Bayesian model averaging for harmful algal bloom prediction

Harmful Algal Blooms (HABs) are a worldwide problem that have been increasing in frequency and extent over the past several decades. HABs severely damage aquatic ecosystems by destroying benthic habitat, reducing invertebrate and fish populations and affecting larger species such as dugong that rely on seagrasses for food. Few statistical models for predicting HAB occurrences have been developed, and in common with most predictive models in ecology, those that have been developed do not fully account for uncertainties in parameters and model structure. This makes management decisions based on these predictions more risky than might be supposed. We used a probit time series model and Bayesian Model Averaging (BMA) to predict occurrences of blooms of Lyngbya majuscula, a toxic cyanophyte, in Deception Bay, Queensland, Australia. We found a suite of useful predictors for HAB occurrence, with Temperature figuring prominently in models with the majority of posterior support, and a model consisting of the single covariate average monthly minimum temperature showed by far the greatest posterior support. A comparison of alternative model averaging strategies was made with one strategy using the full posterior distribution and a simpler approach that utilised the majority of the posterior distribution for predictions but with vastly fewer models. Both BMA approaches showed excellent predictive performance with little difference in their predictive capacity. Applications of BMA are still rare in ecology, particularly in management settings. This study demonstrates the power of BMA as an important management tool that is capable of high predictive performance while fully accounting for both parameter and model uncertainty.

Advanced Gen-1, 2 and 3 biofuels research in Australia - Fuelling advanced biofuels training for international scientists

Flinders University and Queensland University of Technology, biofuels research interests cover a broad range of activities. Both institutions are seeking to overcome the twin evils of "peak oil" (Hubbert 1949 & 1956) and "global warming" (IPPC 2007, Stern 2006, Alison 2010), through development of Generation 1, 2 and 3 (Gen-1, 2 & 3) biofuels (Clarke 2008, Clarke 2010). This includes development of parallel Chemical Biorefinery, value-added, co-product chemical technologies, which can underpin the commercial viability of the biofuel industry. Whilst there is a focused effort to develop Gen-2 & 3 biofuels, thus avoiding the socially unacceptable use of food based Gen-1 biofuels, it must also be recognized that as yet, no country in the world has produced sustainable Gen-2 & 3 biofuel on a commercial basis. For example, in 2008 the United States used 38 billion litres (3.5% of total fuel use) of Gen-1 biofuel; in 2009/2010 this will be 47.5 billion litres (4.5% of fuel use) and in 2018 this has been estimated to rise to 96 billion litres (9% of total US fuel use). Brazil in 2008 produced 24.5 billion litres of ethanol, representing 37.3% of the world’s ethanol use for fuel and Europe, in 2008, produced 11.7 billion litres of biofuel (primarily as biodiesel). Compare this to Australia’s miserly biofuel production in 2008/2009 of 180 million litres of ethanol and 75 million litres of biodiesel, which is 0.4% of our fuel consumption! (Clarke, Graiver and Habibie 2010) To assist in the development of better biofuels technologies in the Asian developing regions the Australian Government recently awarded the Materials & BioEnergy Group from Flinders University, in partnership with the Queensland University of Technology, an Australian Leadership Award (ALA) Biofuel Fellowship program to train scientists from Indonesia and India about all facets of advanced biofuel technology.

Design and implementation of sensor networks for the observation and research of harmful algal blooms in southern California coastal waters

