Avian nest success, mammalian nest predator abundance, and invertebrate prey availability in a fragmented landscape
Bibliographic record
Abstract
Avian nest success is largely determined by predation, but factors affecting the abundance of potential nest predators are rarely studied. We used an information–theoretic approach to assess relative support for models including invertebrate biomass, mammalian nest predator abundance, and percent cover at nests as explanatory variables for nest success of Ovenbirds ( Seiurus aurocapilla (L., 1766)) and Wood Thrush ( Hylocichla mustelina (J.F. Gmelin, 1789)). We ranked models including local vegetation characteristics and landscape composition at two spatial scales (100 and 2000 m) as explanatory variables for the abundance of mammalian nest predator groups and for prey biomass. The nest success of Ovenbirds was best explained by a positive association with percent cover by forbs and seedlings, whereas a positive relationship with prey biomass best explained the nest success of Wood Thrush. Most mammal genera were associated with landscape composition within 100 m of the study sites, and most were positively associated with housing density. Prey biomass was best explained by a positive association with less intensive agriculture within 2000 m. Implementing silvicultural techniques that preserve important habitat features within fragmented forests, limiting housing density within 100 m, and increasing the amount of less intensive agriculture within 2000 m of forest fragments may improve nest success for forest songbirds.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".