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

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

Evaluation of Bottom Trawls as Compared to Acoustics to Assess Adult Lake 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 Center Lake Superior Biological Station 2800 Lakeshore Drive Ashland, Wisconsin 54806 ABSTRACT. We compared density estimates from day bottom trawl tows against night midwater trawl tows and acoustic gear to test the hypothesis that adult lake herring (≥ 250 mm) are underestimated by day bottom trawl tows during the annual USGS spring fish community survey in Lake Superior. We found average density at nine nearshore stations was significantly higher at night (21.3 adult fish/ha) compared to day (1.0 adult fish/ha; p = 0.0119). At nine offshore stations, no lake herring were captured during the day but density averaged 39.6 adult fish/ha at night. At a lakewide scale (n = 18 stations), precision (relative standard error) was much better using night midwater trawls and acoustic gear (37%) compared to day bottom trawls (100%). Moderate sample size increases using the former methodology would likely bring precision within recommended levels (≤ 30%) for stock-recruit data sets. Our results suggest that 1) population abundances of adult lake herring in Lake Superior are much higher than previously considered, 2) the annual spring fish community survey may not provide a relative index of abundance of adult lake herring, 3) night midwater trawls and acoustic gear are necessary for assessing adult lake herring abundance, and 4) previous studies using lake herring data from the annual spring fish community survey need to be re-evaluated in light of these results. Lake herring appear to become progressively more pelagic and less susceptible to bottom trawling as they mature. Day bottom trawls appear to be an adequate tool for estimating relative density of age-1 recruits, although this method still suffers from relatively poor precision. INDEX WORDS:

Lake herring, Coregonus artedi, stock assessment, recruitment, Lake Superior.

INTRODUCTION Historically, lake herring (Coregonus artedi) were found throughout the Great Lakes (Scott and Crossman 1973) and served as a key trophic conduit that linked planktonic crustacean resources to native piscivorous fishes (Dryer and Beil 1964, Brown et al. 1999). At the turn of the 20th Century, lake herring supported major commercial fisheries in the Great Lakes (Baldwin et al. 1979). However, most lake herring stocks subsequently collapsed because of overfishing, habitat degradation, and interactions with exotic species. Lake herring became rare in western Lake Erie (Hartman 1973) and commercially extinct in Lake Ontario (Koelz 1926) by the early 1900s, and severely declined in Lakes

Michigan and Huron in the 1950s (Smith 1972). Lake Superior stocks collapsed in the 1960s (Lawrie and Rahrer 1972, Selgeby 1982), but have at least partially recovered to support commercial fisheries (Bronte et al. 2003). Restoration and rehabilitation of native fish stocks are long-term goals for fishery managers of the Great Lakes and are integral components of the Great Lakes Fishery Commission’s Strategic Vision (Great Lakes Fishery Commission 2001). Lake herring are recognized as a central species in native cold-water fish assemblages and discussions for reestablishment of self-sustaining lake herring populations in all four of the lower Great Lakes have begun (DesJardine et al. 1995, Eshenroder et al. 1995, Stewart et al. 1999, Ryan et al. 2003). In Lake Superior, where lake herring stocks are most numerous and widespread, there is a management need to identify sustainable harvest levels for com-

*Corresponding author. E-mail: [email protected] †Present Address: Grand Portage Band of Chippewa, 27 Store Road, Grand Portage, MN 55605

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Assessment of Lake Herring in Lake Superior mercial fisheries (Ebener 2003). Additionally, restoration of lake trout (Salvelinus namaycush) in the lower four Great Lakes is a focal management goal (DesJardine et al. 1995, Eshenroder et al. 1995, Stewart et al. 1999, Ryan et al. 2003) which may remain elusive until native forage fishes, like lake herring, are reestablished. To make informed decisions for managing and/or restoring lake herring, it is important to understand how lake herring populations function within their environment. Thus, it is critical to ensure that assessment techniques properly characterize population metrics such as abundance, biomass, and size-structure. Without reliable techniques, more complex studies examining ecological processes using baseline data could be compromised. The Lake Superior Biological Station of the U.S. Geological Survey’s (USGS) Great Lakes Science Center has been conducting an annual spring fish community survey in Lake Superior since 1978. This survey uses bottom trawls to assess the relative abundance and biomass of the fish community at 86 stations distributed along the perimeter of Lake Superior. Trawl tows are made across-contour during the day from an average starting depth of 15 m to an average ending depth of 65 m. A major strength of this survey is that it captures many species and life-stages not susceptible to most other surveys on Lake Superior (i.e., gillnet surveys), and thus can provide unique perspectives on fish community dynamics (Gorman and Hoff In press, Bronte et al. 2003). Data from the survey have been used to index survival of young hatchery-reared lake trout (Hansen et al. 1994), estimate recruitment potential of lake whitefish (Coregonus clupeaformis) to commercial fisheries (Curtis et al. 1993), and track the status and distribution of alewives (Alosa pseudoharengus) (Bronte et al. 1991). Data from the spring fish community survey have also been used for a variety of investigations involving lake herring including annual assessment of year-class strength (Bronte et al. 2003, Stockwell et al. 2005, Gorman and Hoff In press), assessment of forage fish supply for native and stocked salmonine predator fish populations (Negus 1995, Ebener 1995), and production of invertebrates (Johnson et al. 1998). Modeling exercises have used these data to develop expectations of fish community responses to management actions, natural events, and invasive species (Kitchell et al. 2000, Cox and Kitchell 2004), and to understand stock-recruit relationships of lake herring (Hoff 2004). Despite indi-

