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+assays/3_destructive_sampling/dataset/.gitkeep filter=lfs diff=lfs merge=lfs -text
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+# Files that might appear in the root of a volume
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+.Trashes
+.VolumeIcon.icns
+.com.apple.timemachine.donotpresent
+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
+Temporary Items
+.apdisk
+# ----- windows rules -----
+# taken from https://github.com/github/gitignore/blob/main/Global/Windows.gitignore
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+# ----- linux rules -----
+# taken from https://github.com/github/gitignore/blob/main/Global/Linux.gitignore
+*~
+# temporary files which can be created if a process still has a handle open of a deleted file
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+# KDE directory preferences
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+# Linux trash folder which might appear on any partition or disk
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+# .nfs files are created when an open file is removed but is still being accessed
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diff --git a/LICENSE b/LICENSE
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diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..7075798fd7ee62c7672d2cb495c8ade1c7be4dde
--- /dev/null
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@@ -0,0 +1,72 @@
+# **2025_mycorrhization_experiment**
+
+## This is the git repository for the publication:
+
+**An Ectomycorrhizal Fungus Alters Development Stages Within Pedunculate Oak’s Endogenous Growth Rhythm**
+
+
+
+## Authors & Affiliations:
+
+**Felix Zimmermann** (*Corresponding Author*; Research Unit Biodiversity and Conservation Biology, Swiss Federal Research Institute WSL, Birmensdorf, Switzerland; felix.zimmermann@wsl.ch; [ORCID: 0009-0002-0762-2454](https://orcid.org/0009-0002-0762-2454))
+
+**Marie-Lara Bouffaud** (Department of Soil Ecology, Helmholtz Centre for Environmental Research - UFZ, Halle/Saale, Germany; [ORCID: 0000-0001-6112-2982](https://orcid.org/0000-0001-6112-2982))
+
+**Sylvie Herrmann** (Department of Soil Ecology, Helmholtz Centre for Environmental Research - UFZ, Halle/Saale, Germany; [ORCID: 0000-0001-8220-3853](https://orcid.org/0000-0001-8220-3853))
+
+**Marco Göttig** (Institute of Biology, Philipps University Marburg, Marburg, Germany)
+
+**René Graf** (Research Unit Biodiversity and Conservation Biology, Swiss Federal Research Institute WSL, Birmensdorf, Switzerland; [ORCID: 0009-0002-9567-9089](https://orcid.org/0009-0002-9567-9089))
+
+**Mika Tarkka** (Department of Soil Ecology, Helmholtz Centre for Environmental Research - UFZ, Halle/Saale, Germany; [ORCID: 0000-0003-4630-351X](https://orcid.org/0000-0003-4630-351X))
+
+**Lars Opgenoorth** (Laboratory of Plant Ecology and Geobotany, Institute of Biology, Philipps University Marburg, Marburg, Germany; [ORCID: 0000-0003-0737-047X](https://orcid.org/0000-0003-0737-047X))
+
+**Daniel Croll** (Laboratory of Evolutionary Genetics, Institute of Biology, University of Neuchâtel, Neuchâtel, Switzerland; [ORCID: 0000-0002-2072-380X](https://orcid.org/0000-0002-2072-380X))
+
+**Martina Peter** (Research Unit Biodiversity and Conservation Biology, Swiss Federal Research Institute WSL, Birmensdorf, Switzerland; [ORCID: 0000-0002-6365-6889](https://orcid.org/0000-0002-6365-6889))
+
+**Benjamin Dauphin** (*Corresponding Author*; Research Unit Biodiversity and Conservation Biology, Swiss Federal Research Institute WSL, Birmensdorf, Switzerland; benjamin.dauphin@wsl.ch; [ORCID: 0000-0003-0982-4252](https://orcid.org/0000-0003-0982-4252))
+
+
+## Content:
+
+### Data:
+
+* Here you can find the [Phenotypic Data File](https://git.nfdi4plants.org/felix.zimmermann/2025_mycorrhization_experiment/-/blob/main/assays/2_destructive_sampling/dataset/pheno_data.csv?ref_type=heads).
+* Here you can find the [Timeseries Data File](https://git.nfdi4plants.org/felix.zimmermann/2025_mycorrhization_experiment/-/blob/main/assays/1_growth_monitoring/dataset/time_series.csv?ref_type=heads).
+* Here you can find the [Experimental Setup Data File](https://git.nfdi4plants.org/felix.zimmermann/2025_mycorrhization_experiment/-/blob/main/assays/3_experimental_setup/dataset/randomization.csv?ref_type=heads).
+
+### Scripts:
+
+* Here you can find scripts for [Figure 1 / Phenotypic Data Analysis](https://git.nfdi4plants.org/felix.zimmermann/2025_mycorrhization_experiment/-/blob/main/workflows/1_phenotypic_data_figure_1/figure1_phenotypicdata.R?ref_type=heads).
+* Here you can find scripts for [Figure 2 / Timeseries Data Analysis](https://git.nfdi4plants.org/felix.zimmermann/2025_mycorrhization_experiment/-/blob/main/workflows/2_timeseries_data_figure2/figure2_timeseries.R?ref_type=heads).
+* Here you can find scripts for the [Supplementary Information Analysis](https://git.nfdi4plants.org/felix.zimmermann/2025_mycorrhization_experiment/-/blob/main/workflows/3_additional_analyses_SI/SI.R?ref_type=heads).
+
+### Results:
+
+* Here you can find results of [Figure 1 / Phenotypic Data Analysis](https://git.nfdi4plants.org/felix.zimmermann/2025_mycorrhization_experiment/-/tree/main/runs/1_phenotypic_data_figure1?ref_type=heads).
+* Here you can find results of the [Figure 2 / Timeseries Data Analysis](https://git.nfdi4plants.org/felix.zimmermann/2025_mycorrhization_experiment/-/tree/main/runs/2_timeseries_data_figure2?ref_type=heads).
+* Here you can find results of the [Supplementary Information Analysis](https://git.nfdi4plants.org/felix.zimmermann/2025_mycorrhization_experiment/-/tree/main/runs/3_additional_analyses_SI?ref_type=heads).
+
+
+## To use our code:
+
+1. Download/clone repository.
+
+2. Open the "2025_Myco_Experiment.Rproj" file with RStudio.
+
+3. Run scripts under "workflows" (you can find "workflows" under "Files" in the "Files/Plots/Packages etc." window).
+
+
+## Citation:
+
+
+
+## Link to publication:
+
+
+
+## ARC DOI:
+
+
diff --git a/assays/1_experimental_setup/README.md b/assays/1_experimental_setup/README.md
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index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
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diff --git a/assays/1_experimental_setup/isa.assay.xlsx b/assays/1_experimental_setup/isa.assay.xlsx
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diff --git a/assays/1_experimental_setup/protocols/experimental_setup_protocol.md b/assays/1_experimental_setup/protocols/experimental_setup_protocol.md
new file mode 100644
index 0000000000000000000000000000000000000000..6be19b530babaed91a2786ed199204512e1356c9
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+++ b/assays/1_experimental_setup/protocols/experimental_setup_protocol.md
@@ -0,0 +1,19 @@
+**Experimental Setup Protocol**
+
+We micropropagated and rooted the pedunculate oak (*Quercus robur* L.) clone DF159 according to Herrmann et al. (1998). In brief, we alternatively used micro-cuttings of axillary and apical buds to maintain the endogenous growth rhythm of oak plants (Herrmann et al., 2016). After micropropagation under sterile conditions in glass vessels, we transferred individual cuttings to sterile glass tubes containing active charcoal medium for in vitro rooting. We performed both the micropropagation and in vitro rooting at 24°C, 50% air humidity, and under long-day conditions (16h of light per day) with a photosynthetic photon flux density of 90-100 µmol m-2⋅s-1.
+After plant production, we prepared the experimental culture system according to Tarkka et al. (2013). In short, soil from an oak forest in the Harz mountains, Saxony Anhalt, Germany (51.400972, 11.125389) from the A0 (humus, 0-10 cm) and A1/A2 (organic, 10–30 cm) horizons was sieved at 2 mm and sterilized at Synergy Health Däniken AG (Däniken, Switzerland) using γ-irradiation with a dosage of 70–90 kGy. We cultivated *P. croceum* strain F1598 (ATCC MYA-4870; Herrmann et al., 1998, 2015) and *C. geophilum strain* 1.58 (CBS 143616; Peter et al., 2016) on Melin Norkrans Modified (MMN; Marx, 1969) agar medium in darkness at 20°C (Herrmann et al., 1998). From the plate cultures, we prepared MMN liquid cultures. Finally, we transferred the liquid cultures onto 10:1 (v/v) mixtures of vermiculite and peat (autoclaved three times) and incubated them in darkness at 20°C for four weeks.
