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I; .- I’ Il”WilliWit]ililelllliiiiiilillilfliliilillll i “W“ S; 3 1293 10596 1506 \Eiu "*5 f;- This is to certify that the dissertation entitled THE BIOLOGY AND ECOLOGY OF ENTOMOPHTHORA MUSCAE (COHN) IN THE ONION AGROECOSYSTEM presented by Raymond I. Carruthers has been accepted towards fulfillment of the requirements for Ph.D. Entomology degree in r / Major /proiessor\J Date December 30, 1981 MS U is an Affirmative Action/Equal Opportunity Institution 0-12771 V D *‘—_ -W v_._.___ _-..—_-_—... OVERDUE FINES: 25¢ per day per item RETURNING LIBRARY MATERIALS: Place in book return to remove charge from circulation records THE BIOLOGY AND ECOLOGY OF ENTOMOPHTHORA MUSCAE (COHN) IN THE ONION AGROECOSYSTEM by Raymond l. Carruthers A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Entomology 1981 ABSTRACT THE BIOLOGY AND ECOLOGY OF ENTOMOPHTHORA MUSCAE (COHN) IN THE ONION AGROECOSYSTEM by Raymond I. Carruthers Entomophthora muscae was identified as a common fungal pathogen of the adult onion maggot, Hylemya antiqua, and the adult seed corn maggot, _lj. m2, throughout Michigan. The general disease cycle is described including the etiology, symptomatology and phenology. lg yilg and i_r1 v_it§g laboratory culture and transmission techniques are also given. Field investigations revealed high levels of _E_. M infection in natural host populations. Although high infection levels were noted, actual _E. 9%- induced mortality in the overall adult pOpulation was significantly lower due to other mortality factors preempting death by mycosis. The importance of g. Egg-induced mortality to population regulation was found to be highly significant, as its effect on the reproductively mature females, thus fecundity was greatest. A conceptual model of g. _m_usc_ae in the onion agroecosystem was developed and used as a basis in hypothesis development for specific experimentation. Functional relationships were identified and quantified for use in the development of a multiple host-pathogen simulation model. Experimental results include characterization of the temperature- dependent disease incubation period, pre-mortality effects of g. muscae on fecundity, and temperature and moisture-induced effects on E. muscae conidial production, rate, and phenology of sporulation, germination and mortality. Further studies examine the spatial interactions of the host and pathogen under field conditions. Identification of important microhabitats and quantification of their impact on disease spread and deveIOpment are also examined. The experimental results of this study have been linked with data from the literature and synthesized into a computer simulation model of the onion maggot-seed corn maggot-g. muscae life system. The model was constructed in free-body form to allow component-by-component evaluation and easy linkage with other models associated with the onion agroecosystem. Model formulation, validation, and application are discussed, with several simulation results presented. DEDICATION To Dr. D. M. MacLeod an inexhaustible source of inspiration 11 ACKNOWLEDGMENTS I wish to express my sincere appreciation to the entire faculty and staff of the Department of Entomology as all of you have greatly added to my professional and personal development. Special thanks are in order for Drs. J. E. Bath, G. W. Bird, W.C. Fulton, E.J. Grafius, D. M. MacLeod, F. Matsumura, and R.L. Tummala for serving on my guidance committee and providing insight throughout the duration of this project. I especially thank Dr. Dean L. Haynes for his constant support, both intellectual and financial, while serving as my major professor. Dean, it's been an honor. I also extend thanks to all of my colleagues, especially Tom Ellis, Julia Pet, Bill Ravlin, and Gary Whitfield, who provided constant support, encouragement, and friendship: you made the hard times easier. To Susan Battenfield, Marie Pane, and Cathy Stewart, thank you for putting this thesis to rest. Most importantly, I express my love and appreciation to my wife Diane, the single person who has given the most toward the completion of this goal. iii Cycles within cycles, independent yet interacting, the large encompassing the small, the small fusing into the large. Each moment of life is shaped by everything that has gone before. No act or circumstance stands alone; each has been influenced by the past and, in turn, will affect the future. There is no living thing, no biological or environmental event, condition or process, that deviates from this universal principle. Interdependence is the supreme law of nature (Edward R. Ricciuti, from The Peaceable Kingdom). iv I. II. III. IV. V. VI. VII. TABLE OF CONTENTS LIST OF TABLES - . ...................... viii LIST OF FIGURES . . . . . . . .............. . xii Introduction. . ........... . . . . . . 1 Objectives ..... . . . . . . . ............ 3 Research Rationale and Approach . . . . ....... . . 3 Background . . . . . . . ..... . ........... 12 System Identification . ....... . . . . . . . 17 Problem Formulation . . . . ......... . ..... 24 Experimental Procedures and Results. . . . . . . . . . . . . 25 A. Etiology and Disease Life System . . . . . ....... 25 B. Response of the Natural System ...... . ...... 39 1. Detection Sampling . ............... 39 2. Field Level Monitoring for gmimection ......... . . . . . 41 3. Host—Pathogen Spatial Patterns and Areas of Interaction .............. 53 a. Infection at Emergence . ........... 54 b. Spatial Patterns Associated With Secondary Infection . .......... 60 4. Host Mortality and the Impact of E. _rrlu_s_c_ag Infection on Fecundity . . ........ 67 TABLE OF CONTENTS, continued VIII. C. Specific Experimentation. . . . . . . . . . . Experiment 1. Laboratory transmission of§.muscae. . . . . . . . . . Experiment 2. Laboratory cross infection of E. muscae between OM and SCM adUItS. O O O O O O O O 0 Experiment 3. Temperature dependent devel- opment of E. muscae i_n vivo . . Experiment (I. The effects of E. muscae on onion maggot fecundity . . . . Experiment 5. Characterization of sporulation rate. . . . . . . . Experiment 6. E. muscae sporulation as a function of temperature. . . . Experiment 7. E. muscae conidial dispersion. , , Experiment 8. The effects of moisture and temperature on _E.muscae spore germination. . . . . . . Experiment 9. Conidial germination and via- bility in various habitats ofconcern. . . . . . . . . . Experiment 10. Host-pathogen interaction. . . Experiment 11. Conidial dose for infection of onion maggot adults . . . . AbstractModeling............... A. B. Simulation Model Formulation . . ...... Computer Implementation and Program Verification . . . . . . . . . . ModelValidation.............. ModelApplication . . . . . . . . . . . . . vi 74 74 76 77 83 84 89 95 102 108 118 121 126 127 131 132 135 TABLE OF CONTENTS, continued IX. summary 0 o o O o o O o ..... o o O o o o o o O o O 0 LITERATURE CITED . . . . . . ...... . . . . ...... APPENDICES Appendix A. Appendix B. Appendix C. Appendix D. Appendix E. Appendix F. Appendix G. Appendix H. Appendix 1. Appendix J. Appendix K. Appendix L. The impact of pesticides on natural enemies of the onion maggot, flylemya antiqua. . . . . . . . . Footnotes for Figure l. . . . . . . . . . . ..... FootnotesforFigure3.. . . . . . . . . . . . . . . Entomophthora muscae field infection data ....... Data for incubation period of _E_. muscae infecting onion maggot adults. . . . . . . . . . . . . Replicates of spore density. . . . . . ........ Spore production for various temperatures. . . . . . . Spore germination data for various temperature and moisture conditions.. . . . . . . . . Spore germination data for various habitats.. . . . . . Host-pathogen interaction data. . . . . . . . . . . . Tgmperature data and DD accumulation (base 4 C) for Grant, Michigan, 1977-1979. . . . . . . . . . Computer source code for E. muscae, OM and SCM simulation program. . . . . . . . . . . . . . . vii 137 142 155 172 179 182 192 195 199 204 208 210 211 215 Table I. Table 2. Table 3. Table 4. Table 5. Table 6. Table 7. Table 8. Table 9. LIST OF TABLES Counties sampled in 1978 Entomophthora muscae detection survey, listed with identified hOSt speCies° o o O o o o o O I o o O o O o o o o o 0 Multivariable regression statistics and variables examined for prediction of lagged E. muscae infeCtion' o o o O O O o O o o o O O o O o 0 O O O 0 Estimated levels of primary E. muscae infection in Hylemya antiqua adults —(pooled by generations across traps) and the between-trap variance/mean ratio for the Grant, Michigan, researchfield.................... Non-parametric test for primary infection levels between onion fields and field borders. ----- - Two-way analysis of variance table for onion maggot adult density in differing habitats through the season (Grant field 4).. . . . ....... Two-way analysis of variance table for seed corn maggot adult density in differing habitats throughtheseason.................. Adult mortality separated into that caused by E. muscae and other mortality factors for the 1979 Grant research field. Estimates of adult half- life, percent of adults living longer than 105 degree days, E. muscae mortality of mature adults (older than 105 DD), and percent reduction in fecundity caused by E. muscae included....................... E. muscae cross infection between Hylemya antiqua and H lemya platura under laboratory conditions. 0 O O O O O O O O O O O O O O O I O O O O One-way analysis of variance for incubation period versus temperature. Student-Newman- Keuls multiple range test included. . . . . . ..... viii 42 52 57 59 62 62 73 78 LIST OF TABLES, continued Table 10. Table 11. Table 12. Table 13. Table 14. Table 15. Table 16. Table 17. Table 18. Table 19. Table 20. Multivariable regression statistics for prediction of the incubation period of the pathogen E. muscaeiflvivo. . . . . . . . . . Three-way analysis of variance table for E. muscae sporulation rate evaluated for day of sample, time of sample, and sex of the Sporulating onion maggot cadaver. , . , , , , Multiple classification analysis for analysis of sporulation rate for day of sample, time of sample, and sex of the sporulating onion maggot cadaver. . . . . . . . . . . . . . . . . . Regression statistics for probit of cumulative sporulation versus time for the data sets pooled across experimental day. . . . . . . . . . . Regression statistics for probit of cumulative sporulation versus time for experimental day 8/8/78. O O O O O O O O O O O O O O O O O O Regression statistics for probit of cumulative sporulation versus time for experimental day 8/17/78. O O O O O O O O O O O O O O O O O Frequency table for continuous hours of 10096 relative humidity within a 24-hour period (12 noon to 12 noon) for Grant, Michigan, from May 1 to September 15 for 1978 and 1979. . , , . , One-way nested analysis of variance for total E. muscae sporulation versus temperature. , , Regression statistics for probit of cumulative sporulation versus temperature. . , , , , , , Spore density measured on a transect through a horizontal plane 50 cm below a point spore source (see Fig‘ 35). O O O O O O O O O O O O Frequency tables for wind velocities between 10 pm and 4 am from May 1 to September 30, 1980. O O O O O O O O O O O O O O O O O O 0 ix 0 O O 81 O O O O O 87 ..... 88 ..... 90 I O O 90 . . . 90 O O O O O 92 O O O O 94 O O O 94 O O O O O 99 O O O O O 103 LIST OF TABLES, continued Table 21. Table 22. Table 23. Table 24. Table 25. Table 26. Table 27. Table 28. Table 29. Analysis of covariance for spore germination by moisture condition (free water, 100% R.I-I.) while controlling for the metric variables of temperature, temperature squared and time. . ..... . 106 Multiple regression statistics for spore germination under free moisture conditions predicted by temperature and time. . , . . , , , , , , , 107 Multiple regression statistics for spore germination under 10096 relative humidity moisture conditions predicted by temperature andtime. 107 One-way analysis of variance for arcsin of observed spore germination in carrot, border andonioncanopies. . . . . . . . . . . . . . ..... 111 Student-Neuman-Keuls multiple range tests for spore germination by Canopy-Habitat treatments.............. ....... .112 One-way analysis of variance and Student- Neuman-Keuls multiple range test for spore germination on July 23 between onion, carrot andbordercanopies. . . . . . . . . . . . . . . . . .115 One-way analysis of variance and Student- Neuman-Keuls multiple range test for observed infection versus time of host-pathogen interaction......................120 Observed and adjusted mortalities for onion maggot adults exposed to various E. muscae conidial doses in field cages. Mortalities adjusted for background infection using the method suggested by Abbott (1925). . . . . . ...... 123 Analysis of covariance for arcsin proportion infection by day of experimentation while controlling for the metric variable, cadaver density.................. ...... 124 LIST OF TABLES, continued Table 30. Table 31. Table A1. Table A2. Analysis of covariance for arcsin proportion infection by day of experiment while controlling for the metric variable, estimated sporedensity..................... Regression statistics for probit mortality versus logsporedose(lmillion). . . . - . . . . . . . . . . . Dose levels (presented as percent solution) for common onion production pesticides evaluated against ELantigua and non-target organisms.. . . . . . . Response levels and LD50 values for pesticide- induced mortality of several organisms in the onion agroecosystem.. . . . . . . . . . ....... Tables 01-29. 1978 and 1979 Entomophthora Table E1. fieldinfection................... Entomophthora muscae incubation data in Tables F1-4. Table G 1. Table H1. Table 11. Table .‘11. Tables K [-3. infected Hylemya antiqua adults.. . . . . . . . . . . . Entomgahthora muscae sporulation rate data. 0 O O O O O O O O O O O O O O O O O O O O O Spore production data for various temperatures. . . . . . Spore germination data for various temperature and moisture conditions. . . . . .. . . . . . . . . . . Spore germination data for various habitats. . . . . . . . Host-pathogen interaction data. . . . . . . . . . . . . Temperature and degree day accumulation data for Grant, Michigan (1977-1979). . . . . . . . . xi 124 124 159 164 182 192 195 199 204 209 210 211 Figure 1. Figure 2. Figure 3. Figure 4. Figure 5. Figure 6. Figure 7. Figure 8. Figure 9. Figure 10. Figure 11. LIST OF FIGURES Structural features showing levels of interaction within an onion agroecosystem design (object of control) and the monitored environment................... Conceptualization of the object of control for an onion agroecosystem using Entomophthora muscae as the reference point. . . . . ...... Functional block diagram of E. muscae-single hostlifesystem. . . . . . . . . . . . Conidial life stage of E. muscae, parasitic on the adult onion maggotTi-lylemya antiqua) ...... Phase contrast micrographs of E. muscae in Grace's insect tissue culture medium supplemented with 596 fetal bovine serum. . . E. muscae in Grace's insect tissue culture media Typical protoplast cultures of Entomophthora spp. provided by Dr. D. M. MacLeod. . . . . . . . Azygospore life stage of E. muscae from natural mycosis of onion maggot adults (Hylemya antiqua). . . . . . . . . . . . . Disease cycle associated with E. muscae mycosis of 1_-I_. antiqua. . . . . . . . . . . . Distribution of E. muscae in Michigan based on detection sampling of infected host flies. . . . Primary research sites monitored for population levels of E. muscae in the onion agrmcosystem. O O O O O O O ...... O O O O xii 19 22 23 27 31 33 36 38 40 43 43 LIST OF FIGURES, continued Figure 12. Figure 13. Figures 14-19. Figure 20. Figure 21. Figure 22. Figure 23. Figure 24. Figure 25. Figure 26. Figure 27. Aerial photograph of the Rice Lake vegetable production region (Grant Township, Newaygo County) with specific sites labeled for identification. . . . . . . Tractor-mounted suction sampler used for live collection of host flies. . . . . . . . . . . . E. muscae infection and host fly population levels in the Grant research field. . . . . . Live catch emergence traps used to capture newly emerging host flies. . . . . . . . . Differential habitat loading of onion maggot adults between field borders and the onion field throughout the 1979 season. . . . . . . . . Spatial pattern of onion maggot adults in a carrot canopy adjacent to an onion field during second generation (research field). . Spatial pattern of E. muscae-infected onion maggot cadavers in onion fields adjacent to carrot canopies and border areas during second generation. . . . . . . . . . . . Graphical representation of analytical method used to calculate total field mortality from pOpulation assessment information. Note that the mortality from all previous time intervals must be used to adjust each estimate. . . . . . . . . . . Total first generation onion maggot adult mortality and the estimated mortality caused byE.muscae. . . . . . . . . . . Total third generation adult mortality and the estimated mortality caused by E. muscae. O O O O O O O O O O O O O I O O Temperature-dependent relationship of the incubation period (percent of total development per day) of E. muscae in adult onionmaggots.............. xiii 48 50 55 63 63 66 70 72 72 82 LIST OF FIGURES, continued Figure 28. Figures 29—30. Figure 31. Figure 32. Figure 33. Figure 34. Figure 35. Figures 36-37. Figures 38-40. Figure 41. Premortality effects of E. muscae mycosis on the fecundity rate -of onion maggot females. 0 O O O O O O O O O O O O O O 0 Cumulative sporulation of E. muscae through a 12-hour period-for two representative sample dates (8/8/78) and 8/17/78). Cumulative sporulation of E. m_u__scae through a 12-hour period for this date pooled across sample dates. . . . . . . Estimated normal distribution compared to actual rate of sporulation. . . . . . . . . Estimated normal distribution fit to actual spore production response of E. muscae- infected onion maggot adults held under differing temperature regimes. . . . . . . Impact spore sampler used to measure E. muscae conidial dispersal from a point source. 0 O O O 0 O O O O O O I O O O O a. Pattern of E. muscae conidial dispersal on a horizontal surface 50 cm below a fixed release point. . . . . . . . . . . b. Sectional view showing the Poisson distributional pattern in a single direction. . . ...... . . . . . . E. muscae conidial germination as a function of time and temperature for both free water conditions (Fig. 36) and 10096 relative humidity (Fig. 37). . . . . . . . . E. muscae conidial germination in three canopy levels for onion, carrot and border habitats. O O O O I O O I O O O O I O O O E. muscae conidial germination through time of day for onion, carrot and border habitats. O O O O O O O O O O O O O O O 0 xiv 85 91 91 91 96 97 101 101 109 114 114 LIST OF FIGURES, continued Figures 42-44. E. muscae conidial mortality as a function Figure 45. Figure 46. Figure 47. Figure 48. Figure 49. Figure 50. Figure 51. Figure 52. Figure 53. Figure 54. Figure 55. Figure 56. of exposure time for three different sample dates during the 1980 season. . . , , , , , The impact of fungicide application on E. muscae conidial mortality (application time indicated by arrow). . . . . . . . . . , , Probit fly mortality versus log of sporulating E. muscae cadavers from field infection studies. , , , . . , . , , , , , Probit fly mortality versus log of estimated spore dose from field infection studies. . . E. muscae-single host life system. . . , , Flow diagram representing the programming structure of the free-body simulation model of the E. muscae-multiple host system. , , Observed and simulated (expected) onion maggot emergence for Grant, Michigan, 1980' O O O O O O O O O O O O O O O O O Observed and simulated onion maggot population levels for Grant, Michigan, 1980. Observed and simulated (expected) onion maggot emergence for Grant, Michigan, 1980' O O O O O O O O O O O O O O O O O Observed and simulated onion maggot adult population levels for Grant, Michigan, 1980. Simulated onion maggot adult pOpulation with the predicted level of E. muscae infection................ Simulated onion maggot adult population with the predicted level of E. muscae infection. 0 O O O O O O O O O O O O O 0 Simulation results showing the augmentation of E. muscae infection due to the inclusion of the seed corn maggot. . . . . . . . . . 117 117 125 125 129 133 O 134 134 134 134 136 136 136 LIST OF FIGURES, continued Figure 57. Simulation results showing the effect of fungicide application on E. muscae infection and the resultant fly population increase. . . Figure A1. Log dose-probit mortality regressions for _H. antiqua, E. platura, C. pallipes exposed to— tigrinia and A. malathion-treated barley......_..........-... Figure A2. Log dose-probit mortality regressions for _A. BallieeSO O O O O O O I O O O O O O O O 0 Figure A3. Residual mortality effects of malathion on H. antigua,_ H. platura, C. tigrinia, and A. pallipesadults. Figure A4. Pesticide-induced differential mortality between E. antiqua and A. pallipes. . . . . Figures D1-6. E. muscae infection and host fly pOpulation for fieldsl- 3, Grant, Michigan, 1979. . . . . Figures D7-12. E. mu__s_cae infection and host fly population levels for fields 4-6, Grant, Michigan, 1979. . 136 161 162 167 169 190 . 191 I. INTRODUCTION Entomogenous fungi have long been known to play an important role in regulating insect numbers (Steinhaus 1954). Over the past century, widespread attention has been drawn to natural epizootics, induced by fungal pathogens, and their potential for suppressing insect pests (Steinhaus 1956), although few controlled successes have been realized (Ferron 1978). One of the primary limitations has been the lack of knowledge associated with the dynamic interactions of the host-pathogen complex under field conditions. Epizooticological studies have been infrequent with few underlying causal mechanisms identified. Such has been the case with Entomophthora muscae (Cohn) Fresenius, a common pathogen of several adult dipterans. Since its first description (Cohn 1855), E. muscae has been noted inducing epizootics in numerous host species including the common house fly, Musca domestica (Thaxter 1888, Yeager 1939, Baird 1957, Miller and McClanahan 1959, Kramer 1971, Berisford and Tsao 1974, Wilding and Lauckner 1974). The incidence of E. muscae in field populations of Hylema antiqua (Meigen), the onion maggot (OM), has been well documented (Miller and McClanahan 1959, Perron and Crete 1960, Kramer 1971, Loosjes 1976). Although it is considered a major mortality factor of adult flies, virtually no research has been conducted on its field biology or its impacts on the host organism. During outbreak years, the OM has been known to cause serious damage to onions (80—9096) (Perron et a1. 1955, Metcalf et a1. 1962). In Michigan, endemic populations annually cause between 5—2096 loss despite intensive control pro- grams (Carruthers et a1. 1981). In response to problems associated with the present control strategies--OM insecticide resistance (Harris and Svec 1976), the economics of current pesticide applications, future pesticide development, and the associated environmental and health hazards (Metcalf l980)-an intensive program was initiated to study the population dynamics of the OM in the onion pest-crop agroecosystem (Haynes et a1. 1980). Due to its importance in the population dynamics of the OM, E. m has been included as a major component of that program. The goal of this study is to research the pathogen's role as it relates to the regulation of OM populations in the onion pest-crop agroecosystem. The "systems approach" (Churchman 1968, Tummala et al. 1975, Manetsch and Park 1977) was used as the underlying research philosophy of study which necessitated a cooperative, transdisciplinary-oriented project. Researchers from various disciplines (botany-plant pathology, economics, entomology, nematology, and electrical engineering-systems science) have been involved in deveIOping an overall research program interlocking the disciplines through the common base of systems modeling. The scope of this subproject has been highly augmented by the cooperative nature of this project within and beyond the Department of Entomology. Specific cooperation with Mr. G. H. Whitfield (PhD candidate, Depart- ment of Entomology) and Ms. J. 3. Pet (MS candidate, Department of Electrical Engineering and Systems Science) on various aspects of the population dynamics of the OM allowed an in-depth evaluation of the impact of E. m in the onion agroecosystem. This evaluation led to the experimentation necessary for developing the biological models for this host-pathogen life system. II. OBJECTIVES 1. Research the basic life system of Entomophthora muscae in the onion agroecosystem. 2. Determine the abiotic and biotic factors that influence the occurrence, viability, movement and mortality of E. muscae. 3. Utilize experimental procedures that produce the quantitative results necessary for the construction of a free-body model. 4. Deve10p a simulation model of the within season dynamics of the E. muscae life system in the onion agroecosystem and utilize this model to aid in the deveIOpment of pest management strategies. III. RESEARCH RATIONALE AND APPROACH CrOp protection in the United States has centered around chemical control supplemented by (1) cultural control methods, (2) chemically-oriented host plant resistance and (3) biological control strategies (McGovran et a1. 1969). The rationale for this approach is based on the notion that chemical controls are inexpensive (based on cheap energy), effective, and implementable with a minimum lag time. However, it has been estimated that approximately $18.2 billion or 3396 (3596 on a worldwide basis) of the crops produced in the United States are lost to insects, pathogens and weed pests despite extensive pest control operations--including an annual application of approximately 1.2 billion pounds of pesticides (Pimentel 1976). Also, during the past thirty years, insecticide usage has increased eleven-fold while preharvest loss to insects has more than doubled (van den Bosch 1978). These statistics indicate that present pest control strategies (chemical control) fail a significant amount of the time. This situation, coupled with the rapid increase in energy costs over the past ten years, casts serious doubt on the viability of the chemical control approach for future crop protection. By conventional definition the onion agroecosystem is one of a group of relatively insignificant components of the American agricultural system, repre- senting only 0.5% of total farm production. By defining the crop's significance from an ecological and energetic perspective, however, this system becomes extremely valuable from a research standpoint as it represents an agricultural microcosm that is the epitome of the state of American agriculture. The existing production system is the result of several decades of energy-intensive cropping practices that have been supported primarily by petroleum-based products. Pest control is no exception. Agrochemical pesticides dominate other management strategies (Haynes et a1. 1980). Insect pests, plant pathogens, and weeds are the three major pests in this ecosystem. On organic soils (e.g., Michigan, Ontario, and New York) the major crop pest is the OM. Efforts to control the OM with chemical agents are numerous, but, with few remaining exceptions, ineffective. The variety of agents used to control the OM represents the evolution of pesticides in the United States: pre-1930's - Natural mortality and cultural practices 1930's — Oil sprays and corrosive sublimates (Kendall 1932) 1940's - Mercurous chloride and oil bordeaux emulsion (Perron and La- France 1960) 1950's - DDT, Aldrin, Dieldrin, etc.: the chlorinated hydrocarbon complex (Doane and Chapman 1952) 1960's - Organophosphates: Parathion, Ethion, etc. (Guyer and Wells 1959, Perron and LaFrance 1962) 1970's - Thiophosphates: Dasanit, Dyfonate, etc. (Cress et a1. 1976). The constant substitution of insecticides does not stem from the develop- ment of more efficient pesticides, but rather to the loss of effectiveness or the failure of an insecticide to control OM infestations over an extended period of time. The reduced effectiveness of chemical control has been attributed to increasing pesticide resistance within OM populations (Brown 1958, Harris 1972, Harris et a1. 1962, Harris and Svec 1976, Harris et a1. 1963, Howitt 1958, Guyer and Wells 1959). Resistance to many chlorinated hydrocarbons (including DDT and the Aldrin-Dieldrin complex) was documented by Drew and Guyer (1958). This resistance stimulated research to find a substitute--the organophosphate (OP) insecticides. OP insecticides were highly effective during the first few years of use, as were the chlorinated hydrocarbons in the early fifties (Guyer and Wells 1959, Allen 1962). However, following the same pattern exhibited by the CH compounds, the effectiveness dropped dramatically with continued use (Harris and Svec 1976). Harris and Svec (1976) noted substantial increases in the levels of OM resistance in relation to many organophosphate and carbamate insecticides between 1965 and 1972, including several pOpulations from Michigan onion- producing regions. Dasanit, a thiophosphate which has been used extensively since 1970 as a soil insecticide for OM control in Michigan, now gives very low levels of control and is no longer being formulated for use on onions (Wells 1977). Carbofuran, a carbamate insecticide, has shown excellent control in field testing, but field resistance is expected to deveIOp (Harris and Svec 1976). Similarly, the synthetic pyrethroid insecticides produced excellent initial control of dipteran pests, but with continued selection pressure resistance deveIOps rapidly (Solomon 1981). Neither chemical type is now felt to offer long-term OM control, and they are no longer being actively pursued for OM registration. If future management is going to be an extension of present-day strate- gies, no long-term pest control can be expected. However, the failure of this approach does not lie exclusively with the use of chemicals. The philosophies and techniques that direct chemical use are equally responsible and need to be altered. Typically, agricultural chemicals are applied in a crop protection mode (often as "insurance" sprays) as opposed to a population regulation mode that utilizes information regarding the pest population and its dynamics. The crop protection philosophy accelerates the normal adaptive processes of the pest directly against the pest control method by applying continual selective pres- sure—even when unnecessary. This same philosophy has been carried directly over to biological control programs--creating a similar set of problems, the most apparent similarity being the deveIOpment of host immunity or resistance to biological control (Ratcliffe 1959, Messenger and van den Bosch 1964, Pimentel and Stone 1968). Less apparent, but probably more important, is the lack of knowledge concerning the true operation of the control system. This lack of understanding allows the system to establish its own unpredictable trajectory, which leads to an unmanageable outcome. Numerous examples of parasite release are cited in the literature where little or no prior knowledge of the host-parasite dynamics existed. The long-term results of such release programs are quite variable with only a few classic examples giving adequate control (the vedalia lady beetle- cottony cushion scale system or Chrysolina-Klamath weed system). Documen- tation of biological control attempts in Canada show only a 10% measureable economic success rate associated with introduced parasitoids and predators (Beirne 1975). Viewed in retrospect, multiple-species parasite releases indicate that direct and indirect interference between competing parasites often lowers overall mortality rates in the host (Turnbull and Chant 1961, Zwolfer 1963, Gage 1974). Actually, any type of mortality (parasitism, predation, or that caused by the abiotic environment) at the right point in a life cycle can raise the overall host survival by relieving the density-dependent mortality stresses of a later life stage. The chemical control philosophy (Doutt and Smith 1969) in biological control has recently focused on the field of insect pathology. A large portion of present microbial research is being directed toward developing spray tank- compatible microbes with long shelf-lives for commercial use. Applying insect microbes in this manner presents no direct problems and may prove to be a valuable method for future pest management programs. However, the problem arises because the unilateral control phIIOSOphy (the use of a single control technique) long associated with chemical control is being directly carried over with other spray technology. Not only are microbes being applied in a unilateral fashion, but they are typically used without an adequate understanding of their Lopulation biology and host interactions. Some leaders in the field (Allen et a1. 