Mountain biodiversity and global change: global to local perspectives
Bibliographic record
Abstract
Human pressures, including land-use change, pollution, and greenhouse gas emissions, are major drivers of climate and environmental change, with profound effects on biodiversity and ecosystem functioning. Mountain ecosystems, despite covering less than one-fifth of Earth’s surface, are particularly important for biodiversity conservation, hosting one-third of terrestrial species diversity and half of the world’s biodiversity hotspots. Yet, their species are highly vulnerable to climate change due to restricted elevational ranges and specialized ecological requirements. This PhD thesis investigates the impacts of global change drivers—climate change, land-use change, and human population density—on mountain ecosystems, combining global- and local-scale analyses to inform conservation strategies. At the global scale, I quantified the exposure of mountain ecosystems to multiple change drivers, showing that tropical mountains, especially in Africa, face the highest risks. Using species niche modeling, I found that future climate instability could be substantially mitigated under low-emission scenarios, reducing the probability of niche shrinkage in mountain carnivores and ungulates. Species distribution models further projected significant range losses for mountain mammals and birds under high-emission scenarios, with tropical regions most at risk, while temperate mountains may function as future refugia. At the local scale, I evaluated the role of unprotected areas adjacent to protected zones in sustaining wildlife populations in the Central Apennines. Analyses of ecological corridors designed for the critically endangered Marsican brown bear revealed their broader importance for multiple mammal species, underscoring the need for connectivity-focused conservation. Additionally, I developed a standardized protocol to map livestock density at fine spatial scales, providing a tool to assess grazing pressures and guide ecosystem management. Overall, this thesis highlights both the vulnerability of mountain biodiversity and the potential of temperate mountain systems to act as climate refugia. By integrating global and local perspectives, it contributes to international policy frameworks such as the Kunming-Montreal Global Biodiversity Framework and the EU Biodiversity Strategy. The findings emphasize the urgency of adaptive, long-term conservation strategies that enhance connectivity, resilience, and ecosystem integrity in mountain environments under accelerating 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".