ORIGINAL PAPER

Mapping the benthos: spatial patterns of seabed-dwellingmegafauna in a Swedish Fjord, as derivedfrom opportunistic video data

Genoveva Gonzalez-Mirelis & Per Bergström &

Tomas Lundälv & Lisbeth Jonsson & Mats Lindegarth

Received: 5 June 2009 /Revised: 7 September 2009 /Accepted: 13 October 2009 /Published online: 3 November 2009# Senckenberg, Gesellschaft für Naturforschung and Springer 2009

Abstract It is widely acknowledged that mapping of benthicdiversity is needed to aid in the management and conservationof marine ecosystems, but the choice of scale is contingentupon the patterns of spatial structure inherent to the benthos,which are often unknown. In this paper, spatial autocorrelationanalysis is used to detect and describe fine-scale patterns ofspatial structure in assemblages of epibenthic megafauna of theseabed below 20 m depth at the Koster fjord/archipelago area(Sweden). Presence/absence of benthic organisms wasobtained from video images, which had been collected bymeans of a remotely operated vehicle. For sample sizes (grain)of <1 m2, and maximum between-sample distance (lag) of200 m, rank-correlograms revealed the presence of patches inall 12 sites surveyed, and faunal homogeneity (positive spatialautocorrelation) was always detected within distances <20 m,though there was variation across sites in the sizes of patches.These findings were further used to resample the data atcoarser grain sizes, to enable exploring of the faunalcomposition of the patches. We conclude that spatial autocor-relation analysis can greatly improve the design of samplingschemes by ensuring parsimoniousness, and maximizing the

chances of detecting patterns. The procedure shown here isespecially well suited to carry out subsequentmapping becauseit can readily discriminate between types of biotopes.

Keywords Spatial structure . Spatial autocorrelationanalysis . Epibenthic megafauna . Underwater video .

Mapping

Introduction

One of the issues ecologists are concerned with iselucidating patterns of distribution of species and assemb-lages, which is done in the interest of science as much asfor developing managerial applications. Regrettably, we areforced to draw our conclusions regarding such big ques-tions from data compiled from relatively small numbers ofscattered, limited observations of the world. Conductingthese observations at the appropriate spatial scales, that is,at the scales at which organisms respond to their environ-ment and interact with one another, is therefore of utmostimportance (Andrew and Mapstone 1987; Underwood andChapman 1996). Not only does sampling with scalingissues in mind boost our chances of detecting the patternsthat we set out to investigate, but it also provides thenecessary knowledge to design sampling schemes that aretime and cost efficient.

This is not at all a new insight: plant ecologists havelong been aware of the effects that sampling designspecifications can have on the data gathered and on theconclusions reached through analyzing those data (e.g.,Greig-Smith 1961; Kershaw 1957). Building from samplingtheory, researchers have considered three spatial elementsof sampling design (Legendre and Legendre 1998): grain,defined as the size of the elementary sampling unit and

G. Gonzalez-Mirelis (*) : P. Bergström : L. JonssonDepartment of Marine Ecology, Tjärnö,University of Gothenburg,Strömstad, Swedene-mail: gegm@marecol.gu.se

M. LindegarthDepartment of Marine Ecology, Tjärnö,University of Gothenburg,Gothenburg, Sweden

T. LundälvSven Lovén Centre for Marine Sciences, Tjärnö,University of Gothenburg,Strömstad, Sweden

Mar Biodiv (2009) 39:291–302DOI 10.1007/s12526-009-0028-1

expressed as the surface area supporting the measurement(see also Wiens 1989; He et al. 1994); sampling interval,defined as the average distance between neighboringsampling units, also referred to as sampling step, or lag;and extent, or the total area included in the study. In marinebenthic environments, marine ecologists are asking thesame fundamental questions, while bearing the burden ofeven more costly gathering of data. Exploring spatial scalesof variation in the benthic landscape, in advance of anyattempts to disclose patterns of distribution of benthic biota,is, then, all the more important.

In Sweden, an agreement was reached recently toestablish the first Marine National Park at the Koster Fjordand archipelago area, off the West Swedish coast (see Fig. 1).The “Koster Sea National Park Project” has prompted muchresearch in recent years so as to lay the basis formanagement and monitoring plans, as well as to providebaseline data against which to measure the impact of thepark. The present paper is a pilot study, in which we reporton the spatial patterns of heterogeneity of benthic biota, withthe aim of providing the knowledge to design adequatesampling programs through which data can be collected toanswer questions concerning the geographic distribution of

benthic diversity in the area. Specifically, we address thequestion: at what spatial scales can heterogeneity be detectedin the composition of benthic assemblages? The issue thatwe tackle more directly is that of the optimal size of thesampling unit, which we will refer to as grain, following thetradition of spatial ecology (O’Niell et al. 1986; Wiens 1989;Turner 1989), from the standpoint of how samplingresolution may influence the classification and subsequentmapping of biotopes.

