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6/01 cJCIRC/DateDue.pGS—p.15

UNCERTAINTY IN ESTIMATING SEA LAMPREY (Petromyzon marinas)
ABUNDANCE IN GREAT LAKES TRIBUTARIES

By

Todd Bradley Steeves

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

2002

ABSTRACT

Great Lakes tributaries are selected for lampricide treatment with a simulation
model that uses sampling information on habitat and lamprey density to estimate larval
lamprey abundance and forecast metamorphosed lamprey production. I examined the
effect of water depth and temperature, larval size and density, and stream conductance on
the efﬁciency of the primary device for sampling sea lamprey larvae, a DC backpack
electroﬁsher. A higher proportion of larvae of all sizes were collected per unit sampling
effort when sample sites were shallower, contained fewer larvae, and were in streams of
lower speciﬁc conductance (P<0.001). Additionally, larvae were more likely to be
included in a sample as the total length increases. Temperature did not affect the efﬁciency
of sampling larvae in this study. The results have implications for the development of a
sampling protocol that uses a single-pass electroﬁshing technique to estimate the number
of sea lamprey larvae in a stream. This estimate is critical to determining the abundance of
larvae with the potential to metamorphose as parasitic lamprey the following year, and,
consequently, the prioritization of streams for chemical treatment.

I used mark-recapture estimates made at the time of lampricide treatment to test the
accuracy of the model forecasts. I also used Monte Carlo methods with the simulation
model to examine the effects that propagation of uncertainty in the input parameters had on
the outputs of the model. The model had poor predictive utility, as the estimates of lamprey
production did not fall within the 95% conﬁdence intervals of the mark-recapture
estimates. The estimates were also highly variable, and were sensitive to correlations
within the sampling data. The results indicate the model structure, the input data, and

model parameters require calibration for the model to be used as a predictive tool.

ACKNOWLEDGEMENTS

I am indebted to my graduate committee, Dr. Michael Jones, Dr. Dan Hayes, and
Dr. Connie Page for their invaluable assistance in the many facets of my thesis. I
appreciate the support of the Great Lakes Fishery Commission and Fisheries and Oceans
Canada — Sea Lamprey Control Centre for ﬁnancial, technical, and physical support, and
generally ﬁnding the means and the time to allow me to complete this career goal.
Thanks also to the US. Fish and Wildlife Service, Marquette and Ludington Biological
stations for the loan of equipment and providing data during the two ﬁeld seasons and
more than two ‘computer’ seasons I spent completing this project.

Thanks to the MSU Statistical Consulting Centre and Dr. Jim Bence for advice
and insight into many of the statistical aspects of this project. Thanks to Scott Cressey,
who provided superb assistance during the dredging and mark-recapture work in 1998
and 1999. Thanks also goes to Tamara Brunger who helped me with the identiﬁcation
and measurement of lamprey larvae in the fall of 1998.

I will miss being a part of the ‘Jones lab’; the quintessential Fisheries and Wildlife
Department lab perched high in our windowless ivory tower on the third ﬂoor. Thanks
for helping to keep everything in perspective by working and laughing at the appropriate
times. And let us never forget the ‘ﬂow chart’.

Finally, for my wife Lisa, who made the same journey at the same time, although
in a different place:
“Come to the moon,
I hope to see you soon,
Half a million miles isn’t far to go
You know I need you so,

I hope you still need me.
Come to the moon.” J. Buffett, M. Utley, and W. Jennings 1984

iii

TABLE OF CONTENTS

List of Tables .................................................................................................................... vi
List of Figures .................................................................................................................. viii
Thesis Introduction ........................................................................................................... 1
Chapter 1.
Sampling sea lamprey (Petromyzon marinas) larvae in wadeable Great Lakes tributaries.
Introduction ............................................................................................... 9
Materials and Methods
Data Collection ............................................................................. 14
Data Analysis ................................................................................ 16
Results ....................................................................................................... 19
Discussion ................................................................................................. 24

Chapter 2.
Uncertainty in the estimation of sea lamprey (Petromyzon marinus) populations
in Great Lakes tributaries.

Introduction ............................................................................................... 34
Materials and Methods
Study streams ................................................................................ 38
Mark-recapture population estimates ............................................ 38
Monte Carlo simulation model parameterization ......................... 42
Habitat and catch resampling ........................................................ 43
Electroﬁsher effectiveness ............................................................ 43
Proportion of Type 1 larval density attributable to
Type 2 habitat ................................................................... 45
Growth Rate .................................................................................. 47
Probability of metamorphosis ....................................................... 47
Evaluation of uncertainties in variables and predictions .............. 49
Results
Mark-recapture population estimates ............................................ 53
Monte-Carlo simulation outputs ................................................... 55
Discussion ................................................................................................. 69

iv

Appendix A
Speciﬁcations of the ESTR model used by the sea lamprey management program to

predict metamorphosed sea lamprey abundance in Great Lakes tributaries ..................... 83
Appendix B

Derivation of the variance of the capture proportion in the backpack electroﬁshing
effectiveness study. .......................................................................................................... 90
Appendix C.

Plot data for the six study streams sampled to examine the sampling effectiveness of
backpack electroﬁshing gear ............................................................................................. 92
Appendix D.

Length and catch data for the six study streams sampled to examine the sampling
effectiveness of backpack electroﬁshing gear. ................................................................. 96
Literature Cited ............................................................................................................... 102

List of Tables
Table 1. Characteristics of the streams used in the electroﬁshing effectiveness study. 15

Table 2a. Sample site information in streams selected for electroﬁsher efﬁciency study.
........................................................................................................................................... 20

Table 2b. Catch and estimated abundance in streams selected for electroﬁsher efﬁciency
study. ................................................................................................................................ 20

Table 3. Logistic regression models of capture proportion versus the individual
explanatory variables. ...................................................................................................... 23

Table 4. Parameter estimates for multivariate logistic regression of length, density,
conductivity, temperature and depth on electroﬁshing capture efﬁciency. ..................... 23

Table 5. Possible interactions to be added to the main effects logistic model. ................ 23

Table 6. F inal parameter estimates for logistic regression model of length, density,

conductivity, and depth on electroﬁshing capture efﬁciency. .......................................... 24
Table 7. Characteristics of the study streams used in the simulation model. .................. 39
Table 8. Nominal values for variables in the ESTR simulation model. .......................... 52

Table 9. Mark-recapture estimates of larval and metamorphosed (or juvenile) lamprey
abundance for the 9 study streams. The upper panel contains the estimate of lamprey
abundance for all species and life stages in each study stream. The middle and bottom
panels contain estimates of larval and juvenile sea lamprey abundance, respectively. The
initial larval and juvenile abundance estimates are the one-year ESTR forecasts from the
survey data, used to set target levels of larvae to mark and recapture. ............................ 54

Table 10. Means and bootstrap percentiles for the coefﬁcients of variation for the larval
and metamorphosed (juvenile) lamprey abundance estimates from the fully randomized
simulation model. ............................................................................................................. 56

Table 1 1. Coefﬁcient of variation of simulation model estimates of larval lamprey
abundance. Bold entries are conditional model CV’s outside of the 95 percentile range of
the CV for the fully randomized simulation model. ........................................................ 68

Table 12. Coefﬁcient of variation of simulation model estimates of metamorphosed
lamprey abundance. Bold entries are conditional model CV’s outside of the 95 percentile

range of the CV for the fully randomized simulation model. .......................................... 68

Table 13. Summary of the sampling data for Club Creek, illustrating an alternative
approach to estimating larval lamprey abundance. .......................................................... 73

vi

Table 14. Summary of the catch and habitat information for Proctor’s Creek. Note that a
higher proportion of the total catch is captured from Site 2, which contains a lower
proportion of the available habitat. This results in higher estimates of larval lamprey
density. ............................................................................................................................. 78

Table A1. Parameter estimates for length-dependent logistic models developed for 11
regions around the Great Lakes. ...................................................................................... 94

vii

List of Figures

Figure 1. Location of electroﬁsher effectiveness study streams around the Great Lakes
Basin. ............................................................................................................................... 14

Figure 2. The logit of the capture proportion 104% 13 )versus the mean length of

larvae within each length bin. .......................................................................................... 21

Figure 3. Scatterplots of the logit of capture proportion, 104% I3 j, of initial plot

abundance versus temperature, conductivity, plot density, and mean depth. The line in
each chart is the best linear ﬁt for the data. ..................................................................... 22

Figure 4. The effects of density on capture proportion, conditioned on length. Capture
proportion decreases as density increases from 0.5 larvae-m'2 (dotted line) to 5 larvae-m'
(dashed line) and 25 larvae-m'2 (solid line). Conductivity and depth are ﬁxed at 200
umhos and 0.3m, respectively. ........................................................................................ 27

2

Figure 5. The effects of depth on capture proportion, conditioned on length. Capture
proportion decreases as depth increases from 0.1m (dotted line) to 0.3m (dashed line) and
0.8m (solid line). Conductivity and lamprey density are ﬁxed at 200 umhos and 5
larvae-m’z, respectively. ................................................................................................... 28

Figure 6. The effects of conductivity on capture proportion, conditioned on length.
Capture proportion decreases as conductivity increases from 50 umhos (dotted line) to
200 umhos (dashed line) and 400 umhos (solid line). Lamprey density and water depth
are ﬁxed at 5 larvae-m'2 and 0.3m, respectively. ............................................................. 29

Figure 7. A comparison of the consequences of ﬁxed versus variable efficiency on

estimates of sea lamprey populations. Stream A represents optimal sampling conditions:

Conductivity=50 umhos, Density=0.5 larvae m'z, Mean Depth=0.l m; stream B

represents extreme sampling conditions: Conductivity =5 50 umhos, Density =20.0 larvae
-2 _

m , Mean Depth —0.8 m. ................................................................................................. 32

Figure 8. Location of the study streams for the veriﬁcation and validation of the ESTR
model. .............................................................................................................................. 39

Figure 9. Hierarchy of stream site, electroﬁshing plot, and capture data used in the
simulation model. During each model iteration the access points were randomly sampled
S times, with replacement. For each access point selected, the associated habitat transects
were randomly sampled T times with replacement, and the electroﬁshing plots were
randomly sampled P times, with replacement. For each electroﬁshing plot sampled, the
associated length data were randomly sampled L times, with replacement ..................... 44

viii

Figure 10. Distribution of the T2/T 1 ratios in the multi-year lamprey study. ................. 46

Figure 11. Regional logistic model to predict probability of metamorphosis (solid line)
versus the logistic models for the individual stream data within the region (dashed lines).
........................................................................................................................................... 48

Figure 12. The number of simulations required to stabilize the coefﬁcient of variation of
the simulation model outputs. .......................................................................................... 50

Figure 13. Scatterplot of variance versus mean for the simulation model abundance
estimates of larval (open circles) and metamorphosed (crosses) lampreys. The values of
mean larval abundance and metamorphosed lamprey variance are scaled by 103, larval
variance by 10'6 to ﬁt common axes. .............................................................................. 51

Figure 14. Comparison of the mark-recapture and mean Monte Carlo simulation model
estimates of larval lamprey abundance. The error bars represent the 95% conﬁdence
interval (horizontal) and the 2.5 and 97.5 percentiles (vertical), for mark-recapture and
Monte Carlo estimates, respectively. The bold line indicates the 1:1 line. ..................... 57

Figure 15. Comparison of the mark-recapture and mean Monte Carlo simulation model
estimates of metamorphosed lamprey abundance. The error bars represent the 95%
conﬁdence interval (horizontal) and the 2.5 and 97.5 percentiles (vertical), for mark-
recapture and Monte Carlo estimates, respectively. The bold line indicates the 1:1 line.
........................................................................................................................................... 58

Figure 16. Boxplots for the fully randomized and conditioned variable simulation model
estimates of larval and metamorphosed sea lampreys in the Batchawana River. The open
boxes represent the 25th and 75th quartiles, the lines within the boxes represent the
median estimates for each set of simulations, and the whiskers are 1.5 times the
interquartile distance. The lines above the uppermost whisker represent observations
greater than 1.5 times the interquartile distance from the median. The diamonds represent
conditional models with CV’s outside of the 95 percentile range of the CV for the fully
randomized model. ........................................................................................................... 59

Figure 17. Boxplots for the fully randomized and conditioned variable simulation model
estimates of larval and metamorphosed sea lampreys in the Club Creek. The open boxes
represent the 25th and 75th quartiles, the lines within the boxes represent the median
estimates for each set of simulations, and the whiskers are 1.5 times the interquartile
distance. The lines above the uppermost whisker represent observations greater than 1.5
times the interquartile distance from the median. The diamonds represent conditional
models with CV’s outside of the 95 percentile range of the CV for the fully randomized
model. .............................................................................................................................. 60

ix

Figure 18. Boxplots for the fully randomized and conditioned variable simulation model
estimates of larval and metamorphosed sea lampreys in the Crystal Creek. The open
boxes represent the 25th and 75th quartiles, the lines within the boxes represent the
median estimates for each set of simulations, and the whiskers are 1.5 times the
interquartile distance. The lines above the uppermost whisker represent observations
greater than 1.5 times the interquartile distance from the median. The diamonds represent
conditional models with CV’s outside of the 95 percentile range of the CV for the fully
randomized model. ........................................................................................................... 61

Figure 19. Boxplots for the fully randomized and conditioned variable simulation model
estimates of larval and metamorphosed sea lampreys in the Gargantua River. The open
boxes represent the 25th and 75th quartiles, the lines within the boxes represent the
median estimates for each set of simulations, and the whiskers are 1.5 times the
interquartile distance. The lines above the uppermost whisker represent observations
greater than 1.5 times the interquartile distance from the median. The diamonds represent
conditional models with CV’s outside of the 95 percentile range of the CV for the fully
randomized model. ........................................................................................................... 62

Figure 20. Boxplots for the fully randomized and conditioned variable simulation model
estimates of larval and metamorphosed sea lampreys in the Meyer’s Creek. The open
boxes represent the 25th and 75th quartiles, the lines within the boxes represent the
median estimates for each set of simulations, and the whiskers are 1.5 times the
interquartile distance. The lines above the uppermost whisker represent observations
greater than 1.5 times the interquartile distance from the median. The diamonds represent
conditional models with CV’s outside of the 95 percentile range of the CV for the fully
randomized model. ........................................................................................................... 63

Figure 21. Boxplots for the fully randomized and conditioned variable simulation model
estimates of larval and metamorphosed sea lampreys in the Proctor’s Creek. The open
boxes represent the 25th and 75th quartiles, the lines within the boxes represent the
median estimates for each set of simulations, and the whiskers are 1.5 times the
interquartile distance. The lines above the uppermost whisker represent observations
greater than 1.5 times the interquartile distance from the median. The diamonds represent
conditional models with CV’s outside of the 95 percentile range of the CV for the fully
randomized model. ........................................................................................................... 64

Figure 22. Boxplots for the fully randomized and conditioned variable simulation model
estimates of larval and metamorphosed sea lampreys in the Salem Creek. The open boxes
represent the 25th and 75th quartiles, the lines within the boxes represent the median
estimates for each set of simulations, and the whiskers are 1.5 times the interquartile
distance. The lines above the uppermost whisker represent observations greater than 1.5
times the interquartile distance from the median. The diamonds represent conditional
models with CV’s outside of the 95 percentile range of the CV for the fully randomized
model. .............................................................................................................................. 65

Figure 23. Boxplots for the fully randomized and conditioned variable simulation model
estimates of larval and metamorphosed sea lampreys in the Watson’s Creek. The open
boxes represent the 25th and 75th quartiles, the lines within the boxes represent the
median estimates for each set of simulations, and the whiskers are 1.5 times the
interquartile distance. The lines above the uppermost whisker represent observations
greater than 1.5 times the interquartile distance from the median. The diamonds represent
conditional models with CV’s outside of the 95 percentile range of the CV for the fully
randomized model. ........................................................................................................... 66

Figure 24. Boxplots for the fully randomized and conditioned variable simulation model
estimates of larval and metamorphosed sea lampreys in the Wilson Creek. The open
boxes represent the 25th and 75th quartiles, the lines within the boxes represent the
median estimates for each set of simulations, and the whiskers are 1.5 times the
interquartile distance. The lines above the uppermost whisker represent observations
greater than 1.5 times the interquartile distance from the median. The diamonds represent
conditional models with CV’s outside of the 95 percentile range of the CV for the fully
randomized model. ........................................................................................................... 67

xi

Thesis Introduction

The sea lamprey (Petromyzon marinas) is a primitive, parasitic ﬁsh species,
indigenous to the Atlantic coasts of North America and Europe (Beamish 1980). It is a
highly adaptable species, as evidenced by its ability to shift from an anadromous to a
completely freshwater life history (Emery 1985, Smith 1971). Freshwater populations of
sea lampreys were reported in the St. Lawrence River, Lake Champlain, and the Finger
Lakes of New York prior to 1920 (Pearce et al. 1980, Scott and Crossman 1973). Sea
lampreys may have been present in the Laurentian Great Lakes as early as 1835, when a
spawning-phase sea lamprey is described in Dufﬁns Creek, a Lake Ontario tributary
(Lark 1971), although this identiﬁcation is controversial (Smith 1995). If sea lampreys
were present in Lake Ontario in the early 1800’s, a population of large and abundant
piscivores combined with degraded or inaccessible spawning habitat likely limited the
effect of the sea lamprey on the freshwater ﬁsh community (Eshenroder and Bumham-
Curtis 1999, Christie and Kolenosky 1980). Sea lampreys did not enter the upper Great
Lakes until the mid-1930’s, after the 1929 modiﬁcation of the Welland Canal allowed sea
lampreys to bypass Niagara Falls (Eshenroder and Bumham-Curtis 1999). The ﬁrst sea
lamprey was observed in Lake Erie in 1931 (Pearce et a1. 1980); by 1947, spawning runs
of sea lampreys were conﬁrmed in all of the upper Great Lakes (Smith and Tibbles
1980).

