The carbon economics of vegetative phase change: why plants make juvenile and adult leaves


 Across plant species and biomes, a conserved set of leaf traits govern the economic strategy used to assimilate and invest carbon. As plants age, they face new challenges that may require shifts in this leaf economic strategy. In this study, we investigate the role of the developmental transition, vegetative phase change (VPC), in altering carbon economics as plants age. We used overexpression of miR156, the master regulator of VPC, to modulate the timing of VPC in Populus tremula x alba, Arabidopsis thaliana and Zea mays to understand the impact of this transition on leaf economic traits, including construction cost, payback time, and return on investment. Here we find that VPC regulates the shift from a low-cost, quick return juvenile strategy to a high-cost, high-return adult strategy. The juvenile strategy is advantageous in light-limited conditions, whereas the adult strategy provides greater returns in high-light. The transition between these strategies is correlated with the developmental decline in the level of miR156, suggesting that is regulated by the miR156/SPL pathway. Our results provide an eco-physiological explanation for the existence of juvenile and adult leaf types, and suggest that natural selection for these alternative economic strategies could be an important factor in plant evolution.


Introduction 27
In the plant world carbon is queen. It is the currency with which they build, barter and 28 operate. Plants acquire this resource through the enzymatic reactions of photosynthesis which 29 harnesses light energy from the sun to convert CO2 into sugars. In order to succeed, plants must 30 photosynthesize efficiently and carbon must be invested wisely. 31 also tend to have lower mass-based Asat, leading to a high investment, slow-return economic 48 strategy, with the opposite being true for leaves with low LMA (Terashima & Hikosaka, 1995;49 Reich et al., 1998;Wright et al., 2004;Terashima et al., 2006). Of course, ROI is not directly 50 tied to these strategies as longer lifespan and faster payback of initial cost both have the potential 51 to lead to greater photosynthetic outputs. 52 While largely ignored, ontogenetic variation in leaf economic strategies is equal in 53 magnitude to that between species (Mason et al., 2013;Hayes et al., 2019;Funk et al., 2020). 54 Shifts in leaf traits from those associated with quick-return to longer-return economic strategies 55 are consistently observed with increasing plant age. Further, trait-trait relationships (i.e. the 56 magnitude with which leaf lifespan increases in response to increasing LMA) are significantly 57 altered across plant development, akin to the alterations induced by environment (Niinemets, 58 2004;Damián et al., 2018;Liu et al., 2019;Funk et al., 2020). These shifts in leaf traits likely 59 have significant ecological impacts altering plant growth, resource acquisition, and 60 environmental interactions across its lifespan. 61 Plant ontogeny includes the juvenile-to-adult vegetative transition known as vegetative 62 phase change (VPC). VPC and its master regulator, microRNA156, have been conserved across 63 plant evolution (Axtell & Bowman, 2008). However, the functional significance of this 64 transition, and its impacts on fitness remains a major question in plant developmental biology. 65 As plants progress from the juvenile to adult vegetative phases, the variations in challenges and 66 resources available likely command distinct economic strategies. Previously, we showed that 67 VPC and miR156 modulates morphological and physiological traits central to carbon economics 68 (Lawrence et al., 2020). Specifically, the changes in miR156 expression that drive VPC alter 69 SLA, leaf N and photosynthetic rates across species. miR156-mediated decreases in SLA 70 (equivalent to increases in LMA) between juvenile and adult phases are consistent with the shift 71 from quick to long-return economic strategies previously described. This suggests miR156 is a 72 regulator of ontogenetic changes in leaf carbon economics and that VPC, and the timing of this 73 developmental transition, has important implications for changes in resource use strategies 74 deployed across a plant's lifespan. As a genetically programmed transition, VPC may impact 75 plant fitness by allowing plants to shift between economic strategies as their physiological 76 demands change with age. 77 In this study we used wildtype and