Distribution of disease caused by Phytophthora cinnamomi in Wilsons Promontory National Park and potential for further impact

The extent of disease caused by Phytophthora cinnamomi was determined within vegetation communities of Wilsons Promontory National Park. Aerial survey of visible symptoms by helicopter and systematic survey along all roads and tracks followed by isolation of the pathogen from soil found that in total 551 ha of moist foothill forest, heath and heathy woodland broad vegetation types were affected by the disease. P. cinnamomi was isolated from 93% of sites that, based on the presence of visible symptoms, were expected to yield the pathogen. The species-rich heathy woodland was most affected with 6.5% of the total area of this type showing symptoms of disease. The size of infestation ranged from 229 ha on the slopes of the Vereker Range in the north to less than 1 ha along the Sealers Cove Walking Track in the south. The potential for disease to spread into uninfested vegetation was estimated for all sites from which P. cinnamomi was isolated. Eight of 18 sites where evidence of disease was found were estimated to have a high potential for further disease spread. This study indicates that even though the disease may be waning in some areas of the Park, the pathogen is active and easily isolated from others and provides a continuing threat to susceptible vegetation communities.

Investigating the distribution of acacia longifolia ssp. sophorae in south-west Victoria using satellite remote sensing and GIS

Mapping and analysis of the distribution of environmental weeds is an important component of strategic weed management. Such information is particularly important in managing 'native invaders', where invasion characteristics must be clearly understood prior to any management action being taken. This paper reports on an investigation of the current distribution of the native invader Acacia longifolia ssp. sophorae (Labill.) Court (coast wattle) in south-west Victoria, using remote sensing and Geographic Information Systems (GIS). Coast wattle was successfully mapped from Landsat ETM imagery using a supervised classification procedure, with 82%, of coast wattle shown on the map accurately depicting coast wattle on the ground. An estimated 11,448 ha were classified as supporting coast wattle, representing 12% of native vegetation in the study area. A more detailed GIS analysis in the Lower Glenelg National Park revealed coast wattle has invaded a limited number of vegetation types, and is more prevalent close to roads and within management zones associated with disturbance. The current regional extent of the species means widespread control is unlikely; hence the immediate focus should be on preventing further spread into areas where it is currently absent. Landsat imagery also proved to be a successful tool for mapping large scale coast wattle distribution, and could be used in long-term monitoring of the species.

Predicting the distribution of discrete spatial events using artificial neural networks

Although the development of geographic information system (GIS) technology and digital data manipulation techniques has enabled practitioners in the geographical and geophysical sciences to make more efficient use of resource information, many of the methods used in forming spatial prediction models are still inherently based on traditional techniques of map stacking in which layers of data are combined under the guidance of a theoretical domain model. This paper describes a data-driven approach by which Artificial Neural Networks (ANNs) can be trained to represent a function characterising the probability that an instance of a discrete event, such as the presence of a mineral deposit or the sighting of an endangered animal species, will occur over some grid element of the spatial area under consideration. A case study describes the application of the technique to the task of mineral prospectivity mapping in the Castlemaine region of Victoria using a range of geological, geophysical and geochemical input variables. Comparison of the maps produced using neural networks with maps produced using a density estimation-based technique demonstrates that the maps can reliably be interpreted as representing probabilities. However, while the neural network model and the density estimation-based model yield similar results under an appropriate choice of values for the respective parameters, the neural network approach has several advantages, especially in high dimensional input spaces.

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.

Distribution and habitat preferences of the rufous bristlebird, Dasyornis broadbenti, in Portland, Victoria

The Rufous Bristlebird Dasyornis broadbenti is a ground-dwelling bird that is listed as nearthreatened (Lower Risk) in Victoria. The species has been observed in a variety of habitats ranging from thickets of shrubs in coastal gullies, shrubland and heathlands on limestone cliffs to sheltered gullies. This study aimed to assess the distribution and habitat preferences of a population of the species in Portland, southwest Victoria. Monthly surveys were conducted on foot in the study area for one hour following sunrise and one hour prior to sunset, and bird presence was recorded on the basis of calls and sightings. Observations outside of the survey times were also recorded to determine habitat utilisation. Vegetation floristics and structure and food availability were measured in areas where birds were present as well as surrounding areas where they were absent to determine habitat preferences. A population size of between 45 and 60 individuals was estimated in the 200ha study area. Bird presence was significantly positively correlated with increasing vegetation density. No significant associations were found between Rufous Bristlebird presence and the floristic associations. Although Rufous Bristlebirds occupy a variety of vegetation communities, results indicate that the key common factor appears to be structure of the vegetation. The findings of this study will be incorporated into a Geographic Information System to develop a spatial model of suitable habitat.

