shoreline residential development and physical … · shoreline residential development and physicd...
TRANSCRIPT
SHORELINE RESIDENTIAL DEVELOPMENT AND
PHYSICAL HABITAT INFLUENCES
ON FISH DENSIW AT THE LAKE EDGE
OF LAKE JOSEPH, ONTARIO
Andrea Marcelline Brown
A thesis submitted in confonnity with the requirernents
for the degree of Master of Science
Graduate Department of Zoology
University of Toronto
O Copyright by Andrea Marcelline Brown 1998
National Libraty 191 of Canada Bibliothèque nationale du Canada
Acquisitions and Acquisitions et Bibliographie Services services bibliographiques 395 Wellington Street 395, rue Wellington Ottawa ON K1A ON4 OttawaON KlAON4 Canada Canada
The author has granted a non- L'auteur a accordé une licence non exclusive licence allowing the exclusive permettant à la National Library of Canada to Bibliothèque nationale du Canada de reproduce, loan, distribute or sel1 reproduire, prêter, distribuer ou copies of this thesis in microfonn, vendre des copies de cette thése sous paper or elecbonic fonnats. la fome de microfiche/fïh, de
reproduction sur papier ou sur format électronique.
The author retains ownership of the L'auteur conserve la propriété du copyright in this thesis. Neither the droit d'auteur qui protège cette thèse. thesis nor substantial extracts fiom it Ni la thèse ni des extraits substantiels may be printed or otherwise de celle-ci ne doivent ê e imprimés reproduced without the author's ou autrement reproduits sans son permission. autorisation.
Shoreline residential development and physicd habitat influences
on fish density at the lake edge of Lake Joseph, Ontario
Master of Science, November 1998
Andrea Marcelline Brown
Department of Zoology
University of Toronto
Fish densities in the littoral fiinge zone (0-2.5m offshore, average depth = 0.53m),
within a large Central Ontario lake, were investigated with respect to shoreline structure
(docks and boathouses) density and physicai habitat characteristics. Both categorical
(ANOVA) and con tinuous (multiple linear regression and regression tree) data analysis
concluded that coarse woody debris ( C m ) was the most important habitat variable for
explaining and predicting densities of totd forage fish. YOY srnaIlmouth bass, were a notable
exception since they appeared to lack a preference for spatially complex habitats. Fish
densities in the fiinge zone and around shoreline structures, showed that in sotne areas the
addition of shoreline structures can increase forage fish densities. This is probably because the
structures add structural complexity which can increase protection from both waves and
predators. The habitat created by crib foundations appeared to be of importance in areas in
which the CWD has been removed by shoreline residential owners.
Acknowledgments
And so the learning continues.. .
It took me 4 years to decide to corne back to school, and although at times it was a
difficult process, it was well worth the effort. Without many people I'm not convinced 1
would have succeeded, and it certainly would not have been as enriching.
1 have been most fortunate, some wouId Say blessed, with my selection of supervisors.
For not only did Nick Collins pull, push and prod when required, he managed to do so with
respect for me as an individual, and not merely as another student. There are many ways to
draw the best from sorneone and Nick was wiiling to consider dmost any method which was
effective. His wide range of knowledge in terrestrial, aquatic and philosophical realms always
made for interesting and enjoyable conversations.
In Muskoka there are many people 1 want to thank: First, my field assistants; Vickî,
Phil, Joel, Dave and you too Matt, who wonderfully managed to divide their time between
serious, detailed work, and enjoyment of the fact that their summer job permitted them to
float along the shoreline counting fish. The staff at the OMNR Fishenes Assessment Unit in
Bracebridge; Warren, Steve, Mark, Rick, Hazel, and Lon who made sure I had the equipment
1 needed, as well as the knowledge of how to correctly use it. In addition, they let me pick
their brains about what and how 1 should be observing fish in the fnnge zone. Those
cottagers who inquired about why we were floating along the shoreline and then were willing
participants in this study. Of course, the cold beverages and munchies which were proffered
were also appreciated after a long day in the Sun. Finally John and Madeline Fielding, who so
generously opened their home to me and in addition to providing a field house which was
beyond compare, also showed me how it was possible to be gracefuI in life.
At Erindale 1 had the good fortune to be associated with the aquatics lab. Agnes,
Cristina, Gary, Jim, Matt, Tanya and Stuart were never too busy to lend a hand when needed,
listen and provide helpful advice (usually), let me bother them when the need arose, bothered
back when necessary, and forced me to work'at home most of the tirne since it was far too
interesting to talk to them when in the lab.
iii
Last but not least, 1 want to let my family know how much I appreciate their support.
Mom and dad are just beginning to truly believe that some day 1 will be able to support myself
doing "fish stuff ', but continually encouraged rny adventures starting with a fateful trip to
Alaska. Sr. Mechtilde has always been an active listener and proof-reader, and has shown me
that al1 things are interconnected if one goes back to the root. And Liam, who has rejoiced in
my successes and graciously borne the brunt of my trials and tribulations. Much to his chagrin
and pride he cm now use statistical and ecological terminology in complete sentences. To my
family 1 dedicate this thesis, for it has been a joy to be able to have day to day interactions
with them once again.
Table of Contents
List of Tables .................................................................. .................................................................. List of Figures
List of Appendices .................................... .. ...................... General Introduction .........................................................
.................................. ...... ........................ Chapter 1 ... .. ............................... ................................ Abstract ....
Introduction ............................................................. .................................................................. Methods
...................................................... Site Location
Data Collection ..................................................... Data Analysis ....................................................
............................ ..................................... Results .. .................................................................. Discussion
Management Recommendations ..........................................
...................................................................... Chapter 2
.................................................................. Abstract
............................................................... Introduction
................................................. Habitat Variables
.................................................................. Methods
..................................................... Site Location
Data Collection and Index Calculations .......................... Data Analysis ....................................................
Multiple Linear Regression Andysis ......................
vii
viii
ix
1
6
6
7
9
9
9
12
14
18
21
Mode1 Selection ................................... Mode1 Significance . permuted i? ................. Mode1 Predictive Ability . cross-validateci f ......
Regression Tree Analysis ................................. Mode1 Selection ................................... Mode1 ~i~nificaice . perrnuted 8 .................. Mode1 Predictive Ability . cross-vaiidnted f ......
Results ................................................................... .............................................. Model Comparisons
............................................. Species Comparisons
................................................................ Discussion
.............................................. Model Comparisons
Habitat Variable Cornparisons .................................. General Conclusions .........................................................
........................................... Suggestions for Future Research
References Cited ..............................................................
List of Tables
Table 2.1 Summary of multipé linear regression and regression tree mode1 results . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . a .. . .. ..
List of Figures
Figure 1.1
Figure 1.2
Figure 1.3
Figure 1.4
Figure 1.5:
Figure 1.6:
Figure 2.1:
Figure 2.2:
.............. Location of transect sites in Lake Joseph, Ontario
Forage fish density / II? from data collected from 30m nearshore transects. The median values (0) with 25"
.................................... and 75& percemiles are shown.
Number of large mes / 30m shoreline. A large tree is assumed to have a diameter of 30cm. The median values e) with 25' and 75' percentiles are shown. * notes a 25'
............................................. percentile equal to O.
Correlation between forage fish density and CWD from nearshore transects. Arrow notes possible threshold indicating minimum number of large trees required for a positive
........................... relationship with forage fish to exist. ... Forage fish densities associated with shoreline structures, in nearshore areas between structures in iess developed areas, and in nearshore areas between structures in developed areas. The median values (0) with 25" and 75' percentiles are shown. * notes a 25' percentile equal to O. Categories with the same
.................. letter indicate they are not statistically different.
Forage fish density combining fish densities from both nearshore transects and shoreline structures. The median values (0) with 25" and 75" percentiles are shown. The median forage fish density values (-) from nearshore transects
................................................... only are shown.
Best subset multiple linear regression analysis. Predicted density, using equation indicated vs. observed density. ........... Final regression tree. At each terminal node the top number is the average number of fish per site, and the bottom nurnber (in brackets) is the number of observations in the node. At non-terminal nodes the variable listed is the variable used to determine the Split. The left branch are those values less than the cnteria, and the right branch are those values greata than the criteria. .......................................................
viii
List of Appendices
Appendix A.l Summary information of human-induced shoreline ......... alteration of Lake Joseph from OMNR survey, 1995. 51
..................... Appendix A.2 Physical Data of Docks and Boathouses 52
Appendix A.3 The box represents the 25th, median, and 75th percentiles. The wisker represents 1 . 5 ~ the interquartile range, any * represents statistical outliers. The number of structures observed is indicated above the graph. The p-value from a I-way ANOVA using Iog lO(x+l) is
................................. indicated below the p p h . .., 53
Appendix B. 1 Relationship between habitat variables and site classification. The dotted Iine (----) represents developed
sites, solid line (-1 represents less developed sites. P-values from two-way ANOVA andysis are indicated below graph; d is deveIopment effect, e is exposure effect,
........................................ d*e is interaction effect. 56
Appendix B.2 Median number (with 29 and 75' percentiles) of fish observed for al1 transects by sampling date. There was no significant difference in the number of fish observed over time or between years, as determined by a l-way ANOVA with Tukey's multiple comparison (df=293, overall alpha P-cO.05) for logio(x+l) data. Therefore we used a measure of central tendency for each of the sixty sites. Chapter 1 used median number per site, while Chapter 2 used the geometric mean number of fish per site. .... 59
Appendix B.3 Median % substrate for different classes of sites. Analysisusing two-way ANOVA's with individual substrate categories indicated no significant difference between development, exposure or an interaction of
.............................. the two categories (not shown). 60
Appendix B.4 Raw data for calculating total prey density / 500m shoreline ...................................... associated with fringe sites 61
Appendix C.1 Scatterplot matrix of fish species geornetric mean ..................................... density versus habitat variables
Appendix C.2 Pearson correlation matrix for habitat variables and ..................................................... f i ~ h g r ~ ~ p ~
Appendix C.3 Geomehic mean for fish density per nf data for 60 .................................................. nearshore fringe sites
Appendix C.4 Habitat variables for 60 nearshore fringe sites .....................
General Introduction One of the greatest threats to our fisheries is habitat loss. The three most prevalent
causes for extinction of freshwater fish in North America are the loss or alteration of habitat
(5O%), the introduction of exotic species (37%) and over-exploitation (8%) (Thomas 1994).
The Canadian Department of Fisheries and Oceans is responsible for maintaining fish
populations and has implemented a policy of "no net loss" of productivity of fish habitat to
fulfill theh responsibility. Development projects O C C U ~ ~ ~ in or around water are legally
required to replace damaged or Iost habitat. Compensation, however, requires that the
relative importance of different types of fish habitat be quantifieci. Additionally, we need an
understanding both of how different types of human development alter fish habitat and of how
individual species respond to the altered habitat.
The impact humans are having on the aquatic environment is an ongoing concern
which has grown from one of water quality to a concern about the entire ecosystern's function
and integrity (Poe et al. 1986; Bryan and Scarnecchia 1992; Loeb. and Spacie 1993; Leslie
and Timmins 1994; Banziger 1995; Bolger et al. 1997; Brazner 1997; Brazner and Beals
1997; Schmude et al. 1998). We know about individual species' abiotic requirements for iife
and reproduction, especially in the cases in which the species is of commercial or recreational
interest. We even understand some of the biotic interactions which exist within an ecosystem.
Although we continue to refine that luiowledge, we are just beginning to apply it to the
management of Our aquatic environment.
Human impacts associated with lake shoreline activities can include construction of
shoreline structures such as docks and boathouses, removal of macrophytes and coarse woody
debris (CWD), construction of erosion controI devices, and disturbance from recreational
activity. These alterations often occur together and the cumulative effects also need to be
determined. AH of these actions will alter fish habitat, although not al1 will be harmful.
Changes to water quality, such as nutrient or pollutant inputs, are beyond the scope of this
thesis.
Most of the impact-generating activities listed above generatetheir effects via changes
in the structural complexities of the habitat. A diverse literature has examined the relationship
between complexity and fish abundance. The best indicators of the importance of habitat
structural complexity come from studies of the littoral zones of small to medium-sized lakes
and streams. These studies have focused on how fish abundance is related to three different
components of structurai complexity: (1) macrophyte abundance; (2) CWD abundance; and
(3) the addition of artificial structures.
