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This is to certify that the

thesis entitled

GENETIC VARIATION WITHIN AND
BETWEEN SYMPATRIC POPULATIONS OF
PISSODES STROBI ON TWO HOST SPECIES
EASTERN WHITE PINE AND JACK PINE

presented by
Charley Adrian Chilcote

has been accepted towards fulfillment
of the requirements for

MASTER OF SCIENCE degree in ENTOMOLOGY

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GENETIC VARIATION WITHIN AND
BETWEEN SYMPATRIC POPULATIONS OF
PISSODES STROBI ON TWO HOST SPECIES:
EASTERN WHITE PINE AND JACK PINE

BY

Charley Adrian Chilcote

A THESIS

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

MASTER OF SCIENCE

Department of Entomology

1985

ABSTRACT

GENETIC VARIATION WITHIN AND
BETWEEN SYMPATRIC POPULATIONS OF
PISSODES STROBI ON TWO HOST SPECIES
EASTERN WHITE PINE AND JACK PINE
BY

Charley Adrian Chilcote

Genetic variability within and between 10 sympatric populations

of Pissodes strobi Peck on two host species were studied in Michigan's

 

lower peninsula. Coefficients of genetic distance were very low and
ranged from 0.000 to 0.007 and genetic distances averaged across hosts

were extremely low, 0.001. Fixation indices (PI and FIT) indicated

8
that some alleles deviated from Hardy-Weinberg equilibria, but
differentiation (PST) between populations was low, 0.018, and ranged
from 0.009 to 0.027. Differentiation between host populations was
even lower, 0.003. Hierarchial analysis provided evidence that most
of the variation in these populations is related to local populations
and not correlated with host or locality. No evidence of host races

was found in this electrophoretic analysis. These populations seem

to represent subpopulations of a polyphagus panmictic population.

To my parents, S.V. and Florence

and my wife, Lois

ii

ACKNOWLEDGEMENTS

I would like to deeply express my appreciation to Gary Simmons
for acting as my major advisor. His guidance, encouragement, and
criticism have helped shape my philosophy of science and my abilities
as a researcher. I also wish to express my gratitude to Guy Bush for
the use of his facilities and his aid in analysis and editing. To
the other members of my guidance committee, Roland Fischer and William
Mattson, my thanks for their advice on many matters.

For technical assistance and early insight on developing a proper
research plan, I am indebted to Daniel J. Howard. His training in
electrophoretic procedures provided a resource uncommonly beneficial
to my project.

I would like to thank those who provided discussion on many
matters that aided in my program. In particular, I thank Todd Bierbaum,
Ray Drapek, Norman Elliott, and Frank Sapio.

I would like to make a special acknowledgement of Gary Rathbun,
who in high school encouraged and helped me develop an interest in

biology and entomology.

iii

TABLE OF C

LIST OF TABLES. . . . . . . . .

LIST OF FIGURES . . . . . . .

INTRODUCTION . . . . . . . . .

Life history and impact of the white pine weevil

ONTENTS

Control of the white pine weevil and host prefernce
Models and hypotheses of sympatric speciation .

STUDY AREAS . . . . . . . . . .

MATERIALS AND METHODS . . . . .

RESULTS . . . . . . . . . . . .

Allele frequencies and heterozygosities . . .
Genetic diversity using genetic distance and similarity

Wright's fixation indices .

Hierarchial and heterogeneity analysis . . .

DISCUSSION . . . . . . . . .
APPENDICES

1. Record of Deposition of
2. White Pine Weevil . . .
3. Gel Electrophoresis . .
Gel preparation . .
Sample preparation .
Gel running . . . .
Staining . . . . . .

LIST OF REFERENCES . . . . . .

iv

Voucher

Page

vii

15
21

21
32
42
47,

52

57
60
63
63
65
67
68

74

LIST OF TABLES

Table Page

1. Study Site Locations And Descriptions For Examina-
tion OfP—o_strObioooooooooooooooooooooooooooooooooooooo ll

2. Enzymes And Buffer Systems Tested For Examination
of PiSSOdeS StrObioooooo ..... ......OCOCCOOOCOOOO ....... 17

 

3. Enzymes And Procedural Information Used In
Examination Of g; strobi................... ......... ... l9

4. Electrophoretic Allele Frequencies Of 10 Populations
0f g; strobi On White Pine And Jack Pine....... ........ 23

5. Genetic Variability At 14 Loci In All Populations
Examined For g;_strobi ..... . ..... .............. ........ 29

6. Significance Test For Whalund Effect.... ..... . ..... .... 33

7. Matrix 0f Genetic Similarity And Distance Coeffi-
cients With EST-1 Included........................ ..... 35

8. Matrix Of Genetic Similarity And Distance Coeffi-
cients Without EST-l Included.......................... 36

9. Matrix of Genetic Distance Coefficient Averaged By
mcalitYOOOOOO......OOOOOCCCOCOOO...00......0.00.0.0... 37

10. Matrix Of Genetic Similarity Coefficient Averaged
By Locality...... ..... ................................. 38

ll. Matrix Of Genetic Distance Coefficient Averaged By

HostOOOOOOO..........OOOOOOOOOOOO0.00.00.00.00......... 39

12. Matrix Of Genetic Similarity Coefficient Averaged
By Host.....0.00.00....0...0..........OOOOOOOOOOOOOOOOO 39

13. Matrix Of Single-locus (ACON-l) Genetic Similarity
coeffiCientoooooooooooooooooooooooooooooooooooooooooooo 4O

14. Matrix Of Single-locus (LAP) Genetic Similarity
Coefficient............ ......... ............. .......... 41

Table Page

15. Matrix Of Single-locus (GOT) Genetic Similarity

coeffiCient 0000000000 .0.........OOOOOOOOOCOIOOOOOO0.... 43
16. FIS(IK) Values For ACON-l In All 10 Populations

Of g; strobi ...... ...... ....... ....... ................ . 44
17. Summary Of F-statistics At All Loci .................... 46

18. Summary Of F-statistics At All Loci For Combined
Host ........................ . .......................... 46

19. Variance Components And F-statistics Combined
Across Loci ............................................ 48

20. Contingency Chi-square Analysis At All Loci ............ 48

21. Hierarchy Contingency Chi-square Analysis At All Loci
(Locality: Adjacent; Host: White Pine)............... 50

22. Hierarchy Contingency Chi-square Analysis At All Loci
(Locality: Adjacent; Host: Jack Pine) ..... ........... 50

23. Hierarchy Contingency Chi-square Analysis At All Loci
(Locality: Outside; Host: Jack Pine)................. 51

24. Hierarchy Contingency Chi-square Analysis At All Loci
(Locality: Outside; Host: White Pine)................ 51

25. Recipes For Buffer Systems Used In Electrophoretic
Studies OfP—CstrobiOOOOOOOOOOO......OOOOOOOOOIOOOOOOOO 64

26. Stain Recipes And Special Instructions For Electro-
Phoretic Analysis OfEO—StrobiOOOOOOOOO00.000.000.00... 69

vi

LIST OF FIGURES

 

Page
Map Of Study Sites For Pissodes strobi ................. 10
Allozyme banding patterns observed in g; strobi
(EST-l)oooooooooooooo ...... 00.00.0000... ..... o ooooooooo 3O
Allozyme banding patterns observed in §;_strobi
(ACON—l)no.oooooooooooooooooooooooooooooooooo oooooooooo 31
Allozyme banding patterns observed in g;_strobi
(PGI).. ......... ..... ..... . ............. ......... ...... 31

vii

INTRODUCTION

Life history and impact of the white pine weevil

 

The white pine weevil, Pissodes strobi Peck, is an important pest

 

of many species of pines and spruces in North America. The most fre-

quently colonized trees include: eastern white pine, Pinus strobus L.,

 

western white pine, §;_monticola Dougl., jack pine, P;_banksiana Lamb.,

Scots pine, E;_sylvestris L., Norway spruce, Picea abies (L.) Karst,

 

 

Sitka spruce, §;_sitchensis (Bong.) Carr, and Engelmann spruce, g;

 

engelmanni Parry. In the East, eastern white pine is considered the

 

preferred host. Sitka spruce is the preferred host in the West.

