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Estuarine, Coastal and Shelf Science 93 (2011) 98e105

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Estuarine, Coastal and Shelf Science

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

Spatial polychaeta habitat potential mapping using probabilistic models

Jong-Kuk Choi a, Hyun-Joo Oh b, Bon Joo Koo c, Joo-Hyung Ryu a, Saro Lee b,*

aKorea Ocean Satellite Centre, Korea Ocean Research & Development Institute, 454 Haean-no, Sangrok-gu, Ansan, Gyeonggi 426-744, Republic of KoreabGeoscience Information Center, Korea Institute of Geoscience & Mineral Resources (KIGAM), 92, Gwahang-no, Yuseong-gu, Daejeon 305-350, Republic of KoreacMarine Living Resources Research Department, Korea Ocean Research & Development Institute, 454 Haean-no, Sangrok-gu, Ansan, Gyeonggi 426-744, Republic of Korea

a r t i c l e i n f o

Article history:Received 21 October 2010Accepted 10 March 2011Available online 17 March 2011

Keywords:polychaetahabitat mappingprobability theoryGISremote sensingKoreaChungcheongnam-doTaean peninsulaCheonsu bay

* Corresponding author.E-mail address: leesaro@kigam.re.kr (S. Lee).

0272-7714/$ e see front matter � 2011 Elsevier Ltd.doi:10.1016/j.ecss.2011.03.006

a b s t r a c t

The purpose of this study was to apply probabilistic models to the mapping of the potential polychaetahabitat area in the Hwangdo tidal flat, Korea. Remote sensing techniques were used to construct spatialdatasets of ecological environments and field observations were carried out to determine the distributionof macrobenthos. Habitat potential mapping was achieved for two polychaeta species, Prionospio japonicaand Prionospio pulchra, and eight control factors relating to the tidal macrobenthos distribution wereselected. These included the intertidal digital elevation model (DEM), slope, aspect, tidal exposure dura-tion, distance from tidal channels, tidal channel density, spectral reflectance of the near infrared (NIR)bands and surface sedimentary facies from satellite imagery. The spatial relationships betweenthe polychaeta species and each control factor were calculated using a frequency ratio and weights-of-evidence combinedwith geographic information system (GIS) data. The species were randomly dividedinto a training set (70%) to analyze habitat potential using frequency ratio and weights-of-evidence, anda test set (30%) to verify the predicted habitat potential map. The relationships were overlaid to producea habitat potential mapwith a polychaeta habitat potential (PHP) index value. Thesemapswere verified bycomparing them to surveyed habitat locations such as the verification data set. For the verification results,the frequency ratio model showed prediction accuracies of 77.71% and 74.87% for P. japonica and P. pulchra,respectively, while those for the weights-of-evidence model were 64.05% and 62.95%. Thus, the frequencyratio model provided a more accurate prediction than the weights-of-evidence model. Our data demon-strate that the frequency ratio and weights-of-evidence models based upon GIS analysis are effective forgenerating habitat potential maps of polychaeta species in a tidal flat. The results of this study can beapplied towards conservation and management initiatives for the macrofauna of tidal flats.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Tidal flats, which are widely distributed along the west coast ofthe Korean Peninsula, are invaluable biological, environmental, andeconomical natural resources. Thus, it is important to developa method to quantitatively evaluate these ecosystems. For example,the macrobenthos distribution among tidal flats is known to beclosely related to the benthic and sedimentary environment, whichhas been utilized as a bioindicator for the evaluation of coastalenvironments (Lee and Park, 1998; Yap et al., 2003).

Geographic information systems (GIS) have been used togenerate habitat maps for a variety of species. It is a useful tool fordetermining the spatial relationships between an event and its

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spatial variables. For example, Brambilla et al. (2009) identified keyhabitat factors and mapped the distribution of suitable habitatsfor conservation of the red-backed shrike Lanius collurio usinga GIS-based model. In addition, Poplar-Jeffers et al. (2009) andOttaviani et al. (2009) used a GIS-based model to determine andquantify the habitats of aquatic animals. Jorgensen and Kollmann(2009) mapped the distribution of Rosa rugosa, an invasive shrubthat has negative effects on biodiversity in dune ecosystems, usinga GIS-based analysis employing aerial photographs and vegetationmaps. Galparsoro et al. (2009) analyzed habitat suitability anddistribution modelling for the European lobster Homarus gamma-rus. Finally, Hatten and Parsley (2009) developed a spatially explicitGIS model to characterize the rearing habitat of the white sturgeonAcipenser transmontanus on the basis of water depth, riverbed slopeand roughness, and also determined fish positions in the field.

