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Ecological Engineering 71 (2014) 335–345

Contents lists available at ScienceDirect

Ecological Engineering

jou rn al hom ep age: www.elsev ier .com/ locate /eco leng

omparison of habitat suitability models using different habitatuitability evaluation methods

ujun Yia,b,∗, Xi Chenga, Silke Wieprechtb, Caihong Tanga

Ministry of Education Key Laboratory of Water and Sediment Science, School of Environment, Beijing Normal University, Beijing 100875, ChinaInstitute for Modelling Hydraulic and Environmental Systems, University of Stuttgart, D-70569 Stuttgart, Germany

r t i c l e i n f o

rticle history:eceived 21 January 2014eceived in revised form 8 May 2014ccepted 11 July 2014

eywords:reference curveuzzy logicata-driven methodabitat suitabilityhinese sturgeon

a b s t r a c t

When simulating the habitat suitability for a species or a community, the selection of the habitat eval-uation index is still a matter of debate. This study examined Chinese sturgeon spawning grounds in theYangtze River as an example. The water level and velocity were simulated by a two-dimensional hydraulicmodel. Five methods, including a two-dimensional preference curve method, one- and two-dimensionalexpert knowledge-based fuzzy logic methods, and one- and two-dimensional data-driven fuzzy logicmethods, were used to calculate the habitat suitability of Chinese sturgeon spawning grounds below theGezhouba Dam. The data-driven fuzzy logic method used a training dataset and the nearest climbingalgorithm to optimize fuzzy sets and fuzzy rules. The weighted usable area (WUA), hydraulic habitat suit-ability index (HHS) and the spatial distribution of suitable Chinese sturgeon spawning grounds at differentdischarges (4000 m3/s, 9000 m3/s, 12,000 m3/s, 16,000 m3/s, 20,000 m3/s, 30,000 m3/s, and 40,000 m3/s)were calculated. The performances of the habitat suitability models based on different methods werecompared. There were few differences in the results regardless of the use of a preference curve method

or a fuzzy logic method. For the data-driven fuzzy logic method, the quality of the training dataset is veryimportant. Therefore, the data-driven fuzzy logic method is a good method when the amount and coverage(e.g., including very high and very low discharges) of the measured dataset are sufficient. However, condi-tions at different discharges can be taken into account comprehensively with the expert knowledge-basedmethod when the available data are not sufficient.

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. Introduction

The influences of dams and reservoirs on stream ecology remainot issues. Especially in China, with its abundant water resourcesnd large number of dams, ecologists and hydrologists are stillearching for a useful way to evaluate the ecosystem responseo dam construction and to protect and repair damaged ecosys-ems. Since the late 1970s, aquatic habitat simulation models haveeen used to analyze fish habitats in water resource managementBovee, 1982). The models are used to evaluate habitat suitabil-ty for aquatic organisms, based on physical variables, such as

ater depth, flow velocity, and substrate (Bovee, 1986; Jowett,997). Physical habitat models are particularly useful for assessinghe impact of hydropower projects, analyzing the effects of water

∗ Corresponding author at: Corresponding author at: Beijing Normal University,chool of Environment, No.19, Xinjiekouwai Street, Beijing 100875, China.el.: +86 10 58801757; fax: +86 10 58801757.

E-mail address: yiyujun@bnu.edu.cn (Y. Yi).

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ttp://dx.doi.org/10.1016/j.ecoleng.2014.07.034925-8574/© 2014 Elsevier B.V. All rights reserved.

© 2014 Elsevier B.V. All rights reserved.

bstraction on river ecology, and determining the minimum flowequirements of aquatic populations (Bockelmann et al., 2004).hese models may also be used to evaluate the impact of restora-ion projects on surrounding environments (Shields et al., 1997;ee and An, 2014).

The Physical Habitat Simulation (PHABSIM) model, which isased on preference curves, was the first fish habitat model and

s now being used worldwide (Bovee, 1986; Spence and Hickley,000). PHABSIM is based on a one-dimensional hydraulic modelnd uses preference curves to provide habitat quality evalua-ion rules. PHABSIM is suitable where the physical habitat limitsopulations, and, although it provides a quantitative output, it

s perhaps most useful in providing a qualitative comparisonetween management options. Other models based on PHAB-IM include RHYHABSIM (Jowett, 1996) and FISU (Yrjänä et al.,999). Parasiewicz (2001) developed Meso-HABSIM for meso-

abitats. Other models based on the preference curve methodre WHYSWESS (Yi et al., 2010a,b), RIVER2D (Im et al., 2011),nd RCHARC (Thoms and Sheldon, 2002). All these models linkhysical variables to habitat suitability by means of uni- or

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36 Y. Yi et al. / Ecological En

ulti- variate preference functions (Bovee, 1986; Pasternack et al.,008).

In contrast to the preference curve method, the fuzzy logicethod uses statements such as “low”, “medium”, and “high” to

erbally describe input variables and mathematical methods forata processing. This method can solve two problems effectively:onlinearity and correlation between variables, which cannot beolved using a preference function (Marsili-Libelli et al., 2013; Chent al., 2011). In the 1990s, the Computer-Aided Simulation Modelor Instream Flow Requirements (CASiMiR), which is based on fuzzyogic, was developed. This model was developed at the University oftuttgart’s Institute for Hydraulic Engineering, using fuzzy sets anduzzy rules to describe habitat suitability. In recent years, CASiMiRas been applied to many projects. It has been proven that CASiMiRan be applied in rivers that are different in size, flow regime, andiver bed structure (Noack et al., 2013). The fuzzy logic method haseen widely used in habitat simulation of fish and benthic animalsMarsili-Libelli et al., 2013). However, it appears to be too subjec-ive to establish fuzzy sets and fuzzy rules absolutely dependingn expert knowledge. To make up for the deficiency, data-drivenethods have been used to optimize fuzzy sets and fuzzy rules

Broekhoven et al., 2006; Mouton et al., 2009; Maeda, 2013).At the same time, other habitat simulation methods, such

s multiple regression, artificial neural network, and multivari-te statistical methods based on factors such as multiple speciesnd community have been used in habitat evaluation (Ahmadi-edushan et al., 2006). Habitat suitability models are widely used

o evaluate the ability of a habitat to support a particular speciesVincenzi et al., 2006; Fukuda, 2009). These models ultimatelyllow the researchers to evaluate the effects of hydraulic struc-ures and river works on their surrounding environments (Moutont al., 2007). Despite the many statistical methods that can be usedo predict the distribution of aquatic organisms, little research hasxamined how to apply these statistical methods to habitat sim-lation or has compared different methods applied to the sameataset.

