Biological Control 51 (2009) 158–168
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Biological Control
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Best linear unbiased prediction of host-range of the facultative parasite Colletotrichum gloeosporioides f. sp. salsolae, a potential biological control agent of Russian thistle
D.K. Berner *, W.L. Bruckart, C.A. Cavin, J.L. Michael, M.L. Carter, D.G. Luster
Foreign Disease-Weed Science Research Unit, Agricultural Research Service, US Department of Agriculture, Ft. Detrick, Frederick, MD 21702, USA article info abstract
Article history: Russian thistle or tumbleweed (Salsola tragus L.) is an introduced invasive weed in N. America. It is widely Received 7 March 2009 distributed in the US and is a target of biological control efforts. Accepted 10 June 2009 The fungus Colletotrichum gloeosporioides (Penz.) Penz. & Sacc. in Penz. f. sp. salsolae (CGS) is a faculta- Available online 14 June 2009 tive parasite under evaluation for classical biological control of this weed. Host-range tests were con- ducted with CGS in quarantine to determine whether the fungus is safe to release in N. America. Keywords: Ninetytwo accessions were analyzed from 19 families: Aizoaceae, Alliaceae, Amaranthaceae, Apiaceae, Animal model Asteraceae, Brassicaceae, Cactaceae, Campanulaceae, Chenopodiaceae, Cucurbitaceae, Cupressaceae, Fab- Anthracnose aceae, Malvaceae, Nyctaginaceae, Phytolaccaceae, Poaceae, Polygonaceae, Sarcobataceae, and Solanaceae BLUP Classical biological control and 10 tribes within the Chenopodiaceae: Atripliceae, Beteae, Camphorosmeae, Chenopodieae, Corisper- Chenopodiaceae meae, Halopepideae, Polycnemeae, Salicornieae, Salsoleae, and Suaedeae. These included 62 genera and Colletotrichum gloeosporioides f. sp. salsolae 120 species. To facilitate interpretation of results, disease reaction data were combined with a relation- Deuteromycotina ship matrix derived from internal transcribed spacer DNA sequences and analyzed with mixed model Disease prediction equations to produce Best Linear Unbiased Predictors (BLUPs) for each species. Twenty-nine species GDATA (30 accessions) from seven closely-related Chenopodiaceae tribes had significant levels of disease sever- Henderson’s mixed model equations ity as indicated by BLUPs, compared to six species determined to be susceptible with least squares means Host-range testing estimates. The 29 susceptible species were: 1 from Atripliceae, 4 from Camphorosmeae, 1 from Halopepi- Invasive weeds Plant pathogens deae, 2 from Polycnemeae, 6 from Salicornieae, 8 from Salsolae, and 7 from Suaedeae. Most species in the PROC MIXED genus Salsola, which are all introduced and weedy, were very susceptible and damaged by CGS. Statistical Quartet puzzling comparisons and contrasts of BLUPs indicated that these Salsola species were significantly more suscep- Reduced animal model tible than non-target species, including 15 species from relatives in the closely-related genera Bassia Relationship matrix (=Kochia), Nitrophila, Salicornia, Sarcocornia, and Suaeda. Of the 29 susceptible species, 10 native or com- Salsola tragus mercially important species in N. America were identified as needing additional tests to determine the SAS extent of any damage caused by infection. Published by Elsevier Inc.
1. Introduction bleweed”. The tumbling habit enables rapid, long-distance dis- persal and may lead to problems with traffic or fire. Large plants Russian thistle (Salsola tragus L., Chenopodiaceae) is a major can roll across highways and dry plants may serve as a source of weed pest in the Western United States (Young, 1991). It is a suit- tinder when they lodge next to buildings. Although herbicide resis- able target for the classical biological control strategy, because it is tance has been reported in this weed (Saari et al., 1992; Stallings introduced and infests large tracts of public or low-value agricul- et al., 1994; Peterson, 1999), there are other major challenges to tural lands. Russian thistle competes with crop species (Blackshaw conventional and chemical management of this pest. Limited eco- et al., 1992; Cudney and Orloff, 1988; Young, 1988; Schillinger and nomic returns from much of the infestation preclude profitable use Young, 2004), competes for water in wheat (Schillinger and Young, of chemical herbicides and application of conventional weed con- 2000), displaces important forage plants (Crompton and Bassett, trol approaches. For these reasons, Russian thistle has been a sub- 1985), is a host of beet leafhoppers and beet curly top virus (Young, ject of research with candidate biological control agents. 1991), and some forms of the plant break off and roll with the wind A number of isolates of Colletotrichum gloeosporioides (Penz.) (Hrusa and Gaskin, 2008), hence the other common name of ‘‘Tum- Penz. & Sacc. in Penz. (Deuteromycotina, Coelomycetes; teleo- morph Glomerella cingulata [Stoneman] Spauld. & H. Schrenk) have * Corresponding author. Fax: +1 301 619 2880. been evaluated for biological control of weeds, despite the fact that E-mail address: [email protected] (D.K. Berner). C. gloeosporioides has been reported on hosts from almost 200 plant
1049-9644/$ - see front matter Published by Elsevier Inc. doi:10.1016/j.biocontrol.2009.06.003 D.K. Berner et al. / Biological Control 51 (2009) 158–168 159 genera (Farr and Rossman, n.d.). Research on isolates of C. gloeospo- The combination of parental performance data and genetic related- rioides for biological control has shown that individual isolates are ness among parents and future progeny allow BLUPs to be gener- reasonably host specific and not damaging to non-target species ated in the absence of performance data, i.e., for future progeny. (Cartwright and Templeton, 1989; Daniel et al., 1973; Killgore In the case of host-range tests in risk assessments of candidate bio- et al., 1999; Mortensen, 1988; Mortensen and Makowski, 1997; logical control organisms, this methodology allows for prediction TeBeest, 1988; Trujillo, 2005; Trujillo et al., 1986). The isolate of of susceptibility of plant species relative to that of the target spe- C. gloeosporioides (96-067) used in this study was first described cies (Berner et al., 2009) and enables predictions of disease reac- on Salsola kali in Hungary (Schwarczinger et al., 1998). Other tion for species that cannot be tested, either because they are genetically similar isolates have been found in both Greece (Berner rare or virtually impossible to grow; a situation analogous to future et al., 2006) and Russia (Kolomiets et al., 2008), so the fungus ap- un-tested progeny. Thus the complete host-range of a pathogen, pears to be widespread in the native range of S. tragus. Isolate using both tested, i.e., inoculated, and not tested species in the 96-067 has been shown to be lethal to S. tragus, and evidence exists analysis, can be predicted. This host-range can then be used to for specificity of this isolate