Buxton silver gum reserve: using geographic information systems to investigate historic change in site integrity

Eucalyptus crenulata is a rare species known from only two populations. The Buxton Silver Gum Reserve was set aside in 1978 for the conservation of the species, but this objective may be compromised by changes in the integrity of the landscape immediately surrounding the Reserve. A time sequence of aerial photos and Geographic Information Systems technology has been used to identify patterns of landscape change, and aid in determining appropriate management strategies to minimize negative impacts caused by landscape fragmentation and habitat exposure

Improving the precision of the accumulated oxygen deficit using VO2-power regression points from below and above the lactate threshold

The accumulated oxygen deficit (AOD) method assumes a linear VO₂-power relationship for exercise intensities increasing from below the lactate threshold (BLT) to above the lactate threshold (ALT). Factors that were likely to effect the linearity of the VO₂-power regression and the precision of the estimated total energy demand (ETED) were investigated. These included the slow component of VO₂ kinetics (SC), a forced resting y-intercept and exercise intensities BLT and ALT. Criteria for linearity and precision included the Pearson correlation coefficient (PCC) of the VO₂-power relationship, the length of the 95% confidence interval (95% CI) of the ETED and the standard error of the predicted value (SEP), respectively. Eight trained male and one trained female triathlete completed the required cycling tests to establish the AOD when pedalling at 80 rev/min. The influence of the SC on the linear extrapolation of the ETED was reduced by measuring VO₂ after three min of exercise. Measuring VO₂ at this time provided a new linear extrapolation method consisting of ten regression points spread evenly from BLT and ALT. This method produced an ETED with increased precision compared to using regression equations developed from intensities BLT with no forced y-intercept value; (95%CI (L), 0.70±0.26 versus 1.85±1.10, P<0.01; SEP(L/Watt), 0.07±0.02 versus 0.28±0.17; P<0.01). Including a forced y-intercept value with five regression points either BLT or ALT increased the precision of estimating the total energy demand to the same level as when using 10 regression points, (5 points BLT + y-intercept versus 5 points ALT + y-intercept versus 10 points; 95%CI(l), 0.61±0.32, 0.87±0.40, 0.70±0.26; SEP(L/Watt), 0.07±0.03, 0.08±0.04, 0.07±0.02; p>0.05). The VO₂-power regression can be designed using a reduced number of regression points... ABSTRACT FROM AUTHOR

Spatial prediction of rufous bristlebird habitat in a coastal heathland: a GIS-based approach

1. To develop a conservation management plan for a species, knowledge of its distribution and spatial arrangement of preferred habitat is essential. This is a difficult task, especially when the species of concern is in low   abundance. In south-western Victoria, Australia, populations of the rare rufous bristlebird Dasyornis broadbenti are threatened by fragmentation of suitable habitat. In order to improve the conservation status of this species, critical habitat requirements must be identified and a system of corridors must be established to link known populations. A predictive spatial model of rufous bristlebird habitat was developed in order to identify critical areas requiring preservation, such as corridors for dispersal.
2. Habitat models generated using generalized linear modelling techniques can assist in delineating the specific habitat requirements of a species. Coupled with geographic information system (GIS) technology, these models can be extrapolated to produce maps displaying the spatial configuration of suitable habitat.
3. Models were generated using logistic regression, with bristlebird presence or absence as the dependent variable and landscape variables, extracted from both GIS data layers and multispectral digital imagery, as the predictors. A multimodel inference approach based on Akaike’s information criterion was used and the resulting model was applied in a GIS to extrapolate predicted likelihood of occurrence across the entire area of concern. The predictive performance of the selected model was evaluated using the receiver operating characteristic (ROC) technique. A hierarchical partitioning protocol was used to identify the predictor variables most likely to influence variation in the dependent variable. Probability of species presence was used as an index of habitat suitability.
4. Negative associations between rufous bristlebird presence and  increasing elevation, 'distance to cree', 'distance to coast' and sun index were evident, suggesting a preference for areas relatively low in altitude, in close proximity to the coastal fringe and drainage lines, and receiving less direct sunlight. A positive association with increasing habitat complexity also suggested that this species prefers areas containing high vertical density of vegetation.
5. The predictive performance of the selected model was shown to be high (area under the curve 0·97), indicating a good fit of the model to the data. Hierarchical partitioning analysis showed that all the variables considered had significant  independent contributions towards explaining the variation in the dependent variable. The proportion of the total study area that was predicted as suitable habitat for the rufous bristlebird (using probability of occurrence at a ≥0·5 level ) was 16%.
6. Synthesis and applications. The spatial model clearly delineated areas predicted as highly suitable rufous bristlebird habitat, with evidence of potential corridors linking coastal and inland populations via gullies. Conservation of this species will depend on management actions that protect the critical habitats identified in the model. A multi-scale  approach to the modelling process is recommended whereby a spatially explicit model is first generated using landscape variables extracted from a GIS, and a second model at site level is developed using fine-scale habitat variables measured on the ground. Where there are constraints on the time and cost involved in measuring finer scale variables, the first step alone can be used for conservation planning.