Although physically smaller, freshwater populations of sea lampreys exhibit the same
life history characteristics as their Atlantic counterparts (Scott and Crossman 1973,
Applegate 1950). In the spring, semelparous adults migrate into tributaries where they

build nests in grave] rifﬂes, then spawn. The eggs incubate within the nest for 18-21 days,

and the emergent larvae are passively carried downstream to depositional areas, where
they burrow into the substrate. The larval stage lasts for approximately 5 years, during
which the lampreys ﬁlter-feed on bioﬁlrn and other organic matter (Sutton and Bowen
1994) until they attain sufﬁcient length and energy stores to metamorphose to the
parasitic juvenile stage and migrate downstream to the lake population (Holmes and
Youson 1998). In their parasitic stage, sea lampreys feed on the blood, body ﬂuids, and
tissue products of host species (Farmer 1980). In the Great Lakes, hosts include all but
the smallest of teleosts, but lampreys preferentially select larger, cold-water species
(Farmer and Beamish 1973), which are often commercially valuable. Combined with
ﬁshing pressure on these species, the parasitic sea lamprey has been at least partially
credited with the demise of commercially and ecologically important ﬁsh stocks in the
Great Lakes (Eshenroder 1992, Coble et al. 1990, Smith and Tibbles 1980).

In 1955, the Great Lakes Fishery Commission (GLFC) was established to co-ordinate
a control program for sea lampreys in the Great Lakes (Smith 1971). Early control efforts
focused on limiting the reproductive potential of the spawning phase of the sea lamprey
with mechanical and electrical barriers installed below spawning areas in streams (Smith
et a1. 1974). These methods had limited success (Smith 1971). The development of a
selective chemical, 3—triﬂouromethyl-4-nitrophenol (TF M), to control stream-dwelling
larval populations re-directed control efforts towards limiting the recruitment of parasitic-
phase lampreys to the lake population (Smith and Tibbles 1980). TFM was ﬁrst applied
to lamprey natal streams on Lake Superior in 195 8, followed by Lakes Michigan and
Huron in 1960, Lake Ontario in 1971, and Lake Erie in 1986, resulting in signiﬁcant

declines in sea lamprey abundance (Dustin et a1. 1990, Smith and Tibbles 1980).

Initially, streams were selected for TFM treatment based upon the established
frequency of TF M application (e. g. every four years), and on a qualitative assessment of
the size structure and abundance of larval sea lamprey populations that had re-established
since the last treatment (Weise and Rugen 1987). Streams were scheduled for treatment
when biologists subjectively estimated that a signiﬁcant portion of the larval population
in the stream would metamorphose to the parasitic stage the following year. This method
of stream selection emphasized the maximum reduction in the overall abundance of
parasitic sea lampreys recruited to the lake population.

Since 1982, the GLF C has implemented a more holistic approach to sea lamprey
control, establishing targets for lamprey abundance that balance the ecological, social,
and economic impacts of control efforts (Koonce et al. 1993, Great Lakes Fishery
' Commission 1992). One goal of this approach is to utilize concepts from the integrated
pest management of insects to develop alternative control strategies that reduce the
dependency on TFM for control of the sea lamprey in the Great Lakes (Great Lakes
Fishery Commission 1992, Sawyer 1980). A decision support system was developed to
assess the outcomes of various lamprey control options, including optimizing the
selection process for streams that will receive the reduced TFM treatment efforts (Koonce
et al. 1993, Koonce and Locci-Hernandez 1989). Within this system, information about
lamprey populations and stream treatment costs allows a cost-beneﬁt approach to
prioritizing streams for treatment, where treatment is recommended for those streams that
prevent the largest number of metamorphosed lampreys from being recruited to the lake

population for each treatment dollar spent. The use of kill-per-unit—cost criteria in stream

rankings provides better control of sea lamprey populations than does the previous
method of maximizing kill (Jones et al. 1987).

The kill-per—unit-cost criterion requires quantitative estimates of the potential
parasitic lamprey production for each stream considered for TFM treatment. The logistics
of treatment scheduling and applying TFM to the selected suite of streams requires that
parasitic production estimates be made and the streams prioritized at least six months
prior to the prospective treatment date. Consequently, lamprey control personnel are
required to forecast the parasitic lamprey production from data gathered the year prior to
proposed treatment, one year prior to the anticipated onset of metamorphosis in the larval
lamprey population.

Lamprey control biologists with Fisheries and Oceans Canada and the US. Fish and
Wildlife Service (the control agents) as well as the GLFC have developed a computer-
based simulation model, the Empiric Stream Ranking System (ESTR), to estimate larval
lamprey p0pulations and predict juvenile lamprey production from annual survey data.
The ESTR model combines the information from the annual sampling of larval lamprey
densities and nursery habitat abundance in Great Lakes tributaries with biological rate
information, such as growth and metamorphosis, estimated from archival data. Using this
information, ESTR produces a deterministic estimate of the number of larval lampreys
within each stream in the year of sampling, and forecasts the number of parasitic
lampreys expected to leave the tributary the year aﬁer sampling (Appendix A).

In conjunction with the prediction model, a sampling protocol has been established to
provide consistent and statistically valid information about larval lamprey abundance in

candidate streams (Slade et al in review, Fodale et al. 2001, unpublished manuscript).

Prior knowledge of stream morphology allows sampling to be divided between deep and
shallow water techniques. Sampling in water over 1 metre in depth is accomplished using
either a bottom-release toxicant or a specialized deep-water electroﬁshing device (Slade
et al, in review). In water less than 1 metre deep, backpack electroﬁshing gear is used as
the sampling tool. In both techniques, the protocol prescribes using standardized effort to
estimate larval lamprey density in a number of randomly located, measured plots, as well
as quantifying the availability of larval nursery habitat from transects located
perpendicular to stream ﬂow at the sample site. The catch from each plot is corrected by
a measure of sampling effectiveness, and plot density is calculated as the corrected catch
divided by the plot area. The mean density of larval lampreys within the stream is
estimated as the total number of lampreys collected divided by the total area sampled.
The ESTR model uses mean density along with the estimated area of available larval
nursery habitat to estimate larval lamprey abundance in each candidate stream.
Backpack electroﬁshing accounts for 84% of the plot-speciﬁc density estimates
collected by the control agents each year. Following the protocol, surveyors determine
the larval lamprey densities within the available larval nursery habitat at randomly
selected sites along the river. Two plots, between 5 and 15 square metres in area, are
measured and marked within suitable larval habitat. A single pass of electroﬁshing, at the
rate of 1.5 minutes per square metre of plot area, is used to collect as many lampreys as
possible from the plots. The corrected electroﬁshing catch is used to estimate the mean
larval density within the stream reach, which is multiplied by the estimated amount of

available habitat to estimate the larval lamprey population.

Initial studies estimated that backpack electroﬁsher sampling collects approximately
48% of the lamprey larvae initially present within a sample plot, under all sampling
conditions (Steeves et al. in review). The control agents recognize that there will be
variability in sampling effectiveness due to plot-speciﬁc sampling conditions, such as
water depth or larval lamprey density, and that this variability will affect the catchability
of larval lampreys. In turn, this will affect the estimates of larval and parasitic lamprey
abundance forecast by the ESTR model, as well as the order of streams produced by the
ranking procedure. Quantitative knowledge of the causes of this variability would allow
more accurate estimates of larval lamprey density to be obtained.

In Chapter 1 of this thesis, I investigate the inﬂuence that water depth, conductivity,
temperature, lamprey density, and lamprey length have on the proportion of lampreys that
are collected using backpack electroﬁshing gear. I illustrate the effects of environmental
variability on lamprey population estimates, and make recommendations regarding the
incorporation of the results into the ESTR model.

Once estimates of the larval lamprey population and potential parasitic lamprey
production are calculated using the ESTR model, the streams are ranked based on the
number of parasitic sea lampreys removed for each treatment dollar spent. As the cost of
TFM treatment prohibits treating all streams that will produce parasitic sea lampreys each
year, the control agents and the GLF C collectively agree on a subset of streams that are
the most cost-effective to treat. The inclusion of a stream on the TFM treatment schedule
is a decision process that balances the beneﬁts of treating the suite of streams to reduce
the recruitment of parasitic lampreys to the lake population with the cost of not treating

the remaining streams that will also produce parasitic lampreys.

The ESTR model produces deterministic estimates by design, and treatment decisions
are based on estimates of lamprey abundance without quantitative information regarding
their inherent uncertainty. However, treatment decisions include subjective evaluation of
the uncertainty in the ESTR forecasts, where informed experience and prior knowledge
of the effectiveness of larval lamprey sampling and lamprey distribution affects the
degree that the control agents are willing to accept the ESTR abundance estimates. This
implicit recognition of uncertainty in the predictive capability of the ESTR model
inﬂuences the decision to include streams on the treatment list the following year.

Uncertainty in the ESTR model stems from multiple sources. First, the accuracy of
the forecasts of larval and metamorphosed lamprey abundance produced by the ESTR
model has never been veriﬁed. Forecasts of abundance are evaluated against expected
measures of abundance given prior experience with the stream-speciﬁc lamprey
population and examination of the current sampling information. Veriﬁcation of the
ESTR model estimates would increase the conﬁdence that the control agents have in the
prioritized stream list. Second, there is uncertainty in the structure of the ESTR model, in
terms of the parameters used in the model as well as in the structural assumptions
regarding how the parameters are linked within the model. These uncertainties may, in
turn, affect the accuracy of the ESTR model forecasts. The quantitative speciﬁcations of
the ESTR model are based on current understanding of lamprey biology and stream
sampling methodologies. Archival data were used to calculate the required sampling
intensity for density and habitat measures, as well as for the rate parameters of growth,
length of growing season, and probability of metamorphosis (Appendix A). These

quantitative components are continually being revised as new information reﬁnes or

reshapes the understanding of lamprey population dynamics. Quantitative knowledge of
the variability in the components could be used to direct research towards those
components that would best reduce the overall uncertainty in the ESTR model, and allow
more accurate estimates to be obtained.

In Chapter 2 of this thesis, I examine the accuracy of the ESTR model estimates of
larval and metamorphosed lamprey abundance. First, I examine the accuracy of the ESTR
model forecasts of metamorphosed sea lampreys produced by a stream by comparing
model forecasts with independent mark-recapture population estimates of larval and
parasitic lampreys made at the time of treatment. Second, I use Monte Carlo methods to
assess the possible contribution of errors in the input variables to uncertainty in the ESTR
model forecast. The analyses illustrate the inﬂuence that each variable in the ESTR
model has on uncertainty in the estimates of larval and parasitic lamprey abundance.
Inﬂuential model inputs and relationships are identiﬁed, and recommendations are made
to improve those components that will most reduce the uncertainty around the ESTR

model estimates.

Sampling sea lamprey (Petromyzon marinas) larvae in wadeable Great Lakes

tributaries.

INTRODUCTION

Information about the presence and abundance of sea lamprey larvae in Great Lakes
tributaries has been used, both qualitatively and quantitatively, to determine a streams
potential production of parasitic sea lampreys. Many techniques have been investigated to
gather this information, including dredges, chemical application (for example, rotenone
and DDT), and remote-operated submersible vehicles equipped with electroﬁshing
electrodes (Weise and Rugen 1987). One of the earliest methods used for detecting larval
sea lamprey presence was to use a shovel to remove larval habitat from the streambed
and deposit it on shore, collecting the larvae as they emerged from the drying sediment
(Weise and Rugen 1987). This method was used in conjunction with visual inventories of
stream characteristics, sampling areas of suitable larval nursery habitat in close
association with spawning gravel and lamprey nests (Loeb and Hall 1952). Most of these
early techniques had detrimental side effects, such as altering the physical and chemical
properties of the stream, or negatively impacting non-target organisms.

Electroﬁshing had been used to collect aquatic biological samples as early as 1863
(Vibert 1967), but was not used for larval lamprey collection until 1947, when a 110 volt
alternating current generator was used to sample lampreys in the Ocqueoc River,
Michigan (Applegate 1950). In 1969, a direct current backpack electroﬁsher was
developed speciﬁcally for sampling larval sea lampreys (Weise and Rugen 1987). The
backpack electroﬁsher had the advantage of greater portability and ease of use than

previous methodologies for sampling larval lampreys in Great Lakes tributaries (Smith et

al 1974). This method of sampling was limited to water less than 1 m deep, but reduced
the unintentional physical, chemical, and biological effects on the stream environment.

Initially, backpack electroﬁshing was used to determine the relative abundance, size
structure, and distribution limits of lamprey larvae in wadeable portions of streams.
Streams were selected for treatment with the selective larvicide 3-triﬂouromethyl-4-
nitrophenol (TF M) when subjective assessments of larval sea lamprey population
structure, based on modal length examination, indicated that a portion of the larval
lampreys sampled were expected to reach 120 mm, a size where metamorphosis was
likely (Smith et a1 1974, Weise and Rugen 1987). Samples of larvae for modal analysis
were taken from areas of the stream that were easily accessible and known to harbour
high numbers of larval lampreys (Weise and Rugen 1987). While this subjective
approach was convenient, it was not statistically rigorous, and comparable quantiﬁcation
of larval abundance among streams was difﬁcult (Smith et al 1974, Weise and Rugen
1987)

In 1990, the Great Lakes Fishery Commission adopted a policy to reduce dependency
on TFM (Great Lakes Fishery Commission 1992). This reduction has been accomplished
in part through more stringent controls on the amount of larvicide applied to a stream,
and through a more judicious selection of the streams that receive TFM treatment each
year. In support of this policy, the focus of larval lamprey sampling has changed from a
qualitative approach to a quantitative assessment of lamprey populations, where a
systematic and repeatable method of sampling provides estimates of lamprey abundance
that are comparable among streams. These estimates are incorporated into a predictive

model, the Empiric Stream Ranking System (ESTR), that uses known treatment and

10

manpower costs to rank the streams for potential lampricide treatments, based on an
evaluation of the cost-effectiveness of eliminating the lamprey population in each stream
(G. Christie, Great Lakes Fishery Commission, Ann Arbor, MI, personal
communication).

Up to 100 Great Lakes tributaries require quantitative assessment each year.
Backpack electroﬁshing is the standard sampling methodology for waters less than 1
metre deep, and accounts for approximately 84% of the annual sampling completed by
both the US. Fish and Wildlife Service (USFWS) and Fisheries and Oceans Canada
(DFO) lamprey control agents each year. Given the number of streams to sample and the
reliance on backpack electroﬁshing as the sampling tool, a rapid, systematic, single-pass
technique is required to complete the quantitative sampling. Slade et al. (in review)
describe a systematic technique for sampling larval sea lampreys within a stream. The
technique prescribes sampling sea lamprey larvae from measured and staked plots, 5 to
15 m2 in area, in the wadeable portions of streams, using a direct current (DC) backpack
electroﬁsher. The observed density of larvae collected during a single-pass episode of
backpack electroﬁshing is corrected by a measure of gear efﬁciency to estimate the
density of larvae prior to electroﬁshing. The corrected densities are combined with
estimates of larval habitat, rates of growth, and probability of metamorphosis in the
ESTR model, to estimate a stream’s capacity to produce parasitic lampreys the following
year. TFM treatment is scheduled for those streams that prevent the largest number of
metamorphosed sea lampreys from being recruited to the parasitic lake population for

each treatment dollar spent.

11

The ESTR model relies on accurate measures of electroﬁshing effectiveness to
estimate the abundance and size structure of larval lampreys within a stream. Previous
estimates of ﬁrst pass sampling efﬁciency for larval lampreys have ranged from 13%
(Sea Lamprey Control Centre, Sault Ste. Marie, ON, unpublished data) to 70%
(Marquette Biological Station, Marquette, MI, unpublished data). Morkert (1987)
reported a recapture of 21% of marked larvae, and Daugherty and Dahl (1986) recovered
between 50 and 85% (mean 69%) of a known number of larvae in a single pass using
backpack electroﬁshers.

