Occurrence of lemurs in landscapes and their species‐specific scale responses to habitat loss
Bibliographic record
Abstract
Studies on the impact of habitat loss on species occurrence consistently find that the amount of habitat (measured as patch area) is a major determinant of species occurrence at a patch-level. However, patch-level research may fail to detect important patterns and processes only observable at a landscape-level. A landscape-level approach that incorporates species-specific scale responses is needed to better understand what drives species occurrence. Our aim was to determine the landscape-level scale of effect of habitat amount on the occurrence of three species of nocturnal lemurs (Cheirogaleus medius, Microcebus murinus, and M. ravelobensis). We surveyed line transects to determine the occurrence of three lemur species within a fragmented landscape of deciduous dry forest and anthropogenic grassland in northwestern Madagascar. To determine the scale of effect of habitat loss on lemur occurrence, we compared logistic regression models of occurrence against habitat amount among eight different landscape scales using Akaike's Information Criterion values. We found differing scale responses among the lemurs in our study. Occurrence of C. medius responded to habitat amount at scales between 0.5-4 ha, M. murinus at scales between 1 and 4 ha and M. ravelobensis at scales between 0.125 and 4 ha. We suggest that the scale of effect for C. medius is mediated by their ability to hibernate. A relatively lower scale-response for Microcebus spp. likely reflect their omnivorous diet, small habitat requirements, and limited dispersal ability. Differences in scale responses between M. murinus and M. ravelobensis are likely a result of differing dispersal ability and responses to edge effects between these species. Our study is among the first on lemurs to show the value of a landscape-level approach when assessing the effects of habitat loss on species occurrence.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".