Comparing different models under a given condition is ben-ficial for understanding the characteristics of each model andan help to select the most suitable model (Ahmadi-Nedushant al., 2006). In this study, the two-dimensional hydraulic modeleveloped by the first author has been used to compute the hydro-ynamic conditions. A preference curve method and fuzzy logicethods (including an expert knowledge-based method and a

ata-driven method) are used to evaluate the habitat suitability ofhinese sturgeon spawning grounds in the middle reach of Yangtzeiver. Based on the field survey of the habitat, water depth andelocity were selected as the two eco-factors. The habitat suitabil-ty of Chinese sturgeon in this reach and its spatial distributiont different discharges were calculated. The relationship betweenhe modeling approach and calculation results was evaluated torovide some guidance for selecting a suitable modeling approach.his study aimed to accomplish the following:

1) Develop preference curves for water depth and velocity anda suitability function for Chinese sturgeon spawning grounds.Fuzzy sets and fuzzy rules based on expert knowledge-basedmethod and data-driven method will be established, respec-tively.

2) Establish a two-dimensional preference curve model, one- andtwo-dimensional expert knowledge-based fuzzy logic models,

and one- and two-dimensional data-driven fuzzy logic models.

3) The habitat suitability of Chinese sturgeon spawning groundsbelow the Gezhouba Dam at different discharges will be calcu-lated by the five models previously mentioned. The distribution

vdTr

ing 71 (2014) 335–345

of the habitat suitability of Chinese sturgeon spawning groundsat different discharges then will be obtained via each model.

4) Analyze and compare the influences of habitat suitability eval-uation methods on the calculated results.

. Fish species and study area

.1. Chinese sturgeon

The Yangtze River basin has a rich diversity of freshwater fish.here are as many as 177 endemic fish species in this area. Of these77 endemic species, 25 of them are endangered. The 25 endan-ered, endemic fish species in the Yangtze River account for 27%f all endangered freshwater fish species in China (Yue and Chen,998). The Chinese sturgeon is one of the endangered endemicncient fish species. The Chinese sturgeon (Acipenser sinensis Gray)s a typical anadromous migratory fish. It originally migrated fromrackish water or the East China Sea to breeding grounds in the Jin-ha River, the upper branch of the Yangtze River (Xie, 2003). Thispecies dates back 1.4 million years, and thus, it is called a “livingossil.”

Since 1983, the Chinese sturgeon has been listed among therst-grade national protection wildlife in China. Some protectiveeasures have been implemented, such as prohibition of fishing

or Chinese sturgeon, and artificial propagation and release haveeen practiced. However, recruitment stocks of juvenile Chineseturgeon and parental sturgeon still decreased by 80% and 90%,espectively, from 1981 to 1999 (Zhang et al., 2007). The sourcef recruitment stocks of juvenile Chinese sturgeon is natural prop-gation; as a result, research on the habitat of Chinese sturgeonpawning has been important for protecting and sustaining theatural populations of Chinese sturgeon.

.2. Study area

With a length of 6300 km and a drainage area of 1.80 millionm2, the Yangtze River is the largest and longest river in China.he landscape of the upper reach of the Yangtze River is moun-ainous, and the river channel is narrow. The river substrate in thepper reach of the Yangtze River mainly consists of gravel. Theonditions of the upper reach are ideal for the spawning of Chineseturgeon. Downstream from Yichang are the middle reaches of theangtze River. In these reaches, the river channel is wide, and theow velocity is low. The substrate of this region mainly consists ofand. According to field investigations, the conditions for Chineseturgeon spawning sites in the middle reaches are inferior com-ared with those in the upper reach. The watershed of and mainpawning sites on the Yangtze River are shown in Fig. 1.

The Chinese sturgeon’s habitat was altered after the completionf the Gezhouba Dam in 1981. The effects of the Gezhouba Dam onhe habitat of Chinese sturgeon in the Yangtze River have been sig-ificant. Chinese sturgeon migration paths were obstructed by thisam, leaving these fish unable to return to their original spawn-

ng sites. Due to the obstruction of migration paths, this species’pawning grounds shrank to a length of 30 km in the Yichang reachownstream of the Gezhouba Dam from a previously recordedtretch of 600 km (Wei et al., 2005). Since 1997, field surveysave revealed that the length of the spawning grounds has shrunko a reach of 4.8 km (from the dam to Zhenjiangge) (Institute ofydrobiology, 2005). After the Three Gorges Project (TGP) reser-

oir began filling in 2003, the flow process downstream from theam was changed. According to the management concept of theGP, the reservoir begins filling in October, and the water levelises from 145 m to 175 m, which results in a reduction of flow

Y. Yi et al. / Ecological Engineering 71 (2014) 335–345 337

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aFaqrvelocity is high, then the suitability index (SI) is low,” the if-part isthe antecedent and the then-part is the consequent. Low, medium,and high values of water depth and velocity are defined by fuzzysets.

