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Status and Trends of Pelagic Prey Fish in Lake Huron, 2017
Timothy P. O’Brien1, David M. Warner1, Peter C. Esselman1, Steve Farha1,
Steve Lenart2, Chris Olds2, and Kristy Phillips1
1U.S. Geological Survey, Great Lakes Science Center
1451 Green Rd. Ann Arbor, MI 48105
2U.S. Fish and Wildlife Service, Fish and Wildlife Conservation Office
480 W. Fletcher St., Alpena, MI 49707
Scientists from the U.S. Geological Survey’s Great Lakes Science Center conducted integrated
acoustic and mid-water trawl surveys of Lake Huron in 1997 and annually from 2004-2017. The
2017 survey was conducted during September and included transects in Lake Huron’s main
basin, Georgian Bay, and North Channel. Mean lake-wide pelagic fish density was 1582 fish/ha
and mean pelagic fish biomass was 10.5 kg/ha in 2017, which represents 96% and 93% of the
long-term mean respectively. Mean lake-wide biomass was 23% higher in 2017 as compared to
2016. The total estimated lake-wide standing stock biomass of pelagic fish species, excluding
cisco, was ~49 kt (± 10.4 kt), consisting almost entirely of bloater (26.8 kt; 55%) and rainbow
smelt (22 kt; 45%), with small contributions from sticklebacks (0.13 kt; 0.26 %), emerald shiner
(0.09 kt; 0.18%), and alewife (0.004kt; <0.005%). Age-0 rainbow smelt abundance increased
from 155 fish/ha in 2016 to 598 fish/ha in 2017. Biomass of age-1+ rainbow smelt increased
from 2.5 kg/ha in 2016 to 4.1 kg/ha in 2017. Age-0 bloater abundance increased from 94 fish/ha
in 2016 to 342 fish/ha in 2017. Biomass of age-1+ bloater in 2017 (5.0 kg/ha) remained at levels
similar to 2016 (5.2 kg/ha). Emerald shiner density decreased from 38.6 fish/ha in 2016 to 19.5
fish/ha in 2017. Emerald shiner biomass remained at 0.02 kg/ha between 2016-2017 which
represented 19% of the long-term mean. Cisco lake-wide mean biomass was estimated at 2.2
kg/ha and mean density was estimated at 5.1 fish/ha in 2017. Bloater and rainbow smelt will
likely continue to be the primary pelagic species available to offshore predators in coming years.
Presented at: Great Lakes Fishery Commission
Lake Huron Committee Meeting
Sault St. Marie, ON, March 19, 2018
See data at: U.S. Geological Survey, Great Lakes Science Center, 2018, Great Lakes Research Vessel Operations 1958-2017
(ver. 2.0, March 2018): U.S. Geological Survey Data Release,
Estimates of fish biomass derived from scientific trawl surveys are critical to understanding
ecosystem dynamics and managing fishery resources (Koslow 2009; Cotter et al. 2009). In Lake
Huron, the U.S. Geological Survey Great Lakes Science Center (GLSC) began conducting
annual trawl surveys of the Lake Huron fish community in the 1970s. These surveys have
tracked broad-scale changes in the benthic fish community and provided valuable information on
prey fish dynamics to fishery managers tasked with balancing predatory demand by native and
introduced salmonines. Integrated acoustic and mid-water trawl surveys were implemented
because it was recognized that a substantial proportion of the prey fish biomass was distributed
in pelagic zones, which could not be measured using bottom trawl gear (Fabrizio et al. 1997,
Stockwell et al. 2007, Yule et al. 2008). Acoustic surveys were first conducted during the 1970s,
but the first lake-wide acoustic survey that included all of Lake Huron’s distinct basins was
conducted in 1997. Annual surveys have been conducted since 2004; however, only the main
basin was sampled during 2006. The purpose of this report is to present 2017 abundance and
biomass estimates for major pelagic offshore prey fish species in Lake Huron and compare these
estimates to previous years (1997, 2004-2016). Furthermore, our purpose is to highlight spatial
patterns in distribution and abundance of these species throughout Lake Huron. We also
summarize cisco Coregonus artedi catch data from acoustic surveys during 2010-2017 and
present information on abundance and spatial patterns of this species in Lake Huron.
