Geomorphological predictors for diversity of juvenile fish in floodplain ...

Ecol. Civil Eng. （ 7 1） , 93-102, 2004

SHORT COMMUNICATION

93

短報

Geomorphological predictors for diversity of juvenile fish in floodplain pools during a low-water period Shingo YAMASHITA１）＊ , Masatoshi DENDA２） and Nobukazu NAKAGOSHI１）１）Graduate School for International Development and Cooperation, Hiroshima University, 1-5-1 Kagamiyama, Higashi-Hiroshima 739-8529, Japan ２）Water Environment Research Group, Independent Administrative Institution Public Works Research Institute, 1-6 Minamihara, Tsukuba 305-8516, Japan 山下慎吾１）＊・傳田正利２）・中越信和１）：低水位期の氾濫原プールにおける稚魚多様性予測子の探索 Ecol. Civil Eng. 7 （1） , 93-102, 2004. １）広島大学大学院国際協力研究科〒７３９―８５２９東広島市鏡山１―５―１２）独立行政法人土木研究所〒３０５―８５１６つくば市南原１―６ Abstract: To explore predictors for juvenile fish diversity in floodplain pools during a lowwater period, we surveyed the relationship between species richness and geomorphological characteristics of floodplain pools. Ten floodplain pools located in the middle reaches of the Chikuma River were chosen for this study. These pools were formed by a flood in September 2001. We surveyed each pool from October 2001 to July 2002. The data at the end of October was used for analysis of the period after flooding in autumn. The data at the end of March 2002 was used for analysis of the period before next flooding in spring. Species richness of juvenile fish was used as the target variable. Thirteen geomorphological parameters of each floodplain pool were measured: area, perimeter, covered edge length, proportion of covered edge length to total perimeter, maximum depth, average depth, coefficient of variation of depth, substrate diversity, length of pool, width of pool, shape index, distance from stream, and distance from nearest pool. From the results of multiple linear regression analyses, the usefulness of two of thirteen geomorphological predictors, maximum depth and substrate diversity, was demonstrated for the species richness of juvenile fishes after flooding in autumn. At the period before flooding in spring, the usefulness of proportion of covered edge length to total perimeter was shown. Maximum depth is considered to be an indicator of refuge function for juvenile fish from flooding. Substrate diversity is assumed the index of habitat heterogeneity of floodplain pools. It appears that the presence of cover is a requirement of juvenile fish for feeding and refuge. Because the number of pools in this study was small（n ＝ 10）, only these three parameters are not useful predictors, but the results of this study suggest the importance of habitat data on maximum depth, substrate diversity, and cover in predicting the function of nursery areas of floodplain pools. Key words: floodplain pool, geomorphological structure, juvenile fish, predictor, species diversity

Recived 27 October 2003; Accepted 23 June 2004 ＊ e-mail: [email protected]

Ecol. Civil Eng. （ 7 1） , 2004

94

Introduction

Methods

A predictive understanding of the relationship between

Study area and survey periods

biodiversity and the structure of the physical environment

The study area（lat. 36°25’N, long. 138°10’E）was located

of floodplains is essential for the conservation of fish habi-

in central Japan, in the middle reaches of the Chikuma

tats and management of riverine environments. Floodplains contain numerous water-filled depressions called

River, where the stream flows through a floodplain that

backwaters（Denda et al. 1999; Shimatani 2000） . Backwa-

contains numerous pools filled by undercurrent water with high transparency during low-flow periods. Ten floodplain

ters without permanent connection to the main stream,

. These pools（A-J）were chosen for this study（Fig.１a）

which are influenced strongly by annual floods, are referred

pools were formed in a flood in September 2001, and were

to as floodplain pools in this study, after Halyk and Balon

reconnected to the main stream on October 23, 2001. We

（1983） . As nursery areas, floodplain pools produce fish

surveyed the pools at the end of every month from October 2001 to July 2002. The data for October 25-27 was used

biomass that can return to the stream during periods of stream-pool connection（Halyk & Balon 1983）. Most fresh-

for analysis of the period after flooding in autumn.

water fish species in Japan spawn from spring to summer

Because the recruitment of juvenile fishes was observed at

（Nakamura 1969; Miyadi et al. 1976; Kawanabe & Mizuno

the end of April 2002 in shallow pools, which had no fish

1989）. It appears that juvenile fishes use floodplain pools

at the end of March 2002, we decided to use the data from March 22-24 for analysis of the period before flooding in

as escape zones during the summer to autumn flooding season, and that these pools play an important role as nursery areas for these juvenile fishes during low-water peri-

spring（Fig. 1b） .

