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Seasonalanddielchangesinfishdistributioninatropicalhydropowerplanttailrace:evidencefromhydroacousticandgillnet...

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Seasonal and diel changes in fish distribution in atropical hydropower plant tailrace: evidence fromhydroacoustic and gillnet samplingR . C . LOURESCemig Gerac�~ao e Transmiss~ao, Belo Horizonte, Minas Gerais, Brazil

P . S . POMPEULaborat�orio de Ecologia de Peixes, Universidade Federal de Lavras, Lavras, Minas Gerais, Brazil

Abstract Hydropower plant operation can lead to direct impacts on fish near the dam, causing injuries and death.In Brazil, it is more common to observe these impacts in the tailrace. To understand temporal and spatial variationsin fish abundance in the tailrace, to assist plant operation management, surveys were performed in Tres MariasHydropower Plant using hydroacoustics and multimesh gillnets. Data were collected in the tailrace backwater over1 year, during 18 field trips, each covering a 24-h cycle with six sampling intervals. A greater abundance of fishwas found in the tailrace during the wet season, mainly explained by total discharge. More fish were detected byhydroacoustics and gill nets during night sampling. Siluriformes, which are known to exhibit nocturnal habits,were the predominant species in both dry and wet seasons. Thus, damaging operational procedures for fish shouldbe planned in the dry season, low-abundance period.

K E Y W O R D S : echo sounding, hydropower plant impact, ichthyofauna, S~ao Francisco River.

Introduction

Damming rivers to install hydropower plants causes aseries of environmental changes (Balciunas & Zdankus2007; Dugan et al. 2010; Garc�ıa et al. 2011) that affectaquatic fauna, especially fish (Woynarovich 1991; Barl-etta et al. 2010). Plant operation is also risky for fish inthe tailrace, which may access the draft tube or reach theturbines (Agostinho et al. 2007; Loures 2009). It is com-mon to observe fish schools in the tailrace of hydro-power plants, which are believed to be attracted byhydraulic conditions in the sections immediately down-stream (Agostinho et al. 2007; Schilt 2007), includingmigrants that intend to swim upriver. Some operationalprocedures, such as draft tube dewatering and stop/startup, impose mortality risk for those fish in the tailraceand can cause death of large numbers of individuals(Andrade et al. 2012). Thus, characterising the ichthyo-fauna downstream from hydropower plants could guidemeasures to minimise the direct effects of plant opera-tion on fish (Loures 2009).

Numerous techniques are available for studying thedistribution, behaviour, abundance and density of fish,such as gillnets, cast nets, telemetry, visual observation,subaquatic cameras and hydroacoustics (echo sounding)(Lucas & Baras 2000). Hydroacoustic assessment orecho sounding involves using transmitted sound to detectorganisms in the water column (Brandt 1996). Thesetechniques are increasingly used in aquatic systems toacquire information from bathymetry and substrateclassification to abundance and distribution of biota,including macrophytes, zooplankton and especiallyfish (Brandt 1996; Godlewska et al. 2004; Simmonds &MacLennan 2005). Most hydroacoustics research,especially the ones conducted in Brazil territory, hasbeen performed in estuary or marine environments (seeKrumme 2004; Sumida et al. 2004; Krumme & Hanning2005; Madureira et al. 2005; Cooke et al. 2007).However, the use of hydroacoustics to assess freshwaterfish stocks and the spatial heterogeneity of their distribu-tion has become more frequent (Brandt 1996; Lyons &Lucas 2002).

