Thirty years of deforestation within the entire ranges of nine endangered lemur species (3 CR, 4 EN, 2 VU) in northwestern Madagascar
Bibliographic record
Abstract
Abstract Forest cover change is of particular concern in tropical regions. In this study, we investigate the degree of deforestation in the entire ranges of nine highly threatened lemur species in northwestern Madagascar. Landsat satellite images were acquired from four different time stages (1990, 2000, 2011, 2020), classified into forest/non-forest, and changes quantified. Forest cover declined from 17.5% to 9.3% within the last 30 years. This decline varied across four protected areas (PAs) investigated: the forest cover of Ankarafantsika National Park (ANP) declined only moderately over time (from 76.3% to 67.4%), while it declined drastically in other PAs (e.g., from 54.9% to 18.9%, Bongolava Forest Corridor). Two lemur taxa are most affected ( Lepilemur otto, Microcebus bongolavensis ) by having only very few isolated forest patches left within their ranges (approximately 542.7 km²). For two other species ( L. ahmansoni, L. aeeclis ), most of the remaining forest is concentrated in two coastal PAs (in total 627.2 and 477.9 km², respectively), while those species occurring inside ANP (5 taxa) experienced rather stable forest coverage until 2020. A reversal of these deforestation trends and active reforestation measures are desperately needed to reduce habitat loss for these nine lemur species. A practical experience-based guideline is therefore provided.
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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 | 0.000 |
| 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".