Embed Size (px)
Citation preview
1
The fisheries and limnology of Oneida Lake 2017
New York Federal Aid in Sport Fish Restoration Job 2-2, Study 2
F-63-R
Prepared by:
J.R. Jackson, A.J. VanDeValk, T.E. Brooking, K.T. Holeck, C. Hotaling, and L.G. Rudstam
Cornell Biological Field Station
Department of Natural Resources Cornell University
900 Shackelton Point Rd. Bridgeport, N. Y. 13030
www.dnr.cornell.edu/fieldst/cbfs.htm.
May 2018
Corresponding author: Randy Jackson: [email protected]
2
Table of contents Abstract 2 Introduction 4 Methods 6 Oneida Lake in 2017 6 Limnology 6 Fish community changes (Gill net) 12 Walleye 13 Yellow perch 20 White perch 23 Smallmouth bass 25 Open water forage fish 27 Lake sturgeon 28 Double-crested cormorants 29 Round goby 30 Nearshore sampling 31 Fyke nets 31 Spring electrofishing with black bass population assessments 32 Shoreline seining 43 Creel survey 45 Recommendations for management and future research directions 55 Literature Cited 60 Appendix 1: Change point analyses of limnological parameters in
Oneida Lake 65 Appendix 2: Impacts of round goby invasion on predation by
Oneida Lake predators. VanDeValk, Pakzad, Jackson, and Brooking. 73
Appendix 3: Data collection methods 80 Appendix 4: Standard data tables 84
Abstract Oneida Lake is New York State’s 2nd most heavily fished lake. Walleye have historically received the majority of targeted effort, with black bass increasing in importance in recent years. Long-term monitoring of the fisheries and limnology of Oneida Lake has captured a series of changes in recent decades that have resulted in pronounced changes in the lake’s physical and biological characteristics, including reductions in nutrient inputs resulting from the Great Lakes Basin water quality agreements; establishment of invasive dreissenid mussels resulting in increases in water clarity; increases in summer water temperatures and decreases in duration of ice cover; establishment of a breeding population of double-crested cormorants; and increases in populations of white perch and gizzard shad. Most recently, round goby have entered the lake.
3
Round gobies were first reported by anglers in 2013 and appeared in our standard surveys in 2014. By the end of the 2016 sampling season, gobies were the most abundant fish in our trawl samples and were common in all other sampling gears, with catches increasing by a factor of 2 or more in all gears. An apparent die-off of round goby occurred over the winter of 2016-2017, but surviving fish reproduced successfully in 2017 and fall catches of age-0 goby in trawls were the highest observed thus far. Long-term analyses show significant changes in production and abundance of many important sport and prey fish species as a result of these changes, including increases in black bass populations and concomitant growth in the black bass fishery. New analyses focus on the period since 2007, following the establishment of quagga mussels, and with the goal of differentiating long-term from ongoing changes. Analyses of data since the year 2007 suggest that the lake has reset at a new, mesotrophic trophic state, with resulting changes in fish populations. Increased water clarity has resulted in increases in littoral macrophytes and a shift of much of the lake’s productivity from pelagic to littoral production. While zooplankton densities did not decrease immediately following establishment of dreissenids, recent data indicate that a significant decrease in Daphnia densities is now occurring. Establishment of double-crested cormorants contributed to declines in yellow perch and walleye populations, but even with an aggressive management program, both populations remain below historic levels, suggesting that current conditions in the lake may no longer support production and survival of walleye and yellow perch needed to achieve population sizes observed prior to nutrient reductions and introduction of dreissenids. Recent levels of recruitment in walleye and yellow perch have been consistently below levels observed before the lake experienced the shift in trophic state and establishment of invasive species. A strong 2010 walleye year class has contributed to an increase in the adult walleye population since 2013. A mark-recapture study conducted in 2016 estimated the adult walleye population to be at 429,000 fish. A strong 2014 year class of walleye could push the population to near 500,000. Despite lower than historic numbers of walleye, angler catch rates have in recent years been characteristic of a very good walleye fishery, as defined by the New York Walleye Management Plan. However, creel survey data in 2016 indicated a 40% decline in targeted catch rates of walleye, with walleye catch rates for all anglers declining by 65%. Following the apparent partial winterkill of round goby, angler catch rates in 2017 rebounded, although not to the levels observed prior to arrival of round goby. The smallmouth bass population has exhibited an increase during the same period that percids have decreased, suggesting that current lake conditions are more favorable for black bass. Spring electrofishing catch rates of both largemouth and smallmouth bass are within the middle third of those observed in other New York waters, and observed length-at-age exceed the mean for most ages of both largemouth and smallmouth bass. Similar to what was observed for walleye, the 2016 targeted catch rate of smallmouth bass declined 44% from rates estimated in 2015, but fully recovered in 2017 to higher rates than recorded in 2015. Despite the changes observed in recent decades, Oneida Lake continues to support quality sustainable fisheries for walleye, yellow
4
perch and black bass. Continued monitoring of the lake’s limnology and fish populations will help keep track of the impacts of establishment of round goby and guide management to ensure that the lake’s fisheries will continue to benefit the regional economy. Introduction Oneida Lake is the largest lake by area entirely within the borders of New York State and is second only to Lake Ontario in total angling effort. Connelly and Brown (2009) estimated 786,000 angler hours/year on Oneida Lake, compared to 1.3 million/year on Lake Ontario. Effort estimates conducted by the Cornell Biological Field Station have produced annual levels of open water effort in excess of 180,000 boat hours in every year since 2010 (see below). Angling on Oneida Lake generates revenues of over 12 million dollars annually, and as such represents an important resource both locally and across the state (Connelly and Brown 2009). Traditionally, walleye Sander vitreus has been the primary focus of the Oneida Lake fishery, with yellow perch Perca flavescens and black bass (smallmouth bass Micropterus dolomieu and largemouth bass M. salmoides) also providing popular fisheries. The walleye population is intensively managed on Oneida Lake, including annual stockings of 150 million walleye fry, management of double-crested cormorants Phalacrocorax auritus, and angling regulations that have been imposed and relaxed with the goals of retaining both a high walleye yield and a yellow perch population capable of providing forage for walleye and larger fish attractive to anglers (Forney 1980). Angling regulations are based on intensive monitoring of the walleye and yellow perch populations and predicted walleye recruitment. Oneida Lake has been the subject of research by the Cornell Biological Field Station (CBFS) since its establishment in 1956. Work on Oneida Lake is an important part of the collaboration between Cornell’s Department of Natural Resources and the New York State Department of Environmental Conservation’s Bureau of Fisheries (NYSDEC). Research and monitoring on Oneida Lake is designed to encompass a range of trophic levels, from nutrients to fish and anglers, and these data are used to improve our understanding of the interactions between the ecosystem and the fishery in Oneida Lake. During the time span that data have been collected on Oneida Lake, a series of perturbations have resulted in fundamental changes in the lake and how it functions. Events that have resulted in demonstrable impacts on Oneida Lake’s dynamics include the Great Lakes Water Quality Agreement of 1972 (with amendments in 1983 and 1987), establishment of a nesting colony of double-crested cormorants in the 1980s, invasion by zebra mussels Dreissena polymorpha in the early 1990s (followed by the invasion by quagga mussels Dreissena rostriformis bugensis in the mid-2000s), and most recently invasion by round goby Neogobius melanostomus in the mid-2010s. This report provides a
5
summary of the standard monitoring data for 2017, along with an appendix with standardized methods for data collection and standard data tables. Over the course of our sampling on Oneida Lake, the occurrence of shifts in conditions over the long-term is well-documented, particularly decreased productivity and increased water clarity resulting from international water quality agreements and establishment of dreissenid mussels. In our recent reports, we have presented analyses of trends in lake biology over more recent years (2000-2016) to assess whether the lake continues to demonstrate changing trends in physical and biological features (Jackson et al. 2017). The shorter time frame for trend analyses was adopted with the intention of allowing detection of responses to recent perturbations free from the influence of more well-established shifts in lake dynamics associated with water quality improvements, double-crested cormorants and the initial colonization of the lake by zebra mussels. Here we use the results of change point analyses on limnological parameters (Appendix 1) to explore what time frame is most appropriate for identifying ongoing changes. Change point analyses identified a shift in soluble reactive phosphorus (SRP) in 1989 correlated with regional water quality plans, shifts in chlorophyll-a and water clarity in the years 1992 and 1993 which are correlated with establishment of zebra mussels, and shifts in Daphnia and total zooplankton biomass in 2007 and 2008 which correlate with the initiation of replacement of zebra mussels by quagga mussels. We therefore have amended our trend analyses to cover the period 2007-present in order to ensure sensitivity to ongoing changes. This period spans the period during which quagga mussels became the dominant dreissenid and also is characterized by ongoing cormorant management efforts and consistent walleye harvest regulations. With this approach, we hope to be able to separate documented trends that were a result of past changes from those that may suggest a response to new changes (e.g., round goby). Several of our data sets are available on the web through the Knowledge Network for Biocomplexity (http://knb.ecoinformatics.org/index.jsp) a data repository that is also a member node of the National Science Foundation DataONE portal (www.dataone.org). A single search for “Oneida Lake” as of April 2018 showed the ten available data sets, which include limnology, phytoplankton, zooplankton, benthos, mussels, ice cover, walleye, yellow perch, gill net and trawl catches. In early 2016, we published a book with summaries and analyses of many of the long-term data sets from our work on Oneida Lake (Rudstam et al. 2016). Collection of data to maintain the long-term database and directed studies aimed at understanding the effects of ecosystem change on the fish populations were continued in 2017 by the Department of Natural Resources of Cornell University as part of the activities of CBFS. Funding was provided by NYSDEC through the Federal Aid in Sport Fish Restoration Program and from the CBFS endowment. Additional support for limnological sampling was made available from a Hatch grant from Cornell University.
6
Methods Most of the data presented in this report result from continuation of long-term sampling protocols established at the outset of the CBFS studies on Oneida Lake. Detailed methods for both limnological and fisheries surveys can be found in Appendix 2. In instances where models have been developed to estimate some index parameters or newly established or modified methods are employed, they are presented in association with the results utilizing them.
RESULTS - ONEIDA LAKE IN 2017 Limnology For the winter of 2016-2017, first complete lake ice cover was observed on December 20. Lake ice broke up and refroze four times between December 20 and March 11, with the final frozen period extending from March 11 to March 31. The total complete ice duration of 63 days was the fourth shortest over our period of record, with only 2001-2002 (no ice cover) and 2011-2012 (21 days) and 2015-2016 (57 days) being shorter (Appendix Table A1). Long-term records of the lake’s winter ice season exhibit trends towards being shorter, and are consistent with warming. Date of first complete ice cover since 1976 is trending later (linear regression: df = 39; F-ratio = 3.84; r2 = 0.09; p = 0.06). Ice off date exhibits no trend (linear regression: df = 40; F-ratio = 1.06; r2 = 0.03; p = 0.31). Recent patterns of multiple freeze and break-up events have contributed to a significant decline in the number of days the lake has complete ice cover (Figure 1; linear regression: df = 40; F-ratio = 5.77; r2 = 0.13; p = 0.02).
Figure 1. Days of complete ice cover, Oneida Lake, New York, 1975-2017.
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
1975 1980 1985 1990 1995 2000 2005 2010 2015
Days o
f co
mp
lete
ice c
ov
er
Year
7
June-August water temperatures at 2 m depth averaged 72.3 oF (22.4 oC) in 2017, slightly above the long-term average of 71.6 oF (22.0°C) (Appendix Table A1). June-August water temperatures have exceeded the long-term average in every year since 2009. Summer water temperature trends are consistent with patterns observed regionally and globally. Average June-August water temperatures since 1975 exhibit a strong and significant increase (Figure 2; linear regression: df = 41; F-ratio = 31.56; r2 = 0.43; p < 0.0001). On average over the period 1975-2017, summer water temperatures have increased at a rate of 0.1° F/year (0.06°C).
Figure 2. Average daily water temperature at 2 m depth at the Shackelton Point station from June 1 to August 31, Oneida Lake, New York, 1975-2017.
While analyses for most of the report focus on the 2007-2017 time period, for limnological parameters we provide an additional comparison covering the period 1993-2017 (Table 1). The longer period represents the period after zebra mussels established, while the more recent period covers replacement of zebra mussels by quagga mussels. Mean annual Secchi depth in 2017 was 3.1 m, just below the long term average of 3.3 m (Figure 3, Appendix Table A1). The mean chlorophyll-a concentration of 5.8 µg/L, was the highest observed since 2007, but still well below the long term average of 6.7 µg/L (Figure 4). High water clarity and low chlorophyll-a concentrations have been typical since 1992, when zebra mussels became abundant in Oneida Lake (Zhu et al. 2006). In approximately 2005, quagga mussels entered Oneida Lake, and by 2008 began displacing zebra mussels.
8
Initially, displacement of zebra mussels by quagga mussels resulted in an increase in total dreissenid biomass at sites historically sampled for zebra mussels, but dreissenid biomass since 2015 has been comparable to what it was prior to quagga mussel establishment. Quagga mussels have colonized softer substrates considered uncolonizable by zebra mussels, creating potential for increases in dreissenid biomass lakewide (Hetherington et al. In review). However, predation on smaller mussels by round goby has impacted total densities of mussels, suggesting that round goby may have potential to reduce mussel populations through reductions in survival to larger sizes (CBFS unpublished data). Chlorophyll-a concentrations declined from 1993-2017, but have not exhibited a significant change since the arrival of quagga mussels. Water clarity has remained stable over the same time period (Table 1). SRP concentrations in 2017 were below the long term average at 7.1 µg/L, and total phosphorus (TP) was also close to the long-term average (Figure 5). Neither SRP nor TP concentrations show significant trends over the period since 2007 when quagga mussels became dominant (Table 1). Following water quality improvement efforts in the 1970s and 1980s and establishment of dreissenids, the productivity of the lake is overall typical of a mesotrophic lake (Carlson 1977; Wetzel 2001; Idrisi et al. 2001; Zhu et al. 2006).
Figure 3. Time trends in Secchi disc measurements in Oneida Lake, New York, 1975-2017.
0.0
1.0
2.0
3.0
4.0
5.0
6.0
1975 1980 1985 1990 1995 2000 2005 2010 2015
Se
cc
hi d
ep
th (
m)
Year
9
Figure 4. Time trends in Chl-a concentration in Oneida Lake, New York, 1975-2017.
Figure 5. Time trends in phosphorus concentration in Oneida Lake, New York, 1975-2017.
Total zooplankton and Daphnia spp. biomass both remained well below their long-term averages, consistent with recent years (Figure 6; Appendix Table A1). Analyses of the years since 2007 do not indicate significant trends in Daphnia
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
1975 1980 1985 1990 1995 2000 2005 2010 2015
Ch
l a
(u
g/L
)
Year
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020Ph
osp
ho
rus c
on
cen
trati
on
(u
g/L
)
Year
SRP
TP
10
spp. biomass or total zooplankton biomass (Table 1). Whereas Daphnia spp. have typically accounted for 55-65% of total zooplankton biomass over much of our data series, in recent years their contribution has fallen below 35%, and in 2017 was at 21%, the lowest observed in our period of record (Figure 7). The decline in representation of Daphnia spp in the Oneida Lake zooplankton community since 2007 is significant (Table 1). Observed reductions in Daphnia spp. and the shift to a more copepod dominated zooplankton community is of concern, as Daphnia spp. are a critical food for supporting growth of early life stages of fish. We will continue to monitor zooplankton levels and look for changes in growth rates of planktivorous fishes.
Figure 6. Time trends in total zooplankton biomass (upper line, with squares) and Daphnia biomass (lower line, with circles) in Oneida Lake, New York, 1975-2017.
0
50
100
150
200
250
300
350
1975 1980 1985 1990 1995 2000 2005 2010 2015
Zo
op
lan
kto
n b
iom
as
s (
ug
/L)
Year
11
Figure 7. Time trend in percentage of total zooplankton biomass represented by Daphnia spp. in Oneida Lake, New York, 1975-2017.
Our analyses of long-term trends in physical and limnological features in Oneida Lake show a lake at a lower state of productivity than when limnological studies were initiated in the 1970s. Reduced nutrient loading, combined with grazing by introduced dreissenid mussels, have led to reduced chlorophyll-a concentrations as compared to the 1970s and 1980s. We are monitoring whether the expansion of the quagga mussel in Oneida Lake will further increase grazing rates, reduce algal concentrations and increase water clarity, but so far we have not seen responses in these parameters. While Oneida Lake was once classified as a eutrophic lake, it now possesses characteristics of a mesotrophic lake. However, summer bluegreen algal blooms still occur, sometimes causing beach closings, attracting media attention. Bluegreen blooms were relatively more abundant in 2017, but bloom production is variable across years (CBFS database). Ongoing studies are directed at determining if bluegreen blooms have increased in recent years, as perceived by much of the public, or whether the perception simply reflects more coverage in the media. While Daphnia spp. biomass did not initially decrease as a result of decreased productivity or establishment of zebra mussels, densities have been below the long-term average since 2005, and this may have implications for production of fish. Hemimysis anomala was found in Oneida Lake in 2009 (Brooking et al. 2010), but no Hemimysis have been observed in fish diets since 2013. To date, there is no indication that Hemimysis will become abundant on Oneida Lake. However, Hemimysis have been documented to have expanded east from the lake into the Erie Canal (Brown et al. 2014).
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
1975 1980 1985 1990 1995 2000 2005 2010 2015
Pe
rce
nta
ge
of
tota
l zo
op
lan
kto
n
rep
res
en
ted
by D
ap
hn
ia
Year
12
Water temperatures in recent years continue to be above long-term averages and ice duration has been below average in ten years since the winter of 2000-2001. These climate-related physical conditions show significant long-term trends. Increases in water temperatures are predicted to increase periods of summer stratification, resulting in anoxic bottom waters and increased phosphorus release from the sediments (Hetherington et al. 2015). Reduced duration of safe ice for anglers will impact winter fishing effort and may reduce harvest of yellow perch, given that historically about 50% of annual yellow perch harvest has been through the ice.
Table 1. Results of change point analysis and regression on Oneida Lake lower trophic level parameters. Year change point detected refers to the first year following the change. Only level 1 changes are reported. Limnological variables are averages of weekly whole water column samples from May through October (Appendix Table A1). Regressions are presented for the time periods 1993-2017 (post- zebra mussel), and 2007-2017 (post- quagga mussel). Significant trends are indicated by bold type. Change point analyses were for time period 1975 to present.
Regression
Parameter 1993 - 2017 2007 - 2017
Secchi depth (m) p=0.77, r2=0.004, n=25 p=0.42, r
2=0.07, n=11
Chlorophyll-a (ug/L) (-) p=0.0468, r2=0.16, n=25 p=0.96, r
2=0.0003, n=11
SRP (ug/L) (+) p=0.0263, r2=0.2, n=24 p=0.84, r
2=0.005, n=10
TP (ug/L) (+) p=0.0178, r2=0.22, n=25 p=0.73, r
2=0.014, n=11
Zooplankton biomass (ug/L) p=0.12, r2=0.06, n=25 p=0.40, r
2=0.08, n=11
Daphnia biomass (ug/L) (-) p<0.0001, r2=0.53, n=25 p=0.37, r
2=0.09, n=11
Daphnia percent biomass (-) p<0.0001, r2=0.69, n=25 (-) p=0.0145, r
2=0.50, n=11
Change point analysis
Average prior Average since
Year(s) change Confidence to change change
Parameter point detected Interval point point
Secchi depth (m) 1993 (1992, 1995) 2.6 3.7
Chlorophyll-a (ug/L) 1992 (1989, 1993) 9.4 5.7
SRP (ug/L) 1989 (1981, 1989) 15.1 5.7
TP (ug/L) none n/a n/a n/a
Zooplankton biomass (ug/L) 2008 (2002, 2008) 220 130
Daphnia biomass (ug/L) 2007 (2001, 2007) 100 47
Daphnia percent biomass 2008 (1993, 2008) 45 28
Fish Community Changes (Gill net) Gill net catches in Oneida Lake are typically dominated by yellow perch, white perch and walleye. These three species have represented over 70% of the total gill net catch in every year since sampling started in 1957. While white perch
13
were the most frequently captured species in gill nets in 2015, they declined in representation to only 9.6% of the total catch in 2016. In 2017, white perch increased to 20% of total gill net catch. Walleye were the most common species in 2017 gill nets, representing 37.2% of catch, followed by yellow perch at 36.0% (Figure 8). Representation of walleye as percentage of total catch was the highest since 1958, and reflects, in part, large 2010 and 2014 year classes. Total number of fish caught in the standard gill nets in 2017 was 1,281, well below the long-term average of 1,940. More common species such as smallmouth bass, gizzard shad Dorosoma cepedianum, white sucker Catastomus commersonii and channel catfish Ictalurus punctatus were captured in numbers within the range of recent years. Freshwater drum Aplodinotus grunniens catches were the lowest observed since 1983.
Figure 8. Proportion of three major fish species in standard gill net sets in Oneida Lake, New York, 1957-2017. Walleye We assess the walleye population in Oneida Lake at several life stages: as larvae (lengths of 9 to 13 mm) with Miller high-speed samplers; as juveniles in the spring, summer and fall with bottom trawls; and as juveniles, sub-adults and adults with gill nets in the summer, supported with mark-recapture for adult fish (age-4 and older) at regular intervals (currently every 3 years, last conducted in 2016).
Abundance of adult walleye (age-4 and older) estimated from the 2016 mark-recapture was 429,200 (Figure 9; Appendix Table A2). In years between mark-recaptures, we estimate walleye numbers using mortality estimates between the
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
1957 1962 1967 1972 1978 1983 1988 1993 1998 2003 2008 2013
Perc
en
t o
f to
tal g
ill n
et
catc
h
Year
Other+Drum
White perch
Yellow perch
Walleye
14
most recent two mark-recaptures. Annual mortality between 2013 and 2016 was estimated at 20%, which would result in a 2017 walleye population estimate of 451,300 fish. However, 2016 walleye harvest declined more than 60% from previous years, presumably due to the establishment of round goby, so annual mortality during 2016 may be well below the 20% estimated at higher harvest levels. If reduced angler harvest resulted in an annual mortality rate of 10%, we would estimate the 2017 adult walleye population at 508,600 fish (Figure 9). The recruitment of the relatively large 2010 year class into the fishery resulted in an increase in the adult walleye population starting in 2014. We estimated that 36% of the fish in the 2016 population were from the 2010 year class. Catches of age-3 walleye from the 2014 year class in 2017 gill net sampling were similar to the age-3 catch of the 2010 year class in 2013, and with a 20% mortality estimate could add 171,000 fish to the fishery in 2018 (192,000 at 10% mortality). Over the full span of our data series, the adult walleye population has exhibited a significant decrease, but has shown no significant trend since 2007 (Table 2).
Figure 9. Density of adult walleye in Oneida Lake, New York, 1957-2017. X denotes estimated density under a 10% annual mortality scenario.
0
10
20
30
40
50
60
1957 1961 1965 1969 1973 1977 1981 1985 1989 1993 1997 2001 2005 2009 2013 2017
Ag
e 4
+ w
alle
ye
den
sit
y (
#/h
a)
Year
x
15
º Table 2. Recent trends (2007-2017) in measurements of walleye abundance in Oneida Lake, New York. Significance levels are based on simple linear regression. Data are presented in Appendix Tables A2 and A4. Trend indicates direction (+ or -) over time, with r2 and p reported for regressions. Significant trends indicated by bold type.
