An evaluation of important plant areas around the world
Bibliographic record
Abstract
Area-based approaches have long dominated biodiversity conservation and have been reinforced by the Kunming-Montreal Global Biodiversity Framework. The important plant area (IPA) approach is a leading framework for the spatial conservation prioritization of plants and fungi, but over 20 years since its launch, its application and conservation outcomes remained unevaluated. Through systematic mapping and semistructured interviews of key informants, we evaluated IPAs globally. We investigated where and how the framework has been applied, to what extent identification has led to plant conservation, how IPAs are perceived by plant conservationists and researchers globally, and key opportunities and challenges for IPAs. We reviewed over 140 relevant sources, spanning scientific publications, reports, websites, and databases, and interviewed 47 key informants. Most publications focused on developing guidance or identifying IPAs. Sixty-four percent of informants were aware of IPAs that had been incorporated into conservation processes, with broader benefits of IPAs also highlighted, such as generating botanical data. Overall perception of IPAs was positive; they were seen to provide a unifying focus for plant conservation and as maintaining a flexible and inclusive approach. Opinions were split on the effectiveness of IPA programs in engaging broader stakeholders or incorporating local ecological knowledge. Key themes affecting participant perceptions and lessons learned for bridging the research-implementation gap were found. Informants recommended that IPA programs globally seek more ambitious and targeted funding; tailor stakeholder communications; invest time in cross-sectoral stakeholder engagement; clarify relationships with key biodiversity areas; and create a single central hub for IPA information. With plants underpinning all terrestrial ecosystems, improved outcomes will have broad benefits for biodiversity protection, particularly as new IPA programs are launched in some of the most biodiverse countries in the world.
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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 | 0.028 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".