Rapid phenotypic differentiation and local adaptation in Japanese knotweed s.l. (Reynoutria japonica and R. × bohemica, Polygonaceae) invading novel habitats

PREMISE Many plant invaders like the Japanese knotweeds are thought to colonize new habitats with low genetic diversity. Such species provide an opportunity to study rapid adaptation to complex environmental conditions. METHODS Using replicate reciprocal transplants of clones across three habitats, we described patterns of phenotypic response and assessed degree of local adaptation. KEY RESULTS We found plants from beach habitats had decreased height, number of leaves, leaf area, and biomass allocation to roots and shoots compared to plants from marsh and roadside habitats when grown in their home habitat. In the marsh habitat, marsh plants were generally larger than beach plants, but not different from roadside plants. There were no differences among plants from different habitats grown in the roadside habitat. Despite this evidence of differentiation in beach and marsh habitats, we found mixed evidence for local adaptation. In their “home site” plants from the marsh habitat had greater biomass than plants from the beaches but not compared to plants from roadsides. Biomass comparisons in other habitats were either maladaptive or not significant. However, plants from the roadside had greater survival in their “home site” compared to foreign plants. There were no differences in survival in the other habitats. CONCLUSIONS We found phenotypic differentiation associated with habitats despite the low reported genetic diversity for these populations. Our results partially support the hypothesis of local adaptation in marsh and roadside habitats. Identifying whether these patterns of differentiation result from genetic or heritable non-genetic mechanisms will require further work.

leaf area, and biomass allocation to roots and shoots compared to plants from marsh and roadside 23 habitats when grown in their home habitat. In the marsh habitat, marsh plants were generally 24 larger than beach plants, but not different from roadside plants. There were no differences among 25 plants from different habitats grown in the roadside habitat. Despite this evidence of 26 differentiation in beach and marsh habitats, we found mixed evidence for local adaptation. In 27 their "home site" plants from the marsh habitat had greater biomass than plants from the beaches 28 but not compared to plants from roadsides. Biomass comparisons in other habitats were either 29 maladaptive or not significant. However, plants from the roadside had greater survival in their 30 "home site" compared to foreign plants. There were no differences in survival in the other 31 habitats. 32 CONCLUSIONS: We found phenotypic differentiation associated with habitats despite the low 33 reported genetic diversity for these populations. Our results partially support the hypothesis of 34 local adaptation in marsh and roadside habitats. Identifying whether these patterns of 35 differentiation result from genetic or heritable non-genetic mechanisms will require further work. 36 The expansion of invasive species challenges our understanding of the process of 42 adaptation given the likelihood of reduced genetic variation following a population bottleneck 43 (Bock et al., 2015;Colautti and Lau, 2015). The classic population genetic assumption is that 44 dramatically reduced genetic variation will severely constrain the evolutionary potential of a 45 given population or species (Sakai et al., 2001;Allendorf and Lundquist, 2003). same genotype. However, our previous studies show that most "genets" within a site also have 156 the same AFLP haplotype and most likely belong to the same individual. Rhizomes were brought 157 to the Stony Brook University greenhouse and cut into pieces of 4-8 grams fresh-weight (12 sites 158 x 7 or 8 rhizomes per site for a total of 95 genets x 18-25 replicates = 2225 rhizome pieces). We 159 planted the rhizome pieces in individual wells in 24-well flats with Pro-mix potting medium 160 (Pro-mix Bx, Quakertown, Pennsylvania, USA), and approximately one teaspoon of slow release 161 fertilizer (15-9-12 Osmocote Plus 8-9 month, Marysville, Ohio). The flats were placed in a 162 temperature-controlled greenhouse under conditions approximating mid-summer in Suffolk 163 County, Long Island and watered as needed to keep the soil moist. Day temperature was 164 maintained at 30˚C and night temperature at 25˚C. We grew the plants in the greenhouse for 165 approximately six weeks to allow for shoots to emerge from the rhizomes and grow to a height 166 of approximately 10-15 cm. 167 The 12 sites were organized into four reciprocal transplant groups, each with one beach, 168 one marsh and one roadside location. Based on the number of plants that emerged, we assigned 169 an equal number of replicate pieces of each rhizome collected at each source site to be 170 transplanted into its home site and into one site of each of the other two habitat types. Therefore, 171 three to eight replicates of each rhizome were assigned randomly across five blocks for each of 172 the three transplant habitats (4 replicate studies x 3 transplant habitats x 3 source habitats x 5-8 173 genets x 2-8 replicates = 1287 plants; Roots were shaken to remove loose dirt in the field and thoroughly washed. 182

