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.

Multi-temporal land use mapping using remotely sensed techniques and the integration of a pollutant load model in a GIS

Estimation of nutrient load production based on multi-temporal remotely sensed land-use data for the Glenelg-Hopkins region in southwest Victoria, Australia, is discussed. Changes in land use were mapped using archived Landsat data and computerized classification techniques. Land-use
change is unparalleled in recent history, with 13% of the region transformed in the last decade. Total nitrogen and phosphorus loading were estimated using an export coefficient model. The analysis demonstrated a disturbing increase in nitrogen and phosphorus loadings from 1995 to 2002. Whilst such increases were suspected from past anecdotal and ad-hoc evidence, our modelling quantitatively estimated such increases and thus demonstrated the enormous potential of using remote sensing and GIS for monitoring land-use change and hence improve land-use management.

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.

Estimates of Heterozostera tasmanica, Zostera muelleri and Ruppia megacarpa distribution and biomass in the Hopkins Estuary, western Victoria, by GIS

Knowledge of the spatial arrangement of the seagrass distribution and biomass within the Hopkins Estuary is an essential step towards gaining an understanding of the functioning of the estuarine ecosystem. This study marks the first attempt to map seagrass distribution and model seagrass biomass and epiphyte biomass along depth gradients by the use of global positioning system (GPS) and geographical information system (GIS) technologies in the estuary. For mapping seagrass in small estuaries, ground-surveying the entire system is feasible. Three species of seagrasses, Heterozostera tasmanica (Martens ex Aschers), Zostera muelleri (Irmisch ex Aschers) and Ruppia megacarpa (Mason), were identified in the Hopkins Estuary. All beds investigated contained a mixed species relationship. Three harvest techniques were trialed in a pilot study, with the 25 × 25-cm quadrat statistically most appropriate. Biomass of seagrasses and epiphytes was found to vary significantly with depth, but not between sites. The average estimate of biomass for total seagrasses and their epiphytes in the estuary in January 2000 was 222.7 g m^–2 (dry weight). Of the total biomass, 50.6% or 112.7 g m^–2 (dry weight) was contributed by seagrasses and 49.4% of the biomass (110.0 g m^–2) were epiphytes. Of the 50.6% of the total biomass represented by seagrasses, 39.3% (87.5 g m^–2) were leaves and 11.3% (25.2 g m^–2) were rhizomes. The total area of seagrasses present in the Hopkins Estuary was estimated to be 0.4 ± 0.005 km², with the total area of the estuary estimated to be 1.6 ± 0.02 km² (25% cover). The total standing crop of seagrasses and epiphytes in the Hopkins Estuary in January 2000 was estimated to be 102.3 ± 57 t in dry weight, 56% (56.9 ± 17 t, dry weight) seagrasses and 44% (45.4 ± 19 t, dry weight) epiphytes. Of the seagrass biomass, 39% (39.7 ± 13 t, dry weight) was contributed by leaves and 17% (17.3 ± 7 t, dry weight) by rhizomes.

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.

Objectively assessing "Walkability" of local communities: using GIS to identify the relevant environmental attributes.

Geographic Information Systems (GIS) may be used to measure objectively, those features of the built environment that may influence walking. Public health research on environmental determinants of physical activity in adults shows that different factors can influence walking for recreation, compared to walking for transport. Most studies have used perceived (self-report) rather than objective measures of potentially relevant environmental attributes. We describe how a previously-developed index of ‘walkability’ was operationalized in an Australian context, using available spatial data.               Attributes believed to be of relevance to walking for transport, that are measurable using GIS, are: Dwelling density (higher-density neighborhoods support greater retail and service variety, resulting in shorter, walkable distances between facilities; driving and parking are more difficult and time consuming). Connectivity (higher intersection densities provide people with a greater variety of potential routes, easier access to major roads where public transport is available and shorter times to get to destinations). Land use mix (the more varied the land use mix and built form, then the more conducive it is to walk to various destinations). Net retail area (there are more options for destinations where goods and services may be purchased and more local employment opportunities that can be reached by walking). The associations of these attributes with walking behaviors can be  examined separately, or in combination. Such GIS data are very helpful in fundamental studies of the environmental determinants of behavior, and also in applied policy research for cities, regions or local communities, to
address public health and environmental issues.

Applying GIS in physical activity research: community walkability and walking behaviours.

Physical activity provides many health benefits, including reduced risk of cardiovascular disease, Type II diabetes and some cancers. Environmental exposure factors (e.g., the built environment) are now receiving ever-increasing attention. Large-scale interdisciplinary studies on the association between attributes of local community environments and residents’ physical activity are being conducted. We will focus on findings from Australia - the Physical Activity in Localities and Community Environments (PLACE) study. PLACE is examining factors that may influence the prevalence and the social and spatial distribution of walking for transport and walking for recreation. A stratified two-stage cluster sampling strategy was used to select 32 urban communities (154 census collection districts), classified as high and low ‘walkable’ using a GISbased walkability index (dwelling density, intersection density, net retail area and land use mix) and matched for socio-economic status. We report data on a sub-sample of 1,216 residents who provided information on the perceived attributes of their community environments (e.g., dwelling density, access to services, street connectivity) and weekly minutes of walking for transport and for recreation. Moderate to strong associations were found between GIS indicators of walkability and the corresponding self-report measures. The walkability index explained the same amount of neighborhood-level variance in walking for transport as did the complete set of self-report measures. No significant associations were found with walking for recreation. Relevant GIS-based indices of walkability, for purposes other than transport need to be   developed.

Producing a GIS based multiple criteria analysis tool for regional sustainability assessment: the problem of weighting

Decision support tools will be useful in guiding regions to sustainability. These need to be simple but effective at identifying, for regional managers, areas most in need of initiatives to progress sustainability. Multiple criteria analysis (MCA) is often used as a decision support tool for a wide range of applications. This method allows many criteria to be considered at one time. It does this by giving a ranking of possible options based on how closely each option meets the criteria. Thus, it is suited to the assessment of regional sustainability as it can consider a number of indicators simultaneously and demonstrates how sustainability can vary at small scales across the region. Coupling MCA with GIS to produce maps, allows this analysis to become visual giving the manager a picture of sustainability across the region. To do this each indicator is standardised to a common scale so that it can be compared to other indicators. A weighting is then applied to each indicator to calculate weighted summation for each area in the region. This paper argues that this is the critical step in developing a useful decision support tool. A study being conducted in south west Victoria demonstrates that the weights chosen can have a dramatic impact on the results of the sustainability assessment. It is therefore imperative that careful consideration be given to determining indicator weights in a way that is objective and fully considers the impact of that indicator on regional sustainability.

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.