Harmful Algal Blooms (HABs) have become an important environmental concern along the western coast of the United States. Toxic and noxious blooms adversely impact the economies of coastal communities in the region, pose risks to human health, and cause mortality events that have resulted in the deaths of thousands of fish, marine mammals and seabirds. One goal of field-based research efforts on this topic is the development of predictive models of HABs that would enable rapid response, mitigation and ultimately prevention of these events. In turn, these objectives are predicated on understanding the environmental conditions that stimulate these transient phenomena. An embedded sensor network (Fig. 1), under development in the San Pedro Shelf region off the Southern California coast, is providing tools for acquiring chemical, physical and biological data at high temporal and spatial resolution to help document the emergence and persistence of HAB events, supporting the design and testing of predictive models, and providing contextual information for experimental studies designed to reveal the environmental conditions promoting HABs. The sensor platforms contained within this network include pier-based sensor arrays, ocean moorings, HF radar stations, along with mobile sensor nodes in the form of surface and subsurface autonomous vehicles. FreewaveTM radio modems facilitate network communication and form a minimally-intrusive, wireless communication infrastructure throughout the Southern California coastal region, allowing rapid and cost-effective data transfer. An emerging focus of this project is the incorporation of a predictive ocean model that assimilates near-real time, in situ data from deployed Autonomous Underwater Vehicles (AUVs). The model then assimilates the data to increase the skill of both nowcasts and forecasts, thus providing insight into bloom initiation as well as the movement of blooms or other oceanic features of interest (e.g., thermoclines, fronts, river discharge, etc.). From these predictions, deployed mobile sensors can be tasked to track a designated feature. This focus has led to the creation of a technology chain in which algorithms are being implemented for the innovative trajectory design for AUVs. Such intelligent mission planning is required to maneuver a vehicle to precise depths and locations that are the sites of active blooms, or physical/chemical features that might be sources of bloom initiation or persistence. The embedded network yields high-resolution, temporal and spatial measurements of pertinent environmental parameters and resulting biology (see Fig. 1). Supplementing this with ocean current information and remotely sensed imagery and meteorological data, we obtain a comprehensive foundation for developing a fundamental understanding of HAB events. This then directs labor- intensive and costly sampling efforts and analyses. Additionally, we provide coastal municipalities, managers and state agencies with detailed information to aid their efforts in providing responsible environmental stewardship of their coastal waters.

Measuring the regional availability of biomass for biofuels and the potential for microalgae

Biomass is an important energy resource for producing bioenergy and growing the global economy whilst minimising greenhouse gas emissions. Many countries, like Australia have a huge amount of biomass with the potential for bioenergy, but non-edible feedstock resources are significantly under-exploited. Hence it is essential to map the availability of these feedstocks to identify the most appropriate bioenergy solution for each region and develop supply chains for biorefineries. Using Australia as a case study,we present the spatial availability and opportunities for second and third generation feedstocks. Considerations included current land use, the presence of existing biomass industries and climatic conditions. Detailed information on the regional availability of biomass was collected from government statistics, technical reports and energy assessments as well as from academic literature. Second generation biofuels have the largest opportunity in New South Wales, Queensland and Victoria (NSW, QLD and VIC) and the regions with the highest potential for microalgae are Western Australia and Northern Territory (WA, NT), based on land use opportunity cost and climate. The approach can be used in other countries with a similar climate. More research is needed to overcome key technical and economic hurdles.

Intellectual property and biofuels: The energy crisis, food security, and climate change

In light of larger public policy debates over intellectual property and climate change, this article considers patent practice, law, and policy in respect of biofuels. This debate has significant implications for public policy discussions in respect of energy independence, food security, and climate change. The first section of the paper provides a network analysis of patents in respect of biofuels across the three generations. It provides empirical research in respect of patent subject matter, ownership, and strategy in respect of biofuels. The second section provides a case study of significant patent litigation over biofuels. There is an examination of the biofuels patent litigation between the Danish company Novozymes, and Danisco and DuPont. The third section examines flexibilities in respect of patent law and clean technologies in the context of the case study of biofuels. In particular, it explores the debate over substantive doctrinal matters in respect of biofuels – such as patentable subject matter, technology transfer, patent pools, compulsory licensing, and disclosure requirements. The conclusion explores the relevance of the debate over patent law and biofuels to the larger public policy discussions over energy independence, food security, and climate change.

