Responses of cavity‐nesting birds to fire: testing a general model with data from the Northern Flicker
Bibliographic record
Abstract
Census‐based studies document changes in population size of animal species in response to wildfires, but mechanisms involving behavior of individuals and the effects on reproductive success are usually unknown. I developed a conceptual model explaining the persistence of cavity‐nesting birds on the landscape after fires depending on carrying capacity (food supply) of the habitat and the philopatry and territoriality of the species. The breeding density, nest site characteristics, and reproductive success of Northern Flickers Colaptes auratus was studied before and after low‐ to moderate‐severity fires on replicated plots. The density and spatial distribution of nests did not change in a consistent way after fires, but the rates of cavity excavation increased and characteristics of nest sites changed as such decay class of the tree. Laying dates were delayed and clutches were smaller in freshly excavated vs. reused cavities on the burned sites. Breeding philopatry caused a shift to older age classes in the population. Depredation of nests by small mammals increased during the first three years after fires, reducing the number of young produced per nest attempt on burned plots. The study shows that, even when fire does not reduce the density of breeding pairs, there may be detrimental effects detected only by monitoring the behavior and reproduction of individuals.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".