Evaluating Landscape Suitability for Golden-Headed Lion Tamarins ( <i>Leontopithecus Chrysomelas</i> ) and Wied's Black Tufted-Ear Marmosets ( <i>Callithrix Kuhlii</i> ) in the Bahian Atlantic Forest
Bibliographic record
Abstract
In southern Bahia, Brazil, rapid deforestation of the Atlantic Forest threatens a variety of endemic wildlife, including the Endangered golden-headed lion tamarin (GHLT; Leontopithecus chrysomelas) and the Near Threatened Wied's black-tufted-ear marmoset (Wied's marmoset; Callithrix kuhlii). Identifying high quality areas in the landscape is critical for mounting efficient conservation programs for these primates. We constructed ecological niche models (ENMs) for GHLTs and Wied's marmosets using the presence-only algorithm Maxent to (1) locate suitable areas for each species, (2) examine the overlap in these areas, and (3) determine the amount of suitable habitat in protected areas. Our models indicate that 36% (10, 659 km 2 ) of the study area is suitable for GHLTs and 53% (15, 642 km 2 ) for Wied's marmosets. Suitable areas were strongly defined by presence of neighboring forest cover for both species, as well as annual temperature range for GHLTs and distance from urban areas for Wied's marmosets. Thirty-three percent of the landscape (9,809 km 2 ) is overlapping suitable habitat. Given that the focal species form mixed-species groups, these areas of shared suitability may be key locations for preserving this important behavioral interaction. Protected areas contained 6% (651 km 2 ) of all suitable habitat for GHLTs and 4% (682 km 2 ) for Wied's marmosets. All protected areas were suitable for the focal species, excepting Serra do Conduru, which had low suitability for GHLTs. Our results highlight that suitable habitat for GHLTs and Wied's marmosets is limited and largely unprotected. Conservation action to protect additional suitable areas will be critical for their persistence.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".