<i>Lemur catta</i> in small forest fragments: Which variables best predict population viability?
Bibliographic record
Abstract
Habitat fragmentation is an increasingly serious issue affecting primates in most regions where they are found today. Populations of Lemur catta (ring-tailed lemur) in Madagascar's south-central region are increasingly restricted to small, isolated forest fragments, surrounded by grasslands or small-scale agriculture. Our aim was to evaluate the potential for population viability of L. catta in nine forest fragments of varying sizes (2-46 ha, population range: 6-210 animals) in south-central Madagascar, using a set of comparative, quantitative ecological measures. We used Poisson regression models with a log link function to examine the effects of fragment size, within-fragment food availability, and abundance of matrix resources (food and water sources) on L. catta population sizes and juvenile recruitment. We found a strong association between overall population size and (a) fragment size and (b) abundance of key food resources Melia azedarach and Ficus spp. (per 100 m along transect lines). Juvenile recruitment was also associated with fragment size and abundance of the two above-mentioned food resources. When the largest population, an outlier, was removed from the analysis, again, the model containing fragment size and abundance of M. azedarach and Ficus spp. was the best fitting, but the model that best predicted juvenile recruitment contained only fragment size. While our results are useful for predicting population presence and possible persistence in these fragments, both the potential for male dispersal and the extent of human disturbance within most fragments play crucial roles regarding the likelihood of long-term L. catta survival. While seven of the nine fragments were reasonably protected from human disturbance, only three offered the strong potential for male dispersal, thus the long-term viability of many of these populations is highly uncertain.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".