Effect of density on spatial distribution, pit formation and pit diameter of Myrmeleon acer Walker, (Neuroptera : Myrmeleontidae): patterns and processes

The effect of increasing population density on the formation of pits, their size and spatial distribution, and on levels of mortality was examined in the antlion Myrmeleon acer Walker. Antlions were kept at densities ranging from 0.4 to 12.8 individuals per 100 cm(2). The distribution of pits was regular or uniform across all densities, but antlions constructed proportionally fewer and smaller pits as density increased. Mortality through cannibalism was very low and only occurred at densities greater than five individuals per 100 cm(2). Antlions in artificially crowded situations frequently relocated their pits and when more space became available, individuals became more dispersed with time. Redistribution of this species results from active avoidance of other antlions and sand throwing associated with pit construction and maintenance, rather than any attempt to optimise prey capture per se.

Target similarity effects: Support for the parallel distributed processing assumptions

Recent research has begun to provide support for the assumptions that memories are stored as a composite and are accessed in parallel (Tehan & Humphreys, 1998). New predictions derived from these assumptions and from the Chappell and Humphreys (1994) implementation of these assumptions were tested. In three experiments, subjects studied relatively short lists of words. Some of the Lists contained two similar targets (thief and theft) or two dissimilar targets (thief and steal) associated with the same cue (ROBBERY). AS predicted, target similarity affected performance in cued recall but not free association. Contrary to predictions, two spaced presentations of a target did not improve performance in free association. Two additional experiments confirmed and extended this finding. Several alternative explanations for the target similarity effect, which incorporate assumptions about separate representations and sequential search, are rejected. The importance of the finding that, in at least one implicit memory paradigm, repetition does not improve performance is also discussed.

X-ray absorption and phase contrast imaging to study the interplay between plant roots and soil structure

Plant performance is, at least partly, linked to the location of roots with respect to soil structure features and the micro-environment surrounding roots. Measurements of root distributions from intact samples, using optical microscopy and field tracings have been partially successful but are imprecise and labour-intensive. Theoretically, X-ray computed micro-tomography represents an ideal solution for non-invasive imaging of plant roots and soil structure. However, before it becomes fast enough and affordable or easily accessible, there is still a need for a diagnostic tool to investigate root/soil interplay. Here, a method for detection of undisturbed plant roots and their immediate physical environment is presented. X-ray absorption and phase contrast imaging are combined to produce projection images of soil sections from which root distributions and soil structure can be analyzed. The clarity of roots on the X-ray film is sufficient to allow manual tracing on an acetate sheet fixed over the film. In its current version, the method suffers limitations mainly related to (i) the degree of subjectivity associated with manual tracing and (ii) the difficulty of separating live and dead roots. The method represents a simple and relatively inexpensive way to detect and quantify roots from intact samples and has scope for further improvements. In this paper, the main steps of the method, sampling, image acquisition and image processing are documented. The potential use of the method in an agronomic perspective is illustrated using surface and sub-surface soil samples from a controlled wheat trial. Quantitative characterization of root attributes, e.g. radius, length density, branching intensity and the complex interplay between roots and soil structure, is presented and discussed.

Monitoring growth in rapidly urbanizing areas using remotely sensed data

Urbanization and the ability to manage for a sustainable future present numerous challenges for geographers and planners in metropolitan regions. Remotely sensed data are inherently suited to provide information on urban land cover characteristics, and their change over time, at various spatial and temporal scales. Data models for establishing the range of urban land cover types and their biophysical composition (vegetation, soil, and impervious surfaces) are integrated to provide a hierarchical approach to classifying land cover within urban environments. These data also provide an essential component for current simulation models of urban growth patterns, as both calibration and validation data. The first stages of the approach have been applied to examine urban growth between 1988 and 1995 for a rapidly developing area in southeast Queensland, Australia. Landsat Thematic Mapper image data provided accurate (83% adjusted overall accuracy) classification of broad land cover types and their change over time. The combination of commonly available remotely sensed data, image processing methods, and emerging urban growth models highlights an important application for current and next generation moderate spatial resolution image data in studies of urban environments.

Integrating attribute and space characteristics in choropleth display and spatial data mining

This paper develops an interactive approach for exploratory spatial data analysis. Measures of attribute similarity and spatial proximity are combined in a clustering model to support the identification of patterns in spatial information. Relationships between the developed clustering approach, spatial data mining and choropleth display are discussed. Analysis of property crime rates in Brisbane, Australia is presented. A surprising finding in this research is that there are substantial inconsistencies in standard choropleth display options found in two widely used commercial geographical information systems, both in terms of definition and performance. The comparative results demonstrate the usefulness and appeal of the developed approach in a geographical information system environment for exploratory spatial data analysis.

