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Improving sea lamprey assessment in the Great Lakes using
adaptive management and historical data

presented by

Gretchen J. Anderson

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IMPROVING LARVAL SEA LAMPREY ASSESSMENT IN THE GREAT
LAKES USING ADAPTIVE MANAGEMENT AND HISTORICAL RECORDS
By

Gretchen J. Anderson

A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

MASTER OF SCIENCE
Department of Fisheries and Wildlife

2006

ABSTRACT
IMPROVING LARVAL SEA LAMPREY ASSESSMENT IN THE GREAT LAKES
USING ADAPTIVE MANAGEMENT AND HISTORICAL RECORDS
By
Gretchen J. Anderson

Sea lampreys in the Great Lakes are managed by treating tributaries with lampricides that
target the larval stage. A resource-intensive but imperfect larval assessment process
(Quantitative Assessment Sampling, QAS) is currently used to determine which streams
to treat annually. I developed an alternative assessment method (Rapid Assessment, RA)
that requires fewer resources, and compared the costs and benefits of RA vs. QAS by
conducting both methods on all wadeable streams requiring assessment in 2005 and 2006
and ranking streams for treatment priority. The use of RA resulted in more treated
streams, and based on population estimates generated by QAS and by capture-recapture
experiments, the use of RA would allow greater suppression of sea lampreys basin-wide.
Assessment expenses could also be reduced through the incorporation of historical
knowledge. Some tributaries are highly regular in their need for treatments, while others
vary widely. I analyzed data collected from 1959 -2005 using mixed-effects models to
test for differences in recruitment and growth to age-1 between regularly and irregularly
treated streams. Recruitment was twice as large in regular streams than in irregular
streams, indicating that year class strength is established early in the sea lamprey life
cycle. I found no consistent differences in growth to age-1 among categories of streams;
however, a variance components analysis showed that Lake Superior streams that are

treated irregularly also exhibit more irregular size at age-1 than streams treated regularly.

ACKNOWLEDGEMENTS

A project of this scale does not come about without the collaboration of many
individuals. I am indebted to the employees of the US Fish and Wildlife Service and to
the Department of Fisheries and Oceans, Canada sea lamprey control offices for their
assistance both with field work and with historical data acquisition. In particular, Jeff
Slade, Mike Steeves, Mike Fodale, and Fraser Neave provided invaluable assistance
throughout this project, and I thank them for their help and their patience. I also thank
Jenny Nelson, Josh Schloesser, and Carrie Bingle for their long hours in the field and lab.

To my advisor, Dr. Michael Jones, I am not sure how to adequately express my
thanks. He has taught and inspired me about the type of scientist I would like to be, and
confirmed with every interaction that I made the right choice in coming to MSU. I also
greatly appreciate the insight and guidance provided on all components of my graduate
career and thesis by my committee members, Dr. Dan Hayes and Dr. Andrew McAdam.

Thanks to my lab mates in the J ones/Bence/QF C conglomerate. From answering
statistical questions to making sure I stayed on task by looking over my shoulder every
couple minutes (watching, judging), I couldn’t have done it without you guys. Thanks to
the rest of my homies here at MSU for keeping the good times rolling, and for solidifying
my belief that one can be a good grad student and still maintain some semblance of a
social life. Thanks to my family for their support and encouragement throughout what is
turning out to be quite a long academic career, and for never questioning (to my face)
why in the world anyone should care about fisheries biology. Finally, I give my most
heartfelt thanks to Jon, for keeping me sane and never wavering in his support,

encouragement, and love. I only hope I do the same for you.

iii

TABLE OF CONTENTS

LIST OF TABLES .................................................................................... v
LIST OF FIGURES ................................................................................ viii
THESIS INTRODUCTION ......................................................................... 1
CHAPTER 1

DEVELOPMENT AND EVALUATION OF AN ALTERNATIVE ASSESSMENT
PROCEDURE FOR LARVAL SEA LAMPREYS: A CASE STUDY IN ADAPTIVE

MANAGEMENT
Introduction ................................................................................... 6
Methods ...................................................................................... 15
Implementation of Rapid Assessment ........................................... 15
Quantitative Assessment Sampling ............................................. 15
Rapid Assessment Sampling ..................................................... 17
Stream Treatment Selection ...................................................... 20
Evaluation of Rapid Assessment ................................................. 22
Results ....................................................................................... 27
Rank Lists and Streams Selected for Treatment .............................. 27
Comparison of ESTR Population Estimates ................................... 37
Capture-Recapture ................................................................ 38
Discussion ................................................................................... 41
CHAPTER 2

DOES DEMOGRAPHIC VARIAION IN LARVAL SEA LAMPREYS DETERMINE
THE REGULARITY OF CHEMICAL TREATMENTS?

Introduction ................................................................................. 50
Methods ...................................................................................... 56
Historical Survey Data ............................................................ 56

Recruitment Analysis ............................................................. 57

Growth Analysis .................................................................. 61

Model Selection ................................................................... 66

Results ....................................................................................... 66
Recruitment Analysis ............................................................. 66

Growth Analysis .................................................................. 71

Discussion ................................................................................... 77
THESIS DISCUSSION ............................................................................ 85
WORKS CITED .................................................................................... 89

iv

LIST OF TABLES

Table 1. Streams that would be selected for treatment based on either assessment method
in 2005, the rank of treatment priority based on RA and QAS, whether or not the stream
would be selected for treatment based on the different assessment methods, and the ESTR

model population estimates for larvae (N L) and transformers (N T). Streams are placed in
order of RA ranking, and only streams that would be selected for treatment based on at
least one method are listed. ....................................................................... 29

Table 2. Streams that would be treated based on the results of one assessment method
but not the other in 2005 given the allocation of 659 additional treatment staff days for
treating streams ranked by RA. The top panel shows the streams that would rank for
treatment based on RA, not QAS, and the bottom panel shows the streams that would
rank for treatment based on QAS, not RA. The ESTR population estimate of
transformers and larvae are shown for each stream, along with the sum of the number of
transformers and larvae present in each set of streams. ...................................... 30

Table 3. Streams that would be selected for treatment based on either assessment method
in 2006, the rank of treatment priority based on RA and QAS, whether or not the stream
would be selected for treatment based on the different assessment methods, and the ESTR

model population estimates for larvae (N L) and transformers (N T). Streams are placed in
order of RA ranking, and only streams that would be selected for treatment based on at
least one method are listed. ...................................................................... 32

Table 4. Streams that would be treated based on the results of one assessment method
but not the other in 2006 given the allocation of 41 6 additional treatment staff days for
treating streams ranked by RA. The top panel shows the streams that would rank for
treatment based on RA only, and the bottom panel shows the streams that would rank for
treatment based on QAS only. The ESTR population estimate of transformers and larvae
are shown for each stream, along with the sum of the number of transformers and larvae
present in each set of streams. .................................................................... 33

Table 5. Capture-recapture estimates of sea lamprey abundance (all life stages) for the 6
study streams. M is the number marked, C is the number collected in the recapture event,
R is the number of recaptures, and N is the Petersen population estimate. (95%
confidence intervals on N are shown for each stream, and ESTR N represents the initial
population estimate of all life stages of sea lampreys generated by the ESTR model from
QAS data. ............................................................................................ 39

Table 6. Capture-recapture estimates for either transformers (T) or large larvae (larvae >
144 rmn, LL) for the 6 streams on which capture—recapture was conducted in 2006.
Transformer estimates were only generated for streams treated afier July 15th, otherwise
large larvae estimates were used. ESTR estimates are of transformer abundance if

transformers were estimated in the capture-recapture study, otherwise the ESTR estimate
is of large larvae abundance as predicted by the ESTR model. .............................. 40

Table 7. Summary of data used for recruitment analysis. For each category, the number
of stream-years of data, the % of occasions in which zero recruitment was observed, and
the mean and standard deviation of the non-zero catch per unit effort values are Shown.
.......................................................................................................... 58

Table 8. Summary of data used for growth analysis. The number of streams falling in
each category, number of individual sea lampreys collected from each category, the mean
length, standard deviation of length, and mean DOY on which a survey was taken are
shown. ................................................................................................ 61

Table 9. Candidate models with different fixed effects in the binary model of recruitment
success of age-1 sea lampreys. Fixed effects are shown with the estimated number of
parameters (K), their AIC values, and the difference between the AIC value of a given
model and that of the best model (AAIC). ...................................................... 67

Table 10. Random effect selection for the binary model of recruitment success of age-1
sea lampreys. The number of estimated parameters (K), AIC value, and the difference
between the AIC value of a model and that of the best model (AAIC) are shown.

Random effects were modeled with a fixed category effect also included. ................ 67

Table 11. Fixed effects estimates, standard error, z-value, and p-values for the binary
model of recruitment success of age-1 sea lampreys. The expected probability of
successful recruitment for each category is also shown. In this model, the intercept refers
to category 1, and the error DF=301. ............................................................ 67

Table 12. Random effect selection for the model of mean recruitment (CPUEA1/4) of
age-1 sea lampreys. The number of estimated parameters (K), AIC value, and the
difference between the AIC value of a model and that of the best model (AAIC) are
shown. Random effects were modeled with fixed effects of category and lake also
included. ............................................................................................. 69

Table 13. Candidate models with different fixed effects for the model of mean
recruitment (CPUEA1/4) of age-1 sea lampreys. The number of estimated parameters
(K), AIC value, and the difference between the AIC value of a model and that of the best
model (AAIC) are shown. .......................................................................... 69

Table 14. Fixed effects estimates, standard errors, t-values, and p-values for each

parameter included in the model of mean recruitment (CPUEA1/4) of age-1 sea lampreys.
In this model, the intercept accounts for the effects of both category 1 and Lake Superior,
and the error DF=248. ............................................................................. 69

Table 15. Random effects for the model of log(length )of age-1 sea lampreys. Random
effects were estimated for the slope (S) and intercept (I) of each level except survey ID,

vi

which only occurred on one day of year (DOY). Candidate models are shown along with
the number of estimated parameters (K), AIC value, and the difference between the AIC
value of a model and that of the best model (AAIC) are shown. Random effects were
modeled with all possible fixed effects also included (DOY, Category, Lake, and a
Category*Lake interaction). ...................................................................... 71

Table 16. Candidate models with different fixed effects for the model of log(length) at
age-1, the number of parameters estimated (K), their AIC values and the difference
between each model's AIC and that of the best fit model (AAIC). All fixed effects were
modeled with random effects included. Random intercepts for stream, year, reach, and
ID, and random slopes for stream and year were included in each model. ................. 71

Table 17. Standard deviation estimates and 95% confidence intervals for all random
effects included in the final model of log(length) at age-1. Fixed effects of DOY and
Lake were also included in this model. .......................................................... 72

Table 18. Fixed effects estimates, standard errors, residual degrees of freedom, t-values,
and p-values for each parameter in the model of log (length) at age-1 of sea lampreys. In
this model, the intercept accounts for the effect of Lake Superior. Random intercepts for
stream, year, reach, and survey, and random slopes for stream and year were also
estimated in this model. ........................................................................... 72

vii

LIST OF FIGURES

Figure 1. Illustration of the trade-off between expenditures on larval assessment versus
lampricide application. As more resources are spent on assessment, fewer resources are
available for lampricide control, as illustrated by the dotted line. On the other hand, as
assessment expenditures increase, the accuracy of that assessment increases and the
streams that are treated are selected with greater confidence, as illustrated by the solid
line. ................................................................................................... 11

Figure 2. Correlation between QAS and RA rank for the 56 streams surveyed with both
methods in 2005. Stream rankings were significantly correlated (Spearman’s rank
correlation = 0.67, p<0.001). Open circles represent streams treated based on both
methods, dark squares represent streams treated based on the RA ranking only, open
triangles represent streams treated based on the QAS list only, and X’s represent streams
not treated based on either method. The dashed line indicates perfect (1:1) correlation.
......................................................................................................... 34

Figure 3. Correlation between QAS and RA rank for the 24 streams that ranked for
treatment based on RA results in 2005 with additional staff days allocated to treat RA
streams. Stream rankings were significantly correlated (Spearman’s rank correlation =
0.50, p = 0.02). Open circles represent streams treated based on both methods, and dark
squares represent streams treated based on the RA ranking only. The dashed line
indicates perfect (1:1) correlation. ............................................................... 35

Figure 4. Correlation between QAS and RA rank for the 46 streams surveyed with both
methods in 2006. Stream rankings were significantly correlated (Spearman’s rank
correlation = 0.56, p<0.001). Open circles represent streams treated based on both
methods, dark squares represent streams treated based on the RA ranking only, open
triangles represent streams treated based on the QAS list only, and X’s represent streams
not treated based on either method. The dashed line indicates perfect (1:1) correlation.
.......................................................................................................... 36

Figure 5. Correlation between QAS and RA rank for the 24 streams that ranked for
treatment based on RA results in 2006 with equal staff days allocated for treatment of RA
streams. Stream rankings were significantly correlated (Spearman’s rank correlation =
0.44, p = 0.03). Open circles represent streams treated based on both methods, and dark
squares represent streams treated based on the RA ranking only. The dashed line
indicates perfect (1:1) correlation. ............................................................... 36

Figure 6. The ratio of RA values to QAS values for labor cost (assessment + control
costs), total estimated number of transformers killed, and total estimated number of
larvae killed when additional treatment staff days are allocated to treat streams ranked by
RA. Light bars represent 2005 values, and dark bars represent 2006 values. Total
transformer and larvae estimates were the sum of the population estimates generated by

viii

the ESTR model for all the streams that would be treated based on the results of each
assessment method. Dashed line indicates where the RA and QAS values are equal;
above this line, RA values are higher, below this line QAS values are higher. ........... 37

Figure 7. The ratio of RA values to QAS values for labor cost (assessment + control
costs), total estimated number of transformers killed, and total estimated number of
larvae killed when equal numbers of treatment staff days are allocated to treat streams
ranked by RA and QAS. Light bars represent 2005 values, and dark bars represent 2006
values. Total transformer and larvae estimates were the sum of the population estimates
generated by the ESTR model for the streams that would be treated based on the results
of each assessment method. Dashed line indicates where the RA and QAS values are
equal; above this line, RA values are higher, below this line QAS values are higher. ...38

Figure 8. The sum and 95% confidence intervals of the capture-recapture population
estimates for a) the total (larval + transformer) population estimates, and b) the large
larvae/transformer population estimates for the streams that ranked on the basis of RA
only (RA streams) and QAS only (QAS Streams) with additional treatment staff days
allocated for the treatment of RA streams. The total RA population estimates include the
populations of 3 RA streams out of the 11 that ranked based on this method and not based
on QAS, and the total QAS population estimates include the populations of all 3 QAS
streams that ranked based on this method and not based on RA. Confidence intervals are
calculated from the summed variance estimates of the three streams used in each
category. Population estimates in two of the three QAS streams are potentially
underestimated due to non-random release of marked animals and non-random recapture
events (see text for further explanation). ....................................................... 41

Figure 9. Probability of successful recruitment and 95% confidence intervals for each
stream category as predicted by binomial model. .............................................. 68

Figure 10. Mean CPUE (catch per hour) and 95% confidence intervals for each stream
category as predicted by the linear model when holding lake constant (values Shown are
for Lake Superior streams). ........................................................................ 70

Figure 11. Mean CPUE and 95% confidence intervals for each lake as predicted by the
linear model when holding category constant (values shown are for category 1 streams).
.......................................................................................................... 70

Figure 12. Mean length at age-1 (bias-corrected) and 95% confidence intervals for each
lake as predicted by the mixed effects growth model. ....................................... 73

Figure 13. Estimates of relative variation and 95% confidence intervals for each lake
except Erie. To estimate variance components, the variance component for Lake
Superior was held constant at 1, and the relative variance components for the other lakes
are estimated. ....................................................................................... 74

ix

Figure 14. Estimates of variance components and 95% confidence intervals for different
stream categories in a) Lake Superior, b) Lake Michigan, c) Lake Ontario, and (1) Lake
Huron. The variance component of category 3 in Lake Superior was held constant at 1,
and others estimated relative to it. ................................................................ 75

Figure 15. Residual variance attributable to within-year variance (light grey bars) and
among-year variance (dark grey bars) for each stream category in a) Lake Superior, b)
Lake Michigan, c) Lake Ontario, and d) Lake Huron. ......................................... 76

THESIS INTRODUCTION

The sea lamprey (Petromyzon marinus) is an invasive species in the Great Lakes
and is the focus of an intensive control program. Sea lampreys are native to the Atlantic
Ocean, and spawn both in North America and Europe (Beamish 1980). Sea lampreys
were first documented in Lake Ontario in the early 18005, although their impacts on other
fish stocks in Lake Ontario appear to have been minimal until the 20th century (Christie
and Kolenosky 1980). Sea lampreys invaded the other Great Lakes through the Welland
canal beginning in the 19203 (Applegate 1950, Christie and Goddard 2003). Spawning
runs of sea lampreys were confirmed in all of the upper Great Lakes by 1947 (Smith and
Tibbles 1980).