miR156 overexpressor mutants in three diverse 78 species, Arabidopsis thaliana, Populus tremula x alba, and Zea mays, to investigate how VPC---79 which is driven by a decline in miR156 expression-is related to ontogenetic shifts in carbon 80 economic strategies. We demonstrate that the previously identified phase-specific changes in leaf 81 morphology and photosynthetic physiology lead to shifts from quick to slow-return economic 82 strategies, and further show that these strategies are likely to be adaptive under different light 83 environments. The evidence that ontogenetic changes in leaf carbon economics are under the 84 regulation of miR156 not only provides a molecular mechanism for this transition in leaf 85 physiology, but also provides an eco-physiological rationale for the existence of vegetative phase 86 change. 87 88

89
Construction cost is higher for adult than juvenile or juvenilized leaves 90 The chemical composition of adult, juvenile and juvenilized leaves in three test species 91 was determined in order to understand how VPC contributes to leaf construction costs. By using 92 juvenilized leaves in miR156 overexpressor lines (those with a juvenile phenotype at leaf 93 positions that would normally be adult), we are able separate the effects of VPC from those 94 related to plant size or age. If a measured trait is developmentally phase-specific, juvenilized 95 leaves at "adult" nodes should be more similar to juvenile leaves than to adult leaves. Per gram 96 of leaf tissue, adult, juvenile and juvenilized leaves require the same amount of glucose (p > 97 0.05, Fig. 1A-B) in P. tremula x alba and A. thaliana; thus the composition of leaves is similar 98 across development (Table 1). One exception to this similarity was the concentration of nitrate in 99 A. thaliana leaves, which was greater in developmentally juvenile leaves (p < 0.05). In Z. mays, 100 adult leaves have a greater construction cost per gram of tissue than juvenile and juvenilized 101 leaves due to phase-specific differences in carbon, nitrogen, and mineral concentrations (p < 102 0.05) (Fig. 1C, Table 1). 103 At the whole leaf level, adult leaves of all three species cost significantly more (p > 0.05) 104 glucose to construct than their juvenile and juvenilized counter parts ( Fig. 1D-F). This phase-105 specific pattern is observed even in P. tremula x alba and A. thaliana where there are no 106 differences in cost per gram of leaf tissue, due to the significantly greater area and mass of adult 107 leaves compared to both juvenile and juvenilized leaves (p > 0.05) (Table S1). Overall, we find 108 that regardless of differences in chemical composition, the effect of VPC on leaf size leads to 109 higher costs for adult compared to juvenile leaves. 110

111
Leaf payback time becomes longer as plants transition from juvenile to adult, but the difference 112 in magnitude depends on light environment. 113 Payback time, the amount of time it takes for a leaf to assimilate the carbon originally 114 invested in its construction, is greater for adult leaves than juvenile or juvenilized leaves across 115 all light levels for all three test species ( Fig. 2A-C). Interestingly, there is a significant 116 interaction between developmental phase and light (p < 0.05) in all three species as the 117 difference between developmental phases is greater as light decreases. Specifically, adult leaves 118 of P. tremula x alba have, respectively, 7.85 and 3.4 fold longer payback time than juvenile and 119 juvenilized leaves under low-light (10 µmol m 2 s -1 ), but only 6.6 and 1.79 fold longer payback 120 time under high-light (1000 µmol m 2 s -1 ). In A. thaliana, the payback time for adult leaves is, 121 respectively, 18.8 and 5.7 fold longer under low-light, and 9.4 and 3.16 fold longer under high-122 light, than for juvenile and juvenilized leaves. Lastly, in Z. mays, adult leaves have, respectively, 123 8.13 and 11.64 fold longer payback time under low-light, and 6.3 and 12 fold longer payback 124 time under high-light than juvenile and juvenilized leaves. Because construction cost remains 125 constant across light levels in our modeled payback time, these differences are a result of 126 photosynthetic responses to light modeled using light response curves (Table S2). Similar 127 relationships between payback time and light are observed on a per gram basis for all three 128 species, although to a lesser extent (Fig. S1). As we saw with construction costs, the greater 129 similarity between juvenilized leaves with juvenile, as opposed to adult leaves, indicates 130 differences in miR156-mediated development, rather than plant size or age, is responsible for the 131 payback differences observed here. 132 133

