Impacts of introduced grasses on breeding season habitat use by northern bobwhite in the South Texas plains
Bibliographic record
Abstract
Abstract Introduced grasses may affect diversity of native fauna and flora adversely, and disrupt ecosystem processes. Many rangelands in South Texas have been seeded to or have been colonized by buffelgrass (Pennisetum ciliare) and Lehmann lovegrass (Eragrostis lehmanniana), perennial bunchgrasses native to Africa. The objective of this research was to quantify impacts that these 2 species of introduced grasses may have on northern bobwhite (Colinus virginianus) habitat use on South Texas rangelands during the breeding period (Apr–Aug). We evaluated the effects of buffelgrass and Lehmann lovegrass on northern bobwhite nest habitat (n = 35 nests) and general habitat use sites (n = 86 radiomarked quail) with logistic regression and habitat selection functions based on simple saddlepoint approximations. Buffelgrass was used as a nesting substrate at 11% of nests; however, vegetation height and visual obstruction between 1 cm and 30 cm were the best predictors of nest site use. Areas of introduced grass coverage ≥15–20% were avoided by northern bobwhites at general habitat use organism‐centered points, but not at nest site use points. Introduced grass coverage and forb coverage were the best predictors of general habitat use, and bobwhites avoided areas with ≥18% introduced grass cover. These results suggest that avoidance of areas with extensive introduced grass cover may indicate a reduction in usable habitat space for northern bobwhite in the western South Texas plains. Maintaining native grass stands while implementing localized control of introduced grasses could be used as a strategy to promote habitat for northern bobwhites. © 2011 The Wildlife Society.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".