Life on deadwood: cut stumps as a model system for the succession and management of lichen diversity
Bibliographic record
Abstract
Abstract Coarse deadwood provides an important habitat for a suite of niche-specialist lichens in old-growth forests, for example, snags (standing dead trees) and fallen logs. Conversely, the scarcity of deadwood in managed forests is a limiting factor to lichen diversity, though cut stumps may provide an alternative habitat for deadwood species. The surface of cut stumps is an ecologically useful study system, facilitating standardized sampling with which to determine the pattern and process of deadwood succession. This study examined vegetation patterns for the surface of cut stumps at Abernethy RSPB Reserve in northern Scotland. We demonstrate the interrelationship between key topographic, management and edaphic factors during a successional process of terrestrialization. Consequently, we recommend that deadwood diversity might be maximized by 1) creating managed plots with varying degrees of canopy openness for sites with different levels of topographic exposure, and 2) providing cut stumps at different heights within plots, to ensure that during a rotational period the process of terrestrialization operates at different speeds among individual microhabitats. The study examined successional processes on cut stumps using two recently accessible and powerful statistical methods: 1) nonparametric multiplicative regression (NPMR), and 2) multivariate regression trees (MRT). The principles on which these techniques are based are becoming the preferred statistical framework with which to provide robust interpretation of field-sampled data; they are unconstrained by prior assumptions as to the form of a species' niche response, and are data-led models evaluated based on cross-validated performance, thereby avoiding the complication of multiple hypothesis tests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".