At most scales, heterogeneity is readily detected in thedistribution of organisms across space, meaning that theprobability of occurrence of the organism under study is notevenly distributed throughout the area. Spatial structure inspecies’ distributions can be caused by contagious bioticprocesses (e.g., predators occurring near their prey), thespatial variation of environmental drivers such as habitat,temperature, etc., and historical factors (Segurado et al.2006; Legendre and Fortin 1989). Depending on which setof processes has the strongest combined effect, and on howbiota respond to those, different types of spatial structuremay appear. Ecological data are in fact a composite ofspatial structures, with trends at macroscales, and patchesand gradients at meso- and local scales (Fortin et al. 2002).It is nevertheless possible to find homogeneous distribu-tions as well (Andrew and Mapstone 1987). Randompatterns are generally found at local and microscales, orsimply below a critical size of grain, where the probabilityof finding a species becomes evenly distributed throughoutthe sample. The theory applies equally, by hierarchicalextension, to the multivariate case of species assemblages,where the covariance of the multiple variables substitutesthe variance in defining heterogeneity (Kenkel et al. 1989).

Spatial structure can be detected and measured by meansof spatial autocorrelation analysis. This type of analysis isgrounded on the fact that, given a combination of grain, lag,and extent, the value of a variable is not independent fromthe value of the same variable in nearby samples, therebypotentially biasing the results of standard statistical tests.This is an issue that has been extensively covered in theliterature (e.g., Dormann et al. 2007; Segurado et al. 2006),but which is not our concern here. When series ofgeographically referenced samples are available, the rela-tionship between spatial autocorrelation and distance can beevaluated, and the scales at which patterns emerge revealed.This relationship can, in turn, be used to determine theamount of spatial detail that can safely be ignored.Accounting for this fact gives the investigator the possibilityof attaining the most parsimonious sampling scheme toexplore an ecological phenomenon.

Few studies have dealt with local scale patchiness ofbiological assemblages in subtidal benthic environments(e.g., Parry et al. 2003), presumably due to the practicaldifficulties of gathering adequate data, but it has long been

Fig. 1 The location of the Koster Fjord and Archipelago, as well asthe bathymetry. Also shown are the remotely operated vehicle (ROV)survey sites whose data were used in this study (solid circles)

292 Mar Biodiv (2009) 39:291–302

hinted at (Angel and Angel 1967; Butman 1987; Eastwoodet al. 2006). Rather, meso-scale patterns dominate in theliterature. These have been inferred from grab samplers(Eastwood et al. 2006; Brown et al. 2002, 2004; Somerfieldand Gage 2000), photographs (Kostylev et al. 2001), beamtrawls (Jennings et al. 1999), and visual censuses throughSCUBA diving or snorkelling (García-Charton and Pérez-Ruzafa 2001; Galzin and Legendre 1987), and scalesconsidered range from tens of meters to kilometres.Underwater video sampling is well suited for capturingsufficiently small samples, sufficiently close to one another,so as to study small scale patterns of similarity of deepbenthic communities. By use of the repository of videofootage accrued over the last few years under the auspicesof the National Park project, this paper explores fine-scalepatterns of spatial structure of epibenthic megafaunalassemblages, including attached fauna, free-living fauna,and demersal fish of the Koster Fjord greater area. We focuson scales from one to hundreds of meters, while the termmicroscale will apply to one to tens of meters, and localscale to ten to hundreds of meters. The purpose of thispaper is twofold: (1) to describe spatial patterns of benthicbiota using existing material from the area, and (2) to drawconclusions useful for the optimization of future studies onpatterns of distribution and abundance of benthic fauna.

Methods

Data collection

The video-material used in this study is labelled “opportunis-tic” because it was not collected for the purpose of studyingspatial patterns of variability of the benthos. Rather, thesurveys from which the data were obtained were of areconnaissance nature, specifically targeted at (1) compilingan overall list of deep benthic species, (2) finding species-richareas, and (3) capturing good photos and attractive footage tobe used in outreach and public awareness activities. Thissetting has not surprisingly resulted in a number of challengesregarding the use of video images for obtaining quantitativedata suitable for statistical analyses. Problems includedvariable distance to the bottom, variable flying speed, andwinding surveying tracks. However, the wealth of data as wellas their immediate accessibility outweighed the above incon-veniences, and ultimately, the nature of the data proved to beof much value. By virtue of the state-of-the-art technologicalequipment in place and the computational power available, itwas possible to acquire, out of the archived underwaterfootage, semi-quantitative data on epibenthic fauna that weregeographically referenced and highly spatially resolved.

The video data had been collected by means of a videocamera mounted on a Sperre Subfighter 7500DC Remotely

Operated Vehicle (ROV). The ROV, deployed from R.V."Lophelia", is fitted with a transponder, which communi-cates with the vessel’s positioning system and sends back astream of positional data. Survey tracks can thus bereconstructed in a Geographic Information System throughstraightforward plotting of the (smoothed) pings.

In the laboratory, video files were viewed and manuallychopped into fragments that could be georeferenced (i.e., thetimecodes had been recorded) whose field of view, asdetected by sight, did not overlap with one another. Theseunits are referred to as “video frames” and they constitute theelementary sampling units of this study. Technically, theywere in fact short video sequences, such that the field of viewof the video frames making up the sequence overlapped atleast in part. This approach ensured that sampling units wereas small as possible, and of equal size. Grain size is thereforedefined by the swath of the camera as <1 m2. Conveniently,in areas of highly diverse fauna, ROV flying speed was low,occasionally stationing at certain spots. These samples havea real-time duration of up to several minutes, which allowedidentification of a great variety of taxa.

The georeferencing of the video frames, i.e., theassignation of geographic coordinates to the samples, wasdone through linear interpolation with the navigation data,where timecodes were used to calculate the expecteddistance to the previous ping and the next. In this process,samples were considered dimensionless (i.e., points).Throughout this study, we used only projected units(meters), from UTM zone 32N.

