Spatial variation in the composition of communities is the product of many biotic and environmental interactions. A neglected factor in the analysis of community distribution patterns is the multi-scale nature of the data, which has implications for understanding ecological processes and the development of conservation and environmental management practice. Drawing on recently established multivariate spatial analyses, we investigate whether including relationships between spatial structure and abiotic variables enable us to better discern patterns of species and communities across scales. Data comprised 1200 macrozoobenthic samples collected over an array of distances (30 cm to 1 km) in three New Zealand harbours, as well as commonly used abiotic variables, such as sediment characteristics and chlorophyll a concentrations, measured at the same scales. Moran’s eigenvector mapping was used to extract spatial scales at which communities were structured. Benthic communities, representing primarily bivalves, polychaetes and crustaceans, were spatially structured at four spatial scales, i.e. >100 m, 50–100 m, 50–15 m, and < 15 m. A broad selection of abiotic variables contributed to the large-scale variation, whereas a more limited set explained part of the fine-scale community structure. Across all scales, less than 30% of the variation in spatial structure was captured by our analysis. The large number of species (48) making up the 10 highest species scores based on redundancy analyses illustrate the variability of species-scale associations. Our results emphasise that abiotic variables and biodiversity are related at all scales investigated and stress the importance of assessing the relationship between environmental variables and the abundance and distribution of biological assemblages across a range of different scales.

A portion of the sandflat (Kaipara Harbour, New Zealand) sampled to assess the spatial distribution of functional attributes in the macrobenthic community. The quadrat is 1 m 2 . Photograph: Roman Zajac

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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.

Abstract. Understanding how the plants and animals that live in the seafloor vary in their spatial patterns of diversity and abundance is fundamental to gaining insight in the role of biodiversity in maintaining ecosystem functioning in coastal ecosystems, as well as advancing the modelling of species distributions under realistic assumptions. Yet, it is virtually unknown how the relationships between abundance patterns and different biotic and environmental processes change depending on spatial scales, which is mainly due to a lack of data. Within the project Spatial Organization of Species Distributions: Hierarchical and Scale-Dependent Patterns and Processes in Coastal Seascapes at the National Institute for Water and Atmospheric Research (NIWA) in New Zealand we collected multi-scale and high-resolution data on macrobenthic biodiversity. We found 146 species, i.e. bivalves, polychaetes and crustaceans (> 500 μm) that live hidden in marine sandflats, and collected point measurements of important environmental variables (sediment grain-size distributions, chlorophyll a concentration, and visible sandflat parameters) in three large intertidal Harbours (Kaipara, Tauranga and Manukau). In each Harbour we sampled 400 points for macrobenthic community composition and abundances, as well as the full set of environmental variables. Using an elaborate sampling design, we were able to cover scales from 30 centimetres to a maximal extent of 1 km. All data and extensive metadata are available from the data publisher PANGAEA via the persistent identifier https://doi.org/10.1594/PANGAEA.903448.