A decision framework to integrate in-situ and ex-situ management for species in the European Union
Bibliographic record
Abstract
Zoos and aquaria in the European Union (EU) can play a crucial role in the conservation of EU species, as they currently hold nearly half (49%) of EU terrestrial vertebrates. In this study, we analyzed the species composition and population sizes of EU zoos and developed a framework to prioritize recommendations for additional ex-situ and in-situ interventions for 277 at-risk EU species. Our results showed that EU zoos currently hold 39% of threatened EU species, 27% of EU endemic species, 62% of EU species vulnerable to climate change, 20% of EU species listed by the Alliance for Zero Extinction (AZE), 25% of Evolutionary Distinct and Globally Endangered (EDGE) EU species, while only 5% are subject to ex-situ conservation. Using our framework, we found that additional captive breeding was recommended for 60-61%% of species while expanding protected areas was recommended for only 2–22%, as 217 out of 277 species already met habitat protection targets. Both interventions were recommended for up to 20% of species, while the remaining 18% required no interventions because captive populations and habitat protection fully met targets. Our flexible framework can support more effective integrated conservation planning decisions for EU species and help identify target species for further in-depth assessment by the IUCN Ex-situ guidelines.
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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 | 0.031 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| 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".