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Marine Environmental Research xxx (2014) 1e7

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Marine Environmental Research

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

High-resolution habitat mapping on mud fields: New approach toquantitative mapping of Ocean quahog

Artem Isachenko a, *, Yana Gubanova b, Alexander Tzetlin a, Vadim Mokievsky c

a MSU, White Sea Biological Station, Russian Federationb MSU, Faculty of Geology, Russian Federationc Shirshov Institute of Oceanology, Russian Academy of Sciences, Russian Federation

Keywords:Arctica islandicaHabitat mappingSide scan sonarWhite SeaMacrobenthosSpatial distribution

* Corresponding author. Leninskiye Gory 1-12, Bionosov, Moscow State University, Moscow 119234,9161888396.

E-mail address: isachenko@wsbs-msu.ru (A. Isach

http://dx.doi.org/10.1016/j.marenvres.2014.05.0050141-1136/© 2014 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Isachenko,Ocean quahog, Marine Environmental Resea

a b s t r a c t

During 2009e2012 stocks of the bivalve Arctica islandica (Linnaeus, 1767) (Ocean quahog) in KandalakshaBay (the White Sea) has been assessed using a side-scan sonar, grab sampling and underwater photoimaging.

Structurally uniform localities were highlighted on the basis of side-scan signal. Each type of a signalreflects combination of sediment type, microtopography and structural characteristics of benthic com-munity. The distribution of A. islandicawas the predominant factor in determining community structure.Seabed attributes considered most significant were defined for each type of substrate type. Relations ofsonar signal and sediment type were used for landscape mapping based on sonar data. Communitycharacteristics at known localities were reliably interpolated to the area of survey using statisticalprocessing of geophysical data.

A method of integrated sonar and sampling data interpretation for high-resolution mapping ofA. islandica by biomass groups, benthic faunal groups and associated habitats was developed.

© 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Arctica islandica (Linnaeus, 1767) e one of the most long livingand slow growing species of marine Bivalvia, age of individuals canreach 350 years (Jones, 1983; Sch€one et al., 2005). It inhabits sub-tropical and boreal water depth of 10e150 m (Roweli et al., 1990;Thompson et al., 1980). It is estimated that life span of this spe-cies in theWhite Sea is much lower (Gerasimova andMaksimovich,1987, 2009). To present time there is evidences of A. islandica set-tlements in the Onega, Kandalaskha and Dvina bays, as well as inthe Gorlo and the White Sea Basin (Naumov, 2006).

The study area is situated in shallowest inner part of Kanda-laksha Bay. This area is one of the rare of the White Sea localitieswith dense A. islandica settlements, which are the subject ofnumerous studies (Brotskaya et al., 1963; Krapivin and Poloskin,2006; Isachenko et al., 2013).

Maximum depth at the area is 29 m; prevailing depths are10e14 m. Vysokiy island coastal slope composed mainly with sand

logical Department of Lomo-Russian Federation. Tel.: þ7

enko).

A., et al., High-resolution habrch (2014), http://dx.doi.org/

ormud and at the depth of 8e10m fine silts start. All over the studyarea soft sediments (highly hydrated silts) prevail and hard-bottomareas cover inconsiderable part.

The study area is occupied by A. islandica community. Differentpolychaetes can be found as subdominant species: Micronephtysminuta, Scoloplos sp., Terebellides stroemi, Alitta virens. A. islandica isa dominant species which define community shape. Therefore weassume that distribution of this bivalvianmollusk is amarker of thiscommunity type.

The main imperative in development of this approach was tocreate a methodology for mining the reliable information ofquantitative settlement characteristics distribution with side-scansonar data. The main difference in comparison to existingmethods of large scale benthic communities habitat mapping (us-ing side-scan sonar) is it's adjustment for mesoscale researches.

