Plant traits – a tool for restoration?
Bibliographic record
Abstract
Abstract Question Most results of restoration efforts are species‐specific and/or site‐specific and therefore are not general enough to be easily applied to other species and other sites. Our research addresses the issue of species‐specific results by investigating the feasibility of using plant traits instead of taxonomic identity to characterize species responses to restoration treatments. Location Ten bunchgrass prairie sites in the Pacific Northwest of North America (Oregon and zashington, USA; British Columbia, Canada). Methods We developed two types of quantitative models for each of ten prairie restoration sites: (1) plant trait models, which related plant traits to species field responses following restoration management treatments; and (2) species identity models, which related species taxonomic identity to species field responses following restoration management treatments. Species identity models determined the maximum amount of variability of field responses that can be explained by differences in individual species' responses to management treatments. Plant trait models determined what proportion of this explanatory power can be attributed to plant traits. The two model types addressed the following specific questions: (1) how much of the variability in field responses (changes in cover) of plants to restoration management treatments is explained by plant traits; and (2) how well do plant traits explain the variability of field responses (changes in cover) following restoration management treatments compared to models relating field responses to species identity? Results (1) The plant trait models explained much of the variability within each of the ten restoration sites, with R2 values ranging between 31% and 69%. (2) The species identity models explained between 47% and 74% of variability of change in cover (R2). Thus, the plant trait models explained nearly as much variability as the species identity models. In seven out of nine sites, the plant trait models were superior to the species identity models, as measured by AIC, i.e. the trait models did well at explaining variability with less model complexity. Conclusion Strong explanatory power of plant trait models supports the feasibility of using plant traits instead of species taxonomic identity as a common language to characterize plant field responses (changes in cover) to restoration treatments.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".