1978) have recently become aware of this trend and are working to establish a more holistic balance within the discipline. An alternative approach to direct chemical or biological control using similar philosophies may be found by studying the pOpulation dynamics of the pest and the natural mortality factors regulating their numbers. In this way, directed pest mortality can be interjected into the production system when necessary with the least amount of disruption to other components within the agroecosystem. If the aforementioned problems are to be avoided, understanding these mortality factors and utilizing them in a multifactorial manner should be the basis for deveIOping and implementing specific pest management strategies. Control strategies for a single pest may act as stimuli not only for the target organism but for many other organisms within the agroecosystem. Management practices other than pest control also exhibit similar impacts on non-target portions of the agroecosystem; therefore, research programs dealing with agricultural management are, or should be, transdisciplinary in nature (Jantsch 1970, Bird 1979). Even if transdisciplinary programs exist, they may provide only short-term solutions unless the dynamics of the overall production system are considered. Such an approach to agroecosystem design and management (Haynes et a1. 1980) is much more complex than what agricultural scientists have dealt with in the past. This complexity necessitates an overall methodology to guide and manage the development and implementation of such projects. Although new to agricultural scientists, the philosophical and practical basis for this methodology has been developed and used for some time in the field of systems engineering. The problem-solving process commonly referred to as the "systems approach" (Churchman 1968, Tummala et al. 1975, Manetsch and Park 1979) offers a highly structured format within which complex problems are considered, needs are identified, alternative solutions are evaluated, and final programs are implemented. The systems approach originated from the military operations research programs of World War II and deveIOped rapidly in the postwar industrial sector (Churchman 1968). Originally, the primary function was designing and imple- menting physical or information processing systems to meet specific user needs. Its utility in an analytical mode soon became evident. This analytical mode of the systems approach gave biologists the ability to consider and evaluate the complex relationships associated with ecological problems. Ecological systems belong to a special subset of real world problems where the system structure is pre-existing but unknown. Scientific experimenta- tion is the primary methodology used to identify the underlying structure and the functional processes linking that structure. Mathematical modeling, a com- ponent of the systems approach, has been used successfully to guide and link such experimentation, thus helping to deveIOp a more comprehensive understanding of the system and its operation (Patten 1971, Tummala et a1. 1975, Ruesink 1976). The systems approach, however, is much more than just applying mathematical modeling to complex problems. It is a problem-solving methodology that begins with a set of objectives and, through a structured process, results in an operational system capable of satisfying original or slightly modified needs based on resource limitations and constraints (Manetsch and Park 1977). The problem-solving process is composed of several interconnected decision-making phases including (1) feasibility evaluation, (2) abstract modeling, (3) implementation design, (4) implementation, and (5) operation. Each phase is iterative in nature and composed of numerous sub-phases (Manetsch and Park 1977). For ecological problems, after carefully outlining objectives, the emphasis 10 is on system identification, the first step of feasibility analysis. Conceptualiza- tion and system-environment dichotomization are the primary processes of this phase which allow the researcher to specify what components are to be considered, their levels of interest, and how they interact with the environment (variables external to the system). The conceptualization process begins with a general description of components in biological terms and ends with the transformation of this model into a functional block diagram that represents the actual structural linkages between components. The second phase of system identification is the parameterization of the functional block diagram, e.g., evaluating the present scientific understanding of the biological components and processes involved in the system. In essence, this is a qualitative and quantitative literature review from which actual system transfer functions can be formulated. In most cases, system conceptualization and parameterization identify areas where data gaps exist and additional research is necessary, which leads to the next sub-phase of feasibility analysis: problem formulation. At this stage, it is clear what research components are necessary to complete the biological model. If no resource limitations exist, research experimentation can be used to identify the missing data for the transfer functions. Normally this is not the case, and the original objectives with the given resource constraints must be reevaluated. This evaluation is the essence of any feasibility analysis and exemplifies the iterative nature of the systems approach. Once objectives, constraints, and system understanding have been bal- anced, model parameterization can be completed and abstract modeling begun. Abstract modeling involves determining model type (e.g., simulation model, 11 mathematical model, stochastic model, etc.), computer implementation, program verification, model validation, and model use (including evaluation of control variables and sensitivity analysis of system parameters). The final results or output of abstract modeling is an identified set of plans for implementation or a set of further research objectives to clarify additional components found to be of importance in system response. The systems approach is a highly structured system that is organized to aid in rational planning and informed decision-making. The two prominent attributes of this approach are: 1) It overtly seeks to include all factors that are important in arriving at an acceptable problem solution, thus promoting a transdisciplinary philosophy. 2) It makes use of qualitative and quantitative models including com- puter representations when deemed appropriate (Manetsch and Park 1977). The systems approach has been used as the overlying methodology in the development of the onion pest-crop agroecosystem project of which this study is a sub-project. The first two phases of the systems approach (feasibility analysis and abstract modeling) were used throughout this study to direct research goals and specific experimentation relating to the population biology of E. m and its major hosts in the onion pest-crop agroecosystem. It is hoped that this study will provide system alternatives to be utilized in the overall onion agroecosystem project, will add biological information to the field of epizootiology, and will provide insight into the use of the systems approach in biological research. 12 IV. BACKGROUND The OM is the major insect pest component of the onion agroecosystem. Records indicate that the OM has been a major pest of onions for over 100 years in Europe and the United States (Fitch 1867). The larval stage feeds directly on the onion bulb. Although the majority of the damage occurs from direct feeding by the larva, the adult is also a key factor in the spread of the damp rot bacteria, Erwinia corotovora (Jones) (Gorlenko et a1. 1956). Scott (1969) assembled an extensive bibliography for 11. m which covers most of the published material with the exception of taxonomic citations and actual spray calendars. Several authors (Doane 1953, Tozloski 1954, Workman 1958, Ellington 1963, Loosjes 1976, Carruthers 1979, Ellis and Ecken- rode 1979) have reviewed and collated much of the important biological literature on E. W. Michigan typically has three distinct generations of _H. 99333 per year which overlap due to the longevity of the adult flies. The adults emerge from overwintering pupae in late April or early May. The exact date and length of the emergence period depends on temperature and depth of the overwintering pupae in the soil. As the soil profile warms, the pupae break diapause. The pupae closest to the surface emerge first. Developmental zero for the diapaused pupae is close to 40°F (Eckenrode, Vea and Stone 1975). Newly emerged adults are soft-bodied and require a day to dry and harden. At this time flies emigrate to field borders and feed on pollen. The preovipositional period lasts about 10 days (Theunissen 1976) varying slightly with microclimatic fluctuations (103 degree day, base 4.4°C) (Robinson and Zurlini 1979, Carruthers 1979). When gravid females move back into the onion field, they lay their eggs on the soil surface around the base of the plant and in the leaf axils. After 13 eclosion, the newly hatched, first instar larvae move into the base of the onion bulb and feed. This disrupts the tissue in the onion bulb, creating acute water stress. Necrotic lesions then Open on the bulb surface which allow microorganisms, primarily soft rot bacteria such as E. corotovora, to enter. The microorganism development increases the rate of- tissue degeneration within the onion and increases symptomatic damage. Doane (1953) described OM soft rot damage symptoms. Onion damage is characterized by flaccid leaves, followed by leaf tip yellowing, and then extreme foliage dehydration. With prolonged damage, the bulb can be completely consumed by the OM soft rot attack, leaving only the desiccated leaf tissue and the outer bulb sheath. At this point the maggot moves into the soil and pupates or migrates to succeeding onions until fully developed (Workman 1958). In the early part of the growing season, one OM may consume up to 28 onion seedlings in the loop stage (Workman 1958), resulting in a high rate of plant damage and mortality. As the season progresses and bulb size increases, one onion can support many more maggots (Loosjes 1976, Carruthers 1979). Kendall (1932) reported that 96.6% of second generation flies reinfest previously infested onions. Perron (1972) discussed several parasitoids and predators of the OM that were present in non-pesticide, organic soil plots in the Ste. Clotilde region of Quebec (1951-1966). A staphylinid beetle, Alerochara bilineata (Gyllo), was most effective. (A. bilineata, as a larval parasitoid, is capable of destroying 20% of the overwintering pupae (Perron 1972). It becomes a predator as an adult.) A braconid wasp, Aphaereta pallipes, was the second most effective parasitoid, capable of destroying 12% of the overwintering pupae. Several other parasitoids and predators were listed along with a short evaluation of each. 14 Ritcey (personal communication, 1979, University of Guelph) stated that less than 10 parasitized individuals were observed from several thousand field- collected pupae in Ontario commercial production areas. This same condition is found throughout Michigan, with the exception of non-pesticidal research plots. Heavy pesticide use (soil treatment at planting and weekly foliar applications) has effectively eliminated the natural enemy complex of the onion maggot from these commercial production areas (see Appendix A). Entomophthora muscae (Cohn) has been identified as a naturally-occurring fungal pathogen of E. antigua (Miller and McCallahan 1959, Perron and Crete I960, Krammer 1971) and is presently the only known natural biotic control agent that has a major impact on populations throughout Michigan's commercial onion production system. MacLeod et a1. (1976) summarized Entomophthora species with muscae-like conidia; life histories, species identification, and a thorough bibliography are included. Entomophthora muscae is believed to overwinter in soil via the environ- mentally-resistant, thick-walled azygospore. In the spring, by a still unknown stimulus-response mechanism, the azygospores germinate in the soil possibly producing germ conidia (Tyrrell and MacLeod, 1975) which cause primary infection of the host. This initial infection takes place in either the developing pupal stage or the newly emerging imago as it moves to the soil surface. After initial contact, the pathogen penetrates the host cuticle and enters the host's body cavity, initiating the disease cycle. Upon entry into the haemocoele, the hyphae begin fragmenting into hyphal bodies. This vegetative stage multiplies rapidly by a budding process and completely fills the host's body cavity before death. An exoenzyme, secreted into the haemocoele, digests most of the host's internal tissue. Eventually, each of these hyphal bodies produces a conidiophore 15 which penetrates the host integument, then produces a single conidium at its tip (MacLeod 1963). The conidia are forcibly discharged with a mucilaginous coating that allows the conidia to adhere to any object they contact. If the conidium contacts a suitable host, it will germinate, produce a germ tube and penetrate the host cuticle, thus initiating a secondary infection cycle. This cycle may be repeated numerous times throughout the growing season. The pathogen is known only to be transmitted between adults; no translar- val transmission has been noted through experimental procedures (Baird 1957). In the late stages of disease deveIOpment, the host's behavior is modified such that dying flies orient to elevated loci (tips of grass leaves, small plants, or other material over 1/3 m above the ground) where they attach themselves to the substrate by salivary secretions, a fungal byssus, and a characteristic body and leg configuration (Berisford and Tsao 1974). Host death and fungal sporulation immediately follow. This peculiar behavior modification just prior to spore discharge characterizes many Entomophthora species-host interactions and enhances fungal inoculum dispersal (Steinhaus 1954, MacLeod 1963). Under still undetermined conditions, conidial formation may be interrupted by a sudden change in morphology (Dustan 1924, 1927) and physiology (Speare and Colley 1912, Ullyott and Schonker 1940) of some of the hyphal bodies. Instead of producing conidiophores, they produce either zygospores or azygospores (MacLeod 1963). With azygospore production in E. antiqua, the fly behavior modification differs from that of conidial production. The flies do not orient to or attach on any substrate, instead they fall to the ground when dead. The insect abdomen, which is completely filled with the thick-walled spores, fragments and releases the spores into the soil where they either germinate immediately or lie dormant as in the overwintering condition. Epizootiological data concerning E. muscae in relation to E. antiqua are 16 lacking, with little, if any, known quantifiable relationships. Numerous qualita- tive assessments of the dynamics of E. M with various hosts appear contradictory as, i.e., epizootics have been typified as occurring only under wet conditions (Gough 1946, Kramer 1971, Petch 1934, Roubaud 1922, Yeager 1939) and as occurring in warm dry conditions, in field (Perron and Crete 1960) and laboratory conditions (Baird 1957). In a more quantitative study, Wilding and Lauckner (1974) found no clear relationship between weather variables and disease incidence in three Entomophthora species, including E. muscae, attacking the wheat bulb fly in England. A significant positive correlation was found between disease levels and host densities. High OM densities have been noted during epizootics (Miller and McClanahan 1959, Perron and Crete 1960) although moderate population levels have also been noted with epizootic fungal conditions (Loosjes 1976). Clearly, disease monitoring and gross correlation with abiotic and biotic environmental factors cannot explain the observed response of such a complex association. Only when detailed analyses are coupled in some structural framework or model, can the operative components of such a system by synthesized, understood, and used effectively in a management program. Classically, insect pathologists and plant pathologists have emphasized the importance of analysis that explores into the microsc0pic depths, factors that cause variations in host-pathogen interactions. Until recently, little effort has been placed on synthesizing this material to a system level. Without such synthesis, the usefulness of in-depth research is extremely limited and possibly misdirected. One of the first comprehensive models synthesizing an insect- pathogen relationship was developed by Kish and Allen (1978). Their approach recognized intricate relationships while keeping the system as a whole in mind. 17 The effect was system-directed analyses that were capable of being linked to provide an overall evaluation of the experimental system. V. SYSTEM IDENTIFICATION The first step in system identification is dichotomizing the universe of concern into the system of interest (object of control) and its environment. This process includes defining controlled and uncontrolled factors that affect the object of control and identifying the structural components of the system and their causal linkages (Manetsch and Park 1977, Tummala and Haynes 1977). The object of control in Figure 1 represents the structural features of an onion pest- crop agroecosystem and the levels of interaction within that system (Haynes et a1. 1980). The onion pest-crop agroecosystem is characterized as a set of interacting components (such as the onion plant, onion maggot, onion leaf blight, etc.) connected through several orders of interaction. First order interactions are the organisms that directly attack or damage the onion (reference point). Historically, agricultural research on crop pests has dealt principally with direct pests or first order interactions. As the research emphasis moves away from this level, it is more difficult to assess the relationships as they affect the reference point. As pointed out earlier, control strategies for any single component stimulate many other components within the same system, thus it is necessary to obtain a more comprehensive view. For example, several different fungicides, such as mancozeb or chloro- thalonil which are recommended for application on onions for protection from the direct attack of Botgtis squamosa, have an undetermined influence on Gliodium roseum, a fungal pathogen of Botrytis; Entomophthora muscae, the reference point of this study; Entomghthora sphaerosperma a fungal pathogen Figure 1. 18 Structural features showing levels of interaction within an onion agroecosystem design (object of control) and the monitored environment. Numbers refer to footnotes containing specific references (Appendix B) (from Haynes et al. 1980). l9 IIIIIIIll'trIIIIIIIIlllliliiliIFlllIilIr IIIIII u 0‘ v- 0 .- 3 i l j.--_____.___.____.__ II'IIIIIIT Jog—.200 no .5260 rwgm showha $33-48.: 1.4183 3.8833 8&3 15¢ :23 .3!€85.- 63:85 .33 anneal-9225... 98 30% EMUISC 9.8 9.333 g - .35 8.6 g 9H3 gamma god“. 0:05 Qua»; .rzws. ZOE>Zm Quack—202 20 of onion thrips; and Arthrobotrys oligospora a fungal predator of the northern root-knot nematode, Meloidogyne hapla, thus the resultant impact of a fungicide treatment on an onion crop is unclear. The goal of the systems approach is to quantify the structural linkages between these biological components so that they may be manipulated in mathematical form to test the effects across levels of interaction. The key to developing an effort of this type is to conduct the necessary research to provide the linkages or transfer functions between each of the components. In this way, individual components or groups of components can be researched in free-body form and then linked together to represent the overall system. A scaled down reorganization of the object of control of Figure 1, with E. muscae as the reference point (Figure 2) creates a more explicit picture of the linkage between E. muscae and other organisms within the onion agroecosystem. To develop an adequate understanding of E. muscae dynamics in the onion agroecosystem, the seed corn maggot (SCM), E. platura (Meigen) must be considered in both research and model form even though it is presently insignificant as a direct pest to the onion plant. Keeping the overall system in mind, a detailed functional block diagram (Figure 3) was constructed showing the relationship between E. muscae and a single host. Wherever possible the transfer functions have been indicated. Initially, specific data concerning E. muscae was very limited; much of the information has been gathered during this study or taken from other Entomoph- m species. In essence, Figure 3 provides the experimental design and literature review of this research project. Each component is documented by the experimental study that provides either the necessary data for implementation or the experimental techniques capable of acquiring that data (numbers refer to 21 Figure 2. Conceptualization of the object of control for an onion agroecosystem using Entomophthora muscae as the refer— ence point. 22 _ _ mmsuam ]_ _ cum: _ _ _ _ _ _ _ _ _ _ was 1_ 23% was? ._ 282528 _. 58% _l _ _ _ _ 5.8 83 _ _ Bommomofio a ma; 1&3 ~33... £6..th9:5;A _ mcxmouwopmizl— _ :5 .p P a _l _ 2223235 _ m0h= s + I “89:0...- >(a-D 08...»!D H ‘ =3: : :32 .m unswam 24 literature in Appendix C). Space, an added dimension not directly depicted in Figure 3, is extremely important in epizootiology, as many of the transfer functions are spatially dependent (i.e., the influence of spatially distributed habitat on microclimatic factors such as temperature and relative humidity, as well as the spatial distribution, thus population overlap, of the host and pathogen). The concept of spatial orientation of pathogens has been considered by numerous researchers primarily in plant pathology. Much of the work has centered on Spore dispersal and disease development in host crOp systems (Ingold 1971, Zadoks and Schein 1979). Insect pathogens present a somewhat different situation as the host may be extremely mobile. Little or no work has been done to evaluate the spatial interactions of such a system, although its importance in disease development, thus management, seems critical. VI. PROBLEM FORMULATION Actual data on E. muscae in the onion agroecosystem are relatively nonexistent other than some documentation of its presence during epizootic conditions for specific years and locales. Basic life history information which defines the actual life cycle and host interactions in the onion agroecosystem is of primary importance. It is also necessary to understand its natural distribution, abundance and impact on host populations. Further detailed experimentation concerning parameter measurement and estimation, as described in Figure 3, is also of extreme importance in defining the exact functional relationships between E. muscae, E. antiqua, E. platura, and the environment within the onion pest-crop system. Explicitly, research needs can be separated into three major categories: 25 a) Identifying unknown system components and their linkage, including disease etiology and definition of the basic life system. b) Studying the response of the natural system, including detection sampling to determine the presence or absence of E. muscae in measureable levels throughout Michigan onion production regions; regional surveys to determine relative abundance, distribution and host identification for different production areas; and field-level sampling to determine the impact of E. muscae on limited host populations and the effect of host and pathogen spatial patterns. c) Conducting specific experiments to parameterize the functional relationships such as the effects of environmental parameters on spore germination and infection, pathogen deveIOpment within the host, host response, and the other relationships graphically described in Figure 3. VII. EXPERIMENTAL PROCEDURES AND RESULTS A. Etiology and Disease Life System Characterization of the natural disease cycle, including identification of the pathogen, specification of its life stages and their relationship to the host organism, is basic to understanding their biology. As discussed by Matanmi and Libby (1975), few investigations have clearly defined host-pathogen interactions as they occur under natural field conditions. This certainly is the case with the infection of root maggot adults by E. mu_sge, even though its importance in pest regulation has been realized for over two decades (Miller and McClanahan 1959, Perron and Crete I960). 26 METHODS: Onion maggot adults, naturally infected with a fungal pathogen later identified as E. m, were first collected from an onion field near Grant, Michigan (Grant Township, Newaygo County), in the spring of 1977. Microscopic examination revealed masses of hyaline, multinucleate ellipsoidal conidia, with a papillate apex and flattened base, protruding from the highly distended abdomen of the flies. Conidiophore penetration was limited to the membranous ventral aspect of the abdomen and dorsally in the intersegmental areas (Figure 4, A-D). One hundred conidia were measured microscopically for width and length and used for pathogen identification (MacLeod et al. 1976). Field-collected adult flies were acquired from the original collection site by sweepnet and taken to the Insect Pathology Research Institute, Sault Sainte Marie, Ontario, Canada. With assistance from Dr. D. M. MacLeod and Ms. M. A. Welton, isolation techniques (Welton and Tyrrell 1975) as described below were followed. Newly sporulating cadavers were attached to the inner lid of a sterile 100 mm petri dish via sterile petroleum jelly. The lid was placed over its original base containing a sterile distilled water-soaked filter paper used to maintain saturated atmospheric conditions within the petri dish. Sporulating conidia were collected over five minute intervals in 5 ml of Grace's insect tissue culture media (supplemented with 5% (v/v) heat denatured, fetal bovine serum and a wide spectrum antibiotic, gentomycin (50 micrograms/ml)), held directly below the sporulating cadaver in a 60 mm diameter petri dish. The medium containing the conidia was then transferred to standard 30 ml tissue culture flasks to which 10 ml of additional media was added. The flasks were incubated at laboratory temperature (ca 21°C) under ambient lighting. At two week intervals, 5 ml portions of the isolate were added to 10 ml of fresh tissue culture media in sterile culture flasks to maintain a continuous supply of the pathogen. 27 Figure 4, AF. Conidial life stage of Entomophthora muscae, para- B-C. sitic on the adult onion maggot (Hylemya antiqua). Female fly infected with E. muscae in the process of sporulation. Note the conidiophore penetration only on the membranous ventral aspect of the abdomen and dorsally between the tergites (fungal growth, whitish area on abdomen, 5x . Lateral views of a male fly infected with E. muscae in the process of sporulation. The area of fungal growth (darkened area in the SEM) is again restricted to the membranous areas of the abdomen (B, 10X and C, 45X). Conidiophore penetration in the intersegmental area of the abdomen. Note the singularly borne conidia in various stages of development from the immature fingerlike conidiophore to the mature bell-shaped conidia (235X). Single E. muscae conidium attached on the thoracic region of an onion maggot adult. The conidia are forcibly ejected from the conidiophores and adhere readily to most substrates. Note the mucilaginous material attached to the base of the conidia and the point of separation from the parent conidiophore. The SEM gives a different perspective, showing a lower separation point, than is seen in light micrographs (Fig. 5A) (1000X). Two E. muscae conidia attached to an abdominal setae of a fly showing the adhesive nature of the mucilaginous protoplasm attached to the conidia (1000X). 28 V q :1; “(if-‘7'. MIMI 29 To insure a stable, long-term supply of the isolate, a subculture was also placed in liquid nitrogen storage at the Forest Pest Management Institute (FPMI), Canadian Forest Service, Environment Canada. One hundred laboratory-reared OM adults were injected in the ventral aspect of the abdomen with 2 microliters of the above culture (five days after culture transfer) using glass-drawn needles. Two control treatments, one with 2 microliters of sterile tissue culture media injected and the other with no injections, were maintained. Newly emerged teneral adults were collected using live catch emergence traps (see section B.3) placed around areas of known OM infestation. These adults, along with adults acquired by sifting pupae from adjacent soil, were held in the laboratory and evaluated for primary E. m infection. Microscopic examination (both SEM and light) were used to characterize the fungal stages _i_r1 yi_v_q. RESULTS: Based on conidial dimensions (mean length: 22.7 1 1.4 pm, mean width: 17.8 i 1.1 um) and shape (Figures 4, E-5A), the pathogen was identified as E. @2933, using the keys provided by MacLeod et al. (1976). Germination of the conidia released into the insect tissue culture media was noted approximately 9-12 hours after transfer to the culture flasks. A variety of germination responses were seen including the production of secondary conidia, many times with elongate, branched germ tubes, germ tubes developing directly from the primary conidia, and conidia-producing vegetative clusters with branchlets growing out in a radial pattern (Figure 5, B-C). Vegetative growth developed readily in the tissue culture media, producing small mycelial- like colonies (Figures 5, D and 6, A, E—F). Conidial formation was never noted in the tissue culture media although, several months after isolation, hyphal 30 Figure 5, A-F. Phase contrast micrographs of Entomophthora muscae in Grace's insect tissue culture medium supplemented with 5% fetal bovine serum. Single conidium just after ejection from conidiophore. Note the mucilaginous protoplasmic material surrounding the conidium and the apparent separation point from the conidiophore (contrast with the SEM of Fig. 4, E-F, 410X). Conidia in the process of germination in tissue culture media. Note the long singular germ tube arising from the secondary conidium in B. The formation of secondary conidia seem to be the normal mode of germination although some conidia produce germ tubes directly, as in C. The germ tubes are found both singularly and branched independent of secondary conidia formation (B + C - 410X). Initial hyphal growth develops from branchlets radiating from the germination point. Hyphal fragments or buds are broken off of the original colony and released into the media (410X). Several months after the initial isolation, numerous hyphal fragments divided, forming spherical bodies, possible precursors of the thick-walled azygospores (410x, 820X). 