# Stomatal Function across Temporal and Spatial Scales: Deep-Time Trends, Land-Atmosphere Coupling and Global Models

Article · Peter J. Franks, Joseph A. Berry, Danica Lombardozzi, Gordon B. Bonan

- Archive id: doi:10.1104/pp.17.00287
- Published: 2017-04-26
- Venue: PLANT PHYSIOLOGY
- DOI: https://doi.org/10.1104/pp.17.00287
- PMID: 28446638
- Citations: 159
- Source URL: https://doi.org/10.1104/pp.17.00287
- Provenance: openalex

## Abstract

The colonization of land by plants and their interaction with biogeochemical and atmospheric processes transformed continental climate and hydrology. Stomata, which evolved to optimize the biological economics of plant carbon uptake in exchange for water loss, play a crucial role in large-scale environmental processes by maintaining a connection between deep soil water reservoirs and the atmosphere, regulating terrestrial carbon sinks and altering surface energy balance as they respond to environmental changes. Stomatal feedback control of leaf gas exchange is observed at multiple temporal and spatial scales, but accurately simulating this dynamic behavior remains challenging, particularly for extreme environmental conditions, including drought. Integration of a more realistic representation of stomatal conductance and its regulation of leaf gas exchange in global models is improving global simulations of carbon, water, and energy fluxes, and these simulations in turn highlight some of the limitations with current leaf-scale models. Our analysis of current leaf-scale models of stomatal conductance representing empirical-based and optimization-based approaches reveals close structural similarities that result in virtually indistinguishable simulations of leaf, canopy, and global fluxes. While acknowledging these similarities, future efforts must focus on more accurate parameterization of stomatal conductance models, informed by studies at multiple temporal and spatial scales, including molecular, fossil, geological, leaf, canopy, and landscape. Stomata first appeared more than 410 million years ago in rudimentary terrestrial plants (Edwards et al., 1998; Raven, 2002; Chater et al., 2016). From then on, they have significantly influenced the fluxes of carbon, water, and energy at the land surface (Berry et al., 2010). After gaining a foothold on land in the late Silurian (Edwards, 1998), the evolution and spread of vascular plants drove one of the most dramatic global climate change events in Earth’s history. The role of plants in changing Earth’s climate was 2-fold: (1) the proliferation of deep-rooting systems and the uptake of nutrients throughout the Devonian enhanced the weathering of Ca-Mg silicate minerals, which facilitated the large-scale transfer of CO2 from the atmosphere to marine carbonates; and (2) unprecedented production of lignin-rich, decay-resistant plant structural material, in conjunction with tectonic basins and wet tropical climates, sequestered massive amounts of carbon in vast coal deposits (Berner, 1997, 2005). The resulting massive drawdown in global atmospheric CO2 concentration from many times that of today, via its reduction of the greenhouse effect, is likely to have contributed significantly to the Permo-Carboniferous glaciation (Royer et al., 2004; Lowry et al., 2014), the most extensive and longest lived glaciation since that time (Crowell, 1978; Scheffler et al., 2003; Montañez and Poulsen, 2013). Ultimately, strong negative feedbacks involving the limitation of terrestrial plant productivity and weathering by low CO2 are likely to have stabilized minimum atmospheric CO2 concentrations above the threshold for severe icehouse conditions (Pagani et al., 2009; Beerling et al., 2012). These megacycles in global vegetation productivity and atmospheric CO2 concentration also are imprinted in the record of plant genome size evolution (Franks et al., 2012a). The colonization of land by plants and extensive greening of the continents marked a dramatic shift in global water and carbon cycles that moderated continental climates and supported a diversity of terrestrial ecosystems (Kleidon et al., 2000; Beerling, 2007; Berry et al., 2010). Stomata played a crucial role in this by maintaining the flow of water from deep soil reservoirs back to the atmosphere, enhancing continental water cycling and cooling of the climate (Berry et al., 2010), a process that continues across terrestrial biomes today (Bonan et al., 1992; Snyder et al., 2004; Feddema et al., 2005; Bala et al., 2007; Arneth et al., 2010). Under current increasing atmospheric CO2 concentration, stomata continue to influence climate as a result of their primary role in optimizing plant water use. Stomatal closure in response to elevated CO2 is a water-conserving mechanism that reduces transpiration but, as a consequence, increases canopy temperature. This physiological forcing adds to the radiative forcing effect of increasing atmospheric CO2 concentration on climate (Sellers et al., 1996a; Betts et al., 1997; Cao et al., 2010). The potential magnitude of physiological