Data analysis

Nematode assemblage structure was described by a number of univariate descriptors, including abundance and different diversity measures such as number of genera (N0),

Margalef’s genus richness, expected number of genera in a sample of 50 individuals (EG50), Shannon-Weaver’s diversity (H’), Simpson’s diversity index (Si), Pielou’s evenness (J), and taxonomic diversity (Δ) and distinctness (Δ∗). N0 is merely the number of genera identified

from each sample, whereas Margalef’s richness lowers the impact of the actual sample size on richness estimates. Nevertheless, even the latter index is not independent of sample size; hence we also used the rarefaction index ‘expected number of genera’ in a hypothetical sample of 50 individuals (EG50). Shannon-Weaver’s diversity combines aspects of richness with evenness, whereas Pielou’s J is a specific evenness index. Finally, Simpson’s index measures the probability that any two randomly sampled individuals belong to the same species. This index varies between 0 (infinite diversity) and 1 (assemblage composed of a single species), and provides a measure of the dominance structure in an assemblage. Taxonomic distinctness (Δ∗) provides a measure of the averaged taxonomic distance among all pairs of genera in an assemblage, whereas taxonomic diversity (Δ) is the average weighted path length between every pair of individuals in a phylogenetic tree (Warwick and Clarke 1998, 2001). All these diversity indices were calculated in PRIMER 6.0 (Clark and Gorley 2006).

We further calculated the frequency of occurrence of nematode genera according to Arasaki et al. (2004). Genera were classified as constant if they occurred in at least half of the samples (F ≥ 50 %), common (25 % ≤ F ≤ 50 %) or rare (F ≤ 25 %). Nematodes were also grouped into feeding types according to the feeding type classification of (Moens and Vincx 1997), which is based on a combination of observations and on the assumption that nematode stoma morphology is an important determinant of food selection. This classification recognizes six feeding types: microvores, deposit feeders, ciliate feeders, epigrowth feeders, facultative predators and predators.

68 distances to pollution were assessed by analysis of variance (ANOVA) in the software Statistica 7 (Statsoft). Prior to analysis, data were tested for normality by means of the Kolmogorov-Smirnov test, and homogeneity of variances using Levene’s test. If the data did not conform to these assumptions, they were log(x+1) transformed; if this data transformation did not solve the issue of normality and/or homoscedasticity, data were analyzed using permutational multivariate analysis of variance (PERMANOVA) (Anderson et al. 2008) with location and distance to pollution as fixed factors (with four and three levels, respectively) using 999 permutations. A Euclidian distance based resemblance matrix was used for PERMANOVA on univariate (i.e. total number of nematode, diversity indices….) data. Homogeneity of multivariate dispersion was assessed using PERMDISP.

ANOVA followed the same two-way factorial design as for PERMANOVA. Tukey’s HSD test was used for pairwise a posteriori comparisons between locations, distances to pollution and their interaction in case of a significant factor or interaction effect in ANOVA. Pairwise comparisons following a significant PERMANOVA result used Monte Carlo permutations of residuals under a full model (if the number of permutations was lower than 150, the Monte Carlo permutation p was used). The same statistical approach was used to test for differences in heavy metal or TOC and TN concentrations among locations and distances to pollution for the 2013 dataset. Sediment granulometry data of 2008 were analysed for differences between locations only using one-way ANOVA.

Non-metric multidimensional scaling (nMDS) based on Bray-Curtis similarities was applied to visually explore differences in nematode assemblage structure between locations and distances to pollution. Nematode assemblage composition was further compared between locations, distances to pollution and their interaction using the same PERMANOVA design as for the univariate assemblage descriptors, but using Bray-Curtis similarities. Genera abundances were square root transformed prior to analysis for a better weighting of the contributions of dominant and rare genera. In addition, the genera contributing most to the dissimilarities between distances to pollution and locations were investigated using the similarity percentages procedure (SIMPER) in PRIMER 6.0, again on square root transformed nematode abundance data.

