Soil and Fertilizer Amendments and Edge Effects on the Floral Succession of Pulverized Fuel Ash

Plots of fresh pulverized fuel ash (PFA, an industrial waste) were inoculated with soils from existing PFA sites and fertilizers in a factorial design, then left unmanaged for 12 years during which time the floral development and soil chemistry were monitored annually. For the first 3 years, the site supported a sparse mix of chenopods (including the scarce Chenopodium glaucum ) and halophytes. As salinity declined, ruderals, legumes, and grasses plus the fire-site moss Funaria hygrometrica colonized, followed by Festuca arundinacea grassland (NVC community MG12) and Hippophae rhamnoides scrub. Dactylorhiza incarnata (orchidacea) appeared after 7 years, but only in plots that had received soil from existing orchid colonies. Four years later, a larger second generation of Dactylorhiza appeared, but only in the central zone of the site where vegetation was thinnest. By year 12, the site was dominated by coarse grasses and scrub, with early successional species persisting only in the sparsely vegetated center, where nitrate levels were lowest. This edge effect is interpreted as centripetal encroachment, a process of potentially wider concern for the conservation of low-fertility habitat patches. Overall, seed bank inoculation seems to have introduced few but desirable species ( D. incarnata , Pyrola rotundifolia , some halophytes, and annuals), whereas initial application of organic fertilizer had long-lasting (≥10 years) effects on cover and soil composition.     

An Efficient Regression Type Estimator in Survey Sampling

For the estimation of population mean in simple random sampling, an efficient regression-type estimator is proposed which is more efficient than the conventional regression estimator and hence than mean per unit estimator, ratio and product estimators and many other estimators proposed by various authors. Some numerical examples are included for illustration.     

Semiparametric Regression in Size-Biased Sampling

Summary .  Size-biased sampling arises when a positive-valued outcome variable is sampled with selection probability proportional to its size. In this article, we propose a semiparametric linear regression model to analyze size-biased outcomes. In our proposed model, the regression parameters of covariates are of major interest, while the distribution of random errors is unspecified. Under the proposed model, we discover that regression parameters are invariant regardless of size-biased sampling. Following this invariance property, we develop a simple estimation procedure for inferences. Our proposed methods are evaluated in simulation studies and applied to two real data analyses.     

Confidence Intervals for the Risk Ratio in Cohort Studies under Inverse Sampling

Three simple interval estimates for the risk ratio in inverse sampling are considered. The first two interval estimates are derived on the basis of Fieller's Theorem and the delta method with the logarithmic transformation, respectively. The third interval estimate is derived on the basis of an F-test statistic proposed by BENNETT (1981) for testing equal probabilities of a disease between two comparison groups when the disease is rare. To evaluate the performance of these three methods, a Monte Carlo simulation is used to compare the actual coverage probability with the nominal confidence level for each method and to estimate the expected length of the corresponding confidence interval in a variety of situations. On the basis of the results found in the simulation, we have concluded that the method with the logarithmic transformation is either equivalent to or better than the other two methods for all situations considered here.     

Genetic structure of a wide-spectrum chicken gene pool

The genetic structure of 65 chicken populations was studied using 29 simple sequence repeat loci. Six main clusters which corresponded to geographical origins and histories were identified: Brown Egg Layers; predominantly Broilers; native Chinese breeds or breeds with recent Asian origin; predominantly breeds of European derivation; a small cluster containing populations with no common history and populations that had breeding history with White Leghorn. Another group of populations that shared their genome with several clusters was defined as 'Multi-clusters'. Gallus gallus gallus (Multi-clusters), one of the subspecies of the Red Jungle Fowl, which was previously suggested to be one of the ancestors of the domesticated chicken, has almost no shared loci with European and White Egg layer populations. In a further sub-clustering of the populations, discrimination between all the 65 populations was possible, and relationships between each were suggested. The genetic variation between populations was found to account for about 34% of the total genetic variation, 11% of the variation being between clusters and 23% being between populations within clusters. The suggested clusters may assist in future studies of genetic aspects of the chicken gene pool.     

Does Mother Nature really prefer rare species or are log‐left‐skewed SADs a sampling artefact?

Intensively sampled species abundance distributions (SADs) show left‐skew on a log scale. That is, there are too many rare species to fit a lognormal distribution. I propose that this log‐left‐skew might be a sampling artefact. Monte Carlo simulations show that taking progressively larger samples from a log‐unskewed distribution (such as the lognormal) causes log‐skew to decrease asymptotically (move towards ?∞) until it reaches the level of the underlying distribution (zero in this case). In contrast, accumulating certain types of repeated small samples results in a log‐skew that becomes progressively more log‐left‐skewed to a level well beyond the underlying distribution. These repeated samples correspond to samples from the same site over many years or from many sites in 1 year. Data from empirical datasets show that log‐skew generally goes from positive (right‐skewed) to negative (left‐skewed) as the number of temporally or spatially replicated samples increases. This suggests caution when interpreting log‐left‐skew as a pattern that needs biological interpretation.