Hierarchical Bayesian models of subtask learning

 The current study used Bayesian hierarchical methods to challenge and extend previous work on subtask learning consistency. A general model of individual-level subtask learning was proposed focusing on power and exponential functions with constraints to test for inconsistency. To study subtask learning, we developed a novel computer-based booking task, which logged participant actions, enabling measurement of strategy use and subtask performance. Model comparison was performed using deviance information criterion (DIC), posterior predictive checks, plots of model fits, and model recovery simulations. Results showed that although learning tended to be monotonically decreasing and decelerating, and approaching an asymptote for all subtasks, there was substantial inconsistency in learning curves both at the group- and individual-levels. This inconsistency was most apparent when constraining both the rate and the ratio of learning to asymptote to be equal across subtasks, thereby giving learning curves only 1 parameter for scaling. The inclusion of 6 strategy covariates provided improved prediction of subtask performance capturing different subtask learning processes and subtask trade-offs. In addition, strategy use partially explained the inconsistency in subtask learning. Overall, the model provided a more nuanced representation of how complex tasks can be decomposed in terms of simpler learning mechanisms.

Measuring chronic condition self-management in an Australian community: factor structure of the revised Partners in Health (PIH) scale

Purpose To evaluate the factor structure of the revised Partners in Health (PIH) scale for measuring chronic condition self-management in a representative sample from the Australian community.

Methods A series of consultations between clinical groups underpinned the revision of the PIH. The factors in the revised instrument were proposed to be: knowledge of illness and treatment, patient–health professional partnership, recognition and management of symptoms and coping with chronic illness. Participants (N = 904) reporting having a chronic illness completed the revised 12-item scale. Two a priori models, the 4-factor and bi-factor models were then evaluated using Bayesian confirmatory factor analysis (BCFA). Final model selection was established on model complexity, posterior predictive p values and deviance information criterion.

Results Both 4-factor and bi-factor BCFA models with small informative priors for cross-loadings provided an acceptable fit with the data. The 4-factor model was shown to provide a better and more parsimonious fit with the observed data in terms of substantive theory. McDonald’s omega coefficients indicated that the reliability of subscale raw scores was mostly in the acceptable range.

Conclusion The findings showed that the PIH scale is a relevant and structurally valid instrument for measuring chronic condition self-management in an Australian community. The PIH scale may help health professionals to introduce the concept of self-management to their patients and provide assessment of areas of self-management. A limitation is the narrow range of validated PIH measurement properties to date. Further research is needed to evaluate other important properties such as test–retest reliability, responsiveness over time and content validity.

A predictive model of avian natal dispersal distance provides prior information for investigating response to landscape change

1. Informative Bayesian priors can improve the precision of estimates in ecological studies or estimate parameters for which little or no information is available. While Bayesian analyses are becoming more popular in ecology, the use of strongly informative priors remains rare, perhaps because examples of informative priors are not readily available in the published literature.
2. Dispersal distance is an important ecological parameter, but is difficult to measure and estimates are scarce. General models that provide informative prior estimates of dispersal distances will therefore be valuable.
3. Using a world-wide data set on birds, we develop a predictive model of median natal dispersal distance that includes body mass, wingspan, sex and feeding guild. This model predicts median dispersal distance well when using the fitted data and an independent test data set, explaining up to 53% of the variation.
4. Using this model, we predict a priori estimates of median dispersal distance for 57 woodland-dependent bird species in northern Victoria, Australia. These estimates are then used to investigate the relationship between dispersal ability and vulnerability to landscape-scale changes in habitat cover and fragmentation.
5. We find evidence that woodland bird species with poor predicted dispersal ability are more vulnerable to habitat fragmentation than those species with longer predicted dispersal distances, thus improving the understanding of this important phenomenon.
6. The value of constructing informative priors from existing information is also demonstrated. When used as informative priors for four example species, predicted dispersal distances reduced the 95% credible intervals of posterior estimates of dispersal distance by 8-19%. Further, should we have wished to collect information on avian dispersal distances and relate it to species' responses to habitat loss and fragmentation, data from 221 individuals across 57 species would have been required to obtain estimates with the same precision as those provided by the general model.

A predictive model of avian natal dispersal distance provides prior information for investigating response to landscape change

1. Informative Bayesian priors can improve the precision of estimates in ecological studies or estimate parameters for which little or no information is available. While Bayesian analyses are becoming more popular in ecology, the use of strongly informative priors remains rare, perhaps because examples of informative priors are not readily available in the published literature.

