Structure, Function and Drought Resilience of Northern Prairie Communities, 50 Years After Grazing Disturbance
Bibliographic record
Abstract
With climate change threatening the function of grassland ecosystems, conservation and restoration strategies are shifting from comparisons of species compositions with baseline conditions, to assessments of ecosystem functions and resilience. Here, I present research from Riding Mountain National Park, Manitoba, to illustrate the links between plant community composition, leaf traits of dominant plants, and grassland community function. I also discuss applications for the management of grassland ecosystems. I use plant community data, collected in 1973, 2010 and 2020, to understand the long-term effects of grazing on the function and resilience of northern fescue prairies. I test whether legacies of historic grazing continue to affect the structure, diversity, and composition of grassland communities, and whether historic grazing predicted community leaf trait composition in fescue grassland ecosystems. I also explore how nutrient and carbon cycling may be influenced by leaf traits of dominant plants, including their leaf carbon and nitrogen concentrations. Fifty years after grazing, heavily grazed prairies continued to have lower plant diversity. However, prairies with light grazing had lower spatial variation in plant composition. By 2020, community leaf trait composition could not be predicted by historic grazing, and instead, plant trait composition was driven by exotic species invasions. Similarity in traits between Poa pratensis and Festuca hallii resulted in a functional redundancy between lightly and heavily grazed grasslands. Invasions of Poa pratensis increased the values of leaf density, and leaf C:N over the years while Bromus inermis increased the value of specific leaf area (SLA), illustrating that changes in grassland composition correlate with changes in the traits of dominant plants that have the potential to affect the resilience of grasslands to drought as well as their function. This study describes how community trait composition can impact grassland drought tolerance and ecosystem functions, and the management implications of those consequences.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 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".