Estimates of a Treatment Effect Based on Censored Data Rank Tests

Linear rank test statistics are applied to the problem of estimating a treatment effect if two sets of censored failure time data are compared and the distributions of the log-failure times of the two samples are assumed to differ only in location. Rank tests for this accelerated failure time model are reviewed and Hodges-Lehmann type estimates for the shift parameter are proposed. Properties of these estimates are investigated, computational aspects are discussed and an example is presented.     

Co‐occurrence patterns of trees along macro‐climatic gradients and their potential influence on the present and future distribution of Fagus sylvatica L.

Aim During recent and future climate change, shifts in large‐scale species ranges are expected due to the hypothesized major role of climatic factors in regulating species distributions. The stress‐gradient hypothesis suggests that biotic interactions may act as major constraints on species distributions under more favourable growing conditions, while climatic constraints may dominate under unfavourable conditions. We tested this hypothesis for one focal tree species having three major competitors using broad‐scale environmental data. We evaluated the variation of species co‐occurrence patterns in climate space and estimated the influence of these patterns on the distribution of the focal species for current and projected future climates. Location Europe. Methods We used ICP Forest Level 1 data as well as climatic, topographic and edaphic variables. First, correlations between the relative abundance of European beech (Fagus sylvatica) and three major competitor species (Picea abies, Pinus sylvestris and Quercus robur) were analysed in environmental space, and then projected to geographic space. Second, a sensitivity analysis was performed using generalized additive models (GAM) to evaluate where and how much the predicted F. sylvatica distribution varied under current and future climates if potential competitor species were included or excluded. We evaluated if these areas coincide with current species co‐occurrence patterns. Results Correlation analyses supported the stress‐gradient hypothesis: towards favourable growing conditions of F. sylvatica, its abundance was strongly linked to the abundance of its competitors, while this link weakened towards unfavourable growing conditions, with stronger correlations in the south and at low elevations than in the north and at high elevations. The sensitivity analysis showed a potential spatial segregation of species with changing climate and a pronounced shift of zones where co‐occurrence patterns may play a major role. Main conclusions Our results demonstrate the importance of species co‐occurrence patterns for calibrating improved species distribution models for use in projections of climate effects. The correlation approach is able to localize European areas where inclusion of biotic predictors is effective. The climate‐induced spatial segregation of the major tree species could have ecological and economic consequences.     

Patterns of intra- and interspecific association in leaf-mining insects on three oak host species

Abstract. 1. We determined mortality and distributional patterns of leaf miners on three oak host species (Quercus falcata, Q.nigra and Q.hemisphaerica) in northern Florida, U.S.A.
2. Patterns of intra- and interspecific occurrence within leaves, and mortality of five most abundant leaf miner species were analysed as a test of competition.
3. Miners co-occurred on leaves more often that expected by chance (P<0.05) in six of ten possible species combinations and log-linear model analysis showed no negative higher-order interactions.
4. All five miner species had highly clumped distributions between leaves (P<0.01).
5. Leaf miner survival was less than expected for four of five species when co-occurring on leaves with conspecifics than when mining with heterospecifics or alone (P<0.05).
6. We conclude that interspecific competition is unapparent within this leaf miner guild and that intraspecific competition occurs in four of the five major leaf miner species. We discuss leaf miner selection of common leaves, perhaps based on chemical/physical leaf characters, as a cause of intra- and interspecific aggregation.     

A study on the minimum number of loci required for genetic evaluation using a finite locus model

For a finite locus model, Markov chain Monte Carlo (MCMC) methods can be used to estimate the conditional mean of genotypic values given phenotypes, which is also known as the best predictor (BP). When computationally feasible, this type of genetic prediction provides an elegant solution to the problem of genetic evaluation under non-additive inheritance, especially for crossbred data. Successful application of MCMC methods for genetic evaluation using finite locus models depends, among other factors, on the number of loci assumed in the model. The effect of the assumed number of loci on evaluations obtained by BP was investigated using data simulated with about 100 loci. For several small pedigrees, genetic evaluations obtained by best linear prediction (BLP) were compared to genetic evaluations obtained by BP. For BLP evaluation, used here as the standard of comparison, only the first and second moments of the joint distribution of the genotypic and phenotypic values must be known. These moments were calculated from the gene frequencies and genotypic effects used in the simulation model. BP evaluation requires the complete distribution to be known. For each model used for BP evaluation, the gene frequencies and genotypic effects, which completely specify the required distribution, were derived such that the genotypic mean, the additive variance, and the dominance variance were the same as in the simulation model. For lowly heritable traits, evaluations obtained by BP under models with up to three loci closely matched the evaluations obtained by BLP for both purebred and crossbred data. For highly heritable traits, models with up to six loci were needed to match the evaluations obtained by BLP.     

Shapes and functions of species–area curves (II): a review of new models and parameterizations

This paper has extended and updated my earlier list and analysis of candidate models used in theoretical modelling and empirical examination of species–area relationships (SARs). I have also reviewed trivariate models that can be applied to include a second independent variable (in addition to area) and discussed extensively the justifications for fitting curves to SARs and the choice of model. There is also a summary of the characteristics of several new candidate models, especially extended power models, logarithmic models and parameterizations of the negative-exponential family and the logistic family. I have, moreover, examined the characteristics and shapes of trivariate linear, logarithmic and power models, including combination variables and interaction terms. The choice of models according to best fit may conflict with problems of non-normality or heteroscedasticity. The need to compare parameter estimates between data sets should also affect model choice. With few data points and large scatter, models with few parameters are often preferable. With narrow-scale windows, even inflexible models such as the power model and the logarithmic model may produce good fits, whereas with wider-scale windows where inflexible models do not fit well, more flexible models such as the second persistence (P2) model and the cumulative Weibull distribution may be preferable. When extrapolations and expected shapes are important, one should consider models with expected shapes, e.g. the power model for sample area curves and the P2 model for isolate curves. The choice of trivariate models poses special challenges, which one can more effectively evaluate by inspecting graphical plots.     

Finite elements/Taguchi method based procedure for the identification of the geometrical parameters significantly affecting the biomechanical behavior of a lumbar disc

The aim of this work is to show a quick and simple procedure able to identify the geometrical parameters of the intervertebral disc that strongly affect the behavior of the FEM model. First, we allocated a selection criterion for the minimum number of geometrical parameters that describe, with a good degree of approximation, a healthy human vertebra. Next, we carried out a sensitivity analysis using the ‘Taguchi orthogonal array’ to arrive at a quick identification of the parameters that strongly affect the behavior of the Fem model.