Biological traits analyses in the study of pollution gradients and ecological functioning of marine soft bottom species assemblages in a fjord ecosystem

Journal of Experimental Marine Biology and Ecology 432–433 (2012) 94–105

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Biological traits analyses in the study of pollution gradients and ecological functioning of marine soft bottom species assemblages in a fjord ecosystem Eivind Oug a,⁎, Annelise Fleddum b, c, Brage Rygg a, Frode Olsgard a, b, 1 a b c

Norwegian Institute for Water Research (NIVA), Gaustadalléen 21, NO-0349 Oslo, Norway Department of Biology, University of Oslo, PO Box 1066, Blindern, NO-0316 Oslo, Norway The Bellona Foundation, Norway

a r t i c l e

i n f o

Article history: Received 21 December 2011 Received in revised form 16 July 2012 Accepted 22 July 2012 Available online 9 August 2012 Keywords: Biological traits analysis Ecological functioning Marine benthic fauna Pollution Soft sediment infauna

a b s t r a c t In the present study, biological traits analysis (BTA) was used to explore and characterise effects of pollution on functional attributes of soft bottom infaunal species assemblages. The data comprised 38 sampling stations in the Oslofjord, Norway, ranging from heavily polluted to minimally impacted areas. At each station, species composition (113 taxa in total), contaminants (cadmium, mercury, lead, DDT, PCB) and sediment parameters were determined. Species functions were analysed for eight biological traits defined for activity and life history features. Traits were scored according to the fuzzy coding technique. The most distinct patterns were shown for mobility, size, sediment dwelling depth, feeding type and larval development in relation to contaminants, sediment physical structure and sediment oxidation status. At high levels of contaminants, particularly cadmium, features such as shallow sediment dwelling depth, small size, subsurface deposit feeding and lecitotroph larval development prevailed, while at low contaminant levels characteristic features included deeper sediment dwelling depth, larger size, surface deposit feeding and permanent attachment. Deep sediment dwelling depth (>15 cm) was related to minimally contaminated oxidised sediments at greater water depths. Mobility and carnivorous feeding prevailed in coarser sediments. The study showed that BTA detected and depicted specific features that correlated with gradients in pollution and may be important for sediment reworking and nutrient cycling. As part of the present work, trait information for >500 macrofaunal taxa have been assembled and entered in a comprehensive database. © 2012 Elsevier B.V. All rights reserved.

1. Introduction Studies of soft bottom species assemblages are fundamental in marine environmental monitoring and assessments of impacts from human activities. The vast majority of studies focus on the structural aspects of species assemblages, such as the numbers and abundances of the constituent species, and seek to relate species patterns and changes to environmental conditions and external factors. Presently, standardised monitoring schemes are under development for the European Water Framework Directive based on well-established routines and data analysis techniques. Whereas there is solid empirical knowledge to interpret faunal changes in relation to environmental factors, these approaches gives few clues to understanding why changes in species assemblages occur, nor what the ecological consequences are. In modern marine resource and environmental management, there is an urgent need to assess the ecological consequences of

⁎ Corresponding author at: Norwegian Institute for Water Research (NIVA), Regional Office Sorlandet, Jon Lilletuns vei 3, NO-4879 Grimstad, Norway. Tel.: +47 98 22 77 80; fax: +47 37 04 45 13. E-mail address: [email protected] (E. Oug). 1 Deceased 19 April 2010. 0022-0981/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jembe.2012.07.019

human influences and in which respects natural systems may be affected (Thrush and Dayton, 2010). Basically all species have their ecological roles, which play parts in the total functioning of ecosystems (Ehrlich and Ehrlich, 1981; Naeem et al., 1995). In broad terms, ecosystem functioning includes all the processes in the system and the chemical, physical and biological components involved (Bremner, 2008; Naeem et al., 2002). In soft sediments, macrofauna are a key biological component which drive important processes such as sediment reworking, bio-irrigation, nutrient matter uptake, and oxygen and dissolved matter transport (Kinzig et al., 2002; Loreau et al., 2002; Naeem and Wright, 2003; Olsgard et al., 2008; Widdicombe et al., 2004). These processes depend on biological features of the resident organisms such as their degree of mobility, burrowing activity, tube construction, feeding methods, food selection and size. Natural phenomena or human influences that affect the organisms may consequently be expected to also lead to changes in the functioning of the ecosystems (e.g. Cooper et al., 2008; Gray et al., 2006; Schratzberger et al., 2007; Tillin et al., 2006). It is a matter of question, however, which functions are affected and to what degree, as this will depend on the biological features of the species that are decreasing or increasing in response to the influence.

E. Oug et al. / Journal of Experimental Marine Biology and Ecology 432–433 (2012) 94–105

Whereas describing or measuring ecosystem functioning is a difficult and overwhelming task, methods to investigate the ecological functioning of components of the systems are more feasible and yet can still enlighten how systems may be influenced. One promising approach, presently known as biological traits analysis (BTA), aims to describe and quantify the ecological functioning of macrofaunal species assemblages. BTA combines structural data for species assemblages (species abundances or biomass) with information on functional features of each species (Bremner et al., 2003, 2006a). The functional information is quantitatively expressed as a series of biological traits where values are given for each species according to life style and mode of activity. By combining the traits with the abundances or biomass values across all species, a quantitative expression of functional features of the species assemblage can be obtained. This expression can then be used for analyses of gradients and relationships to environmental factors in much the same way as the structural data. The approach hence provides a link between species, environment and ecosystem processes. Numerical functional analyses were originally developed and used for freshwater systems (e.g. Charvet et al., 2000; Chevenet et al., 1994; Doledec et al., 1999; Lamouroux et al., 2004; Usseglio-Polatera et al., 2000), but recently there have been several studies using BTA to describe the ecological functioning of marine benthic assemblages as well (e.g. Aarnio et al., 2011; Bremner et al., 2003, 2006a,b; Cooper et al., 2008; Fleddum, 2010; Marchini et al., 2008; Schratzberger et al., 2007; Tillin et al., 2006; Villnäs et al., 2011). In this study, BTA was used to explore and characterise effects of pollution on functional attributes of soft bottom species assemblages, with particular focus on contaminants and organic matter. The analysis was conducted on data from the Oslofjord, Norway, which has been severely influenced by municipal and industrial effluents over an extended time period (Aschan and Skullerud, 1990; Mirza and Gray, 1981) and shows gradients in several contaminants (cadmium, mercury, lead, DDT, PCB) and organic load. The study area stretched from the rather heavily polluted inner areas near the main harbour of the city of Oslo to little impacted areas at a distance from pollution sources. Using multivariate ordination techniques, changes in functional features along gradients in pollution were examined. In particular, function–environment relationships were searched for, in order to assess to what degree specific functional features may be affected by the various pollution components.