within the genus Salsola (Bruckart et establish if any additional non-target plant species should be al., 2004). Because of its specificity within Salsola, isolate 96-067 tested. is referred to in this paper as C. gloeosporioides f. sp. salsolae (CGS). Recently, the MME were used effectively with DNA sequences of Each isolate of C. gloeosporioides described by the aforemen- the internal transcribed spacer 1 (ITS1), 5.8S ribosomal RNA (5.8S tioned authors has been subject to a risk assessment for use as a rRNA), and internal transcribed spacer 2 (ITS2) regions, as mea- biological control agent. Central to the conduct of a risk assessment sures of genetic relationships among species, to clarify the host- is determination of host-range of the candidate agent. For the past range of the obligate parasitic rust fungus Uromyces salsolae, also 30 years, the traditional approach to developing test plant lists for being evaluated for biological control of Russian thistle (Berner et host-range determinations has been the phylogenetic testing al., 2009). Thus it was of interest to see if the same approach could method (Wapshere, 1974). Currently, this process of risk assess- be used as effectively to clarify the host-range of CGS, which is a ment is being scrutinized, and Briese (2005) has called for modern- facultative parasitic fungus, and to see how much BLUPs for disease ization of the process to better translate host-range test results, reaction to the two pathogens were dependent on genetic related- which involve small sample sizes of questionable species represen- ness of the plant species tested versus disease rating data, i.e., see tation tested under artificial conditions, into real-world expecta- how robust BLUPs were for the two pathogens. tions and outcomes. To improve the process, Briese (2005) The objectives of this study were to (1) determine whether the suggests taking into account improved knowledge of plant phylo- MME, based on ITS-5.8S rDNA sequence data and disease ratings, genetic relationships. This is particularly germane for Russian are more useful than least squares methods in delimiting the thistle. host-range of CGS; (2) determine the probable host-range of CGS Only recently has there been resolution of taxonomy and clear among related plant species; (3) determine whether CGS is suffi- identification of species commonly known as Russian thistle in ciently host specific to propose release into N. America for biolog- North America (Ryan and Ayres, 2000; Gaskin et al., 2006; Hrusa ical control or requires additional evaluation; (4) predict and Gaskin, 2008). What was considered previously to be a single susceptibility of species that could not be directly tested because species with many synonyms has now been described as Salsola of rarity or unavailability of propagation material; (5) determine tragus L., Salsola australis R. Br., and a new species, Salsola ryanii the robustness of BLUPs by comparing BLUPs from CGS with those sp. nov. (Hrusa and Gaskin, 2008). Both S. tragus (as ‘‘S. tragus Type from U. salsolae tests on S. tragus and related species. A”, sensu Ryan and Ayres, 2000) and S. australis (as ‘‘S. tragus Type B”, sensu Ryan and Ayres, 2000) are distinct biologically and react very differently to two pathogens (Bruckart et al., 2004) and an in- 2. Materials and methods sect (Sobhian et al., 2003). Issues also remain about phylogeny within the Chenopodiaceae 2.1. The pathogen and inoculation procedures of North America, even though major revisions using molecular ap- proaches have been applied to European species of this family CGS isolate 96-067 (BPI 878740, GenBank # EU805538) col- (Akhani et al., 2007; Pyankov et al., 2001). The position of Salsola lected from a Salsola sp. in Hungary (Schwarczinger et al., 1998) species is much better understood for plants from Europe, but was used in these tests. The isolate was maintained on V-8 JuiceTM there is no similar body of information for North American Cheno- agar. Back-up cultures of CGS were kept either on half-strength podiaceae, and only a limited amount of the European data are use- corn meal agar slants in screw-cap tubes or as agar plugs in vials ful in evaluations of US weed pests. maintained at 80 °C. Inoculum consisted of a suspension of con- Although the objectives of risk assessments have not changed idia harvested from 20% V-8 Juice agar cultures after 2-wk growth. since Wapshere’s method (Wapshere, 1974) was proposed, there Plants were inoculated 4 wk after transplanting by spraying with has been considerable improvement both in understanding plant 106 conidia per ml until the foliage was completely wet. All plants relatedness and in the capability of developing new knowledge were exposed to 12–16 h dew at 25 °C and then transferred to a about phylogeny, primarily on the basis of molecular information. greenhouse (20–25 °C) until symptoms developed. Supplemental In many cases, individual laboratories have the capability to char- lighting was provided to give a 16-h photoperiod. acterize organisms using molecular tools. This enables both confir- mation and generation of data otherwise lacking for North 2.2. Test plant material American Chenopodiaceae, in the case of this study. However, molecular data have not, until recently, been used in conjunction A proposed plant host-range test list has been reviewed by the with disease reaction data to aid in determination of host-range Technical Advisory Group for Biological Control Agents of Weeds of potential biological control pathogens. (TAG) and the USDA, Animal & Plant Health Inspection Service Mixed model equations (MME) (Henderson, 1975, 1977; Har- (USDA, APHIS, 2007), with additional input from the US Dept. Inte- ville, 1976, 1977) have historically been used to generate breeding rior, Fish & Wildlife Service. The list of plants actually tested and values (Best Linear Unbiased Predictors, BLUPs) as predictors of sources of ITS1, 5.8S, and ITS2 sequence information, referred to performance of future (yet-to-be-conceived) progeny based on herein as ITS sequences, for each species analyzed are given in Table the performance of potential parents and their genetic relatedness. 1. Plants inoculated directly in this study are designated in Table 1 160 D.K. Berner et al. / Biological Control 51 (2009) 158–168
