Conserving steppe-land birds under climate change: a gap analysis for the Eurasian Stone-curlew ( <i>Burhinus oedicnemus</i> ) in the Western Palearctic
Bibliographic record
Abstract
ABSTRACT Climate change is having dramatic impacts on the distribution of animals. Birds, and especially steppe-land birds, are particularly sensitive to climate change and identifying areas that are critical for their conservation is pivotal, as well as estimating the expected impact on these areas under different climate and land use change scenarios. In-situ climate refugia (areas suitable under both current and future climates) are especially valuable for the conservation of climate-sensitive species, and is therefore important to identify them and evaluate their coverage by protected areas. Via species distribution modelling, we aimed to identify in-situ climate refugia in the Western Palearctic for the Eurasian Stone-curlew Burhinus oedicnemus , an umbrella steppic species of conservation concern. We used a comprehensive dataset of occurrences in the breeding period to fine-tune a Maxent species distribution model and project it under three carbon emission scenarios of increasing severity for the year 2050. We then identified in-situ climate refugia and performed a gap analysis estimating the percentage of refugia falling within the network of currently protected areas. In all modelled future scenarios a northward expansion of suitable breeding habitats was predicted, and suitable areas had similar extents, with a slight increase of the overall suitability under more severe scenarios. According to our results, the Eurasian Stone-curlew has the potential to maintain viable populations in the Western Palearctic, even though dispersal limitations might hinder the colonization of newly suitable breeding areas. In-situ climate refugia were mainly identified outside protected areas, particularly in Northern Africa and the Middle East. Therefore, we advocate targeted actions in climate refugia to promote the conservation of this and other steppe-land species under global environmental change.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".