Environmental Engineering and Management Journal September 2010, Vol.9, No. 9, 1259-1266 http://omicron.ch.tuiasi.ro/EEMJ/

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IMPLEMENTING EUROPEAN WATER FRAMEWORK DIRECTIVE:

UNCERTAINTY DEGREE OF METRICS FOR MACROINVERTEBRATES IN TRANSITIONAL WATERS

Aurelia Caldararu1∗, Ilaria Rosati2, Enrico Barbone2, Lucian P. Georgescu2, Catalina Iticescu1, Alberto Basset2

1“Dunarea de Jos” University of Galati, European Center of Excellence for the Environment, Faculty of Sciences,

111 Domneasca Street, 800201 Galati, Romania 2University of Salento, Department of Biological and Environmental Sciences and Technologies, Lecce, Italy

Abstract A key step of European Water Framework Directive (WFD) implementation is the ecological status classification and the achievement of ‘good water status’ for all waters, by 2015. Macroinvertebrates are key quality elements, and many metrics have so far been proposed for ecological status classification with benthic invertebrates. In transitional waters, the changing environmental niche induces responses in the macroinvertebrate guilds and macroinvertebrate responses induce uncertainty in the metrics. In this case, the sources of uncertainty in the ecological classification with benthic macroinvertebrates, is addressed by focusing on two major potential sources: spatial heterogeneity and temporal heterogeneity. To this aim, we have studied relative pristine lagoons within protected areas – Le Cesine (Italy) and Sinoe (Romania) - that are characterized by a different degree of component of transitional water internal heterogeneity, i.e. habitat patchiness. Benthic macroinvertebrates were sampled with the same methodology at the two stations, with two sites per station and five replicates, in different seasonal periods and at tree different habitats. On the collected data different metrics were computed and compared. The variability between ecosystems, seasons, habitat types and replicates, was quantified and compared among metrics. Taxonomically based metrics showed a higher variability than non-taxonomic ones, highlighting that the latter better fulfill the requirement of low uncertainty. Key words: benthic macroinvertebrates, metrics, transitional waters, uncertainty, water monitoring Received: May, 2009; Revised final: July, 2010; Accepted: September, 2010

∗ Author to whom all correspondence should be addressed: e-mail: costela.aura@yahoo.com

1. Introduction A main challenge in studies of ecological

assessment is the ability to isolate human-induced effects (signal) from the natural, inherent variability (noise) associated with ecosystem structure and function (Jonson et al., 2006). Stressed systems often show a reduction in species richness, with a change in the number of individuals within a species and a predominance of stress-tolerant species (Jonson et al., 2006). The logical assessment following the Water Framework Directive is based upon the status of biological, hydromorphological and physico-chemical quality elements (Borja et al., 2009; Teodosiu et al., 2009). The biological elements to be considered are

phytoplankton, macroalgae, angiosperms, benthic macroinvertebrates and fishes. Even though all biological quality elements are potentially equally important and a large number of species may be utilized as indicators of stress, zoo-benthic guilds were favoured over both planktonic and nektonic guilds in the last years. The latter are less useful for evaluating long-term contamination episodes because they respond almost immediately to pollution, thus their use is indicated for evaluating the short response of pollution, also the distribution and activity of benthic invertebrates are affected by various abiotic (Basset et al., 2008).

European Union countries are now bound to assess and monitor the quality of their surface and

“Gheorghe Asachi” Technical University of Iasi, Romania

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ground - water bodies through the survey of a set of physical, chemical and biological quality elements defined in the Annex V of the WFD (Blanchet et al., 2008; Lepom et al., 2009; Olaru et al., 2010; Vinke-de Kruijf et al., 2009). One of the innovative aspects of this aquatic Directive is the underpinning role of the use of multiple groups of organisms to detect ecological change (Jonson et al., 2006). As a consequence, numerous bioassessment tools have been developed or adapted to the WFD requirements in recent years, notably in the field of benthic invertebrate ecology because these organisms are generally considered as potentially powerful indicators of aquatic ecosystems health (Barbone et al., 2010; Blanchet et al., 2008).

