Shelter from the storm: Identifying climate change refugia for British Columbia’s coastal birds under future climate scenarios
Bibliographic record
Abstract
The dual crises of biodiversity loss and climate change pose a complex challenge for conservation management. This study highlights the identification of climate change refugia for coastal birds in British Columbia (B.C.), Canada. Climate change refugia are areas of habitat that are predicted to be buffered from severe climate change impacts, thereby providing important areas of habitat into the future and supporting biodiversity. By identifying climate refugia, conservation managers can allocate resources towards those areas and better understand possible future environmental conditions. Here, I use Maxent ecological niche models (ENMs) to identify potential climate change refugia for twelve species of coastal birds in B.C. under future climate scenarios. Model variables include the most recent climate change projections from the International Panel on Climate Change (IPCC) and data representing a suite of habitat characteristics (e.g., coastal slope and elevation, land use type, sea level rise projections, and more). The model outputs indicate the relative probability of bird occurrence across their B.C. range through time and under different climate conditions. I show that under the four IPCC climate scenarios studied, outcomes for B.C.’s coastal birds may vary widely. Under the intensive fossil fuel development scenario (SSP5-8.5), the area (square kilometres) of “optimal habitat” (i.e., where probability of occurrence is between 60-100% per 1x1 kilometre grid cell) declines between mid and late century for eight of twelve species studied. For eight of the twelve species, the SSP5-8.5 scenario also has the lowest overall percent cover of optimal habitat at late century when compared to the other scenarios. However, under the other three climate scenarios, species fare better. Under SSP1-2.6 and SSP3-7.0, eleven out of twelve species gain or have no difference in optimal habitat coverage between mid and late century. Under the “middle of the road” scenario (SSP2-4.5), all twelve species gain optimal habitat between mid and late century. For half of the species studied, models indicate that areas of optimal habitat shift northward over time and under warmer scenarios. For most species, the models do not indicate range expansion outside of birds’ current ranges, but the models do indicate the importance of parks and protected areas as climate refugia into the future.
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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 | 0.001 | 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) | 0.001 | 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".