Adult sea lampreys spawn in streams, where the non-parasitic larvae typically live
for 3-7 years (Potter 1980), although they can remain in streams for as many as 18 years
(Manion and Smith 1978). Upon completion of the larval phase, sea lampreys
metamorphose and migrate downstream into large bodies of water, where they parasitize
other fishes, often injuring or killing the host. An early life history study identified
stream-dwelling larval sea lampreys as the life stage most vulnerable to control
(Applegate 1950); in particular, managers were encouraged to focus control efforts on
larvae undergoing metamorphosis (called transformers) to maximize efficiency (Smith
and Tibbles 1980).

The ecological impacts of sea lampreys on native species of the Great Lakes have
been well documented, including their contribution to the extirpation of lake trout in all
lakes except Superior and Huron (Smith and Tibbles 1980, Pearce et al. 1980). By 1946,

sea lampreys were recognized as a major threat to the fisheries of the Great Lakes

(F etterolf 1980), stimulating the formation of the Great Lakes Fishery Commission
(GLF C) in 1955 to coordinate the management of this species (Christie and Goddard
2003). After several years of limited and relatively unsuccessful attempts to control sea
lampreys using mechanical and electrical barriers to block spawning adults, chemical
control using 3-triflouromethyl-4-nitrophenol (TF M) was initiated in Lake Superior in
1958 (Christie and Goddard 2003). Use of chemical control in Lakes Michigan, Huron,
and Ontario was initiated in the 1960’s and 1970’s, and Lake Erie did not start using
chemical control until 1986 (Christie and Goddard 2003). Sea lamprey populations and
wounding rates of lake trout declined drastically immediately following the initiation of
chemical controls (Smith and Tibbles 1980). Chemical controls are now used in
conjunction with alternative control methods, and adult sea lamprey populations are
judged to be at around 10% of their former abundance (Smith and Tibbles 1980, GLFC
2001, Heinrich et a1. 2003).

Although alternative control methods are currently used to supplement chemical
control techniques, control is achieved mainly through the periodic treatment of sea
lamprey-producing streams with TFM, which typically kills 95-100% of the larvae
present (Smith and Swink 2003). Because larval sea lampreys remain in their natal
streams for several years before becoming parasitic juveniles, it is neither necessary nor
cost-effective to treat every stream each year. Rather, treatments should be applied on a
cycle that matches the duration of the larval phase in a given stream. However, natural
variation in recruitment, growth rates, and survival of larval sea lampreys makes it
impossible to predict with certainty when each stream will require treatment to prevent

the downstream migration of parasitic juveniles. Therefore, each year a group of

candidate streams is assessed to determine which streams have the largest populations of
transformers relative to their treatment cost and thus should be prioritized for treatment
(Slade et a1. 2003).

The current larval assessment methods are costly, yet still produce highly
uncertain population estimates. Recent studies have identified and quantified sources of
this uncertainty (Steeves 2002) and drawn attention to assumptions in the assessment and
stream ranking process that are often violated (Steeves et a1. 2003, Hansen et al. 2003).
Using current assessment and control methods, suppression of sea lampreys to target
levels has yet to be accomplished consistently throughout the Great Lakes (Gavin
Christie, Great Lakes Fishery Commission, personal communication), indicating that the
exploration of alternative methods is warranted. It seems reasonable to assume that an
increase in resources allocated to assessment would result in a corresponding increase in
the accuracy of larval population estimates, and therefore in the certainty of stream
selection decisions. However, high levels of variability in larval growth and
metamorphic rates, combined with the practical limitation that larval assessments must be
conducted in the year prior to a stream treatment, preclude managers from ever being
absolutely certain about which streams to treat, regardless of the level of assessment
expenditures. Additionally, because the GLFC manages sea lampreys with a finite
budget, any increase in assessment costs will result in a corresponding decrease in the
resources available to actually treat streams. An alternative management strategy would
be to allot minimal resources to assessment, accept a high level of uncertainty

surrounding predictions of larval and transformer abundance, but make stream treatment

decisions less sensitive to this uncertainty by using the resources saved on assessment to
treat additional streams.

In Chapter 1 of this thesis I describe the development, implementation, and
evaluation of an alternative assessment and stream selection protocol called Rapid
Assessment (RA). RA requires fewer resources than the current assessment procedure
(Quantitative Assessment Sampling, QAS), and the resources saved on assessment are
used to treat additional streams. The objective of this research was to evaluate the
effectiveness of RA relative to QAS by comparing their costs to the sea lamprey control
program and their benefits in terms of sea lampreys killed. I evaluated the costs and
benefits of RA compared to QAS by implementing both methods on a basin-wide scale
and monitoring the consequences in terms of the streams selected for treatment and the
predicted number of sea lampreys killed. I compared the predicted numbers of sea
lampreys killed using population estimates predicted by QAS as well as population
estimates generated from capture-recapture studies. I compared the two assessment
methods using an adaptive management framework in the sense that the comparisons
were conducted on the scale relevant to management, and involved the use of alternative
management tactics to learn more about the best management strategy to employ in the
future.

Another means through which assessment costs could be reduced is through the
incorporation of historical knowledge into the stream selection process. Larval
assessment surveys have been conducted in Great Lakes tributaries since the inception of
the sea lamprey control program, but these data have never been formally analyzed for

patterns in demographic rates such as recruitment and growth. In Chapter 2, I describe

the analysis of historical survey data collected from 1959 — 2005. Sea lamprey managers
have classified lamprey-producing streams in the Great Lakes into four categories based
on their regularity of lampricide treatments. I used mixed-effects models to analyze
differences in recruitment and growth to age-1 among stream categories. I also used
variance components analyses to determine if differences existed between categories in
the variability of recruitment or growth to age-1. The objectives of this research were to
determine the usefulness of this stream categorization framework in directing assessment
efforts, and to determine which demographic processes of larval sea lampreys have the
greatest influence on the regularity of sea lamprey production and need for treatment in a
stream. The results of these analyses are presented in a management context, and

recommendations for assessment and fiIture analyses based on my results are included.

CHAPTER ONE
DEVELOPMENT AND EVALUATION OF AN ALTERNATIVE ASSESSMENT
PROCEDURE FOR LARVAL SEA LAMPREYS: A CASE STUDY IN ADAPTIVE
MANAGEMENT
Introduction

The sea lamprey (Petromyzon marinas) is an invasive species in the Great Lakes
and is the focus of an intensive control program. Sea lampreys were first documented in
Lake Ontario in the early 18005, and invaded the other Great Lakes through the Welland
canal beginning in the 19203 (Applegate 1950, Christie and Goddard 2003). Their
ecological impacts on native species of the Great Lakes have been well documented,
including their contribution to the extirpation of lake trout in all lakes except Superior
and Huron (Smith and Tibbles 1980, Pearce et al. 1980), prompting the formation of the
Great Lakes Fishery Commission (GLFC) in 1955 to oversee sea lamprey management
(Christie and Goddard 2003).

Adult sea lampreys spawn in streams, where the non-parasitic larvae live for an
average of 3-7 years (Potter 1980), although they can remain in streams for as many as 18
years (Manion and Smith 1978). Upon completion of the larval phase, sea lampreys
metamorphose and migrate downstream into large bodies of water, where they parasitize
other fishes, often injuring or killing the host. An early life history study identified
stream—dwelling larval sea lampreys as the life stage most vulnerable to control
(Applegate 1950); in particular, managers were encouraged to focus control efforts on
larvae undergoing metamorphosis (called transformers) to maximize efficiency (Smith
and Tibbles 1980). Control is currently achieved mainly through the periodic treatment

of streams with the lampricide 3-triflouromethyl-4-nitrophenol (TFM), which typically

kills 95-100% of the larvae present (Smith and Swink 2003). Because larval sea

lampreys remain in their natal streams for several years before becoming parasitic
juveniles, it is neither necessary nor cost-effective to treat every stream each year.
Rather, treatments should be applied on a cycle that matches the duration of the larval
phase in a given stream. However, natural variation in recruitment, growth rates, and
survival of larval sea lampreys makes it impossible to predict with certainty when each
stream will require treatment to prevent the downstream migration of parasitic juveniles.
Therefore, each year a group of candidate streams is assessed to determine which streams
have the largest populations of transformers relative to their treatment cost and thus
should be prioritized for treatment. The current larval assessment methods are costly, yet
still produce highly uncertain population estimates (Steeves 2002). The GLFC has a
finite budget for sea lamprey management, and resources allocated to assessment
diminish those available to implement control strategies. The optimal balance between
assessment and control expenditures has yet to be determined, and is the subject of this
research.

Trade-offs between competing management actions are common to systems
managed under a limited budget. The optimal allocation of resources among two or more
valued activities is a common goal of economic modeling (i.e., Hoy et al. 2001, Varian
2003), but has been formally evaluated infrequently in natural resource management (but
see Cochrane 1999, Shogren et al. 1999). In the case of sea lamprey control, a trade-off
exists between resources allocated to larval assessment, used to determine which streams
need to be chemically treated, and those allocated to the actual treatment of those
streams. The optimal balance between these two management activities can be

determined through testing alternative assessment protocols and monitoring their

efficiency and effectiveness on the scale relevant to management. In this research, I have
initiated an adaptive management experiment to develop, implement, and evaluate one
such alternative assessment method that allocates fewer resources to assessment and
more to treatment.

Before 1995, streams were selected for lampricide treatment based on
unstandardized measures of larval abundance in streams, length-frequency distributions
of larvae derived from non-random sampling, and personal judgments (Slade et al. 2003).
In an effort to standardize assessment procedures so that selection criteria could be more
objective, a method known as quantitative assessment sampling (QAS) was implemented
in 1995 to estimate larval abundances in Great Lakes tributaries. QAS provides data on
larval densities, larval size distributions, and available habitat through intensive,
standardized, random sampling (Slade et al. 2003). These survey data are used in
combination with the Empiric Stream Treatment Ranking (ESTR) model to predict the
abundance of transformers in the year following assessment based on assumptions about
stream-specific growth rates and models of length-based metamorphic probability
(Christie et al. 2003). Streams are then ranked based on the predicted number of
transformers relative to the cost of treating the stream. Streams with the highest
predicted number of transformers killed per dollar of treatment cost are ranked highest,
and streams are treated in rank order until the control budget is exhausted.

Despite the rigorous sampling protocol associated with QAS, it remains an
imperfect assessment method. Larval population estimates obtained from QAS survey
data and the models used in ESTR to predict transformation rates both introduce

uncertainty into stream selection decisions. Recent studies have identified and quantified

sources of this uncertainty (Steeves 2002) and drawn attention to assumptions in the
assessment and stream ranking process that are often violated (Steeves et al. 2003,
Hansen et al. 2003). Hansen et al. (2003) determined that larval growth rates vary
substantially among streams as well as among years. This variation introduces
uncertainty into larval length predictions generated by existing growth models, and this
uncertainty is compounded when these predicted lengths are subsequently used to predict
transformation rates. Therefore, Hansen et al. (2003) recommend investigating
assessment methods that sample larvae near the end of the growing season to reduce the
number of growing days that must be modeled to estimate end-of-year larval lengths.
Reducing the reliance of stream selection decisions on growth models by conducting
assessments later in the year could improve the accuracy of these decisions. However,
given the large number of streams that must be sampled each year, assessment methods
would have to be less time- and effort-intensive than current methods to complete all
assessment surveys in a shorter period of time (i.e., within 60 days of the end of the
growing season). Hansen et al. (2003) also observed high variability in metamorphic
rates, and concluded that reliable prediction of metamorphosis is unlikely in the absence
of stream- and year-specific models. Since the development of such models would be
extremely difficult, they proposed eliminating the use of metamorphosis models
altogether, making the stream treatment selection process independent of metamorphic
rates. In another review of assessment techniques, Slade et al. (2003) called for the
evaluation of alternative methods for estimating larval and transformer abundance that
will constitute the “most prudent use of resources available to control sea lampreys.”

They proposed that assessment could be improved either by making assessment methods

more accurate, or by developing a procedure for ranking and selecting streams for
treatment that is more robust to the variability inherent in the processes that influence the
number of sea lampreys migrating to the Great Lakes.

Any evaluation of alternative assessment techniques will require a consideration
of the economics as well as the biology of sea lamprey control. It seems reasonable to
assume that an increase in resources allocated to assessment would result in a
corresponding increase in the accuracy of larval population estimates and in the certainty
of stream selection decisions (Figure 1). Therefore, one option to reduce uncertainty
about which streams to treat in a given year is to allocate more money to assessment.
The implementation of QAS in 1995 represented an increase in assessment expenses to
increase the reliability of stream selection decisions. In 2006, the GLFC allocated $3.1
million to larval assessment, constituting 16% of the total sea lamprey management
budget (Gavin Christie, Great Lakes Fishery Commission, personal communication).
Despite the current high investment in assessment, critical uncertainties in the stream
selection process still exist (Hansen et al. 2003, Steeves 2002, Steeves et al. 2003).
Further investments in assessment could serve to reduce these uncertainties; however,
high levels of variability in larval growth and metamorphic rates could preclude
managers from ever being absolutely certain about which streams to treat regardless of
the level of assessment expenditures. Additionally, because of the time needed to plan
chemical treatments, the set of streams treated in a given year must be chosen the year
prior to treatment, and therefore the need to forecast future population structures is an
inevitable component of sea lamprey management regardless of the resources spent on

assessment. Any increase in assessment costs will result in a corresponding decrease in

10

the resources available to actually treat streams. An alternative management strategy
would be to allot minimal resources to assessment, accept a high level of uncertainty
surrounding predictions of larval and transformer abundance, but make stream treatment
decisions less sensitive to this uncertainty by using the resources saved on assessment to
treat additional streams — in effect hedging bets against assessment uncertainty.
Presently, the balance between assessment and control expenditures that will maximize
the number of transformers killed is unclear; studies that explore alternative strategies of
resource allocation are needed to evaluate the current balance and determine whether

better strategies could be employed.

A091 11009 IUGUJSSGSSB

 

$ available for lampricide controll
number of streams treated

 

 

 

$ spent on larval assessment

Figure 1. Illustration of the trade-off between expenditures on larval assessment versus
lampricide application. As more resources are spent on assessment, fewer resources are
available for lampricide control, as illustrated by the dotted line. On the other hand, as
assessment expenditures increase, the accuracy of that assessment increases and the
streams that are treated are selected with greater confidence, as illustrated by the solid
line.

The conflict between resources available for assessment of a system and those

available for other management activities is not unique to sea lamprey management.

11

Commercial fisheries managers expend abundant resources on complex stock assessment
techniques and analyses to monitor the status of fisheries and to set firture management
targets. Cotter et al. (2004) argue that these stock assessment models are often too
complicated to be useful, and rely on assumptions that are unjustified by available data.
Despite the complexity of these models, critical uncertainties remain in the predictions
they generate. Because stock assessments and the data collections that support them also
preempt a great deal of effort that could be used to improve management in other ways,
Cotter et al. (2004) advocate a shift to a simpler model of stock assessment when making
policy recommendations. Cochrane (1999) also argues that activities in a management
system should be assessed in terms of their cost-effectiveness, and that doing so could
lead to the adoption of simpler but more effective management measures than are
currently evolving. Additionally, budgetary constraints restrict resources for assessment
for many natural resource managers, making the need for cost-effective assessment
methods all the more urgent. Rapid assessment techniques that are less extensive than
traditional quantitative sampling methods are effective in other systems and have been
advocated as cost-effective means of achieving management goals (e.g., Jones and
Stockwell 1995, Pido et al. 1997, Risk et al. 2001). For example, rapid assessment of
macroinvertebrate species composition allows managers to detect critical changes in
community structure while offering substantial savings in the cost and effort needed to
obtain such information compared to traditional more resource-intensive sampling
techniques (Metzeling et al. 2003). To effectively determine the usefulness of rapid

assessment techniques in sea lamprey management, such techniques must be tested on a

12

scale that is relevant to management decisions. Adaptive management is a tool that lends
itself well to this type of experimentation.

The use of adaptive management in natural resource management has been widely
advocated and adopted in several natural resource management systems (e. g., Walters
and Hilbom 1978, Lee 1993, Cottingham et al. 2001). Adaptive management is based on
the premise that the dynamics of managed ecosystems are complex and difficult to
predict, and that meaningful understanding of these systems cannot be achieved by
dividing systems into simple components that are easily researched using traditional
methods of experimentation (Holling 1978, Walters 1986). Rather, adaptive
management uses alternative management actions themselves as experimental tools to
test hypotheses, decrease uncertainty about managed systems, and optimize management
decisions. Alternative management actions are developed as the result of well-defined
goals; they are then implemented, continuously monitored and evaluated for success in
terms of ecological, economic, and social impacts, and are changed or “adapted” as
necessary (Walters 1986).

The goal of sea lamprey management is to reduce the number of parasitic sea
lampreys in the Great Lakes to levels that allow the realization of fish community
objectives (GLFC 2001, Christie and Goddard 2003). Using current assessment and
control methods, suppression of sea lampreys to target levels has yet to be accomplished
consistently throughout the Great Lakes (Gavin Christie, Great Lakes Fishery
Commission, personal communication), indicating that the exploration of alternative
methods is warranted. QAS has been implemented basin-wide since 1995, but has never

been formally evaluated in terms of its performance relative to other assessment

13

techniques. Additionally, quantifying the impact of management decisions based on
QAS on sea lamprey populations in the Great Lakes has'proven difficult due to the
simultaneous adoption of other large-scale changes in the sea lamprey control program
(i.e., a reduction in the amount of lampricide used to treat streams, Brege et al. 2003).
Given the high levels of uncertainty associated with QAS in Spite of its high resource
demand, and given that it is the basis for stream selection decisions that are of utmost
importance to sea lamprey management, it seems prudent to investigate the effectiveness
of this assessment method relative to that of an alternative method.

