Spatially-explicit decision support tools

A variety of marine animals are being used to investigate seafloor disturbance and recovery from physical stressors

The aim of this project is to develop a framework that helps us decide how to best share marine space. We are using spatial data (data with geographical coordinates) to model trade-offs between different resource uses, objectives, and Māori and stakeholder values, and their impacts on biodiversity and ecosystem health.

Project leader: Carolyn Lundquist, NIWA/University of Auckland

Helping decide how to best share marine space

We are developing spatially-explicit decision support tools to inform decisionmaking. These are models that use spatial data (data that have geographical coordinates) to investigate trade-offs between different resource uses, objectives, and Māori and stakeholder values, and their impacts on biodiversity and ecosystem health.

Because there are many different types of models, we are finding out which models work best in a New Zealand context. We are developing techniques that evaluate whether the uncertainty associated with different representations of data – from the biological (living) and physical (non-living) parts of an ecosystem and socio-cultural values – affect model outcomes, which is a key challenge in largescale management of the marine environment.

We are developing models to examine the cumulative impacts of multiple stressors, such as fishing, mineral extraction and sedimentation, on different marine animals in Tasman and Golden Bays. These models will consider the effects at multiple scales and amount of disturbance.

We are developing these tools with involvement from key policy and management people in government, Māori, and community and stakeholder organisations to ensure they are ‘fit for purpose’ and fully integrated and tested within existing management systems.

Improving how we predict marine fish distributions

To improve management of marine ecosystems and help conservation, decision-makers need to understand how marine life is distributed over large ocean areas. Sustainable Seas research led by Dr Fabrice Stephenson from NIWA has found that a recently developed analysis tool, the Gradient Forest Model, can accurately predict the numbers and distribution of fish species on the ocean floor.

Dr Stephenson tested the new model against real distribution patterns of bottom-dwelling fish, such as hoki, orange roughy and oreos, gathered from more than 27,000 NIWA research surveys conducted over 26 years. As well as fish, the model incorporated detailed environmental data, such as tidal current speeds, temperature gradients, salinity, seafloor roughness and sediment type. It was able to predict fish distributions over a range of environments.

Dr Stephenson worked closely with Dr Carolyn Lundquist and conservation scientist Dr John Leathwick and their findings – recently published in the journal Diversity and Distributions – will be used to enhance existing conservation planning tools. These tools are the ultimate goal of the research as they allow decision-makers to explore ‘what-if’ questions, and determine how future changes in fishing or climate may influence fish distributions within New Zealand’s marine environment.

 

Fish distribution in New Zealand’s Extended Continental Shelf, and Cook Straight (inset). Copyright: Fabrice Stephenson

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