Evaluation of Bottom Trawls as Compared to Acoustics to Assess AdultLake Herring (Coregonus artedi) Abundance in Lake Superior

Jason D. Stockwell*, Daniel L. Yule, Owen T. Gorman, Edmund J. Isaac, and Seth A. Moore†

United States Geological Survey - Great Lakes Science CenterLake Superior Biological Station

2800 Lakeshore DriveAshland, Wisconsin 54806

ABSTRACT. We compared density estimates from day bottom trawl tows against night midwater trawltows and acoustic gear to test the hypothesis that adult lake herring (≥ 250 mm) are underestimated byday bottom trawl tows during the annual USGS spring fish community survey in Lake Superior. We foundaverage density at nine nearshore stations was significantly higher at night (21.3 adult fish/ha) comparedto day (1.0 adult fish/ha; p = 0.0119). At nine offshore stations, no lake herring were captured during theday but density averaged 39.6 adult fish/ha at night. At a lakewide scale (n = 18 stations), precision (rel-ative standard error) was much better using night midwater trawls and acoustic gear (37%) compared today bottom trawls (100%). Moderate sample size increases using the former methodology would likelybring precision within recommended levels (≤ 30%) for stock-recruit data sets. Our results suggest that1) population abundances of adult lake herring in Lake Superior are much higher than previously consid-ered, 2) the annual spring fish community survey may not provide a relative index of abundance of adultlake herring, 3) night midwater trawls and acoustic gear are necessary for assessing adult lake herringabundance, and 4) previous studies using lake herring data from the annual spring fish community surveyneed to be re-evaluated in light of these results. Lake herring appear to become progressively morepelagic and less susceptible to bottom trawling as they mature. Day bottom trawls appear to be an ade-quate tool for estimating relative density of age-1 recruits, although this method still suffers from rela-tively poor precision.

INDEX WORDS: Lake herring, Coregonus artedi, stock assessment, recruitment, Lake Superior.

J. Great Lakes Res. 32:280–292Internat. Assoc. Great Lakes Res., 2006

INTRODUCTION

Historically, lake herring (Coregonus artedi)were found throughout the Great Lakes (Scott andCrossman 1973) and served as a key trophic con-duit that linked planktonic crustacean resources tonative piscivorous fishes (Dryer and Beil 1964,Brown et al. 1999). At the turn of the 20th Century,lake herring supported major commercial fisheriesin the Great Lakes (Baldwin et al. 1979). However,most lake herring stocks subsequently collapsed be-cause of overfishing, habitat degradation, and inter-actions with exotic species. Lake herring becamerare in western Lake Erie (Hartman 1973) and com-mercially extinct in Lake Ontario (Koelz 1926) bythe early 1900s, and severely declined in Lakes

*Corresponding author. E-mail: jstockwell@usgs.gov†Present Address: Grand Portage Band of Chippewa, 27 Store Road,Grand Portage, MN 55605

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Michigan and Huron in the 1950s (Smith 1972).Lake Superior stocks collapsed in the 1960s(Lawrie and Rahrer 1972, Selgeby 1982), but haveat least partially recovered to support commercialfisheries (Bronte et al. 2003).

Restoration and rehabilitation of native fishstocks are long-term goals for fishery managers ofthe Great Lakes and are integral components of theGreat Lakes Fishery Commission’s Strategic Vision(Great Lakes Fishery Commission 2001). Lake her-ring are recognized as a central species in nativecold-water fish assemblages and discussions for re-establishment of self-sustaining lake herring popu-lations in all four of the lower Great Lakes havebegun (DesJardine et al. 1995, Eshenroder et al.1995, Stewart et al. 1999, Ryan et al. 2003). InLake Superior, where lake herring stocks are mostnumerous and widespread, there is a managementneed to identify sustainable harvest levels for com-

Assessment of Lake Herring in Lake Superior 281

mercial fisheries (Ebener 2003). Additionally,restoration of lake trout (Salvelinus namaycush) inthe lower four Great Lakes is a focal managementgoal (DesJardine et al. 1995, Eshenroder et al.1995, Stewart et al. 1999, Ryan et al. 2003) whichmay remain elusive until native forage fishes, likelake herring, are reestablished.

To make informed decisions for managing and/orrestoring lake herring, it is important to understandhow lake herring populations function within theirenvironment. Thus, it is critical to ensure that as-sessment techniques properly characterize popula-tion metrics such as abundance, biomass, andsize-structure. Without reliable techniques, morecomplex studies examining ecological processesusing baseline data could be compromised.

The Lake Superior Biological Station of the U.S.Geological Survey’s (USGS) Great Lakes ScienceCenter has been conducting an annual spring fishcommunity survey in Lake Superior since 1978.This survey uses bottom trawls to assess the rela-tive abundance and biomass of the fish communityat 86 stations distributed along the perimeter ofLake Superior. Trawl tows are made across-contourduring the day from an average starting depth of 15m to an average ending depth of 65 m. A majorstrength of this survey is that it captures manyspecies and life-stages not susceptible to most othersurveys on Lake Superior (i.e., gillnet surveys), andthus can provide unique perspectives on fish com-munity dynamics (Gorman and Hoff In press,Bronte et al. 2003). Data from the survey have beenused to index survival of young hatchery-rearedlake trout (Hansen et al. 1994), estimate recruit-ment potential of lake whitefish (Coregonus clu-peaformis) to commercial fisheries (Curtis et al.1993), and track the status and distribution ofalewives (Alosa pseudoharengus) (Bronte et al.1991).