Mortality disparities between geographic regions : results from a risk percentiles model for coronary heart disease

Background: Planning of disease prevention strategies requires information regarding the distribution of absolute risk in the population to allow targeting of people at high disease risk. It is well known that death rates from coronary heart disease (CHD) are higher in remote areas of Australia compared with major cities. Less well understood is the distribution of the absolute risk of CHD death within the different geographic regions. We present a mathematical model of CHD which projects the lifetime risk of death among individuals in different percentiles of CHD risk. We apply this to model the distribution of CHD risk within different geographic regions.

Methods: Using information from the Framingham1, MRFIT2 and AusDiab3 studies, the Australian population was divided into percentiles of CHD risk within age and gender groups by geographic location. Absolute mortality risk was determined at each percentile using current Australian mortality data. Survival curves were generated for each percentile using these risk estimates. Approximate confidence intervals were derived using bootstrap methods.

Conclusions: The difference in life expectancy at age 25 between those in the lowest decile of CHD risk compared to the highest was 5.8 years (95%CI:4.7,6.7) in major cities compared to 8.5 years (95%CI:7.6,9.7) in remote areas. The difference in risk of premature death (before age 75) was 12% (95%CI:10%,14%) in major cities compared to 33% (95%CI:28%,38%) in remote areas.

Few geographic and socioeconomic variations exist in primary total shoulder arthroplasty: a multi-level study of Australian registry data

BACKGROUND:Associations between socioeconomic position (SEP) and the uptake of primary total shoulder arthroplasty (TSA) is not well understood in the Australian population, thus potentially limiting equitable allocation of healthcare resources. We used the Australian Orthopaedic Association National Joint Replacement Registry (AOA NJRR) to examine whether geographic or socioeconomic variations exist in TSA performed for a diagnosis of osteoarthritis 2007-11 for all Australians aged ≥40 years.

METHODS:Primary anatomical and reverse TSA data were extracted from the AOA NJRR which captures >99 % of all TSA nationally. Residential addresses were cross-referenced to Australian Bureau of Statistics 2011 Census data to identify SEP measured at the area-level (categorised into deciles), and geographic location defined as Australian State/Territory of residence. We used a Poisson distribution for the number of TSA over the study period, and modelled the effects of age, SEP and geographic location using multilevel modelling.

RESULTS:During 2007-11, we observed 6,123 TSA (62.2 % female). For both sexes, TSA showed a proportional increase with advancing age. TSA did not vary by SEP or geographic location, with the exception of greater TSA among men in New South Wales.

CONCLUSIONS:Using a national registry approach we provide the first reliable picture of TSA at a national level. The uptake of TSA was equitable across SEP; however, there was some variation between the States/Territories. With an aging population, it is imperative that monitoring of major surgical procedures continues, and be focused toward determining whether TSA uptake correlates with need across different social and area-based groups.

Sensitivity of fine-scale species distribution models to locational uncertainty in occurrence data across multiple sample sizes

To generate realistic predictions, species distribution models require the accurate coregistration of occurrence data with environmental variables. There is a common assumption that species occurrence data are accurately georeferenced; however, this is often not the case. This study investigates whether locational uncertainty and sample size affect the performance and interpretation of fine-scale species distribution models. This study evaluated the effects of locational uncertainty across multiple sample sizes by subsampling and spatially degrading occurrence data. Distribution models were constructed for kelp (Ecklonia radiata), across a large study site (680 km2) off the coast of southeastern Australia. Generalized additive models were used to predict distributions based on fine-resolution (2·5 m cell size) seafloor variables, generated from multibeam echosounder data sets, and occurrence data from underwater towed video. The effects of different levels of locational uncertainty in combination with sample size were evaluated by comparing model performance and predicted distributions. While locational uncertainty was observed to influence some measures of model performance, in general this was small and varied based on the accuracy metric used. However, simulated locational uncertainty caused changes in variable importance and predicted distributions at fine scales, potentially influencing model interpretation. This was most evident with small sample sizes. Results suggested that seemingly high-performing, fine-scale models can be generated from data containing locational uncertainty, although interpreting their predictions can be misleading if the predictions are interpreted at scales similar to the spatial errors. This study demonstrated the need to consider predictions across geographic space rather than performance alone. The findings are important for conservation managers as they highlight the inherent variation in predictions between equally performing distribution models, and the subsequent restrictions on ecological interpretations.