Adaptive forest conservation in southwest China’s biodiversity hotspot: integrating spatiotemporal dynamics
Bibliographic record
Abstract
Forest fragmentation disrupts ecological processes and negatively impacts ecosystem health and human benefits. Forest landscape dynamics are influenced by both natural factors and human activities. Understanding these dynamics is crucial for sustainable forest management and conservation. This study analyzes the forest landscape dynamics in Yunnan Province, China, in 2000, 2010 and 2020 using Morphological Spatial Pattern Analysis (MSPA). The findings reveal significant changes in forest core areas’ spatial distribution and connectivity, highlighting both successful conservation efforts and ongoing challenges. Forest coverage increased from 47% in 2000 to 67% in 2020, largely due to China’s conservation efforts. MSPA results show core areas becoming more consolidated, with fewer smaller patches and more extensive contiguous areas, particularly in the southwestern and northwest regions. However, the disappearance of core areas in some border regions highlights the need for targeted conservation efforts to mitigate habitat loss and maintain ecological integrity. Key conservation areas include Ailaoshan and Wuliangshan National Nature Reserves in the central region, Wumengshan in the northeast, Xishuangbanna in the south, and Gaoligongshan in the northwest. Moreover, the counties with high forest stability in the southwest region have a positive impact on surrounding counties, whereas urban expansion in the eastern region has a negative impact on forest stability. Efforts to restore and protect forest ecosystems should continue, with a focus on enhancing forest landscape connectivity, particularly in the eastern and central regions, which face significant pressures from urban expansion and land development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".