The role of non‐plant species in ecological succession and implications for restoration
Bibliographic record
Abstract
Animals and fungi have long played an overlooked, yet critical, role in the process of ecological succession, which is a foundational theory underpinning the practice of ecological restoration. Restoration efforts often employ three main strategies to manipulate succession, which we can broadly label as: “jumping the line” “resetting” and “removing barriers.” Despite their potential impact, current applications of animals and fungi to manipulate succession in restoration efforts have been limited. Examples of their current use include the use of mycelial networks to enhance nutrient cycling in forest ecosystems and bison reintroduction to restore grasslands. However, wide‐ranging implementation of taxonomically holistic restoration practices faces various impediments, including logistical challenges, financial constraints, and lack of specialized knowledge. Nevertheless, paradigm shifts toward more inclusive restoration practices that leverage diverse taxa will increase both ecosystem and individual species' resilience, which is especially relevant in the face of the climate and biodiversity crises.
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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".