Abstract
Under benign conditions, as plants grow, size dependent (allometric) scaling changes mass allocation between organs. In the face of resource stress plants grow less but also show plasticity in multiple trait categories, including biomass allocation, morphology and anatomy. The extent to which size dependent (allometric) vs active (beyond allometric) responses to resource limitations are consistent with expectations for increasing resource acquisition potential is poorly understood. Here we assess the impact of allometric scaling on the direction, magnitude and coordination of trait plasticity in response to light and/or nutrient limitations in cultivated sunflower (Helianthus annuus).
We grew seedings of ten sunflower genotypes for three weeks in a factorial of light (50% shade) and nutrient (10% supply) limitation in the greenhouse and measured a suite of allocational, morphological and anatomical traits for leaves, stems, fine roots, and tap roots.
Under resource limitation, more biomass was allocated to the organ capturing the most limiting resource, as expected. Allometric scaling accounted for a substantial portion of many trait responses, especially for anatomical traits. Allometric and active responses were generally aligned in the same direction and for specific leaf area and specific root length under light and nutrient limitation, respectively, this alignment contributed to more acquisitive trait values. However, traits not generally associated with resource limitation showed the greatest active adjustment, e.g. tap root and stem theoretical hydraulic conductance. Although light limitation triggered a more pronounced coordinated trait adjustment than nutrient limitation, factoring out allometric scaling clarified that active coordinated trait responses to both light and nutrient limitation were similar in scale but resource specific.
The substantial contribution of allometric scaling to trait responses that are consistent with a functional increase in the uptake capacity of the most limiting resource suggests that both allometric and active trait adjustments need be considered as potentially adaptive.
Introduction
The extent of plant trait adjustment in response to a changed environment is generally considered as the plant’s phenotypic plasticity (Nicotra et al., 2010; Valladares et al., 2007). According to theory (Gedroc et al., 1996; Shippley & Mezianne 2002; Poorter et al., 2012; Robinson et al., 2010), this plasticity serves to optimize/maximize the uptake of the most limiting resource (Bloom et al.,1985;Chapin, 1991). For example, increased mass allocation to leaves under shade, or to roots under nutrient limitation alleviates some of the stress caused by resource limitation (Shipley & Maziane, 2002; Sugiura & Tateno, 2011). However, since plants are inevitably smaller under resource stress, a factor to consider in understanding these traits shifts under resource limitation is how allometric scaling affects traits (McConnaughay & Coleman 1999; Onsas et al., 2013; Poorter et al. 2015; Reich 2002, 2018; Shipley & Meziane, 2002; Weiner 2004;).
Allometric scaling, as narrowly defined, governs the relationship between mass allocation at differing plant parts (Weiner 2004). Across and within species this scaling relationship is such that smaller plants allocate proportionally more mass to leaves than to roots (Poorter et al., 2015). Thus, in shade, allometric scaling associated with a smaller plant under limiting resources could be seen as a strategy to acquire the most limiting resource at a given plant size (Coleman et al., 1994; Müller et al., 2000; Reich, 2002). This “passive” (Nicotra et al., 2010) or “apparent” (McConnaughay and Coleman 1999) plasticity predisposes the plant to attune its traits with the available resources and resource demand. However, there is evidence for active adjustments (adjustments beyond those accounted for by allometry) that can affect resource uptake. For example, greater plant height for a given mass under low light condition aids in light uptake (Rice & Bazzaz, 1989). The extent to which plasticity in a broader range of trait responses (beyond just mass allocation) during resource limitation is active vs an effect of size dependent (allometric) scaling is poorly understood.
Most research to date governing allometric scaling has focused on broad intraspecific comparisons of species (Poorter et al. 2015). However, at the intraspecific level there could be population or genotypic level differences in these scaling relationships. If responses due to allometric scaling and active adjustments vary in the degree to which they align, and if this variation has a genetic component, then within species and populations there could be variation in tolerating stress at different points in the growth cycle. Moreover, “passive” adjustments in traits associated with size cannot be a priori regarded as or ruled out as adaptive (cf Nicotra et al. 2010, Poorter et al. 2019). If traits responses that are consistent with greater ability to take up the most limiting resource have both allometric and active components, this suggests that the magnitude and alignment of both components need to be considered when evaluating evidence for functional and putatively adaptive responses.