1. High macrophyte abundance in lakes is associated not only with high fish abundance
(Keast et al. 1978; Bryan and Scarnecchia 1992; Leslie and Timmins 1994; Monng and
Nicholson 1994; Randall et ai. 1996; Bramer 1997) but also with; - nursery areas (Keast et al. 1978; Ruiz et al. 1993; Leslie and Timmins 1994)
high fish nchness(Keast et al. 1978; Eadie and Keast 1984; Benke and Wallace 1990;
Benson and Magnuson 1992; Bryan and Scarnecchia 1992; Leslie and Timmins 1994;
Randall et al. 1996; Bramer and Beals 1997)
distinctive fish assemblages(Keast et al. 1978; Poe et al. 1986; Tonn et al. 1992;
Weaver et al. 1997)
decreased foraging success (Cooper and Crowder 1979; Werner et al. 1983b; Heck
and Crowder 1991) - changes in predator-prey interactions(Savino and Stein 1989; Tonn et al. 1992)
O slow fish growth and srnall fish size(Crowder and Cooper 1979; Werner et al. 1983a;
Randall et al. 1996)
2a. High or increased CWD density in streams is associated with the following:
increased fish abundance(Murphy and Hall 1981; Angermeier and Karr 1984; Slaney
et al. 1994; Braaten and Berry 1997; Lehtinen et al. 1997)
O increased fish diversity(Murphy and Hall 1981; Angermeier and Karr 1984; Lobb and
Orth 1991 ; Lehtinen et al. 1997)
O high juvenile fish growth(Fausch and Northcote 1992; Quinn and Peterson 1996) - increased invertebrate abundance (Angemeier and Karr 1984) - increased invertebrate diversity(Murphy and Hall 198 1)
2b. I-Iigh or increased CWD density in estuaries and lakes is associated with the following:
O high fish abundance(Moring et al. 1989; Everett and Ruiz 1993; Peffers 1995; France
1997)
high fish diversity(Everett and Ruiz 1993; France 1997)
3. The addition of artificial structures attracts fish. The following matenals have been used as
artificial stnictwes and al1 attract fish. The following are freshwater exarnples only:
evergreen trees, logs, crib structures (Johnson and Stein 1979; Johnson and Lynch
1992; Bassett 1994; M o ~ g and Nicholson 1994)
automobile tires (Prince and Maughan 1979; Moring and Nicholson 1994)
floats (Helfman 1979)
dredged matenal (Chipps et al. 1997)
cinder blocks (Moring and Nicholson 1994)
Despite the range of habitat information relating to fish abundance, we have lirnited
information about fish density and habitat associations fkom large, oligotrophic lakes which
have low macrophyte abundance. Large lakes possess the potential for a higher variability in
physical conditions, such as hourly or daily changes in conditions associated with strong storrn
surges, upwellings, or seiche activity(Wetze1 1983). There is also a lack of information about
the imporîance of CWD for fish density within lakes. Finally, a high demand for shoreline
residential development provided the impetus to investigate the reliitionship between fish
densities and shoreline structures associated with the nearshore zone,
The nearshore zone focuses on the shallowest part of the littoral zone. It has been
referred to as the land-water ecotone, the "ribbon of life", and the littoral fnnge zone, to name
a few. Generally, it encompasses land to the high-water mark, the wave swash zone, and no
more than 3 rn offshore. It is the area of the lake with the closest associations to the
terrestrial environment, and thus, is also the area most affected by changes to the terrestrial
environment. Work on two srnall (loba), undeveloped lakes indicated that fish (TL~100mm)
had feeding rates about 10x higher in the nearshore zone (depth = 0.2m), than in the spatially
complex areas further offshore (depth = 1-2m). This nearshore zone may be providing as
rnuch food to small fish as the remainder of the littoral zone. (Nick Collins, unpubfished data).
Iî this finding can be generdized to larger lakes, then the impact of shoreline alterations to fish
communities might be more important than has previously been appreciated. Preliminary
work (2 sites) in a larger (SOOOha), lake with residential development dong the shoreline, did
not find higher feeding rates in the nearshore zone compared to offshore areas (Nick Collins,
unpublished data). There were some indications that this contrary finding was related to
higher frequency of waves and alterations to the nearshore habitat by residential owners.
Since the number of sites observed in the larger lake was limited, a more îhorough project was
proposed. This thesis is the result. Although 1 am not comparing nearshore fnnge zone areas
with other areas of the littoral zone, 1 limited my investigation to the nearshore fnnge zone,
comparing areas with differing levels of exposure to prevailing winds and waves, in addition
to human-induced shoreline alteration.
Of the three large "Muskoka lakes" Lake Joseph is the least affected by human
development. In 1995 the mapping of the shoreline of Lake Joseph was completed with a
survey, which located, identified, and recorded dimensions of al1 shoreline structures: docks,
boathouses, ramps, inanicured lawns and shorewalls. At that time only 12% of the shoreline
had been directly altered (Appendix A.l ) , compared to 16% for Lake Rosseau and 17% for
Lake Muskoka. Lake Joseph was chosen as a study lake since it could most easily provide
undeveloped areas for cornparison with those sections which were already developed.
In Chapter 1 1 classify areas within Lake Joseph in relation to the density of shoreline
structures and the 1eveI of exposure to prevailing winds. For each category 1 identified two
levels: high shoreline structure density vs. low shoreline structure density and high exposure
vs. low exposure. Within these 4 classes of sites 1 attempt to determine the relationship which
exists between development and total forage fish abundance. A two-way analysis of variance
(ANOVA) is used to determine the relationships. Appendix B shows the relationships
between individual habitat variables and my classification levels, as well as the raw data for
calculating the density of forage fish in an extended shoreline transect (B.4).
Chapter 2 still investigates the effects of human alteration of shorelines and wind
exposure on fish densities, but determines the relationships using different methods. 1 use
continuous variables to increase the resolution of the results observed in Chapter 1. 1 want to
determine if specific habitat variables can predict fish abundance. 1 also further refine my
conclusions by examining habitat-density relationships for individual species and size classes,
rather than grouping fish togerher as total forage fish. AIthough lnany of the individuals are
potential forage fish, individual species may respond differently to habitat variables based on
life-history differences. Two methods of regression, multiple linear regression and tree
regression, are compared.
Appendices provide background information for the project. Appendix C contains a
scatterplot (C.l) and correlation (C.2) rnatrix showing the relationships between habitat
variables and individual species and size classes of fish. The raw data for the scatterplot
matrix are presented in C.3 and C.4.
Appendix A contains al1 information pertaining to shoreline structures: A. 1 is a
summary of the type and magnitude of hurnan-induced shoreline alterations observed during
the 1995 mapping survey of Lake Joseph completed by the Ontario Minisûy of Natural
Resources; A.2 contains the number and type of structures sampled for this study; A.3 shows
the fish densities associated with structure types as observed for this study.
Chapter One:
Relationship of forage fish density at the lake edge to
density of shoreline structures and exposure to prevailing winds
in Lake Joseph, Ontario.
Abstract High numbers of shoreline structures (docks and boathouses) and high levels of
exposure to prevailing winds are negatively related to the abundance of forage fish in the
littoral fringe area of Lake Joseph, a large, oligotrophic, central Ontario lake. Areas that have
low numbers of shoreline structures and are protected from prevailing winds, have forage fish
densities approximately 6x higher than those found in more developed and wind exposed areas
of the lake. In attempting to identify the mechanism producing this pattern 1 found that coarse
woody debris (CWD) density is also highest in less developed, protected areas, and is
positively correlated with forage fish density at the lake edge. CWD is the predorninant,
naturally occurring physical structure, but is often partialiy or completely removed by
lakefront property owners. Measurements of fish densities dong the lake edge (0-2.5m
offshore; mean depth = 0.53m) and around shoreline structures, show that the addition of
çome shoreline structures can increase forage fish densities, probably because the structures
add to the habitat a structural complexity which can increase protection fiom both waves and
predators. Shoreline structures also concentrate densities of piscivorous fish, which will
increase the possibility for interaction between predators and prey, possibly increasing the
energy flow to sport fishes. In structurally poor lakes, like those found on the Precambrian
ShieId, a simple, inexpensive method of maintaining a high abundance of shoreline forage and
young of the year (YOY) fish is to leave CWD along the lake's shoreline or to restore it if it
has already been removed.
Introduction
The impacts of shoreline residential development in lakes have been an ongoing
concern for decades (Jaakson et ai. 1976; Harker 1 %S), but mainly with regard to nutrient
and sediment input (e.g., Schindler et al. 1971; Schindler 1987) The impact on fish
communities remains uncertain, especially in the littoral fringe arearyan and Scarnecchia
199% Beauchamp et al. 1994). Edge or littoral fringe habitat is the most vulnerable area to
alteration by humans; moreover we know that shallow water is an important feeding and
nursery habitat for young of the year (YOY) and forage fishweast et al. 1978; Ruiz et al.
1993; Leslie and Timmins 1994) The shallow, littoral fringe area is often overlooked when
completing larger scale littoral zone investigations, despite the need for specific knowledge
about mechanisrns operating there.
Human impact can take many forxns: construction of shoreline structures such as
docks and boathouses; removal of macrophytes and CWD; construction of erosion control
measures; and disturbance from recreational activity. These alterations usually do not occur
singly and the cumulative effects also need to be deterrnined. As the demand for shoreline
residences grows so does the potential for irreversible alteration of the fis11 comrnunity.
Actual construction of shoreline structures requires the removal of obstacles such as
macrophytes or CWD. However, removal rarely stops at the minimum area required for
construction. Christensen et al. (1996) established that lakes with shoreline residences have
lower densities of CWD than undeveloped lakes; the greater the shoreline residential density
the lower the CWD density. The literature reports that fish densities are consistently higher in
existing complex habitats tlian in open habitats and that there are increases in local density
when complexity is artificially increased (e.g.Johnson and Stein 1979). Our understanding is
that complex habitat provides refuge from predation(e.g., Cooper and Crowder 1979;
Kerfoot and Sih 1985; Mittlebach 1986) or increases the availability of food(e.g., Werner et
al. 1983a) when compared with simple habitats. Removal of physical structure has well-
documented negative impacts on abundance and species composition i n fish, benthos and
plankton communities in freshwater and marine system$e.g., Crowder and Cooper 1979; Poe
et al. 1986; Everett and Ruiz 1993). However, shoreline development may also add to the
structural complexity. In Ontario, at least one shoreline structure (dock or boathouse) is
added for each shoreline residence. It is expected that these artificial structures wiU at least
attract fish and invertebrates, and at best, increase their production (e.g., Johnson and Stein
1979; Bell et al. 1991; American Fisheries Society 1997) Thus, shoreline structures appear to
have contradictory effects. They increase in-water structural complexity which increases the
amount of refuge area available to small fish, but it is due to the high numbers of small fish
within the structures that large, piscivorous fish cm identify these structures as potential,
reliable food sources.
In freshwater systems, CWD is an important source of physical structure. Physical
structure is positively correlated with high biological abundance and diversirnoring et al.
1989; Everett and Ruiz 1993; Monng and Nicholson 1994) Our information about the role
of CWD in lakes comes from the extensive literature demonstrating the positive relationship
between CWD and fish abundance in Stream systems. To summarize, in Stream systems there
has been a compIete change in management policy regarding CWD. From the early 1900s
CWD was beIieved to act as a barrier to migration of salmonids upstream and was actively
removed from streams. The practice continued until the early 1980s, but research then
indicated that CWD was important tu YOY and juvenile survival and consequently it is now
actively added to streams to increase abundance of small fish(Hannon et al. 1986). Its ability
to provide a refuge from predation is thought to be the main mechanism at work(Cooper and
Crowder 1979; Savino and Stein 1982). The lack of parailel investigations focusing on the
relationships of CWD and fish abundance in lakes may be in part due to the foliowing: (1) the
less dramatic effects CWD has on the surrounding habitat in lakes relative to strearns, (2) the
extensive research on the importance of macrophytes to fish abundance, and (3) the fact that
much of the freshwater research has been carried out on eutrophic or mesotrophic lakes and
not oligotrophic lakes where the macrophyte abundance is lower so that CWD is a relatively
more important source of structural complexity.
This chapter focuses on the relationships of forage fish density to the density of
shoreline structures and exposure to prevailing winds in the littoral finge area. Although
significant effects of wave exposure are demonstrated, management-oriented alterations in
wave exposure are less feasible than management of shoreline structures, therefore this paper
concentrates on the effects of shoreline structures. 1 attempt to explain the observed patterns
of fish abundance through measurements of fish densities associated with shorefine structures
(docks and boathouses) and CWD, and to suggest a "rule of thumb" for rninimizing or
mitigating the impacts of shoreline development on forage fish populations.
Methods
Site Location
Lake Joseph is a large (5375ha), steep sided, deep (maximum depth = 93m, mean depth =
24m), oligotrophic lake, with an average secchi depth in June of approximately 8m, with
typically low macrophyte abundance in south-central Ontcirio, latitude 4510', longitude
79'40' (Fig. 1.1).
Data Collection
Two sets of fish density measurements were made; one from dong the shoreline in the
littoral fringe (&Sm from shore), and the other directly associated with shoreline structures
(docks and boathouses). Al1 fish were observed by a pair of snorkelers with underwater tape
recorders or slates. Fish were identified to species and assigned to size classes; smali (cSOmm
TL), medium (50-99mm), large (>100mm). Al1 small fish, plus al1 medium cyprinids were
classified as forage fish. Al1 smallmouth bass&ficropterus dofornieu, and rock bass,
Ambloplites rupestris, larger than lOOmm were classified as predators. Species observed
were pumpkinseed, Lepomis gibbosus, bluntnose minnow,Pimephales notatus, smallmouth
bass, logperch, Percina caprodes, rock bass, yellow perch, Percaflavescens, spottail shiner,
Nofropis hudsonius, creek chub, Scmotilrrs atrornacufatus, banded killifish,Fundulus
diaphanus, brown bullhead, Arneiurus nebulosus, and white sucker, Catostornus commersoni.
Fish were counted in sixty 30m-long Iittoral fringe transects paralïel to the shoreline
and extending 2.5m offshore. Transect starting points were permanently marked to ensure a
consistent starting point for al1 sarnpling dates. A weighted line was placed on the bottom
1.5m offshore, following the shoreline contours, at kast 30 min. before a transect was swum.
Each site was swum once per week a total of 4 times between July 8 - August 12, 1996 and
Figure 1.1: Map of Lake Joseph - location of nearshore transect sites
once between June 19 - 23,1997. At each site, one observer swarn directly over the
transeet line and was responsible for recording ali fish from the shore to the transect line,
while the second observer swam beside the fast observer and was responsible for recording al1
fish from the transect line to l m farther offshore. The second observer was also responsible
for looking ahead of the observers to note and record any fish swimming away fiom the
snorkelers or any fish swimming into the sampling area from deeper water. If a fish could not
be positively identified to species it was recorded in the appropriate size class as an unknown
fish (2.1% of total fish counted).
