Soybean Rust in the United States

Iowa State University Capstones, Theses and Retrospective Theses and Dissertations Dissertations

2007 Soybean rust in the United States: assess its potential epidemic ranges and frequency based on disease limiting factors, disease attributes, and comparative epidemiology Xun Li Iowa State University

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This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Retrospective Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Soybean rust in the United States: assess its potential epidemic ranges and frequency based on disease limiting factors, disease attributes, and comparative epidemiology

Xun Li

A dissertation submitted to the graduate faculty

in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

Major: Plant Pathology

Program of Study Committee: XiaoBing Yang, Major Professor Mark Gleason William Gutowski, Jr. Mark Kaiser Forrest Nutter, Jr.

Iowa State University

Ames, Iowa

2007

UMI Microform 3259497 Copyright 2007 by ProQuest Information and Learning Company. All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code.

ProQuest Information and Learning Company 300 North Zeeb Road P.O. Box 1346 Ann Arbor, MI 48106-1346 ii

TABLE OF CONTENTS

CHAPTER 1. GENERAL INTRODUCTION 1 Introduction 1 Soybean production 1 Soybean fungal diseases in the U.S. 1 Asian soybean rust 2 Research rationale 2 Research objectives 8 Dissertation organization 8 Literature review 9 History and nomenclature 9 Disease geographical distribution and pathogen host range 10 Pathogen life cycle and morphology 11 Urediospore germination, infection, and survival 12 Disease symptoms, development, and pathogen sporulation 13 Pathogen virulence and physiological races 14 Yield/economic losses and disease management 15 Epidemiology and disease modeling 16 Disease risk assessment 19 Literature cited 20

CHAPTER 2. ESTIMATION OF SOYBEAN RUST UREDIOSPORE TERMINAL VELOCITY, DRY DEPOSITION, AND WET DEPOSITION BY RAINFALL 34 Abstract 34 Introduction 34 Materials and methods 37 Terminal velocity (Vs), density (ρp) of urediospore, and dry deposition 37 Estimation and the fitted model of scavenging coefficient 40 Scavenging efficiency associated with rainfall rate and duration 41 Comparison of urediospore wet deposition and dry deposition 41 Results 42 Terminal velocity of urediospore and estimated urediospore density 42 Urediospore scavenging coefficient by rainfall and the model fitting 43 Change of urediospore concentration during a rainfall event 44 Comparison of urediospore deposition under different dry and rainfall conditions 44 Discussion 45 Acknowledgements 47 Literature cited 47

iii

CHAPTER 3. GROUPING OF FUNGAL DISEASES IN THE UNITED STATES SOYBEAN PRODUCTION REGION: IMPLICATIONS TO THE RISK OF ASIAN SOYBEAN RUST 58 Abstract 58 Introduction 59 Materials and methods 63 Fungal diseases in the U.S. soybean production region 63 Biological and ecological characteristics of the soybean pathosystems 65 Similarity coefficients among the pathosystems and the principal coordinate analysis 67 Pathosystem classification based on cluster analysis 67 Results 68 Similarity coefficients among the soybean pathosystems 68 Principal coordinate analysis and projected pathosystems in a 2-dimension space 68 Pathosystem classification based on cluster analysis 70 Discussion 72 Acknowledgments 76 Literature cited 76

CHAPTER 4. ASSESSING EFFECTS OF TEMPERATURES ON OUTBREAK RISK OF ASIAN SOYBEAN RUST WITH BIOGEOGRAPHY INFORMATION OF RUST DISEASES IN NORTH AMERICA 98 Abstract 98 Introduction 99 Materials and methods 102 Rust diseases in North America and their temperature attributes 102 Operational definition for geographical distribution range of rust diseases in North America 103 Relationships of the optimal infection temperatures of rust pathogens to July mean temperatures along the northern boundaries of disease ranges 103 Assessment of temperature favorability for soybean rust infection based on local historical temperature data 104 Results 106 Relationships of the optimal infection temperatures of rust pathogens to July mean temperatures along the northern boundaries of disease ranges 106 Assessment of potential geographical distribution range of soybean rust in North America 106 Assessment of temperature favorability for soybean rust infection using local historical temperature data 107 Fitted Beta distribution for standardized temperature favorability 108 Discussion 109

Acknowledgements 112 Literature cited 112

CHAPTER 5. COMPARISONS OF SOYBEAN RUST PATHOSYSTEMS IN MAJOR SOYBEAN PRODUCTION REGIONS IN TERMS OF RISKS OF THE DISEASE IN THE UNITED STATES 140 Comparisons of the soybean rust pathosystems and occurrence among China, South America and the United States 140 Soybean production and the other hosts 140 Permanent establishment of soybean rust pathogen 141 Comparisons of the regional climate among China, South America and the United States in terms of the risks of soybean rust 141 Comparisons of climate favorability to soybean rust between China and the U.S. 141 Climate favorability in South America 146 Summary of climate favorability to soybean rust among China, the U.S. and South America 147 Favorability of rainfall and wind to soybean rust in the United States 148 June and July rainfall favorability to soybean rust in the U.S. based on a disease predictive model 148 Prevailing winds to soybean rust 150 Literature cited 151 CHAPTER 6. GENERAL CONCLUSIONS 165 APPENDIX 167

CHAPTER 1. GENERAL INTRODUCTION

Introduction

Soybean production. Soybean (Glycine max L. (Merr.)) is one of the most important crops and commodities in the world that provides food of quality plant proteins for human, forage for domestic animals, and industrial materials (4). Originating in Asia and domesticated by Chinese farmers more than three thousand years ago, soybean has been introduced to many countries in all continents except Antarctic. In particular, soybean has become one of the major crops in some New World countries such as the United States,

Brazil, Argentina, and Canada, which recently contributed about 80% of the world soybean production (4, 26, 88). Soybean planting area and yield have continued increasing in the last several decades, and will very likely keep increasing in the near future (4).

In the U.S., soybean was introduced in 1765 and has became a major crop in the

Midwest and eastern coastal regions (4, 26). Currently the U.S. soybean growing area is from latitude 28 to 49°N with most above 35°N (96). The North Central region is the core production region, which is also called the Soybean Belt including states such as Nebraska,

Minnesota, Iowa, Illinois, Indiana, Ohio, etc. The U.S. was the No. 1 soybean producer, contributing more than 40% to the global yield and export as recorded in the late 1990s (4).

In 2006, total planted area of soybean was about 3.05x107 hectares and total yield was about

8.68x107 metric tons (61).

Soybean fungal diseases in the U.S. Soybean production in the U.S. is frequently attacked by plant diseases, particularly the fungal diseases (26, 55, 98-101, 105). By the end of 1990s, more than thirty fungal diseases had been reported throughout the U.S. (26, 55, 101,

105). Some of them such as Phytophthora rot (Phytophthora sojae), charcoal rot

(Macrophomina phaseolina), brown stem rot (Phialophora gregata), sudden death syndrome

(Fusarium solani f. sp. glycines), and white mold (Sclerotinia sclerotiorum) have been causing great yield damage recently (26, 97-100).

Asian soybean rust. Asian soybean rust, SBR, caused by Phakopsora pachyrhizi

Sydow recently entered the continental U.S. in 2004 (79). International collaborative monitoring programs on soybean rust based on sentinel plots, spore trappers, and mobile scouting teams have been carried out throughout North America since the rust was reported in 2004 (22, 33). Meanwhile, intensive research work has been done in aspects of soybean rust such as possibility of permanent establishment, long-distance dissemination, disease field detection and development, and fungicide control (17, 31, 33, 38, 47, 49, 64-67). By the end of 2006, soybean rust had been reported in 15 states in the continental U.S. (22).

Fortunately no severe yield losses have been reported and a few important soybean production states such Iowa and Minnesota were still free of this disease. Meanwhile, soybean rust is still considered a big threat by producers in major soybean production regions because it has been causing great yield losses in other major soybean production countries, such as China and Brazil (13, 26, 88).

Research rationale. Quantitative soybean rust epidemiology started from effects of temperature and dew period on Phakopsora pachyrhizi urediospore germination and infection. Like other rusts, urediospores of P. pachyrhizi need free moisture (at least 6 h duration) and certain temperature conditions (20 to 25°C) for infection (53, 95). Under controlled conditions, optimal temperature for soybean rust development is 17 to 27°C, under which the latent period is 9 days (43). Besides, previous efforts were made to develop

3 soybean rust simulation model based various approaches with specific environmental factors such as leaf wetness duration (dew period) and physiological day (3, 38, 104, 106). These studies indicated the effects of temperature and moisture to the disease establishment and development, also built up the foundations for further research for disease risk assessment and prediction.

Although SBR is native in Asia and had been found over a hundred years ago, research on this disease particularly the disease epidemiology was scarce probably because the disease was not a concern in major soybean production regions in Asia such as northern and northeastern China. Most research work was done in southern China where soybean rust establishes year-around or frequently occurs, but on the other hand where soybean is a less important crop with limited acreage (86, 88). Available epidemiological studies were on modeling disease development and seasonal forecasting using rainfall and rain days (86). For example, Tan et al developed disease severity predictive model based on linear regression analysis of disease severity vs. rainfall and rain days in the key months of the soybean growing season (Aug or Sep) in Wuhan (87). Similar predictive models were developed in

Lishui and Datian as well on the basis of local disease and rainfall data (86). The fact that these models were based on rainfall as the predictor implied that temperature was not a limiting factor for soybean rust development at these locations, which are in 25 to 30°N adjacent to the year-around established region of soybean rust in China. Under certain circumstances, rainfall data may have a universal power for regional disease prediction, as these locations are scattered hundreds kilometers away from each other. In northern and northeastern China, outbreaks of the rust were sporadically reported, and field observations also implied association of rainfall with the disease outbreak in these regions (86, 88).

Unfortunately, detailed epidemiological research either in local or regional scale in these major soybean production regions was not available.

In Brazil, soybean rust has become a major concern to soybean production since it was reported first in 2001 (112, 113). A similar predictive model for the rust was developed by Del Ponte et al based on Brazilian disease data and rainfall in the key soybean growth stage in 2003 to 2005 (17). The model well explained disease severity by amount of rainfall and rain days without incorporating temperature factors. As Brazil is in a tropical area, where soybean rust pathogen is able to overwinter and establish year-around (65), the absence of temperatures in this predictive model may be reasonable. No other empirical prediction models based on observed disease and weather data are available from other countries.

Soybean rust urediospores need free moisture for infection that can be provided by rainfall. The empirical predictive models from China and Brazil may just reflect this effect.

On the other hand, rainfall may also facilitate airborne urediospores to deposit according to previous studies on other fungal spores (23, 30, 91, 107, 115). Since soybean rust uredopsores are capable of long-distance dissemination, rainfall rate and frequency in a distant area could be associated with the initial disease establishment in the field. Thus, a quantified analysis is necessary for this aspect as soybean rust occurrence in the North

Central regions in the U.S. depends on urediospores from the south by long-distance dissemination.

Before the 1990s when soybean rust had not spread throughout the world, plant pathologists already noticed the potential risks of outbreaks and impact of this disease on soybean production in the Americas (48, 103). Some key conceptual frameworks of plant disease risk assessment were built based on soybean rust research (48, 89, 102, 103).

Generally, disease risk assessment is to identify disease risk source and evaluate possibility of disease establishment, magnitude of outbreak, and disease negative impacts on crops (89,

102, 103). Since the 1990s when the rust started to spread throughout the world from Asia to

Africa, then to South America, and then finally to North America in 2004, soybean rust risk assessment again became a hot area.

The regional risk of soybean rust is mainly associated with 1) availability of hosts, 2) possibility of permanent disease establishment, 3) local environmental favorability for disease establishment and development, and 4) introduction of disease initial inocula. In the

U.S., host availability is not a problem. Right before soybean rust reached the continental

U.S., Pivonia and Yang (65) assessed the general climatic favorability throughout the world and outlined regions where climate conditions meet the requirement of year-around establishment of soybean rust. They indicated that part of the southern U.S. is suitable for

Phakopsora pachyrhizi to overwinter and establish permanently, which was proved correct by field observations after the disease was introduced (22, 65). Pivonia and Yang further used disease temperature attributes and climatic data from four representative locations in the

Midwest to assess the seasonal appearance time of soybean rust (67). The authors suggested that soybean rust would probably show up in the later growing season in the North Central region between the appearance time of common corn rust (Puccinia sorghum) and southern corn rust (Puccinia polysora) (67). This study did not give detailed regional disease risk as only four locations were used, nor give consideration on the factors of rainfall or dew. On the introduction of disease initial inocula, particularly for the North Central region where P. pachyrhizi cannot overwinter, several research groups did similar work based on meteorology models such as the regional climate model (MM5) and the particle transport and

6 dispersion model (HYSPLIT) (32, 47, 64). Their work outlined the possible major routes of urediospore long-distance dissemination from the disease source areas in South America and the southern U.S. However, there is huge uncertainty due to lack of reliable disease intensity information in those source areas. Therefore, a comprehensive risk assessment targeting soybean rust risks in the major soybean production states particularly in the North Central region is necessary but still not available.

Currently disease risk assessment has been done case by case without a universal methodology available (16, 65, 66, 78, 89, 102, 103). As soybean rust was introduced in the

U.S. in late 2004, to assess disease risks based on three years disease occurrence information is certainly very difficult. Alternately, one may conduct risk assessment based on favorability of disease-related regional environmental conditions. For example, Scherm and Yang used temperature, moisture, and cold stress response function to assess risks of sudden death syndrome (SDS, Fusarium solani f. sp. glycines) (78). For soybean rust, which is a very different case in pathogen life cycle and disease cycle from SDS, risk assessment based only on favorable environmental conditions is probably not enough. The reasons are 1) soybean rust is an airborne leaf disease with short-life dispersal units, which implies huge disturbance in the disease cycle from environmental conditions that may result in big variance in seasonal or annual disease occurrence. This is like estimating a sample mean based on a sample with big variation from a population with unknown mean. Thus, a reliable quantitative analysis on such disturbance is necessary but one needs a relatively big disease occurrence dataset that is not available in the U.S. Particularly in a regional scale, as such relationship of disease variance and environmental conditions is not known, the risk assessment based on disease key factors may not give very accurate results unless there is reference information to

7 validate the estimated risks. 2) In regional scale, specific monitored field environmental condition data such as dew period used in previous studies are usually not available, which prevents the soybean rust simulation models built before with such data from directly being used for a larger geographical regional scale risk assessment.

Agriculture systems have many plant diseases, e.g. fungal diseases, rust diseases.

Plant pathologists categorize and compare different plant diseases based on their similarity or dissimilarity in various aspects, such as disease cycle, pathogen life cycle, which have led to the foundation of comparative epidemiology (1, 44-46). The similarity among plant diseases implies that some well-documented diseases may provide more or less valuable information for management and forecasting for similar diseases. For example, one may choose protective fungicide for a newly introduced leaf rust disease based on his/her knowledge of other leaf rust diseases. For the same reason, to risk assessment of a disease, information from other diseases in regard to ecological or biological characteristics would be helpful to bridge the gap of unknown information. The information from other well-known diseases may act as a reference coordinate system, within which the target diseases can be positioned according to disease attributes.

Besides similarity among plant diseases, regional geographical distribution of different diseases may follow certain patterns, due to the attributes of diseases/pathogens and regional environmental conditions. Such geographical patterns are commonly seen in ecology research of species (9, 10). In plant pathology, Yang and Feng found that soybean fungal diseases geographical distribution in the U.S. may follow certain patterns, e.g.

Rapoport’s rule that species centered in higher latitude tend to cover larger geographic area

(105). Further more, a comparative study using modeled disease progress of soybean rust

8 compared with several other rust diseases by Pivonia and Yang indicated that the seasonal appearance time of soybean rust would be between appearance time of common corn rust

(Puccinia sorghi) and southern corn rust (Puccinia polysora) in the North Central region due to their temperature attributes (67). Such sequential appearance of different rusts may be considered as temporal pattern of plant diseases. Similar to what was mentioned above, these patterns may serve as reference coordinate systems to position unknown diseases and help to assess disease risks. Once positioned, the risks of target diseases could then be assessed based on similarities among disease and related information from other diseases.

Research objectives. The study has four objectives: 1) to quantify wet deposition of

Phakopsora pachyrhizi urediospores associated with different rainfall rates and compare the difference between urediospore wet deposition and dry deposition; 2) to understand suitability of soybean rust in aspects of biology, ecology, and epidemiology in the U.S. by comparing the similarities of soybean rust with other soybean fungal diseases in the U.S. based on their biological and ecological characteristics; 3) to determine the potential geographical range of soybean rust by examining the relationship between geographical distribution range and the key temperature characteristics of plant rust diseases in North

America; 4)to assess regional risks of the minimum occurrence and the likelihood of outbreaks of the disease in the U.S. based on results in 1), 2), 3), and other information including historical data of yield losses of other soybean fungal diseases, weather data, and regional climate profiles.

Dissertation organization

The dissertation has six chapters and one appendix. Chapter 1 is the general introduction outlining this research and reviewing literature of soybean rust (Phakopsora

9 pachyrhizi) in biology, ecology, epidemiology, and disease risk assessment. Chapter 2 addresses objective 1, i.e. urediospore wet deposition associated with rainfall. Chapter 3 addresses the work of objective 2, which is the examination of similarity and the grouping of soybean fungal diseases in the U.S. Chapter 4 is to answer the questions in objective 3, which is an examination of relationship between rust diseases geographical distribution range in

North America and disease/pathogen temperature attributes. Chapter 5 is for the comparisons of the related regional climates and disease information from China and South America to address the soybean rust disease risks in the continental U.S. Chapter 6 is the general conclusion summarizing results in Chapter 2-5. The appendix is an abstract for a study done before for historical changes of disease prevalence of five major diseases in wheat and rice associated with long-term temperature changes in China. Although this study was not directly related with soybean rust, such long-term temperature changes due to global warming and their associations with plant diseases may have implications for the future risks or occurrence of soybean rust in the U.S.

Literature review

History and nomenclature. The pathogen of Asian soybean rust, Phakopsora pachyrhizi was originated in Asia and was first found by Torama Yoshinaga in Japan in 1902

(7, 88). Bromfield gave detailed descriptions on the nomenclature in 1984 (7). The fungus was named as Uredo sojae Henn by Hennings in 1903 (7). Then Sydow first used the name of Phakopsora pachyrhizi in 1914 (84). Other synonymies used due to specimen collected from different locations and hosts include Phakopsora sojae Fujikuro, Phakopsora sojae

Sawada, Uredo vignae Bres., Uredo concors Authur, Uromyces sojae Sawada, and

Physopella pachyrhizi (Sydow) Azbukina etc (7, 116). Finally, according to Hiratsuka, Sathe,

10 and Ono, Phakopsora pachyrhizi Sydow was adapted (7, 63, 77, 116). The current classification of P. pachyrhizi is: Kingdom Fungi; Phylum Basidiomycota; Class

Basidiomycetes; Order Uredinales; Family Malampsoraceae; Genus Phakopsora; Species pachyrhizi (7, 62, 88).

Disease geographical distribution and pathogen host range. After first reported in

Japan, soybean rust was found wildly spread in most Asian tropical or subtropical areas where soybean or other leguminous host plants grow (13, 88). In China, soybean rust was found year-around in southern and southwestern regions such as Guangxi, Guangdong,

Hainan, Yunnan, and Taiwan; while the disease frequently occur in eastern and southern middle China, e.g. Hunan, Hubei, Jiangxi, and Zhejiang (87). In temperate climate region of eastern Asia such as northern and northeastern China, the disease presents occasionally due to long-distance dissemination of pathogen urediospores, however, which is not a big concern in these major soybean production regions (88, 114).

Before the 1990s, besides China and Japan, the rust also had been found in many other Asian countries such as Philippines, Thailand, Indonesia, Korea, Malaysia, Burma,

Vietnam, Cambodia, Nepal, India, and in Australia as well (7, 88). The first confirmed soybean rust report in Africa was in 1996 from Kenya, Rwanda, and Uganda (60). Soon it was found in Zambia and Zimbabwe in 1998, Nigeria in 1999, Mozambique in 2000, and

South Africa in 2001 (2, 60, 70). The westward movement of soybean rust continued until it finally reached the Americas. In 2001, Paraguay became the first reported country with soybean rust in South America (60). In the following years, soybean rust spread quickly in

Brazil and Argentina, (68, 72, 112, 113). In the U.S., the disease was reported in Hawaii in

1994, however, until 2004, it was first reported in the continental U.S. (37, 79).

Phakopsora pachyrhizi has a wide host range, including at least 95 species and 42 genera of family Fabaceae (7, 13, 63, 73, 80, 88). Besides soybeans (Glycine spp.), other important hosts include kudzu (Pueraria spp.), and jicama (Pachyrhizus spp.) etc. (86). In some countries such as China, the U.S., and Argentina, the wild or cultivated kudzu plants are particularly important in soybean rust disease cycle as a overwintering host (14, 24, 29,

86, 88).