95

Table 1 List of traits and categories in the traits database.

Activity

Size and shape

Life history

Trait

Abbrev Categories

Adult life habit

AH

Degree of attachment

DA

Adult mobility (relative)

AM

Sediment dwelling depth

SD

Faecal deposition

FD

Body form

BF

Normal adult size

NS

Feeding

FH

Life duration

LD

Reproduction No of reprod per year

NY

2. Methods Reprod period

RP

Reprod technique

RT

Larvae type

LT

2.1. Traits and categories An overview of the traits used to describe the functional features is presented in Table 1. The traits were arranged in four main groups that represent main activity, size and body shape, life history, and reproduction. For each trait a varying number of categories representing included features were characterised. The categories were selected according to the diversity of the life functions that each trait represents and the status of knowledge on the traits. The affinities of the species to the categories were scored according to the ‘fuzzy coding’ procedure (Chevenet et al., 1994). This procedure implies that a species may be given values in more than one category for a trait. A four-stage scale was used: 0 = no affinity, 1 = low importance, 2 = moderately high importance, 3 = dominant. In cases where more than one category was relevant, the values 1 and 2 were used according to the relative importance of each. The value 3 was used in cases when only a single category was appropriate. Traits for which several categories usually were scored included adult life habit (AH), sediment dwelling depth (SD), normal adult size (NS) and feeding mode (FH). In the case of normal adult size (NS), the value 2 (or 3 when one category only applied) was allocated

1 Sessile 2 Permanent tube 3 Semi-permanent tube 4 Burrower 5 Surface crawler 6 Swimmer 1 None 2 Temporary 3 Permanent 1 None 2 Low 3 Medium 4 High 1 0 cm (surface) 2 0–1 cm 3 1–5 cm 4 5–15 cm 5 >15 cm 1 Sediment surface 2 Subsurface 0–5 cm 3 Deep subsurface > 5 cm 1 Short cylindrical 2 Flattened dorsally 3 Flattened laterally 4 Ball-shaped 5 Long, thin threadlike 6 Irregular 1 b0.5 cm 2 0.5–1 cm 3 1–3 cm 4 3–6 cm 5 6–10 cm 6 >10 cm 1 Suspension/filter feeder 2 Scraper/grazer 3 Surface deposit feeder 4 Subsurface deposit feeder 5 Dissolved matter/symbionts 6 Sandlicker/large detritus 7 Scavenger 8 Carnivore/omnivore 9 Parasite/commensal 1 b1 year 2 1–5 years 3 >5 years 1 b1 21 3 2 or more 1 Winter (December–February) 2 Spring (March–May) 3 Summer (June–August) 4 Autumn (September– November) 5 No particular season 1 Asexual (budding) 2 Broadcast spawner 3 Demersal eggs 4 Brooder, viviparous 1 Planktotroph (feeding larvae) 2 Lecitotroph (non-feeding larvae)

to the size group (category) or groups representing the normal size for adult specimens collected in ordinary macrofauna sampling, whereas the value 1 was allocated to the next lower and larger size groups. In the case of feeding mode (FH), where a species may for instance be both a surface deposit and a suspension feeder, the value 2 was allocated to both categories if they were considered to be of about equal importance, or else 2 and 1 for main and subsidiary feeding modes, respectively.

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2.2. Compilation of traits information Traits information was extracted from a comprehensive database of soft-bottom macrofauna in Norwegian waters in which traits information has been assembled along with species data. The database is located at the Norwegian Institute for Water Research (NIVA) and presently comprises 1300 species or species-group taxa. Traits information has been entered for about half of the species. The information has been compiled from a broad selection of literature on natural history and life functions, from numerous studies of the ecology of benthic species in the Oslofjord, and by consulting experts (details may be obtained from the authors). Traits information from the database has previously been used in a study by Cooper et al. (2008). Table 2 gives an overview of the information in the database for major taxonomic groups. For some traits there is no information for several of the represented species, especially the traits on reproduction. The information is complete (i.e. entered for all treated species) or nearly complete for seven of the traits, viz. adult life habit (AH), degree of attachment (DA), adult mobility (AM), sediment dwelling depth (SD), body form (BF), normal adult size (NS), and feeding mode (FH) (Table 2). The information is rather poor, however, for the traits faecal deposition (FD), life duration (LD), number of reproductive cycles per year (NY), and reproductive periods (RP). It is especially among molluscs, crustaceans and echinoderms where this information is lacking. For a total of 49 species, of which most are polychaetes, all traits have been scored. 2.3. Traits analyses The trait analyses were carried out in a multi-step approach. In the first step a species × traits matrix was extracted from the database containing all species in the study and their category scores for the traits. In the next step the traits information was combined with the species abundances at stations to obtain ‘trait profiles’ for the sampling stations. The trait profiles were calculated by multiplying trait Table 2 Numbers of species with scored values for the different traits in major taxonomic groups. A. Total number of species for each trait in the NIVA database. B. Number of species in the present analyses on macrofauna in the Oslofjord. Trait A. Database Number of species Activity Adult life habit (AH) Degree of attachment (DA) Adult mobility (AM) Sediment dwelling depth (SD) Faecal deposition (FD) Size and shape Body form (BF) Normal adult size (NS) Life history Feeding (FH) Life duration (LD) Reproduction No of reprod/year (NY) Reprod period (RP) Reprod technique (RT) Larvae type (LT) All traits B. Oslofjord data Number of species AH, BF, FH, NS, SD (complete data) AM, DA LT