Variable
2007-2017
Trend r2 p
Adult (age 4+) population size + 0.03 0.58 Larval density - 0.07 0.45 October 1 age-0 density + 0.0002 0.97 Spring age-1 density + 0.03 0.61 In past years, we predicted future walleye recruitment using the average of catches in trawls and gill nets of age-1 and age-2 walleye (Appendix Table A2). We estimate density of age-1 to 3 walleye from the average of the estimates from the trawl and the gill net using the age and gear specific catchabilities derived by Irwin et al. (2008) and predict future recruitment using the catchability-adjusted catches of age-1 and age-2 walleye (see Appendix Table A2). In the absence of a significant cormorant effect, the “best” model (determined using the Akaike Information Criterion) given the data for year classes 1957- 2004 includes the natural logarithm of age-1 and age-2 walleye abundance:
Ln(Age-4) = -0.059 + 0.239 Ln(Age-1) + 0.593 Ln(Age-2) (1) where Age-1, Age-2 and Age-4 are densities of walleye age classes in fish/ha. Based on these relationships, our prediction for recruitment to age-4 in 2018 of walleye produced by the 2014 year class is 138,300 fish. Recruitment to age-4 of walleye produced by the 2015 year class in 2019 is predicted to be 166,000 fish.
Adult walleye length-at-age is determined from fish collected in fall (mid-September and later). For much of the data series, samples were primarily collected by bottom trawl, but electrofishing samples were integrated into length-at-age estimates during mark-recapture years and during all years since 2010. Aging was conducted using scales through 2009, after which otoliths have been used. A significant increasing trend in length at age 4 has been observed over the period 2007-2017, (linear regression: df = 9; F-ratio = 8.14; r2 = 0.48; p = 0.02) (Figure 10). Length at age 5 has shown a marginally significant increase over this period (df = 9; F-ratio = 4.39; r2 = 0.33; p = 0.07) and length at age-6 exhibits a significant increase (df = 9; F-ratio = 13.37; r2 = 0.58; p = 0.005). Walleye growth has historically been dependent on availability of yellow perch,
16
with gizzard shad and white perch providing additional forage in recent decades. The increase in the long-term length-at-age for age-6 (other ages showed increasing but non-significant trends) walleye may be a result of the increased availability of food resulting from relatively regular production of summer-hatched gizzard shad year classes, which began in the late 1980s and early 1990s (He et al. 2005). Recent increases in length at age may be associated with the establishment of round goby (see below). Establishment of round gobies in Lake Erie did not result in changes in walleye growth (Johnson et al. 2005), and improved condition of only the largest walleye (>550 mm) was observed in Lake Ontario (Crane et al. 2015). Lake Erie walleye tend to rely more heavily on pelagic prey (Johnson et al. 2005) than in Oneida Lake where walleye have traditionally depended on demersal yellow perch, so it is possible round goby could play a more significant role in walleye diets in Oneida Lake than observed in Lake Erie. We initiated a special study in 2016 to assess the importance of round goby in the diets of Oneida Lake piscivores, and the results of the first two years are presented in Appendix 2).
Figure 10. Observed length-at-age for ages 4-6 walleye from fall trawls and fall electrofishing in Oneida Lake, 1961-2017.
17
The OFCS stocked 173 million walleye fry between April 27 and May 3, 2017. The CBFS Miller sampler estimate of larval walleye density is conducted together with our first estimate of yellow perch larvae (when yellow perch average 8 mm length). In a subset of past years, walleye were assessed earlier, when average walleye lengths were approximately 9 mm (9.4 mm, range 9.0-10.2 mm, N=18). In years when both the 9 mm survey and the yellow perch 8 mm survey were conducted, the larval walleye estimates from the two surveys were correlated (r2 = 0.58, p = 0.01, N=10). With one outlier removed (2002, when few stocked walleye larvae survived a cold period after stocking), the correlation improves (r2 = 0.88, p = 0.0002, N=9). The equation is
WDYP = 203.6 + 0.722 WD9mm (2) where WDYP is walleye density at the 8 mm yellow perch survey and WD9mm is walleye density at the 9 mm survey, both in fish/ha. Our walleye larval index (Appendix Table A4) is the number of walleye larvae at the time of the 8 mm yellow perch survey, either measured directly or calculated from the 9 mm survey with this equation (years 1966, 67, 69, 99, 2000, 03, 04). The walleye larval abundance in 2017 was 581 fish/ha, below the long-term average of 1,552 larvae/ha (31 years, 1966 – 2017; Figure 11). There is no trend in larval walleye abundance since 2007 (Table 2).
Figure 11. Time trends in density of larval walleye, Oneida Lake, New York, 1961-2017.
Age-0 walleye are monitored with bottom trawl surveys at 10 standard stations 3 out of every 4 weeks from July through October. Catch per unit effort is translated to density in fish/ha assuming that each trawl samples an area of 0.1
18
ha and there is no avoidance by young fish. The 2017 age-0 fall walleye density estimate was 5.8 fish/ha on October 1. Average length on October 1 was 149 mm. Age-0 density in 2017 was well below the long-term average of 29.1 fish/ha (Figure 12, Appendix Table A4). Since 2007, there is no significant trend in fall age-0 walleye density (Table 2). During recent years, poor walleye year classes are common, and “good” years represent much smaller year classes than observed prior to the 1990s when zebra mussels first established in the lake. The three largest year classes since 2000 (as measured by trawling) ranged from 14.3-19.2 fish/ha, whereas catches of 30 or more fish/ha were occurring at least every five years prior to 1992 (Figure 12). The 2016 walleye year class density on October 1 was the third largest since 2005 (Figure 12), and captures of yearlings from this year class in May 2017 trawling were also the third highest since 2005 (Figure 13). Gill net catches of age-1 walleye from the 2016 year class were the highest since 1992, and summer trawl catches the highest since 1991, so early signs are that the 2016 year class of walleye could produce a boost to adult walleye numbers in 2020. Densities of yearling walleye in the spring show no declining trend since 2007 (Table 2).
Figure 12. Time trends in density of age-0 walleye on October 1 based on bottom trawls, Oneida Lake, New York, 1961-2017.
19
Figure 13. Time trends in density of yearling walleye in May based on bottom trawls, Oneida Lake, New York, 1961-2017.
The adult walleye population in Oneida Lake declined through the 1990s, and the current population has remained below 500,000 fish despite aggressive cormorant management and a daily harvest limit of three fish. The 2017 adult walleye population estimate of 451,300 fish is similar to levels observed over the past 10 years, despite recruitment of a relatively large 2010 year class into the fishery. The 2014 year class looks to have potential to add over 150,000 fish to the adult population, which may push numbers over 500,000 fish for the first time since 1992 (but note that if mortality rates have decreased substantially due to reduced harvest, the population may already be at 500,000). Survival of age-0 walleye is low compared to years prior to establishment of zebra mussels, with higher mortality between the larval and demersal stages. Reduced first year survival may be attributable to higher predation mortality experienced as a result of clearer water following establishment of zebra mussels. Similarly, reduced production of age-0 yellow perch (see below) may increase predation pressure on fingerling walleye. In addition, increasing numbers of littoral predators such as smallmouth bass and largemouth bass may increase competition for forage. Nonetheless, even under the current levels of recruitment and an adult population consistently below historic levels, the walleye fishery has been sustainable. With round goby now established in the lake (see below) we have seen early signs of declining walleye catch and harvest rates, and will need to assess if this might allow for sustained walleye population growth. If harvest rates decrease substantially, it is likely that the walleye population could increase above current levels.
20
Yellow perch Adult yellow perch numbers are estimated from the catches in standard gill nets adjusted for estimates of catchability (Irwin 2008). Catches of adult yellow perch in 2017 produced a population estimate of 1,146,800, about average for population sizes estimated since 2000 (Figure 14, Appendix Table A5). Catches over the last three summers reflect recovery from a population low observed in 2014. Long-term trends show a significant decline in adult yellow perch population size, but no trend is detectable since 2007, suggesting a more or less stable, but smaller population than was present in the lake in the1960s-1980s (Table 3).
Table 3. Recent trends (2007-2017) in measurements of yellow perch abundance at various stages in Oneida Lake, New York. Significance levels are based on simple linear regression. Data are presented in Appendix Tables A5. Trend indicates direction (+ or -) over time, with r2 and p reported for regressions. Significant trends indicated in bold type.
2007-2017
Variable Trend
r2
p
Adult (age 3+) population size - 0.02 0.67 Larval density - 0.54 0.01 October 15 age-0 density - 0.20 0.17 October 15 mean length + 0.05 0.54 Spring age-1 density + 0.02 0.70 Summer age-1 density + 0.43 0.04
We measure the abundance of yellow perch at the larval stage (two surveys - 8 and 18 mm), and as juveniles in bottom trawls through the summer, and again as yearlings in trawls centered on May 1 (Appendix Table A6). We use the decline in catches in the bottom trawl to estimate age-0 yellow perch abundance on October 15 (or take the average of the three October trawl samples in years where summer catches do not exhibit a significant decline). Larval yellow perch density in 2017 was 18,665/ha, well below the long term average of 67,300 (Figure 15). Fall density of age-0 yellow perch was 518/ha, again below the average of 993 (Figure 16). Spring yearling catches of yellow perch from the 2016 year class indicated a density of 435/ha, the highest observed since 2001 (Figure 17). Long-term numbers show that the yellow perch population has exhibited a significant decline in larval production, fall age-0 densities and summer catches of age-1 fish (Irwin et al. 2009; Rudstam et al. 2016)). As with walleye, the last decade has shown some moderation of the declining trends observed over the long-term, with no significant trends in abundance for fall age-
21
0 density or spring age-1 density, but significant declines in larval and summer age-0 densities (Table 3). The current level of annual production of young yellow perch does not appear sufficient to allow a substantial increase in the adult population. In as much as yellow perch still represent the primary forage for adult walleye prior to gizzard shad recruiting to their diets in late summer and fall, mortality of age-0 yellow perch may remain high. Establishment of round goby (see below) may provide an alternate forage for walleye and could ultimately benefit yellow perch recruitment, a scenario supported by the good survival of the 2016 yellow perch year class from larvae to age-1.
Figure 14. Time trends in age 3+ yellow perch densities (#/ha), Oneida Lake, New York, 1961-2017.
22
Figure 15. Time trends in larval yellow perch densities (#/ha), Oneida Lake, New York, 1961-2017.
Figure 16. Time trends in fall age-0 yellow perch densities (#/ha), Oneida Lake, New York, 1961-2017.
23
Figure 17. Time trends in spring age-1 yellow perch densities (#/ha), Oneida Lake, New York, 1961-2017.
White perch Based on gill net catches, the white perch population in Oneida Lake increased sharply through the late 1990s and early 2000s (Figure 18). White perch catches in gill nets exceeded yellow perch in 2007, 2009-2011, and 2015 (Appendix Table A7). Recruitment is variable, but the white perch recruitment index is suggestive of successful year classes at least once every three years from the early 1990s through 2004, but only one large year class has been observed since 2005 (Figures 18 and 19, Appendix Table A7). Catches of white perch in the 2017 gill nets rebounded from the low observed in 2016, but were still the second lowest since 2000. We have seen a significant decline in adult catches in gill nets since 2007 (linear regression: df = 9; F-ratio = 10.93; r2 = 0.55; p = 0.009). There has been no trend in age-0 white perch catches in bottom trawls since 2007 (linear regression: df = 9; F-ratio = 1.01; r2 = 0.10; p = 0.34). After five years of low production of age-0 white perch, the 2017 year class produced the highest catches observed since 2011. White perch diets are similar to yellow perch, although they appear to feed more on larval and juvenile fish. Increases in white perch could therefore be part of the explanation for increased early mortality of larval percids. Given the relatively small year classes of white perch produced most years since 2005, it is reasonable to expect a decline in adult numbers over the next several years after the 2012 year class works through,
24
which may benefit survival of larval percids (see VanDeValk et al. 2016 for assessment of white perch recruitment in Oneida Lake).
Figure 18. Time trends in gill net catches of white perch, Oneida Lake, New York, 1961-2017. .
Figure 19. Time trends in age-0 white perch densities (#/ha), Oneida Lake, New York, 1961-2017.
25
Smallmouth bass Smallmouth bass have become an important sport fish in Oneida Lake, and can also have large effects on littoral fish communities when abundant (VanderZanden et al. 1999; Lepak et al. 2006). Opening of a spring catch-and-release fishery in 2007 was met with some concern about potential impacts on young-of-year bass production, but our studies of potential impacts of spring fishing showed catches of age-0 smallmouth bass over the first six years following the opening of spring fishing were significantly higher than those from the six years preceding the regulation change (Jackson et al. 2015). Following a high catch of young-of-year smallmouth bass in 2014, the trawl catch in 2015 was among the lowest observed since 2000, only a single young-of-year was caught in trawls in 2016 (Figure 20). The total trawl catch of age-0 smallmouth bass in 2017 was 39 fish. Catches of age-0 smallmouth bass in fyke nets in 2017 were comparable to those observed in 2016 (Figure 21). Age-0 smallmouth bass diets have reflected a transition to newly available round goby, and growth of age-0 smallmouth bass has been higher in post-goby years (Pakzad, Cornell Honor’s Thesis, in prep). It is possible that a shift to foraging on round goby has resulted in a habitat shift by age-0 smallmouth bass. (see Appendix 2). Continued comparisons of trawl and fyke net catches will be necessary to determine if a combination of increased growth and potential habitat shifts may change the reliability of the trawl as an age-0 smallmouth bass index. Catches of adult smallmouth bass in standard gill nets have been variable over recent years, but remain high relative to the 1970s and early 1980s. Catches in 2017 were the lowest observed sine 1996 (Figure 22). Spring electrofishing catches of adult smallmouth bass have been stable since 2012 (see below), so the 2017 gill net catch may simply reflect variability. Over the period 2007-2017, we have observed no trends in young-of-year or adult catches (young-of-year - df = 9; F-ratio = 2.68; r2 = 0.23; p = 0.14; adult - df = 9; F-ratio = 1.17; r2 = 0.12; p = 0.31)., Changes in lake conditions, likely both clearer water facilitating foraging and warmer summer water temperatures contributing to increased year class success, have allowed the smallmouth bass population to reach a higher level than observed in the 1960s-1980s. We anticipate they will continue to be an abundant and important species in the lake’s ecology and fisheries, and it is possible that round goby could enhance smallmouth bass populations. Lake Erie smallmouth bass showed improved condition at all ages after establishment of round goby (Steinhart et al. 2004a; Johnson et al. 2005). However, round goby do pose a potential threat to smallmouth bass as egg predators, and we will continue to monitor year class production to determine if there is a negative impact of round goby on smallmouth bass recruitment (Steinhart et al. 2004b).
26
Figure 20. Time trends of age-0 smallmouth bass catches in bottom trawls, Oneida Lake, New York, 1960-2017.
Figure 21. Time trends of age-0 smallmouth bass catches in fall fyke net samples (combined catch from ¼ in and ½ in mesh nets), Oneida Lake, New York, 2008-2017.
27
Figure 22. Time trends of adult smallmouth bass catches in gill nets, Oneida Lake, New York, 1960-2017.
Open water forage fish (gizzard shad and emerald shiner) Pelagic fish biomass is estimated in the fall using hydroacoustics with supporting small-mesh gill nets and mid-water trawling. Total pelagic fish density in 2017 was estimated to be 10,730 fish/ha (long-term average 11,476) of which 4,312 fish/ha were age-0 emerald shiner Notropis atherinoides, 1,644/ha were age-1 and older emerald shiner, and 4,527/ha were age-0 gizzard shad (Figure 23; Appendix Table A8). Biomass was estimated at 33.8 kg/ha (long-term average 27.6), of which 24.9 kg/ha was gizzard shad and 7.2 kg/ha was age-0 and adult emerald shiner. Pelagic fish surveys indicated alewife Alosa pseudoharengus were present in the lake in 2017 at a density of 247 fish/ha (this density was only exceeded in 1996). Alewife have been captured in pelagic fish surveys on Oneida Lake before (1996, 2003, 2013), but no resident population has yet established. Gizzard shad were the most common diet item of walleye in October in 2017, accounting for 71% of identifiable diet items (Appendix Table A3). Walleye diets included 25% round goby in 2017, no other species exceeded 2% (see Appendix A3). Neither observed density nor biomass of gizzard shad show a significant trend over the period 2007-2017 (density - df = 9,
28
F-ratio = 0.39; r2 = 0.04, p = 0.55; biomass – df = 9, F-ratio = 0.02; r2 = 0.002, p = 0.90). Similarly, no trends have been observed in emerald shiner density or abundance (young-of-year density - df = 9; F-ratio = 0.05; r2 = 0.005; p = 0.83; young-of-year biomass -biomass – df = 9, F-ratio = 0.001; r2 = 0.0002; p = 0.97; adult density - df = 9; F-ratio = 0.53; r2 = 0.06; p = 0.48; adult biomass – df = 9, F-ratio = 0.21; r2 = 0.02; p = 0.65).
Figure 23. Time trends in biomass of open water forage fish from hydroacoustic estimates, Oneida Lake, New York, 1993-2017.
Lake sturgeon May lake sturgeon Acipenser fulvescens catches from directed sampling with large mesh gill nets in 2017 declined more than 60% from 2016 (Appendix Table A9). The May gill net catch in 2017 was 0.11/hr and June catch was 0.05/hr (comparable to 2016). Prior to a stocking of 500 fish in 2014 (which have continued annually), no stockings had been conducted since 2004, so catches might be expected to increase once those fish recruit into the gear. Length and weight data from collected lake sturgeon still indicate a population with fish in excellent condition that are growing at high rates. Our records now include
29
evidence of successful natural reproduction and survival of some young from three years, 2011, 2012, and 2014. There is every reason to believe that some spawning now takes place annually in Fish Creek and possibly other tributaries to the lake.
Double-crested cormorants Double-crested cormorant management evolved through the 2000s, with seasonal management of nesting success and fall migrants during the early 2000s; near complete removal of cormorants from the lake with no successful nesting allowed from 2004-2009; nest control and fall hazing from 2010-2013; and full season hazing 2014-2017. Management since 2010 has been conducted by NYSDEC following a period of management administered by the USDA Wildlife Services on Oneida Lake (2004-2009). Funding for the USDA program was lost prior to the 2010 season. The initial seasons after cessation of the USDA program saw low summer numbers of cormorants and no efforts to nest, but fall hazing was conducted by NYSDEC to push migrants off the lake. In 2013, summer cormorant counts and nesting efforts (12 nests produced chicks) exhibited increases and NYSDEC began implementing a full season hazing program in 2014 with a target goal of no more than 100 cormorants on the lake and no successful nesting. The NYSDEC conducted weekly counts beginning April 27, 2017 through October 2, 2017. The average weekly count over the season was 592 birds (as compared to 361 in 2016), due largely to high counts after the fall migration started. During the spring and summer (pre-fall migration, April through the end of July), counts averaged 166 birds (159 in 2016), and during the migration (August onwards) counts averaged 928 birds (636 in 2016). Analyses of diets were conducted on 220 birds collected from April 4 through October 2. Of the 1,606 diet items, 1,473 were identifiable to species. Of the identifiable items recovered from stomachs, 47% were gizzard shad, 16% were yellow perch, 15% emerald shiner, 12% round goby, and no other species accounted for more than 1.4% of observed diets. Twenty-one walleye were found in cormorant diets, accounting for less than 2% of identifiable food items. By weight, yellow perch made up 36% of cormorant diets, gizzard shad 20%, pumpkinseed Lepomis gibbosus 13% and walleye 11%, with no other species accounting for more than 5%. Our analyses of cormorant diets over a 15-year span have found positive selection for schooling, soft-bodied prey such as gizzard shad when they are available, so buffering of potential impacts on percids by fall migrating cormorants should be realized in years when gizzard shad reproduce successfully (DeBruyne et al. 2013). The 2015 cormorant diets were the first to show a substantial contribution of round goby, and in 2016 round gobies were the most common diet item. The importance of round goby declined in 2017, presumably due to a winter kill of adults (see below), but they were still the 4th most common prey consumed. Round goby may ultimately act as a buffer for more valued sport fishes. Evidence from other systems suggests that round goby could dominate cormorant diets when they become abundant in Oneida Lake (Johnson et al. 2010).
30
Round goby Round goby were confirmed in the Oneida River at the last barrier before Oneida Lake as early as 2010, but no confirmed reports came from within the lake until 2013, when anglers found them in yellow perch stomachs. No sampling by CBFS produced round gobies in 2013 and none were observed in fish diets. By late July 2014, round gobies began to show up in standard trawl samples and by mid-August were encountered regularly throughout the lake. Trawl catches in 2015 indicated that the round goby population was expanding. After a peak catch of 10 round goby in a trawl sample from 2014, catches peaked at 618 on September 15, 2015, 707 on September 19, 2016, and 940 on October 3, 2017 (Figure 24). Low catches of round goby in the spring trawl series (3 in 30 tows compared to 24 in 2016), combined with relative scarcity of round goby in spring diets of predators (see Appendix 2) suggest a die off of round goby in the winter of 2016-2017. Low catches of round goby in early summer trawl samples (as compared to 2016) were consistent with that conclusion (Figure 24). Nonetheless, surviving round goby were able to reproduce successfully in the summer of 2017 and produced fall young of year catches that matched or exceeded those from 2016. We saw no evidence of winter die offs in 2015 or 2016, so it is uncertain if the 2017 die off was an isolated event related to disease or some other factor or if temperatures in Oneida Lake will result in occasional winter die offs in the future. In 2017, round goby occurred in the diets of 33% of lake sturgeon examined, 12% of white perch diets, 29% of freshwater drum Aplodinotus grunniens, 4% of yellow perch, 3% of walleye, and 8% of smallmouth bass. Preliminary analyses suggest round goby may be having a negative impact on benthic invertebrate densities in Oneida Lake (CBFS unpublished data). Additionally, dreissenid mussel length-frequencies from 2015 to 2017 indicate reduced densities of mussels in the size range preferred by round goby, suggesting potential impacts of round goby on mussel recruitment to larger size classes. This may ultimately result in reduced mussel densities and biomass. If round goby follow the pattern observed in other systems, we would expect to see an expanding population over the next few years. Continued sampling should allow us to detect any responses in terms of fish diets, growth and angler catch rates. We should be well-positioned to detect any longer-term changes in the limnology of the lake and its fish community given both pelagic and littoral sampling have been underway for some time in advance of the arrival of round gobies. In Lake Erie, round goby were detected in the diets of all piscivorous species examined, but significant increases in growth were only documented for smallmouth bass, yellow perch and burbot (Steinhart et al. 2004; Johnson et al. 2005).
31
Figure 24. Catches of round gobies in standard bottom trawling in Oneida Lake, New York, 2014-2017.
Nearshore fish community Fall fyke nets Catches in fyke nets in 2017 produced similar results to past years, with a total of 27 species represented. Overall catches of most species were within the range of past years (Appendix Tables A10 and A11). Catches in the 1/4 in mesh nets averaged 187 fish/net-night, well within the range previously observed. Annual catches in this mesh are driven by production of young-of-year Lepomis, and 2017 produced a catch of 125/net night, about the middle of the range observed historically. Other commonly caught species were round goby at 29.4/net-night and young-of year yellow perch at 7.6/net-night. No other species were caught at rates higher than 3/net-night. Catches in the ½ in mesh nets averaged 70 fish/net-night, within the typical range since we began sampling with this gear in 2007. The most commonly caught species were age 1+ yellow perch at 23/net night, pumpkinseed at 14/net-night, bluegill at 11/net night, young-of-year white perch at 4/net-night and rock bass at 3/net-night. Catches of young-of-year largemouth bass in both meshes in 2016 indicated the second largest year class we’ve seen based on this gear, but the 2017 year class produced near-zero catches. Young-of-year smallmouth bass catches were well within the historic catch range.
