Leaf water content contributes to global leaf trait relationships

The theoretical model

The model presented here builds on a recently developed metabolic theory based on biochemical kinetics. It describes a non-linear relationship between plant metabolic rate per unit of dry mass (B_s, nmol g⁻¹ s⁻¹), such as light-saturated photosynthetic rates and dark respiration rates, plant water content (S, g g⁻¹), and temperature (in degrees K)^19,20, i.e.

where g₁ is a normalisation constant, k₁ represents the maximum increase in specific metabolic rates due to changes in water content (i.e. from dehydrated to fully hydrated), K₁ represents the water content when the mean reaction rate of cellular metabolism reaches one-half of its maximum, E is the activation energy, and k is Boltzmann’s constant. In this model S is defined on a dry mass basis (i.e. the ratio of plant water mass to plant dry mass) to broaden its range and better reflect the proportional changes in the amount of water in plant tissues²⁰. Full details of the model’s assumptions can be found in the Methods and Huang et al.^19,20. The model was tested and shown to hold true for a broad range of species and for whole plants and above- and belowground organs²⁰. Here, we first applied the model to describe the quantitative effects of dry mass-based LWC (the ratio of leaf water mass to leaf dry mass) and temperature on the light-saturated leaf photosynthetic rate per unit of dry mass or leaf photosynthetic capacity (P_s, nmol CO₂ g⁻¹ s⁻¹), which can be expressed as

where P_L is the light-saturated whole-leaf photosynthetic rate (nmol s⁻¹). Equation 2 indicates that the log-transformed temperature-corrected P_s (i.e. P_scor) should increase with LWC, following Michaelis-Menten type hyperbolic response, i.e.

Rearranging Eq. (2) shows that the temperature- and LWC-corrected whole-leaf photosynthetic rate (i.e. P_Lcor) should scale isometrically with M_L, i.e.

In this study, we refer to the temperature or water content correction as moving the temperature term ((frac{E}{{kT}})) or water content term ((frac{{k}_{1}cdot {{{{{rm{LWC}}}}}}}{{K}_{1}+{{{{{rm{LWC}}}}}}})) to the left-hand side of the model, an approach that has been commonly used in previous studies²⁸.

Following previous studies^29,30,31, we assume that P_L is proportional to A_L, i.e. ({P}_{L}propto {A}_{L})because leaf area directly determines the light interception capacity¹. Therefore, the quantitative relationship between SLA and LWC and temperature can be described as

where g₂ is another normalisation constant. Given that leaf area is a direct indicator of leaf photosynthetic capacity and that both traits reflect the long-term adaptation of plants to environmental change^3,13, k₁ in Eqs. (2) and (5) represent the maximum increase in mass-specific leaf photosynthetic capacity due to changes in water content. Since the temperature has a direct effect on the metabolic rates³² and productivity of ecosystems³³, it is reasonable to assume that temperature can affect SLA globally^34,35. Therefore, in this context T denotes the mean growing-season temperature (in degrees K), as SLA might be more responsive to long-term changes in temperature. Equation (5) predicts that SLA should increase with both LWC and the growing-season temperature. Rearranging Eq. (5) yields

which predicts that the log-transformed temperature-corrected SLA (i.e. SLA_cor) should increase with LWC following Michaelis-Menten dynamics. Likewise, by moving (frac{{k}_{1}cdot {{{{{rm{LWC}}}}}}}{{K}_{1}+{{{{{rm{LWC}}}}}}}) and (frac{E}{{kT}}) to the left-hand side and M_L to the right-hand side of Eq. (5), we observe that the temperature- and LWC-corrected leaf area (i.e. A_Lcor) should scale isometrically with leaf mass, i.e.

We note that the scaling of P_Lcor and A_Lcor with respect to M_L will reveal how LWC mediates the scaling exponent of leaf trait relationships.