Since the ESTR model population estimates are sensitive to the speciﬁcation of
sampling efﬁciency, the control agents initiated a study in 1996 and 1997, to obtain a
systematic estimate of ﬁrst-pass electroﬁsher efﬁciency in three streams encompassing
the range of conductivity found in Great Lakes tributaries (Steeves et al in review). In this
study, experimental plots were measured and electroﬁshed in three streams following the
methodology described in Slade et al (in review). Techniques were developed to
determine the number of lamprey larvae remaining within the plot aﬁer electroﬁshing
was completed. Plot abundance prior to electroﬁshing was calculated as the sum of the
electroﬁshing catch and the number of lampreys remaining after electroﬁshing. Sampling
efﬁciency for the experimental plots was estimated as the electroﬁshing catch divided by
the initial plot abundance. The study pooled the data from the three streams, and
estimated backpack electroﬁsher sampling efﬁciency as 0.48, or 48% of initial plot
abundance. This value was incorporated into the ESTR model, and is currently used as

the estimate of sampling efﬁciency for backpack electroﬁshing gear.

12

It is unlikely that this estimate of electroﬁshing efﬁciency is consistent among all
lamprey-producing streams. External variables such as conductivity (Pusey et al. 1998,
Hill and Willis 1994), stream width (Kennedy and Strange 1981), ﬁsh size (Zalewski
1985), density (Coble 1992), and temperature (Regis et al. 1981) have been reported to
affect capture efﬁciency in teleosts. Further, laboratory studies have determined that the
response of larval lampreys to electrical stimuli can vary depending upon water
conductance and temperature, substrate composition, and orientation of electrical ﬁeld
(Weisser 1994, Hintz 1993). Sea lamprey larvae are sampled across a wide range of water
depths, temperatures, and conductivities throughout the Great Lakes basin, with each
combination of conditions contributing to potential variability in the proportion of larvae
collected. Inaccurate population estimates could result from applying a ﬁxed value for
electroﬁshing efﬁciency to samples collected under varying sampling conditions. This
could affect the placement of streams on the stream ranking list, allowing parasitic
lampreys to escape to the lake population from streams that should have ranked higher on
the treatment schedule. Reﬁning the estimate of sampling effectiveness using backpack
electroﬁshing gear would help to calibrate the ESTR model and provide more accurate
estimates of larval lamprey abundance and account for among-site differences in
sampling conditions that may affect efﬁciency.

The objectives of this study are:

I) examine the effects of water conductivity and temperature, plot depth and larval
density on the effectiveness of sampling sea lamprey larvae using backpack
electroﬁshing gear

2) estimate the effect of length on sampling effectiveness with backpack electroﬁshers

l3

MATERIALS AND METHODS
Data Collection

Six Great Lakes tributaries, Salem, Proctor’s, Lynde, and Watson’s creeks, and the
Sturgeon and Batchawana rivers (Figure l) were selected to encompass the range of
water conductivity found in streams throughout the Great Lakes. To examine the effects
of larval density and lamprey length on collecting efﬁciency, these streams were selected
with the additional criteria that larval densities could exceed 5 larvae-m'2 and that

multiple year-classes of lamprey larvae were present (Table l).

Batchawana River

Watson’s Creek

 

Sturgeon River
Salem

A ’ Lynde Creek
/ Creek Proctor’s
Creek
5

Figure 1. Location of electroﬁsher effectiveness study streams around the Great
Lakes Basin.

  
     

I measured plots within preferred (Type 1) larval habitat, deﬁned as a combination of
sand, ﬁne organic matter, and detritus or aquatic vegetation, in each stream. Plots were
measured to the nearest 0.1 m in each dimension and stakes were inserted at the comers.
Sample plots were electroﬁshed following the standardized methods and electroﬁsher

settings used by the sea lamprey control agents for quantitative sampling: a single-pass of

14

electroﬁshing at the rate of 1.5 minutes'm'z, 125V, 3 pulses-per-second (pps) slow, 30
pps fast, and 25% duty cycle (Slade et al., in review). As many lampreys as possible were
collected from within the measured sample site during the allotted time. Immediately
after electroﬁshing, the plots were enclosed in 7 thread per cm screening. Electroﬁshed
larvae collected from each plot were anesthetized, measured to total length, and marked
with a tail clip. All marked larvae were released into the respective plots and watched to

ensure that each lamprey burrowed into the substrate within plot boundaries.

Table 1. Characteristics of the streams used in the electroﬁshing effectiveness study.
Data from Fisheries and Oceans Canada and US. Fish and Wildlife Service.

 

 

Mean Annual Alkalinity Number of
Stream Lake Drainage Discharge (mg/L as Conductivity Lamprey

(kmz) (m3s'l) CaC03) (umhos) Age Classes
Batchawana River Superior 1190 23.20 21 50 3
Watson's Creek Huron 6.5 0.65 54 100 2
Sturgeon River Huron 103 1.29 168 285 2
Lynde Creek Ontario 106 0.91 213 525 3
Salem Creek Ontario 52 0.55 183 450 3
Proctor’s Creek Ontario 27 0.78 220 460 3

 

Immediately after the marked larvae had successfully burrowed, the substrate within
the plot was evacuated to a depth of 12 cm using a modiﬁed suction dredge (Bergstedt
and Genovese 1994). This device pumps water through a tapered oriﬁce, creating a high-
velocity jet stream within a ﬂow eduction tube. This produces a strong vacuum within a
ﬂexible intake pipe, allowing sites to be excavated without passing the excavated
materials through the pump. Substrate, detritus, and larvae are transported through the

intake and ﬁltered through a 1mm aperture mesh bag. The substrate from each plot was

15

sorted and larvae removed, identiﬁed to species, examined for marks, measured, and
preserved in 10% formalin solution.
Data Analysis

The electroﬁshing and dredge capture data were used to estimate electroﬁshing
efﬁciency in each of the sample plots. Efﬁciency is deﬁned as the proportion of the sea
lamprey larvae in a plot captured by a single unit of electroﬁshing effort, as described
above. If the dredge technique captured all the larvae in a plot that remained after
electroﬁshing, efﬁciency could be simply calculated as the ratio of electroﬁshing catch to
electroﬁshing+dredge catch. Failure to recapture all of the marked larvae that were
released back into the plots after electroﬁshing, however, would suggest that the dredge
technique was not 100% effective. Some portion of the marked larvae were not
recaptured in the majority of the plots, so an alternative method was required to estimate
electroﬁshing efﬁciency. I applied the data on recovery of marked larvae in the dredge to
a mark-recapture model to estimate the initial abundance of larvae in the plot.

Application of the mark-recapture method to the dredge data was complicated by
evidence that dredge catchability is affected by larval size. In general, it appears that
smaller larvae are less vulnerable to capture than larger animals. To compensate for this
unequal catchability, I divided the larval catches into 5 length bins (0-80mm, 81-100mm,
101-120mm, 121-140mm, and >140mm). Modiﬁed Petersen estimates (Seber 1982) were

calculated for each increment, as:

a_=(Mi+1)*(CDi+1)_1
1 MW”

 

l6

where: 1f], = abundance of larvae in length bin i

M,- = the number of marked lampreys released, corresponding to

electroﬁshing catch

CD,- = the number of lampreys in length bin i examined for marks,
corresponding to the dredge catch

R,- = the number of marked larvae in CD;

The proportion of larval lampreys captured in each bin was estimated as:

. C .
Pi = ‘TEJ'

Ni
where: C5. = electroﬁshing catch in length bin i

M = the estimated abundance in bin i for each plot, i = 1-5

I used a multiple logistic regression model (Collett 1991, Hosmer and Lemeshow
1989) to estimate the effect of larval length (X 1), log(density) (X 2), water temperature

(X3), conductivity (X4),, and depth (X 5) on the proportion of lampreys captured in each
length bin as:

. 1
P1 (1 + exp(-(ﬂ0 + '51 X1 + :62 X2 + ﬂ3X3+ ’64 X 4 + ﬂs X5 + interaction terms)»

This model can be linearized by a logit transformation as:

logit(%_ﬁ.))=ﬂo+ﬂl-X1+ﬂ2-X2 +ﬂ3-X3 +ﬂ4-X4+ﬂ5.X5 +interaction terms
1

To estimate the effect of larval length I calculated the mean length of the larvae
caught by electroﬁshing in each length bin for each individual plot, instead of simply

using the mid-point length for each bin. To estimate the effect of density, I used the

17

mark-recapture estimate of initial plot abundance divided by the measured plot area.
Initial plot abundance was estimated as the sum of the individual length bin estimates in
each plot. The distribution of the density calculations in the data was skewed, indicating a
log-transformation of the density data in the logistic model could improve the model ﬁt
(Kay and Little 1987). A Chi-square test (Neter et al. 1996, Hosmer and Lemeshow
1989) was used to compare the —2*log likelihood (called ‘deviance’ in logistic
regression) of the model ﬁt for density and its loge-transformation against a constant-only
logistic model. The difference in deviance between ﬁtting a constant-only model and a
logistic regression on density is 63.61 , while that for a regression on log.(density) is
115.54, both approximating a Chi-square distribution on 254 degrees of freedom.
Logc(density) was deemed a better predictor of capture proportion, and was used as an
explanatory variable in the model ﬁtting procedure (Collett 1991).

The assumption of homogeneous variances for the dependent variable, 13 i , is not
valid for these data. The variance of 10,. is inversely related to the number of lampreys

recaptured, R, in the second sampling period of a mark-recapture estimate (Krebs 1999).
The number of lampreys recaptured will vary among length increments within a plot, as
well as among plots within and among rivers, resulting in non-homogeneous variance in

19,. and subsequently f), . Because the assumption was violated, I used a weighted logistic

regression model (Neter et al. 1996, Collett 1991, Hosmer and Lemeshow 1989) where
the weighting factor was the inverse of the variance of the capture proportion for each

length increment. This weight was calculated as (Appendix B, Equation B7):

18

 

var(ﬁj) : A 2

Model ﬁtting procedures followed those outlined in Collett (1991) and Hosmer and
Lemeshow (1989). The variables identiﬁed as signiﬁcant using Wald’s Chi-square test
were examined for appropriate scale in the logit, and for interaction terms that were both
statistically signiﬁcant and biologically meaningful. Outliers were identiﬁed using
Pearson residuals, analogous to Studentized residuals in least-squares regression (Hosmer
and Lemeshow 1989). The ﬁnal model of electroﬁsher effectiveness was selected using a
likelihood ratio goodness-of-ﬁt test.

Data analyses were done using the S-Plus (MathSof’t 2000) software package.
RESULTS

Between 6 and 19 plots were sampled in each of the study streams, with the length of
lampreys captured in electroﬁshing comparable to the length of lampreys measured in the
dredge catch (Table 2a). The plots that were electroﬁshed represent the range of depth,
density, and conductivity commonly encountered in wadeable Great Lakes tributaries
(Table 2b). The sampling dates in this study precluded electroﬁshing across the range of
temperatures typical of a ﬁeld season, between April and October each year (Table 2b).

A scatterplot of the logit of capture proportion shows that capture proportion
increases as the length of larvae increases (Figure 2). Scatterplots of the logit of capture
proportion for each length bin versus the site speciﬁc variables show a decrease in
capture proportion as larval lamprey density and water conductivity increase (Figure 3).
Water temperature and plot depth does not appear to affect the capture proportion of

lamprey larvae.

19

 

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20

 

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Logit ofCapture Proportion

 

 

 

I l T T T r I

40 6O 80 100 120 I40 160
Mean of Length Bin (mm)

Figure 2. The logit of the capture proportion 104% ﬁ )versus the mean length of

larvae within each length bin.

Individually ﬁtting each explanatory variable to a logistic model of capture
proportion indicates that each signiﬁcantly reduces the model deviance (Table 3). Based
on these univariate results, all variables are included in the initial multivariate model. In
the multivariate model the effect of temperature, after adjusting for all other terms in the
model, was not signiﬁcant (Table 4). As well, the intercept term does not signiﬁcantly
improve the model ﬁt, and was removed from the model.

After determining the signiﬁcant main effects, each of six potential interaction terms
was individually ﬁt to the model. Three of the interaction terms signiﬁcantly improved
the ﬁt of the model (Table 5). All three terms combined with the main effects model

signiﬁcantly reduce the model deviance.

21

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22

Table 3. Logistic regression models of capture proportion versus the

individual explanatory variables.

 

 

Variable B S.E. ([3) Residual d.f Model X2 p-value
Deviance
NULL 2472.58 259
Mean Length 0.0264 0.001 1755.14 258 644.38 < 0.0001
log(Density) -0.463 0.0229 2044.29 25 8 407.53 < 0.0001
Conductivity -0.002 0.0001 2035.86 258 433 .09 < 0.0001
Temperature -0.043 0.0074 2441.96 258 34.39 < 0.0001
Mean Depth -0.503 0.1149 2453.55 258 19.18 0.00001

 

Table 4. Parameter estimates for multivariate logistic

regression of length, density, conductivity,

temperature and depth on electroﬁshing capture
efﬁciency.

 

Variable

[3

S.E. (a) Wald x’ p-value

 

Intercept
Mean Length
log(Density)
Conductivity
Temperature
Mean Depth

0.3618
0.0209

-0.426
-0.003

0.0158

-1.58

0.2105
0.001 1
0.0261
0.0001
0.0181
0.1515

2.95
367.65
266.23
1011.24

0.76
108.71

0.086
< 0.0001
< 0.0001
< 0.0001

0.38
< 0.0001

 

Residual Deviance: 1023.7 on 254 (if

Table 5. Possible interactions to be added to the main effects logistic model.

 

 

Variable B S.E. ([3) Residual d.f Model x2 p-value
Deviance

Main Effects (excluding --- --- 1027.5 256 --- ---
temperature and intercept
terms)
Mean Length x log(Density) -0.00338 0.0026 0.28 255 1.69 0.194
Mean Length x -0.00002 0.0000049 14.26 255 16.66 0.00016
Conductivity
Mean Length x Mean Depth 0.001 0.0047 0.046 255 0.045 0.829
log(Density) x Conductivity -0.0006 0.0001 35.35 255 36.00 < 0.0001
log(Density) x Mean Depth -0.1889 0.1434 1.73 255 1.74 0.188
Conductivity x Mean Depth -0.0041 0.00083 24.84 255 24.40 < 0.0001

 

Pearson residuals were used to identify 3 potential outliers in the data, corresponding

to length bins within sites 3, 54, and 57. Since estimates of abundance for length bins

23

within the same plot are not independent, the data for all length bins within each plot
were removed when examining the inﬂuence of the outliers. The deletion of plots 3 and
57 did not improve the model ﬁt. Deletion of plot 54 reduced the residual deviance of the
logistic model from 982.18 to 926.20. Lamprey density in plot 54 was estimated to be
71.8 larvae-m'z, which is quite large compared to the densities typically encountered
when sampling for lamprey larvae. This plot was deleted from the data set. The logistic
model of electroﬁsher sampling proportion therefore included the 4 main effects as well
as the 3 interaction terms (Table 6). Although the parameter estimate for conductivity
was no longer signiﬁcant, it was retained in the model due to its interaction with other
variables.

Table 6. Final parameter estimates for logistic regression model of length, density,
conductivity, temperature and depth on electroﬁshing capture efﬁciency.

 

 

Variable [3 S.E. ([3) Wald x2 p—value
Mean Length 0.0225 1.77 x 10-3 161.04 < 0.0001
log(Density) -0.3322 4.16 x 10-2 63.68 < 0.0001
Conductivity 0.00043 4.03 x 10-4 1.12 0.289
Mean Depth -0.8843 1.79 x 10-1 24.40 < 0.0001
Mean Length x Conductivity -0.000013 5.29 x 10-6 6.15 0.013
log(Density) x Conductivity -0.000133 5.54 x 10-5 5.76 0.016
Mean Depth x Conductivity -0.0068 9.54 x 10-4 50.27 < 0.0001

 

Residual Deviance: 579.8 on 253 df

DISCUSSION

Sampling conditions have been found to affect the sampling efﬁciency of
electroﬁshinggear for a variety of species, depending upon the sampling strategy (Bohlin
et al. 1989), type of gear (Bergstedt and Genovese 1994, Weddle and Kessler 1993), and
species sought (Fievet et al. 1999, Pusey et al. 1998, Strange et al. 1989). Sampling

efﬁciency using backpack electroﬁshing gear was signiﬁcantly affected by several

24

variables in this study. Effectiveness increased (P<0.001) as the length of the larvae
increased, but declined (P<0.001) as larval density, water depth, and conductivity
increased. Although temperature is known to affect both conductivity (Weisser 1994,
Bohlin et al. 1989) and larval response to electrical stimulus (Hintz 1993), the
temperature range in this study (10 — 22°C ) did not appear to inﬂuence sampling
effectiveness.