ods lasting until the next flood.

indices of the physical environment of floodplain pools for

Transect lines spanning the width of the floodplain pools were established at 10-m intervals along the entire length

these ecological functions are needed to help with the

of each pool. Twice during each survey period, underwater

conservation of fish habitats and management of the river-

observations were carried out along the transects, and a

ine environment. Halyk and Balon（1983）studied the rela-

hand net（3 mm mesh size）was used to collect samples at

tionship between fish diversity and pool area or stream

the edges of the pools. The number of young fish of each

accessibility during flooding, and found a significant corre-

species observed was counted by each size group classified

lation between species richness and floodplain pool area.

in steps of 30 mm of their total length.

Denda et al.（2002）surveyed fish communities in backwa-

The perimeter and the position of the covered edge of

ters, and noted the influence of winter desiccation and low

each floodplain pool were measured using differential

flooding frequency, which occurs approximately once every

global positioning system （DGPS）instruments: a Path-

three years. However, no other parameters of the physical

finder Pro XR and a Geoexplorer III（Trimble, Sunnyvale,

environment of floodplain pools or backwaters were used

CA. USA） . The covered edge was identified by the pres-

in these studies.

ence of an undercut bank and large woody debris within 50

We hypothesize that certain geomorphological parame-

cm above the water surface（Lewis 1969; Heggenes 1988;

ters of floodplain pools would affect juvenile fish diversity.

Shirvell 1990; Inoue & Nakano 1994）. Depth was meas-

Therefore, to explore predictors for juvenile fish diversity

ured to the nearest centimeter at 1 m intervals along each transect. The nature of the substrate at each depth-meas-

We believe that useful

in floodplain pools at two periods （after flooding in

Measurements in the field

relationship between species diversity and the geomorpho-

urement point was recorded as follows: 1, solid rock or concrete; 2, sand（＜ 2 mm in diameter）; 3, gravel（2-50

logical characteristics of floodplain pools.

mm） ; and 5, cobbles（＞ 300 mm）. mm） ; 4, pebbles（50-300

autumn and before flooding in spring） , we investigated the

Data configuration To describe the diversity of juvenile fish, we employed the number of species observed by twice surveys each period

Yamashita S. et al.: Predictors for juvenile fish in floodplain pools

95

Fig. 1. Characteristics of the study area. （a）Location of the floodplain pools（labeled A-J）in the Chikuma River on October 2001（left）and March 2002（right） .（b）Hydrological conditions from September 2001 to July 2002 based on the water level measured at Ikuta station, which is the station nearest the study area. （MAXD） , average depth （AVD） , coefficient of variation

as an index of species richness. Juveniles in this study were identified as young-of-the-year fishes estimated by

of depth （CVD）, substrate diversity （SUBST） , length of

their total length, using information from the literature

pool（LENG） , width of pool（WIDTH） , shape index（SI）,

（Nakamura 1969; Miyadi et al., 1976; Kawanabe and

distance from stream（DISST） , and distance from nearest

Mizuno 1989）. Because the degree of sampling effort at

pool （DISPL）.

each floodplain pool was not equal, the number of juveniles

LENG. WIDTH. DISST, and DISPL were calculated

was not used as a parameter of diversity.

using ArcView 3.2（Environmental Systems Research Insti-

Thirteen geomorphological parameters of each floodplain

tute, Redlands, CA. USA）geographical information system

pool were measured: area （AREA） , perimeter （PERI）,

（GIS）software. The value of shape index（Patton 1975）

covered edge length （COV） , proportion of covered edge

was calculated as follows:

length to total perimeter （COVP） , maximum depth

The values of AREA. PERI. COV.

Ecol. Civil Eng. （ 7 1）, 2004

96

and a simple linear regression analysis of the relationships （１）

between geomorphological characteristics and the species richness of juvenile fish.

To avoid multicollinearity, we

where P is the perimeter and A is the area of the floodplain

picked up the predictors with a strong correlation.

pool. Thus, SI has a minimum value of 1, which would

selected the significant predictors estimated by simple

describe a circle.

We

SUBST was calculated by using the

linear regression analysis. To identify the predictor within

formula for Simpson’s index of diversity. Simpson’s index

the bounds of possibility, we configured the level of signifi-

has the advantage that sample size has little influence on

cance using simple linear regression analysis at 0.10.（3）

the calculation results （Lande 1996; Morisita 1996） .