Effects of LWC and temperature on leaf trait scaling

The numerical value of the exponent for the P_L versus A_L scaling relationship calculated from the empirical data was 0.99 (Supplementary Fig. 1; 95% CI = 0.95 and 1.02, r² = 0.87), strongly supporting the model assumption that P_L scales isometrically with A_L. We then examined the effect of LWC on leaf trait scaling. The numerical value of the scaling exponent for the P_L versus M_L relationship was 0.95 (Fig. 2a; 95% CI = 0.92 and 0.99, r² = 0.83). The non-linear relationship between P_scor and LWC described by Eq. (3) was supported by the empirical data (Fig. 2b; Supplementary Table 1). The non-linear model (Eq. 3) also had a lower Akaike’s Information Criterion score than the simple linear model between log-transformed P_scor and LWC (i.e. 793.5 versus 1988.8). After LWC and temperature were corrected (see Eq. 4), the numerical value of the scaling exponent became 0.97 (Fig. 2c; 95% CI = 0.94 and 1.01, r² = 0.85), which was statistically indistinguishable from 1.0 (P > 0.05), as predicted by the model. Likewise, the numerical value of the exponent (i.e. α) for the A_L versus M_L scaling relationship was 1.02 (Fig. 2d; 95% CI = 1.02 and 1.03, r² = 0.92). Additional analyses using the pooled dataset showed that log-transformed SLA increased with LWC following Michaelis-Menten dynamics, as predicted by Eq. (6) (Fig. 2e; Supplementary Table 1). After the effects of LWC and temperature were accounted for (using Eq. 7), the numerical value of α became 1.01 (Fig. 2f; 95% CI = 1.00 and 1.01, r² = 0.95). Thus, both of the scaling exponents numerically converged onto 1.0 once LWC and temperature were corrected. In addition, an inspection of the locally weighted smoothing (LOWESS) curves showed that the curvature in both scaling relationships was reduced after the effects of LWC and temperature were corrected, as predicted by the model (Fig. 2).

The results presented here show that temperature and LWC quantitatively correlate with other leaf traits, such as P_s and SLA (or LMA), as predicted by the model. The increases in P_s and SLA attenuate with increasing LWC (Fig. 2b, e), indicating that leaf water availability sets a constraint on the maximum P_s and SLA that leaves can reach. It has long been recognised that SLA is closely correlated with leaf growth rate and metabolic activity^14,36,37. Therefore, it is reasonable to also expect that SLA, as well as P_s, will be quantitatively affected by LWC, which can change as a function of developmental status (such as leaf maturation and the accumulation of lignified tissues) and transiently as a function of evapotranspiration. LWC is also a reflection of species-specific adaptation to environmental conditions in different biomes. Nevertheless, our model, as well as the empirical data used to test it, reveal a broad and statistically robust correlation between critical leaf functional traits and leaf tissue water content. However, the observed variations in P_scor (Fig. 2b) suggest that in addition to temperature and LWC, other factors (e.g. plant phylogeny and soil fertility) may also affect leaf photosynthetic capacity, which is not accounted for in our model and should be critically examined in future research.

Leaf area-mass scaling among different groups

The numerical value of α varied across different plant growth forms, ecosystems, and latitudinal zones (Fig. 3 and Supplementary Table 2). In particular, the leaf area versus mass scaling relationship showed a clear pattern along a latitudinal gradient (Fig. 3c and Supplementary Table 2). The numerical value of α decreased from 1.10 in boreal regions (95% CI = 1.08 and 1.12, r² = 0.91) to 1.00 in temperate regions (95% CI = 0.99 and 1.01, r² = 0.91), and to 0.94 in tropical regions (95% CI = 0.92 and 0.95, r² = 0.91). However, after correcting for the effects of LWC and temperature, as predicted, α converged onto 1.0 across all different groupings, and the r² values of the scaling relationships also increased (Fig. 3 and Supplementary Table 2).

Our analyses show that LWC also affects the numerical values of the exponents of leaf trait scaling relationships, which helps to explain why different works sometimes report significant differences in the exponents governing these relationships^8,10. In particular, the numerical values of the scaling exponent governing the leaf area versus mass scaling relationship differ among different plant growth forms, ecosystems, and latitudinal zones (Fig. 3 and Supplementary Table 2), indicating that no invariant “scaling exponent” (i.e. α) holds true for the leaf area-mass scaling relationship. For example, in our dataset, α is significantly smaller than 1.0 in tropical regions (i.e. in keeping with a “diminishing returns” relationship in leaf area with respect to increasing leaf mass), close to 1.0 in temperate regions (i.e. a break-even relationship), and significantly larger than 1.0 in boreal regions (i.e. an “increasing returns” relationship). This shift in α along a latitudinal gradient may be associated with the different strategies to cope with variations in water availability, e.g. the high evapotranspiration rates in tropical regions may constrain increases in leaf area with increasing leaf mass, therefore resulting in diminishing returns, whereas, in boreal regions, the reduced water stress may enable plants to maximise leaf area to achieve relatively high photosynthetic capacities. Despite the variability in the numerical values of scaling exponents across different groups, the degree of curvature in these scaling relationships is reduced, and exponents converge onto unity after the effects of LWC and temperature are accounted for (Fig. 3), as predicted by the model (Eq. 7). This finding indicates that the variations in the exponent can, at least partially, be ascribed to the effects of LWC on SLA and the relative rate of increase in leaf area versus leaf mass. It is noteworthy that the numerical value of the scaling exponent for the P_L versus M_L relationship is also very close to 1.0, indicating the relatively weak effects of temperature and LWC on leaf photosynthesis-mass scaling. This may be partially attributed to the relatively limited number of data with concurrent LWC measurements and the adaptation of leaf traits to long-term temperature changes (see below for detailed discussion). Nevertheless, more measurements on LWC and leaf photosynthetic rates are needed to further test how LWC mediates leaf photosynthesis-mass scaling.