Correspondence: Raquel Coelho Loures, Cemig Gerac�~ao e Transmiss~ao, Gerencia de Estudos e Manejo da Ictiofauna e Programas Especiais, ProgramaPeixe Vivo. Av. Barbacena, 1200, 13� andar/A1, 30190-131 Belo Horizonte, Minas Gerais, Brazil (e-mail: raquel.fontes@cemig.com.br)

© 2015 John Wiley & Sons Ltd doi: 10.1111/fme.12116

Fisheries Management and Ecology, 2015, 22, 185–196

185

Fisheries Managementand Ecology

Hydroacoustic techniques have various advantagesover other techniques because they are non-intrusive andcan be used to rapidly assess the entire water column ina vertical beaming (Parker-Stetter et al. 2009). However,the fish cannot be directly identified, so alternative tech-niques (e.g. multimesh gillnets, trawls) are used to deter-mine species composition, abundance and size structureand corroborate acoustic data (Zale et al. 2012).Analysis of the hydroacoustics signal may be ham-

pered in noisy environments or by an influx of air form-ing bubbles in the water, which generally restricts thefreshwater application of the technique to reservoirs,lakes and non-turbulent sections of rivers (Lucas &Baras 2000). Thus, the use of hydroacoustics nearhydropower plants is often restricted to the reservoirs(Kubecka & Duncan 1994; Schmidt et al. 2005; Drastiket al. 2009) and to studies assessing the movement offish during downstream migration (Ransom et al. 1996;Steig & Iverson 1998; Spinelli 2010). Few studies haveused echo sounding immediately downstream fromhydropower plants (Oldani et al. 2001; Cornish et al.2006), and it has been reported that the acoustic signalinterference from turbulence and bubbles has impededthe use of this method in such environments (Oldani &Baig�un 2002). However, sometimes low-noise areas canoccur in the tailrace, like in Tres Marias HydropowerPlant (TMHP) (S~ao Francisco River, Brazil) that has anarea beside the power house forming a low-turbulencebackwater.Considering the need to know the dynamics of fish

fauna downstream from hydropower plants and therecognised potential of echo sounding for studying thedistribution and estimating the abundance of fish, thisstudy aimed: (1) to assess temporal and spatial variationsin fish abundance and density in TMHP tailrace, usinghydroacoustics and gillnets; (2) to assess the relationshipbetween fish abundance estimated by hydroacoustics andgillnets catch in the tailrace; (3) to assess the relationshipbetween such variations and abiotic variables.

Materials and methods

Study area

The TMHP, located on the S~ao Francisco River(18°12047.67″S and 45°15044.94″W) in the municipalityof Tres Marias in the Central region of Minas GeraisState, began operating in 1962. The dam is 75 m highand 2700 m long, with installed capacity of 396 MWfrom six generating units of 66 MW each, a hydraulichead mean of 46.1 m, and Kaplan turbines with a maxi-mum nominal discharge of 150 m3�s�1 each. The spill-way is surface type and controlled by seven gates with a

maximum total capacity of 8700 m3�s�1 (Cemig 2006).The river along this stretch is subject to flood control ofthe plant. The rainfall regime and discharge downstreamthe plant over the last 10 years, from March 2001 toMarch 2011, displays a regular discharge over time andtwo well-defined seasons: a wet season (October toMarch) and a dry season (April to September) (Loures& Pompeu 2012). The water that passes through theplant’s turbines is taken from the reservoir’s hypolim-nion (50–60 m deep). The temperature of the river waterdownstream from the plant ranges from 28.5 °C in thewet season to 22.6 °C in the dry season, whereas thedissolved oxygen concentration ranges from 1.89 to7.76 mg�L�1, respectively (Sampaio & L�opez 2003).The sampled area at TMHP tailrace was restricted to

the backwater on the left bank, because it was the clos-est area to the water outflow where bubbles and turbu-lence regime permitted hydroacoustic data acquisitionusing an echosounder. This backwater was formed at thearea prepared during the plant construction in 1962 toinstall two more turbines in the future. Three paralleltransects were conducted (Fig. 1), as empirically deter-mined in a study assessing the degree of coveragefor the sample area (Λ = 4950 km) and the coefficientof variation (CV = 0.225) (Aglen 1989; Simmonds &MacLennan 2005; Loures 2011). The section wassurveyed in a duralumin boat carrying the equipment ata speed of approximately 7 km�h�1.

