mapping the potential distribution of shorebirds in japan: the importance of landscape-level coastal...

Mapping the potential distribution of shorebirds in Japan: theimportance of landscape-level coastal geomorphology

HAZUKI ARAKIDAa,*, HIROMUNE MITSUHASHIb, MAHITO KAMADAc and KAZUO KOYAMAd

aGraduate School of Advanced Technology and Science, The University of Tokushima, Tokushima 770-8506, JapanbMuseum of Nature and Human Activities, Hyogo, Sanda 669-1546, Japan

cDivision of Ecosystem Design, Institute of Technology and Science, The University of Tokushima, Tokushima 770-8506, JapandJapan Bird Research Association, Fuchu 183-0034, Japan

ABSTRACT

1. Several recent studies have predicted potential habitats along coastal areas using large-scale physicalenvironmental variables to identify target areas for conservation. However, no indices or methodologies forpredicting tidal-flat habitats at a large spatial scale have been developed. Tidal flats supporting largepopulations of shorebirds have been identified in semi-enclosed seas. Thus, bays are probably importanttopographic units for evaluating the locations of shorebirds’ non-breeding habitats.

2. A GIS-based methodology was developed to extract ‘bay units’ at any scale from coastline data. Using threeenvironment variables (the area of the bay units at three spatial scales, the percentage of shallow water area in eachbay unit, and the spring-tide range), it was possible to predict tidal-flat habitats for six shorebird species with highaccuracy (AUC> 0.95, sensitivity >90%).

3. Results showed that the percentage of shallow water area in small- and medium-scale bays was the bestpredictor of tidal-flat habitats, followed by the area of bays at a large spatial scale. This indicates that the size(scale) of a bay and the percentage of shallow water present are highly related to the presence of tidal-flat habitats.

4. The prediction maps for individual species of shorebirds clearly showed differences in the distribution patternsof species. These maps were overlaid to identify potentially species-rich areas and thus where conservation andrestoration of the tidal flats in these bays would be important.

5. The model, which uses simple coastal data, is a useful, resource-efficient method for identifying targetconservation and restoration areas across broad scales.Copyright # 2011 John Wiley & Sons, Ltd.

Received 14 January 2011; Revised 9 July 2011; Accepted 24 July 2011

KEY WORDS: hierarchical structure; semi-enclosed sea; shallow water; spring-tide range; tidal flat; topographic unit

INTRODUCTION

Tidal flats are critical stopover sites and wintering habitats forlong-distance migratory shorebirds to forage and rest (Piersmaet al., 1993; Butler et al., 2001). However, undisturbed areas ofnatural coastline have decreased as a result of recent coastaldevelopment, especially for land reclamation and shoreprotection (Gray, 1997; Lotze et al., 2006). As a result of thistrend, shorebird populations have decreased globally, with anespecially marked decline in Asia (Wetlands International,2006). For example, in Japan, the shorebird population is

reported to have declined significantly in the last 30 years(Amano, 2006; Amano et al., 2010), which is probably aresponse to the 40% loss of tidal flats since the 1950s(Environment Agency andMarine Parks Center of Japan, 1994).

Some tidal flats that harbour large migratory populations, suchas the Ramsar Sites (Ramsar Convention, http://www.ramsar.org/pdf/sitelist.pdf) and the Flyway Sites (Partnership for theEast Asian-Australasian Flyway, http://www.eaaflyway.net/),are specifically designated as conservation areas. However, theseareas cover only a small part of shorebird habitat. Conservationareas must be expanded, along with appropriate habitat

*Correspondence to: H. Arakida, Laboratory of Ecosystem Management, Civil and Environmental Engineering, Graduate School of AdvancedTechnology and Science, The University of Tokushima, 2–1 Minamijosanjima-cho, Tokushima 770–8506, Japan. E-mail: [email protected]

Copyright # 2011 John Wiley & Sons, Ltd.

AQUATIC CONSERVATION: MARINE AND FRESHWATER ECOSYSTEMS

Aquatic Conserv: Mar. Freshw. Ecosyst. 21: 553–563 (2011)

Published online in Wiley Online Library(wileyonlinelibrary.com). DOI: 10.1002/aqc.1215

management, to protect populations. Restoration of tidal-flathabitats is needed on highly modified coasts along urbanizedareas (Atkinson, 2003; Erwin and Beck, 2007). Thus, a methodis needed to identify and evaluate ecologically suitable placesfor conservation and restoration from a broad-scale perspective.

For rocky shore habitats, predictive habitat distributionmodels (Guisan and Zimmermann, 2000) have been developed,using the spatial variability of geophysical factors (Isæus, 2004;Bekkby et al., 2008, 2009b). These models have been applied tobroad-scale habitat mapping aimed at identifying targetconservation areas. The models differ from methods usingaerial photographs or satellite imagery and provide a betterunderstanding of the factors and processes that structure coastalhabitats. This knowledge is indispensable for proper habitatmanagement. Thus, indices and methodologies that predicttidal-flat habitats at a large spatial scale should be developed.