Survey and analytical methods
The pelagic prey fish survey in Lake Huron is based on a stratified-random design with acoustic
transects in five geographic strata: eastern main basin (ME), western main basin (MW), southern
main basin (SB), Georgian Bay (GB), and the North Channel (NC) (Figure 1). Within each
stratum, the first transect is selected randomly each year based on latitude and longitude;
subsequent transects are spaced relatively uniformly around the first. Effort (transects per
stratum) is reallocated each year based on stratum area and variability of total biomass in each
stratum from previous surveys (sampling design described in Adams et al. 2006). For analyses,
each transect was divided into 10 m bottom contour intervals and 5-10 m depth layers (1997),
1,000 m distance intervals and 10 m depth layers (2004-2011), or 3,000 m distance units and 10
m depth layers (2012-2017). These comprise the elementary sampling units (ESUs) within
which fish density is summarized along transects.
The 2017 pelagic fisheries survey was completed from 6-29 September. Sampling was
conducted by both the GLSC (R/V Sturgeon) and U.S. Fish and Wildlife Service (USFWS; M/V
Spencer F. Baird). Twenty-six acoustic transects were sampled, resulting in approximately 480
km of acoustic data. Fifty-six mid-water trawl tows were conducted in conjunction with acoustic
data collection.
Fish were collected using a 16.5-m headrope mid-water trawl with 76, 38, 25, and 6.35 mm
stretch meshes (USGS) and a 19.8-m headrope mid-water trawl with 200, 150, 100, 75, 50, and
38 mm stretch mesh with a cod-end liner having 3.175 mm stretch mesh (USFWS). Mid-water
trawl locations and depths were chosen to target fish aggregations, but multiple tows per transect
were conducted when fish were present so that trawl data within a stratum were available from
each scattering layer formed by fish. At a minimum, a single mid-water trawl was conducted on
each transect except in rare instances when very few fish targets were detected. Trawl fishing
depth was monitored using NetmindTM (2004-2015) and Marport M3 (2016-2017) systems
(USGS) and a Simrad PI44 catch monitoring system (USFWS). In 2017, trawling depths ranged
from 7 to 76 m (mean = 28.7 m, mode = 20 m). Most mid-water trawl tows were of 20 minutes
duration, with tow times extended up to 25 or 30 minutes when few fish were present. All fishes
captured in the mid-water trawl tows were identified, counted, and weighed in aggregate (g) by
species. Total length in millimeters was measured on a random subsample (100-200 fish) per
species per tow. Individual fishes were assigned to age categories (age-0 or age 1+) based on the
following length cutoffs: alewife Alosa pseudoharengus =100 mm; rainbow smelt Osmerus
mordax = 90 mm; bloater Coregonus hoyi = 120 mm. These lengths approximate the lengths of
the smallest age-1 fish of these species (USGS 2018).
Figure 1. Location of acoustic transects and mid-water trawls within sampling strata in Lake Huron during
2017. Sampling strata correspond to geographic regions: eastern main basin (ME), western main basin (MW),
southern main basin (SB), Georgian Bay (GB), and the North Channel (NC).
Density (fish/ha) of individual species was estimated for each transect as the product of acoustic
fish density and the proportion of each species (by number) in the mid-water trawl catches at that
location. Total density per species was subdivided into age-0 and age-1+ age-classes by
multiplying total density by the numeric proportions of each age group. Biomass (kg/ha) of each
species was estimated for each transect as the product of density and size-specific mean mass
estimated from fish lengths in trawls, and length-weight relationships. The arithmetic mean and
standard error are presented for total and species-specific density and biomass estimates for the
survey area.
Mean, standard error, and confidence limits for density and biomass for the entire survey area
(all three basins pooled) were estimated using stratified cluster analysis methods in SAS (SAS
Institute Inc. 2007). Cluster sampling techniques are appropriate for acoustic data, which
represent a continuous stream of autocorrelated data (Williamson 1982, Connors and Schwager
2002). Density and biomass values for each ESU in each stratum were weighted by dividing the
stratum area by the number of ESUs in the stratum. Numeric density and biomass density of
cisco were estimated using the R package EchoNet2Fish (R Core Team 2017). Acoustic
equipment specifications, software versions, single target detection parameters, noise levels, and
detection limits can be found in appendices 1 and 2. Supplemental methods on acoustic analysis
methods and acoustic equipment can be found in appendix 3.
Results and Discussion
Density and biomass by species
Alewife Alewife continue to be scarce in mid-water trawl surveys of Lake Huron, including
during 2017 when only three specimens were captured. Alewife densities estimated in 1997,
2005-2006, 2008, and 2013 were considerably higher than other years in the time series.
However, we note that these increases in density did not mean that age-0 alewives were
especially abundant in any survey year (Figure 2). During 1997, the year of their highest
abundance, age-0 alewives were only 2% of total fish density.
Figure 2. Acoustic and mid-water trawl estimates of alewife numeric density (fish/ha; left panel) and biomass
(kg/ha; right panel) in Lake Huron, 1997-2017. Error bars represent ±1 standard error.
