YELLOW PERCH RECRUITMENT AND POTENTIAL INTERACTIONS WITH
SMALLMOUTH BASS IN EASTERN SOUTH DAKOTA GLACIAL LAKES
BY
DANIEL JAY DEMBKOWSKI
A dissertation submitted in partial fulfillment of the requirements for the
Doctor of Philosophy
Major in Wildlife and Fisheries Sciences
South Dakota State University
2014
iii
This dissertation is dedicated to Rob Klumb and Dave Willis, who taught me that the
pursuit of happiness in work and life is important beyond all else.
iv
ACKNOWLEDGMENTS
I would like to first and foremost thank two of my friends and mentors who
passed away during my time at South Dakota State University (SDSU) – Rob Klumb and
Dave Willis. I “cut my fisheries teeth” in 2007 under the supervision of Rob Klumb
while working for the U.S. Fish and Wildlife Service in Pierre, SD. I’ll never forget the
first day I met Rob. As a timid junior-status undergraduate student, I walked into his
office in Pierre to be met with a gruff, “Go grab your s#!t, you’re headed to Valentine.”
While this might seem off-putting to most, I knew I’d come to the right place and would
fit in just fine. From that point up until the day he passed, Rob went out of his way to
look out for me. Be it coming to visit (and inadvertently being “over-served” and
becoming the highlight of a college party) me at my undergraduate alma mater, helping
me navigate the job application process, or lending me boats and equipment for various
research projects, Rob was an all-around great friend and mentor, and I miss him dearly.
Dave Willis was the reason I attended SDSU. While working on my M.S. degree at
Mississippi State University, Dave sent me an email inquiring if I would be interested in
a Ph.D. project working with yellow perch and smallmouth bass at SDSU. Being more
naïve at the time, I waited too long to respond and, by the time I did, the opportunity had
passed. After a brief exchange of emails, I asked Dave to keep me informed of any
future projects that may arise. Unexpectedly, Dave sent another email about 6 months
later, stating that he “dearly hated to miss out on opportunities with promising students”
and that he was able to rustle up some funding for the project we discussed months
before. The fact that he kept me in mind, and went out of his way to secure funding for
the project, attests to his sincerity and generosity in looking out for others over himself.
v
Despite his constant busyness and administrative duties, Dave would always drop
everything when I came to his office wanting to chat about research, teaching strategies,
or recent fishing trips. Dave always asked if I was “happy with and interested in” what I
was doing – I now truly know what he meant. Although Rob and Dave taught me much
about fisheries, they taught me more about life. Both instilled in me the importance of
being happy and of loving what I do, and doing what I love. I miss them both, and I only
hope that I can be half the mentor and friend to others that they were to me.
I am also deeply indebted to the chair and other members of my graduate
committee: Melissa Wuellner, Steve Chipps, Brian Blackwell, Ron Stover, and Dale
Potts. Melissa – thank you for everything you have done and the opportunities you have
provided to ensure my success as a fisheries scientist. I will always value the
conversations we had regarding fisheries, fun, and “growing up.” Thank you also for
always prioritizing my manuscript and presentation edits despite other work piling up on
your desk and for putting up with my endless love of side projects and reining me in
when necessary. Steve – thank you for taking me under your wing, providing me with
countless opportunities, and encouraging me to strive for fisheries greatness. The hours
of hallway and office conversations (some about fisheries and other not) were something
I looked forward to on a weekly basis. Thank you for being a friend and a mentor and for
being on my side when I needed it most. Brian – my project would not have been
possible without your cooperation and aid in idea generation, sampling, and sleeping
quarters in the form of your office floor. I will never forget the soothing sounds of the
water fountain at 3:00 AM. Thank you also for always encouraging me to move forward
with research ideas and taking time to meet at a moment’s notice. Dr. Stover and Dr.
vi
Potts – thank you for remaining involved and interested as graduate faculty
representatives throughout the progression of my education.
Thanks are also owed to Steve Miranda, my M.S. advisor at Mississippi State
University, who continues to be a never-ending source of encouragement, enthusiasm,
information, and humor. Dane Shuman (U.S. Fish and Wildlife Service – Pierre, SD) and
Greg Wanner (U.S. Forest Service – Zigzag, OR) have been mentors from the start and
taught me that having fun on the water is just as important as getting the work done.
Dane and Greg – I try my best to pass on the ‘life lessons’ you passed to me. Mike
Brown, Brian Graeb, and Katie Bertrand are also valued mentors at SDSU and served as
sources of encouragement in research, teaching, and navigating the job application
process. Dave Lucchesi (South Dakota Department of Game, Fish and Parks – Sioux
Falls, SD) was a valuable asset in terms of sampling strategies, local knowledge, history
of the yellow perch trawling database, and brainstorming. I also owe many thanks to
Todd Kaufmann, Steve Kennedy, Ryan Braun, and Ty Moos with the Webster SDGFP
office for helping with sampling on countless occasions. Terri Symens, Di Drake, Kate
Tvedt, and Dawn Van Ballegooyen always brought a smile to my face when wandering
the halls of the Northern Plains Biostress building.
This project, or any research project, for that matter, would not have been possible
without the help of field and lab technicians. I was fortunate enough to have had the
pleasure of working with several outstanding undergraduate scientists. Specifically,
Dalton Benage, Bri Graff, Morgan Kauth, Jake Lindgren, Matt Phayvanh, Craig Schake,
and BJ Schall were instrumental in the success of this and many other projects. Although
I may have been behind the wheel of the project, you were the engine that made it run. It
vii
was an honor to have been able to watch each of you grow as burgeoning scientists.
Thank you for your hours of hard work on the water and in the lab, for dealing with
hectic sampling schedules, and for allowing me to turn every sampling trip into an
adventure in one way or another. Thanks also to Jason Augspurger, Jessica Howell,
Adam Janke, Kris Stahr, and Matt Wagner for assisting with sampling on various
occasions.
My time at SDSU would not have been nearly as enjoyable without the
companionship of my many friends. Justin VanDeHey and Mark Kaemingk made sure I
was up to speed on yellow perch research in the Dakotas and Nebraska. Conversations
with Mark enhanced my understanding and appreciation of nutrient dynamics in aquatic
systems. Thanks also to Jason Breeggemann, Dave Deslauriers, Michael Grey, Cari-Ann
Hayer, and Tobi Rapp for helping to prepare for various exams via our “Comp
Discussion Group.” Participation in various recreational and extracurricular activities
with Brandi Crider, Jake Davis, Eli Felts, Mark Fincel, Mike Greiner, Dan James, Jake
Krause, Alex Letvin, Hilary Meyer, and Josh White made my time here particularly
enjoyable.
Natalie Scheibel, Jeff Grote, and Ace Scheibel deserve special recognition as
members of my South Dakota Family. I would not have been able to complete this
project without the endless conversations, hours of laughs, and weekly family dinners
that we shared together. Words alone cannot express the gratitude I have for our
friendship.
Finally, I thank my mom (Nancy), dad (Walt), and sister (Lauren) for being a
source of never-ending support and encouragement through the trials and tribulations of
viii
graduate school. Thank you for allowing me to “flee the nest” at a young age and follow
my dreams wherever they took me. At heart, I’ll always be the little boy playing with
fish or frogs from the pictures on the fridge.
Each chapter of this dissertation is written in manuscript format. The anticipated
publication outlet of Chapter 2 is the Canadian Journal of Fisheries and Aquatic
Sciences and coauthors include Dave Willis and Melissa Wuellner. The anticipated
publication outlet for Chapter 3 is Transactions of the American Fisheries Society and
coauthors include Dave Willis, Melissa Wuellner, and Mike Weber. The anticipated
publication outlet for Chapter 4 is the North American Journal of Fisheries Management
and coauthors include Dave Willis, Melissa Wuellner, Brian Blackwell, Steve Chipps,
and Tom Bacula.
This project was funded through dollars from the Federal Aid in Sport Fish
Restoration fund (Project F-15-R, Study 1518) administered through the South Dakota
Department of Game, Fish and Parks, and by the Department of Natural Resource
Management at South Dakota State University.
ix
CONTENTS
ABSTRACT.................................................................................................................................... xi
CHAPTER 1. INTRODUCTION .................................................................................................... 1
LITERATURE CITED ................................................................................................................ 6
CHAPTER 2. SYNCHRONY IN LARVAL YELLOW PERCH PRODUCTION: THE
INFLUENCE OF THE MORAN EFFECT DURING EARLY LIFE HISTORY ......................... 10
INTRODUCTION ..................................................................................................................... 10
METHODS ................................................................................................................................ 13
Study area .............................................................................................................................. 13
Larval yellow perch sampling ................................................................................................ 13
Spatial synchrony in larval yellow perch density .................................................................. 14
Factors influencing larval yellow perch density .................................................................... 15
RESULTS .................................................................................................................................. 19
Synchrony in larval yellow perch production ........................................................................ 19
Factors influencing larval yellow perch density .................................................................... 19
DISCUSSION ............................................................................................................................ 21
LITERATURE CITED .............................................................................................................. 30
CHAPTER 3. FACTORS INFLUENCING RECRUITMENT AND GROWTH OF AGE-0
YELLOW PERCH IN EASTERN SOUTH DAKOTA GLACIAL LAKES ................................ 49
INTRODUCTION ..................................................................................................................... 49
METHODS ................................................................................................................................ 52
Data collection ....................................................................................................................... 52
Data analysis ......................................................................................................................... 55
RESULTS .................................................................................................................................. 57
Recruitment ............................................................................................................................ 57
Growth ................................................................................................................................... 58
DISCUSSION ............................................................................................................................ 59
LITERATURE CITED .............................................................................................................. 65
CHAPTER 4. ESTIMATING THE INFLUENCE OF SMALLMOUTH BASS PREDATION ON
RECRUITMENT OF AGE-0 YELLOW PERCH IN SOUTH DAKOTA GLACIAL LAKES ... 80
INTRODUCTION ..................................................................................................................... 80
METHODS ................................................................................................................................ 83
Study lakes ............................................................................................................................. 83
x
Smallmouth bass sampling and diets ..................................................................................... 84
Smallmouth bass population size ........................................................................................... 86
Yellow perch sampling and production estimation ................................................................ 87
Bioenergetics modeling .......................................................................................................... 88
RESULTS .................................................................................................................................. 91
Smallmouth bass diets ............................................................................................................ 91
Smallmouth bass population size estimation ......................................................................... 93
Age-0 yellow perch production .............................................................................................. 93
Bioenergetics modeling .......................................................................................................... 94
DISCUSSION ............................................................................................................................ 95
LITERATURE CITED ............................................................................................................ 102
CHAPTER 5. SUMMARY AND RESEARCH NEEDS ............................................................ 119
LITERATURE CITED ............................................................................................................ 127
xi
ABSTRACT
YELLOW PERCH RECRUITMENT AND POTENTIAL INTERACTIONS WITH
SMALLOUTH BASS IN EASTERN SOUTH DAKOTA GLACIAL LAKES
DANIEL JAY DEMBKOWSKI
2014
Knowledge of spatiotemporal trends in population fluctuations and drivers of
yellow perch Perca flavescens early life history dynamics is important for ecological
understanding and applied management in an aut- and synecological context. Therefore,
the objectives of this study were to: 1) estimate the extent of spatial synchrony in
production of larval yellow perch; 2) estimate the influence of climatological and
hydrological factors on larval perch density, 3) estimate the influence of biotic and
abiotic factors on recruitment and growth dynamics of fall age-0 perch; and 4) estimate
the potential impact of predation by smallmouth bass Micropterus dolomieu on
recruitment of age-0 perch across a range of eastern South Dakota glacial lakes.
Production of larval yellow perch was moderately synchronous among spatially
segregated systems and variation in larval density was influenced by the Moran Effect
during the post-egg mass emergence period. Specifically, increased production of larval
yellow perch corresponded with increased water levels, warmer air temperatures, and low
wind speed.
Biotic factors were more influential than abiotic factors over recruitment and
growth dynamics of fall age-0 yellow perch. Results suggest compensatory density-
xii
dependent regulation of recruitment via potential competition and predation, and growth
via potential intraspecific competition.
Weekly production of age-0 yellow perch ranged from 0.32 kg/ha/week to 1.78
kg/ha/week. Estimates of smallmouth bass consumption measured during the same
intervals ranged from 0.06 kg/ha/week to 0.33 kg/ha/week, equating to consumption of
between 1 and 34% of available yellow perch biomass. Given current conditions relative
to smallmouth bass abundance and consumption dynamics, production of age-0 yellow
perch, and the thermal environment, it does not appear that bass act as a singular factor
limiting recruitment of age-0 perch in my study lakes.
Overall, results of this study demonstrate the complexities involved with
understanding recruitment processes of yellow perch. There is likely a complex
interaction of the variables I examined, along with other unconsidered variables, that act
to limit perch recruitment in these systems. These interactions ultimately add complexity
to the management of yellow perch. Nonetheless, results provide further insight to the
patterns and process that structure yellow perch populations in South Dakota glacial
lakes.
1
CHAPTER 1. INTRODUCTION
Fishery sustainability depends upon successful recruitment of young fish into
catchable, harvestable, or adult sizes (Maceina and Pereira 2007). Among the most
important biological statistics of fish populations identified by Ricker (1975), recruitment
is frequently identified as the most deterministic parameter influencing populations on
the basis that minor fluctuations in recruitment may equate to marked changes in other
parameters (e.g., Carline et al. 1984). Thus, recruitment can substantially influence the
abundance of catchable-size fish and angler success and satisfaction, and biologists and
managers place particular emphasis on understanding recruitment dynamics of fishes and
factors affecting them.
Understanding recruitment dynamics is particularly important when the species of
interest exhibits highly variable or inconsistent recruitment (Parsons et al. 2004).
Inconsistencies in recruitment or weak or missing year classes resulting from recruitment
failure can lead to reduced adult abundance and subsequent angler catch rates.
Population declines and the collapse of a fishery may be the products of recruitment
failure if it occurs over a large enough spatiotemporal scale (Maceina and Pereira 2007).
Alternatively, barring density-dependent growth limitations and mortality, consistent and
high recruitment of young fish into a population can provide balanced size and age
structure, an abundance of catchable-size fish, and high angling success (Ney 1999;
Maceina and Pereira 2007).
Studies of the effects of abiotic and biotic factors on fish recruitment typically
focus on early life stages (i.e., egg, larval, and juvenile life stages; Hjort 1914) because
the absolute number of individuals recruiting to an adult population depends upon the
2
number of pre-recruits (Houde 1989). As such, much existing research suggests that
year-class strength is fixed early in life. Placing emphasis on early life stages is
especially appropriate for fishes such as yellow perch Perca flavescens that produce large
numbers of small progeny with little parental investment.
Yellow perch support important recreational fisheries across much of their range
(Mayer et al. 2000; Isermann et al. 2007; Wilberg et al. 2005; Brown et al. 2009) but are
also an important prey species for predators including walleye Sander vitreus, northern
pike Esox lucius, and smallmouth bass Micropterus dolomieu (Forney 1974; Hansen et al.
1998; Blackwell et al. 1999). Because of their dual role, knowledge of trends in
population fluctuations and drivers of yellow perch early life history dynamics is
important for ecological understanding and applied management in an aut- and
synecological context. Among density-independent factors, yellow perch recruitment has
been linked to lake morphometry characteristics (Isermann 2003), climatological
variables operating during early life stages (Ward et al. 2004; Jansen 2008; Redman et al.
2011; Weber et al. 2011), fluctuations in water levels (Kallemeyn 1987; Dembkowski et
al. 2014), and environmental stochasticity (Clady and Hutchinson 1975). Among
density-dependent factors, yellow perch recruitment has been linked with prey
availability (Jolley et al. 2010; Redman et al. 2011), prey size (Fisher and Willis 1997),
prey assemblage composition (Whiteside et al. 1985), spawning stock characteristics
(Sanderson et al. 1999; Tyson and Knight 2001; Wilberg et al. 2005), competition
(Shroyer and McComish 2000), and predation (Forney 1974).
Yellow perch populations in most South Dakota waters typically exhibit erratic
recruitment, with some lakes having low and inconsistent recruitment and others having
3
high and consistent recruitment (Lott 1991; Isermann et al. 2007). Sporadic and
seemingly unpredictable recruitment leads to an inconsistent fishery and provides
managers with limited predictive capabilities with which to initiate stocking programs or
implement regulations. Anderson et al. (1998) suggested that knowledge of the timing of
year-class establishment and a subsequent index of year-class strength for yellow perch
would allow managers to initiate supplemental stocking to augment weak or missing
year-classes resulting from recruitment failure. Not surprisingly, much of the extant
research concerning eastern South Dakota glacial lakes has investigated early life history
and recruitment of yellow perch.
Most yellow perch recruitment studies in South Dakota waters to date have
attempted to link early life history of perch to a suite of abiotic factors. For example,
Isermann (2003) hypothesized that water temperature, depth, and lake volume may affect
hatch timing, larval growth, and ultimately year-class strength in eastern South Dakota
glacial lakes. Ward et al. (2004) found that higher springtime air temperature and
precipitation and lower springtime wind speed correlated with higher larval yellow perch
abundance. More specifically, Jansen (2008) found that larval yellow perch abundance
was negatively correlated with mean March wind speed and positively correlated with
total April precipitation and mean May temperature. However, these studies were
conducted primarily on a lake-specific basis, limiting inferential space and potentially
overlooking trends in yellow perch recruitment as influenced by broad-scale, spatially
correlated climatic phenomena. Furthermore, few studies have assessed dynamics of
yellow perch populations upon year-class formation (i.e., fall age-0), after natural
mortality has stabilized but prior to the onset of exploitation mortality.