31 32 Figure 6, A-F. Entomghthora muscae in Grace's insect tissue culture media (A—F, 300X7. A, E-F. Typical mode of E. muscae growth in tissue culture flasks. The pathogen grows in colonies that are mycelial-like although it tends to form spheres (hyphal bodies) which continue to bud. B-D. E. muscae held under slight pressure induced by a coverslip tends to develop thread-like forms similar to the protoplast phase of other Entomophthora spp. (see Fig. 7). 34 fragments divided and formed spherical bodies, possibly precursors of thick- walled resting spores (Figure 5, E-F). When vegetative hyphal material is placed under slight pressure (induced by the addition of a cover-slip) the fungal material tends to develop thread-like forms (Figure 6, B-C) similar to the protoplast phase of other Entomophthora spp. (Figure 7, A-D) (Tyrrell and MacLeod 1972, Tyrrell 1977, MacLeod et al. 1980). 1'1 y_i_y_9_ develOpment of E. m is still not well understood, and the presence of protoplasts in the hemolymph of host flies has not yet been identified, although microscopic examination of infected hosts has revealed identifiable hyphal material only the day prior to death. The lack of hyphal bodies or fragments within the abdomen of the host suggests the existence of such a protoplasmic stage during the early stages of E. _r_n_u_sc_a_e infection. Further research is necessary to clarify its mode of i_n 112 develOpment. Injecting OM adults with tissue culture containing the above isolate in a vegetative state resulted in only six flies dying of fungal mycosis (four in the conidial state and two in the azygospore state (Figure 8, A-D)). This mycosis, although low in percentage, was highly significant as it completed the require- ments associated with Koch's postulates. No explanation presently can be given for the low success rate of infection through injection, although subtle changes in osmotic pressure, pH, and nutrient content of the microhabitat are sure to play a distinct role. Infection of teneral adults at the time of emergence is common, although the habitat and its microclimate exert major effects on the observed infection levels (see section B.3). Host flies, obtained by collecting pupae from areas of known E. MES. presence, never became infected when allowed to emerge through laboratory-sterilized soil (N=512), suggesting that host-pathogen inter- 35 Figure 7, AD. Typical protoplast cultures of Entomophthora spp. provided by Dr. D. M. MacLeod. Note the similarity between these protoplasts and the forms produced by E. muscae-induced by the addition of slight pressure. 36 37 Figure 8. Azygospore life stage of Entomophthora muscae from natural mycosis of onion maggot adults (Eylemya antiqua). Female fly containing E. muscae azygospores internally in the abdomen. Note the dark coloration and shriveled nature of the abdomen (7.25X). Azygospores of E. muscae imbedded in the internal abdominal tissue of the host fly (700x, l600X). Azygospore isolated from heavily inoculated muck soil via centrifugation-flotation techniques (2275X). '3 -) C3 39 actions (presumably azygospores or germ conidia originating from azygospores) infect the fly in the soil between the pupal case and soil surface. The overall disease cycle (Figure 9) consists of host emergence in the spring. At this time, a certain proportion are infected (primary infection) from interaction with overwintering azygospores, possibly in the form of germ conidia (Tyrrell and MacLeod 1975). Following the incubation period, host death occurs, and either conidia or azygospores result. Conidial production results in host fly attachment to elevated loci, active spore liberation, germination and, if successful, secondary host infection. If azygospore production occurs, the flies die on the ground, the abdomen turns black, becomes very brittle, and then fractures open, releasing azygospores back into the soil. The cycle is continuous throughout the season, although periods occur when E. mu_sgag infection is below detectable levels. Further details concerning the disease cycle will be discussed in subsequent sections, outlining the phenology and spatial relations of the host- pathogen interactions. B. Response of the Natural System 1. Detection Sampling The impact of E. muscae in the onion agroecosystem is based on its population dynamics and interaction with various host species. Detailed analysis of the dynamics of this system have been conducted in three primary research sites within Michigan (see section 8.2). The full impact on the Michigan onion Production system depends not only on localized population interactions, but also on the occurrence of the pathogen throughout the state. METHODS: To evaluate the natural distribution of E. muscae, a state- wide detection survey was conducted during the spring of 1978 throughout all 40 Basic Life Cycle of ‘Entomopthora Muscae” (Pathogen of the Onion Fly) PATHOGEN DEVELOPI N6 WITHIN THE ONION FLY é; seconoAav .33? i; _ SPORE mooucr ION ..... .uuo ..... t... ..... ----- ..... ccccc ..... ..... ccccc ............. ttttttttttttt ............ .............. ------------- .......... ..... ............... n... ........... nnnnnnnnnnn uuuuuuuuuu nnnnnn \ 2‘ (I‘m ‘ PAW-‘3 I0 . AZYGOSPORES BEING PRODUCED INSIDE THE ONION FLY ONION FLY INFECTED AT TIME OF EMERGEN SPORES DEPOSITED IN SOIL FOR OVERWINTERING ac. , m. Figure 9. Disease cycle associated with Entomophthora muscae mycosis of Iii/lemya antiqua. 41 major onion production areas within Michigan. Pest management field assistants were trained to recognize and collect adult dipteran cadavers exhibiting the characteristic death patterns associated with E. m mycosis (Berisford and Tsao 1974). The specimens collected in the survey were preserved in alcohol, labeled with date and locale, and forwarded to the laboratory for identification of both host and pathogen. Host identification was made using common Dipteran keys (Huckett 1971) and comparative specimens from the Michigan State University insect museum with assistance from Dirk Spillemaeckers. The keys provided by MacLeod et al. (1976) were used for pathogen identification with sample material being verified by Dr. D.M. MacLeod of the Forest Pest Management Institute, Sault Sainte Marie, Ontario, Canada. RESULTS: Twenty-six Michigan counties were sampled during 1978 in the southern lower peninsula. Entomophthora muscae was found naturally occurring in every county sampled including the ten counties where most of Michigan's onions are produced (Table 1, Figure 10). In onion production areas, E. muscae was identified in association with the OM and the SCM. From counties where samples were collected in the absence of onions, the SCM was the primary host identified, although the cluster fly, Pollenia rudis, was also found as a host in six counties, and Coenosia tigrina, the tiger fly, was found in two counties. Numerous other host species are known to be associated with E. muscae, although this survey found no other hosts, probably due to the type of habitats sampled and the level of effort exerted. 2. Field Level Monitoring for E. muscae Infection Entomophthora muscae is known to cause substantial mortality in popula- tions of both the OM (Miller and McClanahan 1959, Perron and Crete 1960, 42 amusm am> OHoumaa mummnsmfinm Omaqawm damage owmamz aowoxmnz aamuunoz condo: omamauq x acumen oxmq uaom OONmEmHmM x domxumh OHGOH x annweH some: oomoaou N aoumm x couaaao ammo anonacu dowuuum huumm N awwuaa< xxx XXNNNNNNNXNNNNNNNNNXNNNNNX N .mflmflmwflm.mmndeOu. wwwsu NHamHHom muauman whamahm mawwuam Omaunmm mamz % a: nuaam uucz u no .mmwumnm umo: wufimfiucmvfi :ufis voumfia .ho>u=m cowuoouuc cabana OHOSuAmOaOuam wan ea voaaawm moauasoo .H manna 43 nuance coumm such x09: 3m: ucmuu .Emumhwouoouwm cowao usu Ea unease wuonunmoaOucm mo OHO>OH aOfiumHOOon How wououanoa mouam nuuwumuu humaaum .HH duamfim .muHHm umos wououmafi mo wcaaaamm Goau quuuv no woman amwficufiz aw omomaa uncannmmaOuam mo coausnfiuumfin m 3!: ad 3 .I- 3!: .In g an M a .g % shag-am 4m D §O~QIOO «0: .OH unawfim 44 Loosjes 1976) and the SCM (Beresford and Tsao 1974), although no detailed assessment of infection level or its effect on the population dynamics of these hosts has been conducted. The response of the natural system under a variety of conditions and a more thorough understanding of its operation is essential if E. m is to be used in pest management programs or even if such programs are deveIOped independent of its effect. METHODS: To determine the incidence of E. m infection in host fly populations, three Michigan onion production regions were monitored during 1978 and 1979 (Figure 11). The primary study site was located in the Rice Lake vegetable production region (Grant Township, Newaygo County) where commer- cially maintained fields, adjacent border areas, and specific research plots were monitored (Figure 12). Because commercial onion fields are typically maintained using large amounts of chemical pesticides for control, a 4-acre field was acquired for research purposes and kept free of foliar insecticides and fungicides. The second study site was located on an organic onion production farm near Eaton Rapids, Michigan (Eaton Rapids Township, Eaton County). This area was maintained completely free of any chemical inputs including pesticides and commercial fertilizers. Pest control was conducted using the standard operating procedures of the grower: primarily cultural control tactics (delayed planting, cultivation, rotation, Strip-planting, etc.). The third study site was located at the Michigan State University, Organic Soils Research Farm near Laingsburg, Michigan (Bath Township, Clinton County). A 4-acre research plot was planted with onions, maintained pesticide-free, and allowed to develOp naturally with no attempt to control pest populations or damage. Figure 12. 45 Aerial photograph of the Rice Lake vegetable production region (Grant Township, Newaygo County), with specific study sites labeled for identification. (R = primary research site; BR = adjacent nonagricultural research site; * = field laboratory; and 1-7 = commercial fields monitored). 46 47 These study sites were used by numerous researchers and evaluated for pest populations, natural enemies, onion growth, crop damage and several other biological parameters of interest in the overall onion agroecosystem project. Environmental monitoring equipment including a 3 point soil thermograph, an air hygrothermograph and a pyreheliometer were maintained at each study site. Leaf wetness meters were also installed, but due to equipment failure (sensi- tivity to electrical storms), very limited data is available. Additional meteoro- logical data including rainfall and wind velocities was available through local National Weather Service monitoring stations. Samples were collected from each of the study sites approximately twice a week, using either a "D-vac" sampler (Dietrick 1961) or a tractor-mounted suction sampler (Figure 13) constructed after the design by Cobb and Ruppel (1976). The suction sampler was driven through the habitat of interest and operated at a speed sufficient to catch adult muscoid flies without causing severe physical injury. The insects captured in the collection net were transferred to cages and transported to the laboratory where samples of flies known to be hosts of E. m were taken. The flies (100 per species when available) were placed by lots of 25 in 20 cm glass lantern globes containing 5-12 cm tall barley seedlings as a substrate. The flies were held under laboratory conditions of 21 i 1°C, 80% R.H. and a 16-8 light-dark photoperiod throughout the incubation period of the disease (see Experiment 3). Water and food (honey and brewer's yeast) were provided in conjunction with daily observations for fly mortality. Dead flies were removed from the containers, examined (microscopically when necessary) for the presence of E. m infection, and data recorded as to host species, sex, date of death and presence of E. @522- At the end of the incubation period, the remaining flies were counted, recorded 9 ex .v. v x 5‘ ..~ . e a ......§§§_,,. . ease .o.. ‘5 .1 . a c .. c 3.7. . e . «pr... s§§§ . _ ..~.”\’o lafl . E z (’90. .c .\\\\. Q s . : ”.1“. .15... a.) . .sr em \i\\ .7 . . .. .4. @ SE SQ EV. sum”. r. ._ ../. r \ / A... .Uuw.x‘n...... . . . ccccc uuuuuuuuuuu ...... ..... ..... ...... ..... T.|II..~|||.. i i .8 i 2.5% hoov— 1 O CO .. .. _ :3. n . £2“. .8 552.8 3 1 86 2.880 8 8283 :8 .325. .253... .9 -r .moaam anon mo Coauoo 8a use: as: 2.5 :3 2m HHOO o>HH you won: Hoamewm coauoam wouasoaiuouumua .MH «Human 49 and then discarded or used for other purposes. Host fly populations were monitored approximately twice a week in each of the study areas using a variety of sampling devices including emergence traps, activity traps, flight interception traps, sticky board traps, rotary flight traps, and sweepnet samples (see Whitfield 1981 for specific trapping details). RESULTS: Fifteen sets of E. mu_sga—e infection data were collected over the 1978-79 sampling seasons for adult populations of both the OM and SCM. Percent infection and the associated sample variance were calculated and are listed by year, collection location, host type, and sample date in Appendix D. These data were used in conjunction with the adult population estimates from Whitfield (1981) (Grant Research Field, Figures 14-19, other fields plotted in Appendix D). Visual examination of these data suggest a lagged, density-dependent response of infection level with host number. This response is clearly reflected in the field data sets from the 1978-1979 Grant research field (Figures 14-19). Increases in adult OM numbers, primarily in the second and third generations, are followed by a lagged disease response (Figure 14). This effect is not noted in the first generation of OM flies until the adult SCM population is considered (Figure 15). Total host density (OM + SCM) clearly suggests that the two populations are ‘ responding to a common pathogen population in a lagged, density-dependent manner (Figure 16). Multivariable techniques were used to regress several independent vari- ables (Table 2) including total host density, several abiotic parameters, and estimated inoculum densities (predicted from the infection data as the number of flies expected to die of E. m over a projected time interval) against the proportion infection, lagged by the length of the incubation period (see Experi- 50 Figures 14-19. Entomophthora muscae infection and host fly popula- tion levels in the Grant research field 1978-1979. O- -----O NDI IDJJNI INJJUJJ 001 El. - A l A A - " " .‘ ,- zsdi \ I U "RH isoo" 2500 DEGREE DRYSI DOSE-.4 .0 C I U ,1 10” v 1070 Grant W run 550 53’ 02 5: 51 51 's d” --- dual/63111 isou 10101 p-----O NDIIGBJNI ”433834 00! 9!. 2560 ‘O‘ CI 1600 1000 DEGREE DRVSI DDS! 600 1070 Grant Rm FiOld 008.1 ISI 1008 8803 0338 .- ----- -O NDIIDIJNI 1N3383J 09‘ 2L 0! A E! g ’I I n I \i 1800 DEGREE DRYSI DRSE _, I 000 v 55o 1070 Grant Rumh Field I v % 814' V 1.4_ o c 4081/011000 iooouu NOINO 3‘1 r-——-4 NDIIJJJNI “‘33!ch 001 TL 4 _. 2000 - zsdil 1500 Y 500 ' 1500" ozone: onrs:ansr=¢.a C) v % 52 5: 01 —— «vi/9311.4 19014 13101 o———-—-. 0011 33"” INBDNBJ 09‘ 2L 1 99 1 j; 2566 v Y 2000 0.0 CI .- ....... y _ I500 : , _- 1000 v -‘-—— 1070 Grant ROOOOT'ch FIOId DEGREE ORYSIBRSE 'I' 500 1. % s: 21 'e '9 ' s JUUIISITDDU uvoa 0339 Lu--- NDIIJBJNI “Band. on: SL 99 sz A 2566 f 0 I I I I I I l I r 2000 I 800 r _. 1000 v 1070 Grant ROOOorch Hold DEGREE DRYSI DRSE=4 .4 C I t 500 v 91 '31 - V '6 - - '0 i 4‘ 't . - dua1IS11nou iooouu naiuo Cb 52 Table 2. Multivariable regression statistics and variables examined for prediction of lagged E. muscae infection. Variables Examined DEN = Total Host Density CAD = Cadaver Density ATEMP = Average Temperature MAXT = Maximum Temperature MINT = Minimwm Temperature AHUM = Average Relative Humidity HOURS = Consecutive Hours Above 95% RH MAXH = Maximum Relative Humidity MINH = Minimum Relative Humidity Variable F to Enter Multiple r2 B L0010(DEN) 28.05*** .5557 13.79 CAD 24.23*** .6508 .49 (Constant) 17.79 y a 17.79 + 13.79LOG10(DEN) + .49CAD *** (p<.001) 53 ment 3). Host density and inoculum density were the only variables eliciting a significant response (p = .05). Abiotic variables, including temperature and moisture levels, were found to have no significant effects in explaining any of the variability associated with pOpulation infection levels during the 1978 season. The effect of abiotic variables will be discussed in some length in other sections, although the lack of significance in this analysis is probably because abiotic conditions conducive to infection are almost always present in the microen- vironment inhabited by these populations. Host and pathogen density seem to be the predominant variables in the development of this disease and many other infections caused by Entomophthora Spp. (Perron and Crete 1960, Wilding and Lauckner 1976, Soper and MacLeod 1980). The relationship of host-pathogen density is much more complex than discussed above, because the "effective density," (that density sensed by the organisms in restricted habitats (Ravlin 1980)), changes differentially throughout the season with respect to absolute density (on a fieldwide per hectare basis). The spatial distribution of host and pathogen is of key importance in regulating the effective density and thus in developing a more complete understanding of the operation of the natural system response. 3. Host—Pathogen Spatial Patterns and Areas of Interaction Fly mycosis occurs naturally by contact between the host and pathogen under favorable environmental conditions. Two distinct modes of contact (Figure 9), primary infection (via contact with azygospores at emergence) and secondary infection (via contact with infective conidia after emergence) produce the infection levels observed under field conditions. As mentioned in the previous section, the spatial distribution of host and 54 pathogen populations is a key factor in disease initiation and development. To better understand the actual host-pathogen spatial interactions, a major effort was made to characterize how these organisms are distributed on a meso scale and in what types of microhabitats they are found, and thus overlap and interact. a. Infection at Emergence Primary infection is induced by host-pathogen contact in the soil, presumably by the interaction of azygospores with teneral adults as they move from the pupal case to the soil surface. The exact mode of contact is still unknown although inoculum density and microhabitat are known to influence the infection rate of other Entomophthora species (Wilding 1975; Soper and MacLeod 1981). METHODS: Centrifugation-flotation techniques commonly used to extract nematodes and spores of endomycorrhizal fungi (Bird 1978, class notes), were evaluated for use in measuring azygospore densities from field soil samples. Silica sand and field-collected muck soil were both heavily inoculated with _E_. was azygospores obtained from infected, field-collected, OM adults. The samples were processed using the techniques described by Bird and evaluated using light microscopy (lactophenol-aniline blue stain) and SEM. Primary infection levels were estimated using live-catch emergence traps (Figure 20) to capture teneral host flies at emergence. The traps (20 per generation) were placed over areas of known onion damage in the Grant research field for all three OM generations during 1979 and 1980. The flies were collected on a daily basis and transferred to laboratory rearing facilities where they were individually held in 20 cm tall glass globes containing five, 11 cm tall barley seedlings. The flies were held in the laboratory under controlled 55 Figure 20. Live catch snergence traps used to capture newly emerging host flies. 56 conditions (21 _+_ 1° c, 8096 R.H. and a 16-8 light-dark cycle) during the disease incubation period (see Experiment 3). The flies were fed, watered and monitored for mortality on a daily basis. Overwintering spatial patterns of E. m and the resultant infection levels stratified by habitats (onion field-field borders) were evaluated using a bioassay technique. One hundred, laboratory-reared, disease-free, OM pupae were placed into diapause and then buried within marked plots within onion fields and field borders in late October of 1979. The following spring (1980), live-catch emergence traps were placed over the plots and teneral adults were trapped and monitored as described above. This procedure was replicated five times within each habitat and across four fields (3 commercial fields and the research field in Grant). RESULTS: Azygosmres were readily extracted and identified from the silica sand, although extractions from the muck soil proved difficult to evaluate due to the diversity of spore types of similar size and configuration to E. muscae azygospores. If such extraction methods are to be used for estimating azygospore densities in field samples, additional experimentation is necessary in the areas of spore separation by flotation or identification using differential staining techniques. Primary infection from the Grant research field was found to be too variable to accurately estimate daily infection rates or correlate to daily weather patterns. The variance was compounded by low emergence numbers on a daily basis in combination with variable infection levels between emergence traps presumably due to the spatial distribution of the pathogen. Aggregating these data by trap across each generation, infection levels for each emergence period were calculated (Table 3). The between-trap, variance-to-mean ratios ere 57 Table 3. Estimated levels of primary _E_. muscae infection in Hylemya antiqua adults (pooled by generations across traps for the Grant, 1C igan, research field). Between-trap variance/mean ratio. Total Flies Estimated Standard Between Trap Emergence Period Trapped (N) 96 Infection Error Variance/Mean Ratio 1_9_7_9_ lst Generation 67 32.84 5.7 17.8 2nd Generation 137 10.2 2.6 21 .3# 3rd Generation 333 27.3 2.1 12.#3 128_0 lst Generation 132 15.2 3.12 14.93 2nd Generation 210 13.33 2.4 18.27 3rd Generation 3550 32.79 0.8 16.77 58 also calculated and used as a measure of the spatial distribution of host infection. These ratios are considered to reflect the spatial pattern of azygospores as environmental conditions between traps were considered stable and fly densities per trap were not significantly different from random, (1 = .05). The large variance-to-mean ratios for each generation (Table 3) suggest an aggregated distribution of azygospores within the field. Such a pattern is expected as spores are released to the soil in aggregates from OM cadavers. Further characterization of this spatial pattern was achieved in combina- tion with the evaluation of the overwintering distribution of E. muscae. It must be noted that the infection levels at emergence in the Grant research field (Table 3) do not reflect the average condition throughout commercial production areas. This field was maintained under different growing practices (see section B.2) resulting in low chemical input and substantial foliage cover in and around the field both during the season and after harvest. For that reason both border samples and onion field samples from the research field were pooled and considered to represent border areas in this analysis. Due to the extended period of time between. establishment of the overwintering plots (late October) and the time of emergence trap placement (late April), several plot markers were lost or destroyed by farm vehicles. The remaining plots (25 out of #0) were monitored between May 1 and June 30. These data could not be normalized as the mode was equal to zero, therefore two nonparametric tests (the median test, and the Mann-Whitney U- Wilcoxon rank sum w test) were used to test for infection level differences between habitats (Siegal 1956). Significant differences were indicated in each test (Table ‘4) which substantiated the hypothesis that there are differences in primary infection betwen habitats (border i = 26.396 infection, onion field SE = 59 Table 4. Non-parametric test for primary infection levels between onion fields and field borders. Median Test - cases above and below the median Onion Fields Field Borders N = 25 Greater than 1. 11. Exact Less than 10. 3. 2-tailed probability=.001 Mann-Whitney U-Wilcoxon Rank Sum W Test Onion Field Field Border MW - U = 14.0 Mean Rank of Subgroup 6.36 18.21 W rank sum = 70.0 2 corrected for ties = 4.1355 Exact 2-tailed Probability = .0001 60 2.0296 infection). Although E. m spore densities were not actually measured and, as mentioned above, differences could be due to differential success in host infection between habitats, it is felt that these results reflect actual spatial patterns of the pathogen. The spatial pattern of the overwintering azygospores is directly dependent on the spatial pattern of the infected flies at the time of death. As they are highly aggregated in border areas during the third generation (see section B.3.b), the observed pattern closely follows the expected pattern. Although further experimentation is necessary to determine the exact causal relationships, the 13-fold increase (26.396 / 2.0296) in primary infection between border areas and onion fields and the similarly high infection levels in the Grant research field (Table 3) suggest management options such as cull manipulation in the fall or companion planting used to manipulate host and pathogen spatial patterns to increase infection. b. Spatial Patterns Associated With Secondary Infection Secondary E. M infection is induced by host contact with the asexually-produced conidia. The areas of contact are variable as both host and pathogen are mobile during this phase of interaction. The spatial patterns of both organisms, therefore, are of key importance in determining when and in what areas population interaction occurs. METHODS: Host fly pOpulations were sampled approximately twice per week in seven different fields in the Grant region during 1979. Within each field, three different microhabitats were sampled: 1) the onion field, 2) adjacent carrot fields (the other major crop in the Grant vegetable production region), and 3) grassy field borders. The samples were collected using the tractor-mounted 61 suction sampler previously described (Figure 13). The suction engine was run at full speed to increase the collection efficiency. High suction causes physical injury to the flies collected in the sampler, but because density determination was the only variable of interest, fly mortality was unimportant. Ten samples 30 m in length were collected from each microhabitat each sampling period. The flies were preserved in alcohol at the collection site and returned to a field laboratory for identification and enumeration. RESULTS: Distinct density differences between microhabitats were apparent throughout the season for both the OM and SCM populations (analysis of variance for Grant field 4 presented, Tables 5 6t 6). A significant interaction term was also noted indicating changes in relative fly densities between habitats through the season. Early in the season (May-June), virtually no habitat cover was available in either the onion or carrot fields due to the small size of the plants and their planting densities. This lack of cover induced high OM adult aggregation in field borders where dense vegetation provided shelter from direct solar radiation and high temperatures. Such aggregation created artifically high densities (artificial in the sense that host numbers associated with large acreages are temporarily restricted to very small areas) within field borders. As the season progressed, the crop canopies developed and provided more suitable cover, thereby allowing the fly populations to become more evenly dispersed. The fly density levels between onion fields and the surrounding border areas followed the general pattern illustrated by field 4 (Figure 21). The largest differences occurred during the first generation when crop canopies were small and again during the third generation after normal onion foliage died back and harvest occurred (Figure 21, Julian day 21:0). 62 Table 5. Two-way analysis of variance table for onion maggot adult den— sity in differing habitats through the season. (Grant Field 4). Source of Variation Sum of Squares DF Mean Square F Main Effects 7745.1 17 455.6 25.18*** Date 5779.7 15 385.3 21.32*** Habitat 2644.8 2 1322.4 73.20*** Interaction Date x Habitat 5197.0 12 433.1 23.90*** Residual 5041.2 279 18.1 Total 17983.3 307 58.6 *** (p<.001) Table 6. Two-way analysis of variance table for seed corn maggot adult density in differing habitats through the season. Source of Variation Sum of Squares DF Mean Square F Main Effects 41343.4 17 2431.9 48.6*** Date 27745.9 15 1849.7 34.8*** Habitat 17443.7 2 8721.5 163.9*** Interaction Date x Habitat 20146.4 12 1678.8 31.6*** Residual 14844.8 279 53.2 Total 76334.5 307 248.7 *** (p<.001) 63 ouuum hommcu chzw wmmhw: we we mc on mu p p p p p p p p p p p 38.1.5.3?» TM a I .6 1 S a a I a Stu. .AwHuHm noumumouv coaumumcuw vacuum wafiusm mamau floHao as ou unmomnvm knoamo uouumo m ca muasvm uowwma :Ofiao Mo ahouuma Hmfiumam .NN wuawfim pu 88313” 08 ISlWHOU 10008" NDINO m>¢o z¢_4:w omN omN CNN 02 02 020 . t . i I a.t3?51 a I a N _ z a f a ,, a 1. 19m 2.: :28 x u x e). i m .a I. r . .. N . I. . . H ~ — .2 a u 1. H a , .g . . no a . n r . . .U a . . w n if .1 .. u r. a m w _ .. ic.mw 1 . _ r if . . + c. a a . .I .6 m y m inumw s . e i, _ m. .. if K p“ 33 5:8 26 w -Susa co unu .Ae eaeauv common mnag may usoswsounu bamfim aofico usu can mnmvuon waofim amm3uun muaowm uowwma :Ofiao mo hufimcun .HN muswfih 64 Due to the openness of the onion canopy, even during mid-season when the onion canOpy was at its peak, the border areas still maintained higher fly densities. This relationship changed through the season in the carrot canopy due to the dense foliage produced after mid-season, creating the significant interaction term noted in the analysis of variance. During mid-season, the carrot canopy provided a habitat very similar to that found in adjacent border areas, thus similar fly densities were noted (Figures 21 6c 22). As OM adults were restricted to onions for oviposition, a gradient effect was seen in adjacent carrot fields. Nearly twice the fly densities associated with onion fields were found in the interface areas, dropping exponentially as the distance into the carrots increased (Figure 22). As the distribution of the pathogen was highly dependent on the infected host, the patterns discussed above were generally followed throughout the incubation period. Departure from this pattern occurred shortly before host death as the infected flies began searching for elevated attachment sites. If suitable attachment sites are available within the habitat, the infected flies attach, die, and sporulate there. If not, they move to an area where suitable sites are available. As spore dispersal under normal conditions was fairly limited (see Experiment 7), the distribution of infective conidia closely resembled that of the cadavers. As mentioned in Section IV, suitable attachment sites typically must be 1/3 m or more above the ground, with the preferred elevation being approximately 3/4 m. Common substrates for attachment include the tips of grass leaves, stems of herbaceous plants, and onion leaves over 1/3 m in length (carrots are not an acceptable site). Under certain conditions, this behavioral specificity in attachment substrates definitely altered the spatial pattern of E. muscae- 65 infected flies. For example, early in the season, both healthy and infected flies were found primarily in grassy border areas. These areas also provided adequate cadaver attachment sites, and thus produced excellent pOpulation overlap and high effective densities (flies/m to cadavers/m) between the host and pathogen. Although this system seems to Operate in favor of onion producers by increasing infection, and thus lowering OM population levels, it is typically disrupted by normal agricultural practices. One standard onion production procedure is border area maintenance, which is primarily a cosmetic practice that includes mowing or spray-down of these areas. Such practices do not eliminate these areas as refuge sites for the OM, but alter the sites in relation to the pathogen density at the time of sporulation. Mowing or herbicide spray-down eliminates adequate cadaver attachment sites which causes an outward migration of diseased flies just prior to sporulation. This practice alters the effective densities of these populations by significantly lowering the population spatial overlap at a critical time. Later in the season, host flies may be found in large numbers in carrot fields where no attachment sites are available. The result is a migration of diseased flies out of the carrots and into areas where such sites exist. The final result is an aggregated pattern of E. muscae cadavers on onion leaf tips along interface areas between onions and carrots (Figure 23). This high aggregation pattern of cadavers along these areas has been noted by several authors (Miller and McClanahan 1959, Perron and Crete 1969, Loosjes 1976) and is felt to enhance infection during mid-season, when host populations are dispersed much more uniformly than either earlier in the season or after harvest. If sporulating cadavers were spread uniformly over the entire area of host distribution (borders, onion fields and carrot fields), the effective density (flies/m to 66 8 o l 0 0.8 4 o l PROPORTION OF MAXIMUM CADAVER DENSITY 0 CU on E119 o m m ' ‘ 1W gift 1 ch 4 8 12 16 20 METERS INTO ONION FIELD Figure 23. Spatial pattern of E, muscae infected onion maggot cadavers in onion fields adjacent to carrot canopies and border areas dur- ing second generation. 