DOI: 10.5061/dryad.wdbrv15qz
title: Data for Rapid phenotypic differentiation and local adaptation in Japanese knotweed s.l. (Reynoutria japonica and R. × bohemica, Polygonaceae) invading novel habitats

description: 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.
description: <em>Collection sites
 and experimental gardens ­–</em>
 In mid-May 2005, we collected
 Japanese knotweed s.l. rhizomes for reciprocal transplant studies between
 beach, marsh and roadside sites across Suffolk County, Long Island, New
 York (Table 1; four sites of each habitat type for 12 total sites were
 used as sources for plant material and as locations of transplants). We
 created four groups of plants for reciprocal transplant in order to
 maximize the ability to replicate each genet in one site of each of the
 habitat types while still testing for superior performance in the original
 “local”
 site. Due to the natural topography of Long Island,
 the beach sites are all located on the northern shore of Long Island,
 while the salt marsh sites and roadside sites are more evenly distributed
 around Suffolk County. The beach sites are separated by 1-65 km, the marsh
 sites are separated by 14–40 km, and the roadside sites are separated by
 20–32 km. At each site, we collected approximately one meter of rhizome
 from seven (Beach 1) or eight genets (all of the other 11 sites) that were
 approximately 10 m apart, to maximize the chances of sampling different
 genotypes and to represent plant distribution at each site. We refer to
 each of these rhizomes as a separate “genet” because replicates cut from the same
 rhizome should have the same genotype. However, our previous studies show
 that most “genets” within a site also have the same AFLP haplotype and
 most likely belong to the same individual. Rhizomes were brought to the
 Stony Brook University greenhouse and cut into pieces of 4-8 grams
 fresh-weight (12 sites x 7 or 8 rhizomes per site for a total of 95 genets
 x 18-25 replicates = 2225 rhizome pieces). We planted the rhizome pieces
 in individual wells in 24-well flats with Pro-mix potting medium (Pro-mix
 Bx, Quakertown, Pennsylvania, USA), and approximately one teaspoon of slow
 release fertilizer (15-9-12 Osmocote Plus 8-9 month, Marysville, Ohio).
 The flats were placed in a temperature-controlled greenhouse under
 conditions approximating mid-summer in Suffolk County, Long Island and
 watered as needed to keep the soil moist. Day temperature was maintained
 at 30˚C and night temperature at 25˚C. We grew the plants in the
 greenhouse for approximately six weeks to allow for shoots to emerge from
 the rhizomes and grow to a height of approximately 10-15
 cm. The
 12 sites were organized into four reciprocal transplant groups, each with
 one beach, one marsh and one roadside location. Based on the number of
 plants that emerged, we assigned an equal number of replicate pieces of
 each rhizome collected at each source site to be transplanted into its
 home site and into one site of each of the other two habitat types.
 Therefore, three to eight replicates of each rhizome were assigned
 randomly across five blocks for each of the three transplant habitats (4
 replicate studies x 3 transplant habitats x 3 source habitats x 5-8 genets
 x 2-8 replicates = 1287
 plants; Table 1). We prepared the transplant gardens by removing only
 above ground vegetation (typically other knotweed plants) to a height of
 less than 2 cm with a machete. The cleared area included a border of 15-30
 cm outside of the transplant blocks.
 Between June 16-21, we
 transplanted the five blocks for each transplant garden into the field (between 80-125 ramets per
 transplant garden). Plants were left to grow in the field from the summer
 of 2005 through the fall of 2006. We measured final height and final
 number of leaves on all plants and harvested above ground and below ground
 for all plants between 11-18 September 2006. Roots were harvested by
 carefully digging to unearth the entire root system. Roots were shaken to
 remove loose dirt in the field and thoroughly
 washed. <em>Traits measured
 –</em> We measured traits related to salt tolerance and
 overall performance for each plant: height, total number of leaves, total leaf area
 (Li-Cor Model LI-3100 Leaf area meter: Li-Cor, Inc., Lincoln, Nebraska,
 USA), succulence (g water in all leaves/ cm2 total leaf area), shoot dry
 biomass, root dry biomass, root:shoot ratio based on dry biomass, and
 total biomass at final harvest. For each plant, all live leaf tissue at
 final harvest was used for calculating total leaf area and succulence.
 Plants were dried in a forced air oven at 60° C for at least 72 h to determine
 shoot, root and total dry
 biomass. We evaluated survival and biomass (as proxies for fitness) to
 assess the degree of adaptation. These taxa have extensive clonal growth
 and many individuals may not flower at all in the field, but persist and
 spread from year to year so biomass is an important indicator of fitness
 (de Kroon
 and Groenendael, 1997).
