R Constantino

M B Gaspar
email: mbgaspar@cripsul.ipimar.pt

J Tata-Regala

S Carvalho

J Cúrdia

T Drago

R Taborda

C C Monteiro

Clam dredging effects and subsequent recovery of benthic

Keywords: Ecosystem disturbance, Environmental impact, Fisheries, Bivalve dredging, Natural variability, Macrobenthic communities, Meiobenthic communities, Southern Portugal

published or not. The documents may come

INTRODUCTION

The deleterious impact of bottom dredge and trawl fisheries on the benthic environment has been extensively documented worldwide (e.g. [START_REF] Dolmer | Evaluation of the Danish mussel fishery: suggestions for an ecosystem management approach[END_REF][START_REF] Carbines | The impact of oyster dredging on the growth of blue cod, Parapercis colias, in Foveaux Strait, New Zealand[END_REF]. According to the studies undertaken in subtidal grounds the magnitude of the impacts caused by such fisheries depends on several factors, namely gear type, habitat, depth, benthic communities and scale of disturbance [START_REF] Collie | A quantitative analysis of fishing impacts on shelf-sea benthos[END_REF]. Therefore, the magnitude of the impacts can range from no detrimental post-fishing effects (e.g. [START_REF] Cranfield | Changes in the distribution of epifaunal reefs and oysters during 130 years of dredging for oysters in Foveaux Strait, southern New Zealand[END_REF][START_REF] Hall-Spencer | Scallop dredging has profound, long-term impacts on maerl habitats[END_REF][START_REF] Hiddink | Effects of suction-dredging for cockles on non-target fauna in the Wadden Sea[END_REF][START_REF] Carbines | The impact of oyster dredging on the growth of blue cod, Parapercis colias, in Foveaux Strait, New Zealand[END_REF] to major disturbance effects in benthic communities (e.g. [START_REF] Kaiser | Changes in megafaunal benthic communities in different habitats after trawling disturbance[END_REF][START_REF] Hall-Spencer | The impact of Rapido trawling for scallops, Pecten jacobaeus (L.), on the benthos of the Gulf of Venice[END_REF][START_REF] Gutiérrez | Environmental heterogeneity and species responses to fishing disturbance: are the effects of clam harvesting spatially consistent[END_REF][START_REF] Pranovi | A multidisciplinary study of the immediate effects of mechanical clam harvesting in the Venice Lagoon[END_REF], with moderate disturbance effects being referenced by other authors (e.g. [START_REF] Hall | The impact of hydraulic dredging for razor clams Ensis sp. on an infaunal community[END_REF][START_REF] Schratzberger | Impacts of trawling on the diversity, biomass and structure of meiofauna assemblages[END_REF][START_REF] Gaspar | Recovery of substrates and macro-benthos after fishing trials with a new Portuguese clam dredge[END_REF].

One of the main issues in fisheries management is the recovery time of the exploited environments. However, most of the conclusions on this issue are rather speculative as, to date, few studies have been designed to specifically assess the recovery time after fishing disturbance (e.g. [START_REF] Kaiser | Changes in megafaunal benthic communities in different habitats after trawling disturbance[END_REF][START_REF] Frid | Long-term changes in the benthic communities on North Sea fishing grounds[END_REF][START_REF] Gilkinson | Immediate and longer-term impacts of hydraulic clam dredging on an offshore sandy seabed: effects on physical habitat and processes of recovery[END_REF][START_REF] Hiddink | Effects of suction-dredging for cockles on non-target fauna in the Wadden Sea[END_REF] and most of them concern short-term impacts (DeAlteries et al., 1999). Nevertheless, some studies have already showed some evidences of long-term persistency of negative effects on marine environment (e.g. [START_REF] Frid | Long-term changes in the benthic communities on North Sea fishing grounds[END_REF]Veale et al., 2000).

Areas closed to clam dredging have been used to study the effects of fishing in marine habitats. Therefore, for scientific purposes, in 2002 an exclusion zone of nearly 6 km 2 located off Vale do Lobo (Algarve, southern Portugal) was closed to commercial fishing by dredge under national legislation. This area was selected in agreement with the fishing sector and was chosen because it was not fished for more than two years due to the collapse of the Spisula solida and Donax trunculus populations in that area.

Such ground provided the adequate field conditions to carry out controlled experiments concerning the recovery periods of shallow dredged fishing grounds. In fact, in Portugal, several clam species are commercially exploited by the dredge fleet that operates mainly between 3 and 20m depth. The present work aims to assess the effects of clam dredging and subsequent recovery in benthic habitats at different depths (6m and 18m) within the dredge fleet operational depth range.

MATERIAL AND METHODS

Sampling strategy and laboratory analyses

Field surveys were carried out in June (6m depth) and from August to October 2003 (18m depth) within the closed area (Fig. 1). Two areas, with approximately 2500m 2 (50m x 50m) each, were established. These areas were ~0.9 km apart and 500m from the closure edges, in order to guarantee the independence of samples and also the freedom from interference of nearby fishing operations. One of the areas was subjected to intense clam dredge-fishing (Impact area), while the other was undisturbed (Control area). During fishing, two clam dredges were towed simultaneously side-by-side for 2h at a commercial towing speed of 2 knots. The dredges used in the present study were similar to the ones used by professional fishermen. This fishing gear weighs around 80 kg and is comprised of a metallic frame, a toothed lower bar (with tooth length of 20 cm, spaced 2.2 cm) at the dredge mouth (64 cm in length) and a rectangular metallic grid box (space between rods of 1.2 cm) to retain the catch (detailed gear specification can be found in [START_REF] Gaspar | Recovery of substrates and macro-benthos after fishing trials with a new Portuguese clam dredge[END_REF]. Sampling strategy followed a BACI design, with both areas being sampled prior to fishing stress and after fishing stress. Samples of macrobenthos, meiobenthos and sediment were collected by SCUBA divers. For the shallower depth (6m), samples were collected before (B), immediately after (IA), 1d, 3d and 17d after dredging. For 18m depth, samples were collected B, IA, 1d, 2d, 5d, 13d, 35d and 90d after dredging. The periodicity of sampling was established taking into account that recovery is hypothetically rapid immediately after impact and slower at deeper depths.

Therefore sampling was intensified soon after the beginning of the experiments and extended longer at 18m. After fishing within the experimental sites, divers have chosen a clearly disturbed area where several dredge tracks were visible. These areas were marked with concrete blocks roped to a surface buoy. The centres of the control sites were also marked at the beginning of the experiment. The position of the buoys was taken with a Differential Global Positioning System (DGPS). The samples were collected randomly around the concrete blocks.

For the study of benthic communities three replicate samples were taken at each area and sampling occasion. In order to account for a patchy distribution of macro and meiobenthos organisms, each replicate was composed of 3 corer samples (total area per replicate of 0.02m 2 ) for macrofauna and 2 smaller core samples (total area per replicate of 0.002m 2 ) for meiofauna. Macrobenthos samples were retaining on a 500µm mesh sieve whereas the meiobenthos were sieved and retained on a 63µm mesh sieve. The retained material was fixed in 4% buffered formalin stained with Rose Bengal. Prior to identification, samples were washed and the organisms were hand sorted into major taxonomic groups, identified to the lowest practical taxonomic level (usually species level for macrofauna and a level higher than order for meiofauna) and counted. Taxonomic nomenclature followed MarBEF Data System (http://www.marbef.org/data/). For macrofauna, biomass expressed in ash-free dry weight (AFDW) was also determined for each taxa and per replicate.

Sediment particle size analysis was based on small corers (5cm i.d.; 20cm depth).

Each corer was opened, described and samples were analysed by standard sieving method (from >2mm to <63 µm). Each fraction was dried and weighed and percentages determined. Grain-size parameters were computed by [START_REF] Folk | Brazos River bar: a study in the significance of grain size parameters[END_REF] method, using the Grangraf program [START_REF] Carvalho | Programa para tratamento de dados granulométricos -GRANGRAF V2.08[END_REF].

Data analysis

Data was analysed using a combination of multivariate and univariate methods.

Macro and meiobenthic community structure was analysed regarding abundance (N), total number of taxa (S), Margalef's species richness (d), and diversity (Shannon-Wiener index H , log e ) indices. These variables were calculated for each area and sampling period.

In order to analyse the impacts of dredging on macrobenthic communities, several categories of different functional traits were assigned to the most abundant taxa following de [START_REF] De Juan | Functional changes as indicators of trawling disturbance on a benthic community located in a fishing ground (NW Mediterranean Sea)[END_REF]. We considered four biological traits: animal size (<1, 1-5, >5cm), body design (vermiform, scale or chitinous plates, shell), presence or absence of an external protection structure, and adult motility (sedentary, low motility and medium motility). Meiobenthic organisms were not analysed as functional traits as this faunal group was identified at high taxonomic levels.