The management action of interest in this study is larval assessment of sea
lampreys. To reduce uncertainty about the optimal allocation of resources between
assessment and control activities, I have developed an alternative larval assessment
method called Rapid Assessment (RA) that is less resource-intensive than QAS. I have
implemented RA alongside QAS on a basin-wide scale for two years, and monitored the
results in the form of the set of streams that would be selected for treatment based on the
results of each assessment method and the predicted number of sea lampreys that would
be killed if those streams were treated. I assumed that the RA method would be less
accurate, but also less costly than QAS. I also assumed that any resources saved in using
RA will be used to chemically treat additional streams.

I hypothesized that the use of Rapid Assessment would lead to greater
suppression of sea lampreys than the use of QAS. To test this hypothesis, I applied two
different “treatments” by conducting both assessment methods on the same set of
streams. I estimated the effect of each treatment by comparing the costs (assessment plus

control costs) and benefits (estimated number of sea lampreys killed) of each method.

14

This experiment is not a traditional example of adaptive management, because
assessment options rather than control options are being compared. However, because I
compared assessment methods that have. a minimal effect on the system being observed, I
was able to apply both treatments to the same set of streams in each year and directly
compare the results. In this chapter, I describe the RA method and its implementation,
evaluation, and implications for the sea lamprey control program in an adaptive
management context.
Methods
Implementation of Rapid Assessment

Great Lakes tributaries are divided into “biological reaches”, which were defined
by sea lamprey managers in 1995 to facilitate larval assessment surveys. A reach is a
section of stream that is relatively homogenous in terms of larval habitat, larval densities,
and control strategies (i.e., above or below a sea lamprey barrier: Slade et al. 2003).
Rapid Assessment (RA) and Quantitative Assessment Sampling (QAS) were both
conducted on all wadeable Great Lakes reaches scheduled for quantitative assessment in
2005 and 2006, and the streams that would be selected for treatment based on the results
of the two methods were compared. In each year of the experiment a small number of
wadeable reaches lacked sufficient larval habitat to conduct both assessment methods
without re-sampling the same habitat areas, and these streams were excluded from this
analysis.
Quantitative Assessment Sampling

Larval Sampling

15

QAS surveys are conducted between April and October and are intended to
provide an estimate of the abundance of larval sea lamprey age-1 and older (Slade et al.
2003). Six access points are randomly selected from all available access points on a
reach. Larval habitat is qualitatively classified into three categories based on its
suitability for supporting larval sea lampreys, and is measured along four randomly
placed transects at each access point. Type-I habitat is considered optimal and consists of
a mixture of sand and fine organic matter, Type-II habitat is acceptable but not preferred
and primarily consists of sand, and Type-III habitat is uninhabitable and consists of hard
packed gravel, bedrock, or other substrates into which larvae cannot burrow (Dustin et al.
1989, Slade et al. 2003). The proportion of each habitat type and the mean stream width
measured at the habitat transects, along with the estimated infested length of the stream,
are used to generate estimates of the available larval habitat in each stream.

Larval lampreys are collected at each access point by systematic sampling with an

ABP-2 backpack electroshocker (University of Wisconsin, Engineering Technical
Services, Madison, WI). Sampled plots are either 15 m2 or 5 m2, depending on available
habitat area. The first habitat encountered of a given type is sampled at an access point,

with no consideration given its quality relative to other areas of the same habitat type.

Two plots of Type-I habitat are demarcated at each site, and sampled at the standardized
rate of 0.67 m2 /min. Two plots of Type-II habitat are measured and sampled at the same

rate at half of the access points for a reach.
Str£_am Treatment Ranking
Population estimates of larvae and transformers are generated from QAS data

using the ESTR model (Christie et al. 2003). In the ESTR model, total larval catch for

16

each stream is adjusted to account for the efficiency of the backpack electrofisher
(Steeves et a1. 2003). Larval density is calculated by dividing this adjusted catch by the
total area sampled, and larval abundance is estimated by multiplying the larval density by
the estimated habitat area of a reach. The projected size structure of the population at the
end of the growing season is forecasted from the size structure of the sea lampreys
collected in QAS surveys using estimates of average daily growth rates and the length of
the growing season for each reach. The number of larvae that will metamorphose in the
following year is estimated from the projected size structure at the end of the growing
season and length-based equations describing the probability of metamorphosis (Slade et
al. 2003, Christie et a1. 2003). The number of metamorphosing sea lampreys predicted to
be in a stream is multiplied by an estimate of treatment effectiveness for that stream to
yield the predicted number of transformers that would be killed if that stream were
treated in the following year (Christie et al. 2003). The cost of treating that stream is then
divided by the predicted number of metamorphosing sea lampreys that would be killed,
resulting in an estimate of cost per transformer killed. Streams are ranked according to
this cost per kill estimate, with streams the lowest cost per kill estimate given the highest
priority for treatment. Streams are then selected for treatment in order of treatment
priority until the control budget is exhausted.
Rapid Assessment Sampling
Larval Sampling

RA surveys were conducted to provide an index of larval abundance for each
stream to be used for comparisons among streams, not to provide actual larval population

estimates. All RA surveys were conducted after August 15‘“. RA surveys were

17

conducted at reference stations subjectively determined by the managing agents to be
representative of the reach as a whole. The number of reference stations sampled in a
reach was proportional to the weighted area of larval habitat in that reach. Weighted

larval habitat area (A) was calculated using the equation:
A = L"‘W'“(PT1+60"‘1’T2) (1)
where L is the infested length of the reach, W is the average width of the reach, P“ is the

proportion of Type-I habitat, PTz is the proportion of Type-II habitat, and a) is the lake-

specific estimate of the ratio of larval density in Type-II to that in Type-I habitats. All
estimates of reach-specific characteristics were based on QAS survey data collected from
1995 to 2004. Lake-specific density ratios were calculated from larval densities in Type-
I and Type-II habitats collected in surveys during 1997-2004 and averaged across all

reaches for a given lake (M. Jones, Michigan State University, East Lansing, MI,

unpublished data). Reaches with less than 50,000 In2 of weighted larval habitat were
sampled at 2 reference stations, reaches with 50,000-200,000 m2 of weighted larval

habitat were sampled at 3 reference stations, and reaches with 2200,000 m2 of weighted

larval habitat were sampled at 4 reference stations. Care was taken to avoid re-sampling

areas that had already been surveyed using QAS. If a QAS survey at the same access
point was also conducted after August 15th, both surveys were performed on the same
day in different sampling plots adjacent to the same access point. If QAS had been
conducted before August 15m, the sampled areas were marked by flagging tape and by

recording the latitude and longitude coordinates, and these previously sampled areas were

avoided when collecting RA samples.

18

RA surveys were conducted using an ABP-2 backpack electroshocker. Reference
stations were sampled by a two-person crew; one crew member sampled upstream and

the other downstream of the access point. Both crew members sampled for 15 min of
shocker time at a rate of 1 mz/min, resulting in a total of 30 m2 of habitat sampled per

reference station. The area sampled was not measured; rather, operators visually
estimated area sampled based on estimated electrofishing rates and time spent shocking.
The highest quality larval habitat available at each access site was sampled. All larvae
observed while shocking were captured and identified to genus. Identification and
measurement of larvae was carried out according to the protocol of the management
agency conducting the assessment. Some larvae were anesthetized in the field using
M8222 and measured immediately to the nearest 1 mm. Others were preserved in 10%
formalin solution and measured 2 72 hours later. If larvae were measured in the field,
live lengths (LL) were converted to preserved lengths (PL) using the equation
PL = (LL + 1.634)/1.602 (2)

(Michael Fodale, United States Fish and Wildlife Service, Marquette, MI, personal
communication).
Stream Treatment Rankflg

Stream-specific estimates of larval growth rates and growing season length from
the ESTR database were used to estimate the length that each larva collected in RA
surveys would have attained by the end of the growing season. The total number of
larvae projected to be 3 100 mm in length by the end of the growing season was summed
for each reach. This number was divided by the area sampled to calculate an index of

population density for the reach, and was then multiplied by the weighted habitat area of

19

the reach to yield an index of abundance of larvae 2100 mm. Weighted habitat area used
for calculating the RA indices of abundance were calculated using equation 1; however,
in the calculations of the indices of abundance a stream-specific estimate of to was used if
two or more estimates of densities in T1 and T11 habitats were available from the ESTR
database. If fewer than two estimates of habitat-specific densities were available, the
lake-specific estimate of a) was used. Indices of abundance for individual reaches were
summed to arrive at a single index for each “treatment unit”; these units are composed of
one or more reaches in a stream and are predetermined by managers to facilitate
treatment decisions. The cost of treating a unit was divided by its index of abundance to
give a cost/kill ratio for larvae 3100 mm. Streams were prioritized for treatment based on
this cost/kill ratio, where the unit with the lowest cost/kill was given the highest treatment
priority.
Stream Treatment Selection

In each year of the study, I compared the two assessment methods by developing
two lists of streams: one in which streams were ranked in order of treatment priority
based on QAS survey data, and a second based on RA survey data. Only streams that
were surveyed using both RA and QAS methods were included in this analysis. Streams
that were selected for treatment on the basis of other criteria1 were not included in the
comparison. I then determined which streams would be treated based on the lists of
treatment priority generated from the results of each assessment method and the budget

available for control given the cost of conducting each assessment method.

 

' Each year, some streams are ranked for treatment based on criteria other than QAS, such as deep-water
survey techniques, the expert opinion of managers, and survey data from past years. These streams were
excluded from my comparison

20

The monetary unit for sea lamprey control is the staff day. To compare the
streams that would be treated based on each method given an equal overall budget (i.e.,
assessment and control costs), I assumed that any resource savings gained from using RA
would be applied directly to the chemical control budget, and would therefore allow for
the treatment of additional streams. Sea lamprey assessment managers estimate that an
average of 14 staff days are required to survey a reach using QAS, and an average of 4.3
staff days are required to survey a reach using RA (Jefi'ey Slade, United States Fish and
Wildlife Service, Ludington, MI, personal communication). These average staff day
estimates were multiplied by the number of reaches surveyed in a given year to estimate
the cost in staff days of conducting assessment basin-wide using each method. The
difference between these two staff day requirements served as the estimate of the
assessment staff days saved through the use of RA. The cost of an assessment staff day
does not equal the cost of a treatment staff day, and treatment staff days are the monetary
unit used in the selection of streams for treatment (Gavin Christie, Great Lakes Fishery
Commission, Ann Arbor, MI, personal communication). Therefore, after calculating the
number of assessment staff days saved through the use of RA, this staff day estimate was
converted to treatment staff days using the cost of deploying a person to the field to do
each type of work. The additional treatment staff days available through the use of RA
were added to the number of staff days budgeted for the treatment of streams assessed by
QAS to determine which streams could be treated if the RA method were employed.
Because of concerns raised by sea lamprey managers regarding whether or not
assessment savings generated from the use of RA would actually translate into additional

resources to be used for treatment, comparisons were also made assuming that the RA

21

savings would not be used to treat additional streams, and that an equal number of
treatment staff days would be available to treat streams regardless of which assessment
method was used.
Evaluation of Rapid Assessment
CorerJarison of rank lists

I used several methods to compare the two lists. The correlation of the RA and
QAS ranks was calculated using Spearman’s rank correlation for all surveyed streams, as
well as for the subset of streams that would rank for treatment based on the RA results.
Population estimates of transformers and larvae predicted by the ESTR model were
summed for all streams that would be treated based on the QAS method and for all
streams that would be treated based on the RA method. The RA population estimates
were calculated both with and without the additional treatment staff days allocated for
treatment based on savings from the RA surveys. The ratios of estimated transformers
and larvae that would be killed in RA streams to those that would be killed in QAS
streams were calculated to give an index of the performance of RA relative to QAS. The
total labor costs (assessment + control) that would be incurred by treating each set of
streams and the ratios of RA to QAS labor costs were also calculated. Assessment staff
days were converted to treatment staff days when calculating total labor costs.
thure-Recapture

Capture-recapture studies were conducted in 2006 on streams ranked for
treatment in 2005 as an independent means of comparing the number of sea lampreys that
would be killed as a result of making treatment decisions based on the two different

assessment methods. Under ideal circumstances, population estimates of the number of

22

sea lampreys present in a stream at the time of treatment would be obtained from capture-
recapture studies on all streams that would have been selected based on one method but
not the other; the sea lamprey populations in streams treated based on both lists are
irrelevant to this comparison because they would have been treated regardless of which
method had been used. However, some streams were not selected for capture-recapture
despite ranking for treatment based on only one assessment method because agency
personnel did not believe it was feasible to conduct a successful capture-recapture
experiment, or because managers elected not to treat the stream in the year following
assessment for practical reasons.

Metamorphosing sea lampreys do not reliably Show physical characteristics of
transformation until late July to early August of the year they begin to metamorphose
(Manion and Stouffer 1970, Youson and Potter 1979). Due to the high number of
streams requiring treatment and practical constraints of management agencies, some
streams were chemically treated before the time when physical signs of metamorphosis
were visible. Therefore, it was not possible to accurately determine the number of
metamorphosing sea lampreys killed as a result of these treatments. In the absence of
information on metamorphosing sea lampreys, comparisons were made of the number of
larvae with a 50% or greater probability of metamorphosing based on their total length as
determined by the ESTR model. For the upper lakes (Superior, Huron, and Michigan),
larvae that were 144 mm had a 50% chance of metamorphosing, and for the lower lakes
(Erie and Ontario) larvae that were 131 mm had a 50% chance of metamorphosing. The

number of larvae that were greater than or equal to these size cutoffs was used as a

23

surrogate for the number of metamorphosing sea lampreys in streams that were treated

prior to July 15th.

The ESTR model larval population estimates were used to develop targets of the
number of larvae to mark and to collect during treatments in each stream. The target
number of sea lampreys to mark and recapture was estimated using the appropriate
nomograph for the desired precision of the population estimate from Figure 6 in Robson
and Regier (1964). The +/- 10% level of accuracy was targeted when possible, although
the +/-25% level of precision was considered acceptable if the effort needed to capture a
sufficient number of sea lampreys to achieve the +/-10% accuracy level was prohibitively
high.

The predetermined number of larval sea lampreys targeted for marking were
collected using an ABP-2 backpack electroshocker, anesthetized using MS-222,
measured (+/- 1 mm), marked by removing a portion of non-vascular tissue at the end of
the caudal fin, and revived in an aerated cooler. Most samples were kept overnight to
observe any post-marking mortality. Upon revival, marked larvae were released
throughout the available habitat of the stream. Larvae used for marking were collected
from the stream of interest whenever possible; however, low larval densities and poor
collecting conditions in some streams necessitated collecting and marking animals from
nearby source streams and importing them into the study stream. Streams were divided
into sections of approximately equal length to facilitate the distribution of marking and
recapture effort. When sea lampreys from outside streams were marked and imported,
the projected size structure of the target stream based on ESTR estimates was matched,

and marked larvae were distributed randomly throughout each stream section in

24

proportion to its area. When larvae were collected and marked from within the source
stream, they were released in proportion to the abundance of larvae captured in each
stream section. Marking was completed two weeks or more in advance of anticipated
lampricide treatment date to allow marked animals to redistribute evenly throughout the
population.

The recapture event took place within 24 hours of lampricide treatment. During
this time period, as many larval and metamorphosed lampreys as possible were collected
by stationary fyke nets and by actively hand dipping with soap nets. Collection efforts
were distributed throughout the infested area of the stream. All collected larvae were
preserved in 10% formalin solution. Preserved lampreys were identified to genus,
examined for marks, measured (+/- 1 mm), classified to the appropriate life stage (i.e.
ammocete, transformer stage 1, transformer stage 2, etc: following Youson and Potter

1979) and counted.

The total sea lamprey population (IV ) of a stream was calculated using the
Chapman modification of the Petersen estimator (Seber 1982):

(M+l)*(C+1)_1 (3)
(R+1)

A7:

 

where M=number of individuals marked, C= total number of individuals in treatment
collection, and R=number of recaptured individuals in the treatment collection. The
variance was estimated as:

2*
(M+l) (C+1)*(C—R) (4)

Vail): 2
(R +1) *(R +2)

 

In streams that were treated after July 15th, the number of metamorphosed sea lampreys

in each population was estimated as:

25

NT=N*— (5)

where Al T = the estimated number of metamorphosed sea lampreys, IV = the Petersen

population estimate, C1=the number of sea lampreys in the treatment collection
exhibiting external signs of metamorphosis, and C= the total number of individuals in the

treatment collection. Similarly, for streams in which the treatment occurred prior to July
15th, the number of sea lampreys over the Size at which 50% or more would be expected

to metamorphose (large larvae) was calculated as:

where Al LL = the estimated number of large sea lampreys, IV = the Petersen population

estimate, CLL=the number of sea lampreys in the treatment collection larger than the

designated size, and C: the total number of individuals in the treatment collection. The

variance of the estimators of population proportions was calculated as:
. . C . C
V(N.;)=(N)2 *V(—’i>+V<N)*(—1)2 (7)
C C
where V(Nx) = the variance of the proportion of interest, either T or LL as appropriate;

N = the Petersen population estimate; V( A7 )=the variance of the Petersen population

Cx* Cx
_ 1__
C ( C)

estimate; V(£’—‘—)= C

 

, the variance of the proportion of interest based on the

binomial distribution; Cx=the number of transformers (T) or large larvae (LL) in the

treatment collection as appropriate; and C= the total number of individuals in the

26

treatment collection. Confidence intervals for the estimates were calculated using the
normal approximation:
(Nx):1.96*,/V(Nx) (8)

I assumed that the capture-recapture estimate provided an unbiased population
estimate at the time of treatment. The total number of sea lampreys, transformers, and
large larvae estimated from the capture-recapture studies was summed for the streams
that were treated based on RA only and for the streams that were treated based on QAS
only. The variances of these population estimates were also summed, and 95%
confidence intervals were calculated for the total populations present in each set of
streams.

