Genetic Variation in Herbivore Resistance Within a Strawberry Crop Wild Relative (Fragaria Vesca

1 Genetic variation in herbivore resistance within a strawberry crop wild relative (Fragaria

2 vesca L.)

3 Daniela Weber1,*, Paul A. Egan1, Anne Muola1,2 & Johan A. Stenberg1

5 1Department of Plant Protection Biology, Swedish University of Agricultural Sciences, Box 102,

6 23053 Alnarp, SWEDEN

7 2 Biodiversity Unit, University of Turku, 20014 Turku, FINLAND

9 Corresponding author: [email protected]

23 bioRxiv preprint doi: https://doi.org/10.1101/728360; this version posted August 7, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

24 Abstract

25 To decrease the dependency on chemical pesticides, the resistance of cultivated strawberry to

26 pests needs to be increased. While genetic resources within domesticated varieties are limited,

27 wild genotypes are predicted to show high heritable variation in useful resistance traits. We

28 collected 86 wild accessions of Fragaria vesca L. from central Sweden and screened this

29 germplasm for antibiosis (pest survival and performance) and antixenosis (pest preference) traits

30 active against the strawberry leaf beetle (Galerucella tenella L.). First, extensive common garden

31 experiments were used to study antibiosis traits in the sampled plant genotypes. Heritable genetic

32 variation among plant genotypes was found for several antibiosis traits. Second, controlled

33 cafeteria experiments were used to test for plant genetic variation in antixenosis traits. The leaf

34 beetles avoided egg laying on plant genotypes possessing high antibiosis. This indicates a high

35 degree of concordance between antibiosis and antixenosis, and that the beetles’ egg-laying

36 behaviour optimizes the fitness of their offspring. The existence of high genetic variation in key

37 resistance traits suggests that wild woodland strawberry contains untapped resources that are

38 sought to reduce pesticide-dependence in cultivated strawberry. Given that only a very small

39 portion of the species’ distribution area was sampled, even higher variation may be expected at

40 the continental scale. As a whole, the genetic resources identified in this study serve to

41 strengthen the position of woodland strawberry as a key crop wild relative.

43 Keywords: Herbivory, oviposition preference, Galerucella tenella, woodland strawberry, crop

44 wild relative, resistance breeding

47 Introduction

48 Higher intrinsic resistance in crop plants against pests is a promising option to gain independence

49 from heavy pesticide use during cultivation (Broekgaarden et al. 2011; Smith and Clement

50 2012). Especially in cultivated strawberry, the demand for alternative pest control approaches is

51 growing, since the fruits are amongst the food with the highest pesticide residues (Fernandes et

52 al. 2011; Godfray et al 2014; Parikka and Tuovinen 2014). Genetic resources for resistance

53 against pests are limited within current strawberry cultivars and breeding programs (Liston et al.

54 2014; Chen et al 2015). A good source to enhance traits for pest resistance is the naturally

55 existing variation among the crop wild relatives (CWR) of cultivated strawberries (Egan et al.

56 2018). Known as ‘rewilding’ or ‘inverse breeding’, beneficial traits such as plant resistance to

57 herbivores can be restored in future varieties by including CWR germplasm in breeding

58 programs (Broekgaarden et al. 2011; Andersen et al. 2015; Palmgren et al. 2015; Dempewolf et

59 al. 2017). For example, a resistance trait to control the cabbage root fly Delia radicum L.

60 (Diptera: Anthomyiidae) was identified in wild white mustard (Sinapis alba L.), and successfully

61 integrated in oilseed rape (Brassica napus L.) and rutabaga (B. napus. var. napobrassica)

62 (Ekuere et al. 2005; Malchev et al. 2010).

63 In recent times, woodland strawberry (Fragaria vesca) has been recognized as a model

64 species and potential source of diversity for Fragaria breeding programs, including those for the

65 garden strawberry (Fragaria × ananassa) (Longhi et al. 2014; Urrutia et al. 2015; Egan et al.

66 2018). Owing to the large geographic distribution of the wild F. vesca (Hilmarsson et al. 2017)

67 and its natural adaptation to a range of habitats, the resulting genetic diversity of F. vesca is a

68 promising source of desirable traits for abiotic and biotic stresses (Amil-Ruiz et al. 2011; Liston

69 et al. 2014). The sequenced genome of F. vesca shares a very high degree of synteny with the

70 hybrid F. × ananassa (Tennessen et al. 2014) and has already permitted greater insight into the

71 molecular mechanisms underpinning flowering (Mouhu et al. 2013; Hollender et al 2014) and

72 fruiting (Kang et al. 2013). In addition, F. vesca itself represents a valuable niche crop with a

73 growing market due to the intense taste of its fruits (Ulrich et al. 2007; Doumett et al. 2011).

74 However, knowledge about resistance against herbivores in F. vesca is still scarce (Muola et al.

75 2017), and, thus, further exploration of resistance in wild germplasm is needed to fully utilize the

76 available gene pool.

77 We used wild Fragaria vesca originating from Sweden to investigate genetic variation in

78 resistance to a chewing herbivore, the strawberry leaf beetle Galerucella tenella (L.)

79 (Coleoptera: Chrysomelidae)(Fig. 1). Galerucella tenella is a pest insect in open strawberry

80 plantations in Russia and Northern Europe, which is commonly controlled by spraying with

81 insecticides (Parikka & Tuovinen 2014; Stenberg 2014). Larvae and adult beetles feed on

82 strawberry leaves as well as on the flowers, which reduces pollination success, and larvae can

83 furthermore damage fruits (Olofsson & Pettersson 1992; Parikka & Tuovinen 2014, Muola et al.

