optimization of stratification scheme for a fishery

Optimization of stratification scheme for a fishery-independentsurvey with multiple objectivesXU Binduo1, 2, REN Yiping1, CHEN Yong2, 1, XUE Ying1, ZHANG Chongliang1, 2, WAN Rong1*1 College of Fisheries, Ocean University of China, Qingdao 266003, China2 School of Marine Sciences, University of Maine, Orono, Maine 04469, USA

Received 30 April 2015; accepted 29 July 2015

©The Chinese Society of Oceanography and Springer-Verlag Berlin Heidelberg 2015

Abstract

Fishery-independent surveys are often used for collecting high quality biological and ecological data to supportfisheries management. A careful optimization of fishery-independent survey design is necessary to improve theprecision of survey estimates with cost-effective sampling efforts. We developed a simulation approach toevaluate and optimize the stratification scheme for a fishery-independent survey with multiple goals includingestimation of abundance indices of individual species and species diversity indices. We compared theperformances of the sampling designs with different stratification schemes for different goals over differentmonths. Gains in precision of survey estimates from the stratification schemes were acquired compared to simplerandom sampling design for most indices. The stratification scheme with five strata performed the best. Thisstudy showed that the loss of precision of survey estimates due to the reduction of sampling efforts could becompensated by improved stratification schemes, which would reduce the cost and negative impacts of surveytrawling on those species with low abundance in the fishery-independent survey. This study also suggests thatoptimization of a survey design differed with different survey objectives. A post-survey analysis can improve thestratification scheme of fishery-independent survey designs.

Key words: fishery-independent survey, optimization, stratified random sampling, stratification scheme,computer simulation

Citation: Xu Binduo, Ren Yiping, Chen Yong, Xue Ying, Zhang Chongliang, Wan Rong. 2015. Optimization of stratification scheme for afishery-independent survey with multiple objectives. Acta Oceanologica Sinica, doi: 10.1007/s13131-015-0739-z

1 IntroductionFishery-independent surveys, often designed based on rigor-

ous statistical principles and at defined spatial and temporalscales (Cochran, 1977), are designed for collecting high qualitybiological and ecological data in species and community levels(Gunderson, 1993; Jennings et al., 2001; Cadima et al., 2005; Blan-chard et al., 2008). In general, fishery-independent surveys tendto be costly and time-consuming in comparison to fishery-de-pendent surveys, which yield data from fisheries-related activit-ies (Scheirer et al., 2004). Optimization of fishery-independentsurvey designs is considered as one necessary way to ensuregood data collection with limited sampling efforts (Hilborn andWalters, 1992; Chen, 1996; Simmonds and Fryer, 1996; Liu et al.,2009). Stratified random sampling, commonly used in fishery-in-dependent surveys, usually divides a target survey area into dif-ferent strata and conducts simple random sampling within eachstratum (Cochran, 1977). Stratification can result in an increasedprecision over a simple random survey design when observa-tions are more homogenous within strata than between strataand the sampling efforts are allocated to strata in proportion tostrata size or strata variance (Ault et al., 1999; Manly et al., 2002;Chen et al., 2006; Miller et al., 2007; Lohr, 2009). Thus, an appro-priate stratification is important to achieve an improved preci-sion of survey estimates (Ault et al., 1999; Smith and Lundy,

2006).Improving precision of survey estimates is critical in the im-

provement of effectiveness of the information collected in thesurveys (Smith and Gavaris, 1993; Smith and Lundy, 2006). Alloc-ating samples in proportion to the strata variance, when the vari-ance estimates from a pilot survey or previous years’ surveys areavailable for a good estimation of the variance for the current sur-vey year, is an efficient way to improve the precision of estimates(Cochran, 1977). On the other hand, there are various adaptivemethods for adding samples during the survey to increase surveyprecisions (Thompson, 1990; Thompson and Seber, 1996; Su andQuinn, 2003; Mier and Picquelle, 2008). Adaptive allocationscheme within a stratified design is also an alternative approachto increase the precision of estimates (Francis, 1984; Smith andLundy, 2006). In stratified random sampling design, precision ofsurvey estimates can be improved by optimizing stratificationschemes or optimizing sample allocation schemes (Gavaris andSmith, 1987; Smith and Robert, 1998; Ault et al., 1999; Folmer andPennington, 2000; Lunsford et al., 2001; Smith and Tremblay,2003; Smith and Lundy, 2006).