A twin study of psychometric intelligence and efficiency of information processing

Intelligence (IQ) can be seen as the efficiency of mental processes or cognition, as can basic information processing (IP) tasks like those used in our ongoing Memory, Attention and Problem Solving (MAPS) study. Measures of IQ and IP are correlated and both have a genetic component, so we are studying how the genetic variance in IQ is related to the genetic variance in IP. We measured intelligence with five subscales of the Multidimensional Aptitude Battery (MAB). The IP tasks included four variants of choice reaction time (CRT) and a visual inspection time (IT). The influence of genetic factors on the variances in each of the IQ, IP, and IT tasks was investigated in 250 identical and nonidentical twin pairs aged 16 years. For a subset of 50 pairs we have test–retest data that allow us to estimate the stability of the measures. MX was used for a multivariate genetic analysis that addresses whether the variance in IQ and IP measures is possibly mediated by common genetic factors. Analyses that show the modeled genetic and environmental influences on these measures of cognitive efficiency will be presented and their relevance to ideas on intelligence will be discussed.

Psychophysiological phenotypes of cognition: The P3 and SW components

As part of a large ongoing project, the Memory, Attention and Problem Solving (MAPS) study, we investigated whether genetic variability explains some of the variance in psychophysiological correlates of brain function, namely, the P3 and SW components of event-related potentials (ERPs). These ERP measures are minute time recordings of brain processes and, because they reflect fundamental cognitive processing, provide a unique window on the millisecondto- millisecond transactions that occur at the cognitive level and taking place in the human brain. The extent to which the variance in P3 and SW components is influenced by genetic factors was examined in 350 identical and nonidentical twin pairs aged 16 years. ERPs were recorded from 15 scalp electrodes during the performance of a visuospatial delayed response task that engages working memory. Multivariate genetic analyses using MX were used to estimate genetic and environmental influences on individual differences in brain functioning and to identify putative genetic factors common to the ERP measures and psychometric IQ. For each of the ERP measures, correlation among electrode sites was high, a spatial pattern was evident, and a large part of the genetic variation in the ERPs appeared to be mediated by a common genetic factor. Moderate within-pair concordance in MZ pairs was found for all ERP measures, with higher correlations found for P3 than SW, and the MZ twin pair correlations were approximately twice the DZ correlations, suggesting a genetic influence. Correlations between ERP measures and psychometric IQ were found and, although moderately low, were evident across electrode site. The analyses show that the ERP components, P3 and SW, are promising phenotypes of the neuroelectrical activity of the brain and have the potential to be used in linkage and association analysis in the search for QTLs influencing cognitive function.

Disaggregation of polygons of surficial geology and soil maps using spatial modelling and legacy data

Examples from the Murray-Darling basin in Australia are used to illustrate different methods of disaggregation of reconnaissance-scale maps. One approach for disaggregation revolves around the de-convolution of the soil-landscape paradigm elaborated during a soil survey. The descriptions of soil ma units and block diagrams in a soil survey report detail soil-landscape relationships or soil toposequences that can be used to disaggregate map units into component landscape elements. Toposequences can be visualised on a computer by combining soil maps with digital elevation data. Expert knowledge or statistics can be used to implement the disaggregation. Use of a restructuring element and k-means clustering are illustrated. Another approach to disaggregation uses training areas to develop rules to extrapolate detailed mapping into other, larger areas where detailed mapping is unavailable. A two-level decision tree example is presented. At one level, the decision tree method is used to capture mapping rules from the training area; at another level, it is used to define the domain over which those rules can be extrapolated. (C) 2001 Elsevier Science B.V. All rights reserved.

Processing of urinary pheromones in Antechinus stuartii (Marsupialia: Dasyuridae): Functional magnetic resonance imaging of the brain

Little is known of the neural mechanisms of marsupial olfaction. However, functional magnetic resonance imaging (fMRI) has made it possible to visualize dynamic brain function in mammals without invasion. In this study, central processing of urinary pheromones was investigated in the brown antechinus, Antechinus stuartii, using fMRI. Images were obtained from 18 subjects (11 males, 7 females) in response to conspecific urinary olfactory stimuli. Significant indiscriminate activation occurred in the accessory olfactory bulb, entorhinal, frontal, and parietal cortices in response to both male and female urine. The paraventricular nucleus of hypothalamus, ventrolateral thalamic nucleus, and medial preoptic area were only activated in response to male urine. Results of this MRI study indicate that projections of accessory olfactory system are activated by chemo-sensory cues. Furthermore, it appears that, based on these experiments, urinary pheromones may act on the hypothalamo-pituitary-adrenocortical axis via the paraventricular nucleus of the hypothalamus and may play an important role in the unique life history pattern of A. stuartii. Finally, this study has demonstrated that fMRI may be a powerful tool for investigations of olfactory processes in mammals.

