Species‐area relationships of lemurs in a fragmented landscape in Madagascar
Bibliographic record
Abstract
We used species-area relationships (SARs) to investigate the effects of habitat loss on lemur biogeography. We measured species richness via visual surveys on line transects within 42 fragments of dry deciduous forest at the Ambanjabe field site in Ankarafantsika National Park, Madagascar. We measured human disturbance and habitat characteristics within 38 of the 42 fragments. We measured the distance between each fragment and the nearest settlement, continuous forest, and nearest neighboring fragment. We fit 10 candidate SAR models to the data using nonlinear least squares regression and compared them using Akaike's Information Criterion (AIC). To determine how habitat characteristics, as well as area, influenced species richness, we ran a hierarchical partitioning procedure to select which variables to include in generalized additive models (GAMs) and compared them using AIC. Contrary to expectations, we found that lemurs form convex SARs, without a "small island effect", and with the power model being the most likely SAR model. Although we found that four variables (area, survey effort, and total human disturbance, and mean tree height) independently contributed greater than 10% of the variation in lemur species richness, only area was included in the most likely model. We suggest that the power model was the most likely SAR model and our inability to detect a "small island effect" are the result of Microcebus spp. being edge tolerant and capable of dispersing through matrix, scale issues in the study design, and low γ-diversity in the landscape. However, more study is needed to determine what role human disturbance plays in influencing species richness in lemurs.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".