Bayesian population dynamics of interacting species: great gerbils and fleas in Kazakhstan

We propose a discrete-time Bayesian hierarchical model for the population dynamics of the great gerbil-flea ecological system. The model accounts for the sampling variability arising from data originally collected for other purposes. The prior for the unknown population densities incorporates specific biological hypotheses regarding the interacting dynamics of the two species, as well as their life cycles, where density-dependent effects are included. Posterior estimates are obtained via Markov chain Monte Carlo. The variance of the observed density estimates is a quadratic function of the unknown density. Our study indicates the presence of a density-dependent growth rate for the gerbil population. For the flea population there is clear evidence of density-dependent over-summer net growth, which is dependent on the flea-to-gerbil ratio at the beginning of the reproductive summer. Over-winter net growth is favored by high density. We estimate that on average 35% of the gerbil population survives the winter. Our study shows that hierarchical Bayesian models can be useful in extracting ecobiological information from observational data.     

A case-cohort design for assessing covariate effects in longitudinal studies

The case-cohort design for longitudinal data consists of a subcohort sampled at the beginning of the study that is followed repeatedly over time, and a case sample that is ascertained through the course of the study. Although some members in the subcohort may experience events over the study period, we refer to it as the "control-cohort." The case sample is a random sample of subjects not in the control-cohort, who have experienced at least one event during the study period. Different correlations among repeated observations on the same individual are accommodated by a two-level random-effects model. This design allows consistent estimation of all parameters estimable in a cohort design and is a cost-effective way to study the effects of covariates on repeated observations of relatively rare binary outcomes when exposure assessment is expensive. It is an extension of the case-cohort design (Prentice, 1986, Biometrika73, 1-11) and the bidirectional case-crossover design (Navidi, 1998, Biometrics54, 596-605). A simulation study compares the efficiency of the longitudinal case-cohort design to a full cohort analysis, and we find that in certain situations up to 90% efficiency can be obtained with half the sample size required for a full cohort analysis. A bootstrap method is presented that permits testing for intra-subject homogeneity in the presence of unidentifiable nuisance parameters in the two-level random-effects model. As an illustration we apply the design to data from an ongoing study of childhood asthma.     

Improving removal-based estimates of abundance by sampling a population of spatially distinct subpopulations

A statistical modeling framework is described for estimating the abundances of spatially distinct subpopulations of animals surveyed using removal sampling. To illustrate this framework, hierarchical models are developed using the Poisson and negative-binomial distributions to model variation in abundance among subpopulations and using the beta distribution to model variation in capture probabilities. These models are fitted to the removal counts observed in a survey of a federally endangered fish species. The resulting estimates of abundance have similar or better precision than those computed using the conventional approach of analyzing the removal counts of each subpopulation separately. Extension of the hierarchical models to include spatial covariates of abundance is straightforward and may be used to identify important features of an animal's habitat or to predict the abundance of animals at unsampled locations.     

Estimation of individual genetic effects from binary observations on relatives applied to a family history of respiratory illnesses and chronic lung disease of newborns

This paper considers methods for estimating the relationship between a binary response Y and the genetic effects responsible for a second binary trait Z. The responses Y are observed only for target individuals, and the responses Z are observed only for the relatives of these targets. The analysis consists of two parts. The first part concerns the analysis of the family data Z and the second part estimates the relation between the genetic effects and Y. For the family data, a generalized linear mixed model with a logit link and Gaussian genetic (random) effects is used. Estimates of the variances of the genetic effects are obtained by using a pseudo-profile log-likelihood method. Estimation of the log likelihood involves averaging over n-dimensional normal distributions, which is done by importance sampling. The methods used in the second part are straightforward. The methods are applied to a data set containing chronic lung disease (CLDN) responses of newborns and asthma (AS), allergy (AL), chronic bronchitis (CB) and eczema (EC) responses observed for the relatives of these newborns. The clinical question is whether genetic effects of AS, AL, CB, and EC have an effect on the risk for CLDN. It can be concluded that for AS, AL, CB, and EC, the influence of genetic effects is significant. However, these genetic predispositions have no significant effect on CLDN.     

Bootstrap confidence intervals for adaptive cluster sampling

Consider a collection of spatially clustered objects where the clusters are geographically rare. Of interest is estimation of the total number of objects on the site from a sample of plots of equal size. Under these spatial conditions, adaptive cluster sampling of plots is generally useful in improving efficiency in estimation over simple random sampling without replacement (SRSWOR). In adaptive cluster sampling, when a sampled plot meets some predefined condition, neighboring plots are added to the sample. When populations are rare and clustered, the usual unbiased estimators based on small samples are often highly skewed and discrete in distribution. Thus, confidence intervals based on asymptotic normal theory may not be appropriate. We investigated several nonparametric bootstrap methods for constructing confidence intervals under adaptive cluster sampling. To perform bootstrapping, we transformed the initial sample in order to include the information from the adaptive portion of the sample yet maintain a fixed sample size. In general, coverages of bootstrap percentile methods were closer to nominal coverage than the normal approximation.     

