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Lookup NU author(s): Dr Fabrice StephensonORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Copyright © 2022 Stephenson, Rowden, Brough, Petersen, Bulmer, Leathwick, Lohrer, Ellis, Bowden, Geange, Funnell, Freeman, Tunley, Tellier, Clark, Lundquist, Greenfield, Tuck, Mouton, Neill, Mackay, Pinkerton, Anderson, Gorman, Mills, Watson, Nelson and Hewitt.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.
Author(s): Stephenson F, Rowden AA, Brough T, Petersen G, Bulmer RH, Leathwick JR, Lohrer AM, Ellis JI, Bowden DA, Geange SW, Funnell GA, Freeman DJ, Tunley K, Tellier P, Clark DE, Lundquist CJ, Greenfield BL, Tuck ID, Mouton TL, Neill KF, Mackay KA, Pinkerton MH, Anderson OF, Gorman RM, Mills S, Watson S, Nelson WA, Hewitt JE
Publication type: Article
Publication status: Published
Journal: Frontiers in Marine Science
Year: 2022
Volume: 8
Print publication date: 01/01/2022
Online publication date: 14/01/2022
Acceptance date: 06/12/2021
Date deposited: 22/11/2023
ISSN (electronic): 2296-7745
Publisher: Frontiers Media S.A.
URL: https://doi.org/10.3389/fmars.2021.792712
DOI: 10.3389/fmars.2021.792712
Data Access Statement: The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doc-deptconservation.opendata.arcgis.com/documents/4050708cbf274e26a978448c4caf2b3d.
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