The role of field margins in microclimatic temperature buffering of temperate Canadian landscapes
Bibliographic record
Abstract
Field margins in agricultural landscapes exhibit considerable structural variation, ranging from herbaceous strips to shrubby borders and tree-rich hedgerows, which can influence microclimatic conditions and wildlife habitat quality. Despite their ecological importance, the microclimatic properties of these diverse field margins remain poorly characterized. By generating fine-scale thermal heterogeneity, field margins may act as microrefugia, buffering organisms against temperature extremes and supporting climate adaptation. We investigated how local vegetation structure and landscape context influence microclimatic variation in a temperate Canadian agricultural landscape. From June to September 2018, we deployed 180 temperature data sensors across 30 fields in 20 1-km2 landscapes in eastern Ontario, Canada, recording conditions in both field margins and adjacent crop fields. We quantified daily maximum and minimum temperature offsets between margins and fields, compared these to regional weather station data, and assessed the sensitivity of measurements to solar radiation shielding. Linear mixed-effects models showed that tree-rich margins strongly reduced maximum temperatures and elevated minimum temperatures, while herbaceous and shrubby margins resulted in limited thermal buffering. Margin width and landscape-level natural vegetation cover had no detectable effects. Local microclimate measurements were consistently more extreme than regional weather data, highlighting the importance of fine-scale monitoring. Although radiation shielding reduced the magnitude of temperature offsets, the overall direction of effects remained consistent. Our findings underscore the ecological value of conserving and expanding treed field margins, which can provide microrefugia and enhance wildlife resilience under climate 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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".