Plant growth in the absence of insects
Matthew A. Barbour
2021-06-24
Last updated: 2021-06-24
Checks: 6 1
Knit directory: genes-to-foodweb-stability/
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Wrangle data
# load data
ChamberNoInsectsDF <- read_csv("data/PreExperimentNoInsectsPlantBiomass.csv") %>%
mutate(Cage = as.character(Cage),
Pot = as.character(Pot))
# conduct analyses at cage level
CageLevelBiomass <- ChamberNoInsectsDF %>%
# sum biomass across both pots
group_by(Cage, Temperature, Richness, Composition, Col, gsm1, AOP2, AOP2.gsoh) %>%
summarise_at(vars(Biomass_g), list(sum)) %>%
# tidy data
ungroup() %>%
select(cage = Cage, temp = Temperature, rich = Richness, com = Composition, Col, gsm1, AOP2, AOP2.gsoh, Biomass_g) %>%
# adjust temp and rich so effect of +1 C is comparable to +1 genotype
mutate(temp = ifelse(temp == "20 C", 0, 3),
rich = rich - 1,
# define orthogonal constrasts to test for above-average allele effects.
# aop2_vs_AOP2 must be included first
aop2_vs_AOP2 = Col + gsm1 - AOP2 - AOP2.gsoh,
mam1_vs_MAM1 = gsm1 - Col, # aop2_vs_AOP2 must be included in model
gsoh_vs_GSOH = AOP2.gsoh - AOP2)Reproduce Table S5
# fit model
log_biomass_lmer <- lmer(log(Biomass_g) ~ temp*(rich + aop2_vs_AOP2 + mam1_vs_MAM1 + gsoh_vs_GSOH) + (1|com/temp),
data = CageLevelBiomass)
# reproduce table S5
anova(log_biomass_lmer, type = "1", ddf = "Kenward-Roger") %>%
kable(., caption = "Analysis of variance for plant biomass (log transformed) in the absence of insects.", booktabs = T) %>%
kable_styling(latex_options = c("striped", "hold_position"))| Sum Sq | Mean Sq | NumDF | DenDF | F value | Pr(>F) | |
|---|---|---|---|---|---|---|
| temp | 4.2342063 | 4.2342063 | 1 | 5.220228 | 52.8748142 | 0.0006393 |
| rich | 0.0110385 | 0.0110385 | 1 | 4.951544 | 0.1378439 | 0.7257894 |
| aop2_vs_AOP2 | 1.2616246 | 1.2616246 | 1 | 5.791912 | 15.7545862 | 0.0079189 |
| mam1_vs_MAM1 | 0.0410509 | 0.0410509 | 1 | 5.791912 | 0.5126251 | 0.5018356 |
| gsoh_vs_GSOH | 0.0148935 | 0.0148935 | 1 | 5.791912 | 0.1859833 | 0.6818684 |
| temp:rich | 0.0097817 | 0.0097817 | 1 | 4.427930 | 0.1221488 | 0.7427133 |
| temp:aop2_vs_AOP2 | 0.0035946 | 0.0035946 | 1 | 5.647776 | 0.0448871 | 0.8396490 |
| temp:mam1_vs_MAM1 | 0.0541709 | 0.0541709 | 1 | 5.647776 | 0.6764608 | 0.4441364 |
| temp:gsoh_vs_GSOH | 0.0669588 | 0.0669588 | 1 | 5.647776 | 0.8361506 | 0.3978575 |
Reproduce Fig. S10
# calculate 95% confidence intervals, but first remove higher-order statistical interactions
summary(lme4::lmer(log(Biomass_g) ~ temp + rich + aop2_vs_AOP2 + (1|com), data = CageLevelBiomass)) # dropped com:temp, because it was singular, it also doesn't matter for estimating confidence intervals for genetic effect, it would matter if we were trying to estimate temp, which we are only controlling for here.Linear mixed model fit by REML ['lmerMod']
Formula: log(Biomass_g) ~ temp + rich + aop2_vs_AOP2 + (1 | com)
Data: CageLevelBiomass
REML criterion at convergence: 39.1
Scaled residuals:
Min 1Q Median 3Q Max
-2.7409 -0.4813 0.1360 0.5751 2.4735
Random effects:
Groups Name Variance Std.Dev.
