Site conditions are more important than abundance for explaining plant invasion impacts on soil nitrogen cycling
Bibliographic record
Abstract
Abstract Invasive plant species can alter critical ecosystem processes including nitrogen transformations, but it is often difficult to anticipate where in an invaded landscape, these effects will occur. Our predictive ability lags because we lack a framework for understanding the multiple pathways through which environmental conditions mediate invader impacts. Here, we present a framework using structural equation modeling to evaluate the impact of an invasive grass, Microstegium vimineum (M.v.), on nitrogen cycling based on a series of invaded sites that varied in invader biomass and non‐M.v. understory biomass, tree basal area, light availability, and soil conditions. Unlike previous studies, we did not find an overall pattern of elevated nitrate concentrations or higher nitrification rates in M.v.‐invaded areas. We found that reference plot conditions mediated differences in mineralization between paired invaded and reference plots at each site through indirect (via M.v. biomass), direct, and interactive pathways; however, the strongest pathways were independent of M.v. biomass. For example, sites with low reference soil nitrate and high non‐M.v. understory biomass tended to have faster mineralization at 5–15 cm in invaded plots. These findings suggest that more attention to reference conditions is needed to understand the impact of invasive species on soil nitrogen cycling and other ecosystem processes and that the greatest impacts will not necessarily be where the invader is most abundant.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".