Economic analyses and policy implications for microalgae biofuels in Australia

The benefits of microalgae biofuels over first and second generation counterparts suggest it has potential as a major biofuel feedstock in Australia. However, the high costs of cultivation and processing has been a major drawback for investors and policymakers. This presentation outlines the economic potential for microalgae biofuels: firstly, through production of microalgae co-products (e.g feed and fertiliser); and secondly, deriving what consumers are willing to pay for microalgae biofuels based on external benefits. These findings will assist decision-makers in both private and public sectors and inform policy development with respect to microalgae as a feedstock for biofuels and other products. This study adds an economics perspective to the current technical literature which has been dominated by biochemical, engineering and financial valuation studies.

Biorefineries – creating economic opportunities from biofuels and bio-products

Biorefineries, producing fuels, green chemicals and bio-products, offer great potential for improving the profitability and sustainability of tropical agricultural industries. Biomass from tropical crops like sugarcane, sweet sorghum, palm and cassava offer great potential because of the high biomass growth potential under favourable climatic conditions. Biorefineries aim to convert waste residues through biochemical and enzymatic processes to low cost fermentable sugars which are a platform for value-adding. Through subsequent fermentation utilising microbial biotechnologies or chemical synthesis, the sugars can be converted to fuels including ethanol and butanol, oils, organic acids such as lactic and levulinic acid and polymer precursors. Other biorefinery products can include food and animal feeds, plastics, fibre products and resins. Pretreatment technologies are a key to unlocking this potential and new technologies are emerging. This paper will address the opportunities available for tropical biorefineries to contribute to the future profitability of tropical agricultural industries. The importance of pretreatment technologies will be discussed.

Biofuels from food processing wastes

Food processing industry generates substantial high organic wastes along with high energy uses. The recovery of food processing wastes as renewable energy sources represents a sustainable option for the substitution of fossil energy, contributing to the transition of food sector towards a low-carbon economy. This article reviews the latest research progress on biofuel production using food processing wastes. While extensive work on laboratory and pilot-scale biosystems for energy production has been reported, this work presents a review of advances in metabolic pathways, key technical issues and bioengineering outcomes in biofuel production from food processing wastes. Research challenges and further prospects associated with the knowledge advances and technology development of biofuel production are discussed.

Sugarcane-Based Biofuels and Bioproducts

Sugarcane has garnered much interest for its potential as a viable renewable energy crop. While the use of sugar juice for ethanol production has been in practice for years, a new focus on using the fibrous co-product known as bagasse for producing renewable fuels and bio-based chemicals is growing in interest. The success of these efforts, and the development of new varieties of energy canes, could greatly increase the use of sugarcane and sugarcane biomass for fuels while enhancing industry sustainability and competitiveness. Sugarcane-Based Biofuels and Bioproducts examines the development of a suite of established and developing biofuels and other renewable products derived from sugarcane and sugarcane-based co-products, such as bagasse. Chapters provide broad-ranging coverage of sugarcane biology, biotechnological advances, and breakthroughs in production and processing techniques. This text brings together essential information regarding the development and utilization of new fuels and bioproducts derived from sugarcane. Authored by experts in the field, Sugarcane-Based Biofuels and Bioproducts is an invaluable resource for researchers studying biofuels, sugarcane, and plant biotechnology as well as sugar and biofuels industry personnel.