The results of this study are consistent with previous studies examining the
relationship between teleost size and electroﬁshing efﬁciency (F ievct et al. 1999,
Anderson 1995, Bohlin et al. 1989, Zalewski 1985, Reynolds 1983, Sullivan 1956), yet
contrasts with laboratory experiments on larval lampreys (Hintz 1993). In the laboratory
experiment, the emergence rate of smaller size classes (65-69 mm) was greater than that
of larger size classes (75-89mm) under constant voltage gradients and water conductivity
at 15 and 20°C (Hintz 1993). However, the laboratory experiment was conducted in clear
aquaria, designed to permit observation of all emergent lampreys. The capture of lamprey
larvae under ﬁeld conditions is quite different, where larvae that emerge from the
substrate may not be seen and, of those that are observed, immobilization and successful
capture are not guaranteed. As well, due to their size, large larvae are more likely to be
seen once they have emerged, increasing the probability that they will be captured in ﬁeld
sampling conditions. Successﬁil capture of lamprey larvae includes inducing lampreys to
emerge from their burrows, followed by observation of the free-swimming larvae,
immobilization, and capture. The effect of increased size may be more important than the
effect of increased emergence rate for successfully capturing emergent larvae under ﬁeld

conditions.

25

My study also showed that increased density of larval lampreys reduced the observed
capture proportion (Figure 4). Electroﬁshing an area of high larval density results in the
simultaneous emergence of multiple larvae. Capture success for an operator faced with
increasing larval lamprey density is similar to a Type II functional response in predator-
prey interactions (Holling 1959). The rate of capture of the operator increases as lamprey
density increases, until physical limitations prohibit the operator from collecting all of the
larvae that emerge. In high density plots some larvae will escape initial capture. Of the
portion that escape, some will swim either out of the plot or to areas of the plot already
sampled, and thus remove themselves from potential capture. The larvae that re-burrow
in the unsampled plot area are more susceptible to narcosis from further electrical stimuli
(Pusey et al. 1998, Cross and Stott 1975), and may not emerge for a second capture
opportunity.

Increasing depth in the sampling plots was found to decrease electroﬁshing efﬁciency
(Figure 5). Bohlin et a1. (1989) summarize the potential for an increase in electroﬁshing
efﬁciency in shallow water when electroﬁshing for teleosts, where the effectiveness of
the electrical ﬁeld changes at the water-air and water-substrate boundaries. Typically, the
current lines of an electrical ﬁeld are uniform between electroﬁshing electrodes, with the
voltage gradient smoothly decreasing as distance from the electrode increases. However,
the current lines are compressed at the water/air interface, resulting in increased
electroﬁsher efﬁciency near the water surface. A ﬁsh that encounters the electrical ﬁeld
while near the surface of the water is more likely to be immobilized due to this effect.
Conversely, stream sediment is usually higher in speciﬁc conductance than the

surrounding water. This results in the current lines of an electrical ﬁeld being attracted

26

into the sediment and reducing the capture efﬁciency for ﬁsh that are encountered close
to the substrate. When electroﬁshing for burrowed lamprey larvae, the increased
conductance of the substrate may increase the probability of stimulating lamprey larvae
to emerge, assuming that the level of stimulus remains below that required to induce
galvanonarcosis. When this occurs in shallower water, the larvae are more likely to be
observed and immobilized for capture due to the increased visibility and efﬁciency of the
electrical ﬁeld near the water surface. Together, the effects of increased emergence,
increased visibility, and a more effective electrical ﬁeld increase the sampling

effectiveness of backpack electroﬁshers in shallow water.

 

013“

015‘

Probability of metamorphosis

OJ!“

 

 

 

 

0.0 I I I I
0 50 100 150 200

Length (mm)

Figure 4. The effects of density on capture proportion, conditioned on length. Capture
proportion decreases as density increases from 0.5 larvae-m’2 (dotted line) to 5
larvae-m'2 (dashed line) and 25 larvae-m'2 (solid line). Conductivity and depth are
ﬁxed at 200 umhos and 0.3m, respectively.

27

 

L0

013‘

Probability of metamorphosis

 

 

 

 

0.0 I T I I
0 50 100 150 200

Length (mm)

Figure 5. The effects of depth on capture proportion, conditioned on length. Capture
proportion decreases as depth increases from 0.1m (dotted line) to 0.3m (dashed line)

and 0.8m (solid line). Conductivity and lamprey density are ﬁxed at 200 umhos and 5
larvae-m'z, respectively.

The effects of conductivity on electroﬁshing efﬁciency are represented as
interaction terms with the other environmental variables in the study, but, in general,
increases in water conductivity result in lower electrofrshing efﬁciency (Figure 6).
Increasing conductivity results in a smaller electrical ﬁeld surrounding the electrodes
with a higher current density between the electrodes (Bohlin et al. 1989, Reynolds 1983).
Given the sedentary nature of lamprey larvae, the electrical ﬁeld in high conductivity
water may not have the current density required to elicit an emergence response until the

electrodes are very close to the burrowed larva. Depending upon the speed the electrodes

28

are being passed over the substrate, the larval burrow may be located between the
electrodes before the larva has the opportunity to emerge. Once this occurs, the current
density may be so large as to induce galvanonarcosis in the individual, and prohibit a

response to the stimulus (Regis et al. 1981).

 

013‘

015“

 

Probability of metamorphosis

0J2“

 

 

 

0.0 I I T I
0 50 100 150 200

Length (mm)

Figure 6. The effects of conductivity on capture proportion, conditioned on length.

Capture proportion decreases as conductivity increases from 50 umhos (dotted line)

to 200 umhos (dashed line) and 400 umhos (solid line). Lamprey density and water

depth are ﬁxed at 5 larvae-m'2 and 0.3m, respectively.

Similar to the effects of density and depth, the effect of conductivity on capture
efﬁciency can be offset as the length of the larvae increases. In this case, a much smaller

current density is required to produce a physiological response to electroﬁshing as the

length of a ﬁsh increases (Bohlin et al. 1989, Zalewski 1985). Therefore, although the

29

generated electrical ﬁeld is smaller and less effective at higher conductivities, larger
lampreys have a higher probability of responding to the stimulus while the electrodes are
further away, increasing their likelihood of being captured. The presence of the
interaction term in the logistic model indicates that these effects are also not consistent
across the range of conductivities and larval lengths examined in this study.

The variables selected for investigation in this study are readily measured using
equipment that is easily portable for ﬁeld use. Although some of the environmental
variables signiﬁcantly reduced the deviance of the logistic model, there are other factors
that inﬂuence electroﬁshing effectiveness. Perhaps the most important of these is
electroﬁshing experience (Hardin and Connor 1992). For example, the density at which
an operator becomes limited in the ability to capture larval lampreys during the ﬁrst pass
of electroﬁshing will vary depending upon physical ability and electroﬁshing experience
(Hardin and Connor 1992). While the sampling protocol speciﬁcation of electroﬁshing at
1.5 minutes-m'2 is intended to minimize the differences in catch due to experience,
veteran electroﬁshing personnel will tend to have a more reﬁned capture technique,
enabling them to collect a higher proportion of lampreys on the ﬁrst pass. Density
estimates from samples collected by experienced personnel would be systematically
higher than those made by less experienced crewmembers, resulting in biased population
estimates. One solution to this problem would be to assess the sampling efﬁciency of
each individual under a variety of sampling conditions, creating an efﬁciency model for
each ﬁeld person. However, the time required to replicate this study for individual ﬁeld
crewmembers makes this an impractical solution. Perhaps the best solution to reduce the

potential bias in population estimates is to ensure that each stream is assessed by multiple

30

crewmembers representing a range of electroﬁshing experience. Although this would
increase the variability in the sampling data, it would also reduce the potential sampling
bias of individual crewmembers in selecting sample plots. This would result in more
comparable estimates of mean stream density and larval lamprey abundance.

Other variables that inﬂuence electroﬁshing effectiveness include turbidity,
vegetative cover, water colour, water surface conditions, glare, fatigue, and even biting
insects. With the exception of turbidity, none of these variables are easily quantiﬁable,
but may be incorporated as categorical variables in future studies. Turbidity can be
quantiﬁed using Secchi disc readings, and the effects of turbidity could be quantiﬁed in
future studies.

The logistic regression incorporates the effects of the sampling conditions and
lamprey density into a prediction of capture success given the size of the lamprey. An
hypothetical example can be used to illustrate how the larval and parasitic population
estimates are affected by using the logistic model of electroﬁsher efﬁciency model to
adjust the observed catch when sampling a larval population across the range of survey
conditions examined in the study (Figure 7). Assume that two different streams are
sampled and the same number and size structure of lampreys are obtained in each
collection. Then assume that the two streams represent, respectively, optimal sampling
conditions of low water depth, larval density, and stream conductivity (stream A) and the
worst sampling conditions, where all sampling attributes have high values (stream B).
Since the observed catch is the same for each stream, the ﬁxed efﬁciency of 48% would
estimate the larval population, and the potential number of parasitic individuals, to be the

same for each stream. If the cost of treatment is comparable for both streams, it would be

31

equally cost-effective to treat either. Incorporating the effects of environmental variables
into the population estimate yields very different population estimates for the two
streams. As we are less effective at sampling larvae under the extreme sampling
conditions of stream B, the observed catch represents a much lower proportion of the true
population. Consequently, the population estimate of larvae and the potential production
of parasitic individuals will be larger than that of stream A. If the cost of treating either
stream is still the same, it becomes more beneﬁcial to treat stream B, as more potential

parasitic lampreys will be removed for each dollar spent.

 

100~

 

 

 

Population Estimate (1000's)
3
\\\\\\\

 

 

Stream A Stream B Stream A Stream B
FIXED EFFICIENCY VARIABLE EFFICIENCY

I Trans El Larvae

Figure 7. A comparison of the consequences of ﬁxed versus variable efﬁciency on
estimates of sea lamprey populations. Stream A represents optimal sampling conditions:
Conductivity=50 umhos, Density=0.5 larvae m'z, Mean Depth=0.1 m; stream B
represents extreme sampling conditions: Conductivity =550 umhos, Density =20.0 larvae
m'z, Mean Depth =0.8 m.

32

Since accounting for environmental variables can have large effects on population
estimates, I recommend that Sea Lamprey Control managers incorporate the current
logistic estimate of electroﬁsher efﬁciency into the Empiric Stream Ranking model.
Incorporation of the model should be done with the understanding that the model reﬂects
the sampling efﬁciency of one individual, uses ﬁxed electroﬁsher settings, and that
investigation of the other variables mentioned could further reﬁne the understanding of
sampling effectiveness using backpack electroﬁshers.

Estimates of available larval habitat range from 500 to 800,000 square metres in
tributaries of the Great Lakes. From a management perspective, an estimated difference
of 0.1 larvae m'2 can affect both the overall population estimate and estimate of parasitic
production derived by the Empiric Stream Ranking model, particularly in large streams.
Since streams are prioritized for treatment based on the cost of eliminating the potential
parasitic population, a change in estimated density of 0.1 larvae rn'2 could make the
difference between TFM treatment the following year or deferral to a later date, allowing
parasitic lampreys to escape to the lake population. Reﬁning our understanding of how
environmental variables inﬂuence the effectiveness of sampling lamprey larvae using
backpack electroﬁshing gear will enable the control agents to more accurately estimate
the larval lamprey population of a stream. This will result in more accurate estimates of
parasitic lamprey production, and a more cost-efﬁcient application of treatment efforts to

control sea lampreys in the Great Lakes.

33

Uncertainty in the estimation of sea lamprey (Petromyzon marinas) populations
in Great Lakes tributaries.
INTRODUCTION
“A 11 models are wrong. Some models are useful. ” George Box

Sea lampreys in the Great Lakes are currently controlled using an integrated
approach, combining biological, physical, and chemical techniques (Great Lakes Fishery
Commission 1992). While biological (e.g. sterile male release) and physical (e.g. low-
head barriers, mechanical traps) methods have the potential to reduce the reproductive
success of sea lampreys, chemical control, with the selective lampricide 3-
triﬂuoromethyl-4-nitrophenol (TFM), remains the primary tool used to reduce sea
lamprey populations (Great Lakes Fishery Commission 1992, Smith and Tibbles 1980).
The controlled application of TFM to Great Lakes tributaries kills larval lampreys in their
natal streams (Smith and Tibbles 1980), before they have the opportunity to
metamorphose to the parasitic stage (Potter 1980).

Presently, the cost of TF M prohibits treating all streams that will produce parasitic
sea lampreys each year. This has led the US. Fish and Wildlife Service, Fisheries and
Oceans Canada (the control agents) and the Great Lakes Fishery Commission (GLFC) to
evaluate and prioritize which Great Lakes tributaries should receive treatment. Using
information about the cost of stream treatment as well as sampling information on
lamprey populations in each candidate stream, the control agents apply a cost-beneﬁt
approach to selecting streams for treatment. Streams where the control agents anticipate
the largest number of parasitic sea lampreys will be killed for each treatment dollar spent

receive the highest prioritization for treatment.

34

A computer-based simulation model, the Empiric Stream Ranking (ESTR) system
(Appendix A), is used to assist with the stream ranking process. The model uses sampling
data collected by assessment crews from approximately 100 streams each year. In
wadeable streams, the control agents begin by randomly selecting 6-10 points in each
stream where the river may be easily accessed, from a pool of all access points available
for the stream. At each access point the crews measure 12 transects perpendicular to
stream ﬂow and subjectively categorize the stream substrate along each transect as
preferred (Type 1), secondary (Type 2), or not usable (Type 3) by larval sea lampreys.
Two sample plots between 5 and 15m2 are measured within the areas of preferred habitat,
and backpack electroﬁshers are used to determine the density of larval lampreys within
the Type 1 habitat. This typically results in 72 habitat transects, 12 electroﬁshed plots,
and 100 or more lamprey larvae captured per stream, but can vary depending upon
stream- and site-speciﬁc conditions. These data are used as inputs to the ESTR model to
estimate the areas of available larval habitat and mean lamprey density. These estimates
are then used to provide a single estimate of larval sea lamprey abundance per stream
(Appendix A).

The ESTR model also uses the length data from the larval sampling to forecast the
number of lampreys in the sample expected to metamorphose the following year
(Appendix A). Larval length in the fall of the year is used as an indicator of the potential
of the larva to metamorphose the following year (Potter 1980, Holmes and Youson
1994). Lamprey larvae are collected throughout the ﬁeld season in the year prior to
expected metamorphosis, requiring the control agents to estimate the amount the larvae

will increase in length by the fall of the current season. This additional growth is

35

estimated as the product of an average daily growth rate and the number of days between
collection and the end of the growing season. The additional growth is added to the
measured length of each larva to estimate fall length. The ESTR model uses this
estimated fall length as an input to a geographically deﬁned, length-dependent logistic
model that estimates the probability of metamorphosis for each larva in the sample. The
ESTR model then calculates the proportion of the sample expected to metamorphose.
This same proportion is applied to the estimate of larval lamprey abundance to forecast
the number of larval lampreys expected to migrate to the lake population the year after
sampling.

Due to uncertainties in the input data as well as the driving variables of sampling
efﬁciency, lamprey density in Type 2 habitat, growth, and probability of metamorphosis,
the forecasts of larval and metamorphosed lampreys are also uncertain quantities.
Although not quantiﬁed, perceived uncertainties in the ESTR predictions currently play a
qualitative role in determining the ﬁnal stream treatment list. Based on experience and
informed knowledge, biologists from the US. Fish and Wildlife Service and Fisheries
and Oceans Canada (the control agents) as well as the Great Lakes Fishery Commission
subjectively assess the ESTR forecasts of parasitic lamprey production. Review of the
stream-speciﬁc sampling information that is the basis for the ESTR prediction is
compared with information regarding the streams’ historical productivity, and the stream
is scheduled for treatment when the informed Opinions of the biologists agree with the
ESTR prediction. Quantiﬁcation of the uncertainty in the ESTR model predictions would
allow the control agents to assess the magnitude and potential consequences of their

stream treatment decisions.

36

In this chapter I examine the sources of uncertainty associated with the ESTR model
forecasts. The ﬁrst is the accuracy of the ESTR model predictions. Although the ESTR
model has been used to prioritize streams for TF M treatment since 1995, the accuracy of
model forecasts has never been veriﬁed by direct comparison with observations at the
time of TFM treatment. Consequently, the control agents tend to use subjective
judgement about the accuracy of the estimates to help establish the stream treatment list.
To determine the accuracy of the ESTR model forecasts, I compared the model estimates
with mark-recapture estimates of larval and metamorphosed lamprey abundance made
during TF M treatment. Verifying the accuracy of the ESTR forecasts will enhance
conﬁdence in the ESTR model as a decision tool for the stream ranking procedure. The
collection of data to compare predicted and observed parasitic lamprey production in the
veriﬁcation process will also enable the control agents to assess any systematic biases in
the ESTR model.