32
The fyke nets continue to produce catches of littoral species not represented in the traditional gears used in our long-term studies (but spring electrofishing, initiated in 2011, now provides an additional littoral sample and annual beach seining was initiated in 2015). They have provided our only index of young-of-year largemouth bass production prior to the initiation of regular shoreline seining in 2015, and also show potential as an index for sunfish (Figure 25) and esocids. Fyke nets do not show potential as an index of adult bass, and an index of largemouth bass, in particular, requires shoreline electrofishing. Given that potential behavioral shifts in young-of-year smallmouth bass could compromise the reliability of our trawl index, fyke nets may also become our primary gear for indexing their production. Over 500 round goby were captured in fyke nets in both 2016 and 2017, and as the population grows we will assess the utility of fyke nets as a potential index gear for them.
Figure 25. Catch rate of centrarchids in ½” mesh fyke net samples, Oneida Lake, New York, 2007-2017.
Spring electrofishing In spring 2011, we initiated a shoreline electrofishing survey directed at centrarchids. Sampling is initiated when water temperatures reach 20ºC. Eight sites were selected to both proportionally represent typical shoreline habitats in the lake and achieve spatial coverage of both the north and south shores. Each site is comprised of an initial 15 minute all fish pick up followed by a 1 hour predator sample and concluded with another 15 minute all fish pick up. Spring centrarchid surveys are scheduled to be conducted 2 of every 3 years, with walleye mark-recapture years excluded.
33
As with past spring surveys, adult largemouth bass were captured at the highest rate among predators (Appendix A12a&b; Figure 26; adult walleye catch rate was higher than largemouth bass in 2015, but in all others years, largemouth bass produced the highest catches). The 2017 largemouth bass catch rate of 18.9/hr was the highest observed since the survey was initiated. Walleye were captured at the second highest rate among predators (9.2/hr), followed by chain pickerel (3.7/hr), smallmouth bass (3.8/hr), and longnose gar Lepisosteous osseus (3.4/hr). More detailed assessments of black bass samples from spring electrofishing can be found below.
Figure 26. Catch rate (#/hr) of predators captured by spring electrofishing in Oneida Lake, New York, 2011-2017. Age-1 and older yellow perch produced the highest catches in all fish runs (Appendix A12a&b; age-1: 64.3/hr; adult: 79.5/hr). As has been typical of past surveys, pumpkinseed were the most commonly captured sunfish (34.3/hr), followed by bluegill (23.8/hr), and rock bass (15.5/hr). Other commonly captured species included brown bullhead (20.0/hr), round goby (10.8/hr), and gizzard shad (7.5/hr).
34
Black bass population assessments Our spring electrofishing surveys are consistent with guidance for NYSDEC black bass surveys. Having now completed five years of surveys, we report below summaries of common population metrics for largemouth and smallmouth bass in Oneida Lake. Catch rates Adult largemouth bass catch rates (stock size and larger bass, weighted mean of all fish and predator runs) were consistently 2x or higher than those for smallmouth bass (Figure 27). Highest observed catch rate of adult largemouth bass was 18.4/hr observed in 2017, while the highest catch rate for smallmouth bass was 4.3/hr in 2016. Catches of both bass species were variable across years, with largemouth bass varying by a factor of 2.3 over the five years of our surveys, while smallmouth bass catches varied by a factor of 2.4. There were no statistically significant differences in catch rates for either species among years (Wilcoxon/Kruskal-Wallis test, largemouth bass – df = 4, X2 = 3.13, p = 0.54; smallmouth bass – df = 4, X2 = 5.73, p = 0.22). Catch rates for yearling largemouth bass fell into the lower third of those observed statewide, but for all other size classes catches fell within the middle third of observed values in New York (Table 4, Brooking et al. 2018). Catches of yearling and preferred smallmouth bass fell in the middle third of state values, while catches of stock and quality fish fell in the lower third (Table 6, Brooking et al. 2018).
Figure 27. Spring electrofishing catch rates (±1 SE) of age-2 and older smallmouth and largemouth bass in Oneida Lake, New York, 2011-2017.
35
Catch rates of yearling largemouth bass and smallmouth bass (all fish runs only, yearling defined as fish ≤ 150 mm) exhibited different patterns than adult catch rates. Catch rates of yearling smallmouth bass were higher than those observed for largemouth bass in three of five years (Figure 28). Yearling smallmouth bass catch rate varied among years by a factor of eight while yearling largemouth bass catch rates varied by a factor of five. There were no statistically significant differences in catch rates for yearling largemouth bass among years, but differences were detected for yearling smallmouth bass (Wilcoxon/Kruskal-Wallis test, largemouth bass – df = 4, X2 = 3.53, p = .47; smallmouth bass – df = 4, X2 = 11.51, p = 0.02).
Figure 28. Spring electrofishing catch rates (±1 SE) of yearling smallmouth and largemouth bass in Oneida Lake, New York, 2011-2017. Yearling black bass catch rates produced a similar picture of relative black bass species abundance to that from age-0 fyke net catches. Yearling smallmouth bass catches averaged 2.6x higher than catches of yearling largemouth bass over the five years of electrofishing, and age-0 catches in ¼” mesh fyke nets averaged 3x higher than largemouth bass over ten years. Age-0 smallmouth bass catches in ½” mesh fyke nets averaged 2.5x higher than largemouth bass over ten years. Similarly, catches of age-0 smallmouth bass have averaged 2x higher than largemouth bass over 15 years of beach seine samples for which comparable data are available. Conversely, catches of age-2 and older largemouth bass in spring electrofishing have averaged 3.3x higher than catches of smallmouth bass. Spring electrofishing provides valuable data on adult bass populations, but timing and habitat sampled can favor largemouth bass, as evidenced by our results, and some caution should be used in interpreting relative catches of adults of the two species.
36
Population structure Largemouth bass Size distributions of largemouth bass captured in spring electrofishing samples were highly variable across the years 2011-2017 (Table 4, Figure 29). Proportional stock densities (PSD) ranged between 60% and 73% from 2011-2014, but increased to 91% in 2015 and then dropped to 27% in 2017. Largemouth bass in Oneida Lake typically recruit into the stock size class at age 2 or 3, so these results reflect little or no recruitment from the 2013 and 2014 year classes. Yearling catches in 2014 were relatively low, but were even lower in 2015, and that year class was well-represented at older ages in the 2017 electrofishing sample. Annual variations in age-0 largemouth bass catches in fyke nets do not appear consistent with observed variations in stock size catches 2-3 years later. These results suggest that survival from age-0 to stock size is variable, and that year class strength of largemouth bass may be influenced by factors that take place after the first growing season. Table 4. Catch rates (fish/h) by length category, PSD and RSD-P for largemouth bass collected in spring electrofishing samples in Oneida Lake, New York, 2011-2017. Based on sample sizes, all PSDs have 80% confidence levels of ±6% or less.
Length category catch-per-unit effort (fish/h) and 95%
confidence interval
Year Number
captured
Total
effort
(h)
All fish
Yearlings
(≤ 150 mm) –
all fish runs
only
≥ Stock size
(200 mm)
≥Quality
size
(300 mm)
≥ Preferred
size
(380 mm)
PSD
RSD-P
2011 127 12 10.6
1.9 – 19.2
1.5
0.0 – 2.99
9.6
2.0 – 17.1
7.1
3.3 – 10.8
3.3
1.6 – 4.9
72.6 33.3
2012 120 12 9.8
1.2 - 18.5
1.3
-0.2 – 2.7
8.0
0.4 – 15.6
5.8
2.0 – 9.5
2.8
1.1 – 4.4
71.9 34.4
2014 146 12 12.1
3.7 - 20.6
0.3
-1.2 – 1.7
11.8
4.4 – 19.1
7.1
3.6 – 10.5
3.0
1.3 – 4.7
60.7 26.4
2015 113 12 9.4
0.8 – 31.1
0.0
9.4
1.8 – 17.0
8.6
4.8 – 12.3
4.3
2.7 – 6.0
91.2 46.0
2017 269 12 22.4
13.8 – 31.1
0.5
-1.0 – 2.0
18.4
10.8 – 26.0
4.9
1.2 – 8.7
3.5
1.8 – 5.2
26.7 19.0
37
Figure 29. Length-frequencies of largemouth bass from spring shoreline electrofishing samples in Oneida Lake, New York, 2011-2017.
0
10
20
30
60 110 160 210 260 310 360 410 460 510
Nu
mb
er
Length class (10 mm)
Stock Quality Preferred 2011
0
10
20
30
60 110 160 210 260 310 360 410 460 510
Nu
mb
er
Length class (10 mm)
2012
0
10
20
30
60 110 160 210 260 310 360 410 460 510
Nu
mb
er
Length class (10 mm)
2014
0
10
20
30
60 110 160 210 260 310 360 410 460 510
Nu
mb
er
Length class (10 mm)
0
10
20
30
60 110 160 210 260 310 360 410 460 510
Nu
mb
er
Length class (10 mm)
2017
2015
38
Age structure of largemouth bass captured in spring electrofishing samples confirm suggestions of variable recruitment indicated by size structure analyses (Table 5). Ages 1-8 were well-represented in samples from 2011 and 2012, but age-1 catches were low in 2014 and no fish ages 1 or 2 were captured in 2015, suggesting poor year classes in 2013 and 2014. Catches in 2017 indicated large year classes in 2015 and 2016. Fyke net catches of age-0 fish did not reflect such large differences between the 2013/2014 year classes (combined mean fyke net catch 2.3 and 4.8/net night, respectively) and the 2015/2016 (combined mean catch of 2.7 and 5.1/net night, respectively) year classes, again suggesting that largemouth bass year class strength at the time of recruitment into the fishery may be dependent on conditions that act on fish after the first growing season. Table 5. Percent composition by age of largemouth bass collected in spring electrofishing samples in Oneida Lake, New York, 2011-2017.
Year
2011 2012 2014 2015 2017
Age
1 7.2 8.8 1.9 24.0
2 6.0 13.3 16.2 13.0
3 14.5 20.4 28.6 9.2 27.0
4 15.7 11.5 10.5 18.4 1.0
5 9.6 6.2 8.6 22.4 4.0
6 18.1 10.6 5.7 5.3 12.0
7 16.9 8.0 11.4 3.9 7.0
8 7.2 14.2 7.6 11.8 4.0
9 2.4 4.4 6.7 21.1 5.0
10 1.2 2.7 1.0 7.9 1.0
11+ 1.2 2.0 2.0
39
Smallmouth bass Size distributions of smallmouth bass captured in spring electrofishing samples were variable across the years 2011-2017 with poor representation of smaller size classes in 2014 and 2015, similar to that observed with largemouth bass. (Table 6, Figure 30). Proportional stock densities (PSD) ranged between 51% and 67% from 2011-2014, but increased to 84% in 2015 and then dropped to 37% in 2017. Smallmouth bass in Oneida Lake typically recruit into the stock size class at age 2, so these results indicate relatively low recruitment from the 2013 and 2014 year classes. Yearling catches (as defined by the 150 mm guide) in 2014 and 2015 were 0, however age-1 fish were collected that exceeded 150 mm. Annual variations in age-0 smallmouth bass catches in fyke nets do not appear consistent with observed variations in stock size catches 2-3 years later. While the 2013 year class produced the lowest fyke net catches we’ve observed, the 2014 year class produced among the highest catches on record. Table 6. Catch rates (fish/h) by length category, PSD and RSD-P for smallmouth bass collected in spring electrofishing samples in Oneida Lake, New York, 2011-2017.
Length category catch-per-unit effort (fish/h) and 95% confidence interval
Year Number captured
Total effort (h)
All fish
Yearlings (≤ 150 mm) – all fish runs
only
≥ Stock size
(200 mm)
≥Quality size
(300 mm)
≥ Preferred size
(380 mm)
PSD
RSD-P
2011 39 12 3.3
1.0 – 5.5 1.3
-0.03 – 2.5 1.8
0.06 – 3.4 1.5
0.3 – 2.7 1.3
0.2 – 2.5 66.6 59.3
2012 56 12 4.7 1.9 – 7.5
2.0 0.9 – 3.9
3.6 1.3 – 5.9
1.8 0.9 – 2.8
1.5 0.7 – 2.3
51.2 41.9
2014 45 12 3.8 1.4 – 6.3
0.0
3.7 1.2 – 6.1
2.0 -0.2 – 4.2
1.7 -0.6 – 3.9
55.8 46.5
2015 52 12 4.0 2.0 – 6.1
0.0
4.3 2.3 – 6.2
3.3 1.6 – 5.0
1.8 0.6 – 3.1
84.0 44.0
2017 86 12 7.2 0.8 – 13.5
8.3 -2.2 – 18.7
3.2 1.6 – 4.7
1.2 0.4 – 1.9
1.1 0.3 – 1.8
36.8 34.2
40
Figure 30. Length-frequencies of smallmouth bass from spring shoreline electrofishing samples in Oneida Lake, New York, 2011-2017.
0
5
10
15
60 110 160 210 260 310 360 410 460 510
Nu
mb
er
Length class (10 mm)
Stock Quality Preferred Memorable 2011
0
5
10
15
60 110 160 210 260 310 360 410 460 510
Nu
mb
er
Length class (10 mm)
2012
0
5
10
15
60 110 160 210 260 310 360 410 460 510
Nu
mb
er
Length class (10 mm)
2014
0
5
10
15
60 110 160 210 260 310 360 410 460 510
Nu
mb
er
Length class (10 mm)
2015
0
5
10
15
60 110 160 210 260 310 360 410 460 510
Nu
mb
er
Length class (10 mm)
2017
41
Age structure of smallmouth bass captured in spring electrofishing samples confirm suggestions of variable recruitment indicated by size structure analyses (Table 7). Ages 1-8 were well-represented in samples from all years, but age-1 catches were low in 2014 and 2015, suggesting poor year classes in 2013 and 2014. Catches in 2017 indicated a large year class in 2016. Large year classes from 2011 and 2012 dominated 2015 samples while in the quality size range. Fyke net catches of these year classes as age-0 fish were about average over the span of years for which we have data, and not indicative of large year classes at that stage. As with largemouth bass, it appears recruitment to the fishery in smallmouth bass is sensitive to conditions after the first growing season. Table 7. Percent composition by age of smallmouth bass collected in spring electrofishing samples in Oneida Lake, New York, 2011-2017.
Age
Year
2011
2012
2014
2015
2017
1 36.7 14.3 2.3 3.9 58.9 2 16.7 30.6 22.7 7.8 16.4 3 3.3 14.3 29.5 29.4 6.8 4 10.0 8.2 4.5 15.7 5 6.7 8.2 2.3 5.9 4.1 6 6.7 6.1 6.8 7.8 5.5 7 6.7 10.8 20.5 13.7 5.5 8 3.3 6.8 9.8 1.4 9 6.7 2.0 3.9 1.4
10 3.3 2.0 4.5 2.0 11+ 4.1
Growth and condition Largemouth bass Largemouth bass typically recruited into the stock size class at age 2, although
some fish had not achieved a length of 200 mm by the spring of their second
year (Table 8). Fish typically did not reach quality length until age 4, and
preferred length until age 6. Significant differences in length-at-age were
observed across years for most ages (ANOVA – age 1 p = 0.001; age 2 p <
0.0001; age 3 p < 0.001; age 4 p < 0.001; 0.31; age 5 p = 0.03; age 6 p = 0.003;
age 7 p = 0.30; age 8 p = 0.18). Excepting ages 3 and 8, highest mean lengths-
at-age were observed in 2017, suggesting a possible effect of round goby on
growth rates. However, while time trends in mean length-at-age were positive for
all ages except age 8, increases in mean length with year were only significant
for age-4 largemouth bass (linear regression – age 1 r2 = .77, p = 0.12; age 2 r2 =
0.48, p = 0.31; age 3 r2 = .37, p = 0.27; age 4 r2 = 0.93, p = 0.01; age 5 r2 = 0.58,
42
p = 0.13; age 6 r2 = 0.45, p = 0.21; age 7 r2 = 0.39, p = 0.26; age 8 r2 = 0.51, p =
0.51). Mean length-at-age for largemouth bass slightly exceeded statewide
averages for all ages (Brooking et al. 2018).
Table 8. Length-at-age for largemouth bass (± 1SE) collected in spring
electrofishing samples in Oneida Lake, New York, 2011-2017.
Year
1
2
3
Age
4
5
6
7
8
2011 127 (±7) 206 (±11) 275 (±9) 310 (±7) 353 (±6) 389 (±5) 406 (±6) 430 (±9)
2012 121 (±6) 165 (±7) 256 (±6) 316 (±7) 337 (±7) 374 (±5) 391 (±7) 409 (±6)
2014 145 (±12) 229 (±6) 299 (±5) 328 (±8) 363 (±6) 391 (±7) 407 (±6) 428 (±8)
2015 296 (±11) 325 (±7) 359 (±4) 384 (±9) 407 (±12) 421 (±8)
2017 149 (±4) 235 (±7) 287 (±6) 345 (±25) 369 (±9) 405 (±5) 412 (±8) 410 (±11)
Condition of largemouth bass, as measured by relative weight, was at or above 100% for all size classes in all years when fish were weighed with the exception of preferred size fish in 2017 (Table 9). Relative weights were highest in 2017 for all size classes, but no significant differences were detectable among years for any size class using simple ANOVA (all p’s ˃ 0.10). Of the available data, only fish captured during 2017 would have had at least one full year of round goby available as prey, and we will continue to monitor whether round goby may have a positive impact on largemouth bass condition. Table 9. Relative weights by size class (± 1SE) of largemouth bass collected in spring electrofishing samples in Oneida Lake, New York, 2014-2017.
Year Stock
Size class Quality
Preferred
2014 116.0 (±1.4) 106.9 (±1.9) 99.0 (±1.4) 2015 115.8 (±3.2) 104.6 (±1.8) 100.1 (±1.2) 2017 119.7 (±1.1) 111.4 (±3.1) 102.5 (±1.5)
Smallmouth bass Smallmouth bass typically recruited into the stock size class by age 2 (Table 10).
Smallmouth bass attained quality length at age 4 (although some age-3 fish
reached this length) and preferred length at age 5. Memorable lengths were
frequently attained by age 7. Yearling smallmouth bass were typically smaller
43
than yearling largemouth bass but by age 5 smallmouth bass were often larger
than largemouth bass and this length-at-age advantage persisted at older ages.
Unlike largemouth bass, no clear patterns in smallmouth bass length-at-age with
time were evident except for age-7 and age-8 fish (linear regression – age 1 r2 =
.01, p = 0.90; age 2 r2 = 0.01, p = 0.87; age 3 r2 = .03, p = 0.77; age 4 r2 = 0.01, p
= 0.91; age 5 r2 = 0.02, p = 0.84; age 6 r2 = 0.02, p = 0.81; age 7 r2 = 0.88, p =
0.02; age 8 r2 = 0.98, p = 0.01). Observed length-at-age exceeded statewide
averages for all age classes (Brooking et al. 2018).
Table 10. Length-at-age for smallmouth bass (± 1SE) collected in spring
electrofishing samples in Oneida Lake, New York, 2011-2017.
Year 1
2
3
Age 4
5
6
7
8
2011 108 (±5) 212(±12) 235 (n=1) 349 (±14) 383 (±4) 421 (±19) 419 (±22) 410 (n=1) 2012 125 (±6) 209 (±6) 250 (±12) 308 (±23) 395 (±4) 411 (±8) 422 (±5) 2014 93 (n=1) 191 (±8) 248 (±12) 367 (±15) 427 (n=1) 423 (±12) 431 (±8) 447 (±3) 2015 89 (4) 191 (±4) 291 (±9) 313 (±9) 366 (±9) 382 (±10) 428 (±9) 451 (±7) 2017 130 (±2) 224 (±11) 234 (±7) 402 (2) 422 (±3) 434 (±6) 471 (n=1)
Condition of smallmouth bass, as measured by relative weight, was at or above 90% for all size classes in all years when fish were weighed with the exception of quality size fish in 2014 and memorable size fish in 2017 (Table 11). Relative weights were highest in 2017 for all size classes except memorable, and significant differences were detectable for the preferred size class using simple ANOVA (preferred p = 0.02; all other p’s ˃ 0.26, quality excluded from analyses due to no data for 2017). Of the available data, only fish captured during 2017 would have had at least one full year of round goby available as prey, and we will continue to monitor whether round goby may have a positive impact on smallmouth bass condition. Table 11. Relative weights by size class (± 1SE) of smallmouth bass collected in spring electrofishing samples in Oneida Lake, New York, 2014-2017.
Year Stock
Size class Quality
Preferred
Memorable
2014 110.5 (±21) 83.0 (±7.1) 94.0 (±1.8) 95.9 (±3.7) 2015 108.8 (±1.4) 106.6 (±2.8) 102.4 (±2.1) 89.3 (±2.7) 2017 115.0 (±2.6) 106.8 (±3.9) 95.9 (n=1)
Shoreline seining To address potential shifts in habitat use by young-of-year yellow perch from offshore areas where they have been indexed by our trawl samples to inshore areas, we implemented a seine survey in 2015 (Fetzer 2013, Fetzer et al. 2015).
44
Daytime seining with a 75 ft beach seine with ¼ in mesh is conducted at 9 sites with available long-term data every fourth week from July through September. Seining in 2017 collected 19 species, lower than the diversity represented in fyke net samples. Seine samples in 2017 were dominated by young-of-year yellow perch in July and August, with catches of 518/haul in July declining to 124/haul in August. By September, young-of-year yellow perch catches declined to 72/haul, and were exceeded by catches of young-of-year Lepomis spp. at 77/haul. Other common species included round goby, banded killifish, and golden shiner Notemigonus crysoleucus. Round goby catches in seine samples from 2017 reflected declines from 2016 catches, consistent with evidence of declines in other gears, so seining may have potential as a round goby index. Fall seine catches indicated densities 44% higher than trawl catches in October in 2017, consistent with past years (Figures 31 and 32; Fetzer et al. 2015).
Figure 31. Fall density (#/ha) of young-of-year yellow perch from bottom trawl samples and shoreline seining, in Oneida Lake, New York, 1961-2017.
45
Figure 32. Ratio of fall trawl and seine densities of young-of-year yellow perch in Oneida Lake, New York, 1961-2017.