Polychaeta Mollusca Crustacea Echinoderm Other 365

95

89

16

35

365 365

95 95

89 89

13 15

35 21

365 365

95 95

89 89

14 7

12 28

154

4

4

3

7

365 365

95 95

89 89

16 14

35 33

365 106

95 7

89 1

13 8

34 0

107 244 198 312 47

5 13 69 67 1

11 13 89 85 0

4 8 11 13 1

0 0 21 12 0

70 70

20 20

9 9

6 6

8 8

70 60

20 15

9 7

6 6

3 5

category scores by species abundances and summing across all constituent species. Trait profiles hence represent the ‘abundances’ of the traits at particular stations and thus reflect the main functional features of the species assemblages. Mathematically this was done by multiplying the station × species matrix by the species × traits matrix to obtain a station × traits matrix. Species abundances were square-root transformed before the calculations to balance the abundance values between abundant and rare species. Trait category scores were standardised so that within a trait the sum of values over categories equalled one. Trait profiles were subjected to multivariate analyses to examine patterns and relationships to environmental factors. Principal component analysis (PCA) was used to describe main patterns and assess associations among functional features. To examine relationships between trait features and environmental factors, Co-inertia analysis (CoI) and Redundancy analysis (RDA) were used. CoI is a general technique for measuring the relation between two separate data sets (Dray et al., 2003). In the present case, CoI was performed on a matrix of correlations between individual trait features (trait categories) and environmental factors calculated as part of the PCA routine (indirect gradient analysis: see ter Braak and Smilauer, 2002 for calculations). RDA is a direct gradient analysis related to PCA, but the axes are constrained by the environmental information in order to extract inter-related variations. Hence RDA will specifically depict trait categories which are maximally related to the environmental factors. PCA and RDA are both linear ordination techniques. It is assumed here that trait features generally show increasing or decreasing trends (linear responses) in relation to environmental gradients. The use of RDA was generally recommended by Kleyer et al. (2012) for studies of traits in species assemblages in relation to environmental gradients. Before performing the traits analyses, the species data (station × species matrix) were subjected to a canonical correspondence analysis (CCA) in order to illustrate the major structural faunal patterns and relationships to environmental factors. The species data were square-root transformed before the analysis to reduce the influence of the most abundant species. All multivariate analyses were performed using Canoco 4.5 software (ter Braak and Smilauer, 2002). During analysis of CCA and RDA, the procedure of forward selection was used to select a subset of environmental factors that were maximally related to the biological data. The calculations were performed on these selected factors, whereas other factors were entered as passive in subsequent analyses to illustrate correlations. The significance of the factors was tested using Monte Carlo permutation tests. A significance level of 10% was chosen for the selection of factors, which implies that factors with moderately strong relationships to species or traits will be among those selected. The selection procedure ensures that the active factors in the analyses are minimally correlated, hence avoiding problems with instability from multiple correlated factors.

2.4. The Oslofjord data: species and environmental parameters The data were collected during a large-scale environmental assessment study in the 1990s that comprised sediment conditions and contaminants in addition to macrofaunal species assemblages (Konieczny, 1994; Olsgard, 1995). Altogether 49 stations ranging from 14 to 160 m were sampled. At each station four replicate samples of macrofauna were taken using a 0.1 m 2 Day grab. The samples were sieved through 1 mm screens and preserved in 4% formaldehyde in seawater in the field. In the laboratory, samples were sorted under a magnifying glass. All organisms were counted and identified to the species level, or alternatively to the lowest possible taxonomic level. Several stations near the city of Oslo were strongly impoverished or had no fauna due to poor oxygen conditions. The

E. Oug et al. / Journal of Experimental Marine Biology and Ecology 432–433 (2012) 94–105

analyses presented here are based on data from 38 stations, omitting stations with no fauna (Fig. 1). In total, 113 species/taxa were recorded at the 38 stations. The number of species at each station varied between 2 and 54, with densities between 140 and 10,800 ind. m −2, and Shannon–Wiener diversity (H′ log2) from 0.14 to 5.22. An overview of the most important species is given in Table 3, while more detailed data can be found in Olsgard (1995). Based on the NQI1 index, which has been developed in Norway for assessments of water quality status according to the European Water Framework Directive (Josefson et al., 2009), the quality status at the stations varied from “high” to “poor”. In the inner part of the fjord the status was mostly “moderate”, but at some stations located close to the main harbour area of the city of Oslo the status was “poor”. In the central and outer part of the fjord the status varied from “moderate” to “high” (Fig. 1). Samples for sediment organics, oxidation status and contaminants were collected in 1992 using a 7 cm diameter Niemistö corer. Sediment organics and contaminants (0–2 cm) were measured from a homogenate of three replicate cores. The contaminants comprised the metals mercury (Hg), cadmium (Cd), lead (Pb) and organic compounds (PCB, DDT). In addition, the metals iron (Fe) and manganese (Mn) were measured. Mn in sediments is redox-sensitive and generally accumulates in oxidised surface sediments overlying reducing conditions (Lynn and Bonatti, 1965). The oxidation status was measured as redox potential (Eh) for the sediment surface (0–0.5 cm). Water oxygen content was not measured, but sediment Eh was taken to represent the oxygen conditions for the fauna. A summary of the environmental information is given in Table 4. Several stations were strongly influenced by metal contaminants and organic pollutants. Hg, Pb and PCB exceeded PNEC (predicted no effect concentration) according to Norwegian environmental quality criteria, which are based on EU risk assessments (SFT, 2007), at most stations. Cd exceeded PNEC at one station close to the harbour

97

Table 3 The most abundant soft-bottom macrobenthic species in the Oslofjord. All species with mean density > 15 ind. m−2 are shown. Geographic location (sector) of the station with the maximum observed abundance is indicated. Species

Nemertinea indet. Priapulida Priapulus caudatus Oligochaeta Tubificoides benedi Polychaeta Amaeana trilobata Amage auricula Ampharete sp. Amphicteis gunneri Anobothrus gracilis Capitella capitata Chaetozone setosa Cirratulus cirratus Diplocirrus glaucus Glycera alba Goniada maculata Heteromastus filiformis Lumbrineris fragilis Maldane sarsi Mediomastus fragilis Melinna cristata Nereimyra punctata Pholoe sp. Pista cristata Polyphysia crassa Prionospio cirrifera Pseudopolydora paucibranchiata Sabellides octocirrata Scoloplos armiger Spiophanes kroeyeri Terebellides stroemi Trichobranchus roseus Mollusca Macoma calcarea Mysella bidentata Nuculoma tenuis Thyasira equalis Thyasira flexuosa Thyasira sarsi Crustacea Philomedes globosus Echinodermata Amphiura filiformis Echinocardium cordatum Labidoplax buski Ophiura spp.

Number of stations

Abundance ind. m−2 Average Max

Sector max abund

29

29

115 G

4

56

198 C

7

171

965 A

10 13 3 16 25 5 30 18 16 32 26 31 23 17 29 30 23 25 27 19 21 32

30 34 45 35 111 599 148 68 19 17 20 271 29 21 109 31 26 20 72 21 25 1033

130 120 98 225 555 2870 1108 345 93 48 65 1895 88 220 860 430 133 40 520 190 140 9843

G E C B B A Q A G E F G H G Q Q B B B C G B

5 9 21 20 14

21 40 82 21 15

73 280 453 70 58

C C F G F

11 7 29 27 16 23

28 27 43 97 166 67

83 155 135 355 890 325

B C E G C C

18

80

598 E

3 10 2 15

39 21 89 66

100 128 143 520

G B G B

of Oslo. Detailed data on the sediment organics and contaminants are given in Konieczny (1994). 2.5. Traits data for macrofauna in the Oslofjord

Fig. 1. Positions of stations in the Oslofjord, Norway. Stations were allocated according to a system of geographic sectors running alphabetically from A near the city of Oslo (north) to H at the narrow passage to the outer fjord (south). Ecological status is according to a classification of the diversity index NQI1 developed for the European Water Framework Directive.