Several structural parameters of benthic invertebrate (i.e. diversity, abundance and proportion of disturbance-sensitive taxa) offer important information about the health of the aquatic ecosystem and about the water quality: high, good, moderate, poor, and bad (Basset et al., 2008; Muxika et al., 2007; Ponti et al., 2008). Ecological monitoring using macro-invertebrate sampling can help in evaluating the history of the water system, by means of abundance, diversity and composition (whether pollution tolerant species are present) of the macro-invertebrate community (Metcalfe, 1989).

Collecting physico-chemical (water quality) samples from the site can provide information on the conditions at the time of sampling. However, macro-invertebrate sampling can indicate the long-term water quality and physical conditions at the site (Yagow et al., 2006). This is because most macro-invertebrates spend all or part of their life in-stream and have a limited spatial distribution. If the water quality or physical habitat at a site deteriorates, certain types of macro-invertebrates may become extinct from the site. Therefore, macro-invertebrates can provide evidence of pollution that existed in the past without being present at the time of monitoring (Oberholster et al., 2008).

There are many advantages of using macroinvertebrates in bio-monitoring: (a) they are ubiquitous (Lenat et al., 1980); (b) they have a basically sedentary behaviour, which allows a spatial analysis of pollutant (Hellawell, 1986); (c) they have long life cycles when compared with other groups, which allows an analysis of temporal changes caused by perturbations (Lenat et al., 1980); (d) they can be affected by environmental perturbations in many different aquatic ecosystems, which allows an analysis of a spectrum of responses to environmental stress (Rosenberg and Resh, 1993; Sangiorgio et al., 2008). Indicators have been used to describe past, present or future conditions at a variety of geographical scales and for a variety of valued resources. Because they are so diversified, development and selection of successful ecological indicators has become a relatively complex process (William et al., 2001).

It is obvious that different indicators cover different aspects of ecosystem health. It may be

necessary to apply several indicators simultaneously to get a correct image of the health or integrity of an ecosystem, since the domain of ecosystem health is vast and it encompasses not only biophysical dimension, but also socio-economic and human aspects. In order to capture all the features needed to give a fully informative assessment of the condition of an ecosystem, some multi-view descriptions have been used. Thus with appropriate adaptation of formulae coefficients and formulae, these indices could be possible tools in decision-making processes since they describe the aggregate pressures affecting the ecosystem, and can evaluate both the state of the ecosystem and the response of managers. They can be used to track progress towards meeting management objectives and facilitate the communication of complex impacts and management processes to a non-specialist audience (Ponti et al., 2009). Indicators and indices, therefore, can and should be used to help direct research and to guide policies and environmental programs.

However, criteria to select the ecological indicator most appropriate to describe ecological status at local sites are required (Dauvin, 2007). The proliferation of indices adds an element of confusion back into what it is hoped to simplify. Some of the confusion arises because of the different processes used for developing, calibrating and validating indices in different regions. This leads to inconsistencies in assessment across regions.

Additional confusion results from indices developed for multiple types of biota, providing managers with multiple, and often conflicting, answers for a single water body. The challenge requires recognition that our field is maturing and responding appropriately. Whereas the last decade was characterized by an explosion of indices, the next decade should be one of consolidation and agreement.

With increased knowledge and understanding of the strengths and weaknesses of competing index approaches, a way must be found to simplify these approaches and continue to provide managers the simple answers they need to use these information effectively and efficiently (Borja et al., 2009).

Indices are based on the assessment of structural properties of benthic macroinvertebrate guilds; macroinvertebrate guilds are known to be affected by many abiotic and biotic parameters of transitional waters, such as salinity, sediment granulometry and organic matter, temperature, dissolved oxygen concentration and presence, density and composition of the phytobenthos components, both as seagrasses and seaweeds.

