Production of Australian freshwater crayfish in earthen-based systems using pelleted diets and forage crops as food

Juvenile Cherax destructor (commonly called theyabby) were cultured in earthen-based ponds and tanks for 70–105d, and were fed pellets and/or a forage crop of the perennialwhiteclover, Trifolium repens. Three supplementary feedingstrategies were evaluated. Yabby growth on pellets consistently exceeded (by67–159%) that obtained on clover. Base-line yields for extensiveproduction systems are around 400 kg ha^–1. Thesupplementary addition of T. repens produced yields of 635kg ha^–1 (in ponds) to 1086 kgha^–1 (in tanks). The sequential addition of cut-cloverto tanks stimulated growth to levels approaching those achieved on pellets.Yabbies stocked into ponds at 17 m^–2 and fed 33%protein pellets for 100 d, resulted in a yield of 1117 kgha^–1.Pellet inputs at a rate of 129–249 g m^–2(dry matter) and 38–83 g m^–2 (protein) over70–100 d resulted in acceptable growth and feed utilisationindices. Clover inputs of 534–682 g m–2 (asdry matter) or 84–177 g m^–2 (as protein)produced reasonable growth rates but poor feed utilisation indices. Aconsiderable quantity of the dry matter and protein content of clover waseitherinefficiently utilised or directed into other production pathways. In tanks,clover inputs from 113–296 g m^–2 (drymatter) and 24–54 g m^–2 (protein) weresufficient to maintain high growth rates for 4 weeks, while in ponds, inputs of21 g m^–2 (dry matter) and 4.3 gm^–2 (protein) were sufficient for 3 weeks. During theearly weeks of production no growth advantage was gained by providing pelletstoanimals cultured in forage-based systems. Forage depletion occurred after3–4 weeks and was probably a major growth limiting factor.

Evaluation of fixed sample-size plans for Plutella xylostella (Lepidoptera: Plutellidae) on broccoli crops in Australia

Fixed sample-size plans for monitoring Plutella xylostella (L.) (Lepidoptera: Plutellidae) on broccoli and other Brassica vegetable crops are popular in Australia for their simplicity and ease of application. But the sample sizes used are often small, ≈10–25 plants per crop, and it may be that they fail to provide sufficient information upon which to base pest control decisions. We tested the performance of seven fixed sample-size plans (10, 15, 20, 30, 35, 40, and 45 plants) by resampling a large data set on P. xylostella in commercial broccoli crops. For each sample size, enumerative and presence-absence plans were assessed. The precision of the plans was assessed in terms of the ratio of the standard error to the mean; and at least 45 and 35 samples were necessary for the enumerative and presence-absence plans, respectively, to attain the generally accepted benchmark of ≤0.3. Sample sizes of 10–20 were highly imprecise. We also assessed the consequences of classifications based on action thresholds (ATs) of 0.2 and 0.8 larvae per plant for the enumerative case, and 0.15 and 0.45 proportion of plants of infested for the presence-absence case. Operating characteristic curves and investigations of the frequency of correct decisions suggest improvements in the performance of plans with increased sample size. In both the enumerative and presence-absence cases, the proportion of incorrect decisions was much higher for the lower of the two ATs assessed, and type II errors (i.e., failure to suggest pest control upon the AT is exceeded) generally accounted for the majority of this error. Type II errors are the most significant from a producer’s standpoint. Further consideration is necessary to determine what is an acceptable type II error rate.

Nest-tree selection by the threatened brush-tailed phascogale (phascogale tapoatafa) (marsupialia: dasyuridae) in a highly fragmented agricultural landscape

The conservation of roosting and nesting resources is of critical concern for many hollow-dependent species around the world. We investigated the nest-tree requirements of the threatened brush-tailed phascogale (Phascogale tapoatafa) in a highly cleared agricultural landscape in south-eastern Australia. We documented the physical characteristics of selected nest trees and describe the spatial and temporal patterns of nest-tree use as revealed by radio-tracking. Nine phascogales (seven females, two males) were radio-tracked between March and July 1999 in an area where most woodland habitat is confined to linear strips along roads and streams or small patches and scattered trees in cleared farmland. Female phascogales were monitored for 13–35 days over periods of 5–15 weeks and two males were monitored for 2 and 9 days respectively. A total of 185 nest-tree fixes was collected and all nests occupied by phascogales were in standing trees. Eighty-three nest trees were identified, ranging in diameter at breast height (dbh) from 25 to 171 cm, with a mean dbh for the trees used by each individual phascogale of >80 cm. Phascogales did not discriminate between canopy tree species in selecting nest trees, but showed highly significant selection for trees in the largest size class. All individuals used multiple nest trees, with the seven females occupying an average of 11.4 nest trees from a mean of 25 diurnal locations. The number of nest trees continued to increase throughout the study, suggesting that more would be identified during a longer or more intensive study. Occupied nest trees were located throughout each individual’s home range, highlighting the importance of a continuous spatial distribution of suitable nest trees across the landscape. Nest trees were also located in adjacent farmland up to 225 m from roadside vegetation, demonstrating the value that scattered clumps and even single trees in farmland can have for wildlife conservation.

