Tree mortality provides early warning sign of ecosystem functional transition in coastal freshwater forested wetlands
Bibliographic record
Abstract
Abstract Globally, coastal forested wetlands are increasingly affected by relative sea level rise. However, the mechanisms underlying coastal wetland degradation remain unclear. Using the eddy covariance approach, we evaluated the long-term (i.e. 2009–2019) net ecosystem exchange (NEE), associated hydrology, and ecosystem structural changes in a forested wetland in North Carolina, USA. We quantified tree mortality in response to changes in hydrology. The ecosystem shifted from a net C sink (NEE = − 3.68 Mg C ha−1 yr−1) pre-2010 to a net C source (NEE = 0.87 Mg C ha−1yr−1–7.59 Mg C ha−1 yr−1) afterward. We ascribe the ecosystem C loss to increasing tree mortality (i.e. from 1.6% in 2009 to 45.8% in 2019), partly due to relative sea level rise (R 2 = 0.62). Tree mortality, and consequent shift of NEE, provide early warning signs of a transition in this ecosystem more than 20 km inland from the coast.
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 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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".