Twelve video surveys were chosen for this paper(Fig. 1), with linear distances ranging between 100 and250 m, and depths between 20 and 180 m (Table 1). Wewill refer to these as tracks. This selection was made basedon whether the material was available in digital format, andin an effort to obtain data from as broad an area as possible.For each one of a total of 1658 video frames, all discernibleorganisms were identified to the species level wheneverpossible, and noted as present. When organisms could notbe assigned to a genus, they were noted as “unknown” andwere excluded from the present analyses.

All video-related data, pertaining to both identified taxaand ROV dive information, was stored in a geodatabase.The structure of this geodatabase allowed for a great deal offlexibility in the production of customized datasets. In someof the analyses performed, samples were aggregated atdifferent levels, representing different scales of analysis(increasing grain size). Details are provided at the relevantpoints.

Data analyses

To test for the presence of spatial structure in the data, anumber of Mantel tests were performed (Mantel 1967;

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Legendre and Fortin 1989; Legendre and Legendre 1998).The non-parametric Mantel test, based on rank-correlations,(hereafter referred to as Mantel test) is a means to correlatethe corresponding elements of two distance matrices, in thepresent case one of biological dissimilarities and anotherone of geographic distances, to describe the relationshipbetween objects (samples) in relation to their speciescomposition and separation distance (Legendre and Fortin1989; Somerfield and Gage 2000).

The Mantel statistic used was the rank-based Spear-man's correlation coefficient (Rho, ρ) between biologicaldissimilarity and geographic distance. The null hypothesisof this test states that there is no relationship betweenbiological resemblance and geographic distance betweensamples, this being the outcome of a lack of spatialstructure. Significance was tested through a permutationprocedure. Thus, the rows in one distance matrix wererandomly shuffled and a new correlation coefficientcalculated, iteratively, for 9,999 runs. The originalcorrelation coefficient was checked against the distribu-tion thus generated, and significance assessed by means ofa two-tailed test.

To set the context of the local scale analyses, it wasfirstly determined whether at the regional scale (1–10 km)there was any spatial structure. For this test, speciespresences and absences in all video frames were aggregatedto yield an overall species composition for the whole track.Biological similarity between tracks was computed basedon presence/absence-transformed data using the Bray-Curtis measure, and then subtracted from 1 in order toobtain a measure of biological distance. A correlation

coefficient was then calculated between dissimilarities, andgeographic distances between the center points of thetracks.

For each one of the 12 tracks, an equivalent test wassubsequently carried out, where the video frames arenow the objects of comparison. Distances betweenobjects included from 0 (adjacent samples) to hundredsof meters. Bray-Curtis (dis)similarity between geolocatedvideo frames was also used here. The distance betweensamples was calculated in meters, and it was simpleEuclidean distance measured in Cartesian space.

Spatial patterns were also quantified by means ofmultivariate rank-correlograms. This type of plot (hereafterreferred to simply as “correlogram”) was originally deve-loped by Sokal and Oden (1978) and Oden and Sokal(1986). It enables the comparison of the average bioticsimilarity of samples within a preset distance class, with theaverage similarity of all other pairs of samples, by plottingcorrelation coefficients, which are now standardized,against distance class. Briefly, correlation coefficients arecomputed sequentially between a group of cells in thesimilarity matrix (those corresponding to the samplesseparated by the distances in the class at hand) andeverything else, one class at a time. The purpose is toapproximate a continuous function of the autocorrelationwith geographic distance, partitioned in intervals. In suchplots, significant autocorrelation minima represent thedistance between samples that are least similar biotically.Significant autocorrelation maxima that occur in distanceclasses greater than an autocorrelation minimum indicatethe distance between the centres of successive patches of

Table 1 Summary of remotely operated vehicle (ROV)-surveyed sites

Label Locality Date (dd/mm/yyyy)a No. of frames checkedb Max. dist (m)c Min. depth (m)d Max. depth (m)e

KH2 O Nordkoster 02/07/2006 108 79.5 80 120

KH3 NO Nordkoster 02/07/2006 120 164.5 73 179

KH5 O Saltholmen 22/07/2006 148 160.4 80 110

KH6 O Ramskär 23/08/2006 186 156.6 130 169

KH8 S Grisbådarne 18/09/2006 195 172.1 66 93

KH9 V Svartskären 26/09/2006 198 224.8 23 123

KH11 S Stenbrottet 09/11/2006 112 129.3 20 44

KH21 SV Syd Hällsö 20/06/2007 185 272.5 58 127

KH30 SV Ursholmen 06/07/2007 105 120.5 26 48

KH31 V Norr Hällsö 11/07/2007 78 78.1 67 134

KH32 O Kalkgrund 13/07/2007 144 124.7 32 66

KH33 V Nordkoster 13/07/2007 79 82.4 32 50

a Date of ROV surveyb Total number of video frames along surveyed track suitable for statistical analysescMaximum between-frame distancedMinimum depth registered along surveyed trackeMaximum depth registered along surveyed track

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biotic similarity (Parry et al. 2003; Legendre and Fortin1989). A spatially homogeneous faunal assemblage will berepresented by a rank-correlogram in which no Spearman’sρ values are significantly different from zero, or in otherwords, by a more or less flat line levelled around the originaxis.