Table 1 Plant species tested for disease reaction to Colletotrichum gloeosporioides f. sp. salsolae, nativity in the United States or Canada, source of test material, source of DNA sequence, and GenBank accession numbers. Species and authority United States or Canadian FDWSRU seed lot Seed or plant source ITS sequence GenBank native planta number source accession Allenrolfea occidentalis (S. Wats.) L ALKOC-1 CDFAb FDWSRU EU643787 Kuntze Allium cepa L. ALLCE-2 Burpee Seed Co. GenBank AM492188 Amaranthus caudatus L. AMACA-1 CDFA GenBank AF210907 Amaranthus hypochondriacus L. AMAHP-1 NCRPISc GenBank AF210917 Amaranthus retroflexus L. L NT NT GenBank L78085 Amaranthus spinosus L. L, P, V NT NT GenBank DQ005962 Aphanisma blitoides Nutt. ex Moq. L NTd NT GenBank AY858591 Arenaria hookeri Nutt. AREHHO-1 RMRPe NAf NA Arthrocnemum glaucum (Del.) NT NT GenBank AY996260 Ungern-Sternb. Atriplex canescens (Pursh) Nutt. L, C ATXCA-2 NAPGRSg GenBank AM420676 Atriplex hortensis L. ATXHD-4 Herbiseed Co. NA NA Atriplex lentiformis (Torr.) S. Watson L ATRCA-2 CDFA-#226 GenBank AM420688 Atriplex leucophylla (Moq.) D. Dietr. L ATLE-1 Rancho Santa Ana Botanic Garden at NA NA Claremont, CA Atriplex patula L. ATXPA-2 Herbiseed Co. GenBank DQ499332.1 Atriplex semibaccata R. Br. ATXSE-2 MFh GenBank AM420700.1 Atriplex suberecta I. Verd. ATRSU-1 MF NA NA Axyris amaranthoides L. NT NT GenBank AM849227 Bassia (Kochia) americana (S. L NT NT GenBank AY489210 Watson) A.J. Scott Bassia hyssopifolia (Pall.) Kuntze BAFHY-1 MF GenBank DQ499333.1 Bassia prostrata (L.) A.J. Scott NT NT GenBank AY489216 Bassia scoparia (L.) A.J. Scott KCHSC-1 CDFA GenBank EF453446 Beta vulgaris L. BEAVD-1 Burpee Seed Co. GenBank AY858597 Brassica oleracea L. BRAOL-1 Burpee Seed Co. GenBank AY722423 Calendula officinalis L. CLDOF-1 Burpee Seed Co. GenBank AF422114.1 Callistephus chinensis CALCH-6 Burpee Seed Co. NA NA Carduus acanthoides L. CRUAC-16 In-house collection GenBank EF123106 Carduus nutans L. CATHO-6 L. McKee NA NA Carduus pycnocephalus L. CRUPY-7 CDFA GenBank EF123105.1 Carthamus tinctorius L. CAVTI-13 CAL WESTSEEDS GenBank EF483949 Centaurea calcitrapa L. CENCA-7 CDFA NA NA Centaurea cyanus L. CENCY-3 Burpee Seed Co. GenBank AY826254 Centaurea diffusa Lam. CENDI-2 D.K. Whaley and G.L. Piper GenBank DQ319108 Centaurea maculosa Lam. CENMA-10 C. Roche NA NA Centaurea melitensis L. CENME-2 CDFA NA NA Centaurea solstitialis L. CENSOL-2 D.K. Whaley and G.L. Piper GenBank DQ319163 Celosia argentea L. CEOAR-1 NCRPIS NA NA Chenopodium album L. L, C CHEAL-15 NCRPIS GenBank L78088 Chenopodium ambrosioides L. CHEAA-2 Bountiful Gardens Co. GenBank DQ005963.1 Chenopodium berlandieri Moq. L, C CHEBE-7 NCRPIS NA NA Chenopodium bonus-henricus L. CHEBH-2 NCRPIS NA NA Chenopodium capitatum (L.) Ambrosi L, A, C CHECA-1 NCRPIS NA NA Chenopodium ficifolium Sm. CHEFI-1 NCRPIS NA NA Chenopodium foetidum Schrad. CHEFO-1 In-house collection NA NA Chenopodium foliosum CHEFL-1 NCRPIS NA NA Chenopodium giganteum D. Don CHEGI-8 NCRPIS NA NA Chenopodium glaucum L. CHEGL-2 NCRPIS NA NA Chenopodium murale L. CHEMU-2 NCRPIS NA NA Chenopodium neomexicanum Standl. L CHENO-2 NCRPIS NA NA Chenopodium pallidicaule Aellen CHEPL-2 NCRPIS NA NA Chenopodium quinoa Willd. CHEQU Herbiseed Co. NA NA Chenopodium rubrum L. L, A, C CHERU-2 NCRPIS NA NA Chenopodium strictum Roth CHEAS-1 NCRPIS NA NA Chenopodium vulvaria L. CHEVU-2 NCRPIS NA NA Corispermum americanum (Nutt.) L, A, C CORAM-2 SSi NA NA Nutt. Corispermum pacificum Mosyakin L NT NT GenBank DQ499337 Crupina vulgaris Cass. CJNVU-5 C. Roche GenBank AY826280 Cucurbita moschata Duchesne CUUMO-1 Burpee Seed Co. NA NA Cupressus abramsiana C. B. Wolf L NT NT GenBank AY988365 Cupressus goveniana Gordon var. L NT NT GenBank AY988380 goveniana Cynara scolymus L. CYUSC-4 Burpee Seed Co. GenBank AJ404744 Daucus carota L. DAUCA-2 Burpee Seed Co. AF07779 Endolepis covillei Standley L NT NT GenBank DQ383881 Froelichia gracilis (Hook.) Moq. L NT NT GenBank AY173410 Glycine max L. GLYXMA-7 Southern States Co. GenBank GMU60551 Gomphrena globosa L. GOMGE-1 NCRPIS NA NA Gossypium barbadense L. GOSBA-1 In-house collection GenBank U12715 Grayia spinosa (Hook.) Moq. L GRASP-1 CDFA GenBank AM849225 D.K. Berner et al. / Biological Control 51 (2009) 158–168 161
Table 1 (continued)
Species and authority United States or Canadian FDWSRU seed lot Seed or plant source ITS sequence GenBank native planta number source accession Halocnemum strobilaceum (Pall.) M. Bieb. NT NT GenBank AY489241 Halogeton glomeratus (M. Bieb.) C.A. Mey. HALGL-1 EIWUj GenBank EF453431 Halostachys caspica (M. Bieb.) C.A. Mey. ex NT NT GenBank AY556427 Schrenk Halothamnus subaphyllus (C.A. Mey.) NT NT GenBank AF318625 Botsch. Haloxylon ammodendron (C.A. Mey.) Bunge NT NT GenBank AY556438 ex Fenzl Haloxylon persicum Bunge ex Boiss. & Buhe NT NT GenBank AF318628 Helichrysum bracteatum (Vent.) Andrews HECBR-1 Burpee Seed Co. NA NA Howellia aquatilis A. Gray NT NT GenBank AF163434 Kalidium foliatum (Pall.) Moq. NT NT GenBank AY489238 Krascheninnikovia ceratoides (L.) KRALA-2 NCRPIS GenBank AM849233 Gueldenst. Lycopersicon esculentum Mill. LYPES-1 Burpee Seed Co. GenBank AY552528 Malacothamnus fasciculatis (Torr. & A. NT NT GenBank AY591839 Gray) Greene Mirabilis multiflora (Torr.) A. Gray L MISMU-1 NCRPIS GenBank AF212002 Monolepis nuttalliana (Schult.) Greene L, A, C MONUT-1 EIWU NA NA Nitrophila mohavensis Munz & Roos L NIRMO-3 EIWU FDWSRU FJ362531 Nitrophila occidentalis (Moq.) S. Watson L NIROC-2 EIWU FDWSRU FJ409841 Opuntia basilaris Engelm. & J.M. Bigelow L OPUBA-1 The Theodore Payne Foundation FDWSRU FJ010627 Opuntia ficus-indica (L.) Mill. NT NT GenBank AB250211 Phytolacca americana L. L, C PHYTL-1 Herbiseed Co. GenBank AB250211 Polycnemum majus A. Braun NT Botanical Garden University of Dr. Gudrun FJ362530 Mainz, Germany Kadereit Polygonum aviculare POLAV-2 Herbiseed Co. GenBank EF653684 Polygonum persicaria POLPE-4 Herbiseed Co. NA NA Salicornia bigelovii Torr. L, P SAABI-1 WRPISk FDWSRU EU682686 Salicornia maritima Wolff and Jefferies (=S. L,C SAAEV-1 McArthur and Sanderson Co. GenBank AY489247 europaea L.) Salicornia virginica L. L, A, C SAAVI-1 CDFA FDWSRU FJ010626 Salsola australis R. Br. (S. tragus type B) SASKR-6 CDFA FDWSRU EU862806 Salsola collina Pall. SASCO-1 EIWU FDWSRU EU643788 Salsola kali L. from UK SASKA-1 Herbiseed Co. FDWSRU EU651836 Salsola kali L. from Maui SASKA-3 MF FDWSRU EU643789 Salsola kali L. (Akhani et al., 2007; Pyankov NT NT GenBank AF318646 et al., 2001) Salsola orientalis S.G. Gmel. SALSO-1 In-house collection GenBank EF453492 Salsola paulsenii Litv. SASPA-5 EIWU GenBank AF318647.1 Salsola soda L. SASSO-3 Dr. Peter Baye, CA FDWSRU EU643790 Salsola soda L. (Akhani et al., 2007) NT NT GenBank EF453497 Salsola tragus L. SASKT-26 EIWU FDWSRU EU373656 Salsola vermiculata L. (Akhani et al., 2007) NT NT GenBank EF453501 Sarcobatus vermiculatus (Hook.) Torr. L, C SAYVE-1 EIWU GenBank EF079501 Sarcocornia fruticosa (L.) A.J. Scott NT NT GenBank DQ340164 Sarcocornia utahensis (Tidestr.) A.J. Scott L SARUT-1 EIWU GenBank AY489260 Sesuvium maritimum (Walter) Britton et al. L, P NT NT GenBank AJ937562 Sidalcea pedata A. Gray NT NT GenBank AJ304924 Silene laciniata Cav. L SILELA-1 Thompson and Morgen Co. NA NA Silene verecunda S. Watson L NT NT GenBank DQ908673 Sorghum bicolor (L.) Moench SORHY-1 Texas A&M GenBank DQ190421 Spinacia oleracea L. SPQOL-11 Burpee Seed Co. GenBank AF062088 Suaeda calceoliformis (Hook.) Moq. L, A, C NT NT GenBank DQ499351 Suaeda californica S. Watson L SUACA-5 Dr. Peter Baye, CA FDWSRU EU862805 Suaeda glauca (Bunge) Bunge NT NT GenBank DQ786333 Suaeda maritima (L.) Dum. (Akhani et al., L, C NT NT GenBank EF453508 2007) Suaeda moquinii (Torr.) Greene L, C SUAMO-2 EIWU GenBank AY181864 Suaeda occidentalis (S. Watson) S. Watson L, A, C NT NT GenBank DQ499353 Suaeda taxifolia (Standl.) Standl. L SUATA-2 EIWU GenBank DQ499354 Suaeda vera Forssk. ex J. F. Gmel. NT NT GenBank AY181868 Suckleya suckleyana (Torr.) Rydb. L, C SUKSU-1 EIWU FDWSRU EU643791 Trifolium trichocalyx A. Heller L NT NT GenBank DQ312180 162 D.K. Berner et al. / Biological Control 51 (2009) 158–168
