dat.co2: Studies on the Effects of Elevated CO2 Levels on Woody Plant Mass

Description

Results from studies examining the effects of elevated CO2 levels on woody plant mass.

Usage

dat.co2

Arguments

format

The data frame contains the following columns: lll{ id numeric observation number paper numeric paper number units numeric paper number genus character genus name species character species name function 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) }

source

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/)

Details

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 varried one or more additional environmental variables (defined by the xtrt and level variables):

NONE = no additional treatment factor
FERT = soil fertility (either aCONTROL,HIGH, orLOWlevel)
LIGHT = light treatment (always aLOWlight level)
FERT+L = soil fertility and light (aLOWlight and soil fertility level)
H2O = well watered vs drought (either aWWorDRTlevel)
TEMP = temperature treatment (either aHIGHorLOWlevel)
OZONE = ozone exposure (either aHIGHorLOWlevel)
UVB = ultraviolet-B radiation exposure (either aHIGHorLOWlevel)

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.

References

Curtis, P. S. & Wang, X. (1998). A meta-analysis of elevated CO2 effects on woody plant mass, form, and physiology. Oecologia, 113, 299--313.

Examples

Run this code

### load CO2 data
data(dat.co2)

### calculate log ratio of means and corresponding sampling variances
dat <- escalc(m1i=m1i, sd1i=sd1i, n1i=n1i, m2i=m2i, sd2i=sd2i, n2i=n2i,
              measure="ROM", data=dat.co2, append=TRUE)
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)

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