Do vegetation boundaries display smooth or abrupt spatial transitions along environmental gradients? Evidence from the prairie–forest biome boundary of historic <scp>M</scp>innesota, <scp>USA</scp>
Bibliographic record
Abstract
Abstract Questions Two alternative mechanisms of abrupt vegetation change across ecological boundaries have been proposed: (1) concomitantly abrupt gradients in physical environmental variables and vegetation across the boundary, and (2) gradual environmental gradients that vegetation responds to in a non‐linear or threshold manner. Here, we evaluate spatial patterns of climate and vegetation across a grassland–forest biome boundary to examine evidence in favour of either of these alternatives. Location Minnesota, USA. Methods Vegetation data represented the presence of prairie vs. forest vegetation in Minnesota from 1847 to 1908, generally prior to European settlement of the region, while the climatic variables represented an index of long‐term average moisture availability (precipitation minus potential evapotranspiration (P – PET). Using linear and sigmoidal regression models, we evaluated spatial patterns of change in vegetation, climate and vegetation–climate relationships across 22 transects (170–400 km) oriented perpendicular to the biome boundary. We also evaluated boundary characteristics in light of dominant topographical controls and position along the boundary. Results Vegetation followed a sigmoidal pattern of change across the boundary, with mean boundary width of ca. 100 km. The P – PET increased by ca. 100 mm across the boundary following a comparatively smooth pattern of change. Climate–vegetation relationships were clearly non‐linear across the boundary, indicating these variables did not change in a common spatial pattern. Regional topographical controls modified relationships between vegetation and climate along the length of the boundary. Conclusions Our results document strong non‐linear relationships between the presence of forest vegetation and its dominant climate control across a grassland–forest biome boundary. An average change of ca. 100 mm in P – PET moving across the boundary is about 40% of the long‐term mean annual range of this variable, suggesting that modest changes to P – PET may potentially cause substantial shifts in the location of the prairie–forest boundary.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".