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cations that the spring survey may under-represent important prey fish populations (Ebener 1995, Negus 1995, Mason et al. 2005), there has been no direct evaluation of the survey to understand possible 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 Crossman 1973, Selgeby 1982, Selgeby and Hoff 1996) have indicated that lake herring are pelagic in Lake Superior and other Great Lakes. Results from Negus (1995) prompted a group of researchers to test the hypothesis that day bottom trawl estimates from the spring survey underestimate biomass of prey fish species. Mason et al. (2005) conducted an acoustic and midwater trawl survey during the night in August 1997 and found prey fish biomass (lake herring, bloater [C. hoyi], kiyi [C. kiyi], and rainbow smelt [Osmerus mordax]) in western Lake Superior to be 2 to 134 times greater than estimates gathered during the May–June 1997 spring fish community survey. In response to the findings of Mason et al. (2005), researchers and managers recommended development of a lakewide night acoustic sampling program for Lake Superior (Ebener 2003). Development of such a program is ongoing, with results from August 2003 and 2004 indicating similar levels of lake herring biomass in offshore and nearshore waters of Lake Superior (Hrabik et al. 2005). In this study, we tested if sampling with bottom trawls during the day in the spring provides similar population metrics for lake herring to sampling with midwater trawls and acoustic gear at night when sampling is done at the same location on the same day. Efforts to capture schooling fish during the day with midwater trawls have proven difficult in Lake Superior (D. Yule, unpublished data). We therefore did not include this sampling strategy in our comparison. Additionally, we did not include night bottom trawl data in the comparison because other studies have demonstrated that lake herring are pelagic at night (Rudstam et al. 1987, Milne et al. 2005). Our null hypothesis was that lake herring density estimates and length-frequency distributions do not vary significantly across sampling strategy (day bottom trawls vs. night midwater trawls and acoustic gear). If refuted, our results would indicate that day bottom trawling is not an effective sampling strategy to monitor lake herring populations in Lake Superior, and that future efforts to meet this objective would require a different sampling strategy. Rejection of the null hypothesis

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

would also have implications for a wide range of studies that make use of data from the spring survey, including stock-recruit models (Hoff 2004), ecosystem simulations for fish community management strategies (Kitchell et al. 2000, Cox and Kitchell 2004), prey selectivity and demand-supply analyses (Ebener 1995, Negus 1995, Ray 2004, Mason et al. 2005), and status of lake herring recovery in Lake Superior (Bronte et al. 2003). METHODS General Sampling Design A total of 18 stations were sampled with day bottom trawls and night midwater trawls and acoustic gear (Fig. 1). Nine stations were categorized as offshore and were selected as part of a binational lower trophic level monitoring effort. Nine stations were in nearshore areas and were a subset (10.5%) of the 86 standard USGS spring bottom trawl survey stations. These stations were selected because they were closest to the offshore stations. The only exception was Station 210 (Fig. 1) which was included to represent the extreme western end of Lake Superior. Sampling occurred between 4 May and 15 June 2005. Day samples were collected after

sunrise and before sunset. Night samples were collected between 0.5 hr after nautical twilight in the evening and 0.5 hr before nautical twilight in the morning. Bottom Trawl Sampling and Biomass Estimates We used a 3/4 Yankee bottom trawl (11.9 m headrope, 15.5 m footrope, and 2.2 m wing lines) with 89- and 64-mm stretch mesh, and a cod end with 13-mm stretch mesh. Estimates of trawl width during sampling were recorded using a NETMIND system (Northstar Technical, Inc., St. John’s, Newfoundland). Bottom trawl tows were run acrosscontour perpendicular to the shore at nearshore stations and towed at 3 to 4 km/hr. Because of differences in bank steepness and bottom characteristics (e.g., rock ledges), starting and ending depths and tow durations varied across nearshore sites. Average starting depth was 21 m (range 15 to 35 m) and average ending depth was 60 m (range 22 to 110 m). Average tow duration was 21 min (range 5 to 38 min). Bottom trawl tows were made alongcontour at offshore sites because banks were not present as in nearshore areas. Average tow duration at offshore sites was 30 min (range 30 to 33 min). Depths of these bottom trawl tows ranged from 95