At a local scale, some models predict suitable foraginghabitats for shorebirds by using detailed physical predictorsthat relate food abundance/availability and predation danger,such as the organic content of the sediment, type of sediment,surface water depth, and distance to cover used by avianpredators (Granadeiro et al., 2004; Kraan et al., 2009;Zharikov et al., 2009). However, obtaining data for thesedetailed environmental variables is not always possible on abroad scale, such as at the national level. Thus, it is necessaryto clarify what types of environmental variables are sufficientto predict the potential habitats of shorebirds at this scale.

Topographical indicators to develop predictive models oftidal flats on a broad scale include the discharge from rivers,wave exposure, and tidal range (Boyd et al., 1992; Harriset al., 2002). The shape of the coastline also affects theformation of a tidal flat. A semi-enclosed bay easily trapssediment because the sea area is protected from ocean swellsand has few tidal currents or locally wind-generated waves(Golbuu et al., 2003). The Biodiversity Center of Japan (2009)reported that the majority of tidal flats with large populationsof shorebirds are in semi-enclosed sea bays. Consequently, the‘enclosedness’ of the coastline should be considered a usefulenvironmental variable for predicting shorebird habitats.

Studies have evaluated the enclosedness of sea areas(International EMECS Center, http://www.emecs.or.jp/index-e.html; Yokoyama, 2003; Bekkby and Isæus, 2008).However, a standardized method to quantify a ‘bay unit’ hasnot yet been devised. Japan’s coastline is indented withinlets and enclosed bays, displaying a hierarchical and fractalstructure; e.g. one large bay may incorporate several smallbays. Therefore, it is necessary to define a bay unit in aspatially hierarchical way.

A GIS-based methodology was developed that canautomatically extract the bay units definable at any scale byusing polygon data of the coastline. Using the occurrence dataof six shorebird species as response variables, a model forpredicting the potential habitats for shorebirds was establishedusing very simple variables of Maxent multi-scale analysis(Phillips et al., 2006) that relate to topography. Bay units wereextracted at different scales, along with other topographicalvariables, including the areas of the bay units at three spatialscales, the percentage of shallow water area in each bay unit,and the spring-tide range. Then, the usability of the methodto extract bay units was considered as well as the spatialdistribution models and their accuracy. The ecologicalsignificance of the environmental variables selected for the

model is discussed in relation to the location of the tidal-flathabitats and the distribution patterns of the six shorebird species.

METHODS

Study area and shorebird data

The study area included the four major islands of Japan:Hokkaido, Honshu, Shikoku, and Kyushu. The Sea ofOkhotsk, the Sea of Japan, the East China Sea, and the PacificOcean surround the islands, covering a length of 1747km fromnorth to south and a width of 1837km from west to east(Figure 1(a)). Coral reef areas, which provide shelter fromwaves (Cochard et al., 2008), were not included in the study area.

Six shorebird species were chosen for analysis: dunlinCalidris alpina, red-necked stint Calidris ruficollis, whimbrelNumenius phaeopus, grey-tailed tattler Heteroscelus brevipes,Kentish plover Charadrius alexandrinus, and grey ploverPluvialis squatarola. These species mainly use tidal flats(Amano, 2006), and are the most common species in Japan(Biodiversity Center of Japan, 2009).

The data for the analysis were based on the results from ashorebird census in Japan from 1999 to 2008 (e.g. WWFJapan, 2007; Biodiversity Center of Japan, 2009). The 63locations (points) chosen for the analysis included mainlytidal flats and all had been reported to support more than 100individuals (parks surrounded by sea walls were not included).In total, 59% (37 points) of the data analysed had alreadybeen selected as Important Bird Areas (IBAs) for shorebirds(Wild Bird Society of Japan, 2007). The training samples ofthe six shorebird species were 60, 32, 32, 25, 29, and 17,respectively (Figure 1, Table 1).

Environmental data sets

To calculate the areas of the bay units at three spatial scalesand the percentage of shallow water area in each bay unit,the bay units were extracted from the Japanese coastlinedata (Marine Information Research Centre, http://www.mirc.jha.or.jp/en/index.html), following the procedure outlinedin Figure 2. First, a buffer was created at X km from thecoastline towards the sea area (Buffer 1), then a buffer wascreated at X km from Buffer 1 towards the coastline (Buffer 2).The area between Buffer 2 and the coastline was then defined asa bay unit.

If there was a wide-open sea area in the bay, Buffer 1became a doughnut polygon. In these situations, ‘open-holebays’ (Figure 3(a)) and ‘closed-hole bays’ (Figure 3(b)) wereexamined. Since the minimum buffer scale to extract theprincipal bays in Japan (e.g. Tokyo Bay, Ise Bay) was 6 km(closed-hole), bay units at three different scales (i.e. 1 km atsmall scale, 3 km at medium scale, and 6 km at large scale)were prepared for the multi-scale analysis. Both open-holebays and closed-hole bays were extracted for each of thesethree scales. Then the areas of the bay units and the shallowwater area (<10m; Marine Information Research Centre,http://www.mirc.jha.or.jp/en/index.html) of each bay unitwere calculated (Table 2). The coastline data and shallow-waterdata were modified before extracting the bay units (Appendix a).Modifications were also made for the extracted bay units(Appendix b).

