New Phytologist

Volume 242, Issue 5 p. 1919-1931

Full paper

Open Access

Variation in leaf carbon economics, energy balance, and heat tolerance traits highlights differing timescales of adaptation and acclimation

Nicole N. Bison,

Corresponding Author

Nicole N. Bison

[email protected]

orcid.org/0000-0002-3810-8223

Department of Botany, The University of British Columbia, Vancouver, BC, V6T 1Z4 Canada

Biodiversity Research Centre, The University of British Columbia, Vancouver, BC, V6T 1Z4 Canada

Author for correspondence:

Nicole N. Bison

Email:[email protected]

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Sean T. Michaletz,

Sean T. Michaletz

orcid.org/0000-0003-2158-6525

Department of Botany, The University of British Columbia, Vancouver, BC, V6T 1Z4 Canada

Biodiversity Research Centre, The University of British Columbia, Vancouver, BC, V6T 1Z4 Canada

Search for more papers by this author

Nicole N. Bison,

Corresponding Author

Nicole N. Bison

[email protected]

orcid.org/0000-0002-3810-8223

Department of Botany, The University of British Columbia, Vancouver, BC, V6T 1Z4 Canada

Biodiversity Research Centre, The University of British Columbia, Vancouver, BC, V6T 1Z4 Canada

Author for correspondence:

Nicole N. Bison

Email:[email protected]

Search for more papers by this author

Sean T. Michaletz,

Sean T. Michaletz

orcid.org/0000-0003-2158-6525

Department of Botany, The University of British Columbia, Vancouver, BC, V6T 1Z4 Canada

Biodiversity Research Centre, The University of British Columbia, Vancouver, BC, V6T 1Z4 Canada

Search for more papers by this author

First published: 26 March 2024

https://doi.org/10.1111/nph.19702

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Summary

Multivariate leaf trait correlations are hypothesized to originate from natural selection on carbon economics traits that control lifetime leaf carbon gain, and energy balance traits governing leaf temperatures, physiological rates, and heat injury. However, it is unclear whether macroevolution of leaf traits primarily reflects selection for lifetime carbon gain or energy balance, and whether photosynthetic heat tolerance is coordinated along these axes.
To evaluate these hypotheses, we measured carbon economics, energy balance, and photosynthetic heat tolerance traits for 177 species (157 families) in a common garden that minimizes co-variation of taxa and climate.
We observed wide variation in carbon economics, energy balance, and heat tolerance traits. Carbon economics and energy balance (but not heat tolerance) traits were phylogenetically structured, suggesting macroevolution of leaf mass per area and leaf dry matter content reflects selection on carbon gain rather than energy balance. Carbon economics and energy balance traits varied along a common axis orthogonal to heat tolerance traits.
Our results highlight a fundamental mismatch in the timescales over which morphological and heat tolerance traits respond to environmental variation. Whereas carbon economics and energy balance traits are constrained by species' evolutionary histories, photosynthetic heat tolerance traits are not and can acclimate readily to leaf microclimates.

Introduction

Multivariate trait correlations characterize evolutionary convergence on a set of general relationships linking the form, function, chemistry, and longevity of leaves from diverse taxa and environments. One prominent example is the leaf economics spectrum (LES), which confines this diversity to a single axis of variation (Reich et al., 1997; Wright et al., 2004; Asner et al., 2016; Díaz et al., 2016). Two prominent theories have been invoked to help understand the potential mechanisms driving multivariate trait correlations: carbon economics theory and energy balance theory.

Carbon economics theory posits that multivariate trait correlations originate from strong selection for leaves to produce a positive net carbon balance over their lifetime (Chabot & Hicks, 1982; Williams et al., 1989; Kikuzawa, 1995; Kikuzawa & Lechowicz, 2006; Blonder et al., 2011; Michaletz et al., 2016). This can be formalized mathematically as

G = \frac{⟨\dot{A}⟩ t_{L}}{LMA} \cdot \frac{k_{1}}{k_{2}} > 1

(Eqn 1)

where G is lifetime carbon gain per unit carbon invested (kg C kg C⁻¹),

⟨\dot{A}⟩

is the time-averaged net carbon assimilation rate per unit leaf area (μmol C m⁻² s⁻¹), t_L is leaf longevity (s), LMA is leaf mass per area (kg m⁻²), k₁ is a molar mass conversion factor (kg C μmol C⁻¹), and k₂ is the carbon mass fraction (kg C kg⁻¹). All mathematical nomenclature is defined in Table 1. Thus, in Eqn 1, the term

⟨\dot{A}⟩

t_L represents the lifetime carbon return, LMA represents the lifetime carbon investment, and

⟨\dot{A}⟩

t_L/LMA represents the lifetime carbon return-on-investment. Eqn 1 shows that to maintain a positive net carbon balance, G must be greater than 1. It also shows that natural selection could increase G by increasing carbon return (

⟨\dot{A}⟩

and/or t_L) or by reducing carbon investment (LMA). Likewise, changes in one or more traits could be counter-balanced by changes in other traits to maintain G > 1. Thus, this theory provides a mechanistic carbon economics framework for helping understand trade-offs of these and other key traits in the LES, from individuals (Fajardo & Siefert, 2018) to species (Neto-Bradley et al., 2021) to the biosphere (Wright et al., 2004; Asner et al., 2016; Díaz et al., 2016).

Table 1. Nomenclature.