0.2

0.4

0.6

0.8

1.0

SI

0.2

0.4

0.6

0.8

1.0

SI

Fig. 1. The main spawning site of

ischarge at the downstream spawning grounds. The Chinese stur-eon spawns generally from October to November. The reduction ofunoff results in the shrinking of spawning areas and consequentlyhe rate of reproduction decreases (Xie, 2003).

This paper presents the habitat simulation results of the reachetween the Gezhouba Dam to Zhenjiangge, a reach of 5 km ashown in Fig. 1. This reach is the main spawning area of Chineseturgeon, including the upper and lower main spawning sites. Thearent sturgeons spawn here every year.

. Methods

.1. Hydraulic model

The 5 km river reach immediately below the Gezhouba Dams the main Chinese sturgeon spawning grounds in recent yearsFig. 1). It was divided into 61 × 46 units, where 61 sections wereet in the reach, and 46 points were set on each section. Grids wereeveloped to discretize the reach.

The velocity field and the distribution of water depth ofeven discharges (4000 m3/s, 9000 m3/s, 12,000 m3/s, 16,000 m3/s,0,000 m3/s, 30,000 m3/s, and 40,000 m3/s) are simulated via awo-dimensional hydraulic model, which is a module of the

HYSWESS (Watershed Hydrology, Hydraulic, Sediment trans-ort, Water Quality and Ecology Simulation System), which is aimulation platform for integrated watershed analysis including

hydrological model (Zhang et al., 2013), hydraulic model (one- two-, and three- dimensional), water quality simulation model,abitat suitability simulation model (benthos, fish, and vegetation),nd virtual reality display model. The two-dimensional hydraulicodule is based on the solution of the two-dimensional shallowater flow equations, including the effects of bottom friction and

urbulence. A detailed introduction of model functions and solu-ions are provided in Yi et al. (2010a).

.2. Habitat model based on preference curve method

Sexually mature fish seek suitable spawning areas. Fertilizedggs attach to stone and hatch approximately 5 to 7 days later.help sturgeons drift with the current, grow slowly in the lower

eaches and river of the Yangtze River. Juvenile sturgeons migrateo the East China Sea and remain there until they reach maturity.

ater depth (H) and velocity (V), the two most important aquatic

cological factors that affect the well-being of spawning activityf Chinese sturgeon, were considered in this model. The habitatuitability module of WHYSWESS was used to calculate the habitatuitability index, HSI.

se sturgeon in the Yangtze River.

The HSI for spawning is determined for each grid point and eachime step in the spawning season. The minimum suitability indexSI) value of water depth or velocity is considered as the controllingactor of the habitat suitability:

SI = min(SIV , SIH) (1)

here the SI curve quantifies physical habitat. The SI ranges fromnsuitable (0) to optimal habitat suitability (1). The intermediatealues represent the suitability range based on a specified hydraulicariable. The definition of SI curves for ecological factors was basedn the research results of experiments and field surveys of biolo-ists. The data sources used to determine the SI curves are listed inable 1, and the SI curves are shown in Fig. 2. A detailed descriptionf eco-factors, SI curve determination, and validation of formula (1)re presented in Yi et al. (2010a).

.3. Fuzzy logic method

Habitat simulation based on the fuzzy logic method must beerformed on the basis of fuzzy sets and fuzzy rules. The fuzzyets that describe habitat conditions (such as depth and velocity)re usually categories such as “low”, “medium”, and “high.” Fuzzyets are defined and described by membership functions. Unlikelassical sets which have definite boundaries, the boundaries ofembership functions of fuzzy sets usually overlap.Fuzzy rules define the relationship between the input vari-

bles and habitat suitability in the life stages of certain species.uzzy rules have two parts, the antecedent and consequent. Thentecedent describes specific habitat conditions and the conse-uent evaluates habitat conditions for organisms to live, grow andeproduce. For example, for the rule “If water depth is high and

0.0403020100

Depth (m)

0.03002001000

Velocity (cm/s)

Fig. 2. Suitability index curves for habitat of Chinese sturgeon.

338 Y. Yi et al. / Ecological Engineering 71 (2014) 335–345

Table 1Information sources on suitability of Eco-factors for Chinese sturgeon.

Variables Eco-factors Sources

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mm

3

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SIH Water depth (spawning)

SIV Flow velocity (spawning)

The common process for habitat evaluation based on the fuzzyogic method is similar to the Mamdani–Assilian process. After theuzzy sets of input variables and fuzzy rules are established, habi-at evaluation can be divided into four steps. First, the membershipegrees of the fuzzy sets of each input variable are calculated. Sec-nd, the membership degrees of the fuzzy sets in the antecedentection are combined together, and then, the degree of fulfillmentDOF) for each fuzzy rule can be obtained. The DOF indicates thenfluence of each rule on the habitat suitability of the computingnit. If the factors in the antecedent part will connected with “and”,he product or minimum value method can be used as the combi-ation method. Third, combining the DOF and the consequent partf the fuzzy rule (fuzzy sets of suitability) by taking the product oretting the minimum value, the graph of the total DOF function thatescribes all fuzzy rules can be obtained. Finally, defuzzification isonducted, wherein a single value that is the SI of the simulationnit and is between 0 and 1 can be obtained by taking the centroidr center of gravity of the graph or other methods.

In this paper, the product approach was chosen as an inferenceethod, and the weighted sum was chosen as the rule combinationethod when two-dimensional calculation was performed.

.4. Data-driven optimization of fuzzy sets and fuzzy rules

In expert knowledge-based methods, fuzzy sets and fuzzy rulesre defined based on the literature and the experiences of experts,hereas in data-driven methods, fuzzy sets and fuzzy rules are

btained using data to conduct optimization training. To ensure alluzzy rules can be properly trained, fuzzy sets of water depth andelocity must be first optimized based on the Shannon–Weaverntropy. The parameters of fuzzy sets of depth and velocity shoulde modified until their Shannon–Weaver entropies are greater than.85. Then, the fuzzy sets can be considered to be distributed uni-ormly on the training dataset, and the preparation for traininguzzy rules is completed.