Static physical variables were measured at al1 sites in July 1996. Overhead cover,
substrate, slope, and aquatic rnacrophytes were measured at the Sm, 15m and 25x11 points
along the 30m transect line parallel to shore. Overhead cover was measured using a l m x
0.5m grid. The grid was placed with the longer edge along the shore and the number of
squares covered by shoreline vegetation was recorded. Substrate categories recorded were
silt (inorganic material finer than sand), sand (rock origin c 0.3cm diameter), grave1 (rock
material between 0.2 - 8cm), rubble (rock material between 8 - 25cm), boulder (rock >25cm),
bedrock (exposed rock with no overburden), muck (organic material with silt and clay),
detritus (organic material with Iarge pieces such as sticks, leaves, and decaying plants).
Percent of each substrate and macrophyte cover were determined using a l m x 0.5m grid
placed over the transect line. Slope was measured in 3 ways: as the depth 1.Sm offshore, the
distance offshore at which the depth reached 20cm, and the distance offshore at which the
lake bottom could no longer be seen from the lake surface (terrned the drop-off point).
In July 1997, al1 in-water CWD (>5crn diameter) within the sampling area was counted
and the diameter was measured to the nearest 2.5cm. CWD total was the sum of the CWD
diameters. For ease in interpretation, CWD totais are reported in large tree equivaients. A
large tree was assumed to have a diameter of 30cm. Therefore a CWD total of 300 per
sampling area corresponds to 10 large tree equivalents. Riparian tree density was measured
from the waterline to 3m onshore. Riparian trees were identified at least to farnily and breast
height diameter size class was recorded; small(10-19cm), medium (20-30cm) and large
(>30cm).
Dynamic physical variables were measured after each transect swim. These included
time of day, wind direction and speed, direction and speed of current, air and water
temperatures, average wave height, and horizontal secchi distance. Al1 measurements were
recorded at the 5m mark dong each transect. Wind direction (N, NE, E, SE, S, SW, W, NW)
was determined with a compass. Average wind speed (m/s) was estimated for a 1 minute
period using a hand-held anemometer. Maximum and minimum speeds over a 1 min. penod
were also recorded. Water current direction was determined with a compass, as well as
relative to the shoreline (onshore, offshore, dong shore). Water speed was measured using a
neutrally buoyant plastic practice golf ball with holes. The distance the golf ball covered in 30
seconds represented an index of the water speed. Air and water temperatures CC) were
recorded with a standard thermotneter in the shade. Average wave height (cm) was
determined by standing a meter stick on the transect line and marking the maximum difference
from crest to trough of a single wave. Wave height measurements were taken in the absence
of boat wakes. Horizontal secchi distance (m) was measured using a horizontaUy mounted
secchi disk attached to a marked string. The secchi disk was placed at the beginning of the
transect and the observer swam away from the disk until it was no longer visible underwater.
Fish density around shoreline structures was detennined by observers swimrning
around eighty-eight shoreline structures. Two snorkelers approaching fiom opposite sides of
the structure each swam the entire perimeter simultaneously. Fish within l m of the structure
were considered associated with the structure and included in the counts. Fish approaching
from greater than l m ftom the structure were not included in the final counts. Counts from
the two observers were averaged for each species and size class, except that the numbers of
fish observed to be moving away from the structure before the two observer's paths crossed
were doubled. The structures' lengths, widths, depths and types were recorded.
Data Analvsis
Shoreline sites were classified as developed vs. less devdoped according to the density
of shoreline structures using data from an unpublished survey of shoreline stmctures
conducted by the Ontario Ministry of Natural Resources. From these maps a development
density for individual sites was determined by counting the number of structures within 250m
on either side of the site. To ensure a wide and even distribution of sites around the lake 1
selected relatively developed and less developed sites in both northem and southern areas.
The southem part of the lake is heavily developed, while in the northern part individual
residences are more widely spaced. Thus, sites classified as less developed in the south may
have sirnilar numbers of shoreline structures as sites classified as developed in the north. This
classification was successful, in that overall there was a significantly greater amount of
shoreline aitered i n developed sites than in less developed sites (Appendix B.l; median
developed = 70m altered/500m shoreline, less developed = 5m altered/500m shoreline; Mann-
Whitney, df= 58, PeO.OOO1). Sites were also classified as exposed vs. protected from
prevailing winds. It was assumed that prevailing winds came from the northwest. This
classification was moderately successful in that the wave heights were statistically significantly
higher in exposed sites than in protected sites, however, the median difference between the
classes rnay be of marginal biological significance (Appendix B. 1; median exposed = 3cm,
protected = 2cm; Mann-Whitney, df=58, PeO.OOO1).
An index of wave exposure was constructed by placing a polar reference overlay of 12
radiating lines 303 apart on a transparent overhead. The overlay was placed with one line
pointing north and the point of origin on the site location of a standard 1:50,000 topographie
rnap issued by Energy, Mines and Resources Canada (Lake Joseph 31 E/4). The distance
dong each line over the water to the first point of land was recorded. "Total fetch" was
deterxnined by summing the length of al1 the lines.
Fish abundance showed no consistent pattern which could be attributed to seasonal
change (Appendix B.2), therefore the median number of fish per site over al1 5 observation
periods was used to represent fish density at each site. For dynamic physical variables the
median value from the 5 measurements recorded at each transect was used for analysis. For
overhead cover, substrate, slope, and macrophytes the average value from the 3 measurements
recorded frorn each site was used for analysis.
The fish data could not be transformed to remove non-nomality or heteroscedasticity
so a resarnpling technique was used for statisticd analyses. 1 completed 1000 permutations of
forage fish density and cdculated a general linear mode1 (GLM) F-statistic for development,
exposure, and the interaction term for each permutation. Following the inethodology of
Manly (1991) 1 perrnuted the raw data among al1 cells, rows and columns. These distributions
of F-statistics were compared to those from the original arrangements of the data. The
reported p-values are the percentages of permuted F-statistics more extreme than that for the
original arrangement of data.
To estirnate average fish densities dong shorelines including shoreline structures,
forage fish densities for both littoral fringe transects and those associated with shoreline
structures were combined. Overail number of fish in a 500 x 2.5m transect was calculated as
follows:
Overall number = (Q x A$ + @f x Ar)
where, Ds = median density of fish associated with shoreline structures and within the first
2.5m offshore
As = sum of the width of al1 shoreline structures for a distance of 250m in both directions
from a littoral fringe transect location x 2.5m offshore
Df = median density of fish in the 5 counts from a 30m x 2.5111 littoral fringe transect
Ar = (500 x 2.5) * A,
Overdl fish density = overall number / (500m x 2.5m )
Results Both development and wave exposure were significantly related to forage fish density
in nearshore transects (Fig. 1.2; GLM permutation; n=6O; development Pa.028, exposure
P=0.003, development*exposure P=0.079). Less developed and less exposed areas had the
highest density (median = 0.13 fish/d). 1 subsequently pursue a senes of questions to
determine how this pattern arose.
CWD density was related to both development and exposure, being highest in less developed
and more protected areas (Fig. 1.3; GLM with log transformation; development Pc0.001,
exposure P=0.015, development*exposure P=0.291). To help identify whether physical or
habitat variables might be the cause for the observed patterns 1 determined whether other
habitat variables were correlated with development and exposure status. 1 found no
significant differences in overhead cover, slope, substrate, aquatic macrophyte
1
less developed
. m . . . . m m . a . . m . m . m . . . m . . m..... aeveio~ea . 1
exposed protected
Fig. 1.2: Interaction graph for development and exposure of forage fish density (4) from data collected from 30rn nearshore transects. The median values @) with 25' and 75' percentiles are shown.
exposed protected
Fig. 1.3: Interaction graph for development and exposure showing number of large tree equivalents per 30m shoreline. A large tree is assumed to have a diameter of 30cm. The median values *) with 25' and 75' percentiles are shown. * notes a 25' percentile equal to O.
density or composition, nparian tree density, drop-off point, or temperature among cells in
our development-exposure classifications (Appendix B. 1; GLM permutation; P>0.05).
The pattern of forage fish distribution with respect to CWD was similar to the pattern
with respect to development and exposure. A significant positive correlation existed between
forage fish density and CWD (Fig. 1.4; Spearman rank-correlation r=0.399, n=60, Pc0.002).
This suggested that the attraction to or protection by physical structure may strongly influence
forage fish densities. The development and exposure responses I observed for forage fish may
be substantially detexmined by the patterns observed of CWD with respect to development
and waves. There appears to be a threshold of 5 large tree equivalents per 30m, above which
forage fish densities are likely to benefit from the addition of CWD. Above this level of large
tree equivalents forage fish were always observed to be present in a 30m transect (Fig. 1.4).
In addition to the natural CWD, significant amounts of structural complexity have
been provided by shoreline structures, >85% of which are crib docks or boathouses. These
structures averaged 1Om x IOm. Their foundations consisted of log walIs with large boulder-
sized rocks within the wood complex (OMNR Bracebridge, unpublished data).
Forage fish associated with shoreline structures were concentrated in littoral fringe
habitats. 72% of the forage fish around shoreline structures were found within 2.5m of the
shoreline, while only 5% of large, piscivorous fish were found within this area. This result
was similar to my findings from littoral fringe transects, where none of the fish observed were
piscivores. 1 compared forage fish density in the first 2.5m offshore in three types of sites;
associated with structures, in less developed sites, and in developed sites. Median forage fish
density associated with structures was not significantly higher than in less developed sites, but
was significantly higher than those in developed sites (Fig. 1.5; median structure = 0.133
fish/d, less developed = 0.053, developed = 0.013; 1-way ANOVA, Tukey's multiple
comparison; df=145, overall alpha Pc0.05). Since structure observations only occwed
around the perimeter, the number of forage fish recorded as being associated with a structure
was conservative. The average width of shoreline structure observed was 10m. For these
structures ( I f Om width), it was not possible to count fish associated with the middle of the
structure. However, this area of the structure remains dark al1 day and it seems reasonable to
assume that a majority of the forage fish would be found at or near the perimeter of the
(large tree equivelents + 1) 130m shorellne
Fig. 1.4: Correlation between forage fish density and CWD from nearshore transects. Arrow notes possible threshold indicating minimum number of large trees required for increased forage fish density.
structure less developed developed
Fig. 1.5: Forage fish densities in three types of sites; associated with shoreline structures, in less developed areas, and in developed areas. The median values @) with 25" and 75' percentiles are shown. * notes a 25h percentile equal to O. Categories with the same letter indicate they are not statistically different.
structure in order to have access to sunlight. Thus, 1 believe, that the forage fish density
associated with structures was only moderately underestimated.
Littoral fringe fish data came from observations along 30m transects, which
intentionally did not include shoreline structures. To evaluate the overall effects of
development on shoreline fish densities 1 needed to add information on fish densities around
structures to those from the littoral fringe transects, because each residence was associated
with at least one structure (OMNR Bracebndge, unpubiished data). Combining data from the
littoral fringe areas and the structures permitted extrapolation to a total number of forage fish
in a 500m x 2.5m area for each site. As expected from the association between forage fish
density and structural complexity, the addition of shoreline structures increased median forage
fish density in al1 classes of sites (Fig. 1.6; GLM permutation; development P=O.155, exposure
P=0.024, development*exposure P=0.024). In fact, the additional fish attracted to the
numerous structures in highly developed areas (comparing Figs. 1.2 and 1.6) appeared to
more than offset the relatively low forage fish densities in littoral fringe transects.
Statistically, consideration of fish around structures reduced the size of the overall
development effect to below the level of significance. However, considering only those sites
protected from prevailing winds, 1 continued to observe significantly higher forage fish
densities in less developed areas (median = 0.18 fish/&) than in developed areas (median =
0.04 fish/n?). If the density of forage fish associated with structures was substantially
underestimated, it is possible that the difference between protected, developed sites and
protected, less developed sites wauld disappear. However, the median forage fish density in
protected, less developed sites was 4 . 5 ~ greater than protected, developed sites. 1 do not
think the underestimation was of this magnitude.
Discussion
High numbers of shoreline structures and high levels of exposure to prevailing winds
were negatively related to the abundance of forage fish in the littoral fringe area of Lake
Joseph (Fig. 1.2). Both of these habitat variables were associated with differences in habitat
complexity, which appeared to be the most important proximal factor determining the
exposed protected
Fig. 1.6: Interaction graph for development and exposure showing forage fish density (4) for extended shoreline transects which combine fish densities from both nearshore transects and shoreline structures. The median values ') with 25' and 75' percentiles are shown. The median forage fish density values (-) from nearshore transects only are shown.
abundance of forage fish present at any single location. Spatially complex substrates, such as
macrophytes or logs, supported a greater abundance and diversity of fish than simpler ones.
Complexity provides small interstitial spaces which act as refuge areas from predation, as well
as increasing the surface area available to support food organisms(e.g., Harker 1982; Werner
et al. 1983a; Kerfoot and Sih 1985; Mittlebach 1986).
The distribution pattern observed for CWD appeared to be the only environmental
variable I examined that was correlated with the observed differences in forage fish density
among classes of sites. 1 found that within a Iake, sites with high numbers of shoreline
structures had lower densities of CWû (Fig. 1.3). This finding concurred with Christensen et
al. (1996). Direct observation showed tliat some individual properties had al1 CWD removed
from the entire length of their shoreline. Inquiries to owners suggested that the main reason
for the removal of al1 CWD was aesthetics. Exposed areas also had lower amounts of CWD
than protected areas. In these areas, the probability of CWD being transported away from the
shoreline point of origin by waves and the effects of ice scour should be greater than in
protected areas.