Assessment of Lake Herring in Lake Superior to 327 m, and distance from shore ranged from 7 to 61 km (Fig. 1). All lake herring were counted, individually measured for total length, weighed in total to the nearest gram, and then frozen. Within 1 month, fish were thawed, and a subset were individually measured (total length) and weighed (nearest 0.1 g) in the laboratory to generate a length-weight relationship. Density (fish/ha) and biomass (kg/ha) were calculated using average bottom trawl width (from mensuration data) and distance the trawl fished on the bottom for each tow. We assumed the catchability coefficient was 1, similar to other studies using the same data set (e.g., Bronte et al. 2003, Hoff 2004). Because we also wanted to estimate biomass of the adult stock, and in some instances these larger fish were weighed in aggregate with smaller sub-adult fish, we used the length-weight relationship to estimate individual weights of larger fish. These data were then used to estimate biomass for the adult stock. Midwater Trawl Sampling The midwater trawl (Gourock Trawls, Ferndale, WA, USA) measured 15.2 × 15.2 m. The mesh tapered from 152 mm (stretch) at the mouth to 13 mm (stretch) at the cod end. Mensuration sensors recorded the midwater trawl head rope depth, head rope temperature, trawl mouth height, and trawl width at approximately 10-s intervals during deployment. The midwater trawl was deployed in a stepped-oblique fashion (Kirn and LaBar 1991), with each stratum fished for approximately equal amounts of time. Average midwater trawl tow duration was 38 min (range 27 to 55 min). The shallowest depth fished (headrope depth) was approximately 5 m for each trawl, and the maximum depth fished was dependent on bathymetry and fish distributions. At offshore sites the midwater trawl was fished no greater than 95 m (footrope depth) because acoustic echograms showed pelagic fish rarely occupied depths greater than 95 m. At nearshore sites, we deployed midwater trawls along the same transects where day bottom trawls were collected. Midwater trawl tows at stations with very short bottom trawl tows (< 15 min; Stations 172 and 411) were extended while still covering similar bathymetric depth zones. Acoustic Sampling and Biomass Estimates Acoustic data (fish and bathymetry) were collected concurrently with midwater trawling using a

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Biosonics (Seattle, WA, USA) DT-X digital echosounder equipped with a 120 kHz split beamtransducer with a nominal beam width of 6.7°. The transducer was mounted on a 1.2-m long tow body and deployed approximately 1 m below the surface. A transmit pulse duration of 0.4 ms was used at all times. Sampling rate was set at 5 pings/sec when trawl transect maximum depths were less than 70 m. For stations with maximum depths ranging from 70 to 150 m, sampling rate was reduced to 3 pings/sec. At bathymetric depths > 150 m we sampled at 1–2 pings/sec. This approach maximized the amount of acoustic information collected while eliminating the problem of acoustic cross talk that occurs when one ping is not allowed to return to the transducer before a subsequent ping is transmitted. The receiving sensitivity of the transducer was calibrated in the field at the onset and end of the survey using a 33 mm calibration sphere with a –40.5 decibels (dB) target strength using techniques recommended by BioSonics. Results of these field tests indicated agreement with laboratory calibrations and consistent sensitivity throughout the survey period. Thresholds were set to allow detection of all echoes exceeding –75 dB on the acoustic axis. Acoustic signals were collected with BioSonics Visual Acquistion Software (version 4.1) and output files were stored to a laptop computer hard drive. Vessel position was measured with an Ashtech BRG2 differentially corrected GPS unit (accurate to 1 m) and this information was embedded in the acoustic data files. Acoustic files were processed with Echoview acoustic post-processing software (version 3.10.132.06, SonarData Pty LTD, Tasmania, Australia). An Echoview algorithm was used to define a line 0.2 m above the bottom. The software-defined bottom line was examined to ensure that bottom echoes were not incorporated into fish backscattering measurements. A surface line was added to each echogram between 2 and 5 m below the surface, depending on sea conditions, to preclude integrating surface noise when measuring fish acoustic backscattering. A fishing-depth line was added that defined the deepest depth fished by the midwater trawl. Echoview was used to measure fish backscattering in the open-water region between the surface and fishing-depth lines. Portions of echograms corrupted with non-fish backscattering were defined as bad-data regions and excluded from analyses. Nonfish acoustic backscattering was minimized by setting a –65 dB minimum backscattering volume

