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1985 Natural Control and Spatial Distribution of Fall Armyworm (Spodoptera Frugiperda) Within Louisiana Corn Fields. Forrest Lee Mitchell Louisiana State University and Agricultural & Mechanical College
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Recommended Citation Mitchell, Forrest Lee, "Natural Control and Spatial Distribution of Fall Armyworm (Spodoptera Frugiperda) Within Louisiana Corn Fields." (1985). LSU Historical Dissertations and Theses. 4143. https://digitalcommons.lsu.edu/gradschool_disstheses/4143
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IMversity Microfilms International
8610653
Mitchell, Forrest Lee
NATURAL CONTROL AND SPATIAL DISTRIBUTION OF FALL ARMYWORM (SPODOPTERA FRUGIPERDA) WITHIN LOUISIANA CORN FIELDS
The Louisiana State University and Agricultural and Mechanical Col. Ph.D. 1985
University Microfilms International300 N. Zeeb Road, Ann Arbor, Ml 48106
Copyright 1986
by Mitchell, Forrest Lee All Rights Reserved
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University Microfilms International
NATURAL CONTROL AND SPATIAL DISTRIBUTION OF FALL ARMYWORM (SPODOPTERA FRUGIPERDA) WITHIN LOUISIANA CORN FIELDS
A Dissertation
Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for the degree of Doctor of Philosophy
in
The Department of Entomology
by Forrest Lee Mitchell B.S., Texas A&M University, 1977 M.S., Texas A&M University, 1980 December 1985 ©1986
FORREST LEE MITCHELL
All Rights Reserved ACKNOWLEDGEMENTS
The completion of a doctoral degree is never an easy task, but I have been fortunate in having many friends and colleagues who assisted me. I greatly appreciate the patience and support of my major professor, Dr. J.R. Fuxa, and my committe members, Dr.
T.C. Sparks, Dr. L.D. Newsom, Dr. J.B. Graves, Dr. B. Wilson, Dr.
J. Larkin, Dr. D. Braymer, and Dr. A. D. Larson. Art Richter and
Greg Warren were both frien d and co-w orker, and w ithout th e ir assistance my research would have gone awry more than once.
Friends too numerous to mention made my time in Baton Rouge enjoyable and le ft me with unforgettable memories. Colleagues in entomological societies were always willing to listen and advise
— they are the main reason entomology w ill always be a colorful and enjoyable profession. The patience and affection of my wife,
Paula, shored me up through the hard times. Without her, I could not have accomplished this work.
ii TABLE OF CONTENTS
page ACKNOWLEDGEMENTS ...... i i
ABSTRACT...... v ii
CHAPTER
1. INTRODUCTION AND LITERATURE REVIEW ...... 1
2. SPATIAL DISTRIBUTION AND SAMPLING OF FALL ARMYWORM IN CORNFIELDS ...... 8 Materials and Methods ...... 9 Results ...... 12 Discussion ...... 32
3. MULTIPLE REGRESSION ANALYSIS OF A NUCLEAR POLYHEDROSIS VIRUS IN POPULATIONS OF FALL ARMYWORM IN CORNFIELDS ...... 34 Materials and Methods * ...... 34 Results ...... 40 Discussion ...... 54
4. MULTIPLE REGRESSION ANALYSIS OF PARASITISM OF FALL ARMYWORM BY THREE SPECIES OF LARVAL PARASITES...... 63 Materials and Methods ...... 64 Results ...... 65 Discussion ...... 83
5. SEASONAL SUSCEPTIBILITY OF FALL ARMYWORM TO A NUCLEAR PLOYHEDROSIS VIRUS ...... 87 Materials and Methods ...... 88 Results ...... 90 Discussion ...... 105
REFERENCES CITED...... 108
APPENDIX 1 ...... 117
APPENDIX 2 ...... 120
APPENDIX 3 ...... 123
VITA...... 126
iii LIST OF TABLES
TABLE page
2.1 Chi-square probability levels for three distri butions fit to total fall armyworm populations collected in one-plant samples ...... 13
2.2 Chi-square probalility levels for three distri butions fit to total fall armyworm populations collected in five-plant samples ...... 14
2.3 Chi-square probability levels for three distri butions fit to small fall armyworm collected from one-plant samples ...... 15
2.4 Chi-square probability levels for three distri butions fit to small fall armyworm collected from five-plant samples ...... 16
2.5 Chi-square probability levels for three distri butions fit to medium fall armyworm collected from one-plant samples ...... 18
2.6 Chi-square probability levels for three distri butions fit to medium fall armyworm collected from five-plant samples ...... 19
2.7 Chi-square probability levels for three distri butions fit to large fall armyworm collected from one-plant samples ...... 20
2.8 Chi-square probability levels for three distri butions fit to large fall armyworm collected from five-plant samples ...... 21
2.9 Number of samples required for p=0.20 accuracy for fa ll armyworm larvae sampled at varying densities (number/plant) in cornfields ...... 31
3.1 Variables tested for inclusion in fall armyworm NPV multiple regression models ...... 39
3.2 Distribution of infection and mortality in field collected larvae ...... 50
3.3 Multiple regression analysis of NPV prevalence in populations of fall armyworm...... 51
3.4 Multiple regression analysis of NPV prevalence in populations of fall armyworm...... 53
iv TABLE page
4.1 Variables tested for inclusion in multiple regression models ...... 66
4.2 Multiple regression anaylsis of parasitism by Cotesia margin!ventris (APANT) and the number of larvae/plant (LNUM) as dependent variables . 79
4.3 Multiple regression analysis of parasitism by Rogas laphygmae (AROGAS) and Che1onus sp. (ACHEL) as dependent variables ...... 80
4.4 Correlation matrix between dependent variables and variables found to be significant in multiple regression models ...... 82
5.1 Results of bioassays of an NPV against progeny of FAW collected as larvae from St. Gabriel ...... 102
5.2 Results of bioassays of an NPV against Fj progeny of FAW collected as larvae from Hammond ...... 104
v LIST OF FIGURES
FIGURE page
2.1 Taylor power function fit for total popula tions in one-plant samples ...... 23
2.2 Taylor power function fit for total popula tions in five-plant samples ...... 25
2.3 Taylor power function fits for small, medium and large larvae in one-plant samples ...... 28
2.4 Taylor power function fits for small, medium and large larvae in five-plant samples ...... 30
3.1 Seasonal density of fall armyworm populations in cornfields at St. Gabriel, LA, 1981 and 1982 ...... 42
3.2 Seasonal density of fall armyworm populations in cornfields at Hammond, LA, 1982 and 1983 ... 44
3.3 Prevalence of a nuclear polyhedrosis virus in populations of fall armyworm feeding in corn fields at St. Gabriel, LA, 1981 and 1982 ...... 46
3.4 Prevalence of a nuclear polyhedrosis virus and granulosis virus in populations of fall army worm feeding in cornfields at Hammond, LA, 1982 and 1983 ...... 48
3.5 Distribution map of an NPV in a cornfield in 1981 ...... 59
3.6 Distribution map of an NPV in a cornfield in 1982 ...... 61
4.1 Percent parasitism of fa ll armyworm by Chelonus sp., Cotesia marginiventris, and Rogas laphyg- mae at St. Gabriel in 1981 ...... 68
4.2 Percent parasitism of fa ll armyworm by Chelonus sp., Cotesia marginiventris, and Rogas laphyg- mae at St. Gabriel in 1982 ...... 70
4.3 Percent parasitism of fa ll armyworm by Chelonus sp., Cotesia margin!vantris, and Rogas laphyg- mae at St. Gabriel in 1983 ...... 73
4.4 Percent parasitism of fa ll armyworm by Chelonus
vi FIGURE page
sp., Cotesia marginiventris, and Rogas laphyg- mae at Hammond in 1982 ...... 75
4.5 Percent parasitism of fa ll armyworm by Chelonus sp., Cotesia marginiventris, and Rogas laphyg- mae at Hammond in 1983 ...... 77
5.1 Probit analysis of response of 1981 f ir s t gen eration St. Gabriel progeny to an NPV...... 93
5.2 Probit analysis of response of progeny of con trol FAW in the second generation to an NPV . . . 95
5.3 Probit analysis of response of progeny of NPV- treated FAW in the second generation to an NPV 97
5.4 Probit analysis of response of progeny of con trol FAW in the third generation to an NPV .... 99
5.5 Probit analysis of response of progeny of NPV- treated FAW in the third generation to an NPV . 101
vii ABSTRACT
Spatial distribution of fall armyworm, Spodoptera frugiperda
(J.E. Smith), (FAW) was studied in cornfields at the St. Gabriel
Experiment Station in Louisiana from 1981 to 1983. Corn plants
were sampled and FAW larvae counted and divided into categories
of small, medium, and large for both single plant and five plant
samples. Count data for each category by date were then fitted to
Poisson, negative binomial, and Neyman type A distributions.
Means and variances for each category by date were also analyzed
with the Taylor Power law. Small larvae were more aggregated than
the medium larvae which were more aggregated than the large
larvae. One plant samples were more efficient than five plant
samples.
Prevalence of a nuclear polyhedrosis virus (NPV) was studied
in 1981 and 1982 at St. Gabriel and in 1982 and 1983 at a private
dairy farm 6 km east of Hammond, Louisiana. Information on FAW
populations, climate, and virus prevalence were analyzed by
multiple regression and regression models made for each location
and for combined data. Important factors at St. Gabriel included
corn height, number of larvae per plant, and temperature variables. Important factors at Hammond were all environmental —
temperature, degree day, rain, and solar radiation. The most important variable for regression models made from the combined data was number of larvae per plant.
Parasitism of FAW larvae by three species of parasite—
viii Cotesia (Apanteles) marginiventris, Rogas laphygmae, and Chelonus
sp. — was studied concurrently with NPV epizootiology. Regres
sion models were constructed for parasitism by each species using
the combined data sets. The R values for the models were low,
not exceeding 0.40. The only variable to appear in all models was
corn height.
The Fj offspring of larvae collected in St. Gabriel in 1981
and Hammond in 1982 were tested by bioassay against the locally
occurring NPV. Heterogeneity with respect to the NPV increased
with time. larvae of parents surviving both NPV applications
and naturally occurring NPV selection often gave bioassay results
too heterogeneous to fit the probit model. Immigration of
susceptible individuals was the most likely cause of the heterogeneity.
ix CHAPTER Is INTRODUCTION AND LITERATURE REVIEW
The number of insect species has been assessed by various
authorities using both subjective and objective criteria. The
main point of agreement is that there are many of them, perhaps
in the millions. Of these, only 6000 to 7000 impinge upon man's
health, welfare, or food supply. In the United States, fewer
than 200 species are considered persistently damaging pests,
while another 500 are occasionally damaging.
This small fraction of the class Insecta receives the
majority of funding for entomological research, amounting to
m illions of dollars annually. Tremendous amounts of literatu re
are generated, and yet still more research is necessary, not only
for practical control measures but for an understanding of the
basic biology of the pest as well. This latter facet of research
has become increasingly important with the realization that pests
cannot be controlled by blankets of insecticides. Methods of
pest control which are based on an understanding of the biology
of the pest and its ecologocal relationships with associated biota are part of a philosophy known as Integrated Pest
Management.
One of these methods, biological control, is especially attractive in that the natural enemies and diseases of the pest insect are used against it, resulting in virtually no damage to the environment. The research investment in biological control projects is not trivial. The dynamic balance of nature must be
1 2
altered, or in some instances repaired, to the advantage of man.
This involves the additional need for information on the biology
and natural history of the organisms concerned, especially as it
r e la te s to the ta rg e t in se c t. The present study was conducted
with the intent of documenting natural control and pertinent
biology of the fall armyworm, Spodoptera frugiperda (J.E. Smith),
in so u th -c e n tra l Louisiana c o rn fie ld s. Corn (Zea mays L.) was
chosen as a host crop in order to compare sim ilar data taken from
grass pastures (Fuxa 1982, Fuxa and Geaghan 1983), and because of
the devastating effect of fall armyworm on the crop. The corn
plant is also easily visualized as a discrete unit in the field,
making absolute samples much easier to acquire.
The dissertation is divided into five chapters. This
chapter fin ish e s w ith a l i t e r a t u r e review on f a l l armyworm
biology, sampling, natural control, and insect virus
epizootiology. Chapter 2 is a study of fall armyworm population distribution in corn plants. Chapter 3 summarizes seasonal prevalence of two viruses that infect fall armyworm. Statistical models of these data are constructed in order to identify factors in the environment and in armyworm larval biology that may affect virus prevalence in the populations. Chapter 4 summarizes similar data for three hymenopterous parasites that commonly parasitize fa ll armyworm. Again, models were constructed to try and identify factors affecting parasitism. Chapter 5 presents data from bioassays of nuclear polyhedrosis virus against fall armyworm larvae, in order to ascertain seasonal susceptibility of the larvae and and determine if virus infection selects for less 3
susceptible individuals in the population.
Literature Review
In 1773, Mr. John Abbott emigrated to the crown colonies in
America, carrying with him a mandate from the Royal Society of
London to conduct research and make collections of the largely unknown American fauna. Notes, specimens, and w atercolor
illustrations were sent to his British and European
correspondents, among whom was Sir John E. Smith, a British naturalist. In 1797 a collaborative effort resulted in "the first extensive monograph entirely devoted to North American entomology" (Wilkinson 1981) entitled "The Natural History of the
Rarer Lepidopterous Insects of Georgia" (Smith and Abbott 1797).
Ironically, it is here that the economically devastating fall armyworm is first described.
The insect soon ceased to be rare. Chittenden (1901) discussed the appearance of the first outbreaks and described in detail an outbreak in 1899. Farmers were nearly helpless against the massive onslaught of this and other pests and sought governmental relief, resulting in the Morrill Act. In congressional debates preceding the passage of the act (Acts of
1862), Senator Wright of Indiana argued for government sponsorship of Agricultural Experiment Stations that would conduct beneficial research to "prevent the ravages of the armyworm" and "the devastation of the wheatfly" (Conover 1924).
However, it was over 100 years after the insect was first described before organized research began at U.S. Department of
Agriculture (USDA) laboratories. 4
The f i r s t n atu ral h is to ry survey of the f a l l armyworm was
published by Chittenden (1901). In addition to studying its
biology, he technically described and illustrated all larval
stages. This work was followed by publication of two other USDA
technical bulletins in 1928 and 1929, by Phillip Luginbill and
R.A. Vickery respectively, which furthered Chittendens' research
and identified many of the parasites associated with the fall
armyworm. These three publications, along with two lesser ones by
Hinds and Dew (1915) and Walton and Luginbill (1917), remained
the major source of information about fall armyworm biology and
natural control until the 1970’s.
