Distributional data for eight taxonomic groups (asteroids, bryozoans, benthic foraminiferans, octocorals, polychaetes, matrix-forming scleractinian corals, sponges, and benthic fish) have been used to train an environmental classification for those parts of New Zealand's 200 n. mile Exclusive Economic Zone (EEZ) with depths of 3000 m or less. A variety of environmental variables were used as input to this process, including estimates of depth, temperature, salinity, sea surface temperature gradient, surface water productivity, suspended sediments, tidal currents, and seafloor sediments and slope. These variables were transformed using results averaged across eight Generalised Dissimilarity Modelling analyses that indicate relationships between species turnover and environment for each species group. The matrix of transformed variables was then classified using k-meDOIds clustering to identify an initial set of 300 groups of cells based on their environmental similarities, with relationships between these groups then described using agglomerative hierarchical clustering. Groups at a fifteen group level of classification appropriate for use at a whole-of-EEZ scale are described; the classification can also be used at other levels of detail, for example when higher levels of classification detail are required to discriminate variation within study areas of more limited extent. Although not formally tested in this analysis, we expect the analytical process used here to increase the biological discrimination of the environmental classification. That is, the resulting environmental groups are more likely to have similar biological characteristics than when the input environmental variables are selected, weighted, and perhaps transformed using qualitative methods. As a consequence, they are more likely to be reliable when used as "habitat classes" for the management of biological values than groups defined using alternative approaches._________________Item Page Created: 2021-03-09 02:48 Item Page Last Modified: 2025-04-05 20:41Owner: NIWA_OpenData