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strength (Sv) threshold. The percent increases in average fish density when changing S v thresholds from –60 to –65 dB, from –65 to –70 dB, and from –70 to –75 dB were 22.5, 6.6, and 3.6%, respectively. Based on these sensitivities and the presence of the macroinvertebrate Mysis relicta in Lake Superior, we selected –65 dB as the Sv threshold. Echoview software measures fish backscattering on an areal basis by calculating a nautical square scattering coefficient (NASC; m2/nautical mile2) equivalent to the total fish backscattering throughout the water column scaled to one square nautical mile at the surface. Fish density was calculated by scaling NASC by the average back-scattering cross section of the average detected fish (i.e., σbs). We used the following 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 average target strength of fish echoes in the open water region, and 343 is the number of ha in a square nautical mile. To estimate TS we used the split-beam single-target detection criteria available in Echoview. The pulse length of a received echo was measured 6 dB down from the peak in the echo envelope. For an echo to be classified as a single target it had to meet several criteria including: 1) the echo TS value had to be a local maximum (i.e., larger than the previous and next digital sample), 2) the echo TS had to exceed a –60 dB threshold that we selected, 3) the echo beam compensation value had to be less than 6 dB, 4) the echo pulse duration had to fall between 0.32 and 0.6 ms, 5) the standard deviation of angles of all samples within the echo envelope had to be less than 1.5, and 6) when two echo envelopes overlapped, the echo with the lowest peak target strength was rejected. For each transect, target strengths of echoes meeting these criteria in the open water region were averaged. By convention, the nominal transducer beam width measured on a one-way polar plot is defined as 3 dB down from the acoustic axis. Therefore, when using a TS threshold of –60 dB targets as small as –54 dB were detectable throughout the nominal beam width. Given the existing knowledge on the relationship between acoustic size and real size of pelagic fish in Lake Superior, the applied TS threshold allowed detection of rainbow smelt exceeding 51 mm (Rudstam et al. 2003), lake herring

exceeding 40 mm (Rudstam et al. 1987), and bloaters exceeding 99 mm (Fleischer et al. 1997). The smallest rainbow smelt, lake herring, and bloater we captured were 29, 52, and 73 mm, respectively. Only 7% of all rainbow smelt captured were ≤ 51 mm and 0.5% of all bloaters captured were ≤ 99 mm. It follows that the applied TS threshold generally allowed detection of the smallest fish targets present in the open water during our spring sampling. Previous studies have shown that when fish are found in high densities, estimates of average TS can be biased upwards and density estimates downwards (Sawada et al. 1993, Appenzeller and Leggett 1992, Rudstam et al. 2003). Fish density estimates using acoustic gear were relatively low at all stations except at nearshore Station 411 (Fig. 1). To assess this potential bias, we calculated the Sawada et al. (1993) Nv index for 10 m vertical by 250 m horizontal cells along Station 411. Only 15 of the 75 cells had Nv values exceeding 0.04 (the cutoff value recommended by Sawada et al. 1993) and average TS values for this station changed little when we included (–45.2 dB) or excluded (–45.7 dB) these 15 cells. We concluded that fish densities were sufficiently low at all sites to allow for unbiased estimates of average TS. To estimate lake herring density, we multiplied the acoustic fish density estimate for the open-water region by the numerical proportion of lake herring in the concurrently deployed stepped-oblique midwater trawl. To estimate lake herring biomass, we multiplied the lake herring density estimate by the average weight of lake herring captured in the midwater trawl. To generate an estimate of adult densities, we calculated density estimates for lake herring ≥ 250 mm using day bottom trawls and night midwater trawls and acoustic gear. This cutoff value was chosen based on the minimum length of mature female lake herring sampled during spawning in fall 2004 (Yule et al. in press). Analyses Because our sample sizes were relatively small for evaluating normality, we used the nonparametric Wilcoxon rank-sum test to test the null hypothesis that density estimates for adult lake herring did not differ between day bottom trawls and night midwater trawls and acoustic gear. We first ran the test using only the nearshore stations to directly evaluate the efficacy of the fish community survey to measure density of adult lake herring. We then

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pooled all stations (nearshore and offshore) to compare lakewide estimates of density and biomass gathered using the two sampling methods. We set α = 0.025 to adjust for the two tests (Bonferroni correction), and we performed one-tailed tests because our expectation was that night estimates would be greater than day estimates. RESULTS At the nine nearshore sites, day bottom trawls captured 1,643 fish (15 species: rainbow smelt = 58% of captured fish, lake whitefish 15%, pygmy whitefish Prosopium coulteri 13%) and night midwater trawls captured 866 fish (11 species: rainbow smelt = 57%, lake herring = 28%). Lake herring represented 2 and 28% of the fish captured for each sampling strategy at the nearshore stations, respectively. At the nine offshore sites, day bottom trawls captured 10,211 fish (13 species: deepwater sculpin Myoxocephalus thompsonii 78%, kiyi 13%) and night midwater trawls captured 212 fish (6 species: kiyi 79%, lake herring 12%). Lake herring represented 0 and 12% of the fish captured for each sampling strategy at the offshore stations, respectively. Lake herring were captured with day bottom trawls at three of the nine nearshore stations. Lake herring were captured with night midwater trawls at eight of these nine stations. For the day bottom trawls, density and biomass estimates ranged from 0 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 was 0.30 (± 0.28) kg/ha. Estimates of density and biomass at the nine nearshore stations using night midwater trawls and acoustic gear ranged from 0 to 795.6 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 average biomass at night (10.70 ± 4.34 kg/ha) was 36 times greater than day estimates. For the nine offshore stations, lake herring were not captured in any day bottom trawls. Lake herring were captured at five of the nine offshore stations using night midwater trawls. Density and biomass at night 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 combined averaged 1.1 (± 0.9) fish/ha and 82.4 (± 43.8) fish/ha for day bottom trawls and night midwater trawls 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.