Can shoreline structures substitute for CWD? Although it appears that they do so in
exposed areas, within protected areas overall forage fish densities in developed sites were 4 . 5 ~
lower than those in less developed sites (Fig. 1.6). This reduction in forage fish was not
related to any of the numerous variables 1 measured, other than intensity of development.
Shoreline structures have potentially contradictory effects. They increase in-water structural
complexity which increases the amount of refuge area available to small fish, but it is due to
the high numbers of small fish within the structures that large, piscivorous fish can identify
these structures as potential, reliable food sources. If there is enhanced piscivory around
structures, this could also contribute to the apparent reduction in forage fish associated with
developed areas. 1 observed no piscivorous fish in the littoral fringe transects, but did observe
some in the littoral fringe zone associated with structures, and 1 observed a high abundance of
piscivorous fish in the deeper water associated with structures. It is possible that shoreline
structures may increase the energy flow to large, piscivorous fish (e.g. game fish) by providing
sufficient refuge for juveniles and forage fish to maintain a healthy diversity and range of life
stages, in addition to acting as a food-attracting dvvice for game fish. However, to fully
answer whether shoreline structures can substitute for CWD investigation at a finer scale may
be necessary. In developed areas spatially complex habitat was concentrated in discrete
patches isolated by shoreline structures, while in less developed areas habitat complexity
provided by CWD was spread over a wider more diffuse area. Thus, within 500m of shoreline
there rnay be overali similar densities of forage fish, but in developed areas the forage fish
would be more heavily concentrated in discrete isolated patches, while in less developed areas
the forage fish would be more evenly distributed.
The conclusions stated above are based on the assumption that shoreline structures
have crib foundations. In central Ontario tliere is a trend away from the construction of crib
structures and towards the construction of pillar or pylon structures (personal observation).
Provincial policy does not require a building permit for construction of a pillar structure, but
does require a permit for a cnb foundation with a foundation footprint area greater than 15d.
The pillar structures consist of a row of metal poles approximately 30cm in diameter
supporting the dock or boathouse fiarne. They do not add the same amount of physical
siructure to the water column as ciib foundations, and are probably less effective substitutes
for the removal of CWD than crib structures. A quantitative evaluation of the relative
attractiveness of crib and pillar structures to forage and piscivorous fishes should be
undertaken before the apparent trend away from crib structures proceeds too far.
Management Recommendations
We are becoming aware of an increasing number of variables that can affect the
survival of individual fish; monitoring al1 of them is an increasingly labour-intensive and
financially expensive undertaking. Keepinp this in mind, I have focused on two factors which
are generaiiy thought to be important for predicting forage fish abundance; shoreline
development density and exposure to prevailing winds. 1 classified sites based on variables
that can be deterrnined from aerial photographs or maps.
My findings suggest that removal of CWD may remove a significant proportion of
available habitat for forage fish. As shoreline residences increasingly intrude into previously
undeveloped areas, habitat limitation for young fish becomes increasingly likely. However,
littoral fringe areas can also be improved during development, by the addition of structures
such as logs or dead trees, or the addition of crib structures on structurally simple substrates.
My evidence indicated that to preserve forage fish populations the most important
habitats to preserve are in areas that are protected from prevailing winds. These areas
supported median forage fish densities 6x higher than those found in other areas of the lake.
New development would have the least negative impact if the shoreline structures were built
in areas exposed to prevailing winds, and if removal of CWD during construction and use was
minimized. As few as 5 large diameter Iogs (>30cm diameter) per 30m (100ft) of shoreline
should provide sufficient physical structure to benefit forage fish (Fig. 1.4). As this CWD is
effective in littoral fringe areas (C 2.5m from shore), it fortunately would be too shallow to
constitute a hazard to boat traffic or navigation. 1 am not advocating that al1 shoreline
development be curtailed, merely that the existing CWD remain dong the shoreline.
Chapter Two:
Relationship of fish density at the lake edge
to physical habitat variables in Lake Joseph, Ontario.
A bs trac t 1 studied associations between fish density and several habitat variables to support
efforts to iimit negative impacts of human-induced alterations to fis11 habitat. Sixty sites in
Lake Joseph, a large, oligotrophic, central Ontario lake, were monitored 5x between Juneand
August. Al1 fish were counted by snorkelers, within the first 2.5m offshore, an area highly
vulnerable to alteration by shoreline development. Purnpkinseed, srnallmouth bass, cyprinids,
and rock bass dominated the counts. Two methods to relate fish densities to habitat variables
were completed; multiple linear regression and tree regression. Tree regression is a cornputer
intensive technique designed to identify, and express in a simple and graphical fonn, non-linear
and non-additive relationships. It does not require assumptions of linearity, but multiple linear
regression does. Neither mode1 was consistently superior in providing higher explanatory or
predictive ability. However, results froin both models indicated that CWD was the most
important variable for explaining and predicting densities of total forage fish, cyprinid
(TL<100mm), small rock bass (TLcSOmm), and small (TLc5Omm) and big (TL>SOmm)
pumpkinseed. YOY smallmouth bass density was highest in areas of high shoreline structure
density. YOY srnailmouth bass was aIso the only fish group whose density was not
significantly related to CWD. Management recommendations to preserve and enhance habitat
would thercfore be different for srnallmouth bass than for rock bass and pumpkinseed.
Introduction
Inventories of fish and fish habitat provide information about fish distribution and
population status, and about the conditions relating to quality and productivity of the
supporting habitats. The Canadian Department of Fisheries and Oceans has as its main goal a
net gain in fish production. This goal is mainly being implemented through the regulation of
impacts of humm development on fish habitat. The department is applying a policy of "no net
loss" of productivity of fish habitat. Development projects are legaily required to replace
damaged or lost fish habitat. However, effective implementation of this policy requires
quantification of the relative importance of different types of fish habitat at different life stages
of both individual species and the fish community as a whole. It also requires an
understanding of how a specific type of development will elitninate or reduce the availability
of specific types of habitat required by the fish comrnunity.
Number or density of fish associated with a particular habitat type is assumed to reflect
fish preference for, or tolerance to, a habitat feature (Krebs 1985; Levin 1992). Many
investigations assume that fish populations are limited by habitat availability. The limitations
could occur at any point during the life cycle of the fish (egg, juvenile or adult) or be related
to a requirement for life (spawning, feeding, growing or rnigrating). If we can identify where
and how a habitat bottleneck occurs, we gain the ability to increase the amount of habitat that
had formerly imposed limitations and thus increase the density of the target populatior(h.linns
et ai. 1996).
Investigations of human-induced alterations began by relating fish responses to water
quality issues such as nutrient addition (e.g.,Schindler et al. 1971), acid deposition (e.g.,
Schindler et aI. 1985) and elevated turbidity (Miner and Stein 1996). Yet many of the changes
in the fish community are related to habitat alteration which are overlooked by the routine
chernical sampling which is most often completed by monitoring agenciecoeb and Spacie
1993). Studies of the relationship between habitat alterations by human development and fish
populations are well advanced for lotic environments but are still preliminary in lakes.
Having completed analysis using broad, general categones for both fish (totai forage
fish) and habitat variables (development and exposure) (Chapter l), 1 shall now attempt to
refine the analyses and increase the resolution of predictions through focusing on responses of
individual species and size classes and using continuous, rather than categorical, habitat
variables.
Multiple linear regression (MLR) and regression tree (RT) analysis are techniques
which quantify the significance and relative importance of severd independent variables
(habitat variables) on one dependent variable (fish density). MLR is most effective when the
reIationships are linear. It produces an equation which will permit quantitative predictions
about fish density for a given set of habitat parameter values, and it provides quantitative
estimates of the magnitude of change in fish density produced by a unit change in each habitat
variable. Tt has been the standard technique used.
RT is a relatively new, flexible, cornputer-intensive approach to predictioflreirnan et
al. 1984; Efron and Tibshirani 1991) The existence of non-linear and non-additive
relationships will not limit the effectiveness of the regression, as occurs with MLR. It does
not require interactions between independent variables to be explicitly specified by the user
before they are considered in the analysis. In addition, the analysis output is a logical,
hierarchical set of decisions which are easily interpreted. It has been proposed that habitat
selection is based on some hierarchical ranking of the habitat variables since it is rare that al1
of the best variables will occur in the same location(Beve1himer 1996) therefore RT should
be able to accurately detect and depict the series of decisions which occur. The RT approach
previously demonstrated better predictions of srnaIlmouth bass nest density from habitat
variables in a large central Ontario lake than did MLR model(Rej wan 1996).
My investigation attempts to describe and predict relationships between habitat
variables and fish density in fringe habitats of an unproductive Canadian Shield lake, both by
species and size class, as well as to investigate whether the conclusions reached by Rejwan
(1996) about RT and MLR can be generaIized to another ecological dataset.
Habitat Variables
1 selected the following 8 independent habitat variables that previous research suggested rnight
be measures of habitat quality for small fislies in the littoral fiinge zone.
Total CWD (smmed diameter of CWD 1 30m shoreline)
CWD is one aspect of habitat complexity and is thought to provide increased food
attachment sites (Werner et al. 1983a) and refuge from predation (Cooper and Crowder 1979;
Crowder and Cooper 1979; Savino and Stein 1982). Physical structure is positively
correlated with high biological abundance and diversit$Moring et al. 1989; Everett and Ruiz
1993; Monng and Nicholson 1994). Extensive literature suggest that the attractiveness of
macrophytes to small fish is related to vertical vegetation complexit$e.g., Eadie and Keast
1984). 1 predicted that CWD would be positively correlated with fish density.
Shorefine Structure Densi0 (total width of al1 dock and boathouses almg 500m of
shoreline centred on sample site)
1 have evidence that human impact is detrimental to small fish throgh removai of
CWD (Chapter 1). In addition the number of docks serves as index for Ievel of human impact
which is not physical in nature, such as boat use, swimming, gas and oil leaked from boats to
the water, nutrient and pesticide inputs, and sewage seepage (Jaakson et al. 1976); none of
which were directly measured in this study. In addition, shoreline structures may directIy alter
wave impacts and water flow regimes. 1 predicted human development to be negatively
correlated with fish density.
Fetch (summed fetch along lines radiating at 30' intervals from a sample site) and wave
height (average wave heightfi-om 5 observation perioh)
Fetch and wave height are both measures of exposure to wind and waves. Fetch
traditionally is used as an indicator of wind and wave exposure. Wave height is a direct
measure of wave exposure, but is more labour-intensive to obtain than fetch. It was used as a
direct measure backup to fetch in case exposure was an important variable but fetch did not
provide sufficient resolution. Wind can have both positive and negative effects, depending on
its magnitude and duration. For example, windward areas of lakes accumulate warm
epilimnetic water which is beneficiawetzel 1983) but are also most exposed to wave action
which can decrease brood sumival (Clady et al. 1979; Rejwan 1996). Wind also drives water
currents which may be beneficial by increasing movement of small food i t e w e t z e l 1983),
but also generates turbidity which reduces visibility for fish foraging and increases siltation
(Miner and Stein 1996). 1 predicted fetch and wave height would be negatively correlated
with fish density.
Temperature (average temperature deviation in "CjFom weekly lake averages)
Temperature is known to be positively correlated with fis11 growth and/or survival of
YOY over the winter (Shuter et al. 1980; Rejwan 1996). At this latitude, warm-water forage
fish, especially YOY, should be attempting to remain in warmer wate4Shuter and Post
1990). 1 predicted that temperature would correlate positively with fish density.
Substrate complexity (substrate vertical complexity)
Substrate complexity is physical structure that can provide refuge from predators.
Complexity may also protect small fisli from waves. Since there are few macrophytes within
Lake Joseph, substrate complexity was the only source of vertical complexitfladie and
Keast 1984) besides CWD. 1 separated substrate complexity from CWD since substrate
cannot be removed or altered as easily and is particularly important during spawning. 1 did
not rneasure the availability of food, but 1 assume substrate complexity and diversity are
correlated to it. Higher abundance and diversity of macroinvertebrate populations have been
found in more complex, 3-D artificial substrate than in less complex, 2-D substrate$Schmude
et al. 1998). 1 predicted substrate complexity to be positively correlated with fish density.
Substrate diversiîy (Simpson's index)
Substrate diversity is correlated with a more diverse invertebrate and plankton
community, which are the main food organisms for sinall fish. Fish species diversity in
southern Ontario lakes was positively related to several measures of habitat heterogeneity
(Eadie and Keast 1984), with the best predictor being substrate diversity and vertical
vegetation complexity. 1 have used the same diversity index for substrate as Eadie (1984). 1
predicted substrate diversity to be positively correlated with fish density.
Siope
In general shallow water wilI be warrner, and can serve as refuge from predation by
inhibiting access by large piscivorous f iswuiz et al. 1993; Randall et al. 1996). 1 predicted
dope to be negatively correlated with small fish (TL4Omm) density, but positively correlated
with big fish (TL>SOmm) density.
Methods
Site Location
Lake Joseph is a large (5375ha), steep sided, deep (maximumdepth = 93m, mean
depth = 24m), oligotrophic lake, with an average secchi depth in June of approximately 8m,
with typically low macrophyte abundance, in south-central Ontario, 4910' N, 7g040'W (Fig.
1.1).
Data Collection and Index Calculations
Fish were counted in sixty 30m-long littoral fringe transects parallel to the shoreline
and extending 2.5m offshore. Transect starting points were pemanently marked to ensure a
consistent starting point for al1 sampling dates. A weighted line was placed on the bottom
1.5m offshore, following the shoreline contours, at least 30 min. before a transect was swum.
