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NF... 3 12 I121 LIBRARY

   
 

This is to certify that the

thesis entitled

Population Sampling and Spatial Distribution of the
Immature Life Stages of the Onion Maggot,

Hylemya Antigua (Meigen)
presented by

 

Raymond I. Carruthers

has been accepted towards fulfillment
of the requirements for

Masters degree in Entomoloqy

 

Major professor

Date May 18; 1979

0-7639

 

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l [I [11'1"].

. ‘
. tilli.‘lllll|d|'§ll

POPULATION SAMPLING AND SPATIAL DISTRIBUTION
OF THE IMMATURE LIFE STAGES OF THE

ONION MAGGOT, HYLEMYA ANTIQUA (MBIGEN)

 

By

Raymond I. Carruthers

A Thesis

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

MASTER OF SCIENCE

Department of Entomology

1979

ABSTRACT

POPULATION SAMPLING AND SPATIAL DISTRIBUTION OF THE
IMMATURE LIFE STAGES OF THE ONION MAGGOT,
HYLEMYA ANTIQUA (MEIGEN)

 

by

Raymond I. Carruthers

The onion maggot, Hylemya antiqua (Meigen) is a continuous prob-

 

lem in onion production in northern United States and southern Canada.
This study examines the spatial distribution of the immature life
stages of this insect pest with the goal Of developing accurate yet
economically reasonable methods Of density estimation.

The spatial distribution of the immature life stages were found
to be highly aggregated at various levels, from the regional distribu-
tion of damage between fields down to the distributional pattern of
maggots between onions. An ovipositional attraction for rotting and/or
previously infested onions was found to exist, with a 20-fold increase
in egg density on previously damaged onions.

Regional and field level sampling techniques were developed for
estimation of both onion maggot plant damage and actual age specific
densities. Sample costs were evaluated for various universes of con—

cern, sample sizes, and levels of precision.

To my family

ACKNOWLEDGEMENTS

I wish to express my sincere appreciation to Dr. James E. Bath,
Department Chairman, whose overwhelming enthusiasm initially attracted
me to Michigan State University, and whose personal guidance has
inspired me ever since.

To Dr. Donald C. Cress, I extend my personal thanks for the gui-
dance received at the onset of this study and wish him the best of
luck with his new endeavors at Kansas State University.

I especially thank Dr. Dean L. Haynes, not only for his constant
support and guidance through this study as my major professor, but for
the continuously stimulating experience of working with him on a daily
basis. No other individual has contributed more to my education.

To Dr. George w. Bird, Dr. Fred W. Stehr, and Dr. R. Lal Tummala,
I would like to extend my appreciation for serving on my guidance
committee and reviewing this manuscript.

I wish to sincerely thank several Of my colleagues, especially
Tom Ellis and Bill Ravlin, as their insight and friendship during this
project was invaluable. Special thanks are extended to John Putnam
(Dr. Coolie) and Mark Silliman (Dr. Labor) whose endless hours in the
field made this study possible.

Lastly, I would like to express my sincere gratitude and love

to my wife, Diane, whose love and patience made all this possible.

TABLE OF CONTENTS

LIST OF TABLES .

LIST OF FIGURES

INTRODUCTION

LITERATURE REVIEW

Taxonomy ,

Geographic Distribution

Life History ,

Developmental Rates

Fecundity and Longevity

Survival in Relation to Abiotic Factors
Parasitoids and Predators

Disease

Rearing and Nutrition

Spatial Distribution and Sampling

METHODS

Plant Damage Sampling , , , , , . . .
Regional Plant Damage Sampling _ , ,
Field Level Plant Damage Sampling

Within Field Sampling for Age Specific Onion

Ovipositional Behavior

0 C O O O O O O 0

RESULTS AND DISCUSSION

Spatial Patterns , , , , , , , , , , , ,
Statistical Distributions
Nearest Neighbor Analysis , , , , ,
Specific Spatial Pattern Studies

Pupal Distribution in Muck Soil

Maggot Density.

Seasonal Distribution of Onion Maggots

Ovipositional Preference , , , ,
Distribution of Initial Attack ,

iv

vi

ix

\OKDGJQQQUILD-bb

[.0
O

11

11
11
15

17
21

23

23
23
41
42
42
47
47
53

TABLE OF CONTENTS (Continued)

Plant Damage Sampling .

Regional Plant Damage Sampling

Field Level Plant Damage Sampling .

Within Field Sampling for Age Specific Density

Biological Monitoring .

SUMMARY . . . .
APPENDICES

Appendix A:
Appendix B:
Appendix C:
Appendix D:
Appendix E:

Appendix F:

LITERATURE CITED

Temporal Distribution .

Onion Maggot Damage .

Regional Plant Damage Sample Data
Mass Rearing Technique

Age Specific Sample Data

Ovipositional Behavior Data .

57
57
75

80
103

111

113
125
150
159
161
191

193

10.

11.

12.

13.

14.

LIST OF TABLES

Sampling phenology for regional plant damage
assessment . . . . . . . . . . . . . . . . . . . . . . .

Predicted adult and larval density peaks for Grant,
Michigan in 1976 and 1977 . . . . . . . . . . . . . . .

Pit of the negative binomial distribution to observed
field level plant damage sampling data . . . . . . . . .

Fit of the negative binomial distribution to observed
within field plant damage sampling data . . . . . . . .

Fit of the negative binomial distribution to actual
onion maggot counts within areas of damaged onions with
application of a common K . . . . . . . . . . . . . . .

Nearest neighbor analysis for within clump plant
damage 0 O O O O O I O O O O O C O O O O O O O O O O O 0

Observed horizontal pupal distribution as compared
with a poisson distribution . . . . . . . . . . . . . .

Observed number of onion maggots per infested bulb
in Grant, Michigan test field . . . . . . . . . . . . .

Analysis of variance table for total eggs and larvae
by onion type and condition . . . . . . . . . . . . . .

Cell statistics for ovipositional preference ANOVA
experiment I O O C O O O O O O O O I O I O C O I C O O 0

Runs test for distance between initial plant damage
and clump centroids . . . . . . . . . . . . . . . . . .

Within and between mean square for four onion regions
and two sampling periods calculated using a one-way

AN OVA 0 O O O O O O O O O O O O O C O C O O O O O O O 0

Regression statistics for within and between field
mean squares for regional plant damage data . . . . . .

Regression statistics for within and between field
mean squares for regional plant damage data . . . . . .

vi

14

16

26

28

38

43

44

48

50

52

56

58

61

61

LIST OF TABLES (continued)

15.

16.

17.

18.

19.

20.

21.

22.

23.

24.

25.

26.

27.

Within field variance to mean relationship as
estimated by log variance—log mean regression for
various sample unit sizes . . . . . . . . . . . . . .

Regression statistics for sampling time versus plant
damage density . . . . . . . . . . . . . . . . . . .

Field level variance to mean relationships as esti—
mated by a log variance-log mean regression for
various sample unit sizes . . . . . . . . . . . . .

Optimum sample unit size as predicted by Equation
19 for various densities and levels of precision . .

Summary for the 100 foot plant damage samples taken
throughout the 1977 growing season in Grant, Michigan

1977 data collection summary for strata 2-5 in the
Grant test field O O O O O O O O O O O O O I O O O 0

Means and multiple range tests for each life stage by
strata (l-S) calculated from data pooled across the

entire growing season . . . . . . . . . . . . . . . .

Analysis of variance table for third instar larvae by
clmp arid Strata O O O O O O O O C O C O O O O O C 0

Regression equation for mean-variance relationship
by life stage for each stratum . . . . . . . . . . .

Comparison of sampling methods for third instar
larvae in Strata 1-5 0 o o o o o o o o o o o o o o 0

Comparison of sampling methods for total immature
population in strata 1-5 . . . . . . . . . . . . . .

Comparison of sampling methods for third instar
larvae in Strata 2-5 0 o o o o o o o o o o o o o o 0

Comparison of sampling methods for total immature
population in strata 2-5 . . . . . . . . . . . . . .

Developmental base temperatures for third instar
larvae and pupae of the onion maggot . . . . . . . .

Mean degree-day requirements for various life
stages of the onion maggot . . . . . . . . . . . . .

vii

68

71

78

78

81

82

87

9O

93

94

96

99

101

118

119

LIST OF TABLES (continued)
A-3. Average degree-day accumulation for adult onion
maggot activity peaks . . . . . . . . . . . . . . . . . . 122

8-1. T-tests comparing population density in the within
field plant damage plots . . . . . . . . . . . . . . . . 131

3-2. T-tests comparing soil parameters of the within
field plant damage plots . . . . . . . . . . . . . . . . 131

8-3. Kruskal-Wallis one-way ANOVA for onion maggot plant
damage by soil type . . . . . . . . . . . . . . . . . . . 134

B-4. Ordering of ranks by soil type for the ANOVA of
Table B-3 0 o o o o o a o o o o o o o o o o o o o o o o o 135

viii

LIST OF FIGURES

1. Michigan map with regional sampling areas
indicated 0 O O O O O O O O O O O O O O O O O O O
2. Onion fields sampled in 1976 and 1977 in Grant,
MiChigan O O O O O I O O O O O O O O O O O O O O
3. Negative binomial parameter k as a function of
the sample mean (100 ft. plant damage samples) .
4. Negative binomial parameter k as a function of
the sample mean (1 meter plant damage samples) .
5. Negative binomial parameter k as a function of
the sample mean for the total immature population
per onion within areas of damage . . . . . . . .
6. Horizontal distribution of onion maggot pupae in
muck soil from onion source . . . . . . . . . . .
7. Vertical distribution of onion maggot pupae in
muck soil from onion source . . . . . . . . . . .
8. Relationship between the mean number of onion
maggots per bulb and the volume of the onion bulb
9. Plot of treatment means for ovipositional at—
traction experiment . . . . . . . . . . . . . . .
10. Mean square to mean relationship for within and
between field components of the regional sampling
data 0 O O O O O O O O O O O O O O O O I O I O O
11. Regional sampling precision plotted as a function
of sample unit size . . . . . . . . . . . . . . .
12. Sampling cost in minutes as a function of plant

damage density . . . . . . . . . . . . . . . . .

12

18

36

36

39

45

46

49

51

6O

69

70

13-14. Necessary subsamples per field for various densities
sample unit sizes, region Sizes, and levels of pre-
cision (regional sampling) . . . . . . . . . . . . .

ix

LIST OF FIGURES (continued)

15-16. Sample cost in minutes for various densities,
sample unit sizes, region size, and levels of
precision (regional sampling) . . . . . . . . . . . . . 73

l7-20. Necessary number of subsamples per field to
achieve specified levels of precision at given
densities and region size (regional sampling) . . . . . 74

21-22. Necessary samples per field for various densities,
sample unit sizes, and levels of precision (field
level smpling) O I O O O I O O O O O O O O O O O O O 0 77

23-24. Sample cost in minutes for field level sampling . . . . 77

2S. Variance to mean relationship of eggs, and first
and second instars in onion strata 2 and 3 . . . . . . 92

26. Pupal and total population plotted for the 1977
growing season . . . . . . . . . . . . . . . . . . . . 104

27. Contour map of onion maggot plant damage in
Grant, Michigan 1976 . . . . . . . . . . . . . . . . . 107

28. Contour map of onion maggot plant damage in
Grant, Michigan 1977 . . . . . . . . . . . . . . . . 108

A-l. Regression method for determination of develop-
mental base temperature for third instar larvae . . . . llS

A-2. Standard error method for determination of
developmental base temperature for second and
third instar larvae . . . . . . . . . . . . . . . . . . 117

A-3. Mean adult trap catch and weighted mean instar
plotted against julian days for 1977 in Grant,
Michigan . . . . . . . . . . . . . . . . . . . . . . . 121

A-4. Mean adult trap catch and weighted mean instar
plotted against degree days for 1977 in Grant,
Michigan . . . . . . . . . . . . . . . . . . . . . . . 122

B—l. Onion fields sampled in 1976 and 1977 in Grant,
Michigan . . . . . . . . . . . . . . . . . . . . . . . 128

INTRODUCTION

The onion maggot, gylemya antiqua (Meigen), is one of Michigan's

 

most economically devastating vegetable insect pests. Mr. William
Riley, Chairman of the Michigan Onion Growers Research Committee

has stated that the onion maggot is the number one problem in the
production of Michigan onions. Unchecked, each onion maggot larva
can destroy up to 28 onion seedlings in the loop stage (Workman 1958).
With adult females producing as many as 250 eggs (McLeod 1965), dam—
age potentials can be extremely high (7,000 onions per female).

Perron et al. (1955) cites crop damage as ranging from 10 to 85 per-
cent depending on the population density. Direct physical damage

such as this, coupled with the fact that the onion maggot is a known

vector of Ervinia carotovora (Jones) (Gorlenko et al. 1956), a soft

 

rot bacterium, causes onion growers to exert much time, effort, and
money towards its control.

Current control strategies consist of a granular soil insecti-
cide at planting and directed foliar sprays for control of the adult
flies. Michigan recommendations (Cress et a1. 1976) call for a 3-day
minimum between foliar applications. Several commercial acreages
are now approaching that rate of application. However, the intense
use of chemical control has caused severe problems in the onion-pest-
crop ecosystem. During the late 1950's and early 1960's, field

studies indicated a high level of onion maggot resistance to cyclodiene

insecticides throughout its North American and European distributions
(Brown 1971, Gostick et al. 1971, Harris and Svec 1976, and Hennequin
1970).

Chapman (1960) states that conditions for the selection of onion
maggot resistance are ideal under commercial field conditions, i.e.,
the onion maggot is confined to one primary host plant which is uni-
versally protected with a single type of insecticide over very large
areas. Harris and Svec (1976) state that high levels of cyclodiene
resistance developed quite rapidly after the initial indication that
resistance was present. Resistance was first noted in Michigan
during 1958 (Guyer and Wells 1959) and a major effort was made to
shift away from the cyclodiene insecticides to the organophosphate
group which immediately was used to control the maggot.

The organophosphates have been used intensively since the early
1960's with a gradual decrease in their effectiveness. Harris and
Svec (1976) attribute this decline in effectiveness to low level re-
sistance. In testing several onion maggot population strains over
the past 12 years for tolerance levels to various insecticides, two
Michigan strains (Gun Marsh and Grant) were found to have significant
increases in their level of parathion tolerance (2.8x and 5.1x,
respectively). The Grant strain was found to have the highest level
of resistance. This coincides with field observations, as Michigan's
most severe onion maggot damage has been in the Grant area. Resis-
tance levels found throughout the tested strains are considered low
level, but highly significant. Brown (1971) points out that organo-
phosphate resistance develops slowly, usually requiring three develop-

mental stages: 1) the development of a latent period involving many

generations of selection, 2) the development of a polyfactorial sys-
tem leading to low level nonspecific resistance, and 3) the develop—
ment of a monofactorial system leading to higher levels of specific
resistance. Harris and Svec (1976) feel that the onion maggot is
closely following the pattern described by Brown. Many growers are
still increasing their insecticidal application rates and application
frequencies with little increased control.

The future of the existing onion maggot control program is ques-
tionable. Other alternatives must be explored and viable means must
be adopted to integrate alternate control procedures into commerCial
operations. Such alternatives can only be designed when adequate
biological information concerning the system dynamics is known.

Basic to population dynamics research and certainly to applied
pest management is the ability to estimate actual insect densities
and their effects in terms of host plant damage. Methods for such
estimates are presently lacking for the onion system; it is the goal
of this report to develop methods by which both plant damage densities
and actual insect densities per life stage can be estimated for

future research and pest management goals.

LITERATURE REVIEW

The literature concerning gylemya antiqua (Meig.) is quite volu-

 

minous with the majority being insecticide oriented and of little
value in the accumulation of biological information. Scott (1969)
assembled an extensive bibliography for g, antiqua covering the
majority of the published material with the exception of taxonomic
citations and actual spray calendars.

Several authors (Doane 1953, Tozloski 1954, Workman 1958, Elling-
ton 1963, and Loosjes 1976) have reviewed and collated much of the

\

important biological literature concerning g, antiqua. The material
presented in the following review is a resume of previous investiga-
tions that lend pertinent information in the areas of taxonomy, geo-
graphic distribution, life history, developmental rates, fecundity
and longevity, survival, parasitoids and predators, diseases, rearing

and nutrition, and spatial distribution and sampling.

Taxonomy

The taxonomic history is given by numerous authors. Most re-
cently descriptions have been given by Huckett (1924), Doane (1953),
Tozloski (1954), and Workman (1958). Keys that are useful in species
identification have been compiled by Brooks (1951), Doane (1953), and

Huckett (1971).

Geographic Distribution

 

A distributional map with a list of the areas inhabited by g.
antigua was published by the Review of Applied Entomology (Distribu-
tion Maps of Insect Pests, Series A, Map No. 75, issued June 1977).
Ellington (1963) gives a brief update of the fly’s distribution in

Europe.

Life History

 

In Michigan there are typically three distinct generations per
year which overlap somewhat due to the longevity of the adult flies.
The adults emerge from overwintering pupae in late April or early May.
The exact date and length of the emergence period is dependent on
temperature and depth of the overwintering pupae in the soil. As the
soil profile warms, the pupae break diapause with the pupae closest
to the surface emerging first. Developmental zero for the diapaused
pupae is close to 40°F (Eckenrode, Ven and Stone 1975).

Newly emerged adults are soft-bodied and require a day to dry
and harden. At this time the fly emigrates to field borders and feeds
on pollen from wild flowers and other weeds. The preovipositional
period lasts about 10 days, varying slightly with micro-climatic
fluctuations (Theunissen 1976).

When gravid females move back into the onion field, they lay
their eggs on the surface of the soil around the base of the plant in
the leaf axils. After ecolosion the newly hatched first instar larvae
move into the base of the onion bulb and feed, quickly disrupting the
plant's vascular system which then shows signs of acute water stress.

Lesions then open on the bulb surface which allows an invasion of

microorganisms, primarily soft rot bacteria such as Erwinia corotovora

 

(Jones). The microorganism development increases the rate of tissue
degeneration within the onion and produces symptomatic damage. (Doane
(1953) gives an indepth description of the onion maggot soft rot dam-
age symptoms.) Onion damage is first characterized by flacid leaves,
followed by leaf tip yellowing, and then complete foliage dehydration.
With prolonged damage the bulb is completely consumed by the onion
maggot soft rot attack, leaving only the desiccated leaf tissue and
the outer bulb sheath. At this point the maggot moves into the soil
and pupates or migrates to succeeding onions until development is
completed (Workman 1958). Kendall (1932) reported that 96.6% of
second generation flies reinfest previously infested onions.

In the early part of the growing season, one maggot may consume
numerous seedlings, resulting in a high rate of plant damage and mor-
tality. As the season progresses and bulb size increases, one onion
will support many more maggots, resulting in a doming of the damage
curve (Loosjes 1976). Many traditional sampling schemes fail to deal
with this functional shift; therefore, population estimates (adults
and larvae) are often erroneously equated with damage predictions
which often results in unnecessary spray applications.

Pupating third instar larvae migrate from the onion plant to the
soil where the puparium is formed. The non-diapausing pupal stage
lasts about 13 days before the following generation adults emerge.
The newly emerged adults of the second and third generations follow
the same developmental pattern as the first. A small percentage of

the second generation pupae and a high percentage of the third enter

diapause (LaFrance and Perron 1959). Diapause is induced by the
exposure of the late developing third instars and early pupae to low

temperatures and a shortened photoperiod (Theunissen 1976).

Developmental Rates

 

Numerous observations concerning developmental rates have been
made under a variety of laboratory and field conditions. Ellington
(1963) reviewed the literature concerning this area and tabulated the
results. Finding the existing data inconsistent, Ellington conducted
laboratory experiments to define the developmental rates for eggs,
larvae and pupae at various constant temperatures. Ellington's
findings as well as other developmental data are discussed further in

Appendix A.

Fecundity and Longevity

 

The ovaries of g. antiqua are meroistic polytrophic which results
in a cyclic ovipositional activity (Missonnier and Stengel 1966).
Due to the gravid female‘s ability to oviposit over an extended period
of time, laboratory fecundity and survival studies have proven unre-
liable in the field. The variability of experimental results suggests

a great need for additional field experimentation in this area.

Survival in Relation to Abiotic Factors

 

Ellington (1963) discusses egg, larval and pupal survival under
a variety of temperature and relative humidity regimes in the labora-
tory. A review of the literature reveals the lack of completed work
associating abiotic field conditions (temperature, relative humidity,

soil moisture, etc.) with onion maggot survival. Although qualitative

assessments linking these phenomena have been noted, no attempt has
been made to quantify them. Workman (1958) qualitatively split soil
moisture into three arbitrary classes (saturated, moist and dry) for
greenhouse experiments. Data of this type is quite common, but is of
little value for estimating actual field survival rates. Using the
work of Ellington (1963) and others (Sleesman 1936, Doane 1953, Gray
1924, and Workman 1958) high moisture situations seem to increase the

survival of egg and larval stages.

Parasitoids and Predators

 

Perron (1972) discusses several parasitoids and predators that
were present in non—pesticided organic soil plots in the Ste. Clotilde

region of Quebec (1951—1966). A staphylinid beetle, Aleochara bilin-

 

gatg_(Gyllo), was most effective. A, bilineata as a larval parasitoid
is capable of destroying 20% of the overwintering pupae (Perron 1972).
It becomes a predator as an adult. A braconid wasp, Asphaereta pal-
lipg§_(Say), was listed as the second most effective parasitoid,
capable of destroying 12% of the overwintering pupae (Perron 1972).
Several other parasitoids and predators were listed with a short
evaluation of each.

Ritcey (personnal communication, University of Guelph) stated
that less than 10 parasitized individuals were observed from;the
thousands of field-collected pupae in Ontario commercial production
areas. It is believed that heavy pesticide usage (soil treatment at
planting and weekly foliar applications) has effectively eliminated

the natural enemy complex of the onion maggot from these areas.

Disease

Entomophthora muscae (Cohn) has been identified as a naturally

 

occuring fungal pathogen of g, antiqua (Perron and Crete 1960, Krammer
1971, and Miller and McCallahan 1959). Perron and Crete (1960) cited
E, muscae as the key factor suppressing outbreak levels of the onion

maggot in Quebec. Infected flies could fly, mate and oviposit but at
highly reduced rates. MacLeod et al. (1976) summarized Entomophthora
species with muscae-like conidia; life histories, species identifica-

tion, and a thorough bibliography are included.

Rearing and Nutrition

 

Rearing and nutritional information concerning g, antigua has
been researched and well documented. Mass rearing programs have been
carried out by several workers (Rawlings 1953, Perron et a1. 1951,
Friend and Patton 1956, WOrkman 1958, Elmosa 1960, and Niemczyk 1964).

Niemczyk (1964) developed a rearing technique for implementation
at the Agriculture Canada, Entomology Laboratory, London, Ontario
where modifications have been made to increase production levels and
efficiency. These implementations increased the facility's rearing
capabilities to 2,000,000 flies per month (Harris personal communication
1976) and are used for rearing laboratory colonies at Michigan State
University (Appendix D).

Friend et al. (1956 and 1957) defined complete nutritional infor-
mation, including amino acid and vitamin requirements. Additional
information concerning the accelerated development of g, antiqua, in
the presence of microorganisms, has also been documented by Friend et

al. (1959).

10

Spatial Distribution and Sampling

Even though little has been published quantifying g. antiqua's
spatial distribution in North America, numerous qualitative descrip-
tions are found in the literature (Perron et al. 1955, Workman 1958,
and Rawlins et al. 1960). These papers describe the maggot population
as being distributed within and between fields in a clumped or an ag-
gregated manner. In agreement with these findings, Loosjes (1976)
examined various sets of sampling data from the Netherlands and cites
the distribution as highly aggregate within fields.

Aggregation, or the tendency to be found in groups, causes signif-
icant increases in sample variation when compared with a randomly
dispersed population (Taylor 1961, Southwood 1966, Pielou 1977, and
Elliott 1977). This increase necessitates a larger sample size (n)
to be collected for estimation of the population density given a fixed
level of precision.

As sample costs can be expensive, several alternate methods of
sampling (simple, multistage, stratified, etc.) have been used to
reduce the sample variance, and thus the sample size (n) (Cochran 1963,
and Jessen 1978).

Southwood (1966) and Elliott (1977) give excellent reviews of
statistical sampling theory as it applies to sampling insect populations.
Southwood also includes descriptions of several sampling methods and
their uses. Several other authors have given excellent reviews of
sampling theory and sampling methods associated with a wide variety of
insect populations. Some of the most helpful publications are: Bliss
(1967), Lewis (1973), Morris (1955 and 1960), Ruesink and Haynes (1973),

and Taylor (1961).

METHODS

Plant Damage Sampling

 

Characteristic plant damage symptoms associated with onion maggot
attack are easily noted in the field and are very useful for monitoring
plant damage spatial patterns and plant damage densities. Plant
damage sampling was conducted at both the field and regional level to
gain insight into the mode, distribution and intensity of onion maggot

attack.

Regional Plant Damage Sampling:

To examine the spatial patterns of onion maggot damage and the
feasibility of developing a regional sampling program for plant dam-
age assessment, an intensive sampling scheme was set up to explore the
allocation of regional variation in plant damage for various densities
and sample unit sizes. Pest management field assistants, trained to
recognize onion maggot damage symptoms, collected the sample data.
Visually unbiased sample locations were selected by throwing an object
into the onion field and using its landing point as the start of a
sample unit. The field assistants paced along a 100 foot sample strip
and recorded the number of damaged onion plants and their respective
locations by one foot increments. This sampling procedure was repeated
10 times per sampled onion field.

Four major onion producing regions (Figure 1) were monitored
four times throughout the growing season. The first sampling period

11

12

 

 

 

 

 

 

 

 

 

 

 

 

 

L
L

 

 

 

 

 

 

 

 

 

 

 

1. Bravo - Allegan Co.

2. Grant - Newaygo Co. 2

3. Imlay City - Lapeer Co. -1—~ 3

4. Eaton Rapids - Eaton Co. I 1
Marshall - Jackson Co. ' ' 7 '
Stockbridge - Ingham Co. 1 I '4

 

 

 

 

III -
o . JJlll

L——-—-‘—_.J L
miles

 

 

 

FIGURE 1. Michigan map with regional sampling areas indicated.

