Conservation planning integrating natural disturbances: Estimating minimum reserve sizes for an insect disturbance in the boreal forest of eastern Canada
Bibliographic record
Abstract
Large natural disturbances such as insect outbreaks and fire are important processes for biodiversity in forest landscapes. However, few methods exist for incorporating natural disturbances into conservation planning. Intact forest landscapes, such as in the North American boreal forest, can produce large natural disturbance footprints. They also have the potential to support large reserves but size estimates based on natural disturbance are needed to guide reserve design. Historical fire data have been used to estimate minimum dynamic reserves, reserve size estimates based on maintaining natural disturbance dynamics and ensuring resilience to large natural disturbance events. While this has been a significant step towards incorporating natural disturbance into reserve design, managers currently lack guidance on how to apply these concepts in areas where fire is not the dominant natural disturbance. We generalize the minimum dynamic reserve framework to accommodate insect outbreaks and demonstrate the framework in a case study for eastern spruce budworm (Choristoneura fumiferana) in the Canadian boreal forest. Our methods use geospatial analysis to identify minimum dynamic reserves based on a set of spatially explicit initial conditions, and simulation models to test for the maintenance of a set of dynamic conditions over time. We found considerable variability in minimum dynamic reserve size depending on the size of historic budworm disturbance events and the spatial patterns of disturbance-prone vegetation types. The minimum dynamic reserve framework provides an approach for incorporating wide-ranging natural disturbances into biodiversity conservation plans for both pro-active planning in intact landscapes, and reactive planning in more developed regions.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".