Development of a Seafloor Community Classification for the New Zealand Region Using a Gradient Forest Approach

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.

Evaluation of biodiversity benefits of proposed marine protected areas from the Sea Change—Tai Timu Tai Pari Hauraki Gulf MarineSpatial Plan

“…For example, biodiversity data exists for some taxonomic groups (e.g., seabirds, shorebirds, and marine mammals; Stephenson et al, 2020) that could be further refined to form more comprehensive layers to be used within a Zonation analysis. A number of datasets have subsequently become available that could further inform MPA network design in the HGMP, including a revised national marine environment classification (Stephenson et al, 2022), and other biodiversity datasets to inform identification of key ecological areas (KEAs), including individual species distribution models for cetaceans, demersal fish, rocky reef fish, macroalgae, and seafloor invertebrates (Lundquist, Stephenson, et al, 2020; Stephenson et al, 2020). These layers could further inform future review and/or planning within the HGMP.…”

Independent statistical validation of the New Zealand Seafloor Community Classification

“…One key strength of classification-based approaches is that they can be created at various hierarchical (nested) levels of group-detail, for example, as a 9-group bioregionalization (Stephenson et al, 2023), to the 75-group community classification (Stephenson et al, 2022), a feature that makes them particularly useful when they need to be applied at differing spatial scales (national to regional to local scales) (Stephenson et al, 2020). The model validation approach developed here can equally be used to assess the classification strength at different spatial scales (and for different classification levels), including for defining classifications with a higher number of groups (200+ groups) which may be more appropriate for regional scale management planning (nested within broader scale classifications), particularly for inshore areas where there is greater heterogeneity in environmental conditions and biological communities.…”

“…The global ANOSIM R values were 0.53 for demersal fish and 0.46 and benthic invertebrates, and both were significant at the 1% level, indicating that the NZSCC groups define biologically distinctive environments as assessed by completely independent evaluation data (Table 1). For context, the global R values for the NZSCC, as assessed by use of the internal training data by Stephenson et al (2022) as part of the development of the NZSCC, was somewhat higher for demersal fish (R value: 0.72) but lower for benthic invertebrates (R value: 0.25).…”

“…That is, where there is more variability in the GF estimates, the resulting predicted classification will be less certain. Together, these uncertainty estimates provide complementary measures of uncertainty to be considered by managers (Stephenson et al, 2022). (Bennion et al, 2023).…”

“…Should the representation of different seafloor communities by the NZSCC be less than desirable, or additional data becomes available to rerun the classification, then it will be important to consider how and when the NZSCC could be updated. Stephenson et al (2022) assessed the NZSCC's ability to discriminate across classification levels using the biological data included in the classification in an analysis of similarities test (ANOSIM) (i.e., using internal data validation). This test showed that the 75-group classification explained the most variation with the fewest number of groups based on data used to develop the models.…”