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Estuarine, Coastal and Shelf Science 92 (2011) 502e520

Contents lists avai

Estuarine, Coastal and Shelf Science

journal homepage: www.elsevier .com/locate/ecss

Benthic habitat mapping: A review of progress towards improved understandingof the spatial ecology of the seafloor using acoustic techniques

Craig J. Brown a,b,*, Stephen J. Smith a, Peter Lawton c, John T. Anderson d

a Fisheries and Oceans Canada, Bedford Institute of Oceanography, PO Box 1006, Dartmouth, NS B2Y 4A2, CanadabCanadian Hydrographic Service, Bedford Institute of Oceanography, PO Box 1006, Dartmouth, NS B2Y 4A2, Canadac Fisheries and Oceans Canada, St. Andrews Biological Station, 531 Brandy Cove, St. Andrews, NB E5B 2L9, Canadad Fisheries and Oceans Canada, Northwest Atlantic Fisheries Centre, PO Box 5667, St. John’s, NF A1C 5X1, Canada

a r t i c l e i n f o

Article history:Received 17 November 2010Accepted 14 February 2011Available online 23 February 2011

Keywords:side scan sonarmulti-beam sonarsingle-beam sonarclassificationremote sensingcommunity

* Corresponding author. Fisheries and Oceans COceanography, PO Box 1006, Dartmouth, NS B2Y 4A2

E-mail address: craig.brown@dfo-mpo.gc.ca (C.J. B

0272-7714/$ e see front matter Crown Copyright � 2doi:10.1016/j.ecss.2011.02.007

a b s t r a c t

This review examines the various strategies and methods used to produce benthic habitat maps usingacoustic remote sensing techniques, coupledwith in situ sampling. Theapplications of threeacoustic surveytechniques are examined in detail: single-beam acoustic ground discrimination systems, sidescan sonarsystems, andmulti-beam echo sounders. Over the past decadewe havewitnessed the nascence of the fieldof benthic habitat mapping and, on the evidence of the literature reviewed in this paper, have seen a rapidevolution in the level of sophistication in our ability to image and thus map seafloor habitats. As acousticsurvey tools have become ever more complex, new methods have been tested to segment, classify andcombine these datawith biological ground truth sample data. Although the specificmethods used toderivehabitat maps vary considerably, the review indicates that studies can generally be categorized into one ofthree over-arching strategies; 1) Abiotic surrogate mapping; 2) Assemble first, predict later (unsupervisedclassification); 3) Predict first, assemble later (supervised classification). Whilst there is still no widelyaccepted agreement on the best way to produce benthic habitat maps, all three strategies provide valuablemap resources to supportmanagement objectives.Whilst there is still considerablework to be done beforewe can answer many of the outstanding technological, methodological, ecological and theoretical ques-tions that have been raised here, the review concludes that the advent of spatial ecological studies foundedon high-resolution environmental data sets will undoubtedly help us to examine patterns in communityand species distributions. This is a vital first step in unraveling ecological complexities and thus providingimproved spatial information for management of marine systems.

Crown Copyright � 2011 Published by Elsevier Ltd. All rights reserved.

1. Introduction

The marine environment is coming under increasing pressuresfrom human activities. Fishing, mining, pollution and other humanactivities cause serious damage to seabed ecosystems and reducebenthic biodiversity. Without immediate action to mitigate theseimpacts, it is predicted that by the middle of the 21st centurycommercial fish and seafood stocks will collapse beyond the pointof recovery (Worm et al., 2006). Furthermore, it is estimated that noarea of the oceans at a global scale is unaffected by human influ-ence, and that a large fraction (41%) is strongly affected by multipleanthropogenic impacts (Halpern et al., 2008). Our knowledge of theextent, geographical range and ecological functioning of benthic

anada, Bedford Institute of, Canada.rown).

011 Published by Elsevier Ltd. All

habitats is still extremely poor due to limitations posed byconventional seabed survey methods, and it is estimated that only5e10% of the seafloor is mappedwith a resolution of similar studieson land (Wright and Heyman, 2008). Consequently, it is difficult tomanage resources effectively, protect ecologically important areasand set legislation to safeguard the oceans. In order to address thismanagement requirement, there is an urgent need to developrobust methods for mapping marine ecosystems to establish theirgeographical location, extent, and condition.

The process of producing maps of the seafloor dates back toearly navigational charting efforts of the thirteenth century, whenseafaring merchants produced pilot charts for the Mediterranean(Blake, 2004). From that time, we have continued charting oceanfloor bathymetry, and efforts continue to this day, especially inshallow coastal regions where changing bathymetry can bea hazard to shipping. Geological and biological investigations of theseafloor began in earnest in the nineteenth century, with early

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C.J. Brown et al. / Estuarine, Coastal and Shelf Science 92 (2011) 502e520 503

efforts relying on samples snatched from the seafloor using prim-itive dredges (Elefteriou andMcIntyre, 2005). Since then, evermoresophisticated methods have been used to study the seafloor. Effortshave been made to identify biophysical patterns and processes inorder to create simpler depictions of the seafloor that facilitate ourunderstanding of how these benthic systems function (Diaz et al.,2004). This is an essential first step toward implementing effec-tive management strategies for ocean systems (Cogan et al., 2009),and one that scientists are continually striving to improve.However, in the sublittoral environment, sampling difficultiesimpair our ability to unravel these complexities due to limitationscaused by conventional, in situ sampling techniques (e.g. grabs,dredges, corers, video, photos and trawls - see ICES, 2007; Van Reinet al., 2009). These methods provide detailed information on thesmall area of seafloor that they sample, but it is often very difficultto derive an accurate representation of the broader spatial config-uration of the seafloor biophysical characteristics within an areawithout extensive and prohibitively expensive survey designsinvolving tightly spaced sampling stations or transects.

The development of aerial and satellite remote sensing tech-niques over the past few decades has increased accessibility andaffordability of optical remote-sensed data for broad-scale ecolog-ical studies, which in turn has dramatically improved our under-standing of the spatial patterns and complexities of the terrestrialrealm (e.g. O’Neill et al., 1999; Dahdouh-Guebas, 2002; Franklin,2009). Application of these techniques in marine benthic systemsis restricted to shallow, coastal waters due to the limited penetra-tion of light through seawater, leaving most of the seabed envi-ronment beyond the scope of these methods. It is only recently,through developments in acoustic survey technologies, that marinescientists have been able to begin matching the quality and reso-lution of terrestrial mapping efforts in the marine realm. Devel-opments in acoustic survey techniques such as single-beamacoustic ground discrimination systems (SB-AGDS), sidescan sonarsystems (SSS), and more recently multi-beam echo sounders(MBES), are providing tools for wide-scale reconnaissance stylesurveying (Hughes Clarke et al., 1996; Lurton, 2002; Mayer, 2006;ICES, 2007; Anderson et al., 2008). Using these techniques, it isnow possible to produce accurate, aerial-like images of the seafloor,and by combining this approach with conventional in situ samplingto characterize the geological and biological seafloor characteris-tics, it is possible to produce thematic seafloor maps for manage-ment applications. Over the past decade, we have witnessed thelarge expansion of high-resolution seafloor mapping as these toolsbecomemorewidely available and affordable, coupled with equallylarge improvements in computing power and Geographic Infor-mation System (GIS) (Mayer, 2006) (see Table 1).

The process of producing seafloor habitat maps cuts across thedisciplines of marine biology, ecology, geology, hydrography, ocean-ographyandgeophysics, and involves the combiningof disparatedatasets from these disciplines to produce simplified spatial representa-tions of the seafloor relating to the distribution of biological charac-teristics. The overall objective of this review is to provide a synopsis ofthe diverse array of methods used to undertake this challenge. Thevariety of specific techniques reported in the literature makes itunfeasible to critically assess each approach in detail. Instead, wedescribe three broad mapping strategies which adequately encom-pass thediverse rangeofmethodsused and intowhicheachstudycanbe placed. The common problems identified across these studies inconnection with how benthic habitat maps are produced are dis-cussed, including issues of scale, resolution (grain), accuracy, andhabitat classification. To keep the review focused, only habitatmapping approaches in the sublittoral zone are covered, concen-trating on the application of acoustic survey methods. However, itshould be noted that in the shallow sublittoral (i.e. <20 m water

depth) where water clarity permits, and in the intertidal zone, aerialand satellite techniques can be used for the same type of application.These methods have been reviewed elsewhere (e.g. Dekker et al.,2007; Godet et al., 2009) and will not be covered in this review.

