Spatial patterns of benthic diversity: is there a latitudinal. gradient along the Norwegian continental shelf? KARI E. ELLINGSEN and JOHN S.

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1 Ecology 2 71, Spatial patterns of benthic diversity: is there a latitudinal Blackwell Science Ltd gradient along the Norwegian continental shelf? KARI E. ELLINGSEN and JOHN S. GRAY Section of Marine Zoology and Marine Chemistry, Department of Biology, University of Oslo, PO Box 164 Blindern, 316 Oslo, Norway Summary 1. We examined data on soft-sediment macrobenthos (organisms retained on a 1-mm sieve) from a transect of c. 196 km along the Norwegian continental shelf (56 71 N), covering a range of water depths ( m) and varying sediment properties. 2. A total of 89 species was recorded from 11 sites. Of these, 36% were restricted to one or two sites, and 29% were represented by one or two individuals. No species spanned the entire transect. Polychaetes were the dominant taxonomic group, followed by crustaceans, molluscs and echinoderms. 3. Alpha diversity (sample species richness) was highly variable ( species) but showed no evidence of a relationship to latitude or other environmental variables. 4. Beta diversity was measured as Whittaker s β W, the number of shared species, complementarity (biotic distinctness) and Bray Curtis similarity, and there was no evidence of a latitudinal trend on the shelf. Beta diversity increased with the level of environmental variability, and was highest in the southern-central area, followed by the most northern area. Change in environmental variables had a stronger effect on beta diversity than spatial distance between sites. 5. Gamma diversity was computed by pooling samples over large areas. There was no convincing evidence of a latitudinal cline in gamma diversity, but gamma diversity increased with the level of environmental heterogeneity. Mean alpha diversity and gamma diversity were not significantly correlated. Whereas mean complementarity and mean Bray Curtis similarity were related to gamma diversity, β W was not. Key-words: alpha diversity, beta diversity, environmental variability, gamma diversity, latitude. Ecology (2) 71, Ecological Society Introduction Oceans cover about 7% of the surface area of the earth, and sedimentary habitats ranging from gravel to fine mud cover most of the sea bottom. However, most studies of biological diversity relate to terrestrial systems, and our knowledge of marine biodiversity lags far behind that on land. As macrobenthos in marine sediments play important roles in ecosystem processes such as nutrient cycling, pollutant metabolism, dispersion and burial, and secondary production (Snelgrove 1998), it is important to improve our understanding of biodiversity in marine sediments. Marine systems differ from terrestrial in a number of ways, and paradigms Correspondence: Kari Elsa Ellingsen, Section of Marine Zoology and Marine Chemistry, Department of Biology, University of Oslo, PO Box 164, Blindern, 316 Oslo, Norway (fax ; k.e.ellingsen@bio.uio.no). concerning terrestrial patterns of biodiversity may not be applicable to marine situations (May 1994; Heip, Warwick & d Ozouville 1998). One important difference is that many benthic species have pelagic larvae that remain floating in the water for days or months, and as, unlike most terrestrial systems, barriers to dispersal are relatively weak, species may disperse over much broader ranges than on land. In terrestrial systems a marked decline in the species richness of many animals and plants from the tropics to the poles is the general rule (Pianka 1966; Rosenzweig 1995; Gaston 1996a). It has long been assumed that a similar trend is also found in the sea. Thorson (1957) reported a pronounced decrease in the species richness of hard substratum epifauna towards arctic areas, whereas the number of soft-sediment infaunal species was roughly the same in tropical, temperate and arctic areas. Latitudinal clines in species richness of shallowwater benthos have been reported for gastropods (Roy

2 374 K.E. Ellingsen & J.S. Gray et al. 1998), bivalve molluscs (Crame ; Roy, Jablonski & Valentine ), in deep-sea benthos (Poore & Wilson 1993; Rex et al. 1993), and for pelagic taxa (Angel 1997; Pierrot-Bults 1997). Yet for shallow-water marine fauna some studies do not show a latitudinal cline (Kendall & Aschan 1993; Boucher & Lambshead 1995). Furthermore, in the southern hemisphere the evidence of a latitudinal gradient of decreasing richness from the tropics to Antarctica is less convincing than in the northern hemisphere (Clarke 1992; Poore & Wilson 1993; Crame ). Thus, there is no convincing evidence of a latitudinal cline across all taxa in the sea comparable to that seen on land (Clarke 1992; Clarke & Crame 1997). The latitudinal gradient has also been demonstrated across more limited geographical areas in terrestrial systems. Schall & Pianka (1978) found a decline in the number of species of both lizards and birds from 31 to 48 N in North America. In a study in Britain, Harrison, Ross & Lawton (1992) showed that there was a clear decline in species richness from south to north (c N) in bees, butterflies, dragonflies, moths, fishes, molluscs, woodlice, orchids, composites, umbellifers and rosaceae. Likewise, Grytnes, Birks & Peglar (1999) found a similar latitudinal cline in the species richness of vascular plants from Denmark to centralnorthern Norway (55 64 N). Here we analyse data covering N from the continental shelf of Norway. The number of species (species richness) has been the traditional measure of biodiversity in ecological and conservation studies, but the abstract concept of biodiversity as the variety of life (Gaston 1996b) cannot be encapsulated by a single measure (Harper & Hawksworth 1994; Warwick & Clarke 1995). Ellingsen (1) evaluated different measures of marine biodiversity, and suggested that, in addition to species richness, distributions of species and community differences should be taken into account. The