H. ARAKIDA ET AL.554

Copyright # 2011 John Wiley & Sons, Ltd. Aquatic Conserv: Mar. Freshw. Ecosyst. 21: 553–563 (2011)

The spring-tide range (i.e. the mean difference in depthbetween high- and low-water springs) was used todetermine the degree of emergence of a tidal flat. Therange in spring-tide data (Japan Coast Guard, 1992) wasinterpolated by applying the inverse distance weighted(IDW) interpolation method with the following settings:power of 2, search radius of 40 km, and cell size of 1 km.The spring-tide ranges on the two sides of channels werevery different and were therefore interpolated separately.Areas where there were data shortages were interpolatedagain from neighbouring values.

Multicollinearity was not found between these environmentalvariables (R2< 0.6). Therefore, all of the variables were arrayed

into grids with 200m resolution for the subsequent analysis. Ifareas that are obviously unsuitable as shorebird habitats areincluded as background data, the AUC value cannot be reliablyevaluated (Phillips et al., 2009). Thus, only data from within theshallow-water areas (<10m) were used.

Model development

Using areas of the bay units at three spatial scales, thepercentage of shallow water area in each bay unit, and thespring-tide range as predictor variables, and occurrence dataof the six shorebird species as response variables, a multi-scaleanalysis was conducted on a national scale. Maximum

0 500 km

d) Grey-tailed tattlerc) Whimbrel

f) Grey plovere) Kentish plover

b) Red-necked stinta) Dunlin

Hokkaido

Honshu

Shikoku

Kyushu

Figure 1. Study area. Each map (a)–(f) represents the actual distribution of the shorebird species. The black points show the census pointswhere over 100 shorebirds were recorded.

MAPPING THE POTENTIAL DISTRIBUTION OF SHOREBIRDS IN JAPAN 555


entropy modelling (Maxent: Phillips et al., 2006) was used forthe analysis, which is an automated learning method, toestimate the most uniform distribution (maximum entropy)across the study area given the constraint that the expectedvalue of each environmental predictor variable under thisestimated distribution matched its empirical average. Thismethod was chosen because it requires only presence data; itsperformance has been evaluated as one of the best whencompared with other distribution-modelling methods,particularly those with small sample sizes (Elith et al., 2006;Hernandez et al., 2006); and it can handle the non-linearity ofenvironmental variables (Phillips et al., 2006).

The performance of the models was evaluated using the areaunder the curve (AUC) approach with receiver operatingcharacteristic analysis (Fielding and Bell, 1997). The accuracyof the models was evaluated by sensitivity using the maximumtraining sensitivity plus specificity logistic thresholds (Manelet al., 2001). The importance of each predictor variable wasevaluated by its contribution to the gain of the models.

Potential habitats of each shorebird species were clarified bymapping the continuous values (ranging from 0 to 1) predictedby Maxent over the extent of the entire area of Japan, and

differences in distribution patterns were compared. Toidentify the potentially species-rich areas, these values werereclassified as ‘1’ if the value was above the selected thresholdand ‘0’ if the value was below the selected threshold, then thevalues of the six species were added and mapped. As thepredicted areas of these national scale maps were very small,the maximum value in each 10 km grid was used for therepresentation. For the potentially species-rich areas, detailedmaps were created to discuss the relationship between thelocation of tidal-flat habitats and the coastal topography.

A geographic information system (GIS: ArcGIS 9.2+SpatialAnalyst, ESRI) was used to prepare the spatial data of theenvironment and species distribution, and for mapping thepotential habitats. Maxent version 3.2.1 (http://www.cs.princeton.edu/~schapire/maxent/) was used for the data analysisand to calculate AUC and thresholds; default parameters wereemployed with the exception that the regularization multipliervalue was set to 1.5 to prevent over-fitting.

RESULTS

Extraction of bay unitsBay units were identified at different scales from the coastlinedata following the procedure outlined in Figures 2 and 3. Thenumbers of the six bay units (BO1, BC1, BO3, BC3, BO6,BC6) were 21601, 19909, 10168, 9140, 5911, and 5331,respectively, and these bay units covered the entire coastalarea of the Japanese islands. The areas of these bay units andthe percentage of the shallow water area in each bay unitwere calculated, and the spring-tide range was interpolated byIDW (Figure 6(h)). Using these predictor variables, potentialhabitats of the six shorebird species were predicted.