Table 1 (continued)
Species and authority United States or Canadian native FDWSRU seed lot Seed or plant ITS sequence GenBank planta number source source accession Triticum aestivum L. TRZAX-7 In-house GenBank AM040486 collection Zea mays L. ZEAMX-3 In-house GenBank DQ683016 collection Zuckia brandegeei (A. Gray) S.L. Welsh & L ZUCBR-1 EIWU FDWSRU EU643792 Stutz
a From USDA, NRCS (2008). The PLANTS Database (http://plants.usda.gov). National Plant Data Center, Baton Rouge, LA 70874-4490, USA. ‘‘A” = native in Alaska, ‘‘C” = native in Canada, ‘‘L” = native in lower 48 United States, ‘‘P” = native in Puerto Rico, ‘‘V” = native in Virgin Islands. b CDFA-California Department of Food and Agriculture. c NCRPIS-USDA-ARS North Central Regional Plant Introduction Station. d Not tested. e Rocky Mountain Rare Plants. f Not available. g USDA-ARS National Arid Plant Genetic Resource Unit. h Mach Fukuda, Kanaha, Maui. i Stewart Sanderson—Great Sand Dunes Nat’l Park Alamasa Co., CO. j Dr. Lincoln Smith, United States Department of Agriculture, Agricultural Research Service, Exotic and Invasive Weed Unit, Albany, CA. k WRPIS-USDA-ARS Western Regional Plant Introduction Station.
and constitute the basic list generated using the method proposed tion reflected a separate inoculation, and from 2 to 10 repetitions by Wapshere (1974). Plant names are in accordance with the were conducted for most species, depending on availability of PLANTS database (USDA, NRCS, 2008). Seeds of plants of S. tragus plant material and relative importance of the species in specificity used in this study were obtained from plants confirmed to be S. tra- tests. Disease data for species for which there were no disease gus by Dr. Fred Hrusa (California Dept. Food & Agriculture). severity ratings were represented as missing values (‘‘.”) in the dataset. 2.3. DNA sequences Ordinal data from the rating scales for disease severity were ranked, using the Rank procedure of SAS (SAS Institute Inc., ITS sequences were either generated at the Foreign Disease- 2004). The variable that was ranked was the disease severity rating Weed Science Research Unit (FDWSRU) of USDA, ARS or obtained for each plant within each species and repetition. The mean and from GenBank (Table 1). For those sequences generated at variance for each repetition and species was computed from the FDWSRU, genomic DNA was extracted either from fresh leaf sam- ranks of disease severity among the plants. The means of each rep- ples or outside samples preserved in silica gel. A 600–700 bp frag- etition were analyzed by ANOVA using the Mixed procedure of SAS ment containing the ITS sequence was extracted, amplified, cloned, with the ANOVAF option and restricted maximum likelihood and sequenced using methods previously described (Berner et al., (REML) estimation (Shah and Madden, 2004). The ANOVAF option 2009). Sequences were truncated to include only the ITS 1, 5.8S, generates F-tests for the effects in the mixed model. The intercept and ITS 2 regions. and species were considered fixed effects and the error term (rep- etition species) a random effect. This ANOVA was weighted by 2.4. Distance matrix generation the inverse of the variance computed from the rankings of plant disease reactions within each repetition and species. Least squares A distance matrix among species was developed using methods means, standard errors, and an F-test for species of plants were described in Berner et al. (2006). ITS sequences of the species were generated from this analysis for an initial assessment of suscepti- aligned with the ClustalW2 tool (Larkin et al., 2007), and the out- bility. Lettered groupings of differences in these least squares put alignment file was then analyzed by quartet puzzling, with means were generated using the PDMIX800 SAS macro (Saxton, TREE-PUZZLE software (Schmidt et al., 2002), to generate both a 1998). A pseudo coefficient of variation (CV) based on standard er- matrix of pairwise maximum likelihood distances among species ror rather than standard deviation) was calculated as (standard er- and a quartet puzzling tree of relationships among species. Allium ror least squares mean) 100. cepa was used as the outlier in this analysis. The distance matrix output from TREE-PUZZLE was used in the predictive MME analy- 2.6. Variance component estimation sis, and a file of maximum likelihood branch lengths was read into TreeViewX software (Page, 1996) to draw a phylogram among The data were then re-analyzed with the Mixed procedure and species. REML estimation with only the intercept as a fixed effect and spe- cies as a random effect. A maximum of five repetitions were used 2.5. Disease evaluation and data collection for each species in this and the subsequent mixed model analysis. The results of this analysis provided the variance estimate for spe- Disease ratings were made each week for 4 wk after each inoc- cies which was subsequently used in the G matrix. ulation (repetition), and ratings from each plant (sample) within each repetition on the second week were used for analysis in this 2.7. Predicted disease reaction using mixed models study. Ratings were based on a scale from 0 to 4, where: 0 = no macroscopic symptoms; 1 = small or isolated lesions, <25% of the The mixed model equations (MME) for the predictive analysis plant diseased; 2 = some coalesced lesions, 25–50% of the plant were, in matrix notation: y = XW + ZU + E. The descriptors and diseased; 3 = many coalesced lesions, >50% of the plant diseased; dimensions of the respective matrices were: y = 187 1 vector of and 4 = severe disease, dead plant. Between one to ten plants were ranks of disease ratings obtained from the Rank procedure as pre- inoculated in each repetition for each species. Plants of S. tragus viously described; X = 187 1 design vector of ‘‘1”s for the fixed were included as a positive check in each repetition. Each repeti- intercept only; W =1 1 vector for the fixed effect parameters, D.K. Berner et al. / Biological Control 51 (2009) 158–168 163 in this case only the intercept; Z = 187 92 design matrix for the of the random effects parameters; E = 187 1 vector of residuals random effects, in this case species; U =92 1 unknown vector (errors). The solutions for the MME (Henderson, 1975; SAS Insti-
Table 2 Least squares mean estimators and mixed model predictors (BLUPs) of disease reaction to Colletotrichum gloeosporioides f. sp. salsolae. The table is arranged in descending order by BLUP value.