Satellite data have been effectively used for quantitative esti-mation of the spatial relationships between surface sedimentaryfacies and tidal flat spectral characteristics based on GIS analysis

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(Choi et al., 2010b). Remote sensing has also been employed toinvestigate the variables that affect the spatial distribution anddynamics of benthic macrofauna in a tidal flat (van der Wal et al.,2008). Many studies have examined the benthic environmentalfactors that influence tidal flat reflectance data (Doerffer andMurphy, 1989; Rainey et al., 2000; Ryu et al., 2004), and attemptsto construct the intertidal topography have been based on remotesensing (Ryu et al., 2002, 2008; Mason et al., 2006). Studies on thespatial relationships between species habitats and various ecolog-ical environments have also been conducted (Kocev et al., 2009;Meixler et al., 2009; Wakamiya and Roya, 2009; Walker et al.,2009; Walters et al., 2009; Beaudrya et al., 2010; Kleinbaueret al., 2010). Fulton et al. (2010) examined the hydraulic influenceof bridge replacement on a mussel habitat by studying factors suchas water depth, current velocity, and their derivatives (shear stress,Froude number), and predicted habitats by establishing correla-tions between the mussel count and the hydraulics using a GIS-based numerical model.

Most studies on macrobenthos have been limited to localdistributions, while investigations of quantitative relationships andmapping-predicted distributions have rarely been attempted.Quantitative estimation of biological distributions in a tidal flatbased upon spatiotemporal relationships within a benthic envi-ronment is fundamental for the effective management of anecosystem and the evaluation of ecological properties. Herein, wepropose the application of a GIS-based frequency ratio andweights-of-evidence models combined with remote sensing and fieldobservations to predict macrofauna in a tidal flat. The frequencyratio and weights-of-evidence are advantageous in that the inputprocessing and calculation can be easily implemented into the GISsoftware and the calculated result can be used as the weight of eachvariable, without converting it into a different data format (Xu et al.,2005; Choi et al., 2010a). The spatial relationships of control factorsinfluencing the habitat of polychaeta can be effectively determinedusing the frequency ratio and weights-of-evidence as a probabilitymodel. Themodels are calculated as the ratio andweight of the area

Fig. 1. (a) The Landsat ETMþ image of the Cheonsu Bay and Hwangdo tidal flat acquired ontidal flat acquired on February 26, 2001 overlaid with 28 sampling positions for macrofaun

where a species has occurred in each class range of each spatialvariable. The frequency ratio and weights-of-evidence were inte-grated as a value of existence vulnerability to produce polychaetahabitat potential maps. Information on the contribution of a strongenvironmental variable that influences the spatial pattern ofa species could provide an estimate for and predict the distributionof a species in the future. Therefore, the frequency ratios andweights-of-evidenceareuseful forproducinghabitatpotentialmapsfor macrofauna using assigned values for each spatial variable.

The process of mapping the habitat potentials of two polychaeta(Prionospio japonica and Prionospio pulchra) in Hwangdo includedsix major steps: (1) A field campaign to obtain in situ samples ofmacrobenthos and sediment, (2) determination and construction ofa control factor database using remote sensing techniquescombined with GIS analysis, (3) a spatial database constructionbased on the two polychaeta species and eight control factorsinfluencing their distribution, (4) polychaeta division into a trainingset (70%) to analyze habitat potential using models as well as a testset (30%) to verify the predicted habitat potential map, (5) dataprocessing using a frequency ratio and weights-of-evidencemodels, and (6) verification of the polychaeta habitat potentialmaps using the known distributions of each polychaeta that werenot used in the analysis.