Symbol	Units	Definition
$\dot{A}$	μmol C m⁻² s⁻¹	Net carbon assimilation rate per unit leaf area
c _{p ,w}	J kg⁻¹ K⁻¹	Specific heat capacity of water
c _{p ,d}	J kg⁻¹ K⁻¹	Specific heat capacity of leaf dry matter
c _{p ,a}	J kg⁻¹ K⁻¹	Specific heat capacity of air
F ₀	Unitless	Basal chlorophyll fluorescence
G	kg C kg C⁻¹	Lifetime carbon gain per unit carbon invested
g _h	m s⁻¹	Boundary layer conductance
h	W m⁻² K⁻¹	Heat transfer coefficient
k ₁	kg C μmol C⁻¹	Molar mass conversion factor
k ₂	kg C kg⁻¹	Carbon mass conversion factor
LDMC	kg kg⁻¹	Leaf dry matter content
t _L	s	Leaf functional longevity
L	m	Characteristic dimension
LMA	kg m⁻²	Leaf dry mass per area
φ	Dimensionless	Ratio of projected to total leaf area
ρ _a	kg m⁻³	Mass density of air
U	m s⁻¹	Wind speed
γ	Pa K⁻¹	Psychrometric constant
τ	s	Thermal time constant
T _crit	°C	Critical temperature of PSII
T _max	°C	Temperature of maximum normalized PSII fluorescence

On the other hand, energy balance theory reveals how some of the same traits central to carbon economics also mediate leaf energy fluxes, temperatures, and ultimately rates of physiology and heat injury (Gates et al., 1964; Ball et al., 1988; Wright et al., 2004; Vogel, 2005; Westoby & Wright, 2006; Michaletz et al., 2015, 2016; Bonan, 2019; Michaletz & Garen, 2024). Leaf temperatures almost never equal ambient air temperatures (Gates et al., 1964; Linacre, 1964; Mahan & Upchurch, 1988; Michaletz et al., 2016; Blonder et al., 2020; Garen et al., 2023), as they are generally buffered against changes in their thermal environment. This buffering effect is determined by a suite of energy balance traits, including the carbon economics traits leaf mass per area (LMA) and leaf dry matter content (LDMC), which influence leaf energy fluxes and storage (Halbritter et al., 2020; Michaletz & Blonder, 2020). These traits can be mechanistically combined into a composite trait, the thermal time constant (τ; s), which quantifies how quickly leaf temperature can respond to changes in the thermal environment. τ can be calculated as (Michaletz et al., 2015; Halbritter et al., 2020; Michaletz & Blonder, 2020)

τ = ϕ LMA [\frac{c_{p, w}}{LDMC \cdot h} + \frac{c_{p, d} - c_{p, w}}{h}]

(Eqn 2)

where LDMC is leaf dry matter content (kg kg⁻¹), c_p,w is the specific heat capacity of water (J kg⁻¹ K⁻¹), and c_p,d is the specific heat capacity of dry leaf matter (J kg⁻¹ K⁻¹). The ratio of projected-to-total leaf area, φ (dimensionless), converts projected area from the denominator of LMA into a total surface area across which heat transfer occurs (Gates, 1980; Chapin et al., 2000; Bonan, 2019). The overall heat transfer coefficient, h (W m⁻² K⁻¹), accounts for convective and net longwave radiation heat transfer from the leaf.

Eqn 2 demonstrates how traits involved in energy storage (mass, specific heat capacity, water content (1−LDMC)) and outgoing energy fluxes (leaf size, shape, stomatal conductance, and emissivity) are mechanistically linked to temporal thermoregulation. Following a change in the thermal environment (e.g. an increase in incident solar radiation), leaves with long τ have a greater capacity to buffer leaf temperatures and thus take longer to reach their new equilibrium temperature. Leaves with short τ will track forcing conditions more closely through time and reach their equilibrium temperature faster (Michaletz et al., 2015). The resultant leaf temperatures then drive rates of leaf physiology, and in turn, the central carbon economics trait $⟨\dot{A}⟩$ (Michaletz et al., 2015, 2016; Michaletz, 2018; Michaletz & Garen, 2024). Since time constants both comprise and control key carbon economics traits, multivariate trait correlations such as those of the LES may reflect some combination of selection via carbon economics and energy balance (Fig. 1a).

Details are in the caption following the image — **Fig. 1**
Open in figure viewer PowerPoint

Coordination of leaf morphological (carbon economics and energy balance) traits and hypothesized coordination with heat tolerance traits. (a) Well-established coordination of leaf morphological traits. (b) Hypothesized coordination of morphological traits with heat tolerance traits based on carbon economics theory. (c) Hypothesized coordination of morphological traits with heat tolerance traits based on energy balance theory. Orange represents carbon economics traits that directly influence lifetime carbon gain (LMA, LDMC), blue represents energy balance traits that directly influence leaf temperature dynamics (τ), and purple represents photosynthetic heat tolerance (T_crit). Leaf images are scanned leaves sampled in this study including *Phyllostachys aureosulcata* (Poaceae; fast) and *Abies grandis* (Pinaceae; slow).