FIG. 2. Density (A) and biomass (B) estimates for lake herring for all 18 stations using day bottom trawls (BTR) and night midwater trawls/acoustic gear (MTR/AC). The offshore stations are denoted by the prefix “LIMN.” The remaining stations are a subset of standard nearshore stations used for the annual USGS spring fish community bottom trawl survey. Of the 18 stations sampled, only one station (Station 139) had comparable size-structure between day bottom trawls and night midwater trawls (Fig. 3A). Two modes were present in each dataset at this station; one at 150 mm and the other at 325 (day bottom trawl) or 350 mm (night midwater trawl). The density estimate from the day bottom trawl (15.8 fish/ha) at Station 139 was one ninth the night midwater trawl and acoustic gear estimate (138.9 fish/ha; Fig. 2A). The day biomass estimate (2.51 kg/ha) was one sixth the night estimate at Station 139 (15.55; Fig. 2B). For the remaining 17 stations, there were not enough fish captured for a comparison of lake herring size-structure between day bottom trawling and night midwater trawling (Fig. 3B). Only 4 lake herring were captured during day bottom trawling compared to 183 captured during night midwater trawling at these 17 stations. The modal lengths for the night midwater trawls (150 mm and 325–375 mm) were comparable to Station 139 (Fig. 3A). Adult lake herring (i.e., ≥ 250 mm) were cap-

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Stockwell et al. TABLE 1. Average densities for lake herring ≥ 250 mm using day bottom trawls or night midwater trawls and acoustic gear in Lake Superior during spring 2005. Standard errors are reported in parentheses. BTR = bottom trawls, MTR/AC = midwater trawls and acoustic gear.

Station Group Nearshore Offshore Total

FIG. 3. Length frequency distributions of lake herring 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. tured with day bottom trawls at only 1 (nearshore Station 139) of the 18 stations sampled, compared to 13 of the 18 stations (eight nearshore and five offshore) using night midwater trawls. For nearshore stations (n = 9), average density (± SE) using day bottom trawls (1.0 ± 1.0 fish/ha) was significantly less than using night midwater trawls and acoustic gear (21.3 ± 11.1 fish/ha; Wilcoxon ranksum test, S = 108.5, p = 0.0119; Table 1). For all stations combined (n = 18), average density for adult lake herring using day bottom trawls (0.5 ± 0.5 fish/ha) was significantly less than using night midwater trawls and acoustic gear (30.4 ± 11.0 fish/ha; Wilcoxon rank-sum test, S = 426.0, p < 0.0003; Table 1). Average density estimates using midwater trawls and acoustic gear at night were almost double at offshore stations (39.6 ± 19.2 fish/ha, n = 9) compared to nearshore stations (21.3 ± 11.1 fish/ha, n = 9; Table 1), but these differences

n 9 9 18

Day BTR Mean Density (fish/ha) 1.0 (1.0) 0 (0) 0.5 (0.5)

Night MTR/AC Mean Density (fish/ha) 21.3 (11.1) 39.6 (19.2) 30.4 (11.0)

were not significant (Wilcoxon rank-sum test, S = 89.0, p = 0.785). All lake herring captured at offshore stations at night exceeded 250 mm. The precision of density estimates for lake herring ≥ 250 mm at nearshore stations, as indicated by the relative standard error (RSE = SE/mean*100), was much greater using day bottom trawls (100%) than midwater trawls and acoustic gear (52%). If we include all 18 stations, the RSE of estimates using day bottom trawls (100%) was still greater than using night midwater trawls and acoustic gear (37%; Table 1). Lake herring were captured at stations with mean bathymetric depths up to 200 m (Fig. 4); well beyond the mean maximum tow depth (65 m) of day

FIG. 4. Biomass (kg/ha) estimates for all lake herring using day bottom trawls (BTR) and night midwater trawls and acoustic gear (MTR/AC) as a function of mean bathymetric depth at each station. The dashed lines indicates the average starting and ending depths of day bottom trawls used in the annual spring fish community survey conducted by USGS from 1978 to the present in nearshore waters of Lake Superior.