Data from the spring fish community survey havealso been used for a variety of investigations in-volving lake herring including annual assessment ofyear-class strength (Bronte et al. 2003, Stockwell etal. 2005, Gorman and Hoff In press), assessment offorage fish supply for native and stocked salmoninepredator fish populations (Negus 1995, Ebener1995), and production of invertebrates (Johnson etal. 1998). Modeling exercises have used these datato develop expectations of fish community re-sponses to management actions, natural events, andinvasive species (Kitchell et al. 2000, Cox andKitchell 2004), and to understand stock-recruit rela-tionships of lake herring (Hoff 2004). Despite indi-

cations that the spring survey may under-representimportant prey fish populations (Ebener 1995,Negus 1995, Mason et al. 2005), there has been nodirect evaluation of the survey to understand possi-ble biases associated with reliance on a single gear(bottom trawls) deployed only during the day,

Numerous studies (Koelz 1929, Smith 1956,Dryer and Beil 1964, Dryer 1966, Scott and Cross-man 1973, Selgeby 1982, Selgeby and Hoff 1996)have indicated that lake herring are pelagic in LakeSuperior and other Great Lakes. Results fromNegus (1995) prompted a group of researchers totest the hypothesis that day bottom trawl estimatesfrom the spring survey underestimate biomass ofprey fish species. Mason et al. (2005) conducted anacoustic and midwater trawl survey during the nightin August 1997 and found prey fish biomass (lakeherring, bloater [C. hoyi], kiyi [C. kiyi], and rain-bow smelt [Osmerus mordax]) in western Lake Su-perior to be 2 to 134 times greater than estimatesgathered during the May–June 1997 spring fishcommunity survey. In response to the findings ofMason et al. (2005), researchers and managers rec-ommended development of a lakewide nightacoustic sampling program for Lake Superior(Ebener 2003). Development of such a program isongoing, with results from August 2003 and 2004indicating similar levels of lake herring biomass inoffshore and nearshore waters of Lake Superior(Hrabik et al. 2005).

In this study, we tested if sampling with bottomtrawls during the day in the spring provides similarpopulation metrics for lake herring to samplingwith midwater trawls and acoustic gear at nightwhen sampling is done at the same location on thesame day. Efforts to capture schooling fish duringthe day with midwater trawls have proven difficultin Lake Superior (D. Yule, unpublished data). Wetherefore did not include this sampling strategy inour comparison. Additionally, we did not includenight bottom trawl data in the comparison becauseother studies have demonstrated that lake herringare pelagic at night (Rudstam et al. 1987, Milne etal. 2005). Our null hypothesis was that lake herringdensity estimates and length-frequency distribu-tions do not vary significantly across samplingstrategy (day bottom trawls vs. night midwatertrawls and acoustic gear). If refuted, our resultswould indicate that day bottom trawling is not aneffective sampling strategy to monitor lake herringpopulations in Lake Superior, and that future effortsto meet this objective would require a differentsampling strategy. Rejection of the null hypothesis

282 Stockwell et al.

would also have implications for a wide range ofstudies that make use of data from the spring sur-vey, including stock-recruit models (Hoff 2004),ecosystem simulations for fish community manage-ment strategies (Kitchell et al. 2000, Cox andKitchell 2004), prey selectivity and demand-supplyanalyses (Ebener 1995, Negus 1995, Ray 2004,Mason et al. 2005), and status of lake herring re-covery in Lake Superior (Bronte et al. 2003).

METHODS

General Sampling Design

A total of 18 stations were sampled with day bot-tom trawls and night midwater trawls and acousticgear (Fig. 1). Nine stations were categorized as off-shore and were selected as part of a binationallower trophic level monitoring effort. Nine stationswere in nearshore areas and were a subset (10.5%)of the 86 standard USGS spring bottom trawl sur-vey stations. These stations were selected becausethey were closest to the offshore stations. The onlyexception was Station 210 (Fig. 1) which was in-cluded to represent the extreme western end ofLake Superior. Sampling occurred between 4 Mayand 15 June 2005. Day samples were collected after

sunrise and before sunset. Night samples were col-lected between 0.5 hr after nautical twilight in theevening and 0.5 hr before nautical twilight in themorning.

Bottom Trawl Sampling and Biomass Estimates

We used a 3/4 Yankee bottom trawl (11.9 mheadrope, 15.5 m footrope, and 2.2 m wing lines)with 89- and 64-mm stretch mesh, and a cod endwith 13-mm stretch mesh. Estimates of trawl widthduring sampling were recorded using a NETMINDsystem (Northstar Technical, Inc., St. John’s, New-foundland). Bottom trawl tows were run across-contour perpendicular to the shore at nearshorestations and towed at 3 to 4 km/hr. Because of dif-ferences in bank steepness and bottom characteris-tics (e.g., rock ledges), starting and ending depthsand tow durations varied across nearshore sites. Av-erage starting depth was 21 m (range 15 to 35 m)and average ending depth was 60 m (range 22 to110 m). Average tow duration was 21 min (range 5to 38 min). Bottom trawl tows were made along-contour at offshore sites because banks were notpresent as in nearshore areas. Average tow durationat offshore sites was 30 min (range 30 to 33 min).Depths of these bottom trawl tows ranged from 95

FIG. 1. Locations of nine nearshore (circles) and nine offshore (stars)stations sampled with day bottom trawls and night midwater trawls/acousticgear. The nine nearshore stations are a subset (10.5%) of the current USGSspring fish community survey operating since 1978 in U.S. waters and 1989in Canadian waters. The offshore sites were sampled as part of a binationallower trophic level monitoring effort, and represent depths (95 to 327 m)not historically sampled during the spring fish community survey.

