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A biometrical study of the effect of nonspecific pathogenicity genes on host and pathogen fitness related… Pope, David D. 1986

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c A BIOMETRICAL STUDY OF THE EFFECT OF NONSPECIFIC PATHOGENICITY GENES ON HOST AND PATHOGEN FITNESS RELATED CHARACTERS IN THE USTILAGO HORDEI-HORDEUM VULGARE SYSTEM. By DAVID D. POPE B.Sc., N o r t h C a r o l i n a S t a t e U n i v e r s i t y , R a l e i g h , 1975 M . S c , The U n i v e r s i t y Of B r i t i s h C o l u m b i a , V a n c o u v e r , 1982 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY i n THE FACULTY OF GRADUATE STUDIES (Department of B o t a n y ) We a c c e p t t h i s t h e s i s a s c o n f o r m i n g t o t h e r e a u i r e c l s t a n d a r d THE UNIVERSITY OF BRITISH COLUMBIA A p r i l 1986 (c ) D a v i d D. Pope, 1986 In presenting t h i s thesis i n p a r t i a l f u l f i l m e n t of the requirements for an advanced degree at the University of B r i t i s h Columbia, I agree that the Library s h a l l make i t f r e e l y a v a i l a b l e for reference and study. I further agree that permission for extensive copying of t h i s thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It i s understood that copying or publ i c a t i o n of t h i s thesis for f i n a n c i a l gain s h a l l not be allowed without my written permission. Department of / ^ T ^ j / ^ QCT-A)t=~y-tc.p) The University of B r i t i s h Columbia 2075 Wesbrook Place Vancouver, Canada V6T 1W5 Date 4j^^8/96 7Q ^  i i ABSTRACT Nine Ustilago hordei sporidia that produced 20 dikaryons were isol a t e d at random from an F2 teliospore (18D1+ x 20C1-) descended from race 7 and race 11. The 20 dikaryons were homozygous for a dominant gene conferring virulence on the barley variety Trebi and were suspected of segregating for nonspecific pathogenicity genes on t h i s variety. V a r i e t i e s Odessa (the universal suscept, with no known s p e c i f i c resistance genes) and Trebi were inoculated with each dikaryon and 58 host and pathogen fi t n e s s related variables were measured. Y i e l d reduction occurred both in diseased and healthy plants as a result of the dikaryon treatments. A s t a t i s t i c a l l y s i g n i f i c a n t negative correlation between host and pathogen reproductivity was found (r=-0.466, P=0.0481) on Trebi but not on Odessa. S t a t i s t i c a l l y s i g n i f i c a n t differences among dikaryons were found for some fitness related variables. The segregation of nonspecific pathogenicity genes with p l e i o t r o p i c e f f e c t s was believed to cause these differences. One of the genes was found to be t i g h t l y linked to the mating locus, coupled with the "-" mating a l l e l e . Analysis of variance revealed s i g n i f i c a n t dominance and/or e p i s t a t i c interaction effects on f i t n e s s related variables. The two v a r i e t i e s reacted d i f f e r e n t l y to the dikaryons. Pathogen i s o l a t e s exhibited s p e c i f i c adaptation to Trebi but not to Odessa. The presence of the nonspecific pathogenicity genes was readily measured s t a t i s t i c a l l y on Trebi, in the background of a matched s p e c i f i c resistance gene but not on Odessa. The t r a d i t i o n a l method of measuring disease damage le v e l (percent smutted plants) was determined to be a r e l i a b l e estimator of pathogen fi t n e s s on Trebi (R2=0.84) and pathogen reproductivity on both v a r i e t i e s (r=0.902, P=0.0001 on Trebi and r=0.8l5, P=0.0001 on Odessa). Due to weak cor r e l a t i o n , prediction of host fitness should not be attempted using values calculated with either of the two t r a d i t i o n a l methods of measuring disease damage l e v e l (percent smutted plants and percent smutted heads). Stepwise regression of various combinations of variables indicated that Trebi, Odessa or smut dikaryon fitness can be accurately estimated with certain predictor v a r i a b l e s . Spearman rank c o r r e l a t i o n tests suggested that "constant (concordant) ranking" of dikaryons for percent smutted plants and for pathogen fitness was evident on Odessa and on Trebi (r=0.871, P=0.0001 and r=0.713, P=0.0004, resp e c t i v e l y ) . iv TABLE OF CONTENTS ABSTRACT i i LIST OF TABLES v i i i LIST OF FIGURES x i i ACKNOWLEDGEMENTS x v i i 1 INTRODUCTION 1 2 GENETICS OF HOST-PARASITE INTERACTIONS 3 2.1 SPECIES COMPATIBILITY 4 2.2 SPECIFIC GENES 4 2.3 NONSPECIFIC GENES 6 2.3.1 CONSTANT RANKING 9 2.4 QUEST FOR DURABLE RESISTANCE 10 3 QUANTITATIVE MEASUREMENT OF DISEASE LEVELS 13 3.1 DEFINITION OF FITNESS 13 3.2 FITNESS IN RUST PATHOSYSTEMS 17 3.2.1 INFECTION FREQUENCY 19 3.2.2 LATENT PERIOD 20 3.2.3 SPORE PRODUCTION 21 4.2.4 INFECTIOUS PERIOD 22 3.2.5 RELATIONSHIPS AMONG COMPONENTS 22 4 THE USTILAGO HORDEI-HORDEUM VULGARE SYSTEM 25 4.1 BIOLOGY OF U. HORDE I 25 4.2 BACKGROUND INFORMATION 26 4.3 QUANTITATIVE INVESTIGATIONS 29 4.4 CURRENT WORK 31 V 5 PURPOSE 34 5.1 OBJECTIVES 37 6 MATERIAL AND METHODS 39 6.1 EXPERIMENTAL DESIGN 39 6.2 SEED PREPARATION 40 6.3 PLANTING 40 6.4 HARVESTING AND DATA RECORDING 40 6.5 HEAD ANALYSIS 42 6.6 SPORIDIA CULTURE MEDIUM 43 6.7 SPORIDIA ISOLATION 43 6.8 LONG-TERM SPORIDIAL STORAGE 44 6.9 INOCULATION 44 6.10 STATISTICAL ANALYSIS 4 5 7 RESULTS 46 7.1 DESCRIPTION OF VARIABLES 46 7.2 REGRESSION OF SPORE NUMBER ON SPORE WEIGHT 46 7.3 DESCRIPTION OF FITNESS VARIABLES 47 7.4 SPORIDIAL TREATMENTS VERSUS CONTROL COMPARISONS 49 7.5 VARIABLE MEAN COMPARISONS FOR THE VARIETIES 50 7.6 ANOVA 52 7.7 MODELS 53 7.7.1 COMPLETE 54 7.7.2 TRADITIONAL 55 7.7.3 PRACTICAL 55 7.7.4 DEVELOPMENTAL 56 7.8 "CONSTANT RANKING" 57 8 DISCUSSION 58 v i 8.1 BIOLOGICAL MATERIAL 58 8.2 SPORIDIAL TREATMENTS VERSUS CONTROL COMPARISONS 59 8.3 VARIABLE MEAN COMPARISONS FOR THE VARIETIES 62 8.4 ANOVA 63 8.5 MODELLING 71 8.5.1 COMPLETE 71 8.5.2 TRADITIONAL 74 8.5.3 PRACTICAL 76 8.5.3.1 MINIMAL COST 76 8.5.3.2 MODERATE COST 76 8.5.3.3 EARLY ASSESSMENT 77 8.5.4 DEVELOPMENTAL 78 8.5.4.1 C (COMPLETELY DISEASED PLANTS) OR H (HEALTHY PLANTS) BASED 78 8.5.4.2 P (PARTIALLY DISEASED PLANTS) BASED: HOST PERSPECTIVE 79 8.5.4.3 P (PARTIALLY DISEASED PLANTS) BASED: PATHOGEN PERSPECTIVE 80 8.6 "CONSTANT RANKING" 81 9 SUMMARY 85 10 REFERENCES CITED 89 11 APPENDICES 109 11.1 APPENDIX A 109 11.1.1 MINIMAL MEDIUM 109 11.1.2 COMPLETE MEDIUM 109 11.1.3 BAUCH MATING TYPE TEST PLATES 109 11.1.4 VOGEL'S SOLUTION 109 v i i 11.1.5 TRACE ELEMENT SOLUTION 109 11.1.6 VITAMIN SOLUTION 110 11.2 APPENDIX B 111 11.3 APPENDIX C 276 v i i i LIST OF TABLES TABLE 1. Variance components and h e r i t a b i l i t i e s for pathogenicity. 112 TABLE 2. Ebba's and Tapke's disease readings for parental teliospores 114 TABLE 3. Eight F1 dikaryotic l i n e (DL) disease readings for the cross between teliospores T1 and T4 on Trebi. ..116 TABLE 4. Description of R (row) related and fi t n e s s (W) variables 118 TABLE 5. Description of H (healthy plant) related variables 120 TABLE 6. Description of C (completely diseased plant) related variables 122 TABLE 7. Description of P ( p a r t i a l l y diseased plant) related variables 124 TABLE 8. Values of the R (row) and fi t n e s s (W) subset of variables on Trebi 126 TABLE 9. Values of the H (healthy plant) subset of variables on Trebi 130 TABLE 10. Values of the C (completely diseased plant) subset of variables on Trebi 133 TABLE 11. Values of the P ( p a r t i a l l y diseased plant) subset of variables on Trebi 135 TABLE 12. Values of the R (row) and fitness (W) subset of variables on Odessa 139 TABLE 13. Values of the H (healthy plant) subset of variables on Odessa 143 TABLE 14. Values of the C (completely diseased plant) subset of variables on Odessa 146 TABLE 15. Values of the P ( p a r t i a l l y diseased plant) subset of variables on Odessa 148 TABLE 16. Single sample t test results between treatment and control means on Trebi 152 TABLE 17. Single sample t test results between treatment and control means on Odessa 154 TABLE 18. One-way ANOVA and Duncan's multiple range test for select variables measured on Trebi 156 TABLE 19. Correlated groups t test results measured on Trebi 160 TABLE 20. One-way ANOVA and Duncan's multiple range test for select variables measured on Odessa 162 TABLE 21. Correlated groups t test results measured on Odessa 166 TABLE 22. Analysis of variance of R (row) and fi t n e s s (W) variables on Trebi 168 TABLE 23. Analysis of variance of H (healthy plant) variables on Trebi 177 TABLE 24. Analysis of variance of C (completely diseased plant) variables on Trebi 182 TABLE 25. Analysis of variance of P ( p a r t i a l l y diseased plant) variables on Trebi 186 TABLE 26. Analysis of variance of R (row) and fi t n e s s (W) X v a r i a b l e s o n O d e s s a . 1 9 3 T A B L E 2 7 . A n a l y s i s o f v a r i a n c e o f H ( h e a l t h y p l a n t ) v a r i a b l e s o n O d e s s a 2 0 2 T A B L E 2 8 . A n a l y s i s o f v a r i a n c e o f C ( c o m p l e t e l y d i s e a s e d p l a n t ) v a r i a b l e s o n O d e s s a 2 0 7 T A B L E 2 9 . A n a l y s i s o f v a r i a n c e o f P ( p a r t i a l l y d i s e a s e d p l a n t ) v a r i a b l e s o n O d e s s a 2 1 1 T A B L E 3 0 . A c o m p a r i s o n o f t h e p a t t e r n o f s i g n i f i c a n t c o m p o n e n t s o f v a r i a b i l i t y o n T r e b i a n d O d e s s a 2 1 8 T A B L E 3 1 . F r e q u e n c i e s o f c o m b i n a t i o n s o f v a r i a n c e c o n t r i b u t i n g s i g n i f i c a n t l y t o t o t a l v a r i a n c e 2 2 2 T A B L E 3 2 . S t e p w i s e r e g r e s s i o n r e s u l t s o f t h e C o m p l e t e m o d e l s f o r t h e d e p e n d e n t v a r i a b l e s W [ P A T H O G E N ] ( p a t h o g e n f i t n e s s ) a n d W [ H O S T ] ( h o s t f i t n e s s ) 2 2 4 T A B L E 3 3 . S t e p w i s e r e g r e s s i o n r e s u l t s o f t h e C o m p l e t e m o d e l s f o r t h e d e p e n d e n t v a r i a b l e s W [ P A T H O G E N ] ( p a t h o g e n f i t n e s s ) a n d W [ H O S T ] ( h o s t f i t n e s s ) 2 2 7 T A B L E 3 4 . S t e p w i s e r e g r e s s i o n r e s u l t s o f t h e T r a d i t i o n a l m o d e l s f o r t h e d e p e n d e n t v a r i a b l e s W [ P A T H O G E N ] ( p a t h o g e n f i t n e s s ) a n d W [ H O S T ] ( h o s t f i t n e s s ) 2 3 0 T A B L E 3 5 . S t e p w i s e r e g r e s s i o n r e s u l t s o f t h e T r a d i t i o n a l m o d e l s f o r t h e d e p e n d e n t v a r i a b l e s W [ P A T H O G E N ] ( p a t h o g e n f i t n e s s ) a n d W [ H O S T ] ( h o s t f i t n e s s ) 2 3 3 T A B L E 3 6 . S t e p w i s e r e g r e s s i o n r e s u l t s o f t h e T r a d i t i o n a l m o d e l s f o r t h e d e p e n d e n t v a r i a b l e s W [ P A T H O G E N ] ( p a t h o g e n f i t n e s s ) a n d W [ H O S T ] ( h o s t f i t n e s s ) 2 3 6 T A B L E 3 7 . S t e p w i s e r e g r e s s i o n r e s u l t s o f t h e P r a c t i c a l Minimal models for the dependent variables W [PATHOGEN] (pathogen fitness) and W [HOST] (host fitness) 239 TABLE 38. Stepwise regression results of the P r a c t i c a l Minimal models for the dependent variables W [PATHOGEN] (pathogen fitness) and W [HOST] (host fitness) 242 TABLE 39. Stepwise regression results of the P r a c t i c a l Moderate models for the dependent variables W [PATHOGEN] (pathogen fitness) and W [HOST] (host fitness) 245 TABLE 40. Stepwise regression results of the P r a c t i c a l Moderate models for the dependent variables W [PATHOGEN] (pathogen fitness) and W [HOST] (host fitness) 248 TABLE 41. Stepwise regression results of the P r a c t i c a l Early models for the dependent variables W [PATHOGEN] (pathogen fitness) and W [HOST] (host fitness) 251 TABLE 48. Spearman rank cor r e l a t i o n c o e f f i c i e n t s and associated p r o b a b i l i t i e s for variables ranked on Trebi and Odessa 272 TABLE 49. Spearman rank c o r r e l a t i o n c o e f f i c i e n t s and associated p r o b a b i l i t i e s for ranking of spe c i f i e d variable p a i r s . 274 x i i LIST OF FIGURES FIGURE 1. L i f e cycle of the smut fungus Ustiiago hordei. .277 FIGURE 2. Schematic representation of the experimental design 278 FIGURE 3. Schematic representation of the rel a t i o n s h i p of the 4 subsets of variables 280 FIGURE 4. Regression of teliospore number vs teliospore weight 282 FIGURE 5. Frequency histograms for variable R1 (germination rate of the 110 treated seeds o r i g i n a l l y planted) 283 FIGURE 6. Frequency histograms for variable R2 (proportion of plants smutted) 285 FIGURE 7. Frequency histograms for variable R3 (number of heads) 286 FIGURE 8. Frequency histograms for variable R4 (proportion of heads smutted) 287 FIGURE 9. Frequency histograms for variable R5 (number of heads from diseased plants) ...288 FIGURE 10. Frequency histograms for variable R6 (average number of heads per plant) 289 FIGURE 11. Frequency histograms for variable R7 (average number of diseased heads per plant) 290 FIGURE 12. Frequency histograms for variable R8 (average number of healthy heads per plant) 291 x i i i FIGURE 13. Frequency histograms for variable R9 (average number of heads per diseased plant) 292 FIGURE 14. Frequency histograms for variable R10 (average number of diseased heads per diseased plant) 293 FIGURE 15. Frequency histograms for variable R11 (average number of healthy heads per diseased plant) 294 FIGURE 16. Frequency histograms for variable R12 (spore weight) 295 FIGURE 17. Frequency histograms for variable R13 (average spore weight per diseased plant) 296 FIGURE 18. Frequency histograms for variable R14 (average spore weight per diseased head) 297 FIGURE 19. Frequency histograms for variable R15 (average spore germination rate per diseased head) 298 FIGURE 20. Frequency histograms for variable R16 (average number of seeds per diseased plant) 299 FIGURE 21. Frequency histograms for variable R17 (average number of seeds per plant) 300 FIGURE 22. Frequency histograms for variable Wp [PATHOGEN] (pathogen f i t n e s s , calculated from P subset of variables) 301 FIGURE 23. Frequency histograms for variable Wc [PATHOGEN] (pathogen f i t n e s s , calculated from C subset of variables) 302 FIGURE 24. Frequency histograms for variable W [PATHOGEN] (to t a l pathogen f i t n e s s , Wp [PATHOGEN]+Wc [PATHOGEN]). .303 FIGURE 25. Frequency histograms for variable Wp [HOST] xiv (host f i t n e s s , calculated from P subset of va r i a b l e s ) . .304 FIGURE 26. Frequency histograms for variable Wh [HOST] (host f i t n e s s , calculated from H subset of va r i a b l e s ) . .305 FIGURE 27. Frequency histograms for variable W [HOST] (t o t a l host f i t n e s s , W [HOST]+Wh [HOST]) 306 FIGURE 28. Frequency histograms for variable H1 (number of healthy plants) 307 FIGURE 29. Frequency histograms for variable H2 (number of heads) 308 FIGURE 30. Frequency histograms for variable H3 (average number of heads per plant) 309 FIGURE 31. Frequency histograms for variable H4 (average number of seeds per plant) 310 FIGURE 32. Frequency histograms for variable H5 (average number of seeds per head) 311 FIGURE 33. Frequency histograms for variable H6 (thousand seed weight, seeds randomly selected from a l l healthy plants) 312 FIGURE 34. Frequency histograms for variable H7 (average seed weight per plant) 313 FIGURE 35. Frequency histograms for variable H8 (average seed weight per head) 314 FIGURE 36. Frequency histograms for variable H9 (seed germination rate for seeds from H6) 315 FIGURE 37. Frequency histograms for variable H10 (number of seeds) 316 FIGURE 38. Frequency histograms for variable C1 (number of X V completely diseased plants) 317 FIGURE 39. Frequency histograms for variable C2 (number of heads) 318 FIGURE 40. Frequency histograms for variable C3 (average number of heads per plant) 319 FIGURE 41. Frequency histograms for variable C4 (spore weight) 320 FIGURE 42. Frequency histograms for variable C5 (average spore weight per plant) ....321 FIGURE 43. Frequency histograms for variable C6 (average spore weight per head) 322 FIGURE 44. Frequency histograms for variable C7 (average spore germination rate per head) 323 FIGURE 45. Frequency histograms for variable P1 (number of diseased plants with seeds) 324 FIGURE 46. Frequency histograms for variable P2 (number of heads) 325 FIGURE 47. Frequency histograms for variable P3 (number of diseased heads) 326 FIGURE 48. Frequency histograms for variable P4 (number of healthy heads) 327 FIGURE 49. Frequency histograms for variable P5 (average number of heads per plant) 328 FIGURE 50. Frequency histograms for variable P6 (average number of diseased heads per plant) 329 FIGURE 51. Frequency histograms for variable P7 (average number of healthy heads per plant) 330 xvi FIGURE 52. Frequency histograms for variable P8 (spore weight) 331 FIGURE 53. Frequency histograms for variable P9 (average spore weight per plant) 332 FIGURE 54. Frequency histograms for variable P10 (average spore weight per head) 333 FIGURE 55. Frequency histograms for variable P11 (average spore germination rate per head) 334 FIGURE 56. Frequency histograms for variable P12 (number of seeds) 335 FIGURE 57. Frequency histograms for variable P13 (average number of seeds per plant) 336 FIGURE 58. Frequency histograms for variable P14 (average number of seeds per healthy head) 337 FIGURE 59. Frequency histograms for variable P15 (seed weight) 338 FIGURE 60. Frequency histograms for variable P16 (average seed weight per plant) 339 FIGURE 61. Frequency histograms for variable P17 (average seed weight per healthy head) 340 FIGURE 62. Frequency histograms for variable P18 (average seed germination rate per healthy head) 341 xvi i ACKNOWLEDGEMENTS I want to express my sincere appreciation and gratitude to co-supervisors Clayton Person and Conrad Wehrhahn for their guidance and patience throughout the evolution of t h i s project. Also, I am indebted to committee members Tony G r i f f i t h s , Ray Peterson and Ryk Ward for invaluable support and assistance. Special thanks go to external, departmental and extra-departmental examiners for their expert review e f f o r t s , and to Frank Williams and Johnathan Woodend for helpful suggestions and stimulating discussions. 1 1 INTRODUCTION Fu l l y one half of a l l l i v i n g species of plants and animals are p a r a s i t i c for at least a portion of their l i f e cycle (Price, 1980). Plant parasites are p a r t i c u l a r l y important because of the impact they can have on the quality of human l i f e . Plants provide 95% of the world's food (Walsh, 1984) and of the 350,000 known plant species i d e n t i f i e d , only about 24 crop plants "stand between people and starvation" (Wittwer, 1980). Plant parasites can appear suddenly, reach epidemic proportions quickly and reduce host y i e l d potentials by d i v e r t i n g host resources for t h e i r own reproductive needs. The FAO (1981) estimated that approximately 1/3 of a l l crops are l o s t to parasites and pests each year. Much research i s targeted at methods of reducing these losses. The most promising r e s u l t s come from the f i e l d of genetics. Interactions between plants and their parasites are known to be mediated by their respective genetic systems. Shortly after the rediscovery of Mendel's work, Biffen (1905, 1907) showed that two recessive resistance genes controlled wheat resistance to the fungal pathogen Puceinia glumarum. Following the discovery of sex in the smut, Ustilago violacea, by Kniep (1919), genetic studies of pathogenicity became more comprehensive. Flor's novel series of experiments (1942, 1947, 1955, 1956) and Person's subsequent th e o r e t i c a l expansion thereof (1959), were important contributions toward understanding fundamental p r i n c i p l e s governing these inter-organism interactions. Their work revealed how discrete 2 autonomous genetic systems could be integrated to regulate disease expression. These interactions (described in more d e t a i l in a la t e r section) provided the basis for the f i r s t wave of disease resistance breeding. As a consequence of the knowledge gained from t h i s work, new discoveries have been made, and innovative theories, and host management strategies have been devised. It i s with some of the these that this work i s concerned. 3 2 GENETICS OF HOST-PARASITE INTERACTIONS Disease expression i s a complex character influenced by genetically controlled resistance in the host and by genetically controlled pathogenicity in the pathogen. Resistance i s shown by a host when the pathogen i s hindered and disease i s reduced (Robinson, 1969). Pathogenicity i s shown when a pathogen can attack a host and disease i s promoted (Robinson, 1969). Breeders have concentrated their e f f o r t s on bolstering host resistance le v e l s without regard for the ramifications of the accompanying genetic changes induced in the pathogen population. C r i t i c a l forces involving "...feedbacks between population genetics and population dynamics over space and time..." (Fleming, 1982) and physiologic mechanisms involved in complex interactions are overlooked or ignored. Future crops are placed at risk because breeders have not adopted a h o l i s t i c approach for managing pathosystems. A pathosystem i s a subsystem of an ecosystem (Robinson, 1976) which involves interactions between plants and their parasites and may be natural (wild pathosystem) or a r t i f i c i a l (crop pathosystem). The important role of pathogen genotype in crop pathosystems i s now being recognized and investigations of plant diseases are now incorporating simultaneous genetic studies of both organisms. 4 2.J_ SPECIES COMPATIBILITY T h e r e a r e t h r e e r e c o g n i z e d t y p e s , l e v e l s , or s u b s y s t e m s w i t h i n a p a t h o s y s t e m , o f g e n e t i c a l l y c o n t r o l l e d i n t e r a c t i o n s between a h o s t and i t s p a t h o g e n . The f i r s t s u b s y s t e m i s one o f s p e c i e s c o m p a t i b i l i t y . B e f o r e any i n d i v i d u a l s of a p a t h o g e n s p e c i e s c a n a t t a c k any i n d i v i d u a l s o f a h o s t s p e c i e s , c o m p a t i b i l i t y between s p e c i e s must e x i s t . F o r i n s t a n c e , p o t a t o i s a n o n h o s t of wheat stem r u s t b e c a u s e o f t h e a b s e n c e o f c o m p a t i b i l i t y between them ( H e a t h , 1985). I t i s c o n s i d e r e d i m p o s s i b l e f o r any n o n p a t h o g e n t o be c a p a b l e o f o v e r c o m i n g t h i s t y p e o f r e s i s t a n c e . R e s e a r c h e r s h y p o t h e s i z e t h a t genes b l o c k i n g s p e c i e s c o m p a t i b i l i t y m i g h t be t r a n s f e r a b l e between h o s t s p e c i e s t o e f f e c t permanent p r o t e c t i o n f r o m some d i s e a s e s ( H e a t h , 1985). As y e t , l i t t l e i s known a b o u t t h e g e n e t i c s o f s p e c i e s c o m p a t i b i l i t y . 2.2 S P E C I F I C GENES The s e c o n d s u b s y s t e m , t h e v e r t i c a l s u b s y s t e m ( R o b i n s o n , 1986), i n v o l v e s s p e c i f i c r e s i s t a n c e genes and s p e c i f i c p a t h o g e n i c i t y genes t h a t i n t e r a c t a c c o r d i n g t o t h e g e n e - f o r - g e n e t h e o r y ( F l o r , 1971; P e r s o n , 1959). S p e c i f i c r e s i s t a n c e and p a t h o g e n i c i t y c a n be r e c o g n i z e d o n l y under c e r t a i n c o n d i t i o n s . A l l e l e s a t a s p e c i f i c r e s i s t a n c e l o c u s i n t h e h o s t i n t e r a c t i n a u n i q u e and p r e d i c t a b l e way w i t h a l l e l e s a t a s p e c i f i c c o m p l e m e n t a r y p a t h o g e n i c i t y ( v i r u l e n c e ) l o c u s i n t h e p a t h o g e n . The p r e s e n c e o r a b s e n c e o f c e r t a i n 5 a l l e l e s at either interacting locus can be detected by virtue of the discrete segregation ra t i o s they produce. Once detected and i d e n t i f i e d , s p e c i f i c genes can be manipulated using c l a s s i c Mendelian techniques. T y p i c a l l y , in gene-for-gene interactions, host resistance a l l e l e s are dominant and host s u s c e p t i b i l i t y a l l e l e s are recessive, although recessive and incomplete resistance have been recorded. Also, pathogen avirulence a l l e l e s are dominant and pathogen virulence a l l e l e s are recessive. Here too, exceptions have been found (Day, 1974; Vanderplank, 1982; Barrett 1985; Person, Christ and Pope, 1986). Barrett (1985) believes that there are more documented examples of systems with dominant resistance than those with recessive resistance because of breeders selection techniques. In a c l a s s i c gene-for-gene interaction the combination of a resistant host genotype with an avirulent pathogen genotype triggers a "stop s i g n a l " (Person and Mayo, 1976) and does not result in a disease phenotype. Any other genotypic combination w i l l result in disease. The e f f e c t of s p e c i f i c resistance i s to reduce the i n i t i a l pathogen inoculum (Vanderplank, 1968). S p e c i f i c resistance genes are used in disease resistance breeding programs and offer temporary resistance against s p e c i f i c virulence genes in the pathogen population. Newly introduced s p e c i f i c resistance genes bring intense selection pressures to bear on the pathogen population (Person, 1968). The matching s p e c i f i c pathogenicity a l l e l e increases in frequency in the pathogen population to 6 epidemic proportions (Person, 1959, 1965). Unfortunately, s p e c i f i c resistance genes involved in gene-for-gene interactions provide short l i v e d protection. Most researchers agree that for some crops, gene-for-gene resistance i s inadequate and that new breeding t a c t i c s should be used. In response to the f a i l u r e of s p e c i f i c resistance in some crops, new theories and host management strategies have been devised. S p e c i f i c resistance i s known by several other names: v e r t i c a l (Vanderplank, 1963, 1968, 1975, 1978, 1984), ra c e - s p e c i f i c , R-gene, q u a l i t a t i v e , oligogenic, major gene, hypersensitive and inoculum reducing resistance. Each of these names has a corresponding s p e c i f i c pathogenicity or virulence counterpart. 2.3 NONSPECIFIC GENES Nonspecific resistance and nonspecific pathogenicity genes comprise the t h i r d subsystem, the horizontal subsystem (Robinson, 1973, 1986). E f f e c t s of nonspecific genes are observable only on susceptible hosts ( i e . in gene-for-gene interactions where s p e c i f i c resistance i s unmatched by s p e c i f i c v i r u l e n c e ) . I d e n t i f i c a t i o n of nonspecific genes i s precluded by the presence of unmatched s p e c i f i c resistance. The action of each nonspecific a l l e l e i s not contingent upon the presence of any a l l e l e in the other organism. Each a l l e l e contributes a small additive increment to the continuously varying disease phenotype (Knutson and Eide, 1961; Habgood, 1973; C l i f f o r d and C l o t h i e r , 1974; Schwarzbach and Wolfe, 1976). Most nonspecific 7 resistance and pathogenicity genes do not display gene-for-gene c h a r a c t e r i s t i c s (Person, 1966). Wolfe (1972) contends that there is no clear cut d i s t i n c t i o n to be made between s p e c i f i c and nonspecific genes. He believes that they represent extremes of a continuum and that a l l genes are of the gene-for-gene type. Genes thought to be nonspecific have not yet been shown to be involved in gene-for-gene interactions. Other researchers agree that there are no nonspecific genes (Riley, 1973; Ellingboe, 1975, 1981; C l i f f o r d , 1975; Nass et a l . , 1981). They believed that the quantitative e f f e c t s of so c a l l e d nonspecific genes are simply the ghost or residual e f f e c t s of s p e c i f i c resistance genes, of the gene-for-gene type, that have been matched and defeated by s p e c i f i c virulence genes. Anderson (1982) c r i t i c i z e d the findings of Nass e_t a_l. Anderson attributed the putative residual e f f e c t s to assumed linkage and genetic d r i f t of quantitative resistance genes during breeding of the near i s o l i n e s . Single nonspecific genes generally do not produce discrete segregation r a t i o s , consequently, s t a t i s t i c a l and quantitative genetic techniques must be used when studying nonspecific genes (Kulkarni and Chopra, 1982). Methods of studying quantitative characters such as those controlled by nonspecific resistance and pathogenicity genes were developed in higher organisms (Mather and Jinks, 1971; Falconer, 1981) and can be applied to most hosts and many pathogens, including fungi (Caten, 1979). It i s rare for a pathosystem to lend i t s e l f readily to a 8 comprehensive genetic study of nonspecific genes, usually because of common b i o l o g i c a l constraints ( i e . the i n a b i l i t y to grow the pathogen in culture, i s o l a t i o n and breeding problems, etc.) and preexisting s p e c i f i c resistance. Despite these problems, nonspecificity has been suggested to be involved in several pathosystems: Cercosporella in wheat (Bruehl et a l . , 1968), Trichometosphaeria turci c a in cereals (Nelson et a l . , 1970), Ustilago hordei in barley (Emara, 1972; Emara and Sidhu, 1974), Phytopthora infestans in potatoes (Caten, 1974; Shattock, 1976), Ceratocystis ulmi in elm (Bassi and Burnett, 1979), and, Gaeumannomyces graminis var t r i t i c i in wheat (Blanch et a l . , 1981). Despite the epidemiologic significance of nonspecific pathogenicity, l i t t l e i s known about how nonspecific resistance would a f f e c t pathogenicity at the population l e v e l . The value of using nonspecific resistance in disease management programs can be ascertained only a f t e r the dynamics of the interplay of nonspecific genes are more thoroughly investigated both experimentally and t h e o r e t i c a l l y . Nonspecific pathogenicity also i s known by other names: horizontal, nonspecific, polygenic, quantitative, minor gene, rate increasing and nonhypersensitive inducing pathogenicity, as well as aggressiveness. Each of these names has a matching nonspecific resistance counterpart. 9 2.3.J_ CONSTANT RANKING According to Vanderplank (1963), pathogenic isol a t e s causing q u a n t i t a t i v e l y d i f f e r e n t smut disease levels on a variety (because of nonspecific pathogenicity gene differences), can be ranked in order of disease severity, provided that gene-for-gene interactions are not involved. This rank order is considered i n d i c a t i v e of the cumulative e f f e c t s of a l l nonspecific pathogenicity genes combined with the cumulative effects of a l l nonspecific resistance genes. Since nonspecific gene e f f e c t s are considered to be additive (Fleming and Person, 1982), rank order i s supposedly maintained on d i f f e r e n t v a r i e t i e s . S i m i l a r l y , host v a r i e t i e s can be ranked in order of their l e v e l of resistance against a series of pathogen is o l a t e s (Driver, 1962). Simultaneous ranking of both organisms i s known as "constant ranking" (Vanderplank, 1963; Robinson, 1976) and i s based exclusively on the l e v e l of disease damage, assessed by measuring variables thought to be correlated to pathogen reproductivity, or by d i r e c t l y measuring pathogen reproductivity. 10 2.4 QUEST FOR DURABLE RESISTANCE Ephemeral disease resistance in economically important crops has sparked a search for the genetic elucidation of durable resistance. Durable resistance i s defined as resistance that remains e f f e c t i v e in a c u l t i v a r over a wide geographic area in an environment favorable to the disease (Johnson and Law, 1973, 1975). Durable resistance, considered to be both temporally and s p a t i a l l y stable, i s now one of the most highly sought aft e r breeding characters in crop plants (Person e_t a l . , 1983). Much attention has been focussed on the genetic causes of durable resistance in plants in attempts to avoid recurring boom-and-bust cycles (Johnson, 1961). Several host management alternatives for combating disease losses have been proposed. Some of these alternatives are thought to provide durable crop resistance through genetic homogeneity and others through s p a t i a l or temporal genetic heterogeneity (thought to cl o s e l y p a r a l l e l natural pathosystems): 1. M u l t i l i n e s (Borlaug, 1958, 1965; Browning and Frey, 1969; Frey et a l . , 1973; Groth and Person, 1977; Marshall and Weir, 1985); 2. Pyramiding of s p e c i f i c resistance genes (Luig and Watson, 1970; Abdalla and Hermsen, 1971; Nelson, 1978); 3. A l l e l e cycling (Person, 1966); 4. Nonspecific resistance (Vanderplank, 1968; Main and Gallegly, 1964; Umaerus, 1969; Eide and Lauer, 1967; Simons and Murphy, 1967; Person et a l . , 1983); 11 5. Combinations of these methods (Graham and Hodgson, 1965; Raymundo and Hooker, 1982). For each alternative l i s t e d above (from Pope, 1982) there are associated p o s i t i v e and negative aspects. Nonspecific resistance promises great e f f i c a c y in reducing disease loss. Studies indicate that nonspecific resistance i s p o t e n t i a l l y durable (Lewellen et a l . , 1967; Caten, 1974; Vanderplank, 1975; P a r l e v l i e t and Zadoks, 1977; Fleming and Person, 1982; Raymundo and Hooker, 1982; Person et a l . , 1983; Robinson, 1986). Under epidemic conditions, a high l e v e l of nonspecific pathogenicity would produce a rapid rate of spread. High leve l s of nonspecific resistance could retard the rate of spread of the epidemic. The large numbers of genes involved in nonspecific resistance could buffer against or dampen, increases in nonspecific pathogenicity (Fleming and Person, 1982). The consensus of opinion i s that nonspecific resistance can help at t a i n durable resistance (Walsh, 1984). Durable resistance can never be conclusively shown to exist in a crop u n t i l i t has persisted in many geographical locations. The threat that t h i s resistance might break down i s constantly present. There are a few examples of crops with suspected durable resistance. One such example is the almost complete protection from stem rust that the Sr6 and Sr9 resistance genes gave Canadian wheat for 20 years (Harlan, 1976). It is interesting to note that the resistance i s not geographically stable. The same genes f a i l e d in Texas. Crops are considered to have potential durable resistance u n t i l such time that the 12 resistance loses i t s effectiveness and i s declared, retrospectively, to have been ephemeral. It i s not yet possible to make factual statements about the precise genetic nature of durable resistance. One popular view is that the combined ef f e c t s of s p e c i f i c and nonspecific resistance genes can produce durable resistance. Before we can f u l l y understand a l l aspects of durable resistance we should dire c t more attention to the least studied components of host-parasite interactions, in p a r t i c u l a r , the horizontal subsystem. 