The formula of Shannon–Weaver entropy is as follows:

ntropy = − 1log2 n

n∑

i=0

pi log2 pi (2)

here n is the number of fuzzy sets, and pi is the number of datahose membership degree to fuzzy set i is greater than 0.5.

The nearest ascent hill-climbing algorithm can be used to opti-ize fuzzy rules. After fuzzy sets are established, the number of

uzzy rules and antecedents of all fuzzy rules in CASiMiR can beetermined. Only the consequent of each fuzzy rule should be opti-ized. The optimization process is as follows. First, consequents of

group of fuzzy rules should be set randomly, and then model per-ormance can be calculated. Second, select a fuzzy rule randomly,hange its consequent to the adjacent fuzzy set (the trans-orm order is as follows: “low” → “medium” → “high” → “low”) andalculate model performance again. Third, the two results are com-ared. If model performance becomes better, optimization shoulde continued by using the changed fuzzy rules. Otherwise, opti-

ization should be continued by using the primary fuzzy rules.

inally, the optimal computation should be continued until modelerformance cannot be improved. If the same group of fuzzy rulesan be found three times to achieve optimal model performance

hw

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rom different starting points, the fuzzy rules are the optimizeduzzy rules.

The percentage of correctly classified instances (%CCI), Kappa,he average deviation (AD), and the adjusted average deviationaAD) are criteria that can be used to evaluate model performance.mong these criteria, %CCI and Kappa require that the consequentsf fuzzy rules must be defined by two fuzzy sets, and fuzzy setshould be changed into classical sets when calculation is conducted.hough AD and aAD allow that the consequents of fuzzy rules cane defined by multiple fuzzy sets, defuzzification is not needed inalculation. In general, AD and aAD are more suitable for this study.D is different from aAD. The criterion aAD can be used to makeinor adjustments to the calculation of model performance based

n the frequency of species occurrence (i.e., the proportion of theampling points with the occurrence of species out of all samp-ing points) to better reflect the actual situation. In this study, therequency of species occurrence was not considered because therocess referred to simulation results. Therefore, AD was selecteds the measurement criterion of model performance. The compu-ational formula of AD is as follows:

AD = 1N

N∑

j=1

n−1∑

i=1

∣∣Di,j

∣∣

Dj =∣∣∣∣∣

i∑

k=1

Ak

(ymodel,j

)−

i∑

k=1

Ak

(ydata,j

)∣∣∣∣∣

(3)

here N is the number of data points; Ak(ymodel,j) is the member-hip degree of the jth output to the kth linguistic output value (low,edium, high); Ak(ydata,j) is the normalized membership degree of

he jth output to the kth linguistic output value (Broekhoven et al.,006). Ak(ydata,j) is the maximum DOF of all fuzzy rules with theame consequent (e.g., the consequent is low) so that the DOF ofhe calculation results of the fuzzy set can be obtained.

.5. Results evaluation

Weighted usable area (WUA) and hydraulic habitat suitabilityndex (HHS) can be used to describe the integrated habitat suitabil-ty of the whole investigated river reach at a certain discharge level.he WUA value can be obtained by multiplying the area of eachesh cell by its habitat suitability index value. The WUA function

s as follows:

UA =n∑

i=1

AiSIi = f (Q )(

unit : m2)

(4)

here i is the order number of cell; n is the number of all cells; Ai

s the area of the ith cell; SIi is habitat suitability index value of theth cell; Q is discharge (m3/s).

The unit of WUA is same as the unit of area. It is important toote that WUA is not the real area of habitat. When all cells have a

abitat suitability of 1.0, the theoretical maximum value of WUA,hich is equal to the total wetted area of a reach, can be obtained.

HHS is the WUA divided by the total wetted area. Comparedith WUA, an advantage of HHS is that the impact of the wetted

Y. Yi et al. / Ecological Engineer

Table 2Fuzzy rules based on the expert knowledge-based method and data-driven method.

Number Antecedent Consequent

Velocity Waterdepth

Expertknowledge-basedmethod

Data-drivenmethod

1 H H L H2 H M M H3 H L L M4 M H H H5 M M H H6 M L L M7 L H L L

aH

H

w

4

4

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8 L M M M9 L L L L

rea which changes with discharge is removed. The formula forHS is as follows:

HS = 1Atotal

n∑

i=1

AiSIi = f (Q ) (5)

here Atotal is the total wetted area.

. Results

.1. Fuzzy sets and fuzzy rules

The fuzzy sets based on expert knowledge and data are shownn Fig. 3. The fuzzy rules are listed in Table 2.

The advantages of fuzzy rules are consistency with humananguage habits and high interpretability (Jorde et al., 2000). Tonalyze the influences of water depth and velocity on habitat suit-bility more intuitively, a three-dimensional histogram was drawn

radg

(a)

Entropy = 0.9348 (b)

0.0

0.2

0.4

0.6

0.8

1.0

302520151050

low

midium

high

Water depth (m)

DOF

0.0

0.2

0.4

0.6

0.8

1.0

3020100

lowmidiumhigh

Water depth (m)

DOF

Fig. 3. Fuzzy sets based on the (a) expert knowledge

ing 71 (2014) 335–345 339

sing MATLAB (Matlab, 2010) to describe the habitat suitability ofifferent combinations of depth and velocity (Fig. 4).