climate forcing varies substantially from tropical to temperate to boreal landscapes (Bonan, 2008). However, its mechanism is not fully understood, particularly with regard to the role of vegetation in land-atmosphere coupling (Andrews et al., 2011; Berg et al., 2016; Byrne and O’Gorman, 2016). Owing to the physical and chemical resilience of the guard cell wall and cuticle, stomata are well preserved in fossil plant remains through at least the last 410 million years of the geologic record (Beerling and Woodward, 1997). This resource has proven to be a valuable key to unlocking the history of vegetation-climate coevolution through the Phanerozoic because it has enabled reconstruction of the evolution of plant gas-exchange capacity and productivity (Beerling and Woodward, 1997; Franks and Beerling, 2009a) as well as global atmospheric CO2 concentration (McElwain et al., 1999; Beerling and Royer, 2002; Grein et al., 2011; Franks et al., 2014; Montañez et al., 2016), the latter a critical boundary condition for simulating paleoclimates using global climate models (Kiehl and Shields, 2013; Meissner et al., 2014; Upchurch et al., 2015). Despite these advances, the record of Earth’s vegetation-climate coevolution is incomplete, with significant knowledge gaps remaining at some of the key periods of global transition (Montañez et al., 2016). Next-generation stomatal gas-exchange CO2 proxy models are now helping to reduce uncertainty in reconstructed paleoatmospheric CO2 concentration (Franks et al., 2014). Results from these studies are revising paleo-CO2 estimates that were once widely disparate (e.g. 200–2,800 µmol mol−1 for the Paleocene-Eocene thermal maximum; McInerney and Wing, 2011) to well-constrained values that are more compatible with global paleoclimate simulations (Meissner et al., 2014; Upchurch et al., 2015). However, current paleo-CO2 proxy methods and paleoclimate simulations require refinement and improvement to achieve better agreement across studies and to reduce uncertainty in model outputs. The scales of stomatal influence are illustrated in Figure 1, with examples of some of the quantities involved and some of the tools applied to different spatial and temporal scales of study. Together with the diversity of technologies involved in data collection at multiple scales, robust, well-validated physiological models describing the complex behavior of stomata at the leaf level are essential for interpretation and simulation. Understanding and predicting larger scale carbon, water, and energy cycles requires accurate estimates of the leaf diffusive (stomatal) to water and CO2 and for these and are in using stomatal conductance models. Despite in the and of these models, some of the most stomatal to environmental as the response to CO2 and water and 2011; Chater et al., 2011; et al., 2011; 2013; et al., Franks and 2016). The scales of influence of temporal and spatial scales of is a in the tools and technologies illustrated are leaf gas-exchange and the latter a of climate data also are examples of the influence of stomatal feedback to at are change in CO2 change in primary productivity change in global primary productivity and change in global surface The of change is illustrated with and the feedback effect of stomata on across scales of stomatal is their role as feedback in Franks et al., 2013). stomata as negative and feedback on the their primary role of maintaining leaf and optimizing the biological economics of stomata are negative feedback control as the of this negative feedback control at the leaf level are as a feedback forcing of climate processes at the for the of stomatal at larger scales remains a on stomatal and of change across temporal and spatial scales, with on methods to for the role of stomata simulating global carbon, water, and energy fluxes in global models. The temporal scale to of and the spatial scale leaf to The fossil record reveals strong of change in stomatal through periods (Edwards et al., 1998; Franks and Beerling, The from on the coupling between vegetation and climate (Bonan, and the influence of stomatal on leaf gas exchange (Franks and are that these in global vegetation and has to the and these particularly in the of they the current of Earth’s environmental of the from the that stomatal of in leaf preserved the last is with the in atmospheric CO2 concentration that time is now well from studies at elevated and a time as as the and of a leaf, plants in response to a change in This is in the that to the physiological of the change in a (e.g. with a increases stomatal conductance to the in CO2 Franks et al., 2013). remains this is to this for the of the response of to change in a has proven time scales, are strong in including of et al., 2003; et al., 2005; et al., as well as in the response and time scales (Franks et al., 2013). This has in to the change in in leaf as a proxy for change in global a 2014). for the response of to a has that of leaf gas-exchange et al., but this and studies that the of to a was that by to for and change in a from to a