13

coincided with initial spring damage and was designed to gather data
on the patterns of initial attack and the viability of the techniques
associated with the sampling program itself. For this sampling period,
field assistants were instructed to sample only one or two fields with
known onion maggot damage and report any difficulties that arose
during the procedure. The remaining three sample periods were planned
to coincide with estimates of peak larval damage of the first, second,
and third generations. Degree-day estimates were made using both
previous trapping data and individual degree-day requirements of each
life stage. These degree-day estimates (see Appendix A) were tracked
in an on-line mode by PETE (Predictive Extension Timing Estimator)
(Welch et a1. 1972) throughout the four onion producing regions
sampled. Automatically generated messages were sent to the respective
field assistants via PMEX (Pest Management EXecutive system) (Croft

et a1. 1976) as their regional sampling dates approached. Sampling
dates were estimated one and two weeks in advance to allow the field
assistants to allocate specific time intervals for an intensive sam-
pling period.

Table 1 summarizes the phenology and amount of monitoring executed
during each of the four sampling periods. Three of the four planned
sampling periods were executed as designated. The final sampling
period was cancelled because visual discrimination between infested
and healthy plants became difficult as nonmal foliage die-back masked
the onion maggot damage symptoms. Appendix C lists the sampling data
for each region and sampling period. These data are listed by the

foot as it was collected by the field assistants.

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15

Thirty six different onion fields were monitored during the 1977
growing season and of those 36, only 18 information data sheets were
completed and returned by the growers. Supplementary information con-
cerning planting rate, planting configuration, seeding date, surround-
ing crops, insecticides used at planting, acreage and geographical

location was collected for each field.

Field Level Plant Damage Sampling:

Field level plant damage was monitored annually in a muck vege-
table producing area near Grant, Michigan. The annual sampling periods
were planned to coincide with peak second generation larval damage
since the cumulative onion damage curve normally approaches its maxi-
mum yearly value (Loosjes 1976) and onion maggot plant damage symptoms
are most easily identified during the mid summer months when water
stress is normally high.

Adult flight activity was monitored throughout the growing season
and was used as a timing indicator of second generation emergence.
Degree-day estimates of second generation peak damage were calculated
using the peak second generation adult trap catch as a baseline to
which the mean physiological time (600 degree-days, base 39) necessary
for third instar development was added (see Appendix A). Degree-day
accumulations were monitored on-line via PMEX (Croft et a1. 1976) and
sampling dates were set as the actual accumulations approached the
estimated plant damage peak. The estimated adult activity peaks and
the actual sampling times (predicted peak damage) for the Grant area

are listed in Table 2 by date and degree days.

16

TABLE 2. Predicted adult and larval density peaks for Grant,

Michigan in 1976 and 1977.

 

Estimated Adult Activity Peak

Actual Sampling Time

 

DATE

DEGREE-DAYS

DATE DEGREE-DAYS

 

 

7/14
1976

7/15
1977

1950

2360

8/3 2550
1976
8/4 2970

1977

 

17

The number of damaged and healthy onion plants was recorded for
50 one-meter samples per field; data on field locations, on surround-
ing crops, and on several other specific observations (i.e., plant
disease occurrence, special soil conditions, heavy wind damage, etc.)
was also recorded.

Twenty three onion fields in 1976 and 17 onion fields in 1977
were sampled. The resulting data is listed in Appendix B along with
an analysis of the effect of soil calcification on onion maggot dam-
age. Field locations varied between years (Figure 2) as rotation
with either carrots, celery, or a cover crop was common. In both
seasons, the sampling was completed in approximately 24 man hours,
including the time spent within fields and the time spent moving be-

tween fields.

Within Field Sampling for Age Specific Onion Maggot Density_

Onion maggots are typically characterized as occurring in an
aggregated pattern within and between fields (Loosjes 1976). Aggre-
gation causes significant increases in sample variation; thus, a
larger sample size (n) must be collected for precise estimation of
population density. For estimation of age specific densities, simple
random sampling is impractical, because the cost of data collection
is extremely high. Individual onions must be pulled and dissected;
the surrounding soil must be sifted; and the immature stages of the
attacking insects must be identified, counted and recorded. There-
fore, the processing cost per onion is quite expensive, and an effi-

cient sampling strategy must avoid large sample sizes.

18

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19

The necessity to make age specific density estimates requires
that an alternate sampling strategy be researched, with the goal of
minimizing the necessary resources while providing a reasonable level
of precision. Many sampling techniques including sample frame selec-
tion, stratified random sampling, and cluster sampling produce signif-
icant gains in overall precision and sample costs, if the proper re-
lationships exist in the population under investigation (Cochran 1963,
and Jessen 1978).

Sawyer and Haynes (1978) have utilized stratified random sampling
to optimize efficiency in estimating the density of the cereal leaf

beetle, Oulema melanopus (L.), in five distinct habitats, as the per

 

unit area means and the relative habitat sizes were of significant
difference.

Two classes or strata of onions (visually healthy and visually
damaged) have already been mentioned; obvious differences are readily
notable in the population parameters (u and 52) that lead to the use
of stratified random sampling. To better examine the habitat struc-
ture each group was subdivided. Under the visually healthy class is,
1) onions which are one or more feet removed from damaged plants, and
2) onions which are within a damage clump or less than one foot re-
moved. Under the visually damaged class is, 3) onions exhibiting
typical signs of onion maggot damage (flacid and slightly yellowed
leaves), 4) onions showing signs of severe degradation from onion
maggot attack (leaves highly dehydrated, yellowing over 75% and typ-
ically decomposing with a soft rot bacteria), and 5) onions missing

(assessed only if within an area of apparent onion maggot damage).

20

Periodic sampling throughout the 1977 growing season was essen-
tially three-part. First, a plant damage survey, as described in the
preceeding within field sampling section, was conducted to estimate
the frequency of damage within the test field. Second, one hundred
visually healthy onions were selected from the field and visually
examined for signs of any onion maggot life stage. These onions were
not removed from the soil, but the onion-soil interface and the leaf
axiles were closely examined for egg deposition and sites of possible
larval feeding. The third, and largest portion of the sample, con-
sisted of grading and monitoring onions within damageclumps. Indi-
vidual onions within a damaged area were numbered and then visually
graded as to classes (described above). The spatial location of each
onion plant in a clump was recorded using a two dimensional (x,y)
coordinate system; (o,o) was set at the northwestern most onion in
the clump. The onions were then removed from the soil and on site
dissections were made whenever possible; when not possible, the
onions were transported, individually packaged, to the laboratory
where they were held at 40°F until they could be processed for deter-
mination of the number and life stage of the specimens within. The
soil beneath each plant was sifted on site with both the number of
viable and previously emerged pupae recorded. The pupae were returned
to the laboratory where they were allowed to emerge for purposes of
identification and parasitoid detection. This process was typically
repeated in several independent clumps of damage to provide an esti-

mate of within and between clump variation.

21

Ovipositional Behavior

 

A study investigating the site selection of the onion maggot was
initiated in 1976 in a heavily infested commercial onion field in
Grant, Michigan. This experiment was designed to test the ovipositional
preferences of the gravid onion maggot female. Observational biology
and the literature (Kendall 1932) suggest that the gravid female
favored a combination of rotting and/or previously infested onions for
oviposition.

A three-way factorial design was utilized within the field (bulb
type, bulb condition, and bulb location). The treatments consisted of:
l) Immature bulbs (small green bulbs, 3/4" in diameter), Mature bulbs
(large green bulbs, 2 3/4" to 3" in diameter), and Mature and Dry bulbs
(large dry bulbs, 2 3/4" to 3" in diameter): 2) Rotting (R), Rotting
and Infested bulbs (R + I), and Normal bulbs (N) (each R + I onion
was preinfested with 3rd instar maggots): and 3) and area outside of,
but along the periphery of field (A), and area within the three bor-
dering rows of field (B), and an area in the geographic middle of
field (C).

The onions were placed in flats containing 3 inches of muck and
were assigned random locations in their respective areas (A, B, or C).
The flats were left in these locations for eight days. It was believed
that this was enough time to obtain sufficient oviposition without

severe alteration of the treatments.

22

At the end of the eight day period the flats were removed from the

field. Dissections were performed to determine the presence of new

larvae and eggs. The implanted 3rd instar larvae were in pupal or pre-

pupal form and were easily distinguished from the newly attacking

larvae.

RESULTS AND DISCUSSION

Spatial Patterns

 

The study of spatial patterns of insect pests is an interesting
aid in the handling of data for statistical analysis, and in gaining
an understanding of the underlying biology which creates such patterns.
An understanding of these factors enhances our ability as managers to
manipulate pest populations within a cropping system.

Onion maggot damage is frequently cited as being dispersed in a
clumped or aggregated manner (Kendall 1932, Perron et a1. 1955, Work-
man 1958, Rawlins et a1. 1960, and Loosjes 1976). These observations
were mainly qualitative assessments of plant damage, with the excep-
tion of Loosjes who used quantitative techniques to evaluate within
field onion maggot damage patterns in the Netherlands.

To further quantify the spatial configuration of the onion mag-
got, its associated plant damage and the underlying biology, the
following analysis utilizes descriptive, analytical, and experimental

techniques.

Statistical Distributions:

The plant damage data and the actual onion maggot counts per
onion have been examined for conformity, or fit, to theoretical prob-
ability density functions. The observed populations are known to be

contagious (s2 > E), thus the negative binomial distribution (NBD)

23

24

was used as the primary model. At extremely low density levels, the
expected model was altered to the poisson, as it has long been con-
sidered the "rare events" distribution by statisticians (Steel and
Torrie 1960).

The fit of the NBD to the observed data sets was evaluated using
the procedures outlined by Elliott (1977). Initial estimates of the
NBD parameter K were calculated using the moment estimation method

(Equation 1)

fi = -7'——*' (1)

and refined by the iterative maximum likelihood estimator (Equation 2)

 

N . 1n (1 + £) =1§ (AA(")) (2)
K x-o K + x
Where: N = total number of samples
x = frequency class
A(x) = total number of counts exceeding x
m = total number of frequency classes

Given K, the expected NBD frequencies were calculated from equa-
tions 3 and 4 then tested against the observed frequencies with a chi-

square goodness of fit test.

-K

 

NC) = (1 +1?) (3)
_ K + (x - 1) i _
P(x) — ( x ) (—-———}_c + KI P(x 1) (4)

In the case of the poisson distribution, the expected frequencies were

calculated from Equation 5, also being tested against the observed data

P(x) = e' -—-— (5)

with a chi-square goodness of fit test.

25

When arbitrary physical units are used to sample in a continuous
universe, as is the case with the plant damage samples, the NBD param-
eter K has no absolute biological significance. The value of K, as
with many other measures of aggregation, differs with changing sample
unit sizes. Although no absolute biological meaning can be related
to these values, they are measures of aggregation within a single
sampling scheme and should only be used to evaluate the deviation
from the random within fixed sampling techniques.

Actual onion maggot counts are also examined on a per onion
basis within clumps of visual damage. The sample unit (the onion) is
considered a discrete unit of habitat, thus a standard from which ag-
gregation can be measured.

Field level aggregation (clumping between fields within a region)
was evaluated using plant damage data sets (III A - 1 + 2) and fre-
quency distributions generated from subsamples pooled on a field basis
for each region and sampling period. The results are presented as
Table 3. The field level analysis clearly shows the high aggregation
noted between fields. Seven of the eight regional data sets fit the
NBD with the eighth set fitting a poisson distribution due to low dam-
age levels (only 1 damaged onion in 15 fields was observed). The two
year analysis from Grant revealed that both data sets easily fit the
NBD. Evaluation of the variance in the parameter K over the range of
sample means indicates that no common clustering is found at the field
level. (Further explanation of a common K will be discussed as it
relates to within field analysis.) The lack of a common K or aggre—

gation coefficient is easily understood, particularly at the field

26

 

 

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27

level. Many variable factors, natural and man manipulated, cause
great environmental variability between fields (i.e., moisture levels,
insecticide types, insecticide rates, planting configurations, etc.),
resulting in extreme variability in the observed spatial pattern (thus
the variability in the parameter K).

Within field aggregation was examined using the same data sets,
only each field was analyzed separately based on the subsamples taken
within fields (regional data 10-100 foot samples per field, Grant data
50-1 meter samples per field). Table 4 presents the results of this
analysis for both data sets. Sixty nine fields from the regional data
set were independently analyzed; 39 recorded no detectable damage,
four recorded only one observation (determining poisson), and 26 re—
corded multiple data observations each of which was successfully fit
to the NBD. Of the 40 fields examined in Grant, six had no detectable
damage, eight had observations of 0 and l, and 26 had multiple fre-
quency classes, all of which were fit with the NBD. Examinations
within each of these data sets for a common K or common aggregation
pattern is again of interest. Figures 3 and 4 show the inverse of the
parameter K plotted against the mean for the regional data set and the
Grant data set, respectively. Elliott (1977) states, the calculation
of a common K is not applicable if there is a relationship between
l/K and the sample mean or if widespread scattering of the data is
prevalent. In both cases no significant linear trend can be found,
but the wide scatter between the points makes the use of a common K

inappropriate.

28

 

 

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33

 

 

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34

 

 

I I I 8 Omho.o mo.o
omz 0m.m v m©H.o oovm.o mm.o
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mam2¢m mmgmzdm
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35

 

 

 

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WNHm DHmHm
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mqmzdm mmnm2¢m
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36

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37

Aggregation or clumping within fields was the dominant spatial
pattern in this study, although no common K or clustering coefficient
was found. These results agree with the within field spatial pattern
analysis carried out by Loosjes (1976) on onion maggot damage in
sandy soils in the Netherlands. Loosjes cites four reasons for within
field clumping:

l. clustered egg deposition,

2. oviposition preference for certain sizes or densities
of onions,

3. strong ovipositional preference for previously damaged
onions, and

4. possible density dependent survival,
but Loosjes states that no common aggregation coefficient can be spec-
ified as various combinations of these factors interact and produce
different patterns. This study fully agrees with his conclusions with
one major addition to the list of factors causing aggregation within
fields. A fifth, and major, cause of damage aggregation in Michigan
onions is the spatial distribution of the granular insecticide placed
in the soil at the time of seeding (this aspect will be discussed in
some detail later in this section).

The observed frequency distributions of actual onion maggot counts
per onion within areas of defined onion maggot damage (strata 2-5 as
discussed on page 19) were also fit to the negative binomial frequency
distribution. Each independent clump of damage was analyzed separately
(results listed in Table 5). All 26 separate clumps analyzed through-
out the 1977 growing season clearly fit the NBD. Plotting l/K against

the sample mean (Figure 5) we find an indication of a common factor

38

 

 

 

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80.0
.04

1/K

39

 

 

FIGURE 5.

I T T ' r V W
2.0 4.0 6.0 8.0 10.0

SRMPLE MERN

Negative binomial parameter k as a function of the sample
mean for the total immature population per onion within
areas of damage.

40

of aggregation. The value of K (l/K as plotted) seems totally inde-
pendent of the sample mean and fairly stable about its mean. In
opposition to this common factor of aggregation are four aberrant
points at the low density range. These points are marked by an aster-
isk in Table 5. Closer examination revealed significant plant damage
in these data sets, although the actual number of non—zero onion mag-
got observations was limited to one or two insects per data set.
Comparing the indicated sample dates with Figure A-3 shows peak second
generation emergence coinciding with these sampling dates. Removal
of the majority of the population from the sampling universe (emergence
as adults) is believed to be the cause of the deviation in the param-
eter K.

A common K (KC) was calculated using all the data sets of Table
5, except those considered as outliers in the previous paragraph. A
common K (KC) of 0.252 was calculated as the arithmetic mean of the
Kis. Table 5 lists the results of the chi-square goodness of fit
test to the negative binomial distribution using the Kc value for the
parameter K in each test. As indicated, the only significant devia-
tions were those four outlying points previously described. The exis-
tance of a common K or common clustering coefficient at this within
clump level, while not at higher levels seems probable for several
reasons, 1) the higher the level examined, the higher the level of
exogenous variability, 2) the environment, within any single clump or
area of damage, is essentially homogeneous, and 3) the within clump
level is the universe within which the immature stages of this insect

actually operate.

41

The within clump study was conducted in a single field; it is
not yet known whether the common aggregation coefficient found in this
study is independent of field differences. Clearly, the significance
of these findings suggests that the onion maggot utilizes a common
mode, within a localized population, to exploit its immediate environ-

ment.

Nearest Neighbor Analysis:

Quadrate sampling was used to analyze between and within field
aggregation patterns. As mentioned earlier, the use of artificial
sample quadrates biases the aggregation coefficient (K) of the NBD.

No comparison between differing quadrate sizes is then possible. Dis-
tance sampling (Clark and Evans 1954, and Pielou 1977) completely
avoids the use of arbitrary sampling units and their associated prob-
lems (Pielou 1977) by examining the distance between individuals with-
in a population (nearest neighbor) or by examining the distance from

a random point to the closest individual. Clark and Evans (1954)

suggest the use of the ratio (Equation 6)

A
R = :—' (6)
I13
where: f = mean distance from random individuals to
A . .
their nearest neighbor
EB = mean distance from random individuals to

their nearest neighbor if the population
were distributed at random

as a means of the degree to which the observed data approaches or de-

parts from random expectation. As Equation 6 clearly reveals, an R

42

value of 1.0 indicates a random distribution. The parameter is also
bounded at both extremes, R = O for absolute aggregation (all individ-
uals at one point) and R = 2.1491 for a uniform pattern.

Application of this technique at the between or within field
level is somewhat awkward as the selection of totally random individ—
uals would be difficult to manage. This technique was utilized on the
data sets collected in the within field sampling study (the same set
utilized for the fit of actual onion maggot count to the NBD). As
described on page 20, the (x,y) coordinates of every onion within a
clump were recorded. For this analysis, only the onions actually
attacked by onion maggots were run through the analysis (algorithm,
Clark and Evans 1954; computer program, Lampert and Untung 1978, and
Untung 1978), which measured the within clump deviation of plant
damage from random. Eighteen individual data sets were analyzed
(results in Table 6). All data sets indicate a high degree of dam-

aged plant aggregation.

Specific Spatial Pattern Studies:

Pupal Distribution in Muck Soil--In conjunction with the age
specific onion maggot density sampling, two sample plots were exca-
vated on August 3, 1977. A third sample plot was excavated on August
12, 1977. Three hundred and twenty nine pupae, surrounding 25 damaged
plants, were extracted from the sample plots. Distances from the
onion source, horizontal and vertical planes, were calculated with the
resultant frequency distributions as given in Table 7 and Figures 6

and 7. A poisson distribution clearly fit the horizontal (Table 7 and

43

 

 

TABLE 6. Nearest neighbor analysis for within clump plant damage
(distance in feet).
DATE CLUMP :— VAR R C"
6-23-77 1 0.1246 0.0075 0.1088 7.625
2 0.1409 0.0106 0.1380 7.375
3 0.1050 0.0053 0.0985 7.713
4 0.1036 0.0028 0.0802 7.869
5 0.1384 0.0048 0.0875 7.807
6—30-77 1 0.1604 0.6272 0.110 7.605
2 0.1609 0.0039 0.0852 7.826
3 0.2047 0.0698 0.1476 7.290
5 0.2228 0.0212 0.1607 7.180
6 0.1463 0.0093 0.1918 6.914
7-7-77 1 0.2249 0.0065 0.2505 6.412
2 0.2332 0.0106 0.1142 7.578
3 0.1251 0.0043 0.0662 7.989
4 0.2228 0.0067 0.1260 7.477
7-29-77 1 0.1805 0.0106 0.2166 6.703
8-3—77 1 0.1407 0.0012 0.1379 7.375
2 0.1269 0.0043 0.1319 7.426
8-12—77 1 0.1114 0.0023 0.1444 7.321

 

* C is compared against the standard variant of the normal dis-
tribution for a particular level of significance (a = 0.05 -
SD = 1.96). C values greater than 1.96 are significantly dif-
ferent from random at the 5 percent level.

44

TABLE 7. Observed horizontal pupal distribution around a source
onion in muck soil as compared with a poisson distribution
(i = 3.21). Tabled values are in terms of inches.

 

 

 

mx g

1 0-1 12 13.27 0.1215
2 1-2 45 42.60 0.1352
3 2-3 61 68.40 0.8006
4 3-4 87 73.20 2.6000
5 445 53 58.70 0.5500
6 5-6 43 37.70 0.7451
7 6-7 11 20.10 4.1200
8 7-8 14 9.25 2.4400
9 8-9 1 3.70 1.9700
10 9-12 2 1.75 0.0360
IE329 13.5100

 

a = 0.05 Tabled x2 = 15.507

II
00

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47

Figure 6) but no distribution was fit to the vertical distribution
(Figure 7) due to the small number of frequency cells. Figure 6 shows
that approximately 90% of the pupae were located within a six inch
radius of the onion source. Figure 7 shows that approximately 100%

of the pupae lie above the six inch depth.

Seasonal Distribution of Onion Maggots Per Bulb--Table 8 lists
the mean number of immature onion maggots found per bulb along with
associated variance. If the data is plotted over the onion bulb
volume, a linear increase is observed (Figure 8), although considerable
variance is noted about the regression line due to changing density

levels as population maturation and adult emergence occur.

Ovipositional Preference-—The experimental data (total eggs and
larvae) was first analyzed using a three—way ANOVA (see Appendix F for
complete data set). As no differences were found due to field loca-
tions, the data was pooled to increase the per cell replication from
6 to 18. A two-way analysis of variance was then used to test for
differences. The analysis of variance was then used to test for
differences. The analysis showed significance for both factors (bulb
size and bulb condition) as well as an interaction (Table 9). A plot
of the means (Figure 9) and the per cell statistics (Table 10) show
the treatment results.

Bulb type showed an obvious effect due to bulb size: small bulbs
were found with a reduced mean, while large green bulbs and large dry
bulbs (high mean) showed no significant differences. All three treat-
ments of bulb condition (R, R + I, N) were significantly different.

Rotting and Infested (R + I) were the most attractive. Rotting (R)

48

TABLE 8. Observed number of onion maggots per infested bulb in the
Grant, Michigan test field.

 

 

DATE MEAN VARIANCE DE32¥$$BEULB
6-23-77 4.40 17.41 20
6-30-77 2.80 7.87 12
7-7-77 3.18 17.67 21
7-21—77 7.11 61.11 22
7-29-77 4.42 24.54 29
8-3-77 7.75 72.07 40
8-12-77 8.64 54.40 39

9-1-77 11.88 110.30 37

 

14
1

UNION HRGGOTS/BULB

 

49

 

 

FIGURE

8.

l l T l 4477
e 16 24 32 4o 48
BULB VOLUME (CU. CM.)

Relationship between the mean volume number of onion maggots
per bulb and the volume of the onion bulb.

TABLE 9. Analysis of variance table for total

50

onion type and onion condition.

eggs and larve by

 

SOURCE OF

SUM OF

MEAN

 

VARIATION SQUARES DF SQ. ACRES F SIGNIFICANCE
Onion Type 3780.48 2 1890.24 31.68 0.001
Onion Condition 12519.51 2 6259.79 104.91 0.001
Interaction 3849.93 4 962.48 16.13 0.001
Error 9128.5 153 59.66
TOTAL 29278.5 161

 

51

35

DLarge Dry Bulbs
ELarge Green Bulbs

 

5:5: Small Green Bulbs

 

30

MEAN ONION MAGGOTS/BULB
15 20 25

10

ROTTEN & INFESTED ROTTEN GOOD
BULBS BULBS BULBS

FIGURE 9. Plot of treatment means for ovipositional attraction
experiment.

52

 

 

 

 

 

 

TABLE 10. Cell statistics for ovipositional preference ANOVA experi-
ment (mean and 95% confidence limits).
Large Dry Bulb Large Green Bulb Small Green Bulb
)7 C.L. i C.L. i C.L.
Rotten 30.77 27.18 12.669 9.069 0.3889 0.0
and
Infested 34.38 16.264 3.98
Rotten 29.167 25.569 10.83 7.236 0.222 0.0
32.764 14.430 3.819
Good 5.889 2.291 4.61 1.014 0.9444 0.0
9.486 8.209 4.54
Grand Mean = 10.6111
Total N = 162

02 of Cell

Means = 1.821

53

onions showed some attraction for oviposition, while healthy onions
were essentially neutral (extremely low means). Although significant
differences were found between R + I and R treatments, the results
must be evaluated with respect to the experiment itself. It is be—
lieved that the differences found between the R and R + I treatments
are partially due to onion desiccation in the R group during the expo-
sure period. The addition of active larvae in the R + I treatment
created higher moisture conditions throughout the test period.‘ It is
not known whether the difference noted was due to the higher moisture
levels in the treatment, the continued maceration of bulb tissue by the
implant larvae (thus an increase in onion volatiles), the presence of
an actual ovipositional stimulant produced by the larvae, or other
unknown factors.

Of significant importance is the verification of an ovipositional
preference for rotting and/or infested onions for which this experi-
ment, in conjunction with the findings of those from the age specific
sampling study (discussed later) which revealed a 20-fold increase in

eggs found on damaged (grade 3) onions as compared to adjacent healthy

onions, gives quantitative proof.

Distribution of Initial Attack--Numerous field observations have
noted a wide range of onion maggot damage patterns. Although aggrega-
tion is typically the rule, the range of aggregation varies highly
between areas. As expected, the field-wide pattern of initial plant
damage is also variable, but a common pattern, near random damage,

was observed within limited areas of initial damage. In other words,

54

some fields showed high initial damage aggregation at the field level
(1 or 2 rows heavily damaged, while the remainder of the field was
damage free) but in areas of apparent damage (heavily damaged rows),
the initial attack approached a random pattern.

To test these observations, early season plant damage samples
collected by the pest management field assistants were analyzed. Six
sets of early season damage samples, consisting of 10-100 foot samples
per set, were collected in mid May (May 15 - May 20) with the number
of damaged plants being recorded by the foot. The distance between
damaged plants was calculated and a Runs test (Siegel 1956) was used
to test for deviation from a random pattern within samples. The sam-
pling distribution being tested under Ho is considered approximately

normal with:

 

_ 2n; n2
Mean 11 fi—Tn— + 1 (8)
1 2
Standard deviation = 0 Vbn n (2n n - n - n ) (8)
r 1 2 1 2 1 2

’—

(n +n)2(n +n -1)
1 2 1 2

where: n number of observations below the sample mean

1

n
2

number of observations above the sample mean
Therefore, the normal score Z (Equation 9) can be tested against the

standard normal distribution for deviation from randomness.