67 cadavers/m) would be extremely low. Aggregation along these interface areas creates much more host-pathogen interaction, as a large number of host flies actually pass back and forth through this area of high cadaver density while moving from the preferred resting habitat of the carrot canopy into the onion fields where oviposition occurs. Further experimentation to quantify host diffusion between these habitats is necessary to fully understand this interaction, although, as shown in experi- ment 10, a great deal of the host-pathogen interaction is linked to host movement within microhabitat areas after sporulation has occurred. Unless host densities are high, as in 1978 (Figure 16), host dispersion over the entire available habitat produces very low effective densities and little host- pathogen interaction occurs. This lowers the infection levels, as in 1979 (Figure 19). For pest management purposes, strip-planting onions and carrots is expected to increase fly infection by increasing interface areas between these crops and allowing more movement of flies through zones of high pathogen density. 4. Host Mortality and the Impact of E. muscae Infection on Fecundity Direct interpretation of E. muscae infection levels measured in the field provides little information regarding its impact on the population dynamics of the host. Infection does not directly translate to host mortality, because other compounding mortality factors (parasites, predators, or abiotic factors) frequently terminate the host before the mycosis. Similarly, the time of host infection after emergence dictates the ovipositional pattern of adult females causing differential ovipositional responses between early-infected and late- infected individuals. Thus the possible extreme of 10096 E. muscae mortality 68 could result in no viable eggs being produced or the maximum number of eggs being produced. Interpretation of the impact of E. ma—e is a confusing problem which requires an independent measurement of host mortality as it impacts fecundity. Cage studies have been used by numerous authors (Perron 1961, Wilding and Lauckner 1974, and Loosjes 1976) to estimate mortality of adult Anthomyiidae, including the OM. However, adult flies maintained in field cages must be supplied with artificial food and water sources, they are sheltered from harsh abiotic conditions, are protected from natural enemies, and do not maintain normal flight activities. Thus mortality estimates made using field cage experiments must be viewed as biased (Loosjes 1976). The need for more realistic adult mortality measurements has required that detailed population assessments be evaluated to provide an alternate method of determination. METHODS: Adult population assessments (Whitfield 1981), including field emergence rates and pOpulation density estimates from adult flight curves, were used jointly to estimate adult mortality through the season. Mortality estimates of adult flies were calculated by using adult emer- gence curves to determine the proportion of new recruitment allocated to the adult population over the intervals between population density estimates. In the absence of mortality, the cumulative emergence curve and the adult population density curve (both plotted as a percent) would track the same function. In reality, mortality naturally occurs following a lagged negative exponential decay function (Robinson and Zurlini 1979). In the field, flies emerge over an extended period of time (several weeks) which compounds the observed mortality because mixed ages of adults occur simultaneously. Mortality of the mixed age 69 population is directly measureable as the difference between the projected population estimated from the emergence curves and the measured population density over the sample interval. Using this method, the assessment of adult mortality is a cumulative process throughout the entire generation as new estimates must be adjusted by previous mortality (Figure 24). Assessing adult mortality in this manner provides an estimate of total adult mortality including that caused by E. m. These mortality estimates are the best that can be derived from the available data. They are not absolute, however, because they depend on several assumptions: 1) population immigration equals emigration; 2) sufficient trap numbers are used to estimate the population means (Whitfield 1981); and 3) environmentally adjusted trapping data (Whitfield 1981) provides stable estimates within any single generation. Separation of the mortality induced by E. £229.33. was accomplished using a computer program which uses the above emergence data, total mortality estimates and percent infection data (Appendix D). The program stochastically simulates a population of 1000 flies emerging and developing through a single adult generation. Using the above mortality estimates, the program first calculates a continuous population density curve which by definition fits the estimated population density data from which it was originally derived. The program then simulates the E. m infection measured in the field over 10 DD intervals. The result is a lower population density curve caused by redundancy of the mortality produced by E. m (E. m_ung mortality was previously accounted for in the original mortality estimates). The program then resimulates the process keeping the E. m levels constant, while iteratively lowering the generalized mortality coefficients until the adjusted curve fits the original population density curve. This process fractionates the mortality into 70 p I PAST MORTALITY } NEW . MORTALITY I EMERGENCE CURVE .4 1.1.1 > 11.1 _I Z i Q i I- I < I s i n- ! : 2 + : DENSITY CURVE 'j' I l TIME ——‘> Figure 24. Graphical representation of analytical method used to calculate total field mortality from population assessment information. Note that the mortality from all previous time intervals must be used to adjust each estimate. 71 the proportion caused by E. mu_sc_a_e and that caused by other factors. Similarly, oviposition was calculated both in the presence and absence of E. m infection, based on the data given by Robinson and Zurlini (1979) and the data of Experiment 4. RESULTS: As emergence data is only available for 1979-1980 and E. m field infection data for 1978-1979, this analysis was restricted to the 1979 Grant research field. These data were evaluated separately for males and females as longevity differences were known to occur (Robinson and Zurlini 1979). The females were also of separate interest due to the effect of mortality on oviposition. The percent mortality by sex was calculated at each sample date and divided by the time interval in DD over which the sample was taken. These mortality coefficients were then used in the program described above tO estimate the actual mortality caused by E. m infection. Although infection levels within the population were high in 1979 (Figure 17), the actual mortality caused by E. we mycosis was rather low until late in each generation (lst and 3rd generation presented as 2nd generation revealed little E. m infection (Figures 25 6c 26)). Calculation of the percent mortality caused by E. m over each generation revealed low levels of mortality in the overall host pOpulation due to the high rate of mortality caused by other factors (Table 7). These average mortality coefficients for each generation were used to calculate the population half-life (median longevity) for each generation. In all cases, the half-life was found to be substantially less than the incubation period of the disease with only a small portion of the population living long enough to die of E. mg mycosis (Table 7). A more relevant measure of the impact of E. muscae infection is the 72 .mmmmmm.nm ma vwmnmo muHHmuHoE kumEHumm mnu mom >ueaMupoE uaacm aofiumumcmw vufizu Hmuoh .om muowfim nu v.¢ mwmmuw>¢o mmmowo coo“ can can so. oou o _ . _ . . . . I 1914 _ . nu £33: . ooomaE . M £33: .28 AHUBBHOBU /A1118180H 1N3383d .wmomoa .m.>n vmmzmo mafiamuuoe uuumafiumo Ono mam muHHmuuoE uaavm uowwme :Ofico cofiumumcmw umuwm Hmuoe 8 Tc wwcmBEo mumowo 2.3. 2.5 2.: 2.x oom— b b 5:33.: oooa::..u £332 .38 h .mN muzwwm AUOBBUOBO /AlIWUlHON 1N33838 73 m.m¢ o.mo m.om .mn m.on H.mm m mum II H.wc H.¢N .Nm c.mw «.0H 2 mum o.ma a.wa m.oH .Hq w.cm N.m m mam II H.mH m.ma .nm m.nm H.N z raw H.qm w.mq m.q~ .qm c.5m «.ma m umH II m.Hq N.oH .oq N.mm m.o z uma :Owuoowum muH=v< unnumz Genoa owfifllmamm huHHmuuoz %ufiamuuoz Now coaumuuamu wwm N mo huHHmuuoz waH>H>u=m coaumasaom umnuo_ mucosa .m vuumawumm mmomsa .m N muasv< N wuumaaumm Hamum>o .wmwoauaw M? .M .3. vmmamo hufivcsomm a.“ coauosvuu unmouma mam .Annmoa amnu umvaov muaavm musums mo Nufiamuuoa.mmmmmmw.m..m%mv mmuwmv moa cmnu umwaoa waw>fia muaovm mo unmouum .OMHHIMHO: quOm mo mmumaHumm .vaHm :oumummm ucmuu mnma Ono How muouomm mafiamuuoa umnuo can wmmmmmvam.hn vumnmo umsu oucfi vuumummwm hufiamuuoa uH=v< .m mHan 74 mortality associated with reproductive females and the associated reduction in fecundity. As the OM has a preovipositional period of approximately the same length as the incubation period of the disease (103 to 105 degree days, respectively), the effect of E. mu_sgag cannot be expressed in younger flies nor can younger flies contribute viable Offspring to the following generation. As described above, only a small portion Of the total female population survive past the preovipositional period (Table 7). Those females that do, have a highly increased probability of E. m mortality. The mortality levels of E. m in mature flies (predicted from above program) are much higher than the levels affecting the overall population. The resulting impact is a substantial reduction Of mature flies by E. m, thus reducing the actual fecundity levels. During 1979, OM fecundity levels were reduced as much as 5096 over the levels estimated in the absence Of E. was. As mortality caused by other factors drops (possible in other years or localities), the high E. m infection levels would produce increased mortality as more adults would live past the 105 DD preovipositional period. C. Specific Experimentation Experiment 1. Laboratory transmission of E. muscae. Transmission studies between E. m-infected and healthy host flies have presented inconsistent results (Gussow 1913, Yeager 1939, Baird 1957, Kramer 1971). In all these studies, healthy flies were infected, but with variable levels of success and no continued repeatability. Kramer (1971) summarized the environmental conditions surrounding these past successful transmissions, which exemplify their extreme variability, and suggested that the inconsistency was due to subtle micro-environmental changes. Under moisture conditions as dry as 75 5096 R.H., fly-to-fly transmission was achieved (Baird 1957). These results seem inconsistent with the data of Experiment 8 where free water was mandatory for spore germination. Although not mentioned by Baird, free moisture must have been available as a water source for the host flies and probably was the medium for spore germination, thus host infection, in this and other studies. METHODS: Free moisture was felt to be the key factor in developing laboratory techniques to produce consistently high levels of disease transmission. To produce adequate free moisture levels similar to those found under field conditions, a cold mist water vaporizer was installed in a semi-airtight, 36 cu ft, plexiglass chamber. Distilled water was run through the vaporizer to form a dense fog cloud. This fog cloud was easily maintained by the vaporizer as the only water loss was due to condensation in the bottom of the chamber. Ten replicates of 25 laboratory-reared, disease free, OM adults were placed in 20 cm tall, glass chimney globes with five E. m-infected OM adults ready to sporulate. The globes were covered with a double layer of cheesecloth to inhibit spore movement between globes (Baird 1957) and then placed over 9 cm diameter Dixie“ cups, each containing five, 12 cm tall barley seedlings. The barley seedlings acted as attachment sites for the E. m_ung sporulating cadavers and as substrates for the healthy flies. Ten control globes were maintained in the infection chamber and in isolation. Flies were maintained in the mist chamber for 12 hours (7:00 p.m.-7:00 am.) at 21 i 2°C under total darkness, after which they were removed from the chamber and incubated under 21 1 2°C, 80% R.H. with a 16-8 light-dark photOperiod maintained with fluorescent lighting. Honey and brewer's yeast were provided as food upon removal from the infection chamber, and water was given daily by wetting the cheesecloth tops of the globes with a mist sprayer. The globes were observed daily for fly death. Dead 76 flies were examined for E. muscae mycosis using microscopic techniques when necessary. RESULTS: Nine of the ten replicates produced 100% mortality of the OM adults due to E. muscae. The tenth replicate indicated 96% mortality from fungal mycosis as only one OM adult did not die from E. muscae infection. No E. muscae infection was found in either control set, giving an overall infection rate of 99.696. Subsequent infection studies have given an average transmission rate well above 9596, with a minimum Of 8796. Eyeriment 2. Laboratory cross infection of E. muscae between OM and SCM adults. Host specificity in the Entomophthorales is highly developed with most Entomgahthora spp. being limited to a narrow set of possible host organisms. The development of races or strains within a given species has been known to increase a pathogen's specificity to limit its function to a single host population (Fargues and Remaudiere 1977). Because E. m was found infecting both E. m and E. M in large numbers throughout the same geographic area, it was felt that a single pathogen population (strain) was probably infective to both hosts. METHODS: Field collections from Grant, Michigan, of E. mag-infected host flies (both OM and SCM adults) were used to acquire the pathogen sources used in the initial cross infections. Four sets of 25 flies (OM and SCM each held separately) were exposed to five E. Essie-sporulating cadavers of the opposite species. Secondary crossback infections of the original host species were also conducted to check for continued pathogen viability. Laboratory-reared, disease-free, OM adults were used in the infection studies. Because laboratory cultures of the SCM were not available, collections from field populations (Eaton 77 Rapids, Michigan) were used and compared to nontreated controls. The glass globes containing the experimental treatments were placed at 21 i 2°C for the length of the incubation period (Experiment 3) and monitored on a daily basis. Test flies showing E. M infection symptoms were removed from the containers before sporulation to inhibit noncontrolled infection. RESULTS: These data (Table 8) indicate that under the laboratory condi- tions described above, field populations of E. m contain sufficient genetic variability to infect both OM and SCM adult populations. Field data seem to further substantiate that cross infection occurs under natural conditions (section VII.B.2). Under current population genetic theory, such strain differentiation would not be expected to occur. Possible differences in relative efficacy between hosts would be expected, yet the development of host-specific strains in an area of continued population intermixing, thus presumably genetic exchange, is doubtful. Experiment 3. Temperature-dependent development of E. muscae in vivo. The developmental phase of E. misc—as from initial spore contact until host death and sporulation is considered to be the incubation period of the disease. Vegetative growth in the Entomophthorales is known to be temperature- dependent (Hall and Bell 1960, 1961) thus affecting the i_n_ M2 incubation period (Stimmann 1968). METHODS: To determine the effect of temperature on the duration of the in 1332 incubation period, 11 sets of 25, laboratory-reared, OM adults (2-5 days old) were placed on a barley substrate (five, 12 cm tall plants) and covered with a cheesecloth-topped, 20 cm glass chimney globe. Five OM adults infected with E. m were added to each globe just prior to sporulation and then placed into the infection chamber described in Experiment 1. The globes containing the test 78 II o.¢ mm Houuaoo II 0.0 mm Houuaoo w.nm m.mm «N c o.o0H o.CCH mm c o.om c.00H mm m o.~a o.~m mu m o.om c.00H mH N o.o0H c.00H mN N w.no o.ca mm H o.om o.om mm H GOHuoumsH coumsnu< GOHuoomaH uaouuom z OOOOHHQOM _=OHuoode woumsfiu< OOHuoomcH udoouom z .ouOOHHaom musuuHm em.ou xuonmmouo oomHusm am.ou xumnmmono II o.o mm Houusoo II o.w mm Houuaoo m.Hm m.Hm mm c o.~m o.o0H mN e c.mw o.ww mu m m.H¢ o.~m mm m o.OOH o.COH mm N ~.Hm 0.0m mu N 0.0m o.om mm H o.~¢ c.00H mm H GOHuoowaH counsfiu< aOHuuomcH udoouom z ouOOHHnoM EOHuoomsH woumsnn< :OHuoomaH udoouom z oumuHHaom «SMHudo am.ou mmouu HmHuHcH unauon mm.ou mmouu HOHuHoH ouaumHm memHhm mauHuco mmamHNw mmHoonm umom HdewHHo .AmNmHv qupn< kn woumomwom mm NuHHMuuoa Houuaoo How woum=nvo GOHuoomaH IAOH Hows: OHOuOHmemEmth was oanuam whEmHmw som3umn GOHuoomcH mmouu mucosa muoaunmwaouam .mSOHquooo >uoumuo .m wanes 79 flies were maintained in the infection chamber at 21 1 2°C (with a saturated atmosphere throughout the 12-hour exposure period). After exposure, the globes were removed and placed with a duplicate nonexposed control at equally spaced temperatures ranging from 4.4°C to 32.22°C (5.6°C increment). The globes were checked daily for fly mortality: abdominal samples were taken from dead flies, stained with lactophenol-aniline blue, and microscopically examined for the presence of E. m_u§c_ag mycosis. In addition to the day of death, and presence of mycosis, data concerning the spore type found in the examinations was recorded. RESULTS: Entomophthora muscae mycosis was noted in 10096 of the 275 OM adults exposed within the infection chamber. No mycosis was noted in the control sets. No premature mortality caused by other factors was present in the treated flies, although four flies in the 32.22°C control died before the experiment ended. Due to the high survival of the treated flies, natural mortality adjustments were not necessary and the control data were discarded. The only spore types found were conidia, although each fly was evaluated for azygospores even in the presence of conidia. The duration data (Appendix E) were found to be homogeneous in a nontransferred form state using both Bartlett's Box F and Cochran's C tests (a = .05). A one-way analysis of variance was used to test for treatment differences (Table 9). These data were then transformed to percent development per day and evaluated using multivariable regression techniques (Table 10). Significance was indicated for a second order polynomial function of temperature (equation 1, Figure 27) providing a multiple r2 of .944 between the data limits of 4.4°C and 30°C. Percent Development 2 0.0152 * Temp - 0.00173 Temp2 - 0.06883 (Eq. 1) 80 Table 9. One-way analysis of variance for incubation period versus temperature. Student—Newman-Keuls multiple range test included. Variable DF Sum of Squares Mean Square F Temperature 10 31875.0 3187.50 1864.4*** Residual 264 451.4 1.71 Total 274 32326.4 *** (p<.001) Temperature Treatment Co Mean Days for Development* 4.40 0.00a 7.20 40.90b 10.00 17.20c 12.81 11.40d 15.60 (7.60e 18.30 7.44e 21.10 7.00ef 23.90 I6.36f 26.70 I 6.24f 29.40 6.16f 32.20 0.00a *Means followed by the same letter are not significantly different (¢=.05) 81 Table 10. Multivariable regression statistics for prediction of the incubation period of the pathogen Entomophthora muscae Eg'vivo. Analysis of Variance DF Sum of Squares Mean Square F Regression 2 .80440 .26813 1389.6*** Residual 247 .04747 .00019 Variable F to Enter Multiple r2 B Temperature 69.12*** .9258 .015203 Temp2 12. 09* . 9717 -. 001729 (Constant) 2 -.06883 y - .015203Temp - .001729Temp - .06883 *(p<. 05) ***(p<.001) 82 a my washcmmeMH mH . o Nm mN vw mu mu b AUG / 1N3N8013A30 lNBOUBd Kw. .muowwma SOHGO uHaom :H omumaa am mo Ahmv you ucoEQOHo>ov Houou mo unmouoav OOHHOQ cOHumnsocH msu mo astaOHuMHOH uaovaonow unaumuoaswa .NN enemas 83 Setting the percent develOpment per day (y) equal to zero and solving equation 1 using the quadratic equation, a developmental base of 4.86°C was estimated. Using this base temperature, DD estimates were obtained on the original 275 flies over the test temperatures. A mean developmental time of 105 DD with 95% confidence limits of 3.14 DD was predicted. Experiment 4. The effects of E. muscae on onion mgggot fecunditb Onion maggot population studies under field cage conditions indicate that E. _Ir_I_u_s£a_e significantly reduces total population fecundity (Perron and Crete 1960). Since OM eggs are deposited in a prolonged, cyclic ovipositional pattern, E. _mu_sc_a_g can reduce total pOpulation fecundity levels in two ways. First, mortality induced by E. w directly shortens adult longevity, thus the number of ovipositional days available for the population. Second, E. MS may induce physiological damage to the adult reproductive organs, thus reducing fecundity rates of infected hosts even before host death. The effect of reduced longevity on fecundity was discussed in section VII.B.4. This experiment was designed to test for and quantify any premortality effects on the overall population fecundity rate. METHODS: Mature OM adults (late 2nd generation, 1980) were collected live from the research field in Grant, Michigan, using the tractor-mounted, insect collection device described in Figure 13. At the time of collection E. M was assessed between 20 and 25% infection in the research field. When the flies were returned to the laboratory, the females were separated out and placed individually in cheesecloth-covered, 40 ml souffle cups containing moist sand and a small onion slice in the bottom. The flies were maintained at 21 i 2°C over the following ten-day experimental period. The flies were transferred daily to clean cups with the old onions and sand closely examined for egg 84 deposition. The fecundity rates for each fly were tabulated individually as was a record of fly mortality including day of death and mortality induced by E. muscae. RESULTS: Seven hundred and two, female, OM adults were captured and reared over the 10-day experimental period. One hundred and seventy three flies (24.6%) were infected with E. m. Fecundity estimates on a per day basis were calculated for OM adults not dying of E. m_usga_e over the experimental period, (thus presumed not infected) and for those flies which died of E. 9.9.523 on each subsequent day after the experiment began. Fecundity was fairly stable over the first 50 DD of infection, with no significant differences from the noninfected prOportion of the population (all represented by the single point on the Y axis (Figure 28)). A significant reduction in fecundity occurred just short of 70°C DD after infection. No egg-laying occurred after that point, which resulted in approximately a 50% fecundity reduction before death. Experiment 5. Characterization of sporulation rate. Host death and the initiation of E. 99393—8 sporulation occurs within very precise time limits during a day. Diseased flies are typically found searching out attachment sites only during early evening hours (5—7 p.m.). Host death occurs approximately 30 minutes after the flies attach to an acceptable substrate. Conidial production and sporulation quickly follow, with conidia being released throughout the night. This phenomenon is widespread in Entomophthora sp. (Hamilton 1959, Wilding 1969, 1970, Newman and Carner 1974) producing high conidial densities during night time hours when environmental conditions are most favorable for conidial germination, thus host infection (Newman and Carner 1974). 85 m1 8 >. < o g y=2,18(1.-EXP(—((120-5"x)/17°5)) 8 m a ‘Q E] 9°“ >- a: c: \. a) CD (5 us Host Deoth I l f 1 j 1 I FE j f fi I Ch 20 40 60 80 100 120 DEGREE DHYS RFTER INFECTION Figure 28. Prenortality effects of E. muscae mycosis on the fecundity rate of onion maggot females. The fecundity of noninfected flies is represented by the point on the Y axis (mean of 529 samples). 86 METHODS: To characterize this sporulation rate, six male and six female E. _r_n_u_sc_ag-infected cadavers were collected from their attachment sites in the Grant research field, prior to the initiation of conidial formation, and placed above hourly-monitored spore traps (adapted from Reidl et al. 1976). Two spore traps, each with six input monitoring ports fitted with a single cadaver (conidia collected by free fall impact on acetate sheets), were maintained under field conditions between 7:30 pm. the night of collection and 7:30 a.m. the following morning. The hourly conidial samples were returned to the laboratory the morning following the experiment where ten randomly selected microsc0pe fields (2.19 sq. mm each) were subsampled for conidial density to estimate sporulation rate per cadaver. RESULTS: Two sampling periods (August 8—9 and August 17-18) were evaluated during the 1978 growing season. These data (Appendix F) were transformed to proportions of the total sporulation over the sampling period by cadaver to equalize any between-cadaver density effects not of interest in this experiment. The data were then adjusted in time to represent the mean release time per sample (conidia trapped between 7:30 and 8:30 are representive of the 8:00 sporulation rate). Bartlett's Box F and Cochran's C tests both indicated that these data were homogeneous; therefore, a 3-way analysis of variance (Table 11) was used to evaluate the effect of sex, day of experimentation, and time of conidial release on the rate of sporulation. No significance (P = .05) was indicated by either host sex or day of experimentation. It should be noted that only two days were evaluated; therefore, between-day differences were possible and even expected due to varying environmental conditions (see Experiment 11). Time of day was found to be highly significant (P = .001) in relation to sporulation rate, with the multiple classification analysis (Table 12) suggesting a 87 Table 11. Three way analysis of variance table for Entomophthora muscae sporulation rate evaluated for day of sample, time of sample, and sex of the sporulating onion maggot cadaver. Source of Variation Sum of Squares DF Mean Square F Main effects .716 13 .055 28.39*** Day .0005 1 .0005 .185 Sex .0005 l .0005 .185 Time .715 11 .065 33.55*** Interactions nonsignificant pooled in residual error Residual .590 215 .0027 Total 1.306 239 .005 *** (P< - 001) 88 Table 12. Multiple classification analysis for analysis of sporulation rate for day of sample, time of sample and sex of the sporulating onion maggot cadaver. Variable Category Sign N Adjusted Deviation Beta Day NS Sex NS Time *** .74 1 20 -.07 2 20 -.05 3 20 -.02 4 20 .04 5 20 .08 6 20 .09 7 20 .06 8 20 .02 9 20 -.01 10 20 -.03 11 20 -.05 12 20 -.07 *** (p<.001) NS = not significant (p>.05) 89 normal distribution of conidial release over the night hours (group 1 = 8:00 p.m., group 2 = 9:00 p.m., . . . , group 12 = 7:00 a.m.). These data were fit to normal distributions by experimental data and pooled across dates using regression analysis on data transformed as probits of the cumulative sporulation versus hours (8:00 p.m. = 1, 9:00 p.m. = 2, . . . , 7:00 a.m. = 12) (Tables 13-15 and Figures 29-31). The regressions over individual sampling dates provided the highest r2 values. This information suggests an increase in variance due to day of sample, but, as was indicated in the analysis of variance, no significance from the pooled analysis can be justified in this case. Using probit values of 5.0 (50%) and 5.99 (84%) in the estimated regression equation of Table 13, the mean and variance of the described normal distribution were calculated as 12:50 a.m. and 4.13 hours, respectively. Therefore, 95% of the sporulation occurs between 9:00 p.m. and 5:00 a.m. the following morning. Using these parameters, the estimated normal distribution was calculated and used to simulate the expected sporulation rate for an example data set (Figure 32). Experiment 6. E. muscae §porulation as a function of temperature. Conidial production in Entomophthora spp. is both temperature and moisture dependent (Newman and Games 1975, Wilding 1970). In the onion agroecosystem, temperature is assumed to be the primary controlling variable as moisture conditions are typically not limiting during the growing season. Eliminating moisture as a variable is justified because: 1) little differential effect in sporulation rate is noted in Entomophthora spp. for atmospheric moisture conditions greater than 80% R.H. (Newman and Carnes 1975), and 2) atmospheric moisture conditions in the low-lying, Michigan muck soil, onion production areas normally reaches saturated conditions during the sporulation period (Table 16). 90 Table 13. Regression statistics for probit of cumulative sporula- tion versus time for the data sets pooled across exper- imental day. Source DF Sum of Squares Mean Square Regression 1 687.20 687.20 Residual 238 74.22 .312 Total 239 761.40 y intercept (a) = 2.158 t .0365 r2 = .9025 slope (b) = .4862 f .01036 y = 2.158 + .4862x Table 14. Regression statistics for probit of cumulative sporula- tion versus time for experimental day 8/8/78. Source DF Sum of Squares Mean Square Regression 1 295.18 295.18 Residual 118 18.72 .294 Total 119 313.90 y intercept (a) 2.78 t .04132 r2 .947 slope (b) .4469 i .01154 y 2.78 + .4469x Table 15. Regression statistics for probit of cumulative sporula- tion versus time for experimental day 8/17/78. Source DF Sum of Squares Mean Square Regression 1 368.20 368.20 Residual 118 14.40 .122 Total 119 382.60 y intercept (a) = 1.78 t .0455 r2 = .96 SIOpe (b) = .5011 t .01318 y = 1.78 + .5011x #1 1 0.. 0.. l 1 0-6 0-8 0-0 0-7 1 l l l CUHULRTIVE SPORULRTION 0-2 1 l 91 1.0 L I 1 0.. 