The study was carried out across the Hwangdo tidal flat inCheonsu Bay, Korea. Cheonsu Bay is characterized by a shallowwater depth of less than 25 m and is located on the central westerncoast of Korea. It is fringed by the Anmyeondo District of TaeanCounty and the Kanweoldo District of Seosan City (Fig.1a). The totalarea originally covered by tidewater was 380 km2, but this wasreduced to 180 km2 following the construction of embankmentsduring the Seosan A and B land reclamation exercise in 1984, theHongseongeBoryeong reclamation in 1991, and the developmentof the freshwater Bunam and Kanweol lakes (So et al., 1998). Thewater depth within Cheonsu Bay has been maintained at over 10 msince dyke construction. Much intertidal area remains around thecoastline.

February 14, 2002. (b) The IKONOS RGB (432 bands) composite image of the Hwangdoa acquired in May, 2009.

J.-K. Choi et al. / Estuarine, Coastal and Shelf Science 93 (2011) 98e105100

The tides are semi-diurnal, with a mean tidal range of 4.59 m(spring tide¼6.33m,neaptide¼2.86m).Themaximumtidalcurrentvelocities in the main tidal channels are approximately 1.00 ms�1

during the flood tide and approximately 0.70 ms�1 during the ebb(Kim and Kim,1996). Sand dunes located in the northwestern sectorof thebayareextensivelyexposedduring theebb (LeeandPark,1998).The central Hwangdo tidal flat is characterized by complex channels.It is 1.65 kmwide and 5.15 km long (Fig. 1b), and the sediment faciesare comprised of mud flats, mixed flats and sand flats from the hightowards the low tide waterline progressively.

2. Data

To map the macrofauna habitat in a tidal flat based on thefrequency ratio and weights-of-evidence model, it was firstnecessary to determine the main factors influencing the macro-benthos distribution in a tidal flat. Themacrobenthos distribution isconsidered to be closely related to the sedimentary facies (Lee andPark, 1998; Yap et al., 2003). According to Koo et al. (2005), thebenthic fauna habitat is significantly dependent on the temperatureconditions, which are mainly affected by exposure duration andintertidal elevation. The temperature in a tidal flat can also beinfluenced by the water content of the surface sediment; thus, thetidal channel networks can be a major control factor for macro-benthos distribution. The following eight control factors wereconsidered in this study: the tidal flat digital elevation model(DEM), slope, aspect, exposure time, distance from tidal channels,tidal channels density, surface sedimentary facies and the spectralreflectance of NIR bands in high spatial resolution satellite data.Table 1 lists the sources for the spatial database used in this study.Slope and aspect were included as topographical components alongwith the tidal flat DEM, and the NIR bands were employed toconsider relativewater content and thus the surface temperature ina tidal flat. Macrofauna samples were collected from the field in2009, and two dominant species of polychaeta (Prionospio japonicaand Prionospio pulchra) were collected for habitat mapping (Fig. 1b,Table 2). The maps of the eight control factors were converted intogrid data with a 4 m resolution grid file (Fig. S1).

TheDEMof theHwangdo tidal flatwas constructed fromLandsatETMþ satellite images using the waterline method, which is aneffective approach for applying remote satellite and remote sensingto topographic mapping of tidal flats (Hoja et al., 2000; Ryu et al.,2002, 2008). The waterline method exploits the different tideconditions that are seen in each image as a topographic contour line.The tidal flat DEM can be generated by stacking all the waterlinesacquired over a given short period. Five Landsat ETMþ imagesobtained from December 2006 to June 2009 were used. Geometricrectification for Landsat ETMþ datawas conducted using a rectifiedIKONOS image by an image-to-image method. The method alsorequired a reference elevation for the extracted waterline (Masonet al., 1997; Chen and Rau, 1998). Levelling data obtained in

Table 1Spatial database of the control factors influencing the macrobenthos distribution inthe tidal flat.