Z=——-—— (9)

where: r = runs = number of sequential data observations
above or below the mean

As multiple observations per sample were necessary for this test, only

samples consisting of five or more damaged plants were evaluated. This

55

reduced the number of fields available for analysis to three. As mid
to late season damage is believed to be built from the same base pat-
tern established from the initial attack, seven sets of data collected
later in the same season were also evaluated. These sets all showed
high aggregation (several damaged plants for each observation) but if
they developed from an initially random base pattern the centroids of
each clump should reflect the initial pattern of damage. In each of
these sets, the distance between the clump centroids was measured and
analyzed as above.

The results (Table 11) indicate that every data set tested (the
initial plant damage and the centroids of damage clumps) showed no
deviation from a random pattern. Therefore it is highly probable
that the initial attack within a limited area of an onion field ap-
proaches a random pattern.

The actual field level damage patterns visualized do not reflect
the total initial attack, only the successful attacks. The variability
noted in the field level damage patterns is believed to be partially
induced by natural environmental factors, but the natural selection
of microhabitat may be overshadowed by pesticide induced mortality.
Large areas of onions are often left vulnerable to onion maggot attack
when, during seeding time, the application equipment, which places
the granular soil insecticides in the furrow, malfunction. By random
oviposition initially in the spring, such unprotected areas become
flagged by damaged plants. This initial random damage quickly evolves

into more highly aggregated patterns as the damaged onions begin

56

 

 

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57

attracting egg laying adults, thus allowing the population to locate
areas of successful survival within a highly toxic environment.

If an insecticide become ineffective, due to improper placement,
leaching, or insect resistance, initial damage is likely to occur ran-
domly throughout the field, and later produce randomly dispersed clumps
of damage throughout the field. Typically some intermediate condition
exists between these two extreme cases, producing the variable inten-

sity of aggregation noted in this study and in the literature.

Plant Damage Sampling

 

Regional Plant Damage Sampling:

The precision with which regional population densities can be
determined is dependent on the amount and type of sample variations
found throughout a region and the quantity of available resources for
data acquisition. Regional sampling variation is essentially two
part, consisting of within and between field variance components
(Morris 1955, and Ruesink and Haynes 1972). The distribution, or re-
lative amount of regional variation, allocated to each variance com-
ponent, sets the structure within whichthe sampling program must be
designed.

The sample variance for each region and sampling date was sepa-
rated into its within and between field components using a one-way
analysis of variance (Table 12) (Sokal and Rohlf 1969, and Jessen
1978). The mean square among (MBA) estimates the between field

variance component (8;) of a region by Equation 10 and the mean square

58

 

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H666.6 6 666.6 66 66H.6 66666
666H.6 6 666.666 66 666.66 o>666 6666 6066666666 660066
E6£mcH I
6666.6 6H 666.6 6HH 666.6 6ou66 - 6066066
6666.6 6 666.66H 66 666.66 6660 66HEH
6666.H 6 666.66 66 666.6H 06666
6666.6 6 666.66H 66 666.66 o>666 x666 60Hu6um666 66666
x 66 662 66 662 on666 oonmm oqumzam

 

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150660 m6oflumm wcflams6m o3u 6:6 mcowmmu 650m Now 666566 C666 A

d

Hm>H6c6 >6suwco 6 6:66: 6mu6a

m

mzv cmm3umn 6:6 A mzv Casufiz .NH mqmce

59

error (MSE) of the ANOVA estimates the within field variance component
(5:) directly (Jessen 1978).
MS —
MSE

S = -———-———- (10)

where: n = number of samples per treatment.

2

Both components of the regional sample variation (S13

and 82) are

w
dependent on the sample mean, as is the total variation of many samples.
A log variance-log mean function (Equation 11) has been used by several
authors (Morris 1955, Wayman 1959, and Taylor 1961) to describe the
variance to mean relationship of sampling data from various populations.

log 02 = log a + b log i

or (11)

2 b

o = a(x)
Parameter "a" depends chiefly on the size of the sampling unit, while
parameter "b" is an index of aggregation varying continuously from 0
for a uniform distribution to plus infinity for extremely contagious
populations ("b" + 1 when the population is randomly dispersed) (Tay-
lor 1961).

The regression of log MS on log mean was used to estimate both
parameters "a" and "b" for the within and between field variance com-
ponents of the cumulative 100 foot samples (see Table 12). As seen
in Figure 10 and the ANOVA Tables 13 and 14, excellent fits (r2 > 0.98)
were given for both mean square components. Finney (1971), Morris
(1955), and Bliss (1967) have shown that the arithmetic means is

underestimated when predicted from the geometric mean of a logrithmic

60

.6u66 @cflHQE6m
H6coflmou 0:0 60 60:0:OQEOU 6H0Hm :003003 6:6 £66063 MOM mechHu6H0u :60E ou 066530 :60: .OH mmDUHm

 

zcmz
ofi o.H H.o H0.0

6 6 _ 6 _ 1 .no

.U

”.0

6

a%&3 1w.
o a nuuu
o a “M
4 a a ”N
11.66
nonu
nu
o o . mw
3.

a
. rmw
no
onuum zuwzhmm a
a

 

abwau znrhmz o r

0001

61

TABLE 13. Regression statistics for predicting within field mean
square for the 100 foot sample unit.

 

 

SOURCE DF SS ms
Regression 1 11.1400 11.14000
Residual 6 0.2167 0.03612
Total 7 11.3600

 

y intercept ("a") 0.9152 1 0.0699

slope ("b")

1.399 1 0.0865

2

r 0.9809

TABLE 14. Regression statistics for predicting between field mean
square for the 100 foot sample unit.

 

 

SOURCE DF SS ms
Regression 1 17.4800 17.480
Residual 6 0.2406 0.041
Total 7 17.7200

 

H-

y intercept ("a") = 1.543 0.0708

slope ("b") 1.719 i 0.0823

2

r 0.9864

62

regression plot. Bliss (1967) suggests Equation 12 to adjust for this
biased regression estimate.
y = Antilog (a + b log x + 1.1513 82) (12)

where: S2 = error mean square from the analysis of variance
table of the regression

The correction factor slightly raises the magnitude of the intercept
(parameter "a") but has no effect on the slope (parameter "b") of the
regression equation.

Equations 13 and 14 represent the Mean Square function predicted
by the log MS log mean regression for within field and between field
components respectively. Equations 15 and 16 represent the same rela-

tionships as Equations 13 and 14, but have been adjusted as suggested

by Bliss.
MSw = 8.28(§)1-“° (13) M8: = 9.13(§)1-“° (15)
MSb = 34.9(701-72 (14) M5; = 38.8(x)1'72 (16)

Morris (1955) after segregating variance components used Equation
17 to solve for the total number of units (Nt) to be sampled, given

several values of Nw and the number of subsamples per unit.

sénw + s:
Nt = (Sy)N (17)
w
where: S? = standard error of the predicted mean

(logrithmic scale)
Morris goes on to show that the Optimal sampling strategy, given
the condition 5; > s: is to take 1 subsample per unit (Nw = 1) as

long as the time spent moving between units was not large compared to

the time spent collecting a single sample within a unit. However, no

63

consideration was given to sampling optimization in a finite universe,
as in this case the sample units (trees in the forest) were essentially
infinite when compared with the number drawn in the sample.

Ruesink and Haynes (1973), considering Nw (subsamples per grain
field) to be 1, as the s; > 5:, used an equation (Equation 18) similar
to that of Morris but included the necessary components to adjust for

sampling in a finite universe for specific levels of precision.

2
5 NF
Nt ‘ s2 + (aimr (18’

 

where: NF = total fields per region
S2 = total variance of the region
a = precision level (Si/x)

Although this equation considers a finite universe, it will give
erroneous results in a two-stage sampling program if the number of
primary sampling units (fields per region) becomes limiting. As the
number of grain fields per region was large, Ruesink and Haynes did
not experience this problem. However, in onion production, the number
of fields per region is much smaller and quickly becomes a limiting
factor necessitating an increase in Nw (subsamples per field).

Cochran (1963) discusses two-stage sampling and offers Equation
19 to describe the total variation by its within and between unit com-
ponents. The approach considers a finite universe for boththe sample

unit and the number of subsamples per unit.
Sb M - m i
—+ ——

 

mm?) = (5246—2).- < M ) m = (a?)2 (19)
where: i = regional mean 5; = between field variance
a = precision (s§/§) s: = within field variance
NF = number fields/region M = possible subsamples/field
n = number fields sampled/region m = number subsamples/field

64

A closed form method for evaluating the optimal number of samples
within and between units is given (Cochran 1963) but it is dependent
on the F distribution, which assumes normality. Normality cannot
always be assumed, nor can a normalizing transformation always be
made when sampling from low-medium density aggregated populations;
therefore, an alternate approach is used. Solving Equation 19 for m

(subsamples per field) we obtain Equation 20.

 

1“ = T (20)

The component Sé/M of the denominator is of little or no signif-
icance in this equation as most commercial onion fields are 10 acres
or larger. Calculation of M (4,000 possible subsamples per 10 acre
field) with division into the highest within field variance noted,
produces an insignificant change in the estimate of m. To be conser-
vative, the component Sé/M is considerd as 0, thus increasing the
value of m for a safer estimate. The resulting expression can be

written as:
52
m: =2 zwn (21)
+ —-
n(au) Sb(NF 1)

 

As the true population parameters, 0, S2

b' and Si, are not known and

their sample estimates must be substituted, the square of one tailed
standard score from the normal distribution (Z) must be included. (If
the corresponding sample size is small, the t statistic must be sub-

stituted. (Karandinos 1976).) The statistics involved in this

65

substitution necessitate the consideration of the probability state-

ment associated with the confidence region about the mean (Equation 22).

' s - s
P = < < + 2' _
r(X) (201/235 Ll X (ZCX/Z.)l/F (l a) (22)
where: H = sample mean
a = probability of type 1 error
Z = the one tailed standard score of the normal

0/2. distribution

Based on the Central Limit Theorem, the assumption of normality will
hold true for the distribution of population means, even though the
Xi's may not be normally distributed (Steel and Torrie 1960). The
value of Z depends on the confidence coefficient which is an arbitrary
variable chosen by the researcher (typically 0.95). Setting the value
at 0.95 a value of 1.96 is obtained for Z. Substituting Equation 15
for 8:, the application of Equations 15 and 16 with Equation 11 for

5;, and with the inclusion of 22 we obtain Equation 23.

(3.84)(9.13)(§)1°”{)
n (38.8(§)‘°72 - 9.13(§)"“°)

Mai-32 +615; - 1)( 10 0

 

m = (23)

 

)

By examining Equation 19 it can be seen that the addition of
sample units at the field level (n) decreases the within and the be—
tween field variation while an equal increase in the number of sub-
samples per field (m) only decreases the within field component of
variation. Jessen (1978) states that when the cost of sampling pri-
maries (fields) essentially equals that of the secondaries (subsam-
ples per field) it is always optimal to increase the number of primary
units to the maximum before increasing the number of subsamples. The

only additional cost in sampling more fields is the cost of moving

66

from one field to the next. The cost involved in moving between onion
fields within a region is essentially zero as the fields are typically
found in large geographic clusters due to the strict dependence on
soil type.

Following the above logic given by Jessen (1978) and using rea-
sonable estimates for i, a, and NF (maximum NF seen < 40), it can al-
ways be seen that every field per region must be sampled before any
reasonable level of precision can be reached.

By sampling every onion field in a region, the between field
variance component can be eliminated from the denominator, leaving

Equation 24, (3 84)Sz
' w

m = "j"?— (24)
NF(ax)

or, in the case of the 100 foot sample unit:

(3.84)(9.13(§)1'“°)
NF(aJ-'?)2

m =
from which the optimal number of samples (m) within each field can now
be directly calculated for various combinations of i, a, and NF.

An additional factor, the sample unit size in linear row feet
(L), must be examined before the calculation of the optimal within
field sample size (m). Taylor (1961) noted that the variance mean
relationship changes as the sample unit size changes, thus directly
affecting the optimal sampling strategy.

Sample data was recorded by one foot increments; sample unit
lengths ranging from 1 to 100 linear row-feet were randomly extracted
from each subsample. As before, an analysis of variance and a log

variance log mean regression (the intercept “a" as adjusted by Bliss

1967) was performed to estimate the variance to mean relationship

67

for all 100 values of L for the within and the between field cases.
Although some decrease in between field variance was noted, the re—
duction was not significant, as even the smallest between field vari—
ance necessitated sampling every field per region.

The effect of L on the within field variance-mean relationship
(Table 15) can be noted in Figure 11; the precision (Si/X) of the
pooled onion maggot damage data is plotted against the sample unit
size (L). Given any set values for m and NF, the sampling precision
steadily improves as L approaches 100 feet. Of greater importance,
is the effect of the sample unit size on the number of samples per
field and the related costs given a set level of precision.

To estimate the cost of sampling, the time involved in collecting
data for various densities of damage was recorded. A linear function
(Figure 12 and Table 16) was found to estimate the time in minutes
necessary to sample 100 linear row feet for the density range examined
(0-100 damaged plants per 100 foot strip). Movement between samples
within a field is independent of the density and was found to take
approximately 1 minute. Coupling these two time components (Equation
25) with Equation 24 and the ten variance-mean relationships of Table
15, the total cost of regional sampling can be compared for various

values of L, 2, NF and a (Si/2)°

Cost = (1. + L(FC/100.))mNF (25)

within field sample size

where: m

L = feet per sample unit
PC = minutes to sample 100 feet
NF = fields per region

68

TABLE 15. Within field variance to mean relationship as estimated by
a log variance-log mean regression for various sample unit

 

 

sizes.

L Adj "a" "b" Deiiiifiiiiifi 7:2)
10 14.24 1.51 0.91
20 14.16 1.54 0-95
30 11.09 1.48 0.94
40 9.74 1.48 0-96
50 9.13 1.45 0.96
60 8.58 1.48 0.97
70 9,11 1.49 0.97
80 9,26 1.43 0.98
90 9,35 1.41 0.98
100 9.13 1.40 0.98

 

- b
General Form S2 = 6100

69

0.40

0.36

L

L

 

0.32

L

 

Si/i (R)
0.24 0.28
1 41 r1 1 11 I 1

0.20

l

 

 

f I T I V

o 20 4o 80 80 100
SRHPLE UNIT SIZE (LI

FIGURE 11. Regional sampling precision plotted as a function of sample
unit size.

70

63-- 100 FOOT SFII‘IPLES

MINUTES

 

 

 

I T r I

 

l
40

r
0 20
DENSITY/100 FO0T PLOT

T l
60 80 100

FIGURE 12. Sampling cost in minutes as a function of plant damage
density.

71

TABLE 16. Regression statistics for sampling time versus plant dam-
age density.

)

 

 

SOURCE DF SS ms
Regression 1 134.6 134.6
Residual 11 12.1 1.1
Total 12 146.7

 

y intercept ("a") 1.741 i 0.2908

slope ("b") 0.09553 i 0.008635

r2 0.92

72

As expected, given any set precision level, the larger sample
unit required fewer samples to be taken per field (see Figures 13 and
14) except when m reaches the minimum value of one sample per field
where L < 100 (possible only at high densities or low precision).

The cost functions (Figures 15 and 16) indicate that the larger 100
foot sample units are the most efficient in terms of time spent sam-
pling, again with the exception where m +1 for L < 100. No sample
unit sizes larger than 100 feet were examined, but as can be seen in
Figures 15 and 16, the cost function has begun leveling off with
minor increases in efficiency as L +100. The exception is where low
densities (& < 1) and high precision ("a" f_0.1) are required.

The sample unit size of 100 row feet will be used to complete
this analysis as it gives the maximum efficiency over the largest range
of densities. Figures 17-20 give the optimal number of samples per
field as calculated from Equation 23 for three levels of precision and
four values of NF (total fields per region).

In this study only a portion of the fields within each of these
regions were monitored, thus prohibiting precise estimates of their
mean damage values. For future utilization of this sampling information
it should be noted that regional means were found to lie in the range
of 1.5 and 6.0 damaged plants per 100 foot sample. Using these ex-
pected mean values and Figures 17-20, the necessary number of subsam-
ples per field (m) can easily be found for the three given precision

levels.

SRHPLES/FIELD (I)

HINUTES

73

   
 

         
 

 

 

 

 

 

 

 

 

 

O. O
8: uraso 8'. ”'=’°
l “LPN“: .1 i KPH“: .2
‘ d
O. 1 O 4
R: a"
4 4
4 4
9‘ .. =I<
8: . 3:
4 D 4
1 —J ‘
04 In a 4
64 7"
6‘ {ggj
1 m ‘
‘ xonk=Io 3 ,
‘ L
2 = 9‘
«1 as 2:
O 4 :
Io-I 9..
'1 ‘ u 1
4 4
4 XMR=IOO ‘
o 4 ° 4 ”“8100
V V Y Y 17? fi V I Y 1 1 v I V V Y 1 T V V ' V I . V V V V V V V V V V v V V V vi V V ' ' I
“b 20. 40. so. 00. I00. ”0. :5. 45. 05. 05. I00.
SRHPLE UNIT SIZE (L) SRHPLE UNIT SHE (L)
FIGURE 13 FIGURE 14
O. a
O O
8“ 8!
l -.
' ‘ “'830 1 ":30
8 4 RLPHflsol 9° 4 “7%.!
o- 8
ID “.1
1 4
1 4
g" -?
0.1
'1 §':
1 1
o ‘ m I
8 4 U 4
8‘ ’5 a.
4 2 . 4
4 H
.. t : xuua
8 4 - 4
8’.“ §-
‘ 4 ”M810
.- I
§‘ "
.3: §< 850
‘ :80 1
j xmflo' 4 Milan
4
. V‘YT‘T I v v T v I v v v v v v v v v v V 0 v ' V v v v v v '
cb- 20- 40- 03- 03. TT30- “b. 25. 45. . . f05.' . . '05 . . Ioo
SRHPLE UNII SIZE IL) snnPLE quI SIZE IL) .
FIGURE 15 FIGURE 16

FIGURE 13-14. Necessary subsamples per field for various densities,
sample unit sizes, region sizes, and levels of precision

(regional sampling).

FIGURE 15-16. Sample cost in minutes for various densities, sample unit
sizes, region size, and levels of precision (regional
sampling).

74

 

NF:10 NF=20

 

SUBSHHPLES/FIELO (H)
[0 IS 20 26 30 88 4
l + l A l A 1 A l L l n
r
SUBSRHPLES/FIELD (HI
I? If 2? 2? 3(1) 3? 4

 

 

 

 

 

 

v

v 1 v v 1 v I w 1 V

0 IO 20 30 40 50 O 10 20 30 40 ‘0
REGIONRL HEBN REGIONAL HEAN

FIGURE 17 FIGURE 18

fi V 1' V

1 NF=30 4 NF=40

I .
84
2.1
4 I
L ..

 

 

SUBSRHPLESIFIELO (H)

D
“~—

SUBSRHPLES/FIELO (H)
II 20
FF 1 6 l
.1 F
I P

I:
It
I:

 

 

 

 

 

 

 

o . I3 4. :5 44* :3 - 45 . 63 o In 23 so 40 so
REGIONHL HERN REGIONHL HER"
FIGURE 19 FIGURE 20

FIGURE 17-20. Necessary number of subsamples per field to achieve
specified levels of precision at given densities and
region size (regional sampling).

75

Field Level Plant Damage Sampling:

Determination of accurate within field plant damage densities,
as with the preceeding regional densities, necessitates an understand-
ing of the variance to mean relationship within the sample universe.
As noted earlier, the variance of a sample mean depends on population
density and dispersion, as well as the structure of the sample unit
itself.

Cochran (1963) defines the variance of a sample mean derived from
simple random sampling as:

VARW) = 1:0? - 02 = —-(—’L'——’l’ = (an-)2 (26)

field mean n number samples/field

where:

precision true population mean

2

Z W XI
ll

possible samples/field S true population variance
The term (N - n)/N compensates for sampling within a finite universe,

but as the sample size, n, is small in comparison to the possible num-
ber of samples, N, the resultant value approaches 1. To be conserva-

tive in estimating n, the value of (N - n)/N was set equal to 1.0,

resulting in Equation 27.

S2 2
VAR(IJ) = :1— = (all) (27)

The true population parameters 0 and S2 are unknown, and the es-
timates i and S2 from sampling data must be used. Karandinos (1976),
adjusting for the estimation of u and S2 and solving for n, transforms

Equation 27 to Equation 28.

(z >st 2
n = -—91-§——2—— on: n = iii—44$ (28)
(ax) (a?)
where: (Za/Z) depends on the confidence coefficient which is an

arbitrary variable chosen by the researcher (typically

0.95, which sets Za/Z = 1.96)

76

Simple means and variances were calculated for numerous values of
L (sample unit size) using the 71 sets of field data (Appendix C). As
before, a log variance-log mean regression was fit for each test value
of L which generated the regressions listed in Table 17. An identical
regression analysis was performed on the 1 meter sample unit data col-
lected from Grant, Michigan (Table 17). As the tabled values suggest,
the resultant variance to mean relationships (3 foot and 1 meter) are
not significantly different (ta = 0.3372, tb = 0.6598, Table t = 1.98;
Cox 1976).

Equation 28 was linked with the variance to mean relationships of
Table 17 (L 6 10 + 100, by 10) to estimate the sample size (n) for
differing densities and precision levels. Figures 21 and 22 indicate,
as expected, N decreasing as L increases. Of particular importance,
apparent in both these figures, is the leveling effect noted in the
slope of the function at the higher densities, which suggests optimum
sample unit sizes less than 100 feet.

The cost function related to the within field sampling program is
essentially equivalent to the regional sampling program, with two
minor adjustments. The multiplicative factor associating the number
of fields per region, NF, may be totally extracted, and the parameter
representing movement between samples within fields is cut from 1
minute to 1/2 minute as the distance between samples is reduced ap-

proximately 50% due to the increase in the sample frequency per field.

SRHPLES/FIELD (N)

MINUTES

77

  
  
    
 

  
 

     

 

 

 

 

 

        

 

 

 

 

 

 

 

 

9-
I I
4
o 4 ‘ flLPNfl:.2
4 4
4 4
.83.- ; E1 reams
4 U 4
4 o 4
4 _’ 4
4 I: 0‘
§: ‘t A:
* xaAA:Io 33 ‘ "“'=1°
4 _J 4
4 0" <
8. E 8.
'4 m “4
4 4
4 4
o 4 4
8‘ K ""‘5" 8‘ xmeso
. ‘ 1
: xannsIoo j xuanexoo
*V‘Y "Tf 'Y‘VIV'V'I"" Yrvv TTf‘V’ vvvv rwrw TYVV
“b 20 45 so so 100 “b 23 .3 .5 63 (Ba
SRHPLE UNIT SIZE (LI SRHPLE UNIT SIZE (LI
FIGURE 21 FIGURE 22
84 84
0, fiLPNflsJ 0.
4 4
‘ xlfllss 4
IO N
< I
. 4
. 4
4 4
O O
9“ 0-4
" “4
‘ xann=Io .
4 a: <
4 u .
8- '5 3.
" . z "4
4 z 4
8L 8:
“- XMR=$O ".
4 M 4
2‘ 8': X xanneso
j ‘ \ H
4 J xannano
O *f'T""I""T""TTr7—'j O"V'T""T*"'I""fi*"1
o 20 40 so so I00 0 20 40 so so I oo
SFIHPLE UNIT SIZE (LI SRHPLE UNIT SIZE (LI
FIGURE 23 FIGURE 24

FIGURE 21—22. Necessary samples per field for various densities. sample
unit sizes, and levels of precision (field level sampling).

FIGURE 23-24. Sample cost in minutes for field level sampling.

78

TABLE 17. Field level variance to mean relationship as estimated by
a log variance-log mean regression for various sample unit
sizes (L).

 

Coefficient of

 

L Adj' "a" "b" Determination (r2)
3 3.01 1.24 0.9733
10 3.43 1.26 0.9703
20 3.57 1.27 0.9727
30 3.99 1.30 0.9761
40 4.21 1.31 0.9802
50 4.39 1.32 0.9815
60 4.60 1.33 0.9859
70 4.76 1.34 0.9857
80 4.85 1.34 0.9866
90 5.02 1.34 0.9867
100 4.98 1.35 0.9877
* 1 meter 3.04 1.26 0.9562

 

*Independent data set from Grant, Michigan (1976-1977).

General Form 82 = a6?)b

TABLE 18. Optimum sample unit size (L) as predicted by Equation 19
for various densities and levels of precision (densities
based on 100 foot plots).

 

10 20 30 40 50 60 7O 80 90 100

 

a=0.l 100' 100' 50' 50' 50' 40' 20' 20' 20' 20'
0.2 100' 100' 50' 40' 50' 50' 40' 20' 20' 20'

0.3 100' 100' 50' 50' 40' 50' 50' 20' 20' 20'

 

79

Adapting Equation 25 as above, we obtain:

Cost (0.5 + L (PC/100.))n (29)

where: L feet per sample unit

n sample size

FC minutes to sample 100 feet (from regression

equation, Table 16)
and thus the curves in Figure 23 and 24. Figures 23 and 24 clearly
indicate optimal sample unit sizes less than 100 feet for two of the
densities graphed. The relationship between the density and optimal
sample unit size (L) seems to be little effected by the precision
levels as indicated in Table 18. Although the values of Table 18 re-
flect the true optimum sample unit size, often the effect of varying
L over a wide range will have little effect on the overall time spent
sampling (Figure 23: i = 50). For other densities, the time savings
can be appreciable (Figure 23: i = 100; savings 8 33%).

The necessary sample size (n) is conditional on the size of the
sample unit selected. If apriori estimates of the field density are
available from previous sampling dates or from preliminary sampling,
Table 18 gives the optimum sample unit length (L). When such informa-
tion is lacking, the lowest density of interest must be selected from
Table 18. In either case, the estimated population variance can be
calculated from the regression coefficients of Table 17 and then used
in Equation 28 for calculation of the necessary sample size n.

If low precision estimation is adequate and the resultant sample
size is less than 30 samples per field, it must be remembered that the

properties of the Central Limit Theorem do not hold and normality

80

cannot be assumed. In that event the use of Chebyshev's Theorem
(Steel and Torrie 1960) allows the estimation of confidence limits for

any type of distribution.

Within Field Sampling for Age Specific Density

 

Within field density sampling was conducted ten times throughout
the 1977 growing season. The first two sample periods (June 10 and
June 14) revealed no observable onion maggot damage within the test
field; therefore, samples were taken to estimate onion maggot density
within stratum 1 (healthy onions). With the first observable onion
maggot damage (June 23), sampling was initiated in strata 2-5. Tables
19 and 20 list the sampling phenology along with a data collection
summary for each sampling period. The complete set of sampling data
is listed in Appendix E. The healthy onion samples (stratum 1) are
not listed in either Table 19 or 20 because only observations of 0.0
were recorded for every sampling date. Although no true means were
established, some insight into the stratum density can be established
using detectable survey techniques.