4
As exemplified above, most extant research regarding yellow perch early life
history in South Dakota waters has been conducted within a paradigmatic framework
emphasizing the importance of abiotic and climatic variables. A factor that has received
comparably less attention is the potential for an introduced species such as smallmouth
bass to affect yellow perch early life history and subsequent recruitment. As apex
predators, smallmouth bass have the potential to create novel interactions with other
fishes in the form of resource competition and predation. Their predatory behavior,
combined with opportunistic feeding habits, enable smallmouth bass to exert top-down
influences on prey populations. Relative to impacts on prey fishes, introductions of
smallmouth bass outside of their native range have been linked with changes in native
fish assemblage trophodynamics (e.g., Whittier et al. 1997; Vander Zanden et al. 1999),
restructuring of aquatic communities (e.g., Vander Zanden et al. 1999; Jackson and
Mandrak 2002), and changes in prey fish population dynamics (e.g., Johnson and Hale
1977; Pflug and Pauly 1984; Tonn and Magnuson 1983; Jackson 2002). While few
studies have assessed the impact of smallmouth bass predation on prey fish populations,
several have documented bass food habits in state waters (e.g., Lott 1996; Blackwell et al.
1999; Bacula 2009; Wuellner et al. 2010), which may provide insight into potential
predatory impacts. For example, Bacula (2009) found that age-0 yellow perch comprised
between 24 and 82% of smallmouth bass diets by weight across a range of eastern South
Dakota glacial lakes, prompting concern that bass predation may negatively influence
perch populations in systems where both species co-occur.
Understanding the influence of an additional predator (i.e., smallmouth bass in
addition to walleye and northern pike) on yellow perch population dynamics has
5
important implications for recreational fisheries in eastern South Dakota glacial lakes.
Substantial predation of juvenile yellow perch by smallmouth bass could be an additional
constraint to recruitment of age-0 perch to sizes preferred by anglers. Thus, knowledge
of the impacts of smallmouth bass on yellow perch populations can assist in appropriate
fish assemblage management strategies. Furthermore, predation coupled with abiotic
influences on yellow perch early life history dynamics has the potential to limit perch
recruitment in South Dakota to unsustainable levels. Therefore, the objectives of this
study were to: 1) estimate the extent of spatial synchrony in production of larval yellow
perch; 2) estimate the influence of climatic and hydrological factors on larval perch
density; 3) estimate biotic and abiotic factors influencing recruitment and growth
dynamics of fall age-0 perch; and 4) estimate the potential impact of smallmouth bass
predation on recruitment of age-0 perch across a range of eastern South Dakota glacial
lakes.
6
LITERATURE CITED
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yellow perch to water-level fluctuations in glacial lakes. Fisheries Management
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Forney, J.L. 1974. Interactions of yellow perch abundance and walleye predation, and
survival of alternate prey from Oneida Lake, New York. Transactions of the
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Hansen, M.J., M.A. Bozek, J.R. Newby, S.P. Newman, and M.D. Staggs. 1998. Factors
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in South Dakota glacial lakes. Ph.D. dissertation. South Dakota State University,
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Isermann, D.A., D.W. Willis, B.G. Blackwell, and D.O. Lucchesi. 2007. Yellow perch in
South Dakota: population variability and predicted effects of creel limit
reductions and minimum length limits. North American Journal of Fisheries
Management 27:918-931.
Jackson, D.A. 2002. Ecological effects of Micropterus introductions: the dark side of
black bass. Pages 221-232 in D.P. Phillip and M.S. Ridgway, editors. Black bass:
ecology, conservation, and management. American Fisheries Society, Symposium
31, Bethesda, Maryland.
Jackson, D.A., and N.E. Mandrak. 2002. Changing fish biodiversity: predicting the loss
of cyprinid diversity due to global climate change. American Fisheries Society
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Jansen, A. C. 2008. Interannual variation in larval yellow perch abundance in eastern
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thesis. South Dakota State University, Brookings.
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vitreum) and smallmouth bass (Micropterus dolomieu) in four northeastern
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34:1626-1632.
Jolley, J.C., D.W. Willis, and R.S. Holland. 2010. Match-mismatch regulation for bluegill
and yellow perch larvae and their prey in Sandhill lakes. Journal of Fish and
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Kallemeyn, L.W. 1987. Correlations of regulated lake levels and climatic factors with
abundance of young-of-the-year walleye and yellow perch in four lakes in
Voyageurs National Park. North American Journal of Fisheries Management
7:513-521.
Lott, J.P. 1991. Food habits of yellow perch in eastern South Dakota glacial lakes. M.S.
thesis. South Dakota State University, Brookings.
Lott, J.P. 1996. Relationships between smallmouth bass feeding ecology and population
structure and dynamics in lower Lake Oahe, South Dakota. South Dakota
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8
Maceina, M.J. and D.L. Pereira. 2007. Recruitment. Pages 121-186 in C.S. Guy and M.L.
Brown, editors. Analysis and interpretation of freshwater fisheries data. American
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Mayer, C.M., A.J. VanDeValk, J.L. Forney, L.G. Rudstam, and E.L. Mills. 2000.
Response of yellow perch (Perca flavescens) in Oneida Lake, New York to the
establishment of zebra mussels (Dreissena polymorpha). Canadian Journal of
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W.A. Hubert, editors. Inland fisheries management in North America, second
edition. American Fisheries Society, Bethesda, Maryland.
Parsons, B.G., J.R. Reed, H.G. Fullhart, V.A. Snook, and J.K. Hirsch. 2004. Factors
affecting black crappie recruitment in four west-central Minnesota lakes.
Minnesota Department of Natural Resources Investigational Report 514, St. Paul.
Pflug, D. E., and G. B. Pauley. 1984. Biology of smallmouth bass (Micropterus
dolomieu) in Lake Sammamish, Washington. Northwest Science 58:118-130.
Redman, R.A., S.J. Czesny, J.M. Dettmers, M.J. Weber, and D. Makauskas. 2011. Old
tales in recent context: current perspective on yellow perch recruitment in Lake
Michigan. Transactions of the American Fisheries Society 140:1277-1289.
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populations. Fisheries Research Board of Canada Bulletin 191, Ottawa.
Sanderson, B.L., T.R. Hrabik, J.J. Magnuson, and D.M. Post. 1999. Cyclic dynamics of a
yellow perch (Perca flavescens) population in an oligotrophic lake: evidence for
the role of intraspecific interactions. Canadian Journal of Fisheries and Aquatic
Sciences 56:1534-1542.
Shroyer, S.M., and T.S. McComish. 2000. Relationship between alewife abundance and
yellow perch recruitment in southern Lake Michigan. North American Journal of
Fisheries Management 20:220-225.
Tonn, W.M., and J.J. Magnuson. 1983. Patterns in the species composition and richness
of fish assemblages in northern Wisconsin lakes. Ecology 63:1149-1166.
Tyson, J.T., and R.L. Knight. 2001. Response of yellow perch to changes in the benthic
invertebrate community of western Lake Erie. Transactions of the American
Fisheries Society 130:766-782.
Vander Zanden, 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.
9
Ward, M.J., M.R. Anderson, S.J. Fisher, D.A Isermann, Q.E. Phelps, and D.W. Willis.
2004. Relations between climatological variables and larval yellow perch
abundance in eastern South Dakota glacial lakes. Journal of Freshwater Ecology
19:213-218.
Weber, M.J., J.M. Dettmers, and D.H. Wahl. 2011. Growth and survival of age-0 yellow
perch across habitats in southwestern Lake Michigan: early life history in a large
freshwater environment. Transactions of the American Fisheries Society
140:1172-1185.
Whiteside, M.C., C.M. Swindoll, and W.L. Doolittle. 1985. Factors affecting the early
life history of yellow perch, Perca flavescens. Environmental Biology of Fishes
12:47-56.
Whittier, T.R., D.B. Halliwell, and S.G. Paulsen. 1997. Cyprinid distributions in
Northeast U.S.A. lakes: evidence of regional-scale minnow biodiversity losses.
Canadian Journal of Fisheries and Aquatic Sciences 54:1593-1607.
Wilberg, M.J., J.R. Bence, B.T. Eggold, D. Makauskas, and D.F. Clapp. 2005. Yellow
perch dynamics in southwestern Lake Michigan during 1986-2002. North
American Journal of Fisheries Management 25:1130-1152.
Wuellner, M.R., S.R. Chipps, D.W. Willis, and W.E. Adams, Jr. 2010. Interactions
between walleyes and smallmouth bass in a Missouri River reservoir with
consideration of the influence of temperature and prey. North American Journal
of Fisheries Management 30:445-463.
10
CHAPTER 2. SYNCHRONY IN LARVAL YELLOW PERCH PRODUCTION: THE
INFLUENCE OF THE MORAN EFFECT DURING EARLY LIFE HISTORY
INTRODUCTION
Fishery sustainability depends upon successful recruitment of young fish into
catchable, harvestable, or adult sizes (Maceina and Pereira 2007). Among the most
important biological statistics of fish populations (i.e., population size, mortality, growth,
recruitment, and surplus production; Ricker 1975), recruitment is frequently identified as
the most important parameter influencing populations on the basis that minor fluctuations
in recruitment may equate to marked changes in other parameters (e.g., Carline et al.
1984; Hansen and Nate 2014). Because the absolute number of individuals recruiting
into an adult population depends on the number of pre-recruits (Houde 1987; Houde
1989), studies of factors influencing fish recruitment typically focus on early life stages
(i.e., egg, larval, and juvenile life stages; Hjort 1914). Furthermore, early life stages are
often more susceptible to critical periods of high natural mortality than are older
conspecifics (sensu Hjort 1914; Hjort 1926). Thus, much existing research suggests that
year-class strength is fixed early in life, after a point at which natural mortality stabilizes
and prior to a cohort entering a recreational fishery (Hjort 1914; Ludsin and Devries
1997; Houde 2008; Isermann and Willis 2008).
Formal evaluation of early life history dynamics and factors influencing
population fluctuations began with Hjort’s (1914) observation that spawning stock egg
production alone was insufficient to explain interannual variation in year-class strength.
Since this seminal work, a multitude of studies have sought to further understand densitydependent and density-independent factors influencing early life stages of fishes. Among
density-dependent factors, early life stages of freshwater fishes have been linked with
11
prey availability (e.g., Graeb et al. 2004; Hoxmeier et al. 2004), prey size (e.g., Fisher
and Willis 1997), and prey assemblage composition (e.g., Whiteside et al. 1985),
spawning stock characteristics (e.g., Sanderson et al. 1999; Hansen et al. 1998; Siepker
and Michaletz 2013), competition (e.g., Hansen et al 1998; Shroyer and McComish
2000), and predation (e.g., Forney 1974; Kim and DeVries 2001). Among densityindependent factors, early life stages of freshwater species have been linked with lake
morphometry characteristics (e.g., Isermann 2003), climatological variables operating
during early life stages (e.g., Ward et al. 2004; Phelps et al. 2008; Siepker and Michaletz
2013), fluctuations in water levels (e.g., Maceina and Stimpert 1998; Clark et al. 2008;
Dembkowski et al. 2014), and environmental stochasticity (e.g., Clady and Hutchinson
1975; Clady 1976; Schupp 2002).
Density-dependent and density-independent factors may lead to synchronous fish
population fluctuations across multiple spatial scales (Grenouillet et al. 2001; Phelps et
al. 2008; Bunnell et al. 2010). One mechanism frequently cited to explain synchronous
population fluctuations is the Moran Effect (Moran 1953), in which spatially correlated
climatic phenomena influences disparate populations in a similar manner (i.e., induces
synchronous fluctuations). The influence of the Moran Effect may be especially
pronounced for fishes such as yellow perch Perca flavescens, which exhibit contracted
spawning and larval emergence windows (Powles and Warlen 1988; Isermann and Willis
2008) and produce large numbers of small progeny with little parental investment. Short
hatch durations and emergence windows may leave the majority of larval yellow perch
vulnerable to periods of unfavorable environmental conditions that may result in
substantial mortality or loss of an entire year class (Isermann and Willis 2008).
12
Yellow perch support important recreational fisheries across much of their range
(Mayer et al. 2000; Isermann et al. 2005; Wilberg et al. 2005; Brown et al. 2009) but are
also an important prey species for predators including walleye Sander vitreus, northern
pike Esox lucius, and smallmouth bass Micropterus dolomieu (Forney 1974; Hansen et al.
1998; Blackwell et al. 1999). Because of their dual role, knowledge of trends in
population fluctuations and drivers of yellow perch early life history dynamics is
important for ecological understanding and applied management in an aut- and
synecological context. Previous studies have demonstrated the influence of climatic
variation on recruitment processes of yellow perch (Ward et al. 2004; Jansen 2008;
Jansen et al. 2009; VanDeHey et al. 2013), but these studies were conducted primarily on
a lake-specific basis and therefore may have overlooked trends in perch recruitment as
influenced by spatially correlated climatic phenomena (i.e., the Moran Effect).
Therefore, the objectives of this study were to evaluate the extent of spatial synchrony in
reproduction of yellow perch and to estimate factors influencing larval perch density
across a range of natural lakes situated in the Northern Great Plains, USA. Because
factors operating prior to, during, or immediately after the contracted hatching period
play a critical role in the yellow perch recruitment process (Isermann and Willis 2008), I
focused on two distinct ontogenetic stages during perch early life history: 1) the period
encompassing egg deposition and embryonic development; and 2) the period immediately
following post-egg skein emergence encompassing larval swim-up and the switch from
endogenous to exogenous feeding. Thus, my analyses not only allowed for identification
of critical life stages but also for estimation of factors influencing temporal trends in
larval yellow perch density.
13
METHODS
Study area
Larval yellow perch were collected from 13 natural lakes across eastern South
Dakota discontinuously during 1995-2013. Lakes varied in surface area, maximum
depth, shoreline development index, and trophic status, and spanned a longitudinal
gradient of approximately 180 km across eastern South Dakota (Table 1). Although
morphometric and physicochemical characteristics vary among lakes, all are generally
shallow, windswept, and productive systems typical of natural lakes in the Prairie Pothole
Region (PPR) of the Northern Great Plains, USA (Stueven and Stewart 1996; Stukel
2003). Additionally, hydrology in these systems is largely tied to regional precipitation
patterns (White et al. 2008), and they are prone to dynamic water-level fluctuations and
extended wet and dry periods (Johnson et al. 2004; Kahara et al. 2009).
Larval yellow perch sampling
Larval yellow perch were sampled using an ichthyoplankton surface trawl
beginning in late April or early May and extending through June. Basic trawl
configuration consisted of a 0.75-m diameter mouth, a 1.5-m conical mesh trawl with
500- or 1,000-µm mesh (bar measure), a plastic collection jar, a three-point towing bridle,
and a mouth-mounted flowmeter (General Oceanics Model 2030R, Miami, FL).
Although samples collected in 1995-1997 used a 500-µm mesh trawl and samples
collected during 2000-2013 used 1,000-µm mesh, Isermann et al. (2002) found no
difference in larval yellow perch density estimates between mesh sizes. The number of
trawl sites per lake varied from three to 20 during 1995-2003 as study objectives changed
(see Fisher and Willis 1997; Anderson et al. 1998; Isermann 2003; Ward et al. 2004), but
14
the number of trawl sites was standardized to three per lake beginning in 2004. Each
trawl site consisted of a nearshore and offshore sample where the trawl was towed in a
circular pattern behind the boat in the upper 1-m of the water column for 3-5 min at a
speed of 1-2 m s-1. Tow duration was adjusted relative to apparent phytoplankton and
zooplankton density; trawl efficiency tended to decrease with increased plankton density
(Jansen 2008). The centroids of the circular tow patterns were approximately 50- and
150-m from shore for nearshore and offshore samples, respectively. At each lake,
trawling occurred at 6- to 12-d intervals encompassing the typical hatching window for
larval yellow perch in the PPR (i.e., mid-May to mid-June; Fisher and Willis 1997;
Isermann and Willis 2008).
Trawl samples were preserved in 70% ethanol and transported to the laboratory
for processing, fish identification, and enumeration. Fishes were identified at least to
genus using keys available in Holland-Bartels et al. (1990), and percids were further
identified to species using keys available in Auer (1982). All larval yellow perch were
counted and a subsample of up to 200 perch per trawl site were measured for total length
(TL; mm). Trawling was discontinued when larval yellow perch exceeded mean TL of
13 mm (Jolley 2009). Lake-, year-, and date-specific larval yellow perch densities were
indexed as the mean number of larvae per 100 m3. Annual larval yellow perch density
was indexed as the highest mean larval density for each lake.
Spatial synchrony in larval yellow perch density
Spatial synchrony in annual larval yellow perch density among lakes was assessed
using a modification of the residual method for analysis of year-class strength (Maceina
1997). For lakes with greater than five sampling events (Table 2), larval yellow perch
15
density was regressed against sample year. Studentized residuals from larval densitysample year regressions were used as relative indices of high and low density, where
positive residuals represented higher relative larval yellow perch density and negative
residuals represented lower relative larval perch density (Maceina 1997; Quist 2007).
Because of the low number of bivariate combinations, Spearman correlation coefficients
were computed for all bivariate combinations of residual values among lakes. Positive
correlation coefficients would suggest synchronous trends in larval yellow perch density
among lakes, whereas negative or non-systematic correlations would suggest lakespecific or asynchronous trends in larval perch density. Spatiotemporal trends in larval
yellow perch density were deemed synchronous if the median correlation coefficient (ρ)
across all bivariate combinations (Cattanèo et al. 2003) was greater than 0.50. Although
other studies report synchronous population fluctuations at varying mean r values (e.g.,
Cattanèo et al. 2003; Edwards et al. 2007; Phelps et al. 2008), no ecologically meaningful
thresholds for determining synchronous population fluctuations exist (Buonaccorsi et al.