Pathogen life cycle and morphology. Phakopsora pachyrhizi is an obligate parasitic fungus (7, 63, 88). Although rust fungi may have five different stages in their life cycle (1), the complete life cycle of P. pachyrhizi is still not fully understood due to unknown aecia and pycnial stage, as well as the alternate hosts, which also leaves whether the rust is autoecious or heteroecious unknown (7, 88). Under natural conditions, the uredinial stage and telial stage of P. pachyrhizi occur, and field observations indicate that teliospores serve as overseasoning structures but with an unknown status of their role in disease epidemics, i.e. only urediospores seem to serve as inoculum (7, 54, 88). Such observations imply that living hosts are necessary for soybean rust pathogen over-seasoning.

Uredinial stage is the most common stage of soybean rust pathogen. Uredinia generally are on the leaf lower surface, subepidermal, and erumpent (7, 88). Color is light tan to reddish brown (7, 88). Urediospores of P. pachyrhizi are ovoid, light brownish or yellowish, usually 17 to 45 µm long and 13 to 29 µm wide (7, 88). Under nature condition, urediospores tend to clump together (7). In Nanning, China, with fresh collected urediospores from wild kudzu leaves, it was found that only 3 to 6% urediospores were single, and under humid environmental condition, urediospores tended to form clumps more easier than under dry condition (51).

Under certain environmental conditions, the fungus can have telial stage, which is rare in tropical area, and the epidemiological effect of telial stage in the disease cycle is not clear (7, 69, 85, 88, 110). Teliospores are single cell spores, light yellowish, about 14 to 30

µm long and 5 to 13 µm wide (7, 69, 88). Germination of teliospores under nature condition is not reported, but under artificial conditions (10 to 12 hours wetting and drying cycles at room temperature), teliospores can germinate and produce 1 to 4 basidiospores (41, 74, 88).

Urediospore germination, infection, and survival. Bonde et al found that urediospores of Phakopsora pachyrhizi germinate in 1 to 2 hours in a dew chamber at 20°C in dark (5). Marchetti et al found that urediospores germinate at 10 to 28.5°C (optimal 15 to

24°C) (53). Isolates from different geographical regions may have slight difference in germination temperature range (88). Singh in India found that minimum temperature for urediospore germination was 20°C; and when urediospore were exposed to 35°C after 6 hours, the urediospores would lose viability (82). However, in Australia, minimum urediospore germination temperature was found to be 8°C, and maximum was 30°C (36).

Generally, urediospores germinate in temperature range 8 to 36°C, optimal range in 15 to

26°C, and 23 to 24°C is the best (7, 88). Bonde reported appressoria formation within 2 hour after germination begins at 20°C (5), Kitani and Inoue however found that in 2.5 to 3 hours appressorium formation begins (7). Koch studied effects of lights on urediospore germination and found negative phototropism of germ tubes to artificial light (40). Urediospore germination tests from Wuhan, China suggest prohibition effects from direct solar light to urediospore germination (87).

P. pachyrhizi urediospore infect plants mostly via direct penetration through the leaf epidermal, a few through stomata (39, 42). Optimal temperature for infection is at 20 to 25°C

13 with 10 to 12 hours dew. Six hours dew is minimum for infection at 20 to 25°C. No infections occur at temperature above 27.5°C (53, 88, 95). Pivonia and Yang suggested that cardinal temperatures of urediospore infection are 10, 22.5, 27.5 °C using fitted classical temperature response curve for biological systems (12, 67, 71). The fitted curve suggests that

15 to 26°C be the optimal range for infections as the relative infection index is larger than 0.5 in this range (67).

Phakopsora pachyrhizi urediospores may lose viability easily under extreme environmental conditions, e.g. high temperature as mentioned above. Tan et al in China found that urediospore could survive in room under nature temperature (13 to 24°C) for two months, while in outdoor environment under 8 to 30°C for only 27 days. Under low temperature (5 to 8°C), they could only live about 18 days (87, 88). Isard et al reported that direct solar light (solar radiation ≥27.3 MJ/m2 or ultraviolet (UV) ≥1.2 MJ/m2) could cause

loss of urediospore viability (31).

Disease symptoms, development, and pathogen sporulation. In early stage of

disease symptom development, lesions on soybean leaf are tiny chlorotic areas (about 0.5 to

1 mm2, lesion density may vary dependent on severity) and then become tan or brown

gradually. Finally slightly raised pustules come out usually on the lower leaf surface with

eruptions in the pustule center where uredinia release urediospores (7, 88). When severity is

high, plants may defoliate early (7).

Keogh found soybean rust latent period about 9 days at night/day temperature

20/25°C with 12 hours light, while 18 days at temperature 15/20°C with 12 hours light (7,

36). Kochman conducted experiments with four night/day temperature regimes (7/17, 12/22,

17/27, and 22/32 °C) for temperature effects on disease development and found that under

14 temperature regime 17/27°C, latent period was 9 days, while 7/17°C it needed 14 days (43).

Generally, latent period may be in a range of 6-10 days under optimal temperature conditions

(88).

The infectious period of soybean rust on soybean plants may be in a range of 10 to 36 days, or even longer (7, 88, 109, 110). Melching et al observed newly produced uredinia even after 52 days of inoculation (58). Tan et al found a short infectious period about 10 days under high temperature in 22 to 28°C, but a longer infectious period about 26 days under temperature at 15°C (87). According to Bromfield et al, the number of uredinia per lesion on the lower leaf surface was greater than upper leaf surface and increases with time (8). Total number of urediospores produced in a lesion was reported by Melching et al and Yeh respectively, but with significant difference (58, 109). According Melching et al, in a 39-day period, total spores per lesion from isolates from Australia, India, Indonesia, and Taiwan was

2028, 3768, 6268, and 6600 respectively in a greenhouse experiment in the U.S. (58). Yeh in

Taiwan however found that in a 36-day period, mean total urediospores production per lesion on soybean cultivars TK5 and PI 230971 in a growth room were 12646 and 9396 respectively (109).

Pathogen virulence and physiological races. Lin first reported 6 pathotypes of

Phakopsora pachyrhizi in different soybean cultivars and other legume hosts in 1966 (52).

McLean and Byth found two races in Queensland, Australia by using cultivars Wills and

PI200492 (56). Singh and Thapliyal further gave a list of about 20 soybean cultivars that may be used as differentials for P. pachyrhizi race identification (81). Bromfield observed host physiological interaction of different P. pachyrhizi isolates from Australia, India, Puerto Rico, and Taiwan on different soybean cultivars and found three infection types, i.e. TAN, with tan

15 lesions about 0.4 mm2 indicating host susceptibility; RB, reddish-brown lesions about 0.4

mm2 indicating resistance associated with hypersensitivity; and type 0, no visible rust

symptoms indicating immunity or near immunity (8). Ding inoculated 12 isolates on 11

soybean cultivars and identified at least five physiological races (88). Yeh used five cultivars

(Aukur, TK5, TN4, PI200492, and PI230971) to differentiate 50 isolates from different

locations in Taiwan and identified three physiological races according to infection types (Tan

and RB) showed on the soybean plants (108).

Yield/economic losses and disease management. Soybean yield losses caused by

soybean rust have been reported world wide, which may be up to 30% to 50% in the field (6,

7, 11, 20, 25-28, 35, 68, 88, 93, 97, 106, 111). However, under certain favorable

environmental conditions, the soybean yield losses in field could be up to 80% to 100% if no

disease control is taken (88). A few studies were conducted to model observed soybean yield

losses associated with disease severity using critical point model or area under disease

progress curve (AUDPC), and AUDPC seemed to give better results (11, 93, 106). Estimated

yield losses in the southern U.S. in years of favorable environmental conditions may be up to

50% (103). The estimated overall economic losses due to soybean rust may be up to $7.2

billion/year in the U.S. (48). In China, disease yield losses were estimated as at least 108 kg/year, and economic loss was 0.2 to 0.3 billion Chinese Yuan per year in the 1990s (114).

In 2003, Brazil, the disease caused economic losses in states of Mato Grosso and Bahia alone were 487.3 million US dollars (113).

Although ideal disease control is to use resistant/tolerant soybean cultivars, most available commercial cultivars are susceptible to Phakopsora pachyrhizi (34, 88). In

Zimbabwe, all local cultivars were reportedly susceptible to soybean rust (90). A few

16 resistant soybean cultivars such as PI200490, PI200492, PI459024, PI459025 have been used in Asia, which provided good yield (88). Four major resistance genes (Rpp1, Rpp2, Rpp3, and Rpp4) were identified in soybean cultivars PI200692, PI230970, PI462312, and

PI459025 (25, 34). Tolerant cultivars were also tested in the field in major soybean production countries such as Brazil and China (15, 88). Tests in west Bahil, Brazil indicated an absence of tolerance in major commercial cultivars there (15).

Soybean rust chemical control has been studied since the 1960s in Asia (88, 94).

Early tests indicated that many protective or eradicative fungicides such as Bayleton, Daconil,

Dithane M-45, etc could be used for soybean rust control, by which the yield losses could be reduced 23% to 50% (88). Currently, the major disease management still relies on fungicides, e.g. in Brazil, about 80% of the soybean fields were sprayed twice a year with fungicides that costs 544 million US dollars (50, 113). Triazoles and strobilurins can provide good protection, but timely application is important (21, 50, 113). A few studies on biological control showed promising results but more work is needed before this approach can be used commercially (7, 75, 76, 88). Modification in cultural practices may be easier for soybean rust control in some regions, such as change planting data, use early maturing groups, and avoid fields that could easily be saturated with moisture (7, 88).

Epidemiology and disease modeling. The pathogen Phakopsora pachyrhizi spreads in the field by airborne urediospores from disease foci. Soybean rust epidemiological research began from quantitative observations of disease foci and spread in the field early on by Japanese plant pathologists (7). In a field experiment, the disease was found to spread with about 1m/day from the disease focus (7). A single diseased leaf may be enough to cause the disease epidemic in a field (7). Apparent infection rate (r) was quantified by several

17 researchers in different countries, which was usually in a range of 0.01-0.376, and associated with environmental conditions and host-pathogen interactions (38, 92, 104, 106).

Temperature, moisture (rainfall, dew, or fog), winds (local winds, low-level jets, or even hurricanes), and light were considered to be important environmental factors affecting disease epidemics (7, 38, 57, 87, 88, 104, 106). Temperature range in 15 to 26°C is optimal for soybean rust disease epidemics (7, 57, 88). High temperature above 27°C hampers disease development by reducing urediospore germination rate and viability (53, 65-67, 88).

In Hubei, China, when early July mean temperature reached 25.5 to 32.8°C, little soybean rust could occur, while until fall, i.e. middle to late September, mean temperature dropped to below 25°C, soybean rust started to build up (88). In Yunan, China, where maximum monthly mean temperature is 19 to 22°C, soybean rust develops very well except in winter

(88).

Previous field observations implied importance of rainfall to soybean rust epidemics

(68, 86, 88). A few studies conducted in different locations in China by Tan et al were on modeling disease development and forecasting disease severity on the basis of rainfall amount and rain days in key months (Aug, Sep, Oct) in soybean growing season (86). Similar predictive model were developed by Del Ponte et al with Brazilian disease data and weather data (17). These models imply that when temperature is not a limiting factor for disease development, rainfall may be used for prediction of disease development, or probably regional risks. Effects of dew (leaf wetness) on pathogen infection and disease development has been well documented (53, 59, 95). Dew was also used for modeling soybean rust development, e.g. Yang et al developed a soybean rust simulation model (SOYRUST) using dew period and temperature as input (104). Unlike rainfall, disease predictive model based

18 on dew is not available currently, probably either due to availability of dew data or different epidemiological effects of dew and rainfall. Besides rainfall and dew, fog has also been observed be a possible factor favoring soybean rust development in mountainous areas in southern China where the disease may be severe even without much rainfall but with plenty of fog (86, 88).

As soybean rust is an airborne disease, winds naturally affect disease epidemics in all scales from fields to geographical regions. In the field, as P. pachyrhizi urediospores tend to clump each other, strong winds could pick up urediospore clumps more easier and increase spore dispersal distance (7, 88). The most important effect of wind to soybean rust epidemics is probably the long-distance dissemination including regional and inter-continental dissemination, which has contributed to the westward movement of soybean rust from Asia to the Americas, and to the U.S. (32, 64, 79, 88). HYSPLIT and MM5 models have been used in various studies on prediction airborne urediospores dissemination in the U.S., which may be used for prediction of possible disease establishment in new regions (32, 47, 64).

However, huge uncertainty exists in aspects of urediospore escape from the canopy, spore survival in high altitude, etc.

Direct solar light has been reported to inhibit Phakopsora pachyrhizi urediospore germination and to increase urediospore mortality greatly (31, 88). Koch also reported negative phototropic response of P. pachyrhizi urediospore germ tubes to artificial light (40).

In China, higher disease severity was observed in mountainous area and shaded area in field where daylight time or light intensity was relatively short or low (86, 88). In a preliminary experiment with different treatments of solar light intensity, Dias et al found higher disease incidence under lower light intensity (18). Dias et al also found possible association between

19 disease development and regional cloudiness in Brazil and South Africa, which implies that the reduced solar light favor disease development (19). However, more detailed quantitative studies are not available on the effects of light on soybean rust epidemics currently.

Disease risk assessment. Although Asian soybean rust was first reported and established in the southern U.S. states in November 2004 (79), as a destructive disease in other soybean production countries, soybean rust has drawn much attention from plant pathologists due to the possible risks to the U.S. soybean industry in the 1980s (7, 48, 103).

Estimated economic losses due to soybean rust to the U.S. soybean industry could be up to billions US dollars (48). Yang et al conducted a series of studies on the risks of soybean rust in the U.S., which not only tried to address the soybean rust risk, but also built the plant disease risk assessment framework using soybean rust as a model system (89, 102-104).

After the rust was reported in South America and caused serious yield losses, more detailed research on disease risk was conducted in various aspects. Pivonia and Yang first assessed the year-round establishment of P. pachyrhizi in the world using CLIMAX, a software package developed by Sutherst and Maywald (83), based on pathogen biological characteristics in aspects of temperatures and moisture, which indicated that the environmental conditions in part of the southern U.S. are suitable for the pathogen permanent establishment (65). By comparing epidemiological factors (overwintering, growing season, wind dispersal, and alternative hosts) with China, Pivonia et al further assessed potential epidemic of soybean rust in the U.S. and indicated likelihood of soybean rust occurrence but with a complicated picture (66). Still using comparative epidemiology approaches, Pivonia and Yang compared soybean rust with four different rust diseases (common corn rust

(Puccinia sorghi), southern corn rust (P. polysora), peanut rust (P. arachidis), and wheat leaf

20 rust (P. triticina)) commonly seen in the U.S. Midwest on the basis of their temperature attributes (67). They suggested that soybean rust may be seen in the North Central regions in the field between the appearance time of common corn rust and southern corn rust (67). The possibility of introduction of urediospores to the northern U.S. were addressed by several research groups indicating the usefulness of meteorology models in soybean rust risk assessment (32, 47, 64).

By November 2006, soybean rust has been reported in 15 states in the continental U.S.

(22). Even though the most important soybean production states such Iowa and Minnesota were still free of this disease and no severe yield losses have been reported, big threats from soybean rust to these major soybean production regions still exist. Much has been answered by previous studies, however, two questions still need be answered: 1) what is the potential geographical distribution range of soybean rust in North America? and 2) how likely are disease outbreaks in the U.S. that would cause great yield losses?

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CHAPTER 2. ESTIMATION OF SOYBEAN RUST UREDIOSPORE TERMINAL VELOCITY, DRY DEPOSITION, AND WET DEPOSITION BY RAINFALL

A paper prepared for submission to Phytopathology

X. Li, X. B. Yang, and J. Y. Mo

Abstract

Terminal velocity of freshly collected Asian soybean rust (Phakopsora pachyrhizi

Syd.) urediospores was measured in Nanning, China. Observed terminal velocities were

fitted by monomolecular models. Single urediospore average terminal velocity is 1.87 cm/s

based the fitted models. Wet deposition for single P. pachyrhizi urediospores due to rainfall was determined for different rainfall rates by coupled models, in which capture efficiency of raindrops to urediospores was based on Slinn’s semi-empirical model, raindrops distribution model was based on log-normal model. Rainfall rate at 0.5 mm/hour, 60 minutes rainfall duration time would result in 50% urediospores removed from the air. Rainfall rate is 5 mm/hour, 10 minutes is enough to remove 50% urediospores. Single urediospores dry deposition was estimated based on simplified scenarios. If urediospore clouds are of 1000 meters thickness and presented in 1000 to 2000 meters height level in the air with uniform concentration, urediospore dry deposition is much less than wet deposition in unit time. In the first 16 hours, almost no urediospore can reach the ground; while wet deposition in 30 minutes with 2 mm/hour rainfall rate is already greater than 24 hours dry deposition.

Introduction

Propagule dissemination is a key step for establishment of fungal diseases by airborne pathogens in new plants, fields, or regions. Soybean rust caused by Phakopsora pachyrhizi

Sydow is a fungal disease where airborne urediospores are capable of long-distance

35 dissemination(8,9,22).AirborneurediosporesofP.pachyrhiziareaerosols,definedas suspensionsofsolidorliquidmatterinair(25).Thesesuspensionparticlesmaylandeither bydrydepositionorbywetdeposition,e.g.scavengedbyrainfall,whichcontributemostof theremovalofaerosols(upto80to90%),andraindropscollectaerosolsthataregreaterthan approximately2mindiameterbyimpactionwithgoodefficiency(25).Thisremoval processforaerosolsimpliesthatrainfallisanimportantfactorforurediosporedeposition.

Observationsforotherfungaldiseaseshaveindicatedthatsporewetdepositiondueto rainfallwasgreaterthandrydepositioninunittime(5,6,23,27).Gregory(5)reportedthat urediosporesofPucciniaspp.couldbecollectedbyanysizeofraindropsandcollection efficiencycouldbeupto80to90%forspores20to30mindiameter.Previousstudies havealsoshowedthatwetdepositiondependsonascavengingcoefficientthatisassociated withaerosoldiameter,aerosoldensity,precipitationrate,andraindropsizes(1,7,14).The equationforthescavengingcoefficient(s–1)isdescribedby:

(Eq.2.1) inwhichλ(dp)istheintegraloverallraindropdiametersdD;E(dD,dp)isthecapture efficiencyforparticlesindiameterdpcollectedbyraindropswithindiameterdD;NdDisthe numberofraindropsindiameterdD(seeTable2.1fordetailedinformation)(1,7,14).The captureefficiency,E,isakeyvariablethatisdependentonrainfallrateandaerosolparticle characteristics.Thecaptureefficiencyindicateshoweasilyraindropsmaycaptureaerosols.

OneexampleofthequantificationoffungalsporescapturedbyrainfallwasAylorandSutton

(1),whousedanonlinearregressionmodeltoestimatethecaptureefficiencyofascospores ofapplescabpathogen(Venturiainaequalis).Furthermore,Slinn(19)developedamore

36 specific semi-empirical model for estimation of capture efficiency E (Eq. 2.2), which has been widely used in atmospheric chemistry and physics for air pollution research (7, 17):

(Eq. 2.2)

In Eq. 2.2 (see Table 2.1 for detailed descriptions for all symbols), the first term stands for

Brownian diffusion, which is associated with particle Brownian diffusivity (DB in Table 2.1).

Brownian diffusion is due to movement (Brownian motion) of aerosols caused by impaction of gas molecules, and is significant when particle size is very small (e.g. 0.1µm) (17). The second term is for interception, which is due to the contact of aerosols with the surface of raindrops when raindrops passing by the aerosols but without direct impactions (17). The third is for direct impaction of raindrops and aerosols when aerosols stay in the path of raindrops (17). Specifically, previous studies (5, 7, 17) have indicated that for particle size of fungal urediospores, capture efficiency was mostly dependent on impaction.

Rainfall is important to facilitate development of soybean rust disease in the field (21,

24). Since airborne urediospores of soybean rust are suitable for removal by precipitation

(due to their size), rainfall not only just provides a source of free moisture necessary for urediospore germination and infection, but also facilitates spore deposition, particularly in the distant area of long-distance dissemination. However, the quantification of soybean rust urediospore wet deposition associated with rainfall, which is very important for determination of initial disease levels in soybean rust forecasting models, is currently not available. Furthermore, dry deposition is another way by which urediospores of Phakopsora pachyrhizi land on plants. A general model for particle dry deposition velocity (Vd) is

Vd=Vs+1/(ra+rb+rarbVs), in which Vs is particle terminal velocity, ra, rb, are the aerodynamic resistance and quasi-laminar layer resistance respectively (17). Because urediospores of P.

pachyrhizi are large particles, the terminal velocity is the dominant factor of dry deposition velocity (17). For this reason, we adopted a simplified model: Vd=(1+LAI)Vs, described by

Aylor and Sutton before (1), in which dry deposition velocity Vd is only associated with

urediospore terminal velocity Vs and soybean leaf area index (LAI). Since dry deposition

velocity and wet deposition velocity are both associated with urediospore terminal velocity

Vs, it is necessary to acquire this value first. However, for urediospores of P. pachyrhizi, this

characteristic is unknown so far. Therefore, the objectives of this study were to: 1) measure P. pachyrhizi urediospore terminal velocity Vs; 2) quantify wet deposition of P. pachyrhizi

urediospores associated with different rainfall rates; and 3) compare P. pachyrhizi

urediospore wet deposition with dry deposition.