In the expert knowledge-based method, fuzzy sets are definedased on observation data and experimental data before the con-truction of the Gezhouba Dam and measured data after theonstruction of the Gezhouba Dam. The range of discharge involveds wide. Therefore, two combinations, high water depth and

edium flow velocity as well as medium water depth and mediumow velocity, are most suitable for the spawning of Chinese stur-eon. Spawning conditions for Chinese sturgeon are not suitable ifater depth and flow velocity are too high or too low. In the data-riven method, the interval of the medium fuzzy set of depth ismaller than the one in the expert knowledge-based method. Thenterval of the medium fuzzy set of flow velocity reduces signifi-antly when velocity is high; for example, the membership degreesf 2 m/s for the “high” and “medium” categories are both 0.5 in thexpert knowledge-based method, but the membership degrees of

m/s for the “high” and “medium” categories are 1 and 0 in theata-driven method, respectively.

The suitability is low when the water depth is low or the flowelocity is low. The training dataset in the data-driven method wasollected after the construction of the Gezhouba Dam, and the datall are in the discharge range of 6500 m3/s to 23,500 m3/s, whichs smaller than the range in the expert knowledge-based method.herefore, the parameters of the fuzzy sets of depth and velocity inhe data-driven method are all low. For example, 1.7 m/s belongs tohe “high” category in the data-driven method, but it belongs to themedium” category in the expert knowledge-based method. Thus,n the data-driven method, based on the fuzzy sets and the fuzzy

ules, four combinations, high depth and high velocity, high depthnd medium velocity, medium depth and high velocity, mediumepth and medium velocity, all are highly suitable for Chinese stur-eon spawning.

Entropy = 0.9361

0.0

0.2

0.4

0.6

0.8

1.0

3210

low

midium

high

Watervelocity(m/s)

DOF

0.0

0.2

0.4

0.6

0.8

1.0

3210

lowmidiumhigh

Watervelocity(m/s)

DOF

-based method, and (b) data-driven method.

340 Y. Yi et al. / Ecological Engineering 71 (2014) 335–345

of ve

4

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4s

(3

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Fig. 4. Three-dimensional histogram

.2. Models validation

Field survey results of 1999 and 2003 were used to validateodels. The mean discharges during the period of Chinese stur-

eon spawning were 14,200 m3/s in 1999, and 9000 m3/s in 2003,espectively. The measured density of sturgeon eggs is indicated byPUEd (quantity of eggs per unit (1000 m3)) (Wei, 2003). The cal-ulated HSI and the observed results CPUEd were compared. TheSI values at 9000 m3/s and 14,200 m3/s were calculated throughve methods. The comparisons of CPUEd and the corresponding HSIbtained with five methods (PC, 1D EK, 1D DD, 2D EK, 2D DD) showhat HSI increases as CPUEd increases, the related coefficients of five

ethods are greater than 0.8, the calculated results (HSI)—CPUEdorrelations of five methods are statistically significant (Fig. 5).

.3. The calculation results of the habitat suitability of Chineseturgeon spawning grounds

The habitat suitability distribution maps at seven discharges4000 m3/s, 9000 m3/s, 12,000 m3/s, 16,000 m3/s, 20,000 m3/s,0,000 m3/s, and 40,000 m3/s) obtained with five methods are

ig. 5. Comparison between HSI obtain with five methods and CPUEd

grain/1000 m3 discharge). The point symbols show the measured data (normal-zed CPUEd value), and the lines show the calculated HSI. (Fitted equations ares follows: PC: y = 0.0028x + 0.0683, R = 0.88; 1D EK: y = 0.0028x + 0.0826, R = 0.85;D DD: y = 0.0028x + 0.1113, R = 0.86; 2D EK: y = 0.0035x + 0.1524, R = 0.88; 2D DD:

= 0.0034x + 0.2244, R = 0.82.)

Amadnst4t

wdot

adtEdaQhcaD1

locity, water depth, and suitability.

hown in Fig. 6. These methods are the two-dimensionalreference curve method (PC) (Fig. 6(I)), one-dimensional expertnowledge-based fuzzy logic method (1D EK) (Fig. 6(II)), two-imensional expert knowledge-based fuzzy logic method (2D EK)Fig. 6(III)), one-dimensional data-driven fuzzy logic method (1DD) (Fig. 6(IV)), and two-dimensional data-driven fuzzy logicethod (2D DD) (Fig. 6(V)), respectively.As shown in Fig. 6, for all simulated discharges, both the suit-

ble area and the value of HSI of Chinese sturgeon spawningites increased gradually from 4000 m3/s to 20,000 m3/s and thenecreased gradually after 20,000 m3/s. The results of the suitabilityrea distribution obtained with the five methods are similar. Whenhe discharge is 4000 m3/s, the suitability of the main channel in theower reach is high and in the upper reach close to the Gezhoubaam is low. When the discharge is 9000 m3/s, the suitability of

he whole reach increases, and the suitable area is enlarged. Whenhe discharge is 12,000 m3/s, the suitable area enlarges further.s the discharge increasing to 16,000 m3/s, the suitability of theain channel close to the Gezhouba Dam increases significantly,

nd the suitability of the whole reach is the highest. When theischarge reaches 20,000 m3/s, the suitability of the main chan-el in the lower reach begins to decrease, and the suitable area anduitability of the upper reach near the Gezhouba Dam continueo increase, but when the discharge increases to 30,000 m3/s and0,000 m3/s, the area with high suitability shrinks gradually, andhe suitability of the whole reach decreases.

Fig. 7 shows the percentage distribution of the HSI obtainedith the different methods at different discharges. The three-imensional histogram clearly shows the percentage distributionf HSI at different discharges, and comparisons can be made amonghem.

