Projected effects of climate change on boreal bird community accentuated by anthropogenic disturbances in western boreal forest, Canada
Bibliographic record
Abstract
Abstract Aim Climate change is expected to influence boreal bird communities significantly, notably through changes in forest habitat (composition and age structure), in the coming decades. How these changes will accumulate and interact with anthropogenic disturbances remains an open question for most species. Location Northeastern Alberta, Canada. Methods We used the LANDIS‐II forest landscape model to project changes in forest landscapes, and associated bird populations (72 passerine species), according to three climatic scenarios (baseline, RCP 4.5 and RCP 8.5) and three forest harvesting scenarios of differing intensity. Results Both forest harvesting and climate‐related drivers were projected to have large impacts on bird communities in this region. As a result of climate‐induced increases in fire activity as well as decreased conifer productivity, our simulations projected that an important proportion of Alberta's boreal forests would transition to treeless habitat (i.e. grass‐ or shrub‐dominated vegetation) while many conifer‐dominated stands would likely be replaced by broadleaf tree cover. Consequently, the abundance of bird species associated with open and deciduous habitats were projected to increase. With a strong anthropogenic climate‐forcing scenario (RCP 8.5), sharp declines in abundance of coniferous trees were also projected, particularly in mature and old forest stands, triggering major declines for bird species associated with coniferous and mixedwood forest types. Main conclusions As the most comprehensive simulation of climate change and harvesting impacts on avian habitats in the North American boreal region to date, our study stresses the importance of considering key habitat characteristics like forest age structure and composition through forest landscape modelling and identifies 18 bird species particularly sensitive to climate change. Our simulations suggest that a change in forest management practices could play an important role in the conservation of boreal bird species vulnerable to climate change. The intensive forest harvesting simulated accelerated declines in bird abundance compared to a “no harvesting” scenario.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".