0.7 0.. O-I L 0.6 l l 1 CUHULRTIVE SPORULHTION 0.2 0-3 0.4 L 1 0.0 0.1 J Figures 29-30. Figure 31. Figure 32. l 1 1' 1 I I I ‘ nun "In tun Sun Sun 7am ‘DHE Figure 29. Figure 31. NORHHLIIED SPORULHIION RRTE c: _31 II ‘;4 9. c: I: O Q‘ 0‘ ’_ c: 5 II a a: 2 '3 3.4 U > o— 1‘ 2; c: _I :3 03‘ g;. D at 0'4 — 6‘ c: (5 ’1 I I I I I lpnt 11'"! Soul all: IIHI 1on1 ‘HME Figure 30. 8 °q 8 6‘ I I . I s; I c: 0.10 Figure 32. Cumulative sporulation of E, muscae through a 12 hour period for two representative sample dates (8/8/78 and 8/17/78). Cumulative sporulation of E, muscae through a 12 hour per— iod for these data pooled across sample dates. sporulation. Estimated normal distribution compared to actual rate of 92 m.~m H.HH NH II II II q.H0 H.HH HH 0.00H e.H em 0.0m «.0m 0H 0.00 H.H mm 0.0m 0.0H m n.50 «.0 mm 0.0m m.n w H.50 «.0 HN m.NH N.m N 0.00 q.H 0N 0.0 m.~ 0 «.mm w.H 0H «.0 H.~ m 0.mm m.m 0H m.e H.H q H.Hm ~.m NH N.m «.0 m 0.50 m.~ 0H 0.N 0.0 N «.mw 0.0 mH m.N «.0 H m.H0 m.¢ 0H H.N H.~ 0 m.nn 0.m 0H hosmsvmum moaoavoum .m.m N00H mo hoaoovmum hocmououm .m.m NO0H mo o>HumHsaso vo>uomno mason moonaHusoo o>HumHsabo vm>homno mason moosdHuaoo .hquHadn o>HumHoH N00H moosoHuaoo mo muoa no mason w o>mn voHuoa mHJu wcHusc mhmu mzu mo N00 uo>o umcu ouoz .mmmH 0cm man How mH Honawumom ou H hm: Scum .cmezqu .uamuu How Aaoo: NH ou soon NHV OOHMOQ usonlww m sHsuHs zunHabn m>HumHmH N00H mo mason moosaHuaoo How OHnmu hocoauouh .0H OHan 93 METHODS: To evaluate the effect of temperature on E. m conidial sporulation, saturated atmospheric conditions were simulated in the laboratory using 40 ml, sealed souffle cups filled with 30 ml of distilled water. Onion maggot cadavers infected in the laboratory (see Experiment 1) were attached via petroleum jelly to the inside lid of each 40 ml container just prior to Sporulation. The cadavers were placed very close to the water's surface (not touching) so the ejected conidia would fall directly into the water beneath. After a 12-hour sporulation period, the cadavers were removed from the containers. Lactophenol-aniline blue stain was added to stop germination, and two drops of Basic H', a surfactant, was added to break the surface tension of the water to augment total spore dispersion. Ten subsamples were extracted from each container after agitation and evaluated for conidial density using a Bright Line hemacytometer. Estimates of the total conidia released per fly were made by weighting each subsample by the total volume (30 ml) of distilled water in each container. RESULTS: These data (Appendix G) were tested for homogeneity of variance and found acceptable (0: = .05) in the nontransformed state. A one-way nested analysis of variance was conducted to test for treatment differences while partitioning out the subsample variation (Table 17). A significant level of variation (P = .001) was found at both levels. No effort was made to differentiate between host sex. Because a size difference between male and female is apparent, some of the between-fly variation is assumed to originate from that source. Visual observation of the data suggested a normal distribution of sporulation as a function of temperature. Transformation to probits of the cumulative curve allowed the application of linear regression to estimate the mean and variance of the observed function 94 Table 17. One-way nested analysis of variance for total sporulation versus temperature with flies (subsamples) nested. Variable DF Sum of Squares Mean Square F Main effects 7 .847E + 14 .121E + 14 5.73** Temperature 7 .847E + 14 .121E + 14 5.73** Nested Flies/Temp. 32 .73952E + 14 .2311E + 13 15.61** Residual 360 .5328E +.14 .l48E + 12 Total 399 .2118R + 15 ** (p<.01) Table I8. Regression statistics for probit of cumulative sporulation versus temperature. Source DF Sum of Squares Mean Square Regression l 36.95 36.95 Residual 6 1.518 .25 Total 7 38.46 y intercept(a) = .7883 1;.1778 slope(b) = .2553 _-|_- .02112 2 r = .96 y .7883 + .2553x 95 (Table 18). Using these estimates, a normal probability density function was calculated and plotted against the mean sporulation over temperature (Figure 33). Under field conditions, this temperature response interacts with the diurnal pattern of spore release (Experiment 5) for total spore production. Within-day temperature differences between 8 p.m. and 7 a.m. are usually not of the magnitude to create major deviations from the diurnal pattern. Between days, however, total spore output is significantly altered (see Experiment 11 and model formation). Experiment 7. Entomophthora muscae conidial dispersion. Conidial dispersion in the Entomophthorales is primarily anemochorous, aided by an active spore liberation mechanism (MacLeod 1963). Active liberation mechanisms are known from various fungi, with some producing ejection velocities up to 100 m/sec (Zadoks and Schein 1979). The combined effect of this ejection mechanism and the air movement at the time of sporulation dictate the dispersal pattern and distance of these prOpagules, which indicates the effective area of possible infection surrounding a sporulating host cadaver. Characterizing the effective area of infection and the resultant conidial densities within this area is extremely important in developing a more thorough understanding of the spatial dimensions associated with host-pathogen interactions and their associated epizootiology. METHODS: To evaluate conidial dispersal under still-air conditions (thus the dispersal caused by active liberation), an impact spore sampler (Raynor 1979) (Figure 34) was designed to capture E. _m_u§_c_ag conidia on plexiglass sample strips (82 cm high x 2.54 cm wide) from 5 to 33 cm (by 2.54 cm intervals) away from a point release source. A cluster of ten sporulating E. muscae-infected, OM 96 MILLIONS OF SPORES/FLY ' I 1 W 1 1 S 10 15 2.0 25 30 35 TEHPERHTUREIC) Figure 33. Estimated normal distribution fit to actual spore produc- tion response of E, muscae infected onion maggot adults held under differing tanperature regimes. Figure 34. 97 L é‘fifiifiififimmfififlfis:I:?:=:i:?:‘<€:=:€:=:i:t «won— x afiammmfi Impact spore sampler used to measure E, muscae conidial dispersal from a point source. 98 cadavers were attached in a radially symmetrical pattern at a central release point, 50 cm above the base of the sampler. The impact sampler with attached cadavers was placed into a plexiglass cloud chamber (see Experiment 1) where saturated atmospheric conditions were maintained at 21 1 2°C, under total darkness for a 12-hour experimental sporulation period. Upon termination of the experimental period, the plexiglass impact strips were microscopically examined, with spore densities evaluated within 32 blocks (2.54 cm intervals) along each sampling strip. Conidial densities were estimated by counting conidia within 3 sub-samples (3-2.l9 sq mrn microscope fields) per block. RESULTS: These data were transformed using a weighting factor (tan -l)* (width of the sample strip/its distance from the source) to equalize the radial area in degrees measured by each plexiglass sampling strip. From the weighted data, the proportional rate of conidial density over distance from the source was calculated for impact at ground level (50 cm below the release point (Table 19)). Assuming uniform dispersion in all directions, the resultant pattern on a horizontal plane 50 cm below a point release source approaches a circular Poisson distribution with a mean of 12.86 cm and variance 12.94 (Figure 35, A and B). The maximum range noted was 34 cm in any direction, which represented an area of influence of approximately .5 sq m. Under still air field conditions, the actual area of influence would be substantially reduced as conidia are expected to impact on and adhere to vegetation well above ground level (Zadoks and Schein 1979). By adding the wind velocity factor, the pattern of conidial dispersal becomes more complex, although rough estimation may provide a reasonable estimate of the area of influence. Weather data from the National Weather Service (Lansing, Michigan) was evaluated from May 1 to September 30, 1980, over the time of maximum 99 .000. 0 wwHo. 0000. Nqu. sumo. qmmo. N00m. 00mm. HNON. owHo. 0 hunnon whomm HOuOH ucoouom 0.0m 0.0m mm.- m0.0H m.NH 0.mH 0.0H c.0H 0.NH m.0 «.0 0 .86 OH uoamumHn .Amm_onstm oomv oousom usoaw uoHoa m BOHon so om oamHm HmuaONHuon o zwsounu poomcmnu u so wouammua_0uHmouw ouonm .0H OHQOH Figure 35. A. 100 Pattern of E. muscae conidial dispersal on a horizontal surface 50 cm below a fixed release point. Sectional view showing the poisson distributional pattern in a single direction (height of graph represents spore density, see Table 19). .o': 9 o“ e‘. . .‘gs *\ :zs 9:. Q“ 0’. T“ o 0 .0 0 0“ 0 s ,0 o 0 ',////’,"o'. }////////I 0’0”! $95333. $5.; ’0 ’I’" ’1’”? N «$1.0M, 0“ 00.1.01 ”It,” “I,“ .331 III ‘III 44:4 101 ‘30 \_\\\\I III” ‘9‘;le \\ I" “$703.9... .:‘I‘\\‘ "N“ $44)?)le 1““ Ill/I11, \0 $1M“ ’I’llll .1 453120 f:‘\‘\ Q‘I‘V“ “‘:\\ “III \\‘ \\\\\\ ““ ‘\‘\\\\\\ \\I\\\\\\ \‘ \\\\‘\‘ \\\\ \ \I\\\\\“ up \I\‘ I, 25516565 '1; ’0’7, Eaters. 55g: ‘\\\‘\“ SOIHCE 102 sporulation (10 p.m. - it a.m.). The data showed extremely low wind velocities (Table 20). Over the time interval examined, the mode was always equal to 0 (34-3896), with over 5096 of the days having wind velocities during the sporulation period of 1.5 m/sec or below. Settling velocities of spores in the g. _rr_]_L_i_s_c_a_e_ conidia size range (ca 20 um) are on the order of 2-5 cm/sec (Chatigny et a1. 1979). Using the average expected settling velocity (3.5 cm/sec), a dispersal distance of 2.86 m is estimated before a 10 cm drop in surrounding vegetation at a wind speed of 1 m/sec. With higher wind velocities, more turbulence is expected and the outcome of spore dispersal is difficult to determine (Zadoks and Schein 1979). Under the majority of weather conditions that occur in Michigan onion production regions, spore dispersal is expected to be short range, producing an effective area similar in size to Figure 35 and in close proximity to the original spore source. Experiment 8. The effects of moisture and temperature on E. muscae spore germination. Spore germination in most fungi directly depends on the availability of free water, although for some fungi (e.g., powdery mildews) free water is actually inhibitory (Zadoks and Schein 1979). In the Entomophthorales, reports indicate a wide range of responses in relation to moisture conditions. Sawyer (1929) found that g. sphaerosperma and other unnamed Entomophthora spp. were capable of conidial germination in relative humidities lower than 7596. Baird (1957) reported that primary conidia of _E_. muscae could germinate even under dry conditions (no quantification given). Yendol (1968) evaluated conidial germination for several Entomophthora spp. and found that extremely high relative humidities were necessary for germination to occur (10096 R.H. for all species examined except _E_. coronata which germinated under 9596 R.H.). Such 103 Table 20. Frequency tables for wind velocities between 10p.m. and 4a.m. from May 1 to September 30, 1980. Note the mode is always zero (34-38%). Frequency km/sec 10:00p.m. 1:00a.m. 4:00a.m. 0.0 34 35 38 .5 13 12 21 1.0 8 11 18 1.5 7 4 12 2.0 8 7 6 2.5 4 7 4 3.0 7 S O 3.5 4 3 O 4.0 6 4 1 4.5 5 4 -_ S-above 4 8 -- 104 high relative humidities suggest the presence of free water in the germination chambers, but this information was not given. METHODS: To examine the germination potential of _E_.. m conidia in relation to both temperature and moisture conditions, slide germination proce- dures (Yendol 1968) were used. Conidia were obtained from field-collected (Grant, Michigan) onion maggot adults. The infected adults were held under mass-caged, laboratory conditions (21 i 2°C, 75% R.H.) until the evening of host death and sporulation. Sporulating cadavers were transferred from the cage, each being attached on the upper, inside surface of a 20 cm disposable petri dish using petroleum jelly. The flies were attached by their heads which allowed the conidia-covered abdomen to be suspended freely over filter paper that was saturated with distilled water. The high relative humidity within the sealed petri dish stimulated sporulation and allowed several hundred conidia to be collected on standard, glass microscope slides during a ten minute time interval. After obtaining an estimated minimum conidial density of 100 per slide, the slides were placed in pint containers, then suspended on plastic racks over sodium hydroxide- distilled water solutions that were used to regulate humidities within the containers (Solomon 1951, Winston and Bates 1960). Distilled water was used to generate 10096 R.H., and free water treatments were obtained by placing water droplets on the upper surface of the slides directly covering the attached conidia. Ten replicates (in some cases only seven) per container were obtained from different sporulating cadavers to insure between-cadaver variation that represented the true population and to provide the necessary number of replicates simultaneously. The pint containers were then sealed and returned to growth chambers for incubation (all containers were maintained at the experi- mental temperature of interest at least three hours prior to testing to allow the III] III" 105 salt solution to stabilize at the test temperature). Temperatures ranging from 0°C to 35°C were evaluated over free water conditions, saturated atmospheric conditions (10096 R.H.) and under a series of atmospheric moisture levels ranging from 0 to 2.11 mm vapor pressure deficit (9596 R.H. over the treatment temperature ranges). Containers were removed from each treatment (tempera- ture x moisture) every 3 hours over the 12-hour experimentation period. When removed from the temperature chambers, germination was terminated using a lactophenol-aniline blue stain and then evaluated for germination under a compound microscope. A minimum germ tube length (2 x spore diameter) or the formation of a secondary conidium was used as the germination criterion. RESULTS: No germ tubes were formed under less than saturated atmospheric conditions, although a limited number of secondary conidia were formed under lower vapor pressure deficits. The upper and lower conidial germination limits were found to be 322°C and 4.4OC, respectively. Conidia below 44°C were found intact but nongerminated, while conidia incubated at 322°C and above were fragmented and no longer easily identified. The dependent variable (data in Appendix H), proportion germination, was linearized using an arcsin transformation, then evaluated for homogeneity of variances using both Cochran's C and Bartlett's F test. The assumption of unequal variances was rejected. The analysis was continued using an analysis of covariance testing for differences between the free water treatment and the 10096 R.H. treatment while controlling for the metric variables, temperature and time. Significant differences (Table 21) were indicated; therefore, these data were divided into two separate sets and evaluated for linear effects using multivariable techniques (Tables 22-23). Significant linear responses were indicated using third order polynomials for both time and temperature. Three 106 Table 21. Analysis of covariance for arcsin spore germination by moisture condition (free water, 100 R.H.) while controlling for the metric variables of temperature, temperature squared and time. Source of Variation Sum of Squares DF Mean Square F Covariates 46962.8 3 15654.3 137.28*** Temp 25677.7 1 25677.7 225.2*** Temp2 27503. 7 1 27503. 7 241. 2*** Time 19146.9 1 19146.8 167.9*** Main effects Water 10287.2 1 10287.2 90.2*** Residual 37631.6 330 114.04 Total 94908.2 334 284.16 *** (p<.001) 107 Table 22. Multiple regression statistics for spore germination under free moisture conditions predicted by temperature and time. 2 Variable F to Enter r B Time 5.49* .3239 -.o4993 Time2 19.71*** .3722 .00620 Temp2 7.51** .3905 .00345 Temps 17.33*** .6915 -.0000932 Constant .05628 y = .05628 - .04993Time + .ooazonme2 + .003451emp2 - .0000932Temp3 * (p< .05) ** OK .01) Wt (p< .001) Table 23. Multiple regression statistics for spore germination under 100% relative humidity moisture conditions predicted by temperature and time. Variable F to Enter r2 B Time 3.78* .1809 .01938 Temp2 6.57** .1999 .001529 Temp3 13.52*** .4246 -.0000388 Constant -.018568 y = -.018568 + .01938Time + .001529Temp2 - .0000388Temp3 * (p<.05) ** (p<.01) *** (p<.001) 108 dimensional surface plots were generated for both moisture conditions using Surface 11 graphics (Sampson 1975) (Figures 36-37). Alteration of these germination rates due to fungicides commonly applied in the onion agroecosystem was also tested (Appendix A and Experiment 9). Biggeriment 9. Conidial germination and viability in various habitats of concern. As discussed in Section VII.B.3, the crop and border area canopy cover changes throughout the season, affecting not only the distribution of the host and pathogen, but also the viability of the spores within them. The vegetation type and canopy cover highly alter temperature and moisture conditions that regulate conidial germination and protect the spores from lethal ultraviolet radiation. Conidial germination and the ability to withstand deleterious environmental conditions is a key factor in understanding the interrelationships associated with host-pathogen interactions under field conditions. METHODS: Conidial germination and viability were evaluated in onion, carrot and border canopies using slide germination tests (Altman 1966). Sporulating OM cadavers were collected from the field and attached via petroleum jelly to the upper surface of petri dishes that contained distilled, water—soaked filter paper to maintain saturated atmOSpheric conditions. Stan- dard glass microscoPe slides were placed directly under the sporulating cadavers to collect a minimum of 100 conidia per slide (approximately 5-10 minutes). Ten slides containing conidia were placed on wooden frame stands set at high (top of canopy), medium (center of canopy) and low (ground level) within the three habitats of interest. The slides were set out into the field at approximately 10 p.m. the night of experimentation. Two slides were removed from each site at 6 a.m., 8 a.m., 10 p.m., 12 p.m. and 6 p.m. One slide from each pair was treated 110 with lactophenol-aniline blue stain and evaluated for percent germination. The other was placed under free moisture conditions (21 i 5°C) and allowed to develop for 12 additional hours after which it was also evaluated for percent germination. Spores were considered germinated if a secondary conidia was present or if the germ tube was 2x the spore diameter. The experimental process was replicated five times for each experimental date. Control treatments were maintained in the field laboratory at 21 i 5°C under free moisture conditions throughout the experimental period. RESULTS: The experiment was conducted four times during the 1980 growing season (June 16, July 16 and 23, and August 21) to characterize the effect of habitat development on spore germination and viability. On the second experimental date (July 16), the grower maintaining the research plots inadvertently applied a fungicide application to the experimental area at 6:30 a.m. just following the morning sampling period. This data set, although not formally designed, represents the impact of fungicide-induced mortality of _E_. m conidia and will be analyzed separately (all four data sets are listed in Appendix 1). Spore germination (after 14 hours of exposure) between habitats and location within habitats was examined using analysis of variance (Table 24). Significant differences were noted between the treatments for each experi- mental date. The multiple range tests (Table 25) reflected the habitat groupings as they affect overall spore germination. Early in the season (June 16), the border areas provided near-optimal conditions (not significantly different from control) for spore germination, while the more open crop canopies were less favorable (ca. 1/3 control) for spore germination. The reduced germination in those spores held at the top of the border canOpy reflect moderation between the 111 Table 24. One-way analysis of variance for arcsin of observed spore germination in carrot, border and onion canopies. June 16 Variable DF Sum of Squares Mean Square F Canopy Habitats 9 75601.4 8400.2 l40.94*** Residuals 40 2382.0 59.6 Total 49 77983.4 ***(p<.001) July 23 Variable DF Sum of Squares Mean Square F Canopy Habitats 9 88996.9 9988.5 106.9*** Residuals 40 3701.6 92.5 Total 49 92698.5 ***(p<.001) August 21 Variable DF Sum of Squares Mean Square F Canopy Habitats 9 94268.5 10474.3 457.4*** Residuals 40 914.0 22.9 Total 49 95182.5 ***(p<.001) 112 Table 25. Student—Newman-Keuls multiple range tests for spore germina- tion by Canopy-Habitat treatments (untransformed mean presented).* Habitat-Canopy Treatment Mean Percent Spore Germination by Test Date June 16 July 23 August 21 Onions-Top 25.1a 26.9d 21.4h Middle 21.4a 22.6d 23.6h Bottom 23.4a 32.7e 23.0h Carrots-Top 21.03 38.1e 39.91 Middle 19.6a 70.1f 80.1jk Bottom 20.2a 78.6fg 73.1j Border-TOp 40.1b 29.4e 52.01 Middle 74.8c 73.4f 73.1j Bottom 74.4c 77.6fg 72.8j Control 83.6c 82.8g 86.2k *Means followed by the same letter are not significantly different (¢=.05). 113 two habitat groupings, probably due to a moderate buffering from dessication and direct exposure to ultraviolet radiation. The third and fourth experimental dates (July 23 and August 21) reflected the same basic germination pattern and will be discussed as a unit. The protection provided by both the border area and carrot canopies produced near optimal (not significantly different from the control) spore germination in the low and mid-canopy heights. The upper portion of the canopies were found to produce significantly less germination than both the control and the lower canopy, although greater than the onions showing the same gradient effect or buffering from harmful environmental conditions shown above. Conidial germination in the onion canopy was still approximately 1/ 3 the level of the control, again reflecting less than optimal environmental conditions. The overall effect of habitat type and spore location on germination is best represented graphically as percent germination by treatment over the experimental dates (Figures 38-40). Further characterization of spore germina- tion within the day provides additional insight into the differential response between habitats. Conidial germination is a cumulative function of time, thus any leveling effect (decrease in germination rate) can be readily identified. A series of one-way ANOVAs (germination by habitat) for each sampling period (6 a.m. - 6 p.m.) was used to examine between-habitat differences over time (a single experimental day, July 23, is presented because the trend holds over all sample dates (Table 26)). No significant differences between habitats were found until the 8:00 sample period, which suggests that favorable environmental conditions were present until approximately the 6:00 sampling period (Figure 41). Differential spore germination between habitats can be explained by either spore mortality, nongermination, or a combination of both. Identifying the 114 100 J 00 l 1 Onion: 4 Border 00 IO 1 - 1 q 8 PERCENT NRXINUH OERHINRTION I PERCENT NRXINUH OERNTNRTION 40 L 00 O o l I I I o o a o o o I I a I a o a ‘ \ \ \ \ L -----.- I -—‘“:.: --.. 20 l 20 l ° 6-16 1-23 3-2: ° 6-16 7-23 5-21 DATE DATE Figure 38. Figure 39. 100 J 100 J A . CARROTS l A .0 l 40 l PERCENT OERHINRTION PERCENT "RX 1 HUN OERNINRT TON 20 l A I ‘ U ' I I I o "‘5 7‘23 8-21 1:009!!! 2:000!“ ‘ 6:000": 10:00!” 2:009” 3:009” DATE ““5 Figure 40. Figure 41. Figures 38-40. jg, muscae conidial germination in three canopy levels for onion, carrot and border habitats. Figure 41. E, muscae conidial germination through time of day for onion, carrot and border habitats. Table 26. 115 July 23, between onion, carrot and border canopies. presented for 6:00 and 8:00 a.m. range tests provided for all five time intervals. One-way analysis of variance for arcsin spore germination on Anova tables Student-Newman-Keuls multiple 6:00 a.m. Variable DF Sum of Squares Mean Square F Habitat 3 15918.0 5306.1 2.51NS Residuals 46 97257.8 2114.3 Total 49 113175.8 NS = Not significant 8:00 a.m. Variable DF Sum of Squares Mean Square F Habitat 3 10528.7 3509.6 3.47* Residuals 46 46524.4 1011.4 Total 49 *(P<-05) Student-Newman-Keuls Multiple Range Test* Time of Sample Treatment 6:00 a.m. 8:00 a.m. 10:00 a.m. 12:00 p.m. 6:00 p.m. Onions 19.4a 20.8c ’22.4 22.4 22.4 Carrots 39.9a 56.3d 61.1 77.1 80.1 Border 38.1a 53.3d 60.6 75.6 78.3 Control 58.0b 70.1e 80.0 82.8 81.9 *means followed by the same letter are not significantly different (9=.05) 116 actual response is important because spore mortality is terminal, while nongermination allows viable spores to accumulate until favorable conditions are present. Conidial mortality was separated from nongermination by comparing actual field germination with germination in the field treatments that were allowed to continue germination under free moisture conditions and the laboratory control. Conidial mortality was estimated as: CM = (1. - (LABG-F1ELDG)/(MAXG-FIELDG)) * 100. Where: CM = percent conidial mortality FIELDG = germination of field sample LABG = germination of field sample held an additional 12 hours under free moisture conditions MAXG = laboratory control germination (maximum possible) Plotting the mean percent spore mortality by habitat (pooled across height within habitats) (Figures 42-44) shows that the majority of spore mortality occurred after 8:00 a.m. Mortality was delayed in protected habitats (those with thick canopy covers) although by the 6:00 p.m. sampling period, spore mortality approached 100% in all habitats. The effect of fungicide applications on spore viability is represented by the July 16 data set where a fungicide treatment was applied directly over the conidia placed within the onion field. The field border plots were also affected as they were directly adjacent to the Sprayed onion plots. The carrot plots were not affected; they were located in plots several hundred meters away from the Sprayed area. The mortality (Figure 45) pooled across height within the habitat approached 100% immediately after spray application (8:00 a.m. samples). The effect of fungicides has been shown under laboratory conditions to cause 100% 100 d .0 1 A 00 1 l PERCENT HORTRL 1 TV 40 117 '81-1 * ------------- f 0' I, l I June 16.1980 1 Joy 23.1980 5 I O I .1 f f " Count: 8 ,1 ' I Border '0 ' , ,_ 1 '1 1' 4 000008 5- f l S o l g 3“ .1 ’1 ' °‘ . I 1 a l ' i I g ‘ O ' . I I h . I ' u o- : l u 9 I I D x 'g I . l u ' I .' ' a . . ' 4 . l . : f ,1 'I I 8d ” d ’ I’ l ......... I ' O V 1 f I r I o ‘ ' Y ' . . 6:00am 10:000m 2:00 6 10mm 2:00am 6:000:11 10:00om 2:oopm 6:00pm 1000919 2001“ ms: pm 00pm TIME Figure 42. Figure 43. 81 "°'. ............ ’ 8% -" fl : —= fl . I .— a ,' / ______.... 4 August 21, 1900 .' ,I 4 July 16. 1900 r' :' / 8‘ 1' I, g- s cm ': ’I 2 I Border ‘ .' ,’ > J 3 . _ . 0‘ . 'J o- ' I a; o_ E . 1: I Border z c 3 1°; 5 A Onion: 2 , 1:1, '5 .5 , °°"°" ; u o-I , 3 3‘ u v ‘ a: s l I U I. 4 ' I n. + 3 g- ' I ‘ I O 10:00:)!“ 2009M 6:009m 10%. 2000111 Figure 45. Figure 44. E, muscae conidial mortality as a function of exposure Figures 42-44. time for three different sample dates during the 1980 season. The impact of fungicide application on g, muscae conidial Figure 45. mortality (application time indicated by arrow). 118 spore mortality down to concentrations as low as 1/ 100 of the recommended field rate (Appendix A). Experiment 10. Host-pathogen interaction. Secondary _E_. muscae infection depends on host contact with the infective conidial stage of the pathogen, successful germination, and penetration of the host's cuticle. Little is known about the actual process of conidia-host contact in nature. Although most authors imply that the conidia directly strike the host at the time of sporulation, the probability of conidia directly striking a viable host the size of an OM adult is extremely small. Assuming one cadaver emitting one million spores over a 1 cu m volume of habitat, the probability of conidia striking any host the size of an OM adult is less than 5%. The addition of dense foliage in the same volume of habitat would lower that probability by aggregating spores closer to the cadaver. An alternative mode of host-pathogen contact could occur during times of host activity (movement through the foliage). This would increase the exposure time (contact after sporulation ends) and the active area of spore influence (increased in terms of the area covered by host movement and the increased surface area of the germinating conidia). The ease with which the germ tube and secondary conidia separate from an attached primary conidia suggests that such a mechanism may have evolved due to an increased success rate in attaching to viable hosts. Since OM adults are not active during the night hours, a comparison of infection over various time intervals should show differences. METHODS: Five OM adult cadavers infected with _E_. muscae in the laboratory were attached to the tips of grass leaves in 12 (6 treatments x 2 replicates) 1 sq m border plots at the Eaton Rapids research field on June 18, 1979. Emergence cages were placed over these plots where sporulation occurred 119 overnight. 