Projected dynamics of breeding habitat suitability for a steppe-land bird warrant anticipatory conservation actions
Bibliographic record
Abstract
ABSTRACT Understanding spatial and temporal variations of habitat suitability is fundamental for species’ conservation under global change. Steppic species are particularly sensitive to anthropogenic change and have undergone large declines in the last decades. We aimed to describe current and future breeding habitat suitability for the Eurasian stone-curlew Burhinus oedicnemus , a steppic species of conservation concern, and to identify critical areas for its conservation. We collected 1628 presence records covering the period 1992-2016. We developed a species distribution model using a dynamic Maxent algorithm and a set of pseudo-absences with a spatial density weighted on a fixed kernel density estimated on the presences, to mitigate the potential sampling bias. We projected this model under a set of carbon emission, socioeconomic and land-use/land-cover scenarios for the years 2030, 2050, 2070 and 2090. Finally, we described the cell-wise and mean change of breeding habitat suitability through consecutive time intervals and identified the areas critical for the species’ conservation. All scenarios predicted a short-term northward shift of suitable areas, followed by a period of stability. We found no consistent trends in the mean change of breeding habitat suitability, and similar extents of suitable areas under current and future scenarios. Critical areas for the conservation of the species are mainly located in Northern Europe, Israel and parts of North Africa, the Iberian Peninsula and Italy. According to our results, the Eurasian stone-curlew has the potential to maintain viable populations in the Western Palearctic, but dispersal limitations might hinder the colonization of shifted suitable areas. Targeted conservation interventions in the critical areas are therefore recommended to secure the future of the species under global 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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".