Genus speciesa Least squares means estimators Mixed model predictors Least squares means estimates Standard error of estimate Lettered grouping BLUPb Standard error of predictionc Pr > |t|d Salsola kali-UK 285.40 41.47 AB 247.92 10.23 <0.0001 Salsola tragus 277.82 37.46 AB 246.73 9.84 <0.0001 Salsola collina 285.98 35.00 A 235.40 70.70 <0.0001 Salsola paulsenii 296.40 34.19 A 225.51 7.59 <0.0001 Salsola kali-Akhani NTe NT 224.75 7.93 <0.0001 Salicornia bigelovii 247.06 17.30 A 213.39 14.95 <0.0001 Salsola australis 111.24 12.72 D 208.14 10.29 <0.0001 Salsola kali-Maui 111.08 18.00 D 207.53 10.58 0.0001 Salicornia maritima 144.18 16.09 CD 205.02 14.86 0.0002 Sarcocornia utahensis NT NT 196.41 20.63 0.0015 Sarcocornia fruticosa NT NT 190.04 21.67 0.0027 Bassia hyssopifolia 239.08 29.50 AB 188.26 17.62 0.0026 Bassia scoparia 110.60 10.35 D 187.80 9.01 0.0036 Nitrophila occidentalis 254.37 81.79 ABCD 184.10 16.80 0.0070 Halothamnus subaphyllus NT NT 176.36 18.05 0.0152 Arthrocnemum glaucum NT NT 176.08 19.17 0.0091 Bassia americana NT NT 176.11 24.97 0.0120 Bassia prostrata NT NT 172.30 20.92 0.0150 Suaeda calceoliformis NT NT 169.86 28.41 0.0269 Kalidium foliatum NT NT 169.85 22.65 0.0159 Spinacia oleracea 180.01 8.93 BC 167.73 14.70 0.0104 Suaeda glauca NT NT 166.54 25.34 0.0261 Polycnemum majus NT NT 165.83 26.21 0.0478 Suaeda occidentalis NT NT 165.42 27.66 0.0361 Suaeda moquinii 110.93 17.97 D 165.27 14.03 0.0119 Suaeda taxifolia 119.07 17.61 D 164.68 13.89 0.0124 Suaeda maritima NT NT 162.11 25.61 0.0399 Halocnemum strobilaceum NT NT 159.38 20.36 0.0259 Suaeda vera NT NT 158.36 25.13 0.0510 Halogeton glomeratus 110.39 17.93 D 153.59 16.78 0.0389 Sesuvium maritimum NT NTf 152.20 29.65 NSg Salsola soda 110.60 12.68 D 145.83 17.62 NS Salsola orientalis 110.60 10.35 144.60 17.65 NS Allenrolfea occidentalis 112.94 3.80 D 143.19 14.29 NS Salicornia virginica 110.60 12.68 D 137.25 13.30 NS Opuntia basilaris 113.17 3.98 D 134.54 21.25 NS Suaeda californica 130.84 16.47 D 129.49 15.80 NS Suckleya suckleyana 110.27 17.86 D 129.11 15.60 NS Calendula officinalis 110.60 12.68 D 123.55 20.58 NS Lycopersicon esculentum 110.60 12.68 D 122.20 22.15 NS Sorghum bicolor 110.60 12.68 D 121.80 22.04 NS Zuckia brandegeei 112.46 5.06 D 120.37 13.22 NS Polygonum aviculare 113.10 2.78 D 119.36 19.10 NS Sarcobatus vermiculatus 112.58 5.27 D 119.29 16.51 NS Cynara scolymus 110.60 12.68 D 118.40 16.44 NS Grayia spinosa 110.60 12.68 D 118.06 13.69 NS Krascheninnikovia ceratoides 81.36 135.63 ABCD 117.97 19.63 NS Mirabilis multiflora 110.60 10.35 D 116.84 19.00 NS Chenopodium album 110.60 12.68 D 116.11 18.39 NS Carthamus tinctorius 110.60 12.68 D 115.49 14.69 NS Amaranthus hypochondriacus 110.60 12.68 D 114.86 19.70 NS Crupina vulgaris 112.46 5.06 D 113.92 17.11 NS Brassica oleracea 110.60 12.68 D 113.87 22.84 NS Glycine max 112.84 3.72 D 113.33 18.02 NS Atriplex canescens 112.46 5.06 D 112.56 13.27 NS Daucus carota 110.60 12.68 D 112.23 22.56 NS Atriplex semibaccata 110.18 17.70 D 111.29 13.54 NS Centaurea diffusa 110.60 12.68 D 111.06 14.74 NS Beta vulgaris 110.60 17.93 D 110.58 15.24 NS Atriplex lentiformis 110.60 10.35 D 109.87 12.88 NS Chenopodium ambrosioides 112.63 3.04 D 109.29 18.69 NS Atriplex patula 110.60 10.35 D 109.19 13.12 NS Carduus pycnocephalus 110.60 12.68 D 108.50 15.80 NS Phytolacca americana 110.60 10.35 D 107.55 18.26 NS Carduus acanthoides 112.94 3.80 D 106.03 15.42 NS Allium cepa 112.58 5.27 D 105.95 20.94 NS Gossypium barbadense 110.60 10.35 D 105.25 20.27 NS Centaurea cyanus 110.60 10.35 D 104.78 14.62 NS Nitrophila mohavensis 100.45 406.91 ABCD 104.73 33.12 NS (continued on next page) 164 D.K. Berner et al. / Biological Control 51 (2009) 158–168
Table 2 (continued) Genus speciesa Least squares means estimators Mixed model predictors Least squares means estimates Standard error of estimate Lettered grouping BLUPb Standard error of predictionc Pr > |t|d Triticum aestivum 110.60 10.35 D 98.35 37.90 NS Centaurea solstitialis 110.60 12.68 D 97.57 15.61 NS Chenopodium foliosum 121.57 12.13 D —h ——
a Species are arranged in order of descending BLUP values. b Best Linear Unbiased Predictor includes fixed intercept estimate to put prediction on same scale as lsmeans estimates. c Standard error of prediction based on BLUP of random species effect plus intercept. d Pr > |t| based on BLUP of random species effect without intercept. e Not tested. f Other species that were not directly tested and which had non-significant (NS) BLUPs were not included in the table. g Not significantly different than zero at P 6 0.05. h Not analyzed with final model; other species not analyzed with the final model were not included in the table.