Multivariate analyses were performed using the PRIMER v5.0 software package [START_REF] Clarke | PRIMER (Plymouth Routines In Multivariate Ecological Research) v5: User Manual/Tutorial[END_REF]. For each sampling period, control and impacted areas were compared using one-way ANOVA. However, prior to ANOVA, data was tested for normality (Anderson Darling test) and homogeneity of variance (Bartlett's method) using SIGMASTAT. Whenever these assumptions were not met, the nonparametric test of Kruskal-Wallis (K-W) ANOVA on RANKS was used. Multiple comparison tests of Student Newman Keuls (S-N-K) were undertaken when null hypothesis was rejected. Similarity between areas and sampling periods was analysed by ordination techniques (non-metric MDS) based on the Bray-Curtis similarity coefficient after square-root transformation of the faunal data. The cluster analysis applied the group-average linkage method. One-way ANOSIM tests were used to test for differences in community structure (macro and meiofaunal data pooled) among areas for each sampling period using abundance data.

Oceanographic setting

Data regarding wave action within the sampling area was analysed in order to characterize the natural hydrodynamic disturbance to which the benthic communities were subjected during the study period.

Tides in the Algarve are semi-diurnal with mean amplitude of 2.8m on spring tides and 1.3m on neap tides. The wave climate is conditioned by the orientation of the coast which provides shelter from the dominant northwest swell generated in the North Atlantic, with mean significant wave height (Hs, defined as the average wave height -trough to crest -of the one-third largest waves) of 1m and a direction predominantly from the W-SW (71%) sector followed by the SE sector (23%) [START_REF] Costa | Contribuição para o estudo do clima de agitação marítima na costa portuguesa. 2 as Jornadas de Engenharia Costeira e Portuária[END_REF]. Wave data collected between 1986 and 2001 showed a strong seasonality, with an average number of storms (Hs > 3.5m) of 9.3 in winter (October-March) and 0.6 in summer (April-September).

Offshore wave data for the surveying period was obtained from Faro wave buoy (maintained by the Portuguese Hydrographical Institute), located at the 80m depth contour, off Cape Santa Maria (Fig. 2). During the experimental period, mainly low energy conditions (Hs < 1m) were observed except for the periods ranging from the 9 th to the 20 th of June (i.e., between the 3d and 17d sampling for 6m depth), where significant wave height exceeded 1.5m, reaching a maximum of 2.5m (Fig. 2A).

Major storm events were also recorded especially between 23 rd of October and 18 th of November (i.e. between 35 and 90d for 18m depth), with waves reaching 6 metres high (Fig. 2D).

RESULTS

6m depth

Macrobenthos

Abundance, number of taxa, diversity and species richness patterns for both areas and sampling periods are shown in Fig. 3. In general, no significant differences were detected between fished and control areas. Although not significant, the disturbed area usually presented lower values than the control area IA dredging, while at the remaining sampling periods, the reverse situation was observed (Fig. 3).

The analysis of the abundance changes within the impacted area after dredging for the taxa accounting for 80% of the total abundance is presented in Table 1. Five out of six taxa were considerably affected by dredging, as their abundance was reduced at least to 40% of the abundance observed before dredging (Urothoe sp.). In contrast, an unidentified nemertinean seemed to be positively affected by dredging, as its abundance increased (Table 1). The isopod Cirolana cranchii, the amphipod Urothoe sp. and an unidentified ophiuroid were the most affected. The latter was completely removed from the impacted area during the two first sampling periods after fishing (Table 1). All these taxa have scales or chitinous plates in their bodies, but in terms of the other functional traits analysed no consistency was found (Table 1).

Meiobenthos

Abundance, number of taxa, diversity and species richness did not show any significant differences between impacted and control areas during the sampling period (Fig. 4).

Community structure

The MDS plot for all community (macro and meiofauna) showed a separation between samples collected before and IA dredging within both control and impacted areas from the remaining. Therefore, no major impacts were detected (Fig. 5). The same pattern was observed concerning meiofauna samples, while the macrofaunal samples collected IA dredging within the impacted area were clearly separated in the MDS plot (Fig. 5).

No significant differences were detected by the ANOSIM test between control and impacted areas for any of the study periods (p>0.05).

Sediment characterization

Bottom sediments at the experiment site were mainly composed of fine sand (40-70%) followed by medium and very fine sand. Coarser sand fractions represented less than 15%. The impact of bottom dredging on the sediment is demonstrated by changes in the median grain-size (φ units) computed for the control and dredged areas 10 (Fig. 6). While in the control area there was not a clear change, in the dredged area there was a sudden decrease in the grain size immediately after dredging (the median increased from 2.2 φ to 2.5 φ). This was followed by a slow increase and 17d after dredging, the median in both areas became quite similar, slightly coarser than 2.2 φ (Fig. 6).

18m depth

Macrobenthos

The impacted area showed significantly lower abundance, number of taxa and Shannon-Wiener diversity (ANOVA, p<0.05) IA dredging in comparison to the control area (Fig. 7). For some of the measures, similar results were detected after 5 (S) and 90 days after dredging (N, S, H') (Fig. 7).

Although most of the taxa were noticeably affected by dredging, an unidentified Eunicidae, Goniadella sp., Parapionosyllis sp. and Lumbrineriopsis sp. were added to the community as they were sampled only after dredging and persisted in it as they were consistently sampled thereafter (Table 2). Within the taxa enhanced by fishing, polychaetes with vermiform shape, without external protection, and carnivory were the dominant functional categories (Table 2). Concerning the taxa negatively affected by dredging, they were mostly crustaceans (mainly amphipods) and polychaetes. The most affected functional categories were the animals that had scales or chitinous bodies, vermiform shape, absence of external protection and deposit-feeding mode (Table 2).

Meiobenthos

According to these results, dredging seems to have caused a decrease in meiofauna abundance and number of taxa that persisted until 13 to 35 days (Fig. 8). This decrease although not significant was more evident for abundance (Fig. 8).

Community structure

Considering both the entire community and also the meiofauna analysed separately, the negative effect of dredging in the benthic community is discernible as most of the samples from the impacted area were separated from the remaining. However, 35d

after dredging the impacts were no longer detected (Fig. 9). Macrofaunal data did not show this clear effect (Fig. 9).

No significant differences were detected by ANOSIM tests between control and impacted areas for any of the study periods (p>0.05).

Sediment characterization

Bottom sediments were dominated by coarse sand and gravel fractions. These fractions represented usually more than 80% of the entire sample, while finer fractions (fine, very fine sand and >4φ sediments) rarely exceed 2%. Sediment was moderately sorted with a median corresponding to coarse and very coarse sand.

Within the study area, samples collected before dredging were characterized by a mean median grain-size of 0.00φ (median grain-size values ranged between -0.25φ and 0.34φ) (Fig. 10). Immediately after dredging, sediment was found to be slightly coarser (0.05φ), ranging within a similar interval (from -0.22φ and 0.21φ) (Fig. 10).

One day after dredging, a major change in sediment grain-size was recorded in the impacted area, as sediment became coarser than the previous samples (-0.45φ) (Fig. 10). Afterwards, and until 13 days after dredging, the sedimentary record showed a gradual trend to become slightly finer (median mean: -0.3φ, maximum: 0.11φ; minimum: -0.67φ) (Fig. 10). From that period onwards, samples showed the same textural pattern with minor fluctuations in median grain-size (mean of -0.06 φ and 0.04 φ for 35 and 90 days after dredging, respectively), revealing a similar textural pattern to the one observed at the beginning of the experience (Fig. 10). During the extent of the experiment, in the control area, sediment median showed a relatively constant pattern with only minor fluctuations (max: 0.34 to -0.13φ; min: 0.19 to -0.31φ; mean: 0.21 to -0.06φ) (Fig. 10). These fluctuations became more evident 2 days and 35 days after dredging, where all collected samples corresponded to medium sand (Fig. 10).

DISCUSSION

Biological impacts

The present study showed that dredging impacts on benthic communities seem to be depth dependent. Moreover, depending on the faunal component used as bioindicator, different results can be observed.