Since no observations larger than 0.0 were made, the actual fit
of any probability density function cannot be tested, but the applica-
tion of the poisson distribution is a reasonable assumption, because
attacks on young virgin onions occur randomly (page 53) and as statis-
ticians have long recognized, the poisson is the "rare events" distri-

bution (Steel and Torrie 1960, and Sokal and Rohlf 1969).

81

TABLE 19. Summary for the 100 foot plant damage sample taken through-
out the 1977 growing season within a single test field in
Grant, Michigan.

 

M... 53333: :33.
6-10 0 0 0
6-14 0 0 0
6-23 42.1 10.90 2,8
6-30 56.9 11.10 3,8
7-7 60.8 10.80 4,1
7-21 69.8 12.50 4,7
7-29 67.0 14.80 4,5
8-3 74.7 16.40 5,0
8-12 70.9 13.76 4,7
9'1 71.1 12.21 4,7

 

* Based on full stand of 15 onions per foot.

82

 

 

TABLE 20. 1977 data collection summary for strata 2-5 in the Grant
test field.
#CLUMPS LIFE
DATE SAMPLED STRATA STAGE MEAN VARIANCE VAR/MEAN
6-23 5 2 193 E 0.0777 0.3325 4.28
l 0 0 -
2 0 0 -
3 0.0104 0.0103 0.99
P 0 0 -
3 36 E 2.0556 14.5111 7.06
1 1.0278 8.3706 8.14
2 1.3056 5.5325 4.24
3 1.1667 1.1714 1.00
P 0.1389 0.1230 0.88
4 78 E 0.0461 0.2166 3.38
1 0.0513 0.2051 3.99
2 1.1795 6.5910 5.88
3 0.6538 1.2682 1.94
P 0.2821 0.3350 1.1875
5 0 None Observed - -
6-30 6 2 229 E 0.0873 0.4046 4.63
l 0 0 -
2 0.0087 0.0087 1.00
3 0.0480 0.0722 1.50
P 0.0087 0.0087 1.00
3 38 E 1.4211 5.7639 4.06
1 0.2895 0.5896 2.04
2 0.9211 4.6152 5.00
3 1.0263 1.6479 1.60
P 0.6053 0.7319 1.21
4 88 E 0 0 -
1 0.0114 0.0114 1.00
2 0.1023 0.7365 7.199
3 0.1364 0.4180 3.06
P 0.4432 0.3646 0.8224
5 0 None Observed - -
7-7 '8 2 E 0.0244 0.0435 1.78
1 0.0488 0.3996 8.18
2 0.0049 0.0049 1.00
3 0.0146 0.0145 1.00
P 0.0049 0.0049 1.00
3 E 0.9600 4.9567 5.16
1 1.3600 10.3233 7.59
2 0.3200 0.6433 2.01
3 0.8400 4.1400 4.90
P 0.9200 1.8267 1.90

83

 

 

TABLE 20. (continued)
#CLUMPS LIFE
DATE SAMPLED STRATA n STAGE MEAN VARIANCE VAR/MEAN
7-7 8 4 36 E 0.0833 0.2500 3.00
l 0 0 -
2 0.1111 0.4444 4.00
3 0.0278 0.0278 1.00
P 0.6111 0.6803 1.11
5 62 P 0.5000 0.6803 1.36
7-21 4 2 52 E 0.1154 0.4962 4.30
l 0 0 -
2 0 0 -
3 0.0385 0.7690 2.00
P 0 0 -
3 9 E 1.6667 6.5000 3.90
1 0.6667 4.0000 6.00
2 3.0000 22.0000 7.33
3 0.4444 0.5278 1.19
P 0.3330 0.2500 0.75
4 5 E 0 0 -
1 0 0 -
2 0 0 -
3 0.2000 0.2000 1.00
P 0 0 -
5 56 P 0 0 -
7-29 1 2 20 E 0.1000 0.2000 2.00
1 0 0 -
2 0.5000 5.0000 10.00
3 0.2500 0.6184 2.47
P 0 0 -
3 2 E 0 0 -
1 0 0 -
2 0 0 -
3 0 0 -
P 6.0000 2.0000 0.33
4 9 E 0 0 -
1 1.0000 9.0000 9.00
2 1.7800 28.4400 15.98
3 0.4400 1.0278 2.34
P 2.8890 6.1110 2.12
5 43 P 1.7200 4.7290 2.75
8-3 2 2 9 E 0 0 -
1 0.2220 0.4440 2.00
2 0.8889 0.6111 0.69
3 1.6670 2.2500 1.35
P 2.1110 11.8611 5.62

84

TABLE 20. (continued)

 

#CLUMPS LIFE

 

DATE SAMPLED STRATA n STAGE MEAN VARIANCE VAR/MEAN
8-3 3 9 E 0 0 -
l 0 o ..
2 2.0000 7.2500 3.63
3 1.3330 1.7500 1.31
P 10.6667 181.5000 17.02
4 9 E 0 0 -
1 0 0 -
2 0.7778 1.9444 2.50
3 0.6667 1.0000 1.50
P 10.8890 72.6111 6.67
5 43 P 2.6512 21.8992 8.26
8-12 1 2 35 E 0 0 -
1 0 0 -
2 0 0 -
3 0.2000 0.6941 3.47
P 0.1429 0.2437 1.71
3 10 E 0 0 -
1 O O -
2 0 0 -
3 1.2000 4.4000 3.67
P 11.1000 122.3220 11.02
4 22 E O 0 -
l 0 0 -
2 0 0 -
3 0.1818 0.2511 1.38
P 7.7273 28.8745 3.74
9-1 2 2 33 E 0.0910 0.2727 3.00
1 0 0 -
2 0 0 -
3 0 0 -
P 0.3939 1.4962 3.80
3 15 E 1.2667 9.2095 7.27
1 1.6000 12.8286 8.02
2 1.8000 20.3143 11.29
3 0.6667 4.3810 6.57
P 11.0000 115.8570 10.53
4 30 E 0 0 -
1 0 0 -
2 0 0 -
3 0.0667 0.1333 2.00
P 12.8670 41.5678 3.23
5 31 P 2.0645 18.0624 8.75

 

85

Assuming these data to be poisson distributed, the probability

(P(r)) of finding r individuals per sample is given by Pielou (1977) as:

Ar A
_ §__ ‘X
P(r) r! e (30)

where: Q expected mean

e base of the natural logarithms
For detection purposes, this function can be rewritten to calculate
the probability (P) of finding at least one organism, probabilistically
one minus the probability of finding zero organisms in N samples (Rue-
sink and Haynes 1973).
P = 1 - e (31)

Lampert (1976). solving Equation 31 for N (Equation 32), was able

to directly calculate the maximum possible value of the population

mean for any given sample size (N) and the level of confidence (P).

x = -lnél - P) (32)

 

Using Equation 32 with a 95% level of confidence, the maximum possible
value of the stratum 1 mean (N = 100) is 0.03. The true value of the
mean may lie well below this level, but no tighter upper limit can be
established without drastically increasing the sample size (N): there-
fore stratum 1 is considered to have a maximum possible density of 0.03
onion maggots per onion thoughout the entire season.

The remaining strata (2-5) each contained non-zero data elements,
thus the computation of means and variances was trivial. Each stratum
was analyzed separately for the immature stages (egg-pupa) as well as
for the cumulative immature population. Analysis of variance was per-

formed on the logarithmic transformation of these data for each sampling

86

data and the pooled sets to determine if the assigned grades truly
represented meaningful strata. Significant differences were found
among the strata in every analysis with the pooled multiple range test
(Student-Newman-Keuls: a = 0.05) for the cumulative immature population
showing complete separation of the class means. Table 21 gives the
stratum means and the multiple range tests of the pooled data for each
life stage and cumulative immature population. Specific life stages
are found occurring more frequently in some strata than in others
(i.e., first instar larvae rarely occur in strata l, 2, 4, and 5,
while typically abundant in stratum 3). Since strata definitions were
based on the evolution of damage through time, it was expected that
these life stages would require different levels of stratification.

0f significant importance is the stratum loading of the egg stage,
whose density is approximately 20 times greater in stratum 3 (damaged
onions) than in any other strata. The stratum loading indicates an
ovipositional preference for previously damaged onions and is believed
to be a key factoriklthe population dynamics of this insect.

As the age structure of the population changes throughout the
season, the expected differences between stratum densities are not
always apparent due to low levels of specific life stages. For exam-
ple, the sample taken on July 21 revealed no significant differences
between any stratum due to the recent emergence of the second genera-
tion adults (Figure A-3). Since individual sampling dates all possess
bias due to the existing age structure, the data sets pooled across

sampling dates are believed to best represent the stratum loading for

87

TABLE 21. Means and multiple range tests (Student—Newman-Keuls) for
each life stage by strata (1-5) calculated from sampling
data pooled across the entire growing season.

 

LIFE STAGES STRATA MEANS & GROUPINGS

 

0.0300 *

0.0646

1.2946

0.0324
0

9)

Eggs

Ulb¢brord
mmo'm

0.0300

0.0162

0.6822

0.0567
0

First Instar

m.b(»tora
mmo‘mm

0.0300

0.0283

1.0465

0.5182
0

Second Instar

Ulabwwl-d
0700079:

0.0300

0.0606

1.0078

0.3239
0

Third Instar

Ulb»uik)h‘
0:0me

0.0300
0.0363
2.1163
1.5263
1.2026

Pupae

mwaH
()0me

0.0300
0.2059
6.1473
2.4576
1.2026

Cumulative
Population

tn.b(»rola
mantra:

 

*Means with the same letter are not significantly different
(P = 0.95).

88

each life stage and their cumulative total. Even though sampling pre-
cision varies somewhat due to the population's shifting age structure,
stratum weighing, if based on yearly averages, should drastically in-
crease the precision over simple random sampling.

Cochran (1963) gives the mean of a stratified sample as:

Ystrat = wth (33)

and the variance of the mean as:

I: 2
var({(strat)gi‘12:{fair-'3“ (34)
h
where: L = number of strata
Wh = proportion of stratum h (§?)
nh = sample size of stratum h
n = total sample size
§n = mean of stratum h
Sh = standard deviation of stratum h

Setting the standard error of the mean equal to a fixed percentage of

the mean (a) we obtain Equation 35.

2
n = (z )2(h-1whsh) (35)

(ai)2
The optimal allocation of n for each stratum (nh), assuming equal

sampling costs per stratum, is given by Cochran (1963) as:

W S
_ h h
opt (nh) — n (XS ) (36)

h

 

The calculation of the total number of samples n and its allocation be-
tween strata (nh) is dependent on the stratum mean (§ STRAT), its
standard deviation (Sh), and its size as a proportion of the total
sample universe (Wh). When sampling apriori knowledge of W can be

h

easily gathered using damage sampling techniques similar to those

89

presented on page 41, but apriori knowledge of both u and $2 is expen-
sive and time consuming. Instead of time specific estimates, prede—
termined expected values can be used. Substituting yearly averages

for expected values is one possible solution to this problem and will

be used here to demonstrate the increased efficiency of sample strati-
fication. If sampling is done frequently through time, a more efficient
method would be to use sample estimates of §h and 8; calculated from

the most recent sampling date.

To evaluate the effects of within field aggregation on sampling
procedures, analysis of variance was also used to test between clump
differences for each life stage. Analyses were made on the stratified
and unstratified data sets. As the total number of analyses exceeded
50 tests, the third instar results which clearly represent the trend
seen in every life stage will be presented. In the unstratified
analysis, significant differences were found in the per onion density
of each life stage between damage clumps. Inclusion of the onion
strata in the analysis (two—way ANOVA life stage x strata x clump)
clearly showed that between clump differences were due to the onion
strata and not the physical clumps. Table 22 shows the results of
five two-way analyses of variance for the third instar population. In
all cases, using the above stratification scheme, the between clump
differences are non-significant. Clumps of damage can be described
as being composed of varying numbers of onions belonging to each of
these strata, and their use as control variables clearly helps elimi-

nate excessive variation between areas.

TABLE 22. Analysis of variance table for third instar larvae by clump

and strata.

90

 

 

SOURCE OF SUM OF MEAN

DATE VARIATION SQUARES DF SQUARES F SIGNIFICANCE

6—23 Clump 1.180 4 0.295 1.741 0.141
Strata 27.068 2 13.534 79.900 0.001*
Error 50.816 300 0.169

6—30 Clump 0.810 5 0.162 1.367 0.236
Strata 14.513 2 7.257 61.245 0.001*
Error 41.115 347 0.118

7-7 Clump 0.518 7 0.074 1.201 0.302
Strata 4.231 3 1.410 22.912 0.001*
Error 19.514 317 0.062

7-21 Clump 0.037 3 0.012 0.265 0.851
Strata 1.210 3 0.403 8.547 0.001*
Error 5.429 115 0.407

8-3 Clump 0.598 1 0.598 2.913 0.093
Strata 10.184 3 3.361 16.374 0.001*
Error 13.343 65 0.205

 

*P < 0.01

91

The variance of the mean, for the third instar larvae and the
total immature population, was calculated based on simple random sam-
pling, stratified proportional sampling, stratified optimal sampling,
and yearly average stratified sampling.

To estimate sample allocation based on yearly averages, log
variance-log mean regressions were used to estimate the variance to
mean relationships for each immature life stage and the cumulative
immature population for each stratum. For some combinations, the re-
gressions of the various life stages per stratum showed no statistical
differences, the data then being pooled and a more generalized model
fit (Figure 25: variance to mean relationship of eggs, first and
second instars in strata 2 and 3).

Table 23 lists the regression statistics for each life stage by
stratum, adjusted as suggested by Bliss (1967). The yearly means
(Table 21) can be used in their respective regression equations of
Table 23 to estimate their expected variances, which are in turn used
in Equation 36 for the determination of sample allocation between
strata (nh). These values, along with Wh (calculated from Tables 19
and 20) and the sample estimates of § STRAT and S2 STRAT were used in
Equation 34 to calculate the actual variance of the mean stratified
by the yearly averages. The results (Tables 24 and 25) are presented
in terms of the standard error to mean ratio for easy comparison. As
the tables indicate, stratification is always more efficient than
simple random sampling, and stratification based on yearly averages
is always better than proportional stratification, but rarely approaches

the precision of optimal allocation. As mentioned earlier, preliminary

L00 VRRIRNCE

FIGURE 25.

2.00
LP;

1

0.80 1.20 1.60

0.40

92

 

 

 

U I

I T 1 ' I T 1
-0026 0000 0026 0050

LOG HEHN

T
”0050

l
-0076

Variance to mean relationship of eggs, and first and second
instars in onion strata 2 and 3.

93

 

 

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94

TABLE 24. Comparison of third instar sampling precision using simple
random (SR), proportional stratification (Prop), yearly
average stratification (Ave), and optimal stratification
(Opt) sampling in strata 1—5.

 

 

DATE 0_ 0_/x
x x
6-23-77
SR 0.000211 0.28
Prop 0.000161 0.24
Ave 0.000112 0.20
Opt 0.000064 0.15
6-30—77
SR 0.000147 0.31
Prop 0.000129 0.29
Ave 0.000095 0.25
Opt 0.000054 0.19
7-7-77
SR 0.000366 0.45
Prop 0.000243 0.36
Ave 0.000108 0.24
Opt 0.000043 0.15
7—21-77
SR 0.000246 0.36
Prop 0.000220 0.34
Ave 0.000190 0.32
Opt 0.000077 0.203
7-29-77
SR 0.000938 0.49
Prop 0.000661 0.41
Ave 0.000556 0.37
Opt 0.000128 0.18

95

TABLE 24. (continued)

 

DATE 0 0_/x

XI
)4

 

8-3—77
SR 0.002768 0.43
Prop 0.000576 0.20
Ave 0.000223 0.12
Opt 0.000084 0.08
8-12-77
SR 0.000943 0.46
Prop 0.000793 0.43
Ave 0.000427 0.31
Opt 0.000135 0.18
9-1-77
SR 0.000939 0.73
Prop 0.000865 0.71
Ave 0.000415 0.49
Opt 0.000229 0.36

 

96

TABLE 25. Comparison of total immature pOpulation sampling precision
using simple random (SR), proportional stratification
(Prop), yearly average stratification (Ave), and optimal
stratification (Opt) sampling in strata 1-5.

 

 

DATE 03 0_/§
x x
6-23-77
SR 0.002150 0.35
Prop 0.001150 0.25
Ave 0.000230 0.11
Opt 0.000080 0.07
6-30-77
SR 0.000901 0.30
Prop 0.000487 0.22
Ave 0.000193 0.14
Opt 0.000064 0.08
7-7-77
SR 0.003141 0.31
Prop 0.002207 0.26
Ave 0.000567 0.13
Opt 0.000126 0.06
7-21-77
SR 0.014377 0.53
Prop 0.009540 0.43
Ave 0.001840 0.19
Opt 0.000410 0.09
7-29-77
Sr 0.036990 0.31
Prop 0.025760 0.26
Ave 0.008090 0.15
Opt 0.001598 0.06

97

TABLE 25. (continued)

 

 

Opt

0.000574

DATE 0_ o_/x
x x
8-3-77
SR 0.117968 0.29
Prop 0.060899 0.21
Ave 0.013086 0.10
Opt 0.003210 0.05
8-12-77
SR 0.048080 0.34
Prop 0.021790 0.23
Ave 0.003540 0.09
Opt 0.000729 0.04
9-1-77
SR 0.062014 0.31
Prop 0.016720 0.16
Ave 0.002783 0.06

0.03

 

98

sampling to estimate 52 STRAT or utilization of data from recent sam-
pling periods could more efficiently allocate the samples between
strata, thus approaching the precision achieved with optimal sample
stratification.

The allocation of samples is heavily weighted toward stratum 1

(approximately 80% for proportional allocation and 70% for average
allocation). The large area covered by this stratum necessitates a
high proportion of the samples allocated even though the mean value
is always low (0.03). The calculation of n, total samples (Znh), for
a moderate level of precision (a = 0.2) yields extremely large values
(approximately 2,000 for third instars and 500 for the total immature
population). Approximately 70-80% of this sample is allocated to find
extremely rare individuals in stratum l where no non-zero observations
were recorded all season and a "maximum" possible density is known to
be below 0.03. Due to the extremely low density found within this
stratum and its effect on the overall sample size, the sampling sta-
tistics will again be calculated with the stratum 1 onions framed out
of the sample universe. Tables 26 and 27 list the variance of the
sample mean and the standard error to mean ratio of the samples for the
third instar larvae and the total immature population respectively.
In most cases the standard error to mean ratio is reduced, but more
importantly, the number of samples necessary to predict the population
mean of strata 2-5 at the same level of precision (a = 0.2) was re-
duced approximately 50%.

Using density estimates calculated only from strata 2—5 and

then weighing those estimates by the proportion of the total universe

99

TABLE 26. Comparison of third instar sampling precision using simple
random (SR), proportional stratification (Prop), yearly
average stratification (Ave), and optimal stratification
(Opt) sampling in strata 2-5.

 

N

 

DATE 0_ O_/x
x x
6-23-77
SR 0.00210 0.15
Prop 0.00150 0.13
Ave 0.00080 0.09
Opt 0.00030 0.06
6-30-77
SR 0.00130 0.09
Prop 0.00100 0.07
Ave 0.00070 0.06
Opt 0.00020 0.03
7-7-77
SR 0.00102 0.41
Prop 0.00080 0.36
Ave 0.00051 0.29
Opt 0.00018 0.17
7-21-77
SR 0.000747 0.50
Prop 0.00060 0.45
Ave 0.00050 0.402
Opt 0.00030 0.312
7-29-77
SR 0.00440 0.51
Prop 0.00410 0.48
Ave 0.00400 0.47
Opt 0.00090 0.22

TABLE 26.

100

 

 

(continued)

DATE 0_ 0_/§
X X

8-3-77
SR 0.01281 0.24
Prop 0.00940 0.21
Ave 0.00930 0.20
Opt 0.00120 0.07

8-12-77
SR 0.01031 0.40
Prop 0.00900 0.37
Ave 0.00770 0.35
Opt 0.00190 0.17

9-1-77
SR 0.00670 0.73
Prop 0.00610 0.69
Ave 0.00200 0.40
Opt 0.00090 0.27

 

101

TABLE 27. Comparison of total immature population sampling precision
using simple random (SR), proportional stratification (Prop),
yearly average stratification (Ave), and optimal stratifi-
cation (Opt), sampling in strata 2-5.

 

 

DATE 0 f 0_/52
x x
6-23—77
SR 0.030200 0.13
Prop 0.018900 0.11
Ave 0.010400 0.08
Opt 0.003530 0.05
6-30-77
SR 0.010340' 0.14
Prop 0.005868 0.10
Ave 0.004150 0.09
Opt 0.001365 0.05
7-7-77
SR 0.015250 0.21
Prop 0.011235 0.18
Ave 0.005624 0.13
Opt 0.001595 0.07
7-21-77
SR 0.061100 0.49
Prop 0.041366 0.40
Ave 0.012673 0.23
Opt 0.004250 0.13
7-29-77
SR 0.235670 0.23
Prop 0.195450 0.21
Ave 0.180640 0.20
Opt 0.045000 0.10

102

TABLE 27. (continued)

 

DATE 0 O /x

XI
X

 

8-3-77
SR 0.972525 0.17
Prop 0.680664 0.15
Ave 0.578580 0.13
Opt 0.179570 0.07
8-12-77
SR 0.491043 0.17
Prop 0.300240 0.14
Ave 0.211651 0.11
Opt 0.055690 0.06
9-1-77
SR 0.656786 0.12
Prop 0.279454 0.08
Ave 0.220135 0.07
Opt 0.062090 0.04

 

103

they represent, allows density estimates of the total universe to be
made. Figure 26 shows the per onion density of both onion maggot pupae
and the cumulative immature population in the Grant, Michigan test
field. Abundance curves for the earlier life stages will not be pre-
sented because the sampling interval used in this study was too large
to estimate their age specific densities. This interval does not
effect the point estimation of density or the methods presented in this
section, it merely eliminates the ability to evaluate total incidence
through time. To make abundance curves for the earlier life stages,
the sampling interval must be reduced at least to the length of the
developmental stadium in question (see Appendix A on temporal distri-

bution for developmental data).

Biological Monitoring

 

Periodic assessment of the biological components (host plant,
pests, parasitoids, etc.) within an agroecosystem is essential for the
development and implementation of crop management programs. Crop loss
assessment as well as effective pest management systems are two such
programs that are highly dependent on effective biological monitoring
schemes.

Biological monitoring programs are typically goal oriented, with
a given objective or set of objectives firmly defined at the onset of
the project. To meet such objectives, which can be quite broad in
scope, it may be necessary to utilize a series of specific sampling
techniques simultaneously or sequentially through time. Sampling meth-
odology is usually very specific in orientation and statistical inter-

pretation. Coordination in the use of such techniques and trade-offs

104

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o
.
DEE 22238.. DEE Tm
lo
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rmo
9
[0
r
10
i.

 

105

between their breadth, precision, and economic costs must be closely
evaluated in terms of the objectives of the overall biological moni-
toring program.

Construction of a biological monitoring program for onion maggots
in Michigan could take innumerable forms depending on the specific
objectives at hand. The possibilities range from small plot damage
estimation to regional density estimates of the insect itself, both
of which can be accomplished using variations of the same sampling
techniques.

For pest management purposes, the needs of an immature onion mag-
got monitoring program are extensive and provide an excellent example
of how several sampling techniques can be structured to work towards
common goals.

In Michigan and other northern states, onion production is pri-
marily limited to organic soils. As formation of these soils typically
occurs in old lake and river beds, (Davis and Lucas 1959) its geograph-
ical distribution is highly aggregated, thus producing similar patterns
in muck grown crops such as onions. A characteristic organic soil
production region is the Rice Lake area near Grant, Michigan. This
muck area, an old lake bed, contains approximately 9 square miles of
organic soil and is farmed by numerous growers. Figure 2 provides
discrete boundaries of the muck area and shows those field which were
planted in onions during the 1976 and 1977 growing season.

The onion maggot, an obligate pest of Allium spp., is found at-

tacking onions throughout the Grant growing region. Few, if any,

106

Allium spp. are present in the surrounding areas; therefore, the onion
maggot population is primarily limited to commercial onion fields.

As adjacent fields are controlled by different owners, their
management policies, including onion maggot control, are usually inde-
pendent of one another. In contrast, onion maggot damage seems to
occur over a broader area, being unrestricted by actual field bound-
aries. Figures 27 and 28 are contour maps showing second generation
onion maggot plant damage over the entire Grant region for 1976 and
1977, respectively. The maps were constructed using the data as listed
in Appendix B with contour lines drawn through points of equal damage.
Each contour line represents the number of injured plants per 1 meter
section of row (approximately 23 onions per meter). These maps indi-
cate onion maggot damage as a regional problem with adjacent fields
showing similar density levels. The adult onion maggot is highly
mobile (Loosjes 1976); therefore, movement between surrounding fields
needs to be considered when designing and executing onion maggot man-
agement strategies.

Metcalf and Luckman (1975) stress the need for development of
pest management systems which operate at the ecosystem level, taking
into account the total pest population, its full effective range, and
other major factors affecting its survival and development. These
principles are presently being researched at Michigan State University
(Haynes et a1. 1977) and it has become obvious that to effectively
develop management strategies for the onion maggot, its density through-
out the total region is of key importance. In Grant the area necessary

for consideration is clearly the entire nine square mile muck region.

107

 

___Az

 

 

 

 

 

 

 

FIGURE 27. Contour map of onion maggot plant damage in Grant, Michigan
1976.

 

 

 

 

 

 

 

77777

 

109

The true onion maggot densities of every onion field within a
region would give complete knowledge to base management decisions on.
In reality, the absolute densities are impossible to acquire, thus
sampling estimates must be substituted. As previously described, data
collection for estimating the absolute immature onion maggot density
is quite expensive and quickly becomes prohibitive when sampling mul—
tiple fields.

An alternative approach to intensive sampling for absolute densi-
ties in every field is the construction of a hierarchical sampling
system. By using a less comprehensive sampling method initially, the
total sampling universe can be divided into portions of variable in-
terest. These subunits can then be dealt with in more specific terms
without impinging unnecessary methods, thus cost, on the total universe
of concern.

