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PHOTO BY S. MANZONI

Eco-hydrological optimality to link water use and carbon gains by plants. Manzoni S. 1,2 , G. Vico 2 , S. Palmroth 3 , G. Katul 3,4 , and A. Porporato 3,4. 1 Physical Geography and Quaternary Geology, Stockholm Univ. 2 Crop Production Ecology and Ecology Dept., SLU, Uppsala

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PHOTO BY S. MANZONI

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  1. Eco-hydrological optimality to link water use and carbon gains by plants Manzoni S.1,2, G. Vico2, S. Palmroth3, G. Katul3,4, and A. Porporato3,4 1Physical Geography and Quaternary Geology, Stockholm Univ. 2Crop Production Ecology and Ecology Dept., SLU, Uppsala 3Nicholas School of the Environment, Duke Univ., USA 4Civil and Environmental Engineering, Duke Univ., USA PHOTO BY S. MANZONI

  2. Carbon uptake Food, fiber, biofuels… Respiration Respiration Soil carbon PHOTO BY S. MANZONI

  3. Stomatal conductance as a “compromise between the need to provide a passage for assimilation and the prevention of excessive transpiration” (Cowan and Troughton, 1971, Planta) Carbon uptake Transpiration A E Rainfall Soil moisture PHOTO BY S. MANZONI

  4. How do plants respond to altered climatic conditions? Can we optimize agro-ecosystem management to balance productivity and resource use? Can we breed crops towards more efficient resource use?

  5. Regulation of water transport Stomatal closure limits evaporation from the leaves LAI gc(P) gc -ψP E gP Lens (2011), New Phytologist -ψP Plant xylem limits transport of liquid water to the leaves Manzoni et al. (2013) Adv. Water Res.

  6. Water use strategies involve tradeoffs • High transpiration allows plants to grow faster → competitive advantage (Eagleson, 2002, Rodriguez-Iturbe and Porporato 2004) BUT: high transpiration lowers soil moisture faster → earlier water stress? • Stomatal closure reduces desiccation risk (Cowan, 1982) BUT: lower stomatal conductance decreases C uptake → carbon starvation?

  7. Tradeoffs require ‘balanced’ solutions Hypothesis: Water use strategies are optimal in a given environment (idea pioneered by Givnish, Cowan and Farquhar) • Objective: maximize photosynthesis (A) • Control: stomatal conductance to CO2 (gC) • Constraint: soil water is limited Process-based optimal control problem

  8. Optimality at different time scales Water use strategies vary with the temporal scale of interest, because environmental drivers fluctuate at different scales Data from Fazenda Tamandua, Brazil R 1) Sub-daily, at ~constant soil moisture 3) Several years and longer: stochastic soil moisture 2) One dry-down (days-weeks)

  9. Stomatal controls on transpiration and photosynthesis Stomatal cavity The water flux is driven by the atmospheric evaporative demand From the xylem C fixation wi ci Guard cells gc The CO2 flux is driven by the gradient between atmospheric and internal CO2 concentrations E A ca wa Biochemical C fixation

  10. Stomatal controls on transpiration and photosynthesis The water flux is driven by the atmospheric evaporative demand Downward concavity! A(gc) E(gc) The CO2 flux is driven by the gradient between atmospheric and internal CO2 concentrations gc Biochemical C fixation

  11. 1) Sub-daily time scale Objective: maximize Soil moisture changes slowly compared to light and VPD  Soil moisture is assumed constant Marginal water use efficiency Optimal stomatal conductance λ is constant, but undetermined! (classical solution by Cowan and Farquhar; Hari and Mäkelä)

  12. λ = constant at given soil moisture Correct scaling gc and E vs. vapor pressure deficit D Katul et al., 2009, PCE Palmroth et al., 1999, Oecologia Proportionality of gc and A (see also Hari et al., 2000, Aus. J. Plant Phys.)

  13. 2) Dry-down time scale (days to weeks) R E Q Objective: maximize Zr Subject to the constraint L Marginal water use efficiency Optimal stomatal conductance with time λ is defined by the boundary conditions of the optimization (Manzoni et al. 2013, AWR)

  14. λ increases with decreasing water availability gc Time Time (Manzoni et al., 2011, Functional Ecol) λ/λww -ψ λ Time λww aΨ -ψ λincreases as drought progresses across species, ecosystems, and climates

  15. 3) Optimal water use in stochastic environments SPAC model Transpiration – moisture curve depends on plant hydraulic traits ψ50 Ψ90,s gP gc LAI gc E -ψP -ψP s → p(s) depends on the E(s) curve and hence also on plant hydraulic traits Constraint: Stochastic rainfall

  16. 3) Optimal water use in stochastic environments → p(s) depends on the E(s) curve and hence also on plant hydraulic traits Constraint: → Plant strategies optimize the long-term mean C uptake Objective: maximize → Focus on stomatal and xylem conductances: What is the optimal shape of gc(P) and gP(P)?

  17. Optimal water use explains plant trait coordination <A> LAI gc ψ50 gP -ψP Ψ90,s gc -ψP Observations are consistent with prediction of coordinated stomatal closure and cavitation occurrence

  18. Conclusions • Sub-daily time scale: optimization explains stomatal responses to air humidity and photosynthesis-transpiration relations • Dry-down time scale: plants optimally down-regulate water losses as soils dry • Long term: coordination among plant hydraulic traits emerges as an optimal evolutionary strategy

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