Computer simulation is usually applied for evaluatingsampling designs in identifying an optimal one (Simmonds andFryer, 1996; Liu et al., 2009; Yu et al., 2012). Some suitable in-dices are identified to measure the performances of different

Acta Oceanol. Sin., 2015

DOI: 10.1007/s13131-015-0739-z

http://www.hyxb.org.cn

E-mail: [email protected]

Foundation item: The Public Science and Technology Research Funds Projects of Ocean under contract No. 201305030; the SpecializedResearch Fund for the Doctoral Program of Higher Education under contract No. 20120132130001; the Fundamental Research Funds for theCentral Universities under contract Nos 201262004 and 201022001.*Corresponding author, E-mail: [email protected]

sampling designs including both accuracy and precision (Taylor,1997; Sokal and Rohlf, 2012). Relative estimation error (REE) is ameasure for comparing both the accuracy and precision and isoften used to compare overall performance of estimators (Chen,1996; Andrew and Chen, 1997). Relative bias (RB) is anothermeasure for comparing the accuracy of estimators (Paloheimoand Chen, 1996).

Shallow coastal waters are essential spawning and nurserygrounds for many commercially important fish species (Tang andYe, 1990; Lazzari et al., 2003), and are also among the most ex-tensively affected aquatic environments due to human activitiesand environmental changes (Blaber et al., 2000; Secor and Rook-er, 2005). It is necessary to conduct fishery-independent surveyto collect reliable information for fisheries stock assessment andmanagement in these waters. A high intensity bottom trawl sur-vey may have negative effects on local ecosystems, in particularon populations with low abundances, juveniles, and spawningindividuals. This calls for a control of sampling efforts during thesurvey.

Although the goals of a survey program are usually to addressecological or/and fisheries management issues, the objectives inoptimization of the survey program may be different. For ex-ample, the optimization objectives can be to yield high qualityestimates for populations of a few commercially important fishspecies for their stock assessment and management (Ault et al.,1999; Smith and Gavaris, 1993) or to provide data for an overallassessment of fish community structure (Greenstreet and Piet,2008). We may not be able to satisfy all the objectives at the sametime in optimization of survey design.

The goals of this study are to: (1) develop a framework forevaluating and optimizing stratification schemes in a fishery-in-dependent survey with the main target estimating abundance ofindividual species and species composition in a shallow coastalwaters with low fish abundances; (2) compare the performancesof different stratification schemes in quantifying the spatial andtemporal variability in fish population abundance and species di-versity; (3) compare the performance of different stratificationschemes when the target indices differ in their spatial distribu-tions; and (4) evaluate the consistency of performances for differ-ent stratification schemes over time.

2 Materials and methods

2.1 Study areaHaizhou Bay is an open bay and located in the western Yel-

low Sea. It was ever an important spawning and feeding groundsfor many fish species, and was also one of the most vital fishinggrounds for commercial fisheries in the Yellow Sea (Chen, 1991).Since the mid-1980s, many traditional fish populations have de-creased in abundance or even been depleted due to the high in-tensity fishing activities and environment degradation in the Yel-low Sea ecosystem (Jin and Tang, 1996; Xu and Jin, 2005). Thereis an urgent need to monitor, evaluate and recover those fishstocks in the coastal waters.

2.2 The stratified random survey in the Haizhou BayIn order to monitor the status of commercially important fish

species and the overall fish community, a fishery-independentbottom trawl survey was conducted in March, May, Septemberand December 2011 in the Haizhou Bay and its adjacent waters.The surveys covered the main life history stages includingspawning adults, juveniles and young fish for most fish species in

the bay. The current survey employed a stratified randomsampling design with five strata divided mainly based on oceano-graphic, regional, and biological characteristics of the survey area(Fig. 1). The survey area was divided into 10 min × 10 minsampling grids, and 24 out of 76 grid cells were selected in eachsurvey. Sampling grid in each stratum were randomly chosenfrom all the grid cells in the stratum. The sample size in eachstratum was determined according to the stratum’s area andstratum importance as critical habitat for fish species as informa-tion on the distribution of fish species was not available prior tothe survey (Chen, 1991). The number of sampling grid for strat-um A, B, C, D and E was 3, 5, 3, 9 and 4, respectively. A trawl haulwas conducted in each sampling grid. The survey was designedfor collecting information on the overall species composition andabundance of some individual fish species.