Acoustic estimates of age-1+ alewife biomass have remained low for the last decade despite
fluctuations in age-0 densities during 2004-2013 (Figure 2). Temporal biomass differences were
largely due to differences in size and age structure between 1997 and other years. Higher
biomass in 1997 was due to higher abundance of age 1+ alewife and low biomass during 2004-
2014 was the result of trawl catches dominated by age-0 fish (Figure 2). Since 2004, alewives
have never comprised more than 2% of pelagic fish biomass. Although sporadic catches of
alewife have continued, recruitment to older age classes appears to be limited based on evidence
from both mid-water and bottom trawl surveys conducted by the GLSC.
Rainbow smelt During 2017, age-0 rainbow smelt density increased from 2016 estimates by
nearly a factor of 4 to 86% of the long-term mean (Figure 3). Age-0 rainbow smelt production
still remains lower than 1997. There has been no clear trend in abundance since 2004. Age 1+
rainbow smelt biomass also increased in 2017 from 2.5 kg/ha in 2016 to 4.1 kg/ha in 2017. This
is roughly 95% of the long-term mean of 4.3 kg/ha, but only 24% of the biomass estimated in
1997 (Figure 3). Rainbow smelt biomass was spatially variable during 2017 and primarily
distributed in the SB, NC, and northern MW strata (Figure 4).
Figure 3. Acoustic and mid-water trawl estimates of rainbow smelt age-0 numeric density (fish/ha; left panel)
and age-1+ biomass (kg/ha; right panel) in Lake Huron, 1997-2017. Error bars represent ±1 standard error.
Figure 4. Geographic distribution of rainbow smelt (left) and bloater (right) biomass summarized within
elementary sampling units (dots) during 2017. Gray lines are 20 m depth intervals.
Bloater Lake-wide mean age-0 bloater density in 2017 was 3.5-times that estimated in 2016
and was the second highest estimate for the time series (Figure 5). Mean biomass of age-1+
bloater decreased from 5.2 kg/ha in 2016 to 5.0 kg/ha in 2017 (Figure 5). Since 2014, age-1+
bloater biomass has remained at or above 5 kg/ha, but standard error around these estimates have
been fairly large indicating lower precision. Similar to results from bottom trawl surveys, age-0
bloater density was variable, but increased during 2004-2015 (average density > 160 fish/ha).
Biomass of age-1+ bloater indicated an increasing trend during 2004-2008, followed by a
decrease from 2009-2010. Although we have estimated somewhat higher bloater biomass during
the past four years, variable spatial distribution across the survey area has resulted in greater
uncertainty in the precision of these estimates. As in the past several years, bloater biomass in
Lake Huron tends to be concentrated in the SB and ME strata and in the northern MW stratum
(Figure 4).
Figure 5. Acoustic and mid-water trawl estimates of bloater age-0 numeric density (fish/ha; left panel) and age-
1+ biomass (kg/ha; right panel) in Lake Huron, 1997-2017. Error bars represent ±1 standard error.
Cisco Cisco catches were sporadic during acoustic surveys in 2010-2013, with few (<10)
specimens caught in most years. During 2014-2017, cisco catches increased (Figure 6). Biomass
increased during 2016 and 2017 due to the increased number of larger fish (>300 mm) in trawl
catches. Cisco caught in trawls during 2010-2017 were mostly > 100 mm (mean 280 mm, median
295 mm) and ranged from 80-471 mm.
Cisco are almost exclusively caught in GB, NC, and northern MW strata during September and
early October (Figure 7). The highest densities of cisco have been observed in NC and GB but
densities have also increased in northern ME and MW strata the last two years.
Figure 6. Acoustic and mid-water trawl estimates of cisco numeric density (fish/ha; left panel) and biomass
(kg/ha; right panel) in Lake Huron, 2010-2017. Error bars represent ±1 standard error.
Figure 7. Geographic distribution of cisco numeric density (mean) estimated from acoustic surveys during
2010-2017. Points are elementary sampling units.
Emerald shiner Mean density of emerald shiner declined moderately in 2017 and was
approximately 24% of the long-term mean. Emerald shiner biomass in 2017 was 0.02 kg/ha and
remained unchanged relative to 2016 (Figure 8). The 2017 biomass estimate was 20% of the
long-term mean of 0.10 kg/ha. Emerald shiner biomass averaged 1.6% of total fish biomass
during 2004-2014, but with the exception of 2006, rarely exceeded 1% of total fish biomass in a
given year.
Figure 8. Acoustic and mid-water trawl estimates of emerald shiner numeric density (fish/ha; left panel) and
biomass (kg/ha; right panel) in Lake Huron, 2004-2017. Error bars represent ±1 standard error.
