Anthropogenic utilization of forest and woodland ecosystems can cause declines in flora and fauna species. It is imperative to restore these ecosystems to mitigate further declines. In this thesis, I focused on a highly degraded region, the Box-Ironbark forests and woodlands of Victoria, Australia. Rather than mature stands with large trees, stands are currently dominated by high densities of small stems. This change has resulted in reduced populations of many flora and fauna species dependent on older-growth forests and woodlands. Managers are interested in restoring mature Box-Ironbark forests and woodlands through three alternative management strategies: allocating land to National Parks and allowing stands to develop naturally without harvesting, modifying timber harvesting regimes to retain more medium and large trees, or a new ecological thinning technique that retains target habitat trees and removes competing trees to encourage growth of retained stems. / The effects of each management strategy are not easy to predict due to complex interactions between intervention and stochastic natural processes. Forest simulation models are often employed to overcome this problem. I constructed state-and-transition simulation models (STSMs) to predict the effects of alternative management actions and natural disturbances on vegetation structure. Due to a lack of empirical data, I relied on the knowledge of experts in Box-Ironbark ecology and management to construct STSMs. Models predicted that the development of mature woodlands under all strategies was minimal over the next 150 years, and neither current harvesting nor ecological thinning is likely to expedite the development of mature stands relative to growth and natural disturbances. However, differences in experts’ opinions led to widely diverging model predictions. / Uncertainty must be acknowledged in model construction because it can affect model predictions. I quantified uncertainty due to four sources – between-expert variation, imperfect expert knowledge, natural stochasticity, and model parameterization – to determine which source caused the most variance in model predictions. I found that models were very uncertain and between-expert uncertainty contributed the majority of variance in model predictions. This brings into question the use of consensus methods in forest management where differences between experts are ignored. / Using uncertain model predictions to make management decisions is problematic because any given action can have many plausible outcomes. I applied several decision criteria to uncertain STSM predictions using a formal decision-making framework to determine the optimal management action in Box-Ironbark forests and woodlands. I found that natural development is the most risk-averse option, while ecological thinning is the most risky option because there is a small likelihood that it will greatly expedite the development of mature woodlands. Rather than selecting one option, managers could rely on a risk-spreading approach where the majority of land is allocated to no-cutting National Parks and a small amount of land is allocated to the other two harvesting strategies. This would allow managers to collect monitoring data for all management strategies in order to learn about effects of harvesting and update model predictions through time using adaptive management.

Rangelands are a major type of land found on all continents. Though they comprise around 70% of the world's land area, knowledge of rangelands is limited and immature. Rangelands supply humans with food and fiber at very low energy costs compared to cultivated lands. They are inherently heterogeneous, highly variable in time and space. Rangeland management needs to consider the impacts of long-term vegetation transition. It needs a conceptual framework defining potential vegetation communities, describing the management induced transition of one vegetation community to another, and documenting the expected benefits provided by the various potential vegetation communities. The most widely used conceptual unit in the rangeland discipline is the "ecological site". Ecological sites can be an effective unit that should respond to management consistently and can help managers understand the site's potential to meet human needs. A state and transition model (STM) brings ecological sites and their potential vegetative states together to build a conceptual framework showing the major causes of transitions between states of an ecological site and thus helping make adaptive management decisions. Within the STM there is a need for an indicator of ecosystem health. Ecosystem services can be important to evaluate alternative states. Ecosystem services do not pass through a market for valuation, though often the cost would be very high if, through mismanagement, the ecosystem is no longer capable of providing those services. Vegetation communities are constantly facing reversible or irreversible transitions triggered by natural events and/or management actions. The framework generated in this study is significant in using remote sensing to generate state and transition models for a large area and in using ecosystem services to evaluate natural and/or management induced transitions as described in the STM. This dissertation addresses the improvement of public rangelands management in the West. It applies geospatial technologies to map ecological sites and states on those sites, characterizes transitions between states and selects a desired state to manage towards based on a systematic assessment of the value of flows of environmental services. The results from this study are an evaluation of improved draft ecological site maps for a larger area using remote sensing images, a simplified state-and-transition model adapted to remote sensing capabilities to study transitions due to climatic events and management practices, and a constrained optimization model that incorporates ecosystem services and the simplified STM to evaluate management costs and conservation benefits. The study showed that brush treatment is the most effective management practice to cause state transitions. The highest increase in the high cover state was by 24%. Areas under grazing and drought show slow transitions from brush to grass and also after prescribed fire vegetation take at least two years to recover.