 

Pissodes strobi is one of the most important factors limiting
reforestation of certain host species in North America (Belyea and
Sullivan 1956). The weevil can do extensive damage to young plant-
ations as well as young natural stands. Injury to a large percentage
of the trees in natural and planted stands results from repeated
attacks during the early life of the trees (3-20 years). Some plant-
ations have shown 50 percent or more of the most vigorously growing
trees to be damaged in one year (Cline and MacAloney 1931; Prentice
and Hildahl 1957). In New York and New Hampshire, it has been estimat-
ed that 70 to 90 percent of all eastern white pine have been damaged
before the age of fifteen years (Graham 1926; Plummer and Pillsbury
1929). Trees, thus, have little value as sawlogs or are scarcely worth
cutting at all (Dirks 1964). In the New England states alone, it is

1

estimated that poor quality of weevilled trees results in a loss in
excess of 7 million dollars annually (Garrett 1972).

The adult weevils overwinter in the duff beneath the trees. When
environmental conditions are optimal, adults emerge and begin to feed
on the terminal growth below the current year's bud. Adults congregate
on the leader to feed and mate. After mating, females lay their eggs
in small cavities beneath the bark. Larvae hatch and feed on the
cambium beneath the bark and as their members increase, aggregate
together to form a feeding ring that encircles the terminal below the
bud. Larvae in the ring orient themselves downward and feed, girdling
the terminal, causing the leader to wilt and turn brown. This dead or
dying leader is the characteristic "shepard's crook" commonly seen in
field examinations of weevilled stands.

As larvae mature, they fall behind the advancing feeding ring and
produce an excelsior-covered chamber in which pupation and transforma-
tion into teneral adults occurs. These adults remain in the chamber
for one to two weeks then emerge and feed on succulent new growth until
environmental conditions force them into overwintering sites. A more
detailed description of the biology of this weevil is presented in
Appendix 2.

In many areas of the northeastern U.S., eastern white pine can be
regenerated naturally with high success. Weevil damage, however, has
been so severe and widespread that it has been difficult to promote
the growth of this species. The weevil has caused serious damage to
both eastern white pine and jack pine in Michigan and Wisconsin. A

1980 survey found 27 percent of the jack pine plantations surveyed

severely damaged (Michigan Pest Report 1980), and in sample plots in
the AuSable State Forest, weevilling rose from 35 percent in 1980 to

65 percent in 1981 (Michigan Pest Report 1981-1982). Weevil damage

was in fact reported in every county in Michigan's Lower Peninsula in
1979 (Michigan Pest Report 1979). Jack pine plantation damage ranged
from 3 percent to 75 percent and many young stands (ten-year-old) in
the Pere Marquette Forest were unmerchantable due to reduced growth and
poor form (Michigan Pest Report 1981—1982).

The damage from the feeding activities and oviposition of the
weevil has been catagorized into either a reduction in the recovered
volume in a stand or planting or the reduction of lumber quality in
recovered volume. The weevil, which normally causes the main terminal
to die, seldom kills trees unless they are young. Laterals below this
dead region then compete for dominance producing a tree with a crooked
stem. If more than one lateral share dominance, the tree may become
forked. Repeated weevilling, year after year, results in a "cabbage"
or a "shrub" tree (Cline and MacAloney 1931). Although a tree may
recover from a crook or fork, lumber cut from these trees may have
serious defects (Belyea and Sullivan 1956) including cross grains,
large knots and compression wood, all of which can be brittle and
check (Spurr and Friend 1941). Reduction in growth, forking and crooks,
and defects in wood thus reduce the quality and quantity of lumber
produced from weevilled stands by as much as 30 percent (Plummer and
Pillsbury 1929). Even if the trees are only used for pulp, large knots

and cross grains create additional expense in processing pulpwood.

Control of the white pine weevil and host preference

 

Many techniques have been developed to control the white pine
weevil. Some of these show merit and are considered feasible, while
others are too costly or impractical. Pruning and reclamation was one
of the first techniques used in the Northeast. This procedure involves
making acceptable products from trees already damaged and is not prac—
tical on a large scale. Later, chemical control was extensively used
in the East and Canada. Compounds such as DDT and Lindane as well as
many others have been used with mixed results. Different spray tech-
niques were tested such as backpack spraying and helicopter spraying.
Currently, chemical control is not employed except on high value stands
such as Christmas trees and high valued lumber trees such as eastern
white pine.

Another control technique currently in use is forest manipulation.
In this category, the stand is modified from the standard planting to a
planting that allows some measure of control. Dense planting of trees
has proven to effectively negate the damage caused by the weevil in
eastern white pine. These plantings force weevilled trees to recover
quickly or lose their competitive place in the stand. A second ap-
proach involving understory plantings of the crop tree in an existing
thinned stand of mature trees has also produced excellent results.
Normally mature hardwoods provide shading for the crop tree during the
early spring when weevil activity is high which reduces oviposition
activity (Sullivan 1959, 1960, 1961). Overstory trees are later removed
when crop trees have outgrown weevil damage or damage is no longer a

problem. Understory planting is probably one of the most practical of

the silvicultural controls available, especially on better sites and
with shade tolerant trees such as eastern white pine (Graham 1926).

In Michigan, jack pine is an important crop tree because it is
adaptable to sandy soils, benefits wildlife, and has excellent pulpwood
qualities. However, it is not shade tolerant and dense plantings do
not provide damage control (Morse 1958). Thus, some other methods for
controlling the weevil on this host are needed. Two methods that have
received only limited study are biological control and host plant
resistance. Because the genetic variability of the white pine weevil
may alter the effectiveness of these methods, a thorough understanding
of the weevil's genetic variability must be gained. Resistance to
the weevil may well be a factor of host preference or selection and
biological control agents (i.e., parasites) that utilize some aspect
of the host plant to find the host may not have the same efficiency
on a new host or on different hosts.

Many studies have been conducted to determine the host preference
of the weevil, but most have not included jack pine (e.g., Anderson
and Fisher 1956), because it was confirmed only recently as a host of
the white pine weevil (Smith and Sugden 1969). In one of the few
experiments using jack pine in a preference test with other species of
of pines, no significant differences were found (Alfaro and Borden
1982). Unfortunately, this study used a forced-feeding technique and
a feeding-choice procedure, which did not reveal much about preference

mechanisms in natural or plantation forest.

Models and hypotheses of sympatric speciation

 

For many years, it has been an accepted hypothesis that speciation
can occur when populations are separated geographically by some kind
of barrier (i.e., allopatric speciation). The primary reason for the
widespread acceptance of this theory is the ease with which it can be
intuitively accepted. It is quite easy to imagine conditions such
that genetic variation caused by different environments and no apparent
gene flow lead to reproductive isolation. However, this theory has
been accepted almost entirely on intuitive logic and very little hard
evidence has been generated to support it (Bush and Howard 1985). It
has not been as easy to accept the notion of speciation occurring within
the cruising range of conspecific populations (i.e., sympatric speci-
ation). In fact, many of the early models of sympatric speciation were
refuted by Mayr (1963) because of "unsupported and unrealistic assump-
tions". Two of the models of sympatric speciation refuted by Mayr
(1963) include: Fisher (1930)- speciation by disruptive selection;
Laven (l959)- speciation by cytoplasmic sterility. Recently, many
new models and evidence have been generated that support sympatric
speciation. Models have been developed that support disruptive selec-
tion (Maynard Smith 1970; Bush 1975; Rice 1984), assortative mating
based on host utilization (Bush 1969; Rice 1984), and reproductive
character displacement (Maynard Smith 1966). The amount of evidence
supporting sympatric speciation is much greater than the evidence
supporting allopatric speciation (Bush and Howard 1985).

Recent sympatric speciation models have incorporated many of the

features of early models and added new aspects and more evidence.

Early principles such as conditioning have been rediscovered. Walsh
(1864) was probably the first to formally present the idea that species
might form in sympatry if the populations adapted (conditioned) to
different hosts. Later, this idea was developed as the Hopkins host
selection principle (Craigehead 1921) which basically stated that an
adult would tend to oviposit on the same host that was used by the im-
mature stage as food. This principle lost credibility for being too
simplistic and being applied too frequently on the basis of host assoc-
iation only. Today, it is still used frequently and more rigorous tests
have provided evidence for its usefulness. Other models have gained new
acceptance and additional evidence is making sympatric speciation seem
plausible. Maynard Smith (1970) pumped new life into the principle of
sympatric speciation by disruptive selection. The important aspect of
Smith's model is the assumption that a population first develops a
stable polymorphism in response to a heterogeneous environment. Many
studies have stressed the importance of this assumption (Maynard Smith
1970; Bush 1975; Tauber and Tauber 1977; Tavormina 1982). Mayr (1963)
had argued that no mechanism (of sympatric speciation) consistent with
the known facts of genetics could be suggested for sympatric speciation.
Smith's model seems to solve one of the biggest problems with previous
models of sympatric speciation and presents a method by which current
genetics can be applied to sympatric speciation. These models have
also been supported by experimental evidence (Pimentel et a1. 1967;
Thoday 1972; Soans et a1. 1974; Bush 1974) and theoretical studies
(Dickinson and Antonovics 1973).