In 1978 and 1980, symposia on the fa ll armyworm were held at
meetings of the Southeastern Branch of the Entomological Society
of America in Gainesville, Florida, and Biloxi, Mississippi,
respectively. The proceedings of these symposia were published in
the Florida Entomologist in two issues {(Vol. 62(2): 53pp. (1979)
and Vol. 63(4): 124pp. (1980)} and are s ig n ific a n t in th at many
areas of research on fall armyworm are brought up to date. One
publication by Sparks (1979) was the first review of fall
armyworm biology in 50 years.
There is no information in U.S. literature on fall armyworm distribution and sampling in field crops. Willson and Young
(1983) refer to collection data fitted to the negative binomial but do not present it. Action thresholds in bermuda grass,
Cynodon dactylon (L.) Pers., and sorghum, Sorghum b ico lo r (L.)
Moench, have been calculated by Martin et al. (1980), but populations of the armyworm were artificially manipulated to achieve desired densities. Linker et al. (1984) compared sampling techniques for fall armyworm feeding in peanut Arachis hypogaea
L., but no sampling scheme was presented. Methods for constructing sampling programs and techniques for data analysis were presented in Southwood (1978). Spatial distribution of insect populations, including history and development of the field, was reviewed by Taylor (1984).
The study of disease epizootics in insect populations was first proposed in detail by Steinhaus (1954). In this landmark publication were gathered terminology, theory, and available supporting information from the literature on both insects and other animals. This literature was expanded by Tanada (1963,
1976). Current theory on invertebrate epizootiology has been translated into mathematically sophisticated models by Anderson and May (1978, 1979, 1980) and May and Anderson (1978, 1979).
Stochastic models of invertebrate host-pathogen systems have been published by Kish and Allen (1978) (velvetbean caterpillar
Anticarsia gemmatalis and the fungus Nomurea rileyi), Anderson and May (1980) (the larch bud moth, Eusosma griseana, m icrosporidian protozoans, and baculoviruses), and Brown and
Nordin (1982) (the alfalfa weevil, Hypera postica, and a fungus in the genus Erynia).
The pathogens infecting fall armyworm have been reviewed by
Gardner and Fuxa (1980). They reported 16 pathogens representing
5 groups (viruses, bacteria, protozoa, fungi, and nematodes) as being capable of inducing disease. The fall armyworm nuclear polyhedrosis virus disease was first reported by Chapman and 6
Glaser (1915) and Allen (1921). Since then, viral natural history and prevalence have been researched in grass pastures(Fuxa
1982), corn (Fuxa 1982, Hamm and Hare 1982) and sorghum (Fuxa
1982, Schwehr and Gardner 1982). Fuxa and Geaghan (1983) developed multiple regression models for factors influencing prevalence of both the nuclear polyhedrosis virus and a granulosis virus in fall armyworm populations feeding in grass pastures.
Resistance occurs in insect populations to various pathogens, at least on a local scale, and has been discussed in many publications (bibliographies and discussions in Tanada 1976 and Briese 1981). Only Martignoni (1957) discussed susceptibility of the host before and after an epizootic using empirical data.
Rush et al. (1981) investigated seasonal changes in s u s c e p tib ility of a cim icid, Oeciacus v ic a r iu s , to Fort Morgan virus. However, the susceptibility was not determined by bioassay, and comparisons were made only between one collection in May and one collection in July. There have been several stu d ie s (David and G ardiner 1960, M artignoni and Schmid 1961,
Reichelderfer and Benton 1974, Briese and Mende 1981, Briese
1982, and others) of changes in susceptibility to a virus between populations of the same insect from different geographical regions. Reichelderfer and Benton (1974) demonstrated that these properties were heritable in fall armyworm and are partially or incompletely dominant. Working with the potato moth, Briese
(1982) provided evidence for a single, dominant autosomal gene controlling susceptibility to a granulosis virus, which segregated according to simple Mendelian ratios. F2 hybrids gave a classical broken probit line when challenged with the virus.
Parasites of the fa ll armyworm were studied and observed by early workers (Luginbill 1928, Vickery 1929), but more recently this complex has been studied in grass and corn in Florida
(Ashley et al. 1980, 1983), and in peanut in Oklahoma (Wall and
Berberet 1975). Ashley (1979), summarizing the information on fall armyworm from the literature, found collection records for
53 species of parasites in two orders and ten families. Ashley et al. (1982) investigated seasonal population density and abundance of parasites attacking fa ll armyworm populations in field corn.
They concluded that fall armyworm populations were capable of escaping parasite regulation. Mitchell et al. (1984) investigated the e ffe c ts of perm eation of a c o rn field w ith f a ll armyworm pheromone on the population dynamics of both fa ll armyworm and its larval parasites. CHAPTER 2: SPATIAL DISTRIBUTION AND SAMPLING OF FALL ARMYWORM IN CORNFIELDS
Spatial distribution is a fundamental property of insect
populations. Due to the simple requirements of the analysis,
information on distribution of a species may be extracted from
data that were taken for other purposes. Knowledge of spatial
distribution is relevant to many areas of research. It is of
major importance to sampling programs. With the advent of
integrated pest management and the need for reliable estimates of
insect abundance, detailed sampling programs for a variety of
insect pests have been developed (Kogan and Herzog 1980, Taylor
1984). Linked with economic injury levels, these programs provide
sound management information for farmers, extension agents, and researchers.
Action thresholds for fall armyworm (FAW), Spodoptera fru g ip erd a J.E. Smith, in bermuda g rass, Cynodon dactylon (L.)
Pers., and sorghum Sorghum b ico lo r (L.) Moench, have been published (Martin et al. 1980) but were based on artificial infestations which were necessary to maintain desired population densities on the host plants. Reference to data gathered on FAW in small grains and fitted to the negative binomial is made in
Willson and Young (1983), but not elaborated. Sweep net and beat cloth techniques were compared for sampling FAW in Florida peanuts by Linker et al. (1984). There are no sampling programs currently available for FAW in field crops, even though estimated losses and cost of control for this pest were $41.35 million in 9
1981, $500,000 in Louisiana alone (Southern 1983). The present study describes spatial distribution of FAW feeding in field corn in south-central Louisiana and uses the information to develop a sampling program for different larval categories.
Materials and Methods
FAW were sampled in small plots on the St. Gabriel
Experiment Station, Iberville Parish, LA. Samples were taken weekly, circumstances permitting, from April until August in 1981 and from April u n til September in 1982 and 1983. A s tr a tif ie d random sampling technique (Southwood 1978) was employed. Fields were divided into four or eight plots, depending on their shapes.
Five plants were sampled from each of 8 or 16 random locations within the plots. On four dates in 1983, a total of five sets of
40 or 80 single plant samples were taken. During periods of low population density or when plants were too large to harvest, sampling was done at the field site. Otherwise, the entire plant from the crown up was harvested, bagged, and returned to the laboratory for examination. All aerial plant parts were examined for the presence of larvae, including the whorl, ear, tassels, and leaf axils.
As each larva was found, it was removed to artificial diet
(Burton 1969, Greene et al. 1976) and placed in a controlled temperature chamber at 27°C, 14:10 photoperiod. Each larva was subjectively classified to instar by comparison with preserved specimens of known age in order to obtain a weighted mean instar.
Larvae were grouped according to length into small (1st and 2nd instars, large (5th and 6th instars, >25mm) categories for all other age dependent analysis. Experiments were conducted as follows: 1981 Two fields were sampled. Field 1 was planted 17 April with Pioneer 3030 seed and measured 42m X 66 rows (0.9m centers) with eight plots, each 10.4m X 33 rows. The outer three rows of each plot were used as buffers. Field 2 was planted 20 May with Pioneer 3030 seed and measured 39m X 68 rows with eight plots, each measuring 9.8m X 34 rows and having three buffer rows. A total of 512 plants on seven sample dates was taken in Field 1, while 613 plants were sampled on ten dates in Field 2. 1982 Three fields were sampled, all of which were plantedwith Pioneer 3030 seed. Field 1 was planted 21 April and measured 183m X 16 rows. Four plots, each 45m X 16 rows, were sampled. The narrowness of the field precluded buffers. Field 2 was planted 12 May and measured 110m X 30 rows. Four plots were sampled, each 27m X 30 rows, without buffer rows. Field 3 was planted 20 July in the same location as Field 1. However, the number of plots was increased to eight, each measuring 22.5m X 16 rows. A total of 530 plants on six dates was sampled in Field 1, 880 plants on nine dates in Field 2, and 680 plants on nine dates in Field3. 1983 Four fields were sampled. Field 1 was planted with Pioneer 3030 seed on 5 May and measured 76m X 64 rows. Only two plots were sampled due to uneven, poor stands in the others. The plots were irregular in outline; one was ca. 76m X 23 rows, while the other was ca. 76m X 41 rows. Field 2 was planted with Pioneer 3030 seed on 17 June and measured 70m X 64 rows. Four plots, each 11 35m X 32 rows (no buffers) were sampled. Fields 3 and 4 were both planted on 11 August. Field 3 measured 140m X 32 rows and was planted in Pioneer 3030 seed. Field 4 measured 69m X 25 rows and was planted in Funk sweet corn. Neither field was subdivided, and only one-plant samples, either from 40 or 80 locations, were taken. A total of 400 plants was examined on six sample dates in Field 1, 480 plants on six sample dates in Field 2, 160 plants on three sample dates in Field 3, and 80 plants on two sample dates in Field 4. Mean and variance were calculated for each field by sample date for small, medium, and large larvae and for total population for five-plant samples. Since larvae had been recorded by individual plant on most dates, means and variances also were c alcu lated for sm all, medium, and larg e larvae and to ta l population for single plants by sample date. Only single plant samples were taken on five dates in 1983; these were analyzed in the same fashion. Count data for each larval category were then tested by sample date against three distributions: poisson, negative binomial, and Neyman type A. The data were tested by a customized SAS (Statistical Analysis Systems, SAS Institute, Cary, NC) procedure developed by the Experimental Statistics Department at Louisiana State University and based on the FORTRAN algorithm of Gates and Ethridge (1972). Chi-square tests were used to test the fit of the distribution. If the probability level fe ll below 0.05 or if insufficient degrees of freedom were available to make the test (due to low population densities), then fit of the model was rejected. 12 The negative binomial parameter k was plotted (as a reciprocal) against the mean to test the effects of density (Taylor et al. 1979) and j u s t i f y the c a lc u la tio n of a common k. Curves were fit with the general linear model (GLM) procedure of SAS. Means and variances also were f i t t e d to the Taylor Power function (Taylor 1961), in which the log of the variance was regressed against the log of the mean. The resulting intercept (a) and the slope (b) were used in the equation: log np = (log a - 2 log p) - (2-b)log m where np = the number of plants to be sampled at the level of accuracy p when the population mean is m (Finch et al. 1975). The means and variances of the five independently drawn one- plant samples taken in 1983 were removed from the data set containing the one-plant samples derived from the five-plant samples. A regression was calculated using the modified data set and the resulting line and its 95% confidence limits were plotted. The five points were then plotted on the line to determine if they fell within the confidence limits. Slopes and intercepts of lines were compared by analysis of covariance with the GLM. If differences were found, t-te s ts were used to determine which slopes or intercepts were significantly d ifferen t. Results No single distribution described FAW populations at the entire range of densities, although the negative binomial and the Neyman type A fit the total population data from more sample 13 TABLE 2.1 Chi-square probability levels for three distributions fit to total fall armyworm populations collected in one-plant samples. A period indicates the distribution did not fit or could not be tested. AVE. MEAN VAR INSTAR Pa NB NA 0.0125 0.0125 6.00 ••• 0.0133 0.0133 6.00 •• 0.0250 0.0250 6.00 o • 0.0260 0.0530 4.00 • • 0.0375 0.0379 5.00 •• 0.0375 0.0375 5.33 • • 0.0750 0.1220 1.33 • • 0.1000 0.1440 5.00 • • 0.1000 0.1430 4.00 • • 0.1250 0.1110 5.66 e • 0.1250 0.1120 5.40 •• 0.2000 0.2150 4.75 • • 0.2250 0.3540 5.11 0 060 0.090 • 0.2250 0.2300 5.22 •• 0.3250 0.4300 4.85 • • 0.3630 1.3730 2.31 0.391 0.152 0.3880 0.8980 1.74 0.868 0.800 0.3880 0.4940 4.10 0 211 • • 0.4070 0.8310 4.07 0.065 • 0.4530 1.0000 4.47 0 133 0.095 • 0.6500 1.2180 4.02 0 057 0.620 0.800 1.2300 1.1530 4.19 0 079 •• 1.3000 1.7570 2.85 • 1.5500 4.5500 1.85 0.720 0.382 1.5700 4.0950 2.10 0.337 • 1.7300 2.1000 2.23 0 630 0.788 0.843 1.8500 4.7970 2.38 0 168 0.477 0.387 2.1500 5.3440 1.90 0.974 0.862 2.3100 4.3180 3.04 0.362 0.378 2.3100 4.3180 3.23 0.200 0.140 2.6500 12.9100 1.98 0.237 • 2.7400 7.2590 3.23 0.129 0.050 3.4000 25.0500 2.30 •• d is trib u tio n s are poisson-P, negative binomial-NB, and Neyman type A-NA. 14 TABLE 2.2 Chi-square probability levels for three distributions fit to total fall armyworm populations collected in five-plant samples. A period indicates the distribution did not fit or could not be tested. AVE. MEAN VAR INSTAR Pa NB NA 0.0625 