Factors affecting species distributions

AUC values and sensitivities were very high for all species(>0.95 and 90%, respectively, Table 1). For each species,the environmental variables SO1 and SO3 made thehighest contributions to the gain of the models (Table 3).That of SO1 was much higher than SO3 for the dunlinand the red-necked stint, whereas SO3 was substantiallyhigher than SO1 for the grey plover. SO1 and SO3produced similar results for the whimbrel, the grey-tailedtattler, and the Kentish plover. BC6 followed SO1 andSO3 for each species, and its contribution was highest forthe grey plover and lowest for the dunlin. BO1 contributedmore than 10% for the whimbrel and the grey-tailedtattler, as did SPR for the Kentish plover. Whereas theresponse curve of BO1 decreased, that of BC6 followed aconvex curve, and those of SO1, SO3 and SPR increased.

Species distributions

To determine the potential habitats for the six species, thepredicted values by Maxent were mapped for the entire areaof Japan (Figure 4). The maps showed that the potentialhabitats for the dunlin, the red-necked stint, and thewhimbrel were widely distributed. Conversely, those for thegrey-tailed tattler, the Kentish plover, and the grey ploverwere partially distributed.

Table 1. Sensitivity at the cut-off point and AUC-values

SpeciesTrainingsamples

Cut-offpoint*

Truepositivesamples

Sensitivity(%) AUC

Dunlin 60 0.12 58 97 0.97Red-neckedstint

32 0.15 31 97 0.97

Whimbrel 32 0.13 31 97 0.97Grey-tailedtattler

25 0.21 23 92 0.97

Kentishplover

29 0.17 28 97 0.98

Greyplover

17 0.33 16 94 0.98

*Maximum training sensitivity plus specificity logistic threshold.

Figure 2. Procedure for bay unit extraction from the coastline data ofthe Marine Information Research Centre. Step (1) A buffer (X km) wascreated from the coastline toward the sea area: Buffer 1. Step (2) Abuffer (X km) was recreated from Buffer 1 towards the coastline:Buffer 2. The area between Buffer 2 and the coastline was definedas a bay unit. The bay unit was extracted if half the distance of the

bay mouth was less than the buffer width.



The potentially species-rich areas, which were determinedby mapping the sum of the extracted values from thesuitable-unsuitable maps of the six species (1/0 data), showedthat these six species were concentrated in East Hokkaido,Tokyo Bay, Ise Bay, Seto Inland Sea, Suo Sea, and the coastal

area of Kyushu (i.e. Hakata Bay, Ariake Sea, and YatsushiroSea), habitats that comprise the major bays, lagoons, and theinland-sea of Japan (Figure 5). Furthermore, lagoon-tidal flatswere extracted for East Hokkaido (Figure 6(a)), and estuarinetidal flats were extracted in other areas (Figures 6(b)–(g)).

Predicted values in some areas, e.g. Tofutsu Lake (Figure 6(a))for the grey-tailed tattler, Sanbanze for the dunlin (Figure 6(b)),and the Arao Coast (Figure 6(g)) for all species, wereunderestimated, even though 100 individuals were recorded.The potential habitat distribution maps also showed that theextent of some other foreshore tidal flats was also underestimated(e.g. Banzu: Figure 6(b), Daijyugarami: Figure 6(g)), whereasoverestimated areas were located in harbours close to urbanizedareas (Figures, 6(b), 6(c), and 6(f)).

DISCUSSION

Spatial distribution models and their accuracy

Using variables related to topography, including bay unitsextracted at different scales as well as other topographicalvariables (i.e. the areas of the bay units at three spatial scales,the percentage of shallow water area in each bay unit, and thespring-tide range), the potential habitats of the six shorebirdswere predicted with very high accuracy (AUC> 0.95, the

a)

b)

Figure 3. Open- and closed-hole bays. The presence of an open sea area in a bay causedBuffer 1 to become a doughnut polygon. (a)Open-hole bay: this bay unitrepresents the proximity of land areas. The small-scale bay in the big bay is extracted. (b) Closed-hole bay: this bay unit represents the width of the bay mouth

facing the open sea.

Table 2. Geographic variables used for modelling shorebirddistributions in Japan

Variables Description

BO1 Bay units at a 1 km scale (open hole) (m2)BC1 Bay units at a 1 km scale (closed hole) (m2)BO3 Bay units at a 3 km scale (open hole) (m2)BC3 Bay units at a 3 km scale (closed hole) (m2)BO6 Bay units at a 6 km scale (open hole) (m2)BC6 Bay units at a 6 km scale (closed hole) (m2)SO1 Shallow water area (<10m) in the bay units at a 1 km scale

(open hole) (%)SC1 Shallow water area (<10m) in the bay units at a 1 km scale

(closed hole) (%)SO3 Shallow water area (<10m) in the bay units at a 3 km scale


(closed hole) (%)SO6 Shallow water area (<10m) in the bay units at a 6 km scale


(closed hole) (%)SPR Spring-tide range (cm)



sensitivity >90%) throughout Japan (Figures 4, 5, and 6). Thepredicted areas included recently surveyed habitats with largepopulation records of shorebirds (Wild Bird Society of Japan,2007; Biodiversity Center of Japan, 2009), as well as otherimportant habitats surveyed in the past, such as the northernpart of the Suo Sea (Figure 6(e)), Kikuchi River (Figure 6(g))and Isahaya Bay before the construction of the flood gate(Figure 6(g); Environment Agency of Japan, 1997). Therefore,despite their simplicity, the variables that were used appear to besufficient for predicting shorebird habitats at large spatial scales.