2. Adding a spatial context to the study of benthic habitats

2.1. Habitat mapping - what is it and how is it done?

Marine habitat mapping has recently been defined as “Plottingthe distribution and extent of habitats to create a map with completecoverageof the seabed showingdistinct boundaries separatingadjacenthabitats” (MESH, 2008a). In this context, the term “habitat” isdefined as “.both the physical and environmental conditions thatsupport a particular biological community together with commu-nity itself” (MESH, 2008a) and the term is therefore used synony-mously with “biotope” (a point we will revisit and discuss underSection 3). This definition makes the assumption that in order torepresent biological patterns spatially we must impose distinctboundaries between adjacent (and therefore discrete) habitat types.This definition has likely materialized from the modus operandi ofseafloor map production within the field of marine geology. Forseveral decades, marine geologists have produced seafloor mapsbased on acoustic survey data where the seafloor is divided into“spatial units” with distinct boundaries representing discrete sedi-ment or bed form characteristics. Many of the earlier habitat mapswere extensions of this geological approach, based on segmentedacoustic data sets and incorporating biological information per-taining to habitat from in situ sampling (e.g. McRea Jr et al., 1999;Kostylev et al., 2001; Brown et al., 2002). Indeed, this approach hasremained popular, andmany studies continue to use thismethod forthe production of benthic habitat maps (see Table 1).

Whilst discontinuous boundaries between seafloor features doexist (e.g. the interface between a rocky reef and surrounding softsediment),weoften see gradational shifts in seafloor characteristics,particularly when we explore benthic community patterns (seeSection 3). Seafloor habitat mapping studies have therefore startedto consider ways to present habitat characteristics in a gradationalmanner, avoiding imposing discrete boundaries and therefore likelyproviding a more realistic representation of how seabed commu-nities are structured.Methods fromterrestrial habitatmappinghavestarted to be tested for marine systems, for example; gradationalhabitat maps produced using species distribution models (SDM)methods showing the spatial prediction of potential habitat wherea particular focal species is likely to occur on the basis of environ-mental conditions (e.g. Galparsoro et al., 2009); gradational mapspredicting community distributions (i.e. biotopes) from modelledenvironmental parameters (e.g. Degraer et al., 2008); and mapsrepresenting purely abiotic environmental patterns in gradationalform fromwhich biological trends can be inferred (e.g. Lucieer andLucieer, 2009). These approaches are discussed in detail later inthe review, but clearly theydonot fall under the definition ofmarinehabitat mapping stated above because they do not display distinctboundaries between adjacent habitat types. Therefore, for thepurpose of this review and to ensure we encompass the diversity ofways in which habitat information can be spatially presented ona map, we offer an alternative definition of “marine habitatmapping”:

“The use of spatially continuous environmental data sets torepresent and predict biological patterns on the seafloor (ina continuous or discontinuous manner)”.

Habitat mapping efforts reported in the scientific literature haveemployed very similar methodological strategies overall, eventhough the way that the habitat information is displayed on a map

Table 1Summary of methods used in benthic habitat mapping studies utilizing acoustic survey techniques from the past decade.

Approach Mappingstrategy(see Fig. 4)

Acoustics 1o data 2o data Segmen-tation

Oceanographic Geographiccoverage

Ground-truthing

Abiotic

‘surrog

ate’

Singlesp

ecies

Com

munity

Strategy

1

Strategy

2

Strategy

3

SSS

SBES

MBES

Other

(incl.n

on-aco

ustic)

Bathym

etry

Backscatter

Other

Slop

e

Asp

ect

Curvature

(incl.B

PI)

Rou

ghness

Other

Subjective

Objective

Temperature

Salin

ity

Curren

ts

Other

Fine(

<1k

m)

Med

ium

(1-10k

m)

Broad

(>10

km)

Geo

logical

Infauna

Epifau

na/flora

Fish

Other

(Allen et al., 2005) x x x x x x x x x(Anderson et al., 2002) x x x x x x x x x x x(Anderson et al., 2007) x x x x x x x x x(Anderson et al., 2009) x x x x x x x x x(Auster et al., 2001) x x x x x x x x x(Barrie et al., 2011) x x x x x x x x x x x x x(Beaman and Harris, 2007) x x x x x x x x x x x x x(Bekkby et al., 2008) x x x x x x x x x x(Blondel and Gomez Sichi, 2009) x x x x x x x x(Brown and Collier, 2008) x x x x x x x x x(Brown et al., 2002) x x x x x x x x x(Brown et al., 2004a) x x x x x x x x x(Brown et al., 2004b) x x x x x x x x x(Brown et al., 2005) x x x x x x x x x x x(Brown et al., 2011) x x x x x x x(Bryan and Metaxas, 2007) x x x x x x x x x x(Buhl-Mortensen et al., 2009) x x x x x x x x x x x x x x x(Butler et al., 2006) x x x x x x x x x x x x(Callaway et al., 2009) x x x x x x x x x x x x(Carey et al., 2003) x x x x x x x(Christensen et al., 2007) x x x x x x x x x x(Christensen et al., 2009) x x x x x x x x x x x x x(Clements et al., 2010) x x x x x x x x x x(Cochrane and Lafferty, 2002) x x x x x x x(Coggan and Diesing, 2011) x x x x x x x x x x x x(Collier and Brown, 2005) x x x x x x x x(Collier and Humber, 2007) x x x x x x x x x x x x(Conway et al., 2007) x x x x x x x x x x x x x(Cook et al., 2008) x x x x x x x x x x x x(Costa et al., 2009) x x x x x x x x x x(Cutter Jr et al., 2003) x x x x x x x x x(Davies et al., 2008) x x x x x x x x x x x(De Falco et al., 2010) x x x x x x x x x x(Degraer et al., 2008) x x x x x x x x x(Diesing et al., 2009) x x x x x x x x x(Dolan et al., 2008) x x x x x x x x x x x x x(Dolan et al., 2009) x x x x x x x x x x x x x x(Downie et al., 1999) x x x x x x x x x x x(Durand et al., 2006) x x x x x x x x(Eastwood et al., 2006) x x x x x x x x x x(Edwards et al., 2003) x x x x x x x x x(Ehrhold et al., 2006) x x x x x x x x x x(Ellingsen et al., 2002) x x x x x x x x(Erdey-Heydorn, 2008) x x x x x x x x x x x x x

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(Eyre and Maher, 2011) x x x x x x x x x x x(Fonseca et al., 2009) x x x x x x x x x x(Foster et al., 2009) x x x x x x x x x(Foster-Smith et al., 2004) x x x x x x x x x x x x x(Foster-Smith and Sotheran, 2003) x x x x x x x x x x x x(Franklin et al., 2003) x x x x x x x x x x x x(Freeman et al., 2004) x x x x x x x(Freitas et al., 2008) x x x x x x x x(Freitas et al., 2006) x x x x x x x x x(Freitas et al., 2003) x x x x x x x x x(Galparsoro et al., 2009) x x x x x x x x x x x x(Galparsoro et al., 2010) x x x x x x x x x x x x x x(Georgiadis et al., 2009) x x x x x x x x x x x x(Geraldi et al., 2009) x x x x x x x x(Greene et al., 2007a) x x x x x x x x x x x(Greene et al., 2007b) x x x x x x x x x x(Greenstreet et al., 1997) x x x x x x x(Greenstreet et al., 2010) x x x x x x x x x x x x x(Guinan et al., 2009a) x x x x x x x x x x x x x(Guinan et al., 2009b) x x x x x x x x x x x(Halley and Bruce, 2007) x x x x x x x(Halley and Jordan, 2007) x x x x x x x x x(Harris, 2007) x x x x x x x x x x x x x(Hewitt et al., 2004) x x x x x x x x x x x(Holmes et al., 2008) x x x x x x x x x x x x x(Huang et al., 2011) x x x x x x x x(Hühnerbach et al., 2007) x x x x x x x x x(Hutin et al., 2005) x x x x x x x x x(Iampietro et al., 2008) x x x x x x x x x x x(Ierodiaconou et al., 2007) x x x x x x x x x x x x x x x(Ierodiaconou et al., 2011) x x x x x x x x x x x x x x(Jordan et al., 2005) x x x x x x x x x x(Kendall et al., 2005) x x x x x x x x x x x(Kennish et al., 2004) x x x x x x x(Kloser et al., 2007) x x x x x x x x x x x x(Komatsu et al., 2003) x x x x x x x(Kostylev et al., 2003) x x x x x x x x x(Kostylev and Hannah, 2007) x x x x x x x x x x x x x x(Kostylev et al., 2001) x x x x x x x x x x x(Kvernevik et al., 2002) x x x x x x x x x(Lindenbaum et al., 2008) x x x x x x x x x x x x x(Lucieer and Pederson, 2008) x x x x x x x x x x(Lucieer, 2007) x x x x x x x(Lucieer, 2008) x x x x x x x x x(Marsh and Brown, 2009) x x x x x x x x x x(McGonigle et al., 2009) x x x x x x x x x x(McGonigle et al., 2010b) x x x x x x x x x(McGonigle et al., 2010a) x x x x x x x x x(McLeod et al., 2007) x x x x x x x x x(McRea Jr et al., 1999) x x x x x x x x x(Monk et al., 2010) x x x x x x x x x x x x x(Moore et al., 2009a) x x x x x x x x(Moore et al., 2009b) x x x x x x x x x x x x x(Mortensen et al., 2001) x x x x x x x x x(Müller et al., 2007) x x x x x x x x(Munoz et al., 2009) x x x x x x x x x(Nicholas et al., 1999) x x x x x x x x x x x(Nitsche et al., 2004) x x x x x x x x x x(Nitsche et al., 2007) x x x x x x x x x x

