The central question addressed in this thesis is whether descriptions of the dynamics of ecological systems at one scale may be effectively used as descriptions of the dynamics of ecological systems at larger scales. This question is addressed in the context of the dynamics of fungal communities. A simple experimental system and complementary theoretical approach, in the form of an individual-based (cellular automaton) model, is presented. Experimental results derived from small-scale systems are used to quantify parameters of the model; results from large-scale experimental systems serve to test the model. The theoretical analyses clearly demonstrate that the dynamics observed are a result of both local and non-local features of the experimental system. In cases such as this the immediate extrapolation of results derived from xperiments conducted out of the context of the community to represent system scale behaviour is not possible. In response to this observation a generic framework is developed to allow the consideration of effects at a range of scales through contextual parameterisation of localised dynamics. The framework is directed toward plant systems where a large body of experimental data exists, and may be parameterised by that experimental data. It represents the essential features of individual interactions in terms of competition for space and resource, and the behaviour of a given plant is described in terms of functional traits. Model runs demonstrate complex community patterns suggestive of a known biological phenomena, succession, that arises as a consequence of the coupling between the community and environment. This coupling may allow the long-term coexistence of species through some particular balance in individual function (traits) across the community. A search mechanism is determined to allow combinations of trait values at the scale of the individual to be assessed for a particular community-scale phenomenon. Initial results demonstrate that this mechanism may identify and converge on combinations of trait values that give rise to, in this case, a simple measure of diversity. The manner in which the generic framework developed may be applied to further the investigation into fungal community dynamics is addressed.

This thesis considers ways to improve biodiversity conservation at the bioregional level in Australia through the use of geospatial science technologies and biological modelling techniques. Following a review of approaches to biodiversity conservation at the bioregional level, including the roles and potential of geospatial science technologies in this regard, I consider biodiversity modelling using a case study of the Burt Plain bioregion in central Australia that focuses on selected taxa, ecosystems and landscapes. The Burt Plain bioregion was chosen since it is one of 19 bioregions nationally that has been given a 'very high' priority status for biological survey, assessment and potential reservation of land for conservation purposes. The specific research objectives for the Burt Plain bioregion study were to: · describe the species composition, distribution and nature of the dominant vegetation communities within the bioregion; · characterise environmental niche of communities with respect to selected environmental and management variables - latitude, longitude, climate, land systems and land units, geology, hydrography, topography, and tenure; · analyse how well or otherwise taxa have been sampled (during previous ground surveys) with respect to geographical and environmental variables; Biodiversity conservation at the bioregional level · develop and compare quantitative habitat models of the potential distribution of selected species based on presence-only distributional data; and · examine the significance of radiometric data as a potential correlate and predictor of the distribution of those selected species. National conservation initiatives such as the bioregional approach and international initiatives such as the biosphere reserves program to support the planning and management of biodiversity conservation are discussed in chapter two. The scientific and related processes underpinning the development of bioregions and strategies across the Australian states and territories are then considered. An important finding arising from this review is the need to improve biological information, especially through systematic surveys and on-going monitoring of ecosystems and populations of species, at the bioregional level to inform land use allocation and management. This finding is consistent with one of the general aims of the thesis to improve the spatial modelling techniques available for bioregional assessment and biodiversity conservation. In chapter three I review the role and limitations of geospatial technologies currently employed for biodiversity conservation management. Current developments and applications of GIS and remote sensing to wildlife research, conservation gap analysis and conservation reserve design are considered. Geographic information systems (GIS) are now routinely used by ecologists to Biodiversity conservation at the bioregional level analyse spatial data. Although various forms of GIS have been available for 15 to 25 years, the biological applications of GIS have figured most prominently in the ecological literature only in the past 15 years. The use of computer-generated models to simulate environmental events can provide a greater understanding of ecosystems, and offers improved predictive powers to conservation and land managers. The decision support offered by computer-based modelling techniques appears likely to underpin conservation and management decisions much more into the future providing that adequate biological and other datasets are available for this purpose. Dominant vegetation communities and various environmental gradients were analysed to characterise environmental niches at the bioregional scale for the Burt Plain bioregion (Chapter 4) and more locally at the catchment scale for the Upper Todd River Catchment (Chapter 5). In Chapters four and five I describe in detail the land tenure and use, land systems, climate soil, geology, topography, hydrology, vegetation