partitioning of species diversity into alpha (α), beta (β) and gamma (γ) components to characterize different aspects or levels of diversity was first proposed by Whittaker (196). The scales describing alpha and gamma diversity vary greatly among authors. Gray () suggested that a uniform notation could be achieved by recognizing four scales of species richness: point, SR P (a single sampling unit); sample, SR S (a number of sampling units from a site of defined area); large area, SR L (including a variety of habitats and assemblages within an area of given size); and biogeographical province, SR B. Most marine studies of species richness have been done on small scales, that of alpha diversity as point species richness (SR P ) or, more usually, sample species richness (SR S ). There are few studies of diversity at different spatial scales in the marine environment (but see Clarke & Lidgard ; Izsak & Price 1). Many terrestrial and freshwater studies (Griffiths 1997; Ricklefs ) have concluded that local species richness is linearly related to regional richness. Whether this is true in marine systems has rarely been tested (but see Cornell & Karlson 1996; Rex, Etter & Stuart 1997). Compared with the knowledge of alpha diversity, beta diversity has been far less studied in marine systems (Gray ). Beta diversity may be based on ratios of species richness of areas of different sizes, or differences in faunal composition between sites or areas, and is not a spatial scale of diversity, in contrast to alpha and gamma diversity. Whittaker (1975) defined beta diversity as the extent of change in species composition of communities among the samples of a data set or along a gradient. Beta diversity can be measured in many different ways (for an overview see Magurran 1988), but Whittaker s (196, 1972) simple statistic (β W ) is one of the most frequently used measures (Wilson & Shmida 1984). Most studies of beta diversity have focused on a single taxon, yet patterns in beta diversity may be expected to vary among taxa (Harrison et al. 1992). Beta diversity has been measured in some marine taxa (bryozoans: Clarke & Lidgard ; polychaetes: Paterson et al. 1998; echinoderms: Price, Keeling & O Callaghan 1999; Izsak & Price 1; ascidians: Naranjo, Carballo & García-Gómez 1998). In marine soft-bottom studies, multivariate methods have proven much more sensitive to small changes in faunal composition than univariate methods (Gray et al. 199; Warwick & Clarke 1991). A multivariate measure of beta diversity, such as the Bray Curtis similarity between sites, may therefore be expected to give additional information to other measures of beta diversity. Recently the relationship between beta diversity and spatial scale has also been studied (Izsak & Price 1). Interactions between beta diversity distance and beta diversity habitat change are also ecologically interesting (Harrison et al. 1992). Ellingsen (2) showed that different measures of beta diversity were more strongly related to change in environmental variables than to spatial distance between sites at a spatial scale of 45 6 km on the Norwegian continental shelf. Here, we present data on patterns at a larger scale, that of the whole continental shelf of Norway. Over the last few decades the relations between softsediment species assemblages and sediment properties have been reviewed by Sanders (1968), Gray (1974), Rhoads (1974), Etter & Grassle (1992) and Snelgrove & Butman (1994). Here, we consider how different measures of biodiversity are related to sediment variables and depth along the shelf, and how they change with different levels of environmental variability (see also Ellingsen 2). In this study soft-sediment macrobenthos data from a transect of 1958 km along the Norwegian continental shelf (56 71 N) were used. Some areas along the Norwegian coast are environmentally homogeneous (Ellingsen 1), whereas others are highly variable (Ellingsen 2). The main objectives of this study were to: (i) determine whether there is any variation in alpha, beta and gamma diversity with latitude over a relatively limited geographical scale of 15 of latitude;

3 375 Spatial patterns of benthic diversity Fig. 1. Geographic position of 11 sampling sites in five large areas at the Norwegian continental shelf. (ii) examine how measures of biodiversity change when environmental variables change. Materials and methods The data used were from a transect along the Norwegian continental shelf, collected as part of routine environmental monitoring surveys of the effects of the oil and gas industry on the seabed. The transect covers approximately 196 km from the North Sea, the Norwegian Sea to the Barents Sea (56 71 N, 1 23 E; Fig. 1). While most continental shelves have a depth limit of about m, this is not so for the Norwegian shelf, where there are deeper areas in the Norwegian Trench, which runs parallel to the Norwegian coast, and in the Skagerrak. The Norwegian Pollution Control Authority (SFT) has divided the Norwegian continental shelf into regions, based on the localization of installations. The data are from region I, sampled in 1996, II (1997), III (1998), IV (1996), VI (1997) and IX (1998). Water depth at 11 sites along the transect ranged from 65 to 434 m, and there was considerable variation in sediment characteristics [silt clay content, 99%; median grain size Mdϕ, ; total organic matter (TOM), %; Table 1]. The data sets were collected and analysed by identical methods. The positioning equipment was a differential global positioning system (GPS). Biological, physical and chemical samples were taken with a 1- m 2 van Veen grab. At each site five replicates for analyses of macrobenthos were taken. Biological samples were washed through a 1-mm round hole diameter sieve, and the retained fauna were fixed in formalin for later identification to lowest practical taxonomic level. Species identification was undertaken by a series of consultants, but synonyms and obvious misidentifications have been eliminated. Three additional grabs were taken at each site for analyses of sediment variables. Subsamples were taken from the upper 5 cm of one grab for analyses of total organic matter, sediment median grain size, sorting, skewness and kurtosis, and from the upper 1 cm of three grabs for chemical analyses of hydrocarbons and metals. Additional details of sampling and analyses are given in the reports of the environmental monitoring surveys (Jensen et al. 1997; Mannvik et al. 1997; Jensen et al. 1998; Mannvik et al. 1998; Jensen et al. 1999; Mannvik et al. 1999). Faunal groups not properly sampled by the methods used, such as nematodes, Foraminifera and colonial groups (Porifera, Hydrozoa, Bryozoa), were not included