Information on traits was complete for all species in the Oslofjord for adult life habit (AH), body form (BF), feeding (FH), normal size (NS) and sediment dwelling depth (SD) (Table 2). For the traits adult mobility (AM) and degree of attachment (DA), information was lacking for a few species, whereas information was lacking for 20 species for larval type (LT). In these cases, zero scores were entered for the missing information, which implies that the species do not contribute to the calculated abundances of the traits in question (Chevenet et al., 1994). For the traits of faecal deposition (FD), life duration (LD), number of reproductive cycles (NY), reproduction period (RP) and reproductive technique (RT), information was lacking for

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Table 4 Summary of environmental information for the macrofauna stations in the Oslofjord. Sector max = geographic location (sector) of the station with the maximum value; PNEC = ‘predicted no effect concentration’ for contaminants according to Norwegian environmental quality criteria (SFT, 2007); Stns >PNEC = number of stations with concentrations exceeding PNEC. Unit m

Range

63

Sector max

PNEC

Stns > PNEC

14–160

F

–

–

% % mg g−1 mg g−1 mg g−1 – mV

32 79 34.6 7.7 9.0 12.2 204

20–55 14–90 8.9–106 3.5–14.6 1–221 0.5–25.3 −149–435

G G H E H F F

– – – – – – –

– – – – – – –

g kg−1 dw g kg−1 dw mg kg−1 dw mg kg−1 dw mg kg−1 dw μg kg−1 dw μg kg−1 dw

2.4 46.9 1.2 0.5 138 109 5

0.5–12.3 21.0–62.1 0.1–4.0 0.1–2.7 38–295 1–764 1–17

D C A A A H B

– – 0.63 2.6 83 17 20

– – 27 1 32 36 0

more than half of the species. These traits were omitted from the analyses as the information was considered insufficient.

A 0.8

Depth Sediment characteristics Sediment dry weight Fine fraction (b0.063 mm) Total organic carbon (TOC) Inorganic carbon (InorgC) Total nitrogen (TN) C/N-ratio Redox potential (Eh) Metals and contaminants Manganese (Mn) Iron (Fe) Mercury (Hg) Cadmium (Cd) Lead (Pb) PCB DDT

Average

Ecological status

Axis 2; 9 %

High

3. Results

Good

TOC

Pb Hg DDT

3.1. Species assemblages and environmental gradients

Poor

PCB Fine fract Fe

C/N

TN

Axis 1; 14 % Cd

InorgC Sed dw

-0.8

Depth Eh

Mn

-0.8

1.5

0.8

B Axis 2; 9 %

Ophiura Thya fle Pb Hg TOC Thya sar DDT Amph gun Pseu pau Medi fra PCB Fi fr Anob gra Chae setCirr cir Glyc alb Goni mac Meli cri Fe C/N Nucu ten TN Lumb fra InorgC Nemertin Sed dw Thya equ Hete fil Tere str Amag aur Depth Mn Prio cir Eh Spio kro

-0.8

On a broad scale, the composition of the species assemblages changed gradually from the inner fjord to the outer areas most distant from the city of Oslo. The stations of poor ecological status closest to the harbour of Oslo were, however, clearly different from the others. The canonical correspondence analysis (CCA) indicated that the main faunal patterns were related to contaminant levels, sediment oxidation status (Eh), depth, and sediment components (Fig. 2). The first axis represented a gradient in contaminants, where in particular the stations near Oslo harbour stood out, but some stations with moderate ecological status also had enhanced levels of contaminants. The second and third axes differentiated among the other stations based on sediment oxidation status (Eh), depth and sediment dry weight. Sediment oxidation status and depth roughly paralleled a geographical gradient from inner fjord (B- and C-stations) with low Eh and enhanced Hg, Pb and DDT to outer fjord (F- and G-stations) with high Eh and low contaminant levels. During forward selection, the factors of Cd, Eh, sediment dry weight and depth (in that order) were found to constitute the best combination of factors. In total, these factors could explain about one third of the total species variance (29%), with 14% and 9% on the first and second axis, respectively. With regard to the ecological status of the stations assessed from the NQI1 index, poor status coincided with high contaminant levels. There was a tendency for good and high status to be associated with high Eh and depth, but the relationships were not very clear (Fig. 2). The species assemblage at the station closest to the city of Oslo was dominated by the pollution-tolerant annelids Capitella capitata and Tubificoides benedii (>70% of specimens). These species were separated on the first axis at high concentrations of Cd as well as other contaminants (Fig. 2). Other species that were found at increased levels of contaminants, but less strongly associated to Cd, included typically tolerant species such as the annelids Cirratulus cirratus, Mediomastus fragilis and Chaetozone setosa. The second axis depicted species changes from the inner contaminated fjord area to the outer fjord with greater depth and generally good oxygen conditions (high Eh). Prevalent species in the inner areas were, for instance, the bivalves Thyasira flexuosa and Thyasira sarsi and the

Moderate

-0.8

Axis 1; 14 % Cd

Tubi ben Capi cap

1.5

Fig. 2. Canonical correspondence analysis (CCA) of soft bottom macrofauna and environmental factors in the Oslofjord. (A) Biplot of stations and environmental factors for the first two axes. Vectors in bold indicate active factors selected during forward selection; other factors were entered as passive to illustrate correlations. Ecological status of stations is according to the classification of the diversity index NQI1. (B) Biplot of most important species and environmental factors. See Table 3 for complete species names.

E. Oug et al. / Journal of Experimental Marine Biology and Ecology 432–433 (2012) 94–105

polychaete Amphicteis gunneri. In outer fjord areas several sedentary tube-building polychaetes such as Amage auricula, Spiophanes kroyeri and Terebellides stroemi characterised the species assemblages. The numerically dominant species Pseudopolydora paucibranchiata was most abundant in the inner fjord (Table 3), but was present all over the area and ranked high at some outer fjord stations as well (see Olsgard, 1995 for details). 3.2. Traits ‘abundances’ and profiles The calculation of the traits illustrated that the most important functional features were quite typical for sediment living faunal assemblages. Fig. 3 shows the calculated “abundances” for each category of traits for Oslofjord fauna. Typically, the species assemblages were dominated by burrowing and tube-building forms (AH) with little or no mobility (AM), and which were largely unattached to the substratum (DA). Most specimens were of medium or small size (NS) and lived in the upper sediment layers or on the sediment surface (SD). Surface and subsurface deposit feeding were the dominant feeding patterns (FH). Larval development was mostly by lecitotroph development (LT). The categories for the traits of adult mobility (AM), sediment dwelling depth (SD) and normal adult size (NS)

Relative importance

0,70 Adult life habit (AH)

0,60 0,50 0,40 0,30 0,20 0,10 0,00

Perm tube Semi tube

Burrow

Surf craw

Swim

Relative importance

0,70 Adult mobility (AM)