Fig. 1. Phylogram from analysis of ITS sequence data based on 50,000 quartet puzzling steps. Branches are based on maximum likelihood branch lengths, and a branch-length measure is indicated in the bottom left. Allium cepa was the designated outlier in the analysis. Tribal memberships within the Chenopodiaceae family are indicated to the right of the species. Species in families other than Chenopodiaceae are indicated as ‘‘Fam. ____”. Species with branch lengths greater than 0.25, are not shown. Tribal designations are from USDA, ARS, National Genetic Resources Program (2008), Akhani et al. (2007), Rilke (1999) and Wilken (1993). D.K. Berner et al. / Biological Control 51 (2009) 158–168 165 tute Inc., 2004) were: Wˆ =(X0Sˆ 1X) X0Sˆ 1y and Û = GZ0Sˆ 1(y XWˆ ) the inoculated species and from an additional 33 species on the ba- as described in Berner et al. (2009). sis of ITS sequence data alone; i.e., the latter set had not been inoc- Only the intercept was considered a fixed effect while species ulated or rated. Another 30 species, which had been inoculated, was considered a random effect with a specified G matrix. The R could not be analyzed using mixed models, because ITS sequences matrix and associated error variance was estimated by iteration were not available. Overall, these analyses included plants repre- (REML) in the final model. The G matrix for variances and covari- senting 19 families in 62 genera and 120 species. ances among species was derived from the distance matrix of ITS Least squares means of ranks of disease severity for each species sequences by subtracting the values of each element from 1 and showed that Salsola paulsenii, Salsola collina, and a UK accession la- then multiplying each element by the variance for species obtained beled S. kali, were the most susceptible to CGS (Table 2). Lettered in the first REML step. The resultant 92 92 matrix of variances groupings from the mixed model analysis used to generate least and covariances was read into the Mixed procedure of SAS, accord- squares means indicated that disease severity rankings were not ing to Berner et al. (2009), to generate BLUPs for each species, stan- significantly different for the target, S. tragus, or for the native spe- dard errors of the BLUPs, and t-tests comparing each BLUP against cies Salicornia bigelovii, Nitrophila occidentalis, and Nitrophila zero. The BLUPs had the same connotation as the least squares mohavensis, and the non-native species Bassia hyssopifolia and Kra- mean estimates, i.e., the higher the BLUP, the more severe the dis- scheninnikovia ceratoides. However, the standard error of the least ease reaction. Estimate statements were written to compare BLUPs squares mean for K. ceratoides was 167.85% of the mean itself while among species and among tribes and clades. A measure of variabil- this ratio was 407.87% for N. mohavensis, even though the least ity (CV) relative to BLUPs for each species was calculated from a squares mean estimates these two species were the lowest of dataset of predictions output from the analysis. The CVs were cal- any species. The range in CVs for the least squares means of all culated in the same way as CVs for the least squares means. 91 accessions inoculated was 3.31–407.87% with a mode of 9.42%. Discounting N. mohavensis and K. ceratoides for reasons gi- ven, only the two native species Salicornia bigelovii and Nitrophila 3. Results occidentalis, within the letter grouping ‘‘A”, were identified for additional testing. In this study, 89 species (=92 accessions; three accessions iden- A phylogram, depicting relationships based on ITS sequences tified by suppliers as ‘‘S. kali”) were inoculated with CGS. Represen- of species tested in this study, is presented in Fig. 1. All ITS se- tatives of plants from 16 plant families in 44 genera were included quences were unique. BLUPs and the standard errors of each pre- among species inoculated, thus circumscribing the target species, dictor, based on disease severity rankings and the relationship as recommended by Wapshere (1974). The MME were used to matrix derived from the ITS sequences, are presented in Table combine sequence information with disease ratings from 59 of 2. The range in CVs for BLUPs of all the 92 accessions analyzed
Table 3 Comparisons of BLUPs of disease reaction to CGS for Salsola tragus with species with non-zero BLUPs and comparisons of BLUPs of Salsolae and the Salsola clade with the most closely-related tribes.
Comparison Difference Standard error P >|t| Species comparisons S. tragus minus S. collina 11.92 10.44 0.2584 S. tragus minus S. paulsenii 21.82 4.05 <0.0001 S. tragus minus S. kali-Akhani 24.58 4.54 <0.0001 S. tragus minus S. kali-Maui 39.80 11.30 0.0009 S. tragus minus S. australis 39.19 11.82 0.0016 S. tragus minus Salicornia bigelovii 33.93 17.58 0.0587 S. tragus minus Salicornia maritima 42.31 17.88 0.0214 S. tragus minus Bassia hyssopifolia 59.07 19.55 0.0038 S. tragus minus Bassia prostrata 75.03 22.53 0.0015 S. tragus minus Bassia scoparia 59.53 20.68 0.0056 S. tragus minus Bassia americana 71.22 26.48 0.0094 S. tragus minus Nitrophila occidentalis 63.23 19.69 0.0022 S. tragus minus Nitrophila mohavensis 111.95 31.34 0.0007 S. tragus minus Polycnemum majus 81.50 27.91 0.0050 S. tragus minus Sarcocornia fruticosa 57.29 23.60 0.0184 S. tragus minus Sarcocornia utahensis 50.92 22.62 0.0283 S. tragus minus Suaeda calceoliformis 77.47 30.10 0.0127 S. tragus minus Suaeda moquinii 82.06 17.23 <0.0001 S. tragus minus Suaeda occidentalis 85.22 27.43 0.0030 S. tragus minus Suaeda taxifolia 82.65 17.13 <0.0001 S. tragus minus Spinacia oleracea 79.59 17.70 <0.0001 S. tragus minus Arthrocnemum glaucum 71.25 21.51 0.0016 S. tragus minus Halothamnus subaphyllus 70.97 19.00 0.0004 S. tragus minus Kalidium foliatum 77.48 24.32 0.0024 Tribe and clade comparisons Salsolae minus Camphorosmeae 5.81 18.04 0.7485 Salsola clade minus Camphorosmeae 46.88 19.20 0.0178 Salsolae minus Salicornieae 12.47 12.99 0.3412 Salsola clade minus Salicornieae 53.54 13.54 0.0002 Salsolae minus Suaedeae 22.32 15.95 0.1672 Salsola clade minus Suaedeae 63.39 16.69 0.0004 Salsolae minus Atripliceae 94.01 14.25 <0.0001 Salsolae minus Polycnemeae 25.16 17.92 0.1657 Salsola clade minus Polycnemeae 66.23 18.50 0.0007 166 D.K. Berner et al. / Biological Control 51 (2009) 158–168 was 3.53 to 43.32% (Cupressus abramsiana) with a mode of 3.98%. non-zero BLUPs were 10 native or commercially important North This range was much narrower than the CVs of least squares American species that were identified for further testing; (d) BLUPs means. BLUPs of 30 of the 92 accessions analyzed with the reflected the disease reactions of each species, as a whole, based on MME were significantly different than zero as indicated by the the disease reactions of the species themselves plus the disease t-tests (Table 2), i.e., these 30 accessions had significant levels reactions of all of the other inter-related species. The relationship of disease severity. Nine of these accessions were in the tribe matrix in the MME was of dimensions 92 92 accessions (89 spe- Salsolae (Fig. 1), and 21 were in other tribes of the Chenopodia- cies), and 8464 interactions (the number of elements in the rela- ceae, to wit the Camphorosmeae (4 spp.), the Salicornieae (6 tionship matrix) were, conceptually, involved in the generation of spp.), the Halopeplideae (which clustered with species in the Sal- each BLUP. These inter-relationships placed the disease reaction icornieae, 1 sp.), the Suaedeae (7 spp.), the Atripliceae (1 sp.), and of each species in context with all species analyzed. the Polycnemeae (2 spp.). Neither N. mohavensis nor K. ceratoides had BLUPs significantly greater than zero. Using this approach, 10 4.2. The probable host-range of CGS native or commercially important species in N. America were identified as needing further assessment. These were, in order Because the MME can generate BLUPs in the absence of ob- of decreasing BLUPs: Salicornia bigelovii, Salicornia maritima, Sarco- served data on a given species, a more complete host-range cornia utahensis, Nitrophila occidentalis, Bassia americana, Suaeda determination is possible, as long as DNA sequence data are avail- calceoliformis, Spinacia oleracea, Suaeda occidentalis (=S. calceolifor- able for all of the species to be analyzed. Results of this study mis in the PLANTS database [USDA, NRCS, 2008]), Suaeda suggest that the probable host-range of CGS among related plant moquinii, and