The trajectory of dispersal research in conservation biology. Systematic review

Dispersal knowledge is essential for conservation management, and demand is growing. But are we accumulating dispersal knowledge at a pace that can meet the demand? To answer this question we tested for changes in dispersal data collection and use over time. Our systematic review of 655 conservation-related publications compared five topics: climate change, habitat restoration, population viability analysis, land planning (systematic conservation planning) and invasive species. We analysed temporal changes in the: (i) questions asked by dispersal-related research; (ii) methods used to study dispersal; (iii) the quality of dispersal data; (iv) extent that dispersal knowledge is lacking, and; (v) likely consequences of limited dispersal knowledge. Research questions have changed little over time; the same problems examined in the 1990s are still being addressed. The most common methods used to study dispersal were occupancy data, expert opinion and modelling, which often provided indirect, low quality information about dispersal. Although use of genetics for estimating dispersal has increased, new ecological and genetic methods for measuring dispersal are not yet widely adopted. Almost half of the papers identified knowledge gaps related to dispersal. Limited dispersal knowledge often made it impossible to discover ecological processes or compromised conservation outcomes. The quality of dispersal data used in climate change research has increased since the 1990s. In comparison, restoration ecology inadequately addresses large-scale process, whilst the gap between knowledge accumulation and growth in applications may be increasing in land planning. To overcome apparent stagnation in collection and use of dispersal knowledge, researchers need to: (i) improve the quality of available data using new approaches; (ii) understand the complementarities of different methods and; (iii) define the value of different kinds of dispersal information for supporting management decisions. Ambitious, multi-disciplinary research programs studying many species are critical for advancing dispersal research.

A hybrid model of fuzzy ARTMAP and genetic algorithm for data classification and rule extraction

A two-stage hybrid model for data classification and rule extraction is proposed. The first stage uses a Fuzzy ARTMAP (FAM) classifier with Q-learning (known as QFAM) for incremental learning of data samples, while the second stage uses a Genetic Algorithm (GA) for rule extraction from QFAM. Given a new data sample, the resulting hybrid model, known as QFAM-GA, is able to provide prediction pertaining to the target class of the data sample as well as to give a fuzzy if-then rule to explain the prediction. To reduce the network complexity, a pruning scheme using Q-values is applied to reduce the number of prototypes generated by QFAM. A 'don't care' technique is employed to minimize the number of input features using the GA. A number of benchmark problems are used to evaluate the effectiveness of QFAM-GA in terms of test accuracy, noise tolerance, model complexity (number of rules and total rule length). The results are comparable, if not better, than many other models reported in the literature. The main significance of this research is a usable and useful intelligent model (i.e., QFAM-GA) for data classification in noisy conditions with the capability of yielding a set of explanatory rules with minimum antecedents. In addition, QFAM-GA is able to maximize accuracy and minimize model complexity simultaneously. The empirical outcome positively demonstrate the potential impact of QFAM-GA in the practical environment, i.e., providing an accurate prediction with a concise justification pertaining to the prediction to the domain users, therefore allowing domain users to adopt QFAM-GA as a useful decision support tool in assisting their decision-making processes.

The role of environmental and genetic influences on fatty acid taste sensitivity

 The studies conducted in this thesis add to the growing body of literature supporting the existence of fatty acid taste. Data contributes to the novel, yet mounting research for a functional role of fat taste in food intake regulation, which may have important implications for overweight and obesity.

Critically low levels of genetic diversity in fragmented populations of the endangered Glenelg spiny freshwater crayfish Euastacus bispinosus

The Glenelg spiny freshwater crayfish Euastacus bispinosus is a large endangered freshwater invertebrate of southeastern Australia that has suffered major population declines over the last century. Disjunct populations in the state of South Australia are in a particularly critical condition, restricted to a few isolated rising-spring habitats and in an ongoing state of decline. We assessed genetic diversity and gene flow within E. bispinosus across its current range using allele frequencies from 11 nuclear microsatellite loci and DNA sequence data from a single mitochon -drial locus (cytochrome oxidase subunit I). Populations were characterized by low levels of genetic diversity and found to be highly structured, with gene flow restricted both within and across catchments, highlighting the species' vulnerability to further habitat fragmentation and the importance of managing environmental threats on local scales across its current natural range. South Australian populations were characterized by critically low levels of genetic diversity generally, highlighting their potential vulnerability to localized extinction. Holistic conservation efforts are necessary to conserve populations, including local habitat management and, potentially, translocations to increase genetic diversity and evolutionary potential, and reduce possible inbreeding effects and the threat of extinction.