Root heterogeneity along an arctic elevational gradient: the importance of resolution
Bibliographic record
Abstract
Summary Spatial heterogeneity affects plant performance and is influenced by plants, but the scale at which fine roots react to or generate spatial heterogeneity has received little attention. Fine roots might be expected to respond to heterogeneity at a scale comparable to their diameter (mm), but studies to date have been conducted at much coarser resolutions (cm – m). Here we quantify root heterogeneity in contrasting habitats with special attention to the influence of resolution. We measured fine root length heterogeneity at resolutions ranging from 1 to 300 mm2, at four elevations along an arctic alpine gradient from 500 m a.s.l. (forest) to 1100 m (tundra). We calculated the magnitude of heterogeneity as the coefficient of variation of root length, and the scale of heterogeneity using semivariance analysis. The magnitude of heterogeneity was about twofold greater at fine than coarse resolution. Further, the magnitude of heterogeneity was generally greatest at the highest elevation, suggesting that soil at 1100 m was less evenly occupied by plant roots than soils at lower elevations. The exception to this was at the 1 mm2 resolution, for which the magnitude of heterogeneity did not vary with elevation, possibly because heterogeneity at this scale is related to ecophysiological processes common to all vegetation types. The scale of root length heterogeneity increased significantly with resolution coarseness, suggesting that roots respond to or generate patchiness at small scales that have not previously been examined. In contrast, the scale of heterogeneity did not vary significantly with elevation and the accompanying turnover in growth form. Our results suggest that roots in four vegetation types respond to or generate very fine scales of spatial heterogeneity, including scales much smaller than those that have previously been examined. Both the magnitude and scale of heterogeneity varied with sampling resolution, suggesting resolutions as small as a few millimetres are relevant to studies of spatial root interactions and below‐ground processes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".