The influence of riparian corridor width on ant and plant assemblages in northern Sydney,Australia

Protecting riparian corridors is a commonly applied environmental policy in urban landscapes. However, empirical data demonstrating their efficacy for biodiversity conservation outcomes is scarce. In this study we investigated whether riparian corridor width influences the diversity and community structure of ant (Hymenoptera: Formicidae) and vascular plant assemblages therein. Eighteen corridors of differing widths were selected from within the Ku-ring-gai Local Government Area, Sydney Australia. Ants were sampled using pitfall traps positioned within rectangular vegetation transects (30 × 10 m). Both ant and plant species richness, when standardised for sampling effort, were unrelated to riparian corridor width. However, significant compositional differences between sites were evident with increased width up to ~50 m. Narrow corridors contained greater abundances of opportunistic ant species and higher proportions of exotic plants. We interpret this to be the result of the greater disturbance/edge influence derived from adjacent upland urban environments. Site beta diversity did not increase with corridor width, suggesting that the exclusion of novel upslope habitats in narrow riparian corridors is not a significant cause of community assemblage modification at these sites. To minimise the impact of deleterious edge effects on ant fauna, environmental managers should seek to retain riparian corridors wider than ~50 m. However, we observed substantial between-site variability of biotic assemblages, irrespective of corridor width. Therefore we recommend that environmental management practice needs to take a catchment-wide approach and consider other parameters that may contribute to riparian health so as to optimise the protection of riparian biodiversity.     

Bayesian Nonparametric Hidden Markov Models with application to the analysis of copy-number-variation in mammalian genomes

We consider the development of Bayesian Nonparametric methods for product partition models such as Hidden Markov Models and change point models. Our approach uses a Mixture of Dirichlet Process (MDP) model for the unknown sampling distribution (likelihood) for the observations arising in each state and a computationally efficient data augmentation scheme to aid inference. The method uses novel MCMC methodology which combines recent retrospective sampling methods with the use of slice sampler variables. The methodology is computationally efficient, both in terms of MCMC mixing properties, and robustness to the length of the time series being investigated. Moreover, the method is easy to implement requiring little or no user-interaction. We apply our methodology to the analysis of genomic copy number variation.     

Introduction to the Issue

Gordon Willard Allport was born more than 100 years ago on November 11, 1897. In honor of the centennial of his birth, this issue of the Journal presents a collection of articles on the social psychology of prejudice and intergroup relations—the field that Allport pioneered with his classic 1954 book, The Nature of Prejudice. The articles provide insights into both perpetrators and targets of prejudice and the conflict that often takes place between members of different societal groups. In the tradition of Gordon Allport, these articles demonstrate that it is possible for social science to make theoretical, empirical, and practical contributions to the understanding and resolution of social problems. This introductory article high-lights Allport's contributions to the social psychology of prejudice and intergroup relations. Overviews of the articles in this issue are also provided, and the recurring theoretical and methodological approaches are considered.     

Technoscientia est Potentia?

Within the realm of nano-, bio-, info- and cogno- (or NBIC) technosciences, the ??power to change the world?? is often invoked. One could dismiss such formulations as ??purely rhetorical??, interpret them as rhetorical and self-fulfilling or view them as an adequate depiction of one of the fundamental characteristics of technoscience. In the latter case, a very specific nexus between science and technology, or, the epistemic and the constructionist realm is envisioned. The following paper focuses on this nexus drawing on theoretical conceptions as well as empirical material. It presents an overview of different technoscientific ways to ??change the world????via contemplation and representation, intervention and control, engineering, construction and creation. It further argues that the hybrid character of technoscience makes it difficult (if not impossible) to separate knowledge production from real world interventions and challenges current science and technology policy approaches in fundamental ways.     

A New Model of Trend Inflation

This article introduces a new model of trend inflation. In contrast to many earlier approaches, which allow for trend inflation to evolve according to a random walk, ours is a bounded model which ensures that trend inflation is constrained to lie in an interval. The bounds of this interval can either be fixed or estimated from the data. Our model also allows for a time-varying degree of persistence in the transitory component of inflation. In an empirical exercise with CPI inflation, we find the model to work well, yielding more sensible measures of trend inflation and forecasting better than popular alternatives such as the unobserved components stochastic volatility model. This article has supplementary materials online.     

Robust estimation for ordinal regression

Ordinal regression is used for modelling an ordinal response variable as a function of some explanatory variables. The classical technique for estimating the unknown parameters of this model is Maximum Likelihood (ML). The lack of robustness of this estimator is formally shown by deriving its breakdown point and its influence function. To robustify the procedure, a weighting step is added to the Maximum Likelihood estimator, yielding an estimator with bounded influence function. We also show that the loss in efficiency due to the weighting step remains limited. A diagnostic plot based on the Weighted Maximum Likelihood estimator allows to detect outliers of different types in a single plot.     

Anderson's classification statistic based on a post-stratified training sample

The performance of Anderson's classification statistic based on a post-stratified random sample is examined. It is assumed that the training sample is a random sample from a stratified population consisting of two strata with unknown stratum weights. The sample is first segregated into the two strata by post-stratification. The unknown parameters for each of the two populations are then estimated and used in the construction of the plug-in discriminant. Under this procedure, it is shown that additional estimation of the stratum weight will not seriously affect the performance of Anderson's classification statistic. Furthermore, our discriminant enjoys a much higher efficiency than the procedure based on an unclassified sample from a mixture of normals investigated by Ganesalingam and McLachlan (1978).     

THE EFFECT OF FEAR OF SEXUAL VICTIMIZATION ON ADOLESCENT FEAR OF CRIME

The author examines the effect of fear of sexual victimization on fear of crime among adolescents. Criminologists have indicated that gender is one of the strongest predictors of fear of crime: Women are more fearful than men. Some authors (Warr 1984, and Ferraro 1995, among others) have suggested that this differential fear among women can mostly be attributed to their inordinate fear of sexual victimization. This relationship, however, has only been examined among adults. Using a sample of 725 adolescents, the effect of fear of sexual victimization and other demographic and contextual variables on fear of criminal victimization is examined. Results indicate that fear of sexual victimization is the best predictor of fear of nonsexual victimization, whereas the effects of other variables are contingent on race and gender. The reasons for this finding, as well as the implications for social policies, are discussed.     

Employee exposure to coworker substance use and negative consequences: the moderating effects of work group membership.

The current study assesses: (1) whether the relationship between individual exposure to coworker substance use and negative consequences resulting from exposure depends on work group membership, and (2) whether group-level characteristics moderate the relationship between exposure and consequences. At the group-level, we assessed occupations involving safety risk or high mobility and social factors of drinking climate and group cohesiveness. We conducted Hierarchical Linear Modeling (HLM) across two samples of municipal employees (ns = 650, 878; n of groups = 50, 49). Our results revealed that groups with higher proportions of jobs involving risk (e.g., machine work) and, to a lesser extent, groups with a higher level of drinking climate were those most vulnerable to consequences under conditions of exposure. Importantly, our findings controlled for individual risk factors (e.g., personal drinking, job stress). Our discussion examines the implications of this study for theory and policy related to workplace substance abuse.