Any measure of ecological quality or status is of little value without some knowledge of its levels of uncertainty (Clarke, 2000). Therefore, there is an intrinsic uncertainty in the assessment of benthic invertebrates structural properties. The sources of variation in the fauna observed at a site can be: sampling variation and sampling method, sample processing and taxonomic identification errors, natural temporal variation, the effects of pollution or

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environmental stress on the biota (Clarke, 2006). Uncertainty is an intrinsic component of all ecological assessment, due to the biological variability and its influence on ecological interactions (Grinnellian and Eltonian species niches) (Gentile et al., 1993). This study addresses the influence of uncertainty in the assessment of the structural descriptive components of benthic macroinvertebrate guilds, including simple metrics and multimetric indices.

The study was performed in transitional water ecosystems of the Mediterranean basin, which are known to have a high degree of both spatial and temporal heterogeneity in the abiotic components. Three main sources of uncertainty were addressed here: geographical location, habitat patchiness and seasonality. Habitat type, particularly sediment habitat type and seasonality are known to affect species distribution and population dynamics in transitional water ecosystems of the Mediterranean ecoregion (Barbone et al., 2010; Basset et al., 2008; Sabetta et al., 2008).

2. Experimental 2.1. Material and methods 2.1.1. Sites

The study was carried out in two relative pristine lagoons within protected areas – Le Cesine (Italy) and Sinoe (Romania) - that are characterized by a different degree of internal heterogeneity. The Cesine Lagoon, a coastal basin located in Puglia (SE Italy; 40°21’05”N, 18°23’05”E; Fig. 1) is a system of several interconnected water bodies (0.9 km2 total area); the largest of them - the Pantano Grande basin - is 2.12 km long, with an approximate surface area of 0.68 km² and a mean depth of 0.80 m. The whole area is included since 1980 in a natural reserve managed by WWF.

Direct water exchange with the Adriatic Sea is prevented by a continuous sand dune; yet, due to the narrowness of the dune itself (< 10 m in some sections) the seawater front diffuse towards the lagoon throughout the year, while occasional dune breaks and direct seawater ingression occur in fall and winter due to storm episodes (Mancinelli et al., 2008). Beside meteoric inputs, freshwater is provided by several ephemeral drainage ditches and underwater springs along the landward coast. The interaction of these marine and freshwater inputs together with strong summer evaporation and reduction in freshwater inputs determines strong seasonal fluctuations in lagoon water salinity: to a mean annual value of 10 - 15 PSU correspond values of 5 - 7 PSU in winter - spring and 28 - 30 PSU during storm events and at summer droughts (Mancinelli et al., 2008).

The second system studied, it was Sinoe Lagoon, which is a principal lagoon ecosystem from Romania, that cover 129.0 km² (Fig. 2). It has two permanent connections: one with Black Sea, and other with Danube. Since 1990, the lagoon is part of

Danube Delta Biosphere Reserve (DDBR), situated in northeastern Dobrogea, south of the Danube Delta and on the coast of the Black Sea (44°47’ and 45°01’ northern latitude and 28º30’ and 29°08’ eastern longitude) (Bretcan et al., 2009).

Fig. 1. Le Cesine Lagoon, study area It appeared and evolved in a former branched

marine gulf, Halmyris, at the basis of a fossilized marine cliff (in the west), being connected to Sfântu Gheorghe branch (in the north) by means of several canals and backwaters and separated from the sea by several alignments of low and weakly consolidated marine bank-ridges, all these characteristics conferring it numerous particularities reflected in the hydrological, thermo, dynamical and hydrochemical regime of the lakes’ water (Bretcan et al., 2009).

2.1.2. Methods

The experimental design consisted of two seasonal samplings (fall 2004 and spring 2005), three habitat types, two stations for each habitat type and five replicates for each station. We defined habitat types using 3 levels of sediment grain size (mud, sand, rock) and 4 levels of vegetation (absent, macro algae, submerged macrophytes, emergent macrophytes) (Fig. 3) (Galuppo et al., 2007). The habitat types are dominate that the submerged vegetation (Le Cesine) and without vegetation (Sinoe), corresponding by the habitat types 7, 11, 12 for Le Cesine and 5, 9, 12 for Sinoe (Fig. 3). Macro-invertebrates were collected using a box-corer of 17 x 17 cm (area 0.029 m²), each sample was sieved through a 0.5 mm mesh sieve and stored in 4% formalin solution.

