Differences in Habitat Quality Drive Behavioral Contrasts in Two Family Groups of the Critically Endangered Hainan Gibbon ( <i>Nomascus hainanus</i> )
Bibliographic record
Abstract
Understanding how spatiotemporal habitat variability shapes endangered species' behavior is crucial for effective conservation. This study examined the impact of fine-scale habitat variation on four behavioral patterns (feeding, resting, social, and traveling) of the critically endangered Hainan gibbon. Year-round behavior data were collected from two groups: GC inhabiting an area with abundant food resources, and GE in a secondary forest with sparse resources. Using 135 monitoring plots, we analyzed variation in 27 habitat variables categorized into food, nutrients, plant diversity, safety and stability, and topography. Linear models revealed crown height, food plant abundance, and crude fat as key variables shaping behaviors. Higher community plant and food plant richness significantly enhanced feeding frequency, while steeper slopes increased traveling. Habitat quality variation shaped distinct behavioral strategies: in GC, feeding and resting were primarily influenced by food, social behavior by safety and stability, and traveling by plant diversity. In GE, resting, social, and traveling behaviors were mainly driven by topography, while feeding was influenced by nutrients. Likewise, food variables dominated during the dry season, whereas safety and stability, and topography variables were more important in the wet season. This study provides the first analysis of Hainan gibbons' behavioral strategies linked to fine-scale habitat variability and seasonal dynamics. The findings highlight the importance of protecting diverse habitats, as different Hainan gibbon groups exhibit distinct behavioral adaptations to their varying resource availability. This underscores the need for habitat-specific primate conservation and management in fragmented landscapes.
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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 | 0.000 | 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".