84 2017).

85 In order to facilitate a robust basis for sustainable pest management we here took both the

86 antibiosis and antixenosis components of plant resistance into account (Stenberg and Muola

87 2017). Antibiosis has an adverse effect on insect herbivore performance, whereas antixenosis

88 targets herbivore behaviour and renders the plant unattractive for feeding and/or oviposition

89 (Painter 1951). To cover both aspects, we monitored oviposition preference as a proxy of

90 antixenosis, as well as three proxies of antibiosis (egg hatching success, larval growth rate, and

91 larval survival) for G. tenella in wild genotypes of F. vesca. In addition, we calculated the broad

92 sense heritability of antibiosis (larval growth rate) in the screened F. vesca genotypes in order to

93 investigate the phenotypic stability of this trait, with the potential view towards ‘rewilding’

94 strawberries. We hypothesized that: 1) wild F. vesca sampled within the native range of G.

95 tenella in Sweden is likely to show high variation in the studied resistance parameters, and 2)

96 that resistance should show significant broad-sense heritability, which would indicate its stability

97 as a trait (i.e. that trait variation shows a significant genetic component) and hence breeding

98 potential. Findings from this screening are likely to be important for the utilization of available

99 genetic resources for restoring herbivore-resistance in cultivated strawberry (Andersen et al.

100 2015; Palmgren et al. 2015).

101

102 Methods

103 Study species

104 The woodland strawberry Fragaria vesca is a low-growing, herbaceous perennial plant. It occurs

105 throughout most of the Holarctic including parts of Scandinavia and grows naturally in various

106 half sunny and sunny habitats such as mountain slopes, roadsides, open forests, forest clearings

107 and edges of forests and farmland (Roiloa & Retuerto 2007; Maliníková et al.2013; Schulze et al.

108 2012). The trifoliate leaves are evergreen, and it is an ever-bearing plant producing flowers and

109 fruits throughout the entire growing season starting in May until late September (Maliníková et

110 al.2013). Flowering peaks in early June and pollination of the white hermaphrodite flowers is

111 carried out by insects, although selfing also commonly occurs alongside cross-pollination (Muola

112 et al. 2017). In addition to the production of animal dispersed fruits, F. vesca is also capable of

113 clonal reproduction and produces a high amount of runners that grow into self-sustaining plants

114 in a short time (Schulze et al. 2012).

115 The oligophagous Galerucella tenella L. (Coleoptera: Chrysomelidae; Fig. 1) feeds on

116 different species of the Rosaceae family, including Fragaria spp. (Stenberg et al. 2006;

117 Hambäck et al. 2013). Wild meadowsweet (Filipendula ulmaria L.) serves as the main host,

118 from which G. tenella commonly spills over to neighboring Rosaceae plants such as Fragaria

119 vesca. Galerucella tenella has become a noticeable pest especially in organic strawberry

120 cultivations. Outbreaks with severe damage have been reported from strawberry cultivations in

121 the Nordic countries (Stenberg and Axelsson 2008; Stenberg 2012; Stenberg 2014), the Baltic

122 States (Kaufmane and Libek 2000), and Russia (Bulukhto and Tsipirig 2004).

123 The adult G. tenella beetles hibernate in the topsoil and emerge during April to May.

124 Mating as well as oviposition takes place directly on the host plant after emerging and the larvae

125 hatch after few weeks from early June on depending on the temperatures in spring (Stenberg et

126 al. 2006). Both adults and larvae feed on the leaves and flowers (Muola et al. 2017). Larvae

127 might also tunnel into ripe berries and gnaw the fruit surface causing brown scaring [own

128 observation, Parikka and Tuovinen 2014). After 2-4 weeks of feeding the larvae leave the host

129 plant to pupate in the upper soil layer from early July on until mid-August in cold (Stenberg et al.

130 2006).

131

132 Collection and experimental organization of wild germplasm

133 We adopted a quantitative genetic framework to study the genetic variation and heritability of

134 resistance against Galerucella tenella using 86 wild Fragaria vesca genotypes. Following the

135 protocol described by Muola et al. (2017) we collected wild plant genotypes from 86 randomly

136 selected and geographically distinct locations (Fig. 2; see Online resource 1 for coordinates)

137 across Uppsala County, Sweden in spring 2012. Uppsala County encompasses 8207 km2 and the

138 distances between the sampling locations varied between 7 and 40 km. One genotype was

139 collected from each site in order to maximize the geographic resolution of the sampled area. A

140 recent microsatellite marker analysis where seven of these collected clones showed that they are

141 true (separate) genotypes (Hilmarsson et al. 2017).

142 We cloned the sampled plant genotypes for several vegetative generations at the SLU

143 Ultuna campus and planted 40 runners per plant genotype in blocks (one runner per plant

144 genotype per block) in sandy soil in an open agricultural field (N59.741°, E17.684°), 15 km

145 south of Uppsala in Autumn 2013. The distance between the plants was 50 cm and the entire

146 common garden was covered with fabric mulch (Weibulls Horto) to reduce weed densities. The

147 common garden was manually weeded when needed, but we did not apply any irrigation or

148 fertilizer. The plants were growing in the common garden for two years before we used them in

149 this study. The plant genotypes were specifically collected for the purpose of this study and

150 corresponding plant material can be obtained from Stenberg on reasonable request.

