A risk-based model of climate change threat: hazard, exposure, and vulnerability in the ecology of lichen epiphytes
Bibliographic record
Abstract
This review positions the biodiversity response to climate change within a social-sciences risk-based framework, integrating the parameters of hazard, exposure, and vulnerability. It uses lichen epiphytes as a case study. In treating human-induced climate change as a hazard, the exposure of lichen epiphytes is considered as their sensitivity to spatial climatic variation, while also seeking congruence between bioclimatic models and observational data supporting distributional change. Improved understanding of exposure could be generated through functional response models, and climate sensitivity should be carefully interpreted against co-occurring hazards (pollution, habitat degradation). Where negative impacts result from exposure to climate change, species vulnerability may be reduced through adaptive forest management. This opportunity is based on a cross-scale interaction between microhabitat specificity and macroclimatic setting. Certain stand types (e.g., old-growth stands) offer greater opportunity for establishment and growth in suboptimal climates, because high microhabitat heterogeneity generates a broader spectrum of microclimatic niches, which buffer an unsuitable macroclimate. Lichen epiphyte vulnerability will nevertheless be dependent on an amalgam of ecological processes considered at the stand scale, including trophic interactions, acclimation, and evolutionary adaptation, and at the landscape scale, including gene flow and dispersal limitation. A trait-focused approach could provide an opportunity to generalize these processes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".