Missing Release Data in Capture-Mark-Recovery Analyses: Consequences for Inference
Bibliographic record
Abstract
Abstract Demographic probabilities, such as annual survival and harvest probability, are key metrics used in research and for monitoring the health of wildlife populations and sustainability of harvest. For waterfowl populations, annual estimates of these probabilities come from mark-recovery analysis of data from coordinated banding operations. The Brownie model is the most commonly used parameterization for analyzing mark-recovery data from harvested species. However, if banded waterfowl are not released during a year of a multiyear banding operation, then estimating annual survival and recovery probabilities from a dead recovery model is a challenge. Due to coronavirus disease 2019, many wildlife monitoring efforts, including annual waterfowl banding programs, were canceled or reduced during 2020 and 2021, highlighting the need for wildlife managers to better understand the consequences of missing data on analyses and regulatory decisions. We summarized methods of model parameterization and use of alternative methods to explore the behavior of demographic parameter estimates when a year of release data was missing. Comparing constrained fixed-effect models (we set parameters during the missing year of data equal to parameters for years with release data) with random-effect models, we found that 1) bias of estimates during a year of missing release data was smaller when using a random-effect model, 2) the direction of the bias was unpredictable, but the expected range in bias could be generally known commensurate to the underlying variability in survival and recovery probabilities, and 3) potential bias was greatest if the missing year of releases occurred during the final year of a time series. We conclude that in some circumstances, various modeling approaches can provide reasonable estimates during a year of missing release data, particularly when underlying demographic parameters, or the parameter constrained in a model, vary little over time (e.g., adult survival in long-lived species), which would result in relatively little bias in the other estimated parameter (e.g., annual recovery probability). We also suggest that using alternative analytical techniques, such as random-effect models, may improve estimates for the demographic parameters of interest when release data are missing. Random-effect models also allowed us to estimate parameters, such as juvenile recovery probabilities, during the year of missing release data, which are not identifiable using standard modeling techniques. Where accurate and precise parameter estimation is important for making harvest management decisions and regardless of the model type or the data used, there is no analytical replacement for missing release data. We suggest that practitioners determine the potential consequences for missing data through simulation by using empirical data and simulated data with known demographic probabilities to determine the best actions to take for analyzing their capture-recovery data when release data are missing.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".