151

152 Screening of antibiosis

153 We measured antibiosis using several different proxies, namely larval survival, larval

154 development time (days from hatching to pupation), pupal weigh (mg) and larval growth rate

155 (mg/day, calculated from the two previous proxies). As growth rate takes both development time

156 and weight into account we think it provides the most complete proxy of antibiosis. Low larval

157 growth rate corresponds to high antibiosis and vice versa. As a final proxy for antibiosis we

158 recorded the egg hatching success, which was done in line with the oviposition (antixenosis)

159 screening and is therefore described under the antixenosis paragraph below.

160 For the screening of larval growth rate and survival we used detached leaves of the 86

161 plant genotypes that were collected from the common garden described above. To obtain neonate

162 larvae for the experiment, we collected adult Galerucella tenella from four distinct natural

163 meadowsweet populations in the vicinity of Uppsala (N59.809°, E17.667°; N59.788°, E17.664°;

164 N59.806°, E17.652°; N59.806°, E17.665°) during early May 2015. The adult beetles were

165 collected from locations with no co-occurring Fragaria vesca population to exclude a previous

166 feeding experience with strawberry plants. This was done to avoid mistaking putative local

167 adaptation of the herbivore with the genetic variation in plant resistance. The collected beetles

168 were randomly mixed and placed in meshed cages containing a mix of F. vesca plants chosen

169 randomly among the 86 genotypes. The cages were kept in a greenhouse (15°C, LD 16:8 h

170 photoperiod, 80% RH) and the beetles were allowed to mate and oviposit freely on the offered

171 plants. In order to rear the larvae, plants with eggs were retrieved and placed in a separate cage

172 and monitored for hatching larvae.

173 We placed the neonate larvae (< 24h) individually in a 30 ml plastic containers and

174 assigned them randomly to feed on one of the 86 plant genotypes. Ten larvae were assigned to

175 each plant genotype, resulting in 860 rearing containers. We provided the larvae with one

176 detached, undamaged, middle aged leaf from the assigned plant genotype. The leaf was replaced

177 every third day and the rearing containers were cleaned in course with the leaf exchange. The

178 rearing containers we kept in a climate chamber until the adult stage (15°C, LD 16:8 h

179 photoperiod, 80% RH). We checked the larvae daily and weighed each larva on the day it

180 reached the pupal stage. Furthermore, we recorded the survival and determined their sex after

181 they reached the adult stage.

182

183 Screening of antixenosis

184 We used oviposition preference as a proxy of antixenosis and addressed host plant suitability in

185 the context of maternal choice. The oviposition preference of Galerucella tenella females was

186 studied in a two-choice experiment. Based on the earlier larval growth rate screening (antibiosis,

187 see description above) we selected eight plant genotypes with low larval growth rate (high

188 antibiosis) and eight with high larval growth rate (low antibiosis) to test if ovipositing beetles

189 discriminate between them. Runners from each of the 16 selected plant genotypes were collected

190 from the common garden and propagated further to produce genetically identical replicates. The

191 runners were potted in 0.2 liter pots with HasselforsTM (Hasselfors, Örebro, Sweden) planting

192 soil and placed in a greenhouse (15°C, 16:8h) for five weeks during April 2016. The plants were

193 then moved outside in early May 2016 one week prior to the experiment to allow them to adjust

194 to the outside conditions. We used ten approximately equal-sized replicates from each plant

195 genotype to exclude plant size as selection criteria for the female beetle, resulting in 80 replicates

196 of both high and low antibiosis plants. The two types (high/low antibiosis) of plant genotypes

197 were randomly paired and placed in meshed cages (40×40×40 cm) located at a sunny, sheltered

198 place at the experimental garden of the SLU Alnarp campus. Within each cage, the two paired

199 plants were spaced 30 cm apart. This was done to avoid leaf contact between the plants so that

200 the female beetle had to choose one or the other. We collected the female G. tenella for the

201 experiment from the same places as the beetles used in the performance experiment with two

202 additional locations (N59.782°, E17.753°; N59.833°, E17.914°) during early May 2016 and

203 selected mating couples with a gravid female to ensure subsequent egg laying. After collection

204 the female beetles were placed centrally on the bottom of the cage and allowed to oviposit freely

205 on the offered plant pair for 48h. For each plant, the number and the position (leaf blades or leaf

206 petioles) of the laid eggs was noted. The position of the egg determines the microclimate for the

207 egg development as well as the exposure of eggs and neonate larvae to predation or conspecific

208 competition. Furthermore, the egg position facilitate or hamper the migration of the neonate

209 larvae to young, developing leaves for feeding and hiding from predators.

210 After all eggs were counted, the plants were placed back into their cages to monitor the

211 survival of the eggs as an additional aspect of antibiosis. Insect eggs can induce plant defences

212 that harm the eggs (Hilker and Fatouros 2015). We measured the egg hatching success by

213 allowing the eggs to hatch naturally and counting the first round of neonate larvae (<24 h) for

214 each plant individual. Plants and G. tenella females were used only once in each oviposition

215 choice trial.

216

217 Statistical analyses

218 All the following data analyses were conducted with R, version 3.3.1 (R Core Team 2016).

219 We used the inverse of herbivore performance measured as larval growth rate as one of three

220 proxies for antibiosis in Fragaria vesca against Galerucella tenella. We analyzed the genetic

221 variation in growth rate with a linear mixed model with larval growth rate as a response variable.

222 Larval growth rate was calculated by dividing pupal weight (mg) by larval developmental time

223 (days). The analyses of genetic variation in these two traits separately are presented in Online

224 resource 2. We included plant genotype as a random factor and the sex of the beetles as a

225 covariate to account for the potential sexual dimorphism in larval growth rate (or in pupal weight

226 and development time). As per previous quantitative genetic analyses in F. vesca (Egan et al.