0.063 6.00 •• 0.0625 0.063 6.00 • • 0.1160 0.125 6.00 •• 0.1880 0.296 5.00 •• 0.1880 0.162 5.33 •• 0.2660 0.133 4.00 • • 0.3750 0.554 1.33 •• 0.5000 1.143 5.00 • • 0.5000 2.000 4.00 • • 0.6250 0.516 5.66 0 365 • • 0.6250 0.268 5.40 • • 0.9330 9.638 3.17 • • 1.0000 1.429 4.75 0 077 • • 1.1250 3.583 5.11 • • 1.1250 1.839 5.22 0 346 0.322 0.310 1.6250 3.982 4.85 •• 1.8120 18.563 2.31 0.163 0.437 1.9370 11.129 1.74 0.718 0.497 1.9370 2.329 4.10 0 828 0.803 0.828 1.9380 6.862 4.07 0.886 0.386 2.2660 5.781 4.47 0 131 0.646 0.609 3.2500 9.400 4.02 0 065 0.751 0.567 5.1250 19.550 5.60 0 626 •• 6.3300 20.242 2.85 0 075 0.587 0.904 6.4160 30.629 1.99 0.535 0.577 7.2500 19.070 1.98 0 235 0.080 0.060 7.7500 49.400 1.85 0.821 0.968 7.8750 50.650 2.10 0.693 0.757 10.5000 46.029 1.90 0.371 0.406 11.3800 62.117 3.23 0.953 0.730 11.4400 33.396 3.04 0 303 0.095 0.130 12.1000 46.540 4.30 0 066 0.291 0.137 13.6900 61.029 3.23 0.859 0.839 17.0000 421.600 2.30 • • 17.6300 136.380 2.10 0.677 0.529 29.6700 431.380 3.30 0.527 0.415 d is trib u tio n s are poisson-P, negative binomial-NB, and Neyman type A-NA. TABLE 2.3 Chi-square probability levels for three distributions fit to small fall armyworm collected from one-plant samples. A period indicates the distribution did not fit or could not be tested. OBS MEAN VAR Pa NB NA 1 0.013 0.013 •• • 2 0.063 0.019 •• • 3 0.075 0.122 • •• 4 0.087 0.612 ••• 5 0.125 0.266 •• • 6 0.263 1.335 • 0.065 0.116 7 0.375 0.896 • 0.776 0.868 8 0.513 0.785 • 0.797 0.874 9 0.613 1.455 • 0.541 0.946 10 0.738 1.934 • 0.143 0.073 11 0.850 2.330 • 0.730 0.702 12 1.063 4.287 * 0.797 0.352 13 1.125 3.224 • 0.529 0.668 14 1.150 1.566 0.4 0.546 0.617 15 1.375 5.174 • 0.545 0.420 16 1.375 4.240 • 0.382 0.194 17 1.86 10.27 • 0.106 • 18 2.350 21.490 • 0.558 • distributions are poisson-P, negative binomial-NB, and Neyman type A-NA. TABLE 2.4 Chi-square probability levels for three distributions fit to small fall armyworm collected from five-plant samples. A period indicates the distribution did not fit or could not be tested. OBS MEAN VAR Pa NB NA 1 0.063 0.063 • • • 2 0.188 0.563 • • • 3 0.300 0.233 • •• 4 0.375 0.544 ••• 5 0.438 3.063 • • • 6 1.313 16.896 • •• 7 1.875 11.317 • 0.193 0.558 8 2.500 9.000 • 0.603 0.191 9 3.063 15.396 • 0.100 0.209 10 3.375 8.783 0.216 0.539 0.562 11 3.688 14.896 • 0.226 0.486 12 4.375 38.517 • 0.313 0.462 13 4.375 13.125 0.335 0.444 0.396 14 5.080 22.083 • 0.070 0.297 15 5.313 43.829 • 0.956 0.600 16 5.625 37.850 • 0.160 • 17 6.813 49.363 • 0.468 0.238 18 11.69 344.63 • 0.132 • 19 12.13 140.55 • 0.271 0.374 distributions are poisson-P, negative binomial-NB, and Neyman type A-NA. 17 dates than did the poisson (Tables 2.1 and 2.2). When total population was divided into small, medium, and large categories, however, a different trend appeared. Small larvae in both one- plant (Table 2.3) and fiv e -p la n t (Table 2.4) samples f i t the negative binomial on 13 dates, while the poisson fit on only one and two dates respectively. For medium larvae in one-plant samples (Table 2.5), the poisson fit the data on nine sample dates, and the negative binomial and Neyman type A fit on seven dates. In five- plant samples (Table 2.6), the negative binomial and Neyman type A distributions fit on 14 sample dates, while the poisson fit on 11 dates. Data for large larvae in one-plant samples (Table 2.7) fit the poisson distribution on six dates, while the other two distributions fit on only one date. In five- plant samples (Table 2.8), the poisson f i t on 15 sample dates and the Neyman type A and negative binomial on ten and nine dates respectively. Of the 52 sets of samples drawn during the study, larvae were present on 38 occasions. When 1/k was plotted against mean density, curves similiar to the type I curve of Taylor et al. (1979) resulted for both the one-plant and five-plant data. A type I curve is expected when the slope of a power law f i t is between 1 and 2, and it indicates that while k varies with density, some k values may fit at more than one density. For this reason, coupled with the fact that the negative binomial did not adequately describe larval distributions on many sample dates, no attempt at calculating a common k was made. A common k is necessary to sampling programs using the negative binomial, as well as for transforming count TABLE 2.5 Chi-square probability levels for three distributions fit to medium fall armyworm collected in one-plant samples. A period indicates the distribution did not fit or could not be tested. OBS MEAN VAR Pa NB NA 1 0.013 0.013 • • 2 0.013 0.013 • 3 0.050 0.049 0.515 4 0.100 0.143 • 5 0.125 0.163 • 6 0.225 0.179 • 7 0.300 0.441 0.265 8 0.325 0.328 0.398 9 0.338 0.353 0.780 10 0.375 0.338 0.351 11 0.488 0.531 0.615 0 265 0 261 12 0.537 0.530 0.644 13 0.625 0.599 0.078 14 0.650 2.559 • 0 331 0 058 15 0.663 1.087 • 0 707 0 572 16 0.775 1.215 • 0 193 0 148 17 1.210 2.190 • 0 194 0 232 18 1.213 1.511 0.171 0 477 0 575 19 1.390 4.088 • 0 .309 0 .688 distributions are poisson-P, negative binomial-NB, and Neyman type a-NA. TABLE 2.6 Chi-square probability levels for three distributions f i t to medium fa ll armyworm collected from five-plant samples. A period indicates the distribution did not fit or could not be tested. OBS MEAN VAR Pa NB NA 1 0.063 0.063 •• • 2 0.063 0.063 •• • 3 0.063 0.063 • • • 4 0.125 0.125 ••• 5 0.125 0.125 • •• 6 0.250 0.214 0.539 •• 7 0.250 0.214 • • • 8 0.266 0.133 • • • 9 0.417 1.356 • • • 10 0.500 2.000 ••• 11 0.625 1.125 0.294 • • 12 0.866 2.266 • 0.111 0.263 13 1.500 1.867 0.271 0.170 0.139 14 1.625 3.450 0.495 0.667 0.849 15 1.870 1.580 0.726 •• 16 1.875 5.554 0.192 0.399 0.257 17 2.375 4.517 0.243 0.684 0.754 18 2.688 3.163 0.542 0.445 0.446 19 3.167 8.515 0.340 0.421 0.590 20 3.688 15.829 0.086 0.393 0.298 21 6.063 36.329 • 0.473 0.342 22 6.750 28.067 0.348 0.617 0.551 23 7.630 33.983 • 0.198 0.131 24 7.900 28.322 • 0.089 0.108 25 9.067 54.638 • 0.330 0.571 26 12.31 53.963 • 0.207 0.610 distributions are poisson-P, negative binomial-NB, and Neyman type A-NA. 20 TABLE 2.7 Chi-square probability levels for three distributions fit to large fall armyworm collected from one-plant samples. A period indicates the distribution did not fit or could not be tested. OBS MEAN VAR Pa NB NA 1 0.013 0.013 • 2 0.025 0.025 • 3 0.038 0.038 • 4 0.071 0.075 • 5 0.075 0.070 • 6 0.075 0.075 • 7 0.075 0.096 • 8 0.081 0.088 • 9 0.125 0.111 • 10 0.138 0.145 * 11 0.150 0.182 « 12 0.175 0.199 • 13 0.200 0.215 • 14 0.225 0.252 0.125 15 0.250 0.342 • 16 0.250 0.244 0.460 17 0.388 0.494 0.211 18 0.475 0.410 0.336 19 0.488 0.506 0.814 28 0 283 20 0.513 0.430 0.319 21 0.613 0.418 • d is trib u tio n s are poisson-P, negative binomial-NB, and Neyman type A-NA. TABLE 2.8 Chi-square probability levels for three distributions fit to large fall armyworm collected from five-plant samples. A period indicates the distribution did not fit or could not be tested. OBS MEAN VAR Pa NB NA 1 0.063 0.623 •• 2 0.063 0.063 • • 3 0.116 0.125 •• 4 0.125 0.116 •• 5 0.188 0.163 • « 6 0.375 0.517 •• 7 0.375 0.554 • • 8 0.375 0.517 •• 9 0.438 0.929 • • 10 0.625 0.268 •• 11 0.667 0.424 0.242 • 12 0.750 0.786 0.233 • 13 0.875 1.268 0.249 • 14 0.916 2.083 0.226 0 225 0.208 15 1.000 1.714 0.203 0.075 16 1.000 2.857 0.115 • 17 1.125 2.250 0.314 0 449 0.348 18 1.130 1.263 0.767 0 220 0.220 19 1.250 2.067 0.444 0 774 0.775 20 1.313 3.296 0.138 0 203 0.114 21 2.438 3.998 0.298 0 561 0.587 22 2.625 3.183 0.588 0 478 0.480 23 3.000 3.733 0.710 0 748 0.746 24 4.300 5.122 0.606 0 449 0.448 25 5.000 18.000 0.639 • 26 8.467 67.552 •• distributions are poisson-P, negative binomial-NB, and Neyman type A-NA. Figure 2.1 Taylor power function fit for total populations in one-plant samples. Squares represent observed points, and the equation for the line is Y=1.1896X + 0.3176. The is 0.96. s> ■-* ro u - h ro LOG VARIANCE i i i i c j i i ro GJ o o m > - a - i £Z data for analysis of variance (Finch et al. 1975). Instead, means and variances were fit by the Taylor Power function. Figure 2.1 demonstrates the fit for one-plant samples (means and variances in Table 2.1). Figure 2.2 shows the fit for five-plant samples (means and variances in Table 2.2). The slopes of the two lines are significantly different at p < 0.05. Each of these lines can be subdivided into separate regressions for small, medium, and large larvae. Figure 2.3 shows these regression lines for one- plant samples. The correlation coefficients are 0.89, 0.95, and 0.98 for sm all, medium, and larg e, larvae resp ectiv ely . The slopes of these three lines are significantly different (p < 0.05), as are the intercepts (p < 0.05). Five-plant samples are shown in Figure 2.4. Correlation coefficients are 0.92, 0.97, and 0.92 for small, medium, and large, larvae respectively. The three regressions have the different slopes (p < 0.05), but the same intercept (p > 0.05). All 8 regressions are significant at p < 0.0001 and all residual scatters are random. Sampling efficiency for different densities of fall armyworm in corn is calculated at a probability of 0.20 in Table 2.9. Number of samples required at p = 0.10 was also calculated, but, except at the very highest densities, these numbers were too unwieldy to be of practical value. Other efficiencies may be calculated at different densities and probabilities with the information presented. The independently drawn one-plant samples were used to determine if the Power law f i t for individual-plant data from the five plant samples was biased. Of the five samples, variances Figure 2.3 Taylor power function fits for small, medium, and large larvae in one-plant samples. Equations for the lines are: small - Y=1.295X + 0.5196 (r2=0.89), medium - Y=1.1282X + 0.184 (R2=0.95), and large - Y=0.9663X - 0.011 (R2=0.98). LOG VARIANCE - 2 0 2 "3 MEDIUM AG a ^ ' LARGE -2 - - - ML O ^ SMALL 1 a 0 O MEAN LOG 1 2 3 00 N5 Figure 2.4 Taylor power function fit for small, medium, and large larvae in five-plant samples. Equations for the lines are: small - Y=1.423X + 0.5445 (R2=0.92), medium - Y= 1.3055X + 0.3092 (R2=0.97), and large - Y=1.2407X + 0.2013 (R2=0.92). LOG VARIANCE - 2-1 2 „ ° ' LARGE 3 0 MEDIUM a - - <>J SHALL 2 1 0 O MEAN LOG 1 2 3 o 31 Table 2.9 Number of samples required for p=0.20 accuracy for fall armyworm larvae sampled at varying densities (number/ plant) in corn. FIVE PLANT-SMALL LARVAE ONE PLANT-SMALL LARVAE AVERAGE DENSITY # OF SAMPLES AVERAGE DENSITY # OF SAMPLES .0625 434 .0125 1809 .1 331 .1 419 .5 131 .5 135 1 878 1 83 5 345 5 27 10 23 10 16 FIVE PLANT-MEDIUM LARVAE ONE PLANT-MEDIUM LARVAE .0625 350 .0125 1742 .1 252 .1 284 .5 82 .5 70 1 51 1 38 5 17 5 9 10 10 10 5 FIVE PLANT-LARGE LARVAE ONE PLANT-LARGE LARVAE 0625 326 .0125 2260 1 228 .1 263 5 67 .5 50 7 52 .7 35 40 1 24 23 2 12 FIVE PLANT-TOTAL LARVAE ONE PLANT-TOTAL LARVAE .0625 320 .0125 1811 .1 222 .1 336 .5 870 .5 91 1 56 1 52 5 21 2 30 10 13 3 21 20 8 5 14 32 from four fell within the 95% confidence limits of a line constructed from the previously mentioned modified data set. The variance of the fifth fell within the 99% confidence interval. This lends support to the use of one-plant samples extracted from five-plant samples. Discussion FAW populations are too variable to expect any one distribution to fit at a range of densities (Tables 2.1-2.8). Taylor (1984), presenting data from McGuire et. al (1957) and Brown and Cameron (1982), demonstrated that the Power law will fit data independently of the distribution. This versatility makes the Power law ideal for the present data and is reflected in the high correlation coefficients for the various equations. The tables also reveal two general trends. The first is that the poisson distribution fit large-larvae data more often than did distributions that reflect aggregation (negative binomial and Neyman type A), while the reverse is true for small larvae. Medium larvae are intermediate. Sequential shifts of distribution with age also have been found in European corn borer (Ostrinia n u b ila lis ) populations (McGuire et al. 1957) and in gypsy moth (Lymantria dispar) egg placement (Taylor 1984 - data from Brown and Cameron 1982). The second trend in the present research is a decrease in average instar with an increase in mean density. It follows from these two trends that small larvae (low average instar, high population density) would tend to aggregate, medium larvae would be more dispersed, and large larvae (high average instar, low population density) would be the most dispersed. Fall armyworm average ca. 140 eggs per mass (Vickery 1929), so that aggregation of small larvae is expected. Early studies (Luginbill 1928, Vickery 1929) have demonstrated that larvae disperse with age; when combined with a proclivity for cannibalism and accumulated natural mortality, older larvae would be expected to be less aggregated. This behavior of dispersal with age affects the sampling efficiency (Table 2.9). For a given density, fewer samples are required to attain the same confidence as larvae grow older. Even though the number of small larvae per plant may be five times as great as the number of large larvae, roughly the same number of samples are required for a 0.20 probability level. In addition, Table 2.9 shows the one-plant samples to be more efficient than the five-plant samples, an observation also made by Finch et al. (1975) for eggs of cabbage root worm fly. The 0.20 probability level was chosen as a compromise between accuracy and labor. Although this level might seem excessively lenient, it is sufficient to detect changes in population density in a pesticide control program (Southwood 1978). More detailed studies would require a higher level of efficiency. CHAPTER 3: MULTIPLE REGRESSION ANALYSIS OF A NUCLEAR POLYHEDROSIS VIRUS IN POPULATIONS OF FALL ARMYWORM IN CORNFIELDS The study of disease epizootiology in insect populations has long been recognized as important for developing descriptive and predictive models of pathogens. Publications by Kish and Allen (1978), Anderson and May (1980), Soper and MacLeod (1981), E n tw istle et al. (1983), and Fuxa and Geaghan (1983) examined quantitative data in various levels of detail, from statistical descriptions to mathematically sophisticated models. All, however, recognized the need to document the underlying ecological mechanisms in epizootics, mechanisms which may be exploited in pest management. Research on the