A second source of uncertainty stems from uncertainty in the model inputs and their
propagation to the ESTR model outputs. The ESTR model components, by design, are
deterministic and do not reﬂect the underlying variability in their values. Values for the
parameters and constants within the ESTR model were initially derived ﬁ'om analyses of
historical data compiled by the control agents, and from analogous studies that paralleled
the purpose of the ESTR model (e.g. Simonson et al 1994). I re-parameterized the ESTR
model using the data sources initially used to develop the model as well as incorporating
more recent data that provided a measure of the variability in the input data. To examine
the propagation of input uncertainty, I combined the re-speciﬁed model with a Monte

Carlo simulation procedure to quantify the uncertainty in the model outputs, as well as to

37

determine which of the input variables contributed most to the uncertainty in model
output (Kleinjen 1996, McKay 1995). The effects of the input variables are examined for
nine representative streams to determine relative importance within the simulation model.
The objectives of this chapter are to:
1) provide an independent estimate of larval and metamorphosed lampreys at the time of
TFM treatment to compare with ESTR model predictions
2) quantify the uncertainty in the ESTR model predictions of metamorphosed lampreys
3) determine the model variables that contribute most to prediction uncertainty
MATERIALS AND METHODS
Study streams
Nine streams were selected for both Petersen mark-recapture estimates of
metamorphosed lamprey production, and to provide input data for simulation modeling
(Table 7, Figure 8). These streams were selected from the list of streams scheduled for
TFM treatment after mid-July in either 1998 or 1999. This timing was necessary to
ensure that external signs of metamorphosis were readily visible on the lampreys and that
sufﬁcient time was available to capture, mark, release and recapture lampreys from. each
stream.
Mark-recapture population estimates
1 used the survey data collected the year prior to treatment, initially used as inputs to
the ESTR model, to estimate a target of larvae to mark and recapture in each stream. The
desired precision for the mark-recapture population estimate was i10%, and I estimated

the target sample sizes from Figure 6 in Robson and Regier (1964).

38

    

Gargantua Batchawana
River River

(23:21 Watson’s
Creek
A .
Wilson
Creek .
1

Club

Creek

Meyer’s

 

Creek

Figure 8. Location of the study streams for the veriﬁcation and validation of the
ESTR model.

Table 7. Characteristics of the study streams used in the simulation model.

 

 

Stream Lake Drainage Mean Annual Alkalinity Conductivity
Discharge
. (ka) (m3s'1) (mg/L CaC03) (umhos)
Batchawana River Superior 1190 23.2 21 50
Club Creek Huron 2.3 0.7‘ 178 360
Crystal Creek Huron 89 0.30‘ 16 54
Gargantua River Superior 97 0.25‘ 20 20
Meyer's Creek Huron 0.5 0.12' 140 250
Proctor's Creek Ontario 27 0.78 220 460
Salem Creek Ontario 52 0.55 183 450
Watson's Creek Huron 6.5 0.65 54 100
Wilson Creek Superior 3.8 0.10 62 140

 

'Mean discharge during TF M treatments

In the year of treatment I collected lamprey larvae from each stream using backpack
electroﬁshing gear, marked them by removing a portion of non-vascular tissue at the end
of the caudal ﬁn, and released them in the approximate area of capture. All collections for
marking were made at least two weeks prior to TFM treatment. Larnpreys were

recaptured either during TFM treatment or within 24 hours alter treatment, using

39

stationary fyke nets or actively collecting with hand dip nets. The recaptured larvae were
identiﬁed to genus, examined for marks and for external signs of metamorphosis.

Population size (N) was estimated for each stream using the bias-corrected Petersen
estimator (Seber 1982):

(M+1)-(C+l)_l
(R+l)

1C]:

 

where: M = number of individuals marked
C = total number of individuals in treatment collection
R = number of marked individuals in the treatment collection

with variance:

V(N)=(M+1)2-(q+1).(C—R)
(R+l) -(R+2)

 

Four of the eight streams selected for the mark-recapture study contained populations
of indigenous lamprey species. Larvae of these species can be difﬁcult to differentiate
from sea lamprey larvae while electroﬁshing or collecting during treatment, but can be
easily identiﬁed using morphometric characteristics alter the collecting effort is
completed. I marked all lampreys collected by electroﬁshing and retained all lampreys
encountered during treatment. The proportion of the overall population estimate that were

sea lampreys was assumed to be the same as the proportion in the treatment collection:

* =10.—
NSL C

where: N31. = the estimated number of sea lampreys;

A7 = the Petersen population estimate of all lampreys;

CSL= number of lampreys in the treatment collection that are sea lampreys; and

40

C = total number of individuals in the treatment collection
Similarly, the number of metamorphosed sea lampreys in each population was

estimated as:

. . CT
=N-—
NT C

where: 107. = the estimated number of metamorphosed sea lampreys;

A7 = the Petersen population estimate;

C1: the number of lampreys in the treatment collection that are;

metamorphosed sea lampreys; and

C = the total number of individuals in the treatment collection

The variance of the estimators of population proportions was calculated as:
Vot )=(1‘7)2-V(9-"-)+Vain-[Q]2
x C C
where: V (10x) = the variance of the proportion interest, either T or SL as appropriate

A7 = the Petersen population estimate;

V(1(I ) = the variance of the Petersen population estimate;

93.0-91)

HQ) = C C , the variance of the proportion of interest, with
C C

 

C, = the number of larvae in the treatment collection equal to T or SL as

appropriate
C = the total number of individuals in the treatment collection

Conﬁdence intervals for the estimates were calculated using the normal approximation:

(Nx)i1°96’V(1\7x)

41

I assumed that the mark-recapture estimate provided an unbiased estimate of lamprey
abundance at the time of TF M treatment. I plotted the estimates of larval and
metamorphosed lamprey abundance from the ESTR model versus the mark-recapture
estimates to compare the relative accuracy of the ESTR forecasts. I considered the ESTR
model estimates of abundance accurate if the plotted points fell along a replacement line
having a slope of 1.

Monte Carlo Simulation Model Parameterization

A Monte Carlo simulation procedure was developed in the S-Plus programming
language (MathSoft Inc. 2000) to examine the inﬂuence of uncertainty in the ESTR
model parameters and on the model’s prediction of parasitic-phase sea lamprey
production. In most cases parametric distributions were not easily ﬁt to the input data, so
the simulation model sampled directly from the input data using resampling procedures
(Davison and Hinkley 1997). Values for each of the input variables were selected, with
replacement, from the data during each iteration of the simulation model. The simulation
model calculated larval and metamorphosed lamprey abundance from the selected values
after each iteration.

The parameters for electroﬁshing efﬁciency, lamprey density in Type 2 habitat,
growth, and probability of metamorphosis are deterministically speciﬁed in the ESTR
model (Appendix A). To examine the effects of uncertainty in these parameters on the
overall uncertainty in the model outputs, a measure of variability was assigned to each, as
outlined in the appropriate sections below. The measure of variance was based on
historical data or, in the case of electroﬁsher efﬁciency, on the model developed in

Chapter 1.

42

Habitat and Catch Resampling

The data to describe habitat, electroﬁshing catch, and lamprey length for each of the 9
study streams were obtained from the control agents. To preserve the potential spatial
correlation that may be present among these variables in a stream, the hierarchy of access
points, habitat transects, plots sampled and observed catch were resampled using a nested
bootstrap approach (Figure 9).

Each stream has sampling data from S access points. For each iteration of the
simulation model, the access point data was randomly sampled S times, with
replacement. Each access point has data for T habitat transects and P electroﬁshing plots.
For each randomly selected access point, T transects and P plots were selected with
replacement from the habitat and plot data speciﬁc to the access point. Each
electroﬁshing plot has length information for L total lampreys captured within the plot.
For each plot P selected from site S, L lengths were randomly selected with replacement
from the plot-speciﬁc length information. The resampled transect and electroﬁshing data
were used to estimate available habitat and larval lamprey density as outlined in
Appendix A.

Electrofisher Effectiveness

The logistic model of electroﬁsher effectiveness (Chapter 1) was incorporated into
the simulation model. The logistic equation models probability of capture of lamprey
larvae as a function of stream conductivity, sampling depth, larval size, and initial density
of larvae within the plot. However, initial larval density is unknown in the data collected
using the quantitative sampling protocol. An iterative procedure was developed that

estimated initial plot density and incrementally adjusted this density within the logistic

43

80800890: :85

.008: I— :0888 388:8 0:03 88 580. 0500:0508 08 8088:: :2: 83808020 8:0 :00: 8:088:80: 8:3 .008:
m 8088:: 388:8 0:03 82: 82808020 08 8:: 8:800:80: 8:3 008: h 838:0 588:8 0:03 80088: 85::
8880000: 08 880200 :80: 00000: 8:0 8.: 8080028: 8:? .008: m 888:0 888:8 0:03 880: 00000: 08 8:80:
7808 8:0 885 :808 88:88:: 05 8 :00: 88 088:0 8: .88 82808020 0:0 8:080 00 80.80:: .0 0.88m

 

 

0 50:3 :0 :2: 0 50:3 N0 8.: g 50:3 .0 :2: :0 33:8. 83::
_ _ _ _
N .5080 :0 :2: N 50:3 N0 .2: N 50:: :0 .2: N0 88:5: 055::
: 508.106.: _ 505...: N0 8.: _ 50:3 :0 :2: :0 38:8: 53::
_
:0 :2: 0:88:82: N0 6.: 0:259:85 a 8.: 0:55:82: _
_II I I I _ _ m 88: 00000<
0 50:3 .8 :2: 0 .5080 N: :2: 0 50:3 8 :2: 8 63:8. 83:: .
_ _ _ _
N 50:3 .8 .2: N 50:3 N: :2: N 50:3 8 .2: N: .888: 053::
_ 50:3. 8 8.: _ 5080. N: 8.: _ 50:3. 8 8.: 8 88:8. 555::
:0 :2: 0:55:52: N: 3.: 05:52:85 8 :2: 05:588.: _
.II I I I _ _ m 88.— 00000<
0 50:3 :< :2: 0 50:3 N... :2: 0 50:3 3. :2: 5. 63:8: 053::
_ _ _ _
N .5050 :< :2: N .5080 N< .2: N 508.: 3. .2: N0: 8.5:. 55:58
_ 50:3. .2 :2: _ 50:3. N5. 8.: _ 50:0.“ 2 .2: _< 8.5:: 555::
:< :2: 05:588.: N... :2: 05:52:85 2 :2: 0:55.82: _
_II I I I F _

 

< 880: 00000<

model until the predicted catch, given the site speciﬁc variables and length of observed
lampreys, was equal to the observed catch. The iteration procedure is stopped when the
difference between the predicted and observed catch, given estimated density, is less than
1 x 10'5.

Variability in electrofisher sampling effectiveness for each length bin is estimated as:
var(EF) = ){hsz {,6} X h (Neter et al. 1996, p 603)

where: 52,06}: the approximate variance-covariance matrix for the logistic

effectiveness model
X h = the vector of values of length, log(density), conductivity, mean

depth, and three interaction terms for each length bin, h, in the sample plot

After iteratively estimating the initial plot density, the simulation model generates a
random normal probability of capture for each length bin, of mean Xkﬂ( where ,6 is the
vector of logistic model parameter estimates) and variance var(EF). The inverse of these
probabilities of capture are summed to estimate the plot densities incorporating variable
sampling efﬁciency, and these densities are used in the ESTR simulation model to
estimate mean density throughout the river.
Proportion of Type 1 Larval Density Attributable to Type 2 Habitat

Backpack electroﬁsher sampling is conducted in an average of 90 streams annually.
To ensure that all streams are sampled, larval lampreys are sampled exclusively from
Type 1 habitat. The sampling protocol speciﬁes that lamprey larvae be sampled from
Type 2 habitat only when there is not enough preferred Type 1 habitat available for
electroﬁshing. The occurrence of insufﬁcient Type 1 habitat for sampling is rare in Great

Lakes tributaries; consequently, the streams selected for the simulation do not have

45

sampling information for lamprey densities in Type 2 habitat. The control agents
currently assign the density of larval lampreys in Type 2 habitat as 0.275 of the larval
lamprey density in Type 1 habitat.

Data are available from a research study in which both Type 1 and Type 2 habitats
were sampled to assess larval lamprey abundance (M. Jones, Michigan State University,
unpublished data). There were 26 streams sampled in the study, with some streams
sampled in multiple years, providing 46 stream-year combinations with comparative

Type 1 and Type 2 larval densities.

 

Frequency

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

P I” _ 0w
0 i
o.o

 

I

4 0.6 0.8 1.0
Larval density in Type habitat/Larval density in Type 1 habitat

III e I

Figure 10. Distribution of the T2/T1 ratios in the multi-year lamprey study.

The distribution of the density ratio data is not easily described parametrically (Figure
10). I assumed that the comparative density data represented a random sample from the
population of density ratios that occur throughout Great Lakes tributaries. In each

iteration of the model, an observation from the data set was randomly sampled and this

46

estimate of the Type 1 density to be attributed to Type 2 habitat was used as R in equation
A.5.
Growth Rate

The control agents use the length of larval lampreys at the end of the season of
collection as an indicator of metamorphic potential (Appendix A). However, larvae are
collected from April through October each year, requiring the control agents to estimate
the length the collections of larvae will be at the end of the growing season. The
procedure for estimating end-of-season larval length uses a stream-speciﬁc daily growth
rate, calculated for each sea lamprey producing tributary in the Great Lakes. Among- and
within-stream variability in the average daily growth rate is expected, however the
current grth rates have been estimated once per stream and do not provide a measure
of this uncertainty.

As growth varies with temperature (Potter 1980), I assumed that the growth rates
would be similar for streams in the same geographic location. The average daily grth
rates for all streams within a region were pooled. The resulting distributions were
approximately normal in distribution, and an overall mean and variance in growth rate
were calculated for the region. For each iteration of the model, average daily grth rate
is determined by randomly sampling from a normal distribution with region-speciﬁc
mean and variance.

Probability of Metamorphosis

Data from samples of larval and metamorphosed sea lampreys collected during 106

separate TFM treatments were used to develop length-dependent logistic models of the

probability of metamorphosis for 11 geographic regions around the Great Lakes. The

47

collections for streams within each region were pooled, and an individual logistic model
ﬁt to the data. The models use the estimated length at the end of the growing season to
predict the probability of metamorphosis for each larvae collected.

The regional probability of metamorphosis models estimated from the pooled data do
not encompass the within- and among-stream variability that is present when each of the
individual stream models of metamorphosis are estimated (Figure 11). To reﬂect this
underlying variability, I assumed that each stream-speciﬁc logistic model represented a
possible model of metamorphosis for the streams in a region. For each iteration of the
simulation model one pair of the stream and year-speciﬁc parameter estimates was
selected from the individual logistic models for that region and used to calculate the

length-dependent probability of metamorphosis.

 

Probability of Metamorphosis

 

 

 

 

100 120 140 160 180 200
Length (mm)

Figure 11. Regional logistic model to predict probability of metamorphosis (solid
line) versus the logistic models for the individual stream data within the region

(dashed lines).

48

Evaluation of uncertainties in variables and predictions

Since some of the parameterizations for the simulation model differ from those in the
current ESTR model, the simulation model outputs may not equal those of the initial
ESTR model. For example, the estimates of density using the logistic model of
electroﬁsher efﬁciency are expected to differ from those obtained using the ﬁxed
estimate of 0.48, resulting in different estimates of lamprey abundance. The simulation
model outputs will be compared with the mark-recapture estimates to determine if these
changes in parameterization provide more accurate abundance estimates.

The number of simulations to use was determined by using the simulation model to
sample one of the data sets 10000 times and observing when the coefficient of variation
of the estimates of metamorphosed lampreys became relatively stable (Figure 12). This
occurred after approximately 5000 simulations. Given that 5000 simulations require
approximately one hour of computing time and there were 81 scenarios to simulate, the
added expense of additional simulations did not substantially improve the coefﬁcient of
variation. The simulation model was used to provide 5000 estimates of larval and
metamorphosed lampreys for each stream in the data set. The 2.5 and 97.5 percentiles
were used to bound the uncertainty in the estimates of larval lamprey abundance and in
the predictions of metamorphosed lamprey production.

The models that predict larval and parasitic lamprey production (Appendix A) cannot
be easily linearized. To evaluate the inﬂuence of variability in the input variables on the

uncertainty in the model prediction I used a nonparametric approach (McKay 1995). The

'49

 

 

 

 

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of the simulation model outputs.
importance of uncertainty in each of the 9 variables on the ammocoete abundance and
parasitic production estimates of the ESTR model was assessed as the difference in the
coefﬁcient of variation of My — the CV of the ESTR model prediction when all inputs y
vary — and the conditional CV’s of Myls — the CV of the ESTR model prediction when
each variable 5 is ﬁxed at its nominal value (McKay 1995).

l assigned nominal values to the model parameters of catchability, growth, lamprey
density in Type 2 habitat, and probability of metamorphosis as the mean values of the
data used for resampling. I assigned nominal values to the hierarchy of access point,

habitat, total catch, and length data by recoding the model to sample the data without

50

replacement when the simulation model was conditioned for the respective variable.
Sampling without replacement forced the simulation model to select the same data
combination and subsequently calculate a consistent value for each conditioned variable
during each iteration of the simulation model. This removed the contribution to output
uncertainty for the conditioned variable.

