The use of the natural range of variability for identifying biodiversity values at risk when implementing a forest management strategy
Bibliographic record
Abstract
The Natural Range of Variability is a concept used under the ecosystem management paradigm that means understanding the disturbance-driven spatial and temporal variability of the ecological systems and mimicking them in management strategies. With this project, we developed a framework that permitted addressing biodiversity issues under the lens of the Natural Range of Variability (NRV) for a managed public forest in central-west Alberta. To do so, we brought together a spatial harvest scheduler, a fire and succession landscape simulator, and a toolbox of biodiversity indicator models. Indicator models, that encompass landscape configuration, ecosystem diversity, stand internal habitat features and speciesspecific habitat supply models, were applied on the outputs of the landscape dynamics simulator to define the NRV. The risk of losing biodiversity values in applying the forest management strategy was addressed by comparing indicators outputs over the simulation horizon with their respective NRV. Results demonstrate that no forest-age-related indicator evaluated on the harvest scheduler output shows an important deviation from the NRV. However, in regards to forest cover types there is obviously a loss in ecosystem diversity, as a direct effect of the stand composition control of the silvicultural strategies. We found that patch size distribution is generally compliant with the NRV, although we observed more large patches and better connectivity for old growth patches under fire-driven landscapes. We also found that downed woody debris volume and many understory vegetation (ground lichen, herb and shrub) covers were at risk. Over the seventeen wildlife species, we detected nine species that could be jeopardized by important loss of habitats. Back-tracking bottleneck forest conditions that put these biodiversity values at risk has allowed development of recommendations with regards to landscape design and adapted practices. Key words: BAP toolbox, fire-driven landscape, natural disturbance regime, ecosystem diversity, landscape configuration, wildlife habitat models, risk analysis
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".