Assessment of Lake Herring in Lake Superior bottom trawls during the annual spring fish community survey. Midwater trawls fished at night captured lake herring at four out of five stations with mean bathymetric depths between 140 and 200 m (Fig. 4). Day bottom trawls and night midwater trawls did not capture any lake herring at the three deepest stations (mean bathymetric depths ranged from 225 to 325 m; Fig. 4). DISCUSSION A number of studies have compared acoustic gear and midwater trawls to bottom trawls to estimate fish densities in the Great Lakes. Argyle (1982) found 20 to 30% of total fish biomass (primarily alewife and rainbow smelt) was above the bottom trawl during day sampling in Lake Huron. In Lake Michigan, Argyle (1992) found night midwater trawl and acoustic estimates were 1.6 times greater for alewife, bloater, and rainbow smelt when compared to day bottom trawl estimates during two separate surveys. Fabrizio et al. (1997) showed much greater density estimates for rainbow smelt and alewife at some lake depths in Lake Michigan using night midwater trawls and acoustic gear, while estimates for bloater were higher using day bottom trawls. Sampling in Fabrizio et al. (1997), however, was limited to only one site. The results from our study, based on 18 sites distributed across all of Lake Superior, indicate average density estimates for adult lake herring are grossly under-estimated using day bottom trawls. We conclude that night sampling with midwater trawls and acoustic gear in both nearshore and offshore waters is necessary to generate more realistic and precise estimates of adult lake herring abundance in Lake Superior. The Lake Superior spring fish community survey is used as a relative index of abundance. However, it does not appear that day bottom trawls provide a relative index of abundance for adult lake herring given the wide range of density estimates at night (2 to 101 adult fish/ha) at locations where zero lake herring were captured during the day. Given previous observations that adult lake herring are pelagic and that these observations come from periods throughout the 20th Century spanning very wide ranges of lake herring abundances (Koelz 1929, Smith 1956, Dryer and Beil 1964, Dryer 1966, Selgeby and Hoff 1996), we have no reason to expect day bottom trawls to provide relative measures of abundance for adult lake herring. Our results indicate that in the spring of 2005 density of adult lake

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herring in offshore waters was similar to, if not greater than, nearshore waters. Given that 75% of Lake Superior is deeper than 80 m, it follows that total abundance of adult lake herring in offshore waters is much greater than in nearshore waters. Although it remains to be demonstrated over time, it appears that the 28-year time series of the spring fish community survey does not provide a measure of relative abundance for adult lake herring. A primary goal of fisheries management and research is to determine the relationship between adult standing stock and subsequent recruits (Ricker 1954, Beverton and Holt 1957, Myers and Barrowman 1996). Without adequate measures of precision of adult standing stocks (or recruits), efforts to understand the stock-recruit relationship will be hampered before they are even initiated. Walters and Ludwig (1981) used a basic model to illustrate how a strong stock-recruit relationship can be obscured when spawner estimates are off by a factor of 2 to 4 (not unusual in most fisheries stock assessments), and that this situation often creates the appearance of independent or dome-shaped relationships between spawners and recruits. Walters and Ludwig (1981) contend this situation often leads researchers to search for other explanations such as environmental factors to explain variability in the stock-recruit relationship, and could be just as deceptive as the analyses of “noisy” stock-recruit data they intend to replace. Walters and Ludwig (1981) recommended precision of abundance estimates to be no greater than ± 30% of the mean when using or publishing stock-recruit data sets. Recently, Hoff (2004) published a stock-recruit model for lake herring in Wisconsin waters of Lake Superior based on lake herring density estimates from the USGS spring fish community survey. Yule et al. (in press) reexamined the adult stock data used in the Hoff (2004) analysis and found the RSE of these data averaged 54% over the 15-year period. The RSE for each year ranged from 41 to 82%. Additionally, our study suggests the relative abundance estimates for adult lake herring from the spring fish community survey may not be relative. Hoff’s (2004) analyses demonstrating the importance of other biotic (lake trout abundance and slimy sculpin biomass) and abiotic (wind speed and air temperature) factors may have been usurped by high sampling variability using an inappropriate sampling gear. Thus, Hoff’s (2004) search for factors other than adult stock to explain lake herring recruitment was likely premature and may be misleading (sensu Walters and Ludwig 1981). Efforts