+Next, we applied four treatments on the clone after plant randomization by development stage, rooting date, and stem length; the control treatment, the *C. geophilum* treatment, the *P. croceum* treatment, and the co-inoculation treatment. Finally, we planted the oaks in 12×12 cm2 squared Petri dishes (roots grown inside, shoots grown outside the Petri dishes), assessed the initial weight and sealed the dishes with parafilm. In total, we prepared two sets of 48 plants each (with 12 plants per treatment, Fig. 1a), with the second set being produced three weeks after the first set. We then grew the plants in a growth chamber at 20–22°C, 80–90% air humidity, and with 16h of light as previously described.
+
+
+**References:**
+
+Herrmann, S., Grams, T. E. E., Tarkka, M. T., Angay, O., Bacht, M., Bönn, M., Feldhahn, L., Graf, M., Kurth, F., Maboreke, H., Mailander, S., Recht, S., Fleischmann, F., Ruess, L., Schädler, M., Scheu, S., Schrey, S., & Buscot, F. (2016). Endogenous rhythmic growth, a trait suitable for the study of interplays between multitrophic interactions and tree development. Perspectives in Plant Ecology, Evolution and Systematics, 19, 40–48. https://doi.org/10.1016/j.ppees.2016.02.003
+
+Herrmann, S., Munch, J. â€C., & Buscot, F. (1998). A gnotobiotic culture system with oak microcuttings to study specific effects of mycobionts on plant morphology before, and in the early phase of, ectomycorrhiza formation by Paxillus involutus and Piloderma croceum. New Phytologist, 138(2), 203–212. https://doi.org/10.1046/j.1469-8137.1998.00105.x
+
+Marx, D. H. (1969). The influence of ectotrophic mycorrhizal fungi on the resistance of pine roots to pathogenic infections. II. Production, identification, and biological activity of antibiotics produced by Leucopaxillus cerealis var. Piceina. Phytopathology, 59(4), 411–417.
+
+Peter, M., Kohler, A., Ohm, R. A., Kuo, A., Krützmann, J., Morin, E., Arend, M., Barry, K. W., Binder, M., Choi, C., Clum, A., Copeland, A., Grisel, N., Haridas, S., Kipfer, T., LaButti, K., Lindquist, E., Lipzen, A., Maire, R., … Martin, F. M. (2016). Ectomycorrhizal ecology is imprinted in the genome of the dominant symbiotic fungus Cenococcum geophilum. Nature Communications, 7(1), 12662. https://doi.org/10.1038/ncomms12662
+
+Tarkka, M. T., Herrmann, S., Wubet, T., Feldhahn, L., Recht, S., Kurth, F., Mailänder, S., Bönn, M., Neef, M., Angay, O., Bacht, M., Graf, M., Maboreke, H., Fleischmann, F., Grams, T. E. E., Ruess, L., Schädler, M., Brandl, R., Scheu, S., … Buscot, F. (2013). OakContig DF 159.1, a reference library for studying differential gene expression in Quercus robur during controlled biotic interactions: Use for quantitative transcriptomic profiling of oak roots in ectomycorrhizal symbiosis. New Phytologist, 199(2), 529–540. https://doi.org/10.1111/nph.12317
+
diff --git a/assays/2_growth_monitoring/README.md b/assays/2_growth_monitoring/README.md
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+version https://git-lfs.github.com/spec/v1
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diff --git a/assays/2_growth_monitoring/isa.assay.xlsx b/assays/2_growth_monitoring/isa.assay.xlsx
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diff --git a/assays/2_growth_monitoring/protocols/growth_monitoring_protocol.md b/assays/2_growth_monitoring/protocols/growth_monitoring_protocol.md
new file mode 100644
index 0000000000000000000000000000000000000000..4956c36c6ba7f295b735952c2678ee4a513d639a
--- /dev/null
+++ b/assays/2_growth_monitoring/protocols/growth_monitoring_protocol.md
@@ -0,0 +1,7 @@
+**Growth Monitoring Protocol**
+
+Day 1 of the experiment is defined as the first day after a four-week acclimatization period in protective plastic bags. Throughout eight weeks of the experiment, we assessed the development stages of the plants twice a week according to Herrmann et al. (2015). In short, *Q. robur* growth is characterized by the succession of developmental stages A (bud rest) and B (bud swelling), corresponding to the root flush of a growth cycle, followed by stages C (shoot elongation) and D (leaf expansion), corresponding to the shoot flush. We weighed the plants weekly and replenished any weight loss with sterilized tap water to maintain optimal growth conditions at a soil water content of 12-15%.
+
+**References:**
+
+Herrmann, S., Recht, S., Boenn, M., Feldhahn, L., Angay, O., Fleischmann, F., Tarkka, M. T., Grams, T. E. E., & Buscot, F. (2015). Endogenous rhythmic growth in oak trees is regulated by internal clocks rather than resource availability. Journal of Experimental Botany, 66(22), 7113–7127. https://doi.org/10.1093/jxb/erv408
\ No newline at end of file
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diff --git a/assays/3_destructive_sampling/protocols/destructive_sampling_protocol.md b/assays/3_destructive_sampling/protocols/destructive_sampling_protocol.md
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index 0000000000000000000000000000000000000000..cb79b1b21d4be78b5f7777aa9fa7c1b344db1817
--- /dev/null
+++ b/assays/3_destructive_sampling/protocols/destructive_sampling_protocol.md
@@ -0,0 +1,3 @@
+**Destructive Sampling Protocol**
+
+After 64 days (set one) and 58 days (set two) of cultivation, we sampled all above- and belowground parts of the plants destructively (Table S2). We measured total plant biomass, followed by a separate weighing of above and below ground plant parts. We further divided above ground biomass into stem, leaves from the newest shoot flush, leaves from the second newest shoot flush, remaining leaves, and buds. We counted leaves from the last two shoot flushes and scanned them using an EPSON Perfection V700 Photo scanner (SEIKO Epson CORPORATION, Tokyo, Japan) to determine leave area. The root system was scanned with the same EPSON Perfection V700 Photo scanner and analyzed with WinRhizo 2003b (Regent Instruments, Québec, Canada) to quantify root parameters (total root length, total root surface area, average root diameter, total root volume, number of root tips, number of root forks, number of root crossings, length of fine roots (0–1mm diameter), surface area of fine roots (0–1 mm diameter), and volume of fine roots (0–1mm diameter)). We separated and weighed principal and lateral roots (the latter defined as diverging more than 1 cm from the principal roots). To assess mycorrhization rate, we examined root tips from ~10% of the lateral roots for colonization by *C. geophilum* and *P. croceum* versus non-mycorrhized tips. Finally, we dried all plant materials at 55°C for two weeks and measured dry weights.