Currently side-scan sonars have been applied in the researchand mapping of various biological objects e from distinct pop-ulations of individual organisms (mussel and oyster beds, coralreefs, etc.) (Wildish et al., 1998; Smith and Greenhawk, 1998, 2001;Kvernevik et al., 2002) to the areal mapping of the benthic com-munities (Freitas et al., 2003). This technique has been used in or-der to monitor changes in landscapes and biota under the pressureof anthropogenic activity (Kenny and Rees, 1996). Side-scan sonar is

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A. Isachenko et al. / Marine Environmental Research xxx (2014) 1e72

a simple and common used method for mapping benthic com-munities in areas where there are pronounced contrasting features.

The mapping of benthic communities using SSS is based on thefollowing principles:

- The existence of a sufficiently strong correlation between thetype of sediment and biota. Major abiotic factors (macro relief,currents, depth) affect sediment distribution at the seabed aswell as biota;

- Patches of sediment mosaic and their borders are clearly iden-tified and mapped;

- SSS back-scatter characterizes cumulative signal of sedimentandmacrobenthic fauna, as well as living benthic organisms andbiogenic structures which could significantly affect bottommicro-relief, altering its acoustic characteristics.

The main goal of present study is to investigate the spatial dis-tribution of the dense community in mesoscale (100e1000 m).Necessity of high-resolutionmapping of A. islandica settlement leadto development of novel approach for side-scan sonar datainterpretation.

2. Methods

During 2009e2012 side-scan sonar surveys, qualitative sedi-ment sampling and underwater photo imaging were carried out atthe test area in Kandalaksha bay, the White Sea.

Within the study area the seafloor is a formation of plains, un-derwater depressions and banks. The results of SSS survey(2009e2010) provided a detail reconstruction of bottom topog-raphy and a spatial distribution of sediment type.

Fig. 1. Scheme of sonar profiles n

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2.1. Side-scan sonar survey

Side-scan sonar «Hydra» was used to produce bathymetry mapof studied polygon and acoustic images of the sea bottom. The side-scan sonar array was permanently attached to the research vesselduring the survey.

Initial data processing was performed in real time and included:

� recording of sonar echoes;� recording of navigation data;� processing echo-signals;� visualization of the results after processing.

For the survey design 50 m interval between the sonar profileswas chosen (Fig. 1). However, due to the complexity of bottomtopography it was not possible to maintain this interval throughoutthe whole survey. In 10% of cases the interval between profilesexceeds 50m (maximum interval is 70m). As this became apparentduring acquisition of the sonar profiles, it was decided to shortenthe interval between the profiles to 25e30 m for more denseprofiling coverage of the target area.

The key factor for the quality of the acquired datawas the choiceof the profiling direction. Latitudinal location of the profiles wasadjusted to the directions of the Kandalaksha Bay currents, whichfacilitated the maintenance of the ships course more accurately. Asthe result of the survey in 2009e2010 14 sonar profiles were per-formed (Fig. 1).

2.2. Sampling

Sampling grid of survey was designed on the basis of bathym-etry data, procured during SSS investigations (2009e2010). During

ear Vysokiy Island in 2010.

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A. Isachenko et al. / Marine Environmental Research xxx (2014) 1e7 3

the SSS survey it was investigated, that bottom topography hascomplex formation, represented by highly patched mosaic ofsediment distribution. We assumed, that the spatial distribution ofthe target species will be related to the heterogeneity of sedimentdistribution. In this case special spatial design of sampling wasneeded. Main aim in distributing sample scheme across the studyarea was to cover several spatial scales. To achieve this sampleswere distributed on two transect (SN, WE).

Sampling was performed using «Okean» grab (Eleftheriou andMcIntyre, 2005) with the sampling area equal to 0.1 m2. A total of106 samples were collected in 2009e2011 within the study area(Fig. 1). Material was washed on a 0.5 mm mesh size sieve. Allmacrobenthos species were preserved in 4% formalin and identifiedto species level where possible (Gaevskaya, 1948; Jirkov, 2001;Naumov, 2006; Starobogatov and Naumov, 1987; Tzetlin et al.,2010). All the species were wet-weighted, A. islandica (as well asother Bivalvia) were weighted with shell.

