Analysis of the needs of the East Coast cluster regional natural resource management bodies in relation to planning for climate change adaptation

The Climate Change Adaptation for Natural Resource Management (NRM) in East Coast Australia Project aims to foster and support an effective “community of practice” for climate change adaptation within the East Coast Cluster NRM regions that will increase the capacity for adaptation to climate change through enhancements in knowledge and skills and through the establishment of long‐term collaborations. It is being delivered by six consortium research partners: * The University of Queensland (project lead) * Griffith University * University of the Sunshine Coast * CSIRO * New South Wales Office of Environment and Heritage * Queensland Department of Science, IT, Innovation and the Arts (Queensland Herbarium). The project relates to the East Coast Cluster, comprising the six coastal NRM regions and regional bodies between Rockhampton and Sydney: * Fitzroy Basin Association (FBA) * Burnett‐Mary Regional Group (BMRG) * SEQ Catchments (SEQC) * Northern Rivers Catchment Management Authority (CMA) (NRCMA) * Hunter‐Central Rivers CMA (HCRCMA) * Hawkesbury Nepean CMA (HNCMA). The aims of this report are to summarise the needs of the regional bodies in relation to NRM planning for climate change adaptation, and provide a basis for developing the detailed work plan for the research consortium. Two primary methods were used to identify the needs of the regional bodies: (1) document analysis of the existing NRM/ Catchment Action Plans (CAPs) and applications by the regional bodies for funding under Stream 1 of the Regional NRM Planning for Climate Change Fund, and; (2) a needs analysis workshop, held in May 2013 involving representatives from the research consortium partners and the regional bodies. The East Coast Cluster includes five of the ten largest significant urban areas in Australia, world heritage listed natural environments, significant agriculture, mining and extensive grazing. The three NSW CMAs have recently completed strategic level CAPs, with implementation plans to be finalised in 2014/2015. SEQC and FBA are beginning a review of their existing NRM Plans, to be completed in 2014 and 2015 respectively; while BMRG is aiming to produce a NRM and Climate Variability Action Strategy. The regional bodies will receive funding from the Australian Government through the Regional NRM Planning for Climate Change Fund (NRM Fund) to improve regional planning for climate change and help guide the location of carbon and biodiversity activities, including wildlife corridors. The bulk of the funding will be available for activities in 2013/2014, with smaller amounts available in subsequent years. Most regional bodies aim to have a large proportion of the planning work complete by the end of 2014. In addition, NSW CMAs are undergoing major structural change and will be incorporated into semi‐autonomous statutory Local Land Services bodies from 2014. Boundaries will align with local government boundaries and there will be significant change in staff and structures. The regional bodies in the cluster have a varying degree of climate knowledge. All plans recognise climate change as a key driver of change, but there are few specific actions or targets addressing climate change. Regional bodies also have varying capacity to analyse large volumes of spatial or modelling data. Due to the complex nature of natural resource management, all regional bodies work with key stakeholders (e.g. local government, industry groups, and community groups) to deliver NRM outcomes. Regional bodies therefore require project outputs that can be used directly in stakeholder engagement activities, and are likely to require some form of capacity building associated with each of the outputs to maximise uptake. Some of the immediate needs of the regional bodies are a summary of information or tools that are able to be used immediately; and a summary of the key outputs and milestone dates for the project, to facilitate alignment of planning activities with research outputs. A project framework is useful to show the linkages between research elements and the relevance of the research to the adaptive management cycle for NRM planning in which the regional bodies are engaged. A draft framework is proposed to stimulate and promote discussion on research elements and linkages; this will be refined during and following the development of the detailed project work plan. The regional bodies strongly emphasised the need to incorporate a shift to a systems based resilience approach to NRM planning, and that approach is included in the framework. The regional bodies identified that information on climate projections would be most useful at regional and subregional scale, to feed into scenario planning and impact analysis. Outputs should be ‘engagement ready’ and there is a need for capacity building to enable regional bodies to understand and use the projections in stakeholder engagement. There was interest in understanding the impacts of climate change projections on ecosystems (e.g. ecosystem shift), and the consequent impacts on the production of ecosystem services. It was emphasised that any modelling should be able to be used by the regional bodies with their stakeholders to allow for community input (i.e. no black box models). The online regrowth benefits tool was of great interest to the regional bodies, as spatial mapping of carbon farming opportunities would be relevant to their funding requirements. The NSW CMAs identified an interest in development of the tool for NSW vegetation types. Needs relating to socio‐economic information included understanding the socio‐economic determinants of carbon farming uptake and managing community expectations. A need was also identified to understand the vulnerability of industry groups as well as community to climate change impacts, and in particular understanding how changes in the flow of ecosystem services would interact with the vulnerability of these groups to impact on the linked ecologicalsocio‐economic system. Responses to disasters (particularly flooding and storm surge) and recovery responses were also identified as being of interest. An ecosystem services framework was highlighted as a useful approach to synthesising biophysical and socioeconomic information in the context of a systems based, resilience approach to NRM planning. A need was identified to develop processes to move towards such an approach to NRM planning from the current asset management approach. Examples of best practice in incorporating climate science into planning, using scenarios for stakeholder engagement in planning and processes for institutionalising learning were also identified as cross‐cutting needs. The over‐arching theme identified was the need for capacity building for the NRM bodies to best use the information available at any point in time. To this end a planners working group has been established to support the building of a network of informed and articulate NRM agents with knowledge of current climate science and capacity to use current tools to engage stakeholders in NRM planning for climate change adaptation. The planners working group would form the core group of the community of practice, with the broader group of stakeholders participating when activities aligned with their interests. In this way, it is anticipated that the Project will contribute to building capacity within the wider community to effectively plan for climate change adaptation.

Automated counting of the Northern Pacific Sea Star in the Derwent using shape recognition

A novel shape recognition algorithm was developed to autonomously classify the Northern Pacific Sea Star (Asterias amurenis) from benthic images that were collected by the Starbug AUV during 6km of transects in the Derwent estuary. Despite the effects of scattering, attenuation, soft focus and motion blur within the underwater images, an optimal joint classification rate of 77.5% and misclassification rate of 13.5% was achieved. The performance of algorithm was largely attributed to its ability to recognise locally deformed sea star shapes that were created during the segmentation of the distorted images.