4 376 K.E. Ellingsen & J.S. Gray Table 1. Summary of the location of the areas, depth and sediment characteristics along the Norwegian continental shelf transect (cf. Fig. 1). Mdϕ, median grain size; Silt clay, fraction of sediment < 63 mm (%); TOM, total organic matter (%); COV, coefficient of variation (standard deviation/mean) multiplied by 1% Depth (m) Mdϕ Silt clay (%) TOM (%) Area No. of sites Latitude ( N) Longitude ( E) Scale (km) Range COV Range COV Range COV Range COV Total km in the data analyses. Likewise, pelagic organisms and juveniles were excluded, and unidentified species were not included if they could be mistaken for an identified species. In soft-sediment studies a single grab (sampling unit), covering only 1 m 2, is known to sample only a small fraction of the species at a site because of small-scale spatial variation. Data analyses were therefore done on species abundance data pooled over five grabs from each site, called a sample. Only data from sites unaffected by oil or gas activities were used. These sites were identified by univariate diversity measures (number of species and individuals, heterogeneity diversity; Hill 1973; Peet 1974) and multivariate analyses of faunal data (CLUSTER, MDS; see description in the following sections), as well as measured concentrations of metals and hydrocarbons (results not shown). A data set of 11 unaffected sites from the whole transect was then selected, based on geographical localization, in order to distribute the sites evenly along the transect. The total data set was divided into five large areas (areas 1, 2, 3, 4 and 5), corresponding to sites from regions I, II, III and IV, VI, and IX, respectively. Area 1 was the southernmost area and area 5 the northernmost (Fig. 1). The spatial scale of the large areas was smaller than 1 26 km. Alpha diversity was sample species richness, SR S in Gray s () terminology, and gamma diversity was the species richness in large areas (SR L ). The five large areas together constituted the largest scale studied, called the total area species richness (SR T ). We determined species restricted to a single site, uniques ; species occurring at exactly two sites only, duplicates ; species represented by a single individual, singletons ; and species represented by only two individuals, doubletons, following the terminology of Colwell & Coddington (1994), using the EstimateS software (Colwell 1997). The nonparametric Chao2 method (Colwell & Coddington 1994) was used to estimate the theoretical number of species expected within the whole transect. Here Chao2 = Sobs + (Q 1 2 /2Q 2 ), where Sobs is the number of species observed in all samples pooled and Q 1 and Q 2 are the frequency of uniques and duplicates, respectively. Four measures of beta diversity were used in this study. First, Whittaker s (196, 1972) original beta diversity measure, β W = (γ/ᾱ) 1, where γ is the total number of species resulting from merging a number of individual samples and ᾱ is the average number of species per individual sample. This measures the proportion by which a given area is richer than the average of samples within it. β W was measured over two scales, large area β W = (SR L /SR S ) and total area β W = (SR T / SR S ), where SR S is mean alpha diversity. Secondly, the number of species shared (V jk ) for each possible pair of samples j and k. Thirdly, biotic distinctness, or complementarity (C jk ; Colwell & Coddington 1994), between all pairwise combinations of sites. Here complementarity between two sites is the total number of unshared species divided by the total species richness for the two sites, ranging from (identical samples) to 1%

5 377 Spatial patterns of benthic diversity (completely distinct). Chao2, V jk and C jk were calculated using the EstimateS software (Colwell 1997). Fourthly, multivariate statistical analyses were used to determine other aspects of beta diversity. A similarity matrix was constructed using square root transformation and the Bray Curtis coefficient (Bray & Curtis 1957). The Bray Curtis similarity between two samples j and k is defined as S jk = 1[1 Σ y ij y ik /Σ(y ij + y ik )] with the summations being over species i = 1,..., p. Here y ij (and y ik ) represents the abundance for the ith species in the jth (and kth) sample. Hierarchical, agglomerative classification (CLUSTER), employing group-average linking (Clifford & Stephenson 1975) and ordination by nonmetric multidimensional scaling (MDS) based on the Bray Curtis similarity matrix (Kruskal & Wish 1978; Clarke & Green 1988), were used to provide a graphical presentation of how faunal similarity changes along the Norwegian coast. Low similarities within a dendrogram denote high beta diversity. However, because a comparison of dendrograms is difficult, the similarity between all pairwise permutations of sites (from the similarity matrix) was used as the fourth measure of beta diversity. The Bray Curtis coefficient ranges from (completely dissimilar) to 1% (identical samples), thus varying inversely with complementarity. The relationships between faunal patterns, using the Bray Curtis similarity matrix, and different subsets of environmental variables (matrices computed using normalized Euclidean distance), were examined using the BIO-ENV procedure (Clarke & Ainsworth 1993). For the above analyses we used