For the construction of the correlograms, classes weremade on the basis of numbers of video frames (ratherthan equal distance intervals), to enable a direct comparisonbetween classes across the entire range of spatial scales.Thus, the number of two-way connections in all classesof all correlograms is approximately 1,000, the numberof classes varying greatly from track to track, from asfew as 2 classes (KH33) to as many as 13 (KH21). Thesignificance of the Mantel statistic was tested for eachdistance class using 10,000 permutations with replace-ment for a two-tailed test, in a way analogous to thatused for the overall Mantel tests (see above). Globalsignificance was then assessed by checking that at leastone class was significant at the Bonferroni-correctedlevel, based on the number of distance classes in therelevant correlogram.

Because we were interested in overall patterns, regard-less of potential differences in driving processes, the testswere done using data from all taxa, both attached and free-living biota, together. This approach also served to reducethe impact of stochastic variation. Keeping in line with theaims of the study, no distinction was made betweensubstrate types or depth classes.

The most interesting output from a rank-correlogram isinsight into the scale at which heterogeneity can bedetected, which is given by the intercept of the autocorre-lation function with the x-axis. This value of distance, inmeters, was used to re-sample the data at coarser grainsizes. Species presences and absences were lumpedtogether for the whole set of contiguous video frames thatfit inside a circular area, dubbed “bubble” for simplicity,whose diameter was the distance determined by theautocorrelation intercept (Fig. 2). We wanted to ensure thatsharp corners in the tracks would not end up as twoconsecutive sections, and thus overlapping in the area thatthey represented, but rather, in one single sample. Thismethod guaranteed the presence of a neighbourhood aroundthe data points, of which the data are assumed to berepresentative. The samples thus obtained, hitherto referredto as “bubbles”, represent variability at the local scale, andwere used to carry out further multivariate analyses.

The multivariate, local scale analysis remains largelyexploratory, mainly because there was variation in the sizeof the sampling units (the sizes match local patterns ofheterogeneity, which vary across sites). A hierarchicalcluster analysis combined with a SIMPROF test wasperformed (see Clarke et al. 2008 for details on how this

test works), using group average as clustering method. Thisfinal analysis enabled us to describe the patterns ofassociation among taxa which emerge at the scale ofanalysis chosen. Furthermore, it allowed us to describe thecomposition of the faunal patches.

The Mantel tests were done by means of a script writtenfor MATLAB. All other analyses were carried out inPRIMER 6, where the RELATE function was used, as wellas the built-in suite of multivariate analysis tools.

Results

General observations

A total of 153 taxa (both genus and species levels) wereidentified from the ROV footage of the seabed below20 m, including one seemingly undescribed species ofAscidia. On average, 42 taxa were identified per site, withlittle variability in taxa richness among sites. The mostcommon taxon was Pandalus sp., of which two speciesexist in the area, the more common P. borealis (NorthernShrimp), and P. montagui (Pink Shrimp). The images didnot always allow this distinction, and the two species werecollapsed into one genus in our analyses. This taxon wasfollowed by Sabella pavonina (Peacock Worm), and twosponges: Axinella rugosa and Phakellia ventilabrum. Over40 taxa were encountered only once, among which wereAphrodita aculeata (Sea Mouse), Astropecten irregularis(Sand Star), Myoxocephalus scorpius (Short-spined SeaScorpion), Thorogobius ephippiatus (Leopard-spottedGoby), Zeugopterus punctatus (Topknot), and Gobiusniger (Black Goby).

Fig. 2 The scales of analysis used. The solid line depicts the surveytrack, as interpolated from the navigation data. Solid circles representgeoreferenced video frames. The procedure for aggregating frame-by-frame data into large grain size samples using the “bubble” method isillustrated

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Spatial structure

Analyses of spatial autocorrelation revealed homogeneity atthe regional scale (ρ=0.09, p value=0.6). This means thatfor a grain size of approximately 2 ha (based on the size ofa minimum bounding rectangle fit around the filmed track),and lag distances between 700 m and 23 km, there are nogradients or trends in species composition of benthicassemblages at depths below 20 m throughout the area.Nevertheless, these are very few data points for a very largearea (approximately 500 km2), so this finding should betaken with much caution.

At the local scale, spatial structure was found in all sites,and this was always highly significant (Table 2). Theweakest signal was found at KH6, according to the intensityof the relationship as is inferred from the value of ρ, alongwith the significance level of the test. KH9 featured thestrongest relationship between biotic dissimilarity anddistance, indicating strong spatial structure (or positivespatial autocorrelation) of biological assemblages.

Spatial patterns

All rank-correlograms are displayed jointly in Fig. 3, wherecorrelation coefficients have been plotted against the mid-point of their corresponding distance class, so that allautocorrelation profiles can be portrayed together along onecontinuous abscise axis (the vertical scale is the same, sinceall correlations have been performed on equal samplesizes).

All but one (KH6) correlograms were globally signifi-cant at the α=0.05 level (and in fact, not just one, but themajority of the values therein where also significant at theα/v level, where v was the number of classes for thatcorrelogram), indicating that spatial assemblage heteroge-neity was indeed captured at the studied range of scales.

While for KH6, biotic similarity between samples was nohigher or lower in any distance class than in any other, allother sites showed highly positive autocorrelation acrossthe short distance intervals (left-most end of the plot), andhighly negative autocorrelation in those distance classesimmediately following the first group, occasionally reach-ing the minimum at this point.

