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
Abstract Food resources are crucial for conserving endangered species. Quantifying the distribution of food plants and their driving factors in Hainan gibbon habitats helps to understand food supply characteristics and guide targeted habitat restoration. This study was based on a survey of 122 grid plots (20 × 20 m), categorized as food plants by size (large: diameter at breast height [DBH] ≥ 15 cm; small: 1 cm ≤ DBH < 15 cm) and seasonality (dry season and preferred food plants). The results showed: 1) Significant differences in the ecological factors between high-altitude and low-altitude habitats (t = − 9.04–11.03, P < 0.05). 2) Significantly higher species richness (t = 2.08–5.02, P < 0.05) in high-altitude habitats than low-altitude ones, with a long dry season, and preferred food plants were more abundant in the A and C family groups. 3) Key factors affecting the spatial distribution of food plants included elevation, the soil carbon-to-nitrogen ratio, average temperature, and annual precipitation, with effects varying by food plant type and elevation. These findings indicate that successional low-altitude secondary forests are potential habitats for Hainan gibbons (e.g., family group E) but require further restoration for population growth and spatial expansion. In contrast, high-altitude areas remain ideal. This research underscores the importance of tailored restoration strategies for different food plant groups. Practices, such as thinning, are recommended to enhance food plant diversity and ensure a stable food supply. Future research should focus on human disturbances and plant interactions to fully understand the food plant distribution patterns.
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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 | 0.000 |
| 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 | 0.000 |
| 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".