the PRIMER package (Clarke & Warwick 1994). Geographic distances in km were computed between all pairwise combinations of sites, using the R package (Legendre & Vaudor 1991). Results SPECIES RICHNESS Alpha diversity (sample species richness, SR S ) at 11 sites along the Norwegian shelf transect of 1958 km was highly variable, ranging from 35 to 148 species (Fig. 2) but there was no clear relation to latitude (r s = 24, P < 1, n = 11). Although depth and type of sediment varied considerably along the shelf (Table 1), the relationships between alpha diversity and environmental variables were either weak or not significant (Table 2). A number of environmental variables were strongly positively related to each other, especially silt clay content vs. kurtosis, depth vs. latitude and 16 1 Number of species Sample number Fig. 2. Sample species richness (SR S, alpha diversity). Samples ordered in a sequence from south (56 N) to north (71 N). Solid squares, area 1; solid triangles, area 2; open triangles, area 3; open circles, area 4; solid diamonds, area 5. Table 2. Pairwise Spearman rank correlations (r s ) between alpha diversity (SR S, sample species richness) and environmental variables, with significant (P < 1) coefficients in bold (n for all correlations = 11). Silt clay, fraction of sediment < 63 mm (%); Mdϕ, median grain size; Sk I, skewness; σ I, sorting; K G, kurtosis; TOM, total organic matter (%) Latitude Longitude Depth (m) Silt clay Mdϕ Sk I σ I K G TOM Longitude 7 Depth (m) Silt clay Mdϕ Sk I σ I K G TOM SR S

6 378 K.E. Ellingsen & J.S. Gray total organic matter, and total organic matter vs. sorting (Table 2). Similarly, mean alpha diversity (SR S ) over the five large areas studied showed no systematic relation to latitude (Table 3). SR S was highest in area 5, followed by area 2, where 12 out of and 21 sites, respectively, had more than 1 species, and lowest in area 1, where the highest sample species richness was 78 (Fig. 2). There was no clear evidence of a relationship between SR S and environmental variability [coefficient of variation (COV) for depth, median grain size, silt clay content and total organic matter in Table 1 and SR S in Table 3 were not significantly correlated, coefficients not shown]. Gamma diversity (large area species richness, SR L ) was highly variable, ranging from 177 species in area 1 to 5 in area 5 and 477 in area 3 (Table 3). Thus, gamma diversity showed no systematic geographical variation. However, area 3 had the highest level of environmental variability, followed by area 5, whereas area 1 was environmentally homogeneous (Table 1), suggesting that SR L increases with environmental heterogeneity. There was no significant relationship between SR S and SR L (r s = 7, P = 19, n = 5), although area 1 showed the lowest values of both (Fig. 3). The species accumulation curves for each large area showed little sign of stabilizing towards asymptotic values, although Mean alpha diversity r s = 7, P = 19, n = Gamma diversity Fig. 3. Relationship between mean alpha diversity (SR S ) and gamma diversity (SR L, species richness large area). Bars indicate ±95% confidence intervals (CI). the slopes of the curves varied (Fig. 4). The estimated species richness in only 1 2 m 2 in area 5 and area 3 was higher than the total number of species observed in area 1 (Fig. 4). The data from the total area comprised individuals and 89 species (SR T ) in 5 5 m 2. The Chao2 estimate of actual species richness gave 135 ± 42 (mean ± SD). Neither the species accumulation curve nor the Chao2 estimate stabilized towards asymptotic values (Fig. 5a). The polychaetes (344 species) 6 Cumulative number of species Area 1 Area 5 Area 2 Area 4 Area Area (m 2 ) Fig. 4. Species accumulation curves for the large areas at the Norwegian shelf (cf. Fig. 1). Plotted values are means of 5 estimates based on 5 randomizations of sample accumulation order (without replacement). Table 3. Species richness (SR S, sample species richness = alpha diversity; SR S, mean alpha diversity; SR L, large area; SR T, total area), the number of individuals (n), and the proportion of rare species. Area 1: the southernmost area; area 5: the northernmost area. Uniques, species restricted to a single site; duplicates, species occurring at exactly two sites; singletons, species represented by a single individual; doubletons, species represented by only two individuals. CI 95% confidence intervals SR S Area Range SR S ± CI SR L n Uniques (%) Duplicates (%) Singletons (%) Doubletons (%) ± ± ± ± ± Total ± 4 9 SR T

7 379 Spatial patterns of benthic diversity Species richness estimator (a) Chao2 Sobs Cumulative number of species (b) Pol Cru Mol Ech 52 Area (m 2 ) Fig. 5. Species accumulation curves for the total area. (a) Estimators of species richness are the total number of all species (Sobs) and the Chao2 estimator of true richness. (b) The four dominant taxonomic groups: Pol, polychaetes; Cru, crustaceans; Mol, molluscs; Ech, echinoderms. Plotted values are means of 5 estimates based on 5 randomizations of sample accumulation order (without replacement). Bars indicate ± SD. Number of species Number of sites occupied Fig. 6. Distribution of species range sizes. Range size is the number of sites occupied by a species out of a total of 11 sites (cf. Fig. 1). constituted 43% of the total number of species, whereas the crustaceans (23 species), molluscs (163 species) and echinoderms (35 species) comprised 28%, % and 4%, respectively. The species accumulation curves for the dominant taxonomic groups showed that the echinoderms with low species richness reached an asymptotic value, whereas the curves for the other groups did not (Fig. 5b). DISTRIBUTIONS OF SPECIES No species spanned the entire sampling area, and only 18 species (2 2%) were represented at more than 5 sites (Fig. 6). These relatively widespread species, dominated by polychaetes (16 species), were among the most abundant. Conversely, 1 species, or 25% of the total number of species, were restricted to a single site