Results
Rank Lists and Streams Selected for Treatment

In 2005, 104 reaches in 56 streams were surveyed using both QAS and RA.
Based on the average number of staff days required to conduct each type of assessment
method, 1456 staff days were required to conduct QAS and 447 staff days were required
to conduct RA on these reaches. Therefore, the use of RA resulted in a savings of 1009
assessment staff days. To convert these assessment staff day savings into staff days to be
used to treat additional streams, a conversion factor of 1.00 assessment staff day per 0.65
treatment staff days was used based on the different costs of each activity (Gavin
Christie, Great Lakes Fishery Commission, Ann Arbor, MI, personal communication).
This conversion resulted in an estimated 656 additional treatment staff days that would be
available to treat additional streams if the RA method were used for assessment. In 2006,
68 reaches in 46 streams were surveyed using both QAS and RA, with a cost of 952

assessment staff days to conduct QAS and 292 staff days to conduct RA. The use of RA

27

resulted in a savings of 660 assessment staff days, or 429 treatment staff days to be used
to treat additional streams.

The 16 top-ranked streams from the QAS treatment rank list were selected for
treatment in 2005 using the baseline level of treatment effort of 1409 treatment staff days
(Table 1). This baseline level of treatment effort reflects only the number of staff days
needed to treat streams that ranked for treatment on the basis of QAS surveys; the actual
treatment budget is much higher than 1409 treatment staff days, but includes the cost of
treating streams that ranked based on assessment methods other than QAS. Given the
same 1409 treatment staff days plus the 656 additional staff days available from the use
of RA, 24 streams would be selected for treatment based on the RA rankings (Table 1).
Of these 24 streams, 11 would be treated regardless of which assessment method was
used. Thirteen streams would be treated only based on the RA method, and three streams

would be treated only based on the QAS method (Table 2).

28

Table 1. Streams that would be selected for treatment based on either assessment method
in 2005, the rank of treatment priority based on RA and QAS, whether or not the stream
would be selected for treatment based on the different assessment methods, and the ESTR
model population estimates for larvae (N L) and transformers (NT). Streams are placed in

order of RA ranking and only streams that would be selected for treatment based on at
least one method are listed.

 

 

 

Treated
RA RA
(extra (equal
RA QAS staff staff
Stream Name Rank Rank QAS days) days) NL NT

Garden River (entire) 1 1 1 X X X 641,883 1,281
Oshawa Creek (entire) 2 l X X X 47,339 19,791
Millecoquins River (Furlong) 3 2 X X X 31,236 1,949
Cloud River (entire) 4 4 X X X 17,908 1,840
Chocolay River (entire) 5 6 X X X 407,574 1,933
Mindemoya River (entire) 6 10 X X X 31,215 280
Au Train River (upper) 7 13 X X X 58,059 737
Traverse River (entire) 8 18 X X 137,697 491
Sucker River (entire) 9 14 X X X 40,167 1,463
Pere Marquette River (no
Middle) 10 12 X X X 145,960 3,860
Betsie River (below barrier) l l 34 X X 157,020 234
Carp River (entire) 12 9 X X X 23,265 403
Boyne River (mainstream) 13 45 X X 114,767 59
Kaministiquia (entire) 14 20 X X 748,191 1,671
Trail Creek (entire) 15 17 X X 5,084 986
Platte River (middle) 16 27 X X 50,281 158
Jordan River (entire) 17 26 X 139,858 665
Red Cliff Creek (entire) 18 30 X X 2,205 43
Crow River (entire) 19 5 X X 23,782 695
Whitefish River (entire) 20 16 X X 218,965 2,479
Beaver Lake Creek (Lowney) 21 39 X 3,982 19
Trent River (Mayhew Creek) 22 3 X X 27,796 910
Saginaw R. (Big Salt, Bluff,
Home) 23 21 X 58,153 1,254
Lindsey Creek (entire) 26 19 X 7,306 323
Lincoln River (entire) 27 15 X 13,431 1,086
Little Munuscong River
(entire) 32 8 X 49,137 1,018
Big Munuscong River

(Taylor) 5 5 7 X 14,583 514

 

29

Table 2. Streams that would be treated based on the results of one assessment

method but not the other in 2005 given the allocation of 659 additional staff days for
treating streams ranked by RA. The top panel shows the streams that would rank for
treatment based on RA, not QAS, and the bottom panel shows the streams that would

rank for treatment based onQAS, not RA. The ESTR population estimate of

transformers and larvae are shown for each stream, along with the sum of the number
of transformers and larvae present in each set of streams.

 

 

 

 

 

 

 

 

 

 

RA only
ESTR ESTR
QAS RA Transformer larval
rank rank Name estimate estimate
18 8 Traverse River 491 137,697
34 1 l Betsie River 234 157,020
45 13 Boyne River 59 114,767
20 14 Kaministiquia 1,671 748,191
17 15 Trail Creek 986 5,084
27 16 Platte River (middle) 158 50,281
26 17 Jordan River 665 139,858
30 18 Red Cliff Creek 43 2,205
39 21 Beaver Lake Ck 19 3,982
21 23 Saginaw River (Big Salt, Bluff, & Home Drain) 1,254 58,153
19 26 Lindsey Creek 323 7,306
TOTALS 5,902 1,424,543
QAS only
ESTR ESTR
QAS RA Transformer larval
rank rank Name estimate estimate
7 55 Big Munuscong River (Taylor Ck) 514 14,583
8 32 Little Munuscong River 1,018 49,137
15 27 Lincoln River 1,086 13,431
TOTALS 2,619 77,152

 

In 2006, the 21 top-ranked streams from the QAS treatment rank list were selected for

treatment using the baseline treatment effort level of 1735 treatment staff days (Table 3).

Again, this effort level reflects the cost of treating only the streams that were ranked

based on current QAS transformer estimates; streams ranked through other methods were

excluded from the calculation of treatment costs. Given the same 1735 treatment staff

days plus the additional 429 additional staff days available through the use of RA, 29

30

streams would be selected for treatment based on the RA rankings (Table 3). Of these 29
streams, 19 would be treated based on the QAS results as well. Ten streams would only
be selected for treatment based on RA results, and two streams would be selected for

treatment only based on QAS results (Table 4).

31

Table 3. Streams that would be selected for treatment based on either assessment
method in 2006, the rank of treatment priority based on RA and QAS, whether or not
the stream would be selected for treatment based on the different assessment methods,
and the ESTR model population estimates for larvae (N L) and transformers (NT).
Streams are placed in order of RA ranking, and only streams that would be selected
for treatment based on at least one method are listed.

 

 

Treated
RA RA
(extra (equal
RA QAS staff staff

Stream Name Rank Rank QAS days) days) NL NT
Bighead River (entire) 1 1 X X X 1,705,376 80,899
Bad River (fall-sturgeon) 2 3 X X X 1,795,270 18,713
Poplar River (entire) 3 23 X X 56,502 228
Platte River (entire) 4 10 X X X 1,210,067 4,157
Fishdam River (entire) 5 34 X X 26,352 26
Coldwater Creek (entire) 6 12 X X X 92,139 567
Augres River (entire) 7 1 1 X X X 272,453 3,015
Sturgeon River (entire) 8 7 X X X 12,602 4,933
White River (main and N.
Branch) 9 6 X X X 30,642 10,61 1
Galloway Creek (entire) 10 41 X X 226 1
Middle River (barrier down) 1 1 8 X X X 28,694 782
McKay Creek (entire) 12 4 X X X 24,522 2,943
Cypress (entire) 13 14 X X X 40,029 434
Cheboygan River (Maple) 14 17 X X X 46,112 637
Good Harbor Creek (main) 15 31 X X 38,351 38
Wolf River 16 29 X X 24,210 92
Long Lake Creek (lower) 17 5 X X X 30,571 1,286
Kalamazoo River (Mann) 18 19 X X X 1,387 93
Cheboygan River (Pigeon) 19 15 X X X 90,341 2,092
Martineau Creek (entire) 20 16 X X X 1,684 166
Neebing-Mclntyre Floodway 21 28 X X 28,269 148
Au Sable River (lower) 22 39 X 146,110 27
Boyne River (main) 23 32 X X 274 25
Saginaw River (Carroll
Creek) 24 24 X X 62 1 14 1
Cedar River (main) 25 21 X X 261,516 1,308
Swan River (entire) 26 20 X X X 148,364 601
Pentwater River (North,
Cedar, Crystal) 27 2 X X 77,418 8,491
Rouge River (entire) 28 25 X 334 154
Grand River (Norris, Rhymer,
Sullivan) 30 9 X X 1,195 744
Grand River (Sand) 41 18 X 1,279 521
Bark River (entire) 46 13 X 85,694 718

 

32

Table 4. Streams that would be treated based on the results of one assessment
method but not the other in 2006 given the allocation of 41 6 additional treatment
staff days for treating streams ranked by RA. The top panel shows the streams that
would rank for treatment based on RA only, and the bottom panel shows the streams
that would rank for treatment based on QAS only. The ESTR population estimate of
transformers and larvae are Shown for each stream, along with the sum of the number
of transformers and larvae present in each set of streams.

 

 

 

 

 

 

 

 

 

 

RA not QAS
ESTR ESTR
RA QAS Transformer larval
rank rank Name estimate estimate
3 23 Poplar River (entire) 228 56,502
5 34 Fishdam River (entire) 26 26,352
10 41 Galloway Creek (entire) 148 28,269
15 31 Good Harbor Creek (main) 1 226
16 29 Wolf River 38 38,351
21 28 Neebing-Mclntyre Floodway 92 24,210
22 39 Au Sable River (lower) 27 146,110
23 32 Boyne River (main) 25 274
24 24 Saginaw River (Carroll Creek) 141 621
28 25 Rouge River (entire) 154 334
TOTALS 878 321,248
QAS not RA
ESTR ESTR
RA QAS Transformer larval
rank rank Name estimate estimate
41 18 Grand River (Sand) 521 1,279
46 13 Bark River (entire) 718 85,694
TOTALS 1,239 86,974

 

For each year of the comparison, I also considered the treatment scenario with an
equal number of treatment staff days budgeted to treat streams ranked by either method.
Under this equal treatment staff day scenario, in 2005, 17 streams would be treated based
on the RA rankings (Table 1). Ten of these 17 streams would be treated regardless of
which assessment method was used. Seven streams would be treated only based on the
RA results, and an additional six streams would be treated based on the QAS results only.

In 2006, 24 streams would be treated based on the RA rankings, 16 of which would be

33

treated regardless of which assessment method was used (Table 3). Eight streams would
be treated based only on RA, and five streams would be treated based only on QAS.

The RA and QAS rankings of the full set of 56 streams surveyed in 2005 were
significantly correlated (Spearman’s rank correlation = 0.67, p<0.001, Figure 2). The RA
and QAS ranks of the subset of 24 streams that would be treated based on the RA results
with the allocation of 659 additional staff days in 2005 were also significantly correlated,

although the correlation was not as strong (Spearman’s rank correlation = 0.50, p<0.02,

 

 

Figure3).
60 1 xx"!
A Xxx x”;x
50 r x X x" X X
x x Xx’ x
x 40 _l X X I”, X X
C X ”'X x
«I X X
A ’z

a: 30 1 x X x X
< A ' x
M o .,.X

20 - O o’,”” . . I

o ”x”. - . '
10 ‘ .I’x’ooo . '
’6
0’9‘
0 o” to I T T T l
0 10 20 30 40 50 60
QAS Rank

Figure 2. Correlation between QAS and RA rank for the 56 streams surveyed with both
methods in 2005. Stream rankings were significantly correlated (Spearman’s rank
correlation = 0.67, p<0.001). Open circles represent streams treated based on both
methods, dark squares represent streams treated based on the RA ranking only, Open
triangles represent streams treated based on the QAS list only, and X’s represent streams
not treated based on either method. The dashed line indicates perfect (1:1) correlation.

34

50 . ,.

 

 

4o 4
x x”,
r: 30 ~
< 1 o ,x’ I
m 20 O 0’”! I I
” - I
I’ .
o ,I’ I I
10 ~ ,” 0 o .
,0” 0 o
,0
99' o
0 T T l l 1
O 10 20 30 40 50
QAS Rank

Figure 3. Correlation between QAS and RA rank for the 24 streams that ranked for
treatment based on RA results in 2005 with additional staff days allocated to treat RA
streams. Stream rankings were significantly correlated (Spearman’s rank correlation =
0.50, p = 0.02). Open circles represent streams treated based on both methods, and dark
squares represent streams treated based on the RA ranking only. The dashed line
indicates perfect (1:1) correlation.

The RA and QAS rankings of the full set of 46 streams surveyed in 2006 were
also significantly correlated (Spearman’s rank correlation = 0.56, p<0.001, Figure 4).
The rankings of the subset of 29 streams that would be treated based on RA given
additional staff days for treatment were not significantly correlated (Spearman’s rank
correlation = 0.29, p=0.13) although the 24 streams that would be treated based on RA

given equal staff days for treatment were significantly correlated (Spearman’s rank

correlation = 0.44, p = 0.03, Figure 5).

35

501l ,

454 A x x—"

401 A

35 X . .—

30 i 0 Xx , "x X x
l o ' .—'

251‘ 0° ,3" .

20,1! 0 00/-0"’ I

151 "'o ' I

10 i o x‘” I
l .1°' 0

5 1L0: o 0 ' I

D 7" i'i'f 7 7 7 7 7 7 ,fi,fi,fi#717 * if ‘ri” i 7.,fi,_T_.

0 10 20 30 40 50
QAS Rank

RA Rank

 

Figure 4. Correlation between QAS and RA rank for the 46 streams surveyed with both
methods in 2006. Stream rankings were significantly correlated (Spearman’s rank
correlation = 0.56, p<0.001). Open circles represent streams treated based on both
methods, dark squares represent streams treated based on the RA ranking only, open
triangles represent streams treated based on the QAS list only, and X’s represent streams
not treated based on either method. The dashed line indicates perfect (1:1) correlation.

50 -
40 ~
30 —

20 ‘ 00 r" I

RA Rank
0
I

10— ° .. '

 

 

0 10 20 30 40 50
QAS Rank

Figure 5. Correlation between QAS and RA rank for the 24 streams that ranked for
treatment based on RA results in 2006 with equal staff days allocated for treatment of RA
streams. Stream rankings were significantly correlated (Spearman’s rank correlation =
0.44, p = 0.03). Open circles represent streams treated based on both methods, and dark
squares represent streams treated based on the RA ranking only. The dashed line
indicates perfect (1:1) correlation.

36

Comparison of ES T R Population Estimates

If the staff days saved by using RA were used to treat additional streams, the total labor
costs (assessment + control costs) of using each method would be equal. Under this
scenario, based on the ESTR model single-year population forecasts, in 2005 the use of
RA would allow for 1.1 times as many transformers and 1.8 times as many larvae to be
killed as compared to the QAS method (Figure 6). In 2006, under equal labor costs, the
RA method would allow for the same amount of transformers and 4% more larvae to be

killed as compared to the QAS method according to ESTR model predictions (Figure 6).

2.0 -
1.8 <
1.6 -
1.4 ~
1.2 ~
1.0 4 -----
0.8 -
0.6 i
0.4 4
0.2 ~
0.0

RA value/QAS value

 

 

 

 

 

 

 

 

Labor Cost Transformers Larvae

Figure 6. The ratio of RA values to QAS values for labor cost (assessment + control
costs), total estimated number of transformers killed, and total estimated number of
larvae killed when additional treatment staff days are allocated to treat streams ranked by
RA. Light bars represent 2005 values, and dark bars represent 2006 values. Total
transformer and larvae estimates were the sum of the population estimates generated by
the ESTR model for all the streams that would be treated based on the results of each
assessment method. Dashed line indicates where the RA and QAS values are equal;
above this line, RA values are higher, below this line QAS values are higher.

If the staff days saved by using RA were not used to treat additional streams, the
labor cost (assessment + control cost) of using RA would be approximately 30% less than

that of using QAS in 2005. Under this scenario, based on the ESTR model single-year

37

population forecasts, the use of RA would allow for 0.9 times as many transformers and
1.5 times as many larvae to be killed as compared to the QAS method (Figure 7). In
2006, if the staff day savings generated by RA were not used to treat more streams, the
labor cost of assessment and treatment of RA would be approximately 20% less than that
of QAS, resulting in 0.9 times as many transformers and approximately the same number
of larvae killed as compared to the QAS method (Figure 7).

