Causes of pattern in plant communities where environmental change is rapid and species longevity is short
Bibliographic record
Abstract
Abstract. Questions: To what extent can spatial structure and its causes be determined in a highly disturbed environment? What are the main determinants of pattern and are these species‐specific? How much do spatial patterns change over generations? Location: Wimmera region of southern Australia. Methods: Broad‐leaved weeds were counted in 225 000 contiguous 20‐cm square quadrats. A substantial number of these quadrats were recorded again after two and four years. An hierarchical ‘adaptive analysis’ approach was used to select spatial analytical methods to examine specific aspects of pattern and variation in pattern from year to year. Results: Patterns varied among species and included both dense and sparse patches surrounded by areas of zero density, diffuse gradations of density and clear anisotropy. Patterns inErodium botrysandOxalis pes‐capraepersisted over years, whereas patterns inArctotheca calendulawere less pronounced and varied over time. Edaphic factors appeared to have only a minor influence over the spatial distribution of the weed community as a whole. InOxalis pes‐caprae, whose patches were hypothesized to have been shaped by cultivation, there was no spread in four years, despite further tillage. Outlying plants ofO. pes‐capraefailed to establish new patches, even in the year of greatest population increase. Little evidence of localised recruitment events was found. Conclusions: Despite repeated annual disturbances by natural and anthropogenic mechanisms, clear and interpretable spatial structure develops in annual weeds over a range of spatial resolutions. Adaptive analysis is a useful approach to the characterization of such patterns.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".