13 3 QUANTITATIVE MEASUREMENT OF DISEASE LEVELS 3.J_ DEFINITION OF FITNESS Fitness i s a measure of the a b i l i t y of an individual to pass on a l l e l e s to i t s o f f s p r i n g . The absolute f i t n e s s of an individual i s the f i n a l outcome of a l l i t s developmental and physiological processes (Falconer, 1981). Absolute f i t n e s s i s greater than or equal to 0 and i s the expected number of offspring that an individual w i l l contribute to the next generation (Roughgarden, 1983). Individuals within a population d i f f e r in absolute f i t n e s s . Relative f i t n e s s i s the " r e l a t i v e a b i l i t y of d i f f e r e n t genotypes to pass on their a l l e l e s to future generations" (Hedrick, 1983). The net result of the ef f e c t s of a number of variable characters, which may be influenced by genetic va r i a t i o n in combination with environmental components, i s a measure of r e l a t i v e f i t n e s s (Hedrick, 1983). Variation in metric characters can r e f l e c t v a r i a t i o n in fi t n e s s to d i f f e r e n t degrees (Falconer, 1981). Relative f i t n e s s of an individual i s the absolute f i t n e s s of that i n d i v i d u a l divided by the highest absolute f i t n e s s in the population. The fundamental theorem of natural selection states that the average r e l a t i v e f i t n e s s increases each generation to a peak value ( i e . i t i s maximized). This i s not considered r e a l i s t i c because an individual's r e l a t i v e fitness does not remain constant through time (because of frequency-dependent and density-dependent selection e f f e c t s ) . 14 For a t r a i t to be selected i t must increase the r e l a t i v e fitness of the bearer and not just the absolute f i t n e s s (Wilson, 1980). Fitness i s a function of the t r a i t under selection and the size of the population. Another measure of genotypic f i t n e s s i s derived from r- and K-selection (Andrews, 1984). Selection for high r- t r a i t s , is associated with populations in the exponential phase of growth and promote increased growth rate for a population under conditions of low density. Selection for high K- t r a i t s i s associated with populations that are near or at the carrying capacity of the environment and promote high equilibrium population size for a population under conditions of high density. Density-dependent selection causes the evolution of high K- t r a i t s while density-independent selection causes evolution of high r- t r a i t s which occur in a low density population when i t i s expanding (Dobzhansky, 1950). In pathosystems, p a r a s i t i c f i t n e s s can concern the a b i l i t y of isolates or genotypes within a pathogen population to compete successfully and to p e r s i s t over time (Nelson, 1979). Host attributes conferring nonspecific resistance influence certain components of p a r a s i t i c f i t n e s s . A reduction in one or more components of p a r a s i t i c f itness can be caused by nonspecific resistance. Istock (1982)indicated that primary f i t n e s s characters include survival p r o b a b i l i t i e s , development times and f e r t i l i t i e s associated with p a r t i c u l a r genotypes in certain environments. "Natural populations may store large reservoirs 1 5 of v a r i a t i o n , in the polygenic form, which i s manifest only with environmental change. At t h i s point, such speculations serve mostly to emphasize our need to know much more about the nature of polygenic v a r i a t i o n " (Istock, 1982). Biometrical analysis is an important and useful tool for the study of f i t n e s s characters because these characters usually show continuous variation as a result of the underlying polygenic determination. Mendelian a l l e l e s make additive, dominance and e p i s t a t i c contributions to the phenotypic values of individuals and of the population. These contributions can be neutral, p o s i t i v e or negative (Mather, 1971; Falconer, 1981). There appears to be a decline in the additive genetic variance and h e r i t a b i l i t y as one studies characters closer and closer to the primary f i t n e s s characters. This phenomenon may not be generalizable to natural populations, because most supporting information comes from studies of domesticated animals which l i v e in f a i r l y stable environments. Developmental characters t y p i c a l l y have h e r i t a b i l i t i e s of 0.1 to 0.4. F e r t i l i t y measures t y p i c a l l y have lower h e r i t a b i l i t i e s of 0.05 to 0.25 (Istock, 1982). MacKenzie (1978) stated that one obvious measure of p a r a s i t i c f i t n e s s i s the apparent infection rate, r, as defined by Vanderplank (1963,1968). Differences in r, among populations, i s o l a t e s , biotypes, strains or races, r e f l e c t differences in p a r a s i t i c f i t n e s s , when tested on the same host genotype under i d e n t i c a l environmental conditions (MacKenzie, 1978). Fleming (1982) concurred that f i t n e s s i s linked to the 16 rate of disease progress in exponential growth models. It i s interesting to note that some individuals continue to use.these fi t n e s s differences to dis t i n g u i s h pathogenic biotypes or races (Nelson, 1979). Quantitative measurements of disease phenotype can be made in d i f f e r e n t ways depending on the system involved and may indicate the type of resistance operating (Kranz, 1983). The measure of the disease phenotype represents the outcome of the interaction between the host and the pathogen and i s indi c a t i v e of the fit n e s s of each. The current b e l i e f i s that host f i t n e s s i s expected to be negatively correlated with pathogen f i t n e s s (Pimentel, 1961). Data supporting t h i s b e l i e f i s furnished by Hoy et a l . (1985) for the smut-sugarcane system. A high disease reading translates to a high pathogen reproductivity and low host reproductivity. Conversely, a low disease reading indicates high host reproductivity and low pathogen reproductivity. Durable resistance i s a c h a r a c t e r i s t i c of a pathosystem and not just of the host population as the expression implies. Durable resistance i s measured in terms of the disease l e v e l or quantity and is influenced by the resistance of the host and by the pathogenicity of the pathogen (Johnson, 1981). Therefore, pathogen fi t n e s s i s an important component in studies of durable resistance. 17 3.2 FITNESS IN RUST PATHOSYSTEMS Attempts to measure fit n e s s can be found in some epidemiologically related papers. The largest body of knowledge concerning epidemiology in plant pathosystems involves studies of the cereal rust fungi. Rusts are basidiomycetes with complex l i f e cycles that can show great v a r i a b i l i t y (Ingold, 1973). The asexual repeating uredial stage of these spec i a l i z e d obligate parasites allows them to reproduce and spread rapidly. Urediospores increase in numbers exponentially on healthy tissue (Vanderplank, 1963). They cause host y i e l d depression and can spread up to 300 miles in a few days. Urediospores are wind dispersed. They contact host tissue, germinate, penetrate and colonize. A l l t h i s occurs in 7-14 days (Katsuya and Green, 1967; Leonard, 1969). Disease l e v e l or severity i s assessed in terms of infection types which are routinely measured on a r e l a t i v e scale of 0-4. A reading of ITO (infection type 0) corresponds to a host resistant reaction with necrotic or ch l o r o t i c flecks and no sporulation (hypersensitive or s p e c i f i c resistance and s p e c i f i c avirulence). Infection type 4 i s a f u l l y susceptible reaction with a sporulating pustule without chlorosis or necrosis. Infection type i s affected by temperature, l i g h t , host genotype, pathogen genotype, humidity, i n f e c t i o n density, plant age and differences in experimental methods (Luig and Rajaram, 1972). Infection type as a measure of disease was developed by Stakman and co-workers around 1919 (Hoerner, 1919). Stakman's system has undergone minor modifications and i s used extensively 18 for most of the cereal rusts (Roelfs, 1984). Disease l e v e l or severity i s the cumulative result of the e f f e c t s of several factors or components. These components are: infec t i o n frequency, latent period, spore production and infectious period. Variations in a l l four of these components have been recorded and are purported to affect host and pathogen fitnesses. Few studies have d i r e c t l y measured t o t a l spore and seed production at the end of a growing season ( i e . pathogen and host fitnesses) and compared these t o t a l s with component measurements taken at various periods during the season. Controlled inoculum experiments provide the best approach for measuring components of resistance. The c r i t i c a l components of quantitative resistance can be thought of as resistance that reduces infec t i o n e f f i c i e n c y , extends the latent period from inoculation to sporulation, and reduces sporulation. ( P a r l e v l i e t , 1979). Rouse et. a l . (1980) noted that d i r e c t measurement of components of nonspecific resistance were tedious, time consuming and p r o h i b i t i v e to the plant breeder. They suggested using alternate approaches for rapid, precise sampling of individual selections. Infection e f f i c i e n c y , latent period and spore production per lesion parameters can be measured accurately for c u l t i v a r s with d i f f e r i n g levels of nonspecific resistance. Measurements of components of resistance, no matter how accurate, are not s u f f i c i e n t in themselves for r e l i a b l e assessment of their combined e f f e c t s on resistance in a variety (Leonard and Mundt, 19 1984). S t a t i s t i c a l l y s i g n i f i c a n t interactions between components of rate reducing resistance and epidemiologic f i t n e s s have been demonstrated in some host-parasite systems (Johnson and Taylor, 1976; P a r l e v l i e t , 1979; Rouse et a l , 1980). 3.2._J_ INFECTION FREQUENCY Infection frequency i s defined as the proportion of spores that result in sporulation lesions. Resistance to f i r s t contact and to colonization w i l l decrease the i n f e c t i o n frequency. Differences in i n f e c t i o n frequency r e f l e c t differences accumulated over various development stages ( P a r l e v l i e t , 1979). These developmental stages start from the time of the i n i t i a l establishment phase and end just prior to spore formation. Infection frequency varies with host genotype and developmental stage of the host. When infections occur in low frequency there i s an approximate linear r e l a t i o n s h i p between the number of sporulating infections and the t o t a l number of spores produced. When the density increases, the number of spores produced per infection decreases. Relative f i t n e s s changes from generation to generation and i t s average over several generations might d i f f e r considerably from r e l a t i v e fitness defined above. Increasing infec t i o n density, by applying higher doses of inoculum, shortens the latent period from infect i o n to sporulation (Yarwood, 1961; Lapwood and McKee, 1966; Katsuya and Green, 1967; Leonard, 1969; Mehta and Zadoks, 1970; P a r l e v l i e t , 20 1975). 3.2.2 LATENT PERIOD Latent period i s the time from i n f e c t i o n to spore production and i s sometimes confused with the incubation period. Incubation period i s the time between inoculation and the f i r s t v i s i b l e symptoms. Latent period increases from the primary leaf to the young f l a g leaf stage for a l l c u l t i v a r s after which i t decreases again. Differences among c u l t i v a r s are small in the seedling stage and large at the adult stage. Latent period i s thought to r e f l e c t the growth rate of the pathogen. Latent period i s the c r u c i a l component determining the apparent i n f e c t i o n rate when a large number of reproductive cycles (macrocyclic) are required to complete the epidemic. P a r l e v l i e t (1979) stated that for pathogens with fewer reproductive cycles, the e f f e c t of the other components becomes more important in the i n t e r a c t i o n . Ultimately, when only one reproductive cycle occurs per reproductive cycle of the plant (monocyclic), the i n f e c t i o n frequency and spore production are the most important f i t n e s s determining factors. Examples of monocyclic diseases include the smuts and bunts in cereals. Latent period i s governed by polygenes and i t i s l i k e l y that i n f e c t i o n frequency and sporulation capacity are s i m i l a r l y determined ( P a r l e v l i e t , 1981). Shaner and Finney (1980) noted that latent period was the component that could be measured with least error and was s i g n i f i c a n t l y correlated with disease increase in the f i e l d . 21 3.2.3 SPORE PRODUCTION Spore production i s the number of spores produced per lesion or per unit area of infected tissue. Spore production can be measured in s p e c i f i c intervals of time or over the entire infectious period. Spore production i s usually measured in spores produced per leaf area, per lesion or pustule, per unit area of lesion or per unit area of sporulating surface (Johnson and Taylor, 1976). Spore production represents the t o t a l e f f e c t of a l l the components of resistance and may be the most useful c r i t e r i o n upon which to base selection (Johnson and Taylor, 1976). A count of propagules i s considered an al t e r n a t i v e for, or a complement to, disease assessment. Zadoks (1972) stated that the measurement of the t o t a l spore production of pathogens provides an accurate measure of the resistance of the host. Johnson and Taylor (1976) agreed with Zadoks that cumulative spore counts, in quantitative analyses, are analogous to disease resistance and went on to say that spore counts also provide a measure of pathogenicity of the pathogen. They conclude that obtaining cumulative spore counts i s too laborious to be used as a routine, e f f i c i e n t selection method. Lesions are formed when uredia break the plant surface and sporulate during infectious periods of 2-3 weeks (Chester, 1946; Leonard, 1969). Lesion size i s the area of host tissue showing disease symptoms. The colony size i s the area actually showing signs of the presence of the pathogen. Area, diameter and length are frequently measured and used as estimators of 22 pathogen f i t n e s s . Lesion size i s thought to r e f l e c t the growth rate of the pathogen in the host and therefore i t i s thought to r e f l e c t i t s net spore production. Lesions eventually become senescent and lose the a b i l i t y to become reinfected. 4.2.4 INFECTIOUS PERIOD Infectious period i s the period of time during which pustules produce and release spores. Since so few studies have concentrated on measuring the numbers of spores produced by the end of the growing season, there i s l i t t l e data available concerning aspects related to the infectious period. 3.2.5 RELATIONSHIPS AMONG COMPONENTS Deshmukh and Howard (1956) and Lapwood (1963) found a close co r r e l a t i o n between the resistance to growth of mycelium through host tissues and resistance to production of spores or sporangia. Lapwood (1961) determined that the rates of growth of Phytophthora infestans in potato leaf tissue was the same for some c u l t i v a r s but that the number of spores per lesion varied. The amount of sporulation corresponded more c l o s e l y with resistance of c u l t i v a r s in the f i e l d than did growth rate of mycelium. Heagle and Moore (1970) reported that Puceinia coronata produced fewer pustules, smaller pustules with fewer spores, and retarded hyphal growth and a longer latent period on a resistant oat variety than on a susceptible one. C l i f f o r d (1972) and 23 P a r l e v l i e t and Van Ommeren (1975) found that Puceinia hordei produced fewer pustules and had a longer latent period on a resistant barley c u l t i v a r than on a susceptible one. Similar results were described for potatoes and Phytophthora infestans (Lapwood, 1966). Lee and Shaner (1985) found a negative c o r r e l a t i o n between latent period and lesion s i z e . R a p i l l y (1979) determined that both latent period and sporulation greatly condition the rate of epidemic progression. He found that the t o t a l number of spores produced depends on the duration of t h e i r production and on the speed of spore formation and pustule enlargement. Both components were considered as contributors to aggressiveness. Umaermus (1970) used increased latent period and reduced sporulation capacity of Phytophthora infestans to select for high l e v e l s of horizontal resistance in potatoes. Jones (1978) suggested using v i s u a l inspection for increased latent period of Erysiphe graminis f.sp. avenae was possible on the t h i r d or higher leaf of adult plants. Gregory et a l . (1982) and Gregory et §_1. (1984) studied the e f f e c t of corn genotype on the estimates of r e l a t i v e p a r a s i t i c f i t n e s s among populations of Helminthosporium carbonum by measuring lesion size as an a t t r i b u t e of p a r a s i t i c f i t n e s s to determine the v a r i a t i o n in four populations of H. carbonum, race 3. They noticed that host genotype had a great influence on the evaluation of p a r a s i t i c f i t n e s s . They found that less susceptible host genotypes were more e f f e c t i v e in detecting differences • among populations. They stated that p a r a s i t i c 24 f i t n e s s in the pathogen may be analogous to rate-reducing resistance in the host. They also think that increased f i t n e s s corresponds to an increase in lesion size. Therefore, i t may not be appropriate to base race designation on lesion s i z e . Also, they suggested that p a r a s i t i c f i t n e s s should be monitored to detect s h i f t s towards increased f i t n e s s and to detect and avoid destructive epidemics. As previously mentioned, some of the components associated with the macrocyclic rust pathogens are not found in a l l systems. In the monocyclic barley-smut system, there i s only one latent period. The l i f e cycle of the smut coincides with that of i t s host. Spore production has never been measured d i r e c t l y . The infectious period i s irrelevant in this system because only 1 crop of spores i s produced per growing season. The large differences between macrocyclic and monocyclic diseases led P a r l e v l i e t (1979) to state that t o t a l spore count may provide an accurate measure of pathogenicity only for monocyclic diseases. For macrocyclic diseases, measurements of the individual components of the epidemic development were more important. P a r l e v l i e t showed that values obtained from measuring components at various stages during the growing season can vary s i g n i f i c a n t l y and that these differences could be caused by any number of contributing factors. 25 4 THE USTILAGO HORDEI-HORDEUM VULGARE SYSTEM 4 .J_ BIOLOGY OF TJ. HORDE I Ustilago hordei (Pers.) Lagerh. is a bipolar smut fungus, obligately p a r a s i t i c on barley. In the past, smuts have caused extensive damage to various c u l t i v a t e d plant species. U. hordei i s well suited for biometric and population genetic studies of pathogenicity because i t i s easy to culture, store and harvest. Teliospores of U. hordei are generally 5-11 u in diameter, smooth and l i g h t colored on one side (Fischer, 1953). Upon germination, the d i p l o i d nucleus moves out into the long slender promycelium, where i t undergoes the f i r s t meiotic d i v i s i o n (Fischer and Holton, 1957).- A wall forms across the promycelium between the n u c l e i . The nuclei undergo the second meiotic d i v i s i o n and two more dividing walls are formed. This results in four l i n e a r l y arranged, uninucleate c e l l s being produced, with the basal c e l l extending into the teliospore. Haploid sporidia, representing the products of meiosis, bud continuously from the four promycelial c e l l s . Each bud, in turn, can divide to produce clones (Fischer and Holton, 1957). Sporidia of opposite mating type can fuse to form dikaryotic hyphae which can penetrate and infect barley seedlings (Fischer and Holton, 1957). The dikaryotic mycelium grows i n t e r c e l l u l a r l y in association with the a p i c a l t i p (Kozar, 1969) and forms sori in the s p i k e l e t s , replacing the seeds with smut b a l l s consisting of m i l l i o n s of spores. Mechanical harvesting techniques rupture the basal part of the glumes that 26 encase the smut b a l l s (Stevens, 1913) and spread the spores to other seeds. Seeds can become infected when sown (Tapke and Bever, 1942; Groth and Person, 1976). See figure 1 for the l i f e cycle of U. hordei. The barley host, Hordeum vulgare L. i s a p r o l i f i c , s e l f - f e r t i l i z i n g crop plant. Highly isogenic c u l t i v a r s are readily available (Pope, 1982). 4.2 BACKGROUND INFORMATION Current studies involving covered smut of barley use the "percent of plants smutted" as a measure of disease damage l e v e l . Tapke (1929, 1931) used both smutted head and smutted plant counts in his work. Clark et a_l. (1933) found a high co r r e l a t i o n (r=0.741) between the percent of plants bunted and percent of heads bunted with T i l l e t i a caries and concluded that either could be used, despite values based on head counts being consistently lower than values based on plant counts. Ruttle (1934) found a similar c o r r e l a t i o n in Ustilago hordei. Groth (1974, 1976) stated that i t is not v a l i d to e s t a b l i s h a c o r r e l a t i o n between the percentage smutted heads and the percentage smutted plants because the two variables are not independent. Gaines (1923) decided that head count accurately assessed the impact of smut on crops ( i e . on crop y i e l d s ) . While Briggs (1926) and Churchward (1937-1938) maintained that smutted plants should be used for assessment of smut p a r t i c u l a r l y for genetic reasons ( i e . for the i d e n t i f i c a t i o n of p a r t i c u l a r genes). 27 A reduction in t i l l e r i n g was reported for nearly a l l cereal smuts (Welsh, 1932; Mather and Hansing, 1960; Gaunt and Manners, 1971). Ruttle (1934) found reduced t i l l e r i n g of barley in plants smutted with Ustilago hordei. No difference in t i l l e r i n g between inoculated smutted and inoculated nonsmutted plants was found in the barley-smut system (Groth, 1974; Groth and Person, 1978). Groth i d e n t i f i e d two d i s t i n c t hurdles that the smut must overcome in order to produce spores. If the smut can overcome both hurdles then i t can smut a l l or nearly a l l the culms. The f i r s t hurdle might equate to s p e c i f i c resistance and the second., to nonspecific resistance. When resistance was high, nearly a l l culms were healthy. When disease severity was high, most plants were usually either t o t a l l y smutted or nonsmutted. Groth inoculated 12 v a r i e t i e s of barley with 21 d i f f e r e n t dikaryons, some from Tapke's physiologic races (Tapke, 1937, 1945). Occasionally the level s of smutting d i f f e r e d from Tapke's. Groth discovered that an inverse r e l a t i o n s h i p existed between the within-plant disease severity and the average number of culms produced. In other words the higher the percentage of smutted heads a plant had, the lower the number of t i l l e r s i t had. Smutted heads occurred nonrandomly in smutted plants. T i l l e r families tended to be either a l l healthy or a l l smutted. Older culms remained healthy more frequently than did younger culms, both within and between t i l l e r f a m i l i e s . The pathway of smuts to parts of the crown i s thought to be highly variable. There i s probably a close connection between growth to and through the crown, and d i s t r i b u t i o n of smutted culms. He 28 believed that the fungus could be present in a l l seedlings just after inoculation and postulated that y i e l d loss may occur even in the absence of fungal sporulation. Batts and Jeater (1958) stated that only a limited amount of mycelium was produced in any plant and that in high t i l l e r i n g plants only a limited amount of mycelium would find i t s way into a t i l l e r . As a result only a few t i l l e r s would become diseased. The seedling could outgrow the smut mycelium (Ohms and Bever, 1955). Mycelia might need to reach a c r i t i c a l point, grow at a p a r t i c u l a r rate or be in a s p e c i f i c location during a plant developmental stage before a t i l l e r or t i l l e r family w i l l become smutted (Person, personal communication). Deep sowing and cool temperatures at germination might tend to slow plant growth and extend the c r i t i c a l period. Tapke (1938, 1941) determined that smut leve l s on barley could be affected by the post germination environment. The deeper the seeds were sown, the greater the number of smutted plants and the greater the number of smutted t i l l e r s that were produced. Woodward and Tingle (1941) observed that less f e r t i l e s o i l produced high smutting in Ustilago hordei. Ebba (1975) found evidence of genotype by environment interactions. An iso l a t e that produced a low l e v e l of smut in Vancouver produced high smutting on the same c u l t i v a r in C a l i f o r n i a . Multiple infections were demonstrated in oats infected with Ustilago k o l l e r i (Person and Cherewick, 1964). Multiple infections also occur in Ustilago hordei (Megginson and Person, 1974; Mylyk and Person, unpublished). 29 4.3 QUANTITATIVE INVESTIGATIONS Quantitative techniques were f i r s t employed by Emara (1972) to investigate aggressiveness in Ustilago hordei. Odessa seeds were inoculated with representatives from Tapke's 13 races (1937, 1945). The percent infected spikes was measured for rows of 30 plants each (an unusually small number). A considerable amount of genetic v a r i a b i l i t y was detected. Most of i t was additive with a small contribution by dominance and e p i s t a t i c e f f e c t s . Narrow sense and broad sense h e r i t a b i l i t i e s were calculated (table 1). Emara and Sidhu (1974) studied the polygenic inheritance of aggressiveness in Ustilago hordei by s e l f i n g and crossing two teliospores and using the resulting 16 dikaryons to inoculate the susceptible barley c u l t i v a r Vantage. Aggressiveness, defined as the degree of i n f e c t i o n , was found to be a continuous character g e n e t i c a l l y controlled by polygenes which modified the expression of the recessive virulence a l l e l e Uhv4. A large amount of variance was found both among and within teliospores. The dikaryons produced by crossing were more aggressive than those from s e l f i n g . This implied the operation of heterosis. Additive, dominance and e p i s t a t i c e f f e c t s occurred (table 1). Sixteen smut dikaryons from 8 meiotic products of 2 teliospores were constructed by Emara and Freake (1981). These dikaryons were used on the compatible barley v a r i e t y , Hannchen, in 5 d i f f e r e n t macro-environments. Analysis of variance revealed s i g n i f i c a n t differences among dikaryons and among macro-environments. Interactions between parasites and macro-30 environments were not s i g n i f i c a n t . Genetic v a r i a b i l i t y was 28.1%, micro-environmental v a r i a b i l i t y was 41.4% and macro-environmental v a r i a b i l i t y was 30.5% of t o t a l v a r i a b i l i t y . They concluded that pathogenicity of Ustilago hordei i s a highly variable character which i s also sensitive to environmental conditions. They stated that disease incidence of covered smut of barley in the same environment on the same c u l t i v a r i s a d i r e c t indication of pathogenicity of d i f f e r e n t genotypes of Ustilago hordei (table 1). Caten et a_l. ( 1984) examined genetic determination of a quantitative component of pathogenicity ( i e . aggressiveness) in Ustilago hordei. They measured the proportion of smutted plants produced from inoculated seeds of a susceptible barley c u l t i v a r in progeny populations derived from 3 parent dikaryons. The race 10 s t r a i n parental dikaryon was found to be highly homozygous for genes a f f e c t i n g aggressiveness. Highly variable progeny resulted when sporidia from t h i s parent were mated with unrelated s p o r i d i a . Aggressiveness was found to be determined by a polygenic system that involved both additive and dominance e f f e c t s . The number of genes was not determined. A factor a f f e c t i n g aggressiveness was found linked t i g h t l y to the mating type locus. Dominance was b i d i r e c t i o n a l and genotypes with an intermediate genotype were most f i t . They stated that aggressiveness i s important in determining the severity of epidemics of susceptible hosts. Also, they believe that aggressiveness i s a major component of f i t n e s s and may even influence the frequency of virulence factors in pathogen 31 populations and the evolution of new races (table 1). 