In general, no clear impacts were observed for the shallower area either considering univariate and multivariate methods and also macro and meiofaunal data. Despite the lack of clear differences between control and dredged areas, a general decrease in abundance of the most abundant taxa was observed after dredging. Regarding 18m depth, the results of this study clearly demonstrated that clam dredging causes an immediate effect on the meio-and macrobenthic communities. Considering macrofauna, all biological variables showed a significant decrease immediately after dredging, probably due to the removal of target and non-target species by the gear and/or spatial redistribution of macrobenthic fauna in the dredged area [START_REF] Hiddink | Effects of suction-dredging for cockles on non-target fauna in the Wadden Sea[END_REF]. Several authors described similar results for different kinds of bottom fishing gears. For example, [START_REF] Pranovi | A multidisciplinary study of the immediate effects of mechanical clam harvesting in the Venice Lagoon[END_REF] in a multidisciplinary study of the immediate effects of mechanical clam harvesting in the Venice lagoon detected a significant decrease in the total number of individuals after the haul. [START_REF] Thrush | The impact of habitat disturbance by scallop dredging on marine benthic communities: What can be predicted from the results of experiments?[END_REF] observed a decrease in macrofaunal taxa due to scallop dredging and [START_REF] Kaiser | The effects of beam-trawl disturbance on infaunal communities in different habitats[END_REF] reported a general decrease in biodiversity after beam trawling.

Biodiversity loss may persist over long periods of time, namely due to the reduction of habitat complexity, resulting from the removal of tubicolous organisms and epibenthic species [START_REF] Thrush | The impact of habitat disturbance by scallop dredging on marine benthic communities: What can be predicted from the results of experiments?[END_REF][START_REF] Kaiser | Chronic fishing disturbance has changed shelf sea benthic community structure[END_REF]. These organisms are particularly important as they provide protection for commercial species, and their removal will potentially affect fisheries [START_REF] Kaiser | Chronic fishing disturbance has changed shelf sea benthic community structure[END_REF].

Most fishing impact studies focused only on changes of macrofauna, whereas smaller benthic components have been largely neglected. Since meiofauna are among the smallest animals in benthic communities and have very fast turnover times, they may be expected to show little or no response to dredging [START_REF] Schratzberger | Impacts of trawling on the diversity, biomass and structure of meiofauna assemblages[END_REF]. In fact, such absence of significance and/or sensitivity to mobile gear-driven changes of meiobenthos when compared with macrobenthos has also been noted by other authors (e.g.: [START_REF] Schratzberger | Impacts of trawling on the diversity, biomass and structure of meiofauna assemblages[END_REF][START_REF] Pranovi | A multidisciplinary study of the immediate effects of mechanical clam harvesting in the Venice Lagoon[END_REF]. Nevertheless, in the present study multivariate analyses performed with both macro-and meiofaunal data showed that the latter may have higher sensitivity to dredging impacts. While for macrofauna, dredging impacts were only apparent IA fishing, for meiofauna these impacts were detected until 35d after dredging. Therefore, the detection of benthic impacts may be hampered by the faunal component used during a monitoring study.

In general, the most affected taxa had bodies both with scales or chitinous plates and vermiform shape especially crustaceans, polychaetes and ophiuroids. Moreover, most of these taxa did not have external protection but were mainly deposit-feeders. In contrast, some polychaete taxa without external protection and with a carnivorous feeding mode were enhanced by fishing. Shifts in dominance of feeding modes have been extensively described in relation to commercial fisheries [START_REF] Lindeboom | The effects of different types of fisheries on the North Sea and Irish Sea benthic ecosystems[END_REF]de Groot 1998, Jennings et al. 2001). The passage of the dredge across the sediment floor will kill or injure some organisms that will then be exposed to potential predators/scavengers [START_REF] Frid | Long-term changes in the benthic communities on North Sea fishing grounds[END_REF], Veale et al. 2000). Therefore, the enhancement of carnivore taxa after dredging is not surprising. The persistence of disturbance will benefit these opportunistic organisms, increasing their abundance [START_REF] Frid | Long-term changes in the benthic communities on North Sea fishing grounds[END_REF] and potentially changing the trophic structure of the benthic communities.

Impact on the sediment

The analysis of bottom sediment grain-size data suggested that dredge fishery impacts both sediment morphology and texture. The passage of the dredge on the bottom produced a slightly depressed track, about 30cm wide and 10cm depth, where the sedimentary structures were disrupted. Previous studies (e.g. Watling et al., 2001;[START_REF] Tuck | The impact of Water-jet dredging for razor clam dredging, Ensis spp, in a shallow sandy subtidal environmental[END_REF] reported that the dredging activities tend to induce the resuspension of fine-grained particles thus resulting in sediment coarsening. This effect was not consistently observed in the present study. In fact, within the shallower area (6m depth experiment) the measured textural differences were relatively small, with a slight decrease (increase in phi units) in the mean grain-size immediately after dredging. Nevertheless, the observed changes are within bottom sediment variability.

After 17 days, differences between control and dredged areas were minimal. On the other hand, at 18m depth, 1day after dredging, a slight increase in grain size was detected within the dredged area. This result might be related to the subsequent infilling of the dredge tracks by coarse particles that were dislodged to the sides of the trench during dredging [START_REF] Caddy | Underwater observations on track dredges and trawls and some effects of dredging on a scallop grounds[END_REF][START_REF] Eleftheriou | The effects of experimental scallop dredging on the fauna physical environment of a shallow sandy community[END_REF][START_REF] Currie | Changes to benthic communities over 20 years in Port Phillip Bay, Victoria, Australia[END_REF]. Subsequently, there was a progressive increase in gravel content, which reduced the differences between control and dredged areas; after 13 days, mean sediment grain size in the disturbed area was similar to the undisturbed sediments in deeper waters.

The low level of perturbations induced by the dredging activities can be related to a multiplicity of factors. In a wave-dominated environment, like the one examined in the study, this effect might be essentially related with the activity of surface waves on the bottom. If wave time-series are compared to the changes in benthic fauna, it becomes clear that the most significant changes were related to the more energetic events, which occurred between the 9 th and the 20 th of June and between the 23 rd of October and the 18 th of November, and masking fishing effects. The highly dynamic nature of this environment can be demonstrated by the wave orbital velocities that affect the bottom. For example, for the shallow water areas (average depth of 8m), considering a significant wave height of 1m with a period of 5s, linear wave theory (e.g. [START_REF] Dean | Water Wave Mechanics for Engineers and Scientists[END_REF] predicts a maximum orbital bottom velocity of 0.29m s -1 , which exceed the [START_REF] Hallermeier | Sand motion initiation by water waves. Two asymptotes[END_REF] threshold for 500µm quartz particles.

These results showed that at 6m depth, sediments were mobile during a large part of the experiment, which may explain the fast recovery recorded for the sediment and for benthic communities. Sediments from deeper areas were also affected by storm events. Using linear wave theory maximum bottom orbital velocities at the 18m contour were computed from deepwater wave parameters recorded by the wave buoy located near the study area. At the beginning of the experiment, energy levels at the bottom were relatively weak (< 0.08m s -1 ). Between the 5 and 13 days after dredging and during a large part of October, there were major gaps in available wave data, thus preventing estimates of bottom energy levels during these periods. Significant bottom sediment mobility was only observed after mid September and particularly during the 25 th to 29 th October storm where bottom orbital velocities attained 1.39 m s -1 and exceeded the threshold criteria for the entire sedimentary spectrum.

From these considerations it can be inferred that the initial sediment textural changes for the 18m depth experiment should have been essentially gravity driven (i.e. related to the morphological disturbance of the dredging activities) as bottom energy levels were insufficient to move most of sedimentary particles. This process should have a very limited spatial and temporal scope, and therefore might have played a minor role in the overall recovery of the disturbed area. The significant recovery observed from 5 days to 13 days after dredging, can be related to an increase in bottom energy levels partially represented in the data (unfortunately there is a gap in wave period data between the 25 th and 31 st August) and can explain the relative increase in the finer sand fractions. After 13 days, several high energy events were observed which induced bottom orbital velocities exceeding the threshold of the entire grain-size spectrum and therefore easily masking the sedimentary perturbation induced by the dredging activities.

The relationship between depth and benthic impacts

In the present study, the relationship between depth and fishing impacts seemed to be highly dependent on the ecosystem characteristics, namely wave action. For instance, when considering sediments and macrobenthic fauna, an apparent immediate effect of fishing disturbance can be recorded, but a considerably faster recovery of macrobenthos was observed within the shallower area. According to several studies, macrobenthic communities from high-energy environments tend to be less affected to fishing as they are naturally and consistently subjected to disturbance (e.g. [START_REF] Currie | Effects of scallop dredging on a soft sediment community: a large experimental study[END_REF][START_REF] Kaiser | The effects of beam-trawl disturbance on infaunal communities in different habitats[END_REF]Zajac and Whitlatch, 2003). Nevertheless, in a moderately disturbed environment, [START_REF] Morello | Medium-term impacts of hydraulic clam dredgers on a macrobenthic community of the Adriatic Sea (Italy)[END_REF] found that fishing impacts on benthic community structure were still distinguishable from those resulting from natural variation. Therefore, the frequency and intensity of environmental disturbances such as storms may be among the key factors determining the resilience of the benthic community to fishing [START_REF] Morello | Medium-term impacts of hydraulic clam dredgers on a macrobenthic community of the Adriatic Sea (Italy)[END_REF]. Conversely, with depth increase the frequency and intensity of natural disturbance events tend to decrease.