\ No newline at end of file
diff --git a/runs/1_phenotypic_data_figure1/Figure1.pdf b/runs/1_phenotypic_data_figure1/Figure1.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..7b775f623e7914c957060958b3fa9b131a800360
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diff --git a/runs/1_phenotypic_data_figure1/Figure1_session_info.txt b/runs/1_phenotypic_data_figure1/Figure1_session_info.txt
new file mode 100644
index 0000000000000000000000000000000000000000..64b307e0db58bd2b04fcdf6ddc5656d1c847c3b6
--- /dev/null
+++ b/runs/1_phenotypic_data_figure1/Figure1_session_info.txt
@@ -0,0 +1,35 @@
+R version 4.4.1 (2024-06-14)
+Platform: aarch64-apple-darwin20
+Running under: macOS Sonoma 14.6.1
+
+Matrix products: default
+BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
+LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
+
+locale:
+[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
+
+time zone: Europe/Zurich
+tzcode source: internal
+
+attached base packages:
+[1] grid stats graphics grDevices utils datasets methods base
+
+other attached packages:
+ [1] patchwork_1.2.0 ggbeeswarm_0.7.2 emmeans_1.10.4 MASS_7.3-61 rstatix_0.7.2
+ [6] psych_2.4.12 ggpubr_0.6.0 magick_2.8.5 viridis_0.6.5 viridisLite_0.4.2
+[11] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2
+[16] readr_2.1.5 tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0
+
+loaded via a namespace (and not attached):
+ [1] beeswarm_0.4.0 gtable_0.3.5 lattice_0.22-6 tzdb_0.4.0 vctrs_0.6.5
+ [6] tools_4.4.1 generics_0.1.3 parallel_4.4.1 sandwich_3.1-0 fansi_1.0.6
+[11] pkgconfig_2.0.3 Matrix_1.7-0 lifecycle_1.0.4 compiler_4.4.1 munsell_0.5.1
+[16] mnormt_2.1.1 codetools_0.2-20 carData_3.0-5 vipor_0.4.7 pillar_1.9.0
+[21] car_3.1-2 abind_1.4-5 multcomp_1.4-26 nlme_3.1-166 tidyselect_1.2.1
+[26] mvtnorm_1.3-1 stringi_1.8.4 splines_4.4.1 colorspace_2.1-1 cli_3.6.3
+[31] magrittr_2.0.3 survival_3.7-0 utf8_1.2.4 broom_1.0.6 TH.data_1.1-2
+[36] withr_3.0.1 scales_1.3.0 backports_1.5.0 timechange_0.3.0 estimability_1.5.1
+[41] gridExtra_2.3 ggsignif_0.6.4 zoo_1.8-12 hms_1.1.3 coda_0.19-4.1
+[46] rlang_1.1.4 Rcpp_1.0.13 xtable_1.8-4 glue_1.8.0 rstudioapi_0.16.0
+[51] R6_2.5.1
diff --git a/runs/1_phenotypic_data_figure1/above_below_ground_wilcoxon.csv b/runs/1_phenotypic_data_figure1/above_below_ground_wilcoxon.csv
new file mode 100644
index 0000000000000000000000000000000000000000..a501ca5d5cb18e6c39a79686dabe309d1647d0f2
--- /dev/null
+++ b/runs/1_phenotypic_data_figure1/above_below_ground_wilcoxon.csv
@@ -0,0 +1,13 @@
+".y.","group1","group2","n1","n2","statistic","p","p.adj","p.adj.signif","Part"
+"DW_Shoot","Control","Pilo",19,21,142,0.124,0.124,"ns","Shoot"
+"DW_Shoot","Control","Ceno",19,24,328,0.015,0.03,"*","Shoot"
+"DW_Shoot","Control","Co_Inoc",19,23,282,0.112,0.124,"ns","Shoot"
+"DW_Shoot","Pilo","Ceno",21,24,479,2.56e-07,1.536e-06,"****","Shoot"
+"DW_Shoot","Pilo","Co_Inoc",21,23,400,0.000103,0.000309,"***","Shoot"
+"DW_Shoot","Ceno","Co_Inoc",24,23,201,0.113,0.124,"ns","Shoot"
+"DW_Roots_Total","Control","Pilo",19,21,115.5,0.024,0.048,"*","Root"
+"DW_Roots_Total","Control","Ceno",19,24,291,0.127,0.1905,"ns","Root"
+"DW_Roots_Total","Control","Co_Inoc",19,23,246,0.495,0.594,"ns","Root"
+"DW_Roots_Total","Pilo","Ceno",21,24,430.5,5.13e-05,0.0003078,"***","Root"
+"DW_Roots_Total","Pilo","Co_Inoc",21,23,373,0.002,0.006,"**","Root"
+"DW_Roots_Total","Ceno","Co_Inoc",24,23,252,0.62,0.62,"ns","Root"
diff --git a/runs/1_phenotypic_data_figure1/dw_plant_wilcoxon.csv b/runs/1_phenotypic_data_figure1/dw_plant_wilcoxon.csv
new file mode 100644
index 0000000000000000000000000000000000000000..c9320b98dfd3152c09d6297839603b055a453062
--- /dev/null
+++ b/runs/1_phenotypic_data_figure1/dw_plant_wilcoxon.csv
@@ -0,0 +1,7 @@
+"group1","group2","n1","n2","statistic","p","p.adj","p.adj.signif"
+"Ceno","Co_Inoc",24,23,211,0.171,0.2052,"ns"
+"Ceno","Control",24,19,132,0.018,0.036,"*"
+"Ceno","Pilo",24,21,69,1.01e-05,6.06e-05,"****"
+"Co_Inoc","Control",23,19,175,0.28,0.28,"ns"
+"Co_Inoc","Pilo",23,21,100,0.000626,0.001878,"**"
+"Control","Pilo",19,21,127,0.05,0.075,"ns"
diff --git a/runs/1_phenotypic_data_figure1/emm_resource_allocation.csv b/runs/1_phenotypic_data_figure1/emm_resource_allocation.csv
new file mode 100644
index 0000000000000000000000000000000000000000..26b91539f3317333bc67fa8150811ad785fb6e44
--- /dev/null
+++ b/runs/1_phenotypic_data_figure1/emm_resource_allocation.csv
@@ -0,0 +1,9 @@
+"Treatment","response","SE","df","lower.CL","upper.CL","Part"
+"Control",0.453567514597558,0.0275055266994483,82,0.383318509711376,0.52381651948374,"Shoot"
+"Pilo",0.47366276586997,0.0291267922686154,82,0.399273055221825,0.548052476518115,"Shoot"
+"Ceno",0.351858330159944,0.0256275976469571,82,0.286405548542847,0.41731111177704,"Shoot"
+"Co_Inoc",0.395743444153842,0.0253353168124786,82,0.331037146615882,0.460449741691802,"Shoot"
+"Control",0.332049163229227,0.0261926790038616,82,0.265153166092717,0.398945160365736,"Roots"
+"Pilo",0.385646437453976,0.0277365610424506,82,0.314807371782272,0.456485503125679,"Roots"
+"Ceno",0.323345119635379,0.0244043841131146,82,0.261016420478317,0.385673818792442,"Roots"
+"Co_Inoc",0.337522080037482,0.0241260539375056,82,0.27590423503275,0.399139925042215,"Roots"
diff --git a/runs/1_phenotypic_data_figure1/pairwise_comparison_resource_allocation.csv b/runs/1_phenotypic_data_figure1/pairwise_comparison_resource_allocation.csv
new file mode 100644
index 0000000000000000000000000000000000000000..586bee92586a6370eacc6bfa7fadd7ca3fc1d0d6
--- /dev/null
+++ b/runs/1_phenotypic_data_figure1/pairwise_comparison_resource_allocation.csv
@@ -0,0 +1,13 @@
+"contrast","estimate","SE","df","t.ratio","p.value","Part"
+"Control - Pilo",-0.0200952512724119,0.0399122214934747,82,-0.503486163397278,0.615972401884668,"Shoot"
+"Control - Ceno",0.101709184437615,0.0376885469832781,82,2.69867619154279,0.0253534911944604,"Shoot"
+"Control - Co_Inoc",0.0578240704437162,0.0374470841340624,82,1.54415415194153,0.189602536279991,"Shoot"
+"Pilo - Ceno",0.121804435710026,0.0412341308669473,82,2.95397121629798,0.0245565260402311,"Shoot"
+"Pilo - Co_Inoc",0.0779193217161281,0.0399514120665197,82,1.950352132395,0.109101535219373,"Shoot"
+"Ceno - Co_Inoc",-0.0438851139938983,0.0351525595011496,82,-1.24841873868283,0.258516587745045,"Shoot"
+"Control - Pilo",-0.0535972742247492,0.0380071982381118,82,-1.41018745683297,0.418945708063114,"Roots"
+"Control - Ceno",0.00870404359384718,0.0358896604323072,82,0.242522316706344,0.878399868322438,"Roots"
+"Control - Co_Inoc",-0.00547291680825584,0.0356597226830697,82,-0.153476146096174,0.878399868322438,"Roots"
+"Pilo - Ceno",0.0623013178185964,0.0392660124491575,82,1.5866474320321,0.418945708063114,"Roots"
+"Pilo - Co_Inoc",0.0481243574164934,0.0380445182324155,82,1.26494853010096,0.418945708063114,"Roots"
+"Ceno - Co_Inoc",-0.014176960402103,0.0334747164538472,82,-0.423512486555317,0.878399868322438,"Roots"
diff --git a/runs/2_timeseries_data_figure2/Figure2.pdf b/runs/2_timeseries_data_figure2/Figure2.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..7669aee55b337165461c93b033cb28e796411997
Binary files /dev/null and b/runs/2_timeseries_data_figure2/Figure2.pdf differ