8 K.E. Ellingsen & J.S. Gray (a) Bray-Curtis similarity (%) Area 3 Area 2 Area 2 Area 3 Area 4 Area 5 Area 1 Area (b) 39 Area Area Area Stress:,12 75 Area 5 Area 4 38 Fig. 7. (a) Hierarchical, agglomerative clustering of square root transformed macrobenthos data from 11 sites using groupaverage linking on Bray Curtis similarities (%). (b) Multidimensional scaling ordination for square root-transformed macrobenthos data based on Bray Curtis similarities (stress = 12). Samples ordered from 1 (the southernmost site) to 11 (the northernmost site). (uniques) and 89 species (11%) were restricted to only two sites (duplicates) (Fig. 6). Twenty per cent (158 species) of the total number of species were singletons (represented by a single individual), and 9% (69 species) were doubletons (two individuals). Only % and 23% of the total number of polychaetes and echinoderms, respectively, were restricted to a single site, 26% of the molluscs were uniques, whereas as much as 33% of the crustaceans were uniques. At the scale of the large areas the unique species comprised between 22% and 38% of the benthos, and the singletons between 16% and 29% (Table 3). FAUNAL ASSEMBLAGES IN SPACE Clusters occurred over a wide range of similarities (1 75%; Fig. 7a). At 1% similarity the sites were divided into three clusters, and at 14% into two main groups, to a large extent reflecting change in depth. One group comprised a cluster of the sites in area 4 (depth m), a cluster of the sites in area 5 ( m), and the deepest sites in area 3 ( m). The other group comprised a cluster of the sites in area 1 (65 74 m), two clusters of the sites in area 2 ( m), as well as the shallowest sites in area 3 ( m). Site number 38 (14 m) in area 3 was clearly separated from all other sites. The sites in area 1 showed highest similarity to each other, whereas the sites in area 3 were most dissimilar. The MDS ordination showed a change in the faunal composition from south to north (Fig. 7b). However, the distances between the sites in area 3 showed that they had different faunal patterns, despite the relatively small geographical distances between them (< 147 km; Fig. 1). At the scale of the five large areas, the rank correlations between single environmental factors and the faunal patterns (Bray Curtis similarity matrix) varied (BIO-ENV analysis; Table 4a). Depth and silt clay

9 381 Spatial patterns of benthic diversity Table 4. Summary of results from BIO-ENV analyses. (a) The five large areas for all taxa pooled. (b) All 11 sites from the Norwegian shelf transect for all taxa pooled and for the dominant taxonomic groups. Spearman rank correlations (r s ) between biotic and abiotic similarity matrices, with highest correlations in bold. Lower correlations are omitted from the table. Biotic data square root transformed, abiotic data log (1 + n) transformed with the exception of latitude, longitude and depth. Silt clay, fraction of sediment < 63 mm (%); Mdϕ, median grain size; Sk I, skewness; σ I, sorting; K G, kurtosis; TOM, total organic matter (%); Lat, latitude; Long, longitude (a) Area 1 Area 2 Area 3 Area 4 Area 5 Latitude Longitude Depth (m) Silt clay Mdϕ Sk I σ I K G TOM Maximum correlation Lat ( 36) Silt clay, σ I, Depth, Lat ( 82) Depth, Silt clay, σ I, Mdϕ, Lat ( 85) Lat, Sk I, TOM ( 45) Long, Depth, Lat, Silt clay ( 66) (b) All taxa Crustacea Echinodermata Mollusca Polychaeta Latitude Longitude Depth (m) Silt clay Mdϕ Sk I σ I K G TOM Maximum correlation σ I, Depth, Silt clay, Lat ( 86) Depth, σ I, Silt clay, TOM, Lat ( 78) Depth, TOM, Silt clay, σ I, Lat ( 75) Depth, σ I, Lat ( 8) σ I, TOM, Lat ( 81) Table 5. Whittaker s beta diversity (β W ) for the large areas (SR L /SR S ) and for the total area (SR T /SR S ) for all taxa pooled and for the dominant taxonomic groups Area All taxa Crustacea Echinodermata Mollusca Polychaeta Total content showed the strongest correlations with the faunal patterns in area 3, silt clay content in area 2, longitude in area 5, and latitude in area 1 and area 4, although the three latter relations were weak. At the scale of the whole transect the rank correlations ranged from 8 to 74 (Table 4b). Sorting showed the strongest correlation with the faunal patterns, followed by depth, silt clay content and total organic matter. Thus, latitude showed a weaker relation to the faunal patterns (r s = 62) than depth and a range of sediment characteristics. The subset of environmental factors that best explained the biotic composition included sorting, depth, silt clay content and latitude (r s = 86). The faunal patterns of the four dominant taxonomic groups showed close relations to subsets of environmental variables (Table 4b). Polychaetes showed the strongest correlation with sorting, whereas crustaceans, molluscs and echinoderms were more strongly correlated with depth. BETA DIVERSITY Table 5 shows that Whittaker s β W at the scale of the large areas (SR L /SR S ) varied between the dominant taxonomic groups and was greatest for crustaceans (range ). There was no relationship apparent between β W at this scale and latitude (Table 5). Within each taxonomic group and for all taxa pooled β W was highest in area 3, and lowest in area 1. β W was higher at the largest scale (SR T /SR S ) than at the scale of the large areas (Table 5). β W (SR T /SR S ) also varied between the dominant taxonomic groups, and was highest for