Current models stress the need for stable polymorphisms developing.

They further incorporate the assumption that host selection occurs in
conjunction with mate selection (Diehl and Bush 1984). In this way the
host is not chosen completely on the basis of its own merits (i.e.,
chemical cues, shape, etc.). This type of mate selection can easily
lead to assortative mating. Mutations in genes that control host selec-
tion may lead to host shifts. Other mutations in genes controlling the
surviveability of larvae on a host and timing of development can also
lead to reduced gene flow between populations (Diehl and Bush 1984).
Coupled with assortative mating, new host races may form that even-
tually lead to new species.

The new push for information about sympatric speciation has lead
to new and detailed information about many populations that were once
believed to be one wide ranging species. The white pine weevil is one
of these species. It ranges from Nova Scotia to British Columbia and
its host range is wide. In the East, it was given the species name of

its preferred host, Pissodes strobi for Pinus strobus, eastern white

 

 

pine. In the West, it received the names of the two hosts it most fre-

quented, P;_sitchensis for Picea sitchensis, Sitka spruce, and Ba

 

 

engelmanni for Picea engelmanni, Engelmann spruce. Recent evidence has

 

 

made it clear that these species actually represent one species (Smith
and Sugden 1969; Phillips 1985). This insect choses its mate in con-
junction with its host which makes it a good candidate for host shifts.
Detailed study of the possible correlation of genetic distance and geo-
graphic distance has been carried out by Phillips (1985). It was the
intention of this study to gain information about the genetic varia-
bility within the cruising range of the insect and to establish the

relative variability associated with host utilization.

STUDY AREAS

Field collection of specimens was conducted in northern Lower
Michigan. Two counties were selected as study areas, Wexford and
Manistee. A preliminary examination of eastern white pine and jack
pine stands provided 10 that were deemed adequate for study. Figure
1 is a map of these counties and the numerical labels are the sites
described in Table l. Plantations were chosen on the basis of stand
uniformity (i.e., white pine only or jack pine only) and their rela-
tive distances from different stands of the same host. The choices
of stands are represented in the hierarchy presented below;

White Pine
Adjacent
Site B W Pine
Site C W Pine
Close
Site D W Pine
Outside
Site A W Pine
Site E W Pine

Jack Pine
Adjacent
Site B J Pine
Site C J Pine
Close
Site A J Pine
Outside
Site D J Pine
Site E J Pine

Two stands of each host were chosen as adjacent stands (i.e., stands

separated by only a few hundred feet at the most). One stand was chosen

10

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13

for its close proximity to the adjacent stands of the same host (i.e.,
not more than two miles from the adjacent stands). Two additional
stands of each host were selected to be not more than 20 miles away
from the adjacent stands of the same host.

Stands were chosen to allow examination of genetic variation
within a small geographic area (i.e., less than 300 square miles).
Selection of stands at various distances was made to test for subtle
differences in genetic variation which could result from host race
formation at various levels and geographic distances.

Within these stands, weevilled trees were selected at random.

To accomplish this, a starting point was chosen along the perimeter

of the stand. A random number was chosen from a random number table
and converted to a vector based on its relation to a compass reading
(i.e., 45 became 45 degrees when standing on the west side of the stand
and 135 degrees when standing on the north side of the stand). Weevil-
1ed trees along this vector were the trees to be sampled and their
leaders were clipped and numbered for later reference. If a vector
did not produce sufficient leaders upon going though the stand, a

new vector was chosen at the point of exit from the stand and the
procedure repeated until sufficient leaders were sampled. Leaders

were surveyed until the larvae within them were starting to pupate.
When this occurred, approximately 30 leaders from each stand were
collected which allowed for the collection of mature larvae. The
monitoring and collection of larvae was accomplished by cutting the
bark away and removing the larvae from beneath it. About 25 larvae

were removed from each leader. Those to be used in electrophoretic

14

analysis were placed in a cold vial and submerged in liquid nitrogen
until the samples could be transferred to an ultra-cold freezer for
storage at -80 degrees C. Monitoring continued until more than 50%
of the insects in the leaders had pupated. At that time, leaders
were clipped and placed in rearing chambers. Since birds often strip
leaders in the field and eat many of the pupae, this chamber allowed
collection of adults from the stands. Two hundred forty adults from
each site were collected from these chambers, 20 leaders/ stand; 12
individuals/ leader. Again adults were placed in liquid nitrogen

until transfer to an ultra-cold freezer.

MATERIALS AND METHODS

The electophoretic procedure used in this study was similar to
those used by Berlocker and Bush (1982), Coyne et a1. (1979), and
Howard (1982). An 11.75% horizontal starch gel, which consisted of 47
grams of Electostarch, lot #392, (Electrostarch Co., Madison, WI)
added to 400 ml. of the appropriate buffer solution, was used. The
solution was heated until about 7 seconds after a phase change had
occurred. The starch was aspirated and poured into a mold about 1
cm. thick and allowed to set. After allowing the gels to set for
24 hours, slits were out though the gels toward one end. Wicks of
filter paper (2 X 9 mm.) containing a small amount of insect homogen-
ate were placed into the slit. A D.C. electric current then was
applied to the gels which caused the enzymes contained within the
homogenate to migrate though the gels. The amount of current de-
pended on the buffer system used. Appendix 3 gives a detailed des-
cription of the electrophoretic procedure as well as the buffers used,
currents applied, and run times.

Individuals from each population were crushed in a grinding
buffer, 0.05 M tris/HCl pH 7.00, and the homogenate was applied to
rectangular filter paper dits (2 by 9 mm.). Dits were placed into
slits in the gels as described by Coyne et al. (1979). On each gel,
twenty-three individual white pine weevils were run consisting of
four individuals from each of the five populations chosen for that

15

16

runs and three individuals from the reference population. The reference
population was the population chosen to be used as a standard and the
other populations were scored relative to this population. During the
enzyme survey, the most common allele for each locus in this population
was noted and designated as 1.00. During subsequent runs alleles were
measured relative to this allele (i.e., and allele measuring 1.50 cm.
relative to the reference of 3.00 cm. was designated 0.50). Three
individuals from the reference population were placed on the gel in

the first, the tenth, and the twenty-third positions from the left

which reduced errors in scoring gels.

Initially an enzyme—system survey was performed in which forty
enzymes were tested on ten buffer systems (Table 2). This procedure
allowed for evaluation of the proper buffer systems and enzyme stains
for this insect. The enzymes which gave good resolution and strong
banding under the systems tested are listed in Table 3. Ten enzymes,
representing fourteen loci, were found to produce consistantly good
results and were used in the analysis of genetic variation. Two
enzymes, PEP and SDH, representing three loci, were later found to
provide adequate banding, but were not used in the analysis.

Scoring of the bands was accomplished by measurement of the dis-
tance of individual bands from the origin. Individual weevils were
assessed at each locus for the alleles they expressed. Those enzymes
that were dimers expressed three bands in heterozygotes and one band in
homozygotes. Monomers exhibited two bands in heterzygotes and one in
homozygotes. Presumed subunit components for the enzymes examined in

this study are presented in Table 3. The reason for the banding

17

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18

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20

pattern expressed by these subunits are discussed by Brewer (1970).
Relative relationships of electromorphs discussed earlier were used to
assign allelic designations to each individual. Later these design-
ations were converted to an alphabetic system with the slowest allele
designated as "A" and each subsequent allele was given the next alpha-
betic character. This was done to facilitate analysis by computer.

The genetic basis for most of the enzymes in this study have been
worked out independently by Phillips (1985). However, the genetic
basis for ACON and LAP were assessed on the basis of the family group
segregation (i.e., each leader sampled represented a family of weevils,
since, in most cases, only one female oviposits in a leader-- results
from data on electrophoretic study of 25 individuals from each leader
produced no individuals that appeared to be from different parents; by
testing members from family groups, segregation of genotypes could be
examined and if it fit with normal Mendelian segregation, a genetic
relationship was assumed for electromorphs under study).

