Missing Release Data in Capture-Mark-Recovery Analyses: Consequences for Inference
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».