AREA SENSITIVITY IN GRASSLAND PASSERINES: EFFECTS OF PATCH SIZE, PATCH SHAPE, AND VEGETATION STRUCTURE ON BIRD ABUNDANCE AND OCCURRENCE IN SOUTHERN SASKATCHEWAN
Bibliographic record
Abstract
Abstract Information on area sensitivity and effects of habitat fragmentation has come largely from forest and tallgrass-prairie habitats. Research from other ecosystems is required to determine whether the fragmentation paradigm derived from those studies is applicable to passerine communities elsewhere. I examined the effects of habitat fragmentation on abundance and occurrence of nine species of mixed-grass prairie passerines in southern Saskatchewan. I conducted 190 point-counts in 1996 and 1997 on 89 pastures ranging in size from 8 to 6,475 ha. Sprague's Pipit (Anthus spragueii), Baird's Sparrow (Ammodramus bairdii), Grasshopper Sparrow (A. savannarum), and Chestnut-collared Longspur (Calcarius ornatus) were found to be area-sensitive, in that they were more abundant or occurred more frequently, or both, in larger patches of mixed-grass prairie. However, the ratio of edge to interior habitat was a better predictor of area sensitivity than patch size in most cases. Horned Lark (Eremophila alpestris), Savannah Sparrow (Passerculus sandwichensis), Clay-colored Sparrow (Spizella pallida), Western Meadowlark (Sturnella neglecta), and Brown-headed Cowbird (Molothrus ater) were insensitive to patch size, though occurrence of Clay-colored Sparrow and Western Meadowlark tended to be greater in smaller pastures. Vegetation structure was also found to be an important predictor of grassland songbird abundance and occurrence, in that it explained additional variation not accounted for by patch size or the ratio of edge to interior habitat. Although protection of large contiguous tracts of habitat is essential to conservation of native species, small native-prairie patches with minimal edge habitat also play a vital role in conservation of grassland birds.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".