Among plant traits, anatomical traits are often overlooked due to time and budget constraints. However, variation in anatomical traits underlies or contributes to variation in morphological and physiological traits that have received much attention (John et al., 2017; Kong et al., 2014; Scoffoni et al., 2015). For example, palisade parenchyma thickness is positively correlated to leaf thickness (Catoni et al., 2015) and photosynthetic rate (Chatelet et al., 2013). A thicker cortex could provide a relative larger site for mycorrhizal infection and higher resource uptake in thicker roots, especially for the arbuscular mycorrhiza (Kong et al., 2014). Root cortex thickness, due to the size of cortical cells (Eissenstat & Achor, 1999) strongly affects fine root diameter (Gu et al., 2014; Guo et al., 2008). Exploring how responses in anatomical traits align with those of other traits will enhance our understanding of how plants adjust to changing environmental conditions from tissue, to organ, to architecture.
Across species or populations within a species, plant functional traits are generally expected to exhibit a coordinated shift due to resource limitation in their typical habitats, i.e., the leaf, stem, root and whole-plant economics spectrum with acquisitive traits at high resource conditions (Díaz et al., 2016; Fortunel et al., 2012; Reich, 2014; Wright et al., 2004;). Moreover, trait values are thought to show a coordinated response across all organs, supporting the idea of a whole-plant-based strategy (Reich, 2014). However, within species, much remains unclear about how the expectation of integrated trait responses at the ‘whole plant economic spectrum’ (Reich, 2014) level plays out at the level of environmentally induced plasticity. Especially in terms of coordination among a broad set of traits and plant organs and how size induced, allometric, plasticity affects this.
To add to our understanding of plant response to resource limitation, here we examined trait responses to light and nutrient limitation of traits across different trait categories (biomass allocation, morphology and anatomy) and organs (leaf, stem and root) in cultivated sunflower. Prior research has shown strong plastic responses to resource limitation and other environmental factors in Helianthus (Bowsher et al., 2017; Donovan et al., 2014; Masalia et al. 2018; Temme et al., 2019). Specifically, we sought to answer the following questions:
How do mass allocation, organ morphology, and anatomy change with above and below ground resource limitation, and what role does size scaling of traits play in this?
How do traits compare for magnitude of plasticity and what role does size scaling of traits play in this?
Do traits show a coordinated shift due to resource limitation across all organs and what role does size scaling of traits play in this?
Material and Methods
Experimental design
To address these questions we selected a set of 10 cultivated sunflower genotypes, varying broadly in biomass based on prior work (Table S1) from a larger diversity panel used for genomic dissection of traits (Mandel et al., 2011; Masalia et al., 2018; Nambeesan et al., 2015). We conducted a factorial design of two nutrient treatments (rich and poor) and two light treatments (sun and shade) at the Botany greenhouses of The University of Georgia USA in March 2018. Achenes were sown in seedling trays and allowed to grow for seven days, after which each seedling was transplanted to 5 liter (1.3 gallon) pot filled with a 3:1 sand:calcinated clay mixture (Turface MVP, Turface Athletics, Buffalo Grove, IL). Pots were arranged in a split plot design of 6 replicate blocks or whole-plot. The light treatment was applied as the whole-plot factor, with 2 sub-plots in each plot randomly assigned to unshaded or 50% shade generated with high density woven polyethylene cloth (Fig. S1). Within each subplot, two pots of each genotype were randomly distributed and supplied with either 40g or 4g fertilizer (Osmocote Plus 15-9-12 with micronutrients, Scotts, Marysville, OH, USA), totaling 240 pots (plants). Greenhouse temperature controls were set to maintain 18–24 °C, and supplemental lighting was provided to maintain a 15/9-h photoperiod.