Analysis of genetic identity and distance was performed with the
aid of BIOSYSl, a computer program provided by Swofford and Selander
(1981). This program calculates allele frequencies, Nei's genetic
identities, Nei's genetic distances, F-statistics, Hardy-Weinberg
equilibria, Chi-square goodness of fit for Hardy-Weinberg, as well as
other functions not used for final analysis. A detailed description
of the formulas and analysis techniques can be found in the User's
Guide (Swofford and Selander 1981). This program allowed for mani-
pulation of population structure to give more detailed information

about the genetic variability within and between the populations.

RESULTS

Allele frequencies and heterozygosities

 

Ten enzymes, representing 14 loci, could be routinely resolved
and scored. Most of these enzymes produced strong bands easily assign-
ed to an electromorph category. Extremely long gel runs (i.e., about
12 hours) were needed to pull some bands apart sufficiently to deter-
mine heterozygotes (e.g., for ACON and LAP). Clear heterozygotes were
expressed by most loci (ACON-1, ACON-2, MDH-l, MDH-Z, IDH-l, IDH-2, AK,
ADH-l, LAP, PGI, GAPDH, EST-l, and GOT). Four enzymes expressed no
heterozygotes in any individuals (i.e., tetrazolium oxidase, TO; Glucose
6-phosphate dehydrogenase, G6PDH; Lactate dehydrogenase, LDH; and NADH
diaphorase, DIA) and, because bands were indistinct or smeared, were
seen as unreliable and not used in the analysis. Only one locus, ADH-2,
was considered to be monomorphic as it did not express any heterozygote
individuals in contrast to ADH-l, which did show heterozygotes on the
same gel. Two additinal enzymes, SDH (sorbitol dehydrogenase) and PEP
(peptidase), were found late in the study and provided good banding
and exhibited heterozygosity. These two enzymes, however, were dis-
covered too late to be included in analysis. Another locus, EST-2,
was found to provide good resolution and banding, but it was difficult
to determine a reference point from which to score the other alleles
and it was not included in final examination.

The enzymes examined were highly variable and represented by a

21

22

wide number of alleles (Table 4). ACON-l, PGI, and EST-l were extreme-
ly variable, exhibiting an average of 10 alleles each. EST-l had 13
distinct alleles that combined to form electromorphs (Figure 2). Some
of the observed combinations of alleles for ACON-l and PGI are present-
ed in Figures 3 and 4. Most of the other loci expressed 3 or more
alleles.

Allele frequencies observed in the 10 populations of §;_strobi
are presented in Table 4. ACON-2, MDH-2, IDH-l, ADH-Z, and ADH-l
expressed very little frequency variation between populations. PGI,
ACON-l, and EST-1 displayed a high level of variation between popula-
tions. The "D" allele in ACON-l had a range of frequencies from 0.389
in population E WP to 0.641 in population A JP. PGI also varied con-
siderably with the "E" allele having a frequency of 0.481 in population
E JP and 0.649 in population C JP. EST-1 expressed the highest degree
of allele frequency variation. An extreme example of variation from
EST-1 is found in the "J" allele, which varied from 0.083 in D JP to
0.432 in D WP. Except for these few cases, the allele frequencies were
very similar in all populations studied. Mean heterozygosities, cal-
culated by direct count, were quite similar in all populations and
ranged from 0.210 in D WP to 0.307 in A WP. Table 5 presents mean
heterozygosities and expected values calculated from Hardy-Weinberg
expectations (Nei 1978). None of the direct-count measures of mean
heterozygosity varied to any great extent from those expected under
Hardy-Weinberg.

Using Levene's (1949) correction for small sample, it was found

that 12 of the 112 possible variable loci in the 10 populations did

23

 

 

 

 

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24

 

 

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26

 

 

 

 

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27

 

 

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5m0.0 000.0 5N0.0 000.0 000.0 HHH.0 050.0 mmH.0 H50.0 v0H.0 A.0.000
5m0.0 000.0 5N0.0 000.0 000.0 00H.0 m50.0 00H.0 H50.0 m5H.0 A0200:
0m0.0 000.0 5N0.0 000.0 000.0 00H.0 N50.0 50H.0 050.0 H5H.0 0
02 02 02 02 02 02 02 a 02 02 00350:
000.0 000.0 000.0 000.0 000.0 000.0 000.0 000.0 000.0 mH0.0 0
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000.0 000.0 000.0 000.0 000.0 0H0.0 000.0 000.0 000.0 000.0 N
A5N0 ANNV H5m0 AmN0 vav A5N0 H5N0 A0m0 “0N0 Ammv H20 .olzoz.
on m on 0 on 0 no 0 mo 0 m3 0 oz 0 03 0 m3 0 03 4 moHeHHm anooq
HnoHueHnmom

 

 

.oonnHunou

.v OHQeB

.5uHmO05Nowouon unnoo =uoouHo= u 0.0.00: «H050H H020 muHmomwwoneuen

ooueEHumo oomeHonD M 002000 “wuHmo05Nououon oouoomxe 0uennH03I5ouem u 0 .H00.0v0 we uneonHn0Hm

u «a “H0.0v0 ue uneoHMHanm u « «H0.0v0 we uneoHMHanm uon u 02 “ESHHQHHHsUe 0uennHe3I>oHe0 on
ooneEHOMnoo you A0v0H0 umeu m.ono>o0 op muemow 003000 .oonHEexe mHenoH>HonH mo nonenn ou mnemou sz m
.uxeu nH no>H0 meooo mnooH How mnoHuen0HmeQ N

 

.nEeH enewane0.4M .onHm xoen u 00 «.0 monoupm mnnHm .onHm oanz nweumeo u 03 "meeuu umom H

 

28

 

 

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02 02 02 02 02 02 02 02 02 02 003000
000.0 mv0.0 5N0.0 000.0 0H0.0 000.0 000.0 0m0.0 N00.0 5N0.0 0
000.0 000.0 000.0 000.0 000.0 000.0 000.0 000.0 0H0.0 000.0 0
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A5N0 ANNV A5m0 AmNV Ammv A5N0 A5NV H0m0 AHMV A5m0 2 M0
0vH.0 0MH.0 m00.0 mv0.0 0N0.0 000.0 000.0 00H.0 000.0 «00.0 0.0.000
mvH.0 mmH.0 N00.0 mv0.0 0m0.0 000.0 000.0 000.0 000.0 «00.0 002000
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02 02 02 02 02 02 02 02 02 02 003000
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A5N0 ANNV HNmV Ava Ammv A5N0 A5N0 Home AON0 A5m0 sz .HIeoa.
00 m 00 0 00 0 00 m 00 n 03 m 03 0 03 U 03 0 03 n moHeHHfl Nmnooa
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. v 3.090

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.00.0 ooooxo uon mooo eHeHHe noenoo umofi on» no 5onenoeuw onu «H UHnmuoE>Hom oewoonnoo mH mnooH 4 a
.muouuo oueoneuw one momonunouem nH monHe> H

29

 

 

 

 

 

.mno.oo 50N.o Ammo.o. 5v~.o m.He ne.o0 m.m 05.0. e.mm me n euHm oH
0050.00 m5m.o 0500.00 Hm~.o m.mm A5.o0 ~.m 0m.ov 0.H~ an o euHm m
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xn5o.ov m5~.o nm5o.ov 0mm.o ~.ev 00.00 H.m 00.00 n.5m n: m ouHm m
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«aoouoomxm unnou aoHnmuoemwon mnooq woo mnoon noHueHnmom
ensue: -uoouHo Hoon no meoHHa 00 non euHm
Nunmomsuoumuem new: mmeucmouma .02 new: eHneem new:
.mmmmmm.qm Hon emcHeexm mcoHueHsnon HHe :H Hoon VH be NuHHHneHue> oHuwnoo .m oHnee

H

30

...‘IU

 

.oounomonmon one mnnouuem oo>nowno HHe uoz
.Hnonum 4m nH oo>noobo mnnouuem 0nHone0 oE5NoHH¢

.N onnon

 

IE

33

30

"If“

>15)

>13

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H8

93

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DD

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PH

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20030

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0

H0000

7.50

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31

genotype

‘5 3 8 8 ‘3 8 E 8 EL“ 8 E B 8 E
H III
C II. II II
F I-I - I-I I-II- II
E l-
D (I. III I-III- III II III II
C -- II III
B - .-
A — -

69
T

 

ORIGIN

Figure 3. Allozyme banding patterns observed in E; strobi.
Not all observed patterns are represented.

t
gemypeas‘asaeeoessmas
PGI _
IH II II
G II
F' II == III .-
E IIIIII ... II III III II
II
I) II 'II III III III III III .-
.. II
@C --- ---
E III III III
T‘ A» II .-
ORIGIN

 

Figure 4. Allozyme banding patterns observed in E; strobi.
Not all observed patterns are represented.

