Transcript Nonlinear Dynamics of Soil Moisture and Mineral Nitrogen

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
2Crop
Production Ecology and Ecology Dept., SLU, Uppsala
3Nicholas
4Civil
Geography and Quaternary Geology, Stockholm Univ.
School of the Environment, Duke Univ., USA
and Environmental Engineering, Duke Univ., USA
PHOTO BY S. MANZONI
Carbon uptake
Food, fiber,
biofuels…
Respiration
Respiration
Soil
carbon
PHOTO BY S. MANZONI
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
How do plants respond to
altered climatic conditions?
Can we optimize agroecosystem management to
balance productivity and
resource use?
Can we breed crops towards more efficient resource use?
Regulation of water transport
Stomatal closure limits
evaporation from the
leaves
gc
LAI gc(P)
-ψP
gP
E
-ψP
Manzoni et al. (2013) Adv. Water Res.
Lens (2011), New Phytologist
Plant xylem limits transport
of liquid water to the leaves
Water use strategies involve tradeoffs
1) 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?
2) Stomatal closure reduces desiccation risk (Cowan, 1982)
BUT: lower stomatal conductance decreases C uptake
→ carbon starvation?
Tradeoffs require ‘balanced’ solutions
Hypothesis:
Water use strategies are optimal in a given environment
(idea pioneered by Givnish, Cowan and Farquhar)
1) Objective: maximize photosynthesis (A)
2) Control: stomatal conductance to CO2 (gC)
3) Constraint: soil water is limited
Process-based optimal control problem
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
1) Sub-daily, at
~constant soil
moisture
2) One dry-down
(days-weeks)
R
3) Several years and longer:
stochastic soil moisture
Stomatal controls on transpiration and photosynthesis
Stomatal cavity
From the
xylem
C fixation
wi ci
Guard
cells
gc
E
wa
A
ca
The water flux is driven by the
atmospheric evaporative demand
E  agC wi  wa   agC D
The CO2 flux is driven by the
gradient between atmospheric and
internal CO2 concentrations
A  gC ca  ci   k Qci
Biochemical C fixation
Stomatal controls on transpiration and photosynthesis
Downward
concavity!
The water flux is driven by the
atmospheric evaporative demand
E  agC wi  wa   agC D
A(gc)
E(gc)
gc
g C kca
Ag C  
k  gC
The CO2 flux is driven by the
gradient between atmospheric and
internal CO2 concentrations
A  gC ca  ci   k Qci
Biochemical C fixation
1) Sub-daily time scale
T
Objective: maximize
 Ag dt
c
0
Soil moisture changes slowly compared to light and VPD
 Soil moisture is assumed constant
Optimal stomatal conductance
 ca

g C  k 
 1
 aD

Marginal water use efficiency
A
 t  
 0
E
λ is constant, but undetermined!
(classical solution by Cowan and Farquhar; Hari and Mäkelä)
λ = constant at given soil moisture
Palmroth et al., 1999, Oecologia
Correct scaling gc and E vs.
vapor pressure deficit D
Katul et al., 2009, PCE
Proportionality of gc and A
(see also Hari et al., 2000, Aus. J. Plant Phys.)
2) Dry-down time scale (days to weeks)
R E
T
Objective: maximize
 Ag dt
Q
c
0
Subject to the
constraint
ds
nZ r
 R  E g c   L  Q
dt
s
Zr
L
Optimal stomatal conductance
Marginal water use efficiency
 ca

g C  k 
 1
 aD

 t   with time
λ is defined by the boundary conditions of the optimization
(Manzoni et al. 2013, AWR)
(Manzoni et al., 2011, Functional Ecol)
λ increases with decreasing water availability
-ψ
gc
-ψ
λ/λww
λww
Water use efficiency
λ
  wwea
aΨ
Water stress
λ increases as drought progresses across
species, ecosystems, and climates
3) Optimal water use in stochastic environments
SPAC model
gc
LAI gc
Ψ90,s
Transpiration – moisture
curve depends on plant
hydraulic traits
gP
ψ50
E
-ψP
-ψP
s
→ p(s) depends on the
ds
 R  E  L  Q E(s) curve and hence also
Constraint: nZ r
dt
on plant hydraulic traits
Stochastic rainfall
3) Optimal water use in stochastic environments
→ p(s) depends on the
ds
 R  E  L  Q E(s) curve and hence also
Constraint: nZ r
dt
on plant hydraulic traits
→ Plant strategies optimize the long-term mean C uptake

Objective: maximize
A   As  ps ds
0
→ Focus on stomatal and xylem conductances:
What is the optimal shape of gc(P) and gP(P)?
ψ50
<A>
gP
Optimal water use
explains plant trait
coordination
A
-ψP
Observations are consistent with
prediction of coordinated stomatal
closure and cavitation occurrence
Ψ90,s
gc
LAI gc
-ψP
Conclusions
1. Sub-daily time scale: optimization explains
stomatal responses to air humidity and
photosynthesis-transpiration relations
2. Dry-down time scale: plants optimally
down-regulate water losses as soils dry
3. Long term: coordination among plant
hydraulic traits emerges as an optimal
evolutionary strategy