Other species - Other species captured during acoustic and mid-water trawl surveys included
threespine stickleback Gasterosteus aculeatus, ninespine stickleback Pungitius pungitius,
chinook salmon Oncorhynchus tshawytscha, lake whitefish Coregonus clupeaformis, and lake
trout Salvelinus namaycush. These species typically compose a small proportion of the mid-
water trawl catch.
Among-basin comparisons of fish biomass
Biomass in the North Channel (22.9 kg/ha) in September of 2017 was roughly double that
estimated in 2016 and was driven solely by increased biomass of rainbow smelt (Figure 9).
Biomass in the main basin (MW, SB, ME strata combined, 11.6 kg/ha) increased marginally
from 2016 estimates, and was due to small increases in rainbow smelt biomass. Biomass in
Georgian Bay (7.7 kg/ha) changed little between 2016 and 2017, with increases in rainbow smelt
biomass but decreases in bloater biomass (Figure 9). Over the long-term, total pelagic fish
biomass in both Georgian Bay and the main basin remains lower than in 1997. There is no clear
evidence of a declining trend in the North Channel (Figure 9).
Biomass in Georgian Bay has been primarily composed of rainbow smelt (58% average), while
biomass in the main basin has consisted of varying proportions of rainbow smelt and bloater.
Since 2012, bloater has been the dominant contributor in the main basin, averaging 75% of
pelagic fish biomass annually. In the North Channel, rainbow smelt have averaged 75% of
annual biomass since 1997.
Figure 9. Biomass (kg/ha) of major pelagic fish species in Georgian Bay, main basin, and North Channel during
1997-2017. Horizontal lines denote 1997-2016 mean density.
Lake-wide fish density and biomass
Lake-wide mean pelagic fish density increased from 775 fish/ha in 2016 to 1582 fish/ha in 2017,
representing roughly 60% of the long-term mean (Figure 10). The 2017 pelagic fish density
estimate represented roughly 30% of the 1997 estimate. The 2017 lake-wide mean pelagic fish
biomass estimate was 10.4 kg/ha, a 23% increase from 2016. Total standing stock biomass in
2017 was estimated at 49 kt (SE 10.4 kt) (Figure 10). The increase in standing stock biomass in
2017 was driven primarily by increased rainbow smelt biomass. In general, acoustic estimates of
pelagic fish biomass in Lake Huron have been relatively stable between 2004 and 2017.
Figure 10. Acoustic and mid-water trawl estimates of lake-wide numeric density (fish/ha; left panel) and
standing stock biomass (kilotonnes; right panel) in Lake Huron, 1997-2017. Error bars represent ±1 standard
Fish population estimates derived from the lake-wide acoustic survey, as with any other type of
fishery survey, include assumptions about the sampling and data analysis techniques. For
example, we assumed that the areas sampled were representative of the respective basins. This
survey sampled areas of Lake Huron from 10 to 250 m in depth. These depths encompass 85% of
the range of depths in Lake Huron, although sampling is limited in shallower (<20 m) areas of
the lake. For example, nearshore zones and large shallow embayments, especially Thunder Bay,
Saginaw Bay, and Parry Sound, are not sampled. These areas could be responsible for high rates
of pelagic fish production (Fielder and Thomas 2014, Höök et al. 2001, Klumb et al. 2003), but
could not be sampled safely due to the draft of our research vessel (3 m). Given the small surface
areas of these shallow-water embayments relative to the total surface area, densities would need
to be considerable to influence the lake-wide mean. We conducted sufficient mid-water trawls to
achieve an acceptable degree of confidence in fish community composition, according to
guidelines in Warner et al. (2012). An additional assumption was that fish size was a reasonable
proxy for age-0 or age-1+ groupings. We used size to assign age and assumed no overlap in age
among size classes. This assumption was likely violated, especially for rainbow smelt. While this
might have slight effects on our estimates of age-0 versus age-1+ density and biomass, it would
have no impact on our estimates of total density or biomass for a species.
Lake-wide biomass of common pelagic species in Lake Huron continues to consist of primarily
bloater and rainbow smelt, with bloater making up more of the biomass in recent years.
Distribution of preyfish biomass also continues to be patchy, with high areas of biomass in the
North Channel (rainbow smelt) and the southern main basin (bloater). Since 2012, acoustic-
derived estimates of lake-wide prey fish biomass in Lake Huron have remained relatively stable,
with biomass fluctuating by 1-2 kg/ha per year. At the basin level, annual biomass continues to
show some variation, but this is mostly for the North Channel.
