Cerulean Warbler Reproduction, Survival, and Models of Population Decline
Bibliographic record
Abstract
Abstract: We present and compare demographic data for cerulean warblers ( Dendroica cerulea ) from 5 study sites across the range of the species from 1992 to 2006. We conducted field studies to collect data on daily nest survival, nest success, and young fledged per successful nest, and we used data to estimate fecundity. Daily nest survival, nest success, young fledged, and fecundity varied widely across the cerulean range and among years. Study sites in agriculture‐dominated landscapes (Mississippi Alluvial Valley, IN, and MI, USA) had negative growth rates in all years monitored because measured values of nest success and young produced per successful nest were incapable of offsetting apparent mortality. Ontario (Canada) and Tennessee (USA) populations had greater nest success and fecundity but still appeared to be incapable of producing stable populations (λ = 1) under field‐measured and assumed conditions. We had survival data only for one site (Ontario); thus, additional survival data are greatly needed to enable more reliable estimates of population growth. Conservation strategies for cerulean warblers in agriculture‐dominated landscapes (e.g., Mississippi Alluvial Valley, IN, and MI) may require major landscape‐level habitat reconfiguration to change agriculture‐dominated landscapes to forest‐dominated landscapes to increase fecundity. Conservation strategies in predominantly forested landscapes in the core of the range (e.g., TN) require a focus on minimizing habitat loss and developing management prescriptions capable of improving fecundity. In both cases, based on sensitivity and elasticity analyses, efforts to improve survival during the nonbreeding season would have the greatest positive effect on population growth. (JOURNAL OF WILDLIFE MANAGEMENT 72(3):646–653; 2008)
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".