From coverage to extension: Evaluating indices for biodiversity monitoring in cities to reflect global and EU biodiversity targets
Bibliographic record
Abstract
• Evaluation of biodiversity indices for monitoring urban biodiversity. • Urban biodiversity monitoring supports national and EU policy frameworks. • Biodiversity indices reveal gaps in Essential Biodiversity Variables (EBVs) coverage. • Proposed extensions to an index for better alignment with EBV dimensions. • Toolbox of indices aids in tracking urban biodiversity restoration progress. Urbanisation has a major environmental impact and leads to novel ecosystems composed of native and non-native biodiversity, often resulting in ecosystem functions and services that differ from those outside of cities. International policy goals, such as the Kunming-Montreal Global Biodiversity Framework, the EU Biodiversity Strategy for 2030, the EU Habitats Directive, the European Green Deal, and the EU Nature Restoration Law have urban targets, as does the Berlin Urban Nature Pact. In order to assess the achievement of targets, it is necessary to monitor changes in urban biodiversity in its different dimensions, which are described by Essential Biodiversity Variables (EBVs). Biodiversity indices provide the means to combine different elements of change and can therefore be used to monitor biodiversity change. Here we provide an overview of biodiversity indices that can be applied at the city scale: The Living Planet Index, the Diversity-weighted Living Planet Index, the Red List Index, the City Biodiversity Index and the IUCN Urban Nature Indexes, and present a concept for extending one of the indices to include additional EBVs. Our study sheds light on the indicator toolbox currently available to help cities to meet policy targets for biodiversity recovery, so that the effectiveness of monitoring progress towards biodiversity and ecosystem restoration in cities can be improved.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".