Materials and methods

All constants, variables, and symbols used in various models including Eqs. 2.1 and

2.2 are listed in Table 2.1. More specific information can be found in cited literatures.

Terminal velocity (Vs), density (ρp) of urediospore, and dry deposition. Terminal velocity of a spherical particle (Vs) in still air is primarily dependent upon particle size (dp) and density (ρp). To determine P. pachyrhizi urediospore terminal velocity theoretically,

Stokes’s law (5, 17) could be used in which the terminal velocity of a smooth spherical

particle in still air was determined as:

2 Vs = gdp (ρp – ρa)/18µa (Eq. 2.3)

Urediospores of P. pachyrhizi are ovoid, 17 to 45 µm long and 15 to 27 µm wide (13, 21). In

this study, urediospores were treated as spherical particles of diameter 24 µm. Given that

38 there is no knowledge of an exact urediospore density value to determine the terminal velocity of urediospores of P. pachyrhizi based on Stokes’s law (Eq. 2.3), in our study, their terminal velocities were measured in a urediospore terminal velocity measurement experiments instead of using Stokes’s law. Based on experimental results, urediospore density was derived by Stokes’s law.

The experiments to measure P. pachyrhizi urediospore terminal velocity were conducted in the midnight in the room conditions to minimum the affections of air currents.

The conceptual design of experimental device and process of urediospore deposition are illustrated in Fig. 2.1. A plastic (polyvinyl chloride, PVC) cylinder that was 19 cm high, with a 5 cm inside diameter, was used to create a perpendicular tower of still air. The top of the cylinder was left open to facilitate the release of urediospores. The lower end of the cylinder was mostly sealed by paper (shaded area in Fig. 2.1 at bottom of the cylinder) only with a square opening of size 1 cm2 in the center. A gliding plastic plate covered by blue wax paper

was placed under the cylinder to collect urediospores falling from the top through the square

opening. Sequential square grids of size 1 cm2 were drawn on the blue wax paper.

As urediospores of P. pachyrhizi often form clumps (11), different urediospores or

spore clumps would have different terminal velocity because of their size or density

difference. Urediospores or clumps that have high terminal velocity would fall quickly and

reach the gliding plate earlier than those with low terminal velocity. To measure the terminal

velocity, when urediospores were released from the top, the gliding plate started to move

horizontally (as shown in Fig. 2.1 by an arrow) at speed of 1cm/s. For each second, the

square opening at the bottom of the cylinder would scan over one grid drawn on the plate.

Suppose an urediospore clump need three seconds to reach the gliding plate, as the plate is

39 moving at 1 cm/s speed, this clump will deposit in the third grid on the plate. Finally, time for all urediospores falling from the top to reach the plate was correlated with the location of the grids in which they deposited respectively. Therefore, terminal velocity of an urediospore or spore clump was derived based on location of the grid where it was.

To determine relationship between terminal velocity and clump size, urediospores in each grid were examined under dissecting microscope with magnifications 50 to 200 times and spore clump sizes were determined. The velocity values for all urediospore clumps were averaged by the clump size and then fitted by nonlinear models for sizes of clumps vs. average terminal velocity of certain clump size. Finally, single urediospore terminal velocity was determined by the fitted models.

Diseased kudzu leaf samples were collected from a wild kudzu field with natural soybean rust infection in Nanning, China in March 2006. Disease severity (defined by area of pustules on leaves) of sampled leaves ranged from 10% to 50%. Leaves were placed in a moisture chamber for 1 day, under dark, and then moved to room temperature for a few hours.

Urediospores were released by tapping leaves gently on the top of PVC cylinder.

Urediospore terminal velocity of P. pachyrhizi measurement was conducted under two different environmental conditions (uncontrolled): air temperature at 19°C and relative humidity (RH) at 50% using urediospores from 11 leaflets, and air temperature at 20°C and

RH at 90% with urediospores from 13 leaflets.

An exact value of density of P. pachyrhizi urediospores is not available in our study.

Its value is estimated based on urediospore terminal velocity measured in previous experiment and Stokes’s law. Dry deposition was also determined using the terminal velocity.

Furthermore, because the assessment of urediospore wet deposition by rainfall for

40 urediospore clumps is quite complicated, we only dealt with situation of single urediospores in our analyses.

Estimation and the fitted model of scavenging coefficient. Several models are necessary in order to compute the scavenging coefficient. These models and the parameter settings are described as follows: Rainfall rate R is set in a range of 0.5 to 50 mm per hour.

Raindrop diameter ranges from 0.1 to 6 mm. Typically, for raindrop size, the exponential distribution model is used, as shown by Marshall and Palmer (10, 15, 17), however, because their model may overestimate small raindrops less than 1.2 mm (17), a lognormal model of raindrop distribution from Cerro was used in our study (3):

(Eq. 2.4)

0.30 0.23 0.5 in which NT = 194R , Dr = 0.630R , σ= (0.191-0.01lnR) , and R is rainfall rate (see

Table 2.1). A regression model from Willis (26) was used to calculate raindrops terminal velocity:

VD=4.854dDexp(0.195dD). (Eq. 2.5)

Capture efficiency, E, was calculated according to Slinn’s model (Eq. 2.2) for each raindrop size. The model for scavenging coefficients was described in Eq. 2.1. For each rainfall rate R, the scavenging coefficient λ(dp)was determined by integrating over all raindrop diameters

(dD) in the raindrop distribution model (Eq. 2.4). This integral was determined numerically using a Trapezoid sums approximation (16), with error no larger than 10-5. Finally, for different rainfall rates, the scavenging coefficients were fitted with a nonlinear model:

b λ(dp)= aR , (Eq. 2.6)

41 in which a and b are coefficients determined by model fitting as described by Aylor and

Sutton (1).

Scavenging efficiency associated with rainfall rate and duration. Assume that distribution of raindrops does not vary with time, using the fitted model for scavenging coefficient, the change of Phakopsora pachyrhizi urediospores left in the air during a rainfall event is estimated by:

Ns(t) = Ns(0)exp[–λ(dp) t], (Eq. 2.7)

in which Ns(0) is the initial urediospore concentration and Ns(t) is the concentration at time t.

If treat Ns as fraction instead of concentration, then Ns(0) is 1, Ns(t) is fraction of

urediospores at time t (2, 4, 14, 18). Substitution of λ(dp) in Eq. 2.7 with the fitted scavenging coefficient model leads to the estimation of urediospore concentration at time t when rainfall rate is known. All computation was conducted with MatLab (The MathWorks, Inc. Natick,

MA, USA).

Comparison of urediospore wet deposition and dry deposition. In order to simplify the system, we assumed that Phakopsora pachyrhizi urediospore cloud from a long- distance dissemination source area was the only initial inoculum source for a soybean field and all urediospores were single. Thickness of the urediospore cloud was 1000 meters and height of the cloud bottom was 1000 meter above soybean field. The concentration of urediospores was 2 urediospore/m3 and uniform for all height levels. If uredopsores were not

deposited within 24 hours, we assumed that they were diluted in the air and lose availability

for deposition in this field. For wet deposition, raindrops fall from height level above 2000

meters. Based on Eqs. 2.1-2.7, the number of urediospore deposited in unit area on ground

under different rainfall conditions (0.5, 1, 5, 10, 20 mm/h rainfall rate, 30 min duration) was

estimated. For dry deposition, it was mentioned that dry deposition velocity Vd is associated

with Vs and leaf area index (LAI). With our assumptions that the urediospores are from outer

sources and present in the air at 1000 to 2000 m height level, the soybean leaf area index

does not affect the urediospore deposition in a unit area, thus the dry deposition velocity Vd is further simplified as Vd=Vs in our estimates. Then for different dry deposition duration time

(8, 16, and 24 hours) in one day, number of urediospores due to dry deposition in a unit area

was estimated based on urediospore terminal velocity Vs and compared with the wet

deposition.

Results

Terminal velocity of urediospore and estimated urediospore density. In the

experiment under temperature condition at 19°C and RH 50%, 222 single Phakopsora

pachyrhizi urediospores were collected, which the terminal velocities ranged in 1.09 to 3.45 cm/s with average at 1.86 cm/s. Under temperature condition at 20°C and RH 90%, 137 single urediospores were collected, which the terminal velocities were in 1.15 to 5.43 cm/s and average at 2.25 cm/s. Average terminal velocity for all 359 single urediospores was 2.01 cm/s. The size of urediospore clumps ranged from 1 to 100 urediospore/clump and terminal velocity ranged from 1.09 to 7.6 cm/s. Average size of urediospore clumps at 19°C and RH

50% was 8.31 urediospore/clump, which is significant smaller (P=0.0096) than average clump size, 9.94 urediospore/clump, at condition of 20°C and RH 90% (data not shown).

Analysis of variance showed that the average terminal velocity (including all single urediospores or urediospore clumps) under temperature of 20°C and RH 90% condition was significant larger (P<0.0001) than those under 19°C and RH 50% (data not shown). These

43 results indicate that urediospore may tend to form clumps more easily under humid condition and have larger terminal velocity.

Generally, P. pachyrhizi urediospore terminal velocity increases as urediospore clump size increases and follows a monomolecular curve (Fig. 2.2). Highest terminal velocity was 7.6 cm/s. Considering that errors of estimation of the clump size under dissecting microscope increase with clump size and clumps greater than 50 urediospore/clump had similar velocity at 7.6 cm/s, model fitting was conducted only for clump size ranged over 1to 60 urediospore/clump. Table 2.2 gives parameters estimates of the two fitted monomolecular models (Vs=a-c·EXP(-bx)) for the two different experimental conditions. Fig. 2.2 A and B show the measured velocities of different clumps and their model predicted values by monomolecular models. An averaged value, 1.87 cm/s, as a representative single urediospore terminal velocity based on single urediospore terminal velocity predicted by the two models was used for computation in the next steps. This derived single urediospore terminal velocity value (1.87 cm/s) was smaller than the averaged single urediospore terminal velocity by measurement (2.01 cm/s). With terminal velocity at

1.87 cm/s for single urediospores, urediospore density was estimated as 1.07 g/cm3 according

to Stokes’s law. Based on this single urediospore terminal velocity, without other resistances

in the atmosphere, an urediospore falling from 1000 m height level with velocity at 1.87 cm/s

would take 14.8 hours to reach the ground, while spores at 2000 m height would take more

than one day.

Urediospore scavenging coefficient by rainfall and the model fitting. Scavenging

coefficients (λ(dp)) were calculated based on Eqs. 2.1, 2.2, 2.4, and 2.6. Fig. 2.3 gives scavenging coefficients over rainfall rate from 0.5 to 50 mm/h. λ(dp) increases when rainfall

-1 rate increases. When rainfall rate is 0.5 mm/h, λ(dp) is only about 0.0002s . At 50 mm/s, it

reaches 0.094s-1. Fitted model based on Eq. 2.6 for the scavenging coefficients is

0.8391 2 λ(dp)=0.0003563R with r =0.999, as the curve shown in Fig. 2.3.

Change of urediospore concentration during a rainfall event. Based on the fitted

0.8391 model of scavenging coefficient (as shown in Fig. 2.3, λ(dp) = 0.0003563R ), assume

initial urediospore in the air is 100% and all are single urediospore, rainfall rate is constant

during the rainfall events, percentage of total urediospores left in the air at time t was

estimated by Eq. 2.7. Fig. 2.4 illustrates the percentage vs. time (0 to 60 minutes) for 6

different rainfall rates (0.5 to 15mm/h). When rainfall rate is 0.5 mm/h, it would take 60

minutes to remove 50% urediospores from the air. When rainfall rate is about 5 mm/h, 10

minutes is enough to remove 50% urediospores. This indicates fast urediospore scavenging

during a moderate rainfall event. However, in reality, rainfall rate always varies during a

rainfall event and results in uncertainty of the time.

Comparison of urediospore deposition under different dry and rainfall

conditions. Under simplified situation with dry deposition velocity at 1.87 cm/s, the number

of total urediospores deposited on ground by dry and wet deposition from 1000 to 2000 m

height levels in different time and different rainfall conditions are given in Table 2.3. Clearly,

dry deposition is much less than wet deposition in same time length. For example, in the first

16 hours, almost no urediospores can reach the ground from the 1000 meters height; while

wet deposition by 2 mm/h rainfall with 30 minutes duration is already greater than dry

deposition in 24 hour. The urediospore at 2000 meters height cannot even reach the ground in

one day with terminal velocity at 1.87 cm/s. At 5 mm/h rainfall rate, 90% urediospore in

1000 to 2000 m height can deposit in 30 min. After 30 minutes rainfall, higher rainfall rates

(>10 mm/h) make no significant difference in wet deposition.

Discussion

Our work focused on the theoretical estimation with simplified experiments and situations. The results indicated that Phakopsora pachyrhizi urediospore wet deposition is much greater than dry deposition. In reality, characteristics of urediospores and rainfall rate always change under different environmental conditions. However, a reasonable conclusion can be made according to our calculation is that rainfall removes urediospore quickly, associated with moisture it brings to the field, rainfall gives double effects to disease initial establishment in a long-distance dissemination target area.

Experiment data of wet deposition of soybean rust urediospores are scarce.

Observations of other fungal spores or plant pollens may give more or less validation on our calculation. An hourly observation of Cladosporium spp. conidia spores concentration in

Tulsa, Oklahoma showed a decrease of spore concentration from 3000 spore/m3 to almost

zero after 30 mm rainfall and another case of decrease from 10000 spore/m3 to below 1000 spore/m3 after 23 mm rainfall (23). Observations on several kinds of particles washed down

during different time periods in a thunderstorm event ending a 7-day spell of warm, dry

weather in Rothamsted Experimental Station in July 22, 1951 showed similar results (5).

These observations generally fit our theoretical calculations. Ascospores of apple scab

pathogen are much smaller than soybean rust urediospores (1). Aylor’s previous work on

apple scab pathogen ascospores gave fitted scavenging coefficient model as

0.7873 λ(dp)=0.000272R (1), which indicated smaller scavenging coefficients for ascospores

0.8391 compared with urediospores of soybean rust (λ(dp) = 0.0003563R ) and this is consistent

46 with implication of Slinn’s model (Eq. 2.2) that larger spores are easier to be removed by rainfall.

Terminal velocity of single urediospores showed relatively large range, i.e. 1.09 to

5.43 cm/s. This may be due to variability of fresh spores particularly the size or experimental errors. Generally, the urediospore density (1.07 g/cm3) derived by the fitted models is similar

to other rust fungal spores (5). According to Slinn’s model, terminal velocity of urediospore

has little affection to capture efficiency of raindrops to urediospores when their difference is

very large. However, this velocity is important for dry deposition and urediospore taking-off

from disease field. More precisely controlled conditions would be necessary for better

measurement of these urediospore characteristics.

The model for wet deposition would be useful for disease forecast in long-distance

dissemination target area, where rainfall would be the major mechanism of spore deposition

(12, 20, 22, 28). If urediospore concentration in the air could be predicted in a region, by

monitoring rainfall events, one could predict initial disease level before seeing disease

symptoms. Slow dry deposition indicates that even urediospores present in the air, disease

may still not be able to establish in the field, as long time exposure under solar radiation

would reduce urediospore viability greatly. For area already with soybean rust disease, as a

polycyclic disease with short latent period, high scavenging coefficient of urediospore imply

that rainfall would have negative effects on long distance dispersal of local urediospore by

keeping removing urediospore from the air but positive effects for bringing more

urediospores back for secondary infection and help local disease development.

Because scavenging coefficient of urediospores of soybean rust is very high (Fig. 2.2),

not only rainfall, but also overhead irrigation may act as a washing-down factor. Tchanz (24)

47 reported a much higher disease level in the field with overhead irrigation than the field with furrow irrigation. Fog could induce wet deposition too. Some observations from southern

China mountainous areas with frequent fog events indicated severe soybean rust development though rainfall was not abundant (22). More research is needed for these phenomena. Rainfall can also flush spores off from plant surface especially by a heavy rainfall event. Meanwhile, a heavy rainfall may splash spores that have already deposited on the soil surface to the plants. Although urediospore scavenging coefficient increases as rainfall rate increases, what is the optimal rainfall rate for overall disease development would be an interesting problem.

Acknowledgements

We thank the National Natural Science Foundation of China (NSFC) for funding. We are grateful to Dr. Paul Esker in Iowa State University and Mr. T. X. Guo in Guangxi, China for help.

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Table 2.1. List of variables and symbols with definitions and equations used in all models. All variables dependent on temperature are of values at 20°C. All units in calculation and equations are SI base units except rainfall rate and raindrop. Symbol Variable (unit) Symbol Variable (unit)

Ns(0), Urediospore concentration at time 0 and t Vs Terminal velocity of urediospore in still Ns(t) (m-3) air (m s-1) dp Aerodynamic diameter of urediospores VD Terminal velocity of raindrop in still air (m) (m s-1) dD Diameter of raindrops (mm) E Capture efficiency

ρa, ρw, Density of air = 1.27; water = 1000; and g Acceleration of gravity: -3 -2 ρp urediospores (kg m ) 9.8 (m s ) -5 -23 -1 µa, µw Viscosity of air =1.8x10 and water k Boltzmann constant: 1.38x10 (J K ) =1.002x10-3 (kg m-1s-1)

NT Parameter of lognormal distribution of Cc Cunningham correction factor (≈1 for raindrops (m-3mm-1) particle size of urediospores (17)) -1 N Number of raindrops in a certain raindrop DB Particle Brownian diffusivity (J s kg ) -3 -1 diameter range (dD+d(dD)) (m mm ) DB=kTCc/3πµadp T is temperature in degrees of Kelvin R Rainfall rate (mm h-1) σ Variance of raindrop size

Dr Representative raindrop diameter (mm) Re Raindrop Reynolds number for radius (dimensionless) Re=dD VD ρa/2µa

τ Particle relaxation time (s) Sc Aerosol particle Schmidt number 2 τ=(ρa - ρp)dp Cc/18µa (dimensionless) Sc=µa/ρaDB

St Stoke’s number (dimensionless) ω Viscosity ratio (ω = µw/µa) St=2τ(VD - Vs)/dD -1 λ(dp) Scavenging coefficient (s ) φ Diameter ratio (φ = dp/dD) Critical Stokes number for impaction S* (dimensionless) S* = [1.2 + (1/12)ln(1 + Re)]/[1+ln(1+Re)]

Table 2.2. Parameters of the two fitted monomolecular models Vs=a-c·EXP(-b·x), in which x is urediospore clump size, for terminal velocity measured under different environmental conditions. Environmental Model Parameter SSEa R-square RMSEb DFEc condition variable estimate

Temp = 19°C a 7.734 7.1153 0.9092 0.5821 21 RH = 50% b 0.05646 c 6.253 Temp = 20°C a 7. 619 2.1108 0.9668 0.3524 17 RH = 90% b 0.09864 c 6.27 a SSE = sum of squares; b RMSE = root mean squared error; c DFE = degree of freedom.

Table 2.3. Comparisons of urediospore deposition under different conditions. Urediospore cloud is assumed in height level of 1000 to 2000 m with uniform concentration of 2 urediospore/m3. Urediospore terminal velocity is 1.87 cm/s. Urediospore deposition under different conditions (# urediospore/m2) Dry deposition (hours) Wet deposition in 30 minutes (mm/h) 8 16 24 0.5 1 2 5 10 15 0 144 1231 557 903 13341825 1976 1996

Fig. 2.1. The experimental device design for Phakopsora pachyrhizi urediospore terminal velocity measurement and illustration of urediospore deposition process.

Fig. 2.2. The measured velocity of different Phakopsora pachyrhizi urediospore or clumps and predicted velocity by monomolecular models for experiments under different environmental conditions. A: under temperature 19°C and RH 50%. B: under temperature 20°C and RH 90%.

Fig. 2.3. Scavenging coefficients of single Phakopsora pachyrhizi urediospore calculated based on Slinn’s model and lognormal raindrop distribution model for rainfall rate over 0.5 to 50 mm/h.

Fig. 2.4. Percentage of single Phakopsora pachyrhizi urediospores left in the air at different time t during 6 different rainfall events in 60 minutes.

