Late 21st‐Century Climate and Land Use Driven Loss of Plant Diversity in African Mountains
Bibliographic record
Abstract
With the 1.5°C-5°C increase in global temperature projected for this century, many plant species are expected to shift their distribution ranges to track their environmental requirements. Across several mountain regions, responses to climate change like upslope shifts may result in accelerated rates of species turnover, species richness increases in upper montane belts, and amplified habitat losses. Yet, evidence of how such processes may influence plant diversity in Africa is still scarce. Here, using a species distribution modeling approach, we quantify and map how different scenarios of climatic and land-use changes may affect plant species ranges in African mountains. Using individually tuned models and dispersal buffers, we compared distribution losses and potential expansion through dispersal across 607 vascular plant species under three shared socioeconomic pathways for the end of the century. Our projections indicate that keeping warming under 2°C until 2100 under a sustainability scenario (SSP1.26), almost half (49.3%) of the species would experience a contraction in suitable areas, compared to 71%-75.6% in case these targets are not met (SSP3.70 and SSP5.85). Among these losses, mean contractions between 19% and 50.4% are predicted depending on the scenario. We project rates of upslope shifts that may be up to three times higher than the global calculated average. Contractions will be higher for species occurring at upper elevations, and trees and shrubs will show lower declines. Our findings align with previously reported trends of upslope shifts of species distributions but suggest that accelerated rates of change may limit the capacity of some species to track their niche based solely on their natural dispersal capacity. This implies that further efforts to improve habitat connectivity, restoration, and assisted migration may be necessary.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".