Assessment of Lake Herring in Lake Superior 283

to 327 m, and distance from shore ranged from 7 to61 km (Fig. 1).

All lake herring were counted, individually mea-sured for total length, weighed in total to the near-est gram, and then frozen. Within 1 month, fishwere thawed, and a subset were individually mea-sured (total length) and weighed (nearest 0.1 g) inthe laboratory to generate a length-weight relation-ship. Density (fish/ha) and biomass (kg/ha) werecalculated using average bottom trawl width (frommensuration data) and distance the trawl fished onthe bottom for each tow. We assumed the catchabil-ity coefficient was 1, similar to other studies usingthe same data set (e.g., Bronte et al. 2003, Hoff2004). Because we also wanted to estimate biomassof the adult stock, and in some instances theselarger fish were weighed in aggregate with smallersub-adult fish, we used the length-weight relation-ship to estimate individual weights of larger fish.These data were then used to estimate biomass forthe adult stock.

Midwater Trawl Sampling

The midwater trawl (Gourock Trawls, Ferndale,WA, USA) measured 15.2 × 15.2 m. The mesh ta-pered from 152 mm (stretch) at the mouth to 13 mm(stretch) at the cod end. Mensuration sensorsrecorded the midwater trawl head rope depth, headrope temperature, trawl mouth height, and trawlwidth at approximately 10-s intervals during deploy-ment. The midwater trawl was deployed in astepped-oblique fashion (Kirn and LaBar 1991), witheach stratum fished for approximately equal amountsof time. Average midwater trawl tow duration was 38min (range 27 to 55 min). The shallowest depthfished (headrope depth) was approximately 5 m foreach trawl, and the maximum depth fished was de-pendent on bathymetry and fish distributions. At off-shore sites the midwater trawl was fished no greaterthan 95 m (footrope depth) because acousticechograms showed pelagic fish rarely occupieddepths greater than 95 m. At nearshore sites, we de-ployed midwater trawls along the same transectswhere day bottom trawls were collected. Midwatertrawl tows at stations with very short bottom trawltows (< 15 min; Stations 172 and 411) were extendedwhile still covering similar bathymetric depth zones.

Acoustic Sampling and Biomass Estimates

Acoustic data (fish and bathymetry) were col-lected concurrently with midwater trawling using a

Biosonics (Seattle, WA, USA) DT-X digitalechosounder equipped with a 120 kHz split beam-transducer with a nominal beam width of 6.7°. Thetransducer was mounted on a 1.2-m long tow bodyand deployed approximately 1 m below the surface.

A transmit pulse duration of 0.4 ms was used atall times. Sampling rate was set at 5 pings/sec whentrawl transect maximum depths were less than 70m. For stations with maximum depths ranging from70 to 150 m, sampling rate was reduced to 3pings/sec. At bathymetric depths > 150 m we sam-pled at 1–2 pings/sec. This approach maximized theamount of acoustic information collected whileeliminating the problem of acoustic cross talk thatoccurs when one ping is not allowed to return to thetransducer before a subsequent ping is transmitted.

The receiving sensitivity of the transducer wascalibrated in the field at the onset and end of thesurvey using a 33 mm calibration sphere with a–40.5 decibels (dB) target strength using techniquesrecommended by BioSonics. Results of these fieldtests indicated agreement with laboratory calibra-tions and consistent sensitivity throughout the sur-vey period. Thresholds were set to allow detectionof all echoes exceeding –75 dB on the acousticaxis. Acoustic signals were collected with BioSon-ics Visual Acquistion Software (version 4.1) andoutput files were stored to a laptop computer harddrive. Vessel position was measured with anAshtech BRG2 differentially corrected GPS unit(accurate to 1 m) and this information was embed-ded in the acoustic data files.

Acoustic files were processed with Echoviewacoustic post-processing software (version3.10.132.06, SonarData Pty LTD, Tasmania, Aus-tralia). An Echoview algorithm was used to define aline 0.2 m above the bottom. The software-definedbottom line was examined to ensure that bottomechoes were not incorporated into fish backscatter-ing measurements. A surface line was added toeach echogram between 2 and 5 m below the sur-face, depending on sea conditions, to preclude inte-grating surface noise when measuring fish acousticbackscattering. A fishing-depth line was added thatdefined the deepest depth fished by the midwatertrawl. Echoview was used to measure fish backscat-tering in the open-water region between the surfaceand fishing-depth lines. Portions of echograms cor-rupted with non-fish backscattering were defined asbad-data regions and excluded from analyses. Non-fish acoustic backscattering was minimized by set-ting a –65 dB minimum backscattering volume

284 Stockwell et al.

strength (Sv) threshold. The percent increases in av-erage fish density when changing Sv thresholdsfrom –60 to –65 dB, from –65 to –70 dB, and from–70 to –75 dB were 22.5, 6.6, and 3.6%, respec-tively. Based on these sensitivities and the presenceof the macroinvertebrate Mysis relicta in Lake Su-perior, we selected –65 dB as the Sv threshold.