10 382 K.E. Ellingsen & J.S. Gray Table 6. Measures of beta diversity between all pairwise combinations of sites. CI 95% confidence intervals No. of species shared Complementarity (%) Bray Curtis similarity (%) Area n Range Mean ± CI COV Range Mean ± CI Range Mean ± CI ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 1 3 Total ± ± ± 2 9 Table 7. Measures of beta diversity related to distance and change in environmental variables between all pairwise combinations of sites. Measure of correlation is the product moment correlation r, with significant (P < 1) coefficients in bold (*P = 2). V jk, number of species shared; C jk, complementarity or biotic distinctness (%); B-C, Bray Curtis similarity (%); σ I, sorting; silt clay, fraction of sediment < 63 mm (%); TOM, total organic matter (%) Area Beta diversity measure n Distance (km) Depth (m) σ I Silt clay TOM Total V jk C jk B-C B-C B-C B-C B-C * 4 5 B-C crustaceans (14 5), followed by molluscs (9 4), echinoderms (8 8) and polychaetes (6 3), whereas β W for all taxonomic groups pooled was 8 3. The number of shared species, the complementarity (biotic distinctness) and the Bray Curtis similarity illustrated different aspects of beta diversity. At the scale of the five large areas the mean complementarity and mean Bray Curtis similarity showed that beta diversity was highest in area 3, followed by area 5, and lowest in area 1 (Table 6). Thus, there was no evidence of a latitudinal cline in large area beta diversity, rather beta diversity increased with environmental heterogeneity (Table 1). In addition, the variability in the number of species shared was highest in area 3 and lowest in area 1 (Table 6). The relations between Bray Curtis similarity at the scale of the large areas, and in turn environmental variables and distance between all combinations of sites, are shown in Table 7. The faunal similarities between all combinations of sites showed strongest relations to change in depth in area 3, change in silt clay content in area 2, and spatial distance in area 5, area 1 and area 4, although the two latter relations were weak (Table 7). β W (SR L /SR S ) was not significantly correlated to gamma diversity (SR L ), although there was a tendency for β W to be lower with lower SR L (Fig. 8a). Mean complementarity and mean Bray Curtis similarity were strongly negatively correlated, and both were highly related to SR L (Fig. 8b,c). For all combinations of sites along the shelf transect the number of species shared varied between 1 and 93 (mean 25; Table 6), the complementarity values showed highly variable levels of biotic distinctness (38 99%, mean 82%), and the Bray Curtis similarities ranged from 2% to 75% (mean 24%). This component of beta diversity, the faunal differences between sites, was higher at this scale than at the scale of the large areas. These three measures of beta diversity showed weak relations to spatial distance between sites, but the relations to change in environmental variables, notably depth, followed by sorting, were stronger (Table 7). Sites located at the same depth, or with similar sediment sorting, shared significantly more species, showed lower biotic distinctness and were more similar to each other on average than sites located at different depths, or with different sorting. The Bray Curtis similarity was more strongly related to environmental variables than complementarity and the number of shared species. However, a plot of the Bray Curtis similarities between all possible combinations of sites related to change in depth revealed considerable scatter (Fig. 9). In Fig. 1a, the Bray Curtis similarities between all combinations of sites were averaged over increasing distance (unit 1 km), and mean similarity showed a strong inverse relationship to distance (logarithmic function r 2 = 77, n = ). The relationship in Fig. 1a implies that, on average, the similarity of the 1-km scale faunas on the shelf was halved (i.e. 42 2%/ 2 = 21 1%) over a distance of about 8 km. Moreover, mean Bray Curtis similarity between all combinations of sites pooled over increasing change in depth (unit m) showed a clean inverse relationship to depth difference between sites (logarithmic function r 2 = 98, n = 19; Fig. 1b). The relationship in Fig. 1b implies

11 383 Spatial patterns of benthic diversity that, on average, the similarity of the -m depth interval faunas on the shelf was halved (42 8%/2 = 21 4%) over a change in depth of about 13 m. Discussion Recently, there has been an upsurge in interest in whether or not there are latitudinal diversity clines in marine systems. Whereas most marine studies have involved large spatial scales from the poles to the tropics, and studied specific taxonomic groups (e.g. gastropods: Roy et al. 1998; bivalves: Roy et al. ; asteroids: Price et al. 1999; bryozoans: Clarke & Lidgard ), we have taken a smaller geographical scale (15 of latitude) and analysed species richness and beta diversity at different spatial scales. SPECIES RICHNESS AND SPATIAL SCALES Fig. 8. Relations between gamma diversity (SR L ) and measures of beta diversity. (a) Whittaker s beta diversity (β W = SR L /SR S ). (b) Mean complementarity (%). (c) Mean Bray Curtis similarity (%). Bars indicate ± 95% CI. The smallest scale examined was alpha diversity (SR S ), the sum of five grabs from a site ( 5 m 2 ). There was no evidence of a latitudinal cline in SR S (Table 2) or in mean alpha diversity (SR S ) over the five large areas (Table 3) along the transect (56 71 N). SR S was highest in area 5 (15 8), the northernmost area, followed by area 2 (11 2), and lowest in area 1 (65 9), the southernmost area. Yet SR S was highly variable within each of the large areas, ranging from 61 to 148 species in area 5 and from 56 to 135 species in area 2. This high variability at small scales is typical for marine data where large numbers of samples have been taken (Cornell & Karlson 1996; Clarke & Lidgard ). The high variability in SR S ( species) at the 11 sites indicates that, in order to make reliable assessments of latitudinal gradients in species richness, extensive sampling is necessary. However, this is rarely done. Kendall & Aschan (1993) compared macrobenthos data at an arctic (Spitzbergen, 78 N), a temperate (Northumberland, 55 N), and a tropical site (Java, 7 S), using just one sample, in the terminology used here, to characterize each area. They concluded that there was no difference in diversity between sites and that their results must Bray-Curtis similarity (%) r = 67, P < 1, n = Depth difference between sites (m) Fig. 9. Bray Curtis similarity (%) related to depth difference (m) between all pairwise combinations of 11 sites.

