Document Detail


Modeling age and nest-specific survival using a hierarchical Bayesian approach.
MedLine Citation:
PMID:  19302407     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Recent studies have shown that grassland birds are declining more rapidly than any other group of terrestrial birds. Current methods of estimating avian age-specific nest survival rates require knowing the ages of nests, assuming homogeneous nests in terms of nest survival rates, or treating the hazard function as a piecewise step function. In this article, we propose a Bayesian hierarchical model with nest-specific covariates to estimate age-specific daily survival probabilities without the above requirements. The model provides a smooth estimate of the nest survival curve and identifies the factors that are related to the nest survival. The model can handle irregular visiting schedules and it has the least restrictive assumptions compared to existing methods. Without assuming proportional hazards, we use a multinomial semiparametric logit model to specify a direct relation between age-specific nest failure probability and nest-specific covariates. An intrinsic autoregressive prior is employed for the nest age effect. This nonparametric prior provides a more flexible alternative to the parametric assumptions. The Bayesian computation is efficient because the full conditional posterior distributions either have closed forms or are log concave. We use the method to analyze a Missouri dickcissel dataset and find that (1) nest survival is not homogeneous during the nesting period, and it reaches its lowest at the transition from incubation to nestling; and (2) nest survival is related to grass cover and vegetation height in the study area.
Authors:
Jing Cao; Chong Z He; Kimberly M Suedkamp Wells; Joshua J Millspaugh; Mark R Ryan
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Publication Detail:
Type:  Journal Article; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
Title:  Biometrics     Volume:  65     ISSN:  1541-0420     ISO Abbreviation:  Biometrics     Publication Date:  2009 Dec 
Date Detail:
Created Date:  2009-12-16     Completed Date:  2010-03-04     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1052-62     Citation Subset:  IM    
Affiliation:
Department of Statistical Science, Southern Methodist University, Dallas, Texas 75275, USA. jcao@smu.edu
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MeSH Terms
Descriptor/Qualifier:
Age Factors
Animals
Bayes Theorem*
Biometry / methods*
Birds*
Female
Male
Models, Statistical*
Population Dynamics
Statistics, Nonparametric
Survival Analysis

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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