Strengths and complementarity of systematic conservation planning and Key Biodiversity Area approaches for spatial planning
Bibliographic record
Abstract
Developing biodiversity-inclusive spatial plans at a national level is the focus of Target 1 of the Kunming-Montreal Global Biodiversity Framework (KMGBF). There are 2 general approaches to identifying areas of value for biodiversity plans: criteria-based, such as the Key Biodiversity Areas (KBA) process, and systematic conservation planning (SCP) approaches, which apply complementarity to efficiently achieve specific quantitative targets. We examined the benefits of both approaches and considered how the KBA approach can best complement SCP. We reviewed 200 papers articles that applied SCP to real-world data with the Marxan conservation design software. Our review showed that targets for biodiversity elements are poorly selected in many SCP publications, with more than 75% of the studies applying uniform percentage target amounts to planning elements. Uniform targets favor more widespread species and ecosystems that are likely to be more common and less important for conservation. The strengths and complementarities of KBA and SCP approaches were reviewed and we identified the elements from both approaches that should be considered for spatial planning to achieve Target 1 in the KMGBF. In particular, the global approach of KBAs (i.e., identifying sites of global significance for species or ecosystems) better complements SCP, which often has a national or subnational focus. The KMGBF will fail if conservation of globally significant sites is not targeted and these sites are not incorporated in national spatial planning.
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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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".