32

not conform to Hardy—Weinberg and were significant at P<0.01. EST-1
was significant at P<0.001 in 5 of the 10 populations. Only one other
locus was significant at P<0.001 using Levene's test (i.e., population
E WP for PGI). However, using exact probabilities (analogous to
Fisher's (1930) exact test), only one population was significant at
P<0.001 for the Chi-square test for Hardy-Weinberg conformance for any
locus (i.e., population E JP for EST-1). Levene's test is suspect
when expected frequencies of some classes are low (Swofford and Selander
1981) and exact probabilities may obscure real deviations from Hardy-
Weinberg expectations because of the pooling involved in its calcula-
tion. To examine the significance of Hardy-Weinberg deviations, a test
was conducted to determine if the populations conformed to the Whalund
Principle (Crow and Kimura 1970). For this test, all populations were
combined and treated as one large population. A Hardy-Weinberg equil-
ibrium test was performed with some loci exhibiting an excess of homo-
zygotes. No loci were found to differ significantly from expected
values using exact probabilities (Table 6). The populations do not
seem to exhibit a reduction in homozygosity expected for partially

isolated isolates that were pooled into a single panmictic population.

Genetic diversity using_genetic distance and similarity

Measurement of genetic diversity for conspecific populations was
calculated by pairwise comparisons. Nei's (1978) estimates for unbias—
ed genetic identity and distance were used to compare the mean number
of allele differences at three levels of hierarchy described earlier.

At the individual population level, demes (=subpopulations) were treated

33

Table 6. Significance Test For Whalund Effect.l

 

 

 

Locus R1 R2 R3 P

ACON-1 89 128 64 0.186
ACON-2 264 17 0 1.000
MDH-l 275 19 1 0.308
MDH-2 285 5 0 1.000
IDH-l 268 28 1 0.537
IDH-Z 289 8 0 1.000
ADH-l 268 16 0 1.000
PGI 92 148 56 0.906
AK 262 34 0 0.610
LAP 167 76 16 0.088
GOT 126 118 43 0.093
GAPDH 184 50 5 0.390

 

1 Test using exact probabilities; (Swofford and Selander 1981)
R1 = Homozygotes for most common allele
R2 = Common/ rare heterozygotes
R3 = Rare homozygotes and other heterozygotes

34

as independent populations and genetic distances ranged between 0.000
and 0.007 with EST-1 included (Table 7). The same distances were be-
tween 0.000 and 0.004 when EST-l was removed from the analysis (Table
8). The greatest distance occurred between populations within the
same host. No pattern of divergence was evident from deme level
genetic distances. These levels are very low and are well within
those expressed for within population variation.

Genetic distances expressed for the hierarchial level of locality
were also very low (Table 9) and ranged between 0.000 and 0.003. The
greatest distance was found between the two adjacent populations on
white pine and between the close population on jack pine and the out-
side populations on white pine. These results indicate that the varia—
tion between populations in this level are not significantly different.
No direct relationship was found for any locality and genetic distance.

Estimates of genetic distance averaged by host exhibited no dif-
ferences between the two hosts (Table 11). With each of five popula-
tions from each host being used in the average means, the within
host variation was greater than between host distances. Host popula-
tions were essentially identical with the distance between the two
hosts equal to 0.001, which was not significantly different from the
within host distances of 0.001 for white pine and 0.000 for jack pine.

Single-loci genetic identities showed no significant patterns.
Identities between populations for ACON-1 ranged from 0.931 to 1.000
with the least identity expressed between populations E WP and A JP
(Table 13). LAP, GOT, GAPDH exhibited some differences in identity

between populations with GOT differing only in populations collected

35

 

 

 

 

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36

 

 

 

 

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37

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39

Table 11. Matrix Of Genetic Distance Coefficient Averaged By Host.1

 

 

 

No. Of
Host Pops. 1 2
1 White Pine 5 0.001
(0.000-0.003)
2 Jack Pine 5 0.001 0.000

(0.000-0.004) (0.000-0.001)

 

l Nei (1978) Unbiased Genetic Distance

(Values in parentheses represent the range of values expressed in
the level examined)

Table 12. Matrix Of Genetic Similarity Coefficient Averaged By Host.l

 

 

 

No. Of
Host Pops. 1 2
1 White Pine 5 0.999
(0.997-l.000)
2 Jack Pine 5 0.999 1.000

 

1 Nei (1978) Unbiased Genetic Identity

(Values in parentheses represent the range of values expressed in
the level examined)

 

 

 

 

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41

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42

from jack pine (Table 15). All other loci displayed no differences
in identity between populations. None of the loci were significantly

different in respect to host or locality.

Wright's fixation indices

Wright's (1965, 1978) fixation indices were used to assess the
levels of fixation between the populations. These indices are defined
in terms of expected and observed heterozygosities (Nei 1977). The
formula

1 - FIT = (1 - PIS )(1 - PST ) (1)
provides the basis for this analysis. FIS is the fixation index for
individuals relative to the subpopulation. FIT represents the index
of fixation for individuals relative to the total population. F15
and FIT measure the deviations of genotype frequencies from Hardy—
Weinberg proportions in the subpopulations and in the total popula-
tion. FST provides a measure of the amount of differentiation among
subpopulations relative to the limiting amount under complete fix-
ation (Nei 1973).

Fixation indices for each locus were calculated separately.
FIS(IK) values for all loci were used to examine fixation of individual
alleles within each subpopulation (i.e., FIS for the K-th allele in
the I-th subpopulation). Using chi-square analysis (Li 1955), it was
found that most of the FIS(IK) values were insignificant in all sub-
populations for all alleles. The prime exception to this occurred in
ACON-1 (Table 16), where two alleles were significantly different from

PIS = 0 at P<0.001. Allele "G" was significant in two subpopulations,

43

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44

 

 

 

 

 

 

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45

B WP and E JP, suggesting an excess of this allele in these subpopula—
tions. The only other case of an allele that was significant at
P<0.001 was the "F" allele of ACON—l in subpopulation E WP (Table 16).
Again the suggestion from chi-square test was that there was an excess
of this allele in that population. Other alleles that were signifi-
cant at P<0.025 included: alleles "A" and "B" for IDH-l in E JP;
allele "B" for LAP in C WP and E WP; allele "D" for LAP and GOT in D JP.

Weighted averages (across subpopulations) for FISK , FITK , and
FSTK were calculated using the formulas of Wright (1965, 1978) and
Nei (1977). Although these averages varied considerably for alleles
in different subpopulations, all excesses or deficiencies were in-
significant at P<0.01. Extreme values for FISK and FITK occurred in
LAP, with both the "B" allele and "C" allele expressing a level of
excess. With a FISK of 0.104 for both the "B" and "C" alleles, these
values were significant only at P<0.10. However, FITK values for these
alleles were both significant at P<0.05, indicating that these alleles
occurred in excess to the expected Hardy-Weinberg proportions at that
level. GOT also had an allele that was significant at P<0.05 for FITK
(allele "D"). No value of FSTK was significant for any allele in any
subpopulation, indicating very little differentiation between subpop-
ulations.

PIS , FIT , and FST values (weighted averages of FISK , FITK , and
FSTK across alleles) were found to be insignificant for all loci
(Table 17). The greatest values for these were again found in LAP and
GOT (i.e., 0.072 for PIS and 0.099 for FIT in LAP; 0.087 for FIS and

0.095 for FIT in GOT). Again, these values indicate that these alleles

46

 

 

 

 

 

 

 

 

 

Table 17. Summary Of F-statistics At All Loci.
Locus F(IS) F(IT) F(ST)
ACON-l 0.030 0.049 0.020
ACON-2 -0.036 -0.029 0.007
MDH-l 0.001 0.027 0.027
MDH-2 -0.021 -0.008 0.013
IDH—l 0.022 0.033 0.012
IDH-2 -0.022 ~0.011 0.011
ADH-l -0.040 -0.019 0.020
PGI -0.029 -0.019 0.009
AK -0.063 -0.040 0.022
LAP 0.072 0.099 0.028
GOT 0.087 0.095 0.010
GAPDH -0.001 0.033 0.034
Mean 0.022 0.040 0.018
Table 18. Summary Of F—statistics At All Loci For Combined Hosts.
Locus F(IS) F(IT) F(ST)
ACON-l 0.043 0.049 0.006
ACON-2 -0.029 -0.028 0.002
MDH-l 0.008 0.024 0.016
MDH-2 -0.009 -0.009 0.000
IDH-l 0.021 0.024 0.002
IDH-Z -0.011 -0.011 0.000
ADH-l -0.023 -0.019 0.003
PGI -0.023 -0.022 0.000
AK -0.044 -0.041 0.003
LAP 0.078 0.083 0.005
GOT 0.081 0.082 0.002
GAPDH 0.045 0.045 0.000
Mean 0.032 0.035 0.003

 

47

deviate from Hardy-Weinberg proportions by some amount. However, none
of these values were significant even at P<0.10.

