Tundra shrub expansion in a warming climate and the influence of data type on models of habitat suitability
Bibliographic record
Abstract
Warming across the low Arctic is increasing tundra vegetation productivity and facilitating the expansion of upright shrubs. We modeled the effects of warming on habitat suitability in green alder, dwarf birch, Labrador tea, bog bilberry, and lingonberry and assessed the influence of data type (true absence or pseudo-absence) on species distribution models (SDMs). We generated SDMs using the two absence data types under current (1970–2000) and future (2061–2080) climate projections. Our results show that warming leads to range expansion of all shrubs, though responses vary in magnitude and extent, with mean increases in suitability ranging from 0.080 (Labrador tea) to 0.369 (lingonberry) with true absences. Differences in driving variables and suitability projections suggest that physiological and ecological variability between species mediate responses to warming. Between data types, we observed inconsistencies in model performance, suitability projections, and variable importance. Bog bilberry and lingonberry exhibited larger differences in suitability (0.201 and 0.288, respectively), whereas alder showed similar responses (difference of 0.01). These results are important to consider when assessing changes in habitat suitability or identifying environmental or climatic determinants of species’ distributions. We suggest further development of open data repositories, facilitating access to true absence data to support conservation and land use planning.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".