Category Extracted factors Data type

Benthic Macrofauna Prionospio japonica PointPrionospio pulchra

DEM Tidal flat DEM GridSlopeAspectExposure time

IKONOS Distance from tidal channel GridDensity of tidal channelSpectral reflectance of NIR bandsSurface sedimentary facies

September 2009 within the centre part of the study area at ebb tidewere used for the reference elevation in the study.We extracted ourwaterlines by applying the procedures of Ryu et al. (2002). Fig. S1ashows the DEM of the study area constructed using the waterlinemethod. The overall trend shows a high elevation in the centre;whereas there are considerable gradients in the west, the topog-raphy has relatively low relief towards the east of the study area.Fig. S1b and c are maps of the slope gradient and aspects, respec-tively, which were generated using the DEM.

Exposure time (Fig. S1d) at each macrofauna sampling site wascalculated by comparing the tidal height acquired from the Bor-yeang tidal station during 2008with in situ observations of sea levelat each site. An exposure duration database was generated usingthe DEM and the total duration for which the location was exposedabove a given sea level during the measurement time intervals. TheFORTRAN program was used to calculate the exposure time (hour)at each specific sea level.

The tidal channel networks of the study area were extractedfrom the IKONOS image acquired on February 26, 2001. A multi-spectral image with a spatial resolution of 1 mwas generated fromthe panchromatic band image with a spatial resolution of 1 mcombined with the visible-to-NIR band image with a spatial reso-lution of 4 m. From the spectrally enhanced image, the tidalchannels were extracted and digitized. A map of distance from thetidal channels was generated from the tidal channels database asshown in Fig. S1e using Euclidean distance method widely used inGIS analysis. A map of the tidal channels density (m/m2) for thestudy area was also constructed from the tidal channels databaseusing a surface analysis (Fig. S1f). Fig. S1e and f show the obvioustrend that the tidal channels are more densely distributed in thearea of mud flat facies where the relatively fine-grained particlesdominate, rather than in the area of coarse-grained sand flats.

Each NIR band reflectance of the IKONOS imagewas constructedas a raster database. The NIR band can be an indicator of thesedimentary environment in the tidal flat by indicating watercontent and remnant surface water, and it is closely related to thesurface sediment facies and the surface temperature in a tidal flat(Ryu et al., 2004; Koo et al., 2005). We used the digital number (DN)value instead of the spectral reflectance, because we could notacquire the calibration coefficient to convert the radiance (DNvalue) into optical reflectance for the satellite images. The value ofeach cell in the grid file was a DN value of IKONOS band 4 (Fig. S1g).

The surface sedimentary facies in the Hwangdo tidal flat wasmapped using the March 2004 grain size data based upon high-resolution IKONOS satellite data. The image was acquired at 11:20AM local time on February 26, 2001,whichwas 0.5 h before low tide,when the tide was at 0.78 m (Boryeong station). Mapping of thesediment distribution was achieved using an object-based classifi-cation method, which has been used previously for image classifi-cation based on high-resolution satellite images (Flanders et al.,2003). IKONOS bands 2, 3 and 4 were used to map surface sedi-ment, in consideration of the strong sensitivity to soil and watercontent relating to the distribution of tidal channels and thetopography in the coastal region. Fig. S1h shows the distribution ofthe surface sediment, which was composed of five classes: mud flat(I), mud flat (II), mixed flat, sand flat and sand shoal. Mud flats weredifferentiated into two classes based on a small difference in theirreflectance.

3. Methodology

3.1. In situ sampling

Fieldwork was conducted to collect macrobenthos and surfacesediment samples from the study area. Macrobenthos samples

Table 2Abundance of Prionospio japonica and Prionospio pulchra in each site (unit: individuals per 0.1 square metre).

Name of species H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19 H20 H21 H22 H23 H24 H25 H26 H27 H28

Prionospio japonica 0 0 0 3 42 0 4 4 5 0 27 22 2 8 0 1 28 0 4 0 4 11 91 34 0 11 66 22Prionospio pulchra 0 0 0 0 0 0 19 0 2 0 19 87 0 0 0 20 108 46 22 0 0 7 13 6 0 0 0 0

Total number of Prionospio japonica: 389.Total number of Prionospio pulchra: 349.Division into training and verification set: 70% and 30%.Training set: 272 (Prionospio japonica) and 241 (Prionospio pulchra).Verification set (bold): 117 (Prionospio japonica) and 108 (Prionospio pulchra).