prevalence of baculoviruses (nuclear polyhedrosis viruses - NPVs, and granulosis viruses - GVs) in fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith), populations has been conducted in grass pastures (Fuxa 1982), corn (Hamm and Hare 1982, Fuxa 1982), and sorghum (Schwehr and Gardner 1982, Fuxa 1982). Fuxa and Geaghan (1983) developed multiple regression models for factors influencing prevalence of NPV and GV diseases in FAW populations feeding in grass pastures in Louisiana. The present study was conducted at two of the same locations in Louisiana that were utilized by Fuxa (1982) and Fuxa and Geaghan (1983), and expands the research to another host plant of the FAW. Materials and Methods 34 Samples of FAW were taken from cornfields at two locations in southeastern Louisiana. Cornfields at the St. Gabriel Experiment Station were planted specifically for this study. Planting began in early spring, and as corn plants began to die in one field, another field was planted. Samples were taken in 1981 from 2 May until 7 August in two fields, and in three fields from 11 May until 26 September in 1982. The other field site was 6 km east of Hammond at a private dairy farm. Fields were planted simultaneously rather than sequentially. Four fields were sampled from 19 April until 27 July in 1982, and four fields were sampled from 24 May until 26 July in 1983. At the St. Gabriel location, each field was subdivided into four or eight plots based on its size and shape, and a stratified random sampling technique (Southwood 1978) was employed for weekly FAW collection. One or two randomly chosen samples of five plants each were taken in each plot, and 80 plants total were harvested on each sampling date, except for the last three sampling dates in 1981, when only 40 plants were examined. During periods of low population density or when p lan ts were large (greater than four or five feet), FAW were removed from the plant and placed individually in cups with artificial diet at the field site. Otherwise, the entire plant from the crown up was removed, bagged, and returned to the laboratory for examination. Sampling was always destructive; leaf axils, whorls, tassels, and ears were all examined for FAW. Collected larvae were subjectively categorized to instar by comparison to preserved specimens so that a weighted mean in s ta r could be c alcu lated . They were divided by length into categories of small (< 11mm), medium (11- 25 mm), and large (> 25 mm) for all other age-dependent analysis. All subjective assignments to instar were made by the same person for the sake of uniformity. The number of FAW per plant (by size category) and the location of the sample in the field were recorded. Weather data taken on site were temperature, relative humidity, rainfall, and solar radiation. Collected larvae were held in incubators at 27°C and 14:10 light:dark photperiod until death or adult emergence. All larvae that did not die from a known cause were dissected, a portion of fat body removed, and a wet mount made and examined under phase-contrast microscopy for the presence of pathogens. Dissected larvae also were examined for the presence of immature parasites. Since larvae were recorded individually at St. Gabriel, the size category at capture of those larvae dying from virosis could be determined, as well as size category at death. The size category of larvae most likely to acquire virosis could be determined in this manner. At Hammond, fields were divided into four plots. The stratified sampling technique was similiar to that at St. Gabriel, except that the number of larvae per plant by size category and the location of the sample in the field were not recorded. An average number per plant was obtained by dividing the number of larvae collected by the number of plants sampled. At least one field was sampled weekly from this location. Environmental variables were measured as at St. Gabriel. All collected larvae were treated as those collected from St. Gabriel. Soil samples were taken from both the St. Gabriel and Hammond fields. These samples were collected, processed, and bioassayed as in Fuxa and Geaghan (1983) in order to estimate the amount of NPV in the soil at the onset of each season. Soil was assayed from all fields in 1981 and 1982, and from two of the four fields in 1983. Data were collected for the variables in Table 3.1. The percentages of mortality caused by NPV and GV infections and parasitism by three parasites (identified at LSU) were transformed to an arcsin square root due to the many low percentages obtained. The date function, which uses 1 January 1960 as a baseline, calculates the number of days which have passed between the baseline and the sample date. All variables dealing with number of larvae/plant were converted to their log values. Variables 11-14 were not used for Hammond because individual fields were not necessarily sampled weekly, and information from the previous week was not always available for a given field. Variables 15 and 16 were not measured for Hammond. Degree day was calculated as in Fuxa and Geaghan (1983), except th at a threshhold tem perature of 15°C was used, which more closely corresponds to the developmental threshhold of FAW (Barfield et al. 1978). Both average weekly temperature and degree day were investigated in the event that a temperature dependent process which was not linked to FAW development might be important. Solar radiation was measured with a mechanical pyronometer as gm/cals of energy per second. 38 For sample dates at St. Gabriel, the mean and variance of the number of larvae in each of the five-plant samples was calcu lated . The variance to mean r a tio was then calcu lated and used as a measure of aggregation (Southwood 1978, Taylor 1984). This factor is of interest when investigating communicable diseases. Data were analyzed with procedures from the Statistical Analysis Sytem, SAS (SAS Institute, Cary, N.C.). Variables were selected for multiple regression by use of R values, Mallows Cp statistic (Daniel and Wood 1971), and forward stepwise regression. Cp is calculated as an option under both the RSQUARE and STEPWISE procedures. An RSQUARE analysis was run f ir s t with either ANPV or AGV as the dependent variable and variates 3-23 (Table 3.1) as the regressor variables. Forward stepwise regression was then run. Variables that had an R relative to the dependent variable of less than 0.02 and were not selected by the stepwise regression were removed from the regressor variable list, and the RSQUARE analysis was run again. Combinations of variables were selected from the stepwise regression and both RSQUARE analyses by th e ir associated R and Cp values and inserted into multiple regression models. When Cp was calculated, the error mean square for the complete model (a model in which all variables in the regressor or independent variable list are included, irrespective of whether STEPWISE or RSQUARE indicates they are important) was used as a measure of variance. When the regressor variable l is t includes variables which have no effect on the model, then this number is biased with respect to the 39 TABLE 3.1 Variables tested for inclusion in fall armyworm NPV multiple regression models. Number Variate Source Transformation 1 % mortality due to NPV Arcsin Sqr M PX* 2 AGV % mortality due to GV Arcsin Sqr 3 ACHEL % m ortality due to Chelonus sp. Arcsin Sqr 4 AROGAS % mortality due to R. laphygmae Arcsin Sqr 5 APANT % mortality due to C. marginiventris Arcsin Sqr 6 DATE # of days elapsed since 1 Jan. 1960 7 LSMALL # of small larvae/plant log 8 LMED # of medium larvae/plant log 9 LLARGE # of large larvae/plant log LNUM total # of larvae/plant 10* log LAGSMALL # of small larvae/plant last sample U* log 12 LAGMED # of medium larvae/plant last sample log LAGLRG // of large larvae/plant last sample 13* log 14* LAGNUM total # of larvae/plant last sample log AVINSTAR weighted mean instar 15* 16 VMRATIO variance/mean ratio (index of aggregation) 17 HOSTHT height of corn plant 18 SOILNPV % infection from soil bioassay sample 19 AVTEMP mean temperature since last sample 20 DEGDAY degree days accumulated since last sample 21 RAIN rainfall accumulated since last sample 22 AVRH mean relative humidity since last sample 23 SOLRAD mean solar radiation since last sample St. Gabriel data only ** Hammond data only calculation of Cp, and models that will generate significant parameter values are overlooked. When these unimportant variables are eliminated (the 0.02 value is arbitrary), then more variable combinations emerge for multiple regression analysis. Results In the St. Gabriel cornfields, 754 and 1403 FAW were c o lle c te d in 1981 (15 sample dates) and 1982 (20 dates), respectively. In Hammond, the numbers were 2177 and 385 collected in 1982 (31 samples on 14 dates) and 1983 (9 samples, 7 dates), respectively. FAW larvae were most abundant in corn at both sites towards the end of the growing season (Figs. 3.1 and 3.2). At least three generations per year appeared in St. Gabriel. Discrete generations were harder to discern at Hammond due to overlapping oviposition. Peak populations were more dense at St. Gabriel but appeared earlier in the season at Hammond. Figures 3.3 and 3.4 present the seasonal prevalence of the NPV disease at St. Gabriel and Hammond, respectively. Figure 3.4 also shows the percent infection by a GV during 1983 in Hammond. The GV disease was rare at St. Gabriel and occurred only sporadi cally at Hammond in 1982. Patterns of NPV prevalence were dif ferent at the two locations. At St. Gabriel, NPV infections were detected later in the season, and prevalence peaked abruptly. In Hammond NPV infections were detected relatively early in the season, and prevalence increased at an almost constant rate to higher levels than at St. Gabriel. Mortality never reached 15% at St. Gabriel, while it was over 40% at Hammond. 41 Figure 3.1 Seasonal density of fall armyworm populations in corn fields at St. Gabriel, LA., 1981 and 1982. 6 -r ST. GABRIEL 5- □ 1981 A1982 4- 0 98 119 140 161 182203 224 245 266 287 WEEK ENDING JULIAN DATE -P>ho Figure 3.2 Seasonal density of fall armyworm populations in corn fie ld s at Hammond, LA., 1982 and 1983. Each lin e re p resen ts the average of the fields sampled on a given date. NUMBER PER PLANT 0 0.5- 2 2.5- 3.0- 1 1.5- . . . 0 0 0 0 16 4 18 8 210 189 168 147 126 105 - - - HAMMOND 1983A 1982n EK NIG UIN DATE JULIAN ENDING WEEK 231 45 Figure 3.3 Prevalence of a nuclear polyhedrosis virus in populations of fall armyworm feeding in cornfields at St. Gabriel, LA, 1981 and 1982. PERCENT INFECTION 15 12 0 3 6 9 8 1 10 6 12 0 24 4 26 287 266 245 224 203 182 161 140 119 98 T GABRIEL ST. EK NIG UIN DATE JULIAN ENDING WEEK p A- S jL| -ffl' |B yjSL iS -a 'A I B Ip □ 1981 982 M Figure 3.4 Prevalence of a nuclear polyhedrosis virus and granulosis virus in populations of fall armyworm feeding in cornfields at Hammond, LA., 1982 and 1983. Each line represents the average of the fields sampled on a given date. PERCENT INFECTION 501 30- 40- 0 2 10 105 - - EK NIG UIN DATE JULIAN ENDING WEEK 126 4 168 147 189 210 231 00 The prevalence curves generally resemble those of Fuxa (1982), Hamm and Hare (1982), and Schwehr and Gardiner (1982) for armyworm feeding in corn and sorghum, in that NPV prevalence was greatest later in the season. The curves from Hammond do not have a descending arm (postepizootic phase - Steinhaus 1954) because the corn was harvested and further samples could not be taken. One of the main points under study was the determination of which larvae acquired virus infection. Of the NPV-infected larvae collected at St. Gabriel, only 16% had acquired the virus in the sm all stage (Table 3.2). Death was delayed u n til the medium or large stage. Of all larvae collected at St. Gabriel (both infected and uninfected), 40% were small, 41% were medium, and 19% were large. Figures 3.5 and 3.6 show the spread of NPV disease in FAW at St. Gabriel. Each dot represents a sampling location where diseased insects were recovered, and points accumulate from week to week. When the disease was first found in a population, it apparently radiated from a single locus. However, other loci may soon form, and the disease spreads throughout the population. Data from the two field sites were analyzed independently by multiple regression (Table 3.3). SOILNPV was a significant factor only in model A, where i t had a negative influence. When SOILNPV o was removed from consideration, model B emerges. The best R that could be obtained with the Hammond data was 0.45, but the models contain fewer variables than those constructed from the St. Gabriel data. Two models are shown, one of which contains only climatic variables. Except for variable ACHEL in model C (the TABLE 3.2 Distribution of infection and mortality in field collected larvae. All larvae summarized below had a virus infection when captured. Field collected larvae Field collected larvae Size category with infection dying from infection Small 16% 0% Medium 58% 57% Large 26% 43% TABLE 3.3 Multiple regression analyses of NPV prevalence in populations of fall armyworm. St. Gabriel and Hammond data analyzed separately.3 St. Gabriel Hammond Model A Model B Model C Model D Parameter Parameter Parameter Parameter Variates Estimates p Estimates p Estimates p Estimates ACHEL 0.5703*.2327 .018 DATE 0.00025*.00008 .004 LLARGE 0.0920±.0311 .008 LNUM 0.1026*.0315 .005 LAGLRG 0.0910*.0307 .009 AVINSTAR 0.0369±.0144 .018 0.0597*.0183 .005 HOSTHT 0.0016*.0006 .010 0.0013*.0006 .040 SOILNPV -0.0502*.0160 .005 AVTEMP 0.0127*.0039 .004 0.0222*.0074 .004 DEGDAY 0.00094*.0004 .039 0.0039*.0010 <.001 RAIN -0.0437*.0184 .022 -0.0352*.0188 .067 SOLRAD -0.4682*.1377 .001 -0.4296*.1432 .004 Intercept -0.9346 -2.3030 -0.9771 0.2304 0.69 0.77 0.45 0.41 P 0.0002 0.0001 0.0001 0.0001 df 25 24 50 50 aS0ILNPV included in models A,C, and D. Model D results if variable ACHEL is not included in model C. best model found), NPV prevalence in Hammond is best explained with climatic variables. Rain and solar radiation appeared to be the most important variables and had a negative influence. Degree day was replaced by average temperature in model C, but both had a positive effect on prevalence. A model could not be found for GV prevalence in Hammond in 1983. Although several were tested, significant parameter estimates