Using plant damage as an indicator of actual immature onion mag-
got densities, a regional survey involving every field within a region,
can be used to identify fields above and below a predetermined critical
density level. Field level plant damage sampling techniques give the
number of subsamples per field necessary for precision estimation at
the field level. Extraction of 10-100 foot plant damage subsamples
per field allows a damage density as low as 3% to be estimated with
a precision range of approximately 3.0. The exact time involved in
completion of a regional sampling program is dependent on the size of
the region and can be estimated from Equation 25. Using ten 100 foot

samples per field, the approximate completion time per field is less

110

than one hour, thus requiring approximately 25 man hours to sample a
growing area the size of Grant, Michigan.

Fields revealing damage levels below the level of interest should
then be eliminated from the sampling universe. Those fields showing
higher densities should be more closely evaluated using the extensive
sampling techniques for estimation of actual immature onion maggot
densities (see pages 80 to 102).

A total biological monitoring program for onion pest management
or even for onion maggot control may have many more components than
the above example, as many more biological entities are sure to be
involved (Haynes et al. 1977). With the addition of more components,
the costs of the monitoring system quickly inflates, thus coordination
or structuring of the system to meet multiple objectives simultaneously
is manditory.

Biological monitoring is much more than a simple sampling proce-
dure. It is a management system divised for optimization of specific
biological data collection given standard sampling techniques, re-

stricted resources, and a set of closely defined objectives.

SUMMARY

The spatial distribution of the immature onion maggot was evalu-
ated at various geographic levels. Aggregation or clumping was found
to predominate from the regional distribution of plant damage between
fields down to the distributional pattern of the maggot within damaged
onions. The negative binomial frequency distribution was utilized to
describe the majority of the observed sampling data. Although the NBD
typically fit quite well, no KC or common aggregation coefficient was
indicated above the within clump level. A common K was found for the
actual onion maggot counts within areas of damage, but it is not known
if the pattern holds between fields.

Ovipositional attraction was tested and preference for rotting
and/or rotting and infested onions was found to exist. These experi-
mental results are heavily supported from independent field data which
shows a 20-fold increase in egg density on previously damaged onions
over adjacent healthy onions. This behavioral biology in combination
with the spatial pattern of initial plant damage is felt to play a key
role in the mode and the distribution of onion maggot attack through-
out the season.

Sampling techniques were developed for estimation of both onion
maggot induced plant damage and actual age specific onion maggot den-
sities. Two stage sampling techniques were utilized for determination

of the optimal sample unit size and the optimal number of samples to

111

112

be drawn for precision estimation of regional onion maggot plant dam-
age. Sampling costs, evaluated in terms of time units, were also
measured and incorporated into the overall analysis. A similar anal-
ysis followed for determination of the optimal sampling methods for
within field plant damage sampling. As with the preceeding section,
sample unit lengths, sample sizes, and sampling costs were all evalu-
ated.

Stratified random sampling techniques were used in the develop-
ment of sampling methods for age specific onion maggot density estima-
tion. Stratification was based on visual plant damage symptoms pro-
duced by onion maggot larvae feeding in the onion bulb. A comparison
of age specific sampling using simple random sampling, proportional
stratification, yearly average stratification, and optimal stratifica-
tion clearly showed the utility of the techniques: sampling precision
was increased while it reduced the number of samples actually extracted.

Development of more comprehensive biological monitoring programs
was discussed in general terms, stressing the differences between
standard sampling methodology and the more inclusive objectives of a
biological monitoring program. An example program for regional onion
maggot density estimation, using a hierarchical sampling scheme, was
also presented.

This study was designed to address several questions relating to
immature population monitoring of the onion maggot. It is hoped that
these findings will serve future researchers in their studies pertain-
ing to the population biology and eventually the population management

of this insect pest.

APPENDICES

All Data Files in the following Appendices can be found in a User

Permanent File, 7-track tape, VRN=UP1200.

APPENDIX A

Temporal Distribution

 

The onion maggot is multivoltine with a variable number of gen-
erations found throughout its geographic distribution. In Michigan,
typically three generations per year are noted. The females exhibit
a cyclic ovipositional pattern and remain gravid over an extended
period of time (Missonier and Stengel 1966). This extended oviposi-
tional activity allows an overlapping of life stages and under some
conditions an overlapping of generations.

Although the temporal distribution is not the main thrust of this
study, population phenology is important in various types of entomolog-
ical studies. It is believed that the inclusion of such information
will aid in future interpretations of this study and will develop a

better understanding of the onion maggot biology as a whole.

Developmental Zeros and Heat Accumulation Requirements:

Numerous observations concerning the developmental rates of g,
antiqua have been made under a variety of laboratory and field condi-
tions. Ellington (1963) reviewed the literature concerning this area
and tabulated the results. Finding the existing data inconsistent,
Ellington conducted laboratory experiments to define the developmental

rates for eggs, larvae, pupae, and preovipositional adults at various

113

114

constant temperatures. The data presented by Ellington was in the

. 0
form of days for development for a series of temperatures (50 , 600,

70°, 80°, and 90°F).

Additional developmental data was obtained through the University
of Guelph (Ritchey, personal communication 1977). The data consisted
of mean days for development of the egg, the first, second, and third

instars and the pupal stage given six temperatures (50°, 54.50, 590,

65.5°, 68°, and 77°F).

To determine degree-day accumulations it is necessary to first
establish lower limit thresholds, below which no development occurs.
Threshold determination is typically done by plotting percent develop-
ment per day over a range of temperatures, finding the point at which
the regression line crosses the x axis, and defining that point as the
lower threshold.

The accuracy of this method depends on two major assumptions:

1) that the data (original or transformed) is linear, and 2) that the
test temperatures include or approach the suspected minimum develop-
mental threshold.

Both data sets used a low temperature of 50°F (100 higher than
the suspected threshold base). Since the range of extrapolation is
large, care must be taken in the use of regression analysis. Regres-
sion analysis was performed on the linear portion of the data to ap-
proximate the base temperature (Figure A-l). Another method, standard

error determination (Casagrande 1971) was also used to estimate the

base temperature. This method uses several temperatures bracketing

PERCENT DEVELOPMENT/DRY

115

   

3RD INSTRR ONION HRGGOTS

fi-i

 

 

T l T
30 4o 50 60 70 80 90
TEMPERATURE (F)

FIGURE A-l. Regression method for determination of developmental base
temperature for third instar larvae.

116

the suspected true base temperature for calculation of degree-day
accumulations. Standard errors are calculated for each base tempera-
ture (Figure A—2). The minima of the standard error function deter—
mines the base temperature that best fits the given data set.

The short developmental stadiums for the egg, first instar and
second instar necessitate very short periods of time between samples
if the data is to be used for threshold determination. Ellington's
daily sampling was not precise enough to use for such calculations.
The longer third instar and pupal stadium did provide suitable data
for this analysis. Table A-1 presents this data along with the mean
and its 90% confidence limits.

Mean degree—day accumulations for the egg, larval (first, second,
and third instars), pupal, and preovipositional adult stages are
listed in Table A-2 along with their sources. Ellington's data for

instar l and instar 2 was omitted because of low sampling frequency.

Population Maturity

 

Fulton (1973) discusses several methods for evaluating population
age distributions or maturity through time. The weighted mean instar
(WMI) maturity scale was extremely useful as the population age struc-
ture is represented as a single number. Weighted mean instar is

calculated as in Equation A-l.

t . t
P _
WMI = , i 1 Ni/. PiNi (A 1'
1:1 1=1

where: Pi proportion of total developmental stadium spent

in life stage i

2
II

number of individuals in life stage i

number of life stages being evaluated

117

64
1

4 380. INOTRRS

STRNDRRD ERROR (Sy)
2
1

 

 

cga

°°‘ 240. tusmns
.J

O ‘ I ' T fl T i T ' I '7 r ‘r 1
24 20 32 36 4o 44 40 52

DEGREE-DRY BHSES (F‘)

FIGURE A-2. Standard error method for determination of developmental
base temperature for second and third instar larvae.

118

TABLE A-l. Developmental base temperatures of third instars and pupae.

 

 

Using U. of Guelph's Data Third Instar Pupae
Linear regression 39.0 42.1
Standard error analysis 39.5 41.0

Using Ellington's Data

 

 

Linear regression 37.5 37.0
Standard error analysis 36.0 38.0
i = 38.76 s = 2.06
8x = 0.728 t90% = 1.86

§ with 90% confidence limits 38.76 i 1.37

 

119

 

 

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120

Computation of WMI for the immature onion maggot population
(0 = E, l = first instar...4 = pupa) from the data collected in Grant,
Michigan is plotted along with the mean adult activity trap catch.
from the same area (Figure A-3).

Adult population phenology has been evaluated using degree days
by several researchers (Eckenrode et al. 1975, Libby unpublished, and
Vail unpublished) and has been suggested as a method to help time adult
control measures (Eckenrode et al. 1975). These studies, conducted
in New York and in Michigan, report similar degree-day requirements
for peak adult flight activity (Table A-3).

Although the heat accumulation between these peaks corresponds
closely with that necessary to complete a full generation, care must
be taken when predicting second and third generation emergence. This
method does not account for major population time shifts due to mor-
tality. Figure A-4 shows the expected adult activity peak (arrows)
using the degree-day model of Table 21 (Michigan values). As the
figure clearly indicates, large deviations from the expected peaks can
be noted. This deviation from the expected is thought to have been
caused by high immature mortality early in the growing season. Larval
damage was expected as early as 700 degree—days (initial emergence
and preovipositional period) but was not encountered until 1650 degree-
days, well in excess of the required accumulation. The hot dry con—
ditions which persisted through the early growing season, in combina-

tion with the granular insecticide placed at planting, were presumably

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TABLE A-3. Average degree-day accumulation for adult onion maggot
activity peaks from New York and Michigan. (Eckenrode
et a1. 1975, and Vail unpublished).

 

 

lst GENERATION 2nd GENERATION 3rd GENERATION
LOCATION PEAK PEAK PEAK
New York 0 710 1900 3150
base = 40 F
Michigan 670 1710 2920

base = 39 F

 

124

responsible for the high mortality rates. Shifting the expected second
and third generation peaks by this 900 degree-day deviation (stars)
explains the majority of the observed deviation.

In summary, phenology models which operate on developmental rates
exclusively must be used with caution; critical deviations from the
expected are possible. With the inclusion of environmental mortality

or on-line monitoring such models could produce more reliable results.

APPENDIX B

Introduction

 

Current onion maggot control strategies consist of a granular
soil insecticide at planting for control of larvae and directed foliar
sprays for control of the adult flies. Michigan recommendations (Cress
et al. 1976) call for one of three currently registered granular soil
insecticides (Dansanit, Dyfonate, or Ethion) to be applied at planting.
In addition to these recommendations a warning was issued which states:

Dansanit has given erratic control for the past
year or two, particularly in the Grant area.
Growers are advised to use maximum rates and
correct applications.

Dansanit gave spotty control in several fields which were composed
of various mixtures of Martisco, Houghton, Edwards, Deford, and Tawas
muck soils (soil types determined from Mokma and Whiteside 1973).
Although the damage was not quantified within or between fields, local
onion growers and county extension agents felt that a significantly
higher amount of onion maggot damage was found in connection with
tiled drainage ditches and fields high in marl content. Edwards,
Martisco, and Houghton mucks are all found overlaying a marl base.
Marl, a highly alkaline material, is also found in areas of the Grant
swamp. Fields composed chiefly of Martisco mucks (marl less than 16"

from the soil surface) are usually avoided for onion production, but

both Edwards-Martisco combinations (soil type E, Mokma and Whiteside

125

126

1972) and Houghton (HM) mucks are used extensively in Grant, Michigan
for onion production.

The fields containing the marly drainage ditches in question were
composed mainly of Edwards and Houghton mucks (marl 16" to 54" from
soil surface). These tile lines were visually apparent in the field
and appeared as whitish-gray strips 3 to 4 feet wide and extending the
majority of the field's length. The color differential is due to the
calcium carbonate particulate matter found within the strips. The
material was lifted from the underlying marl base at the time of tile
installation and was mixed throughout the trench backfill.

Speculation by both Dr. Don Cress, vegetable extension entomolo-
gist (Michigan State University) and Bob VanKlompenberg, district
extenstion horticulture agent, suggests that the increased alkalinity
within these marly strips causes an acceleration in the chemical de-
gradation of the acidic reacting insecticides. An acceleration of
the chemical breakdown could account for increased onion maggot sur-

vival in such areas, thus explaining the 1974-75 observations.

Methods

A project was designed in the spring of 1976 to test the hypothesis
of early chemical degradation in high alkaline tile line ditches using
population damage as an indicator. The experimentation was conducted
in the Rice Lake muck area near Grant, Michigan where many of the
initial observations concerning this hypothesis were first noted.

In 1976, the initial year of this project, Dansanit was removed

from the market in the formulation registered for use in onion maggot

127

control. Most growers replaced Dansanit was another organophosphate
insecticide, Dyfonate. Dyfonate was never cited in the 1974-75 obser-
vations of damage in marly soil. However, because the chemical reac-
tion of the two insecticides in the soil is thought to be similar, it
was felt that this study should continue (Cress personal communication

1976).

Within Field Analysis

 

A field in section 10 of Grant Township (Field #10, Figure B-l)
which was composed primarily of Houghton muck, was selected for the
within field experimentation. The field was chosen for three reasons:
1) the field was noted in 1975 as having moderately high damage levels
from the onion maggot with a substantial over-wintering population
known to exist for the 1976 growing season, 2) some of the preliminary
observations concerning the hypothesis in question came from the
adjoining fields, and 3) the grower was willing to allow the removal
of onions from his field for sampling purposes.

The field was tiled in a north—south direction with a drainage
canal on the southern parameter of the field. Existing tile lines
could easily be located. As expected, the location of the tile lines
coincided with the whitish-gray marl streaks visible in the field.

The onions (downing yellow globe) were planted parallel to the
drainage tile extending north from the canal approximately 1/4 mile.
The onions were planted in six foot bands consisting of eight rows per
band with a 1 foot tractor break between each band. A broadcase ap-

plication of 300 pounds per acre of potash was placed before planting

128

 

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129

and then supplemented by a within furrow application of an 8-32-16
fertilizer mix with 3% Manganese at the rate of 600 pounds per acre.
Dyfonate was also applied as a within furrow application at the rate
of 15 pounds per acre.

Two sample plots were laid out with respect to tile line location
and areas of the field. Plot A was an onion band exactly overlaying
an existing marly tile line, while plot B was equal distance from two
such lines, thus making a nearly independent area with respect to the
marly areas. Both plots were 200 feet in length and were kept 200
feet away from the edge of the field to avoid any edge effect in the
distributional pattern of the maggot.

Ten one—row meter long samples of onion plants were randomly
removed from each plot four times during the growing season. These
plants were examined for onion maggot damage and the percent damage
was calculated. In addition to the plant damage samples, ten soil
samples were collected within each plot at the time that significant
damage was first noted (July 1, 1976). These samples were submitted
to the soil testing lab on campus where the analysis was performed.
The soil parameters measured were pH, phosphorus, potassium, calcium,

and magnesium.

Between Field Analysis

Between field sampling was also carried out in connection with a
regional monitoring program. The objective was to compare field
damage estimates with field soil types as given by Mokma and Whiteside

(1973). The soil types analyzed were: 1) MF/CM (muck overlaying shallow

130

sand), 2) CM (muck 16" to 51" deep overlaying sand), 3) E (Martisco-
Edwards muck 0" to 16" deep overlaying marl), and 4) HM (Houghton muck
16" to 51" deep, overlaying marl). For detailed descriptions of each
soil type (MF, CM, E, and HM) see Mokma and Whiteside (1973).

Twenty three fields were sampled in 1976, and 17 fields were
sampled in 1977 (see Figure B-l). Fifty random samples (1 meter in
length) were taken in each field. The number of damaged and healthy
onions in each sample was recorded along with notes concerning any
special observations (i.e., occurrence of other diseases, special soil

conditions, heavy wind damage, etc.).
Results

Within Field Analysis:
The sample results from the within field damage were analyzed as

paired sets through time. The null hypothesis Ho: u = “B with the

A
alternative hypothesis H1: “A # “B was tested for each sampling date.
The results of these comparisons are in Table B-1. The null hypothe-
sis (Ho: pl = 02) was easily accepted for every sampling date at the
95% confidence level (0 = 0.05).

The results of the soil sample analyses conducted on July 20,
1976 are presented in Table B-2. The analysis of soil plots A and B

closely followed the procedure set for the bioassays. Tests were con-

ducted for each variable between plots A and B. The null hypothesis

11:11

0 A = “B was tested and accepted at the 95% level of confidence

(0 = 0.05).

131

 

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132

It should be noted that for soil pH at the 90% level of confidence,
the null hypothesis could be rejected and the alternative hypothesis
Hi: “a # “b accepted. As the predetermined level of confidence was
set at 95%, the null hypothesis is still accepted.

Between Field Analysis:

The between field damage sampling analysis was more complex than
the within field study due to the field distribution of onion maggot
populations. Onion maggot damage appears in a clumped or aggregated
pattern within and between fields. There are several reasons for
this aggregated distribution of onion maggot damage: 1) the irregular-
ity of chemical insecticide application at planting (missing of spots
within the application area), 2) the variability of abiotic factors
enhancing survival (soil moisture levels, etc.), 3) the ovipositional
attraction of gravid females to previously infested and/or rotting
onions, and 4) the higher survival rates of larvae attacking previously
damaged onions. This clumping or aggregation causes problems in the
application of parametric statistics such as Analysis of Variance.

The sampling data fit a negative binomial distributions with no
common K (see pages 24 to 42). By using the transformation log (x + 1),
many negative binomial distributions can be normalized and the assump—
tion of the analysis of variance met. Due to the high aggregation of
the onion maggot population under these field conditions and the low
population densities found in many of the sampled fields, these data

could not be normalized, which rules out the use of parametric statis-

tics.

133

Several non-parametric statistical tests are available for anal—
ysis of such data. The Kruskal-Wallis one-way ANOVA was chosen
(Siegel 1956, and Nje et al. 1975). The analysis examined damage
levels and soil types within each year and in the two years pooled.
Table B-3 summarizes the data of that analysis. Although some other
differences are noted throughout the analysis, the one soil type that
is significantly different (0 = 0.05) throughout every test is soil
type B. This soil type was found having significantly lower damage
levels (see Table B-4) than the other soil types examined. Soil type
CM was found low in damage during 1976, but had higher damage esti-

mates in 1977 and its mean rank was readjusted.

Conclusion

 

The paired bioassay analyses of the marly tile area versus the
area between tile lines indicated no damage difference between the
plots throughout the growing season. Moderate damage levels were
noted in many areas of the field and visual observations of those
damaged areas showed no noticeable preference for marly areas.

The soil analysis performed indicated there was little difference
between the plots in the parameters that were measured. Many of the
other unmeasured micronutrients may actually be more important in
the breakdown of a soil insecticide. If the bioassays would have in-
dicated a significant difference, more intense soil analyses and chem-
ical testing would have been pertinent.

The between field analyses seem to indicate an effect opposite

that which was expected from the hypothesis in question. The soil

134

 

 

 

 

 

 

 

 

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135

TABLE B-4. Ordering of ranks (onion maggot damage) by soil type for
the Kruskal-Wallis ANOVA of Table B-3.

 

SOIL TYPES AND ORDER OF MEAN RANK (1 = high)

 

YEAR ......................
MF-CM CM E HM
1976 1 3 4 2
1977 3 1 4 2
1976-77 3 2 4 1

Pooled

 

136

type highest in marl content seemed to consistently show lower damage
levels than any other soil type examined. Overall, this study seems
to indicate that areas high in marl concentrations did not increase
the insecticide decomposition rate to a level detectable by natural

population difference.

Grant Onion Maggot Survey Data

1976

Field Number

Soil Type (1-4 as defined in methods section)

N
F
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FN ST DP

FN ST DP

ST DP

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1111111111111111111111111111111111111111111111

137

138

1976 Grant Onion Maggot Survey Data (continued)

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139

1976 Grant Onion Maggot Survey Data (continued)

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1976 Grant Onion Maggot Survey Data (continued)

10
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10
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10
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140

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1976 Grant Onion Maggot Survey Data (continued)

13
13
13
13
13
13
13
13
11

-J—8A-A—h—J—b-JNNNNNNNNAJNN[UNNNNNNNNNNNNNNNNNNNNNR)N.UNNIUNNNNN

AOOOOOOOOOOOOOOCOtOCOOO—i—bOOONOWOIVOOOOOOCOOOOOOOOOOOO

1L1
111
1L1
1L1
1L1
1L1
111
111
111
111
1L1
1L1
111
1L1
121
1L1
1L1
1L1
111
111
111
111
111
111
1L1
111
111
1L1
1L1
111
1L1
1L1
1L1
111
111
111
111
111
1a
1L1
121
1L1
15
15
15
15
15
15
15
15
15
15

NNNNNNNNNMAAAAAAAAAAAAAAAAAAAAAA_.s_a_a_;_s_s_s_;_4_.s_sd_a_s_a_s_s_a_a_;

141

NNNNNNNNNNNNIUNNIUNN1UK1NNNNNNNNNNNNIVNIUNIUNNIUNNIvNNIUNNNNNN

OOCOOONOOO-‘OOOOOU‘AOCOOOOOOCOOWNOOOOOOOOOOOOOOOC‘CWN-‘O

' d
1976 Grant Onion Maggot Survey Data (continue )

16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
16
17
17
17
17
17
17
17
17
17
17
17
17
17
17

NIUNN.UNN1\J
NNNNNNIU

lUf\)1\)1\)Nl\)l\)

\JNlUNNN

\)l\)f\)&)1\)|\)l

:31\)|\)|\)1\)I

333333

33:34::

, OOC
OC-JO-‘OC—b
NNOO-‘ON

COOOCO

ONIJOOO

1

17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
17
1'7
17
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18

142

"‘ «1:34:53:
32:34-4‘:
3:42.534:
4: “42.233332235523333
A—I-J—J—b—b—A—é 4.
A—J-J-J—bd—tu-A

O—‘C‘w
O-AOOOwO
L'OOOOOO
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.30»-

, OOOONU‘O

OOOOOC‘O

Add—:44...)
_.s_s_.|._s._s._n_s._h_a
_.|_s_a_s._|_a_.|_a__|

._|_.L._L_b._t_.s__|_s

34:53:54

1:53:47:

3:47:55:

O-A-‘O
ozwoz—so

. OOOONNO

OOOOOOC-fi

OwOOOO—b

OOOOCOO

NOOOOO

OOOOOO

1976 Grant Onion Maggot Survey Data (continued)

NNNNNNNNNNNNNNNNNNNIUJ:1:34:31:35:55:233353523353334:23:33

O—‘OOOOC‘OOOOOOOOC‘OOOOOOfiOOCddwodOOW—bOOOOOWOOOOOdO—IOO-é

20
20
2O
2O
20
2O
2O
2O
20
2O
20
20
20
20
20
2O
2O
2O
2O
20
20
20
2O
20
20
20
2O
20
20
20
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21

’21

21
21

143

2

#2::33334:332:33:txtt-z-B’NNNNNNNNNNNNNNNNNNNNNNNNNNI'UNN

21
21

A

21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22

22
22
22
22
22

. mmmmmmmmmmmmmmmmmmmmmmmmzzzxrzzzzz:zzzzzzzzzxrzzzzzn:4:4:-

OO—J—‘OOOOOON—‘OOUJOANOOO'JOC‘C‘OOOOOCOOCOOOO—‘OOOOCOO—‘OOO—‘OO

144

1976 Grant Onion Maggot Survey Data (continued)

 

m
I
X
3
,UAU,U,U,UAU.I,UAU,U”U,UAU.U“UrUAUA2ru“UAUnUAUAUrU.I “no;nu,U.UFU,UAUAUAU.UAUnUnUnUnUnUnUnUAUnU U
no
u
22222222222222222222222222 2pT33333333333333333333
Q6
I
QJQJQJQJQJQ.232525QJQJQJQJQJQJQJQJQJQJQJQJQJ3 3,UnU XHN u.u.u.u.u.u.u.u‘u.u.Ozo/o/QJo/QIQJQIQIQ,
9.9.9.92929.9.929.929.22122222222212“2222222222 9 P.
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UF
Sm
9.9.9.9.9.9.9.9.9.9.7_Q_9_9_9_929.92929.929.929.92929292 +.T.W“5)?)QJ25239)a;239353QJQJQJQJQJQJQJQJQJQJ
mom
7
3333333333333333333333333333 87N44444444444444444444
9.929.929.92/_9.92 2:29.929./_2.21222n2n2n2n2n2n2n2n2a2 aam_f
MUD
C
n
O
.l
n
no
pl
A2AUHUA2nunUAU.I“Un2rofl2hqu.l,U.2,U,UnurunU,UnU,UnUnU,U +L nuAUAUAUAUAU“UAU“UAUAUAUPUAU.UAU“UAUAUAUAU
n
m T
22222222222222.22222222222222 G 8339,33333333333333333
9.9_?_9_92927.2 9.9.2 2_/_ .222225212222 2.22212 2A2 2A2 N”nwnwuvu.u.u.u.uwu.utn.u.u.u.u.u.u.u.u.u.
n2n2 2:2 2 2.2.2 222222222 212 2222222, 2 2.222222222 at

1977

1977 Grant Onion Maggot Survey Data (continued)

__s_.;_s_;_s_s_.a_a_a_s
OOOOOOOOOOQQOOOOOOOOOOOOQ

-|
C.‘

_L_s_.s_.s_s_s_.|_.s_s_|_s_a_s_.s__s_;_s_a..s_s..|_s_s_.s_s_.r

OOOC’OOOOOC’OOOOOOOC‘OOOOCOOOCOOOOOOOOOOOOOOOOOOOOOOOOO

1O
10
1O
1O
1O
1O
1O
1O
1O
1O
1O
10
1O
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11
11

145

NNNNNNNRDIU[\1[\1|\)I\)1\)1\31\)l\\|\)1\)l\)IUN|\1|\)R3NR1K3R1NNNNNIVR3K3R3NSSS-fitttkczttk

JOOOOOOOC‘AGO—‘OOOOO—boOOONOOOOWOO401UO—‘OOOO—‘OOOOOOOCOOO

11
11
11
11
11
11
11
11
11
11

11
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
19

12
12
12
12
111
111
111
1a
1L1
1L1
1L1
111
1L1
111
111
1L1
111
111
1L1
1L1

d-J—as—A—A—I—b—J—b—8--|--b—I-|—I>—-‘fit-l:J?12:32:53.1:fizzc'ztz-C’E£334:1\)|\)I\)I\JNI\3NNNNR)