There was a high degree of agreement across sites atshort to medium distances, where autocorrelation exhibiteda regular monotonic decline with distance at similar rates(with the exception of KH9, whose rate of decreaseappeared lower). In other words, homogeneity is found atthe micro-scale (one to tens of meters) throughout the studyarea, and a mosaic of patches can be detected at scaleslarger than those. Furthermore, the interpolated profiles allcross the zero level within a narrow window of distances atan average of 38 m (Fig. 3, Table 3), indicating thepresence of patches of similar dimensions in almost alllocalities.

When moving into larger distance classes (local scale),the various autocorrelation profiles show some differences.In KH8, similarity between video frames increases, until itbecomes higher than expected by chance (significantpositive autocorrelation) again, beyond 100 m. Thisindicates the entry into another patch of the same kind asthe previous one (“wide wave” sensu Legendre and Fortin1989). From this it can be inferred that at this site, thecenters of patches are closer together than at all other sites.

Fig. 3 Composite multivariate rank-correlogram, for equally-sizeddistance classes. Spearman’s correlation coefficients (ρ) are plottedagainst the mid-point of their corresponding distance class. Solidtriangles on the x-axis represent the intercepts of the interpolatedprofiles. Values of ρ significantly different from 0 are represented bysolid circles, while non-significant ones are represented by blankcircles

Track ρ

KH2 0.25***

KH3 0.26***

KH5 0.35***

KH6 0.09**

KH8 0.08***

KH9 0.59***

KH11 0.25***

KH21 0.12***

KH30 0.30***

KH31 0.38***

KH32 0.11***

KH33 0.36***

Table 2 Spearman’s correlationcoefficients between biologicaldissimilarity and distancebetween video frames

***p value<0.001, **0.01>p value>0.001

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KH32 reaches a second, lower peak at ≈75 m, possiblysuggesting a nested pattern of patches. KH21 seems tostabilize beyond 40 m, similarity remaining lower thanexpected by chance all the way through the classes studied.This is could be the result of an ample transition zonebetween patches.

All other sites present a similar type of structure where,for the scales measured, the relationship between similarityand distance continues to decline, indicating that we haveleft the patch, but not entered a new one (“sharp step” sensuLegendre and Fortin 1989), obscuring what the distancebetween patches might be, or, indeed, the size of the patchwe are in (e.g., KH30, KH31, KH33).

The geographic distance where the autocorrelationbecomes not significant measures the distance at whichsamples become structurally independent. Another way ofinterpreting this distance is as the average “zone ofinfluence” (Legendre and Fortin 1989; Wilkinson and Edds2001) of a given combination of biophysical processes, asis manifest in the presence of a particular benthicassemblage (i.e. the diameter of the “core” area of thepatch). The intercepts are summarized in Table 3, as well asdepicted in Fig. 3. The mean zone of influence of localprocesses across the greater Koster area was 38.6 m (95%CI: 27.5–49.8). If the value for KH9 is removed, the meanbecomes 34.4 m (95% CI: 27.6–41.2).

Megafaunal assemblages

The hierarchical cluster analysis was based on standardizedfrequencies (sum of presences divided by number of videoframes contained in each “bubble”). Between 3 and8 “bubbles” were obtained for each track. Site KH6 wasexcluded from this analysis, owing to the fact that no patchstructure was found there at the measured scales. When thedendrogram was cut off at the (arbitrarily chosen) 20%similarity level, six clusters of >2 members could be

identified (see Fig. 4). We interpret these groupings asbiological assemblages, and describe them below accordingto their typical compositions, i.e., species that wereencountered most frequently, and species that were veryabundant.

Assemblage 1 was largely dominated by Pandalus sp.,which co-occurred with Sabella pavonina, Parastichopustremulus, Axinella rugosa, Mesothuria intestinalis, andPachycerianthus multiplicatus among other less frequentspecies. The most abundant taxa, occurring in much lowernumbers than Pandalus sp., were M. intestinalis, Kophobe-lemnon stelliferum, and P. tremulus. This cluster alsoincluded the only occurrences of the fish Myxine glutinosa.

The most frequent, as well as most abundant, taxa ofAssemblage 2 included, in decreasing order of abundance,S. pavonina, Phakellia ventilabrum, Ascidia mentula, A.rugosa, Terebratulina retusa, and Novocrania anomala.

Assemblage 3 featured the following taxa as mostfrequently encountered: A. mentula, P. ventilabrum,A. rugosa, Porania pulvillus, Caryophyllia smithii,Ctenolabrus rupestris, Asterias rubens, and Labrus mix-tus. The most abundant species were C. rupestris, andAlcyonium digitatum.

Assemblage 4 was characterized by the presence ofGadus morhua (Cod) and Suberites virgultosus whichwere both the most frequent and most abundant, alongsideof A. mentula, S. pavonina, N. anomala, Lumpenuslampretaeformis, and Tetilla cranium.

The most abundant, as well as frequently-encounteredspecies of Assemblage 5 were M. intestinalis, V. mirabilis,K. stelliferum, P. pulvillus, Funiculina quadrangularis, andAstropecten irregularis. Present in large numbers wereP. pulvillus, and F. quadrangularis. There are only threemembers of this assemblage, and they are all at the same site(KH32).