Collection and processing of acoustic data

Data collection covered 1 year, performed monthly fromApril to September 2010 (dry season) and fortnightlyfrom October to March 2011 (wet season). Each fieldtrip covered one 24-h cycle and consisted of six sam-pling intervals starting at 21:00, 01:00, 05:00 (night sam-ples), and at 09:00, 13:00, 17:00 (daytime samples).Each sampling lasted about 20 min.A BioSonics DT-X Digital Scientific echosounder,

Biosonics Inc., Seattle, Washington, USA equipped witha split beam 6° digital transducer at 123 kHz was used.It was calibrated using a tungsten carbide sphere follow-ing international protocols described by Foote et al.(1987) and BioSonics Inc. (2004). The transducer waspositioned on the side of a duralumin boat 0.5 m belowthe water surface oriented vertically towards the bottom(vertical beaming). The echosounder was directly con-nected to a GPS system, the 179 HVS NMEA 0183Garmin high sensitivity antenna. The program VisualAcquisition version 6.0 (Biosonics Inc.) was used toacquire the acoustic data, and the parameters configura-tion were standardised for all surveys: the maximumexpected depth in the area was 20 m; a rate of 12 pings

© 2015 John Wiley & Sons Ltd

R. C. LOURES & P. S. POMPEU186

s�1 was used, with a pulse length of 0.3 ms and a Svthreshold of �130 dB; the beamwidth was 6.8°9 6.8°,and the equivalent two-way beam angle was �20.97 (dBre 1 steradian). Water temperature and pH were set ateach survey accordingly to measures taken a few min-utes before the acoustic data acquisition (data notshown).Acoustic data were processed in Echoview version

4.9, Myriax Software Pty Ltd., Hobart, Tasmania, Aus-tralia. The abundance of fish in the study area during thesurveys was determined by two methods: echo countingof single fish echoes (fish tracks) and echo integration.Echo counting was used when the single target echoesdid not overlap, which generally occurred when the fishwere at lower densities (Simmonds & MacLennan2005). A fish track (FT) was considered a combinationof at least three single targets from a single fish(Ehrenberg & Torkelson 1996) (Fig. 2). Considering thatfish target strength (TS) generally ranges from 25 to�65 dB (Brandt 1996), the Echoview software was con-figured to detect single targets using a TS threshold of�65 dB, 6.0 dB for pulse length determination level and12 dB for maximum beam compensation. The singleechoes were combined into fish tracks based on the tar-get tracking algorithm of Blackman (1986) used by thesoftware. More than three echoes had to be separatedfrom each other by less than three pings to be consid-ered a track. The mean in situ TS was determined by thesoftware for each fish track. The use of fish tracks mini-mises the variance for estimates TS of backscattered ech-oes (Ehrenberg & Torkelson 1996). In this study, fishdensity was estimated considering the mean TS of thefish tracks detected in the respective month and time torepresent the fish better in the area. The noise level mea-sured in Sv domain was �80 dB, so TSu domain at 1 m

depth was �107.5 dB, calculated by equation 24 in Par-ker-Stetter et al. (2009).When fish were aggregated (here fish aggregation is

considered any collection of fish that have gatheredtogether in some locality), the density was generallyextremely high, and the single target echoes overlappedthereby impeding or preventing the separation of the fishtracks (Fig. 2). In such cases, echo counting does notyield reliable fish abundance estimates; thus, echointegration is the more appropriate processing method(Taylor & Maxwell 2007). Echo integration is the totalsum of backscattering cross sections within a samplingarea or volume (Brandt 1996). The density per aggrega-tion area was calculated using the following equation(Parker-Stetter et al. 2009):

qa ¼ABCrbs

; ð1Þ

where: qa = areal fish density (individuals m�2),rbs = backscattering cross section (m2) = 10TS/10, ABC(sa) = area backscattering coefficient (m�2) .The abundance estimate by echo integration (N) was

calculated using the following equation (Parker-Stetteret al. 2009):

N ¼ A� �q; ð2Þwhere: A = aggregation area in m2 (corrected area

parameter in the Echoview software), �q = mean densityper area.The corrected area parameter represents the aggrega-

tion area corrected for the acoustic beam geometry, fol-lowing the system of Diner (1998).