Creel survey – full season effort and walleye harvest Methodology Analyses of seasonal patterns in walleye catch and harvest rates estimated during roving creel surveys for the entire open water season indicated a good predictive relationship existed between rates observed in June and July and full open water season rates. The full open water season walleye catch rate can be predicted by the relationship:
CR = 0.700(JJCR) + 0.043 where CR is the catch rate predicted for the entire open water season and JJCR is the mean of June and July catch rates (r2 = 0.94; p = 0.0002). The open water walleye harvest rate was predicted by the relationship:
HR = 0.761(JJHR) + 0.012 where HR is the harvest rate predicted for the entire open water season and JJHR is the mean of June and July harvest rates (r2 = 0.85; p = 0.003). Beginning with the 2016 season, we also predict full open water season based on counts from a subset of months. We found that effort estimates from May, June and July predicted total season effort, accounting for 96% of the variability observed over 11 seasons of full effort data. The relationship was;
OWEFFORT = 31,302.9 + (1.2(ME+JnE+JuE))
46
where OWEFFORT is effort predicted for the entire open water season and ME, JnE and JuE are May, June and July effort, respectively (r2 = 0.96; p < 0.0001). While open water season walleye catch and harvest rates are predictable from summer data, no reliable relationship was identified for black bass or yellow perch. The current approach is to conduct the summer survey each year, basing effort on boat counts from May, June and July and basing catch and harvest on exit interviews conducted during June and July. A complementary full open water roving survey is conducted every fifth year (scheduled for 2018). Effort is estimated by fixed point boat counts conducted from a tower on the CBFS property. Counts are conducted at two random times on two randomly selected weekdays and both weekend days through the counting season and effort in boat hours calculated following the methods described in Krueger et al. (2009). Boat hours are converted to angler hours by multiplying boat hours by the average party size calculated from exit interviews in June and July (1.88 in 2017). Exit interviews are conducted on two randomly selected weekdays and both weekend days during either a morning shift (0800-1400) or afternoon shift (1400-2000), also randomly selected. Exit interviews are conducted at three boat launches, South Shore Boat Launch, Godfrey Point Boat Launch and Oneida Shores, and location for each day is randomly selected. Catch and harvest rates are calculated using the ratio of means following methods described by Krueger et al. (2009). Angler Effort Effort during the 2017 open water season was estimated at 184,731 boat hours, nearly a 5% drop from 2016 effort (Appendix Table A13). Effort in all eight years of our shortened creel survey (2010-2017) has been higher than observed in any year during the 2002-2007 full creel survey, but 2017 was the lowest effort observed during the years of the shortened survey. May effort was conspicuously below recent levels, and likely accounts for most of the decline in open water fishing effort in 2017. Anecdotally, May effort appears to be driven by weekend weather conditions, and 2017 was characterized by poor weather or windy weekend conditions for most of the month, most notably on opening day, when we observed the lowest opener effort in our recent records. Effort in June and July was within the range observed in the new survey, but June was slightly lower than 2016, while July effort was somewhat higher than 2016 (see below).
Species sought Total number of access interviews conducted during June and July 2017 was 488. Of these anglers, 253 (52%) strictly sought walleye, while 163 (33%) sought only black bass. Anglers who sought some combination of walleye, black
47
bass, yellow perch and panfish comprised the rest of the sample. Of anglers seeking black bass, 30 (18% of bass anglers) indicated they were fishing in a tournament. These results indicate a decline in relative preference for black bass as compared to the 2016 survey, when 49% of anglers sought black bass exclusively (compared to 40% seeking walleye), with 49% of bass anglers participating in tournaments. It seems likely that annual variability in tournament participation effort, and by extension black bass effort, is in part driven by whether random interview schedules place creel clerks at the one ramp where tournament weigh-ins are commonly held on weekend afternoons. Based on past comparisons, access interviews can tend to reflect a higher percentage of effort directed at bass than roving interviews, likely because black bass fishing attracts a higher percentage of anglers from outside the immediate lake area who use public boat launches to access the lake, thereby increasing the likelihood of being interviewed. Additionally, if the clerk encounters a black bass tournament at their assigned interview site, interviews for that day will be heavily weighted towards black bass.
Catch and harvest rates and 2017 walleye harvest estimate Festa et al. (1987), based on a survey of walleye fisheries in New York, suggested that walleye catch rates of 0.10-0.25/hr were characteristic of good to very good fisheries, with catch rates exceeding 0.25/hr considered excellent. For targeted catch rates, rates exceeding 0.20/hr were above average and rates approaching 0.50/hr were considered excellent. Estimated catch rate for walleye (all anglers) from access surveys in the 2017 open water season was 0.11/hr in June and 0.23 in July (mean targeted catch rate for the June was 0.24/hr and for July 0.31/hr). Smallmouth bass catch rate (all anglers) was 0.37/hr in June and 0.20/hr in July (mean targeted catch rate was 0.60/hr in June and 0.56/hr in July). Walleye catch rates for all anglers dropped 65% to the lowest observed during our surveys from 2015 to 2016, but rebounded in 2017, although still below the rates observed from 2012-2015 (Figure 29). Targeted catch rates for walleye only increased 17% from lows observed in 2016 (Figure 30). Catch rates for black bass were the highest observed sine 2014 (Figures 33, 34).
48
Figure 33. Overall angler catch rates for walleye and smallmouth bass in Oneida Lake, 2012-2017.
Figure 34. Targeted angler catch rates for walleye and smallmouth bass in Oneida Lake, 2012-2017. Open water harvest rate for walleye for the June/July 2017 period used to predict harvest was 0.11/hr, more than twice that observed in 2016, but still just below that observed before round goby established in the lake (2012-2014 average 0.15/hour). Estimated total harvest of walleye for the 2017 open water season was 34,388 fish. The 2017 harvest was almost double that observed in 2016, but
49
well below levels observed prior to establishment of round goby (2012-2014 average 58,124; Figure 35). Smallmouth bass harvest rates have remained stable at 0.01/hr for the June/July period.
Figure 35. Open water walleye harvest in Oneida Lake, 2012-2017.
Creel data from 2016 indicated sharp declines from previous years in walleye catch rates and harvest. Targeted smallmouth bass catch rates also exhibited a decline, although overall smallmouth bass catch rates were unchanged from 2015. Data from 2017 show an improvement in catch rates for both fisheries, but walleye rates did not return to pre-round goby levels. While additional years of data will be needed to confirm the trend, the correlation between the reduced catch and harvest of walleye and the increase in densities of round goby suggests that round goby may have a negative impact on angler success. The improvement in catches following the apparent winter die-off of some portion of the round goby population during the winter of 2016-2017 is consistent with the potential negative impact of round goby on angler catch rates. Walleye catch rates in Oneida Lake were previously shown to be sensitive to the availability of natural forage for walleye, with increased forage resulting in reduced catch rates (VanDeValk et al. 2005). If round goby contribute to the same relationship, and winter die-offs are not a regular phenomenon, their increasing densities could result in declines in the quality of the walleye fishery on Oneida Lake.
Angler opinion and behavior survey As a complement to the catch and harvest rate data collected from angler interviews, we added additional survey questions in 2013 directed at assessing angler opinions about the quality of the Oneida Lake fishery and its management as well as angler activities in areas of recent or current management concern. Five questions were developed with NYSDEC staff and incorporated into angler
50
interviews. Some questions were designed to allow tracking of opinions over time, and we report here the results of the first five years of the program. Question 1. On a scale of 1 to 5, with 5 being very satisfied, how satisfied are you with the overall quality of fishing on Oneida Lake? From 2013 through 2015, anglers indicated a high level of satisfaction with the quality of angling on Oneida Lake, with average scores of 4 or higher (Figure 36). Satisfaction scores fell to 3.2 in 2016, but rebounded to 3.9 in 2017. The percentage of anglers providing quality scores of 1 or 2 dropped from 30% in 2016 to 11% in 2017. We anticipated that angler satisfaction would be related to catch rates, but with five years of data and the improved data spread provided by the low 2016 catch rates, we see that the relationship is even stronger than might have been anticipated (linear regression: percent of anglers providing a 5 score to fishery quality as a function of catch rates - df = 3; F-ratio = 270.83; r2 = 0.99; p = 0.0005; mean quality score - df = 3; F-ratio = 110.43; r2 = 0.97; p = 0.002).
Figure 36. Angler level of satisfaction with overall quality of fishing on Oneida Lake from access interviews. 2013 survey N=489, 2014 N=475, 2015 N=320, 2016 N=276, 2017 N=427.
Question 2a,b. Have you fished for black bass during the spring catch and release season on any New York waters since the regulation changed? Have you fished the spring season for black bass on Oneida Lake? Responses indicated substantial participation in the spring catch and release season for black bass (Figure 37). Interviews in 2017 were consistent with past
51
years, and indicated that 24% of 2017 Oneida anglers had taken advantage of the spring bass season on at least one New York water. Similarly, 2017 responses were consistent with past years in showing a lower percentage of anglers interviewed had fished Oneida Lake for black bass during the catch and release season (Figure 38; 2017– 17%). The lower use of the spring season on Oneida Lake relative to other waters may reflect that anglers are less apt to travel for the catch and release season, and tend to fish waters closer to home prior to the tournament season.
Figure 37. Percent of anglers interviewed who indicated they had fished for black bass during the spring catch and release season on at least one New York water since the regulation changed. 2013 N=490, 2014 N=475, 2015 N=320, 2016 N=281, 2017 N=427.
Figure 38. Percent of anglers interviewed who indicated they had fished for black bass during the spring catch and release season on Oneida Lake. 2013 N=489, 2014 N=475, 2015 N=320, 2016 – N=281, 2017 N=427.
0
10
20
30
40
50
60
70
80
90
2013 Access 2014 Access 2015 Access 2016 Access 2017 Access
Perc
en
t o
f re
sp
on
den
ts
Year and survey
No Yes
0
10
20
30
40
50
60
70
80
90
2013 Access 2014 Access 2015 Access 2016 Access 2017 Access
Per
cen
t o
f re
spo
nd
ents
Year and survey
No Yes
52
Question 3. What is your opinion about the current daily possession limit for walleye on Oneida Lake? a – continue as is, b – change to statewide possession limit. Results indicated that a majority of anglers from all years of interviews felt the current 3 fish possession limit for walleye should continue (Figure 39). Interestingly, the percentage of anglers expressing this opinion has increased steadily in each year of the survey.
Figure 39. Angler opinions on the current daily possession limit for walleye on Oneida Lake (3 fish) as opposed to a change to the statewide limit (5 fish). 2013 N=399, 2014 N=338, 2015 N=320, 2016 N=281, 2017 N=427.
0
10
20
30
40
50
60
70
80
90
2013 Access 2014 Access 2015 Access 2016 Access 2017 Access
Perc
en
t o
f re
sp
on
den
ts
Year and survey
Continue as is
Change to statewide
53
Question 4. On this fishing trip, which of the following have you fished with? a – artificial lures, b – natural baits, c – both. Artificial lures were the most commonly used technique in all years of interviews (Figure 40). Natural baits were commonly used and 18% or more anglers have reported using both, likely a function of the popularity of worm harnesses and jigs tipped with worms in the Oneida Lake walleye fishery. There was an increase in use of artificial lures concurrent with a reduction in worm-oriented baits in 2016 which persisted into 2017. This may be attributable to the presence of round goby, which make fishing worms on the bottom more difficult and less productive. Of natural baits used in 2017, interviews revealed 208 anglers using worms, 1 using leeches, 11 using baitfish, and 6 using crayfish.
Figure 40. Percentage use of artificial lures and natural baits by anglers on Oneida Lake. 2013 N=736, 2014 N=475, 2015 N=320, 2016 N=281, 2017 N=426).
0
10
20
30
40
50
60
70
Artificial lures Natural baits Both
Pe
rce
nt
of re
sp
on
den
ts
Type of bait
2013 Access
2014 Access
2015 Access
2016 Access
2017 Access
54
Question 5. On a scale of 1 to 5, with 5 being very satisfied, how satisfied are you with the job the DEC Bureau of Fisheries does managing Oneida Lake? Anglers indicated a high level of satisfaction with DEC’s management of Oneida Lake in 2017, with an average satisfaction score of 4.1 out of 5 (Figure 41). The average score in 2016 did reflect a drop from previous years, though this seemed to be a function of scores of 5 shifting to scores of 3 or 4, and not an increase in the number of anglers expressing outright dissatisfaction with DEC management. Responses in 2017 exhibited a shift to more anglers responding with a score of 5 Satisfaction with DEC management of Oneida Lake has exceeded satisfaction with the quality of the fishery from all years of interviews. As with satisfaction with the fishery of the lake, satisfaction with the Agency is strongly correlated with catch rates (linear regression: percent of anglers providing a 5 score to management quality as a function of catch rates - df = 3; F-ratio = 49.64; r2 = 0.94; p = 0.006; mean quality score - df = 3; F-ratio = 10.8; r2 = 0.78; p = 0.05).
Figure 41. Angler level of satisfaction with management of Oneida Lake by the DEC Bureau of Fisheries. 2013 N=899, 2014 N=438, 2015 N=320, 2016 N=281, 2017 N=426.
0
10
20
30
40
50
60
1 2 3 4 5
Pe
rce
nt
of
res
po
nd
en
ts
Score
2013 Access %
2014 Access %
2015 Access %
2016 Access %
2017 Access %
55
Recommendations for management and future research directions.
Over the duration of our research on Oneida Lake, we have identified several ecological changes, many ongoing, that are likely to affect the fish community in Oneida Lake. These have included warming water temperatures, species invasions, and increased water clarity resulting from dreissenid mussels and reduced nutrient inputs. The data collected in 2017 are consistent with previous indications that the lake has undergone changes in physical characteristics and productivity at the lower trophic levels. Water temperatures and ice duration continued to reflect warmer conditions than when studies were first initiated, and water clarity remained well above levels observed in the earliest years of our studies. Oneida Lake presently fits the overall characteristics of a mesotrophic system, with reduced primary production from the early decades of our studies when the lake was classified as eutrophic. Much of the productivity has shifted from the pelagic to the littoral zones, including increases in littoral macrophytes and benthic algae production (Cecala et al. 2008), with concomitant increases in abundance of nearshore fish species. We are now seeing signs of reduced Daphnia spp. production, a typical response to dreissenid colonization, but one that was not evident in the first decade following mussel establishment. Continued declines in Daphnia densities and conversion of the zooplankton community to one dominated by copepods may have implications for planktivores and planktivorous life stages of important sport fishes. Clearer water conditions appear to have reduced survival of pelagic walleye and yellow perch fry, resulting in lower average year class size and recruitment to subadult stages than was typical of the lake before major ecological changes were observed. Double-crested cormorant predation on subadult percids resulted in decreases in recruitment to the fishery, and the establishment of a management program contributed to increases in adult walleye numbers, but we have not seen an increase in yellow perch numbers as double-crested cormorant management has continued. NYSDEC hazing efforts contribute to keeping predation pressure by double-crested cormorants at levels that are well below those observed prior to any management being implemented, and gizzard shad commonly buffer much of the impact in the fall. At present, double-crested cormorant management efforts have likely minimized impacts on sport fish species. Round goby, now established in the lake, were for the first time in 2016, numerically more common in double-crested cormorant diets than any other species. Round goby appear to have experienced a partial winterkill in the winter of 2016-2017, and were less common in cormorant diets, but still ranked fourth in numerical abundance in cormorant diets in 2017. Round goby may provide an additional buffer for sport fish species against double-crested cormorant predation, and one that would be available all season, as opposed to the late-summer and fall availability of gizzard shad. Additional years of data will be required to determine if round goby die-offs such as observed in the winter of 2016-2017 will be an isolated or regular event, and the availability of larger round goby to double-crested cormorants
56
early in the growing season will likely determine their value as a buffer for sport fish. While the lake supports an excellent fishery for walleye, and should continue to do so even under the new conditions, our past analyses have suggested that recruitment was no longer sufficient given harvest rates to expect the population to rebuild to levels observed in the 1960s and 1970s. Similarly, yellow perch recruitment has also declined to a new, lower, average level in the last decade, and it seemed likely that the adult perch population would also stay well below its historic highs. The presence of round goby could alter these dynamics. Harvest of walleye in 2016 dropped sharply from previous years, and if this was attributable to round goby and continues, current levels of walleye recruitment could produce increases in the adult population with the reduced harvest levels. A large walleye year class from 2014 will recruit into the fishery in 2018, and there appears to be another large year class from 2016 that would recruit into the fishery in 2020. If harvest rates in the presence of round goby remain reduced from the pre-goby levels, these year classes could support an increase in adult walleye numbers to well over 500,000. Survival of young yellow perch could increase if round goby provide an alternate prey resource for the lake’s piscivores, and it does appear that the 2016 year class of yellow perch has remained high relative to most previous years. Under this scenario, it may be possible that the adult yellow perch population could increase. If yellow perch densities are in part limited by productivity, it is also possible that historic increases in the white perch population may have acted as a constraint on the size of the adult yellow perch population, both through competition and through direct predation on yellow perch fry by white perch. Consistently poor white perch year classes in recent years have resulted in a decline in the white perch adult population. If this trend persists, there may be enhanced survival of both yellow perch and walleye larvae, which could also contribute to population growth. However, it must be noted that the 2017 catch of age-0 white perch was the largest observed since 2011. The relationship between age-0 white perch catches and the recruitment index is weak, so additional years of sampling will be required to determine if the 2017 year class of white perch will lead to an increase in adult numbers. Smallmouth bass and largemouth bass have benefited from changes in the lake, and their populations have reached higher levels than were observed in the 1960s and 1970s. Based on trends in young-of-year production, there is no reason to think this will not remain the case. The presence of round goby as potential nest predators of bass eggs and larvae will need to be monitored, but based on record catches of young smallmouth bass in fyke nets in 2015 and of largemouth bass young in 2016, there are no early indications of a detrimental impact of round goby on production of young bass.
57
Oneida Lake offers diverse, high quality fishing opportunities, and should continue to do so, but all indications are that the fish community has changed as a result of larger ecological events. With increased production of littoral species and reduced abundances of pelagic species, it does not appear practical to use benchmarks established in the 1960s and 1970s as gauges of what is realistic today. While the walleye fishery remains predominant, the black bass fishery has received national attention and gains in popularity. The increased abundance of chain pickerel also offers an alternative to the traditional fisheries of Oneida Lake, but we see little evidence that this species is a popular target for anglers. The round goby is now established in the lake, and if it reaches high densities we may see several responses in our data series. Growth of adult walleye, yellow perch and bass may increase if gobies provide an abundant food resource. The round goby spawns multiple times in a growing season, and may provide a prey resource for young-of-year walleye and black bass throughout the summer which could improve growth, and presumably survival. We have seen indications of improved young-of-year smallmouth bass growth with round goby appearing in diets. However, round goby appear to have negatively impacted angler catch rates by providing an abundant food source for piscivores Continuing analysis and monitoring of the Oneida Lake data set should give us information on the response to these ongoing ecological changes that are relevant not only to Oneida Lake, but also to the northeastern US and southeastern Canada. A baseline data series is essential for evaluating system responses to ecological change. In addition, we have recently completed analyzing cormorant-percid interactions by observing the response of the Oneida Lake fish community, in particular walleye and yellow perch, to the removal of most cormorants from the lake. This represents a whole lake management experiment, and it was important that this effort was evaluated thoroughly. Results show that cormorants can increase mortality of subadult life stages of percids, thereby reducing recruitment, and that the management of cormorants on Oneida Lake returned subadult percid mortality rates to pre-cormorant levels. Full results can be found in Coleman et al. (2016). These results should inform similar cormorant management activities that are ongoing in other lakes in the US and Canada. 2016 angler data indicated a large decrease in walleye harvest, with an estimated harvest of only 18,000 fish, as compared to 57,000 or more in each of the previous four years. We attributed this decline to the presence of round goby, and following the partial winterkill of round goby, we saw improvement in angler catch rates in 2017, although not a return to levels observed pre-round goby. Historically, anglers have enjoyed high catch and harvest rates in the early and mid- seasons prior to age-0 gizzard shad becoming available as prey for walleye in the late summer. Round goby, which are available throughout the year, could negatively impact early season success of anglers and reduce
58
harvest. If reduced harvest rates persist, it may be possible for the adult walleye population to grow. Recommendation for current management: Fisheries management on Oneida Lake includes stocking of walleye larvae, size and creel limits for walleyes, black bass, and other species, and control of double-crested cormorants. We recommend maintaining these efforts and regulations at current levels in 2018-2019. Stocking of walleye larvae. Continue stocking at current levels. This will maintain a consistent supply of walleye larvae to the lake and makes walleye less sensitive to potential increases in egg predation from a future abundant round goby population. Our best estimates suggest that the number of naturally produced walleye larvae in the lake is about 33% of the numbers stocked. Size and creel limits for walleye. The adult walleye population was estimated at 429,000 fish in the 2016 mark-recapture study. Our estimate for 2017 numbers is between 450,000 and 500,000 fish. Even under historic harvest rates, we feel that the population will exhibit continued growth in 2018, potentially reaching 600,000 or more fish. Reduced harvests in the last two years likely combined with a large 2014 year class may result in the adult walleye population to reach numbers not observed since the early 1990s . Impacts of round goby on angler catch and harvest rates may allow even smaller walleye year classes to contribute to growth of the walleye population. Similarly, round goby may provide a buffer for predation for young yellow perch, and walleye management has historically been directed at maintaining a balance between walleye and their yellow perch prey base. This dynamic may no longer be the primary predator-prey relationship in Oneida Lake if round goby become an important prey resource for walleye. While we continue to collect data to assess potential changes in these relationships, we suggest maintaining the current size limit for Oneida Lake walleye at 15 inches. The 3 fish creel limit is a conservative approach to reduce impacts of poor walleye recruitment and enjoys strong support from anglers. Double-crested cormorant control. We have observed an increase in the walleye and yellow perch populations concomitant with more intensive double-crested cormorant control, although not to historic levels. This suggests that removing double-crested cormorants does increase percid recruitment to the fishery. More intensive double-crested cormorant control by APHIS was conducted from 2004-2010. Our data do show that a rebuilding of summer double-crested cormorant numbers will likely reduce subadult walleye and yellow perch survival, and potentially reduce populations to the point where recent harvest rates are not sustainable (DeBruyne 2014, Coleman et al. 2016). Continued efforts should be made to find a workable approach to limiting cormorant numbers and preventing the rebuilding of a large summer population.
59
Given the results and discussion in this report, we recommend the following research and monitoring activities in 2018: 1) Continue standard sampling program. This program includes limnological surveys, two larval fish sampling surveys (8 and 18 mm yellow perch surveys), spring centrarchid electrofishing survey, 15 standard gill nets, weekly trawl surveys from mid-July through October, pelagic prey fish survey with acoustics, midwater trawl and pelagic gill nets at the end of August, fyke net sampling for nearshore fish in September, and large mesh gill nets for sturgeon. 2) Increase attention to the importance of changing spatial distributions of age-0 yellow perch. Continue summer seining to complement trawling in order to sample yellow perch in both offshore and near shore habitats. Replace one weekly trawl sample per month with a seine survey one week per month from July to September. Combined trawling and seining may also provide a potential inshore/offshore index of round goby. 4) Continue fyke net sampling as a means to monitor changes in the nearshore fish community, particularly with the arrival of round gobies. Continue spring centrarchid electrofishing surveys in non-walleye mark-recapture years. 5) Conduct a full season open water creel survey in 2018, complemented by the abbreviated June-July exit survey. Conduct a winter ice fishing creel survey in the winter of 2018-2019. 6) Continue diet analyses of double-crested cormorants in coordination with NYSDEC. 6) Continue the targeted collection of predator diet data initiated in 2016 to assess impacts of round goby on predator-prey dynamics in the lake. This study is designed to end in 2018, when three years of data will be available to compare to an equivalent three year effort before round goby arrived. Continued monitoring of round goby populations will be used to determine if additional years of diet studies are necessary in response to round goby achieving higher densities than those observed during the current study. 7) Conduct a lakewide video-based assessment of round goby densities in early summer for comparison to trawl catches.