Leaf lifespan is longer for adult leaves than juvenile leaves 134
To understand how phase-specific differences in leaf construction cost and payback time 135 impact the overall economic strategy of juvenile and adult leaves, we also measured the 136 photosynthetic lifespan of these leaves. Previously, we determined that SLA, which is closely 137 connected to lifespan, is a phase-specific trait, with adult leaves having lower SLA (or higher 138 LMA) compared to both juvenile and juvenilized leaves of all three test species (Table S1,  139 Lawrence et al., 2020b). Across species, thicker, more dense adult leaves (low SLA) had 140 significantly longer (p < 0.05) lifespans than juvenile leaves ( Fig. 2D-F). Lifespan differences 141 between juvenile and adult leaves ranged from 26 and 23 days in P. tremula x alba and Z. mays, 142 respectively, to 8 days in A. thaliana. Of note, the low-light payback time for adult Z. mays 143 leaves (which approaches 52 days) far exceeds the 34-day average lifespan of these leaves. 144

145
Light environment alters the phase-specific relationship of leaf return on investment 146 Despite higher construction cost and longer payback time, lifespan differences produce a 147 higher return on investment (ROI) for adult leaves than for juvenile leaves, across most light 148 environments ( Fig. 2G-I). Adult leaves outperform juvenile leaves in high-light environments 149 (1000 µmol m 2 s -1 ) with 14, 2.8 and 18 times more glucose returns for P.tremula x alba, A. 150 thaliana and Z. mays respectively. However, in low-light environments (10 µmol m 2 s -1 ), adult 151 leaves experience net carbon loss with 4.3, 4.6 and 5.1 times fewer returns than juvenile leaves. 152 Furthermore, the ROI for juvenile leaves is less sensitive to light environment than is the case for 153 adult leaves. Juvenile leaves approach their maximum ROI at a PPFD around 10 mol m -2 day -1 154 whereas, with the exception of A. thaliana, which was grown under low-light conditions for this 155 experiment, the ROI for adult leaves continues to increase as PPFD increases, well past 40 mol 156 m -2 day -1 . ROI per gram of tissue display similar patterns across light environments to leaf-based 157 measures in P. tremula x alba and Z. mays. However, in A. thaliana, juvenile tissue maintains a 158 higher ROI than adult tissue across all light environments (Fig. S1). light, juvenile leaves of both species more quickly reached higher photosynthetic rates and 166 induction states than adult leaves (Fig. 3). The relaxation times for Rubisco activation were not 167 significantly different (p < 0.05) between juvenile and adult leaves (Table S3), indicating that 168 developmental differences in induction occur prior to 1 min, during the 'fast-phase' of induction. 169 Alternatively, developmental differences in induction could be due to a combination of 170 traits, such as stomatal conductance and Calvin cycle intermediate accumulation, rather than 171 solely to the activation of Rubsico. In P. tremula x alba, differences in photosynthetic induction 172 are apparent by 1 min of high-light exposure, suggesting phase-specific differences in induction 173 are likely present before exposure to high-light, or during the first minute, when buildup of 174 intermediates in the Calvin cycle are most important. This is not the case for Z. mays, where 175 juvenile leaves reach a higher induction state than adult leaves during this 'slow-phase' period of 176 minutes 1-10 (Fig. 3D). These results suggest that while juvenile leaves of both species have 177 faster photosynthetic induction properties than their adult counter parts, the mechanisms behind 178 these differences may vary between species. were much more affected by PPFD compared to juvenile leaves, as indicated by the smaller 188 slope of the negative relationship with payback time, and the larger slope of the positive 189 relationship with ROI for adult leaves of both species (Table S4). Similar trends are observed on 190 a per gram of tissue basis, although differences between juvenile and adult leaf payback time 191 across PPFD are not significant (p > 0.05) for P. tremula x alba (Fig. S3, Table S4). 