Plant harvest and trait measurements
Plants were harvested 3 weeks after transplanting (4 weeks after germination). At harvest, plant height (from soil surface to top of apical meristem) and stem diameter (midway between cotyledons and first leaf pair) were measured. Plants were separated into root, leaf (including cotyledons), and stem (including bud if present—rarely) for organ anatomy, morphology and allocation measurements. In order to determine the relative magnitude of adjustments to anatomy, morphology and allocation traits we assigned all measured traits to one of these three categories. While we believe our assignment of traits to categories is defensible, we acknowledge that this is somewhat arbitrary and that different groupings could influence results associated with comparisons among categories.
For plant anatomical traits, from each replicate plant, one recently matured fully expanded leaf was sampled, cutting a 1×0.5 cm rectangle out of the leaf center, a 5 mm length stem segment (centered between the cotyledon and the first leaf pair) was cut, a 1 cm tap root segment was cut 4 cm below the root/stem junction, and from a single lateral root attached to the tap root near to the root/stem junction with an intact root tip, a 1 cm fine root segment was cut 2 cm from the apex of the root. All tissue subsamples were fixed in formalin–acetic acid-alcohol solution, FAA, (50% ethanol (95%), 5% glacial acetic acid, 10% formaldehyde (37%) and 35% distilled water).
Fixed subsamples were processed for anatomy at the University of Georgia Veterinary Histology Laboratory. Each sample was embedded and gradually infiltrated with paraffin, sliced with a sledge microtome, mounted to a slide, and stained with safranin and fast green dye. Slides were imaged with a camera mounted Zeiss light microscope using ZEN software (Carl Zeiss Microscopy, Oberkochen, Germany). Cell (i.e., conduit) and tissue (i.e., palisade and spongy, cortex and vascular or stele) dimensions of leaf, stem, fine and tap roots were traced using Motic Images Advanced 3.2 software (Motic Corporation, Xiamen, China). Theoretical hydraulic conductivity (Ks, kg·s-1·m-1·MPa-1) for each sample was calculated, based on the Hagen-Poseuille equation (Tyree & Ewers, 1991): . where ρ is the density of water (988.3 kg·m-3 at 20 °C); η is the viscosity of water (1.002×10-9 MPa·s at 20 °C); Aw is the stele (vascular) cross-section area, d is the diameter of the ith vessel and n is the number of conduits in the xylem.
For plant morphological traits, after anatomical trait samples were collected, the rest of the leaf, stem, and fine/tap root of each plant were scanned (Espon, Expression1680, Japan). Total root length and volume of fine root, tap root and stem, as well as leaf area were measured using WinRhizo (v. 2002c, Regent Instruments, Quebec, Canada), respectively. Then, the subsamples were dried at 60°C for 48 h and weighed. Specific leaf area (SLA, cm2·g-1) and specific root length (SRL, m·g-1) were calculated as the ratios of leaf area to leaf dry mass, and root length to root dry mass, respectively. Tissue density (g·cm-3) was calculated as the ratio of dry mass to volume for stem, tap and fine root, respectively. Leaf dry matter content (LDMC, mg·g-1), used as a proxy for leaf tissue density, was measured as leaf dry mass divided by leaf fresh mass (Kramer-Walter et al., 2016; Wilson et al., 1999).
For allocational traits, total plant dry mass was calculated as the sum of all plant parts, including the subsamples for anatomical and morphological traits. Once anatomy and morphology subsamples were collected, the remaining tissue organs were dried at 60°C for 48 h and weighed. Prior to fixed in FAA, the fresh biomass of subsamples for anatomical analysis was measured and converted to dry biomass based on the ratio of fresh/dry biomass of remaining tissue organs. Finally, the mass fractions for each tissue were calculated as proportions of total plant dry mass (g·g-1).
