Multivariate modelling of landuse on in-stream salinity over multiple spatial scales

An impediment to sustainable dryland salinity management is the lack of information on contributing factors. GIS and satellite imagery now offer a cost-effective means of generating relevant land and water resource information for integrated regional management of salinity. In this paper the relationships between patterns in land uselcover distribution and base flow salt concentration in streams (indicated by EC) are investigated and modelled. The Glenelg-Hopkins area is a large regional watershed in southwest Victoria, Australia, covering approximately 2.6 million ha. It is currently estimated that 27,400 ha of land is affected by dryland salinity and this is predicted to rapidly increase in the next decade' if current conditions prevail. Salt concentration data from five gauging stations were analysed with multi-temporal land use maps obtained from satellite imagery. Multiple regression analyses demonstrated that the variables Native Vegetation and Dry/and Grain Cropping were the most significant influences on in~stream salinity in the whole catchment (1=88.9%) and 500 m V=88.3%) and 100 m riparian buffers (1=86.9%) during times of base flow. The implications for future land use planning, effectiveness of riparian zones and revegetation programmes is discussed. This work also demonstrates the utility of applying nmltivariate statistical analyses, spatial statistics, and remote sensing with data integrated in a GIS framework for the purpose of predicting and managing the regional salinity threat.

Comparative sexuality of conspecific stream-side and stream-rock mosses

The sexuality of conspecific stream-rock and stream-side bryophytes at Cement Creek in the Yarra Ranges National Park, Victoria, Australia was examined in terms of sex ratios and sporophyte production. Generally, stream-side colonies had higher numbers of stems, inflorescences and gametangia than their stream-rock counterparts. A higher number of stream-side than stream-rock species produced sporophytes although Cyathophorum bulbosum (Hedw.) Mull.Hai. produced more sporophytes on stream-rocks than in the stream-side habitat. Sex ratios generally showed a female bias with regard to stem numbers and number of inflorescences.

The effects of land use on leaf-litter processing by macro-invertebrates in an Australian temperate coastal stream

Replacement of riparian vegetation by pasture has occurred worldwide and is predicted to have strong effects on macroinvertebrate community structure and function in streams, but this has rarely been examined. In this study, leaf processing and macroinvertebrate community structure were examined in a single stream using experimental leaf-packs and surveys of natural leaf-packs. Two sites in each of three land use categories were selected to represent reaches in forest, pasture and forest-pasture boundary regions. In two experiments using tethered leaf-packs, no differences were found in mean leaf breakdown between land use types. However, shredding invertebrates were absent from the pasture sites, so leaf breakdown in pasture resulted from chemical, physical and microbial processes only. Amounts of fine particulate organic matter in experimental leaf-packs were higher in pasture reaches than the forest and boundary reaches but did not influence leaf breakdown. Macroinvertebrate species richness did not differ between land uses. A predictive model developed for species richness and total abundance enabled direct comparison of assemblages on experimental packs to natural leaf-packs. In the forest reach and at the forest-pasture boundary, macroinvertebrate species richness and total abundance increased proportionally with the number of leaves within a pack, but this relationship was not observed in the pasture reach. Pasture land use on Skenes Creek was therefore associated with weakened relationships between allochthonous inputs and macroinvertebrate communities, but this did not alter leaf breakdown.

Agents and stream data mining: a new perspective

Many organizations struggle with the massive amount of data they collect. Today, data does more than serve as the ingredients for churning out statistical reports. They help support efficient operations in many organizations, and to some extent, data provide the competitive intelligence organizations need to survive in today's economy. Data mining can't always deliver timely and relevant results because data are constantly changing. However, stream-data processing might be more effective, judging by the Matrix project.

The relationship between native vegetation and in-stream salinity: An Australian case study

The Glenelg-Hopkins area is a large regional watershed (2.6 million ha) in southwest Victoria that has been extensively cleared for agriculture. In-stream electrical conductivity (EC) in relation to remnant native vegetation is examined from the headwaters to the upper extent of the estuary of the Glenelg River. Five water quality gauging stations were selected. Their contributing subcatchments represent a continuum of disturbance. Proportions of native vegetation ranged from ∼100% at the headwaters of the river to ∼30% at the furthest downstream gauge station. The relationship between remnant vegetation and in-stream EC was examined using aggregated and non-aggregated land use statistics over a period of 22 years from three land use maps. Increased proportions of native vegetation were significantly negatively correlated with in-stream EC and were consistent across all scenarios investigated.