227 2018), this model structure – in which genotype is fitted as a random effect – permitted

228 environmental (within-genotype) variation to be partitioned from genetic (between-genotype)

229 variation (Hill 2010) in order to predict a ‘total genetic value’ for each genotype (Piepho et al.

230 2008). These values were used to visualize genetic variation in plots, or as an input to additional

231 analyses, as detailed below. The interaction term between plant genotype and the covariate was

232 insignificant and therefore removed from the final model. We assessed normality by visual

233 examination and conducted a Levene’s test to check for equality of variances of the residuals.

234 As a second proxy of antibiosis we used one-way ANOVA to test whether larval growth

235 rate (working with genotype genetic values as predicted in the previous model) is associated with

236 larval survival rate, i.e. the percentage of larvae surviving per plant genotype. Due to the high

237 rate of larval survival, we used the larval growth rate as response variable and grouped the

238 survival rate into three categories of 100%, 90%, and less than 90% survival. This categorical

239 factor was then used as an explanatory variable to avoid zero inflation in the model. We

240 validated the model for normality and equality of variances as described above, and compared

241 the survival levels in a posthoc comparison based on a Tukey’s pairwise analysis.

242 As a third proxy for antibiosis, we examined the effect of plant genotype on the survival

243 of the eggs that were laid in the antixenosis screening (see below). As response variable we

244 calculated the number of eggs successfully hatched as a proportion of the total amount of eggs

245 per plant. We fitted the proportional response of egg hatching success in a generalized linear

246 mixed model (logit link function) with a binomial error structure, and used antibiosis (i.e. larval

247 growth rate) level (susceptible vs. resistant) as a fixed factor and individual plant genotype

248 nested within the antibiosis level as a random factor. We performed a likelihood ratio test to

249 assess the significance of the random effect of genotype. We validated the model with the

250 Anderson Darling test and visual examination of the residuals.

251 To examine the antixenosis of Fragaria vesca genotypes we tested whether antibiosis

252 affected the oviposition choice of Galerucella tenella females. Since the female beetles laid

253 different absolute numbers of eggs on different plants, as well as on different tissues (i.e. the leaf

254 blade and leaf petiole), we calculated the number of eggs laid on each tissue of a plant as a

255 proportion of the total amount of eggs laid in the cage (following the set up in cages containing

256 one resistant and one susceptible plant genotype). We used egg proportion per plant tissue (i.e.

257 eggs laid on the leaf blade / all eggs laid in the cage, and, eggs laid on the leaf petiole / all eggs

258 laid in the cage) as a proportional response variable in a binomial generalized mixed model (R-

259 package “lme4”), in which plant antibiosis level (susceptible vs. resistant), plant tissue type, and

260 their interaction were fitted as a fixed factor. Both plant individual nested within genotype, and

261 genotype nested within the plant antibiosis level (susceptible vs. resistant) were included as

262 random effects; in order to account for the non-independence of egg laying locations within the

263 same plant individual, and the genotypic effect in the resistance level, respectively. Due to the

264 significant interaction term between plant antibiosis level and plant tissue type, we obtained

265 estimated marginal means and computed contrasts with the ‘emmeans’ package for R. As only

266 four of the six possible combinations were of interest (e.g. resistant leaf blade vs. resistant leaf

267 petiole; resistant leaf blade vs. susceptible leaf blade), we used the ‘holm’ method to adjust for

268 four pairwise comparisons. To test whether the random effect of genotype had a significant

269 effect, we ran a likelihood ratio test to compare the full model to a null model in which this

270 random effect was removed. We validated the model through the Anderson Darling test for

271 normality and visual examination of the residuals.

272 To examine broad-sense heritability (or clonal repeatability) in antibiosis (larval growth

273 rate) among the tested plant genotypes, broad-sense heritability (H2) estimates were calculated

274 from a linear mixed effects model. The tests for broad-sense heritability in larval development

275 time and pupal weight separately are presented in Online resource 2. The model was fitted using

276 the rptR package (Holger et al. 2016) in R (function ‘rpt’), and used the same model structure as

277 specified for the resistance model above (i.e. ‘growth rate’ predicted by the random effect of

278 ‘genotype’, controlling for the fixed effect of ‘sex’). The standard error of H2 was estimated

279 based on 1000 parametric bootstraps, and the significance of H2 was tested via a likelihood ratio

280 test.

281

282 Results

283 Antibiosis

284 We found genetic variation in antibiosis levels against Galerucella tenella in Fragaria vesca as

285 indicated by statistically significant variation in the growth rate of G. tenella larvae reared on 86

286 different plant genotypes (χ2 =78.6, d.f.=1, P <0.001, Fig. 3). Furthermore, there was a heritable

287 component to this variation, as broad-sense heritability in antibiosis (measured as larval growth

288 rate) differed significantly from zero (H2 = 0.22 ± 0.04 SE). Similarly, both larval development

289 time and pupal weight showed significant genetic variation between the tested plant genotypes

290 and there was a heritable component in the observed variation (Online resource 2). The average

291 larval growth rate varied from 0.204 ± 0.03 mg/day on the most susceptible plant genotype, to

292 0.129 ± 0.016 mg/day on the most resistant plant genotype. Female pupae were significantly

293 heavier than male pupae (LMM: t-value =-13. 5, d.f.=733, P<0.001, Online resource 2) and,

294 accordingly, females also had a significantly higher larval growth rate than males (F=139.52,

295 d.f.=1, 717, P <0.001).