192 Surprisingly, there was no significant interaction between developmental phase and 193 number of sunflecks for payback time or ROI in P. tremula x alba (Fig. 4B,D) (Table S4). While 194 significant interactions (p < 0.05) were present for these relationships in Z. mays, the low R 2 195 values for both developmental phases and economic traits (R 2 ≤ 0.05), indicate that sunflecks 196 have a minor effect on payback time and ROI (Table S4). There were no significant differences 197 in the way juvenile and adult tissue responded to sunflecks on a per gram basis, and any 198 significant relationships between payback time or ROI and sunflecks for either developmental 199 stage was minor (R 2 ≤ 0.05) (Fig. S3, S4, Table S4). 200 Despite there being no meaningful relationship between carbon economics and number of 201 sunflecks, lags in photosynthetic response to light fluctuations due to the rate of induction 202 resulted in assimilation loss for both developmental phases in both species. As the number of 203 sunflecks increased and plants were exposed to more rapid changes in light, the assimilation lost 204 due to a lag in induction also increased ( Fig. 6). In both species, the faster induction rate in 205 juvenile leaves resulted in lower assimilation losses compared to adult leaves (p < 0.05). 206 Nevertheless, the impact of these losses on carbon economics in these simulated environments is 207 minimal compared to the effect of overall changes in PPFD. 208 209

Discussion 210
Vegetative phase change alters plant economic strategies through miR156-mediated 211 changes in leaf morphology and physiology. Juvenile leaves-which have high levels of 212 miR156-use a low-cost, quick-return economic strategy, whereas adult leaves-which have 213 low levels of miR156-use a high-cost, slow-return strategy. The adult strategy carries more 214 risk than the juvenile strategy, but has the potential to provide high ROI (Fig. 7). This 215 developmental shift in strategy is brought about by the same traits that govern leaf economics 216 across species and environments in the LES, namely leaf lifespan and LMA (Wright et al., 217 2004). Across species, adult leaves have high LMA and long lifespan while juvenile leaves have 218 low LMA and shorter lifespan ( Fig. 2D-F , Table S1). In Z. mays, leaf N and the photosynthetic 219 rates of juvenile and adult leaves follow the established trait relationships within the LES as the 220 low LMA juvenile leaves also have higher mass-based measures of N and Asat compared to adult 221 leaves (Table 1, Lawrence et al., 2020b). As previously reported, trait-trait relationships of the 222 LES are not always conserved at smaller than global scales (Edwards et al., 2014;Mason & 223 Donovan, 2015b;Anderegg et al., 2018). We find this to be the case for developmental changes 224 in leaf N and Asat in P. tremula x alba and A. thaliana, as these leaves have no significant 225 differences in mass-based measures of N and Asat despite their differences in LMA. It is unclear 226 why the expected negative relationships between LMA and leaf N or Asat are lacking, as LMA 227 increases during VPC in these species. However, previous work found no phase-specific changes 228 in photosynthetic nitrogen use efficiency (PNUE) (Lawrence et al., 2020), indicating adult leaves 229 somehow compensate for the structural changes that often reduce PNUE in high LMA leaves, 230 potentially through their increased stomatal density which could reduce resistance to CO2 231 diffusion (Hikosaka, 2004;Feng et al., 2016;Lawrence et al., 2021). 232 These developmentally programmed changes in leaf carbon economic strategy are likely 233 to have ecological implications because plants face different biotic and abiotic challenges during 234 their lifetime. For example, juvenile leaves, which have a photosynthetic advantage over adult 235 leaves under low-light (Lawrence et al., 2020), are more likely to be found in low, highly 236 dynamic, light environments because young plants are often shaded by their neighbors, and 237 quickly self-shade due to their relatively rapid rate of leaf production (Wang et al., 2008). Here 238 we find that the economic strategy of juvenile leaves further adds to this low-light advantage as 239 these leaves are able to maintain a positive carbon balance even at very low light levels (Fig. 2,  240   7). The payback time of adult leaves dramatically increases under low-light, greatly reducing 241 ROI and, in some cases, exceeds the lifespan of a leaf, resulting in net carbon loss. On the other 242 hand, the magnitude with which adult ROI exceeds that of juvenile leaves increases