Data analysis
The statistical analysis for the phenotypic data was performed using R v3.5.1 (R Core Team). To obtain genotype means from our split plot design, a mixed effects model was fitted using the package lme4 (Bates et al., 2018) with genotype, light and nutrient level and all their interactions as fixed effects and light treatment within block as random factor. Least-square (LS) means of all trait values without random factor were estimated from this model using the R package emmeans (Lenth et al., 2018). To test the effect of genotype and treatment on measured traits we fitted a less expansive mixed effects model with genotype, light and nutrient level as well as the interaction between light and nutrient level as fixed effects (following Freschet et al., 2018) and light treatment within block as random factor. From this model, fixed effects were then tested using a Walds Chisquare test in a type III Anova using the package car (Fox et al., 2018). Differences between treatments were tested using a Tukey test corrected for multiple comparisons. We then estimated the influence of plant allometry on the significance of nutrient and light limitation effects on traits by adding (log-transformed) plant biomass as a fixed factor to both models and recalculating means and significance (Ryser & Eek, 2000; Wahl et al., 2001). The relative shift in trait value per genotype was calculated as the value in control (high nutrient & unshaded conditions) minus that in each treatment, and divided by the value at control.
To quantify the plastic response of each trait to each resource limitation treatment, we calculated the relative distance plasticity index (RDPI, Valladares et al., 2006, Scoffoni et al., 2015) as , where xi and are the mean trait values of genotypes grown under control (high-light and high-nutrient) and resource limited condition. In addition, the RDPI for each traits and resource limitation treatment was recalculated after correcting for plant size (total biomass). Significant values of RDPI (difference from zero, no plasticity) were determined using T-test on genotype averages.
A principal component analysis (PCA) was applied to the trait data before and after correcting for size, to determine major sources of variation across multiple traits and identify whether there were concerted trait adjustments to limitation in above or belowground resources. Differences between treatments were tested using, Bonferroni corrected, Hotellings-t test on the first two principal components. Data visualizations were made using ggplot2 (Wickham et al., 2018).
Results
Across all genotypes, plant biomass decreased by 21.4%, 53.3%, and 65.8% under light, nutrient, and combined limitation, respectively, with additive effects of both stresses on whole plant biomass (Fig. 1a, Table 1). Moreover, traits frequently scaled with biomass and genotypes differed in the effect of biomass on trait value (Table 1).
Allometric and active responses to resource limitation
Allocational traits were affected in contrasting ways by above and belowground resource limitation. The ratio of leaf mass to fine root mass (LFRratio) increased under light limitation (+82.6%) due to both increased mass allocation to leaf mass (LMF, +3.7%) and decreased allocation to fine roots mass (FineRMF, −41.4%). In contrast, LFRratio decreased under nutrient limitation (−45.7%) due to decreased LMF (−12.1%) and an increased FineRMF (+71.4%). A significant interaction of light and nutrient limitation was found on allocational traits, e.g., LMF, stem mass fraction (SMF) and FineRMF (Table 1). After allometric scaling was taken into account by statistically correcting for the effect of total plant dry mass on trait values, the effect of light limitation on leaf mass fraction disappeared, indicating that the increased LMF response was largely driven by the reduction in plant size and not an active adjustment to resource limitation (Table 1).
Light limitation affected all morphological traits except for fine root diameter (FRD) (Table 1). The responses led to strong increases in acquisitive values for resource acquiring traits such as SLA (+40.5%, Fig. 2a), SRL (fine root, +31.8%, Fig. 2b; tap root +86.9%), and a moderate increase in plant height (Height, +15.0%) (Table 2). This was accompanied by strong decreases in organ dimensions, e.g., leaf thickness (LT, −10.8%), stem diameter (SD, −18.02%), tap root diameter (TRD, −28.84%), as well as tissue density assessed as leaf dry matter content (LDMC, −20.57%); stem tissue density (STD, −22.89%), fine root tissue density (FineRTD, −18.76%). After allometric scaling was factored out, the effect of light limitation on largely disappeared, indicating that the root trait adjustments were mainly driven by allometric scaling (Table 2).
In contrast to the effect of light limitation, nutrient limitation affected only 4 out of 12 morphological traits (Table 1). Nutrient limitation decreased SD (−15.6%) and FRD (−7.3%), and increased the fine root specific root length (FineSRL, +19.8%) (Fig. 2b, Table 2) and TapRTD (+16.6%, Table 2). After allometric scaling was factored out, the effect of nutrient limitation on these traits remained, indicating active adjustments.
