Modelling workflow from: "Using rare mosses to resolve barriers in the use of species distribution models for climate change vulnerability assessments"
Bibliographic record
Abstract
guided-ESM-workflow This repository contains reusable versions of the scripts used to create (Ensemble of Small Models) ESM ecological niche models and microclimate adjustments used by Menchions et al. (2025). The code heavily relies on the previous work of Breiner et al.(2015), Di Cola et al.(2016), and Thuillier et al.(2016). Breiner, F. T., Guisan, A., Bergamini, A., & Nobis, M. P. (2015). Overcoming limitations of modelling rare species by using ensembles of small models. Methods in Ecology and Evolution, 6(10), 1210-1218. Di Cola, V., Broennimann, O., Petitpierre, B., Breiner, F. T., d'Amen, M., Randin, C., ... & Guisan, A. (2017). ecospat: an R package to support spatial analyses and modeling of species niches and distributions. Ecography, 40(6), 774-787. Thuiller, W., Georges, D., Engler, R., Breiner, F., Georges, M. D., & Thuiller, C. W. (2016). Package ‘biomod2’. Species Distrib. Model. within an ensemble Forecast. Framew. Overview We recommend using this process for species with few occurrences (< 50 presences), but for species with more than 50 presences, more streamlined and traditional modeling procedures might be more efficient (Breiner et al., 2015). *An additional guide directs users through the different components. See the "Workflow_Guide" in this repository* The workflow consists of 7 components (R scripts): - Part 0 - Setup- Part 1 - Occurrence data preparation - Part 2 - Environmental variable preparation- Part 3 - Modeling- Part 4 - Broad-range shift calculations (continuous and thresholded) - Part 5 - Novel areas removal - Part 6 - Rough Plotting- Custom function: ThresholdIndShifts.R After completion of Part 5, we recommend mapping and site-specific analyses to be performed in ArcGIS Pro or other GIS software. Still, we included a sixth script that can produce rough maps. Additionally provided, but not included in the guided instructions, is the script used for the microclimatic adjustments in Menchions et al. (2025). It can be used to add heterogeneity into climate data due to topography and canopy cover when data has only been statistically downscaled using elevation information. In Menchions et al. (2025), this was implemented at a 1-arc-second resolution, adjusting for estimated buffering and decoupling influences from canopy cover, topography concavity and slope aspects in mountainous terrain. It requires adaptation to be generalized. Acknowledgements This research was funded by the National Sciences and Engineering Research Council of Canada (NSERC).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.144 | 0.079 |
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".