The gaps between the predicted areas and the actual habitatswere examined statistically by the cut-off point and visually bythe potential habitat distribution maps. The underestimatedareas included some foreshore tidal flats. Most estuarine tidalflats situated next to foreshore tidal flats were evaluatedadequately. Thus, the topographic condition of the location offoreshore tidal flats does not greatly contradict the resultsfrom this analysis. To improve the model, the sediment supplyof estuaries (Boyd et al., 1992) and coastal slope (Kang et al.,2002), which are strongly related to the formation of foreshoretidal flats, should be included in future analyses. Theoverestimated areas were located in harbours close tourbanized areas (Figures, 6(b), 6(c), and 6(f)), probablybecause these areas have been highly modified from natural toartificial coasts with no shallow water (<1m) remaining.However, these are prime areas to target for habitat restoration.

The ecological significance of the environmental variables

SO1 and SO3 exhibited the highest contributions to the models,followed by BC6 for each species. Conversely, the contributionof SPR was above 10% only for the Kentish plover (Table 3).These results indicate that the bay units at the threespatial scales were the most important topographic factor.The response curves of the best predictors, SO1 and SO3,increased; indeed, the most suitable potential habitats forthe shorebirds were small- and medium-scale bays withplenty of shallow water (Figure 6). The combination of thebay units and total shallow-water area was important,probably not only because the bay units indicate areassheltered from waves but also because sediment is easily

deposited in shallow-water areas. BC6 reflected large-scalebays facing the open sea, which tend to contain largeareas of shallow water (Figure 6) with decreasing exposureto waves from the open sea within the bay leading toincreased sedimentation (Golbuu et al., 2003). Therefore,our results indicate that the hierarchical structures of thebays as well as the existence of large areas of shallowwater correlate with the presence of tidal flats.

The optimal combination of the scale of a bay and thepercentage of shallow water in the bay differed betweenspecies. SO1 was much higher than SO3 for the dunlinand the red-necked stint (Table 3). The bay at the 1 kmscale included river mouths (Figure 6). Therefore, the riveritself may affect the habitats of these two species, ratherthan the hierarchical structure of the bay, which weakenswave impact. In contrast, SO3 was substantially higherthan SO1, and BC6 was also high for the grey plover.Consequently, the potential habitats of the grey plover(Figure 4(f)) were more likely to be in large-scale bays andlagoons (Figure 6) than the potential habitats of the dunlinand red-necked stint (Figures 4(a) and 4(b)). This indicatesthat the grey plover prefers tidal flats located in bays thathave hierarchical structure. The grey plover belongs to theCharadriidae family, which detects prey using visual cues,and some studies indicate that surface dwellingmacrobenthos (epifauna) are an important food source ofthis species (Pienkowski, 1983; Jing et al., 2007).Therefore, the hierarchical structure of the bay may drivethe distribution of epifauna. For the remaining species, thedifferences between SO1 and SO3 were negligible(Table 3); however the distribution pattern of the whimbrelwas similar to that of the dunlin and the red-necked stint,while those of the grey-tailed tattler and the Kentishplover were similar to that of the grey plover (Figure 4).Overlaying the different distribution patterns of the sixspecies showed that the species-rich areas in Figure 5represent the principal semi-enclosed seas in Japan. Thus,it is imperative to conserve and restore the tidal flats inthese sea areas to preserve species diversity.

The contributions of the variables differed greatlybetween open-hole bays and closed-hole bays (Table 3).

Table 3. Contribution of each environmental variable to the models

Variables

Species

Dunlin Red-necked stint Whimbrel Grey-tailed tattler Kentish plover Grey plover

Bay unitsBO1 5 7 10* 13* 6 7BC1 0 0 0 0 0 0BO3 0 2 1 0 2 1BC3 1 0 0 2 1 1BO6 1 2 0 0 0 3BC6 6 10* 10* 14* 14* 16*Percentage of shallow water areaSO1 62* 59* 35* 29* 30* 21*SC1 8 2 5 2 3 0SO3 14* 16* 32* 35* 34* 47*SC3 0 0 0 0 0 0SO6 1 0 2 0 2 0SC6 0 0 0 0 0 0Spring-tide rangeSPR 4 3 6 4 10* 3

*Contribution greater than 10%.