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to couple fish population data with environmental factors to predict recruitment appear promising (e.g., Axenrot and Hansson 2003) and may prove appropriate for lake herring populations in Lake Superior. However, our results indicate that day bottom trawling is not an effective sampling strategy to assess adult lake herring. It follows that any stockrecruit model based on these data, with or without environmental factors, is tenuous at best. Measures of precision (RSE) using night midwater trawls and acoustic gear (52%) were much better than estimates using day bottom trawls (100%) for the nine nearshore stations. The RSE for the midwater trawl and acoustic gear density estimate was still greater than the maximum recommended (30%) by Walters and Ludwig (1981). However, the RSE for night midwater trawling and acoustic gear decreased to 37% when all 18 stations were used to estimate average density for adult lake herring. This is promising given the 18 stations span all regions of Lake Superior and thus likely represent the range of adult densities likely to be encountered anywhere in the lake during a survey. Assuming the observed mean and variance from the 18 stations remain constant, sample size analysis indicates that 26 samples would be needed to achieve 30% RSE, 37 samples to achieve 25% RSE, and 58 samples to achieve 20% RSE. This indicates 1) a diminishing return on precision with increased effort, and 2) 20 to 30% RSE is probably the best expectation at a lakewide scale with the level of effort we allocated at each station. Given our experience with acoustic sampling, we could expect to collect 30 to 40 samples from across Lake Superior in a 3-week period (the annual spring fish community survey lasts about 6 weeks). For comparison, we performed the same exercise for the 18 day bottom trawls and found that 58 day bottom trawl samples (same sample size that resulted in 20% RSE for midwater trawl and acoustic gear) would result in a 56% RSE. These results suggest bottom trawls fished during the day in the spring are not sufficient to adequately sample adult lake herring populations with acceptable levels of precision. Our night biomass estimates for lake herring are comparable to those found by Mason et al. (2005) in August 1997. They sampled the western arm of Lake Superior at night using midwater trawls and acoustic gear and estimated coregonine (lake herring, bloater, and kiyi combined) biomass in the Apostle Islands, the Duluth-Superior region, and the 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 Stations 172, LIMN 201, LIMN 171) kg/ha. Mason et al. (2005) also found that during August, biomass dropped rapidly at bathymetric depths exceeding 100 m (see their Table 3 and Fig. 5). In contrast, our spring results indicate nearly equal biomass of lake herring in nearshore (10.7 kg/ha) versus offshore (11.9 kg/ha) locations. Three of our seven offshore stations outside of the western portion of Lake Superior had biomass > 20 kg/ha, and demonstrate the need to examine spatial variability at regional scales to understand patterns in fish distributions. Density estimates for age-1 lake herring from the spring fish community survey have fluctuated by a factor of greater than 4,000 in U.S. waters between 1978 and 2003 (Gorman and Hoff In press). Age-1 lake herring appear to readily recruit to day bottom trawls in the spring (e.g., see Bronte et al. 2003, Gorman and Hoff In press). Day and night work during the summer of 2004 also indicate age-1 lake herring are susceptible to day bottom trawling and night midwater trawling in the same area (D. Yule, unpublished data). Thus it appears the spring fish community survey (i.e., day bottom trawling) may be an adequate method for measuring relative abundance of this life-stage. However, we calculated RSE for the 1984 to 1998 age-1 density estimates (USGS file data) used by Hoff (2004), and found average RSE over this time period to be 48%. The RSE for each year ranged from 28 to 100% (interquartile range of 36 to 49%). Even though day bottom trawls appear to be an adequate method for capturing age-1 lake herring, the RSE for density estimates from this gear for Wisconsin waters (n = 15 stations) is higher than recommended by Walters and Ludwig (1981). One solution to improve precision for age-1 lake herring is to increase sample sizes. Sample size analysis using the age-1 lake herring data from the 15 stations in Wisconsin waters from 1984 to 1998 indicates an average of 28 stations (in Wisconsin waters) would be required to reach a 30% RSE over the 15-year period. This represents an 86% increase in effort for this part of Lake Superior, and would require a careful examination of how resources are allocated for assessment. We hypothesize that lake herring undergo an ontogenetic shift in habitat use. We propose that age-1 lake herring are demersal in nearshore areas during the day and that they perform a vertical migration at

Assessment of Lake Herring in Lake Superior night. As lake herring grow to age-2 and age-3 fish, we hypothesize they become more pelagic and thus decreasingly susceptible to capture with day bottom trawls. Once lake herring become age-4 or older, they adopt a mostly pelagic lifestyle and are infrequently captured with bottom trawls. This behavior would explain 1) the relatively quick decrease in lake herring biomass indicated by the spring fish community survey within several years of the appearance of strong year-classes (Bronte et al. 2003, Gorman and Hoff In press), and 2) the capture of only 8 lake herring > 40 cm in over 1,900 bottom trawl tows conducted during the spring fish community survey since 1978 (USGS file data) compared to 7 lake herring > 40 cm captured in only 18 midwater trawl tows in this study. Mason et al. (2005) suggest diel vertical migration may take lake herring greater than 100 m during the day, making them unsusceptible to spring survey bottom trawls. However, we did not capture any lake herring in day bottom trawls at any depths greater than 100 m. An alternative explanation is that lake herring remain demersal during the day but better evade capture with bottom trawls as they grow, and captures at night using midwater trawls are a result of diel vertical migration. Efforts to critically test these ideas are hampered by apparent evasion of midwater trawls by schooling fish during daylight hours (D. Yule, unpublished data). A number of other studies have used lake herring data from the spring bottom trawl survey. Negus (1995) first drew attention to the possibility of under-representation of prey fish abundance and biomass by comparing consumption estimates of predator fish to prey fish abundance estimates based on the spring bottom trawl survey. Mason et al. (2005) found summer estimates of coregonines in the western arm of Lake Superior, based on acoustic gear and midwater trawls, were greater than spring bottom trawl estimates in the same region and year, supporting the conclusion by Negus (1995). Results from our study directly confirm results of both studies. Bronte et al. (2003) compared lake herring biomass estimates from the spring fish community survey to estimates of peak commercial harvest removal from 1941 to 1950 and suggested that full recovery of lake herring in Lake Superior is likely impossible without more frequent recruitment events. It is apparent from our results that measurements of harvestable biomass based on spring bottom trawl data are highly conservative and comparisons with historical harvest data to make

