Direct estimates of downslope deadwood movement over 30 years in a temperature forest illustrate impacts of treefall on forest ecosystem dynamics
Bibliographic record
Abstract
Deadwood plays important roles in forest ecosystems by storing carbon, influencing hydrology, and provisioning countless organisms. Models for these processes often assume that deadwood does not move and ignore redistribution that occurs when trees fall. To evaluate the effects of treefall, we provide the first direct estimates for the magnitude, direction, and drivers of deadwood movement in a long-term oak–hickory forest dynamics plot in Missouri, USA. Among 1871 total pieces of deadwood, logs today pointed downslope more often than branches and occurred at lower elevation than snags. Of these, 477 logs retained tags from which we reconstructed movement using new formulae for reconciling survey coordinates and calculating log shape. Relocated logs occurred at lower elevation than their original rooting location, with the magnitude of the drop dependent on log size, degree of decay, and slope. Although changes in elevation were modest, the log centroids moved up to several meters horizontally. Consequently, as large trees fall, they predictably redistribute deadwood downhill, suggesting that models of deadwood dynamics in small inventory plots may gain accuracy by incorporating import and export along with recruitment and decay. We highlight implications of small-scale deadwood movement for forest inventories, carbon dynamics, and biodiversity.
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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 | 0.001 |
| 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".