Assemblage 6 was distinguished by the following widelyoccurring taxa: M. intestinalis, G. morhua, A. rubens, Hyassp., and Pennatula phosphorea. The following speciesappeared also in large numbers: A. rubens, P. phosphorea,M. intestinalis, Liocarcinus sp., Melanogrammus aeglefinus,and Taurulus bubalis. There are only two members of thisassemblage, and they are both at the same site (KH33),which is, additionally, in the neighborhood of KH32.

A SIMPROF test carried out in combination with theclustering revealed that clusters 1, 3, 4, 5, and 6 aresignificant groups and that therefore, further subdivisions ofthem would be as good as arbitrary. Conversely, cluster 2could be further split into three meaningful groups (of threeor more members each), which we regard as different“phases” of Assemblage 2. Once completed, this procedurerendered a total of eight classes.

Assemblage 2.1 was defined by the presence of Geodiabarretti, N. anomala, Pandalus sp., and S. pavonina, while

Track Distance (m)

KH2 21.6

KH3 33.2

KH5 39.2

KH6 NA

KH8 36.3

KH9 81.1

KH11 27.9

KH21 50.3

KH30 46.1

KH31 32.4

KH32 22.0

KH33 34.8

Table 3 Estimated values forthe intercepts of the autocorre-lation profiles

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the latter was dominant. This assemblage was present atfive sites, all situated on the edges of the Fjord (at the topof the slope). This was the cluster with most cluster-specific taxa, including Polycarpa pomaria, Ascidia callosa,Bonellia viridis, Clathrina sp., Dendrodoa grossularia,Geryon trispinosus, Guancha sp., Lebbeus polaris, andMacandrevia canium.

Assemblage 2.2 was characterized by the extensivepresence of S. pavonina, P. ventilabrum, A. mentula, andT. retusa. The most abundant species in this assemblagewere P. ventilabrum, T. retusa, and A. mentula. Lastly,Assemblage 2.3 featured S. pavonina and A. mentula,alongside others less abundant such as N. anomala.

Two samples in the overarching Assemblage 2 becomesingled out by the SIMPROF test as a significant grouping.We did not include it in our description for containing toofew samples; however, it is worth pointing out the highnumber of unique species present there, such as Euden-drium rameus, Galathea sp., Trisopterus luscus, andZeugopterus punctatus. Because this assemblage appearedat the site farthest away from the fjord trough (KH8), it

appears that more exhaustive sampling might reveal a largescale East-to-West trend in species composition.

Epibenthic megafaunal assemblage patchinessand distribution

The site that showed strongest spatial structure was alsothat which exhibited the most unique autocorrelationprofile: KH9. This points to a region of large, well definedpatches, where possibly a suite of unique processes operate.Conversely, in one site no evidence of heterogeneity wasfound (KH6), leading us to believe that patches around thatarea, the bottom of the fjord, are larger than the scalesstudied. It is possible, though no evidence is available, thatrepeated trawling in that area has had an effect insmoothing out any faunal discontinuities.

The results from the cluster analysis provide ancillaryevidence to support the claim that faunal patches aredistinguishable at the scale ten to hundreds of meters.Spatial structure resulted always in the presence of severalsignificant assemblages. In addition, the autocorrelation

Fig. 4 Hierarchical, group average, cluster classification. Samples(“bubble” level) are labeled with the track site name, followed by anumerical indicating the sample number for that site. Gray branches

indicate the partitionings where the SIMPROF test was not significant.Clusters that are discussed in the text are also labeled, using numbersfrom 1 to 6

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profiles predict rather robustly which way the classificationof the “bubble” samples will go. For example, in KH8,where the autocorrelation function drops and then starts toascend again, the same assemblage can be found near bothends of the track (Assemblage 2), while a different oneoccupies the middle (Assemblage 1). In KH21, whereautocorrelation shows a very slight upwards trend, whichwe interpreted as a stabilizing profile, only one of the“bubbles” along the track turned out to be of a differentkind (Assemblage 2.1), while all others were the same(Assemblage 1). Moreover, the sample classified as 2.1 lay45 m from the end of the track, as expected from the pointwhere the autocorrelation profile reached its lowest value.In KH2 autocorrelation goes up very quickly after theminimum and then starts to drop again immediately. There,a sort of intrusion of Assemblage 2.1 into Assemblage 1can be detected. All other sites shared a declining profile,and accordingly, featured a series of different assemblagesoccurring along the track, none of them appearing morethan once.

The most widespread assemblage (Assemblage 1) was aPandalus-dominated one, involving mostly, though notonly, organisms associated with soft sediments (e.g.,Pachycerianthus multiplicatus, Parastichopus tremulus,Kophobelemnon stelliferum). It was present at five sites,both at the edges of the fjord, and on the site farthest awayfrom it, emphasizing its ubiquitousness. One other assem-blage (5) included organisms that are known to beassociated with soft sediments (the sea pens Virgulariamirabilis, K. stelliferum, and Funiculina quadrangularis),and in this case Pandalus was entirely absent. This can bedifferentiated from the singleton KH5_1, already distin-guishable at the 20% similarity level (see Fig. 4), where thedominant taxon was Pandalus sp. co-occurring withBolocera tuediae, and which seemed much moresubstrate-specialised, including strictly organisms typicalfrom unconsolidated sediments (e.g., Nephrops norvegicus).Thus, the presence of the shrimps, possibly in combinationwith depth, emerges as one potentially important factordriving the composition of the benthos across areas ofsimilar substrate type.

