Sugar Maple Litter Decay Rates Are Reduced More Strongly by Drought Than by American Beech Proliferation in the Understory
Bibliographic record
Abstract
ABSTRACT Recurrent drought threatens high‐latitude deciduous forests in eastern North America. The decline of sugar maple (Acer saccharum) at its northern limits under additional stress imposed by changing climate regimes and its replacement by American beech (Fagus grandifolia) cannot maintain the ecosystem services that the former provides, including its nutrient‐rich leaf litter. In 2022, we incubated litter bags in three maple stands (Kenauk Reserve, Quebec) where beech saplings proliferated (Proliferation = Yes) versus three beech‐free ones (Proliferation = No), in which paired plots were established with rainfall excluders versus rainfall accessibility in summer 2021 and 2022. Moisture was two‐fold higher in rainfall‐accessible soils (Rainfall exclusion, No), but half as spatially variable (CV%) as excluder plot soils (Exclusion, Yes). Mesh bags (mm apertures: 50, Large; 20, Medium; 0.1, Small) that were filled with maple or beech leaves, or their 50:50 mixtures (Species), were deployed in June 2022 within the 12 plots, with 30‐, 60‐, and 90‐day removals. Mass loss did not change with mesh size in a consistent manner over 90 days (initial prediction: L > M > S). We estimated k‐values (year−1) by extending the linearized exponential decay model to 12 Proliferation‐Exclusion‐Species combinations. Maple decayed 16%–30% faster than beech when soil moisture was not limited. Mass loss rates were 2.7–4.13 (beech saplings, Yes) and 2.3–2.9 (beech, No) times higher under rainfall than under moisture deficiency, and ordered: maple ≥ mixed > beech. Separate k‐values for mixed‐bag species were higher than their monospecific counterparts, suggesting synergistic behavior. Early leaching is drought‐sensitive; mass losses were 2.5–2.9 times higher under accessible rainfall versus rainfall exclusion. Furthermore, moisture and maple decay may be increased under slowly decaying beech “mulch.” Indeed, slightly higher maple loss rates were observed beneath beech understories, despite possible negative effects of leaf and litter leachates produced by the latter. Recurring drought and shifting stand composition through continued beech‐maple competition may threaten the persistence and productivity of northern hardwood communities, thereby affecting ecosystem functioning, including decomposition and associated biogeochemical transformations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".