(a) (b)

Figure 1. (a) Tres Marias Hydropower Plant (TMHP). Survey area, tailrace backwater are highlighted. (b) Survey area contour in aerial image mapfrom TMHP. Dotted lines represent transects performed in acoustic surveys (Source: Image from Google Earth).

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TAILRACE FISH ASSESSMENT BY ECHO SOUNDING 187

Fish collection

Fish were sampled concomitantly with each acoustic sur-vey. At each survey, gillnets were set in the tailracebackwater for a 24-h cycle and checked for fishes duringthe six sampling intervals starting at 21:00, 01:00, 05:00(night samples), and at 09:00, 13:00, 17:00 (daytimesamples). Gillnets (10 m long and 1.6 m high) of differ-ent mesh sizes: 3, 4, 5, 6, 7, 8, 10, 12, 14, 16 cm(stretch mesh) total area of 160 m² were used. The nets

were set perpendicularly from the riverbank. Fish wereidentified and measured in total length, and voucherindividuals were deposited at the Ichthyological Collec-tion of the Federal University of Lavras (UniversidadeFederal de Lavras: CI-UFLA).

Abiotic data sampling

Limnology and hydrology data were used to assess therelationships between fish abundance and abiotic factorsin the TMHP tailrace. Therefore, the data acquisitionfollowed the days and times of each hydroacoustic pro-spection in the tailrace backwater.A multiparameter probe (Horiba U10, Irvine, Califor-

nia, USA) was used to acquire the water parameters,including the pH, dissolved oxygen (mg�L�1) and tem-perature (°C). Precipitation and discharges (turbinate andspill) data for the study period were obtained fromCemig Gerac�~ao e Transmiss~ao S.A (see Loures & Pom-peu 2012). Mean depth variation over the study periodwas 0.19 m and was only higher than 0.75 m in March2011 when the spillway gates were open and due to spilldischarge depth variation almost reached 1.5 m in tail-race.

Data analysis

Linear regression analyses were performed to test fora relationship between the abundance of fish capturedin the gillnets (Loures & Pompeu 2012) and the abun-dance determined by echo counting, echo integration,and for the sum of abundance determinate by thesetwo methods. The same analysis was used to test fora relationship between the abiotic variables and theabundance of fish measured by hydroacoustics andgillnets.Prior studies demonstrated that the fish community in

the study area varied between the dry and the wetmonths (Loures & Pompeu 2012). Thus, a factorialANOVA, was performed to test for possible differences inthe amount of fish (based on fish tracks counting)between seasons (dry and wet), diel cycle (day andnight) and four depth classes (0–5 m; 5–10 m; 10–15 m; 15–20 m). This analysis was followed by Tu-key’s HSD (honest significant difference) post hoc testfor multiple comparisons of the means from the testedgroups. Similar tests were performed to assess fish trackTS, as an indirect indicator of fish size. The greater theTS, the stronger is the echo relative to the transmission,hence bigger the target (Simmonds & MacLennan2005).All statistical analyses were performed using Statistica

10.0, and the level of significance (a) was set at 0.05.

(a)

(b)

(c)

Figure 2. Echograms examples from acoustic surveys in Tres MariasHydropower Plant tailrace, illustrating the interpretation of echoes indata processing techniques performed in Echoview 4.9 software. TSthreshold was set to �65 dB to detect single targets. (a) Sv Echogram(June 2010) (b) SV Echogram displays single fish echoes before fishtrack detection (January 2011) (c) Single Target Echogram after fishtrack detection (January 2011). (A) Surface line corresponding to thenearfield. Data above this line were excluded from processing; (B) Bot-tom line, bottomfield. Data below this line were excluded from process-ing; (C) Aggregated fish; (D) Fish Tracks; (E) Single fish echoes.

