Nitrogen alters effects of disturbance on annual grassland community diversity: Implications for restoration
Bibliographic record
Abstract
Abstract In an era of anthropogenically altered disturbance regimes and increased nutrient loads, understanding how communities respond to these perturbations is essential for successful habitat restoration. Disturbance and resource supply can affect community diversity by altering community assembly processes, such as recruitment, mortality or competitive inequalities. The mechanisms behind community responses to these drivers will differentially affect multiple facets of diversity. Here we examine how factorial manipulations of disturbance (raking to remove above‐ground vegetation) and nitrogen supply affect taxonomic and phylogenetic diversity of predominantly annual California grassland communities spanning a 500‐km latitudinal and twofold rainfall gradient. The disturbance caused density‐independent biomass removal and increased access to resources such as space and light, thus mimicking demographic effects of disturbance as considered in ecological models and broadly applicable to empirical systems. We used paired metrics of richness, evenness and community composition to compare evidence from taxonomy and phylogeny. Disturbance increased species and phylogenetic diversity (richness and evenness metrics). However, nitrogen addition interacted with disturbance to reduce species richness and phylogenetic diversity. Undisturbed communities were more strongly clustered phylogenetically, but disturbance eroded this clustering such that communities became more random (i.e. indistinguishable from a null model of assembly). Species composition differed between disturbed and undisturbed communities, and many species were observed in only one community type. Disturbance interacted with nitrogen supply to alter phylogenetic composition of communities, and recently disturbed communities were more spatially variable in phylogenetic composition than undisturbed communities. Phylogenetic composition of communities also differed among nitrogen treatments. Synthesis. Our results suggest that disturbing these grassland communities by removing above‐ground vegetation increased community diversity by increasing recruitment. Seed addition following this type of disturbance is thus likely to be an effective restoration technique. However, we have shown that disturbance combined with nitrogen enrichment reduces community diversity. The mechanism for this enrichment effect does not appear to be linked to increased productivity leading to light limitation. This work suggests restoration efforts employing biomass removal must take nutrient availability into account to maximize local community diversity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".