4.4 CURRENT WORK Ebba (1974) i n i t i a t e d an investigation into the inheritance of pathogenicity on the barley variety Trebi, in descendents of a cross between two Ustilago hordei teliospores, one from race 11 and one from race 7 (Tapke, 1937, 1945), later renamed T1 and T4, respectively (table 2). Eight F1 dikaryotic l i n e s numbered 17 through 24 were formed by crossing the products of meiosis from the T1 teliospore with those of the T4 teliospore, in a l l compatible combinations (table 3). Results from his crosses and backcrosses led him to conclude that a series of a l l e l e s , at a single locus and with a hierarchy of dominance, controlled pathogenicity on Trebi. Subsequent c l a s s i c a l genetic analysis by Person (personal communication) uncovered the segregation of a single dominant s p e c i f i c virulence gene in the descendents of the T1 x T4 parental cross. Person, Ebba and Christ (1986) found that in addition to the s p e c i f i c virulence gene, other nonspecific pathogenicity genes were segregating and that i s o l a t e s could be ranked according to the magnitude of their disease phenotype. Ranking r e f l e c t s the nonspecific genotype of the individual (Vanderplank, 1963, 1968, 1984; Person, 1983). Biometrical analysis of the F2 progeny from the parental cross showed s t a t i s t i c a l l y s i g n i f i c a n t variety x dikaryon and inter-dikaryon differences (Pope, 1982). The variety x dikaryon differences were att r i b u t e d to the segregation of the s p e c i f i c virulence 32 gene and the inter-dikaryon differences were indicative of the segregation of nonspecific pathogenicity genes. The number of genes was estimated to be between 2 and 4. The nonspecific pathogenicity genes exhibited dominance and e p i s t a t i c interactions, ambidirectional dominance, interactions with environmental components and possible interactions with the virulence gene. At least 1 gene with a large effect was found to be t i g h t l y linked with the mating locus. Person iso l a t e d 24 sporidia of both mating types (12 with and 12 without the dominant virulence gene) from the 8 F1 dikaryotic l i n e s and crossed them in every possible way to produce a 12 x 12 matrix of 144, F2 dikaryons. The F2 dikaryons could be divided into 3 groups according to virulence genotype. Dikaryons homozygous for the dominant virulence a l l e l e produced higher disease readings, on average, than did dikaryons that were heterozygous for the virulence a l l e l e . Virulence gene heterozygotes produced higher disease readings than did the recessive homozygotes. Each F1 sporidium was ranked according to the average quantitative measure of the magnitude of the disease phenotype when combined with a l l other sporidia of the opposite mating type, on the barley variety Trebi. This represents the f i r s t  time that concordant ranking of polyqenically controlled  nonspecif ic pathogenic i t y has been conclusively shown. Bi o l o g i c a l material generated from t h i s study offers the unique and exciting opportunity to study absolute and r e l a t i v e measures of host and pathogen fitnesses and to test the hypothesis of "constant ranking" 34 5 PURPOSE The purpose of t h i s study was investigate important aspects of nonspecific pathogenicity gene e f f e c t s in a pathosystem. In cereal-rust systems, as previously described, a r e l a t i v e l y large body of information i s available concerning the f i t n e s s related components in f e c t i o n frequency, latent period, spore production and infectious period. In the monocyclic barley-smut system very l i t t l e information has been generated concerning characters other than the percent of plants smutted. This d i s p a r i t y i s explained by fact that host-pathogen systems involving rust are more prevalent in agriculture than those involving smut. A greater amount of time, energy, and resources have been directed toward investigating rust systems. Also, because of the macrocyclic nature of the l i f e cycle of the rusts, d i r e c t measurements of pathogen fi t n e s s are v i r t u a l l y impossible. Consequently, attention must be focused on characters that are believed to be c l o s e l y related to pathogen f itness. The monocyclic l i f e cycle of the smut pathogen lends i t s e l f to d i r e c t measurement of reproductivity. Spores counts can be taken at the end of the growing season. As yet, d i r e c t measurements have never been made. Several interesting questions are raised about smut systems in l i g h t of the absence of d e t a i l e d information concerning f i t n e s s related components. Do fitness related components similar to those in rust systems exist in the barley-smut system? Can they be i d e n t i f i e d ? Can any of the pathogen or 35 host f i t n e s s related variables provide accurate and cost e f f e c t i v e estimations of host and pathogen fitnesses? These questions are addressed in t h i s study. Other questions s p e c i f i c a l l y t a i l o r e d to suit the unique b i o l o g i c a l material chosen for t h i s study also w i l l be addressed. These questions include the following. W i l l d i f f e r e n t nonspecific pathogenicity gene combinations have an ef f e c t on any of these pathogen fitness related variables? How do nonspecific pathogenicity gene differences a f f e c t host fitness related variables? In cereal-rust host-pathogen systems disease l e v e l readings are e a s i l y obtained by scoring one or a few plants treated with the rust is o l a t e s of in t e r e s t . Some systems even permit in  v i t r o detached leaf techniques. The disease l e v e l readings obtained from these systems are absolute and are easy to handle qu a n t i t a t i v e l y . Conceptually these readings are easy to grasp because genotypically i d e n t i c a l plants produce i d e n t i c a l disease readings under similar conditions. Also, a minimum of b i o l o g i c a l material ( i e . a small number of plants) i s necessary to obtain these readings. In the barley-smut system, a r e l a t i v e l y large number of plants must be treated with the i s o l a t e s of interest before disease lev e l s can be assessed. A large number of plants must be inoculated with each smut iso l a t e because t r a d i t i o n a l l y the disease l e v e l has been measured as the percentage of treated plants, or less frequently, as the percentage of smutted heads that show signs of disease. 36 These measures of disease l e v e l involve p r o b a b i l i t i e s . In the case of the percent plants smutted, the measure of disease damage i s the p r o b a b i l i t y that a single susceptible plant w i l l show signs of disease. Conceptually t h i s measure of disease i s more d i f f i c u l t to grasp and deal with than that for the cereal-rust system. The conceptual d i f f i c u l t y a r ises because a diseased plant, treated with a v i r u l e n t smut i s o l a t e , can show signs of disease while another plant of the same genotype treated in an i d e n t i c a l way can show no signs of disease. Accurate assessments of disease l e v e l s can not be obtained by using one or a few plants because any single plant w i l l be scored either as diseased or not diseased. In order to obtain a measure of disease l e v e l many genet i c a l l y i d e n t i c a l plants must be scored as either diseased or not diseased and the r a t i o (percentage) of diseased plants to nondiseased plants becomes the p r o b a b i l i t y of a plant of that genotype becoming diseased. Probabi l i t y (percentage) values have become accepted at face value as indicators of reproductivity (aggressiveness or fitness) of the pathogenic i s o l a t e s . Their appropriateness as accurate measures of pathogen reproductivity or f i t n e s s has never been assessed. Another untested r e l a t i o n s h i p in the barley-smut system i s that between host f i t n e s s and pathogen f i t n e s s . I n t u i t i v e l y , one would expect a negative co r r e l a t i o n between pathogen fi t n e s s and host f i t n e s s . The existence of t h i s c o r r e l a t i o n has not been tested in the barley-smut system. Data gathered from this experiment w i l l also allow a test of the "constant ranking" hypothesis not only for the 37 t r a d i t i o n a l measures of percent plants smutted and percent heads smutted but also for pathogen f i t n e s s and host f i t n e s s 5.1 OBJECTIVES In l i g h t of the questions and challenges described above the objectives of th i s study are 1 to measure and compare a t o t a l of 58 putative f i t n e s s related variables in order to i d e n t i f y those which may be clos e l y related to host and/or pathogen f i t n e s s , 1.1 to determine the rel a t i o n s h i p between the t r a d i t i o n a l measures of disease l e v e l and a di r e c t quantitative measure of pathogen f itness, 1.2 to make di r e c t measurements of host f i t n e s s to determine i f the re l a t i o n s h i p between host and pathogen fitnesses can be determined, 1.3 to generate data dealing with f i t n e s s and/or reproductive differences among pathogenic i s o l a t e s for estimating selection ("s") values in future modeling experiments, 1.4 to id e n t i f y p a r t i c u l a r subsets of fit n e s s related variables that can be used to make useful predictions of host and pathogen f itnesses, 1.4.1 to reveal aspects of the underlying biology involved in the codevelopment of the host and the pathogen which are controlled by nonspecific pathogenicity genes, 2 to determine i f additive or nonadditive gene eff e c t s of the nonspecific pathogenicity genes play an important role in the expression of any of the fi t n e s s related variables, 2.1 to establish i f d i f f e r e n t nonspecific pathogenicity gene combinations cause p l e i t r o p i c e f f e c t s of any of the putative f i t n e s s related variables, to test the "constant ranking" hypothesis in t h i s system. 39 6 MATERIAL AND METHODS 6.J_ EXPERIMENTAL DESIGN A single F2 dikaryon was chosen for analysis from Person's ranked matrix (previously described). Parental sporidia of t h i s dikaryon were la b e l l e d 18D1+ and 20C1-. The dikaryon was homozygous for the dominant virulence a l l e l e . The choice of the dikaryon was based on the consistently high disease readings on Trebi of one of i t s parents, T1 (race 11), as well as on the low readings of the other parent T2 (race 7), when paired with compatible sporidia known to have the dominant virulence a l l e l e . This dikaryon was expected to be highly heterozygous for nonspecific pathogenicity on the barley variety Trebi. Ten sporidia were isolated at random from the chosen F2 dikaryon, 5 of each mating type. One sporidium was subsequently lo s t during subculturing. The remaining 9 sporidia were combined in a l l compatible ways to produce a 5 x 4 North Carolina Mating Design II matrix (Comstock and Robinson, 1952; Singh, 1979). The r e s u l t i n g 20 dikaryotic treatment combinations were used to inoculate Trebi and Odessa seeds (subsequently referred to as T and 0 r e s p e c t i v e l y ) . Consult figure 2 for a diagram of the experimental design. Odessa was included in t h i s experiment because i t i s considered a universal suscept ( i e . without known s p e c i f i c resistance). Also, Odessa was expected to have a low l e v e l of nonspecific resistance based on prior performance. Plots were planted in replicated, randomized complete blocks in the f i e l d . 40 There were three r e p l i c a t e s . 6.2 SEED PREPARATION Single seed progeny of the barley v a r i e t i e s Odessa and Trebi were soaked in a d i l u t e formalin solution (0.12 %) for 30 minutes and then washed thoroughly in tap water for 60 minutes. The seeds were spread t h i n l y on newspaper and allowed to a i r dry for 48 hours before being placed in 110 seeds l o t s into 25 ml p l a s t i c v i a l s . 6.3 PLANTING During May, seeds were sown in the f i e l d in replicated, randomized complete blocks designs. Rows were aligned in an east to west d i r e c t i o n . A hand operated, belt driven, single row seeder was used to space seeds evenly in 10 foot rows at a depth of approximately 2 cm. Plots were weeded and watered as necessary. 6.4 HARVESTING AND DATA RECORDING Approximately 3-4 months after planting, when heads were golden in color and very dry, measurements made on each treatment row were recorded. Some of the measurements made on the f i r s t 50 plants in the row included the following: - number of diseased plants - number of heads per plant - number of diseased heads per plant - weight of spores per diseased head 41 - spore germination rate per diseased head - number of healthy heads - number of seeds per healthy head - seed weight per healthy head - seed germination rate per healthy head - thousand seed weight In addition to those l i s t e d above, other variables were measured. From the recorded data, s t i l l other variables were constructed that related s p e c i f i c a l l y to plant and t i l l e r averages, and, host and pathogen fitnesses. The complete set of variables was subdivided into 4 subsets. A mnemonic code character (R,H,C or P) was assigned to each of the subsets for ease of handling and analysis. The R subset of variables relates to aspects of the "row" in general. H variables involve "healthy" plant measurements. Variables in the C subset were made on "completely" diseased plants ( i e . with every head diseased). The P subset of variables was obtained from " p a r t i a l l y " diseased plants ( i e . with at least 1 healthy head and 1 diseased one). Some variables, normally expressed as percentages, were transformed using a modified Freeman and Tukey (1950) angular transformation (Zar, 1984): p' = 1/2 [arcsin( (x/(n+1 ) " 2 ) + .erwisiw ( ( ( * H )/(?»+1 ) 1 / 2 ) ] where p' = transformed percent smutted plants, x = the number of smutted plants, and n = the number of plants scored. Consult tables 4 to 7 for a more detailed description of the variables used. Figure 3 i s a schema showing the in t e r r e l a t i o n s h i p of the subsets of variables. 42 6.5 HEAD ANALYSIS Smutted heads were ground in a mortar to release the teliospores. Plant debris was manually removed and the teliospores were brushed into a weighboat and were weighed on a Mettler balance. The weight of the weighboat was subtracted from the reading to give the actual teliospore weight. When possible, 1 mg of teliospores from each smutted head was placed in 5 ml of s t e r i l e water for 30 minutes. Two drops of t h i s suspension were spread on a p e t r i plate containing complete medium and the plate was incubated at 22°C for 18 hours. A straight reference l i n e was drawn on the bottom of the plate. The f i r s t 100 teliospores touching the l i n e (moving from east to west) were scored for the presence of promycelium with at least one f u l l y formed sporidium. Teliospores with at least one sporidium were considered to have successfully germinated. The seed number, seed weight and seed germination rate was recorded for healthy heads from diseased plants. Seed germination rates were assessed by c a l c u l a t i n g the percentage germinating after 4 days in large p e t r i plates containing moist vermiculate. Seeds from the f i r s t 50 healthy plants were pooled with seeds from remaining healthy plants in each row. A random sample of 1000 of these seeds was taken and weighed to measure the thousand seed weight variable (H6). The germination rate of a random selection of 100 of these seeds was measured. 43 6.6 SPORIDIA CULTURE MEDIUM Three media types were employed in this study: minimal agar medium, complete agar medium and complete l i q u i d medium. See Appendix A for recipes. Minimal medium was usually used for short term cu l t u r i n g while complete medium was used for procedures l a s t i n g more than 2 days. 6.7 SPORIDIA ISOLATION Smutted heads were surface s t e r i l i z e d in a 1% sodium hypochlorite (household bleach) solution for 30 seconds then rinsed in s t e r i l e water for 2-3 minutes. The heads were cut open and teliospores, c e n t r a l l y located within a sorus, were teased out and allowed to imbibe s t e r i l e water for 30 minutes. Under s t e r i l e conditions, droplets of the teliospore suspension were placed in the center of 20 mm x 20 mm x 3 mm blocks of minimal medium agar. The agar blocks were mounted on 25 sq mm coverslips and incubated at 22°C, 100% r e l a t i v e humidity, for 18 hours to promote germination and sporidia production. Each agar block containing c o v e r s l i p was inverted and placed on a moveable stage microscope at 150x magnification. A haploid sporidium was coaxed to each edge of a block with a bulbous tipped, fine glass needle, mounted in a de Fronbrue micromanipulator (CH. Beaudouin, P a r i s ) . After 3-4 days of incubation at 22°C s p o r i d i a l microcolonies were v i s i b l e at block edges. These microcolonies were transferred to p e t r i plates containing complete agar medium. The mating type of each iso l a t e was 44 determined by compatibility with known standards using a modified Bauch test (Bauch, 1932). The appearance of microscopic hyphae ("Suchfaden") signalled compatibility ( i e . opposite mating types). 6.8 LONG-TERM SPORIDIAL STORAGE Ce l l s from each s p o r i d i a l colony were transferred to s t e r i l e complete medium, slant agar tubes. After 4 days, c e l l s were emulsified in 1 ml of s t e r i l e water plus 1 ml of double strength skim milk. Screw capped tubes were half f i l l e d with s i l i c a gel (Perkins, 1962), loosely capped and s t e r i l i z e d in an oven at 180°C for 90 minutes. Caps were tightened and tubes were allowed to a i r cool to room temperature. One ml aliquots of the s p o r i d i a l c e l l suspension were pipetted into each tube. Tubes were shaken u n t i l a l l traces of moisture disappeared. Tubes were then placed on ice and l a t e r stored at 4°C. 6.9 INOCULATION Spor i d i a l i s o l a t e s were placed in tubes containing 5 ml of s t e r i l e complete medium with t e t r a c y c l i n e HC1 at a concentration of 0.075 mg/ml and shaken at 22°C for 48 hours. One ml of each suspension was transferred to separate 250 ml flasks containing 60 ml complete medium and t e t r a c y c l i n e HC1 at a 0.075 mg/ml concentration. Flasks were shaken at 22°C for 48 hours. 45 Experimental treatments, consisting of every possible pairwise combination of compatible sporidia were premixed in s t e r i l e f l a s k s . V i a l s containing 110 seeds were inoculated with 5 ml volumes of c e l l suspension treatments. V i a l s were then subjected to a negative pressure in a b e l l jar for 30 minutes (Groth and Person, 1976). The excess l i q u i d was drained from the v i a l s and the wet seeds were transferred to l a b e l l e d coin envelopes and allowed to a i r dry for 48 hours prior to sowing. 6. U) STATISTICAL ANALYSIS The data were analysed on an Amdahl computer with the SAS s t a t i s t i c a l package (1981, 1982a, 1982b). Some variables were changed to angles using a modified angular transformation to s a t i s f y the a p r i o r i assumption of normality. A general li n e a r modelling approach was used in the analysis. S t a t i s t i c a l techniques employed included the t test, analysis of variance, Duncan's multiple range test, multivariate analysis of variance, c o r r e l a t i o n c o e f f i c i e n t s , Spearman rank c o r r e l a t i o n c o e f f i c i e n t and stepwise li n e a r regression. 46 7 RESULTS 7.J_ DESCRIPTION OF VARIABLES The l i s t i n g and description of the 4 subsets of variables i s found in tables 4 to 7. A schema in representation of the relati o n s h i p among the 4 subsets i s shown in figure 3. Mean values of treatments and controls for each variable are presented in tables 8 to 15. 7.2 REGRESSION OF SPORE NUMBER ON SPORE WEIGHT A regression of teliospore number on teliospore weight was made to determine i f the expected linear relationship between the two spore related variables existed. The regression indicated that the relationship was linear and that a high po s i t i v e c o r r e l a t i o n of 0.9931 existed between the two variables. The intercept was forced through the o r i g i n , and the slope was s i g n i f i c a n t l y d i f f e r e n t from 0 (figure 4, Tcalc=33.84, P=0.0001). The teliospore weight c o e f f i c i e n t , 1.241E10 (+/-3.667E8), could have been used as a m u l t i p l i e r to convert raw teliospore weights to teliospore numbers. Each milligram of teliospores on average consisted of 1,241,000 teliospores. The simple linear r e l a t i o n s h i p between the two variables made i t unnecessary to actually convert the weight values. Instead, teliospore weight was used in place of teliospore number throughout the remainder of the analysis. 47 7.3 DESCRIPTION OF FITNESS VARIABLES Many of the variables measured in th i s system were expected to be highly correlated with pathogen and/or host f i t n e s s . Based on t h i s expectation, six composite fitness variables were constructed from others within the set of measured variables. The f i t n e s s variables were s p e c i f i c a l l y constructed for use as dependent variables in subsequent multivariate analysis. Three of the six composite variables (Wp [PATHOGEN], Wc [PATHOGEN] and W [PATHOGEN]) are absolute measures of pathogen f i t n e s s . The f i r s t of these three variables was derived from pathogen performance on p a r t i a l l y diseased plants (Wp [HOST]). The second variable represents pathogen fi t n e s s on completely diseased plants (Wc [PATHOGEN]). The t h i r d variable i s a measure of t o t a l pathogen f i t n e s s on a l l plants (W [PATHOGEN]) and was created by summing the f i r s t two pathogen fi t n e s s values. The remaining three composite variables (Wp [HOST], Wc [HOST] and W [HOST]) quantify aspects of host f i t n e s s . One of these variables represents host f i t n e s s on p a r t i a l l y diseased plants (Wp [HOST]). Another variable quantifies host fi t n e s s on healthy plants (Wh [HOST]). The sixth variable combines the f i r s t two as a measure of the t o t a l f i t n e s s of the host (W [HOST]). The formulae for these composite f i t n e s s values are described below. Calculating pathogen fi t n e s s on p a r t i a l l y diseased plants for each row involved the following variables: the weight of teliospores from p a r t i a l l y diseased plants (P8) and the average teliospore germination percent per diseased head 48 of p a r t i a l l y diseased plants converted to decimal form (P11/100). The exact formula i s Wp [PATHOGEN] = P8 x PI 1/100 The following variables were used to calculate pathogen fi t n e s s on completely diseased plants: the weight of teliospores from completely diseased plants (C4) and the average teliospore germination rate per head from completely diseased plants converted to a decimal (C7/100). The formula for Wc [PATHOGEN] i s Wc [PATHOGEN] = C4 x C7/100 The sum of Wp [PATHOGEN] and Wc [PATHOGEN] to t a l s W [PATHOGEN], the t o t a l pathogen f i t n e s s . Variables Wp [HOST] to W [HOST] represent host fitnesses for p a r t i a l l y diseased plants, completely diseased plants, and a l l types of plants, respectively and were b u i l t from the following measurements: the number of seeds from p a r t i a l l y diseased plants (P12), the average seed germination rate for p a r t i a l l y diseased plants in decimal form (P18/100), the number of seeds from healthy plants (H10) and the average seed germination rate of seeds from healthy plants expressed as a decimal (H9/100). Formulae for Wp [HOST] to W [HOST] are Wp [HOST] = P12 x (P18/100), Wh [HOST] = H10 x H9/100, and W [HOST] = Wp [HOST] x Wh [HOST] Values for Wp [PATHOGEN] to W [HOST] are in tables 8 and 12 for 49 Trebi and Odessa, respectively. 7.4 SPORIDIAL TREATMENTS VERSUS CONTROL COMPARISONS Based on prior observations, seeds treated with sporidia were expected to suffer reductions, r e l a t i v e to control, for many of the measurements. One-tailed t tests were performed to determine i f s t a t i s t i c a l l y s i g n i f i c a n t reductions occurred r e l a t i v e to control. The n u l l hypothesis of no difference between a variable's value and that of the corresponding control was tested at the 95% confidence l e v e l for 19 degrees of freedom and was rejected when calculated t values exceeded 1.729. Variables obtained from p a r t i a l l y and completely diseased plants had no matching control variables with which they could be compared. This i s because control rows were free of disease, as expected. To circumvent t h i s s i t u a t i o n variables from subsets C (completely diseased plants) and P ( p a r t i a l l y diseased plants) were tested against c l o s e l y related means from control rows. Test results are found in tables 16 and 17. S i g n i f i c a n t reductions were observed in the following variables: TH1, TH2, TH9, TC3, TP12, TP13, TP14, TP16, TP17, TP18, OR1, OR6, 0R8, OWh [HOST], OW [HOST], OH1, OH2, 0H3, OH5, OHIO, OC3, 0P12, OP13, 0P14, OP16, 0P17 and 0P18. 50 7.5 VARIABLE MEAN COMPARISONS FOR THE VARIETIES Equality of variable means was tested either with a correlated groups t-test or with a one-way ANOVA. The t-test n u l l hypothesis was for no difference between paired scores. At an alpha of 0.05, with 19 degrees of freedom the nu l l hypothesis was rejected i f the calculated t value was more extreme than the tabulated t value of +/-2.093. The ANOVA n u l l hypothesis was for no difference among means. An F value was calculated by placing the "tested means" mean square (with 2 degrees of freedom) over the "error" mean square (with 57 degrees of freedom) and comparing i t with the appropriate tabulated F value (alpha=0.05). The ANOVA was used as a simple alternative to performing three t - t e s t s . S t a t i s t i c a l differences among these means were expected to r e f l e c t the range of ef f e c t s that the treatments had on C (completely diseased), P ( p a r t i a l l y diseased) and H (healthy) plants and how these e f f e c t s varied between v a r i e t i e s . Important differences were found between many of the variables. Tables 18 to 21 catalogue the results of the comparison of means. S t a t i s t i c a l l y s i g n i f i c a n t differences were revealed in the t-tests between variable pairs on Trebi and Odessa. The pairs tested are as follows: - the average number of diseased heads (R7) and healthy heads per plant (R8), - the number of healthy heads from healthy (H2) and p a r t i a l l y diseased plants (P4), 51 - the average number of seeds per plant for healthy (H4) and p a r t i a l l y diseased plants (H13), - the average number of seeds per head for healthy (H5) and p a r t i a l l y diseased plants (P14), - the average seed weight per plant for healthy (H7) and p a r t i a l l y diseased plants (P16), - the average seed weight per head for healthy (H8) and p a r t i a l l y diseased plants (P17), - the average seed germination rate per head for healthy (H9) and p a r t i a l l y diseased plants (P18), - the number of seeds from healthy (H10) and p a r t i a l l y diseased plants (P12), - the number of diseased heads from completely diseased plants (C2) and from p a r t i a l l y diseased plants (P3), - the average number of diseased heads per plant for completely (C3) and p a r t i a l l y diseased plants (P6), - t o t a l spore weight for completely (C4) and p a r t i a l l y diseased plants (P8), - average spore weight per plant for completely (C5) and p a r t i a l l y diseased plants (P9), - the average spore weight per head for completely (C6) and p a r t i a l l y diseased plants (P10), - the average spore germination rate per diseased head for completely (C7) and p a r t i a l l y diseased plants (P11), - the number of diseased (P3) and healthy heads for p a r t i a l l y diseased plants (P4), - the average number of healthy (P6) and diseased heads for p a r t i a l l y diseased plants (P7), - the pathogen's fi t n e s s on p a r t i a l l y (Wp) and completely diseased plants (Wc) and the fi t n e s s of the host on p a r t i a l l y diseased (Wp [HOST]) and healthy plants (Wh [HOST]). Other differences among means were found in the three ANOVA's involving: the number of healthy (H1), completely 52 d i s e a s e d (C1) and p a r t i a l l y d i s e a s e d p l a n t s ( P 1 ) , t h e number o f h e a ds from h e a l t h y (H2), c o m p l e t e l y d i s e a s e d ( C 2 ) , and p a r t i a l l y d i s e a s e d p l a n t s ( P 2 ) , and, t h e a v e r a g e number of h e ads p e r p l a n t f o r h e a l t h y (H3), c o m p l e t e l y d i s e a s e d (C3) and p a r t i a l l y d i s e a s e d p l a n t s ( P 5 ) . 7.6 ANOVA E a c h v a r i a b l e underwent a d i f f e r e n t and s e p a r a t e a n a l y s i s o f v a r i a n c e . The o b j e c t i v e was t o p a r t i t i o n t o t a l v a r i a n c e f o r e a c h v a r i a b l e i n t o t h e s e v e n p o s s i b l e c o n t r i b u t i n g s o u r c e s . T h e s e s o u r c e components were "+" s p o r i d i a , "-" s p o r i d i a , " r e p " ( r e p l i c a t e s ) , "+x-" s p o r i d i a i n t e r a c t i o n s , "+xrep" i n t e r a c t i o n s , " - x r e p " i n t e r a c t i o n s and "+x-xrep" i n t e r a c t i o n s , w h i c h was r e d e f i n e d a s t h e e r r o r component. The n u l l h y p o t h e s i s was f o r t h e e q u a l i t y o f g r o u p means. The l e v e l o f s i g n i f i c a n c e was s e t a t a l p h a = 0 . 0 5 . Pseudo-F v a l u e s were c a l c u l a t e d and u s e d t o t e s t t h e main e f f e c t s components b e c a u s e a p p r o p r i a t e d e n o m i n a t o r mean s q u a r e s were n o t a v a i l a b l e . The i n t e r a c t i o n components were t e s t e d a g a i n s t t h e e r r o r mean s q u a r e . R e l a t i v e c o n t r i b u t i o n s o f e a c h v a r i a n c e component t o t o t a l v a r i a b i l i t y was a s s e s s e d u s i n g t h e e x p e c t e d mean s q u a r e t a b l e and was e x p r e s s e d as a p e r c e n t a g e ( t a b l e s 22 t o 2 9 ) . T a b l e 30 was c o n s t r u c t e d t o summarize t h e r e s u l t s o f t h e ANOVA's. An a s t e r i s k was p l a c e d i n t h e a p p r o p r i a t e column f o r v a r i a n c e components t h a t had s i g n i f i c a n t F v a l u e s . T a b l e 31 p r e s e n t s t h e f r e q u e n c i e s o f s p e c i f i c c o m b i n a t i o n s o f s i g n i f i c a n t components f o r e a c h s u b s e t o f v a r i a b l e s and f o r e a c h v a r i e t y . 53 7.7 MODELS Four groups of models, determined by stepwise regression and constructed from s p e c i f i c subsets of independent variables, were designed to estimate host (W [PATHOGEN]) and pathogen fi t n e s s (W [HOST]) values and to provide explanations for the underlying biology associated with aspects of f i t n e s s . These groups are subsequently referred to as: - COMPLETE - TRADITIONAL - PRACTICAL - DEVELOPMENTAL The c r i t e r i a for inclusion of a variable in a model was that i t had an F value with a p r o b a b i l i t y of no more than 0.15, and that t h i s p r o b a b i l i t y was maintained when the variable was included in the model. Variables were excluded or removed from a model i f these c r i t e r i a were not s a t i s f i e d . Models chosen as being "best" were those that had no term with an F value p r o b a b i l i t y greater than 0.05. "Best" models were chosen so that models with more terms did not have s i g n i f i c a n t l y larger R2 values. Whenever possible, models were r e f i t t e d with select independent variables known to be influenced by nonspecific pathogenicity ( i e . in ANOVA's these variables had s t a t i s t i c a l l y s i g n i f i c a n t genetic related components). Models with these independent variables are s i g n i f i e d with the l e t t e r "G" next to 54 the dependent variable. Residual analysis of a l l models revealed no unusual outlying values. Therefore, a p r i o r i assumptions about the normal d i s t r i b u t i o n of error values about a mean of zero were supported. Regression results are found in tables 32 to 47. 7.7.J_ COMPLETE These models involved nearly the entire set of variables measured for th i s host-parasite system (tables 32 and 33). Obvious problems with m u l t i c o l i n e a r i t y were avoided by excluding select constructed fitness variables (Wp [PATHOGEN], Wc [PATHOGEN], W [PATHOGEN], Wp [HOST], Wc [HOST], W [HOST]) as independent variables. Which variables were excluded depended upon which dependent variable was used in the model. For example, when pathogen fitness (W [PATHOGEN]) was the dependent variable, Wp [PATHOGEN], Wc [PATHOGEN], W [PATHOGEN] were dropped. Variables Wp [HOST], Wc [HOST] and W [HOST]) were dropped from the model when host fitness (W [HOST]) was the dependent variable. For each of the v a r i e t i e s , 4 s p e c i f i c models were b u i l t . The f i r s t was meant to find the best combination of independent variables that estimated pathogen f i t n e s s (W [PATHOGEN]). The second model was meant to predict host f i t n e s s (W [HOST]). The la s t two equations were similar to the f i r s t two, except that only variables known to be controlled by genetic differences among treatment sporidia were used as independent variables. Note that for Trebi and Odessa, the independent terms in the 55 models can d i f f e r markedly. 7.7.2 TRADITIONAL This series of models compared the two best known methods of assessing disease damage that involve variables believed to be correlated with pathogen f i t n e s s (tables 34 to 36). The two methods are the percent smutted plants (R2), currently the most popular and commonly used, and, the percent smutted heads (R4). R e l i a b i l i t y of using these same two independent variables to estimate host f i t n e s s was assessed. The effectiveness of combining these two variables as estimators of fitnesses was investigated. 7.7.3 PRACTICAL This series of models was arranged expressly to investigate ce r t a i n combinations of independent variables associated with p r a c t i c a l and technical aspects of performing t h i s experiment (tables 37 to 41). The models were divided into 3 subgroups: - MINIMAL COST - MODERATE COST - EARLY ASSESSMENT The f i r s t subgroup (MINIMAL COST) involves independent variables that were obtained with minimal cost, in terms of man hours, equipment and financing. The MODERATE COST models 56 i n c o r p o r a t e v a r i a b l e s used i n the MINIMAL COST model p l u s a few o t h e r s which were o b t a i n e d a t a moderate c o s t . The EARLY ASSESSMENT model t e s t s the adequacy of u s i n g c e r t a i n v a r i a b l e s , o b t a i n a b l e w e l l i n advance of h a r v e s t , as a c c u r a t e p r e d i c t o r s of host and pathogen f i t n e s s e s . 7.7.4 DEVELOPMENTAL Models i n v o l v i n g independent v a r i a b l e s a s s o c i a t e d w i t h , or r e f l e c t i n g , s e q u e n t i a l s t a g e s i n t h e development of the h o s t / p a r a s i t e a s s o c i a t i o n c o m p r i s e d the DEVELOPMENTAL group of e q u a t i o n s . These models were used i n an attempt t o i d e n t i f y p a r t i c u l a r s t a g e s of development i n the host which might be a s s o c i a t e d w i t h p h y s i o l o g i c mechanisms a f f e c t i n g h o s t and pathogen f i t n e s s e s ( t a b l e s 42 t o 4 7 ) . There were 2 subgroups of models w i t h i n t h i s group: - C (COMPLETELY DISEASED PLANTS) OR H (HEALTHY PLANTS) BASED - P (PARTIALLY DISEASED PLANTS) BASED: HOST PERSPECTIVE - P (PARTIALLY DISEASED PLANTS) BASED: PATHOGEN PERSPECTIVE C (COMPLETELY DISEASED PLANTS) OR H (HEALTHY PLANTS) BASED models i n c o r p o r a t e d independent v a r i a b l e s from e i t h e r the C ( c o m p l e t e l y d i s e a s e d p l a n t s ) or t h e H ( h e a l t h y p l a n t s ) s u bset of v a r i a b l e s depending on which dependent v a r i a b l e was i n v o l v e d . When the dependent v a r i a b l e was W [PATHOGEN] (pathogen f i t n e s s ) , C ( c o m p l e t e l y d i s e a s e d p l a n t s ) based, pathogen r e l a t e d , 57 independent variables were used. S i m i l a r l y , H (healthy plants) based, host fitness related independent variables were needed when W [HOST] (host fitness) was the dependent variable. The second subgroup of models involved independent variables from the P ( p a r t i a l l y diseased plants) subset. These variables were either host related or pathogen related. 