Towards Global Biodiversity Framework Target 12: mapping urban biodiversity monitoring programmes – a case study for Berlin, Germany
Bibliographic record
Abstract
Abstract Urbanisation is a major environmental transformation, leading to various fundamental changes in biodiversity in all its dimensions. Therefore, the 15th Conference of Parties (COP-15) to the Convention on Biological Diversity (CBD) of the United Nations formulated a target especially related to urban areas. Target 12 refers to biodiversity-inclusive urban planning measures for enhancing native biodiversity to improve human health until the year 2030. To understand if and to which extent this target is reached, the mapping of biodiversity time-series is a first crucial step as it provides an overview on existing monitoring programmes, and allows to uncover gaps for future assessments. In our study, we aimed to map all biodiversity monitoring data for the German capital city, Berlin, following the concept of Essential Biodiversity Variables (EBVs). In total, we found 89 biodiversity monitoring programmes, covering 10 taxonomic groups and five of the six EBVs. We also detected taxonomic groups and EBVs, that are over- or underrepresented in monitoring programmes, as several are taxonomically biased. Biodiversity monitoring programmes are strongly focussed on species occurrence, while especially time series related to the benefits of biodiversity on human health are largely lacking and still need to be conceptualised. In conclusion, our study shows that there is an urgent need for action related to long-term biodiversity monitoring programmes, so that they are really able to inform about achievements towards Target 12 of the Kunming-Montreal Global Biodiversity Framework.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".