could not be generated. The combined St. Gabriel and Hammond data also were analyzed by multiple regression (Table 3.4). Four models are presented which show different variables of interest and their affect on 2 the R and on the dependent variable. Variables were almost evenly split between those relating to environment and those relating to host. The number of larvae per plant in the population is now found in all four models. Relative humidity, which did not appear in the individual site analyses, emerged as an important variable with a negative effect. Degree day disappeared completely in favor of average temperature. The number of small larvae in the population had a negative influence on prevalence and was a s ig n ific a n t v a ria b le in models 1 and 2. Solar radiation, the most significant variable in Hammond, is found in model 4, again with a negative effect. The Cp s ta tis tic proved useful in selecting models that had significant parameter estimates. However, it never chose the model with the highest R as the optimum model. For instance, in Table 3.3, Cp indicated that models A and D were less biased than models B and C, and that model 2 in Table 3.4 was less biased TABLE 3.4 Multiple regression analysis of NPV prevalence in populations of fall armyworm. St. Gabriel and Hammond combined.a Model 1 Model 2 Model 3 Model 4 Parameter Parameter Parameter Parameter Variates Estimates P Estimates P Estimates P Estimates P LSMALL -0.053U.0258 .043 -0.0675±.0257 .011 LNUM 0.07521,Q323 .023 0.11441.0285 <.001 0.07001.0329 .037 0.07821.0334 .022 HOSTHT 0.00151.0007 .033 AVTEMP 0.0145±.0059 .016 0.01671.0059 .006 0.01251.0057 .030 AVRH -0.0095±.0025 <.001 -0.00581.0023 .013 -0.0100i.0026 <.001 SOLRAD -0.02221.0066 .001 Intercept -0.3172 0.4910 -0.4266 -0.7259 R2 0.34 0.32 0.30 0.27 P 0.0001 0.0001 0.0001 0.0001 df 75 75 75 75 aThese models are the four best from the analysis. All are presented to demonstrate the effect of variables of interest on the R value and on the dependent variable. wU 1 than model 1. Discussion When the analyses from the individual field sites are examined, conflicting conclusions result. The Hammond analysis supports the conclusion that NPV prevalence is abiotically regulated and that population density plays no role, whereas hos-t population parameters account for much of the variation at St. Gabriel. Although six of the population parameters at St. Gabriel could not be examined at Hammond, only one, LAGLRG, appeared in the St. Gabriel analyses. When data from both areas are combined, the most important variable, LNUM, is a host population variable. However, three environmental variables still appear in the model. Considering the information in Table 3.2, it is not surprising that small larvae have a negative impact on virus prevalence. Although small larvae are several thousand times more suceptible than medium larvae and may be over a million times more susceptible than large larvae to NPV infection (Mitchell and Fuxa, unpublished), this size group does not often acquire or die from NPV infection. In the St. Gabriel models (Table 3.3, A and B), density of large larvae, either LLARGE or LAGLRG, entered as significant variables, which is in agreement with Table 3.2. Medium larvae did not enter into any of the models, except when combined into the variable LNUM. SOILNPV appears only in St. Gabriel model A, where its negative effect is both counter-intuitive and at odds with results from pastures (Fuxa and Geaghan 1983). There is no good explanation for this result, and it is likely an error of small sample size. This variable was not significant in the Hammond or combined analyses, and model B, which has a higher R , is available for St. Gabriel and does not include it. Variables dealing with moisture - rain and average relative humidity - both had negative effects on NPV prevalence. Relative humidity probably did not directly affect the virus but rather influenced the host or perhaps the host plant or microclimate. Rain may wash away in fected larvae which are weak or have just died, but it is unlikely that polyhedra are removed from the plant su rfaces by w ater action (Jaques 1977). Fuxa and Geaghan (1983) found rain to be a positive influence on virus prevalence in FAW populations feeding in grass pastures. They hypothesized that rain splashed virus from the soil on to the host plants, a conclusion that is supported by the fact that soil NPV levels were an important positive effect and host height a negative effect. In the current study, the effect of rain, soil NPV levels, and host height are all reversed. Since host height and number of larvae are both positive influences on NPV prevalence in corn, it is probable that bigger plants simply have more surface (ears, tassels, leaf axils) and will support more larvae and therefore more virus. The negative influence of solar radiation on baculoviruses has been documented (David 1978, Young 1978). Although this variable did not appear in the St. Gabriel models or the optimum combined models, its influence was negative when present. Degree day appeared in both site analyses- Although it is replaced by average tem perature in the combined an aly sis. Fuxa and Geaghan 56 (1983) found that date suppressed the entry of degree day into the regression models for NPV prevalence in pastures, but in the current study date entered only in the St. Gabriel analyses, in accompaniment with degree day. The presence of average temperature in models for the combined data sets may indicate a temperature dependent process that is not related to FAW development threshholds. One variable that did not enter into the St. Gabriel models is VMRATIO, an index of aggregation. Although other indices are available, variance to mean ratios are the simplest (Taylor 1984). Iwao (1970), using m-m regressions, found that an NPV disease in populations of the western tent caterpillar (Malacosoma calif ornicum) also did not have a contagious dispersion, or, in other words, its appearance in colonies was random. This unexpected finding was hypothesized to be the result of latent or transovarial infections. Transovarial transmission of the FAW NPV has not been noted in this laboratory, but McKinley et al. (1981) provide strong evidence of NPV being latent in Spodoptera littoralis. Effects of parasitism by the three most common species of parasite - Cotesia (Apanteles) marginiventris, Rogas laphygmae, and Chelonus sp. - were tested. C^_ marginiventris, which oviposits in first instar larvae, is a known vector of lawn armyworm (Spodoptera exempta) NPV (Tamashiro 1968), while R. laphygmae parasitizes late f i r s t in s ta r larvae of FAW (Vickery 1929) and may also be capable of mechanically moving the virus on its ovipositor. Chelonus sp. parasitizes the eggs of FAW and is not a likely candidate for virus vectoring. However, it was the only species to appear in a model (Table 3.3, model C). The positive influence may be best explained by a weakening of the larvae, predisposing them to infection. Chelonus sp. emerges from the fourth instar of its host (Vickery 1929), a stage prone to acquire virus (Table 3.2). Since both C^_ marginiventris and R. laphygmae parasitize the small hosts, they would be unlikely to encounter NPV infected larvae from which to become contaminated. The fields used for studying NPV disease spread, depicted in Figures 3.5 and 3.6, were too small for reliably ascertaining direction of movement. In 1981, it is difficult to determine if there was more than one epicenter of disease, or if the disease moved in a wave-like fashion from a single locus. The 1982 field had several distinct epicenters, with the diseased FAW often appearing at the field margins. The edges of both fields were largely composed of signalgrass, Brachiaria platyphylla (Grisebach) Nash, one of the grasses which Fuxa and Geaghan (1983) found to be a positively correlated with NPV prevalence. Early in the morning, large FAW larvae were observed moving in the grass at the field borders and could easily have moved the virus into the cornfield. Fuxa (1982) documented a lag in NPV prevalence in corn and sorghum versus pastures and hypothesized it was caused by host plant height preventing abiotic agents from moving the NPV. Since host plant height and number per plant were significantly positive factors in this study, and since diseased FAW appeared often at field margins, it may be that continuous inoculation of cornfields by NPV infected larvae from grass is Figure 3.5 Distribution map of an NPV in a cornfield in 1981. Points are cumulative from week to week and represent a sampling location where NPV was found infecting FAW larvae. JULY 10 JULY 16 JULY 23 JULY 31 Figure 3.6 Distribution map of an NPV in a cornfield in 1982. Points are cumulative from week to week and represent a sampling location where NPV was found infecting FAW larvae. 61 AUG. 20 • • O AUG. 27 • • » • SEPT. 3 • • • • • • • • SEPT. 10 © • © • • © © © © •1 • • ••© • • • SEPT. 17 • • © • © • • • • • © © • • •• • • • important in initiating the disease in corn-feeding larvae. Under the right conditions, disease can then spread and be maintained in the cornfield. A threshold for disease maintenance was postulated by Hamm and Hare (1982) when natural infestations of FAW invaded their field plots of corn. More diseased larvae were found in plots where NPV had been applied to larvae through irrig atio n pipes than in fields where NPV applications were not made and only natural virus was present. Although host plant size was not considered, population density of FAW for maintaining and increasing NPV levels in the field was hypothesized to be important. Epizootic thresholds were not calculated for NPV infecting FAW in cornfields, due to the annual nature of the crop and the o low R of the combined models. Fuxa (1982) defined an epizootic to be that level of infection 1.64 SD units above the expected percent infection for two consecutive weeks. The best model for both Hammond and St. Gabriel combined explains only 32% of the variance, which is not enough to calculate an expected percent mortality based on the combination of variables used in the model. Although factors significant to NPV prevalence are identified in this study, more work is clearly needed to improve understanding of the complex interactions involved. Experimental approaches using the information collected in this and other surveys will be necessary to gain the additional detail required to stochastically model NPV prevalence and manipulate the virus. CHAPTER 4: MULTIPLE REGRESSION ANALYSIS OF PARASITISM OF FALL ARMYWORM BY THREE SPECIES OF LARVAL PARASITES The natural history of fall armyworm, Spodoptera frugiperda (J.E. Smith) (FAW), and the taxonomy and biology of its parasites have received the attention of many researchers. Early workers (L uginbill 1928, Vickery 1929) documented seasonal h isto ry and made behavioral observations on both host and parasites. More recently this complex has been studied in grass and corn in Florida (Ashley et al. 1980, 1983), and in peanut in Oklahoma (Wall and Berberet 1975). Ashley (1979), summarizing the information on FAW from the lite ra tu re , found collection records for 53 species of parasites in two orders and ten families. In recent years, pest management philosophy has shifted research emphasis to include quantitative evaluations of parasite ability to regulate host populations. Ashley et al. (1982) investigated seasonal density and abundance of parasites attacking FAW populations in field corn. Two species Chelonus insularis (Cresson) and Temelucha sp., were responsible for 97% of the observed parasitism. Parasitism rates generally mirrored FAW density, but the study concluded that FAW populations were capable of escaping parasite regulation. Mitchell et al. (1984) investigated the effects of FAW pheromone permeation of a cornfield on the population dynamics of both FAW and its larval parasites. Although the pheromone had no effect on parasitism, the level of parasitism proved predictable by instar for the 63 f ir s t four instars. Other results were in agreement with Ashley et al. (1982). Parasitism of larvae and the effect of parasites on host populations are generally determined by sampling the host at regular intervals and maintaining captured larvae until the parasites emerge or the larval stage otherwise ends. Efficiency of the parasite in regulating the host can then be determined either by correlation of host population changes with the parasite or by analysis of life table mortality data (DeBach and Bartlett 1964). Life tables require a level of detail that is generally beyond most field studies, but changes in insect population density can be detected with less effort by a reliable sampling program (Southwood 1978), and regression analysis of the data. The objectives of the current study were to 1) identify factors that influence larval population density of FAW feeding in field corn, and 2) identify factors affecting parasitism of larval FAW by three species of parasites - Cotesia (Apanteles) m arginiventris, Rogas laphygmae, and Chelonus sp. M aterials and Methods Samples of FAW were taken from cornfields at two locations in southeastern Louisiana. One field site was at the St. Gabriel Experiment Station, while the other was located at a private dairy 6 km east of Hammond. Data were the same as used in Chapter 4 for analysis of FAW mortality caused by a nuclear polyhedrosis virus (NPV), with the following exceptions. The 1983 Hammond data include two fields not used in the previous study. These represent an additional nine samples which were averaged in with 65 the data. Data from St. Gabriel in 1983 were not used in the NPV study due to low mortality associated with the virus, but parasites were present and the data are included in this analysis. Data were collected for the variables in Table 4.1 as in Chapter 4. Multiple regression models were constructed with the number of FAW larvae per plant and percent parasitism by Chelonus sp., marginiventris, and laphygmae as the dependent variables, and the variables in Table 4.1 as the regressor variables. Variable selection for the regression models and the multiple regression analyses were conducted in the same fashion as Chapter 4. Data were analyzed with procedures from the Statistical Analysis Systems, SAS (SAS