diff --git a/runs/2_timeseries_data_figure2/Figure2_session_info.txt b/runs/2_timeseries_data_figure2/Figure2_session_info.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9c567124c8fbbe2a2259ab300cb86659cd354130
--- /dev/null
+++ b/runs/2_timeseries_data_figure2/Figure2_session_info.txt
@@ -0,0 +1,42 @@
+R version 4.4.1 (2024-06-14)
+Platform: aarch64-apple-darwin20
+Running under: macOS Sonoma 14.6.1
+
+Matrix products: default
+BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
+LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
+
+locale:
+[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
+
+time zone: Europe/Zurich
+tzcode source: internal
+
+attached base packages:
+[1] grid stats graphics grDevices utils datasets methods base
+
+other attached packages:
+ [1] patchwork_1.2.0 ggsignif_0.6.4 rstatix_0.7.2 ggtext_0.1.2 tidybayes_3.0.7
+ [6] bayestestR_0.15.0 bayesnec_2.1.3.0 posterior_1.6.1 brms_2.22.0 Rcpp_1.0.13
+[11] ggpubr_0.6.0 viridis_0.6.5 viridisLite_0.4.2 lubridate_1.9.3 forcats_1.0.0
+[16] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1
+[21] tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0
+
+loaded via a namespace (and not attached):
+ [1] svUnit_1.0.6 tidyselect_1.2.1 loo_2.8.0 TH.data_1.1-2
+ [5] tensorA_0.36.2.1 estimability_1.5.1 timechange_0.3.0 lifecycle_1.0.4
+ [9] survival_3.7-0 magrittr_2.0.3 compiler_4.4.1 rlang_1.1.4
+[13] tools_4.4.1 utf8_1.2.4 bridgesampling_1.1-2 xml2_1.3.6
+[17] abind_1.4-5 multcomp_1.4-26 withr_3.0.1 fansi_1.0.6
+[21] xtable_1.8-4 colorspace_2.1-1 emmeans_1.10.4 scales_1.3.0
+[25] MASS_7.3-61 insight_1.0.1 cli_3.6.3 mvtnorm_1.3-1
+[29] generics_0.1.3 RcppParallel_5.1.9 rstudioapi_0.16.0 tzdb_0.4.0
+[33] splines_4.4.1 operator.tools_1.6.3 bayesplot_1.11.1 parallel_4.4.1
+[37] matrixStats_1.3.0 vctrs_0.6.5 Matrix_1.7-0 sandwich_3.1-0
+[41] carData_3.0-5 car_3.1-2 hms_1.1.3 arrayhelpers_1.1-0
+[45] ggdist_3.3.2 glue_1.8.0 chk_0.10.0 codetools_0.2-20
+[49] distributional_0.5.0 stringi_1.8.4 gtable_0.3.5 munsell_0.5.1
+[53] pillar_1.9.0 Brobdingnag_1.2-9 R6_2.5.1 formula.tools_1.7.1
+[57] evaluate_0.24.0 lattice_0.22-6 backports_1.5.0 gridtext_0.1.5
+[61] broom_1.0.6 rstantools_2.4.0 coda_0.19-4.1 gridExtra_2.3
+[65] nlme_3.1-166 checkmate_2.3.2 zoo_1.8-12 pkgconfig_2.0.3
diff --git a/runs/2_timeseries_data_figure2/model_predictions_timeseries.csv b/runs/2_timeseries_data_figure2/model_predictions_timeseries.csv
new file mode 100644
index 0000000000000000000000000000000000000000..910a8a6bc358fe20693100712c89ac93609fabfc
--- /dev/null
+++ b/runs/2_timeseries_data_figure2/model_predictions_timeseries.csv
@@ -0,0 +1,401 @@
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diff --git a/runs/2_timeseries_data_figure2/overall_treatment_effects.csv b/runs/2_timeseries_data_figure2/overall_treatment_effects.csv
new file mode 100644
index 0000000000000000000000000000000000000000..a6adf09ed9365caf3f6d1a85e9a993da27958f0c
--- /dev/null
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diff --git a/runs/2_timeseries_data_figure2/pairwise_treatment_differences_over_time.csv b/runs/2_timeseries_data_figure2/pairwise_treatment_differences_over_time.csv
new file mode 100644
index 0000000000000000000000000000000000000000..8cdf769c6dcfcbf1089bffa37d60f87fea7f9deb
--- /dev/null
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+"Ceno","Pilo",56,-3.69453189966358,-1.91899728186224,TRUE
+"Pilo","Co_Inoc",1,-0.00829256580590761,1.04386375829179,FALSE
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+"Pilo","Co_Inoc",4,6.31324248490108e-07,1.16884703924248,TRUE
+"Pilo","Co_Inoc",5,5.56444460014238e-06,1.19795253834032,TRUE
+"Pilo","Co_Inoc",6,1.54640210337009e-05,1.23140658302732,TRUE
+"Pilo","Co_Inoc",7,4.16967079400366e-05,1.2613556357033,TRUE
+"Pilo","Co_Inoc",8,0.000139662330144097,1.32309757932139,TRUE
+"Pilo","Co_Inoc",9,0.000277497308627944,1.33757737487661,TRUE
+"Pilo","Co_Inoc",10,0.000471316530414628,1.39645566057787,TRUE
+"Pilo","Co_Inoc",11,0.000610037467438906,1.42907831322369,TRUE
+"Pilo","Co_Inoc",12,0.00217033900573025,1.47286265082116,TRUE
+"Pilo","Co_Inoc",13,0.00314509899072675,1.48830615544308,TRUE
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+"Pilo","Co_Inoc",16,0.0126591616950834,1.63718583359092,TRUE
+"Pilo","Co_Inoc",17,0.0174690158245141,1.65738331725628,TRUE
+"Pilo","Co_Inoc",18,0.0189260272755379,1.69184889343729,TRUE
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+"Pilo","Co_Inoc",41,0.687172145488601,2.33224807470371,TRUE
+"Pilo","Co_Inoc",42,0.724702358095486,2.34641480091153,TRUE
+"Pilo","Co_Inoc",43,0.726298870908657,2.36430658612171,TRUE
+"Pilo","Co_Inoc",44,0.748834615230805,2.38841060439595,TRUE
+"Pilo","Co_Inoc",45,0.755887303065962,2.38593289626244,TRUE
+"Pilo","Co_Inoc",46,0.769450834268547,2.41068052701225,TRUE
+"Pilo","Co_Inoc",47,0.776371506056993,2.42151645685909,TRUE
+"Pilo","Co_Inoc",48,0.804915600680162,2.42783922427744,TRUE
+"Pilo","Co_Inoc",49,0.807628605505593,2.44203932477424,TRUE
+"Pilo","Co_Inoc",50,0.821468610593464,2.43988527698565,TRUE
+"Pilo","Co_Inoc",51,0.820293894709928,2.46071019335235,TRUE
+"Pilo","Co_Inoc",52,0.829803700863557,2.46094832580031,TRUE
+"Pilo","Co_Inoc",53,0.8401886363905,2.46139035232705,TRUE
+"Pilo","Co_Inoc",54,0.851171051014411,2.49300876153795,TRUE
+"Pilo","Co_Inoc",55,0.84522969305191,2.47670460919566,TRUE
+"Pilo","Co_Inoc",56,0.862460119877953,2.47735566414307,TRUE
diff --git a/runs/2_timeseries_data_figure2/significance_days.csv b/runs/2_timeseries_data_figure2/significance_days.csv
new file mode 100644
index 0000000000000000000000000000000000000000..a57a023bdb990bb492be93f2ba3b9e6e90b83c2d
--- /dev/null
+++ b/runs/2_timeseries_data_figure2/significance_days.csv
@@ -0,0 +1,7 @@
+"Treatment1","Treatment2","First_Significant_Day"
+"Control","Ceno",21
+"Control","Pilo",33
+"Control","Co_Inoc",45
+"Ceno","Pilo",6
+"Ceno","Co_Inoc",36
+"Pilo","Co_Inoc",4
diff --git a/runs/2_timeseries_data_figure2/stage_duration_wilcoxon.csv b/runs/2_timeseries_data_figure2/stage_duration_wilcoxon.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e61da70a4134a7152ff9fb886426decb975a2668
--- /dev/null
+++ b/runs/2_timeseries_data_figure2/stage_duration_wilcoxon.csv
@@ -0,0 +1,25 @@
+"group1","group2","n1","n2","statistic","p","p.adj","p.adj.signif","stage"
+"Ceno","Co_Inoc",25,23,385,0.045,0.0675,"ns","A"
+"Ceno","Control",25,21,393.5,0.004,0.012,"*","A"
+"Ceno","Pilo",25,27,540.5,2e-04,0.0012,"**","A"
+"Co_Inoc","Control",23,21,292,0.238,0.2856,"ns","A"
+"Co_Inoc","Pilo",23,27,427.5,0.022,0.044,"*","A"
+"Control","Pilo",21,27,325,0.392,0.392,"ns","A"
+"Ceno","Co_Inoc",10,18,102,0.572,0.7236,"ns","B"
+"Ceno","Control",10,18,89,0.98,0.98,"ns","B"
+"Ceno","Pilo",10,23,144.5,0.226,0.678,"ns","B"
+"Co_Inoc","Control",18,18,145.5,0.603,0.7236,"ns","B"
+"Co_Inoc","Pilo",18,23,231,0.518,0.7236,"ns","B"
+"Control","Pilo",18,23,259.5,0.149,0.678,"ns","B"
+"Ceno","Co_Inoc",14,23,65.5,0.002,0.012,"*","C"
+"Ceno","Control",14,24,118,0.117,0.234,"ns","C"
+"Ceno","Pilo",14,29,108,0.008,0.024,"*","C"
+"Co_Inoc","Control",23,24,314.5,0.409,0.4908,"ns","C"
+"Co_Inoc","Pilo",23,29,388,0.301,0.4515,"ns","C"
