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The USGS Great Lakes Science Center has conducted integrated acoustic and mid-water trawl surveys of Lake Huron during 1997 and annually from 2004-2015. The 2015 survey was conducted during September and included transects in Lake Huron's main basin, Georgian Bay, and North Channel. Mean lake-wide total pelagic fish density was 1,313 fish/ha and mean total pelagic fish biomass was 10.7 kg/h in 2015, which represents 77% and 92%, respectively of the long-term mean. Mean lake-wide biomass was 13% higher in 2015 as compared to 2014. The total estimated lake-wide standing stock biomass of pelagic fish species was ~50 kt, consisting almost entirely of bloater (36.8 kt; 74%) and rainbow smelt (12.5 kt; 25%). No alewives were captured during the 2015 survey. Age-0 rainbow smelt abundance increased from 129 fish/ha in 2014 to 475 fish/ha in 2015. Biomass of age-1+ rainbow smelt decreased from 2.8 kg/ha in 2014 to 2.2 kg/ha in 2015. Age-0 bloater abundance increased from 35 fish/ha in 2014 to 315 fish/ha in 2015. Biomass of age-1+ bloater increased from 6.2 kg/ha in 2014 to 7.
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Status and Trends of Pelagic Prey Fish in Lake Huron, 2015
Timothy P. O’Brien1, David M. Warner1, Steve Lenart2,
Peter Esselman1, Lynn Ogilvie1, and Chris Olds2
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
The USGS Great Lakes Science Center has conducted integrated acoustic and mid-water trawl
surveys of Lake Huron during 1997 and annually from 2004-2015. The 2015 survey was
conducted during September and included transects in Lake Huron’s main basin, Georgian Bay,
and North Channel. Mean lake-wide total pelagic fish density was 1,313 fish/ha and mean total
pelagic fish biomass was 10.7 kg/h in 2015, which represents 77% and 92%, respectively of the
long-term mean. Mean lake-wide biomass was 13% higher in 2015 as compared to 2014. The
total estimated lake-wide standing stock biomass of pelagic fish species was ~50 kt, consisting
almost entirely of bloater (36.8 kt; 74%) and rainbow smelt (12.5 kt; 25%). No alewives were
captured during the 2015 survey. Age-0 rainbow smelt abundance increased from 129 fish/ha in
2014 to 475 fish/ha in 2015. Biomass of age-1+ rainbow smelt decreased from 2.8 kg/ha in 2014
to 2.2 kg/ha in 2015. Age-0 bloater abundance increased from 35 fish/ha in 2014 to 315 fish/ha
in 2015. Biomass of age-1+ bloater increased from 6.2 kg/ha in 2014 to 7.1 kg/ha in 2015.
Emerald shiner density increased from 0.1 fish/ha in 2014 to 37 fish/ha in 2015 and biomass
increased from < 0.001 kg/ha in 2014 to 0.02 kg/ha in 2015. Bloater and rainbow smelt will
continue to be the primary pelagic species available to offshore predators in coming years, with
reduced numbers of rainbow smelt if recruitment to older ages remains poor. Pelagic fish
biomass in Lake Huron is greater than that observed in recent lake-wide acoustic surveys of Lake
Michigan and Lake Superior, but species composition differs among the three lakes. Of the three
upper Great Lakes, Lake Superior had the greatest pelagic prey fish diversity and occurrence of
native species, while Lake Michigan had the lowest species diversity and lowest native fish
prevalence, whereas Lake Huron was intermediate in regards to both. .
Presented at: Great Lakes Fishery Commission
Lake Huron Committee Meeting
Milwaukee, WI, March 21, 2016
The U.S. Geological Survey’s Great Lakes Science Center (GLSC) has conducted bottom trawl
surveys of the Lake Huron fish community since the 1970s. These surveys have tracked broad-
scale changes in the fish community and provided valuable information on prey fish dynamics to
fishery managers tasked with balancing predatory demand by native and introduced salmonines.
Although bottom trawling has been an important tool for monitoring long-term trends in fish
populations, 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. Recent research has further shown that
acoustic and mid-water trawling methods are better at assessing species or life stages that are
pelagic, particularly over lake areas with rough bottom (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 provide abundance and biomass estimates
for major pelagic offshore prey fish species in Lake Huron which constitute the bulk of prey
resources for introduced and native piscivores.