Echoview software measures fish backscatteringon an areal basis by calculating a nautical squarescattering coefficient (NASC; m2/nautical mile2)equivalent to the total fish backscattering through-out the water column scaled to one square nauticalmile at the surface.

Fish density was calculated by scaling NASC bythe average back-scattering cross section of the av-erage detected fish (i.e., σbs). We used the follow-ing algorithm to estimate fish density from NASC(MacLennan and Simmonds 1992):

Fish density (fish/ha) = NASC / (4π*σbs*343).

By convention, σbs = 10(TS/10), where TS is the av-erage target strength of fish echoes in the openwater region, and 343 is the number of ha in asquare nautical mile.

To estimate TS we used the split-beam single-tar-get detection criteria available in Echoview. Thepulse length of a received echo was measured 6 dBdown from the peak in the echo envelope. For anecho to be classified as a single target it had to meetseveral criteria including: 1) the echo TS value hadto be a local maximum (i.e., larger than the previ-ous and next digital sample), 2) the echo TS had toexceed a –60 dB threshold that we selected, 3) theecho beam compensation value had to be less than6 dB, 4) the echo pulse duration had to fall between0.32 and 0.6 ms, 5) the standard deviation of anglesof all samples within the echo envelope had to beless than 1.5, and 6) when two echo envelopesoverlapped, the echo with the lowest peak targetstrength was rejected. For each transect, targetstrengths of echoes meeting these criteria in theopen water region were averaged.

By convention, the nominal transducer beamwidth measured on a one-way polar plot is definedas 3 dB down from the acoustic axis. Therefore,when using a TS threshold of –60 dB targets assmall as –54 dB were detectable throughout thenominal beam width. Given the existing knowledgeon the relationship between acoustic size and realsize of pelagic fish in Lake Superior, the applied TSthreshold allowed detection of rainbow smelt ex-ceeding 51 mm (Rudstam et al. 2003), lake herring

exceeding 40 mm (Rudstam et al. 1987), andbloaters exceeding 99 mm (Fleischer et al. 1997).The smallest rainbow smelt, lake herring, andbloater we captured were 29, 52, and 73 mm, re-spectively. Only 7% of all rainbow smelt capturedwere ≤ 51 mm and 0.5% of all bloaters capturedwere ≤ 99 mm. It follows that the applied TSthreshold generally allowed detection of the small-est fish targets present in the open water during ourspring sampling.

Previous studies have shown that when fish arefound in high densities, estimates of average TS canbe biased upwards and density estimates down-wards (Sawada et al. 1993, Appenzeller andLeggett 1992, Rudstam et al. 2003). Fish densityestimates using acoustic gear were relatively low atall stations except at nearshore Station 411 (Fig. 1).To assess this potential bias, we calculated theSawada et al. (1993) Nv index for 10 m vertical by250 m horizontal cells along Station 411. Only 15of the 75 cells had Nv values exceeding 0.04 (thecutoff value recommended by Sawada et al. 1993)and average TS values for this station changed littlewhen we included (–45.2 dB) or excluded (–45.7dB) these 15 cells. We concluded that fish densitieswere sufficiently low at all sites to allow for unbi-ased estimates of average TS.

To estimate lake herring density, we multipliedthe acoustic fish density estimate for the open-waterregion by the numerical proportion of lake herringin the concurrently deployed stepped-oblique mid-water trawl. To estimate lake herring biomass, wemultiplied the lake herring density estimate by theaverage weight of lake herring captured in the mid-water trawl. To generate an estimate of adult densi-ties, we calculated density estimates for lakeherring ≥ 250 mm using day bottom trawls andnight midwater trawls and acoustic gear. This cutoffvalue was chosen based on the minimum length ofmature female lake herring sampled during spawn-ing in fall 2004 (Yule et al. in press).

Analyses

Because our sample sizes were relatively smallfor evaluating normality, we used the nonparamet-ric Wilcoxon rank-sum test to test the null hypothe-sis that density estimates for adult lake herring didnot differ between day bottom trawls and nightmidwater trawls and acoustic gear. We first ran thetest using only the nearshore stations to directlyevaluate the efficacy of the fish community surveyto measure density of adult lake herring. We then

Assessment of Lake Herring in Lake Superior 285

pooled all stations (nearshore and offshore) to com-pare lakewide estimates of density and biomassgathered using the two sampling methods. We set α = 0.025 to adjust for the two tests (Bonferronicorrection), and we performed one-tailed tests be-cause our expectation was that night estimateswould be greater than day estimates.

RESULTS

At the nine nearshore sites, day bottom trawlscaptured 1,643 fish (15 species: rainbow smelt =58% of captured fish, lake whitefish 15%, pygmywhitefish Prosopium coulteri 13%) and night mid-water trawls captured 866 fish (11 species: rainbowsmelt = 57%, lake herring = 28%). Lake herringrepresented 2 and 28% of the fish captured for eachsampling strategy at the nearshore stations, respec-tively. At the nine offshore sites, day bottom trawlscaptured 10,211 fish (13 species: deepwater sculpinMyoxocephalus thompsonii 78%, kiyi 13%) andnight midwater trawls captured 212 fish (6 species:kiyi 79%, lake herring 12%). Lake herring repre-sented 0 and 12% of the fish captured for each sam-pling strategy at the offshore stations, respectively.