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from 28 sites were collected on the Hwangdo tidal flat in May 2009during a low spring tide. The sites were selected by considering thetidal height and sediment facies, important factors controlling thespatial distribution of macrobenthos. Sediment was sampled fourtimes at each site using can cores (sampling area 0.025 m2, depth0.3 m, four replications) for analyses of grain size and macrofaunalpolychaeta. A differential global positioning system (GPS) (Path-finder Pro XR Trimble Co., CA, USA) with 1 m horizontal accuracywas used for accurate positioning of the sample sites. For poly-chaeta analysis, the sediment samples were sieved througha 1.0 mm mesh screen by washing with seawater, and the residueon the screen was preserved in 10% neutralized formalin solution.In the laboratory, the sampled specimens were sorted into majorfaunal groups, and the crustaceans were identified at the specieslevel, counted andweighed.We also counted the number of burrowopenings in the sediments made by deep-dwelling species that aredifficult to sample using cores, and these values were included inthe subsequent analysis. Two dominant species in terms of abun-dance and biomass (Table 3) were determined among the identifiedpolychaeta, and these were then used for mapping the potentialpolychaeta habitats by remote sensing and GIS. Table 2 shows theabundance of Prionospio japonica and Prionospio pulchra at eachsample site. For the grain size analysis, the sand and mud fractionswere separated by wet sieving through a 63 micron stainless-steelsieve after removing the organic material and carbonate byimmersion in solutions of 10% H2O2 and 0.1 N HCl. Grain-sizedistributions were determined by using standard sieving (Folk,1980) and a Sedigraph-5100 for the sand and mud fractions,respectively. An inclusive graphic method was used to determinethe sediment type, mean grain size, and sorting (Folk and Ward,1957).

A total of 43 samples were collected in March 2004 for the grainsize analysis to map the surface sediment distribution in the studyarea. Twenty-one samples were obtained using a shipboard grabsampler during the flood tide, and 14 samples were obtained byhand at the ebb tide. At least three samples were collected withina 10 m radius at each site within the top 5 mm of the surfacesediment. Based on the percentage of grains larger than very finesand (0.0625 mm), grain sizes were classified into three faciestypes: sand flat (above 70%), mixed flat (30e70%) and mud flat(0e30%) after Folk (1968).

A total of 59 polychaeta species comprising 3697 specimenswere identified by the in situ data analysis, and their mean densityand biomass were 1320 inds/m2 and 18.3 gWWt/m2, respectively.The dominant species was Prionospio japonica, accounting for 10.5%of the total abundance. Prionospio pulchra showed a similar distri-bution to P. japonica in terms of total abundance and total biomass.

Table 3Dominant polychaeta in terms of abundance and biomass in the study area. Occurring freqsites.

Species Total abundance % Mean density (ind./m2)

Prionospio japonica 389 10.5 139Prionospio pulchra 349 9.4 125

These two species also occurred mainly within the lower tidal flatwith the highest densities at the H23 and H17 sites for P. pulchraand P. japonica, respectively (Table 2).

3.2. Frequency ratio and weighs-of-evidence

Using the probability model, the spatial relationship betweenpolychaeta occurrence location and each related factorwas derived.The correlation ratings were calculated from a relation analysisbetween the polychaeta locations and the relevant factors. Therating of each factor type or range was assigned as the relationshipbetween polychaeta location and each factor, this being the ratio ofpolychaeta-free to event-evident cells. The seventh column (Ratio)of Tables S1 and S2 shows the frequency ratio value for each factortype or range for the two species. The polychaeta habitat potentialindex (PHPFR), Eq. (1), was calculated by a summation of each factorratio value (Lee and Min, 2001):

PHPFR ¼X

FR (1)

where FR is the rating of each factor type or range.The relation analysis shows the ratio of the area where poly-

chaeta occurred to the total area, such that a value of 1 indicates anaverage value. If the value is greater than 1, there is a high corre-lation, while a value less than 1 indicates a lower correlation. If theprobability is high, there is a greater probability of polychaeta;a lower value indicates a lower probability.