(continued on next page)

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xx

x

x

x

Table 1 (continued)

Approach Mappingstrategy(see Fig. 4)

Acoustics 1o data 2o data Segmen-tation

Oceanographic Geographiccoverage

Ground-truthing

Abiotic

‘surrog

ate’

Singlesp

ecies

Com

munity

Strategy

1

Strategy

2

Strategy

3

SSS

SBES

MBES

Other

(incl.n

on-aco

ustic)

Bathym

etry

Backscatter

Other

Slop

e

Asp

ect

Curvature

(incl.B

PI)

Rou

ghness

Other

Subjective

Objective

Temperature

Salin

ity

Curren

ts

Other

Fine(

<1k

m)

Med

ium

(1-10k

m)

Broad

(>10

km)

Geo

logical

Infauna

Epifau

na/flora

Fish

Other

(O’Connell et al., 2007) x x x x x x x x x(Ojeda et al., 2004) x x x x x x x x(O’Reilly et al., 2003) x x x x x x x x x(Orlowski, 2007) x x x x x x x x(Phillips et al., 1990) x x x x x x x x x x(Pinn and Robertson, 2003) x x x x x x x x x x(Pinn et al., 1998) x x x x x x x x x x(Pittman et al., 2007) x x x x x x x x x x(Preston, 2009) x x x x x x x x x(Rattray et al., 2009) x x x x x x x x x x x x x x(Reed et al., 2006) x x x x x x x x x x x x(Riegl and Purkis, 2005) x x x x x x x x(Roberts et al., 2009) x x x x x x x x x x x x(Roberts et al., 2008) x x x x x x x x x(Roberts et al., 2005) x x x x x x x x x x x(Roberts et al., 2003) x x x x x x x x x x x x(Robinson et al., 2011) x x x x x x x x x x x x x x(Roff et al., 2003) x x x x x x x x x x x(Romsos et al., 2007) x x x x x x x x x x x x(Rooper and Zimmermann, 2007) x x x x x x x x x x x x(Ryan et al., 2007) x x x x x x x x x(Schimel et al., 2010) x x x x x x x x x x x(Shotwell et al., 2007) x x x x x x x x x(Shumchenia and King, 2010) x x x x x x x x x x x(Simons and Snellen, 2009) x x x x x x x(Smith et al., 2001) x x x x x x x x x x x x(Smith and Greenhawk, 1998) x x x x x x x x x x x x(Smith et al., 2009) x x x x x x x x x(Sotheran et al., 1997) x x x x x x x x x x(Sutherland et al., 2007) x x x x x x x x x x(Todd and Kostylev, 2011) x x x x x x x x x x x x x x x x x x x(Tremblay et al., 2009) x x x x x x x x x(van Overmeeren et al., 2009) x x x x x x x(Verfaillie et al., 2009) x x x x x x x x x x x x x x(Walker et al., 2008) x x x x x x x x x x(Watt and Greene, 2007) x x x x x x x x(Wheeler et al., 2008) x x x x x x x x(White et al., 2003) x x x x x x x x x(Whitmire et al., 2007) x x x x x x x x x x x x x x(Wildish et al., 2008) x x x x x x x x x x(Wilson et al., 2007) x x x x x x x x x x x x x(Zajac et al., 2000) x x x x x x x x(Zajac et al., 2003) x x x x x x x x(Zieger et al., 2009) x x x x x x x x x x x x x

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differs between studies (i.e. gradational versus discrete entities). Inthemajority of cases,maps are produced by interpreting continuouscoverage, environmental data layers, often derived from remotesensing data, and using biological information about habitatsobtained from in situ sampling and observation of the seabed (aprocess commonly referred to as ground-truthing) (Fig. 1). Theground-truthingprocess only samples avery small proportion of theseafloor, and the complete coverage of habitats is therefore inferredfrom the association between the remotely sensed environmentaldata and the in situ sample data. In this way, the final map isapredictionof thedistribution of seabedhabitats,with the completecoverage environmental data acting as a proxy for the habitat data(MESH, 2008a) (Fig. 1). Therefore, for the production of any form ofbenthic habitat map, we need continuous coverage environmentaldata layers and in situ (habitat) sample data of some form.

2.2. Environmental data layers

Benthic biology is influenced by both seafloor geological andmorphological characteristics and the overlying water columnattributes; the environmental data layers used for the production ofseafloor habitat maps focus primarily on providing spatial infor-mation on these parameters. A summary of the types of environ-mental data sets are illustrated in Fig. 2, and these data can becollected using a variety of methods. Acoustic survey methods arenow routinely used to collect information on the geomorphologicalcharacteristics of the seafloor (Pickrill and Todd, 2003). There areseveral types of acoustic systems in common use, and each methodcomes with its own strengths and weaknesses regarding applica-tions to benthic habitat studies. Technical information regardingthese surveymethods have been reviewed adequately and at lengthelsewhere (see: Lurton, 2002; Kenny et al., 2003; Elefteriou andMcIntyre, 2005; ICES, 2007; Sherman and Butler, 2007; Andersonet al., 2008; MESH, 2008b; Blondel, 2009; Le Bas and Huvenne,2009; Pandian et al., 2009; Van Rein et al., 2009), and this reviewassumes a prior knowledge of some of the survey techniques, whilstfocusingon three basic types of acoustic surveymethodswhichhavebeen most widely used for habitat mapping: 1) Single-beam,Acoustic Ground Discrimination Systems (SB-AGDS); 2) Sidescansonar systems (SSS); 3) Multibeam echosounders (MBES). Theseacoustic survey methods record information relating to seafloorbathymetry, acoustic backscatter strength, or a combination of thesetwo features, and from these primary data sets a number ofsecondary data layers (e.g. Bathymetry: slope, aspect, terrain vari-ability, etc; Backscatter: hardness, roughness, acoustic class, etc.)can be generated that have been used for the production of seafloorhabitat maps (Fig. 2). The resolution at which these systems acquiredata is dependant on the specification of the systemandmayalso beaffected by water depth, but generally speaking they can be used toresolve horizontal features in the order of tens of centimetres to tensof metres in dimension (Kenny et al., 2003; Diaz et al., 2004; ICES,2007; Anderson et al., 2008).

Oceanographic parameters have an equally important influenceon seafloor biological characteristics. Spatial oceanographic datatends to be derived from a variety of sources. For example,measured continuous coverage data may be available from satellitedata (e.g. inferred chlorophyll content from optical satellite signa-tures which can be used as an indicator of productivity); data maybe interpolated from point sample oceanographic measurements(e.g. temperature, salinity, oxygen content); or, continuouscoverage data may be modelled (e.g. bottom currents) (Fig. 2).Unlike the acoustic data sets described above, the horizontal spatialresolution of oceanographic data tends to be coarser, in the regionof tens of metres to tens of kilometres (Kenny et al., 2003). This hasa profound influence on the scale of the habitat features that can be

resolved using these data, and habitat mapping studies incorpo-rating, or based primarily on oceanographic parameters tend to beconducted at much broader-scales (Vincent et al., 2004; Kostylevand Hannah, 2007; Degraer et al., 2008; Verfaillie et al., 2009).

