ICES Journal of Marine Science: Journal du Conseil Advance Access originally published online on July 26, 2008
ICES Journal of Marine Science: Journal du Conseil 2008 65(8):1414-1420; doi:10.1093/icesjms/fsn125
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This article appears in the following ICES Journal of Marine Science issue: Marine Environmental Indicators: Utility in Meeting Regulatory Needs [View the issue table of contents]
Biological indicators of disturbance at a dredged-material disposal site in Liverpool Bay, UK: an assessment using time-series data
1 Centre for Environment, Fisheries and Aquaculture Science, Lowestoft Laboratory, Pakefield Road, Lowestoft Suffolk, NR33 OHT, UK
2 Napier University, School of Life Sciences, 10 Colinton Road, Edinburgh E10 5DT, UK
Correspondence to P. Whomersley: tel: +44 1502 524304; fax: +44 1502 513865; e-mail: p.whomersley{at}cefas.co.uk
Whomersley, P., Ware, S., Rees, H. L., Mason, C., Bolam, T., Huxham, M., and Bates, H. 2008. Biological indicators of disturbance at a dredged-material disposal site in Liverpool Bay, UK: an assessment using time-series data. – ICES Journal of Marine Science, 65: 1414–1420.The development of reliable indicators of disturbance in the marine environment is essential because of increasing anthropogenic pressures and the need for more effective regulation. Our objective was to evaluate 13 nationally and internationally recommended metrics, using a large dataset derived from annual (1996–2003) macro-invertebrate infaunal surveys of a Liverpool Bay dredged-material disposal site. The primary and derived univariate metrics, along with multivariate derivations, were assessed and scored against six selection criteria. Metrics that did not correlate with the pressure indicator (the annual quantity of material disposed of) were discounted from further analysis. Of the 13 metrics evaluated, only measures of species number and Margalef’s species richness index were significantly correlated. Although assemblage types were significantly different between stations, common time patterns were observed, indicating that underlying larger scale, low-frequency events may have influenced assemblage development at all locations.
Keywords: biological indicators, dredged-material disposal, macrofauna, time-series data
Received 23 November 2007; accepted 17 April 2008; advance access publication 26 July 2008.
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Introduction |
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 Top Introduction Material and methodsResultsDiscussionReferences |
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Increasing utilization and exploitation of the marine environment make the development of reliable indicators of disturbance essential to meeting the associated need for more effective regulation of all these activities (Rogers and Greenaway, 2005). Major drivers of indicator development include political commitments to achieving international ecosystem targets set, for instance, within the OSPAR Biological Diversity and Ecosystems Strategy and the EU Water Framework Directive.
Environmental scientists have a wide range of analytical tools for measuring both physical and biological shifts within marine ecosystems (Washington, 1984; Elliot, 1994; Danilov and Ekelund, 2001; Quintino et al., 2006). Difficulties arise when trying to identify the most informative and reliable metrics to use in a given situation (Salas et al., 2006). Univariate tools available for assessing the health of biological communities include primary and derived indices, such as number of individuals (N), species number (S), Margalef’s species richness index (d), Shannon–Wiener species diversity (H’), and taxonomic distinctness and diversity (Clarke and Warwick, 2001). Multivariate techniques include multidimensional scaling (MDS), similarity percentage (SIMPER), and the RELATE procedure (Clarke and Gorley, 2006). In addition, biological metrics for the benthic fauna take into account the pollution tolerances and ecological strategies displayed by individual species, e.g. the AZTI marine biological index (AMBI; Borja et al., 2003) and the infaunal trophic index (ITI; Word, 1979; Maurer et al., 1999).
In an indicator context, these metrics can be used in several ways. For instance, they can be applied in monitoring to assess natural variation within a biological community (MEMG, 2004; Schratzberger et al., 2004) or to assess the impact of anthropogenic activities on local communities, such as at a dredged-material disposal site (Rees et al., 1992, 2006b). They may also be used for assessing the effectiveness of management practice (Whomersley et al., 2007).
Appropriate sampling designs accompanied by a good understanding of the recent history of the human activities of interest are a prerequisite for effective indicator application. For the initial evaluation of the usefulness of different metrics as indicators of disturbance, the potential for confounding natural and anthropogenically induced variation must first be discounted, typically through the selection of appropriate reference sites. For dredged-material disposal, impacts may be the result of the physical act of burial (amount deposited), a change in sediment (type of sediment being deposited), the presence of contaminated material, or by a combination of all these factors. The type of impact expected is clearly important when selecting the metric(s) that will be the most effective as indicators. Many selection criteria have already been suggested and used to evaluate metric performance under varying circumstances (ICES, 2001; Defra, 2004; EEA, 2005; Sneddon et al., 2006), including their scientific validity, correlation with manageable human activities, ease of communication, relevance to decision making, sensitivity and ability to show spatial and temporal trends, and cost-effectiveness.