Better delineation of cisco stocks and estimates of their abundance continue to be a focus of the
acoustic program on Lake Huron. Based on catches in mid-water trawls during 2010-2017, cisco
in offshore areas appear to be mostly confined to northern Lake Huron, Georgian Bay, and the
North Channel. Extant cisco stocks in Lake Huron are not well understood but acoustic surveys
have served to help better define offshore habitat use by this species. Most information on cisco
spatial distribution and abundance in Lake Huron has resulted from collections made during the
late fall when fish are aggregated for spawning purposes. We anticipate acoustic surveys to
continue providing important information on ecology and habitat use of cisco during other seasons.
To provide accurate estimates of available prey fish resources in Lake Huron, the continuation of
acoustic surveys will be instrumental in assessing the pelagic component of the prey fish
community, while complementing bottom trawl surveys that better estimate benthic prey
resources. The information gathered from acoustic surveys that sample areas where bottom
trawling is not feasible will increase our understanding of variation in prey fish biomass across
large temporal and spatial scales (i.e., all of Lake Huron’s basins). As no single gear is best for
assessing all species, life stages, or habitats, estimates of fish biomass from multiple gear types
will lead to a better understanding of fish population dynamics.
We thank the vessel crews and biologists of the R/V Sturgeon, R/V Grayling, and M/V Spencer
Baird for their assistance with field surveys. We thank Capt. Joe Bergan, Lyle Grivicich, and
Brad Briggs for vessel support during 2017 field operations. Scott Nelson, Dan Benes, and Limei
Zhang provided computer and database support. The Ontario Ministry of Natural Resources and
Forestry provided support for field operations. Mark Vinson and Arunas Liskauskas provided
helpful reviews of this report. Any use of trade, product, or firm names is for descriptive
purposes only and does not imply endorsement by the U.S. Government. All GLSC sampling
and handling of fish during research are carried out in accordance with guidelines for the care
and use of fishes by the American Fisheries Society (
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Appendix 1. Single target detection parameters used in acoustic data analyses in 2017
TS threshold (dB)
Pulse length determination level (dB)
Minimum normalized pulse length
Maximum normalized pulse length
Maximum beam compensation (dB)
Maximum standard deviation of minor-axis angles
Maximum standard deviation of major-axis angles
1 Only targets -60 dB were included in analysis
Appendix 2. Noise levels, detection limits, and acoustic equipment specifications in Lake
Huron, 2017
R/V Sturgeon
M/V Spencer Baird
Collection software
Visual Acquisition 6.0
ER60 2.2
Transducer beam angle (3dB)
8.28º split beam
6.53º split beam
Frequency (kHz)
Pulse length (ms)
Sv noise at 1 m (dB)
2 way equivalent beam angle
Detection limit (m) for -60 dB target2
2 Assuming 3 dB signal-to-noise ratio.
Appendix 3. Supplement to methods
Acoustic data collected in 1997 were analyzed using custom software (Argyle et al. 1998). Data
collected in 2004 and later years were analyzed using EchoviewTM software, which provided fish
density estimates for each sampling unit. Fish density was calculated as:
hafishDensity 4
where ABC was the area backscattering coefficient (m2 / m2) of each 10 m high by 1000-3,000 m
long cell, and σ was the mean backscattering cross section (m2) of all targets between -60 and -30
dB in each cell. The lower threshold should have included any age-0 alewives present (Warner et
al. 2002), but may have underestimated age-0 rainbow smelt density (Rudstam et al. 2003). The
upper threshold excluded fish larger than our species of interest.
In 1997, a BioSonics model 102 dual-beam echosounder was used to collect acoustic data during
pelagic fish surveys. During 2004-2005 and 2007-2008 acoustic data were collected during
September through early October with a BioSonics split-beam 120 kHz echosounder deployed
from the Research Vessel (R/V) Sturgeon. During 2006, acoustic data were collected during
August with a 70 kHz echosounder and a transducer deployed via towfish from the R/V Grayling.
During 2009, the survey was performed with a 38 kHz echosounder because the 120 kHz
transducer failed field calibration tests. In 2010-2015, we used both a 38 and 120 kHz echosounder
to facilitate frequency comparisons, but with the exception of 2009, only 120 kHz data are
presented in this report. Comparison of paired 120 kHz and 38 kHz data revealed that a) density
estimates from 38 kHz are higher than from 120 kHz, b) this difference does not vary among fish
species, and c) fish density estimates from the two frequencies are highly correlated (r2 =0.77). In
order to provide estimates for 2009 that would have been equivalent to 120 kHz, we predicted the
2009 fish density estimates using the 38 kHz estimates and a regression model relating the two
from data collected in subsequent years. Additionally, studentized residual plots indicated that the
model was acceptable. During 2011-2012 and 2014-2017, the survey was carried out jointly
between GLSC and the United States Fish and Wildlife Service (USFWS). USFWS used 70 kHz
and 120 kHz split-beam echosounders (Simrad EK60) to sample transects located in the MW
stratum. In all years, sampling was initiated one hour after sunset and ended no later than one hour
before sunrise. A threshold equivalent to uncompensated target strength (TS) of -70 decibels (dB)
was applied to Sv data.