Each site was swum weekly a total of 4 times between July 8 - August 12,1996 and once
between June 19 - 23, 1997. Al1 fish were observed by a pair of snorkelers with underwater
tape recorders or slates. At each site, one observer swam directly over the transect line and
was responsible for recording al1 fish from the shore to the transect line, while the second
observer swam beside the first observer and was responsible for recording al1 fish from the
transect line to lm farther offshore. The second observer was also responsible for looking
ahead of the observers to note and record any fish swimming away from the snorkelers or any
fish swimming into the sampling area from deeper water.
Fish were identified to species and assigned to size classes; srna11 (c50mm TL), big
(>50mrn). Species observed were pumpkinseed,lepornis gibbosus; bluntnose rninnow,
Pimephales notufus; smallmou t h bass, Micropteru do 10 mieu; logperch, Percina cuprodes;
rock bass, Amblop fites rupestris; yellow perch, Perca flavescens; spottail shiner, Notropis
hudsonius; creek chub, Semotifus atromaculafus; banded killifish,Fundufus diaphanus;
brown bullhead, Ameiurus nebulosus; and white sucker, Cutostomm commersoni. If a fish
could not be positively identified to species it was recorded in the appropriate size class as an
unknown fish (2.1% of total fish counted), but not included in any of the individual species
analysis. Cyprinids were positively identified if possible, but it is acknowledged that visual
identification of cyprinids through underwater observation is difficult. Therefore al1 cyprinids
were grouped together for analysis. Al1 small fish, plus ail cyprinids (c100mm) were classified
as forage fish. A total of 300 transects were swuin, and 4,415 fish were observed. Of the
total fish observed 39% were cyprinids or unknown fry, 38% pumpkinseed, 10% rock bass,
7% smallmouth bass, and 3% logperch. The remaining species individualIy accounted for less
than 1% of the total observed.
Fish abundance showed no consistent pattern which could be attributed to seasonal
change (Appendix B.2), therefore a single estirnate of centrai tendency was calculated to
represent each site's fish density. A positive skew in the distribution of data within a site
suggested the use of an index of central tendency other than the average. The use of medians
eliminated any distinction between sites where no fish were ever observed and those where
fish had been observed once or twice during the 5 observation periods. Using median fish
densities total forage fish was the only dependent variable which had more than 30% of the 60
sites with a forage fish density greater than O. As a less extreme transformation 1 used the
geometric mean; back-transformed average of 1080 (fish per site +l) for the 5 observation
periods. My resolution increased and the incidence of fish density greater than O for all 8
dependent fish variables was greater than 45%.
Substrate was measured at the Sm, 15m and 25m points along the 30m transect line
parallel to shore. Percent of each substrate was measured using a lm x O.Sm grid placed 1Sm
offshore. Substrate categories recorded were silt (inorganic material finer than sand), Sand
(rock origin < 0.3cm diameter), grave1 (rock material between 0.2 - 8cm), rubble (rock
material between 8 - 25cm), boulder (rock >25cm), bedrock (exposed rock with no
overburden), muck (organic material with silt and clay), detitus (organic matenal with large
pieces such as sticks, leaves, and decaying plants). Two indices of substrate were calculated
for use in analysis. Substrate complexity represents the availability of vertical physical habitat
structure. Each substrate type was assigned a weighting factor value according to the amount
of vertical physical structure it provides. Silt, sand, and bedrock were assigned a value of 1,
gravel, muck and detritus were assigned a value of 2, and rubble and boulder were assigned a
value of 3.
complexity = C weighting factor x proportion substrate
Substrate diversity was calculated using Simpson's diversity i n d e w e b s 1985).
diversity = 1
proportion substrate *
Slope was measured as the average depth 1.5m offshore at the Sm, 15m and 25m
points along the 30111 transect line parallel to shore.
Water temperature (OC) was recorded after each transect swim with a mercury
thermometer in the shade. For each week an average temperature among sites was calculated.
This is referred to as the arnong-site average temperature. The temperature deviation was
calculated by subtracting the site temperature frorn the among-site average for each week.
The average of the 5 deviations was used as temperature deviation.
Average wave height (cm) was determined by standing a meter stick on the transect
line and marking the maximum difference from crest to trough of a single wave. Wave height
measurements were taken between boat wakes. The average wave height for the 5
observation periods was calculated. An additional index of wave exposure hereafter referred
to as total fetch was coiistructed by placing a polar reference overlay of 12 radiating lines 30'
apart on a transparent overhead. The overlay was placed with one line pointing north and
with the point of origin on the site location on a standard 150,000 topographie map issued by
Energy, Mines and Resources Canada (Lake Joseph 31 E/4). The distance along each line
over the water to the first point of land was recorded. Total fetch was determined by
summing the length of al1 the lines.
A shoreline structures density index was constructed from an unpublished survey of
shoreline structures conducted by the Ontario Ministry of Natural Resources. It is the sum of
all shoreline structure (dock or boathouse) widths within 250m in both directions from the
site, and represents the amount of shoreline directly altered by the presence of shoreline
structures.
Finally, al1 in-water CWD ( > k m diameter) within the sarnpling area was counted and
the diameter was measured to the nearest 2.5crn. C W total was the sum of the CWD
diameters. For ease in interpretation, CWD totals are reported in large tree equivalents. A
large tree was assumed to have a diameter of 30cm. Therefore a CWD total of 300 per
sampling area corresponds to 10 large tree equivalents.
Data Analvsis
Sufficient nuinbers of fish were counted to pennit individual analysis of total forage
fish, cyprinids, rock bus, pumpkinseed and smallmouth bass. Independent variables evaluated
were: total CWD, shoreline structure density, total fetch, wave height, temperature deviation, .
substrate complexity, substrate diversity, and slope.
Multiple Linear Re ession Analysis N L R )
MLR analysis was used to deterinine the relationship between fish density and the
associated habitat measures. Best subsets analysis (Minitab, Version 11) was used to
determine which variables to include in the multiple regression. This method involved
cdculating the best model for 1 variable, for two variables, and so on until the model including
al1 variables was calculated. Generally, the equation with the highest adjusted?was used for
further analysis. However if the adjusted i value increased by only a small increment with the
addition of a variable, the benefit gained by increased explanation was not considered to be
worth the increased cost and effort required for rneasurement. For this study, an additional
predictor had to increase the adjusted 8 by at least 296, in order to be incorporated into the
final model. Once the "best subset" of variables was determined, a MLR % was calculated.
Model Sign ficunce - permuted r2
The level of significance of the model was determined by comparing the multiple2r
value with those generated by random permutation procedure (Edgington 1987; Manly 199 1;
Good 1994). This method was used since it does not require the sarne level of adherence to
the assumptions of normality and heteroscedasticity, as the estimates of p-values provided by
statistics software packages. The fish density values were randomly pemuted lOOOx while
the order of the habitat rneasures was held constant. For each permutation a best subset
model with the same number of variables as the original model was calculated. The ?
corresponding multiple ? values for al1 permutations were used as a reference distribution for
cornparison with the original multiple i. The level of significance for the model was the
proportion of random multiple ? values which were larger than the original multiple 1. The average froin the 1000 permuted 2 values represented a measure of the variation
explained by the model complexity alone. The net $value was calculated by subtracting
average permuted ? from the original multiple i. It was a more accurate method of
quantifying the amount of variation which can be associated with habitat variables.
Model Predictive A biliiy - cross-va fidateci r? Cross-validation was completed to estimate the model's abilityto predict fish density
for sites not in the calibration dataset. This method accounted for the possibility that a part of
the standard r2 for a dataset represents the ability of the model to explain the unique
peculiarities of the particular sample of independent and dependent variable measurements.
This component of model structure would not help to predict variables in a new dataset.
Cross-validation involved dividing the data into 10 equal subsets of 6 sites. 1 excluded one
subset (test group), determined the best subset regression equation (with the number of
habitat variables detennined from the best subsets analysis) using the remaining 90% of the
data (calibration group) and predicted the fish density values for each site in the test group.
This was completed for each of the 10 subsets. The differences between predicted and
observed values were squared and summed. The total sum of squared differences was the
model SS. The r2 value for the 10-fold cross-validation set was calculated as
?= 1 - model SS
total s S
This procedure was repeated 100 times, each with a different random allocation of data into
subsets. The cross-validation ? for the linear model was the average * of the 100 10-fold
cross-validations.
Remession Tree Analysis
Mode1 Seleetion
RT analysis was also used to determine the relationship between fish density and the
associated habitat measures. The tree started with the full data set (root), and had a series of
binary splits into two subsets (nodes). A node which could not be split any further was a
terminai node (leaf). The average of the habitat variable at each terminal node served as the
prediction for future observations. Each individual split was based on a single habitat variable.
The habitat variable was chosen to minimize the sum of the node SS of the two resulting
subsets, and maximized the difference between the two nodes. A node continued to be split if
the deviance (residual SS) within a node was greater than a specified level(O.O1) or the
number of observations per node reached a specified minimum (5). In other words, the first
habitat variable is used to divide the fish density data into two groups which have the least
intragroup variability but are most different from one another, and that have at least 5
observations. The SS for the two resuIting groups (node SS) are then summed. This process
is repeated for each of the 8 habitat variables. The variable with the smallest summed node SS
is the variable used to split the fish density data into two groups. Each of the resulting groups
is subjected to the same process with al1 of the habitat variables included in the analysis.
Attention to the minimum node size was important since our data set was small
(n400). 1 was concemed that a smaller minimum node size rnight produce alternative and
additional significant splits. For our dataset, decreasing the minimum node size to 1 increased
the number of texminal nodes, and therefore the amount of variance explained for the specific
data set, but did not increase the model's predictive ability as measured by cross-validation
(see below).
The resulting tree was a series of threshold criteria. If the value of the habitat variable
specified at a particular node was larger than the cnterion, it belonged to the right subset,
while those sites with values smaller than the criterion belonged to the left subset. The length
of the vertical branches was proportional to the arnount of variance explained by the criterion
(e.g., Fig. 2d).
As with the MLR, selection of the "best" tree size was required. This was completed
through cross-vaiidation of trees of different sizes. Cross-validation again involved dividing
the data into 10 equal subsets of 6 sites, excluding one subset (test group), growing a tree to
al1 of the different sizes possible using the remaining 90% of the data and testing the tree with
the excluded data. The unexplained variance for the test group at each tree size was
calculated. This was completed for each of the 10 test subsets. This 10-fold cross-validation
was completed 50 times, each with a different random allocation of data into subsets. For any
given tree, as the number of variables included in the tree increased, the model's ability to
explain the specific dataset increased. Variables added later to the tree increased explanation
of the specific data set, but did not increase the predictive power and therefore were removed
(pruned) fiom the tree. Thus, the best tree size was determined by the resulting pruned tree as
determined through cross-validation. The amount of variation explained by the pruned
regression tree (r2) was as;
2 = 1 - 1 deviance for al1 terminal nodes from the pruned tree
total deviance in the fish density dataset
Model Signficance - pcrmuted r2
The level of significance of the mode1 was detennined using the same method as with
the MLR, by comparing the ? value to those generated by a random permutation procedure.
The fish density values were randomly perrnuted 500x and for each permutation the best
regression tree with the same number of temiinal nodes as the original mode1 was calculated.
The net 2 value for the original tree was calculated as with the MLR.
Mode1 Predictive Ability - cross-validated $
The RT's ability to predict fish density for sites not in the calibration dataset was
determined using the previously generated cross-validation data.
cross-validation 2 = 1 - median unexplained variance from cross-validation at best tree sire
median of the total unexplained variance from calibrdion datasets
Results Mode1 Com~arisons
Big rock bass (TL>SOmm), and big smallmouth bass (TL>SOmm) will not be discussed
further as there were no significant models using either MLR or RT analysis (Table 2.1).
Total forage fish and cyprinid models included CWD as the only significant habitat variable,
and the associated net 8 and cross-validation 3 were also similar (Table 2.1 and Figs. 2. la,
2.2a, 2.1b, 2.2b). Big pumpkinseed and YOY smallmouth bass produced significant RT
models, but did not produce significant MLR models. Small rock bass and small pumpkinseed
produced significant MLR and RT models, but they differed in the number of significant
habitat variables, i n the variables which were significant, in nethalues and in cross-
validation ? values (Table 2.1 and Figs. 2. lc, 2.2c, 2.ld, 2.2d). Overall, neither MLR or RT
models produced consistently higher cross-validation ?'S.
RT consistently had higher permuted # values than the corresponding MLR models,
reflecting the fact that RT models are inherently more coinplex than MLR models even when
the number of variables in the model was identical. As expected for both methods, the
permuted r2 values increased as the number of independent variables included in the model
increased.
S~ecies Cornparisons
Within Lake Joseph, CWD was the most important variable for explaining and
predicting 5 fish densities: total forage fish, cyprinid, small rock bass, and small and big
pumpkinseed (Table 2.1). In contrast, YOY smallmouth bass density was highest in areas of
high shoreline structure density, which was associated with low CWD abundance (Chapter 1)
Fig. 2.la
y = 1.19 + 0.047 CWD net r2 = 0.39 cv 3 = 0.32
Fig. 2.lb
y = -0.074 + 0.018 CWD net r2 = 0.28 cv f = 0.09
predicted cyprinind
Fig. 2.lc
9.5 4 1- 4.5 0.0 0.5 1 .O 1.5 2 0 2 5
predicted u ~ l l rockbers
y = -0.662 + 0.002 CWD -i- 0.02 slop + 0.75 diversi ty
net r2 = 0.307 cv 8 = 0.290
Fig. 2.1: Best subset multiple linear regression analysis. Predicted density, using equation indicated vs. observed density.