© 2015 John Wiley & Sons Ltd

R. C. LOURES & P. S. POMPEU188

Bathymetry of the TMHP tailrace backwater was per-formed on 22 August 2010 to characterise the environ-ment in terms of depth. A BioSonics DT-X DigitalScientific echosounder was used with the same configu-ration as for acoustic data collection. Water level down-stream from the plant was 516 mASL (metres above sealevel) with a total discharge of 435 m3�s�1. Data wereprocessed using Echoview 4.9, and depth informationalong with the XY coordinates was exported to Surferversion 10.0 (Golden Software Inc. 2011) to constructthe bathymetric chart.The outline of the plant’s backwater was determined in

Surfer from Google Earth image, and a bathymetric chartwas generated using a kriging data interpolation. Krigingis a process of estimation by moving means of values forvariables distributed in space based on adjacent values(Landim & Sturaro 2002). Descriptive statistics of thearea were obtained during data processing in Surfer.For each season, a map of fish density was generated,

considering fish aggregations, to verify their distributionin the TMHP tailrace backwater. To this end, Surfer10.0 was used to generate density maps using InverseDistance to a Power as an interpolation method. WithInverse Distance to a Power, data are weighted duringinterpolation, so that the influence of one point, relativeto another, declines with the distance from the grid node.Normally, Inverse Distance to a Power behaves as anexact interpolator (Franke 1982; Landim 2000).

Results

Seasonal and diel changes in fish abundance, densityand distribution

The number of fish estimated by echo counting in thetailrace differed significantly between dry and wet sea-sons, day and night, and between the four depth classes(Table 1). There were fewer fish at depths >10 m duringthe dry season, and there was an increase in the numberof fish closer to the water surface (<5 m) at night(Fig. 3). Fish abundance significantly increased duringthe wet season, especially in deeper layers, with no dif-ferences between day and night.Echo integration revealed that the number of fish

aggregations was lower during the dry season than thewet season, but fish abundance estimates in aggregationswere higher in dry season (Table 2). The tailrace back-water bathymetry demonstrated that the median depthwas 10.1 m, and only 25% of the area was below13.6 m, whereas 25% was above 3.3 m (Fig. 4). Densitymaps revealed a greater concentration of fish nearTMHP powerhouse wall, where depth was >10 m, withhigher densities during the dry season (Fig. 4). Fish

aggregations were more dispersed throughout the tailracebackwater during the wet season, with a less clear pref-erence for deeper areas.

Relationship between hydroacoustics and gillnetsabundances estimates

Fish abundance estimated by echo counting rangedfrom 162 to 3204 individuals in the study area. Echointegration abundance estimated 97–10 664 fish inaggregations, during the study period, with the excep-tion of April and May when no aggregations weredetected (Fig. 5). Gillnets captured 995 fish from 33species, and the great majority of individuals were fromthe two orders: Siluriformes (69.7%) and Characiformes(29.8%) (see Loures & Pompeu 2012 for more gillnetcatch results).Both survey methods, hydroacoustics and gillnets,

found that the amount of fish in tailrace backwater wasgreater in the wet season (Fig. 5). The mean abundancedetermined by echo counting and echo integration wassignificantly correlated with fish abundance estimated bygillnets captures (P < 0.001). The abundance measuredby counting fish tracks (FT) accounted for 20% of thefish in nets, whereas the abundance estimated from fishaggregations accounted for 31%. However, a better cor-relation (r2 = 0.35) was observed for the sum of thoseabundance estimates (FT + fish in aggregations), whenboth individual fish as well as those aggregated wereconsidered.The mean TS of fish tracks detected in the study per-

iod differed significantly between night and day, andbetween the four depth classes in the tailrace (Table 3).Generally, the TS values increased with depth duringboth seasons (Fig. 6). The mean size of fish captured ingillnets were 218 mm, with higher variation in the wetseason, 81–708 mm, compared with the dry season,108–423 mm (Fig. 7).

Table 1. Factorial ANOVA for differences in fish track abundance(log10N FT+1) between the temporal factors, the season (dry 9 wet)and the diel cycle (day 9 night), and the spatial factor (depth classes).