This will result in more stable environments with communities that are usually less resilient to environmental changes.

The vulnerability of the sampling area to wave action appears to be corroborated by the visual recognition of the dredge track, which, in this study, was not apparent 24 hours after dredging at 6m depth. On the other hand, at 18m depth, tracks were still visible 13 days after dredging. Thus, oceanographic forcing can also be responsible for differences observed in macro-and meiobenthos. The occurrence of major storm events recorded especially between the last two sampling surveys for the 18m depth areas appeared to produce greater changes in macrobenthic communities than the fishing operations, as samples from control and impact area were distinct from the remaining samples. In fact, the effect of wave action has been previously suggested as a forcing function for the restoration and recovery of fisheries-disturbed grounds [START_REF] Kaiser | Changes in megafaunal benthic communities in different habitats after trawling disturbance[END_REF][START_REF] Dealteris | The significance of seabed disturbance by mobile fishing gear relative to natural processes: a case study in Narragansett Bay, Rhode Island[END_REF][START_REF] Gaspar | Recovery of substrates and macro-benthos after fishing trials with a new Portuguese clam dredge[END_REF][START_REF] Flåten | Assigning environmental variables to observed biological changes[END_REF]. It has been reported that the effects of storms may be much greater that anthropogenic stress [START_REF] Hall | The impact of hydraulic dredging for razor clams Ensis sp. on an infaunal community[END_REF]Brylinsky et al., 1994;[START_REF] Dolmer | Evaluation of the Danish mussel fishery: suggestions for an ecosystem management approach[END_REF].

Moreover, animals adapted to highly dynamic seabed environment are more resistant to disturbance [START_REF] Boesch | Response to stress in marine benthic communities[END_REF] and may actually not be affected by fishing gears [START_REF] Dealteris | The significance of seabed disturbance by mobile fishing gear relative to natural processes: a case study in Narragansett Bay, Rhode Island[END_REF]. Furthermore, the short-lived track configuration also reinforces the importance of wave action and currents on the habitat restoration, as previously suggested by [START_REF] Gaspar | Recovery of substrates and macro-benthos after fishing trials with a new Portuguese clam dredge[END_REF] also studying Portuguese clam-dredge fishing effects.

CONCLUSIONS

From the present case study, it can be assumed that clam-dredging activity disturbs the seabed, but the significance of that disturbance can be compared in magnitude and frequency to natural seabed disturbances. In fact, bottom energy levels at these depths, particularly during storms, are relatively high and may be responsible for the relatively rapid elimination of the morphological and sedimentary impact related to the dredging activities. This study was only possible due to the existence of a similar unexploited area (control) that had been closed to dredging since 2002 and where natural temporal changes could be studied. The absence of a suitable control area has been highlighted as a major constraint in similar studies. In most cases, such works have been undertaken in areas already modified by bottom fishing gears, and consequently benthic communities remaining in these areas are generally impoverished and resilient to fishing (Currie andParry, 1996, Tuck et al., 1998).

Thus, we uphold the statement of [START_REF] Dealteris | The significance of seabed disturbance by mobile fishing gear relative to natural processes: a case study in Narragansett Bay, Rhode Island[END_REF], arguing that the relative significance of dredge fishery-driven disturbance should be scaled against the magnitude and frequency of natural habitat-stressing phenomena. Notwithstanding, care must be taken, as causal relationships between biological and environmental variables are not straightforward [START_REF] Flåten | Assigning environmental variables to observed biological changes[END_REF].

Future management strategies for subtidal highly hydrodynamic fishing grounds should consider alternatives to permanently closed areas given the considerably fast recovery of communities, associated with the apparent ability for organisms to cope with physical stress of greater magnitude. Nevertheless, more studies are needed namely on the impacts caused by long-term cumulative fishing stress.

  .O., Hill, A.S., Hawkins, S.J., Brand, A.R., 2000. Effects of long-term physical disturbance by commercial scallop fishing on subtidal epifaunal assemblages and habitats. Marine Biology 137, 325-337. Watling, L., Findlay, R.H., Mayer, L.M. Schick, D.F., 2001. Impact of a scallop drag on the sediment chemistry, microbiota, and faunal assemblages of a shallow subtidal marine benthic community. Journal of Sea Research 46 (3-4), 309-324.

Figure captions Figure 1 .

 captions1 Figure captions

Figure 2 .

 2 Figure 2. Wave height recorded during the sampling period. A -6m depth

Figure 3 .

 3 Figure 3. Variation of mean abundance (N, ind×0.02m -2 ), number of taxa (S, taxa×0.02m -2 ), species richness (d) and diversity (H´) for macrobenthic communities

Figure 4

 4 Figure4Variation of mean abundance (N, ind×0.002m -2 ), number of taxa (S, taxa×0.002m -2 ), species richness (d) and diversity (H´) for meiobenthic communities

Figure 5 .

 5 Figure 5. Multidimensional scaling ordination diagrams for all community,

Figure 6 .

 6 Figure 6. Median grain-size variations in the control and impact areas for 6m depth

Figure 7 .

 7 Figure 7. Variation of mean abundance (N, ind×0.02m -2 ), number of taxa (S, taxa×0.02m -2 ), species richness (d) and diversity (H´) for macrobenthic communities

Figure 8 .

 8 Figure 8. Variation of mean abundance (N, ind×0.002m -2 ), number of taxa (S, taxa×0.002m -2 ), species richness (d) and diversity (H´) for meiobenthic communities

Figure 9 .

 9 Figure 9. Multidimensional scaling ordination diagrams for all community,

Figure 10 .

 10 Figure 10. Median grain-size variations in the control and impact areas 18m depth

  Figure 6

Table 1 -

 1 Percentage of abundance in relation to the sample before dredging at 6m depth. Reductions > 40% are highlighted, while increases > 40% are presented in bold. AMP -

	Amphipoda; DEC -Decapoda; ISO -Isopoda; NEM -Nemertinea; ECH -
	Echinodermata; Mot -motility; FM -feeding mode; EP -external BS -body
	shape; C -carnivory; SF -suspension-feeding; DF -deposit-feeding; N -P -
	protection; S -shell; S/CP -scales/chitinous plates; V -vermiform. IA -immediately
	after; d -days.
				After fishing			Functional traits
	Group	Taxa	IA	1d	3d	17d	Mot	FM	EP	Size (cm)	BS
	AMP Bathyporeia spp.	25	106 169 163	low	DF	N	0-1	S/CP
	DEC Diogenes pugilator 36	93	136	14	medium SF,DF,C	P	1-5	V
	ISO Cirolana cranchii	12	29	35	6	low	C	P	0-1	S/CP
	NEM Nemertina sp.1	150 150 150 150	medium	C	N	1-5	V
	AMP	Urothoe sp.	40	140	0	20	low	DF	N	0-1	S/CP
	ECH Ophiuroidea sp.1	0	0	50	50	medium C,DF	N	>5	S/CP

Table 2 -

 2 Percentage of abundance in relation to the sample before dredging at 18m depth. Reductions > are highlighted, while increases > 40% are presented in bold. AMP -Amphipoda; NEB -Nebaliacea; POL Polychaeta; ECH -Echinodermata; OLI -Oligochaeta; NEM -Nemertinea; SIP -Sipunculida; BIV -Bivalvia; ISO -Isopoda; CEP -Cephalochordata; TAN -Tanaidacea; Mot -motility; FM -feeding mode; EP -external protection; BSbody shape; C -carnivory; SF -feeding; DF -deposit-feeding; H -herbivory; N -none; P -protection; S -shell; S/CP -scales/chitinous plates; V -vermiform. IA -immediately after; d -days.