2. Dispersal distance is an important ecological parameter, but is difficult to measure and estimates are scarce. General models that provide informative prior estimates of dispersal distances will therefore be valuable.

3. Using a world-wide data set on birds, we develop a predictive model of median natal dispersal distance that includes body mass, wingspan, sex and feeding guild. This model predicts median dispersal distance well when using the fitted data and an independent test data set, explaining up to 53% of the variation.

4. Using this model, we predict a priori estimates of median dispersal distance for 57 woodland-dependent bird species in northern Victoria, Australia. These estimates are then used to investigate the relationship between dispersal ability and vulnerability to landscape-scale changes in habitat cover and fragmentation.

5. We find evidence that woodland bird species with poor predicted dispersal ability are more vulnerable to habitat fragmentation than those species with longer predicted dispersal distances, thus improving the understanding of this important phenomenon.

6. The value of constructing informative priors from existing information is also demonstrated. When used as informative priors for four example species, predicted dispersal distances reduced the 95% credible intervals of posterior estimates of dispersal distance by 8-19%. Further, should we have wished to collect information on avian dispersal distances and relate it to species' responses to habitat loss and fragmentation, data from 221 individuals across 57 species would have been required to obtain estimates with the same precision as those provided by the general model.

The reliability, validity, discriminant and predictive properties of the social Phobia Inventory (SoPhI)

This paper reports on the psychometric properties of the Social Phobic Inventory (SoPhI) a 21-item scale that was designed to measure social anxiety according to the criteria of DSM-IV (American Psychiatric Association, APA (1994) Diagnostic and Statistical Manual of Mental Disorder , 4th Edn., Washington). Factor analysis of the SoPhI using data from a clinical sample of respondents with social phobia revealed one factor which explained approximately 59% of variance and which demonstrated strong internal reliability ( agr= 0.93). The SoPhI demonstrated concurrent validity with the SPAI ( r = 0.86) and convergent validity with the Fear of Negative Evaluations-Revised ( r = 0.68). The predictive utility of the scale was demonstrated in a sample of university students classified as extroverted, normal, shy/introverted, and phobic/withdrawn ( -2 57%). Multivariate Analysis of Variance (MANOVA) revealed that the combined university sample differed from the clinical sample on the summated scores on the SoPhI and that 43% ( -2 ) of this difference was attributable to group membership. This figure rose to 58% attributable to group membership when these same groups were compared for differences on the 21 individual items. Scores of the SoPhI that are indicative of concern and of possible diagnostic criteria, as well as suggestions for future research, are discussed.

Evaluation of the robustness of predictive yield models based on catchment characteristics using GIS for reservoir fisheries in Sri Lanka

Land-use patterns in the catchment areas of Sri Lankan reservoirs, which were quantified using Geographical Information Systems (GIS), were used to develop quantitative models for yield prediction. The validity of these models was evaluated through the application to five reservoirs that were not used in the development of the models, and by comparing with the actual fish yield data of these reservoirs collected by an independent body. The robustness of the predictive models developed was tested by principal component analysis (PCA) on limnological characteristics, land-use patterns of the catchments and fish yields. The predicted fish yields in five Sri Lankan reservoirs, using the empirical models based on the ratios of forest cover and/or shrub cover to reservoir capacity or reservoir area were in close agreement with the observed fish yields. The scores of PCA ordination of productivity-related limnological parameters and those of land-use patterns were linearly related to fish yields. The relationship between the PCA scores of limnological characteristics and land-use types had the appropriate algebraic form, which substantiates the influence of the limnological factors and land-use types on reservoir fish yields. It is suggested that the relatively high predictive power of the models developed on the basis of GIS methodologies can be used for more accurate assessment of reservoir fisheries. The study supports the importance and the need for an integrated management strategy for the whole watershed to enhance fish yields.

Generality is predictive of prediction accuracy

During knowledge acquisition multiple alternative potential rules all appear equally credible. This paper addresses the dearth of formal analysis about how to select between such alternatives. It presents two hypotheses about the expected impact of selecting between classification rules of differing levels of generality in the absence of other evidence about their likely relative performance on unseen data. It is argued that the accuracy on unseen data of the more general rule will tend to be closer to that of a default rule for the class than will that of the more specific rule. It is also argued that in comparison to the more general rule, the accuracy of the more specific rule on unseen cases will tend to be closer to the accuracy obtained on training data. Experimental evidence is provided in support of these hypotheses. We argue that these hypotheses can be of use in selecting between rules in order to achieve specific knowledge acquisition objectives.