In the laboratory, benthic macro-invertebrates collected were separated from inorganic and organic particles and preserved in 4% formalin solution. All invertebrates were identified to the lowest possible level, measured (body length) and their body mass determined. Identification and biometry of every individual was carried out under Leica MZ6 stereoscopes.

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Fig. 2. Sinoe Lagoon, study area

Fig. 3. Habytat types (7, 11, 12 for Le Cesine and 5, 9, 12 for Sinoe)

Data collected were used to calculate the

following metrics: Shannon-Wiener index, is based on the information theory, it assumes that individuals are sampled at random, out of an “indefinitely large” community, and that all the species are represented in the sample (Shannon and Wiener, 1963) Margalef index, quantifies the diversity relating specific richness to the total number of individuals; the expected number of species (ES) in a randomly selected subset of individuals, e.g. 50 (hence ES(50)) (Hurlbert, 1971; Rosenberg et al., 2004) and also we us standard descriptive parameters (median, 10th and 90th percentiles).

Body size is one of the most important traits of an organism determining, to a large extent, the type and the strength of ecological interactions to which individuals are subjected (de Roos et al., 2003).

3. Results and discussion

Thirty one benthic macro-invertebrate taxa,

belonging to 3 phyla, were sampled from the two transitional water ecosystems considered in this study. Pooling all samples together, taxonomic richness ranged from 14 taxa in the Cesine lagoon to 21 taxa in the Sinoe lagoon (Tables 1, 2).

A common pattern of macro-invertebrate guilds at both studied ecosystems was rarity; most taxa were rare, being sampled at very low densities and/or in a few sampling occasion, and very few were both abundant and spatially widespread. Chironomus plumosus and Gammarus insensibilis were the two dominant taxa at Le Cesine, while Oligochaeta was the dominant taxa at Sinoe.

These measures of taxonomic similarity were usedin order to quantify the heterogeneity among stations and habitat types at the ecosystem level (Figs. 4, 5).

Within each ecosystem, taxonomic similarity is higher for Le Cesine than Sinoe (Le Cesine 59.24±1.72; Sinoe 42.70±2.71). Clustering techniques show qualitative patterns of variation of taxonomic similarity, with samples from different seasons grouped into different clusters at Le Cesine while at Sinoe spatial variations among habitat types seems more relevant than temporal variation.

However, differences were not statistically significant (Mds Cesine: stress= 0.09; Mds Sinoe: stress= 0.05). Spatial and temporal heterogeneity was also assessed for quantitative descriptors (metrics) of macroinvertebrate guilds. In this latter case, the variability is used as a measure of uncertainty.

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Table 1. List of macroinvertebrate taxa collected in fall and spring (Le Cesine)

Fall Spring Taxon 7 11 12 7 11 12 Annelida Hediste diversicolor 109 15 1 12 41 15 Arthropoda Chironomidae 13 Chironomus plumosus 24 280 162 604 246 724 Diamesinae 1 Ficopomatus enigmaticus 3 25 14 265 70 4 Gammarus insensibilis 33 1 17 194 63 146 Lekanesphaera hookeri 6 6 3 30 18 16 Nymphula nymphaeata 1 Psychomyiidae 6 Stratiomyidae 1 19 Tabanidae 2 Tanypodinae 3 4 2 Mollusca Cerastoderma glaucum 37 3 3 6 Ventrosia ventrosa 41

Table 2. List of macroinvertebrate taxa collected in fall and spring (Sinoe)

Fall Spring Taxon 9 5 9 12 Annelida Hediste diversicolor 4 4 Nematomorpha 2 Nereididae 5 Oligochaeta 234 395 249 638 Sabellidae 1 Arthropoda Acentria sp. 1 Caenis sp. 1 Ceratopogonidae 1 3 Chironomidae 3 1 Chironomus sp. 117 10 83 57 Coleoptera 2 10 Corophium sp. 31 302 2 1 Cumacea 188 4 27 Diamesinae 2 19 Diptera 1 1 Ecnomidae 3 5 Gammarus sp. 2 1 5 Tanypodinae 27 9 Mollusca Dreissena sp. 37 7 Mytilus sp. 2 Ruditapes sp. 1

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Absolute uncertainty was evaluated from the

overall variance of the different metrics, showing that metrics including sensitivity measures, both as size class sensitivity and as taxonomic sensitivity, have a lower uncertainty than both taxonomic diversity and simple body size spectra descriptive metrics (Table 3).

