Undesirable woody establishment is driven by herbaceous cover phylogenetics and abiotic conditions
Bibliographic record
Abstract
Abstract Revegetation with herbaceous covers is widely used to inhibit tree encroachment in managed areas. However, most studies tend to focus on species selection for inclusion in seed mixtures, neglecting the effect of local contingencies such as soil conditions and native vegetation emerging from the seed bank. Here, we tested the joint effect of sown herbaceous mixtures and local site conditions on the encroachment of undesirable woody species. In a recently cleared right-of-way, we sowed mixtures containing either 3, 5, or 8 species. Over two consecutive years, we allowed spontaneous vegetation to be established. We measured three facets of its diversity: taxonomic, functional and phylogenetic. We also evaluated how woody encroachment was spatially structured and influenced by soil physico-chemical properties. Our study illustrates how successful revegetation strategies for inhibiting tree species should account for both mixture effectiveness in ideal conditions and their ecological versatility (i.e., their ability to regenerate and thrive in various soil types and climatic conditions). The inhibition of woody species also involved multiple mechanisms, with phylogenetic diversity metrics being the dominant predictors of tree encroachment in the first year. However, the significance of abiotic mechanisms intensified in the second year. This temporal shift added complexity to identifying the most effective vegetation management strategies for inhibiting invasive and undesirable species. Hence, an effective prevention of woody species establishment on bare soil sites linked to infrastructure projects necessitates a multifaceted strategy, incorporating not only sown diversity but also a comprehensive understanding of soil properties and resident vegetation diversity.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".