296 The overall survival rate of Galerucella tenella from egg hatching until pupation was

297 high (93.2 %). However, larval growth rate and survival were associated: a higher than average

298 larval growth rate was observed for those plant genotypes where all beetles survived, in contrast

299 to below average growth rates observed in cases where one or more larvae died (F=9.63, d.f.=2,

300 P<0.001; Fig. 4), indicating that antibiosis also affects survival through its effects on growth rate.

301 Interestingly, egg hatching success was not affected by plant antibiosis level (Z=1.38; df= 160,

302 P= 0.169; Fig. 5a), in which a predicted average of 47.4 % of eggs successfully hatched on a

303 resistant genotype, and 56.5% on a susceptible genotype. However, hatching success was

304 affected by plant genotype (χ2 =6.31, d.f.=1, P <0.012).

305

306 Antixenosis

307 We used oviposition preference as a measure of antixenosis. We found that the location of eggs –

308 whether located on the leaf blade or leaf petiole – was affected by antibiosis, as shown by a

309 significant interaction between plant antibiosis level (resistant or susceptible) and leaf tissue type

310 (leaf blade or leaf petiole; Z= 2.15, df=160, P=0.032, Fig. 5b). Female beetles oviposit on average

311 1.9 times more eggs on leaf blades and 5.3 times more on leaf petioles of susceptible plants

312 compared to resistant plants. Overall, female beetles were 3.2 times more likely to lay eggs on

313 plant genotypes classified as susceptible (averaged across different tissue types), compared

314 to resistant plants (Z= 2.774, df=320, P=0.006; Fig. 5b). In addition to plant antibiosis level,

315 oviposition preference was affected by plant genotype (χ2 =1.77, d.f.=1, P=0.036).

316

317

318

319

320 Discussion

321 Theory suggests that relatively high genetic variation can be found even in small geographic

322 areas (Thompson 2013; Egan et al. 2018). Concordantly, we found significant genetic variation

323 in the measured resistance traits against the Northern European pest insect Galerucella tenella in

324 our screening of wild woodland strawberry, Fragaria vesca. In our study, we observed a large

325 gradient in resistance, ranging from susceptible to highly resistant, suggesting that an untapped

326 genetic resource is available in wild germplasm from Nordic populations of this plant species.

327 However, the sampled area represents only a small fraction of this species’ Eurasian and North

328 American distribution, suggesting that even higher variation could be harnessed if germplasm

329 from the whole distribution was screened.

330 In order to obtain robust resistance in plants against pest insects, plant traits underlying

331 strong antibiosis as well as antixenosis should be identified and optimized simultaneously

332 (Stenberg and Muola 2017). We scored antibiosis using three different proxies: egg hatching

333 success, larval growth rate and larval survival. The herbivore growth rate varied markedly and

334 significantly between the plant genotypes. Furthermore, it correlated positively with the larval

335 survival; i.e. plant genotypes that supported higher herbivore growth rate also contributed to

336 higher herbivore survival. Although the mortality was generally low, on certain genotypes the

337 herbivore growth rate was significantly reduced which can further act as a sub‐lethal plant

338 defence by prolonging the window of vulnerability of G. tenella to natural enemy attack (Clancy

339 and Price 1987; Williams 1991). In addition, both larval development time and pupal weight, i.e.

340 the intrinsic components of larval growth rate, also show individually significant genetic

341 variation (Online resource

342 2). The plant traits and mechanisms underlying these antibiosis processes are yet to be

343 identified, but our findings suggest that true resistance traits (e.g. chemical or physical defences),

344 rather than low nutritional content, underlie antibiosis in woodland strawberry (Haukioja et al.

345 1991). Egg hatching success was unaffected by plant antibiosis level.

346 Our study of antixenosis – measured as oviposition preference – showed that this aspect

347 of plant resistance also varied between plant genotypes. The female beetles showed a clear

348 preference for genotypes with high host plant quality (low antibiosis), and thus by extension, an

349 ability to make optimal choices for their offspring (cf. the mother knows best hypothesis)

350 (Jaenike 1978; Valladares and Lawton 1991). Furthermore, this result connects the antixenosis

351 component of G. tenella resistance in F. vesca positively with the antibiosis component. The fact

352 that antibiosis and antixenosis correlate with each other is also beneficial from a future breeding

353 perspective, as this opens up the possibility to simultaneously optimize both aspects of resistance

354 in the same plant, providing robust protection against the target herbivore (Stenberg & Muola

355 2017).

356 Reverse breeding or rewilding is one promising way to provide modern crops with new

357 or improved traits – such as resistance – which may have been lost or degraded during

358 domestication. Given the continuous need to develop resistant strawberry cultivars, rewilding

359 would allow cultivation of strawberry with reduced input of chemical pesticides. In the case of

360 antibiosis against G. tenella we found different levels of broad-sense heritability (or clonal

361 repeatability; the stability or ‘repeatability’ of this trait amongst clonal replicates of the same

362 genotype) depending on which proxy that was used. Larval development time showed the

363 highest level of heritability, while larval growth rate showed moderate levels, and pupal weight

364 low heritability (Online resource 2). Which of the three proxies that is biologically most relevant

365 is up to debate, and all of them have previously been used and recommended for other plant

366 species (Stenberg and Muola 2017).