Survey and analytical methods
The pelagic prey fish survey in Lake Huron is based on a stratifiedrandom 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). Saginaw Bay was
omitted because of its shallow depth and its prey fish community is surveyed by other methods
(Fielder and Thomas 2014). Within each stratum, the first transect was selected randomly each
year based on latitude and longitude; subsequent transects were spaced evenly around the first.
Effort (transects per stratum) was allocated based on stratum area and variability of total biomass
in each stratum from previous surveys (Adams et al. 2006). For analysis, 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
The 2015 pelagic fisheries survey was completed from 8-29 September. Sampling was
conducted by both the GLSC (R/V Sturgeon) and USFWS (M/V Spencer F. Baird). Twenty-five
acoustic transects were sampled, resulting in approximately 460 km of acoustic data. Thirty-
seven mid-water trawl tows were conducted in conjunction with acoustic data collection.
Fish species were collected using a 15-m headrope mid-water trawl (USGS) and a 21-m
headrope mid-water trawl (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 a NetmindTM
system (GLSC) and a Simrad PI44 catch monitoring system (USFWS). In 2015, trawling depths
ranged from 4 to 70 m (mean = 28 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 fish 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 fish 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. Based on previous age estimates for these
species, these lengths approximate the lengths of the smallest age-1 fish of these species.
Figure 1. Location of acoustic transects and mid-water trawls, and delineation of sampling strata in Lake
Huron during 2015 (left) and location of acoustic transects during surveys in 2004-2014 (right).
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 elementary sampling unit (ESU) in each stratum
were weighted by dividing the stratum area by the number of ESUs in the stratum.
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.
Density and biomass by species
Alewife During 2015, no alewives were caught in mid-water trawls that sampled a broad range
of depths in Lake Huron. Alewife densities estimated in 1997, 2005-2006, 2008, and 2013 were
considerably higher than other years in the time series. However, we note that density
differences, though substantial, did not mean that alewives have been especially abundant in any
survey year (Figure 2). During 1997, the year of highest abundance, alewives were only 3.1% of
total fish density.
Acoustic estimates of alewife biomass have remained low for the last decade despite large
fluctuations in density during 2004-2013 (Figure 2). Temporal biomass differences were largely
due to differences in size and age structure between 1997 and other years. In 1997, age 1+
alewife was captured, but 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 mid-water trawl catches of age-0 alewives occurred during
some acoustic surveys, recruitment has been limited and alewives have shown no sign of
returning to higher abundance. Our findings are consistent with results from annual bottom trawl
surveys (Roseman et al. 2015), which indicated that alewife density and biomass remain low in
the open waters of Lake Huron (i.e., < 1 fish/ha, < 1kg/ha, respectively).
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, 1997-2015. Error bars represent ±1 standard error.
Rainbow smelt During 2015, age-0 rainbow smelt density increased from 2014 estimates to
66% of the long-term mean (Figure 3). Age-0 rainbow smelt populations are considerably less
than the high observed in 1997, but there has been no clear trend in abundance since 2004. Age
1+ rainbow smelt biomass decreased from 2.8 kg/ha in 2014 to 2.2 kg/ha in 2015. This was
roughly 50% of the long-term mean of 4.4 kg/ha (Figure 3) and substantially less than that
observed in 1997.
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, 1997-2015. Error bars represent ±1 standard error.
Bloater Estimates of age-0 bloater numeric density showed a nine-fold increase between 2014
and 2015 (Figure 4). Estimated biomass of age-1+ bloater increased from 6.2 kg/ha in 2014 to
7.1 kg/ha in 2015 (Figure 4) however, the standard error around this estimate was large,
indicating lower precision. Acoustic estimates of age-0 bloater were low during 1997 (<4 fish/ha,
Figure 4). Similar to results from bottom trawl surveys, age-0 bloater density was variable but
increased during 2004-2014 (average density > 160 fish/ha). Biomass of age-1+ bloater showed
an increasing trend from 2004-2008, followed by a decrease from 2009-2010. Abundance of age-
1+ bloater remained relatively unchanged during 2011-2013. Although we have seen increased
bloater biomass during the past two years, relative standard error for these estimates ranged from
40-50% indicating low equitability in distribution of biomass throughout Lake Huron. Much of
the biomass is driven by bloater aggregations in the southern main basin.