2.3. Utilization of environmental data sets: high-resolution acousticdata

An important first step in the production of a benthic habitat mapis to organize the environmental data into a suitable format forintegration with the in situ habitat information. Although habitatpatterns on the seafloor can be presented as continuous or discreteentities (as discussed above), in the majority of cases high-resolutionacoustic data sets tend to be divided into “spatial units” prior to theincorporation and integration of habitat information. For the purposeof this review we will therefore refer to this process as “segmenta-tion”, although it should be noted that the process has been referredto using other terms (e.g. acoustic seabed classification (ASC) -Anderson et al., 2008; delineation - Brown and Blondel, 2009).

2.3.1. Backscatter analysis approachesAcoustic backscatter data are by far the most widely used form

of remote-sensed data for habitat characterization and mapping,and the majority of studies reported in the scientific literatureutilize this type of data in some way (Table 1 and Fig. 2). Thesimplest acoustic survey systems which measure the backscattercomponent of the returning seafloor echo are SB-AGDS. SB-AGDSare vertical-incidence systems using a single-beam transducer andtypically range in frequency between 30 and 200 kHz (Lurton,2002; Brown, 2007). There are a number of proprietary systemswhich have been used for habitat studies (e.g. RoxAnn, QTC-View,EchoPlus), all of which record depth and some measure of thebackscatter signal relating to the properties of the seafloor (thedetails of which depend on the system - see Brown, 2007; ICES,2007). The data from SB-AGDS are usually divided into acousticclasses using signal-based segmentation of the returning echo-grams (Fig. 2: e.g. segmentation based on the strength of the firstand second seabed echo returns in the case of RoxAnn; or based onthe characteristic features of the first echo return in the case of QTC-View). For habitat mapping applications, the acoustic classes arethen linked to specific seafloor habitat characteristics from ground-truthing data sets (see Table 1 for a comprehensive list of habitatmapping studies using SB-AGDS).

The backscatter from these vertical-incidence systems is mucheasier to segment compared to the backscatter from SSS and MBES.For swath systems (SSS and MBES), interaction of the off-axisacoustic signal with the seafloor is very complex, making segmen-tation more challenging. This offers an advantage to using SB-AGDSover the more costly and complex swath systems. However, onedisadvantage is that to produce continuous coverage data layers ofthe segmented acoustic classes from SB-AGDS it is necessary tointerpolate the acoustic data between survey lines, even if surveylines are tightly spaced. Unless the seafloor is very homogeneous(something which is often unknown before a survey is conducted),interpolation has the potential to miss discrete features in the non-surveyed areas. In addition, as water depth increases so does theacoustic footprint, which can often mean that for single-beamsystems, where the beam angle tends to be relatively large (e.g.15�e25� for SBES compared to 0.5�e3� for MBES), resolvability ofseafloor features and attributes may be limited (Foster-Smith andSotheran, 2003; Brown et al., 2005; Brown, 2007). In many cases,single-beam systems have largely been superceded by swathacoustic systems (SSS andMBES) for seabedhabitatmapping studies(Table 1: see Riegl and Purkis, 2005; Orlowski, 2007; Freitas et al.,

Fig. 1. Generalized approach for the production of benthic habitat maps (modified from MESH, 2008a). Example data from Stanton Banks, UK (McGonigle et al., 2009).

Fig. 2. Spatial data sets used for habitat segmentation. Primary data (bathymetry and backscatter), and secondary layers (white boxes). Oceanographic data can also be used, but ismore difficult to measure at a spatial scale required for effective habitat delineation. Modified from Wilson et al. (2007).

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2008; Lindenbaum et al., 2008; Walker et al., 2008; Greenstreetet al., 2010; Quintino et al., 2010, for continued usage of SB-AGDS).

The acoustic backscatter signal from SSS has been used bygeologists for many years to segment the seafloor into geologicalclasses (i.e. surficial sediment types), and a close association isreported between acoustic backscatter strength and geotechnicalproperties of the seafloor (Collier and Brown, 2005; Ferrini andFlood, 2006; Fonseca and Mayer, 2007; Brown and Collier, 2008).Sidescan sonar systems were developed in the 1940s (Kenny et al.,2003), and operate at relatively high frequencies (100e500 kHz),recording acoustic backscatter data from a swath of seafloor inorder to produce a textural image pertaining to the surficial seabedcharacteristics (for a detailed technical account of how they oper-ate, see: Lurton, 2002; Kenny et al., 2003; ICES, 2007; Le Bas andHuvenne, 2009). By designing acoustic surveys with parallel tracklines and with line spacing based on the swath dimensions of theSSS, it is relatively easy to collect continuous coverage data from anarea of interest. This overcomes the issues of inaccuracy caused byinterpolation raised above in connection with SB-AGDS. Using themosaicked imagery, segmentation of the backscatter data hasconventionally been done by expert interpretation, whereby theimagery is divided into regions of similar texture or backscatterstrength “by eye” (Fig. 2). The acoustic segments (some timesreferred to as “acoustic facies” - Olenin and Ducrotoy, 2006) arethen linked to geological attributes from ground-truthing samples(Todd et al., 1999; Blondel, 2009). Segmentation by expert inter-pretation has also been employed for mapping discrete biologicalcharacteristics from a range of environments (see Table 1 for a fulllist of references, with examples including: coastal waters - Allenet al., 2005; Brown et al., 2002; continental shelf waters -Conway et al., 2007; Cook et al., 2008; deep-water - Greene et al.,2007b; and estuaries - Nitsche et al., 2004; Nitsche et al., 2007).

More recently, automated methods of segmenting SSS back-scatter data have been explored, driven largely by the advantages ofusing objective classification algorithms applied to the backscatterdata, thus eliminating the subjectivity of the expert segmentationprocess (Ehrhold et al., 2006; Brown and Collier, 2008; Lucieer,2008; van Overmeeren et al., 2009). Automated segmentationmethods can be broadly divided into two types; 1) image-basedsegmentation based on the division of a backscatter image intoregions of similar backscatter characteristics (e.g. surface features,backscatter intensity, textural features etc.); 2) Signal-basedsegmentation where changes in the backscatter intensity withincreasing grazing angle fromnadir are analyzed to classify the datain some way (Fig. 2). Several image-based segmentationapproaches have been utilized for SSS data in the context of benthichabitat studies, for example; segmentation methods using texturalanalysis based on grey level co-occurrence matrices (Cochrane andLafferty, 2002; Huvenne et al., 2002; Hühnerbach et al., 2007);unsupervised classification techniques using combinations ofbackscatter metrics (Allen et al., 2005; Lucieer, 2007; Brown andCollier, 2008); supervised classification techniques linking habitatcharacteristics from ground-truth stations to distinctive acousticsignatures (Allen et al., 2005); and segmentation methods usingprincipal components analysis of multiple backscatter image-basedattributes within proprietary software packages (QTC-Sideview)(Yeung and McConnaughey, 2008). There are also examples ofstudies where the focal habitats display distinctive backscattersignatures which are particularly suited to segmentation usingautomated image-based routines (e.g. shellfish beds - Allen et al.,2005; van Overmeeren et al., 2009).

In contrast, there are fewer examples of signal-based segmen-tation methods for SSS. Signal-based segmentation methods werevalidated initially for single-beam echosounders (e.g. van Walreeet al., 2005), but have recently been applied to swath acoustic

data sets (Brown and Blondel, 2009). However, signal-basedsegmentation approaches are difficult to conduct on SSS data due tothe fact that for most SSS (with the exception of interferometricsystems), seafloor bathymetry is not measured. The absence ofgeometrical data needed to determine angular dependencies of thesignal make signal-based segmentation for SSS difficult (Lurton,2002). However, signal-based segmentation methods have gainedmomentum for MBES data, and are discussed below.

MBES are fast becoming the survey tool of choice in the generalfield of seafloor habitat mapping, in part due to their ability tosimultaneously collect seafloor bathymetry and backscatter infor-mation over a swath of seafloor (Pickrill and Todd, 2003; Mayer,2006). In a comparable way to SSS, continuous coverage data ofthe seafloor can be collected by running parallel survey tracks atappropriate line spacing for the system. MBES tend to be fairlycomplex systems requiring sophisticated motion reference units inorder to rectifyvessel pitch, roll andheavewhenpositioning thedatarelative to the seafloor (Lurton, 2002; ICES, 2007; Le Bas andHuvenne, 2009; van Overmeeren et al., 2009). There are a widevariety of different systems on the market, each designed forparticular applications and water depths (for a detailed account ofhow they operate, see: Lurton, 2002; Kenny et al., 2003; ICES, 2007;Le Bas and Huvenne, 2009; Pandian et al., 2009). Until recently, theuse of MBES for habitat mapping was fairly restricted due to therelative high cost of data acquisition, and technical difficultiesassociated with data storage and processing (Kenny et al., 2003; LeBas and Huvenne, 2009). With increased computing power,cheaper data storagemedia, andwider availability of systems,MBEShas gained in popularity as a survey tool, with the derived seabedimagery (bathymetryandbackscatter) providing the template for anincreasing number of habitat mapping studies (Table 1).

