TY - JOUR
T1 - Modeling age and nest-specific survival using a hierarchical bayesian approach
AU - Cao, Jing
AU - He, Chong Z.
AU - Suedkamp Wells, Kimberly M.
AU - Millspaugh, Joshua J.
AU - Ryan, Mark R.
PY - 2009/12
Y1 - 2009/12
N2 - 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.
AB - 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.
KW - Age-specific survival probability
KW - Bayesian hierarchical model
KW - Intrinsic autoregressive prior
KW - Nest survival
KW - Nest-specific covariate
UR - http://www.scopus.com/inward/record.url?scp=70450233460&partnerID=8YFLogxK
U2 - 10.1111/j.1541-0420.2009.01204.x
DO - 10.1111/j.1541-0420.2009.01204.x
M3 - Article
C2 - 19302407
AN - SCOPUS:70450233460
SN - 0006-341X
VL - 65
SP - 1052
EP - 1062
JO - Biometrics
JF - Biometrics
IS - 4
ER -