The duration of capture and restraint during anesthesia and euthanasia influences glucocorticoid levels in male golden hamsters

Deep litter has been shown to decrease stereotypic wire-gnawing in male golden hamsters, suggesting that increased litter depth may be associated with decreased chronic stress levels. To determine the relationship between litter depth and stress levels in hamsters, the authors measured serum levels of corticosterone, cortisol, and ACTH in male golden hamsters kept in cages with three different depths of litter. The duration of handling the hamsters significantly increased the concentrations of corticosterone, cortisol, and the ratio of cortisol/corticosterone. It took longer to catch hamsters housed in cages with deep litter and the ACTH levels were higher in these hamsters. The positive effect of the enrichment (deep litter) was diminished by methodological problems during handling/anesthesia.

Evaluation of a Bayesian MCMC Random Effects Inference Methodology for Capture-Mark-Recapture Data

Monte Carlo simulation was used to evaluate properties of a simple Bayesian MCMC analysis of the random effects model for single group Cormack-Jolly-Seber capture-recapture data. The MCMC method is applied to the model via a logit link, so parameters p, S are on a logit scale, where logit(S) is assumed to have, and is generated from, a normal distribution with mean μ and variance σ2 . Marginal prior distributions on logit(p) and μ were independent normal with mean zero and standard deviation 1.75 for logit(p) and 100 for μ ; hence minimally informative. Marginal prior distribution on σ2 was placed on τ2=1/σ2 as a gamma distribution with α=β=0.001 . The study design has 432 points spread over 5 factors: occasions (t) , new releases per occasion (u), p, μ , and σ . At each design point 100 independent trials were completed (hence 43,200 trials in total), each with sample size n=10,000 from the parameter posterior distribution. At 128 of these design points comparisons are made to previously reported results from a method of moments procedure. We looked at properties of point and interval inference on μ , and σ based on the posterior mean, median, and mode and equal-tailed 95% credibility interval. Bayesian inference did very well for the parameter μ , but under the conditions used here, MCMC inference performance for σ was mixed: poor for sparse data (i.e., only 7 occasions) or σ=0 , but good when there were sufficient data and not small σ .