Landscape characteristics associated with species richness and occurrence of small native mammals inhabiting a coastal heathland: a spatial modelling approach

In the coastal region of south-western Victoria, Australia, populations of native small mammal species are restricted to patches of suitable habitat in a highly fragmented landscape. The size and spatial arrangement of these patches is likely to influence both the occupancy and richness of species at a location. Geographic Information System (GIS)-based habitat models of the species richness of native small mammals, and individual species  occurrences, were developed to produce maps displaying the spatial  configuration of suitable habitat. Models were generated using either generalised linear Poisson regression (for species richness) or logistic regression (for species occurrences) with species richness or  presence/absence as the dependent variable and landscape variables, extracted from both GIS data layers and multi-spectral digital imagery, as the predictor variables. A multi-model inference approach based on the Akaike Information Criterion was used and the resulting model was applied in a GIS framework to extrapolate predicted richness/likelihood of occurrence across the entire area of the study. A negative association between species  richness and elevation, habitat complexity and sun index indicated that richness within the study area decreases with increasing altitude, vertical vegetation structure and exposure to solar radiation. Landform  characteristics were important (to varying degrees) in determining habitat occupancy for all of the species examined, while the influence of habitat complexity was important for only one of the species. Performance of all but one of the models generated using presence/absence data was high, as indicated by the area under the curve of a receiver-operating characteristic plot. The effective conservation of the small mammal species in the area of concern is likely to depend on management actions that promote the protection of the critical habitats identified in the models.

Modelling habitat suitability of the swamp antechinus (Antechinus minimus maritimus) in the coastal heathlands of southern Victoria, Australia

In recent years, predictive habitat distribution models, derived by combining multivariate statistical analyses with Geographic Information System (GIS) technology, have been recognised for their utility in conservation planning. The size and spatial arrangement of suitable habitat can influence the long-term persistence of some faunal species. In southwestern Victoria, Australia, populations of the rare swamp antechinus (Antechinus minimus maritimus) are threatened by further fragmentation of suitable habitat. In the current study, a spatially explicit habitat suitability model was developed for A. minimus that incorporated a measure of vegetation structure. Models were generated using logistic regression with species presence or absence as the dependent variable and landscape variables, extracted from both GIS data layers and multi-spectral digital imagery, as the predictors. The most parsimonious model, based on the Akaike Information Criterion, was spatially extrapolated in the GIS. Probability of species presence was used as an index of habitat suitability. A negative association between A. minimus presence and both elevation and habitat complexity was evidenced, suggesting a preference for relatively low altitudes and a vegetation structure of low vertical complexity. The predictive performance of the selected model was shown to be high (91%), indicating a good fit of the model to the data. The proportion of the study area predicted as suitable habitat for A. minimus (Probability of occurrence greater-or-equal, slanted0.5) was 11.7%. Habitat suitability maps not only provide baseline information about the spatial arrangement of potentially suitable habitat for a species, but they also help to refine the search for other populations, making them an important conservation tool.

Walkability of local communities: using geographic information systems to objectively assess relevant environmental attributes.

Geographic Information Systems (GIS) can be used to objectively measure features of the built environment that may influence adults’ physical activity, which is an important determinant of chronic disease. We describe how a previously developed index of walkability was operationalised in an Australian context, using available spatial data. The index was used to generate a stratified sampling frame for the selection of households from 32 communities for the PLACE (Physical Activity in Localities and Community Environments) study. GIS data have the potential to be used to construct measures of environmental attributes and to develop indices of walkability for cities, regions or local communities.

Solving regression problems using competitive ensemble models

The use of ensemble models in many problem domains has increased significantly in the last fewyears. The ensemble modeling, in particularly boosting, has shown a great promise in improving predictive performance of a model. Combining the ensemble members is normally done in a co-operative fashion where each of the ensemble members performs the same task and their predictions are aggregated to obtain the improved performance. However, it is also possible to combine the ensemble members in a competitive fashion where the best prediction of a relevant ensemble member is selected for a particular input. This option has been previously somewhat overlooked. The aim of this article is to investigate and compare the competitive and co-operative approaches to combining the models in the ensemble. A comparison is made between a competitive ensemble model and that of MARS with bagging, mixture of experts, hierarchical mixture of experts and a neural network ensemble over several public domain regression problems that have a high degree of nonlinearity and noise. The empirical results showa substantial advantage of competitive learning versus the co-operative learning for all the regression problems investigated. The requirements for creating the efficient ensembles and the available guidelines are also discussed.