All the bottom trawl surveys were conducted using a 220 kilo-watt otter trawler during the day time. The towing speed was atabout 2.5 knots and the duration of the trawl haul was about 1hour on average. The open width of the sampling net was 25 mand the mesh size for the codend was 17 mm. At each haul the allthe individuals caught were identified to species where possible,measured, and the length, weight, and species abundance werealso recorded.

2.3 Simulation procedureWe evaluated the performances of alternative stratification

schemes and the possibility of reducing the sampling efforts withdifferent stratification schemes which still maintained the qual-ity of the data for quantifying the individual species abundanceand species diversity of demersal fish community.

Two types of indices were selected as the estimating object-ives in the fishery-independent surveys: abundance indices offour individual species and species diversity indices for the de-mersal fish community (Table 1). The four individual species, in-cluding small yellow croaker (Larimichthys polyactis), fat green-ling (Hexagrammos otakii), whitespotted conger (Conger myri-aster) and pinkgray goby (Amblychaeturichthys hexanema), weremost abundant or of great ecological importance and had differ-ent spatial distribution patterns in the study area (Chen, 1991;Wang et al., 2013; Sun et al., 2014). Species diversity indices in-cluded the following three indices: Margalef’s species richnessindex d, Pielou’s evenness index J’ and Shannon’s diversity index

Fig. 1. Schematic stratified random survey stations andbathymetric contours in the Haizhou Bay.

2 XU Binduo et al. Acta Oceanol. Sin., 2015

H’ (Ludwig and Reynolds, 1988) (Table 1).We assumed that the current survey design could represent

the “true” spatial distribution of fish species in the Haizhou Bay.The current sampling effort allocation scheme was followed inthe simulation study. As revealed by a previous study, samplingefforts in a stratified random survey could be reduced from 24 to18 while still achieving relatively high precision and accuracy formost indices (Xu et al., 2015). For the sake of simplification of theanalysis, only four levels of sample sizes were considered in or-der to examine the consistency of stratification schemes whenthe sampling efforts changed in the simulation study. For a spe-cific level of sampling effort evaluated, the sample size in eachstratum was systematically scaled down in the simulation study(Table 2). It was assumed that the original survey data could rep-resent the ‘true’ data for the two types of indices, and the “true”means of the indices were computed using the original survey

data.A simulation framework for stratification scheme optimiza-

tion was developed (Fig. 2). In the simulation study, 10 stratifica-tion schemes, from simple random survey design (1 stratum) tostratified random survey design with 2 to 5 strata, in the samplingdesign for the fishery-independent survey were considered, inwhich strata were defined based on depth and geographic area(Table 3). Stratification scheme 1 was a simple random surveydesign without stratification. The numbers of strata for eachscenario were listed in brackets in Table 3. Strata A to E weredefined in the current survey design in Table 1, and stratum ABreferred to the combination of strata A and B in which the samplesize was equal to the sum of sample sizes in strata A and B. We re-sampled the original survey data for 1 000 times with replace-ment for different stratification schemes and sampling efforts,and calculated the means of the selected indices with the re-

Table 1. Summary of the key indices for the stratification scheme optimization in the simulation study. The CV is the average coef-ficient and variation (%) (ranges in the four sampling months are in parentheses) for the original indices and the mean index value(ranges in the four sampling months are in parentheses)

Types of index Specific index Species/group codes CV/%Mean index value for

species/g·h–1

Abundance index ofindividual species

Small yellow croaker(Larimichthys polyactis)

LP 15.5 (10.8–24.1) 718.5 (0–1426.1)

Fat greenling(Hexagrammos otakii)

HO 17.7 (8.0–23.6) 378.6 (66.9–1145.1)

Whitespotted conger(Conger myriaster)

CM 18.4 (15.5–23.9) 329.7 (38.5–651.5)