In order to assign fish species and size composition to acoustic data, we used a technique
described by Warner et al. (2009), with different approaches depending on the vertical position
in the water column. For cells with depth < 40 m, mid-water trawl and acoustic data were
matched according to transect, depth layer (0-10, 10-20 m, etc., depending on headrope depth
and upper depth of the acoustic cell), and by bottom depth. For acoustic cells without matching
trawl data, we assigned the mean of each depth layer and bottom depth combination from the
same transect. If acoustic data still had no matching trawl data, we assigned the mean of each
depth layer and bottom depth combination within the same geographic stratum. Finally, if
acoustic data still had no matching trawl data, we used a lake-wide mean for each depth layer.
Mean mass of species/size groups at depths < 40 m were estimated using weight-length
equations from mid-water trawl data. For depths 40 m, we assumed that acoustic targets were
large bloater if mean TS was > -45 dB (TeWinkel and Fleischer 1999). Mean mass of bloater in
these cells was estimated using the mass-TS equation of Fleischer et al. (1997). If mean TS was
-45 dB, we assumed the fish were large rainbow smelt and estimated mean mass from mean
length, predicted using a TS-length equation (Rudstam et al. 2003).
As recommended by the Great Lakes Acoustic Standard Operating Procedures (Parker-Stetter et
al. 2009, Rudstam et al. 2009), we used a number of techniques to assess or improve acoustic data
quality. We used the Nv index of Sawada et al. (1993) to determine if conditions in each acoustic
analysis cell were suitable for estimation of in situ TS. We defined suitability as an Nv value < 0.1
and assumed mean TS in cells at or above 0.1 were biased. We replaced mean TS in these cells
with mean TS from cells that were in the same depth layer and transect having Nv < 0.1. To help
reduce the influence of noise, we estimated Sv noise at 1 m on each transect using either passive
data collection or echo integration of data below the bottom echoes. We then used noise at 1 m to
estimate noise at all depths, which we subtracted from the echo integration data. Additionally, we
estimated the detection limit (depth) for the smallest targets we include in our analyses.
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Acoustic estimation of absolute fish abundance depends on knowledge of the relationship between target strength (TS) and size for the species of interest. We have derived a relationship between in situ TS and both length (L, cm) and mass (W, g) for alewives Alosa pseudoharengus in Lake Ontario and eight inland lakes in New York to provide equations for predicting one variable from the other. The pelagic fish community in these lakes was dominated by alewives (≥80% numerically). Target strength distributions from fish populations investigated in 25 surveys were multimodal, whereas those for individual fish were unimodal, indicating that each mode for the populations corresponded to a size-group of alewives (range, 2.5-15.2 cm). The positive relationship between mean TS and mean length was highly significant (TS = 20.53 log10L− 64.25), as was the relationship between mean TS and mean mass (TS = 6.98 log10W− 50.07). These equations are similar to one often-used TS-length relationship but differ substantially from other relationships in the literature. Predictions of TS from our equations were 8.2 decibels greater than those from commonly used equations for marine clupeids. Our equations also differ for fish smaller than 10 cm compared with the equations available for mixed species of Great Lakes forage fish (alewives, rainbow smelt Osmerus mordax, and bloater Coregonus hoyi).
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An annual daytime bottom trawl survey of the Lake Superior fish community designed in 1978 does not adequately assess the entire community. Whereas recent studies have recommended that pelagic species be surveyed with a combination of acoustic and midwater trawling methods (AC–MT), we used bottom trawling to study the effects of depth, diel period, and season on biomass estimates and the sizes of bottom-oriented species. Day and night bottom trawl samples were collected within 48 h at three depths (30, 60, and 120 m) at a Lake Superior site during eight sampling periods that included two seasons each year (early summer and late summer to early fall) for 2 years (2004 and 2005). Depth significantly affected the biomass of seven of the eight species analyzed, while diel period affected the biomass of six species. For most species, average biomass levels were higher at night. The effect of season on biomass was comparatively low (three species were significantly affected). Depth significantly affected the sizes of six bottom-oriented species, as the average length of most species increased with depth. The effects of diel period (three species) and season (one species) on average length were comparatively small. By adding night bottom trawl samples to night AC–MT collections, the entire fish community of Lake Superior can be monitored with a single lakewide survey employing multiple gears. The establishment of offshore sampling (i.e., where depths exceed 80 m) will provide estimates of deepwater species that have been largely undersampled by the 1978-designed survey. We recommend that the present fish community survey be maintained, albeit at a reduced level, until a nighttime survey time series is well established (in 3–5 years).