Effect F P

Season 90.42 <0.001Diel 36.69 <0.001Depth 28.91 <0.001Season 9 Diel 2.62 0.107Season 9 Depth 23.23 <0.001Diel 9 Depth 2.05 0.111Season 9 Diel 9 Depth 1.60 0.192

Significative p values are in bold.

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TAILRACE FISH ASSESSMENT BY ECHO SOUNDING 189

Factors influencing changes in fish abundance

The physico-chemical parameters of water quality in thetailrace did not explain the temporal variation in fish abun-dance estimated by hydroacoustics, which was explained(r2 = 0.51) only by gillnets fish abundance (t = 3.87;P = 0.002) and a negative effect of total discharge (tur-bine flow and spill) (t = �2.31; P = 0.035). Total dis-charge and precipitation explained 73% of the varianceobserved in species richness and abundance of individualscaptured by the gillnets (Loures & Pompeu 2012).

Discussion

Seasonal and diel changes in fish abundance, densityand distribution

Abundance estimated by counting fish tracks as well asthe number of fish in aggregations estimated by echointegration differed significantly between the dry andwet seasons, with higher values registered from Novem-

ber to March (wet season). The captures by gillnetsshowed the same pattern (Loures & Pompeu 2012). Var-iation in fish abundance throughout the year may reflectthe seasonality of tropical waters. In rivers, seasonality isprimarily caused by changes in the water level (Lowe-McConnell 1999) and the availability of floodplain habi-tat (Junk et al. 1989), although the rain and temperaturecan trigger migration to spawn (Parkinson et al. 1999),even in dammed rivers (Pompeu & Martinez 2006). Themonths with the greatest detected abundance coincidedwith the reproductive period of numerous species in theS~ao Francisco River (Alves et al. 2011). Thus, schedul-ing damaging operational procedures for fish during theperiod of lower abundance in the tailrace, that is monthswith low precipitation (dry season), may reduce directimpact of plant operation on fish.More fish were detected by hydroacoustics and gill-

nets during night sampling. Siluriformes, which areknown to exhibit nocturnal habits (Burguess 1989), werethe predominant species detected.

Figure 3. Distribution of fish tracks abundance (N FT) during dry and wet seasons, diel cycles and in four depth classes in Tres Marias Hydropow-er Plant tailrace. SE = standard error and SD = standard deviation. Depth means followed by the same letter do not differ significantly by Tukey’stest at a 5% significance level. Lowercase letters compare day 9 night during the dry season, uppercase letters compare day 9 night during the wetseason and italicised letters compare the dry x wet seasons during day (xyzw) and night (tuv).

© 2015 John Wiley & Sons Ltd

R. C. LOURES & P. S. POMPEU190

Fish aggregations were more dispersed throughout theTMHP tailrace backwater region more concentrated in thewet season, but were only found in n some locations ofthe backwater during the dry season. There were also sea-sonal differences in fish distribution in water column. Dur-ing the dry months, the number of fish decreased atgreater depths, while in the wet months, fish were moreconcentrated from 5 to 15 m depth. Density maps demon-strated that fish were more aggregated (greater density) indeeper areas than shallow regions (<7 m). Given that thebackwater bathymetry indicated that 50% of the area wasapproximately 10.1 m deep, the fish likely occupied areascloser to the bottom. Gillnet data did not disclose anyinformation on fish distribution in the water columnsupporting the observation that most fish detected byhydroacoustics were Siluriformes. Many species of thisorder have demersal habit, including the two mostabundant species found in wet season: Franciscodorasmarmoratus (Reinhardt,) and Pimelodus maculatusLac�ep�ede (Santos et al. 2008; Kirchheim & Goulart 2010;Loures & Pompeu 2012).