2001). To examine the spatial scale of potential synchronous larval yellow perch density
among lakes, inter-lake correlations were plotted relative to Euclidean distance between
lakes. Regression and correlation analyses were performed using the Statistical Analysis
System software package (SAS Institute 2010) and decision probability was set at α =
0.10.
Factors influencing larval yellow perch density
The degree of synchrony in trends in larval yellow perch production was used to
determine the scale at which to model variation in larval perch production. If trends in
production were synchronous among lakes, explanatory models were applied to a pooled
16
dataset. However, if trends in larval yellow perch density were asynchronous, lakespecific explanatory models were developed. Annual estimates of larval yellow perch
density were assessed relative to climatic and hydrological variables that may influence
variability in density based on previous research. Climatic variables used herein included
springtime air temperature and wind speed as described by Ward et al. (2004) and Jansen
(2008). Air temperature and wind data were obtained from nearby National Oceanic and
Atmospheric Association (NOAA) or municipal airport weather stations
(http://www.wunderground.com; accessed on June 13, 2014). Because of a lack of lakespecific water temperature data throughout trawling duration (i.e., 1995-2013), air
temperature was used as a surrogate for water temperature. The use of such data is
appropriate as larval yellow perch trawling sites are typically shallow (~1.5 m) and thus
water temperature was assumed to be reflective of air temperature.
Annual lake-specific water level elevation data were also used as a predictor
variable on the basis that water-level fluctuations have been demonstrated to influence
dynamics of early life stages of yellow perch (e.g., Kallemeyn 1987; Dembkowski et al.
2014). Furthermore, water-level fluctuations may influence spawning substrate
availability (Miranda et al. 1984; Kohler et al. 1993) and system productivity (Grimard
and Jones 1982; Ploskey 1986), which may, in turn, influence production of larval yellow
perch. Previous evaluations of the influence of climatic variables on larval yellow perch
density have used springtime precipitation data as a surrogate for water level elevation
data (Ward et al. 2004; Jansen 2008); however, direct measurements of water level
elevation data were available following completion of these studies. Therefore, mean
annual water surface elevation data (m above sea level) were obtained from the South
17
Dakota Department of Environment and Natural Resources
(http://denr.sd.gov/des/wr/dblakesearch.aspx). For each lake, annual water surface
elevation was regressed against sample year and Studentized residuals representing yearspecific deviations in water levels from long-term trends were used as relative indices of
high- and low-water years; positive residuals represented high-water years whereas
negative residuals represented low-water years.
To model the influence of the aforementioned climatic and hydrological variables
on larval perch density, the variables were combined into competing models with
different a priori hypotheses representative of conditions during two different
ontogenetic stages. The egg deposition and embryonic development (EDED) models
consisted of variables that were thought to influence yellow perch egg deposition and
embryonic development prior to emergence of free-swimming larvae. Because adult
yellow perch populations in the PPR typically deposit egg skeins from mid-April to midMay (Fisher and Willis 1997; Hanchin et al. 2003), these models included mean annual
air temperature and wind speed from 15 April to 15 May. The post-emergence (PE)
models consisted of variables that were thought to influence larval yellow perch during
the post-emergence swim-up period and during the ontogenetic switch from endogenous
to exogenous feeding. For the PE models, mean air temperature and wind speed were
calculated during the 14-d period prior to the date of peak mean larval density at each
lake in a given year (Table 2). For years in which no larval yellow perch were collected,
the mean lake-specific date of peak larval density was used in place. Annual water level
residual values were included in all EDED and PE models.
18
All competing models assumed the general linear form Y = a+ b(X), where Y is
larval yellow perch density, a is the intercept, X is the predictor variable, and b is the
slope. An information theoretic approach was chosen over more traditional regression
approaches because it provides a broader understanding of factors and competing models
by allowing for the identification of multiple models that may explain observed
variability instead of relying on one “best” model identified through traditional regression
analyses (Burnham and Anderson 2002; Whittingham et al. 2006). All possible models
were compared based on Akaike Information Criterion (AIC; Akaike 1973) values
corrected for small sample size (AICc), Δi (AICi – AICmin, where AICmin is the smallest
AICc value among the models), and wi (relative weight of support for a given model;
Burnham and Anderson 2002). A Δi value of 0-2 provides “substantial” support in
explaining variation in the given data whereas a value of 4-7 provides “considerably less”
support (Burnham and Anderson 2002). Evidence ratios (w1/wj) were also used to
evaluate the likelihood that the jth model was better supported that the estimated bestsupported model, where w1/wj < 2.7 suggest substantial support (Burnham and Anderson
2002). After determining which of the EDED or PE models was most supported, posthoc modeling analysis was used to further deconstruct different combinations of and
interactions between variables in that model (Burnham and Anderson 2002). The mostsupported model was evaluated by estimating larval yellow perch density using
regression model parameters and comparing observed and predicted larval density.
Variables were log-transformed as necessary to approximate normality and homogenize
error terms. All model selection analyses were performed using the Statistical Analysis
System software package (SAS Institute 2010).
19
RESULTS
Synchrony in larval yellow perch production
Throughout the study duration, density of larval yellow perch varied substantially
both among years and lakes (Table 2; Figure 1). Among lakes, density of larval yellow
perch varied by over two orders of magnitude within a single year (Table 2). However,
overall trends in larval yellow perch density were generally similar among lakes across
years (Figure 1). Correlation coefficients (ρ) for the 15 bivariate combinations of
Studentized residuals were all positive and ranged from 0.14 to 0.89 (median = 0.51),
with 8 of the 15 being significant at α = 0.10. Furthermore, 53% of correlations were
greater than 0.50 and 20% of correlations were greater than 0.70. The magnitude of
correlation coefficients among lakes suggests a moderate degree of spatial synchrony in
larval yellow perch production among lakes. The extent of synchrony was not related to
distance between lakes, at least not at the spatial scale included in this study (r = 0.01; P
= 0.76; Figure 2).
Factors influencing larval yellow perch density
Because interannual trends in larval yellow perch production were synchronous
among lakes, larval trawling data from all 13 study lakes were pooled to examine the
influence of climatic and hydrological variables on larval perch density. The resulting
dataset consisted of 98 lake-year observations and corresponding climatic and
hydrological data. Among the candidate models explaining variability in larval yellow
perch density, the PE models received substantially more support than the EDED models
(Table 4). Specifically, the PE model including temperature, wind, and water level
residual values was the most-supported, followed by the PE model including only
20
temperature and water level residual values (Table 3). The EDED models examining the
influence of climatic and hydrological conditions during egg deposition and embryonic
development received substantially less support (Δi > 2.0; w1/wj > 2.7; Table 3). In
general, yellow perch larval density was greater during years with elevated water levels
and with higher temperature and lower wind speed during the swim-up period and the
switch from endogenous to exogenous feeding (Figure 3). Although the full PE model
was most-supported, it only moderately predicted larval yellow perch density (Figure 4),
suggesting that larval perch density may be influenced by variables other than, or in
addition to, those already included in the model.
Post-hoc deconstruction of the most-supported PE model suggested that the
interaction between wind speed during the post-emergence period and annual variation in
water levels was most influential in explaining variability in larval yellow perch density
(Table 3). Other post-hoc models receiving substantial support included the annual water
level residual value and the interaction between temperature and water level. Other
single-variable, additive, and interactive models received substantially less support
(Table 3). In all instances, yellow perch density was positively related to both
temperature and water levels and negatively related to wind speed. The influence of
water levels is particularly apparent given the presence of the water level residual value
in all models receiving substantial support (Table 3). Furthermore, temporal trends in
larval yellow perch density seem to align with temporal trends in the regional hydrograph
(Figure 1; Figure 5), with peaks in larval perch density corresponding with peaks in the
hydrograph (e.g., 2001 and 2011).
21
DISCUSSION
Long-term sampling offered a unique opportunity to examine factors influencing
spatiotemporal trends in density of larval yellow perch across a range of natural lakes in
the Northern Great Plains. Overall, results suggest a moderate degree of spatial
synchrony in production of larval perch among lakes that was unrelated to distance
between lakes. Synchronous population fluctuations were driven by climatic variables
(i.e., the Moran Effect) including temperature, wind, and water levels (tied to regional
precipitation patterns; White et al. 2008). Because temperature, wind, and water level
residual values were included in single-variable, additive, and interactive models that
received substantial support relative to other candidate and post-hoc models, results
suggest that no single climatological or hydrological variable was related to larval yellow
perch density. Rather, the effects of any one predictor variable likely depend on the
magnitude of the other variables in a given year. Additionally, results suggest that
environmental phenomena during the post-emergence period may be more important than
those during the egg mass deposition and embryonic development period in influencing
eventual larval yellow perch density.
Synchronous population fluctuations in freshwater fishes have received
considerably less attention than those in marine and anadromous species (Myers et al.
1995; Myers et al. 1997; Cattanèo et al. 2003). Despite this disparity, synchronous
population fluctuations have been demonstrated for freshwater fishes including walleye
(Koonce et al. 1977; Colby et al. 1979; Schupp 2002), walleye × sauger Sander
canadensis hybrids (Graeb et al. 2010), brown trout Salmo trutta (Cattanèo et al. 2003),
roach Rutilus rutilus (Grenouillet et al. 2001), and common carp Cyprinus carpio (Phelps
22
et al. 2008). Among the limited available literature examining synchronous population
fluctuations in freshwater fishes, even less attention has been paid to synchronous
fluctuations during early life stages, and this is the first of my knowledge to demonstrate
synchrony in production of a larval cohort of a freshwater species.
Phelps et al. (2008) hypothesized that there may be a gradient in recruitment
synchrony among fishes. Populations exhibiting asynchronous population fluctuations
may be more influenced by localized climatic events or biotic factors (e.g., predation; Ims
and Steen 1990; Myers et al. 1997), whereas populations exhibiting synchronous
population fluctuations are more likely influenced by broad-scale climatic phenomena
(i.e., the Moran Effect). Exemplifying this gradient, common carp populations in the
Northern Great Plains exhibited synchronous recruitment patterns and year-class strength
was influenced by climatic phenomena during the first growing season (Phelps et al.
2008). Conversely, Edwards et al. (2007) determined that bluegill Lepomis macrochirus
populations exhibited asynchronous recruitment and found no evidence of the influence
of broad-scale climatic variables on year-class strength, instead concluding that biotic
factors such as competition or predation were most likely influencing the observed
recruitment patterns. Given the synchronous trends in larval yellow perch production
among lakes and evidence to suggest the influence of climatic and hydrological factors
over interannual variation in larval perch density, my findings suggest that recruitment
patterns of yellow perch may align more with those of common carp than with those of
bluegill. The disparity in recruitment patterns between fishes such as bluegill (i.e.,
asynchronous recruitment) and yellow perch and common carp (i.e., synchronous
recruitment) may be driven by differences in life-history characteristics - specifically,
23
greater parental investment by bluegill as compared to perch and carp (Scott and
Crossman 1973; Jennings et al. 1997; Spotte 2007). Greater parental investment by adult
male bluegill may infer a survival advantage upon early life stages (e.g., larval and fry
stages) and offset environmentally-related mortality that may otherwise serve to
synchronize population fluctuations. Further research is needed to examine the potential
gradient in recruitment synchrony and mechanisms for disparities in recruitment patterns
among freshwater fishes.
As is frequently cited as the underlying mechanism influencing synchronous
population fluctuations, interannual variation in density of larval yellow perch among
lakes appeared to be driven by the Moran Effect, specifically water levels (tied to
regional precipitation; White et al. 2008), temperature, and wind. Other mechanisms
proposed to synchronize populations include dispersal among populations (Ranta et al.
1995) or mobile predators (Ims and Steen 1990). However, most studies conclude that
the Moran Effect is the most likely mechanism influencing synchrony among disparate
freshwater fish populations (Myers et al. 1997; Grenouillet et al. 2001; Tedesco et al.
2004; Phelps et al. 2008), as dispersal and predation generally require connectivity
among populations.
Interestingly, my modeling efforts revealed that environmental phenomena during
the post-emergence period may be more important in influencing eventual larval yellow
perch density than those during the egg mass deposition and embryonic development
period. Previous evaluations attempted to explain variability in larval yellow perch
density using explanatory variables measured during an extended time period (i.e.,
March-May; Ward et al. 2004; Jansen 2008) that encompasses both the EDED and PE
24
periods. Thus, more resolute identification of critical life stages was not possible during
previous analyses. While temperature, water levels and wind may exert some degree of
influence during the egg mass deposition and embryonic development period, results
herein suggest that yellow perch egg masses may be more resistant or resilient to
environmental phenomena than previously estimated. Jansen et al. (2009) observed no
difference in hatching success of yellow perch egg masses subject to acute temperature
reductions relative to a control group, which suggests that perch eggs are more resistant
to or resilient against environmental stressors than previously hypothesized.
Reproductive success and subsequent recruitment of various fishes are generally
greatest during or following periods of high water (Boxrucker et al. 2005; Clark et al.
2008). Several previous studies have found positive relationships between increased
water levels and dynamics of early life stages of yellow perch. For example, Kallemeyn
(1987) found positive correlations between catch rate of age-0 yellow perch and water
levels in lakes in Voyageurs National Park, Minnesota, USA. Similarly, Henderson
(1985) found that recruitment of yellow perch in South Bay, Lake Huron was largely a
function of water levels and Dembkowski et al. (2014) hypothesized that increased
abundance of adult perch during high water years in a range of PPR natural lakes was due
to increased recruitment. In a cursory analysis of factors influencing larval yellow perch
abundance, Ward et al. (2004) found positive correlations between larval perch
abundance and springtime precipitation levels. Positive relationships between population
characteristics and increased water levels are commonly attributed to mechanisms
including increased spawning substrate (Miranda et al. 1984; Ploskey 1986; Kohler et al.
1993; Hardie 2013) and system productivity following inundation of shoreline vegetation
25
(Grimard and Jones 1982; Ploskey 1986). In other PPR lakes similar to those included in
this study, adult yellow perch typically deposited egg mass on recently submerged (i.e.,
periphyton-free) coarse woody debris. Thus, years with high water levels may see a
substantial increase in availability of preferred spawning substrate, which may, in turn,
result in increased egg mass deposition and larval yellow perch density (and vice-versa;
sensu Hardie 2013; Gaeta et al. 2014). Alternatively, inundation of terrestrial vegetation
during high-water years may result in a trophic upsurge-type phenomenon (Baranov
1961; Ostrofsky 1978), wherein elevated nutrient inputs from inundated soil and
vegetation increase production throughout the food web beginning at lower trophic
levels. Increased production of zooplankters, which are typically the first exogenous
food source for free-swimming larval yellow perch (Fisher and Willis 1997; Graeb et al.
2004), may result in increased growth, survival, and subsequent recruitment of early life
stages of perch.
Density of larval yellow perch was generally greater during years with warmer
temperatures during the post-egg mass emergence period. Several other studies have also
indicated a positive relationship between abundance of early life stage of yellow perch
and temperature. Ward et al. (2004) and Jansen (2008) found positive relationships
between springtime temperature and abundance of larval yellow perch in a range of PPR
natural lakes, and Eschenroder (1977) found that springtime temperature played an
important role in reproductive success of yellow perch in Saginaw Bay, Lake Huron.
Pope et al. (1996) found that recruitment of age-0 yellow perch in PPR natural lakes was
negatively related to variability in springtime temperature, suggesting that variability in
temperature may be more influential over dynamics of early life stages of perch than
26
mean annual trends. Although trends in abundance of early life stages of yellow perch as
a function of temperature are generally understood, underlying mechanisms eliciting the
population-level responses are not. Nonetheless, several hypotheses have been put forth
as potential mechanistic explanations. Given that dynamics of embryonic stages and
larvae of yellow perch are dependent upon temperature (Hokanson 1977), increased
water temperature may ultimately result in increased survival and recruitment via the
“stage duration” (Houde 1987; Leggett and DeBlois 1994) or “growth rate” (Ware 1975;
Anderson 1988) hypotheses. Newsome and Aalto (1987) reported that suboptimal
temperatures for early development may result in gill, jaw, and body-size deformities.
Alternatively, suboptimal temperatures may indirectly influence survival of larval yellow
perch by eliciting a behavioral response. In an experimental setting, VanDeHey et al.
(2013) compared survivorship of yellow perch yolk-sac fry across temperature reduction
treatments ranging from 0°C to 8°C in a 24-h period. While no differences in larval
yellow perch survival were found among treatments, behavioral differences were noted
wherein locomotory activity of larvae subjected to the temperature reduction of 8°C
ceased and larvae settled to the bottom of microcosms. Although the temperature
reduction did not cause direct mortality, VanDeHey et al. (2013) concluded that acute
temperature reductions may act as a sub-lethal stressor and increase the risk of starvation
or predation. Additionally, cooler springtime water temperatures may delay primary
productivity (Wetzel 1975), potentially decreasing zooplankter prey availability (e.g.,
match-mismatch hypothesis; Cushing 1975; Cushing 1990), resulting in reduced growth
rates and increased mortality.
27
I found a negative relationship between density of larval yellow perch and wind
speed during the post-egg mass emergence period, which is in agreement with the
prevailing paradigm that wind can negatively influence abundance of larval yellow perch
by dislodging egg mass and causing mortality via physical destruction on hard substrata,
desiccation if egg masses are washed ashore, or suffocation from siltation or
sedimentation (Clady and Hutchinson 1975; Clady 1976; Aalto and Newsome 1993).
However, explanatory models including wind speed measured during the egg mass
deposition and embryonic development periods were not well supported in our analyses.