1.6 ~

1.4a
1.24

    

RA alueIQAS value

 

 

 

 

 

 

 

Labor Cost Transformers Larvae

Figure 7. The ratio of RA values to QAS values for labor cost (assessment + control
costs), total estimated number of transformers killed, and total estimated number of
larvae killed when equal numbers of treatment staff days are allocated to treat streams
ranked by RA and QAS. Light bars represent 2005 values, and dark bars represent 2006
values. Total transformer and larvae estimates were the sum of the population estimates
generated by the ESTR model for the streams that would be treated based on the results
of each assessment method. Dashed line indicates where the RA and QAS values are
equal; above this line, RA values are higher, below this line QAS values are higher.

Capture-Recapture

Streams were selected for capture-recapture from the set of streams that ranked on
the basis of one method of assessment, but not the other in 2005 (Table 2) to compare the
difference in the number of sea lampreys that would be killed as a result of making

treatment decisions based on RA or QAS. Because of logistical constraints preventing

38

capture-recaptures on all of these streams, a subset of streams was chosen (Table 5).
Three streams were chosen from the 11 that would rank for treatment based only on RA,
given additional staff days for treatment, (RA streams), and 3 streams were chosen that
would rank for treatment based only on QAS (QAS streams). The accuracy of the
capture-recapture results for two streams (the Little Munuscong and the Big Munuscong)
are suspect because neither the release of marked animals nor the recapture effort was
distributed randomly throughout the stream, and the population estimates from these
streams should be treated as a minimum estimate rather than an unbiased population
estimate.

Table 5. Capture-recapture estimates of sea lamprey abundance (all life stages) for

the 6 study streams. M is the number marked, C is the number collected in the
recapture event, R is the number of recaptures, and N is the Petersen population

estimate. (95% Confidence intervals on N are shown for each stream, and ESTR N

represents the initial population estimate of all life stages of sea lampreys generated
by the ESTR model from QAS data.

 

 

95% CI ESTR
RA streams M C R N Lower Upper estimate
Boyne River 2,012 5,321 107 99,195 80,763 117,628 114,826
Trail Creek 888 1,394 23 59,821 36,546 83,097 6,070
Betsie River 2,892 5,439 34 449,654 303,240 596,068 157,254
QAS streams
Lincoln River 1,458 1,730 30 81,468 53,494 109,441 14,517
Little Munuscong“ 2,517 1,649 328 12,627 11,408 13,846 32,280
Big Munuscong“ 1,488 299 125 3,544 3,075 4,014 15,097

 

*Streams for which population estimates are suspect due to non-random release and recapture of larvae

The initial ESTR model population estimate of the total stream population falls
within the 95% confidence intervals of the capture-recapture population estimate in only
one of the six streams (Table 5). The ESTR model population estimate for transformers
or large larvae does not fall within the 95% confidence intervals of the capture-recapture
population estimate for any of the six streams (Table 6). The summed capture-recapture

population estimates Show that when RA savings are used to treat additional streams, the

39

RA streams contain more sea lampreys and more large larvae/transformers than the QAS

streams (Figure 8).

Table 6. Capture-recapture estimates for either transformers (T) or large larvae
(larvae > 144 mm, LL) for the 6 streams on which capture-recapture Was conducted
in 2006. Transformer estimates were only generated for streams treated afier July
15th, otherwise large larvae estimates were used. ESTR estimates are of transformer
abundance if transformers were estimated in the capture-recapture study, otherwise
the ESTR estimate is of large larvae abundance as predicted by the ESTR model.

 

 

Proportion
Treatment Life- of NT 95% CI ESTR
N as T or
RA streams Date stage LL or N LL lower upper estimate
Boyne River May 23, 2006 LL 0.002 224 91 357 0
July 29-Aug 2,
Trail Creek 2006 T 0.081 4,818 2,782 6,854 986
Betsie River Sept 8, 2006 T 0.014 6,283 3,803 8,764 234
QAS streams
Lincoln River July 5-6, 2006 LL 0.010 801 333 1,268 1,519
Little June 28-29,
Munuscong"I 2006 LL 0.050 636 489 782 396
Big June 27-28,
Munuscgg“ 2006 LL 0.067 237 132 342 448

 

Streams for which the population estimates are suspect due to non-random release and recapture of
larvae

40

800000 ‘
700000
600000
500000
400000 ~
300000
200000 .
100000 «

ID
v

 

nggngnxgggglrgp J
H—

 

 

16000

14000 l b)

12000 1

10000 1

8000 l

6000 l
l
1

Population
1
i
l
I
l
l
l
E

4000
2000 1

lg--.._,-, r———f———i
RA Streams QAS Streams

 

Figure 8. The sum and 95% confidence intervals of the capture-recapture population
estimates for a) the total (larval + transformer) population estimates, and b) the large
larvae/transformer population estimates for the streams that ranked on the basis of RA
only (RA streams) and QAS only (QAS Streams) with additional treatment staff days
allocated for the treatment of RA streams. The total RA population estimates include the
populations of 3 RA streams out of the 11 that ranked based on this method and not based
on QAS, and the total QAS population estimates include the populations of all 3 QAS
streams that ranked based on this method and not based on RA. Confidence intervals are
calculated from the summed variance estimates of the three streams used in each
category. Population estimates in two of the three QAS streams are potentially
underestimated due to non-random release of marked animals and non-random recapture
events (see text for further explanation).

Discussion
The acquisition of knowledge to inform decision—makers about the optimal course

of action is a common goal of scientific inquiry. Often it is assumed that the more

41

knowledge acquired, the better the decisions will be. However, in situations of limited
resources, that increased knowledge can come at the expense of the ability to carry out
the very actions the increased knowledge was intended to inform. When resources are
limited, it is important to analyze the trade-off between resources used to assess a system
and resources used to carry out management actions. In the case of sea lamprey control,
streams are chemically treated to kill larval sea lampreys to achieve management goals.
Assessment is needed to inform managers which streams, if treated, would provide the
greatest benefit to the lamprey program in terms of sea lampreys killed. Finding the
optimal balance between resources spent on this assessment and resources reserved for
treating streams requires testing alternative frameworks of resource allocation and
monitoring the consequences. In this research, I have tested one such alternative method
and observed the consequences in terms of the streams that would be selected for
treatment and the estimated number of sea lampreys that would be killed. After two
years of conducting RA and QAS concurrently, l have concluded that the use of RA to
assess and select streams for treatment allows managers to kill more sea lampreys at
equal or lesser costs to the GLFC. While finding the optimal balance of assessment and
control resources will require further inquiry, the use of RA is an improvement over the
current allocation of resources.

On average, RA surveys cost about 70% less than QAS surveys to conduct. I
expected the RA surveys to be less accurate than the QAS surveys given the lower level
of effort needed to conduct them, and the rationale behind conducting the RA surveys
was that this loss of accuracy would be compensated for with additional resources

available to treat streams. However, I found that the information obtained from the two

42

types of surveys in terms of the ranking of stream treatment priority was similar despite
the lower costs associated with RA. In 2005, basing treatment decisions on the RA data
would result in the treatment of all but three of the streams that would be treated under
QAS, with the addition of 11 more streams under the RA list. In 2006, the use of RA
would result in the treatment of all but two of the streams that would be treated under
QAS, and ten additional streams would be treated under RA. Qualitatively, RA is more
cost effective in terms of the number of streams it allows managers to treat, and not much
information is lost in using RA since the majority of streams that rank for treatment under
QAS also rank under RA.

Using the ESTR model population estimates as a basis for comparison, under
equal labor costs to the sea lamprey program, the use of RA results in at least as many, if
not more transformers and more larvae to be killed than does the use of QAS (Figure 6).
Even if the savings resulting from the use of RA were not used to treat more streams, the
use of RA still results in almost as many transformers and larvae killed as compared to
streams treated based on QAS (Figure 7). These ESTR-based estimates of the relative
performance of RA are conservative, because they are generated from the QAS surveys
on which the QAS rank list is based, and therefore will tend to favor the QAS surveys. In
calculating the relative costs of the two assessment methods, chemical costs of treating
streams were not included. Currently, the sea lamprey control program possesses a
surplus of lampricide chemicals used to treat streams, and therefore chemical costs are
not a limiting factor in selecting streams for treatment (Gavin Christie, Great Lakes
Fishery Commission, Ann Arbor, MI, personal communication). However, if RA were to

be adopted by the GLFC and on average more streams per year were to be treated, the

43

cost of lampricide may become a limiting factor. If in the future RA is adopted, some of
the savings generated through the use of RA may need to be used to purchase additional
lampricides, reducing the number of additional streams treated.

The capture-recapture portion of this study was intended to provide an additional,
independent means of comparing the benefits of making decisions based on the two
assessment methods. By conducting capture-recapture on all of the streams that ranked
for treatment on the basis of only QAS (QAS streams), I expected to be able to quantify
the lower bound of the number of larvae or transformers that would have to be killed
based on the RA method in order for it to outperform QAS. I also conducted capture-
recapture studies on three of the eleven streams that ranked for treatment based only on
RA (RA streams). If the capture-recapture population estimates were unbiased, I would
simply sum the population estimates for all three QAS streams, sum the population
estimates for the subset of three RA streams, and compare the two totals. If the total
population for the RA streams were higher, I would be confident that making treatment
decisions based on RA would allow managers to kill more sea lampreys, especially given
that there are eight additional streams that would be treated based on only RA that would
contribute to the total number of sea lampreys killed. If this method is followed, it is
clear that at least in the first year of the study, more larvae and more transformers would
be killed if streams were treated based on the results of RA rather than QAS (Figure 6).

Unfortunately, the estimates obtained from the capture-recapture studies on the
Little Munuscong and Big Munuscong Rivers, two of the three streams that rank for
treatment based on only QAS, were suspect because neither the release of marked

animals nor the recapture effort was distributed randomly throughout the streams. The

44

accuracy of a capture-recapture population estimate requires that either the marked
animals or the recapture effort are randomly distributed throughout the population being
sampled (Ricker 1975). In these two streams, the marked animals were highly
concentrated in certain areas, and the subsequent recapture efforts were also generally
concentrated in these same areas. Violating the assumption of equal distribution of
marked animals amongst unmarked animals in a capture recapture experiment,
particularly when the recapture effort is also unequal and focuses on these same
concentrated areas, can result in a high proportion of marks collected on the second
collection event and hence a low population estimate. For these two streams called into
question because of the marking methods, the population estimate obtained from the
capture-recapture experiment was significantly lower than that generated by ESTR (Table
6). Because one of the major assumptions of a capture-recapture experiment was violated
for these two streams, their population estimates cannot be treated as unbiased.

In the absence of a reliable population estimate for these two streams, the
comparison of the number of sea lampreys killed based on the two methods becomes
more equivocal. However, assuming that the population estimates for the Little
Munuscong and Big Munuscong rivers are uninforrnative, some level of comparison is
still possible. The combined larval and transformer populations of these two streams
would have to be approximately 528,000 (11 times larger than their combined ESTR
model population estimates) for the total population of the 3 QAS streams to equal that of
the 3 RA streams. While this seems unlikely, it is not impossible, especially given the
capture-recapture results of this and another study (Steeves 2002), which showed that in

some cases the capture-recapture population estimates were eight to nine times higher

45

than the ESTR population predictions. However, there are eight additional streams that
would be treated on the basis of RA and not QAS with sea lamprey populations that will
also contribute to the total number killed in RA streams. Given the populations estimated
from the capture-recapture studies and the existence of these eight additional RA streams,
it seems reasonable to conclude that the use of RA surveys to rank streams for treatment
and the subsequent treatment of those streams would result in higher total numbers of sea
lampreys killed than the use of QAS.

A similar situation exists when comparing the number of large
larvae/transformers that would be killed if treatment decisions were based on RA or
QAS. The subset of three RA streams have almost seven times as many large
larvae/transformers as the full set of three QAS streams, and there are still eight RA
streams for which I have no capture-recapture population estimates. Assuming we know
nothing about the number of transformers in the Little or Big Munuscong Rivers, they
would need to contain over 10,000 transformers (approximately nine times the ESTR
model transformer estimate for these streams) to equal those in the subset of RA streams
for which we have data. Coupled with the fact that there are eight additional RA streams
that would contribute to the total number of transformers killed as a result of treating
streams based on RA, this seems highly unlikely. Therefore I conclude that making
stream treatment decisions based on RA results would allow managers to kill more large
larvae/transformers than would making stream treatment decisions based on QAS results.

In addition to providing a basis for comparison of the outcome of using an
alternative assessment method, the capture-recapture population estimates also serve to

illustrate the inaccuracies that exist in the QAS and ESTR population estimates despite

46

their high resource demand. The capture-recapture population estimates ranged from 0.9-
10 times the ESTR total population estimates, and from 05-27 times the ESTR large
larvae/transformer estimates. These results could serve as a warning for managers
against putting too much confidence in ESTR population estimates; however, there are
reasons to approach these results with caution. In four of the six streams on which
capture-recapture experiments were conducted, the marked sea lampreys were imported
from an outside source stream. A major assumption of any recapture study involving the
importation of marked subjects is that the behavior of the marked imports must be
indistinguishable from that of unmarked members of the target population. Therefore,
this methodology should only be applied when there are adequate grounds for believing
that this assumption is a reasonable approximation of reality (Goudie 1995). This
assumption has not been formally evaluated for sea lampreys. We have reason to believe
that sea lampreys imported from other streams will behave the same as residents of that
stream, given the high survival rate of marked sea lampreys even when kept in target
stream water, and given the agencies’ long history of keeping larvae alive in a variety of
waters (Jejfi‘ey Slade, USFWS, Ludington, MI, personal communication). However, this
assumption has not been formally tested, and warrants firrther investigation.

In this study, I have implemented an alternative larval assessment and stream
treatment selection method, observed the results in the form of the set of streams that
would be selected for treatment, and compared the results to those obtained from the use
of the current assessment method. In doing so, I have initiated an adaptive management
experiment that can provide insight into how to improve the balance of resources

allocated to sea lamprey larval assessment and those allocated to control activities. This

47

experiment is not a traditional example of adaptive management, because the
management action of interest in this case is the assessment of a system. However, the
principles of adaptive management still apply (Walters 1986). An alternative
management action (assessment) was implemented on a scale relevant to management
decisions, and the consequences of implementing this action were monitored in the form
of the streams that would be selected for treatment. Monitoring these consequences have
shown that RA is a more efficient use of resources for sea lamprey control than QAS in
that it allows for more streams to be treated resulting in more sea lampreys killed at equal
program costs. The ESTR population estimates alone demonstrate that making stream
treatment decisions based on RA results in just as many, if not more, larvae and
transformers killed than making stream treatment decisions based on QAS results. While
not a complete or perfect picture, the capture-recapture population estimates lend
additional support to this idea. This study will continue for one more year of RA surveys
and two more years of capture-recapture experiments. Following the acquisition of these
additional data, it will be possible to more definitively determine the assessment method
that best serves the goals of sea lamprey management, and the GLFC will be in a better
position to rationalize the assessment program that they employ.

The balance between resources spent to learn more about a system and resources
spent to actually manage that system are applicable to other natural resource situations.
Rapid assessment techniques have also been shown to be effective in other systems
(Jones and Stockwell 1995, Metzeling et al. 2003), and could potentially be applied even
more broadly. Detailed stock assessments of commercial fisheries, evaluation of the

status of an endangered species, and determining the ideal location for reserves and

48

protected areas are a few examples of situations in which a conflict could exist between
resources allocated to learn more about the system and resources allocated to the
management, conservation, or protection of that system. Based on the results found in
this study of sea lamprey management, it is not necessarily always the best strategy to
allocate large amounts of resources to learn more before acting. Further research into the
optimal allocation of limited resources in such situations and the development of
strategies for determining the point at which additional information ceases to be valuable
will lead to better management of natural resource systems. The use of adaptive
management to test new methods of assessment and resource allocation is a means
through which the optimal balance of resource demands can be determined, and should

be applied to other systems.

49

CHAPTER TWO
DOES DEMOGRAPHIC VARIATION IN LARVAL SEA LAMPREYS
DETERMINE THE REGULARITY OF CHEMICAL TREATMENTS IN GREAT
LAKES STREMS?
Introduction
Variation in population abundance is widespread among fish species, and

understanding how growth and recruitment affect fluctuations in population size is a
common and important goal of fisheries science (Rothschild 1986, Houde 1987, Hilbom
and Walters 1992, Myers 2001). Variation in life history parameters has been well
studied, both among species (Pauly 1980, Roff 1984, Winemiller and Rose 1992), and
among populations within species (e. g. Hutchings and Jones 1998, Shuter et al.1998,
Berg and Pedersen 2001 , Purchase et al. 2005). This research indicates that
understanding variation in demographic rates such as growth and recruitment among
populations can be used to improve management policies (Winemiller 2005). For most
fisheries, knowledge of population variation is used to develop better harvest strategies;
however, in the case of an undesirable fish species, accounting for differences in
demographic rates among populations can also be used to aid suppression and allow for
more effective use of resources in controlling that species. Variation in recruitment and
other demographic rates is common among vertebrate pest species, and accounting for
this variation and that of other demographic rates can influence the effectiveness of
control efforts on these and other species (e. g. European rabbits, Oryctolagus cuniculus,
Twigg and Williams 1999; great cormorants, Phalacrocorax carbo sinensis, F rederiksen

et al. 2001, sea lampreys, Petromyzon marinus , Jones et al. 2003; carp, Cyprinus carpio

50

L., Brown et al. 2005; and brushtail possums, Trichosurus velpecula, Ramsey 2005) .
However, as in desired fish populations, oftentimes variation in recruitment that is
essential for successful management is not well understood.

