Composition and structure of fescue prairie respond to burning and environmental conditions more than to grazing or burning plus grazing in the short-term
Bibliographic record
Abstract
Burning and grazing are key processes in the natural disturbance regime of the Fescue Prairie. Burning, grazing and their interacting effects on plant species diversity (Hʼ), species richness, and heterogeneity in species composition were studied at two spatial scales for two years in a remnant Fescue Prairie near Saskatoon, Saskatchewan. Cattle distribution in relation to plant communities was also studied. At the plot scale (100 m²), burning increased Hʼ (P<0.01) on species richness; richness varied between years (P=0.04), averaging 14.2 species m⁻² in year one versus 15.8 species m⁻² in year two (S.E.±0.65). Spatial heterogeneity (P>0.25; x=46%; S.E.±3.0) and temporal heterogeneity in species composition (P>0.21; x=42%; S.E.±3.8) were not affected by burning, grazing, or their interaction. Burning + grazing increased tiller densities in Elymus lanceolatus (68%) and those of Festuca hallii (11%) (P<0.001) compared to the control. Burning decreased total \naboveground net primary production (ANPP) (P<0.001) (x=305 g m⁻²) compared to unburned treatments (x=500 g m⁻²; S.E.±30.8). Grazing and burning + grazing had no effect on total ANPP or graminoid ANPP (P≥0.36). At the scale of Kernen Prairie (130 ha), H' increased between 1996 (P<0.05) (x=1.10) and 2005 (x =1.40; S.E.±0.094). Species richness increased from 5.2 species 0.25 m⁻² in 1996, to 6.8 species 0.25 m⁻² in 2005 (S.E.±0.505). Heterogeneity in plant species composition tended to increase after prescribed burning was started in 1986 and \nafter grazing began in 2006. Cattle preferred Bromus inermis- and Poa pratensis-dominated \nplant communities, areas with intermediate amounts of total aboveground standing crop of \nplants, and areas in which shrub densities exceeded 16 stems 0.25 m⁻². In the short term, burning \nand environmental conditions had greater effects on species diversity, richness, and heterogeneity in species composition than grazing or the interaction of burning and grazing. Different responses may be expected with different combinations of timing, frequency, and intensity of burning and grazing at different sites under ever changing environmental conditions.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".