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Evaluation of the biases in sampling methodology is essential for understanding the limitations of abundance and biomass estimates of fish populations. Estimates from surveys that rely solely on bottom trawls may be particularly vulnerable to bias if pelagic fish are numerous. We evaluated the variability in the vertical distribution of fish biomass during the U.S. Geological Survey's annual spring bottom trawl survey of Lake Superior using concurrent hydroacoustic observations to (1) test the assumption that fish are generally demersal during the day and (2) evaluate the potential for predictive models to improve bottom trawl–determined biomass estimates. Our results indicate that the assumption that fish exhibit demersal behavior during the annual spring bottom trawl survey in Lake Superior is unfounded. Bottom trawl biomass (BBT) estimates (mean ± SE) for species known to exhibit pelagic behavior (cisco Coregonus artedi, bloater C. hoyi, kiyi C. kiyi, and rainbow smelt Osmerus mordax; 3.01 ± 0.73 kg/ha) were not significantly greater than mean acoustic pelagic zone biomass (BAPZ) estimates (6.39 ± 2.03 kg/ha). Mean BAPZ estimates were 1.6- to 4.8-fold greater than mean BBT estimates over 4 years of sampling. The relationship between concurrent BAPZ and BBT estimates was marginally significant and highly variable. Predicted BAPZ estimates using cross-validation models were sensitive to adjustments for back-transforming from the logarithmic to the linear scale and poorly corresponded to observed BAPZ estimates. We conclude that statistical models to predict BAPZ from day BBT cannot be developed. We propose that night sampling with multiple gears will be necessary to generate better biomass estimates for management needs.
Acoustic methods are used to estimate the density of pelagic fish in large lakes with results of midwater trawling used to assign species composition. Apportionment in lakes having mixed species can be challenging because only a small fraction of the water sampled acoustically is sampled with trawl gear. Here we describe a new method where single echo detections (SEDs) are assigned to species based on classification tree models developed from catch data that separate species based on fish size and the spatial habitats they occupy. During the summer of 2011, we conducted a spatially-balanced lake-wide acoustic and midwater trawl survey of Lake Superior. A total of 51 sites in four bathymetric depth strata (0–30 m, 30–100 m, 100–200 m, and >200 m) were sampled. We developed classification tree models for each stratum and found fish length was the most important variable for separating species. To apply these trees to the acoustic data, we needed to identify a target strength to length (TS-to-L) relationship appropriate for all abundant Lake Superior pelagic species. We tested performance of 7 general (i.e., multi-species) relationships derived from three published studies. The best-performing relationship was identified by comparing predicted and observed catch compositions using a second independent Lake Superior data set. Once identified, the relationship was used to predict lengths of SEDs from the lake-wide survey, and the classification tree models were used to assign each SED to a species. Exotic rainbow smelt (Osmerus mordax) were the most common species at bathymetric depths <100 m with their population estimated at 755 million (3.4 kt). Kiyi (Coregonus kiyi) were the most abundant species at depths >100 m (384 million; 6.0 kt). Cisco (Coregonus artedi) were widely distributed over all strata with their population estimated at 182 million (44 kt). The apportionment method we describe should be transferable to other large lakes provided fish are not tightly aggregated, and an appropriate TS-to-L relationship for abundant pelagic fish species can be determined.
Because it is not possible to identify species with echosounders alone, trawling is widely used as a method for collecting species and size composition data for allocating acoustic fish density estimates to species or size groups. In the Laurentian Great Lakes, data from midwater trawls are commonly used for such allocations. However, there are no rules for how much midwater trawling effort is required to adequately describe species and size composition of the pelagic fish communities in these lakes, so the balance between acoustic sampling effort and trawling effort has been unguided. We used midwater trawl data collected between 1986 and 2008 in lakes Michigan and Huron and a variety of analytical techniques to develop guidance for appropriate levels of trawl effort. We used multivariate regression trees and re-sampling techniques to i. identify factors that influence species and size composition of the pelagic fish communities in these lakes, ii. identify stratification schemes for the two lakes, iii. determine if there was a relationship between uncertainty in catch composition and the number of tows made, and iv. predict the number of tows required to reach desired uncertainty targets. We found that depth occupied by fish below the surface was the most influential explanatory variable. Catch composition varied between lakes at depths <38.5 m below the surface, but not at depths ≥38.5 m below the surface. Year, latitude, and bottom depth influenced catch composition in the near-surface waters of Lake Michigan, while only year was important for Lake Huron surface waters. There was an inverse relationship between RSE [relative standard error = 100 × (SE/mean)] and the number of tows made for the proportions of the different size and species groups. We found for the fifth (Lake Huron) and sixth (Lake Michigan) largest lakes in the world, 15–35 tows were adequate to achieve target RSEs (15% and 30%) for ubiquitous species, but rarer species required much higher, and at times, impractical effort levels to reach these targets.