7.8 "CONSTANT RANKING" The Spearman rank c o r r e l a t i o n c o e f f i c i e n t , r, for the ranking of the 20 dikaryons on Trebi and Odessa was p o s i t i v e , high and s i g n i f i c a n t (r=0.8714, P=0.0001) for percent smutted plants (R2, table 48). When disease levels were represented by percent smutted t i l l e r s the Spearman co r r e l a t i o n c o e f f i c i e n t was 0.8526 (P=0.0001). Two other variables, used for ranking, generated s i g n i f i c a n t c o r r e l a t i o n s : pathogen f i t n e s s on p a r t i a l l y diseased plants (Wc [PATHOGEN]) and pathogen f i t n e s s on completely diseased plants (W [PATHOGEN]). Rank co r r e l a t i o n of pathogen fi t n e s s values with host f i t n e s s values was not s t a t i s t i c a l l y s i g n i f i c a n t on either Trebi or on Odessa (table 49). 58 8 DISCUSSION 8.J_ BIOLOGICAL MATERIAL Va r i e t i e s Trebi and Odessa are s u f f i c i e n t l y d i s s i m i l a r g e n e t i c a l l y , that their reactions to smut i s o l a t e s d i f f e r markedly. Odessa possesses no known s p e c i f i c resistance, while Trebi has s p e c i f i c resistance to race 7, among others (Tapke, 1937, 1945). When challenged with s p e c i f i c virulence genes, Trebi can exhibit d i f f e r e n t i a l interactions (Tapke, 1937, 1945). When the two v a r i e t i e s show s u s c e p t i b i l i t y to the same is o l a t e s , Trebi invariably has a higher, quantitative l e v e l of disease. Non-specific genetic differences were found to control t h i s variation in disease l e v e l s (Pope, 1982). A l l treatment dikaryons used in t h i s experiment possessed the dominant virulence a l l e l e conferring pathogenicity on Trebi (Person, 1983). No known virulence genes are required for pathogenicity on Odessa. Therefore, a l l treatment dikaryons had the a b i l i t y to produce disease damage on both v a r i e t i e s . The lev e l of disease damage caused by any dikaryon depended on the combined e f f e c t s of nonspecific pathogenicity and nonspecific resistance. 5 9 8.2 SPORIDIAL TREATMENTS VERSUS CONTROL COMPARISONS Effe c t s of s p o r i d i a l treatment common to both v a r i e t i e s were: a reduction in the number of healthy plants and heads, a reduction in t i l l e r i n g of completely and p a r t i a l l y diseased plants, a dramatic reduction in seed number, weight and germination rate for p a r t i a l l y diseased plants, and a decrease in healthy plant f i t n e s s . These e f f e c t s were present regardless of the resistance genotype of the host. It appears that a l l plants, diseased or apparently healthy, suffered reductions in seed biomass and reproductivity. Since a l l plants of one variety were homozygous for nonspecific resistance, i t was expected that each plant would have the same pr o b a b i l i t y of showing disease signs. The fact that not a l l plants in a row produced spores indicates that some fa c t o r ( s ) , other than plant genotype, was important in c o n t r o l l i n g the p r o b a b i l i t y of a susceptible plant becoming diseased. The factors most l i k e l y to be involved are pathogen genotype, and environmental influences, random events and/or combinations of these. A plausible explanation for disease escape i s that an important, as yet unidentified event(s), during a c r i t i c a l period(s) of host development retards, excludes, or removes the smut fungus. Should a dikaryon have the genetic means to escape, avoid or prevent these event(s) i t can produce teliospores. A dikaryon could continue to grow and act as a physiological sink and could eventually show signs of i t s presence by producing teliospores. 60 Quantitative d i s s i m i l a r i t i e s between the v a r i e t i e s were noticed upon comparison of several variables. Trebi had fewer s t a t i s t i c a l l y d i f f e r e n t departures from control means than Odessa. For Odessa these departures included: a decrease in the number of treated seeds that germinated and reached maturity, and, for healthy plants, a reduction in t i l l e r i n g , seed production and seed weight. Also, a large decrease in host f i t n e s s for Odessa was recorded. Seeds from healthy Trebi plants suffered a reduced germination rate. No such reduction was observed for Odessa. This result would have a n o n t r i v i a l impact on the contribution of healthy Trebi plants to the next generation ( i e . f i t n e s s ) , r e l a t i v e to uninoculated plants. Treatment of Odessa caused several effects to occur that were not seen on Trebi. Following planting, but prior to maturation, inoculated Odessa seeds showed a pronounced reduction in rate of germination compared with seeds of Trebi. It i s hypothesized that Trebi possesses genetically conferred resistance that manifests i t s e l f at some time following germination but prior to maturation. It i s most l i k e l y that t h i s resistance becomes ef f e c t i v e soon after germination because there were no immature plants present at harvest and no obvious deaths of immature plants prior to harvest. Also, genetic d i s s i m i l a r i t i e s between Trebi and Odessa are considered to be responsible for causing healthy plants to have lower t i l l e r i n g , seed production and seed weight than control plants. Trebi did not suffer large reductions in these fitness 61 related variables. It appears that the plant may pay a price for r e s i s t i n g the disease in order to remain healthy (Person, personal communication). In an infected plant a "... greatly increased biosynthetic a c t i v i t y occurs at the expense of stored host energy and may ultimately l i m i t plant growth and y i e l d r e l a t i v e to potential growth and y i e l d " (Smedegaard-Petersen, 1985). An apparent discrepancy was found in the Trebi data. The mean number of healthy plants and heads from healthy plants was di f f e r e n t than the mean number for contro l . Since the average number of heads per healthy plant was similar to that for the control, i t was expected that, either a s i g n i f i c a n t decrease in the t o t a l number of seeds from healthy plants or an increase in the average seed number per healthy plant, would occur. Neither of these situations happened, indicating a possible Type II error in one, some or a l l of the following variables: the average number of heads per plant (TH3), the average number of seeds per plant (TH4), the average number of seeds per head (TH5), the t o t a l number of seeds (TH10). Two variables, the average number of heads per plant (TH3) and the t o t a l number of seeds (TH10), had T(calc) values that were very close to the T(tab) value and were most l i k e l y the ones to have been involved in a Type II error. Based on these facts, the parsimonious explanation for t h i s discrepancy involves only 1 of these suspected variables in a Type II error, namely TH10. 62 8.3 VARIABLE MEAN COMPARISONS FOR THE VARIETIES Interestingly, s t a t i s t i c a l l y s i g n i f i c a n t differences were found when comparisons were made between variable means. There were large differences among the plant types ( i e . healthy, completely diseased and p a r t i a l l y diseased). There also were s t r i k i n g s i m i l a r i t i e s between the two v a r i e t i e s in terms of relationships between certain variables (compare tables 19 and 21). The only differences between the v a r i e t i e s were for plant type numbers and average t i l l e r numbers. These differences are expected and are consistent with the genetic d i s s i m i l a r i t y between the two v a r i e t i e s . In terms of f i t n e s s , completely diseased plants make a larger contribution to pathogen f i t n e s s than p a r t i a l l y diseased plants. This i s explained by a combination of events: a greater number of completely, as compared with p a r t i a l l y diseased plants (for Odessa only), a greater number of diseased t i l l e r s per plant for completely diseased plants, a greater average spore weight per t i l l e r for completely diseased plants and f i n a l l y , a larger average spore germination rate for completely diseased plants. Host f i t n e s s values calculated from healthy plants were larger than those from p a r t i a l l y diseased plants because of: a larger number of healthy plants, a greater average number of healthy t i l l e r s per plant, a larger average number of seeds per t i l l e r and a larger average seed germination rate. The average seed weight per t i l l e r was greater for healthy plants than for p a r t i a l l y diseased plants. Although not 63 s t r i c t l y related to host or pathogen f i t n e s s , as defined in t h i s study, t h i s difference i s believed to be important in re l a t i o n to the qual i t y of seed set and the y i e l d at harvest. The presence of inoculum at the time of planting may not lead to the production of large amounts of teliospores on p a r t i a l l y diseased plants but does cause a dramatic depression of expected y i e l d for p a r t i a l l y diseased plants. From these data, i t can not be determined i f the pathogen, in diseased t i l l e r s of p a r t i a l l y diseased plants, acts as a metabolic sink and reduces healthy t i l l e r seed weight or i f the pathogen continues to l i v e and grow in tissue of healthy t i l l e r s . 8.4 ANOVA Six possible components of va r i a t i o n were measured for a l l variables. These components were - "+" sporidia main ef f e c t s (corresponds to nonspecific pathogenicity) - "-" sporidia main e f f e c t s (corresponds to nonspecific pathogenicity) - "rep" r e p l i c a t e e f f e c t s (corresponds to environmental differences among blocks) - "+x~" sporidia interaction (corresponds to dominance and e p i s t a t i c interaction of pathogenicity genes) - "+xrep" sporidia by re p l i c a t e interaction (corresponds to genotype by environment interaction) - "-xrep" sporidia by rep l i c a t e interaction (corresponds to genotype by environment interaction) Forty variables from Trebi and 41 from Odessa, with 30 of these held in common, revealed s t a t i s t i c a l l y s i g n i f i c a n t 64 differences for, at least, one source component. For each of the 3 main effect components, "+", "rep", and the "+x-" interaction component, s i g n i f i c a n t F values appeared in 1, 23, 21 and 7 variables, respectively, on Trebi and 1, 17, 32 and 8 variables, respectively, on Odessa. There were more variables for Odessa than for Trebi where differences among replicates were important in af f e c t i n g v a r i a b i l i t y . For Trebi, more variables were affected by differences among the "-" sporidia than for Odessa. Replicate differences ("rep") appeared more frequently in ANOVA's than any of the other 6 components of v a r i a t i o n . Replicate differences r e f l e c t the effect of environmental heterogeneity on v a r i a b i l i t y . . Replicate blocks were handled as s i m i l a r l y as possible and were placed in f i e l d locations which were as uniform as possible. Some interplot differences were evident. The experimental f i e l d had a slope of about 2 degrees from south to north. A clay hardpan existed at depths varying from approximately 20 to 30 cm below s o i l surface. These two factors could have affected water drainage and might be the most important environmental factors contributing to v a r i a b i l i t y . Other factors that might have contributed to rep l i c a t e related v a r i a b i l i t y include: technical (seed preparation, inoculation, planting, e t c . ) , f e r t i l i z e r d i s t r i b u t i o n , plant density, s o i l dwelling organisms, and above s o i l organisms ( p a r t i c u l a r l y mildew and barley yellow-dwarf). S i g n i f i c a n t F values for the re p l i c a t e component appeared in 13 variables for Trebi and 24 for Odessa (2 were common to both v a r i e t i e s ) . Environmental 65 factors played a larger role in generating v a r i a b i l i t y for variables on Odessa than on Trebi. Genetic differences among treatment sporidia also were an important factor in generating v a r i a b i l i t y . The most frequently occurring genetic component was the "-" mating type component and represents the nonspecific pathogenicity gene differences among sporidia. A large F value was found for the "-" component only, for 15 variables on Trebi and for 4 on Odessa, 2 of which were held in common. Genetic differences among sporidia were more important for producing v a r i a b i l i t y on Trebi than on Odessa. A t o t a l of 26 variables on Trebi and 18 on Odessa was shown by ANOVA to be controlled, to some extent, by nonspecific pathogenicity genes. It i s interesting to note that for Trebi these variables were mainly from the R (row), C (completely diseased plants), and P ( p a r t i a l l y diseased plants) subsets indicating the possibly consequential involvement of these genetic differences in determining host f i t n e s s . For Odessa, mainly the R (row) and C (completely diseased plants) subsets had variables with important genetic components. This indicates involvement of nonpathogen related factors in p a r t i a l l y diseased plants, possibly heretofore undetected host resistance. No variable was found where the "+" sporidia component was the sole source of v a r i a b i l i t y . It i s highly probable that nonspecific pathogenicity gene(s) are t i g h t l y linked to the mating locus, coupled with the "-" mating a l l e l e . This result i s consistent with that found in an e a r l i e r studies .(Pope, 1982; 66 Caten et a l , 1984). The fact that genetically related components for the percent of plants smutted (R2) and for pathogen fitness (W [PATHOGEN]) were s i g n i f i c a n t on Trebi but not on Odessa permits inter e s t i n g speculation. Genetic differences among dikaryons were observed on Trebi but not on Odessa. Nonspecific pathogenicity genes segregating in the dikaryons were e f f e c t i v e on Trebi but not on Odessa. According to expectations based on Vanderplank's d e f i n i t i o n of horizontal genes (1982, 1984), in the absence of gene-for-gene interactions a l l nonspecific pathogenicity genes are e f f e c t i v e against a l l nonspecific resistance genes regardless of their o r i g i n or number. Results from t h i s experiment deviate from these expectations. Smut dikaryons are more variable on Trebi than on Odessa which indicates that race 11 (parental teliospore T1) i s better adapted to Trebi than to Odessa. Adaptation to Trebi i s possibly a result of selection in race 11 for pathogenicity a l l e l e s that optimize interactions on Trebi. The history of race 11 is not a v a i l a b l e . Therefore, t h i s p o s s i b i l i t y can not be v e r i f i e d . The e f f e c t s of the Trebi related "subset" of nonspecific pathogenicity a l l e l e s are not present on Odessa. Other reasons for t h i s reaction on Odessa can include the following: Odessa might have the unusual dynamic capacity to interact with the is o l a t e s in strengths d i r e c t l y related to the aggressiveness l e v e l of each i s o l a t e , thereby causing i s o l a t e s to appear to be gene t i c a l l y i d e n t i c a l , or the universal suscept could be devoid 67 of active resistance polygenes, negating the interaction of pathogenicity and resistance genes (this assumes that these interactions are a prerequisite to the resolution of nonspecific genetic differences among i s o l a t e s ) . The f i r s t s i t u a t i o n i s inconsistent with the d e f i n i t i o n of nonspecific polygenes. The second si t u a t i o n i s not supported by experimental evidence here or in the l i t e r a t u r e . Interactions among pathogen iso l a t e s of the same race and susceptible hosts, similar to those found here, were recorded in the potato-late blight system (Bruyn, 1 947; Jeffrey e_t a_l, 1962; Caten, 1974) and in the barley-rust system ( C l i f f o r d and Clothier, 1974; P a r l e v l i e t , 1978). These researchers concluded that pathogenic strains may be s p e c i f i c a l l y adapted to v a r i e t i e s from which they were i s o l a t e d . There i s one major difference between those investigations and t h i s one. For each of those investigations the hosts were consistantly of one type, in terms of s p e c i f i c resistance. They were either without known s p e c i f i c resistance ( i e . RO hosts; Caten, 1974) or they had i d e n t i c a l but defeated s p e c i f i c resistance genes ( C l i f f o r d and Cl o t h i e r , 1974). In t h i s study, one host (Odessa) was without known s p e c i f i c resistance and the other (Trebi) had a defeated s p e c i f i c resistance gene. This difference plays a non t r i v i a l role in the following in t e r p r e t a t i o n . The presence of a defeated s p e c i f i c resistance gene in the background of Trebi but not in the background of Odessa could be an important determinant of how, when or i f the segregating 68 nonspecific pathogenicity genes function. The d i f f e r e n t s p e c i f i c gene backgrounds could be involved in generating s t a t i s t i c a l l y s i g n i f i c a n t differences among dikaryons for certain variables but not others. Consequently, measurements of host and pathogen reproductivities and fitnesses would d i f f e r markedly for the two v a r i e t i e s . The nonspecific pathogenicity genes could be operative ( i e . active in host-pathogen interactions and s t a t i s t i c a l l y measurable) only when a certain matching s p e c i f i c resistance/virulence gene combination are present in the background. The nonspecific pathogenicity genes might not adapt an i s o l a t e to any variety. They might adapt an i s o l a t e to a host variety with a certain defeated s p e c i f i c resistance gene. This variety or background s p e c i f i c i t y i s not the same as, and should not be confused with, gene-for-gene s p e c i f i c i t y . These conclusions are supported by subsequent analyses described in a lat e r section. One can only speculate as to how widespread t h i s type of interaction i s in any pathosystem. Perhaps every s p e c i f i c gene has i t s own subset of modifying nonspecific genes; perhaps only a few do. Can a nonspecific gene be a member of more than one subset? Must a nonspecific gene necessarily be a member of any subset. Many other pertinent questions are raised. Conceivably, under the right conditions, interactions described here could exhibit a quadratic check. Since the check i s evidence for a gene-for-gene interaction (Person, 1959; Ellingboe, 1981), care should be taken not confuse these 69 interactions with those generated by genuine gene-for-gene interactions. Exactly how these interactions w i l l affect the Vanderplankian d e f i n i t i o n of nonspecific pathogenicity genes and "constant ranking" awaits el u c i d a t i o n . It i s possible that some v a r i e t i e s with long l i v e d resistance which i s known to be s p e c i f i c ( v e r t i c a l ) in nature, are actually protected from severe damage from compatible pathogenic genotypes because of the absence of disease enhancing a l l e l e s within the appropriate subset of nonspecific genes. Individuals in the pathogen population would have to undergo many mutational events, each with a low pr o b a b i l i t y of occurance, to develop the a b i l i t y to produce severe disease damage. Further investigation of the interaction between pathogenicity and resistance genes, in thi s and other systems, should be undertaken e s p e c i a l l y in view of i t s involvement in some durably resistant natural and crop pathosystems. As well, useful information could be obtained from studies of the molecular and physiological nature of the interactions. The following combinations of s i g n i f i c a n t components existed: "-" with "rep" (3 for Trebi, 4 for Odessa, 1 in common), "-" with "+x-" (1 on Trebi, 5 on Odessa), "rep" with "+x-" (1 on Trebi), "+", with "-", and "rep" (1 on Trebi, 1 on Odessa), and, "-", with "rep" and "+x-" (3 on Trebi, 3 on Odessa). There were no instances of s i g n i f i c a n t contributions to v a r i a b i l i t y by the "+" component alone or by the following 70 combination of components: "+" with "+" with "rep", "+" with "+x-", "+" with "rep" and "+x-", and, "+" with "-", "rep" and "+x-". Plus by minus sporidia was the only interaction component that was important in causing v a r i a b i l i t y , with one exception, "-xrep" on Trebi. A s i g n i f i c a n t "+x-" interaction indicates that s p e c i f i c sporidia of the "-" mating type in combination with cert a i n sporidia of the "+" mating type generate a r e l a t i v e l y sizable amount of v a r i a b i l i t y because of e p i s t a t i c and/or dominance interaction. The interactions can be of two types, synergistic or interference (Sokal and Rohlf, 1981). Most s i g n i f i c a n t "+x-" interactions occurred in association with other s i g n i f i c a n t components, p a r t i c u l a r l y with "-" main ef f e c t s . Care should be taken when interpreting such r e s u l t s . The simultaneous occurrence of s i g n i f i c a n t differences among "-" sporidia and "+x-" interactions for a variable does not necessarily mean that both components are important contributors to v a r i a b i l i t y . Finney (1947) stated that s i g n i f i c a n t interaction e f f e c t s ("+x-" interactions) are intimately t i e d with the main ef f e c t s components ( i e . "+" and "-" main e f f e c t s ) . A s i g n i f i c a n t "+" and/or "-" component may be an a r t i f a c t of a si g n i f i c a n t "+x-" interaction. Therefore, testing for the significance of the main e f f e c t s might not be very meaningful (Gilbert, 1973; Sokal and Rohlf, 1981). If the s i g n i f i c a n t "+x-" interaction was absent, s i g n i f i c a n t main ef f e c t s components might disappear too, especially when the main ef f e c t s F values are close to the c r i t i c a l F value. When the difference 71 between the interaction and a main ef f e c t s F value i s large, so is the prob a b i l i t y that the main e f f e c t s component i s not an a r t i f a c t of the interaction. This s i t u a t i o n applies not only to the "+x-" interactions but also to any f i r s t - o r d e r i n t e r a c t i o n . Two variables on Trebi revealed only s i g n i f i c a n t "+x-" e f f e c t s . There were none on Odessa. 8.5 MODELLING At the start of th i s section i t should be noted that where possible, models involving independent variables with known s t a t i s t i c a l l y s i g n i f i c a n t genetic components are favored over models where some or a l l independent variables have no s t a t i s t i c a l l y s i g n i f i c a n t components. Models with one or more independent variables without s i g n i f i c a n t genetic components generally have large environmental components. Such variables are unreliable for inclusion in prediction related models because they are affected by uncontrolled environmental factors. 8.5.1 COMPLETE Tables 32 and 33 show that three independent variables chosen by the stepwise program predict pathogen f i t n e s s (W [PATHOGEN]) on Trebi. These were t o t a l spore weight (R12), the number of completely smutted plants (C1) and the average spore weight per t i l l e r (P10). Together they account for 99.1% of the variance in the dependent variable. On Odessa, 98.4% of the v a r i a b i l i t y of pathogen f i t n e s s was accounted for by three 72 variables: the average number of diseased heads per plant (R7), the number of heads from completely smutted plants (C2) and the t o t a l spore weight from completely diseased plants (C4). Host f i t n e s s (W [HOST]) was weakly correlated with pathogen fi t n e s s on completely diseased plants (Wc [PATHOGEN]) on Trebi (R2=0.253). On Odessa three variables, the percent of plants smutted (R2), the percent of heads smutted (R4) and the number of completely diseased plants (C1) generated on R2 value of 0.674. From t h i s group of models i t appears that nonspecific genetic differences among treatment sporidia played an important role in c o n t r o l l i n g pathogen f i t n e s s but not host f i t n e s s . The cor r e l a t i o n between host and pathogen fitnesses was -0.437 (P=0.0538) for Trebi and -0.231 (P=0.3280) for Odessa, both not s t a t i s t i c a l l y s i g n i f i c a n t . Most pathogen is o l a t e s w i l l depress host fi t n e s s (yield) to a certain extent, but an i s o l a t e with high fi t n e s s w i l l not necessarily depress host f i t n e s s more than one with low f i t n e s s . These results are based on the d e f i n i t i o n of f i t n e s s provided in the RESULTS section of t h i s study. The lack of a high p o s i t i v e c o r r e l a t i o n between the fitnesses of the interacting organisms does not mean that the c o r r e l a t i o n between their r e p r o d u c t i v i t i e s i s necessarily i n s i g n i f i c a n t . In fact, the r value for spore number (R12) vs seed number (H10) i s -0.466 (P=0.0383) and for percent smutted plants (R2) vs seed number (H10) r i s -0.447 (P=0.0481) on Trebi. The same correlations on Odessa are -0.317 (P=0.1736) and -0.423 73 (P=0.0633). The s i g n i f i c a n t correlations on Trebi indicate that in a farmer's f i e l d a large increase in the number of spores ( i e . pathogen reproductivity) and/or in the number of smutted plants ( i e . disease damage l e v e l ) , w i l l result in a reduction in host reproductivity. No such re l a t i o n s h i p exists for Odessa. These correlations between re p r o d u c t i v i t i e s do not involve . any measure of the a b i l i t y of the spores or the seeds to germinate and survive for the next season. Similar results were obtained by Hoy, H o l l i e r and Fontenot (1985) in the smut-sugarcane system. They found a highly s i g n i f i c a n t c o r r e l a t i o n between l e v e l s of smut in f e c t i o n and sugarcane y i e l d . Smut reduced the number of healthy canes in diseased p l o t s . For Trebi the two interpretations of these data seem contradictory at f i r s t . In terms of fi t n e s s as defined in thi s study there i s no s i g n i f i c a n t c o r r e l a t i o n between fitnesses of the interacting organisms. In terms of reproductivity (a component usually referred to as fi t n e s s by pathologists and epidemiologists), the c o r r e l a t i o n i s s i g n i f i c a n t and negative. It appears that, on Odessa, selection does not favor high f i t n e s s or high aggressiveness in isolat e s ( s t a t i s t i c a l l y speaking). On Trebi, extending the d e f i n i t i o n of fit n e s s to include aspects of post-harvest s u r v i v a l which are not involved in or implied by the d e f i n i t i o n of aggressiveness or reproductivity, causes the c o r r e l a t i o n between the fitnesses of the organisms to disappear. This disappearance indicates that 74 the most aggressive pathogenic i s o l a t e s w i l l not necessarily contribute r e l a t i v e l y more to the establishment of the epidemic in the next season than less aggressive i s o l a t e s . In fact t h i s i s very weak evidence for the absence of selection in favor of the most aggressive isolates ( i e . those with the highest r e p r o d u c t i v i t i e s ) , or in favor of the least aggressive i s o l a t e s . A l l i s o l a t e s would be considered equally f i t based on the d e f i n i t i o n of fitness used in t h i s study. There are two additional, less weak, explanations for t h i s phenomenon that stem from the fact that the reproductivity and f i t n e s s correlations on Trebi are both borderline in terms of their p r o b a b i l i t i e s . It is possible that a s t a t i s t i c a l error occurred or that the tests had i n s u f f i c i e n t power to find both r values s i g n i f i c a n t . 8.5.2 TRADIT1ONAL T r a d i t i o n a l l y , two methods of measuring disease damage have been used in the Ustilago hordei-Hordeum vulgare host-parasite system and both are believed to be highly correlated with pathogen f i t n e s s . The methods are: the percent smutted plants and the percent smutted t i l l e r s . The former, at present i s the one most commonly employed. This group of models was constructed to compare and contrast the two methods and to determine their usefulness for estimating host and pathogen fitnesses (tables 34-36). The accuracy of estimating pathogen fi t n e s s on Odessa using the percentage smutted plants (R2) was poor (R2=0.57), whereas, on Trebi, i t was much better (R2=0.84). 75 The c o r r e l a t i o n between percent smutted plants (R2) and pathogen reproductivity (R12, aggressiveness) was 0.815 (P=0.0001) and 0.902 (P=0.0001) on Odessa and Trebi, respectively. In my opinion, a measure of the percent smutted plants provides a r e l i a b l e estimate of pathogen fi t n e s s on Trebi but a less r e l i a b l e one on Odessa; at least for the dikaryons involved in t h i s study. The high posi t i v e c o r r e l a t i o n between percent smutted plants and pathogen f i t n e s s was expected in l i g h t of the fact that both had s t a t i s t i c a l l y s i g n i f i c a n t genetic components. In short, nonspecific pathogenicity genes segregating in the sporidia can e l i c i t a response in pathogen f i t n e s s . Host f i t n e s s , on the other hand, i s poorly represented or estimated on both v a r i e t i e s by percent smutted plants (R2, R2<0.16). The independent variable, R4 (percent smutted t i l l e r s ) , i s marginally better than R2 as an estimator of pathogen fi t n e s s on Odessa (R2=0.600) and s l i g h t l y better on Trebi (R2=0.905). As estimators of host f i t n e s s , both R4 and R2 alone are poor (R2<0.24). The percent plants smutted (R2) and the percent heads smutted (R4) combined act as a moderately accurate predictors of host f i t n e s s (W [HOST]) on Odessa (R2=0.541). 76 8.5.3 PRACTICAL 8.5.3.J_ MINIMAL COST This series of models (tables 37-38) was developed to determine i f certain e a s i l y obtained independent variables would provide good estimates of fitness for both organisms. Five variables were c l a s s i f i e d as being e a s i l y obtainable: germination rate of treated seeds (R5), percentage smutted plants (R2), t o t a l heads in the row (R3), percentage smutted heads (R4) and the t o t a l number of t i l l e r s from diseased plants (R5). From these f i v e independent variables, only -1--- model, involving the percent of heads smutted (R4) offers an acceptable estimation of pathogen fitness on Odessa (R2=0.600). The R4 (percent of heads smutted) variable turned out to be the only independent variable involved in the "best" model for predicting pathogen f i t n e s s on Trebi (R2=0.905). Host fi t n e s s (W [HOST]) i s poorly predicted by percent of heads smutted (R4, R2=0.238) on Trebi and moderately well by the combination of percent plants smutted (R2) and percent heads smutted (R4) on Odessa (R2=0.541). 8.5.3.2 MODERATE COST The MODERATE COST models (tables 39-40) promise better estimates of fitnesses because of the inclusion of independent variables along with those already in the MINIMAL COST models. 77 Two models involving independent variables with large genetic components were noteworthy. Pathogen fitness assessed on Trebi was estimated adequately (R2=0.959) by the average number of diseased t i l l e r s per plant (R7) and the number of t i l l e r s from completely diseased plants (C2). On Odessa 79.6% of the v a r i a b i l i t y of pathogen fi t n e s s (W [PATHOGEN]) was explained by the average number of diseased heads per plant (R7). Host fi t n e s s (W [HOST]) i s poorly predicted by the percent heads smutted (R4) and the average number of diseased heads per plant (R7) on Trebi (R2=0.384). Host f i t n e s s (W [HOST]) i s predicted moderately well on Odessa (R2=0.674) by the percent plants smutted (R2), the percent heads smutted (R4) and the number of completely diseased plants (C1). 8.5.3.3 EARLY ASSESSMENT Testing the f e a s i b i l i t y of accurately estimating fitnesses with pre-harvest variables was the objective of the EARLY ASSESSMENT models (table 41). Approximately 84.0% of pathogen fi t n e s s on Trebi was accounted for by the percent smutted plants (R2). On Odessa, the percent smutted plants was attributed with producing only 57.0% of the v a r i a b i l i t y . Variable R2 can be used to estimate pathogen f i t n e s s on Trebi prior to harvest, provided that for every smutted plant at least one smutted head emerges from the boot and is e a s i l y scored. Heads emerged from the boot within a r e l a t i v e l y short span of time prior to harvest, making this method of estimating pathogen fi t n e s s a po t e n t i a l l y useful time saver. 78 Host f i t n e s s (W [HOST]) models involved independent variables with no s t a t i s t i c a l l y s i g n i f i c a n t genetic component and were considered unreliable. It i s estimated that for any growing season, between 2 to 4 weeks of time can be saved by estimating fitnesses using the predictor variables described above. 8.5.4 DEVELOPMENTAL Unidentified developmental events in the interaction between the host and the pathogen were expected to influence pathogen f i t n e s s . Developments within the infected host that leads to disease expression are not yet separable into discrete events or stages. The elucidation of physiological mechanisms is not yet possible. 