The owls are coming: positive effects of climate change in Northern ecosystems depend on grassland protection
Bibliographic record
Abstract
Climate-driven migrations towards Northern latitudes are expected to reorganize biotic communities as result of range shift dynamics. However, the establishment of healthy populations of migrating species depends on habitat provision by receptor landscapes. Here, we ask if the rising temperatures and changes in precipitation regimes in western North America are likely to lead to an expansion of warm and dry-affiliated species, using the burrowing owl (Athene cunicularia) as a study case. This migratory species depends on grassland habitats for nesting and breeding, so we test for the effect of the lack of grasslands on the occupancy of future suitable environments. To estimate the burrowing owl's potential distribution, we used ecological niche models (ENMs) calibrated with climate and soil information and projected onto future scenarios of climate change (low versus high greenhouse gas emission). Then, we simulated environmental sorting using habitat filter masks derived from information on habitat use and forecasts of future land use change, focusing on grasslands as nesting and breeding habitat. We found that the burrowing owl could expand its geographic distribution by 3 to 10-fold towards Northern latitudes, especially under high-emission scenarios of climate change. However, nearly half of the suitable environments (up to 53,593 km2 of locations with suitable climate and soil) might not be covered by grasslands, due to conversion to agriculture and other human land uses which may prevent the establishment of breeding populations. Our results shed light on the pervasive effects of neglecting the preservation of grasslands across western North America, which could provide critically needed habitat for migrating species from lower latitudes. Enhancing and facilitating the colonization of novel species is a shift in the static paradigm of biodiversity conservation and a proactive measure for climate change adaptation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".