Open-hole bays (SO1, SO3) were greater contributors toshallow water areas in small- and medium-scale bays thanwere closed-hole bays (SC1, SC3). In contrast, closed-holebays (BC6) made a greater contribution to the area oflarge-scale bays than did open-hole bays (BO6). These

results indicate that at the small and medium scales theproximity of land areas, and at the large scale the size ofthe bay mouth that faces the open sea, sufficiently predictthe enclosedness of sea areas on the Japanese coastline.However, coastal topography differs between countries;

c) Whimbrel

d) Grey-tailedtattler

e) Kentish plover

f) Grey plover

Probability

Probability

Probability

Probability

Probability

Probability

0.15-0.5>0.5

0.13-0.5>0.5

0.21-0.5>0.5

0.17-0.5>0.5

0.33-0.5>0.5

a) Dunlin

b) Red-neckedstint

0 500 km

0.12-0.5>0.5

Figure 4. Potential habitat-distribution maps for six shorebird species: maximum probability in each 10� 10 km grid. White squares show probabilitiesover a logistic threshold of less than 0.5. Black squares show probabilities greater than 0.5.



thus, for future analyses, an evaluation of both open-holebays and closed-hole bays is recommended.

The bay unit as an indicator

In the UK and Scandinavia, the openness of the coastline shapehas been used as a topographical wave-exposure index forrocky shore habitats exposed to waves. This indicator has beendeveloped through coastal engineering studies, and theBaardseth Index, which uses open angles (Ruuskanen et al.,1999), or the fetch indicator (i.e. the distance to the nearestshore or island) as well as wind (Keddy, 1982; Thomas, 1986)have been applied to habitat evaluation. Several recent studiespredict seagrass and kelp habitats (Isæus, 2004; Bekkby et al.,2008, 2009b), rocky-shore community structures (Burrowset al., 2008), rocky seabeds (Bekkby et al., 2009a), and largeshallow inlets and bays (Bekkby and Isæus, 2008) at largespatial scales using GIS-based fetch and wind.

Both the bay unit devised here and the above-mentionedwave-exposure index are based on the shape of the coastline.However, the wave-exposure index reflects the wave strengthitself at each point, while the bay unit indicates the area ofsemi-enclosed sea. A methodology was developed that canautomatically extract bay units definable at any scale, i.e. notonly large-scale bays that are affected by waves from the opensea but also smaller-scale bays within the large-scale bays,which can be evaluated hierarchically. In particular, the areaof shallow water in smaller-scale bays was the highestcontributing variable to the prediction models (Table 3).Indeed, smaller-scale bays with extensive shallow waterexhibited tidal flats (Figure 6), which are areas where sedimentis readily deposited. Thus, the method appears to be valid forevaluating soft-sediment habitats located in inner bays.

Application to tidal flat habitat mapping and conservationplanning

As in the GIS-based model of rocky shores (Isæus, 2004; Bekkbyet al., 2008, 2009b), the method developed in this study is aresource-efficient method for identifying target conservationand restoration areas across broad scales. The predictive map ofpotentially species-rich areas identifies the principal bays to beconserved in Japan. The map also indicates highly modifiedbays with only a few tidal flats remaining that should berestored. Despite urbanization that has modified bays such asTokyo Bay, the potential for restoration is very high. Someplaces with high potential have been excluded from themonitoring programme in Japan, and these should be included.

The proposed models used very simple coastal data (i.e.coastline and shallow-water data), but predicted potentialshorebird habitats at broad scales with considerable accuracy.The method using bay units was relevant for hierarchicallyevaluating the locations of tidal flats: hence, it should beapplicable to other tidal flat species. The coastline andbathymetry data are easily obtained worldwide (e.g. NOAA:http://www.ngdc.noaa.gov/mgg/bathymetry/relief.html), andthus the method is globally applicable.

ACKNOWLEDGEMENTS

We appreciate the shorebird counters as well as the Ministry ofthe Environment of Japan for the collection of bird data. Wealso thank Dr. Susumu Nakano for his advice on physicalenvironmental factors that affect tidal-flat formation, TakeshiOsawa for help with data preparation, the anonymous refereeand Dr John Baxter for their constructive suggestions in

East Hokkaido

Tokyo BayIse Bay

Suo Sea

Hakata Bay

Seto Inland Sea

Ariake Sea and Yatsushiro Sea500 km0

123456

Probability

Figure 5. The predicted occurrence of the six species defined by the threshold values in Table 2. The map shows the maximum probability in each10� 10 km grid.



!!

!!!!!!!! !!!!!!!!!!!

Tofutsu Lake

Sanbanze

Bay (3km scale, open hole)Shallow water (<10m)

a) b)

c) d)

e)

g) h)

0 500 km

0 20 km 0 10 km

0 10 km

0 10 km

0 5 km

0 10 km0 10 km

Spring range (m)

0 - 100

100 - 200

200 - 300

300 - 500

Banzu

Daijyugarami

IsahayaBay

Floodgate

Arao Coast

1

2

3

Probability

4

5

6

Kikuchi River

Yatsu Tidal Flat

a) East Hokkaido

b) Tokyo Bay

f) Hakata Bay

e) Suo Sea

c) Ise Bay

d) Seto Inland Sea

g) Ariake Sea and Yatsushiro sea

f)

Figure 6. Detailed maps showing the predicted number of occurrences of the six species. The circles show the census points where more than 100individuals of any species were recorded.



revising this manuscript, and Mayumi Sato and SarahGwillym-Margianto for improving the English.

