Developing An Independent Shallow-water Survey For The Western Rock Lobster Fishery: Tracking Pre-recruitment Abundance And Habitat Change
Funder
Fisheries Research and Development Corporation
Funding Amount
$445,500.00
Summary
The current method of measuring undersize lobster abundance (PRA) is based on catch data adjusted for multiple biases inherent in commercial monitoring, namely: water depth, water temperature, swell, fisher experience, pot type, escape gaps, pot pulling time, month and location. Biases are exacerbated by recent poor sample sizes, as many fishers choose not to fish in shallow-water areas. Developing a standardized, repeatable survey in shallow areas will provide an improved index of PRA that ca ....The current method of measuring undersize lobster abundance (PRA) is based on catch data adjusted for multiple biases inherent in commercial monitoring, namely: water depth, water temperature, swell, fisher experience, pot type, escape gaps, pot pulling time, month and location. Biases are exacerbated by recent poor sample sizes, as many fishers choose not to fish in shallow-water areas. Developing a standardized, repeatable survey in shallow areas will provide an improved index of PRA that can be incorporated into the stock modelling: this would improve the overall assessment. Currently fishery-independent survey data collection requires a minimum of two staff to process the catch and record information. Data entry is conducted by a third staff member. To increase cost effectiveness (for this and other lobster surveys), a more efficient data collection system is needed. Initial scoping has identified a number of possible solutions (e.g. app-based entry). This project will expand on this original scoping work to develop a solution. This will also benefit commercial lobster monitoring work conducted by DPIRD as well as a range of other surveys in similar pot based fisheries. This survey will also provide a platform for monitoring inshore WRL habitats. This will establish a baseline against which further research into the relationships between WRL and their near shore habitats can be undertaken. This will assist with detecting and quantifying habitat shifts that may impact the fishery. For example, there is anecdotal evidence that the heatwave temporarily modified some of these near shore habitats, leading to the altered relationship between puerulus and lobster recruitment. Long-term monitoring of juvenile habitats will likely provide a useful indicator of one of the factors affecting recruitment to the fishery. Objectives: 1. Produce accurate measures of pre-recruit abundance throughout the West Coast Rock Lobster Managed Fishery. Compare with commercial monitoring undersize lobster abundance and puerulus settlement data. 2. Design and construct a more efficient data recording tool to increase the accuracy and speed of data collection. 3. Develop base-line habitat descriptions at all potting locations throughout the shallow water survey. 4. Determine the relationship between sampling rate required to detect different magnitudes of marine habitat change at these survey locations Read moreRead less
Developing A Cost-effective Monitoring Regime And Stock Assessment For Sand Flathead In Tasmania
Funder
Fisheries Research and Development Corporation
Funding Amount
$703,700.00
Summary
Sand Flathead account for well over half of the total catch (by numbers) taken by marine recreational fishers in Tasmania and represent the mainstay of Tasmania's recreational fishery. Furthermore, since the recreational catch of sand flathead is more than 20 times the commercial catch, trends in commercial catch and catch rates are of little value in inferring changes in stock status. This has meant that fishery independent or novel assessment methods are required. To date, IMAS has implemente ....Sand Flathead account for well over half of the total catch (by numbers) taken by marine recreational fishers in Tasmania and represent the mainstay of Tasmania's recreational fishery. Furthermore, since the recreational catch of sand flathead is more than 20 times the commercial catch, trends in commercial catch and catch rates are of little value in inferring changes in stock status. This has meant that fishery independent or novel assessment methods are required. To date, IMAS has implemented a research program focused on sand flathead in the south-east of the state that provides a spatially restricted, perspective and semi-quantitative evaluation of stock condition. Given the significance of the species and a status of 'depleting' in the latest stock assessment report, there is a need to implement a more comprehensive stock monitoring approach throughout the state that can support the development of a spatially explicit quantitative stock assessment model. There is also a need to determine the appropriate spatial resolution to apply to the stock assessment model. As such, there is a need to understand the extent of adult movement and ontogenetic connectivity of regional sub-populations of sand flathead throughout Tasmania. In addition, by collation of biological data sets from historical studies and surveys conducted around Tasmania the extent and direction of potential changes in population size structures, and life history characteristics will be investigated. Where possible collated biological data will be used to assess spatial and temporal changes in life history characteristics to assess the implications of selective excessive fishing pressure and/or past and future climate change effects for this species. Objectives: 1. Review and collate available biological and fishery data collected on sand flathead within Tasmanian waters 2. Design, implement and assess the effectiveness of fishery dependent and fishery independent biological sample collection techniques for sand flathead 3. Determine the spatial and temporal variability of key life history characteristics and population structures of sand flathead 4. Investigate movement and connectivity of sand flathead within Tasmania 5. Develop a quantitative region-age-sex structured fishery assessment model for sand flathead 6. Identify management scenarios for consideration Read moreRead less
