Anthropogenic disturbance modifies tree functional traits in the only remnant swamp forest of Bangladesh
Bibliographic record
Abstract
The relationship between ecosystem functions and plant functional traits has been well documented and is considered to be one of the most vital topics in ecology. However, the correlation between anthropogenic disturbance and tree functional trait diversity remains largely unclear. The present study investigates the role of anthropogenic disturbance on selected tree functional traits in Ratargul Swamp Forest (RSF) – the only remnant tropical freshwater swamp forest ecosystem in Bangladesh. We established 50 plots and collected six plant traits, i.e., tree height, specific leaf area (SLA), wood density (WD), leaf dry matter content (LDMC), seed mass, and bark thickness. A two-way analysis of variance (ANOVA) was carried out to test the interaction between plant functional traits and anthropogenic disturbance in RSF. Our study found that anthropogenic disturbance has a significant (p< 0.05) influence on deciduous swamp forest tree species’ functional traits but none on evergreen tree species’ functional traits. For deciduous trees, disturbance has a significant positive effect on CWM.SLA (p< 0.05) and CWM.Height (p< 0.05) and a negative impact on CWM.Bark-thickness and CWM.Seed-mass (p< 0.05). There were no significant effects of disturbance on CWM.WD and CWM.LDMC. We believe the present study will contribute toward improving our understanding of the effect of disturbances on tree functional trait diversity in tropical human-dominated landscapes where anthropogenic pressure is increasing at an unprecedented rate. It will also function as an essential conservation for related decision-making of Ratargul Swamp Forest in northeastern Bangladesh and elsewhere in the tropics with a similar context.
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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 | 0.000 |
| 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 | 0.001 |
| 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".