Bayesian methods for analysing ringing data

A major recent development in statistics has been the use of fast computational methods of Markov chain Monte Carlo. These procedures allow Bayesian methods to be used in quite complex modelling situations. In this paper, we shall use a range of real data examples involving lapwings, shags, teal, dippers, and herring gulls, to illustrate the power and range of Bayesian techniques. The topics include: prior sensitivity; the use of reversible-jump MCMC for constructing model probabilities and comparing models, with particular reference to models with random effects; model-averaging; and the construction of Bayesian measures of goodness-of-fit. Throughout, there will be discussion of the practical aspects of the work - for instance explaining when and when not to use the BUGS package.     

Aggregate, Disaggregate, and Hybrid Analyses of Ecological Risk Perceptions

Laypeople's perceptions of health and safety risks have been widely studied, but only a few studies have addressed perceptions of ecological hazards. We assembled a list of 39 attributes of ecological hazards from the literatures on comparative risk assessment, ecological health, environmental conservation and management, environmental psychology, and risk perception. In Study 1, 125 laypeople evaluated 83 hazards on subsets of this attribute set. Factor analysis of attribute ratings (averaged over participants) revealed six oblique factors: ecological impacts, human impacts, human benefits, aesthetic impacts, scientific understanding, and controllability. These factors predicted mean judgments of overall riskiness, ecological riskiness, acceptability, and regulatory strictness. In Study 2, 30 laypeople each evaluated 34 hazards on 17 attributes and 3 dependent variables. Aggregate-level factor analysis of these data replicated the appropriate portion of the factor solution and yielded similar regression results. Parallel analyses at the individual-participant level yielded factors that explained less variance in judgments of overall riskiness, ecological riskiness, and acceptability. However, the decrease in explanatory power was much less than is often reported for disaggregate-level analyses of psychometric data. This discrepancy illustrates the importance of distinguishing between the level of analysis (aggregate versus disaggregate) and the focus of analysis (distinctions among hazards versus distinctions among participants). In a hybrid analysis, aggregate-level factor scores predicted individual participants' riskiness judgments reasonably well. Psychometric studies such as these provide a sound empirical basis for selecting attributes of ecological hazards for use in comparative risk assessment.     

The efficient integration of abundance and demographic data

Summary. A drawback of a new method for integrating abundance and mark–recapture–recovery data is the need to combine likelihoods describing the different data sets. Often these likelihoods will be formed by using specialist computer programs, which is an obstacle to the joint analysis. This difficulty is easily circumvented by the use of a multivariate normal approximation. We show that it is only necessary to make the approximation for the parameters of interest in the joint analysis. The approximation is evaluated on data sets for two bird species and is shown to be efficient and accurate.     

Revisiting 'communities in Britain'

In this article I re‐examine Frankenberg’s Communities in Britain (1966) and his use of a ‘morphological continuum’ within his analysis. This analysis is itself a re‐assessment of some influential community studies conducted within the British Isles. I look at some particular ideas within the more theoretical part of this book, especially the ideas of ‘social redundancy’ and the distinction between ‘complex’ and ‘complicated’. I then consider some themes arising from some more recent studies. These themes include identity, networks and social capital and time and I argue that Frankenberg's analysis can still provide the reader with useful tools to think through the ideas and practices of community.     

Bayesian methods for analysing ringing data

A major recent development in statistics has been the use of fast computational methods of Markov chain Monte Carlo. These procedures allow Bayesian methods to be used in quite complex modelling situations. In this paper, we shall use a range of real data examples involving lapwings, shags, teal, dippers, and herring gulls, to illustrate the power and range of Bayesian techniques. The topics include: prior sensitivity; the use of reversible-jump MCMC for constructing model probabilities and comparing models, with particular reference to models with random effects; model-averaging; and the construction of Bayesian measures of goodness-of-fit. Throughout, there will be discussion of the practical aspects of the work - for instance explaining when and when not to use the BUGS package.     

What Do People Know About Global Climate Change? 2. Survey Studies of Educated Laypeople

Drawing on results from earlier studies that used open-ended interviews, a questionnaire was developed to examine laypeople's knowledge about the possible causes and effects of global warming, as well as the likely efficacy of possible interventions. It was administered to two well-educated opportunity samples of laypeople. Subjects had a poor appreciation of the facts that (1) if significant global warming occurs, it will be primarily the result of an increase in the concentration of carbon dioxide in the earth's atmosphere, and (2) the single most important source of additional carbon dioxide is the combustion of fossil fuels, most notably coal and oil. In addition, their understanding of the climate issue was encumbered with secondary, irrelevant, and incorrect beliefs. Of these, the two most critical are confusion with the problems of stratospheric ozone and difficulty in differentiating between causes and actions specific to climate and more general good environmental practice.     

Multiple‐Family Group Intervention for Incarcerated Male Adolescents Who Sexually Offend and Their Families: Change in Maladaptive Emotion Regulation Predicts Adaptive Change in Adolescent Behaviors

The multiple‐family group intervention is an effective, yet affordable, 8‐week treatment that is conducted in a juvenile correctional institution in Alabama with adolescents who sexually offend and their families. Data from 115 incarcerated male adolescents and their male and female caregivers collected at pre‐, post‐, and 1‐year follow‐up were used to determine that problem behaviors (internalizing, externalizing) decreased over pre‐ and posttest and the significant decreases in maladaptive emotion regulation predicted those changes. Adolescent‐reported anxiety over abandonment and attachment dependence on parents increased significantly; these changes were predicted by decreases in maladaptive emotion regulation. Linear growth models were also fit over the 3 time points and indicate decreases in adolescent problem behavior and maladaptive emotion regulation.