Climate modifies response of non-native and native species richness to nutrient enrichment
Bibliographic record
Abstract
Ecosystem eutrophication often increases domination by non-natives and causes displacement of native taxa. However, variation in environmental conditions may affect the outcome of interactions between native and non-native taxa in environments where nutrient supply is elevated. We examined the interactive effects of eutrophication, climate variability and climate average conditions on the success of native and non-native plant species using experimental nutrient manipulations replicated at 32 grassland sites on four continents. We hypothesized that effects of nutrient addition would be greatest where climate was stable and benign, owing to reduced niche partitioning. We found that the abundance of non-native species increased with nutrient addition independent of climate; however, nutrient addition increased non-native species richness and decreased native species richness, with these effects dampened in warmer or wetter sites. Eutrophication also altered the time scale in which grassland invasion responded to climate, decreasing the importance of long-term climate and increasing that of annual climate. Thus, climatic conditions mediate the responses of native and non-native flora to nutrient enrichment. Our results suggest that the negative effect of nutrient addition on native abundance is decoupled from its effect on richness, and reduces the time scale of the links between climate and compositional change.
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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 | 0.000 | 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) | 0.001 | 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".