+"Control","Pilo",24,29,333.5,0.797,0.797,"ns","C"
+"Ceno","Co_Inoc",23,31,420.5,0.263,0.3156,"ns","D"
+"Ceno","Control",23,27,437,0.013,0.078,"ns","D"
+"Ceno","Pilo",23,35,534.5,0.034,0.102,"ns","D"
+"Co_Inoc","Control",31,27,522.5,0.102,0.204,"ns","D"
+"Co_Inoc","Pilo",31,35,635.5,0.229,0.3156,"ns","D"
+"Control","Pilo",27,35,413,0.392,0.392,"ns","D"
diff --git a/runs/3_additional_analyses_SI/Correlation_Plot.pdf b/runs/3_additional_analyses_SI/Correlation_Plot.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..ca381a0fe453098da7f416c25a89d1a297499c75
Binary files /dev/null and b/runs/3_additional_analyses_SI/Correlation_Plot.pdf differ
diff --git a/runs/3_additional_analyses_SI/Randomization_Plot.pdf b/runs/3_additional_analyses_SI/Randomization_Plot.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..b4b559c4cd966878349ea995bdbd533a36fd66fa
Binary files /dev/null and b/runs/3_additional_analyses_SI/Randomization_Plot.pdf differ
diff --git a/runs/3_additional_analyses_SI/Resource_Allocation_Proportions.pdf b/runs/3_additional_analyses_SI/Resource_Allocation_Proportions.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..a0a0f3d35be384681d56774655ae7c8d06a4304e
Binary files /dev/null and b/runs/3_additional_analyses_SI/Resource_Allocation_Proportions.pdf differ
diff --git a/runs/3_additional_analyses_SI/SI_session_info.txt b/runs/3_additional_analyses_SI/SI_session_info.txt
new file mode 100644
index 0000000000000000000000000000000000000000..eeacb131f26fcfce548bb097b39213cbd18dc72a
--- /dev/null
+++ b/runs/3_additional_analyses_SI/SI_session_info.txt
@@ -0,0 +1,32 @@
+R version 4.4.1 (2024-06-14)
+Platform: aarch64-apple-darwin20
+Running under: macOS Sonoma 14.6.1
+
+Matrix products: default
+BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
+LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
+
+locale:
+[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
+
+time zone: Europe/Zurich
+tzcode source: internal
+
+attached base packages:
+[1] stats graphics grDevices utils datasets methods base
+
+other attached packages:
+ [1] patchwork_1.2.0 ggpubr_0.6.0 corrplot_0.94 psych_2.4.12 viridis_0.6.5
+ [6] viridisLite_0.4.2 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4
+[11] purrr_1.0.2 readr_2.1.5 tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1
+[16] tidyverse_2.0.0
+
+loaded via a namespace (and not attached):
+ [1] utf8_1.2.4 generics_0.1.3 rstatix_0.7.2 stringi_1.8.4 lattice_0.22-6
+ [6] hms_1.1.3 magrittr_2.0.3 grid_4.4.1 timechange_0.3.0 backports_1.5.0
+[11] gridExtra_2.3 fansi_1.0.6 scales_1.3.0 abind_1.4-5 mnormt_2.1.1
+[16] cli_3.6.3 rlang_1.1.4 munsell_0.5.1 withr_3.0.1 tools_4.4.1
+[21] parallel_4.4.1 tzdb_0.4.0 ggsignif_0.6.4 colorspace_2.1-1 broom_1.0.6
+[26] vctrs_0.6.5 R6_2.5.1 lifecycle_1.0.4 car_3.1-2 pkgconfig_2.0.3
+[31] pillar_1.9.0 gtable_0.3.5 glue_1.8.0 tidyselect_1.2.1 rstudioapi_0.16.0
+[36] nlme_3.1-166 carData_3.0-5 compiler_4.4.1
diff --git a/runs/3_additional_analyses_SI/Spearman_R.csv b/runs/3_additional_analyses_SI/Spearman_R.csv
new file mode 100644
index 0000000000000000000000000000000000000000..11ba36bba635f6f5bb6b7416a541e910191883f6
--- /dev/null
+++ b/runs/3_additional_analyses_SI/Spearman_R.csv
@@ -0,0 +1,40 @@
+var,nSF,n_L_SF1,n_L_SF2,Shoot_Length_Init,Shoot_Length,Delta_Length,FW_Plant,DW_Plant,FW_Shoot,DW_Shoot,FW_Stem,DW_Stem,FW_L_SF1,DW_L_SF1,FW_L_SF2,DW_L_SF2,DW_L_Rest,FW_Bud,DW_Bud,FW_Roots_Total,DW_Roots_Total,FW_PR,DW_PR,FW_LR,DW_LR,A_L_SF1,A_L_SF2,N_Ceno,N_Pilo,Length_Tot,Area_Tot,Diameter_Avg,Root_Vol,Tips,Forks,Crossings,Length_Fine,SA_Fine,Vol_Fine
+nSF,1,-0.2909351171829085,-0.32049661212218017,0.3923126585913846,-0.08247860988423225,-0.3303685546032712,0.1649572197684645,0.24743582965269675,0.24743582965269675,0.329914439536929,0.1649572197684645,0.329914439536929,0.08247860988423225,-0.20619652471058064,0.4948716593053935,0.45363235436327737,0,0.24743582965269675,0.10469228644697122,0.28867513459481287,0.37115374447904514,0.28867513459481287,0.329914439536929,0.28867513459481287,0.24743582965269675,-0.12371791482634838,0.5361109642475097,-0.3687083775950658,0.1952106623114296,0.1649572197684645,0.28867513459481287,0.041239304942116126,0.28867513459481287,0.24743582965269675,0.20619652471058064,0.20619652471058064,0.1649572197684645,0.24743582965269675,0.1649572197684645
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diff --git a/runs/3_additional_analyses_SI/Spearman_p.csv b/runs/3_additional_analyses_SI/Spearman_p.csv
new file mode 100644
index 0000000000000000000000000000000000000000..856fb07c1f714207498a8a5ac6dc5b1267992177
--- /dev/null
+++ b/runs/3_additional_analyses_SI/Spearman_p.csv
@@ -0,0 +1,40 @@
+var,nSF,n_L_SF1,n_L_SF2,Shoot_Length_Init,Shoot_Length,Delta_Length,FW_Plant,DW_Plant,FW_Shoot,DW_Shoot,FW_Stem,DW_Stem,FW_L_SF1,DW_L_SF1,FW_L_SF2,DW_L_SF2,DW_L_Rest,FW_Bud,DW_Bud,FW_Roots_Total,DW_Roots_Total,FW_PR,DW_PR,FW_LR,DW_LR,A_L_SF1,A_L_SF2,N_Ceno,N_Pilo,Length_Tot,Area_Tot,Diameter_Avg,Root_Vol,Tips,Forks,Crossings,Length_Fine,SA_Fine,Vol_Fine
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+n_L_SF1,0.33486119124497316,-0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+n_L_SF2,0.2856913407523003,0.08148440308957387,-0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+Shoot_Length_Init,0.18487564831866724,0.5026783254364396,0.028216630791249506,-0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+Shoot_Length,0.7887978280460586,0.7343890279929199,0.8825377466723056,0.14053110184653045,-0,0.9908001194509485,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+Delta_Length,0.2702600365148612,0.7266941740334484,0.15100636373614656,0.5644924108111397,0.0014174536758954914,-0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
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diff --git a/studies/mycorrhization_experiment/README.md b/studies/mycorrhization_experiment/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/studies/mycorrhization_experiment/isa.study.xlsx b/studies/mycorrhization_experiment/isa.study.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..9fe62c82d61de6ce8dc2c732361a7def557a2efc
Binary files /dev/null and b/studies/mycorrhization_experiment/isa.study.xlsx differ
diff --git a/studies/mycorrhization_experiment/protocols/.gitkeep b/studies/mycorrhization_experiment/protocols/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/studies/mycorrhization_experiment/protocols/mycorrhization_experiment_protocol.md b/studies/mycorrhization_experiment/protocols/mycorrhization_experiment_protocol.md
new file mode 100644
index 0000000000000000000000000000000000000000..88689e3ad7b2e2f1372b5b70b9c1aa6b5a099cde
--- /dev/null
+++ b/studies/mycorrhization_experiment/protocols/mycorrhization_experiment_protocol.md
@@ -0,0 +1,5 @@
+**Mycorrhization Experiment Protocol**
+
+In this experiment, we investigated the inoculation-effects of two ectomycorrhizal fungi (EMF) species with contrasting evolutionary histories, ecological properties, host-preferences, and habitat-ranges on phenotypic responses and growth dynamics of pedunculate oak (Quercus robur), an ecologically and economically important temperate forest tree species.
