Inferring current and Last Glacial Maximum distributions are improved by physiology‐relevant climatic variables in cold‐adapted ectotherms
Bibliographic record
Abstract
Abstract Aim Ecological niche‐based models (ENM) frequently rely on bioclimatic variables (BioV) to reconstruct biogeographic scenarios for species evolution, ignoring mechanistic relations. We tested if climatic predictors relevant to species hydric and thermal physiology better proximate distribution patterns and support location of Pleistocene refugia derived from phylogeographic studies. Location The Western Palaearctic. Taxon Vipera berus and Zootoca vivipara , two cold‐adapted species. Methods We used two sets of variables, that is physiologically meaningful climatic variables (PMV) and BioV, in a multi‐algorithm ENM approach, to compare their ability to predict current and Last Glacial Maximum (LGM) species ranges. We estimated current and LGM permafrost extent to address spatially the cold hardiness dissimilarity between both species. Results PMV explained more accurately the current distribution of these two cold‐adapted species and identified the importance of summer temperature and solar radiation that constrain activity in cold habitats. PMV also provide a better insight than BioV predictors on LGM distribution. By including notably, the permafrost extent, PMV‐based models gave parsimonious putative arrangement and validity of refugia for each clade and subclade in accordance with phylogeographic data. Northern refugia were also identified from 48 to 52° N for V. berus and from 50 to 54° N for Z. vivipara . Main Conclusions Our hybrid approach based on PMV generated more realistic predictions for both current (biogeographical validation) and past distributions (phylogeographic validation). By combining constraints during the activity period (summer climatic niche) and those inherent to the wintering period (freeze tolerance), we managed to identify glacial refuges in agreement with phylogeographic hypotheses concerning post‐glacial routes and colonization scenarios.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".