Consumption of crops by feral pigs (Sus scrofa) in a fragmented agricultural landscape

Feral pigs (Sus scrofa) consume and damage crops and impact the environment through predation, competition and habitat disturbance, although supporting dietary data are lacking in agricultural landscapes. This study was undertaken to determine the relative importance of food items in the diet of feral pigs in a fragmented agricultural landscape, particularly to assist in predicting the breadth of likely impacts. Diet composition was assessed from the stomach contents of 196 feral pigs from agricultural properties in southern Queensland. Feral pigs were herbivorous, with plant matter comprising >99% of biomass consumed. Crops were consumed more frequently than non-crop species, and comprised >60% of dietary biomass, indicating a clear potential for direct economic losses. Consumption of pasture and forage species also suggests potential competition for pasture with domestic stock. There is little evidence of direct predation on native fauna, but feral pig feeding activities may impact environmental values. Seasonal differences in consumption of crop, pasture or animal food groups probably reflect the changing availability of food items. We recommend that future dietary studies examine food availability to determine any dietary preferences to assist in determining the foods most susceptible to damage. The outcomes of this study are important for developing techniques for monitoring the impacts of feral pigs, essential for developing management options to reduce feral pig damage on agricultural lands.

Molecular Breeding for Complex Adaptive Traits: How Integrating Crop Ecophysiology and Modelling Can Enhance Efficiency

Progress in crop improvement is limited by the ability to identify favourable combinations of genotypes (G) and management practices (M) in relevant target environments (E) given the resources available to search among the myriad of possible combinations. To underpin yield advance we require prediction of phenotype based on genotype. In plant breeding, traditional phenotypic selection methods have involved measuring phenotypic performance of large segregating populations in multi-environment trials and applying rigorous statistical procedures based on quantitative genetic theory to identify superior individuals. Recent developments in the ability to inexpensively and densely map/sequence genomes have facilitated a shift from the level of the individual (genotype) to the level of the genomic region. Molecular breeding strategies using genome wide prediction and genomic selection approaches have developed rapidly. However, their applicability to complex traits remains constrained by gene-gene and gene-environment interactions, which restrict the predictive power of associations of genomic regions with phenotypic responses. Here it is argued that crop ecophysiology and functional whole plant modelling can provide an effective link between molecular and organism scales and enhance molecular breeding by adding value to genetic prediction approaches. A physiological framework that facilitates dissection and modelling of complex traits can inform phenotyping methods for marker/gene detection and underpin prediction of likely phenotypic consequences of trait and genetic variation in target environments. This approach holds considerable promise for more effectively linking genotype to phenotype for complex adaptive traits. Specific examples focused on drought adaptation are presented to highlight the concepts.

Optimal adaptive sequential spatial sampling of soil using pair-copulas

A spatial sampling design that uses pair-copulas is presented that aims to reduce prediction uncertainty by selecting additional sampling locations based on both the spatial configuration of existing locations and the values of the observations at those locations. The novelty of the approach arises in the use of pair-copulas to estimate uncertainty at unsampled locations. Spatial pair-copulas are able to more accurately capture spatial dependence compared to other types of spatial copula models. Additionally, unlike traditional kriging variance, uncertainty estimates from the pair-copula account for influence from measurement values and not just the configuration of observations. This feature is beneficial, for example, for more accurate identification of soil contamination zones where high contamination measurements are located near measurements of varying contamination. The proposed design methodology is applied to a soil contamination example from the Swiss Jura region. A partial redesign of the original sampling configuration demonstrates the potential of the proposed methodology.

Transcending disciplinary, cultural and national boundaries: Emergent technologies, new education landscape and the cloud workshop project

As technology continues to become more accessible, miniaturised and diffused into the environment, the potential of wearable technology to impact our lives in significant ways becomes increasingly viable. Wearables afford unique interaction, communication and functional capabilities between users, their environment as well as access to information and digital data. Wearables also demand an inter-disciplinary approach and, depending on the purpose, can be fashioned to transcend cultural, national and spatial boundaries. This paper presents the Cloud Workshop project based on the theme of ‘Wearables and Wellbeing; Enriching connections between citizens in the Asia-Pacific region’, initiated through a cooperative partnership between Queensland University of Technology (QUT), Hong Kong Baptist University (HKBU) and Griffith University (GU). The project was unique due to its inter-disciplinary, inter-cultural and inter-national scope that occurred simultaneously between Australia and Hong Kong.