Adaptive spike detection for resilient data stream mining

Automated adversarial detection systems can fail when under attack by adversaries. As part of a resilient data stream mining system to reduce the possibility of such failure, adaptive spike detection is attribute ranking and selection without class-labels. The first part of adaptive spike detection requires weighing all attributes for spiky-ness to rank them. The second part involves filtering some attributes with extreme weights to choose the best ones for computing each example’s suspicion score. Within an identity crime detection domain, adaptive spike detection is validated on a few million real credit applications with adversarial activity. The results are F-measure curves on eleven experiments and relative weights discussion on the best experiment. The results reinforce adaptive spike detection’s effectiveness for class-label-free attribute ranking and selection.

Algebraic attacks on clock-controlled stream ciphers

We present an algebraic attack approach to a family of irregularly clock-controlled bit-based linear feedback shift register systems. In the general set-up, we assume that the output bit of one shift register controls the clocking of other registers in the system and produces a family of equations relating the output bits to the internal state bits. We then apply this general theory to four specific stream ciphers: the (strengthened) stop-and-go generator, the alternating step generator, the self-decimated generator and the step1/step2 generator. In the case of the strengthened stop-and-go generator and of the self-decimated generator, we obtain the initial state of the registers in a significantly faster time than any other known attack. In the other two situations, we do better than or as well as all attacks but the correlation attack. In all cases, we demonstrate that the degree of a functional relationship between the registers can be bounded by two. Finally, we determine the effective key length of all four systems.

Bayesian classification of catchments using spatial data: a first step to improved modelling of catchment effects on stream ecological condition

A major challenge facing freshwater ecologists and managers is the development of models that link stream ecological condition to catchment scale effects, such as land use. Previous attempts to make such models have followed two general approaches. The bottom-up approach employs mechanistic models, which can quickly become too complex to be useful. The top-down approach employs empirical models derived from large data sets, and has often suffered from large amounts of unexplained variation in stream condition.

We believe that the lack of success of both modelling approaches may be at least partly explained by scientists considering too wide a breadth of catchment type. Thus, we believe that by stratifying large sets of catchments into groups of similar types prior to modelling, both types of models may be improved. This paper describes preliminary work using a Bayesian classification software package, ‘Autoclass’ (Cheeseman and Stutz 1996) to create classes of catchments within the Murray Darling Basin based on physiographic data.

Autoclass uses a model-based classification method that employs finite mixture modelling and trades off model fit versus complexity, leading to a parsimonious solution. The software provides information on the posterior probability that the classification is ‘correct’ and also probabilities for alternative classifications. The importance of each attribute in defining the individual classes is calculated and presented, assisting description of the classes. Each case is ‘assigned’ to a class based on membership probability, but the probability of membership of other classes is also provided. This feature deals very well with cases that do not fit neatly into a larger class. Lastly, Autoclass requires the user to specify the measurement error of continuous variables.

Catchments were derived from the Australian digital elevation model. Physiographic data werederived from national spatial data sets. There was very little information on measurement errors for the spatial data, and so a conservative error of 5% of data range was adopted for all continuous attributes. The incorporation of uncertainty into spatial data sets remains a research challenge.

The results of the classification were very encouraging. The software found nine classes of catchments in the Murray Darling Basin. The classes grouped together geographically, and followed altitude and latitude gradients, despite the fact that these variables were not included in the classification. Descriptions of the classes reveal very different physiographic environments, ranging from dry and flat catchments (i.e. lowlands), through to wet and hilly catchments (i.e. mountainous areas). Rainfall and slope were two important discriminators between classes. These two attributes, in particular, will affect the ways in which the stream interacts with the catchment, and can thus be expected to modify the effects of land use change on ecological condition. Thus, realistic models of the effects of land use change on streams would differ between the different types of catchments, and sound management practices will differ.

A small number of catchments were assigned to their primary class with relatively low probability. These catchments lie on the boundaries of groups of catchments, with the second most likely class being an adjacent group. The locations of these ‘uncertain’ catchments show that the Bayesian classification dealt well with cases that do not fit neatly into larger classes.

Although the results are intuitive, we cannot yet assess whether the classifications described in this paper would assist the modelling of catchment scale effects on stream ecological condition. It is most likely that catchment classification and modelling will be an iterative process, where the needs of the model are used to guide classification, and the results of classifications used to suggest further refinements to models.

Islands in the stream : Australia and Japan face globalisation

A major study of how Australia and Japan, two island nations, face the currents of globalisation. Fascinating chapters by distinguished students of Australia and Japan, and they explore the impact of globalisation.