Predictive distribution models and their application in wildlife conservation

Wildlife managers are often faced with the difficult task of determining the distribution of species, and their preferred habitats, at large spatial scales. This task is even more challenging when the species of concern is in low abundance and/or the terrain is largely inaccessible. Spatially explicit distribution models, derived from multivariate statistical analyses and implemented in a geographic information system (GIS), can be used to predict the distributions of species and their habitats, thus making them a useful conservation tool. We present two such models: one for a dasyurid, the Swamp Antechinus (Antechinus minimus), and the other for a ground-dwelling bird, the Rufous Bristlebird (Dasyornis broadbenti), both of which are rare species occurring in the coastal heathlands of south-western Victoria. Models were generated using generalized linear modelling (GLM) techniques with species presence or absence as the independent variable and a series of landscape variables derived from GIS layers and high-resolution imagery as the predictors. The most parsimonious model, based on the Akaike Information Criterion, for each species then was extrapolated spatially in a GIS. Probability of species presence was used as an index of habitat suitability. Because habitat fragmentation is thought to be one of the major threats to these species, an assessment of the spatial distribution of suitable habitat across the landscape is vital in prescribing management actions to prevent further habitat fragmentation.

Objective versus perceived walking distances to destinations : correspondence and predictive validity

Judgments concerning features of environments do not always correspond accurately with objective measures of those same features. Moreover, perceived and objectively assessed environmental attributes, including proximity of destinations, may influence walking behavior in different ways. This study compares perceived and objectively assessed distance to several different destinations and examines whether correspondence between objective and perceived distance is influenced by age, gender, neighborhood walkability, and walking behavior. Distances to most destinations close to home are overestimated, whereas distances to those farther away are underestimated. Perceived and objective distances to certain types of destinations are differentially associated with walking behavior. Perceived environmental attributes do not consistently reflect objectively assessed attributes, and both appear to have differential effects on physical activity behavior.

Pregnant women's alcohol consumption : the predictive utility of intention to drink and prepregnancy drinking behaviour

Objective: This study had two aims: (1) to examine pregnant women's alcohol consumption across time from prepregnancy until childbirth and (2) to explore whether prepregnancy drinking and intention to drink predict prenatal alcohol consumption while controlling for relevant demographic variables.

Methods: At 17–21 weeks, 248 pregnant women completed questions about demographics, intention to drink alcohol during the subsequent pregnancy, and retrospective measures of prepregnancy and early pregnancy consumption. After this time, calendars were sent fortnightly assessing daily alcohol consumption until birth.

Results: For women who drank both prepregnancy and postpregnancy confirmation, average fortnight alcohol consumption in the first weeks of pregnancy was lower than during prepregnancy, and consumption continued to decrease between gestational weeks 1 and 8, particularly following pregnancy confirmation, after which it remained relatively stable. When predicting whether women drank in late pregnancy, intention accounted for unique variance after controlling for income and prepregnancy drinking. For women who drank after pregnancy confirmation, prepregnancy drinking quantity significantly predicted intention to drink, which in turn predicted fortnight alcohol consumption in later pregnancy, after controlling for prepregnancy drinking and income.

Conclusions: Findings highlight the need to measure alcohol consumption at multiple time points across pregnancy, the need for educating and supporting women to reduce consumption when planning pregnancies, and the usefulness of intention to drink as a predictor of drinking during pregnancy.

Predictive mapping of powerful owl (Ninox strenua) breeding sites using Geographical Information Systems (GIS) in urban Melbourne, Australia

Urban expansion is a principal process threatening biodiversity globally. It is predicted that over half of the world's population will reside in urban centres by 2010. If we are to conserve biodiversity, a shift in perspective from traditional ecological studies based in natural environments, to studies based in less natural environments is paramount. To effectively conserve species which occur in urban environments, comprehensive analysis is necessary to determine the processes that are driving this urban usage. Geographical Information Systems (GIS) technology provides a valuable tool for efficient spatial analysis and predictive mapping of species distributions.

This study used GIS to analyze current breeding sites for the powerful owl, a vulnerable top order predator in urban Melbourne, Australia. GIS analysis suggests that a number of ecological attributes were influencing powerful owl usage of urban environments. Using these ecological attributes, predictive mapping was undertaken, which identified a number of potential breeding sites for powerful owls within urbanized Melbourne.

Urban environments are traditionally perceived as “the wastelands” of natural environments, however, this study demonstrates that they have the potential to support apex predators, an important finding for the management of rare and threatened species.