Banding age ratios reveal prairie waterfowl fecundity is affected by climate, density dependence and predator–prey dynamics
Bibliographic record
Abstract
Abstract Fecundity estimates for demographic modelling are difficult to acquire at the regional spatial scales that correspond to climate shifts, land use impacts or habitat management programmes. Yet they are important for evaluating such effects. While waterfowl managers have historically used harvest‐based age ratios to assess fecundity at continental scales, widely available age ratios from late summer banding (ringing) data present an underutilized opportunity to examine a regional fecundity index with broad temporal replication. We used age ratios from banding data and hierarchical mixed‐effect models to examine how fecundity of five North American dabbling duck species was affected by temporal variation in hydrological cycles, intra‐ and interspecific density dependence and alternate prey availability, and whether those relationships were consistent across a broad geographic area. Model‐estimated fecundity was within the range of traditional harvest‐based fecundity estimates for each species. Ecological covariates explained between 16% and 53% of the temporal variation in fecundity, dependent on species. Increasing wetland inundation and an indicator of vole population irruptions were consistent predictors of increasing fecundity across all species. Species exhibited mixed positive and negative responses to interspecific and intraspecific breeding pair densities hypothesized to affect nest and brood survival respectively, highlighting the importance of integrating brood survival into fecundity metrics for precocial species. Declines in fecundity over time and across space at more northern latitudes may reflect stronger policies for grassland and wetland protection in the U.S. versus Canadian portions of the prairies over the time period of our study. Maintaining the capacity of less permanent basins to rehydrate in wetter periods through easement protection benefits fecundity, particularly for late‐nesting species that acquire a greater proportion of their reproductive energy on the breeding grounds. Synthesis and applications . Age ratios derived from postbreeding banding operations allowed us to attribute variation in waterfowl fecundity to temporal ecological variables. Effects of habitat management for waterfowl may be masked unless analysts account for this temporal variation. Postbreeding‐pulse age ratios at capture could be useful as fecundity metrics in integrated population models and for evaluating population dynamics of extensively banded nongame species, especially if adjusted for capture vulnerability using within‐season recapture data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".