0,60

3.3. Trait patterns and relationships The PCA on trait profiles indicated that the most distinctive patterns were shown among life habit (AH), mobility (AM), feeding type (FH), sediment dwelling depth (SD) and degree of attachment (DA) (Fig. 5). The first axis distinguished burrowing subsurface deposit feeders (AH4, FH4; right-hand side of plot) from non-mobile tube-building surface deposit feeders (AH2, AM1, FH3; left-hand side of plot). The second axis distinguished rather shallow digging threadlike forms (SD2, BF5; bottom of plot) from deeper digging

0,90 0,80 0,70 0,60 0,50 0,40 0,30 0,20 0,10 0,00

Degree of attachment (DA)

None

Temp

Perm

0,50 Sediment dwelling depth (SD)

0,30

0,40 0,30

0,20

0,20 0,10

0,10 None

Low

Med

High

0,00

Surf

0-1 cm

1-5 cm

5-15 cm

>15 cm

0,50

0,60 Relative importance

followed roughly bell-shaped distributions, illustrating that medium trait expressions were the most abundant. Trait profiles were largely similar for many of the stations, but there were several clear differences, especially between stations from polluted and minimally influenced environments. As an example, a comparison of the profiles for a station located near Oslo harbour and a station in the outer fjord is shown in Fig. 4. It may be noted for instance that the outer fjord station had a higher proportion of tube living forms feeding on surface deposit matter, whereas the station near Oslo had a higher proportion of burrowing forms feeding on subsurface deposit and dissolved matter.

0,40

0,50

0,00

Body form (BF)

0,50

Normal adult size (NS)

0,40

0,40

0,30

0,30 0,20 0,20 0,10

0,10 0,00

Relative importance

99

0,80 0,70 0,60 0,50 0,40 0,30 0,20 0,10 0,00

0,00 Cylind

Flat dors

Flat lat

Ball

Thread

Irreg

< 0.5 cm 0.5-1 cm

1-3 cm

3-6 cm

6-10 cm

>10 cm

0,60 Feeding (FH)

Larval type (LT)

0,50 0,40 0,30 0,20 0,10 0,00

Plankt

Lecit

Susp

Surf dep

Subsurf

Dis- Sandlick Scav solv

Carn Parasite

Fig. 3. Relative importance of trait categories for the traits used in the present analyses, calculated as average “abundances” (+/−SD) across stations.

E. Oug et al. / Journal of Experimental Marine Biology and Ecology 432–433 (2012) 94–105

Adult life habit and feeding mode

and moderate ecological status, which suggests that these features were less clearly related to the ecological conditions in the area.

Relative importance 0,00

0,20

0,40

0,60

3.4. Trait–environment relationships

Permanent tube

Both the co-inertia analysis (CoI) and the redundancy analysis (RDA) indicated that trait patterns could be related to contaminants, sediment components and depth. The most distinctive patterns were depicted for feeding type, sediment dwelling depth, degree of attachment, size and larval development (Fig. 6). In particular, surface and shallow sediment dwelling depth, subsurface deposit feeding and lecitotroph larval development (SD1, SD2, FH4, LT2) were associated with high levels of contaminants (right-hand side of plots), whereas deep sediment dwelling depth, large size, permanent attachment and to some degree surface deposit feeding, (SD3–5, NS5–6, DA3, FH3) were associated with oxidised sediments (high Eh) and low levels of contaminants (left-hand side of plots). Carnivorous feeding (FH8) was associated with coarser and low-porosity sediments (high sediment dry weight) in mostly shallow water (top of plot). Ranking of factors in RDA indicated that Cd was the best explaining factor, followed by Pb, Eh, DDT and sediment dry weight (Table 5). Cd alone

Semiperm tube Burrower Surface crawler Swimmer

Susp / filer Surf deposit Subsurf deposit Dissolved matter Sandlicker Scavenger Carniv/omniv

outer fjord (G)

Parasite /com

near Oslo (B)

A

Fig. 4. Examples of trait profiles comparing stations in the outer fjord (area G) and near Oslo (area B). Details of adult life habit (AH) and feeding mode (FH) are shown.

1.0

100

Axis 2, 19 %

AM4 High mobility AM3 Medium mobility

AH4 Burrower

Sed dw

FH8 Carnivore

SD1 Surface dwelling

C/N

SD2 Sed dwell 0-1 cm

NS6 Size > 10 cm

Cd TN

DA1 No attachment

PCB

NS3 size 1-3 cm

InorgC FH3 Surf dep feeder DA3 Perm attachm NS5 Size 6-10 cm Eh

Hg

Axis 1, 66 %

FH4 Subsurf dep feed

Fine fra Pb

SD3 Sed dwell 1-5 cm

Fe

Depth AM1 No mobility

LT2 Lecit larvae FH1 Susp feeder DA2 Temp attachm

Mn

SD4 Sed dwell 5-15 cm

-1.0

DDT

TOC

SD5 Sed dwell > 15 cm

NS2 Size 0.5-1 cm AH2 Perm tube

-1.4

1.4

B 0.7

forms (SD3, SD4) and surface crawling carnivores (FH8, AH5; top of plot). Both axes represented fairly strong functional patterns by extracting, respectively, 40% and 26% of the variation in the traits data. The results suggest that feeding, life habits and sediment dwelling depth represent the most discriminating biological features in the area. A few categories of other traits also figured among the most important, such as lecitotroph larval development (LT2) and size (NS6). Several functional features appeared to be related to the ecological status at the sampling localities as measured by the NQI1 index (Fig. 5). Subsurface deposit feeding, shallow digging depth, lecitotroph larval development and temporary attachment coincided with poor and moderate ecological status, whereas carnivorous feeding, surface crawling and no attachment mostly coincided with good and high ecological status. Surface deposit feeding, permanent tube and planktotroph larval development, however, were found at both high

Axis 2; 12 %

1.0

AH4 Burrower

Axis 2, 26 %

AH5 Surf crawler FH8 Carnivore DA1 No attachm NS6 Size > 10 cm SD3 Sed dwell 1-5 cm DA3 Perm attachm SD4 Sed dwell 5-15 cm FH3 Surf dep feed

Ecological status SD3 Sed dwell 1-5 cm AH5 Surf crawler FH8 Carnivore DA1 No attachment Sed dw

High Good Moderate

NS6 Size > 10 cm NS5 Size 6-10 cm SD4 Sed dwell 5-15 cm

Poor

LT1 Plankt larvae

FH4 Subsurf dep feed

LT2 Lecit larvae

TN

Axis 1, 40 %

Fine fra

Cd Axis 1; 21 %

Fe

Eh

TOC

AH2 Perm tube

SD1 Surface dwelling DDT NS3 Size 1-3 cm

FH3 Surf dep feed

Mn

AH4 Burrower

FH1 Susp feed

SD1 Surface dwelling FH4 Subsurf dep feed

-1.0

BF5 Body threadlike LT2 Lecit larvae SD2 Sed dwell 0-1 cm

-1.2

BF2 Body flat dors LT1 Plankt larvae AH2 Perm tube

-0.7

DA2 Temp attachm

Fig. 5. Principal component analysis (PCA) on trait features of macrofauna in the Oslofjord: biplot of trait categories and stations for the first two axes. All trait categories with > 50% fit on the two first axes are shown. Ecological status according to the diversity index NQI1 is shown for the stations.