CHAPTER 3. GROUPING OF FUNGAL DISEASES IN THE UNITED STATES SOYBEAN PRODUCTION REGION: IMPLICATIONS TO THE RISK OF ASIAN SOYBEAN RUST

A paper prepared for submission to Phytopathology

X. Li, and X. B. Yang

Abstract

Ten biological or ecological characteristics of pathogen/disease were used to quantitatively describe 34 pathosystems of soybean fungal diseases in the U.S. These characteristics describe optimal temperature for disease development, host ranges, free moisture requirement for infection, secondary infection, means of pathogen dispersal, and capacities of pathogen survival. Gower’s general similarity coefficient for each two of the pathosystems was determined. Principal coordinate analysis (PCoA) based on these similarity coefficients projected these pathosystems in a 2-dimension space, in which significant patterns were identified for some of the characteristic variables, such as means of dispersal and free moisture requirement. Similarity coefficients and PCoA indicated that soybean rust disease (Phakopsora pachyrhizi) is mostly similar to soybean downy mildew

(Peronospora manshurica) and Leptosphaerulina leaf spot (Leptosphaerulina trifolii).

Cluster analysis grouped all pathosystems into three major clusters. The first cluster mostly consists of leaf diseases. The second one consists of leaf/pod diseases, including soybean rust, brown spot (Septoria glycines), frogeye leaf spot (Cercospora sojina), Phyllosticta leaf spot

(Phyllosticta sojicola), purple seed stain (Cercospora kikuchii), downy mildew (Peronospora manshurica), and Leptosphaerulina leaf spot etc. The third one includes seedborne root diseases. Phytophthora rot (Phytophthora sojae) was independent of any of the three major clusters indicating its uncommonness. Estimated soybean yield losses in the U.S. in 1996 to

2005 showed that moderate to severe yield losses were mostly caused by diseases in the first and third clusters. Only light to moderate yield losses were caused by diseases in the second cluster. Based on the information of yield losses and geographical distribution of other diseases in the second cluster, the cluster analysis implies that the potential geographical distribution range of soybean rust may reach the North Central region, yield losses would be light in the northern U.S. however moderate in the south if environmental conditions are favorable.

Introduction

Disease risk assessment is to identify disease risk source and evaluate possibility of disease establishment, magnitude of outbreak, and disease negative impacts on crops (64, 72,

73). The conceptual framework of risk assessment of plant disease has been established before (64, 72, 73, 74). Since then risk assessment of different diseases has been done case by case without a universal methodology available (12, 45, 46, 56, 73). For risk assessment of a threatening disease in new geographic regions, to know two aspects of the disease: 1) the disease range that generally refers the distribution of disease occurrence over a large geographic region and 2) the likelihood of disease outbreaks and yield losses followed is always of interest but challenging to plant pathologists when limited information is available for the disease particularly a new disease (72, 74).

An agriculture system has many plant diseases, e.g. fungal diseases. Different diseases may have similar or even same characteristics, such as favorable temperature for disease development, means of inoculum dissemination. Comparative epidemiology has been focused on examination of similarities or dissimilarities among diseases or disease progresses

(3, 31, 32, 33, 47). Some diseases were already well documented, providing valuable

60 information for disease management. For those for which risk assessment is necessary but with limited disease information, other diseases bearing similar characteristics would be helpful to bridge the gap of unknown information.

Some comparative epidemiology studies focused on components of disease cycle and disease modeling such as infection rate or infectious period (31, 32, 47). Approaches such as cluster analysis, discrimination analysis has been used in comparative epidemiology (31-33).

Cluster analysis is also widely used in ecological research to classify objects based on their characteristics, or often called attributes as well (25, 37, 52). In plant pathology, Kranz first employed cluster analysis to examine epidemic patterns of 40 pathosystems based on 10 quantitative attributes of their disease progress curves (32). In other aspects of plant pathology, cluster analysis was used on such as detection of genetic similarity of fungal pathogens and pathogen geographical zoning (36, 49). Sache and Vallavieille-Pope used cluster analysis to group 24 airborne diseases such as powdery mildew and rust diseases in several crops based on their biological characteristics (53). Despite these studies did not have disease risk assessment in their objectives; they showed the similarities among pathosystems with quantitative approaches.

Regional or global patterns in species, such as latitudinal gradients in species richness, species-energy relationships, and species-area curve are commonly seen in ecology (8, 15, 22,

39). In plant pathology, a previous study by Yang and Feng (74) indicated that geographical distribution (defined as disease range) of soybean (Glycine max L. (Merr.)) fungal diseases in

North America follows certain patterns (51). Disease patterns in a large scale are not limited to the spatial patterns. Other patterns such as temporal patterns may exist (75, 76). A pattern in large scale such as geographical distribution patterns of diseases should result from the

61 coexistence of pathogens, hosts, and environmental conditions over a long period. Also, a pattern of many of diseases would be associated with factors such as biological or ecological attribute of the pathogen, limiting biotic or abiotic conditions of the environment in spatial dimension or temporal dimension. Suppose we have various diseases in an agroecosystem such as soybean fungal diseases in the U.S., using comparative epidemiology or appropriate approaches in other disciplines, we can examine similarity or dissimilarity of various diseases based on their attributes, (31-33, 37) which would provide certain links among these diseases, with or without knowing the mechanisms. After that, we may examine whether or not there are certain patterns, which is associated with any aspects of these diseases, such as regional prevalence, yield losses, pathogen dispersal, and overseasoning. Then the hypothesis is that with the quantified similarity among the diseases and the knowledge of their patterns in terms of the biological, ecological, spatial, or temporal characteristics of the diseases, we may follow the patterns to assess unknown information of a specific disease according to it similarity with other diseases.

Asian soybean rust (Phakopsora pachyrhizi Sydow) was first reported and established in the southern U.S. in November 2004 (58). Being a destructive disease in other soybean production regions, soybean rust had drawn the attention from plant pathologists for its possible adverse impacts to the U.S. soybean industry long before it reached this continent

(34, 73). More detailed assessment on the year-round establishment of P. pachyrhizi based on its characteristics of temperatures and moisture has been done (45). The pathogen overwintering range in the U.S. is limited in southern states such as Florida, Texas, and

Louisiana (45). However, the airborne urediospores of P. pachyrhizi may spread to the northern soybean production regions by long distance dissemination. By November 2006,

62 soybean rust has been reported in 15 states in the continental U.S. (16). Being a new disease in the continental U.S., soybean rust would probably take few years or more to reach its potential geographical range. Two questions in the risk assessment of soybean rust need be answered: 1) would its potential geographical range cover the main soybean production area in the North Central region; and 2) how likely disease outbreaks would occur in the U.S. to cause yield losses. Answering these two questions at its early stage of entry would greatly help soybean producers to manage the risk of this disease.

Because soybean rust pathogen was first reported in the continental U.S. in 2004, it would be unreliable to assess the disease range and likelihood of outbreak using two-year disease monitoring data. Pivonia and Yang assessed the seasonal appearance time of soybean rust using modeled disease progress of soybean rust compared with several other rust diseases based on their temperatures attributes. They suggested that soybean rust would appear in the Soybean Belt between the appearance time of common corn rust (Puccinia sorghi) and southern corn rust (Puccinia. polysora), (47). Sache and Vallavieille-Pope used cluster analysis to compare 24 airborne leaf diseases based on pathogen sporulation and infection characteristics, in which soybean rust pathogen was grouped together with sorghum rust (Puccinia purpurea), common corn rust, and rice blast (Magnaporthe grisea) (53). More comprehensive comparative studies based on the whole disease cycle and other disease ecological attributes are not available. As information of epidemics of soybean rust in the continental U.S. is limited, the potential geographical distribution and likelihood of outbreak for soybean rust could be assessed based on comparisons of similarity in biological and ecological attributes with other soybean diseases that are native or already established in the

U.S. for a longer time and more information are available. Therefore, the objectives of this

63 study were: 1) to examine similarities among soybean fungal pathosystems in the U.S. based on their biological and ecological characteristics; 2) to examine possible biological, ecological, or geographical patterns among these pathosystems by classification approaches.

Based on results in 1), 2), and other information of diseases geographic distribution and yield loss data, potential geographical range and likelihood of outbreak of soybean rust in the continental U.S. was assessed.

Materials and methods

Fungal diseases in the U.S. soybean production region. More than 30 soybean fungal diseases have been reported in the U.S. (9, 23, 41). Some of the diseases are caused by several fungal species, such as Pythium rot with major casual agents including Pythium aphanidermatum, P. ultimum, and P. irregulare, or by different varieties in same species such as northern stem canker (Diaporthe phaseolorum var. caulivora) and southern stem canker (Diaporthe phaseolorum var. meridionalis) (23). For Rhizoctonia aerial blight, the major causal agent is Rhizoctonia solani anastomosis group 1 (AG1). For Rhizoctonia root rot, major causal agent is Rhizoctonia solani anastomosis group 4 (AG4). All these major causal agents including species, varieties, and anastomosis groups were included separately as long as information is available. Finally, 34 soybean fungal disease pathogens including varieties and anastomosis groups that cause 32 soybean diseases (Table 3.1) were included for the analysis. For convenience in the analysis, they are treated as 34 pathosystems, in which each pathosystem contains one host and one causal agent, either a species, a variety, or an anastomosis group. Pathosystem, disease, and pathogen are different concepts. We use

‘pathosystem’ to refer the entity of a disease and its causal pathogen in the analysis. We use the terms of disease or pathogen when mention certain biological or ecological attributes, or

64 when historical disease and pathogen geographical distribution and yield loss information is involved. Each pathosystem was assigned with a code, based on the disease common name or the causal pathogens’ Latin names if there are multiple causal pathogens. We generally do not discriminate the use of disease common name and codes; they may refer the pathosystem, the disease, or the pathogen, dependent on the context.

Besides the soybean pathosystems in reality, we designed three virtual pathosystems, which do not exist in the nature. The first one is called Average Disease, for which the characteristic values were designated based on the averaged and then rounded-up values of the characteristics of all other real pathosystems, say, it has the most frequently presented biological and ecological characteristics among all the real pathosystems. The second one is called Super Disease, which has the characteristics favorable for disease epidemics and pathogen survival. Determination of what characteristics are favorable for disease epidemic was based on general biological knowledge and expert estimation. For example, one may think that a pathogen would be favored for epidemics and survival with characteristics of broad host range, long-term survival without hosts, multiple means of dispersal (seed, wind, debris, etc). The third designed disease is Weak Disease, which has characteristics unfavorable for disease epidemics and pathogen survival compared with the Super Disease.

Optimal disease development temperatures for these three virtual pathosystems were set at

25°C, which is the averaged value for all real soybean pathosystems. The three virtual pathosystems served as markers in our analyses for better understanding the attributes of each group. Also, because they do not exist in the nature, what would be the fundamental biological and ecological reasons for their absence may give some clues for assessment of the occurrence of other diseases such as soybean rust.

Biological and ecological characteristics of the soybean pathosystems.

Characterization of soybean pathosystems was based on quantified descriptive variables that describe certain biological and ecological attributes of these pathosystems. No universal criteria are available for how to characterize a pathosystem by biological or ecological attributes. Kranz suggested some descriptors to describe pathosystems (31). Generally, one would describe a pathosystem according to disease cycle, pathogen and host attributes. In our study, four criteria were used for selecting biological and ecological characteristics of the pathosystems. 1) It must be descriptive and intrinsic characteristic of the pathosystem, instead of functional. For example, whether a pathogen is seedborne is descriptive intrinsic, while disease infection rate r is functional, because r is associated with environment; 2) it must not change frequently. For example, pathogen virulence may change quite frequently, though it is intrinsic biological characteristic. In contrast, optimal temperature for disease development is a much stable characteristic. Literature information of a frequently changing characteristic may be inconsistent in different historical time, which is difficult to quantify such characteristics; 3) its spatial scale must be at least equal or larger than field scale, e.g. spatial scale of disease gradients within plant rows is too small; 4) the information is available and reliable. Some biological characteristics such as optimal environmental relative humidity for disease development had to be excluded from analysis due to lack of availability or reliability. Besides the four criteria, measurement levels are important in characterization of soybean pathosystems. Generally, the measurement levels used for variables describing certain characteristics are 1) binary, including symmetric binary, e.g. female/male and asymmetric binary, e.g. red color/other color; 2) nominal, e.g. yellow/red/green; 3) ordinal, e.g. 1st/2nd/3rd; 4) interval, e.g. temperatures; and 5) ratio, e.g. percentage (25, 52). In our

66 study, efforts were made to define variables of pathosystem characteristics as binary or nominal variables because quantification of some characteristics as interval or ratio variables may not be reliable, though this would lose information in analysis.

According to the biological and ecological attributes of soybean plant, soybean diseases cycle, and pathogens, ten variables were defined to describe characteristics of these pathosystems. First is optimal disease development temperature (OT, interval, °C), which is defined as the environmental temperature that favors the disease development in soybean plants. Second is host range (HR, ordinal), which describes the categories of the hosts that can be infected by a soybean disease pathogen. Because some pathogens can infect hundreds species of plants, host range was reduced to three levels. Host range is 1 when soybean

(Glycine max) or the plants in genus Glycine are the hosts, 2 when other leguminous plants can be the hosts, 3 when plants from other families can be the hosts. Third is free moisture requirement (FM, binary), which is defined as whether free moisture is needed to facilitate a pathogen to infect. For example, leaf wetness is needed for soybean rust pathogen (P. pachyrhizi) to infect soybean leaves. Forth is major infected plant parts (IP, binary), which describes the parts of soybean plant mostly infected by a pathogen. The value is 1 for upper stem, leaf, or pods, 0 for root or lower stem. Fifth is growth stage of infection (GS, asymmetric binary), which describes the soybean growth stages when plants mostly infected by a pathogen. If infections generally occur in any stage, the value is 1; if infection occurs mostly in vegetative stage or reproductive stage, value is 0. Sixth is secondary infection (SI, binary), defined as whether secondary infections occur, which Yes = 1, No = 0. Seventh is wind dispersal (WD, asymmetric binary), defined as whether the pathogen dispersal units can be dispersed by wind in field, which Yes = 1, No = 0. Eighth is seedborne (SB, binary),

67 defined as whether the pathogen can infect soybean seed, which Yes = 1, No = 0. Ninth is survival in host residue (SR, binary), defined as whether pathogen can survive saprophytically in soybean residue after harvesting and serve as initial inoculum source for the next growing season, which Yes = 1, No = 0. Tenth is long time survival in soil (LS, asymmetric binary), defined as whether the pathogen can survive saprophytically in the soil without host, or can be parasite in other microbes such as nematodes, or can produce long- time dormancy structures such as sclerotia that can survive for a few years, which Yes = 1,

No = 0. All these variables of characteristics and the values are summarized in Table 3.1.

Similarity coefficients among the pathosystems and the principal coordinate analysis. Similarity coefficient (S) describes how similar two objects are to each other, which its complement (1-S) is dissimilarity coefficient (D), or sometimes is called distance as well (25, 37, 40, 52). The similarity between two soybean pathosystems is based on their characteristics in Table 3.1. Given the facts that there are different levels of measurements of pathosystem characteristics, similarity coefficients among them were determined based on

Gower’s general coefficient of similarity that is a value of 0 to 1 and fits all levels of measurements (18, 25, 37, 52).

Principal coordinate analysis was conducted to project each pathosystem in a 2- dimension space as a visualized profile of all pathosystems, with which we can examine possible patterns among them (37). In this space, pathosystems with larger similarity coefficients among them stay closer. Calculation of similarity coefficients and principal coordinate analysis were conducted with SAS (SAS Institute, Cary, NC).

Pathosystem classification based on cluster analysis. Based on distance among the pathosystems, hierarchical cluster analysis was conducted with SAS (SAS Institute, Cary,

NC) by unweighted average clustering (or called unweighted pair-group method using arithmetic averages, UPGMA) (25, 37, 40, 52). First, only real soybean pathosystems in the nature were subjected to cluster analysis. Then the three virtual pathosystems were incorporated into the dataset. Grouped pathosystems are illustrated in dendrogrammes.

Results

Similarity coefficients among the soybean pathosystems. All the similarity coefficients among all soybean pathosystems including the three virtual pathosystems were given in Table 3.2, in which 13 coefficients greater than 0.9 and 3 coefficients less than 0.1 were underlined. The two maximums are equal to 0.993, between frogeye leaf spot (FLS) and brown spot (BrS) as well as Rhizoctonia root rot (RRR) and Fusarium pod and collar rot

(FPC). The two minimums are equal to 0.074, between soybean rust (SbR) and charcoal rot

(ChR) as well as soybean rust and Fusarium root rot (FRR). The third smallest similarity coefficient is 0.078, which is between the virtual Weak Disease (WkD) and target spot (TaS).

Soybean rust has the maximum similarity coefficient of 0.833 with Leptosphaerulina leaf spot (LLS). It is interesting that no pathosystems are either highly similar (similarity coefficient > 0.9) to the virtual Super Disease (SuD), or to the Average Disease (AvD). The maximum similarity coefficient between a real pathosystem and the Super Disease is 0.894 for Thielaviopsis root rot (TRR), while for the Average Disease, maximum similarity coefficient is 0.868, between it and Rhizoctonia aerial blight (RAB). For the Weak Disease,

Phytophthora rot (PpR) is the closest one, with a relatively small similarity coefficient of

0.617.

Principal coordinate analysis and projected pathosystems in a 2-dimension space.

All soybean pathosystems were plotted in Fig. 3.1 using principal coordinate analysis based

69 on the first and the second dimensions. Eigenvalues for the first five dimensions were 2.34,

1.26, 0.59, 0.51, and 0.30 respectively, indicating that the first two dimensions used in Fig.

3.1 represent most of the variance among the soybean pathosystems. Soybean rust (SbR) is close to downy mildew (DwM) and Leptosphaerulina leaf spot (LLS), and several other leaf diseases as well. However, its location is at the lower right side, a very end of the cloud. In contrast, SbR is far away from charcoal rot (ChR) and Fusarium root rot (FRR) at the upper left corner in Fig. 3.1, which is consistent with their very small similarity coefficients (0.074) in Table 3.2. The virtual Average Disease (AvD) is located in the middle of the cloud of all real pathosystems, which reflects its definition. Super Disease (SuD) is at the upper part of the cloud, but not close to any real pathosystems. Not surprisingly, the virtual Weak Disease

(WkD) is far away from any pathosystems, indicating its absence in the nature is reasonable.

At the same time, it is interesting that Phytophthora rot (PpR) stays far away from other pathosystems too, though much closer to others compared with Weak Disease.

Different variables of characteristics have different distribution patterns. The general pattern is that most pathosystems of leaf disease fall into the right part in Fig. 3.1, while root disease pathosystems fall into left part. Similar pattern can be found for the long time survival (LS) attribute of the pathosystems (data not shown in Fig. 3.1, see Table 3.1). The attributes of optimal disease development temperature (OT), host range (HR), growth stage of infection (GS), survival on plant residue (SR), did not show striking distribution patterns

(data not shown). Distributions of the other four attributes showed striking patterns. In Fig.

3.2, pathosystems that free moisture, e.g. leaf wetness is needed for pathogen infections are located at lower right part. In Fig. 3.3, pathosystems that do not have secondary infections in disease cycle are located on the left part. In Fig. 3.4, pathosystems that the pathogens can be

70 wind dispersed in the field are located in the right part, mixed with some pathosystems that do not have wind dispersal. In Fig. 3.5, pathosystems that the pathogens cannot infect seeds are located at the lower edge of the cloud of projected pathosystems.

Pathosystem classification based on cluster analysis. Cluster analysis (UPGMA) first was conducted without the three virtual pathosystem, which gave a dendrogramme in

Fig. 3.6. In this dendrogramme, three major clusters are identified. From the top, the first one has 8 members, including Alternaria leaf spot (ALS), target spot (TaS), anthracnose (Anr),

Myrothecium leaf spot (MLS), pod and stem blight (PSB), Choanephora leaf blight (CLB),

Phomopsis seed decay (PSD), and Rhizoctonia aerial blight (RAB). Most of these are typical leaf/pod or upper stem disease pathosystems except pod and stem blight, and they are located in the upper part in Fig. 3.1. The second major cluster has 10 pathosystems, including brown spot (BrS), frogeye leaf spot (FLS), Phyllosticta leaf spot (PLS), purple seed stain (PSS), downy mildew (DwM), Leptosphaerulina leaf spot (LLS), soybean rust (SbR), Sclerotinia stem rot (SSR), northern stem canker (NSC), and southern stem canker (SSC). Most of them are leaf disease pathosystems as well, and they are located at the middle to right part in Fig.