Relationship between hydroacoustics and gillnetsabundances estimates

Temporal variation in fish abundance estimated by gill-nets was similar to that estimated by hydroacoustics,

using both methods echo counting and echo integration.When abundance estimates by those two methods weresummed, a better correlation was found with fish gillnetscaptures. This finding suggests that gillnet sampling andhydroacoustics surveys can be used to assess seasonalityof fish in TMHP tailrace backwater. The combined useof these techniques may yield better results because eachtechnique possesses limitations. One example of theselimitations was evident in the TS amplitude for fishdetected by hydroacoustics, which suggested a greatervariation in individual size compared with fish sizestructure captured by gillnets. Furthermore, it is notpossible to identify fish species that are insonified byhydroacoustics, species richness and frequency in thecommunity in tailrace can be determined using nets.Numerous studies, in addition to acoustic techniques,used alternative methods that enable fish identificationand collection of biological data to supplement acousticestimates (Ransom et al. 1996; Brandt 1996; Cornishet al. 2006; Matveev 2007; Spinelli 2010; Jurveliuset al. 2011).Distribution analysis of fish tracks mean TS suggested

that fish size increased with depth because the greatestTS values were detected in the deepest layers. Thatincrease was not as notable during the dry season as inthe wet season, but a difference in TS values was readilyobservable beyond 15 m, suggesting that there were

Table 2. Echo integration results on fish abundance and fish density (qa) in aggregations. Fish track (FT) mean target strength (TS) measuredin situ, in each field trip, was used to calculate the density. Mean depth of aggregations are shown. Fortnightly sampling is indicated by 1 and 2 aftereach month

Season Total abundance No. of aggregations

qa (individuals m�2)

Mean TS (dB) Mean depth (m)Min Max Mean

Dry*June 562 3 1.84 14.95 10.56 �37.78 5.8July 97 7 0.10 18.55 5.07 �40.41 9.7August 188 10 0.03 11.97 5.17 �40.57 5.7September 944 24 0.03 90.74 6.01 �45.33 4.7Dry mean 448 11 0.50 34.05 6.7 �43.62 6.5WetOctober 1 774 12 0.01 35.71 7.66 �43.37 6.0October 2 217 22 0.09 9.33 1.60 �43.01 5.9November 1 699 27 0.04 10.63 2.25 �43.01 4.8November 2 2932 29 0.02 23.78 3.20 �42.12 10.5December 1 7958 38 0.04 31.91 5.03 �42.22 6.9December 2 8262 64 0.01 81.01 5.22 �44.25 6.0January 1 10664 51 0.22 21.41 2.33 �43.4 7.7January 2 4730 39 0.01 20.87 2.84 �44.3 9.4February 1 3731 34 0.01 7.24 1.78 �41.86 10.3February 2 7516 34 0.02 11.40 2.71 �42.81 10.2March 1 3948 51 0.04 7.18 1.48 �42.87 10.0March 2 10365 64 0.01 16.20 1.55 �43.13 9.6Wet mean 5150 39 0.04 23.06 2.84 �43.92 8.1

*No aggregations were registered in April and May.

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TAILRACE FISH ASSESSMENT BY ECHO SOUNDING 191

(a)

(b)

Figure 4. (a) Bathymetry of the tailrace backwater (water level at 516 m above sea level and total discharge 435 m³�s�1). (b) Density maps ofaggregated fish during the dry season and wet season in the Tres Marias Hydropower Plant tailrace backwater. The dry season map was generatedwith fish aggregation data from June to September/2010 (in April and May no fish aggregation was detected), and the wet season map was generatedby the same procedure for the months of November to December 2010 and January to March 2011.

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R. C. LOURES & P. S. POMPEU192

larger fish in this region. Gillnets samples could not cor-roborate these data because the nets were set perpendicu-larly from the riverbank at one depth.

Factors influencing changes in fish abundance

Water physico-chemical parameters did not account forfish abundance variation in the TMHP tailrace. Abun-dance in the tailrace estimated by gillnets catches waspositively correlated with total discharge and precipita-tion (Loures & Pompeu 2012). By contrast, abundanceestimated by hydroacoustics was negatively correlatedwith total discharge, but positively with fish catches inthe nets. As flow in the tailrace increased during damoperation in March 2011, turbulence in the waterbecame more intense and interfered with fish detections.

