Network governance and climate change adaptation: collaborative responses to the Queensland floods

Abstract This research examines ways to build adaptive capacity to climate change, through a case study of organisations that participated in the response to Queensland’s major flood disaster in Queensland in 2010/11. The research applied a network governance approach, including social network analysis and qualitative investigations, to the communities of Rockhampton, Emerald and Brisbane. The study was designed to compare social networks across a range of different geographical; functional; and institutional and regulatory contexts.Primary data were obtained from organisations involved in disaster management and water management, through a telephone survey conducted March – September 2012. The network analyses examined collaboration and communication patterns; changes in the network structure from routine management to flood operations; similarities and differences between the geographic regions, and whether collaboration was correlated with trust. A cultural values analysis was then performed to identify the key values of the network actors in each region. Two workshops were conducted in Rockhampton and Brisbane to disseminate the findings to stakeholders, as well as to obtain feedback through group activities.A total of 63 organisations participated in the study. As the network analyses and visualisations indicated that the Rockhampton and Emerald networks were tightly interconnected, a single ‘Central Queensland’ (CQ) network was used for all subsequent analyses. In both Brisbane and CQ, slightly higher levels of collaboration amongst organisations were recorded during flood periods compared with routine operations; and organisations tended to provide, as well as receive, information and/or resources from their collaborators. Overall, both networks appeared to feature high trust, with only a low level of difficult ties (problematic relationships) being reported.The cultural analyses identified patterns of common values amongst participating organisations. In Brisbane, respondents placed a high value on shared information systems and resources; shared communication and language; as well as on collaboration and flexibility. In the CQ network, there was a greater emphasis on local solutions, community wellbeing and longitudinal issues (such as post-disaster supply chains for recovery). The workshop activities suggested that the current structure of Local Disaster Management Groups was heavily influential on broader network participation; and that defining an ‘effective’ disaster response was a complex issue.This study has demonstrated that a network governance approach can provide new ways of understanding the core elements of adaptive capacity, in areas such as enablers and barriers to adaptation, and translating capacity into adaptation. The key implications for policy and practice include the need for stakeholders to drive adaptation to climate change through collaboration and communication; the need for stakeholders to share a common goal and language; the need for better engagement with community, diversity and Indigenous organisations; the need to establish collaboration outside of disaster events; and the need for network governance systems to play an important role in helping to facilitate climate change adaptation. The areas identified for future research included further methodological development and longitudinal studies of social networks, understanding effective modes of communication, and the influence of the changing nature of regional Australian communities on climate change adaptation.Please cite this report as:Kinnear, S, Patison, K, Mann, J, Malone, E, Ross, V 2013, Network governance and climate change adaptation: collaborative responses to the Queensland floods, National Climate Change Adaptation Research Facility, Gold Coast, pp. 113.This research examines ways to build adaptive capacity to climate change, through a case study of organisations that participated in the response to Queensland’s major flood disaster in Queensland in 2010/11. The research applied a network governance approach, including social network analysis and qualitative investigations, to the communities of Rockhampton, Emerald and Brisbane. The study was designed to compare social networks across a range of different geographical; functional; and institutional and regulatory contexts.Primary data were obtained from organisations involved in disaster management and water management, through a telephone survey conducted March – September 2012. The network analyses examined collaboration and communication patterns; changes in the network structure from routine management to flood operations; similarities and differences between the geographic regions, and whether collaboration was correlated with trust. A cultural values analysis was then performed to identify the key values of the network actors in each region. Two workshops were conducted in Rockhampton and Brisbane to disseminate the findings to stakeholders, as well as to obtain feedback through group activities.A total of 63 organisations participated in the study. As the network analyses and visualisations indicated that the Rockhampton and Emerald networks were tightly interconnected, a single ‘Central Queensland’ (CQ) network was used for all subsequent analyses. In both Brisbane and CQ, slightly higher levels of collaboration amongst organisations were recorded during flood periods compared with routine operations; and organisations tended to provide, as well as receive, information and/or resources from their collaborators. Overall, both networks appeared to feature high trust, with only a low level of difficult ties (problematic relationships) being reported.The cultural analyses identified patterns of common values amongst participating organisations. In Brisbane, respondents placed a high value on shared information systems and resources; shared communication and language; as well as on collaboration and flexibility. In the CQ network, there was a greater emphasis on local solutions, community wellbeing and longitudinal issues (such as post-disaster supply chains for recovery). The workshop activities suggested that the current structure of Local Disaster Management Groups was heavily influential on broader network participation; and that defining an ‘effective’ disaster response was a complex issue.This study has demonstrated that a network governance approach can provide new ways of understanding the core elements of adaptive capacity, in areas such as enablers and barriers to adaptation, and translating capacity into adaptation. The key implications for policy and practice include the need for stakeholders to drive adaptation to climate change through collaboration and communication; the need for stakeholders to share a common goal and language; the need for better engagement with community, diversity and Indigenous organisations; the need to establish collaboration outside of disaster events; and the need for network governance systems to play an important role in helping to facilitate climate change adaptation. The areas identified for future research included further methodological development and longitudinal studies of social networks, understanding effective modes of communication, and the influence of the changing nature of regional Australian communities on climate change adaptation