significantly 243 with increasing irradiance (Fig. 2). Overall, the economic strategy of juvenile leaves appears to 244 be less sensitive to light environment, making it a low-risk, low-reward strategy that is likely 245 beneficial for a young plant with minimal carbon reserves. Although the high-cost strategy of 246 adult leaves incurs greater risk because of long-term environmental variability, the high-reward 247 potential of this strategy may outweigh this risk. 248 Surprisingly, the ability of juvenile leaves to respond more quickly to sunflecks than 249 adult leaves had little effect on the carbon economic relationships between these leaves in our 250 dynamic light models (Fig. 4,5). In our simulated environments, we held the total time leaves 251 were exposed to sunflecks relatively constant but allowed the number of sunflecks to vary 252 dramatically. As a result, there was no correlation between the number of sunflecks and daily 253 integrated PPFD. It may be that developmental differences in induction rate have a greater 254 influence on carbon economic relationships when sunflecks play a large role in determining daily 255 PPFD, such as in a rainforest understory where sunflecks can account for 52% of daily light 256 (Chazdon & Pearcy, 1991). 257 The developmental differences in carbon economics described here indicate that 258 genotypic variation in miR156 expression, and subsequently the timing of VPC, could have 259 significant consequences for plant ecology and evolution. Among other things, leaf economic 260 strategies alter plant growth and survival in response to nutrient and water availability, 261 herbivory, competition and light environment (Coley, 1988;Poorter et al., 2006;Reich, 2014;262 Mason & Donovan, 2015a;Russo & Kitajima, 2016;Adams et al., 2020). naturally juvenile and adult leaves in wild-type lines and "juvenilized" leaves, those in miR156 307 overexpressor lines with a juvenile phenotype at leaf positions that would normally be adult, 308 were sampled. In P. tremula x alba, developmental stage was determined by petiole shape and 309 abaxial trichome density as described in Lawrence et al., (2021). Juvenile leaves were sampled 310 from wild-type node 10 and adult from node 25, and juvenilized leaves were sampled from 311 overexpressor node 25. In Z. mays, developmental stage was determined by the presence or 312 absence of epicuticular wax and trichomes as described in Poethig (1988). Juvenile leaves were 313 sampled from node 4 and adult from node 11 in wildtype plants, and juvenilized leaves sampled 314 from node 4 in Cg1 mutants. In A. thaliana, developmental stage was determined by the presence 315 or absence of abaxial trichomes. Juvenile leaves were sampled from node 5 for physiological and 316 morphological measurements and nodes 2-5 for construction cost measures, and adult and 317 juvenilized from node 10 and 10-15 in wildtype and miR156 overexpressors, respectively. 318 319

Leaf Construction Cost Determination 320
Area of fresh leaf samples was determined from photographs using FIJI software 321 (Schindelin et al., 2012). Samples were then dried at 60°C until consistent mass, ground using a 322 Willey Mill until small enough to pass through a 2 mm grinding mesh, and then ground further 323 using a mortar and pestle. Each Z. mays sample consisted of ~100 mg tissue from one leaf, P. 324 tremula x alba samples consisted of ~100-120 mg tissue from between 1 and 4 leaves, and A. For nitrate determination, 20 mg of sample was added to 2 ml 80°C water for 20 mins for 331 nitrate extraction. Samples were centrifuged at 5000 rcf for 15 mins. 0.2 ml of supernatant was 332 mixed with 0.8 ml of 5% (w/v) salicylic acid in H2SO4 and incubated at room temperature for 20 333 mins. Following incubation, 19 ml of 2N NaOH was added to samples. Absorbance of 410 nm 334 was determined for 0.2 ml aliquots of each sample and NO -3-N standards of 1 to 200 µg, used to 335 create a standard curve. 336 The remaining tissue was weighed and used for mineral determination. Samples were 337 ashed at 550°C in a muffle furnace for 6 hrs and weighed again. Ash alkalinity was determined 338 in duplicate for each sample to measure CO3 -2 that formed when oxides from the plant tissue 339 reacted with CO2 upon cooling. 