146

1977 Grant Onion Maggot Survey Data (continued)

14 1 0 16 2 O 17 u 0
1L; 1 0 1o 2 o 17 u 0
1M 1 0 16 2 O 17 u 1
1h 1 0 16 2 O 17 u 0
1H 1 O 16 2 O 20 2 0
1M 1 0 16 2 0 20 2 1
14 1 3 17 u 0 20 2 1
14 1 O 17 u 0 20 2 2
16 2 0 17 H 2 20 2 3
16 2 O 17 u 1 2O 2 1
16 2 1 17 4 O 20 2 O
16 2 0 17 14 0 2O 2 O
16 2 0 17 H O 20 2 1
16 2 0 17 u 0 20 2 O
16 2 O 17 M 0 2O 2 O
16 2 O 17 u 0 2O 2 0
16 2 U 17 H O 20 2 O
16 2 1 17 h 0 20 2 0
16 2 O 17 u 0 2O 2 O
16 2 0 17 u 0 20 2 O
16 2 O 17 4 ,0 20 2 1
16 2 1 17 11 0 2O 2 U
16 2 0 17 u 0 20 2 O
16 2 O 17 4 0 2O 2 O
16 2 0 17 M O 20 2 1
16 2 O 17 14 0 2O 2 0
16 2 1 17 11 2 2O 2 O
16 2 O 17 H 2 20 2 0
16 2 0 17 3+ U 20 2 0
16 2 0 17 u 0 2O 2 1
16 2 1 17 H 1 2O 2 O
16 2 O 1'7 N O 20 2 O
16 2 O 17 11 0 2O 2 2
16 2 1 17 u 0 2o 2 0
16 2 1 17 u 0 20 2 1
16 2 0 17 N O 20 2 8
16 2 1 17 H 0 20 2 1H
16 2 0 17 u ' 0 20 2 2
16 2 0 17 U 0 20 2 0
16 2 0 17 U 3 20 2 1
16 2 2 17 u 3 20 2 1
16 2 1 17 H 2 2O 2 O
16 2 O 17 u 0 20 2 3
16 2 0 17 N O 20 2 O
16 2 O 17 H 0 2O 2 1
16 2 0 17 u 0 20 2 0
16 2 O 17 H 0 2O 2 O
16 2 O 17 11 O 20 2 0
16 2 o 17 l: o 20 2 o
16 2 0 17 N 0 20 2 1
16 2 O 17 4 0 2O 2 0

1977 Grant Onion Maggot Survey Data (continued)

20
2O
2O
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22
22

[\1NNNNN1VIVNNNNNNR)1\)1\)I\JI\)1\)|\)1\)IUNR1I\)I\11\3I\)I\3NNNN1\)1\)N1\)N1\)1\)NN1\1NNNNNIUN1\1

OOOOOOOCOON-‘OOO—‘OOCOCOOOCOOOO—A-fi—i-‘O[VA—IOWUW—‘WN-l—‘OOOOOOC‘

22
23
23
23
23
23
23
23
23
23
23
23
23
23
23

147

HNNNNNIVIUIU[UNNNNIVNNNIVNNIUNNNI’UNNNIVR‘NR1R1NNNNNNNNNIVNIVNNNNNN

OOOOOOCCOOAOOOOOOOO—‘OOOOOOOC‘OOO-‘OC‘OOOOOOOOOOOOOOOOOO

211
211
211
211
211
211
211
214
211
214
2’4
214
211
214
211
211
211
211
211
214
2’4
214
211
211
211
2’4
211
211
211
2’4
211
211
2’4
211
2’4
211
211
2’4
211

211
211
211
214
211
2’4
211
211
211
25

25

dédédéd—k—J—h—I—ld—é—Q—fi—Q—IAAA—JAdd—J—Q—I—b—I—I-A—Iu—k—J—AA—I—h—A—I—b—Ad—J—JAAJAAJ

OOOOOOCO-‘OC’OOOOOOOOOOOOOOOOOOOOOO—‘OOOOOR’OOO—‘NOOOOOOO

ontinued)
t Onion Maggot Survey Data (c
1977 Gran

25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
25
26
26
26
26
26

u—J—J-J
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A

A—A—J—A _3 A

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d—J—J

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NNNR1|UJ

0000
00000
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OOOOO
OOOCOOOOOOOOOO

'OOOO

OOOOC

OOOOO

COO—3d

26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
26
27
27
27
27
27
27
27

148

NM
[VIVN’IVN
NEUNIVN
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OOOCO

OOCOO

1977 Grant Onion Maggot Survey Data (continued)

28
28
28
28
28
28
23
28
28
23
28
28
28
28
28
28
29
29
29
29
29
29

COOOOOOOWOOIVOOOOOOCDOOO

29
29
29
29
29
29
2Q
29
29
29

149

WWWWWWWgflWWWWWWWWWLJWWWW

OOOOOOC’OOOOOOOOCOOC‘OOO

wwwwwwwwwwwwwwwwwwwwww

OCOOOOOOOOOOOOOOOOOOOO

APPENDIX C
Regional Plant Damage Sample Data

(REP = Replicate, DIS = Distance, OBS = Observation)

Bravo 5/26/77 - 6/2/77
CDDAOMSURVEYBRAVOTl (12,13,12)

 

REP DIS OBS REP DIS OBS REP DIS OBS
1 60 1 h 92 5 2 75 1
2 o D 5 64 1 2 8M 1
3 o o 6 3n 3 2 97 2
u 25 1 7 o O 3 6 1
h 96 2 8 o O 3 12 1
5 o o 9 59 8 3 24 2
6 9o 2 9 68 10 3 25 2
7 o 0 10 o o 3 27 2
8 D 0 1 o o 3 3n 1
9 28 1 2 50 6 3 39 3

1o 10 1 3 o 0 3 52 A
1 o 0 u o o 3 6o 1
2 o 0 5 o o 3 6n 2
3 O O 6 2H 5 3 66 1
b O O 6 28 2 3 68 1
5 O O 6 29 2 3 7O 1
6 o 0 6 3o 1 3 96 1
7 O 0 6 31 1 A o o
8 O O 6 38 8 5 1 1
9 O O 6 60 6 5 1o 1

10 o 0 6 61 6 5 32 3
1 o o 7 0 o 5 33 2
2 o o 3 o 0 5 90 3
3 0 0 9 o 0 5 100 1
A o 0 10 o o 6 2 1
5 o o 1 7 1 6 7 1
6 O U 1 37 2 6 28 1
7 o O 1 60 u 6 he 3
8 0 o 1 6h 1 6 80 2
9 O O 1 7h 1 7 76 2

10 o o 1 86 1 7 79 2
1 o o 1 90 3 8 9 1
2 0 0 2 8 2 8 3o 2
3 10 u 2 16 1 8 M3 1
3 8M 10 2 26 2 8 8M 1
u 6 5 2 27 2 9 3H 1
5 us 2 2 u3 3 9 6n 2
N 68 10 2 M6 3 9 95 1
u 88 7 2 M8 1 10 5a 1

150

151

Regional Plant Damage Sample Data (continued)

6/2/77 - 6/7/77

Grant

(12.13.12)

CDDAOMSURVEYGRANTTl

SOOOOOO2O.UO1OOn/_hfi0000000

u._000000300070052_U0.UOOO0

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1 All All

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510.06 0.0 Q... .

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7. 0,0, .4

1
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5 .10 4138

1.QaiJu.RJho7.Qoo/no1.9.2;h.R1h671QJQ/no1.1.1.1.
4' 1|

5/31/77 - 6/15/77

Lapeer

(IZ,I3,I2)

CDDAOMSURVEYLAPTI

112011111u1¢11112200111rd

09. 00“?

071 «4134171008 27..
88 1117.31.45 8

7| “.3 .b0
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23u5078901n43u5.b789011
1| 1|

OOOOOOOUOOOOOOOOOOOOO

OOOOOOOOOOOOOOOOOOOOO

17.3.4r9578901n43145678901
41 1|

Regional Plant Damage Sample Data (continued)

7
8
9
1O
1O

.4
O

4.1.4.4.)..- __‘
kWNNd-J-J—‘d-d444—3100OOOOOOOOOOOOOOOOOOOJDJ)Q-Qowzwm-‘O

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13
61-1
77
86
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100
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80
100
110
L11
1
38
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1

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61
67
71
75
81
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91
92
93
16
211'
25
26
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L1
36
66
67
68
69
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71
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100
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99

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118

Noggggaa—bafl—Am-xl—tK3-J—‘Nr-fifi‘o—‘NW—‘K-AUJK1—‘K1-J—‘N—3-JdK‘OO—‘OOW—‘N-JAOOO

152

51-1
52
98
100
36
96
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28
32
116
88
22
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214
28
30
311
36
116
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58
60
611
72
78
88
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82
98

6
8

MRS-am—bwwtO—I—‘NAN-ANt-A—bw-A—b—le—sdamawklwWK14aam—s1vwr—sm—s—s—smrvo...

9
16
10
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000

10
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100
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38
110
62
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22
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22
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811

611

ad—‘WNOOOOOO-‘CJN—‘dOOOOOOC-OAOON—b—S—IUONO—bxlNO—é-AOdw—I—b—b—IN—b—t-A

153

Regional Plant Damage Sample Data (continued)

Tricounties

6/10/77

6/6/77 —

(12,13,12)

CDDAOMSURVEYTRITI

OOOOOOOOOOOOOOUOOOOOOOOO0000510000241Ond2213321u13

00000000000000UOOOOOOOOO.UOOO1OOOOOM30AJQQJ1
5.1 15 1n

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«1 1| 1 14.111114111111411

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000000000000000000000000060.0000anOOOOOOOOOOOOOfiwO

7890123“5.b78901nd3u5107890123..“567890123856789012Fd3
4|. 1| 1| all 1|

oondnUOOOOOOndnCOOOOOOOOOn/TUU00000000n¢190000000000000

n u

123u5.b7-8901123h5678901”(3195.1078901233u567890123u56
«1 1.. 4| 1

154

Regional Plant Damage Survey Data (continued)

7/7/77

Bravo

(I2,I3,12)

CDDAOMSURVEYBRAVOTZ

000000000550000058

00.000000001OOOOOAIO
“U. 50,

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1.

069.
146

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1| 4|.

1|4l|

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1.1.1.1.L.U.RJQquOJO/O/ 414124

11111122rd2fi423ur3555r96

7/20/77

7/19/77 -

Grant

(I2,I3,12)

CDDAOMSURVEYGRANTTZ

00000.UOOOOOOOOOOOOOOOO.UOO

OOOOOUOOOOOOOOnUOOOOOOOUOO

b789012385.678901nd3u567890
1| 41 1|

0000000OOOOOOOOOOOOOOOOOO

00000006000OOOOOOOOOOOOOOO

1231a.Sbfl-80;0123.u.5678901123u.5
«1 1|

02.20.1361.O210.UOOIH~/.OOOOOO1IO

00005800000000
7-00 0

1|

1.rial“.55556778901223u567890
41 1|

155

Regional Plant Damage Sample Data (continued)

(12,I3,I2)

CDDAOMSURVEYLAPTZ

7/13/77 — 7/22/77

Lapeer

3125ndu111,1032221|2262~C2211.121111211/011111u1113132111

”01u025n50225.5u. 5:130 0U.01U.9.U.Q102U. 257573.50) 1140u6 0208
89 111659 2L». :778 955 111:3 5.0106889 22333 “L456

100

“.IH55555555.b.b6.00.06666677..88Q.8888888888888999999999

nun69au.9.?49.9.9.9.1.L.Ru1.1.1.1.RJKJ1.1.QJ1.1.1.1.1.1.1.R1921.1.1.QJQJ1.1.1.9.9.9.92U.9.9.9.1.

12070.46 80 2.“.
1112

102 0:8 0.7.00
21414 14555 510781

20

0 2: La.
36 689 7

90111111111111111112hdnd9.7.29.7.2222222222,/.»/_33333U.U.14r4
1

000000000000000000000.0000000000000000.00000000000

000000000000000000000000000000000000000000000000

17.3“5Au7890123.45.57.59019.385.578901r43u56789019....555678
1 1 1 1

156

Regional Plant Damage Sample Data (continued)

0000101111.U101111111121131.40

“U
7 89

2

000450,.‘5uubflah5.b_b7-0
9 11:2 2333“““66

9

4| 1|

003u210200001105000n451110000

3827. u 25 2 830.“.0
4|. 1|

567788901”d3uuu567899001nd3h56
1| 1|1|

332..”6119—31122212301300111113

2

001...“..0288
"(9333.14

Tricounties

7/6/77 — 7/20/77

(12,13,12)

CDDAOMSURVEYTRITZ

OOOOO.UOOOOOOOOOO

OOOODUDODOOOOOOO

3h567890123u5ro78
1:

0000000000000000

0000000000000000

7890123..“56789012
1| 1

1.000000000000000

3000000000000000

1n/_3.h.5.078904|23u.5ro
1|

157

Regional Plant Damage Sample Data (continued)

0O0000000000000OOOOOOOOOOOOOOOOOOO

00000000HUD00000000nU000000000000000

78901nd3|95ro7890123Ju5678901~43u5.67890
1| 1| 1 1|

OO0000000000000oonofluOOOOOOOOOOPUoooo

00000000000000OOOOVOOOOOOOOOOOOOOOO

3.“.5.ID78901I2A<JHHr910789017.3“5/07-8904|r¢3145.10
1| 1 1|

OOfluOOOOOOOOOOUOOOOOOODOOOOOOODUOOOO

OOMUOOOOOOOOOOOOOOOOOOOOOOOOOO00000

90123u5r07QJQJnU1I9.35510789_U1n/_3|u.Bran/.89HU17.
1| 4| 4|: 1|

158
Regional Plant Damage Sample Data (continued)

Bravo 5/13/77 - 5/1u/77
CDDAOMSURVEYBRAVOTO (12,13,12)

1 A 1 3 0 O 9 M6 3
1 13 2 h 13 1 1O 0 O
1 19 1 5 27 17 1 O O
1 2M 1 5 3H 2 2 O O
1 27 1 5 A6 1 3 O U
1 #3 1 5 62 1 u 0 0
1 9“ 1 5 83 1 5 O O
1 71 1 6 53 1 6 O 0
1 78 2 7 0 O 7 O O
1 83 1 8 0 0 8 0 O
2 7 1 9 3 1 9 O O
2 93 1 9 A2 1 10 O 0
Grant 5/17/77
CDDAOMSURVEYGRANTTO (I 2 , I 3 , I 2)
1 8 1 h 87 1 7 O O
1 93 1 h 90 1 8 21 1
2 O O h 91 1 9 O 0
3 O O 5 5h 2 10 3 6
A 13 1 5 71 12 1O 31 1
A 51 2 5 78 1 1O 37 1
A 77 1 5 9h 1 10 AD 1
h 8% 1 6 O 0
Lapeer 5/17/77
CDDAOMSURVEYLAPTO (IZ,I3,I2)
1 O O 2 0 O 5 0 O
2 O O 3 12 1 6 H 1
3 0 0 3 19 1 6 7 1
h D 0 3 28 1 6 26 1
5 O O 3 31 1 6 60 2
6 0 O 3 L7 1 7 O O
7 0 O 3 58 2 8 0 0
8 O 0 3 68 1 9 0 0
9 0 O 3 77 1 1O 13 1
10 O O 3 82 1 10 6h 1
1 15 1 3 87 1 1D 77 2
1 71 1 3 9h 2 1C 96 1
1 8O 1 3 98 2
1 87 2 A O O
Tricounties 5/20/77
CDDAOMSURVEYTRITO (I 2 , I 3 , I 2)
1 O O 5 O O 9 0 O
2 O O 6 D 0 1O 0 0
3 O 0 7 O 0
h 57 1 8 0 0

APPENDI X D

Mass Rearing Technique

 

Mass rearing programs have been carried out by several researchers
(Perron et al. 1951, Rawlins 1953, Friend and Patton 1956, Workman
1948, Elmosa 1960, and Niemczyk 1964) using somewhat different tech-
niques. Several of these methods have been explored at Michigan State
University and a modification of the method presented by Niemczyk (1964)
was found as the most desirable.

Niemczyk's rearing technique was developed for implementation at
the Agriculture Canada Entomology Laboratory, London, Ontario. Recent
modifications to Niemczyk's technique have been made to increase pro-
duction levels and efficiency levels at the London laboratory. These
modifications have resulted in increasing the rearing capacity of the
London facility to 2,000,000 flies per month (Harris 1976, personal
communication) and have produced similar results at Michigan State

University.

Modifications to Niemczyk's Rearing Technique:

Adults (1000-2000 per 30 cm3 cage) were held at :26°c and 16 h
photoperiod and provided with honey, a mixture of yeast, yeast hydrol-
ysate, soya bean meal and water. The flies were transferred to clean
cages weekly. For oviposition 15 cm plastic petri dishes were filled

with 1:1 muck/sand kept moist via a dental wick extending beneath the

159

160

container to a water reservoir. Two sliced onion halves (oviposition-
al attractants) were placed on top of the soil. Eggs were collected
twice weekly. Larvae were reared in plastic dishpans 30 x 22 x 15 cm
deep containing several 5 mm holes in the bottom for drainage of
excess moisture. The pan was filled with onion sleeves arranged on a
3 cm layer of dry sand. Eggs, mixed in 1:1 sand/muck, were deposited
around the sliced onions. A layer of plastic film was placed over the
sliced onions to maintain a moisture level. Additional food was

added 10 days later by removing the container contents, adding dry
sand and sliced onions and replacing the original contents on top.
When the original food supply was exhausted, larvae moved readily to
the new onions below. The resultant pupae were removed and placed in
containers for emergence. Larvae and pupae were reared at iZZOC and
16 h photoperiod with the two procedures requiring approximately 20

and 12 days respectively.

APPENDIX E CDDAALLONIONCLUMP (1X,I3,12,7(2X,I3) ,2F10.3)
Age Specific Data (see 11,8)
NOTE: Parameter Y is double its true value in days
188-244. (C = clump, G = grade, E = eggs, 1, 2,

 

+3 = 1-3 instars, P=puoae, X and Y coordinates)

DAY C G E 1 2 #3. P X Y

174 1 1 0 0 3 0 0 .040 -.025
174 1 1 0 0 3 0 0 .095 -.100
174 1 1 0 0 3 0 0 .141 -.002
174 1 1 0 0 3 0 0 .217 .022
174 1 3 0 0 3 2 0 .293 .020
174 1 3 1 4 6 O O .368 .069
174 1 3 0 0 3 0 1 .444 .093
174 1 1 o o o o o .535 -.033
174 1 3 0 0 3 1 0 .601 .041
174 1 3 0 0 2 1 0 .701 .039
174 1 2 0 3 3 2 0 .787 .038
174 1 2 3 0 0 1 0 .888 —.013
174 1 1 0 0 0 0 0 .964 -.039
174 1 1 0 0 3 0 0 1.049 -.040
174 1 3 0 0 3 1 0 1.120 .008
174 1 3 0 0 2 1 0 1.206 .007
174 1 3 0 0 3 6 0 1.302 -.044
174 1 1 0 0 3 0 0 1.403 -.021
174 1 1 0 0 3 0 0 1.488 -.047
174 1 1 0 0 3 0 0 1.559 .002
174 1 1 0 0 3 0 0 1.615 .001
174 1 1 0 0 ’3 0 0 1.655 .000
174 1 1 0 0 3 0 0 .041 .417
174 1 1 0 0 3 0 0 .137 .519
174 1 2 6 0 3 1 0 .208 .468
174 1 2 0 8 4 0 0 .298 .442
174 1 3 0 0 3 1 0 .384 .490
174 1 3 0 0 3 2 0 .475 .414
174 1 3 0 O 3 O O .536 .463
174 1 1 0 0 3 0 0 .611 .487
174 1 1 0 0 3 0 0 .722 .485
174 1 1 0 0 3 0 0 .808 .459
174 1 1 0 0 0 0 0 .879 .600
174 1 1 4 0 0 0 0 .884 .334
17a 1 1 o o o o o .929 .532
174 1 1 0 0 3 0 0 .979 .357
174 1 2 0 4 0 2 0 1.045 .406
174 1 3 4 0 0 1 1 1.116 .504
174 1 3 0 0 0 0 0 1.207 .478
174 1 1 0 O 0 O O 1.277 .452
174 1 1 0 0 3 0 0 1.338 .526
174 1 2 3 0 0 0 0 1.418 .400
174 1 1 0 0 3 0 0 1.489 .375
174 1 1 0 0 3 0 0 1.550 .423
174 1 1 0 0 3 0 0 1.615 .373
174 1 1 0 0 3 0 0 1.661 .397
174 1 0 0 0 3 0 0 0.000 0.000

162

Age Specific Data (continued)

179 2 1 0 0 0 0 0 .045 -.02u
174 2 1 0 0 0 0 0 .105 -.231
179 2 1 0 0 0 0 0 .156 -.046
17a 2 2 0 0 u 0 1 .216 -.122
17a 2 2 0 0 0 3 0 .296 -.172
17a 2 2 6 0 6 0 1 .386 - 142
17a 2 1 0 0 o 0 0 .452 — 192
17a 2 3 0 0 0 0 0 .537 - 084
17u 2 3 0 0 0 u 0 .698 -.026
17a 2 3 0 0 0 2 0 .788 -.101
17a 2 1 0 0 0 0 0 1.130 - 088
179 2 2 0 0 0 2 0 1.225 - 085
17a 2 1 0 0 0 0 0 1.310 -.108
17a 2 3 0 0 0 0 0 1.396 —.052
17u 2 3 0 0 0 0 1 1.481 -.099
17a 2 1 0 0 0 0 0 1.878 -.165
17a 2 1 0 0 0 0 0 1.963 -.083
17a 2 1 0 0 o 0 0 2.058 -.210
17a 2 1 0 0 0 0 0 2.129 -.129
174 2 1 0 0 0 0 0 2.209 -.126
1714 2 1 0 0 0 0 0 2.2514 -.200
17a 2 1 0 0 0 0 0 2.305 -.123
17a 2 1 0 0 0 0 0 2.380 -.120
17a 2 1 0 0 0 0 0 2.480 -.090
17a 2 1 0 0 0 0 0 2.551 -.192
17a 2 3 0 0 12 0 0 2.646 -.110
17a 2 3 0 0 u 0 0 2.721 -.133
17a 2 3 0 0 0 0 2 2.802 -.130
17u 2 2 3 2 0 0 0 2.872 -.206
17a 2 2 0 0 0 3 0 2.972 -.202
17a 2 3 0 0 0 0 0 3.063 -.199
17a 2 2 0 3 0 2 0 3.193 -.092
17u 2 3 0 0 0 2 0 3.229 .092
17a 2 3 0 0 0 0 1 3.309 .019
17a 2 3 0 0 0 0 0 3.379 -.109
17a 2 1 0 0 0 0 0 3.460 -.106
17a 2 1 0 0 0 0 0 .0u1 .u19
17a 2 1 0 0 0 0 0 .131 .396
17a 2 1 0 0 0 0 0 .197 .399
17a 2 1 0 0 0 0 0 .257 .u27
17a 2 1 0 0 0 0 0 .322 .u03
17a 2 3 0 0 0 0 0 .392 .328
17a 2 1 0 0 0 0 0 .483 .488
17a 2 3 0 0 3 1 0 .593 .464
174 2 2 9 0 2 1 0 .950 .401
174 2 1 0 0 0 0 0 1.126 .u07
17u 2 1 3 0 0 0 0 1.211 .u10
17a 2 3 0 0 0 0 0 1.301 .362
17a 2 3 0 0 0 0 0 1.617 .321
17a 2 3 0 0 0 0 2 1.708 .377
174 2 3 0 0 0 0 0 1.803 .35u
17a 2 1 0 0 0 0 0 1.869 .383

Age Specific Data (continued)

1711
1714
1711
17:1
1711
1711
1711
1714
17L1
174
1711
1711
1711
1711
1711
1721
1711
1711
1721
174
171:
17a
1711
178
179
1711
1711
1714
1711
1711
17L:
1711
1714
17L1
17:1
1711
17a
17a
1711
1711
1711
1711
1711
1721
1711
1711
1711
1711
1711
179
1711
1711

wwwwwwwwwwwwwwwwwwwwwwwwcuuuwww1\)mmmmmmmmmmmmmmmmmmmmwN

AA‘J-J-i-A-J—JW—Ad.—bdamamAANAAJ.4—3—3—3AO‘AA-bd-ANNWWWW—I-A—éd—Jd—bd&4

C‘OOOOOOOOOOOOOOOOOOOOOOOQOOCOOOOOOOOCOOOOOOOOOOOOOOO

COOOOOOOOC’OOOOOOOOOC‘O‘OOC‘OOOC‘OOOOOOOOOOWO‘OOOOOOOOOOOO

163

OOOOOOOOOOOOOOOOONOOOOOOC‘C‘OOOOOOOCOO-‘AOWONOOOOOOOOOO

COOOOOOOONOOOOC‘OOOOOOOC‘OOOOC‘OC‘OOOOOOOOOOOOC‘OOOOOOOOO

1.909
1.974
2 039
2.095
2. 210
2. 246
2.290
2.376
2.441
2.496
2.571
2.657
2.722
2.808
2.878
2.973
3.054
3.119
3. 209
3. 280
3 370
3.460
0. 000
.030
.090
.148
.215
.270
.280
.325
.375
.430
.490
.555
.700
.780
.865
950
1.040
1.085
1.095
1.145
1.210
1.305
1.375
1.400
1.420
1.470
1.530
1.560
1.605
1.640

.500
.439
.415
.339
.317
.490
.294
.271
.221
.197

334
.284
366
.342
.346
375
377
.407
357
.308
.338
0.000
-.051
.124
-.102
.021
-.080
.220
.045
.019
.068
.117

.038
.062
.036
.034

.008

.082

- .280
.006
.030
.053

- .048

- .210

.101

- .150

- .201
.048
‘0152

Age Specific Data (continued)

17"
17M
17H
17H
17H
17H
17H
17H

17“,

171:
17a
17a
17L:
17a
17L:
17a
17a
17a
1m
1714
171:
1724
171:
17L:
17a
17a
1714
1724
1714
171:
174
1714
1714
1714
1714
17m
171;
17a
17L:
174
1711
171:
17L:
17a
17a
17L:
1714
171:
17a
1714
171:
17a