Our objective is to apply these national and international recommendations in the evaluation of a suite of potential metrics, using a large dataset derived from annual (1996–2003) macro-invertebrate infaunal surveys of a Liverpool Bay dredged-material disposal site. The main questions addressed are:
- What measurable environmental impacts associated with the disposal can be linked to or correlated with a faunal response?
- Does an individual metric suffice or should a suite of metrics be employed when assessing the effects of disposal?
- What are the wider lessons for indicator application?
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Material and methods |
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TopIntroductionMaterial and methodsResultsDiscussionReferences |
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Site Z in Liverpool Bay on the west coast of the UK (Figure 1) was first licensed for disposal of dredged material in 1982, following closure of a nearby site owing to shoaling. The disposal site is located in shallow water (10 m) and is exposed to wave action principally from westerly to northerly winds (Rees et al., 1992; Somerfield et al., 1995), with residual bottom currents flowing in a predominantly landward (eastward) direction (Ramster, 1973). In 1996, Site Z was extended to the west, also because of shoaling in the centre of the licensed area. From 1996 to 2003, this site received 15 million wet tonnes of dredged material, an average of 2 million wet tonnes per annum. This material originated largely from maintenance dredging of docks or navigational channels in the Mersey estuary and its approaches (Somerfield et al., 1995).
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A macro-invertebrate infaunal dataset was produced from four replicate samples collected annually in September 1996–2003 from one station within the disposal ground (D) and from two nearfield reference stations to the south and to the north (Rs and Rn, respectively; Figure 1). Sediment samples were also collected for particle size analysis, and organic (carbon and nitrogen) and metal (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) content.
Thirteen metrics were then calculated from the biological data for each station. Normality of the distributions of the estimates was tested using the Anderson–Darling test. Two groups of criteria for judging their utility were selected from those identified by a variety of national and international organizations (ICES, 2001; Defra, 2004; EEA, 2005):
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If a metric was considered not to be scientifically valid (i.e. relevant to the objective of measuring disturbance), or not to be tightly linked to the disturbance in question, then the metric was rejected from further analysis. Metrics that passed the Group 1 criteria were then evaluated using criteria from Group 2. Scores (1 = very poor, 2 = poor, 3 = good, 4 = very good, and 5 = excellent) were subjectively allocated in consultation with four other experienced benthic ecologists based at Cefas. Only the two highest scoring metrics were used to evaluate the disturbance effects at the disposal site.
The initial list of indicators used was produced by the authors using expert judgement and existing literature (Aubry and Elliot, 2006), and certainly was not exhaustive. The choice of the criteria used may be biased towards specific activities and management objectives (Rice and Rochet, 2005), and the method of scoring based on expert judgement will be sensitive to the experiences of those involved (Rochet and Rice, 2005). We accept the subjective nature of this approach. However, the increasing need for advice on the implications of human activity within the marine environment, combined with the large array of potential indicators and the restrictions on resources available for regulators, demands that alternative methodologies of developing and assessing indicator performance be explored.
Univariate analysis
Because of the absence of any significant differences in contaminant levels and sediment type at the three sites, we decided that the most appropriate pressure indicator of disturbance to link to the state and response of environmental parameters was the amount of material disposed per annum.
To assess whether the chosen metrics were tightly linked to dredged-material disposal, the Pearson product-moment correlations were calculated between each metric and the annual quantity of material deposited in the same year and with a 1-year time-lag. A general linear model (GLM) with factors station, year, sample (nested in station), and an interaction term of station*year was then constructed to evaluate differences between years and stations. Metrics displaying a significant (p < 0.05) correlation (positive or negative) with disposal quantities were then tested for significant interaction terms. Those between years and sites were of particular interest, because they imply different temporal trajectories at different stations. In addition, Tukey’s multiple comparison tests were carried out to investigate differences between stations within years. Treatment–reference (T/R) ratios (in fact we used 100*[T/R–1], so that zero represents equality) were calculated using a pairwise comparison of annual measures to investigate the degree of community change and synchrony at the three stations. A mean cumulative species abundance plot was constructed to investigate species dominance within each station.
