Effects of ecological factors on the spatial distribution of food plants in the habitat of Hainan gibbons (Nomascus hainanus): Insights for conservation and habitat restoration
Bibliographic record
Abstract
Understanding the availability of food resources is essential for effectively conserving endangered species. This study quantified the distribution of food plants within the Hainan gibbon habitat and assessed the environmental drivers of these distributions to guide targeted habitat restoration efforts. A total of 122 habitat plots were surveyed across five gibbon groups to collect the environment and food plant diversity data. Groups A to D occupied tropical montane rainforests (800–1200 m), while group E inhabited secondary lowland rainforests (500–700 m). Results revealed: 1) Climate and soil factors differed significantly between high- and low-altitude habitats. 2) Food plant species richness was higher in high-altitude habitats, while dry-season foods and preferred foods were more abundant in A and C groups. 3) Elevation, soil C/N ratio, soil alkaline dissolved nitrogen, and soil fast-acting phosphorus significantly affected food plant distribution. Soil content and climate are key drivers, with varying effects across different altitudes and food plant types. These findings indicate that successional low-altitude secondary forests are potential habitats for Hainan gibbons (e.g., group E) but require further restoration in lower quality areas. Our study highlights the need for habitat-specific restoration: in low-altitude forests, improving soil conditions (i.e., introducing native nitrogen-fixing species such as Albizia spp. to reduce C/N ratios and enhance alkaline dissolved nitrogen) can promote key food plant growth. In high-altitude forests, introducing climate-resilient species (e.g., Ficus spp. ) can offset temperature and precipitation limitations. Such targeted actions are critical to ensuring food stability and supporting Hainan gibbon conservation. • Understanding ecological drivers of food plant distribution informs conservation and habitat restoration of Hainan gibbons. • Hainan gibbons thrive successfully in low-altitude secondary forests albeit with lower habitat quality. • Elevation, C/N ratio, alkaline dissolved nitrogen, and soil fast-acting phosphorus significantly affect food plant distribution. • Soil content and climate are key drivers of food plant distribution, with varying effects across altitudes and plant types. • Targeted habitat restoration in low-altitude forests by selective thinning and planting large food tree seedlings is needed.
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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 | 0.001 |
| 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) | 0.000 | 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".