Lake herring were captured with day bottomtrawls at three of the nine nearshore stations. Lakeherring were captured with night midwater trawls ateight of these nine stations. For the day bottomtrawls, density and biomass estimates ranged from0 to 15.8 fish/ha and 0 to 2.51 kg/ha, respectively(Fig. 2). Average density (± one standard error, SE)was 2.1 (± 1.7) fish/ha and average biomass was0.30 (± 0.28) kg/ha. Estimates of density and bio-mass at the nine nearshore stations using night mid-water trawls and acoustic gear ranged from 0 to795.6 fish/ha and 0 to 34.66 kg/ha, respectively(Fig. 2). Average density at night (124 ± 86 fish/ha)was 59 times greater than day estimates, and aver-age biomass at night (10.70 ± 4.34 kg/ha) was 36times greater than day estimates.

For the nine offshore stations, lake herring werenot captured in any day bottom trawls. Lake herringwere captured at five of the nine offshore stationsusing night midwater trawls. Density and biomass atnight ranged from 0 to 155.1 fish/ha (average = 41.3± 20.1 fish/ha) and 0 to 43.28 kg/ha (average = 11.93± 5.45 kg/ha; Fig. 2). Density for all 18 stations com-bined averaged 1.1 (± 0.9) fish/ha and 82.4 (± 43.8)fish/ha for day bottom trawls and night midwatertrawls and acoustic gear, respectively. For biomass,the averages were 0.15 (± 0.14) and 11.32 (± 3.38)kg/ha for day and night estimates, respectively.

Of the 18 stations sampled, only one station (Sta-tion 139) had comparable size-structure between daybottom trawls and night midwater trawls (Fig. 3A).Two modes were present in each dataset at this sta-tion; one at 150 mm and the other at 325 (day bottomtrawl) or 350 mm (night midwater trawl). The den-sity estimate from the day bottom trawl (15.8 fish/ha)at Station 139 was one ninth the night midwatertrawl and acoustic gear estimate (138.9 fish/ha; Fig.2A). The day biomass estimate (2.51 kg/ha) was onesixth the night estimate at Station 139 (15.55; Fig.2B). For the remaining 17 stations, there were notenough fish captured for a comparison of lake her-ring size-structure between day bottom trawling andnight midwater trawling (Fig. 3B). Only 4 lake her-ring were captured during day bottom trawling com-pared to 183 captured during night midwatertrawling at these 17 stations. The modal lengths forthe night midwater trawls (150 mm and 325–375mm) were comparable to Station 139 (Fig. 3A).

Adult lake herring (i.e., ≥ 250 mm) were cap-

FIG. 2. Density (A) and biomass (B) estimatesfor lake herring for all 18 stations using day bot-tom trawls (BTR) and night midwatertrawls/acoustic gear (MTR/AC). The offshore sta-tions are denoted by the prefix “LIMN.” Theremaining stations are a subset of standardnearshore stations used for the annual USGSspring fish community bottom trawl survey.

286 Stockwell et al.

tured with day bottom trawls at only 1 (nearshoreStation 139) of the 18 stations sampled, comparedto 13 of the 18 stations (eight nearshore and fiveoffshore) using night midwater trawls. Fornearshore stations (n = 9), average density (± SE)using day bottom trawls (1.0 ± 1.0 fish/ha) was sig-nificantly less than using night midwater trawls andacoustic gear (21.3 ± 11.1 fish/ha; Wilcoxon rank-sum test, S = 108.5, p = 0.0119; Table 1). For allstations combined (n = 18), average density foradult lake herring using day bottom trawls (0.5 ±0.5 fish/ha) was significantly less than using nightmidwater trawls and acoustic gear (30.4 ± 11.0fish/ha; Wilcoxon rank-sum test, S = 426.0, p <0.0003; Table 1). Average density estimates usingmidwater trawls and acoustic gear at night were al-most double at offshore stations (39.6 ± 19.2fish/ha, n = 9) compared to nearshore stations (21.3± 11.1 fish/ha, n = 9; Table 1), but these differences

were not significant (Wilcoxon rank-sum test, S =89.0, p = 0.785). All lake herring captured at off-shore stations at night exceeded 250 mm.

The precision of density estimates for lake her-ring ≥ 250 mm at nearshore stations, as indicated bythe relative standard error (RSE = SE/mean*100),was much greater using day bottom trawls (100%)than midwater trawls and acoustic gear (52%). Ifwe include all 18 stations, the RSE of estimatesusing day bottom trawls (100%) was still greaterthan using night midwater trawls and acoustic gear(37%; Table 1).

Lake herring were captured at stations with meanbathymetric depths up to 200 m (Fig. 4); well be-yond the mean maximum tow depth (65 m) of day

FIG. 3. Length frequency distributions of lakeherring captured with day bottom trawls (BTR)and night midwater trawls (MTR) at Station 139(A) and the other 17 remaining stations combined(B) sampled in May and June 2005.

TABLE 1. Average densities for lake herring ≥250 mm using day bottom trawls or night midwa-ter trawls and acoustic gear in Lake Superior dur-ing spring 2005. Standard errors are reported inparentheses. BTR = bottom trawls, MTR/AC =midwater trawls and acoustic gear.