and biodiversity of the Burt Plain bioregion and Upper Todd River Catchment. The bioregion contains some ephemeral watercourses, which are generally in fair to good condition, but are afforded little protection from a range of threatening processes, including grazing and trampling by feral animals and livestock and weed infestation. The major river systems occurring in the bioregion include parts of the Plenty, Hanson, Sandover and Lander Rivers. In the Upper Todd River Catchment the major watercourses Biodiversity conservation at the bioregional level are the Todd River and Station Creek, which exit the area via two narrow gaps in the low rocky hills on the southern boundary of the bioregion. The dominant geology can be summarised as plains and low rocky ranges of Pre-Cambrian granites on red earths. The bioregion has approximately 200 - 250 mm of summer rainfall, with rainfall occurring on 20 - 30 days per year. There is a high variability and range of temperatures, with an annual mean temperature of approximately 22-23°C. In Chapter six I consider a range of species found within the Burt Plain bioregion using existing survey data and techniques that enables the prediction of the spatial distribution of taxa. Using GLM and GAM models, Black-footed Rock- Wallaby (Petrogale lateralis), Spinifex Hopping Mouse (Notomys alexis) and Spencers Frog (Limnodynastes spenceri) were chosen for a more in-depth analysis. Environmental variables correlated with the presence of each species are then described and prediction maps showing the probability or likelihood of the presence of the species within the bioregion developed. In Chapter seven I examine the utility of radiometric data for wildlife habitat modelling. Statistical relationships are tested between the concentrations of the elements uranium, thorium and potassium and terrain characteristics such as position in the landscape, slope and aspect as well as other climatic variables. Radiometric data were found to be useful for developing statistical predictive Biodiversity conservation at the bioregional level models of six species: Red Kangaroo (Macropus rufus), Desert Dunnart (Sminthopsis youngsoni), Rabbit (Orcytolagus cuniculus), Brown Honeyeater (Lichmera indistincta), Little Spotted Snake (Suda punctata) and Southern Boobook (Ninox novaeseelandiae). I suggest that the utility of radiometric data for wildlife habitat modelling would appear significant and should be explored further using alternative quantitative modelling techniques and presence/ absence records for target faunal species. Predictions of species distributions may be useful for prioritising land acquisitions for reservation as well as in the future design of biological surveys. The thesis concludes with a synthesis of the major research findings, discussion of the limitations of the datasets available for the study, perspectives on management issues in the Burt Plain bioregion, and possible future research directions. It is important that purposefully-designed biological survey research be undertaken across the bioregions of the arid zone of Australia to enhance basic understanding of biodiversity patterns and their relationships to environmental heterogeneity and site-landscape level processes. Geospatial modelling techniques can assist such biodiversity survey and evaluation and make their conduct more cost-efficient and the inferences drawn from subsequent data analyses more powerful. This knowledge is required to contribute to the emergent concepts and theory of ecosystem dynamics and associated biodiversity patterns in arid Australia and, most significantly, to enhance the conservation and management of the unique biological complement and systems found in this region.

Marine ecosystem-based management requires end to end models, which are models capable of representing the entire ecosystem including physical, chemical and biological processes, anthropogenic activities, and multiple species with different sizes, life histories and from different trophic levels. To adequately represent ecosystem dynamics in shallow coastal regions, end-to-end models may need to include macrobiota species (wild and aquacultured) and may have to allow feedbacks (i.e. two-way coupling) between macrobiota and planktonic ecosystem dynamics. This is because the biomass of macrobiota can locally exceed the biomass of plankton, thus influencing the distribution of planktonic ecosystem tracers and altering the overall food web structure. Here, I describe a hybrid (Eulerian/Individual-Based) ecosystem framework, implemented in the Regional Ocean Modeling System (ROMS), a state-of-the-art 3-D ocean circulation model. The framework was applied to a model of a synthetic embayment containing seagrass, rockweed and kelp beds, a wild oyster reef, a mussel ranch and a fish farm. I found that two-way coupling is essential to reproduce expected spatial patterns of all variables and to conserve mass in the system. I also developed a shellfish ecophysiology model (SHELL E) and compared its results against water samples collected over 5 years in Ship Harbour, a fjord with mussel aquaculture in Nova Scotia, Eastern Canada. Also, from a high-resolution bio-optical survey of the fjord, I found that mussels decrease phytoplankton biomass inside the farm, but also cause a bloom of phytoplankton outside the farm. Using ROMS/SHELL-E, I determined that the increase of phytoplankton around the farm is caused by the waste products of the farmed bivalves, which have a fertilization effect, enhancing phytoplankton production outside the farm during nutrient-limited and light-replete conditions (i.e. late spring to late fall in Ship Harbour). The main conclusion of this thesis is that—in shallow coastal regions—ecosystem models must represent bilateral interactions between macrobiota and physical-planktonic dynamics, in a spatially-explicit setting, to adequately represent mass flows and ecosystem dynamics. The hybrid end-to-end modelling system provides a computationally efficient framework for describing these interactions and, through careful comparisons against observations, can be a powerful tool to test hypotheses and generate insights into coastal ecosystems.

