Forest buffer-strips mitigate the negative impact of oil palm plantations on stream communities
Bibliographic record
Abstract
The global area cultivated with oil palm has doubled in the past two decades, causing deforestation, land-use change, freshwater pollution, and species loss in tropical ecosystems worldwide. Despite the palm-oil industry been linked to severe deterioration of freshwater ecosystems, most studies have focused on terrestrial environments, while freshwaters have been significantly less studied. We evaluated these impacts by contrasting freshwater macroinvertebrate communities and habitat conditions in 19 streams from primary forests (7), grazing lands (6), and oil palm plantations (6). In each stream, we measured environmental characteristics, e.g., habitat composition, canopy cover, substrate, water temperature, and water quality; and we identified and quantified the assemblage of macroinvertebrates. Streams in oil palm plantations lacking riparian forest strips showed warmer and more variable temperatures, higher turbidity, lower silica content, and poorer macroinvertebrate taxon richness than primary forests. Grazing lands showed higher conductivity and temperature, and lower dissolved oxygen and macroinvertebrate taxon richness than primary forests. In contrast, streams in oil palm plantations that conserved a riparian forest, showed a substrate composition, temperature, and canopy cover more similar to the ones in primary forests. These habitat improvements by riparian forests in the plantations increased macroinvertebrate taxon richness and maintained a community resembling more the one in primary forests. Therefore, the conversion of grazing lands (instead of primary forests) to oil palm plantations can increase freshwater taxon richness only if riparian native forests are safeguarded.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".