Recovery time and state change of terrestrial carbon cycle after disturbance
Bibliographic record
Abstract
Ecosystems usually recover from disturbance until a stable state, during which carbon (C) is accumulated to compensate for the C loss associated with disturbance events. However, it is not well understood how likely it is for an ecosystem to recover to an alternative state and how long it takes to recover toward a stable state. Here, we synthesized the results from 77 peer-reviewed case studies that examined ecosystem recovery following disturbances to quantify state change (relative changes between pre-disturbance and fully recovered states) and recovery times for various C cycle variables and disturbance types. We found that most ecosystem C pools and fluxes fully recovered to a stable state that was not significantly different from the pre-disturbance state, except for leaf area index and net primary productivity, which were 10% and 35% higher than the pre-disturbance value, respectively, in forest ecosystem. Recovery times varied largely among variables and disturbance types in the forest, with the longest recovery time required for total biomass (104 ± 33 years) and the shortest time required for C fluxes (23 ± 5 years). The longest and shortest recovery times for different disturbance types are deforestation (101 ± 28 years) and drought (10 ± 1 years), respectively. The recovery time was related to disturbance severity with severer disturbances requiring longer recovery times. However, in the long term, recovery had a strong tendency to drive ecosystem C accumulation towards an equilibrium state. Although we assumed disturbances are static, the recovery-related estimates and relationships revealed in this study are crucial for improving the estimates of disturbance impacts and long-term C balance in terrestrial ecosystems within a disturbance-recovery cycle.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".