Stepwise multiple linear regression analysis was conducted

Simpson’s index of diversity（D）was calculated as follows:

using predictors after screening. We configured the level of injection at 0.05, with the level of elimination of 0.10 in the

（２）

stepwise selection.

In addition, the Akaike information

criterion（AIC）was calculated for help of model selection. where S is the total number of substrate type, N is the total number of points and Ni is the number of points with substrate type i.

Results

Data analysis

Seasonal change of the species richness

To extract the main predictor variables, we carried out step-

The species richness of juvenile fishes in the floodplain

wise multiple linear regression analyses between geomor-

pools in October and that in November were approximately

phological characteristics and the species richness of

equal（Fig.２）. Zacco platypus was distributed in all pools,

juvenile fishes in three steps as follows.（1）A logarithmic

but Hemibarbus barbus and Liobagrus reini were observed

transformation of the exact values of the predictor vari-

only in the pools at the right side of the main stream. The

ables was performed before the analyses to standardize

species richness was decreased by influence of winter desic-

variances and improve the normality of the data

cation in December. At the end of March, ten pools were

distribution.

filled by water, but the species richness in March was

Percentile data were arcsine square root

transformed.（2）To screen the predictors before multiple

approximately equal to that in February.

linear regression analysis, we conducted a correlation analy-

April, the recruitment of juvenile fishes was observed, that

sis （Pearson’s r）among geomorphological characteristics

was notable in the pools at the right side of the main

At the end of

Fig. 2. Seasonal change of the species richness of juvenile fishes in the floodplain pools Values are expressed as mean ±SD. R: the pools（labeled A-E）in the floodplain at the right side of the main stream. L: the pools（labeled F-J）in the floodplain at the left side of the main stream.


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Table 1. Juvenile fishes observed in the study area on October 2001 and March 2002. TL: the class of total length assigned as young-of-the-year fish for each species. Numbers of individuals show the average value of twice observations of each month. Family

TL （mm）

Species

Cyprinidae

Cobitidae Amblycipitidae Gobiidae

Carassius auratus langsdorfii Zacco platypus Phoxinus lagowskii steindachneri Tribolodon hakonensis Pseudorasbora parva Pseudogobio esocinus esocinus Hemibarbus barbus Misgurnus anguillicaudatus Cobitis biwae Liobagrus reini Rhinogobius sp．OR

＜９０＜９０＜６０＜９０＜３０＜６０＜９０＜９０＜６０＜３０＜３０

Number of individuals October

March

４５．０５４４３．０２０７．５１０３．５９１．０４．５１４２．０９．５３０．０４．５２．０

２６．０３３７２．０８５．５２１．０７７．０２．００．０３．５８．５０．００．０

Table 2. Geomorphological characteristics of floodplain pools in October 2001 and March 2002. Values are expressed as mean ±SD, and the range is shown in parentheses. Predictor Area（m２）

AREA

Perimeter（m）

PERI

Covered edge length（m） Covered edge proportion（％） Maximum Depth （cm） Average Depth （cm） Coefficient of variation of depth

MAXD

Substrate diversity

SUBST

Length（m）

LENG

Maximum Width （m）

WIDTH

Shape index

SI

Distance from stream（m） Distance from nearest pool（m）

COV COVP

AVD CVD

DISST DISPL

October （n ＝１０）

March （n ＝１０）

９４８．８±１０９６．５（８１．５−３５４９．２）２６５．０±２１０．３（５４．２−７３８．７）３９．１±２７．５（５．３−９３．４）２０．１±１６．０（４．８−５９．０）５５．５±２９．１（２１．０−１１２．０）１５．９±８．４（７．３−３７．４）０．８３±０．１６（０．５８−１．０８）０．５８±０．１３（０．３０−０．７３）１０４．７±７９．６（２５．３−２８４．８）１１．９±６．５（４．６−２３．７）２．５１±０．６９（１．６９−３．５０）７４．６±７０．３（０．０−１８６．９）１９．６±１７．５（４．０−５４．７）

２５４．０±４７２．１（２．７−１４９０．４）１０６．４±１１９．５（８．０−３１８．１）９．６±１１．３（０．０−３５．９）１５．４±１９．８（０．０−６１．７）３４．４±３０．３（３．０−１０８．０）１５．６±１６．３（２．０−５９．１）０．６７±０．１９（０．４２−０．９７）０．４４±０．２５（０．００−０．７０）４６．４±５５．６（３．８−１５４．１）５．０±３．５（１．３−１２．９）２．１７±０．９０（１．１４−３．８１）８８．２±７３．１（０．０−２１８．５）６０．６±３６．３（２３．１−１３６．８）


98

stream especially. New recruitment of juvenile fishes was

DISST （Table３） .

observed in the pools at both side of the main stream in

AREA. PERI. COV. MAXD. AVD. SUBST, and WIDTH

July.

correlated positively with species richness of juveniles

Survey after flooding in autumn

（Table４）. Three geomorphological parameters: AREA.

