Optimization of root distribution

Functions spwb_ldrExploration and spwb_ldrOptimization are used to find optimum the species root distribution within spwb, given the arguments x, meteo and psi_crit.

Usage

spwb_ldrExploration(
  x,
  meteo,
  cohorts = NULL,
  RZmin = 301,
  RZmax = 4000,
  V1min = 0.01,
  V1max = 0.94,
  resolution = 10,
  heat_stop = 0,
  transformation = "identity",
  verbose = FALSE,
  ...
)

spwb_ldrOptimization(y, psi_crit, opt_mode = 1)

Arguments

x

An object of class spwbInput.

meteo

A data frame with daily meteorological data series (see spwb).

cohorts

A character string with the names of cohorts to be explored. If NULL then all cohorts are explored.

RZmin

The minimum value of RZ (the rooting depth) to be explored (in mm)

RZmax

The maximum value of RZ (the rooting depth) to be explored (in mm)

V1min

The minimum value of V1 (the root proportion in the first soil layer) to be explored

V1max

The maximum value of V1 (the root proportion in the first soil layer) to be explored

resolution

An integer defining the number of values to obtain by discretization of the root parameters RZ and V1. The number of parameter combinations and therefore the computation cost increases increase with the square of resolution

heat_stop

An integer defining the number of days during to discard from the calculation of the optimal root distribution. Usefull if the soil water content initialization is not certain

transformation

Function to modify the size of Z intervals to be explored (by default, bins are equal).

verbose

A logical value. Print the internal messages of the function?

...

Additional parameters to function spwb.

y

The result of calling spwb_ldrExploration.

psi_crit

A numerical vector of length iqual to the number of species in the plot containing the species values of water potential inducing hydraulic failure (in MPa). Use NA values to skip optimization for particular plant cohorts.

opt_mode

Optimization mode:

opt_mode = 1 maximizes transpiration along the line of stress equal to psi_crit (Cabon et al. 2018). The optimization is based on the eco-hydrological equilibrium hypothesis (Eagleson, 1982), which is formulated here as the root distribution for which plant transpiration is maximized while the plant water potential is close to the species-defined critical value psi_crit (Cabon et al.,2018).
opt_mode = 2 maximizes transpiration among combinations with stress according to psi_crit).
opt_mode = 3 maximizes photosynthesis among combinations with stress according to psi_crit).
opt_mode = 4 maximizes transpiration, subject to root construction constrains, among combinations with stress according to psi_crit).
opt_mode = 5 maximizes photosynthesis, subject to root construction constrains, among combinations with stress according to psi_crit).

Value

Function spwb_ldrExploration returns a list containing a list containing the explored RZ and V1 combinations as well as arrays with the values of average daily plant transpiration, average daily net photosynthesis and the minimum plant water potential for each cohort and parameter combination.

Function spwb_ldrOptimization returns a data frame with containing the species index used in medfate, psi_crit and the optimized values of V1 and the LDR parameters Z50 and Z95 (see root_ldrDistribution) and as many rows as the number of species.

Details

For each combination of the parameters RZ and V1 the function spwb_ldrExploration runs spwb, setting the total soil depth equal to RZ. The root proportion in each soil layer is derived from V1, the depth of the first soil layer and RZ using the LDR root distribution model (Schenk and Jackson, 2002) and assuming that the depth containing 95 percent of the roots is equal to RZ. Function spwb_ldrOptimization takes the result of the exploration and tries to find optimum root distribution parameters. psi_crit, the species specific water potential inducing hydraulic failure, can be approached by the water potential inducing 50 percent of loss of conductance for the and gymnosperms and 88 percent for the angiosperms (Urli et al., 2013, Brodribb et al., 2010). Details of the hypothesis and limitations of the optimization method are given in Cabon et al. (2019).

References

Brodribb, T.J., Bowman, D.J.M.S., Nichols, S., Delzon, S., Burlett, R., 2010. Xylem function and growth rate interact to determine recovery rates after exposure to extreme water deficit. New Phytol. 188, 533–542. doi:10.1111/j.1469-8137.2010.03393.x

Cabon, A., Martínez-Vilalta, J., Poyatos, R., Martínez de Aragón, J., De Cáceres, M. (2018) Applying the eco-hydrological equilibrium hypothesis to estimate root ditribution in water-limited forests. Ecohydrology 11: e2015.

Eagleson, P.S., 1982. Ecological optimality in water-limited natural soil-vegetation systems: 1. Theory and hypothesis. Water Resour. Res. 18, 325–340. doi:10.1029/WR018i002p00325

Schenk, H.J., Jackson, R.B., 2002. The Global Biogeography of Roots. Ecol. Monogr. 72, 311. doi:10.2307/3100092

Urli, M., Porte, A.J., Cochard, H., Guengant, Y., Burlett, R., Delzon, S., 2013. Xylem embolism threshold for catastrophic hydraulic failure in angiosperm trees. Tree Physiol. 33, 672–683. doi:10.1093/treephys/tpt030

Author

Antoine Cabon, WSL