Understanding Stomatal Conductance Responses to Long-Term Environmental Changes: a Bayesian Framework That Combines

Tree Physiology 33, 119–122 doi:10.1093/treephys/tpt008

Commentary

Understanding stomatal conductance responses to long-term environmental changes: a Bayesian framework that combines

patterns and processes Downloaded from

Brent E. Ewers1,2

1Department of Botany and Program in Ecology, University of Wyoming, Laramie, WY 82071, USA; 2Corresponding author ([email protected]) http://treephys.oxfordjournals.org/

Received January 6, 2013; accepted January 22, 2013; handling Editor: Danielle Way

When stomata are open, the trade-off between water loss Such models require sufficient empirical data for appropriate through transpiration and CO2 uptake via photosynthesis is a calibration and thus cannot independently predict stomatal result of the evolutionary history of land plants from green controls over water and carbon exchange without parameter- algae. To supply the up to 1000-fold higher flux of water leav- ization. Empirical estimates of stomatal conductance are at University of Wyoming Libraries on June 27, 2016 ing the leaf compared with CO2 molecules entering the leaf divided into two types, gas exchange and sap flux. The bene- during leaf gas exchange (Nobel 2009), plant hydraulics has fits of gas exchange measurements are that they include both evolved to be highly efficient. The intimate connection between transpiration and photosynthesis, and they permit manipulation plant water transport and photosynthesis has been explained of some environmental conditions including light, temperature, elegantly with a theory of stomata themselves (Cowan and CO2, wind speed and humidity (Long and Bernacchi 2003); Farquhar 1977) and expanded to connect plant hydraulics and however, the disadvantage is that the measurements disturb photosynthesis directly through an economic approach (Katul the environment of the leaf, sample a small area of the canopy et al. 2009). This theory is well supported by data including and are limited in time. In contrast, sap flux measurements are correlations between plant hydraulic conductance and stomatal relatively continuous, sample a relatively large area of the conductance (Meinzer and Grantz 1990), mesophyll conduc- xylem pipes supporting the canopy and do not disturb the tance to CO2 (Peguero-Pina et al. 2012) and the quantum yield environment of the leaves. The disadvantages of sap flux mea- of photosystem II (Brodribb and Feild 2000). These studies surements include potentially biased measurements based on illustrate that the connections between plant hydraulics and xylem anatomy and sensor type (Bush et al. 2010, Steppe photosynthesis include both biochemical and gas exchange et al. 2010), consideration of capacitance between the mea- components of photosynthesis. However, the main plant physi- surement point somewhere in the stem and the loss of water ological regulation over the photosynthesis and transpiration from the leaves (Phillips et al. 1997, Meinzer et al. 2003, compromise, stomatal conductance, is still without a full mech- Zweifel et al. 2007), and estimation of the environment of the anistic basis from genes to environmental responses. As a whole canopy (Ewers et al. 2007). Both methods, neither of result, our predictive understanding of plant hydraulic and pho- which are direct measurements of stomatal conductance, also tosynthetic responses to anthropogenic changes to climate require some type of scaling from the area of water loss esti- and historical disturbance regimes is still hindered even in the mated (i.e. part of the stem sapwood system or a subset of most recent soil–plant–atmosphere models (Berry et al. 2010). leaves) to the area of water loss under investigation (i.e. whole Modern models of stomatal conductance are, very often, still canopy, stands, watersheds or landscapes). Given the known based on more than three decades old concepts of multiplica- issues in sap flux-based estimates of stomatal conductance tive constraints (Jarvis 1976) and semi-mechanistic parameter- (Ewers and Oren 2000) and still incomplete knowledge con- izations (Ball et al. 1987, Leuning 1995, Buckley et al. 2012). cerning environmental regulation over stomatal conductance

( Berry et al. 2010), a method that quantitatively connects both patterns and processes would be ideal for improving predictive understanding of stomatal conductance. Current approaches generally estimate stomatal conductance from empirical measurements to quantify patterns and then test process models independently of the measurements (Figure 1, black arrows). However, knowledge is contained in both the patterns of stomatal conductance and the processes that explain the patterns. Thus, one wants to know both how well the data inform the parameters of the model and how well the model outputs predict the data. In Bayesian statistics, the probability of a particular model structure and parameter set is determined based Figure 1. The understanding of stomatal conductance at any point in time is a function of the measurements that provide estimates for the on a collected set of data. Bayesian statistics have been

