Assessing the species habitats in Colombia’s tropical dry forest over a 20 years period
Bibliographic record
Abstract
Countries worldwide are collaborating under the Convention on Biological Diversity to address biodiversity loss. As part of this effort, the monitoring framework of the Kunming-Montreal Global Biodiversity Framework (K-M GBF) includes a set of indicators to track progress toward its goals and targets. One of these is the Species Habitat Index (SHI), a component indicator supporting Goal A, which measures changes in habitat extent and connectivity for multiple species. In this study, we applied the SHI to assess the status and trends of species' habitats in Colombia’s Tropical Dry Forests (TDF) from 2000 to 2020. These forests have undergone extensive degradation and fragmentation, being reduced to less than 2% of their original extent in some regions, with much of their original extent reduced to small, isolated patches. Overall, we found that Colombia’s TDF has lost nearly one-third of its cover since 1990, despite modest gains between 2010 and 2018. Most forest loss resulted from conversion to pasture, although some recovery of degraded forest was observed. We calculated SHI values for 755 bird (237), mammal (68), and plant (450) species using land cover data. To assess habitat connectivity, we used GISFrag and Omniscape and compared outputs. Across the potential TDF area, habitat and connectivity declined by approximately 20% between 2000 and 2020, leaving only ~800,000 ha of habitat. Species associated with natural habitats showed lower SHI values than those adapted to artificial environments, and mammals, many of which are threatened, had the lowest scores overall. We also evaluated the representativeness of protected areas and found that less than 13% of the remaining habitat lies within protected areas. The increasing extent of successional forests, now over 1,000,000 ha, presents an opportunity for ecological restoration. These results underscore the urgency of implementing nature-based solutions. Regionally tailored strategies will be critical to maintaining connectivity in this highly fragmented ecosystem.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".