Fig. 2.ld
p r d c t e d big pmpklnasad
y = 2.69 + 0.018 CWD - 0.21 fetch net r2 = 0.24 cv 8 = 0.22
Fig. 2.le
y = 1.79 + 0.004 CWD - 0.06 fetch - 1.95 diversi ty
* not significant
Fig. 2.1 f
y = 0.5 1 + 0.005 structure density - 0.025 fetch
* not significant
piadlcled YOY smllmouth bars
Fig. 2.1: continued
CWD = 262.5
1 I
100 300 500
total CWD
CWD = 305 1 I
Fig. 2.2a
net = 0.39 cv 3 = 0.41
Fig. 2.2b
net r2 = 0.23 cv r2 = 0.05
Figure 2.2: Final regression tree. At each terminal node the top number is the average number of fish per site, and the bottom number (in brackets) is the number of observations in the node. At non-terminal nodes the variable listed is the variable used to determine the split. The left branch are those values less than the criteria, and the right branch are those vaIues greater than the criteria.
CWD = 227.5 I 1
total CWD
Fig. 2 . 2 ~
net r2 = 0.23 cv $= 0.15
CWD = 152.5
Fig. 2.2d
net r2 = 0.46 cv r2 = 0.26
1 I
P
3
100 300 500 slope = 32.3
total CWD avg = 9.7
0.23 (45)
4.98 30
slope 60
19.06 (1 0) (5)
Fig. 2.2: continued
CWD = 262.5 1 I
100 300 500
total CWD
shoreline structure = 97.5m / 500m
I I
shoreline devleopment
Fig. 2 . 2 ~
Fig. 2.2f
net r2 = 0.19 cv 2 = 0.15
Fig. 2.2: continued
(Table 2.1). YOY smallmouth bass was also the only fish group whose density was not
significantly related to CWD.
Big rock bass, big smallmouth bass and big pumpkinseed (MLR only) densities were
not significantly associated with any habitat variable (Table 2.1). These larger fish did not
appear to have the same habitat restrictions or associations as smaller fish. However, this
finding may also be an artifact of low nurnbers observed in the fringe zone (0-2.5m offshore)
and thus 1 had insufficient power to detect associations.
Discussion Mode1 Cornparisons
Closer inspection of models in which the results differed provide some insight into the
sensitivities of the methods. MLR could not produce signifiant explanatory models for big
purnpkinseed and YOY smallmouth bass, whereas RT could. Both analyses possessed at least
one predicted value which acted as an outlier (Figs. 2. l e and 2. If). These points appeared to
have a larger negative influence on the explanatory power and predictive ability of the MLR
rnodel, than on the RT rnodel. It is possible that a transformation could reduce their influence,
but transformations can create alternative violations to the assumptions of MLR and make the
regression coefficients difficult to interpret ecologically. Transformations are not necessary
for RT models, and this is one of its advantages. For our data the R T approach appears to be
more robust to outliers than MLR.
For small rock bass the MLR model only provided a slightly higher explanatory result
(MLR net r2 = 0.31, RT net r2 = 0.23) but a substantially higher predictive ability (MLR cross-
validation I? = 0.29, RT cross-validation ? = 0.15) than the RT model. The MLR
incorporates 3 variables while the best RT model uses only one. A possible explanation is that
the relationship between small rock bass and several of the habitat variables is Iinear, and since
the MLR model is most effective with linear relationships, the MLR model can include
additional variables to the model increasing the net i value. Linear relationships are not
apparent, however, when looking at scatterplots comparing srna11 rock bass density with
individual habitat variables and small rock bass. Additionally 1 cannot understand why small
rock bass would be the only group of fish observed that had a distinctly linear relationship
with severd habitat variables.
Small purnpkinseed show the opposite result. The RT model provided almost double
the explanatory ability of the MLR model (MLR net f = 0.46, RT net r2 = 0.24). Closer
inspection of the RT graph (Fig. 2.2d) shows that those sites with high CWD (>152.5) could
be further split based on dope; this increased the amount of variance which could be explained
by the RT model. However, since the cross-validation f for both models was sirnilm and
substantially lower than the RT net $ value, it appears that the explanatory power of the
model is related to the uniqueness of the fitted dataset.
These differences reinforce my conclusion that neither the MLR or RT approach is
consistently superior to the other in contrast with the finding ofRejwan (1996).
Initially I felt that RT provided a superior management tool since it specifies threshold
criteria, and complex interactions can be represented visually. RT models permit an easy way
to express and communicate information to lake managers and their constituents. This may be
particularly appealing for rehabilitation or restoration programs since a threshold goal is set.
MLR model results rnay not be as immediately understandable, and complex models with
more than three habitat variables cannot be visually represented. A 3-dimensional graph can
illustrate two habitat variables, but with more than three variables the graph cannot present
real, measured variables on the axes. For any model, however, it is important to remember
that the purpose of the model is to determine relationships between fish density (dependent
variable) and habitat variables (independent variables). Conclusions will be more readily
understandable if the habitat variables are recorcied in a form which can be easily related to
concrete terms, such as, number of shoreline structures per 500m shoreline, number of large
trees / 30m shoreline or average distance over open-water.
To demonstrate how the results from the two metliods can be interpreted for lake
managers or their constih~ents, 1 will elaborate upon the results obtained for small
pumpkinseed (YOY and 1+, TL4Ornrn) density. Both models had approximately the same
ability to predict new data (MLR cross-validation ? = 0.22, RT cross-validation ? = 0.26),
although the RT model explained almost twice as much variance in the data set as the MLR
model (MLR net 8 = 0.24, RT net r2 = 0.46).
RT indicated that CWD and slope are the significant habitat variables (Fig. 2.2d). The
CWD variable was the sum of dl diameters for al1 CWD dong 30m (100ft) of shoreline. A
CWD value of 300 wouId be equivalent to approximately 10 large trees, based on the
assumption that a large tree has a diameter of 30cm. Slope was the depth 1.5m offshore,
which means the greater the slope the deeper the water. RT calculateci the first threshold to
be 152.5 CWD which corresponds to approximately 5 large trees. Areas with at least 5 large
trees / 30m had small pumpkinseed densities approximately 40x greater than areas with fewer
than 5 large trees per 30m shoreline. The second threshold was a slope of 32.3cm, but this is
of significance only for those areas with more than 5 large trees. At these locations, areas
with high slope (> 32.5cm 1.5m offshore) had almost 4x more srnail pumpkinseed than areas
which were less steep (<32.5cm). The management message would be that to increase smaii
pumpkinseed density in the lake, at least 5 large trees are required / 30m shoreline. Although
litile can be easily done to alter slope, the benefit for small pumpkinseed should be even
greater if areas witli a depth greater than 32.5cm, 1.5m offshore are the areas identifleci for the
maintenance of at least 5 large trees / 30m or the addition of CWD to ensure a minimum of 5
large trees / 30m shoreline.
MLR, for these same data, indicated that CWD and fetch were the significant habitat
variables (Fig. 2.ld). Fetch is the sum of distance (cm on a topographie map) of 12 lines
radiating from the site at 300 intervals (lcrn on map corresponds to 500m actual distance).
However, a sum of the distance of open-water in 12 potential directions may be difficult to
conceptualize, therefore another way of representing this measure is to discuss the average
arnount of open-water which can be seen while standing at the site location. Thus, a fetch
value of 1 actually corresponds to 500m / 12 = 41.7m. MLR indicated the significant
equation for predicting small pumpkinseed density as;
small pumpkinseed density = 2.69 + 0.018*CWD - 0.2l"fetch
Translated into a management goal this means areas with high CWD have high small
pumpkinseed density, while areas with high fetch have lower small pumpkinseed density.
However, the magnitude of the habitat variables are not obvious from the equation.
Specifically, each additional lcrn of diarneter of CWD will increase the small pumpkinseed
density by 0.018. Therefore, the addition of approximately two large trees is required to add
1 smali pumpkinseed to the population. Exposure to each additional 41.7m of open-water
will decrease the small purnpkinseed density by 0.21. Therefore, for every additional 200m of
exposure to open-water there will be 1 fewer smdl pumpkinseed. Both the arnount of CWD
and level of exposure to open-water must be considered before the density of small
pumpkinseed can be predicted. The management message would be that to increase small
pumpkinseed density in the lake CWD can be added to the lake and although there is little
which can be done to alter fetch, areas with a low fetch values should be protected from
alteration, since these areas have higher small pumpkinseed density. Thus, both techniques
c m be explained in relatively simple, easy to understand ternis, although MLR cannot be
easily depicted graphically . Overall, RT models should prove useful wheneve there is reason to expect non-linear
or non-additive relationships between dependent and independent variables. MLR analysis
can capture the same information, but the models can be considerably more difficult to
develop and interpret, especially if independent variables interact. However, MLR models
may predict continuous variables better than RT model. Datasets which are moderate to large
(n>100), have independent variables which interact, and employ independent variables in
easily visualizable units rather than as indices, should make full use of the strengths of RT
andysis. RT may also be useful as a screening tool for interesting or unusud relationships
among variables when using a large number of independent variables.
Habitat Variable Cornparisons
Areas with high CWD supported the highest density of cyprinids, srnall rock bass,
small and big pumpkinseed, and total forage fish. Areas with CWD are spatially cornplex,
even more so than those areas with spatial complexity provided by substrate alone. This result
supports the conclusions that physical structure in general, and CWD in particuIar, is an
important habitat feature for fish (Chapter 1). In fact, correlations with any of the fish groups
identified as possessing a significant relationship with CWD (forage fish, cyprinid, small rock
bas , small pumpkinseed, and big pumpkinseed), were approximately twice as strong with
CWD, than with substrate complexity (Appendix C.2). It is not just small fish which are
found in high densities in areas of high CWD, but also big pumpkinseed. This variety in
species and size classes which show positive associations with CWD suggests a broad range
of benefits provided by CWD - more than serving as a refuge for small fish - such as providing
attachment sites for food organisms.
It was surprising that wave height or fetch were not significant variables, since
exposure was significantly related to total forage fish density in Chapter 1. Fetch was only a
significant variable for small puinpkinseed, and even then it was the second variable of
importance.
Since Lake Joseph is a relatively northern lake with an epilimnetic temperatures
seldom greater than 24OC and an extended winter period, I was also surprised that
temperature was not a significant variable for any of the species since temperature has been
shown to be positively correlated relationships with YOY growth for yellow perch and
smallmouth bass (Shuter and Post 1990). However, my temperature measurements were not
continuous and 1 may not have had the resolution to detect any relationships between
temperature and fish density.
YOY smallmouth bass (TLcSOmrn) appear to be much bolder in the face of predation
risk than other small fishes. In addition to being associated with areas of high shoreline
structure density, where there are low CWD densities, they were the only small fish
consistently observed in close proximity to large, piscivorous fish. They were observed
offshore in open water, and 65% of those observed to be associated with shoreline structures
were in water deeper than 2.5m. In the same shallow water areas near shoreline structures
only 39% of small pumpkinseed and no cyprinids or YOY yellow perch were observed.
Whether YOY smallmouth bass actually expenence lower predation risk is unknown. The
association of YOY smallmoiith bass with development has been recorded in a large,
eutrophic lake in Iowa (Bryan and Scarnecchia 1992) and in coastal areas of Green Bay
(Brazner and Beals 1997).
General Conclusions
Several studies have focused on the impact of human development on fish abundance.
These studies classifieci areas as developed (altered) or undeveloped (unaltered) by humans.
An area was altered either through the addition (docks, boathouse, bridges, landfill, roads) or
removal (macrophytes, CWD) of material dong the shoreline. Despite the variety in type of
alteration, developed areas consistently were more homogenous in habitat characteristics and
had a narrower range in the number species observed than undeveloped areas. They had
lower macrophyte or CWD density, higher turbidity levels, higher densities of "generalist" fish
species such as rock bass and smallmouth bass and lower densities of YOY fisyPoe et al.
1986; Bryan and Scarnecchia 1992; Leslie and Timinins 1994; Brazner 1997; Brazner and
Beals 1997). 1 observed YOY smallmouth bass in higher densities in developed areas than in
less developed areas, but 1 did not observe rock bass (any age or size class) or smallmouth
bass (>YOY) in higher densities in developed areas. Not only were YOY smallmouth bass
found in different locations than other small fish, they were bolder in their response to
predation risk.
L i e undeveloped, vegetated areas in Iowa (Bryan and Scarnecchia 1992) and wetland
areas in Green Bay (Brazner 1997) undeveloped areas with high CWD in Lake Joseph
supported high abundance of pumpkinseed, rock bass, and cyprinids. CWD was identified as
a significant habitat correlate both with categorical and continuous variables. Although the
specific threshold level differed, both chapters indicated that fish were responding to a CWD
threshold. Analysis of forage fish median values (Chapter 1) suggested a minimum of 5 large
trees / 30m shoreline, while analysis of forage fish geometric mean values (Chapter 2)
indicated a minimum of 8.5 large trees / 30m shoreline, before high forage fish densities were
observed. Identification of CWD as an important habitat variable using continuous data d s o
supports the interpretation from Chapter 1 that the development effect we originally saw is
driven by CWD variation.
Shoreline structures are not inherently h m f u l to fish populations. In some areas,
specifically areas exposed to prevailing winds, the addition of shoreline structures can increase
the densities of some species, by increasing the structural complexity i n that area. Another
potential benefit is that shoreline structures increase the interactions between forage and
piscivorous fish attracted to the structure, which can in turn, increases the energy transfer
between trophic levels. Structures may provide sufficient refuge areas from predation to
maintain a strong forage fish population, while providing a foraging area for piscivores.