Effects of environmental variation on ecological and evolutionary dynamics

Environmental variation is a fact of life for all the species on earth: for any population of any particular species, the local environmental conditions are liable to vary in both time and space. In today's world, anthropogenic activity is causing habitat loss and fragmentation for many species, which may profoundly alter the characteristics of environmental variation in remaining habitat. Previous research indicates that, as habitat is lost, the spatial configuration of remaining habitat will increasingly affect the dynamics by which populations are governed. Through the use of mathematical models, this thesis asks how environmental variation interacts with species properties to influence population dynamics, local adaptation, and dispersal evolution. More specifically, we couple continuous-time continuous-space stochastic population dynamic models to landscape models. We manipulate environmental variation via parameters such as mean patch size, patch density, and patch longevity. Among other findings, we show that a mixture of high and low quality habitat is commonly better for a population than uniformly mediocre habitat. This conclusion is justified by purely ecological arguments, yet the positive effects of landscape heterogeneity may be enhanced further by local adaptation, and by the evolution of short-ranged dispersal. The predicted evolutionary responses to environmental variation are complex, however, since they involve numerous conflicting factors. We discuss why the species that have high levels of local adaptation within their ranges may not be the same species that benefit from local adaptation during range expansion. We show how habitat loss can lead to either increased or decreased selection for dispersal depending on the type of habitat and the manner in which it is lost. To study the models, we develop a recent analytical method, Perturbation expansion, to enable the incorporation of environmental variation. Within this context, we use two methods to address evolutionary dynamics: Adaptive dynamics, which assumes mutations occur infrequently so that the ecological and evolutionary timescales can be separated, and via Genotype distributions, which assume mutations are more frequent. The two approaches generally lead to similar predictions yet, exceptionally, we show how the evolutionary response of dispersal behaviour to habitat turnover may qualitatively depend on the mutation rate.

Phylogeography and Adaptive Divergence of Three-spined Stickleback Populations

Genetic studies on phylogeography and adaptive divergence in Northern Hemisphere fish species such as three-spined stickleback (Gasterosteus aculeatus) provide an excellent opportunity to investigate genetic mechanisms underlying population differentiation. According to the theory, the process of population differentiation results from a complex interplay between random and deterministic processes as well historical factors. The main scope in this thesis was to study how historical factors like the Pleistocene ice ages have shaped the patterns molecular diversity in three-spined stickleback populations in Europe and how this information could be utilized in the conservation genetic context. Furthermore, identifying footprints of natural selection at the DNA level might be used in identifying genes involved in evolutionary change. Overall, the results from phylogeographic studies indicate that the three-spined stickleback has colonized the Atlantic basin relatively recently but constitutes three major evolutionary lineages in Europe. In addition, the colonization of freshwater appears to result from multiple and independent invasions by the marine conspecifics. Molecular data together with morphology suggest that the most divergent freshwater populations are located in the Balkan Peninsula and these populations deserve a special conservation genetic status without warranting further taxonomical classification. In order to investigate the adaptive divergence in Fennoscandian three-spined stickleback populations several approaches were used. First, sequence variability in the Eda-gene, coding for the number of lateral plates, was concordant with the previously observed global pattern. Full plated allele is in high frequencies among marine populations whereas low plated allele dominates in the freshwater populations. Second, a microsatellite based genome scan identified both indications of balancing and directional selection in the three-spined stickleback genome, i.e. loci with unusually similar or unusually different allele frequencies over populations. The directionally selected loci were mainly associated with the adaptation to freshwater. A follow up study conducting a more detailed analysis in a chromosome region containing a putatively selected gene locus identified a fairly large genomic region affected by natural selection. However, this region contained several gene predictions, all of which might be the actual target of natural selection. All in all, the phylogeographic and adaptive divergence studies indicate that most of the genetic divergence has occurred in the freshwater populations whereas the marine populations have remained relatively uniform.

