Environmental classifications are potentially useful tools for summarising broad-scale spatial patterns in ecological and environmental gradients, particularly when biological data are limited in availability. Such classifications can be tuned with respect to specific faunal groups, but their usefulness depends on the validity of the assumption that biological distributions are correlated with gradients in the biophysical environment. Two marine environment classifications with relevance to benthic invertebrate distributions have been developed for New Zealand’s Exclusive Economic Zone (EEZ): the Marine Environment Classification (MEC) and the Benthic Optimised Marine Environment Classification (BOMEC). Until now, however, the ability of the MEC and the BOMEC to map benthic habitats and fauna has not been evaluated against independent sample data. We used benthic invertebrate faunal data from video and epibenthic sled samples collected during Ocean Survey 20/20 (OS 20/20) surveys of Chatham Rise and Challenger Plateau to assess whether the MEC and BOMEC provide a reliable means of mapping benthic habitats and faunal assemblage composition. We also generated a new environmental classification (“Chat/Chall”), tuned solely to the OS 20/20 sample data, to assess the effect of sampling of this type on classification performance. First, we compared the three environmental classifications (MEC, BOMEC, and Chat/Chall) with the OS 20/20 sample sites assigned to a set of 12 biotic habitats which were derived independently by clustering of the faunal data alone. Comparisons were made both visually and using chi-squared tests of goodness-of-fit. Second, using the full multivariate detail of the OS 20/20 faunal data, we used ANOSIM R and homogeneity statistics to assess how well each classification grouped the OS 20/20 sites at all classification levels up to 60 classes. Third, we compared how well each of the environmental classifications grouped the OS 20/20 sample sites in relation to a set of univariate biodiversity metrics calculated for each site from the sample data. None of the three environmental classifications discriminated well between biotic habitats at the site level, but visual comparisons showed consistent patterns which broadly matched distributions of the biotic habitats at larger spatial scales (100–1000 km) in all classifications except the MEC at the 20-class level. These patterns included differentiation between Chatham Rise and Challenger Plateau, and between the north and south flanks of Chatham Rise. ANOSIM R and homogeneity statistic values were low for all three classifications at all class levels, indicating poor ability to map benthic distributions at the spatial scale and taxonomic resolution of the OS 20/20 samples. The classifications also showed poor ability to discriminate the OS 20/20 sites on the basis of biodiversity metrics. The BOMEC and Chat/Chall classifications were generally similar in performance and both were better than the MEC. Our main conclusions from these results are: (1) the BOMEC is an improvement over the MEC for mapping benthic distributions; (2) neither the BOMEC nor the MEC provide reliable information at the spatial scale of individual OS 20/20 sample sites; (3) at larger spatial scales (ca. over 100s km) both MEC and BOMEC classifications produced patterns that were broadly consistent with sampled benthic distributions, suggesting that they might have applications in regional-scale assessment of benthic habitats; (4) to be useful in management or planning applications, objective criteria for determining appropriate, ecologically relevant, classification levels and spatial scales are needed, and (5) further OS 20/20-style surveys could be effective for expanding the scope and generality of existing marine environment classifications.

Identifications and abundances of sessile benthic epifauna from DTIS still camera photographs during OS 20/20 voyages TAN0705 (Chatham Rise) and TAN0707 (Challenger Plateau). DTIS is NIWA's Deep Towed Imaging System.

Surveys to map the distributions of marine benthic assemblages require sampling strategies that yield information at spatial scales and taxonomic resolutions relevant to research questions. Allocation of sampling effort, however, involves trade-offs between factors including the number of sites sampled, the sampling methods used, the taxonomic resolution achieved, and the primary purpose of the survey. We used data sets from extensive Ocean Survey 20/20 (OS 20/20) benthic surveys of Chatham Rise and Challenger Plateau, New Zealand, to evaluate different approaches to survey design. To check that the survey data would be useful for informing design criteria for other locations, we first compared the variance structure of each data set at a range of spatial scales. Next, we used multiple regression, for univariate measures, and Canonical Correspondence Analysis (CCA), for multivariate community data, to evaluate the proportion of variance in the test data sets that was explained by a suite of oceanographic and seabed variables. These variables were used in three forms: (1) the unclassified data, (2) after non-hierarchical k-means clustering, (3) after hierarchical average-linkage clustering. We also evaluated the design strata that were actually used for the OS 20/20 surveys, and a classification based on variables derived from multibeam echosounder (MBES) acoustic data alone, and then re-evaluated the proportion of explained variance after incorporation of the spatial distance between samples as a predictor variable. We used power analysis to estimate the number of samples needed to detect differences between classes reliably, and evaluated the effect of increased numbers of samples on the precision of estimates for a suite of biodiversity metrics. Finally, we constructed correlograms to evaluate rates of change in assemblage similarity with spatial distance across Chatham Rise only. For most data sets, variances did not differ significantly between or within locations but the magnitude of variances differed with both sampling method and spatial scale. Variance in the OS 20/20 faunal data was best explained by regression against the unclassified environmental variables (24.1% to 48.7% explained depending on metric) but classes derived from the k-means clustering method performed similarly (26.3% to 42.7%). Hierarchical clustering and MBES classes performed poorly by comparison. Increases in the power to differentiate between habitats and the precision of assemblage metric estimates were observed with all increases of sampling effort up to the limit possible using the OS 20/20 data. Spatial distance explained 11% of the total variance, whereas environment alone explained 29 % and only 1% was explained by interaction of the two. We conclude that the Chatham-Challenger OS 20/20 data provide a valuable test set for evaluating different approaches to survey design and provide insights into how broad-scale biodiversity surveys in New Zealand might be planned in future. The results demonstrate that increasing the density of sampling (decreasing sample lag) is likely to be the most effective way of improving the accuracy of biodiversity maps. In practice, this would entail sampling with a smaller number of methods. Of the methods evaluated here, data sets from video and epibenthic sled were the most informative, primarily because they combined the smallest sample lag with fine taxonomic resolution. While environmental data can serve as a sound basis for initial survey design, our analyses show that a proportion of sampling effort should also be allocated purely on the basis of spatial distance between sites, or by reference to characteristics of the area that are not captured by the environmental data.

Identifications and abundances of mobile benthic epifauna from DTIS still camera photographs during OS 20/20 voyages TAN0705 (Chatham Rise) and TAN0707 (Challenger Plateau). DTIS is NIWA's Deep Towed Imaging System.