				After fishing				Functional traits
	Group Taxa	IA	1d	2d	5d	13d 35d 90d	Mot	FM	EP Size (cm) BS
	AMP Cheirocratus sundevalli	6	35	47	41	59	18	0	low	H, DF	N	1-5	S/CP
	AMP Atylus vedlomensis	0	60	30	30	0	40	10	low	C, DF	P	0-1	S/CP
	AMP Megamphopus cornutus	0	25	19	13	0	13	0	low	SF, DF	N	0-1	S/CP
	NEB Nebalia sp.	8	0	8	17	25	17	25	low	F	P	0-1	S/CP
	POL Nereididae	17	17	8	8	17	8	0	medium		N		V
	ECH Ophiura ophiura	0	30	0	20	10	20	10	medium C, DF	N	>5	S/CP
	AMP Photis cf. longipes	7	24	20	24	30	65	2	low	DF	N	0-1	S/CP
	POL Spiophanes bombyx	4	17	13	17	8	92	13	sed	DF	P	1-5	V
	OLI Oligochaeta sp. 1	7	0	33	20	0	100	0	sed	DF	N	0-1	V
	NEM Nemertina sp. 1	0	0	7	63	41	15	22	medium	C	N	1-5	V
	SIP Aspidosiphonida sp.	59	48	58	41	66	81	47	sed	DF, SF	N	>5	V
	POL Hyalinoecia tubicola	42	58	50	42	100 100	25	sed	C, DF	N	>5	V
	BIV Corbula gibba	25	92	17	25	17	8	33	sed	SF	N	1-5	S
	AMP Leucothoe incisa	0	75	42	17	58	108	17	low	DF	N	0-1	S/CP
	POL Micronephtys sp.	20	120	40	40	0	60	20	medium	C	N	0-1	V
	AMP Melitidae sp. 1	114	42	19	31	13	31	31	low		P		S/CP
	POL Pomatoceros sp.	63	38	50	13	88	25	0	sed	SF	P	1-5	V
	ISO Eurydice pulchra	12	76	41	71	41	53	24	medium	C	P	0-1	S/CP
	AMP Leptocheirus pectinatus	32	76	45	42	66	163	3	sed	SF	P	0-1	S/CP
	POL Sphaerosyllis sp.	63	33	46	46	88	138	71	medium	DF	N	0-1	V
	CEP Branchiostoma lanceolatum	27	81	68	71	56	78	123	medium C, DF	N	1-5	V
	POL Notomastus sp.	38	74	110	49	71	109	53	sed	DF	N	>5	V
	BIV Gouldia minima	40	160	20	20	100	40	40	sed	SF	N	1-5	S
	POL Syllis sp.	62	46	54	138 108 115	62	medium	C	N	1-5	V
	POL Maldanidae	0	300 100	0	50	350 100	sed	DF	N		V
	AMP Monoculodes carinatus	50	150 200	50	350	50	0	low	C	P	1-5	S/CP
	AMP Amphipoda sp. 1	50	150 150 100 200 300	0			P		S/CP
	POL Eunicidae	appeared						low		N		V
	POL Goniadella sp.	appeared						medium	C	N	1-5	V
	POL Parapionosyllis sp.	appeared						medium	C	N	0-1	V
	POL Lumbrineriopsis sp.	appeared						medium	C	N	1-5	V
	POL Protodorvillea kefersteini	153 284 163 274 226 274 216	medium	C	N	0-1	V
	POL Ophelia sp.	114 171 143 171 129 514 557	sed	DF	N	>5	V
	POL Nephtys sp.	67	200	67	233 167 267	67	medium	C	N	>5	V
	AMP Ampelisca spp.	120 300 180 260 380 220	0	sed	SF, DF	P	1-5	S/CP
	POL Pisione remota	400 407 336 200 429 671 929	low	DF, H	N	0-1	V
	POL Eunicida	195 284 195 263 216 300 311	low		N		V
	TAN Apseudes latreillii	300 400 400 300	0	0	0	sed	DF	P	0-1	S/CP

ACKNOWLEDGEMENTS

We would like to thank the crews of the RV "Donax" and the Fishing Vessel "Família Santa" for their skilful help. This study was conducted within the "DREDIMPACT" project (POCTI MGS-42319/2001). The authors thank to Pedro Fernandes, Selma Gabriel and Sílvia Serina for their helpful contribution for the laboratory grain-size analysis. Thanks are due to the referees for the constructive comments that significantly improved the manuscript. 7.


MPhil Emma Patterson
email: emma.patterson@ki.se

PhD Julia Wärnberg

MSc Eric Poortvliet

PhD John M Kearney

Michael Sjöström

Ass Prof

Dietary energy density as a marker of dietary quality in Swedish children and adolescents: the European Youth Heart Study

Keywords: 24-h recall, dietary patterns, epidemiology, foods, nutrients

Objective: To investigate if dietary energy density is associated with measures of dietary quality (food group, micronutrient and macronutrient intakes) in children and adolescents.

Methods: 551 children (mean age 9.6y, 52% girls) and 569 adolescents (15.5y, 55% girls), sampled from schools in Sweden, completed a single 24-h dietary recall. Dietary energy density (kJ/g) was calculated as the energy from all food consumed divided by the weight of all food consumed. Beverages were excluded from the calculation. Food and micronutrient intakes were adjusted for energy intake. A one-way ANOVA tested for differences in food group and nutrient intakes across age-and gender-specific tertiles of energy density.

Discriminant analysis was used to confirm the groupings formed by tertiles.

Results: Subjects with low energy density diets were significantly more likely to consume fruits; vegetables; pasta, rice, potatoes; cereals; and less likely to consume sweetened drinks; sweets and chocolate. After energy adjustment, their intakes of many foods recommended in Swedish food-based-dietary-guidelines were higher and intakes of nutrient-poor foods were lower. The macronutrient energy profile (% energy) of low energy density diets was closest to the recommended level. Low energy density diets contained greater amounts of most micronutrients. Discriminant analysis confirmed the existence of heterogeneous dietary patterns and the likelihood of correct classification by energy density in 65% of cases.

Conclusions:

Lower dietary energy density is associated with better dietary quality in children and adolescents. Energy density has advantages over other whole-diet analysis methods and may be suitable as a simple proxy of diet quality.

Introduction

No simple, well-accepted criteria exist that allow diets to be classified as healthy or unhealthy. Attempts to assess overall diet quality have often relied on scores or indexes [START_REF] Kant | Indexes of overall diet quality: a review[END_REF]) and more recently, data-driven methods (e.g. cluster or factor analyses) [START_REF] Hoffmann | Application of a new statistical method to derive dietary patterns in nutritional epidemiology[END_REF][START_REF] Hu | Dietary pattern analysis: a new direction in nutritional epidemiology[END_REF][START_REF] Kant | Dietary patterns and health outcomes[END_REF][START_REF] Newby | Empirically derived eating patterns using factor or cluster analysis: a review[END_REF]. These approaches attempt to capture the dietary pattern rather than focusing on certain foods or nutrients. The disadvantages are that data must be structured in a way that allows the score to be calculated or, in the case of data-driven methods, that the results are sample-specific. Dietary energy density has the advantage of being simply calculated and available from all types of dietary data where food and beverage energy and intakes are available.

The energy density of a food/diet is a measure of the amount of energy provided per unit weight and primarily determined by its water and fat content [START_REF] Drewnowski | The role of energy density[END_REF][START_REF] Stubbs | Energy density of foods: effects on energy intake[END_REF]. Dietary energy density has been associated with higher dietary quality in adults (Kant and Graubard 2005b, Ledikwe et al 2006b[START_REF] Maillot | Low energy density and high nutritional quality are each associated with higher diet costs in French adults[END_REF], Schröder et al 2008b), and also with more objective measures of health, such as the metabolic syndrome, overweight and predictors of obesity in adults and children [START_REF] Howarth | Dietary energy density is associated with overweight status among 5 ethnic groups in the multiethnic cohort study[END_REF], Kant and Graubard 2005b, Ledikwe et al 2006a[START_REF] Mendoza | Dietary energy density is associated with selected predictors of obesity in U.S. Children[END_REF][START_REF] Mendoza | Dietary energy density is associated with obesity and the metabolic syndrome in U.S. adults[END_REF].

The aim of this study was to examine the association of dietary energy density with dietary quality, namely food group intake, vitamin and mineral intakes and macronutrient energy profile, in Swedish children and adolescents. The ability of energy density to discriminate heterogeneous dietary patterns in the population was also tested.

Subjects and methods

Participants

Subjects were Swedish participants of the European Youth Heart Study, conducted in 1998-99. Over two thousand (n = 2 313) children (from grade 3 (9 years old)) and adolescents (grade 9 (15 years old)) were sampled from classes selected from forty-two schools in southern Stockholm and Örebro, and 1 137 students consented to participate [START_REF] Wennlöf | Sampling procedure, participation rates and representativeness in the Swedish part of the European Youth Heart Study (EYHS)[END_REF]. Permission was obtained from the local ethical committees (Huddinge University Hospital no. 474/98, Örebro City Council no. 690/98). Written consent was provided by a parent or legal guardian; verbal consent was provided by the subjects. Data collection was performed at the school.