The following formulation of the Bayesian probability model,known as the weights-of-evidence model, was applied to thepotential polychaeta habitat analysis as synthesized from Bonham-carter et al. (1989), and Bonham-carter (1994). The approachinvolves extraction of binary predictor patterns based on thequantified spatial correlation between a set of relevant factors andevents. To generate binary predictor patterns for each of the factorsin the study, the spatial databasewas classified into a binarymap bycalculating Wþ and W� from Eqs. (2) and (3), which show favour-able and unfavourable areas (see Fig. S2 and Table S1).

Wþ ¼ loge½PfBjDg=PfBjDg� (2)

W� ¼ loge½PfBjDg=PfBjDg� (3)

where P is probability, B is presence of binary pattern, B is absenceof binary pattern, D is presence of event occurrence and D isabsence of event occurrence. Wþ and W� are the weights ofevidence when a factor is present (relevant) and absent (not rele-vant), respectively.

uency means the number of sites inwhich each species occurred among the total 28

Total biomass % Mean biomass (gWWt/m2) Occurring frequency

0.60 1.2 0.21 190.42 0.8 0.15 11

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The binary predictor patterns were also assigned weights(Tables S1 and S2) with maximum C/S(C) and were calculatedaccording to Eq. (4). The optimum cut-off for the binary patternwasdetermined by calculating the C/S(C), studentized value of contrast,which is estimated as the ratio of the contrast to its standarddeviation such as C=

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiS2ðWþÞ þ S2ðW�Þ

p. The magnitude of the

contrast, C, was determined from the difference, Wþ and W�, andprovided a measure of the spatial association between a set ofpolychaeta and the patterns (see Fig. S2 and Table S1).

PHPWOE ¼X

WOE (4)

where PHPWOE ¼ Polychaeta Habitat Potential index, WOE ¼ Wþ

and W� of the binary pattern for a range of each factor values orfactor class at Max. C/S(C).

3.3. Application of GIS

The spatial distribution of macrobenthic species has continu-ously changed due to the spatial variation in benthic environmentalfactors in tidal flats. GIS can be effectively applied as a basic analysistool to assess the spatial relationships between the occurrence ofmacrobenthos species and related environmental factors. In thisstudy, maps were constructed as a spatial database using ArcGIS9.0, and these data were used in a frequency ratio and weights-of-evidence model. The spatial database included the DEM, slope,aspect, exposure time, distance from tidal channels, tidal channelsdensity, NIR bands (from IKONOS) and sedimentary facies. Tidal flatmacrofauna was classified into two species including Prionospiojaponica and Prionospio pulchra. Each extracted factor was con-verted into a 4 m by 4 m grid file using a conversion tool in ArcGIS.The dimensions of the study area grid were 1238 rows by 503columns; thus the total number of cells was 384,932. The frequencyratio and weights-of-evidence were based on the observed

Fig. 2. Habitat potential maps for Prionospio japonica generated fro

relationships between each factor and the distribution of species.Tables S1 and S2 show the spatial relationship between speciesoccurrence and each factor.

4. Results

4.1. Habitat potential mapping

The spatial relationship between species occurrence and species-related factors was derived using the frequency ratio and weights-of-evidence model. The ratios and weights value represents thepossibility of species occurrence. Therefore, it can be assigned asa relative ratio for mapping the habitat potential for the species. Inthe case of the frequency ratio model, the Polychaeta HabitatPotential index (PHPFR) was calculated by summing each factor ratiovalue using Eq. (1). In the case of the weights-of-evidence model,the Polychaeta Habitat Potential index (PHPWOE) was calculated bysummingWþ andW� of the binary pattern (see Fig. S2 and Table S1)for a range of factor values or factor classes using Eq. (4). High PHPFRand PHPWOE index values indicated a higher potential for a species;a lower value suggested a lower potential for a species. Figs. 2 and 3represent the habitat potential map for Prionospio japonica andPrionospio pulchra, in which a PHPFR and PHPWOE index value wasassigned to each pixel. The indices of each potential map weresorted in descending order and categorized into four classes for easyvisual interpretation: very high 5%, high 15%, medium 30%, andlow 60%.

