Studies on the Effects of Elevated CO2 Levels on Woody Plant Mass
Results from studies examining the effects of elevated CO2 levels on woody plant mass.
dat.curtis1998
The data frame contains the following columns:
| id | numeric |
observation number |
| paper | numeric |
paper number |
| genus | character |
genus name |
| species | character |
species name |
| fungrp | character |
plant functional group |
| co2.ambi | numeric |
ambient CO2 level (control group) |
| co2.elev | numeric |
elevated CO2 level (treatment group) |
| units | character |
units for CO2 exposure levels |
| time | numeric |
maximum length of time (days) of CO2 exposure |
| pot | character |
growing method (see below) |
| method | character |
CO2 exposure facility (see below) |
| stock | character |
planting stock code |
| xtrt | character |
interacting treatment code (see below) |
| level | character |
interacting treatment level codes (see below) |
| m1i | numeric |
mean plant mass under elevated CO2 level (treatment group) |
| sd1i | numeric |
standard deviation of plant mass underelevated CO2 level (treatment group) |
| n1i | numeric |
number of observations under elevated CO2 level (treatment group) |
| m2i | numeric |
mean plant mass under ambient CO2 level (control group) |
| sd2i | numeric |
standard deviation of plant mass under ambient CO2 level (control group) |
| n2i | numeric |
number of observations under ambient CO2 level (control group) |
The studies included in this dataset compared the total above- plus below-ground biomass (in grams) for plants that were either exposed to ambient (around 35 Pa) and elevated CO2 levels (around twice the ambient level). The co2.ambi and co2.elev variables indicate the CO2 levels in the control and treatment groups, respectively (with the units variable specifying the units for the CO2 exposure levels). Many of the studies also varied one or more additional environmental variables (defined by the xtrt and level variables):
NONE = no additional treatment factor
FERT = soil fertility (either a CONTROL, HIGH, or LOW level)
LIGHT = light treatment (always a LOW light level)
FERT+L = soil fertility and light (a LOW light and soil fertility level)
H2O = well watered vs drought (either a WW or DRT level)
TEMP = temperature treatment (either a HIGH or LOW level)
OZONE = ozone exposure (either a HIGH or LOW level)
UVB = ultraviolet-B radiation exposure (either a HIGH or LOW level)
In addition, the studies differed with respect to various design variables, including CO2 exposure duration (time), growing method (pot: number = pot size in liters; GRND = plants rooted in ground; HYDRO = solution or aeroponic culture), CO2 exposure facility (method: GC = growth chamber; GH = greenhouse; OTC = field-based open-top chamber), and planting stock (stock: SEED = plants started from seeds; SAP = plants started from cuttings). The goal of the meta-analysis was to examine the effects of elevated CO2 levels on plant physiology and growth and the interacting effects of the environmental (and design) variables.
Hedges, L. V., Gurevitch, J., & Curtis, P. S. (1999). The meta-analysis of response ratios in experimental ecology. Ecology, 80, 1150–1156. (data obtained from Ecological Archives, E080-008-S1, at: http://www.esapubs.org/archive/ecol/E080/008/)
Curtis, P. S., & Wang, X. (1998). A meta-analysis of elevated CO2 effects on woody plant mass, form, and physiology. Oecologia, 113, 299–313.
### copy data into 'dat'
dat <- dat.curtis1998
### calculate log ratio of means and corresponding sampling variances
dat <- escalc(measure="ROM", m1i=m1i, sd1i=sd1i, n1i=n1i,
m2i=m2i, sd2i=sd2i, n2i=n2i, data=dat)
dat
### meta-analysis of log ratio of means using a random-effects model
res <- rma(yi, vi, method="DL", data=dat)
res
### average ratio of means with 95% CI
predict(res, transf=exp, digits=2)
### meta-analysis for plants grown under nutrient stress
res <- rma(yi, vi, method="DL", data=dat, subset=(xtrt=="FERT" & level=="LOW"))
predict(res, transf=exp, digits=2)
### meta-analysis for plants grown under low light conditions
res <- rma(yi, vi, method="DL", data=dat, subset=(xtrt=="LIGHT" & level=="LOW"))
predict(res, transf=exp, digits=2)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.