Pb

DA2 Temp attachm

AM1 No mobility

-1.0

1.5

Hg BF5 Body threadlike

InorgC Depth

FH1 Susp feed

SD2 Sed dwell 0-1 cm

C/N

DA3 Perm attachm SD5 Sed dwell > 15 cm

AM1 No mobility

PCB

1.0

Fig. 6. Relationship between macrofauna traits and environmental factors in the Oslofjord. (A) Co-inertia analysis on correlations between trait categories and environmental factors. (B) Redundancy analysis (RDA) for extracting inter-related variations. Trait categories with > 75% fit (CoI) or > 25% fit (RDA) are shown. Vectors for trait categories are omitted for clarity. Environmental vectors (RDA) in bold indicate active factors selected during forward selection. Other vectors indicate passive factors to illustrate correlations.

E. Oug et al. / Journal of Experimental Marine Biology and Ecology 432–433 (2012) 94–105

could account for 19% of the traits variation, whereas the other contaminants, Eh and sediment dry weight could each account for 5–10% of the variation. Organic carbon and nitrogen (TOC, TN) and percent fine particulate materials (fine fraction) were poorly related to the traits. During forward selection the factors Cd, Pb, Hg, sediment dry weight and DDT (in that order) were found to constitute the best combination of explanatory factors. The selected factors could in total explain more than one third (39%) of the total variance in the traits data (Table 5). Co-inertia analysis does not facilitate a ranking of environmental factors as in RDA. The analysis nevertheless indicated that Cd, Eh, sediment dry weight and depth were among the most important factors, indicated by their distal placement on the two first axes (Fig. 6A). With regard to the traits, the results of CoI and RDA were generally similar, but CoI indicated a stronger relevance of mobility (AM) and a somewhat lesser importance of life habit (AH5). CoI represented the trait–environment correlations well (85% on two axes). It may be noted that the first axis in CoI was similar to the second axis in

Table 5 Explanatory power of environmental factors in RDA. Explained variance = variance attributable to each factor taken singly; forward selection = order of selection and added variance accounted for by factors selected one by one in sequence. Selected factors were found statistically significant at the 10% significance level.

Order of selection

Added variance %

19 10 9 7 6 5 4 3 3 b2

1 2 – 5 4 3 – – – –

19 6 – 4 5 5 – – – – 39

1.0

Forward selection

1.0

Cd Pb Eh DDT Sediment dry weight Hg Depth Fe Mn Fine fraction, TOC, TN, C/N-ratio, inorgC, PCB Sum

Explained variance %

Axis 2; 12 %

DA3 Perm attachment

Fine fra C/N TOC

Sed dw

Eh

Fine fra C/N

Eh Fe InoC Depth

DDT Mn

TOC

Pb

-1.0

PCB

InoC

Cd Hg

Depth

Cd

-1.0

Axis 1; 21 %

Hg

1.6

1.0

TN

Sed dw

101

DDT

LT2 Lecitotroph larvae

Ecological status

Mn

High Fine fra C/N TOC

Sed dw

Good

Pb

-1.0

Moderate

InoC

Eh

Poor

Cd Hg DDT

Depth Mn

Pb

1.6

-1.0

-1.0

Fine fra C/N TOC Hg

InoC Depth

Eh

DDT

Fine fra C/N TOC InoC

SD3 Sed dwell 1-5 cm

Sed dw

Eh

DDT Mn

Fine fra C/N TOC Hg InoC

SD5 Sed dwell > 15 cm

Eh

Mn

InoC

Cd Hg DDT

Depth

Pb

-1.0 -1.0

Fine fra C/N TOC

Mn

Pb

1.6

1.6

Sed dw

DDT

Depth

Pb

-1.0

Cd

-1.0

-1.0

1.6

Hg

Pb

-1.0

-1.0

Cd

DDT

Depth Mn

Pb

1.0

SD2 Sed dwell 0-1 cm

Depth

DDT

-1.0

-1.0

1.6

1.0

-1.0

Eh

Eh

Depth

Cd Hg

InoC

Mn

Pb

Sed dw

Hg

InoC

Mn

Fine fra C/N TOC

Sed dw

Cd

-1.0

Eh

Fine fra C/N TOC

Sed dw

Cd

FH8 Carnivore

1.0

Sed dw

1.6

1.0

FH3 Surf dep feeder

1.0

1.0

-1.0 FH4 Subsurf dep feeder

1.6

-1.0

1.6

Fig. 7. Redundancy analysis (RDA) on macrofauna in the Oslofjord: plot of stations and environmental factors (upper left) and attribute plots for selected individual traits (upper right and bottom). Ecological status of stations is according to the classification of the diversity index NQI1. Attribute plots illustrate the relative importance of individual traits at the stations in relation to environmental factors. Symbol sizes in attribute plots are scaled to represent the relative ‘abundance’ of the traits at each station.

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The most important factors were contaminants, particularly cadmium, sediment physical structure and sediment oxidation status. Taken singly, cadmium was the factor that could account for the largest part of the variance. At high levels of cadmium, trait features such as small size, shallow sediment dwelling, subsurface deposit feeding and lecitotroph larval development were prevalent, while at low levels, permanent attachment, and to some extent surface deposit feeding and planktotroph larval development were typical. In short, it can be said that ecological functioning changed from shallow subsurface deposit feeding to deep digging minimally mobile surface deposit feeding along a gradient in cadmium from high to low levels. Other contaminants such as mercury, lead and DDT also showed fairly strong relationships to size and sediment dwelling depth, whereas relationships to feeding type and larval development were not as clear as for cadmium. For surface deposit feeding this was partly a consequence of surface deposit feeding being a widely distributed and important feature in the area, including at sites with fairly high levels of contaminants. With regard to other traits, it was found that long threadlike body form and temporary attachment also characterised contaminated areas, although these features represented less strong patterns. Obviously the stations from the most polluted area close to the main harbour of Oslo contributed significantly to the strength of the functional relationships with regard to contaminants. These stations were dominated by the small annelids C. capitata and T. benedii, which are subsurface deposit feeders with lecitotroph larvae, and at the same time had high concentrations of cadmium, mercury and lead. Capitella and Tubificoides, however, were present at other stations with increased levels of contaminants, as well as there being other subsurface deposit feeders at the same stations, illustrating a certain generality of the relationships. It may be envisaged that these stations essentially depicted those features that were best adapted to strong pollution. Quite similar patterns have been observed elsewhere. In studies of estuarine macrobenthos, Gaston et al. (1998) and Rakocinski et al. (2000) found a preponderance of subsurface deposit feeders in sites containing high levels of metals and organic contaminants, while the proportion of other trophic groups like surface deposit feeders, suspension feeders and carnivores was low. In their studies, the same groups of species dominated as in the present study (capitellid polychaetes, tubificid oligochaetes), especially the polychaete C. capitata, which is recognised as a general indicator of disturbance (Gray, 1979). Gaston et al. (1998) suggested that subsurface deposit feeders would be the trophic group most likely to