The echo level from targets (fish, invertebrate, bottom)should be greater than the noise from the environment(undesirable signals such as bubbles or boat motor inter-ference) to be detected and provide interpretable data(Signal to noise ratio – SNR) (Simmonds & MacLennan2005; Parker-Stetter et al. 2009). Thus, because morenoise was present as discharge increased, the detectionof echoes from fish may have been compromised,thereby reducing the power of estimating abundance anddensity under such conditions. Moreover, such aninverse relationship between the total discharge andabundance measured hydroacustically may be reflectedin the low correlation observed between the gillnet andthe hydroacoustics abundances. A study using an echo-sounder and gillnets to assess upstream to downstreamfish passage at the Yacyret�a Dam, the largest on theParan�a River, could not sample in the area near the plantdue to the flow and the presence of bubbles (Oldani &Baigun 2002).This study demonstrated temporal and spatial varia-

tions in fish abundance and density in TMHP tailracebackwater, which can assist operation of the hydro-power plant. Damaging operational procedures for fishat TMHP, such as turbine dewatering, should beplanned for periods of low fish abundance, that is fromApril to October (dry season). A reduction in the num-ber of turbine stop/start-ups from November to Marchwould also be beneficial because greater fish abundancein the tailrace increases the probability of injury ordeath by turbine operation. Additionally, density mapscombined with bathymetry of the area may eventuallyguide the selection for the best sites for installing a fishpassage along the dam if future installation is required.Further studies to measure the hydraulic geometry ofthe tailrace combined with information on the fishdistribution in the water column, such as thoseobtained in the present study, can improve the under-standing of how fish respond to water flow pattern inthis area.In some cases, hydroacoustics can be a useful tech-

nique for estimating fish abundance downstream fromplants, when the interference from bubbles or flow doesnot impede echo detection. The present study demon-strated that using alternative techniques, such as gillnets,for collecting biological data could supplement the infor-mation obtained by hydroacoustics. Using an echosoun-der and techniques for processing acoustic data allowanalysis of multiple aspects of the fish community in aspecific area, including estimation of fish body weight,behavioural characteristics from speed and the directionof fish movement to the morphology of schools anddepth distribution.

Figure 5. Temporal abundance variation: fish tracks were detectedusing echo counting (FT = fish track); fish aggregations were estimatedusing echo integration (FA = fish aggregation) and fish caught in gill-nets (FN = fish nets). Grey area corresponds to wet season and fort-nightly sampling are indicated by 1 and 2 after each month.

Table 3. Factorial ANOVA for differences in the mean acoustic back-scatter (TS in dB) of the fish tracks between the temporal factors, theseason (dry 9 wet) and the diel cycle (day 9 night), and the spatialfactor (depth classes).

Effect F P

Season 0.01 0.921Diel 7.80 0.006Depth 51.16 <0.001Season 9 Diel 0.10 0.750Season 9 Depth 4.40 0.006Diel 9 Depth 0.95 0.416Season 9 Diel 9 Depth 3.16 0.027

Significative p values are in bold.

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TAILRACE FISH ASSESSMENT BY ECHO SOUNDING 193

Figure 6. Distribution of fish track (FT) mean target strength (TS in dB) during dry and wet seasons, diel cycle and in four depth classes in TresMarias Hydropower Plant tailrace. SE = standard error and SD = standard deviation. Depth means followed by the same letter do not differ signifi-cantly by Tukey’s test at a 5% significance level. Lowercase letters compare day 9 night during the dry season, uppercase letters compareday 9 night during the wet season and italicised letters compare the dry 9 wet seasons during day (xyzw) and night (tuv).

Figure 7. Frequency distribution of fish abundance captured by gillnets in fish length classes (mm).

© 2015 John Wiley & Sons Ltd

R. C. LOURES & P. S. POMPEU194

Acknowledgments

We thank the fisher Waldir Alves dos Santos and thebiologists Ivo Prado, Igor Boratto and Francisco And-rade, for their support in data collection. We also thankthe environmental analyst Renato Junio Constancio andthe Tres Marias Hydropower Plant operational and main-tenance team for their assistance in field work, and thePeixe Vivo Program of CEMIG for providing financialsupport.

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