The role of climate in shaping vegetation dynamics and carbon dioxide fluxes in global protected forest landscapes
Bibliographic record
Abstract
Protected forest areas (PAs) are vital for biodiversity conservation, climate regulation, and carbon sequestration. Yet their ecological resilience faces increasing threats from climate change and human disturbances. Despite international efforts to expand PA coverage, the effectiveness of existing PAs in maintaining ecological functions under climate stress remains uncertain. To address this, we analyzed climate-driven vegetation dynamics, tree cover loss, and carbon dioxide (CO 2 ) fluxes across eight globally distributed tropical and temperate PAs between 2001 and 2023. Using climate datasets, MODIS-derived Gross Primary Productivity (GPP), vegetation indices (NDVI and EVI), tree cover loss products, and spatial carbon flux estimates, we assessed site-specific ecosystem responses to climate variability and forest degradation. Significant warming trends occurred at four sites (Crater Mountain, Białowieża, Tasmania, and Wolong), but significant precipitation changes were limited, decreasing in Crater Mountain and increasing in Wolong. GPP showed nonlinear temperature responses, peaking at 22–27 °C and declining sharply above 28 °C, signaling emerging productivity thresholds. NDVI exhibited consistent temperature sensitivity (Jaú, R 2 = 0.32; Tasmania, R 2 = 0.43) but weak precipitation relationships. Substantial tree cover loss occurred primarily in Tasmania and Yellowstone, coinciding with significant emission increases in Kahuzi-Biega, Crater Mountain, Yellowstone, Wolong, and Jaú (R 2 = 0.35–0.70; p < 0.05). Critically, despite rising emissions, most PAs remained net carbon sinks, except Gunung Leuser, which became a net carbon source despite minimal forest loss. Our findings indicate a critical decoupling between forest structure and carbon balance, underscoring the urgent need for adaptive strategies to safeguard ecological resilience in protected forests. • Warming reduces productivity thresholds in global protected forest areas. • Forest productivity closely follows temperature rather than rainfall trends. • Despite forest loss, most protected forests remain carbon sinks. • Minimal forest loss can still lead to carbon emissions under climate stress. • Functional forest health is crucial to global climate mitigation goals.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".