Eqns 1 and 2 also hypothesize mechanistic, predictable coordination between leaf carbon economics/energy balance traits and physiology (Michaletz et al., 2016). From a carbon economics perspective, ‘slow’ leaves with high LMA (often evergreen) require longer life spans for a positive net carbon return-on-investment (Wright et al., 2004; Reich, 2014). One way to invest in prolonged leaf life span is through physiological and biochemical adjustments to withstand extreme temperatures, particularly to protect sensitive photosynthetic machinery (Knight & Ackerly, 2003; Sastry & Barua, 2017). Indeed, a positive association between photosynthetic heat tolerance and leaf life span was observed in woody savanna species (Zhang et al., 2012). Thus, it has been hypothesized that photosynthetic heat tolerance should increase with LMA in order to prolong leaf life span and lifetime carbon gain (Fig. 1b; Knight & Ackerly, 2003; Sastry et al., 2018). However, support for this hypothesis is mixed, with some studies finding a positive association between heat tolerance and LMA (Knight & Ackerly, 2003; Sastry & Barua, 2017; Sastry et al., 2018; Slot et al., 2021; Li et al., 2022) and others finding no such relationship (O'Sullivan et al., 2017; Fadrique et al., 2022; Münchinger et al., 2023).

From an energy balance perspective, time constants increase proportionally with LMA (all else equal; Eqn 2), resulting in greater thermal buffering capacity and reduced variation in extreme leaf temperatures (Michaletz et al., 2016, Leigh et al., 2017 but see Leigh et al., 2012). Photosynthetic heat tolerance has been proposed to be more strongly associated with extreme (rather than average) temperatures, such that greater maximum leaf temperatures should drive higher photosynthetic heat tolerance (Perez & Feeley, 2020). Thus, energy balance theory leads to the hypothesis that photosynthetic heat tolerance should decrease with time constants, due to increased thermal buffering and decreased maximum leaf temperatures (Fig. 1c; Vogel, 2005, 2009; Michaletz et al., 2016; Slot et al., 2021). However, studies examining the relationship between photosynthetic heat tolerance and energy balance traits (i.e. τ) are extremely limited (but see Slot et al., 2021). A negative relationship between τ and heat tolerance would be consistent with selection acting primarily via energy balance, whereas a positive relationship between LMA and heat tolerance would be consistent with selection acting primarily via carbon economics. However, tests of these predictions are often limited in taxonomic breadth, and/or use trait data collected across climate gradients that introduce acclimation and trait filtering as potentially confounding variables (Knight & Ackerly, 2003; Michaletz et al., 2016; Sastry & Barua, 2017; Slot et al., 2021; Fadrique et al., 2022).

Since leaf carbon economics and energy balance share common traits and are inextricably linked, the extent to which the evolution of leaf traits reflects selective pressure via carbon economics or energy balance is unclear. Here, we examine these questions using a diverse set of 177 plant species from 157 families in a common garden that maximizes taxonomic sampling and breadth while minimizing co-variation of traits and taxa with climate (Kumarathunge et al., 2019). Specifically, we (1) quantify macroevolutionary variation in key carbon economics, energy balance, and heat tolerance traits, (2) assess phylogenetic signal in traits, and (3) test for coordination of carbon economics, energy balance, and heat tolerance traits. Given their roles in both carbon economics and energy balance, we analyze LMA and LDMC both separately as carbon economics traits and combined as constituents of an energy balance trait (the thermal time constant). To our knowledge, this is the largest and most taxonomically diverse dataset containing paired measurements of leaf-level carbon economics, energy balance, and physiological heat tolerance traits produced to date. This is also the first study to evaluate whether macroevolution of key leaf morphological traits is more consistent with selection on carbon economics or energy balance constraints.

Materials and Methods

Site and species selection

Samples were collected in Vancouver, BC, Canada, from the University of British Columbia Botanical Garden (49.2541°N, 123.2508°W) and the VanDusen Botanical Garden. These collections include taxa from around the world, representing most major biomes, with a particular emphasis on temperate regions of North America and Asia. The gardens are located at the same elevation (c. 100 m) within 10 km of each other and share a common macroclimate (49.2396°N, 123.1325°W).

Overall, our dataset comprised 1593 leaves from 177 species and 157 families. We focused sampling at the family level to maximize the taxonomic breadth and variation in LES strategies (Anderegg et al., 2018) within our dataset. We randomly selected one species from each family plus an additional 6–7 species in each of Dryopteridaceae, Ericaceae, and Pinaceae to increase the range of phylogenetic divergence times in our dataset (Neto-Bradley et al., 2021).

For each species, three leaves from each of three individuals (cf. Baraloto et al., 2010; Hulshof & Swenson, 2010) were sampled between 01 June 2022 and 13 July 2022. The sampling period was made as short as possible to minimize seasonal variation in environment and trait variables (Bison et al., 2022). To further control for any potential confounding influence of these variables, we randomly sampled 8–10 species d⁻¹ of the sampling period to help ensure independence of phylogeny with any temporal environment or trait variation. Between 07:30 h and 10:30 h on each sampling day, we selected the most recent, fully expanded, sunlit upper crown leaves from the study subjects. Pole pruners were used to obtain branches of tall trees. The leaves were excised with sharp scissors, placed in a sealed plastic bag with a moist paper towel, and stored in an ice-filled cooler. Leaves remained in the cooler (in between each measurement) until all fresh leaf measurements were complete (within 3–5 h of sampling).

Leaf trait measurements

Morphological (carbon economics and energy balance) traits LMA and LDMC were measured on each leaf following standard methods (Pérez-Harguindeguy et al., 2013). Surface area was measured using a flatbed scanner (LiDE 400; Canon, Melville, NY, USA) at a resolution of 300 pixels per inch. Images were converted to leaf area using the LeafArea package in R (Katabuchi, 2015). Leaf fresh and dry mass were measured using a Sartorius Entris II Advanced analytical balance (precision of 0.1 mg). Fresh mass was measured immediately after sampling on fully hydrated leaves, and dry mass was taken after drying for 72 h in a 60°C oven (Pérez-Harguindeguy et al., 2013). Petioles were included in all morphological trait measurements.