Traits measured -183
We measured traits related to salt tolerance and overall performance for each plant: 184 height, total number of leaves, total leaf area (Li-Cor Model LI-3100 Leaf area meter: Li-Cor, 185 Inc., Lincoln, Nebraska, USA), succulence (g water in all leaves/ cm 2 total leaf area), shoot dry 186 biomass, root dry biomass, root:shoot ratio based on dry biomass, and total biomass at final 187 harvest. For each plant, all live leaf tissue at final harvest was used for calculating total leaf area 188 and succulence. Plants were dried in a forced air oven at 60° C for at least 72 h to determine 189 shoot, root and total dry biomass. We evaluated survival and biomass (as proxies for fitness) to 190 assess the degree of adaptation. These taxa have extensive clonal growth and many individuals 191 may not flower at all in the field, but persist and spread from year to year so biomass is an 192 important indicator of fitness (de Kroon and Groenendael, 1997 We did not model root to shoot ratio directly, but instead we used the ratio of the estimates of 209 mean and variance for root and shoot to assess significance within the Bayesian framework 210 (Korner-Nievergelt et al., 2015). 211 In the LMM and GLMM models, "SOURCE.type" is the origin habitat type (beach, 212 marsh , road), "GARDEN.type" is the transplant habitat type (beach, marsh, road). These effects 213 as well as their interaction terms were modeled as fixed effects. The origin site, the transplant 214 site, and the individual genets ("genetfactor") were initially included as random terms. To avoid 215 overfitting, we removed random effect terms that effectively explained no variance. This was 216 true for the genet term for all traits and for the "Origin.site" term, which was removed for 217 number of leaves, succulence and shoot biomass (see Table S1 for final models). 218 We examined the correlation matrix for each model to evaluate auto correlation between 219 terms. To test the significance of the fixed effects of "SOURCE.type" and "GARDEN.type", we 220 Our design is constrained by the fact that origin site and transplant sites are nested within 246 levels of "Transplant group" (see discussion here Long, 2021). To examine the importance of 247 this design constraint, we also reran the LMER and GLMER models for each trait with the fixed 248 term "Transplant group". To properly test for the effects in this nesting design, we should ideally 249 fit random intercepts for the sites nested within groups, but we did not have enough replication 250 within groups to do so. We assume that fitting the fixed effect of the "Transplant group" also 251 controls for the non-independence of the origin site and transplant site within groups (Long, 252 2021). By comparing the modeling with and without the fixed term of Transplant group, we 253 evaluated how these random terms impact the main effects of interest which are the fixed effects 254 of the habitats of the source and transplant gardens (i.e., "SOURCE.type" and "GARDEN.type"). 255 We tested for local adaptation with two fitness proxies: total biomass and survival. We 256 ran a "local vs. foreign" test using the Bayesian fitted values for biomass obtained from the same 257

