The use of dynamic landscape metapopulation models for forest management: a case study of the red-backed salamander
Bibliographic record
Abstract
Spatial models of population dynamics have been proposed as a useful method for predicting the impacts of environmental change on biodiversity. Here, we demonstrate advances in dynamic landscape metapopulation modelling and its use as a decision support tool for evaluating the impacts of forest management scenarios. This novel modelling framework incorporates both landscape and metapopulation model stochasticity and allows their relative contributions to model output variance to be characterized. It includes a detailed sensitivity analysis, allowing defensible uncertainty bounds and the prioritization of future data gathering to reduce model uncertainties. We demonstrate this framework by modelling the landscape-level impacts of eight forest management scenarios on the red-backed salamander ( Plethodon cinereus (Green, 1818)) in the boreal forest of Ontario, Canada, using the RAMAS Landscape package. The 100 year forest management scenarios ranged in intensity of timber harvesting and fire suppression. All scenarios including harvesting predicted decreases in salamander population size and the current style of forest management is predicted to produce a 9%–17% decrease in expected minimum population size compared with scenarios without harvesting. This method is amenable to incorporating many forms of environmental change and allows a meaningful treatment of uncertainty.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".