Impact of Climate Change on the Distributional Potential of the Endemic Species <i>Tamarix dubia</i> Bunge and Conservation Implications for the Irano‐Turanian Region
Bibliographic record
Abstract
ABSTRACT Climate change significantly influences species distribution patterns. Utilizing Ecological Niche Models (ENMs) in climate change research provides valuable insights into species–environment relationships and can inform conservation management decisions. We analyzed climate change effects on the geographic distribution of Tamarix dubia Bunge, an endemic species in the Irano‐Turanian region, using ENM approaches. We modeled the current and future suitable areas for T. dubia using the Maxent algorithm under two shared socioeconomic pathways (SSP2‐4.5 and SSP5‐8.5) for the period 2041–2060. The results revealed that the mean temperature of the warmest quarter (bio10) and precipitation of the wettest quarter (bio16) were determined to be the most important explanatory climatic variables affecting T. dubia distribution. Under both future scenarios, we predicted a decrease in the suitable habitat range of T. dubia in the period 2041–2060. Moreover, a relatively high loss of suitability was anticipated in the actual ranges of species. The results indicated that the distribution of T. dubia , a drought‐tolerant plant species, is likely to be affected significantly by climate change. This study supports future management plans for T. dubia and provides insights into the impacts of climate change on endemic species in arid and semi‐arid regions, which are valuable for understanding distribution patterns and informing future research in the Irano‐Turanian region.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".