Phenotypic response to reciprocal transplants -268
We found that analyses of all traits resulted in large credible intervals (CrI) around the 269 estimates of the means within source-by-garden combinations (Figure 1). This could be due to 270 the large variance among transplant sites ( Table 2, Table S2). Despite this large variance, we 271 found differences in responses depended on the source habitat and the garden habitat. For every 272 trait, at least one comparison met our significance criteria based on nonoverlapping of CrI of a 273 group with the mean of another group. 274 In the beach gardens, plants originally from this habitat had only one-third the height, less 275 than half the number of leaves, 30% less leaf area, one-fourth the shoot biomass and half as 276 much root biomass as plants from the roadside habitats ( Figure 1; Table S3). Plants from the 277 beach habitat also had half as many leaves and nearly half the root biomass of plants from the 278 marsh habitat when grown in the beach gardens. In the marsh gardens, plants from marsh 279 habitats had two and half times the height and twice the number of leaves as plants from beach 280 habitats. In addition, plants from beach habitats had greater succulence than plants from the 281 roadside habitats (but not greater than plants from marsh habitats) when grown in the marsh 282 gardens. In roadside garden, we found no differences among the groups for any of these traits. 283 We also discovered that the responses of plants from marsh and roadside habitats were largely 284 indistinguishable in any garden (Figures 1 and 2). 285 These findings were supported by examining simulated values of the differences between 286 the groups of plants for each trait (Figure 2). In the beach habitat, beach plants were almost 287 always shorter, had fewer leaves, had less leaf area, less root and shoot biomass than plants from 288 marsh or roadside habitats (beach < marsh plants in 99 or 100% of the simulations). In the marsh garden, marsh plants were taller, had more leaves, greater leaf area, shoot and root biomass than 290 beach plants in 100% of the simulations. Compared to roadside plants transplanted in the marsh 291 garden, marsh plants were also usually taller (97% of the simulations), had more leaves (90%), 292 greater leaf area (79%), shoot (91%) and root (87%) biomass but the differences in response 293 were not as strong as comparisons to beach plants and did not meet our threshold for significance 294 ( Figure 1). In the roadside garden, again simulations did not support significant differences in 295 pairwise comparisons of plants from different habitats. 296

Explanatory power of the models of phenotypic variance -297
Our linear mixed models explained 18 to 45% of the variation in the traits we measured 298 (Table 2). However, we found that the majority of the variance was explained by the random 299 effects (r2_nakagawa in Table 2) and in particular the "Transplant.site" which alone explained 300 14-43% of the variance in these traits. Only 2 to 8% of the variance was explained by the fixed 301 effects of "Source type" or "Garden type" (rptR in Table 2) which were our main interest to test 302 the general effects of habitats. Together the fixed effects were best able to explain variance in 303 height and number of leaves (8%) and least predictive of succulence (2%). We used the 304 commonalityCoefficients program to further examine the amount explained by each of the fixed 305 effects. The source type explained twice as much of the variance as transplant garden type for 306 height, succulence and total biomass, and three times the variance in shoot biomass, but less of 307 the variance than transplant garden for the number of leaves. Source type explained a similar 308 amount of the variance as transplant garden for leaf area. 309 When we evaluated the "Transplant group" as a fixed effect, the overall R 2 changed very 310 little (Table S2). On average the models changed by only 0.2%. The largest change in R 2 was in 311 the model for succulence which decreased from 29% in the original model without the effect of 312 transplant group (Table 2) to 26% with the effect (Table S2). On average the Transplant group 313 effect increased the amount of variance explained by fixed effects by 17%. Using 314 commonalityCoefficients, we found that the unique contribution of transplant group was 29-65% 315 of the variance explained by the combined fixed effects. For several traits (e.g., height, 316 succulence, shoot root and total biomass) most of the variance explained by fixed effects was due 317 uniquely to the transplant group effect (Table S2). Adding this effect also changed the amount of 318 variance explained by "Source type" or "Garden type". When the transplant group was included, 319 the unique contribution of source type still explained twice as much of the variance as that of 320 transplant garden type for shoot biomass, but a similar amount of variance as garden type for 321 height, leaf area, succulence and total biomass. Using this model, source type explained half as 322 much of the variance as transplant garden for the number of leaves. 323

