Application of the Conservation Planning Tool Zonation to Inform Retention Planning in the Boreal Forest of Western Canada
Bibliographic record
Abstract
Retention forestry is an approach in which live trees and other components of forest structure are retained within harvested areas. A primary objective of retention forestry is to maintain biodiversity and to hasten post-harvest recovery of forest structure and function. Retention is now a key element in sustainable forest management practices in many regions of the world. However, locating where retention should be placed to best achieve management objectives is a challenging problem, and evidence-based approaches to operational applications are rare. We suggest here that harvest planners could benefit from the use of systematic conservation planning principles and methods to inform retention design. Specifically, we used a conservation planning—or prioritization—tool, Zonation, to create spatially-explicit scenarios of retention harvesting in a boreal mixedwood forest in northwestern Alberta, Canada. Scenarios were informed by several environmental variables related to site productivity; in particular, we used a metric of wetness (depth-to-water from the Wet Areas Mapping algorithm) that is based on airborne lidar-derived terrain models previously shown to correlate with patterns in post-harvest forest regeneration and biodiversity. The nine retention scenarios examined here related to the placement of retention focused to drier, mesic, or wetter sites in combination with other prioritization constraints. Results were compared with an existing harvest plan to assess differences in the spatial pattern of retention (e.g., percent overlapping area, number of patches, size of the patches). We also tested for the homogeneity of forest attributes (e.g., tree species, deciduous density) between scenarios and the existing harvest plan using multivariate dispersion analysis. Our results showed limited commonalities among scenarios compared to the existing harvest plan; they were characterized as having limited spatial overlap, and more and smaller patches with the use of a timber-cost constraint further affecting retention patterns. While modeling results significantly differed from current retention practices, the approach presented here offers flexibility in testing different scenarios and assessing trade-offs between timber production and conservation goals using a standardized conservation planning toolkit.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".