12 384 K.E. Ellingsen & J.S. Gray Mean Bray-Curtis similarity (%) (a) y = ln(x) r 2 = 77, n = (b) Distance between sites (km) y = ln(x) r 2 = 98, n = Depth difference between sites (m) Fig. 1. (a) Bray Curtis similarity (%) between all combinations of sites averaged over increasing distance (unit 1 km) plotted as a function of distance. Range of n in intervals: (b) Bray Curtis similarity (%) between all combinations of sites averaged over increasing depth difference (unit m) between sites plotted as a function of change in depth. Range of n in intervals: Bars indicate ±95% CI. serve to weaken the hypothesis of a latitudinal cline in benthic diversity. Later, Kendall (1996) showed that within a single Arctic region, Spitzbergen, there was substantial local variability in benthic diversity. Comparisons based on small numbers of alpha diversity are unlikely to contribute to resolving whether or not there is a latitudinal cline in species richness. In a recent analysis of deep-sea isopods, gastropods and bivalves in the North Atlantic (5 m depth), Rex, Stuart & Coyne () reported significant relationships between epibenthic sled sample species richness and latitude. The number of species was extremely low in the Norwegian Sea, and the sample species richness of isopods increased steadily to the tropics. However, the bivalves and gastropods showed no clear evidence of an increase in species richness from c. N to the equator, and the variability in sample species richness was high at about 1 N and N. Clarke & Lidgard () showed a high degree of variability in bryozoan sample species richness with no increase in diversity north to c. 55 N. Further north sample species richness tended to be lower, but the paucity of samples between 55 and 9 N precluded any conclusion concerning latitudinal trends. These studies show that extensive sampling is necessary to represent adequately the variability in alpha diversity in each latitudinal band. Although depth and sediment properties varied considerably on the Norwegian shelf, there was no clear evidence of a relationship between alpha diversity (SR S ) or mean alpha diversity (SR S ) for the five large areas and environmental factors (Table 2; COV for environmental variables in Table 1; SR S in Table 3). Likewise, Gray (1994) found no relation between sample species richness and in turn depth and sediment properties along a transect of 1 km at the Norwegian shelf. For gamma diversity (large area species richness, SR L ) our data provided no evidence in support of a latitudinal cline along the shelf transect. Gamma diversity was highest in area 3 (477 species), followed by area 5, and lowest in area 1 (177 species). Area 3 had greatest environmental variability (Table 1; COV for depth, Mdϕ, silt clay content and TOM) followed by area 5,

13 385 Spatial patterns of benthic diversity whereas area 1 was environmentally homogeneous. These findings suggest that gamma diversity increased with habitat heterogeneity. Differences in environmental heterogeneity are almost always correlated with differences in species diversity (Huston 1994; Rosenzweig 1995). However, our data showed that this was the case for gamma diversity but not for alpha diversity. It is clear from these data that the factors that influence species richness act at different scales (Levin 1992), and it is difficult to scale up from the results of small-scale studies to conclusions that are relevant to ecological patterns and processes at larger spatial scales (Thrush & Warwick 1997). In a terrestrial study in Britain, Harrison et al. (1992) found strong geographical gradients in 5 5-km squares that they called alpha, in contrast with our study where alpha diversity was described over only 5 m 2. Clearly the ecological processes that affect these two scales must be different (Gray ), and Harrison et al. s (1992) alpha diversity is in fact more comparable to our gamma diversity. The low species richness that has been reported from the deep part (25 m) of the Norwegian Sea (Bouchet & Warén 1979; Dahl 1979; Rex et al. ) is in marked contrast to the shelf data shown here, where species richness is relatively high in both the Norwegian Sea (area 4, N) and in the Barents Sea (area 5, 7 71 N). Our finding of relatively high species richness at high latitudes along the shelf is also in marked contrast to Grytnes et al. s (1999) terrestrial data on vascular plants. They showed that species richness in km squares in data collated from Denmark, Norway, Sweden and Finland decreased significantly from 55 to 64 N, but did not decrease from 64 to 71 N. They suggested that this pattern was a result of both history and climate. About 1 12 years ago much of the North Atlantic (north of the Arctic circle) was open water, at least during the summer, when pack ice covered the southernmost coasts of Norway, and much of the North Sea was still dry land (Andersen & Borns 1997). Thus, the first areas to become ice-free in these northern areas were the northernmost parts of Finmark and some areas along the west-coast of northern Norway (see Grytnes et al and references therein). Long-distance dispersal from, for example, Novaya Zemlya (with ice-free areas as early as 15 years ago) or from areas to the north and west could have provided a pool of species to the coast of northern Norway. This may have led to earlier recolonization of northern than more southern areas of the Norwegian continental shelf, although more data are needed before such a speculative argument can be tested. In recent years a number of studies have concluded that local richness is directly proportional to regional richness (often equated with alpha and gamma diversity, respectively) (Cornell & Lawton 1992; Cornell & Karlson 1996; Griffiths 1997; Loreau ). This implies that regional processes may be more important than local processes (e.g. competition and predation) in determining local species richness (Lawton 1999). In this study there was no evidence of a relationship between mean alpha diversity (SR S ) and gamma diversity (SR L ) (Fig. 3), although area 1 had both lower alpha and gamma diversity than the other areas. However, only five gamma diversity values were used and gamma diversity was described over a relatively limited spatial scale (< 1 26 km). The total data set (SR T ) comprised 89 species from individuals in 5 5 m 2, and polychaetes were the dominant taxonomic group, followed by crustaceans, molluscs and echinoderms. The Chao2 estimate of true species richness gave 135 species. However, neither the species accumulation curve nor the Chao2 reached an asymptote (Fig. 5a), and Chao2 was almost certainly an underestimate. The Norwegian continental shelf has been monitored since the late 197s and the database comprises over 25 macrobenthos species. This suggests that a larger number of samples is needed before Chao2 can give a reliable estimate in soft sediments. Applications of the Chao2 method in marine studies are few (Paterson et al. 1998; Gray ; Ellingsen 1; Ellingsen 2), but most suggest that Chao2 underestimates the actual species richness. In ecological data sets most species are represented by a small number of individuals, most individuals belong to a few abundant species, and at larger scales the frequency distributions of species range sizes are typically strongly right-skewed (Gaston 1994), as was the case with our data (Fig. 6). The group of restrictedrange species comprised 36% of the benthos