Dietary assessment

All subjects completed a single interviewer-mediated 24-h recall. The younger children kept a 1-d qualitative food diary with the assistance of their parents on the day before the interview, to aid as a prompt in case of difficulties with the recall. A food atlas with pictures of common foods in various portion sizes was available during the interview, along with standard household units to help estimate quantities accurately. Data from the recall was analysed using software (StorMats,version 4.02;Rudans Lättdata,Västerås,Sweden) based on the Swedish Food Administration's nutritional database (version 99.1; www.slv.se). Each food consumed was allocated to a food group based on nutritional or dietary similarities and intakes are presented at food group level. The food groups were originally based on the Swedish Food Administration's groups but were modified to allow more specific classification in some cases and less in others. The "milk, fil, yoghurt" group includes fil, a soured-milk product similar to yoghurt, common in Nordic countries. "Sweetened drinks" refers to carbonated or cordial-based drinks; fruit juices are concentrated or fresh juices. The "other sweet foods" group includes desserts, ice-cream, sweet soups, jams and added sugar.

"Burgers, sausages" are processed meat products and are a subsection of the "meat, meat dishes" group. "Cereals" refer to breakfast cereals (both sweetened and unsweetened). Fried potatoes are included in the "chips, crisps" group. Someone who reported eating any amount of a food group was considered a consumer.

Calculation of energy density

Energy density was calculated as energy (kJ) divided by weight (g). Food was defined as solid food and liquids consumed as food (e.g. soups, yoghurt). All beverages, both energycontaining (e.g. milk, sweetened drinks, fruit juices) and non-energy-containing (e.g. water, coffee, diet beverages) were excluded. Because the energy density differed by age and gender, tertiles were created that were age-and gender-specific. The diets of these groups were referred to as low, middle and high energy density diets.

Recalls of suspected poor quality or inadequate energy

On completion, the recall was immediately rated by the interviewer based on the subject's perceived interest and motivation and the level of detail provided. Interviews with a score of less than three (out of five) were suspected to be of low quality. The plausibility of energy intakes reported was tested by comparing the reported intakes to a theoretical intake that is predicted from basal metabolic requirements (BMR), and age-and gender-specific physical activity levels, assuming weight stability. A confidence interval is constructed around the predicted intake that takes into account the number of days of diet recorded [START_REF] Goldberg | Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify under-recording[END_REF]. BMR was estimated [START_REF] Schofield | Predicting basal metabolic rate, new standards and review of previous work[END_REF] using weight and height that had been measured using standardised techniques, and reference physical activity levels were taken from [START_REF] Black | The sensitivity and specificity of the Goldberg cut-off for EI:BMR for identifying diet reports of poor validity[END_REF]. Subjects with energy intakes below the lower confidence interval cut-offs were considered possible under-reporters, and these cut-offs were equivalent to a ratio of reported energy to BMR of 0.97, 0.98, 0.98 and 1.03 in 9-year-old girls and boys, and 15-year-old girls and boys, respectively. Due to the uncertainty in identifying under-reporters, particularly when only one day of dietary data is available [START_REF] Black | The sensitivity and specificity of the Goldberg cut-off for EI:BMR for identifying diet reports of poor validity[END_REF], we chose not to exclude these subjects. Instead, we considered the effect exclusion might have on the results.

Statistical analysis

An initial correlation analysis confirmed a positive and significant association between energy intake and both food and nutrient intakes. As energy was also a component of the predictor variable -energy density -any association between foods and/or nutrients with energy density might have been due to the effect of increasing energy. To overcome this, food group and micronutrient intakes were regressed on total energy intakes and the residuals of these linear regressions were used as the response variables in subsequent analyses [START_REF] Willett | Adjustment for total energy intake in epidemiologic studies[END_REF].

The residuals are by definition uncorrelated with energy and represent the differences between the observed and predicted intakes, i.e. the difference between an individual's actual food or nutrient intake and what it would be expected to be, given their energy intake.

The association of energy density tertiles with food group and micronutrient intake residuals were analysed by one-way ANOVA. Differences in energy density between age-and gendersubgroups were investigated by one-way ANOVA with Tukey's post-hoc test. A χ 2 test checked for differences in the frequency of food group consumers across tertiles. Energy density (after square-root transformation to normalise the distribution) was tested between under-and adequate reporters with a Student's t-test.

To further check that the tertiles represented heterogeneous dietary patterns, we performed discriminant function analysis. This tests whether cases are classified as predicted from a set of discriminating variables (the food residuals) into predefined groups of a criterion variable (the energy density tertiles). The predicted classification was compared to the original classification. It also calculates structure coefficients of the discriminating variables, which indicate how much they predict the groups.

Prior to regressing the foods/nutrients on energy, the intakes were transformed (square root or natural log) so that the residuals of the regression were as homogeneous as possible across tertiles, a requirement of both discriminant analysis and ANOVA. Due to the skewness of the data, intakes of food groups are presented in Tables 2a and2b as geometric means and 95% confidence intervals, for consumers only. The geometric mean is equivalent to the antilog of the arithmetic mean of log-transformed data. All other tables use (arithmetic) mean and s.d.

All analysis was performed using SPSS for Windows, version 16 (SPSS Inc, Chicago, US) and the level of significance was set at P < 0.05.

Results

Data was available for 551 children (52% girls, mean age 9.6 years) and 569 adolescents (55% girls, mean age 15.5 years). The prevalence of overweight was 12% (highest in 9-yearold boys at 14%; lowest in 15-year-old girls at 9%), and the prevalence of obesity was 2% (highest in 15-year-old boys at 3%; lowest in 15-year-old girls at 1%).

As the diet increased in energy density, energy intake increased, and total food intake decreased, across each tertile, in all age-and gender-subgroups (Table 1). These relationships were linear, except in 15-year-old boys. Only the low energy density diet group met the population goals for energy from macronutrients (Nordic Council of Ministers 2004, WHO 2003)(Figure 1).

Significant differences between energy density tertiles were seen in both the proportion of food group consumers and in the amounts consumed (Tables 2a and2b). χ 2 tests showed that, for each of the following food groups, in at least three of the age-and gender-subgroups subjects with the lowest energy density were more likely to consume: fruit; vegetables; pasta, rice and potatoes; and cereals, and were less likely to consume sweets and chocolate, and sweetened drinks. After adjustment for energy, intakes of most food groups differed significantly across tertiles of energy density (Table 2b). The food group that differed most across tertiles, with the largest F-value, was fruit, followed by pasta, rice, potatoes; vegetables; sweets and chocolate; and milk, fil, yoghurt. Low, mid and high energy density diets also differed significantly in terms of micronutrient and fibre intake (Tables 3a and3b).

The discriminant function analysis confirmed that significant differences between energy density tertiles existed (overall Wilks' λ = 0.49, P<0.001). The largest structure coefficients were for fruit (0.430); pasta, rice, potatoes (0.387); vegetables (0.352); sweets and chocolate (-0.275); sweetened drinks (-0.245); and milk, fil, yoghurt (0.220). The discriminant analysis was also able to classify subjects into groups that agreed well with the energy density tertiles.

Using all available food intake data, 65% of subjects were classified to the "correct" (same) group as when only the dietary energy density variable was used, exceeding the value for classification based on chance (33%). Reanalysis with the extreme tertiles only (i.e. low and high) correctly classified 89%.

Potential energy under-reporting occurred more in the older age-group (data not shown).

Under-reporters had significantly lower dietary energy density compared to adequate reporters in the older age-group only (1.57 vs 1.71 kJ/g, P = 0.010), however removing all under-reporters (n = 84) did not change any associations between energy density and either food groups or micronutrients. While the younger age group were more likely to have a lower quality interview, energy density did not differ between low and high quality recalls, and excluding these recalls (n = 149) also had no effect on the main analysis.

Discussion

Associations between dietary energy density and diet quality

The low energy density diet had a macronutrient energy profile closest to population goals and higher intakes of most micronutrients examined. In the majority of age-and gendersubgroups, it was characterised by higher and more frequent intakes of many of the food groups recommended in Sweden. The discriminatory analysis confirmed that the tertiles, particular the two extreme tertiles, were considerably different from each other. Taken together, the overall picture of a lower energy density diet is one of higher dietary quality.