+
+We set up the mycorrhization experiment using 96 plants of a genetically uniform Q. robur clone (DF159) and applied four treatments: A control treatment; an inoculation-treatment with the cosmopolitan Ascomycete Cenococcum geophilum, which is commonly found in oak forests; an inoculation-treatment with the Basidiomycete Piloderma croceum, which has not yet been found in natural oak forests but has been shown in previous experiments to support oak growth; and a co-inoculation treatment. We then assessed growth stage development of the trees over eight experimental weeks and measured various phenotypic traits during a destructive sampling.
diff --git a/studies/mycorrhization_experiment/resources/.gitkeep b/studies/mycorrhization_experiment/resources/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/workflows/1_phenotypic_data_figure_1/.Rhistory b/workflows/1_phenotypic_data_figure_1/.Rhistory
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/workflows/1_phenotypic_data_figure_1/figure1_phenotypicdata.R b/workflows/1_phenotypic_data_figure_1/figure1_phenotypicdata.R
new file mode 100644
index 0000000000000000000000000000000000000000..48f3acdf45553ae07710b4734e93faaaaecfd929
--- /dev/null
+++ b/workflows/1_phenotypic_data_figure_1/figure1_phenotypicdata.R
@@ -0,0 +1,484 @@
+# ------------------------------------------------------------------
+# Script Name: figure1_phenotypicdata.R
+# Purpose: Reproduces Figure 1 from "An Ectomycorrhizal Fungus Alters Development Stages Within Pedunculate Oak’s Endogenous Growth Rhythm"
+# Author: Felix Zimmermann
+# Date: 2025-05-07
+# Contact: felix.zimmermann@wsl.ch
+# License: CC BY
+# ------------------------------------------------------------------
+
+
+##### BASICS #####
+
+# load packages
+library(tidyverse)
+library(viridis)
+library(magick)
+library(grid)
+library(ggpubr)
+library(psych)
+library(rstatix)
+library(MASS)
+library(emmeans)
+library(ggbeeswarm)
+library(patchwork)
+
+# check and save session info
+sessionInfo() %>%
+ capture.output(file = "runs/1_phenotypic_data_figure1/Figure1_session_info.txt")
+
+
+##### DATA LOADING, CLEANING & SCALING #####
+
+# laod data
+pheno_data <- read_csv("assays/3_destructive_sampling/dataset/pheno_data.csv")
+
+# exclude contaminated and dead plants
+pheno_data_final <- subset(pheno_data, Exclude == "no")
+
+
+##### DEFINE BASIC PLOTTING PARAMETERS #####
+
+# define treatment labels
+treatment_labels <- c(
+ "Ceno" = expression(italic("Cenococcum geophilum")),
+ "Pilo" = expression(italic("Piloderma croceum")),
+ "Co_Inoc" = "Co-Inoculation",
+ "Control" = "Control"
+)
+
+# define legend order
+treatment_order <- c("Control", "Pilo", "Ceno", "Co_Inoc")
+
+# define custom colors
+custom_colors <- c(
+ "Control" = "#4B8C6F",
+ "Ceno" = "#8C5E8C",
+ "Pilo" = "#D6C9A0",
+ "Co_Inoc" = "#5E96A6"
+)
+
+
+##### PLOT DRY WEIGHT COMPARISONS - FIGURE 1B #####
+
+### do statistical comparison between treatments for durations of each stage ###
+
+# do wilcoxon test with Benjamini-Hochberg p adjustment for plant dry weight
+stat_test_dw_plant <- pheno_data_final %>%
+ wilcox_test(DW_Plant ~ Treatment) %>%
+ adjust_pvalue(method = "BH") %>%
+ add_significance()
+
+# save_results
+dw_plant_results <- subset(stat_test_dw_plant, select = -c(.y.))
+write.csv(
+ dw_plant_results,
+ "runs/1_phenotypic_data_figure1/dw_plant_wilcoxon.csv",
+ row.names = FALSE
+)
+
+
+### plot results ###
+
+# set plot order
+pheno_data_final$Treatment <- factor(
+ pheno_data_final$Treatment,
+ levels = treatment_order
+)
+
+# create plot
+dw_plant_plot <- ggplot(
+ pheno_data_final,
+ aes(x = Treatment, y = DW_Plant, fill = Treatment)
+) +
+ geom_boxplot(
+ outlier.shape = 16,
+ outlier.size = 2,
+ alpha = 0.5,
+ lwd = 0.5,
+ aes(color = Treatment)
+ ) +
+ scale_fill_manual(
+ values = custom_colors,
+ labels = treatment_labels,
+ breaks = treatment_order
+ ) +
+ scale_color_manual(
+ values = custom_colors,
+ labels = treatment_labels,
+ breaks = treatment_order
+ ) +
+ scale_x_discrete(labels = treatment_labels) + # format x-axis
+ theme_classic() +
+ geom_signif(
+ y_position = c(1.8, 1.94, 1.66),
+ xmin = c(2, 2, 1),
+ xmax = c(3, 4, 3),
+ annotation = c("< 0.001", "0.002", "0.036"),
+ textsize = 4
+ ) +
+ labs(
+ title = "b",
+ x = "Treatment",
+ y = "Dry Weight (g)"
+ ) +
+ theme_pubr() +
+ theme(
+ legend.position = "none",
+ axis.text.x = element_text(size = 12),
+ axis.text.y = element_text(size = 12),
+ axis.title = element_text(size = 14),
+ plot.title = element_text(
+ face = "bold",
+ size = 20,
+ vjust = 2,
+ hjust = -0.075
+ ),
+ panel.grid.major.y = element_line(color = "gray90"),
+ panel.grid.minor.y = element_line(color = "gray95")
+ )
+
+# show plot
+dw_plant_plot
+
+
+##### PLOT DRY WEIGHT ABOVE AND BELOW GROUND - FIGURE 1C #####
+
+### do statistical comparison between treatments for durations of each stage ###
+
+# do wilcoxon test with Benjamini-Hochberg p adjustment for plant dry weight
+stat_test_above <- pheno_data_final %>%
+ wilcox_test(DW_Shoot ~ Treatment) %>%
+ adjust_pvalue(method = "BH") %>%
+ add_significance()
+stat_test_above$Part <- "Shoot"
+stat_test_below <- pheno_data_final %>%
+ wilcox_test(DW_Roots_Total ~ Treatment) %>%
+ adjust_pvalue(method = "BH") %>%
+ add_significance()
+stat_test_below$Part <- "Root"
+
+# combine results
+wilcoxon_results_above_below <- rbind(stat_test_above, stat_test_below)
+print(wilcoxon_results_above_below)
+
+# save_results
+wilxcoxon_reuslts_above_below <- subset(
+ wilcoxon_results_above_below,
+ select = -c(.y.)
+)
+write.csv(
+ wilcoxon_results_above_below,
+ "runs/1_phenotypic_data_figure1/above_below_ground_wilcoxon.csv",
+ row.names = FALSE
+)
+
+
+### plot results ###
+
+# create long format
+pheno_data_long_above_below <- pivot_longer(
+ pheno_data_final,
+ cols = c("DW_Roots_Total", "DW_Shoot"),
+ names_to = "Part",
+ values_to = "Part_DW"
+)
+
+# set treatment labels
+treatment_labels_above_below <- c("Shoot", "Root")
+
+# set plot order
+pheno_data_long_above_below$Part <- factor(
+ pheno_data_long_above_below$Part,
+ levels = c("DW_Shoot", "DW_Roots_Total")
+)
+treatment_order_above_below <- c("Shoot", "Root")
+
+# create plot
+dw_above_below_plot <- ggplot(
+ pheno_data_long_above_below,
+ aes(x = Part, y = Part_DW, fill = Treatment)
+) +
+ geom_boxplot(
+ outlier.shape = 16,
+ outlier.size = 2,
+ alpha = 0.5,
+ lwd = 0.5,
+ aes(color = Treatment)
+ ) +
+ scale_fill_manual(
+ values = custom_colors,
+ labels = treatment_labels,
+ breaks = treatment_order
+ ) +
+ scale_color_manual(
+ values = custom_colors,
+ labels = treatment_labels,
+ breaks = treatment_order
+ ) +
+ scale_x_discrete(labels = treatment_labels_above_below) +
+ geom_signif(
+ y_position = c(1.05, 1.12, 0.98, 0.93, 1, 0.86),
+ xmin = c(0.906, 0.906, 0.716, 1.906, 1.906, 1.72),
+ xmax = c(1.094, 1.282, 1.094, 2.0935, 2.282, 1.906),
+ annotation = c("< 0.001", "< 0.001", "0.030", "< 0.001", "0.006", "0.048"),
+ textsize = 4
+ ) +
+ labs(
+ title = "c",
+ x = "Plant Part",
+ y = "Dry Weight (g)"
+ ) +
+ theme_pubr() +
+ theme(
+ legend.position = "right",
+ axis.text.x = element_text(size = 12),
+ axis.text.y = element_text(size = 12),
+ axis.title = element_text(size = 14),
+ legend.title = element_text(size = 14),
+ legend.text = element_text(size = 12),
+ plot.title = element_text(
+ face = "bold",
+ size = 20,
+ hjust = -0.075,
+ vjust = 2
+ ),
+ panel.grid.major.y = element_line(color = "gray90"),
