Posted on 30 November 2020

How ‘forests’ can help marine spatial planning

New guidance for marine managers explains how Gradient Forest models – a new ‘classification tree’ technique that predicts species composition in marine environments – can help predict biodiversity hotspots in marine areas where there is little to no available data.

Although we have comprehensive data for much of the coast and and some offshore areas like the Chatham Rise, large parts of the EEZ (exclusive economic zone) remain unsampled because marine surveys in deep offshore habitats are logistically difficult and expensive. Gradient Forest models can help bridge this gap. 

View/download: Filling gaps in marine data using Gradient Forest models

New Zealand has hundreds of detailed species distribution models, which is unusual,” says Fabrice Stephenson, the lead author and a marine modeller at NIWA. “But these models only cover a tiny fraction of our marine species, and don’t include rare species that are most in need of protection. For most of our vast marine area – which is around 4.1 million km2 – we have no idea what species are where, and in what numbers. This level of uncertainty makes life extremely difficult for marine managers.” 

This guidance, based on two peer-reviewed scientific papers, shows how GF models can help marine managers bridge these data gaps to make robust decisions about biodiversity conservation and resource use by: 

  • Improving our understanding of the distribution of most species, particularly rare species or those found only in a few locations 
  • Identifying biodiversity ‘hotspots’ 

It breaks the EEZ down into easily comprehensible units that are practically much easier to manage, and require less data to run, than considering hundreds of species individually,” says Carolyn Lundquist, who led the Spatially-explicit decision support tools project. It’s also more holisticthe communities better reflect the real ecosystem, because species are never in isolation – they interact with each other and their environment; and the assemblages act as proxies for rare species that we have no data for.” 

Marine spatial planning 

The researchers used the assemblages to determine optimal locations for biodiversity conservation, and to explore trade-offs between resource use and biodiversity conservation. 

As well as supporting biodiversity and conservation planning, the findings will help businesses with their investment planning by providing more certainty and robust decision-making about where commercial (and other) activities can/can’t take place. 

Journal papers 
  • Filling gaps in marine data with GF models - best for print

    4 MB | Adobe Acrobat PDF file

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Spatially-explicit decision support tools
Credit: Crispin Middleton NIWA 2018
Spatially-explicit decision support tools
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We developed tools to help decision-makers explore how best to use and share marine spaces.

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