A framework for prioritizing areas for conservation in tropical montane cloud forests
Bibliographic record
Abstract
Tropical cloud forests are under severe distress, as deforestation leads to forest fragmentation and degradation. This represents a severe threat to small-ranged, forest-dependent species, as they are at risk of losing habitat and connectivity between populations. These detrimental effects are aggravated by upslope range shifts caused by climate change, as further habitat loss is expected. To mitigate these threats, the preservation of habitat and connectivity becomes necessary. Here, we present a novel framework for identifying future key areas offering high-quality habitat and connectivity. The framework combines data on the composition of forests, their configuration in the landscape, as well as dispersal abilities and altitudinal range for several focal species. Importantly, the framework integrates projections of future range shifts. Thus, it prioritizes a network of areas with high-conservation value robust to climate change. We applied the framework to the cloud forest in Ecuador, using two endemic bird species to identify areas for mitigating the adverse effects of climate change. Our approach allows targeting reforestation measures effectively to areas of high-conservation value. The framework presented here can be applied to different ecosystems and geographical locations, and therefore contribute to making informed decisions about the implementation of robust conservation measures.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".