2.3. Underwater imaging

SCUBA based underwater photography was used to observehard-substrate habitats and small-scale distribution of A. islandica.Images were taken with a Canon 5D Mark 2 in an underwaterhousingwith 2 Inon Z240 strobes. Map of observed sites is providedin Fig. 1. A. islandica population density was assessed with 1 m2

frame temporally placed on bottom. At the same time underwaterlandscape photographs were made. All the detected species wereincluded in habitat description.

2.4. Developing mapping methodology

For determining an accurate assessment of A. islandica stocks, anovel method of SSS data processing was developed. The followingbiological and geophysical data were taken for interpretation andanalysis.

2.4.1. Geophysical characteristicsThe idea of the proposed interpretation method of SSS data is

based on attribute computations within the survey area. Usingcluster analysis, a specific group of SSS attributes were assigned toeach of three levels of A. islandica density distribution. Each level ofdistribution was provided with geological interpretation in accor-dance with the data, acquired.

Attributes of the sonogramwere selected according to followingprinciples:

1. absence of correlation between the sonogram's attributes(Table 1);

2. “a relationship” between sonogram's attributes and character-istics of the sediments.

The following attributes were chosen for analysis:

1. depth;2. average distribution e characteristics of sediment reflective

power: soft sediments (mud) have low reflectivity, while moresolid sediments (sand, gravel) have high reflectance;

3. dispersion e characteristic of sediment homogeneity: high in-dexes of homogeneity indicates poorly sorted sediments, i.e.containing equal proportions of different particle-size fractions;

4. distribution asymmetry e index connected with sedimentgrading level: negative indexes indicates predominance of smallparticle size fraction, positive indexes indicates predominanceof coarse deposit;

5. kurtosis e measure of main sediment fraction predominance;

Please cite this article in press as: Isachenko, A., et al., High-resolution habOcean quahog, Marine Environmental Research (2014), http://dx.doi.org/

6. central frequency e measure of sediments attenuation: lowvalues of index indicates high absorption ability of thesediment;

7. mean distance between peaks of sonogram e complex responseof fraction's spatial distribution;

8. ratio of high and low frequencies pulse e ratio of reflectivepower of thin and coarse sediments.

For details on how to calculate these attributes please seeGubanova et al., 2013.

2.4.2. Biological characteristicsA. islandica biomass distribution was considered as a main bio-

logical characteristic. Three levels of A. islandica distribution pat-terns were identified: a. samples with high biomass of the mollusc(more than 1800 g per m2); b. samples with average biomass(30e1400 g per m2); samples biomass of A. islandica equal to0 g per m2.

2.4.3. Algorithm of data interpretation

1. Corresponding biological (sampling) and geophysical data.

The GPS positions of sampling stations and sonar profiles werecompared for unification of A. islandica distribution and the resultsof sonar survey. Radius for data unification was chosen equal to30 m in accordance to accuracy of benthic sampling stations posi-tioning. In total, the biomass data from 67 samples were used inanalysis, while rest of the samples were excluded from analysisbecause of far distance from SSS profiles. These samples were usedlater on to test developed methodology. The number of samplesvaried inside each level of A. islandica biomass distribution: thelevel with max. Biomass was presented with 3 sampling points, thelevel with average biomass ewith 50 points, level with no biomasse with 14 points.

2. Additional points with A. islandica zero biomass were added toanalysis to outline the shallowswith nomollusks andwithmorecoarse sediments.

Bottom depressions occupy a significant area at the study site(5e10%), which were identified from SSS data. These areas at thesite are not inhabited with A. islandica; this was confirmed withsampling data and photos taken during the dives. Three pointsbased on the dives (depths of 22e25 m) were added to the totalsample database. A summary table with 70 points with knowngeophysical and biological parameters was formed after thesemanipulations. A summary table of the rest 3045 points wereformed with only data for attributes of the sonar signal (all thetables provided in Electronic supplement).

3. Checking the attributes. Selected attributes were tested forcorrespondence with identified biological clusters, i.e. doessonogram attributes respond to different levels of A. islandicabiomass distribution? For such examination pairwise charts ofall attributes and biomass were produced. The level with noA. islandica biomass was clearly identified by depth factor(Fig. 2).