In regards to the percentage of the region with high habitat suit-bility (HSI > 0.7), the following results were noted: (1) when theischarge is 4000 m3/s, the PC yields the minimum percentage (9%),he fuzzy logic method based on expert knowledge (1D EK and 2DK) are between 30% and 40%, and the fuzzy logic method based onata (1D DD and 2D DD) are between 15% and 20%. The percent-ges of the region with HSI > 0.9 of all methods are less than 8% at

= 4000 m3/s. (2) As discharge increases, the area of the region withigh habitat suitability (HSI > 0.7) enlarges gradually. When the dis-

harge is 9000 m3/s, the results of PC (57.1%) and 2D EK (57.2%)re similar, the 1D EK result (63.6%) is the maximum, and the 2DD result (39.3%) is the minimum. (3) When discharge reaches2,000 m3/s, the area of the region with high habitat suitability

Y. Yi et al. / Ecological Engineering 71 (2014) 335–345 341

Fig. 6. The calculated results of five habitat suitability methods at seven discharges (I) preference curve method; (II) CASiMiR 1D expert knowledge-based fuzzy logic method;(III) CASiMiR 2D expert knowledge-based fuzzy logic method; (IV) CASiMiR 1D data-driven fuzzy logic method; (V) CASiMiR 2D data-driven fuzzy logic method. From theleft to the right, the discharges are 4000 m3/s, 9000 m3/s, 12,000 m3/s, 16,000 m3/s, 20,000 m3/s, 30,000 m3/s, and 40,000 m3/s, respectively).

342 Y. Yi et al. / Ecological Engineering 71 (2014) 335–345

Fig. 7. The percentage distribution of HSI obtained with the different methodsat different discharges (PC: two-dimensional preference curve method; 1D DD:ome

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xpert knowledge method).

HSI > 0.7) continues to enlarge. The results of PC (67.5%), 1D DD62.6%), and 2D EK (65.8%) are similar; 1D EK (76.3%) still provides

he maximum; and 2D DD (49.8%) still provides the minimum. (4)

hen discharge reaches 16,000 m3/s, the area of the region withigh habitat suitability (HSI > 0.7) enlarges further. The results of PC71.1%), 2D EK (74.0%) and 1D DD (71.6%) are similar; 1D EK (81.9%)

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ig. 8. WUA and HHS as a function of the discharge. EK: expert knowledge; DD:ata-driven; PC: preference curve.

till provides the maximum; and 2D DD (60.7%) still provides theinimum. (5) When the discharge reaches 20,000 m3/s, the area of

he region with low habitat suitability does not change much, therea of the region with high habitat suitability (HSI > 0.7) shrinkslightly, and the area of the region with medium habitat suitabil-ty enlarges slightly. The results of PC (70.3%) and 1D DD (71.0%)re similar; 2D EK is 57.1%; 1D EK (79.7%) still provides the maxi-um; and 2D DD (56.7%) still provides the minimum. The results

f PC, 1D DD, and 1D EK are all over 70%. (6) When the discharge is0,000 m3/s, the percentages of the region with HSI > 0.7 obtainedith all methods shrink dramatically. At this moment, the percent-

ges of the region with HSI > 0.7 obtained with different methodsisplay different tendencies, and 1D EK decreases to 56.6%, which

s similar to PC value of 58.9%. The results of 2D DD is 50.3%; 1DD (64.8%) yields the maximum value; and 2D EK (42%) yields theinimum value. Previously, for a discharge of 20,000 m3/s, the per-

entage of the region with HSI > 0.7 of 1D EK was the highest. (7)hen the discharge is 40,000 m3/s, the 2D EK result is 34.7%, the

D DD result (57.6%) is the maximum. In addition, the results of 1DK (45.5%) and 2D DD (43.6%) are similar. PC (29.5%) yields the min-mum. Only the percentage of the region with HSI between 0 and.1 of 1D EK is high, and there are certain percentages of the regionsith medium suitability obtained with the other four methods.

In general, when predicting high-quality habitat, 1D EK yieldshe largest value when the discharge is below 20,000 m3/s. PCields the most conservative prediction at the lowest (4000 m3/s)nd highest (40,000 m3/s) discharges, but moderate values for dis-harges between 9000 and 30,000 m3/s. 1D DD yields moderatealues when the discharge is medium or low, but yields the highestalues when the discharge is larger than 20,000 m3/s. In summary,he results for PC are moderate, whereas 1D EK is on the high sidehen the discharge is medium or low, and 1D DD is on the high

ide when the discharge is high.

WUA and HHS are a function of the discharge as shown in Fig. 8.

s discharge increases from 4000 m3/s to 40,000 m3/s, the WUAnd HHS obtained by expert knowledge-based methods (including

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C, 1D EK, and 2D EK) increase with the discharge from 4000 m3/sp to 16,000 m3/s, and then decrease with the discharge from6,000 m3/s up to 40,000 m3/s. The WUA and HHS are highestt 16,000 m3/s, whereas the WUA obtained from the data-drivenethods, are highest at 30,000 m3/s, but the HHS, similar to the

xpert knowledge-based methods, is highest when the discharges 16,000 m3/s.

According to previous field observations, the conventional Chi-ese sturgeon spawning grounds are near Gezhouba Dam (Fig. 1),nd the lower straight reach is a sporadic spawning ground.lthough the suitability of the lower straight reach is relatively hight low discharge, Chinese sturgeon may not choose that locationor spawning. When the increased discharge makes the suitabilityf the reach near Gezhouba Dam high, it is beneficial for Chineseturgeon spawning. The preference for this reach might be becausehinese sturgeon prefers a spawning region with coarse bed mate-ial, and high oxygen concentration which is mainly caused by highurbulence below the dam. Because the topography of the reachelow Gezhouba Dam is complicated and it has been influenced byhe flow released from the dam, the flow pattern is more turbulenthan the lower straight reach.