The impact of the botanical gardens on theaeroplankton of the city of Cagliari, Italy

In this study the authors analyse the incidence inthe airborne pollen from exotic plants that grow inthe Botanical Gardens of the University of Cagliari.It was possible to collect the pollen grains of 52 ofthe species cultivated in the Botanical Gardens, mostof which belong to the Leguminosae, the Moraceae and the Myrtaceae. Casuarinastricta Aiton of Australian origin shows the highestconcentration with 137 grains per cubic metre of air.     

Dynamics of the Hck-SH3 domain: comparison of experiment with multiple molecular dynamics simulations

Molecular dynamics calculations provide a method by which the dynamic properties of molecules can be explored over timescales and at a level of detail that cannot be obtained experimentally from NMR or X-ray analyses. Recent work (Philippopoulos M, Mandel AM, Palmer AG III, Lim C, 1997, Proteins 28:481-493) has indicated that the accuracy of these simulations is high, as measured by the correspondence of parameters extracted from these calculations to those determined through experimental means. Here, we investigate the dynamic behavior of the Src homology 3 (SH3) domain of hematopoietic cell kinase (Hck) via 5N backbone relaxation NMR studies and a set of four independent 4 ns solvated molecular dynamics calculations. We also find that molecular dynamics simulations accurately reproduce fast motion dynamics as estimated from generalized order parameter (S2) analysis for regions of the protein that have experimentally well-defined coordinates (i.e., stable secondary structural elements). However, for regions where the coordinates are not well defined, as indicated by high local root-mean-square deviations among NMR-determined structural family members or high B-factors/low electron density in X-ray crystallography determined structures, the parameters calculated from a short to moderate length (less than 5-10 ns) molecular dynamics trajectory are dependent on the particular coordinates chosen as a starting point for the simulation.     

Clonal and fine-scale genetic structure in populations of a restricted Korean endemic, Hosta jonesii (Liliaceae) and the implications for conservation

BACKGROUND AND AIMS: In plant populations the magnitude of spatial genetic structure of apparent individuals (including clonal ramets) can be different from that of sexual individuals (genets). Thus, distinguishing the effects of clonal versus sexual individuals in population genetic analyses could provide important insights for evolutionary biology and conservation. To investigate the effects of clonal spread on the fine-scale spatial genetic structure within plant populations, Hosta jonesii (Liliaceae), an endemic species to Korea, was chosen as a study species. METHODS: Using allozymes as genetic markers, spatial autocorrelation analysis of ramets and of genets was conducted to quantify the spatial scale of clonal spread and genotype distribution in two populations of H. jonesii. KEY RESULTS: Join-count statistics revealed that most clones are significantly aggregated at < 3-m interplant distance. Spatial autocorrelation analysis of all individuals resulted in significantly higher Moran's I values at 0-3-m interplant distance than analyses of population samples in which clones were excluded. However, significant fine-scale genetic structure was still observed when clones were excluded. CONCLUSIONS: These results suggest that clones enhance the magnitude of spatial autocorrelation due to localized clonal spread. The significant fine-scale genetic structure detected in samples excluding clones is consistent with the biological and ecological traits exhibited by H. jonesii including bee pollination and limited seed dispersal. For conservation purposes, genetic diversity would be maximized in local populations of H. jonesii by collecting or preserving individuals that are spaced at least 5 m apart.     

Ex vivo gingival-biofilm consortia

AIMS: To develop a protocol for harvesting ex vivo samples of gingival-biofilm consortia and to investigate their basic characteristics. METHODS AND RESULTS: Gingival epithelial cells with attached biofilm were collected from healthy subjects by taking a smear. The bacterial viability was estimated via the alteration of the membrane permeability and metabolic activity via the double/single-stranded nucleic acid ratio using a confocal laser-scanning microscope. Morphological analysis was performed by scanning and transmission electron microscopy. Additionally, microbiological estimations were made. The electron microscopy revealed fimbriae-mediated adhesion and the formation of a biofilm matrix. Most bacteria were viable and had a high metabolic activity. CONCLUSIONS: The presented study offers an easy to follow approach for harvesting samples of gingival-biofilm consortia. The latter differs considerably from the supragingival plaque in viability and zonal distribution. Related to free-living and in vitro-grown biofilms, the gingiva-associated biofilm revealed an atypically high metabolic activity. SIGNIFICANCE AND IMPACT OF THE STUDY: Biofilm fragments should possess the basic features of the entire gingiva-associated biofilm; which as yet cannot be simulated in vitro. Thus, samples of ex vivo gingival-biofilm consortia can be used to investigate the resistance of oral biofilms against antibiotics and biocides.