60
Literature Cited Brooking, T.E., J.R. Jackson, L.G. Rudstam, and A.J. VanDeValk. 2014. Habitat
mapping of Oneida and Canadarago Lakes. Final Report, Study 2, Job 4. New York State Department of Environmental Conservation, Albany, New York.
Brooking, T.E., L.G. Rudstam, S.D. Krueger, J.R. Jackson, A.B.Welsh, W.W.
Fetzer. 2010. First occurrence of the mysid Hemimysis anomala in an inland lake in North America, Oneida Lake, NY. Journal of Great Lakes Research 36(2010):577-581.
Brown, M., B. Boscarion, J. Roellke, E. Stapylton, and A. Driller-Colangelo.
2014. Fifteen miles on the erie Canal: the spread of Hemimysis anaomala G.O. Sras, 1907 (bloody red shrimp) in the New York State canal system. BioInvasion Records 3:261-267.
Carlson, R.E. 1977. A trophic state index for lakes. Limnology and
Oceanography 22:361-369. Cecala, R.K., C.M. Mayer, K.L. Schultz, and e.L. Mills. 2008. Increased benthic
algal production in response to the invasive zebra mussel (Dreissena polymorpha) in a productive ecosystem, Oneida Lake, New York. Journal of Integrative Plant Biology 50:1452-1466.
Chevalier, J. R. 1973. Cannibalism as a factor in first-year survival of walleyes in
Oneida Lake. Transactions of the American Fisheries Society 103:739-744.
Coleman, J. T. H. 2009. Diving behavior, predator-prey dynamics, and
management efficacy of Double-crested Cormorants in New York State. PhD thesis, Cornell University, Ithaca, New York.
Coleman, J.T.H., R.L. DeBruyne, L.G. Rudstam, J.R. Jackson, A.J. VanDeValk,
T.E. Brooking, C.M. Adams, and M.E. Richmond. 2016. Evaluating the influence of double-crested cormorants on walleye and yellow perch populations in Oneida Lake, New York. Pages 397-424 in Rudstam, L.G., E.L. Mills, J.R. Jackson, and D.J. Stewart, editors. 2016. Oneida Lake: Long-term dynamics of a managed ecosystem and its fishery. American Fisheries Society, Bethesda, Maryland.
Connelly, N.A. and T.L. Brown. 2009. New York Statewide Angler Survey 2007,
Report 1: Angler effort and expenditures. New York State Department of Environmental Conservation, Albany.
61
Crane, N.A., J.M. Farrell, D.W. Einhouse, J.R. Lantry, and J.L. Markham. 2015. Trends in body composition of native piscivores following invasion of Lake Erie and Ontario by the round goby. Freshwater Biology 60:111-124.
DeBruyne, R.L., J.T.H. Coleman, J.R. Jackson, L.G. Rudstam, and A.J.
VanDeValk. 2013. Analysis of prey selection by double-crested cormorants: a 15-year diet study in Oneida Lake, New York. Transactions of the American Fisheries Society 142:430-446.
Festa, P.J., J.L. Forney, and R.T. Colesante. 1987. Walleye management in
New York State: A plan for restoration and enhancement. New York State Department of Environmental Conservation, Albany.
Fetzer, W.W. 2013. Disentangling the effects of multiple ecosystem changes on
fish population and community dynamics. Ph.D. Thesis, Cornell University.
Fetzer, W.W., T.E. Brooking, J.R. Jackson and L.G. Rudstam. 2011. Over-
winter mortality of gizzard shad: evaluation of starvation and cold temperature shock. Transactions of the American Fisheries Society 140:1460-1471.
Fetzer, W.W., M.M. Luebs, J.R. Jackson, and L.G. Rudstam. 2015. Intraspecific
Niche Partitioning and Ecosystem State Drive Carbon Pathways Supporting Lake Food Webs. Ecosystems 18:1440-1454.
Forney, J. L. 1980. Evolution of a management strategy for the walleye in Oneida
Lake, New York. New York Fish and Game Journal 27:105-141. Forney, J. L., L. G. Rudstam, D. M. Green, and D. L. Stang. 1994. Percid
sampling manual. Fish sampling manual. New York Department of Environmental Conservation, Albany, New York.
Hetherington, A.L., R.L. Schneider, L.G. Rudstam, G. Gal, A.T. DeGaetano, and
M.T. Walter. 2015. Modeling climate change impacts on the thermal dynamics of ploymictic Oneida Lake, New York. Ecological Modeling 300:1-11.
Hetherington, A.L., Rudstam, L.G., Schneider, R.L., Holeck, K.T., Hotaling, C.W.,
Cooper, J.E., Jackson, J.R. In review. Population dynamics of zebra mussels (Dreissena polymorpha) and quagga 1 mussels (Dreissena rostriformis bugensis) in polymictic Oneida Lake, NY, U.S.A. Biological Invasions.
62
He, J.X., L.G. Rudstam, J.L. Forney, A.J. VanDeValk, and D.J. Stewart. 2005. Long-term patterns in growth of Oneida Lake walleye: a multivariate and stage explicit approach for applying the von Bertalanffy function. Journal of Fish Biology 66:1459-1470.
Idrisi, N., E. L. Mills, L. G. Rudstam, and D. J. Stewart. 2001. Impact of zebra
mussels, Dreissena polymorpha, on the pelagic lower trophic levels of Oneida Lake, New York. Canadian Journal of Fisheries and Aquatic Sciences 58:1430-1441.
Irwin, B. J., T. J. Treska, L. G. Rudstam, P. J. Sullivan, J. R. Jackson, A. J.
VanDeValk, and J. L. Forney. 2008. Estimating walleye (Sander vitreus) density, gear catchability, and mortality using three fishery-independent data sets for Oneida Lake, New York. Canadian Journal of Fisheries and Aquatic Sciences 65:1366-1378.
Irwin, B.J., L.G. Rudstam, J.R. Jackson, A.J. VanDeValk, J.L. Forney, and D.G.
Fitzgerald. 2009. Depensatory mortality, density-dependent growth, and delayed compensation: disentangling the interplay of mortality, growth, and density during early life stages of yellow perch. Transactions of the American Fisheries Society 139:99-110.
Jackson, J.R., D.W. Einhouse, A.J. VanDeValk, and T.E. Brooking. 2015. Year-
class production of black bass before and after opening of a spring catch-and-release season in New York: case studies from three lakes. Pages 181-191 in M.D. Tringali, J.M. Long, T.W. Birdsong, and M.S. Allen, editors. Black bass diversity: multidisciplinary science for conservation. American Fisheries Society, Symposium 82, Bethesda, Maryland.
Jackson, J.R., A.J. VanDeValk, T.E. Brooking, K.T. Holeck, C. Hotaling, and L.G.
Rudstam,. 2017. The fisheries and limnology of Oneida Lake 2016. New York State Department of Environmental Conservation. 91 pp.
Johnson, J.H., R.M. Ross, R.D. McCullough, and A. Mathers. 2010. Diet shift of
double-crested cormorants in eastern Lake Ontario associated with the expansion of the invasive round goby. Journal of Great Lakes research 36:242-247.
Johnson, T. B. and D. O. Evans. 1991. Behavior, energetics and associated
mortality of young-of-the-year white perch (Morone americana) and yellow perch (Perca flavescens) under simulated winter conditions. Canadian Journal of Fisheries and Aquatic Sciences 48:672-680.
Johnson, T.B., D.B. Bunnell, and C.T. Knight.2005. A potential new energy
pathway in central Lake Erie; the round goby connection. Journal of Great Lakes Research 31(Suppl. 2):238-251.
63
Krueger, S. D., J. R. Jackson, A. J. VanDeValk, and L. G. Rudstam. 2009. The
Oneida Lake Creel Survey, 2002-2007. New York State Department of Environmental Conservation, Albany, NY.
Lepak, J. M., C. E. Kraft, and B. C. Weldel. 2006. Rapid food web recovery in
response to removal of an introduced apex predator. Canadian Journal of Fisheries and Aquatic Sciences 63:569-575.
Mayer, C.M., L.E. Burlakova, P. Eklov, D. Fitzgerald, A.Y. Karatayev, S.A.
Ludsin, S. Millard, E.L. Mills, A.P. Ostapenya, L.G. rudstam, B. Zhu, and T.V. Zhukova. 2014. The benthification of freshwater lakes: exotic mussles turning ecosystems upside down. Page 575-585 in T.F. Nalepa and D.W. Schloesser, editors. Quagga and zebra mussels: biology, impacts, and control. Second edition. CRC Press, Boca raton, Florida.
Rudstam, L. G., D. M. Green, J. L. Forney, D. L. Stang, and J. T. Evans. 1996.
Evidence of biotic interactions between walleye and yellow perch in New York State lakes. Annales Zoologica Fennici 33:443-449.
Rudstam, L. G., A. J. VanDeValk, C. M. Adams, J. T. H. Coleman, J. L. Forney,
and M. E. Richmond. 2004. Cormorant predation and the population dynamics of walleye and yellow perch in Oneida Lake. Ecological Applications 14:149-163.
Rudstam, L.G., J.R. Jackson, T.E. Brooking, S.D. Krueger, J.L. Forney, W.W.
Fetzer, R. DeBruyne, E.L. Mills, J. Swan, A. Seifert, and K. Holeck. 2009. Oneida Lake and its fishery in 2008. New York State Department of Environmental Cnservation, Albany.
Rudstam, L.G., E.L. Mills, J.R. Jackson, and D.J. Stewart, editors. 2016. Oneida
Lake: Long-term dynamics of a managed ecosystem and its fishery. American Fisheries Society, Bethesda, Maryland.
Rudstam, L.G., J.R. Jackson, A.J. VanDeValk, T.E. Brooking, W.W. Fetzer, B.J.
Irwin, and J.L. Forney. 2016. Walleye and yellow perch in Oneida Lake. Pages 319-354 in Rudstam, L.G., E.L. Mills, J.R. Jackson, and D.J. Stewart, editors. 2016. Oneida Lake: Long-term dynamics of a managed ecosystem and its fishery. American Fisheries Society, Bethesda, Maryland.
Steinhart, G.B., E.A. Marschall, and R.A. Stein. 2004b. Round goby predation
on smallmouth bass offspring in nests during simulated catch-and-release angling. Transactions of the American Fisheries Society 133:121-131.
64
Steinhart, G.B., R.A. Stein, and E.A. Marschall. 2004a. High growth rate of young-of-year smallmouth bass in Lake Erie: a result of the round goby invasion? Journal of Great Lakes research 30:381-389.
VanderZanden, M. J., J. M. Casselman, and J. B. Rasmussen. 1999. Stable
isotope evidence for the food web consequences of species invasions in lakes. Nature 401:464-467.
VanDeValk, A.J., J.L. Forney, J.R. Jackson, L.G. Rudstam, T.E. Brooking, and
S.D. Krueger. 2005. Angler catch rates and catchability of walleye in Oneida Lake, New York. North American Journal of Fisheries Management 25:1441-1447.
VanDeValk, A.J., J.L. Forney, T.E. Brooking, J.R. Jackson, and L.G. Rudstam.
2016. First-year density and growth as they relate to recruitment of white perch to the adult stock in Oneida Lake, New York, 1968–2011. Transactions of the American Fisheries Society 145:416-426.
Wetzel, R. G. 2001. Limnology. Lake and river ecosystems. 3rd edition.
Academic Press, San Diego, CA, USA. Zhu, B., D. G. Fitzgerald, C. M. Mayer, L. G. Rudstam, and E. L. Mills. 2006.
Alteration of ecosystem function by zebra mussels in Oneida Lake: Impacts on submerged macrophytes. Ecosystems 9:1017-1028.
65
Appendix 1:
Change point analyses of limnological parameters in Oneida Lake
Change point analysis is a method used to evaluate if a change occurred, when the change occurred, and with what confidence the change occurred (Taylor 2003; http://www.variantion.com). It is typically used in industry along with control charting, but by itself, is a powerful tool for evaluating change in historical data. First, bootstrapping is used to detect change points, and then CUSUM analysis is performed to determine when the changes occurred. In the first graphic below (Secchi depth), a change was detected in 1993. This represents the first year after the change occurred (a second change was detected in 1980, but here we focus only on Level 1 changes which are changes found on the initial pass through the data). The confidence level indicates the likelihood that a change occurred. We are 100% confident this change occurred. The confidence interval (set at 95%) is the range during which the change occurred. We are 95% confident that the change occurred between 1992 and 1995. The blue shading represents the area expected to contain all the values, based on the model, that the two changes should capture. Since all points fall within this region, this model fully explains the variation in the data.
66
Plot of secchi depth (m)5
3
1secch
i d
ep
th (
m)
1975 1981 1987 1993 1999 2005 2011 2017
yearTable of Significant Changes for secchi depth (m)
Confidence Level for Candidate Changes = 50%, Confidence Level for Inclusion in Table = 90%, Confidence Interval = 95%,
Bootstraps = 1000, Without Replacement, MSE Estimates
year Confidence Interval Conf. Level From To Level
1980 (1976, 1992) 91% 2.88 2.5538 3
1993 (1992, 1995) 100% 2.5538 3.656 1
67
Plot of chl-a (ug/L)15
7
-1
ch
l-a (
ug
/L)
1975 1981 1987 1993 1999 2005 2011 2017
yearTable of Significant Changes for chl-a (ug/L)
Confidence Level for Candidate Changes = 50%, Confidence Level for Inclusion in Table = 90%, Confidence Interval = 95%,
Bootstraps = 1000, Without Replacement, MSE Estimates
year Confidence Interval Conf. Level From To Level
1992 (1989, 1993) 100% 9.3588 5.7312 1
2008 (2002, 2010) 98% 5.7312 3.9 2
68
Plot of SRP (ug/L)60
15
-30
SR
P (
ug
/L)
1975 1981 1987 1993 1999 2005 2011 2017
yearTable of Significant Changes for SRP (ug/L)
Confidence Level for Candidate Changes = 50%, Confidence Level for Inclusion in Table = 90%, Confidence Interval = 95%,
Bootstraps = 1000, Without Replacement, MSE Estimates
year Confidence Interval Conf. Level From To Level
1989 (1981, 1989) 100% 15.05 5.6583 1
2001 (1999, 2005) 100% 5.6583 10.262 2
69
Plot of TP (ug/L)90
40
-10
TP
(u
g/L
)
1975 1981 1987 1993 1999 2005 2011 2017
yearTable of Significant Changes for TP (ug/L)
Confidence Level for Candidate Changes = 50%, Confidence Level for Inclusion in Table = 90%, Confidence Interval = 95%,
Bootstraps = 1000, Without Replacement, MSE Estimates
year Confidence Interval Conf. Level From To Level
1987 (1976, 1987) 100% 42.467 31.1 3
1989 (1989, 1992) 98% 31.1 20.658 6
2001 (1996, 2004) 100% 20.658 27.129 4
70
Plot of Zoo Biomass340
175
10
Zo
o B
iom
ass
1975 1981 1987 1993 1999 2005 2011 2017
yearTable of Significant Changes for Zoo Biomass
Confidence Level for Candidate Changes = 50%, Confidence Level for Inclusion in Table = 90%, Confidence Interval = 95%,
Bootstraps = 1000, Without Replacement, MSE Estimates
year Confidence Interval Conf. Level From To Level
2008 (2002, 2008) 93% 219.67 129.5 1
2012 (2010, 2013) 92% 129.5 184 2
71
Plot of Daphnia biomass200
85
-30Dap
hn
ia b
iom
ass
1975 1981 1987 1993 1999 2005 2011 2017
yearTable of Significant Changes for Daphnia biomass
Confidence Level for Candidate Changes = 50%, Confidence Level for Inclusion in Table = 90%, Confidence Interval = 95%,
Bootstraps = 1000, Without Replacement, MSE Estimates
year Confidence Interval Conf. Level From To Level
2007 (2001, 2007) 99% 99.719 46.727 1
72
Plot of summer temp26
22
18
su
mm
er
tem
p
1975 1980 1985 1990 1995 2000 2005 2010 2015
yearTable of Significant Changes for summer temp
Confidence Level for Candidate Changes = 50%, Confidence Level for Inclusion in Table = 90%, Confidence Interval = 95%,
Bootstraps = 1000, Without Replacement, MSE Estimates
year Confidence Interval Conf. Level From To Level
1994 (1989, 1997) 100% 21.247 22.609 1
73
Appendix 2:
Impacts of round goby invasion on predation by Oneida Lake predators
T. VanDeValk, I. Pakzad, R. Jackson, and T. Brooking Targeted assessment of the impacts of the round goby Neogobius melanostomus on predation by Oneida Lake predators was continued in 2017. We completed Year 2 of a 3-year diet study that compares age-1 and older predator consumption during 3 years prior to the arrival of the round goby (Fetzer et al. 2016) to 3 years after the arrival of the round goby. Fetzer et al. (2016) estimated consumption by age-1 and older smallmouth bass Micropterus dolomieu, largemouth bass M. salmoides and inshore and offshore walleye Sander vitreus populations from June through October between 2007-2009, years before round goby invasion, and estimated the relative contribution of each predator to age-0 yellow perch Perca flavescens mortality. Predator diets and abundances were integrated with temperature and growth rates in a bioenergetics model to estimate annual consumption of major diet items. Predator diets were assessed using sunrise and sunset electrofishing (inshore) and sunrise trawling (offshore, walleye only) surveys. Predator abundances, age structure and growth were estimated using long-term gill-net surveys complimented by walleye mark-recapture estimates every 2-3 years. Age-0 yellow perch abundances and growth rates were tracked using high-speed Miller samplers for larvae in June and standardized bottom trawl and beach seine surveys in July through October for offshore and inshore habitats. A more detailed description of the methods is provided by Fetzer et al. (2016). We used the same methods as Fetzer et al. (2016) to estimate predator consumption for the current study with two exceptions: 1) we added May sampling to explore possible buffering by the round goby on predation of age-1 yellow perch, and 2) we added chain pickerel Esox niger to our inshore predator group. Chain pickerel have increased in our inshore gear catches in recent years as have anglers’ comments regarding their abundance. Results from Fetzer et al. (2016) revealed a seasonal transition in predator consumption from age-1 yellow perch in the spring to age-0 yellow perch in the summer to age-0 gizzard shad Dorosoma cepedianum in the fall for all 3 species of predators examined. Crayfish were also important to both bass species across seasons. Within each year, predation by all three species accounted for all observed age-0 yellow perch mortality in summer and fall, but not in early summer, suggesting other predators in the lake likely predate on the youngest, most vulnerable yellow perch. Walleye were the
74
dominant predator in both offshore and inshore habitats while smallmouth bass and largemouth bass were also important inshore predators. Walleye consumption of age-0 yellow perch was on average 10 and 27 times higher than smallmouth bass and largemouth bass, respectively, despite having an estimated population size of only 2 times smallmouth bass and approximately 4 times largemouth bass. Results from the first 2 years of the 3-year post-round goby time period were consistent with Fetzer et al. (2016) in that consumption by predators followed the general seasonal pattern of consumption of age-1 yellow perch in spring and transitioned to age-0 yellow perch in summer to age-0 gizzard shad in fall. However, round gobies contributed substantially to the diets of all 4 inshore predator species throughout the year, with minimal contributions to offshore walleye diets. In 2016, monthly percent composition by weight of round gobies in diets of largemouth bass averaged 37% (range: 0-74), 59% (range: 3-100) for smallmouth bass, 26% (range: 0-100) for chain pickerel, 21% (range: 0-47) for inshore walleye and 7% (range: 0-22) for offshore walleye (Jackson et al. 2017). In 2017, monthly percent composition by weight of round gobies in diets of largemouth bass averaged 21% (range: 6-32), 35% (range: 6-65) for smallmouth bass, 29% (range: 0-77) for chain pickerel, 7% (range: 0-27) for inshore walleye and 5% (range: 0-18) for offshore walleye (Table 1). In 2017, round gobies accounted for less of the biomass consumed for all predators except chain pickerel than in 2016, which may be, at least in part, due to the 2016-17 overwinter decrease in round goby densities discussed earlier in this report. Age-0 walleye diets have been monitored in Oneida Lake since 1961. In the 1960s and 1970s, age-0 yellow perch were the predominant planktivore and the primary preyfish often constituting over 80% of the catch in bottom trawls and in the diets of age-0 walleyes (Forney 1974). In the mid-1980s, the Oneida Lake preyfish community shifted from a community dominated by young percids to a more diverse community with an increase in later spawning species (Hall and Rudstam 1999). In response to this change as well as an observed increase in age-0 yellow perch growth, age-0 yellow perch accounted for only 6% on average of all fish eaten by age-0 walleye between 1986 and 2013, and tessellated darters Etheostoma olmstedi became the most common species in age-0 walleye diets (33%), followed by age-0 Morone spp. (22%), Lepomis spp. (11%), age-0 gizzard shad (9%), trout-perch Percopsis omiscomaycus (9%), emerald shiner Notropis atherinoides (6%), and age-0 freshwater drum Aplodinotus grunniens (3%).
75
Since the arrival of the round goby, age-0 walleye diets shifted again with round gobies becoming the most common species found in age-0 walleye stomachs. In 2014, the first year round gobies were detected by Cornell sampling gear in Oneida Lake, round gobies accounted for 1% of all fish identified in age-0 walleye stomachs. The proportion of round gobies increased to 67% in 2015 and to 97% in 2016, but then decreased to 62% in 2017 (Figure 1). The mean length of age-0 walleyes collected in October also decreased in 2017 (Figure 2). Again, the decrease in round goby densities discussed earlier in this report may explain the lower occurrence of round gobies in diets of age-0 walleye and lower first year growth observed in 2017. Continued monitoring combined with analyses of ration size are required to assess the impacts of the round goby on age-0 walleye diet and growth relative to other preyfish species. If round gobies are increasing ration size, we expect age-0 walleye growth will increase as round goby densities increase.
Figure 1. Occurrence of fish in stomachs of age-0 walleye caught in bottom trawls since 1961.
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
1961 1971 1976 1983 1989 1994 1999 2005 2010 2015
#/st
om
ach
exa
min
ed
Year
Round Goby
Other fish
Yellow Perch
76
Figure 2. Mean length of age-0 walleye caught in October electrofishing and 12.2 m trawl surveys since 1959. In 2015, efforts to monitor diets of age-0 smallmouth bass and largemouth bass (here after referred to as black bass) were initiated. The transition to feeding on round gobies was not as dramatic for young black bass as was observed for walleye; however, round gobies were consumed by both black bass species and may be contributing to increased age-0 black bass growth. The diets of age-0 black bass collected in beach seines, fyke nets and bottom trawls in 2015-2017 were examined and compared to diets from black bass in archived samples (2010-2012). During the pre-round goby years, the most common preyfish found in young bass diets were tessellated darters followed by age-0 Lepomis spp. In 2015, no round gobies were found in largemouth bass diets but round gobies comprised 84% of all identifiable fish in age-0 smallmouth bass diets. Occurrence increased in 2016 as round gobies comprised 20% of all fish identified in age-0 largemouth bass diets and 67% of all fish in smallmouth bass diets, but decreased in 2017 to 6% in largemouth bass diets and 63% in smallmouth bass diets. Mean September lengths of age-0 smallmouth bass and largemouth bass caught in fyke net surveys in 2010 – 2012 and 2015 – 2017 were compared using Tukey-Kramer Honest Significant Difference test (HSD). The HSD test results indicate smallmouth bass mean lengths were significantly greater in 2 of the 3 round goby years than during the 3 pre-goby years (Figure 3). The lone round goby year that did not exhibit higher growth was 2017, the year following the overwinter decrease in round goby abundance.
