High levels of green‐tree retention are required to preserve ground beetle biodiversity in boreal mixedwood forests
Bibliographic record
Abstract
Recovery of biodiversity and other ecosystem functions to pre-disturbance levels is a central goal of natural disturbance-based approaches to ecosystem management. In boreal mixedwood forests, green-tree retention has been proposed as an alternative approach to traditional clearcutting that may minimize initial displacement of species assemblages and speed recovery of the biota. Here we evaluated the effectiveness of six levels of dispersed greentree retention for conservation of ground beetle biodiversity in four boreal mixedwood cover types that span a gradient of stand development following wildfire. Each cover type X treatment combination was replicated three times in an operational scale experiment using 10-ha compartments. Ground beetle assemblages (59 species and 45 419 individuals) responded to increasing levels of dispersed, green-tree retention, but even relatively high levels of retention (up to 50% retention) did not retain species assemblages characteristic of uncut forest stands. This latter effect was most pronounced in compartments in later successional stages; i.e., those with developing conifer understories, or mixed and/or conifer-dominated overstories. Beetle assemblages in high levels of retention (50-75%) were statistically similar across all cover types, although we detected modest differences among the 5-year recovery of assemblages, based on initial cover type differences. Thus, recovery to initial conditions likely will be slower in mixed and conifer stands than in deciduous stands. We suggest that recovery of beetle assemblages is strongly linked to stand reinitiation through deciduous "suckering" post-harvest. Increasing levels of harvest appear to homogenize carabid assemblages across the four dominant cover types, and thus higher levels of retention (>50%) will be required to preserve assemblages of later successional stages. Regional renewal of assemblages, however, will require landscape-level planning.
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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 | 0.001 | 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".