+ panel.grid.minor.y = element_line(color = "gray95")
+ )
+
+# show plot
+dw_above_below_plot
+
+
+##### BIOMASS ALLOCATION PATTERNS - FIGURE 1D ######
+
+### do statistical test ###
+
+# create long format
+pheno_data_long_allocation <- pivot_longer(
+ pheno_data_final,
+ cols = c("DW_Shoot", "DW_Roots_Total"),
+ names_to = "Part",
+ values_to = "Part_DW"
+)
+
+# create data subsets
+subset_DW_Shoot <- subset(pheno_data_long_allocation, Part == "DW_Shoot")
+subset_DW_Roots <- subset(pheno_data_long_allocation, Part == "DW_Roots_Total")
+
+# calculate GLMs (sum)
+model_DW_Shoot_no <- glm(
+ (Part_DW) ~ Treatment + DW_Plant,
+ data = subset_DW_Shoot,
+ family = gaussian()
+)
+model_DW_Roots_no <- glm(
+ (Part_DW) ~ Treatment + DW_Plant,
+ data = subset_DW_Roots,
+ family = gaussian()
+)
+bc_shoot <- boxcox(Part_DW ~ Treatment + DW_Plant, data = subset_DW_Shoot)
+lambda_shoot <- bc_shoot$x[which.max(bc_shoot$y)]
+model_DW_Shoot_bc <- glm(
+ ((Part_DW^lambda_shoot - 1) / lambda_shoot) ~ Treatment + DW_Plant,
+ data = subset_DW_Shoot,
+ family = gaussian()
+)
+bc_roots <- boxcox(Part_DW ~ Treatment + DW_Plant, data = subset_DW_Roots)
+lambda_roots <- bc_roots$x[which.max(bc_roots$y)]
+model_DW_Roots_bc <- glm(
+ ((Part_DW^lambda_roots - 1) / lambda_roots) ~ Treatment + DW_Plant,
+ data = subset_DW_Roots,
+ family = gaussian()
+)
+model_DW_Shoot_log <- glm(
+ log(Part_DW) ~ Treatment + DW_Plant,
+ data = subset_DW_Shoot,
+ family = gaussian()
+)
+model_DW_Roots_log <- glm(
+ log(Part_DW) ~ Treatment + DW_Plant,
+ data = subset_DW_Roots,
+ family = gaussian()
+)
+
+# check AIC and BIC
+AIC(model_DW_Shoot_bc, model_DW_Shoot_log, model_DW_Shoot_no)
+AIC(model_DW_Roots_bc, model_DW_Roots_log, model_DW_Roots_no)
+BIC(model_DW_Shoot_bc, model_DW_Shoot_log, model_DW_Shoot_no)
+BIC(model_DW_Roots_bc, model_DW_Roots_log, model_DW_Roots_no)
+# use no transformation (best BIC and AIC)
+
+# check distributions of residuals
+hist(residuals(model_DW_Shoot_no))
+qqnorm(residuals(model_DW_Shoot_no))
+qqline(residuals(model_DW_Shoot_no))
+hist(residuals(model_DW_Roots_no))
+qqnorm(residuals(model_DW_Roots_no))
+qqline(residuals(model_DW_Roots_no))
+
+# get summary statistics
+summary(model_DW_Shoot_no)
+summary(model_DW_Roots_no)
+
+# calculate and plot estimated means
+emm_DW_Shoot <- emmeans(
+ model_DW_Shoot_no,
+ ~ Treatment | DW_Plant,
+ type = "response",
+ adjust = "BH"
+)
+plot(emm_DW_Shoot, comparisons = TRUE)
+emm_DW_Shoot_results <- as.data.frame(emm_DW_Shoot)
+emm_DW_Shoot_results$Part <- "Shoot"
+emm_DW_Roots <- emmeans(
+ model_DW_Roots_no,
+ ~ Treatment | DW_Plant,
+ type = "response",
+ adjust = "BH"
+)
+plot(emm_DW_Roots, comparisons = TRUE)
+emm_DW_Roots_results <- as.data.frame(emm_DW_Roots)
+emm_DW_Roots_results$Part <- "Roots"
+
+# combine results
+emm_results <- rbind(emm_DW_Shoot_results, emm_DW_Roots_results)
+
+# save_results
+emm_results <- subset(emm_results, select = -c(DW_Plant))
+write.csv(
+ emm_results,
+ "runs/1_phenotypic_data_figure1/emm_resource_allocation.csv",
+ row.names = FALSE
+)
+
+# calculate pairwise comparison statistics with Benjamini-Hochberg correction
+pairs_Shoot <- as.data.frame(pairs(
+ emm_DW_Shoot,
+ adjust = "BH",
+ type = "response"
+))
+pairs_Shoot$Part <- "Shoot"
+pairs_Roots <- as.data.frame(pairs(
+ emm_DW_Roots,
+ adjust = "BH",
+ type = "response"
+))
+pairs_Roots$Part <- "Roots"
+
+# combine results
+emm_pairwise <- rbind(pairs_Shoot, pairs_Roots)
+
+# save_results
+emm_pairwise <- subset(emm_pairwise, select = -c(DW_Plant))
+write.csv(
+ emm_pairwise,
+ "runs/1_phenotypic_data_figure1/pairwise_comparison_resource_allocation.csv",
+ row.names = FALSE
+)
+
+
+### plot results ###
+
+# define plot order
+plot_order_allocation <- c("Shoot", "Roots")
+
+# set treatment order
+pheno_data_long_allocation$Part <- factor(pheno_data_long_allocation$Part)
+levels(pheno_data_long_allocation$Part) <- c("Roots", "Shoot")
+
+# set part order
+emm_results$Part <- factor(emm_results$Part, levels = plot_order_allocation)
+
+# set treatment labels
+treatment_labels_allocation <- c("Shoot", "Root")
+
+# create plot
+emm_plot <- ggplot() +
+ geom_point(
+ data = emm_results,
+ aes(x = Part, y = response, color = Treatment),
+ position = position_dodge(width = 0.75),
+ size = 3
+ ) +
+ geom_linerange(
+ data = emm_results,
+ aes(
+ x = Part,
+ y = response,
+ color = Treatment,
+ ymin = lower.CL,
+ ymax = upper.CL
+ ),
+ position = position_dodge(width = 0.75),
+ size = 1.2
+ ) +
+ geom_beeswarm(
+ data = pheno_data_long_allocation,
+ aes(x = Part, y = Part_DW, color = Treatment),
+ alpha = 0.5,
+ dodge.width = 0.75,
+ shape = 16
+ ) +
+ scale_color_manual(
+ values = custom_colors,
+ labels = treatment_labels,
+ breaks = treatment_order
+ ) +
+ scale_x_discrete(labels = c("Shoot", "Root")) +
+ labs(
+ title = "d",
+ x = "Plant Part",
+ y = "Dry Weight Estimated Marginal Mean (g)",
+ color = "Treatment"
+ ) +
+ annotate("segment", x = 0.905, xend = 1.095, y = 0.95, yend = 0.95) +
+ annotate("segment", x = 0.905, xend = 0.905, y = 0.922, yend = 0.95) +
+ annotate("segment", x = 1.095, xend = 1.095, y = 0.922, yend = 0.95) +
+ annotate("text", x = 1.0, y = 0.98, label = "0.025", size = 4) +
+ annotate("segment", x = 0.725, xend = 1.095, y = 1.04, yend = 1.04) +
+ annotate("segment", x = 0.725, xend = 0.725, y = 1.012, yend = 1.04) +
+ annotate("segment", x = 1.095, xend = 1.095, y = 1.012, yend = 1.04) +
+ annotate("text", x = 0.91, y = 1.07, label = "0.025", size = 4) +
+ theme_pubr() +
+ theme(
+ axis.text.x = element_text(size = 12),
+ axis.text.y = element_text(size = 12),
+ axis.title = element_text(size = 14),
+ legend.position = "right",
+ legend.title = element_text(size = 14),
+ legend.text = element_text(size = 12),
+ plot.title = element_text(
+ face = "bold",
+ size = 20,
+ hjust = -0.075,
+ vjust = 2
+ ),
+ panel.grid.major.y = element_line(color = "gray90"),
+ panel.grid.minor.y = element_line(color = "gray95")
+ )
+
+# show plot
+emm_plot
+
+
+##### FINAL VISUALIZATION - FIGURE 1 #####
+
+# create combined plot
+combined_plot_pheno <- dw_plant_plot /
+ dw_above_below_plot /
+ emm_plot +
+ plot_layout(widths = c(5), heights = (10))
+
+# show plot
+combined_plot_pheno
+
+# save combined plot (has to be combined with experimental setup plot)
+ggsave(
+ "runs/1_phenotypic_data_figure1/Figure1.pdf",
+ plot = combined_plot_pheno,
+ width = 12.5,
+ height = 15
+)
+dev.off()
diff --git a/workflows/2_timeseries_data_figure2/.Rhistory b/workflows/2_timeseries_data_figure2/.Rhistory
new file mode 100644
index 0000000000000000000000000000000000000000..17263bcc9c261e22fd661238bc79cef73eb542ca
--- /dev/null
+++ b/workflows/2_timeseries_data_figure2/.Rhistory
@@ -0,0 +1,20 @@
+# ------------------------------------------------------------------
+# Script Name: SI.R
+# Purpose: Reproduces SI figures from "An Ectomycorrhizal Fungus Alters Development Stages Within Pedunculate Oak’s Endogenous Growth Rhythm"
+# Author: Felix Zimmermann
+# Date: 2025-05-07
+# Contact: felix.zimmermann@wsl.ch
+# License: CC BY
+# ------------------------------------------------------------------
+##### BASICS #####
+# load packages
+library(tidyverse)
+library(viridis)
+library(psych)
+library(corrplot)
+library(ggpubr)
+library(patchwork)
+library(forcats)
+# check and save session info
+sessionInfo() %>%
+capture.output(file = "runs/3_additional_analyses_SI/SI_session_info.txt")
