The local‐ and regional‐based forms of anthropogenic change reducing grassland diversity are generally identified, but these scale‐dependent processes tend to co‐occur with unclear interactive effects. Here, we explicitly test how common local and regional perturbations simultaneously affect plant alpha and beta diversity in a multiyear community assembly experiment using fragments of grassland habitat of various sizes. We hypothesized that local disturbances and decreasing patch size would interact, suppressing local diversity while homogenizing composition among patches. Location North America. Methods We conducted a three‐year grassland assembly experiment, factorially manipulating local perturbation (nitrogen addition and mowing) and patch area for 36 patches over 13 ha. We quantified the individual and interactive effects of these local and regional factors on plant alpha and beta diversity within (quadrat scale) and among patches (patch scale). We also used a null model approach to disentangle between stochastic‐ and niche‐based assembly mechanisms. Results We detected a gradient of assembly outcomes driven by two non‐interacting factors—the effects of N fertilization on alpha (negative) and beta (positive) diversity regardless of spatial scale and the scale‐dependant effect of increasing patch size on alpha (positive) and beta (positive) diversity. These effects unfolded over time, with the constraints on richness and composition shifting from dispersal‐based during the first sampling year to perturbation‐and size‐based factors at year two and three. Fertilization effects were driven by a mixture of deterministic (i.e., selection at the species level) and stochastic (i.e., random extinctions) processes resulting in a decline in local richness but an increase in spatial heterogeneity in species composition. Area appeared to influence alpha diversity mainly via stochastic “sampling effect”—larger patches represented a larger sample of the regional pool. Niche‐based processes, however, led to convergence in beta diversity among smaller patches driving a positive overall effect of area on beta diversity. Main conclusion Our results illustrate how diversity regulation in contemporary grasslands can be simultaneously shaped by local and regional factors acting additively but via contrasting assembly mechanisms that operate at different spatial and temporal scales.
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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 | 0.001 | 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".