Thus, the negative influence of wind speed on larval yellow perch density in my study
may instead be a function of physical destruction of free-swimming larvae or transport of
larvae into areas with unfavorable temperatures (Clady 1976) or prey densities
(Kaemingk et al. 2011). In an evaluation of the influence of high sustained wind speed
on distribution of larval yellow perch, bluegill, and zooplankton in a small (332 ha)
Nebraska, USA natural lake, Kaemingk et al. (2011) found no differences in spatial
distribution of larval perch between windward and leeward shores and no evidence of
spatial mismatch between perch and zooplankton prey, suggesting that wind did not act
as a significant transport mechanism of larval perch away from available prey items.
Conversely, Dettmers et al. (2005) posited that offshore transport (> 20 km) of larval
yellow perch via wind-induced currents, in conjunction with changes to the food web,
contributed to an extended period of poor perch recruitment in southern Lake Michigan,
and hypothesized that age-0 perch suffered substantial mortality because they were
unable to return to littoral areas following an ontogenetic diet shift from zooplankton to
benthic organisms. Although offshore transport of larval yellow perch, hydrodynamics,
28
and spatial match-mismatch between perch and prey resources have yet to be explored in
South Dakota glacial lakes, the relatively small scale of most glacial lakes decreases the
likelihood that juvenile yellow perch would not be able to return to littoral areas
following a diet shift to benthic prey. Alternatively, wind may function in concert with
temperature to reduce density of larval yellow perch. Because acute reductions in
temperature (i.e., cold fronts) are often accompanied by high winds (Browning and Monk
1982), wind- and wave-induced mortality of larval yellow perch may be exacerbated
during these climatic phenomena due to the close proximity of larvae to substrata and
their physiological inability to avoid physical destruction (e.g., VanDeHey et al. 2013).
It is likely that factors other than, or in addition to, climatic and hydrological
variables were influential over larval yellow perch density in our study lakes. I speculate
that relationships between larval yellow perch density and broad-scale temperature, wind
speed, and water level phenomena may in some cases be mediated by system-specific
characteristics (e.g., physical habitat or fish assemblage structure). For example, wave
energy and current velocity are reduced within macrophyte stands in lake littoral zones
(Losee and Wetzel 1993; Madsen et al. 2001). Therefore, the negative impacts of high
wind speed on larval yellow perch may be buffered by stands of macrophytes or other
structural habitat features in some natural lakes. Relative to system-specific biotic
influences, the biotic-abiotic constraining hypothesis (BACH; Quist et al. 2003) posits
that predation and competition can have an overriding negative influence on a species
even when abiotic conditions are favorable. Regardless of the mechanism, further
research regarding the interaction of broad-scale climatic phenomena with localized
29
abiotic or biotic characteristics would greatly improve our understanding of factors
influencing dynamics of fishes during early life stages.
My results represent an important advance in our understanding of yellow perch
early life history. Isermann and Willis (2008) posited that factors operating prior to,
during, or immediately after the contracted hatching period play a critical role in the
yellow perch recruitment process. Given that my results suggest a greater likelihood that
factors operating during the PE period, rather than factors operating during the EDED
period, have a larger bearing on eventual density of larval yellow perch, I have refined
previous hypotheses, thereby narrowing the focus of future research relative to drivers of
perch early life history, recruitment, and year-class strength. My research highlights the
importance of the Moran Effect in driving spatiotemporal trends in early life stages of an
important recreational and ecological species. Given the observed response of yellow
perch early life stages to climatic and hydrological phenomena, researchers may be able
to forecast trends in adult perch population and community dynamics relative to
environmental variation during early life stages. However, the unpredictable nature of
climatic and hydrological phenomena ultimately adds complexity to management of
yellow perch. Nonetheless, further research is needed for a mechanistic understanding of
the responses of yellow perch early life stages to climatic and hydrological variation,
which will ultimately provide a more comprehensive understanding of patterns and
processes that structure yellow perch populations.
30
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39
Table 1. Morphometric and physicochemical variables collected from eastern South
Dakota glacial lakes sampled for larval yellow perch, 1995-2013. Trophic status was
based on Carlson’s (1977) trophic status index. SDI = shoreline development index; N/A
= data not available. Data were summarized from Stueven and Stewart (1996) and Stukel
(2003).
Lake
Surface area (ha)
Maximum depth (m)
SDI
Trophic status
Brant
420
4.3
1.5
Mesotrophic
Clear
474
6.7
1.5
Mesotrophic
Cochrane
144
7.3
1.4
Eutrophic
East 81
133
4.9
2.0
Eutrophic
Enemy Swim
868
7.9
2.5
Mesotrophic
1,069
4.9
2.2
Eutrophic
160
1.8
1.6
Eutrophic
Pelican
1,131
2.4
1.4
Eutrophic
Pickerel
397
12.5
2.2
Mesotrophic
Sinai
696
10.1
2.4
Eutrophic
South Buffalo
724
4.3
3.8
Eutrophic
6,293
10.7
4.5
Eutrophic
436
3.0
2.5
Eutrophic
Madison
Oak
Waubay
West Oakwood
40
Table 2. Larval yellow perch density and trawl characteristics from sampling efforts on
13 South Dakota glacial lakes, 1995-2013. Density of yellow perch was indexed as the
number of larvae per 100 m3. For lakes sampled during only one year, values reported for
mean density and mean date of peak larval density were representative of single point-intime sampling events.
Lake
Years trawled
2001-2008
Mean
density
1.81
Minimum
density
0.00
Maximum
density
6.29
Mean date of
peak larval density
28-May
Brant
Clear
2011-2013
520.41
26.23
1,891.20
30-May
Cochrane
2005-2006
52.43
19.96
84.90
18-May
East 81
2000-2003
32.77
0.14
82.90
26-May
Enemy
Swim
1997, 2000-2013
153.76
2.20
633.56
29-May
2000-2013
2.17
0.00
9.30
26-May
1997
324.00
Pelican
1995-1996, 2008
35.00
0.00
55.00
3-June
Pickerel
1995-1997,
2000-2013
88.01
5.34
193.20
30-May
1997, 2000-2013
199.24
0.06
3,097.00
28-May
South
Buffalo
1997
259.00
Waubay
2000-2013
19.06
West
Oakwood
1997
39.00
Madison
Oak
Sinai
27-May
27-May
0.00
224.00
6-June
27-May
41
Table 3. Rankings of models to explain variation in density of larval yellow perch in
South Dakota glacial lakes. The full post-emergence (PE) model included mean air
temperature and wind speed during the 14-d period prior to the date of peak mean larval
density at each lake in a given year and the water level residual value indicative of annual
water surface elevation. The full egg deposition and embryonic development model
(EDED) included mean air temperature and wind speed during the typical period of
yellow perch egg mass deposition in the Northern Great Plains, USA (15 April - 15 May)
and the water level residual value. Reduced models only included temperature and water
level variables. The post-hoc models were as indicated. N = number of observations; K
= number of parameters estimated; AICc = Akaike’s Information Criterion corrected for
small sample sizes; ΔAICc = the difference between the AICc value of model i and that of
the most-supported model; wi = relative support for model i given the parameters; w1/wj =
evidence ratio indicative of the likelihood that the jth model was better supported that the
estimated best-supported model; see text for additional details.
Model
N
K
AICc
ΔAICc
wi
w1/wj
A priori models
PE – full
5
98
-12.09
0.00
0.50
1.00
PE – reduced
4
98
-11.59
0.50
0.39
1.28
EDED – reduced
4
98
-8.55
3.54
0.08
5.87
EDED – full
5
98
-6.76
5.34
0.03
14.41
Post-hoc models
Water × wind
3
98
-11.76
0.00
0.20
2.03
Water
3
98
-11.62
0.14
0.18
2.14
42
Table 3. Continued.
Model
N
K
AICc
ΔAICc
wi
w1/wj
Temperature × water
3
98
-11.46
0.29
0.17
2.37
Temperature
3
98
-10.68
1.08
0.12
4.30
Wind + water
4
98
-10.47
1.28
0.10
4.86
Wind
3
98
-10.34
1.42
0.10
5.10
Temperature + wind
4
98
-9.51
2.25
0.06
7.73
Temperature + wind +
water
5
98
-9.31
2.44
0.06
8.52
Temperature × wind
3
98
-6.02
5.74
0.01
44.21
43
Figure 1. Spatiotemporal trends in density of larval yellow perch collected from 6 eastern
South Dakota glacial lakes, 1995-2013. Residual values are indicative of relative withinlake yellow perch density; positive residuals are representative of high density whereas
negative residuals are representative of low density.
44
Figure 2. Extent of spatial synchrony in larval yellow perch density as a function of
Euclidean distance between lakes.
45
46
Figure 3. Larval yellow perch density [log10(number/100 m3)] as a function of
temperature, wind speed, and regional water levels. Dashed lines represent trend lines as
estimated by simple linear regression of log10 larval yellow perch density on each
predictor variable.
47
Figure 4. Relationship between observed larval yellow perch density (number/100 m3)
and predicted larval perch density (number/100 m3) based on regression parameter
estimates from the most-supported candidate model (see Results and Table 3). The
horizontal line reference line represents a 1:1 relationship.
48
Figure 5. Regional mean hydroperiod data for eastern South Dakota glacial lakes, 19962014. Water level residual values are Studentized residuals representing year-specific
deviations in water levels from long-term trends and were used as relative indices of
high- and low-water years; positive residuals represented high-water years whereas
negative residuals represented low-water years.
49
CHAPTER 3. FACTORS INFLUENCING RECRUITMENT AND GROWTH OF AGE0 YELLOW PERCH IN EASTERN SOUTH DAKOTA GLACIAL LAKES
INTRODUCTION
Fishery sustainability depends upon successful recruitment of young fish into
catchable, harvestable, or adult sizes (Maceina and Pereira 2007). Among the most
important dynamics and demographics of fish populations identified by Ricker (1975),
recruitment is frequently identified as the most deterministic parameter influencing
populations on the basis that minor fluctuations in recruitment may equate to marked
changes in other parameters (e.g., Carline et al. 1984). Recruitment dynamics are often
driven by density-dependent (i.e., biotic) and density-independent (i.e., abiotic) factors
and, within this biotic-abiotic framework, recruitment may be regulated by direct or
indirect processes (e.g., Ludsin et al. 2014). Research evaluating factors that influence
fish recruitment typically focus on early life stages (i.e., egg, larval, and juvenile; Hjort
1914) because the absolute number of individuals recruiting to an adult population
depends on the number of pre-recruits (Ludsin and Devries 1997; Houde 1987; Houde
1989). Furthermore, density-dependent and density-independent factors often inflict high
mortality upon a cohort during early life stages (Hjort 1914; Houde 1989). As such,
much existing research suggests that year-class strength is fixed early in life, after a point
at which natural mortality stabilizes and prior to a cohort entering a recreational fishery
(e.g., Ludsin and Devries 1997; Isermann and Willis 2008).
In addition to the number of pre-recruits, recruitment to the adult population may
also be mediated by the size of pre-recruits. For many fishes, faster growth, and thus a
larger size-at-age, infers greater survival and recruitment to subsequent ontogenetic
stages (e.g., Anderson 1988). Two hypotheses have been proposed to explain the
50
survival advantage inferred upon faster growing individuals and populations. In a biotic
context, the “growth rate” hypothesis (Ware 1975; Anderson 1988) postulates that faster
growing and subsequently larger individuals are less susceptible to predation and are
more likely to survive and recruit to subsequent ontogenetic stages than smaller
individuals. The “stage duration” hypothesis (Houde 1987; Leggett and DeBlois 1994)
postulates that faster growth rates reduce the time that a cohort spends in an ontogenetic
stage vulnerable to high mortality caused by unfavorable environmental conditions or
periods of high predation. Both hypotheses assert that faster growth during early
ontogeny infers greater survival, and therefore increased recruitment to subsequent life
stages. For example, larval fish of certain taxa that hatch later in the growing season may
experience more favorable temperatures and prey densities, resulting in increased growth,
decreased stage duration, and increased survival compared to earlier-hatched larvae
(Crecco and Savoy 1985; Rice et al. 1987; Kaemingk et al 2014b).
Placing emphasis on recruitment and growth dynamics during early life stages is
especially appropriate for fishes such as yellow perch Perca flavescens that produce large
numbers of small progeny with little parental investment. Yellow perch support
important recreational fisheries across much of their range (Mayer et al. 2000; Wilberg et
al. 2005; Isermann and Willis 2008) and are also an important prey species for predators
including walleye Sander vitreus, northern pike Esox lucius, and smallmouth bass
Micropterus dolomieu (Hansen et al. 1998; Blackwell et al. 1999). Erratic recruitment
has been documented in yellow perch populations across their range (Forney 1971;
Kallemeyn 1987; Sanderson et al. 1999; Isermann and Willis 2008) and for their
congeners Eurasian perch Perca fluviatilis (Le Cren et al. 1977), resulting in
51
unpredictable fisheries and prey availability. Among density-independent factors, yellow
perch recruitment has been linked to lake morphometry characteristics (Isermann 2003),
climatological variables operating during early life stages (Ward et al. 2004; Jansen 2008;
Redman et al. 2011; Weber et al. 2011), fluctuations in water levels (Kallemeyn 1987;
Dembkowski et al. 2014), and environmental stochasticity (Clady and Hutchinson 1975).
Among density-dependent factors, yellow perch recruitment has been linked with prey
availability (Jolley et al. 2010; Redman et al. 2011), prey size (Fisher and Willis 1997),
and prey assemblage composition (Whiteside et al. 1985), spawning stock characteristics
(Sanderson et al. 1999; Tyson and Knight 2001; Wilberg et al. 2005), competition
(Shroyer and McComish 2000), and predation (Forney 1974). Because of the intrinsic
linkage between growth and recruitment of juvenile fishes via the “growth rate” (Ware
1975; Anderson 1988) and “stage-duration” (Leggett and DeBlois 1994) hypotheses,
many of the factors that influence recruitment may also influence growth (e.g., Weber et
al. 2011).
To further identify factors influencing yellow perch recruitment and growth
dynamics during early ontogeny, I modeled recruitment and growth variation of age-0
(i.e., < 120 mm total length [TL]) yellow perch across a range of eastern South Dakota
glacial lakes from 2008 to 2010 to estimate the relative importance of density-dependent
versus density-independent factors. Because year-class strength for yellow perch is set
early in life (i.e., fall age-0; Fisher and Willis 1997; Anderson et al 1998; Isermann et al.
2007), I focus on recruitment and growth of yellow perch upon year-class formation after
natural mortality has stabilized but prior to the onset of exploitation mortality. While
previous research has estimated factors influencing larval yellow perch production and
52
growth (e.g., Whiteside et al. 1985; Mills et al. 1989; Ward et al. 2004; Isermann and
Willis 2008; Redman et al. 2011) and recruitment and growth of adult perch (e.g.,
Shroyer and McComish 1998; Wilberg et al. 2005; Isermann et al. 2007), considerably
less research has focused on factors influencing recruitment and growth of age-0 perch
upon year-class formation. Furthermore, most previous research estimating determinants
of recruitment and growth dynamics of yellow perch populations has occurred within a
paradigmatic framework emphasizing the importance of abiotic and climatological
variables (e.g., Ward et al. 2004; Isermann and Willis 2008; VanDeHey et al. 2013), and
much variation remains to be explained. Thus, relationships between density-dependent
factors (e.g., spawning stock abundance, competition, and predation) and recruitment and
growth of age-0 yellow perch were of particular interest.
METHODS
Data collection
Age-0 yellow perch were collected in backwater habitats with pulsed DC
electrofishing during the last week of the month from July through September in lakes
Brant, Campbell, East Oakwood, Goldsmith, Herman, Madison, Oak, West Oakwood,
and Whitewood from 2008-2010, resulting in a total of 27 lake-year observations.
Because yellow perch recruitment in prairie pothole lakes is determined early in life
(Isermann and Willis 2008), I assumed fall age-0 yellow perch abundance provided a
useful index of year-class strength. The first 100 age-0 yellow perch collected were
measured for total length (TL; mm); if fewer than 100 individuals were collected on a
given date, all individuals were measured. Longhenry (2006) estimated that that age-0
53
yellow perch reach a maximum of 130 mm TL by the fall of their first growing season.
To be conservative, I defined yellow perch < 120 mm TL as age-0 fish. Age-0 bluegill
Lepomis macrochirus, black crappie Pomoxis nigromaculatus, and common carp
Cyprinus carpio were collected concurrently with age-0 yellow perch to index potential
competitor abundance. Relative abundance of all age-0 fishes was expressed as the
number of individuals per species collected per hour of electrofishing.
Relative abundance of adult yellow perch, walleye, northern pike, bluegill, black
bullhead Ameiurus melas, black crappie, and common carp were assessed annually in
each lake as indices of spawning stock size, potential predation (walleye and northern
pike), and potential competition (bluegill, black bullhead, and common carp) using
standardized trap net and experimental gill net surveys (St. Sauver et al. 2008). Trap nets
were constructed with 0.9-m high by 1.5-m wide frames, 18.3-m leads, and 19-mm bar
mesh netting and were used to sample bluegill, black crappie, black bullhead, and
northern pike. Experimental gill nets were 45.7 m long and 1.8 m deep with one 7.6-m
panel each of 13-, 19-, 25-, 32-, 38-, and 51-mm bar mesh monofilament netting and were
used to sample yellow perch and walleye. Effort deployed varied by lake and increased
as a function of lake surface area but all locations were randomly selected and nets were
soaked for approximately 24 h (St. Sauver et al. 2008). Relative abundance of all fishes
except adult yellow perch was indexed as the mean number of individuals per species per
net night.
Yellow perch spawning stock biomass was estimated from standardized gill net
sampling data following the methods of Redman et al. (2011). Briefly, all adult (i.e., >
120 mm TL) yellow perch were counted and measured to TL; a subsample of ten perch
54
per 10-mm length group were weighed to the nearest g. Mean weight of yellow perch
was calculated for each 10-mm length-group in the weighed subsample and was
extrapolated to the un-weighed fish. In the instance that weights were not recorded
within a given length-group, weights were estimated using population- and year-specific
length-weight regression equations. Annual spawning stock biomass was expressed as
the mean biomass (g of yellow perch/gill net) of yellow perch from all gill net sets in a
given lake in a given year.