PCA. This indirectly illustrates that important trait patterns depicted on the first axis in PCA were not accounted for by environmental factors, which is also apparent from the lower explained variance in RDA compared to PCA. Several trait–environment relationships are illustrated in more detail in Fig. 7, showing attribute plots of selected traits from the RDA analysis. It appears, for instance, that permanent attachment (DA3) prevailed at stations with oxidised sediments (high Eh) and low values of contaminants at mostly high sediment dry weight or greater depths. For feeding strategies, surface deposit feeding (FH3) appeared to be important at most sites, but decreased somewhat at high levels of contaminants, whereas subsurface deposit feeding (FH4) prevailed at high levels of contaminants. Carnivores (FH8) were mostly found in shallow areas with high sediment dry weight. For sediment dwelling depth, shallow depth (SD2) prevailed at high levels of contaminants, whereas deep depth (SD5) was found at low levels of contaminants and oxidised sediments. Table 6 shows correlations between the most important features and environmental factors. Factors which were not found to be related to functional features included sediment organic carbon (TOC) and PCB. Apparently the variations in these factors differed from the distribution of the species assemblages and their functional features. In particular, both factors had high values at a few stations (especially area H) that were not characterised by distinctive species assemblages.

4. Discussion The present analyses of macrofauna in the Oslofjord detected functional patterns that were correlated to pollution factors and environmental conditions. The most distinctive patterns were shown for mobility, size, sediment dwelling depth, feeding type and larval development. On a general basis, the results agree with studies elsewhere where traits related to feeding and mobility have been found to be primarily responsible for differentiating between species assemblages (Bremner et al., 2003; Marchini et al., 2008). Feeding, in particular, has often been considered an important variable of benthic community diversity (de Juan et al., 2007; Frid et al., 2000; Pearson and Rosenberg, 1987). The functional patterns were broadly concurrent with the distribution of species assemblages, indicating that the species changes along the environmental gradients also resulted in changes in the ecological functioning of the species assemblages in the area.

Table 6 Correlations between selected traits and environmental factors. Correlations below +/−0.30 are not shown (−). Depth

AM1 no mobility AM3 medium mobility AM4 high mobility DA1 no attachment DA2 temp attachment DA3 perm attachment NS3 size 1–3 cm NS4 size 3–6 cm NS5 size 6–10 cm SD1 surface dwelling SD2 sed dwell 0–1 cm SD3 sed dwell 1–5 cm SD4 sed dwell 5–15 cm SD5 sed dwell > 15 cm FH1 suspension feeding FH3 surf dep feeding FH4 subsurf dep feeding FH8 carnivores LT1 planktotroph larvae LT2 lecitotroph larvae

0.33 – −0.37 – – – −0.30 – 0.31 −0.38 – 0.33 – 0.35 – – – – – –

Sediment characteristics Finefr

Sedwt

– −0.36 – – – – – – – – – – – – – – – – – –

–

Contaminants Eh 0.32

0.51 0.43 – −0.32 – – – 0.32 – – – – – – – – 0.50 – −0.31

– – 0.33 – 0.48 −0.50 – 0.64 −0.42 −0.37 0.42 0.31 0.59 – 0.45 −0.37 0.30 – –

TOC

Cd

Hg

Pb

DDT

– – – – – –

– – – −0.54 0.41 −0.60 0.67 – −0.69 0.51 0.75 −0.69 −0.57 −0.63 – −0.34 0.49 −0.54 – 0.51

– – – – – −0.32 0.68 −0.33 −0.57 0.37 – −0.40 – −0.58 – – – −0.31 – –

– −0.32 – −0.37 0.48 −0.41 0.73 – −0.60 0.41 0.37 −0.48 – −0.50 0.35 – – −0.48 – –

– – – −0.33 0.35 −0.35 0.59 – −0.56 0.52 0.42 −0.53 −0.38 −0.61 – – – – – –

0.33 – −0.31 – – – – – – – – – – –

E. Oug et al. / Journal of Experimental Marine Biology and Ecology 432–433 (2012) 94–105

develop pollution tolerance because they may regularly encounter toxic metals and organic pollutants released from sediments. It is not clear, however, why cadmium appears to be the most important of the contaminant factors. With regard to the environmental criteria, cadmium concentrations exceeded the PNEC level at one station only, whereas mercury and lead exceeded PNEC at most stations and hence would be expected to exert a stronger influence on the fauna. In recent field experimental studies on soft bottom species communities from the Oslofjord, no clues to lethal effects of cadmium have been detected even at considerably higher concentration levels (9–23 mg Cd kg −1) than in the present study (Trannum et al., 2004). Several experimental studies have not detected toxic effects of cadmium at environmentally relevant concentrations (McLeese et al., 1987 and references therein; Olla et al., 1988; Selck et al., 1998). It is possible, however, that cadmium per se is not the causative factor, but may represent the effect of several pollutants acting synergistically. In a study on field data from the Norwegian shelf, Leung et al. (2005) established effects levels for cadmium, and lead as well, which were far lower than PNEC used in national guidelines, pointing out that their levels represented responses in the presence of other contaminants acting together. Bjørgesæter and Gray (2008) subsequently showed that threshold levels for cadmium and lead varied with sediment types and depth. In the present study all contaminants showed more or less correlated patterns, which is quite typical for urban and industrialised areas where several contaminants may originate from the same sources. Consequently, it can be difficult to distinguish further between the effects of each contaminant. In addition, field sampling variations can be considerable and add an uncertainty to the calculated relationships. Eutrophication due to high organic effluents has been one of the major environmental problems for years in the Oslofjord. Previous studies (Aschan and Skullerud, 1990; Mirza and Gray, 1981) demonstrated that soft bottom species assemblages were affected by organic effluents and showed distance-related gradients from the most polluted areas. In the present study, few relationships between the organic load and functional features were found. Sediment factors such as organic carbon (TOC), nitrogen (TN) and C/N-ratio, which are routinely used to characterise organic load, showed little or no relationship to the functional features. The only patterns were detected for sediment oxidation status (Eh), where no-mobility, permanent attachment, large size, deep sediment dwelling depth and surface deposit feeding were associated with high Eh, i.e. deep location of the redox border in the sediments. This relationship was generally opposite to that for contaminants, reflecting that the contaminated sites often had poorer sediment oxygen status. It may be noted, however, that Aschan and Skullerud (1990), though relating faunal changes to distance from effluent discharge outlets, were not able to correlate faunal changes to sediment carbon content (TOC), probably because of low variations in sediment quality. Some trait features were related to natural environmental factors such as depth and sediment components (fine fraction, dry-weight). Mobility and carnivorous feeding, in particular, were prevalent in environments with mostly coarser sediments (high dry-weight) and may represent adaptations to the influence of currents or physical stress. In contrast, permanent tubes and temporary attachment were related to sediments with higher water content (less dry-weight). Most natural factors were roughly uncorrelated to the contaminants, which meant that the effects of pollution on the functional features could be separated from those due to natural factors. Surface deposit feeding was the dominant feeding strategy in the fjord. Its relative importance increased for high Eh and low contaminant concentrations, but several of the moderately contaminated sites also had a high proportion of surface deposit feeding. This was largely due to the occurrence of the spionid polychaete P. paucibranchiata, which showed high densities at several sites with enhanced levels of contaminants. Previous studies in the Oslofjord have found that