367 The genetic diversity of wild Fragaria species has previously provided novel genetic

368 resources that were integrated into new cultivars (Liston et al. 2014). Among the wild Fragaria

369 species, the woodland strawberry F. vesca is of special interest since its genome is sequenced

370 (Shulaev et al. 2011) and displays a high level of collinearity with the cultivated garden

371 strawberry in comparative mapping studies (Rousseau-Gueutin et al. 2008). New molecular

372 methods such as CRISPR/Cas (Fonfara et al. 2016) and advances in plant breeding may soon

373 offer solutions to overcome the multi gene nature of many resistance mechanisms and the

374 differences in ploidy between wild and cultivated strawberry, and thus smooth the way for

375 introgression of desired traits from wild relatives into modern crop varieties (Borges et al. 2018;

376 Martinez et al. 2018).

377

378 Conclusion

379 In conclusion, this study contributes to a growing body of literature showing that crop wild

380 relatives in general, and woodland strawberry in particular, contain genetic resources that could

381 contribute to resistance breeding in modern crops. In particular, we demonstrated considerable

382 genotypic variation in each measured proxy of antibiosis (egg hatching success, larval growth

383 rate and larval survival) and antixenosis (oviposition preference) as components of plant

384 resistance against the pest insect Galerucella tenella. In terms of their combined potential for

385 pest suppression, antibiosis and antixenosis either held no specific relation to each other, or else

386 showed good compatibility – e.g. where genotypes which possessed high antixenosis levels

387 against G. tenella larvae furthermore tended to be avoided by female adults as oviposition host

388 plants. These results together highlight the large potential value of wild germplasm for resistance

389 breeding. If breeding challenges can be overcome, we expect that “wild” resistance traits will

390 play important role in future re-wilded strawberry cultivars, and contribute to a more sustainable

391 crop production with reduced dependency on pesticides.

392

393 Acknowledgements

394 We thank Joubin Haji Mirza Ali, Linn Holmstedt, Sara Janbrink, Wera Kleve, Joel Lönnqvist,

395 Milda Norkute, Erika Qvarfordt, and Brenda Vidal Estévezfor technical assistance and Jan-Erik

396 Englund and Adam Flöhr for valuable statistical discussions. The authors are funded by The

397 Swedish Research Council Formas (grant nos. 217 - 2014-541 and 216 - 00223) and Maj and Tor

398 Nessling Foundation (grant no. 201800048).

399

400 Availability of data and material

401 Until publication, the data supporting our conclusions are available from the corresponding

402 author on request. After publication data supporting our conclusions will be deposited to Dryad.

403

404 Authors’ contributions

405 All authors conceived, organized and planned the research; JAS collected the wild accessions

406 and established the common garden; DW undertook the experimental work and collected the

407 data; DW, PAE, and AM analysed the data; DW led the writing of the manuscript. All authors

408 contributed critically to the drafts and gave final approval for publication.

409

410

411 Conflict of Interest

412 The authors declare that they have no conflict of interest.

413

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540 24:490–495.

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560 Figure Captions

561 Fig. 1 On the left, copulating strawberry leaf beetles, Galerucella tenella. On the right, eggs of G.

562 tenella on strawberry leaf. Photo credits: Alejandro Ruete.

563

564 Fig. 2 Map of the collection sites of 86 Fragaria vesca genotypes used in this study.

565

566 Fig. 3 Genetic variation in plant resistance (antibiosis) against Galerucella tenella in 86 wild

567 collected Fragaria vesca genotypes. Antibiosis was measured as inverse of herbivore

568 performance. Larval growth rate (calculated as pupal weight divided by the larval development

569 time from hatching until pupation) was used as a measure of herbivore performance. In the figure,

570 the mean larval growth rate on each plant genotype is compared to the overall mean (standardized

571 on zero) of all 86 plant genotypes used. Negative values i.e. larval growth rate lower than the

572 overall mean indicate more resistant plant genotypes, while positive values indicate more

573 susceptible plant genotypes. The red and blue bars denote the plant genotypes selected for

574 experiments testing oviposition preference and egg survival. Predicted mean (total genetic value)

575 + SE.

576

577 Fig. 4 Association between larval growth rate and survival of Galerucella tenella larvae. The

578 inverse of larval growth rate (calculated as pupal weight divided by the larval development time

579 from hatching until pupation) was used as a proxy of plant resistance (antibiosis). Larval survival

580 was divided into three classes according the number of larvae that survived when assigned to feed

581 on the certain plant genotype. Statistical significance levels have been obtained by pairwise

582 Tukey’s posthoc tests. Estimated marginal means + SE. ** 0.05 < P < 0.001; *** P <0.001.

583

584 Fig. 5 Effect of plant resistance (antibiosis) on a) egg hatching success (antibiosis), and b) the

585 interactive effects of leaf tissue (leaf blade vs. leaf petiole) and plant antibiosis level on oviposition

586 preference (antixenosis) of Galerucella tenella. Plant resistance (antibiosis) was measured as

587 inverse of herbivore performance i.e. larval growth rate calculated as pupal weight divided by the

588 larval development time from hatching until pupation. Pairwise a priori contrast comparisons were

589 conducted to compare differences between resistant leaf blade vs. resistant leaf petiole, susceptible

590 leaf blade vs. susceptible leaf petiole, resistant leaf blade vs. susceptible leaf blade and resistant

591 leaf petiole vs. susceptible leaf petiole. Estimated marginal means + SE. ** 0.05 < P < 0.001; ***

592 P <0.001.

593

26 bioRxiv preprint doi: https://doi.org/10.1101/728360; this version posted August 7, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. bioRxiv preprint doi: https://doi.org/10.1101/728360; this version posted August 7, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. bioRxiv preprint doi: https://doi.org/10.1101/728360; this version posted August 7, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. bioRxiv preprint doi: https://doi.org/10.1101/728360; this version posted August 7, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. bioRxiv preprint doi: https://doi.org/10.1101/728360; this version posted August 7, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. bioRxiv preprint doi: https://doi.org/10.1101/728360; this version posted August 7, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. bioRxiv preprint doi: https://doi.org/10.1101/728360; this version posted August 7, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Online resource 1.

