Threshold dynamics in plant succession after tree planting in agricultural riparian zones
Bibliographic record
Abstract
Summary Trajectories of plant communities can be described by different models of plant succession. While a Clementsian (gradual continuum model) or Gleasonian approach (relay floristics model) has traditionally been used to inform restoration outcomes, alternative succession models developed recently may better represent restoration trajectories. The threshold dynamics succession model, which predicts an abrupt species turnover after an environmental threshold is crossed, has never been used in a restoration context. This model might, however, better describe shifts in plant competitive ranking and facilitation interactions during species turnover. Fifty‐three riparian zones, planted with trees 3–17 years prior to sampling, and 14 natural riparian forests were studied in two agricultural watersheds of south‐eastern Québec (Canada). The cover of vegetation strata was assessed at the site scale, and the cover of plant species was estimated in a total of 784 1‐m 2 plots. Canopy cover was measured stereoscopically for each plot. As revealed by Principal Response Curves and broken stick models, herbaceous species composition was stable during the first 12–13 years after tree planting, but then abruptly shifted. This two‐step pattern in species turnover followed the increase in canopy cover after tree planting. Once canopy cover passed a threshold of ca 40%, plant succession started and led to the re‐establishment of forest communities 17 years after planting. Following herbaceous species turnover, the cover of ecological groups changed significantly towards covers of natural riparian forests: shade‐tolerant species generally increased, while light‐demanding and non‐native species decreased. Vegetation structure was also significantly affected by tree planting: tree and shrub cover increased, while monocot cover decreased. Synthesis and applications . Tree planting efficiently restored herbaceous forest communities in riparian zones by inducing a species turnover mediated by light availability corresponding to the threshold dynamics model in plant succession. Fostering and monitoring canopy closure in tree‐planted riparian zones should improve restoration success and the design of alternative strategies. The innovative statistical approach of this study aiming to identify succession patterns and their associated theoretical models can guide future restoration in any type of ecosystem around the world to bridge the gap between science and management.
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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".