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3.1

Sediment granulometry and total element analysis

Table 2-1. Sediment granulometry (measured on samples from December 2008), total organic carbon (TOC) and total nitrogen (TN) (measured on samples from August 2013) and genus richness (number of genera, N0, determined on samples from December 2008) of the four

beaches studied. Data on granulometry are means ± 1 standard deviation of three stations per beach, with one sample per station. Data on TOC and TN are means ± 1 standard deviation of three stations per beach, with three samples per station. All statistically significant differences indicated by different letters.

Suro Haghani Dolat Park Terminal

median grain size (µm) 155±8.9 b 175±18.6 b 177±17.5 b 119±9.4 a mean grain size (µm) 185.6±28.3 b 194.9±23.4 b 198.1±11.1 b 175.0±2.6 a

% silt 0.4±0.1 b 1.0±1.0 b 5.9±3.9 b 17.8±3.0 a

%very fine sand 31.4±2.8 23.9±7.0 19.6±5.5 34.8±1.2

%fine sand 53.6±4.0 b 53.2±2.1 b 48.2±8.3 ab 29.5±4.2 a

%medium sand 11.4±3.4 20.3±7.1 25.0±3.5 12.61±1.3

%coarse sand 1.9±1.9 1.6±1.2 1.2±0.8 3.4±0.8

%very coarse sand 0.6±0.6 0.0±0.0 0.0±0.0 0.9±0.3

%TOC 0.1±0.0 b 0.2±0.1 ab 0.1±0.0 b 0.3±0.1 a

%TN 0.03±0.0 0.02±0.0 0.03±0.01 0.03±0.0

genus richness 10.44±1.59 a 5.6±0.28 bc 9±0.78 ad 8.1±0.9 cd

The sediment of the four locations was mainly composed of very fine sand and fine sand throughout. Significant differences in median grain size between locations were observed (df = 3, pseudo-F = 3.57, p = 0.05), Terminal having a lower median grain size and higher silt fraction than the three other locations (Table 2-1 and Table S2-3 ).

Total nitrogen (TN) concentrations were always < 0.1 % and did not differ depending on the location-by-distance interaction (df = 6, pseudo-F = 0.99, p = 0.50), nor between locations (df = 3, pseudo-F = 1.04, p = 0.7) or with distance from pollution (df = 2, pseudo-F = 0.61, p = 0.57). Total organic carbon (TOC) concentrations varied from 0.1 to 0.7 % and were not

70 however, they differed significantly between locations (df = 3, pseudo-F = 4.2, p < 0.02) as well as between distances from pollution (df = 2, pseudo-F = 4.46, p < 0.02). Terminal showed a significantly higher TOC concentration than Suro (p = 0.01) and Dolat Park (p = 0.02) (Table 1), and stations nearest pollution sources and at 100 m away both had significantly higher % TOC than the most distant station (both p < 0.05). The significant distance-to-pollution effect was largely due to Haghani and Terminal, where the station nearest the pollution point source had, respectively, more than and nearly threefold higher TOC concentrations than the two more distant stations.

The elements As, Be, Bi, Cd, Co, Hg, Ni and Pb were present at concentrations < 10 ppm throughout our study area (data not shown). Ba, Cr, Cu, Sr, Zn, Y and S were obtained in higher concentrations and varied among locations, distances to pollution and/or their interaction (supplemental material – Table S2-1), except Cu. However, no single location or distance to pollution had clearly higher or lower levels of multiple elements. Haghani had significantly higher concentrations of Cr than all other locations, while both Haghani and Suro had significantly higher concentrations of Sr, which could be considered in line with the expected higher pollution at Haghani and to a lesser extent Suro, but other metals did not always follow those trends. Likewise, elemental concentrations did not exhibit clear trends with distance to a pollution source (Table S2-1). Cu, which is often linked to sewage and other anthropogenic pollution sources, did not vary significantly between locations and distances to pollution.