Modelling and monitoring: examining the utility of dynamic landscape metapopulation models for sustainable forest management
Bibliographic record
Abstract
Abstract Reliable, practical and affordable means of assessing the sustainability of forest management remain elusive. Monitoring of biological indicators is an important element, but sufficiently powerful monitoring strategies are expensive and monitoring alone may not provide answers in time to avloid irreversible environmental or ecological damage. We propose a model-based approach to assessing sustainability using bio-indicators of ecosystem condition to provide timely feed-back to managers about the sustainability of current and alternative forest management options, but acknowledge that assessment of biological indicators is only one component of the assessment of sustainability. This model-based approach can also support the development of better-targeted and more relevant monitoring systems. Dynamic landscape meta-population (DLMP) models integrate spatial models of forest change (also known as landscape dynamic models or forest succession models) with meta-population models. which describe demographic and biological attributes of species and the dynamic consequences of migration and habitat change. DLMP models may be used to predict the meta-population level consequences of management actions on bio-indicator species, and as such. may provide a relatively efficient and inexpensive approach to assessing an important aspect of the potential sustainability of forest management. We review some of the criticisms of monitoring for trend in indicators and the advantages and problems associated with using DLMP models of bio-indicator species to evaluate the sustainability of forest management options. We draw on results of a case study of the brown creeper (Certhis americana) in northern Ontario, Canada, that explored the sustainability of competing forest management scenarios. Based on case study results, DLMP models of bio-indicator species appear to be useful for (1) assessing and ranking the sustainability of management options, (2) quantifying the stresses placed on ecosystems by particular management activities, (3) targeting future research and data collection, and (4) dealing explicitly with both environmental and model uncertainties.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".