Response of foodborne Salmonella spp. marker strains inoculated on egg shell surfaces to disinfectants in a commercial egg washer.

The objective of this study was to determine the effectiveness of an iodine based disinfectant (IBD, Iocide, Biomedical Development Corporation, San Antonio, TX) on Salmonella enteritidis and S. typhimurium inoculated on egg shell surfaces under simulated industry egg processing conditions with a commercial egg washer used as the sanitizer delivery system. Re-circulated egg washer water containing 1.40-2.85 g/l total dissolved solids was obtained from a commercial egg processing. Sanitizing treatments consisted of distilled deionized water (DDW), IBD, and chlorine (CL; 200 ppm). All treatments (DDW, IBD and CL) significantly (p < 0.05) decreased Salmonella spp. populations on the shell compared to dry (no spray) egg controls. However, efficacy of egg sanitizers appeared to be dependent on the level of total dissolved solids in the egg wash water.     

Fitting complex population models by combining particle filters with Markov chain Monte Carlo

We show how a recent framework combining Markov chain Monte Carlo (MCMC) with particle filters (PFMCMC) may be used to estimate population state-space models. With the purpose of utilizing the strengths of each method, PFMCMC explores hidden states by particle filters, while process and observation parameters are estimated using an MCMC algorithm. PFMCMC is exemplified by analyzing time series data on a red kangaroo (Macropus rufus) population in New South Wales, Australia, using MCMC over model parameters based on an adaptive Metropolis-Hastings algorithm. We fit three population models to these data; a density-dependent logistic diffusion model with environmental variance, an unregulated stochastic exponential growth model, and a random-walk model. Bayes factors and posterior model probabilities show that there is little support for density dependence and that the random-walk model is the most parsimonious model. The particle filter Metropolis-Hastings algorithm is a brute-force method that may be used to fit a range of complex population models. Implementation is straightforward and less involved than standard MCMC for many models, and marginal densities for model selection can be obtained with little additional effort. The cost is mainly computational, resulting in long running times that may be improved by parallelizing the algorithm.     

Effects of choice of baseline on the uncertainty of population and biodiversity indices

Environmental and Ecological Statistics - Many monitoring programs provide annual indices of relative change over time in some quantitative measure of ecological status, such as population...     

Temporal trends in opportunistic citizen science reports across multiple taxa

Opportunistic reporting of species observations to online platforms provide one of the most extensive sources of information about the distribution and status of organisms in the wild. The lack of a clear sampling design, and changes in reporting over time, leads to challenges when analysing these data for temporal change in organisms. To better understand temporal changes in reporting, we use records submitted to an online platform in Sweden (Artportalen), currently containing 80 million records. Focussing on five taxonomic groups, fungi, plants, beetles, butterflies and birds, we decompose change in reporting into long-term and seasonal trends, and effects of weekdays, holidays and weather variables. The large surge in number of records since the launch of the, initially taxa-specific, portals is accompanied by non-trivial long-term and seasonal changes that differ between the taxonomic groups and are likely due to changes in, and differences between, the user communities and observer behaviour. Supplementary InformationThe online version contains supplementary material available at 10.1007/s13280-021-01550-w.     

Individual heterogeneity and senescence in silvereyes on Heron Island

Individual heterogeneity and correlations between life history traits play a fundamental role in life history evolution and population dynamics. Unobserved individual heterogeneity in survival can be a nuisance for estimation of age effects at the individual level by causing bias due to mortality selection. We jointly analyze survival and breeding output from successful breeding attempts in an island population of Silvereyes (Zosterops lateralis chlorocephalus) by fitting models that incorporate age effects and individual heterogeneity via random effects. The number of offspring produced increased with age of parents in their first years of life but then eventually declined with age. A similar pattern was found for the probability of successful breeding. Annual survival declined with age even when individual heterogeneity was not accounted for. The rate of senescence in survival, however, depends on the variance of individual heterogeneity and vice versa; hence, both cannot be simultaneously estimated with precision. Model selection supported individual heterogeneity in breeding performance, but we found no correlation between individual heterogeneity in survival and breeding performance. We argue that individual random effects, unless unambiguously identified, should be treated as statistical nuisance or taken as a starting point in a search for mechanisms rather than given direct biological interpretation.