The level with a maximum biomass was clearly separated bydispersion and large negative kurtosis values (Fig. 3). The meanvalues of the variance and kurtosis within the group with averageand maximum values of biomass were different, which should leadto the separation of these groups for further analysis. Привестирезультаты теста

itat mapping onmud fields: Newapproach to quantitativemapping of10.1016/j.marenvres.2014.05.005

Table 1Correlation matrix for attributes of side scan sonogram signal.

Depth Mean Var Skwe Kurt f_prin Mean_dist HL_FRrange

Depth 1.00Mean 0.37 1.00Var 0.57 0.90 1.00Skwe 0.21 �0.66 �0.32 1.00Kurt 0.07 �0.62 �0.50 0.69 1.00f_prin �0.15 �0.05 �0.09 �0.06 0.02 1.00Mean_dist 0.26 0.12 0.22 0.11 �0.06 �0.06 1.00HL_FRrange �0.20 �0.19 �0.21 0.04 0.10 �0.09 �0.08 1.00

A. Isachenko et al. / Marine Environmental Research xxx (2014) 1e74

4. Examine the homogeneity of main parameters (depth, disper-sion, kurtosis) of the detached groups. Leading parameters ofpoints with high or average biomass were homogenous(Table 2), but group with absence of the mollusc clearly sepa-rates into two because of depth (Fig. 4).

5. Algorithm of unifying pointed biological and areal spreadgeophysical data was based on discriminant analysis. Discrimi-nant functions of all marked biological groups were used foranalysis of all points bearing sonar data attributes. Error levelwas calculated by comparing results of a posteriori and a prioriclassification. Overall error level for this method was less then10% (test results see in Electronic supplement).

SSS mapping provided detailed map of A. islandica settlementbiomass distribution as well as outlined contours of homogenousunderwater landscape elements (Fig. 5). To make completedescription of each biotope highlighted underwater imaging duringSCUBA diving.

Fig. 2. Correlation of A. islandica biomass and depth. Group with absence of themollusc separates into two subgroups in shallow and deep parts of the polygon.

3. Results

3.1. Benthic community

The study area is dominated by soft sediments (loosely packed,highly hydrated silt sediments), while hard substrate areas (rep-resented by moraine deposits) have a limited distribution. Bottomcurrent speeds are generally low (0.02e0.17 m/sec, the averagevalue e 0.11 m/sec for middle of tidal cycle). Slow current speedsand features of the bottom topography (composition of de-pressions) form the hydrological conditions in which the off-bottom temperatures are elevated (12e15 �C) in the summermonths. The content of organic carbon in surface sediments is2.75 ± 0.31%.

An A. islandica community generally dominates the study area.45 invertebrates taxa were observed in the macrozoobenthos:Polychaeta e 27, Crustaceae 8, Bivalvia e 7, Gastropoda, Nemertea,Ascidiaceae 1. The benthos was dominated (both in number and inbiomass) by bivalves, particularly A. islandica, and polychaetes.While A. islandicawas the dominant species at most stations, otherthe species with which it occurred changed from station to stationbut were predominantly polychaetes such asM. minuta, A. virens, T.stroemi and Scoloplos ex gr. acutus.

The total number of species in samples varied from 5 to 15,average number was 10 species per sample. The number of speciesand individuals per sample was not correlated with the depth(r ¼ �0.02). Samples with a maximum number of individuals weretaken in the central part of the area with varied depths.

Uneven distribution of target species can be explained by thecomplicity of the underwater topography and high heterogeneity ofsediment distribution. The highest A. islandica population densitywas found on the banks periphery and slopes, such places are themost favorable for the settlement not only this, but also otherseston feeders as long as it forms good conditions for effective

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filtration (supply and aeration) due to greater current speed. Largefiltration efficiency with increasing flow intensity for various spe-cies of bivalves (Walne, 1972).

The average population density is 532 ind./m2, and the averagebiomass e 419 g/m2. The average biomass at those stations withA. islandica was 495.2 g/m2, and the contribution of A. islandica tothe biomass averaged more than 90%.