Relative to abiotic factors, seasonal changes in water level elevation (m above sea
level) were used to estimate availability of newly-inundated terrestrial vegetation that
may influence spawning habitat availability (Miranda et al. 1984; Kohler et al. 1993),
increased system productivity (Grimard and Jones 1982; Ploskey 1986), and ultimately
recruitment success (Kallemeyn 1987; Dembkowski et al. 2014). Water level elevation
data were obtained from the South Dakota Department of Environment and Natural
Resources (http://denr.sd.gov/des/wr/dblakesearch.aspx). Spring water level fluctuation
was calculated as the difference in water level elevation from September through May. I
also calculated summer water level fluctuation as the difference in water level elevation
from May through September that may increase system productivity (Grimard and Jones
1982; Ploskey 1986). While the influence of water levels on fish populations is welldocumented for impounded systems, considerably less attention has been paid to the
influence of water levels on fish populations in dynamic and unregulated glacial systems
(but see Dembkowski et al. 2014).
Air temperature and wind data were obtained from an online weather database
(www.wunderground.com; accessed on November 28, 2011). Because climatological
55
variables may influence fish recruitment on a large spatial scale (Phelps et al. 2008) and
due to the lack of lake specific climate data, air temperature and wind speed data
collected at the monitoring station in Brookings, South Dakota was used for all sampling
locations (maximum common distance between lake and monitoring station was 51 km).
Embayments sampled for age-0 yellow perch were shallow (~1.5 m); thus, air
temperature was assumed to provide a relative approximation for water temperature.
Variation in water temperature may be an important factor influencing yellow perch
recruitment (Jansen et al. 2009; Forsythe et al. 2012). Therefore, I calculated the
coefficient of variation of temperature from March 1 through May 31, which coincides
with documented yellow perch spawning phenology in eastern South Dakota (Isermann
and Willis 2008). Because wind events during spawning and larval stages may have a
negative effect on yellow perch recruitment (Ward et al. 2004; Jansen 2008), wind speed
was indexed as the cumulative wind speed for March 1 through May 31.
Data analysis
To evaluate potential mechanisms affecting annual variation in age-0 yellow
perch relative abundance, I considered a simple Ricker stock-recruitment relationship
without environmental variables:
R = Se [a –βS]eε
where R is the relative abundance of age-0 yellow perch, S is yellow perch spawning
stock biomass, a is a density-independent term describing the slope of the curve at the
origin, β is a term reflective of density dependence between stock size and recruitment,
and eε is the lognormal error term (Maceina and Pereira 2007). Error about the Ricker
curve tends to be lognormally distributed; therefore, I log-transformed the equation
56
(logeR/S = a – bS + ε). Next, I modified the Ricker stock-recruitment model to include
environmental variables as logeR/S = a – bS + c1Xi + ε, where Xi represents an
environmental variable and Ci represents its associated parameter (Maceina and Pereira
2007; Redman et al. 2011).
Akaike’s Information Criteria (AIC; Akaike 1973) was used to evaluate seven
separate a priori model combinations: 1) spawning stock biomass; 2) potential predator
(walleye and northern pike) relative abundance; 3) potential adult interspecific competitor
(bluegill, black crappie, black bullhead, and common carp) relative abundance; 4)
potential age-0 interspecific competitor (age-0 bluegill, black crappie, and common carp)
relative abundance; 5) abiotic factors (lake depth and surface area, temperature, wind
events, September through May water level change, and May through September water
level change), 6) biotic factors (spawner biomass, potential predation, potential adult
competition, potential age-0 competition), and 7) a global model that included all of the
aforementioned variables. I also used an AIC approach to evaluate the effects of age-0
yellow perch abundance, spawning stock biomass, abundance of potential competitors,
temperature, and wind on mean total length of age-0 yellow perch at the end of August.
Size of age-0 yellow perch in August was evaluated due to larger sample size of fish
collected at this time compared to September, which provided a more accurate
representation of average fish size. An information theoretic approach was chosen over
more traditional regression approaches because it allows for the identification of multiple
models that may explain observed variability instead of relying on one “best” model
identified through regression analyses (Burnham and Anderson 1998; Whittingham et al.
2006). All possible models were assessed based on AIC values corrected for small
57
sample size (AICc), Δi (AICi – AICmin, where AICmin is the smallest AICc value among
the models), and the relative weight of support for a given model (wi; Burnham and
Anderson 1998). A Δi value of 0-2 indicates “substantial” support in explaining variation
in the given data whereas a value of 4-7 indicates “considerably less” support (Burnham
and Anderson 1998). Evidence ratios (w1/wj) were also used to evaluate the likelihood
that the jth model was better supported that the estimated best-supported model, where a
ratio < 2.7 suggests substantial support. The most-supported models explaining variation
in age-0 yellow perch recruitment and growth were evaluated by estimating recruitment
and growth using regression model parameters and comparing observed values to
predicted values. All statistical analyses were performed using the Statistical Analysis
System software package (SAS Institute 2010).
RESULTS
Recruitment
A wide range of age-0 yellow perch fall relative abundance and spawning stock
biomass were collected across the 22 lake-year observations (Table 1). Age-0 yellow
perch abundance varied between 0 and 402.7 individuals captured per hour of
electrofishing (Table 1). Mean relative abundance of age-0 yellow perch across systems
and years was 65.53/h (SE = 22.23) and mean adult yellow perch biomass was 4,986.0
g/gill net (SE = 1,243.39). The singular Ricker-based model that included only spawning
stock biomass had the lowest AICc and was best supported (Table 2; Figure 1). The
highest abundance of recruits per spawner coincided with the lowest relative abundance
of adult yellow perch and recruitment declined with increasing spawning stock biomass.
58
The model predicted general trends in recruitment and did not tend to under- or
overestimate recruit abundance (Figure 2). However, the fit of the model was predicated
upon two relatively large recruitment events at the highest two levels of observed
spawning stock biomass (Figure 1; Figure 2). An additional three models that included
the abundance of potential age-0 and adult competitors and abundance of potential
predators received moderate support for explaining yellow perch recruitment variation,
but these models had considerably lower Akaike’s weights (< 0.20). A priori models that
included abundance of potential competitors and predators and lake morphology,
temperature, wind variables were not well supported (Table 1).
Growth
Mean TL of age-0 yellow perch in August varied substantially (Table 1),
indicating considerable variation in growth rates among systems. Akaike’s Information
Criterion indicated that two models received substantial supported in explaining variation
in yellow perch size. Age-0 yellow perch abundance received the most support (Table 3)
and was negatively related to age-0 fish size (Figure 3). The model did a reasonable job
predicting age-0 yellow perch total length and did not tend to under- or overestimate fish
size (Figure 2). Spawning stock biomass and the abundance of potential competitors also
received moderate support in explaining age-0 perch size, but the model had considerably
lower AIC weight (Table 3). Other models with biotic, abiotic, and all variables received
considerably less support (Table 3).
59
DISCUSSION
Overall, results suggest that the number of age-0 yellow perch surviving through
the first growing season in eastern South Dakota glacial lakes is regulated primarily by
biotic factors including spawning stock biomass, potential inter- and intraspecific
competition, and potential predation. Given the negative relationship between relative
abundance and total length of age-0 yellow perch in the fall, my analyses also suggest
that the size, and thus growth rates, during the first growing season of age-0 perch
recruits is regulated by biotic factors, specifically, potential intraspecific competition.
Although abiotic characteristics (e.g., lake morphometry, climatological, and
hydrological variables) have been demonstrated as influential over various aspects of
yellow perch early ontogeny (Kallemeyn 1987; Ward et al. 2004; Weber et al. 2011),
explanatory models including abiotic variables were not well-supported in my analyses.
The significant negative relationship between abundance of age-0 yellow perch
and perch spawning stock biomass suggests compensatory density-dependent mortality
(Hansen et al. 1998; Quinn and Deriso 1999). Most analyses of recruit-spawner
relationships for yellow perch are restricted to large dynamic systems (e.g., the
Laurentian Great Lakes) where recruitment dynamics may be driven by coupled physical
and biological processes much like those in marine systems (e.g., Dettmers et al. 2005;
Redman et al. 2011; Weber et al. 2011; Ludsin et al. 2014). Among studies of yellow
perch recruit-spawner relationships in large and dynamic systems, my results agree with
those of Redman et al. (2011) but are in contrast to those of Wilberg et al. (2005) who
found a positive relationship between spawning stock biomass and recruitment to age-0
in southwestern Lake Michigan. However, adult yellow perch stocks in southern Lake
60
Michigan were at extremely low biomass due to overexploitation (Wilberg et al. 2005;
Marsden and Robillard 2004), and thus recruitment may have been functioning in a
depensatory manner such that any increase in spawner abundance would equate to an
increase in recruits. Henderson (1985) and Henderson and Nepszy (1988) found no
evidence of compensatory density-dependent recruitment for yellow perch populations in
Lake Huron and Lake Erie, respectively, and instead concluded that recruitment was
driven by density-independent factors. Among studies of recruit-spawner relationships
on smaller spatial scales, my findings agree with those of Le Cren et al. (1977) who
found an inverse relationship between spawning stock biomass and abundance of age-0
Eurasian perch in Lake Windermere, UK, but are in contrast to those of Sanderson et al.
(1999), who demonstrated positive relationships between abundance of age-0 yellow
perch and spawning stock abundance in Crystal Lake, Wisconsin.
Compensatory density-dependent mortality may manifest directly or indirectly via
processes including predatory and competitive interactions and both mechanisms were
moderately supported by our models. Ricker (1954) posited that an inverse relationship
between recruit and spawner abundance may be attributed to cannibalism. While
cannibalism has been observed in yellow perch populations in New York (Tarby 1974),
Minnesota (Fullhart et al. 2002), and Michigan (Clady 1974), cannibalism has not been
documented in yellow perch populations in South Dakota waters (e.g., Lott et al. 1996;
Schoenebeck and Brown 2010). Perhaps more importantly, however, a decline in
recruitment at high spawner abundance may arise from predation by other species
(Maceina and Pereira 2007). In South Dakota, yellow perch constitute a primary prey
resource for other sport fishes including walleye and northern pike (Blackwell et al.
61
1999). Development of strong year-classes of yellow perch was strongly influenced by
walleye predation in Oneida Lake, New York (Forney 1971). While juvenile yellow
perch have been identified as a preferred prey item of walleye, Ney (1978) postulated that
the dominant prey item of a walleye population may be a function of prey availability
rather than preference. If it is a function of prey availability, walleye predation may only
regulate yellow perch year-class strength periodically due to erratic perch recruitment
patterns (Sanderson et al. 1999; Isermann and Willis 2008). Recent evidence also
suggests the potential for substantial predation of juvenile yellow perch by smallmouth
bass in South Dakota (Bacula 2009), but the potential for bass predation to limit perch
recruitment has not yet been fully explored.
Intra- and interspecific competition may also contribute to the observed
recruitment and growth patterns. Growth of yellow perch is often density-dependent
(Pierce et al. 2006; Headley and Lauer 2008; Irwin et al. 2009), and periodic strong year
classes may increase the potential for intraspecific competition. Bluegill, black crappie,
black bullhead, and common carp were considered as potential prey resource competitors
of yellow perch on the basis that all consume macroinvertebrates at the age-0 and adult
stages (Becker 1983). Variable densities of interspecific competitors may exacerbate
competitive interactions and result in further-reduced growth rates of juvenile yellow
perch. For example, Kaemingk and Willis (2012) found high overlap in both habitat and
prey resource use between age-0 yellow perch and age-0 bluegill in Nebraska glacial
lakes, leading to an inverse relationship between growth of age-0 perch and abundance of
age-0 bluegill. A similar inverse relationship was found between growth rates of yellow
perch congener Eurasian perch and abundance of roach Rutilus rutilus, with increased
62
competitive interactions in systems with higher roach densities (Persson 1983; Bergman
1990). Regardless of the competitive mechanism (i.e., inter- or intraspecific
competition), competitive interactions influence growth rates which may in turn mediate
recruitment via the “growth rate” (Ware 1975; Anderson 1988) or “stage duration”
(Houde 1987; Leggett and DeBlois 1994) hypotheses. In my study lakes, reduced age-0
yellow perch growth rates as a function of increased abundance of conspecifics and
interspecific competitors may increase the duration of susceptibility to predation or
unfavorable environmental conditions during early ontogeny, thereby reducing survival
and recruitment to subsequent life stages.
Although increased mortality during early ontogeny may reduce the overall
number of recruits, increased growth rates of the remaining cohort may serve as a process
of delayed compensation by allowing individuals to obtain a larger size at the end of the
first growing season, which may reduce mortality during later life stages (e.g., the first
overwinter period of life; Irwin et al. 2009). The first overwinter period of life has been
previously identified as a critical period of high mortality for many teleost fishes (Post
and Evans 1989; Sogard 1997). Most extant research suggests that overwinter mortality
is a size-dependent process wherein smaller individuals are selectively removed from a
cohort (Sogard 1997). Presumably, larger members of a cohort have increased
physiological capabilities to tolerate temperature extremes and can endure longer periods
without food (due to elevated lipid reserves) compared to smaller conspecifics (Sogard
1997). For example, cohorts of juvenile walleye (Forney 1976; Chevalier 1973),
smallmouth bass (Shuter et al. 1980), and largemouth bass Micropterus salmoides
(Toneys and Coble 1979) exhibited size-selective overwinter mortality where larger
63
individuals (i.e., those with faster growth rates during the first growing season) exhibited
greater overwinter survival than smaller conspecifics. Furthermore, Post and Evans
(1989) demonstrated size-selective overwinter mortality of juvenile yellow perch in
laboratory, in-situ, and field experiments. Therefore, faster growth may not only infer a
survival (and thus, recruitment) advantage during proximate life stages (i.e., from larval
to fall age-0), but also through subsequent life stages (i.e., through the first overwinter
period) as well.
Consideration of abiotic characteristics such as lake morphometry, climatological,
and hydrological variables herein was given because several previous studies have
identified them as important drivers of age-0 yellow perch abundance and growth (e.g.,
Ward et al. 2004; Weber et al. 2011). However, explanatory models including abiotic
variables were not well supported in my recruitment or growth analyses. This may be
due to a disparity between factors influencing early ontogeny and later life stages.
Previous evaluations of the influence of abiotic characteristics on yellow perch
recruitment and growth in South Dakota waters have focused on the larval stage (Ward et
al. 2004; Isermann and Willis 2008; Jansen et al. 2009; VanDeHey et al. 2013), where
fish may not be physiologically capable of tolerating stochastic environmental conditions
including variable temperature and wind events (Clady and Hutchinson 1975; Aalto and
Newsome 1993; VanDeHey et al. 2013). Because I chose to analyze factors influencing
recruitment and growth of age-0 yellow perch upon year-class formation (Anderson et al.
1998; Isermann et al. 2005), my analysis likely occurred after perch had recruited through
earlier critical periods of high mortality due to stochastic environmental events (e.g.,
hatching, switch from endogenous to exogenous feeding; Hjort 1914) and are now more
64
proximately influenced by biotic factors (e.g., predation and competition). Kaemingk et
al. (2014a) found that growth and recruitment of yellow perch larvae were primarily
influenced by abiotic factors (e.g., temperature) and found an increasing influence of
biotic factors as fish recruited to subsequent ontogenetic stages.
Recruitment and growth dynamics may be driven by density-dependent (i.e.,
biotic) factors, density-independent (i.e., abiotic) factors, or by complex interactions
between both. Herein, I demonstrate the influence of biotic factors affecting recruitment
and growth of age-0 yellow perch in eastern South Dakota glacial lakes, namely
spawning stock characteristics, competition, and predation. Given the substantial
influence of recruitment on other population dynamics (Carline et al. 1984), the influence
of juvenile growth dynamics on recruitment via the “growth rate” (Ware 1975; Anderson
1988) and “stage duration” hypotheses (Houde 1987; Leggett and DeBlois 1994), and the
dependence of predator populations on yellow perch as prey resources (Blackwell et al.
1999), biologists and managers should continually monitor yellow perch spawning stock
abundance as well as potential predators and competitors as drivers of perch recruitment
and growth, which in turn influence adult perch population characteristics, predator
population dynamics, and ultimately the dynamics of the recreational fisheries for these
species.
65
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Table 1. Summary statistics for biotic and abiotic variables collected from 27 lake-years
of sampling effort in eastern South Dakota glacial lakes. YEP = yellow perch; CPUE =
catch per unit effort; TL = total length; SSB = spawning stock biomass.
Variable
Mean
Standard error
Minimum
Maximum
Age-0 YEP CPUE (number/h)
65.5
22.2
0.0
402.7
Age-0 YEP TL (mm)
84.3
2.3
68.9
101.0
YEP SSB (g/net night)
4,986.0
1,243.4
409.3
23,062.0
655.4
103.8
104.0
1,893.0
2.6
0.4
1.2
7.2
Walleye + northern pike
(number/net night)
19.1
5.2
1.0
94.7
Adult competitors (number/net
night)
71.7
16.0
9.9
293.7
Age-0 competitors (number/h)
266.5
77.9
4.4
1,555.0
4,015.0
59.1
3,676.0
4,332.0
Temperature CV
30.0
0.0
30.0
30.0
Cumulative wind speed (km/h)
986.0
14.3
884.0
1,036.0
Yellow perch variables
Lake morphometry variables
Lake surface area (km2)
Lake depth (m)
Predator/competitor variables
Climatological variables
Cumulative temperature (°C)
74
Table 1. Continued.
