Using rare mosses to resolve barriers in the use of species distribution models for climate change vulnerability assessments
Bibliographic record
Abstract
Abstract Climate change vulnerability assessments (CCVAs) provide a framework to assess the threat of climate change and inform conservation decisions. Species distribution models (SDMs) can be informative for a primary component of CCVAs: estimating climate change exposure (hereafter exposure). Despite their utility, SDMs are inconsistently applied. Limitations of few occurrences and difficulty obtaining microclimate‐informed predictors relevant in topographically complex and heterogeneous landscapes challenge their use and may lead to inaccurate exposure estimates. To address this, we develop SDMs with a technique adapted for few occurrences for two rare mosses, Bartramia aprica and Bartramia halleriana , and use a simple method for representing microclimates for the latter, which occurs in mountainous regions. We estimate exposure from models with varying microclimatic detail, spatial resolution, and extent, and explore additional uncertainty by comparing estimate types, scenarios, and potential for extrapolation to novel climates. We found that including microclimate data, smaller spatial extents, and finer resolutions predicted less exposure and produced the best‐performing models. We additionally found that B. halleriana may face greater exposure regardless of the scenario, model, or exposure estimate used. Based on our findings, we introduce a framework suggesting approaches for these difficult cases to enhance the consistent implementation of SDMs in CCVAs.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".