Impacts of fire and climate change on long-term nitrogen availability and forest productivity in the New Jersey Pine Barrens
Bibliographic record
Abstract
Increased wildfires and temperatures due to climate change are expected to have profound effects on forest productivity and nitrogen (N) cycling. Forecasts about how wildfire and climate change will affect forests seldom consider N availability, which may limit forest response to climate change, particularly in fire-prone landscapes. The overall objective of this study was to examine how wildfire and climate change affect long-term mineral N availability in a fire-prone landscape. We employed a commonly used landscape simulation model (LANDIS-II) in the New Jersey Pine Barrens, a landscape characterized by frequent small fires and fire-resilient vegetation. We found that fire had little effect on mineral N, whereas climate change and fire together reduced mineral N by the end of the century. Though N initially limited forest productivity, mineral N was no longer limiting after 50 years. Our results suggest that mineral N is resilient to fire under our current climate but not under climate change. Also, predictions that do not consider N limitation may underestimate short-term but not long-term productivity responses to climate change. Together these results illustrate the importance of including N dynamics when simulating the effects of climate change on forest productivity, particularly in fire-prone regions such as the New Jersey Pine Barrens.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".