Mathematical models have been used to capture the behaviour of biological systems, from low-level biochemical reactions to multi-scale whole-organ models. Models are typically based on experimentally-derived data, attempting to reproduce the observed behaviour through mathematical constructs, e.g. using Ordinary Differential Equations (ODEs) for spatially-homogeneous systems. These models are developed and published as mathematical equations, yet are of such complexity that they necessitate computational simulation. This computational model development is often performed in an ad hoc fashion by modellers who lack extensive software engineering experience, resulting in brittle, inefficient model code that is hard to extend and reuse. Several Domain Specific Languages (DSLs) exist to aid capturing such biological models, including CellML and SBML; however these DSLs are designed to facilitate model curation rather than simplify model development. We present research into the application of techniques from software engineering to this domain; starting with the design, development and implementation of a DSL, termed Ode, to aid the creation of ODE-based biological models. This introduces features beneficial to model development, such as model verification and reproducible results. We compare and contrast model development to large-scale software development, focussing on extensibility and reuse. This work results in a module system that enables the independent construction and combination of model components. We further investigate the use of software engineering processes and patterns to develop complex modular cardiac models. Model simulation is increasingly computationally demanding, thus models are often created in complex low-level languages such as C/C++. We introduce a highly-efficient, optimising native-code compiler for Ode that generates custom, model-specific simulation code and allows use of our structured modelling features without degrading performance. Finally, in certain contexts the stochastic nature of biological systems becomes relevant. We introduce stochastic constructs to the Ode DSL that enable models to use Stochastic Differential Equations (SDEs), the Stochastic Simulation Algorithm (SSA), and hybrid methods. These use our native-code implementation and demonstrate highly-efficient stochastic simulation, beneficial as stochastic simulation is highly computationally intensive. We introduce a further DSL to model ion channels declaratively, demonstrating the benefits of DSLs in the biological domain. This thesis demonstrates the application of software engineering methodologies, and in particular DSLs, to facilitate the development of both deterministic and stochastic biological models. We demonstrate their benefits with several features that enable the construction of large-scale, reusable and extensible models. This is accomplished whilst providing efficient simulation, creating new opportunities for biological model development, investigation and experimentation.

In a changing climate, it is increasingly important to be able to model environmental effects on food webs, and to do that, one must have appropriate dynamic models. I present a shallow lake ecosystem model where producers, grazers, carnivores, piscivores, and detritivores are coupled through resource (light, nutrients and detritus) fluxes between the benthic and pelagic habitats and through carnivore life history events (ontogenetic habitat and diet shifts). The two habitats each contain primary producers, grazers, carnivores and detritivores. Within the habitats, there is strong top-down regulation, but across habitat boundaries, bottom-up interactions drive production. In the absence of piscivores, stage-structured carnivores cause intriguing patters of alternative stable states. Notably, the model predicts a lesser dependence on benthic production with detritus presence. Model predictions are largely in agreement with empirical studies. The results have implications for management of freshwater, and for the interpretation of previous models.

Esta pesquisa de doutorado apresenta resultados em três áreas distintas: (i) Ciência da Computação e Estatística -- devido ao desenvolvimento de uma nova solução para o problema de seleção de características, um problema conhecido em Reconhecimento de Padrões; (ii) Bioinformática -- em razão da construção de um método baseado em um \\textit de algoritmos, incluindo o de seleção de características, visando abordar o problema de identificação de arquiteturas de redes de expressão gênica; e (iii) Biologia -- ao relacionar o estrógeno com uma nova função biológica, após analisar informações extraídas de séries temporais de \\textit pelas novas ferramentas computacionais-estatísticas desenvolvidas. O estrógeno possui um importante papel nos tecidos reprodutivos. O crescimento das gândulas mamárias e do endométrio durante a gravidez e o ciclo menstrual são estrógeno dependentes. O crescimento das células tumorais nesses órgãos podem ser estimuladas pela simples presença de estrógeno; mais de $300$ genes são conhecidos por terem regulação positiva ou negativa devido a sua presença. A motivação inicial desta pesquisa foi a construção de um método que possa servir de ferramenta