To test the significance of allele excesses and deficiencies
across hosts, subpopulations within each host were combined and the
F-statistics presented above were recalculated. Values for FIS(IK)
varied from -0.084 to 0.170 in alleles for LAP in white pine. This
was the greatest variance expressed for any alleles in either white
pine or jack pine. Values for FISK and FITK differed considerably,

with some alleles more variable than others. FSTK values were very low
for most alleles, below 0.007, with the greatest value found for the

D allele in MDH-l, 0.024. Means of FISK , FITK , and PSTK values
were not significant using Li's (1955) chi-square test for significance.

This result indicates that the variance within each host and between

hosts was very low.

Hierarchial and heterogeneity analysis

 

A hierarchial analysis of population differentiation was computed
using Wright's (1978) formulation. Variance components and F-statistics
were calculated for each level of the hierarchy, used for sample col-
lection, relative to other levels. Individual loci values for variance
components were highly variable. Values for each allele were also very
variable but no direct relationship could be made between any pairs of
levels and individual loci or alleles. The greatest variance component
across all loci was expressed for the demes relative to the locality
(Table 19). Of the six pairs of levels, the three comparing demes to

other levels had variance components at least 3 times greater than any

48

 

 

 

 

 

 

 

 

 

Table 19. Variance Components And F-statistics Combined Across Loci.
Comparison
Variance FXY
x Y Component
Demes Locality 0.01294 0.005
_Demes Host 0.01126 0.004
Demes - Total 0.00942 0.004
Locality Host -0.00168 -0.001
Locality Total -0.00352 -0.001
Host Total -0.00184 -0.001
Table 20. Contingency Chi-square Analysis At All Loci.
No. Of
Locus Alleles Chi-square D.F. P
ACON-l 8 79.458 63 0.079
ACON~2 4 17.196 27 0.926
MDH-l 6 49.954 45 0.283
MDH-2 2 7.343 9 0.601
IDH-l 3 20.353 18 0.313
IDH-Z 4 27.043 27 0.461
ADH-l 5 35.884 36 0.474
PGI 9 63.863 72 0.742
AK 5 52.503 36 0.037
LAP 5 48.204 36 0.084
GOT 6 30.031 45 0.958
GAPDH 2 16.652 9 0.054
(TOTALS) 448.483 423 0.189

 

49

other comparisons, indicating that the greatest variance is related to
the demes. However, values of FXY were extremely low and none of the
pairwise comparisons were significant.

A chi-square test for a M x N contingency table with (M-1)(N-1)
degrees of freedom, where M is the number of populations and N the
number of alleles, was employed to test heterogeneity. This analysis
revealed that none of the loci differed significantly from expected
values and that heterogeneity among populations was very low (Table 20).
This test was also employed for the second level of the hierarchy and
results indicated that heterogeneity between adjacent populations
(Tables 21 and 22) and between outside populations (Tables 23 and 24)
are extremely similar for both hosts. Neither of the locality levels
were significantly different from expected values using Hardy-Weinberg

equilibrium.

Table 21. Hierarchy Contingency Chi-square Analysis At All Loci.l

 

 

No. Of

Locus Alleles Chi-square D.F. P

 

 

ACON—1 8 10.554 7 0.159
ACON-2 2 0.005 1 0.942
MDH-l 4 1.873 3 0.599
MDH-Z 2 0.974 1 0.324
IDH-l 2 0.342 1 0.559
IDH-2 2 0.342 1 0.559
ADH-l 3 2.672 2 0.263
PGI 8 7.692 7 0.366
AK 3 1.202 2 0.548
LAP 4 2.875 3 0.411
GOT 4 2.881 3 0.410
GAPDH 2 8.457 1 0.004
(TOTALS) 39.810 32 0.161

 

1 Locality: Adjacent; Host: White Pine

Table 22. Hierarchy Contingency Chi-square Analysis At All Loci.1

 

 

No. Of

Locus Alleles Chi-square D.F. P

 

 

 

ACON-1 6 3.323 5 0.650
ACON-2 2 2.922 1 0.087
MDH-l 2 0.681 1 0.409
MDH-Z 2 1.554 1 0.212
IDH-l 2 0.020 1 0.888
IDH-2 2 0.079 1 0.778
ADH-l 3 2.831 2 0.243
PGI 4 4.617 3 0.202
AK 4 2.418 3 0.490
LAP 5 6.354 4 0.174
GOT 5 0.782 4 0.941
GAPDH 2 0.280 1 0.597
(TOTALS) 25.862 27 0.526
1 Locality: Adjacent; Host: Jack Pine

51

 

 

 

 

Table 23. Hierarchy Contingency Chi-square Analysis At All Loci.l
No. Of

Locus Alleles Chi-square D.F. P
ACON-l 7 3.870 6 0.694
ACON-2 2 0.167 1 0.682
MDH—l 2 0.823 1 0.364
IDH-l 2 1.320 1 0.250
IDH-2 2 0.823 1 0.364
ADH-l 5 2.226 4 0.694
PGI 6 2.603 5 0.761
AK 2 2.506 1 0.113
LAP 4 2.361 3 0.501
GOT 5 4.621 4 0.328
GAPDH 2 0.042 1 0.838
(TOTALS) 21.362 28 0.810

 

1 Locality: Outside; Host: Jack Pine

 

 

 

 

 

Table 24. Hierarchy Contingency Chi-square Analysis At All Loci.1
No. Of
Locus Alleles Chi-square D.F. P
ACON-1 8 11.226 7 0.129
ACON—2 4 1.594 3 0.661
MDH-l 5 6.496 4 0.165
MDH-Z 2 1.443 1 0.230
IDH-l 3 2.947 2 0.229
IDH-2 2 1.381 1 0.240
ADH-l 3 1.483 2 0.476
PGI 7 5.514 6 0.480
AK 4 1.444 3 0.695
LAP 4 3.943 3 0.268
GOT 5 2.417 4 0.659
GAPDH 2 1.991 1 0.158
(TOTALS) 41.880 37 0.267
1 Locality: Outside; Host: White Pine

Discussion

Genetic variability within and between the 10 populations appears
to extremely low using Nei's (1978) unbiased estimates. In the con-
text of this study, identity between all populations was notably high.
Even when the 5 populations from each host were combined, genetic
identity was essentially the same as when populations were examined
separately. From this analysis, it appears that these populations re-
present only a fraction of a panmictic population. At no sample hier-
archy level was there any significant differences between populations
or levels, with locality and host levels expressing indistinguishable
identities. The only analysis, using Nei's estimates, that indicated
differences was the single-locus analysis with loci exhibiting vary-
ing degrees of identity between populations. This may well indicate
that genetic drift may be influencing the results for this analysis.
Random genetic drift occurring independently in subpopulations can
lead to genetic differentiation between subpopulations (Falconer, 1981).
However, it seems that this drift may not manifest itself when allele
frequency differences are averaged, as in Nei's (1978) estimates. It
must be noted that other factors may be influencing these differences
(i.e., selection, inbreeding, and effective population size).

Additional evidence for subpopulation differentiation is found
when examining Wright's (1978) F-statistics. Levels of differentia-

tion between subpopulations is extremely low. However, the levels

52

53

found indicate that subpopulations do differ from expected for a pan-
mictic population (i.e., PST does not equal 0). More important than

the between subpopulation variation is the within subpopulation varia-
tion. PIS and FIT values are significant in a few subpopulations.

These values measure the deviation from expected Hardy-Weinberg.
Deviations expressed indicate that within these subpopulations allele
frequencies cannot be explained fully by expected Hardy-Weinberg equili-
bria. Something is causing either an excess or a deficiency of alleles
within these subpopulations. Genetic drift, natural selection, inbreed-
ing, or some other factor or any combination may be causing the subpop-
ulations to express differences in allele frequencies.