8.5.4.1 C (COMPLETELY DISEASED PLANTS) OR H (HEALTHY PLANTS) BASED The average spore germination rate per t i l l e r (C7) was the only variable among those tested that made a s i g n i f i c a n t contribution to pathogen f i t n e s s on Trebi (table 42-43). This variable i s related to the l a s t step measurable in the developmental sequence of events which might have an e f f e c t of the fitnesses of the interacting organisms. Any number of events occurring between the time of inoculation and the germination of seeds from treated plants could have influenced pathogen f i t n e s s . Obviously, t h i s result does not implicate any 79 s p e c i f i c physiological event as being involved in c o n t r o l l i n g or a f f e c t i n g pathogen f i t n e s s . The fact that t h i s variable was influenced by the pathogen genotype complicates the interpretation of t h i s r e s u l t . The genetic differences among dikaryons could have been the major cause of the differences in spore germination rate. It is not clear i f physiological events i n i t i a t e d by, mediated by, or involving the host, affected spore germination rate. On Odessa, an event or events leading up to the production of spores (average spore weight per plant, C5) regulates pathogen fi t n e s s l e v e l s . The independent variable C7 was not included in the equation indicating that the germination rate of the spores i s not an important factor in determining pathogen fit n e s s on Odessa. Also, i t implies that the environment Trebi provides for the dikaryon i s d i f f e r e n t than that provided by Odessa and that the genetically heterogeneous dikaryons react very d i f f e r e n t l y to the two host environments. The conclusion reached in a previous section concerning nonspecific pathogenicity gene subsets i s supported by these r e s u l t s . No models accurately predict host f i t n e s s (W [HOST]) on either v a r i e t y . 8.5.4.2 P (PARTIALLY DISEASED PLANTS) BASED: HOST PERSPECTIVE Average seed germination rate for p a r t i a l l y diseased plants (P18), which has a large genetic component for the control of pathogen fi t n e s s on Trebi, does not provide a d e f i n i t e clue as to where, , developmentally, the genetic differences among 80 dikaryons manifest themselves (tables 44-45). On Odessa, the average number of t i l l e r s per p a r t i a l l y diseased plant (P5), which has no genetic component, suggests that some plant mediated event(s) leading to, and/or involving, the determination of t i l l e r number, af f e c t s pathogen f i t n e s s . As a universal suscept, Odessa should be attacked by a l l dikaryons. This does not imply that Odessa i s without any measure of resistance. It appears that Odessa has a barely discernable l e v e l of resistance that i s operative early, following i n f e c t i o n . It i s obvious that t h i s resistance i s weak and inconsistent, by v i r t u e of the fact that s i g n i f i c a n t l y more Odessa plants can become completely diseased than p a r t i a l l y diseased. 8.5.4.3 P (PARTIALLY DISEASED PLANTS) BASED: PATHOGEN  PERSPECTIVE Genetic differences among dikaryons for spore germination (P11), on p a r t i a l l y diseased Trebi, suggest no s p e c i f i c early events influencing pathogen f i t n e s s (tables 46-47). On Odessa variables in the model had no s t a t i s t i c a l l y s i g n i f i c a n t genetic components. No models predicting host f i t n e s s were generated. 81 8.6 "CONSTANT RANKING" Wehrhahn (1986, personal communication) believes the term "constant ranking" is not "operationally useful" because i t implies a r i g i d consistency in rank order, a d i f f i c u l t condition to f i n d in practice, e s p e c i a l l y when large confounding environmental and random error e f f e c t s are possible. In practice ranking can involve occasional rank reversals of near neighbors in an array of interacting genotypes. The term "concordant ranking" was suggested by Wehrhahn as an alte r n a t i v e for "constant ranking" because the new term, s t a t i s t i c a l l y speaking, implies the p o s s i b i l i t y of less r i g i d i t y . The term "concordant" i s derived from the f i e l d of s t a t i s t i c s ( i e . Kendall's concordant c o r r e l a t i o n ) . "Constant (concordant) ranking" i s based on the assumption that pathogen reproductivity i s negatively correlated with host reproductivity. "Constant (concordant) ranking" ignores effects of pathogen damage to the host. More pre c i s e l y , i t ignores possible attendant reduction in host reproductivity. "Constant (concordant) ranking" also ignores the possible presence of tolerance. Tolerence i s a component of horizontal resistance. A tolerant plant can sustain a certa i n l e v e l of disease and s t i l l have a high y i e l d , while a less tolerant plant can have the same l e v e l of disease and a lower y i e l d . In the barley-smut system, "constant (concordant) ranking" occurs for disease damage variables: percent smutted plants (R2), percent smutted t i l l e r s (R4) and pathogen fitness (Wc [PATHOGEN] and W [PATHOGEN]; table 48). Although the genetic 82 differences among dikaryons were not large enough to generate s i g n i f i c a n t F values on Odessa, the dikaryons s t i l l maintained a rank order that was highly correlated with that on Trebi. Current evidence for the presence of "constant (concordant) ranking" in t h i s system, supports an e a r l i e r finding, the f i r s t  ever recorded, involving a select population of T1 x T4 descendents (Person et a l . , 1983). Host f i t n e s s values (Wp [HOST], Wh [HOST] and W [HOST]) were tested for compliance with the fundamental concept of "constant (concordant) ranking". No s i g n i f i c a n t rank cor r e l a t i o n resulted. Ranking of host and pathogen fitnesses was tested on Trebi and on Odessa with negative results (table 49). Therefore, pathogen f i t n e s s rankings can not be used to rate expected host performance (fi t n e s s or y i e l d ) . This finding i s not surprising because of the poor performance of pathogen fi t n e s s values in predicting host f i t n e s s values, in the models discussed e a r l i e r . Regardless of how common polygene subsets targeting d i f f e r e n t v a r i e t i e s or s p e c i f i c gene backgrounds are, the concept of "constant (concordant) ranking" i s s t i l l v a l i d . Imagine a s i t u a t i o n where an i s o l a t e with a subset of pathogenicity polygenes can target variety A. The same i s o l a t e also might have a second subset of polygenes targeted for variety B. The genes in both subsets are not necessarily mutually exclusive. That i s , a p a r t i c u l a r gene can be a member of both subsets and can be functional and contribute to pathogenicity on both v a r i e t i e s . Under the simplifying 83 assumption of equality in magnitude of a l l e l e action, ranking of v a r i e t i e s w i l l s t i l l occur. On the other hand, i f the numbers, and d i r e c t i o n and magnitude of action of polygenes in the 2 subsets d i f f e r greatly, then the v a r i e t i e s may not display "constant (concordant) ranking". Another important issue concerning "constant (concordant) ranking", peripheral to the present study, but s t i l l an integral complication with i t s use, is the method of ranking. Jenns et a l (1982) and Jenns and Leonard (1985) also recognized that problems with ranking can occur. It i s my opinion that because of the use of phenotypic pathogenicity values (disease l e v e l values) for ranking, ranking according to nonspecific genotype is not as accurate as i t could be. This t i e s in with Wehrhahn's b e l i e f , described e a r l i e r concerning the lack of r i g i d i t y of ranking. Ranking based on additive gene e f f e c t s (breeding values) would be more appropriate. The confounding effects of superfluous genetic (dominance and/or e p i s t a t i c interaction) and nongenetic (environmental and other interaction) e f f e c t s , known to be associated with phenotypic values, would be excluded from ranking. In other words, ranking of disease phenotypes i s expected to be less accurate for assessing host or pathogen performance than ranking of additive gene e f f e c t s . This i s a novel idea to the f i e l d of host-pathogen interactions and warrants further consideration. If we expect to reduce pathogen induced host y i e l d losses with e f f e c t i v e host management strategies, a l l theoretical and p r a c t i c a l information concerning every aspect of host-parasite interactions should be made readily available to breeders. 85 9 SUMMARY The following i s a summary of the important conclusions reached and hypotheses constructed in th i s study. These conclusions and hypotheses have been divided into three groups according to the objectives described in the PURPOSE section. Conclusions and hypotheses that are new to the f i e l d of host-pathogen interactions or to the Hordeum vulgare-Ustilaqo  hordei system, in p a r t i c u l a r , are suffixed with the term "[DISCOVERY]". Conclusions that support previously reported results are suffixed with the term "[CORROBORATIVE FINDING]". 1 Some fit n e s s related variables measured on treated rows d i f f e r e d s i g n i f i c a n t l y from those measured on untreated control rows. [DISCOVERY] 1.1 Comparison of fitness related variables indicated that the two v a r i e t i e s reacted in dramatically d i f f e r e n t ways to the dikaryons. [CORROBORATIVE FINDING] 1.1.1 The t r a d i t i o n a l measure of the l e v e l of disease damage (percent plants smutted, R2) in t h i s system was found to be a r e l i a b l e estimator of pathogen f i t n e s s on Trebi and reproductivity on both v a r i e t i e s . The other less frequently used measure (percent heads smutted, R4), was a s l i g h t l y better estimator. [DISCOVERY] 1.2 Inoculation with the pathogen caused reduced host f i t n e s s in both diseased and healthy plants. [DISCOVERY] 1.2.1 A s t a t i s t i c a l l y s i g n i f i c a n t negative c o r r e l a t i o n was found between the reproductivity of the host and the pathogen on Trebi. [CORROBORATIVE FINDING] 1.2.2 There was no s i g n i f i c a n t c o r r e l a t i o n between host and pathogen fitnesses (as defined in t h i s study) on either variety. [DISCOVERY] 3 Individual selection favored neither high nor low reproductivity (aggressiveness) on Odessa. [DISCOVERY] 1.3.1 Nonspecific pathogenicity gene differences among dikaryons indicate that selection ("s") values can be calculated for these data for future modeling experiments. [DISCOVERY] 4 Modelling 1.4.1 Neither t r a d i t i o n a l method of measuring disease damage le v e l should be used to predict host f i t n e s s . [DISCOVERY] 1.4.2 Three variables, the t o t a l spore weight (R12), the number of completely diseased plants (C1) and the average spore weight per head (P10) on Trebi, and three variables on Odessa, the average number of diseased heads per plant (R7), the number of heads from completely diseased plants (C2) and the t o t a l spore weight from completely diseased plants (C4), can be used as r e l i a b l e predictors of pathogen f i t n e s s (W [PATHOGEN]). [DISCOVERY] 1.4.3 The variables, percent plants smutted (R2), percent smutted heads (R4) and the number of completely diseased plants (C1) should produce moderately accurate predictions of host fitness (W [HOST]) on Odessa. [DISCOVERY] 1.4.4 Variables c o l l e c t a b l e with minimal cost provide acceptable estimations of fitnesses. S p e c i f i c a l l y , the percent heads smutted (R4) produce accurate and moderately accurate predictions of pathogen f i t n e s s on Trebi and on Odessa (respectively). The combination of the percent plants smutted (R2) and the percent heads smutted (R4) on Odessa produce moderately accurate estimates of host f i t n e s s (W [HOST]). [DISCOVERY] 1.4.5 Variables c o l l e c t a b l e with a moderate cost provide even more accurate predictors of pathogen fi t n e s s (W [PATHOGEN]). These variables are the average number of diseased heads per plant (R7) and the t o t a l number of heads from completely diseased heads (C2) on Trebi and the average number of diseased heads per plant (R7) on Odessa. The percent plants smutted (R2), the percent heads smutted (R4) and the number of completely diseased plants (C1) are moderately accurate at predicting host fi t n e s s on Odessa (W [HOST]). [DISCOVERY] 1.4.6 Under conditions of early head emergence, a preharvest variable, percent smutted plants (R2), can be coll e c t e d to provide time saving, accurate estimates of pathogen fi t n e s s (W [PATHOGEN]) on Trebi and moderately accurate predictions on Odessa. [DISCOVERY] 1.4.7 There i s l i t t l e evidence to i d e n t i f y s p e c i f i c events during the development of both the host and the pathogen that af f e c t fitnesses on Trebi and on Odessa. [DISCOVERY] S t a t i s t i c a l l y s i g n i f i c a n t genetic differences among dikaryons were displayed for 26 variables on Trebi and for 17 variables on Odessa. These differences were attributed to segregating nonspecific pathogenicity genes with p l e i o t r o p i c e f f e c t s . [DISCOVERY] 2.1 Biometrical analyses uncovered s i g n i f i c a n t additive gene ef f e c t s for 15 variables on Trebi and 4 on Odessa. [DISCOVERY] 2.2 Si g n i f i c a n t interaction components existed for many of the fitness related variables (9 on Trebi and 12 on Odessa) indicating the importance of dominance and e p i s t a t i c interactions. [DISCOVERY] 2.3 Nonspecific genetic differences among dikaryons played an important role in c o n t r o l l i n g pathogen f i t n e s s but not host f i t n e s s . [DISCOVERY] 2.4 Environmental (replicate) differences alone, generated large amounts of v a r i a b i l i t y for 13 variables on Trebi and 24 variables on Odessa. [DISCOVERY] 2.5 D i f f e r e n t i a l variety reaction to nonspecific pathogenicity genes indicate that Ebba's parental teliospore from race 11 was probably better adapted to Trebi than to Odessa. [DISCOVERY] 2.6 It i s speculated that nonspecific pathogenicity genes in th i s b i o l o g i c a l material may be targeted to certa i n v a r i e t i e s or to s p e c i f i c resistance ( v e r t i c a l gene) backgrounds. [DISCOVERY] 2.7 A nonspecific pathogenicity gene(s), t i g h t l y linked with the mating locus was revealed (coupled with the "-" mating a l l e l e ) . [CORROBORATIVE FINDING] There i s "constant (concordant) ranking" of percent of plants smutted (R2), percent of t i l l e r s smutted (R4) and pathogen f i t n e s s (Wc [PATHOGEN] and W [PATHOGEN]) on the two v a r i e t i e s . [DISCOVERY] 3.1 "Constant (concordant) ranking" of additive gene e f f e c t s (breeding values) i s suggested. [DISCOVERY] 89 10 REFERENCES CITED Abdalla, M.M.F. and J.G.T. 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J _ . 3 BAUCH MATING TYPE TEST PLATES Same as Minimal Medium but with only 2 gm Dextrose. U_.j_.4 VOGEL' S SOLUTION (50x; VOGEL, 1956) Na2 c i t r a t e 2H20 123 gm K2HP04 250 gm NH4N03 anhyd. 100 gm MgS04.7H20 10 gm CaC12.2H20 5 gm Trace element solution 5 ml D i s t i l l e d Water 750 ml Chloroform 2 ml Heat solution and add chemicals gradually with s t i r r i n g . Store solution at room temperature in a stoppered bo t t l e . 11.1.5 TRACE ELEMENT SOLUTION C i t r i c Acid 1H20 5 gm Zn S04.7H20 5 gm Fe(NH4)2.(S04)2.6H20 1 gm CuS04.5H20 0.25 gm MnS04.1H20 0.05 gm H3B03 anhyd. 0.05 gm Na2Mo04.2H20 0.05 gm Chloroform 1 D i s t i l l e d Water 9 5 ™ J Store at 4°C in t i g h t l y stoppered b o t t l e . H-1.6 VITAMIN SOLUTION (BEADLE AND TATUM, 1945) Thiamin Riboflavin 5 0 m g Pyridoxine 5 0 m | Ca Pantothenate 200 mg p-ammo-benzoic Acid 50 m q Ni c o t i n i c Acid 2 0 0 m * Choline chloride 200 mg n 9 50 mg D i s t i l l e d Water 1 0 0 0 m f Dispense in 10 ml aliquot s . Store at -20°C. 111 JM . 2 APPENDIX B T h i s appendix c o n t a i n s a l l t a b l e s a s s o c i a t e d w i t h t h i s s t u d y . 112 TABLE 1. Compilation of variance components and estimations of h e r i t a b i l i t i e s of the percent smutted plants from experiments on pathogenicity in the smut-barley system. Bracketed values represent the percent contribution of the component to the t o t a l phenotypic variance. (Emara (1972); Emara and Sidhu (1974); Pope (1982); Caten et a l . (1984)) Vt : Vg • Va : Vna Ve : H2 : h2 : s = i = n = T1 7 T21 T23 01 7 021 023 = t o t a l variance; = genetic variance; = additive genetic variance; = nonadditive genetic variance = environmental variance; = broad sense h e r i t a b i l i t y ; = narrow sense h e r i t a b i l i t y ; selfed teliospore; inbred teliospore; natural i s o l a t e ; = teliospore from F1 from F1 from F1 from F1 from F1 from F1 teliospore teliospore teliospore teliospore teliospore dikaryotic l i n e 17 (on dikaryotic l i n e 21 (on dikaryotic l i n e 23 (on dikaryotic l i n e 17 (on dikaryotic l i n e 21 (on dikaryotic l i n e 23 (on Trebi); Trebi); Trebi); Odessa) Odessa) Odessa) TABLE 1 VARIABILITY COMPONENTS HERITABILITY RESEARCHER Vt Vg Va Vna Ve H2 h2 Emara and 301 . 69 196.76 1 32 . 52 64 . 23 104.93 0 .65 0 .44 S idhu ( 6 5 . 2 ) ( 4 3 . 9) (21 . 3) ( 3 4 . 8 ) Emara 7 2 . 1 4 36 .96 ( 5 1 . 2 ) 30 . ( 4 2 . 35 1 ) 6 . ( 9 . 61 1 ) 35 . 18 ( 4 8 . 8 ) 0 .51 0.42 Caten ( s ) 51 . 40 23 .80 27 .60 0 .46 e t a l . ( 4 6 . 0 ) ( 5 4 . 9 ) ( i ) 28 . 30 1 .50 ( 5 . 0 ) 26 .80 ( 9 5 . 0 ) 0 .05 ( i ) 31 . 60 5.90 ( 1 9 . 9 ) 25 .70 ( 8 1 . 0 ) 0 .19 (n) 35 . 00 3.20 ( 9 . 0 ) 31 .80 ( 9 1 . 0 ) 0 .09 (n ) 40 . 40 4 .10 ( 1 0 . 0 ) 36 .30 ( 9 0 . 0 ) 0 .10 (n ) 45 . 50 1 .60 ( 4 . 0 ) 43 .90 ( 9 6 . 0 ) 0 .04 (n ) 40 . 10 0.00 ( 0 . 0 ) 40 . 1 0 ( 1 0 0 . 0 ) 0 .00 Pope (T17) 63 . 12 27.63 ( 4 3 . 8 ) 19. ( 3 0 . 49 9) 8 . ( 1 2 . 1 4 9) 33. 1 4 ( 5 2 . 5 ) 0 .44 0.31 (T21 ) 79 . 72 48 .39 ( 6 0 . 7 ) 33 . (41 . 23 7) 15. ( 1 9 . 26 0) 31 .33 ( 3 9 . 3 ) 0 .61 0.42 (T23) 69 . 01 49.61 ( 7 1 . 9 ) 28 . (41 . 83 8) 20 . ( 3 0 . 78 1 ) 17.10 ( 2 4 . 8 ) 0 .72 0.42 (017) 60 . 33 38 .03 ( 6 3 . 0 ) 1 . ( 3 . 97 3) 36 . ( 5 9 . 06 8) 1 9 .62 ( 3 2 . 5 ) 0 .63 0.03 (021 ) 73 . 22 30 .60 ( 4 1 . 8 ) 17. ( 2 3 . 52 9) 13. ( 1 7 . 08 8) 35 .56 ( 4 8 . 6 ) 0 .42 0.24 (023) 57 . 59 17.23 ( 3 0 . 0 ) 8 . ( 1 4 . 13 1) 9. ( 1 5 . 10 8) 39 .05 ( 6 7 . 8 ) 0 .30 0.14 TABLE 2. Ebba's and Tapke's disease readings from s e l f i n g teliospores T1 and T4 ( i e . race 11 and 7, r e s p e c t i v e l y ) . Disease readings on Trebi and Odessa are expressed as the percentage of plants smutted. TELIOSPORE RACE TABLE 2 TREBI ODESSA SELF EBBA % TAPKE % TAPKE % 11 T1 -1 X T1-2 42 - --1 X - 4 49 - --3 X - 2 44 - --3 X - 4 43 - -1 a v g . = 44 43 39 7 T4-1 X T4-3 2 -1 X - 4 3 - --2 X - 3 2 - --2 X - 4 3 - -a v g . = 2 .5 5 34 3. Eight F1 dikaryotic l i n e (DL) disease readings for the cross between teliospores T1 and T4 on Trebi (Ebba, 1974) . TABLE 3 CROSS DL % -1 x T4 -3 1 7 37 -1 X -4 18 49 -2 x -1 19 44 -2 x -2 20 48 -3 x -3 21 49 -3 x -4 22 47 -4 x -1 23 43 -4 x -2 24 43 avg. = 45 118 TABLE 4. Description of row (R) related and fi t n e s s (W) variables. Codes and descriptions of the R subset of variables are catalogued. Most variables were measured for, or, were expressed in r e l a t i o n to, the f i r s t 50 plants scored in each row. A l l weights are in mg units. TABLE 4 CODE DESCRIPTION RVtw germination rate of the 110 treated seeds o r i g i n a l l y planted R2t percent of plants smutted R3 number of heads R4t percent of heads smutted R5 number of heads from diseased plants R6 average number of heads per plant R7 average number of diseased heads per plant R8 average number of healthy heads per plant R9 average number of heads per diseased plant R10 average number of diseased heads per diseased plant R11 average number of healthy heads per diseased plant R12 spore weight R13 average spore weight per diseased plant R14 average spore weight per diseased head R15t average spore germination rate per diseased head R16 average number of seeds per diseased plant R17 average number of seeds per plant Wp [PATHOGEN] pathogen f i t n e s s (calculated from P subset of variables) Wc [PATHOGEN] pathogen f i t n e s s (calculated from C subset of variables) W [PATHOGEN] t o t a l pathogen f i t n e s s (Wp+Wc) Wp [HOST] host f i t n e s s (calculated from P subset of variables) Wh [HOST] host fitness (calculated from H subset of variables) W [HOST] t o t a l host f i t n e s s (Wp+Wh) t = modified angular transformation w = measurement may have involved other plants in the row in addition to the f i r s t 50 ( i e . the whole row) 1 20 TABLE 5. Description of healthy plant (H) related variables. Codes and descriptions of the H subset of variables are given. Most variables were measured for, or, were expressed in r e l a t i o n to, the f i r s t 50 plants scored in each row. A l l weights are in mg units. TABLE 5 CODE DESCRIPTION H1 number of healthy plants H2 number of heads H3 average number of heads per plant H4 average number of seeds per plant H5 average number of seeds per head H6w thousand seed weight, seeds randomly selected from a l l healthy plants H7 average seed weight per plant H8 average seed weight per head H9t seed germination rate (for seeds from H6) H1 0 number of seeds t = modified angular transformation w = measurement may have involved other plants in the row in addition to the f i r s t 50 ( i e . the whole row) 122 TABLE 6. Description of completely diseased plant (C) related variables. Codes and descriptions of the C subset of variables are given. Most variables were measured for, or, were expressed in re l a t i o n to, the f i r s t 50 plants scored in each row. A l l weights are in mg units. TABLE 6 CODE DESCRIPTION Ci number of completely diseased plants C2 number of heads C3 average number of heads per plant C4 spore weight C5 average spore weight per plant C6 average spore weight per head C7t average spore germination rate per head t = modified angular transformation 1 24 TABLE 7. Description of p a r t i a l l y diseased plant (P) related variables. Codes and descriptions of the P subset of variables are given. Most variables were measured for, or, were expressed in r e l a t i o n to, the f i r s t 50 plants scored in each row. A l l weights are in mg units. CODE TABLE 7 DESCRIPTION P1 number of diseased plants with seeds P2 number of heads P3 number of diseased heads P4 number of healthy heads P5 average number of heads per plant P6 average number of diseased heads per plant P7 average number of healthy heads per plant P8 spore weight P9 average spore weight per plant P10 average spore weight per head P1 11 average spore germination rate per head P12 number of seeds P13 average number of seeds per plant P14 average number of seeds per healthy head P15 seed weight P16 average seed weight per plant P17 average seed weight per healthy head Pl8t average seed germination rate per healthy head t = modified angular transformation 126 TABLE 8. Mean v a l u e s o f t he T r e b i row (R) and f i t n e s s (W) subse t o f v a r i a b l e s . Column " T " i d e n t i f i e s t h e t r e a t m e n t number ( c = c o n t r o l ) . Columns " + " and " - " i d e n t i f y t h e p a r t i c u l a r s p o r i d i a l c o m b i n a t i o n f o r each t r e a t m e n t . 1 27 TABLE 8 ROW (R) VARIABLE ON TREBI T + - 1 2 3 4 5 6 7 8 c _ _ 47.5 111. 0 2.2 2.2 1 1 1 48.8 13.5 100. 0 11.9 5.3 2.0 0.1 1.9 2 1 2 46.9 12.2 1 15. 0 10.0 4.3 2.3 0.1 2.2 3 1 3 47.8 11.5 119. 7 8.7 11.0 2.4 0.1 2.3 4 1 4 51 .2 11.5 1 06. 7 7.8 7.0 2.2 0.1 2.1 5 1 5 49.6 16.7 111. 0 14.3 10.0 2.2 0.2 2.0 6 2 1 44.5 26.2 1 45. 3 18.4 32.0 2.9 0.3 2.6 7 2 2 45.0 4.0 1 59. 0 2.3 0.0 3.2 0.0 3.2 8 2 3 48. 1 12.0 95. 0 9.8 4.3 1.9 0.1 1.8 9 2 4 46.9 4.0 105. 0 2.8 0.0 2.1 0.0 2.1 10 2 5 49.0 15.7 93. 3 17.7 9.3 1 .8 0.2 1 .7 1 1 3 1 48.8 24.0 99. 0 20.5 16.0 2.0 0.2 1 .7 1 2 3 2 46.9 5.9 1 17. 3 4.3 0.3 2.3 0.0 2.3 13 3 3 48.9 13.9 97. 7 12.0 8.3 2.0 0.1 1.9 1 4 3 4 46.3 12.5 117. 3 8.2 2.0 2.3 0.0 2.3 15 3 5 49. 1 29.8 88. 7 27.6 20.0 1 .8 0.4 1 .4 1 6 4 1 45.7 25.2 97. 3 25.4 22.7 2.0 0.4 1 .6 17 4 2 46.2 4.0 93. 0 3.0 0.0 1.9 0.0 1 .9 18 4 3 47.6 14.7 1 14. 7 10.4 6.0 2.3 0.1 2.2 19 4 4 46.7 23.6 99. 0 20.4 14.3 2.0 0.2 1 .7 20 4 5 47.0 24.4 94. 0 22.2 14.3 1 .9 0.3 1 .6 TABLE 8 (continued) ROW (R) VARIABLE ON TREBI T + - 9 10 11 1 2 13 1 4 1 5 1 6 1 7 c 1 1 1 2.4 1 .8 0 .6 0 .6050 0. 2933 0. 1323 46. 8 1 1 . 6 62. 2 2 1 2 1 .8 1 .7 0 .2 0 .3233 0. 1 188 0. 0641 41 . 8 5. 3 76. 5 3 1 3 2.1 1 .0 1 . 1 0 .5077 0. 0929 0. 051 5 31 . 7 36. 6 75. 6 4 1 4 1 .7 0 .7 1 .0 0 .1847 0. 0424 0. 0384 32. 8 26. 4 64. 0 5 1 5 1 .3 1 .2 0 .2 1 .2057 0. 1493 0. 0860 28. 5 4. 2 71 . 9 6 2 1 2.8 1 .5 1 .3 1 .3613 0. 1 347 0. 0902 48. 1 44. 4 90. 0 7 2 2 0.0 0 .0 0 .0 0 .0 0. 0 0. 0 2. 9 0. 0 120. 1 8 2 3 1 . 1 0 .9 0 .2 0 .4180 0. 0981 0. 0704 34. 0 8. 4 51 . 3 9 2 4 0.0 0 .0 0 .0 0 .0 0. 0 0. 0 2. 9 0. 0 65. 3 10 2 5 3.1 2 .5 0 .6 0 .8490 0. 3004 0. 1 1 73 47. 1 17. 9 53. 2 1 1 3 1 2.0 1 .5 0 .5 1 .0233 0. 1 221 0. 0827 45. 7 13. 9 55. 9 12 3 2 0.3 0 .3 0 .0 0 .0057 0. 0057 0. 0057 12. 6 0. 0 68. 8 13 3 3 2.7 1 .6 1 . 1 0 .6703 0. 2155 0. 1240 47. 7 32. 5 57. 5 14 3 4 1 .0 1 .0 0 .0 0 .0737 0. 0595 0. 0595 43. 9 0. 0 66. 1 1 5 3 5 1 .6 1 .5 0 .2 2 .21 37 0. 1765 0. 1 1 96 49. 2 3. 7 44. 6 16 4 1 2.2 2 .0 0 .3 2 .6020 0. 2403 0. 1 199 44. 8 8. 2 49. 2 17 4 2 0.0 0 .0 0 .0 0 .0 0. 0 0. 0 2. 9 0. 0 48. 3 18 4 3 1 .7 1 .2 0 .5 0 .3490 0. 1 039 0. 0882 50. 9 14. 2 74. 2 19 4 4 1 .6 1 .4 0 .3 1 .3927 0. 1499 0. 1061 46. 2 9. 6 54. 6 20 4 5 1 .8 1 .6 0 . 1 1 .81 53 0. 2244 0. 1392 47. 8 4. 3 49. 1 1 29 TABLE 8 (continued) ROW (R) VARIABLE ON TREBI [PATHOGEN] [HOST] T + - Wp Wc W Wp Wh W _ — _ _ — — 1 c - - - - - — 3258 .3 3258. 3 1 1 1 0. 1 224 0. 2070 0. 3294 22. 8 2884 .6 2907. 4 2 1 2 0. 0107 0. 1 322 0. 1429 20. 1 3538 .6 3558. 7 3 1 3 0. 1 693 0. 0776 0. 2469 213. 4 3341 . 1 3554. 5 4 1 4 0. 0401 0. 0603 0. 1 004 114. 5 2840 .2 2954. 6 5 1 5 0. 0755 0. 4588 0. 5343 34. 6 3284 .7 3319. 2 6 2 1 0. 5315 0. 2455 0. 7769 541 . 5 3500 .4 4041 . 9 7 2 2 0 0 0 0 5605 .4 5605. 4 8 2 3 0. 1263 0. 1 450 0. 2712 40. 4 2322 .2 2362. 7 9 2 4 0 0 0 0 3068 .3 3068. 3 10 2 5 0. 1048 0. 3487 0. 4534 41 . 0 2474 . 1 251 5. 0 1 1 3 1 0. 1491 0. 3782 0. 5273 94. 3 251 0 .5 2604. 8 12 3 2 0 0. 0016 0. 0016 0 3201 .6 3201 . 6 13 3 3 0. 2488 0. 1358 0. 3846 108. 1 2612 .4 2720. 5 1 4 3 4 0 0. 0385 0. 0385 0 3131 .4 3131 . 4 1 5 3 5 0. 1 134 1 . 1877 1. 301 1 28. 6 2053 . 1 2081 . 7 16 4 1 0. 3425 0. 9590 1. 301 5 114. 9 2165 .6 2280. 5 17 4 2 0 0 0 0 2201 .7 2201 . 7 18 4 3 0. 1698 0. 0376 0. 2074 54. 5 3407 .9 3462. 5 1 9 4 4 0. 1 080 0. 6046 0. 7125 80. 7 2451 .8 2532. 5 20 4 5 0. 1 061 0. 8898 0. 9959 36. 3 2237 .2 2273. 6 1 30 TABLE 9. Mean v a l u e s of t he T r e b i h e a l t h y p l a n t (H) subse t o f v a r i a b l e s . Column " T " i d e n t i f i e s t h e t r e a t m e n t number ( c = c o n t r o l ) . Columns " + " and " - " i d e n t i f y t h e p a r t i c u l a r s p o r i d i a l c o m b i n a t i o n f o r each t r e a t m e n t . TABLE 9 HEALTHY PLANT (H) VARIABLE ON TREBI T + - 1 2 3 4 5 6 7 c — _ 50. 0 1 1 1 . 0 2 .2 68 . 8 31 .0 4 7 . 9 3 . 2957 1 1 1 47 . 7 94 . 7 2 .0 64 . 5 31 .9 48 .8 3. 1013 2 1 2 48. 0 110. 7 2 .3 7 9 . 9 32 .8 4 8 . 4 3 . 91 47 3 1 3 47 . 7 108. 7 2 .3 76 . 6 30 .5 47 . 1 3 . 7182 4 1 4 47. 7 99 . 7 2 .1 65 . 6 30 .9 46 .0 3 . 0710 5 1 5 45 . 0 101 . 0 2 .2 78 . 9 35.2 48 .3 3. 8282 6 2 1 40. 3 113. 3 2 .8 96 . 8 34 .9 4 5 . 0 4 . 3510 7 2 2 50 . 0 159. 0 3 .2 120. 1 37.7 4 5 . 9 5. 5371 8 2 3 47. 7 90 . 7 1 .9 5 2 . 9 27 .3 4 3 . 6 2 . 2984 9 2 4 50. 0 105. 0 2 .1 6 5 . 3 30 .9 4 8 . 5 3 . 1798 10 2 5 46. 7 84 . 0 1 .8 56 . 3 31 .8 49 .8 2 . 7888 1 1 3 1 42 . 0 83 . 0 2 .0 64 . 0 32 .5 4 8 . 2 3. 0762 1 2 3 2 49. 7 1 17. 0 2 .3 69 . 0 27.3 43 .7 3 . 1487 1 3 3 3 47 . 3 8 9 . 3 1 .9 58 . 5 30.7 49 . 1 2 . 8833 1 4 3 4 48 . 0 115. 3 2 .4 69 . 1 28 .9 4 8 . 4 3 . 3459 15 3 5 37. 7 68 . 7 1 .8 56 . 8 31 .5 4 7 . 6 2 . 7195 1 6 4 1 40 . 7 74 . 7 1 .8 58 . 3 30.0 4 8 . 9 2 . 8917 17 4 2 50 . 0 93 . 0 1 .9 48 . 3 24.8 47 .0 2 . 3628 18 4 3 47 . 0 108. 7 2 .3 77 . 9 33 .5 4 8 . 0 3 . 7356 19 4 4 42 . 0 84 . 7 2 .0 61 . 4 30 .5 4 7 . 5 2 . 9857 20 4 5 41 . 7 79 . 7 1.9 57 . 4 30.2 47 . 1 2 . 7028 TABLE 9 (continued) HEALTHY PLANT (H) VARIABLE ON TREBI T + - 8 9 10 c _ _ 1.4854 76. 5 3440 .2 1 1 1 1.5490 74. 6 3082 .6 2 1 2 1.5975 75. 4 3803 . 1 3 1 3 1.4574 74. 3 3561 .4 4 1 4 1.4242 73. 2 3081 .8 5 1 5 1.7065 73. 3 3559 .8 6 2 1 1.5710 71 . 0 3920 .8 7 2 2 1.7310 75. 2 6006 .7 8 2 3 1 . 1875 72. 9 2 521 .4 9 2 4 1.5093 75. 4 3265 .0 10 2 5 1.5839 76. 1 2618 .8 1 1 3 1 1.5643 75. 0 2685 .2 12 3 2 1.2259 73. 6 3440 .6 1 3 3 3 1.5129 76. 6 2752 .7 14 3 4 1.3965 76. 1 3306 .2 1 5 3 5 1.5044 74. 2 2189 .2 16 4 1 1.4811 73. 7 2334 .5 17 4 2 1.1976 71 . 1 241 5 .0 18 4 3 1.6051 74. 6 3642 . 1 19 4 4 1.4635 74. 5 2642 .8 20 4 5 1.4180 74. 5 2415 .7 1 33 TABLE 10. Mean values of the Trebi completely diseased plant (C) subset of variables. Column "T" i d e n t i f i e s the treatment number (c=control). Columns "+" and "-" id e n t i f y the p a r t i c u l a r s p o r i d i a l combination for each treatment. 1 34 TABLE 10 COMPLETELY DISEASED PLANT (C) VARIABLE ON TREBI T + - 1 2 3 4 5 6 7 c 1 1 1 1 .0 2.0 2 .0 0 .3830 0. 3830 0. 1605 46.3 2 1 2 1 .3 3.0 2 .0 0 .2903 0. 1705 0. 0699 42.4 3 1 3 1 .0 1 .7 1 .0 0 . 1 520 0. 0810 0. 0455 32. 1 4 1 4 0.7 1 .0 0 .5 0 . 1 143 0. 0572 0. 0381 17.4 5 1 5 4.0 7.0 1 .2 1 .0540 0. 1 628 0. 0930 28. 1 6 2 1 4.3 6.3 1 .5 0 .4737 0. 1114 0. 0743 45.2 7 2 2 0.0 0.0 0 .0 0 .0 0. 0 0. 0 2.9 8 2 3 2.0 2.7 0 .8 0 .2540 0. 0756 0. 0584 33. 1 9 2 4 0.0 0.0 0 .0 0 .0 0. 0 0. 0 2.9 10 2 5 2.7 6.3 . 2 .5 0 .6583 0. 2939 0. 1 1 42 47.7 1 1 3 1 5.0 8.0 1 .6 0 .7347 0. 1433 0. 0923 46. 1 12 3 2 0.3 0.3 0 .3 0 .0057 0. 0057 0. 0057 12.6 13 3 3 1 .0 2.0 1 .2 0 .2710 0. 1593 0. 0923 32.5 1 4 3 4 2.0 2.0 1 .0 0 .0737 0. 0595 0. 0595 43.9 15 3 5 11.0 16.3 1 .5 2 .0350 0. 1776 0. 1 193 48.6 16 4 1 7.0 14.7 2 . 1 1 .9473 0. 2563 0. 1 185 44.-4 17 4 2 0.0 0.0 0 .0 0 .0 0. 0 0. 0 2.9 18 4 3 1 .7 1 .0 0 .7 0 .0930 0. 0595 0. 0470 32.9 19 4 4 6.3 9.7 1 .5 1 .21 07 0. 1733 0. 1 1 33 46.8 20 4 5 7.3 11.7 1 .7 1 .6020 0. 2321 0. 1 377 48.0 1 35 TABLE 11. Mean values of the Trebi p a r t i a l l y diseased plant (P) subset of variables. Column "T" i d e n t i f i e s the treatment number (c=control). Columns "+" and "-" id e n t i f y the pa r t i c u l a r s p o r i d i a l combination for each treatment. TABLE 11 PARTIALLY DISEASED PLANT (P) VARIABLE ON TREBI T + - 1 2 3 4 5 6 7 8 c 1 1 1 1.3 3.3 2.0 1 .3 2.7 1.7 1 .0 0.2220 2 1 2 0.7 1.3 0.7 0.7 0.7 0.3 0.3 0.0330 3 1 3 1 .3 9.3 2.7 6.7 2.3 0.7 1 .7 0.3557 4 1 4 1 .7 6.0 1 .7 4.3 2.0 0.7 1 .3 0.0703 5 1 5 1 .0 3.0 1 .7 1 .3 2.0 1 .2 0.8 0.1517 6 2 1 5.3 25.7 8.7 17.0 3.7 1 .4 2.2 0.8877 7 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 8 2 3 0.3 1.7 0.7 1.0 1 .7 0.7 1 .0 0.1640 9 2 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10 2 5 0.7 3.0 1 .7 1 .3 3.0 1.7 1 .3 0.1907 1 1 3 1 3.0 8.0 4.0 4.0 2.6 1.3 1 .3 0.2887 1 2 3 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 3 3 3 1.7 6.3 2.3 4.0 2.1 0.8 1 .3 0.3993 1 4 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 5 3 5 1 .3 3.7 1 .7 2.0 2.8 1.3 1.5 0.1787 16 4 1 2.3 8.0 4.0 4.0 2.2 1.2 0.9 0.6547 17 4 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 8 4 3 1 .3 5.3 2.7 2.7 2.4 1 .3 1 . 1 0.2560 19 4 4 1 .7 4.7 2.0 2.7 1 .9 0.8 1 .1 0.1820 20 4 5 1 .0 2.7 1 .3 1 .3 1 .7 0.8 0.8 0.2133 TABLE 11 (continued) PARTIALLY DISEASED PLANT (P) VARIABLE ON TREBI T + - 9 10 11 12 13 14 c — — — — 1 1 1 0. 2167 0. 1 1 38 2 1 2 0. 01 65 0. 0165 3 1 3 0. 0889 0. 0445 4 1 4 0. 0281 0. 0281 5 1 5 0. 0955 0. 0571 6 2 1 0. 1514 0. 1 1 25 7 2 2 0. 0 0. 0 8 2 3 0. 1 640 0. 0820 9 2 4 0. 0 0. 0 10 2 5 0. 1 907 0. 0732 1 1 3 1 0. 0848 0. 0626 12 3 2 0. 0 0. 0 1 3 3 3 0. 1 1 33 0. 081 6 14 3 4 0. 0 0. 0 1 5 3 5 0. 1 572 0. 1110 16 4 1 0. 1 658 0. 0927 17 4 2 0. 0 0. 0 18 4 3 0. 1 238 0. 0619 19 4 4 0. 061 6 0. 0469 20 4 5 0. 1080 0. 0729 47 .0 26. 0 21 .8 21 .8 13 .6 21 . 3 10 .7 10.7 16 .5 219. 3 54 .8 11.0 33 .2 118. 7 35 .7 17.2 30 .0 37. 0 20 .8 15.4 48 .4 578. 0 74 .3 32.5 2 .9 0. 0 0 .0 0.0 22 .3 42. 0 42 .0 14.0 2 .9 0. 0 0 .0 0.0 30 .9 42. 0 42 .0 20.9 44 .6 1 06. 7 38 .2 30.7 2 .9 0. 0 0 .0 0.0 34 .2 1 22. 3 38 . 1 20.2 2 .9 0. 0 0 .0 0.0 50 .7 42. 3 32 .3 22.8 33 .9 1 20. 3 24 .8 16.1 2 .9 0. 0 0 .0 0.0 36 .8 65. 0 27 .4 16.7 27 .5 86. 0 36 .2 21 .5 31 .3 39. 7 25 .8 21.2 138 TABLE 11 ( c o n t i n u e d ) PARTIALLY DISEASED PLANT (P) VARIABLE ON TREBI T + - — 15 - 16 - 17 18 c 1 1 1 1 .1557 1 .0225 1 .0225 7 3 . 2 2 1 2 1 .1010 0 .5505 0 .5505 27 .0 3 1 3 10 .9037 2 .7259 0 .5452 28 .5 4 1 4 5 .6017 1 .5947 0 .7042 4 7 . 9 5 1 5 1 .8583 1 .0038 0 .7190 44 .8 6 2 1 28 .6410 3 .6745 1 .5967 7 8 . 2 7 2 2 0 .0 0 .0 0 .0 2 .9 8 2 3 2 .4120 2 .4120 0 .8040 28 .0 9 2 4 0 .0 0 .0 0 .0 2 .9 10 2 5 2 .221 3 2 .221 3 1 .0849 53 .4 1 1 3 1 4 .6863 1 .6907 1 .3577 70 .3 1 2 3 2 0 .0 0 .0 0 .0 2 .9 1 3 3 3 5 .9787 1 .8432 0 .9659 45 .5 1 4 3 4 0 .0 0 .0 0 .0 2 .9 1 5 3 5 1 .5177 1 .281 0 0 .9253 61 .0 16 4 1 6 .4373 1 .2571 0 .7861 51 .8 17 4 2 0 .0 0 .0 0 .0 2 .9 18 4 3 3 .2487 1 .3407 0 .7955 4 8 . 5 19 4 4 4 .1373 1 .7347 1 .0343 51 .2 20 4 5 1 .8667 1 .2630 1 .0618 5 0 . 9 1 39 TABLE 12. Mean values of the Odessa row (R) and f i t n e s s (W) subset of variab l e s . Column "T" i d e n t i f i e s the treatment number (c=control). Columns "+" and "-" iden t i f y the p a r t i c u l a r s p o r i d i a l combination for each treatment. TABLE 12 ROW (R) VARIABLE ON ODESSA T + - 1 2 3 4 5 6 7 8 c _ _ 56.2 78.7 1 .6 1 .6 1 1 1 49.7 16.6 88.0 15.6 6.7 1.7 0.1 1 .6 2 1 2 50.2 18.7 79.0 16.8 7.7 1 .6 0.1 1 .4 3 1 3 50.9 11.0 82.3 10.1 3.3 1.7 0.1 1 .6 4 1 4 55.7 17.0 66.3 16.5 5.3 1 .3 0.1 1 .2 5 1 5 55.9 26.5 56.7 27. 1 12.7 1 . 1 0.3 0.9 6 2 1 51.1 25.0 56.7 26.6 11.7 1 . 