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

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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

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