Improving an inverse model of sheet metal forming by neural network based regression

An inverse model for a sheet meta l forming process aims to determine the initial parameter levels required to form the final formed shape. This is a difficult problem that is usually approached by traditional methods such as finite element analysis. Formulating the problem as a classification problem makes it possible to use well established classification algorithms, such as decision trees. Classification is, however, generally based on a winner-takes-all approach when associating the output value with the corresponding class. On the other hand, when formulating the problem as a regression task, all the output values are combined to produce the corresponding class value. For a multi-class problem, this may result in very different associations compared with classification between the output of the model and the corresponding class. Such formulation makes it possible to use well known regression algorithms, such as neural networks. In this paper, we develop a neural network based inverse model of a sheet forming process, and compare its performance with that of a linear model. Both models are used in two modes, classification mode and a function estimation mode, to investigate the advantage of re-formulating the problem as a function estimation. This results in large improvements in the recognition rate of set-up parameters of a sheet metal forming process for both models, with a neural network model achieving much more accurate parameter recognition than a linear model.

Terrain characteristics influencing aquifer salinisation of the Glenelg-Hopkins CMA region, using GIS

Salinisation of aquifers is an issue of great concern in the Glenelg-Hopkins region. The GlenelgHopkins region is located in south-west Victoria, south of the Great Dividing Range and covers 2.6 million hectares. The area receives an annual average rainfall of 500-910 mm and experiences a Mediterranean climate, with hot, dry summers and cold wet winters and has varied geology and soil types. Terrain characteristics, such as soil type, geology, depth-to-water table, land use and topography have been integrated into a Geographic Information System (GIS). A geostatistical approach, including the use of multiple linear regression is used to analyse the spatial variability of the relationships between aquifer salinity and terrain characteristics across the entire region. Results from this study should greatly improve knowledge of aquifer salinisation across the region. It is expected that this work will enable managers to determine the most appropriate mitigating measures for each specific area affected.

Associations of children's perceived neighborhood environments with walking and physical activity

Purpose. To examine associations between children's perceptions of the neighborhood environment and walking and physical activity.
Design. Cross-sectional study of a school-based sample.
Setting. Elementary schools in Melbourne, Australia.
Subjects. 280 children aged 10 years (response rate 78%).
Measures. A self-report survey assessed children's perceptions of the neighborhood physical and social environments and their weekly walking frequency. Physical activity was also objectively measured using accelerometers.
Results. Multiple linear regression analyses showed a positive association between walking frequency and the number of accessible destinations in the neighborhood among boys; having a neighborhood that was easy to walk/cycle around and perceiving lots of graffiti were positively associated with walking frequency among girls. Perceiving lots of litter and rubbish was positively associated with boys' overall physical activity, but no environmental variables were associated with girls' overall physical activity.
Conclusion. Several different environmental factors were associated with walking and physical activity. Perceptions of the neighborhood environment were more strongly associated with girls' walking than with objectively-measured physical activity. Future studies should confirm these findings using objective measures and prospective study designs.

Neighborhood walkability and TV viewing time among Australian adults

Background
Built-environment attributes of a neighborhood are associated with participation in physical activity and may also influence time spent in sedentary behaviors. Associations of neighborhood walkability (based on dwelling density, street connectivity, land-use mix, and net retail area) and television viewing time were compared in a large, spatially-derived sample of Australian adults.

Methods
Neighborhood-level variables (walkability and socioeconomic status [SES]) were calculated in 154 Australian census collection districts using Geographic Information Systems. Individual-level variables (TV viewing time, time spent in leisure-time physical activity, height, weight, and sociodemographic variables) were collected from adults living in urban areas of Adelaide, Australia using a mail survey (N=2224) in 2003–2004. Multilevel linear regression analysis was conducted in 2006 separately for men and women to examine variations in TV viewing time across tertiles of walkability.

Results
Neighborhood walkability was negatively associated with TV viewing time in women, but not in men. After controlling for neighborhood SES, body mass index, physical activity, and sociodemographic variables, women living in medium- and high-walkable neighborhoods reported significantly less TV viewing time per day (14 minutes and 17 minutes, respectively) compared to those residing in low-walkable neighborhoods.

Conclusions
Built-environment attributes of neighborhoods that are related to physical activity also may play an important role in influencing sedentary behavior, particularly among women. Considering the effects of prolonged sedentary time on health risks, which are independent of physical activity, there is the need for further research to explore how environmental characteristics may contribute to the amount of time spent in sedentary behavior.

Geographic routing with cooperative relaying and leapfrogging in wireless sensor networks

A novel geographic routing protocol for multi-hop wireless sensor networks is presented. It exploits the broadcast nature of the wireless channel to enable on-demand cooperative relaying and leapfrogging for circumventing weak radio links. In order to achieve energy efficiency, a metric is introduced for next-hop selection that takes into account information on the residual battery energy, the geographical position of the sensor nodes, and the channel quality of the involved radio links when available. Performance results show that the completely decentralized protocol offers significant benefits by reducing the number of (re)transmissions required to reach the destination. This translates into network-wide energy savings that extend the network lifetime.