Similar to allocational and morphological traits, responses of anatomical traits to resource limitation were also organ-specific. Light limitation affected leaf, stem, and tap root anatomical traits, while nutrient limitation predominately affected fine root traits. More specifically for light limitation, aboveground traits responded by decreasing leaf palisade parenchyma layer thickness (LPT, −12.1%, Fig. 3a), stem cortex thickness (SCT, −11.6%), stem vascular bundle thickness (SVT, −26.2%, Fig. 3b) and stem xylem thickness (SXT, −24.7%) (Table 2). Belowground traits responded by reducing tap root cortex thickness (TRCT, −12.9%) and stele diameter (TRSD, −33.6%) (Table 2). Vascular tissue adjustment to light limitation led to changes in theoretical hydraulic conductivity, with for decreased leaf hydraulic conductivity (LKs, −19.0%) but increased stem hydraulic conductivity (SKs, +32.4%) (Table 2). After allometric scaling was factored out, the effect of light limitation on SCT, SVT, TRCT and TRSD was driven mainly by allometric scaling (Table 2), but there was active adjustment for greater stem and tap root hydraulic conductivity (SKs, +95.9%; TRKs, +94.1%) (Table 2).
In contrast to light limitation, nutrient limitation predominately affected fine root anatomy. Nutrient limitation decreased fine root cortex thickness (FRCT, −7.72%, Fig. 3c) and fine root stele diameter (FRSD, −7.95%) (Table 2). After allometric scaling was factored out, the effects of nutrient limitation on FRCT remained in both light conditions, however the effect of nutrient limitation on FRSD disappeared under low light condition (Table 2).
Allometric and active trait plasticity
Trait categories differed in magnitude and direction of plasticity depending on resource limitation (Fig. 4). Based on simple means per category in absolute relative distance plasticity index (RDPI), biomass allocational traits showed relatively larger magnitude in plasticity, followed by morphology, and last anatomy (Fig. 4a). On average, light limitation induced a higher RDPI values than nutrient limitation (Fig. 4). When allometric scaling is factored out, the average magnitude of RDPI values decreased for most traits (Fig. S2). Notable exceptions were, plant height, SKs and TRKs, for which RDPI increased after taking plant biomass into account indicating active and passive responses to be in opposite directions (Fig. 4b, Fig. S2).
Coordinated trait adjustment to resource limitation
When the genotypes means for traits in all three traits categories (allocation, morphology, and anatomy) and all treatments were included in a principal component analysis, the first two axis explained 34.4% and 13.4% of the variation (Fig. 5a).Light and nutrient limitations moved the key set of traits to be more acquisitive along the first and second axis, respectively, e.g., higher SLA under shade, higher FineSRL under nutrient limitation (Fig. 5a).Overall, light limitation triggered more pronounced coordinated trait adjustment than nutrient limitation (Fig. 5a). However, after allometric scaling was factored out, active coordinated trait responses to both light and nutrient limitation were more similar in scale and resource specific.
Discussion
Here we assessed the impact of allometric scaling on the direction, magnitude, and coordination of allocation, morphological and anatomical trait responses to light or/and nutrient limitation in cultivated sunflower. We found more biomass was allocated to the organs acquiring the most limiting resource (roots for nutrient stress and leaves for shade) and that morphological and anatomical trait adjustments generally led to more acquisitive trait values (higher SLA in shade and higher SRL in nutrient stress). Although portions of the plant trait responses were accounted for by allometric scaling (i.e., trait changes due lower biomass of stressed plants), there was additional active adjustments of key traits beyond allometric scaling. Interestingly, traits that showed the greatest active adjustment were traits that are not often discussed in relation to light or light or nutrient limitation, namely tap root and stem theoretical hydraulic conductance. We found evidence for coordination of both allometric and active adjustments for many traits in response to resource stress.