1n treatments 1 and 2, 100 laboratory-reared, OM adults were sealed in the emergence cages prior to sporulation and removed at 6 a.m. and 2 p.m., respectively, the following day. A single control treatment with 100 flies and no cadavers was maintained throughout the experiment. The emergence cages were removed from treatments 4-6 at 6 a.m. to allow normal drying and sunlight exposure to the plot beneath. The cadavers were removed from all treatments at 6 a.m. The cages were replaced as the plots were used later in the day. In treatments 3-6, 100 flies were added at 6 a.m., 8 a.m., 10 a.m. and 12 p.m., respectively. All flies were removed, and the experiment terminated at 2 p.m. The flies were taken to the laboratory immediately after being removed from the emergence cages and maintained under 21 i 2°C, 75% R.H., with an 16-8 hour photoperiod (treatment 2 flies which were removed at 6 a.m. were moved to untreated plots and held in the same type of habitat until 2 p.m.). The flies were fed, watered and monitored daily for presence of _E_. muscae. RESULTS: No _E. muscae-induced mortality was found in the control; therefore, the original data (Appendix J) were transformed to arcsins to meet the assumptions of the analysis of variance. A one-way analysis based on exposure time was performed with significant differences indicated between the means (P = .05). A Student-Newman-Keuls multiple range test was used to separate the treatment differences (Table 27). As hypothesized, treatment 1 caused significantly less E. muscae infection than treatment 2, indicating host-pathogen contact after sporulation ceased. Treatments 3-6 substantiated this hypothesis as the flies within those treatments were never exposed to sporulating cadavers. It was hoped that there would be differences between treatments 3-6 so that number of active hours could be 120 Table 27. One-way analysis of variance for arcsin of observed infection versus time of host-pathogen interaction. Student-Newman-Keuls multiple range test given on untransformed means. Variable DF Sum of Squares Mean Square F Exposure Times 5 5767.5 1153.5 26.2*** Residuals 12 528.0 44.0 Total 17 6295.5 *** (p<.001) Treatment Exposure Time, Mean Proportion Infected* 1 7:00pm-6:00am .1560a 2 7:00pm-2:00pm .7450b 3 6:00am-2:00pm .5147c 4 8:00am—2:00pm .5660c 5 10:00am-2:00pm .5410c 6 12:00am92:00pm .0255d *Means followed by the same letter are not significantly different (¢=.05) 121 related to infection. The only significant difference, however, was that associated with treatment 6. Infection levels in treatment 6 (exposure between 12 a.m. and 2 p.m.) were below 3% infection—much lower than any of the other treatments. Several hypotheses for this difference have been generated. The most probable is that actual conidial mortality due to environmental factors, such as ultraviolet radiation and dessication, had occurred (see Experiment 9). Experiment 11. Conidial dose for infection of onion maggot adults. Numerous methods including injection, topical application of a conidial suspension, exposure to conidia discharged from i_n_ y_i_tr_o cultures and exposure to conidia discharged from infected insects have been used to evaluate conidial density to infection in test insects. These procedures, although accurate, are of little use in relating field conidial densities to natural infection, because they do not account for the mode and habitat of host-pathogen interaction. Measuring infection under field conditions is extremely important, because the habitat of infection exerts a dominant role in the contact, germination and infection process. METHODS: Adult OM cadavers infected with _E. m were collected from the research field in Grant, Michigan, just prior to the time of sporulation. These cadavers were attached to the tips of grass leaves within 1 sq m cheesecloth enclosures (.75 m high). The enclosures were situated in a field border area representative of an area where the majority of host infection was believed to occur (see section VII.B.3). Treatment densities were composed of 0, 1, 2, 4, 6, 8 and 10 cadavers per enclosure, each replicated three times. One hundred OM adults, collected from field 1 in Grant (an area of low E. m infection), were placed within each enclosure for 18 hours (8 p.m. - 2 p.m.). At the end of the experimental period, the flies were removed and maintained under 122 laboratory conditions (21 i 2°C, R.H. 75 i 3%, 16-8 hour photoperiod) over the necessary incubation period. The flies were fed, watered and evaluated daily for mortality. This experimental procedure was repeated twice during the 1980 growing season (June 16, 1980 and June 28, 1980). RESULTS: Over the exposure period and while removing flies from the field cages, several individuals escaped from each cage, which produced an unbalanced data set. Although numerous cases were missing, it presented no analysis problems as the data was evaluated using the proportion infected of the total recaptured. Control mortalities (0 cadavers per enclosure) of 8.9% and 12.03% were measured from the first and second sample period data sets, respectively. The data were corrected for control mortalities using the methods suggested by Abbott (1925) (Table 28). An analysis of covariance was used to evaluate between-day infection differences while controlling for cadaver density using the arcsin of proportion infection. A significant difference between days (p = .05) was indicated (Table 29). A probit regression conducted on the pooled data sets (Figure 46) shows the variation caused between days. Because the weather conditions were not in ranges that limited spore germination, an assumption was made that actual number of conidia produced per cadaver might be different between the experimental periods. Linking the sporulation rate curve (Experiment 5) with the data on cumulative sporulation as an instantaneous function of temperature (Experiment 6) and evaluating the relationship using hourly temperature data from the research site, a 2.53 fold difference in total conidia per cadaver was estimated. Weighting each cadaver by the estimated number of conidia produced on the day of experimentation provided a variance stabilizing factor between experimental periods (see analysis of covariance, Table 30). It also predicted the 123 111 N.mH Hm m 111 m.m mm m .11 m.o ma N AHouuaouv -11 a.mH on N «Houuaoov 111 H.0H mm H o 111 m.m me H o ¢.m¢ N.qm Nm m o.oN N.NN cc m m.wm N.mm Hm N 0.5m m.ma nm N m.wm m.wm mm H OH o.OOH o.ooH Nm H OH ¢.Hm ¢.Na Nm m m.NN m.mm aN m o.o0H o.o0H mm N o.Nm o.wm on N H.Nm «.mm on H w o.COH o.OOH mm H w m.oa n.0m Hm m n.9m ¢.Nm mm m o.COH c.00H mm N m.Ho a.mo nm N N.om ¢.Ha cm H o o.wN o.ow on H o m.qo H.ao mm m a.mn e.Hw me n N.mo m.NN an N m.om w.¢n Hm N N.om m.Hm He H q o.mvmu wmumsfiv< unmouom z oumoHHnum mum>mvmo ome .wN wash ommH .cH wash lumowwsm wosuoa wnu waHm: aOHuommcH wasouwxomn you wmumsmcm meuHHmuuoz IHaoo owomaa am.m50Hnn> ou vomoaxo nustm uomwma :OHco you moHuHHmuuoa woumshvm was vo>uomno .AmNmHV uuonn< hp v0 .mowmo VHme :H momov H36 .wN oHnmy Table 29. 124 Analysis of covariance for arcsin proportion infection by day of experimentation while controlling for the metric variable, cadaver density. Source of Variation Sum of Squares DF Mean Square F Covariate Cadaver density 24.01 1 24.01 85.64*** Main Effects Days 2.99 l 2.99 10.68** Residual 9.25 33 .28 Total 36.25 35 1.036 ** (p<.01) *** (p<.001) Table 30. Analysis of covariance for arcsin proportion infection by day of experiment while controlling for the metric variable, estimated spore density. Source of Variation Sum of Squares DF Mean Squares F Covariate Estimated Spore Density 21.24 1 21.24 49.46*** Main Effects Days .84 1 .84 1.96NS Residual 14.17 33 .429 Total 36.25 35 1.036 *** (p<.001) NS = Not significant (p>.10) Table 31. Regression statistics for probit mortality versaslog spore dose (*1 million). Source DF Sum of Squares Mean Square Regression l 24.15 24.15 Residual 34 9.99 .294 Total 35 34.15 y intercept(a) = 4.86 i .019 r2 - .7072 slope(b) = 2.03 -_l-_ .224 y = 4.86 + 2.03x 125 .z aw\wzogmmuzuwmoo macaw on; 8w. wave 8.... 3.x. 8.x. 3...“. 8.? z Iliaw 8 1| d 17¢ . w a o .o l 9 m 1. I. .N. .9. 1 _l .1. _M “w. 19.1 w w nu [:4 Nu nu no mm 11. w -3. 0' I ”M .0: I. .U .A L T ”w: I I I I 10 .moHvsum GOHuuum IaH uHon scum omov ouoam woumEHumo mo on msmuw> huHHmuuoa me anoum .Ne ouame z.am\mmw>¢ocu oz_p¢4:momw 004 D.— O.D 0.0 v.0 N.O 0.0 N.OI F b . . E h z 10% m 0. ud O 3 w. m. m. .0: W 0 lb: 0 m “M “We m1. - I I S AlllUlHON 113 llQOUd .mvSum :OHuoomaH vaHm scum muo>mvmo mmomsa am.w:HuMH=Hoam mo NOH mamuo> muHHMuuoE hHm anoum .cq unamfim 126 actual conidia density to produce the observed infection. A probit regression analysis was applied to the spore density-percent infection data (Table 31, Figure 47) (note the reduced variation due to the differential conidial production between days). Using this relationship to predict host infection under differing conidial loads, the infection process can be modeled. For example, 1.17 million spores (less than that produced by 1 cadaver under optimal conditions) per meter produces 50% host mortality. This experiment may have overestimated the actual field infection due to the buffered environment within the experimental enclosures. V111. ABSTRACT MODELING The complexity of biological processes, particularly at the ecosystem level, hampers direct conceptual interpretation of overall system response to changing environmental conditions. Populations of specific organisms respond differentially to various environmental stimuli, as do individuals within each of these populations. Characterization of these responses both within and between species is important in developing a comprehensive understanding of how organisms interact within their environment. Although numerous detailed studies have been conducted for many organisms, synthesis of this information back to a system level has been hindered primarily due to a lack of available techniques. The development of biological modeling (Patten 1971 and Tummala et a1. 1975) has recently provided a medium for such synthesis. Individual subcomponents of a system may be evaluated in detail, described mathematically and linked through computer modeling with other subcomponents for evaluation of overall system response. The necessity to develOp more precise agricultural management programs, 127 including pest management, requires a thorough understanding of overall system response to both controlled and uncontrolled stimuli. Agricultural design and management are the focus of the onion pest crop agroecosystem project (Haynes et al. 1980), thus necessitating such a level of understanding. Systems modeling is being used in this project as the primary integration method between subcomponents. Each subcomponent is modeled and evaluated in free-body form (Tummala et al. 1975) and then linked to provide a better understanding of overall system response. Model development in this subproject follows the above format closely. The three primary components, OM, SCM and E. r_nu_sca£, are each conceptu- alized and modeled in free-body form and then linked to provide insight into their response as a unit. Model development and evaluation was conducted in cooperation with Ms. J.J. Pet and Mr. G.H. Whitfield. Only a brief synopsis of model formation validation and use will follow as it will be the central theme for Ms. J.J. Pet's Master of Science degree in the Department of Electrical Engineering and System Science at Michigan State University. (For more technical details, complete validation and use, please refer to Ms. Pet's thesis (projected completion date, 1982).) A. Simulation Model Formulation Actual model formulation involves the transformation of biological con- cepts and processes into mathematical form which can be linked and evaluated numerically. Numerous techniques are available to construct such models, including both "black box" and structural approaches (Manetsch and Park 1977). The structural approach, which explicitly defines subcomponents, their interconnections and constraints, provides the most biologically intuitive methods, and thus was used throughout this study whenever possible. When in- 128 depth system structure was not necessary or was unknown, time series were evaluated and black box approximations used (Manetsch and Park 1977, Varadarajan 1979). The modeling process begins with system conceptualization (section V, Figures 1-3), where the exact components of interest and the level of considera- tion are determined. For this model, the OM and E. m were considered in the detail described by the exact functional block diagram representing their life system (Figure 48). The SCM was considered to have the same response to E. m although only the adult life stages were modeled (time series representation) as much of its population dynamics occurs externally to the onion system and is presently unknown. The pest and pathogen components of the system are each composed of several life stages (Figure 48). These life stages (eggs, larvae, adults, Spores, etc.) represent "states" (Patten 1971) of the biological system (the density of the organism by life stage) at any given time (t). The transformation of these organisms between different states is controlled by various biological processes referred to as rates. Common to each life stage is the process of development (physiological maturation). The develOpment of each life stage is highly correlated with prevailing environmental conditions, predominantly temperature (Experiment 3, Carruthers 1979). Development occurs only when the temperature, T(t), exceeds a threshold, To, that is characteristic of each particular organism. Thus, each life stage requires a specific amount of heat accumulation above the threshold in order to complete development and pass from one life stage to the next. Once these developmental rates are estimated (Experiment 3, Carruthers 1979), the development can be modeled as discrete delays using difference 129 acupgcu 3.on p a u + £22... 0.: .8; was..- “.3.me Sq. 33.8.93 © 3.... .m.» whswfih any! hid a... 43.”... V ”we...“ 22:3 has”. .H .H 0 Cub-0.0 / + 1 3.28:: x n . . - >(d-O V . :1. ¢ l +1 II: Am Oba’fl‘ Oahu-.8. Lbl LEI 5.... as: 32.38. pin» 0 :3... / _m H _a :1. a. a.m.-uaxauo— .\ H- ., T 2...... , NW . @l , 5.... a... H.HH VII 38 .\:J 2:1. 9 130 equations or delay-differential equations. The underlying assumption in using a discrete delay to model insect development is that _a__ll individual entities of the population at any one stage develop in an identical manner. In other words, we assume that the insects move as homogeneous groups from one life stage to another. In reality, this seldom occurs. Organisms develop as heterogeneous groups; therefore, development differs between individuals. It is more realistic to model development considering aggregative behavior, which is characterized by a mean delay and associated variance. This mean delay is dependent on temperature, and hence, is time-varying. Consequently, the basic building blocks of the model are time-varying distributed delays (Forrester 1961, Abkin et al. 1972, Manetsch 1976, Ravlin et al. 1978, and Ravlin 1980) linked serially to form the basic model structure. Parameterization of the model subcomponents was accomplished using the data provided by the experimental sections of this study in combination with previously known information (Figure 3, Appendix C). Stage-specific mortality (Ellington 1963, Whitfield 1981) was applied at the end of each immature life stage and as an instantaneous mortality rate (Fulton 1978) in the adult stage. Host fly populations were allocated between specific habitats (onion fields and border areas) using the data collected in relation to host spatial patterns (section VII.B.3, Figure 21). Relative field-border dimensions were considered as a model variable and adjusted differentially to examine the effect of limiting habitat size on overall system response. Entomophthora muscae densities were initiated through host infection at the time of emergence and allowed to change freely based on interaction with host populations in the areas of population overlap. Conidial formation and sporulation were based on the time and temperature relationships outlined in 131 section VII.C (Experiments 5 6c 6). Conidial densities associated with the sporulation period were integrated and allowed to interact with host fly densities. This interaction produced infection based on the experimental probit infection-spore density relationship (section VII.C, Experiment 11). Two major deviations from the proposed model (Figure 48) were necessary for implementation: 1) the structural characterization of the azygospore section of the life cycle was not feasible due to the present lack of understanding of the biological processes controlling its operation, thus time series (section VI.B.3) were used to simulate primary host infection; and 2) further structural characterization of the adult OM was necessary to separate preovipositional and ovipositional adults. The preovipositional adult stage in the OM lasts approximately 105°C DD, necessitating separate consideration in model form. Because infection time is critical in determining if adults will live long enough to reach ovipositional maturity, a special modeling component was necessary to describe the host- pathogen interaction during this stage. This component can best be described as two biological processes (adult development and pathogen development) simultaneously competing for the same resource. The process was modeled by developing a 2-dimensional extension of a standard, time-varying distributed delay. B. Computer Implementation and Program Verification The computer implementation of any model increases its abstraction from the real world process by imposing a variety of technological constraints on the conceptualized system (Manetsch and Park 1977). Care must be taken when choosing the programming language and the machine for implementation as well as critically evaluating the resultant output. The E. muscae-host life system 132 model described above was programmed in cooperation with Ms. J.J. Pet (Master of Science candidate, Department of Electrical Engineering and Systems Science) in Fortran V and implemented on the Michigan State University Cyber 750 computer. The program was constructed in a hierarchical manner with a driver program controlling free-body submodels of each biological component (Figure 49, program source code listed in Appendix L). Program verification, a rigorous and time-consuming step, involved individual verification of each model subroutine before implementation into the final program followed by an overall evaluation when linked together through the driver program. Programming errors and model stability under various time increments (DT) were scrutinized for accuracy. Further program verification is linked with model validation before the model is acceptable for use. C. Model Validation Validation establishes whether or not the model is a valid representation of the actual system. The degree of model accuracy depends on the level of precision necessary for model use. For our purposes, conceptual evaluation, high precision (exact quantitative fit) was not mandatory, although desirable. In this instance, where the model was designed from experimentally-derived structural components, comparison with actual field data provided a measure of validity. Each component of the free-body model was evaluated separately and in combination. Initial response of the OM component provided a rough approximation of the observed natural system. Emergence and population density estimates (Figures 50 & 51) predicted by the model accurately described the initiation of each generation, although as the season progressed, the model response deviated markedly from the observed field data. The pathogen component of the model increased accuracy by providing adult mortality that 133 MAIN PROGRAM /.3 ———f ENTMOP \ I\ 051.20 loans] 1, INFECT2 \ H/ NORMAL 1' LE Lssreupj L ATEMP 1 \Uesmw j SCMAG /\ COMMON BLOCK 1- COMMON/PASS1/TEMP,LX,DT 2- COMMON/PASSZIHTIMEDMAG 3- COMMON/PASS3/STRG,SQ|.SQ2 4- COMMON/PASS4/RATES,DEL,DEGGS,SADULTS,OMOUT 5- COMMON/PASS5/SCRATE,DELSC.SCINF 6- COMMONIPASSGISCSTOR 7- COMMONIPASS7IOM,STMP,PSUM 8- COMMON/PASSBIPUPA,PUPA1,PUPA2,CUMDD \LFNL ] MAGGOT /\ \— INORMALI IDEVDELI I DELAY I Figure 49. Programming structure of Free Bod Ho st-Patho e Sim Model listed in Appendix L. y g n Ulation 134 ..- 51 MODEL-- 23' 2 nab—— 2° .13 u a H C) DO 8 CE ‘iz’ .1 019‘ 5 0° ON 3 "e. K LO L O m'u z >.0 o I ~~~~~ v- 1— C m.- .1 0‘3. 2.. 8° 36‘ I: U 0. O O O "‘ r*fi1""vfi"'v'*'*T'r"1 “to 100 150 200 280 300 “to 100 150 200 250 300 JULIRN DRTE JULIRN DRTE Figure 50. Figure 51. V D O .2 6‘1 noon—- h,. m nan — U. F”. 2° .Jo 11.1 D .3 0 00 M C U 5': a’ o l— o: Eu 2 9+ Leo O. D “1.. z >° O 1- : C! as... .J OD...) 3°: 8° 3 0° E I\\ =2 8 . \._ cwfvv V-.. vvwv'vvvv"'*'1 “to 100 150 200 260 :00 “to 100 150 200 250 30c JULIRN DRTE JULIRN DRTE Figure 52. Figure 53. Fi ure 50. Observed and simulated (ex ected) onion ma ot emergence for 4. Figure 51. Figure 52. Figure 53. Grant 1980. E, muscae not included in the simulation. Observed and simulated onion maggot adult population levels for Grant 1930. .E. muscae not included in the simulation. Observed and simulated (expected) onion maggot emergence for Grant 1980. E, muscae included in the simulation. Observed and simulated onion maggot adult population levels for Grant 1980. E, muscae included in the simulation. 135 was inherent in the natural system. The effect was a shifting of the predicted population curves and a much more realistic model response (Figures 52 6c 53). Population infection levels (Figure 54) also followed observed field patterns (Figures 14-19) responding in a lagged, density-dependent manner (Section VII.B.2). Further model validation including the response of the immature OM life stages, and sensitivity analysis of the entire model is given by Whitfield (1981) and Pet (1982, projected date of masters thesis completion). D. Model Application An understanding of the system response to various environmental and human-induced conditions is necessary to deve10p and implement pest management strategies. Simulation of these conditions, although not absolute, can provide insight into the system response at an extremely small cost. Such insight may be useful for management decision-making or for developing hypotheses to be tested under field conditions. Although the model is still in the process of being validated, example outputs (Figures 54—56) were generated using the 1980 Grant weather data as a standard for comparison. The effect of altering the initial OM population (Figures 54 Gr 55, note differing scales) on E. m follows the expected density-dependent pattern discussed in section VII.B. An initial population size of 5000 flies resulted in infection levels approaching 50%. The resultant second and third generations and the associated E. M infection levels were reduced to much lower densities. Simulation of the initial population density of 500 flies produced rather stable numbers between all three generations. The reduced infection level during the second generation is always less than the other two generations, due to the spatial aggregation (Figure 21) in border areas in early spring and late fall. 136 8 -s 9 -§ 0] ’ } F P g r E i ‘ l g. l I ‘ ' U) m E: " z 5 z 1': E 2 r. * 2 g 8 f;‘ p E s E 8 ’ 'i (D :4 _ o :8 u-I ° .. o .— 8 If! 8 8" 1 a a u \ u u 3% :5 :3 > :5 3 Nd & j l .' f." “ "o'- 94 1 §4 . I I \ , I‘ -_ I \ l \\ I \\/I \\ l J H V-.- ---- *vvv "" "" fi"j""1" ‘Y""Yfi" a “so 130 1150 :30 do no" °su m 150 :00 :so :00 JULIRN ORYS JULIRN ORYS Figure 54. Figure 55. g] '§ §' '§ p P | . . 1 g4 ' g4 : 1 U) ‘0 a: ‘ z :1 §« 2 1:34 . U P" U o— 1: 0- : :- U U - r: a t a; z "’ El 2 d ~ 0 _ o F- 8 E 8 2 IO U ‘0 U ‘§ ‘62 kg :2 U) 1: m a: r: 1 1: d d 5. g. °so °u JULIRN ORYS JULIRN ORYC Figure 56. Figure 57. Figure 54. Figure 55. Figure 56. Figure 57. Simulated onion maggot adult population with the predicted level of E. muscae infection. (Initial OM population = 5000). Simulated onion maggot adult population with the predicted level 0f.§3 muscae infection. (Initial OM population = 5000). Simulation results showing the augmentation of E, muscae infection due to the inclusion of the seed corn maggot. (Initial populations: OM = 5000; SC = 5000). Simulation results showing the effect of fungicide application on E, muscae infection and the resultant fly pOpulation increase. 137 The impact of an alternate disease host can be clearly noted (Figure 56) with the addition of the SCM population. Using the same input parameters as in Figure 5#, with the addition of 5000 SCM adults substantial increases in the infection levels were simulated, causing a further reduction in the second and third generations. The model was also developed to simulate the utilization of both insecticides and fungicides on the three populations. Two applications of an early season fungicide (applied on days 150 and 170 for downy mildew (Figure 57)) greatly reduced g. mu_sgag levels, which altered both OM and SCM from their previously projected population levels (Figure 56). These examples represent only a small proportion of the analysis possible upon final validation of the model. After completion, the model is to be linked to other models associated with the onion agroecosystem for evaluation of overall system response and the development of ecosystem level management strategies. IX. SUMMARY Entomophthora muscae was identified as a common fungal pathogen of the adult onion maggot, Hylemya antiqua, and the adult seed corn maggot, fl. p_l_a_tgr3, during each of 4 study years (1977-80). Positive identifications made in every sample area indicated a well established, widespread distribution of the pathogen throughout Michigan's southern lower peninsula. In addition, two other flies commonly found in onion production regions, the cluster fly (Pollenia rudis) and the tiger fly (Coenosia tigrina) were identified as common hosts. Clearly, g. muscae is a common and integral biological component of the onion agroecosystem. Intensive field-level monitoring revealed variable infection levels 138 throughout the season. Disease incidence, on the average, was highest during the spring and fall and lower during midsummer. Multivariable techniques were used to assess the effect of both abiotic and biotic environmental variables on disease occurrence. Host and inoculum densities were the only variables found to produce statistically significant infection patterns. Further experimentation indicated that changes in host and pathogen spatial patterns throughout the season directly affected the amount of pOpulation overlap. These changes in interaction in turn resulted in variable infection levels. Although E. muscae infection levels were high under many conditions, overall host mortality due to mycosis was significantly reduced by other preempting mortality factors. Estimates ranging between 2.196 and 23.196 mortality due to mycosis were noted in the overall fl. antigua population during 1979. By considering only the adult flies reaching maturity (females older than the 105 DD preoviposition period), the range of g. muscae-induced mortality is increased to 15.1-68.196. The significance of this pathogen from a population dynamics standpoint must then be considered as it affects the succeeding generation through a reduction in population fecundity. Using simulation methods, estimates of between 13.6 and l19.396 reduction in fecundity were obtained for the 1979 ii. antigua pOpulations. In years when other mortality factors are not as effective and do not preempt g. muscae mycosis, the potential impact may be greater. The development of a conceptual g. muscae/single-host life system model helped specify what further experimentation was necessary to parameterize the transfer functions used to complete an actual simulation model. _Ifl xiv_o and i_n _\_ri_tr_o culture and transmission techniques were also developed to aid in specific experimentation. 139 A second order polynomial expression adequately described the temperature-dependent developmental relationship of E. muscae i_n vivo. Entomophthora muscae also caused a premortality reduction in E. antiqua fecundity. Noticeable reductions in fecundity of mature females occurred #0 DD after infection, with complete suspension around 70 DD (approximately #0 DD before host death). Host death and the initiation of g. m_usgag conidial sporulation occurred only during evening hours. Dying flies locate attachment sites, fasten themselves to the substrate, and die between 5 and 8 p.m. Conidial production and sporulation quickly follow. Conidia are released throughout the night. Sporulation follows a cumulative normal distribution centered around 1:00 a.m., with 9596 of the spores released between 9:00 p.m. and 5:00 a.m. the following morning. Although the sporulation phenology followed this basic pattern, the absolute number of spores produced was temperature and moisture dependent. Under optimal conditions, approximately 1.5 million spores per fly are produced. For modeling purposes, the phenology and density of conidial production was evaluated on an hourly basis for synchrony with environmental conditions producing spore dispersal and germination. Conidial germination was evaluated under laboratory and field conditions. Free moisture was found to be necessary for conidial germination; temperature regulated the rate of development. Polynomial regressions were used to simulate the time-temperature-germination response surfaces. Conidial germination and survival were evaluated in onion, carrot, and field border plant canopies. Germination and survival rates were highest in carrot and border canopies (the primary site of host-pathogen interaction) due to increased canopy cover. Canopy cover provided protection from harsh environmental conditions. 140 In each of four experimental dates, almost complete conidial mortality occurred within a 24-hour period following the initiation of sporulation. This suggested that host-pathogen interactions must occur the day following sporulation for infection to deve10p. Host-pathogen interactions were examined in border habitats. Experimental results indicated that a significant amount of infection occurs from host contact with conidia deposited on vegetation rather than only by direct contact at the time of sporulation. Field cage studies were used to estimate host mortality under differing conidial doses. Using probit analysis, 5096 host infection level can be expected with a conidial load of 1.17 million conidia per meter. Under optimal conditions, 1.17 million conidia per meter converts to less than 1 cadaver per meter, which is well within observed field densities. The multiple host-pathogen life system was simulated using free-body modeling techniques developed in cooperation with Dr. G.H. Whitfield and Ms. J.J. Pet. Preliminary model validation has been conducted component by comonent and in combined form. Results indicate that the model provides a reasonable approximation of the desired system. Further validation and testing are currently underway and readers are referred to Pet (in prep.) for a more detailed evaluation of results. In conclusion, 1:. muscae is of prime importance in the population regulation of both OM and SCM in the onion agroecosystem. 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Path. 10:116-21. Zadoks, J.C. and RD. Schein. 1979. Epidemiology and Plant Disease Manage- ment. Oxford Press, New York. 427 pp. Zwolfer, H. 1963. The structure of the parasite complexes of some Lepidop- tera. Zeit Angew. Entomol. 51:346-57. APPENDIX A The Impact of Pesticides On Natural Enemies of the Onion Maggot, HylemLa antigg The Impact of Pesticides On Natural Enemies of the Onion Maggot (Hylemya antiqua (Meigen))l’ 2 By R. I. Carruthers 6: D. L. Haynes ABSTRACT Pesticide-induced differential mortalities between the onion maggot (Hylemya antiqua (Meigen)) and several organisms associated with its natural control (E. platura (Meigen), Coenosia tigrina (Fabricius), Entomophthora muscae (Cohn), and Aphaereta pallipes (Say)) were evaluated under simulated field conditions. Direct and residual differential mortalities were examined for three herbicides (Chloro-IPC, nitrofen, and CDAA), three fungicides (maneb, chlorothalonil, and copper sulfate), and an insecticide (malathion). The recommended field application rates of these chemicals produced high differential mortality levels between E. an_ti_gu_a and some of the natural mortality agents. Chloro—IPC, a preplant herbicide, induced 10096 differential mortality between E. m_tqug and _A_. £11m over the three-day residual test period. 155 156 INTRODUCTION An intensive use of chemical insecticides has long been known to disrupt the natural balance of host-parasitoid systems (Nicholson 1939, Pickett 1949, Ripper 1956, Van den Bosh and Stern 1962). Insecticide use also causes differential mortality, the temporary elimination of natural enemy food resources (host insects) and the secondary poisoning of natural enemies-—known mechanisms of pest resurgence (Croft and Brown 1975). Disruptions such as these dominate contemporary agricultural production systems (Metcalf 1980). Less apparent, yet just as significant, are the impacts associated with agrochemicals applied for purposes other than direct insect control. Of the present agricultural pesticide usage, herbicides compose ca. 5296, insecticides ca. 3896, and fungicides ca. 1096 (McEwen and Stevenson 1979). The accelerated use of herbicides in recent years, combined with the current and predicted trend toward chemically maintained no-till production systems, is certain to cause increased side effects in the associated agroecosystem (Barney 1980). Direct toxicity as well as indirect effects (elimination of habitat, food resources, etc.) on non-target organisms are probable and must be considered when designing and managing agricultural production systems. The onion production system in Michigan is a prime example of an agroecosystem with several direct pests as the focus of intensive, chemical control programs. Haynes et a1. (1980) described the structural organization of the onion pest-crop agroecosystem, accentuating the interrelationship between the biotic components and the onion plant. In this system, antagonism between traditional disciplinary-oriented control strategies is apparent as many important biological control populations (parasitoids, pathogens, and predators) are frequently disrupted by the application of common agrochemicals. fl 157 This paper will quantify the differential effects of specific pesticides between Hylemya antiqua (Meigen), the primary insect pest associated with northern onion production, and several organisms associated with its natural mortality: Hylemya platura (Meigen) (alternate pathogen host), Entomophthora muscae (Cohn) (fungal pathogen), Coenosia tigrina (Fabricius) (predator) and [Mahaereta filipes (Say) (parasitoid). METHODS TEST SPECIES A vacuum-operated, self-propelled, insect collection machine (Cobb and Ruppel 1976) was used to mass collect E. M, E. 3131153, and E. M adults from onion fields in Grant, Michigan. The flies were anesthetized with C02, sorted to species, and caged for transportation to the laboratory. The test species were maintained at 21 i 2°C for a 48 h period prior to testing (food and water provided). Individuals identified as being infected with E. m were transferred to alternate storage areas before sporulation and used as pathogen sources for conidial sporulation and germination studies. One hundred _A. Wparasitized onion maggot (OM) pupae were collected from onion fields in Grant Township, Newaygo County, Michigan, and reared through a single cohort of laboratory-reared OM larvae (Niemczyk 1964) to acquire the quantity necessary for testing. APPLICATION TECHNIQUES The pesticides tested represent the chemicals presently used in the Michigan onion production system. Commercially available formulations were used in all experiments with treatment doses designed to bracket recommended 158 field application rates (Cress et a1. 