Relationship between plant species diversity and grassland condition
Bibliographic record
Abstract
Although the maintenance of biodiversity has become one of the goals in ecosystem management, the relationships of diversity to ecosystem characteristics such as level of herbivory, productivity, and vegetation structure are still poorly understood. We examined these relationships in 8 native grassland sites differing in grazing histories and range condition in the Mixed Grassland (6), Moist Mixed Grassland (1) and Aspen Parkland (1) ecoregions of southern Saskatchewan. Range condition, assessed using standard methods, ranged from fair to excellent. The Shannon's diversity index followed a curvi-linear relationship with range condition, increasing from fair to good, but decreasing from good to excellent condition, within a range between 0.66 and 2.58. Species evenness was affected by range condition in a similar manner ranging from 0.44 to 0.86. Species richness varied among sites and plots between 4 and 28 plants 0.25 m(-2), but changed little with range condition. Most structural parameters, such as the cover, height, or thickness of standing plants (live or dead) and litter, increased with range condition especially from good to excellent. The Shannon's diversity index was positively correlated with forb biomass, but not with biomass of any other group or their combination. Grazing regimes that maintain good range condition also maintain species and structural diversity of grasslands.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".