The WUA and HHS obtained via the two-dimensional preferenceurve method and the two-dimensional expert knowledge-baseduzzy logic method are very close. The trends for WUA and HHSbtained via the one-dimensional expert knowledge-based fuzzyogic method is similar to the trends obtained via the two previous

ethods, but the overall values obtained via the one-dimensionalxpert knowledge-based fuzzy logic method are higher than theesults of two-dimensional methods. In the data-driven fuzzy logicethods, the predicted WUA and HHS were on the high side when

he discharge higher than 20,000 m3/s. The reason is that the train-ng datasets for data-driven methods were obtained for dischargeetween 6500 m3/s and 23,500 m3/s. Among the training dataset,he discharge of 23,500 m3/s is the highest one in the datasets, andas good habitat suitability. Thus, the prediction results obtainedia data-driven methods were maintained at a good level when theischarges were higher than 20,000 m3/s. As a result, the integratedabitat suitability of the reach at a certain discharge obtainedhrough the data-driven fuzzy logic methods is on the high sidet high discharges (>20,000 m3/s).

Therefore, generally speaking, regardless of the use of the pref-rence curve method or the fuzzy logic method, the calculatedesults have small differences. According to the comparison of thexpert knowledge-based fuzzy logic method and the data-drivenuzzy logic method, the quality of the original datasets is a verymportant factor for data-driven fuzzy logic method. Therefore, theata-driven fuzzy logic method is a good method when the quantitynd quality of the datasets are good. Conditions at different dis-harges can be taken into account comprehensively in the expertnowledge-based fuzzy logic method when the available data arensufficient.

. Discussion

The habitat suitability of the lower reach of the studied areas high when the discharge is between 4000 m3/s and 9000 m3/s.he suitability of the upper reach increased with the increase of dis-harge until 20,000 m3/s. According to the historical measured dataWei et al., 2005), the main Chinese sturgeon spawning grounds areow concentrated in the region below Gezhouba Dam. The upper

pawning ground and the lower spawning ground are shown inig. 1. The main channel of the lower reach is a sporadic spawninground. The Chinese sturgeon might prefer places with high turbu-ence and, thus, high dissolved oxygen to spawn. The reach close

Gtdd

s before impoundment of Gezhouba dam; 1982–1998 is after impoundment ofezhouba dam and before impoundment of Three Gorges Dam; 2003–2007 is after

mpoundment of Three Gorges Dam).

o the dam has high turbulence because of the deep pools and theow influenced by the tail water from the hydropower station.

The expert knowledge-based method indicates that a velocityetween 1.2 m/s and 1.5 m/s provides the most suitable area and auitability index larger than 0.7 results when velocity is in the rangef 1.0–1.8 m/s. Based on the monitoring undertaken after the con-truction of Gezhouba Dam, the current velocity above the bottomayer should be between 1.0 and 1.7 m/s to stimulate fish spawningYang et al., 2007). According to the research result of this paper,

water depth of 8–15 m is the most suitable range, providing auitability index larger than 0.7 when the depth is between 7 and9 m. Based on the daily flow field in spawning sites during spawn-

ng seasons in 1998–2002, Yang (2007) suggested that 1.3–1.5 m/ss the most suitable velocity range for Chinese sturgeon spawning,nd the most suitable water depth range is 9–12 m.

In this study, the WUA and HHS indicate that when the dis-harge is between 9000 m3/s and 25,000 m3/s, the habitat qualitys good, and the best quality is reached at 16,000 m3/s. From 1983o 2004, Chinese sturgeon spawning took place 37 times. The dis-harge variation range at Yichang segment during the spawningeriod were 7170–26,000 m3/s (Yang et al., 2007). Historical mon-

toring data indicate that Chinese sturgeons usually spawn whenhe discharge between 10,000 and 20,000 m3/s. After the impound-

ent of the TGP in 2003, the flow process downstream fromhe dam was changed. In October, the reservoir was impoundednd the water level rose from 145 m to 175 m, which resultedn a reduction of flow discharge at the spawning ground. Fig. 9hows the average monthly discharge of multiyear in the Yichangtation. The Chinese sturgeon spawns generally from October toovember. Before the filling of Gezhouba Dam (1960–1969), theverage discharges during Chinese sturgeon spawning season athe Yichang station was during 20,000 m3/s and 11,000 m3/s; afterhe operation of Gezhouba Dam and before the filling of the Threeorges Reservoir TGR (1982–1998), the average discharges wasuring 17,000 m3/s and 8500 m3/s; and after the filling of the TGR2003–2007), the average discharges was between 15,000 m3/s and500 m3/s (Fig. 9). The reduction of runoff resulted in the shrinkingf spawning areas, and consequently decreased the rate of repro-uction (Xie, 2003).

The definition of suitable ranges of water depth and flow veloc-ty in this paper was based on the discharges during sturgeonpawning season observed both before and after Gezhouba Damonstruction. Because considering the conditions before and after

ezhouba Dam construction, the discharge range considered in

his study is wider than the range only including conditions afteram construction. At the same time, the suitable ranges of waterepth and velocity are relatively large, especially in the region with

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igh water depth and high flow velocity. After the construction ofhe Gezhouba Dam, especially after the filling of the TGR, the dis-harge in the reach below the dam during the Chinese sturgeonpawning period has decreased because of natural drought and thelling of the reservoir, among other factors. Ban and Li (2007) men-ioned that both the discharge and the water level decreased duringhe Chinese sturgeon spawning period after the filling of the TGR,nd the discharge was between 10,000 and 17,000 m3/s, and theater level was approximately 42 m. Cai et al. (2010) suggested

he maximum WUA would be obtained when the discharge was5,000 m3/s, and habitat quality was good when discharge wasetween 13,000 m3/s and 19,000 m3/s. The results in this paperave small difference with Cai et al.’s (2010), this is caused by the

arge discharge range we considered.When comparing the WUA and HHS calculated by five meth-

ds, the results from the PC and 2D EK methods were similar andower than the results of other three methods. When the discharges lower than 20,000 m3/s, the results of 2D DD were similar to theesults of PC and 2D EK, and the results for both 1D DD and 1D EKre higher than the results obtained with the other three meth-ds. Because the discharge ranges of the training datasets wereimited, the result values obtained with the data-driven methods

ere significantly larger than the results from the expert knowl-dge methods at high discharge (>20,000 m3/s).