Eleven species were found in the floodplain pools（Table

MAXD, and SUBST were added to stepwise multiple linear

１）. The species with great abundance were Zacco platypus,

regression analysis.

Phoxinus lagowskii steindachneri, and Hemibarbus barbus.

results of the multiple linear regression analyses at the

The average value of pool area was approximately 945 m2,

period after flooding in autumn（Table５） . The relation-

the average value of maximum depth was approximately

ship between species richness and MAXD was described

56 cm, the pools varied in size（Table２）.

using a linear regression model（labeled 1; R2 ＝ 0.655, P ＝

A strong correlation was found among AREA. PERI.

0.005）.

LENG. WIDTH, and SI. MAXD was correlated with both

MAXD and SUBST was closely described by a multiple

AVD and WIDTH. SI also correlated with SUBST or

. linear regression model（labeled 2; R2 ＝ 0.844, P ＝ 0.001）

Seven geomorphological parameters:

Two models were created from the

The relationship between species richness and

Table 3. Matrix of correlation coefficients among geomorphological predictors at the period after flooding in autumn. AREA PERI COV COVP MAXD AVD CVD SUBST LENG WIDTH SI DISST DISPL

０．９８４０．５３３ −０．４７９０．４１１０．３８０ −０．３１１０．５６３０．９６７０．８４９０．８２４ −０．３７９ −０．１４１

PERI

COV

０．５４６ −０．４８１０．４３６０．３１６０．３７２０．２８８０．０１５ −０．３３００．０６１０．６３７０．６３７０．９８９０．４４８０．７７６０．４１７０．９１００．５０３ −０．５０２ −０．１６１ −０．１１３ −０．４７９

COVP

MAXD

AVD

０．１３５ −０．１９８０．３９６０．０１５ −０．５６８ −０．３６４ −０．４１８０．３０１ −０．１７６

０．８７００．４５５０．４１３０．２５００．７２３０．０７３０．１１９０．０５７

０．２３００．２７２０．２６４０．６８５０．０６９０．１２００．２８４

CVD

SUBST

LENG

WIDTH

SI

DISST

０．１１９ −０．３５４０．６０８ −０．０５６０．５１３０．７３２ −０．３３１０．７２４０．９１５０．５０２０．０８８ −０．４１４ −０．５１７ −０．０３８ −０．７３７ −０．１０８ −０．１０１ −０．０４８ −０．１９９０．００８ −０．３６６

Table 4. Results of simple linear regression analyses of species richness with geomorphological predictors at the period after flooding in autumn. Predictor

Coefficient

r2

F

P

AREA PERI COV COVP MAXD AVD CVD SUBST LENG WIDTH SI DISST DISPL

２．２０１３．０５４３．４１６１．９３２６．８３１６．４２１３．２８９１１．７３８２．９６２５．８８６１．３８９ −０．６０４０．９３４

０．３２２０．３０８０．３７２０．０３５０．６５５０．３６５０．０７００．５３４０．２５６０．４１１０．２２３０．０６９０．０３７

３．８０１３．５５６４．７３００．２９２１５．２１６４．５９３０．６００９．１５８２．７５２５．５８５２．２９７０．５９２０．３０４

０．０８７０．０９６０．０６１０．６０４０．００５０．０６４０．４６１０．０１６０．１３６０．０４６０．１６８０．４６４０．５９６


99

Table 5. Results of multiple linear regression analyses of species richness with geomorphological predictors at the period after flooding in autumn.