MBES backscatter imagery is roughly similar to SSS backscatterimagery. However, the backscatter data from an MBES was, untilrecently, of an inferior quality compared to the imagery from anequivalent sidescan system. This was mainly due to the loweralong-track resolution of MBES systems (1e3�) compared to side-scan systems (less than 1�), and the optimal range of incidenceangles for backscatter measurement achieved by a towed sidescansonar system (which have lower grazing angles) compared toa hull-mounted MBES (Brown and Blondel, 2009). However, recenton-going developments in data collection and processing of MBES,combined with the availability of co-registered bathymetry, havedrastically improved the quality of the imagery, giving as much ormore information than is available with sidescan sonar alone (LeBas and Huvenne, 2009).

In the context of seafloor habitat mapping, segmentationapproaches for MBES backscatter data are broadly analogous tosegmentationmethods for SSS outlined above and in Fig. 2. Many ofthe earlier studies adopted conventional “expert” interpretation ofMBES backscatter data to segment the mosaics into regions ofsimilar backscatter characteristics (e.g. Kostylev et al., 2001;Kostylev et al., 2003; Pickrill and Todd, 2003; Roberts et al.,2005). More recently, various image-based segmentationmethods have been tested, including but not limited to: neural-network techniques (Ojeda et al., 2004; Marsh and Brown, 2009);segmentation methods using principal components analysis ofmultiple backscatter image-based attributes within proprietarysoftware packages (QTC-Multiview) (McGonigle et al., 2009, 2010a,2010b; Preston, 2009; Brown et al., 2011; McGonigle et al., 2011);Bayesian decision rules (Simons and Snellen, 2009); textural anal-ysis based on grey level co-occurrence matrices (Blondel andGomez Sichi, 2009); and creation of synthetic colour imagesderived by applying high and low pass filters to the backscatterimage (HIS) (Ierodiaconou et al., 2007; Rattray et al., 2009). Whilsta variety of image-based segmentation methods have been tested,

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no widely accepted agreement on the best way to segment the datahas yet been reached.

Signal-based methods (Fig. 2) have also been used to extractquantitative information from the returning MBES backscattersignal (Fonseca and Mayer, 2007; Fonseca et al., 2009; Lamarcheet al., 2011). The variation of the backscatter strength with theangle of incidence is an intrinsic property of the seafloor, which canbe used as a robust method for acoustic seafloor characterization.Although multi-beam sonars acquire backscatter over a wide rangeof incidence angles, the angular information is lost during standardbackscatter processing and mosaicking. Signal-based classificationworks by extracting several parameters from stacks of consecutivesonar pings. The average angular response is then compared toformal mathematical models that link acoustic backscatter obser-vations to seafloor properties. The inversion of the model can beused to produce estimates of various seafloor geotechnical prop-erties, which can be used to predict the substratum properties ofthe seabed (Fonseca and Mayer, 2007; Fonseca et al., 2009;Lamarche et al., 2011). This general approach shows a great dealof promise for seafloor habitat mapping applications (Fonseca et al.,2009; Lamarche et al., 2011), although is yet to be rigorously testedover a range of benthic marine ecosystems.

The vitality of research into both SSS and MBES objective back-scatter processing methods is evident from the examples outlinedabove, representative of a much wider set of publication in thisgeneral area (Table 1). Indeed, this topic of research has been thefocus of a recent special issue exploring the application of MBESbackscatter data for habitatmapping (see issue of Applied Acoustics -Brown and Blondel, 2009, and references therein).

2.3.2. Bathymetric analysis approachesBenthic species show preferences for certain depths and topo-

graphic conditions, and therefore bathymetry can be used tosegment an area into regions which reflect distinctive biologicalcharacteristics (Fig. 2). This type of segmentation is often referred toas “morphometric analysis”, and has been used to map benthichabitats froma rangeofmarine systems including: estuarine (CutterJr et al., 2003), coastal (Holmes et al., 2008; Iampietro et al., 2008;Diesing et al., 2009; Rattray et al., 2009; Verfaillie et al., 2009;Zieger et al., 2009), and deeper water environments (Wilson et al.,2007; Buhl-Mortensen et al., 2009; Guinan et al., 2009b). Withinthese studies, a number of secondary-derived bathymetric layershave been used to help segment the seafloor into biologically-relevant units (e.g. slope, orientation, curvature and terrainvariability - Fig. 2 and Wilson et al., 2007). A number of thesebathymetric parameters (e.g. terrain variability) can be calculated indifferent ways and at different spatial scales, which have importantimplications for what the map layers show and how they relate tothe distribution of the benthic organisms (Wright and Heyman,2008; Dunn and Halpin, 2009). As GIS tools are developed for thistype of analysis we are starting to witness an exploration of thedifferent methods to relate bathymetric data for benthic habitatcharacterization. Recent studies have also started to exploreways toobjectively combine both backscatter and bathymetry segmenteddata layers for habitat mapping, with some promising outcomes(e.g. Rattray et al., 2009).

2.4. Utilization of environmental data sets: oceanographic data

Benthic ecosystems are not only influenced by the physical char-acteristics of the seafloor, but are also strongly affected by the over-lyingwater column conditions. This “third dimension” of the benthicecosystem is important for the supply of food, nutrients, gametes andnew recruits for sessile benthic organisms, and it is therefore possibleto usepatterns in theoverlyingwater column conditions asproxies to

predict the likely distribution of biological characteristics. Fig. 2summarizes some of the important oceanographic parameterswhich have been used in this way. As noted above, the resolution ofoceanographic parameters is considerably coarser when comparedwith the seafloor parametersmeasurable using acousticsmethods, inthe region of tens of metres to tens of kilometres (Kenny et al., 2003;Diazet al., 2004).Over these scales, patterns and trendswithineachofthese oceanographic variables tend to be more gradual. However,there is likely much finer-scale variability in these variables (i.e. localchanges in bottom currents caused by fine-scale geomorphologicalfeatures) that are not detected using current survey methods andwhichmay have a profound affect on the biology of the seafloor. Thisis also complicated by the high temporal variability of oceanographicparameters (compared to the scale at which benthic characteristicsmeasured using acoustic survey methods change). Nonetheless, it isstill possible to identify oceanographic patterns for the purpose ofbroad-scale habitat delineation, exemplified by the studies outlinedbelow. Often, the use of broad-scale oceanographic (and seafloor)data in this way is referred to as a “marine-landscape” or “seascape”approach.

Roff and Taylor (2000) and Roff et al. (2003) list and describe theoceanographic variables they considered useful for the productionof “seascape” maps at a continental shelf scale within Canadianwaters (e.g. ice cover, temperature, salinity, light, water masses,stratification patterns, nutrients, tidal amplitudes and exposure).They used a hierarchical classification approach within a GIS tosegment the seafloor environment based on recognizable patternsin the various data layers (also bringing inbroad seafloor parameterssuch as depth and sediment grain size from low-resolution datasources). Similar “seascape” segmentation approaches using broad-scale oceanographic parameters (and using unsupervised classifi-cation methods) are presented in Australian (Harris, 2007; Harriset al., 2008), United Kingdom (Vincent et al., 2004; Robinson et al.,2011) and Belgian waters (Verfaillie et al., 2009). More sophisti-cated segmentationmethods based on the same type of broad-scaleoceanographic and seafloor data layers, but presenting the results ina gradational manner, have also been tested with promising results(e.g. fuzzy classification methods - Lucieer and Lucieer, 2009).

Another novel way to utilize broad-scale oceanographic (andseafloor) data is presented by Kostylev and Hannah (2007). Theyadopt a habitat template approach based on the earlier theoreticalwork of Southwood (1977,1988). This uses arguments based aroundevolutionaryadaptation of species to environmental conditions, anduses environmental data layers as surrogates to predict the likelylife-history traits of “successful” or “persistent” organisms withina given environmental domain (i.e. whether or not they can respondand recover fromdisturbance). The environmental layers are used asproxies for natural disturbance and as indicators of potential scopefor recovery. In thisway theycan be used as predictors of the general“types” of habitat characteristics likely to be found within a broadgeographic region (i.e. habitats dominated by K or r strategists). Theresulting maps present the information in a gradational way alongtwo axes (disturbance vs scope for growth), and the resulting clas-sification can be used to identify the likely sensitivity of an area todisturbance (Kostylev and Hannah, 2007).