4 mg of ash was mixed in 5 mL of deionized H2O and 2-3 drops 340 of 0.5% phenolphthalein were added. The solution was titrated with 0.5N HCl until the pink 341 indicator color disappeared. An additional volume of HCl, equal to that needed for titration plus 342 an additional 2 mL, was added to the sample. The solution was then boiled for 5 mins, cooled to 343 room temperature and an additional 2-3 drops of phenolphthalein were added. Samples were then 344 back titrated with 0.5N NaOH until a faint pink color persisted. The average alkalinity from the 345 two replicates of each sample was used in calculations. level changes and data logging. Net photosynthetic rate (Anet) in A. thaliana was measured at 357 light levels of 1000,800,600,300,200,150,100,75,50,25, 0 µmol m -2 s -1 at a flow rate of 300 358 µmol air sec -1 , in Zea mays at light levels of 1800, 1500, 1200, 1000, 800, 600, 300, 200, 150, 359 100, 75, 50, 25, 0 µmol m -2 s -1 at a flow rate of 400 µmol air sec -1 , and P. tremula x alba at light 360 levels of 1500,1200,1000,800,600,300,200,150,100,75,50,25,10  Photosynthetic induction was measured on leaves exposed to light levels less than 20 365 µmol m -2 s -1 for a minimum of 20 mins. Induction was measured by logging every 10 seconds as 366 leaves were exposed to 20 µmol m -2 s -1 of light for 2 mins and then shifted to saturating light, 367 1800 µmol m -2 s -1 for P. tremula x alba and Z. mays or 1000 µmol m -2 s -1 for A. thaliana, for 20 368 mins. 369 370

Carbon Economics Calculations 371
All equations for calculations are described in Poorter, (1994) andPoorter et al., (2006) 372 and presented in the appendix. Assimilation and respiration rates were converted from µmol CO2 373 m -2 to grams of glucose per gram of tissue using equation (3.1). Specific leaf area (SLA) used in 374 this equation was calculated by dividing the fresh leaf area by its dry weight. Payback time, the 375 time in days required for leaves to assimilate the equivalent glucose needed to construct it, was 376 determined using equation (4.1). Return on investment (ROI) was calculated using equation 377 Part 1 of the model determines light levels across the day based on Campbell & Norman, 391 (1998), Zhu et al., (2004) and Salter et al., (2019. Solar declination angle (eq. 6.1), hour angle 392 (eq. 6.2), and solar elevation angle (eq. 6.3) were calculated using a latitude of Philadelphia, PA, 393 USA (39.95°N or 0.697 rads) and Julian day of 180. Direct and diffuse light were calculated 394 using equations (6.4) and (6.5) respectively. Solar constant was assumed to be 2600 µmol m -2 s -1 395 and atmospheric transmissivity 0.75. Light levels during sun and shade flecks were determined 396 using equations (6.6) and (6.7) respectively. Leaf area index (LAI) varied between 0.5 and 8 for 397 each simulation and are reported in Table S5. 398 Part 2 of the model determines when light switches between sun and shadeflecks using 399 equations (7.1) and (7.2) described in Salter et al., (2019). Day light began at 6:00 and ended at 400 18:00 with simulations set to begin with a sunfleck. Initial sunfleck lengths varied between 401 simulations and are reported Table S5 vs Time for minutes 1-10 of induction upon exposure to high-light (eq. 8.3). The initial minute of 410 induction, often referred to as the 'fast phase', was excluded from our model because 1) 411 increases in Anet during this phase are primarily governed by increases in the pool of RuBP and 412 therefore, Rubsico kinetics cannot accurately be estimated using gas exchange, and 2) at times 413 greater than 1 min, which is the resolution of our model, the contribution of this phase to Anet is 414 negligible and can be excluded (Woodrow & Mott, 1989). At times greater than 10 mins, most 415 changes in Anet are governed by stomatal opening, and therefore A * shows little change 416 (Woodrow & Mott, 1989). Tau during decreases in light describes the deactivation of Rubisco. 417 Because Anet decreases more quickly than Rubisco deactivation when light levels are reduced, 418 tau during deactivation is difficult to estimate using gas exchange. Woodrow & Mott, (1989) 419 showed that when measured biochemically, tau during deactivation was roughly 5x tau during 420 induction, we therefore estimated our values in this way. Induction state, representing the percent 421 of Asat instantaneous assimilation is at during induction was calculated by equation (8.4). 422 Anet throughout the day required the calculation of the potential maximum assimilation 423 rate (Af, eq. 9.1) and initial assimilation rate prior to induction (Ai, eq. 9.2) for each 1 min 424 interval as described in Woodrow & Mott, (1989) and Taylor    Supplemental Tables  644  645   Table S1. Leaf morphological traits 646