IMAGE UNDERSTANDING OF MOLAR PREGNANCY BASED ON ANOMALIES DETECTION

Cancer occurs when normal cells grow and multiply without normal control. As the cells multiply, they form an area of abnormal cells, known as a tumour. Many tumours exhibit abnormal chromosomal segregation at cell division. These anomalies play an important role in detecting molar pregnancy cancer. Molar pregnancy, also known as hydatidiform mole, can be categorised into partial (PHM) and complete (CHM) mole, persistent gestational trophoblastic and choriocarcinoma. Hydatidiform moles are most commonly found in women under the age of 17 or over the age of 35. Hydatidiform moles can be detected by morphological and histopathological examination. Even experienced pathologists cannot easily classify between complete and partial hydatidiform moles. However, the distinction between complete and partial hydatidiform moles is important in order to recommend the appropriate treatment method. Therefore, research into molar pregnancy image analysis and understanding is critical. The hypothesis of this research project is that an anomaly detection approach to analyse molar pregnancy images can improve image analysis and classification of normal PHM and CHM villi. The primary aim of this research project is to develop a novel method, based on anomaly detection, to identify and classify anomalous villi in molar pregnancy stained images. The novel method is developed to simulate expert pathologists’ approach in diagnosis of anomalous villi. The knowledge and heuristics elicited from two expert pathologists are combined with the morphological domain knowledge of molar pregnancy, to develop a heuristic multi-neural network architecture designed to classify the villi into their appropriated anomalous types. This study confirmed that a single feature cannot give enough discriminative power for villi classification. Whereas expert pathologists consider the size and shape before textural features, this thesis demonstrated that the textural feature has a higher discriminative power than size and shape. The first heuristic-based multi-neural network, which was based on 15 elicited features, achieved an improved average accuracy of 81.2%, compared to the traditional multi-layer perceptron (80.5%); however, the recall of CHM villi class was still low (64.3%). Two further textural features, which were elicited and added to the second heuristic-based multi-neural network, have improved the average accuracy from 81.2% to 86.1% and the recall of CHM villi class from 64.3% to 73.5%. The precision of the multi-neural network II has also increased from 82.7% to 89.5% for normal villi class, from 81.3% to 84.7% for PHM villi class and from 80.8% to 86% for CHM villi class. To support pathologists to visualise the results of the segmentation, a software tool, Hydatidiform Mole Analysis Tool (HYMAT), was developed compiling the morphological and pathological data for each villus analysis

Applying proximity sensors to monitor beef cattle social behaviour as an indicator of animal welfare