1 0.2 0.9 7 2 2 49.8 4.0 83.3 3.2 0.0 1.7 0.0 1 .7 8 2 3 51 .5 18.2 81 .3 19.5 15.7 1 .6 0.2 1 .4 9 2 4 50.3 16.0 80.7 14.1 5.3 1 .6 0.1 1 .5 1 0 2 5 50.7 25.8 68.3 24.5 12.3 1 .4 0.2 1 . 1 1 1 3 1 45.5 26.2 63.7 27.9 17.0 1 .3 0.3 1 .0 12 3 2 51 .7 4.0 63.0 3.6 0.0 1 .3 0.0 1 .3 13 3 3 52.6 21.4 92.7 18.1 13.7 1.9 0.2 1 .6 14 3 4 50.7 22. 1 81.3 20.5 12.7 1 .6 0.2 1 .4 15 3 5 50.4 23.5 57.7 23.3 1 1 .7 1 .2 0.2 1 .0 16 4 1 50. 1 27. 1 63.7 26.7 12.3 1 .3 0.2 1 .0 1 7 4 2 53.7 4.0 64.0 3.6 0.0 1 .3 0.0 1 .3 18 4 3 51 .2 22.5 68.7 22.9 11.7 1 .4 0.2 1 .2 19 4 4 48.7 27.2 101.7 24.7 22.0 2.0 0.3 1 .7 20 4 5 50.4 25.8 76.3 25.3 20.3 1 .5 0.3 1 .2 TABLE 12 (continued) ROW (R) VARIABLE ON ODESSA T + - 9 10 11 12 13 14 15 16 17 c — — — — — — 1 1 1 1 .8 1 .6 0. 2 1 .0490 2 1 2 1 .4 1 .3 0. 2 0 .5487 3 1 3 0 .9 0 .9 0. 0 0 .4293 4 1 4 1 .3 1 .3 0. 0 0 .4797 5 1 5 1 .2 1 .2 0. 0 1 .3643 6 2 1 1 .3 1 .3 0. 0 1 .3787 7 2 2 0 .0 0 .0 0. 0 0 .0 8 2 3 2 .4 1 .9 0. 5 1 .9600 9 2 4 1 .4 1 .2 0. 2 0 . 1423 10 2 5 1 .4 1 .2 0. 1 1 .1980 1 1 3 1 1 .3 1 .3 0. 0 2 .5190 1 2 3 2 0 .0 0 .0 0. 0 0 .0 1 3 3 3 1 .6 1 .4 0. 2 1 .5870 1 4 3 4 1 .7 1 .4 0. 3 0 .8783 15 3 5 1 .3 1 . 1 0. 2 1 .4137 1 6 4 1 1 .2 1 .2 0. 1 1 .491 3 1 7 4 2 0 .0 0 .0 0. 0 0 .0 18 4 3 1 .5 1 .4 0. 1 1 .901 3 19 4 4 2 . 1 1 .6 0. 5 2 .3137 20 4 5 2 . 1 1 .8 0. 3 1 .9123 0. 2822 0. 1677 36 .0 5. 8 64. 3 0. 0940 0. 0652 40 .3 3. 6 49. 9 0. 0880 0. 0501 25 .2 0. 0 60. 2 0. 1 1 02 0. 0849 45 .7 0. 0 46. 0 0. 1246 0. 1068 41 .2 0. 0 27. 5 0. 1 508 0. 1141 46 .4 0. 0 28. 7 0. 0 0. 0 2 .9 0. 0 65. 1 0. 2822 0. 1 396 53 . 1 18. 3 49. 8 0. 0338 0. 0278 43 .8 6. 2 57. 3 0. 1310 0. 1 066 49 .6 6. 7 39. 1 0. 1911 0. 1 439 54 .7 1 . 1 32. 3 0. 0 0. 0 2 .9 0. 0 37. 7 0. 1 907 0. 1 340 49 .9 6. 6 64. 0 0. 1 045 0. 0744 46 .3 9. 5 51 . 5 0. 1702 0. 1 492 47 .3 6. 7 33. 5 0. 1630 0. 1340 48 .8 0. 9 36. 4 0. 0 0. 0 2 .9 0. 0 41 . 9 0. 2434 0. 1709 52 .0 3. 4 42. 8 0. 2181 0. 1 340 51 . 1 16. 0 60. 4 0. 2413 0. 1 300 45 .8 10. 8 39. 9 1 42 TABLE 12 ( c o n t i n u e d ) ROW (R) VARIABLE ON ODESSA [PATHOGEN] [HOST] T + : Wp Wc W Wp Wh W c — _ — — _ 2755. 5 2755. 5 1 1 1 0. 1558 0 .2506 0. 4064 18. 8 3109. 0 31 27. 7 2 1 2 0. 0149 0 .2258 0. 2407 20 . 2 2388. 1 2408. 4 3 1 3 0 0 .2207 0. 2207 0 2905. 1 2905. 1 4 1 4 0 0 .2512 0. 251 2 0 221 3. 4 221 3. 4 5 1 5 0 0 .5769 0. 5769 0 1269. 2 1269. 2 6 2 1 0 0 .7497 0. 7497 0 1413. 9 1413. 9 7 2 2 0 0 0 0 3093. 2 3093. 2 8 2 3 0. 5466 0 .6780 1 . 2247 120. 6 2220. 9 2341 . 5 9 2 4 0. 01 22 0 .0500 0. 0622 21 . 7 2750. 4 2772. 1 10 2 5 0 . 1007 0 .6006 0. 701 2 51 . 0 1827. 7 1878. 6 1 1 3 1 0 . 1313 1 .5212 1 . 6526 17. 0 1 541 . 8 1558. 8 12 3 2 0 0 0 0 1766. 5 1766. 5 1 3 3 3 0 . 3621 0 .6796 1 . 041 7 85 . 6 2907. 1 2992. 7 14 3 4 0. 0377 0 .4582 0. 4959 50 . 4 2450. 6 2501 . 0 1 5 3 5 0 . 0873 0 .7513 0. 8386 77 . 4 1528. 8 1606. 2 16 4 1 0. 0596 0 .7868 0. 8464 4 . 6 1 755 . 1 1759. 7 17 4 2 0 0 0 0 1 941 . 9 1 941 . 9 18 4 3 0. 0958 1 .1014 1 . 1 972 27 . 1 2038. 7 2065. 8 19 4 4 0 . 3502 1 .0542 1 . 4044 1 54 . 9 2715. 0 2869. 9 20 4 5 0 . 2459 0 .8026 1 . 0485 1 39 . 9 1744. 6 1884. 4 1 43 TABLE 13. Mean values of the Odessa healthy plant (H) subset of variables. Column "T" i d e n t i f i e s the treatment number (c=control). Columns "+" and "-" i d e n t i f y the p a r t i c u l a r s p o r i d i a l combination for each treatment. TABLE 13 HEALTHY PLANT (H> VARIABLE ON ODESSA T + - 1 2 3 4 5 6 7 c _ — 50.0 78 .7 1 .6 57 .8 37 .0 39 .2 2 .2922 1 1 1 46 .3 81 .3 1 .8 69 .0 39 .2 40 .0 2 .8382 2 1 2 45 .0 71 .3 1 .6 55 .1 34 .9 40 .0 2 .2333 3 1 3 48 .0 79 .0 1 .7 63 .0 38 .7 4 0 . 5 2 .5522 4 1 4 46 .0 61 .0 1 .3 50 .4 36 .7 40 .4 2 .0815 5 1 5 39 .7 44 .0 1 .1 34 .8 3 1 . 6 3 6 . 2 1 .2614 6 2 1 41 .0 45 .0 1 .1 34 .5 31 .5 41 .0 1 .4037 7 2 2 50 .0 83 .3 1 .7 65 . 1 38 .4 38 .5 2 .5475 8 2 3 45 .0 65 .7 1 .5 53 .2 3 6 . 2 3 8 . 9 2 . 1 139 9 2 4 46 .3 75 .3 1 .6 60 .9 37 .3 41 .4 2 .5064 10 2 5 40 .7 56 .0 1 .4 46 . 1 3 3 . 2 37 .2 1 .7308 1 1 3 1 39 .0 46 .7 1 .2 40 .2 34 .4 39 .7 1 .5872 12 3 2 50 .0 63 .0 1 .3 37 .7 29 . 1 33 .3 1 .3688 13 3 3 43 .0 79 .0 1 .9 76 .8 38 .9 41 .4 3 .2846 14 3 4 43 .0 68.7 1 .6 59 .4 36 .6 4 0 . 2 2 .4448 15 3 5 42 .0 46 .0 1 . 1 37 .6 34 .9 39 .7 1 .4876 16 4 1 39 .7 51 .3 1 .3 44 .0 33 . 1 38 .0 1 .6586 17 4 .2 50 .0 64 .0 1 .3 4 1 . 9 32 .0 37 .6 1 .61 33 18 4 3 42 .3 57 .0 1 .3 49 . 1 3 5 . 9 38 .5 1 .8938 19 4 4 39 .7 79 .7 2 .0 73 .0 36 .3 39 .5 2 .9518 20 4 5 39 .7 56 .0 1 .4 48 .2 33 .0 40 .9 2 .0286 TABLE 13 ( c o n t i n u e d ) HEALTHY PLANT (H) VARIABLE ON ODESSA T + - 8 9 10 c _ _ 1.4453 77 .0 2889. 0 1 1 1 1.5697 80 .4 3189. 8 2 1 2 1.3952 78 .4 2472. 7 3 1 3 1.5593 78 .9 3 0 1 1 . 7 4 1 4 1.4927 77 .2 2302. 0 5 1 5 1.1469 73 .5 1373. 8 6 2 1 1.2865 81 .7 1435. 6 7 2 2 1.4771 76 . 1 3255. 0 8 2 3 1.4118 76 .4 2354. 4 9 2 4 1.5416 78 .7 2841 . 2 1 0 2 5 1.2189 78 .5 1891 . 7 1 1 3 1 1.3638 78 .7 1 601 . 5 12 3 2 1.0251 7 4 . 5 1883. 3 1 3 3 3 1.6158 78 .9 31 . 5 . 5 14 3 4 1.4802 79 .8 251 4 . 9 15 3 5 1.3788 78 .5 1591 . 9 1 6 4 1 1.2579 77 .4 1813. 5 1 7 4 2 1.2183 74 .2 2093. 3 18 4 3 1.3805 8 0 . 9 2107. 8 19 4 4 1.4453 77 .6 2851 . 8 20 4 5 1.3664 76 .9 1835. 1 TABLE 14. Mean values of the Odessa completely diseased plant (C) subset of variables. Column "T" i d e n t i f i e s the treatment number (c=control). Columns "+" and "-" ide n t i f y the pa r t i c u l a r s p o r i d i a l combination for each treatment. TABLE 14 COMPLETELY DISEASED PLANT (C) VARIABLE ON ODESSA T + - 1 2 3 4 5 6 7 c 1 1 1 3 .0 4 .3 1 .6 0 .6763 0. 2535 0. 1562 36 .0 2 1 2 4 .3 6.0 1.3 0 .5097 0. 1032 0. 0671 40.3 3 1 3 2 .0 3.3 0 .9 0 .4293 0. 0880 0. 0501 25 .2 4 1 4 4 .0 5.3 1 .3 0 .4797 0 . 1 102 0. 0849 45 .7 5 1 5 10 .3 12.7 1.2 1 .3643 0. 1246 0. 1068 41 .2 6 2 1 9 .0 11.7 1 .3 1 .3787 0 . 1508 0. 1141 46 .4 7 2 2 0 .0 0.0 0 .0 0 .0 0 . 0 0 . 0 2 .9 8 2 3 3 .7 7.0 1.7 1 . 1 237 0. 2462 0. 1435 52.7 9 2 4 3 .3 4 .0 1 . 1 0 .1170 0. 0289 0. 0251 44 .6 10 2 5 8 .7 10.0 1 .2 1 .0607 0. 1 291 0. 1 058 48 .6 1 1 3 1 10 .3 15.3 1 .3 2 .3280 0. 1880 0 . 1 420 54.7 12 3 2 0 .0 0 .0 0 .0 0 .0 0 . 0 0 . 0 2 .9 13 3 3 5 .0 6.7 1.3 1 .0577 0. 1849 0 . 1401 49 .9 14 3 4 5 .7 9.7 1 .5 0 .8090 0. 1161 0. 0783 46 .5 1 5 3 5 7 .3 8 .7 1 . 1 1 .2980 0. 1 698 0. 1 481 47 .2 16 4 1 10 .0 11.7 1.2 1 .4050 0. 1 624 0. 1 306 48 .6 17 4 2 0 .0 0.0 0 .0 0 .0 0 . 0- 0. 0 2 .9 18 4 3 7 .0 9.7 1 .4 1 .7683 0 . 2531 0 . 1765 51 .8 19 4 4 8 .3 12.7 1 .5 1 .7383 0. 2067 0. 1 341 51 .3 20 4 5 7 .3 12.7 1.9 1 .5250 0. 2513 0. 1285 44 .0 148 TABLE 15. Mean values of the Trebi p a r t i a l l y diseased plant (P) subset of variables. Column "T" i d e n t i f i e s the treatment number (c=control). Columns "+" and "-" id e n t i f y the p a r t i c u l a r s p o r i d i a l combination for each treatment. TABLE 15 PARTIALLY DISEASED PLANT (P) VARIABLE ON ODESSA T + - 1 2 3 4 5 6 7 8 c 1 1 1 0 .7 2 .3 1 .7 0.7 1 .2 0.8 0 .3 0. 3727 2 1 2 0 .7 1 .7 0 .7 1.0 1 .7 0.7 1.0 0. 0390 3 1 3 0 .0 0 .0 0 .0 0 .0 0 .0 0.0 0 .0 0. 0 4 1 4 0.0 0 .0 0 .0 0 .0 0 .0 0.0 0 .0 0. 0 5 1 5 0.0 0.0 0 .0 0 .0 0 .0 0 .0 0 .0 0. 0 6 2 1 0.0 0 .0 0 .0 0.0 0 .0 0 .0 0 .0 0. 0 7 2 2 0.0 0.0 0 .0 0.0 0 .0 0 .0 0 .0 0. 0 8 2 3 1 .3 8 .7 5 .0 3.7 3 .8 1.9 1 .9 0. 8363 9 2 4 0 .3 1 .3 0 .7 0.7 1 .3 0.7 0 .7 0. 0253 10 2 5 0.7 2 .3 1 .0 1 .3 1 .2 0 .5 0 .7 0. 1 373 1 1 3 1 0.7 1 .7 1 .0 0 .7 1 .7 1 .0 0 .7 0. 1910 12 3 2 0 .0 0 .0 0 .0 0 .0 0 .0 0 .0 0 .0 0 . 0 1 3 3 3 2 .0 7.0 3 .7 3.3 1 .2 0 .6 0 .6 0. 5293 14 3 4 1 .3 3 .0 1 .3 1 .7 2 .3 1 .0 1 .3 0. 0693 15 3 5 0 .7 3 .0 0 .7 2 .3 1 .5 0.3 1.2 0. 1 157 16 4 1 0 .3 0.7 0 .3 0 .3 0 .7 0.3 0 .3 0. 0863 17 4 2 0.0 0 .0 0 .0 0 .0 0 .0 0.0 0 .0 0. 0 18 4 3 0.7 2 .0 1 .0 1.0 2 .0 1 .0 1 .0 0. 1 330 19 4 4 2 .0 9.3 4 . 3 5.0 3.1 1 .4 1 .7 0. 5753 20 4 5 3.0 7.7 3 .3 4 .3 1 .7 0.7 0 .9 0. 3873 150 TABLE 15 ( c o n t i n u e d ) PARTIALLY DISEASED PLANT (P) VARIABLE ON ODESSA T + - 9 10 1 1 1 2 13 14 c 1 1 1 0. 1863 0. 0745 15. 4 23 .3 1 1 . 7 1 1 .7 2 1 2 0. 0390 0. 0390 26 . 7 22 .0 2 2 . 0 1 3 .2 3 1 3 0. 0 0. 0 2 . 9 0 .0 0 . 0 0 .0 4 1 4 0. 0 0. 0 2 . 9 0 .0 0 . 0 0 .0 5 1 5 0. 0 0. 0 2 . 9 0 .0 0 . 0 0 .0 6 2 1 0 . 0 0. 0 2 . 9 0 .0 0 . 0 0 .0 7 2 2 0 . 0 0 . 0 2 . 9 0 .0 0 . 0 0 .0 8 2 3 0. 2908 0. 0765 34 . 0 1 37 .0 68 . 6 24 .3 9 2 4 0. 0253 0. 01 27 16. 6 24 .7 24 . 7 1 2 .3 10 2 5 0. 0687 0. 0458 21 . 5 60 .7 30 . 3 1 5 .2 1 1 3 1 0. 1910 0. 1205 36 . 5 17 .3 17. 3 17 .3 12 3 2 0. 0 0. 0 2 . 9 0 .0 0 . 0 0 .0 13 3 3 0. 0882 0. 0481 20 . 5 93 .0 15. 5 9 .3 1 4 3 4 0. 0527 0. 0527 45 . 2 61 .0 46 . 0 38 .5 15 3 5 0 . 0578 0. 0578 2 2 . 0 80 .7 40 . 3 1 1 .5 16 4 1 0. 0863 0. 0863 20 . 6 5 .7 5 . 7 5 .7 17 4 2 0. 0 0. 0 2 . 9 0 .0 0 . 0 0 .0 18 4 3 0. 1330 0. 0682 36 . 3 32 .7 3 2 . 7 24 .8 19 4 4 0. 1899 0. 0895 34 . 7 1 68 .0 56 . 8 22 .7 20 4 5 0. 0859 0. 0797 36 . 4 159 .3 33 . 4 23 .4 TABLE 15 ( c o n t i n u e d ) PARTIALLY DISEASED PLANT (P) VARIABLE ON ODESSA T + -— 15 — 16 1 7 18 c 1 1 1 0. 7880 0. 3940 0. 3940 23 . 1 2 1 2 0. 9540 0. 9540 0. 5630 53 . 7 3 1 3 0. 0 0. 0 0 . 0 2 . 9 4 1 4 0. 0 0. 0 0. 0 2 . 9 5 1 5 0. 0 0. 0 0. 0 2 . 9 6 2 1 0. 0 0. 0 0. 0 2 . 9 7 2 2 0. 0 0. 0 0. 0 2 . 9 8 2 3 5. 6077 2 . 81 92 0. 9978 49 . 1 9 2 4 0. 9070 0. 9070 0. 4535 25 . 1 10 2 5 1 . 6727 0. 8363 0. 4182 24 . 0 1 1 3 1 0. 6260 0. 6260 0. 6260 55 . 5 1 2 3 2 0. 0 0 . 0 0 . 0 2 . 9 1 3 3 3 3. 8843 0. 6474 0. 3884 26 . 3 1 4 3 4 2. 1 137 1 . 5147 1 . 2593 65 . 3 15 3 5 3. 4253 1 . 7127 0. 4893 27 . 8 1 6 4 1 0. 1 780 0. 1780 0. 1780 23 . 5 17 4 2 0. 0 0. 0 0. 0 2 . 9 18 4 3 1 . 2853 1 . 2853 0. 9920 44 . 6 19 4 4 7. 0423 2 . 41 14 0. 9646 50 . 2 20 4 5 6. 4520 1 . 3514 0. 9469 48 . 0 152 TABLE 16. Single sample t test results between treatment and control means on Trebi (T). Means of select variables were tested for the p r o b a b i l i t y of s t a t i s t i c a l l y s i g n i f i c a n t difference from mean control values. S i g n i f i c a n t differences between means are shown by an asterisk in the "SIG" column. Absence of an asterisk indicates no s i g n i f i c a n t difference between the means. (Ttab(a=.05(1),df=19) = 1 .729)) [P] = [PATHOGEN], [H] = [HOST] TABLE 16 VARIABLE CONTROL VARIABLE MEAN SE MEAN T c a l c SIG TR1 4 7 . 6 0.36 4 7 . 5 0. 138 TR3 108.4 3.89 111.0 0.669 TR6 2.18 0.077 2 .2 0 .326 TR8 2.03 0.891 2 . 2 1 .963 * TWh[H] 2941 .6 172.95 3258 .3 1 .831 * TW[H] 3018 .9 176.74 3258 .3 1 .354 TH 1 45 .85 0.814 50 .0 5. 106 * TH2 99 . 1 4 .34 111.0 2 .753 * TH3 2 .2 0.77 2 .2 1 .650 TH4 68 .8 3.61 68 .8 0.220 TH5 3 1 . 2 0.64 31 .0 0.298 TH6 4 7 . 4 0.38 4 7 . 9 1 .479 TH7 3.2820 0.16265 3.2957 0.084 TH8 1.4843 0.32460 1 .4854 0.033 TH9 92 .8 0.31 94 .7 6.129 * TH1 0 3162.3 186.18 3440 .2 1 .493 TC3 1 .2 0. 16 2 .2 6.441 * TP5 1 .7 0.25 2 .2 2 .014 * TP1 2 8 3 . 3 28 .25 3440 .2 118.829 * TP 1 3 2 6 . 2 4 .44 68 .8 9.595 * TP1 4 14.6 2 .22 31 .0 7.376 * TP1 6 1.2808 0.22167 3.2957 9.090 * TP1 7 0.6977 0.10460 1 .4854 7.530 * TP 1 8 4 5 . 9 7.17 94 .7 6.806 * 154 TABLE 17. Single sample t test results between treatment and control means on Odessa (0). Means of select variables were tested for the pro b a b i l i t y of s t a t i s t i c a l l y s i g n i f i c a n t difference from mean control values. S i g n i f i c a n t differences between means are shown by an asterisk in the "SIG" column. Absence of an asterisk indicates no s i g n i f i c a n t difference between the means. (Ttab(a=.05(1),df=19)=1.729)) [P] = [PATHOGEN], [H] = [HOST] TABLE 17 VARIABLE CONTROL VARIABLE MEAN SE MEAN T c a l c SIG 0R1 51 .0 0 .50 56 .2 10.413 * OR 3 73 .8 2 .77 78 .7 1 .781 * OR6 1 .5 0 .06 1 .6 2 . 198 * OR8 1 .3 0 .06 1.6 5 .222 * OWh[H] 2179.1 127.20 2755.5 4 .532 * OW[H] 2218 .5 128.69 2755.5 4 .173 * OH1 43.8 .82 50.0 7 .555 * OH 2 63 .7 2 .87 78 .7 5.231 * OH 3 1 .5 0 .06 1.6 2 .394 * OH 4 52 .0 2 .81 57.8 2 .063 * OH 5 35 . 1 0 .62 37 .0 3 .096 * OH 6 39. 1 0.43 39.2 0. 1 27 OH 7 2.0794 0. 12594 2.2922 1 .689 OH 8 1.3816 0.03332 1.4453 1.912 * OH 9 9 5 . 6 0 .35 95 .3 0 .857 OHIO 2232.7 125.18 2889.0 5.243 * OC3 1 . 1 0 .12 1 .6 3.913 * OP5 1 .2 2 .42 1 .6 1 .779 * OP1 2 44 .27 12.12 2889.0 234.674 * OP1 3 20 .3 4 .58 57.8 8 .205 * OP14 11.5 2 .42 37.0 10.518 * OP16 0.7819 0. 18488 2.2922 8 . 169 * OP17 0.4336 0.09083 1 .4453 11.139 * OP 18 31 .1 6 .25 95.3 10.272 * TABLE 18. One-way ANOVA and Duncan's multiple range test for select variables measured on Trebi (T). The pro b a b i l i t y of s t a t i s t i c a l l y s i g n i f i c a n t differences among variable means was calculated. The "TEST" label in the source column represents the among means source of v a r i a b i l i t y . If s i g n i f i c a n t differences exist between one variable mean and at least one of the other two, an asterisk i s found in the "SIG" column. Means were grouped with Duncan's multiple range test and were assigned an alphabetic character in the "GROUPING" column. Means not d i f f e r i n g s i g n i f i c a n t l y have the same alphabetic character. TABLE 18 ANOVA ( V a r i a b l e s TH1,TC1 and TP1) SOURCE DF SS MS F PR > F SIG TEST 2 25549.733333 12774.866670 1546.95 0.0001 ERROR 57 470.711111 8.258089 TOTAL 59 26020.444444 DUNCAN'S MULTIPLE RANGE TEST VARIABLE GROUPING MEAN N TH1 A 45.833 20 TC1 B 2 .933 20 TP1 B 1.233 20 TABLE 18 ( c o n t i n u e d ) ANOVA ( V a r i a b l e s TH2,TC2 and TP2) SOURCE DF SS MS F PR > F SIG TEST 2 118671.670370 59335.835150 390.50 0.0001 ERROR 57 8661.061111 151.948440 TOTAL 59 127332.731481 DUNCAN'S MULTIPLE RANGE TEST VARIABLE GROUPING MEAN N TH2 A 99 .033 20 TC2 B 4 .783 20 TP2 B 4 .600 20 TABLE 18 ( c o n t i n u e d ) ANOVA ( V a r i a b l e s TH3,TC3 and TP5) SOURCE DF SS MS F PR > F SIG TEST 2 9.82059259 4.91029630 7.27 0 .0015 * ERROR 57 38.49294444 0.67531481 TOTAL 59 48.31353704 DUNCAN'S MULTIPLE RANGE TEST VARIABLE GROUPING MEAN N TH3 A 2.1417 20 TC3 A 1.6850 20 TP5 B 1.1517 20 160 TABLE 19. Correlated groups t test results measured on Trebi (T). The p r o b a b i l i t y of a s t a t i s t i c a l l y s i g n i f i c a n t difference between paired scores of certain variables was calculated. A s i g n i f i c a n t difference between paired scores was shown with an asterisk in the "SIG" column. (N=20,Ttab(a=.05(2),df=l9)=+/~ 2.093)) [P] = [PATHOGEN], [H] = [HOST] TABLE 19 PAIRED MEAN VARIABLES N DIFFERENCE SE T c a l c SIG TR7 vs TR8 20 - 1 . 9 0. 1 1 - 1 7 . 7 2 * TH2 vs TP4 20 96 .3 4 . 55 21 .18 * TH3 vs TP7 20 1 .3 0. 18 7.11 * TH4 vs TP 1 3 20 42 .6 5. 94 7.18 * TH5 vs TP14 20 16.5 2 . 16 7 .66 * TH7 vs TP 1 6 20 2.0013 0. 29042 6 .89 * TH8 vs TP 1 7 20 0.7866 0. 10270 7.66 * TH9 vs TP 18 20 35 .5 5. 65 6 .29 * TH1 0 vs TP1 2 20 2968.4 236 . 02 12.58 * TC2 vs TP3 20 2 .9 1 . 05 2 .77 * TC3 vs TP6 20 0.4 0 . 1 1 3 .12 * TC4 vs TP8 20 0.3553 0. 1 3741 2 .59 * TC5 vs TP9 20 0.0418 0. 01 553 2 .69 * TC6 vs TP1 0 20 0.0191 0. 00668 2 .86 * TC7 vs TP1 1 20 7.1 2 . 95 2 .41 * TP3 vs TP4 20 - 0 . 8 0. 47 -1 .79 TP6 vs TP7 20 - 0 . 1 0. 09 -1 .07 TWp[P] vs TWc[P] 20 - 0 . 1 7 4 8 0. 07533 - 2 . 3 2 * TWp[H] vs TWh[H] 20 - 2 8 5 8 . 9 177. 14 - 1 6 . 1 4 * 1 62 TABLE 20 . One-way ANOVA and Duncan ' s m u l t i p l e range t e s t f o r s e l e c t v a r i a b l e s measured on Odessa ( 0 ) . The p r o b a b i l i t y o f s t a t i s t i c a l l y s i g n i f i c a n t d i f f e r e n c e s among v a r i a b l e means was c a l c u l a t e d . The "TEST" l a b e l i n t h e sou rce column r e p r e s e n t s t h e among means source o f v a r i a b i l i t y . I f s i g n i f i c a n t d i f f e r e n c e s e x i s t between one v a r i a b l e mean and a t l e a s t one o f t h e o t h e r t w o , an a s t e r i s k i s f o u n d i n t h e "S IG" c o l u m n . Means were g rouped w i t h Duncan 's m u l t i p l e range t e s t and were a s s i g n e d an a l p h a b e t i c c h a r a c t e r i n t h e "GROUPING" c o l u m n . Means no t d i f f e r i n g s i g n i f i c a n t l y have t h e same a l p h a b e t i c c h a r a c t e r . TABLE 20 ANOVA ( V a r i a b l e s OH1,OC1 and OP1) SOURCE DF SS MS F PR > F SIG TEST 2 22339.300000 11169.650000 1248.84 0.0001 ERROR 57 509.811111 8.944054 TOTAL 59 22849.111111 2.990661 DUNCAN'S MULTIPLE RANGE TEST VARIABLE GROUPING MEAN N OH1 A 43 .817 20 OC1 B 5.467 20 OP1 C 0.717 20 TABLE 20 ( c o n t i n u e d ) ANOVA (VARIABLES OH2,OC2 and OP2) SOURCE DF SS MS F PR > F SIG TEST 2 46065.525925 23032.762960 336.74 0.0001 ERROR 57 3898.777777 68.399610 TOTAL 59 49964.303703 DUNCAN'S MULTIPLE RANGE TEST VARIABLE GROUPING MEAN N OH2 A 63.667 20 OC2 B 7.567 20 OP2 B 2.533 20 TABLE 20 ( c o n t i n u e d ) ANOVA ( V a r i a b l e s OH3,OC3 and OP5) SOURCE DF SS MS F PR > F SIG TEST 2 1.21737037 0.60868519 1.15 0.3231 ERROR 57 30.10816667 0.52821345 TOTAL 59 31.32553704 DUNCAN'S MULTIPLE RANGE TEST VARIABLE GROUPING MEAN N OH3 A 1.4517 20 OC3 A 1.1600 20 OP5 A 1.1400 20 166 TABLE 21. Correlated groups t test results measured on Odessa (0). The pro b a b i l i t y of a s t a t i s t i c a l l y s i g n i f i c a n t difference between paired scores of certain variables was calculated. A s i g n i f i c a n t difference between paired scores was shown with an asterisk in the "SIG" column. (N=20,Ttab(a=.05(2),df=19)=+/- 2.093)) [P] = [PATHOGEN], [H] = [HOST] TABLE 21 PAIRED MEAN VARIABLES N DIFFERENCE SE T c a l c SIG 0R7 vs OR8 20 -1 . 1 0. 07 - 1 5 . 85 * OH 2 vs OP4 20 62 . 4 2 . 91 21 . 42 * OH 3 vs OP7 20 0. 8 0. 13 6. 53 * OH 4 vs OP1 3 20 31 . 8 4 . 87 6. 51 * OH 5 vs OP1 4 20 23 . 6 2. 42 9 . 75 * OH 7 vs OP1 6 20 1 . 2975 0. 20050 6. 47 * OH 8 vs OP1 7 20 0. 9480 0. 08970 10. 57 * OH 9 vs OP18 20 51 . 0 4 . 69 10. 89 * OH1 0 vs OP1 2 20 2081 . 4 130. 76 15. 92 * OC2 vs OP3 20 6. 3 1 . 00 6. 39 * OC3 vs OP6 20 0. 6 0. 10 5. 75 * OC4 vs OP8 20 0. 7785 0. 13729 5. 67 * OC5 vs OP9 20 0. 0636 0. 01311 4 . 85 * OC6 vs OP1 0 20 0. 0540 0. 00847 6. 38 * OC7 vs OP1.1 20 19. 8 3. 03 6. 55 * OP3 vs OP4 20 - 0 . 1 0 . 1 4 -o . 48 OP6 vs OP7 20 -o . 1 0. 06 -1 . 03 OWp[P] vs OWc[P J 20 - 0 . 4263 0. 08265 - 5 . 1 6 * OWp[H] vs OWh[H] 20 - 2 1 4 7 . 5 1 3 1 . 61 - 1 6 . 32 * 168 TABLE 22. Analysis of variance of R and fit n e s s (W) variables on Trebi (T). Sources of v a r i a b i l i t y include three main e f f e c t s components; plus sporidia (+), minus sporidia (-), and replicates (rep); as well as a l l possible second order interactions; s p o r i d i a l interactions (+x-), and two types of sporidia r e p l i c a t e interactions (+xrep, and -xrep). The t h i r d order interaction component (+x-xrep) was redefined as the error component. Degrees of freedom, mean squares, F and pseudo-F values were calculated. It was necessary to calculate pseudo-F values for the three main ef f e c t s components because of the absence of suitable denominator mean squares. Components with s t a t i s t i c a l l y s i g n i f i c a n t F values (alpha=.05) have an asterisk in the "SIG" column. The r e l a t i v e contribution of each component to t o t a l v a r i a b i l i t y (% VAR) was determined using the following expected mean squares table: EMS + EMS-EMSrep EMS+x-EMS+xrep EMS-xrep EMSerror Verror Verror Verror Verror Verror + 3V+x-3V+x-+ 5V+xrep + 15V+ • ^» ~ + 4V-xrep + 12V-+ 5V+xrep + 4V-xrep + 20Vrep + 3V+x-+ 5V+xrep Verror + 4V-xrep Verror EMS = expected mean square V = variance TABLE 22 VARIABLE TR1 GERMINATION RATE OF THE 110 TREATED SEEDS ORIGINALLY PLANTED # SOURCE DF MS F SIG % VAR 1 + 3 17.31305556 2 .25 7.1 2 - 4 11.06266667 1.28 2 .6 3 r e p 2 77.72866667 5 .39 * 26 .7 4 + x - 12 5.03777778 .64 0 5 + x r e p 6 6.14222222 .79 0 6 - x r e p 8 9.72679167 1.24 3.6 7 e r r o r 24 7.82423611 59 .9 VARIABLE TR2 PERCENT OF PLANTS SMUTTED # SOURCE DF MS F SIG % VAR 1 + 3 132.56861111 1 .27 2 .3 2 - 4 527.62775000 5.56 * 38 .5 3 r e p 2 165.37016667 3.78 7.3 4 + x - 1 2 88.26819444 2 .95 19.6 5 + x r e p 6 39.66727778 1 .32 2 .0 6 - x r e p 8 11.96225000 .40 0 7 e r r o r 24 29.95852778 30 .3 VARIABLE TR3 NUMBER OF HEADS # SOURCE DF MS 1 + 3 2 - 4 3 r e p 2 4 + x - 12 5 + x r e p 6 6 - x r e p 8 7 e r r o r 24 F SIG % VAR 1125.11111111 1.30 3 .0 916.01666667 1.24 2 .8 2599.80000000 4 .50 * 14.1 923.52777778 1.53 12.0 409.04444445 .68 0 303.09166667 .50 0 605.16944444 68 .2 TABLE 22 ( c o n t i n u e d ) VARIABLE TR4 PERCENT OF HEADS SMUTTED # SOURCE DF MS F SIG % VAR 1 + 3 134.50733333 • 1 .66 4 .8 2 - 4 527.75275000 7.10 * 43 .6 3 r e p 2 93.73216667 2 .83 4 . 4 4 + x - 1 2 67.23219444 2 .17 13.2 5 + x r e p 6 32.62150000 1 .05 .4 6 - x r e p 8 11 .45737500 .37 0 7 e r r o r 24 30.96448611 33 .7 VARIABLE TR5 NUMBER OF HEADS FROM DISEASED PLANTS # SOURCE DF MS 1 + 3 2 - 4 3 r e p 2 4 + x - 12 5 + x r e p 6 6 - x r e p 8 7 e r r o r 24 F SIG % VAR 39.13333333 .65 0 578.19166667 2 .58 19.4 619.81666667 3 .95 * 15.0 143.14722222 1.52 9.2 63.28333333 .67 0 117.50416667 1.25 3.3 94.02638889 53 .1 VARIABLE TR6 AVERAGE NUMBER OF HEADS PER PLANT # SOURCE DF MS F SIG % VAR 1 + 3 .41022222 1.24 2 .3 2 - 4 .39225000 1.32 3 .6 3 r e p 2 1.03516667 4 .50 * 13.6 4 + x - 12 .37258333 1.44 10.3 5 + x r e p 6 .16738889 .65 0 6 - x r e p 8 .12037500 .47 0 7 e r r o r 24 .25870833 7 0 . 2 TABLE 22 ( c o n t i n u e d ) VARIABLE TR7 AVERAGE NUMBER OF DISEASED HEADS PER PLANT # SOURCE DF MS F • SIG % VAR 1 + 3 .02775111 1 .49 3.2 2 - 4 .12836667 4 .49 * 34.0 3 r e p 2 .05952667 4 .31 * 10.2 4 + x - 1 2 .02022889 1 .88 1 1 .9 5 + x r e p 6 .00557111 .52 0 6 - x r e p 8 .01075167 1 .00 0 7 e r r o r 24 .01077389 40 .7 VARIABLE TR8 AVERAGE NUMBER OF HEALTHY HEADS PER PLANT # SOURCE DF MS F SIG % VAR 1 + 3 .63794444 1 .65 6 .3 2 - 4 .76141667 1 .89 10.8 3 r e p 2 .71266667 2.91 8 .6 4 + x - 12 .35863889 1 .66 13.4 5 + x r e p 6 .15977778 .74 0 6 - x r e p 8 .15954167 .74 0 7 e r r o r 24 .21609722 60 .9 VARIABLE TR9 AVERAGE NUMBER OF HEADS PER DISEASED PLANT # SOURCE DF MS F SIG % VAR 1 + 3 .70444444 .58 0 2 - 4 6.44666667 2 .55 21 .3 3 r e p 2 2.98716667 1.75 4 .8 4 + x - 12 1.75722222 1.84 15.2 5 + x r e p 6 1.11027778 1.16 1.7 6 - x r e p 8 1.14716667 1.20 2 .7 7 e r r o r 24 .95638889 54.3 TABLE 22 ( c o n t i n u e d ) VARIABLE TR10 AVERAGE NUMBER OF DISEASED HEADS PER DISEASED PLANT # SOURCE DF MS F SIG % VAR 1 + 3 .28416667 .56 0 2 - 4 3.44766667 2 .82 * 25 .1 3 r e p 2 .18150000 1.08 .3 4 + x - 12 1.02722222 2.50 * 24 .9 5 + x r e p 6 .20750000 .50 0 6 - x r e p 8 .34129167 .83 0 7 e r r o r 24 .41118056 49 .8 VARIABLE TR11 AVERAGE NUMBER OF HEALTHY HEADS PER DISEASED PLANT # SOURCE DF MS F SIG % VAR 1 + 3 .33814444 .85 0 2 - 4 .97423917 1.57 7 .9 3 r e p 2 1.75704000 2.43 12.0 4 + x - 12 .41792917 1.08 1.9 5 + x r e p 6 .43588445 1.12 1.8 6 - x r e p 8 .44748792 1.15 2 .8 7 e r r o r 24 .38831292 73 .7 VARIABLE TR12 SPORE WEIGHT # SOURCE DF MS F SIG % VAR 1 + 3 1.57609766 2 .35 6 .5 2 - 4 4.91638560 4 .29 * 25 .8 3 r e p 2 2.23564712 3.94 * 8 .1 4 + x - 12 .79862030 .93 0 5 + x r e p 6 .23776090 .28 0 6 - x r e p 8 .54646264 .64 0 7 e r r o r 24 .85448630 59 .7 TABLE 22 ( c o n t i n u e d ) VARIABLE TR13 AVERAGE SPORE WEIGHT PER DISEASED PLANT # SOURCE DF MS F SIG % VAR 1 + 3 .00484705 .90 0 2 - 4 .07684853 3.61 * 2 6 . 4 3 r e p 2 .00773068 1.36 1.4 4 + x - 12 .01538425 1.03 .7 5 + x r e p 6 .00663535 .44 0 6 - x r e p 8 .01002367 .67 0 7 e r r o r 24 .01496982 7 1 . 5 VARIABLE TR14 AVERAGE SPORE WEIGHT PER DISEASED HEAD # SOURCE DF MS F SIG % VAR 1 + 3 .00316823 1.42 2 .5 2 - 4 .01973971 4 .82 * 34 .1 3 r e p 2 .00385515 2 .32 4 .1 4 + x - 12 .00290201 1.23 4 . 2 5 + x r e p 6 .00099843 .42 0 6 - x r e p 8 .00167958 .71 0 7 e r r o r 24 .00235890 55 .1 VARIABLE TR15 AVERAGE SPORE GERMINATION RATE PER DISEASED HEAD # SOURCE DF MS F SIG % VAR 1 + 3 503.79066667 .97 0 2 - 4 1926.41108333 2.56 2 5 . 4 3 r e p 2 310.32516667 1.41 1.5 4 + x - 12 568.66997222 4.61 * 36 .3 5 + x r e p 6 75.22516667 .61 0 6 - x r e p 8 231.81808333 1.88 6 .6 7 e r r o r 24 123.32113889 30 .1 TABLE 22 ( c o n t i n u e d ) VARIABLE TR16 AVERAGE NUMBER OF SEEDS PER DISEASED PLANT # SOURCE DF MS F SIG % VAR 1 + 3 271.52061111 .81 0 2 - 4 954.36983333 1 .50 7.0 3 r e p 2 1808.96550000 2 .52 12.3 4 + x - 12 441.67672222 1 .05 1 .4 5 + x r e p 6 413.24994445 .99 0 6 - x r e p 8 471.96383333 1.13 2 .4 7 e r r o r 24 418.81022222 76 .8 VARIABLE TR17 AVERAGE NUMBER OF SEEDS PER PLANT # SOURCE DF MS F SIG % VAR 1 + 3 1430.05483333 1 .78 6 .9 2 - 4 879.81641667 1 .25 2.8 3 r e p 2 2593.72516667 4 .99 * 15.0 4 + x - 12 809.04775000 1 .49 10.6 5 + x r e p 6 299.95050000 .55 0 6 - x r e p 8 329.17954167 .61 0 7 e r r o r 24 543.44904167 64 .8 VARIABLE TWp [PATHOGEN] PATHOGEN FITNESS (CALCULATED FROM P SUBSET OF VARIABLES) # SOURCE DF MS F SIG % VAR 1 + 3 .01665366 1.33 1.5 2 - 4 .15645464 1.94 12.2 3 r e p 2 .15823547 2 .13 8.1 4 + x - 12 .02720927 .61 0 5 + x r e p 6 .01887408 .42 0 6 - x r e p 8 .07610722 1.71 11.8 7 e r r o r 24 .04442487 66 .4 175 TABLE 22 ( c o n t i n u e d ) VARIABLE TWc [PATHOGEN] PATHOGEN FITNESS (CALCULATED FROM C SUBSET OF VARIABLES) # SOURCE DF MS F SIG % VAR 1 + 3 .38715007 2 .08 7.3 2 - 4 .97784340 5.23 * 3 0 . 6 3 r e p 2 .15266710 2 .58 3 .5 4 + x - 12 . 17482332 1 .39 6.7 5 + x r e p 6 .07180571 .57 0 6 - x r e p 8 .03645610 .29 0 7 e r r o r 24 .12611129 51 .9 VARIABLE TW [PATHOGEN] TOTAL PATHOGEN FITNESS (Wp [PATHOGEN] + Wc [PATHOGEN]) # SOURCE DF MS F SIG % VAR 1 + 3 .43708659 2 .34 6 .4 2 - 4 1.40965597 4 .35 * 26 .6 3 r e p 2 .59755085 4 .31 * 8.0 4 + x - 1 2 .23556903 1 .02 .3 5 + x r e p 6 .05059528 .22 0 6 - x r e p 8 .14175317 .61 0 7 e r r o r 24 .23157868 58 .5 VARIABLE TWp [HOST] HOST FITNESS (CALCULATED FROM P SUBSET OF VARIABLES) # SOURCE DF MS F SIG % VAR 1 + 3 18083.99743427 .81 0 2 - 4 66032.07611782 1 .27 3.8 3 r e p 2 153248.09576362 3.12 13.5 4 + x - 1 2 44277.72421213 1.18 4 .7 5 + x r e p 6 23927.03935402 .64 0 6 - x r e p 8 37174.06968837 .99 0 7 e r r o r 24 37470.31685946 78 .0 TABLE 22 ( c o n t i n u e d ) VARIABLE TWh [HOST] HOST FITNESS (CALCULATED FROM H SUBSET OF VARIABLES) # SOURCE DF MS F SIG % VAR 1 + 3 2597099.20169904 1 .63 5 .7 2 - 4 2111770.22994770 1 .29 3 .6 3 r e p 2 5260971.73765141 4 .34 * 1 4'. 6 4 + x - 1 2 1637807.68537828 1 .50 10.9 5 + x r e p 6 622548.47648524 .57 0 6 - x r e p 8 840503.07338025 .77 0 7 e r r o r 24 1089905.48815647 65 .2 VARIABLE TW [HOST] TOTAL HOST FITNESS (Wp [HOST] + Wh [HOST]) # SOURCE DF MS F SIG % VAR 1 + 3 3002297. 28216660 1 .80 6 .6 2 - 4 1870533. 04314847 1 .26 2 .9 3 r e p 2 6196930. 11705426 5.66 * 16.2 4 + x - 12 1749705. 85335652 1 .42 9 .1 5 + x r e p 6 6 0 7 6 5 1 . 50908114 .49 0 6 - x r e p 8 705162. 13728359 .57 0 7 e r r o r 24 1233359. 50130055 65 .2 177 TABLE 23. Analysis of variance of H variables on Trebi (T). Sources of v a r i a b i l i t y include three main e f f e c t s components; plus sporidia (+), minus sporidia (-), and repl i c a t e s (rep); as well as a l l possible second order interactions; s p o r i d i a l interactions (+x-), and two types of sporidia r e p l i c a t e interactions (+xrep, and -xrep). The t h i r d order interaction component (+x-xrep) was redefined as the error component. Degrees of freedom, mean squares, F and pseudo-F values were calculated. It was necessary to calculate pseudo-F values for the three main ef f e c t s components because of the absence of suitable denominator mean squares. Components with s t a t i s t i c a l l y s i g n i f i c a n t F values (alpha=.05) have an asterisk in the "SIG" column. The r e l a t i v e contribution of each component to t o t a l v a r i a b i l i t y (% VAR) was determined using the following expected mean square table: EMS+ EMS-EMSrep EMS+x-EMS+xrep EMS-xrep EMSerror Verror Verror Verror Verror Verror + 3V+x-+ 3V+x-+ 5V+xrep + 15V+ ~ + 4V-xrep + 12V-+ 5V+xrep + 4V-xrep + 20Vrep + 3 V + x -3V+x + 5V+xrep Verror + 4V-xrep Verror EMS = expected mean square V = variance TABLE 23 VARIABLE TH1 NUMBER OF HEALTHY PLANTS # SOURCE DF MS F SIG % VAR 1 + 3 3 1 . 7 1 1 1 1 1 1 1 1 .39 3.3 2 - 4 1 08 . 16666667 4 .29 * 32.0 3 r e p 2 40.81666667 4 .22 * 8.1 4 + x - 12 22.26666667 2 .55 * 19.3 5 + x r e p 6 6.72777778 .77 0 6 - x r e p 8 5.00416667 .57 0 7 e r r o r 24 8.72083333 37 .3 VARIABLE TH2 NUMBER OF HEADS # SOURCE DF MS F SIG % VAR 1 + 3 1411.44444444 1.54 5.5 2 - 4 2235.77500000 2 .00 14.1 3 r e p 2 1312.71666667 1.77 4 .9 4 + x - 12 789.31944444 1.64 12.7 5 + x r e p 6 442.29444445 .92 0 6 - x r e p 8 570.92500000 1.18 2 .7 7 e r r o r 24 482.66944444 60.1 VARIABLE TH3 AVERAGE NUMBER OF HEADS PER PLANT # SOURCE DF MS F SIG % VAR 1 + 3 .40994444 1.18 2 .0 2 - 4 .40041667 1.21 2 .9 3 r e p 2 .79216667 2 .86 10.4 4 + x - 12 .35286111 1.53 12.7 5 + x r e p 6 .19061111 .83 0 6 - x r e p 8 .16716667 .72 0 7 e r r o r 24 .23061111 72 .0 TABLE 23 ( c o n t i n u e d ) VARIABLE TH4 AVERAGE NUMBER OF SEEDS PER PLANT # SOURCE DF MS F SIG % VAR 1 + 3 1012.02911111 1.29 2.8 2 - 4 522.91125000 .92 0 3 r e p 2 2956.13316667 5.23 * 16.8 4 + x - 12 879.60980556 1.50 11.6 5 + x r e p 6 355.77361111 .61 0 6 - x r e p 8 321.83462500 .55 0 7 e r r o r 24 584.85034722 68 .8 VARIABLE TH5 AVERAGE NUMBER OF SEEDS PER HEAD # SOURCE DF MS F SIG % VAR 1 + 3 29.56194444 .89 0 2 - 4 11.33025000 .47 0 3 r e p 2 232.23316667 12.42 * 41 .4 4 + x - 12 29.54791667 4 .55 * 29 .0 5 + x r e p 6 10.98694445 1.69 3 .4 6 - x r e p 8 8.24020833 1.27 1.6 7 e r r o r 24 6.49695833 24 .5 VARIABLE TH6 THOUSAND SEED WEIGHT, SEEDS RANDOMLY SELECTED FROM ALL HEALTHY PLANTS # SOURCE DF MS F SIG % VAR 1 + 3 4 .46905556 .77 0 2 - 4 6.73641667 .78 0 3 r e p 2 150.89116667 13.70 * 46 .8 4 + x - 12 10.67919444 1.50 7 .6 5 + x r e p 6 4 .35938889 .61 0 6 - x r e p 8 7.16929167 1.01 .1 7 e r r o r 24 7.09723611 45 .4 TABLE 23 ( c o n t i n u e d ) VARIABLE TH7 AVERAGE SEED WEIGHT PER PLANT # SOURCE DF MS F SIG % VAR 1 + 3 1 .81376015 1.18 1 .6 2 - 4 .97111379 .92 0 3 r e p 2 10.64086477 7.20 * 24 .2 4 + x - 12 1 .86823488 1 .28 6.3 5 + x r e p 6 .90255312 .62 0 6 - x r e p 8 .77737790 .53 0 7 e r r o r 24 1 .46140572 67 .9 VARIABLE TH8 AVERAGE SEED WEIGHT PER HEAD # SOURCE DF MS F SIG % VAR 1 + 3 .04737899 1.19 1 .0 2 - 4 .04000611 1 .00 .0 3 r e p 2 1.01391597 14.50 * 37 . 1 4 + x - 12 .08017736 .94 0 5 + x r e p 6 .03134368 .37 0 6 - x r e p 8 .04444303 .52 0 7 e r r o r 24 .08516773 61 .8 VARIABLE TH9 SEED GERMINATION RATE (FOR SEEDS FROM H6) # SOURCE DF MS F SIG % VAR 1 + 3 5.46727778 .70 0 2 - 4 3.43858333 .66 0 3 r e p 2 65.65216667 3 .05 19.2 4 + x - 12 7.94880556 .85 0 5 + x r e p 6 13.17594445 1 .41 5 .9 6 - x r e p 8 11.38820833 1 .22 3 .9 7 e r r o r 24 9.32143056 71 .0 TABLE 23 ( c o n t i n u e d ) VARIABLE TH10 NUMBER OF SEEDS # SOURCE DF MS F SIG % VAR 1 + 3 3126059. 88711178 1 .63 6.1 2 - 4 2460746. 50558384 1 .27 3 .6 3 r e p 2 5351548. 64216757 3 .63 * 12.9 4 + x - 1 2 1864419. 80002758 1 .54 11.8 5 + x r e p 6 792952. 22861079 .65 0 6 - x r e p 8 1016804. 55070808 .84 0 7 e r r o r 24 1211146. 45298617 65 .7 182 TABLE 24. Analysis of variance of C variables on Trebi (T). Sources of v a r i a b i l i t y include three main e f f e c t s components; plus sporidia ( + ), minus sporidia (-), and repl i c a t e s (rep); as well as a l l possible second order interactions; s p o r i d i a l interactions (+x-), and two types of sporidia r e p l i c a t e interactions (+xrep, and -xrep). The t h i r d order interaction component (+x-xrep) was redefined as the error component. Degrees of freedom, mean squares, F and pseudo-F values were calculated. It was necessary to calculate pseudo-F values for the three main ef f e c t s components because of the absence of suitable denominator mean squares. Components with s t a t i s t i c a l l y s i g n i f i c a n t F values (alpha=.05) have an asterisk in the "SIG" column, The r e l a t i v e contribution of each component to t o t a l v a r i a b i l i t y (% VAR) was determined using the following expected mean squares table: EMS + EMS-EMSrep EMS+x-EMS+xrep EMS-xrep EMSerror Verror + 3V+x- + 5V+xrep + 15V+ Verror + 3V+x- + 4V-xrep + 12V-Verror + 5V+xrep + 4V-xrep + 20Vrep Verror + 3V+x-Verror + 5V+xrep Verror + 4V-xrep Verror EMS = expected mean square V = variance TABLE 24 VARIABLE TC 1 NUMBER OF COMPLETELY DISEASED PLANTS # SOURCE DF MS F SIG % VAR 1 + 3 31 .42222222 2.24 9.3 2 - 4 66.18333333 4 .69 * 32 .3 3 r e p 2 7.31666667 3.15 * 2.9 4 + x - 12 13.78333333 2.67 * 19.8 5 + x r e p 6 2 .53888889 .49 0 6 - x r e p 8 1 .42083333 .27 0 7 e r r o r 24 5.17083333 35 .7 VARIABLE TC2 NUMBER OF HEADS # SOURCE DF MS F SIG % VAR 1 + 3 70.59444444 2.03 7.0 2 - 4 199.56666667 4 .93 * 3 4 . 6 3 r e p 2 39.21666667 3.88 * 4 . 9 4 + x - 12 35.84444444 2 .37 * 16.8 5 + x r e p 6 6.32777778 .42 0 6 - x r e p 8 7 .65416667 .51 0 7 e r r o r 24 15.09861111 36 .7 VARIABLE TC3 AVERAGE NUMBER OF HEADS PER PLANT # SOURCE DF MS F SIG % VAR 1 + 3 .35261111 .59 0 2 - 4 3 .85391667 2.62 21 .6 3 r e p 2 .59116667 1.46 1.8 4 + x - 12 1.25191667 2 .09 20 .5 5 + x r e p 6 .37094445 .62 0 6 - x r e p 8 .44429167 .74 0 7 e r r o r 24 .59795833 56 .2 TABLE 24 ( c o n t i n u e d ) VARIABLE TC4 SPORE WEIGHT # SOURCE DF MS F SIG % VAR 1 + 3 1 .39219033 5.11 * 9 .6 2 - 4 3.37456973 12.46 * 28 .4 3 r e p 2 3.53597800 10.34 * 17.4 4 + x - 1 2 .14230979 .31 0 5 + x r e p 6 .22092943 .48 0 6 - x r e p 8 .16569707 .36 0 7 e r r o r 24 .46309152 44 .6 VARIABLE TC5 AVERAGE SPORE WEIGHT PER PLANT # SOURCE DF MS F SIG % VAR 1 + 3 .01728456 1 .09 .8 2 - 4 .08472522 2.82 * 21 .3 3 r e p 2 .01455143 1.13 .8 4 + x - 12 .02050058 1 .00 0 5 + x r e p 6 .01413103 .69 0 6 - x r e p 8 .01685805 .82 0 7 e r r o r 24 .02053559 77 .2 VARIABLE TC6 AVERAGE SPORE WEIGHT PER HEAD # SOURCE DF MS F SIG % VAR 1 + 3 .00365424 1 .02 .2 2 - 4 .02042098 4 .07 * 34. 1 3 r e p 2 .00646162 1 .92 4 . 9 4 + x - 1 2 .00329793 1 .70 10.9 5 + x r e p 6 .00218005 1.12 1 . 1 6 - x r e p 8 .00219975 1.13 1.5 7 e r r o r 24 .00194486 47 .2 185 TABLE 24 ( c o n t i n u e d ) VARIABLE TC7 AVERAGE SPORE GERMINATION RATE PER HEAD # SOURCE DF MS F SIG % VAR 1 + 3 310.81661111 .60 0 2 - 4 1809.78641667 2.42 21 .9 3 r e p 2 . 