Effects of management and landscape structure on biodiversity in boreal agricultural farmland

Intensified agricultural practises introduced after the Second World War are identified as a major cause of global biodiversity declines. In several European countries agri-environment support schemes have been introduced to counteract the ongoing biodiversity declines. Farmers participating in agri-environment schemes are financially compensated for decreasing the intensity of farming practises leading to smaller yields and lower income. The Finnish agri-environment support scheme is composed of a set of measures, such as widened field margins along main ditches (obligatory measure), management of features increasing landscape diversity, management of semi-natural grasslands, and organic farming (special agreement measures). The magnitude of the benefits for biodiversity depends on landscape context and the properties of individual schemes. In this thesis I studied whether one agri-environment scheme, organic farming, is beneficial for species diversity and abundance of diurnal lepidopterans, bumblebees, carabid beetles and arable weeds. I found that organic farming did not enhance species richness of selected insect taxa, although bumblebee species richness tended to be higher in organic farms. Abundance of lepidopterans and bumblebees was not enhanced by organic farming, but carabid beetle abundance was higher in mixed farms with both cereal crop production and animal husbandry. Both species richness and abundance of arable weeds were higher in organic farms. My second objective was to study how landscape structure shapes farmland butterfly communities. I found that the percentage of habitat specialists and species with poor dispersal abilities in butterfly assemblages decreased with increasing arable field cover, leading to a dramatic decrease in butterfly beta diversity. In field boundaries local species richness of butterflies was linearly related to landscape species richness in geographic regions with high arable field cover, indicating that butterfly species richness in field boundaries is more limited by landscape factors than local habitat factors. In study landscapes containing semi-natural grasslands the relationship decelerated at high landscape species richness, suggesting that local species richness of butterflies in field boundaries is limited by habitat factors (demanding habitat specialists that occurred in semi-natural grasslands were absent in field margins). My results suggest that management options in field margins will affect mainly generalists, and species with good dispersal abilities, in landscapes with high arable field cover. Habitat specialists and species with poor dispersal abilities may benefit of management options if these are applied in the vicinity of source populations.

trans gene regulation in adaptive evolution: A genetic algorithm model

This is a continuation of earlier studies on the evolution of infinite populations of haploid genotypes within a genetic algorithm framework. We had previously explored the evolutionary consequences of the existence of indeterminate—“plastic”—loci, where a plastic locus had a finite probability in each generation of functioning (being switched “on”) or not functioning (being switched “off”). The relative probabilities of the two outcomes were assigned on a stochastic basis. The present paper examines what happens when the transition probabilities are biased by the presence of regulatory genes. We find that under certain conditions regulatory genes can improve the adaptation of the population and speed up the rate of evolution (on occasion at the cost of lowering the degree of adaptation). Also, the existence of regulatory loci potentiates selection in favour of plasticity. There is a synergistic effect of regulatory genes on plastic alleles: the frequency of such alleles increases when regulatory loci are present. Thus, phenotypic selection alone can be a potentiating factor in a favour of better adaptation.

The Temporal Landscape of Residential Aged Care Facilities – Implications for Context-Sensitive Health Technology

Health information technology (IT) can have a profound effect on the temporal flow and organisation of work. Yet research into the context, meaning and significance of temporal factors remains limited, most likely because of its complexity. This study outlines the role of communications in the context of the temporal and organizational landscape of seven Australian residential aged care facilities displaying a range of information exchange practices and health IT capacity. The study used qualitative and observational methods to identify temporal factors associated with internal and external modes of communication across the facilities and to explore the use of artifacts. The study concludes with a depiction of the temporal landscape of residential aged care particularly in regards to the way that work is allocated, prioritized, sequenced and coordinated. We argue that the temporal landscape involves key context-sensitive factors that are critical to understanding the way that humans accommodate to, and deal with health technologies, and which are therefore important for the delivery of safe and effective care.