of to response in this of From the many of studies that have it that the is than as as to in a on (Franks et al., This of to a be in the interpretation of studies involving the of particularly a is by a of as in many CO2 key to in the of changing a on stomatal is its with stomatal size the diffusive conductance to CO2 and water and in to be to in and it is the of and that and is a negative between and at scales, including a (Franks et al., across in a and Woodward, 2003; et al., 2010), through the fossil record (Franks and Beerling, and across of a is to by et al., et al., 2014; Franks et al., 2015). This negative as a negative the and evolution of stomata forcing by environmental leaf gas achieve a and plants and the of a negative that the of leaf to gas exchange et al., 2016). for stomatal conductance is to and (Franks and Beerling, The by et has its of physical by the and of stomata and the size of the genome that is the of guard be fully stomata to be by at least one the et al., 2000; et al., 2014), and stomatal guard be than their vascular of the than in stomatal size stomatal conductance (Franks and Beerling, is to the evolution of plant genome size et al., and Franks et al., and the This widely observed between stomatal guard cell and plant genome size has the to be that for stomatal conductance for in and genome size (Franks et al., The role of that size and also must be including in the et al., 2013). genome size a on stomatal size and gas-exchange then the of genome size be a significant in the current of across landscapes and of the most of focus has the evolution of the stomatal control in the of of stomatal to CO2 and water as well as by the This is because and models of stomatal control the and of and of studies have that stomatal to CO2 and evolved first in plants and and are in vascular plants of more as and et al., 2009; and 2013; and 2015). These studies the evolution of stomatal particularly in response to water was to as the and 2011; also as the model in and 2012). This has in a of studies a of including and leaf gas-exchange which that stomatal to and CO2 are to land plant et al., 2011; et al., 2011; et al., Franks and 2016; et al., et al., and control are in land plant this that they one stomatal control mechanism (Franks and 2016). This model for stomatal control is illustrated in Figure the model be as a of the model in which of the and with the stomatal response to elevated CO2 and water are from plants now to CO2 in and Franks and 2016; in However, some remains on stomatal is et al., 2016). current models for the evolution of stomatal the and models. models the key of the stomatal control and the evolution of these as plant The plant with are with to their not to The between the models is that the model in some of the of and stomatal control in plant the model that stomatal control including to elevated CO2 and evolved first in in the of and are in involved in of the stomatal control and their feedback control in the regulation of stomatal different resulting from response to response to water response to water potential by and response to elevated model and control in land plants not stomatal the of and feedback control in stomata of as well as of these have to as land plants as a many plant However, the and of stomata in land plant guard that with to for gas a stomatal control model a to the and processes that stomatal and physiological is to fully the and of and stomatal control across the diversity of vascular and plant gas exchange requires a of the of the behavior of stomata for the of plant gas exchange requires some of the stomatal control mechanism and its accurate models of stomatal from of processes the behavior of leaf gas-exchange is the for of guard cell et al., 2012). from of guard cell and has not to the behavior of stomata but to processes the guard cell that were improving the of stomatal et al., et al., 2014). of this more across plant on accurate of the processes stomatal behavior in plant some of these processes are to be in then the and of models have to be Stomatal conductance models applied to larger spatial scales et al., et al., and et al., 2009; et al., The because stomatal conductance models are on physiological and of and models require in of the models one of has but is now a in the of these approaches and with on studies have applied to model plant gas exchange and et al., et al., 2011; et al., 2014), it has not as widely as have from Stomatal control of leaf gas exchange was in of ago and but stomatal control has using approaches et al., 2010). with the of is the of of the of transpiration and CO2 to in stomatal conductance to water as the et al., Stomatal conductance is at some it Stomatal these it to the plant has a of with the least of water in with and on the of this which the of a for to of plant and to the a methods have for for in gas-exchange models stomatal but this requires some the of and in the plant vegetation of as well as a of that be with the et al., This is to a of to atmospheric CO2 for a model the in the model et