1976, Putnam 1979). The chemical doses are presented as concentrations (percent solution) based on the amount of active ingredient applied to 1 ha in 470 liters of water (Table A1). Bioassay procedures associated with the insect species tested used a Beltsville chain-drive sprayer to apply the test chemicals to barley seedlings. Wooden greenhouse flats containing 20 cups (each containing five 12 cm tall plants per 9 cm diameter Dixie' cup) were run through the sprayer at 40.5 m/min. A Tee Jet’ 8004B nozzle, emitting 1.46 liter/min (27 g/cm2 liquid pressure) was used to acquire the desired spray volume (470 liter/ha) (Anonymous 1977). A11 sprayer specifications were held constant throughout the experiment with the treatment doses adjusted by altering the spray solution concentration. Test insects were placed on the barley substrate (1 cup/rep) immediately after spray application and covered with a screen-topped glass globe. The globes were held at 21 1 2°C for the test period with mortality assessments made at 12, 24, 36, and 48 h after initial contact. The insects were considered dead if they were unable to right themselves. The pesticide test series was compared to three control treatments for each chemical-insect combination: a) no spray, b) water spray, and c) non-contact. The non-contact control treatment consisted of treated barley (2x field rate) covered by glass lantern globes fitted with an internal screen divider separating the treated plants from the test insects. This was used to evaluate the contact versus fumigative mortality. Additional barley cups, treated at the recommended field rates, were placed outdoors and allowed to age under natural field conditions (14 h photoperiod, clear skies, 0 precipitation, 27-5°C temperature range). Residual activities were evaluated over a three-day period by introducing test insects after specific aging periods. 159 TABLE A1. Treatment levels (presented as percent solution) for common onion production pesticides. Malathion Nitrofen CDAA CIPC Chlorothalonil Maneb C°PPer (I) (H) (H) (H) (F) (F) Sulfate (F) a .576 .960 2.88 2.88 1.08 1.14 .240C .2886a .480a 1.44a 1.44a .54a .57a .024c .1444 .240 .720 .72 .27 .285 .0024C .0722 .120 .360 .36 .14 .143 .0361 .06 .180 .18 .07 .072 .018 .054c .057C .009 .0054C .0057C .0045 “00‘!” "111111 11:: recommended field application rate treatment applied only to _A. pallipes treatment applied only to E. muscae conidia insecticide herbicide fungicide 160 Standard slide germination tests (Altman 1966) were used to evaluate pesticide-induced disruption of E. muscae conidial germination. Glass microscope slides were dipped into chemicals at the test concentrations, air- dried, and then held under sporulating, E. muscae-infected, _I_-I_. antigua adults until a minimum of 100 conidia per slide was obtained. Five independent pathogen sources (infected flies) were used as replicates within each treatment. The slides were incubated by treatment in enclosed containers maintained at 21 1: 2°C and 10096 R.H. under a 14-10 light-dark cycle. One hundred conidia per replicate were evaluated for spore germination. A minimum germ tube length (2x spore diameter) or the formation of a secondary conidium was used as the criterion for germination. RESULTS AND DISCUSSION Response to treatment stabilized after 24 hr, and this time interval was used for mortality assessment. Mortality levels between controls (no spray, water spray, and non contact) were not significantly different (analysis of variance-arcsin transformation) at the 9596 level of confidence. Control mortalities were pooled and used to adjust each treatment replicate for natural mortality (Abbott 1925). Corrected dose-mortality data were linearized using log dose, probit mortality transformations and evaluated using regression analysis where sufficient mortality data was available (Figures A1 and A2). A complete summary of the experimental results, LD50 with 95% confidence limits, the no effect treatments, and the 10096 mortality treatments are given in Table A2. Malathion-induced differential mortality levels between 11. m and the other test insects are readily apparent (Figure Al). Due to organophosphate 161 "3 C. tigrina to 1 2 3.4644142): o " as loturo A. pallipes ' P “I- 6.8 +2.13): m 33.33%. 2x {.193 10 _ c: -- 1\ >1: '9 H. antiqua H 5. 6+.78 3' {1.37 x 12 ° , o g 05" In p. a: t O currently recommended 05 u?— applicator: rate , o 0.. v '0 9 '_ » e w I I ' l I "’ .001 .01 .1 1 10 100 PERCENT MADKHflON SOLUflON Figure A1. Log dose-probit mortality regressions for H. antigua, E, platura, E. tigrina, and E. pallipes exposed to PERCENT MORTALITY malathion-treated barley (see Table 2 for sample sizes). PROB I T HORTRLITY 162 ‘9 to- malathion _ o 9.7 +2.32 °’ 61.93 x 9‘ pddo «o 5.5 3. #:334- 27x w maneb I o " =5.5 +3.2 " w {2:33 7" 0 5‘ r 8 nitrofen 5.94+2. 9 2$.96 2 "3- 0 V 1»- chlorotholonil n 5:3;th ‘3 .f 11 9 ID a; I 1 I I c, .001 .01 .1 1 10 100 PERCENT PESTICIDE SOLUTION Figure A2. Log dose-probit mortality regressions for E, pallipes (see Table 2 for sample sizes). *Recommended field application rates. PERCENT MORTALITY TABLE A2. 163 Response levels and LD50 values with 95% confidence limits (CL) for all experimental treatments. m4W.“ “H— E _ .‘—— ‘A— h—H-. .... 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N 3!... «:20 asap 1 m. .m 0881 I 81 1008 N803 0338 4381/81 1005 10003“ N01N0 o---—--¢ N011 33!"! 10133034 o-—--—-o #4011333!” 1N33U3d .mfinusa mumsmfim .ma mpswfim .u v.0uun¢..n>¢o Hague emu Smu calm. 92.: more 9. dm\ 1/ \Aa ‘1 \ v . m < i V n .c u; h n 3 . 0 A ..m u i I w; . m ..m . c I I. u u. . a an A V — 22L «coco onu— WL fin .mn muswfim .u v.vuua¢o.u>¢o uncoun— 88 88 83 88 So an .. V . s v ~~ Vol 0 R4 .. m. . fl 0 s N _N i u w. J m. n f 1 n f m A _. 10 m . u f v / L .n 1 9.. k . W In. J _ 22... .86 22 . wk .m Appendix E. 192 Data for incubation period of E. muscae infecting onion maggot adults (See Experiment 3). Appendix E. CDDALATENTPERIODEMUSCAE (1X, 3F10.5) Table E1. 0 0 Days for o 0 Days for f F 0‘ 'Developmegg ' F' ' ' -' ' .C' 'Develgpment 40260000 4.44444 0.00000 50.00000 10.00000 16.00000 40.00000 4.44444 0.00000 50.00000 10.00000 16.00000 40.00000 4.44444 0.00000 50.00000 10.00000 17.00000 40.00000 4.44444 0.00000 50.00000 10.00000 17.00000 40.00000 4.44444 0.00000 ~50.00000 10.00000 17.00000 40.00000 4.44444 0.00000 50.00000 10.00000 17.00000 40.00000 4.44444 0.00000 50.00000 10.00000 18.00000 40.00000 4.44444 0.00000 50.00000 10.00000 18.00000 40.00000 4.44444 0.00000 50.00000 10.00000 18.00000 40.00000 4.44444 0.00000 50.00000 10.00000 19.00000 40.00000 4.44444 0.00000 50.00000 10.00000 19.00000 40.00000 4.44444 0.00000 50.00000 10.00000 19.00000 40.00000 4.44444 0.00000 50.00000 10.00000 20.00000 40.00000 4.44444 0.00000 50.00000 10.00000 20.00000 40.00000 4.44444 0.00000 50.00000 10.00000 21.00000 40.00000 4.44444 0.00000 50.00000 10.00000 21.00000 40.00000 4.44444 0.00000 50.00000 10.00000 22.00000 40.00000 4.44444 0.00000 55.00000 12.77778 9.00000 40.00000 4.44444 0.00000 55.00000 12.77778 10.00000 40.00000 4.44444 0.00000 55.00000 12.77778 10.00000 40.00000 4.44444 0.00000 55.00000 12.77778 10.00000 40.00000 4.44444 0.00000 55.00000 12.77778 10.00000 40.00000 4.44444 0.00000 55.00000 12.77778 11.00000 40.00000 4.44444 0.00000 55.00000 12.77778 11.00000 40.00000 4.44444 0.00000 55.00000 12.77778 11.00000 45.00000 7.22222 36.00000 55.00000 12.77778 11.00000 45.00000 7.22222 36.00000 55.00000 12.77778 11.00000 45.00000 7.22222 37.00000 55.00000 12.77778 11.00000 45.00000 7.22222 38.00000 55.00000 12.77778 11.00000 45.00000 7.22222 38.00000 55.00000 12.77778 11.00000 45.00000 7.22222 39.00000 55.00000 12.77778 11.00000 45.00000 7.22222 39.00000 55.00000 12.77778 12.00000 45.00000 7.22222 40.00000 55.00000 12.77778 12.00000 45.00000 7.22222 40.00000 55.00000 12.77778 12.00000 45.00000 7.22222 40.00000 55.00000 12.77778 12.00000 45.66000 7.22222 41.00000 55.00000 12.77778 12.00000 45.00000 7.22222 41.00000 55.00000 12.77778 12.00000 45.00000 7.22222 41.00000 55.00000 12.77778 12.00000 45.00000 7.22222 41.00000 55.00000 12.77778 13.00000 45.00000 7.22222 41.00000 55.00000 12.77778 13.00000 45.00000 7.22222 42.00000 55.00000 12.77778 13.00000 45.00000 7.22222 42.00000 55.00000 12.77778 14.00000 45.00000 7.22222 42.00000 60.00000 15.55556 6.00000 45.00000 7.22222 43.00000 60.00000 15.55556 7.00000 45.00000 7.22222 43.00000 60.00000 15.55556 7.00000 45.00000 7.22222 43.00000 60.00000 15.55556 7.00000 45.00000 7.22222 44.00000 60.00000 15.55556 7.00000 45.00000 7.22222 45.00000 SG-GGGCG 15.55556 7.00000 45.00000 7.22222 45.00000 60.00000 15.55556 7.00000 45.00000 7.22222 47.00000 60.00000 15.55556 7.00000 50.00000 10.00000 12.00000 60.00000 15.55556 7.00000 50.00000 10.00000 13.00000 60.00000 15.55556 7.00000 50.00000 10.00000 14.00000 60.00000 15.55556 7.00000 50.00000 10.00000 14.00000 60.00000 15.55556 8.00000 50.00000 10.00000 15.00000 60.00000 15.55556 8.00000 50.00000 10.00000 15.00000 60.00000 15.55556 8.00000 50.00000 10.00000 16.00000 60.00000 15.55556 8.00000 50.00000 10.00000 16.00000 60.00000 15.55556 8.00000 193 Appendix E. (con.) 0 0 Days for o 0 Days for F C Development F C Development 60.00000 15.55556 8.00000 75.00000 23.33339 3.00000 60.00000 15.55556 8.00000 75.00000 23.88889 7.00000 60.00000 15.55556 8.06000 75.00000 23.88889 7.00000 60.00000 15.55556 8.00000 75.00000 23.33339 7.00000 60.00000 15.55556 9.00000 75.00000 23.33339 7.00000 60.00000 15.55556 9.00000 75.00000 23.88889 7.00000 60.00000 15.55556 9.00000 75.00000 23.33339 7.00000 60.00000 15.55556 9.00000 75.00000 23.33339 7.00000 60.00000 15.55556 10.00000 75.00000 23.33339 7.00000 65.00000 18.33333 6.00000 75.00000 23.33339 5.00000 65.00000 18.33333 7.00000 75.00000 23.33339 6.00000 65.00000 18.33333 7.00000 75.00000 23.33339 5.00000 65.00000 18.33333 7.00000 75.00000 23.88889 6.00000 65.00000 18.33333 7.00000 75.00000 23.33339 5.00000 65.00000 18.33333 7.00000 75.00000 23.88889 6.00000 65.00000 18.33333 7.00000 75.00000 23.33339 5.00000 65.00000 18.33333 7.00000 75.00000 23.88889 5.00000 65.00000 18.33333 7.00000 75.00000 23.88889 6.00000 65.00000 18.33333 7.00000 75.00000 23.88889 6.00000 65.00000 18.33333 7.00000 75.05000 23.88889 6.00000 65.00000 18.33333 7.00000 75.00000 23.88889 6.00000 55.00000 18.33333 7.00000 75.50000 23.88889 6.00000 55.00000 18.33333 7.00000 75.00000 23.88889 5.00000 65.00000 18.33333 7.00000 75.00050 23.88889 6.00000 65.00000 18.33333 7.00000 30.00000 26.66667 5.00000 65.00000 18.33333 8.00000 30.00000 25.55557 5.00000 65.00000 18.33333 8.00000 30.00000 26.66667 7.00000 65.00000 18.33333 8.00000 30.00000 25.55557 7.00000 65.00000 18.33333 8.00000 30.00000 25.55357 7.00000 65.00000 18.33333 8.00000 80.00000 26.66667 7.00000 65.00000 18.33333 8.00000 30.00000 26.66667 7.00000 65.00000 18.33333 9.00000 30.00000 26.66667 7.00000 65.00000 18.33333 9.00000 30.00000 26.66667 7.00000 65.00000 18.33333 9.00000 30.00000 26.66667 7.00000 70.00000 21.11111 5-00000 80.00000 26.66667 6.00000 70.00000 21.11111 6.00000 30.00000 26.66667 6.00000 70.00000 21.11111 6.00000 80.00000 26.66667 5.00000 70.00000 21.11111 7-00000 80.00000 26.66667 6.00000 70.00000 21.11111 8.00000 80.00000 26.65667 6.00000 70.00000 21.11111 8.00000 80.00000 26.66667 6.00000 70.00000 21.11111 8.00000 80.00000 26.66667 6.00000 70.00000 21.11111 8.00000 80.00000 26.66667 6.00000 70.00000 21.11111 7.00000 80.00000 26.66667 6.00000 70.00000 21.11111 7.00000 80.00000 26.66667 6.00000 70.00000 21.11111 7.00000 80.00000 26.66667 6.00000 70.00000 21.11111 7.00000 80.00000 26.66667 6.00000 70.00000 21.11111 7.00000 80.00000 26.66667 6.00000 70.00000 21.11111 7.00000 85.00000 29.44444 5.00000 70.00000 21.11111 7.00000 85.00000 29.44444 5.00000 70.00000 21.11111 7.00000 35.00000 29.44444 7.00000 70.00000 21.11111 7.00000 35.00005 29.44444 7.00000 70.00000 21.11111 7.00000 35.00000 29.44444 7.00000 70.00000 21.11111 7.00000 35.00000 29.44444 7.00050 70.00000 21.11111 7.00000 85.00000 29.44444 7.00000 70.00000 21.11111 7.00000 35.00000 29.44444 7.00000 70.00000 21.11111 7.00000 35.00000 29.44444 7.00000 75.00000 23.88889 5.00000 35.00000 29.44444 6.00000 Appendix E. (con.) Days for o o F C Development 85.00000 29.44444 6.00000 ' 85.00000 29.44444 6.00000 85.00000 29.44444 6.00000 85.00000 29.44444 6.00000 85.00000 29.44444 6.00000 85.00000 29.44444 6.00000 85.00000 29.44444 6.00000 85.00000 29.44444 6.00000 85.00000 29.44444 6.00000 85.00000 29.44444 6.00000 85.00000 29.44444 6.00000 85.00000 29.44444 6.00000 85.00000 29.44444 6.00000 85.00000 29.44444 6.00000 90.00000 32.22222 0.00000 90.00000 32.22222 0.00000 90.00000 32.22222 0.00000 90.00000 32.22222 0.00000 90.00000 32.22222 0.00000 90.00000 32.22222 0.00000 90.00000 32.22222 0.00000 90.00000 32.22222 0.00000 90.00000 32.22222 0.00000 90.00000 32.22222 0.00000 90.00000 32.22222 0.00000 90.00000 32.22222 0.00000 90.00000 32.22222 0.00000 90.00000 32.22222 0.00000 90.00000 32.22222 0.00000 90.00000 32.22222 0.00000 90.00000 32.22222 0.00000 90.00000 32.22222 0.00000 90.00000 32.22222 0.00000 90.00000 32.22222 0.00000 90.00000 32.22222 0.00000 90.00000 32.22222 0.00000 90.00000 32.22222 0.00000 90.00000 32.22222 0.00000 90.00000 32.22222 0.00000 194 Appendix F. Entomophthora muscae sporulation rate data (See Experiment 5). CDDASLEASEMALEAUGB. CDDASLEASEFEMALEAUGB. CDDASLEASEMALEAUGl7. CDDASLEASEFEMALEAUG17. (1X, 11F5.0) -999 = missing data 195 MMMVQVGWIDIDLDWQQWI‘NFFQGJQCDQ .5 .H .N .6 .3 .85 .m .m .6 .88 .m .mm .3. .86 .mm .58 .mm .mm .mm .mm .m .8 .85 .6 .m .6 .8 .8 .5 .8 .85 .8 .58 .85 .85 .88 .66 .88 .588 .558 .858 .5 .m .6. .m .m .m .m .m .8 .8 .88 .8 .mm .Nm .mma .mm .3. .6m .vm .mm .3. .m 0% .0 oVfl as. .m 0N av 0? .Q oNH 0N o®OH .Nm 0V6 ohv 0mm 0mm 0ND .FV oh? cm .6 .88 .5 .8H .58 .6 .88 .55 .85 .55 .8 .58 .58 .88 .66 .85 .58 .88 .558 .688 .m .m .8 I: .8 .m .m .m .m .m .3 .3 .mm .mm .mm .mm .mm .mm .8H .mm .mm .m .5 .8 .5 .6 .8 .68 .8 .8 .5 .55 .555 .65 .668 .58 .55 .66 .555 .88 .858 .858 .m .8 .58 .5 .8 .6 .6 .5 .8 .m .88 .58 .65 .855 .86 Jam .58 .58 .mm .58 .65 .5 .m .85 .8 .58 .88 .55 .85 .5 .6 .58 .mm .86 .88 .86 .mm .mm .555 .55 .55 .65 .6 .8 .m .3 .2 .5 .ma .MH .6 .58 I: .86 .mm .3 .mm .mm .NH .m~ .9... .m3 .6m .6 .3 .3 .8: .5” .6H .8 .m .8 .m .3 .mm .mm .3. .8: .mm .3. .mm .mn .558 65H .6 .58 .58 .88 .58 .m .5 .6 .8 .8 .55 .65 .86 .88 .58 .mm .mm .58 .mm .mm .66 .6 .5 .58 .m .8~ .8 .8 .5 .58 .85 .m .56 .56 .65 .58 .58 .5“ .5 .m .8 .8 .8 .m8 .55 .65 .5. .5H .8 .5 .AH .88 .6 .58 .58 .58 .88 .88 .58 .68 .58 .86 .88 .8 .55 .6 .m .m .8 .8 .6 .8 .88 .6 .58 .68 .58 .58 .mm .mm .55 .65 .88 .68 .8 .88 .m .6 .m .8 .5 .6 .8 .8 .m .868 .888 .68 .86 .68 .65 .5 .5 .5 .8 .8 .58 .85 .8 .56 .88 .56 .5 .68 .58 .5 .5 .5 .8 .8 .8 .5 .85 .8 .58 .m .8 .86 .86 .88 .88 .86 .58 .88 .58 .88 .5 .5 .8 .8 .8 .8 .5 .5 .8 .8 .5 .8 .58 .55 .88 .88 .55 .65 .6 .5 .5 .8 .58 .5 .5 .55 .5 .88 .85 .m .5 .5 .8 .mm .58 .58 .mm .65 .58 .85 .mm .88 .5 .8 .5 .5 .5 .5 .5 .5 .5 .5 .5 .8 .58 .mm .mm .mm .86 .58 .86 .86 .86 .8 .6 .8 .m .m .8 .5 .6 .5 .6 .6 .8 .658 .mm .86 .58 .58 .85 .58 .mm .58 .8 .5 .5 .5 .5 .5 .5 .5 .5 .8 .8 .5 .6 .m .68 .8 .5 .8 .68 .85 .86 .8 .5 .8 .5 .5 .5 .5 .5 .5 .5 .5 .5 .86 .86 .86 .8“ .88 .mm .58 .86 .86 .8 .5 .5 .5 .5 .5 .5 ,wo .5 .5 .5 .8 05H. UEHH. wwfimcov 080mm mo moumofiamou coy huamcov muomm mo moumoadmuu :09 .meS .w um5w3< .Hm manna 196 o o o o HQNHVNV 0.0.0... QVOWMQMQ H HMN . O m v.1 O‘v v-c .80" .565 .m5 .55 .556 .mm .NH .mm .5 .8 .8 .5 .5 .5 .5 .8 .5 .5 .5 .8 .m .w .8. .h .w .v .5 .5 .5 .5 .5 .8 .5 .8 .5 .5 .8 .8 .5 .8 .8 .8 .5 .8 .5 .6 .5 .5 .8 .5 .58 .88 .58 .65 .55 .88 .8 .58 .58 .8 .5 .65 .66 .56 .86 .56 .65 .55 .5 .6 .8 .8 .5 .5 .8 .5 .5 .5 .5 .8 om 0N 0N 0? ON on .5 .5 .5 .55 .65 .8 .6 .5 .8 .55 .55 .5 .5 .5 .8 .8 .5 .5 .85 .5 .5 .5 .6 .6 .6 .8 .8 .6 .5 .8 .6 .6 .6 .5 .8 .6 .65 .68 .55 .58 .88 .58 .5 .5 .68 .86 .58 .58 0H 0N om 0H 0N 0M .5 .55 .5 .55 .5 .6 ON 0V ON 0? om 0N .88 .55 .6 .5 .85 .5 .58 .58 .88 .85 .55 .585 ”86 .88 .55 .5 .58 .68 .5 .8 .5 .6 .55 .58 8 .5 .5 .65 .8 .55 .58 .58 .58 .55 .58 .55 .mm .58 .88 .55 .58 .55 .555 .mNH .on .m- .mNH .mo~ .vwa .HHN .mmn .655 .Nm .woa ”on .mm .mm .Nm .mmm .on a .m .05 .85 .HN .vw .Nn .mm .mm .MN .mm .mm mufimaou 080mm mo moumoadmuu 8 .8 .85 .8 .5 .85 .5 .5 .N5 .o .H .- .8 .8 .85 .8 .5 .85 .8 .8 .55 .Hv .ma .am .55 .85 .55 .88 .56 .55 .m .6 .55 .m .ma .55 .8 .8 .55 .65 .85 .55 .Hm .HN .9“ .m .5 .95 .5 .8 .55 .8 .8 .55 .m .m .m .56 .68 .5 .mm .8N .m .8 .8 .5 .m .m .m .m .m .m .85 .mm .m .88 .55 .5 .0m .Nv .m .mfl .om .m .5 .55 .5 .55 .68 .5 .mm .ooa .8 .vm .556 .8 .08 .565 .8 .65 .855 .8 .vm .ma .8 .55 .55 .8 mafia aoH .55 .6@ .m- .mom .va .onm .~m .Nmm .v¢ .hv .m~ .nN .hv .ham .vm .on .98 .mm .wv .hm .mm .Nm .5m .58 .mm .58 .Nm .vm .mm .85 .MN .NH .mm .mm .58 .85 .555 .555 .58 .85 .55 .86 .65 .85 .585 .855 .555 .585 .855 .65 .565 .555 .558 .585 .855 .565 .855 .555 .565 .005 .505 .hmm .Nmm .mma .Nma .Nmm .58 .85 .58 .85 .555 .86 .58 .55 .585 .555 .55 .55 .85 .85 .85 .855 .558 .588 .555 .555 .588 .565 .555 .555 .555 .655 .558 .555 .655 .85 .55 .86 .585 .555 .555 .85 .585 .58 .588 .858 .555 .555 .558 .555 .888 .558 .555 .555 .58 .58 .58 .58 .55 .86 .86 .85 .oh .55N .mmm .565 .Nm5 .aam .Nm .vma .85 .85 .65 .86 .85 .85 .555 .86 .86 .855 .58 .85 .565 .855 .655 .585 .86 .86 .mm .vm .mm .mm .Nw .oh .085 .wvm .va .mom .vm .mNH .vm .m- .55 .865 .555 .885 .88 .85 .56 .55 .58 .585 .558 .585 .85 .855 .58 .85 .55 .86 .58 .58 .58 .58 .85 .58 .58 .88 .85 .55 .55 .555 .55 .55 .66 .66 .55 .55 .85 .85 .86 .85 .588 .66 .58 .65 .5 .55 .55 .55 .mm .86 .mm .86 .mm .hv .05 .mm .855 .85 .55 .555 .65 .85 .555 .855 .88 .85 .55 .88 .5 .55 .55 .56 .58 .88 .58 .86 .86 .55 .55 .86 .88 .58 .58 .65 .65 .85 .58 .58 .55 .58 .58 .85 .55 .555 .555 .58 .88 .55 .58 .58 .68 .55 .86 .58 .58 .65 .86 .88 .55 .55 .85 .555 .58 .86 .85 .86 .58 .86 .58 .86 .88 .85 .5 .8 .56 .5 .55 .5 .58 .86 .85 .55 .86 .58 .58 .555 .85 .88 .58 .85 .58 .55 .65 .86 .58 .68 .55 .55 .55 .85 .5 .85 .58 .55 .55 .88 .86 .58 .86 .58 .Nuwmaov ouomm mo mwumowamuu coy .o5uwgwm .555 .mm .565 .mmm .86 .mm .mmm .woa .585 .mmw .oh .ham .vm .vm .bv .va .Nm .58 .mm .05 .om .55 .86 .Nm .8m .86 .won .0m .6m .60 UNIV 0 .m5 . .855 . .mw .m .mm 255.5. 00000000000000. HHflH—«HNNNNNNMMMMMMVVVVVfl'mmmmmmw‘DWWWm .w unswa< .Nm macaw 197 .555 .55 .58 .85 .58 .65 .58 .555 .555 .86 .5 8 .8 .8 .8 .6 .8 .6 .8 .8 .5 .85 .55 .58 .58 .86 .58 .58 .58 .88 .86 .55 .5 .8 .8 .8 .8 .8 .8 .5 .8 .5 .6 .85 .55 .58 .86 .86 .86 .58 .86 .58 .55 .86 .5 .6 .5 .5 .5 .5 .6 .8 .5 .8 .5 .85 .58 .86 .86 .58 .58 .86 .86 .86 .86 .58 .5 .55 .6 .5 .5 .8 .6 .5 .5 .8 .6 .85 .58 .55 .88 .58 .555 .55 .58 .55 .86 .65 .5 .8 .8 .6 .8 .5 .8 .8 .8 .8 .5 .55 .88 .58 .58 .88 .88 .58 .88 .58 .58 .86 .5 .58 .565 .655 .555 .55 .6 .8 .6 .5 .5 .55 .55 .58 .55 .85 .55 .86 .58 .86 .58 .86 .5 .8 .55 .55 .65 .88 .68 .58 .85 .88 .85 .55 .58 .88 .58 .86 .58 .58 .86 .86 .55 .55 .5 .55 .55 .55 .55 .85 .55 .5 .8 .5 .5 .55 .86 .55 .86 .86 .86 .55 .86 .58 .86 .58 .6 .5 .5 .5 .55 .55 .88 .58 .55 .55 .5 .55 .55 .58 .56 .88 .86 .58 .86 .58 .58 .58 .6 .55 .58 .86 .86 .58 .58 .86 .58 .86 .86 .55 .88 .86 .58 .86 .88 .88 .86 .88 .88 .86 .6 .58 .55 .55 .85 .55 .5 .5 .8 .8 .55 .55 .88 .86 .88 .58 .58 .86 .58 .88 .85 .58 .6 .55 .86 .55 .86 .58 .58 .58 .58 .55 .65 .55 .0 ON. 0N om am am 0N on on 0H 0” oh? ohv oMN 09” 005. 05V 0mm oMN on am” am .58 .86 .86 .88 .86 .58 .58 .58 .86 .58 .8 .58 .55 .58 .55 .86 .85 .86 .55 .85 .58 .5 .58 .58 .58 .58 .55 .58 .58 .88 .58 .55 .8 .58 .58 .55 .65 .55 .5 .85 .5 .86 .58 .5 .5 .5 .8 .55 .6 .5 .85 .85 .5 .5 .8 .88 .55 .55 .58 .58 .86 .86 .85 .55 .55 .5 .8 .8 .5 .5 .5 .55 .6 .6 .5 .55 .8 .86 .86 .58 .58 .55 .58 .55 .58 .555 .55 .5 .86 .85 .65 .58 .86 .86 .58 .6 .5 .6 .8 .555 .655 .85 .855 .58 .58 .55 .85 .58 .58 .5 .8 .6 .8 .5 .5 .6 .5 .55 .5 .8 .8 .86 .58 .58 .86 .58 .58 .58 .86 .58 .58 .5 .8 .8 .8 .8 .6 .5 .8 .6 .8 .8 .8 .85 .85 .55 .65 .65 .58 .58 .85 .86 .85 .5 .5 .5 .8 .5 .5 .8 .5 .5 .5 .5 .5 .55 .65 .565 .86 .55 .55 .86 .85 .85 .58 .8 .8 .8 .8 .6 .8 .8 .8 .8 .8 .8 .5 .58 .86 .55 .86 .55 .58 .565 .55 .55 .58 .8 .5 .6 .8 .5 .5 .5 .8 .8 .8 .5 .5 .86 .55 .55 .86 .88 .58 .86 .58 .58 .86 .8 .8 .5 .5 .8 .8 .8 .5 .5 .8 .5 .5 .55 .86 .58 .58 .58 .85 .86 .58 .58 .58 .8 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Spore germination data for various temperature and moisture conditions (See Experiment 8). 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00000. 00000.00 00000. 00000.00 000cc. 00000.00 00000. 00000.00 00000. 00000.00 00000. 00000.0 00000. 00000.0 00000. 00000.0 00000. 00000.0 00000. 00000.0 00000. 00000.0 00000. 00000.0 00000. 00000.0 00000. 00000.0 00000. 00000.0 00000. 00000.0 00000. 00000.0 00000. 00000.0 00000. 00000.0 00000. 00000.0 00000. 00000.0 00000. 00000.0 00000. 00000.0 00000. 00000.0 00000. 00000.0 00000.0 00000.0 00000.0 00000.0 00000.0 00000.0 00000.0 00000.0 00000. 00000.0 00000. 00000.0 aowumawahmo . mafia coauuoaoum 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 00000.00 00000.00 00000.0 90 mo uamauwwua unaumfioz Appendix I. —-—-.-....-—- .. w . 208 Appendix I. Spore germination data for various habitats (Experiment 9). CDDAGERMFIELDZ (22F3.0) Data coded as follows: A-Day l=6-l6-80 2=7-16-80 3=7-23-80 4=8-21-80 B-Time '1=6:00a.m. =8:00a.m. 3=lO:OOa.m, 4=12z00p.m. 5=6:00p.m. C-Carrots, ground level, evaluated at time t D-Carrots, ground level, evaluated at time t+6 hours E-Carrots, mid canopy, evaluated at time t F-Carrots, mid canopy, evaluated at time t+6 hours G-Carrots, top of canopy, evaluated at time t H-Carrots, top of canopy, evaluated at time t+6 hours I-Border, ground level, evaluated at time t J-Border, ground level, evaluated at time t+6 hours KrBorder, mid canopy, evaluated at time t L-Border, mid canopy, evaluated at time t+6 hours M-Border, top of canopy, evaluated at time t N-Border, top of canopy, evaluated at time t+6 hours O-Onions ground level, evaluated at time t P-Onions, ground level, evaluated at time t+6 hours Q-Onions, mid canopy, evaluated at time t R-Onions, mid canopy, evaluated at time t+6 hours S-Onions, top of canopy, evaluated at time t T-Onions, top of canopy, 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. 210 Appendix J. Host-pathogen interaction data (Experiment 10). CDDAEMUSCAEINFECTIONTIME (1X, 5F10.4) Table 31. Exposure JNumber Total Proportion Arcsin Treatment Infected Number Infected Infection 1.0000 64.0000 85.0000 .7530 60.1989 1.0000 50.0000 73.0000 .6850 55.8578 1.0000 51.0000 64.0000 .7970 63.2207 2.0000 5.0000 51.0000 .5930 18.2431 2.0000 14.0000 97.0000 .1440 22.3011 2.0000 21.0000 93.0000 .2260 28.3851 3.0000 34.0000 79.0000 .4300 40.9761 3.0000 39.0000 64.0000 .6090 51.2958 3.0000 47.0000 93.0000 .5050 45.2865 4.0000 33.0000 65.0000 .5080 45.4584 4.0000 42.0000 70.0000 .6000 50.7685 4.0000 48.0000 81.0000 .5905 50.1849 5.0000 17-0000 42.0000 .4050 39.5236 5.5005 24.0000 53.0000 .4530 42.3031 5.0000 39.0000 51.0000 .7650 61.0027 6.0000 5.0000 80.0000 .0525 14.4775 6.0000 0~0000 76.0000 0.0000 0.0000 6.0000 1-0000 72.0000 .0140 6.7952 Appendix K. Temperature data and Degree Day Accumulation (base 4.40C) for Grant, Michigan. l977-1979J TAIL! 1 . DATE ACC. MAX HI! 08608! 7888 T!!! 4/ 1/78 4.5 45 44 4/ 2/78 4.5 34 25 4/ 3/78 5.2 43 31 4/ 4/78 8.9 51 31 4/ 5/78 14.2 56 27 4/ 6/78 17.7 49 35 4/ 7/78 25.0 57 35 4/ 8/78 27.0 47 31 4/ 9/78 27.2 41 28 4/10/78 33.5 59 24 4/11/78 36.5 48 36 4/12/78 43.2 59 27 4/13/78 44.6 44 37 4/14/78 46.6 47 30 4/15/78 47.7 45 26 4/16/78 52.5 56 21 4/17/78 57.4 56 21 4/18/78 59.3 45 38 4/19/78 69.8 60 41 4/20/78 71.8 45 38 4/21/78 77.7 55 31 4/22/78 83.3 57 25 4/23/78 87.4 so 37 4/24/78 99.4 63 41 4/25/78 109.6 65 32 4/26/78 122.7 69 35 4/27/78 134.4 69 30 4/28/78 145.3 68 28 4/29/78 158.9 71 34 4/30/78 163.0 53 27 5/ 1/78 168.2 56 25 5/ 2/78 176.3 60 33 5/ 3/78 187.6 66 35 5/ 4/78 195.1 55 4o 5/ 5/78 199.0 49 38 5/ 6/78 208.0 62 33 5/ 7/78 220.0 61 43 5/ 8/78 235.5 64 47 5/ 9/78 246.0 55 55 5/10/78 261.0 66 44 5/11/78 278.0 68 46 5/12/78 298.0 64 56 5/13/78 319.5 66 57 5/14/78 335.0 59 52 5/15/78 354.0 64 54 5/16/78 373.0 65 53 5/17/78 393.0 7 43 5/18/78 419.0 81 51 5/19/78 442.5 81 46 5/20/78 470.0 7 61 5/21/78 482.1 65 39 5/22/78 496.5 71 37 5/23/78 516.5 76 44 5/24/78 541.0 83 46 5/25/78 565.0 83 45 5/26/78 595.5 86 55 5/27/78 628.0 88 57 5/28/78 661.5 90 57 5/29/78 697.5 90 62 5/30/78 726.0 80 57 5/31/78 753.5 79 56 6/ 1/78 784.0 82 59 6/ 2/78 803.0 68 50 6/ 3/78 815.6 68 36 6/ 4/78 834.1 75 42 6/ 5/78 851.1 73 41 6/ 6/78 874.1 81 45 6/ 7/78 902.1 79 57 6/ 8/78 919.6 71 44 8/ 9/78 936.6 72 42 6/10/78 958.1 78 45 6/11/78 991.6 85 62 6/12/78 1014.6 66 60 6/13/78 1026.1 64 39 6/14/78 1034.3 61 31 6/15/78 1059.8 79 52 6/16/78 1084.3 74 55 6/17/78 1110.8 72 61 6/18/78 1136.3 76 55 6/19/78 1161.3 79 51 TABLE 1. courxuutn DAT! ace. 88x 818 0860A! TEMP 7888 6/20/78 1189.8 83 54 6/21/78 1212.8 70 56 6/22/78 1231.3 75 42 6/23/78 1250.8 74 45 6/24/78 1276.3 82 49 6/25/78 1305.8 74 65 6/26/78 1337.3 79 64 6/27/78 1371.8 83 66 6/28/78 1401.8 85 55 6/29/78 1432.3 86 55 6/30/78 1462.8 84 7 7/ 1/78 1482.3 62 57 7/ 2/78 1504.3 64 60 7/ 3/78 1530.3 74 58 7/ 4/78 1555.3 82 48 7/ 5/78 1582.8 82 53 7/ 6/78 1615.8 86 60 7/ 7/78 1649.8 83 65 7/ 8/78 1678.3 75 62 7/ 9/78 1702.8 80 49 7/10/78 1722.3 70 49 7/11/78 1739.3 73 41 7/12/78 1759.3 79 41 7/13/78 1792.3 81 65 7/14/78 1819.8 82 53 7/15/78 1848.3 84 53 7/16/78 1871.3 79 47 7/17/78 1895.3 82 46 7/18/78 1927.8 82 63 7/19/78 1965.3 85 70 7/20/78 2003.8 91 66 7/21/78 2037.8 82 66 7/22/78 2073.3 85 66 7/23/78 2100.3 75 59 7/24/78 2125.3 82 48 7/25/78 2152.3 1 53 7/26/78 2180.8 77 60 7/27/78 2211.8 78 64 7/28/78 2228.9 75 39 7/29/78 2252.9 74 54 7/30/78 2272.9 71 49 7/31/78 2293.4 77 44 8/ 1/78 2319.9 81 52 8/ 2/78 2344.9 78 52 8/ 3/78 2367.4 75 50 8/ 4/78 2383.9 73 40 8/ 5/78 2401.9 76 40 8/ 6/78 2427.4 85 46 8/ 7/78 2456.9 83 56 8/ 8/78 2484.4 83 52 8/ 9/78 2514.4 82 58 8/10/78 2536.4 79 45 8/11/78 2559.4 80 46 8/12/78 2590.4 84 58 8/13/78 2622.4 88 56 8/14/78 2656.4 90 58 8/15/78 2692.4 89 63 8/16/78 2723.9 78 65 8/17/78 2756.4 85 60 8/18/78 2786.4 79 61 8/19/78 2815.4 71 67 8/20/78 2835.9 75 46 8/21/78 2859.9 82 46 8/22/78 2888.4 85 52 8/23/78 2919.9 85 58 8/24/78 2953.9 85 63 8/25/78 2977.4 72 55 8/26/78 3007.4 78 62 8/27/78 3036.4 74 64 8/28/78 3068.4 78 66 8/29/78 3097.4 77 61 8/30/78 3122.4 78 52 8/31/78 3144.9 76 49 9/ 1/78 3168.9 80 48 9/ 2/78 3197.4 79 58 9/ 3/78 3225.4 74 62 9/ 4/78 3246.4 77 45 9/ 5/78 3275.4 86 52 9/ 6/78 3305.9 84 57 9/ 7/78 3338.9 88 58 TAIL! 1 . COITIIUID cc. “AX "I" 0": 0:6DAY 7889 71!? 61 9/ 5/78 3373.9 '9 9/ 9/73 3402.4 :3 :i 9/10/78 3437.9 88 ‘2 9,11/78 3472.9 63 ‘2 9/12/78 3495.4 63 52 9/13/78 3512.9 72 51 9/14/78‘ 3534.4 73 ‘9 9/15/78 3555.4 75 ‘9 9/16/78 3577.4 69 5. 9/17/78 3600.9 75 5‘ 9/18/78 3626.4 81 55 9/19/78 3654.4 82 57 9/20/78 3683.9 61 53 9/21/78 3700.9 ‘1 3713.4 64 9/22/73 57 35 9/23/78 3725.2 7‘ ‘2 9/24/78 3743.2 6‘ ‘1 9/25/78 3755.7 69 37 9/26/78 3769.1 62 52 9/27/78 3786.1 65 30 9/28/78 3795-0 62 35 9/29/78 3806.2 65 53 9/30/78 3825.2 62 51 10/ 1/78 3841.7 70 3‘ 10/ 2/78 3855.9 65 ‘3 10/ 3/78 3869.9 61 ‘0 10/ 4/78 3880.4 62 ‘6 10/ 5/73 3894.4 50 ‘5 10/ 6/78 3902.4 ‘7 37 10/ 7/78 3905.1 49 3‘ 10/ 8/78 3908.3 61 3° 10/ 9/78 3916.3 68 32 10/10/78 3928.0 62 ‘5 10/11/78 3941-5 60 .5 10/12/78 3955.5 55 35 10/13/78 3961.6 “ 35 10/14/78 3962.8 58 35 10/15/78 3970-3 5, 3; 10/16/78 3975-‘ 55 25 10/17/7 3980.2 39 10/18/78 3988.8 58 35 10/19/78 3995.3 56 29 10/20/78 4000.1 54 39 10/21/78 4014.7 70 ‘1 10/22/78 4029.2 68 ‘3 10/23/78 4035.2 49 22 10/24/78 4038.5 52 31 10/25/78 4044.0 55 ‘0 10/26/78 4048.0 48 32 10/27/78 4054.0 56 2' 10/28/78 4056.6 49 2‘ 10/29/78 4058.7 43 36 10/30/78 4070.3 66 35 10/31/78 4078.8 60 TABLE 2:. DATE 80:. MAX 818 0860A! 2889 2888 4/ 1/79 0.0 38 32 4/ 2/79 0.0 36 3o 4/ 3/79 2.7 50 24 4/ 4/79 2.7 40 33 4/ 5/79 4.0 45 30 4/ 6/79 4.0 28 15 4/ 7/79 4.0 38 14 4/ 8/79 4.0 32 30 4/ 9/79 4.9 44 28 4/10/79 7.3 50 18 4/11/79 7.9 43 28 4/12/79 22.7 73 35 4/13/79 30.7 54 42 4/14/79 33.9 49 34 4/15/79 37.3 48 38 4/16/79 44.1 55 38 18808 2 . 008118080 DA?! 800. HAX 818 3 88608! 7888 1889 4/17/79 51.5 61 26 4/18/79 60.7 65 26 4/19/79 71.1 68 25 4/20/79 84.7 73 30 4/21/79 99.2 58 51 4/22/79 110.0 67 30 4/23/79 125.5 75 34 4/24/79 140.0 67 42 4/25/79 161.5 65 58 4/26/79 169.5 52 44 4/27/79 175.4 56 31 4/28/79 175.8 42 34 4/29/79 179.3 52 24 4/30/79 180.5 44 35 5/ 1/79 186.1 56 29 5/ 2/79 201.6 69 42 5/ 3/79 210.6 53 45 5/ 4/79 215.2 53 31 5/ 5/79 219.1 53 24 5/ 6/79 238.1 70 48 5/ 7/79 263.1 80 50 5/ 8/79 296.6 86 61 5 9/79 331.1 83 66 5/10/79 364.6 84 63 5/11/79 392.6 71 65 5/12/79 398.4 53 38 5/13/79 407.8 64 30 5/14/79 423.8 67 45 5/19/79 433.9 63 36 5/16/79 444.8 68 28 5/17/79 459.0 70 38 5/18/79 486.5 82 53 5/19/79 508.5 70 54 5/20/79 522.1 70 36 5/21/79 534.1 62 42 5/22/79 544.7 67 29 5/23/79 560.7 65 47 5/24/79 571.7 59 43 5/25/79 583.9 66 38 5/26/79 596.4 63 42 5/27/79 607.9 60 43 5/28/79 619.4 59 44 5/29/79 634.2 74 33 5/30/79 649.2 64 46 5/31/79 675.2 79 53 6/ 1/79 696.7 66 57 6/ 2/79 713.4 75 38 6/ 3/79 737.9 81 48 6/ 4/79 762.9 80 50 6/ 5/79 787.9 77 53 6/ 6/79 816.4 83 54 6/ 7/79 848.9 80 65 6/ 8/79 882.9 84 64 6 9/79 914.4 75 68 6/10/79 945.4 74 68 6/11/79 963.9 73 44 6/12/79 979.2 73 37 6/13/79 996.4 76 38 6/14/79 1023.9 84 51 6/15/79 1057.4 85 62 6/16/79 1090.9 87 60 6/17/79 1117.9 68 66 6/18/79 1141.4 74 53 6/19/79 1164.9 81 46 6/20/79 1192.9 78 58 6/21/79 1224.9 79 65 6/22/79 1246.4 68 55 6/23/79 1259.4 64 42 6/24/79 1276.9 73 42 6/25/79 1292.5 74 36 6/26/79 1313.0 79 42 6/27/79 1342.0 81 57 6/28/79 1369.0 82 52 6/29/79 1397.0 76 60 6/30/79 1423.5 75 58 7/ 1/79 1441.5 62 54 7/ 2/79 1466.0 79 50 7/ 3/79 1488.0 76 48 7/ 4/79 1514.0 76 56 7/ 5/79 1530.2 74 38 7/ 6/79 1548.0 78 37 7/ 7/79 1569.5 81 42 7/ 8/79 1594.0 80 49 7/ 9/79 1624.0 84 56 7/10/79 1654.5 85 56 7/11/79 1687.5 85 61 7/12/79 1722.5 89 61 7/13/79 1758.5 89 63 7/14/79 1794.0 84 67 TAIL! 2 . COITIIUBD acc. 88x urn DAT: 08608! 