We found mean target strength to be a reliable in situ predictor of fish weight, which allows direct estimation of the pelagic planktivore fish biomass from target strength measurements. Fish were collected by midwater trawling concurrent with target strength measurements (120-kHz frequency) in Lake Michigan. The mean weight of fish caught ranged from 2 to 71 g and mean target strength ranged from –54.9 to –38.0 decibels. Changes in mean target strength explained 73% of the variability in mean weight for combinations of various planktivore species, principally rainbow smelt Osrnerus mordax, bloaters Coregonus hovi, and alewives Alosa pseudoharengus. Bloaters were found to be less acoustically reflective than the other pelagic species, and a linear regression model with a classification variable was used to predict weight from target strength for bloaters and for the other species. We demonstrated that variations in the backscattering properties of different fish species must be considered to obtain accurate acoustic-based estimates of fish biomass.
Resource managers are often required to estimate the size of a wildlife population based on sampling surveys. This problem is especially critical in fisheries, where stock-size estimation forms the basis for key policy decisions. This study looks at design-based methods for a hydroacoustic fisheries survey, with the goal of improving estimation when the target stock has a patchy spatial distribution. In particular, we examine the efficiency and feasibility of a relatively new design-based method known as adaptive cluster sampling (ACS). A simulation experiment looks at the relative efficiency of ACS and traditional sampling designs in a hydroacoustic survey setting. Fish densities with known spatial covariance are generated and subjected to repeated sampling. The distributions of the different estimators are compared. Hydroacoustic data frequently display strong serial correlation along transects and so traditional designs based on one-stage cluster sampling are appropriate. Estimates of total stock size for these designs had a markedly skewed distribution. ACS designs performed better than traditional designs for all stocks with small-scale spatial correlation in fish density, yielding estimates with lower variance. ACS estimators were not skewed and had a lower frequency of large errors. For the most variable stock the use of ACS reduced the coefficient of variation (CV) of the stock size estimate from over 0.9 to around 0.5. Differences between traditional and ACS designs were consistent over multiple realizations of each spatial covariance model. A survey of rainbow smelt ( Osmerus mordax ) in the eastern basin of Lake Erie was used as a case study for development of a survey design. A field trial showed that use of ACS for the survey is feasible but pointed out some areas for further research. The biggest drawback to use of ACS is uncertainty in the final sample size. This can be partially controlled by applying ACS within a stratified design. ACS retains the unbiased and non-parametric properties of design-based estimation but allows increased sampling in high-density areas that are of greater biological interest. For stocks with an aggregated or patchy spatial distribution ACS can provide a more precise estimate of stock size than traditional survey methods.
The vertical migration and nighttime vertical distribution of adult bloaters Coregonus hoyi were investigated during late summer in Lake Michigan using acoustics simultaneously with either midwater or bottom trawling. Bloaters remained on or near bottom during the day. At night, bloaters were distributed throughout 30–65 m of water, depending on bottom depth. Shallowest depths of migration were not related to water temperature or incident light. Maximum distances of migration increased with increasing bottom depth. Nighttime midwater densities ranged from 0.00 to 6.61 fish/1,000 m and decreased with increasing bottom depth. Comparisons of length distributions showed that migrating and nonmigrating bloaters did not differ in size. However, at most sites, daytime bottom catches collected a greater proportion of larger individuals compared with nighttime midwater or bottom catches. Mean target strengths by 5-m strata indicated that migrating bloaters did not stratify by size in the water column at night. Overall, patterns in frequency of empty stomachs and mean digestive state of prey indicated that a portion of the bloater population fed in the water column at night. Bloater diet composition indicated both midwater feeding and bottom feeding. In sum, although a portion of the bloater population fed in the water column at night, bloaters were not limited to feeding at this time. This research confirmed that bloaters are opportunistic feeders and did not fully support the previously proposed hypothesis that bloater vertical migration is driven by the vertically migrating macroinvertebrate the opossom shrimp Mysis relicta.