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Hierarchical clustering Use of benthic invertebrate community for the anthropic impact assessment in marine coastal environments. (1) INTRODUCTION Benthic.

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Presentation on theme: "Hierarchical clustering Use of benthic invertebrate community for the anthropic impact assessment in marine coastal environments. (1) INTRODUCTION Benthic."— Presentation transcript:

1 Hierarchical clustering Use of benthic invertebrate community for the anthropic impact assessment in marine coastal environments. (1) INTRODUCTION Benthic macroinvertebrates are often used for ecological assessment as they are strongly linked to the substrate and are therefore potentially influenced by toxic and persistent pollutant accumulated into the sediments. Use of marine macrobenthos for ecological assessment is widespread, although in marine ecosystems natural variability (spatial patchiness and temporal variations) in the distribution of benthic organisms can be very high. Also many other sediment- related environmental variables such as particle size often show deep spatial variability which can be hard to quantify prior to sampling. All this variability can hide the possible relationship between pollution and macroinvertebrate community structure.Whenever the existence of patchiness is not adequately taken into account in sampling programmes planning, the variability can be undervaluated, leading to incorrect description of the impacts. This inconvenience can be overcome using a nested hierarchical sampling design, which requires the adoption of hierarchical-organized spatial scales and multiple control sites (Underwood, 1981; Andrew e Mapstone, 1987, Morrisey et al., 1992). (3) CHEMICAL QUALITY Firstly, chemical analyses, performed on the sediment, confirmed the perturbed status of the putatively impacted area CIV, due to higher concentrations of PAHs and PCBs. PCB 0.0 5.0 10.0 15.0 20.0 25.0 TAR ATAR BTAR CSAL ASAL BSAL CAGO AAGO BAGO CCIV ACIV BCIV C site μg / kg IPA 0 500 1000 1500 2000 2500 3000 3500 TARSALAGO CIV site μ g / kg (7) CONCLUSIONS Community structure expresses a clear answer only in presence of a hard anthropogenic impact, otherwise the differences could appear not so evident and require more complex data treatments. Data of macrobenthic community are intrinsically multivariate, so it would be better to consider them globally, without dispersing their informative content. In fact, more sensitive evaluations have been obtained by using multivariate methods. One of the more significative approaches seems to be that of identifying indicator species or taxa, even if this approach requires in-depth and not even available knowledge about sensibility of macrobenthonic species. Whatever the chosen indicator may be, the use of a complex sampling design, with multiple spatial scales and multiple control sites appears to be of great importance. In this study the choice of a complex sampling plan, has allowed us to highlight the existence of a large interval of variability, causes of which may be only partially identified. Multi-Dimensional Scaling (MDS) Samples can be then ordered in a dendrogram by applying a Hierarchical Cluster Analysis. An analogous approach can be used to generate a Multi Dimensional Scaling (MDS) plot, where samples are displaced in a two- dimensional space following their relative similarity. By looking at the examples in the Figures it can be observed how samples within areas are grouped together. Differences between treatments (impact vs control) and among areas were statistically tested by an analysis of similarity, ANOSIM (Clarke 1993, Clarke and Warwick 1994) applied to the ranked similarity matrix. The algorithm for ANOSIM provided by the software Primer 5.0 involves the calculation of a global test statistic which compares variability among replicates within groups selected on an a-priori basis, to variability between groups. In this case a two way nested ANOSIM was applied to test differences in assemblage similarity between sites nested in area belonging to putatively impacted condition (CIV), and sites nested in areas belonging to control condition (TAR, SAL, AGO). E. Garofalo, C. Bonollo 1, L. Gilli 2. 1 CESI Environment Unit, Milan, Italy. 2 Envitech S.r.l., Novara, Italy Analysis of variance Area H' AGOCIVSALTAR 1.0 1.5 2.0 2.5 3.0 SVSSglVFP State 0.741 4.000.183 Area 0.3720.190.660.544 Site 2.2580.282.610.023 Replicate 3.88360.11 Total 7.2547 There is evidence to reject the null hypothesis only at the p < 10% level (Clarke and Warwick 1994). Finally, a taxonomic analysis was applied to investigate for th existance of indicator taxa. This analysis revealed that density of the polychaete Capitella capitata in the impacted area was significantly higher than in the control areas in two campaigns out of three. Capitella capitata is an opportunistic species and one of the most typical members of the benthic communities in organically enriched sediments. Abundances distribution of Capitellidae Results suggest that the study site is characterised by a high variability due to various factors in addition to the anthropic disturb of Civitavecchia area. We have tried to understand which environmental variables could be responsible for the detected variability by