Energy balance traits

Leaf τ were calculated following Eqn 2, where φ was taken as 0.5 for morphologies that approximate flat plates (broadleaves and scale-leaves) and 1/π for morphologies that approximate cylinders (needle leaves; Gates, 1980; Chapin et al., 2000; Bonan, 2019; Halbritter et al., 2020; Michaletz & Blonder, 2020). Specific heat capacities c_p,w and c_p,d were taken as 4181 and 1902.6 J kg⁻¹ K⁻¹, respectively (Michaletz et al., 2016). The use of a constant c_p,d effectively ‘controls out’ any potential influence of variation in c_p,d on variation in τ. This is a common approach (Ball et al., 1988; Fauset et al., 2018; Slot et al., 2021; Bison et al., 2022), and a suitable first approximation because the specific heat capacity of a fresh leaf primarily reflects that of water (c_p,w), given that c_p,w is ca. double that of dry matter (c_p,d), and water accounts for the majority of fresh leaf mass (mean of 76% in our data). However, species-specific values of c_p,d could also be used. The heat transfer coefficient was calculated as

h = ρ_{a} c_{p, a} g_{h}

(Eqn 3)

where ρ _a (1.224 kg m⁻³) is mass density of air, c_p,a is the specific heat capacity of air (1007 J kg⁻¹ K⁻¹), and g _h (m s⁻¹) is boundary layer conductance to heat. This approach does not account for variation in leaf emissivity, which is generally assumed constant for leaves (Campbell & Norman, 1998; Jones, 2014). It also intentionally neglects the influence of stomatal conductance on latent (transpiration) heat fluxes (Michaletz et al., 2016; Halbritter et al., 2020; Michaletz & Blonder, 2020), since previous analyses suggested stomatal conductance explained only negligible amounts of variation in τ relative to other constituent traits (Michaletz et al., 2016). g_h varies with the size and geometry of leaves, such that broadleaf and needleleaf heat conductances are, respectively, given as

g_{h} = \frac{(1.5) (6.62) {[\frac{U}{L}]}^{0.5}}{1000}

(Eqn 4)

g_{h} = \frac{(1.5) (4.03) [\frac{U^{0.6}}{L^{0.4}}]}{1000}

(Eqn 5)

where the factor of 1.5 accounts for outdoor conditions such as turbulence (Jones, 2014; Bonan, 2019), U is windspeed taken as 0.3 m s⁻¹ (Michaletz et al., 2016), and L is a characteristic dimension (m) that controls boundary layer development and thus convective flux (Michaletz et al., 2016; Leigh et al., 2017). This characteristic dimension accounts for the influence of leaf shape and dissection on boundary layer formation. For example, assuming a constant total leaf area, a highly dissected pteridophyte leaf, would have a smaller characteristic dimension, thinner boundary layer, and greater convective conductance than a simple broadleaf. Following Nobel (1999), L was quantified as the diameter of the maximum inscribed circle within the leaf.

As shown in Eqns 2 and 3, leaf absorptance to shortwave (solar) radiation does not influence the thermal time constant. This is because the thermal time constant is derived as the difference between two energy balance equations: one that neglects heat storage and another that includes heat storage (Jones, 2014; Bonan, 2019). Both equations contain the same radiative forcing term (the product of absorptance and incident shortwave radiation), which is independent of time and leaf temperature. Calculating the difference between these energy balances causes the radiative forcing terms to cancel out. This effectively isolates the impact of heat storage from the effects of shortwave radiation absorption. The result is a third energy balance equation that does not contain a radiative forcing term; instead, it contains only the heat fluxes that depend on leaf temperature itself (convection and longwave radiation), and the leaf traits that mediate those fluxes. It is this time-dependent energy balance equation that defines the thermal time constant, showing how changes in leaf temperature are governed by the leaf's own heat storage and loss processes. Thus, while absorptivity influences the leaf energy balance by determining how much shortwave radiation is absorbed, this absorbed radiation (independent of leaf temperate) does not affect the thermal inertia or thermal time constant of the leaf.

Photosynthetic heat tolerance

Photosystems I and II are protein complexes that play central roles in photosynthetic light reactions and are particularly sensitive to temperature (Havaux, 1993). Photosynthetic heat tolerance is often quantified as the critical temperature above which photosystem II (PSII) sustains irreversible damage based on minimal chlorophyll a fluorescence experiments (T_crit (°C); Schreiber & Berry, 1977, O'Sullivan et al., 2017, Rao et al., 2023). Due to their potential influence on fitness in hotter environments, these critical limits are of interest for understanding species' fundamental niches (Aspinwall et al., 2019; Feeley et al., 2020) and sensitivity to extreme events such as heatwaves (Andrew et al., 2022).

F₀-rise experiments were performed to measure photosynthetic heat tolerance using a Closed FluorCam FC 800-C imaging fluorometer from Photon Systems Instruments (Schreiber & Berry, 1977; Arnold et al., 2021). On the same leaves used to measure traits, one disk was removed using a 5.5-mm hole punch per leaf and loaded into a 8 × 12 cm TR2000 thermoregulating block containing 45 1-cm-diameter wells each filled with 0.9 ml of distilled water (Moran et al., 2023). The use of excised disks is common in the heat tolerance literature (Schreiber & Berry, 1977; Havaux, 1993; Krause et al., 2013; Slot et al., 2021; Hernández et al., 2022; Posch et al., 2022), though heat tolerance estimated for excised disks may underestimate that measured on intact plants (Buchner et al., 2017). After a 30-min dark acclimation period inside the chamber, the thermoregulating block temperature was ramped from 30 to 60°C over 1 h (0.5°C min⁻¹) following Arnold et al. (2021). Basal chlorophyll fluorescence (F₀) was measured at 30-s intervals using short (10 μs) flashes of very weak blue light to detect F₀ without inducing PSII photochemistry (Schreiber & Berry, 1977; Arnold et al., 2021).