103

Pseudopolydora and a couple of species of Polydora are among the dominants in the fjord, most prominently in the polluted areas (Aschan and Skullerud, 1990; Mirza and Gray, 1981; Ramberg and Schram, 1983). At low levels of contaminants, a suite of species encompassing various spionid and terebellomorph polychaetes (e.g. S. kroyeri, A. auricula, T. stroemi), crustaceans and molluscs were present. It appears that the increased numbers of surface deposit feeding species at distances from the polluted areas may have contributed significantly to the main pattern for this feature in the fjord. Deep uncontaminated areas were generally characterised by features such as large size, deep sediment dwelling depth and permanent attachment. These features are also typical of the spionid and terebellomorph polychaetes from the areas of the fjord minimally affected by pollution. Olsgard et al. (2003) showed that terebellomorph polychaetes were suitable as a surrogate indicator group for benthic species richness. Whereas their findings provide a link from a selected group of polychaetes to species diversity, the present study depicts a link from the functional features of the same group to environmental conditions. High species diversity is generally taken as an indicator of unperturbed environments in monitoring (Gray et al., 1992; Mirza and Gray, 1981; Pearson and Rosenberg, 1978). The present results illustrate which functional aspects of highly diverse species assemblages are particularly affected by contaminants. With regard to sediment dwelling depth, the change from shallow to deep sediment dwelling depths fits well with the well-known Pearson–Rosenberg model for faunal change along an organic pollution gradient (Pearson and Rosenberg, 1978), which has also been shown to be applicable for chemical contaminants (Rakocinski et al., 2000). In the present study some traits were not complete for all species. The consequence of incomplete traits is that the calculated ‘abundance’ values are decreased (Chevenet et al. 1994), but as long as values are missing for only a few species the effects are assumed to be of little importance. Traits that were incomplete for many species, however, were omitted from the present analysis. In studies of soft-bottom macrofauna it is generally expected that traits will range from those which are complete to those lacking data for a majority of species. The question of where to set the limits for inclusion of incomplete traits may be important to address. The choice of limits is essentially a trade-off between the desire to have as many traits as possible represented in the analysis, and the confidence associated with analyses based on traits that are complete for all species. It is a major difficulty in traits analysis, however, that obtaining reliable information on all traits involves a considerable amount of time and effort and is presently not even feasible, due to gaps of knowledge in species biology (Marchini et al., 2008). At present it may seem unrealistic to perform functional analyses incorporating all possible functional aspects that could be of importance. The considerations linked with including specific trait features may be illustrated by the particular case of the most contaminated stations close to the harbour of Oslo. In the analysis of community structure (CCA), these stations figured as ‘outliers’ because of the high dominance of the annelids C. capitata and T. benedii, which were not abundant at other stations. In the functional analyses the stations were more in line with the main patterns. This was because the functional features used were rather general and adapted for most species. The marked shift in species composition suggests that Capitella and Tubificoides possess specific features that allow them to tolerate the particular conditions at the station. For Capitella, its extreme tolerance to pollution has been attributed to its life history strategies (e.g. Gray, 1979; Gray and Mirza, 1979) involving e.g. rapid reproduction in combination with egg brooding (Tsutsumi, 1987), rather than high physiological tolerance to the pollutants. Possibly, therefore, if traits encompassing life style features of particular relevance to pollution had been included in the analyses, more specific functional changes related to levels of contaminants might have been detected. The question of incorporating traits that may respond

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to particular conditions becomes a matter of whether general functional features or specific affinities are of interest, in addition to the efforts and possibilities of assembling the relevant information. The present study on macrofauna in the Oslofjord showed that the BTA technique detected specific functional features that correlated with gradients in pollution. Some of the features described well established relationships, for instance the change from small forms living close to the sediment surface to larger non-moving forms living deeper in the sediment along pollution gradients. Of particular interest is the demonstration of relationships between contaminants and main life features (degree of attachment, construction of tubes), feeding type and mobility. These traits all express aspects of activity of the organisms which are important for sediment reworking and nutrient matter uptake and turnover. Detecting such relationships is thus a first step toward understanding how contaminants can affect ecosystem functioning, and may form the basis for developing hypotheses on effects on community functioning and processes on a larger scale. In general terms, obtaining more information about specific factors may form the basis for developing predictive models of ecosystem functioning to be used in monitoring and environmental management (see Bremner, 2008).

Acknowledgements We would like to acknowledge our dear friend and colleague the late Dr. Frode Olsgard (1957–2010) who carried through a series of studies on effects of human influence on structure and function of marine soft-bottom ecosystems. He was central in the development of the traits database at the Norwegian Institute for Water Research (NIVA) and established the present project based on data from his studies on impacts of pollution in the Oslofjord. The project was funded by Statoil (previously Norsk Hydro prior to the merger of the companies) through a grant to Akvaplan-niva, Tromsø, which is hereby gratefully acknowledged. The main content of the traits information was assembled as part of a project “Functional role of macrofauna on contaminated sediments, and the potential of fauna recovery following sediment remediation” funded by the Norwegian Research Council in 2002–2004. We are indebted to Tom Pearson for supplying information on traits and checking trait scores, and to Hilde Trannum for suggestions about the manuscript. Two anonymous reviewers provided input that allowed the manuscript to be considerably improved. [ST]

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