Day BTR Night MTR/AC Mean Density Mean Density

Station Group n (fish/ha) (fish/ha)

Nearshore 9 1.0 (1.0) 21.3 (11.1)Offshore 9 0 (0) 39.6 (19.2)

Total 18 0.5 (0.5) 30.4 (11.0)

FIG. 4. Biomass (kg/ha) estimates for all lakeherring using day bottom trawls (BTR) and nightmidwater trawls and acoustic gear (MTR/AC) as afunction of mean bathymetric depth at each sta-tion. The dashed lines indicates the average start-ing and ending depths of day bottom trawls usedin the annual spring fish community survey con-ducted by USGS from 1978 to the present innearshore waters of Lake Superior.

Assessment of Lake Herring in Lake Superior 287

bottom trawls during the annual spring fish commu-nity survey. Midwater trawls fished at night cap-tured lake herring at four out of five stations withmean bathymetric depths between 140 and 200 m(Fig. 4). Day bottom trawls and night midwatertrawls did not capture any lake herring at the threedeepest stations (mean bathymetric depths rangedfrom 225 to 325 m; Fig. 4).

DISCUSSION

A number of studies have compared acousticgear and midwater trawls to bottom trawls to esti-mate fish densities in the Great Lakes. Argyle(1982) found 20 to 30% of total fish biomass (pri-marily alewife and rainbow smelt) was above thebottom trawl during day sampling in Lake Huron.In Lake Michigan, Argyle (1992) found night mid-water trawl and acoustic estimates were 1.6 timesgreater for alewife, bloater, and rainbow smeltwhen compared to day bottom trawl estimates dur-ing two separate surveys. Fabrizio et al. (1997)showed much greater density estimates for rainbowsmelt and alewife at some lake depths in LakeMichigan using night midwater trawls and acousticgear, while estimates for bloater were higher usingday bottom trawls. Sampling in Fabrizio et al.(1997), however, was limited to only one site. Theresults from our study, based on 18 sites distributedacross all of Lake Superior, indicate average den-sity estimates for adult lake herring are grosslyunder-estimated using day bottom trawls. We con-clude that night sampling with midwater trawls andacoustic gear in both nearshore and offshore watersis necessary to generate more realistic and preciseestimates of adult lake herring abundance in LakeSuperior.

The Lake Superior spring fish community surveyis used as a relative index of abundance. However,it does not appear that day bottom trawls provide arelative index of abundance for adult lake herringgiven the wide range of density estimates at night(2 to 101 adult fish/ha) at locations where zero lakeherring were captured during the day. Given previ-ous observations that adult lake herring are pelagicand that these observations come from periodsthroughout the 20th Century spanning very wideranges of lake herring abundances (Koelz 1929,Smith 1956, Dryer and Beil 1964, Dryer 1966, Sel-geby and Hoff 1996), we have no reason to expectday bottom trawls to provide relative measures ofabundance for adult lake herring. Our results indi-cate that in the spring of 2005 density of adult lake

herring in offshore waters was similar to, if notgreater than, nearshore waters. Given that 75% ofLake Superior is deeper than 80 m, it follows thattotal abundance of adult lake herring in offshorewaters is much greater than in nearshore waters. Al-though it remains to be demonstrated over time, itappears that the 28-year time series of the springfish community survey does not provide a measureof relative abundance for adult lake herring.

A primary goal of fisheries management and re-search is to determine the relationship betweenadult standing stock and subsequent recruits(Ricker 1954, Beverton and Holt 1957, Myers andBarrowman 1996). Without adequate measures ofprecision of adult standing stocks (or recruits), ef-forts to understand the stock-recruit relationshipwill be hampered before they are even initiated.Walters and Ludwig (1981) used a basic model toillustrate how a strong stock-recruit relationship canbe obscured when spawner estimates are off by afactor of 2 to 4 (not unusual in most fisheries stockassessments), and that this situation often createsthe appearance of independent or dome-shaped re-lationships between spawners and recruits. Waltersand Ludwig (1981) contend this situation oftenleads researchers to search for other explanationssuch as environmental factors to explain variabilityin the stock-recruit relationship, and could be justas deceptive as the analyses of “noisy” stock-recruitdata they intend to replace. Walters and Ludwig(1981) recommended precision of abundance esti-mates to be no greater than ± 30% of the meanwhen using or publishing stock-recruit data sets.

Recently, Hoff (2004) published a stock-recruitmodel for lake herring in Wisconsin waters of LakeSuperior based on lake herring density estimatesfrom the USGS spring fish community survey. Yuleet al. (in press) reexamined the adult stock dataused in the Hoff (2004) analysis and found the RSEof these data averaged 54% over the 15-year period.The RSE for each year ranged from 41 to 82%. Ad-ditionally, our study suggests the relative abun-dance estimates for adult lake herring from thespring fish community survey may not be relative.Hoff’s (2004) analyses demonstrating the impor-tance of other biotic (lake trout abundance andslimy sculpin biomass) and abiotic (wind speed andair temperature) factors may have been usurped byhigh sampling variability using an inappropriatesampling gear. Thus, Hoff’s (2004) search for fac-tors other than adult stock to explain lake herringrecruitment was likely premature and may be mis-leading (sensu Walters and Ludwig 1981). Efforts

288 Stockwell et al.

to couple fish population data with environmentalfactors to predict recruitment appear promising(e.g., Axenrot and Hansson 2003) and may proveappropriate for lake herring populations in Lake Su-perior. However, our results indicate that day bot-tom trawling is not an effective sampling strategy toassess adult lake herring. It follows that any stock-recruit model based on these data, with or withoutenvironmental factors, is tenuous at best.

