Assessing the functional role of plant diversity in grasslands: a trait-based approach.
Bibliographic record
Abstract
The objective of this chapter is to provide some insights into the functional role of plant diversity in grasslands. To do so, we will follow a trait-based approach to species functioning, and show how it can be used, in combination with community structure, to infer some properties of ecosystems, and to serve as a basis for grassland management. We will stick to the idea that a trait is measured at the level of an individual organism (see McGill et al., 2006, for more details; Lavorel et al., 2007), with the following defi nition: ‘any morphological, physio logical or phenological feature measur able at the individual level, from the cell to the whole-organism level’ (Violle et al., 2007). We will present recent developments in grassland ecology using traits, largely based on the response-effect framework proposed by Lavorel and Garnier (2002) and further refi ned by Suding et al. (2008). A simplifi ed scheme of this framework, incorporating ecosystem services provided by grasslands, is pre sented in Fig. 15.1: environmental drivers act as fi lters sorting species according to the value of their traits (so-called ‘response traits’), which results in a functional structure of communities depending on the type and strength of these fi lters. In turn, the functional structure of communities, defi ned as the value, range and relative abundance of traits, has various impacts on ecosystem properties (via so-called ‘effect traits’) and services (Diaz et al., 2007b). We examine below how this general framework applies to grasslands.
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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 | 0.000 | 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) | 0.000 | 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".