patterns and the processes that regulate those patterns. Measurements Downloaded from described extensively in books and papers meant for statistical include both direct estimates as well as scalars (e.g. leaf area and sap- experts (Gelman et al. 2004). Given the importance of Bayesian wood area) needed to estimate stomatal conductance at different spatial scales. Processes include regulators operating in both external and statistics to modern science, other sources have arisen for sci- internal plant conditions. The black arrows represent an independent entists who may not be as well-versed in statistics. Kruschke means of understanding stomatal conductance either through direct empirical estimation or through current, yet limited understanding of the (2011) describes the Bayesian approach to statistics using the http://treephys.oxfordjournals.org/ regulators of stomatal conductance. The hierarchical Bayesian model in following components: the likelihood is the probability that the Ward et al. (2013a) presents an approach that combines information data could be generated by the model with a particular struc- from both the patterns and processes simultaneously (white arrows). In ture and set of parameters, the prior is the strength of belief in the face of incomplete knowledge of processes such as the mechanistic the model before new data are collected and is expressed as a basis of stomata responses to environmental regulators from genes to whole plants and measurement errors, the Bayesian hierarchical model probability distribution, and the posterior is the strength of provides new biological insights compared with commonly used meth- belief in the model once new data have been included and is ods that investigate the two black arrows independently. also expressed as a probability distribution. This Bayesian comparison of models and data is represented as white arrows replacements that are necessary for any sap flux study, both of at University of Wyoming Libraries on June 27, 2016 in Figure 1. which are vexing to all frequentist (i.e. ANOVA-type) statistical One application of this approach to statistics is a Bayesian studies. The advance in understanding of stomatal conduc- starvation analysis in which complex models are compared tance from this analytical approach and enormous data set can with simpler models to quantify which model explains data the be divided into two main areas. most parsimoniously. In a recent example, 16 different models, First, the role of leaf vs. whole-plant regulation of stomatal ranging from a fully coupled photosynthesis and stomatal con- conductance in response to elevated CO2 levels and soil N ductance process model with five parameters to single- changes with species. In the case of the angiosperm parameter, big leaf models, were tested against sap flux data Liquidambar styraciflua L., the decline in stomatal conductance

(Mackay et al. 2012). The priors were the maximum and mini- itself with elevated CO2 levels is most important. In the case of mum of the parameters of the models based on the literature. the gymnosperm Pinus taeda L., the hydraulic regulation of sto- The most likely model based on a comparison of the posteriors matal conductance governed by changes in resources, espe- with the means of data representing particular parameters was cially those contributing to hydraulic supply, partitioning over the model with four parameters, notably neither the most com- the 11 years of the data is most important. Further, the hydrau- plex nor the least complex model. The work of Ward et al. lic contribution to stomatal conductance in P. taeda includes (2013a) provides another approach to directly test models changes in the capacitance of the stem in which the treatment against data: hierarchical Bayesian modeling. This method and species differences are greater when soil moisture is high investigates the contribution of measurement error and neces- (Ward et al. 2013b). The hydraulic dynamics over the whole sary scalars to empirical estimates of stomatal conductance data set includes changes in both resources partitioning and errors in the process model at any given time in the face of between the supply of water via roots and sapwood area and different plant species, soil nutrition and elevated CO2 levels. In the demand for water via leaves. Such changes have been support of this method, these authors have assembled the shown in shorter time scale studies (i.e. from a growing season largest sap flux data set ever published; more than 20,000 to a few years) in which the rate of transpiration per unit leaf measurement time steps representing millions of data points area is different from the rate of transpiration per sapwood over more than a decade—a time scale relevant to tree area or per tree (Ewers et al. 2001, Angstmann et al. 2012). responses to climate change. Their hierarchical Bayesian model Second, the response of stomatal conductance to environ- is able to accommodate both missing data and the sensor mental regulators changes over the 11 years of the record. The

Tree Physiology Volume 33, 2013 Understanding stomatal conductance responses to long-term environmental changes 121

initial response to elevated CO2 and soil nutrients in the first States Geological Service through the University of Wyoming year is not the same as the long term. This shows that long- Office of Water Programs. lived plant species should be studied with approaches that allow inferences at time scales that are relevant to the question. References In the case of elevated CO2 levels and soil nutrients, the study must last long enough for the plant to not only acclimate its Allen CD, Macalady AK, Chenchouni H et al. (2010) A global overview stomatal conductance, but also its structural components such of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For Ecol Manag 259:660–684. as leaf to sapwood or root area ratios. From the work of Ward Angstmann JL, Ewers BE, Kwon H (2012) Size-mediated tree transpi- et al. (2013a), this time scale would be shorter for L. styraciflua ration along soil drainage gradients in a boreal black spruce forest than for P. taeda because the former has responses to elevated wildfire chronosequence. Tree Physiol 32:599–611. Ball JT, Woodrow IE, Berry JA (1987) A model predicting stomatal con- CO2 levels (i.e. lower stomatal conductance) that occur on shorter time scales than the changes in resource allocation that ductance and its contribution to the control of photosynthesis under different environmental conditions. In: Biggins J (ed.) Progress in pho- occur in the latter. Caution must also be taken if results are

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Tree Physiology Volume 33, 2013