Spatial data mining for enhanced soil map modelling

The principle of using induction rules based on spatial environmental data to model a soil map has previously been demonstrated Whilst the general pattern of classes of large spatial extent and those with close association with geology were delineated small classes and the detailed spatial pattern of the map were less well rendered Here we examine several strategies to improve the quality of the soil map models generated by rule induction Terrain attributes that are better suited to landscape description at a resolution of 250 m are introduced as predictors of soil type A map sampling strategy is developed Classification error is reduced by using boosting rather than cross validation to improve the model Further the benefit of incorporating the local spatial context for each environmental variable into the rule induction is examined The best model was achieved by sampling in proportion to the spatial extent of the mapped classes boosting the decision trees and using spatial contextual information extracted from the environmental variables.

A strategy to fill gaps in soil survey over large spatial extents: an example from the Murray-Darling basin of Australia

We re-mapped the soils of the Murray-Darling Basin (MDB) in 1995-1998 with a minimum of new fieldwork, making the most out of existing data. We collated existing digital soil maps and used inductive spatial modelling to predict soil types from those maps combined with environmental predictor variables. Lithology, Landsat Multi Spectral Scanner (Landsat MSS), the 9-s digital elevation model (DEM) of Australia and derived terrain attributes, all gridded to 250-m pixels, were the predictor variables. Because the basin-wide datasets were very large data mining software was used for modelling. Rule induction by data mining was also used to define the spatial domain of extrapolation for the extension of soil-landscape models from existing soil maps. Procedures to estimate the uncertainty associated with the predictions and quality of information for the new soil-landforms map of the MDB are described. (C) 2002 Elsevier Science B.V. All rights reserved.

Spatial and temporal variation in distribution of mangroves in Moreton Bay, subtropical Australia: a comparison of pattern metrics and change detection analyses based on aerial photographs

An assessment of the changes in the distribution and extent of mangroves within Moreton Bay, southeast Queensland, Australia, was carried out. Two assessment methods were evaluated: spatial and temporal pattern metrics analysis, and change detection analysis. Currently, about 15,000 ha of mangroves are present in Moreton Bay. These mangroves are important ecosystems, but are subject to disturbance from a number of sources. Over the past 25 years, there has been a loss of more than 3800 ha, as a result of natural losses and mangrove clearing (e.g. for urban and industrial development, agriculture and aquaculture). However, areas of new mangroves have become established over the same time period, offsetting these losses to create a net loss of about 200 ha. These new mangroves have mainly appeared in the southern bay region and the bay islands, particularly on the landward edge of existing mangroves. In addition, spatial patterns and species composition of mangrove patches have changed. The pattern metrics analysis provided an overview of mangrove distribution and change in the form of single metric values, while the change detection analysis gave a more detailed and spatially explicit description of change. An analysis of the effects of spatial scales on the pattern metrics indicated that they were relatively insensitive to scale at spatial resolutions less than 50 m, but that most metrics became sensitive at coarser resolutions, a finding which has implications for mapping of mangroves based on remotely sensed data. (C) 2003 Elsevier Science B.V. All rights reserved.

Simultaneous X-ray imaging of plant root growth and water uptake in thin-slab systems

Direct and simultaneous observation of root growth and plant water uptake is difficult because soils are opaque. X-ray imaging techniques such as projection radiography or Computer Tomography (CT) offer a partial alternative to such limitations. Nevertheless, there is a trade-off between resolution, large field-of-view and 3-dimensionality: With the current state of the technology, it is possible to have any two. In this study, we used X-ray transmission through thin-slab systems to monitor transient saturation fields that develop around roots as plants grow. Although restricted to 2-dimensions, this approach offers a large field-of-view together with high spatial and dynamic resolutions. To illustrate the potential of this technology, we grew peas in 1 cm thick containers filled with soil and imaged them at regular intervals. The dynamics of both the root growth and the water content field that developed around the roots could be conveniently monitored. Compared to other techniques such as X-ray CT, our system is relatively inexpensive and easy to implement. It can potentially be applied to study many agronomic problems, such as issues related to the impact of soil constraints (physical, chemical or biological) on root development.