Modeled Seed Accumulation Patterns Explain Spatial Heterogeneity of Shrub Recruitment Within the Taiga‐Tundra Ecotone
Bibliographic record
Abstract
Abstract Arctic shrub productivity trends display variability at multiple spatial scales. Fine‐scale studies have generally observed the greatest shrub expansion in landscape positions that accumulate water and nutrients. While considerable work has focused on the mediating effect of these resources on growth responses to warming, less is known about the mechanisms constraining recruitment‐driven expansion. Given the low seed viability of many Arctic shrubs, spatial patterns of seed dispersal may play an important role in constraining fine‐scale variability of shrub recruitment. This variability may also be driven by ground cover suitability, though these relationships are understudied in undisturbed sites. Here, we developed models representing seed accumulation mechanisms around Alnus alnobetula (green alder) patches within the taiga‐tundra ecotone of the Northwest Territories and compared these with observations of seed and seedling density. We also investigated relationships between seedling abundance, topographic position, and ground cover. Observed patterns of recruitment were complex, with preferential expansion occurring beneath alder patches only on the steepest slopes. Seed accumulation models representing overland flow, wind, and source distance were important predictors of seedling recruitment. This provides indirect evidence of localized seed limitation around patches, suggesting future recruitment may not respond as expected to changing environmental conditions. Sphagnum cover also predicted recruitment, indicating the importance of seedbed conditions for establishment. We propose that developing models of shrub expansion that include both dispersal and environmental constraints may increase our ability to predict patterns and rates of expansion. Such predictions are necessary to understand future biosphere‐atmosphere interactions in a rapidly changing Arctic.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".