Embracing Changes for Responsive Video-sharing Services. Video-sharing platforms are a critical information channel for the public. Increasing scale and shifts in user base, with Generation Z now as the dominant user, have resulted in an unprecedented amount of ubiquitous changes in the content and users of these platforms which greatly challenges the responsiveness and quality of the services provided. This project aims to design innovative algorithms to effectively predict and leverage changes ....Embracing Changes for Responsive Video-sharing Services. Video-sharing platforms are a critical information channel for the public. Increasing scale and shifts in user base, with Generation Z now as the dominant user, have resulted in an unprecedented amount of ubiquitous changes in the content and users of these platforms which greatly challenges the responsiveness and quality of the services provided. This project aims to design innovative algorithms to effectively predict and leverage changes, optimise the value of changes, and extract insights from changes for diverse downstream applications of video-sharing platforms. The expected outcomes will create new-generation representation learning techniques, and provide practical tools to amplify the socioeconomic values of video-sharing platforms.Read moreRead less
Differential Evolution Framework for Intelligent Charging Scheduling. Smart charging scheduling is a vital challenge as dynamic environment with traffic networks and various unexpected issues. This project aims to develop a differential evolution framework for intelligent charging scheduling. The framework consists of a comprehensive charging scheduling model with various road networks and factors. The project outcomes include a distributed evolutionary computation framework, differential evolut ....Differential Evolution Framework for Intelligent Charging Scheduling. Smart charging scheduling is a vital challenge as dynamic environment with traffic networks and various unexpected issues. This project aims to develop a differential evolution framework for intelligent charging scheduling. The framework consists of a comprehensive charging scheduling model with various road networks and factors. The project outcomes include a distributed evolutionary computation framework, differential evolution algorithms, and cooperative co-evolutionary strategies. The outcome results will be demonstrated by practical evaluations over public datasets and comparisons to related works. The project is beneficial to the nation in both theory of artificial intelligence techniques and applications of real transport systems.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE240100131
Funder
Australian Research Council
Funding Amount
$539,000.00
Summary
Federated Omniverse Facilities for Smart Digital Futures. A world-first trans-disciplinary, -domain, and -institutional smart 3D omniverse R&D ecosystem AuVerse will be built in NSW, affiliated with Queensland, and accessible to academia and industry. AuVerse will support cloud-based, reality-virtuality-fused, immersive, interactive and secure future-oriented digital design, development, training and society. In the new era of digital innovation and paradigm shift, AuVerse will substantially boo ....Federated Omniverse Facilities for Smart Digital Futures. A world-first trans-disciplinary, -domain, and -institutional smart 3D omniverse R&D ecosystem AuVerse will be built in NSW, affiliated with Queensland, and accessible to academia and industry. AuVerse will support cloud-based, reality-virtuality-fused, immersive, interactive and secure future-oriented digital design, development, training and society. In the new era of digital innovation and paradigm shift, AuVerse will substantially boost Australia’s pivotal research leadership and business competitiveness in nurturing new-generation, collaborative and transformative digital R&D and talent pipeline. It will enable large-scale strategic business innovation and transformation including smart manufacturing and Industry 4.0.Read moreRead less
Towards Generalisable and Unbiased Dynamic Recommender Systems. This project aims to develop the foundations, including models, methodology, and algorithms for building generalisable and unbiased dynamic recommender systems to facilitate intelligent decision-making, prompt contextualised and personalised strategic plans, and support context-aware action recourse. To ensure that fundamental principles, such as fairness and transparency, are respected, a set of algorithms and techniques are propos ....Towards Generalisable and Unbiased Dynamic Recommender Systems. This project aims to develop the foundations, including models, methodology, and algorithms for building generalisable and unbiased dynamic recommender systems to facilitate intelligent decision-making, prompt contextualised and personalised strategic plans, and support context-aware action recourse. To ensure that fundamental principles, such as fairness and transparency, are respected, a set of algorithms and techniques are proposed to develop recommender systems in a more responsible manner. The result of this project will not only maintain Australia's leadership in this frontier research area, but also serve as an excellent vehicle for the education and training of Australia's next generation of scholars and engineers.Read moreRead less
