dat.curtis1998.RdResults from studies examining the effects of elevated CO2 levels on woody plant mass.
dat.curtis1998The 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 ‘Details’) |
| method | character | CO2 exposure facility (see ‘Details’) |
| stock | character | planting stock code |
| xtrt | character | interacting treatment code (see ‘Details’) |
| level | character | interacting treatment level codes (see ‘Details’) |
| 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(4), 1150–1156. https://doi.org/10.1890/0012-9658(1999)080[1150:TMAORR]2.0.CO;2 (data obtained from Ecological Archives, E080-008-S1, at: https://doi.org/10.6084/m9.figshare.c.3297278)
Curtis, P. S., & Wang, X. (1998). A meta-analysis of elevated CO2 effects on woody plant mass, form, and physiology. Oecologia, 113(3), 299–313. https://doi.org/10.1007/s004420050381
ecology, ratios of means
### copy data into 'dat' and examine data
dat <- dat.curtis1998
head(dat)
#> id paper genus species fungrp co2.ambi co2.elev units time pot method stock xtrt level
#> 1 21 44 ALNUS RUBRA N2FIX 350 650 ul/l 47 0.5 GC SEED FERT HIGH
#> 2 22 44 ALNUS RUBRA N2FIX 350 650 ul/l 47 0.5 GC SEED FERT CONTROL
#> 3 27 121 ACER RUBRUM ANGIO 350 700 ppm 59 2.6 GH SEED NONE .
#> 4 32 121 QUERCUS PRINUS ANGIO 350 700 ppm 70 2.6 GH SEED NONE .
#> 5 35 121 MALUS DOMESTICA ANGIO 350 700 ppm 64 2.6 GH SEED NONE .
#> 6 38 121 ACER SACCHARINUM ANGIO 350 700 ppm 50 2.6 GH SEED NONE .
#> m1i sd1i n1i m2i sd2i n2i
#> 1 6.8169 1.7699820 3 3.9450 1.1157970 5
#> 2 2.5961 0.6674662 5 2.2512 0.3275839 5
#> 3 2.9900 0.8560000 5 1.9300 0.5520000 5
#> 4 5.9100 1.7420000 5 6.6200 1.6310000 5
#> 5 4.6100 1.4070000 4 4.1000 1.2570000 4
#> 6 10.7800 1.1630000 5 6.4200 2.0260000 3
### load metafor package
library(metafor)
### calculate (log transformed) ratios of means and corresponding sampling variances
dat <- escalc(measure="ROM", m1i=m1i, sd1i=sd1i, n1i=n1i,
m2i=m2i, sd2i=sd2i, n2i=n2i, data=dat)
head(dat)
#>
#> id paper genus species fungrp co2.ambi co2.elev units time pot method stock xtrt level
#> 1 21 44 ALNUS RUBRA N2FIX 350 650 ul/l 47 0.5 GC SEED FERT HIGH
#> 2 22 44 ALNUS RUBRA N2FIX 350 650 ul/l 47 0.5 GC SEED FERT CONTROL
#> 3 27 121 ACER RUBRUM ANGIO 350 700 ppm 59 2.6 GH SEED NONE .
#> 4 32 121 QUERCUS PRINUS ANGIO 350 700 ppm 70 2.6 GH SEED NONE .
#> 5 35 121 MALUS DOMESTICA ANGIO 350 700 ppm 64 2.6 GH SEED NONE .
#> 6 38 121 ACER SACCHARINUM ANGIO 350 700 ppm 50 2.6 GH SEED NONE .
#> m1i sd1i n1i m2i sd2i n2i yi vi
#> 1 6.8169 1.7699820 3 3.9450 1.1157970 5 0.5470 0.0385
#> 2 2.5961 0.6674662 5 2.2512 0.3275839 5 0.1425 0.0175
#> 3 2.9900 0.8560000 5 1.9300 0.5520000 5 0.4378 0.0328
#> 4 5.9100 1.7420000 5 6.6200 1.6310000 5 -0.1134 0.0295
#> 5 4.6100 1.4070000 4 4.1000 1.2570000 4 0.1172 0.0468
#> 6 10.7800 1.1630000 5 6.4200 2.0260000 3 0.5183 0.0355
#>
### meta-analysis using a random-effects model
res <- rma(yi, vi, method="DL", data=dat)
res
#>
#> Random-Effects Model (k = 102; tau^2 estimator: DL)
#>
#> tau^2 (estimated amount of total heterogeneity): 0.0216 (SE = 0.0088)
#> tau (square root of estimated tau^2 value): 0.1471
#> I^2 (total heterogeneity / total variability): 86.87%
#> H^2 (total variability / sampling variability): 7.61
#>
#> Test for Heterogeneity:
#> Q(df = 101) = 769.0185, p-val < .0001
#>
#> Model Results:
#>
#> estimate se zval pval ci.lb ci.ub
#> 0.2531 0.0185 13.6965 <.0001 0.2168 0.2893 ***
#>
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
### average ratio of means with 95% CI
predict(res, transf=exp, digits=2)
#>
#> pred ci.lb ci.ub pi.lb pi.ub
#> 1.29 1.24 1.34 0.96 1.72
#>
### 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)
#>
#> pred ci.lb ci.ub pi.lb pi.ub
#> 1.14 1.04 1.26 0.82 1.59
#>
### 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)
#>
#> pred ci.lb ci.ub pi.lb pi.ub
#> 1.57 1.34 1.85 0.94 2.63
#>