superimposing values of some measured variables on sites, represented in MDS plots. In the plots, the circles size is proportional to the values of the variables taken into account: PAHs and PCBs concentration in sediment, mean particles size and site depth.The plots show that all the imposed variables are involved to determine the relative position of area CIV and suggest that further environmental factors have to be investigated. September - PAHs superimposed SAL C AGO C AGO B AGO A CIV A TAR A TAR B TAR C SAL A SAL B CIV BCIV C September - PCBs superimposed AGO C AGO B AGO ACIV BCIV C CIV A SAL C TAR A TAR B TAR C SAL A SAL B AGO C AGO B AGO A CIV A SAL C TAR A TAR B TAR C SAL A SAL B CIV CCIV B September - Granulometry superimposed AGO C AGO B AGO A CIV C CIV A SAL C TAR A TAR B TAR C SAL A SAL B CIV B September - Depth superimposed (4) UNIVARIATE INDICES Some univariate indices were applied on the biological matrix (total abundance, species richness, Shannon-Wiener index, Simpson index, Pielou's evenness index, etc.). To compare the numerical values obtained for each site, we have carried out an asimmetrical three factors ANOVA. ANOVA did not reveal significative differences between treatments (impacted versus controls) and among areas. (2) EXPERIMENTAL DESIGN Study site is located in the Tyrrhenian Sea (Lazio, Italy), in an open coastal area with unvegetated soft sediments and a depth of 5 -10 m. Within this reach are located one potential impacted area (CIV) offshore the city of Civitavecchia and three control areas (TAR, SAL, AGO) offshore a scarcely anthropized region. 5 km CIV AGO SAL TAR Spatial level: area Spatial level: site 100 m A B C Spatial level: replicate 10 m 1 2 3 4 The impacted area so as the controls have been defined a priori by looking at the maps reporting the prevailing land use. The impacted area was supposed to be influenced by the discharges of a thermoelectric power plant. The experimental work was based on a Nested Sampling Design that comprises three spatial scales, as illustrated aside. Sites within areas have been located trying to minimize variation in physical parameters correlated to the sediment such as depth and granulometry. The same sampling design was applied in each cruise campaign, carried out in May, July and September 2000. On the whole 144 macroinvertebrates samples were collected; organisms were isolated and classified to species level. The basic assumption of an anthropic disturb in area CIV, was tested by chemical analyses carried out to detect the presence of some classes of pollutants. Bray-Curtis similarity index calculated on fourth - root transformed abundance data (5) MULTIVARIATE ANALYSIS A multivariate approach was applied with the aid of the software Primer 5.0, developed by the Plymouth University. A triangular similarity matrix was generated by calculating similarities between each couple of samples. Then we tried to investigate differences in macrobenthic community assemblages by applying different data treatment approaches (univariate and multivariate methods) on the whole data set, and by testing them statistically. In other words, impacted condition and control condition appear statistically different but only when adopting a high confidence level. (6)TAXONOMIC ANALYSIS Environmental stress such as chemical pollution is considered to decrease community diversity and species richness and to increase abundance of opportunist species. Benthic macroinvertebrates are often used for ecological assessment as they are strongly linked to the substrate and are therefore potentially influenced by toxic and persistent pollutant accumulated into the sediments. In marine ecosystems, natural variability (patchiness) in the distribution of benthic organisms can be very high, at least as high as the variability in other sediment-related environmental variables such as particle size. Use of marine macrobenthos for ecological assessment has been widespread, sometimes without taking into account the existence of patchiness when field sampling programmes were planned. An experimental work was planned using a Nested Sampling Design that comprises three spatial scales, and three temporal replications. This design was applied to a potential impacted area and to three control areas. Study site was located in an open coastal area, in the Tyrrhenian Sea (Lazio, Italy) characterized by unvegetated soft sediments and an average depth of 5-10 m. Three field campaigns were carried out in May, July and September 2000. On the whole 12 sediment samples and 144 macroinvertebrates samples were collected; a total of more then 13000 organismes were isolated and classified to species level. Chemical analyses, performed on the sediment, confirmed the perturbed status of impacted area, due to higher level of PAHs and PCBs. Various statistical analyses were applied on the whole data set, using different data treatment approaches (univariate indices and multivariate methods). The asimmetrical three factors ANOVA for univariate indices did not revealed significative differences between areas. On the contrary, for multivariate analysis variations within areas were less than differences between areas; however no significative differences were detected between conditions (impacted versus control).


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