com (Intercept) 0.009476 0.09735
Residual 0.081126 0.28483
Number of obs: 60, groups: com, 11
Fixed effects:
Estimate Std. Error t value
(Intercept) -0.05210 0.07843 -0.664
temp -0.21085 0.02451 -8.601
rich -0.02374 0.05456 -0.435
aop2_vs_AOP2 0.19691 0.04554 4.324
Correlation of Fixed Effects:
(Intr) temp rich
temp -0.469
rich -0.645 0.000
ap2_vs_AOP2 0.000 0.000 0.000aop2_CI <- tidy(lmer(log(Biomass_g) ~ -1 + temp + I(AOP2 + AOP2.gsoh) + I(Col + gsm1) + (1|com),
data = CageLevelBiomass),
conf.int = T, conf.level = 0.95) %>%
filter(term %in% c("I(AOP2 + AOP2.gsoh)","I(Col + gsm1)")) %>%
mutate(allele = c("AOP2","aop2"))
# note that I back transform to original scale for plotting
exp(aop2_CI$estimate[2])[1] 1.17452# get the effect of each genotype
mean_geno <- tidy(lmer(log(Biomass_g) ~ -1 + temp + AOP2 + AOP2.gsoh + Col + gsm1 + (1|com),
data = CageLevelBiomass),
conf.int = T, conf.level = 0.95) %>%
filter(term %in% c("AOP2","AOP2.gsoh","Col","gsm1")) %>%
mutate(allele = c("AOP2","AOP2","aop2","aop2"),
term = factor(term, levels = c("Col","gsm1","AOP2","AOP2.gsoh"), labels = c("Col","gsm1","AOP2","AOP2/gsoh")))
# plot on original scale
# adding a genotype with an aop2 allele to the population doubles the likelihood of species persistence
plot_AOP2_growth_no_insects <- ggplot(aop2_CI, aes(x = allele, y = exp(estimate))) +
geom_point(size = 5) +
geom_point(data = mean_geno, aes(color = term), size = 5, position = position_dodge(width = 0.3)) +
geom_linerange(aes(ymax = exp(estimate + std.error), ymin = exp(estimate - std.error)), size = 1.5) +
geom_linerange(aes(ymax = exp(conf.high), ymin = exp(conf.low))) +
scale_x_discrete(labels = c("AOP2\u2013","AOP2+")) +
scale_y_continuous("Plant biomass (g)") +
xlab("Allele") +
scale_color_manual(values = c("darkgreen","steelblue","darkorange","firebrick1"), name = "") +
theme_cowplot(font_size = 18, line_size = 1)
x11(); plot_AOP2_growth_no_insects
ggsave(plot = plot_AOP2_growth_no_insects, filename = "figures/AOP2-growth-no-insects.pdf", height = 6, width = 8, device=cairo_pdf)
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.7 LTS
Matrix products: default
BLAS: /usr/lib/libblas/libblas.so.3.6.0
LAPACK: /usr/lib/lapack/liblapack.so.3.6.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] broom.mixed_0.2.6 lmerTest_3.1-3 lme4_1.1-27 Matrix_1.3-4
[5] cowplot_1.1.1 kableExtra_1.3.4 forcats_0.5.1 stringr_1.4.0
[9] dplyr_1.0.6 purrr_0.3.4 readr_1.4.0 tidyr_1.1.3
[13] tibble_3.1.2 ggplot2_3.3.3 tidyverse_1.3.1 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] nlme_3.1-152 pbkrtest_0.5.1 fs_1.5.0
[4] lubridate_1.7.10 webshot_0.5.2 httr_1.4.2
[7] rprojroot_2.0.2 numDeriv_2016.8-1.1 tools_4.1.0
[10] TMB_1.7.20 backports_1.2.1 bslib_0.2.5.1
[13] utf8_1.2.1 R6_2.5.0 DBI_1.1.1
[16] colorspace_2.0-1 withr_2.4.2 tidyselect_1.1.1
[19] compiler_4.1.0 git2r_0.28.0 cli_2.5.0
[22] rvest_1.0.0 xml2_1.3.2 labeling_0.4.2
[25] sass_0.4.0 scales_1.1.1 systemfonts_1.0.2
[28] digest_0.6.27 minqa_1.2.4 rmarkdown_2.8
[31] svglite_1.2.3.2 pkgconfig_2.0.3 htmltools_0.5.1.1
[34] highr_0.9 dbplyr_2.1.1 rlang_0.4.11
[37] readxl_1.3.1 rstudioapi_0.13 farver_2.1.0
[40] jquerylib_0.1.4 generics_0.1.0 jsonlite_1.7.2
[43] magrittr_2.0.1 Rcpp_1.0.6 munsell_0.5.0
[46] fansi_0.5.0 gdtools_0.2.3 lifecycle_1.0.0
[49] stringi_1.6.2 yaml_2.2.1 MASS_7.3-54
[52] plyr_1.8.6 grid_4.1.0 parallel_4.1.0
[55] promises_1.2.0.1 crayon_1.4.1 lattice_0.20-44
[58] haven_2.4.1 splines_4.1.0 hms_1.1.0
[61] knitr_1.33 pillar_1.6.1 boot_1.3-28
[64] reshape2_1.4.4 reprex_2.0.0 glue_1.4.2
[67] evaluate_0.14 modelr_0.1.8 vctrs_0.3.8
[70] nloptr_1.2.2.2 httpuv_1.6.1 cellranger_1.1.0
[73] gtable_0.3.0 assertthat_0.2.1 xfun_0.23
[76] broom_0.7.6 coda_0.19-4 later_1.2.0
[79] viridisLite_0.4.0 ellipsis_0.3.2