Sea lampreys (Petromyzon marinas) invaded the Great Lakes in the 1920’s, and
their negative impacts on the native fish community have been well documented (i.e.
Smith and Tibbles 1980, Youngs 1980, Heinrich et al. 2003). Adult sea lampreys spawn
in streams, where the non-parasitic larvae live for an average of 3-7 years (Potter 1980),
although they can remain in streams for as many as 18 years (Manion and Smith 1978).
Upon completion of the larval phase, sea lampreys metamorphose and migrate
downstream into large bodies of water, where they parasitize other fishes, often injuring
or killing the host. An early life history study identified stream-dwelling larval sea
lampreys as the life stage most vulnerable to control (Applegate 1950); in particular,
larvae undergoing metamorphosis (called transformers) are the life stage on which
managers were encouraged to focus control efforts to maximize efficiency (Smith and
Tibbles 1980).

Sea lampreys have been the focus of intensive control efforts since the early
1950’s (Smith and Tibbles 1980). The majority of control efforts currently being used
target the non-parasitic, stream-dwelling larval phase of sea lampreys through the
periodic treatment of streams with the lampricide 3-triflouromethyl-4-nitrophenol (TF M).
The application of TF M usually kills from 95-100% of larvae present in the stream at the
time of treatment (Christie et a1. 2003). Because larval sea lampreys remain in their
natal streams for several years before becoming parasitic, it is neither necessary nor cost

effective to treat every stream each year. Rather, treatments should ideally be applied on

51

a cycle that matches the duration of the larval phase in a given stream. However, natural
variability in recruitment, growth rates, and survival within each stream results in
inconsistency in the length of time before streams require treatment to prevent the
escapement of parasitic sea lampreys; therefore, subsets of streams are assessed annually
to determine their need for treatment (Slade et al. 2003).

Assessment of larval, stream-dwelling lamprey populations is conducted to
provide managers with estimates of sea lamprey numbers and size structure within
streams in order to direct stream treatments. Larval assessment is a costly yet uncertain
process, and resources allocated to assessment reduce those available to carry out control
efforts and research new methods of control. Although some level of larval assessment
is certainly needed to direct stream treatments, recent studies have drawn attention to the
uncertainty inherent in the current assessment and stream selection process (Hansen et al.
2003, Steeves et al. 2003). The incorporation of historical data into assessment and
stream selection procedures may provide a means for managers to make effective
treatment decisions with minimal expenses on assessment, thereby freeing up resources
to be used in other ways that could improve the overall effectiveness of the sea lamprey
control program.

For the purposes of these analyses, I considered larval sea lampreys within
different streams to be distinct populations, despite the fact that sea lampreys mix as one
population within the lake environment and do not home to natal streams (Bergstedt and
Seelye 1995). In spite of this mixing during juvenile and adult life stages, sea lampreys
spend the duration of their larval phase in the same stream, and demographic rates such

as growth and incidence of metamorphosis are known to differ among streams (Hansen et

52

al. 2003). Because genetic differences among stream populations are unlikely to exist
due to the absence of homing in sea lampreys, demographic variation among larval
populations is likely to be a consequence of differences among stream environments.
Larval assessment surveys have been conducted since the late 1950’s to estimate
population levels and size structure, direct lampricide treatments to the appropriate
streams, and evaluate treatment effectiveness (Slade et al. 2003). Despite the plethora of
historical data available, these data have not yet been used to examine demographic ’4
patterns in stream-dwelling sea lamprey populations. Ideally, lampricide treatment cycles
should match the cycles of recolonization, growth, and maturation of sea lampreys
following treatment events (hereafter referred to as “lamprey production”) in individual
streams. Most lamprey-producing streams are treated on a 3-5 year cycle, but streams
differ in the regularity with which large populations of transformers develop (Heinrich et
al. 2003, Lavis et al. 2003, Morse et al. 2003). In other words, some streams are highly
regular in their cycles of lamprey production and need for treatment, while others vary
widely. Previous authors have suggested that differences in recruitment, growth, and
survival following lampricide treatments contribute to differences in treatment regularity
(Heinrich et al. 2003, Lavis et al. 2003); however, these assertions have never been
formally tested. Through this research, I will test whether streams with irregular lamprey
production and treatment cycles have more variable recruitment and/or growth rates than
streams with naturally regular cycles of lamprey production. Understanding the
population-level causes of variation in lamprey production could allow for better

prediction of the need for treatment in irregularly producing streams, help to shape a

53

more cost-effective and efficient assessment procedure, and increase general
understanding of sea lamprey ecology.

Researchers and sea lamprey managers together have divided streams considered
for chemical control into four categories based on their regularity of lamprey production
inferred from the historic regularity of chemical treatments and from the expert opinion
of assessment biologists who work on these streams. Category 1 streams are very
predictable in their lamprey production cycle and their treatment schedule. These have
also recently been referred to as “expert judgment” streams, because decisions regarding
their treatment have been based on prior knowledge rather than on assessment data.
Category 2 streams are somewhat variable in their lamprey production cycle and
treatment schedule, but can be somewhat predictable. Category 3 streams are highly
variable in their production of sea lampreys and treatment schedule. Category 4 streams
are streams in which sea lampreys have been found in the past, but do not currently
support sea lamprey populations and are no longer treated.

This categorization was created in part to direct assessment efforts to the streams
that need them most. Category 1 streams are likely to require minimal or no assessment
to effectively predict their need for treatment, and in the future managers could
potentially rely heavily on historical patterns to make treatment decisions for these
streams. Category 3 streams are likely to require the most assessment to determine their
need for treatment. As useful as these categorizations could be to direct assessment
activities, they were created in a subjective manner based on the expertise of sea lamprey
biologists. Before directing assessment resources preferentially to certain stream

categories, a formal evaluation of the demographic basis for differences in variability in

54

 

>7,.__--,¥,

 

sea lamprey populations seems appropriate. The two demographic processes that can be
examined using historical surveys are growth to age-1 and recruitment to age-l as
measured by catch per unit effort (CPUE). I have analyzed data from historical surveys
conducted between 1959 and 2005 to determine whether the stream categorization is
supported empirically as demonstrated by the existence of measurable differences in
growth and/or recruitment among stream categories. In particular, I have looked for
differences in the variability of growth and recruitment rates, as well as differences in the
mean growth and recruitment rates across stream categories.

This analysis of differences in growth and recruitment will i) assess the usefulness
of the stream categorization developed by managers for directing assessment activities, ii)
determine whether growth or recruitment is the more important driver of lamprey
production, and iii) will help to shape an assessment protocol that targets the larval stage
that is most influential in determining lamprey production. For example, if differences
in lamprey production and treatment regularity are driven by differences in larval
recruitment, larval assessment could focus on early life stages, and the detection of a re-
established larval population of a certain threshold size within a stream could serve as the
main treatment selection criterion. Alternatively, if differences in growth rates are
associated with treatment regularity, treatment schedules based on recruitment will be
less effective and larval assessment would more likely focus on later life stages. Further,
if sea lampreys from different stream categories differ in these vital demographic rates,
an understanding of these differences can allow for a more cost-effective and efficient
assessment procedure by preferentially directing assessment resources to stream types

exhibiting higher levels of variation and higher uncertainty in their need for treatment.

55

flw‘

Finally, this type of analysis could serve as a precursor to the use of a more formal
Bayesian approach to selecting streams for treatment, in which managers could calculate
an expected larval population based on prior surveys and patterns to be used in
combination with current assessment data.
Methods
Historical Survey Data

Over 30,000 larval sea lamprey assessment surveys were conducted between 1959
and 2005 by the United States Fish and Wildlife Service (U SFWS) and the Department of
Fisheries and Oceans, Canada (DFO). I obtained the results of subsets of these surveys
determined by the timing criteria described below, and analyzed them separately for
larval growth and recruitment. Several types of larval assessment surveys exist (i.e.
index surveys, Quantitative Assessment Surveys, biocollection surveys), and all types
were initially obtained from the USFWS and DFO. Only age-1 individuals were used for
these analyses because it was difficult to distinguish reliably between older age-classes of
larval sea lampreys based on length-frequency histograms; however, generally the first
two age classes are more clearly separable (Potter 1980). To increase the likelihood of
only age-1 and younger larvae being present in an assessment collection, only surveys
that followed fall lampricide treatments were used in these analyses, since treatments that
occur in the fall are more consistent than spring or summer treatments in their elimination
of that year’s recruits (D. C uddy, Department of Fisheries and Oceans, Sault Ste. Marie,
Ontario, personal communication). Surveys that took place two years after fall
treatments were selected for analysis because the first opportunity for a year class to re-

establish after a fall treatment is in the spring of the year following treatment, and two

56

years after the treatment that year class would be age-1. At the time of these surveys, the
streams should have contained a maximum of two year classes (age-0 and age-1).
However, streams might have also contained residual sea lampreys that survived the
lampricide treatment. I examined length-frequency histograms for each stream and year
to determine which individuals were age-l and should be included for further analysis.

Streams with two or more years of survey data that fit the timing criteria were included in

i,

this analysis. No surveys from Lake Erie were included in any analyses due to the

F'fnl twp-.94? lf-LEI'I‘I

paucity of data from Lake Erie streamsz.

in

Recruitment Analysis

Recruitment was analyzed using a relative measurement of catch per unit effort
(CPUE). To standardize for effort, I only used index surveys to calculate CPUE,
resulting in a total of 900 surveys collected in 305 stream-years for this analysis. Index
surveys have been conducted at the same access points for many years with a relatively
consistent level of sampling effort. The CPUE value used as an index of recruitment for
each stream-year was calculated using the total number of age-1 sea lampreys caught in
all the surveys in a given stream-year divided by the total time (in hours) spent
electrofishing to collect them (meter time). Some surveys reported effort as “collecting
time”, which is a measure of total time spent at a site rather than time spent
electrofishing. These measures of collecting time were converted to meter time using a
conversion factor of 1.595 units of collecting time for every 1.0 unit of meter time,

developed by USFWS-Marquette sea lamprey control (M. Fodale, USFWS, Marquette,

 

2 Chemical treatments have only been used in Lake Erie tributaries since 1986, and only two Lake Erie
streams had more than one year of data that fit the timing criteria required for this analysis. This paucity of
data made the establishment of patterns in variation of population level processes among stream categories
impossible.

57

MI, unpublished data). Summary statistics of the data used for the recruitment analysis

are shown in Table 7.

Table 7. Summary of data used for recruitment analysis. For each
category, the number of stream-years of data, the % of occasions
in which zero recruitment was observed, and the mean and standard
deviations of the non-zero catch per unit effort values are shown.

 

 

CPUE (catch/hr)

Catggrry N % zero recruitment mean" SD*
1 158 10.13 50.7 61.7

2 43 16.28 35.1 38.6

3 76 14.47 30 40.8

4 28 57.14 10.5 9.7

 

* = mean and SD are calculated for only non-zero CPUE values.

The recruitment analysis was conducted as a two-step process using the delta
approach (Maunder and Punt 2004). First, differences among stream categories in the
probability of occurrence of an age-1 year class in the second year following a chemical
treatment were analyzed using a binary response variable indicating whether any age-1
sea lampreys were caught in the surveys (yes = 1; no = 0). Then, non-zero CPUE values
were examined for differences in mean CPUE as well as variation in CPUE among
stream categories.

Probability of Successful Recruitment

The objective of this analysis was to determine if differences exist among stream
categories in the establishment of a cohort following the chemical treatment of a stream.
Streams with no age-1 sea lampreys collected two years following a fall treatment were
assumed to have no recruitment, and recruitment was assumed to have occurred in
streams with one or more age-1 sea lampreys collected. Recruitment events (no

recruitment = 0, recruitment event=1) were modeled using generalized linear mixed

58

effects models with a binary response variable and a logit link function (Schall 1991). In
addition to stream category, the lake into which a stream flows was included as a
potential fixed effect in the model. For this analysis, fixed effects were selected prior to
random effects due to the inability of the model to converge with all fixed effects and
random effects included. After the fixed effects structure was determined, the
significance of stream and year as non-nested random effects was evaluated. After the
model that best explained the data was selected, probability of successful recruitment and
95% confidence intervals were calculated from the parameter estimates using the logit
link function (Faraway 2006).
flab/sis of Non-Zero Recruitment
Analysis of mean CPUE

The objective of this analysis was to determine if significant differences existed in
mean CPUE among stream categories. All CPUE values > zero were modeled using
linear mixed effects models. Due to non-normality of error terms, CPUE was
transformed prior to analysis. The data were heavily skewed, and error terms remained
non-nonnally distributed after using either a square root or cubed root transformation;
therefore, data were transformed using a quarter-root transformation, resulting in
normally distributed residuals. To account for non-independence in recruitment data,
stream and year were tested as potential non-nested random effects. Stream category and
lake were included as potential fixed effects. The full model against which other models

were tested was:
yjklmnzflo+fl1j+fl2k+b1+bm+ejk1mm (9)

j=11mr4ik=1w~a5§l=1,.--,95;m=1,...,44,n=1,...,255;

59

b1 ~ N(0,0'12 ), bm ~ N(0,a§' ), 5ijklm ~ N(0,0'2),
where y jklmn is the quarter-root transformed CPUE from stream year n, ,80 is the
overall mean CPUE or intercept, ,61 j is the fixed effect of stream category j, ’32 k is the

fixed effect of lake k, b, is the random stream effect, 1; m is the random year effect, and

gjklmn is the unexplained residual error. All random effects and error terms were

assumed to be normally distributed with a mean of zero and a variance estimated by the
model.
Analysis of variation in CPUE

The objective of this analysis was to determine if stream categories differed
significantly in recruitment variation. After selecting the best model to describe mean
CPUE (above), differences in variation of CPUE among categories were tested by
modeling standard deviation ratios of the within group errors using variance covariates
(Pinheiro and Bates 2000). The same fixed and random effects selected in the analysis of
mean CPUE described above were used in this model. The error structure in the variance

components model was represented by:

2 2
e n ~N(0,a 5]. ), (10)

jklm

where j =1,. . .,4. e j is the residual error for each sample from stream category j, and 6 j

is the variance component estimated for stream category j. In order to achieve
identifiably of all parameters, restrictions must be placed on 5 . The variance component

of the first category was held constant at one (61 = l ), and the estimates of the other

60

variance components represent the ratio between their standard deviations and the
standard deviation of the first stratum (Pinheiro and Bates 2000).

Categories were determined to have significantly different levels of variation in
CPUE if the model allowing different levels of variance modeled for each category was a
significantly better fit to the data than the model with a constant level of variance for all '

stream categories. The relative fit of the two models to the data was assessed using a

L!

likelihood ratio test (0r=0.05).

Growth Analysis

”I? 'r-r‘rr rr' '“ T1

A total of 2405 larval assessment surveys that collected 60,281 age-1 larvae were
chosen that took place two years following fall treatments. All types of larval assessment
surveys were used for the growth analysis, resulting in more surveys available for
analysis than in the recruitment analyses. The streams and individual sea lampreys
included in this analysis are summarized in Table 8. The preponderance of Category 1
streams in the dataset was due to the higher number of surveys that fit the timing criteria
on these types of streams that are by definition treated more regularly than other

categories of streams.

Table 8. Summary of data used for growth analysis. The number of
streams falling in each category, number of individual sea lampreys collected
from each category, the mean length, standard deviation of length, and

mean DOY on which a survey was taken are shown.

 

Length (mm)
Categogt N streams N individuals Mean SD mean DOY
1 57 46310 44.52 12.08 216.75
2 21 5158 44.50 13.88 208.16
3 30 6455 42.80 12.07 216.80
4 8 2226 50.96 10.01 223.21

 

61

An_alysis of mean length at age-1

The aim of this statistical analysis was to determine if significant differences
existed in mean length at age-l among stream categories. I evaluated differences in mean
length using linear mixed effects models. Length was log transformed to correct for non-
norrnality and heteroscedasticity of residuals. When reporting results, estimates of back-
transforrned mean effect sizes were bias corrected (Beauchamp and Olsen 1973). The
assessment surveys used for this analysis were conducted between May 1St and October
31". The Julian day on which a survey was conducted (day of year, DOY) was included
as a continuous fixed effect in all models to correct for differences in length due to
different collection dates. DOY was centered around the mean survey DOY
(mean=216.3, N=60,281, sd=45.5) to avoid correlation among estimates of random
slopes and intercepts (Pinheiro and Bates 2000). Category was included as a potential
fixed effect in the model to test for differences among stream categories in mean length at
age-1. The lake into which a streams flows was also included as a potential fixed effect.
Initially, all possible interactions among fixed effects were also included as fixed effects.
However, the inclusion of category by lake and DOY by lake interactions caused models
to not converge. Therefore, these interactions were not considered as potential fixed
effects in model selection.