Pinkgray goby(Amblychaeturichthys hexanema)

AH 16.1 (11.5–21.8) 244.0 (61.7–785.1)

Species diversity index Margalef’s richness index d 2.1 (1.7–2.6) 3.2 (3.1–3.3)

Pielou’s evenness index J’ 1.5 (0.9–2.4) 0.6 (0.6–0.7)

Shannon’s diversity index H’ 1.4 (1.3–2.4) 2.2 (2.0–2.4)

Table 2. The distribution of the total sample size among strata (from A to E) defined in this study. Nh is the total number of possiblesample unit in stratum h. Wh is weighting factor of stratum h

Stratum Strata description Wh Nh 24 21 18 15A <20 m, northern, coastal currents 0.13 8 3 3 2 2

B <20 m, central, coastal currents 0.21 12 5 4 4 3

C <20 m, southern, coastal currents 0.13 9 3 3 2 2

D 20–30 m, cold water mass 0.38 29 9 8 7 6

E >30 m, cold water mass 0.17 18 4 3 3 2

Fig. 2. The flowchart of the simulation study summarizing the simulation framework for the optimization of stratification schemesfor fishery-independent survey with multiple objectives.

XU Binduo et al. Acta Oceanol. Sin., 2015 3

sampled data for each simulation run.

2.4 Measures for evaluating performanceThe coefficient of variation (CV), REE and RB were calculated

to compare different stratification schemes in the simulationstudy. CV of abundance indices of individual species and speciesdiversity indices calculated based on the resampled data wasused to measure dispersions related to the indices in the simula-tion study (Cochran, 1977). The CV was regarded as a relativestandard error and calculated as the ratio of the standard error tothe stratified mean of the estimate (Som, 1973),

CV=

sHP

h=1

W 2h

1-f hnh

S 2h

HPh=1

WhX h

(1)

X h =

nhXi=1

x hi=nh

S 2h =

nhXi=1

¡x hi ¡ X h

¢2= (nh ¡ 1)

where Wh is the weighting factor of stratum h, H is the number ofstrata, nh is the number of hauls in stratum h, Nh is the total num-ber of possible hauls in stratum h, fh=nh/Nh is the sampling frac-

tion in stratum h, is the estimated mean indices

in stratum h, xhi is the observed index in haul i in stratum h, and

is the estimated variance in strat-

um h.REE can evaluate the accuracy and precision of estimated in-

dices and is calculated as follows (Chen, 1996; Liu et al., 2009),

R EE =

sRP

i=1

¡Y estimated

i -Y true¢2

=R

Y true£ 100%; (2)

Y estimatedi

where Ytrue is the “true” index value derived from the originaldata, is the index value estimated from the resampleddata in the ith simulation run, and R is the number of runs of sim-ulation for each scenario (Cochran, 1977; Chen, 1996).

RB, evaluating the accuracy of estimates, can be calculated bythe following formula (Paloheimo and Chen, 1996; Jiao et al.,2004),

R B =

RPi=1

Y estimatedi =R -Y true

Y true£ 100%: (3)

A survey design yields an estimate with small CVs, REE andRB values implies that the design performs well and vice versa(Chen, 1996). The RB value can also indicate whether a surveydesign tends to underestimate or overestimate the “true” values.

3 Results

3.1 Comparison of CVsFor each survey month, the CVs of the selected indices

showed different changes with respect to the same stratificationscheme, i.e., one strata design was optimal for some indices, butwas suboptimal for others in terms of CVs (Fig. 3). In general, theCVs for abundance indices of individual species were high, andthe CVs of species diversity indices were low.

The CVs of selected indices were obviously reduced by strati-fication in comparison with simple random sampling design (i.e.,Design 1) and the CVs of most indices generally kept relativelystable or showed slight decrease from stratification schemeDesigns 2 to 10 in the four survey months (Fig. 3). Large fluctu-ations in CVs of abundance index of pinkgray goby (AH) for strat-ification scheme Designs 2 to 10 were observed in the Septemberand May surveys. The CVs of most indices from the stratificationscheme currently used (i.e., Design 10) were the lowest amongthese 10 stratification scheme designs evaluated. When thesampling efforts were reduced to 21, 18 and 15, the perform-ances of different designs were still relatively consistent for mostof the indices in the four month surveys (Fig. 3).