Conversely, all clusters belonging to the overarchingAssemblage 2 appear to include organisms known to occuronly in areas of rock outcrops. Notably, the cluster with themost cluster-specific taxa was found exclusively at siteslocated at the top of the slope on the edges of the fjord. Thisunderlines the high degree of distinctness of this type ofhabitat, seemingly constrained to this depth range andsubstrate type.

Ctenolabrus rupestris (Goldsinny-wrasse) was thedominant species of Assemblage 3, together with Alcyo-nium digitatum (Dead man’s fingers), and was found onlyat the most shallow areas. The C. rupestris/A. digitatum

assemblage exhibited a high number of species thatoccurred nowhere else in the study area, though this waspresumably because all other sites were located at higherdepths.

Lastly, there was one more assemblage (4) that featuredmostly hard-substrate organisms, as well as many fishspecies, both from the cod family, and flat fishes.Assemblage 6, present only at one site (KH33) appears tohave a mixture of species both from hard substrate habitats,and soft bottoms, and it also seems to be associated withmore fish species than other assemblages.

At the lowest level of similarity, the cluster analysis splitup the data into two main classes, one containing clusters 5and 6, and another one encompassing everything else.These two assemblages are not only similar in theircomposition, but they are also very close in space, as theyoccur in neighbouring sites KH32 and KH33. Furthermore,both span a similar range of depths, from 30 to 60 m.Considered together, sites KH32, KH33, KH2 and KH5,located in the same area northeast of the Koster archipelagoand bordering the edge of the fjord, on a platform made upof small mounds, contained four assemblages that do notoccur anywhere else in the whole of the study area, namelyclusters 4, 5, and 6, as well as sample KH5_1 which wasclassified as a group on its own, including a number ofcommercial species (Cod, and Norwegian Lobster).

Discussion

In the Koster Fjord area, the use of underwater videographyin combination with Geographic Information Systems hasenabled bridging the gap from a wealth of data of a non-spatial nature, to in-depth knowledge of fine-scale spatialpatterns of benthic assemblages. The main merits of thispaper are related with improving the allocation of time andmoney resources in benthic research: On one hand, noadditional data were needed to carry out this study; on theother, we hope our findings will help increase the efficiencyof further data gathering efforts in the Koster Fjord area.Our findings are also secondarily discussed in the contextof theoretical considerations of ecological heterogeneity.

The value of pilot studies in ecological research has beenpointed out for decades (e.g., Fortin et al. 1989), but anumber of questions remain unsolved (Legendre andLegendre 1998), among which is the size of the samplingunit necessary to give an adequate analysis of an ecologicalphenomenon. This deficiency is more accentuated in thebenthic environment (Rhoads and Germano 1982). Con-ducting a study using an inadequate size of plot, quadrat, orsome other type of sampling framework, may result in oneof two consequences: the need to sample an area toointensively (i.e., inefficiently), or the risk of overlooking

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existing patterns (Wiens 1989). Knowledge of the type andintensity of the spatial structure of the variable of interestcan help shed light on this question.

When the objective of the research program is relatedwith the recognition of patterns, rather than its counterpartin the arena of statistical inference, the estimation ofparameters, it has been suggested that the size of thesampling unit should be smaller than the structuresresulting from the unit process (e.g., a patch) (Kenkel etal. 1989; Dungan et al. 2002). Furthermore, if grain size ismade to be equal to, or smaller than the smallest availableareal unit with homogeneous community composition, wewill obtain the greatest spatial resolution possible.

For Wiens (1989), the whole spectrum of scales couldbe divided into regions over which, “for a particularphenomenon in a particular ecological system, patternseither do not change or change monotonically withchanges in scale” (p. 392). These so-called “domains ofscale” are in turn separated by “relatively sharp transitionsfrom dominance by one set of factors to dominance byother sets”. In his review paper, he pleaded for thedevelopment of methods to incontrovertibly identify thoseboundaries of scale domains, as he thought the neglect ofthese to be a key stumbling stone in the process ofunderstanding ecological phenomena. Spatial correlo-grams, used in the way shown here, are just one suchmethod.

A multivariate spatial correlogram can be regarded as asummary of megafaunal assemblage variation in space, andit can then be used for delimiting geographic regions thatare either homogeneous or patchy (Diniz-Filho and Telles2002), with respect to their megafaunal composition. Byrepresenting patterns of similarity across spatial scales, thisplot clearly depicts the threshold at which we would movefrom a homogeneous environment to a heterogeneous,patchy one. Our analyses lead us to conclude that any areaof the seafloor approximately 38 m across is likely to besufficiently homogeneous so as to consider variation withinit largely as noise or unnecessary spatial detail, in theregional context.