A Control Strategy for Reference Wave Adaptive Current Generation

Speed control of ac motors requires variable frequency, variable current, or variable voltage supply. Variable frequency supply can be obtained directly from a fixed frequency supply by using a frequency converter or from a dc source using inverters. In this paper a control technique for reference wave adaptive-current generation by modulating the inverter voltage is explained. Extension of this technique for three-phase induction-motor speed control is briefly explained. The oscillograms of the current waveforms obtained from the experimental setup are also shown.

Assimilation of remote sensing and hydrological data using adaptive filtering techniques for watershed modelling

The knowledge of hydrological variables (e. g. soil moisture, evapotranspiration) are of pronounced importance in various applications including flood control, agricultural production and effective water resources management. These applications require the accurate prediction of hydrological variables spatially and temporally in watershed/basin. Though hydrological models can simulate these variables at desired resolution (spatial and temporal), often they are validated against the variables, which are either sparse in resolution (e. g. soil moisture) or averaged over large regions (e. g. runoff). A combination of the distributed hydrological model (DHM) and remote sensing (RS) has the potential to improve resolution. Data assimilation schemes can optimally combine DHM and RS. Retrieval of hydrological variables (e. g. soil moisture) from remote sensing and assimilating it in hydrological model requires validation of algorithms using field studies. Here we present a review of methodologies developed to assimilate RS in DHM and demonstrate the application for soil moisture in a small experimental watershed in south India.

An adaptive drug delivery design using neural networks for effective treatment of infectious diseases: A simulation study

An adaptive drug delivery design is presented in this paper using neural networks for effective treatment of infectious diseases. The generic mathematical model used describes the coupled evolution of concentration of pathogens, plasma cells, antibodies and a numerical value that indicates the relative characteristic of a damaged organ due to the disease under the influence of external drugs. From a system theoretic point of view, the external drugs can be interpreted as control inputs, which can be designed based on control theoretic concepts. In this study, assuming a set of nominal parameters in the mathematical model, first a nonlinear controller (drug administration) is designed based on the principle of dynamic inversion. This nominal drug administration plan was found to be effective in curing "nominal model patients" (patients whose immunological dynamics conform to the mathematical model used for the control design exactly. However, it was found to be ineffective in curing "realistic model patients" (patients whose immunological dynamics may have off-nominal parameter values and possibly unwanted inputs) in general. Hence, to make the drug delivery dosage design more effective for realistic model patients, a model-following adaptive control design is carried out next by taking the help of neural networks, that are trained online. Simulation studies indicate that the adaptive controller proposed in this paper holds promise in killing the invading pathogens and healing the damaged organ even in the presence of parameter uncertainties and continued pathogen attack. Note that the computational requirements for computing the control are very minimal and all associated computations (including the training of neural networks) can be carried out online. However it assumes that the required diagnosis process can be carried out at a sufficient faster rate so that all the states are available for control computation.

An optimal dynamic inversion-based neuro-adaptive approach for treatment of chronic myelogenous leukemia

Combining the advanced techniques of optimal dynamic inversion and model-following neuro-adaptive control design, an innovative technique is presented to design an automatic drug administration strategy for effective treatment of chronic myelogenous leukemia (CML). A recently developed nonlinear mathematical model for cell dynamics is used to design the controller (medication dosage). First, a nominal controller is designed based on the principle of optimal dynamic inversion. This controller can treat the nominal model patients (patients who can be described by the mathematical model used here with the nominal parameter values) effectively. However, since the system parameters for a realistic model patient can be different from that of the nominal model patients, simulation studies for such patients indicate that the nominal controller is either inefficient or, worse, ineffective; i.e. the trajectory of the number of cancer cells either shows non-satisfactory transient behavior or it grows in an unstable manner. Hence, to make the drug dosage history more realistic and patient-specific, a model-following neuro-adaptive controller is augmented to the nominal controller. In this adaptive approach, a neural network trained online facilitates a new adaptive controller. The training process of the neural network is based on Lyapunov stability theory, which guarantees both stability of the cancer cell dynamics as well as boundedness of the network weights. From simulation studies, this adaptive control design approach is found to be very effective to treat the CML disease for realistic patients. Sufficient generality is retained in the mathematical developments so that the technique can be applied to other similar nonlinear control design problems as well.