Temperature and fluorescence data from the F₀-rise experiment were processed and exported using the manufacturer-provided software (FluorCam v.7.0). Pixels in each disk were extracted using circular masks, fit slightly smaller than the diameter of the disk to exclude pixels along the edges of each disk. Fluorescence values were normalized so that minimum fluorescence was assigned as 0 and maximum fluorescence was assigned as 1. T_crit and T_max values were extracted from time series of F₀ by temperature using a breakpoint analysis (segmented package in R; Muggeo, 2008) following Arnold et al. (2021) (Supporting Information Fig. S1). T_crit was taken as the breakpoint from piecewise linear regression and T_max was taken as the temperature of maximum fluorescence. One hundred and forty curves (c. 7% of total) did not show a clear breakpoint and were excluded from further analyses.

Statistical analyses

All analyses were performed in R (v.4.3.2; R Core Team, 2021) at the species level (n = 177), with trait data averaged across nine samples (three leaves from each of three individuals). Traits were log-transformed before statistical analysis to resolve non-normality.

To quantify variation in key carbon economics, energy balance, and heat tolerance traits, we summarized trait distributions for a small number of clades including angiosperms, gymnosperms, and pteridophytes. Our dataset contained 150 angiosperm species, 14 gymnosperm species, and 13 pteridophyte species. Despite the greater number of angiosperm species, we note that gymnosperms and pteridophytes were not underrepresented; indeed, the dataset contains almost all families within these groups, which are c. 10–40 times less diverse at the family level than angiosperms (The World Flora Online Consortium et al., 2023). Statistical differences in species-level means across clades were determined using one-way ANOVA. For traits that differed significantly across clades, post hoc pairwise comparisons were conducted using the Tukey's honestly significant difference test to control Type I error rate for multiple statistical tests.

To assess relative phylogenetic signal in traits, we built a maximum likelihood phylogeny using the most recently updated megaphylogeny for vascular plants using the v.phylomaker package in R (Zanne et al., 2014; Smith & Brown, 2018; Jin & Qian, 2022). Tips were attached halfway between the family branch, consistent with Phylomatic and BLADJ (Qian & Jin, 2016). Phylogenetic signal was estimated using Pagel's λ (Pagel, 1999), as it is a robust method for inferring phylogenetic signal in ecologically and evolutionarily relevant traits (Molina-Venegas & Rodríguez, 2017). Pagel's λ was calculated using the caper package (Orme et al., 2018). Since photosynthetic heat tolerance may acclimate to leaf temperatures over short timescales (Posch et al., 2022; Zhu et al., 2024), estimates of λ controlled for variation in mean air temperature (a proxy of leaf temperature) leading up to the sampling date. Mean daily temperature throughout the 6-wk sampling period was extracted from a nearby weather station (7.87 km from the UBC Botanical Garden). To determine the best pre-sampling window over which to calculate average air temperature, we compared linear regression model fits for the effect of mean air temperature on T_crit using mean temperature over the days preceding sampling (1–30 d before sampling). Mean air temperature was taken as the average air temperature over the 3 d preceding sampling date, as this window provided the best model fits compared to other possible averaging windows (Fig. S2). Both T_crit and T_max varied significantly with mean temperature before sampling (Fig. S3), with mean air temperature averaged over 3 d preceding the sampling date for each species explaining 32.1 and 25.2% of variation in T_crit and T_max, respectively. A phylogeny with trait values was generated using the ggtree package (Yu, 2022). Trait data were binned into 0.05, 0.25, 0.50, 0.75, and 0.95 quantile bins to aid visualization.

To assess whether carbon economics and energy balance traits are coordinated with photosynthetic heat tolerance, we used two approaches that provide different but complimentary perspectives. First, we regressed T_crit on LMA and τ using phylogenetic linear regression (phylolm package; Ho & Ané, 2014) to account for statistical non-independence of our samples due to phylogenetic history (Felsenstein, 1985; Garland Jr et al., 1992). This analysis was also repeated for T_max (Fig. S4). Additionally, we performed a second phylogenetic linear regression for heat tolerance (T_crit and T_max) with 3-d average air temperature to assess acclimation to seasonal variation in air temperature throughout our study period. Second, we used a principal component analysis (PCA) to test for coordination of leaf heat tolerance with carbon economics and energy balance traits. Principal component analysis was performed on heat tolerance traits T_crit and T_max, and morphological (carbon economics and/or energy balance) traits LMA, LDMC, A, and L and the energy balance trait, τ using factominer, factoextra, packages (Lê et al., 2008; Kassambara & Mundt, 2020). A standard PCA compliments results from the phylogenetic linear regression because it fully characterizes both the evolutionary and environmental variation present in our dataset, as opposed to phylogenetic regression that accounts for the expected covariance structure based on the phylogeny. However, we additionally performed a phylogenetic PCA to analyze trait variation in all traits while accounting for shared evolutionary history (Revell, 2009).