Genetic variation in herbivore resistance within a strawberry crop wild relative (Fragaria

vesca)

Daniela Weber1,*, Paul A. Egan1, Anne Muola1,2 & Johan A. Stenberg1

1Department of Plant Protection Biology, Swedish University of Agricultural Sciences, Box

102, 23053 Alnarp, SWEDEN

2 Biodiversity Unit, University of Turku, 20014 Turku, FINLAND

Corresponding author: Daniela Weber

Online resource 1 table 1. Collection site coordinates of woodland strawberry genotypes.

Wild woodland strawberry genotypes used in the study were collected from 86 randomly

selected and geographically distinct locations across Uppsala County, Sweden in spring 2012.

Uppsala County encompasses 8207 km2 and the distances between the sampling locations

varied between 7 and 40 km. Coordinates are given in decimal degree format and based on

the international WGS 85 (World Geodetic System).

Plant genotype Longitude, latitude 01A 17.4607833, 60.58625 01F 17.4834, 60.60395 02A 17.8758334, 60.54 02F 17.8725, 60.56275 03A 18.42595, 60.4368667 03F 18.4074, 60.4506833 04A 17.4682, 60.4927166 05A 17.6124333, 60.4346833 05F 17.6016167, 60.4420666 06A 17.7712667, 60.4465167 06F 17.7980833, 60.4621667 07F 18.0191667, 60.4395833 08A 18.47625, 60.3466 08F 18.5696667, 60.3076166 09A 17.389, 60.3295167 10A 17.5596667, 60.3738 10F 17.5703334, 60.3893333 11A 17.73245, 60.3002167 11F 17.8081166, 60.35955 bioRxiv preprint doi: https://doi.org/10.1101/728360; this version posted August 7, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

12F 18.0151833, 60.304 13F 18.2925833, 60.2146834 14A 18.52, 60.2357333 14F 18.5572, 60.2360167 15A 17.2910167, 60.30075 15F 17.2302, 60.3340167 16A 17.47525, 60.27365 16F 17.4905167, 60.2555834 17A 17.6836667, 60.2357667 18A 18.0061166, 60.1617667 19A 18.2141, 60.1582667 19F 18.1865667, 60.1976166 20A 18.46335, 60.0990167 20F 18.3433333, 60.1018 21A 16.9916833, 60.1747 22A 17.28555, 60.1787833 23A 17.4720834, 60.1559666 23F 17.4416, 60.1385666 24A 17.65225, 60.0747 26A 18.0904833, 60.0699 26F 18.1888667, 60.0159833 27A 18.2636, 59.9927333 27F 18.38825, 60.0328334 28A 17.00835, 60.0569 28F 16.96025, 60.0477833 29A 17.2344167, 59.9971667 29F 17.1817166, 60.0146333 30A 17.4146333, 60.0020833 30F 17.4263, 59.9881167 31A 17.5480167, 60.00615 31F 17.6086, 60.0101333 32A 17.8997167, 59.95075 32F 17.78635, 59.97075 33A 18.1150333, 59.9483833 34A 18.2148833, 59.9123334 34F 18.2901, 59.88625 35A 16.8895833, 59.9361666 35F 16.8041, 59.9318167 36A 17.0124, 59.8995 36F 17.1441, 59.9155333 37A 17.4046334, 59.9254666 37F 17.3795834, 59.9105334 38A 17.4986, 59.8919333 38F 17.4916667, 59.8976334 39A 17.75185, 59.8263334 39F 17.7754, 59.86815 40A 18.0445, 59.7585834 40F 18.0685166, 59.80065 41A 16.9236834, 59.8421 41F 16.8869667, 59.8261 bioRxiv preprint doi: https://doi.org/10.1101/728360; this version posted August 7, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

42A 17.1145667, 59.8065667 42F 17.0513, 59.8384 43A 17.34075, 59.8165667 43F 17.3420666, 59.76215 44F 17.5492167, 59.78835 45A 17.7025, 59.73325 45F 17.7243833, 59.7614 46A 17.0682667, 59.6654167 46F 16.93955, 59.7118833 47A 17.2483166, 59.6669167 47F 17.2943334, 59.6778666 48A 17.36685, 59.6789333 48F 17.3085166, 59.6393334 49A 17.1435833, 59.5527667 49F 17.166, 59.5894833 50A 17.3894666, 59.4987667 50F 17.3277, 59.5279 bioRxiv preprint doi: https://doi.org/10.1101/728360; this version posted August 7, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Online Resource 2

Genetic variation in herbivore resistance within a strawberry crop wild relative (Fragaria

vesca)

Daniela Weber1,*, Paul A. Egan1, Anne Muola1,2 & Johan A. Stenberg1

1Department of Plant Protection Biology, Swedish University of Agricultural Sciences, Box

102, 23053 Alnarp, SWEDEN

2 Biodiversity Unit, University of Turku, 20014 Turku, FINLAND

Corresponding author: Daniela Weber

Larval development time and pupal weight as proxies for antibiosis

We used 86 wild Fragaria vesca genotypes to study the genetic variation and heritability of

plant resistance against Galerucella tenella. As one proxy for resistance, we used the inverse

of herbivore performance measured as larval growth rate (i.e. antibiosis; reported in more

details in the main text). Larval growth rate is composed of two measurements namely larval

development time (days from hatching to pupation) and pupal weight (mg). Here, we report

the results separately for these two traits.

