A closer look at Carver and White's BIS/BAS scales: Factor analysis and age group differences

The Behavioural Inhibition and Behavioural Activation System (BIS/BAS) scales were developed by Carver and White (1994) and comprise four scales which measure individual differences in personality (Gray 1982, 1991). More recent modifications, namely the five-factor model derived from Gray and McNaughton's (2000) revised Reward Sensitivity Theory (RST) suggests that Anxiety and Fear are separable components of inhibition. This study employed exploratory and confirmatory factor analyses on the scales in order to test whether the four or five-factor model was the better fit in a sample of 994 participants aged 11–30 years. Consistent with RST, superior model fit was shown for the five-factor model with all variables correlated. Significant age effects were observed for BIS Fear and BIS Anxiety, with scores peaking in middle and late adolescence respectively. The BAS subscales showed differential effects of age group. Significantly increasing scores from early to mid and from mid to late adolescence were found for Drive, but the effect of age on Fun Seeking and Reward Responsiveness was not significant.

Predicting forest attributes In southeast Alaska using artificial neural networks

Artificial neural network (ANN) methods are used to predict forest characteristics. The data source is the Southeast Alaska (SEAK) Grid Inventory, a ground survey compiled by the USDA Forest Service at several thousand sites. The main objective of this article is to predict characteristics at unsurveyed locations between grid sites. A secondary objective is to evaluate the relative performance of different ANNs. Data from the grid sites are used to train six ANNs: multilayer perceptron, fuzzy ARTMAP, probabilistic, generalized regression, radial basis function, and learning vector quantization. A classification and regression tree method is used for comparison. Topographic variables are used to construct models: latitude and longitude coordinates, elevation, slope, and aspect. The models classify three forest characteristics: crown closure, species land cover, and tree size/structure. Models are constructed using n-fold cross-validation. Predictive accuracy is calculated using a method that accounts for the influence of misclassification as well as measuring correct classifications. The probabilistic and generalized regression networks are found to be the most accurate. The predictions of the ANN models are compared with a classification of the Tongass national forest in southeast Alaska based on the interpretation of satellite imagery and are found to be of similar accuracy.

Are Alaskan trees found in locally more favourable sites in marginal areas?

Aim Species generally become rarer and more patchily distributed as the margins of their ranges are approached. We predicted that in such marginal sites, tree species would tend to occur where some key environmental factors are at particularly favourable levels, compensating in part for the low overall suitability of marginal sites.

Location The article considers the spatial distributions of trees in Southeast Alaska (the Alaskan 'panhandle').

Methods We quantified range marginality using spatial distributions of eight tree species across more than one thousand surveyed sites in Southeast Alaska. For each species we derived a site core/margin index using a three-dimensional trend surface generated from logistic regression on site coordinates. For each species, the relationships between the environmental factors slope, aspect and site marginality were then compared for occupied and unoccupied sets of sites.

Results We found that site slope is important for more Alaskan tree species than aspect. Three out of eight had a significant core/margin by occupied/unoccupied interaction, tending to be present in significantly shallower-sloped (more favourable) sites in the marginal areas than the simple core/margin trend predicted. For site aspect, one species had a significant interaction, selecting potentially more favourable northerly aspects in marginal areas. A finer-scale analysis based on the same data came to the same overall conclusions.

Conclusions There is evidence that several tree species in Alaska tend to occur in especially favourable sites in marginal areas. In these marginal areas, these species amplify habitat preferences shown in core areas.