Identification of biotopes

The production of habitat maps for use in Marine Protected Area network design requires the ability to map the biology at an appropriate level for use in planning, given the resolution of the underlying acoustic data. Due to the coarse resolution of acoustic data from the deep sea, mapping at a ‘community’ level is necessary to cover the vast area involved. To achieve this, biological assemblages or ‘biotopes’ are used as mapping units, where they represent distinct biological assemblages associated with certain environmental factors such as substratum and depth (Dahl 1908).

Biotopes were defined using multivariate analysis [PRIMER v. 6 (Clarke and Gorley 2006)] applied to quantitative morphospecies data derived from image analysis. Cluster analysis is a method for finding hierarchical grouping in multivariate datasets, and SIMPER [similarity percentage (Clarke 1993)] is a simple method for assessing which taxa are primarily responsible for an observed difference between groups of samples identified from the cluster analysis (Clarke 1993). SIMPER is not a statistical testing framework, but an exploratory analysis that indicates which species are principally responsible either for an observed clustering pattern, or for differences between sets of samples (Clarke and Warwick 2001). The ratio between the contribution each species makes to the average similarity within a group (sim) and the standard deviation (SD) of its contribution can be used to determine those species that typify a group; those species that are consistently abundant throughout, where the SD of its contribution is low, and the ratio between sim/SD is high is used to identify those species which characterise that cluster (Clarke 1993), additional species had to contribute > 5% to that clusters to be considered a characterising species. The SIMPROF [similarity profile (Clarke et al.

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2008)] routine looks for statistically-significant evidence of genuine clustering in samples which are unstructured a priori.

As the purpose was to define benthic assemblages, highly mobile species such as fish were removed from the dataset prior to the analysis. Cluster analysis was used to identify significant clusters; the SIMPROF routine was undertaken on transformed, Bray-Curtis similarity matrices. Once significant clusters were identified, the SIMPER routine was used to determine which species were driving the clustering. Data were combined with available environmental data to define benthic assemblages.

Due to the nature of hierarchical analysis, samples may cluster together on the basis of a single, often abundant, species. The resulting cluster may not represent a distinct benthic assemblage that easily functions as a mapping unit. A number of criteria were used to accept / reject those clusters identified by SIMPROF, to remove outliers and to remove those which did not serve as coherent biological assemblages that could be used as mapping units:

1. Outlier clusters were taken at a 1% Bray-Curtis similarity level on the dendrogram and discarded.

2. Clusters that contained less than 7 images were deemed as not adequately representing a coherent assemblage and were also discarded.

3. Those clusters that have an average similarity (SIMPER) of less than 15% were defined as not being coherent.

4. In line with habitat classification, SIMPROF clusters can be split on the basis of substratum.

5. SIMPROF clusters can be combined at a lower similarity node on the dendrogram to produce more practical mapping units (appropriate scale).

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Biotopes were defined as distinct benthic assemblages occurring on a specific substratum, or range of substrata, over a given depth, these definitions were used to biotope map the video footage.

An essential element of biotopes is that they are practical, mappable units that can be easily recognised visually and applied at an appropriate scale for management of SACs in order to allow cost effective, rapid assessment and monitoring of sites. With sparsity and issues relating to the acquisition of deep-sea data, it may not always be feasible to strictly adhere to statistical rigour, and thus additional criteria may need to be implemented – although this process needs to be transparent. Therefore following standard multivariate analysis, faunally distinct clusters (as assessed using the criteria described above) were assessed against a second set of criteria to determine their use as mapping units. Only those clusters that subsequently met these criteria were further analysed in terms of their faunal composition and diversity. To function as a mapping unit assemblages must 1) occur at a scale relevant to the resolution of the acoustic data and the scale of existing widely accepted benthic communities, such as cold water coral reefs (e.g. 10 m scale), and 2) be easily identified from video data.

To aid in the applied use of biotope, this requires a comprehensive description which can be recognised by others. Simply using species that typify a biotope identified by the SIMPER routine may not adequately reflect the assemblage. This is particularly true in instances where the sample size is too small to fully capture larger, conspicuous fauna, which in descriptive terms is necessary for the biotope to be recognised temporally and spatially. As biotopes need to be easily recognised from video data, in addition to using the results from the SIMPER analysis, those biotopes where the SIMPER analysis had not sufficiently identified the conspicuous taxa (i.e. due to sample size being too small)

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names could be derived during video mapping. Coherent clusters that related to biotopes were given a code (for ease of interpretation) according to the characterising species (morpho-types), using a maximum of two, taking the first 3 letters of each. For example, if the chacterising species were Lophelia pertusa and Madrepora oculata, the code would be Lop.Mad. As biotopes are being described to produce standard terms to be used as mapping units, and thus recognisable, a full name was given that encapsulated the dominant taxa and associated substratum.