para a identificação de genes que tenham seu nível de expressão alterado a partir de uma resposta induzida por estrógeno, mais precisamente, um método para modelar os inter-relacionamentos entre os diversos genes dependentes do estrógeno. Apresentamos um novo \\textit de algoritmos que, a partir de dados temporais de \\textit e um conjunto inicial de genes que compartilham algumas características comuns, denominados de \\textit{genes sementes}, devolve como saída a arquitetura de uma rede gênica representada por um grafo dirigido. Para cada nó da rede, uma tabela de predição do gene representado pelo nó em função dos seus genes preditores (genes que apontam para ele) pode ser obtida. O método foi aplicado em estudo de série-temporal de \\textit para uma cultura de células \\textit submetidas a tratamento com estrógeno, e uma possível rede de regulação foi obtida. Encontrar o melhor subconjunto preditor de genes para um dado gene pode ser estudado como um problema de seleção de características, no qual o espaço de busca pode ser representado por um reticulado Booleano e cada um de seus elementos representa um subconjunto candidato. Uma característica importante desse problema é o fato de que para cada elemento existe uma função custo associada, e esta possui forma de curva em U para qualquer cadeia maximal do reticulado. Para esse problema, apresentamos um nova solução, o algoritmo ewindex. Esse algoritmo é um método do tipo \\textit, o qual utiliza a estrutura do reticulado Booleano e a característica de curva em U da função custo para explorar um subconjunto do espaço de busca equivalente à busca completa. Nosso método obteve excelentes resultados em eficiência e valores quando comparado com as heurísticas mais utilizadas (SFFS e SFS). A partir de um método baseado no \\textit e de um conjunto inicial de genes regulados \\textit pelo estrógeno, identificamos uma evidência de envolvimento do estrógeno em um processo biológico ainda não relacionado: a adesão celular. Esse resultado pode direcionar os estudos sobre estrógeno e câncer à investigação de processo metastático, o qual é influenciado por genes relacionados à adesão celular. / This Phd. research presents in three distinct areas: (i) Computer Science and Statistics -- on the development of a new solution for the feature selection problem which is an important problem in Pattern Recognition; (ii) Bioinformatics -- for the construction of a pipeline of algorithms, including the feature selection solution, to address the problem of identification the architecture of a genetic expression network and; (iii) Biology -- relating estrogen to a new biological function, from the results obtained by the new computational-statistic tools developed and applied to a time-series microarray data. Estrogen has an important role in reproductive tissues. The growth mammary glands and endometrial growing during menstrual cycle and pregnancy are estrogen dependent. The growth of tumor cells in those organs can be stimulated by the simple presence of estrogen. Over $300$ genes are known by their positive or negative regulation by estrogen. The initial motivation of this research was the construction of a method that can serve as a tool for the identification of genes that have changed their level of expression changed by a response induced by estrogen, more specifically, a method to model the inter-relationships between the several genes dependent on estrogen. We present a new pipeline of algorithms that from the data of a time-series microarray experiment and from an initial set of genes that share some common characteristics, known as \\textit{seed genes}, gives as an output an architecture of the genetic expression network represented by a directed graph. For each node of the network, a prediction table of the gene, represented by the node, in function of its predictors genes (genes that link to it) can be obtained. The method was applied in a study of time-series microarray for a cell line \\textit submitted to a estrogen treatment and a possible regulation network was obtained. Finding the best predictor subset of genes for a given gene can be studied as a problem of feature selection where the search space can be represented by a Boolean lattice and each one of its elements represents a possible subset. An important characteristic of this problem is: for each element in the lattice there is a cost function associated to it and this function has a U-shape in any maximal chain of the search space. For this problem we present a new solution, the \\textit algorithm. This algorithm is a branch-and-bound solution which uses the structure of the Boolean lattice and U-shaped curves to explore a subset of the search space that is equivalent to the full search. Our method obtained excellent results in performance and values when compared with the most commonly used heuristics (SFFS and SFS). From a method based on the pipeline of algorithms and from an initial set of genes direct regulated by estrogen, we identified an evidence of involvement of estrogen in a biological process not yet related to estrogen: the cell adhesion. This result can guide studies on estrogen and cancer to research in metastatic process, which is affected by cell adhesion related genes.