A Data-Centric Mobile Edge Platform for Resilient Logistics & Supply Chain. This project aims to develop a secure mobile edge computing platform for resilient logistic and supply chain management. It consists of easy-used functions that help businesses realise low latency, high reliability, low cost, and high security in their logistics and supply chain system. To cope with the vast generated application data, we invent new data replication, placement, and deduplication techniques to optimise th ....A Data-Centric Mobile Edge Platform for Resilient Logistics & Supply Chain. This project aims to develop a secure mobile edge computing platform for resilient logistic and supply chain management. It consists of easy-used functions that help businesses realise low latency, high reliability, low cost, and high security in their logistics and supply chain system. To cope with the vast generated application data, we invent new data replication, placement, and deduplication techniques to optimise the mobile edge computing platform from the computation, storage, and network aspects. The invented mobile edge computing platform will enable more intelligent business applications for various industries, e.g., IT, manufacturing, and media, to appear, thus benefiting both the economy of Australia.Read moreRead less
Situated Anomaly Detection in an Open Environment. This project aims to investigate situated anomaly detection in an open environment. Existing anomaly detection techniques follow the setting of conventional machine learning and discover anomalies from a set of collected data. In contrast, this project proposes to develop the next-generation of anomaly detection algorithms by learning from interactions with an open environment, which enables the discovery of new anomalies and the early detection ....Situated Anomaly Detection in an Open Environment. This project aims to investigate situated anomaly detection in an open environment. Existing anomaly detection techniques follow the setting of conventional machine learning and discover anomalies from a set of collected data. In contrast, this project proposes to develop the next-generation of anomaly detection algorithms by learning from interactions with an open environment, which enables the discovery of new anomalies and the early detection of anomalies. The established theories and developed algorithms will advance frontier technologies in machine intelligence. The success of the project will contribute to a wide range of real applications in cybersecurity, defence and finance, bringing massive social and economic benefits. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100105
Funder
Australian Research Council
Funding Amount
$458,823.00
Summary
Towards Evolvable and Sustainable Multimodal Machine Learning. Machine learning is commonly limited to a single operational modality. To enable image, sound and language comprehension simultaneously would require machines to reuse knowledge and understand concepts from multimodal data. The project aims to build a sparse model and present a set of innovative algorithms to enhance model generalisation for addressing distributional and semantic shifts and minimise the computational and labelling co ....Towards Evolvable and Sustainable Multimodal Machine Learning. Machine learning is commonly limited to a single operational modality. To enable image, sound and language comprehension simultaneously would require machines to reuse knowledge and understand concepts from multimodal data. The project aims to build a sparse model and present a set of innovative algorithms to enhance model generalisation for addressing distributional and semantic shifts and minimise the computational and labelling costs for training multimodal systems. Its outcomes will enable evolvable learning of models to suit varying testing scenarios after deployment and whilst reducing energy consumption and carbon emission. The application of these techniques could benefit sectors such as E-commerce, agriculture and transport.Read moreRead less
Efficient and effective methods for classifying massive time series data. This project aims to transform the theory and practice of time series classification. The current state of the art cannot handle the massive numbers of time series that describe many critical problems facing humanity, such as disease transmission and climate change. This project seeks to develop methods that can analyse dynamic processes at global scale, delivering the most accurate classifiers feasible within a given comp ....Efficient and effective methods for classifying massive time series data. This project aims to transform the theory and practice of time series classification. The current state of the art cannot handle the massive numbers of time series that describe many critical problems facing humanity, such as disease transmission and climate change. This project seeks to develop methods that can analyse dynamic processes at global scale, delivering the most accurate classifiers feasible within a given computational budget. Expected outcomes of this project include efficient, effective and broadly applicable time series classification technologies. This should provide significant benefits to myriad sectors, transforming data science for time series problems and supporting innovation in industry, commerce and government.Read moreRead less