Figure 4. 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, 1997-2015. Error bars represent ±1 standard error.
Emerald shiner In 2015, emerald shiner biomass increased from 2014 estimates and was 24%
of the long-term mean of 0.10 kg/ha (Figure 5). Mean biomass of emerald shiner was estimated
to be < 0.01 % of total pelagic fish biomass in 2014, but increased to 0.22 % of total biomass in
2015. 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 5. Acoustic and mid-water trawl estimates of emerald shiner numeric density (fish/ha; left panel) and
biomass (kg/ha; right panel) in Lake Huron, 2004-2014. Error bars represent ±1 standard error.
Other species - Other species captured during acoustic and mid-water trawl surveys included
threespine stickleback Gasterosteus aculeatus, lake whitefish Coregonus clupeaformis, lake trout
Salvelinus namaycush, and cisco Coregonus artedi. These species compose a small proportion of
the mid-water trawl catch. In the case of cisco, catches have occurred in most years during
acoustic surveys but their density remains low in open waters of the lake during September and
October. During October in northern Lake Huron, cisco are primarily distributed in shallow, near
shore areas (M.P. Ebener, Chippewa Ottawa Resource Authority, personal communication). Our
acoustic and mid-water trawl surveys primarily operate in deeper waters (>15 m) during the fall,
and therefore do not effectively sample cisco that are likely more concentrated in nearshore
areas. Cisco are occasionally caught in mid-water trawls but catches are too sporadic to be able
to use trawl proportions to apportion acoustic densities. During 2015, several small cisco (< 200
mm TL) were caught in the North Channel and two larger cisco (441 mm and 376 mm TL) were
caught offshore in Georgian Bay. During 2004-2014, catches of cisco were similarly low during
acoustic surveys.
Among-basin comparisons of fish biomass
In 2015, pelagic fish biomass increased in the main basin and decreased in both the North
Channel and Georgian Bay. Biomass in the North Channel (12.1 kg/ha) was roughly 63% of the
long-term mean and decreases were driven by lower biomass of both age-1+ rainbow smelt and
bloater (Figure 6.). Main basin biomass (12.9 kg/ha) showed a 28% increase from 2014 due to
increases in age-1+ bloater and a slight increase in age-0 rainbow smelt. Biomass in Georgian
Bay (4.1 kg/ha) declined to 37% of the long-term mean due to decreases in age-1+ rainbow
smelt. Bloater biomass increased slightly in Georgian Bay during 2015 (Figure 6). In addition to
differences in fish biomass, the three basins have had different temporal trends in biomass and
community composition. In both Georgian Bay and the main basin, fish biomass has declined
relative to 1997, but there is no evidence of a declining trend in the North Channel (Figure 6).
Community composition differences are predominantly the result of variation in the proportion
of biomass comprised by rainbow smelt and bloater. Most biomass in Georgian Bay has been in
the form of rainbow smelt (54% 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 72% of pelagic fish biomass (Figure 6). In the North
Channel rainbow smelt have comprised 73% of biomass on average.
Figure 6. Biomass (kg/ha) of major pelagic fish species in Georgian Bay (panel A), main basin (panel B), and
North Channel (panel C) during 1997-2015. Horizontal lines denote 1997-2014 mean density.
Lake-wide fish density and biomass
Lake-wide mean pelagic fish density increased from 729 fish/ha in 2014 to 1,313 fish/ha in 2015,
representing 77% of the long-term mean (Figure 7). The 2015 pelagic fish density estimate
represented 26% of that observed in 1997. The 2015 lake-wide mean pelagic fish biomass
estimate was 10.6 kg/ha, a 12.5% increase from 2014. Total standing stock biomass in 2015 was
estimated at 50 kt (SE 16.3 kt) (Figure 7). This was slightly greater than that observed in 2014
(Figure 7) and was driven by higher biomass of age-1+ bloater in the main basin. In general,
acoustic estimates of pelagic fish biomass in Lake Huron have shown no consistent trend
between 2004 and 2015. However, biomass has been considerably lower than in 1997 when
rainbow smelt and bloater were more abundant in Georgian Bay and the main basin, and alewife
was more abundant throughout the lake.