Schneider and Peter (1999) compared the preference curveethod and fuzzy logic method and found that the preference

urve method cannot effectively consider the diversity of the com-inations of physical variables. In this study, the difference betweenhe preference curve method and the fuzzy logic method is not sig-ificant. Fuzzy sets and fuzzy rules impact the calculated results.o develop a reliable model, fuzzy sets, fuzzy rules, and the rangend span of the simulated discharge should be comprehensivelyonsidered. Because of the large variation of discharge in thepawning sites of Chinese sturgeon, fuzzy sets of water depth andelocity using three grades (“low”, “medium” and “high”) cannoteflect the entire condition. Fuzzy sets divided into more gradese.g., “very low”, “low”, “medium”, “high”, “very high”) is a pos-ibility, and corresponding fuzzy rules can be set to improve theccuracy of the model. Monitoring data of spawning activity of Chi-ese sturgeon are difficult to obtain, so the available datasets are

imited. As a result, the effectiveness of the data-driven method inptimizing the model is limited. Mouton et al. (2009) comparedith calculation results of fish habitat via two methods (expert

nowledge-based fuzzy logic method and data-driven fuzzy logicethod) and found the same conclusion as this paper. The data-

riven fuzzy logic method provided superior performance whenata were sufficient, but the expert knowledge-based method wasetter when the data were insufficient.

Of course, other factors, such as turbulence and river topogra-hy, also influence the habitat selection of Chinese sturgeon. Sometudies have examined this factor separately. The vorticity strengthelection of Chinese sturgeon spawning can enhance our under-tanding of the egg fertilization rate and enhance the protectionf fertilized eggs (Yang et al., 2008). River bed topography, such aslope and bedload size composition, will also influence spawningYi et al., 2013). Future research can potentially add to these twoactors.

. Conclusions

Through the work in this paper, the main conclusions are as

ollows:

1) When the discharge is 4000 m3/s, the suitability of the mainchannel in the lower reach is high, and the suitability of the

ing 71 (2014) 335–345

upper reach near the Gezhouba Dam is low. When the dis-charge is 9000 m3/s, the suitability of the whole reach increases,and the suitable area is enlarged. When the discharge is12,000 m3/s, the suitable area enlarges further. As the dis-charge increases to 16,000 m3/s, the suitability of the mainchannel in the upper reach close to the Gezhouba Dam increasessignificantly, and the suitability of the whole reach reachesthe highest. When the discharge increases to 20,000 m3/s, thesuitability of the main channel of the lower reach begins todecrease, and the suitable area and the suitability of the upperreach near Gezhouba Dam continue to increase. If the dischargecontinues to increase, the area with high suitability shrinks.The condition of Chinese sturgeon spawning is good when thedischarge is between 9000 m3/s and 25,000 m3/s. Among thesimulated discharges, 16,000 m3/s is the best for Chinese stur-geon spawning.

2) For the prediction results concerning high-quality habi-tat (HSI > 0.7), the results of the one-dimensional expertknowledge-based model provide the maximum values whenthe discharge is lower than 30,000 m3/s among the five meth-ods. The WUA of the preference curve method is the minimumwhen the discharge is 4000 m3/s (the minimum discharge) or40,000 m3/s (the maximum discharge), and the results of thepreference curve method are medium at other discharges. Thedata-driven methods provide moderate results when the dis-charge is medium or low, but the prediction results of thedata-driven methods are on the high side when the discharge ishigher than 20,000 m3/s. On the whole, the results of the pref-erence curve method are moderate and the one-dimensionalexpert knowledge-based model is on the high side when thedischarge is medium or low, and the one-dimensional data-driven method results are on the high side when the dischargeis high.

3) The WUA and HHS obtained via the two-dimensional prefer-ence curve method and two-dimensional expert knowledge-based fuzzy logic method are very close. The trend of thesetwo parameters obtained from the one-dimensional expertknowledge-based fuzzy logic method is similar to the trendsobtained via the two foregoing methods, but the overall numer-ical values of the one-dimensional expert knowledge-basedfuzzy logic method are higher than the results of the two-dimensional methods. With regards to the data-driven fuzzylogic method, when the discharge is larger than 20,000 m3/s,the WUA and HHS values become higher than the valuesof other three expert knowledge-based methods. The rea-son is that the training data of the data-driven method isbased on conditions for discharge values between 6500 m3/sand 23,500 m3/s, and the discharge of 23,500 m3/s is thehighest discharge in the datasets, with relatively good habi-tat suitability. Thus, the suitability obtained through thedata-driven method is higher when extrapolated to highdischarges.

4) The calculation results remained at a good level when the dis-charges were higher than 20,000 m3/s applying the data-drivenmethod. Therefore, generally speaking, regardless of the use ofthe preference curve method or the fuzzy logic method, thecalculated results displayed only small differences. For the com-parison of the expert knowledge-based fuzzy logic method andthe data-driven fuzzy logic method, the quality of the origi-nal datasets is a very important factor. Thus, the data-drivenfuzzy logic method is a good method when the quantity and

quality of the available datasets are good. Conditions at dif-ferent discharges can be taken into account comprehensivelywhen using the expert knowledge-based fuzzy logic methodespecially when the data are insufficient.

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Appropriate methods are important for solving problems,nowing them better is helpful. To solve problems in complex natu-al systems, combining sufficient experimental or field survey datand rich expert knowledge is of great help when seeking the mosteasonable solution.

onflict of interest statement

No conflict of interest exits in the submission of this manuscript.

cknowledgements

This study is supported by Nonprofit Environment Protectionpecific Project of China (No. 201209029-4); National Science andechnology Support Program (No. 2011BAC12B02); the Funda-ental Research Funds for the Central Universities (No. 2013YB17),

nd Alexander von Humboldt Foundation.

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