3.1. It is interesting that soybean powdery mildew (PwM) is close to the second cluster in Fig.

3.6 but not included, while in Fig. 3.1, PwM is among those pathosystems in the second cluster. The third cluster has 14 pathosystems, including brown stem rot (BSR), southern blight (SnB), Thielaviopsis root rot (TRR), charcoal rot (ChR), Fusarium root rot (FRR),

Fusarium pod and collar rot (FPC), Rhizoctonia root rot (RRR), Fusarium wilt (FWi), red crown rot (RCR), sudden death syndrome (SDS), Neocosmospora stem rot (NSR), and the three Pythium root pathogens (Pythium aphanidermatum, PyA; Pythium irregulare, PyI;

Pythium ultimum, PyU). These are all pathosystems of soybean root or lower stem, and they

71 are located in the left part in Fig. 3.1. Finally, Phytophthora rot (PpR) lays out side of the three clusters; until it finally joins the third cluster at a larger distance level to form a bigger cluster.

Same cluster analysis was conducted again after adding three virtual pathosystems with the dendrogramme shown in Fig. 3.7. Generally, there are still three major clusters. The first cluster expanded significantly, having 16 disease pathosystems including the virtual

Average Disease and Super Disease. This cluster occupies the upper middle part of the cloud in Fig. 3.1. Three pathosystems (SSR, NSC, and SSC), previously in the second cluster in Fig.

3.6, joined the first cluster in Fig. 3.7, and another three (BSR, SnB, and TRR) from previously the third cluster also joined the first cluster in Fig. 3.7. The Super Disease is right beside Thielaviopsis root rot (TRR), while the Average Disease is with brown stem rot (BSR) and southern blight (SnB). These five disease pathogens (SuD, TRR, AvD, BSR, and SnB) also form a sub-cluster.

The second cluster in Fig. 3.7 has 8 pathosystems of typical leaf disease, including soybean rust. Powdery mildew is in this cluster this time. Seven of them (BrS, FLS, PLS,

PSS, DwM, LLS, and SbR) were grouped together both in Figs. 3.6 and 3.7. One interesting feature of this 7-member group is that 6 of them have relatively narrow host range (ranked 1 to 2 in Table 3.1), except purple seed stain (PSS).

The third cluster in Fig. 3.7 has 11 members after lost three pathosystems to the first cluster. There are two sub-clusters in the third cluster, which are same in Figs. 3.6 and 3.7. In

Fig. 3.1, Phytophthora rot and the virtual Weak Disease are far from other pathosystems. In

Fig. 3.7, they are grouped together and still independent from all the major clusters until all

72 pathosystems finally merge into one cluster. This implies that Phytophthora rot may be significant different from other fungal pathosystem in the nature.

Discussion

Information of disease or pathogen geographical distribution and historical yield loss has been documented in literatures (9, 23, 41, 60, 66-70, 74). Although more than thirty soybean fungal diseases have been reported in the U.S., yield loss data are available only for the major diseases frequently causing severe losses. In Fig. 3.8, several major soybean fungal pathosystems that caused major soybean yield losses in 1996 to 2005 (averaged annual estimation data, 68) were highlighted by solid dots and underlines. Those caused most severe damage (annually yield losses >107 bushels) were double underlined. Those caused moderate damage (annually yield losses in 106 to 107 bushels) were underlined. Soybean downy

mildew (DwM) and southern blight (SnB) only caused minor yield losses (<106 bushels),

which are marked by half closed circles in Fig. 3.8 without underlines for their codes.

It is clear that most damage was from the pathosystems in the first cluster and the

third cluster in Fig. 3.7. In the first cluster, most of these pathosystems are located around the

virtual Average Disease and Super Diseases indicating that pathogens of these pathosystems

may have the best fitness in the soybean cropping system in the U.S. For the second cluster

that contains soybean rust in Fig. 3.7, only brown spot (BwS) and frogeye leaf spot (FLS)

caused moderate damage. The relatively light damage from the diseases in the second cluster

implies that diseases in this cluster may have limited potential to cause severe damage in the

U.S. soybean production region. This limit may be associated with the geographical

environment in the U.S. and the ecological and biological attributes of these pathosystems

such as that their pathogens need free moisture for infection, cannot survive for long time

73 without host, and have relatively narrow host range (HR, most of them ranked in 1 to 2 in

Table 3.1). Close to downy mildew (DwM) and Leptosphaerulina leaf spot (LSS) that caused light yield losses, soybean rust falls in this cluster. Therefore, according to other members in this cluster, the likelihood of a severe damage caused by soybean rust disease throughout the

U.S. soybean production region would be low. However, soybean rust is located at the very end of this cluster (see Fig. 3.1). Would it be another extreme case like Phytophthora rot

(PpR), which causes sever damage, is an interesting question yet to be answered.

Geographical distribution of a fungal species consists two aspects: 1) the maximum latitudinal or longitudinal range; and 2) regional disease prevalence. The maximum range is strongly associated with the regional environmental condition such as temperature and moisture to the disease occurrence. Regional disease prevalence may be more associated with pathogen dispersal and pathogen suitability in hosts. However, information for disease regional prevalence is quite limited. Simply compare the pathosystems in the second cluster in Fig. 3.7, their disease geographical distribution in the U.S. is significantly different.

Soybean rust (SbR) has been reported in 15 states from Gulf Coast region to Indiana (16).

Leptosphaerulina leaf spot (LLS) has a limited geographical distribution range in Missouri,

Maryland reported as a soybean disease (9, 23), while the species has been reported on other host in Georgia as well (14, 19). Downy mildew (DwM), brown spot (BrS), frogeye leaf spot

(FLS), and purple seed stain (PSS) have been reported widely spread from Gulf Coast to the northern U.S. (9, 23) implicating large absolute range and high prevalence. Phyllosticta leaf spot (PLS) is distributed in most regions in soybean belt especially in cool region (23).

Powdery mildew (PwM) has been reported in southeastern and the Midwestern U.S. such as

Georgia, Iowa, and Wisconsin (23). Among these diseases, Leptosphaerulina leaf spot has

74 the most limited geographical distribution, which would be due to its low optimal temperature (18°C) for disease development. Downy mildew, brown spot, frogeye leaf spot, and purple seed stain are widely spread. They have optimal temperatures for disease development at 21, 25, 26, and 27 °C respectively, which are close to the average optimal temperature of all real pathosystems in this study. Furthermore, they are capable of seedborne, which further facilitate their wide range distribution and high regional prevalence.

Phyllosticta leaf spot and powdery mildew have limited geographical distribution compared with the four most widely spread ones. For Phyllosticta leaf spot, this may be due to its narrow host range. For powdery mildew, it cannot be seedborne, survive on host residual, and survive without host, also has a cool optimal temperature for development (20°C), which may limit its distribution. On the other hand, it has a broad host range (ranked as 3) and does not require high moisture for infection, which may compensate the disadvantages of other aspects.

Compared with the four most severe ones, soybean rust has optimal disease development temperature at 26°C, similar host range (ranked as 2), but cannot be seedborne and long-time survive without host or in plant residue. Synthesize all these characteristics, one may expect that soybean rust would have the maximum geographical distribution range similar to the four most widely spread diseases, but limited regional prevalence level. At certain favorable weather conditions that extremely favor its wind dispersal, soybean rust may have the potential to reach further northern regions such as Iowa or Minnesota. However, even it reaches these regions, the yield losses would be minor according to the pattern of yield losses in Fig. 3.8 and the other pathosystems in the second cluster in Fig.3.7. As soybean rust has an optimal disease development temperature at 26°C, in the southern U.S.

75 close to its overwintering region, soybean rust would likely cause moderate yield losses when weather conditions are favorable.

Those around the Average Disease and Super Disease causing great damage in soybean production may be due to their relatively balanced characteristics. Charcoal rot

(ChR), Fusarium root rot (FRR), sudden death syndrome (SDS), and Phytophthora rot (PpR) also cause severe damage in soybean. This may be due to their capabilities of survive in adverse environmental conditions and the fact that infections from these diseases can kill the whole plant. In principal coordinate analysis, the Weak Disease is far away from other real pathosystems, which gives no surprises, as it is absent in the nature. Similarly, Phytophthora rot is also located far away from other fungal diseases though much closer compared with the

Weak Disease. Along with Pythium spp. and downy mildew pathogen, Phytophthora sojae is in Oomycetes, which may be part of the reason that it is not like the others but it still causes great yield damage. However, why it is still unique compared with Pythium spp. and downy mildew pathogen remains an interesting question. Furthermore, the patterns shown in Figs.

3.1-3.5 and Fig. 3.8 may guide us in the analyses of other exotic fungal pathogens and to identify possible risks if they are projected close to those causing great damage in the U.S.

Our study has been focus on soybean pathosystems in the U.S. The analyses showed striking patterns and significant differences among clusters of pathosystems. The application of Gower’s general similarity coefficient and principal coordinate analysis in comparative epidemiology would provide us more comprehensive and visualized disease profiles with the quantified links and patterns among the diseases. Furthermore, using these approaches, we have the ability to build a new type of coordinate systems based on the biological and ecological attributes of the diseases. The new coordinate systems provide us new spaces in

76 which we may identify patterns of the diseases other than that in the 4-D temporal-spatial space. For example, the yield losses pattern in Fig. 3.8 would change in other soybean production regions such as China and South America due to different characteristics of environmental conditions. Studies to compare all the soybean fungal diseases in the three regions respectively are worthy doing.

Due to limited information, some of the important pathosystem attributes such as optimal environmental humidity, host resistance were not included in our study. How to quantify these remains a problem. A few minor soybean diseases were not included, which may need further study because inclusion of these minor diseases may help us better understand why they are not suitable to the soybean cropping system in the U.S.

For soybean rust, Phakopsora pachyrhizi has a different life cycle from other soybean fungal pathogens, though its life cycle may be microcyclic as known so far, which is relatively simple. Further study to compare soybean rust pathosystem with other rust pathosystems, such as wheat leaf rust (Puccinia triticina), corn common rust (Puccinia sorghi), etc for their geographical distribution and outbreak frequencies may shade light on this important topic.

Acknowledgments

We thank the National Natural Science Foundation of China (NSFC) for funding.

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Table 3.1. The disease and pathogen names, disease codes, and variable values of descriptive characteristics of soybean fungal pathosystems in North America. Disease name Pathogen name Code OT1 HR2 FM3 IP4 GS5 SI6 WD7 SB8 SR9 LS10 Data Source Alternaria leaf spot Alternaria alternata ALS 25 3 0 1 0 1 1 1 1 0 21, 23, 24, 28 Anthracnose Colletotrichum truncatum Anr 28 3 0 1 1 1 0 1 1 0 9, 23, 26, 61 Brown spot Septoria glycines BrS 25 2 1 1 1 1 1 1 1 0 23 Brown stem rot Phialophora gregata BSR 23 2 0 0 1 1 0 1 1 1 9,10, 23 Charcoal rot Macrophomina phaseolina ChR 34 3 0 0 0 0 0 1 1 1 23, 41,2, 42 Choanephora leaf blight Choanephora infundibulifera CLB 28 3 1 1 1 1 0 0 1 0 41 Downy mildew Peronospora manshurica DwM 21 1 1 1 1 1 1 1 0 0 9, 23, 41, 71 Frogeye leaf spot Cercospora sojina FLS 26 2 1 1 1 1 1 1 1 0 9, 38 Fusarium pod and collar rot Fusarium pallidoroseum FPC 24 3 0 0 1 0 0 1 1 1 9, 23 Fusarium root rot Fusarium solani FRR 18 3 0 0 0 0 0 1 1 1 9, 23 85 1 OT: optimal temperature (°C) for disease development. 2 HR: host ranges; 1=hosts in genus Glycine; 2=hosts in other leguminous plants; 3=host in other families. 3 FM: whether free moisture is needed to facilitate the pathogen to infect; 1=yes, 0=no. 4 IP: what parts of soybean plant are mostly infected by the pathogen; 1=upper stem, leaf, and pods; 0=root and lower stem. 5 GS: what plant growth stage the pathogen infects soybean plants mostly; 1=all stage; 0=vegetative stage or reproductive stage. 6 SI: whether secondary infections occur; 1=yes, 0=no. 7 WD: whether the pathogen can be dispersed by wind in field; 1=yes, 0=no. 8 SB: whether the pathogen can infect soybean seed; 1=yes, 0=no. 9 SR: whether the pathogen can survive saprophytically in soybean residue and serve as initial inoculum source for the next growing season; 1=yes, 0=no. 10 LS: whether the pathogen can survive saprophytically in the soil without host, or can parasite other microbes such as nematodes, or can produce dormancy structures such as sclerotia that can survive for a few years; 1=yes, 0=no. * AG1: anastomosis group 1. † AG4: anastomosis group 4.

Table 3.1. (continued) Disease name Pathogen name Code OT HR FM IP GS SI WD SB SR LS Data Source Fusarium wilt Fusarium oxysporum f.sp. tracheiphilum FWi 22 2 0 0 1 0 0 1 1 1 9, 23, 43 Leptosphaerulina leaf spot Leptosphaerulina trifolii LLS 18 2 1 1 1 1 1 0 1 0 9, 6 Myrothecium leaf spot Myrothecium roridum MLS 27 3 0 1 1 1 0 1 1 0 4, 9, 35, 41 Neocosmospora stem rot Neocosmospora vasinfecta NSR 27 2 0 0 0 0 0 0 1 1 11, 23, 44 Northern stem canker Diaporthe phaseolorum var. caulivora NSC 23 3 1 0 1 1 1 1 1 0 23, 27, 65 Phomopsis seed decay Phomopsis longicolla PSD 30 3 1 1 1 1 0 1 1 0 5, 17, 23, 41 Phyllosticta leaf spot Phyllosticta sojicola PLS 22 1 1 1 1 1 1 1 1 0 9, 41 Phytophthora rot Phytophthora sojae PpR 26 1 1 0 0 1 0 1 0 1 9, 23 Pod and stem blight Diaporthe phaseolorum var. sojae PSB 25 3 0 0 1 1 0 1 1 0 23, 29, 41,63 Powdery mildew Microsphaera diffusa PwM 20 3 0 1 1 1 1 0 0 0 23 Purple seed stain Cercospora kikuchii PSS 27 3 1 1 1 1 1 1 1 0 9, 23, 59 Pythium root rot Pythium aphanidermatum PyA 33 3 1 0 0 0 0 0 1 1 23

Pythium root rot Pythium irregulare PyI 30 3 1 0 0 0 0 0 1 1 9, 23 86 Pythium root rot Pythium ultimum PyU 22 3 1 0 0 0 0 0 1 1 23,57 Red crown rot Cylindrocladium crotalariae RCR 26 3 0 0 1 0 0 0 1 1 23, 41 Rhizoctonia aerial blight Rhizoctonia solani (AG1)* RAB 28 3 1 1 1 1 0 1 1 1 9, 23, 41, 62 Rhizoctonia root rot Rhizoctonia solani (AG4)† RRR 25 3 0 0 1 0 0 1 1 1 13, 23, 30 Sclerotinia stem rot Sclerotinia sclerotiorum SSR 22 3 1 0 0 1 1 1 1 1 1, 9, 23 Southern blight Sclerotium rolfsii SnB 30 3 0 0 1 1 0 1 1 1 7, 23, 41, 50, 54 Southern stem canker Diaporthe phaseolorum var. meridionalis SSC 31 3 1 0 1 1 1 1 1 0 23, 48 Soybean rust Phakopsora pachyrhizi SbR 26 2 1 1 1 1 1 0 0 0 23, 45, 47, 73 Sudden death syndrome Fusarium solani f.sp. glycines SDS 22 3 0 0 1 0 0 0 1 1 23, 41, 55 Target spot Corynespora cassiicola TaS 19 3 0 1 0 1 0 1 1 1 23, 41 Thielaviopsis root rot Thielaviopsis basicola TRR 24 3 0 0 1 1 1 1 1 1 9, 20, 23, 41 Weak disease - WkD 25 1 1 0 0 0 0 0 0 0 - Super disease - SuD 25 3 0 1 1 1 1 1 1 1 - Average disease - AvD 25 3 1 0 1 1 0 1 1 1 -

Table 3.2. Gower’s general similarity coefficients for all pathosystems of the soybean fungal diseases in North America. Thirteen similarity coefficients greater than 0.9 and three less than 0.1 are underlined. See Table 3.1 for detail information of the codes.

Similarity coefficient Code ALS Anr BrS BSR ChR CLB DwM FLS FPC Anr 0.757 ------BrS 0.694 0.673 ------BSR 0.512 0.659 0.588 ------ChR 0.493 0.514 0.2680.617 - - - - - CLB 0.535 0.750 0.6730.437 0.292 - - - - DwM 0.528 0.507 0.834 0.463 0.119 0.507 - - - FLS 0.687 0.680 0.993 0.581 0.274 0.680 0.827 - - FPC 0.494 0.639 0.418 0.798 0.819 0.417 0.281 0.412 - FRR 0.507 0.486 0.281 0.659 0.875 0.264 0.181 0.274 0.847 FWi 0.406 0.541 0.481 0.882 0.722 0.319 0.369 0.475 0.902 LLS 0.534 0.513 0.840 0.469 0.124 0.735 0.730 0.833 0.287 MLS 0.764 0.992 0.680 0.666 0.507 0.742 0.514 0.687 0.646 NSR 0.347 0.354 0.288 0.639 0.726 0.354 0.138 0.294 0.673 NSC 0.653 0.632 0.791 0.624 0.431 0.632 0.653 0.784 0.594 PSD 0.632 0.859 0.770 0.534 0.417 0.859 0.604 0.777 0.514 PLS 0.646 0.625 0.952 0.569 0.225 0.625 0.882 0.945 0.388 PpR 0.326 0.319 0.469 0.619 0.438 0.319 0.569 0.476 0.431 PSB 0.667 0.852 0.583 0.791 0.604 0.602 0.417 0.576 0.771 PwM 0.632 0.611 0.548 0.406 0.213 0.611 0.660 0.541 0.375 PSS 0.764 0.771 0.902 0.499 0.356 0.771 0.736 0.909 0.481 PyA 0.278 0.299 0.274 0.402 0.742 0.521 0.125 0.281 0.604 PyI 0.299 0.319 0.293 0.423 0.719 0.542 0.144 0.299 0.625 PyU 0.313 0.292 0.306 0.465 0.656 0.514 0.194 0.299 0.653 RCR 0.394 0.542 0.318 0.673 0.722 0.542 0.169 0.324 0.875 RAB 0.581 0.778 0.706 0.659 0.514 0.778 0.556 0.712 0.639 RRR 0.500 0.646 0.424 0.791 0.826 0.424 0.275 0.418 0.993 SSR 0.646 0.463 0.606 0.618 0.583 0.463 0.494 0.599 0.588 SnB 0.569 0.764 0.493 0.867 0.750 0.542 0.344 0.499 0.847 SSC 0.625 0.646 0.763 0.574 0.481 0.646 0.597 0.770 0.556 SbR 0.465 0.458 0.771 0.381 0.074 0.680 0.827 0.778 0.212 SDS 0.381 0.514 0.306 0.687 0.694 0.514 0.194 0.299 0.875 TaS 0.736 0.715 0.487 0.666 0.633 0.493 0.388 0.481 0.632 TRR 0.694 0.675 0.618 0.818 0.638 0.475 0.481 0.612 0.800 WkD 0.125 0.102 0.306 0.292 0.305 0.352 0.417 0.299 0.326 SuD 0.800 0.781 0.724 0.712 0.544 0.581 0.575 0.718 0.694 AvD 0.500 0.646 0.624 0.791 0.604 0.646 0.475 0.618 0.771

Table 3.2. (continued)

Similarity coefficient Code FRR FWi LLS MLS NSR NSC PSD PLS PpR FWi 0.778 ------LLS 0.225 0.375 ------MLS 0.493 0.549 0.521 ------NSR 0.711 0.743 0.3440.361 - - - - - NSC 0.469 0.519 0.6600.639 0.300 - - - - PSD 0.361 0.417 0.611 0.852 0.229 0.729 - - - PLS 0.275 0.475 0.8330.632 0.244 0.771 0.722 - - PpR 0.438 0.500 0.3250.326 0.461 0.481 0.417 0.475 - PSB 0.618 0.674 0.424 0.859 0.458 0.764 0.711 0.535 0.438 PwM 0.288 0.313 0.681 0.618 0.281 0.535 0.486 0.542 0.263 PSS 0.344 0.394 0.743 0.778 0.225 0.861 0.868 0.854 0.394 PyA 0.633 0.507 0.331 0.292 0.734 0.438 0.424 0.231 0.445 PyI 0.656 0.528 0.350 0.313 0.758 0.456 0.444 0.250 0.469 PyU 0.719 0.583 0.400 0.299 0.742 0.494 0.389 0.300 0.469 RCR 0.722 0.778 0.375 0.549 0.799 0.481 0.417 0.275 0.333 RAB 0.486 0.542 0.563 0.771 0.354 0.669 0.875 0.663 0.542 RRR 0.840 0.896 0.281 0.653 0.681 0.588 0.521 0.381 0.438 SSR 0.639 0.525 0.500 0.469 0.438 0.794 0.550 0.600 0.639 SnB 0.694 0.750 0.350 0.757 0.563 0.656 0.667 0.450 0.528 SSC 0.419 0.469 0.6040.639 0.300 0.944 0.771 0.715 0.469 SbR 0.074 0.275 0.833 0.465 0.294 0.563 0.556 0.722 0.475 SDS 0.750 0.806 0.400 0.521 0.771 0.494 0.389 0.300 0.306 TaS 0.742 0.563 0.419 0.722 0.469 0.475 0.590 0.481 0.445 TRR 0.663 0.713 0.488 0.681 0.506 0.794 0.563 0.588 0.488 WkD 0.320 0.396 0.368 0.109 0.578 0.319 0.211 0.313 0.617 SuD 0.556 0.606 0.581 0.788 0.413 0.688 0.669 0.681 0.394 AvD 0.618 0.674 0.481 0.653 0.458 0.788 0.743 0.581 0.660