Suaeda taxifolia. species appears restricted to the tribes Salsolae, Camphorosmeae, Results from inoculations and analysis using MME indicated ex- Halopepideae, Salicornieae, Suaedeae, and Polycnemeae. Spinacia tremes in susceptibility of three accessions of S. kali. The S. kali oleracea was the only species in the tribe Atripliceae with a accession from the UK was determined to be S. tragus on the basis non-zero BLUP, but extensive testing of this species indicated that of disease response, morphology, and molecular analysis of ITS se- disease is correlated with plant senescence (data not shown); i.e., quences. Alignment of the two sequences with BLAST (Altschul all plants tested in the greenhouse were bolting, and evidence et al., 1990) indicated 600/601 identities with no gaps (99.8%), from limited studies suggested that non-senescent plants were and the BLUPs (Table 2) for the two accessions were similar. The not susceptible. sequences of S. kali-Akhani and S. kali-Maui compared less favor- Nitrophila occidentalis was susceptible to CGS, but there was also ably to the sequence of S. tragus, having 411/416 identities with a lot of variability associated with the least squares mean, as evi- no gaps (98.8%) and 399/410 identities with no gaps (97.3%), denced by a ratio of 31.2% between the standard error and least respectively. The BLUPs for these two accessions also were also sig- squares mean for the ranked analysis (Table 2). Variability was nificantly lower than that of S. tragus (Table 3). The S. kali-Akhani considerably less (9.0%) for the BLUP for N. occidentalis (Table 2). and S. kali-Maui accessions shared 405/416 identities with no gaps Although an explanation is not clearly evident, the variability asso- (97.4%) and, based on this alignment and similar BLUPs, were prob- ciated with the least squares mean for N. occidentalis along with ably the same species, S. kali. the relatively poor relationship of this species to Nitrophila mohav- Salsola tragus (including S. kali-UK) was compared with the ensis and Polycnemum majus (Fig. 1) seem likely to contribute to other species that had non-zero BLUPs by estimating the difference this outcome. The presence of Sesuvium maritimum, of the family in BLUPs for each pair and generating P >|t| values for the differ- Aizoaceae, within this clade was also surprising. One explanation ences. Results of the statistical analysis of these differences are is that the S. maritimum sequence, which was from GenBank, presented in Table 3. Only Salsola collina and Salicornia bigelovii may have been misidentified. were not significantly (P 6 0.05) different than S. tragus, although the difference between Salsola tragus and Salicornia bigelovii was al- 4.3. The safety of CGS for release in N. America most significantly different (P = 0.0587). Comparisons between the tribe Salsolae and the clade of Salsola Results from preliminary inoculation studies with CGS indi- species (Fig. 1) and related tribes, i.e., Camphorosmeae, Sali- cated distinct variability in susceptibility among the Salsola species cornieae, Suaedeae, and Atripliceae, are also presented in Table 3. from the US (Bruckart et al., 2004), but CGS caused extensive dam- The BLUP for the tribe Salsolae was not significantly different than age and death to S. tragus, S. collina, and S. paulsenii inoculated with those of the Camphorosmeae, Polycnemeae, Salicornieae, or Suae- CGS in this study. However, there were some native, non-target deae. However, the BLUP for the Salsola clade, encompassing only North American species that developed symptoms, and these have the group of the most closely-related species in the genus Salsola raised concern about deployment of this fungus. These plants were (Fig. 1), was significantly greater than BLUPs for each of these members of the genera Salicornia, Suaeda, and Nitrophila, genera tribes. that included two species, Suaeda californica and Nitrophila mohav- ensis, both listed as ‘‘Endangered” by the federal government (TESS, 2006). 4. Discussion Species of Bassia, Nitrophila, Salicornia, Sarcocornia, and Suaeda had non-zero BLUPs. Of the Bassia spp., only B. americana is native 4.1. The usefulness of the MME in delimiting the host-range of CGS to N. America, and the introduced species, Bassia scoparia, has been declared noxious in three states (USDA, NRCS, 2008). B. americana, The MME enabled a more complete analysis of host-range be- had a non-zero BLUP, but it was significantly less than S. tragus. cause: (a) Plants representing more genera were analyzed within Species of Salicornia and Sarcocornia that had non-zero BLUPs were the Chenopodiaceae, i.e., 29 genera versus 19 genera analyzed by significantly less diseased than S. tragus, and all of these species are the least squares method; (b) Representatives of two more genera widely distributed in N. America. Salicornia bigelovii is present in and two more families were analyzed outside of the Chenopodia- one Canadian province, 20 US states, and Puerto Rico (USDA, NRCS, ceae; (c) More species were considered susceptible, with non-zero 2008). Salicornia maritima (=S. europea in the PLANTS database BLUPs, on the basis of MME, compared with least squares means [USDA, NRCS, 2008]) is present in six Canadian provinces and the (Table 2). BLUPs were, therefore, more conservative than least state of Maine, and Sarcocornia utahensis is present in five US states squares means in predicting susceptibility. Among the species with (USDA, NRCS, 2008). D.K. Berner et al. / Biological Control 51 (2009) 158–168 167
Although most species with non-zero BLUPs were significantly out this confirmation, results from inoculations would have been less diseased than Salsola tragus and the Salsola clade (Table 3), confusing. Also, it was clear from the phylogenic analysis that most additional direct inoculation tests are being conducted to deter- Salsola species separate into a single clade, with the exceptions of S. mine if any of the native species identified as susceptible in this soda, which groups with Halogeton, Haloxylon, and Halothamnus, study are actually damaged and therefore at risk from CGS, if it is and S. orientalis and S. vermiculata which group in a clade interme- deployed for biological control. Tests are being conducted with diate to Camphorosmeae and Salicornieae. None of these three Sal- the native species to compare above- and below-ground biomass sola spp. were susceptible. of inoculated plants with non-inoculated controls as a measure Similarly, the use of congeners has been proposed for testing in of damage by CGS. The MME analysis has reduced the need for lieu of plant species that are listed as endangered, threatened, rare, intensive biomass reduction tests from a possible 92 species to or are otherwise difficult to grow. The example in this study concerns 10 native or commercially important species, and results of bio- N. occidentalis, which is related to N. mohavensis, a species listed as mass reduction tests on these species will ultimately determine ‘‘Endangered” in the US and in California, and as ‘‘Fully Protected” whether CGS is safe to release in N. America. in Nevada. Although N. mohavensis was tested and found not suscep- tible, it is clear from phylogenetic analysis in this study (Fig. 1) that 4.4. The ability of the MME to predict susceptibility of un-tested germ these species are not so closely related. Alignment of N. occidentalis plasm and N. mohavensis using the BLAST algorithm revealed only a 76% match (236 of 309 base pairs, with 5 gaps), which was similar to BLUPs have historically been used to predict breeding values of the comparison between N. mohavensis with P. majus (a 78% match; high-value commodities, e.g., animals, but BLUPs also have rele- 239/306 base pairs, with 4 gaps) and between N. occidentalis and P. vance in predicting disease responses of high-value rare plant spe- majus (a 90% match; 364/404 base pairs and 2 gaps). These three spe- cies. Predictions of disease reaction for numerous un-tested species cies grouped in a separate clade, the tribe Polycnemeae, despite the in this study (Table 1) were made possible by incorporating ITS se- differences (Fig. 1), but the poor matches in DNA sequences indicate quences into the MME. Predictions of disease reaction should be that they might all be different genera. possible for any other species as long as plant material or ITS se- Results presented herein are based on disease severity ratings quence data are available. made with a 