The complexity of technical systems has increased significantly in order to address evolving customer needs and environmental concerns. From a product development process viewpoint, the pervasive nature of multi-disciplinary systems (i.e. mechanical, electrical, electronic, control, software) has brought some important integration challenges to overcome conventional disciplinary boundaries imposed by discipline specific approaches. This research focuses on functional reasoning, aiming to develop a structured framework based on the System State Flow Diagram (SSFD) for function modelling of complex multidisciplinary systems on a practical and straightforward basis. The framework is developed at two stages. 1) The development of a prototype for the SSFD framework. The proposed SSFD framework are tested and validated through application to selected desktop case studies. 2) Further development and extension of the SSFD framework for the analysis of complex multidisciplinary systems with multiple operation modes and functional requirements. The developed framework is validated on real world case studies collaborated with industrial partners. The main conclusion of this research is that the SSFD framework offers a rigorous and coherent function modelling methodology for the analysis of complex multidisciplinary systems. Further advantages of the SSFD framework is that 1) the effectiveness of the Failure Mode Avoidance (FMA) process can be enhanced by integrating the SSFD framework with relevant tools of the FMA process, and 2) the integration of the SSFD with the SysML systems engineering diagrams is doable, which can promote the take-up of the approach in industry.

Broad-scale forest die-off associated with drought and heat has now been reported from every forested continent, posing a global-scale challenge to forest management. Climate-driven die-off is frequently compounded with other drivers of tree mortality, such as altered land use, wildfire, and invasive species, making forest management increasingly complex. Facing similar challenges, rangeland managers have widely adopted the approach of developing conceptual models that identify key ecosystem states and major types of transitions between those states, known as "state-and-transition models" (S&T models). Using expert opinion and available research, the development of such conceptual S&T models has proven useful in anticipating ecosystem changes and identifying management actions to undertake or to avoid. In cases where detailed data are available, S&T models can be developed into probabilistic predictions, but even where data are insufficient to predict transition probabilities, conceptual S&T models can provide valuable insights for managing a given ecosystem and for comparing and contrasting different ecosystem dynamics. We assembled a synthesis of 14 forest die-off case studies from around the globe, each with sufficient information to infer impacts on forest dynamics and to inform management options following a forest die-off event. For each, we developed a conceptual S&T model to identify alternative ecosystem states, pathways of ecosystem change, and points where management interventions have been, or may be, successful in arresting or reversing undesirable changes. We found that our diverse set of mortality case studies fit into three broad classes of ecosystem trajectories: (1) single-state transition shifts, (2) ecological cascading responses and feedbacks, and (3) complex dynamics where multiple interactions, mortality drivers, and impacts create a range of possible state transition responses. We integrate monitoring and management goals in a framework aimed to facilitate development of conceptual S&T models for other forest die-off events. Our results highlight that although forest die-off events across the globe encompass many different underlying drivers and pathways of ecosystem change, there are commonalities in opportunities for successful management intervention.

The complexity of technical systems has increased significantly in order to address evolving customer needs and environmental concerns. From a product development process viewpoint, the pervasive nature of multi-disciplinary systems (i.e. mechanical, electrical, electronic, control, software) has brought some important integration challenges to overcome conventional disciplinary boundaries imposed by discipline specific approaches. This research focuses on functional reasoning, aiming to develop a structured framework based on the System State Flow Diagram (SSFD) for function modelling of complex multidisciplinary systems on a practical and straightforward basis. The framework is developed at two stages. 1) The development of a prototype for the SSFD framework. The proposed SSFD framework are tested and validated through application to selected desktop case studies. 2) Further development and extension of the SSFD framework for the analysis of complex multidisciplinary systems with multiple operation modes and functional requirements. The developed framework is validated on real world case studies collaborated with industrial partners. The main conclusion of this research is that the SSFD framework offers a rigorous and coherent function modelling methodology for the analysis of complex multidisciplinary systems. Further advantages of the SSFD framework is that 1) the effectiveness of the Failure Mode Avoidance (FMA) process can be enhanced by integrating the SSFD framework with relevant tools of the FMA process, and 2) the integration of the SSFD with the SysML systems engineering diagrams is doable, which can promote the take-up of the approach in industry. / Automotive Research Centre

