This record provides an overview of the scope and research output of NESP Marine Biodiversity Hub Project D1 - "Ecosystem
understanding to support sustainable use, management and monitoring of marine assets in the North and North-west regions".
For specific data outputs from this project, please see child records associated with this metadata.
Effective management of marine assets requires an understanding of ecosystems and the processes that influence patterns of
biodiversity. Focusing on the North and North-west regions, this project will leverage previous research to improve ecosystem
understanding through a synthesis of existing information and by making testable predictions about the character and extent
of conservation values, including for key ecological features (KEFs) and Commonwealth Marine Reserves. End-users and stakeholders
will benefit from improved regional descriptions of marine ecosystems and uncertainty statements. In turn, this will inform
prioritisation of future investments in monitoring marine ecosystems and State of the Environment reporting.
• A report on the synthesis (based on collations completed in 2015) of datasets and models for the North and NW identifying
areas of greatest information coverage, gaps and themed to CMRs and KEFs in those regions. This report will also describe
key spatial patterns in biodiversity (benthic and pelagic) and associations between benthic environments, fish and megafauna
and large scale processes (e.g. oceanography).
• Predictions and related products (maps) of the spatial distribution of biodiversity across the Oceanic Shoals CMR that
encompasses benthic habitat, pelagic and demersal fish and megafauna communities. This will provide an example/test case
at the National Prioritisation Workshop of how confidently predictive modelling can be used to describe assets and values
in data poor areas to inform management and monitoring.
• An updated conceptual model of ecosystem processes (benthic and pelagic) within the Oceanic Shoals CMR based on extension
of modelling into pelagics.
• A review of existing knowledge of the Ancient Coastline KEF.
• A qualitative model of Glomar Shoal KEF (to be confirmed in consultation with DOE).
• Communication products that capture activities and general interest stories of scientific results disseminated through
NW Atlas social media links.
• Upload of new relevant spatial data layers in NW Atlas for management and planning, and engagement with end users to maximize
uptake of the NW Atlas products.
The main focus of this project is to leverage prior investment in data collection and collation to apply and refine spatial
models previously developed under CERF and NERP. This project will enable predictions of the extent of benthic habitats,
pelagic hotspots and connectivity in priority areas in the North and NW regions, particularly for KEFs and CMRs where data
is particularly sparse (Figure 1). Through data collation and the application of predictive models, key information gaps
relevant to the management and the CMR network will be derived and used, in consultation with DOE staff, to prioritize and
plan future activities necessary to fill the most important data gaps. These activities in combination with integration
of our current understanding (observed and predicted) of benthic, pelagic, megafauna movement/behaviour and oceanographic
patterns will help identify and characterise Biologically Important Areas (BIAs) where we see concentrations of species/biodiversity
within the region. Over the three years, the project will be achieved as a three step process:
• Apply existing models generated through CERF, NERP, WAMSI and elsewhere to predict key ecosystem attributes at locations
(e.g. CMRs and KEFs) identified and prioritised through science and stakeholder workshops;
• Identify and prioritise opportunities to fill data gaps, based on the information needs of DOE, through targeted field
programs in ways that will maximize the gain of new information for these priority areas and to consolidate our understanding
of biodiversity values and ecosystem function; and
• Assess the application of equivalent approaches nationally, based on regional learnings.