Functional response to land use change in grasslands: Comparing species and trait data
Bibliographic record
Abstract
ABSTRACT The search for general plant community patterns that explain plant responses to land use changes is at present a major focus of studies related to grassland conservation. Traditionally, vegetation change has been documented by identifying species-based changes over time. Recent studies suggest that species-based assessments should be complemented with functional assessments of species characteristics. We examined land use change along a successional gradient by comparing grasslands with long grazing history, abandoned formerly grazed grasslands, and pastures with a short grazing history by using i) species data, i.e., species richness, composition, growth form, and homogenization, and ii) functional characteristics, using three core traits, seed mass, specific leaf area (SLA), and plant height. Analyzing species data directly in terms of species richness, composition, or growth form was a more straightforward tool than analyzing species function based on selected core traits. No functional groups, based on the investigated traits, were supported. Functional response trends were, however, detected, as height increased and SLA decreased along the successional gradient. Analyses based on species data followed documented response patterns associated with grassland succession, i.e., decrease in species richness, decrease in the number of plant species favoured by grazing, and shifts in species composition and growth form towards less dominance of herbs. Due to idiosyncrasy of individual species responses, we question the benefit of using a small number of response traits or functional groupings compared to species-based analyses for documenting vegetation changes in grazing systems over time.
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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 | 0.000 |
| 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".