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. This depth range encompasses about 85% of the total
surface area of Lake Huron. However, 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
Figure 7. 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, 1997-2015. Error bars represent ±1 standard
Higher age-0 production and adult biomass during 2015 indicate bloater will continue to be the
most available pelagic prey species in the offshore zone of Lake Huron. Although lake-wide
preyfish biomass increased in 2015, we note that biomass was only 30% of the 1997 estimate.
This decline is primarily due to reduced biomass of rainbow smelt, which in 2015 was only 13%
of the 1997 estimate of 21 kg/ha. Biomass of rainbow smelt in the main basin will likely remain
low during 2016 given recent declining trends in recruitment for this species (O’Brien et al.
2014) and lower adult biomass in 2015. During 2016, pelagic prey available to piscivores will
likely be similar to that seen in recent years, although offshore predators such as lake trout will
have increased numbers of adult bloater available as forage.
Lake-wide pelagic biomass in Lake Huron during 2015 was higher than that estimated for Lake
Michigan during 2015 (4.2 kg/ha, Warner et al. 2016) and Lake Superior during 2011 (6.8 kg/ha,
Yule et al. 2013). In addition to differences in lake-wide biomass in recent years, pelagic fish
community composition differs considerably between the three lakes. In Lake Michigan, alewife
is still prevalent and comprises about 70% of the pelagic biomass, while in lakes Huron and
Superior, the biomass of this species is negligible. Additionally, native coregonines and other
species are at historic low levels in Lake Michigan. Native species constitute much higher
proportions of total biomass in lakes Huron and Superior. In the case of Lake Superior, kiyi
(Coregonus kiyi) are numerically dominant at depths > 100 m, while cisco are most of the
biomass (Yule et al. 2013). In Lake Huron, rainbow smelt are numerically more abundant, while
rainbow smelt and bloater have been alternating roles as the dominant contributor to total
biomass, with bloater contributing more in recent years. Additionally, there have been relatively
consistent (but low) catches of emerald shiner and cisco in Lake Huron mid-water trawling. In
the case of emerald shiner, it is likely that their reappearance was the result of a release from
predation on fry following the collapse of alewife (Madenjian et al. 2008; Schaeffer et al. 2008).
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 ecosystem dynamics.
Acknowledgements: We thank the vessel crews and biologists of the R/V Sturgeon, R/V
Grayling, and M/V Spencer Baird for their assistance with the night field surveys. We thank
Capt. Shawn Parsons, Lyle Grivicich, Erin Grivicich, and Deirdre Jordan for their support of
2015 field operations. Scott Nelson, Dan Benes, and Limei Zhang provided computer and
database support. Mark Vinson and Chris Davis 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.
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Appendix 1. Single target detection parameters used in acoustic data analyses in 2015.
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,
M/V Spencer Baird
Collection software
ER60 2.2
Transducer beam angle (3dB)
6.18º 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 2014-2015, 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
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Technical Report
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Saginaw Bay is a large, shallow water embayment in the Michigan waters of Lake Huron that has historically sustained a coolwater fish community that in turn supports important recreational and commercial fisheries focused primarily on percids and coregonids. The fish community has undergone enormous change and collapses of key fisheries since the mid-Twentieth Century. Between 2005 and 2011 the Saginaw Bay walleye population continued to grow in abundance, achieving or exceeding recovery targets. Walleye growth rate has declined in response to increased abundance. Several strong walleye cohorts have been produced but year-class strength has become more variable. Total annual mortality rate has increased during this time in spite of the increased abundance. Yellow perch reproductive success also increased but this has not translated into recruitment to the population or fisheries. These gains in percid reproductive success are attributed to the ongoing absence of alewives from the bay. Recruitment failures in yellow perch are attributed to high mortality between age-0 and age-1 life stages, primarily attributed to heavy predation by predators in the bay. Growth rate of age-0 yellow perch has declined but growth of yearling and older yellow perch has increased substantially as density has declined. Previously the bay was characterized by low predator abundance and over abundant prey species. During this reporting period, the prey base has declined and an overall more balanced predator / prey dynamic has been achieved in the Saginaw Bay fish community. Long time staples of predator diets, alewives and rainbow smelt have declined or disappeared but round gobies have become firmly established as part of the prey fish community. While some 37 species were documented in the trawling and gill-net series, notably absent were cisco and lake sturgeon, both of which were historically abundant.
