Oil palm plantations fail to support mammal diversity
Bibliographic record
Abstract
Agricultural expansion is the largest threat to global biodiversity. In particular, the rapid spread of tree plantations is a primary driver of deforestation in hyperdiverse tropical regions. Plantations tend to support considerably lower biodiversity than native forest, but it remains unclear whether plantation traits affect their ability to sustain native wildlife populations, particularly for threatened taxa. If animal diversity varies across plantations with different characteristics, these traits could be manipulated to make plantations more "wildlife friendly." The degree to which plantations create edge effects that degrade habitat quality in adjacent forest also remains unclear, limiting our ability to predict wildlife persistence in mixed-use landscapes. We used systematic camera trapping to investigate mammal occurrence and diversity in oil palm plantations and adjacent forest in Sabah, Malaysian Borneo. Mammals within plantations were largely constrained to locations near native forest; the occurrence of most species and overall species richness declined abruptly with decreasing forest proximity from an estimated 14 species at the forest ecotone to -1 species 2 km into the plantation. Neither tree height nor canopy cover within plantations strongly affected mammal diversity or occurrence, suggesting that manipulating tree spacing or planting cycles might not make plantations more wildlife friendly. Plantations did not appear to generate strong edge effects; mammal richness within forest remained high and consistent up to the plantation ecotone. Our results suggest that land-sparing strategies, as opposed to efforts to make plantations more wildlife-friendly, are required for regional wildlife conservation in biodiverse tropical ecosystems.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.014 |
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; both teacher heads agree on what is shown here.
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".