Located just east of Missoula, Montana, Milltown Dam stood from 1908 to 2008 immediately downstream of the Clark Fork River’s confluence with the Blackfoot River. After the discovery of arsenic-contaminated groundwater in the nearby community of Milltown, as well as extensive deposits of contaminated sediment in the dam’s upstream reservoir, in 1981, the area was designated a Superfund site – along with much of the Upper Clark Fork Watershed. This motivated the eventual decision to remove the dam, perform environmental remediation, and reconstruct approximately five kilometers of the Clark Fork River and its floodplain. This study is part conceptual and part empirical. It describes a state-and-transition framework equipped to investigate channel evolution as well as the adjustment trajectories of other socio-biophysical landscapes. This framework is then applied to understand the post-restoration channel evolution of the Clark Fork River’s mainstem, secondary channels, and floodplain. Adopting a state-and-transition framework to conceptualize landscape evolution lets environmental managers more effectively anticipate river response under multiple disturbence scenarios and therefore use more improvisational and adaptive management techniques that do not attempt to guide the landscape toward a single and permanent end state. State-and-transition models can also be used to highlight the spatially explicit patterns of complex biophysical response. The state-and-transition models developed for the Clark Fork River demonstrate the possibility of multiple evolutionary trajectories. Neither the secondary channels nor the main channel have responded in a linear, monotonic fashion, and future responses will be contingent upon hydrogeomorphic and climatic variability and chance disturbances. The biogeomorphic adjustments observed so far suggest divergent evolutionary trajectories and that in some instances the long-term fates of the mainstem, floodplain, and secondary channels are inescapably enmeshed with one another.

No description available.

Monitoring and identifying the state of rangelands on a landscape scale can be a time consuming process. In this thesis, remote sensing imagery has been used to show how the process of classifying different ecological sites and states can be done on a per pixel basis for a large landscape. Twenty-seven years' worth of remotely sensed imagery was collected, atmospherically corrected, and radiometrically normalized. Several vegetation indices were extracted from the imagery along with derivatives from a digital elevation model. Dominant vegetation components from five major ecological sites in Rich County, Utah, were chosen for study. The vegetation components were Aspen, Douglas-fir, Utah juniper, mountain big sagebrush, and Wyoming big sagebrush. Training sites were extracted from within map units with a majority of one of the five ecological sites. A Random Forests decision tree model was developed using an attribute table populated with spectral biophysical variables derived from the training sites. The overall out-of-bag accuracy for the Random Forests model was 97.2%. The model was then applied to the predictor spectral and biophysical variables to spatially map the five major vegetation components for all of Rich County. Each vegetation class had greater than 90% accuracies except for Utah juniper at 81%. This process is further explained in chapter 2. As a follow-on effort, we attempted to classify vegetation ecological states within a single ecological site (Wyoming big sagebrush). This was done using field data collected by previous studies as training data for all five ecological states documented for our chosen ecological site. A Maximum Likelihood classifier was applied to four years of Landsat 5 Thematic Mapper imagery to map each ecological state to pixels coincident to the map units correlated to the Wyoming big sagebrush ecological site. We used the Mahalanobis distance metric as an indicator of pixel membership to the Wyoming big sagebrush ecological site. Overall classification accuracy for the different ecological states was 64.7% for pixels with low Mahalanobis distance and less than 25% for higher distances.

