Biomass accumulation in trees and downed wood in northern hardwood forests: Repeated measures of a successional chronosequence in New Hampshire, USA
Bibliographic record
Abstract
Successional, second-growth forests dominate much of eastern North America; thus, patterns of biomass accumulation in standing trees and downed wood are of great interest for forest management and carbon accounting. The timing and magnitude of biomass accumulation in later stages of forest development are not fully understood. We applied a “chronosequence with resampling” approach to characterize live and dead biomass accumulation in 16 northern hardwood stands in the White Mountains of New Hampshire. Live aboveground biomass increased rapidly and leveled off at about 350 Mg/ha by 145 years. Downed wood biomass fluctuated between 10 and 35 Mg/ha depending on disturbances. The species composition of downed wood varied predictably with overstory succession, and total mass of downed wood increased with stand age and the concomitant production of larger material. Fine woody debris peaked at 30–50 years during the self-thinning of early successional species, notably pin cherry. Our data support a model of northern hardwood forest development wherein live tree biomass accumulates asymptotically and begins to level off at ∼140–150 years. Still, 145-year-old second-growth stands differed from old-growth forests in their live ( p = 0.09) and downed tree diameter distributions ( p = 0.06). These patterns of forest biomass accumulation would be difficult to detect without a time series of repeated measurements of stands of different ages.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".