Steppe-land birds under global change: Insights from the Eurasian Stone-curlew (Burhinus oedicnemus) in the Western Palearctic
Bibliographic record
Abstract
Global change is having dramatic impacts on the distribution of animals. Birds, and especially steppe-land birds, are particularly sensitive to climate and land-use/land-cover 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/land-cover scenarios. In-situ refugia (areas suitable under both current and future conditions) are especially valuable for the conservation of species sensitive to global change, and is therefore important to identify them and evaluate their coverage by protected areas. Via species distribution modelling, we aimed to identify in-situ refugia in the Western Palearctic for the Eurasian Stone-curlew [ Burhinus oedicnemus oedicnemus (Linnaeus, 1758)], 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 two carbon emission and land-use/land-cover scenarios of increasing severity for the year 2050. We then identified in-situ refugia and performed a gap analysis estimating the percentage of refugia falling within the network of currently protected areas. Climate change is expected to increase habitat suitability and land-use/land-cover change to decrease it. Given the low relevance of land-use/land-cover in the model, the climate change model is more supported and an increase of suitability, especially at Northern latitudes, is expected. 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 refugia were mainly identified outside protected areas, particularly in Northern Africa and the Middle East. Therefore, we advocate targeted actions in refugia to promote the conservation of this and other steppe-land species under global environmental change. • Steppe-land birds are particularly sensitive to anthropogenically driven global change. • By means of species distribution models and future projections we identify in-situ refugia for the Eurasian Stone-curlew ( Burhinus oedicnemus ). • We perform a gap analysis to identify how much protected areas cover the in-situ refugia. • Climate change is expected to increase habitat suitability and land-use/land-cover change to decrease it. • In-situ refugia are found mainly outside protected areas and we advocate for targeted conservation actions in these areas.
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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 | 0.000 |
| Research integrity | 0.000 | 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 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".