Multiple streams from each category were sampled, and within streams there are
often many subsections (reaches). Each stream had at least two years of survey data, and
in most cases more than one survey was conducted on a given reach in a given year.
Multiple individuals were collected from each survey. Because of the hierarchical nature

of the data, nested random effects were included in the model to account for the structure

62

of the data and to correct for the lack of independence among individuals from the same
stream, reach, year, and survey.

The full model against which other models were tested is Shown below. The
stream, reach, year, and survey ID in which a sample was collected were tested as
potential random effects and all were nested within the next highest level. Random

slopes (representing the effect on the relationship between length and DOY) and random

Sat
0 O I 3“
Intercepts were estimated for stream, year, and reach, and random Intercepts were

t.

g,
estimated for survey ID. The full model is represented by the equation: it

yijklmno = 40 + (fir + flzn +5131 +bjk.1 ”111,011+ 43.1 + 440 +bj,2+bjk,2
+bjkl,2+bjklm+€ijklmnor (11)

2 2 2
b 131 ~ 100,012), b jg ~ N(0,0'2), b M ~ N(0,a3 ), b 171.2 ~ N(0,a4),

2 2 2
b jkl,1 ~ N(0,0'32 ), b jkl,2 ~ N(0,0'6 ), b jklm ~ N(0,0'7 ), Eijklmno ~ N(0,0' ),
where yijklmno is the log-transformed length of individual sea lampreyi

(i=1,. . .,60281); ,60 is the overall mean length or intercept; 61 is the fixed day of year

effect for the day of year x,- for individual i, centered around the mean day of year;
62,, is the fixed interaction effect of category n (n=1,. . .,4) by day of year x; ,6 3n is the

fixed effect of category ,8 4 0 is the fixed effect of lake 0 (o=1,. . .,4); b j is the random
effect of stream j (f=1,...,118), where b j,1 is the random Slope and b 132 is the random
intercept; bjk is the random effect of year k within stream j (k=l,...,NJ-), where b jk,1 is

the random slope and b jk,2 is the random intercept; bjkl is the random effect of reach I

63

within year k and stream j (l=1 ,. . .,Njk), where b jkl,1 is the random slope and b jkl,2 is
the random intercept; bjklm is the random effect of survey m nested within reach 1, year
k, and stream j (m=1,.. .,NJ-kl); and g ijklmno is the unexplained residual error. All

random effects and error terms were assumed to be normally distributed with a mean of
zero and a variance estimated by the model.

Analysis of variation in length at age-1

The aim of this statistical analysis was to test for different levels of variation in

fig.-_-_-:-__-:-: efi

mean length at age-1 among stream categories and among lakes. Preliminary analysis
showed that the relationship between stream category and variance in growth differed
among lakes. In order to test for differences in variation, different residual variances
were estimated for each level of a stratification variable (Pinheiro and Bates 2000). To
determine if the within group variance in length at age-1 differed significantly among
lakes, variance components 6 were estimated for each lake using stream and reach as
random effects. To determine if within group variance in length at age-1 also differed
among stream categories within lakes, variance covariates were then estimated for each
category and lake combination, again including stream and reach as random effects. The

error structure of these models is represented by:

2 2
gijklmno N(0,0' 6 p ), (12)

where p =1 ,. . .,N; and 61 = 1. 8.. is the residual error for each individual sea
yklmno

lamprey i from strata p, p is the stratification variable in which an individual was

collected, either the lake or the stream category and lake combination, and 6p is the

64

variance component estimate for variable p. The residual variance for each category and

lake combination was calculated by multiplying the variance parameter estimate (62 ) by
the residual variance of the model.

I tested the significance of the separate variance components by testing the
models with separate variance components against the simpler models using likelihood
ratio tests. If likelihood ratio tests were significant, indicating a better model fit when
separate variance components were estimated for different strata, I used 95% confidence
intervals on the estimates of variance components for each stratum to determine which
strata differed from one another in their variance component estimates. For these
variance models, the same fixed effects selected in the analysis of mean growth from
equation 11 were used, random slopes and intercepts were estimated for stream, and
random intercepts were estimated for reach.

The variance component analysis that included stream and reach as random
effects determined whether or not lakes, and categories within lakes, differed in their
residual variances, composed of both within- and among-year variance. Both types of
variance are important to sea lamprey managers, although the among-year variance is of
most interest for this analysis. To determine the relative contribution of within- and
among-year variance to the overall differences in variance observed among strata, an
additional model was created that estimated random slopes and intercepts for each year in
addition to the random effects estimated for stream and reach. Variance components
were again estimated for each category and lake combination. Because of the inclusion

of year as a random effect, the variance components estimated in this model encompassed

within-year variance only. The 62 estimated for each stratification factor was

65

multiplied by the residual variance of the model to estimate the within year variance for
each category and lake combination, and compared to the estimate of the total residual
variance obtained from the model in which only stream and reach were included as
random effects.
Model Selection

The significance of random and fixed terms were evaluated using Akaike’s

Information Criterion (AIC), and effects were considered significant if their inclusion

‘T..W"w
IA

resulted in a decrease in AIC value of 32 (Bumham and Anderson 1998). Random

r1171“? 1‘7. ' ‘

effects were modeled with all possible fixed effects included except when otherwise
noted. Significance of individual random effects were evaluated using AIC values for
individual models using the restricted maximum likelihood (REML) method of
estimation of model fit (Pinheiro and Bates 2000). After determining the appropriate
random effects structure for each model, significance of individual fixed effects were
determined by sequentially removing fixed effects from the model and comparing AIC
values. All tests for fixed effects were performed using the maximum likelihood (ML)
method of estimation of model fit (Pinheiro and Bates 2000). Diagnostics of all selected
models were examined to ensure no assumptions were violated. All modeling and
statistical analyses were performed using R V.2.1.1 (R Core Development Team 2005).
M2113
Recruitment Analysis
Probability of Successful Recruitment

The probability of a successful recruitment event was best explained by a model

including only category as a fixed effect (Table 9). Including stream as a random effect

66

did not improve model fit, and including year as a random effect only marginally
improved model fit (AAIC=1.1), so neither random effect was included in the final model
(Table 10). Models with both year and stream as random effects could not be fit to the
data due to insufficient sample number. Category 4 streams were half as likely to have
successful recruitment events as any other type of stream, and categories 1-3 did not

differ in their probability of a successful recruitment event (Table l 1, Figure 9).

Table 9. Candidate models with different fixed effects in the binary model of
Recruitment success of age-1 sea lampreys. Fixed effects are shown with ‘

 

 

the estimated number of parameters (K), their AIC values, 52‘
and the difference between the AIC value of a given model and that of the best
model (AAIC).
Model Fixed Effects K AIC A AIC

1 Category 5 250.90 0

2 Category+Lake 8 253.43 2.53

3 (Intercept) 2 274. 14 23 .24

4 Lake 4 275.95 25.05

 

Table 10. Random effect selection for the binary model of
recruitment success of age-1 sea lampreys. The number of estimated
parameters (K), AIC value, and the difference between the AIC value
of a model and that of the best model (AAIC) are shown. Random
effects were modeled with a fixed category effect also included.

 

Model Random effect K AIC A AIC
1 Year 5 249.8 NA
2 None 4 250.9 1 .1
3 Stream 5 254.9 5. 1

 

Table 11. Fixed effects estimates, standard error, z-value, and p-values for the
binary model of recruitment success of age-1 sea lampreys. The expected
probability of successful recruitment for each category is also shown. In this
model, the intercept refers to category 1, and the error DF=301.

 

 

Effect Estimate SE 2 p—value Category J(success)
Intercept 2.180 0.264 8.28 <.001 1 0.899
Category 2 -0.546 0.49 -1 .1 1 0.266 2 0.837
Category 3 -0.407 0.419 -0.97 0.332 3 0.855
Categpg/ 4 -O.247 0.464 -0.532 <.001 4 0.429

 

67

1.0 -
I l I

0.8 -

0.6 ~

0.4 4

0.2 «

Probability of successful
recruitment

 

0.0 l I l
0 1 2 3 4

Category

 

..J

Figure 9. Probability of successful recruitment and 95% confidence intervals for each
stream category as predicted by binomial model.
An_alysis of Non-Zero Recruitment
Mean CPUE

The mean CPUE of a stream was influenced by the stream category and the lake
into which a stream flows. The model that best explained mean CPUE included no
random effects (Table 12) and category and lake as fixed effects (Table 13). Category 1
streams had the highest level of mean recruitment of any stream category, and Lake
Ontario streams had the highest mean recruitment of any lake (Table 14). When held
constant for lake, the mean recruitment level in category 1 streams was almost twice as
large as that in category 3 streams, and nearly 5 times as high as that in category 4
streams (Figure 10). When held constant for category, the mean recruitment in Lake
Ontario streams was more than twice that of streams in any other lake (Figure 11). While
this model explained significant differences in mean recruitment, it did not explain the

majority of recruitment variation (Multiple R2=0.13).

68

Table 12. Random effect selection for the model of mean
recruitment (CPUEA1/4)of age-1 sea lampreys. The number of
estimated parameters (K), AIC value, and the difference
between the AIC value of a model and that of the best model
(AAIC) are shown. Random effects were modeled with fixed
effects of category and lake also included.

 

 

Model Random Effects K AIC A AIC
1 None 9 499.3 0
2 Stream+Year 1 1 508.4 9.1
3 Stream 10 511.2 11.9
4 Year 10 516.6 17 .3

 

Table 13. Candidate models with different fixed effects for the model of
mean recruitment (CPUEAI/4) of age-1 sea lampreys. The number of
estimated parameters (K), AIC value, and the difference between the
AIC value of a model and that of the best model (AAIC) are shown.

 

Model Fixed effects K AIC A AIC
1 Category+Lake 8 497.72 0
2 Category 5 500.54 2.82
3 Lake 5 518.74 21.02
4 intercept) 1 520.74 23.02

 

Table 14. Fixed effects estimates, standard errors, t-values,
and p-values for each parameter included in the model of mean
recruitment (CPUEA1/4) of age-1 sea lampreys. In this model,
the intercept accounts for the effects of both category 1 and
Lake Superior, and the error DF=248.

 

 

Parameter Estimate St. Error t value p value
Intercept 2.410 0.069 35.07 <0.001
Category 2 -0.283 0.121 -2.35 0.020
Category 3 -0.380 0.095 -3.99 <0.001
Category 4 -0.732 0.191 -3.83 <0.001
Lake Michigan 0026 0.093 -0.28 0.780
Lake Huron 0.049 0.111 0.44 0.661
Lake Ontario 0.599 0.210 2.84 0.005

 

69

CPUE

20.
15-

0 I I l l
0 1 2 3 4

Category

 

 

Figure 10. Mean CPUE (catch per hour) and 95% confidence intervals for each stream
category as predicted by the linear model when holding lake constant (values shown are
for Lake Superior streams).

 

 

 

160]
E 140 T
E 120‘“
43 100‘ 4
9. 80~
ur -
'2 60 i
o 401 i i i

20—

O l l T l 1

Superior Michigan Huron Ontario
Lake

Figure 11. Mean CPUE and 95% confidence intervals for each lake as predicted by the
linear model when holding category constant (values shown are for category 1 streams).

Variation in CPUE
Stream categories did not differ significantly in their variation in CPUE; the
model allowing for different levels of variation for each category did not have greater

support than the model with constant variance (Likelihood ratio=3.3, DF=3, p=0.35).

70

Growth Analysis
Analysis of mean length gt age-1
Mean length at age-1 was best explained by a model including stream, year,

reach, and survey ID as random effects (Table 15). Random slopes and intercepts were
estimated for stream and year, and random intercepts were estimated for reach and survey
ID. DOY and lake were included in the model as fixed effects (Table 16). The standard
deviation of log(length) at age-1 explained by each random effect is shown in Table 17.

Table 15. Random effects for the model of log(length )of age-1 sea lampreys.

Random effects were estimated for the slope (S) and intercept (I) of each level

except survey ID, which only occurred on one day of year (DOY). Candidate

models are shown along with the number of estimated parameters (K), AIC value,

and the difference between the AIC value of a model and that of the best model

(AAIC) are shown. Random effects were modeled with all possible fixed effects
also included (DOY, Category, Lake, and a Category*Lake interaction).

 

Model Random effects K AIC AAIC
Stream(l)+Stream(S)+Year(I)+Year(S)+Reach(l)

1 +ID(1) 17 -79316.0 0.0
Stream(l)+Stream(S)+Year(l)+Year(S)+Reach(S)+

2 Reach(l)+lD(l) 18 -793 1 3 .0 3.0
Stream(l)+Stream(S)+Year(1)+Year(S)+Reach(l)+

3 Reach(S) 17 -72875.3 6440.7

4 Stream(l)+Stream(S)+Year(1)+Year(S)+Reach(l) 16 -72592.2 6723.7

5 Stream(l)+Stream(S)+Year(I)+Year(S) 15 -68616.5 10699.5

6 Stream(1)+Stream(S)+Year(I) 14 -63624.3 15691.7

7 Stream(l)+Stream(S) 13 -47047.5 32268.5

8 Stream(l) 12 -39910.6 39405.4

9 None 11 -7883.1 71432.9

 

Table 16. Candidate models with different fixed effects for the model of log(length)
at age-l, the number of parameters estimated (K), their AIC values and the difference
between each model's AIC and that of the best fit model (AAIC). All fixed effects
were modeled with random effects included. Random intercepts for stream, year,
reach, and ID, and random slopes for stream and year were included in each model.

 

Model Fixed Effects K AIC AAIC
1 DOY+Lake 1 1 -79412.6 0
2 DOY+Category+Lake 14 -79408. 1 4.5
3 DOY 8 -79403.7 8.9
4 DOY+Category+Lake+Category*DOY 1 7 -79403 .4 9.3
5 DOY+Category l 1 -79401 .1 l 1.5
6 DOY+CategorL+Category*DOY 14 -793 96.2 1 6.4

 

71

Table 17. Standard deviation estimates and 95%

confidence intervals for all random effects included in
the final model of log(length) at age-1. Fixed effects of
DOY and Lake were also included in this model.

 

 

Random effects 95% CI
Level Term SD lower upper
Stream Intercept 0.1414 0.1164 0.1717
Slope 0.0009 0.0006 0.0015
Year Intercept 0. 1 160 0.0998 0. 1340
Slope 0.0015 0.0012 0.0019
Reach Intercept 0.0730 0.0623 0.0857
ID Intercept 0.0787 0.0749 0.0826
Residual 0.1190 0.1183 0.1197

 

Sea lampreys from Lake Ontario were on average 30% larger than those from
Lake Superior (Table 18, Figure 12). Sea lampreys from Lakes Michigan and Huron did
not differ significantly in their mean length at age-1 from Lake Superior sea lampreys

(Table 18, Figure 12). The day that a survey was conducted positively influenced mean

length at age-1 (Table 18).

Table 18. Fixed effects estimates, standard errors, residual degrees of
freedom, t-values, and p-values for each parameter in the model of log
(length) at age-1 of sea lampreys. In this model, the intercept accounts
for the effect of Lake Superior. Random intercepts for stream, year,
reach, and survey, and random slopes for stream and year were also

estimated in this model.

 

 

Parameter Estimate St. Error DF
Intercept 3.740 0.0229 57743
DOY-216.3 0.004 0.0002 57 743
Lake Michigan 0.004 0.0368 112
Lake Huron 0.042 0.0427 112
Lake Ontario 0.260 0.0676 1 12

 

72

65-
604
554

ham
0010
111
PH
l-O-l
H—l

Length at age-1 (mm)

354

 

(a)
O

 

Superior Michigan Huron Ontario
Lake

Figure 12. Mean length at age-1 (bias-corrected) and 95% confidence intervals for each
lake as predicted by the mixed effects growth model.
Analysis of variation in length at age-1

Length at age-1 was better explained by the model with separate variance
components for each lake than the model with no variance covariates (Likelihood
ratio=65.5, df=3, p<0.001). Likewise, modeling separate variance components for
category/lake combinations better explained variation in length at age-l than modeling
variance components for lake only (Likelihood ratio=487.8, df=10, p<0.001), indicating
that variation in length at age-l differed significantly among lakes and among categories
within lakes. Sea lampreys from Lake Huron and Lake Ontario were 94% and 90% as
variable in length at age-1 (on the log scale) as sea lampreys from Lake Superior,
respectively (Figure 13). Sea lampreys from Lake Michigan did not differ significantly

from those from Lake Superior in their variability in length at age-1. The relative

variability in length at age-1 observed in sea lampreys from different stream categories

73

differed among lakes, and all but one lake exhibited Significant differences in variability
of length at age-1 among categories. In Lake Superior, sea lampreys from category 3
exhibited higher levels of variability in mean length at age-1 than sea lampreys from
other types of streams (Figure 14a). The majority of this variation was due to within year
variance, although among-year variance was also highest in category 3 streams (Figure
15a). In Lake Michigan and Lake Ontario, sea lampreys from category 1 streams were
significantly more variable in length at age-1 than individuals from any other stream
category (Figures 14b and 14c). In these two lakes, category 1 sea lampreys had the
highest levels of both within- and among-year variance in length at age-l (Figures 15b
and 15c). Lake Huron sea lampreys showed no evidence of differences in overall
variation in length at age-1 among stream categories (Figure 14d), although sea lampreys
from category 3 streams did have higher among-year variance than any other category of

streams in Lake Huron (Figure 15d).