Variable
Mean
Standard error
Minimum
Maximum
May-September water level
change (m)
0.1
0.3
-4.5
3.8
September-May water level
change (m)
-0.8
0.2
-4.4
1.1
Hydrological variables
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Table 2. Rankings of linear models to explain variation in recruitment of age-0 yellow
perch in eastern South Dakota glacial lakes. Model ranks were determined using the
Akaike information criterion corrected for small sample size (AICc), where the smallest
value indicates the most supported model. RSS = residual sum of squares; K = the
number of slope parameters plus error and intercept; Δi = the difference between the AICc
value for the model in question and that of the model with the minimum AICc value; wi =
the relative weight of evidence for the model in question; w1/wj = evidence ratio
indicating the likelihood that the jth model was better supported that the estimated bestsupported model. See Methods for model explanations.
Candidate models
RSS
K
AICc
Δi
wi
w1/wj
Spawning stock
12.20
3
-0.65
0.00
0.55
1.00
Potential age-0
competition
11.49
5
1.56
2.21
0.18
3.01
Potential adult
competition
12.20
6
2.21
2.86
0.13
4.17
Potential predation
12.16
4
2.17
2.82
0.13
4.10
Biotic
11.47
7
12.13
12.78
0.00
594.51
Abiotic
8.31
6
18.04
18.69
0.00
11,431.02
Global
7.00
12
36.18
36.83
0.00
99,214,928.05
76
Table 3. Rankings of linear models to explain variation in recruitment of age-0 yellow
perch in eastern South Dakota glacial lakes. Model ranks were determined using the
Akaike information criterion corrected for small sample size (AICc), where the smallest
value indicates the most supported model. See Methods for model explanations and Table
2 for abbreviations.
Candidate models
RSS
K
AICc
Δi
wi
w1/wj
Potential intraspecific
competition
0.04
3
-63.12
0.00
0.48
1.00
Spawning stock
0.04
3
-62.65
0.47
0.38
1.26
Potential interspecific
competition
0.04
5
-60.14
2.99
0.11
4.45
Biotic
0.03
7
-57.32
5.80
0.03
18.19
Abiotic
0.03
6
-53.46
9.67
0.00
125.71
Global
0.02
11
-35.13
27.99
0.00
1,198,247.07
77
Figure 1. Age-0 yellow perch recruits per spawner (upper panel) and recruits (lower
panel) produced as a function of spawning stock biomass in eastern South Dakota glacial
lakes, 2008-2010.
78
Figure 2. Relationship between observed and predicted recruitment (upper panel) and
growth (lower panel) of age-0 yellow perch in eastern South Dakota glacial lakes, 20082010. Recruitment and growth were predicted using regression model parameters from
the most-supported models explaining variation in abundance and total length of age-0
yellow perch. See Results, Table 2, and Table 3 for details. The solid 45° line represents
a 1:1 relationship between observed and predicted values.
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Figure 3. Relationship and associated Pearson correlation statistics between age-0 yellow
perch abundance and size in eastern South Dakota glacial lakes, 2008-2010.
80
CHAPTER 4. ESTIMATING THE INFLUENCE OF SMALLMOUTH BASS
PREDATION ON RECRUITMENT OF AGE-0 YELLOW PERCH IN SOUTH
DAKOTA GLACIAL LAKES
INTRODUCTION
Fisheries management programs have transplanted popular sport fishes including
northern pike Esox lucius (McMahon and Bennett 1996), rainbow trout Oncorhynchus
mykiss (Hitt et al. 2003), brook trout Salvelinus fontinalis (Holton 1990), brown trout
Salmo trutta (Taylor et al. 1984), and bluegill Lepomis macrochirus (Quinn and Paukert
2009), among many others, outside of their native ranges to provide novel angling
opportunities. Black basses, especially largemouth bass Micropterus salmoides and
smallmouth bass Micropterus dolomieu, are some of the most commonly introduced
species worldwide (Jackson 2002). In the U.S., smallmouth bass are native to 23 states
but have been stocked into an additional 22 states (Rahel 2000). Despite a long history of
smallmouth bass introductions, formal evaluation of the ecological consequences of these
introductions has only recently begun (Jackson 2002).
As apex predators, smallmouth bass have the potential to create novel interactions
with other fishes in the form of resource competition and predation. Their predatory
behavior, combined with opportunistic feeding habits, enable smallmouth bass to exert
top-down influences on prey populations. Relative to impacts on prey fishes,
introductions of smallmouth bass outside of their native range have been linked with
changes in native fish assemblage trophodynamics (e.g., Whittier et al. 1997; Vander
Zanden et al. 1999), restructuring of aquatic communities (e.g., Vander Zanden et al.
1999), and changes in prey fish population dynamics (e.g., Johnson and Hale 1977; Pflug
and Pauly 1984; Tonn and Magnuson 1983).
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The magnitude of predatory impacts of smallmouth bass on prey fish populations
encompasses a broad spectrum, ranging from minimal impacts to complete extirpation
(e.g., Jackson 2002). In a study of the influence of predation by the predatory fish
assemblage on abundance of out-migrating salmonid smolts in John Day Reservoir, Vigg
et al. (1999) found that introduced smallmouth bass had the lowest mean seasonal
consumption of salmonids in the reservoir and consumed only 0.04 salmonid
prey/predator/day. Similarly, Fayram and Sibley (2000) concluded that smallmouth bass
predation on juvenile sockeye salmon Oncorhynchus nerka had little impact on an
observed temporal decrease in sockeye salmon abundance in Lake Washington, WA. On
the opposite end of the spectrum, the presence of smallmouth bass in a range of
Minnesota lakes corresponded to a substantial reduction in walleye abundance, possibly
attributed to predation of walleye Sander vitreus by smallmouth bass (Johnson and Hale
1977; Zimmerman 1999). Several studies report extensive predation on native cyprinids
by smallmouth bass in Ontario lakes, with predation so high in some instances as to cause
extirpation of cyprinids in some systems (Jackson 2002; Jackson and Mandrak 2002).
In South Dakota, smallmouth bass are native to the Minnesota River drainage
basin but were introduced outside of their native range beginning in the mid-1980s to
diversify angling opportunities (Milewski and Willis 1990; Berry and Young 2004). Due
to their short residence time, relatively little is known about smallmouth bass interactions
with native species in most state waters. However, several studies examined potential
predatory and competitive interactions between smallmouth bass and walleye in field and
laboratory settings as a response to angler concerns that bass were predating upon
juvenile walleye and reducing abundance, condition, and growth rates of adult walleye
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via interspecific resource competition (Wuellner et al. 2010; Wuellner et al. 2011a;
Wuellner et al. 2011b; Galster et al. 2012). While few studies have assessed the impact
of smallmouth bass predation on prey fish populations, several have documented bass
food habits in state waters (e.g., Lott 1996; Blackwell et al. 1999; Bacula 2009), which
may provide insight into potential predatory impacts. For example, Bacula (2009) found
that age-0 yellow perch Perca flavescens comprised between 24 and 82% of smallmouth
bass diets by weight across a range of eastern South Dakota glacial lakes, prompting
concern that bass predation may negatively influence perch populations in systems where
both species co-occur.
Yellow perch are an important ecological and recreational component of fish
assemblages in South Dakota and throughout much of their range (Hansen et al. 1998;
Blackwell et al. 1999; Mayer et al. 2000; Isermann et al. 2007; Gigliotti 2007).
Recruitment patterns of yellow perch are typically characterized as erratic or inconsistent,
with a large degree of interannual variation in year-class strength (Anderson et al. 1998;
Isermann et al. 2007; Isermann and Willis 2008). Several studies have attempted to link
interannual variation in year-class strength with abiotic and climatological factors, but a
large amount of variation remains unexplained (e.g., Ward et al. 2004; Isermann and
Willis 2008; Jansen 2008). The findings of Bacula (2009) prompted concern that
smallmouth bass predation upon age-0 yellow perch may contribute to interannual
variation in perch year-class strength and ultimately act as a factor limiting recruitment of
yellow perch to sizes preferred by the recreational fishery. To address these concerns, I
estimated the potential impact of smallmouth bass predation on recruitment of age-0
yellow perch in two northeastern South Dakota glacial lakes. Specifically, I used
83
smallmouth bass population size and diet information as inputs in a bioenergetics model
to estimate a likely range in consumption of age-0 yellow perch. Consumption estimates
were compared to estimates of age-0 yellow perch production to estimate the proportion
of the age-0 perch cohort consumed by the smallmouth bass populations.
METHODS
Study lakes
This study was conducted on two glacial lakes in northeastern South Dakota
during 2012 and 2013. Pickerel Lake (Day County, South Dakota, USA) was sampled
during May-September 2012, and Clear Lake (Marshall County, South Dakota, USA)
was sampled during the same months in 2013. Pickerel Lake is considered mesotrophic
and Clear Lake is considered eutrophic (Carlson 1977). Pickerel Lake has a surface area
of 397 ha, mean depth of 4.8 m, maximum depth of 12.5 m, and shoreline development
index of 2.2; Clear Lake has a surface area of 474 ha, mean depth of 3.8 m, maximum
depth of 6.7 m, and shoreline development index of 1.5 (Stueven and Stewart 1996;
Stukel 2003). Pickerel Lake has a relatively steep basin morphometry relative to Clear
Lake. Natural riparian vegetation surrounding both lakes is limited owing to
anthropogenic development, and littoral habitat consists largely of bare rock and sand
substrate interrupted by stands of submerged (sago pondweed Stuckenia pectinata and
coontail Ceratophyllum demersum) and emergent (bulrushes Scirpus spp. and cattails
Typha spp.) macrophytes.
Fish assemblages in both lakes are relatively simple and consist primarily of
centrarchids, percids, ictalurids, cyprinids, esocids, moronids, and catostomids. Most
84
species present in Pickerel and Clear lakes are ubiquitous within glacial lakes throughout
northeastern South Dakota (B. Blackwell, South Dakota Department of Game, Fish and
Parks, personal communication). Pickerel Lake is managed as a panfish (i.e., black
crappie Pomoxis nigromaculatus, bluegill, and yellow perch), smallmouth bass, and
walleye fishery. Management objectives in Clear Lake also focus on walleye and
smallmouth bass but also include largemouth bass. Smallmouth bass regulations on both
lakes include a five-fish daily bag limit and a 355 mm – 457 mm (14” – 18”) protected
slot limit.
Smallmouth bass sampling and diets
Smallmouth bass were sampled from Pickerel and Clear lakes every 7-10 d during
May-September using nighttime boat electrofishing. Littoral transects with rocky
substrate were randomly selected at the onset of sampling and were fixed for the study
duration at each lake per the recommendations of Bacula et al. (2011). Specifically, six
10-min transects were sampled using a boat electrofisher equipped with a Smith-Root 7.5
GPP pulsator unit (Smith-Root, Inc., Vancouver, Washington). Pulsed DC electricity
was cycled at 60 Hz with voltage output adjusted according to the specific conductance at
each lake and sampling event to maintain a constant output of 7-9 A.
All smallmouth bass were measured to total length (TL; mm) and wet weight (W;
g). Scales were removed from a subsample of smallmouth bass for age, growth, and
mortality analyses. Sagittal otoliths were removed from smallmouth bass subjected to
incidental mortality for verification of scale-based age estimates. Diet items were
collected from a target of 20 smallmouth bass per length group in August and September.
Diet sampling was only conducted during August and September because previous
85
studies indicated that smallmouth bass consumption of age-0 yellow perch peaked during
the late-summer and early-fall period (Bacula 2009). Therefore, I assumed that the
potential impact of smallmouth bass predation on age-0 yellow perch would be greatest
during this time period. Length groups consisted of substock (SS; < 180 mm TL), stockquality (S-Q; 180-279 mm TL), quality-preferred (Q-P; 280-349 mm TL), and preferred
or greater (P+; ≥ 350 mm TL; Gabelhouse 1984). Diets from smallmouth bass ≥ 180 mm
TL were collected using gastric lavage, a nonlethal sampling technique found to
effectively remove over 90% of diet items from black basses (Hakala and Johnson 2004).
Stomach contents were flushed onto a 500-µm sieve, transferred to a sample container,
and preserved in 70% ethyl alcohol. Smallmouth bass < 180 mm TL were sacrificed and
preserved in 70% ethyl alcohol for laboratory processing because small esophageal
diameter inhibited efficiency of the gastric lavage device. Stomachs and esophagi were
removed at a later date.
Stomach contents were identified to lowest appropriate taxonomic level,
enumerated, and weighed. Prey fishes were identified to species (Becker 1983) and
macroinvertebrates were identified to order (Merritt and Cummins 1996) when possible.
Unidentifiable prey fishes, miscellaneous fish parts, and underrepresented fish species
were pooled as “other fish.” Unidentifiable fishes with obvious centrarchid body
morphology were pooled as “unidentified centrarchids.” All prey items were blotted to
remove excess liquid and weighed to the nearest 0.01 g. Length-group-stratified diets
were characterized using mean proportion of dominant diet items by wet weight (Hanson
et al. 1997; Chipps and Garvey 2007; Hartman and Hayward 2007).
86
Smallmouth bass population size
Smallmouth bass population size was estimated using a multiple census markrecapture design. During electrofishing sampling events, all smallmouth bass were given
a pectoral fin clip. Electrofishing samples were periodically supplemented with catches
of smallmouth bass from mini-fyke nets and hook-and-line angling to reduce gear
selectivity biases. Smallmouth bass population size at each lake was estimated using the
Schumacher-Eschmeyer modification of the Schnabel population estimator (Schnabel
1938; Schumacher and Eschmeyer 1943; Ricker 1975):
𝑡
N=
∑𝑖=2(𝑛𝑖 ∗ 𝑀𝑖 )
𝑡
∑𝑖=2(𝑚𝑖 +1)
,
where N is the estimated population size, t is the number of sampling events, ni is the
number of fish caught in the ith sample, mi is the number of fish with marks caught in the
ith sample, and Mi is the number of marked fish at large for the ith sample. Population
size was estimated for whole smallmouth bass populations and stratified by individual
cohorts; cohort size estimates were dependent on recapture rates of fish within individual
cohorts. Cohorts were assigned based on month- and population-specific age-length keys
developed from ages estimated from scale samples (Ricker 1975; Isley and Grabowski
2007). Cohort size was estimated using a scalar modification of the Schnabel population
estimator (Schnabel 1938; Schumacher and Eschmeyer 1943; Ricker 1975):
𝑡
𝑁𝑗 =
∑
(𝑛𝑖𝑗 ∗𝑀𝑖𝑗 )
∑
(𝑚𝑖𝑗 +1)
𝑖=2
𝑡
𝑖=2
,
where Nj is the estimated size of cohort j, t is the number of sampling events, nij is the
number of fish of cohort j caught in the ith sample, mij is the number of fish of cohort j
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with marks caught in the ith sample, and Mij is the number of marked fish of cohort j at
large for the ith sample. Asymmetrical 95% Poisson confidence intervals (CI)
surrounding each population or cohort size estimate were computed following equations
in Ricker (1975).
Yellow perch sampling and production estimation
Age-0 yellow perch were sampled at each lake at 6-10 d intervals during August
and September using a beach seine (Dembkowski et al. 2012). A 27.4 × 1.8 m bag seine
was deployed in a circle by wading, using an onshore point as the starting and ending
point. To enclose the sample, the lead-line was pulled toward shore from both ends until
the collected fish were confined in the seine bag. Dembkowski et al. (2011) found that
age-0 yellow perch in Pickerel and Clear lakes were distributed around patches of
submerged vegetation in nearshore areas and maintain a mostly demersal existence, thus,
vegetated shoreline areas were selected as seine sample sites. Prior to seine sampling at
each lake, the extent of submerged littoral vegetation cover was estimated as the mean
percentage of lake shoreline transects with significant macrophyte coverage (Stevenson
and Bain 1999). During each seine haul, density (number/m2) and W (g) of age-0 yellow
perch were recorded.
Production of age-0 yellow perch in Pickerel and Clear lakes was estimated using
the instantaneous growth rate method (Ricker 1975; Hayes et al. 2007):
𝑃̂ = 𝐺̂ 𝐵̅ ,
where 𝑃̂ is the estimated production for a given cohort within a specified interval, 𝐺̂ is
the estimated instantaneous growth rate for the cohort from time t to time t + 1, and 𝐵̅ is
the estimated arithmetic mean cohort biomass from time t to time t + 1. Estimation of the
88
𝐺̂ and 𝐵̅ parameters involved three components. First, mean density of age-0 yellow
perch estimated by weekly seine hauls was extrapolated to the vegetated littoral area
using the area-density method (Van Den Avyle and Hayward 1999). Stukel (2003)
estimated that approximately 45% of the total surface area of Pickerel and Clear lakes
consisted of littoral area. Thus, littoral area was estimated as the product of 0.45 and 397
ha and 474 ha for Pickerel and Clear lakes, respectively. I assumed that production of
age-0 yellow perch was spatially limited to the vegetated littoral area of each lake based
on the results of Dembkowski et al. (2011). Thus, littoral lake area was multiplied by our
estimates of the percentage of lake shoreline with significant macrophyte coverage
(approximately 40% at each lake). Lake-wide estimates of age-0 yellow perch
abundance were then computed as the product of vegetated littoral area (m2) and mean
density of age-0 perch (number/m2). Second, biomass of the age-0 yellow perch cohort
was estimated as the product of mean weight of age-0 perch and abundance of age-0
perch in vegetated littoral areas. Finally, instantaneous growth rates were estimated as
̅ t+1 - loge 𝑊
̅ t (Ricker 1975). Production of age-0 yellow perch was expressed as
loge 𝑊
kg/ha and was estimated during the period between each seine sampling event. Variance
surrounding each estimate of production was estimated using equations available in
Hayes et al. (2007).