3.2. Mapping results

Four types of habitats of underwater landscape were distin-guished during SSS interpretation: a. submerged banks; b. mudfields; c. sea-bed depressions (Gubanova et al., 2013). These habi-tats were groundtruthed using photography and grab samples.

3.2.1. Underwater banksTwo types of submerged banks are described within the area.

These banks are of the same geological morphology, but at differentstages of development.

The first type of bank has ridges which are covered with recentmarine sediments (muddy sand, modal fracture ¼ 0.001 mm).Banks depth near ridge is approx. 10 m. Macrobenthos of first type

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Fig. 3. Correlation of A. islandica biomass, dispersion and kurtosis.

A. Isachenko et al. / Marine Environmental Research xxx (2014) 1e7 5

of banks consists of echinoderm Asterias rubens, the gastropod e

Buccinum spp., tunicates and unindentified sponges. These placeshave maximum biomass of A. islandica. M. minuta, T. stroemi, S.acutus, A. virens were commonly found as subdominant species.

Ridges of the second type submerged banks are more complexby structure on the top and lie at depth less than 6 m. Ridges ofthese banks are composed by coarse deposit of moraine debris(modal fraction 1.5 cme1.5 m). These types of banks are very welldefined at the SSS images because of high acoustic reflectivity of thecoarse material. Large boulders are fouled with sponges (Haliclonaaquaeductus as example), kelp (Laminaria saccharina) and otherbrown sea weeds (i.e. Chorda sp.). The sedentary benthos isdominated by the tunicate Styela rustica. Coarse material on thebank ridges is coveredwith a thin veneer of silt. The large sedentarypolychaete Amphitrite figulis which forms small mud lumps arecommon on the top of the banks. Other macrobenthic species at thebank ridges are the echinoderm A. rubens, the hydroid Obelia gen-iculata, the gastropod Buccinum spp. and the crustacean Pandalusmontagui. A. islandica was not recorded at the tops of these banks.

Around the periphery of these banks (5e7 m depth) werecovered with aleurite (modal fraction 0.01e0.05 mm) and littered

Table 2Variation of attribute parameters in selected biomass clusters.

Variation%

A. islandica nobiomass

Averagebiomass

Maximumbiomass

Depth 59 20 4Median 48 33 12Dispersion 79 43 10STD/Med 33 14 10Distribution asymmetry 146 49 297Kurtosis 62 46 5Central frequency 106 93 83Mean distance between

peaks of sonogram7 4 6

Ratio of high and lowfrequencies pulse

18 14 9

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with numerous dead shells of A. islandica. Live A. islandica in-dividuals were founded in a very small number (0e1 ind. per m2).Other common macrobenthic species here is the ascidian S. rustica.

Bank slopes (9e12 m depth) are of the same type of the sedi-ment as banks' periphery. These places have the same maximaldensity of the A. islandica settlement as first type banks ridges (alsothe same depth).

3.2.2. Mud fieldsMud fields (plains) were located in the central and east part of

the study area. Mud fields are the most frequently recorded habitatwithin the study area and are characterized by uniform topographyand sediment type (The prevailing sediment type is silt/clay withsmall amounts of sand (<6%)). A. islandica occurs in moderate

Fig. 4. Depth factor in group with absence of the mollusc. Subgroup 1 characterizedwith shallow depth (less than 9 m), subgroup 2 with depth more than 21 m.

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Fig. 5. Underwater landscape of Vysokiy island vicinities (Rugozerskaya bay, the White Sea). Main habitats indicated.

A. Isachenko et al. / Marine Environmental Research xxx (2014) 1e76

densities within the mud fields (approx 100 per m2). Single gas-tropods, tunicates and echinoderms A. rubenswere detected duringimage analysis.

3.2.3. Underwater depressionsDepressions were located in the central and eastern part of the

area and they oriented NNW to SSE. Depressions represent thedeepest parts of the study area (15e22 m). The prevailing sedimenttype within depressions is highly hydrated silt, while aggregationsof large boulders commonly occur in the deepest areas. Whereboulders accumulate they form a separate habitat with increasedspecies diversity and density when compared to the surroundingsilt dominated areas. The large hydroid Tubularia indivisa (up to6 ind./m2), dense settlements of S. rustica and a variety of uniden-tified sponges were found on these boulders. Settlements ofA. islandicawere not founded at the bottom of depressions near thegroups of boulders and as well at the silty substrate around.