The simulation model was reiterated 5000 times for each of the 8 variables ﬁxed at
their nominal values (Table 8). The estimates of mean and variance of the abundance
estimates for both larval and metamorphosed lampreys are not independent (Figure 13).
For this reason, I chose to use the coefﬁcient of variation (CV) to compare the difference
in variability between the fully randomized simulation model outputs and outputs of each

conditioned model.

 

 

 

 

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Figure 13. Scatterplot of variance versus mean for the simulation model abundance
estimates of larval (open circles) and metamorphosed (crosses) lampreys. The values
of mean larval abundance and metamorphosed lamprey variance are scaled by 103,
larval variance by 10'6 to ﬁt common axes.

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52

To determine which conditioned variables produced a signiﬁcant reduction in output
variability, I used a bootstrap procedure to determine the 2.5 and 97.5 percentiles of the
CV for the fully randomized model outputs. For each stream, I resampled the 5000
estimates of larval and metamorphosed lampreys from the fully randomized model 1000
times, with replacement, and calculated the CV for each resample. From the 1000
bootstrap CV’s I calculated the 2.5 and 97.5 percentiles of the CV for the fully
randomized model. A CV for the conditional model that fell outside of this 95 percentile
range was considered a signiﬁcant deviation.

RESULTS
Mark-recapture population estimates

The mark-recapture estimates of larval and metamorphosed lamprey abundance are
presented in Table 9. The initial ESTR model forecasts, used to determine the target
number of larvae to mark and release, do not accurately predict larval or metamorphosed
lamprey abundance in the majority of the streams. For larval abundance forecasts, the
initial ESTR model estimates do not fall within the 95% conﬁdence intervals of mark-
recapture larval abundance estimates for any stream. The initial ESTR model forecasts of
metamorphosed lampreys lie within the 95% conﬁdence intervals of the mark-recapture
estimates for 2 of the 9 streams; Batchawana River and Meyer’s Creek.

Since the parameterization of the simulation model for the Monte Carlo approach to
quantifying model uncertainties differs from the original EST R model speciﬁcation, I
also compared the relative accuracy of the simulation model forecasts with the mark-
recapture estimates of abundance (Figures 14 and15). The comparisons where the both

the vertical and horizontal error bars encompass the replacement line represent those

53

Table 9. Mark-recapture estimates of larval and metamorphosed (or juvenile) lamprey
abundance for the 9 study streams. The upper panel contains the estimate of lamprey
abundance for all species and life stages in each study stream. The middle and bottom
panels contain estimates of larval and juvenile sea lamprey abundance, respectively. The
initial larval and juvenile abundance estimates are the one-year ESTR forecasts from the
survey data, used to set target levels of larvae to mark and recapture.

 

 

 

 

 

 

 

 

 

 

Stream Treatment 95% C.I.*

Name Date M R C N * Lower Upper
Batchawana R. Oct 14-16, 1998 2618 41 5102 318209 223489 412928
Club Cr. Sept 6, 1999 1386 187 3500 25829 22247 29411
Crystal Cr. Sept 20-22, 1999 1608 144 5023 55749 46837 64661
Gargantua R. Aug 3-4, 1999 692 214 1573 5073 4445 5702
Meyer's Cr. Oct 5, 1999 393 28 382 5204 3413 6994
Proctor's Cr. Aug 19-21, 1998 1068 112 2398 22695 18628 26762
Salem Cr. Aug 19-20,1998 1309 43 3770 112273 79661 144885
Watson's Cr. Sept 30, 1998 1339 154 3350 28970 24530 33410
Wilson Cr. July 26-28, 1999 3276 135 11547 278256 231936 324576
1 Population estimate includes all species and life stages of lampreys

Estimate of Initial Larval

Stream Proportion of N as Larval 95% C.I. Abundance

Name larval sea lampreys Abundance Lower Upper Estimate
Batchawana R. 0.289 92120 64392 118974 29344
Club Cr. 0.990 25572 22025 29118 33432
Crystal Cr. 0.975 54336 45650 63022 26906
Gargantua R. 0.999 5067 4439 5695 26403
Meyer's Cr. 0.657 3419 1992 4084 1533
Proctor's Cr. 0.997 22629 18574 26684 13622
Salem Cr. 0.999 112154 79577 144731 13384
Watson's Cr. 0.939 27189 23021 31356 8417
Wilson Cr. 0.999 277894 231635 324154 142339

Estimate of Initial Juvenile

Stream Proportion of N as Juvenile 95% C.I. Abundance

Name juvenile sea lampreys Abundance Lower Upper Estimate
Batchawana R. 0.00137 437 88 785 164
Club Cr. 0.00997 258 165 350 92
Crystal Cr. 0.02534 1413 1082 1744 164
Gargantua R. 0.00127 6 0 15 327
Meyer's Cr. 0.0733 381 192 570 427
Proctor's Cr. 0.00292 66 16 117 496
Salem Cr. 0.00106 119 0 241 453
Watson's Cr. 0 0 - - 298
Wilson Cr. 0.0013 361 169 554 1862

 

54

streams where the mean simulation model estimate of lamprey abundance falls within the
95% CI of the mark-recapture estimate. Two of the estimates of larval abundance fall
near the replacement line (Figure 14); the Batchawana River and Wilson Creek. Similar
to the ESTR model estimates, two of the simulation model estimates of metamorphosed
lamprey abundance lie within the 95% CI of the mark-recapture estimates and fall near
the replacement line (Figure 15); the Batchawana River and Meyer’s Creek. In all
comparisons, the Monte Carlo simulation model estimates are more variable than the
mark-recapture estimates, demonstrated by the greater range between the 2.5 and 97.5
percentiles of the simulation forecasts than the 95% conﬁdence intervals of the mark-
recapture estimates. Conversely, in 16 of 18 comparisons, the mark-recapture estimate
falls within the 95-percentile range of the simulation model estimates. There is no
evidence of systematic differences between the simulation and mark-recapture estimates
of lamprey abundance.

Monte Carlo Simulation model outputs

The larval and metamorphosed lamprey abundance estimates from the 5000 simulations
for all models are presented for each stream in Figures 16 through 24. In all cases the
median values of abundance are approximately equal, indicating that the model
consistently estimates the same central measure of lamprey abundance whether the model
is fully randomized or a conditioned on a nominal value for each variable. The
coefﬁcients of variation for estimates of metamorphosed lamprey abundance are higher
than the coefﬁcients of variation for estimates of larval abundance within the same

stream (Table 10). This may be due to the contribution of the added variability from

55

growth rates and probability of metamorphosis in the metamorphosed lamprey abundance

estimates that does not affect the estimates of larval lamprey abundance.

Table 10. Means and bootstrap percentiles for the coefﬁcients of variation for the
larval and metamorphosed (juvenile) lamprey abundance estimates from the fully

randomized simulation model.

 

Larval Estimates

Juvenile Estimates

 

 

 

 

Percentiles Percentiles
Stream Mean 2.5 97.5 Mean 2.5 97.5
Batchawana R. 0.798 0.776 0.817 1.917 1.747 2.152
Club Cr. 0.532 0.520 0.544 1.832 1.778 1.886
Crystal Cr. 0.766 0.748 0.786 1.777 1.690 1.864
Gargantua R. 0.771 0.746 0.793 1.613 1.557 1.705
Meyer's Cr. 0.621 0.607 0.635 1.252 1.221 1.284
Proctor's Cr. 0.626 0.612 0.639 1.053 1.018 1.088
Salem Cr. 0.717 0.699 0.733 1.199 1.162 1.236
Watson's Cr. 0.775 0.760 0.791 0.880 0.853 0.909
Wilson Cr. 0.423 0.415 0.430 0.967 0.942 0.993

 

Three variables, the access points sampled, observed catch, and the ratio of Type 1
larval density assigned to Type 2 habitat, reduced the coefﬁcient of variation in most
streams below the 2.5 percentile of the fully randomized model estimates of larval
lamprey abundance (Table 11). The CV of larval lamprey estimates was signiﬁcantly
different in 5 of 9 streams when the variability in the amount of habitat was removed
from the model. In Club Creek, the model conditioned on length also had a signiﬁcantly
lower CV than the CV for the fully randomized model.

Seven of the 8 conditioned simulation models changed the coefﬁcient of variation for
estimates of metamorphosed lamprey abundance in at least 6 of the 9 streams in this
study (Table 12). Conditioning on the Type lzType 2 ratio again reduced the CV for
metamorphosed lamprey estimates in all 9 streams, followed by the models conditioned
on access point and observed catch for 8 of the 9 streams. Notably, ﬁxed capture

effectiveness signiﬁcantly changes the CV for metamorphosed lamprey abundance

56

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Figure 16. Boxplots for the fully randomized and conditioned variable simulation
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Figure 18. Boxplots for the fully randomized and conditioned variable simulation
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61

 

       

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

    

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62

 

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Figure 20. Boxplots for the fully randomized and conditioned variable simulation
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63

 

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Figure 21. Boxplots for the fully randomized and conditioned variable simulation
model estimates of larval and metamorphosed sea lampreys in the Proctor’s Creek.
The open boxes represent the 25th and 75th quartiles, the lines within the boxes
represent the median estimates for each set of simulations, and the whiskers are 1.5
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of the CV for the fully randomized model.

64

 

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66

 

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67

 

 

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estimates in Salem Creek and does not reduce the CV for larval lamprey estimates in any
stream.

In the case of Proctor’s Creek, ﬁxing the amount of habitat at nominal values resulted
in an increase in the CV for estimates of larval and metamorphosed lamprey abundance.
When the simulation model is conditioned on habitat area, Type 1 and 2 habitat areas are
assigned as constant values, while the remaining access point variables (site, catch, and
length) are sampled with replacement. If larval lamprey length or abundance is correlated
with the amount of habitat available at an access point, this sampling approach removes
any correlation between habitat area and the remaining access point variables. In turn,
this may artiﬁcially increase the variability in the sampling data, where, for example,
larval lamprey catches may be assigned to areas where the actual stream data do not
reﬂect this relationship. This is an indication of the importance of maintaining the
structure when resampling the survey data, where removing correlation within the data by
relaxing the sampling scheme can result in more variable model outputs.

DISCUSSION

Uncertainty in prediction models is derived ﬁ'om three general sources (Sargent 1998,
Rykiel 1996, Draper 1995, McKay 1995, Power 1993, Grant 1986). The ﬁrst is
uncertainty in the model parameters, and how this parameter uncertainty is propagated
through the model to the model output. The second is uncertainty in the structural
assumptions of the model that deﬁnes the relationships between the model components.
The third is uncertainty in the accuracy of the model forecasts, and may be influenced by

the uncertainty from the ﬁrst two sources.

69

Validation, calibration, and veriﬁcation of prediction models is an ongoing process
that reduces these uncertainties, where model components and relationships are updated
as new information reﬁnes or reshapes the understanding of model users (Kleijnen 1996,
Rykiel 1996, McKay 1995, Grant 1986). Validation consists of determining if the model
predictions reﬂect the behaviour of the system, sometimes deﬁned as the accuracy of the
model. Calibration ensures that the parameters and constants within the model are
appropriately speciﬁed to provide agreement between model output and the system being
modeled, improving model accuracy. Calibrating the model can also reduce the
propagation of uncertainty in the model parameters through the model to the outputs,
improving the ability to prioritize streams for TF M treatment based on the model
forecasts. Veriﬁcation consists of examining the model for structural and logical
integrity, ensuring that the model formalism is correct. I will discuss how the results of
the mark-recapture study and the simulation model analyses may be used to validate,
calibrate, and verify the ESTR model, improving the utility of the model forecasts for
treatment prioritization purposes.

Assuming that the mark-recapture estimates are unbiased estimators of lamprey
abundance at the time of treatment, the current ESTR model (Appendix A) estimates of
lamprey abundance are comparable in 2 of 18 cases. The simulation model, which
contains slightly different parameters than the ESTR model, does not fare much better,
providing comparable estimates of lamprey abundance in 4 of 18 comparisons. The lack
of systematic differences between the mark-recapture and the ESTR or simulation model
estimates indicates that there is no consistent source of error that may be reduced to

increase the accuracy of the predictions (Power 1993). For example, the ESTR model

70

does not account for mortality between the time of sampling and the time of treatment. If
the mark-recapture estimates were consistently lower than the model predictions,
incorporating a mortality term into the model could provide some improvement in
prediction accuracy. Comparing the mark-recapture estimates with the ESTR model
outputs to validate the model indicates that the current model does not reproduce the
behaviour of the system (Sargent 1998, Rykiel 1996, Power 1993). Consequently, the
current ESTR model is invalid for the purpose of providing reliable forecasts of lamprey
abundance as a component of the cost-beneﬁt ranking of streams for TFM treatment.

Several sources may contribute to the inaccuracy in the ESTR model predictions.
Invalid data provided by the sampling protocol, improper values assigned to the driving
variables within the model, or an improper model structure used to predict lamprey
abundance would result in inaccurate model outputs (Sargent 1998, McKay 1996, Draper
1995). With respect to model structure, the speciﬁcation of the ESTR model is one of
many plausible alternative models that could be used to predict parasitic sea lamprey
production. Alternative models may include additional parameters, such as the
aforementioned mortality parameter, or may specify different relationships among the
input variables (Morgan and Henrion 1990).

The importance of model structure is illustrated by an examination of how the
sampling data are used to estimate larval lamprey abundance. The habitat and density
data are currently collected from discrete sites within a stream, and may be many
hundreds of metres apart. The ESTR model pools the sampling data for all sites to
calculate stream-wide estimates of habitat area and mean larval lamprey density. By

pooling the data there is an implicit assumption that there is no correlation between the

71

habitat and larval density found at a speciﬁc access point. I will use the sampling
information from Club Creek to illustrate a potential for error in this assumption.

Table 13 contains a summary of the catch and habitat information for Club Creek.
The ESTR model estimate of larval lamprey abundance is calculated following the
formula (Appendix A):

(Mean density x Type 1 habitat area) + (Mean density x Type 2 habitat area x 0.1)
and results in an estimated 33,432 lamprey larvae. An alternative method for estimating
larval lamprey abundance is to treat each sample site as an individual section of the
stream, estimate the abundance for each section using the above formula, and sum across
sections to estimate the overall stream abundance of larval lampreys. I have done this for
each of the 6 sites in Club Creek. For illustrative purposes I have assumed that the sites
were evenly spaced along the length of the stream, giving equal weight to the population
estimate for each stream section. The incremental estimate of 27,911 lampreys now falls
within the 95% conﬁdence interval of the mark-recapture estimate for the Club Creek
population (22025,29118; Table 9). However, an improved estimate of larval abundance
is not always the case when using the incremental approach to estimating abundance for
every stream in the study. The same technique applied to the data for Salem Creek results
in an abundance estimate of 11,758 larval lampreys, which is further from the mark-
recapture abundance estimate. As well, the improved estimate of larval abundance does
not necessarily result in an improved estimate of metamorphosed lamprey abundance.
Since the same catch information is used to estimate the proportion of larvae that will
metamorphose in the two approaches, I expect that 92/33,432 or 0.00275 of the

incremental larval population estimate, approximately 77 larvae, would metamorphose in

72

Club Creek. As with the Salem Creek larval abundance estimates, this is further from the

mark-recapture estimate of metamorphosed lamprey abundance for Club Creek. This

illustration is not intended to prescribe a method that results in improved accuracy in the

ESTR model predictions. Rather, it is intended to demonstrate the effect that verifying

the structural assumptions of the model can have on the accuracy of model predictions.
Table 13. Summary of the sampling data for Club Creek, illustrating an

alternative approach to estimating larval lamprey abundance.
Incremental Method

Density Habitat Areas (m2) Population
Site Catch (#-m'2) Type 1 Type 2 Type 3 Estimate

 

 

 

l 1 0.069 646.1 3100.3 507.3 66

2 46 3.194 381.6 2636.4 238.5 2061
3 88 6.111 841.2 2445.6 0.0 6635
4 114 7.917 524.7 2541.0 60.7 6165
5 76 5.278 2129.0 2575.7 43.4 12596
6 2 0.139 2597.3 1933.9 99.7 388

Incremental Total 27911

ESTR model estimates
1-6 327 3.785 7302.0 15315.1 711.1 33432

 

As well as being inaccurate, the model outputs have a high degree of uncertainty
(Figures 16-24). The range of values between the 2.5 and 97.5 percentiles for the
simulation model outputs are from 1.5 to 90 times greater than the mark-recapture
conﬁdence intervals in the estimates of larval abundance, and from 2 to more than 200
times greater in the metamorphosed lamprey estimates. The quantiﬁcation of the
uncertainty around the model outputs illustrates the potential risk of not treating streams
that may have a high abundance of metamorphosing sea lampreys (Morgan and Henrion

1990).

