A multivariate analysis of bird species composition and abundance between crop types and seasons in southern Ontario, Canada
Bibliographic record
Abstract
Many farmland bird species are declining in North America and Europe, yet there are few data documenting bird use of agricultural landscapes, especially in Canada. This information is needed in order to identify candidate factors contributing to declines. We examined the influence of crop type and adjacent habitat on birds in fields of four crop types in three southern Ontario counties during the 1988 breeding (May-July) and 1987 and 1988 migration (August-September) seasons, using canonical correspondence analysis (CCA). Crops included apple Malus spp. orchards in Norfolk, soybeans Glycine max in Essex, vineyards Vitae spp. in Niagara and corn Zea mays (maize) in all three counties. Bird assemblages differed between counties because corn in Norfolk had more adjacent wetlands and woodlands than those in Essex. During the breeding season (1988), significant habitat variables explaining variation in bird assemblages (in order of importance) were adjacent apple orchards, wetlands, and “other” wooded habitats and apple as the crop (as distinct from adjacent apple orchards). During migration, apple as the crop was most important, followed by crop type corn (distinct from adjacent corn), adjacent wetlands and adjacent other crops in 1988. Apple as the crop was most important, followed by grape as the crop (distinct from adjacent vineyards) and wetlands in 1987. Based on median vector distances in ordination space as a measure of the difference between breeding and migration periods, bird assemblages in soybean and corn in Essex changed most, while birds assemblages in apple orchards changed least, although differences were not significant among crops. Our results emphasize the importance of non-crop and crop habitats for birds during both breeding and migration seasons.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".