The silt dominated slopes of depressions supported a similarmacrofaunal community to the mud fields with A. islandica occur-ring in moderate densities.

4. Discussion

Currently technology of habitat mapping based on geophysicaldata is widely applied in research of separate biological objects andgeneral large-scale community mapping. Commonly usedgeophysical data sources are broad acoustic beam swath systems(such as side-scan sonar) (Fish and Carr, 1990; Kenny, 1998); single-beam echo sounders (such as RoxAnn)(Chivers et al., 1990) andmulti beam swath bathymetric systems (Hughes Clarke, 1998).

Existing mapping method is generally based on the followingprinciples: 1) creating the sediment distribution maps; 2)descripting biological characteristics (species composition, speciesrichness) at number of sampling stations; 3) corresponding knownbenthic habitat characteristics to investigated during sonar surveybottom topography and/or sediment distribution; 4) extrapolationof biological data to the whole area of survey (Freitas et al., 2003;Pandian et al., 2009; Kenny et al., 2003).

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Our aim was in simplifying existed technology by direct corre-sponding target species distribution (A. islandica) and sonogramcharacteristics.

Similar approach is used in analysis of optic signal (satellitebased surveys in tropical waters) (Mumby et al., 1997). This allowspassing from complex system of multiple correlations to arrangingthe straight connection between characteristics of sonar signal andspecies distribution. The approach allows to increase the surveyresolution.

Traditionally surveys on small-scale spatial distribution in softsubstrate sediments assume even arrangement of target speciesindividuals. In nature this never happens due to inter-speciesrelation, bottom microtopography and many other factors(Thrush et al., 1989), and species distribution reveals uncovered.

The solution of this problem lies in increasing number of sam-ples and decreasing the distance between sampling points(Morrisey et al., 1992) or developing of novel methodologies formapping these types of benthic communities.

Among existed suitable methodologies are underwaterphotography. It allows to collect the information on spatial distri-bution of different species along the transects (Dayton et al., 1974).But in case of lowwater transparency the frame areawith sufficientimage resolution will be very small.

5. Conclusions

Significant spatial aggregation was found in dense A. islandicapopulation in survey area. Within the area of 3.5 km2 the speciesoccurs on slopes and never on top of the banks and in bottom ofdepressions. Within the distribution range the highest biomassvalues was associate with bank ridges which are covered withrecent marine sediments (muddy sand, modalfracture ¼ 0.001 mm) and slopes at 9e12 m depth with the sametype of the sediment.

The methodology developed during this study, using a combi-nation of acoustic, biological and underwater video/photographicsampling techniques, proved to be successful in mapping sea-bedbiotopes within the survey area.

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A. Isachenko et al. / Marine Environmental Research xxx (2014) 1e7 7

A comprehensive method of mapping was used for this study.The method described is based on integrated interpretation oftraditionally procured data (grab sampling), results of underwaterphotography and remote sensing studies using side-scan sonar.

Detailed bathymetry was procured from SSS «first brake» anal-ysis. Characteristics and distribution of homogenous underwaterlandscape elements were derived from SSS data interpretation.

This approach allows for the creation of highly detailed map ofspatial distribution of the bottom landscapes and it provides in-formation on quantitative distribution of the dominant speciessettlements as well as morphological characteristics of the seabed.

Acknowledgments

We are grateful to everyone who participated in sampling andprocessing of the materials for present work and to staff membersof Pertsov White Sea Biological Station of LMSU, V.P. Shevchenko,M.Yu. Tokarev, A.A. Shmatkov, Ya.E. Gubanova and V.V. Kozlovskiy.

Appendix A. Supplementary data

Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.marenvres.2014.05.005.

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itat mapping onmud fields: Newapproach to quantitativemapping of10.1016/j.marenvres.2014.05.005