As to the same index, the performance of the same stratifica-tion scheme was different in the four survey months (Fig. 3). Forinstance, the CVs of the abundance index of pinkgray goby (AH)for stratification scheme Designs 5, 7 and 9 were low in Septem-ber survey, and the CVs from Designs 5 and 9 were high in Maysurvey; however, relatively constant CVs for Designs 2 to 10 wereshown in March and December surveys.

3.2 Comparison of REEFor each month survey, the REE values of species abundance

indices and species diversity indices were different for a givenstratification scheme design, i.e., one stratification schemedesign was optimal for one index, but was suboptimal for otherindices (Fig. 4). In general, the REE for abundance indices of in-dividual species were high, and the REE values for species di-versity indices were low.

The REE values of all indices were reduced by different strati-

Table 3. Different designs of stratification schemes in the sampling design for the fishery-independent survey. Strata are defined bas-ed on depth and geographic area. Design 1 is a simple random survey design in comparison with stratified random survey designs. Thenumbers of strata in each design scenario were listed in brackets. Strata A–E are defined in the current survey design in Table 1, and ABmeans combination of strata A and B in which the sampling effort is equal to the sum of sample size in stratum A and B

Design Stratification schemes Strata description1(1) ABCDE Simple random sampling

2(2) ABC/DE <20 m, >20 m

3(2) ABCD/E <30 m, >30 m

4(3) AB/C/DE <20 m northern-central, <20 m southern, >20 m

5(3) A/BC/DE <20 m northern, <20 m central-southern, >20 m

6(3) ABC/D/E <20 m, 20–30 m, >30 m

7(4) A/B/C/DE <20 m northern, <20 m central, <20 m southern, >20 m

8(4) AB/C/D/E <20 m northern-central, <20 m southern, 20–30 m, >30 m

9(4) A/BC/D/E <20 m northern, <20 m central-southern, 20–30 m, >30 m

10(5) A/B/C/D/E <20 m northern, <20 m central, <20 m southern, 20–30 m, >30 m


Fig. 3. Estimated CVs for different indices with 4 levels of sampling efforts, 10 stratification scheme designs in different surveymonths. a. March; b. May; c. September and d. December. The abbreviations of the specific indices are: Larimichthys polyactis, LP;Hexagrammos otakii, HO; Conger myriaster, CM; Amblychaeturichthys hexanema, AH; Margalef’s richness index, d; Pielou’sevenness index, J’ and Shannon’s diversity index, H’.


fication schemes compared with simple random survey design(Design 1). The REE values of the selected indices were relativelyconstant or exhibited decrease to a certain extent from stratifica-tion scheme Designs 2 to 10 in the four survey months (Fig. 4).The REE of most indices from the stratification scheme currentlyused (i.e., Design 10) was the lowest among these 10 Designs.The performance of each stratification scheme design was relat-ively consistent with different levels of sampling efforts for mostof the indices in the four survey months (Fig. 4).

For a given index, the performances of the same stratificationscheme differed in different survey months (Fig. 4). For example,

the REE of the abundance index of whitespotted conger (CM)from Design 4 was lower in March survey, but it was higher inother months compared to the other Designs, which indicatedthat Design 4 was optimal in March, but was not the most suit-able design in other months in terms of estimating abundanceindex of whitespotted conger (CM).

3.3 Comparison of RBFor four levels of sampling efforts at 15, 18, 21 and 24

sampling sites, the RB values for all the indices with differentDesigns were low, ranging between -8% and 10% without exhibit-


ing consistently positive or negative trends with Designs in all thefour sampling months (Fig. 5). This result indicated that the es-timation of all the indices was unbiased.

The absolute maximum RB values for all the species diversityindices were less than 2% in all the four survey months. The ab-solute maximum RB values for the abundance indices of pink-gray goby in March, small yellow croaker and whitespotted con-ger in December were less than 4%, and most of other indiceshad relatively high RB values (Fig. 5).