The studies outlined above which have utilized oceanographicdata sets, all have a limit to the scale that they can resolve seafloorcharacteristics based on the maximum resolution of the data(usually tens of metres to tens of kilometres. See summary tables in- Kenny et al., 2003; Diaz et al., 2004; ICES, 2007). There is thereforea disparity in the scale of features that can be resolved betweenhigh-resolution acoustic data sets described under section 2.3, andthe broader oceanographic data sets described here. This posesa significant challenge when combining acoustic and oceano-graphic data in producing habitat maps.

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2.5. Ground-truthing (i.e. adding the benthic biologicalinformation)

With a few exceptions (i.e. reef forming organisms such ascorals, or densely aggregating species such as shellfish), most bio-logical attributes on the seafloor are not directly measurable usingremote survey techniques. For the production of a benthic habitatmap, it is therefore always necessary to measure the biologicalcharacteristics of the seafloor using in situ ground-truthingmethods, which can then be linked in some way to the environ-mental data layers. The choice of in situ sampling technique willdepend on a large number of factors (e.g. purpose of the survey,availability of gear, survey platform, cost etc.) but ultimately thiswill have a profound effect on the ability to characterize the biologyof an area in a way that can be linked to the remotely sensed datasets (Solan et al., 2003). It is beyond the scope of this review todescribe the vast variety of in situ sampling tools that have beenused in seafloor habitat mapping studies; the variety of techniquesavailable are documented adequately elsewhere (Elefteriou andMcIntyre, 2005; ICES, 2007; Van Rein et al., 2009). Regardless ofthe specific sampling technique used (e.g. grab sampling, trawls,underwater photographs or video) the multivariate nature of thebiological data is usually reduced in some way to a single site-specific category that represents a summary of the physical andbiological characteristics when describing a habitat.

No matter how the environmental data layers are analyzed, it isstill necessary at some stage to incorporate biological informationin order to create a benthic habitat map. However, before we dealwith this issue (section 4) we need a clear understanding of whatwe are attempting to map. The term “habitat” is often used in anambiguous way, and we need to address this point carefully. Whatexactly do wemean by the term “habitat”, and what is the best wayto label the (segmented) environmental data sets to provide themost useful map(s) for management applications? How do weextract the information needed from the ground-truthing data tobest relate it to the remotely sensed environmental data layers?There must be a clear relationship between the scale of thesegmented environmental data layers and the biological attributesthat are used to label these segmented “units”.

3. Benthic habitat?

3.1. The problem of spatial scale

Benthic species, as with other organisms, have evolved to fitcertain environmental conditions where survival for that specieswill be optimal; in essence, the ecological niche concept developedby Grinnell (1917) and Hutchinson (1957) which essentially definesthe niche of a species based on an n-dimensional hypervolume ofenvironmental conditions (Begon et al., 2005). With increasingdistance away from the optimal conditions, numbers will decreaseuntil environmental parameters become uninhabitable for thatspecies. Overlapping niches of each species therefore definea community. Communities will shift in composition as the n-dimensional hypervolume changes along environmental (and biotic)gradients. This can make patterns in community composition verydifficult to predict (Fig. 3).

The term “habitat” can be defined as a place where an organismis ordinarily found (Begon et al., 2005). The concept of habitatincorporates aspects of both the abiotic and biotic environment, isspecies-specific and is therefore dependant on spatial scale.Consequently, in the context of benthic habitat mapping, the termhas no fixed definition and can be used to describe a range ofdifferent attributes from the same geographical space at differentspatial and temporal scales (e.g. an individual boulder on the

seafloor comprising the habitat for a barnacle; the sand beneath theboulder comprising the habitat for an infaunal polychaete worm;the region of mixed substrata of cobbles and sand forming thefeeding habitat for a demersal fish species, etc.). This can thereforelead to a great deal of confusion relating to the empirical meaningof the term, and many habitat mapping studies reported in thescientific literature fail to clearly define the terminology they use,or the scales over which they operate (see Table 1). It is notsurprising, therefore, that the term “habitat” is often used in anambiguous manner within the scientific literature, with follow-onimplications for how we map benthic “habitat”.

In the terrestrial realm, habitat is often defined and structuredby the dominant vegetation types or by human structures, whichprovide the physical setting and 3-dimensional structure of thehabitat for associated fauna (Zajac, 2008). Optical remote sensingmethods used for terrestrial mapping programs are often able todistinguish and delineate vegetation type at the scale of landscapefeatures, on the basis of spectral signatures combined with otherassociated remotely sensed measures (e.g. elevation, slope, etc.)(Turner et al., 2001). When producing maps of these features, thereis often a close relationship between the spectral signatures fromthe optical airborne or satellite sensors and the dominant vegeta-tion types which define the habitats. In this way, the scale of themap is defined by the plant communities (the focal species) ata scale and resolution (grain) that is linked to the survey method-ology. In essence, the remote environmental data layers aresegmented based on the niche characteristics of the dominant plantspecies which structure the terrestrial environment. In contrast,marine benthic habitats tend to be structured by their two- orthree-dimensional geomorphological characteristics coupled withoverlying hydrographic parameters, whichmakes themmuchmorechallenging to map (Zajac, 2008). The exceptions are biogenicstructures (e.g. coral reefs, sponge reefs, mussel beds) or shallowwater habitats which are dominated by vegetation (e.g. kelp forests,sea grass beds), where the remotely detected environmental datalayers can be used as a direct spatial measure of species/habitatdistributions (e.g. Huvenne et al., 2002; Roberts et al., 2005;Conway et al., 2007; Lo Iocano et al., 2008). Thus, in most casesbenthic biological characteristics have to be inferred frommeasuresof spatial correlation and/or modelled distributions between thespecies and abiotic conditions based on known niche characteris-tics of focal species.

When attempting to map benthic “habitat”, studies can gener-ally be divided into three broad categories, each with differentimplications of how scale affects the final map products, and withassociated difficulties relating to how the maps are produced andtheir usefulness for management.

3.2. Single species habitat mapping

Single species habitat maps are essentially spatial representa-tions of where the environmental conditions match the n-dimen-sional hypervolume of the focal species in essence, mapping therealized niche of the focal species (Guisan and Thuiller, 2005;Franklin, 2009 - Fig. 3). In this way, appropriate segmentation ofthe continuous coverage environmental data sets can be used asa proxy to identify the habitat of the focal species. In theory, this isthe simplest approach to producing a benthic habitat map becausethe scale of the map is defined by the focal species, which makes itconceptually easier to link the life history traits of the species to theabiotic characteristics (i.e. niche characteristics) of the benthicenvironment. In practice, the task is considerably more difficult asinformation relating to specieseenvironment interactions is oftenlacking, especially for marine benthic species (see reviews by:Richmond and Seed, 1991; Wilson, 1991; Snelgrove and Butman,

Fig. 3. The concept of discrete communities versus continua. Each species is evolved to fit a certain ecological niche. The geographical distributions for each species in thecommunity represent the realized niche of each type of organism, taking account of biotic interactions (Guisan and Thuiller, 2005). Offshore, ecosystems are mostly characterized bygradational changes in environmental characteristics, making community patterns difficult to predict.

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1994; Underwood, 2000; Levin et al., 2001). This makes it difficultto select and determine how the environmental data layers shouldbe analyzed and segmented. In addition, not all of the environ-mental data layers will be available at a quality, spatial extent and/or resolution appropriate for prediction of the focal species (seeSection 2.2 above).

This type of habitat mapping approach, usually referred to asspecies distribution modelling (SDM), has been widely andsuccessfully applied in terrestrial applications where good qualityenvironmental data layers are usuallymore readily available than inthe marine realm (for a detailed account of SDM, see: Elith and

Leathwick, 2009; Franklin, 2009). Nonetheless, a number ofbenthic habitatmapping studies have reported successful outcomesadopting a single species approach. In particular, the approachworks well for sessile species such as corals, attached epifaunalorganisms, or slow moving infaunal or epifaunal species, wherethere is likelya close correlationbetween substratumcharacteristicsand the geographical distribution of the organisms. This type ofapproach has been used for mapping both shallow- (Phillips et al.,1990; White et al., 2003), and deep-water (Mortensen et al., 2001;Hovland et al., 2002; Roberts et al., 2005; Hühnerbach et al., 2007;Wilson et al., 2007; Guinan et al., 2009a) reef-forming coral

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species with a great deal of success. Reef-forming coral species tendto have very distinctive bathymetric and backscatter signatures,making it easier to chart their distribution and extent when goodquality acoustic data are available. Similarly, other biogenic reefstructures havebeenmappedusing this type of approach, including;surpulid reefs (Serpula vermicularis) (Moore et al., 2009a), bivalvereefs (Modiolusmodiolus) (Service,1998;Wildish et al., 2008); oysterreefs (Crassostrea virginica) (Smith et al., 2001; Allen et al., 2005;Grizzle et al., 2008); sponge reefs (Aphrocallistes vastus and Hetero-chone calyx) (Conway et al., 2007; Cook et al., 2008); and corallinealgal reefs (Georgiadis et al., 2009).