Measures of precision (RSE) using night midwa-ter trawls and acoustic gear (52%) were much betterthan estimates using day bottom trawls (100%) forthe nine nearshore stations. The RSE for the mid-water trawl and acoustic gear density estimate wasstill greater than the maximum recommended(30%) by Walters and Ludwig (1981). However, theRSE for night midwater trawling and acoustic geardecreased to 37% when all 18 stations were used toestimate average density for adult lake herring. Thisis promising given the 18 stations span all regionsof Lake Superior and thus likely represent the rangeof adult densities likely to be encountered anywherein the lake during a survey. Assuming the observedmean and variance from the 18 stations remain con-stant, sample size analysis indicates that 26 sampleswould be needed to achieve 30% RSE, 37 samplesto achieve 25% RSE, and 58 samples to achieve20% RSE. This indicates 1) a diminishing return onprecision with increased effort, and 2) 20 to 30%RSE is probably the best expectation at a lakewidescale with the level of effort we allocated at eachstation. Given our experience with acoustic sam-pling, we could expect to collect 30 to 40 samplesfrom across Lake Superior in a 3-week period (theannual spring fish community survey lasts about 6weeks). For comparison, we performed the sameexercise for the 18 day bottom trawls and foundthat 58 day bottom trawl samples (same sample sizethat resulted in 20% RSE for midwater trawl andacoustic gear) would result in a 56% RSE. Theseresults suggest bottom trawls fished during the dayin the spring are not sufficient to adequately sampleadult lake herring populations with acceptable lev-els of precision.

Our night biomass estimates for lake herring arecomparable to those found by Mason et al. (2005)in August 1997. They sampled the western arm ofLake Superior at night using midwater trawls andacoustic gear and estimated coregonine (lake her-ring, bloater, and kiyi combined) biomass in theApostle Islands, the Duluth-Superior region, andthe open-lake region to be 24.4, 17.9, and 7.9 kg/ha,respectively. Our biomass estimates for lake herring

(all sizes) from stations within their three regions(in the preceding order) were 15.6 (Station 139),15.8 (Station 210), and 2.9 (average from Stations172, LIMN 201, LIMN 171) kg/ha. Mason et al.(2005) also found that during August, biomassdropped rapidly at bathymetric depths exceeding100 m (see their Table 3 and Fig. 5). In contrast,our spring results indicate nearly equal biomass oflake herring in nearshore (10.7 kg/ha) versus off-shore (11.9 kg/ha) locations. Three of our sevenoffshore stations outside of the western portion ofLake Superior had biomass > 20 kg/ha, and demon-strate the need to examine spatial variability at re-gional scales to understand patterns in fishdistributions.

Density estimates for age-1 lake herring from thespring fish community survey have fluctuated by afactor of greater than 4,000 in U.S. waters between1978 and 2003 (Gorman and Hoff In press). Age-1lake herring appear to readily recruit to day bottomtrawls in the spring (e.g., see Bronte et al. 2003,Gorman and Hoff In press). Day and night workduring the summer of 2004 also indicate age-1 lakeherring are susceptible to day bottom trawling andnight midwater trawling in the same area (D. Yule,unpublished data). Thus it appears the spring fishcommunity survey (i.e., day bottom trawling) maybe an adequate method for measuring relative abun-dance of this life-stage. However, we calculatedRSE for the 1984 to 1998 age-1 density estimates(USGS file data) used by Hoff (2004), and foundaverage RSE over this time period to be 48%. TheRSE for each year ranged from 28 to 100% (inter-quartile range of 36 to 49%). Even though day bot-tom trawls appear to be an adequate method forcapturing age-1 lake herring, the RSE for densityestimates from this gear for Wisconsin waters (n =15 stations) is higher than recommended by Waltersand Ludwig (1981). One solution to improve preci-sion for age-1 lake herring is to increase samplesizes. Sample size analysis using the age-1 lake her-ring data from the 15 stations in Wisconsin watersfrom 1984 to 1998 indicates an average of 28 sta-tions (in Wisconsin waters) would be required toreach a 30% RSE over the 15-year period. This rep-resents an 86% increase in effort for this part ofLake Superior, and would require a careful exami-nation of how resources are allocated for assess-ment.

We hypothesize that lake herring undergo an on-togenetic shift in habitat use. We propose that age-1lake herring are demersal in nearshore areas duringthe day and that they perform a vertical migration at

Assessment of Lake Herring in Lake Superior 289

night. As lake herring grow to age-2 and age-3 fish,we hypothesize they become more pelagic and thusdecreasingly susceptible to capture with day bottomtrawls. Once lake herring become age-4 or older,they adopt a mostly pelagic lifestyle and are infre-quently captured with bottom trawls. This behaviorwould explain 1) the relatively quick decrease inlake herring biomass indicated by the spring fishcommunity survey within several years of the ap-pearance of strong year-classes (Bronte et al. 2003,Gorman and Hoff In press), and 2) the capture ofonly 8 lake herring > 40 cm in over 1,900 bottomtrawl tows conducted during the spring fish com-munity survey since 1978 (USGS file data) com-pared to 7 lake herring > 40 cm captured in only 18midwater trawl tows in this study. Mason et al.(2005) suggest diel vertical migration may take lakeherring greater than 100 m during the day, makingthem unsusceptible to spring survey bottom trawls.However, we did not capture any lake herring inday bottom trawls at any depths greater than 100 m.An alternative explanation is that lake herring re-main demersal during the day but better evade cap-ture with bottom trawls as they grow, and capturesat night using midwater trawls are a result of dielvertical migration. Efforts to critically test theseideas are hampered by apparent evasion of midwa-ter trawls by schooling fish during daylight hours(D. Yule, unpublished data).

