bigleaf (version 0.8.2)

optimum.temperature: Optimum temperature of Gross Primary Productivity

Description

Calculates the relationship between Gross Primary Productivity (GPP) and Air Temperature (Tair) using boundary line analysis and derives the thermal optima. This function can also be used to find the boundary line relationship and optima of other variables such as NPP and NEP.

Usage

optimum.temperature(
  data,
  GPP = "GPP",
  Tair = "Tair",
  BLine = 0.9,
  Obs_filter = 30
)

Value

A list containing the following objects:

1. df.bl: A four column dataframe:

   • Tair_bin: air temperature bins in 1 degree increments

   • GPP_Bline: Value of GPP at the BLine

   • n_obs: number of observations in the air temperature bin

   • GPP_Bline_smooth: Value of GPP at the smoothed Bline

2. opt.temp: A named vector with two elements:

   • Topt: Thermal optima of GPP - the air temperature bin with maximum GPP along the smoothed Bline

   • GPP_bl: The boundary line GPP observation at Topt

Arguments

data

Dataframe containing the Gross Primary Productivity and Air Temperature observations

GPP

Name of column (in quotations, eg. "GPP") containing the Gross Primary Productivity observations (umol CO2 m-2 s-1).

Tair

Name of column (in quotations, eg. "Tair") containing the air temperature (degrees celcius) observations.

BLine

Quantile at which to place the boundary line in format "0.XX". Defaults to 0.90.

Obs_filter

Filter to remove air temperature bins with an insufficient number of observations. Defaults to 30.

Details

This function works by first binning GPP and air temperature observations to 1 degree temperature bins and then deriving the relationship between GPP and air temperature at a defined quantile using boundary line analysis. Observations are binned using a rounding function, so that each bin is centered on the degree integer value (eg. bin 18 contains values between 17.5 and 18.49). The boundary line is usually placed at the upper boundary of the distribution (see Webb 1972) however this functional allows the user to select any quantile, with the default of 0.9 selected for use with eddy covariance flux observations due to the high level of noise in these data (see Bennett et al, 2021). After binning observations, the function removes temperature bins with fewer observations than the default of 30 (this value can also be user defined). It then calculates the smoothed curve between GPP and air temperature using the loess function and derives the thermal optima of GPP (Topt). Topt is defined as the temperature bin at which GPP reaches its maximum along the smoothed boundary line.

References

Bennett A. et al., 2021: Thermal optima of gross primary productivity are closely aligned with mean air temperatures across Australian wooded ecosystems. Global Change Biology 32(3), 280-293

Webb, R. A. 1972. Use of the Boundary Line in the analysis of biological data. Journal of Horticultural Science 47, 309-319

Examples

 # Locate the relationship between GPP and air temperature using default values 
 # for BLine and observation filter.

 Gpp_ta <- optimum.temperature(data=AT_Neu_Jul_2010, GPP="GPP", Tair="Tair")
 
 # Locate the relationship between GPP and air temperature at the 50th percentile, 
 # filtering temperature bins with fewer than 10 observations
 
 Gpp_ta <- optimum.temperature(data=AT_Neu_Jul_2010, 
                               GPP="GPP", Tair="Tair", BLine=0.50, Obs_filter=10)