Plasticity and allometry
Our results demonstrated that plants increase biomass allocation to the plant part acquiring the most limiting resource, both through allometric scaling and additional active responses (Bloom et al., 1985; Poorter et al., 2012). Lower light availability resulted in increased relative investment in aboveground plant organs (higher LMF and SMF) and lower nutrient availability resulted in increased relative investment in belowground (FineRMF). This allocation pattern has been confirmed in other growth forms (e.g., grasses, Siebenkäs et al., 2015; shrubs, Valladares et al., 2000; trees, Reich et al., 1998, Kramer-Walter & Laughlin, 2017). Both morphology and anatomy adjustments resulted in a higher specific area of light and nutrient capture per biomass invested under each stress respectively. Additionally, anatomical adjustments in leaf and root, both decreased LPT and FRCT, reduce tissue metabolic and maintenance costs (Guo et al., 2006, Galindo-Castañeda et al., 2018; Jaramillo et al., 2013). Thus, greater capacity for resource acquisition was coupled with reduced costs.
The relatively high SKs and TRKs in shaded plants was associated with the functional hydraulic continuum with taller plants. Both plant height and SLA of shaded plants were significantly greater than the control (Tables 1, 2), resulting in an increase in axial transportation distance and total transpiring surface. Theoretical hydraulic conductance, SKs and TRKs of shaded plant were increased by 95.9% and 94.1% when taking biomass into account indicating active adjustments beyond allometry (Table 2). This suggests that a coordinated increase in axial above- and below-ground hydraulic conductance offsets the greater transportation resistance (Plavcová & Hacke, 2012), ensuring efficient movement of carbon to roots and nutrients and water to leaves (Maurel et al., 2010; Rodríguez-Gamir et al., 2016; Wahl et al., 2001).
Besides a response to resource limitation, many traits showed a strong correlation with plant biomass indicating allometric scaling, with larger plants having a thicker leaf, stem and tap root (Table 1). Consistent with Poorter et al. 2012, we found that light limitation effects were more accounted for by allometry then nutrient limitation effects. However, even when accounting for plant biomass by using total biomass as a covariate, treatment effects remained significant for major traits, such as SLA, FineSRL, LPT and FRCT, indicating active adjustments as well. This demonstrates that light and nutrient limitation had substantial active effect on plant adjustments to the major morphological and anatomical traits influencing resource acquisition indicate allometry. Generally, allometric adjustments went in a similar direction as the active adjustments (Fig 3). Thus, both allometric scaling as well as active trait adjustments were consistent with trait adjustments that serve to ameliorate some of the effects of resource limitation.
Magnitude of trait adjustments
Consistent with other resource limitation studies, plasticity of mass allocational traits was largest, followed by morphological traits (Kramer-Walter & Laughlin, 2017, Valladares et al., 2000), and smallest in anatomical traits (Catoni et al., 2015; Cai et al., 2017; Xu et al., 2015). It should be noted that differences in the extent of plasticity for different categories of traits may be species specific or based on the traits included. For example, oak (Quercus robur) seedlings were more plastic in physiological traits under shade, yet beech (Fagus sylvatica) was more plastic in morphological traits (Valladares et al., 2002). Differences among trait categories for the magnitude of plasticity may reflect an inherent hierarchy originating from the internal structural framework of any specific organ. For example, leaf thickness is highly correlated with palisade parenchyma (Scoffoni et al., 2015) and root diameter with cortex and/or stele thickness (Kong et al., 2014). Thus, small shifts in individual component anatomical traits could add up to larger shifts in morphological traits which in turn affect allocational traits.
In general, pulling out allometric effects decreased the magnitude of morphological and anatomical traits adjustment. Only the RDPIs of the plant height, SKs and TRKs increased especially under shade when the allometry was accounted for (Fig. 4, Fig. S2). Given that height increased under shade, a higher efficiency of stem and root transportation could offset greater resistance due to a longer transportation distance (Plavcová & Hacke, 2012). It should be noted, however, that this increased stem Ks may come at the cost of increased risk of xylem embolism in shaded plants (Tyree & Zimmermann, 2002). However, the opposite response in leaf Ks provides a puzzling counterpoint that would be worth assessing in more experiments. Additionally, the anatomical dataset collected in this work provides an excellent resource for anatomical water flow models (Couvreur et al., 2018) to further shed light on the consequences of these anatomical trait adjustments for plant hydraulics.
