Chapter 2: General methods
2.6 Data analysis
The dataset from 2001-2004 was available from Boyd et al. (2004) and Cooper et al. (2005). The analyses were carried out by combining these datasets with the 2007 dataset analysed by the author.
2.6.1 Traditional statistical analyses
A variety of traditional analyses were used to assess the recovery of macrofaunal assemblages after dredging. These analyses were the species richness (S), abundance (N), biomass (B) (based on Ash Free Dry Weight (AFDW)), Margalef’s diversity index (Dm), Simpson’s diversity index (Ds) and Taxonomic Distinctness (TD). Further description of the selected indices can be found in Chapter 3. An analysis of variance (ANOVA) test was applied (after assessing that the assumptions of ANOVA were met) using the mean values for each of the above indices to verify the significant difference between different sampling stations and years.
2.6.2 Functional analyses
Cooper et al. (2008) reviewed 12 functional analyses to quantify functional diversity and recommended 5 techniques as being suitable for use with benthic macrofaunal data which was collected from the Hastings Shingle Bank. The selected techniques were Infaunal Trophic Index (ITI), Somatic Production (Ps), Biological Traits Analysis
(BTA), and Rao’s Quadratic Entropy coefficient (Rao’s Q), and Taxonomic Distinctness (TD). Due to the similar nature of the seabed characteristic between these studies, the same indices were selected to be used in the present study with some slight modifications where TD was classified as a traditional index. In addition, another recent index, Functional Diversity (FD), was also selected. Each of the selected techniques is thoroughly described in Chapter 4.
2.6.3 Multivariate analyses
A multivariate statistical approach was used to examine temporal and spatial variation in macrofaunal assemblages and sediment distribution. A similarity matrix of the biological data (e.g. abundance, species richness and the values of functional indices) was constructed using the Bray-Curtis similarity measure. Non-metric multi-
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dimensional scaling (MDS) ordination was applied to the similarity matrix to produce a two-dimensional ordination plot representing the similarity between samples/plots. The distance between samples indicates the relative similarity of the multivariate data where samples that are clustered together are more similar than samples that are far apart. Occasionally, the similarity between samples, based on biological data, was presented using cluster analysis. This analysis produces a dendrogram that clusters the samples according to their similarity, such that samples that are clustered into the same branch are more similar than samples that are clustered within other branches. Analysis of similarity (ANOSIM) was performed to test the null hypothesis (H0) that
there was no significant difference between samples collected from different stations. This test produces a value (R value) ranging from -1 to 1; where the value close to 0 indicates the high similarity between samples while the value closer to 1 indicates that the samples are becoming less similar. In unusual cases, where the similarity between samples is higher than the similarity within samples, the ANOSIM test produces a negative R-value. The nature of the community groupings identified in the MDS ordinations was further explored by applying the similarity percentages program (SIMPER) to determine the contribution of individual species to the average of dissimilarity between samples. In contrast to the MDS that was used for biological data, a principle component analysis (PCA), based on Euclidean distance was applied to sediment distribution data to identify any group of samples with similar sediment characteristics. The correlation between macrofaunal assemblages and environmental variables was analysed using Bio-Env routine. This technique was used to investigate if the same groups of species or the same functional traits have any relation with the size of the sediments. This is done by produce a rank correlation of two similarity matrices (biotic and environmental) to determine which sediment particle attributes that best explains the distribution pattern of macrofaunal community. The Bio-Env procedure was also applied to functional groups data (Chapter 6) to determine which set of functional groups best explains the observed macrofaunal community. All multivariate analyses were carried out using PRIMER (Plymouth Routines In Multivariate Analysis of Variance) package version 6 (Clarke and Gorley, 2006).
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