Succession theory and vegetation restoration
Bibliographic record
Abstract
Kunming-Montreal Global Biodiversity Framework proposes to protect 30% and restore 30% of the land with high quality and maximize the goal of conserving biodiversity and mitigating climate change. Succession theory and vegetation restoration can serve the targets of 30% protection and restoration. Succession theory is the core theory in vegetation ecology. Succession refers to the process that the structure or composition of a group of different species in a site change with time. Vegetation restoration is the process of restoring or recovering or naturally renewing plant communities, mainly based on plant planting and configuration. Vegetation restoration is the process of changing the structure and function of ecosystem from simple to complex, from low level to high level, and the ultimate goal is to establish healthy and stable plant communities. Succession is the foundation of vegetation restoration, and vegetation restoration can be seen as the manipulation of the succession process to achieve the goal of restoring damaged vegetation ecosystem. Succession theory can guide vegetation restoration. Vegetation restoration is also beneficial to the development of succession theory. Succession theory and vegetation restoration differ in scale, theme and paradigms. Succession often emphasizes disturbances related to nature, while vegetation restoration focuses on disturbances related to humans. The succession can be divided into primary succession and secondary succession according to the nature of bare land. The restoration process is suggested to be regarded as the tertiary succession, which will help to understand the management options for promoting the success of vegetation restoration through human intervention, especially by emphasizing the management options which may improve success, especially by addressing environmental and biological legacies. Artificial intervention based on succession theory can accelerate vegetation restoration, avoid early positive promotion of degraded vegetation ecosystems to pre-degraded levels in poor habitats, and also avoid resource waste caused by disordered competition and low efficiency among communities. This paper also puts forward the scientific and technical issues on the theories of vegetation restoration and succession in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".