A general rooted tree drawing algorithm

A general rooted tree drawing algorithm is designed in this paper. It satisfies the basic aesthetic criteria and can be well applied to binary trees. Given an area, any complex tree can be drawn within the area in users' favorite styles. The algorithm is efficient with O(LxNxlogN) time complexity and self-adaptive as well.

Optimal multicast tree routing for cluster computing in hypercube interconnection networks

Cluster computation has been used in the applications that demand performance, reliability, and availability, such as cluster server groups, large-scale scientific computations, distributed databases, distributed media-on-demand servers and search engines etc. In those applications, multicast can play the vital roles for the information dissemination among groups of servers and users. This paper proposes a set of novel efficient fault-tolerant multicast routing algorithms on hypercube interconnection of cluster computers using multicast shared tree approach. We present some new algorithms for selecting an optimal core (root) and constructing the shared tree so as to minimize the average delay for multicast messages. Simulation results indicate that our algorithms are efficient in the senses of short end-to-end average delay, load balance and less resource utilizations over hypercube cluster interconnection networks.

Using decision-tree to automatically construct learned-heuristics for events classification in sports video

Automatic events classification is an essential requirement for constructing an effective sports video summary. It has become a well-known theory that the high-level semantics in sport video can be “computationally interpreted” based on the occurrences of specific audio and visual features which can be extracted automatically. State-of-the-art solutions for features-based event classification have only relied on either manual-knowledge based heuristics or machine learning. To bridge the gaps, we have successfully combined the two approaches by using learning-based heuristics. The heuristics are constructed automatically using decision tree while manual supervision is only required to check the features and highlight contained in each training segment. Thus, fully automated construction of classification system for sports video events has been achieved. A comprehensive experiment on 10 hours video dataset, with five full-match soccer and five full-match basketball videos, has demonstrated the effectiveness/robustness of our algorithms.

Coastal marine habitat mapping using multi-beam acoustics, video data and a decision tree classifier on the temperate south-east Australian continental shelf

In this habitat mapping study, multi-beam acoustic data are integrated with extensive, precisely geo-referenced video validation data in a GIS environment to classify benthic substrates and biota at a 33km2 site in the near shore waters of Victoria, Australia. Using an automated decision-tree classification method, 5 representative biotic groups were identified in the Cape Nelson survey area using a combination of multi-beam bathymetry, backscatter and derivative products. Rigorous error assessment of derived, classified maps produced high overall accuracies (>85%) for all mapping products. In addition, a discrete multivariate analysis technique (kappa analysis) was used to assess classification accuracy. High-resolution (2.5m cell-size) representation of sea floor morphology and textural characteristics provided by multi-beam bathymetry and backscatter datasets, allowed the interpretation of benthic substrates of the Cape Nelson site and the communities of sessile organisms that populate them. Non-parametric multivariate statistical analysis (ANOSIM) revealed a significant difference in biotic composition between depth strata, and between substrate types. Incorporated with other descriptive measures, these results indicate that depth and substrate are important factors in the distributional ecology of the biotic communities at the Cape Nelson study site. BIOENV analysis indicates that derivatives of both multi-beam datasets (bathymetry and backscatter) are correlated with distribution and density of biotic communities. Results from this study provide new tools for research and management of the coastal zone.

Deterministic versus stochastic quantitative microbial Risk Assessment models for wastewater irrigation of food crops

Reuse of wastewater to irrigate food crops is being practiced in many parts of the world and is becoming more commonplace as the competition for, and stresses on, freshwater resources intensify. But there are risks associated with wastewater irrigation, including the possibility of transmission of pathogens causing infectious disease, to both workers in the field and to consumers buying and eating produce irrigated with wastewater. To manage these risks appropriately we need objective and quantitative estimates of them. This is typically achieved through one of two modelling approaches: deterministic or stochastic. Each parameter in a deterministic model is represented by a single value, whereas in stochastic models probability functions are used. Stochastic models are theoretically superior because they account for variability and uncertainty, but they are computationally demanding and not readily accessible to water resource and public health managers. We constructed models to estimate risk of enteric virus infection arising from the consumption of wastewater-irrigated horticultural crops (broccoli, cucumber and lettuce), and compared the resultant levels of risk between the deterministic and stochastic approaches. Several scenarios were tested for each crop, accounting for different concentrations of enteric viruses and different lengths of environmental exposure (i.e. the time between the last irrigation event and harvest, when the viruses are liable to decay or inactivation). In most situations modelled the two approaches yielded similar estimates of risk (within 1 order-of-magnitude). The two methods diverged most markedly, up to around 2 orders-of-magnitude, when there was large uncertainty associated with the estimate of virus concentration and the exposure period was short (1 day). Therefore, in some circumstances deterministic modelling may offer water resource managers a pragmatic alternative to stochastic modelling, but its usefulness as a surrogate will depend upon the level of uncertainty in the model parameters.