7888 8889 7/15/79 1830.0 88 64 7/16/79 1857.5 82 53 7/17/79 1881.5 76 52 7/18/79 1901.5 80 40 7/19/79 1924.0 84 41 7/20/79 1948.5 86 43 7/21/79 1976.5 86 50 7/22/79 2007.0 86 55 7/23/79 2039.5 89 56 7/24/79 2071.0 79 64 7/25/79 2104.0 76 70 7/26/79 2132.0 79 57 7/27/79 2161.0 87 51 7/28/79 2193.0 83 61 7/29/79 2220.5 85 50 7/30/79 2249.5 82 56 7/31/79 2281.5 80 64 8/ 1/79 2309.0 74 6: 8/ 2/79 2337.5 81 57 8/ 3/79 2366.5 81 5 8/ 4/79 2397.0 82 59 8/ 5/79 2426.0 82 sg 8/ 6/79 2452.0 79 5 8/ 7/79 2486.5 88 61 8/ 8/79 2516.5 78 62 8/ 9/79 2543.0 80 53 8/10/79 2573.5 76 65 8/11/79 2591.0 71 4: 8/12/79 2606.8 73 3 8/13/79 2628.8 70 54 8/14/79 2644.3 63 48 8/15/79 2660.8 70 43 8/16/79 2674.6 73 31 8/17/79 2691.1 63 50 8/18/79 2716.6 76 55 8/19/79 2742.1 77 5 8/20/79 2768.1 72 60 8/21/79 2794.6 79 54 8/22/79 2821.6 76 58 8/23/79 2853.1 79 64 8/24/79 2879.1 69 63 8/25/79 2902.1 72 54 8/26/79 2923.1 78 44 8/27/79 2941.6 65 52 8/28/79 2969.6 74 62 8/29/79 2999.6 78 62 8/30/79 3030.1 83 58 8/31/79 3060.6 83 58 9/ 1/79 3090.6 80 60 9/ 2/79 3123.6 79 67 9/ 3/79 3149.6 78 54 9/ 4/79 3176.6 78 56 9/ 5/79 3204.1 83 52 9/ 6/79 3232.1 82 54 9/ 7/79 3247.6 59 52 9/ 8/79 3257.5 64 33 9/ 9/79 3272.1 70 39 9/10/79 3297.6 73 58 9/11/79 3320.1 75 50 9/12/79 3350.1 84 56 9/13/79 3377.6 71 64 9/14/79 3394.1 64 49 9/15/79 3405.5 66 35 9/16/79 3423.1 77 38 9/17/79 3442.6 77 42 9/18/79 3467.6 76 54 9/19/79 3477.0 65 27 9/20/79 3493.7 75 38 9/21/79 3513.7 68 52 9/22/79 3524.1 67 28 9/23/79 3534.9 67 30 9/24/79 3550.1 72 38 9/25/79 3566.6 73 40 9/26/79 3584.3 79 35 9/27/79 3604.4 81 39 9/28/79 3624.9 80 41 9/29/79 3652.4 83 52 9/30/79 3676.9 77 52 10/ 1/79 3691.2 74 31 10/ 2/79 3710.2 64 54 10/ 3/79 3723.2 62 44 10/ 4/79 3731.2 60 33 10/ 5/79 3738.3 59 3o 10/ 6/79 3747.3 55 43 10/ 7/79 3752.8 51 40 '213 28802 2 . COUTIIUID DATE ACC. MAX 818 08608! 7888 2888 10/ 8/79 3758.1 54 33 10/ 9/79 3763.4 52 38 10/10/79 3766.4 49 32 10/11/79 3767.4 44 32 10/12/79 3770.3 47 38 10/13/79 3772.0 45 36 10/14/79 3774.4 49 25 10/15/79 3784.4 64 33 10/16/79 3790.5 55 35 10/17/79 3805.5 60 50 10/18/79 3818.0 60 45 10/19/79 3838.5 66 55 10/20/79 3860.5 70 54 10/21/79 3890.0 73 66 10/22/79 3920.5 75 66 10/23/79 3924.0 45 42 10/24/79 3925.7 45 36 10/25/79 3925.7 38 33 10/26/79 3926.7 44 32 10/27/79 3929.8 50 29 10/28/79 3935.8 51 41 10/29/79 3940.7 55 26 10/30/79 3947.5 57 34 10/31/79 3964.5 72 42 28808 3. DA?! ACC. HAX 818 080082 7889 ?£HP 4/ 1/80 2.1 48 26 4/ 2/80 10.9 61 34 4/ 3/80 11.0 41 33 4/ 4/80 12.4 45 32 4/ 5/80 18.7 57 31 4/ 6/80 28.9 64 34 4/ 7/80 41.4 59 46 4/ 8/80 54.4 59 47 4/ 9/80 55.0 42 38 4/10/80 55.0 38 32 4/11/80 56.2 44 35 4/12/80 56.7 42 36 4/13/80 57.9 45 28 4/14/80 57.9 37 28 4/15/80 58.3 42 32 4/16/80 60.0 47 24 4/17/80 63.5 52 24 4/18/80 63.5 37 36 4/19/80 76.4 68 37 4/20/80 90.9 74 32 4/21/80 106.9 72 40 4/22/80 133.9 84 50 4/23/80 141.5 56 39 4/24/80 141.6 41 26 4/25/80 145.4 52 27 4/26/80 151.4 54 37 4/27/80 159.4 55 41 4/28/80 167.4 55 41 4/29/80 175.4 53 43 4/30/80 186.9 58 45 5/ 1/80 203.9 68 46 5/ 2/80 220.0 73 39 5/ 3/80 240.5 82 39 5/ 4/80 262.5 84 40 5/ 5/80 285.5 83 43 5/ 6/80 298.1 68 36 5/ 7/80 302.1 51 33 5/ 8/80 306.0 52 29 5/ 9/80 312.7 57 33 5/10/80 326.4 69 38 TAIL! 3 COITIIUID DI?! ACC. MAX III DIGDAY TEMP TIIP 5/11/80 344.4 67 49 5/12/80 360.4 70 42 5/13/80 376.4 63 49 5/14/80 384.0 59 33 5/15/80 393.2 62 34 5/16/80 404.8 68 32 5/17/80 418.3 61 46 5/18/80 437.3 64 54 5/19/80 458.3 75 47 5/20/80 478.3 75 45 5/21/80 498.0 83 35 5/22/80 521.0 85 41 5/23/80 546.0 85 45 5/24/80 580.0 84 64 5/25/80 608.0 82 54 5/26/80 625.0 77 36 5/27/80 645.0 80 40 5/28/80 670.0 80 50 5/29/80 695.5 73 58 5/30/80 723.5 79 57 5/31/80 747.5 71 57 6/ 1/80 760.0 58 47 6/ 2/80 782.5 73 52 6/ 3/80 806.5 73 55 6/ 4/80 823.5 74 40 6/ 5/80 841.0 71 44 6/ 6/80 867.5 78 55 6/ 7/80 895.0 79 56 6/ 8/80 905.0 58 42 6/ 9/80 917.0 64 40 6/10/80 924.0 59 29 6/11/80 937.1 72 30 6/12/80 955.2 77 39 6/13/80 976.7 80 43 6/14/80 1005.2 81 56 6/15/80 1026.7 63 60 6/16/80 1041.2 69 40 6/17/80 1054.5 71 33 6/18/80 1078.0 77 50 6/19/80 1094.0 57 55 6/20/80 1110.2 74 38 6/21/80 1132.7 78 47 6/22/80 1161.7 82 56 6/23/80 1192.2 87 54 6/24/80 1225.7 88 59 6/25/80 1259.2 88 59 6/26/80 1296.7 89 66 6/27/80 1324.7 80 56 6/28/80 1354.7 84 56 6/29/80 1382.7 76 60 6/30/80 1407.7 75 55 7/ 1/80 1433.7 83 49 7/ 2/80 1461.2 81 54 7/ 3/80 1486.2 87 43 7/ 4/80 1516.7 85 56 7/ 5/80 1550.7 84 64 7/ 6/80 1574.2 79 48 7/ 7/80 1606.2 88 56 7/ 8/80 1638.2 85 59 7/ 9/80 1665.7 83 52 7/10/80 1696.7 89 53 7/11/80 1735.7 93 65 7/12/80 1772.2 85 68 7/13/80 1805.7 89 58 7/14/80 1843.7 94 62 7/15/80 1882.7 86 72 7/16/80 1915.2 86 59 7/17/80 1947.2 80 64 7/18/80 1975.7 86 51 7/19/80 2014.7 92 66 7/20/80 2057.7 96 70 7/21/80 2089.2 75 68 7/22/80 2119.7 79 62 7/23/80 2145.7 80 52 7/24/80 2170.2 82 47 7/25/80 2199.2 86 52 7/26/80 2228.2 72 66 7/27/80 2257.2 76 62 7/28/80 2292.2 83 67 7/29/80 2323.2 82 60 7/30/80 2345.7 76 49 7/31/80 2376.7 84 58 8/ 1/80 2411.2 87 62 8/ 2/80 2438.2 75 59 8/ 3/80 2468.7 83 58 8/ 4/80 2495.2 86 47 8/ 5/80 2525.2 83 57 8/ 6/80 2556.7 87 56 8/ 7/80 2593.2 87 66 TADL! 3. COITIHUID DAT! ACC. MAX "IN 08608! 18!? TEHP 8/ 8/80 2627.7 86 63 8/ 9/80 2662.7 84 66 8/10/80 2697.7 84 66 8/11/80 2724.7 73 61 8/12/80 2751.2 76 57 8/13/80 2771.2 72 48 8/14/80 2800.2 81 57 8/15/80 2823.2 76 50 8/16/80 2841.2 74 42 8/17/80 2865.7 67 62 8/18/80 2897.7 1 63 8/19/80 2925.2 86 49 8/20/80 2956.7 86 57 8/21/80 2990.2 81 66 8/22/80 3016.7 81 52 8/23/80 3042.7 87 45 8/24/80 3068.2 84 47 8/25/80 3096.2 75 61 8/26/80 3129.7 85 62 8/27/80 3163.2 87 60 8/28/80 3197.7 84 65 8/29/80 3234.7 89 65 8/30/80 3268.2 82 65 8/31/80 3298.7 74 67 9/ 1/80 3327.7 74 64 9/ 2/80 3358.7 76 66 9/ 3/80 3383.2 80 49 9/ 4/80 3410.2 82 52 9/ 5/80 3432.7 78 47 9/ 6/80 3457.7 80 50 9/ 7/80 3483.7 81 51 9/ 8/80 3512.2 84 53 9/ 9/80 3534.7 71 54 9/10/80 3549.2 70 39 9/11/80 3566.7 73 42 9/12/80 3584.2 65 50 9/13/80 3614.2 81 59 9/14/80 3641.2 74 60 9/15/80 3658.7 62 53 9/16/80 3676.7 64 52 9/17/80 3690.2 61 46 9/18/80 3702.9 68 36 9/19/80 3719.9 72 42 9/20/80 3747.4 75 60 9/21/80 3776.9 75 64 9/22/80 3795.4 69 48 9/23/80 3806.4 61 41 9/24/80 3817.8 65 37 9/25/80 3831.8 66 42 9/26/80 3838.8 56 37 9/27/80 3849.0 64 34 9/28/80 3862.5 63 44 9/29/80 3880.5 73 43 9/30/80 3900.0 76 43 10/ 1/80 3920.5 73 48 10/ 2/80 3933.5 64 42 10/ 3/80 3936.1 46 39 10/ 4/80 3937.6 45 34 10/ 5/80 3941.1 49 36 10/ 6/80 3947.4 57 31 10/ 7/80 3957.6 66 29 10/ 8/80 3962.2 53 32 10/ 9/80 3969.3 59 30 10/10/80 3979.5 64 34 10/11/80 3988.0 49 48 10/12/80 3994.5 53 40 10/13/80 4002.2 60 31 10/14/80 4002.6 42 33 10/15/80 4005.9 49 35 10/16/80 4017.9 63 41 10/17/80 4034.9 67 47 10/18/80 4042.9 50 46 10/19/80 4049.9 52 42 10/20/80 4053.9 48 40 10/21/80 4057.9 50 36 10/22/80 '4061.4 52 25 10/23/80 4068.0 55 37 10/24/80 4077.9 62 37 10/25/80 4079.9 43 41 10/26/80 4079.9 39 36 10/27/80 4080.0 41 28 10/28/80 4080.1 41 25 10/29/80 4081.3 46 16 10/30/80 4083.5 49 19 10/31/80 4088.8 52 38 Appendix L. Computer Source, Code for E. muscae, 0M and SCM Simulation Program. 215 PROGRAM DRIVE(INPUT,OUTPUT,TAPEI,TAPE2,TAPE3,TAPE4) Cinit!attatttatnttutuauttutuunuauauuatuuntuaat VARIABLES USED : DRIVE BASE - BASE TEMPERATURE, BELOW WHICH NO D-DAYS ACCUMULATE CUMDD - CUMULATIVE D-DAYS DMAG - NUMBER OF DISEASED DEAD ONION MAGGOT ADULTS DT - TIME INCREMENT FRMDD - DEGREE DAYS HOUR - (PI)/12. USED IN TEMPERATURE SINE WAVE HRANG - DAILY TEMPERATURE RANGE HTIME - TIME OF DAY, @-24 IDAY - JULIAN DAY IDP - NUMBER OF DT'S IN ONE DAY LX - PARAMETER, 0=GO THROUGH INITIALIZAPION, I=SKIP INITIALIZAPION NDAY - NUMBER OF DAYS PROGRAM RUNS OM - INITIAL ONION MAGGOT PUPAE COUNT PINFCT - PERCENT INFECTION PSUM - EMERGENCE RATE PUPAI - INITIAL SEED CORN MAGGOT COUNT, IST GENERATION PUPAZ - INITIAL SEED CORN MAGGOT COUNT, 2ND GENERATION SCSTOR - NUMBER OF SEED CORN MAGGOT ADULTS SCUMDD - SOIL CUMULATIVE D-DAYS SQ] - NUMBER OF LIVING DISEASED ONION MAGGOT ADULTS SQZ - NUMBER OF LIVING DISEASED PRE-OVIPOSITIONAL ONION MAGGOT ADULTS STEMP - SOIL TEMPERATURE STRG - ARRAY OF NUMBERS OF ONION MAGGOTS IN EACH LIFE STAGE TEMP - TEMPERATURE IN EACH DT THETA - ARGUMENT OF SINE FUCTION 7 (HTIME-9)*HOUR TMAX - MAXIMUM DAILY TEMPERATURE TMIN - MINIMUM DAILY TEMPERATURE TOTAL - TOTAL NUMBER OF ADULT ONION MAGGOTS Z] - NUMBER OF HOURS IN ONE DT atttfitntuttaauutaututnituuanuttauuauaunt: Ittfiflttfittttttt3ttflxfltifififittfitttlttttfitti. SUBROUTINES CALLED : DRIVE DEGDAY - COMPUTES NUMBER OF DEGREE DAYS ACCUMULATED PER DAY SCMAG - COMPUTES SEED CORN MAGGOT ADULT EMERGENCE AND DEVELOPMENT ENTMOP - COMPUTES ENTOMOPHTHORA DISEASE DEVELOPMENT MAGGOT - COMPUTES ONION MAGGOT DEVELOPMENT FOR EACH LIFE STAGE ATEMP - READS AIR TEMPERATURE FROM TAPE] STEMP - READS SOIL TEMPERATURE FROM TAPEZ ENL - TABLE LOOK-UP FUNCTION Ittlflflfittttt!*ttttlfifl‘lflflfltflt**tfifl*t*fi‘lttt 00000000000000000000000OOOOOOCOOOCOOOOOOOOOOOOOO 0000 66 216 DIMENSION STRG(6),STEMP(I3),IDAYSP(28),IFDAY(26) COMMON/PASSl/TEMP,LX,DT,EGGT COMMON/PAssz/HTIME,DNAG,FAREA,BAREA COMMON/PASS3/STRG,SQI,SQZ COMMON/PAsse/SCSTOR COMMON/PASS7/OH,ONPUPA,STMP,PSUM COMMON/PASSB/PUPAI,PUPA2,CUMDD,ISPNURT COMMON/PRPARM/OMPR,SCMPR,ENTPR COMMON/FUNG/IFMORT INTEGER OMPR,SCMPR,ENTPR fl*tfltt‘lflflfit‘lt‘lfltIlltflhifittflfifliii“luttnt‘ktitufi‘lfififlfiuufin DATA BASE/4.44/,Lx/D/ REWIND 1 REWlND 2 REWIND 3 REWIND 4 REWIND 5 titttitXflflflhifltfiflIt‘ktuttfitt‘lttfifilttttttttt*iltfi******l ****tIttflfl‘k'l‘ktfiItt*‘ktttttfltu*‘kufl‘ktfitttfitfixttttfltfifl‘ktu‘l PROGRAM INITIALIZATION DO 77 I=I,20 IFDAY(I)=0 IDAYSP(I)=@ PR1NT',“WHAT IS THE TIME INCREMENT,DT ?“ READ‘,DT PR1NT*,”WHA” IS THE INITIAL PUPAE COUNT 2“ READ‘,OM OM=OM/DT PR1NT*,“WHAT IS THE SEED CORN MAGGOT DENSITY?“ PR1NT*,“GENERATION 1?” READ*,PUPA1 PR1NT',“GENERAT10N 2?" READ*,PUPA2 PR1NT',“WHAT IS THE FIELD AREA (M2)?” READ‘,FAREA PR1NT“,”WHAT IS THE BORDER AREA (M2)?” READ*,BAREA PR1NT*,“ENTER NUMBER OF DAYS To SPRAY WITH lNSECTICIDE-” READ*,ISPRAY DO 66 1=1,ISPRAY PR1NT',“ENTER JULIAN DATE FOR SPRAY-”,1 READ*,IDAYSP(I) ISPMORT=0 IZ=I PR1NT*,“ENTER NUMBER OF DAYS To SPRAY WITH FUNG]C1DE-“ READ*,1FUNG DO 99 1=],1FUNG PR1NT*,“ENTER JULIAN DATE FOR SPRAY-“,1 99 000 00000 C 217 READ*,1FDAY(I) lFMORT=0 1FZ=1 tuttiuuuuuttututunauuuunxtutanuauuuunutuuuxuuauununxan OMPR=D SCMPR=D ENTPR=0 TIME SETTING Z1=DT*24. lDT=1NT(1./DT+.0@5) HOUR=(22./7.)/12. CUMDD=O. SCUMDD=@. PR1NT',“HOW MANY DAYS SHALL IT RUN?” READ',NDAYS FRMDD=8. Itltfifltflfifltfittttflttuttltfiflu*IttluttitiflfittIltfittfitufltt MAIN DAY LOOP DO 1660 L=I,NDAYS HTIME=0. PSUM=O . C WEATHER INPUT AND CENTIGRADE CONVERSION WITH DDAY CALC. (1‘: (I) 97 C CALL ATEMP(IDAY,TMIN,TMAX,TMEAN,HRANG) CALL SSTEMP(IIDAY,STEMP,LX) CALL DEGDAY(TMAX,TM1N,BASE,FRMDD) IF(1DAY.NE.IDAYSP(IZ))GO TO 88 IZ=IZ+I ISPMORT=1 CONTINUE IF(IDAY.NE.IFDAY(1FZ))GO TO 97 IFZ=IFZ+I IFMORT=I CONTINUE C WITHIN DAY LOOP J=I,IDT C C DO 900 J=I,IDT INSTANTANEOUS TEMPERATURE AND DEVELOPMENT ESTIMATES CUMDD=CUMDD+FRMDD*DT HTIME=HTIME+ZI THETA=(HTIME-9.)*HOUR TEMP=TMEAN+HRANG‘SIN(THETA) STMP=FNL(STEMP,0 . , 2 . ,1 3,HTIME) 218 CTIME=HT1ME—l/2*Z1 STF=FNL(STEMF,8.,2.,13,CTIME) 1F (STF.GT.BASE) SFRMDD=(STP-BASB)*DT IF (STP.LE.BASE) SFRMDD=.08881 SCUMDD=SCUMDD+SFRMDD CALL MAGGOT CALL SCMAG CALL ENTMOP ISPMORT=8 Lx=1 988 CONTINUE TOTAL=STRG(2)+STRG(1)+SQl+SQZ P1NFCT=(SQ]+SQZ)/TOTAL IF(TOTAL.LE.8)FINFCT=8. WRITE (3,180) 1DAY,CUMDD,STRG(5),STRG(4),STRG(3),STRG(2), +STRG(1),SOI,Soz,DMAG,SCSTOR 188 FORMAT (1x,15,18(2x,F9.1)) WRITE (4,116) IDAY,CUMDD,PSUM,TOTAL,PINFCT 118 FORMAT (Ix,15,4(2x,F10.3)) 1800 CONTINUE OWFUFA=OwPUPA+STRG(4)+STRG(3) PRINT*,“OVERH1NTER1NG PUPAE=”,OWPUPA PR1NT*,“TH1RD EGGS EGGS LAID=",EGGT END atuna*xutxxnnauaunuunn:tantnnnutuuxntuauuukuututnauxatnx xflatutuuuraittuntuxxtuSUBRUUTINEStatuttatttuautttuuuxnxfi wit:ttuuattuttuxtxuuuutuuutttutti!axuutuuxuauttauufluuxunn 0000000 SUBROUTINE DEVDEL(VIN,VOUT,R,STRG,DEL,DELP,DT,K,STORE) VIN - UNLAGGED INPUT VARIABLE VOUT - LAGGED OUTPUT VARIABLE R - ARRAY OF INTERMEDIATE RATES STRG - TOTAL IN STORAGE DEL - MEAN DELAY DELP - PREVIOUS MEAN DELAY DT - TIME INCREMENT K - NUMBER OF STAGES IN PROCESS STORE - ARRAY OF INTERMEDIATE STORAGE 00000000000 DIMENSION R(K),STORE(K) FK=FLOAT(K) A=DT'FK/DEL v=VIN DELD=(DEL-DELP)/(DT'FK) DELP=DEL Do 18 I=1,R 10 N G 0000000 000000 16 N G 0000 000000 219 DR=R(I) R(I)=DR+A'(V-DR*(I.+DELD)) V=DR CONTINUE VOUT=R(K) STRG=O. DO 20 I=I,K STORE(I)=R(I)‘DEL/FK STRG=STRG+STORE(I) CONTINUE RETURN END titti*I‘fi‘klflflflunfli‘lxtfiflfifilfitfitflfifltfitt*ttfifl‘hfiufitfifittltutfitfl SUBROUTINE DELAY(TEMP,DEL,A,B) TEMP - TEMERATURE AT TIME OF EVALUATION DEL - ARRAY OF DELAY RATES FOR.ALL STAGES A - A COEFFICIENTS FOR ALL STAGES B - B COEFFICIENTS FOR.ALL STAGES REAL A(6), B(6), DEL(5) 1F (TEMP .LE. 4.44) DEL(1)=188 IF (TEMP .GT. 4.44) DEL(1)=A(1)+B(1)/TEMP Do 18 I=2,3 IF (TEMP .LE. 4.44) DEL(1)=108 IF (TEMP .GT. 4.44) DEL(1)=A(1)*(TEMP'*B(I)) CONTINUE Do 28 1:4,5 IF (TEMP .LE. 4.44) DEL(I)=188 IF (TEMP .GT. 4.44) DEL(I)=A(I)+B(I)/TEMP CONTINUE RETURN END utaut:*ttuuttttttfikttuuauuthtuttuituntxtuttnuauunnnxnat SUBROUTINE DEGDAY(XMAX,XMIN,BASE,XHEAT) XMAX - MAXIMIM TEMPERATURE XMI N - MI N I MUM TEMPERATURE BASE - BASE TEMPERATURE, BELOW WHICH NO D-DAYS ACCUMULATE 220 C XHEAT - D-DAYS C DATA TPIE/6.283185388/, HPIE/I.578796327/ IF (XMAX .GT. BASE) GOTO 16 XHEA =.88801 RETURN 16 CONTINUE Z=XMAX-XM1N XM=XMAX+XMIN IF (XMIN .LT. BASE) GOTO 28 XHEAT=XM/2.—BASE IF (XHEAT .GT. 8.) GOTO 38 XHEAT=.88881 39 RETURN 28 CONTINUE TBASE=BASE*2. A=ASIN((TBASE-XM)/Z) XHEAT=(2.*COS(A)-(TBASE-XM)*(HPlE—A))/TPIE IF (XHEAT .GT. 8.) GOTU 48 XHEAT=.88881 48 RETURN END C C CIIt'll3'8Ill‘lt*XII'l'l‘l'kfi'kIt‘flflfififlfiflfl‘k‘lflflflfi‘lfitfi!8%filt‘kfiflfififi C SUBROUTINE MAGGOT Ct*‘ktfi*t'ttfltittfi‘l‘ufl‘tttwttfitfltttit'kfilu‘luXI‘k VARIABLES USED : MAGGOT A - ARRAY OF “A“ COEFFICIENTS FOR ALL STAGES OF OM B - ARRAY OF ”B“ COEFFICIENTS FOR ALL STAGES OF OM BASE - BASE TEMPERATURE, BELOW WHICH NO D-DAYS ACCUMULATE DEGGS - #OF EGGS PRODUCED BY DISEASED OM DEL - ARRAY OF MEAN DELAY VALUES FOR ALL STAGES OF OM DT - TIME INCREMENT EEC - FECUNDITY RATE FOR HEALTHY ADULT OM KS - NUMBER OF STAGES IN OVERWINTERING DELAY PROCESS LX - PARAMETER ; 0=GO THROUGH INITIALIZATION, I=SKIP INITIALIZAPION OM - INITIAL PUPAE COUNT FOR OM OMOUT - ARRAY OF LAGGED OUTPUTS FOR EACH LIFE STAGE PREVDEL - PREVIOUS MEAN DELAY VALUE PUPAE - NUMBER OF OVERWINTERING PUPAE IN DELAY PROCESS RATES - ARRAY OF INTERMEDIATE RATES FOR EACH LIFE STAGE OF OM SADULTS - NUMBER OF ADULTS LEAVING OVERWINTERING DELAY SDEL - MEAN DELAY VALUE FOR OVERWINTERING PUPAE SPDEL - PREVIOUS MEAN DELAY VALUE FOR OVERHINTERING PUPAE SQI - NUMBER OF LIVING DISEASED ADULTS 0000000000000000000000 221 C 802 - NUMBER OF LIVING DISEASED PREOVIPOSITIONAL ADULTS C SRATES - ARRAY OF INTERMEDIATE RATES FOR C OVERWINTERING PUPAE C STORE - ARRAY OF INTERMEDIATE STORAGE FOR EACH LIFE STAGE C STRG - NUMBER OF INDIVIDUALS IN EACH LIFE STAGE C SURVIVE - ARRAY OF SURVIVAL RATES FOR EACH LIFE STAGE C TEMP - TEMPERATURE AT EACH DT Cuunwanttxatuntuuatntuanauunxanauututtuautu C cu*tututtttuntiutransit:tuxuutxttutuuunnuu'48 C SUBROUTINES CALLED : MAGGOT C DELAY - COMPUTES MEAN DELAY VALUES FOR EACH LIFESTAGE OF C C OM BASED ON TEMP DEVDEL - TIME-VARYING DISTRIBUTED DELAY Ctautxuatxunututhuutauuatttnnuuuaututxtuut C REAL A(6),B(6),OMOUT(6),RATES(20,5),STRG(6),DEL(5), +SURVIVE(5),PREVDEL(5),SRATES(20),STORE(28),ARG(4),VAL +(4),VAL2(4) COMMON/PASSI/TEMP,LX,DT,EGGT COMMON/PASS3/STRG,SQI,Soz COMMON/PASS4/RATES,DEL,DEGGS,OMOUT,SURADT COMMON/PASS7/OM,OWPUPA,STMP,PSUM COMMON/PASSB/PUPAI,PUPA2,CUMDD,ISPMORT COMMON/PRPARM/OMPR,SCMPR,ENTPR INTEGER OMPR,SCMPR,ENTPR DATA A/-37.6,899.9,1167.4,-4.41,-1.85,3828./ DATA B/1475.2,-1.6,-1.4,478.7,112.4,-1.8/,BASE/4.44/ DATA SURVIVE/1.,1.,1.,1.,1./ DATA ARC/18.,15.56,26.67,32.22/ DATA VALI/-.8687,—.2157,-.2727,-.62262/ DATA VAL2/-.6058.-.028.-.0672.-.1@95/ C PROGRAM INITIALIZATION C C 60 IF(LX.GT.U)GO TO 810 OVERWINTERING PUPAE OWPUPA=D . DO 60 I=I,2@ STORE(I)=0. SRATES(I)=D. SADUUTS=PUPAE=Z. KS=20 SPDEL=10@. SDEL=@. SQI=0. ONION MAGGOT EGGS-ADULTS DEGGS=0. FEC=6. EGGT=O. 222 OMOUT(6)=@. STRG(6)=Z. DO I I=1,5 PREVDEL(I)=IGD. DEL(I)=Z. STRG(I)=D. OMOUT(I)=U. I CONTINUE DO 10 II=I,5 DO 13 I=I,20 10 RATES(I,II)=@. 810 CONTINUE C C INSTANTANEOUS DEVELOPMENTAL ESTIMATES CALL DELAY(TEMP,DEL,A,B) CALL OWDEL(STMP,A(3),B(3),DEL(3)) C EGGT=EGGT+DT‘OMOUT(6) C ONION MAGGOT DELAYS, EGGS-ADULTS AND OW PUPAE DO 800 I=I,5 CALL DEVDEL(OMOUT(I+1),OMOUT(I),RATES(I,I),STRG(I), +DEL(I),PREVDEL(I),DT,KS,STORE) OMOUT(I)=OMOUT(I)‘SURVIVE(I) 809 CONTINUE C COMPUTE SURVIVAL TSURI=TABLE(VALI,ARG,TEMP,4) TSUR2=TABLE(VAL2,ARG,TEMP,4) SUREGG=AMINI(EXP(TSURI*DT),I.) SRPUPA=AMINI(EXP(TSUR2‘DT),1.) SURADT=SRPUPA IF (TEMP.GE.3I.I) SURADT=.9 IF(ISPMORT.EQ.I)SURADT=SURADT'.7 DO 103% I=I,2@ RATES(I,5)=RATES(I,5)*SUREGG RATES(I,4)=RATES(I,4)‘SRPUPA RATES(I,2)=RATES(I,2)'SURADT RATES(I,I)=RATES(I,I)*SURADT 100% CONTINUE CALL DEVDEL(OM,SADULTS,SRATES(I),PUPAE,DEL(3),SPDEL, +DT,KS,STORE) OMOUT(3)=OMOUT(3)+SADULTS PSUM=PSUM+OMOUT(3) OM=Z. OMOUT(6)=DT‘(STRG(I)“FEC+DEGGS) IF(CUMDD.LT.ZOGO) GO TO 820 OWPUPA=OWPUPA+DT'OMOUT(3) OMOUT(3)=O. 82$ RETURN END SUBROUTINE ENTMOP CI*‘kfiflfilfiflflfifitittififl**$**ttutnfiu*‘flfififitfii‘I! 00000000000000000000000000000000000000000000000000 223 VARIABLES USED : ENTMOP C - ARRAY OF FECUNDITY RATES FOR DISEASED OM DEGGS - # OF EGGS PRODUCED BY DISEASED OM DEL - ARRAY OF MEAN DELAY VALUES FOR OM DELSC - MEAN DELAY VALUE FOR SEED CORN MAGGOT DELT2 - MEAN DELAY VALUE FOR ENTOMOTHPHORA DELIA - ARRAY OF DELAY VALUES OF OM ADULTS FOR 2-D DELAY DELIB - ARRAY OF DELAY VALUES OF OM PREOVIPOSITIONAL ADULTS FOR 2-D DELAY DELZ - ARRAY OF DELAY VALUES ON ENTOMOTHPHORA FOR 2-D DELAY DMAG - NUMBER OF DEAD DISEASED OM DSC - NUMBER OF DEAD DISEASED SEED CORN MAGGOTS DT - TIME INCREMENT HTIME - TIME OF DAY, 0-24 INFSC - ARRAY OF INFECTED SEED CORN MAGGOTS INFT - INFECTION RATE K - NUMBER OF STAGES IN 2-D DELAY PROCESS (LEFT TO RIGHT) KZ - NUMBER OF STAGES IN 2-D DELAY PROCESS (TOP TO BOTTOM) LX - PARAMETER ; Z= GO THROUGH INITIALIZATION PROCESS, I= SKIP INITIALIZATION NUMBER - TOTAL NUMBER OF SCM AND OM.ADULTS OBI - ARRAY OF DISEASED OM ADULTS LEAVING 2-D DELAY, OUT BOTTOM 0B2 - ARRAY OF DISEASED PREOVIPOSITIONAL OM ADULTS LEAVING 2-D DELAY, OUT BOTTOM ORI - ARRAY OF DISEASED OM ADULTS LEAVING 2-D DELAY, OUT RIGHT 0R2 - ARRAY OF DISEASED PREOVIPOSITIONAL OM ADULTS LEAVING 2-D DELAY, OUT RIGHT OUTB] - NUMBER OF DISEASED DEAD OM ADULTS LEAVING 2-D DELAY OUTBZ - NUMBER OF DISEASED DEAD PREOVIPOSITIONAL OM ADULTS LEAVING 2-D DELAY OUTR] - NUMBER OF LIVING DISEASED OM ADULTS LEAVING OUTR2 - NUMBER OF LIVING DISEASED PREOVIPOSITIONAL OM ADULTS LEAVING QDUMI - TEMPORARY STORAGE FOR 2-D DELAY, OM ADULTS QDUM2 - TEMPORARY STORAGE FOR 2-D DELAY, PREOVIPOSITIONAL OM ADULTS Q] - ARRAY OF INTERMEDIATE STORAGE FOR DISEASED OM ADULTS QZ - ARRAY OF INTERMEDIATE STORAGE FOR DISEASED PREOVIPOSITIONAL OM ADULTS RATES - ARRAY OF INTERMEDIATE RATES FOR HEALTHY OM SCRATE - ARRAY OF INTERMEDIATE RATES FOR HEALTHY SCM SCSTOR - NUMBER OF SCM ADULTS SPORES - TOTAL NUMBER OF SPORES SQI - NUMBER OF LIVING DISEASED OM ADULTS SQZ - NUMBER OF LIVING PREOVIPOSITIONAL OM ADULTS STRG - NUMBER OF HEALTHY INDIVIDUALS IN EACH LIFE STAGE OF OM 0000000000000000 224 TEMP - TEMPERATURE AT EACH DT TRINI - INPUT ARRAY DISEASED ADULTS FOR 2-D DELAY TRINZ - INPUT ARRAY OF DISEASED PREOVIPOSITIONAL ADULTS FOR 2-D ARRAY TSTORI - ARRAY OF INFECTED OM.ADULTS FOR INPUT IN TO 2-D DELAY TSTOR2 - ARRAY OF INFECTED PREOVIPOSITIONAL OM ADULTS FOR 2-D DELAY utint:utttfittttnuuttatatuanuuxuutanxttuuxu II!!!“*3tt*tiflflfiflfifittinflt*tfiflttfitfittflfifitfit SUBROUTINES USED : ENTMOP INFECT - COMPUTES INFECTION RATE DELAYZ - COMPUTES DELT2-MEAN DELAY VALUE FOR ENTOMOTHPHORA DEL2D - TWO DIMENSIONAL TIME-VARYING DISTRIBUTED DELAY PROCESS CIuttuttttufiuutuautuau*uuauutuuuauutuuuuttu REAL RATES(20,5),DEL(5),STRG(6),TSTORI(20),TRINI(2@,20) +TRIN2(20,20),DEL1A(28,28),DEL2(20,28),DELIB(2M,28), +02(20,20),QDUM1(20).QDUM2(2D),OR1(20),OR2(26),OB1(28), +TSTOR2(28),INFT,INFSC(20),SCRATE(20),C(20),TQI(28), +01(26,28),OB2(28),OMOUT(6) REAL INFSTG,INFSTO(2@),DSCRAT(28) COMMON/PASS]/TEMP,LX,DT,EGGT COMMON/PAssz/HTIME,DMAG,FAREA,BAREA COMMON/PASSB/STRG,SQI,SQZ COMMON/PASS4/RATES,DEL,DEGGS,OMOUT,SURADT COMMON/PASSS/SC,SCRATE,DELSC,SC1NF COMMON/PASS6/SCSTOR COMMON/PASSB/PUPAI,PUPA2,CUHDD,ISPMORT COMMON/PRPARM/OMPR,SCMPR,ENTPR INTEGER OMPR,SCMPR,ENTPR DATA c/8.,8.,0.,8.,0.,6.,.06,.86,.86,1.8,1.8,1.8, +2.7,2.7,2.7,2.7,2.7,2.7,2.7,2.7/ C PROGRAM INITIALIZATION K=ZD K2=2U IF (LX.GT.U) GO TO 109 D0 3 I=I,K DO 2 J=17K2 TRINI(I,J)=O. TRIN2(I,J)=U. DELIA(I,J)=IOO. DELIB(I,J)=I@@. DEL2(I,J)=IOO. 01(18J)=00 QZ(I,J)=G. CONTINUE CONTINUE OUTRI=U. DELT=IUU. 4 10 C C C C 16 26 C 225 1NFSTG=@. OUTBI=@. OUTR2=@. OUT82=D. 501:0. SQZ=0. K2=20 DMAG=0. DSC=0. SCINF=0. 1NFT=O. DO 4 1:1,20 OR1(I)=D. OR2(1)=@. OBI(I)=0. OBZ(1)= D. QDUM1(1) E. QDUH2(1) 0. DSCRAT(I)=U 1NFSC(I)- 0. INFST0(I)= D. CONTINUE O CONTINUE tututuuxututtuaauuuxuuuuututatanxiuautuxunuttuuttxuaauutuuu COMPUTE INFECTION DELAYS autttuxutatxuxnuxanuatnuanutttttxtittxuaututanstxtauitstat: NUMBER=STRG(1)+STRG(2)+SQ]+SQZ+SCSTOR CDMPUTE INFECTION RATE DFLIES=DSC+DMAG CALL INFECTZ(DFLIES,HT1ME,TEMP,CUMDD,LX,BAREA,FAREA,DT, +1NFT) FK=FLOAT(K) COMPUTE NUMBER OF ADULTS INFECTED DO 10 J=1,K TSTOR](J)=1NFT*RATES(J,1) TRIN](1,J)=TSTOR1(J)*DEL(1)/FK RATES(J,1)=RATES(J,1)-TSTOR1(J) TSTOR2(J)=1NFT'RATES(J,2) TR1N2(],J)=TSTOR2(J)*DEL(2)/FK RATES(J,2)=RATES(J,2)-TSTOR2(J) CONTINUE TRINJ(1,1)=OR2(1)+TR1N1(1,1) DO 20 J=2.K2 TRINI(J,1)=OR2(J) CONTINUE COMPUTE NUMBER OF SC ADULTS INFECTED DSC=@. DO 25 I=1,20 INFSC(1)=1NFT*SCRATE(1) SCRATE(I)=SCRATE(I)-1NFSC(1) 226 DSCRAT(I)=DSCRAT(I)+INFSC(I) 25 CONTINUE SCINF=SCINF/DT C COMPUTE NUMBER OF INFECTED OM.AT EMERGENCE AZYINF=.]'DT IF (CUNDD.GT.15GO.) AZYINF=.3“DT TOMOUT=AZYINF-OMOUT(3) OMOUT(3)=OMOUT(3)-TUMOUT TR1N2(1,I)=TR1N2(1,1)+TOMOUT*DEL(3)/FK C COMPUTE DELAY RATE FOR ENTOMOP. CALL DELAY2(TEMP,DELT2) DO 40 1:1,26 DO 39 J=1,2G DEL1A(1,J)=K/DBL(1) DELIB(1,J)=K/DEL(2) DEL2(1,J)=K2/DELT2 Ql(1,J)=QI(1,J)*SURADT Q2(1,J)=QZ(1,J)*SURADT 3G CONTINUE 4E CONTINUE C ENTOMOP. INFECTION DELAYS CALL DEVDEL(SC1NF,DSC,1NFSC,1NFSTG,DELT2,DELT, +DT,K,1NFSTO) CALL DEL2D(TR1N1,0UTR1,0UTB],DEL]A,DEL2,QI,SQI,DT,K,K2, +QDUM],OR1,0BI) CALL DEL2D(TR1N2,0UTR2,0UTBZ,DEL]B,DEL2,QZ,SQZ,DT,K,K2, +QDUM2,OR2,OB2) DMAG=OUTBI+OUTB2 C COMPUTE # OF EGGS PRODUCED BY DISEASED OM-—DEGGS DEGGS=D. DO 60 1:1,20 TQI(1)=D. D0 50 J=1.2@ T01(1)=TQI(1)+01(1.J)/DT 50 CONTINUE DEGGS=DEGGS+C(1)“TQI(I) 60 CONTINUE RETURN END SUBROUTINE SCMAG Cutatttatutattattuntfiutuutuuuunatunnatutttat VARIABLES USED : SCMAG AOUT - LAGGEDD OUTPUT FOR SEED CORN MAGGOT DELAY CUMDD - CUMULATIVE DEGREE DAYS DELSC - MEAN DELAY VALUE FOR SEED CORN MAGGOT DT - TIME INCREMENT EMERGE - NUMBER OF EMERGED SCM TO ENTER ADULT DELAY LX - PARAMETER ; U = GO THROUGH INITIALIZATION, 1 = SKIP INITIALIZATION MEAN - CUMDD AT AT 5% EMERGENCE FOR GEN] AND GEN2 0000000000 227 OF SEED CORN MAGGOT PDEV - PERCENT EMERGENCE OF SCM POPULATION PREDEV - PREVIOUS CUMULATIVE PERCENT EMERGENCE PREVDELSC - PREVIOUS MEAN DELAY VALUE PROB - CUMULATIVE PERCENT EMERGENCE PUPAI - NUMBER OF SCM IN IST GENERATION PUPAZ - NUMBER OF SCM IN 2ND GENERATION SCRATE - ARRAY OF INTERMEDIATE RATES FOR SCM DELAY SCSTOR - NUMBER OF SCM ADULTS SIGMA - STANDARD DEVIATION WITH RESPECT TO MEAN TEMP - TEMPERATURE AT EACH DT Z - INPUT FOR NORMAL DISTRIBUTION USED TO COMPUTE PROB Ct‘lflflfitfifiu'k‘Rfltflfl‘nfl‘I‘kflItiflluxflttflfififlfl‘lllfi‘fl C Cttit:tattttttutttttuttutatttttttattttutuutn C SUBROUTINES CALLED : SCMAG c DELAY3 - COMPUTES DELSC; MEAN DELAY VALUE; ACCORDING C To TEMP C DEVDEL - TIME-VARYING DISTRIBUTED DELAY c NORMAL - COMPUTES PROB USING CURRENT VALUE OF 2 cufittinttttttanttaxitauxattxtattaauautnuuuta DIMENSION SCRATE(2O),PROB(2),STORE(2D),XMEAN(2),SIGMA(2) DIMENSION PREDEV(2),Z(2),PDEV(2) COMMON/PASSl/TEMP,LX,DT,EGGT COMMON/PASSS/SC,SCRATE,DELSC,SC1NF COMMON/PASS6/SCSTOR COMMON/PASSB/PUPAI,PUPA2,CUMDD,ISPMDRT COMMON/PRPARM/OMPR,SCMPR,ENTPR INTEGER OMPR,SCMPR,ENTPR DATA XMEAN(I)/2G2.78/,XMEAN(2)/1511.II/,SIGMA(I)/77.78/, +SIGMA(2)/I38.89/,K/2G/ C PROGRAM INITIALIZATION IF (LX.GT.0)G0 TO 160 PROB(1)=PROB(2)=0. PREVDEL=IGD. DELSC=E. AOUT=E. DO 5 1=1.2@ SCRATE(I)=D. STORE(I)=D. 5 CONTINUE 109 CONTINUE c COMPUTE SC ADULT EMERGENCE D0 10 I=I,2 PREDEV(I)=PROB(I) Z(1)=(CUMDD-XMEAN(1))/SIGMA(1) CALL NORMAL(Z(I),PROB(I),DEN) PDEV(I)=PROB(I)-PREDEV(1) ID CONTINUE EMERGE=(PDEV(1)*PUPA1+PDEV(2)*PUPA2)/DT 000000000000 228 C COMPUTE INFECTION BY ENT AT EMERGENCE TSCINF=.25*DT'EMERGE EMERGE=EMERGE-TSCINF SCINF=TSCINF*DT C APPLICATION OF SPRAY MORTALITY IF(ISPMORT.NE.I)GO TO 1009 DO 6 IS=I,29 6 SCRATE(IS)=SCRATE(IS)*.2 C COMPUTE SC ADULT DELAY 1600 CALL DELAY3(TEMP,DELSC) CALL DEVDEL(EMERGE,AOUT,SCRATE,SCSTOR,DELSC,PREVDEL: +DT,K,STORE) RETURN END SUBROUTINE NORMAL(X,P,D) C C C COMPUTES Y=P(X)=PROBABILITY THAT THE RANDOM VARIABLE C DISTRIBUTED NORMALLY (O,I) IS LESS THAN OR EQUAL TO X. C THE ORDINATE OF THE NORMAL DENSITY AT X, IS ALSO COMPUTED. C C Ctuttttuufi*tttttuxttuiutnuutuutxnttttttuxutu C VARIABLES USED : NORMAL C C X - INPUT SCALAR FOR WHICH P(X) IS COMPUTED C P - OUTPUT PROBABILITY C D - OUTPUT DENSITY C'snintuutxnuttatuxuuuttuuutwannauututtuutuux AX=ABS(X) T=I.D/(1.0+.23164I9*AX) D=D.3989423*EXP(-X*X/2.9) P=1.0-D*T*((((I.333274*T-1.821256)‘T+1.781478)*T- +0.3565638)*T+0.3193815) 1F (X) 1.2.2 2 RETURN END SUBROUTINE DELAY3(TEMP,DEL) Cututtiatutuxtutttatututaa*aa‘auuauuttuauuunut C VARIABLES USED : DELAY3 C C TEMP - TEMPERATURE AT EACH DT C A - “A“ COEFFICIENT FOR SEED CORN MAGGOT C B - ”B“ COEFFICIENT FOR SEED CORN MAGGOT C DEL - MEAN DELAY VALUE Cututtauututtutuuttxuttttttattuaatttaauuuuuxa A=-37.662 B=1475.231 1F (TEMP.LE.4.44) DEL=IOO. 229 IF (TEMP.GT.4.44) DEL=A+B/TEMP RETURN END SUBROUTINE DELAY2(TEMP,DEL2) Cta‘ktattuttttttttutuflttttttttttttxltxtutttttx C VARIABLES USED : DELAY2 C C TEMP - TEMPERATURE AT EACH DT C A - “A“ FOR ENT C B - “B“ FOR ENT C DELZ - MEAN DELAY VALUE FOR ENT Cttutituttutttutututauuafinututuutxutuuutuuntt =-.02518 B=153.55 IF (TEMP.LE.4.44) DEL2=109. IF (TEMP.GT.4.44) DEL2=A+B/TEMP RE TU RN END SUBROUTINE DEL2D(TRIN,OUTR,OUTB,DELI,DEL2pQ.SQ,DT,KI, Cttttfltflt***tut*ttfltfiitfiflIttuflfififittttlttxtttt 00000000000000000 VARIABLES USED : DEL2D DELI - MEAN DELAY VALUE FOR LEFT - RIGHT FLOW DELZ - MEAN DELAY VALUE FOR TOP - BOTTOM FLOW KI - NUMBER OF DELAY STAGES, LEFT - RIGHT K2 - NUMBER OF DELAY STAGES, TOP - BOTTOM OBI - ARRAY OF OUTPUT, OUT BOTTOM OR] - ARRAY OF OUTPUT, OUT RIGHT OUTB - OUTPUT OF STAGE K2, BOTTOMMOST STAGE OUTR - OUTPUT OF STAGE KI, RIGHTMOST STAGE Q - ARRAY OF K1 * K2 STORAGES IN DELAY PROCESS RIL - RATE IN LEFT RIT - RATE IN TOP ROB- RATE OUT BOTTOM ROR - RATE OUT RIGHT SO - SUM OF ALL STORAGES IN THE DELAY PROCESS TRIN - K1 BY K2 ARRAY OF INPUTS TO THE KI * K2 STAGES C!*fltflIfltlflfltfltfl*Ifliflflfltfltfififlfltflflflfltflfllttxfiut +K2, QDUM, ORI , OBI ) DIMENSION TRIN(KI,K2),Q(KI,K2),DELI(KI,K2),DEL2(K],K2) +,QDUM(K2),ORI(20),OBI(20) OUTR=OUTB=O. SQ=0. DO 8% II=I,KI DO 70.JJ=I,K2 ROR=DELI(II,JJ)*Q(II,JJ) OUTRI=0. IF (II.EQ.KI) OUTRI=ROR IF (II.EQ.KI) ORI(JJ)=OUTRI ROB=DEL2(II,JJ)*Q(II,JJ) 70 80 230 OUTB1=D. IF (JJ.EQ.K2) OUTBI=ROB IF (JJ.EQ.K2) OB](II)=OUTB] RIL=D. IF (II.NE.I) R1L=DEL1(II-I,JJ)*QDUM(JJ) RIT=@. IF (JJ.NE.I) RIT=DEL2(II,JJ-])‘QDUM(JJ-]) QDUM(JJ)=Q(II,JJ) Q(11,JJ)=Q(II,JJ)+DT*(TRIN(II,JJ)+RIL+RIT-ROR-ROB) OUTR=OUTR+OUTR1 OUTB=OUTB+OUTBI SQ=SQ+Q(II,JJ)/DT CONTINUE CONTINUE RETURN END SUBROUTINE INFECT(NUMBER,SPORES,INFT,HT1ME) REAL INFT INFT=.DI RETURN END SUBROUTINE ATEMP(IDAY,TMIN,TMAX,TMEAN,BRANG) Cutitanutttuutxttaunt:nauuttuxuaununuuuxuxtututninflux: C C C C C C C 50 VARIABLES USED: ATEMP IDAY - JULIAN DAY TMIN - MINIMUM TEMPERATURE TMAX - MAXIMUM TEMPERATURE TMEAN - MEAN TEMPERATURE HRANG - TEMPERATURE RANGE **ttti*Iflitflfiit*!I%I*I*Ii***33**tfifitIufltfifitfittiuiflttt READ(I,50) IDAY,TMAX,TMIN FORMAT(1X,5X,I3,2(5X,F3.0)) TMAX=(TMAX-32.)*5./9. TMIN=(TMIN-32.)*5./9. TMEAN=(TMAX+TMIN)/2. HRANG=(TMAX—TM1N)/2. RETURN END SUBROUTINE SSTEMP(IIDAY,STEMP,LX) CItfltflttitfifltflttiIflfitlfififi*ttfilfitttt*ttfifixfiflhlfilututtfltfitfllfl C VARIABLES USED: STEMP C IIDAY - JULIAN DAY C STEMP - SOIL TEMPERATURE Cuttuutttttttttututuuuutatxuaatuuuuuatutu:uuutuuuuuunxtuxaa 10 DIMENSION STEMP(]3),DAY(I3) IF (LX.GT.O) GO TO 10% DO 10 I=I,13 READ (2,50) STEMP(I) STEMP(I)=(STEMP(1)—32.)‘5./9. CONTINUE 169 2G 30 00000000000 15 26 25 231 GO TO 30 CONTINUE STEMP(I)=STEMP(I3) DO 20 l=2,13 READ (2.5%) STEMP(I) STEMP(I)=(STEMP(I)-32.)*5./9. CONTINUE CONTINUE FORMAT(F2.0) RETURN END FUNCTION FNL(DVAL,XS,DX,N,X) TABLE LOOK-UP FUNCTION : GIVES Y-VALUE FOR ANY X-VALUE INPUT, GIVEN POINTS ON A CURVE (DVAL). VARIABLES : FNL - ARRAY CONTAINING Y-VALUES XS - FIRST X-VALUE INCREMENT IN X-VALUES - NUMBER OF POINTS X - X-VALUE INPUT DIMENSION DVAL(I) IF (x—xs) 10,10,5 IF (X—XS-N‘DX) 29.15.15 FNL=DVAL(I) GOTO 25 FNL=DVAL(N+1) GOTO 25 XD=X-XS I=I.D+XD/Dx FNL=(XD-FLOAT(1-1)‘DX)*(DVAL(I+1)-DVAL(1))/DX+DVAL(I) RETURN END SUBROUTINE ONDEL(STMP,A,B,SDEL) CIflfifltfilfii*8**tfi*Ififlflflflflflfififllttfitfi*t‘ufiflfilfifl'fiXII‘lfiflit C C C C C C C VARIABLES USED: OWDEL STMP - SOIL TEMPERATURE A.- A COEFFICIENT FOR PUPAE B - B COEFFICIENT FOR PUPAE SDEL - DELAY RATE FOR PUPAE atututtttntuttuuuuutuuuuuttnntuaauuxautuuxnunnautat IF (STMP.LE.4.44) SDEL=IOO. IF (STMP.GT.4.44) SDEL=A*(STMP**B) RETURN END FUNCTION TABLE(VAL,ARG,DUMMY,K) C ai:ataxia:ututtuutttutnutruutuntutuuanaauatuuxtaunauuttx 0000000000000 00 00000000000000000000000 232 flfififiII***‘K*I*I*Ifitnxfififit*fifitflfitfifififlfifixfiflflflflfitl3*!Ilfifi VARIABLES USED: TABLE VAL - ARRAY OF Y-VALUES ARG - ARRAY OF X-VALUES DUMMY - X-VALUE INPUT K - DIMENSION OF VAL AND ARG tattutttttuutxxtattaututuuuxauttaxnttutttatutxataxiaan THIS FUNCTION COMPUTES THE Y VALUE FOR ANY X VALUE GIVEN. THE ELEMENTS OF THE ARGUMENT ARRAY DO NOT HAVE TO BE EQUALLY SPACED AND THIS FUNCTION DOES NOT ESTRAPOLATE BEYOND THE END POINTS. tttnauanuuxttuauuutttuuuatataxiaaaauuattitttttuutuatttt tuttutnutuatuttututituututuaauntunutuaxuuuunutaauxnu DIMENSION VAL(I),ARG(I) DUM=AMAX1(AMINI(DUMMY,ARG(K)),ARG(I)) DO I I=2,K IF (DUM.GT.ARG(I)) GO TO I TABLE=(DUM—ARG(I-1))*(VAL(I)-VAL(I-I))/ +(ARG(I)-ARG(I-I))+VAL(I-1) RETURN CONTINUE RETURN END SUBROUTINE INFECT2(DFLIES,HT1ME,TEMP,CUMDD,LX,BAREA, +FATEA,DT,1NFT) *tuatttttattuttntututttuxnttuanuttxtauaauatttxnaaaaaattuxat VARIABLES USED: INFECTZ DFLIES - NUMBER OF DEAD DISEASED FLIES HTIME - TIME OF DAY IN HOURS TEMP - TEMPERATURE CUMDD - CUMULATIVE DEGREE DAYS LX - PARAMETER PASSED FOR INITIALIZATION BAREA - BORDER AREA FAREA - FIELD AREA DT - TIME INCREMENT SUMFLY - # OF DEAD FLIES IN ONE DAY TOTCAD - TOTAL NUMBER OF CADAVERS PER DAY CADM2 - NUMBER OF CADAVERS PER METER SQUARED SPORE - SPORES PER DT TOTAL - TOTAL NUMBER OF SPORES XLOAD - HABITAT LOADING INFT - INFECTION RATE tuttittituttlntinttttttttintfiuttttufitfitttfitttttautttnttit! SUBROUTINES CALLED: NORMAL TABLE - TABLE LOOK UP FUNCTION fiflflflttflu*$%***“itttiItfiufifltttttfifltflflfitfltfittfittfittittfifitnut tttflfifltiutifltflfi*tfiflflfifllfltflflfitflflfliflfififl*filtktti*tflutfltttfitut DIMENSION VAL ( 21 ),ARG ( 21) 233 COMMON/FUNG/IFMORT REAL IN FT DATA XMTEMP/Ie.5/,XSTEMP/3.878/,XMTIME/5.85/,XSTIME/2.O32/ DATA VAL/.DBI,.DS,.I,.15,.2,.25,.3,.35,.4,.45,.5,.55,.6, +.65,.7,.75,.8,.85,.9,.95,.99/ DATA ARG/0.,3.36,3.72,3.96,4.16,4.33,4.48,4.61,4.75,4.87, +5.0@,5.13,5.25,5.39,5.52.5.67,5.84,6.04,6.28,6.64,7.33/ C PROGRAM INITIALIZATION IF (LX.GT.0) GO TO 10 SUMFLY=O . Y=24‘DT+I9. Z=24'DT+7. CADM2=O. TOTCAD=O. POLD=O. TOTAL=O. C COMPUTE SUM OF FLIES DAILY 10 SUMFLY=SUMFLY+DFLIES IF (HTIME.GT.19..AND.HTIME.LT.Y) TOTCAD=SUMFLY IF (HTIME.GT.I9..AND.HTIME.LT.Y) SUMFLY=O. CADM2=TOTCAD/BAREA C COMPUTE NUMBER OF SPORES PER FLY XZ=(TEMP-XMTEMP)/XSTEMP CALL NORMAL(xz,P,D) SPORE=D*3SDEDDG. IF (HTIME.GT.7..AND.HT1ME.LT.19.) GO TO 106 XI=HTIME-I9. IF (HTIME.LT.I9.) X1=HTIME+5. XZ=(XI-XMTIME)/XSTIME CALL NORMAL(XZ,P,D) WEIGHT=P-POLD WEIGHT=ABS(WEIGHT) POLD=P SPORE=SPORE*WEIGHT TOTAL=TOTAL+SPORE C COMPUTE INFECTION RATE WHEN IT=I IOU 1T=0 IF (HTIME.GT.7..AND.HT1ME.LT.Z) IT=1 IF (IT.EQ.D) GO TO 209 TSPORE=TOTAL‘CADM2 TOTAL=0. IF (TSPORE.LE.G.) TSPORE=.I TSPORE=TSPORE/IEDDDDD. PMORT=4.86+2.029*(ALOGID(TSPORE)) PERINF=TABLE(VAL,ARG,PMORT,21) XLOAD=3D. 1F (CUMDD.GT.361.) XLOAD=44.55-.@4D3*CUMDD IF (CUMDD.GT.1055.) XLOAD=2. IF (CUMDD.GT.1805.) XLOAD=-850.2+.472*CUMDD IF (CUMDD.GT.2@55.) XLOAD=120. 234 TBAREA=XLOAD*BAREA INFT=TBAREA/(FAREA+TBAREA)*PERINF C APPLICATION OF FUNGICIDE MORTALITY TO GERMINATING SPORES IF(IFMORT.GT.I)INFT=O. IF(IFMORT.EQ.3)IFMORT=0 IF(IFMORT.EQ.2)IFMORT=3 IF(IFMORT.EQ.I)1FMORT=2 WRITE (5,50%) CUMDD,INFT,PERINF,CADM2,TSPORE 50D FORMAT(IX,5(2X,FIO.4)) GO TO 390 20% INFT=O. 300 RETURN END MICHIGAN STATE UNIV. LIBRARIES mWINIIIWW‘IWIW(IIIIHIINIVINWIHIIWI 31293105961506