＊

Standardized partial coefficient

Model

Predictor

Partial coefficient

１

MAXD

６．８３１＊＊

０．８１０

０．６５５１５．２１６０．００５３５．９０

２

MAXD SUBST

５．１６６＊＊７．６７２＊

０．６１２０．４７８

０．８４４１９．００１０．００１２９．３９

P ＜０．０５,

＊＊

R2

F

P

AIC

P ＜０．０１

Table 6. Matrix of correlation coefficients among geomorphological predictors at the period before flooding in spring. AREA

PERI

COV

COVP

MAXD

AVD

CVD

SUBST

LENG

WIDTH

SI

DISST

PERI ０．９８１ COV ０．５２９０．５４３ COVP ０．０５９０．０１３０．７７４ MAXD ０．８２５０．７４８０．７３１０．５０３ AVD ０．８１００．７１３０．６５３０．４２８０．９７８ CVD −０．０３７０．００４０．３３７０．４７４０．０５０ −０．１１８ SUBST ０．８７７０．８５８０．７７００．４４８０．８３２０．７８９０．２４２ LENG ０．９８１０．９９３０．４７４ −０．０５７０．７４１０．７１１ −０．０２５０．８１８ WIDTH ０．９４５０．８９３０．５１４０．１９３０．８３４０．８３４０．０３７０．９１９０．８８２ SI ０．７１５０．８３３０．４７７ −０．０７８０．３７８０．２９６０．１２５０．６３３０．７９３０．５７９ DISST −０．５００ −０．５８１０．０６１０．３８１ −０．０４９０．０１３ −０．０８６ −０．３３２ −０．５６４ −０．４４５ −０．７５３ DISPL ０．３５７０．２７１ −０．２１２ −０．１４１０．３１００．３５３ −０．４０７０．１５３０．２８７０．４３００．０７０ −０．４５５

Table 7. Results of simple linear regression analyses of species richness with geomorphological predictors at the period before flooding in spring. Predictor

Coefficient

r2

F

P

AREA PERI COV COVP MAXD AVD CVD SUBST LENG WIDTH SI DISST DISPL

１．１７９１．５９８３．１４６５．５８８３．１０１３．１５５５．６０７５．７０６１．４０４３．９０２０．７０４ −０．３８１１．３９４

０．２４３０．２０１０．５６２０．６２６０．５４１０．４０５０．２８００．４９８０．１５３０．３３４０．０９５０．０１７０．０３０

２．５７２２．０１２１２．５５２１３．４１６９．４４２５．４３６３．１１０７．９２５１．４４０４．００７０．８４００．１３７０．２５１

０．１４７０．１９４０．００８０．００６０．０１５０．０４８０．１１６０．０２３０．２６５０．０８００．３８６０．７２１０．６３０


100

Table 8. Results of multiple linear regression analyses of species richness with geomorphological predictors at the period before flooding in spring.

＊

Model

Predictor

１

COVP

P ＜０．０５,

＊＊

Partial coefficient

Standardized partial coefficient

５．５５８＊＊

０．７９１

R２

F

P

０．６２６１３．４１６０．００６

P ＜０．０１

The value of AIC of the model 2 was smaller than that of

nels that had formed during flooding.

the model 1.

vary with position or time in depressions in secondary

Survey before flooding in spring

channels during flooding, it is assumed that the diversity

Eight species were found in the floodplain pools（Table１） .

of current velocity in deep depression is higher than shal-

The dominant species were Zacco platypus, Phoxinus

low depression in secondary channels.

lagowskii steindachneri, and Pseudorasbora parva. The aver-

background, MAXD of floodplain pools is regarded as an

age value of pool area was 254 m2, the average value of

indicator of degree of refuge for juvenile fish from flooding.

maximum depth was approximately 34 cm.

Current velocities

Considering this

The sizes of

SUBST is regarded as an index of habitat heterogeneity.

pools in March were smaller than that in October（Table２）.

The biogeographical principle that a larger area supports

A strong correlation was found among AREA. PERI.

more species has been revealed in past studies（MacArthur

MAXD. AVD. SUBST. LENG. WIDTH, and SI. COV was

& Wilson 1967; Johnson & Raven 1973; Rey 1981; Rosenz-

correlated with COVP. MAXD and SUBST（Table６） . Six

weig 1995）. On the other hand, Power （1972）demon-

of the thirteen geomorphological variables: COV. COVP.

strated that the species richness of land birds breeding on

MAXD. AVD. SUBST, and WIDTH accounted for signifi-

islands off the California coast was best predicted by the

cant proportions of the variation in species richness

number of plant species（76％ of the variance） , whereas

（Table７）. Two geomorphological parameters, COVP and

area accounted for 58％ of the variance and latitude for

MAXD, were added to stepwise multiple linear regression

25％ . In this case, the number of plant species was inter-

analysis.

preted as a measure of habitat variety.