OOOOOOOOC‘OOWOOOOONOOOOOOOOOOOOC‘OOOOC‘OOOOOOOOOOOOOOOO

OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO

OWOOOOOOOOOOOOOOOC‘OOOOOOOOOOC‘OOOOOC‘OOOOOOOOOC‘OOOOOOO

164

OOOOOO-‘O-fi—‘O—bOOOOOC‘OOOOOOOOOOOOOOOOOCOOOOOOOOC‘OOOOOOO

ON—‘OOOOOOOOOOOOOOC‘OC‘OOOOOOOOC‘OOOOOOOOOOOOOOOC‘OOOOOOO

.700
.710
.771
.755
.830
.880
.925

NNNNNNNNNNNNNNN—bd—I—b—-I—s—s—4—I—s
_I
U1
Q

.fl81
.fl29
.HSH
.u77
.580
.476

Age Specific Data (continued)

171:
1711
1714
17L:
1714
174
17L:
17:4
171:
1714
174
17a
17a
17a
1724
17a
1714
m
174
1724
171:
174
m
174
1.7L;
17L:
17a
17a
17m
174
1711
m
17L:
174
171:
17a
Wt:
174
17L:
17!:
mt
1714
174
17a
17a
174
174
17a
1714
174
17a
17L:

1:333:54:-22:4:4:4:4:24:24:4:4:1::J:zwwwwwwwwwwwwwwwwwwwwwwwwwwww

d—s—I—b—bé—Idewmd—‘NN—‘WNAd-J-J-JO—‘QA—‘AUUIUUUUUVUWJAWAWWWNNWWWWWWUU

_I

.3
OOOOOOOOOC’OOOOC’WOONOOOOOOOOOOOO—‘OOOOCOOOOOOOOOOOOOOO

OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOQNOOOOOOOOOOOOOOOC‘OOOO

165

OOOOOOOOONOMOO:AOWNOOOOOOOOOOOOONOO—JOOOO-JJOUJNC‘NOONOO

OOOOOOOOO-J-JOOOOOOOC‘OOOOOOOOOOOOO—IO—‘AOOOOOOOOO—‘C‘ONOOO

1.489
1.549
1.609
1.659
1.724
1.774
1.854
1.939
2.019
2.104
2.124
2.159
2.209
2.219
2.304
2.374
2.469
2.544
2.564
2.609
2.684
2.744
2.799
2.844
2.864
2.929
2.969
0. 000
.040
135
.210
.254
.309
374
.464
.559
.638
.713
.778
.828
.893
.958
1.242
1.312
1.367
1.431
1.466
1.536
1.611
1.686
1.726
1.761

166

Age Specific Data (continued)

174 4 1 0 0 0 0 0 1.796 .029
174 4 1 0 0 0 0 0 1.865 -.046
174 4 1 0 0 0 0 0 1.925 .075
174 4 1 0 0 0 0 0 1.955 -.121
174 4 1 0 0 0 0 0 .045 .487
174 4 1 0 0 0 0 0 .105 .412
174 4 1 0 0 0 0 0 .205 .434
174 4 1 0 0 0 0 0 .279 .383
174 4 3 0 0 6 0 0 .369 .430
174 4 3 0 0 0 0 0 .444 .453
174 4 2 0 15 3 0 1 .539 .377
174 4 1 0 0 0 1 0 .628 .424
174 4 3 0 0 6 3 0 .703 .373
174 4 3 0 0 0 0 0 .788 .395
174 4 3 0 0 2 0 0 .888 .369
174 4 3 0 0 0 0 0 .948 .440
174 4 3 0 0 0 0 0 1.102 .485
174 4 1 0 0 0 0 0 1.302 .529
174 4 1 0 0 0 0 0 1.367 .503
174 4 1 0 0 0 0 0 1.436 .331
174 4 1 0 0 0 0 0 1.471 .525
174 4 1 0 0 0 0 0 1.546 .426
174 4 1 0 0 0 0 0 1.606 .351
174 4 3 0 0 0 0 0 1.631 .374
174 4 3 0 0 3 0 0 1.746 .421
174 4 3 0 0 0 2 1 1.825 .395
174 11 2 0 0 0 2 0 1.915 .344
174 4 1 0 0 0 0 0 1.970 .367
174 4 0 0 0 0 0 0 0.000 0.000
174 5 1 0 0 0 0 0 .035 0.000
174 5 1 0 0 0 0 0 .090 -.074
174 5 1 6 0 0 0 0 .200 -.074
174 5 1 0 0 0 0 0 .290 -.074
174 5 1 0 0 0 0 0 .360 -.074
174 5 1 C1 0 0 0 0 JWS -450
174 5 1 0 0 0 0 0 .460 -.099
174 5 2 0 0 0 1 0 .535 -.074
174 5 1 0 0 0 0 0 .615 -.025
174 5 1 0 0 0 0 0 .695 -.074
174 5 1 0 0 0 1 0 .760 -.074
174 5 1 0 0 0 0 0 .825 -.025
174 5 1 0 0 0 0 0 .875 -.050
174 5 1 0 0 0 0 0 .975 0.000
174 5 1 0 0 0 0 0 .044 .397
174 5 1 0 0 0 0 0 .129 .372
174 5 1 0 0 0 0 0 .199 .372
174 5 2 0 0 2 0 0 .269 .422
174 5 2 4 0 7 1 0 .349 .422
174 5 2 0 0 0 0 1 .449 .397
174 5 2 0 0 0 0 1 .549 .422
174 5 3 0 0 0 0 0 .614 .471
174 5 3 0 0 13 0 0 .684 .422

Age Specific Data (continued)

174
174
174
174
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181

NNNNNNNNNIvNOAAAA_|—I—I—l—J—b-—I.—Ia—I-t—b-a-t—J-tdA-A—IAAA—laAA—b—bd—h—hmmmm

wad—b—Id—D—J—AAAO—l—h—IdAddmmwwwwwwwwfldwaau—i—AAAAAWW—bNM—b—b—bdmww

OOOOOOOOOOOOOOOOOOOOOOOOCOOONOOOOOOOOOOOOOOOO‘OOOOOOO

OOOOOOOOOOOOC‘OOOOOOWOOOOOOOOAOOOOOOOOOOCOOOOOOOOOOOO

OOOOOOOOOOOOC‘OOOOOOOOOOOOOOOOOOOOOOOOOOC‘OOOOOOOOOOWO

167

OOOCOOC‘OOOOOC‘OOOOOO-‘NONOOOOONOOOOOOOOOOCOOO—IUOOOOO—‘OO

OOOOOOOOOOOOC‘OOOOOOOOOOO-J-‘-3-*OOONOOOOOOOC‘O-AOOOOOOOOOO

.749
.799
.874
.959
.045
.131
.211
.287
373
.463
.539
.624

.795
976

1.047
1.138
1.213
1. 268
1. 294
1. 374
1. 445
.050
.131
.206
.307
378
.463
.629
.725
.800
.881
.966
.047
.128
.193
.253
.314
L 384
1.480
0.000
.065
.146
.237
.322
.473
.554
.635
.746
.816
.897
.972

d—SA—b—II

.422
.446
.422
.546
.022
.016
.036
031
.025
.019
.014
.034
.022
.017
.011
.030
.024
.019
-.012
- .011
.008
.029
.501
.495
.490
.509
.504
.498
.487
.481
.476
.445
490
.509
.504
.500
.470
.466
.462
.455
0.000
-.027
-.029
-.031
-.032
—.061
-.037
-.012
-.015
-.016
.009
.007

168

Age Specific Data (continued)

181 2 1 0 0 0 0 0 1.043 .006
181 2 1 0 0 0 0 0 1.118 .004
181 2 1 0 0 0 0 0 1.194 .003
181 2 1 0 0 0 0 0 1.244 .002
181 2 1 0 0 0 0 0 1.315 .000
181 2 1 0 0 0 0 0 .052 .547
181 2 3 0 0 0 0 0 .153 .545
181 2 2 4 0 0 1 0 .239 .517
181 2 1 0 0 0 0 0 .324 .542
181 2 1 0 0 0 0 0 .400 .514
181 2 1 2 0 0 2 0 .475 .513
181 2 2 0 0 0 1 0 .566 .537
181 2 1 0 0 0 0 0 .647 .535
181 2 2 0 0 0 0 1 .727 .508
181 2 2 0 0 0 0 1 .813 .532
181 2 3 0 0 0 0 0 .883 .557
181 2 3 0 0 0 0 3 .959 .555
181 2 3 0 0 0 0 0 1.035 .580
181 2 3 0 0 0 0 0 1.105 .579
181 2 3 0 0 0 0 1 1.186 .577
181 2 1 0 0 0 0 0 1.256 .576
181 2 1 0 0 0 0 0 1.312 .575
181 0 0 0 0 0 0 0 0.000 0.000
181 3 1 0 0 0 0 0 .050 -.028
181 3 1 0 0 0 0 0 .136 —.007
181 3 1 0 0 0 1 0 .216 -.011
181 3 1 0 0 0 0 0 .306 -.016
181 3 1 0 0 0 0 0 .878 -.019
181 3 3 0 0 0 0 0 .963 -.024
181 3 3 0 0 0 0 0 1.033 -.002
181 3 3 0 0 0 0 0 1.109 -.006
181 3 3 0 0 0 0 0 1.194 .016
181 3 3 0 0 0 0 0 1.279 —.014
181 3 3 0 0 0 0 1 1.355 .007
181 3 3 0 0 0 0 1 1.455 -.023
181 3 3 0 0 0 0 1 1.535 -.027
181 3 1 0 0 0 0 0 1.636 -.032
181 3 3 0 0 0 0 1 1.721 -.037
181 3 3 0 0 0 0 1 1.816 -.016
181 3 2 3 0 0 1 0 1.962 .002
181 3 2 0 0 1 0 2 2.067 -.003
181 3 3 0 0 0 0 1 2.167 .017
181 3 3 0 0 0 0 1 2.232 -.012
181 3 3 0 0 0 0 0 2.318 -.016
181 3 3 0 0 0 0 0 2.403 -.020
181 3 3 0 0 0 0 1 2.508 -.000
181 3 3 0 0 0 0 0 2.604 -.031
181 3 3 0 0 0 0 1 2.694 -.035
181 3 3 0 0 0 0 0 2.779 .011
181 3 1 O 0 0 0 0 2.880 -.045
181 3 1 0 0 0 0 0 2.970 .027
181 3 1 0 0 0 0 0 .039 .608

Age Specific Data (continued)

181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181

wwwwwwmmaawmamdwaAWA-A-s._|04—34—34Adda—aaaaamwwwmc‘Awaéiuww

OOOOOOO GOOCDC’OOOOOOOOOOOOC‘OOOC‘NOOOOOOOOOOOOOOOOOOC‘NOO

OOOOOOOOOOC‘JOOOOOOOOOOOOOCOOOOOOOOOOOC‘OOOOWOOOOOO-‘OO

169

ONOOOOON—bOC‘OOKNOOOU‘OOOOOOOOOOOOOOOOOOC‘OOOOOOOOOOOOOC‘

COOOAONOOOC‘JOOOOOOOOOOOCOOOOOOOOOOOOOC—AOO—I—fiOOOOOONOO

NMNNN-fi—b—b—Jd—A—bd

CNN—“dd

.150
.245

.600
.715
.805
.890
.970
.060
.195
.295
.420
.500
.615
.755
.915
.030
.160
.280
.415
.545

.629

.033

Iona

Age Specific Data (continued)

181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181

o~0\0\o~oxo~o~0\0\oxoxo~c>0101010101010101010101010101010101uwuwuwuwuwuwuwuwuw01010101010101010101010101

_L_s|\)(n_aAdd—SadaodaAAAAAJAAAAJAAAAAAAAAAAAW”Wam(n(n(.)_s_s_a(n_g(n

OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOWOOOOOOOOOL‘OOOOOOOOOO

OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO

OONOOCOOOOOOOOOOOOOOOOOC‘OOOOOOOOOOOOOOOOOOOOOOOOOOC‘O

170

OOONNOOOOOOOOOOOOOOOOOOC‘OOOOOOOOOOOOOOOOOOR)OOOOOOOCO

c>c>—-c>—sc>c>c>c>c>c>c>c>c>c>c>c~c>c>c>c>c>c’c~c~c~c>c>c>c>c>c>c>c>c>c>c>c>c~c»c>c>c>c>—ac>c>c>c>c>c~c>

.650
.725
.790
.875
.960
.051
.126
.221
.311
.461
.581
.706
.811
.891
.971

.136
.211
.301
.371
.456
.546
.631
.711
.796
.866
.956
.036
.136
.196
.306
.376
.471
.541
.641
.721
.796
.866
.956
.000
.178
.293
.464
.055
.140
.306
2.466
2.707
2.782
2.948
3.303
3.374

NNNNN

NMN-J—A-JONNNNNNNNNNNN-Jd—A—‘d—t—b—b—I—s—I—s

064
042
018

.049
.561
.589
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.570
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604

.609
.587
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618
570
547
600
603

H
\J
p.»

Age Specific Data (continued)

181 6 1 0 0 0 0 0 3.464 -.032
181 6 1 0 0 0 0 0 3.549 -.031
181 6 1 0 0 0 0 0 .047 .535
181 6 3 0 0 0 0 1 .298 .538
181 6 3 0 0 0 0 1 .368 .539
181 6 3 0 0 0 0 1 .463 .565
181 6 3 0 0 0 0 1 .539 .541
181 6 3 0 0 0 0 1 .629 .593
181 6 1 0 0 0 0 0 .960 .546
181 6 3 0 0 0 0 0 1.065 .547
181 6 3 0 0 0 0 0 1.185 .549
181 6 1 3 0 0 1 0 1.306 .551
181 6 2 0 0 0 0 3 1.456 .552
181 6 3 0 0 1 0 0 1.771 .607
181 6 1 0 0 0 0 0 2.283 .512
181 6 1 0 0 0 0 0 2.388 .539
181 6 1 0 0 0 0 0 2.388 .513
181 6 3 0 0 0 0 0 2.709 .518
181 6 1 0 0 0 0 0 2.950 .521
181 6 1 0 0 0 0 0 3.065 .547
181 6 1 0 0 0 0 0 3.150 .523
181 6 1 2 0 0 0 0 3.256 .524
181 6 2 6 0 0 2 0 3.401 .526
181 6 2 0 0 1 0 0 3.526 .553
181 6 2 3 0 0 3 0 3.642 .504
181 6 2 2 0 2 5 0 3.797 .557
181 6 3 0 0 0 0 0 3.952 .559
181 6 1 0 0 0 0 0 4.075 .050
181 6 1 0 0 0 0 0 4.140 -.055
181 6 1 0 0 0 0 0 4.216 -.057
181 6 1 0 0 0 0 0 4.291 -.034
181 6 1 0 0 0 0 0 4.406 -.O38
181 6 1 0 0 0 0 0 4.472 -.015
181 6 1 0 0 0 0 0 4.567 -.043
181 6 1 0 0 0 0 0 4.652 -.071
181 6 1 0 0 0 0 0 4.718 .003
181 6 1 0 0 0 0 0 4.798 -.025
181 6 1 0 0 0 0 0 4.878 -.028
181 6 1 0 0 0 0 0 4.964 -.030
181 5 1 0 0 0 0 1 5.084 -.009
181 6 3 0 O 0 0 0 5.205 .012
181 6 2 0 0 0 0 1 5.410 .006
181 6 1 0 0 0 0 0 5.646 -.001
181 6 1 0 0 0 0 0 5.802 .019
181 6 1 0 0 0 0 0 5.927 .040
181 6 1 0 0 0 0 0 6.053 .011
181 6 1 0 0 0 0 0 6.133 .034
181 6 1 0 0 0 0 0 6.218 .031
181 6 1 0 0 0 0 0 6.304 .003
181 6 1 0 0 0 0 0 6.454 -.002
181 6 1 0 0 0 0 O 6.570 -.005
181 6 1 0 0 0 0 0 6.655 -.008

Age Specific Data (continued)

181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181
181

_s...|_§_.|_.|_.|_b.;_s_n_.|ANdewwmwwWA_s_s_.\[\)_;[\)_a_s_¢_s_a_s_t.;_t_s_a._s._s_;_.s_s_;_s_s_;._|_s_s

OOOOOOOOOOOONOCOOOOOOOOOOOOOU‘CDOOC‘OOOOOOOOC‘OOOOOOOOOO

OOC’OOC’OOOOOOOOC‘OOONOOOC‘OOOOOC‘C‘OOOOOOOOOOOC‘OOOOOOOOOO

OOOOOOOOOOOOOOCOC‘OOOOOOOOOOOOOOOOC‘OOOOOOOCOCOOOOOC’OO

172

OOOOOOOOOOOO—ba‘ocOOOOOOOOOOOOO—IOOOOOOOOOOOOC‘OOOOOOOOOC"

OOOOOOCOOOOOOOC‘O-‘OW—‘d—iOOOOAOOOOOOOOOOOOOOC‘OOOCOOOOOO

.735
.936
.046
.051
.147
.232

.423
.593
.679
.819
075
.056
.126
.217
.307
.402
.478
.558
.653
.724
.799
.894
.980
.080
.210
.421
.557
.712
.049
.134
.194
.269
.460
.646
.726
.801
.957
.404
.564

N440oooooooommmmmguzzzzz=23224fl44444<4400

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8. 445
8. 575
8.705
9.115
9.220
9. 305
9. 395
9. 505
9.705

.015
-.042
-.121

.030

.027

.024
-.004

.018
-.038
-.040

.006
.001
.502

.500

.523

.495

.517

.489

.512
.484
.481
.504
.501
.499
.495
.491
.485
.506

.515

.513
.486
.534
.477
.522
.519
.517
.537
.624
.594
.591
.587
.011
-.017

.026
.012
.016
.011
-.022

-.063

173

Age Specific Data (continued)

181 6 1 O O 0 0 0 9.800 -.007
181 6 1 0 0 0 0 0 9.880 .019
181 6 1 0 0 0 0 0 9.975 -.O14
181 6 1 0 0 0 0 0 10.070 -.077
181 6 3 0 0 0 0 0 10.160 -.021
181 6 1 0 0 0 0 0 10.250 -.024
181 6 1 0 0 0 0 0 10.690 -.040
181 6 1 0 0 0 0 0 11.070 -.054
181 6 3 0 0 0 0 1 11.220 .020
181 6 3 0 0 0 0 0 11.310 -.034
181 6 1 0 0 0 0 0 11.620 .014
181 6 1 0 0 O 0 0 11.750 .039
181 6 1 0 0 0 0 0 11.900 -.026
181 6 1 0 0 0 0 0 8.130 .649
181 6 1 0 0 0 0 0 8.230 .645
181 6 1 0 0 0 0 0 8.300 .642
181 6 1 0 0 0 0 0 8.395 .639
181 6 1 0 0 0 0 0 8.500 .605
181 6 1 0 0 0 0 0 8.655 .659
181 6 1 0 0 0 0 0 8.805 .683
181 6 1 0 O 0 0 0 8.925 .708
181 6 3 0 0 0 0 2 9.075 .673
181 6 3 0 0 0 0 0 9.155 .729
181 6 1 0 0 0 0 0 9.300 .724
181 6 1 0 0 0 0 0 9.390 .750
181 6 2 0 0 3 0 1 9.580 .684
181 6 1 0 O 0 0 0 9.840 .734
181 6 1 0 0 0 0 0 9.950 .759
181 6 1 0 O 1 0 0 10.060 .726
181 6 2 0 0 8 1 0 10.160 .752
181 6 3 O 0 0 0 0 10.280 .747
181 6 1 0 0 0 0 0 10.430 .682
181 6 1 0 0 0 0 0 10.530 .738
181 6 1 0 0 0 0 0 10.640 .734
181 6 1 0 0 0 0 0 10.720 .731
181 6 1 0 0 0 1 0 10.870 .606
181 6 2 0 0 0 0 1 10.930 .723
181 6 2 0 0 0 0 1 10.970 .781
181 6 3 0 0 0 0 0 11.080 .806
181 6 3 0 0 0 0 1 11.410 .765
181 6 2 7 0 0 1 0 11.890 .747
181 6 3 0 0 0 0 0 12.170 0.000
181 6 1 0 0 0 O 0 12.290 0.000
181 6 3 0 0 0 0 1 12.450 -.025
181 6 3 0 0 0 0 1 12.620 0.000
181 6 3 0 0 O 0 0 12.780 -.025
181 6 1 0 0 O O 0 12.280 .550
181 6 1 0 O 0 0 0 12.370 .525
181 6 1 0 0 0 O 0 12.470 .550
181 6 1 0 O 0 1 0 12.690 .525
181 6 3 0 1 8 1 1 12.780 .550
181 6 2 0 0 1 3 0 12.950 .525

Age Specific Data (continued)

188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188

1

1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

Ngwaaaaazmézzwm54Ad—JJzzmzzzaaazdwzzd:Naaaa-s-s-a-t-s-s-s—swz—L

OOOOOOOOOOOOOOOOOOC‘OOOOOOOOOOOOOOOOOOOOOOOOOOCOOOOOO

OOOOOOOOOOOOOONOOOC‘OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO

COOOOOOOOOOOOOWC’OOOOOOOOOOOCOOOOOOOOOOOOOOOOOOOOOOOO

174

OOOOOOOOOOOOOO—SC‘OOOOOOOC‘OOOOOOOOOOOOOOOOOOOOOC‘O—AOOOO

.050
.251
.462
.557
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.149
.199
.269
.390
.540
.645
.756
.816
.957
.102
.323
.463
.594
.714
.945

.231
.371
.587
.753
.933
.089
.234
.430
.580
.701
.791
.866
.952
.107
.328
.458
.599
.769
.950
.046
.243
.373
.444
.534
.634
.661
.841
.068
5.214

U1zzzz-BzzSWWWWWWNNNNNNNNddddd—‘WWWWWWNNNNNNNNNd—fid-J-J—h—b

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0000000000
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.547
.544
.516
.539
.511
.533
.530
.528
.527
.526
.524
.496
.518
.516
.513
.510
.533
-.507
-.509
-.536
-.512
-.513
-.641
-.388
-.517
-.469
-.522

175

Age Specific Data (continued)

188 1 3 0 0 0 0 1 5.355 -.524
188 1 3 0 0 0 0 0 5.637 -.476
188 1 4 6 0 0 0 0 5.748 -.453
188 1 4 0 0 0 0 c: 5.924 -.429
188 1 4 0 0 0 0 0 6.084 -.482
188 1 3 0 0 0 0 0 6.256 -.459
188 1 4 0 0 0 0 0 6.432 —.461
188 1 4 0 0 0 0 1 6.598 -.438
188 1 4 0 0 0 0 0 6.784 -.491
188 1 1 0 0 6 0 0 6.959 -.544
188 1 1 0 0 0 0 0 7.065 -.520
188 1 3 0 0 0 0 0 7.297 -.497
188 1 4 0 0 0 0 0 7.443 -.499
188 1 4 0 6 0 0 0 7.594 -.501
188 1 4 0 0 0 0 0 7.775 -.504
188 1 4 0 0 0 0 0 7.935 -.556
188 1 1 0 6 6 0 6 4.644 .455
188 1 4 6 6 6 6 0 4.245 .478
188 1 1 6 6 6 6 6 4.391 .476
188 1 1 6 6 6 0 C) 4.456 .475
188 1 4 6 6 6 6 6 4.597 .499
188 1 1 6 6 6 6 6 4.793 .446
188 1 1 6 6 C) 6 6 4.955 .494
188 1 1 6 0 6 6 6 5.675 .493
188 1 1 6 6 0 6 0 5.211 .491
188 1 1 0 0 0 0 0 5.347 .515
188 1 1 6 6 C) 6 0 5.488 .488
188 1 1 6 6 C) 6 6 5.553 .461
188 1 1 2 6 6 6 C) 5.644 .486
188 1 2 2 12 6 6 1 5.746 .510
188 1 3 0 0 6 0 3 5.916 .482
188 1 4 6 6 6 6 1 6.162 .505
188 1 4 6 c. 6 (1 6 6.278 .478
188 1 3 6 6 6 6 6 6.424 .501
189 1 ‘3 6 0 c) 0 6 6.505 .525
188 1 3 6 6 6 6 6 6.655 .473
188 1 3 0 6 6 6 6 6.746 .497
188 1 1 6 6 0 6 6 6.957 .469
188 1 1 6 6 0 0 0 7.068 .468
188 1 1 0 0 0 0 0 7.304 .465
188 1 1 0 9 0 1 0 7.385 .489
188 1 2 0 0 0 0 1 7.541 .512
188 1 4 0 0 0 0 1 7.762 .459
188 1 1 0 0 0 0 0 7.973 .456
188 1 4 0 0 0 0 0 8.093 -.479
188 1 4 0 0 0 0 0 8.254 -.460
188 1 4 0 0 0 0 0 8.410 -.490
188 1 4 0 0 0 0 0 8.565 -.496
188 1 4 0 0 0 0 0 8.766 -.529
188 1 4 0 0 0 0 2 8.912 -.509
188 1 4 0 0 0 6 1 9.688 -.491
188 1 4 0 0 0 0 0 9.249 -.547

Age Specific Data (continued)

'188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188

.2
NNNNNK’NNNNNNNNNNNN1UNNNNNNNNNIUQJ_I_A_s_.0_;_s_n_s_5_s.3_.n_s._|.a_s_0_s_s_s

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OOOOOOOOOOOOOOtOOO-JOOOOOOOO—‘OOOONONOOOOOOOOOOOOOOOOO

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176

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OOOOOOOOOOOOOOOOOOOOOOOOOOOOOCOU‘OOO-‘OOOOOOOOOOO

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9.394
9.505
9. 646
9. 776
9. 937
8. 093
8. 234
8 304
8.435
8.560
8.641
8.751
8.817
8.977
9.113
9 259
9.375
9. 485
9.565
9.671
9. 782
9. 977
0.000

.111
.221
.302

.548

.663

.794

.970
1.106
1.302
1.397
1.503
1.578
1.633
1.724
1.940

.045

.116

.206

.281

.377

.457

.553
.628

.935
1. 095
1. 251

-.527
-.531
-.512
-.517
—.473
.497
.516
.539
.484

177

Age Specific Data (continued)