3.4 Combination of different strata designs and sampling effortsThe simulation study suggests that the loss of precision due to

the reduction of sampling efforts could be compensated throughdifferent stratification schemes in the optimization of the fishery-independent survey. The optimal combinations of stratificationschemes with sampling efforts for a stratified random surveydesign differed with survey objectives because different indiceshad different spatial distributions. Contour plots with varyingsampling efforts and stratification scheme designs for CV ofabundance index of fat greenling (HO) were demonstrated as ex-ample in Fig. 6. With the same requirement of precision for afishery-independent survey, simple random sampling design(Design 1), Designs 3 and 7 needed higher sampling efforts thanother designs in this study. Designs 6, 8 and 10 required a samplesize of about 15 sites to gain a precision with CV at 18%, whileDesigns 3 and 7 needed a sample size of about 23 and 21, re-spectively. The negative effects of trawl sampling survey on thepopulation with low abundance would be reduced by improvedstratification scheme with lower sampling efforts.

4 DiscussionWe conducted a simulation study to evaluate different strati-

fication schemes and to identify ways to reduce sampling effortswith little to no loss in precision with respect to different surveyobjectives. The study indicated that choices of stratificationschemes could influence the performance of a stratified randomsurvey design and the different objectives (i.e., different indices)in the survey design might lead to different optimization of strati-fications (Fig. 3).

Large-scale distribution patterns of fish species were often as-sociated with bathymetric and hydrographic conditions (Chen,1991). Therefore, strata in a fishery-independent survey were of-ten defined based on the environmental variables which wereimportant in influencing fish distribution, such as depth, sub-strate type and geographic area (Ault et al., 1999; Gavaris and

Smith, 1987; Zhang et al., 2011). The precision of survey estim-ates could be improved by using stratification, and many fishery-independent surveys were based on stratified sampling designs.For instance, the Maine-New Hampshire inshore trawl survey ac-tually used depth and area to stratify the survey (Chen et al.,2006; Zhang et al., 2011). Other methods were used in the con-struction of strata based on the frequency distribution of the vari-ables of interest, such as the historical spatial distribution ofabundance of target species, recognizing that spatial pattern ap-peared to persist from one year to the next (Cochran, 1977; Smithand Gavaris, 1993). Such an approach would be difficult whenone tried to define the strata boundaries for a new survey pro-gram because of lack of prior information and uncertainty in thepersistence of spatial distribution of fish community. In ourstudy, we only had one year survey data, and were not sure if thespatial distribution of fish species and species diversity was con-sistent over time, and could not reliably forecast the distributionand variance in each stratum during the surveys (Cochran, 1977).

The major gains in sampling efficiency, i.e., improved preci-sion at a lower sample size, usually occur as a result of employ-ing an optimal sampling efforts allocation scheme that accountsfor strata size as well as strata variance (Cochran, 1977; Ault et al.,1999; Smith and Lundy, 2006; Smith et al., 2011). Due to lack ofreliable previous survey data showing persistent spatial distribu-tion of fish species, we could not calculate the specific stratumvariance for target species in our study. According to the knownhistorical information that depth and bottom type were import-ant variables for defining the habitat of many fish species in thestudy area (Chen, 1991; Tang and Ye, 1990), and therefore thesevariables were considered in defining strata in the current surveydesign. The allocation of sampling efforts was apportioned ac-cording to the stratum size and its importance as critical habitatsfor individual fish species in the current survey. This simulationstudy was mainly focused on the optimization of stratificationschemes. We did not combine stratification with effort allocationin this study to focus on the evaluation of stratification which wasdetermined rather arbitrarily because of lack of reliable informa-tion of spatial distribution of fish species prior to this study. Oth-er studies had covered the ways to improve precision throughstratification or sampling efforts allocation schemes. However,these studies were mainly done for an individual species. For ex-ample, higher precisions were derived through optimizingsample allocation schemes than through stratification in the sur-vey designs for Atlantic cod (Gadus morhua) on the Scotian Shelf

Fig. 4. The relative estimation error (REE) for different indices with different stratification schemes in the four survey months. Theabbreviations of the specific indices are: Larimichthys polyactis, LP; Hexagrammos otakii, HO; Conger myriaster, CM;Amblychaeturichthys hexanema, AH; Margalef’s richness index, d; Pielou’s evenness index, J’ and Shannon’s diversity index, H’.