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
10.00
1950 1960 1970 1980 1990 2000 2010 2020
Mean length
(in
)
Year
77
Age-0 largemouth bass growth did not reflect a round goby effect. Water temperatures have been shown to affect growth rates of both species (Forney 1972, Rypel 2009). An analysis including all fyke net years (2007-2017) found no correlation between water temperature and age-0 largemouth bass lengths in the fall but there was a significant positive relationship between water temperature and age-0 smallmouth bass lengths in the fall (r2 = 0.32, p = 0.04). Continued monitoring is required to better understand the roles of prey availability as it relates to round goby abundance and water temperature on age-0 black bass growth.
Figure 3. September mean length of age-0 smallmouth bass in pre-goby years (2010-2012) and goby years (2015-2017). Error bars represent 1 SE. Letters above bars indicate which years are different (years sharing a letter are not different).
Preliminary assessment of the impacts of the round goby on predator diets indicates that predators do consume round gobies at all ages and during all seasons, and predation rate appears to increase as round goby density increases. While it is still too early for our data to detect increases in adult predator growth rates, we do have evidence of increased age-0 smallmouth bass growth likely due to the round goby invasion, and we expect age-0 walleye growth rates to follow suit if round goby densities continue to increase. As multiple event
0
1
2
3
4
5
6
2010 2011 2012 2015 2016 2017
Mean
len
gth
(in
)
Year
D CD A B C E
78
spawners, the round goby will likely provide young throughout the summer that are suitable size for age-0 predators. We also expect adult predator growth rates to respond to increasing round goby densities and we will use predator length-at-age to assess the effects of round gobies on adult predator growth. We expect this assessment to become more conclusive with additional years of growth estimates providing round goby densities remain high. Finally, we expect predation on round gobies to buffer predation on other preyfish species, and completion of the 3-year post-round goby diet study should allow us to test this expectation.
Fetzer, W. W., C. J. Farrell, J. R. Jackson, and L. G. Rudstam. 2016. Year-class variation drives interactions between warm-water predators and yellow perch. Canadian Journal of Fisheries and Aquatic Sciences 73:1330-1341.
Forney, J. L. 1972. Biology and management of smallmouth bass in
Oneida Lake, New York. New York Fish and Game Journal 19:132-154.
Forney, J. L. 1974. Interactions between yellow perch abundance,
walleye predation, and survival of alternate prey in Oneida Lake, New York. Transactions of the American Fisheries Society 103:15-24.
Hall, S. R. and L. G. Rudstam. 1999. Habitat use and recruitment: a
comparison of long-term recruitment patterns among fish species in a shallow eutrophic lake, Oneida Lake, NY, U.S.A. Hydrobiologia 408/409:101-113.
Jackson, R. J., A. J. VanDeValk, T. E. Brooking, K. T. Holeck, C.
Hotaling, and L. G. Rudstam. 2016. The fisheries and limnology of Oneida Lake 2016. New York State Department of Environmental Conservation, Albany, New York.
Rypel, A. L. 2009. Climate-growth relationships for largemouth bass
(Micropterus salmoides) across three southeastern USA states. Ecology of Freshwater Fish 18:620-628.
79
Table 1. Summary of predator diets collected in 2017. Percent composition (by weight) provided for select prey species (YP-yellow perch, Gizz-gizzard shad and RG-round goby) in diets of inshore predators (LMB-largemouth bass, SMB-smallmouth bass, CP-chain pickerel and WE-walleye) collected by electrofishing and offshore walleye collected by trawling. % composition (biomass) Habitat Species Month N # empty YP0 YP>0 Gizz0 Crayfish RG Other
Inshore LMB May 6 1 0 11 0 40 6 42 June 12 3 0 21 14* 0 15 50 July 35 5 11 47 0 4 23 16 August 31 5 33 0 2 9 32 24 September 28 8 3 0 54 6 32 5 October 21 9 0 0 79 0 18 3
SMB May 21 7 0 65 0 10 24 0 June 6 4 0 35 0 0 65 0 July 9 4 0 0 28 50 22 0 August 13 6 0 0 40 0 60 0 September 24 13 0 0 57 13 30 0 October 26 6 0 0 94 0 6 0
CP May 1 0 0 0 0 0 0 100 June 5 5 0 0 0 0 0 0 July 14 8 3 97 0 0 0 0 August 10 5 0 20 4 0 77 0 September 27 24 5 0 0 0 66 29 October 23 10 4 0 96 0 0 0
WE May 63 18 0 80 0 0 1 19 June 53 20 21 42 0 0 27 10 July 74 22 43 33 1 0 15 8 August 84 41 0 6 94 0 0 0 September 87 31 5 3 86 0 2 4 October 139 16 3 0 97 0 0 1
Offshore WE May 40 13 0 96 0 0 0 4 June 45 1 80 0 0 0 0 20 July 16 2 93 0 0 0 7 0 August 2 2 0 0 0 0 0 0 September 1 0 0 0 88 0 0 12 October 16 0 5 0 68 0 18 9
*indicates age-1 gizzard shad “Other” includes age-0 walleye, Lepomis spp., age-0 white perch Morone americana, age-0 brown bullhead Ameiurus nebulosus, banded killifish Fundulus diaphanus, logperch Percina caprodes, age-0 smallmouth bass, and emerald shiner.
80
Appendix 3: Data collection methods
Limnology. Zooplankton samples are collected weekly (May-October) from 1-5 sites with a 153 um mesh nylon net (0.5 m diameter) using a vertical tow from 0.5 m above the bottom to the water surface. Samples are preserved in 70% ethyl alcohol (8% sugar-formalin solution 1975-1996). Zooplankton are identified, counted, and measured (to the nearest µm) using a digitizing tablet and microscope (1998 – present). Previous methods include use of a dissecting microscope and calipers (1975-1982), and a touch screen setup with computer-assisted plankton analysis system WSAM (1983-1997) (Hambright and Fridman 1994). Mean May-October biomass is calculated from weekly averages using the length – weight regressions in Watkins et al. (2011). These values are in dry weight. Integrated water samples for total phosphorous (TP) and soluble reactive phosphorous (SRP) are collected using a 1.9 cm inside diameter Nalgene tube lowered to a depth of 1 meter above bottom, and frozen for later analysis. In the lab, a 50 mL aliquot of unfiltered water is analyzed for TP using the persulfate digestion method (Menzel and Corwin 1965). For SRP, lake water is filtered through a Whatman 934-AH glass fiber filter and a 50 mL aliquot is analyzed using the molybdate method of Strickland and Parsons (1972). For chlorophyll-a measurements, lake water (up to 2.0 L) is filtered through Whatman 934-AH glass fiber filters and the filters are assayed using the acetone extraction method (Strickland and Parsons 1972). Annual averages are calculated as the average of weekly values collected at 1 to 5 stations from May to October. All 5 stations are included when available, except for Secchi depth from the shallow station (Three Mile Bay) because the Secchi disk is sometimes observed on the bottom. Beginning in 2010, one site (Buoy 117) was dropped from standard sampling and a new sampling protocol for water chemistry was developed. Four sites were sampled each week, and on week 1 water samples were processed by individual stations as in 1975-2009. On weeks 2-4, water from all four sites was pooled for analysis. This rotation was maintained throughout the sampling season. Samples were pooled for water chemistry only, not zooplankton. Beginning in 2009, nutrients samples were analyzed at the Upstate Freshwater Institute (UFI). This EPA approved laboratory uses SM 18-20 4500-P E for TP and SRP, and SM 20 4500-SiO2 C for SRS (APHA). Beginning in 2013, chlorophyll were analyzed with a Turner Design fluorometer after extractions following the EPA standard operating procedure LG405 with the exception that all samples are run during the winter after the completion of the field season.
APHA. 1998. Standard Methods for the Examination of Water and Wastewater. 20th edition.
EPA LG405 Standard Operating Procedure for In Vitro Determination of Chlorophyll a in Freshwater Phytoplankton by Fluorescence
81
Larval fish surveys: Miller high-speed sampler surveys are designed to estimate abundance of larval walleye and yellow perch. Larval walleye and yellow perch are sampled when yellow perch reach approximately 8 mm and again at approximately 18 mm. For each survey, the lake is divided into two or more horizontal and vertical depth strata and samples taken at a total of 46 randomly selected sites within designated strata. At each site, four Miller samplers are towed simultaneously at different depths and catches are pooled by stratum. Distance towed is about 1.6 km at a speed of 3.6 m/s. Larval fish captured are identified, counted, and measured. Density estimates are calculated for each strata based on catch and volume of water strained. Catches of yellow perch in the 18 mm survey are adjusted for size-specific gear avoidance (Noble 1970). Gill net surveys: Standard gill net catches provide an index of the adult walleye and yellow perch populations as well as relative abundance estimates of various other species. A variable mesh multifilament gill net is fished overnight at a different standard site each week for 15 consecutive weeks starting in the beginning of June and continuing through mid-September. The net consists of four gangs 45.75 m long by 1.83 m deep sewn together to form one 183 m long net. Each gang consists of six 7.6 m panels with 38, 51, 64, 76, 89 and 102 mm stretch mesh. The net is set around sunset, fished on the bottom, and retrieved in the morning at about 0730. The time fished varies somewhat with season but has been identical for each location each year. All fish (or a subsample of at least 60 individuals of a species) are measured (total length in mm), weighed (g), sexed, stomach contents recorded, and scales taken. Large mesh gill nets were used to monitor sturgeon reproductive status and abundance and growth in 4 different substrate types. Variable (152, 203, 254, and 305 mm stretch mesh) mesh monofilament gill nets 61 m in length were set for approximately 4 hours at 12 sites monthly in May and June. All sturgeon caught were examined for tags, measured, weighed, a fin ray section removed for age determination, diet recorded using gastric lavage, tagged with both a Carlin dangler tag and PIT tag, and released. Trawl surveys: The catch in trawls provides an estimate of year class abundance for young-of-year (age-0) and yearling walleye as well as prey species, primarily young yellow perch. Trawling begins around the middle of July when age-0 yellow perch become demersal (at about 1 g in weight) and weekly surveys continue until three October surveys are completed. A 5.5 m otter trawl is towed for 5 minutes, sampling approximately 0.10 ha per haul. Ten standard sites are sampled in each survey. Age-0 fish are identified, counted, total weight by species recorded to the nearest gram, and a subsample of fish measured for length. Lengths are recorded and scale samples taken on all older fish. A series of three trawl surveys at the same sites centered around May 1 is also conducted to assess age-1 walleye and yellow perch abundance.
82
Hydroacoustic surveys: Pelagic fish biomass is estimated in the end of August–beginning of September using hydroacoustics. Surveys are conducted using a 123 kHz split beam unit (Biosonics DT-X, pulse length 0.4 ms, 7.8o beam width) along a set of approximately 8 transects from the east to the west ends of the lake. Surveys are typically conducted during two consecutive nights starting one hour after sunset. Acoustic data are analyzed with EchoView (v5.4 in 2016). Echograms are checked for problems associated with poor bottom detection, bubbles from waves, echoes from macrophytes, and other sources of noise. Questionable areas are removed from the analysis. Attempts are made to sample as close to the bottom as possible by re-defining the bottom at high magnification when needed. All densities are calculated from in situ backscattering cross section (average for targets larger than –60dB) and echo integration according to the standard operating procedure for Great Lakes acoustics (Parker-Stetter et al. 2009). Noise level at 16 m, the maximum depth in Oneida Lake is estimated to be –85 dB (uncompensated TS) thus satisfying a 15 dB signal to noise ratio throughout the water column for the smallest targets included in the analysis as recommended by Rudstam et al. (2009). Analyses are conducted using each transect as cluster of elementary sampling units of 500 m. Cluster analysis was used to estimate mean density and standard error using standard formulas (Scheaffer et al. 2006) and a program available on the web site “Acoustics Unpacked” (www.acousticsunpacked.org, Sullivan and Rudstam 2008). Fish are sampled in association with acoustic surveys using a midwater fry trawl and fine mesh gill nets. These gears are used to assess the species composition of young fish in the pelagic zone. The trawl measures 2 m x 2 m at the mouth and is mounted in a metal frame. The first 2 m of the net is comprised of 12.7 mm stretch mesh, the next 2 m of 6.4 mm stretch mesh, and the cod end of the net consists of a 0.5 m plankton net and bucket with 1 mm mesh. At each site, one haul divided into 2.5 minutes at 4.3 to 6.1 m depth and 2.5 minutes at 2 to 3.8 m depth (determined from rope angles) and a second 5 minute haul at the surface (sampling the top 2 m of the water column) are conducted. Two trawl hauls are completed at each of 10 sites, and fish are preserved in formalin and returned to the lab for species identification, enumeration, and measurement. Fine mesh gill nets, 21 m long, are set either on bottom or suspended from the surface. Each gill net consists of seven 3 m wide by 6 m deep panels of different mesh sizes (6.2, 8.0, 10.0, 12.5, 15.0, 18.7 and 25.0 mm bar mesh). Paired (1 surface and 1 bottom) gill nets are set at each of 4 deep stations, and 4 shallow stations are sampled with only 1 net that samples the entire water column. Acoustic density estimates are apportioned to emerald shiners, gizzard shad, and other fish based on catches in vertical gill nets and midwater trawls after accounting for the relative length selectivity and effort of the two gears. Fish in the top 2 m of the water column that are not surveyed with acoustics are accounted for by calculating the proportion of gizzard shad and emerald shiners caught in the top 2 m in vertical gillnets set compared to the rest of the water cloumn.
83
Fyke Net Surveys: We sample 18 sites around the lake representative of nearshore habitat types. Sites were selected to represent the common substrates in the nearshore in the proportions they occur and distributed around both shores of the lake as evenly as possible while still achieving substrate representation. Each site is sampled via approximately 24 hour sets of a fyke net comprised of a 0.9 m x 1.5 m frame fitted with 12.7 mm (1/2”) delta knotless mesh. In 2008, we concurrently sampled 14 sites with a fyke net comprised of a 0.9 m x 1.5 m frame fitted with 5 mm (1/4”) delta knotless mesh From 2009 onwards, all 18 sites were sampled with nets of both mesh sizes. Sampling is typically conducted in September of each year.
84
Appendix 4:
Standard Data Tables
85
Table A1. Physical, chemical and biological characteristics of Oneida Lake since 1975. Secchi depth (m), chlorophyll-a (µg/L), total phosphorous (TP, µg/L) soluble reactive phosphorous (SRP, µg/L), total zooplankton biomass (µg/L), and Daphnia spp. biomass (µg/L) are averages of weekly data from 1 to 5 stations from May to October. Ice freeze day (day since Dec 1), ice duration and ice out day (day of year) are noted at CBFS and refer to the year of ice break-up. The lake was not completely frozen over in the winter of 2001. Summer temperature (oC) is the average temperature from June to Aug measured every hour at 2 m depth at a site near Shackelton Point.
Year Secchi Chl-a SRP TP Zoopl.
Biomass Daphnia Biomass
First Freeze Day
Ice Duration
Ice Out Day Sum Temp
1975 2.8 9.0 17.6 45.9 211 107 no data no data 87 22.2 1976 2.8 9.9 3.3 29.5 241 163 19 99 87 20.6 1977 2.6 11.2 5.2 36.2 209 53 3 118 90 20.9 1978 2.9 7.7 16.5 42.5 116 73 15 121 105 22.0 1979 3.3 7.6 29.0 56.9 226 101 29 96 94 19.8 1980 2.6 12.7 10.2 45.2 257 126 35 91 95 20.4 1981 2.3 11.7 13.8 31.3 243 45 15 95 76 21.6 1982 2.2 9.0 15.2 48.0 260 93 20 118 107 20.8 1983 2.6 8.0 21.7 38.6 261 107 13 74 87 22.3 1984 2.3 9.2 14.7 30.1 231 104 21 111 101 21.6 1985 2.2 10.5 11.6 38.3 261 82 40 79 88 20.4 1986 2.4 10.3 27.5 67.1 304 178 19 104 92 20.4 1987 2.9 6.5 7.3 27.6 178 97 35 86 90 21.7 1988 2.7 9.4 17.1 34.6 248 99 34 91 94 20.8 1989 3.4 5.2 9.4 24.1 185 81 16 102 84 21.9 1990 2.4 9.5 4.8 22.0 221 65 5 107 81 21.7 1991 2.4 11.7 4.6 23.2 188 67 31 78 78 23.0 1992 2.8 7.1 1.8 20.1 315 196 25 93 102 20.2 1993 3.9 5.1 5.9 15.8 157 64 24 99 105 21.4 1994 3.7 6.6 6.2 30.4 193 103 27 113 109 22.0 1995 4.9 3.2 10.0 22.9 207 140 39 75 97 23.2 1996 3.6 5.5 6.0 19.9 222 128 32 100 101 22.0 1997 3.6 5.3 3.3 14.7 300 135 39 88 96 21.6 1998 3.0 5.2 5.2 21.5 161 57 48 58 86 22.5 1999 3.3 6.0 6.3 15.2 206 82 33 94 96 23.3 2000 2.9 6.5 4.4 18.1 154 85 45 63 77 21.3 2001 3.6 5.3 10.4 27.8 237 101 12 117 103 22.4
2002 3.7 4.8 7.0 27.2 162 75 no freeze 0 no
freeze 23.0 2003 3.7 6.5 9.8 27.3 209 92 10 104 105 22.2 2004 3.4 7.7 10.8 29.0 233 99 21 90 95 21.5 2005 4.2 3.8 16.4 29.4 259 116 26 97 98 24.2 2006 3.1 7.3 10.6 29.2 209 77 18 72 91 22.9 2007 3.5 5.8 6.4 20.9 185 68 54 71 94 22.6 2008 4.2 3.8 12.2 24.6 165 45 19 83 92 22.5 2009 3.7 4.0 24.4 117 40 24 85 81 21.7 2010 4.4 2.6 11.1 28.5 139 48 23 85 81 23.4 2011 3.2 4.9 7.9 30.5 97 22 17 107 94 23.3 2012 4.7 2.9 15.3 31.5 183 63 46 21 53 24.1
86
Year Secchi Chl-a SRP TP Zoopl.
Biomass Daphnia Biomass
First Freeze Day
Ice Duration
Ice Out Day Sum Temp
2013 3.2 3.9 10.7 30.8 209 64 30 80 91 22.8 2014 3.3 4.2 11.1 28.0 176 43 30 118 103 22.3 2015 3.2 3.2 6.5 22.5 214
47
37 99 105 22.6
2016 4.3 3.7 7.8 24.5 167
42
45 74 57 23.4 2017 3.1 5.8 10.2 25.1 155
1 32
1 20 63 90 22.4
Avg
3.3 6.7 10.5 29.8 206 86 26.7 88.1 91.8 22.0
1 Shackelton Point site only.
87
Table A2. Walleye age-specific density estimates since 1957 (in fish/ha). Age 1, 2 and 3 are estimated from the average of trawl and gill net estimates using catchabilities in Irwin et al. (2008). Bold values are from mark-recapture estimates. Densities of walleyes for intervening years were approximated from the distribution of mortality between successive population estimates (for 2011 estimates annual mortality of 20% was used). Estimates from 1978-1987 and 1992-1994 from Irwin et al. (2008).
Year Age 1 Age 2 Age 3 Age 4 Age 5 Age 6 Age ≥ 7
Total (Age-≥4)
1957 no
data no
data no
data 0.4 6.22 0.97 4.62 12.21 1958 9.18 1.55 4.72 37.82 0.6 6.12 5.59 50.13 1959 0.60 12.23 3.72 2.69 34.12 0.27 9.47 46.54 1960 4.94 3.62 22.70 1.8 2.36 15.74 4.8 24.7 1961 27.87 18.72 4.76 20.82 2.45 2.14 14.9 40.31 1962 15.84 14.49 12.62 3.15 13.93 1.71 10.35 29.14 1963 24.58 16.31 17.92 13.26 2.56 15.32 7.61 38.75 1964 34.61 19.28 10.36 9.03 8.85 1.71 15.29 34.88 1965 43.68 19.15 12.78 8.69 5.53 7.18 10.53 31.93 1966 22.09 31.64 15.05 11.61 7.1 4.52 14.48 37.71 1967 3.99 19.27 29.23 10.29 8.17 3.77 10.42 32.66 1968 22.35 2.89 20.67 17.37 5.66 3.88 8.88 35.79 1969 93.66 31.11 4.87 12.74 13.83 4.65 9.44 40.65 1970 3.10 37.77 10.75 1.18 8.41 9.53 9.05 28.16 1971 4.07 0.53 8.00 9.53 1.01 5.48 12.1 28.12 1972 80.32 9.21 1.54 23.09 6.19 0.86 11.42 41.55 1973 0.65 43.68 4.58 1.41 12.63 3.63 7.17 24.84 1974 6.08 2.18 47.64 2.65 0.37 2.52 3.48 9.02 1975 1.56 3.68 1.08 29.91 2.6 0.36 5.88 38.76 1976 92.71 3.61 3.23 1.08 27.76 2.11 5.06 36.00 1977 0.70 55.05 2.56 1.92 0.49 15.08 3.9 21.39 1978 36.75 0.96 31.26 1.56 1.64 0.36 16.67 20.24 1979 3.35 30.20 1.04 22.17 1.24 1.23 11.27 35.91 1980 2.48 4.41 22.30 0.98 14.45 0.81 8.2 24.44 1981 39.70 4.52 5.71 21.39 0.64 9.45 5.87 37.35 1982 26.88 22.72 3.87 3.53 17.5 0.45 10.37 31.85 1983 14.32 33.19 30.66 2.58 2.88 12.55 7.12 25.13 1984 9.79 9.43 20.79 13.77 2.13 2.06 13.33 31.28 1985 10.23 6.85 14.32 26.89 11.68 1.55 10.27 50.4 1986 15.01 9.07 7.03 9.96 22.41 8.7 7.77 48.84 1987 3.09 13.46 6.31 7.89 8.05 16.75 11.13 43.82 1988 105.80 2.14 12.59 10.34 5.02 9.66 22.32 47.34 1989 3.88 50.90 2.80 8.16 7.58 7.68 13.29 36.71 1990 7.98 8.29 49.85 1.16 5.99 5.41 14.54 27.1
88
Year Age 1 Age 2 Age 3 Age 4 Age 5 Age 6 Age ≥ 7
Total (Age-≥4)
1991 12.22 7.31 3.91 19.75 2.78 4.84 13.15 40.52 1992 45.62 9.25 6.74 1.79 16.71 1.15 10.45 30.1 1993 3.55 26.66 2.87 1.8 1.42 11.57 7.57 22.36 1994 8.64 2.40 23.14 2.29 1.19 0.98 12.83 17.29 1995 5.81 5.31 1.14 5.65 1.56 0.65 6.22 14.08 1996 9.66 2.65 2.78 1.32 5.52 0.92 4.87 12.63 1997 3.67 4.82 3.01 1.57 1.2 5.39 4.00 12.16 1998 22.17 1.43 4.16 0.59 1.50 1.17 7.33 10.59 1999 13.65 7.68 1.88 1.54 0.57 1.44 6.86 10.42 2000 9.58 11.81 6.41 0.33 1.82 0.63 7.17 9.95 2001 7.26 12.47 5.99 3.9 0.38 2.15 6.9 13.33 2002 32.13 9.23 8.39 3.65 3.25 0.88 7.54 15.33 2003 10.87 14.43 3.65 2.78 3.85 2.71 8.32 17.66 2004 6.39 12.94 12.19 4.74 3.92 2.59 6.89 18.15 2005 8.52 1.59 4.65 6.15 4.97 5.53 6.05 22.71 2006 5.53 9.68 1.17 1.17 6.60 2.75 9.48 20.00 2007 9.24 6.67 4.41 1.27 1.03 7.09 9.28 18.67 2008 2.07 2.32 0.87 4.77 1.12 0.91 14.46 21.27 2009 0.11 5.51 2.67 3.32 4.21 0.99 13.58 22.11 2010 1.81 5.13 3.69 4.52 2.94 3.72 12.88 24.06 2011 8.37 2.73 6.95 2.60 3.38 2.20 12.40 20.57 2012 3.95 7.65 2.49 4.50 1.94 2.52 10.90 19.86 2013 5.54 6.43 9.71 2.25 3.36 1.45 9.93 16.99 2014 9.40 10.98 4.36 8.98 1.91 2.86 9.67 23.42 2015 11.43 10.32 5.64 3.29 8.25 1.63 10.65 24.36 2016 12.18 10.23 6.57 2.34 3.29 7.52 7.58 20.73 2017 32.96 13.53 10.33 5.25 1.87 2.63 12.08 21.84
89
Table A3. Fish observed in stomachs of yearling and older walleye taken by trawls and electrofishing during October and November since 1971, expressed as numbers per kg of walleye (YP-yellow perch; Morone-white perch or white bass; Gizz-gizzard shad; ES-emerald shiner; RG-round goby)..
