Filling gaps in marine data using Gradient Forest models
This describes how large numbers of different data sets can be effectively pooled to group marine species into 'community assemblages'. These assemblages can be used to identify biodiversity hotspots and explore trade-offs resulting from management decisions.
Aotearoa New Zealand’s waters cover a vast area, 4.1 million km2, most of which is deep sea.
While many coastal areas and some offshore areas like the Chatham Rise have been well characterised, large parts of the EEZ (exclusive economic zone) remain unsampled as marine surveys in deep, offshore habitats are logistically difficult and expensive.
The lack of comprehensive information about what species are where, and in what numbers, makes it difficult to:
- Understand the distribution of most species, particularly rare species or those found only in a few locations
- Identify biodiversity ‘hotspots’
- Make robust management decisions about resource use and conservation
This guidance describes how our researchers pooled large numbers of existing data sets to group 253 species of demersal (bottom-dwelling) fish into 30 community assemblages, according to the oceanographic and environmental conditions in which they live – ie species that tend to be found in the same habitats (eg cold, deep water) at the same time.
They used these assemblages:
- As a proxy to estimate patterns of species diversity in inaccessible areas – using information from common species to ‘fill the gaps’ where data was limited for less common species
- To determine optimal locations for biodiversity conservation, and to explore trade-offs between resource use and biodiversity conservation
Advantages of this approach
- It requires less data to run than considering 100s of species individually
- It provides a limited number of groups of species (community assemblages) for decision makers to consider, which is:
- Easier than an individual assessment for 100s of species
- More holistic as these species interact and affect one another
- It predicts assemblages that serve as proxies for rare species that cannot be modelled as they are poorly represented in available data