The frequency ratio and weights-of-evidence value of Prionospiojaponica suggested that the species was centred around areas oflow elevation and steep slopes (Table S1). This species appeared tobe mainly distributed away from the tidal channels with sufficientremnant surface water and short exposure times. The 202e225range of ‘DEM’, the 15.36e18.25 range of ‘Slope’, the NW-facing of

m the frequency ratio (a) and weights-of-evidence (b) model.

Fig. 3. Habitat potential maps for Prionospio pulchra generated from the frequency ratio (a) and weights-of-evidence (b) model.

Fig. 4. Success rate curves showing the cumulative percentage of each speciesoccurrence (y-axis) for the descending ordered polychaeta habitat potential index(PHP) rank (x-axis) in the case of Prionospio japonica and Prionospio pulchra based onfrequency ratio and weights-of-evidence.

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‘Aspect’, the 6350e7068 range of ‘Exposure time’, the16,079e27,784 range of ‘Distance from tidal channels’, the zerovalue of ‘Density of tidal channels’, the sand flat class of ‘Sedi-mentary facies’ and the 202e209 range of ‘Spectral reflectance’showed the highest ratios and C/S(C) values. These results provideimportant indicator classes or ranges in terms of the habitatpotential of P. japonica. This species was located in habitats rela-tively far from the tidal channels with short exposure times. Prio-nospio pulchra also tended to be found in areas of low elevationwith plenty of remnant surface water, however the species did notappear to prefer a distinct tidal channel distribution or exposureduration.

4.2. Verification

Our predictions of the habitat potentials of Prionospio japonicaand Prionospio pulchrawere verified using known species locationsthat were not used in the analysis. Verification was performed bycomparing the species data with the polychaeta habitat potential(PHP) map. The comparison results are shown in Fig. 4 as a linegraph. The success rates illustrated in Fig. 4 explain how well themodel and factors predicted the species habitat. To obtain therelative ranks for each prediction pattern, the calculated PHP valuesof all the cells in the study area were sorted in descending order.The ordered cell values were then divided into 100 classes, withaccumulated 1% intervals. The above procedure was also adaptedfor the species occurrence cells by comparing the 100 classesobtained with the distribution in the study area. For example, in thecase of frequency ratio (Fig. 4), the 90e100% (10%) class of the studyarea where the species habitat potential index had a higher rankcould explain 19% of all P. japonica. In addition, the 80e100% (20%)class of the study area where the species habitat potential indexhad a higher rank could explain 72% of all P. japonica. To quanti-tatively estimate the accuracy of the predicted habitat potential, the

ratio of the area under the curve to the total area of the graph wascalculated. If 100% of the P. japonica occurrence belonged to the firstclass, indicating the top 1% of the PHP values, the area under thecurve was equal to the total area of the graph, which means thatthe prediction accuracy was 100% (Lee et al., 2004). The area underthe curve ratio can therefore be used to assess the predictionaccuracy. The area under the curve ratio and the prediction accu-racy are shown in Fig. 4. In the case of the frequency ratio model,

J.-K. Choi et al. / Estuarine, Coastal and Shelf Science 93 (2011) 98e105104

the macrobenthic species habitat map shows an accuracy of 77.71%and 74.87% for P. japonica and P. pulchra, respectively. In addition,for the weights-of-evidence model, the macrobenthic specieshabitat map shows an accuracy of 64.05% and 62.95% for P. japonicaand P. pulchra, respectively.

5. Conclusion and discussion

The macrobenthos distributions in the Hwangdo tidal flat,Korea, were quantitatively estimated by GIS-based probabilisticmodels along with remote sensing techniques and field observa-tions. Data for two polychaeta species and eight control factorswere effectively applied to potential mapping using the frequencyratio and weights-of-evidence model. The probabilistic modelswere easily applied to determine the ratio and weight of eachspatial variable based on the spatial relationships between thespecies occurrence seen from field collections and each spatialvariable influencing the species distribution. The ratio and weightof each variable was easily integrated into a polychaeta habitatpotential index value to generate potential maps. The potentialmaps were then verified by comparing them to existing specieslocations that were not used in the analysis. The conclusions drawnfrom this study are as follows:

(1) Field observation identified 59 polychaeta comprising 3697species, among which Nephths polybranchia was the mostdominant species in the Hwangdo tidal flat; most species wereobserved in low elevation areas. Prionospio japonica and Prio-nospio pulchra showed similar patterns of distribution in termsof total abundance and biomass, and were also observed inrelatively low elevation areas.

