Potential extinction risk of Juniperus phoenicea under global climate change: Towards conservation planning
Bibliographic record
Abstract
Global change effects on species are most pronounced when there is a large mismatch between past climate conditions, and the present climate, and this chasm will grow as global change proceeds without mitigation. Global change encompasses the alteration of temperature and precipitation patterns worldwide and these drivers can both increase the risk of species extirpation, and extinction. Juniperus phoenicea is an endemic plant species in the Mediterranean region of high conservation concern. Ensemble distribution models and the potential impact of future climate scenarios revealed that temperature, isothermality, and precipitation are the only significant bioclimatic factors affecting the geographical distribution of J. phoenicea. To study the potential impact of global change, we constrained the SDMs with a combination of two shared socio-economic pathways (SSPs) climate scenarios in the near (2030) and far (2090) future, together with two dispersal scenarios (full and limited). After removing incompatible regions based on current land-use distribution, the comparison of the current and future areas of occupancy revealed strong declines in the distribution of J. phoenicea. Applying the IUCN criteria, the species is predicted in all scenarios to be up-listed from the currently "least-concern" status to the "vulnerable", and potentially to the "critically endangered" status under the highest emission scenario in 2090. The range shifts predicted by our analysis draws attention to regions with stable distribution, and others predicted to become favorable for the species establishment. This information is essential for future conservation planning, including afforestation and reforestation programs.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".