Given that LWC was weakly correlated with M_L (Supplementary Fig. 2, log-log slope = −0.01, 95% CI = −0.02 and 0, r² < 0.01, P = 0.17), it is reasonable to assume that LWC is independent of M_L in order to derive the change in temperature-corrected P_L and A_L with respect to differences in M_L i.e. (partial left({P}_{{{{{{rm{L}}}}}}}{e}^{E/{kT}}right)/partial {M}_{{{{{{rm{L}}}}}}}={g}_{1}{e}^{{k}_{1}cdot {{{{{rm{LWC}}}}}}/left({K}_{1}+{{{{{rm{LWC}}}}}}right)}) and (partial left({{A}_{{{{{{rm{L}}}}}}}e}^{E/{kT}}right)/partial {M}_{{{{{{rm{L}}}}}}}={g}_{2}{e}^{{k}_{1}cdot {{{{{rm{LWC}}}}}}/left({K}_{1}+{{{{{rm{LWC}}}}}}right)}). These equations predict that increases in temperature-corrected P_L and A_L with respect to M_L should become faster with increasing LWC when LWC is relatively low, and achieve a relatively constant rate (plateau) when LWC is saturated, such that ({k}_{1}cdot {{{{{rm{LWC}}}}}}/left({K}_{1}+{{{{{rm{LWC}}}}}}right)approx 1) (Supplementary Fig. 3). Therefore, LWC plays an essential role in regulating the dynamics of whole-leaf photosynthesis and area.

Leaf water mass is a robust predictor of leaf photosynthesis and area

We used the China Plant Trait Database³⁸ (see Methods) to explore whether whole-leaf N and P mass (g) or water mass (M_W, g) show stronger scaling relationships with P_L and A_L. This dataset was specifically used for this purpose because it includes paired data required for this comparison. The numerical values of the scaling exponents of the relationships of P_L versus leaf N and P were 0.94 (Fig. 4a; 95% CI = 0.90 and 0.98, r² = 0.80) and 0.76 (Fig. 4b; 95% CI = 0.72 and 0.82, r² = 0.71), respectively. Likewise, the exponents for the scaling of A_L versus leaf N and P were 0.95 (Fig. 4d; 95% CI = 0.92 and 0.99, r² = 0.86) and 0.81 (Fig. 4e; 95% CI = 0.77 and 0.86, r² = 0.78), respectively. However, P_L and A_L scaled as the 0.95 power (Fig. 4c; 95% CI = 0.91 and 0.99, r² = 0.81) and as the 0.97 power of M_W (Fig. 4f; 95% CI = 0.94 and 0.99, r² = 0.91), respectively. Thus, M_W provided a higher explanatory power in predicting P_L and A_L with an exponent closer to 1.0 than either leaf N or P. Moreover, an inspection of the LOWESS curves (blue lines in the graphs of Fig. 4) showed that the scaling of P_L and A_L with respect to both leaf N and P had a stronger degree of curvature than the scaling of P_L and A_L with respect to M_W. Thus, water appears to have a higher hierarchical status than nutrients in terrestrial ecosystems where almost all biological activities, including nutrient uptake, depend on water availability¹⁵.