REFERENCES

Amano H. 2006. Status of migratory birds that use mud flats.Chikyu Kankyo 11: 215–226 (in Japanese).

Amano T, Székely T, Koyama K, Amano H, Sutherland WJ.2010. A framework for monitoring the status of populations:An example from wader populations in the East Asian-Australasian flyway. Biological Conservation 143: 2238–2247.

Atkinson PW. 2003. Can we recreate or restore intertidal habitatsfor shorebirds? Wader Study Group Bulletin 100: 67–72.

Bekkby T, Isæus M. 2008. Mapping large, shallow inlets andbays: modelling a Natura 2000 habitat with digital terrainand wave-exposure models. ICES Journal of Marine Science65: 238–241.

Bekkby T, Rinde E, Erikstad L, Bakkestuen V, Longva O,Christensen O, Isæus M, Isachsen PE. 2008. Spatial probabilitymodelling of eelgrass (Zostera marina) distribution on thewest coast of Norway. ICES Journal of Marine Science 65:1093–1101.

Bekkby T, Moy FE, Kroglund T, Gitmark JK, Walday M,Rinde E, Norderhaug KM. 2009a. Identifying rocky seabedusing GIS-modeled predictor variables. Marine Geodesy 32:379–390.

Bekkby T, Rinde E, Erikstad L, Bakkestuen V. 2009b. Spatialpredictive distribution modelling of the kelp species Laminariahyperborea. ICES Journal of Marine Science 66: 2106–2115.

Biodiversity Center of Japan. 2009. The annual report of the‘Monitoring Sites 1000’ in Japan: shorebird monitoring.Biodiversity Center of Japan, Ministry of the Environmentof Japan, Yamanashi (in Japanese).

Boyd R, Dalrymple R, Zaitlin BA. 1992. Classification ofclastic coastal depositional environments. SedimentaryGeology 80: 139–150.

Burrows MT, Harvey R, Robb L. 2008. Wave exposureindices from digital coastlines and the prediction of rockyshore community structure. Marine Ecology ProgressSeries 353: 1–12.

Butler RW, Davidson NC, Morrison RIG. 2001. Global-scaleshorebird distribution in relation to productivity of near-shore ocean waters. Waterbirds 24: 224–232.

Cochard R, Ranamukhaarachchi SL, Shivakoti GP, Shipin OV,Edwards PJ, Seeland KT. 2008. The 2004 tsunami in Aceh andSouthern Thailand: a review on coastal ecosystems, wavehazards and vulnerability. Perspectives in Plant Ecology,Evolution and Systematics 10: 3–40.

Elith J, Graham CH, Anderson RP, Dudík M, Ferrier S, GuisanA, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A,et al. 2006. Novel methods improve prediction of species’distributions from occurrence data. Ecography 29: 129–151.

Environment Agency and Marine Parks Center of Japan. 1994.The report of marine biotic environment survey in the 4thnational survey on the natural environment: Vol. 1, TidalFlats. Nature Conservation Bureau, Environment Agencyof Japan, Tokyo (in Japanese).

Environment Agency of Japan. 1997. Inventory of WetlandsUsed by Migratory Shorebirds. Nature ConservationBureau, Environment Agency of Japan: Tokyo (in Japanese).

Erwin RM, Beck RA. 2007. Restoration of waterbird habitatsin Chesapeake Bay: Great expectations or Sisyphusrevisited? Waterbirds 30: 163–176.

Fielding AH, Bell JF. 1997. A review of methods for theassessment of prediction errors in conservation presence/absence models. Environmental Conservation 24: 38–49.

Golbuu Y, Victor S, Wolanski E, Richmond RH. 2003.Trapping of fine sediment in a semi-enclosed bay, Palau,Micronesia. Estuarine, Coastal and Shelf Science 57:941–949.

Granadeiro JP, Andrade J, Palmeirim JM. 2004. Modelling thedistribution of shorebirds in estuarine areas using generalisedadditive models. Journal of Sea Research 52: 227–240.

Gray JS. 1997. Marine biodiversity: patterns, threats andconservation needs. Biodiversity and Conservation 6: 153–175.

Guisan A, Zimmermann NE. 2000. Predictive habitatdistribution models in ecology. Ecological Modelling 135:147–186.

Harris PT, Heap AD, Bryce SM, Porter-Smith R, Ryan DA,Heggie DT. 2002. Classification of Australian clastic coastaldepositional environments based upon a quantitativeanalysis of wave, tidal, and river power. Journal ofSedimentary Research 72: 858–870.

Hernandez PA, Graham CH, Master LL, Albert DL. 2006.The effect of sample size and species characteristics onperformance of different species distribution modelingmethods. Ecography 29: 773–785.

Isæus M. 2004. Factors structuring Fucus communities at openand complex coastlines in the Baltic Sea. Doctoral thesis,Department of Botany, Stockholm University, Stockholm.