On the affinity of Chuaria–Tawuia complex : a multidisciplinary study

In this study, biometric and structural engineering tool have been used to examine a possible relationship within Chuaria–Tawuia complex and micro-FTIR (Fourier Transform Infrared Spectroscopy) analyses to understand the biological affinity of Chuaria circularis Walcott, collected from the Mesoproterozoic Suket Shales of the Vindhyan Supergroup and the Neoproterozoic Halkal Shales of the Bhima Group of peninsular India. Biometric analyses of well preserved carbonized specimens show wide variation in morphology and uni-modal distribution. We believe and demonstrate to a reasonable extent that C. circularis most likely was a part of Tawuia-like cylindrical body of algal origin. Specimens with notch/cleft and overlapping preservation, mostly recorded in the size range of 3–5 mm, are of special interest. Five different models proposed earlier on the life cycle of C. circularis are discussed. A new model, termed as ‘Hybrid model’ based on present multidisciplinary study assessing cylindrical and spherical shapes suggesting variable cell wall strength and algal affinity is proposed. This model discusses and demonstrates varied geometrical morphologies assumed by Chuaria and Tawuia, and also shows the inter-relationship of Chuaria–Tawuia complex. Structural engineering tool (thin walled pressure vessel theory) was applied to investigate the implications of possible geometrical shapes (sphere and cylinder), membrane (cell wall) stresses and ambient pressure environment on morphologically similar C. circularis and Tawuia. The results suggest that membrane stresses developed on the structures similar to Chuaria–Tawuia complex were directly proportional to radius and inversely proportional to the thickness in both cases. In case of hollow cylindrical structure, the membrane stresses in circumferential direction (hoop stress) are twice of the longitudinal direction indicating that rupture or fragmentation in the body of Tawuia would have occurred due to hoop stress. It appears that notches and discontinuities seen in some of the specimens of Chuaria may be related to rupture suggesting their possible location in 3D Chuaria. The micro-FTIR spectra of C. circularis are characterized by both aliphatic and aromatic absorption bands. The aliphaticity is indicated by prominent alkyl group bands between 2800–3000 and 1300–1500 cm−1. The prominent absorption signals at 700–900 cm−1 (peaking at 875 and 860 cm−1) are due to aromatic CH out of plane deformation. A narrow, strong band is centred at 1540 cm−1 which could be COOH band. The presence of strong aliphatic bands in FTIR spectra suggests that the biogeopolymer of C. circularis is of aliphatic nature. The wall chemistry indicates the presence of ‘algaenan’—a biopolymer of algae.

Integrated Bayesian network frameworks for modelling complex ecological issues

Ecological problems are typically multi faceted and need to be addressed from a scientific and a management perspective. There is a wealth of modelling and simulation software available, each designed to address a particular aspect of the issue of concern. Choosing the appropriate tool, making sense of the disparate outputs, and taking decisions when little or no empirical data is available, are everyday challenges facing the ecologist and environmental manager. Bayesian Networks provide a statistical modelling framework that enables analysis and integration of information in its own right as well as integration of a variety of models addressing different aspects of a common overall problem. There has been increased interest in the use of BNs to model environmental systems and issues of concern. However, the development of more sophisticated BNs, utilising dynamic and object oriented (OO) features, is still at the frontier of ecological research. Such features are particularly appealing in an ecological context, since the underlying facts are often spatial and temporal in nature. This thesis focuses on an integrated BN approach which facilitates OO modelling. Our research devises a new heuristic method, the Iterative Bayesian Network Development Cycle (IBNDC), for the development of BN models within a multi-field and multi-expert context. Expert elicitation is a popular method used to quantify BNs when data is sparse, but expert knowledge is abundant. The resulting BNs need to be substantiated and validated taking this uncertainty into account. Our research demonstrates the application of the IBNDC approach to support these aspects of BN modelling. The complex nature of environmental issues makes them ideal case studies for the proposed integrated approach to modelling. Moreover, they lend themselves to a series of integrated sub-networks describing different scientific components, combining scientific and management perspectives, or pooling similar contributions developed in different locations by different research groups. In southern Africa the two largest free-ranging cheetah (Acinonyx jubatus) populations are in Namibia and Botswana, where the majority of cheetahs are located outside protected areas. Consequently, cheetah conservation in these two countries is focussed primarily on the free-ranging populations as well as the mitigation of conflict between humans and cheetahs. In contrast, in neighbouring South Africa, the majority of cheetahs are found in fenced reserves. Nonetheless, conflict between humans and cheetahs remains an issue here. Conservation effort in South Africa is also focussed on managing the geographically isolated cheetah populations as one large meta-population. Relocation is one option among a suite of tools used to resolve human-cheetah conflict in southern Africa. Successfully relocating captured problem cheetahs, and maintaining a viable free-ranging cheetah population, are two environmental issues in cheetah conservation forming the first case study in this thesis. The second case study involves the initiation of blooms of Lyngbya majuscula, a blue-green algae, in Deception Bay, Australia. L. majuscula is a toxic algal bloom which has severe health, ecological and economic impacts on the community located in the vicinity of this algal bloom. Deception Bay is an important tourist destination with its proximity to Brisbane, Australia’s third largest city. Lyngbya is one of several algae considered to be a Harmful Algal Bloom (HAB). This group of algae includes other widespread blooms such as red tides. The occurrence of Lyngbya blooms is not a local phenomenon, but blooms of this toxic weed occur in coastal waters worldwide. With the increase in frequency and extent of these HAB blooms, it is important to gain a better understanding of the underlying factors contributing to the initiation and sustenance of these blooms. This knowledge will contribute to better management practices and the identification of those management actions which could prevent or diminish the severity of these blooms.