Leaf N and P are considered important leaf traits in the leaf economics spectrum because of their close correlations with other leaf traits, such as P_L and A_L^1,9,10. However, the numerical values of scaling exponents are known to vary significantly across different plant groups⁹, and no mechanistic model has been proposed to explain this variation. In this study, we found that the scaling relationships of P_L and A_L, with respect to leaf N and P, exhibited curvature, whereas using M_W resulted in much more robust and linear scaling relationships (Fig. 4). These analyses indicate that leaf water availability may be a better indicator of P_L and A_L compared to leaf N and P, probably because (i) water serves as an essential biochemical reactant, solvent, and nutrient carrier in plants^15,20, which indicates that leaf water availability plays a more fundamental role in determining leaf growth and metabolism than leaf N and P; and (ii) LWC is more sensitive to changes in endogenous (e.g. developmental age) and exogenous factors (e.g. drought stress) thereby providing a better reflection of plant growth status and changes in other leaf traits^15,39. Leaf N shows a more robust scaling relationship with P_L than P (Fig. 4a, b), which is reasonable because N is a major component of key enzymes involved in photosynthesis, such as Rubisco.

The important role of LWC in the leaf economics spectrum

Although LWC is a relatively understudied leaf trait, its important role in the leaf economics spectrum is indicated by the strong correlations between LDMC, a trait that is functionally related to LWC, and other traits, such as SLA⁴⁰. Recent work reveals that LDMC also affects the ability of leaves to store and exchange heat in response to changes in surface energy fluxes, which further influences leaf carbon assimilation²⁶. In addition, LDMC is predicted to be an effective indicator of plant resource use⁴¹ and ecosystem net primary production²², which corroborates the crucial role of LWC in regulating leaf growth and metabolism and, therefore, influencing productivity at the whole-plant and at the ecosystem levels. Although leaf functional traits have been extensively explored in ecological studies^{1,7,8,9,10,11,12}, leaf trait variation across species or ecosystems, as well as the quantitative relationships among leaf traits, are not well understood due to the lack of a general model that captures the fundamental mechanism governing leaf trait variation. Our findings indicate that LWC is an important leaf trait that shows non-linear quantitative relationships with other important leaf traits, i.e. P_s and SLA (or LMA), and directly regulates the leaf trait scaling relationships. Although temporal variations in LWC might exist in response to instantaneous changes in microclimate conditions, previous work indicates that variation in LDMC (and thus LWC) is relatively conserved across seasons⁴². Further investigations should be conducted to better understand the temporal (e.g. seasonal and yearly) dynamics of LWC and its link to the dynamics of other leaf traits across different species and ecosystems.

Relatively weak effects of temperature and precipitation

It is worth noting that the energy activation E in the Boltzmann factor is observed to be very close to 0 for both the leaf photosynthesis-mass scaling (−0.13, 95% CI = −0.40 and 0.15) and leaf area-mass scaling relationships (0.03, 95% CI = 0.01 and 0.05), which is consistent with previous work showing that both individual tree growth and ecosystem productivity are weakly correlated with growing-season air temperature^33,43. This observed weak effect of temperature may be probably attributed to species-specific short-term phenotypic acclimation and long-term adaptation of plant photosynthetic traits to changes in ambient temperature^43,44. However, as discussed elsewhere^33,43, we used long-term mean air temperature for the SLA analyses, which may not effectively reflect the leaf temperature at which leaf traits were measured. Future investigations are needed to critically examine the effect of leaf temperature on leaf trait variations and trait scaling relationships.

Although the effect of precipitation was not directly quantified in our model, it might be partially accounted for by the effect of LWC because the plant water content is closely correlated with a variety of hydrological processes such as precipitation and evapotranspiration, soil water status, and plant growth^16,20. Our analyses show that both LWC and SLA_cor are overall weakly correlated with mean annual precipitation (Supplementary Fig. 4), indicating that precipitation alone might not be able to fully explain leaf trait variation. In contrast, LWC shows a strong non-linear correlation with other leaf traits as predicted by our model and as shown by using a global dataset, demonstrating that LWC is an integrative trait that plays a critical role in mediating leaf trait variability.

In this study, we combined a theoretical model and a global leaf trait dataset to quantify the effects of LWC and temperature on leaf trait scaling relationships (Fig. 5). The theoretical framework presented here provides a mechanistically deeper insight into the integrated functional traits of leaves and into how future changes in global climate may affect leaf traits such as P_s and SLA (and LMA). Empirical observations and global climate simulations predict an overall rise in temperature and especially an intensification of drought stress in many regions of the world^45,46. These climatic changes will undoubtedly influence leaf traits, including P_s and SLA, directly through the effect of temperature or indirectly through the drought-induced changes in leaf structure and leaf water status. Our study highlights the importance of LWC as a simple but powerful trait that can be easily measured, and that quantitatively correlates with other important leaf traits. Consequently, LWC should be included in the leaf economics spectrum to better understand the trait variations and scaling relationships.

Source: Ecology - nature.com