Year #
examined %
empty YP Morone Gizz ES
RG Other UID Total
1971 240 37 3.92 0.01 0.00 0.00 0.00 0.06 1.59 5.58 1972 163 58 1.02 0.10 0.00 0.00 0.00 0.62 0.89 2.63 1973 295 32 0.69 1.36 0.00 0.00 0.00 0.43 1.35 3.83 1974 228 27 2.11 1.15 0.01 0.11 0.00 0.38 1.76 5.52 1975 204 68 0.20 0.13 0.00 0.02 0.00 0.17 0.24 0.76 1976 156 36 1.31 0.89 0.00 0.16 0.00 0.75 1.17 4.28 1977 70 19 3.14 1.25 0.00 0.00 0.00 0.14 0.89 5.42 1978 85 56 0.51 0.12 0.00 0.00 0.00 0.47 0.74 1.84 1981 88 66 1.52 0.16 0.00 0.00 0.00 0.00 0.56 2.24 1982 122 11 0.38 5.27 0.00 0.00 0.00 0.00 0.54 6.19 1983 117 62 0.19 0.79 0.00 0.00 0.00 0.00 0.30 1.28 1984 148 59 0.21 0.45 0.97 0.00 0.00 0.07 0.46 2.16 1985 151 50 1.60 0.04 0.36 0.00 0.00 0.13 0.44 2.57 1986 193 45 1.60 0.16 0.05 0.00 0.00 0.15 0.49 2.45 1987 194 23 0.05 0.64 1.96 0.00 0.00 0.02 0.54 3.21 1988 180 55 0.36 0.00 0.30 0.00 0.00 0.07 0.33 1.06 1989 193 26 0.00 0.18 5.42 0.00 0.00 0.03 0.83 6.46 1990 179 28 0.03 0.00 4.91 0.01 0.00 0.00 0.66 5.61 1991 137 20 0.02 0.01 3.81 0.00 0.00 0.10 0.77 4.71 1992 65 58 0.17 0.02 0.22 0.00 0.00 0.07 0.32 0.80 1993 134 25 2.13 0.51 0.01 0.42 0.00 0.81 1.28 5.16 1994 120 55 0.36 0.06 0.71 0.17 0.00 0.04 0.75 2.09 1995 86 45 0.44 0.35 0.06 0.02 0.00 0.13 0.67 1.67 1996 184 32 0.85 0.37 0.10 0.07 0.00 0.52 1.39 3.30 1997 75 45 0.28 0.36 0.00 0.02 0.00 0.26 1.15 2.07 1998 78 40 0.28 0.15 0.00 0.10 0.00 0.18 0.66 1.37 1999 64 42 0.25 0.03 0.25 0.75 0.00 0.03 0.61 1.92 2000 134 21 0.04 0.28 2.32 0.01 0.00 0.01 0.92 3.58 2001 123 28 0.40 0.18 0.88 0.17 0.00 0.24 0.36 2.23 2002 83 41 0.03 0.04 1.03 0.16 0.00 0.03 0.31 1.60 2003 183 39 0.84 0.09 0.36 0.04 0.00 0.21 0.52 2.06 2004 135 13 0.30 0.38 2.36 0.57 0.00 0.06 0.91 4.58 2005 134 30 1.08 0.11 0.70 0.31 0.00 0.13 0.52 2.85 2006 110 25 0.37 0.29 2.50 0.15 0.00 0.09 0.51 3.91 2007 264 50 0.87 0.00 0.67 0.02 0.00 0.08 0.45 2.09 2008 324 16 0.58 0.08 3.54 0.02 0.00 0.08 1.39 5.69 2009 308 44 1.21 0.05 1.63 0.02 0.00 0.05 0.26 3.21 2010 164 13 0.03 0.01 3.93 0.04 0.00 0.01 1.12 5.15 2011 207 37 0.22 0.58 0.93 0.15 0.00 0.08 0.65 2.60 2012 206 21 0.03 0.00 2.25 0.03 0.00 0.02 0.93 3.25 2013 234 63 0.14 0.03 3.12 0.01 0.00 0.58 0.45 4.33 2014 196 30 0.49 0.09 1.84 0.00 0.00 0.07 1.08 3.56 2015 152 14 0.13 0.00 2.91 0.37 0.06 0.49 1.88 5.80 2016 150 21 0.31 0.01 1.79 0.22 0.38 0.03 0.97 3.71 2017 202 12 0.17 0.08 5.76 0.01 2.05 0.08 1.18 9.33
90
Table A4. Young-of-year and age-1 walleye density estimates and mean lengths. Larval walleye density (at the time of the 8 mm perch survey) are from Miller sampler surveys at that time or calculated from the 9 mm larval walleye survey. Age-0 walleye densities (#/ha) and mean lengths (TL, mm) on October 1 are from trawl surveys surrounding the Oct 1 date (5 dates, 50 trawls), and age-1 walleye densities (#/ha) and mean lengths on May 1 are from trawl surveys around May 1 (3 dates, 30 trawls). Densities calculated based on area swept (0.1 ha per trawl) assuming no avoidance.
Year Class
Larval Density
Oct 1 Age 0
Density Oct 1 Age 0 Length
May 1 Age 1 Density
May 1 Age 1 Length
1961 114.5 140.6 1962 135.9 142.9 44.2 158.8 1963 98.5 124.2 37.9 153.6 1964 80.6 137.5 73.4 161.3 1965 79.4 153.8 133.0 163.7 1966 1,348 6.3 138.5 9.0 148.1 1967 967 82.4 126.6 1968 1,580 219.0 143.9 184.2 163.8 1969 559 50.0 142.7 17.0 161.0 1970 2,271 25.8 120.7 24.5 166.7 1971 309 42.0 167.0 124 180.6 1972 1,599 6.0 120.6 12.5 156.0 1973 222 1.6 164.2 4.5 174.0 1974 1,464 14.8 99.6 6 143.6 1975 1,362 148.4 171.2 59 184.6 1976 2,327 1.6 133.2 1.5 158.5 1977 660 71.6 136.5 108 167.8 1978 14.6 123.0 1979 4.6 145.9 1980 17.8 154.5 28.0 165.1 1981 57.8 149.4 1982 22.4 162.0 27.7 175.6 1983 28.0 154.9 25.5 166.8 1984 6.0 132.8 26.3 151.3 1985 31.0 141.0 31.5 159.1 1986 5.4 140.4 3.8 165.3 1987 29.8 176.7 25.0 186.5 1988 10.4 142.3 2.2 146.0 1989 3.0 160.3 17.0 154.2 1990 14.4 173.8 15.0 177.6 1991 46.7 173.2 44.0 175.2 1992 333 12.4 150.4 5.0 157.1 1993 10.4 147.1 13.0 168.2 1994 11.4 130.8 11.5 163.3 1995 13.6 135.4 11.3 165.7
91
Year Class
Larval Density
Oct 1 Age 0
Density Oct 1 Age 0 Length
May 1 Age 1 Density
May 1 Age 1 Length
1996 1.8 150.3 5.0 168.3 1997 8.0 158.8 0.7 141.5 1998 275 2.4 207.4 3.0 189.0 1999 1,773 12.4 144.3 2.7 121.8 2000 1,208 3.0 176.5 14.3 180.7 2001 2,541 19.2 153.0 33.0 154.0 2002 213 3.7 173.6 12.3 179.3 2003 986 2.5 139.1 19.5 167.4 2004 3,196 15.3 150.7 21.0 161.8 2005 8,106 5.6 106.5 13.3 143.2 2006 1,304 2.2 163.0 9.3 173.3 2007 942 7.5 131.9 3.3 183.3 2008 5,102 5.4 129.7 3.67 132.04 2009 957 1.6 154.2 1.67 144.0 2010 898 14.3 132.3 27.5 162.0 2011 251 2.5 183.0 0.0 - 2012 2,694 1.0 104.0 0.3 140 2013 530 1.7 167.0 3.0 175 2014 1457 11.8 146.0 0.7 133 2015 1660 2.2 138.0 1.0 122 2016 1546 9.0 122.0 5.0 137 2017 581 5.8 149
92
Table A5. Yellow perch density estimates since 1961. Data are from mark-recapture (bold) or based on the catch in gill nets using size specific net selectivity.
Year Density (#/ha) at age Total (age3+) 2 3 4 5 6 >6
1961 51.2 11.3 56.7 9.4 10.2 17.5 105.1 1962 18.9 38.4 27.8 40.2 12.8 6.5 125.7 1963 15.6 26.7 40.1 32.7 33.5 16.1 149.1 1964 10.5 11.3 45.0 44.4 32.8 12.4 146.0 1965 11.3 44.2 12.7 67.2 41.4 14.1 179.7 1966 34.3 19.6 28.6 20.9 39.2 12.7 120.9 1967 1.4 50.1 28.1 27.2 20.4 20.3 146.0 1968 37.0 3.5 70.2 16.0 17.1 24.6 131.4 1969 33.2 21.7 7.3 54.3 18.9 18.9 121.2 1970 6.7 48.0 23.1 7.5 61.9 37.9 178.4 1971 1.9 7.7 52.6 17.1 3.0 30.2 110.7 1972 41.5 1.8 7.6 26.9 9.0 11.9 57.1 1973 4.6 144.4 3.9 7.2 17.7 26.2 199.5 1974 No gill netting 1975 39.0 0.9 5.7 61.3 2.5 15.0 85.5 1976 5.3 56.5 2.8 11.2 51.2 14.4 136.1 1977 2.7 12.9 40.0 0.5 2.2 24.2 79.7 1978 19.7 3.9 8.6 41.7 3.6 28.8 86.6 1979 99.1 12.5 5.4 6.1 33.9 10.3 68.1 1980 4.9 179.2 16.3 8.6 14.5 41.3 260.0 1981 16.0 16.3 134.4 23.2 3.7 24.9 202.5 1982 31.2 10.3 10.6 99.6 4.3 8.0 132.8 1983 2.8 27.7 8.2 5.2 54.4 5.8 101.4 1984 18.6 12.6 48.3 17.2 10.3 36.0 124.5 1985 29.8 7.6 5.0 22.2 3.3 12.2 50.3 1986 29.5 24.0 10.3 8.1 28.9 9.0 80.3 1987 15.4 31.7 29.0 11.1 5.0 35.7 112.5 1988 10.0 15.5 24.7 18.9 4.3 21.9 85.4 1989 27.8 7.1 18.6 31.0 23.5 24.2 104.4 1990 8.7 33.5 2.9 5.8 17.2 18.0 77.4 1991 3.4 3.7 18.5 5.9 9.0 22.3 59.4 1992 47.9 5.5 5.2 18.4 6.5 10.0 45.5 1993 29.5 28.2 7.5 4.8 13.7 10.8 65.1 1994 1.7 10.4 8.9 1.5 0.8 6.5 28.1 1995 13.9 4.3 16.1 5.9 1.4 4.0 31.7 1996 26.4 10.7 4.0 8.5 3.6 3.8 30.6 1997 21.3 26.3 7.0 1.4 2.7 1.7 39.0 1998 13.2 23.9 22.0 10.4 4.2 3.9 64.3 1999 4.3 10.5 13.1 8.9 2.7 1.7 37.0 2000 20.3 8.9 15.2 19.4 10.5 7.3 61.4
93
Year Density (#/ha) at age Total (age3+) 2 3 4 5 6 >6
2001 3.7 21.5 7.1 4.8 5.8 6.5 45.7 2002 5.7 7.9 46.0 11.5 10.7 24.6 100.8 2003 1.7 2.3 7.1 21.7 6.1 9.8 47.0 2004 3.4 5.4 5.5 8.3 17.0 19.5 55.7 2005 2.9 13.4 12.4 4.9 9.2 32.6 72.5 2006 15.5 11.0 12.6 8.1 2.9 18.5 53.0 2007 38.2 15.0 7.1 6.5 3.5 11.7 43.8 2008 14.7 41.7 16.0 5.6 3.7 13.4 80.4 2009 8.3 14.8 12.1 3 5.8 3.4 39.1 2010 17.5 14.2 5.5 11.8 9.0 7.9 48.3 2011 33.2 8.5 12.9 6.5 13.8 7.9 49.5 2012 15.0 7.7 19.9 17.6 4.2 18.8 68.2 2013 58.1 23.4 8.1 11.4 18.0 19.7 80.6 2014 47.3 9.0 5.2 2.2 5.4 6.9 28.7 2015 20.0 24.9 8.8 4.2 1.0 5.7 44.6 2016 43.4 9.0 25.9 4.3 1.6 3.1 43.9 2017 13.8 11.0 6.8 12.1 4.4 21.1 55.5
94
Table A6. Young-of-year and age-1 yellow perch density estimates and mean lengths. Larval yellow perch densities (at 18 mm, #/ha) are estimated from Miller sampler surveys. Age-0 yellow perch densities (#/ha), age-0 mean lengths (TL, mm) are estimates for October 15 obtained from regression analysis of weekly catches throughout the season. Age-1 yellow perch density are from trawl surveys around May 1 and from mid-July through October (#/ha). Age-1 yellow perch mean lengths are from spring trawl surveys centered on May 1 since 1961.
Year class
Larval density October age-0 Age-1
density mean length
density spring
mean length
density summer
1961 2,850 60 19.4 1962 4,260 73 486 76 186.8 1963 780 60 71 15.8 1964 3,520 71 849 73 585.9 1965 140,100 2,610 60 30 2.0 1966 40,200 170 73 25 74 39.3 1967 61,200 2,240 72 136.5 1968 141,800 6,700 67 598 75 57.2 1969 69,200 210 65 2 0.5 1970 80,000 930 77 158 85 44.5 1971 216,400 3,520 57 52 62 30.5 1972 120,700 100 67 4 77 0.8 1973 16,600 510 86 63 90 46.0 1974 32,000 320 72 33 74 9.3 1975 188,700 450 65 5 75 4.3 1976 46,600 180 72 12 77 4.8 1977 65,200 4,140 69 3385 70 241.5 1978 180 73 13.5 1979 103,200 360 75 6.4 1980 131,600 500 81 118 81 100.9 1981 208,200 2,590 57 4.6 1982 353,400 980 63 25 68 10.6 1983 45,600 710 79 95 79 26.3 1984 16,000 810 71 103 73 32.1 1985 91,100 2,700 68 174 74 29.8 1986 14,600 70 82 2 84 1.8 1987 3,700 220 68 128 70 97.9 1988 76,200 220 81 19 83 4.5 1989 3,700 20 81 17 82 13.1 1990 117,000 460 73 184 70 121.9 1991 34,000 340 82 705 84 166.5 1992 60,800 100 73 13 79 5.4 1993 32,800 320 85 70 84 56.4 1994 21,800 280 83 281 83 30.1 1995 15,100 90 90 373 89 58.5
95
Year class
Larval density October age-0 Age-1
density
mean length
density spring
mean length
density summer
1996 43,600 80 80 74 81 24.3 1997 4,600 30 80 23 80 17.5 1998 57,100 700 83 457 84 99.3 1999 42,100 1,080 81 18 84 17.8 2000 19,300 140 78 73 79 7.2 2001 36,200 270 84 466 86 6.3 2002 23,400 1,660 76 380 80 17.6 2003 68,500 60 85 38 84 5.3 2004 60,700 180 86 36 84 5.7 2005 36,300 410 93 280 93 13.5 2006 58,502 240 79 117 79 19.6 2007 135,990 1,842 81 243 85 6.2 2008 67,420 720 73 104 76 9.1 2009 112,712 1,454 74 1.67 88 6.9 2010 62,853 829 72 47 78 17.8 2011 4,713 21 72 16 73 2.7 2012 22,297 18 88 20 84 28.8 2013 6,654 131 84 62 84 13.6 2014 12,035 427 78 19 77 8.42 2015 28,162 58 82 22 80 22.5 2016 33,477 1,270 74 435 77 46.6 2017 18,665 518 78
96
Table A7. Relative abundance of white perch year classes at successive stages of development. Age-0 white perch abundance represented by the calculated density from area swept in trawls in August-September, Age-0 Length is from October trawls, Age-1 Spring is from the CPUE in spring trawls, and age-1 and older are catches in standard gill nets. These values are data for the year of collection. The recruitment index (RI) is the sum of the gill net catch at age 2 and 3 of fish born that year (Fitzgerald et al. 2006). For example the RI value for years 1961 is the sum of the gill net catch of age 2 in 1963 and age 3 in 1964. Bold RI numbers for 1971 and 1972 includes an extrapolation of gill net catches for 1974 when gill nets were not used (see Fitzgerald et al. 2006).
Year Age-0 Age-0 Length
Age-1 Spring
Age-1 Age-2 Age-3 Age-4 Age-5 Age-6 Age7+ RI sum GN
1961 1,236 84 2 9 20 94 6 8 39 10 178 1962 316 94 0 15 2 34 66 10 13 114 140 1963 49 68 0 5 28 5 83 62 15 12 198 1964 56 79 0.4 0 55 5 36 20 62 54 54 232 1965 963 77 9.3 6 7 59 9 56 43 74 9 254 1966 1,318 78 0.0 0 19 5 28 8 54 19 5 133 1967 132 85 0.0 0 3 35 9 15 26 19 16 107 1968 12 86 0 0 6 18 6 20 22 7 72 1969 81 80 0.0 0 0 5 10 21 6 23 3 65 1970 263 81 0.0 0 4 16 20 46 37 56 25 179 1971 85 78 0.0 1 0 3 7 2 9 23 69 45 1972 30 84 0.5 0 11 3 0 0 2 8 9 24 1973 2,106 85 0.0 0 6 14 1 1 0 6 551 28 1974 355 72 3.0 No Gill Net Data 15 1975 208 87 0.0 0 240 5 143 14 2 11 3 415 1976 314 64 0.0 0 4 311 5 101 39 41 8 501 1977 957 77 0.0 0 1 11 128 4 52 11 517 207 1978 40 85 12.0 23 0 2 3 53 1 18 12 100 1979 1,740 78 1 224 8 17 1 228 30 6 509 1980 6,432 79 0 8 293 0 1 3 48 59 353 1981 278 75 2.0 0 1 4 775 28 22 132 10 962 1982 4,824 75 0 21 5 10 411 8 31 29 486 1983 6,669 80 0.0 0 0 38 5 6 343 28 249 420 1984 359 72 0.0 0 6 10 141 10 13 244 297 424 1985 102 70 0.5 1 31 23 12 212 15 372 38 666 1986 17 72 0.5 4 142 218 29 26 195 309 15 923 1987 5,259 62 0.0 1 27 155 31 11 11 69 17 305 1988 4 81 0.0 0 1 11 7 0 3 8 3 30 1989 886 78 0.0 3 4 14 34 4 0 8 8 67 1990 74 72 0.0 2 0 13 19 56 18 17 2 125 1991 86 90 0.0 6 4 3 1 4 19 19 6 56 1992 48 70 0.0 0 0 4 1 10 4 59 0 78 1993 797 79 0.0 0 3 2 2 1 18 40 15 66 1994 66 80 0.0 1 0 3 3 0 2 31 19 40 1995 613 97 0.5 2 4 0 6 2 4 18 243 36 1996 54 87 14.7 2 2 11 3 8 0 14 13 40 1997 956 76 0.0 0 155 17 8 1 5 14 415 200 1998 125 85 0.0 87 0 88 4 10 2 6 202 197 1999 8 97 0.7 40 315 13 122 9 0 4 132 502 2000 590 78 0.7 2 50 100 4 47 2 3 211 208 2001 59 84 2.3 6 56 152 211 14 55 0 283 494 2002 1,145 82 8.3 32 122 76 65 120 7 26 72 448 2003 59 84 5.3 0 106 89 46 52 36 17 5 346 2004 1,413 79 0.0 0 33 177 61 38 27 40 590 376 2005 81 98 1.3 44 1 39 227 40 53 17 210 421 2006 137 76 1.7 16 261 4 32 214 50 10 115 587 2007 140 81 0.0 12 111 329 20 34 198 67 78 771
97
Year Age-0 Age-0 Length
Age-1 Spring
Age-1 Age-2 Age-3 Age-4 Age-5 Age-6 Age7+ RI sum GN
2008 335 75 1.0 5 16 99 126 11 42 74 163 373 2009 196 73 0.7 32 39 99 138 277 38 150 22 773 2010 48 89 2.5 4 79 39 45 71 184 67 93 489 2011 1,181 76 0 22 10 84 32 28 84 282 5 541 2012 144 100 0 0 25 12 68 22 33 216 270 376 2013 7 86 0.3 44 0 68 47 97 29 261 50 546 2014 49 81 0.0 3 108 5 25 20 64 97 34 322 2015 19 88 0.0 0 26 162 17 35 37 168 445 2016 15 93 0.0 7 12 24 40 3 6 26 118 2017 896 74 0.0 37 36 22 27 77 7 43 250
98
Table A8. Abundance and biomass of pelagic fish (emerald shiners (ES), gizzard shad, and Alosa spp. (blueback herring and alewife) in Oneida Lake since 1994.
Abundance (fish/ha) Biomass (kg/ha)
Year ES
Age-0 ES
Age1+ Gizzard
shad Alosa spp.
Sum ES Age-0
ES Age1+
Gizzard shad
Alosa spp.