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inferences about the level of recovery are likely suspect. Until we have better population estimates, other metrics such as (density-dependent changes in) fecundity (e.g., Bowen et al. 1991, Yule et al. in press) or growth may be useful indicators of lake herring recovery levels. Two ecosystem simulation studies have included lake herring data from the spring fish community survey to examine Lake Superior fish community responses to alternative management actions, natural events, and invasive species (Kitchell et al. 2000, Cox and Kitchell 2004). Based on our evaluation of lake herring data from the spring fish community survey, it appears we cannot yet define a proper stock-recruit model for lake herring, and thus there is a strong probability that the lake herring component to each of these modeling exercises is an inadequate representation of the Lake Superior system. The extent to which our results would significantly alter the conclusions of Kitchell et al. (2000) and Cox and Kitchell (2004) is unknown and warrants further study. Evaluation of how lake herring data from the spring survey are used in the future is necessary. We have demonstrated strong differences in the behavior and distribution of lake herring during the spring in Lake Superior by sampling with multiple gears in different bathymetric zones at different times of the day. Day bottom trawls do not appear to represent adult standing stocks in relative or absolute terms. These findings indicate that the spring fish community survey, with regard to lake herring, violates several important rules for abundance surveys: 1) a survey should sample over the entire spatial range where the stock(s) of interest might be found, 2) they should permit mapping of the spatial distribution of density within the entire range, and 3) current distribution of fishing activity should never be a key criterion for setting the boundaries of the survey area (Hilborn and Walters 1992). The spring fish community survey was not intended to provide estimates of fish abundance across the entire range of individual fish species, but rather a repeated snapshot of the nearshore fish community to monitor relative fluctuations. However, we have demonstrated that if the spring fish community survey is to be used to assess adult lake herring populations as attempted by Hoff (2004), it must be expanded to offshore waters and incorporate night sampling with midwater trawls and acoustic gear. An alternative assessment approach would be to survey lake herring spawning grounds in the fall. Yule et al. (in press) demonstrated the feasibility of

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using midwater trawls and acoustic gear to assess spawning female lake herring. When combined with commercial catch data, results from such a survey can provide direct estimates of fishing mortality on both adult female lake herring and their eggs (Yule et al. in press). Lake herring are a high priority species for managers on Lake Superior. Restoration of lake herring in the other four Great Lakes is also of high interest to respective management agencies. To improve lake herring management efforts on Lake Superior, and aid future restoration efforts on the lower Great Lakes, it is imperative to develop reliable assessment techniques. Results from this study, Yule et al. (in press), and an ongoing project to generate lakewide biomass estimates for pelagic fish species in Lake Superior (Hrabik et al. 2005) are providing baseline assessments of such techniques with associated measures of precision. These efforts are improving our understanding how to best sample lake herring and will ultimately improve our understanding of lake herring ecology and management in the Great Lakes. ACKNOWLEDGMENTS We thank Captain Joe Walters, First Mate Mike McCann, and Engineer Keith Peterson of the R/V Kiyi for their tireless work during field collections. Gary Cholwek, Lori Evrard, Lindsey Lesmeister, Jared Myers, and Allison Gamble were instrumental in collecting and processing samples. Jean Adams, Charles Bronte, John Janssen, and two anonymous reviewers provided helpful comments to improve the manuscript. This article is Contribution 1355 of the USGS Great Lakes Science Center. REFERENCES Appenzeller, A.R., and Leggett, W.C. 1992. Bias in hydroacoustic estimates of fish abundance due to acoustic shadowing: evidence from day-night surveys of vertically migrating fish. Can. J. Fish. Aquat. Sci. 49:2179–2189. Argyle, R.L. 1982. Alewives and rainbow smelt in Lake Huron: mid-water and bottom aggregations and estimates of standing stocks. Trans. Am. Fish. Soc. 111:267–285. ——— . 1992. Acoustics as a tool for the assessment of Great Lakes forage fishes. Fish. Res. (Amst.) 14:179–196. Axenrot, T., and Hansson, S. 2003. Predicting herring recruitment from young-of-the-year densities, spawning stock biomass, and climate. Limnol. Oceanogr. 48:1716–1720.

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