188 2 4 0 0 0 0 0 1. 432 .499
188 2 1 0 0 0 0 0 1. 638 .509
188 2 1 O O 0 0 0 1. 688 .429
188 2 1 0 0 0 0 0 1. 704 .630
188 2 1 0 0 0 0 0 1.764 .423
188 2 1 0 0 0 0 0 1.809 .369
188 2 1 0 0 0 0 0 1.814 .571
188 2 1 O 0 0 0 0 1.889 .388
188 2 1 0 0 0 0 0 1.910 .589
188 2 1 0 0 0 0 0 1.965 .458
188 0 0 0 0 0 0 0 0. 000 0.000
188 3 4 0 0 0 0 0 .074 -. 444
188 3 1 0 0 0 0 0.193 -.500
188 3 1 0 0 0 O 0 .263 -.529
188 3 1 0 0 0 0 0 .328 -.505
188 3 2 5 0 0 1 0 .397 -.507
188 3 2 0 0 0 2 0 .482 -.536
188 3 3 0 0 O 0 0 .557 - 513
188 3 2 O 0 O 2 0 .627 -.489
188 3 1 0 0 0 0 0.701 —.492
188 3 1 0 0 0 0 0.771 -.520
188 3 1 0 0 0 0 0 .851 -.523
188 3 2 0 0 0 0 3 .931 -.526
188 3 3 O 0 0 0 0 1.005 -.502
188 3 1 0 0 0 0 0 1.055 -.478
188 3 1 0 0 0 0 0 L 100 -.558
188 3 1 0 0 0 0 0 L 115 -.428
188 3 1 0 0 0 0 O 1.160 -.560
188 3 1 0 0 0 0 O 1.175 -.378
188 3 1 0 0 O 0 0 1.214 -.588
188 3 1 0 0 0 0 0 1.230 - .432
188 3 1 0 0 0 O 0 1.269 - .563
188 3 1 0 0 0 0 0 1. 290 -.408
188 3 1 0 0 0 O 0 1. 354 -.489
188 3 1 0 0 0 0 0 1. 404 -.412
188 3 1 0 0 0 0 0 1. 459 - .492
188 3 1 0 0 0 0 0.051.518
188 3 1 0 0 0 0 0.116.516
188 3 1 0 0 0 0 0.181.513
188 3 1 O O 0 0 0 .260 .511
188 3 1 0 O 0 0 0 .330 .534
188 3 1 0 0 0 0 0 .405 .506
188 3 1 0 0 O 0 0 .475 .503
188 3 1 0 O 0 0 0 .535 .501
188 3 1 0 O O 0 0 .614 .498
188 3 1 0 0 0 0 0 .724 .494
188 3 1 0 0 O 0 0 .814 .491
188 3 3 0 0 0 0 1 .933 .487
188 3 2 2 0 0 O 1 1.048 .483
188 3 3 0 0 0 1 0 1.137 .506
188 3 1 0 0 0 0 0 1.262 .528
188 3 1 0 0 0 0 0 1.317.526

Age Specific Data (continued)

188
188
188
188
188
188
133
188
188
188
183
188
188
188
188
188
188
188
183
188
188
183
188
188
188
188
188
188
188
188
183
188
183
183
188
138
188
183
188
188
188
188
188
188
188
188
188
188
188
188
188
188

EKOWW
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' fizzzddzdzaadgaaamAAd
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OC‘OOO
OOOOOOOOC’OOOOOOOOOOOOC‘OOOOOOOOOOOOOOOOOOOOOOOOO

OOC‘OOO
OOOOOOOOOOOOOOOOOOC’OOC‘OOOOOOOOOOOOOOOOOOOC‘OOC’O

178

0000000
OOOOOOOOOOOOOOOC‘OOOOOC‘OOOOOOOOOOOOOC‘OOOOOOOOO

C‘OC‘O
OOOOOOOOO—e—‘OOONOéw—lOIUUUOONONOOOOOOOOOOOOOOOOOC‘COO

1.372
1.11117
0.000
.043
.103
.1911
.275
.386
.496
.582
.683
.789
.880
1.021
1.132
1.207
1.323
1.11511
1.580
1.731
1.857
1.968
2.01111
2.1311
.113

.466
.582
.668
.789
.925
1.106
1.263
1.439
1.590
1.757
1.933
2.099
0.000
.055
.136
.221
.311
.391
.467
.562
.652
.763
.953
1.048
1.144
1.244

0' O. 0' CO 0'
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179

Age Specific Data (continued)

188 5 1 0 0 0 0 0 1.400 -.552
188 5 1 O 0 6 6 6 1. 475 -.556
188 5 1 0 0 O 0 0 .055 .554
188 5 1 0 0 0 0 0 .110 .551
188 5 1 O O O O 0.191 .547
188 5 1 0 0 0 O 0.266 .518
188 5 1 O 0 0 0 0.336 .540
188 5 1 0 0 0 0 0 .431 .510
188 5 1 0 0 O O 0.512 .506
188 5 1 0 0 0 0 0.602 .501
188 5 1 0 0 0 0 0.662 .523
188 5 1 0 0 0 0 0 .717 .546
188 5 1 0 0 0 0 0 .803 .542
188 5 1 0 0 0 0 0 .858 .514
188 5 1 0 O 0 O O .943 .509
188 5 3 O 0 0 O 1 .988 .507
188 5 2 0 0 0 0 1 1.064 .453
188 5 1 0 0 0 0 0 1.129 .500
188 5 1 0 0 0 0 0 1.189 .497
188 5 1 0 0 0 0 0 1.294 .567
188 5 1 0 0 O 0 O 1.380 .563
188 0 0 0 O 0 0 0 0.000 0.000
188 6 1 0 0 0 0 0 .044 -.565
188 6 1 0 O 0 O 0 .105 -.592
188 6 1 0 0 0 0 0 .135 -.u39
188 6 1 0 0 0 0 0 .231 —.519
188 6 1 0 0 0 0 0 .302 -.495
188 6 1 0 0 0 0 0 .402 -.651
188 6 1 0 0 0 0 0 .519 -.424
188 6 2 0 0 1 0 2 .610 -.426
188 6 1 0 0 0 0 0 .696 -.377
188 6 1 O 0 0 0 0 .736 -.606
188 6 1 0 0 O 0 0.807 -.457
188 6 4 0 0 0 0 0 .923 -.485
188 6 1 0 0 0 0 0 .042 .512
188 6 1 0 0 0 0 0 .107 .536
188 6 1 0 0 0 0 0 .142 .381
188 6 1 O O 0 0 O .198 .277
188 6 1 0 0 0 O 0 .208 .456
188 6 1 0 0 0 O 0.284.480
188 6 1 0 0 0 0 0.365 .478
188 6 1 0 0 0 0 0.451 .476
188 6 1 0 0 0 0 0 .537 .499
188 6 1 0 0 0 0 0 .592 .472
188 6 1 0 O O 0 0 .653 .496
188 6 1 0 0 0 0 0 .713 .520
188 6 1 0 O 0 0 0.824 .440
188 6 1 0 0 0 0 0 .946 .1137
188 6 4 O 0 0 0 0.835.500
188 0 0 0 0 0 0 0 0.000 0. 000
188 7 1 0 0 0 0 0 .049 - .569

Age Specific Data (continued)

188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
188
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202

N—‘dzdzth-JARES-5:34:34tW-E-ZJ‘JZK—fi-Jdd-JJ—‘A—‘NN-AAO—é—b—b—I—fi—b—b—s—AN.4

C‘OOOOOOOOOOOOOOOOC’OOOOOOOOOC‘OOOOOOOOOOOOOOOOOOOOOOOO

—b

MOOOOOOOOOOOOOOOOOOOOOOOOOOCOCOOOOOOOOOOOOOOOOOOOONO

180

C‘NOOOOOOOOOOOOOOOOOOOOOOOOOC‘OOOOOOOOOOOOOOOOOOOOOOCO

.130
.226
.307
377
A53

.128
.213
.299
.375
.441
0.000
.080
.162
.259
.366
.468
.058
.149
.236
.302
.399
.476

.142
277
.457
.587
.778
.918
1.073
1.248
1.449
1.594
1.749
1.914
2.100
2.260
2.410
2.591
2.736
2.961
.089
.249
.504
.615
.780
.960
1.090
1. 246
1. 371
1.461

-.577
-.562
-.570
-.577
-.585
.482
.474
491
.508
500
.468
0.000
—.540
-.548

551
542
558
.526
.542
.534
-.492
-.490
-.486
-.482
’0 ”78
-.525
-.470
- .466
- A61

Age Specific Data (continued)

202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202

WWWWWWWWWWWWWWUUONNNIUNNNNNNIUNIUNNNNNNNNNNNNO—fid—b—A—J—b—I—s—I—i

zzzzzzzztzwm-I—fiAOAw—h—A-J-b—fith—I—h—A—A—A—A—ASSSNde—JO—I—Izz-lt—s—I—az

OOOOOOOOOOOSOCOOOOOOOOOOOC’OOOOOOOOOOOOOOOOOOOOOOOOOO

OOOOOOOOOOOO‘OOOOOOOOOOOOOOOOOCOOOOOOOOOOOOOOOOOOOOOO

—3
OOCOOOOOOOOOOOOOOOOOOOOOOOOOOC‘OOOOOOOOOOOOOOOOOOOOOO

181

OOCOOOOOOOONOOOOC‘AOOOOOOOOOOOC‘OOOOOO—AOOOOOOOOOOOOOOO

OOCOOOCOOOOOOOOOOOOOOOOOO-‘OOOC‘OOOOOO—bOOOOOOC‘OOOOOCOO

.601
.746
.887
.092
.247
.418
.558
.768
.878
.963
.000
.072
.162
.267
.367
.542
.747
.917
1. 087
1.182
1.247
1.342
1.447
.062
.408
.497
.692
.762
.912
1.037
1.087
1.167
1.227
1. 302
1. 372
1.452
0. 000
.055
.115
.190
.350
.470
.600
.755
.925
1. 095
1. 240
1. 410
1.575
1.745
1.915
2.080

ONNNNNNN—t—sa

.561
.565
.569

556

Age Specific Data (continued)

202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
202
210
210
210
210
210
210
210
210

.aa—aa-a—s—aazzzzzzzzzzzzzzzzzzzowwwwwwwwwwwwwwwwwwwwwwww

z—‘Bz-B’JI-fl’t-S—‘dtz-LT—im—b-‘d—‘d—fiJ:—J-‘-—|—|CttJr-Irkt-Irfiwwzzzzt—bN—é—b—b—bz—bm

OOOOOOOOOOOOOOOU‘IOOOOOOOOOOQOOOOOC‘OOOOOOCOOOOO-‘COOOOO

OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO

OOOOOOOOOOOOOOCWOOOOOOOOOOOOOOOOOOOOOOOOOCOONC‘OOOOOO

182

OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOCOO-‘OOOOOOO

OOOOOOOOOOOOOOCOOOOOOOOOOOOOOOOOOOOOOOOOOCOO—IOOOOOOO

2.365
2.500
2.725
2. 940

.120
.200
.305
.405
.575
.760
.905
1. 085
1. 245
1. 440
L 555
L 755
1. 910
2.085
2. 255
2. 400
2. 580
2.755
2.925
0.000
.070
.171
.283
.370
.593
.765
.872
1.080
1.192
1.283
1. 380
.169

.600
.743
.900
L 083
1.174
1.418

.248
.429
.605
.756
.903

1.044
1.271

-.416
-.439
-.460
-.406
.533
.559
.560
.537
.539
.541
.519
.547
.550
.553
.556
.558
.536
.564
.516
.570
.572
.550
.528
.530
0. 000
- .456
- .456
-.481
-.430
-.430
-.456
-.456
-.481
-.430
-.405
-.430
.506
.532
.557
.532
.532
.532
.532
.532
-.508
-.521
-.537
-.552
-.565
-.498
-.537
-.530

Age Specific Data (continued)

210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210
210

A

JAJJAd—JAAAAAAAAAAA.‘dJ—JA—J-J—J-J—A-J—A—bdd.J—JAA—I—h—A—J—A—ld—I—Jdddd‘

.=.=-a.=u=.=Lo-ax:x::::=::¢:UJsrcnzuszLD-AL».z-a.:.=.=.=.=.:tu-a-a-a.z-a-a-a-a-a.zLu.=-A.=L»-a-AL».4.=

OOOOOOOOOOC‘OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOONOOO

OOOOOOOOOOC‘OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO

OOOOOOOOOOOOOOOOOOOCOOOOOOOOOOOJ‘OOOOOOOOOOOOOOCOOOOO

183

OOOOOOOOOOOOOOWOOOOCOOOOOOOOOC’OAOOOOOOONOO-AOOOC‘OOOUOO

OOOOOOOOWONU‘WBad-fid-fiN—AO—JOOON—fiwz—‘wOOOSOOOOOUWO‘NOUTO‘COO‘OO

1.426
1.593
1.674
1.754
1.875
2.041
2.253
2.474
2.595
2.797
2.923
3.094
3.235
3.311
3.497
3.553
3.638
3.819
3.886
3.971

.085

.271

432

.604

.750

.941
L 072
L 223
1.394
1.521
1. 732
2. 059
2.155
2. 261
2. 412
2. 589
2. 775
2.977
3.112
3.269
3.430
3.601
3.772
3. 944
4. 054
4. 296
4. 442
4.578
4.764
5.011
5.248
5.419

-.569
-.531
-.538
-.545
-.555
-0 5,43
-.561
-.580
-.564
-.555
-.566
-.554
-.593
-.520
-.589
-.488
-.522
-.590
-.438
-.550
.545
.582
.516
.527
.594
.577
.566
.553
.539
.554
.562
.508
.526
.517
.504
.568
.552
.588
497
.563
.523
.534
.546
.531

- .524
-.517
-.502
-.513
-.527
-.546
-.487
-.526

Age Specific Data (continued)

210
210
210
210
210
210
210
210
210
210
210
210
210
210
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215

NNNOJQ-L‘_.|_3_s_.|_a..s_a_.s_;_n_s_a_;_s_a_s_3_s_s__|_s_.\_.s_s_a_s_s.4...|_.\.A_s_a._|_s._s._s_.s_a_5_.s._s_s_t

Stzozzz-Z-JW-t—‘NWWWDb-‘NE-P-‘d-S’NNWUON-J-AtNKZEZZ-fi—Jttbké4:4:NNJ‘J-B’J:

OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO

OOOOOOOOOOONOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO

oooooooo—smowm-s:ooo—sooo—‘oooooxlooozooooocooooooooooooo

184

OOOOOOOOUOI’UOUO—‘NNOOONOOOsz-‘OO-‘OOOWOOOOOC‘OOOOOOOOOOOCO

NOOC‘O—‘OONOO‘O‘U‘NOOO

—J

.A

NN
O—‘éOOOOOOOMOUUU‘ZWUT—‘NAWWOOOOOGJOUTOONUTW-J

5. 581
5. 777
5. 9113
11.107
11. 3211
11. 586
11. 757
4.984
5.2111
5.1127
5.583
5. 7119
5. 901
5. 9111
.0911
.21111
.1150
.591
.751
.962
1.107
1.228
1. 328
1. 669
1. 830
1.930
2. 206
2. 316
2. 517
2.698
2.908
.097
.287
.1173
.588
.759

1. 246
1. 441
1.682
1. 862
1. 993
2.133
2. 299
2.449
2.560
2.740
2.941

.098

.239

.414

.585

-.513
-.476
-.541
.537
.520
.526
.512
.521
.553
.512
.526
.539
.501
.576
-.551
-.560
-.547
-.581
-.591
-.578
-.587
-.542
-.574
-.569
-.553
-.533
-.549
-.556
-.516
- .579
- .566
566
.528
.517
.562
.551
.537
.548
536

. 51 1
. 555
.520
.510
.11119
.11911
. 483
. 1197
. 1199

.541
498

mllll
65‘.

Age Specific Data (continued)

215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
215
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224

\OU‘KICDCDO—buJ1

MEN
EON

_b
WOOOOOONOOOOOOOK]SWONAOOUWNAAOOO—b—SOONNJ‘JNNEN

_J

_._._._._._._._6_..._-_s_s_a—s_-_:—1—-mmmmwmmmmwammmmmmmmmmmmmmmmmmmmm
—-I

zzzzczzmazz-A-aaawwwwzzz-a-a:zzzazzzzzzwwzzmzzmww24:31:22.1:
COOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOC‘OOOOOOOO
OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOC’OOOOOOOOOOOOOOOOO
oooooooooooooooooooooom—tocoooooooooomooooooooooooooo
OOOOOOOWOOOOOOOO-‘OOOOON-‘OCOOOOOOC’OOOUUOOOOOOOOOOOOOOO

_§

.736
.922
.083

.420
.606
.787
.857
.043
.254
.435
.616
.751
.922
.109
.264
.420
.591
.762
.932
.103
.249
.445
.596
.767
.933
.098
.334
.505
.606
.777
.928
.928
.116
.221
.317
.477
.563
.643
1.719
1.814
1.935
2.080
2.211
2.307
2.432
2.603
2.759
2.935
3.101
3.261
3.452

NNNNNN—‘A—h—Idd

A—b—I—J—AdNNNNNNN—A—Jd—h—l—J

-.506
-.515
-.524
-.507
-.515
-.499
-.456
-.460
-.496
-.481
-.464
-.474
-.533
-.567
.592
.610
.550
.593
.532
.549
.566
.559
.548
.541
.584
.575
.540
.528
.571
.592
.583
.575
.575
-.481
-.487
-.492
-.502
-.507
-.512
-.490
-.522
-.476
-.459
—.466
-.472
-.506
-.490
-.525
-.509
-.519
-.529
-.540

Age Specific Data (continued)

224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224
224

d44dd—b—h—é—b—J—A—b—J—Dd—hA—I—I—A—J‘A—l—I—I‘d—IJ—A—b—bJ—Jg—Iédd—S—AdA—D—l-Jd‘d‘d

wwwzd-J—fi—‘d-‘d—‘dA-JAAAWWWNNNWW-Egkz-CNNN-‘dd—bJ=J=—*d—*dd-‘WWWWWW

OOOOOOOOOOOOOOOOOOC‘OOOOOOOOOOOOOOOOOOOOOOOOOOCOOOOC‘O

OOOOOOOOOOOOOOOOOOOOOOOOOOOCOOOOOOOOOOOOOOOOOOOOOOOO

COOOOOOOOOOOOOOOOOOOOOOOOOOCOOOOOC’OOOOOOOOOOOOOOOOOO

186

COOOOOOOOOOOOOUJOOOOOOOOUOOOOCOOOOOOBOOOOOOOOOOOONOOOO

CKOOUJ—fi—h-BOONOOOOOOOONO‘WOAO‘

-|
le-‘O‘OOOOOOOONOOOOONP-‘U‘

3.568
3.643
3.729
3.814
3.879
3.980
11.1111
4.312
4.417
4.477
4.558
11.633
11.7711
11.925
1.121
1.226
1. 307
1. 402
1. 5118
1. 628
1.7011
1.910
2.090
2.256
2.1117
2.588
2. 729
2. 804
2. 889
2. 975
3.075
3.151
3.231
3.317
3. 397
3. 467
3. 553
3.6118
3.719
3.809
3.8911
3.975
4.055
11.2116
11.1172
4.558
4.804
4. 970
5.064
5. 2111
5.294
5.384

-.547
-0 551
-.556

-.538
-.518
-.522
-.553
-.531
-.513
-.548
.572
.539
.561
.529

.5112
.564
.551

.5111
.584
.5118
.538
.529
.551
5.20
.5111
.535
.531
.526
5.21
511.2
.538
.533
.527
.523
.51111
.513
.561
.556
.518
.531
.553
.564
.5511

-.565

-.575

-.5511

-.561

187

Age Specific Data (continued)

224 1 2 0 0 0 6 2 5.539 -.518
224 1 3 0 0 0 1 11 5.639 -.525
224 1 2 0 0 0 0 6 5.714 -.531
224 1 4 0 0 0 0 0 5.899 -.517
224 1 1 0 0 0 0 0 6.034 -.500
224 1 1 0 0 0 0 0 6.279 -.518
224 1 1 0 0 0 0 0 6.439 -.556
224 1 1 0 0 0 0 0 5.031 .531
224 1 4 0 0 0 0 12 5.261 .541
224 1 3 0 0 0 0 16 5.386 .533
224 1 3 0 0 0 0 4 5.466 .554
224 1 3 0 0 0 0 3 5.551 .574
224 1 2 0 0 0 0 39 5.631 .595
224 1 4 0 0 0 0 2 5.761 .586
224 1 4 0 0 0 0 0 5.912 .602
224 1 4 0 0 0 0 0 6.087 .616
224 1 4 0 0 0 0 0 6.232 .606
224 1 1 0 0 0 0 0 6.357 .624
224 1 1 0 0 0 0 0 6.452 .617
244 1 1 0 0 0 0 0 .036 -.514
244 1 1 0 0 0 0 2 .126 -.515
244 1 3 0 0 0 0 17 .201 -.516
244 1 3 0 0 0 0 9 .301 -.490
244 1 4 0 0 0 0 4 .411 -.491
244 1 3 0 0 0 0 13 .546 -.519
244 1 4 0 0 0 0 3 .736 -.494
244 1 2 0 2 0 0 6 .951 -.497
244 1 4 0 0 0 0 3 1.076 -.525
244 1 4 0 0 0 0 11 1.246 -.527
244 1 3 0 0 0 0 22 1.451 -.529
244 1 3 0 0 0 0 7 1.541 -.530
244 1 2 2 0 6 0 3 1.646 -.558
244 1 1 0 0 0 0 3 1.791 -.500
244 1 1 0 0 0 0 0 1.861 -.534
244 1 1 0 0 0 0 0 1.956 -.535
244 1 1 0 0 0 0 0 2.026 -.535
244 1 4 0 0 0 0 0 2.226 -.538
244 1 4 0 0 0 0 0 2.401 -.540
244 1 2 0 0 0 0 13 .124 .566
244 1 3 0 0 0 0 6 .204 .565
244 1 4 0 0 0 0 17 .409 .563
244 1 3 0 0 0 0 10 .629 .588
244 1 3 0 0 0 0 22 .714 .587
244 1 2 0 0 0 0 3 .789 .586
244 1 2 0 0 0 0 19 .859 .585
244 1 2 0 0 0 0 11 .964 .557
244 1 2 0 0 0 0 31 1.044 .583
244 1 1 3 0 0 0 6 1.133 .609
244 1 1 0 0 0 0 0 1.209 .582
244 1 1 0 0 0 0 0 1.295 .581
244 1 1 0 0 0 0 0 1.378 .607
244 1 1 0 0 0 0 0 1.459 .579
244 1 1 0 0 0 0 0 1.544 .578

Age Specific Data (continued)

2411
244
244
244
244
244
244
244
244
244
244
244
244
244
244
244
244
244
244
244
244
244
244
244
244
244
244
244
244
244
244
244
244
244
244
244
244
2411
244
244
244
244
244
244
244
244
244
244
244
244
21111
244

R‘IUR‘K‘K’NNR3R‘I\)I\)I\3NI\31\)I\1[\3l\)f\)1\31\3[\3l\)1\)l\)l\)l\)f\‘1\)l\‘l\)l\1l\)l\\1\3l\‘l\\l\)l\‘l\)l\.)l\\l\1l\\o4.3.3.5444

A—A-A—b-A-J-i—A-‘d—S—S—‘UJUU—J-JAFrWWR1-E’WWW1UR1-é-AAJ?trJ—‘I’EP-fi'wwwwoJTJ—‘Jr—JLAJNUU

OOOOOCOOOOCOOOOOOOOOOOOOOCOO«JOOOCOOOOOOOOOOOOOOOOOOO

OOOOOCOOOOOOOOOOOOCCOOWOOOOO‘CDOOCCOOOOOOOOOOOOOOOOOWO

OOOOOOOOOOOOOOOOOOOOOOIUOCOOOOOLDOOOOOOOOOOOOOOCOOOOOO

188

OOOOCOOOOOOOOOOOOOOOOOOOOCOOR‘OOCCOOOOOOOOOOOOOOOOOON

—J A
[UN-4

[\1—A ....|_a

NA .2
OOOOOOOOOOOOOR‘R11VOOdeO‘R1thoUTUJW-KOOOOOOOOOOtNO‘m—AOOOOO

1.674
1.774
1.859
1.943
2.089
2.239
2.394
0.000

.034

.109

.215
.472
.587
.748
.915
1. 081
1.237
1.403
1.584
1.755
1.865
1.951
2.052
2.147
2.218
2.298
2.374
2.464
2.595
2.726
2.806
2.967
3.093
3.254
3.385
3.470
3.541
3.637
3.803
3.893
3.969
.037
.122
.223
.298
.369
.464
.540
.630
.696
.791
.882

.577
.575
.548
.601
5.72
.597
5.96
O. 000
-.526
-.526
-.526
-.500
-. 579
-.500
-.500
-. 500
-. 474
-. 4117
- .500
- .474
-.500
-. 500
-.500
.474
5.26
.474
-.500
-.500
-.11711
-. 474
-. 474
-.4711
-. 4711
-.500
-. 474
-.500
-. 473
-. 474
-. 447
-. 447
-. 395
.526
.526
.579
.553
.553
.579
.605
.553
5.79
.579
.553

Age Specific Data (continued)

244
244
244
244
2411
244
244
244
244
2411
244
244
244
244
21111
244
244
2411
244
244
21111
244
244
2411

[\1l\)l\)1\)l\)f\)l\)l\)|\)l\3|\)l\)l\3l\)l\)l'\)[\3f\\|\)l\)l\)l\)l\3l\)

—I
OOOCOOOOOOOOOOCOOOOOOOOO

OOOCOOOOOOOOOOOOOOOOOOOO

_I
OOOR’QOOOOOOOOOCOOOOOOOOO

189

OOCOUJOOOOOOOOOCOOOOOOOOO

—aou
u1::u1~acac>c>n)c>c>c>

a _s
oer‘NO‘OOWN

.973
.093
.259
.390
.566
.752
.938
.145
.215
.381
.462
.547
.633
.723
.819
.945
.076
.257
.398
.468
.544
.644

WWWWWWNNNR‘NR‘R‘NN—b—I—I—h—I—a

w
.4
N
O

3.936

.553
.579
.579
.579
.579
.526
.526
.579
.553
.579

.553

.526
.553
.553
.553
.553

.553
.579
.579
.605
.605
.605
.579
.605

APPENDIX F

 

Ovipositional Behavior Data

The data is listed as i,j, k where i stands for
bulb condition (1 = rotting + infested 2 = rotting,
3 = normal), j stands for bulb type ( l = large dry
bulbs, 2 = large green bulbs, 3 = small green bulb.)
and K equals the total number of immature onion

maggots associated with each bulb.

CDDAONIONACT (5X , 3F5 . O)

190

 

191

Ovipositional Behavior Data

 

 

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