However, 1 am not convinced that any and al1 docks and boathouses will provide similar
benefits. Approximately 83% of the docks and boathouses observed in Lake Joseph have crib
foundations. Docks and boathouses with pillar foundations cannot add the same level of
habitat complexity as crib foundations, and thus may not be able to provide the benefits 1
observed in Lake Joseph.
Other aspects of human development can negatively affect fish populations,
specifically the removal or loss of CWD from the shore's edge. CWD appears to function
similarly to macrophytes, in that moderately high densities provide small fish with refuge areas
fiom predation. Areas which have low CWD have low forage fish density. In Lake Joseph
physical structure appears to be limited in availability and the percentage of shoreline altered is
relatively low, therefore if shoreline residential owners minimize the removal of dead trees
from their shoreline property, ghysical structure within the lake is only enhanced through the
addition of shoreline structures. My work indicates that this enhancement of available
structure should at least maintain the existing densities of fish.
Finally, tliere are two further significant findings from this thesis. First, larger fish
seem to be less constrained in habitat associations than smaller fish at the lake's edge.
Second, although regression tree analysis is a potentially valuable technique, it was not
consistently superior to multiple linear regression analysis in either its abiIity to explain the
variance in a specific dataset or in its ability to predict new data.
Suggestions for Future Research The ability of shoreline structures to increase or at least provide habitat complexity
requires further investigation. Recent changes to provincial legislation (November 1996)
concerning the building of docks and boathouses have occurred.
"1. Docks and boathouses which will not require a work permit:
Q Cantilever docks
Floating docks and floating boathouses
Docks and boathouses supported by posts, stilts, or poles
Boathouses built above the high water mark
Q Crib docks and crib boathouses where the total supporting crib
structure (including historical crib structures) does not exceed 15 sq.
metres in surface area.
Any combination of the above (e-g. a floating dock with a cribcl5 sq.
meses).
Boat lifts and marine railways
Removal of an old dock or boathouse
2. Docks and boathouses which will require a work permit:
Crib docks andor boathouses where the total surface of al1 historical
cribs and the proposed new cribs exceeds 15 sq. metres in surface area.
r Docks with solid foundations (cg. concrete), jetty docks, or docks
constructed with steel sheeting
Boathouses with solid foundations (e-g. concrete) " (Ministry of
Natural Resources 1998)
In addition, the Ontario Ministry of Natural Resources produced a Fact Sheet
"Working Around Water? What you should know about Fish Habitat and Building Docks and
Boathouses", which proposes several best management practices. They include the two
following:
"Select a structure which minimizes disturbance to the river or lake
bottom: Cantilever, fioating and post-supported boathouses and docks do not
disturb river or lake bottoms or restrict the rnovement of water near the shore.
These structures c m actually improve fish habitat by providing fish with extra
shelter fiom predators. From a fish habitat perspective, these structures are
preferred.
Limit the size of crib foundations: Crib foundations for docks and
boathouses are acceptable if there is bndging between them which enables
water to circulate. Small cribs are preferred. Vertical planking is not
recommended along the dock, because it c m restrict water movement. "
(Ministry of Natural Resources 1998)
1 have been unable to find any studies completed which have looked directly at the
relationship between different types of docks or boathouses and fish abundance. Given the
volume of studies supporting the conclusion that areas with complex habitat are beneficial or
at least associated with high abundance and diversity of fish, 1 find it difficult to accept that
floating and post-supported docks and boathouses can improve fish habitat by providing
shelter frorn predators. 1 had hypothesized that differences in small fish densities associated
with the different types of structures would be greater than differences in Iarge fish densities,
but this hypothesis could not be substantiated. Visual observations from 86 docks and
boathouses showed no differences in abundance between structure types for any individual
species, total fish, total forage fish or total predatory fish (Appendix A.3). There appears to
be a difference in total fish density, with cnb > pi1Ia.r > floating, however, the number of
observations made around pillar (n=5) and floating (n=3) structures was so low that the
power to detect differences was negligible. However, the trend observed for totai fish, the
existing policy of O M M , and the continued addition of new shoreline structures indicate that
this specific issue should be investigated more thoroughly.
Appendix A. 1 :
Summary information of human-induced shorehe alteration of Lake Joseph h m OMNR survey, 1995.
To ta1 S horeline m
Main Island 130884 82075.5
Structure and Type Number Width (m) Area (II?)
Wet Boathouses
Docks Crib Pillar Floating Fill Can tilever Covered Combination Other Total Cri b PilIar Fill Other Total
Rarnps Wood Cernent Pillar Other To ta1
Manicured Lawns Unbuffered Buffered Totai
S horewalls Stone Wood Cemen t Other To ta1
Man-made Beaches Other
Total altered shoreline (m) % of shoreline dtered
Appendix A.2: Physical Data of Docks and Boathouses
The box represents the SSth, median, and 75th percentiles. The wisker represents 1.5~ the interquartiie range, any * represents statistical outliers. The iiumber of structures observed is indicated above the graph. The p-value from a 1-way ANOVA using log,,(x+l) is indicated below the graph.
Appendix A.3: Fish Densities by Structure Type
A total of 86 structures was sampled and a total of 6,147 fish was observed. Of the total fish observed 57% were rock bass, 21 % were pumpkinseed, 8% were cyprinids and 7% were smalimouth bas. Cyprinids were observed at only 19% of the structures and therefore could not be analyzed M e r . The remainder of the species were observed nt greater than 74% of the structures.
The box represents the 25th, median, and 75th percentiles. The wisker represents 1 . 5 ~ the interquartile range, any * represents stritistical outliers. The number of structures observed is indicated above the graph. The p-value from a 1-way ANOVA using loglO(x+l) is indicated below the graph.
Appendix A.3: continued
71 7 3 5 71 7 3 5 4 + I I
1 .O00
0.1 00 i f - 1 $ 7
Appendix A.3: continued
Appendix B. 1 :
Relationship between habitat variables and site classification. The dotted line (----) represents developed sites, solid line +) represents less developed sites. P-values fiom No-way ANOVA analysis are indicated below graph; d is development effect, e is exposure effect, d*e is interaction effect.
1 .'
exposeà pro tected
exposed protected
exposed protected
exposed protected
Appendix B. 1 : continued
- P d .
exposed protected
exposed protected
exposed protected
exposed protected
Appendix B. 1 : continued
exposed protecteù
exposed protected
exposed protected
exposed protected
exposed protected
Appendix B.2: Fish density seasond pattern
Median number (with 29' and 75' percentiles) of fish observed for al1 transects by sampling date. There was no significant difference in the number of fîsh observed over tirne or between years, as determined by a 1-way ANOVA with Tukey's multiple cornparison (df=293, overall aIpha Pc0.05) for loglo(x+l) data. Therefore we used a rneasure of central tendency for each of the sixty sites. Chapter 1 used median number per site, while Chapter 2 used the geometric mean number of fish per site.
O ! I I I June 16,1997 July 8, 1996 July 15,1996 July 29,1996 August 5,1996
Appendix B.3: substrate data
Median % substrate for different classes of sites. Anaiysis using two-way ANOVA's with individuai substrate categories indicated no significant difference between development, exposure or an interaction of the two categories (not shown).
dw exp undev exp dev prot undev pro(
Appendix B.4:
Raw data for calculating total prey density/500m shoreline associated with fringe sites
site develop exposure prey /m2 prey # dock dock # prey # prey # prey total dock lm2 1500m distance fringe dock 500 prey
fringe /SOOm /rn2 2 15 970.00 4.88 974.88 0.780
Appendix C. 1 :
Scatterplot matrix of fish species geornetxic mean density versus habitat variables
Developmenl
Fetch
Wave
Temperature
Complexiîy
Diversity
SI ope
Fmge Cyprinid fish
.- . . m . . . .. m... .. - rr . œ 0 . -. L - . 1:.
Non-zero fish density observations
Number %
RB big PS sm PS big SB YOY SB big
- - .. P.. . h.". . ' -.. i .
* a b . . - 0 . O..' . @. ?. * s ' . i" * a * = ' : O
Appendix C.2:
Pearson Correlation matrix for habitat variables and fish groups
CWD
shoreline structures
fetch
wave height
temperature deviation
substrate complexity
substrate diversity
slope
forage
fish
0.67
-0.10
-0.39
-0.3 1
0.27
-0.33
0.11
-0.03
cypnnid
0.59
0.00
-0.22
-0.18
0.27
-0.32
0.04
-0.13
srnaIl
rock bass
0.53
-0.19
-0.33
-0.3 1
0.09
-0.27
0.24
0.26
big
rock bass
O. 19
-0.07
-0.15
-0.19
-0.07
0.07
0.10
0.30
srnall
pumpkinseed
0.54
-0.16
-0.39
-0.33
O. 17
-0.25
0.11
0.03
big
pumpkinseed
0.36
-0.09
-0.3 1
-0.27
0.1 1
-0.13
-0.14
0.04
YOY big
smallmouth bass smallrnouth bass
-0.05 0.06
0.27 0.01
-0.23 -0.01
-0.04 O. 16
0.12 o. 17
-0.10 0.04
o. 19 0.1 1
0.08 0.30
Appendix C.3:
Geometric mean for fish density per d data for 60 nearshore fringe sites
site
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 3 1 32 33 34 35 36 37 38 39 40 41 42 43 44 45
cyprinid rock bass rock bass pumpkinseed pumpkinseed smallmouth smallmouth small 2.20 0.00 0.25 0.38 0.32 0.1 5 0.32 1.17 0.00 0.00 0.00 0.00 0.58 0.89 1.35 0.1 5 0.00 0.25 3.09 0.78 0.1 5 0.00 0.1 5 2.47 0.00 0.00 2.35 0.64 0.00 0.62 1.24 0.00 0.43 0.1 5 0.26 0.32 0.74 0.52 0.15 0.97 0.1 5 0.1 5 0.32 0.1 5 0.32
big 0.00 0.00 0.15 0.70 0.38 0.52 0.58 1.76 0.15 0.00 0.00 0.00 0.1 5 0.32 0.25 0.32 0.00 0.25 4.01 0.55 0.55 0.1 5 0.32 0.38 0.00 0.00 0.64 0.55 0.1 5 0.25 2.44 0.00 0.00 1 .O5 0.41 0.00 0.00 0.1 5 0.00 1.17 0.1 5 1 .O5 1.64 0.1 5 0.64
small 19.75 0.00 0.89 20.45 0.15 0.25 0.1 5 0.00 0.00 0.00 0.38 0.00 0.00 1.77 12.93 0.1 5 1-05 0.00 30.14 0.55 0.00 0.00 0.78 2.20 0.00 0.00 1 1.42 0.1 5 0.00 1.58 1 6.03 1.33 0.00 0.00 0.00 10.58 0.1 5 0.1 5 0.00 0.64 0.00 0.00 0.1 5 0.00 17.26
big 2.87 0.00 0.86 1 .O0 0.25 0.32 0.43 0.1 5 0.00 0.00 0.00 0.00 0.00 1.76 3.07 0.00 0.52 0.84 6.44 0.32 0.1 5 0.00 0.1 5 0.00 0.00 0.00 1 .O8 0.32 0.00 0.00 2.02 0.00 0.00 0.62 0.00 1.20 0.00 0.00 0.00 0.15 0.00 0.00 0.15 0.00 12.97
bass small 0.00 0.00 0.52 0.32 0.43 0.00 0.64 0.38 0.15 0.32 1 .O0 0.74 0.74 1.64 0.58 0.00 0.32 0.32 1.39 0.1 5 0.00 0.58 0.1 5 1 .O2 0.72 0.55 0.43 0.52 0.25 3.31 0.00 3.1 9 0.25 0.1 5 1.33 0.25 0.52 0.93 0.55 1.18 0.38 0.52 0.25 0.00 0.64
bass big 0.00 0.00 0.25 0.00 0.32 0.00 0.00 0.32 0.15 0.00 0.1 5 0.00 0.00 0.1 5 0.1 5 0.1 5 0.00 0.1 5 0.00 0.25 0.43 0.1 5 0.1 5 0.1 5 0.00 0.00 0.1 5 0.64 0.00 0.15 0.32 0.00 0.00 0.15 0.1 2 0.00 0.00 0.00 0.00 0.1 5 0.1 5 0.15 0.43 0.00 0.25
Appendix C.4:
Habitat variable raw data for 60 nearshore fringe sites
site CWD shoreline fetch wave temperature substrate substrate dope structure density height deviation complexity diversity
1 170 15 0.7 86.67 0.66 19.0
References Cited
American Fisheries Soceity (1997). Artificial habitat. Fisheries22: 5-36.
Angermeier, P.L. and J.R. K m (1984). Relationships between woody debris and fish habitat in a smail warrnwater stream. Transactions of the American Fisheries SocietSlS: 716- 726.
Banziger, R. (1995). A comparative study of the zoobenthos of eight land-water interfaces (Lake of Geneva). Hydrobiologia 3OO/3Ol: 133- 140.
Bassett, C.E. (1994). Use and evaluation of fish habitat structures in lakes of the eastem United States by the USDA Forest Service. Bulletin of Marine ScienceSS(2-3): 1137- 1148.
Beaucharnp, D.A., E.R. Byron and W.A. Wurtsbaugh (1994). Summer habitat use by littoral- zone fishes in Lake Tahoe and the effects of shoreline structures. North American Journal of Fisheries Management ld(2): 385-394.