The differences in F-statistics do not seem to be correlated to
any level of the sampling hierarchy. This indicates that the genetic
differences within subpopulations seem to be occurring randomly and
are not related to host species association. The prime support for this
comes from the hierarchy analysis of Wright (1978). Subpopulations
(=Demes) seem to carry most of the variance with them. Variance com-
ponents (when the deme level is included) are 3 times greater than any
other combinations which lends support to the concept that populations
once believed to be panmictic are really composed of small demes
(Selander and Kaufman, 1973; Nei, 1975; Wright, 1978).

This substructuring of populations has become an important feature
in population genetic theory (Phillips, 1985). Many recent studies have
set out to examine the levels of substructuring in many different species
(Bush et a1., 1977- Rhagoletis; Nei and Imaizumi, 1965- Humans). This

type of substructuring can lead to reduced gene flow between demes (Nei.

54

1975) which increases the possibility of genetic divergence (Diehl and
Bush, 1984). It can also lead to inbreeding within demes, especially if
the effective population size of demes was low (Falconer, 1981). If
gene flow was greatly reduced and effective population size was low

then substantial genetic drift could accumulate (i.e., founder effect).
Substructuring that resulted in host race formation could also be very
important (Bush, 1969) and demes that are host specific may become
reproductively isolated from demes on other hosts. Accumulation of
genetic diversity could be enhanced by genetic variation that effective-
ly isolates subpopulations on the basis of hosts. Host selection

based on chemical cues specific for the parent population may be altered
by new genes that result from mutations. If gene flow is reduced and
inbreeding is common, the new host selection genes may become common

in the new demes (Bush, 1969). Substructuring of populations may be

one of the first steps in the formation of races and species in sympatric
populations.

If a population is composed of demes, it may be easy to imagine the
fate of these demes if various factors influence genetic diversity be-
tween them. A reduction in gene flow could and probably would result
in genetic drift between the demes. The same result would occur if the
dispersal rate of the species was low (Phillips, 1985). This would in
effect be similar to ecological isolation and might be regarded as
allopatric isolation based on a lack of interbreeding. Within the demes,
inbreeding could lead to reproductive isolation if gene flow between
demes was greatly restricted. Thus, demic differentiation may result

from any number of isolating mechanisms. Random drift within a deme,

55

inbreeding, assortative mating based on deme structure, low levels of
dispersal may be the processes by which a deme may diverge from other
demes.

In the current study, the demic differentiation was moderate.
The hierarchy examined presented one was of trying to get a handle on
the factors influencing differentiation. Demic differences could not
be correlated with the hosts examined. However, it must be noted that
the chi-square test utilized in this study has been the subject of some
debate. Ward and Sing (1970) have stated that it may be necessary to
examine large numbers of individuals (>103) in order to detect signifi-
cant levels of differences from F-statistics. Small sample sizes and
resulting sampling errors in this study may have influenced the results.
Although some of the F—statistics were significant using this analysis,
results do not provide adequate information about the causes of devi-
ations from Hardy-Weinberg. Significant chi-square values obtained are
such that it would be illogical to attribute the deviations from Hardy-
Weinberg to inbreeding. Demic differentiation may be lower than would
be predicted from the F-statistics. Despite high variation within demes
and deviations from Hardy-Weinberg, PST values indicate that between
deme differentiation is low. Values for all F-statistics were even
lower when all host specific demes were combined, implying that gene-
tic variation within and between demes is extremely low and not related
to host specificity. It would seem that this group of populations is
acting as one panmictic polyphagus species.

The importance of these results to managers of forests or private

plantations may be limited. Controlling the weevil on one of the hosts

56

may not be as effective if the other host in in close proximity.
Measures currently employed to control the weevil on eastern white
pine may be subverted by high populations of weevils on nearby jack
pine plantations. However, the greatest importance of this study may
well be to managers trying to find resistant varieties. Trying to find
varieties resistant to weevil infestation may be a very difficult
endeavor. This insect would seem to be able to infest most species of
pines with a degree of consistancy. Varieties of host species may
prove ineffective in providing any measure of resistance to this type
of insect. This is not to say that current programs utilizing host
resistance are not worth continuing. However, the hope of finding
varieties that provide control may be low. Pines such as red pine,

Pinus resinosa Ait., do show resistance to weevil infestation and a

 

thorough understanding of why they do may lead to varieties of other
species that show resistance. Managers may want to be cautious about
planting species of trees in close proximity to one another and to

undertake programs for resistance with similar caution.

APPENDICES

57
APPENDIX 1

Record of Deposition of Voucher Specimens*

The specimens listed on the following sheet(s) have been deposited in
the named museum(s) as samples of those species or other taxa which were
used in this research. Voucher recognition labels bearing the Voucher
No. have been attached or included in fluid-preserved specimens.

Voucher No.: 1985'2

 

Title of thesis or dissertation (or other research projects):

GENETIC VARIATION WITHIN AND
BETWEEN SYMPATRIC POPULATIONS OE
PISSODES STROBI ON TWO HOST SPECIES
EASTERN WHITE PINE AND JACK PINE

Museum(s) where deposited and abbreviations for table on following sheets:

Entomology Museum, Michigan State University (MSU)

Other Museums:

Investigator's Name (5) (typed)

 

Charley Adrian Chilcote

 

 

*Reference: Yoshimoto, C. M. 1978. Voucher Specimens for Entomology in
North America. Bull. Entomol. Soc. Amer. 24:141-42.

Deposit as follows:

Original: Include as Appendix 1 in ribbon copy of thesis or
. dissertation.
Copies: Included as Appendix 1 in copies of thesis or dissertation.
Museum(s) files.
Research project files.

This form is available from and the Voucher No. is assigned by the Curator,
Michigan State University Entomology Miseum.

58

APPENDIX 1.1

Voucher Specimen Data

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Pages

59
APPENDIX 1.1
2 of 2

Voucher Specimen Data

Page

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Appendix 2

White Pine Weevil

The white pine weevil, Pissodes strobi Peck, belongs to the family

 

Curculionidae (Coleoptera). This family reportedly contains more
species than any other in the animal Kingdom. The family consists of
some 60 subfamilies, which differ greatly in their biology and habits.
Most plants are colonized by at least one species of this family and
their damage to plants often cause extensive economic loss if not con-
trolled (e.g., boll weevil). Damage can be inflicted to the fruit,
nuts, stems, buds, and other tissue of the host plants as well as on
forest, shade, ornamental trees and seedlings in natural stands, small
ornamental plantings, and plantations. Adult weevil usually drill holes
into the structure by feeding from the outside of the host and larvae
usually live within the same host tissue.

The white pine weevil is an elongate, brownish insect about 4 to
6 mm. long. It is easily identified by irregular groups of brown and
white scales that are found on the body and elytra. Their eggs are
pearly white, usually about 1 mm. long and normally found in groups of
two or three. Larvae are yellowish—white, legless scarabaeiform grubs,
and are slightly longer than the adults. The pupae are creamy-white,
exarate pupae and are about as long as the adults.

From March to May, adult weevils emerge from overwintering sites,
in the duff beneath the host trees. Upon emergence, they make their
way to the succulent new growth of their host. It is here that they
feed until ready to copulate. Normally only a few days of feeding occur

60

61

before the male and female copulate. Feeding generally is confined to
the area within 15 to 20 cm. of the dormant bud. During feeding and
copulation, adults are gregarious and several may be found on the same
leader.

Eggs are deposited in tiny cavities in the bark which are created
by the female chewing out the inner bark with her curved snout. This
produces a puncture in the outer bark with a larger chamber beneath.
Upon completion of the cavity, one to three eggs are deposited within
the chamber. This process of oviposition is normally confined to areas
where the female has fed, but it is not uncommon for the female to
oviposit on several trees.

Larvae eclose in about 7 to 10 days and begin to feed on the phloem
and cambium in a random fashion. As more larvae emerge, they begin to
orient themselves downward. Eventually the number of larvae is great
enough that they arrange themselves in a compact feeding ring around
the stem of the leader. This ring increases the chance of individuals
surviving as feeding in a concentrated ring effectively stops resin flow
that might drown individual larvae. However, the feeding ring leaves
no nutrient behind, and individuals not at the forefront of the ring
starve. As larvae mature, they fall behind the ring, bore into the
wood, and use the wood chips produced to cover their chamber. The
length of the larval period can vary greatly, depending on environmental
factors, but usually lasts from 4 to 6 weeks.

