Soil homogenization and microedges: perspectives on soil‐based drivers of plant diversity and ecosystem processes
Bibliographic record
Abstract
Abstract Disturbance caused by agriculture and resource extraction has resulted in widespread homogenization of soils at the local (within‐site) scale. Here, we describe how experimental manipulation of heterogeneity at the local scale has had inconsistent effects on plant species diversity. Moreover, we discuss how soil homogenization per se typically has not been accounted for in the study of heterogeneity–diversity relationships, and how disturbance, often viewed as an artifact in soil heterogeneity experiments, can be a key driver of soil homogenization. We propose a conceptual model for describing the relationship between plant size, patch size, and diversity, and we discuss how factors such as disturbance, productivity, and competition among species either should be controlled or accounted for in soil heterogeneity–plant diversity experiments. Finally, we consider the concept of soil patch microedges, which may provide unique microsites for increased plant diversity, and how these biogeochemical and hydrological interfaces could potentially drive ecosystem processes in a manner unique from the adjacent patches. Overall, this synthesis integrates perspectives on the functional links between plant‐driven processes, and soil patterns and processes.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".