Bell, S.S., E.D. McCoy and H.R. Mushinsky, eds. (1991). Habitat Structures. New York, NY, Chapman and Hall.
Benke, A.C. and J.B. Wallace (1990). Wood dynamics in coastal plain blackwater streams. Canadian Journal of Fisheries and Aquatic Scienced7: 92-99.
Benson, B.J. and J.J. Magnuson (1992). Spatial heterogeneity of littoral fish assemblages in lakes: relation to species diversity and habitat structure. Canadian Journal of Fisheries and Aquatic Sciences 49: 14%- 1500.
Bevelhimer, M.S. (1996). Relative importance of temperature, food, and physical structure to habitat choice by smallmouth bass in laboratory experiments. Transactions of the American Fishenes Societyl25(2): 274-283.
Bolger, D.T., A.C. Alberts, R.M. Sauvajot, P. Potenza, C. McCalvin, D. Tran, S. Mazzoni and M.E. Soule (1997). Response of rodents to habitat fragmentation in costal southern California. Ecological Applicationfl(2): 552-563.
Braaten, P.J. and C.R. Berry (1997). Fish associations with four habitat types in a South Dakota prairie stream. Journal of Freshwater Ecology 12(3): 477-489.
Bramer, J.C. (1997). Regional, habitat, and human development influences on coastal wetland and beach fish assemblages in Green Bay, Lake Michigan. Journal of Great Lakes Research 23(1): 36-5 1.
Brazner, J.C. and E.W. Beals (1997). Patterns in fish assemblages from coastal wetland and beach habitats in Green Bay, Lake Michigan: a multivariate analysis of abiotic and biotic forcing factors. Canadian Journal of Fisheries and Aquatic Sciences54(8): 1743-1761.
Breiman, L., J.H. Friedman, R.A. Olshen and C.J. Stone (1984). Classification and Regression Trees. Belmont, CA, Wadsworth, Inc.
Bryan, M.D. and D.L. Scarnecchia (1992). Species richness, composition, and abundance of fish larvae and juveniles inhabithg natural and developed shorelines of a glacial Iowa lake. Environmentai Biology of Fishes35(4): 329-341.
Chipps, S.R., D.H. Bennett and J. Thomas J. Dresser (1997). Patterns of fish abundance associated with a dredge disposal Island: Implications for fish habitat enhancement in a large reservoir. North American Journal of Fisheries Managementl7: 378-386.
Christensen, D.L., B.R. Herwig, D.E. Schindler and S.R. Carpenter (1996). Impacts of lakeshore residential development on coarse woody debris in north temperate lakes. Ecological Applicationsd(4): 1 143- 1 149.
Clady, MD., R.C. Sumrnerfelt and R. Tafanelli (1979). Effectiveness of floating tire breakwaters for increasing density of young largemouth bass in covers of an Oklahoma reservoir. in Response of fish to habitat structure in standing water. D. L. Johnson and R. A. Stein, North Cental Division American Fisheries SocietySpecial Publication 6: 38- 43.
Cooper, W.E. and L.B. Crowder (1979). Patterns of predation in simple and complex environments. in Predator-Prey Systems in Fisheries Management. H. Clepper. Washington, D.C., Sport Fishing Institute: 257-267.
Crowder, L.B. and W.E. Cooper (1979). Stnictural complexity and fish-prey interactions in ponds: A point of view. in Response of fish to habitat structure in standing water. D. L. Johnson and R. A. Stein, North Central Division American Fisheries SocietySpecial Publication 6: 2-10.
Eadie, J.M. and A. Keast (1984). Resource heterogeneity and fish species diversity in lakes. Canadian Journal of Zoology 62: 1689- 1695.
Edgington, E.S. (1987). Randomization Tests, Marcel Dekker.
Efron, B. and R. Tibshirani (1991). Statistical data analysis in the cornputer age. Scienca53: 290-305.
Everett, R.A. and G.M. Ruiz (1993). Coarse woody debris as a refuge from predation in aquatic communities. Oecologia93: 475-486.
Fausch, K.D. and T.G. Northcote (1992). Large woody debris and salmonid habitat in a small coastal British Columbia Stream. Canadian Journal of Fisheries and Aquatic Scienced9: 682-693.
France, R.L. (1997). The importance of beaver lodges in stnict\ll-ing littoral communities in boreal headwater lakes. Canadian Journal of Zoology75(7): 1009-1013.
Good, P. (1994). Permutation Tests: A Pratical Guide to Resampling Methods for Testing Hypotheses. NY, NY Springer-Verlag Inc.
Harker, J.M. (1982). Littoral zone study final report. Lakeshore capacity study fisheries component.
Harmon, M.E., J.F. Franklin, F.J. Swanson, P. Sollins, S.V. Gregory, J.D. Lattin, N.H. Anderson, S.P. Cline, N.G. Aumen, J.R. Sedell (1 986). Ecology of coarse woody debris in temperate ecosystems. Advances in Ecological ResearchlS: 133-302.
Heck, H.L., Jr. and L.B. Crowder (1991). Habitat structure and predator -prey interactions in vegetated aquatic systems. in Habitat Structure: the physical arrangement of objects in space. Bell, S.S., E.D. McCoy and H.R. Mushinsky, eds 28 1-299.
Helfman, G.S. (1979). Fish attraction to floating objects in 1akes.in Response of fish to habitat structure in standing water. D. L. Johnson and R. A. Stein, North Central Division American Fishenes Society.Specia1 Publication 6: 49-57.
Lobb, M.D. and D.J. Orth (1991). Habitat use by an assemblage of fish in a large warmwater Stream. Transactions of the American Fisheries Societyl20: 65-78.
Jaakson, R., M.D. Buszynski and D. Botting (1976). Canying capacity and lake recreation planning. Town Planning Review: 359-373.
Johnson, D.L. and R.A. Stein, eds. (1979). Response of fish to habitat structure in standing water. Bethesda, MD, North Central Division, American Fisheries Society.
Johnson, D.L. and J. William E. Lynch (1992). Panfish use of and angier success at evergreen tree, brush, and stake-bed structures. North American Journal of Fisheries Management 12: 222-229.
Keast, A., 3. Harker and D. Turnbull(1978). Neasshore fish habitat utilization and species associations in Lake Opinicon (Ontario, Canada). Environmental Biology of FisheB(2): 173- 184.
Kerfoot, W.C. and A. Sih, eds. (1985). Predation: Direct and indirect impacts on aquatic communities. Hanover, New Hampshire, USA, University Press of New England.
Krebs, C.J. (1985). Ecology: The Experimental Analysis of Distribution and Abundance. N'Y, NY, Harper & Row Publishers.
Lehtinen, R.M., N.D. Mundahl and J.C. Madejczyk (1997). Autumn use of woody snags by fishes in backwater and channel border habitats of a large river. Envrionmental Biology of Fishes 49(1): 7- 19.
Leslie, J.K. and C. A.Timmins (1994). Ecology of YOY Fishes in Severn Sound, Lake Huron. Canadian Journal of Zoology 72(ll): 1887- 1897.
Levin, S.A. (1992). The problem of pattern and scale in ecology. EcoIogy73(6): 1943-1967.
Loeb, S.L. and A. Spacie (1993). Biological monitoring of aquatic systems. Boca Raton, FL Lewis Publishers381.
Manly, B.F. (1991). Randomization and Monte Car10 methods in biology. London; New York, Chapman and Hall.
Miner, J.G. and R.A. Stein (1996). Detection of predators and habitat choice by small bluegills: Effects of turbidity and alternative prey. Transactions of the American Fisheries Society 125(1): 97- 103.
Ministry of Natural Resources (1998) http://www.mnr.gov.on.ca/MNR/csb/news/crown5.html
Minns, C.K., J.R.M. Kelso and R.G. Randall(1996). Detecting the response of fish to habitat alterations in freshwater ecosystems. Canadian Journal of Fisheries and Aquatic Sciences 53(Suppl. 1): 403-414.
Mittlebach, G. (1986). Predator-mediated habitat use: some consequences for species interactions. Envrionmental biology of fishesl6(1-3): 159- 169.
Moring, J.R., M.T. Negus, R.D. McCullough and S.W. Herke (1989). Large concentations of submerged puIpwood logs as fish attraction structures in a reservoir. Bulletin of Marine Science 44(2): 609-61 5.
Moring, J.R. and P.H. Nicholson (1994). Evaluation of three types of artificial habitats for fishes in a freshwater pond in Maine, USA. Bulletin of Marine SciencBS(2-3): 1149- 1159.
Murphy, M.L. and J.D. Hall (1981). Varied effects of clear-cut logging on predators and their habitat in smdl streams of the Cascade Mountains, Oregon. Canadian Journal of Fisheries and Aquatic Sciences 38(l37- 145).
Peffers, B.M. (1995). Distribution of in-lake coarse woody debris within old-growth and second-growth forest settings. M.Sc. Thesis. Fisheries and Wildiife, Michigan State University: 63pp.
Poe, T.P., C.O. Hatcher, C.L. Brown and S.W. Schloesser (1986). Cornparison of Species Composition and richness of fish assemblages in altered and unaltered littoral habitats. Journal of Freshwater Ecology 3(4): 525-536.
Prince, E.D. and O.E. Maughan (1979). Attraction of fishes to artificial tire reefs in Smith Mountain Lake, Virginia. in Response of fish to habitat structure in standing water. D. L. Johnson and R. A. Stein, North Cental Division American Fisheries SocietySpecial hblication 6: 19-25.
Quinn, T.P. and N.P. Peterson (1996). The influence of habitat complexity and fish size on over-winter survival and growth of indivudually mareked juvenile coho salmon (Oncorhynchus kisutch) in Big Beef Creek, Washington. Canadian Joumal of Fisheries and Aquatic Sciences 53: 1555-1564.
Randdl, R.G., C.K. Minns, V.W. Cairns and J.E. Moore (1996). The relationship between an index of fish production and submerged macrophytes and other habitat features at three littoral areas in the Great Lakes. Canadian Journal of Fisheries and Aquatic Sciences 96(53; supplement 1): 35-44.
Rejwan, C. (1996). The relations between smallmouth bass (i4icropterus dofornieu) nest distributions and characteristics of their habitat in Lake Opeongo, Ontario. M.Sc. Thesis. Zoology, University of Toronto. 8 lpp.
Ruiz, G.M., A.H. Hines and M.H. Posey (1993). ShaUow water as a refuge habitat for fish and crustaceans in non-vegetated estuaries: an example from Chesapeake Bay. Marine Ecology Progress Series 99: 1-16.
Savino, J.F. and R.A. Stein (1989). Behavior of fish predators and their prey: habitat choice between open water and dense vegetation. Environmental Biology of Fishes24(4): 287- 293. .
Savino, J.F. and T.A. Stein (1982). Predator-prey interaction between largemouth bass and bluegills as influenced by simulated, submerged vegetation. Transactions of the American Fisheries Society I l l : 255-266.
Schindler, D.W. (1987). Detecting ecosystem responses to anthropogenic stress. Canadian Journal of Fisheries and Aquatic Sciences44(Supplement 1): 6-25.
Schindler, D.W., F.A.J. Armstrong, S.K. Holmgren and G.J. Brunskill(1971). Eutrophication of Lake 227, Experimental Lakes area, Northwestern Ontario, by addition of phosphate and nitrate. Journal Fisheries Research Board of Canada28: 1763-1782.
Schindler, D. W., K.H. Mills, D.F. Mdley, S.L. Findlay, J. A. Shearer, 1. J. Davies, M. A. Turner, G.A. Linsey and D.R. Cruikshank (1985). Long-term ecosystem stress: The effects of years of experimental acidification on a small lake. Science28: 1395-1401.
Schmude, K.L., M.J. Jennings, K.J. Ottis and R.R. Piette (1998). Effects of habitat complexity on macroinvertebrate colonization of artificial substrates in north temperate lakes. Jomal of North American Benthological Societyl7(1): 73-80.
Shuter, B.J., J.A. MacLean, F.E.J. Fry and H.A. Regier (1980). Stochastic simulation of temperature effects on first-year survival of smallrnouth bass. Transactions of the Arnerican Fishenes Societyl09: 1-34.
Shuter, B.J. and J.R. Post (1990). Climate, population viability, and the zoogeography of temperate fishes. Transactions of the American Fisheries Societyll9: 314-336.
Slaney, P.A., B.O. Rublee, C.J. Perrin and H. Goldberg (1994). Debris structure placements and whole-river fertilization for salmonids in a Iarge regulated Stream in British Columbia. Bulletin of Marine ScienceS(2-3): 1 160- 1 180.
Thomas, C.D. (1994). Extinction, colonization, and metapopulations: Environmental tracking by rare species. Conservation Biology8(2): 373-378.
Tonn, W., C. Paskowski and 1. Holopainen (1992). Piscivory and recruitment: Mechanisms structuring prey populations in small lakes. Ecology73(3): 951-958.
Weaver, M.J., J.J. Magnuson and M.K. Clayton (1997). Distribution of littoral fishes in structurally complex rnacrophytes. Canadian Journal of Fisheries and Aquaîic Sciences 54(10): 2277-2289.
Werner, E.E., J.F. Gilliam, D.J. Hall and G.G. Mittlebach (1983a). An experimental test of the effects of predation risk on habitat use in fish. Ecology64: 1540-1548.
Werner, E.E., G.G. Mittlebach, D.J. Hall and J.F. Gilliam (1983b). Experimental tests of optimal habitat use in fish: the role of relative habitat profitability. EcoIog$4: 1525- 1539.
Wetzel, R.G. (1983). Limnology. Fort Worth, Texas, Saunders College Publishing.