73

The control agents review the ESTR model forecasts of metamorphosed lamprey
abundance, and base their treatment decisions on an evaluation of whether they believe
the ESTR model estimates are accurate. The control agents reject the estimates and place
a lower priority on streams that they believe the ESTR model has overestimated the
abundance of metamorphosed lampreys. This most oﬁen occurs in the larger streams
where the 12 plots electroﬁshed for lamprey larvae represents a very small proportion of
the available lamprey habitat. In these larger streams, lamprey density is typically low,
and few (e.g. less than 50) lampreys are obtained from the sample plots. This relatively
small sample of lamprey larvae would contain a low number of larvae that are greater
than 100 mm in length and have the potential to metamorphose the following year.
Extrapolating these few large larvae in the sample over a large amount of habitat results
in a large estimate of lampreys with the potential to metamorphose. Intuitively, the
uncertainty in this estimate is also large, since the addition (or removal) of one potentially
metamorphic larva would have large effects on the forecast of metamorphosed lampreys.

As mentioned, this most often occurs in large streams, which are also expensive to
treat with TFM. The control agents’ approach to stream prioritization attempts to
minimize the chance of the worst possible outcome (Morgan and Henrion 1990), which
they view as the treatment of a stream when the forecast of metamorphosed lamprey
abundance overestimates the true population. In these situations, the control agents
subjectively balance the cost of treating a stream where few metamorphic lampreys exist
against the risk of not treating the stream when the lampreys are truly there. This method

of stream prioritization using a qualitative estimate of uncertainty in the model forecasts

74

clearly indicates the need to acquire better information on which to base treatment
decisions (Morgan and Henrion 1990).

One solution to these problems may be to collect more samples from the streams that
contain the most uncertainty, thereby reducing the variability in the model forecasts as
well as potentially increasing the accuracy of the estimate. However, with up to 100
streams requiring a quantitative estimate of lamprey abundance each year, increasing the
number of samples is an expensive proposition.

Additional sampling of the streams with the highest uncertainty may be accomplished
through the development of a more optimal allocation of sampling effort. The control
agents know which streams will be quantitatively sampled prior to the sampling year.
This list of streams usually contains streams that consistently produce high numbers of
sea lampreys and are cost-effective to treat, as well as streams that have historically had
uncertain abundance estimates and may require additional sampling each year.
Developing a methodology to assess the productive streams more rapidly, such as a rapid,
single-pass index of abundance on fewer sites per stream (Jones and Stockwell 1995,
Lobon-Cervia and Utrilla 1993), would reduce the amount of time and effort required to
determine the relative abundance of lampreys in the streams known to be cost-effective
for treatment. The effort could then be used in streams that are expected to have a high
degree of uncertainty in the estimates of lamprey abundance. This method of allocating
sampling resources is only worthwhile if the additional sampling effort on streams with
high output uncertainty improves the accuracy and precision of the estimates of larval

and metamorphosed lamprey abundance.

75

The ﬁnal analysis examines which of the input variables contributes most to output
uncertainty and would be the most beneﬁcial to calibrate within the model. An
examination of the contribution of parameter uncertainty to output uncertainty can be
misleading when there is uncertainty about the structure of the model and the accuracy of
the outputs (Morgan and Henrion 1990). However, modeling is an iterative process (Pace
2001, Rykiel 1996), and calibrating those parameters that most affect output uncertainty
could lead to more accurate and more precise model outputs.

Input uncertainty in the simulation model can be divided into two categories:
uncertainty in the sampling data and uncertainty in the parameters within the model. I
will ﬁrst consider how these two categories affect the estimate of larval lamprey
abundance. The explanations for the observed effect that reducing uncertainty in the
model components has on the uncertainty in larval lamprey estimates are valid for the
forecasts of metamorphosed lamprey abundance. Calibrating the model components that
signiﬁcantly inﬂuence the estimate of the larval lamprey population may be a signiﬁcant
step towards more accurate and precise estimates of potentially metamorphic lampreys.

Forcing the simulation model to select the same combination of sites for each
iteration consistently reduced the variability in the estimates of larval and
metamorphosed lampreys (Table 11, Table 12). This is likely due to correlation between
the amount of habitat and the number of lampreys found at discrete access points within
the stream. During the development of the backpack electroﬁshing protocol, this potential
for longitudinal correlation was investigated using archival sampling data for US.
streams (M. F odale, US. Fish and Wildlife Service, Marquette, Michigan, personal

communication). There was no evidence of longitudinal correlation of habitat, catch, or

76

length of lampreys and the sampling location within a stream, which lead to the protocol
assumption that random selection of access points would yield similar forecasts of
metamorphosed lamprey production regardless of the location of the access points. The
results of the simulation model conditioned on the site data indicate that there is a
considerable amount of among-site variability in the sampling data, and the selection of
access sites can inﬂuence the model outputs.

The inﬂuence of the site-speciﬁc data on the uncertainty in the estimates of
abundance can be explained when one of three situations occurs within the data. Habitat
is the most inﬂuential of the site-speciﬁc variables when there is a disparity between the
relative abundance of Type 1 and Type 2 habitat among the sites. This occurs, for
example, when the random selection of access sites selects a point that is associated with
the estuary of a stream. Stream estuaries tend to have greater stream width, lower water
velocities, and a higher proportion of Type 1 habitat than do the rest of the stream. The
option of including (or not including) an estuarine site when resampling the data using
the fully randomized model creates variability in the estimated amount of available larval
habitat and the associated estimate of abundance.

Similar arguments exist for the inﬂuence of the catch and length information on
model outputs. In those streams where the reducing the variability in the catch
information reduces variability in the model outputs, there is at least one sample site
where the number of lampreys collected differs considerably from the other sample sites.
Similarly, in those streams where reducing variability in the length information reduces
uncertainty in the forecasts of metamorphosed lamprey abundance, at least one

electroﬁshed plot contains a higher abundance of large lamprey larvae than do other

77

electroﬁshed plots in the data set. The inﬂuence of uncertainty in length data is discussed
in more detail along with catchability later in this discussion.

If there were no within-site correlation among data at an access site, the nested
resampling scheme would not be necessary. Length, plot, and habitat data could be
randomly resampled without contributing additional variability to the estimates of
lamprey abundance. In this case, the ESTR model approach of pooling the data to obtain
estimates of mean habitat area and lamprey abundance would not affect the model
outputs. However, as both the example using Club Creek data as well as the output of the
model conditioned on the site variables shows, the selection of the access site and the
catch and habitat data that accompany it are important to the model outputs. This
relationship is further illustrated with an examination of the data for Proctor’s Creek
(Table 14), where conditioning the simulation model on the estimated amount of habitat
results in an increased coefficient of variation of the estimates of larval and
metamorphosed lampreys.

Table 14. Summary of the catch and habitat information for Proctor’s Creek. Note that a
higher proportion of the total catch is captured from Site 2, which contains a lower

proportion of the available habitat. This results in higher estimates of larval lamprey
density.

 

Estimated
, Number Total Proportion of Proportion of Proportion of Type 1
Site of Plots Catch Total Catch of Type 1 Area of Type 2 Area Density (#-m'2)

 

l 4 38 0.157 0.359 0.243 1.9
2 4 175 0.723 0.214 0.060 8.75
3 4 29 0.120 0.427 0.697 1.45

 

In the Proctor’s Creek data, the catch from sample site 2 contained over 70% of the
lampreys collected, while the habitat data for the site represents less than 20% of the

estimated available habitat. The model conditioned on habitat ignores this correlation

78

between habitat and catch at each site. Consequently, as many as 3 samples of catch data
from site 2 can be incorporated into the density estimates without the correlated value for
habitat area. This results in the highest density of lampreys being applied to areas of high
habitat abundance within the model, when the association does not exist in the data. The
range of estimates for larval and metamorphosed lampreys is greater in Proctor’s Creek
for the model conditioned on the habitat variable, resulting in increased variability in the
model outputs and a higher coefﬁcient of variation.

The examination of the driving variables in the simulation model shows that the
variability in the estimates of larval and metamorphosed lamprey abundance is
signiﬁcantly reduced when the variability of the density of lampreys in Type 2 habitat is
removed (Table 11, Table 12). This is not unexpected, as most tributaries to the Great
Lakes, including the ones used in this study, have higher abundance of Type 2 habitat.
Consequently, the proportion of lampreys in Type 1 habitat assigned to Type 2 habitat
will signiﬁcantly impact the estimates of lamprey abundance. The effect varies as the
proportion of Type 2 habitat increases. In the Batchawana River and Crystal Creek,
where the ratio of Type 2:Type 1 habitat area is 11.9 and 13.7 respectively, reducing the
variability in the proportion of lampreys assigned to Type 2 habitat causes the largest
reduction in variability in the estimates of larval lamprey abundance.

Notably, the reduction in variability in either catchability or length is not propagated
through the model to the uncertainty around larval lamprey estimates. With respect to
catchability, this is most likely due to the speciﬁcation of the model, where the reduction
in the variance of catchability is achieved by removing the inﬂuence of the variance-

covariance matrix in the logistic model of electroﬁsher effectiveness. Since there is little

79

variability around the logistic model it makes sense that removing this small source of
uncertainty does not signiﬁcantly reduce the uncertainty in the estimate of larval
lampreys.

The effect of reducing the variability associated with randomly resampling the length
data is also associated with the logistic model of sampling efﬁciency. Although the length
of the lampreys captured will inﬂuence the iterative procedure in the logistic model, with
smaller lampreys equating to a greater estimate of density, resampling the length
information results in a relatively consistent length distribution. Resampling the length
distribution within each sample plot does not contribute enough variability to propagate
through the logistic model of electroﬁsher effectiveness and signiﬁcantly inﬂuence
uncertainty in estimates of larval lamprey abundance. The estimates of larval lamprey
abundance are primarily inﬂuenced by the absolute abundance of the lampreys collected,
not the size structure in the catch.

Conversely, the uncertainty in the forecasts of metamorphosed lampreys is sensitive
to the parameters that affect the number of larvae of potentially metamorphic size. This
includes the catch and habitat parameters that affect the estimate of larval abundance, and
also includes length, growth and probability of metamorphosis parameters, that affect the
estimated proportion of the catch that will metamorphose. For example, in the
Batchawana River and Club and Crystal creeks, larvae greater than 100mm make up a
small proportion of the total collection, and are contained within one or two
electroﬁshing samples. The effect of resampling these data by ﬁrst selecting the plot data
and then resampling the length data can geometrically increase the number of large larvae

in the simulation. When the variability in the simulation model is reduced by

80

conditioning on length, the number of large larvae in the collection is signiﬁcantly less
variable, and results in more precise estimates of metamorphosed lampreys. Similarly,
variation in the average daily grth rate will affect the number of larvae that are
expected to be over 100m at the end of the growing season. The speciﬁc model selected
to estimate probability of metamorphosis will, in turn, affect the forecast of number of
these larvae expected to metamorphose.

The analysis of the propagation of parameter uncertainty demonstrates that the
contribution of one parameter to uncertainty in the model output may depend upon the
value assigned to another parameter, or may be affected by the structure of the sampling
data used as model inputs. This has been demonstrated for the hierarchy within the access
point data used in this model. Although not done in this study, the effects of the co-
dependency of additional parameters, such as between grth rate and probability of
metamorphosis, can be explicitly modeled and examined, and may warrant further
investigation in the reduction of uncertainty in ESTR model estimates (Warren-Hicks and
Moore 1998, McKay 1995, Morgan and Henrion 1990).

The inclusion of a stream on the TFM treatment schedule is a decision process that
balances the risks of treating a subset of streams to remove the parasitic lamprey
population at the cost of not treating the remaining streams that will also produce
parasitic lampreys. The ESTR model currently used by the control agents of the sea
lamprey control program does not accurately forecast the abundance of either larval or
metamorphosed sea lampreys, and is not adequate for the task of providing sound
quantitative estimates as the basis for treatment decisions. To improve the accuracy of the

ESTR forecasts the control agents must continue to develop their understanding of the

81

processes that affect lamprey production, and use this improved understanding to reﬁne
the ESTR model parameters and underlying model structure. To do this, I recommend
that the control agents begin by reﬁning the sampling protocol and ESTR model structure
to estimate larval lamprey abundance with a predetermined level of accuracy. This should
be accompanied by methodologies that quantify the amount of uncertainty in the
estimates, so that the potential consequences of treatment decisions can be assessed. Once
a reliable model of larval lamprey abundance has been developed, the parameters to
forecast the expected number of metamorphosed lampreys can be evaluated. By
quantifying the uncertainty in the predictions as well as improving the accuracy of the
ESTR model, uncertainty in stream treatment decisions can be better assessed and
balanced against the ecological, social, and economic needs of Great Lakes ﬁsheries

management.

82

APPENDIX A

Specifications of the ESTR model used by the sea lamprey management program to
predict metamorphosed sea lamprey abundance in Great Lakes tributaries.

1. Sample Allocation

The objective of the ESTR model is to use habitat and larval density data to forecast
the number of larval and metamorphosed lampreys in Great Lakes tributaries the year
after sampling. To meet this objective, a larval assessment sampling protocol has been
established to formalize how the stream-speciﬁc habitat and larval density data are
collected. The sampling protocol for backpack electroﬁshing speciﬁes that information
describing 12 habitat transects and 2 electroﬁshing plots be collected at 6 access points,
such as road or trail crossings, in each stream. The 6 access points are selected randomly
from a list of all available access points. When fewer than 6 access points are available,
the number of habitat transects measured in the stream is reduced to 12 transects at each
access point (e. g. 4 access points x 12 transects = 48 habitat transects). To ensure that an
adequate number of larval lampreys are collected to describe the year-class structure of
the larval population, the minimum number of electroﬁshing plots remains at 12. In the
case where fewer than 6 access points are sampled, two electroﬁshing plots are assigned
to each available access point, and the remaining plots are evenly distributed among the
reduced number of access points. In the case of 5 access points, where the 12
electroﬁshing plots cannot be evenly divided among access points, the 2 remaining plots
are randomly assigned to 2 of the 5 access points.
2. Habitat Estimates

Stream substrate is subjectively classiﬁed as one of three habitat types available to

larval lampreys along 12 transects at each access point. Preferred larval habitat (Type 1)

83

APPENDIX A (continued)
is found in depositional areas of the stream, where slower water velocity allows ﬁne silt,
sediments, and detritus to accumulate. Secondary larval habitat (Type 2) is composed of
coarse sand and ﬁne gravel, and is typically associated with higher water velocities. Type
3 habitat is composed of hardpan clay or bedrock, substrates that larval lampreys cannot
burrow into.

Transects are located perpendicular to stream ﬂow, and an observer measures the
amount of each habitat type encountered along each transect. The transects are spaced at
three mean stream widths if the stream is less than 5m wide, and at two mean stream
widths if the stream is greater than 5m wide. The transects are used to calculate the area

of each habitat type as:

M 2

Z ZTi,j

H.="—=¥f‘——csL (AA)
M

where: Hi = area of stream that is habitat type j, j = l or 2;

TiJ = length of transect i that is habitat type j, in metres;
M = number of transects measured in stream; and
SL = the measured length of the stream that contains sea lamprey larvae, in
metres

3. Larval Lamprey Density Estimates

Larval lampreys are electroﬁshed from 12 measured plots located in Type 1 habitat.
The observed catch is adjusted by the estimated sampling efﬁciency for backpack

electroﬁshing gear, and the number of lampreys in each plot is calculated as:

84

APPENDIX A (continued)

C
CA = — (A.2)
q

where: CA = observed catch adjusted for sampling effectiveness;
C = the number of lampreys collected from plot Ir,
q = 0.48, the estimate of sampling effectiveness calculated from the 1996-

97 data from the initial electroﬁsher efﬁciency studies (unpublished data)

The mean density of sea lamprey larvae in the stream is calculated as the mean of the plot

densities using the adjusted catch, as:

g [2;]
k=l q ' ak (A.3)

where: D; = mean density;

Ck = the number of lampreys collected from plot k;

ak = area of plot k;

P = the number of plots electroﬁshed
4. The proportion of larval lamprey density in Type 1 habitat that is attributed to
Type 2 habitat.
As mentioned in section A3, larval lampreys are sampled in Type 1 habitat. The density
of larval lampreys in Type 2 habitat is estimated as a proportion of the gear-adjusted
mean density estimated in Type 1 habitat, as:

R = 0.275 V (A.4)

85

APPENDIX A (continued)
where: R = the proportion of larval lampreys in Type 1 habitat that is attributed to
Type 2
The proportion, R, is the mean proportion of Type2/Type 1 lamprey densities from an
independent study that examined larval lamprey density in both habitat types.
5. Estimate of larval lamprey population.
Habitat area, larval lamprey density, and the estimated proportion of lampreys in
Type 2 habitat are used to estimate the larval sea lamprey population in each stream.

