Abstract

To support ongoing marine spatial planning in New Zealand, a numerical environmental classification using Gradient Forest models was developed using a broad suite of biotic and high-resolution environmental predictor variables. Gradient Forest modeling uses species distribution data to control the selection, weighting and transformation of environmental predictors to maximise their correlation with species compositional turnover. A total of 630,997 records (39,766 unique locations) of 1,716 taxa living on or near the seafloor were used to inform the transformation of 20 gridded environmental variables to represent spatial patterns of compositional turnover in four biotic groups and the overall seafloor community. Compositional turnover of the overall community was classified using a hierarchical procedure to define groups at different levels of classification detail. The 75-group level classification was assessed as representing the highest number of groups that captured the majority of the variation across the New Zealand marine environment. We refer to this classification as the New Zealand “Seafloor Community Classification” (SCC). Associated uncertainty estimates of compositional turnover for each of the biotic groups and overall community were also produced, and an added measure of uncertainty – coverage of the environmental space – was developed to further highlight geographic areas where predictions may be less certain owing to low sampling effort. Environmental differences among the deep-water New Zealand SCC groups were relatively muted, but greater environmental differences were evident among groups at intermediate depths in line with well-defined oceanographic patterns observed in New Zealand’s oceans. Environmental differences became even more pronounced at shallow depths, where variation in more localised environmental conditions such as productivity, seafloor topography, seabed disturbance and tidal currents were important differentiating factors. Environmental similarities in New Zealand SCC groups were mirrored by their biological compositions. The New Zealand SCC is a significant advance on previous numerical classifications and includes a substantially wider range of biological and environmental data than has been attempted previously. The classification is critically appraised and considerations for use in spatial management are discussed.

Highlights

  • Robust identification of priority areas for marine spatial planning is often hampered by a lack of comprehensive knowledge of biodiversity patterns (Ferrier et al, 2007; Arponen et al, 2008; Hortal et al, 2015)

  • Tidal current speed was important in Gradient Forests (GF) models of demersal fish, benthic invertebrates and the community GF model

  • For the first time globally, spatial estimates of confidence were provided for the predicted compositional turnover through the use of bootstrapping techniques, which can in turn be used to partially assess the confidence that can be placed in the individual New Zealand SCC groups

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Summary

Introduction

Robust identification of priority areas for marine spatial planning is often hampered by a lack of comprehensive knowledge of biodiversity patterns (Ferrier et al, 2007; Arponen et al, 2008; Hortal et al, 2015). Multivariate or community-based modelling methods, which account for multiple species, can be used to summarize biodiversity patterns by classifying readily available environmental data into groups that are likely to have similar biological characteristics (e.g., Gregr and Bodtker, 2007; Dunstan et al, 2012; Leathwick et al, 2012) One such method, Gradient Forests (GF; Ellis et al, 2012; Pitcher et al, 2012), uses species distribution data to control the selection, weighting and transformation of environmental predictors to maximise their correlation with species compositional turnover and establish where along the range of environmental gradients important compositional changes occur (Ellis et al, 2012). Using a large set of independent data for evaluation, this 30-group classification was found to be highly effective at summarising spatial variation in both the composition of demersal fish assemblages and species turnover (Stephenson et al, 2018b)

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