A review and meta‐analysis of the effects of climate change on Holarctic mountain and upland bird populations
Bibliographic record
Abstract
Mountain regions are globally important areas for biodiversity but are subject to multiple human‐induced threats, including climate change, which has been more severe at higher elevations. We reviewed evidence for impacts of climate change on Holarctic mountain bird populations in terms of physiology, phenology, trophic interactions, demography and observed and projected distribution shifts, including effects of other factors that interact with climate change. We developed an objective classification of high‐elevation, mountain specialist and generalist species, based on the proportion of their breeding range occurring in mountain regions. Our review found evidence of responses of mountain bird populations to climate (extreme weather events, temperature, rainfall and snow) and environmental (i.e. land use) change, but we know little about either the underlying mechanisms or the synergistic effects of climate and land use. Long‐term studies assessing reproductive success or survival of mountain birds in relation to climate change were rare. Few studies have considered shifts in elevational distribution over time and a meta‐analysis did not find a consistent direction in elevation change. A meta‐analysis carried out on future projections of distribution shifts suggested that birds whose breeding distributions are largely restricted to mountains are likely to be more negatively impacted than other species. Adaptation responses to climate change rely mostly on managing and extending current protected areas for both species already present, and for expected colonizing species that are losing habitat and climate space at lower elevation. However, developing effective management actions requires an improvement in the current knowledge of mountain species ecology, in the quality of climate data and in understanding the role of interacting factors. Furthermore, the evidence was mostly based on widespread species rather than mountain specialists. Scientists should provide valuable tools to assess the status of mountain birds, for example through the development of a mountain bird population index, and policy‐makers should influence legislation to develop efficient agri‐environment schemes and forestry practices for mountain birds, as well as to regulate leisure activities at higher elevations.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.014 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".