To the best of our knowledge, no other studies comprehensively describing dietary quality and energy density in children under free-living conditions have been published, although Johnson et al. (2008a) did report a relationship between dietary energy density and percent energy from total fat (positive) and fibre (negative) in a sample of UK children. Also in children, a cross-sectional association between energy density and selected predictors of obesity [START_REF] Mendoza | Dietary energy density is associated with selected predictors of obesity in U.S. Children[END_REF], and a longitudinal association with overweight (Johnson et al 2008a[START_REF] Mccaffrey | Energy density of the diet and change in body fatness from childhood to adolescence; is there a relation?[END_REF], have been reported. In adults, low energy density diets have been associated with higher diet quality (based on micronutrient, macronutrient and food intakes) in two large US cohorts: the NHANES III and the CSFII 1994-1996 surveys (Kant andGraubard 2005b, Ledikwe et al 2006b). In almost 2 000 French adults, dietary energy density was found to correlate with the mean adequacy ratio, an index of quality based on recommended nutrient intakes [START_REF] Maillot | Low energy density and high nutritional quality are each associated with higher diet costs in French adults[END_REF]. [START_REF] Cucó | Food and nutritional profile of high energy density consumers in an adult Mediterranean population[END_REF] defined energy density as kJ per cm 3 , and included beverages, and found high energy density was associated with higher intakes of fat and certain food groups in Spanish adults. Using kJ/g and excluding beverages, Schröder et al. also found low energy density diets were of higher quality in Spanish adults, including elderly (Schröder et al 2008a, Schröder et al 2008b).

Both the ANOVA and the discriminant analysis suggested that fruit; pasta, rice and potatoes; vegetables; milk, fil and yoghurt; sweets and chocolate; and sweetened drinks are the most important food groups in determining dietary energy density. Johnson et al. (2008b) noted in their study that fruit and vegetables (combined) had twice as heavy a loading on a dietary pattern score that included energy density than crisps and confectionary did. They propose that this should "reinforce efforts to encourage consumption of fruit and vegetables" rather than "focusing on the exclusion" of energy-dense foods, but whether this would be any more effective from a public health perspective is open to debate [START_REF] Verbeke | Impact of communication on consumers' food choices[END_REF]. For many foods the trend across energy density tertiles was clear, but for burgers and sausages, and crisps and chips, it was not. These foods may reflect home-cooking as well as fast food consumption and so the association with diet quality may not be as straightforward.

The mean energy density values in our study are slightly lower than those published by Johnson et al. (2008a) and [START_REF] Mccaffrey | Energy density of the diet and change in body fatness from childhood to adolescence; is there a relation?[END_REF] but the dietary assessment methods used differed from ours and their subjects were younger. [START_REF] Mendoza | Dietary energy density is associated with selected predictors of obesity in U.S. Children[END_REF] reported much lower values but did not exclude all beverages in their calculations. As with our younger agegroup, no significant gender differences in energy density were reported in these studies.

Despite also consuming the most foods and beverages, 15-year-old boys had significantly more energy dense diets than either the children or 15-year-old girls. Their mean energy intake was 12.4 MJ, over 3.8 MJ more than the 15-year-old girls (data not shown).

Differences of a similar magnitude have been seen in another study of Swedish 15-year-olds [START_REF] Sjöberg | Assessment of habitual meal pattern and intake of foods, energy and nutrients in Swedish adolescent girls: comparison of diet history with 7-day record[END_REF]. Over the life course, energy density has been shown to decline after 15-17 years of age and 13-15 years in U.S. and Spanish adolescents, respectively [START_REF] Drewnowski | Sensory control of energy density at different life stages[END_REF][START_REF] Martí-Henneberg | Energy density of the diet, food volume and energy intake by age and sex in a healthy population[END_REF].

Defining a healthy diet

There is no accepted way to identify a "healthy" diet. Defining a food or food group as healthy is also challenging (Drewnowski 2005, Lobstein and[START_REF] Lobstein | Defining and labelling 'healthy' and 'unhealthy' food[END_REF] but food-based dietary guidelines provide some direction [START_REF] Becker | Dietary guidelines and patterns of food and nutrient intake in Sweden[END_REF], FAO/WHO 1998). The Nordic Nutrition Recommendations (NNR) recommend the consumption of fruits and vegetables, cereals, fish, milk and milk products (mainly lean varieties) and potatoes (Nordic Council of Ministers 2004). They advise that the consumption of energy dense foods should be limited.

More recently, an interest in whole-diet analysis has led to the development of dietary pattern methods that can be broadly classified as data-driven and knowledge-driven. The former are sample-specific and require subjective interpretation of the patterns that emerge (Hu 2002, Newby and[START_REF] Newby | Empirically derived eating patterns using factor or cluster analysis: a review[END_REF]; the latter, such as scores and indexes, are based on a priori assumptions about diet and health, and are not applicable in every setting. Some require information on portions, compliance with recommended daily allowances or intakes of foods or beverages (e.g. alcoholic), which may not be appropriate in all countries or age groups [START_REF] Kant | Indexes of overall diet quality: a review[END_REF][START_REF] Kant | Dietary patterns and health outcomes[END_REF], Kant and Graubard 2005a). Energy density has the advantage of being easy to calculate in every dietary study where energy and weight of food and beverages consumed is available. If these results are replicated in other studies, using different dietary assessment methods and in other populations, the use of energy density as a simple proxy marker for a diet of better quality might be supported. It could be used to identify upper and lower quantiles within a population and to thereby discriminate between diets that are generally of lower and higher quality, respectively.

Methodological considerations

The most appropriate method of calculating dietary energy density remains to be defined. Not only are beverages much less energy dense than most foods, but the regulation of beverage intakes is different to that of food [START_REF] Rolls | Changing the energy density of the diet as a strategy for weight management[END_REF] . We chose to exclude all beverages from the calculation, and this has been recommended to enhance comparability with other studies [START_REF] Johnson | Reflections from a systematic review of dietary energy density and weight gain: is the inclusion of drinks valid?[END_REF], Ledikwe et al 2005). We repeated the analysis using energy density calculated with milk, and with milk and other energy-containing beverages included. The association with 3-6 food groups (out of 20) and 2-3 micronutrients (out of 13) changed; some became associated, some lost association (data not shown), but our overall conclusion did not change.

It should be noted that high energy-dense foods are not nutritionally equal. For example, a person with a high intake of "healthy" fats would be as likely to be in the high energy dense diet category as someone with a high intake of saturated fats, and this is one limitation of dietary energy density. The relatively large sample size and the use of both an objective and subjective method of identifying recalls of potentially lower quality are strengths of the study.

The limitations of 24-h recalls in capturing habitual intake at individual level are well known [START_REF] Beaton | Errors in the interpretation of dietary assessments[END_REF][START_REF] Bingham | Limitations of the various methods for collecting dietary intake data[END_REF], due to the large day-to-day variation in intake that exists.

However, for the purpose of this study, a 24-h recall on a reasonably large group, with no obvious bias, was deemed appropriate to demonstrate an association with energy density and dietary quality.

Conclusions

The use of dietary energy density to categorise the diets resulted in groups characterised by quite different dietary patterns. Subjects with lower energy density diets are more likely to consume recommended food groups, have higher intakes of micronutrients and recommended food groups and have the most favourable macronutrient energy profile. Replication of these findings in other studies could support the use of dietary energy density as a simple marker of dietary quality. 325 Recommended upper limits for saturated fat and sucrose: 10%; population goals for protein: 15%; fat: 30%, and carbohydrates: 55% (NNR, 2004) All means differed significantly across tertiles (P < 0.001)

Figure 1

 1 Figure 1 Mean percent energy from macronutrients across tertiles of energy density

Table 1

 1 Energy density, food intake and energy intake across energy density tertiles, by age and gender

		Energy density 1	Food only 2	All food and beverages
				kJ/g	MJ	g	MJ	g
		Tertile n	Mean (s.d.)	Mean (s.d.) Mean (s.d.)	Mean (s.d.)	Mean (s.d.)
	Girls, 9 years a Low	95	5.21 (0.60)	6.57 (1.84) 1261 (319)	7.90 (1.90)	(494)
		Mid	96	6.62 (0.34)	6.83 (1.76) 1034 (268)	8.29 (1.95)	(562)
		High	95	8.65 (1.17)	6.93 (1.92)	817 (256)	8.49 (2.12)	(448)
	Boys, 9 years a Low	88	5.15 (0.56)	6.70 (1.97) 1307 (387)	8.32 (2.26)	(688)
		Mid	89	6.57 (0.37)	7.41 (1.99) 1133 (316)	9.12 (2.30)	(624)
		High	88	8.68 (1.16)	7.92 (2.06)	930 (274)	9.44 (2.27)	(543)
	Girls, 15 years	a Low	104	5.12 (0.58)	6.64 (2.05) 1312 (438)	7.89 (2.36)	(952)
		Mid	104	6.61 (0.44)	7.15 (2.27) 1082 (339)	8.63 (2.54)	(723)
		High 104	9.17 (1.43)	7.78 (2.72)	870 (330)	9.28 (3.05)	(810)
	Boys, 15 years	b Low	85	5.65 (0.66)	9.72 (3.43) 1727 (587)	11.63 (3.78)	(1102)
		Mid	86	7.37 (0.43)	10.70 (3.19) 1456 (439)	13.19 (3.72)	(1115)
		High	86	9.43 (1.17)	9.95 (3.31) 1073 (386)	12.49 (3.72)	(897)
	1 Calculated from food only
	2 All beverages excluded
	Different superscripts indicate significant differences in energy density between age and gender groups
	1 MJ = 1000 kJ = 239 kcals

