Effects of restoration thinning on spatial heterogeneity in mixed-conifer forest
Bibliographic record
Abstract
Spatial pattern is an essential attribute of forest ecosystems and influences many ecological processes and functions. We hypothesized that restoration thinning conducted in fire-excluded ponderosa pine ( Pinus ponderosa Douglas ex P. Lawson & C. Lawson) – western larch ( Larix occidentalis Nutt.) – mixed-conifer forest would restore spatial patterns characteristic of active fire regime old-growth. We evaluated effects of thinning on spatial patterns and also compared post-treatment patterns with reconstructions of pre-suppression patterns at nearby old-growth reference sites that developed in the historical mixed-severity fire regime. Restoration thinning reduced spatial aggregation and resulted in globally random tree patterns comprised of local tree clumps, openings, and widely spaced single trees, similar to reference conditions. Post-treatment spatial patterns in the replicate treatment units spanned the range of variability bounded by the reference sites. Our analyses indicate that, under certain circumstances, restoration of spatial heterogeneity in unlogged, fire-excluded forests can be achieved by retaining live legacy pre-suppression trees during thinning treatments. However, success is not assured. Restoration of spatial heterogeneity in forests where few live pre-suppression trees remain due to past mortality or harvest, a common condition of candidate restoration sites, presents a greater silvicultural challenge. Thus, we recommend that, as a general rule, managers deliberately address spatial pattern when crafting forest restoration treatment objectives and prescriptions.
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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".