Institute, Cary, N.C.). Results The parasitism and host density data for the five field- years are plotted in Figures 4.1-4.5. Population density of FAW was the highest of the study at St. Gabriel in 1981 (Figure 4.1), peaking at 5.5 larvae per plant. Parasitism by Chelonus sp. peaked six weeks earlier, and only a few FAW larvae were parasitized by this species during the highest host population density. Ch_ marginiventris and laphygmae parasitism remained low throughout the year, peaking at 10% and 6% respectively. This graph represents a total of 754 larvae taken on 15 sample dates. Of these, 33 were parasitized by Chelonus sp., three were parasitized by C^ marginiventris and four by R;_ laphygmae. FAW population density also peaked in the latter half of 1982 at St. Gabriel (Figure 4.2). Parasitism by Chelonus sp. 66 TABLE 4.1 Variables tested for inclusion in multiple regression models. Number Variate Source Transformal 1 ANPV % mortality due to NPV Arcsin Sqr 2 ACHEL % m ortality due to Chelonus sp. Arcsin Sqr 3 AROGAS % m ortality due to R. laphygmae Arcsin Sqr 4 APANT % mortality due to C. marginiventris Arcsin Sqr 5 DATE # of days elapsed since 1 Jan. 1960 6 LSMALL # of small larvae/plant log 7 LMED # of medium larvae/plant log 8 LLARGE # of large larvae/plant log 9 LNUM total # of larvae/plant log 10 HOSTHT height of corn plant 11 AVTEMP mean temperature since last sample 12 DEGDAY degree days accumulated since last sample 13 RAIN rainfall accumulated since last sample 14 AVRH mean relative humidity since last sample 15 SOLRAD mean solar radiation since last sample Figure 4.1 Percent parasitism of fa ll armyworm by Chelonus sp., Cotesia marginiventris, and Rogas laphygmae at St. Gabriel in 1981. Number of FAW per plant is multiplied by 10. PERCENT OR NUMBER/PLANT 0 2 60 40- 819140 119 98 - ' x ROGAS a + CHELONUS - NO ./PLANT COTESIA EK NIG ULIAN DATE N A I L JU ENDING WEEK 161 182 203 224 00 Ox Figure 4.2 Percent parasitism of fall armyworm by Chelonus sp., Cotesia marginiventris, and Rogas laphygmae at St. Gabriel in 1982. Number of FAW per plant is multiplied by 10. PERCENT OR N U 11 B E R / P L A N T 40- - 60- 80-i— 0 2 — 0 119 - . □ x + NO ./PLANT ROGAS COTESIA CHELONUS EK NIG UIN DATE JULIAN ENDING WEEK 4 15 0 21 259 231 203 175 147 287 reached its highest percentage of the study on day 210 at 75%. Both C. marginiventris and R. laphygmae appeared early in the season and remained at low levels in the host population. Twenty samples were taken on 20 dates and 1403 FAW larvae were captured. Of these, 1234 were examined for parasites. A total of 330 FAW larvae were parasitized by Chelonus sp., 14 by (L_ marginiventris, and 11 by R. laphygmae. In 1983, FAW population density was again greatest in the second half of the year (Figure 4.3). Chelonus sp. were virtually absent during this season, but C^_ marginiventris and R^_ laphygmae reached their highest population densities at the St. Gabriel location. Thirteen samples on 13 dates yielded 547 FAW larvae, 53 of which were parasitized by C^_ marginiventris, 36 by R. laphygmae, and one by Chelonus sp. FAW larvae at Hammond in 1982 rarely exceeded one per plant (Figure 4.4). Parasitism by Chelonus sp. was lower than at St. Gabriel and was always lower than 20%. C^_ marginiventris appeared early in the year, returning at low levels in mid-season. R. laphygmae parasitism remained at less than 3% for the season. A total of 2177 FAW larvae were collected in 31 samples on 14 dates. Of these, 110 were parasitized by Chelonus sp., 44 by C. m arginiventris, and 14 by laphygmae. FAW population densities at Hammond were lower in 1983 than in 1982 (Figure 4.5). Number of larvae per plant remained below one on all sample dates but two. Larvae parasitized by Chelonus sp. were only collected twice, once early and once late in the season. Parasitism by this species did not exceed 3%. Parasitism Figure 4.3 Percent parasitism of fa ll armyworm by Chelonus sp., Cotesia marginiventris, and Rogas laphygmae at St. Gabriel in 1983. Number of FAW per plant is m ultiplied by 10. PERCENT OR NUMBER/PLANT 20 30 40 50 10 0 2 17 6 19 1 21 252 231 210 189 168 147 126 EK NIG UIN DATE JULIAN ENDING WEEK - NO x./PLANT ROGAS ° COTESIA + CHELONUS Figure 4.4 Percent parasitism of fall armyworm by Chelonus sp. Cotesia m arginiventris, and Rogas laphygmae at Hammond in 1982 Number of FAW per plant is multiplied by 10. PERCENT OR N U II B E R / P L A N T 25 20 10 15 0 5 4 1 10 6 16 224 196 168 140 112 84 EK NIG UIN DATE JULIAN ENDING WEEK N.PAT xROGAS COTESIA □ -NO./PLANT + CHELONUS Ln 76 Figure 4.5 Percent parasitism of fa ll armyworm by Chelonus sp., Cotesia m arginiventris, and Rogas laphygmae at Hammond in 1983. Number of FAW per plant is multiplied by 10. PERCENT OR NUMBER/PLANT 25 -r 0 2 15- 10 0 5- 4 14 6 12 9 210 196 182 168 154 140 - - - EK NIG UIN DATE JULIAN ENDING WEEK x ROGAS °COTESIA + CHELONUS - NO ./PLANT 224 by both of the other species of parasites was more prevalent than in the previous year. Larvae parasitized by C^_ marginiventris were taken on every sample date, while laphygmae was recovered on all dates but two. A total of 1304 larvae were collected 118 of which were parasitized by marginiventris, 23 by R. laphygmae, and two by Chelonus sp. Factors that influenced the number of FAW larvae per plant are listed in Table 4.2 (Model 1). Two biotic agents - Chelonus sp. and the NPV - were positively related to changes in host population density. Two abiotic factors - average temperature and average relative humidity - also correlated with host population. Temperature had a positive coefficient, while that of relative humidity was negative. These four variables explain 42% of the variation in variable LNUM. Two analyses were conducted with APANT as the dependent variable (Table 4.2, models 2 and 3). In model 2, parasitization by R^ laphygmae appears as a significant positive variable. Since no mechanism for an increase in parasitism by CL_ marginiventris caused by R^ laphygmae could be derived from the data, variables AROGAS and ACHEL were removed and the analysis run again. This resulted in model 3, which has only two variables - DATE and HOSTHT. The coefficients retain the same signs as in model 2, but variable AVRH has now dropped out. Though the analysis is s till o significant, the R has fallen to 0.29. A similiar situation occurred in the analysis of parasitism by R. laphygmae (Table 4.3, models 4 and 5). In model 4, parasi tism by both (L_ marginiventris and Chelonus sp. was significant. TABLE 4.2 Multiple regression analysis of parasitism by Cotesia marginiventris (APANT) and the number of FAW larvae per plant (LNUM) as dependent variables. Model 1 Model 2 Model 3 LNUM APANT APANT Parameter Parameter Parameter Variates Estimates p Estimates p Estimates P DATE 0.0001*8 E-5 .117 0.0003*6 E-5 .001 HOSTHT -0.0014*.0005 .011 -0.0016*.0005 .003 AVRH -0.0236*.0097 .018 -0.0055*.0025 .033 AVTEMP 0.0820*.0158 <.001 ACHEL 1.1380*.2981 <.001 AROGAS 0.4220*.1403 .004 ANPV 0.5879*.3516 .098 Intercept -5.4985 -0.5629 -2.1843 R2 0.42 0.37 0.29 P 0.0001 0.0001 0.0001 df 85 85 85 TABLE 4.3 Multiple regression analysis of parasitism by Rogas laphygmae (AROGAS) and Chelonus sp. (ACHEL) as dependent variables. Model 4 Model 5 Model 6 AROGAS AROGAS ACHEL Parameter Parameter Parameter Variates Estimates p Estimates p Estimates p DATE 0.0002*6 E-5 .007 HOSTHT -0.0011*.0004 .012 -0.0025*.0007 <.001 AVRH -0.0044*.0020 .028 0.0132*.0032 <.001 SOLRAD 0.0084*.0033 .014 -0.0120*.0079 .130 ACHEL 0.1245*.0494 .014 APANT 0.3031*.0649 <.001 LNUM 0.0442*.0216 .044 0.1094*.0271 <.001 LLARGE -0.0501*.0242 .042 Intercept -0.0087 -1.6494 -0.5918 R2 0.30 0.25 0.40 P 0.0001 0.0004 0.0001 df 85 85 86 Again, no hypothesis for this positive effect could be generated from available information, so variables APANT and ACHEL were removed. The second regression produced model 5, which has more variables but a lower R than model 4. Variable SOLRAD was not selected in the second model, and environmental factors were represented only by relative humidity. DATE and HOSTHT were again present, as in the (L_ marginiventris analysis, and two variables relating to the host - LLARGE and LNUM - appear. The coefficient for LLARGE was negative and may be explained by the fact that L. laphygmae does not parasitize this stage. The coefficient for LNUM was positive, indicating an increase in parasitism with an increase in the host numbers per plant. Parasitism by R. laphygmae, however, did not appear in model 1 where LNUM was the dependent variable. A nalysis of p a ra sitism by Chelonus sp. appears in model 6 (Table 4.3). Only one analysis was run, since all variables in the model seemed reasonable. All four significant variables - AVRH, HOSTHT, LNUM, and SOLRAD - appear in previous models. The 2 R for the Chelonus sp. analysis was 0.40, the highest for the parasite regressions. The variable HOSTHT was a significant factor for all three parasites. The coefficient was always negative, as were the correlation coefficients (Table 4.4), indicating that parasitism decreased as host size increased. Degree day was absent from all analyses. Average temperature appeared only in model 1, and is also correlated with ACHEL, though not included in the model for that variable. TABLE 4.4 Correlation matrix between dependent variables and variables found to be significant in mul tiple regression models. VARIABLE LNUM APANT ACHEL AROGAS ** ACHEL .362 -.064 1.00 .271 ** APANT .127 1.00 -.064 .396 ** AROGAS .133 .398 .271 1.00 ** ANPV .338 .041 .039 -.077 ** DATE .085 .454 -. 211* . 211' ** ** ** HOSTHT .010 -.330 -.352 -.345 ** LNUM 1.00 .127 .362 .133 ** ** LLARGE .671 .041 .271 -.028 ** ** AVRH .060 -.320 .422 .098 ** ** AVTEMP .514 -.110 .212 .093 ** ** SOLRAD .052 -.093 .232 .258 Significant at p<0.05 Significant at p<.01 83 Discussion The regression models in Tables 4.2 and 4.3 demonstrate that different combinations of regressor variables are important to parasitism by different species. The environmental variables - AVTEMP, AVRH, and SOLRAD - and the v a ria b le HOSTHT are a ll logical components in the various parasite models. As mentioned previously, host height had a negative influence on parasitism, probably as a result of the increased surface area a growing plant produces that must be searched by the adult parasite. The appearance of average temperature in model 1 may indicate the presence of a temperature dependent process that is not reliant on the FAW developmental threshold. Developmental thresholds were not calculated for the three species of parasites, but since average temperature did not generally affect parasitism, degree day calculations for the parasites may prove important. Relative humidity may play a role in preventing (or allowing for) desiccation of immature parasites in the pupal or prepupal stage. Solar radiation has a less obvious role, but likely acts on adult parasites, affecting searching behavior. Of the three parasites, only parasitism by Chelonus sp. was significantly correlated with host population density (Table 4.4). Variable LNUM was selected in the model for Chelonus sp. parasitism (model 4.6), and Chelonus sp. parasitism was selected in the model for to tal number of FAW larvae per plant (model 1). Weseloh (1983) interpreted sim ilar data for hyperparasitism of Apanteles melanoscelus (Ratzeburg) cocoons by Eurytoma appendigaster (Swederus) and u n identified species of Eulophidae to indicate a density dependent response by the hyperparasites. In his study, percent parasitism by appendigaster and the eulophids had significant, positive regression coefficients in a multiple regression model in which the number of cocoons per tree was the dependent variable. Chelonus sp. virtu ally disappeared from cornfields in both locations in 1983 (Figures 4.3 and 4.5) at the same time C. marginiventris and R^_ laphygmae reached their highest percentages in this year. However, a related species - Chelonus texanus Cresson (and presumably others in the genus) - is a weak inter specific competitor for host larvae (Vinson and Abies 1980). In addition, the lack of significant negative correlations between Chelonus sp. and the other two parasite species make i t unlikely that Chelonus sp. is capable of supressing other parasites. Ashley et al. (1982) inferred competition between Chelonus insu- la ris Cresson and Temelucha sp. by the fact that percent parasi- tization curves for the two species were mirror images. In this instance it was C^_ insularis that was being suppressed. Mitchell et al. (1984) found that percent parasitism in the first four larval instars of FAW could be predicted with the percent of the population which that instar represented. This relationship is unusual in that 1) it was arithmetic (and linear) and 2) no transformations were involved. No such relationship existed in the current study. Correlations were not significant between percent parasitism (both transformed and untransformed) for each parasite versus percent small or medium larvae in the population (transformed and untransformed) (data not shown). 