Agglutinins from marine macroalgae of the southeastern United States

Protein extracts from 22 species of marine macroalgae from Florida and North Carolina were compared for their abilities to agglutinate sheep and rabbit erythrocytes. Protein extracts from 21 algal species agglutinated rabbit erythrocytes compared to 19 for sheep erythrocytes. However, agglutination by brown algal extracts was variable. The agglutination produced by protein extracts from Dictyota dichotoma could be blocked by addition of polyvinylpyrrolidone. Protein extracts from North Carolina macroalgae were also tested against five bacterial species. Three of these agglutinated bacterial cells. Ulva curvata and Bryopsis plumosa agglutinated all five species. Protein extracts from five species of Florida algae were tested for their effects on mitogenesis in mouse splenocytes and human lymphocytes. Gracilaria tikvahiae HBOI Strain G-5, Ulva rigida and Gracilaria verrucosa HBOI Strain G-16S stimulated mitogenesis in mouse splenocytes, while Gracilaria tikvahiae HBOI Strain G-16stimulated mitogenesis in human lymphocytes.

Planning and implementing trajectories for autonomous underwater vehicles to track evolving ocean processes based on predictions from a regional ocean model

Path planning and trajectory design for autonomous underwater vehicles (AUVs) is of great importance to the oceanographic research community because automated data collection is becoming more prevalent. Intelligent planning is required to maneuver a vehicle to high-valued locations to perform data collection. In this paper, we present algorithms that determine paths for AUVs to track evolving features of interest in the ocean by considering the output of predictive ocean models. While traversing the computed path, the vehicle provides near-real-time, in situ measurements back to the model, with the intent to increase the skill of future predictions in the local region. The results presented here extend prelim- inary developments of the path planning portion of an end-to-end autonomous prediction and tasking system for aquatic, mobile sensor networks. This extension is the incorporation of multiple vehicles to track the centroid and the boundary of the extent of a feature of interest. Similar algorithms to those presented here are under development to consider additional locations for multiple types of features. The primary focus here is on algorithm development utilizing model predictions to assist in solving the motion planning problem of steering an AUV to high-valued locations, with respect to the data desired. We discuss the design technique to generate the paths, present simulation results and provide experimental data from field deployments for tracking dynamic features by use of an AUV in the Southern California coastal ocean.

Cooperative multi-AUV tracking of phytoplankton blooms based on ocean model predictions

In recent years, ocean scientists have started to employ many new forms of technology as integral pieces in oceanographic data collection for the study and prediction of complex and dynamic ocean phenomena. One area of technological advancement in ocean sampling if the use of Autonomous Underwater Vehicles (AUVs) as mobile sensor plat- forms. Currently, most AUV deployments execute a lawnmower- type pattern or repeated transects for surveys and sampling missions. An advantage of these missions is that the regularity of the trajectory design generally makes it easier to extract the exact path of the vehicle via post-processing. However, if the deployment region for the pattern is poorly selected, the AUV can entirely miss collecting data during an event of specific interest. Here, we consider an innovative technology toolchain to assist in determining the deployment location and executed paths for AUVs to maximize scientific information gain about dynamically evolving ocean phenomena. In particular, we provide an assessment of computed paths based on ocean model predictions designed to put AUVs in the right place at the right time to gather data related to the understanding of algal and phytoplankton blooms.