How much do local factors matter for predicting transient ecosystem dynamics? Suggestions from permafrost formation in boreal peatlands
Bibliographic record
Abstract
Summary With rapid climate warming, ecosystems will probably exhibit complex dynamics because local factors and life history attributes of species mediate the effects of regional climate change. To assess the relative importance of local vs. regional processes on permafrost formation in boreal peatlands, I sampled for permafrost and factors affecting its formation in 38 collapse scars across a 4 °C mean annual temperature (MAT) gradient in the discontinuous permafrost zone of northern Manitoba, Canada. Three complimentary approaches were used to model factors important to permafrost formation at both local and regional scales. In the first analysis, a mechanistic, spatial model of permafrost formation was developed as a function of Picea mariana size and proximity. In the second approach, permafrost formation was modelled as a function of two local factors, diameter of Picea mariana trees and emergent organic matter depth, and the regional factor, mean annual temperature (MAT). Finally, published aerial photography data were used to determine whether the proportion of bogs with permafrost changes across a MAT gradient. Results show that permafrost formation in boreal permafrost peatlands is best described as a locally driven process within regional climatic constraints. At local scales of 1–2 meters, the spatial and size distributions of trees controlled the spatial distribution of permafrost. At regional scales, tree size was a significantly better predictor than emergent organic matter or MAT. These results suggest that transient models of discontinuous permafrost based only on climate may poorly predict changes in vegetation and permafrost.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".