Other species-centric studies have identified and mappedhabitats for motile benthic species which show a high degree ofhabitat fidelity with limited range traits. A study by Kostylev et al.(2003) reported a highly significant correlation between MBESbackscatter intensity, substratum characteristics and scallopabundance, suggesting that the habitat preference of adult scallop,comprising gravel lag deposits, could be spatially mapped fromMBES backscatter mosaics. Smith et al. (2009) also examined thedistribution of scallops and related abundance to geologicalsubstratum maps with some success. However, in this study therelationship was masked by the effects of fishing.

A more challenging task is to identify and map the habitats ofmore motile and wider ranging benthic species. Unlike the seden-tary or low-ranging benthic species referred to above, demersal fishand some motile megabenthos (e.g. lobsters) utilize seafloorresources in a variety of ways for different life history activities (e.g.feeding, refuge, breeding, nursery grounds etc.). An in-depthunderstanding of the life habits of the focal species are thereforerequired in order to produce appropriate and accurate habitatmaps.The maps may therefore need to reflect temporal or life historyvariations in habitat preferences caused by seasonal or ontogeneticcues. Nonetheless, a number of studies have undertaken this chal-lenge with various degrees of success e.g. American and Europeanlobster (Galparsoro et al., 2009; Tremblay et al., 2009), crab (Yeungand McConnaughey, 2008), prawn (Callaway et al., 2009), andnumerous fish species (Auster et al., 2001; Anderson et al., 2002,2009; Iampietro et al., 2008; Yeung and McConnaughey, 2008).

3.3. Benthic community mapping

Identification of distinct and predictable marine communitytypes in association with characteristic environmental conditionshas led to the development of various habitat classificationschemes over the course of the last century (Costello, 2009). Thisnotion essentially defines the concept of a biotope, which can bedescribed as “a combination of an abiotic habitat and its associatedcommunity of species” (Connor et al., 2004). However, it should benoted that a wide variety of other terms appear in the scientificliterature with essentially the same meaning as biotope (e.g. eco-tope, biocenosis, biogeocenosis etc. - see reviews by: Diaz et al.,2004; Olenin and Ducrotoy, 2006, for a detailed discussion onthis issue). Terminology definitions for many of these terms areprovided by Dauvin et al. (2008a, b), who highlight the confusioncreated through poorly defined terminology, and the use of termsin a relaxed and ambiguous manner.

The concept of a biotope is an effective idiom to conveycommunity information spatially in the form of a benthiccommunity map. Recently, Olenin and Ducrotoy (2006) reopenedthe discussion on the concept of a biotope in marine ecology, andthere are additional discussions on this topic in, Dauvin et al.(2008a, b), Fraschetti et al. (2008) and Costello (2009). The issueis whether or not discrete communities exist versus continua inindividual species’ distributions that lead to perceived assemblagestructures; this has long been debated in a wide range of ecological

systems. In the intertidal region, patterns in benthic assemblagecomposition at various spatial scales are easily recognizable(Ballantine, 1961; Underwood, 2000; Fraschetti et al., 2005), anddistinctive biotopes are comprehensively documented within thisenvironment (Connor et al., 2004). In contrast, community patternsoffshore tend to be less predictable due to the absence of strongabiotic changes caused by tidal fluctuations. Glémarec (1973),based on available studies at the time, concluded that offshorethere are no sharp distinctions between neighbouring communitiesbut rather gradual changes in the composition of the faunawithoutdiscontinuities. Other studies in the scientific literature supportthis view (e.g. Basford et al., 1989; Basford et al., 1990; Van Hoeyet al., 2004) reporting that communities often grade into oneanother along continuous environmental gradients.

Nonetheless, discrete assemblages offshore can be identifiedstatistically comprising characteristic species and environmentalconditions, and there are many examples of biotope mapping usinghigh-resolution acoustic survey methods (e.g. Kostylev et al., 2001;Brown et al., 2002, 2004b; Roberts et al., 2005; Degraer et al., 2008;McGonigle et al., 2009 - and see Table 1). However, in many of thesestudies, boundaries are drawn between neighboring communitiesthat represent the broad patterns where the biotope classesrepresent the nodes of frequency of species clusters. In some cases,assemblage similarities within each class can be very low (<30%similarity in assemblage structure - Brown et al., 2002; Brown andCollier, 2008; McGonigle et al., 2009). Study sites characterized bydiscontinuous patterns in abiotic conditions undoubtedly reflectclearer community patterns (e.g. Brown et al., 2004b) compared toregions characterized by gradational environmental conditions(e.g. Freitas et al., 2006; Brown and Collier, 2008) or where there isa high degree of fine-scale environmental heterogeneity whichmay mask community patterns (e.g. Brown et al., 2004a).

There is a clear desire to develop accepted habitat classificationschemes based around the basic “biotope” concept, evident fromthe number of habitat classifications that have appeared in recentyears (Greene et al., 1999; Allee et al., 2000; Connor et al., 2004;Valentine et al., 2005; Last et al., 2010). Many of these classifica-tions are hierarchical in structure, with higher levels defined bybroad-scale abiotic characteristics (primarily geological classesbased on accepted geological classification schemes), with lowerlevels introducing species information at a biotope level. Fraschettiet al. (2008) suggest that marine classification schemes proposedover the past 50 years are either too vague (referring mainly togeological features of the seafloor), or too detailed (referring tobiodiversity at the species level) and often do not consider naturalseason variation in assemblage structure for effective imple-mentation of conservation initiatives. The debate regarding thetheoretical notion of a biotope in the sublittoral zone willundoubtedly continue, and is unlikely to be resolved until newknowledge and information from seafloor mapping studies rigor-ously test the concept and help to further define community-basedclassification schemes. Nonetheless, a wide variety of studies haveattempted to map benthic communities using acoustic surveymethods and a comprehensive list of these is provided in Table 1.

3.4. “Abiotic” habitat mapping

Methods to segment continuous coverage environmental datalayers into regions where environmental conditions are similar anddifferent from neighboring regions has been described above underSection 2. It is tempting to assume that these regions reflectdifferences in biological characteristics, and this is essentially whatwe mean by “abiotic habitat mapping”. The expectation is that theenvironmental differencewill result in distinctive shifts in the bioticcomposition (i.e. a shift in the n-dimensional hypervolume which

C.J. Brown et al. / Estuarine, Coastal and Shelf Science 92 (2011) 502e520514

will result in the presence of different species). This is a majorassumption since specieseenvironmental interactions are notalways known or straight forward. Reviews of soft sediment (e.g.Wilson, 1991; Snelgrove and Butman, 1994; Levin et al., 2001), andhard substratum (e.g. Richmond and Seed,1991; Underwood, 2000;Brown, 2005) environments have not yet revealed any universallypredictive and convincing explanations of animalesubstratumrelationships. Thismay reflect the inherent difficulties in unravelingthese interactions at relevant scales, offshore sampling difficultieswhich lead to limitations in the quality and quantity of data, orsimply the inherent complexities of a system thatmay defy a simpleparadigm.

Nonetheless, this approach could provide useful representationsof environmental patterns for management. The abiotic maps areoften reflected in the upper levels of the habitat classificationschemes referred to above, which often have geologically-centricterms. They may reflect broader landscape scale differences whichmay be useful for management at a national or even internationallevel, and are exemplified by the seascape-scale mapping studiesreferred to under section 2.4 (Roff et al., 2003; Vincent et al., 2004;Harris, 2007; Harris et al., 2008; Verfaillie et al., 2009; Robinsonet al., 2011). However, care should be taken when inferring bio-logical patterns on the seafloor from these studies. There are rarelyone-to-one relationships between abiotic maps at this scale andspecies/community distribution patterns. Broad-scale maps ofabiotic features are not habitat maps, and only become so with theaddition of biological data.