To determine which traits primarily drive variation in thermal time constants, we conducted an independent effects analysis (Murray & Conner, 2009) using the R package hier.part (Nally & Walsh, 2004). This technique uses hierarchical partitioning, which compares the fits of all possible models that include and exclude each predictor variable. This approach enables us to isolate the independent contribution of each trait in driving variation in τ.

Results

Variation in carbon economics, energy balance, and heat tolerance traits

Distributions for morphological traits LMA and LDMC are shown in Fig. 2(a,b), and associated central moments are provided in Table S1. Carbon economics traits varied widely among taxa in our dataset, with LMA varying 35-fold from 0.011 to 0.387 kg m⁻², and LDMC varying 15-fold from 0.034 to 0.532 kg kg⁻¹ (Fig. 2a,b). Mean LMA was 0.062 ± 2.801e−04 standard error (SE) kg m⁻², and varied significantly between clades (P = 2.55e−06, F = 13.88, df = 2), with gymnosperms having a significantly greater mean LMA than angiosperms (P = 3.53e−05) and pteridophytes (P = 3.60e−06). Mean LDMC was 0.244 (±5.450e−04 SE) kg kg⁻¹ and was not significantly different between clades (P = 0.152, F = 1.90, df = 2).

Variation in τ is shown in Fig. 2(c). τ varied strongly among leaves, from 2.862 to 48.805 s, with a mean of 13.270 (± 0.043 SE) s. Mean τ varied significantly across clades (P = 3.38e−05, F = 10.93, df = 2). Pteridophytes had the lowest τ compared with gymnosperms (P = 0.05) and then angiosperms (P = 3.00e−05), with no significant difference detected between gymnosperms and angiosperms (P = 0.316). An independent effect analysis revealed that most of the variation in τ was explained by LMA (40.3%) and LDMC (39.9%) followed by L (19.8%; Fig. S5).

T_crit ranged from 39.965 to 51.130°C with a mean of 45.989 (± 0.012 SE) °C, with mean T_crit being relatively consistent across clades (Fig. 2d). T_crit did not vary across clades (P = 0.509, F = 0.678, df = 2), nor did T_max (P = 0.864, F = 0.146, df = 2).

Phylogenetic signal in carbon economics, energy balance, and heat tolerance traits

Leaf morphological traits were phylogenetically structured (Fig. 3a,b; Table 2). Pagel's λ for LMA (λ = 0.569, P = 2.523e−05) and LDMC (λ = 0.805, P = 0.011) had phylogenetic structure, but less than expected under Brownian motion (Table 2). When leaf carbon economics traits were combined into the energy balance trait τ, phylogenetic signal weakened to much less than expected under Brownian motion (Fig. 3b, λ = 0.264, P = 0.004). Additional morphological traits within τ were phylogenetically structured close to expected under Brownian motion (Table S2), including leaf surface area (λ = 1.000, P = 2.220e−16) and L (λ = 0.871, P = 2.220e−16).

Table 2. Phylogenetic structure in carbon economics and energy balance traits but not heat tolerance traits.

	Trait	Value	P-value
Carbon economics	LMA	0.569	2.523e−05
Carbon economics	LDMC	0.805	0.011
Energy balance	τ	0.264	0.004
Heat tolerance	T _crit	0.000	1
Heat tolerance	T _max	0.593	1

Pagel's λ indicates the degree to which variation in traits are explained by phylogenetic relatedness. Orange represents carbon economics traits that directly influence lifetime carbon gain (LMA, LDMC), blue represents energy balance traits that directly influence leaf temperature dynamics (τ), and purple represents photosynthetic heat tolerance (T_crit). Bold font indicates values that are statistically significant.

We found no evidence of phylogenetic structure in T_crit based on Pagel's λ (Fig. 3c; Table 2, λ = 0.000, P = 1). T_max was also not phylogenetically structured.

Coordination of carbon economics, energy balance, and heat tolerance traits

Neither LMA (r² = 0.031) nor τ (r² = 0.005) explained substantial variation in T_crit in our dataset (Fig. 4a,b). Although there was a statistically significant relationship between T_crit and LMA (P = 0.019), LMA explained almost no variation in T_crit (r² = 0.031). Similar results were obtained with T_max (Fig. S4).

A PCA further showed that heat tolerance traits are orthogonal to morphological traits (LMA, LDMC, A, L) and thermal time constants (Fig. 5). PC1 corresponded to a carbon economics/energy balance axis and explained 35.9% of variation in our dataset (Table S3), with the highest loadings for morphological traits LMA, LDMC, A, L, and τ of −0.508, −0.393, 0.529, 0.530, 0.138, respectively (Table S4). PC2 corresponded to a photosynthetic heat tolerance axis, explaining 26.3% of variation in our dataset with loadings of 0.641, and 0.678 for T_crit and T_max, respectively. A phylogenetic PCA showed similar results (Tables S5, S6).