Sum
1994 3,589 1,352 2,515 607 7,457 2.2 4.1 15.6 6.1 28.0 1995 350 792 538 575 2,255 0.6 3.2 17.2 9.3 30.3 1996 2,909 280 22 492 3,704 2.3 1.0 1.3 5.3 10.0 1997 16,936 1,760 101 14 18,811 15.0 6.4 0.6 0.2 22.3 1998 2,254 5,668 41 3 7,966 1.0 16.1 0.4 0 17.5 1999 7,539 4,093 726 0 12,358 6.1 10.0 8.7 0 24.8 2000 3,463 1,836 1,936 0 7,235 3.4 5.2 6.3 0 14.9 2001 16,112 2,441 2,458 0 21,010 15.2 9.0 23.5 0 47.8 2002 20,529 2,516 2,924 0 25,969 9.4 7.2 5.6 0 22.2 2003 2,645 8,149 2,474 0 13,268 1.7 23.3 13.8 0 38.7 2004 9,057 1,407 2,664 0 13,128 10.1 4.9 14.6 0 29.6 2005 2,597 1,307 2,215 0 6,119 2.6 4.9 47.8 0 55.3
2006 2,651 666 1,716 0 5,033 2.6 2.3 15.5 0 20.4
2007 417 215 1,431 0 2,065 0.6 0.3 14.3 0 15.2
2008 7,900 381 2,073 0 10,354 6.6 1.3 14.6 0 22.5
2009 1,001 1,521 5,969 5 8,496 0.7 4.4 18.8 0.5 24.0
2010 8,350 2,032 7,643 26 18,051 7.0 6.3 24.7 0.2 38.2
2011 35,918 4,067 4,679 0 44,664 23.4 11.6 15.0 0 50.0
2012 2,749 1,224 2,773 0 6,746 3.2 3.8 29.9 0.0 36.9
2013 738 106 1,236 5 2,085 0.7 0.4 7.0 0.1 8.0
2014 2,031 774 4,949 0 7,754 3.0 1.1 11.4 0.0 16.0
2015 3,163 1,206 1,143 0 5,512 4.4 1.7 6.8 0.0 12.9
2016 7,257 2,766 1,539 1 11,563 6.4 2.4 16.5 0.1 25.4
2017 4,312 1,644 4,527 247 10,730 5.2 2.0 24.9 1.7 33.8
99
Table A9. Catch/hour of lake sturgeon in large mesh gill nets at 12 standard sites.
Year Month May June July August September October November
2002 - 0.39 0.35 0.10 0.10 0.16 - 2003 0.32 0.17 0.09 0.09 0.56 - - 2004 0.35 0.39 0.08 0.37 0.15 - - 2005 0.18 0.11 - - - - - 2006 0.31 0.11 - - - - 0.06 2007 0.30 0.11 - - - 0.07 - 2008 0.17 0.13 - - - - - 2009 0.20 0.14 - - - - - 2010 0.20 0.04 - - - - - 2011 0.34 0.04 2012 0.40 0.15 2013 0.20 0.19 2014 0.09 0.06 2015 0.24 0.03 2016 0.34 0.06 2017 0.11 0.05
100
Table A10a. Catches in ½” mesh fyke nets, Oneida Lake, 2007-2015 (n=18 sites). Mean Catch/Net
Scientific Name
2007
2008
2009
2010
2011
2012
2013
2014
2015
Family Lepisosteidae Longnose gar (Adult) <0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Family Amiidae Bowfin (Adult) 0.1 0.2 0.2 0.1 0.3 0.4 0.2 0.1 0.1 Family Clupeidae Gizzard shad (All) 0.2 0.4 0.3 4.1 35.3 3.4 25.1 0.5 0.9 Family Cyprinidae Common carp (All) 0.0 <0.1 0.2 0.0 <0.1 0.0 <0.1 0.0 0.0 Golden shiner (All) 0.2 0.5 0.3 0.2 0.2 0.0 0.1 0.1 0.0 Emerald shiner (All) 0.0 0.0 0.0 0.0 0.0 <0.1 <0.1 0.1 0.0 Spottail shiner (All) 0.1 0.0 0.2 0.2 0.0 0.1 0.0 0.0 0.2 Bluntnose minnow (All) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Family Catostomidae Longnose sucker (All) <0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 White sucker (All) 0.6 0.8 0.4 0.8 0.7 0.4 0.4 0.2 0.4 Creek chubsucker (All) <0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 Greater redhorse (All) <0.1 <0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Family Ictaluridae Yellow bullhead (All) 0.2 0.4 0.5 0.4 0.2 0.2 0.1 0.3 0.1 Brown bullhead (All) 0.8 0.9 0.8 0.6 0.5 0.3 1.8 0.5 0.9 Family Esocidae Chain pickerel (All) 0.4 <0.1 <0.1 0.7 0.6 0.5 1.0 0.5 0.8 Family Gadidae Burbot (Adult) <0.1 <0.1 0.1 0.1 0.0 <0.1 <0.1 0.1 0.1 Family Fundulidae Banded killifish (All) <0.1 0.0 0.0 0.0 <0.1 0.0 0.0 0.0 0.0 Family Percichthyidae White perch (YOY) 1.4 0.1 5.4 <0.1 4.5 0.0 0.0 0.1 0.9 White perch (Adult) <0.1 <0.1 <0.1 <0.1 0.4 <0.1 0.3 0.1 0.0 Family Centrarchidae Rock bass (Adult) 3.1 3.0 2.6 3.3 2.0 2.5 3.7 3.3 2.8 Green sunfish (Adult) 0.2 0.1 0.2 0.3 0.4 0.4 0.4 0.1 0.1 Pumpkinseed (Adult) 9.5 13.4 16.8 7.0 7.5 4.5 10.1 7.0 6.4 Bluegill (Adult) 2.6 3.8 9.3 3.5 4.3 4.4 19.5 5.2 1.7 (YOY - <75mm) 3.3 1.1 2.1 3.2 10.7 2.9 3.0 1.0 4.7 Smallmouth bass (YOY) 11.6 4.8 2.4 1.3 1.0 1.0 0.4 1.9 14.3 Smallmouth bass (Adult) 0.0 0.1 0.0 0.2 0.1 0.1 0.0 0.1 0.1 Largemouth bass (YOY) 1.2 2.2 0.7 0.8 0.8 1.0 1.3 2.9 1.8
101
Largemouth bass (Adult) 0.1 0.0 0.1 <0.1 <0.1 0.1 0.2 0.1 0.0 Black crappie (All) 2.4 0.9 0.9 0.8 3.3 0.9 6.2 2.2 1.4 Family Percidae Yellow perch (YOY) 18.2 1.5 0.5 0.3 1.0 6.2 0.3 0.8 4.5 Yellow perch (Adult) 16.0 26.1 24.5 19.5 19.6 16.3 24.5 14.9 25.7 Logperch (All) 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Tesselated darter (All) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Walleye (YOY) 0.2 0.4 0.1 0.2 0.1 0.1 <0.1 0.1 0.1 Walleye (Adult) 0.5 0.5 0.8 0.5 1.6 1.1 1.0 1.4 0.7 Family Sciaenidae Freshwater drum (Adult) <0.1 <0.1 0.1 0.3 0.4 0.4 0.4 0.2 0.1 Family Gobiidae Round goby 0.0 0.0 0.0 0.0 0.0 0.0 0.0 <0.1
102
Table A10b. Catches in ½” mesh fyke nets, Oneida Lake, 2016- (n=18 sites). Scientific Name
Mean Catch/Net
2016
2017
Family Lepisosteidae Longnose gar (Adult) 0.1 0.0 Family Amiidae Bowfin (Adult) 0.1 0.3 Family Clupeidae Gizzard shad (All) 0.1 2.5 Family Cyprinidae Common carp (All) 0.1 0.0 Golden shiner (All) 0.1 0.0 Emerald shiner (All) 0.0 0.0 Spottail shiner (All) 0.1 0.0 Bluntnose minnow (All) 0.0 0.0 Family Catostomidae Longnose sucker (All) 0.0 0.0 White sucker (All) 0.7 0.2 Creek chubsucker (All) 0.3 0.0 Greater redhorse (All) 0.0 0.0 Family Ictaluridae Yellow bullhead (All) 0.3 1.1 Brown bullhead (All) 0.4 1.1 Family Esocidae Chain pickerel (All) 0.8 0.1 Family Gadidae Burbot (Adult) 0.0 0.0 Family Fundulidae Banded killifish (All) 0.1 0.0 Family Percichthyidae White perch (YOY) 0.0 4.2 White perch (Adult) 0.1 0.1 Family Centrarchidae Rock bass (Adult) 3.1 2.7 Green sunfish (Adult) 0.1 0.1 Pumpkinseed (Adult) 9.1 14.3 Bluegill (Adult) 5.1 10.6 (YOY - <75mm) 7.7 0.2 Smallmouth bass (YOY) 3.0 3.2 Smallmouth bass (Adult) 0.3 0.0 Largemouth bass (YOY) 1.7 0.1 Largemouth bass (Adult) 0.1 1.2
103
Scientific Name
Mean Catch/Net Scientific Name
2016
2017
Black crappie (All) 1.5 2.9 Family Percidae Yellow perch (YOY) 0.4 0.0 Yellow perch (Adult) 26.7 23.4 Logperch (All) 0.0 0.0 Tesselated darter (All) 0.0 0.0 Walleye (YOY) 0.4 0.1 Walleye (Adult) 0.7 1.1 Family Sciaenidae Freshwater drum (Adult) 0.2 0.4 Family Gobiidae Round goby 1.6 0.4
104
Table A11a. Catches in 1/4” mesh fyke nets, Oneida Lake, 2008-2015 (2008: n=14 sites; 2009- : n=18 sites). Scientific Name
Mean Catch/Net
2008
2009
2010
2011
2012
2013
2014
2015
Family Lepisosteidae Longnose gar (Adult) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 Family Amiidae Bowfin (Adult) 0.1 0.3 0.0 <0.1 <0.1 0.0 0.0 0.0 Family Clupeidae Gizzard shad (All) 0.1 0.4 0.1 465.1 5.1 168.9 0.3 0.1 Family Cyprinidae Common carp (All) 0.0 <0.1 0.0 0.0 0.0 0.0 0.0 0.0 Golden shiner (All) 0.1 <0.1 0.1 0.4 0.1 0.2 0.1 0.0 Emerald shiner (All) 0.1 <0.1 <0.1 0.2 0.1 5.2 0.1 0.7 Spottail shiner (All) 0.0 0.8 0.1 2.8 0.2 0.7 0.0 0.0 Bluntnose minnow (All) 0.6 7.1 0.3 0.3 0.1 0.7 0.1 0.0 Family Catostomidae Longnose sucker (All) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 White sucker (All) 0.0 0.1 <0.1 0.1 <0.1 <0.1 0.0 0.2 Creek chubsucker (All) 0.0 <0.1 0.0 0.0 0.0 0.0 0.0 0.0 Greater redhorse (All) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Family Ictaluridae Yellow bullhead (All) 0.6 0.3 0.0 0.1 0.2 0.6 0.4 0.3 Brown bullhead (All) 0.5 0.2 0.2 0.2 0.3 0.4 0.3 0.3 Family Esocidae Chain pickerel (All) 0.0 <0.1 0.1 0.1 0.1 <0.1 0.1 0.1 Family Gadidae Burbot (Adult) 0.2 0.0 0.0 0.0 0.2 0.1 0.2 0.0 Family Fundulidae Banded killifish (All) 0.4 1.2 5.1 0.3 1.8 3.5 0.1 2.1 Family Percichthyidae White perch (YOY) 0.0 0.1 0.0 6.0 0.0 0.0 0.1 0.2 White perch (Adult) 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 Family Centrarchidae Rock bass (Adult) 3.6 2.5 3.3 3.0 3.6 2.9 4.2 2.6 Green sunfish (Adult) 0.3 0.3 <0.1 0.3 0.5 0.4 0.2 0.2 Pumpkinseed (Adult) 2.4 1.7 4.3 3.1 1.4 2.3 5.8 1.7 Bluegill (Adult) 2.4 4.4 5.3 3.5 3.5 10.0 4.7 0.6 (YOY - <75mm) 42.9 237.1 21.1 139.0 38.1 1009.7 89.3 176.3 Smallmouth bass (YOY) 11.2 2.5 2.5 5.0 6.2 0.8 9.1 4.2 Smallmouth bass (Adult) 0.9 0.0 0.1 <0.1 <0.1 <0.1 0.0 0.1
105
Scientific Name
Mean Catch/Net
2008
2009
2010
2011
2012
2013
2014
2015
Largemouth bass (YOY) 2.6 0.4 1.5 2.3 1.8 1.0 2.9 0.9 Largemouth bass (Adult) 0.0 0.0 0.0 0.1 <0.1 0.0 0.0 0.0 Black crappie (All) 0.3 0.1 0.5 0.9 0.2 0.9 0.7 0.6 Family Percidae Yellow perch (YOY) 18.3 22.4 25.7 11.8 16.5 9.2 18.0 39.1 Yellow perch (Adult) 1.9 9.4 1.9 3.8 2.0 7.0 6.9 2.6 Logperch (All) 0.1 0.4 <0.1 0.3 0.5 1.0 0.5 0.3 Tesselated darter (All) 0.1 0.6 0.1 0.1 0.1 0.5 0.3 0.2 Walleye (YOY) 0.0 0.0 0.2 <0.1 0.3 0.0 0.0 0.1 Walleye (Adult) 0.0 <0.1 0.1 0.2 0.0 0.1 0.1 0.1 Family Sciaenidae Freshwater drum (Adult) 0.0 0.0 <0.1 <0.1 0.0 0.0 0.0 0.1
Family Gobiidae Round goby 0.0 0.0 0.0 0.0 0.0 0.0 <0.1 7.6
106
Table A11b. Catches in 1/4” mesh fyke nets, Oneida Lake 2016- (n=18 sites). Scientific Name
Mean Catch/Net
2016
2017
Family Lepisosteidae Longnose gar (Adult) 0.0 0.0 Family Amiidae Bowfin (Adult) 0.0 0.1 Family Clupeidae Gizzard shad (All) 0.0 0.7 Family Cyprinidae Common carp (All) 0.0 0.0 Golden shiner (All) 0.0 0.1 Emerald shiner (All) 0.2 1.8 Spottail shiner (All) 0.8 0.3 Bluntnose minnow (All) 1.1 0.3 Family Catostomidae Longnose sucker (All) 0.0 0.0 White sucker (All) 0.0 0.1 Creek chubsucker (All) 0.1 0.0 Greater redhorse (All) 0.0 0.0 Family Ictaluridae Yellow bullhead (All) 0.2 0.7 Brown bullhead (All) 0.7 0.3 Family Esocidae Chain pickerel (All) 0.1 0.1 Family Gadidae Burbot (Adult) 0.0 0.1 Family Fundulidae Banded killifish (All) 4.9 4.9 Family Percichthyidae White perch (YOY) 0.0 0.3 White perch (Adult) 0.0 0.0 Family Centrarchidae Rock bass (Adult) 2.7 2.9 Green sunfish (Adult) 0.2 0.6 Pumpkinseed (Adult) 0.9 1.6 Bluegill (Adult) 1.2 2.4 (YOY - <75mm) 68.7 125.4 Smallmouth bass (YOY) 1.7 1.3 Smallmouth bass (Adult) 0.1 0.0 Largemouth bass (YOY) 3.4 0.0 Largemouth bass (Adult) 0.0 1.3
107
Scientific Name
Mean Catch/Net Scientific name 2016 2017
Black crappie (All) 0.2 0.8 Family Percidae Yellow perch (YOY) 31.6 7.6 Yellow perch (Adult) 5.4 2.3 Logperch (All) 0.2 0.9 Tesselated darter (All) 0.1 0.0 Walleye (YOY) 0.1 0.1 Walleye (Adult) 0.0 0.0 Family Sciaenidae Freshwater drum (Adult) 0.0 0.2
Family Gobiidae Round goby 27.1 29.4
108
Table A12a. Catches (#/hr) from spring shoreline electrofishing, Oneida Lake 2011-2015. Catches are means from 8 sites, with each site comprised of a 1 hour predator run and 2 15 minute all fish runs (total predator effort = 12 hours, effort for non-predators = 4 hours).
Scientific Name
Common Name
Catch/Hour
2011
2012
2014
2015
Predators Family Lepisosteidae Lepisosteus osseus Longnose gar (Adult) 1.8 2.0 2.2 3.1 Family Amiidae Amia calva Bowfin (Adult) 2.6 1.6 2.6 1.8 Family Ictaluride Ictalurus punctatus Channel catfish (All) 0.1 0.2 0.2 0.0 Family Esocidae Esox niger Chain pickerel (Age-1) 0.5 0.0 0.6 0.3 Chain pickerel (Adult) 4.0 3.7 6.6 9.3 Family Gadidae Lota lota Burbot (Adult) 3.8 1.0 0.6 0.9 Family Centrarchidae Micropterus dolomieu Smallmouth bass (Age-1) 0.9 1.0 0.2 0.2 Smallmouth bass (Adult) 2.3 3.7 3.7 4.3 Micropterus salmoides Largemouth bass (Age-1) 0.9 2.1 0.2 0.0 Largemouth bass (Adult) 9.6 8.2 12.0 9.7 Family Percidae Sander vitreus Walleye (Age-1) 4.0 1.5 3.3 3.9 Walleye (Adult) 4.7 8.0 4.8 11.7 Family Sciaenidae Aplodinotus grunniens Freshwater drum (Adult) 1.8 5.4 1.8 2.8
109
Scientific Name
Common Name
Catch/Hour
2011
2012
2014
2015
OTHER SPECIES Family Clupeidae Dorosoma cepedianum Gizzard shad (All) 9.5 10.0 3.0 1.5 Family Cyprinidae Notemigonus crysoleucas Golden shiner (All) 4.0 4.5 3.0 6.0 Notropis atherinoides Emerald shiner (All) 15.0 30.3 3.0 117.8 Notropis hudsonius Spottail shiner (All) 4.0 2.0 0.0 0.3 Pimephales notatus Bluntnose minnow (All) 1.8 0.8 0.8 0.0 Family Catostomidae Catostomus commersoni White sucker (All) 4.0 1.3 0.5 0.5 Erimyzon oblongus Creek chubsucker (All) 0.3 0.0 0.3 0.0 Family Ictaluridae Ameiurus natalis Yellow bullhead (All) 0.5 1.0 2.0 0.5 Ameiurus nubulosus Brown bullhead (All) 28.0 37.0 44.0 35.0 Family Atherinopsidea Labidesthes sicculus Brook silverside (All) 0.3 0.3 0.3 0.0 Family Fundulidae Fundulus diaphanus Banded killifish (All) 20.3 3.0 2.3 2.5 Family Percichthyidae Morone americana White perch (All) 0.8 0.3 0.8 0.5 Family Centrarchidae Ambloplites rupestris Rock bass (Age-1) 3.8 0.3 0.0 1.0 Rock bass (Adult) 6.5 5.5 15.0 8.0 Lepomis cyanellus Green sunfish (Adult) 0.8 1.3 1.8 0.3 Lepomis gibbosus Pumpkinseed (Age-1) 4.3 7.8 0.5 1.8 Pumpkinseed (adult) 62.8 22.8 42.0 35.0 Lepomis macrochirus Bluegill (Age-1) 2.8 5.5 0.3 0.0 Bluegill (Adult) 24.5 6.5 12.8 3.0 Pomoxis nigromaculatus Black crappie (Age-1) 0.3 0.0 0.0 0.0 Black crappie (Adult) 0.8 0.5 0.5 0.5 Family Percidae Perca flavescens Yellow perch (Age-1) 129.0 24.5 51.5 106.8
110
Scientific Name
Common Name
Catch/Hour
2011
2012
2014
2015
Percina caprodes
Yellow perch (Adult) Logperch (All)
90.5 25.0
19.3 26.8
98.0 8.0
46.3 14.3
Etheostoma olmstedi Tesselated darter (All) 1.8 1.0 0.0 2.3 Family Gobiidae Neogobius melanostomus Round goby 0.0 0.0 0.0 1.3
111
Table A12b. Catches (#/hr) from spring shoreline electrofishing, Oneida Lake 2017. Catches are means from 8 sites, with each site comprised of a 1 hour predator run and 2 15 minute all fish runs (total predator effort = 12 hours, effort for non-predators = 4 hours).
Scientific Name
Common Name
Catch/Hour
2017
Predators Family Lepisosteidae Lepisosteus osseus Longnose gar (Adult) 3.4 Family Amiidae Amia calva Bowfin (Adult) 2.9 Family Ictaluride Ictalurus punctatus Channel catfish (All) 0.0 Family Esocidae Esox niger Chain pickerel (Age-1) 0.1 Chain pickerel (Adult) 3.7 Family Gadidae Lota lota Burbot (Adult) 0.4 Family Centrarchidae Micropterus dolomieu Smallmouth bass (Age-1) 3.3 Smallmouth bass (Adult) 3.8 Micropterus salmoides Largemouth bass (Age-1) 3.5 Largemouth bass (Adult) 18.9 Family Percidae Sander vitreus Walleye (Age-1) 5.6 Walleye (Adult) 9.2 Family Sciaenidae Aplodinotus grunniens Freshwater drum (Adult) 2.7
112
Scientific Name
Common Name
Catch/Hour
2017
OTHER SPECIES Family Clupeidae Dorosoma cepedianum Gizzard shad (All) 7.5 Family Cyprinidae Notemigonus crysoleucas Golden shiner (All) 3.0 Notropis atherinoides Emerald shiner (All) 3.3 Notropis hudsonius Spottail shiner (All) 0.8 Pimephales notatus Bluntnose minnow (All) 0.0 Family Catostomidae Catostomus commersoni White sucker (All) 1.3 Erimyzon oblongus Creek chubsucker (All) 0.0 Family Ictaluridae Ameiurus natalis Yellow bullhead (All) 0.5 Ameiurus nubulosus Brown bullhead (All) 20.0 Family Atherinopsidea Labidesthes sicculus Brook silverside (All) 0.8 Family Fundulidae Fundulus diaphanus Banded killifish (All) 6.3 Family Percichthyidae Morone americana White perch (All) 0.3 Family Centrarchidae Ambloplites rupestris Rock bass (Age-1) 8.8 Rock bass (Adult) 15.5 Lepomis cyanellus Green sunfish (Adult) 0.5 Lepomis gibbosus Pumpkinseed (Adult) 34.3 Lepomis macrochirus Bluegill (Adult) 23.8 Lepomis spp. Age-1 33.3 Pomoxis nigromaculatus Black crappie (Age-1) 0.3 Black crappie (Adult) 0.0 Family Percidae Perca flavescens Percina caprodes
Yellow perch (Age-1) Yellow perch (Adult) Logperch (All)
64.3 79.5 6.8
113
Scientific Name
Common Name
Catch/Hour
2017
Etheostoma olmstedi Tesselated darter (All) 0,0 Family Gobiidae Neogobius melanostomus Round goby 10.8
114
Table A13. Open water angling effort (boat hours) as determined by tower counts, Oneida Lake.
Year Month May June July August September TOTAL
2002 12,773 21,132 24,983 19,156 15,465 93,509 2003 15,675 24,041 33,281 28,375 20,859 122,231 2004 22,230 37,240 34,681 32,012 17,925 144,088 2005 30,738 35,344 38,622 29,799 21,564 156,069 2006 25,004 41,381 63,308 30,230 19,807 179,730 2007 30,942 40,203 41,183 35,748 26,844 174,921
2010 49,180 40,749 43,819 48,552 26,179 208,479 2011 58,774 41,997 52,025 38,090 23,774 214,660 2012 53,554 49,933 56,295 35,629 18,159 213,570 2013 42,479 59,037 62,224 35,169 19,480 218,389 2014 43,253 57,078 55,955 40,951 20,312 217,548 2015 50,372 51,784 58,005 48,020 24,957 233,139 2016 32,828 50,517 52,422 194,366 2017 23,396 49,789 54,560 184,731
