DOI: 10.25349/d9rw3g
title: Spenders versus savers: climate-induced carbon allocation tradeoffs in a recently introduced woody plant

description: 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.
description: We sampled stem and root tissues in the P<sub>g</sub>
 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<sup>th</sup> and October
 21<sup>st</sup>, 2016 (n=18 for each population, table S1).
 For the seasonal sampling of P<sub>f</sub>, collections
 occurred in one of the three blocks used in the P<sub>g</sub>
 sampling and used the same eight individuals throughout the sampling
 period (n=8 for each population, table S1). Stem and root tissues from
 P<sub>f</sub> (Fig. 1) were collected on May
 26<sup>th</sup> (Spring), July 22<sup>nd</sup>
 (Summer) and October 21<sup>st </sup>(Fall) in 2016 and
 February 26<sup>th</sup> (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<sup>st</sup> 2016 and March 1<sup>st</sup> 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 <i>et al. </i>(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<sup>th</sup> 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, r<sup>2</sup> = 0.94, F<sub>1,25
 </sub>= 336.4, p &lt; 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 <i>Tamarix </i>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<sup>st</sup> and June
 1<sup>st</sup> when the minimum temperature was below 0 °C and
 the maximum temperature from the proceeding day was above 0 °C. We chose
 January 1<sup>st</sup> 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<sub>g</sub> 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 <i>Tamarix
 </i>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<sub>g</sub> genotypes, and included those individuals
 sampled for the NSC monitoring. <i>Tamarix </i>has small
 (&lt;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<sub>g</sub> 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<sup>th</sup> 2016) and again in the fall (October
 25<sup>th</sup> 2016). All stem diameters were measured with a
 caliper at 10 cm above the soil surface to determine the basal area
 (A<sub>b</sub>). 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; mm<sup>2</sup>
 <i>d</i><sup>-1</sup>) were calculated according
 to Lambers, Chapin, & Pons (2008) where A<sub>bf
 </sub>is the final measured basal area, A<sub>bi</sub>
 is the initial basal area and <i>d</i> is the number of days
 between A<sub>bf</sub> and
 A<sub>bi</sub>:                                                                                                             (1) Canopy volume was measured on May 25<sup>th</sup>, 2016 for the same individuals used for basal area and NSC sampling (n = 18 for each P<sub>g</sub>) by measuring the width of the trees at their widest point and the corresponding orthogonal width. The canopy volume (Vol<sub>can</sub>; m<sup>3</sup>) was calculated using the radii of two widths (W<sub>1</sub>, W<sub>2</sub>) and the max height (H) of the tree and the formula for the volume of ellipse