Rainfall Modifies the Disturbance Effects on Regulating Ecosystem Services in Tropical Forests of Bangladesh
Bibliographic record
Abstract
Tropical forests are critical for ecosystem functioning and are the major provider of global ecosystem services. The combined effects of climate change and anthropogenic disturbances in the tropics, however, have caused a mass decline in major ecosystem services in recent decades. Nevertheless, most previous studies focused entirely on these disturbances’ effects on forest ecosystem services without taking into consideration the combined effects of climate change and disturbances on key ecosystem services’ indicators. This is more pronounced in Bangladesh, which is one of the countries most adversely affected by global climate change. We investigated anthropogenic disturbances’ effects on regulating ecosystem services under different rainfall regimes in Khadimnagar National Park (KNP) and Satchari National Park (SNP), two major forested, protected areas in Northeast Bangladesh. We considered twelve indicators for five aboveground ecosystem services (i.e., primary production, carbon storage, biodiversity, wildlife habitat, and timber production) and four indicators for four belowground ecosystem services (i.e., organic carbon storage, soil aggregate stability, soil physical support, and soil water regulation) in our study. The effects of disturbances on the indicators of four aboveground ecosystem services (i.e., primary production, tree carbon storage, biodiversity conservation, and timber production) and one belowground ecosystem service (soil physical support) differed in terms of rainfall regimes. Disturbances negatively influenced indicators of primary production (p = 0.03, r = −0.28), tree carbon storage (p = 0.02, r = −0.29), and timber production (p = 0.002, r = −0.39), and positively impacted soil organic carbon storage (p = 0.04, r = 0.27) only under a medium-rainfall regime. Interestingly, a significant positive impact of disturbances on soil physical support (p = 0.001, r = 0.39) was observed under heavy rainfall in our study. In addition, disturbances were significantly associated with biodiversity conservation (p < 0.05) and wildlife habitat quality (p < 0.05) under both rainfall classes. Our findings provide critical insights into the synergistic role of anthropogenic perturbations in regulating ecosystem services with rainfall pattern as a key climatic parameter, which can be useful in restoring large tracts of degraded tropical forests in the United Nations (UN) Decade on Ecosystem Restoration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".