Nesting Ecology of White-throated Sparrows (Zonotrichia albicollis) : The Effects of Variation in Clutch-initiation Date and the Application of Prescribed Fire on Nesting Success
Bibliographic record
Abstract
The study of nesting success is one of the most widely-used methods for examining the factors governing recruitment in birds. Many ecological influences act together to increase or decrease nesting success, and knowledge of these mechanisms is crucial for determining the habitat or conservation requirements of a given species. One ecological influence is the timing of breeding, which in many species is related to a decline in nest success later in the breeding season. Much of the research on this subject has focused on cavity-nesting species, and the hypothesis that this seasonal decline is caused by a mismatch between clutch-initiation date and peak food availability for young. I tested this hypothesis in a population of white-throated sparrows (Zonotrichia albicollis) in Algonquin Provincial Park, Ontario, and I offer the alternate hypothesis that this decline may be caused by increased predation later in the season. Another influence on nest survival is disturbance caused by habitat management, such as the use of prescribed fire to conserve open- and early-successional habitats. While the goal of these restoration initiatives is to maintain landscape heterogeneity, the level of disturbance required to improve a habitat cannot exceed the tolerance of target species. Much of the research on this subject has focused on community-level studies quantifying the change in species abundances after a fire event. These studies capture only the most coarse-grained effects of fire, and may not be used to elucidate the mechanisms driving these changes in population size. I investigate the effect of spring burning on the nesting success of a single species, white-throated sparrows, and reveal that, while overall nesting success is not affected, fire has a substantial impact on nest site selection. This has implications for future controlled burn initiatives, since a matrix of suitable nesting habitat must be left untouched in order to allow breeding in the first year after the burn. Observing the same population in both a natural and managed system represents a valuable contribution to our knowledge of the effects of management strategies on avian nesting success.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".