Modelling Climate Change Impacts on Tropical Dry Forest Fauna
Bibliographic record
Abstract
Tropical dry forests are among the most threatened ecosystems in the world, and those occurring in the insular Caribbean are particularly vulnerable. Climate change represents a significant threat for the Caribbean region and for small islands like Jamaica. Using the Hellshire Hills protected area in Jamaica, a simple model was developed to project future abundance of arthropods and lizards based on current sensitivities to climate variables derived from rainfall and temperature records. The abundances of 20 modelled taxa were predicted more often by rainfall variables than temperature, but both were found to have strong impacts on arthropod and lizard abundance. Most taxa were projected to decrease in abundance by the end of the century under drier and warmer conditions. Where an increase in abundance was projected under a low emissions scenario, this change was reduced or reversed under a high emissions climate change scenario. The validation process showed that, even for a small population, there was reasonable skill in predicting its annual variability. Results of this study show that this simple model can be used to identify the vulnerability of similar sites to the effects of shifting climate and, by extension, their conservation needs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".