A number of other studies have used lake herringdata from the spring bottom trawl survey. Negus(1995) first drew attention to the possibility ofunder-representation of prey fish abundance andbiomass by comparing consumption estimates ofpredator fish to prey fish abundance estimatesbased on the spring bottom trawl survey. Mason etal. (2005) found summer estimates of coregoninesin the western arm of Lake Superior, based onacoustic gear and midwater trawls, were greaterthan spring bottom trawl estimates in the same re-gion and year, supporting the conclusion by Negus(1995). Results from our study directly confirm re-sults of both studies.

Bronte et al. (2003) compared lake herring bio-mass estimates from the spring fish community sur-vey to estimates of peak commercial harvestremoval from 1941 to 1950 and suggested that fullrecovery of lake herring in Lake Superior is likelyimpossible without more frequent recruitmentevents. It is apparent from our results that measure-ments of harvestable biomass based on spring bot-tom trawl data are highly conservative andcomparisons with historical harvest data to make

inferences about the level of recovery are likelysuspect. Until we have better population estimates,other metrics such as (density-dependent changesin) fecundity (e.g., Bowen et al. 1991, Yule et al. inpress) or growth may be useful indicators of lakeherring recovery levels.

Two ecosystem simulation studies have includedlake herring data from the spring fish communitysurvey to examine Lake Superior fish communityresponses to alternative management actions, nat-ural events, and invasive species (Kitchell et al.2000, Cox and Kitchell 2004). Based on our evalua-tion of lake herring data from the spring fish com-munity survey, it appears we cannot yet define aproper stock-recruit model for lake herring, andthus there is a strong probability that the lake her-ring component to each of these modeling exercisesis an inadequate representation of the Lake Superiorsystem. The extent to which our results would sig-nificantly alter the conclusions of Kitchell et al.(2000) and Cox and Kitchell (2004) is unknownand warrants further study.

Evaluation of how lake herring data from thespring survey are used in the future is necessary.We have demonstrated strong differences in the be-havior and distribution of lake herring during thespring in Lake Superior by sampling with multiplegears in different bathymetric zones at differenttimes of the day. Day bottom trawls do not appearto represent adult standing stocks in relative or ab-solute terms. These findings indicate that the springfish community survey, with regard to lake herring,violates several important rules for abundance sur-veys: 1) a survey should sample over the entire spa-tial range where the stock(s) of interest might befound, 2) they should permit mapping of the spatialdistribution of density within the entire range, and3) current distribution of fishing activity shouldnever be a key criterion for setting the boundariesof the survey area (Hilborn and Walters 1992). Thespring fish community survey was not intended toprovide estimates of fish abundance across the en-tire range of individual fish species, but rather a re-peated snapshot of the nearshore fish community tomonitor relative fluctuations. However, we havedemonstrated that if the spring fish community sur-vey is to be used to assess adult lake herring popu-lations as attempted by Hoff (2004), it must beexpanded to offshore waters and incorporate nightsampling with midwater trawls and acoustic gear.An alternative assessment approach would be tosurvey lake herring spawning grounds in the fall.Yule et al. (in press) demonstrated the feasibility of

290 Stockwell et al.

using midwater trawls and acoustic gear to assessspawning female lake herring. When combinedwith commercial catch data, results from such asurvey can provide direct estimates of fishing mor-tality on both adult female lake herring and theireggs (Yule et al. in press).

Lake herring are a high priority species for man-agers on Lake Superior. Restoration of lake herringin the other four Great Lakes is also of high interestto respective management agencies. To improvelake herring management efforts on Lake Superior,and aid future restoration efforts on the lower GreatLakes, it is imperative to develop reliable assess-ment techniques. Results from this study, Yule et al.(in press), and an ongoing project to generatelakewide biomass estimates for pelagic fish speciesin Lake Superior (Hrabik et al. 2005) are providingbaseline assessments of such techniques with asso-ciated measures of precision. These efforts are im-proving our understanding how to best sample lakeherring and will ultimately improve our understand-ing of lake herring ecology and management in theGreat Lakes.

ACKNOWLEDGMENTS

We thank Captain Joe Walters, First Mate MikeMcCann, and Engineer Keith Peterson of the R/VKiyi for their tireless work during field collections.Gary Cholwek, Lori Evrard, Lindsey Lesmeister,Jared Myers, and Allison Gamble were instrumentalin collecting and processing samples. Jean Adams,Charles Bronte, John Janssen, and two anonymousreviewers provided helpful comments to improvethe manuscript. This article is Contribution 1355 ofthe USGS Great Lakes Science Center.

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Submitted: 6 October 2005Accepted: 21 February 2006Editorial handling: John Janssen