85 Parasitism of FAW in Florida (Ashley et al. 1982, Mitchell et al. 1984) had different trends than in Louisiana. In Florida, parasitization of the first four larval instars was heavy and consistent, with few larvae surviving to fifth and sixth instar (large larvae). In the current study, 1774 of 6185 larvae c o l l e c t e d (29%) were c l a s s i f i e d as la rg e . P a r a s itis m is inconsistent but occasionally heavy in the first four instars (small and medium larvae). Mitchell et al. (1984) found a negative correlation between the percent small larvae in the population and host plant height. This correlation was not found in the current study (data not shown). The factors that most affected population density are shown in Table 4.2. o The R calculations for the parasite regressions were generally low. While the current models may assist future research and can be compared to other studies, more information is needed to explain parasitism. Considering that percent parasitism as a dependent variable embodies both adult parasite behavior and larval parasite competitiveness and vigor, it may prove that much of the variation could be removed by studies of parasite biology, rather than by more detailed sampling or further environmental measurements. The use of percent parasitism (apparent parasitism) as an indicator of parasite efficacy in host population regulation is not without its pitfalls (Simmonds 1948, Marston 1980). It is of value, however, when comparing the effect of independent factors on parasitism. The difference in FAW population regulation and biology between Florida and Louisiana is an indication of the variation in natural control over broad geographical areas. It highlights the need for redundant research in other habitats of FAW. A clearer understanding of the natural history and parasitism of this insect will allow for more informed decisions in its management. CHAPTER 5: SEASONAL SUSCEPTIBILITY OF FALL ARMYWORM TO A NUCLEAR POLYHEDROSIS VIRUS The relationship between host and pathogen is an important aspect of insecr. epizootiology. This topic may be divided into several research areas, one of which is the susceptibility of the host insect population to the pathogen and how it may change. Resistance to pathogens occurs in insect populations and has been discussed in many publications (see Tanada 1976 and Briese 1981). Several studies (David and Gardiner 1960, Martignoni and Schmid 1961, R eichelderfer and Benton 1974, B riese and Mende 1981, Briese 1982, and others) have addressed differences in susceptibility to a baculovirus between populations of the same insect species from different geographical regions. Reichelderfer and Benton (1974) demonstrated that susceptibility to a nuclear polyhedrosis virus (NPV) was heritable in fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith), and was partially or incompletely dominant. Briese (1982) provided evidence for a single dominant autosomal gene controlling susceptibility of Phthorimaea opercullela (Zeller), to a granulosis virus (GV). Burges (1971) stated that it is inconceivable for populations of insects to retain the same susceptibility to pathogens over time. Briese and Mende (1981) indicated that the variability between geographically separate populations of P. opercullela may be a reflection of the past history of selection pressure at each location. In order to test this hypothesis, Steinhaus (1954) proposed examining "the nature and status of 87 88 survivors in insect populations that have suffered epizootics," which would involve measuring host susceptibility over time at the same geographical locations. Populations of FAW in south-central Louisiana are susceptible to an NPV which is able to produce epizootics (Fuxa 1982, Fuxa and Geaghan 1983, Chapter 3). FAW populations vary in response to at le a s t one NPV (R eicheldefer and Benton 1974) and thus are suitable candidates with which to study seasonal susceptibility at a single location. Materials and Methods Fall armyworm were sampled in cornfields at the St. Gabriel Experiment Station in 1981 and at a private dairy 6 km east of Hammond, LA in 1982. In 1981, two treatm en ts of 40.47 larval equivalents (LE)/hectare ( = 100 LE/acre) of NPV were applied to two separate plots of 0.127 ha in size. The first treatment was applied on 5 June, corresponding with the appearance of the second generation. The second plot was treated on 8 July, corresponding with the third generation. The NPV was applied in water with a CO2 sprayer. Sampling methodology and larval rearing are described in Chapter 3. Field collected larvae that survived to adulthood were sorted by collection date and confined in one lite r soft drink cups. These cups were lined with paper towelling and covered by a single paper towel secured with a rubber band. Moths were provided with a 10% sucrose solution in 30 ml cups filled with cotton. The paper towels served as an oviposition site and were changed daily. Towels containing eggs were sealed in plastic bags until hatch. Larvae from a single night's 89 oviposition generally hatched within 3-4 hours of one another. A fiber optic light tube placed at one end of the plastic bag concentrated the larvae for removal with a camels hair brush. Inocula used in the bioassays were prepared from the locally occurring strain of NPV, acquired in 1980. NPV was grown in colony insects, harvested, and purified on 40-63% sucrose gradients (Summers and Smith 1978). Concentrations of NPV inocula were determined with a Petroff-Hauser counting chamber, and all inocula were stored at -20°C until use. F^ larvae less than 8 hours old were bioassayed by the larval drinking technique (Mitchell 1980, Hughes and Wood 1981, M itchell and Smith 1985). Four to six doses and a control were used in each assay. When possible, 30 larvae per dose, plus extras, were used. Otherwise, all available larvae, down to a minimum of 15/dose were used. When a larva died within 24 hours of inoculation, or died from causes other than infection, it was replaced in the bioassay with one of the extras. The number of larvae tested and responding at each dose were added together when assays were combined. Data were analyzed by the probit analysis procedure of SAS (Statistical Analysis System, SAS Institute, Cary NC), and by a custom probit program written at Louisiana State University for use on microcomputers. Fit of the probit model was determined by the chi-squared goodness of fit. A group of 50 freshly hatched Fj larvae was fed a suspension containing a mixture of tritiated amino acids (21000 DPM). These larvae were placed individually in scintillation vials with a cocktail solution and allowed to stand for 30 minutes. Counts 90 were then made with a liquid scintillation counter. The ratio of the original tritiated mixture to larval scintillation counts determined the average amount of inoculum ingested by larvae. This information was used in turn to estimate the number of polyhedra ingested per insect and thereby calibrate the bioassays. Results Larvae were present in corn at St. Gabriel from mid-April until the first week in August, when the plants died. Generations were discernable at this location, and bioassays were grouped accordingly. Seven assays were conducted on F^ larvae from first- generation parents collected as larvae on four sample dates (29 April, 2 May, 7 May, and 13 May). Larvae in the second generation were divided into two groups, those treated with NPV and untreated controls. Three assays were conducted on larvae from the untreated group; parents were collected as larvae on two dates in June (18 June and 25 June). Two assays were conducted on F^ larvae from the treated group. The parents of this group were collected as larvae on 25 June, the first survivors collected from the spray plot. Larvae in the third generation also were divided into treated and untreated groups. Parents for all F^ larvae assayed in both groups were collected on 10 July. Prevalence of naturally occurring virus was low at St. Gabriel in 1981. Diseased larvae were not found in generation 1 or the untreated larvae in generation 2. Untreated larvae in generation 3 had less than 1% virus infection at collection. Larvae treated with NPV in generation 2 had mortality levels as high as 85% prior to sampling, while mortality was at 16% in the treated larvae of generation 3 at the time the parent stock was collected. Prevalence of naturally occurring virus peaked at 13% in untreated larvae of generation 3, and at 56% in treated larvae. Figures 5.1-5.5 illustrate the sequential changes in the probit line at the St. Gabriel site. Figure 5.1 demonstrates the homogeneous response of the first generation to NPV inoculation. The bioassay data f i t the probit model, and an LC^g of 10 with a slope of 2.03 resu lted . F iducial lim its are in Table 5.1. Assay data from untreated larvae in generation 2 also fit the probit model (Figure 5.2). The slope decreased to 1.68 and the LC^q to 6. Figure 5.3 illustrates the response of the treated larvae in the second generation to NPV inoculation. Data did not fit the probit model and were fit by means of the heterogeneity factor (Finney 1971). The percent mortality at doses 4, 45, and 108 polyhedra/insect are superimposable on the analogous responses by larvae from the untreated group (Figure 5.2), while the responses at doses 8 and 27 have shifted. Bioassay data from FAW not treated in generation 3 also required adjustment by the hetero geneity factor to fit the probit model (Figure 5.4). Two new dose levels, 15 and 40, were added and the dose of 108 removed. The responses at the doses of 4, 8, 27, and 45 are superimposable on the responses to the same doses by the progeny of the NPV treated larvae in generation 2 (Figure 5.3). Figure 5.5 presents the response of the F^ progeny of FAW treated in generation 3. Again, the response is heterogeneous enough to require adjustment, and Figure 5.1 Probit analysis of response of 1981 first generation St. Gabriel progeny to an NPV. 95- > h* 3 70 GENERATION I < LD50 ■ 10 Slop*-2 .0 3 0£ 7 Assays n ■ 980 o 50 z * 30- 5 T' " I------1— 4 8 29 LOG DOSE Figure 5.2 Probit analysis of response of progeny of control FAW the in second generation to an NPV. 9 0 - -J 7 0' GENERATION 2 < L050 ■ 6 Slope « 1.68 o 50- 3 Assays n ■ 452 Z * 30- 5- T —r "i " 1 i 4 8 2 7 4 5 io8 LOG DOSE VOUl Figure 5.3 Probit analysis of response of progeny of FAW in the second generation to an NPV. Parent stock was treated in corn fields with NPV. / 95- / • z. 7 / / 7 0 - ✓ GENERATION 2 (Treated) LD50 » 8 Slope ■ 1.52 g 50 2 Assays n ■ 325 2E * 30- 5 T T I 4 8 27 45 108 LOG DOSE VO Figure 5.4 Probit analysis of response of progeny of control FAW in the second generation to an NPV. 9 5 - 70- GENERATION 3 LD50> 9 Slope-I. g 50 2 Assays n > 4 0 0 2 * 30- 5- T T 4 8 Figure 5.5 Probit analysis of response of progeny of FAW in the third generation to an NPV. Parent stock was treated in corn fields with NPV. 95- 3 7 5 T T 4 8 15 Table 5.1 Results of bloassays of an NPV against progeny of FAW collected as larvae from St. Gabriel. Fiducial Generation # assays # insects* LC50 Limits Slope NPV^ 1 7 980 10 8 to 11 2.03 0 2.92 2 3 452 6 4 to 8 1.68 0 2.61 2 treated 2 325 8 1 to 16 1.52 85% 6.39* 3 2 400 9 4 to 14 1.55 0 10.02* 3 treated 4 710 16 7 to 35 1.68 16% 29.71 ^Includes control larvae. 2 Greatest amount of NPV mortality for the generation prior to sampling. •ff Significant chi-square value — analysis completed with heterogeneity factor. 103 the probit line is broken. Responses are no longer superimposable, and the probit has shifted downward on the graph, with the break in the line now occurring below the LC^q. FAW were collected at the Hammond location in 1 82 due to higher population levels of FAW early in the season and generally higher prevalence of the NPV than at St. Gabriel. Oviposition was continuous at this site in 1982, and generations could not be separated. Bioassay results were therefore grouped by the sample date of the parent stock. Results are presented in Table 5.1. Larvae were field collected for two months, May and June. Viral prevalence was low during this study, reaching 12% on 29 June. Data from the bioassays f i t the probit model except on 8 June and 15 June, corresponding with the appearance of the virus in the population. Bioassay results from 29 June fit the probit model, even though viral prevalence was at its highest point of the study. The LC^q's generally increased with time, while the slopes decreased. The chi-square values for the bioassays were generally low, but increased to significance when NPV infected larvae apppeared in the population (Tables 5.1 and 5.2). The one exception occurred on 29 June 1982 when the value was non significant and NPV prevalence was 12%. The chi-square test normally serves as a measure of the goodness of f i t of the probit line. In this study, lack of fit indicated heterogeneity in the insect population to NPV infection, demonstrated by the characteristic inflection of the broken probit line, rather than other sources of bias or error. Data from the 1982 and 1983 St. Gabriel collections were too Table 5.2 Results of bloassays of an NPV against progeny of FAW collected as larvae from Hammond. Fiducial Sample day // assays # insects* LC50 Limits Slope NPV2 X2 3 May 3 602 6 5 - 7 1.80 0 2.22 12 May 2 385 5 4 - 6 1.45 0 1.02 20 May 2 397 11 8 - 20 1.25 0 1.83 25 May 2 394 11 9 - 18 1.66 0 6.11 8 June 4 665 13 11 - 45 1.21 2% 8.15 15 June 3 494 10 9 - 46 1.17 5% 7.91 29 June 2 384 15 10 - 26 1.51 12% 0.87 ^Includes control larvae. 2 Prevalence of virus for the week. Significant chi-square value — analysis completed with heterogeneity factor 105 sparse to include in this study. Hammond data for 1983 was confusing due to too few assays and also not included in the study. Discussion The LC^q's remained stable throughout the course of the study, changing by less than a factor of three. The ratio of the broadest range of fiducial limits yielded possible 35 fold d ifferen c es in 1981 and 11.5 fold d ifferen c es in 1982. However, limits and LC^q’s calculated by use of the heterogeneity factor should, in this instance, be viewed with caution. The non-fit of the probit line that invoked the H-factor was probably not due to random error, and the results best are used for graphical and non-statistical comparisons. When slope and the chi-square statistic are taken into account, FAW populations appear less stable than indicated by LCcjq alone. Slope is a measure of homogeneity (Finney 1971), and in this study initial assays in both years had a slope at or near two, which is considered by Hughes and Wood (1981) as the theoretical upper limit for this technique with an unsynergized pathogen. The slopes generally decreased until the probit line broke, as indicated by the significant chi-squared statistic. This break or inflection in the probit line is characteristic of a hybrid cross between insecticide resistant and susceptible insects, where resistance is controlled by a single, dominant gene allele (Croft 1977). Briese (1982) obtained a similiar pro b it response when r e s is ta n t and su sc e p tib le P. o percullela were crossed and the F 2 were bioassayed with a GV. 106 Apparently, two populations of FAW were present in the field — susceptible and "less susceptible" — with respect to infection by the NPV. Representatives of both groups or crosses between the groups were included in the heterogeneous assays, causing the break in the probit line. It is noteworthy that the breaks occurred only when viral infections appear in the parent population, with one exception. Yet it seems unlikely that the low percentage of infection in 1982 could have affected the genetic composition of FAW populations and resulted in the heterogeneous responses. The most likely explanation for the results is immigration of a susceptible genotype. FAW apparently migrate into Louisiana from both Texas and Florida (Young 1979), and smaller scale movements are easily conceivable. In 1981, heavy local selection for less susceptible FAW via insecticidal virus treatment (generation 2 treated) followed by an influx of local, untreated FAW may be responsible for the first break in the probit line. Continued pressure resulted in more selection and more heterogeneity, reflected in the high chi-squared statistics. In 1982, when virus was not artificially applied, disease prevalence was initially low and then increased. Significant chi-square values would not be expected from bioassays if selection by the NPV was solely responsible for inducing heterogeneity. However, immigration of a susceptible genotype would explain the simultaneous low prevalence and significant chi-square values. Each larva in every bioassay was challenged with NPV, and probit analysis detected heterogeneity in populations with mixed susceptibility. In the field, fewer larvae were exposed to NPV and, even though susceptible insects were present, prevalence was low. If this line of reasoning is followed however, it does not explain why the bioassays of 29 June 1982 did not produce a broken probit line. These results support the observations of previous researchers (Briese and Mende 1981, Burges 1971) concerning the dynamic nature of pathogen susceptibility at a given location. There were no dramatic changes in LC^q, but changes in the population were indicated by non-fit of the probit model and the increasing chi-square values that became significant as the season progressed. 