al., The widely observed of a for a leaf conditions, a by the control of stomatal conductance for some conditions, has applied in the of plant gas including time (Franks et al., 2014). that a some it with water et al., and across it to be in plants with capacity (Franks and a is and as with it is not for of the of and on plant and for the of must be and for the plant vegetation of in the of these in different plant the capacity of stomatal models to plant gas must be the of approaches with surface models the fluxes of and CO2 between the land and These models, with models of atmospheric and the terrestrial of models. to models is the regulation of transpiration and by The of is in to the model as However, it is from that is of the the of et al., also is to it be that plants with 1, and this has observed et al., 2015). 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The model with with the than the model and better the and the model the water also the data between the and models. This result is with the leaf simulations in Figure but model simulations with the data are the uncertainty in of simulations of using the and models, data above the the as by et model with model with the model with the water model with from to model with from to model with from to water The is the for the the is the for the and the is the from the in the model to with in the model to with and in the model to water CO2 with a more realistic between the models. a that with the in the the of the model to the data and was to the model and and values to with the of for the from the the and model simulations were to with and it is that parameterization of 1, and in the and models, and in the and models, the land surface model to data from simulations with the and models in the potential influence that in the parameterization of and have on estimates of water and carbon fluxes. simulations using and atmospheric forcing data for to from the and for data et al., 2016). is agreement between simulations using the stomatal conductance model in and but some simulations using with the values for different plant to simulations using with a global of for plants and for result in primary productivity in the and and surface is in the and and with this increases transpiration to in the that were with of the model for in et al., et al., 2015). simulations with also but the model reduces in model et al., 2015). these are substantially plant in simulations using the stomatal conductance model are different values that with the values for plant than a of for plants and with and and and with and The are transpiration increases that has surface with the simulations using values in and with for the leaf and canopy scales these global simulations that vegetation models the model for stomatal conductance the are values for plant the of empirical-based and optimization-based models of in global land surface models in to methods are of stomatal model parameterization on global were in the global model the et stomatal conductance model with values in simulations using the stomatal conductance model with a global of for plants and for The is as were by using and and for atmospheric forcing data for to and primary productivity and surface for of the and in and but for the are parameterization of stomatal conductance models in global The of global simulations was as in Figure that of using a of for simulations with the stomatal conductance different plant were values for 1, from the values for plant using This the between simulations using the stomatal conductance The and of stomatal conductance models is for in the of soil is to stomatal closure in models diffusive limitations in response to soil by 1, with a shift to at a more physiological stomatal conductance drought. models limitations and reduce stomatal conductance by as soil increases models scale with than with limitation on et al., et al., 2011; et al., and diffusive and limitations must be et al., 2013). is also uncertainty the of the soil and 2014). a is model to in land surface models the process of plant water uptake and the of plant on stomata (Bonan et al., 2014; et al., 2016; et al., 2016). The role of stomata in processes and the coevolution of this have with knowledge and better of geological, and global and at scale of but is a role for studies at one scale to at the level are the in some the of of the stomatal conductance control mechanism and its behavior multiple and environmental The of more realistic representation of stomatal conductance and its regulation of leaf gas exchange in global models is improving global simulations of carbon, water, and energy fluxes, and these simulations with in turn highlight some of the limitations with current leaf-scale models. Our analysis of current leaf-scale models of stomatal conductance representing empirical-based optimization-based approaches reveals close structural similarities that virtually and indistinguishable in simulations of leaf, canopy, and global fluxes. for land model

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