The lowest value of the autocorrelation coefficient, whenplotted against distance, has been used as a measure of theminimum radius of a faunal patch (e.g., Parry et al. 2003).We did not find any correlation coefficients lower than thatfound for pairs of video frames 60 m apart (ρ=−0.28,p value=0), in track KH31 (see Fig. 3). However, it appearsthat, had data at higher distance classes been available forsome of the tracks (e.g., KH31, KH33), still lower values ofthe Mantel statistic would have been found. Here, wepropose a view of the landscape as encompassing peaks ofsimilarity within a continuous gradient of faunal composi-tion and have therefore placed the focus on the scales overwhich there is no evidence of autocorrelation (i.e., the

distance classes at which our autocorrelation profiles crossthe x-axis). We regard this as the average linear dimensionof the “core” area of a patch of seabed-dwelling megafauna.This is also a conservative approach that we felt necessarygiven the “opportunistic” nature of the data. The meanintercept distance may then be used as an upper limit ofoptimal grain size, when the ecological question beinginvestigated through the sampling has to do with theidentification of patterns, since we have found that theseare discernible at scales of tens to hundreds of meters. Ourfindings indicate that samples of epibenthic megafauna ofsome regular shape, representing an area of approximately314 m2 (i.e., that of a circle of 20 m in diameter), up toapproximately 5,024 m2 (diameter of 80 m) will efficientlycapture relationships between environmental factors andbiota, and appropriately describe the system; samples muchsmaller (at least one order of magnitude difference) will bemore affected by stochastic processes, and samples muchlarger than that are likely to smooth out faunal disconti-nuities and depict, instead, regional trends.

Reliability of our results is enhanced by the fact thatseveral (12) correlograms were built from our data (whichin turn represented the co-variation of some 150 variables),making it possible to supply a probability distribution forthis estimate. Furthermore, and perhaps most enlightening,values were tightly distributed around the mean, even moreso if the intercept for KH9 (81 m), which strongly suggestsa different behavior, is removed. Notwithstanding the widthof the distribution, it remains a robust method to determinethe size of the influence zone of local ecological processes.The fact that the range was relatively narrow in our systempoints to further ecological (or possibly geological) ques-tions, related with the factors driving these patterns,deserving of further attention in future studies.

How much smaller than 20 m across should/could grainsize be, while still ensuring that micro-scale noise is mostlyabsent from the data? In this case, the underlying issue isthat observations must be large enough so variabilityamong samples is maximal (Kenkel et al. 1989). Indeed,when we performed, in an exploratory phase of the study,an Analysis of Similarities (ANOSIM) on the frame-by-frame data, setting site as factor, the test returnedsignificant, implying that on average there was morevariation between sites that within sites (i.e., variationbetween video frames was very small). Only after grainsize was increased, did variation appear at the desiredlevel (i.e., between sites). Our approach was a step in theright direction, but fine-tuning of the sample sizes wouldbe useful, as the “bubble” scale did not completelyremove the track (site) signal in the patterns of variation.Thus, the answer to the above question becomes: samplesize should be as close to 20 m as time and budgetaryconstraints allow.

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The heterogeneity of benthic communities has beensuccessfully explained by invoking the action of small-scaletemporal disturbances; to the extent that they create amosaic of micro-environments to whose spectra of varyingconditions, different sets of species are well adapted. Thismodel was first proposed by Johnson (1970). In our studyarea, patches of hard substrate are found interspersed withareas of unconsolidated sediment, limiting the presence ofthe more specialised organisms to the areas where theirsuitable habitat appears. Similarly, depth imposes varyingambient pressure conditions on the benthic biota. Thealignment of the structure of benthic communities with thismosaic of environmental conditions is then readily pre-dicted by the theory. Most striking is the finding that theinclusion of mobile species, such as fish and shrimps, didnot deter spatial structure from appearing. Further studiesare needed to unravel the relative importance of thedifferent factors driving this pattern, among which mustbe “the heterogeneity of the physical environment, thevagaries of larval recruitment, and biological interactions”(Johnson 1970).

(Re)sampling the benthic environment at the scalesstemming from the spatial autocorrelation analysis hasenabled us to define a meaningful level to describe patternsof association between species. Interspecific associationsare known to vary greatly as grain size is changed, and as aresult species classifications and ordinations are also proneto change (Kenkel et al. 1989). A procedure that attempts tomaximize between-sample variation, while minimizing thevariation within the sample, is especially well suited tocarry out subsequent mapping because it can readilydiscriminate between types of biotopes, taking into accountthat properties such as species composition, diversity, andenvironmental factors are gradational in space, and thathowever one defines classes, there will be variation amongclasses and within them (Levin 1992). Because patchesdisplayed by maps can only be interpreted with respect tothe scale of the sampling program, it is vital that this is inaccord with the scale of processes shaping the structure ofthe landscape (Legendre and Legendre 1998). The approachproposed here ensures simultaneously sampling efficiency,and maximal mapping resolution.

It is worth driving home the point that “scale should notbe considered an intrinsic property of the ecosystem, butrather, a methodological decision we must make asobservers, keeping in mind that most ecological processesare multiscaled in nature” (García-Charton and Pérez-Ruzafa 2001, p. 918). In other words, there is no correctscale for describing a system, but the description ofvariability is contingent upon the window through whichthe system is viewed. In a homogeneous environment,spatial variability will be a function of grain size, in suchway that as grain size is increased variability decays, and

the rate at which this will happen is in turn determined bythe way spatial autocorrelation falls off with distance(Levin 1992). In this paper, we have presented a robustmethod upon which to base such a fundamental decision asthe size of the window through which to look at a system,justifiably balancing the amount of simplification permis-sible and the amount of aggregation needed, and therebydetermining the level of detail necessary to understand andfurther explore patterns.

Acknowledgements This study was financially supported byNaturvårdsverket’s project Korterhavet, and FORMAS throughcontract 217-2006-357 to M. Lindegarth. Many thanks to PerNilsson for his useful comments, and to Andy Hennell for writingand re-writing many MATLAB scripts.

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