P P
z z[é—;] m... z[ a]

A [:1 k=1 k i=1 k=l q .ak
.= - : - + - : ~ - -R A.5
N' M P SI" M P SL ( )

 

 

 

where: 10, = the estimate of larval sea lamprey abundance, and other variables

are as previously deﬁned
6. Length of observed catch at end of growing season.

Lamprey larvae have been shown not to increase in length in the year of
metamorphosis (Youson 1988, Potter 1980) and the total length of an ammocoete after
growth has ceased is an indicator of metamorphic potential the following year. However,
lamprey larvae used to forecast parasitic production are captured between April and
October each year, while there is still time remaining in the season for larvae to grow.
Since virtually all of the grth of larval lampreys occurs in the warmer months (Potter
1980), the control agents are required to estimate the length the larvae will be at the end
of the growing season, typically late October to mid-November. For each stream, an

average daily grth rate in mm-day'l has been calculated from data for the ﬁrst larval

86

APPENDIX A (continued)
cohort re-established in the stream after TFM treatment. The approximate end of the
larval lamprey growing season was estimated from climatic data, as the date when
average daily air temperature drops below 5°C. To calculate the larval length at the
end of the growing season, the product of the stream-speciﬁc average daily growth rate
and the number of days from capture to the projected end of the larval growing season is
added to the measured length at the time of capture. This ‘ﬁnal’ length is used in the
logistic equation to calculate probability of metamorphosis.
LF = LC+ (DF - DC)'GD (A-6)
where: Lp = length of lamprey larvae at the end of the growing season;
L5 = length of lamprey larvae at the time of capture;
D1: = date of the end of the growing season;
Dc = the date of sampling;
GD = the average daily growth rate, in mm-day’1
7. Probability of metamorphosis.

Probability of metamorphosis is estimated from lamprey length and life-stage data
collected from 106 TFM treatments between 1969 and 1996. Using this data, length-
dependent logistic regression models were developed for 11 separate geographic regions
around the Great Lakes (Table A1). Probability of metamorphosis is estimated for each
individual larva as:

I
1+exp_(ﬂo+ﬂl'LF)T

l -

 

1

Pr{Met} .—. (A.7)

 

 

87

APPENDIX A (continued)

where: Pr{Met} = the length-dependent probability of metamorphosis for a larva;

[30,131 = parameter estimates for the regional logistic model

Table A1. Parameter estimates for length-dependent logistic models

developed for 11 regions around the Great Lakes.

 

 

Geographic Region [30 [31
Lake Ontario South Shore 0.201 -25.821
Lake Ontario/Lake Erie North Shore 0.267 -33.917
Lake Erie South Shore 0.299 -39.875
Lake Huron Main Basin & Georgian Bay (Can) 0.155 -23.636
Lake Huron North Channel (Can) 0.218 -30.596
Lake Michigan/Lake Huron North (U.S.) 0.163 -24.821
Lake Michigan/Lake Huron South (U.S.) 0.204 -29.075
Superior East (Can) 0.118 -l6.59l
Superior Central & West (Can) 0.122 -l8.842
Lake Superior East (U .8.) 0.239 -34.484
Lake Superior West (U.S.) 0.233 -32.458

 

8. Proportion of catch that is expected to metamorphose.

The proportion of the electroﬁshing catch that is expected to metamorphose is used to
infer the proportion of the estimated larval population that will metamorphose. The
proportion of the catch that will metamorphose is equal to the sum of the probability of
metamorphosis for each larva in the whole-stream collection divided by the total catch

for the stream, and is estimated as:

 

(A.8)
C T

where: C M = the proportion of the observed catch expected to metamorphose;

(L p)a = the ﬁnal length of ammocoete a;

88

APPENDIX A (continued)

q a = the plot-speciﬁc catchability of ammocoete a;
Pr {Met}a = the length-dependent probability of metamorphosis of

ammocoete a

CT = the total number of lampreys collected from the stream

9. Expected number of metamorphosed lampreys one-year post-sampling.
The larval lamprey population estimate and the proportion of catch expected to
metamorphose are used to forecast the expected number of metamorphosed lampreys the

year after sampling, as:

 

 

 

 

 

 

 

 

 

 

M = 1V,- - CM (A.9)
— - _ _ E (Lp), 1
M P M P “ .
ZTil Z _Qk_ ZT,2 Z[ Ck :l “:1 qa [1+exp_(ﬂo+ﬂl'LF)]
= i=1 ’ 'k=qu.ak"S +i=l ’ .k=1 q-ak "S 'R . L _
M P L M P L c

 

 

89

APPENDIX B

Derivation of the variance of the capture proportion in the backpack electrofishing
effectiveness study.

Capture proportion p for each length increment i is estimated as:

.. M:
Pi =‘._1 03-”
Ni

where: M; = the electroﬁshing catch in increment i

10,. = estimated abundance of larvae in increment i

Lockwood et al. (1999, p15) derive the variance of a proportion as:

 

 

 

2
var(%) = (g) .|:var(2a) + “22”] where a,b are independent (B2)
0
2 A 2 .
WW) -[var<ti>+mfr>]=[w) (Le)
N i M i N i N i N i

(M:+1)(C:+1)
Ri+1

 

where: var( 10,.) = var[ ], with M,- and C,- known, and R,- is Poisson

distributed for low values of R (Krebs 1999), so that:

 

var( 19,.) = [(Mi+l)(C,-+ 1)]2.var[ (Ki-1+0] (13.4)

90

APPENDIX B (continued)

 

 

 

2
From B.2, var[ 1 j: [‘1‘] ' var(21)+ var(R)2 (3.5),
(Ri+ 1) Ri+1 (1) (R,+l)
which simpliﬁes to: var(G—ilﬁjj = (15:04 (B.6)

 

war(Ni)=[(Mi+1)(Ci+1)]2- Rf ’N __

 

 

 

 

2 (IV )2 R
i 2
and var(ﬁi)=(—M:';) (RziH)
Ni (Ni)
R
M%- 2
2 (if?) (M)

91

 

 

 

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95

Appendix D. Length and catch data for the six study streams sampled to examine
the sampling effectiveness of backpack electroﬁshing gear.

 

 

Mean
Length Electroﬁshing Dredge Recaptures Length
Site Stream Increment Catch (M) Catch (C) (R) (mm)
1 Watson's 80 50 80 20 53.62
100 6 3 2 86.17
2 Watson's 80 1 l 39 10 57.64
100 1 1 l 93.00
120 l 2 1 105.00
3 Watson's 80 2 4 1 70.00
4 Watson's 80 86 1 19 59 52.27
100 3 4 3 92.67
120 2 3 2 107.00
5 Watson's 80 76 l 10 56 52.46
100 2 3 2 86.00
6 Watson's 80 47 70 28 55.79
100 3 4 3 88.33
7 Sturgeon 80 l 1 l 73.00
100 1 1 1 94.00
140 1 1 1 135.00
8 Sturgeon 100 1 l 1 88.00
120 3 2 2 110.33
9 Sturgeon 80 l 1 l 79.00
120 l l 1 101.00
10 Sturgeon 100 2 2 2 99.00
120 2 2 2 111.50
1 l Sturgeon 100 l 1 1 91 .00
12 Sturgeon 120 1 l l 101 .00
13 Sturgeon 80 3 2 2 52.67
100 9 6 6 92.11
120 3 4 3 104.67
14 Sturgeon 100 7 7 6 96.86
120 3 4 3 107.33
15 Sturgeon 80 l 2 1 42.00
100 5 7 4 97.20
120 3 7 2 110.33
140 2 2 2 126.00
16 Sturgeon 100 16 17 11 93.81

 

96

Appendix D (continued)

 

 

Mean
Length Electrofishing Dredge Recaptures Length
Site Stream Increment Catch (M) Catch (C) (R) (mm)
17 Sturgeon 80 2 2 1 63.00
100 6 14 6 92.83
120 2 2 1 105.00
1 8 Sturgeon 80 l 2 1 63 .00
100 5 6 5 93.40
120 7 5 5 108.14
19 Sturgeon 80 4 8 4 60.50
100 4 4 4 89.00
120 5 6 5 110.20
140 1 1 1 122.00
20 Sturgeon 80 4 5 2 62.25
100 6 6 5 89.83
120 7 4 3 105.57
21 Lynde 80 3 5 2 63.00
22 Lynde 80 l 2 1 56.00
23 Lynde 80 3 4 2 60.67
120 2 2 2 104.00
24 Lynde 80 2 2 1 68.50
25 Lynde 80 9 3 3 63.11
140 2 3 2 134.50
26 Lynde 80 6 5 2 63.17
120 l 2 1 104.00
27 Lynde 80 5 4 3 66.60
100 l l 1 90.00
120 l 2 1 120.00
140 2 2 2 133.50
200 l 2 1 143.00
28 Lynde 80 3 5 2 66.67
29 Lynde 80 2 2 2 70.50
140 1 1 1 127.00
30 Lynde 80 3 4 3 59.67
100 2 2 2 87.50
140 1 1 1 125.00
200 1 l 1 141.00
31 Lynde 80 6 4 4 66.83
140 3 3 3 131.00

 

97

Appendix D (continued)

 

 

Mean

Length Electroﬁshing Dredge Recaptures Length

Site Stream Increment Catch (M) Catch (C) (R) (mm)
32 Lynde 80 6 10 5 64.17
100 l 2 1 95.00

120 2 l 1 110.50

33 Lynde 80 2 5 2 68.50
100 1 l l 85.00

34 Lynde 80 1 3 1 63.00
120 1 1 1 119.00

35 Batchawana 80 16 18 4 51.75
100 9 7 6 89.44

120 3 3 2 106.67

140 2 2 2 123.50

36 Batchawana 80 24 54 18 64.71
100 15 13 13 92.00

120 4 6 4 111.25

140 5 4 4 129.80

200 2 2 2 158.50

37 Batchawana 80 25 18 14 50.12
100 12 10 8 95.42

120 10 8 7 106.10

140 5 6 5 131.60

200 3 3 3 154.33

38 Batchawana 80 19' 19 15 60.95
100 7 5 5 89.71

140 1 1 1 132.00

39 Batchawana 80 35 35 21 46.80
100 4 6 4 88.75

120 4 4 4 107.00

140 2 2 2 129.50

200 l 1 1 157.00

40 Batchawana 80 22 52 20 59.36
100 5 5 4 86.00

120 4 5 4 111.75

140 2 1 1 124.50

200 1 1 1 166.00

200 2 2 2 152.00

 

98

Appendix D (continued)

 

 

Mean

Length Electrofishing Dredge Recaptures Length

Site Stream Increment Catch (M) Catch (C) (R) (mm)
41 Batchawana 80 15 31 10 49.47
100 8 10 7 91.00

120 2 l 1 114.50

140 2 2 1 130.00

200 2 1 1 150.50

42 Batchawana 80 13 23 10 50.69
100 3 4 3 85.67

43 Batchawana 80 24 45 17 46.00
100 6 9 5 88.00

120 3 3 3 110.00

140 6 7 6 132.50

200 2 1 1 147.50

44 Batchawana 80 15 46 6 52.27
100 4 5 3 90.25

120 l 1 1 107.00

140 2 2 1 128.00

200 4 3 3 160.75

45 Batchawana 80 18 34 11 45.00
100 l 2 1 82.00

120 2 3 2 116.00

140 2 2 2 130.50

200 3 2 2 165.33

46 Batchawana 80 24 41 16 52.38
100 6 5 4 91.00

120 4 2 2 111.50

140 2 2 2 126.50

200 2 4 2 160.50

47 Batchawana 80 27 47 23 61.19
100 8 11 6 89.38

120 3 4 3 110.67

140 5 5 4 130.20

200 4 3 3 149.00

48 Batchawana 80 37 51 29 62.24
100 5 8 5 91.40

120 3 4 3 108.33

140 3 3 2 127.33

 

99

Appendix D (continued)

 

 

Mean

Length Electrofishing Dredge Recaptures Length

Site Stream Increment Catch (M) Catch (C) (R) (mm)
49 Batchawana 80 38 37 28 52.76
100 7 8 7 89.00

120 6 5 4 106.50

, 140 5 5 4 128.40
50 Batchawana 80 64 75 45 57.00
100 17 11 9 87.41

120 3 2 1 106.00

140 4 2 2 132.00

200 3 2 1 148.67

51 Batchawana 80 14 16 8 56.86
100 9 7 6 89.78

120 5 2 2 114.60

52 Batchawana 80 41 61 32 55.10
100 16 22 15 89.69

120 12 12 8 111.83

140 3 1 1 130.67

200 5 5 4 156.00

53 Batchawana 80 45 63 30 59.84
100 13 16 11 88.00

120 6 7 4 111.50

140 2 1 1 130.00

200 4 3 3 146.00

54 Salem 80 40 221 27 53.13
100 10 21 8 89.70

120 2 4 2 112.00

140 4 5 4 128.50

55 Salem 80 8 24 7 50.63
100 2 3 2 92.50

120 1 1 1 112.00

140 1 1 1 135.00

56 Salem 80 32 59 30 48.38
100 1 2 1 83.00

57 Salem 80 18 92 15 51.50
100 6 14 6 91.83

120 2 2 2 103.00

140 1 3 1 138.00

200 2 3 2 142.50

 

100

Appendix D (continued)

 

 

Mean

Length Electroﬁshing Dredge Recaptures Length

Site Stream Increment Catch (M) Catch (C) (R) (mm)
58 Salem 80 29 39 21 50.79
100 2 4 2 98.00

120 3 3 3 112.33

140 1 2 1 137.00

59 Salem 80 29 52 17 48.00
100 5 12 5 89.20

120 4 7 4 107.25

140 2 6 2 135.00

200 3 5 3 148.67

60 Salem 80 6 29 3 55.83
100 8 11 7 88.88

120 4 4 4 106.25

61 Salem 80 22 78 16 52.27
100 16 23 13 90.44

120 3 5 2 101.67

140 2 3 2 133.50

62 Salem 80 23 65 19 50.26
100 4 11 4 91.25

120 3 5 3 103.33

140 1 3 1 127.00
63 Salem 80 21 49 21 48.71
100 4 6 3 91.00

120 4 2 2 104.50

200 2 2 1 150.50

64 Salem 80 12 19 9 48.75
100 1 2 1 94.00

140 2 2 2 135.50

200 1 1 1 162.00

65 Salem 80 6 49 3 46. 17
66 Salem 80 8 25 6 55.25
100 3 6 3 87.67

120 2 3 2 113.50

140 1 2 1 132.00

67 Salem 80 5 12 5 62.00

 

101

Appendix D (continued)

 

 

Mean

Length Electroﬁshing Dredge Recaptures Length

Site Stream Increment Catch (M) Catch (C) (R) (mm)
68 Salem 80 5 l3 3 63.40
100 2 7 2 97.50

120 4 10 4 107.00

140 2 2 2 126.50

69 Salem 80 21 43 19 65.05
100 6 18 6 88.17

120 4 11 3 110.75

140 1 2 1 137.00

70 Salem 80 14 12 8 63.50
100 6 6 5 91.50

120 3 4 3 116.33

7 1 Proctor's 80 3 5 3 69.67
72 Proctor's 80 2 l 1 59.00
100 3 3 3 86.00

120 2 4 2 104.50

73 Proctor's 100 2 3 2 98.00
74 Proctor's 80 2 2 1 67.00
100 1 3 1 96.00

75 Proctor's 100 1 2 1 96.00
76 Proctor's 80 1 5 1 70.00
120 2 3 2 108.50

140 1 l 1 128.00
77 Proctor's 100 4 8 3 89.75
78 Proctor's 80 5 9 3 63.60
100 8 14 8 92.00

120 1 1 1 111.00

140 1 2 1 121.00

200 l l 1 149.00

79 Proctor's 80 6 4 3 58.50
100 7 8 6 89.71

80 Proctor's 80 10 6 4 59.40
100 4 5 2 94.00

140 l 1 1 135.00
8 1 Proctor's 80 8 8 4 59.88
100 7 7 6 93.86

120 3 3 3 107.67

 

102

Appendix D (continued)

 

 

Mean

Length Electrofishing Dredge Recaptures Length
Site Stream Increment Catch (M) Catch (C) (R) (mm)
82 Proctor's 80 13 19 12 56.00
100 2 3 2 85.50

120 1 2 1 1 1 1.00

140 2 2 2 134.00
83 Proctor's 80 1 4 1 54.00
100 1 2 1 98.00

140 3 5 2 135.67

200 l l 1 141.00
84 Proctor's 80 18 12 10 57.1 1
100 4 5 4 92.25

120 6 6 4 107.17

200 l 1 1 149.00

 

103

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109

 

 

Ill