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APPENDIX 1 St. Gabriel 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1 81 5 06 0 4 14 18 18 5.1 0.225 0 71.4 77.5 7.49 2 81 5 12 0 0 10 10 10 5.7 0.125 0 71.0 74.0 8.05 3 81 5 21 0 0 1 1 1 6.0 0.013 0 74.1 71.0 8.61 4 81 6 04 28 1 0 29 29 1.7 0.388 0 80.6 78.5 8.05 5 81 6 11 1 24 6 31 31 4.1 0.388 0 82.2 85.0 6.44 6 81 6 18 3 14 17 31 34 4.5 0.453 0 84.8 82.5 8.75 7 81 6 25 18 1 7 26 26 2.3 0.363 0 83.4 84.0 7.49 8 81 7 04 54 5 10 64 69 2.0 1.260 0 83.5 83.0 9.24 9 81 7 10 46 146 16 208 208 3.1 3.350 2 81.7 85.5 4.76 10 81 7 16 71 53 42 167 167 2.9 5.470 1 84.7 81.0 7.84 11 81 7 23 3 74 43 121 121 4.3 2.500 15 89.3 74.5 6.79 12 81 7 31 0 1 39 40 40 5.6 1.010 5 86.2 77.5 8.47 13 82 5 19 0 2 0 2 2 4.0 0.026 0 73.7 76.5 7.98 14 83 5 20 9 2 0 11 11 1.8 0.029 0 75.6 74.3 6.51 15 83 5 27 3 32 4 39 39 3.5 0.010 0 72.9 72.4 7.77 16 82 6 02 12 36 33 81 81 4.0 0.229 0 82.1 77.9 1.00 17 82 6 10 0 1 2 3 3 5.0 0.038 0 81.3 75.6 9.73 18 82 6 16 0 0 1 1 1 6.0 0.013 0 81.5 78.4 9.38 19 82 6 23 0 1 11 12 12 5.3 0.038 0 80.0 81.0 8.33 20 82 6 30 0 0 6 6 6 6.0 0.025 0 77.9 82.6 7.56 21 82 7 29 89 2 0 90 91 1.1 0.990 0 80.7 81.6 6.79 22 82 8 04 104 61 0 157 165 2.1 2.150 1 80.1 83.0 6.72 23 82 8 12 90 30 6 106 126 1.9 1.570 0 78.9 82.4 7.56 24 82 8 20 57 110 21 167 188 3.0 2.313 2 79.6 79.4 7.14 25 82 8 27 188 43 41 192 272 2.1 3.400 1 82.8 78.4 7.91 26 82 9 03 59 110 49 194 218 3.2 2.740 4 81.9 76.7 7.91 27 82 9 10 49 96 37 170 182 3.2 2.310 12 78.1 79.9 7.14 28 82 9 17 7 27 18 49 52 4.0 0.650 5 78.7 82.9 6.30 29 82 9 26 0 0 4 4 4 5.8 0.050 0 71.3 76.9 8.61 30 83 6 05 0 4 19 23 23 5.3 0.100 0 75.5 69.1 8.89 31 83 6 10 0 0 12 10 10 5.6 0.010 0 73.0 71.3 8.54 32 83 7 03 83 39 0 122 122 1.9 1.555 0 81.8 75.1 6.58 33 83 7 07 28 37 7 72 72 2.9 1.300 0 84.0 69.2 8.61 34 83 7 18 155 51 11 217 217 2.0 2.650 0 82.9 69.2 9.41 35 83 7 26 5 24 19 48 48 4.2 1.230 0 86.7 70.7 8.38 36 83 8 18 5 0 0 5 5 1.0 0.063 0 85.6 75.1 6.02 37 83 8 23 59 22 0 28 81 1.9 1.000 0 86.2 71.0 7.00 38 83 8 30 99 22 17 138 138 2.1 1.790 3 86.6 72.1 8.33 117 APPENDIX 1 15 16 17 18 19 20 21 22 23 1 1.2 48 81126 57 0 0 0 3.184 4.2 2 0.7 54 81132 65 0 0 0 0.826 4.2 3 0.7 78 81141 138 0 0 0 1.000 4.2 4 0.5 8 81155 157 0 15 1 5.746 4.2 5 0.4 12 81162 171 1 10 1 1.202 4.2 6 0.7 24 81169 191 0 3 2 2.551 4.2 7 0.9 48 81176 182 0 0 0 10.245 4.2 8 0.5 84 81185 215 0 2 0 4.774 4.2 9 1.2 96 81191 134 2 2 0 7.738 4.2 10 0.9 96 81197 161 0 1 0 11.880 4.2 11 0.0 96 81204 204 0 0 0 3.846 4.2 12 1.0 96 81212 205 0 0 0 4.000 4.2 13 0.6 24 82139 107 0 0 0 2.008 1.8 14 2.1 8 83140 109 5 0 0 •• 15 3.0 12 83147 92 12 0 15 • • 16 0.0 60 82153 132 3 0 2 1.621 1.8 17 0.0 18 82161 169 0 0 0 1.575 3.7 18 0.0 40 82167 129 0 0 0 1.000 3.7 19 2.5 48 82174 144 0 0 1 0.867 3.7 20 3.0 78 82181 125 0 0 0 0.867 3.7 21 0.8 4 82210 166 3 68 1 0.000 1.8 22 0.5 7 82216 121 5 104 17 4.518 1.8 23 1.9 12 82224 152 0 62 2 6.098 1.8 24 4.2 20 82232 156 0 49 5 2.797 1.8 25 0.0 30 82239 160 1 1 4 16.245 1.8 26 0.1 40 82246 154 2 22 2 4.459 1.8 27 0.0 60 82253 126 0 24 0 5.461 1.8 28 0.1 72 82260 131 0 0 0 2.892 1.8 29 0.0 80 82269 115 12 0 15 0.000 1.8 30 0.5 36 83156 149 0 0 3 • • 31 0.2 50 83161 65 0 0 0 • • 32 6.0 12 83184 114 9 0 3 •• 33 0.8 18 83118 72 7 0 6 • • 34 1.2 48 83199 257 20 1 9 • • 35 0.7 60 83207 216 0 0 0 • • 36 0.7 5 83230 179 0 0 0 • • 37 0.4 12 83235 137 0 0 0 •• 38 0.4 18 83242 185 12 5 2 • • APPENDIX 1 Column legend - l:year 2:month 3:day 4:number of small larvae captured 5:number of medium larvae captured 6:number of large larvae captured 7:laboratory count 8:field count (larvae that were killed or escaped in the field were included in the field count, but not the laboratory count) 9:average instar 10:total number of larvae per plant 11:number of larvae infected by NPV 12:average temperature 13:averaee percent relative humidity 14:solar radiation (calories/cm /min) 15:inches of rain 16:host plant height in inches 17:julian date 18:degree days between sam ples ((max temp. + min. temp)/2 - threshold threshold=50°F) 19:number of Cotesia marginiventris captured 20:number of Chelo- nus sp. captured 21:nur.ber of Rogas laphygmae captured 22:vari- ance to mean ratio23:soil NPV (percent infection in soil bioassay) APPENDIX 2 Hammond 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 82 4 19 0 2 8 10 0.02 0 0 68 65 1.20 1.45 13 2 82 5 3 0 0 47 47 0.01 0 0 70 65 1.20 1.45 24 3 82 5 3 0 0 7 7 0.01 0 0 70 65 1.20 1.50 24 4 82 5 12 6 22 15 43 0.21 0 0 70 68 1.40 0.10 36 5 82 5 12 13 35 21 69 0.45 0 0 70 68 1.40 0.10 36 6 82 5 12 2 13 3 18 0.10 1 0 70 68 1.40 0.10 20 7 82 5 20 0 6 52 58 0.13 0 0 74 71 1.20 1.50 66 8 82 5 20 0 24 62 86 0.11 0 0 74 71 1.18 1.50 60 9 82 5 20 0 2 34 36 0.04 0 0 74 71 1.18 1.50 27 10 82 5 25 0 0 34 34 0.12 0 0 75 67 1.35 0.50 72 11 82 5 25 4 11 32 47 0.09 0 0 75 67 1.35 0.50 39 12 82 6 1 0 3 12 15 0.15 0 0 80 71 1.31 0.40 84 13 82 6 1 0 13 31 44 0.52 0 0 80 71 1.31 0.40 54 14 82 6 1 23 167 42 232 0.57 2 0 80 71 1.31 0.40 6 15 82 6 8 0 2 12 14 0.03 0 0 80 70 1.32 0.60 84 16 82 6 8 0 14 24 38 0.20 0 0 80 70 1.32 0.60 84 17 82 6 8 0 0 11 11 0.06 0 0 80 70 1.32 0.60 72 18 82 6 8 37 99 47 183 1.03 4 0 80 70 1.32 0.60 15 19 82 6 15 0 8 21 29 0.15 1 0 77 71 1.26 0.60 96 20 82 6 15 2 13 19 34 0.19 0 0 77 71 1.26 0.60 12 21 82 6 15 2 5 6 23 0.24 0 0 77 71 1.26 0.60 84 22 82 6 15 17 96 88 201 1.12 14 0 77 71 1.26 0.60 18 23 82 6 22 0 1 23 24 0.10 0 0 80 72 1.18 5.50 96 24 82 6 22 1 2 4 7 0.08 0 0 80 72 1.18 5.50 84 25 82 6 22 59 74 79 212 0.94 18 0 80 72 1.18 5.50 36 26 82 6 29 12 136 129 257 1.93 30 0 79 73 0.97 2.20 48 27 82 7 6 1 3 12 16 0.09 1 0 81 69 1.25 0.60 84 28 82 7 6 2 11 66 79 0.36 16 0 81 69 1.25 0.60 36 29 82 7 13 6 9 25 40 0.08 10 0 81 69 1.19 2.50 72 30 82 7 20 43 82 29 154 1.18 61 0 81 71 1.11 0.00 84 31 82 7 27 17 46 46 109 0.37 47 0 81 70 0.99 0.00 84 32 83 5 24 0 18 0 18 0.12 0 0 74 61 0.84 3.05 48 33 83 5 24 0 16 2 18 0.15 0 0 74 61 0.84 3.05 36 34 83 5 31 0 7 22 29 0.20 0 0 74 57 1.08 0.00 66 35 83 5 31 0 33 17 50 0.50 0 0 74 57 1.08 0.00 54 36 83 6 10 0 19 13 32 0.20 0 0 75 56 1.28 2.01 78 37 83 6 10 0 46 16 62 0.58 0 3 75 56 1.28 2.01 68 38 83 6 16 0 4 45 49 0.22 5 0 76 55 1.19 0.65 84 39 83 6 16 9 45 1 55 0.38 2 0 76 55 1.19 0.65 80 40 83 6 16 0 61 11 72 1.36 1 4 76 55 1.19 0.65 8 41 83 6 20 0 3 8 12 0.12 3 0 78 61 0.84 1.96 96 42 83 6 20 100 20 0 120 1.68 5 0 78 61 0.84 1.96 66 43 83 6 20 1 13 0 14 0.14 0 1 78 61 0.84 2.01 92 44 83 6 20 121 27 0 148 1.05 8 2 78 61 0.84 1.96 24 45 83 6 27 5 54 16 75 1.61 5 0 78 62 0.80 0.11 78 120 121 APPENDIX 2 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 46 83 6 27 29 133 57 217 1.45 20 1 78 58 0.80 0.11 36 47 83 7 18 59 2 0 61 1.79 7 0 76 55 0.92 0.00 86 48 83 7 18 104 51 4 159 1.05 63 0 76 55 0.92 0.00 48 49 83 7 26 4 73 36 113 0.75 50 1 82 62 1.03 1.89 60 16 17 18 19 20 21 22 1 82109 57 2 0 0 9.0 HC1 2 82123 68 0 0 0 9.0 HCl 3 82123 68 0 0 0 6.9 HC2 4 82132 69 0 0 0 9.0 HCl 5 82132 69 0 0 0 6.9 HC2 6 82132 69 0 0 0 4.6 HC3 7 82140 98 0 0 0 9.0HCl 8 82140 98 0 0 0 6.9 HC2 9 82140 98 0 0 0 4.6 HC3 10 82145 107 0 0 0 6.9 HC2 11 82145 107 0 0 0 4.6 HC3 12 82152 138 0 0 0 6.9 HC2 13 82152 138 0 0 0 4.6 HC3 14 82152 138 9 8 5 7.0 HC4 15 82159 141 0 0 0 9.0 HCl 16 82159 141 0 0 1 6.9 HC2 17 82159 141 0 0 0 4.6 HC3 18 82159 141 19 0 0 7.0 HC4 19 82166 135 0 1 0 9.0 HCl 20 82166 135 0 2 0 6.9 HC2 21 82166 135 0 0 0 4.6 HC3 22 82166 135 8 5 0 7.0 HC4 23 82173 141 0 0 0 6.9 HC2 24 82173 141 0 0 0 4.6 HC3 25 82173 141 0 45 1 7.0 HC4 26 82180 126 4 26 1 7.0 HC4 27 82187 149 0 0 0 4.6 HC3 28 82187 149 0 1 0 7.0 HC4 29 82194 150 0 0 0 7.0 HC4 30 82201 144 2 9 5 7.0 HC4 31 82208 149 0 13 1 7.0 HC4 32 83144 102 1 0 1 14.3 HCl 33 83144 102 2 0 0 15.0 HC2 34 83151 100 0 1 0 14.3 HCl 35 83151 100 1 0 2 • HC3 36 83161 102 0 0 0 14.3 HCl 37 83161 102 4 0 0 • HC3 38 83167 114 13 0 0 14.3 HCl 39 83167 114 4 0 0 • HC3 40 83167 114 20 0 2 • HC4 41 83171 124 0 0 0 14.3 HCl 42 83171 124 19 0 7 15.0 HC2 APPENDIX 2 16 17 18 19 20 21 22 43 83171 124 0 0 0 • HC3 44 83171 124 22 0 8 • HC4 45 83178 127 6 0 2 15.0 HC2 46 83178 127 20 0 0 • HC4 47 83199 149 0 0 0 15.0 HC2 48 83199 149 4 1 1 • HC4 49 83207 160 2 0 0 • HC4 Column legend - l:year 2:month 3:day 4:number of small larvae captured 5:number of medium larvae captured 6:number of large larvae captured 7:total number of larvae captured8:total number of larvae per plant 9:number of larvae infected by NPV 10:number of larvae infected by GV ll:average temperature 12:average percent relativehumidity 13:solar radiation (calories/cm /min) 14:inches ofrain 15:host plant height in inches 16:julian date 17:degree days between samples ((max. temp. + min. temp.)/2 - threshold threshold=50°F) 18:number of Cotesia margin!ventris captured 19:number of Che1onus sp. captured 20:number of Rogas laphygmae captured 21:soil NPV percent infection in soil bioassay 22.’field ID APPENDIX 3 St. Gabriel 1981 Fiducial Date of P^ Number per Slope LC50 Limits Capture Plant Generation 1 1 1.74 6 2-9 4/22-4/29 .013 2 2.09 13 9-19 5/2/81 .368 3 4.88 8 6-12 5/2/81 .368 4 2.98 12 9-16 5/7/81 .225 5 2.52 14 10-24 5/7/81 .225 6 2.06 8 6-10 5/2/81 .368 7 2.25 13 10-18 5/13/81 .125 Generation 2 8 1.59 6 3-9 6/18/81 .453 9 1.30 6 1-10 6/25/81 .363 10 3.70 7 5-9 6/25/81 .363 Generation 2 (treated) 11 1.45 5 3-8 6/25/81 .088 12 1.97 13 8-19 6/25/81 .088 Generation 3 13 1.39 9 5-13 7/10/81 3.35 14 1.6 8 5-12 7/10/81 3.35 Generation 3 (treated) 15 3.42 6 4-8 7/10/81 2.38 16 2.07 18 13-23 7/10/81 2.38 17 1.73 15 6-40 7/10/81 2.38 18 7/10/81 2.38 123 APPENDIX 3 Hammond 1982 Assay Fiducial Number per Number Slope LC50 Limits Plant Week of 3 May 1 2.02 5 2-20 .01 2 2.19 9 7-16 .01 3 1.51 4 3-5 .01 Week of 12 May 4 1.78 7 5-12 .34 5 1.55 9 6-18 .34 Week of 20 May 6 1.38 10 7-22 .07 7 1.15 14 8-135 .07 Week of 25 May 8 1.40 18 - .10 9 1.52 9 6-17 .10 Week of 8 June 10 1.80 10 6-25 .24 11 0.84 33 11- .24 12 1.13 8 5-22 .24 13 1.23 11 7-31 .24 Week of 15 June 14 1.15 5 - .44 15 0.97 36 13- .44 16 1.5 8 2- .44 Week of 29 June 17 1.26 32 14-500 .12 18 2.02 10 7-16 .12 125 APPENDIX 3 Data not used in study Assay Fiducial Number/ Date of in Chi- Number Slope LC50 Limits Plant Capture assay square St. Gabriel 1982 1 2.06 3 2-4 .125 5/11 199 5.93 2 1.36 1 1-2 .229 6/2 188 5.34 3 1.05 38 13- .038 6/23 170 0.16 St . Gabriel 1983 1 1.39 2 2-53 5/20 188 1.42 2 3.60 2 1-2 .350 6/5 191 3.85 3 0.48 1 - 1.550 7/3 222 14.62 4 1.00 1 1-3 1.55 7/3 (D7) 189 5.19 Hammond 1983 1 3.80 1 1-2 .13 5/24 187 1.02 2 1.87 15 11 -22 .32 5/31 (D7) 126 1.40 3 3.23 10 8-12 .32 5/31 (D7) 190 6.48 4 2.40 3 1-4 .32 5/31 (D7) 192 0.47 5 1.00 1 1-2 1.49 6/27 180 3.54 6 2.00 1 1-2 1.49 6/27 222 15.54’ 7 2.40 4 2- - 8/9 224 13.52’ 8 2.00 6 5-16 - 8/9 224 2.10 *Significant chi-square value — analysis completed with heterogeneity factor VITA Forrest Lee Mitchell, the son of Daniel F. and Thelma Mitchell was born December 21, 1955 in Brownsville, Texas. He attended Brownsville High School and graduated in May, 1973. Subsequently enrolling in Texas A&M University, he graduated in May, 1977 with the degree of Bachelor of Science in Wildlife and Fisheries Science. He began graduate study in the department of Entomology at Texas A&M in August, 1977, and graduated with a Master of Science degree under the direction of Dr. J.W. Smith, Jr. in December, 1980. Transferring to the department of Entomology at Louisiana Stats University in September, 1980, he initiated work in the Ph.D. program. In July, 1984, he accepted employment with the Texas Agricultural Experiment Station in Stephenville, Texas. The dissertation was completed in December, 1985. The author was married to the former Paula Lynn Levin in Bryan, Texas on July 28, 1979. They have one son, Robert F o rrest, born on August 7, 1983 in Baton Rouge, Louisiana. The author is a member of the Entomological Society of America, the American Association for the Advancement of Science, the Southwestern Entomological Society, the Kansas Entomological Society, the Louisiana Entomological Society, the Society for Invertebrate Pathology, the American Peanut Researchers and Educators Society, and the Texas Fruit Growers Organization. 126 DOCTORAL EXAMINATION AND DISSERTATION REPORT Candidate: Forrest L. Mitchell Major Field: Entomology Title of Dissertation: Natural Control and Spatial Distribution of Fall Armyworm (Spodoptera frugiperda) within Louisiana Corn Fields Approved: /f Major Professor and Chairman cho olDean of the Graduate choolDean EXAMINING COMMITTEE: - y Date of Examination: August 26, 1985