Conservation value of cacao agroforestry for amphibians and reptiles in South‐East Asia: combining correlative models with follow‐up field experiments
Bibliographic record
Abstract
Summary 1. Although agricultural expansion is a primary threat to tropical biodiversity, experimental studies evaluating the conservation value of tropical agricultural habitats are scarce. In particular, little is known about the sensitivity of amphibians and reptiles to habitat disturbance in areas of very high diversity such as South‐East Asia. 2. We used a two‐step approach to determine the relationship between habitat complexity and conservation value of cacao agroforestry for herpetological diversity in Sulawesi (Indonesia). Indonesia is the third largest cacao‐exporting country globally and forest conversion to cacao plantations is a major threat to its biodiversity. We first sampled 43 cacao plantations six times to determine the environmental variables that best explained herpetofaunal diversity patterns using a Bayesian model selection approach. Based on these results, we experimentally manipulated leaf litter thickness (LLT), number of branch piles (LOGS) and LLT + LOGS combinations in the cacao plots. The experimental data were analysed using Bayesian hierarchical regression. 3. The best supported correlative models incorporated LLT, LOGS, air temperature and the ratio between leaf litter and shrub cover, showing the importance of habitat heterogeneity and suggesting climate change sensitivity. The subsequent structural manipulation of these attributes changed amphibian and reptile species richness, and reptile abundance, but only addition of leaf litter did so in a biologically meaningful way, providing microhabitat resources. However, the main beneficiaries were common disturbance‐tolerant reptiles. 4. Synthesis and applications. The different results from the correlative model and the independent manipulative experiments showed how important such a combined approach is to derive adequate conservation management recommendations. Increasing leaf litter in cacao agroforestry will work best if implemented on a landscape scale to incorporate sufficient environmental variation and species life histories. This will mainly enhance the richness and abundance of disturbance‐tolerant species, which still may maintain ecosystem functions such as pest removal. Particularly for rare species, native forests remain critical for herpetological richness. The direct temperature sensitivity suggests that future climate change impacts may be severe for herpetological diversity in plantation habitats and, hence, demand further research.
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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 | 0.000 |
| 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".