Non-structural carbohydrate(NSC) storage may be under strong selection in woody plant species that occur across strong environmental gradients. We therefore investigated carbon allocation strategies in a widely distributed, introduced woody plant. We predicted genotypes from cold climates with exposure to episodic freeze events, would have elevated NSC concentrations with the tradeoff of reduced growth and reproduction relative to warm-adapted genotypes. We established an experimental common garden using genotypes of Tamarix spp., sourced across a large thermal gradient within their introduced range. We measured seasonal NSC storage in coarse roots and stems, above-ground growth and flower production. Autumn NSC concentrations were 50% higher in genotypes from sites with spring freeze events compared to genotypes from warmer sites. Cold-adapted genotypes also had a 2.3-fold higher starch to soluble sugar ratio than warm-adapted genotypes. Across all genotypes and seasons, NSC storage was inversely correlated with growth and reproduction. Results suggest that Tamarix from colder locations cope with freeze events by maintaining large storage pools to support tissue regrowth, but with the tradeoff of reduced growth and reproduction. Our results provide evidence of rapid selection in carbon allocation strategies in response to climate in introduced woody species.

We sampled stem and root tissues in the P_g individuals in the spring and fall to assess relationships between provenance site and NSC concentrations across populations. Six individuals were randomly selected from three different blocks for NSC analysis and were sampled on May 26^th and October 21^st, 2016 (n=18 for each population, table S1). For the seasonal sampling of P_f, collections occurred in one of the three blocks used in the P_g sampling and used the same eight individuals throughout the sampling period (n=8 for each population, table S1). Stem and root tissues from P_f (Fig. 1) were collected on May 26^th (Spring), July 22^nd (Summer) and October 21^st (Fall) in 2016 and February 26^th (Winter) in 2017. Temperature ranges for the collection dates were as follows: Spring 16-31°C, Summer 31-45°C, Fall 21-36°C, Winter 12-21C (Fig S2). Total precipitation between March 1^st 2016 and March 1^st 2017 was 86.9 mm, and all plots were flood irrigated to supplement precipitation and maintain soil moisture. At the start of first sampling for NSCs the trees were over 2 m tall (SF1). Mature second year stem samples (7-12 mm diam, 3-5 cm length) were collected at breast height (1.37 m above soil surface) and coarse root tissue samples (5-10 mm diam, 3-5 cm length) were collected by excavating roots from the soil at a depth range of 10-15 cm within 25 cm from the tree base. Three roots and stem samples were collected from each individual and pooled together for analysis, using the entire collected sample including bark and cambial layers.

For the NSC analysis, we used the methods adapted from Quentin et al. (2015). Samples were placed on dry ice immediately after excising from the tree and microwaved within 24 hours to prevent enzymatic degradation. Samples were then oven dried at 60 °C for 48 hours and kept in a freezer (-20 °C) until they were prepared for extractions. Samples were first coarse ground with a #20 mesh Wiley Mini-Mill (Thomas Scientific, Swedesboro, NJ), and then fine ground with a dental amalgamator (Wig-L-Bug, Dentsply Rinn, Charlotte, NC). A sub-sample of 50 mg (± 1.5 mg) was used for extractions with the final weight recorded. For every 10^th sample, a second sub sample was taken for quality control in addition to using glucose (Sigma-Aldrich G7528) and starch (Sigma-Aldrich S5926Fluka) lab standards. The lab standards for starch and glucose were used to verify that our methods were robust (quantified concentration = -1.70 + 1.08*actual concentration, r² = 0.94, F_1,25 = 336.4, p < 0.001) by mixing the two to achieve a total of 50 mg (± 1.5 mg) at various ratios ranging from absent to 100% of either standard. Across our sub sample duplicates we found little variation in final concentrations for sugar or starch (mean errors 0.74%, SE = 1.67 and 0.067%, SE = 1.79, respectively), indicating that adequate homogenization of samples had been achieved. Low molecular weight sugars were extracted using 80% ethanol in an 85 °C water bath. After being centrifuged, the supernatant was removed and saved for later analysis; this process of extraction was repeated three times. Total soluble sugars were quantified using a phenol- sulfuric acid reaction to determine the relative sugar concentration by quantifying at 490 nm (Chow & Landhäusser, 2004). Any remaining ethanol after the third extraction was evaporated and an enzymatic digestion using Alpha-amylase and amyglucosidase was used to degrade starches into glucose. The glucose concentration was quantified using the peroxidase-glucose oxidase/o-diansidine enzyme method at 525 nm (Chow and Landhäusser 2004). The glucose concentration per unit dry mass was equivalent to the starch concentration in the sample.