4. Map production

Though most benthic habitat mapping studies follow the basicapproach outlined in Fig. 1, the specific details on how the variousdisparate data sets are combined varies tremendously. There aremany similarities between the methods used to map terrestrialenvironments (Guisan and Zimmermann, 2000; Guisan andThuiller, 2005; Elith and Leathwick, 2009), and those which havegained popularity in marine applications. Nonetheless, the lack ofhigh-resolution spatial environmental data (until recently) has ledto the field of benthic habitat mapping lagging somewhat behindits terrestrial counterpart. As a consequence, the majority of marinehabitat mapping studies have tended to rely on simple statisticalmodels to determine associations between ground-truthing andenvironmental data sets (Table 1). However, more recent studieshave started to evaluate more complex statistical approaches(Kraan et al., 2010) or have turned to methods such as speciesdistribution modelling (SDM) or community level modelling,developed from terrestrial research, with very promising results.

Expanding on the generalized approach in Fig. 1, a review of theliterature from the past decade reveals three basic map productionstrategies (Fig. 4 - developed from Ferrier and Guisan, 2006). Eachstrategy has relative merits and problems which we discuss below,and Table 1 categorizes studies presented in the scientific literatureinto one of these three basic strategies.

4.1. Strategy 1 abiotic surrogates (unsupervised classification -limited or no ground validation)

Strictly speaking, this strategy does not deal with integrating insitu biological/geological data with environmental data layers, andtherefore does not follow the generalized approach presented inFig. 1. However, it is important to include as there are severalstudies which have developed integration methods based entirelyon the abiotic data layers (discussed above under section 3.4) (Roffet al., 2003; Vincent et al., 2004; Harris, 2007; Harris et al., 2008;Dunn and Halpin, 2009; Verfaillie et al., 2009; Robinson et al.,

2011). This strategy adopts unsupervised classification methodsto look for “patterns” in the environmental data, and usuallyinvolves little or no in situ ground-truthing. It is usually applied ata broad-scale and has been successfully used over continental shelfregions, but there are also examples of this strategy at finer spatialscales (e.g. Cochrane and Lafferty, 2002; Lucieer, 2007; Marsh andBrown, 2009). The output maps (often termed marine landscapeor seascape maps when conducted at broad spatial scales) may beuseful for identification of broad physiographic features (e.g. areasof rocky reef, canyons, banks etc.) from which species occurrencescan be inferred. However, at this scale they tend to be poorpredictors of species patterns and are limited in application tonational management objectives.

4.2. Strategy 2: assemble first, predict later (unsupervisedclassification)

This strategy is by far the most common strategy represented inthe literature over the past decade (Table 1), and can be used toproduce single species maps, community maps, or maps of gener-alized habitat classes based on observed geological/biologicalseafloor characteristics using a benthic habitat classificationscheme (see section 3.3) (Strategy 2 - Fig. 4). The strategy takesa top-down approach, whereby the environmental data and in situbiological/geological data are organized before they are combined(an unsupervised classification strategy). This involves thesegmentation of the environmental data into spatial units, usingeither expert interpretation by eye or an objective based approachas outlined under Section 2.3 above. In the case of single specieshabitat mapping, a focal species is selected and its presence/absence at the ground-truthing locations is then modelled againstthe segmented environmental data. The modelling usually takesthe form of simple statistical assessment of the correlation betweenthe data sets. Species presence can then be extrapolated based onthe segmented environmental data where there is geographicalconcurrence between the data sets. Community mapping is done ina comparable way, where the ground-truthing data are usuallyorganized into classes using (multivariate) statistical methods.Similarly, the in situ biological (and geological) data may be clas-sified using expert judgment or statistical methods based on anexisting habitat (biotope) classification scheme. The community orhabitat (biotope) classes are then compared against the segmentedenvironmental data set in a comparable way to the single speciesexample outlined previously. In this way the classes can beextrapolated on the basis of the segmented environmental data.

4.3. Strategy 3: predict first, assemble later (supervisedclassification)

The third strategy adopts a bottom up approach whereby the insitu biological/geological ground-truthing data are used to informthe organization and segmentation of the environmental data (asupervised classification strategy), and can be applied from a singlespecies or a community standpoint (Strategy3 - Fig. 4). Froma singlespecies perspective, the presence of the chosen focal species fromthe ground-truthing data is modelled as a function of the environ-mental predictors, thereby generating a predicted species distribu-tion map (effectively generating an SDM - Elith and Leathwick,2009). This is a more sophisticated and objective mappingapproach and several of the more recent benthic mapping studieshave started to employ this type of strategy (e.g. Bryan andMetaxas,2007; Davies et al., 2008; Iampietro et al., 2008; Galparsoro et al.,2009), with some success. It is also theoretically possible togenerate community maps using this approach by producing singlespecies habitat maps one at a time, and then combine the resulting

Fig. 4. Three basic strategies for the production of benthic habitat maps (developed from Ferrier and Guisan, 2006).

C.J. Brown et al. / Estuarine, Coastal and Shelf Science 92 (2011) 502e520 515

stack of species distribution maps to produce a community distri-bution map (dashed arrow under Strategy 3 in Fig. 4). This is a fairlynew approach, and whilst examples of this strategy are emergingfrom terrestrial systems (e.g. Peppler-Lisbach and Schröder, 2004),as yet it has not been tested with marine data sets.

More commonly, community maps are generated by firstorganizing the in situ data into classes using (multivariate) statis-tical methods, or by classifying the data using expert judgment orstatistical methods based on an existing habitat (biotope) classifi-cation scheme. These classes are then used to perform some form ofsupervised classification on the environmental data sets tosegment the continuous coverage variables. This approach waswidely used to produce maps from SB-AGDS data based on bothbiologically and geologically focused in situ data sets (e.g. Sotheranet al., 1997; Foster-Smith et al., 2004; Brown et al., 2005). Morerecently, sophisticated habitat suitability modelling techniqueshave been applied to community classes derived from multivariateanalysis of benthic sample data (Degraer et al., 2008), with prom-ising results.

5. Conclusions

Withonly5e10%of theworld’s seafloormappedat a resolutionofsimilar terrestrial studies (Wright and Heyman, 2008) addinga spatial dimension to benthic habitat studies is still a significantchallenge. Over the past decade we have witnessed the nascence ofthe field of benthic habitat mapping and, on the evidence of theliterature reviewed in this paper, have seen a rapid evolution in thelevel of sophistication in our ability to image and thus map seafloorhabitats. As acoustic survey tools have become ever more complex,new methods have been tested to segment, classify and combine

these data with biological ground truth sample data. The threestrategies outlined under Section 4 offer a variety of methods forthe production of benthic habitat maps, and whilst we are stillsome way off standardizing and agreeing on the best way toproduce benthic habitat maps, all three strategies provide valu-able map resources to support management objectives. We stillhave a long way to go before we answer many of the outstandingtechnological, methodological, ecological and theoretical ques-tions that have been raised here and there is still a great deal ofresearch still to be done. Nonetheless, the advent of spatialecological studies founded on high-resolution environmentaldata sets will undoubtedly help us to examine patterns incommunity and species distributions, which is a vital first step inunraveling complexities. The spatial nature of the mapsproduced from any of the mapping strategies outlined above(Fig. 4) will facilitate quantification of biologically-relevantpatterns on the seafloor (e.g. relative richness of habitats,proportion of study area occupied, diversity and dominance,connectivity, contagion and other patch-based metrics - Forman,1995; Zajac, 2008). This will help to address important ecologicalquestions pertaining to animaleenvironment interactions,and facilitate our ability to manage benthic ecosystems effec-tively through the implementation of Ecosystem BasedManagement (EBM) (Cogan et al., 2009). With continuedresearch effort and a move towards the acquisition of high-resolution MBES data sets as part of national mapping programs(e.g. INFOMAR - http://www.infomar.ie; Joint Irish BathymetricSurvey - http://www.marine.ie/home/services/surveys/seabed/JIBS.htm), we have the potential to significantly advance ourunderstanding of seafloor ecosystems from what is achievableusing conventional in situ sampling alone.

C.J. Brown et al. / Estuarine, Coastal and Shelf Science 92 (2011) 502e520516

Acknowledgements

The work undertaken as part of this review was supported byScience Sector of the Department of Fisheries and Oceans throughits Ecosystem Research Initiative. The manuscript benefited frominternal DFO reviews by Don Gordon and Melisa Wong, and twoanonymous external referees.

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