There are currently no approved monitoring programs in the beef industry that use paddock based behaviour as an indicator of animal welfare. Current animal welfare assessments are conducted at a single point in time, such as supplying food and water and treating illnesses as these needs arise. These aspects comply with the five freedoms that animals should have when addressing animal welfare, however, the assessments are infrequent. Of the five freedoms, the freedom to express normal behaviour can be a subjective measure, due to differences in the way individual animals express certain behaviours. There is a need for continual monitoring of welfare indicators in modern animal assessment methods to objectively measure behaviour and address public concerns about the welfare state of animals. The experiment commenced in June 2017 to assess changes in cattle social interaction patterns in response to social stress created by regrouping four groups of eight heifers. Previous research with cattle has provided evidence that social contact and spatial behaviour differ when novel individuals are introduced (Patison et al., 2010b), and re-grouped animals continue to experience stress until the social hierarchy is re-established after regrouping (Kondo and Hurnik, 1990). Proximity sensors that record the frequency and duration of close proximity contacts (<4 m) will be used to remotely collect animal association data, while blood cortisol concentrations will be used as an independent measure of stress. Responses to stress will be compared with a group of heifers where re-grouping does not occur. This paper outlines the background and methodology to explore the potential for proximity sensors as a continual welfare monitoring device, related to an animal’s freedom to express normal behaviour. Preliminary results of the project will be presented at The International Tri-Conference for Precision Agriculture held in New Zealand in October, 2017

Sex and Stature Estimation from Adult Lumbar Vertebrae in a Thai Population Based on Image Analysis

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Efficiency of dry bone inspection compared with two-dimensional os coxal images for age estimation in a Thai population

The auricular surface and pubic symphysis are commonly used in age estimation. This study aimed to compare the results of age estimation between dry bones and 2D images of the os coxae and to develop a tool specifically for Thai individuals. The total samples were 250 left os coxal dry bones divided into 200 samples (100 males, 100 females) for the training set and 50 samples for the test set. The age range was 26 – 94 years. We used the Suchey-Brooks method and Berg method for observing the pubic symphysis and the Buckberry-Chamberlain method for observing the auricular surface. Afterward we compared the dry bones and photo parts. Our results showed sex did not play a significant role in estimating the age-at-death. In both parts, the auricular surface yielded the highest accuracy (80 – 84%) with SEE = 13.99 – 14.24 years. The pubic symphysis showed an accuracy of 74 – 76% and SEE = 14.37 – 14.44 years. The results of the dry bone and photo parts did not differ significantly. In both dry bone and photo parts, the intra-observer agreement performed moderate to almost perfect agreement. On the other hand, the inter-observer agreement was slight to fair. In conclusion, our study can be potentially applied for distant consultation for age estimation using 2D pelvic images with a forensic anthropologist for estimating biological profiles

Monitoring Animal Behaviour and Environmental Interactions Using Wireless Sensor Networks, GPS Collars and Satellite Remote Sensing

Remote monitoring of animal behaviour in the environment can assist in managing both the animal and its environmental impact. GPS collars which record animal locations with high temporal frequency allow researchers to monitor both animal behaviour and interactions with the environment. These ground-based sensors can be combined with remotely-sensed satellite images to understand animal-landscape interactions. The key to combining these technologies is communication methods such as wireless sensor networks (WSNs). We explore this concept using a case-study from an extensive cattle enterprise in northern Australia and demonstrate the potential for combining GPS collars and satellite images in a WSN to monitor behavioural preferences and social behaviour of cattle

A Relational Event Approach to Modeling Behavioral Dynamics

This chapter provides an introduction to the analysis of relational event data (i.e., actions, interactions, or other events involving multiple actors that occur over time) within the R/statnet platform. We begin by reviewing the basics of relational event modeling, with an emphasis on models with piecewise constant hazards. We then discuss estimation for dyadic and more general relational event models using the relevent package, with an emphasis on hands-on applications of the methods and interpretation of results. Statnet is a collection of packages for the R statistical computing system that supports the representation, manipulation, visualization, modeling, simulation, and analysis of relational data. Statnet packages are contributed by a team of volunteer developers, and are made freely available under the GNU Public License. These packages are written for the R statistical computing environment, and can be used with any computing platform that supports R (including Windows, Linux, and Mac).