We used the inverse of herbivore performance measured as larval development time

and pupal weight as proxies for antibiosis in F. vesca against G. tenella. We analysed the

genetic variation in larval development time and pupal weight with linear mixed model

separately for each proxy. We included plant genotype as a random factor and the sex of the

beetles as a covariate to account for the potential sexual dimorphism in larval development

time or pupal weight. In addition, given the common inter-dependency between larval

development time and pupal weight (i.e. that a larva needs to obtain a certain threshold

weight before it can pupate), we included pupal weight as an additional covariate to the

model analysing genetic variation in larval development time – and vice versa for the model bioRxiv preprint doi: https://doi.org/10.1101/728360; this version posted August 7, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

analysing genetic variation in pupal weight. As such, these models hence quantified the

extent to which unique (as opposed to overlapping) genetic variation existed for these traits.

As per previous quantitative genetic analyses in F. vesca (Egan et al. 2018), this model

structure – in which genotype is fitted as a random effect – permitted environmental (within-

genotype) variation to be partitioned from genetic (between-genotype) variation (Hill 2010)

in order to predict a ‘total genetic value’ for each genotype (Piepho et al. 2008). These values

were used to visualize genetic variation in plots, or as an input to additional analyses, as

detailed below. The interaction term between plant genotype and the covariate was

insignificant and therefore removed from the final model. Larval development time did not

fulfil the assumptions of normally distributed data, and, thus, we used the log-transformation.

We assessed normality by visual examination and conducted a Levene’s test to check for

equality of variances of the residuals.

To examine broad-sense heritability (or clonal repeatability) in larval development

time and pupal weight among the tested plant genotypes, broad-sense heritability (H2)

estimates werecalculated from a linear mixed effects model. The models were fitted using the

rptR package (Holger et al. 2016) in R (function ‘rpt’), and used the same model structure as

specified for the ‘larval development time’ and ‘pupal weight’ models above. The standard

error of H2 was estimated based on 1000 parametric bootstraps, and the significance of H2

was tested via a likelihood ratio test.

We found genetic variation in antibiosis levels against G. tenella in F. vesca indicated

by the statistically significant variation in both the development time (χ2 = 161.66, d.f.= 1,

P<0.001) and pupal weight (χ2 = 25.01, d.f.= 1, P<0.001) of G. tenella larvae reared on 86

different plant genotypes (Supplementary Fig. 1a & b). The average larval development time

varied from 16.8 days ± 1.0 SE on the most susceptible plant genotype, to 22.0 days ± 1.0 SE

on the most resistant plant genotype. Pupal weight and larval development time were bioRxiv preprint doi: https://doi.org/10.1101/728360; this version posted August 7, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

significantly related (LMM: t-value= -3.56, d.f.=741, P<0.001), in which larval development

shortened by a predicted 0.02 days for every mg increase in pupal weight. Avarage pupal

weight varied from 4.84 mg ± 0.11 SE when feeding on the leaves of the most susceptible

plant genotypes to 3.82 mg ± 0.16 SE when feeding on the most resistant plant genotypes.

Larval development time and pupal weight were significantly related (LMM: t-value= -4.31,

d.f.=654, P<0.001), in which pupal weight was a predicted 0.04 mg lighter for every day’s

increase in development time. Female pupae (4.46 mg ± 0.03 SE) were significantly heavier

than male pupae (3.99 mg ± 0.03 SE) (LMM: t-value =-13. 5, d.f.=733, P<0.001).

Furthermore, there was a heritable component in this variation, as broad-sense heritability

both in larval development time and pupal weight differed significantly from zero (H2 = 0.36

± 0.046 SE for larval development time, and H2 =0.112 ± 0.031 SE for pupal weight).

Online Resource 2 Figure 1.

Genetic variation in plant resistance (antibiosis) against Galerucella tenella in 86 wild

collected Fragaria vesca genotypes. Antibiosis was measured as inverse of herbivore

performance. Larval development time from hatching until pupation (1a) and pupal weight

(mg; 1b) were used as measures of herbivore performance. In the figure, the mean development

time (1a) and the mean pupal weight (1b) on each plant genotype is compared to the overall

mean (standardized on zero). Negative values indicate more resistant plant genotypes, while

positive values indicate more susceptible plant genotypes. Predicted mean (total genetic value)

+ SE.

References

Egan PA, Muola A, Stenberg JA (2018) Capturing genetic variation in crop wild relatives:

An evolutionary approach. Evol Appl 11:1293–1304. bioRxiv preprint doi: https://doi.org/10.1101/728360; this version posted August 7, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Hill WG (2010) Understanding and using quantitative genetic variation. Philos Trans R Soc

Lond B Biol Sci 365:73–85.

Holger S, Stoffel M, Nakagawa S (2016) rptR: Repeatability Estimation for Gaussian and

Non-Gaussian Data. R package version 0.9.0. 2016. https://CRAN.R

project.org/package=rptR Accessed 14 Aug 2018.

Piepho HP, Moehring J, Melchinger AE, Buechse A (2008) BLUP for phenotypic selection in

plant breeding and variety testing. Euphytica 161:209–228.

bioRxiv preprint doi: https://doi.org/10.1101/728360; this version posted August 7, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. bioRxiv preprint doi: https://doi.org/10.1101/728360; this version posted August 7, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.