Table 3.2. (continued)

Similarity coefficient Code PSB PwM PSS PyA PyI PyU RCR RAB RRR PwM 0.521 ------PSS 0.653 0.618 ------PyA 0.389 0.219 0.363 ------PyI 0.410 0.238 0.3810.977 - - - - - PyU 0.424 0.288 0.3690.914 0.938 - - - - RCR 0.660 0.463 0.3940.729 0.750 0.750 - - - RAB 0.646 0.450 0.7940.521 0.542 0.514 0.542 - - RRR 0.778 0.369 0.488 0.611 0.632 0.646 0.882 0.646 - SSR 0.581 0.388 0.669 0.590 0.611 0.667 0.475 0.663 0.581 SnB 0.854 0.438 0.581 0.535 0.556 0.500 0.750 0.764 0.854 SSC 0.736 0.479 0.861 0.488 0.494 0.444 0.469 0.681 0.563 SbR 0.354 0.764 0.688 0.281 0.300 0.300 0.325 0.513 0.219 SDS 0.646 0.488 0.3690.701 0.722 0.778 0.972 0.514 0.868 TaS 0.625 0.494 0.550 0.391 0.414 0.477 0.507 0.715 0.625 TRR 0.794 0.575 0.681 0.444 0.463 0.488 0.688 0.675 0.794 WkD 0.250 0.299 0.208 0.563 0.586 0.602 0.438 0.201 0.333 SuD 0.700 0.669 0.788 0.350 0.369 0.381 0.594 0.781 0.700 AvD 0.778 0.369 0.688 0.611 0.632 0.646 0.660 0.868 0.778

Table 3.2. (continued)

Similarity coefficient Code SSR SnB SSC SbR SDS TaS TRR WkD SuD SnB 0.650 ------SSC 0.744 0.694 ------SbR 0.400 0.300 0.549 ------SDS 0.500 0.722 0.4440.300 - - - - - TaS 0.646 0.701 0.4250.281 0.535 - - - - TRR 0.788 0.863 0.7560.413 0.688 0.669 - - - WkD 0.313 0.188 0.2920.521 0.424 0.078 0.194 - - SuD 0.681 0.769 0.663 0.519 0.581 0.763 0.894 0.100 - AvD 0.781 0.854 0.763 0.419 0.646 0.625 0.794 0.333 0.700

Fig. 3.1. Projection of all soybean pathosystems including the three virtual pathosystems in a 2-dimension space by principal coordinate analysis based on Gower’s general similarity coefficients in Table 3.2. Open circles represent the pathosystems, labeled with codes (see Table 3.1). Circles may overlap due to large similarity coefficient.

Fig. 3.2. Same principal coordinate analysis projection as Fig. 3.1. Solid circles represent the pathosystems in which free moisture (FM) is needed for completion of pathogen infection.

Fig. 3.3. Same principal coordinate analysis projection as Fig. 3.1. Solid circles represent the pathosystems in which the pathogens do not have secondary infections (SI) or second disease cycle in a growing season.

Fig. 3.4. Same principal coordinate analysis projection as Fig. 3.1. Solid circles represent the pathosystems in which the pathogens can be disseminated by wind (WD).

Fig. 3.5. Same principal coordinate analysis projection as Fig. 3.1. Solid circles represent the pathosystems in which pathogens cannot infect soybean seeds (SB).

Fig. 3.6. Illustration of grouped real soybean pathosystems in a dendrogramme by cluster analysis (UPGMA) based on pathosystems’ characteristics values in Table 3.1 and Gower’s similarity coefficients. Pathosystems are labeled by disease code (see Table 3.1 for detailed information). Three major clusters can be identified, except soybean powdery mildew (PwM) and Phytophthora rot (PpR).

Fig. 3.7. Illustration of grouped soybean pathosystems including the three virtual pathosystems in a dendrogramme by cluster analysis (UPGMA) based on pathosystems’ characteristics values in Table 3.1 and Gower’s similarity coefficients. There are three major clusters, except the virtual Weak Disease (WkD) and Phytophthora rot (PpR).

Fig. 3.8. Same principal coordinate analysis projection as Fig. 3.1. Solid circles represent the diseases that caused significant yield damage in soybean based on averaged estimates of soybean yield losses in the U.S. in 1996 to 2005. The codes of those caused severe yield damage are double underlined. Those caused moderate yield damage are underlined. Soybean downy mildew (DwM) and southern blight (SnB) only caused minor yield losses, which are only marked with half closed circles.

CHAPTER 4. ASSESSING EFFECTS OF TEMPERATURES ON OUTBREAK RISK OF ASIAN SOYBEAN RUST WITH BIOGEOGRAPHY INFORMATION OF RUST DISEASES IN NORTH AMERICA

A paper prepared for submission to Phytopathology

X. Li, and X. B. Yang

Abstract

Optimal infection temperatures of rusts and the average/maximum July mean temperatures from 1968 to 1996 along the northern boundary of geographic distribution range of rusts in North America were examined. Regression analysis showed that the optimum temperatures for infection were highly correlated with July mean temperatures along the northern boundary of disease range for the rusts examined. The potential distribution range of soybean rust (SBR) in North America was modeled using the regression models based on other rusts with similar disease cycle to SBR and the optimal infection temperature of SBR. This potential range was defined by either of two contours: where average July mean temperature was 21°C or maximum July mean temperature was 24°C during years from 1968 to 1996. Both contours defined the potential range without significant differences. This range includes most of the U.S. soybean production areas.

Temperature favorability for SBR infection within this potential range was assessed using local daily data from 1961 to 2000. The southeastern U.S. has the highest temperature favorability. The southern Midwest in west of Mississippi River is less favorable. The North

Central region along Soybean Belt is moderate. Southern Texas and the northern states have low favorability. Fitted Beta distribution for the frequency distribution of standardized temperature favorability was skewed to the right side.

Introduction

Information on geographical distribution range of a disease is critical to regional

disease management, particularly important to risk assessment of new diseases and

reemerging diseases (19, 20, 111, 118). Asian soybean rust (SBR), caused by Phakopsora

pachyrhizi Sydow recently entered the continental United States (93). The disease has established in the southern U.S. along the Gulf Coast (32, 79, 119). By 2006, soybean rust has been reported in the U.S. in 15 states (32). Although the life cycle of P. pachyrhizi in

nature is not fully understood, field and laboratory observations indicated that it is similar to

wheat stripe rust (Puccinia striiformis f.sp. tritici) (6, 89, 102). In the field, only urediospores are observed to serve as the initial inocula and temperature conditions are important to P. pachyrhizi infection (66, 79, 80, 102). With long-distance dissemination (102), soybean rust may extend its current distribution range in North America. However, its potential range in this continent is still unknown. Given the facts that many rust diseases have similar pathogen life cycles and disease cycles to soybean rust (1, 6, 89), examination of their geographical distribution ranges may shed light on the assessment of potential range of SBR.

Many rust pathogens are capable of long-distance dissemination, which facilitate them to establish in a vast geographical range (1, 52, 100). Presence of a disease in a geographical region usually implies suitable local conditions for the disease to occur.

Theoretically, the geographical distribution range of a disease should reflect the geographical distribution of limiting biotic or abiotic factors to the disease. Distribution range of a rust may be limited by pathogen dispersal, moisture availability, temperature conditions, and host distribution. For instance, in North America, moisture availability decreases from east to west that may define the western boundary of disease range longitudinally (5, 88). In contrast,

100 temperature would be more important than moisture to define the latitudinal boundary of disease range.

Wherever a rust occurs, temperatures must be at least suitable for infection, which may just need a few days to result in minimum occurrence of the disease. Minimum disease occurrence should be an indication of minimum disease risk (104, 118). However, it is difficult to know the exact temperature condition for the minimum occurrence of a disease in the field, especially for a new disease. Rapid development of a polycyclic airborne disease needs frequent secondary infections favored by optimal environmental temperature for pathogen infections (1). Although a rust may infect hosts under its minimum infection temperature, because for many rust diseases their optimal temperatures for disease development are higher than their optimal temperatures for infection (1, 6, 9, 80, 89, 100), the subsequent disease development would be retarded at such low temperatures and level of the disease may be so low that detection is unlikely. Since the reported disease distribution usually implies relatively significant disease occurrence, one would expect that when environmental temperature is optimal for infection for a certain time, e.g. one month that is longer than the latent period of many rusts (1, 9, 89, 100), the disease occurrence would eventually reach a detectable level. Whereas if environmental temperature never rises to the optimal infection temperature even in the warmest month (July for North America), a rust would probably not occur or be under the detectable level even if it occurs. Therefore, the hypothesis is that the observed disease geographical range of a disease, particularly for the rusts, is highly associated with regional temperature condition, which may result in occurrence of the disease above a detectable level. Specifically, the northern boundary of this disease range is associated with July mean temperature favorable for infection. Along this

101 boundary, regional temperature in other months is too low to allow the disease to occur even if other conditions are favorable, unless in July, the mean temperature would reach optimal infection temperature of the pathogen disease and result in detectable disease occurrence in a given year.

Due to temperature fluctuations, the northern boundary of disease range may vary from year to year. The shift of this boundary over years defines a potential disease range, i.e. the reported disease range, which has favorable regions where regional temperature reaches the optimal infection temperature frequently and marginal regions where the favorable temperature condition is uncommon. Beyond this boundary, regional temperature never reaches the favorable temperature range for infection of the disease even over a long period and thus no disease occurs. If we can determine the boundary constraint conditions, which offer chances for minimum occurrence of a disease, and probability of these conditions over a long period in a region, we may know whether or not a disease can occur in the region. As mentioned above, July mean temperatures may be temperature constraint conditions for minimum occurrence of a disease along the northern boundary of its distribution range. Since infection temperatures of a pathogen determine what regional temperature would facilitate occurrence of the disease, infection temperatures of the pathogen should be associated with northern boundary of the corresponding disease range. With determination of how the northern boundary of disease range is associated with pathogen infection temperatures, the potential disease distribution range of a new disease, e.g. soybean rust could be assessed based on its infection temperatures.

The assessed potential range indicates where the disease may occur. Within this potential range, we may further analyze detailed weather data and other disease related

102

information to examine more specific temperature favorability in a finer scale, which would

be defined as the temperature favorability for occurrence of the disease at specific locations.

For instance, the temperature favorability for soybean rust infection would be associated with

the local night length and night temperature because germination of SBR urediospore is

inhibited by lights and the infections need free moisture from dew or rainfall (6, 102).

Therefore, in this study, the objectives were: 1) to assess relationships between

geographical distribution range of rusts and optimal temperatures for their pathogen infection in North America; 2) determine how regional temperatures may be associated geographical distribution range of soybean rust in North America; 3) assess temperature favorability for the occurrence of SBR in the U.S. based on historical weather data. The conceptual framework to assess soybean rust potential range and temperature favorability for P.

pachyrhizi infection is given in Fig. 4.1. The potential range of SBR was assessed by comparative epidemiology approaches based on information of other rust diseases distribution and is to project where soybean rust would occur. Temperature favorability was based on local historical weather data and biological attributes of P. pachyrhizi to quantify favorability of local temperature condition for the infection. As temperature is available and reliable in most weather datasets, while other variables such as relative humidity, dew duration are less available and reliable; to build the link of temperature and disease distribution range seems a more feasible approach in disease risk assessment.

Materials and methods

Rust diseases in North America and their temperature attributes. Rust diseases reported in North America were used in this study and listed in Table 4.1. Values of their optimal temperatures for infection of various spore types were from literature. Temperatures

103 for germination were used when information for infection was not available for a particular disease.

Operational definition for geographical distribution range of rust diseases in

North America. As mentioned above, disease distribution is the result of regional temperature conditions that determine disease infection. Therefore, regional temperature conditions should be associated with disease distribution range. Thus, instead of using geographical indices such as latitude, we used temperature contours as the measure of distribution range of these rusts. The disease distribution information was gathered from literature and the CABI Crop Protection Compendium online database (9). Two contour maps for the average July mean temperature (AveJulT) and the maximum July mean temperature (MaxJulT) during 1968 to 1996 were generated based on climatic data from the

National Center for Environmental Prediction (NCEP) and the National Center for

Atmospheric Research (NCAR) of the National Oceanic & Atmospheric Administration

(NOAA) (47). By plotting the reported disease range of a rust on both contour maps, we could identify several temperature contours across the northern boundary of the range. This northern boundary of each rust was then defined by the lowest temperature contours of

AveJulT and MaxJulT in this period along the northern boundary (Table 4.1).

Relationships of the optimal infection temperatures of rust pathogens to July mean temperatures along the northern boundaries of disease ranges. To determine the relationship between the optimal infection temperatures of these rusts (soybean rust not included) and the July mean temperatures (AveJulT and MaxJulT) during 1968 to 1996 along the northern boundary of the disease range, simple linear regression analysis (101) was conducted with SAS (SAS Institute, Cary, NC). Because a rust pathogen may have several

104

spore types each with its own optimal infection/germination temperature (Table 4.1), a

simple arithmetic average of optimal infection/germination temperatures (AveOpT) of all

spore types was derived. First, linear regression was conducted to examine the relationship

between AveOpT of these rusts and AveJulT as well as MaxJulT. In the field, observations

of soybean rust indicated that only urediospores serve as initial inoculum for long-distance

dissemination (66, 102). Some rust pathogens resemble P. pachyrhizi in regard to their long-

distance dissemination and heteroecious life cycle (1, 9, 89, 100). Their natural geographic

ranges may be good indicators for that of soybean rust. Therefore, the linear relationship of

optimal infection/germination temperature of urediospores (UredioOpT) of these rust

diseases and the two July mean temperature variables was examined as well. The linear

regression models were then used to determine the northern boundary of SBR potential range

in North America.

Assessment of temperature favorability for soybean rust infection based on local

historical temperature data. Historical daily temperature data from the U.S. Historical

Climatology Network (USHCN) were acquired from the National Climatic Data Center

(NCDC) (48), including daily maximum temperature and minimum temperature from 1961 to 2000. Daily weather data in 2006 were from Global Surface Summary of Day acquired from NCDC as well. Previous research indicated the importance of night temperature to

Phakopsora pachyrhizi infection (50, 102). Besides, free moisture provided either by rainfall or dew is necessary for P. pachyrhizi infection and dew occurs generally in the night (6, 102).

Thus, P. pachyrhizi infections would occur mainly in the night. To fully address the temperature favorability for P. pachyrhizi infection, we used a comprehensive approach instead using single daily mean or night temperature.

105

First, given latitude of each weather station and the date of temperature records, the sunset time and night length were calculated using sun angle and day length models (15).

Then the temperature at any given time in a day was calculated with a temperature temporal variation model (15). Pivonia and Yang developed a relative infection index (RII, scale 0 to 1) model based on classical response curve for biological systems and the cardinal temperatures for P. pachyrhizi infection (10, 22.5, and 27.5 °C) (80). Using the RII and temperature models, RII in any given time in the night was calculated. Numerical integral was then conducted by Trapezoid approximation sums (92) with error <10-3 to give the daily RII integral over the night. We defined this RII integral as the daily temperature favorability.

Daily RII over the summer (June, July, and August) was accumulated for each year from

1961 to 2000 to represent the annual summer temperature favorability. Finally, for each station, there were a series of accumulated RIIs. The 10% and 90% quantiles, as well as the medians of these accumulated RIIs of each location were plotted to illustrate the temperature favorability for soybean rust infection in the U.S. The accumulated RII in 2006 summer was calculated as well.

Meanwhile, daily theoretical maximum temperature favorability occurs if the night temperature is always optimal, which results in relative infection index to be 1. Then each location has a maximum accumulated RII over summer. This maximum temperature favorability is only associated with latitude and decreases from low latitude to high latitude in summer because of night length. The proportion of actual temperature favorability to the maximum temperature favorability is actually a standardized temperature favorability index.

Knowing the probability distribution of these standardized indices over the 40 years may help us to understand what would be the chances for certain temperature favorability to occur.

106

To examine the probability distribution of the standardized temperature favorability indices at different locations, we defined sub-regions according to climate types in the U.S. and distribution of major soybean production areas (90). For all weather stations in each sub- region, a histogram was generated with all standardized temperature favorability indices. The frequency distribution of these indices for each sub-region was then fitted by Beta distribution to examine the profile of the temperature favorability over these years (54).

Calculation of temperature favorability was conducted by Microsoft Excel (Microsoft

Corporation, Redmond, WA) and Beta distribution fitting was by SAS (SAS Institute, Cary,

NC).

Results

Relationships of the optimal infection temperatures of rust pathogens to July mean temperatures along the northern boundaries of disease ranges. As expected, temperature attributes of rust pathogens were highly correlated with July mean temperatures either the average or the maximum along the north boundary of distribution range of these rusts (regression models given in Table 4.2 and Figs. 4.2-4.5). The two regression models for average July mean temperature (AveJulT) (Figs. 4.2 and 4.3) have slope less than 1; while the two models in Figs. 4.4 and 4.5 for maximum July mean temperature (MaxJulT) have slope very close to 1, which may indicate the differences in association of these two variables with the disease northern boundary.

Assessment of potential geographical distribution range of soybean rust in North

America. Because optimal infection temperature of soybean rust urediospores is about 23°C, using the two linear regression models for UredioOpT (Table 4.2) which were generated from data of rust diseases similar to SBR in disease cycle and pathogen life cycle, we

107

calculated that the northern boundary of potential geographical distribution range of SBR in

North America would be along the temperature contours where either average July mean

temperature was 21°C or maximum July mean temperature was 24°C in the period from

1968 to 1996. Similarly, the northern boundary of temperature favorable geographic range of soybean rust would be along the contour where average July mean temperature was 23°C in same period. These temperature contours are plotted in Fig. 4.6 as well as Figs. 4.7-4.12.

Although the two contours (AveJulT=21°C and MaxJulT=24°C) in Fig. 4.6 were determined by different regression models, they overlap generally and outline the SBR potential range without significant differences. This indicates similar power for AveJulT and

MaxJulT to assess SBR potential range. The average July mean temperature contour of 23°C outlines the temperature favorable region for SBR in North America where the favorable temperature would be frequent at least in July, which may result in frequent occurrence of soybean rust. This region includes the Gulf Coast region, the Mississippi Delta region, the southern portion of North Central region, the southeastern coastal region, as well as southern

South Dakota, southwestern Iowa, and most of Illinois, Indiana, Kentucky, Virginia and southern New Jersey. The marginal region covers southern North Dakota, northern South

Dakota, southwestern Minnesota, northeastern Iowa, southern Wisconsin, southern Michigan,

Ohio, West Virginia, southern Pennsylvania, and northern New Jersey. In this region, July mean temperature would not always favor SBR. However, due to temperature fluctuations,

SBR may occur occasionally in years with favorable July mean temperature.

Assessment of temperature favorability for soybean rust infection using local historical temperature data. The accumulated relative infection indices (RIIs) based on local historical weather data at each location were plotted in Figs 4.7-4.10 to illustrate the

108 profile of summer (Jun-Aug) temperature favorability for soybean rust in the U.S. from 1961 to 2000 and in 2006. The contour map of medians of the accumulated RIIs in Fig. 4.7 indicates that most favorable regions are southeastern coastal region and east of lower

Mississippi River. The southern Midwest in west of Mississippi is less favorable compared with the southeast. The North Central region along the average July mean temperature contour of 23°C is moderate favorable. Southern Texas and the northern most states have the minimum favorability. The 10% and 90% quantiles of the accumulated RIIs were plotted in

Figs. 4.8 and 4.9, which illustrate the lowest or highest temperature favorability occurred with 10% chances in the 40 years. Both have general patterns similar to that in Fig. 4.7. The temperature favorability in 2006 is given in Fig. 4.10. Compared with the medians, 2006 season had less favorability in the Gulf Coast but more favorability in the Soybean Belt.

Texas had extreme low favorability compared with the map of the 10% quantiles.

Fitted Beta distribution for standardized temperature favorability. Five sub- regions different in risk levels of soybean rust were defined according to climate types, major soybean production area, and the contour where average July mean temperature was 23°C

(Fig. 4.11). The five histograms are summarized in Fig. 4.12, (A-E) with the fitted Beta distribution curves and parameters.

