Abstract:

Effective management of marine assets requires an understanding of ecosystems and the processes that influence patterns of biodiversity. Project D1 of the NESP Marine Biodiversity Hub has been collating and synthesising existing data through 2015/16, focusing on Commonwealth Marine Reserves (CMRs) and Key Ecological Features (KEFs) of the North and North-west regions of Australia’s marine estate, with three main objectives:

  1. Increase the accessibility of existing research and data products to end users including managers, regulators and the general public
  2. Identify knowledge gaps and develop strategies to address these
  3. Improve ecosystem understanding of KEFS and CMRS through predictive modelling

Building on the North West Atlas (www.northwestatlas.org) as a communication platform, we collated 179 data sets for the North and NW Regions, and these are now accessible online. Targeted syntheses of knowledge for the Oceanic Shoals CMR and the Ancient Coastline KEF were used to demonstrate the value of this approach for informing marine planning and management and highlighting uncertainty.

Based on collated data sets, we undertook a formal gap analysis across CMRs and KEFs of the North and NW regions to identify those areas for which there exists sufficient data to underpin spatial predictive modelling in future years. Our results highlight the patchiness of available biophysical information, and large differences in coverage among taxa across the CMR network. We considered that the Kimberly CMR was the only area across the North and NW region for which existing data might underpin accurate spatial predictive modelling in the future. Our gap analysis did highlight CMRs and KEFs for which information coverage is greatest, as well as areas in which targeted empirical data collection would both inform future management and planning and enhance our capacity to use predictive models for ecological inference.

We used the Oceanic Shoals CMR as a case study for assessing the value of spatial predictive models in delivering knowledge of habitats and species distributions in remote, unsampled areas. We predicted the distribution of a range of biological and physical characteristics across the entire CMR, including benthic habitats, pelagic species, sponge diversity, and sediment type and hardness. This exercise shows the value of this approach for identifying assets in the marine estate where it is impossible to collect comprehensive data, and is a guide for stakeholders in identifying future data needs and tools required to adopt a similar approach nationally. The Oceanic Shoals predictive modelling example also provides a perspective on how modelling performance needs to be considered in the interpretation of predictive model outputs and maps.

Innovative science continues to support the effective management of Australia’s marine estate. In addition to the data collation, synthesis and modelling, the Project D1 team has been developing a range of manuscripts for publication in the peer-reviewed literature. A summary of key findings and progress of eight papers that collectively value-add to past NERP and present NESP research in the North and NW Regions is provided. Novel science discoveries include the identification of pelagic fish hotspots, environmental predictors of flatback turtle behaviour, impacts of cyclones on turtle movements, and descriptions of potential biological and geomorphic values in the Oceanic Shoals CMR.

The work undertaken to date as part of Project D1 has created an easily accessible knowledge framework for the Oceanic Shoals CMR and the Ancient Coastline KEF that will directly inform the development of management and monitoring plans in these areas.

We have demonstrated how spatial predictive modelling can be used to fill knowledge gaps and hence form a foundation for the evolution from precautionary management based on minimal information to more effective management based on a more rigorous scientific understanding of ecosystems. We also identified CMRs and KEFs where similar approaches can be implemented easily or with minimal additional investment in field data capture. The methods illustrated here for the North and NW regions provide a template for the application of similar approaches to other regions of Australia, where similar data are available or could be obtained, in particularly for supporting additional KEF characterisation and CMR monitoring and management.

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