Estimating the population size of lemurs based on their mutualistic food trees
Bibliographic record
Abstract
Abstract Aim Species’ distributions and abundances are primarily determined by the suitability of environmental conditions, including climate and interactions with sympatric species, but also increasingly by human activities. Modelling tools can help in assessing the extinction risk of affected species. By combining species distribution modelling of abiotic and biotic niches with population size modelling, we estimated the abundance of 19 lemur taxa in three regions, especially focusing on 10 species that are considered Endangered or Critically Endangered. Location Madagascar. Taxa Lemurs (Primates) and angiosperm trees. Methods We used climate data, field samples, and published occurrence data on trees to construct species distribution models (SDM) for lemur food tree species. We then inferred the SDMs for lemurs based on the probability of occurrence of their food trees as well as climate. Finally, we used tree SDMs, topography, distance to the forest edge, and field estimates of lemur population density to predict lemur abundance in general linear models. Results The SDMs of lemur food trees were stronger predictors of the occurrence of lemurs than climate. The predicted probability of presence of food trees, slope, elevation, and distance from the forest edge were significant correlates of lemur density. We found that sixteen species had minimum estimated abundances greater than 10,000 individuals over >1,000km2. Three lemur species are especially threatened, with less than 2,500 individuals predicted for Cheirogaleus sibreei, and heavy hunting pressure for the relatively small populations of Indri indri and Hapalemur occidentalis. Main conclusions Biotic interactors were important variables in SDMs for lemurs, allowing refined estimates of ranges and abundances. This paper provides an analytical workflow that can be applied to other taxonomic groups to substantiate estimates of species’ vulnerability to extinction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".