Bioenergetics modeling
A bioenergetics model (Hanson et al. 1997) was used to estimate age-specific
consumption of age-0 yellow perch by the smallmouth bass populations in Pickerel and
Clear lakes. Bioenergetics models are highly applicable and function on the basis of an
energy mass- balance equation:
89
Consumption (C) = Growth (G) + Respiration (R) + Egestion (F) + Excretion (U),
where units of G are derived through population measurements; R is a function of fish
weight, temperature, diet, and activity; and F and U are functions of temperature and diet
(Kitchell et al. 1977; Hanson et al. 1997; Hartman and Hayward 2007; Fincel et al. 2014).
Consumption modeling minimally requires a set of predator-specific physiological
parameters, predator food habits, abundance, and growth, predator and prey energy
densities, and water temperature. Species-specific physiological parameters were
included implicitly in the bioenergetics modeling software (Fish Bioenergetics 3.0;
Hanson et al. 1997). Smallmouth bass food habits and cohort abundance were derived
from our population sampling and diet collections during August and September. To
provide an estimate of the greatest potential impact of smallmouth bass predation on
recruitment of age-0 yellow perch, the upper bounds (i.e., upper 95% CI) of bass cohort
size estimates were used as bioenergetics inputs (Table 1). Additionally, prior
smallmouth bass food habits from Pickerel and Clear lakes from 2008 were compiled
from Bacula (2009); population and diet sampling were conducted in a standardized
fashion in 2008, 2012, and 2013. During this time period, age-0 yellow perch constituted
24-82% of smallmouth bass diets by wet weight (Bacula 2009). For both lakes in 2008,
August and September diets were comprised primarily of prey fishes, but other important
prey items included decapods and various other macroinvertebrates (Figure 1). To
incorporate this temporal dimension of smallmouth bass consumption dynamics, we
simply replaced our empirical food habits data with those collected during 2008; all other
bioenergetics inputs were derived herein (smallmouth bass growth and abundance, and
water temperature data were not collected during the 2008 study). Because estimates of
90
smallmouth bass abundance were stratified by age and food habits were stratified by
length group, diets were assigned to cohorts based on mean TL of fish in each cohort.
Growth of smallmouth bass was expressed as the difference between final and initial
weight (g) of each cohort. Energy densities (J/g wet weight) of individual prey items
were gathered from the literature for macroinvertebrates (Cummins and Wuycheck 1971;
Hill 1997) and fish (Kitchell et al. 1974; Rice et al. 1983; Bevelheimer et al. 1985; Bryan
et al. 1996; Liao et al. 2004; Table 2). For pooled diet items (e.g., unidentified
centrarchids), energy density was estimated as the mean energy density of taxonomically
similar diet items of known identity (Table 2). Daily mean littoral water temperatures
were recorded throughout the growing season by stationary data loggers affixed to dock
pilings or cinderblock mounts.
Age-specific consumption of age-0 yellow perch by the smallmouth bass
populations was expressed as the weight (g) of age-0 perch consumed by the smallmouth
bass cohorts on any given day. Total consumption of age-0 yellow perch was estimated
as the sum of daily consumption estimates for each smallmouth bass cohort throughout
the simulation period. Because previous studies indicated that predation of age-0 yellow
perch by the smallmouth bass populations peaked during August and September (Bacula
2009), consumption of age-0 perch was modeled from 1-August to 30-September, the
period that directly corresponded to our estimates of age-0 yellow perch production. To
facilitate direct comparison of age-0 yellow perch production versus age-0 yellow perch
consumption, age-specific and cumulative consumption of age-0 perch by the
smallmouth bass populations were expressed in kg/ha and were extracted from the
bioenergetics output during the same intervals during which perch production was
91
estimated. Therefore, I directly estimated the proportion of weekly age-0 yellow perch
production consumed by the smallmouth bass populations. The percent of total
consumption of age-0 yellow perch by each smallmouth bass cohort was estimated to
further investigate intra-population consumption dynamics and estimate which segments
of the bass population contributed most to perch consumption.
RESULTS
Smallmouth bass diets
I collected and analyzed stomach contents from 174 and 168 smallmouth bass at
Pickerel and Clear lakes, respectively. At Pickerel Lake 21 (12%) stomachs were empty,
whereas at Clear Lake, 15 (9%) were empty. Based on mean TL of smallmouth bass in
each cohort, SS diets were assigned to the age 0-2 cohort, S-Q diets were assigned to the
age-3 cohort, Q-P diets were assigned to the age-4 cohort, and P+ diets were assigned to
the age-5 and age-6+ cohorts at both Pickerel and Clear lakes. Prey composition of
smallmouth bass varied temporally through August and September and among cohorts
within each lake (Figure 2).
At Pickerel Lake, consumption of prey fishes (i.e., bluegill, black crappie,
unidentified centrarchids, and yellow perch) was generally greatest for smallmouth bass
less than quality length (i.e., cohorts 0-2 and 3), and food habits analysis showed an
increasing reliance on decapods as bass grew, with decapods comprising over 70% of
stomachs by wet weight for bass larger than minimum preferred length (i.e., cohorts 5
and 6+; Figure 2). During August, age-0 yellow perch constituted between 5 and 13% of
smallmouth bass diets by weight; consumption of age-0 perch was highest for
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smallmouth bass less than minimum stock length (i.e., cohort 0-2). During September,
age-0 yellow perch constituted between 0 and 11% of smallmouth bass diets by weight,
with the highest consumption of age-0 perch stemming from bass of stock-quality length
(i.e., cohort 3).
Similar trends were observed at Clear Lake, with a decreasing reliance on prey
fish and an increasing reliance on decapods through progressive smallmouth bass cohorts
(Figure 2). During August, consumption of age-0 yellow perch was greater than that
observed at Pickerel Lake. Age-0 yellow perch constituted between 0 and 20% of
smallmouth bass diets by weight; consumption of age-0 perch was greatest for bass of
stock-quality length (i.e., cohort 3). During September, age-0 yellow perch constituted
between 0 and 9% of smallmouth diets by weight, with the highest consumption
stemming from stock-quality length bass (i.e., cohort 3).
The contribution of age-0 yellow perch to smallmouth bass diets in Pickerel and
Clear lakes was substantially greater in 2008 (Figure 1). In Pickerel Lake, age-0 yellow
perch constituted between 3 and 42% of bass diets by weight during August and between
0 and 17% of diets by weight during September. In Clear Lake, age-0 yellow perch
constituted between 17 and 36% of smallmouth bass diets by weight during August and
between 0 and 12% of diets by weight in September. In contrast to consumption patterns
observed in Pickerel and Clear lakes in 2012 and 2013, consumption of age-0 yellow
perch was spread relatively evenly throughout smallmouth bass cohorts and not limited to
younger and smaller bass.
93
Smallmouth bass population size estimation
Throughout the sampling period, 1,206 and 893 smallmouth bass were marked at
Pickerel and Clear lakes, respectively. Recapture rates of smallmouth bass were 13% at
Pickerel Lake and 11% at Clear Lake. Estimated smallmouth bass population size was
5,321 (upper 95% CI = 6,288; lower 95% CI = 4,529) at Pickerel Lake and 3,695 (upper
95% CI = 4,346; lower 95% CI = 3,197) at Clear Lake. The population size estimate at
Pickerel Lake yielded population density and standing crop estimates of 13.7/ha and 3.3
kg/ha, respectively. At Clear Lake, population density was 7.7/ha and standing crop was
3.3 kg/ha.
Based on sample size and recapture rates among age-groups at each lake, cohort
size was estimated for smallmouth bass age- 0-2, 3, 4, 5, and 6+. At Pickerel Lake, the 02 cohort was most abundant, followed by a trend of decreasing abundance of cohorts 36+ (Table 1). A similar trend was observed at Clear Lake, but abundance of the age-5
and age-6+ cohorts was increased relative to abundance of age-3 smallmouth bass (Table
1). Although 95% CI’s varied substantially for cohort size estimates at each lake, there
were no instances when the CI’s encompassed zero. At Pickerel Lake, mean TL for the
0-2 smallmouth bass cohort was 168 mm, followed by 239 mm, 322 mm, 376 mm, and
398 mm for the 3, 4, 5, and 6+ cohorts, respectively. At Clear Lake, mean TL for the 0-2
cohort was 174 mm, followed by 257 mm, 346 mm, 389 mm, and 424 mm for the 3, 4, 5,
and 6+ cohorts, respectively.
Age-0 yellow perch production
Seine sampling was conducted during 10 occasions each at Pickerel Lake in 2012
and Clear Lake in 2013. At Pickerel Lake, mean density of age-0 yellow perch in seine
94
hauls decreased from 2.29 perch/m2 (SD = 2.92) on 2-August to 0.68 perch/m2 (SD =
0.72) on 4-October. Conversely, mean W of age-0 yellow perch increased from 0.91 g
(SD = 0.14) to 2.94 g (SD = 0.67) throughout the sampling period. At Clear Lake, mean
density of age-0 yellow perch in seine hauls decreased from 4.06 perch/m2 (SD = 3.89)
on 30-July to 0.46 perch/m2 (SD = 0.47) on 2-October. Mean W of age-0 yellow perch
increased from 0.68 g (SD = 0.10) to 2.46 g (SD = 0.52) throughout the sampling period.
Production of age-0 yellow perch was estimated during 9 intervals at each lake
ranging temporally from 6 to 10 d (hereafter, week). At both lakes, weekly production
estimates were variable but showed a general decrease from early August through late
September (Figure 3; Figure 4). Estimates of age-0 yellow perch production were
comparable between both lakes, with production estimates at Pickerel Lake ranging from
0.48 kg/ha/week (± 0.07 kg/ha/week) to 1.38 kg/ha/week (± 0.29 kg/ha/week; Figure 3)
and production estimates at Clear Lake ranging from 0.32 kg/ha/week (± 0.04
kg/ha/week) to 1.78 kg/ha/week (± 0.29 kg/ha/week; Figure 4).
Bioenergetics modeling
Consumption of age-0 yellow perch by the smallmouth bass populations were
modeled during the same 9 intervals over which perch production was estimated. At
Pickerel Lake during 2012, weekly population-level consumption of age-0 yellow perch
(estimated as the sum of daily age-specific consumption) ranged from 0.01 kg/ha/week to
0.10 kg/ha/week (Figure 5), equating to consumption of between 1 and 7% of available
perch biomass. When 2012 diets were replaced with those from 2008 (the period when
age-0 perch constituted a substantially greater proportion of smallmouth bass stomach
contents), weekly consumption of age-0 yellow perch ranged from 0.06 kg/ha/week to
95
0.33 kg/ha/week (Figure 5), equating to consumption of between 7 and 34% of available
perch flesh. At Clear Lake during 2013, weekly population-level consumption of age-0
yellow perch ranged from 0.02 kg/ha/week to 0.04 kg/ha/week (Figure 6), equating to
consumption of between 2 and 6% of available perch biomass. When 2013 diets were
replaced with those from 2008, population-level consumption ranged from 0.11
kg/ha/week to 0.28 kg/ha/week (Figure 6), equating to between 14 and 34% of available
perch biomass.
Intra-population consumption dynamics of age-0 yellow perch by smallmouth
bass differed between the periods of low (2012 and 2013) and high (2008) perch
consumption. During 2012 and 2013 at Pickerel and Clear lakes, greater than 80% of
total age-0 yellow perch consumption was from smallmouth bass < 280 mm TL (Table
3). Conversely, consumption of age-0 yellow perch during 2008 was spread relatively
equally throughout the smallmouth bass cohorts, and bass > 280 mm TL were responsible
for greater than 50% of total age-0 perch consumption (Table 3).
DISCUSSION
I estimated a likely range in consumption of age-0 yellow perch by smallmouth
bass in two South Dakota glacial lakes using bioenergetics modeling simulations
parameterized with age-specific smallmouth bass abundance and diet data representing
periods of relatively high (2008) and low (2012 and 2013) consumption of age-0 perch.
Given current conditions relative to smallmouth bass abundance and consumption
dynamics, production of age-0 yellow perch, and the thermal environment, it does not
appear that smallmouth bass act as a singular substantial factor limiting recruitment of
96
age-0 yellow perch in my study lakes. Few other studies have examined consumption
dynamics of smallmouth bass populations relative to prey production. Liao et al. (2004)
found that smallmouth bass consumption in Spirit Lake, IA concentrated mainly on
yellow perch and that summer and fall consumption of perch ranged from approximately
0.08 kg/ha to 0.38 kg/ha during 1995-1997, which were comparable to our findings.
However, Liao et al. (2004) did not estimate yellow perch production or availability and
thus were unable to conclude whether or not smallmouth bass predation represented a
substantial influence on the perch population.
The extent of predatory interactions is likely dictated by the degree of spatial and
temporal overlap between smallmouth bass and age-0 yellow perch (sensu Cushing 1975;
Cushing 1990; Chick and Van Den Avyle 1999; Romare et al. 2003; Beauchamp et al.
2004). Dembkowski et al. (2011) found that age-0 yellow perch in my study lakes were
distributed in littoral areas primarily around patches of submerged macrophytes and
maintained a mostly demersal existence. In contrast, smallmouth bass generally occupy
sites with cobble substrates devoid of macrophytes (George and Hadley 1979; Rankin
1986; Olson et al. 2003; Brown et al. 2009). However, smallmouth bass do exhibit
cover-seeking behavior at all life-stages and show no preference for cover type (e.g.,
Hubert and Lackey 1980; Edwards et al. 1983), and juvenile bass (i.e., < 180 mm TL)
were periodically sampled from vegetated areas during age-0 yellow perch sampling
efforts (D. Dembkowski, unpublished data).
Temporally, consumption of age-0 yellow perch by smallmouth bass in eastern
South Dakota lakes increased in August and September compared to May, June, and July
(Bacula 2009), presumably because perch had grown to a size large enough to be used by
97
bass as prey items and to be energetically profitable enough so as to outweigh the
energetic costs associated with pursuit and handling (i.e., optimal forage; MacArthur and
Pianka 1966). Although diets of smallmouth bass were not sampled outside of the
primary growing season (i.e., May-September), my results show a general decrease in
consumption of age-0 yellow perch by smallmouth bass from August to September, and I
hypothesize that this trend continues throughout later autumn months. Additionally, it is
likely that some age-0 yellow perch will outgrow gape limitations of a proportion of
smaller smallmouth bass by the end of the growing season, thus reducing their relative
vulnerability to predation (e.g., Hambright 1991). Exemplifying this as an extreme case,
age-0 gizzard shad Dorosoma cepdianum typically outgrow gape limitations of a
majority of their predators by the end of the first growing season, leaving them
invulnerable to predation (e.g., Carline et al. 1984; Allen et al. 1999; Kim and DeVries
2000). Given the relatively late appearance of age-0 yellow perch in smallmouth bass
diets in the growing season, the decreasing trend in consumption of age-0 perch
throughout the study duration observed herein, and the potential for age-0 perch to
outgrow bass gape limitations by the end of the growing season, it is likely that the
duration of substantial predation on age-0 perch by bass is relatively short. This short
duration of predation, combined with the relatively low degree of spatial overlap between
smallmouth bass and age-0 yellow perch, makes it relatively unsurprising that bass
predation does not impose stricter limitations upon the perch populations in our study
lakes.
The relative influence of smallmouth bass predation may also vary depending on
availability of age-0 yellow perch. Yellow perch populations in the Northern Great
98
Plains are often characterized by a large degree of interannual variation in recruitment
(e.g., Isermann et al. 2007; Isermann and Willis 2008). Thus, availability of age-0 yellow
perch, and subsequent consumption by smallmouth bass, may vary substantially from
year to year. Specifically, the impact of predation may be greater if predation pressure is
depensatory (i.e., consumption increases as a negative function of age-0 yellow perch
abundance; sensu Irwin et al. 2009) rather than compensatory (i.e., consumption
increases as a positive function of age-0 perch abundance; sensu Rose et al. 2001; Irwin
et al. 2009). Given the opportunistic feeding behavior of smallmouth bass, it is likely that
bass predation pressure functions in a compensatory manner. If it is a function of prey
availability, smallmouth bass predation may only strongly influence yellow perch yearclass strength periodically due to erratic perch recruitment patterns (resulting in variable
interannual abundance of prey items; Sanderson et al. 1999; Isermann and Willis 2008).
Differences in age-0 yellow perch availability may have contributed to the
observed differences in intra-population consumption dynamics when using diets data
from periods when perch comprised relatively high (2008) and low (2012 and 2013)
proportions of bass diets. I hypothesize that differences in intra-population consumption
dynamics between the two time periods were mediated by trends in aquatic macrophyte
abundance. During 2008, submerged aquatic macrophyte coverage in the study lakes was
substantially less than that in 2012 and 2013 (B. Blackwell, South Dakota Department of
Game, Fish and Parks, personal communication). Age-0 yellow perch predation
vulnerability may have increased as a function of decreased protective habitat provided
by submerged macrophytes, thereby explaining the increased consumption by
smallmouth bass of all sizes. Similarly, Gaeta et al. (2014) posited that consumption of
99
yellow perch by largemouth bass in a northern Wisconsin lake may have increased due to
a reduction in protective habitat (coarse woody debris) that increased predator-prey
encounter rates. In 2012 and 2013, a majority of the age-0 yellow perch cohort may have
been invulnerable to predation by smallmouth bass due to a substantial amount of
protective macrophyte coverage, and consumption was restricted primarily to the bass
cohorts exhibiting direct spatial overlap with the age-0 perch.