Fig. 5. Relative bias (RB) with different stratification schemes for different indices in the four survey months. a. March; b. May; c.September and d. December. The abbreviations of the specific indices are: Larimichthys polyactis, LP; Hexagrammos otakii, HO;Conger myriaster, CM; Amblychaeturichthys hexanema, AH; Margalef’s richness index, d; Pielou’s evenness index, J’ and Shannon’sdiversity index, H’.


(Gavaris and Smith, 1987), Pacific Ocean perch (Sebastes alutus)(Lunsford et al., 2001), shrimp (Penaeus duorarum) (Ault et al.,1999), West Greenland shrimp (Pandalus borealis) (Carlsson etal., 2000; Folmer and Pennington, 2000) and offshore sea scal-lops (Placopecten magellanicus) (Smith and Robert, 1998); whilestratification schemes for lobster (Homarus americanus) trapsurvey design acquired greater gains in precision than could berealized from the changes to the allocation schemes (Smith andTremblay, 2003).

The main objectives were to estimate abundance of multiplespecies and the overall species composition in a shallow and dy-namic coastal ecosystem in the current fishery-independent sur-vey. We used the abundance index of individual fish species andspecies diversity indices to measure different survey objectives inthe optimization of the survey design. The study suggested thatan optimal stratification design identified for one target indexmight not necessarily be for others (Figs 3 and 4), which wasmainly caused by the different spatial distribution of thesampling objectives. For example, the CVs and REEs for abund-ance index of pinkgray goby Amblychaeturichthys hexanema(AH) with stratification scheme 5 and 7 in September were low,but they were not the case for other indices. On the contrary, theCVs and REEs for abundance index of pinkgray goby Ambly-chaeturichthys hexanema (AH) with stratification scheme 6 and 8in September were high, but they were relatively low for other in-dices. Other studies suggested that in different fisheries, the bestsurvey design could be different. The multispecies survey designwith emphasis on the distribution of one species usually wasmore optimal for the target species than for others in abundanceestimates of these species (Smith and Gavaris, 1993; Yu et al.,2012). Thus, this study showed that it was difficult to identify anoptimal stratification scheme in survey design to satisfy all thesurvey objectives, and it was critical to identify the key objectiveof survey program prior to survey designs. For a survey programwith multiple survey objectives, it was necessary to identify themost important objective that the survey design should addressand then evaluated the robustness of such a design in achievingthe other survey objectives. If no primary objective could bedefined, a utility function might need to be developed in compar-ing different survey designs.

The same stratification scheme performed differently in dif-ferent survey months (Figs 3 and 4). This resulted from the distri-

bution of selected indices which was more variable in one monththan the others. There were seasonal changes in the spatial distri-bution of fish species, especially when they were in different lifehistory stages such as spawning, nursing and feeding in thecoastal waters (Chen, 1991; Tang and Ye, 1990). Seasonal variab-ility in the performance of different stratification schemes neededfurther study.

In conclusion, our simulation study suggested that the beststratification scheme in the stratified random survey designmight be different with the different objectives in a fishery-inde-pendent survey. The optimal stratification schemes and samplingefforts must consider the spatial and temporal distribution pat-tern of the organisms as well as for accuracy and the cost of thesurvey. Stratification provided increased precision over simplerandom survey design (Design 1) and the stratification refine-ment from Designs 2 to 10 appeared to gain the precision, andthe original survey strategy currently used (Design 10) for mostsurvey objectives performed better than the simple randomsampling (1 strata) and all stratified random strategies with lessthan 5 strata. Furthermore, the simulation study suggested thatthe loss of precision due to the reduction of sampling effortscould be compensated through different stratification schemes inthe optimization of the fishery-independent survey, which wouldreduce the cost of the fishery-independent survey and the negat-ive effects of trawl sampling on the populations with low abund-ance.

AcknowledgementsThe authors would like to thank all scientific staff and crew for

their assistance in data collection during the trawl surveys. Thefirst author is grateful to the China Scholarship Council for thefunding when he was a visiting scholar at the University of Maine,Orono. Supports from the Ocean University of China and theUniversity of Maine are also appreciated greatly. We also wouldlike to thank the anonymous reviewers for their constructivecomments that greatly improved the initial manuscript.

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