Trait coordination and resource strategy
Contrary to the evolutionarily conservative strategy generally expected under low resource conditions (Díaz et al., 2016; Reich 2014), our results at the scale of environmentally induced plasticity showed that traits shift towards values generally thought to be resource acquisitive under stress (Fig. 2–4; Table 1,2), consistent with other resource manipulation studies for commonly measured traits (Freschet et al. 2015). Our plants growing under lower resource conditions, with lower biomass, had thinner leaves, stem, fine and tap roots, as well as narrower leaf palisade, stem vascular tissue, fine root cortex and tap root stele, but higher SLA and SRL. These coordinated trait shifts depended on the limiting resource or organ. For example, higher SLA and thinner LPT were only significant under shade, while higher FineSRL and thinner FRCT and FRSD were only significant under poor nutrient condition (Table 2). Thus, while there was some coordination, shifts were not such that the resource use strategy at the whole-plant level shifted from conservative to acquisitive, even though individual organs showed more acquisitive traits consistent with increased capture ability for the limiting resource.
Conclusion
Few studies have assessed the phenotypic response of whole plants in terms of biomass allocation, organ morphology and anatomy simultaneously, and even less under multiple resource limitation (Givnish 1988; Pratt et al., 2010). Here, our research demonstrates that major traits from all three categories shift in response to resource limitation. The resource specific extent, direction, and coordination of the responses is driven not only by changes in plant size (allometric scaling), but often includes a variable amount of active adjustment that is sometimes, but not always, aligned in the same direction as the allometric component. The substantial contribution of allometric scaling to trait responses that are consistent with a functional increase in the uptake capacity of the most limiting resource suggests that both allometric and active trait adjustments need be considered as potentially adaptive.
Author contributions
YW, AT, and LD designed the experiment. YW carried out the experiment and took all measurements. YW and AT analyzed the results. YW wrote the initial manuscript draft with revisions by AT and LD. All authors contributed to manuscript revision, and read and approved the submitted version
Supplementary materials
Table S1. List of genotypes used in this study, it’s common name, the corresponding plant ID from the USDA GRIN Database for each genotype, and the market type of each genotype.
Fig S1. Comparison of light levels between shaded and unshaded treatment. On average the low-light treatment received 50% of the photon flux density of the high-light treatment. Light intensities in each treatment were measured with a handheld light meter (LI-189; LI-COR, Lincoln, NE). Readings were taken from 8:00 AM to 7:00 PM. The light sensor was held near the soil level in each plot. We presented representative data from May 15th, a cloud-free day.
Fig S2. Effect of accounting for size scaling in traits when calculating relative distance plasticity index (RDPI). RDPI changes are shown per treatment (Light/Shade, full sun/50% shade; Rich/Poor, full nutrients/10% nutrients) and whether RDPI measures were positive or negative when initially compared to control (Light-Rich). For positive RDPI values (traits that increase in trait value with stress) a positive change when taking size scaling into account shows that size scaling decreased apparent (as measured initially) plasticity (i.e taking size scaling into account shows increased plasticity). A negative change on the other hand shows that the apparent plasticity was enhanced by size scaling (i.e taking size scaling into account shows decreased plasticity. For negative RDPI values (traits that decrease in trait value with stress) the effect of the sign of change is reversed. Symbols indicate whether RDPI significance (different from zero) is gained (circles), lost (squares), or remains unchanged (triangles, when taking biomass into account.
Fig S3. Scaling relationship between biomass and trait value among and between genotypes and treatments. (a) Scaling relationships between individual plants per genotypes and treatment (dotted lines) and (b) scaling relationships between genotypes per treatment.
Acknowledgements
We would like to thank M. Boyd and K. Tarner for assistance with plant growth and data collection, K. Bettinger, S. Chhajed, G. Manning, J. Parrilli, N. Reisinger and J. Kobylanski for assistance with experimental setup and data collection, and the greater Donovan lab group for comments that improved this study and manuscript. Additionally, we would like to thank the reviewers for their time and constructive comments. This work was financially supported by grant NSF1444522 to LAD and a China Scholarship Council (CSC) grant to YW.
Footnotes
Thanks to reviewer feedback updated the direction and focus of the paper towards allometric scaling and plasticity due to resource limitation.