364.28816667 1.18 1.0 4 + x - 12 596.97063889 3.20 * 30 .7 5 + x r e p 6 237.68461111 1.27 2.3 6 - x r e p 8 227.69004167 1.22 2.3 7 e r r o r 24 186.62676389 41 .9 186 TABLE 25. Analysis of variance of P variables on Trebi (T). Sources of v a r i a b i l i t y include three main e f f e c t s components; plus sporidia (+), minus sporidia (-), and repl i c a t e s (rep); as well as a l l possible second order interactions; s p o r i d i a l interactions (+x-), and two types of sporidia replicate interactions (+xrep, and -xrep). The t h i r d order interaction component (+x-xrep) was redefined as the error component. Degrees of freedom, mean squares, F and pseudo-F values were calculated. It was necessary to calculate pseudo-F values for the three main effects components because of the absence of suitable denominator mean squares. Components with s t a t i s t i c a l l y s i g n i f i c a n t F values (alpha=.05) have an asterisk in the "SIG" column. The r e l a t i v e contribution of each component to t o t a l v a r i a b i l i t y (% VAR) was determined using the following expected mean squares table: EMS+ EMS-EMSrep EMS+x-EMS+xrep EMS-xrep EMSerror Verror + 3V+x- + 5V+xrep + 15V+ Verror + 3V+x- + 4V-xrep + 12V-Verror + 5V+xrep + 4V-xrep + 20Vrep Verror + 3V+x-Verror + 5V+xrep Verror + 4V-xrep Verror EMS = expected mean square V = variance TABLE 25 VARIABLE TP 1 NUMBER OF DISEASED PLANTS WITH SEEDS # SOURCE DF MS F SIG % VAR 1 + 3 .02222222 .46 0 2 - 4 1 3.43333333 2.41 18.0 3 r e p 2 16.11666667 3.89 * 16.1 4 + x - 1 2 3.24444444 1 .52 8 .8 5 + x r e p 6 1 .47222222 .69 0 6 - x r e p 8 3.22083333 1 .51 6 .5 7 e r r o r 24 2.13194444 50 .6 VARIABLE TP2 NUMBER OF HEADS # SOURCE DF MS F SIG % VAR 1 + 3 16.84444444 .71 0 2 - 4 208.80833333 1 .62 8 .5 3 r e p 2 358.35000000 3.13 13.9 4 + x - 1 2 83.99722222 1.11 2 .6 5 + x r e p 6 46.86111111 .62 0 6 - x r e p 8 91.87083333 1.21 3.8 7 e r r o r 24 75.82638889 71 .3 VARIABLE TP3 NUMBER OF DISEASED HEADS # SOURCE DF MS F SIG % VAR 1 + 3 1.08333333 .72 0 2 - 4 35.27500000 2 .22 16.3 3 r e p 2 37.61666667 2 .74 12.0 4 + x - 12 7.37500000 .98 0 5 + x r e p 6 4.55000000 .61 0 6 - x r e p 8 11.88750000 1.59 9 .2 7 e r r o r 24 7.48750000 6 2 . 5 TABLE 25 ( c o n t i n u e d ) VARIABLE TP4 NUMBER OF HEALTHY HEADS # SOURCE DF MS F SIG % VAR 1 + 3 10.99444444 .74 0 2 - 4 73.85833333 1.38 5.2 3 r e p 2 163.81666667 3.27 14.3 4 + x - 12 42.48055556 1.13 3 .4 5 + x r e p 6 23.06111 1 1 1 .61 0 6 - x r e p 8 38.48333333 1.03 .5 7 e r r o r 24 37.50555556 76 .7 VARIABLE TP5 AVERAGE NUMBER OF HEADS PER PLANT # SOURCE DF MS F SIG .% VAR 1 + 3 .48238889 .66 0 2 - 4 14.02391667 3.95 * 24 .0 3 r e p 2 9.01550000 1.93 6.5 4 + x - 12 1.61447222 .55 0 5 + x r e p 6 3.52172222 1.20 2 .7 6 - x r e p 8 2.67404167 .91 0 7 e r r o r 24 2.92776389 66.7 VARIABLE TP6 AVERAGE NUMBER OF DISEASED HEADS PER PLANT # SOURCE DF MS F SIG % VAR 1 + 3 .12977778 .98 0 2 - 4 3.82975000 7.27 * 29 .7 3 r e p 2 .56816667 1.46 1.9 4 + x - 12 .31241667 .41 0 5 + x r e p 6 .59394445 .78 0 6 - x r e p 8 .31900000 .42 0 7 e r r o r 24 .76200000 68 .5 TABLE 2 5 ( c o n t i n u e d ) VARIABLE TP7 AVERAGE NUMBER OF HEALTHY HEADS PER PLANT # SOURCE DF MS F SIG % VAR 1 + 3 .17298833 .53 0 2 - 4 3.46061833 2.18 13.4 3 r e p 2 5. 12148167 2.31 11.6 4 + x - 12 .77145500 .81 0 5 + x r e p 6 1.36824167 1 .43 5 .5 6 - x r e p 8 1 .25847333 1 .32 5.1 7 e r r o r 24 .95688333 64 .4 VARIABLE TP8 SPORE WEIGHT # SOURCE DF MS F SIG % VAR 1 + 3 .03656099 1 .57 2 .0 2 - 4 .48579490 2 .06 12.7 3 r e p 2 .50772827 2 .53 9 .3 4 + x - 12 .07926974 .57 0 5 + x r e p 6 .03240491 .23 0 6 - x r e p 8 .22345354 1 .61 10.0 7 e r r o r 24 .13881002 65 .9 VARIABLE TP9 AVERAGE SPORE WEIGHT PER PLANT # SOURCE DF MS F SIG % VAR 1 + 3 .00238528 1 .38 1 .6 2 - 4 .05836304 4 .07 * 20 .3 3 r e p 2 .00666079 1.01 0 4 + x - 12 .00450695 .24 0 5 + x r e p 6 .01068481 .57 0 6 - x r e p 8 .01441916 .77 0 7 e r r o r 24 .01863158 78 .1 TABLE 25 ( c o n t i n u e d ) VARIABLE TP 10 AVERAGE SPORE WEIGHT PER HEAD # SOURCE DF MS F SIG % VAR 1 + 3 .00004321 1 .36 1 .3 2 - 4 .01865939 4 .16 * 22 .3 3 r e p 2 .00371102 1 .36 1 .7 4 + x - 12 .00150026 .30 0 5 + x r e p 6 .00221715 .44 0 6 - x r e p 8 .00418991 .84 0 7 e r r o r 24 .00501163 7 4 . 6 VARIABLE TP11 AVERAGE SPORE GERMINATION RATE PER HEAD # SOURCE DF MS F SIG % VAR 1 + 3 128. 72994444 .56 0 2 - 4 2719. 19875000 5.85 * 33 .3 3 r e p 2 749. 96016667 1 .59 3.3 4 + x - 12 359. 02230556 .96 0 5 + x r e p 6 535. 59927778 1 .43 5.0 6 - x r e p 8 170. 02537500 .45 0 7 e r r o r 24 375. 05143056 58 .4 VARIABLE TP12 NUMBER OF SEEDS # SOURCE DF MS F SIG % VAR 1 + 3 18501 . 55555556 .80 0 2 - 4 75857. 20833333 1 .28 4 .0 3 r e p 2 173688. 46666667 3.17 13.6 4 + x - 12 49894. 37500000 1.18 4 .8 5 + x r e p 6 25883. 48888889 .61 0 6 - x r e p 8 42259. 00833333 1 .00 . 1 7 e r r o r 24 42105. 97500000 7 7 . 6 TABLE 25 ( c o n t i n u e d ) VARIABLE TP 13 AVERAGE NUMBER OF SEEDS PER PLANT # SOURCE DF MS F SIG % VAR 1 + 3 339.11438889 .60 0 2 - 4 3088.60308333 1 .75 9 .4 3 r e p 2 5729.90016667 2.33 12.4 4 + x - 1 2 853.77786111 .84 0 5 + x r e p 6 1400.94972222 1 .38 4 . 9 6 - x r e p 8 1494.56245833 1 .47 7.7 7 e r r o r 24 1017.43923611 65 .5 VARIABLE TP14 AVERAGE NUMBER OF SEEDS PER HEALTHY HEAD # SOURCE DF MS F SIG % VAR 1 + 3 9.54866667 .51 0 2 - 4 935.39891667 4 .50 * 24 .0 3 r e p 2 431 .23266667 1 .97 5.1 4 + x - 12 179.09547222 .85 0 5 + x r e p 6 251.19000000 1.19 2 .5 6 - x r e p 8 75.96204167 .36 0 7 e r r o r 24 21 1 .86326389 68 .4 VARIABLE TP 15 SEED WEIGHT # SOURCE DF MS F SIG % VAR 1 + 3 51.09973451 .83 0 2 - 4 186.98489757 1.27 3.8 3 r e p 2 430.33494527 3.08 13.5 4 + x - 12 121.30637884 1.17 4 . 5 5 + x r e p 6 65.28459505 .63 0 6 - x r e p 8 108.18709952 1.05 .9 7 e r r o r 24 103.42683238 77 .4 TABLE 25 ( c o n t i n u e d ) VARIABLE TP 16 AVERAGE SEED WEIGHT PER PLANT # SOURCE DF MS F SIG % VAR 1 + 3 1 .40939377 .71 0 2 - 4 7.71249796 1 .65 8 .2 3 r e p 2 14.93139679 2.17 11.5 4 + x - 12 1.99044076 .74 0 5 + x r e p 6 3.79408857 1 .40 5.3 6 - x r e p 8 4.32460045 1 .60 9.8 7 e r r o r 24 2.70237471 65 .2 VARIABLE TP17 AVERAGE SEED WEIGHT PER HEALTHY HEAD # SOURCE DF MS F SIG % VAR 1 + 3 .01921060 .54 0 2 - 4 2.08465134 4 .14 * 21 .0 3 r e p 2 1.17580962 1 .87 5.1 4 + x - 12 .39450527 .70 0 5 + x r e p 6 .68476766 1 .22 3.0 6 - x r e p 8 .24494189 .43 0 7 e r r o r 24 .56345846 70 .8 VARIABLE TP18 AVERAGE SEED GERMINATION RATE PER : HEALTHY : HEAD # SOURCE DF MS F SIG % VAR 1 + 3 365.70061111 .67 0 2 - 4 6346.54150000 5.91 * 31.2 3 r e p 2 2050.60266667 1 .77 4 .1 4 + x - 12 784.38727778 .78 0 5 + x r e p 6 1264.16711111 1 .26 3.2 6 - x r e p 8 459.46037500 .46 0 7 e r r o r 24 1001.24731944 61 .5 193 TABLE 26. Analysis of variance of R and fit n e s s (W) variables on Odessa (0). Sources of v a r i a b i l i t y include three main ef f e c t s components; plus sporidia (+), minus sporidia (-), and repl i c a t e s (rep); as well as a l l possible second order interactions; s p o r i d i a l interactions (+x-), and two types of sporidia r e p l i c a t e interactions (+xrep, and -xrep). The t h i r d order interaction component (+x-xrep) was redefined as the error component. Degrees of freedom, mean squares, F and pseudo-F values were calculated. It was necessary to calculate pseudo-F values for the three main ef f e c t s components because of the absence of suitable denominator mean squares. Components with s t a t i s t i c a l l y s i g n i f i c a n t F values (alpha=.05) have an asterisk in the "SIG" column. The r e l a t i v e contribution of each component to t o t a l v a r i a b i l i t y (% VAR) was determined using the following expected mean squares table: EMS+ EMS-EMSrep EMS+x-EMS+xrep EMS-xrep EMSerror Verror Verror Verror Verror Verror Verror Verror + 3V+x-+ 3V+x-+ 5V+xrep + 3V+x-+ 5V+xrep + 4V-xrep + 5V+xrep + 15V+ + 4V-xrep + 12V-+ 4V-xrep + 20Vrep EMS = expected mean square V = variance TABLE 26 VARIABLE 0R1 GERMINATION RATE OF THE 110 TREATED SEEDS ORIGINALLY PLANTED # SOURCE DF MS F SIG % VAR 1 + 3 14.67927778 1 .23 1 .4 2 - 4 14.48541667 .45 0 3 r e p 2 87.51616667 2.14 11.3 4 + x - 1 2 16.04441667 1 .43 6 .9 5 + x r e p 6 4.93994445 .44 0 6 - x r e p 8 41 .27616667 3.67 * 32 .2 7 e r r o r 24 1 1 .23216667 48 .2 VARIABLE OR2 PROPORTION OF PLANTS SMUTTED # SOURCE DF MS F SIG % VAR 1 + 3 40 . 21822222 .69 0 2 - 4 583. 17650000 5.00 * 33 .3 3 r e p 2 637. 79850000 14.54 * 25 .3 4 + x - 1 2 89 . 56350000 2.91 * 15.9 5 + x r e p 6 12. 83405556 .42 0 6 - x r e p 8 33. 15787500 1 .08 .5 7 e r r o r 24 30 . 74287500 25 .0 VARIABLE OR3 NUMBER OF HEADS # SOURCE DF MS F SIG % VAR 1 + 3 31 .51 0 2 - 4 746.64166667 .93 0 3 r e p 2 2527.01666667 2 .55 14.2 4 + x - 12 510.70833333 1.16 3 .8 5 + x r e p 6 402.75000000 .92 0 6 - x r e p 8 760.57916667 1.73 12.7 7 e r r o r 24 439.39583333 69 .3 TABLE 26 ( c o n t i n u e d ) VARIABLE OR4 PROPORTION OF HEADS SMUTTED # SOURCE DF MS F SIG % VAR 1 + 3 35.94088889 .71 0 2 - 4 638.83041667 4 .96 * 34.7 3 r e p 2 552.26616667 8.91 * 2 0 . 2 4 + x - 12 89.41630556 2 .18 * 12.4 5 + x r e p 6 18.92438889 .46 0 6 - x r e p 8 47.64179167 1.16 1 .3 7 e r r o r 24 40.94834722 31 .4 VARIABLE OR5 NUMBER OF HEADS FROM DISEASED PLANTS # SOURCE DF MS F SIG % VAR 1 + 3 104.24444444 1 .35 2 .3 2 - 4 269.93333333 2 .59 13.5 3 r e p 2 778.95000000 9 .03 * 29 .7 4 + x - 12 79.74444444 1 .26 4 . 4 5 + x r e p 6 44.32777778 .70 0 6 - x r e p 8 48.97083333 .78 0 7 e r r o r 24 63.18194444 50 . 1 VARIABLE OR6 AVERAGE NUMBER OF HEADS PER PLANT # SOURCE DF MS F SIG % VAR 1 + 3 .01172222 .50 0 2 - 4 .30141667 .95 0 3 r e p 2 1.06016667 2 .64 15.2 4 + x - 12 .20297222 1.17 3.8 5 + x r e p 6 .16972222 .97 0 6 - x r e p 8 .29704167 1.71 12.2 7 e r r o r 24 .174097.22 68 .9 TABLE 26 ( c o n t i n u e d ) VARIABLE OR7 AVERAGE NUMBER OF DISEASED HEADS PER PLANT # SOURCE DF MS F SIG % VAR 1 + 3 .0213511 1 1.13 .8 2 - 4 .08477667 2.74 * 15.9 3 r e p 2 .22232000 8.28 * 31 .3 4 + x - 12 .0203011 1 1 .30 4 .7 5 + x r e p 6 .0124711 1 .80 0 6 - x r e p 8 .01626167 1 .04 .5 7 e r r o r 24 .01557944 46 .7 VARIABLE OR8 AVERAGE NUMBER OF HEALTHY HEADS PER PLANT #. SOURCE DF MS F SIG % VAR 1 + 3 .03127778 .60 0 2 - 4 .42225000 1.12 2 .2 3 r e p 2 .47016667 1 .38 3 .9 4 + x - 1 2 . 18391 667 1 .22 4 . 9 5 + x r e p 6 .12127778 - .80 0 6 - x r e p 8 .33037500 2.18 20 .3 7 e r r o r 24 .15120833 68 .6 VARIABLE OR9 AVERAGE NUMBER OF HEADS PER DISEASED PLANT # SOURCE DF MS F SIG % VAR 1 + 3 .10977778 .48 0 2 - 4 3.36016667 3.29 * 26 .6 3 r e p 2 .89716667 3.38 * 5.5 4 + x - 12 .89505556 2.33 * 20 .8 5 + x r e p 6 .13494445 .35 0 6 - x r e p 8 .24404167 .63 0 7 e r r o r 24 .38459722 47 . 1 TABLE 26 ( c o n t i n u e d ) VARIABLE OR10 AVERAGE NUMBER OF DISEASED HEADS PER DISEASED PLANT # SOURCE DF MS F SIG % VAR 1 + 3 .12994444 .58 0 2 - 4 2.62350000 4 .58 * 35 .3 3 r e p 2 .22316667 1 .82 2 .0 4 + x - 1 2 .50550000 2.07 16.5 5 + x r e p 6 .13561111 .56 0 6 - x r e p 8 .12087500 .50 0 7 e r r o r 24 .24387500 46 .2 VARIABLE OR11 AVERAGE NUMBER OF HEALTHY HEADS PER DISEASED PLANT # SOURCE DF MS F SIG % VAR 1 + 3 .04534444 1.17 1 .3 2 - 4 .08850583 1 .08 1 .2 3 r e p 2 .26132667 4 .47 * 16.1 4 + x - 1 2 .07346250 1 .34 8 .1 5 + x r e p 6 .01186445 .22 0 6 - x r e p 8 .05890583 1 .07 1 .3 7 e r r o r 24 .05485750 71 .9 VARIABLE OR12 SPORE WEIGHT # SOURCE DF MS F SIG % VAR 1 + 3 1.70851133 1.34 2 .6 2 - 4 4 .43700396 2 .44 14.6 3 r e p 2 9.30956402 4 .64 * 21 .9 4 + x - 12 1 .06771 579 .96 0 5 + x r e p 6 1.03879182 .93 0 6 - x r e p 8 1.20947725 1.09 1.3 7 e r r o r 24 1.11266455 59 .6 TABLE 26 ( c o n t i n u e d ) VARIABLE OR 13 AVERAGE SPORE WEIGHT PER DISEASED PLANT # SOURCE DF MS F SIG % VAR 1 + 3 .00796154 .75 0 2 - 4 .06535069 3.44 * 29 .5 3 r e p 2 .00643195 .98 0 4 + x - 12 .01412462 1.64 12.3 5 + x r e p 6 .00806812 .93 0 6 - x r e p 8 .00735857 .85 0 7 e r r o r 24 .00863637 58 .2 VARIABLE OR14 AVERAGE SPORE WEIGHT PER DISEASED HEAD # SOURCE DF MS F SIG % VAR 1 + 3 .00335809 .91 0 2 - 4 .03008313 5.77 * 50.8 3 r e p 2 .00063911 .81 0 4 + x - 12 .00407365 4 .45 * 25.1 5 + x r e p 6 .00062684 .69 0 6 - x r e p 8 .00130209 1.42 2.3 7 e r r o r 24 .00091452 21 .8 VARIABLE OR15 AVERAGE SPORE GERMINATION RATE PER DISEASED HEAD # SOURCE DF MS F SIG % VAR 1 + 3 20.89422222 .18 0 2 - 4 2747.64150000 5.81 * 50 .2 3 r e p 2 .56466667 1.17 .1 4 + x - 12 451.67172222 7.28 * 33 .6 5 + x r e p 6 21.60155556 .35 0 6 - x r e p 8 31.89987500 .51 0 7 e r r o r 24 62.03093056 16.1 TABLE 26 ( c o n t i n u e d ) VARIABLE OR 16 AVERAGE NUMBER OF SEEDS PER DISEASED PLANT # SOURCE DF MS F SIG % VAR 1 + 3 63.26950000 1.19 1 .5 2 - 4 119.51608333 1 .36 4 .4 3 r e p 2 288.61316667 4 .80 * 14.9 4 + x - 12 87.45963889 1 .27 6.6 5 + x r e p 6 23.33983333 .34 0 6 - x r e p 8 51.19045833 .74 0 7 e r r o r 24 68.80684722 72 .6 VARIABLE OR 17 AVERAGE NUMBER OF SEEDS PER PLANT # SOURCE DF MS F SIG % VAR 1 + 3 120. 12111111 .71 0 2 - 4 860 . 80600000 1.11 2 .2 3 r e p 2 462 . 23450000 .87 0 4 + x - 12 402. 17000000 1.19 4 .8 5 + x r e p 6 239. 98561111 .71 0 6 - x r e p 8 673. 44137500 2 .00 18.6 7 e r r o r 24 336. 90804167 74 .4 VARIABLE OWp [PATHOGEN] PATHOGEN FITNESS (CALCULATED FROM P SUBSET OF VARIABLES) # SOURCE DF MS F SIG % VAR 1 + 3 .04023427 1 .06 .5 2 - 4 .09557794 1 .80 6.4 3 r e p 2 .16386699 2 .72 7.4 4 + x - 12 .06928482 .73 0 5 + x r e p 6 .05851552 .61 0 6 - x r e p 8 .03668752 .38 0 7 e r r o r 24 .09545937 85 .8 TABLE 26 ( c o n t i n u e d ) VARIABLE OWc [PATHOGEN] PATHOGEN FITNESS (CALCULATED FROM C SUBSET OF VARIABLES) # SOURCE DF MS F SIG % VAR 1 + 3 .67255677 1 .76 5.7 2 - 4 1.08300876 2.15 12.6 3 r e p 2 2. 10390383 3.97 * 18.3 4 + x - 12 .29429220 .98 0 5 + x r e p 6 .25714967 .86 0 6 - x r e p 8 .34795047 1.16 2 .5 7 e r r o r 24 .29931392 61 .0 VARIABLE OW [PATHOGEN] TOTAL PATHOGEN FITNESS (Wp [PATHOGEN] + Wc [PATHOGEN]) # SOURCE DF MS F SIG % VAR 1 + 3 .96751576 1 .54 4 .5 2 - 4 1.56063792 2 .20 12.3 3 r e p 2 3. 10532772 3.58 * 17.1 4 + x - 12 .44170166 .89 0 5 + x r e p 6 .51042054 1 .02 .3 6 - x r e p 8 .49659848 1 .00 0 7 e r r o r 24 .49896609 65.8 VARIABLE OWp [HOST] HOST FITNESS (CALCULATED FROM P SUBSET OF VARIABLES) # SOURCE DF MS F SIG % VAR 1 + 3 8569.43529033 1 .74 5.0 2 - 4 10383.70323932 1 .73 7.0 3 r e p 2 20464.46626115 4 .82 * 12.7 4 + x - 12 6317.10778844 1 .00 . 1 5 + x r e p 6 2233.41231140 .35 0 6 - x r e p 8 3316.96389569 .53 0 7 e r r o r 24 6294.72001496 75 .2 TABLE 26 ( c o n t i n u e d ) VARIABLE OWh [HOST] HOST FITNESS (CALCULATED FROM H SUBSET OF VARIABLES) # SOURCE DF MS F SIG % VAR 1 + 3 425748. 21119924 .82 0 2 - 4 1943631. 35600586 1.11 2.1 3 r e p 2 845109. 14484295 .76 0 4 + x - 12 863660. 96068215 1.21 5 .0 5 + x r e p 6 5 2 5 0 3 1 . 87772750 .74 0 6 - x r e p 8 1538370. 42225049 2 .15 20 .8 7 e r r o r 24 714149. 44967887 72 .0 VARIABLE OW [HOST] TOTAL HOST FITNESS (Wp [HOST] + Wh [HOST]) # SOURCE DF MS F SIG % VAR 1 + 3 325583. 74482073 .75 0 2 - 4 1946995. 42645697 1 .08 1 .7 3 r e p 2 1062041. 41176370 .87 0 4 + x - 1 2 925795. 61069632 1 .20 4 . 9 5 + x r e p 6 534066. 65325863 .69 0 6 - x r e p 8 1586665. 13315334 2 .05 19.5 7 e r r o r 24 772457. 77180752 74 .0 202 TABLE 27. Analysis of variance of H variables on Odessa (0). Sources of v a r i a b i l i t y include three main e f f e c t s components; plus sporidia (+), minus sporidia (-), and replicates (rep); as well as a l l possible second order interactions; s p o r i d i a l interactions (+x-), and two types of sporidia replicate interactions (+xrep, and -xrep). The t h i r d order interaction component (+x-xrep) was redefined as the error component. Degrees of freedom, mean squares, F and pseudo-F values were calculated. It was necessary to calculate pseudo-F values for the three main effects components because of the absence of suitable denominator mean squares. Components with s t a t i s t i c a l l y s i g n i f i c a n t F values (alpha=.05) have an asterisk in the "SIG" column. The r e l a t i v e contribution of each component to t o t a l v a r i a b i l i t y (% VAR) was determined using the following expected mean squares table: EMS+ EMS-EMSrep EMS+x-EMS+xrep EMS-xrep EMSerror Verror Verror Verror Verror Verror + 3V+x-+ 3V+x-+ 5V+xrep + 15V+ • j»• A + 4V-xrep + 12V-+ 5V+xrep + 4V-xrep + 20Vrep + 3V+x- + 5V+xrep Verror + 4V-xrep Verror EMS = expected mean square V = variance TABLE 27 VARIABLE 0H1 NUMBER OF HEALTHY PLANTS # SOURCE DF MS F SIG % VAR 1 + 3 22.95000000 1.30 1.5 2 - 4 123.89166667 3.77 * 23 .3 3 r e p 2 231.11666667 10.78 * 30 .9 4 + x - 12 20.26944444 1.66 7 .5 5 + x r e p 6 6.71666667 .55 0 6 - x r e p 8 15.86666667 1.30 2 .5 7 e r r o r 24 12.24444444 34 .3 VARIABLE OH2 NUMBER OF HEADS # SOURCE DF MS F SIG % VAR 1 + 3 143.37777778 .67 0 2 - 4 1124.79166667 1.22 4 .3 3 r e p 2 929.21666667 1.17 1.8 4 + x - 12 416.00277778 1.25 5.5 5 + x r e p 6 293.66111111 .88 0 6 - x r e p 8 782.59166667 2 .35 22 .3 7 e r r o r 24 333.20277778 66 .1 VARIABLE OH3 AVERAGE NUMBER OF HEADS PER PLANT # SOURCE DF MS F SIG % VAR 1 + 3 .01527778 .49 0 2 - 4 .35475000 .88 0 3 r e p 2 .88066667 1.96 9 .4 4 + x - 12 .23875000 1.27 6 .2 5 + x r e p 6 .17177778 .92 0 6 - x r e p 8 .37400000 2.00 16.8 7 e r r o r 24 .18733333 67 .6 TABLE 27 ( c o n t i n u e d ) VARIABLE 0H4 AVERAGE NUMBER OF SEEDS PER PLANT # SOURCE DF MS F SIG % VAR 1 + 3 46.83133333 .52 0 2 - 4 869.62900000 .98 0 3 r e p 2 945.34016667 1.12 1.4 4 + x - 12 490.78466667 1.19 4 .9 5 + x r e p 6 396.39016667 .97 0 6 - x r e p 8 809.94162500 1.97 18.3 7 e r r o r 24 410.71995833 75 .4 VARIABLE OH5 AVERAGE NUMBER OF SEEDS PER HEAD # SOURCE DF MS F SIG % VAR 1 + 3 12.30283333 .65 0 2 - 4 43.42691667 1.67 10.4 3 r e p 2 13.57800000 .86 0 4 + x - 12 20.42325000 1.67 15.3 5 + x r e p 6 17.10866667 1.40 5.5 6 - x r e p 8 12.83150000 1.05 .9 7 e r r o r 24 12.20905556 68.0 VARIABLE OH6 THOUSAND SEED WEIGHT, SEEDS RANDOMLY SELECTED FROM ALL HEALTHY PLANTS # SOURCE DF MS F SIG % VAR 1 + 3 1.38994444 .54 0 2 - 4 17.76858333 1.24 1.9 3 r e p 2 246.83750000 18.00 * 54 .2 4 + x - 12 12.37869444 1.49 6.1 5 + x r e p 6 5.58461111 .67 0 6 - x r e p 8 8.58833333 1.03 .3 7 e r r o r 24 8.31127778 37 .4 TABLE 27 ( c o n t i n u e d ) VARIABLE OH7 AVERAGE SEED WEIGHT PER PLANT # SOURCE DF MS F SIG % VAR 1 + 3 .08928833 .48 0 2 - 4 1.75870841 1.01 . 1 3 r e p 2 3.69165893 1 .85 8 .9 4 + x - 12 .97864675 1 .24 5 .5 5 + x r e p 6 .86281089 1.10 1 .3 6 - x r e p 8 1.55269703 1 .97 16.5 7 e r r o r 24 .78753073 67 .8 VARIABLE OH8 AVERAGE SEED WEIGHT PER HEAD # SOURCE DF MS F SIG % VAR 1 + 3 .02511772 .98 0 2 - 4 .13610154 1 .38 5 .5 3 r e p 2 .20194638 2.20 8 .7 4 + x - 12 .05934858 .89 0 5 + x r e p 6 .03444043 .52 0 6 - x r e p 8 .08783123 1 .32 6.3 7 e r r o r 24 .06665947 79 .5 VARIABLE OH9 SEED GERMINATION RATE (FOR SEEDS FROM H6) # SOURCE DF MS F SIG % VAR 1 + 3 2.48022222 .86 0 2 - 4 27.47941667 1 .58 6 .7 3 r e p 2 6.52866667 .85 0 4 + x - 12 12.74897222 .62 0 5 + x r e p 6 14.31688889 .69 0 6 - x r e p 8 17.69116667 .86 0 7 e r r o r 24 20.67105556 93 .3 TABLE 27 ( c o n t i n u e d ) VARIABLE OHIO NUMBER OF SEEDS # SOURCE DF MS F SIG % VAR 1 + 3 405144. 08777807 .77 0 2 - 4 2148896. 21233355 1.14 2.8 3 r e p 2 897264. 65150047 .75 0 4 + x - 12 930321 . 95555548 1 .21 5.1 5 + x r e p 6 584074. 40594429 .76 0 6 - x r e p 8 1628950. 16295821 2. 12 20 .2 7 e r r o r 24 766701 . 29018058 71 .9 207 TABLE 28. Analysis of variance of C variables on Odessa (0). Sources of v a r i a b i l i t y include (three main e f f e c t s components; plus sporidia ( + ), minus sporidia (-), and repl i c a t e s (rep); as well as a l l possible second order interactions; s p o r i d i a l interactions (+x-), and two types of sporidia r e p l i c a t e interactions (+xrep, and -xrep). The t h i r d order interaction component (+x-xrep) was redefined as the error component. Degrees of freedom, mean squares, F and pseudo-F values were calculated. It was necessary to calculate pseudo-F values for the three main ef f e c t s components because of the absence of suitable denominator mean squares. Components with s t a t i s t i c a l l y s i g n i f i c a n t F values (alpha=.05) have an asterisk in the "SIG" column. The r e l a t i v e contribution of each component to t o t a l v a r i a b i l i t y (% VAR) was determined using the following expected mean squares table: EMS+ EMS-EMSrep EMS+x-EMS+xrep EMS-xrep EMSerror Verror Verror Verror Verror Verror + 5V+xrep + 15V+ • J V - A + 4V-xrep + 12V-+ 5V+xrep + 4V-xrep + 20Vrep + 3V+x-+ 3V+x-+ 3V+x-+x _ + 5V+xrep Verror + 4V-xrep = Verror EMS = expected mean square V = variance TABLE 28 VARIABLE 0C1 NUMBER OF COMPLETELY DISEASED PLANTS # SOURCE DF MS F SIG % VAR 1 + 3 1 0 .88 0 2 - 4 107.65000000 3.42 * 24 .0 3 r e p 2 165.26666667 9.04 * 27 . 1 4 + x - 1 2 18.47222223 1 .96 10.5 5 + x r e p 6 3.53333333 .37 0 6 - x r e p 8 15.78750000 1 .67 5 .5 7 e r r o r 24 9.44305556 32 .9 VARIABLE OC2 NUMBER OF HEADS # SOURCE DF MS F SIG % VAR 1 + 3 29 . 80000000 1.12 .7 2 - 4 178. 97500000 2 .96 * 18.5 3 r e p 2 396. 86666667 8.91 * 30 .7 4 + x - 12 35 . 23055556 1 .40 5 .5 5 + x r e p 6 13. 66666667 .54 0 6 - x r e p 8 33 . 70000000 1 .34 3.5 7 e r r o r 24 25 . 13888889 41 .2 VARIABLE OC3 AVERAGE NUMBER OF HEADS PER PLANT # SOURCE DF MS F SIG % VAR 1 + 3 .18177778 .58 0 2 - 4 2 .44058333 4 .24 * 35 .8 3 r e p 2 .18050000 1.46 1.2 4 + x - 12 .50247222 2 .64 * 2 2 . 2 5 + x r e p 6 .13627778 .72 0 6 - x r e p 8 .11758333 .62 0 7 e r r o r 24 .19030556 40 .7 TABLE 28 ( c o n t i n u e d ) VARIABLE 0C4 SPORE WEIGHT # SOURCE DF MS F SIG % VAR 1 + 3 1.24118798 1.31 2 .4 2 - 4 3.30745789 3.45 * 19.1 3 r e p 2 4 . 18379995 2.31 11.2 4 + x - 12 .25063370 .38 0 5 + x r e p 6 1 .19797666 1 .84 8 .9 6 - x r e p 8 .89848249 1 .38 5 .0 7 e r r o r 24 .65186424 53 .3 VARIABLE OC5 AVERAGE SPORE WEIGHT PER PLANT # SOURCE DF MS F SIG % VAR 1 + 3 .01059327 .89 0 2 - 4 .05891309 3.46 * 3 1 . 9 3 r e p 2 .00558766 .85 0 4 + x - 12 .01185602 2 .09 17.2 5 + x r e p 6 .00638486 1.13 1.2 6 - x r e p 8 .00682236 1 .20 2 .4 7 e r r o r 24 .00567392 47 .3 VARIABLE OC6 AVERAGE SPORE WEIGHT PER HEAD # SOURCE DF MS F SIG % VAR 1 + 3 .00348274 .91 0 2 - 4 .02932797 5.10 * 48 .3 3 r e p 2 .00119590 .91 0 4 + x - 12 .00421105 5.28 * 2 7 . 2 5 + x r e p 6 .00051235 .64 0 6 - x r e p 8 .00169060 2.12 5 .3 7 e r r o r 24 .00079805 19.1 TABLE 28 ( c o n t i n u e d ) VARIABLE OC7 AVERAGE SPORE GERMINATION RATE PER HEAD # SOURCE DF MS F SIG % VAR 1 + 3 18.51172222 .18 0 2 - 4 2729.20041667 5.82 * 49 .9 3 r e p 2 1.15800000 1.23 .2 4 + x - 12 448.00852778 6.88 * 33 .0 5 + x r e p 6 21.69088889 .33 0 6 - x r e p 8 32.18716667 .49 0 7 e r r o r 24 65.14394444 16.9 21 1 TABLE 29. Analysis of variance of P variables on Odessa (0). Sources of v a r i a b i l i t y include three main e f f e c t s components; plus sporidia (+), minus sporidia (-), and replicates (rep); as well as a l l possible second order interactions; s p o r i d i a l interactions (+x-), and two types of sporidia r e p l i c a t e interactions (+xrep, and -xrep). The t h i r d order interaction component (+x-xrep) was redefined as the error component. Degrees of freedom, mean squares, F and pseudo-F values were calculated. It was necessary to calculate pseudo-F values for the three main effects components because of the absence of suitable denominator mean squares. Components with s t a t i s t i c a l l y s i g n i f i c a n t F values (alpha=.05) have an asterisk in the "SIG" column. The r e l a t i v e contribution of each component to t o t a l v a r i a b i l i t y (% VAR) was determined using the following expected mean squares table: EMS+ EMS-EMSrep EMS+x-EMS+xrep EMS-xrep EMSerror Verror Verror Verror Verror Verror Verror Verror + 3V+x-+ 3V+x-+ 5V+xrep + 3V+x-+ 5V+xrep + 4V-xrep + 5V+xrep + 4V-xrep + 15V+ + 12V-+ 4V-xrep + 20Vrep EMS = expected mean square V = variance TABLE 29 VARIABLE 0P1 NUMBER OF DISEASED PLANTS WITH SEEDS # SOURCE DF MS F SIG % VAR 1 + 3 2.72777778 1.58 4 .8 2 - 4 1.94166667 1.21 2 .4 3 r e p 2 5.81666667 3.91 * 12.5 4 + x - 12 1.96388889 1.14 3 .6 5 + x rep" 6 .861 1 1 1 1 1 .50 0 6 - x r e p 8 1.06666667 .62 0 7 e r r o r 24 1.72222222 76 .7 VARIABLE OP2 NUMBER OF HEADS # SOURCE DF MS F SIG % VAR 1 + 3 25.64444444 1.34 2 . 6 2 - 4 33.56666667 1.52 5 .0 3 r e p 2 82.91666667 4 .41 * 12.1 4 + x - 12 27.97777778 .98 0 5 + x r e p 6 12.49444445 .44 0 6 - x r e p 8 12.79166667 .45 0 7 e r r o r 24 28.53611111 80 .3 VARIABLE OP3 NUMBER OF DISEASED HEADS # SOURCE DF MS F SIG % VAR 1 + 3 4 .64444444 .99 0 2 - 4 8 .68333333 1.54 5.1 3 r e p 2 20.06666667 3 .19 9 .9 4 + x - 12 7.67222222 .91 0 5 + x r e p 6 5.51111111 .66 0 6 - x r e p 8 3.42083333 .41 0 7 e r r o r 24 8 .39305556 85 .1 TABLE 29 ( c o n t i n u e d ) VARIABLE OP4 NUMBER OF HEALTHY HEADS # SOURCE DF MS F SIG % VAR 1 + 3 8 .73333333 1.74 5.1 2 - 4 9 .44166667 1.50 5 .2 3 r e p 2 21.65000000 4 .60 * 12.9 4 + x - 12 6 .66388889 1.02 .5 5 + x r e p 6 2 .11666667 .32 0 6 - x r e p 8 4 .00416667 .61 0 7 e r r o r 24 6 .52638889 76 .3 VARIABLE OP5 AVERAGE NUMBER OF HEADS PER PLANT # SOURCE DF MS F SIG % VAR 1 + 3 2 .48755556 1.20 1.4 2 - 4 3 .75308333 1.28 2 . 9 3 r e p 2 14.88200000 9.68 * 18.9 4 + x - 12 3.93186111 1.36 8 .2 5 + x r e p 6 .56422222 .20 0 6 - x r e p 8 1.27220833 .44 0 7 e r r o r 24 2 .89331944 68 .6 VARIABLE OP6 AVERAGE NUMBER OF DISEASED HEADS PER PLANT # SOURCE DF MS F SIG % VAR 1 + 3 .45177778 .97 0 2 - 4 .98500000 1.40 3 .9 3 r e p 2 3 .48750000 5.73 * 16.2 4 + x - 12 .95066667 1.12 3 .0 5 + x r e p 6 .39194445 .46 0 6 - x r e p 8 .36562500 .43 0 7 e r r o r 24 .85229167 76 .9 TABLE 29 ( c o n t i n u e d ) VARIABLE OP7 AVERAGE NUMBER OF HEALTHY HEADS PER PLANT # SOURCE DF MS F SIG % VAR 1 + 3 .84565500 1.15 1 .2 2 - 4 1.11180667 1 .07 1 .0 3 r e p 2 4.05798167 5.27 * 17.8 4 + x - 12 1.11138000 1 .47 10.9 5 + x r e p 6 .28448833 .38 0 6 - x r e p 8 .62931292 .83 0 7 e r r o r 24 .75404458 69 .0 VARIABLE OP8 SPORE WEIGHT # SOURCE DF MS F SIG % VAR 1 + 3 .06504438 .94 0 2 - 4 .20282789 1 .61 5.1 3 r e p 2 .44130515 3.19 * 8 .6 4 + x - 12 .18827850 .81 0 5 + x r e p 6 .12888513 .56 0 6 - x r e p 8 .08198650 .35 0 7 e r r o r 24 .23173982 86 .3 VARIABLE OP9 AVERAGE SPORE WEIGHT PER PLANT # SOURCE DF MS F SIG % VAR 1 + 3 .00742927 1 .09 .6 2 - 4 .02785290 1 .57 4 . 9 3 r e p 2 .07084056 3.89 * 10.6 4 + x - 12 .02220151 .76 0 5 + x r e p 6 .01149667 .39 0 6 - x r e p 8 .01424267 .49 0 7 e r r o r 24 .02934757 8 3 . 8 TABLE 29 ( c o n t i n u e d ) VARIABLE OP 10 AVERAGE SPORE WEIGHT PER HEAD # SOURCE DF MS F SIG % VAR 1 + 3 .00652572 2.68 * 6.3 2 - 4 .00571639 2.50 * 7.0 3 r e p 2 .01278546 6.61 * 9.3 4 + x - 12 .00355481 .51 0 5 + x r e p 6 .00147161 .21 0 6 - x r e p 8 .00151332 .22 0 7 e r r o r 24 .00695823 77 .4 VARIABLE OP11 AVERAGE SPORE GERMINATION RATE PER HEAD # SOURCE DF MS F SIG % VAR 1 + 3 91 1 .31755556 2 .22 7.1 2 - 4 477.21150000 1 .27 2 .4 3 r e p 2 1923.06050000 9.18 * 14.5 4 + x - 12 606.62005556 1 .07 1 .8 5 + x r e p 6 59.37938889 . 1 1 0 6 - x r e p 8 211.65987500 .37 0 7 e r r o r 24 564.97459722 74 . 1 VARIABLE OP 12 NUMBER OF SEEDS # SOURCE DF MS F SIG % VAR 1 + 3 10549 .91111111 1 .74 5 .0 2 - 4 13051 .64166667 1 .74 7.1 3 r e p 2 25258 .61666667 4 .59 * 12.4 4 + x - 12 7707 .84166667 .97 0 5 + x r e p 6 2897 .86111111 .37 0 6 - x r e p 8 4320 .49166667 .55 0 7 e r r o r 24 7908 .87500000 75 .5 TABLE 29 ( c o n t i n u e d ) VARIABLE OP 13 AVERAGE NUMBER OF SEEDS PER PLANT # SOURCE DF MS F SIG % VAR 1 + 3 1226.18533333 1.40 3.0 2 - 4 1798.93358333 1.64 6 .4 3 r e p 2 4473.22066667 6.13 * 16.2 4 + x - 12 1187.41936111 1.22 5 .0 5 + x r e p 6 388.13200000 .40 0 6 - x r e p 8 501.40045833 .51 0 7 e r r o r 24 976.97123611 69 .4 VARIABLE OP14 AVERAGE NUMBER OF SEEDS PER HEALTHY HEAD # SOURCE DF MS F SIG % VAR 1 + 3 366.68505556 1.39 3.1 2 - 4 400.38733333 1.44 4 . 5 3 r e p 2 1038.61550000 7.03 * 14.7 4 + x - 12 363.16644444 1.39 8 .9 5 + x r e p 6 89.48505556 .34 0 6 - x r e p 8 95.49570833 .36 0 7 e r r o r 24 261.64331944 68 .9 VARIABLE OP15 SEED WEIGHT # SOURCE DF MS F SIG % VAR 1 + 3 17.93020739 1.62 4 . 6 2 - 4 20.65359102 1.68 6 .6 3 r e p 2 41.40557082 4 .69 * 12.4 4 + x - 12 13.67217384 1.06 1.5 5 + x r e p 6 5.33826566 .41 0 6 - x r e p 8 6.24042582 .48 0 7 e r r o r 24 12.87820711 7 4 . 9 TABLE 29 ( c o n t i n u e d ) VARIABLE OP 16 AVERAGE SEED WEIGHT PER PLANT # SOURCE DF MS F SIG % VAR 1 + 3 1.81415413 1 .27 2 .1 2 - 4 2.73604594 1 .49 5.2 3 rep 2 7.52095115 6 .74 * 17.2 4 + x - 12 2.05253858 1 .37 8 .3 5 + x r e p 6 .54956405 .37 0 6 - x r e p 8 .78920615 .53 0 7 e r r o r 24 1.49650386 67 . 1 VARIABLE OP17 AVERAGE SEED WEIGHT PER HEALTHY HEAD # SOURCE DF MS F SIG % VAR 1 + 3 .54884986 1 .43 3.3 2 - 4 .55839826 1 .47 4 . 5 3 r e p 2 1.83318920 8 .47 * 17.7 4 + x - 12 .50164149 1 .39 8 .5 5 + x r e p 6 . 13488174 .37 0 6 - x r e p 8 . 12421348 .34 0 7 e r r o r 24 .36170730 66 .0 VARIABLE OP18 AVERAGE SEED GERMINATION RATE I PER HEALTHY : HEAD # SOURCE DF MS F SIG % VAR 1 + 3 1282.60727778 1 .35 2 .5 2 - 4 673.45691667 .84 0 3 rep 2 4845.36866667 13.53 * 17.4 4 + x - 12 1674.48936111 1 .60 13.3 5 + x r e p 6 56.53444445 .05 0 6 - x r e p 8 378.83116667 .36 0 7 e r r o r 24 1046.92527778 66 .7 218 TABLE 30. A comparison of the pattern of s i g n i f i c a n t components of v a r i a b i l i t y on Trebi (T) and Odessa (O). Variance components 1 to 6 (1=+ sporidia, 2=- sporidia, 3=replicates, 4=+x- interactions, 5=+xreplicate interactions, 6=-xreplicate interactions) are l i s t e d . When s t a t i s t i c a l l y s i g n i f i c a n t differences among constituent members of a component were detected, an asteri x was placed in the corresponding column. The simi l a r variables measured on Trebi and on Odessa are shown side by side for ease of comparison. [P] = [PATHOGEN], [H] = [HOST] TABLE 30 VARIANCE COMPONENT TR 1 2 3 4 5 6 OR 1 * 1 2 * 2 3 * 3 4 * 4 5 * 5 6 * 6 7 * * 7 8 8 9 9 10 * * 10 11 11 12 * * 12 13 * 13 14 * 14 15 * 15 16 16 17 * 17 Wp[P] Wp[P] Wc [ P ] * Wc [ P ] W[P] * * W[P] Wp[H] Wp[H] Wh[H] * Wh[H] W[H] * W[H] VARIANCE COMPONENT 1 2 3 4 5 6 * * * * * * * * * * * * * * * * * * * * * * * * * TABLE 30 ( c o n t i n u e d ) VARIANCE COMPONENT VARIANCE COMPONENT TH 1 2 3 4 5 6 OH 1 2 3 4 5 6 1 * * * 1 * * 2 2 3 3 4 * 4 5 * * 5 6 * 6 * 7 * 7 8 * 8 9 9 10 * 10 VARIANCE COMPONENT VARIANCE COMPONENT TC 1 2 3 4 5 6 OC 1 2 3 4 5 6 1 * * * 1 * * 2 * * * 2 * * TABLE 30 ( c o n t i n u e d ) VARIANCE COMPONENT TP 1 2 3 4 5 6 1 2 3 4 5 * 6 * 7 8 9 * 10 * 1 1 * 12 13 14 * 15 16 17 * 18 * VARIANCE COMPONENT OP 1 2 3 4 5 6 1 * 2 * 3 4 * 5 * 6 * 7 * 8 * 9 * 10 * * * 1 1 * 1 2 * 1 3 * 14 * 15 * 1 6 * 17 * 18 * 222 TABLE 31. Frequencies of combinations of variance components contributing s i g n i f i c a n t l y to t o t a l variance. Frequencies of a l l possible combinations of variance components 1 to 4 are shown for each individual subset. The t o t a l number of types of combinations for each subset i s given as well as the t o t a l number of types of combinations for each variety. 223 TABLE 31 TREBI ODESSA VARIABLE SUBSET VARIABLE SUBSET COMPONENT* COMBINATIONS R H C P TOTAL R H C P TOTAL 1 _ 2 5 - 2 8 15 2 - 2 - 4 3 7 5 - 1 13 7 1 - 16 24 4 1 - 1 - 2 - - - - -12 - - - - - - - - - -13 1 4 23 — — — — — - - — — — 3 - - - 3 1 1 2 - 4 24 1 - - - 1 2 - 3 - 5 34 - 1 - - 1 - - - - -1 23 - - 1 - 1 - - - 1 1 134 - - - - - - - - - -234 - 1 2 - 3 3 - - - 3 1 234 — — — — — — — — — — TOTAL 1 7 7 6 9 39 1 5 2 7 17 41 * 1 = + sporidia; 2 = - sporidia; 3 = r e p l i c a t e s ; 4 = + x - s p o r i d i a l inte r a c t i o n ; 12 = + sporidia and - sporidia 234 = - sporidia, r e p l i c a t e s and + x - s p o r i d i a l interactions; etc. 224 TABLE 32. Stepwise regression results of the COMPLETE models for the dependent variables W [PATHOGEN] (pathogen fitness) and W [HOST] (host f i t n e s s ) . Independent variables employed in these models are shown above the R SQUARE value. The TERMS column contains the Y intercept and any independent variable with a s t a t i s t i c a l l y s i g n i f i c a n t F value. Intercept and term c o e f f i c i e n t s are in the B column. Remaining columns hold the standard error (SE), sum of squares (SS), F, and p r o b a b i l i t y of significance values (PROB>F). TABLE 32 COMPLETE: PATHOGEN FITNESS (W [PATHOGEN]) ON TREBI RI R2 R3 R4 R5 R6 R7 R8 R9 R10 R1 1 R12 R13 R14 R15 R16 R17 Wp [HOST] Wh [HOST] W [HOST] H1 H2 H3 H4 H5 H6 H7 H8 H9 H1 0 Cl C2 C3 C4 C5 C6 C7 P1 P2 P3 P4 P5 P6 P7 P8 P9 P1 0 P1 1 P1 2 PI 3 P1 4 P1 5 PI 6 P1 7 P1 8 R SQUARE = 0.99409972 TERMS B SE SS F PROB>F INTERCEPT -0 .02529980 R12 0.40560957 0.03103345 0.20529614 170.83 0.0001 C1 0.02526359 0.00688430 0.01618432 13.47 0.0021 P10 0.96666443 0.28802940 0.01353637 11.26 0.0040 COMPLETE: PATHOGEN FITNESS (W [PATHOGEN]) ON ODESSA RI R2 R3 R4 R5 R6 R7 R8 R9 R10 R1 1 R1 2 R1 3 R1 4 R1 5 R1 6 R1 7 Wp [HOST] Wh [HOST] W [HOST] H1 H2 H3 H4 H5 H6 H7 H8 H9T H10 Cl C2 C3 C4 C5 C6 C7 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 P15 P16 P17 P18 R SQUARE = 0.98374694 TERMS B SE SS F PROB>F INTERCEPT 0.06577155 R4 - 0 . 0 1 0 7 6 0 0 9 0.00362184 0.04317179 8.83 0.0090 R12 0.80254383 0.05129552 1.19730763 244.78 0.0001 R13 -0 .87927227 0.34046053 0.03262424 6.67 0.0200 TABLE 32 ( c o n t i n u e d ) COMPLETE: HOST FITNESS (W [HOST]) ON TREBI R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R1 1 R1 2 R1 3 R1 4 R1 5 R1 6 R1 7 Wp [PATHOGEN] Wc [PATHOGEN] W [PATHOGEN] H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 Cl C2 C3 C4 C5 C6 C7 PI P2 P3 P4 P5 P6 P7 P8 P9 PlO P11 P12 P13 P14 P15 P16 P17 P18 R SQUARE = 0.99729422 TERMS B SE SS F PROB>F INTERCEPT 19. 983571 R1 7 46 . 