Multivariate analysis
To complement the two high-scoring metrics and to assess whether or not disposal had a significant effect on macrofauna community structure, the Spearman rank correlations were calculated between Bray–Curtis similarity matrices derived from the samples for each station. This allowed the investigation (through the RELATE procedure, which constructs a seriation model to assess to what extent samples follow a simple linear trend) of directional changes in macrofauna community composition over time at the three stations, with a significant correlation indicating comparable temporal trends.
Non-metric MDS ordinations derived from Bray–Curtis similarity matrices were carried out to display differences in the structure of macrofauna communities. Two-way analysis of similarities (ANOSIM) was performed to assess differences in macrofauna community structure between stations and over time. The SIMPER procedure was utilized to identify the main species contributing to the community patterns observed. The BEST procedure, which searches for high correlations between matrices typically created from species assemblages and environmental variables presumed to include those thought to be driving the assemblage structure, was also employed to ascertain if the same groups of species from the different stations correlated with the annual quantity of material disposed of. All multivariate analyses were performed on double square-root transformed species abundance data, using PRIMER version 6.1.5 (Clarke and Gorley, 2006).
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Results |
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TopIntroductionMaterial and methodsResultsDiscussionReferences |
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Univariate metrics
There were no significant differences between mean values of sediment characteristics (median particle size, percentage silt/clay, organic carbon and nitrogen, and the content of several metals; data not shown), when reference stations and the disposal station were compared. Species number S (r = –0.83; p = 0.02) and species richness d (r = –0.82; p = 0.02) were the only metrics to correlate significantly with amounts deposited lagged 1 year (Table 1).
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= yes; - = no.Throughout the time-series, S and d tended to be higher at the reference stations than at the disposal station (Figure 2). Both metrics also scored high (mean score: 4 = very good) when assessed using the Group 2 criteria (Table 2). Therefore, these two were selected to assess further the effects of dredged-material disposal.
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The GLM results revealed significant differences in S (F = 97; p < 0.01) and d (F = 89; p < 0.01) between sites, as well as over time (F = 14, p < 0.01; and F = 11, p < 0.01, respectively). Moreover, the significant station*year interaction terms (S: F = 5.7, p < 0.001; and d: F = 5.1, p < 0.001) indicate that the three stations behaved differently over time. Tukey’s multiple comparisons test also revealed several significant differences between disposal and reference stations within years for both metrics (Table 3).
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T–R ratios remained below equality (zero) for most of the time-series, indicating that values of both metrics were lower within the disposal site, except for 1999 when T–R values for S and d both rose above zero for station Rs (Figure 3).
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The mean species-accumulation abundance curves suggest that, apart from a lower overall abundance, abundance is more evenly spread at Station D (Figure 4). Densities of the dominant species, the polychaete Lagis koreni, at all three stations showed large fluctuations over time. Although the maxima at various stations appear to coincide with the years when disposal was relatively low (Figure 5), correlations for individual stations were not significant.
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Multivariate metrics
Comparison of Bray–Curtis similarity matrices derived from the biotic time-series data using the RELATE procedure resulted in significant R-values (Rn/D: r = 0.55, p < 0.01; Rs/D: r = 0.49, p < 0.01; Rn/Rs: r = 0.61, p < 0.01), indicating that there were common time patterns among all stations (Figure 6).
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Results from the two-way ANOSIM with replication revealed significant differences in community structure between all three stations (r = 0.79; p < 0.01). Further analysis using a one-way ANOSIM also showed significant differences between stations within years (Table 3).
SIMPER analyses showed that the species contributing most to the dissimilarity between stations were L. koreni and the bivalve Spisula subtruncata. The main dissimilarities (17%) between stations were in the abundances of these two species. Results from the BVSTEP routine identified different groups of species that correlated with the quantity of material disposed of each year. At Station D, the group correlating most strongly with the quantity disposed of (r = 0.91) was represented mainly by predatory polychaete species from the families Glyceridae, Nephtyidae, and by deposit-feeding species from the family Ampharetidae. At the reference stations (r = 0.90 in both cases), the groups with the highest correlations were represented mainly by ophiuroids, amphiurids, and bivalves.
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Discussion |
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TopIntroductionMaterial and methodsResultsDiscussionReferences |
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Potential impacts of dredged-material disposal on the receiving environment depend on the nature and quantity of the material deposited (Rees et al., 1992, 2006b; Rees and Rowlatt, 1994; Bolam and Rees, 2003; Bolam et al., 2006). These impacts include burial, changes in sediment type, and organic enrichment. Because the stations did not differ significantly in sediment type, trace metal, and organic content, the amounts deposited per annum were considered the most appropriate pressure indicator to use in our assessment of the effects at Site Z.