Fig. 4. Average similarity (Bray-Curtis similarity; UMPGA clustering technique) of benthic invertebrate guilds at Le

Cesine lagoon, accounting for temporal (seasons) and spatial (habitat types) potential sources of variability

Fig. 5. Average similarity (Bray-Curtis similarity; UMPGA clustering technique) of benthic invertebrate guilds at Sinoe

lagoon, accounting for temporal (seasons) and spatial (habitat types) potential sources of variability

Table 3. Absolute uncertainty of the different metrics and statistical comparisons among uncertainties

(F ratios; n.s., P<0.05; P<0.01)

Median Size

’10-90 d H’ ES50 M-

ISS б² 1.11 0.72 0.45 0.33 0.27 0.18

Median Size

’10-90 1.55 d 2.50 1.61

H’ 3.35 2.16 1.34 ES50 4.14 2.67 1.66 1.24 M-ISS 6.15 3.97 2.46 1.84 1.48

Variance of normalised metrics ranged

between 0.18 for M-ISS and 1.11 for the median of the size spectra. Relative uncertainty was also assessed, from the partitioning of the absolute uncertainty among sources of variation. For Cesine lagoons, diversity measures (d, ES50, H`) have showed a higher average percentage of unexplained variance (Fig. 6) than size related measures (Median size, range 10-90, M-ISS).

The habitat type have showed a more homogeneous explained variance for all measures than season and consequently, for the interaction between season and habitat. For Sinoe lagoon, the uncertainty metrics situation is a bit different, we see that the highest percentage of error is the MedianSize (> 50%), followed by the rest of metrics with a smaller percentage (< 30%): H’, ES50, d, percentiles 10-90 and M-ISS (Fig. 7). The interaction between season and habitat type is the factor that more explained metric variance.

Fig. 6. Explained absolute uncertainty (%) of different metrics (d, ES50, H’, MedianSize, percentiles 10-90, M-ISS) describing benthic macroinvertebrates guilds at Le

Cesine lagoon, due to seasonal, spatial and interactive factors

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Fig. 7. Explained absolute uncertainty (%) of different metrics (d, ES50, H’, MedianSize, percentiles 10-90, M-

ISS) describing benthic macroinvertebrates guilds at Sinoe lagoon, due to seasonal, spatial and interactive factors

4. Conclusions The monitoring using macroinvertebrate

sampling can help in evaluating the history of the water system, by means of abundance, diversity and composition of the macroinvertebrate community, that can be affected by many abiotic and biotic parameters of transitional waters, such us salinity, sediment granulometry and organic matter, temperature, dissolved oxygen concentration and presence, density and composition of the phytobenthos components, both as seagrasses and seaweeds. Uncertainty is an intrinsic component of all ecological assessment, due to the biological variability and its influence on ecologica interactions (i.e., Grinnellian and Eltonian species niches). The sources of uncertainty observed in this study, are: geographical location, habitat patchiness and seasonality; also is there an intrinsic uncertainty in the assessment of benthic invertebrates structural properties. Potential metrics describing benthic invertebrate guild in transitional water ecosystems have therefore an intrinsic uncertainty, as well.

The results from this paper, which compare macro-invertebrate guilds at two geographically distinct lagoon ecosystems, show that metrics largely diverge both in their absolute and in their relative uncertainty. At both ecosystems, size spectra derived metrics generally showed a lower relative uncertainty than taxonomically based metrics, even accounting for the fact that taxonomic diversity is generally the less variable taxonomically based metric.

The results highlight that metrics based on size spectra, and particularly accounting also for size class sensitivity, have lower uncertainty than other taxonomically based metrics. Acknowldegements

This study was supported by the Grant SOP HRD/6/1.5/S/15 SIMBAD co financed from ESF of EC, Romanian Government and University Dunarea de Jos of Galati.

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