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Rainbow Smelt Osmerus mordax are native to northeastern Atlantic and Pacific–Arctic drainages and have been widely introduced throughout North America. In the Great Lakes region, Rainbow Smelt are known predators and competitors of native fish and a primary prey species in pelagic food webs. Despite their widespread distribution, importance as a prey species, and potential to negatively interact with native fish species, there is limited information concerning stock–recruitment relationships for Rainbow Smelt. To better understand recruitment mechanisms, we evaluated potential ecological factors determining recruitment dynamics for Rainbow Smelt in Lake Huron using data from bottom trawl catches. We specifically evaluated influence of stock size, environmental factors (water temperature, lake levels, and precipitation), and salmonine predation on the production of age-0 recruits from 1976 to 2010. Rainbow Smelt recruitment was negatively related to stock size exceeding 10 kg/ha, indicating that compensatory, density-dependent mortality from cannibalism or intraspecific competition was an important factor related to the production of age-0 recruits. Recruitment was positively related to spring precipitation suggesting that the amount of stream-spawning habitat as determined by precipitation was important for the production of strong Rainbow Smelt recruitment. Additionally, density of age-0 Rainbow Smelt was positively related to Lake Trout Salvelinus namaycush abundance. However, spawning stock biomass of Rainbow Smelt, which declined substantially from 1989 to 2010, was negatively associated with Lake Trout catch per effort suggesting predation was an important factor related to the decline of age-2 and older Rainbow Smelt in Lake Huron. As such, we found that recruitment of Rainbow Smelt in Lake Huron was regulated by competition with or cannibalism by older conspecifics, spring precipitation influencing stream spawning habitats, and predation by Lake Trout on age-2 and older Rainbow Smelt.Received June 27, 2013; accepted December 16, 2013
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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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The alewife Alosa pseudoharengus, an invader to the Laurentian Great Lakes from the Atlantic Ocean, has been blamed for causing major disruptions of Great Lakes fish communities during the past 50 years. We reviewed the literature and examined long-term data on fish abundances in the Great Lakes to develop a new synthesis on the negative effects of alewives on Great Lakes fish communities. The results indicated that certain fish populations are substantially more vulnerable to the effects of alewives than others. More specifically, the effects of alewives on other fish populations appeared to follow a continuum—from such fishes as slimy sculpin Cottus cognatus, lake whitefish Coregonus clupeaformis, and bloater Coregonus hoyi, which were relatively unsusceptible—to Atlantic salmon Salmo salar, lake trout Salvelinus namaycush, and emerald shiner Notropis atherinoides, which were highly susceptible. Intermediate species in this continuum included yellow perch Perca flavescens, deepwater sculpin Myoxocephalus thompsonii, and burbot Lota lota. The predominant mechanism by which alewives exerted their negative effect appeared to be predation on the larvae of other fishes. The key factor in the extirpation of Atlantic salmon from Lake Ontario, however, was probably early mortality syndrome induced by a diet rich in alewives. We conclude that the degree of restoration of the native Great Lakes fish community depends in part on the degree of control of the alewife population.
Conditions for precise measurement of in situ fish target strength (TS) are empirically studied and two indexes are introduced for this purpose. One is the number of fish in the effective reverberation volume which contributes echo formation at a certain instant and the other is the percentage of the multiple echoes which is derived from a residual of the single echo extraction. With the decrease of both indexes measured target strength approach a certain asymptotic value which is admitted as reliable from the past study. This shows the existence of some threshold values and below these threshold values TS measurement will be successful. The effectiveness of both indexes is confirmed by the data set obtained from one large same fish school in the eastern shelf of Bering sea during the intership calibration between Japanese and U.S. vessels on 15 and 16 August 1991.
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.