The two Group 1 criteria represent prerequisites necessary for the use of any metric as an indicator of disturbance: any initial selection must involve an informed decision on whether or not the measurement is likely to be relevant in the given situation. In our case, this involved considering the basic logic of the metric and looking for evidence of some sensitivity to the load of dredged material. The Group 2 criteria facilitate the mutual weighting, ensuring that the metrics chosen are fit for the purpose, e.g. by providing managers with accurate, cost-effective, and easily communicable results. This procedure raises important questions, including how can we ensure that observed changes represent a cause–effect relationship and therefore that the metrics are tightly linked to the manageable anthropogenic activity in question?
Only two metrics (species number and species richness) of 13 investigated passed the Group 1 criteria. The remaining metrics did not show a correlation with the amount of material deposited in the same year or lagged by 1 year. This is hardly surprising in so far as some metrics (such as AMBI and ITI) address impacts of organic enrichment, which appear not to be represented by the pressure data. However, the absence of a biological response in organic-enrichment indicators may in itself have value in evaluating the actual cause–effect relationships at this particular site in Liverpool Bay.
Both univariate (GLM with species number and richness as responses) and multivariate (ANOSIM) analyses showed persistent significant differences between all three stations. However, when multivariate community data were analysed using the RELATE routine, no significant differences in the macrofaunal communities over time were observed. The finding that communities at the three stations, despite significant differences, exhibited common time patterns over an 8-year period suggests that factors other than quantities of dredged material, such as natural variation (Hall et al., 1994) and climate change (Rees et al., 2006b), have contributed to the variation observed. To ascertain the effects of changing climatic conditions, further correlations with the species metrics could have included the North Atlantic Oscillation (NAO) index and sea surface and bottom temperature (Rees et al., 2007). This illustrates the central challenge in interpreting long-term datasets: to discriminate between low-amplitude, low-frequency drivers associated with climate change and high-frequency, point-source impacts caused by fishing, aggregate extraction, or dredged-material disposal.
Species number and Margalef’s richness index were lower within the disposal station than at the reference stations. The SIMPER analysis showed that L. koreni and S. subtruncata were the dominant species at all three sites. Lagis koreni has been identified previously as a possible indicator of disturbance within the Liverpool Bay dredged-material disposal ground and is thought to dominate in such disturbed areas because of its opportunistic life cycle, which enables it to colonize recently deposited material (Rowlatt et al., 1990; Rees and Rowlatt, 1994). However, as L. koreni was present in numbers greater at the two reference stations than at the disposal station, this may be an example of how disturbance may increase evenness by preventing the benthic community from reaching a climax represented by a few well-adapted dominant species (Paine, 1974; Connell, 1978).
The identification of metrics capable of detecting and quantifying the effects of dredged-material disposal (or any other point-source impact) generally relies on the comparison of impacted and reference stations. The method used here for calculating treatment–reference ratios relative to a no-effect zero line yields an easily communicable output and permits changes in primary metrics to be summarized and communicated to environmental managers in an unambiguous way. This approach may help to set action levels to guide managerial decisions relating to the future use of the site. The setting of such action levels (environmental quality standards) has been applied previously to other marine disposal activities (MAFF, 1993; Rees et al., 2006a), based on an acceptable deviation from reference values of primary and derived metrics. When assessing impacts of licensed activities, their sphere of influence is an important issue. Any measurable effect on the benthic community within a licensed area, although interesting scientifically, would be considered to be acceptable under the licensing agreement. However, if the activity began to affect areas outside the disposal site, then action would need to be taken. Using our method of treatment–reference ratios, it would be possible to set action levels based on the distance from equality. A reduction in the magnitude of the difference could indicate that communities were being affected at the reference stations, and further assessments would be necessary. An important management decision would relate to the choice of the critical threshold that should precipitate action. It is likely that these thresholds would be site-specific and would need to be adapted over time, e.g. in response to systematic changes in disposal practices.
Similar analyses for other disposal sites and for other types of human activities would be helpful in refining the use of disturbance indicators. Only metrics proven to be linked to the disturbance in question should be used if the aim is to monitor the extent of the disturbance, rather than to detect the disturbance. The two univariate indices evaluated here, species number and Margalef’s species richness index, should be used as front-line indicators to monitor future biological effects of dredging disposal on macrofaunal communities within the Site Z disposal ground and at the far-field reference stations.
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