 

 

1.10 -
E 1.05 -
d9
3
a 100 — 0 i
g 095 I
o .
3 0.90 ~ {
C
.2
a 0.85 4
>
0.80 r , , j 1
Superior Michigan Huron Ontario
Lake

Figure 13. Estimates of relative variation and 95% confidence intervals for each lake
except Erie. To estimate variance components, the variance component for Lake
Superior was held constant at 1, and the relative variance components for the other lakes
are estimated.

74

1.05- a)

0.951 I

0.85“ I
0.75 I
0.65 -

 

0.55 I T

 

.4
.J

1.05. b)
0.95.
0.85 . i
0.75 .
0.55 1 I I f

0.55

 

 

1.05 r C)

0.95 4

0.85 « f

0.75 -

I I

0.55 LL“ , _ _- ._ _#e__. ,

Variance Component
N
U
s.

 

1.05 «
0.95 l

0.85 4 f I i

0.75 ~
0.65 .

 

055 I f I fl

 

Category

Figure 14. Estimates of variance components and 95% confidence intervals for different
stream categories in a) Lake Superior, b) Lake Michigan, c) Lake Ontario, and (1) Lake
Huron. The variance component of category 3 in Lake Superior was held constant at 1,
and others estimated relative to it.

75

3.5 E-02 . a)
30502 ~
2.55-02 -
205-02 1
1.5E-02 -
1.0E-02 -
5.0E-03 ~
0.0E+00

 

 

 

3.5 E-02 ‘ b)
305-02 1
255-02 “
2.05-02 ‘
1.55-02 ‘
1.05-02 1
505-03 4
0.0E+00

 

 

3.5 E-02 '
3.0E-02 ‘
2.5E-02 4
2.0E-02 ‘
1.55-02 ‘
1.0E-02 ‘
5.0E-03 T
0.0E+00

Variance

 

 

3.5 E-02 -
3.0E-02 .
2.5E-02 ~
2.0E-02 ~
1.5E-02 -
1.0E-02 «
5.0E-03 .
0.0E+00 -

 

 

Figure 15. Residual variance attributable to within-year variance (light grey bars) and
among-year variance (dark grey bars) for each stream category in a) Lake Superior, b)
Lake Michigan, 0) Lake Ontario, and d) Lake Huron.

76

Discussion

Variation in recruitment can influence the success or failure of management strategies,
whether the management goal is to sustain a population or to suppress it. In the case of
sea lampreys, variation in recruitment can determine the effectiveness of alternative
control techniques (Jones et al. 2003). Additionally, I have demonstrated that differences
in recruitment to age-1 influence the regularity of lamprey production and the need for
chemical treatments by showing that streams with highly regular treatment cycles
(category 1 streams) also tend to have higher levels of recruitment. The regularity of
stream treatments appears to also be associated with the regularity of larval growth in
Lake Superior streams, although not in other lakes. Overall, successful recruitment
above a certain threshold level is more important than early larval growth in determining
the regularity of lamprey production.

Category 4 streams (those that in the past have required treatment, but no longer
support sea lamprey populations) were more likely to have no recruitment following a
lampricide treatment than any other category of stream. Category 4 streams also had the
lowest mean recruitment of any type of stream. This propensity for failed recruitment
years could explain why these streams no longer need to be treated. Sea lamprey
numbers throughout the Great Lakes have been reduced dramatically in the past 45 years
(Smith and Tibbles 1980, Larson et al. 2003, Sullivan et al. 2003), and streams with
lower average densities of age-1 larvae and higher probability of failed recruitment than
other types of streams could likely no longer support viable sea lamprey populations once
the lake-wide density of sea lampreys was reduced past a certain point. It is possible that

these category 4 streams have some common environmental characteristics that make

77

 

them less hospitable to sea lamprey larvae (i.e. habitat types, temperature regimes, Young
et al. 1990), and only under high density conditions are these types of streams used by sea
lampreys. Further research into the environmental characteristics of these types of
streams and what makes them relatively inhospitable to sea lampreys could be useful in
developing strategies to eliminate sea lamprey populations from other types of rivers.
Categories 1-3 did not differ in their probability of successful recruitment; these
types of streams had approximately an 85% chance of successful recruitment of an age-1
year class following the chemical treatment of the stream. However, stream categories
did differ in their mean recruitment as measured by CPUE. The mean CPUE in category
1 streams was almost twice as large as that in category 3 streams, and almost 5 times as
large as that in category 4 streams. However, much variation in CPUE remained
unexplained even by the best model, indicating that even within stream categories
recruitment varies widely. This variation could be due to actual variation in sea lamprey
recruitment; sea lamprey recruitment can vary up to three orders of magnitude even with
a constant number of spawning females (Jones et al. 2003). The high levels of
unexplained variation could also be due to the imprecision of CPUE as an index of
recruitment. Although CPUE provides a rather imprecise index of recruitment and
provides little information regarding the actual size of the age-1 year class, it is useful for
comparative purposes, and CPUE has been widely used as an index of population size in
fisheries (N ey 1993). The identification of a clear pattern in recruitment among stream
categories in spite of the high levels of variation that would tend to obscure any patterns,
due to both natural fluctuations and the imprecise metric used to measure recruitment,

indicates that differences in recruitment among stream categories are indeed quite

78

pronounced. Observed differences in recruitment to age-1 could potentially be used in
management to make stream treatment decisions.

The association of the regularity of lamprey production with my index of
recruitment suggests that variations in the size of a year class at age-1 persists in
subsequent years, a pattern that has been demonstrated in other fish populations (e. g.
Helle et al. 2000, Smith et al. 2005). Other researchers have emphasized the utility of
sampling juvenile fishes in an attempt to index year-class strength of a cohort before they
reach the age of management interest due to the importance of the larval stage in the
determination of year-class strength (Rijnsdorp et al. 1985, Uphoff 1989, Sammons and
Bettoli 1998). In the case of sea lampreys, the correlation between age-1 year-class
strength and the regularity of chemical treatment indicates that larval assessment could be
conducted several years before a stream might need to be treated, and the relative
abundance of young larvae could serve as an indicator of the future transformer
abundance on which managers could base treatment decisions.

The variability in CPUE of age-1 sea lampreys did not differ among stream
categories. The most regularly treated streams (category 1) did not have more consistent
recruitment, but they did have higher mean recruitment. A threshold cohort size may be
necessary for a year class to persist in sufficient numbers to warrant treatment as the
cohort approaches metamorphosis. Below this threshold size normal variations in cohort
survival and grth may result in an inconsistent need for treatment. Category 2-4
streams may achieve this threshold level of recruitment less consistently than Category 1
streams. The strong pattern observed of higher CPUE in regularly treated streams could

allow for the identification of this threshold CPUE value to be used for management

79

purposes. If such a threshold could be identified, streams could be surveyed one or two
years following treatment to quantify recruitment to age-1, and if the threshold catch rate
was observed, managers would schedule the stream for treatment some number of years
later. The number of years between survey and treatment would be determined by the
historical growth and metamorphosis cycle of the stream. Identification of this threshold
CPUE value will require an analysis of the observed CPUE of age-l larvae vs. selection
for treatment in subsequent years, and should be the subject of future investigation.

Sea lampreys from different stream categories did not differ in their mean length
at age-1, although sea lampreys from the Lake Ontario were significantly larger at age-1
than those from the upper lakes (Superior, Michigan, and Huron). Lower lakes sea
lampreys are known to achieve larger sizes more quickly than upper lakes sea lampreys
(Potter 1980, Hansen et al. 2003, Slade et al. 2003), so the existence of larger sea
lampreys in Lake Ontario was not surprising. I used mean size at age-1 as a surrogate for
early larval growth, under the assumption that larger individuals must have grown faster
in order to achieve that larger size. This assumption may not be correct, as larvae could
hatch out at larger sizes or experience longer growing seasons in certain types of streams
or in tributaries to certain lakes, allowing them to achieve larger sizes despite equivalent
or even slower grth rates. Within-year growth of age-1 larvae was measured in my
analysis through the relationship between the Julian day of sampling and the mean length
of the larvae collected; however, this measure of growth was fairly crude, as collections
from different streams and years were combined, and the range of dates sampled within a
given stream and year were ofien too small to reliably predict growth rates. I found no

significant interaction between stream category and the day of sampling, indicating that,

80

at least with this crude measure of growth, within-year growth did not differ among
stream categories. Within-year growth did differ among streams and years, as indicated
by the random effects of stream and year on the relationship between day of sampling
and length (random slope), as would be expected as a result of different growing
conditions.

The relationship between variability in length at age-1 and stream category
differed among lakes. In Lake Superior, sea lampreys from category 3 streams exhibited
the most variability in length at age-l. In other lakes, either no relationship existed
between category and variability in length at age-1 (Lake Huron), or sea lampreys from
category 1 streams were the most variable in length at age-1 (Lakes Michigan and
Ontario). Lake Superior streams have been treated for the longest time period of any lake
(Heinrich et al. 2003), and Lake Superior contains more streams included in this analysis
than any other lake. It is possible that streams from other lakes will exhibit similar
grth patterns given more treatment cycles or the inclusion of more streams that fit the
timing criteria required for this analysis. Alternatively, it is possible that because of their
longer treatment history, Lake Superior streams exhibit more clear distinctions in
treatment regularity and lamprey production, lending them more readily to a useful
categorization.

In all lakes and all categories, the majority of variation in growth was a result of
within-year variation. Larvae of the same age in the same stream at the same time show
considerable variation in length, indicating the need for large sample sizes when
conducting assessment surveys if a precise estimate of the size-structure of the stream

population is desired. Despite accounting for the majority of residual variation, the

81

relative contribution of within-year variation to overall variation was fairly consistent

among categories within a lake. Most of the differences among categories in variation in

length at age-1 resulted from differences in variation among years. Variation among

years in length at age-1 was highest in category 3 streams in Lakes Superior, but highest

in category 1 streams in Lakes Michigan and Ontario, indicating no consistent growth

pattern within stream categories across all lakes. Therefore, the stream categorization

framework is not supported by growth differences in any lake except Superior. Again, 1"“
Lake Superior streams could be easier to categorize due to their longer treatment history.
Alternatively, growth differences could be less important than recruitment differences in
determining treatment regularity in the Great Lakes other than Lake Superior.

The stream categorization system developed by sea lamprey managers is
consistent with demographic patterns in recruitment, and could be useful for directing
assessment needs. The relationship between categories and grth varies by lake, and
may not be consistent enough to be useful for assessment purposes. My results suggest
that growth to age-1 of sea lampreys in category 3 streams are more variable in Lake
Superior, which implies a greater need for assessment to focus on later life stages in these
streams. Of more use for sea lamprey managers is the observation that category 1
streams have higher levels of recruitment across all lakes. Category 1 streams could
likely be selected for treatment with little to no assessment, allowing more resources to
be targeted to Category 2 and 3 streams, which could be assessed using a method
designed to detect the presence or absence of a year class of a certain threshold size in

order to determine a stream’s need for treatment.

82

Recruitment and grth are two of the three primary factors that determine fish
population dynamics (mortality is the other). Understanding grth and recruitment and
their variability are vital to managing fisheries (Houde 1987, Quinn and Deriso 1999,
Myers 2001). Stable recruitment can reduce the complexity of fisheries management, but
many fish populations have highly variable recruitment (Ricker 1954, Hilbom and
Walters 1992, Myers 1998, MacKenzie et al. 2003). If not properly accounted for, this
variability can cause high inter-annual variation in yield or catch rates in the case of
desired fisheries, and high annual variation in control success in pest species such as sea
lampreys. Variation in growth can also contribute to variable success of fisheries
management strategies (e. g. Houde 1987, Campana 1996, Van den Avyle and Hayward
1999, Scharf 2000). By improving our understanding of the variability in recruitment
and growth within and among economically important fish populations, it should be
possible to design policies for exploitation and control that more effectively account for
this variation. The analyses presented in this chapter provide an example of how such
knowledge can be used to improve management.

This study represents the utility of historical data in understanding the dynamics
of a managed population, and could be extended within the field of sea lamprey
management. Based on this analysis, historical sea lamprey assessment data exhibit
patterns across years that can inform future assessment activities and resource allocation.
In the future, historical surveys could be used in a more rigorous manner to direct stream
treatment decisions. A threshold level of recruitment could be identified above which
chemical treatments would be applied, directing assessment efforts to early (age-1) life

stages of sea lampreys and providing an additional objective metric on which to base

83

treatment decisions. Alternatively, a Bayesian approach in which historical data are used
to create informative prior probabilities of a stream’s need for treatment could be
employed, and combined with less-intensive data collection to make stream treatment
decisions. This type of Bayesian assessment would be less costly than current assessment
since it would rely less heavily on conducting surveys and more heavily on the wealth of
data that have already been collected. Continued research into the use of historical
survey data to make present-day decisions is warranted within sea lamprey management

and in other systems for which informative historical records are available.

84

THESIS DISCUSSION

The acquisition of knowledge to determine the optimal course of action is a common
goal of scientific inquiry. Often it is assumed that the more knowledge acquired, the
better the decisions will be. However, in situations of limited resources, the gathering of
information to increase knowledge can come at the expense of the ability to carry out the
very actions the increased knowledge was intended to inform. When resources are
limited, it is important to analyze the trade-off between resources used to assess a system
and resources used to carry out management actions. Testing alternative strategies of
resource allocation on the scale relevant to management and monitoring their
consequences is a way to determine the optimal balance between competing management
goals. Additionally, one means of reducing reliance on present-day assessment and
information gathering is to use historical knowledge to inform decision making. In many
managed systems, data have been collected for various purposes throughout the history of
management, which can be used to direct management decisions or to better understand
population dynamics, reducing the reliance on information gathered from present-day
formal assessments (Myers et al. 1995, Patton et al. 1997, Swetnam et al. 1999).

In the case of sea lamprey control, streams are chemically treated to kill larval sea
lampreys to achieve management goals. Assessment is needed to inform managers which
streams, if treated, would provide the greatest benefit to the sea lamprey program in terms
of sea lampreys killed. Finding the optimal balance between resources spent on this
assessment and resources reserved for treating streams requires testing alternative
frameworks of resource allocation and monitoring the consequences. Based on the

results presented in chapter one, sea lamprey managers could allocate fewer resources to

85

assessment and more to control and achieve greater suppression of sea lampreys. The
rapid assessment procedure described in this chapter is one of a potentially infinite
number of alternative assessment methods. RA may not represent the optimal balance
between assessment and control, but it appears to be at least an improvement over the
current allocation of resources in that it allows for greater numbers of sea lampreys to be
killed than the current assessment method. The use of adaptive management to test
alternative means of resource allocation and assessment will allow for the direct
application of the results of this experiment to sea lamprey management decisions.
Adaptive management is a tool that should be used more often to test alternative
management actions and their results in real world systems, allowing for the continuous
refinement of management actions in order to approach the optimal course of action.
Larval assessment surveys have been conducted to direct sea lamprey management
since the inception of sea lamprey management. Based on the results presented chapter
two, historical data can be useful in identifying demographic patterns in larval sea
lamprey populations, and potentially in improving management, even if the data were
originally collected for other purposes. The categories describing the regularity of
lamprey production and treatment cycles developed by managers are supported by
differences in recruitment to age-1, even when recruitment is measured on a very crude
scale. Differences in growth rates are significantly related to treatment regularity only in
Lake Superior streams, where irregularly treated streams exhibit the highest variation in
mean length at age-l. Chemical treatments have been occurring longest in Lake Superior
tributaries, and therefore these streams may be more easily categorized, or different

population dynamics may be driving differences in treatments in Lake Superior streams

86

than in tributaries to other lakes.
Further refinements to the Rapid Assessment method could be achieved by

incorporating historical information. For example, category 1 streams may require an

 

even less resource-intensive assessment method, aimed simply at identifying the presence
or absence of a year class. Because differences in recruitment to age-1 appear to be

driving differences in lamprey production across stream categories, it may be possible to

'

identify a threshold level of recruitment above which a stream will require treatment, and

develop an assessment procedure that identifies whether or not this threshold level has

I, l‘.‘"‘-"‘.““ T’"?"’I
1?...

been achieved in category 2 and 3 streams. Alternatively, historical data could be used in
a Bayesian framework, in which prior probabilities of a strearn’s need for treatment are
formed using historical data, and combined with data collected from a rapid assessment
procedure to determine which streams require treatment. I
Understanding how best to balance resources used to gather information and those
used to manage is important in many natural resource systems. Stock assessments of
commercial fisheries, evaluations of the status of endangered species, and the
determination of the optimal location for reserves and protected areas are examples of

situations in which a conflict could arise between resources allocated to learn more about

 

a system and those allocated to the management, conservation, or protection of that

system. The use of historical data to identify demographic patterns in populations and/or
to improve management may be a means through which managers could spend fewer ‘
resources on assessment, thereby freeing up resources to be used for other purposes.

Studies that examine the tradeoff between assessment and management will assist

managers in making critical decisions in situations of limited resources, and should be

87

initiated in other systems in which competing management goals exist and in which

historical records are available.

88

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