195638 0 .567151 12461355.625647 6634.43 0.0001 COMPLETE: HOST FITNESS (W [HOST]) ON ODESSA R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R1 1 R1 2 R1 3 R1 4 R1 5 R1 6 R1 7 Wp [PATHOGEN] Wc [PATHOGEN] W [PATHOGEN] H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 C1 C2 C3 C4 C5 C6 C7 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 P15 P16 P17 P18 R SQUARE = = 0.99648852 TERMS B SE SS F PROB>F INTERCEPT - 4 . 7 9 2 6 6 3 R17 47.905550 0.670283 6601497.392234 5108.05 0.0001 227 TABLE 33 . S t e p w i s e r e g r e s s i o n r e s u l t s o f t h e COMPLETE models f o r t h e dependent v a r i a b l e s W [PATHOGEN]G (pa thogen f i t n e s s ) and W [HOST]G ( h o s t f i t n e s s ) . I ndependen t v a r i a b l e s employed i n t h e s e models were t h o s e w i t h s t a t i s t i c a l l y s i g n i f i c a n t g e n e t i c componen t (s ) and a r e shown above t h e R SQUARE v a l u e . The TERMS column c o n t a i n s t h e Y i n t e r c e p t and any i ndependen t v a r i a b l e w i t h a s t a t i s t i c a l l y s i g n i f i c a n t F v a l u e . I n t e r c e p t and t e r m c o e f f i c i e n t s a r e i n t h e B c o l u m n . Remain ing columns h o l d t he s t a n d a r d e r r o r ( S E ) , sum of squares ( S S ) , F, and p r o b a b i l i t y o f s i g n i f i c a n c e v a l u e s (PROB>F). TABLE 33 COMPLETE: PATHOGEN FITNESS (W [PATHOGEN]G) ON TREBI R2 R4 R7 R1 0 R1 2 R1 3 R1 4 R1 5 H1 Cl C2 C4 C5 C6 C7 P5 P6 P9 P1 0 P1 1 P1 4 P1 7 P1 8 R SQUARE = 0.99409972 TERMS B SE SS F PROB>F INTERCEPT -0 .02529980 R12 0.40560957 0.03103345 0.20529614 170.83 0.0001 C1 0.02526359 0.00688430 0.01618432 13.47 0.0021 P10 0.96666443 0.28802940 0.01353637 11.26 0.0040 COMPLETE: PATHOGEN FITNESS (W [PATHOGEN]G) ON ODESSA R1 R2 R4 R7 R9 R10 R13 R14 R15 H1 C1 C2 C3 C4 C5 C6 C7 P1 0 R SQUARE = 0.98093828 TERMS B SE SS F PROB>F INTERCEPT -0 .01198516 R7 4.63272255 0.60986663 0331021991 57 .70 0.0001 C2 -0 .12873443 0.01447878 0.45350218 79 .05 0.0001 C4 0.85865437 0.07441344 0.76381553 133.15 0.0001 229 TABLE 33 ( c o n t i n u e ) COMPLETE: HOST FITNESS (W [HOST]G) ON TREBI R2 R4 R7 R10 R12 R13 R14 R15 Wc [PATHOGEN] W [PATHOGEN] H1 C1 C2 C4 C5 C6 C7 P5 P6 P9 P10 P11 P14 P17 P18 R SQUARE = 0.25327987 TERMS B SE SS F PROB>F INTERCEPT 3358.812774 Wc [ P ] -1150 .670752 465.686247 3164773.719389 6.11 0.0237 COMPLETE: HOST FITNESS (W [HOST]G) ON ODESSA R1 R2 R4 R7 R9 R10 R13 R14 R15 H1 C1 C2 C3 C4 C5 C6 C7 P10 R SQUARE = 0.67439628 TERMS B SE SS F PROB>F INTERCEPT 1962.578000 R2 256.156791 67.034552 1968583.549921 14.60 0.0015 R4 - 1 7 8 . 4 9 2 4 1 5 78.531384 696454.193075 5.17 0.0372 C1 - 2 4 3 . 9 7 5 4 5 4 95.279342 883963.425815 6 .56 0 .0209 230 TABLE 34 . S tepw ise r e g r e s s i o n r e s u l t s o f t h e TRADITIONAL models f o r t h e dependent v a r i a b l e s W [PATHOGEN] (pa thogen f i t n e s s ) and W [HOST] ( h o s t f i t n e s s ) . On ly 1 i ndependen t v a r i a b l e was employed (R2) and i s shown above t h e R SQUARE v a l u e . The TERMS column c o n t a i n s t h e Y i n t e r c e p t and t h e i ndependen t v a r i a b l e , i f i t had a s t a t i s t i c a l l y s i g n i f i c a n t F v a l u e . I n t e r c e p t and t e r m c o e f f i c i e n t s a r e i n t h e B c o l u m n . Remain ing co lumns h o l d t h e s t a n d a r d e r r o r ( S E ) , sum of squares ( S S ) , F, and p r o b a b i l i t y o f s i g n i f i c a n c e v a l u e s (PROB>F). TRADITIONAL: PATHOGEN TABLE 34 FITNESS (W [PATHOGEN]) ON TREBI R2 R SQUARE = 0.83622239 TERMS B SE SS F PROB>F INTERCEPT R2 - 0 . 0 . 31431579 04787175 0.00499355 2.72516849 91 .91 0.0001 TRADITIONAL: PATHOGEN FITNESS (W [PATHOGEN]) ON ODESSA R2 R SQUARE = 0.57033495 TERMS B SE SS F PROB>F INTERCEPT R2 - 0 . 0 . 27599497 04832365 0.00988607 2.74626142 23 .89 0.0001 TABLE 34 ( c o n t i n u e d ) TRADITIONAL: HOST FITNESS (W [HOST]) ON TREBI R2 R SQUARE = 0. 15130233 TERMS B SE SS F PROB>F INTERCEPT 3627.452314 R2 -39 .872768 22.258394 1890547.575347 3.21 0.0901 TRADITIONAL: HOST FITNESS (W [HOST]) ON ODESSA R2 R SQUARE = 0. 13125757 TERMS B SE SS F PROB>F INTERCEPT 2738.408861 R2 - 2 7 . 1 9 1 6 8 0 16.488575 869549.905872 2 .72 0.1165 35 . S tepwise r e g r e s s i o n r e s u l t s o f t h e TRADITIONAL models f o r t h e dependent v a r i a b l e s W [PATHOGEN] (pa thogen f i t n e s s ) and W [HOST] ( h o s t f i t n e s s ) . On ly 1 i ndependen t v a r i a b l e was employed (R4) and i s shown above the R SQUARE v a l u e . The TERMS column c o n t a i n s t h e Y i n t e r c e p t and t h e independen t v a r i a b l e , i f i t had a s t a t i s t i c a l l y s i g n i f i c a n t F v a l u e . I n t e r c e p t and t e r m c o e f f i c i e n t s a r e i n t h e B c o l u m n . Remain ing columns h o l d t h e s t a n d a r d e r r o r ( S E ) , sum of squares ( S S ) , F, and p r o b a b i l i t y o f s i g n i f i c a n c e v a l u e s (PROB>F). 234 TABLE 35 TRADITIONAL : PATHOGEN FITNESS (W [PATHOGEN]) ON TREBI R4 R SQUARE = 0.90528508 TERMS B SE SS F PROB>F INTERCEPT - 0 . R4 0. 24946328 05162188 0.00393563 2.95023716 172.04 0.0001 TRADITIONAL: PATHOGEN FITNESS (W [PATHOGEN]) ON ODESSA R4 R SQUARE = 0.59992019 TERMS B SE SS F PROB>F INTERCEPT R4 - 0 . 2 4 4 6 4 3 4 9 0.04816169 0.00927026 2.88871949 26 .99 0.0001 TABLE 35 ( c o n t i n u e d ) TRADITIONAL: HOST FITNESS (W [HOST]) ON TREBI R4 R SQUARE = 0.23843417 TERMS B SE SS F PROB>F INTERCEPT 3687.945767 R4 - 5 1 . 8 7 5 2 9 2 21.852130 2979274.293383 5 .64 0.0289 TRADITIONAL: HOST FITNESS (W [HOST]) ON ODESSA R4 R SQUARE = 0. .21172367 TERMS B SE SS F PROB>F INTERCEPT 2840.478148 R4 - 3 3 . 5 5 9 7 6 0 15.262902 1402618.535973 4 .83 0.0412 236 TABLE 36. Stepwise regression results of the TRADITIONAL models for the dependent variables W [PATHOGEN] (pathogen fitness) and W [HOST] (host f i t n e s s ) . Two independent variables were employed (R2 and R4) and are shown above the R SQUARE value. The TERMS column contains the Y intercept and any independent variable with a s t a t i s t i c a l l y s i g n i f i c a n t F value. Intercept and term c o e f f i c i e n t s are in the B column. Remaining columns hold the standard error (SE), sum of squares (SS), F, and pro b a b i l i t y of significance values (PROB>F). TABLE 36 TRADITIONAL : PATHOGEN FITNESS (W [PATHOGEN]) ON TREBI R2 R4 R SQUARE = 0.90528508 TERMS B SE SS F PROB>F INTERCEPT - 0 . R4 0. 24946328 05162188 0.00393563 2.95023716 172.04 0.0001 TRADITIONAL: PATHOGEN FITNESS (W [PATHOGEN]) ON ODESSA R2 R4 R SQUARE = 0.59992019 TERMS B SE SS F PROB>F INTERCEPT -0 .24464349 R4 0.04816169 0.00927026 2.88871949 26 .99 0.0001 238 TABLE 36 ( c o n t i n u e d ) TRADITIONAL: HOST FITNESS (W [HOST]) ON TREBI R2 R4 R SQUARE = 0.23843417 TERMS B SE SS F PROB>F INTERCEPT 3687.945767 R4 - 5 1 . 8 7 5 2 9 2 21.852130 2979274.293383 5.64 0 .0289 TRADITIONAL: HOST FITNESS (W [HOST]) ON ODESSA R2 R4 R SQUARE = 0.54096301 TERMS B SE SS F PROB>F INTERCEPT 2479.498203 R2 268.888345 77.004393 2181131.598273 12.19 0.0028 R4 - 2 9 1 . 4 8 2 3 4 3 74.830004 2714200.228374 15.17 0.0012 37. S tepw ise r e g r e s s i o n r e s u l t s o f t h e PRACTICAL: MINIMAL COST models f o r t h e dependent v a r i a b l e s W [PATHOGEN] (pa thogen f i t n e s s ) and W [HOST] ( h o s t f i t n e s s ) . I ndependen t v a r i a b l e s employed i n t h e s e models a r e shown above t h e R SQUARE v a l u e . The TERMS column c o n t a i n s t h e Y i n t e r c e p t and any independen t v a r i a b l e w i t h a s t a t i s t i c a l l y s i g n i f i c a n t F v a l u e . I n t e r c e p t and t e r m c o e f f i c i e n t s a r e i n t h e B c o l u m n . Remain ing co lumns h o l d t he s t a n d a r d e r r o r ( S E ) , sum of squares ( S S ) , F, and p r o b a b i l i t y o f s i g n i f i c a n c e v a l u e s (PROB>F). TABLE 37 PRACTICAL-MINIMAL COST: PATHOGEN FITNESS (W [PATHOGEN]) ON TREBI R1 R2 R3 R4 R5 R SQUARE = 0.90528508 TERMS B SE SS F PROB>F INTERCEPT -0 .24946328 R4 0.05162188 0.00393563 2.95023716 172.04 0.0001 PRACTICAL-MINIMAL COST: PATHOGEN FITNESS (W [PATHOGEN]) ON ODESSA R1 R2 R3 R4 R5 R SQUARE = 0.80171475 TERMS B SE SS F PROB>F INTERCEPT -0 .06116624 R5 0.07020985 0.00822995 3.86039517 72 .78 0.0001 TABLE 37 ( c o n t i n u e d ) PRACTICAL-MINIMAL COST: HOST FITNESS (W [HOST]) ON TREBI R1 R2 R3 R4 R5 R SQUARE = .94494173 TERMS B SE SS F PROB>F INTERCEPT -1546 .463710 R3 43.439697 2.588944 11393151.5556 281.53 0.0001 R5 - 1 5 . 3 1 7 3 0 1 5.411363 324240.1646 8.01 0.0115 PRACTICAL-MINIMAL COST: HOST FITNESS (W [HOST]) ON ODESSA R1 R2 R3 R4 R5 R SQUARE = 0.94998665 TERMS B SE SS F PROB>F INTERCEPT - 7 4 3 . 7 3 2 8 4 7 R3 44 .929569 2.559785 6004329.957726 308.08 0.0001 R5 - 3 4 . 8 5 8 1 9 2 5.067681 922143.020531 47.31 0.0001 242 TABLE 38. Stepwise regression results of the PRACTICAL: MINIMAL COST models for the dependent variables W [PATHOGEN]G (pathogen fitness) and W [HOST]G (host f i t n e s s ) . Independent variables employed in these models had s i g n i f i c a n t genetic components and are shown above the R SQUARE value. The TERMS column contains the Y intercept and any independent variable with a s t a t i s t i c a l l y s i g n i f i c a n t F value. Intercept and term c o e f f i c i e n t s are in the B column. Remaining columns hold the standard error (SE), sum of squares (SS), F, and p r o b a b i l i t y of significance values (PROB>F). TABLE 38 PRACTICAL-MINIMAL COST: PATHOGEN FITNESS (W [PATHOGEN]G) ON TREBI R2 R4 SAME AS TRADITIONAL MODEL TW [PATHOGEN] (SEE TABLE 36) PRACTICAL-MINIMAL COST: PATHOGEN FITNESS (W [PATHOGEN]G) ON ODESSA R1 R2 R4 R SQUARE = 0.59992019 TERMS B SE SS F PROB>F INTERCEPT - 0 . 2 4 4 6 4 3 4 9 R4 0.04816169 0.00927026 2.88871949 26 .99 0.0001 TABLE 38 ( c o n t i n u e d ) PRACTICAL-MINIMAL COST: HOST FITNESS (W [HOST]G) ON TREBI R2 R4 SAME AS TRADITIONAL MODEL TW [HOST] (SEE TABLE 36) PRACTICAL-MINIMAL COST: HOST FITNESS (W [HOST]G) ON ODESSA R1 R2 R4 R SQUARE = 0.54096301 TERMS B SE SS F PROB>F INTERCEPT R2 R4 2479 268 -291 .498203 .888345 .482343 77.004393 74.830004 2181 131 .598273 2714200.228374 12. 15. 19 17 0.0028 0.0012 245 TABLE 39 . S tepw ise r e g r e s s i o n r e s u l t s o f t h e PRACTICAL: MODERATE COST models f o r t h e dependent v a r i a b l e s W [PATHOGEN] (pa thogen f i t n e s s ) and W [HOST] ( h o s t f i t n e s s ) . I ndependen t v a r i a b l e s employed i n t h e s e models a r e shown above t h e R SQUARE v a l u e . The TERMS column c o n t a i n s t h e Y i n t e r c e p t and any i ndependen t v a r i a b l e w i t h a s t a t i s t i c a l l y s i g n i f i c a n t F v a l u e . I n t e r c e p t and t e r m c o e f f i c i e n t s a r e i n t he B c o l u m n . Remain ing co lumns h o l d t h e s t a n d a r d e r r o r ( S E ) , sum o f squares ( S S ) , F, and p r o b a b i l i t y o f s i g n i f i c a n c e v a l u e s (PROB>F). TABLE 39 PRACTICAL-MODERATE COST: PATHOGEN FITNESS (W [PATHOGEN]) ON TREBI R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 H1 H2 H3 C1 C2 C3 PI P2 P3 P4 P5 P6 P7 R SQUARE = 0.96195777 TERMS B SE SS F PROB>F INTERCEPT - 0 . 0 0 5 0 7 1 4 9 R7 3.74983604 0.21441740 2.23045331 305.85 0.0001 P1 - 0 . 0 6 3 7 4 5 8 0 0.02040064 0.07120395 9 .76 0.0062 PRACTICAL-MODERATE COST: PATHOGEN FITNESS (W [PATHOGEN]) ON ODESSA R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 H1 H2 H3 C1 C2 C3 P1 P2 P3 P4 P5 P6 P7 R SQUARE = 0.80171475 TERMS B SE SS F PROB>F INTERCEPT - 0 . 0 6 1 1 6 6 2 4 R5 0.07020985 0.00822995 3.86039517 72 .78 0.0001 247 TABLE 39 ( c o n t i n u e d ) PRACTICAL-MODERATE COST: HOST FITNESS (W [HOST]) ON TREBI R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 H1 H2 H3 C1 C2 C3 P1 P2 P3 P4 P5 P6 P7 R SQUARE = 0.93088798 TERMS B SE SS F PROB>F INTERCEPT -1740 .45405 H3 2222.27928 142.721850 11631598.6730 242 .45 0.0001 PRACTICAL-MODERATE COST: HOST FITNESS (W [HOST]) ON ODESSA RI R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 H1 H2 H3 C1 C2 C3 P1 P2 P3 P4 P5 P6 P7 R SQUARE = 0.96367010 TERMS B SE SS F PROB>F INTERCEPT -808 .428914 H2 44.420031 2.091957 6383185.19730 450 .87 0.0001 C3 174.436444 51.552209 162093.39193 11.45 0.0035 40. Stepwise regression results of the PRACTICAL: MODERATE COST models for the dependent variables W [PATHOGEN]G (pathogen fitness) and W [HOST]G (host f i t n e s s ) . Independent variables employed in these models had s i g n i f i c a n t genetic components and are shown above the R SQUARE value. The TERMS column contains the Y intercept and any independent variable with a s t a t i s t i c a l l y s i g n i f i c a n t F value. Intercept and term c o e f f i c i e n t s are in the B column. Remaining columns hold the standard error (SE), sum of squares (SS), F, and p r o b a b i l i t y of significance values (PROB>F). TABLE 40 PRACTICAL-MODERATE COST: PATHOGEN FITNESS (W [PATHOGEN]G) ON TREBI R2 R4 R7 R10 HI C1 C2 P5 P6 R SQUARE = 0.95899962 TERMS B SE SS F PROB>F INTERCEPT - 0 .00780581 R7 1 .86926327 0.54100846 0.09383036 11.94 0.0030 C2 0 .03655558 0.01306162 0.06156363 7.83 0.0123 PRACTICAL-MODERATE COST: PATHOGEN FITNESS (W [PATHOGEN]G) ON ODESSA R1 R2 R4 R7 R9 R10 H1 C1 C2 C3 R SQUARE = 0.79592837 TERMS B SE SS F PROB>F INTERCEPT - 0 . 0 9 4 1 0 8 7 6 R7 4.21626124 0.50320574 3.83253276 70 .20 0.0001 250 TABLE 40 ( c o n t i n u e d ) PRACTICAL-MODERATE COST: HOST FITNESS (W [HOST]G) ON TREBI R2 R4 H1 C1 C2 R7 P5 R1 0 P6 R SQUARE = = 0 .38410192 TERMS B SE SS F PROB>F INTERCEPT 4331.736400 R4 - 2 0 4 . 4 7 1 2 3 2 78.741865 3052494.948951 6 .74 0.0188 R7 9931.412577 4952.896846 1820142.520083 4 . 0 2 0.0611 PRACTICAL-MODERATE COST: HOST FITNESS (W [HOST]G) ON ODESSA R1 R2 R4 H1 C1 C2 C3 R7 R9 R10 R SQUARE = = 0 .67439628 TERMS B SE SS F PROB>F INTERCEPT 1962.578000 R2 256.156791 67.034552 1968583.549921 14.60 0.0015 R4 - 1 7 8 . 4 9 2 4 1 5 78.531384 696454.193075 5 .17 0.0372 C1 - 2 4 3 . 9 7 5 4 5 4 95.279342 883963.425815 6 .56 0 .0209 41. Stepwise regression r e s u l t s of the PRACTICAL: EARLY ASSESSMENT models for the dependent variables W [PATHOGEN] (pathogen fitness) and W [HOST] (host f i t n e s s ) . Independent variables employed in these models are shown above the R SQUARE value. The TERMS column contains the Y intercept and any independent variable with a s t a t i s t i c a l l y s i g n i f i c a n t F value. Intercept and term c o e f f i c i e n t s are in the B column. Remaining columns hold the standard error (SE), sum of squares (SS), F, and p r o b a b i l i t y of significance values (PROB>F). TABLE 41 PRACTICAL-EARLY ASSESSMENT: PATHOGEN FITNESS (W [PATHOGEN]) ON TREBI R1 R2 R3 R SQUARE = 0.83622239 TERMS B SE SS F PROB>F INTERCEPT - 0 . 3 1 4 3 1 5 7 9 R2 0.04787175 0.00499355 2.72516849 91 .91 0.0001 PRACTICAL-EARLY ASSESSMENT: PATHOGEN FITNESS (W [PATHOGEN]) ON ODESSA R1 R2 R3 R SQUARE = 0.57033495 TERMS B SE SS F PROB>F INTERCEPT -0 .27599497 R2 0.04832365 0.00988607 2.74626142 23 .89 0.0001 TABLE 41 ( c o n t i n u e d ) PRACTICAL-EARLY ASSESSMENT: HOST FITNESS (W [HOST]) ON TREBI R1 R2 R3 R SQUARE = 0.93593024 TERMS B SE SS F PROB>F INTERCEPT -5666 .986634 R1 74.906008 35.333830 211640.1419 4 .49 0.0490 R3 47.269452 3.285085 9750214.0476 207 .05 0.0001 PRACTICAL-EARLY ASSESSMENT: HOST FITNESS (W [HOST]) ON ODESSA R1 R2 R3 R SQUARE = 0.89578095 TERMS B SE SS F PROB>F INTERCEPT -367 .867456 R2 - 2 1 . 9 5 0 3 0 0 5.895271 563043.254653 13.86 0.0017 R3 40.750942 3.649147 5064783.977911 124.71 0.0001 254 TABLE 42. Stepwise regression results of the DEVELOPMENTAL: C (COMPLETELY DISEASED PLANTS) OR H (HEALTHY PLANTS) BASED models for the dependent variables W [PATHOGEN] (pathogen fitness) and W [HOST] (host f i t n e s s ) . Independent variables employed in these models are shown above the R SQUARE value. The TERMS column contains the Y intercept and any independent variable with a s t a t i s t i c a l l y s i g n i f i c a n t F value. Intercept and term c o e f f i c i e n t s are in the B column. Remaining columns hold the standard error (SE), sum of squares (SS), F, and p r o b a b i l i t y of significance values (PROB>F). TABLE 42 DEVELOPMENTAL: PATHOGEN FITNESS (W [PATHOGEN]) ON TREBI R1 C3 C5 C7 R SQUARE = 0.442 46371 TERMS B SE SS F PROB>F INTERCEPT -o. ,13541350 C7 0. ,01679538 0.00444377 1 .44194676 14.28 0 .0014 DEVELOPMENTAL: PATHOGEN FITNESS (W [PATHOGEN]) ON ODESSA R1 C3 C5 C7 R SQUARE = 0.6593 4546 TERMS B SE SS F PROB>F INTERCEPT - 0 . .01904564 C5 4. .82156232 0.81686843 3. 1 7486245 34 .84 0.0001 256 TABLE 42 ( c o n t i n u e d ) DEVELOPMENTAL: HOST FITNESS (W [HOST]) ON TREBI R1 H3 H4 H7 H9 R SQUARE = 0.97013702 TERMS B SE SS F PROB>F INTERCEPT -5376 .783602 H3 1132.967435 369.140380 219688.268372 9.42 0.0073 H4 24.548005 7.807633 230541.325208 9.89 0.0063 H9 57.611473 24.655888 127330.307603 5.46 0.0328 DEVELOPMENTAL: HOST FITNESS (W [HOST]) ON ODESSA R1 H3 H4 H7 H9 R SQUARE = 0.91899888 TERMS B SE SS F PROB>F INTERCEPT - 6 0 . 3 9 1 4 2 8 H4 43.827720 3.066907 6088147.116651 204.22 0.0001 257 TABLE 43. Stepwise regression results of the DEVELOPMENTAL: C (COMPLETELY DISEASED PLANTS) OR H (HEALTHY PLANTS) BASED models for the dependent variables W [PATHOGEN]G (pathogen fitness) and W [HOST]G (host f i t n e s s ) . Independent variables employed in t h i s model had s i g n i f i c a n t genetic components and are shown above the R SQUARE value. The TERMS column contains the Y intercept and any independent variable with a s t a t i s t i c a l l y s i g n i f i c a n t F value. Intercept and term c o e f f i c i e n t s are in the B column. Remaining columns hold the standard error (SE), sum of squares (SS), F, and p r o b a b i l i t y of significance values (PROB>F). 258 TABLE 43 DEVELOPMENTAL: PATHOGEN FITNESS (W [PATHOGEN]G) ON TREBI C5 C7 R SQUARE = 0.44246371 TERMS B SE SS F PROB>F INTERCEPT - 0 . 13541350 C7 0 .01679538 0.00444377 1.44194676 1 4 .28 0.0014 DEVELOPMENTAL: PATHOGEN FITNESS (W [PATHOGEN]G) ON ODESSA R1 C3 C5 C7 R SQUARE = 0.65934546 TERMS B SE SS F PROB>F INTERCEPT - 0 .01904564 C5 4 .82156232 0.81686843 3.17486245 34 .84 0.0001 TABLE 43 ( c o n t i n u e d ) DEVELOPMENTAL: HOST FITNESS (W [HOST]G) ON TREBI NO APPROPRIATE INDEPENDENT VARIABLES NO MODEL GENERATED DEVELOPMENTAL: HOST FITNESS (W [HOST]G) ON ODESSA R1 NO MODEL GENERATED 260 TABLE 44 . Stepwise regression results of the DEVELOPMENTAL: P (PARTIALLY DISEASED PLANTS) BASED (HOST PERSPECTIVE) models for the dependent variables W [PATHOGEN] (pathogen fitness) and W [HOST] (host f i t n e s s ) . Independent variables employed in these models are shown above the R SQUARE value. The TERMS column contains the Y intercept and any independent variable with a s t a t i s t i c a l l y s i g n i f i c a n t F value. Intercept and term c o e f f i c i e n t s are in the B column. Remaining columns hold the standard error (SE), sum of squares (SS), F, and p r o b a b i l i t y of significance values (PROB>F). TABLE 44 DEVELOPMENTAL: PATHOGEN FITNESS (W [PATHOGEN]) ON TREBI R1 P5 P7 P13 P16 P18 R SQUARE = 0 .45024903 TERMS B SE SS F PROB>F INTERCEPT - 0 . 0 1 2 6 9 7 2 9 P18 0.01107389 0.00288417 1.46731835 14.74 0.0012 DEVELOPMENTAL: PATHOGEN FITNESS (W [PATHOGEN]) ON ODESSA R1 P5 P7 P13 P16 P18 R SQUARE = 0 .41374006 TERMS B SE SS F PROB>F INTERCEPT 0.30816765 P5 0.29291859 0.08218490 1.99222996 12.70 0.0022 TABLE 44 ( c o n t i n u e d ) DEVELOPMENTAL: HOST FITNESS (W [HOST]) ON TREBI R1 P5 P7 P13 P16 P18 NO MODEL GENERATED DEVELOPMENTAL: HOST FITNESS (W [HOST]) ON ODESSA R1 P5 P7 P13 P16 P18 NO MODEL GENERATED 263 TABLE 45 . Stepwise regression results of the DEVELOPMENTAL: P (PARTIALLY DISEASED PLANTS) BASED (HOST PERSPECTIVE) models for the dependent variables W [PATHOGEN]G (pathogen fitness) and W [HOST]G (host f i t n e s s ) . Independent variables employed in these models had s i g n i f i c a n t genetic components and are shown above the R SQUARE value. The TERMS column contains the Y intercept and any independent variable with a s t a t i s t i c a l l y s i g n i f i c a n t F value. Intercept and term c o e f f i c i e n t s are in the B column. Remaining columns hold the standard error (SE), sum of squares (SS), F, and p r o b a b i l i t y of significance values (PROB>F). TABLE 45 DEVELOPMENTAL: PATHOGEN FITNESS (W [PATHOGEN]G) ON TREBI P5 P18 R SQUARE = 0.45024903 TERMS B SE SS F PROB>F INTERCEPT - 0 . 0 1 2 6 9 7 2 9 P18 0.01107389 0.00288417 1.46731835 14.74 0.0012 DEVELOPMENTAL: HOST FITNESS (W [HOST]G) ON ODESSA R1 NO MODEL GENERATED TABLE 45 ( c o n t i n u e d ) DEVELOPMENTAL: HOST FITNESS (W [HOST]G) ON TREBI P5 P18 NO MODEL GENERATED DEVELOPMENTAL: HOST FITNESS (W [HOST]G) ON ODESSA R1 NO MODEL GENERATED 266 TABLE 46. Stepwise regression results of the DEVELOPMENTAL: P (PARTIALLY DISEASED PLANTS) BASED (PATHOGEN PERSPECTIVE) models for the dependent variables W [PATHOGEN] (pathogen fitness) and W [HOST] (host f i t n e s s ) . Independent variables employed in these models are shown above the R SQUARE value. The TERMS column contains the Y intercept and any independent variable with a s t a t i s t i c a l l y s i g n i f i c a n t F value. Intercept and term c o e f f i c i e n t s are in the B column. Remaining columns hold the standard error (SE), sum of squares (SS), F, and prob a b i l i t y of significance values (PROB>F). TABLE 46 DEVELOPMENTAL: PATHOGEN FITNESS (W [PATHOGEN]) ON TREBI R1 P5 P6 P9 PI 1 R SQUARE = 0.47067011 TERMS B SE SS F PROB>F INTERCEPT -0 .02651632 P11 0.01719068 0.00429696 1.53386871 16.01 0.0008 DEVELOPMENTAL: PATHOGEN FITNESS (W [PATHOGEN]) ON ODESSA R1 P5 P6 P9 P11 R SQUARE = 0.70754410 TERMS B SE SS F PROB>F INTERCEPT 0.13319956 P6 - 0 . 9 5 0 0 3 2 0 5 0.42169327 0.44672007 5.08 0.0387 P9 6.77381373 1.99178439 1.01796803 11.57 0.0037 P11 0.02746657 0.00932473 0.76363961 8 .68 0.0095 TABLE 46 ( c o n t i n u e d ) DEVELOPMENTAL: HOST FITNESS (W [HOST]) ON TREBI R1 P5 P6 P9 P1 1 NO MODEL GENERATED DEVELOPMENTAL: HOST FITNESS (W [HOST]) ON ODESSA R1 P5 P6 P9 P11 NO MODEL GENERATED 269 TABLE 47. S tepw ise r e g r e s s i o n r e s u l t s of t h e DEVELOPMENTAL: P (PARTIALLY DISEASED PLANTS) BASED (PATHOGEN PERSPECTIVE) models f o r t h e dependent v a r i a b l e s W [PATHOGEN]G (pa thogen f i t n e s s ) and W [HOST]G ( h o s t f i t n e s s ) . I ndependen t v a r i a b l e s employed i n t h e s e models had s i g n i f i c a n t g e n e t i c components and a r e shown above t h e R SQUARE v a l u e . The TERMS column c o n t a i n s t h e Y i n t e r c e p t and any i ndependen t v a r i a b l e w i t h a s t a t i s t i c a l l y s i g n i f i c a n t F v a l u e . I n t e r c e p t and t e r m c o e f f i c i e n t s a r e i n t h e B c o l u m n . Remain ing co lumns h o l d t h e s t a n d a r d e r r o r ( S E ) , sum of squares ( S S ) , F, and p r o b a b i l i t y o f s i g n i f i c a n c e v a l u e s (PROB>F). TABLE 47 DEVELOPMENTAL: PATHOGEN FITNESS (W [PATHOGEN]G) ON TREBI P5 P6 P9 PI 1 R SQUARE = 0 .470 6701 1 TERMS B SE SS F PROB>F INTERCEPT -o. .02651632 P1 1 0. .01719068 0. 004296 96 1 .53386871 16.01 0.0008 DEVELOPMENTAL: PATHOGEN FITNESS (W [PATHOGEN]G) ON ODESSA R1 NO MODEL GENERATED TABLE 47 ( c o n t i n u e d ) DEVELOPMENTAL: HOST FITNESS (W [HOST]G) ON TREBI P5 P6 P9 P11 NO MODEL GENERATED DEVELOPMENTAL: HOST FITNESS (W [HOST]G) ON ODESSA R1 NO MODEL GENERATED 272 TABLE 48. Spearman rank correlation c o e f f i c i e n t s (r) and associated p r o b a b i l i t i e s (P) for variables ranked on v a r i e t i e s Trebi and Odessa. TABLE 48 VARIABLE r P R2 0. 8714 0. 0001 R4 0. 8526 0. 0001 Wp [PATHOGEN] 0. 4118 0. 0712 Wc [PATHOGEN] 0. 7180 0. 0004 W [PATHOGEN] 0. 71 34 0. 0004 Wp [HOST] 0. 0016 0. 9948 Wh [HOST] 0. 2692 0. 251 1 W [HOST] 0. 2165 0. 3591 274 TABLE 49. Spearman rank correlation c o e f f i c i e n t s (r) and associated p r o b a b i l i t i e s (P) for ranking of sp e c i f i e d variable pairs on Trebi (T) and on Odessa (O). TABLE 49 VARIABLES TW [PATHOGEN] TW [HOST] - 0 . 4 0 9 6 0 .0729 OW [PATHOGEN] OW [HOST] - 0 . 2 5 6 0 0 .2759 276 T h i s appendix s t u d y . n_.3 APPENDIX C c o n t a i n s a l l f i g u r e s a s s o c i a t e d w i t h t h i s 277 F I G U R E 1 A Schematic Representation of the Life Cycle of Ustilago hordei (from Ebba, 1974) germination promycelium formation YU) <S>g> 1x2 1x4 C&^P 3x2 3x4 (selfing) o < D x sporidial \ multiplication spor production t t + (n) conjugation teliospore (2n) Non-parasitic Dipbphase Non-parasitic Haplophase Parasitic Dikaryophase teliospore formation dikaryon (n + n) 278 FIGURE 2. Schematic representation of the experimental design. Teliospores (2N) are represented by squares and sporidia (1N) are represented by c i r c l e s . Genetic crosses are shown by an "X". Teliospore and sporidium genotype i s indicated by the virulence a l l e l e symbols (V or v). Parental teliospores T1 (W) and T4 (vv) were crossed to produce 8 F1 dikaryotic l i n e s . Each F1 l i n e was heterozygous for the virulence gene (Vv). Two sporidia containing the dominant virulence a l l e l e (V), but d i f f e r i n g in their nonspecific pathogenicity (Person, 1983), were isolated from the F1 population and crossed to produce F2 teliospores (W). Ten F2 sporidia, 5 of each mating type ("+" and " - " ) , were isolated at random from the F2. A sporidium of the "+" mating type was subsequently l o s t . The remaining 9 sporidia were combined in a l l possible ways to produce 20 treatment dikaryons that were expected to vary.for nonspecific pathogenicity. Seeds of the v a r i e t i e s Trebi and Odessa were inoculated with the treatment dikaryons. 2 7 9 F I G U R E 2 r © - 0 - 0 • • r n 20 280 FIGURE 3. Schematic representation of the relationship of the 4 subsets of variables. Located in the center of the diagram i s a thin v e r t i c a l rectangle delimited with s o l i d and broken l i n e s . This rectangle is a symbolic representation of a treatment row consisting of a l l treated plants at harvest. The rectangle or row i s subdivided into 2 areas. The f i r s t area i s bounded by s o l i d l i n e s and corresponds to the f i r s t 50 plants in the row. The second smaller area i s bounded by broken li n e s and corresponds to a l l plants other than the f i r s t 50 plants. A l l variables calculated from the f i r s t 50 plants were catagorized as R subset variables (row). One R variable (the germination rate of the 110 treated seeds o r i g i n a l l y planted, R1) was calculated from a l l plants in the row. The f i r s t area bounded by s o l i d l i n e s was further subdivided into three smaller areas by d i f f e r e n t i a l shading (unshaded, completely shaded and p a r t i a l l y shaded). The areas are l a b e l l e d H, C, and P to correspond to variables calculated from healthy, completely diseased and p a r t i a l l y diseased plants. 2 8 1 F I G U R E 3 TELIOSPORE WEIGHT (GM) REGRESSION: TELIOSPORE NUMBER v s T E L I O S P O R E WEIGHT 283 FIGURES 5 to 62. The following graphs are frequency histograms for the 58 fit n e s s related variables. Inverted tria n g l e s indicate variable means. F I G U R E 5 Odessa o CD tD OJ -O -r 4 0 4 5 5 0 5 5 6 0 T r e b i o CD 10 OJ H o r 4 0 V 4 5 5 0 5 5 6 0 R l t F I G U R E 6 Odessa m 0 10 15 20 25 30 T r e b i rn OJ o J V 0 10 15 20 25 30 R 2 t F I G U R E 7 Odessa CJ cu c co ID OJ V 50 100 150 200 T r e b i CO 10 cr cu c_ LL OJ V 50 100 150 200 R3 F I G U R E Odessa 8 cr cu r_ l i . m o l r n 1 0 1 0 1 5 2 0 2 5 3 0 T r e b i cr CD t_ L L m O J 1—I—I—I 1—I—I—I—I—I—I 1—I—I—I—I 1—I—I—I i 1 1 1 1 1 1 0 5 1 0 1 5 2 0 2 5 3 0 R4t F I G U R E 9 Odessa in •nT rn 10 20 30 40 Tneb i in on OJ 10 20 30 40 R5 289 FIGURE 10 Odessa cr cu c U. QD 10 V 1.0 1.5 2.0 2.5 3.0 3.5 T r e b i CD I D CJ CU o J 1.0 1.5 2.0 2.5 3.0 3.5 R6 290 FIGURE 11 Odessa CD ID or OJ V 0 . 0 0 . 1 0 . 2 0 . 3 0 . 4 0 . 5 Tneb i CO ID cr cu Li-CU 0 . 0 0 . 1 0 . 2 0 . 3 0 . 4 0 . 5 R7 FIGURE 12 Odessa in m rvi 0 Treb i in m OJ R B 292 FIGURE 13 Odessa i 1 1 1 1 1 1 1 0 . 0 0 . 5 1.0 1 .5 2 . 0 2 . 5 3 . 0 3 . 5 T r e b i i v i 1 1 1 1 1 1 1 0 . 0 0 . 5 1.0 1.5 2 . 0 2 . 5 3 . 0 3 . 5 R9 c r cu c_ L L ID ID m c r cu c LL ID in m CM 293 FIGURE 14 Odessa in m. OJ 0 . 0 0 . 5 1.0 1.5 2 . 0 2 . 5 3 . 0 Tneb i in -j cn OJ r-l O J n 0 . 0 0 . 5 1.0 1 .5 2 . 0 2 . 5 3 . 0 RIO FIGURE 15 Odessa CO ID cu o J t r ~~i r-0 . 0 0 . 2 0 . 4 0 . 6 0 . 8 1 .0 1 . 2 1 . 4 1 Tneb i CO ID i r~ -i 1 1 r 0 . 0 0 . 2 0 . 4 0 . 6 O.B 1 . 0 1 . 2 1 . 4 1 R l l 295 FIGURE IB Odessa CT CU c_ ID ID m C\J V 0 . 0 0 . 5 1.0 1 .5 2 . 0 2 . 5 3 . 0 T r e b i cr cu r_ L i _ CD ID m ru 0 . 0 0 . 5 1 .0 1 .5 2 . 0 2 . 5 3 . 0 R12 FIGURE 17 Odessa in m OJ O 0 . 0 0 . 0 5 0 . 1 0 0 . 1 5 0 . 2 0 0 . 2 5 0 . 3 0 0 . 3 5 T r e b i in m OJ 0 . 0 0 . 0 5 0 . 1 0 0 . 1 5 0 . 2 0 0 . 2 5 0 . 3 0 0 . 3 5 R13 F I G U R E 1 8 Odessa in OJ V 0 . 0 0 . 0 5 0 . 10 0 . 15 0 . 2 0 Tneb i in -i m OJ T-I O 0 . 0 0 . 0 5 0 . 10 0 . 15 0 . 2 0 R14 298 F I G U R E 1 9 Odessa CD CD CU 10 20 30 40 50 60 Treb i CD ID C T Cu c_ Ll_ OJ O J V 10 20 30 40 50 60 R15t F I G U R E 2 0 Odessa CD CO T OJ 0 10 20 30 40 50 T r e b i o CD CD T OJ 10 20 30 40 50 R16 F I G U R E 2 1 300 cr cu r_ LL. 10 I D NT ro CM V Odessa 20 40 60 BO 100 120 140 160 T r e b i or cu c_ L L CO in m OJ o 20 40 60 B0 100 120 140 160 R17 FIGURE 22 Odessa o rH CO UD CM V 0 . 0 0 . 1 0 . 2 0 . 3 0 . 4 0 . 5 0 . 6 T r e b i o rH V co UD OJ 0 . 0 0 . 1 0 . 2 0 . 3 0 . 4 0 . 5 0 . 6 Wp [PATHOGEN] F I G U R E 2 3 302 CO V tD cr L L CU CD V CD cr tu Odessa o.o 0 . 5 1.0 1.5 2 . 0 T r e b i o.o 0 . 5 1.0 1.5 2 . 0 Wc [PATHOGEN] 303 FIGURE 24 Odessa v o . o 0 . 5 1.0 1.5 2 . 0 T r e b i cr tu Ll_ If) m OJ V 0 . 0 0 . 5 1.0 1 .5 2 . 0 W [ P A T H O G E N ] FIGURE 25 Odessa v 100 200 300 400 500 600 T r e b i o CO ID T o V 0 100 200 300 400 500 600 Wp [HOST] FIGURE 26 Odessa v OJ 1 0 0 0 2 0 0 0 3 0 0 0 4 0 0 0 5 0 0 0 6 0 0 0 m OJ T r e b i v 1 0 0 0 2 0 0 0 3 0 0 0 4 0 0 0 5 0 0 0 6 0 0 0 Wh [HOST] F I G U R E 2 7 Odessa LO m cu V 1000 2000 3000 4000 5000 6000 T r e b i ID m cu V 1000 2000 3000 4000 5000 6000 W [HOST] 10 CJ CO c_ C\J 307 F I G U R E 2 8 Odessa CD V 30 35 40 45 50 55 60 T r e b i CD CO or cu c_ Li_ OJ 30 35 40 45 50 55 60 H I F I G U R E 2 9 C D CD C U Odessa 50 100 150 200 CD CD OJ Treb i 50 100 150 200 H2 F I G U R E 3 0 Odessa CO 10 OJ T r e b i CO ID OJ -O J H3 FIGURE 31 ID OJ Odessa CD V 20 40 60 80 100 120 140 T r e b i CD CD OJ 20 40 60 80 100 120 140 H4 FIGURE 32 Odessa CD LO OJ V 20 25 30 35 40 T r e b i CO ID OJ 20 25 30 35 40 H5 FIGURE 33 cr cu c_ CO CD OI O Odessa o V 30 35 40 45 50 Tneb i cr cu c_ o CD CD OJ V 30 35 40 45 50 H 6 FIGURE 34 Odessa CD in ro OJ O J T r e b i ID in m OJ V H7 FIGURE 35 Odessa 1.0 1.2 1.4 1.6 T r e b i 1.0 1.2 1.4 1.6 I . H B FIGURE 36 Odessa CO LO CV1 O J V 65 70 75 80 85 Treb i CO ID OJ o 65 70 75 80 85 H9t FIGURE 37 316 Odessa CT CU ID m CM V 1000 2000 3000 4000 5000 6000 7000 CT CU r_ L L LT) cn CM o T r e b i 1000 2000 3000 4000 5000 6000 7000 H10 FIGURE 38 Odessa cr cu r_ Lu in m cu V 0 B 10 12 T r e b i cr cu c_ LL m rn cu O 0 V 8 10 12 C l 3 1 8 FIGURE 39 Odessa cr cu c L L ID cn OJ V 10 15 20 T r e b i cr cu c L L ID rn OJ 10 15 20 C2 319 FIGURE 40 Odessa o.o 0 . 5 1.0 1 . 5 2 . 0 2 . 5 3 . 0 T r e b i cr cu L L 10 in m OJ 0 . 0 0 . 5 1.0 1 .5 2 . 0 2 . 5 3 . 0 C3 320 FIGURE 41 Odessa cr cu c li_ tD in ro OJ 0.0 0.5 1.0 1.5 2.0 2.5 T r e b i c r cu c_ LL CD in cn cu 0.0 0.5 1.0 1.5 2.0 2.5 C 4 FIGURE 42 Odessa in m 0 . 0 0 . 1 0 . 2 0 . 3 T r e b i in m OJ 0 . 0 0 . 1 0 . 2 0 . 3 C5 FIGURE 4 3 Odessa ID in m V 0 . 0 0 . 0 5 0 . 1 0 0 . 1 5 0 . 2 0 T r e b i CO in cn C X I o -J V 0 . 0 0 . 0 5 0 . 1 0 0 . 1 5 0 . 2 0 C6 FIGURE 4 4 Odessa ID m cu 10 20 30 40 50 60 T r e b i in m OJ 10 20 30 40 50 60 C7t FIGURE 45 Odessa CO ID OJ V 0 T r e b i CO CO OJ o P l FIGURE 46 Odessa CD ID cr cu c_ LL CVI V 0 10 15 20 25 30 T r e b i or cu r_ LL CD CD CVl O J V 10 15 20 25 30 P2 FIGURE 47 Odessa CO 10 CT cu t_ LL CM 0 B 10 Tneb i cr cu c_ LL C O CO OJ o B 10 P3 FIGURE 48 Odessa CD CD -vT CXI o 10 15 20 T r e b i CO CD CVJ o J Z L 10 15 20 P4 FIGURE 4 9 Odessa CO 10 OJ 0 0 Treb i CO ID CU O P5 329 FIGURE 50 Odessa cr cu r_ U. CO CO V -CU -V 0 . 0 0 . 5 1.0 1 .5 2 . 0 T r e b i cr cu LL CO CD CU 0 . 0 0 . 5 1.0 1 .5 2 . 0 P6 330 FIGURE 51 Odessa cr cu c_ LL CD CD C\J 0 . 0 0 . 5 1.0 1 .5 2 . 0 2 . 5 Tneb i CD CD LT CU r_ Li_ CM 0 . 0 0 . 5 1.0 1 .5 2 . 0 2 . 5 P7 331 FIGURE 52 Odessa cr cu r_ Ll_ CO ID CU 0 . 0 0 . 2 0 . 4 0 . 6 O .B 1 .0 T r e b i cr cu c_ LL CO LO \r ru 0 . 0 0 . 2 0 . 4 0 . 6 O .B 1 .0 PB FIGURE 53 Odessa CD tD V -CVl V 0 . 0 0 . 0 5 0 . 1 0 0 . 1 5 0 . 2 0 0 . 2 5 0 . 3 0 T r e b i CD ID CVl V 0 . 0 0 . 0 5 0 . 1 0 0 . 1 5 0 . 2 0 0 . 2 5 0 . 3 0 P9 FIGURE 5 4 Odessa CD ID o J ' -I r -i r -i 1 0 . 0 0 . 0 2 0 . 0 4 0 . 0 6 0 . 0 8 0 . 1 0 0 . 1 2 0 . 1 4 0 . 1 6 T r e b i CD ID OJ i i i 1 1 1 1 1 1 0 . 0 0 . 0 2 0 . 0 4 0 . 0 6 0 . 0 8 0 . 1 0 0 . 1 2 0 . 1 4 0 . 1 6 P10 FIGURE 5 5 Odessa CO n ID CU -1 0 2 0 3 0 4 0 5 0 6 0 T r e b i CO ID CVl V 0 10 2 0 3 0 4 0 5 0 6 0 P l l t F I G U R E 5 6 Odessa ioo 200 300 400 500 600 T r e b i CO ID "\T CU o n £1 0 100 200 300 400 500 600 P12 F I G U R E 5 7 Odessa CO 10 CJ cu r_ LL OJ 0 2 0 4 0 6 0 BO T r e b i CO CO CT CU t_ LL-CM 2 0 4 0 6 0 8 0 P13 337 F I G U R E 5 8 Odessa 03 n 10 or tu c_ LL CM V 0 10 20 30 40 T r e b i CO V ID -c r LL CM -O J l 1 1 1 • • 1 1 1 1 1 1 1 I 1 1 1 1 0 10 20 30 40 P14 F I G U R E 5 9 Odessa 338 10 15 20 25 30 T r e b i cr cu r_ LL CO ID o n 10 15 20 25 30 P15 F I G U R E 6 0 Odessa CO 10 CM -T r e b i CO to cu o -J 0 P 1 B F I G U R E 6 1 Odessa CO to CVl 0 . 0 0 . 5 1 .0 1 .5 2 . 0 T r e b i CO 10 CVl 0 . 0 0 . 5 1 .0 1 .5 2 . 0 P17 341 F I G U R E 6 2 Odessa cr tu L i _ CO n 10 OJ 20 40 60 BO T r e b i cr tu LI-CO 10 "vT -OJ V 20 40 60 80 P I B t 

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