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 in Erodium botrys and Oxalis pes‐caprae persisted over years, whereas patterns in Arctotheca calendula were 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. In Oxalis pes‐caprae , whose patches were hypothesized to have been shaped by cultivation, there was no spread in four years, despite further tillage. Outlying plants of O. pes‐caprae failed 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.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".