Japan Coast Guard. 1992. Tidal Harmonic Constants Tablesfor the Japanese Coast. Hydrographic Publication 742 (inJapanese).

Jing K, Ma Z, Li B, Li J, Chen J. 2007. Foraging strategiesinvolved in habitat use of shorebirds at the intertidal areaof Chongming Dongtan, China. Ecological Research 22:559–570.

Kang YK, Takahashi S, Kuroda T, Okudaira A. 2002.Characteristics of the topography and soil in foreshore tidalflats and its type classification: stability of foreshore tidalflats against wave action. Proceedings of Civil Engineeringin the Ocean 18: 13–18 (in Japanese).

Keddy PA. 1982. Quantifying within-lake gradients of waveenergy: interrelationships of wave energy, substrate particlesize and shoreline plants in Axe Lake, Ontario. AquaticBotany 14: 41–58.

Kraan C, Gils JAV, Spaans B, Dekinga A, Bijleveld AI,Roomen MV, Kleefstra R, Piersma T. 2009. Landscape-scaleexperiment demonstrates that Wadden Sea intertidalflats are used to capacity by molluscivore migrantshorebirds. Journal of Animal Ecology 78: 1259–1268.

Lotze HK, Lenihan HS, Bourque BJ, Bradbury RH, CookeRG, Kay MC, Kidwell SM, Kirby MX, Peterson CH,Jackson JBC. 2006. Depletion, degradation, and recoverypotential of estuaries and coastal seas. Science 312:1806–1809.

Manel S, Williams HC, Ormerod SJ. 2001. Evaluatingpresence-absence models in ecology: the need to account forprevalence. Journal of Applied Ecology 38: 921–931.

Phillips SJ, Anderson RP, Schapire RE. 2006. Maximumentropy modeling of species geographic distributions.Ecological Modelling 190: 231–259.

Phillips SJ, Dudík M, Elith J, Graham CH, Lehmann A,Leathwick J, Ferrier S. 2009. Sample selection bias andpresence-only distribution models: implications for backgroundand pseudo-absence data. Ecological Applications 19: 181–197.

Pienkowski MW. 1983. Surface activity of some intertidalinvertebrates in relation to temperature and the foragingbehaviour of their shorebird predators. Marine EcologyProgress Series 11: 141–150.

Piersma T, Goeij PD, Tulp I. 1993. An evaluation of intertidalfeeding habitats from a shorebird perspective: towardsrelevant comparisons between temperate and tropicalmudflats. Netherlands Journal of Sea Research 31: 503–512.



Ruuskanen A, Bäck S, Reitalu T. 1999. A comparison of twocartographic exposure methods using Fucus vesiculosus asan indicator. Marine Biology 134: 139–145.

Thomas MLH. 1986. A physically derived exposure index formarine shorelines. Ophelia 25: 1–13.

Wetlands International. 2006. Waterbird Population Estimates,4th edn. Wetlands International: Wegeningen, NL.

Wild Bird Society of Japan. 2007. Important Bird Areas inJapan. Wild Bird Society of Japan: Tokyo (in Japanese).

WWF Japan. 2007. The comprehensive report of the shorebirdpopulation monitoring census 1999-2003 in Japan. WWFJapan, Tokyo (in Japanese).

Yokoyama, H. 2003. Environmental quality criteria for fishfarms in Japan. Aquaculture 226: 45–56.

Zharikov Y, Elner RW, Shepherd PCF, Lank DB. 2009.Interplay between physical and predator landscapes affectstransferability of shorebird distribution models. LandscapeEcology 24: 129–144.

APPENDIX 1: MODIFICATIONS TO THE COASTLINE AND SHALLOW WATER DATA BEFORE THEEXTRACTION OF THE BAY UNITS AND MODIFICATIONS TO THE BAY UNITS AFTER EXTRACTION

(a) Modifications to the coastline and shallow water dataRemoval of small coastal features

Islands under 10 ha, channels less than 100m wide and bridges were eliminated because these relatively small features greatly affected thecalculation of bay units.

Addition of brackish lakes with shallow waterSome brackish lakes with shallow water (<10m) that were missing were added to the coastline data.

Modification of the upper limit of the riversThe upper limit of the rivers was defined as the limit of the tidal rise or barrages (e.g. river mouth barrages and floodgates). If thesedata were not available, a height of 2m was set as the limit.

Floodgate of Isahaya BayThe recently constructed floodgate for the dike in Isahaya Bay was not included in the coastline data so that the habitat potential ofthis area could be evaluated under natural conditions (Figure 6(g)).

(b) Modifications to the extracted bay unitElimination of some bay units

Units under 100m² were eliminated.Units were cut off at channels or capes

The bay units were cut off if the channels connected bays that faced different open seas, or if the bays were connected at the capeand the distance between the cape and bay mouth was less than 300m.



mapping the potential distribution of shorebirds in japan: the importance of landscape-level coastal...

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