The fitted Beta distributions for sub-regions 1 and 2 have similar means and skew to the right side, but the frequency of high RIIs is higher in sub-region 1 indicating greater temperature favorability for SBR infection. Sub-regions 3 and 4 have almost same fitted Beta distribution parameters indicating no significant differences of temperature favorability between these two sub-regions. But compared with sub-regions 1 and 2, sub-regions 3 and 4 have smaller mean values indicating a lower temperature favorability level. Thus temperature

109 favorability for soybean rust is lower in these regions. The fitted Beta distribution in sub- region 5 is significant different from those in the others, with a much lower mean, and less concentrated frequency distribution. Given the fact that this region is outside the temperature favorable region, such differences in standardized RII distribution is expected.

Discussion

This study used a comparative epidemiological approach to analyze the relationship between rust disease geographical distribution ranges and their temperature attributes in

North America. The results indicated the association of the disease ranges of these rusts with their temperature attributes. To our best knowledge, this is a new approach in comparative epidemiology and the study was the first to build the link between regional temperature profiles and disease distribution range across a large number of plant diseases in a continental scale. Although this study only used rusts in North America, which is a relatively simplified situation, the approach may be applicable to other pathosystems to help us understand disease geographical distribution. Our study focused on temperature effects on soybean rust. Other factors such as moisture and spore dispersal may limit soybean rust distribution. Effects of other factors are yet to be determined in this pathosystem.

In this study, the potential geographical distribution range of soybean rust was modeled, which is substantially larger than current SBR distribution range in North America

(32). It is reasonable to anticipate that soybean rust may move further north. The coverage of the temperature favorable region in Fig. 4.6 implies the risk of SBR outbreaks in Iowa and other states along the boundary of this region would be low. In the marginal region, risks of outbreaks would be even lower. Areas outside the potential region, such as soybean production areas in Canada, would be safe from soybean rust. Nevertheless, global warming

110 is a serious issue for agriculture (20, 27, 45). If the regional temperature increases by 1°C, according to our models and maps, the SBR potential range would then expand toward north by about 150 km, which brings Iowa more risks.

Corn is another important crop in the U.S. soybean production region (112). The two rusts of corn (Table 4.1) are like SBR dependent on urediospores for long-distance dissemination (9, 112). Southern corn rust (Puccinia polysora) has an optimal temperature of

23°C for urediospore infection; whereas common corn rust (P. sorghi) has an optimal temperature of 16°C. Common corn rust is commonly seen in the North Central region in the

U.S. as well as Ontario and Quebec in Canada (9, 112, 121); whereas southern corn rust occasionally appears in the North Central region as well as southern and eastern Ontario in

Canada (9, 112, 120). Pivonia and Yang (80) suggested that the time of appearance of soybean rust in field across the U.S. Soybean Belt may be between the appearance of common corn rust and southern corn rust. Their estimates generally agree with our results.

Peanut rust (P. arachidis) and daylily rust (P. hemerocallidis) also have optimum temperature requirement similar to SBR (Table 4.1). Peanut has limited distribution in the south, which may not provide much information for SBR. Currently, daylily rust shows a distribution range similar to southern corn rust (9). Daylily rust was first reported in 2001 in the U.S. (113), which is also a relatively new rust disease. It seems that it has already reached most parts of its potential range according to the assessment based on our regression model

(Figs. 4.4-4.6 and Table 4.2). Although occurrence of daylily rust should still be limited the regional temperatures, the man-made dissemination of the pathogen among nurseries may have accelerated its quick spread to the north (42, 68, 113).

111

The two MaxJulT models were built without including wheat stripe rust (P.

striiformis f.sp. tritici) and barley yellow rust (P. striiformis f.sp. hordei) as shown by open

squares in Figs. 4.3 and 4.4 because they seem to be outliers. This is probably because that

their distribution is limited by the cropland distribution in Canada instead of temperature.

Otherwise, both diseases may reach further north as they have very low optimal infection temperature (Table 4.1).

The four regression models in Table 4.2 have similar high R2 values. But they have

significant differences in regard to the slope values. The two models for AveJulT have a

slope less than 1 and for MaxJulT have a slope close to 1 (Table 4.2). This implies MaxJulT

may be more closely associated with the northern boundary of disease distribution range, but

both AveJulT and MaxJulT have similar power for assessment of SBR range because they

gave almost same assessed potential ranges as shown in Fig. 4.6. It is interesting that

intercepts of the two MaxJulT models are also significantly larger than 0. Because MaxJulT

represents an extreme rare case of favorable temperature occurrence in many years, the

intercepts imply that when such a rare favorable temperature occurs in the north, the northern

boundary of disease distribution range may not necessarily reach further north due to other

limiting factors.

The RIIs in 2006 seem to be consistent with the observed disease development in the

year (32). The Appalachian region and Ohio River Valley had a higher than normal

temperature favorability (Figs. 4.7 and 4.10), which may explain the high prevalence of SBR

in late 2006 (32). The skewed Beta distributions as shown in Fig. 4.12 indicate the medians

of the accumulated RIIs would be better than the averages represent the most frequently

occurred situation. With the assumption that the fitted Beta distribution for each sub-region

112 in Fig. 4.12 fits all the locations in the sub-region, we may further estimate the probability of a lower accumulated RII compared with the actual value in 2006 that may occur in other years. Furthermore, each location has a time series of the accumulated RIIs over many years.

It would be worthy to examine possible temporal patterns with these time series for cyclic patterns or monotonic patterns for assessment of long-term soybean rust risk.

Acknowledgements

Thanks are given to NOAA/OAR/ESRL PSD, Boulder, Colorado, USA for NCEP

Reanalysis Derived data and to National Science Foundation of China for supports.

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Disease Survey, 85:31-34. Online publication.

Table 4.1. Temperature parameters of major rust diseases in North America

Host and life cycle Ave Max Pathogen temperature attributesd Data source Disease Pathogen b c Host name AltHosta JulT JulT UredioOpT AecioOpT BasidioOpT Temp. Distribution Alfalfa rust Uromyces striatus Medicago sativa Yes 18 20 18 - - 97, 109 57, 74 Asparagus rust Puccinia asparagi Asparagus officinalis Autoec 15 17 15 - - 4, 94 9, 12, 33, 91 Barley leaf rust Puccinia hordei Hordeum vulgare Yes 13 16 15 - - 89, 96, 103 9, 87 Barley yellow rust Puccinia striiformis Hordeum vulgare Yes 12 16 9 - - 7, 59 7, 9, 64 f.sp. hordei Beet rust Uromyces beticola Beta vulgaris Autoec 12 16 15 - - 9, 22 9, 14 Corn common rust Puccinia sorghi Zea mays Yes 17 19 16 - - 9, 40, 65, 121 89,80 Southern corn rust Puccinia polysora Zea mays Unkwn 19 22 23 - - 41, 80 9, 120 Cotton southwestern Puccinia cacabata Gossypium spp. Yes 25 27 - - 24 75 9, 71 rust Daylily rust Puccinia Hemerocallis spp. Yes 20 23 22 - - 68 9, 42, 68, 125 hemerocallidis 113 Dry Bean rust Uromyces Phaseolus vulgaris Autoec 18 20 18 - - 21, 43 114, 117 appendiculatus Pea rust Uromyces viciae- Vicia faba Autoec 15 17 18 G 15 G 17 TG 46, 83, 108 11, 17, 115, fabae 116 Flax rust Melampsora lini Linum usitatissimum Autoec 15 17 17 18 G 17 TG 38, 39, 82 10, 86 a AltHost: Status of pathogen alternate host; Autoec for autoecious rust; Unkwn for alternate host unknown currently. b AveJulT: Value of the temperature contour of the average July mean temperature in 1968-1996 along the northern boundary of the disease range. c MaxJulT: Value of the temperature contour of the maximum July mean temperature in 1968-1996 along the northern boundary of the disease range. d UredioOpT, AecioOpT, BasidioOpT: Optimal temperature for infection of urediospore, aeciospore, and basidiospore respectively. -: Data not available for the pathogen or in literature. G Optimal temperature for germination. TG Optimal temperature for teliospore germination.

Table 4.1. (continued)

Host and life cycle Ave Max Pathogen temperature attributes Data source Disease Pathogen Host name AltHost JulT JulT UredioOpT AecioOpT BasidioOpT Temp. Distribution Grape leaf rust Phakopsora euvitis Vitis spp. Yes 23 26 24 G - 20 TG 9, 58, 72 9, 18, 67 Oat crown rust Puccinia coronata Avena sativa Yes 15 17 18 - - 77, 81 9, 16, 24 Oat stem rust Puccinia graminis Avena sativa Yes 18 20 20 - - 51, 89 23, 29-31 f.sp. avenae Peanut rust Puccinia arachidis Arachis hypogaea Unkwn 22 25 22 - - 8, 80 9, 110 Poplar leaf rust Melampsora Populus deltoides Yes 18 20 17 G - 16 TG 55, 56, 99 35, 44, 69 larici-populina Poplar leaf rust Melampsora Populus deltoides Yes 15 17 18 G - 17 TG 99, 105 9 medusae Raspberry yellow rust Phragmidium Rubus idaeus Autoec 15 17 20 G - 16 TG 2, 3, 9 25, 34, 70 rubi-idaei

Tall fescue stem rust Puccinia graminis Festuca arundinacea Yes 18 20 20 - - 78, 98 78, 98 126 ssp. graminicola Sorghum rust Puccinia purpurea Sorghum bicolor Yes 23 26 23 - - 49 9 Soybean rust Phakopsora Glycine max Unkwn 23 26 23 - - 61, 62, 79, 32 pachyrhizi 80 Sunflower rust Puccinia helianthi Helianthus annus Autoec 18 20 17 16 - 53, 95 84, 85 Wheat leaf rust Puccinia triticina Triticum spp. Yes 15 17 15 - - 28, 80 13, 63 Wheat stem rust Puccinia graminis Triticum spp. Yes 16 18 18 - - 89 9, 26, 37 f.sp. tritici Wheat stripe rust Puccinia striiformis Triticum spp. Unkwn 12 16 11 - - 9, 59, 89 9, 73, 89 f.sp. tritici White Pine blister rust Cronartium ribicola Pinus spp. Yes 12 16 20 16 12 9, 36, 9, 60, 106 76,107

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Table 4.2. Linear regression between temperature attributes of rust diseases and July temperatures along northern boundary of these diseases Model Variable Parameter SEe P>|t| R2 Root estimates MSEf AveJulTa Constant 1.9388 1.6662 0.7777 1.7341 AveOpTc 0.8475 0.0925 <0.0001 AveJulT Constant 3.6538 1.6776 0.8450 1.4877 UredioOpTd 0.7525 0.0894 <0.0001 MaxJulTb Constant 1.0312 1.9978 0.8026 1.6114 AveOpT 1.0200 0.1079 <0.0001 MaxJulT Constant 1.8270 2.7128 0.8244 1.4859 UredioOpT 0.9862 0.1372 <0.0001 a AveJulT: Lowest contour value of average July mean temperature in 1968-1996 along the disease range northern boundary. b MaxJulT: Lowest contour value of maximum July mean temperature in 1968-1996 along the disease range northern boundary. c AveOpt: Averaged optimal infection/germination temperature of all forms of spores of a rust pathogen. d UredioOpT: Urediospore optimal infection temperature for those rusts that only urediospores serve as inocula in long-distance dissemination and the pathogens have heteroecious life cycle. e SE: standard error. f Root MSE: Root mean square error.

Distribution Relationship of range and pathogen infection July mean infection Soybean rust optimal temperature and temperatures infection temperatures temperatures of rust distribution other rusts

Assessed potential range of SBR (favorable region + SBR occurrence in the future

marginal region) 128

Night temperature +

Temperature favorability

for SBR infection at SBR occurrence in a specific year

SBR infection cardinal specific locations temperatures Overall temperature favorability for SBR infection in the U.S.

Fig. 4.1. The conceptual framework to assess soyhean rust potential range and the temperature favorability for soybean rust infection in the U.S.

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Fig. 4.2. Relationship between average optimal infection/germination temperature (AveOpT) of various spore types of rust fungi (X axis) and the average July mean temperature for the period of 1968-1996 along the northern boundary of rust diseases geographical range (Y axis).

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Fig. 4.3. Relationship between urediospore optimal infection/germination temperature (UredioOpT) of rust fungi (X axis) and the average July mean temperature for the period of 1968-1996 along the northern boundary of geographical range (Y axis) of rust diseases which have similar characteristics in life cycle and disease cycle, i.e. the disease is not autoecious with urediospores for long-distance dissemination.

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Fig. 4.4. Relationship between average optimal infection/germination temperature (AveOpT) of various spore types of rust fungi (X axis) and the maximum July mean temperature for the period of 1968-1996 along the northern boundary of rust diseases geographical range (Y axis). The two open squares are for wheat stripe rust (Puccinia striiformis f.sp. tritici) and barley yellow rust (Puccinia striiformis f.sp. hordei), which were not included in regression analysis.

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Fig. 4.5. Relationship between urediospore optimal infection/germination temperature (UredioOpT) of rust fungi (X axis) and the maximum July mean temperature for the period of 1968-1996 along the northern boundary of rust diseases geographical range (Y axis). These rust diseases have similar characteristics in life cycle and disease cycle, i.e. not autoecious and with urediospore for long-distance dissemination. The two open squares are for wheat stripe rust (Puccinia striiformis f.sp. tritici) and barley yellow rust (Puccinia striiformis f.sp. hordei), which were not included in regression analysis.

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Fig. 4.6. Potential distribution range including the favorable region and the marginal region for the occurrence of soybean rust in North America. Based on the regression models in Figs. 4.3 and 4.5, the potential range is in south of the contour where average July mean temperature (AveJulT) was 21°C (dashed line) or maximum July mean temperature (MaxJulT) was 24°C (dot line) for the period from 1968 to 1996. Favorable region is in south of the contour where average July mean temperature was 23°C for the same period (solid line).

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Fig. 4.7. Median temperature favorability (accumulated relative infection index (RII) of soybean rust in summer (Jun-Aug) in each year) for the period from 1961 to 2000 in the U.S. RII is the relative amount of SBR infection resulted from observed temperature to the maximum infection result from optimal temperature according to Pivonia and Yang (80). The green and purple temperature contours are same as those in Fig. 4.6 to illustrate the modeled potential and favorable SBR range.

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Fig. 4.8. 10% quantiles of the temperature favorability (accumulated relative infection index (RII) of soybean rust in summer (Jun-Aug) in each year) for the period from 1961 to 2000 in the U.S. RII is the relative amount of SBR infection resulted from observed temperature to the maximum infection result from optimal temperature according to Pivonia and Yang (80). The green and purple temperature contours are same as those in Fig. 4.6 to illustrate the modeled potential and favorable SBR range.

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Fig. 4.9. 90% quantiles of the temperature favorability (accumulated relative infection index (RII) of soybean rust in summer (Jun-Aug) in each year) for the period from 1961 to 2000 in the U.S. RII is the relative amount of SBR infection resulted from observed temperature to the maximum infection result from optimal temperature according to Pivonia and Yang (80). The green and purple temperature contours are same as those in Fig. 4.6 to illustrate the modeled potential and favorable SBR range.

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Fig. 4.10. Temperature favorability (accumulated relative infection index (RII) of soybean rust in summer (Jun-Aug)) in 2006 in the U.S. RII is the relative amount of SBR infection resulted from observed temperature to the maximum infection result from optimal temperature according to Pivonia and Yang (80). The green and purple temperature contours are same as those in Fig. 4.6 to illustrate the modeled potential and favorable SBR range.

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Fig. 4.11. Five sub-regions that differ in the risk of soybean rust in the United States. The sub-regions are in shaded areas. Sub-regions 1 and 2 are within subtropical wet (Cr) climate region. Sub-regions 3, 4, and 5 are within temperate continental (Dc) climate region, in which sub-regions 3 and 4 are in south of the contour where average July mean temperature was 23°C for the period from 1968 to 1996, while sub-region 5 is in north of this contour. These sub-regions are in major soybean production areas in the U.S.

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Fig. 4.12. Histograms and the fitted Beta distribution curves for the five sub-regions (A-E for sub-region 1-5 respectively; See Fig. 4.11 for detail geographic information of each sub- region). Parameters of the distribution curves are in each graph. Histogram was generated for each sub-region with the standardized accumulated relative infection index (RII) of each year for the period from 1961 to 2000 using data of all weather stations in the sub-region.

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CHAPTER 5. COMPARISONS OF SOYBEAN RUST PATHOSYSTEMS IN MAJOR SOYBEAN PRODUCTION REGIONS IN TERMS OF RISKS OF THE DISEASE IN THE UNITED STATES

Comparisons of the soybean rust pathosystems

and occurrence among China, South America and the United States

Soybean production and the other hosts. Both China and the U.S. have soybean production regions in a wide latitude range (20 to 50°N in China, 28 to 49°N in the U.S.) but major growing areas in the north (above 35°N) (21). In South America, soybean planting area is more uniformly distributed throughout latitude range of 8 to 40°S (21). In 2004, the soybean planting area in China, the U.S., Brazil, and Argentina was 9,580, 29,932, 21,539, and 14,371 1000-hectare respectively (data source: FAOSTAT online database, Food and

Agriculture Organization of the United Nations;

URL: http://faostat.fao.org/site/336/default.aspx; access date: 23 Feb 2007).

In northern China, soybean is planted in spring and summer. In southern China it is planted in spring, summer, and fall, or even winter in the southernmost Hainan province, but planting area in the south is only about 25% of the total area in the country (2, 20). The U.S. plants soybean in spring mostly as a full season crop (2). In Brazil, most planting is in spring, but may also be in the other seasons (21). In Argentina, soybean is planted in spring and summer (11, 21). Besides commercial soybeans, volunteer soybeans or alternative hosts, in which kudzu probably is the most important one, may act as important bridge hosts in the disease cycle to keep Phakopsora pachyrhizi reproduction available after growing season in these countries (4, 7, 10, 14, 19, 20, 22). In the U.S., kudzu is widely spread in the south from FL to MO, IL, etc, and total area could be more than 2x106 hectares (1). The area of

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kudzu, including wild and cultivated, in China is unknown. However, as kudzu is native in

Asia, the acreage is very likely higher than that in the U.S. How much kudzu in South

America is not well documented. At least in Argentina, kudzu has been observed as a host of

P. pachyrhizi (4). Importance of other alternative hosts is not clear in these countries.

Permanent establishment of soybean rust pathogen. Pivonia and Yang suggested

that the potential areas for year-around establishment of soybean rust in terms of climatic

suitability of Phakopsora pachyrhizi are greatest in South America, then China, and the last the U.S. (15). Incorporated with the host availability, South America seems to have the greatest host availability due to the tropical climate for the fungus to establish year-around in most of the region. China would be the second one with a large area suitable for the pathogen overwintering in southern China below 25°N in wild alternative hosts, particularly kudzu, and limited soybean. The U.S. is the third with limited pathogen year-around establishment region (limited along the Gulf Coast) and limited host availability in the south. Because the pathogen year-around establishment region essentially provide the initial inoculum to the adjacent major soybean production regions where the pathogen cannot overwinter, with huge acreage of cultivated soybean in the tropical region, soybean rust pathosystem in South

America, particularly Brazil, probably has the strongest initial inocula source compared with

China and the U.S. The U.S. has limited inoculum source in the south, which limits the disease outbreak possibility in the northern soybean production regions.

Comparisons of the regional climate among China, South America

and the United States in terms of the risks of soybean rust

Comparisons of climate favorability to soybean rust between China and the U.S.

Regional climate is also associated with the disease occurrence. Same climate types may

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have similar climatic favorability to soybean rust. Comparisons of climates should provide

valuable information for soybean rust risk assessment, though this information may not be

sufficient for disease forecasting. According to Chinese soybean rust information compiled

by Tan et al, the frequent occurrence regions of soybean rust in China include the year-

around establishment region (see Pivonia and Yang 2004, 15) and the adjacent region

(meshed area in Fig. 5.1). The frequent occurrence regions are featured by major climate

types Cw (subtropical wet), Cr (subtropical summer rain), and Aw (tropical wet and dry) (Fig.

5.2) (20, 21). The overall characteristic of these climate types is the warm weather (Cw and

Cr: 8 to 12 month mean temperature >9°C; mean temperature of warmest month ≥22°C; Aw: coolest month mean temperature ≥17°C) and a rainy summer or a relatively uniform monthly rainfall distribution (see Figs. 5.2 and 5.3) (17, 18). Although warm weather in part of southern China may not be optimal for soybean rust in summer, it favors soybean rust year- around establishment and development in early growing seasons. For example, in Wuhan with Cr climate (Wuhan in Figs. 5.2 and 5.3), when early July mean temperature reached

25.5 to 32.8°C, little soybean rust could occur (20). In Yunnan, where Cw climate is dominant and maximum monthly mean temperature is 19 to 22°C (Kunming in Figs. 5.2 and

5.3), soybean rust develops very well except in winter (20). In northern China, climate type is

Dc, which is warm in summer but cooler in other seasons compared with the south, and much less rainfall (Figs. 5.2 and 5.3) (17, 18). Such climate obviously does not provide a long favorable season for soybean rust, but may meet the basic requirements for the rust pathogen to colonize in the field for probably a few weeks, as soybean rust has been reported occasionally in this region.

