Practical actions for applied systematic conservation planning
Bibliographic record
Abstract
Systematic conservation planning is intended to inform spatially explicit decision making. Doing so requires that it be integrated into complex regulatory and governance processes, and there are limited instances where this has been achieved effectively. South Africa is a global leader in the application of conservation plans, the outputs of which are widely used for spatial planning and decision making in many spheres of government. We aimed to determine how conservation planning in the country progressed from theory to implementation, and to identify practical actions that enabled this transition, by assessing temporal trends in the characteristics of conservation plans (1990-2017, n = 94). Since 2010 conservation planning has entered an operational period characterized by government leadership of plans, administrative rather than ecological planning domains, decreasing size of planning units, increasing emphasis on end-user products, and scheduled revision of plans. Key actions that enabled this progression include transitioning leadership of plans from scientists to practitioners, building capacity within implementing agencies, creating opportunities to integrate plans in legislative processes, establishing a strong community of practice, adopting implementation-focused methods, and balancing standardization with innovation. Learning from this model will allow other countries, particularly those with a similar megadiverse, developing context, to operationalize conservation planning into spatial planning and decision making.
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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 | 0.304 | 0.291 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.009 | 0.029 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.007 | 0.026 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".