(2) The spatial relationships between the occurrences of the twopolychaeta species and species-related factors were calculatedusing the frequency ratio and weights-of-evidence model. The‘sand flat’ class for both species showed the highest ratio valueamong the relative ratios for the species habitat potential in‘sedimentary facies’, which implies that polychaeta is mainlydistributed in sand flat facies in the study area. Specifically, theratios and weight values of the two polychaeta species sug-gested that topographical elements, such as elevation and tidalchannels distribution, are important indicators of habitatpotentials for these species in the study area.

(3) The relative ratios of each factor class for the related factorswere integrated to generate species habitat potential maps.Prionospio japonica was centred around areas of low elevationand steep slopes, and was mainly located away from the tidalchannels with sufficient remnant surface water and shortexposure times. Prionospio pulchra also appeared to preferareas of low elevation with substantial remnant surface water,although this species did not show a preference for tidalchannel distribution or exposure duration.

(4) The frequency ratio results showed accuracies of 77.71% and74.87% for Prionospio japonica and Prionospio pulchra, respec-tively. The weights-of-evidence results showed accuracies of64.05% and 62.95% for each macrobenthic species. Thefrequency ratios were 13.66% and 11.92% better than theweights-of-evidence for predicting the habitats of P. japonicaand P. pulchra, respectively. The P. japonica habitat potentialmap showed the best result, with 77.71% and 64.05% accuracyusing frequency ratios and weights-of-evidence, respectively.

This studydemonstrated that probabilisticmodels alongwithGISand remote sensing techniques are effective for predicting thehabitat potentials of macrofauna species in a tidal flat. The methodprovides quantitative estimates of the future locations of species

basedonspatial information, previously knownspeciesdistributionsand species-related spatial environmental information. Generally,the macrobenthic species locations predicted based on frequencyratios showedhigher accuracies than those predicted usingweights-of-evidence. The generalization or reclassification of geospatial datainto binary maps may result in more distortion and possible loss ofvaluable information compared to multi-class maps. Therefore, theaccuracy estimated by combining the multi-class ratio could behigher than that estimated using binary Wþ and W� maps. If manymacrofauna samples are available, these methods may be appro-priate formapping the habitat potentials of macrofauna. However, itis difficult to obtain a large numberof sample locations in studyareaswith limited access.

We tested habitat mapping within the benthic environment ofa tidal flat. A tidal flat is characterized by the repetition of flood andebb tides, and thus its benthic environment is quite different fromthose of other coastal environments such as lagoons, estuaries, andriver systems. The macrobenthos and other habitat-related factorsof tidal flats also differ from those of other coastal areas. Therefore,we cannot expect that the factors examined in this study can beapplied directly to other types of ecosystems. However, the GIS-based model is a feasible approach for any kind of habitat mappingin coastal regions. Given that we examined a benthic environmentthat is influenced by tides, the model used in this study can beeffectively applied when mapping macrobenthos habitat potentialin other ecosystems if the appropriate habitat-related factors areconsidered.

This study provides an effective method for estimating theinfluence of the benthic environment on the macrofauna distri-bution in a coastal region. The method can be used to conserve andmanage the biological properties of tidal flats. Specifically, themacrofaunal potential map could be used for effective managerialdecision-making regarding the protection and conservation of tidalenvironments.

Acknowledgements

This research was supported by the Basic Research Project of theKorea Institute of Geoscience and Mineral Resources (KIGAM)funded by the Ministry of Knowledge and Economy of Korea andthe Functional Improvement of Korea Ocean Satellite Center project(PE98620) funded by the Korea Ocean Research & DevelopmentInstitute (KORDI).

Appendix. Supplementary material

Supplementary data related to this article can be found online atdoi:10.1016/j.ecss.2011.03.006.

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