Discussion

Multivariate leaf trait correlations such as the LES are a ubiquitous feature of biology. Key functional traits LMA and LDMC influence not only lifetime carbon gain (carbon economics theory; Eqn 1) but also leaf temperature dynamics (energy balance theory; Eqn 2). However, the intricate links between leaf carbon economics and energy balance have made it unclear which of these processes primarily shape the evolution of leaf traits. We used a common garden to describe macroevolutionary patterns in leaf traits and help evaluate whether selection via carbon economics or energy balance shapes the evolution of leaf traits. Using a diverse set of 177 plant species from 157 families, we found strong variation in carbon economics, energy balance, and heat tolerance traits. LMA and LDMC, involved in both leaf carbon economics and energy balance, showed strong phylogenetic signal when analyzed independently. However, when combined mechanistically into thermal time constants, phylogenetic signal weakened considerably, suggesting that selection on leaf traits operates primarily via carbon economics, and not energy balance. Importantly, photosynthetic heat tolerance was not coordinated with (and was orthogonal to) carbon economics and energy balance traits. Instead, heat tolerance acclimated to short-term variation in temperature, reflecting a strong capacity of physiology to acclimate to environmental variation (Curtis et al., 2019; Cook et al., 2021; Vinod et al., 2023).

We observed wide variation in photosynthetic (PSII) heat tolerance within a common garden that was not explained by phylogenetic history. The range of variation within our common garden (c. 40 to 51°C) slightly exceeds that observed across globally distributed sites spanning 43°S to 68°N latitude (c. 42 to 51°C; O'Sullivan et al., 2017). We suspect that this reflects acclimation of heat tolerance to local variation in leaf microclimates (Curtis et al., 2019; Ahrens et al., 2021; Posch et al., 2022; Andrew et al., 2022; Milner et al., 2023). Indeed, microclimate can also vary as much within sites as macroclimate varies across sites (Blonder et al., 2020; Zellweger et al., 2020). Photosynthetic heat tolerance also acclimated strongly to variation in temperature over short timescales in our study (i.e. 3-d moving average, R² = 0.32; Fig. S3), with residual variation likely explained by additional microclimate variables that control leaf energy balance and resulting leaf temperatures (Curtis et al., 2016; Andrew et al., 2022; Posch et al., 2022). Microclimate has a strong influence on leaf temperatures via solar radiation, air temperature, vapor pressure deficit, soil moisture (stomatal conductance), relative humidity, and wind speed (Gates, 1980; Campbell & Norman, 1998; Jones, 2014; Bonan, 2019). For example, Curtis et al. (2016) found that microclimatic water availability predicted species-level heat tolerance better than macroclimate. Similarly, Cook et al. (2021) found that plant water status influenced leaf temperatures and photosynthetic heat tolerance in a desert field experiment. Together, these results suggest that greater soil water availability allows for relatively cooler leaf temperatures to be maintained via latent heat effects of evapotranspiration (Michaletz et al., 2015, 2016). Leaf microclimates are also known to vary considerably within plant canopies, which influences leaf temperatures and physiological rates (Leuzinger & Körner, 2007; Michaletz, 2018; Blonder et al., 2020; Vinod et al., 2023; Michaletz & Garen, 2024). For example, heat tolerance tends to be higher in sun leaves than in shade leaves (Curtis et al., 2019; Slot et al., 2019). Overall, microclimate interacts with leaf traits to control energy balance, leaf temperatures, and heat tolerance limits (Michaletz et al., 2016). Future work should consider leaf microclimates alongside regional climate for predicting tolerance to high temperatures (e.g. Cook et al., 2021).

Our results are inconsistent with some previous observations of phylogenetic signal in photosynthetic heat tolerance. For example, Hernández et al. (2022) found that evolutionary history constrained photosynthetic heat tolerance among 39 species within six families in the order Zingiberales. Zhu et al. (2018) reported higher T_crit in species from warmer biomes in a common garden study of 62 species. Similarly, Ahrens et al. (2021) showed that tree populations from warmer regions better able to avoid photosystem II damage during a moderate heatwave than populations from cooler regions. In addition to controlling out variation in macroclimate, our study differs from these previous studies in terms of taxonomic scale. Studies reporting phylogenetic conservatism in heat tolerance have typically been conducted at finer phylogenetic scales, focusing on a smaller number of species in specific clades, providing important insight into potential evolutionary dynamics of certain clades or communities. By contrast, our study is conducted at a much broader taxonomic scale, maximizing variation at the family level instead at the species level. Thus, our results may reflect more general macroevolutionary patterns but may not represent phylogenetic structure present at finer phylogenetic scales.

Contrary to recent hypotheses that physiological heat tolerance is coordinated with carbon economics (Knight & Ackerly, 2003; Sastry & Barua, 2017; Fadrique et al., 2022) and energy balance (Slot et al., 2021) traits, we observed that photosynthetic heat tolerance traits were orthogonal to carbon economics and energy balance traits. Although we observed remarkable variation in photosynthetic heat tolerance in our common garden (c. 40 to 51°C), this variation was not explained by phylogenetic structure. This suggests that macroecological variation in heat tolerance traits does not reflect adaptation on longer evolutionary timescales, but rather reflects acclimation to local microclimate on shorter timescales (Vinod et al., 2023). By contrast, carbon economics traits LMA and LDMC were phylogenetically structured. This pattern is consistent with recent work on plant–water relation traits, which found that morphological traits such as stomatal length and density were strongly phylogenetically structured, whereas physiological traits such as stomatal conductance and water potential at turgor loss did not show phylogenetic signal (Ávila-Lovera et al., 2023).

Together, our results highlight a fundamental mismatch in the timescales over which morphological (carbon economics) and physiological (heat tolerance) traits respond to environmental variation. Whereas morphological traits appear to be constrained by species' evolutionary histories via carbon economics, physiological traits are not, and appear to acclimate readily to changes in leaf microclimates over short timescales (Fig. S3) While this study focuses specifically on traits related to carbon economics, energy balance and thermal tolerance, consistent patterns have also been reported for water use traits (Ávila-Lovera et al., 2023) as well as photosynthetic capacity (Neto-Bradley et al., 2021) and its temperature response (Kumarathunge et al., 2019). The different timescales at which morphology and physiology respond to the environment (consistent with adaptation and acclimation, respectively) could arise for several reasons, including lower heritability in physiological traits compared with morphological traits (Ávila-Lovera et al., 2023).