Pupation occurs within the excelsior covered chamber with the pupal
stage usually lasting approximately 2 to 3 weeks. Mature pupae change

into pharate adults which remain within the pupal chamber for about 10

62

days. Mature adults emerge from the damaged leader in late July to
mid-September, and sometimes as late as early October whereupon they
feed on the succulent growth of the host. As winter approaches adults
migrate down the trees into the duff where overwintering occurs in the
pine litter below the crown dripline. Most overwintering sites are
located at the interface level, an area that lies below the dry pine
needles and above the moist organic debris zone.

Weevils go through only one generation per year as adults normally
die shortly after mating and oviposition have occurred. Occasionally
a weevil may live for several years, but this is not frequent (McMullen

and Condrashoff, 1973).

Appendix 3

Gel Electrophoresis

Gelgpreparation

 

The preparation of starch gels follows those used by Howard (1982).
An 11.75% horizontal starch gel was prepared using 47 grams of Electro-
starch (The Electrostarch Co., Madison, Wisconsin), lot #392, in approx-
imately 400 ml. of the appropriate gel buffer (Table 25). Buffer
systems used were the ones found to give good results in the initial
enzyme survey. In System 3, it was necessary to add an extra 10-15 ml.
of buffer to the original amount to reduce the incidence of splitting
at the origin which resulted from the gel contracting during the run.
Extra buffer made the gel 2-3 mm. higher than the mold which allowed
the ice container to press down directly onto the gel, keeping it
together during the run.

Starch was placed into a 1000 ml. Ehrlenmyer flask and the con-
tents swirlled until all large clumps of starch were dissolved. This
prevented clumps from forming in the gel that could disrupt migration
of the enzymes. The flask was then heated over a standard bunsen
burner and vigorously swirlled until a noticeable phase change occurred.
This change was recognized by the transformation of the milky white
suspension of the starch to a clearer, more viscous, gelatinous fluid.
For seven seconds after the phase change, the flask remained over the
flame and swirlling continued. Because this mixture was boiling during
the time after the phase change, many small air bubbles became trapped

in the solution which could interfere with the migration of the enzymes

63

64

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65

and thus had to be removed. After the gel was removed from the flame,
a vacuum was applied to the flask to remove the unwanted air bubbles.
Vacuuming caused the gel to boil vigorously as most of the air trap-
ped in the suspension escaped. This procedure was continued until
large, regular bubbles rose from the suspension (i.e., about 15-28
min.; depending on the buffer used). The aspirated starch was then
poured unformly and quickly into the mold in order to prevent premature
setting. After all the gel had been poured, the mold was shaken gently
side to side to smooth the surface and to insure all corners were fill-
ed. This also helped mend any cracks in the gel formed during pouring.
Air bubbles and debris that might have been missed or introduced dur-
ing aspiration or pouring were removed using a pasteur pipet. Only
large bubbles and debris were removed, because care had to be taken

not to produce holes in the gel that set up before they could be filled.
After debris had been removed, the mold was once again shaken gently to
fill in holes and smooth the gel surface then left to cool for about

20 minutes. After cooling, it was covered with Saran Wrap and placed
in a cold room (about 4 degrees C.) until used, usually not less than

4 hours and not more than 48 hours after preparation.

Sample preparation

 

On the day of a gel run, individual adult and larval weevils had
to be prepared. Vials containing samples of either larvae or adults
were removed from the -80 degree C. storage and placed on crushed ice.
One individual from each vial was transferred to spot plates which

were placed on crushed ice to keep the enzymes from degrading.

66

Four individuals from each of the five populations under study were
placed in individual spots on the plates. In addition, three individ-
uals from the reference population (population A WP) were placed in the
first, the tenth, and the twenty-third positions on the plates. These
individuals were used to make sure that all gels were scored the same.
After all individuals were transferred to spot plates, one drop of
grinding buffer (0.05 M tris/HCl pH 7.0) was added to each spot with

a pasteur pipet. Each weevil was then ground using a clean tissue
grinding rod to insure that enzymes were not transferred between spots.
Wicks of filter paper (2 by 9 mm.) were then added to each spot, the
number of wicks varying, depending on the number of gels to be run.

If two different systems of buffers were being used for different en-
zymes, two wicks were added to each spot. The wicks soaked up the
homogenate containing the enzymes. In a few cases, it was necessary to
retain some of the homogenate for future study. In which case, capil-
lary tubes were used to collect a small amount of the fluid and the
tubes held in a test tube submerged in a cold ice bath until all were
collected. They were then transferred to an ultra-cold freezer for
future use. When needed the tubes were removed, allowed to thaw, and
the fluid emptied directly onto wicks placed in spot plates.

When all individuals were ground and wicks prepared, gels were out
near one edge. The cut was made using a scalple along a plastic guide,
through the gel gel to the bottom of the mold. The cut (from here on
termed the origin) was used to place wicks into the gel in preparation
for electrophoresis. Wicks were removed from spots on the plates

individually with care taken not to confuse individual wicks (i.e.,

67

each individual was assigned a particular position on the gel; individ-
ual #1 had to be placed into position #1 on each and every gel used).
Each individual weevil provided sufficient homogenate to test for many
enzymes. The fluid wetted enough wicks to be used on several gels.
Each gel provided a test for up to four enzymes and each individual
provided enough wicks for up to seven gels. When all wicks were

placed into the gel, it was ready to be run.

Gel running

 

Running the gel was performed in a cold room at 4 degrees C..
Electrode trays were set up so that all electric leads led out of the
cold room to power units. Electrode buffers, listed in Table 25, were
placed in the appropriate trays. About 160 ml. of buffer were placed
into each side of the tray. The amount of buffer was sometimes increas—
ed because fluid levels were lower than desired. Buffer levels were
occasionally checked during long runs to insure that capillary action
did not reduce the level to the point where electric contact was
reduced. When ready to start a gel, protective mold feet were removed
to allow good electric contact. Gel molds containing the gel with wicks
in place were placed into the electrode trays. Electrodes were placed
into trays with the red (+) electrode placed at the end of the gel
away from the origin. Electrodes consisted of a small piece of plat—
anum wire stretched on plastic legs and connected to a lead terminal.
After the gels were in place and electrodes connected, they were ready
to run. Run voltages and times depended on the buffers used and were

those used by Howard (1982). Run times were increased in those cases

68

where bands were too close together to determine heterozygotes. An ice
box was placed on each gel when it was determined that the gel was run-
ning properly (i.e., the power unit displayed a potential that was
right for the system used). The ice box was filled with crushed ice
and used to cool the gel during the run and in the cases described
earlier, to press on the gel to keep the origin from separating. Pe-
riodic checks during the run were made to insure good electrical con-
tact, proper electrode buffer level, and sufficient ice. All through
the run, Saran Wrap was kept between the gel and ice box which reduced
moisture loss from gels and prevented the gels from degrading. Thus,
enzymes were not denatured or their movement though the gel impaired.
After the run was completed, gels were removed from electrode
trays and sliced at the edge of each of the feet to remove them. A
notch was cut at the origin on the left-hand side to provide a refer-
ence point when gel slices were examined later. Gels were then removed
from the mold and placed on a slicing board. A slicer was then used
to take four slices (about 2 mm. thick) from each gel. The front slice
(wide slice) and back slice (narrow slice) were both cut and placed
into individual stain boxes. In most cases only the front slice con-

tained enzymes used in the study.

Staining

Stains used in this study were modified from those of Howard (1982).
Table 26 gives the recipes and special instructions used to give good
results. After slices were made, the proper stains for the systems

used were added to stain boxes. Stain boxes were either placed into an

69

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73

incubator immediately or transferred to one after an appropriate time.
Banding patterns were watched to judge when to stop the staining
process. In some cases this was almost immediate and in others it
took several hours (Table 26). After gels were stained for the ap-
propriate enzymes, they were removed from the incubator and either
scored immediately or fixed. The fixative that was used consisted

of a 5:5:1 mixture of water to methanol to acetic acid. Some stained
gels faded when placed into this solution and had to be scored im-
mediately. Others were fixed and scored later. A mixture of methanol
and water (1:1) was used to fix gels stained for peptidase. In most
cases, pictures of stained gels were taken for future reference.
Later, it was found that photocopies of gels could be taken on a stand-
ard copying machine. These provided for quick reference and main-

tained banding patterns for future examination.

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