Many factors can cause non-systemic errors in NSC measurements, including matrix effects of woody plant material and secondary metabolites, as well as other pools of carbon storage not accounted for in our extraction methods (e.g. hemi-cellulose or lipids), and some soluble sugars measured may not be readily available (Germino, 2015; Quentin et al., 2015). We minimized these potential errors by using starch, glucose, and internal standards to test for reproducibility of data. Differences in secondary metabolites should also have been minimized by using only one species and growing all of the populations in the common garden with similar environmental conditions. Further, secondary metabolites in Tamarix were not found to vary across a broad latitudinal gradient of source populations in a similar common garden study (Hussey, Kimball, & Friedman, 2011).

We used the risk of freeze events as a predictor of carbon allocation. We defined freeze events as any day between January 1^st and June 1^st when the minimum temperature was below 0 °C and the maximum temperature from the proceeding day was above 0 °C. We chose January 1^st as the start date to emphasize the importance of freeze events as plants become metabolically active as dormancy breaks and would be exposed to potentially damaging temperatures via freeze-thaw or cell death of new growth. To determine the number of events where temperatures would rise above, and then drop below the freezing point, we interpolated daily maximum and minimum temperatures from 1992-2012 for all P_g sites using data from the PRISM Climate Group, Oregon State University (http://prism.oregonstate.edu, created 10 Jan 2018). The freeze-thaw risk of each site was calculated as the mean number of total freeze events at a given site divided by the mean number of events at the site with the highest number of freeze events (42.2 events). This metric allowed us to compare relative risks across sites as a proportion of the highest risk sites.

The Tamarix hybrid complex in the North America is a deciduous group that has shown differences in timing of spring flowering and leaf flush and across it’s introduced range, as well as in common gardens (Friedman, Roelle, & Cade, 2011; Long et al., 2017). Bimonthly spring phenological observations were made from February 2016 until June 2016 on 12 individual plants from three randomly selected blocks of each of the P_g genotypes, and included those individuals sampled for the NSC monitoring. Tamarix has small (<2 mm), perfect (bisexual) flowers that are on secondary racemes, each supporting an average of 50-60 flowers although some can support as many as 750,000 flowers on a single individual (Andersen & Nelson, 2013; Gaskin & Schaal, 2002; Warren & Turner, 1975). Due to this high number of flowers per plant, reproductive output was evaluated by estimating the number of flowering racemes on each individual during each measurement period. Measurements were calibrated during each sampling event for each population by estimating the area that would represent one hundred racemes and then counting each raceme within that area for six different individuals. If there was a discrepancy of greater than ± 10% between the estimation and actual number of racemes then the area was recalibrated.

We also recorded the progression of canopy development during the spring of 2016 by assigning the canopy a greenness score ranging from 0-4 (0 = dormant, 1 = any green expanding tissue present, 2 = most of buds expanding, 3 = fully expanded leaves, and 4 = full canopy). In the fall we documented the progression from green to yellow or senesced foliage every three weeks, using a percentage of canopy as yellow or senesced from October 2016 until January 2017 on the same individuals used for spring phenology measurements.

The basal area on the same six individuals used for NSC analysis from each P_g was measured in three different blocks (n = 18 for each population) to determine basal area mean growth increments of each population during the growing season. Initial measurements were taken in late spring (May 25^th 2016) and again in the fall (October 25^th 2016). All stem diameters were measured with a caliper at 10 cm above the soil surface to determine the basal area (A_b). Due to irregular stem shapes, two measurements were taken at orthogonal directions from each other and area was calculated as an ellipse. Three representative stems on each individual were selected for repeat measurements and marked with paint pens at ten cm above the ground surface so that they could be re-measured at the same points. Mean basal area increment was calculated for each population from the measurements at each of the three stems. Basal area increments (BAI; mm² d^-1) were calculated according to Lambers, Chapin, & Pons (2008) where A_bf is the final measured basal area, A_bi is the initial basal area and d is the number of days between A_bf and A_bi:

                                                                                                            (1)

Canopy volume was measured on May 25^th, 2016 for the same individuals used for basal area and NSC sampling (n = 18 for each P_g) by measuring the width of the trees at their widest point and the corresponding orthogonal width. The canopy volume (Vol_can; m³) was calculated using the radii of two widths (W₁, W₂) and the max height (H) of the tree and the formula for the volume of ellipse