The macroevolutionary patterns in leaf traits presented here are consistent with theory and empirical data that LMA and LDMC are acted upon by natural selection to maximize net carbon gain under differing conditions (Chabot & Hicks, 1982; Williams et al., 1989; Kikuzawa, 1995; Ackerly & Reich, 1999; Westoby et al., 2002; Wright et al., 2004; Kikuzawa & Lechowicz, 2006; Blonder et al., 2011; Michaletz et al., 2016; Sartori et al., 2019; Wang et al., 2023). Specifically, the lack of strong macroevolutionary pattern in leaf thermal time constants demonstrates that variation in leaf morphological traits LMA and LDMC are likely driven by selection on carbon economics rather than energy balance. Thus, it appears as though morphological traits primarily evolve via selection on carbon economics traits over longer timescales, and then influence leaf energy balance and temperatures, to which physiology must then acclimate over shorter timescales. In other words, the ability of physiology to acclimate on short timescales appears to exist because it is necessary for dealing with energy balance constraints imposed by selection on carbon economics traits.

Future work is recommended in two areas. First, steady-state leaf temperatures should be considered alongside energy balance traits that control temporal dynamics to capture other strategies that plants employ to mediate maximum temperatures, for example variation in absorptivity (not included in τ) or acute adjustments in stomatal conductance, which are considered fixed parameters in τ (Krich et al., 2022). Specifically, the relative importance of traits comprising the thermal time constant should be assessed against traits such as absorptance that influence leaf equilibrium temperatures but do not affect thermal time constants. Second, we argue for the need to directly test whether carbon gain, G, is phylogenetically structured in order to determine whether the macroevolution of leaf morphology reflects selection on carbon economics. Our methods cannot determine whether evolutionary variation in traits may be adaptive (caused by natural selection) or are conserved through neutral processes (e.g. genetic drift). Thus, our findings highlight a need to test the evolutionary mechanisms underlying these patterns (e.g. Friedman et al., 2015; Buckley & Huey, 2016; Porcelli et al., 2016).

Acknowledgements

The authors thank Andy Leigh and two anonymous reviewers for constructive comments on earlier drafts of the paper. We also thank the University of British Columbia Botanical Garden and the VanDusen Botanical Garden for supporting this research, and especially Cynthia Cyr, Daniel Mosquin, Adriana Lopez Villalobos, Andy Hill, Douglas Justice, Ben Storms, Laura Caddy, Tim Chipchar, Andrew Fleming, and Casey Werfl. The authors would also like to thank Kahsennaró:roks (Maddy) Deom, Julien Grébert, Katica Naude, Ria Raut, and Timothy Wong who assisted with data collection and to Josef Garen for sharing code and advice on statistical analysis. NNB was supported by two NSERC scholarships (CGS-M and CGS-D). This research was supported by an NSERC Discovery Grant to STM.

Competing interests

None declared.

Author contributions

NNB and STM designed the research. NNB performed experiments, conducted fieldwork and performed analyses with guidance from STM. NNB wrote the first draft of the manuscript with guidance from STM. NNB and STM revised the manuscript.

Open Research

Data availability

The data and scripts that support the findings of this study are available on GitHub (https://github.com/MichaletzLab/thermal_traits) and OSF (https://osf.io/t3hdw).

Supporting Information

Filename

Description

nph19702-sup-0001-Supinfo.pdfPDF document, 676.5 KB

Fig. S1 Example of F0-rise curves with fitted T_crit, and T_max parameters.

Fig. S2 Explanatory power (R²) for phylogenetic linear regression of T_crit, and T_max with mean air temperature averaged over varying number of days preceding the sampling date.

Fig. S3 Positive correlation between photosynthetic heat tolerance (T_crit, and T_max) and mean air temperature averaged over 3 d before sampling date.

Fig. S4 No relationship between photosynthetic heat tolerance (T_max) and leaf mass per area or thermal time constant (τ).

Fig. S5 Independent effect analysis of the relative contribution of morphological traits to variation in thermal time constants.

Table S1 Statistical moments for leaf mass per area, leaf dry matter content, τ, T_crit, and T_max.

Table S2 Results from phylogenetic analysis for additional morphological traits within τ.

Table S3 Variance explained by each component in a principal component analysis containing carbon economics, energy balance, and photosynthetic heat tolerance traits.

Table S4 Loadings for heat tolerance and energy balance traits in a principal component analysis containing carbon economics, energy balance, and photosynthetic heat tolerance traits.

Table S5 Variance explained by each component in a phylogenetic principal component analysis containing carbon economics, energy balance, and photosynthetic heat tolerance traits.

Table S6 Loadings for heat tolerance and energy balance traits in a phylogenetic principal component analysis containing carbon economics, energy balance, and photosynthetic heat tolerance traits.

Please note: Wiley is not responsible for the content or functionality of any Supporting Information supplied by the authors. Any queries (other than missing material) should be directed to the New Phytologist Central Office.

Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

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Volume242, Issue5

June 2024

Pages 1919-1931

Variation in leaf carbon economics, energy balance, and heat tolerance traits highlights differing timescales of adaptation and acclimation

Summary

Introduction