Table８ summarizes the results of the multiple

Nichols et al.

linear regression analyses at the period before flooding in

（1997）demonstrated that soil drainage heterogeneity

spring. Only one predictor variable, COVP, was selected by

exerted a greater effect than area on total plant species rich-

stepwise selection. The relationship between species rich-

ness and was therefore a more precise independent predic-

ness and covered edge proportion was described using a

tor of biodiversity.

. linear regression model（R2 ＝ 0.626, P ＝ 0.006）

support habitat heterogeneity being better than area as the

The results of our analysis also

predictor of species richness for juvenile fish.

Discussion

However,

because AREA shows a correlate with SI, it suggests a possibility of use as the index of habitat heterogeneity（Tonn

Predictors after flooding in autumn

& Magnuson 1982; Eadie & Keast 1984） .

We considered the model 2 was better than the model 1

Predictors before flooding in spring

from the value of AIC. The usefulness of at least two of

The usefulness of at least COVP of thirteen geomorphologi-

thirteen geomorphological predictors, MAXD and SUBST,

cal predictors was demonstrated for the species richness of

was demonstrated for the species richness of juvenile fishes

juvenile fishes at the period before flooding in spring.

at the period after flooding in autumn.

The preference of juvenile fish for cover is well known

As well as side pools formed on sand bars in the Kizu

（DeVore & White 1978; McMahon & Hartman 1989; Shir-

River （Tsujimoto & Teramoto 2000） , floodplain pools in

vell 1990; Inoue et al. 1997） . Helfman（1981）studied the

this study were located in depressions in secondary chan-

advantages to fish of hovering in shade. He found that a


101

fish hovering in shade is better able to see approaching

バー水際線率，最大水深，平均水深，水深の変動係数，

objects and is simultaneously more difficult to see.

底質多様度，プールの長さ，プールの最大幅，形状指数，主

Damant（1921）found that plankton was more easily seen

流路からの距離および最近傍プールからの距離の１３変

by a shaded than by an unshaded observer.

Thus, it

数を設定した．主要な予測変数を抽出するための手法と

appears that the presence of cover is a requirement of juve-

してステップワイズ重回帰分析を用いた．重回帰分析に

nile fish for feeding and refuge. In the light of these facts,

投入する予測変数については，相関係数と単回帰分析に

COVP of floodplain pools is regarded as an indicator of degree of sustenance for juvenile fish during low-water

よるスクリーニングを行った．解析の結果，秋出水直後

period lasting until the next flood.

最大水深と底質多様度の有用性が示された．最大水深は

Use of these predictors

遊泳力の小さい稚魚の避難場機能の指標となることが推

Because the number of pools in this study was small（n ＝

察された．底質多様性はハビタットの異質性を示してい

10） , only these three parameters are not useful predictors,

ることが考えられた．また，春出水前においては，カバ

but the results of this study suggest the importance of habi-

ー水際線率の有用性が示唆された．解析対象プール数が

tat data on maximum depth, substrate diversity, and cover

少ないため（n ＝１０），他の有用かもしれない変数を採択

in predicting the function of nursery areas of floodplain

できていない可能性があるが，本研究の結果により，氾

pools. The target of river restoration is to restore the equi-

濫原プールの稚魚生育場機能の予測評価を行うためには，

librium dynamic system to keep the function of habitat

少なくとも最大水深，底質多様性，カバーに関するデー

quality（Shimatani 1999） . It requires a predictive under-

タが重要であることが示唆された．

の氾濫原プールにおいては，稚魚多様度の予測子として

standing of habitat data over wide area. We believe that the development of the index using these predictors and

References

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摘要低水位期の氾濫原プールにおける稚魚多様性の予測子を探索するため，秋出水直後と春出水前における地形構造と種多様性との関係を調べた．現地調査は，２００１年９月の台風出水で形成された１０箇所の氾濫原プールにおいて，水位が安定した１０月末から翌年７月末まで実施し，そのうち２００１年１０月末のデータを秋出水直後として，２００２年３月末のデータを春出水直前に該当するものとして解析に用いた．調査地における魚類の種別個体数を体長群（全長３０mm 間隔）別に記録し，文献情報に基づく当歳魚のサイズに最も近い体長群までを本研究対象の稚魚として，その種数を目的変数として用いた．予測変数としてはプールの面積，水際線長，カバー水際線長，カ

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