Table 2a

 2a Intakes of food groups across diets of low, mid and high energy density in 9 year olds (n = 551), by gender

						Girls									Boys
			Low		Mid			High			Low			Mid	High
			% 1 Mean	CI 2	% Mean	CI	% Mean	CI	% Mean	CI	% Mean	CI	% Mean	CI
	Food group (g)
	Milk, fil, yoghurt	b	97	394-560 96 432 380-491 94 462 404-529	99 630 551-721 99 603 527-690 92 436 377-505
	-full fat		60	321-443 47 264 215-324 46 299 250-357	66 420 357-494 58 331 273-402 50 247 198-310
	-reduced fat		77	239-363 77 371 323-427 76 385 331-448	83 361 294-444 78 507 436-589 72 376 317-445
	Cheese	b	41	25-42	53	33 25-43	46	35 25-47	28	25 18-36	51	47 38-59	53	47 35-62
	Bread		93	52-70	97	76 65-90	98	91 77-107	89	67 56-79	94	92 78-109 97 119 103-138
	Meat, meat dishes		79	93-151 88 125 104-150 83 107 89-128	89 131 105-165 84 139 113-171 87 130 109-154
	-burgers, sausages		58	90-136 69 117 97-142 57	93 75-115	72 102 81-130 64 120 95-151 66 110 87-139
	Fish, fish dishes	b	35	60-119 33	55 41-73	28	42 26-67	30	60 40-89	37	56 37-84	19	36 19-68
	Pasta, rice, potatoes	b	80	194-259 84 200 174-231 74 138 118-161	91 235 204-271 84 217 185-254 72 146 124-173
	Cereals	a	51	30-63	34	45 30-68	24	37 23-59	50	47 33-68	40	74 50-109 38	62 40-94
	Pizza, pies, pancakes		16	128-225 21 175 139-221 27 186 154-223	16 138 89-214 18 193 144-260 30 204 172-241
	Vegetables	b	88	58-87	80	48 39-59	81	34 28-42	80	58 46-72	72	65 51-82	55	33 24-44
	Fruit	a,b 83	189-261 63 158 133-187 42 132 111-157	75 178 144-219 51 129 100-165 43 116 94-143
	Fruit juice	a	36	176-292 45 202 158-260 26 202 164-250	40 222 180-273 27 277 222-345 33 234 158-347
	Sweetened drinks	a	39	173-271 54 250 193-323 58 316 269-372	43 311 264-366 35 300 252-358 52 328 267-404
	Spreads and oils	b	89	10-13	90	13 11-16	91	16 13-19	80	13 11-15	91	15 12-17	94	19 16-22
	Chips, crisps		16	22-136 21	63 33-122 27	62 39-97	10	51 17-152 16	83 44-158 22	49 30-83
	Other sweet foods	b	62	33-69	72	50 37-67	63	51 39-68	74	66 47-92	63	65 46-93	80	65 51-83
	Cakes, biscuits		42	32-57	50	41 32-53	50	45 34-60	39	33 23-46	32	39 27-58	39	57 43-75
	Sweets, chocolate	a,b 55	8-16	74	15 12-19	83	23 18-29	53	8 6-11	72	13 9-17	65	23 17-32
	1 Percent (%) consuming food group in each tertile
	a, b χ2 for trend in % consumers across low, mid and high energy density diets significant in girls and boys, respectively
	2 Mean and confidence interval (CI) is the geometric mean and 95% confidence interval, for consumers only

Table 3a

 3a Intakes of nutrients across diets of low, mid and high energy density in 9 year olds (n = 551), by gender

			Girls			Boys
		Low	Mid	High	Low	Mid	High
	Nutrient
	Vitamin C (mg)	130 (89)	92 (66)	66 (64)	122 (102)	80 (70)	72 (74)
	Folic acid (μg)	224 (79)	193 (57)	179 (65)	223 (95)	212 (81)	185 (67)
	Vitamin B12 (μg)	5.0 (2.7)	4.4 (2.0)	4.7 (2.7)	6.2 (10.3)	5.6 (2.7)	5.0 (2.8)
	Vitamin E (mg)	6.4 (2.3)	6.4 (2.5)	7.1 (3.1)	6.5 (2.5)	6.8 (2.4)	7.3 (3.0)
	Vitamin D (μg)	4.5 (2.6)	4.7 (2.9)	5.4 (3.3)	4.9 (2.9)	6.7 (9.2)	5.5 (3.5)
	Retinol equivalents (μg) 1139 (756)	931 (500) 1045 (648)	1174 (2128) 1119 (653)	1106 (933)
	Iron (mg)	8.7 (3.2)	8.7 (3.8)	8.3 (3.4)	10.5 (5.7)	11.5 (8.3)	9.6 (5.8)
	Calcium (mg)	1173 (468)	1115 (522) 1109 (500)	1224 (512)	1417 (591)	1191 (538)
	Zinc (mg)	10.3 (3.5)	10.5 (3.9)	10.1 (3.7)	11.4 (4.6)	12.2 (4.6)	11.3 (4.3)
	Selenium (μg)	32 (16)	28 (11)	28 (12)	33 (17)	37 (18)	28 (14)
	Sodium (mg)	3119 (1120) 3004 (956) 2562 (823)	3011 (1140) 3333 (1061) 3056 (1007)
	Cholesterol (mg)	269 (131)	252 (102)	282 (154)	263 (144)	310 (177)	308 (156)
	Fibre (g)	16.1 (6.2)	14.1 (5.6)	13.5 (5.4)	15.8 (5.5)	15.0 (6.2)	14.1 (5.1)
	Results are presented as mean (s.d.)

Table 3b

 3b Intakes of nutrients across diets of low, mid and high energy density in 15 year olds (n = 569), by gender Results are presented as mean (s.d.) 1 One-way ANOVA for all subjects (n = 1 120), with tertiles as independent variable, and residuals of nutrients as dependents Arrow indicates direction of change in residuals as dietary energy density increases from low to high

			Girls			Boys		ANOVA
		Low	Mid	High	Low	Mid	High	F P-value 1
	Nutrient
	Vitamin C (mg)	165 (131)	108 (94)	87 (89)	148 (132)	120 (114)	85 (93)	< 0.001 
	Folic acid (μg)	259 (115)	221 (95)	189 (97)	314 (121)	287 (109)	260 (121) < 0.001 
	Vitamin B12 (μg)	4.0 (2.3)	4.7 (2.7)	4.0 (2.5)	8.1 (11.0)	7.1 (4.6)	8.3 (23.5)	< 0.001 
	Vitamin E (mg)	7.3 (3.4)	7.4 (3.6)	9.1 (15.1)	8.8 (4.0)	10.5 (4.7)	9.6 (4.1)	0.419 -
	Vitamin D (μg)	4.3 (3.0)	5.3 (3.8)	4.8 (3.6)	5.9 (2.9)	6.7 (3.0)	7.1 (4.5)	0.096 -
	Retinol equivalents (μg) 1043 (630)	1015 (646)	877 (594)	1548 (2191) 1374 (1075) 1605 (3128)	< 0.001 
	Iron (mg)	9.7 (3.4)	9.7 (4.4)	9.2 (4.3)	14.4 (6.3)	13.9 (5.3)	12.3 (8.1)	< 0.001 
	Calcium (mg)	1172 (575)	1168 (527) 1233 (658)	1766 (812)	1789 (854)	1732 (929)	< 0.001 
	Zinc (mg)	10.0 (4.3)	10.6 (3.9)	10.4 (4.6)	16.9 (7.5)	18.5 (10)	15.1 (6.3)	< 0.001 
	Selenium (μg)	30 (21.3)	31 (16.2)	26 (17)	42 (21)	44 (22.2)	34 (15.4)	< 0.001 
	Sodium (mg)	2924 (1315) 2925 (925) 2806 (1228)	4740 (1814) 4671 (1707) 3805 (1423) < 0.001 
	Cholesterol (mg)	216 (129)	255 (132)	254 (187)	320 (170)	395 (177)	338 (178)	0.258 -
	Fibre (g)	19.4 (9.4)	15.8 (7.1)	13.7 (7.4)	23.2 (9.7)	19.8 (8.3)	17.6 (7.3)	< 0.001 

Acknowledgements

We thank Associate Prof. Olle Carlsson and Prof. Juni Palmgren for their suggestions regarding the statistical analysis. We also thank the participants of this study and colleagues who collected the data. This work was supported by Stockholm County Council. 330

This study was funded by Stockholm