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Great similarity in climate types exists between China and the U.S. as shown in Figs.

5.2, 5.3, 5.4, and 5.5. The climate type in the southern U.S. in east of Rocky Mountain is Cr, same as that in southeastern China. While in North Central U.S., climate type is Dc, same as that in northern China. Despite the commonality of these climate types, there is difference in regard to number of climate types and their monthly temperature/precipitation pattern. First, a Cw climate region as the Cw one in China (Kunming in Figs. 5.2 and 5.3) is absent in the southern U.S. In China, such a region is an important soybean rust inoculum source in summer due to high altitude of mountains and plateau as well as ample moisture air from

Indian Ocean, which result in cool and moist weather (6, 17, 20). When the weather in other adjacent regions of Cr climate is unfavorably warm for soybean rust development, the Cw region keeps providing urediospores for the northern soybean production regions. In Fig. 5.5, mean temperature in Lafayette, Orlando, and Memphis exceeds 25°C in June indicating unfavorable warm weather. Because there is not an alternative regional inoculum source like the Cw region in China, soybean rust development in the southern U.S. may be retarded easily by high temperature and therefore the strength of inoculum moving northward is reduced as well. However, it is worth noting that a quantitative comparison of the available inoculum source and strength between the two countries is not available though the year- around establishment region of soybean rust is much larger in southern China than that in the southern U.S. On the other hand, retarded disease development in the southern U.S. can still continuously provide small amount of inoculum. As strong low-level jets in the U.S. may facilitate long-distance dissemination better (16), with the sequential inputs of small amounts of inoculum, disease severity level in the distant areas in the north may still build up in the field if there are favorable environmental conditions.

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The rainfall pattern is different between the two Cr climate regions in China and the

U.S. Climate data in Memphis, Lafayette, and New Bern showed a relatively large amount of annual rainfall and uniform monthly rainfall distribution pattern. While in Orlando, June to

September is much wet than other months (Fig. 5.5). Compared with the rainfall pattern in

Cr climate region in the U.S., the four representative locations in Cr region in China, i.e.

Changsha, Guangzhou, Wuhan, and Hangzhou as shown in Fig. 5.3 have more concentrated monthly rainfall distribution pattern due to East Asia monsoon. In Changsha, raining season starts in April when monsoon trough approaches. When front of monsoon arrives in Wuhan, raining season starts in June.

Changsha, Wuhan, and Hangzhou are in the soybean rust frequent occurrence regions in China (Fig. 5.1). Tan et al development a predictive model using rainfall amount and rain days in the key months (Aug or Sep) of soybean growing season to predict soybean rust severity in Wuhan (20). When monthly rainfall in August is more than 150mm and rain days is more than 15 days, soybean rust would be moderate to severe in the late fall (20). In provinces of Zhejiang (capital is Hangzhou in Fig. 5.2), Fujian (in south of Zhejiang), and

Jiangxi (in west of Zhejiang), several similar predictive models were developed. Soybean rust may reach high severity level when there are more than 300mm rainfall and 20 rain days in September and October in these regions (20). According to the historical disease information from these locations and the temperature/rainfall patterns, mean temperature in

18 to 25°C, 150 mm rainfall, and 10 to 15 rain days in a month may provide soybean rust a good environment to develop quickly with sufficient initial inoculum. Compared with the south, in northern Chinese Dc climate region where soybean rust was reported occasionally,

Xian, Heze, and Shenyang (Figs. 5.2 and 5.3) have less annual rainfall, but Heze and

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Shenyang have concentrated large amount rainfall (about 150 mm) in July and August due to

East Asian monsoon, which may meet the minimum conditions for soybean rust to develop.

In the U.S. Cr climate region, Orlando, Lafayette, and Memphis are so warm that is

unfavorable to soybean rust in summer, in addition, Memphis has relative insufficient rainfall

in summer (about 100 mm) (Fig. 5.5). In New Bern, in June to September, temperature is

24.4, 26.3, 25.9, and 23.1 °C, while rainfall is 137, 178, 167, 130 mm respectively. Thus only

New Bern seems to have relatively favorable environmental conditions for soybean rust in

summer compared with the other three locations. As for North Central, Ottumwa, and Dayton

have favorable temperature in summer, but rainfall is relatively insufficient according to the

disease historical information in China. Fargo and Flint have even lower temperature and less

rainfall than Ottumwa and Dayton, which may not be favorable for soybean rust.

In China, soybean rust was mostly reported in late summer and fall though soybean is

grown from spring. No effort has been made to determine if soybean rust in southern China

is associated with the monsoon. Although monsoon brings abundant rainfall favorable for

soybean rust in the early summer, the steadily northward-moving system with a rain belt at

the front of the monsoon may act as a filter, which keeps washing out airborne Phakopsora

pachyrhizi urediospores (see Chapter 2 for detailed information). This effect may block long- distance dissemination of urediospores to the north in spring and early summer. Therefore, for example in Wuhan, it is possible that major urediospore clouds reach there only after monsoon reaches there. So is for Heze and Shenyang. While in the U.S. Midwest, convective rainfall in summer is dominant that might not block long-distance urediospores dissemination to the north as much as East Asian monsoon does (17). If this is true, plus the low-level jets, the northern U.S. may have more chances to have P. pachyrhizi urediospores in the early

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season, which may favor early occurrence of soybean rust, though the temperature is usually

unfavorably low at this time.

Climate favorability in South America. Inter-Tropical Convergence Zone (ITCZ) in

summer provides Brazil a cloudy region with abundant rainfall along latitude 15°S (8, 17).

Such a cloudy and rainy region favors soybean rust, though in vast area mean temperature in

Dec, Jan, and Feb may exceed optimal disease development temperature (>25-26°C) (17).

The climate types in South American major soybean production regions are Aw in most

Brazilian tropical region, Cr in southern Brazil and northern Argentina, and BS in central

Argentina (Fig. 5.6). The Brazilian Aw climate region where most Brazilian soybean is

planted is much larger than the Aw region in southwestern China. The Aw regions in both

countries are soybean rust year-around establishment regions (15, 20, 23). According to

Godoy et al, in Brazil, most severe soybean rust occurred in Aw climate regions (Fig. 5.6) (9).

In Argentina, soybean rust is more severe in Cr regions than in Bs region (9).

Figures 5.6 and 5.7 show eight representative locations and their temperature/rainfall patterns in South America. Cuiaba, Goiania, Campo Grade, and Pocos de Caldas in Aw climate region are featured by large amount of rainfall (200 to 300 mm monthly) in summer and a dry winter. Except Cuiaba that is very warm in summer (mean temperature >25°C probably due to its low altitude (21)), the other three have very good summer temperature regimes (20 to 25°C) optimal to soybean rust. Such favorable temperature and moisture condition is consistent with the observed severe rust occurrence in these regions (22, 23).

In the south, Puerto Stroessner in Cr climate region has temperature in summer higher than 25°C, but winter is warm as well. Besides, summer rainfall is abundant (usually more than 150 mm) and monthly rainfall distribution pattern is relatively uniform. Such warm and

147 humid environment favors soybean rust pathogen establishment year-around, which well explains the rust disease outbreaks in this region every year (9, 13, 22, 23). With more rainfall, a cooler summer, and a warmer winter, climate in Puerto Stroessner is somewhat similar to the climate in Lafayette in the U.S. However, the climate in Puerto Stroessner certainly favors soybean rust much more than the climate in Lafayette. In further south,

Rosario has cooler summer and colder winter than Puerto Stroessner, and much less rainfall, which reduces climate favorability to soybean rust. Nevertheless, the summer rainfall is still in 100-150 mm and winter is warm enough (>10°C) for Phakopsora pachyrhizi to overwinter

(7). Compared with Wuhan in China, Rosario has less rainfall in summer, but a much warmer winter, which facilitates the pathogen to overwinter. Thus in Rosario, as observed in the field, outbreak of soybean rust still occurred (7). In the U.S., the climate in New Bern (temperature

20 to 25°C, rainfall 120 to 180 mm) is probably similar to that in Rosario in summer, but is colder in winter (Figs. 5.5 and 5.7).

The two locations in BS climate regions, Tucuman and Rio Cuarto have dry but mild winter (mean temperature about 12°C and 9°C respectively, rainfall about 10 to 15 mm), which does not favor soybean rust even the pathogen can overwinter there. In summer, temperatures are good in both locations and in Tucuman, rainfall is abundant as well (150 to

200 mm), while Rio Cuarto has much less rainfall (100 to 150 mm) but still enough for soybean rust development compared with Chinese situation. Soybean rust has been reported in regions around Tucuman, but not in Rio Cuarto though the disease was reported nearby in the east (9). There is a potential for soybean rust to reach Rio Cuarto.

Summary of climate favorability to soybean rust among China, the U.S. and

South America. Among the three landmasses, the most favorable region for soybean rust

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year-around development is South America including regions in Brazil and northern

Argentina plus Paraguay (Puerto Stroessner in Fig. 5.6) where summer is cool, winter is warm, and rainfall is abundant. The second favorable one is southwestern China with Cw climate where summer is cool, rainfall is abundant, and winter is relatively cold but still warm for pathogen overwintering. South and southeastern China with Cr climate, central

Argentina with Cr and BS climate, and southern/southeastern U.S. are the third best where the pathogen may or may not overwinter, summer is often unfavorably warm (>25°C) with relatively enough rainfall (100 to 150 mm/month), however the disease outbreak still can occur under certain conducive weather conditions. Northern China and the northern U.S. where the pathogen cannot overwinter and summer is often unfavorably warm (northern

China) or with less rainfall (<100 mm/month, the northern U.S.) are the regions with low chances of disease outbreaks. However, the annual fluctuation of climate may give some years of certain very favorable weather conditions for these regions, which may result in disease occurrence. A situation with warm winter and spring, cool summer (mean temperature <25°C), with abundant rainfall in growing season (150 mm/month) in the U.S.

Midwest would probably favor the occurrence of soybean rust.

Favorability of rainfall and wind to soybean rust in the United States

June and July rainfall favorability to soybean rust in the U.S. based on a disease predictive model. Besides regional climate favorability, more specific disease favorability associated with rainfall amount and rain days in key months of growing season could further address the overall regional risks of soybean rust. Tan et al in China and Del Ponte et al in

Brazil developed several predictive models based on observed soybean rust severity and local rainfall data in key months in growing season (5, 19, 20). As these rainfall predictive models

149 have been used in several locations in southern China and Brazil, they may have certain power to address disease risks associated with rainfall in the U.S.

Since climates in the Midwest and eastern U.S. are similar to those in southeastern and northern China, we employed a predictive model developed by Tan et al in Wuhan,

China (Fig. 5.2), where soybean rust pathogen cannot overwinter, but the disease occurs frequently (20), based on monthly rainfall amount and rain days to address the partial risk of soybean rust associated with rainfall in the U.S. (20). The original model is

DS=0.49+0.009RA+0.093RD, in which DS is the soybean rust disease severity in scale 0 to

5, RA is monthly total rainfall amount in millimeter, RD is monthly rain days (20). U.S.

Historical Climatology Network (USHCN) daily rainfall data in June and July from 1951 to

2000 were acquired from the National Climatic Data Center (NCDC) (12). It must be clarified that the employment of this model in the U.S. was only for assessment of the partial disease risk associated with rainfall. The original model implies that the temperature is not a limiting factor of soybean rust occurrence in Wuhan, which may not be true in the northern

U.S. Besides, although the original model output is disease severity, in current assessment, the model output shall not be considered as disease severity, rather than an index of disease risk in scale 0 to 5, which higher value represents higher risk.

The frequency of years with the index of disease risk larger than 3 in June and July from 1951 to 2000 was derived (Figs. 5.8 and 5.9). In Fig. 5.8, the highest risk (frequency

>20 in 50 years) occurred only in Florida. The Gulf Coast region, southeastern coastal region

(Georgia, South Carolina), south of Appalachia Mountain, and western Missouri/eastern

Kansas had 15 years out of 50 years with favorable rainfall conditions in June. Iowa,

Minnesota, and western Wisconsin had about 10 favorable years. Other regions in Middle

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West had only 5 to 10 favorable years.

The distribution pattern of favorable years in July (Fig. 5.9) seems more concentrated than that in June (Fig. 5.8). There were more favorable years (>20 years) in Florida, the Gulf

Coast region, southeastern coastal region (Georgia, South Carolina, North Carolina, and

Virginia), and Appalachia. So was for southeastern Mississippi River and Ohio River valley.

While in the North Central and southwestern Midwest, the frequency of favorable year decreased to 5 to 10 years. Part of Texas even had no favorable year.

In general, major soybean production area in the North Central region may have better moisture condition in June, but less favorable temperature condition (see the contours of July mean temperature in Figs. 5.8 and 5.9). While in July, moisture condition in this region turns to less favorable, though temperature condition becomes better. However, the relationship between monthly temperature and rainfall was not examined.

Prevailing winds to soybean rust. In the southern U.S., the generally wind pattern in late spring and summer is from south to northeast or north (3). Pivonia and Yang pointed out that compared with China, the U.S. may have stronger and more southerly winds (in particular the low-level jets in the night) facilitating the northward dissemination of P. pachyrhizi urediospores due to the topographical characters (16). Fig. 5.10 shows four prediction maps generated based on weather data (data source: Z. T. Pan in St. Louis

University, unpublished) and observed soybean rust inoculum source data in 2006 summer.

Since the main soybean rust inoculum source is in Florida and other states along the Gulf

Coast, most urediospores would be carried to the southeastern coastal regions by prevailing winds, i.e. Georgia, South Carolina, and North Carolina. While in China, mountainous area in the south may have adverse effects on urediospore initial take-off; also in the middle

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China (lower Yangtze River valley), several east-west orientation mountains may block low-

level winds to reduce the chances of urediospores to reach further north. Unfortunately, a

quantitative comparison of such topographical impact on soybean rust epidemics in the two

countries is not available currently. In South America, summer wind pattern is controlled by

shift of ITCZ, which results in a prevailing winds from north northeast to south southwest (8).

This wind direction is favorable for P. pachyrhizi urediospore dissemination from Brazil to

Argentina, but the strength compared with winds in China and the U.S. is unknown as well.

Literature cited

1. Blaustein, R. J. 2001. Kudzu's invasion into Southern United States life and culture.

Pages 55-62 In: The great reshuffling; human dimensions of invasive species. J. A.

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Fig. 5.1. Regional occurrence of soybean rust in China reproduced from Y. J. Tan et al. Meshed regions are with frequent occurrence, areas with dots represent severe occurrence locations. Region in stripes is occasional occurrence region (20).

156

Fig. 5.2. Major climate types in China and eight representative weather stations reproduced from W. Rudloff and World Agricultural Outlook Board of the USDA (18, 21).

157

Fig. 5.3. Monthly temperature and rainfall at the eight representative weather stations in China from World Agricultural Outlook Board of the USDA (21).

158

Fig. 5.4. Major climate types in the U.S. and eight representative weather stations reproduced from W. Rudloff and World Agricultural Outlook Board of the USDA (18, 21).

159

Fig. 5.5. Monthly temperature and rainfall at the eight representative weather stations in the U.S. from World Agricultural Outlook Board of the USDA (21).

160

Fig. 5.6. Major climate types in South America and eight representative weather stations reproduced from W. Rudloff and World Agricultural Outlook Board of the USDA (18, 21).

161

Fig. 5.7. Monthly temperature and rainfall at the eight representative weather stations in South America from World Agricultural Outlook Board of the USDA (21).

162

Fig. 5.8. Number of years with the disease risk index >3, which was calculated with rainfall- based model by Tan et al, in June for the period of 1951 to 2000 (rainfall data source: U.S. Historical Climatology Network (USHCN)) (20).

163

Fig. 5.9. Number of years with the disease risk index >3, which was calculated with rainfall- based model by Tan et al, in July for the period of 1951 to 2000 (rainfall data source: U.S. Historical Climatology Network (USHCN)) (20).

164

Fig. 5.10. Weekly prediction maps from June 24 to July 15 in 2006 on the rainfall favorability to soybean rust and urediospore dispersal (data source: Z. T. Pan in St. Louis University, unpublished). Shaded area is for urediospore concentration >10-4 spore/m3 at the ground surface. Disease rainfall favorability is in scale of 0 to 100, which is equivalent to the disease risk index of 0 to 5 scale used in Figs. 5.8 and 5.9. Soybean rust urediospore source areas were in states along the Gulf Coast. The maps illustrate that the major directions of urediospore dispersal were to the southeastern U.S. coastal regions.

165

CHAPTER 6. GENERAL CONCLUTIONS

In our study, approaches of comparative epidemiology, numerical modeling, and statistical modeling were used to assess the risks of soybean rust in the United States in a comprehensive way. The major factors associated with the risk that have been involved in the study include the pathogen suitability of biology and ecology, environmental favorability of temperature and rainfall, urediospore wet deposition and dry deposition, and the regional climates. The assessments of all these factors provided a relatively full understanding of this disease in association with of its risk of outbreak in the United States.

The importance of wet deposition of Phakopsora pachyrhizi urediospores facilitated by rainfall has been addressed in Chapter 2, which indicated frequent rainfall events would provide favorable environmental conditions for the initial establishment and the subsequent development of soybean rust. Dry deposition of the urediospores of soybean rust has been considered less acceptable in regard to the survival of the urediospores.

The biological and ecological suitability of soybean rust compared with other soybean diseases was addressed in Chapter 3. Soybean rust may have limited capability of causing severe yield losses in the U.S. according to other similar soybean fungal diseases.

However, the regional environmental condition would be critical for the disease outbreaks.

The temperature favorability for P. pachyrhizi infection and subsequent disease development in regard to the long-term regional temperature profile have been described in detail in

Chapter 4. The modeled potential soybean rust distribution range in Chapter 4 is a relatively vast area which covers most of the U.S. soybean production regions. Chapter 5 further addressed the risks associated with regional host availability, regional climates patterns, and

166 rainfall patterns. Comparisons of climates among China, the Unites States, and South

America indicated the uniqueness of the regional climatic condition in South America which would be directly associated with its severe soybean rust outbreaks in that continent. The

United States seems to have less favorable climatic condition compared with China and

South America. Spore dispersal associated with prevailing winds in the three continents was discussed briefly.

With the integration of regional temperature and rainfall regional climate patterns, spore dispersal routes, and rust overwintering regions, we believe that within the U.S. soybean production regions, the highest risk of soybean rust is in the southeastern coastal region, e.g. the region around or in south of New Bern in Fig. 5.4. In the Mississippi River valley, the risk is moderate compared with southeastern coastal regions due to drier conditions and less inoculum input. The North Central Region has the lowest risk. With the establishment of soybean rust in Texas and Mexico, that region may produce inoculum airborne to Iowa and other states in the North Central region around Iowa due to the prevailing wind patterns. Since climate in Texas is unfavorably warm and dry for soybean rust occurrence, the amount of urediospores from Texas may be very low and the risks of regional outbreak in Iowa surrounding states would be still low in general.

167

APPENDIX. AN ABSTRACT OF A PREVIOUS STUDY CONDUCTED AS PART OF PHD WORK

A paper accepted for publication by Phytopathology

Abstract

Li, X., P. Sun, B. H. Hu, and X. B. Yang. 0000. Changes in disease prevalence in association with temperature changes in China from 1952 to 2002. Phytopathology 00:000-000

Disease data of stripe rust (Puccinia striiformis), powdery mildew (Blumeria graminis f.sp. tritici), and scab (Fusarium spp.) on wheat (Triticum spp.), blast

(Magnaporthe grisea) and sheath blight (Rhizoctonia solani) on rice (Oryza spp.) in China from 1952 to 2002 were analyzed along with temperature data. These data were annual prevalence of the five diseases and a quantified Pandemic Index of wheat stripe rust collected systematically through a national monitoring system. Temperature data included the minimum, maximum, and mean of the annual and seasonal temperatures as well as diurnal temperature range (DTR). Four warm-temperature diseases (powdery mildew, scab, blast, and sheath blight) showed significant trends of increasing prevalence that were consistent with the trends of increasing temperatures. Correlations between temperature variables and disease occurrence were determined. Multivariable regression models showed that prevalence of warm-temperature diseases was positively correlated with increasing minimum and mean temperatures, particularly in winter and spring, indicating increasing temperatures were associated with disease increases. For the cold-temperature disease (stripe rust), the

Pandemic Index was positively correlated with the decrease of DTR caused by the increase in minimum temperature, indicating warmer temperatures would have suppressed wheat stripe rust during the past few decades.