tnzm_a_1660384_sm2907.docx (2.76 MB)

A New Zealand demersal fish classification using Gradient Forest models

Download (2.76 MB)
journal contribution
posted on 10.09.2019, 02:53 by Fabrice Stephenson, John R. Leathwick, Malcolm P. Francis, Carolyn J. Lundquist

Spatial classifications of the environment have previously been used to characterise biodiversity and to facilitate management planning at large spatial scales. Such classifications are more likely to be adopted if they can demonstrate integration of real patterns in habitats or biotic assemblages, in addition to environment. A previous classification used Gradient Forest analysis to derive 30 classes based on demersal fish assemblage patterns and environmental gradients. Here we provide a detailed description of the similarities and differences in the environment and fish assemblages of classes resulting from an updated classification using the same methodology. Environmental differences were associated with varying levels of differences in the distributions of fish species. At broad spatial scales, assemblages are differentiated primarily according to oceanographic conditions such as temperature and depth; at finer scales, patterns in species assemblages are more closely associated with more localised environmental conditions such as productivity, sea-surface temperature gradients and tidal currents. The 30-group classification allows complex biodiversity information to be summarised in ways accessible to stakeholder and environmental managers. Given the hierarchical nature of the classification, there is considerable scope to use a larger number of groups for applications at regional to local scales.


This work was funded by the National Institute of Water and Atmospheric Research (NIWA - Coasts and Oceans Research Programme 5, SCI 2018/19) and aligned funding from the Ministry of Business, Innovation and Employment (MBIE; Contract No. C01X1515) as part of the Sustainable Seas National Science Challenge, Managed Seas program, Project ‘Spatially Explicit Decision Support Tools’ (SUSS16203).