along the shelf transect, 29% of the species were represented by one or two individuals, and no species spanned the entire transect. Furthermore, the number of species and particularly the number of individuals (i.e. dominance patterns) varied considerably from year to year in soft sediments of the Norwegian continental shelf (Pearson & Mannvik 1998). BETA DIVERSITY Beta diversity is usually associated with betweenhabitat diversity, but defining what is a habitat in soft sediments is not a simple task. Subtidally, sediments tend to grade into each other and the extent of a habitat or assemblage often cannot be determined, and one is usually sampling remotely and blindly (Gray ). The beta diversity measures used here were therefore a mixture of between-habitat and within-habitat differences. New remote acoustic techniques (Collins, Gregory & Anderson 1996) that collect continuous data from large areas relatively quickly, as well as underwater cameras, will give additional information to traditional grab or core point samples and will be highly relevant to future diversity studies. The finding that β W (SR T /SR S ) varied between the dominant taxonomic groups (Table 5) shows that a single taxonomic group cannot be taken to represent

14 386 K.E. Ellingsen & J.S. Gray overall beta diversity. In fact, at this scale β W was highest for those taxonomic groups with the highest proportion of restricted-range species (Table 3). In a study of North Atlantic bryozoans Clarke & Lidgard () pooled data into bins of 1 degrees of latitude and found that β W for the 5 6 N and 6 7 N bins was lower than 4. They also found that β W for a biogeographic province with mean latitude of approximately 6 N was about 5, compared with 6 3 (polychaetes), 8 8 (echinoderms), 9 4 (molluscs) and 14 5 (crustaceans) for SR T /SR S in this study. This suggests that β W for the dominant groups at the Norwegian shelf was relatively high. However, there are, as yet, few studies that can be used as a comparison, and we do not know enough about beta diversity in the sea to decide what is a high and what is a low value. In a terrestrial study in Britain, Harrison et al. (1992) compared beta diversity in plants, invertebrates and vertebrates across distance gradients, and found low values of β W. They showed a relationship between beta diversity and distance for most of the taxa, but distance was correlated with environmental dissimilarity, thus providing an alternative explanation for distance effects. The fact that alpha and gamma diversity were described over different scales in Harrison et al. s (1992) study compared with this study makes a direct comparison of β W values between the different ecological systems problematic. Additional measures of beta diversity used here were the number of shared species, complementarity (biotic distinctness; Colwell & Coddington 1994) and Bray Curtis similarity (Bray & Curtis 1957) between sites. At the scale of the large areas there was no evidence of a latitudinal cline in beta diversity. Mean complementarity and mean Bray Curtis similarity showed that beta diversity was highest in area 3, followed by area 5, and lowest in area 1. It is difficult to compare the number of shared species between areas, but the variability in the number of shared species was highest in area 3 and lowest in area 1. β W (SR L /SR S ) was also highest in area 3 and lowest in area 1. Thus, both gamma (SR L ) and beta diversity increased with greater environmental heterogeneity, whereas alpha diversity did not. In the present study both mean complementarity and mean Bray Curtis similarity were related to gamma diversity, whereas β W was not (Fig. 8a,b,c). In a study of lacustrine fish in North America, Griffiths (1997) similarly found that β W was not significantly correlated with regional species richness. Thus, biotic distinctness and Bray Curtis similarity gave additional information to β W. At the scale of the large areas the relationship between the faunal patterns and in turn environmental variables and spatial distance (Table 7) and environmental factors (BIO-ENV analyses; Table 4a) varied between areas. No single mechanism can explain faunal patterns observed across the different areas, and at any given location a number of different interacting factors are involved (Snelgrove & Butman 1994). Schlacher et al. (1998) found, in a study of soft-sediment fauna in a coral lagoon, that the number of shared species between all pairwise permutations of sites was low (i.e. low beta diversity) but weakly related to distance at a spatial scale of km. The low mean number of species shared (25 species), low mean Bray Curtis similarity (24%), and high mean biotic distinctness (82%) between all sites on the shelf showed that beta diversity was relatively high, a result that is in accordance with β W values. These three measures of beta diversity showed weak relations to distance, but the relations to change in environmental variables, notably depth, followed by sorting, were stronger. Likewise, a multivariate analysis (BIO-ENV, based on Bray Curtis similarities) showed that the faunal patterns were more closely related to sorting and depth than to latitude (Table 4b). In the present study, β W, the mean number of species shared, mean complementarity and mean Bray Curtis similarity showed that beta diversity was higher at the largest scale (1958 km transect) than at the scale of the large areas (< 1 26 km). Similarly, Ellingsen (1) found that mean Bray Curtis similarity between sites was lower at a scale of 7 13 km than at smaller scales within the same geographical area. Our findings differ from Izsak & Price s (1) study of echinoderms in the Indo-West Pacific, who found higher beta diversity at a small scale ( 1 km) compared with the intermediate scale (1s 1 km) and province/oceanic scale (1s 1s km). Mean Bray Curtis similarity between all combinations of sites pooled into increasing distance units of 1 km showed a strong inverse relationship to distance. The overall relationship (Fig. 1a) implies that, on average, the similarity of the 1-km scale faunas on the shelf was halved over a distance of about 8 km. In a study of the benthic fauna at the Norwegian continental shelf, Gray () found that the similarity was halved over a distance of only approximately 15 km, but Gray included similarities between sites at a much smaller scale, where mean similarities were as high as 6%. This clearly shows the role of spatial scale in the measurement of biodiversity. Clarke & Lidgard () found that similarity was halved over c. 5 km with bryozoans, and Paterson et al. (1998) suggested that Atlantic and Pacific assemblages of polychaetes changed over a scale of 5 1 km. In the present study mean Bray Curtis similarity between all combinations of sites pooled over increasing changes in depth (unit m) was closely negatively related to depth differences between sites (Fig. 1b), and this relationship was stronger than for similarity averaged over distance (Fig. 1a). The overall relationship (Fig. 1b) implies that, on average, the similarity of the -m depth interval faunas was halved over a change in depth of about 13 m. Thus, depth was a critical factor influencing the faunal patterns, and must be considered when measuring diversity patterns from the tropics to the poles. Large ocean-scale relationships between beta diversity and spatial distance (Sheppard 1998; Price et al. 1999; Clarke & Lidgard ) may be