0–4 scale. Accuracy of the BLUPs might be improved if disease severity data were more representative of relative differ- 4.5. Robustness of BLUPs ences in susceptibility noted in this study. For example, use of a fi- ner scale for disease ratings, e.g., from 0 to 10, or describing disease Comparison of BLUPs for U. salsolae (Berner et al., 2009) and CGS response in terms of a quantitative variable, would be expected to tests on S. tragus and related species showed that seven species of produce BLUPs that more accurately describe the range in suscep- Salsola were susceptible to U. salsolae and 29 to CGS. Because CGS is tibility among species. a necrotophic fungus, less specificity was expected, and found, The theory and historical development of the MME as a predic- than with the obligate rust fungus. Susceptibility among species, tive tool in animal breeding have been extensively documented by based on BLUPs, for disease reaction to the two fungi was also dif- Henderson (1975, 1977), Harville (1976, 1977), and others. The ferent. For both fungi, species in the genus Salsola were the most major difference between the original animal breeding models susceptible, followed by other species in the tribe Salsolae, in the and the application of the MME described both in this study and case of U. salsolae, or by species in the tribes Atripliceae, Campho- in Berner et al. (2009), is that genetic relatedness among animals rosmeae, Halopepideae, Polycnemeae, Salicornieae, Salsolae, and in the original models was determined through coefficients of co- Suaedeae in the case of CGS. For U. salsolae, only species of Salsola ancestry, derived from pedigrees among animals, while the genetic had non-zero BLUPs while species in other tribes (previously relatedness among plant species, for determination of pathogen listed) had non-zero BLUPs for CGS. These differences in degree host-range, was determined with DNA sequences and generation of susceptibility to two different pathogens indicate that the of a distance matrix based on these sequences. In both cases, the MME are robust in generating BLUPs that depend not only on the matrices derived from pedigrees or DNA sequences were used in relatedness of the plant material analyzed but also on the disease generation of respective G matrices. As the quantity and quality reactions of the different species. of DNA sequence information in public databases improves, and Only one isolate of each pathogen was evaluated in each study as better protocols for in-house DNA extraction and characteriza- because only a single isolate can be approved for release by APHIS. tion develop, the MME and BLUPs are expected to become more Although differences in susceptibility among plant species might accurate and widely used for host-range determination in the fu- occur with different pathogen isolates, these potential differences ture. With increased usage, more nuances and differences in BLUPs would be expected, at worst, to be intermediate to the differences versus other estimates in host-range determination will be found between the two pathogen species. More likely, results described. would be more closely aligned with other isolates of the same spe- cies. Therefore, a test plant list for candidate biological control pathogens should not be based solely on ITS, or any other DNA se- Acknowledgments quences, because prediction of disease reaction depends in equal part on the pathogen being evaluated, i.e., some measure of plant The authors thank Dr. John Phillips, Statistician General, USDA, response to inoculation. ARS, North Atlantic Area, Wyndmoor, PA for proofing the data and statistical analyses. We also thank Dr. Arnold Saxton, Professor, 4.6. Other considerations Statistical Genomics, Animal Science Department, University of Tennessee, in Knoxville, for making valuable suggestions on the There were other advantages from incorporation of molecular analyses information into the MME for host-range determination of CGS. Molecular information enabled confirmation or clarification of host References plant identifications, and there was clarification of phylogeny that facilitated interpretation of results. It was determined that one Akhani, H., Edwards, G., Roalson, E.H., 2007. Diversification of the old world accession identified as S. kali from the UK was S. tragus, and with- Salsoleae s.l. (Chenopodiaceae): molecular phylogenetic analysis of nuclear and 168 D.K. Berner et al. / Biological Control 51 (2009) 158–168
chloroplast data sets and a revised classification. International Journal of Plant Page, R.D.M., 1996. TREEVIEW: an application to display phylogenetic trees on Science 168, 931–956. personal computers. Computer Applications in the Biosciences 12, 357–358. Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J., 1990. Basic local Peterson, D.E., 1999. The impact of herbicide-resistant weeds on Kansas agriculture. alignment search tool. Journal of Molecular Biology 215, 403–410. Weed Technology 13, 632–635. Berner, D.K., Bruckart, W.L., Cavin, C.A., Michael, J.L., 2009. Mixed model analysis Pyankov, V.I., Artyusheva, E.G., Edwards, G.E., Black Jr., C.C., Soltis, P.S., 2001. combining disease ratings and DNA sequences to determine host range of Phylogenetic analysis of tribe Salsoleae (Chenopodiaceae) based on ribosomal Uromyces salsolae for biological control of Russian thistle. Biological Control 49, ITS sequences: implications for the evolution of photosynthesis types. American 68–76. Journal of Botany 88, 1189–1198. Berner, D.K., Cavin, C.A., McMahon, M.B., Loumbourdis, I., 2006. First report of Rilke, S., 1999. Revision der Sektion Salsola s.l. der Gattung Salsola anthracnose of Salsola tragus caused by Colletotrichum gloeosporioides in Greece. (Chenopodiaceae). Bibliotheca botanica, Heft 149. E. Schweizerbart’sche Plant Disease 90, 971. Verlagsbuchhandlung (Nagele u. Obermiller), Stuttgart. Blackshaw, R.E., Morrison, R.J., Muendel, H.-H., Blair, T.R., 1992. Weed control in Ryan, F., Ayres, D., 2000. Molecular markers indicate two cryptic genetically safflower (Carthamus tinctorius) with Flurtamone. Weed Science 40, 110– divergent populations of Russian thistle (Salsola tragus). Canadian Journal of 114. Botany 78, 59–67. Briese, D.T., 2005. Translating host-specificity test results into the real world: the Saari, L.L., Cotterman, J.C., Smith, W.F., Primiani, M.M., 1992. Sulfonylurea herbicide need to harmonize the yin and yang of current testing procedures. Biological resistance in common chickweed, perennial ryegrass, and Russian thistle. Control 35, 208–214. Pesticide Biochemistry and Physiology 42, 110–118. Bruckart, W., Cavin, C., Vajna, L., Schwarczinger, I., Ryan, F., 2004. Differential SAS Institute Inc., 2004. SAS OnlineDocÒ 9.1.3. SAS Institute Inc., Cary, NC. susceptibility of Russian thistle accessions to Colletotrichum gloeosporioides. Saxton, A.M., 1998. A macro for converting mean separation output to letter Biological Control 30, 306–311. groupings in Proc Mixed. In: Proc. 23rd SAS Users Group Intl., SAS Institute, Cartwright, D.K., Templeton, G.E., 1989. Preliminary evaluation of a dodder Cary, NC, pp. 1243–1246. anthracnose fungus from China as a mycoherbicide for dodder control in the Schmidt, H.A., Strimmer, K., Vingron, M., von Haeseler, A., 2002. TREE-PUZZLE: US Proc.. Arkansas Academy of Science 43, 15–17. maximum likelihood phylogenetic analysis using quartets and parallel Crompton, C.W., Bassett, I.J., 1985. The biology of Canadian weeds. 65. Salsola computing. Bioinformatics 18, 502–504. pestifer A. Nels. Canadian Journal of Plant Science 65, 379–388. Schwarczinger, I., Vajna, L., Bruckart, W.L., 1998. First report of Colletotrichum Cudney, D.W., Orloff, S.B., 1988. Russian thistle, Salsola iberica, control in onion, gloeosporioides on Russian thistle. Plant Disease 82, 1405. Allium cepa. Weed Technology 2, 375–378. Schillinger, W.F., Young, D.L., 2000. Soil water use and growth of Russian thistle Daniel, J.T., Templeton, G.E., Smith Jr., R.J., Fox, W.T., 1973. Biological control of after wheat harvest. Agronomy Journal 92, 167–172. northern jointvetch in rice with an endemic fungal disease. Weed Science 21, Schillinger, W.F., Young, D.L., 2004. Cropping systems research in the world’s driest 303–307. rainfed wheat region. Agronomy Journal 96, 1182–1187. Farr, D.F., Rossman, A.Y., n.d. Fungal Databases, Systematic Botany and Mycology Shah, D.A., Madden, L.V., 2004. Nonparametric analysis of ordinal data in designed Laboratory, ARS, USDA.