The effect of transplant habitats on fitness proxies -324
We investigated the fitness proxies of total biomass (g) and survival. In the beach 325 gardens, plants from beach habitats accumulated less biomass than plants from either marsh or 326 roadside habitats in 100% of the simulations, contrary to predictions of local adaptation. On the 327 other hand, in the marsh gardens, plants from the marsh habitat accumulated more biomass than 328 plants from beaches (100% of the simulations) and tended to grow bigger than plants from 329 roadsides (in 90% of the simulations but the effect size was smaller; Figure 3a). We found little 330 support for differences in biomass among groups when grown in the roadside gardens. 331 Our model explained approximately 38% of the variance in total biomass. This variance 332 was largely determined by the random term "transplant garden site": 43% of the variance was 333 attributed to "transplant site" when "transplant group" was not included (Table 2), 36% when type explained almost twice as much as that of transplant garden habitat type, but combined they 336 explained only 6% of the variance in biomass (Table 2). When transplant group is included as a 337 fixed effect, the R 2 jumps to 24% explained by combined fixed effects (according to results of 338 r2_nakagawa, Table S2) and the effect of origin habitat type still explains more than that of 339 transplant garden habitat type (19% compared to 13% of the variance due to fixed effects which 340 translates to approximately 4% and 3% of the overall variance in this model). 341 Mortality was high across the experiment, but particularly in the beach habitat garden 342 sites (average 89% mortality; Table 3 We did find a few differences elicited in the marsh transplant gardens where marsh plants 396 tended to be larger than the others. We expected that succulence could be important for invasion 397 of the saline marsh habitat because the ability to become succulent, and dilute the toxic effect of 398 concentrated salt ions, is essential for many species in salt environments (Flowers et al., 1977). 399 For example, Salsola kali originating from different habitats was found to have dramatic 400 intraspecific variation in succulence and the salt tolerant subspecies S. kali traga was able to increase succulence more than the non-salt tolerant S. kali ruthenica (Reimann and Breckle, 402 1995). However, we did not find consistent response in succulence in our previous work with 403 these knotweed taxa. Instead, we found a lot of variation among knotweed genets for succulence 404 in response to salt treatments, including several genets that seemed to display no change in 405 succulence (Richards et al., 2008). In this study, the only difference in succulence we found was 406 that the beach plants were more succulent than the roadside plants in the marsh transplant 407 garden. Succulence could aid in the adaptation to saline habitats in Reynoutria, but could be 408 specific to certain genets or conditions that we did not explore with our current design. The differences in phenotype elicited by these habitats could result in adaptive 416 differentiation if there is heritable variation for these traits within the populations. We did not 417 detect any genet-level variation within populations in this study. However, our power to detect 418 this level of variation was limited by the high mortality. The random effect of source site did not 419 explain much of the variation, but the source habitat type was a better predictor of variation than 420 transplant garden habitat type for most traits.  We also found support for local adaptation among roadside plants, which were better able 444 to survive in their home sites. Roadside plants had the best survival odds when compared to 445 foreign plants. This is somewhat surprising considering that plants from roadside habitats are 446 largely indistinguishable from plants from marsh habitats for most traits that we measured.

Sources of phenotypic differentiation -448
In our previous work, we used cytology and AFLP markers to show that most of these 449 Despite our mixed evidence, adaptive processes could still be important for most of the 516 populations, since transplants maintained biomass across at least two if not all three habitats. The 517 current study confirms our findings from the greenhouse that there is phenotypic differentiation 518 among these populations of Japanese knotweed, some of which is attributed to their source 519 habitat. Some of the plasticity in these traits and in fitness are likely to be passive responses to 520 resource limitation and stress, but "active" or adaptive plasticity in underlying morphological 521 and physiological traits may help to minimize the fitness loss in these environments