Estimating Survivorship when the Subjects are Visited Periodically

Abstract

Statistical methods for estimating and comparing constant survival rates are developed here for sampling designs in which survival of a subject is checked at irregular intervals. The maximum likelihood estimator is derived and shown to be readily calculated using an iterative procedure that starts with the Mayfield (1975) estimate as a trial value. Sampling distributions of this estimator and of the product of two or more estimates are skewed, and normalizing transformations are provided to facilitate valid confidence interval estimation. The sampling distribution of the difference between two independent estimates is found to be sufficiently normal without transformation to allow valid use of conventional normal theory procedures for testing differences and determining sample size for specified power. Statistical validity under the variable intensity sampling design does require that the duration of intervisit periods vary independently of observer perceptions concerning the survival status of the subject and, in order to achive robustness with respect to the assumption of constant survivorship, sampling intensity must vary independently of any temporal changes in the daily survival rate. Investigators are warned not to return earlier than planned to subjects thought to have died, as this observer behavior may cause serious bias in the survivorship estimate.

Have Lesser Scaup, <em>Aythya affinis</em>, Reproductive Rates Declined in Parkland Manitoba?

“…Nests of non-radiomarked females were visited every 4 to 6 days to determine fate (Bart and Robson 1982). For nests of radiomarked females, we used female locations to monitor nest status.…”

Effectiveness of management on the breeding success of Chatham Island oystercatchers (Haematopus chathamensis)

“…We used the logit-link function for analysis of nest survival. This approach extends the survival models developed by Mayfield (1961), Johnson (1979), and Bart & Robson (1982) by permitting direct evaluation of the influence of specific covariates on DSR (Dinsmore et al 2002). Akaike's information Criterion (AiC) and AiC weights (w i ) (Burnham & Anderson 2002) were used to evaluate individual models.…”

Winter Distribution, Movements, and Annual Survival of Radiomarked Vancouver Canada Geese in Southeast Alaska

“…Most studies of nest survival have used Mayfield's ad hoc estimator of DSR or the maximum likelihood estimator of Johnson (1979) or Bart and Robson (1982), and these methods assume that DSR is the same for all nests on all dates and for all nest ages (nest fates are independent and identically distributed: iid) within the sample. To attempt to meet the assumption of homogeneity of fates within a given sample, one can stratify data prior to analysis and create sets for which fates can reasonably be considered iid.…”

“…Subsequent to Mayfield's (1961Mayfield's ( , 1975 publications, a variety of authors published a maximumlikelihood approach to estimating daily survival probabilities and nesting success for data from nests that were visited periodically (Johnson 1979, Hensler and Nichols 1981, Bart and Robson 1982. The method is based in statistical theory and provides estimates of the mean and variance of daily survival rate.…”