BIODIVERSITY CONSIDERATIONS IN CONSERVATION SYSTEM PLANNING: MAP-BASED APPROACH FOR NOVA SCOTIA, CANADA
Bibliographic record
Abstract
Biodiversity considerations in conservation system planning include three main criteria: representation, special elements, and focal species. A GIS-based approach utilizing simple models was used to assess existing biophysical data relative to these criteria for conservation system planning in Nova Scotia, Canada, with potential utility in applications elsewhere. Representative samples of natural landscapes were identified on the basis of size (≥10 000 ha) and degree of naturalness (natural cover, uneven-aged forests, low or zero road density). Special elements were selected, including hotspots of diversity and rarity, critical habitat for species at risk, significant wetlands, old and unique forests, and ecosites. Habitat requirements of viable populations of focal species (American moose, American marten, and Northern Goshawk) were identified using species distribution data, habitat suitability, and population viability analyses. Priority core areas for biodiversity conservation system planning were identified on the basis of these three sets of criteria. Key areas of habitat connectivity were delineated by selecting the least-cost paths for focal species between relevant core areas through cost–distance analyses based on habitat suitability, road density, and minimum corridor width. Collectively, these biodiversity considerations indicate that ∼60% of Nova Scotia, including 32% in core areas, should be managed for conservation objectives to maintain genes, species, and ecosystems over time. Although data and modeling limitations require that our analysis of richness and diversity, habitat suitability, population viability, and core area selection be verified, the area calculations and other results are consistent with those in similar studies. Consequently, the system design and other information generated are useful for local and regional biodiversity conservation planning and management, and the methodological approach is of potential use in other regions where the necessary field-based data may be made available.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".