Esta pesquisa de doutorado apresenta resultados em três áreas distintas: (i) Ciência da Computação e Estatística -- devido ao desenvolvimento de uma nova solução para o problema de seleção de características, um problema conhecido em Reconhecimento de Padrões; (ii) Bioinformática -- em razão da construção de um método baseado em um \\textit de algoritmos, incluindo o de seleção de características, visando abordar o problema de identificação de arquiteturas de redes de expressão gênica; e (iii) Biologia -- ao relacionar o estrógeno com uma nova função biológica, após analisar informações extraídas de séries temporais de \\textit pelas novas ferramentas computacionais-estatísticas desenvolvidas. O estrógeno possui um importante papel nos tecidos reprodutivos. O crescimento das gândulas mamárias e do endométrio durante a gravidez e o ciclo menstrual são estrógeno dependentes. O crescimento das células tumorais nesses órgãos podem ser estimuladas pela simples presença de estrógeno; mais de $300$ genes são conhecidos por terem regulação positiva ou negativa devido a sua presença. A motivação inicial desta pesquisa foi a construção de um método que possa servir de ferramenta para a identificação de genes que tenham seu nível de expressão alterado a partir de uma resposta induzida por estrógeno, mais precisamente, um método para modelar os inter-relacionamentos entre os diversos genes dependentes do estrógeno. Apresentamos um novo \\textit de algoritmos que, a partir de dados temporais de \\textit e um conjunto inicial de genes que compartilham algumas características comuns, denominados de \\textit{genes sementes}, devolve como saída a arquitetura de uma rede gênica representada por um grafo dirigido. Para cada nó da rede, uma tabela de predição do gene representado pelo nó em função dos seus genes preditores (genes que apontam para ele) pode ser obtida. O método foi aplicado em estudo de série-temporal de \\textit para uma cultura de células \\textit submetidas a tratamento com estrógeno, e uma possível rede de regulação foi obtida. Encontrar o melhor subconjunto preditor de genes para um dado gene pode ser estudado como um problema de seleção de características, no qual o espaço de busca pode ser representado por um reticulado Booleano e cada um de seus elementos representa um subconjunto candidato. Uma característica importante desse problema é o fato de que para cada elemento existe uma função custo associada, e esta possui forma de curva em U para qualquer cadeia maximal do reticulado. Para esse problema, apresentamos um nova solução, o algoritmo ewindex. Esse algoritmo é um método do tipo \\textit, o qual utiliza a estrutura do reticulado Booleano e a característica de curva em U da função custo para explorar um subconjunto do espaço de busca equivalente à busca completa. Nosso método obteve excelentes resultados em eficiência e valores quando comparado com as heurísticas mais utilizadas (SFFS e SFS). A partir de um método baseado no \\textit e de um conjunto inicial de genes regulados \\textit pelo estrógeno, identificamos uma evidência de envolvimento do estrógeno em um processo biológico ainda não relacionado: a adesão celular. Esse resultado pode direcionar os estudos sobre estrógeno e câncer à investigação de processo metastático, o qual é influenciado por genes relacionados à adesão celular. / This Phd. research presents in three distinct areas: (i) Computer Science and Statistics -- on the development of a new solution for the feature selection problem which is an important problem in Pattern Recognition; (ii) Bioinformatics -- for the construction of a pipeline of algorithms, including the feature selection solution, to address the problem of identification the architecture of a genetic expression network and; (iii) Biology -- relating estrogen to a new biological function, from the results obtained by the new computational-statistic tools developed and applied to a time-series microarray data. Estrogen has an important role in reproductive tissues. The growth mammary glands and endometrial growing during menstrual cycle and pregnancy are estrogen dependent. The growth of tumor cells in those organs can be stimulated by the simple presence of estrogen. Over $300$ genes are known by their positive or negative regulation by estrogen. The initial motivation of this research was the construction of a method that can serve as a tool for the identification of genes that have changed their level of expression changed by a response induced by estrogen, more specifically, a method to model the inter-relationships between the several genes dependent on estrogen. We present a new pipeline of algorithms that from the data of a time-series microarray experiment and from an initial set of genes that share some common characteristics, known as \\textit{seed genes}, gives as an output an architecture of the genetic expression network represented by a directed graph. For each node of the network, a prediction table of the gene, represented by the node, in function of its predictors genes (genes that link to it) can be obtained. The method was applied in a study of time-series microarray for a cell line \\textit submitted to a estrogen treatment and a possible regulation network was obtained. Finding the best predictor subset of genes for a given gene can be studied as a problem of feature selection where the search space can be represented by a Boolean lattice and each one of its elements represents a possible subset. An important characteristic of this problem is: for each element in the lattice there is a cost function associated to it and this function has a U-shape in any maximal chain of the search space. For this problem we present a new solution, the \\textit algorithm. This algorithm is a branch-and-bound solution which uses the structure of the Boolean lattice and U-shaped curves to explore a subset of the search space that is equivalent to the full search. Our method obtained excellent results in performance and values when compared with the most commonly used heuristics (SFFS and SFS). From a method based on the pipeline of algorithms and from an initial set of genes direct regulated by estrogen, we identified an evidence of involvement of estrogen in a biological process not yet related to estrogen: the cell adhesion. This result can guide studies on estrogen and cancer to research in metastatic process, which is affected by cell adhesion related genes.