Looking Back to Plan for the Future: A Short History of Systematic Conservation Planning and Its Increasing Importance in Abating Earth's Biodiversity Crisis
Bibliographic record
Abstract
ABSTRACT The Convention on Biological Diversity Kunming‐Montreal Global Biodiversity Framework (KM‐GBF) was endorsed in 2022. This framework was designed to guide biodiversity conservation efforts globally, to not just halt the loss of biodiversity but to improve it. The emerging prominence of spatial planning as a means to these lofty ends is notable in the KM‐GBF, as it requires signatories to ensure that all areas within a country are under Participatory Integrated Biodiversity Inclusive Spatial Planning (PI‐BISP) to bring the loss of areas of high biodiversity importance and ecosystems of high integrity close to zero by 2030, while respecting the rights of Indigenous Peoples and local communities (Target 1). Furthermore, there are spatial planning requirements in at least a further five targets which guide on‐the‐ground management and broader policy for protecting, conserving, managing, and restoring nature. Here, we review the history and context of systematic conservation planning, as a discipline born out of an application (and subsequent publication) by Jamie Kirkpatrick in 1983 across the central east coast of Tasmania. In doing so we consider the planning theory which influenced systematic conservation planning, the tools which have been developed to bridge plans and implementation, and forward‐looking innovations needed to deliver on the KM‐GBF goals.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".