 <em>Data Analysis
 –</em> We performed all statistical analyses in the R
 statistical programming environment version 4.0.0 (R Core Team,
 2020), using the Linear-Mixed-Model (LMM) or Generalized
 Linear-Mixed-Model (GLMM) framework (lme4 package, Bates et al.,
 2015) and the Bayesian simulation package arm (Gelman and Su,
 2020). We checked the residuals to assess the appropriateness of
 the model and performed data transformations on traits as appropriate: we
 did not transform succulence and final height, but we performed log
 10-transformation on leaf area, and log 2 transformation on shoot, root
 and total biomass. For these traits we fitted LMM with the
 model: trait&lt;lmer(trait~SOURCE.type+GARDEN.type+SOURCE.type:GARDEN.type+(1|Origin.site)+(1|Transplant.site)+(1|genetfactor),data=data,REML=F). We used GLMM to model the number of leaves, with the negative binomial distribution with the model: modlfnum&lt;glmer.nb(lf.number~SOURCE.type+GARDEN.type+SOURCE.type:GARDEN.type+ (1|Origin.site)+(1|Transplant.site) + (1|genetfactor),data=data, REML=F). We did not model root to shoot ratio directly, but instead we used the ratio of the estimates of mean and variance for root and shoot to assess significance within the Bayesian framework (Korner-Nievergelt et al., 2015). In the LMM and GLMM models, “SOURCE.type” is the origin habitat type (beach, marsh , road), “GARDEN.type” is the transplant habitat type (beach, marsh, road). These effects as well as their interaction terms were modeled as fixed effects. The origin site, the transplant site, and the individual genets (“genetfactor”) were initially included as random terms. To avoid overfitting, we removed random effect terms that effectively explained no variance. This was true for the genet term for all traits and for the “Origin.site” term, which was removed for number of leaves, succulence and shoot biomass (see Table S1 for final models). We examined the correlation matrix for each model to evaluate auto correlation between terms. To test the significance of the fixed effects of “SOURCE.type” and “GARDEN.type”, we used 95% credible intervals (CrI), a Bayesian analogue of confidence intervals (Bolker et al., 2009). For each response variable, we obtained the model estimates from the back-transformed effect sizes. We calculated the associated 95% Credible intervals (CrI) for the modeled effects by performing 10,000 iterations of Bayesian simulation of the mean and variance of each estimate, using the sim function in the R package arm with non-informative priors (Korner-Nievergelt et al., 2015). For each model, we examined distributions of simulated fitted values predicted by the fixed effect terms. We used the values corresponding to the 2.5 and 97.5 quantiles of the distribution to designate the lower- and upper-boundary of the 95% CrI. If the CrI of a group did not overlap with the mean of the other group within transplant gardens, we considered the difference between these groups to be significant (sensu Bucharova et al., 2016, 2017). In order to understand how much of the variance was explained by the random and fixed effects in our model, we used several approaches. First, we used the package r2_nakagawa: Nakagawa's R2 for mixed models (Nakagawa and Schielzeth, 2013; Nakagawa et al., 2017) to determine the conditional R2 (the variance explained by both the fixed and random effects) and marginal R2 (the variance explained by the combined fixed effects). The random effect variances calculated in this package are the mean random effect variances, and appropriate for mixed models with nested random effects (Johnson, 2014). We also used the package rptR (Stoffel et al., 2017) to further evaluate the components of variance for each of the random effects separately (i.e., “Origin site” and “Transplant site”. We used the package commonalityCoefficients (Nimon et al., 2008) to examine the amount of variance explained by the separate fixed effects of the habitats of the source and transplant gardens (i.e., “SOURCE.type” and “GARDEN.type”). This approach does not include information about the random effects of origin site and transplant site which are nested within the source type and garden type. However, this approach is valuable for evaluating the relative contribution of each separate fixed effect. Our design is constrained by the fact that origin site and transplant sites are nested within levels of “Transplant group” (see discussion here Long, 2021). To examine the importance of this design constraint, we also reran the LMER and GLMER models for each trait with the fixed term “Transplant group”. To properly test for the effects in this nesting design, we should ideally fit random intercepts for the sites nested within groups, but we did not have enough replication within groups to do so. We assume that fitting the fixed effect of the “Transplant group” also controls for the non-independence of the origin site and transplant site within groups (Long, 2021). By comparing the modeling with and without the fixed term of Transplant group, we evaluated how these random terms impact the main effects of interest which are the fixed effects of the habitats of the source and transplant gardens (i.e., “SOURCE.type” and “GARDEN.type”). We tested for local adaptation with two fitness proxies: total biomass and survival. We ran a “local vs. foreign” test using the Bayesian fitted values for biomass obtained from the same LMM model: totalbiomass&lt;-lmer(trait~SOURCE.type+GARDEN.type+SOURCE.type:GARDEN.type+(1|Origin.site)+(1|Transplant.site)+(1|genetfactor),data=data,REML=F). For each garden type we performed random pairwise contrasts as the differences between Bayesian fitted values of the local plants with those from foreign habitats. For survival, we performed random pairwise contrast by calculating the log of odds ratio between local and foreign plants.  We reported the mean, 95% CrI, and percentage of contrasts showing superior performance of plants grown in their home site compared to plants from each of the other habitats as magnitude of local adaptation. 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description: R scripts are anotated with results that we obtained.