It is important to note that I only estimated the potential singular influence of
smallmouth bass predation on recruitment of age-0 yellow perch. Although I conclude
that predation by smallmouth bass alone does not represent a substantial factor limiting
recruitment of age-0 yellow perch, bass predation may have an additive or cumulative
influence when combined with consumption dynamics of the rest of the predatory fish
assemblages in these systems. For example, although predation by smallmouth bass
alone on out-migrating salmonids did not substantially contribute to the annual loss of
out-migrating salmonids (Vigg et al. 1999), predation did substantially contribute to the
overall loss when consumption dynamics of the entire predatory fish assemblage were
considered collectively rather than individually (Rieman et al. 1991). Other apex
predators in my study lakes included walleye and northern pike; both are opportunistic
predators but are generally more piscivorous than smallmouth bass (Becker 1983;
Blackwell et al. 1999). Blackwell et al. (1999) found that yellow perch constituted
between 30 and 50% of walleye and northern pike diets by weight in a study of seasonal
diets of top-level predators in a range of South Dakota glacial lakes similar to those
included in my study. If walleye and northern pike consumption of yellow perch in my
study lakes is of a similar magnitude, the influence of predation by the collective
100
predatory fish assemblage (i.e., smallmouth bass, walleye, and northern pike) may act to
limit recruitment of yellow perch. Alternatively, if interspecific resource competition
among smallmouth bass, walleye, and northern pike is strong enough, the opportunistic
feeding habits of smallmouth bass may enable them to exploit other prey items (e.g.,
decapods), thus lessening the collective impact of predation on age-0 yellow perch.
Clearly, further research is warranted to estimate the collective influence of smallmouth
bass, walleye, and northern pike predation on recruitment of age-0 yellow perch in my
study lakes.
While results demonstrate that predation by smallmouth bass does not represent a
substantial factor limiting recruitment of age-0 yellow perch under the observed
conditions in our study lakes, further research is needed to investigate potential changes
in smallmouth bass consumption dynamics in response to variable yellow perch densities.
Furthermore, and given the presence of other apex predators in our study lakes (e.g.,
walleye and northern pike), holistic investigation of the impact of predation by the
collective predatory fish assemblage on prey fish population dynamics is needed. If the
impact of predatory interactions on recruitment of age-0 yellow perch remains a concern
in the future, managers may ultimately need to consider holistic predatory fish
assemblage management options to ensure the sustainability of both predator (i.e.,
smallmouth bass, walleye and northern pike) and prey (i.e., yellow perch) fisheries in
South Dakota glacial lakes. Regardless, future research and management initiatives
should recognize that the long-term impact of smallmouth bass predation is not static and
will likely fluctuate depending on environmental (i.e., temperature) and biotic (i.e., trends
101
in macrophyte abundance, predator and prey population structure and abundance, and
predatory fish assemblage dynamics) characteristics.
102
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Table 1. Smallmouth bass population and cohort size estimates for Pickerel Lake, SD
during 2012 and Clear Lake, SD during 2013. Population and cohort sizes were
estimated using the Shumacher-Eschmeyer modification of the Schnabel population
estimator (Schnabel 1938; Schumacher and Eschmeyer 1943; Ricker 1975). CI =
asymmetrical 95% Poisson confidence interval.
Lake
Pickerel
Clear
Population segment
Lower 95% CI
Nominal estimate
Upper 95% CI
Population
4,529
5,321
6,288
Age 0-2
2,636
3,374
4,559
Age-3
954
1,185
1,565
Age-4
397
563
901
Age-5
93
155
348
Age-6+
35
64
192
Population
3,197
3,695
4,346
Age 0-2
1,013
1,361
1,985
Age-3
753
935
1,213
Age-4
222
319
524
Age-5
264
441
993
Age-6+
490
735
1,323
111
Table 2. Energy densities and sources of information of prey items consumed by
smallmouth bass in Pickerel and Clear lakes during August and September, 2008, 2012,
and 2013. Energy densities for unidentified (UID) invertebrates, fish, or general prey
items were calculated as the mean energy density of known invertebrates, fish, or all prey
items. Energy density for unidentified fish with obvious centrarchid body morphology
was calculated as the mean energy density of known centrarchids observed in the diets.
Group
Taxonomic
classification
Invertebrates Amphipoda
Coleoptera
Decapoda
Diptera
Ephemeroptera
Hemiptera
Heteroptera
Hymenoptera
Odonata
Trichoptera
UID invert
Fish
Black bullhead
Black crappie
Bluegill
Johnny darter
Yellow perch
UID centrarchid
UID fish
Other
Terrestrial
vertebrate
UID prey
Energy density
(J/g)
4,429.0
5,523.0
3,062.7
2,566.5
4,705.0
5,523.0
5,523.0
5,523.0
3,176.0
3,176.0
4,086.0
3,694.0
5,812.0
3,807.0
6,108.0
5,097.0
4,809.5
4,903.6
7,753.0
4,570.7
Source
Cummins and Wuycheck (1971)
Hill (1997)
Lott (1996)
Cummins and Wuycheck (1971)
Cummins and Wuycheck (1971)
Hill (1997)
Hill (1997)
Hill (1997)
Hill (1997)
Hill (1997)
Liao et al. (2004)
Liao et al. (2004)
Liao et al. (2004)
Liao et al. (2004)
Liao et al. (2004)
Cummins and Wuycheck (1971)
112
Table 3. Age-specific consumption estimates of age-0 yellow perch during August and
September by smallmouth bass in Pickerel and Clear lakes, SD. Numbers in parentheses
represent the percent of total consumption of age-0 yellow perch by each smallmouth
bass cohort. Total consumption was estimated as the sum of weekly age-specific
consumption values across all sample dates.
Smallmouth bass consumption of age-0 yellow perch (kg/ha)
Lake
Year
Age 0-2
Age-3
Age-4
Age-5
Age-6+
Total
Pickerel
2008
0.34
0.32
0.04
0.37
0.26
1.33
(26)
(24)
(3)
(28)
(20)
(100)
0.08
0.10
0.02
0.01
0.01
0.23
(38)
(43)
(9)
(5)
(5)
(100)
0.07
0.27
0.20
0.41
0.63
1.57
(4)
(17)
(13)
(26)
(40)
(100)
0.07
0.16
0.01
0
0
0.24
(29)
(67)
(4)
(0)
(0)
(100)
Pickerel
Clear
Clear
2012
2008
2013
113
Figure 1. August and September prey composition (percent by wet weight) for
smallmouth bass from Pickerel and Clear lakes collected during 2008. Consumption is
stratified by length group [Gabelhouse 1984; substock (SS) < 180 mm TL; stock-quality
(S-Q) = 180-279 mm TL; quality-preferred (Q-P) = 280-349 mm TL; greater than
preferred (P+) > 350 mm TL].
114
Figure 2. August and September prey composition (percent by wet weight) for
smallmouth bass from Pickerel Lake during 2012 and Clear Lake during 2013.
Consumption is stratified by length group [Gabelhouse 1984; substock (SS) < 180 mm
TL; stock-quality (S-Q) = 180-279 mm TL; quality-preferred (Q-P) = 280-349 mm TL;
greater than preferred (P+) > 350 mm TL].
115
Figure 3. Weekly production of age-0 yellow perch during August and September 2012
in Pickerel Lake, SD. Error bars represent variance as estimated by equations provided in
Hayes et al. (2007).
116
Figure 4. Weekly production of age-0 yellow perch during August and September 2013
in Clear Lake, SD. Error bars represent variance as estimated by equations provided in
Hayes et al. (2007).
117
Figure 5. Proportion of age-0 yellow perch production consumed by smallmouth bass at
Pickerel Lake during August and September, 2008 and 2012. Smallmouth bass
consumption was modeled over the same intervals during which age-0 yellow perch
production was estimated.
118
Figure 6. Proportion of age-0 yellow perch production consumed by smallmouth bass at
Clear Lake during August and September, 2008 and 2013. Smallmouth bass
consumption was modeled over the same intervals during which age-0 yellow perch
production was estimated.
119
CHAPTER 5. SUMMARY AND RESEARCH NEEDS
The importance of factors operating during early ontogeny in the overall yellow
perch recruitment process cannot be overstated. In any assessment of recruitment
dynamics, integration of multiple life stages and consideration of multiple factors is
important because the number of individuals recruiting to a given ontogenetic stage is
ultimately a function of the abundance of individuals and factors operating during prior
ontogenetic stages (Houde 1989). Accordingly, this research examined biotic and abiotic
factors influencing recruitment of larval and fall age-0 yellow perch and provided
important insights to patterns and processes that drive perch population fluctuations in
South Dakota glacial lakes. Ultimately, results from each specific objective can be
synthesized to create an early life history model identifying critical periods of high
mortality and factors constraining recruitment of yellow perch in eastern South Dakota
glacial lakes.
In a novel assessment of factors influencing density of larval yellow perch, I
found that one of the earliest critical periods of high mortality was the period
immediately following post-egg skein emergence encompassing larval swim-up and the
switch from endogenous to exogenous feeding. During this period, larval yellow perch
were influenced by climatic variables including temperature, wind, and variation in water
levels (tied to regional precipitation patterns; White et al. 2008). I also found a moderate
degree of spatial synchrony in production of larval yellow perch among spatially
segregated systems that was likely induced by broad scale, spatially correlated climatic
phenomena (i.e., the Moran Effect; Moran 1953). Isermann and Willis (2008) posited
that factors operating prior to, during, or immediately after the contracted hatching period
120
play a critical role in the yellow perch recruitment process. Given that my results suggest
a greater likelihood that factors operating during the post-emergence period, rather than
factors operating during the egg deposition and embryonic development period, have a
larger bearing on eventual density of larval yellow perch, I have refined previous
hypotheses, thereby narrowing the focus of future research relative to drivers of perch
early life history, recruitment, and year-class strength This is also the first study of my
knowledge that has demonstrated synchrony in production of a larval cohort of a
freshwater species.
Recruitment of yellow perch from the larval stage to the fall age-0 stage was
further constrained by biotic factors including spawning stock biomass, potential interand intraspecific competition, and potential predation. Given the negative relationship
between relative abundance and total length of age-0 yellow perch in the fall, my
analyses also suggest that the size, and thus growth rates, during the first growing season
of age-0 perch recruits is regulated by biotic factors, specifically, potential intraspecific
competition. I expected the abundance of recruits at the fall age-0 stage to be related to
climatic factors because of the relationships I identified between larval yellow perch
abundance and climatic phenomena. However, explanatory models including abiotic
variables were not well-supported in my analyses, representing a disparity between
factors influencing early (i.e., larval) and later (i.e., fall age-0) life stages. Although the
abundance of fall age-0 yellow perch recruits is tautologically a function of the
abundance of pre-recruits (i.e., larval perch) and factors operating at earlier life stages, it
is likely that fall age-0 recruits were influenced more proximately by biotic factors in my
121
analyses. System-specific biotic characteristics may serve to de-synchronize population
fluctuations following the larval stage, but this potential has not been fully investigated.
Although I found that the abundance of fall age-0 yellow perch was constrained
by potential predation, I did not find sufficient evidence to conclude that predation by
smallmouth bass alone represented a singular substantial factor limiting recruitment of
age-0 perch in my study lakes. Given current conditions relative to smallmouth bass
abundance and consumption dynamics, production of age-0 yellow perch, and the
thermal environment, perch production exceeded bass consumption by approximately
70%. Smallmouth bass consumption dynamics of age-0 yellow perch are likely driven by
variation in perch availability as influenced by either variation in year-class strength or
trends in macrophyte abundance. Given the presence of other apex predators in my study
lakes (e.g., walleye and northern pike), holistic investigation of the impact of predation
by the collective predatory fish assemblage on prey fish population dynamics is needed.
Although the unpredictable and dynamic nature of factors that influence yellow
perch recruitment ultimately adds complexity to perch management, findings presented
herein are valuable and may be of interest to future and current biologists from a
management perspective. Given the observed response of yellow perch early life stages
to climatic and hydrological phenomena, researchers may be able to forecast trends in
adult perch population dynamics relative to environmental variation during early life
stages. Because of the substantial influence of recruitment on other population dynamics
(Carline et al. 1984; Hansen and Nate 2014), the influence of juvenile growth dynamics
on recruitment via the “growth rate” (Ware 1975; Anderson 1988) and “stage duration”
hypotheses (Houde 1987; Leggett and DeBlois 1994), and the dependence of predator
122
populations on yellow perch as prey resources (Blackwell et al. 1999), biologists and
managers should continually monitor yellow perch spawning stock abundance as well as
potential predators and competitors as drivers of perch recruitment and growth. Further,
while smallmouth bass predation does not represent a contemporary constraint to yellow
perch recruitment, future research and management initiatives should recognize that the
long-term impact of smallmouth bass predation is not static and will likely fluctuate
depending on environmental (i.e., temperature) and biotic (i.e., trends in macrophyte
abundance, predator and prey population structure and abundance, and predatory fish
assemblage dynamics) characteristics. If the impact of predatory interactions on
recruitment of age-0 yellow perch remains a concern in the future, managers may
ultimately need to consider holistic predatory fish assemblage management options.
My research provided important insights to the patterns and processes that
structure yellow perch populations in eastern South Dakota glacial lakes. However, there
are still stages of the yellow perch recruitment process during early ontogeny that remain
to be understood. Furthermore, it is unclear how factors limiting yellow perch
recruitment during early ontogeny will influence long-term population dynamics. For
example, although mortality during early ontogeny as a function of climatic phenomena,
potential inter- or intraspecific competition, and potential predation may reduce the
overall number of recruits, increased growth rates of the remaining cohort may serve as a
process of delayed compensation (sensu Irwin et al. 2009) by allowing individuals to
obtain a larger size at the end of the first growing season, which may reduce mortality
and ultimately increase recruitment during later life stages. These and other areas
outlined below provide exciting avenues for future research necessary to equip fisheries
123
scientists with a better understanding of yellow perch recruitment dynamics and improve
predictive capabilities required for appropriate management initiatives to ensure the
sustainability of both predator (e.g., smallmouth bass, walleye, and northern pike) and
prey (yellow perch) fisheries in eastern South Dakota glacial lakes.
Research needs:
1. Although responses of larval yellow perch cohorts to environmental phenomena
(i.e., temperature, wind, and water-level fluctuations) are generally clear,
mechanisms for the observed responses are not well understood. A mechanistic
understanding would provide further insights to the basic ecology of yellow perch
during early ontogeny and likely aid in understanding the intricacies of the perch
recruitment process.
2.
Tracking correlations between successive ontogenetic stages for individual
cohorts of yellow perch would aid in identification of critical stages of high
mortality which may provide an early index of year-class strength (sensu
Isermann 2003). Accompanying research would likely involve evaluations of
sampling gears for effective collection of life stages that are not frequently
targeted in standard fisheries surveys (i.e., age-1 yellow perch).
3. Assessing the influence of hatch date on larval yellow perch density and
subsequent recruitment may aid in improving predictability of eventual year-class
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strength. Cursory analyses suggest that late hatching generally corresponds with
low larval densities and weak year-classes, but formal evaluation is lacking.
4.
Evaluating yellow perch recruitment dynamics in simple versus complex
systems. Previous analyses (including this study) have focused on eastern South
Dakota lakes pooled as a collective data set, thereby potentially failing to account
for variability that may otherwise be explained by inter-lake differences in
structural habitat and fish assemblage complexity. Stratification of recruitment
analyses based on habitat and assemblage complexity metrics may provide further
insight as to the relative importance of biotic and abiotic factors in driving yellow
perch recruitment processes and the potential need for system-specific
management initiatives.
5. Estimating the response of larval yellow perch density to habitat manipulations;
specifically, the addition of spawning substrates (e.g., tree reefs). Increased
production of larvae as a function of increased spawning substrate may offset
losses due to natural mortality during early ontogeny, ultimately promoting
recruitment. Furthermore, this research would fill a critical gap in the literature
regarding the relationship between habitat area and recruitment.
6. Formal assessment of the extent of overwinter mortality of age-0 yellow perch.
The first overwinter period is typically identified as the final critical period of
high natural mortality, but formal evaluation in South Dakota yellow perch
125
populations is lacking. Cursory analyses on a subset of eastern South Dakota
yellow perch populations do, however, suggest that overwinter mortality
selectively removes smaller individuals from the age-0 cohort.
7. Estimating the collective impact of predation on yellow perch recruitment; that is,
quantitatively examining consumption dynamics of the entire predatory fish
assemblage (i.e., smallmouth bass, largemouth bass, walleye, white bass, and
northern pike). Although predation by smallmouth bass alone does not represent
a substantial factor limiting recruitment of age-0 yellow perch, bass predation
may have an additive or cumulative influence when combined with consumption
dynamics of the rest of the predatory fish assemblages in these systems.
8. Estimating potential compensatory dynamics of mortality (as a function of
environmental phenomena, inter- or intraspecific competition, and predation)
during yellow perch early life history. Although increased mortality during early
ontogeny may reduce the overall number of recruits, increased growth rates of the
remaining cohort may serve as a process of delayed compensation (sensu Irwin et
al. 2009) by allowing individuals to obtain a larger size at the end of the first
growing season, which may reduce mortality during later life stages.
9. Estimating the potential future impact of smallmouth bass predation on
recruitment of yellow perch under various climate change scenarios. Dynamics of
smallmouth bass consumption and production of age-0 yellow perch may change
126
as a response to forecasted changes in the thermal environment, and awareness of
a likely range of potential predatory impacts would help managers prepare
mitigation plans.
10. Estimating the relative vulnerability of various prey fishes (e.g., yellow perch,
black crappie, and bluegill) to predation by smallmouth bass. Knowledge of the
proportion of prey fish cohorts vulnerable to smallmouth bass predation will aid
in fully understanding the potential impact of bass predation on prey populations.
11. Identifying angler attitudes and beliefs towards negative interactions between
smallmouth bass and yellow perch. Although this study demonstrated minimal
negative interactions between smallmouth bass and yellow perch, anglers may
maintain incorrect perceptions of the extent of the interactions. This information
can be used to produce effective information campaigns, reduce conflicts, and
manage angler expectations.
127
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