This study tested the performance of classification, regression, and ordination techniques to evaluate the spatiotemporal dynamics of threats to big sagebrush ecological sites. The research was focused on invasion by annual exotic grasses and encroachment by woodlands. We sought to identify those areas that have had a persistent coverage of cheatgrass (Bromus tectorum) in big sagebrush ecological sites. We took advantage of the contrast in greenness between multi-temporal (within one year) remotely sensed vegetation indices captured in the spring and summer to find a distinct phenological signature that allowed mapping cheatgrass. We utilized support vector machines (SVM) to classify three temporal scenarios for which field data sets were available. SVM performed very well with accuracies of 70% (producer's) and 95% (user's) for the class of interest (presence of cheatgrass). This was the focus of chapter 2. In chapter 3 we report the development of vegetation continuous fields (VCF) for three years of interest 1996, 2001, and 2007 in order to detect active woodland encroachment. We prepared VCF for shrubs, trees, herbaceous vegetation, and bare ground using a suite of remotely sensed spectral reflectance, vegetation indices, and transformations. We compared the performance of multivariate regression trees (MRT) and random forests (RF) to develop the VCF multi-temporal series. RF outperformed MRT in both accuracy and ability to appropriately map the continuum of percent cover across large landscapes. We estimate that 17,570 hectares of big sagebrush lands showed encroachment by woodlands. Our goal in chapter 4 was to develop a similarity index for large rangeland landscapes. Trend assessments field sites and a long-term annual series (1984 - 2008) of remotely sensed imagery were used in conjunction with multidimensional scaling (MDS) to measure ecological distance to undesired states such as invasion by exotic annuals and encroachment by woodlands. In this chapter our units of analysis were soil-mapping units, which were predominantly composed of one ecological site (>60%). Our MDS results show that different ecological sites can be identified in the reduced MDS statistical space. The observed transitions and trajectories of mountain, Wyoming, and basin big sagebrush sites correlated well with the ecological expectation in semiarid lands. We anticipate that managers can use our protocols to update ecological site descriptions and state and transition models from a remotely sensed perspective.

Made available in DSpace on 2015-03-03T15:15:25Z (GMT). No. of bitstreams: 1 MarceloBS_DISSERT.pdf: 2228867 bytes, checksum: 46ad012b7ecf9d333c9b9a88bbfb0411 (MD5) Previous issue date: 2010-08-12 / Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico / The diffusive epidemic process (PED) is a nonequilibrium stochastic model which, exhibits a phase trnasition to an absorbing state. In the model, healthy (A) and sick (B) individuals diffuse on a lattice with diffusion constants DA and DB, respectively. According to a Wilson renormalization calculation, the system presents a first-order phase transition, for the case DA > DB. Several researches performed simulation works for test this is conjecture, but it was not possible to observe this first-order phase transition. The explanation given was that we needed to perform simulation to higher dimensions. In this work had the motivation to investigate the critical behavior of a diffusive epidemic propagation with L?vy interaction(PEDL), in one-dimension. The L?vy distribution has the interaction of diffusion of all sizes taking the one-dimensional system for a higher-dimensional. We try to explain this is controversy that remains unresolved, for the case DA > DB. For this work, we use the Monte Carlo Method with resuscitation. This is method is to add a sick individual in the system when the order parameter (sick density) go to zero. We apply a finite size scalling for estimates the critical point and the exponent critical =, e z, for the case DA > DB / O processo epid?mico difusivo (PED) ? um modelo estoc?stico de n?o equil?brio que se inspira no processo de contato e que exibe uma transi??o de fase para um estado absorvente. No modelo, temos indiv?duos saud?veis (A) e indiv?duos doentes (B) se difundindo numa rede unidimensional com uma difus?o constante DA e DB, respectivamente. De acordo com os c?lculos do grupo de renormaliza??o, o sistema apresentou uma transi??o de fase de primeira ordem, para o caso DA > DB. V?rios pesquisadores realizaram trabalhos de simula??o para testar esta conjectura e n?o conseguiram observar esta transi??o de primeira ordem. A explica??o dada era que precis?vamos realizar simula??o para dimens?es maiores. Por isso, neste trabalho tivemos a motiva??o de investigarmos o comportamento cr?tico de um processo de propaga??o epid?mico difusivo com intera??o de L?vy (PEDL) em uma dimens?o. A distribui??o de L?vy tem intera??o de difus?o de todos os tamanhos levando o sistema unidimensional a um sistema de dimens?es maiores. Com isso, poderemos tentar explicar esta controv?rsia que existe at? hoje, para o caso DA > DB. Para este trabalho utilizamos o M?todo de Monte Carlo com ressuscitamento. Este m?todo consiste em acrescentar um indiv?duo doente no sistema quando o par?metro de ordem (densidade de doente) vai ? zero. Aplicamos a t?cnica de an?lise de escala de tamanho finito para determinarmos com boa precis?o o ponto cr?tico e os expoentes cr?ticos ??/v, v e z, para o caso DA > DB