diff --git a/workflows/2_timeseries_data_figure2/figure2_timeseries.R b/workflows/2_timeseries_data_figure2/figure2_timeseries.R
new file mode 100644
index 0000000000000000000000000000000000000000..01d74021902603277836c08c98bb698ec7c4bd14
--- /dev/null
+++ b/workflows/2_timeseries_data_figure2/figure2_timeseries.R
@@ -0,0 +1,1304 @@
+# ------------------------------------------------------------------
+# Script Name: figure2_timeseries.R
+# Purpose: Reproduces Figure 2 from "An Ectomycorrhizal Fungus Alters Development Stages Within Pedunculate Oak’s Endogenous Growth Rhythm"
+# Author: Felix Zimmermann
+# Date: 2025-05-07
+# Contact: felix.zimmermann@wsl.ch
+# License: CC BY
+# ------------------------------------------------------------------
+
+##### BASICS #####
+
+# load packages
+library(tidyverse)
+library(viridis)
+library(ggpubr)
+library(brms)
+library(posterior)
+library(bayesnec)
+library(bayestestR)
+library(tidybayes)
+library(ggtext)
+library(rstatix)
+library(ggsignif)
+library(grid)
+library(patchwork)
+
+# check and save session info
+sessionInfo() %>%
+ capture.output(file = "runs/2_timeseries_data_figure2/Figure2_session_info.txt")
+
+
+##### DATA LOADING, CLEANING & SCALING #####
+
+# laod data (dead plants are excluded in time_seriess.csv file)
+time_data <- read_csv("assays/2_growth_monitoring/dataset/time_series.csv")
+
+# create stage_reference for calculation of stages reached
+stage_reference <- data.frame(
+ Stage = c(paste0(rep(1:5, each = 4), rep(c("A", "B", "C", "D"), 5))),
+ stage_index = 1:20
+)
+
+# calculate the number of stages reached for each date
+time_data <- time_data %>%
+ left_join(stage_reference, by = "Stage") %>%
+ group_by(ID) %>%
+ arrange(ID, Days) %>%
+ mutate(
+ initial_index = first(stage_index),
+ Stages_Reached = ifelse(Days == 0, 0, stage_index - initial_index)
+ ) %>%
+ dplyr::select(-c(stage_index, initial_index))
+
+# create a column where total number of stages reached is added to each plant ID
+time_data <- time_data %>%
+ group_by(ID) %>%
+ mutate(Max_Stages_Reached = max(Stages_Reached)) %>%
+ ungroup()
+
+# exclude contaminated plants, add +1 to Stages_Reached (beginning stage = stage 1)
+time_data_final <- subset(time_data, Contaminated == "no")
+time_data_final$Stages_Reached <- time_data_final$Stages_Reached + 1
+
+# scale time variable, define plant ID & Treatment as factor
+time_data_scaled <- time_data_final
+time_data_scaled$days_scaled <- scale(time_data_final$Days)
+time_data_scaled$ID <- factor(time_data_scaled$ID)
+time_data_scaled$Treatment <- factor(time_data_scaled$Treatment)
+
+
+##### DEFINE BASIC PLOTTING PARAMETERS #####
+
+# define treatment labels
+treatment_labels <- c(
+ "Ceno" = expression(italic("Cenococcum geophilum")),
+ "Pilo" = expression(italic("Piloderma croceum")),
+ "Co_Inoc" = "Co-Inoculation",
+ "Control" = "Control"
+)
+
+# define legend order
+treatment_order <- c("Control", "Pilo", "Ceno", "Co_Inoc")
+
+# define custom colors
+custom_colors <- c(
+ "Control" = "#4B8C6F",
+ "Ceno" = "#8C5E8C",
+ "Pilo" = "#D6C9A0",
+ "Co_Inoc" = "#5E96A6"
+)
+
+
+##### CREATE BASIC GROWTH STAGE DEVELOPMENT PLOT - FIGURE 2B #####
+
+# remove week 9 & 10 (week 8 was last week where both sets were measured twice)
+time_data_basic_plot <- subset(time_data_final, Week < 9)
+
+# create basic plot for growth stage development (mean and standard error)
+basic_growth_plot <- ggplot(
+ time_data_basic_plot,
+ aes(x = Week, y = Stages_Reached, color = Treatment)
+) +
+ stat_summary(fun = mean, geom = "line", linewidth = 1) +
+ stat_summary(
+ fun.data = mean_se,
+ geom = "ribbon",
+ alpha = 0.5,
+ aes(fill = Treatment)
+ ) +
+ scale_color_manual(
+ values = custom_colors,
+ labels = treatment_labels,
+ breaks = treatment_order
+ ) +
+ scale_fill_manual(
+ values = custom_colors,
+ labels = treatment_labels,
+ breaks = treatment_order
+ ) +
+ scale_x_continuous(
+ breaks = (c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)),
+ expand = (c(0, 0))
+ ) +
+ scale_y_continuous() +
+ labs(
+ title = "b",
+ y = "N Stages Reached",
+ x = "N Weeks",
+ color = "Treatment",
+ fill = "Treatment"
+ ) +
+ theme_pubr() +
+ theme(
+ axis.text.x = element_text(size = 12),
+ axis.text.y = element_text(size = 12),
+ axis.title = element_text(size = 14),
+ legend.position = "right",
+ legend.title = element_text(size = 14),
+ legend.text = element_text(size = 12),
+ plot.title = element_text(
+ face = "bold",
+ size = 20,
+ hjust = -0.075,
+ vjust = 2
+ ),
+ plot.subtitle = element_text(size = 14, face = "italic"),
+ panel.grid.major.y = element_line(color = "gray90"),
+ panel.grid.minor.y = element_line(color = "gray95")
+ )
+
+# show plot
+basic_growth_plot
+
+
+##### GROWTH MODEL - BRM (Cumulative Distribution Family) - FIGURE 2C #####
+
+### FIT MODELS WITH DIFFERENT LEVELS OF COMPLEXITY ###
+
+# set control treatment as reference
+time_data_scaled$Treatment <- relevel(
+ time_data_scaled$Treatment,
+ ref = "Control"
+)
+
+# fit growth curve model with cumulative distribution family using bayesian regression (Treatment * days_scaled + random intercept factor ID)
+bay_model <- brm(
+ Stages_Reached ~ Treatment * days_scaled + (1 | ID),
+ ## treatment effect with linear time trend and random intercepts
+ # treatment and days_scaled interaction as fixed effects
+ # random intercept-factor ID to account for non-independence of repeated measurements
+ family = cumulative(), # cumulative distribution family to account for cumulative nature of growth-stage succession
+ data = time_data_scaled, # use scaled numeric explanatory variable in model
+ chains = 4, # use 4 independent fitting procedures
+ iter = 6000, # use 6000 iterations
+ warmup = 3000, # use 50% of iterations as warm-up
+ seed = 2025, # use seed
+)
+
+# check model summary statistics and plot posterior distribution
+summary(bay_model)
+rhat(bay_model)
+fixef(bay_model)
+ranef(bay_model)
+bayes_R2(bay_model)
+plot(bay_model)
+plot(conditional_effects(bay_model))
+loo(bay_model)
+
+# fincrease complexity by incorporating random slope (Treatment * days_scaled + random intercept-slope factor ID)
+bay_model_slope <- brm(
+ Stages_Reached ~ Treatment * days_scaled + (1 + days_scaled | ID),
+ ## treatment effect with linear time trend, random intercepts and random slopes
+ # treatment and days_scaled interaction as fixed effects
+ # random intercept-factor ID to account for non-independence of repeated measurements
+ # random slope-factor ID to account for heterogeneity in growth rates between plants
+ family = cumulative(), # cumulative distribution family to account for cumulative nature of growth-stage succession
+ data = time_data_scaled, # use scaled numeric explanatory variable in model
+ chains = 4, # use 4 independent fitting procedures
+ iter = 6000, # use 6000 iterations
+ warmup = 3000, # use 50% of iterations as warm-up
+ seed = 2025, # use seed
+)
+
+# check model summary statistics and plot posterior distribution
+summary(bay_model_slope)
+rhat(bay_model_slope)
+fixef(bay_model_slope)
+ranef(bay_model_slope)
+bayes_R2(bay_model_slope)
+plot(bay_model_slope)
+plot(conditional_effects(bay_model_slope))
+loo(bay_model_slope)
+
+## increase complexity by incorporating auto-regressive term (Treatment * days_scaled + auto-regressive term + random intercep factor ID)
+bay_model_ar <- brm(
+ Stages_Reached ~
+ Treatment * days_scaled + ar(time = days_scaled, gr = ID) + (1 | ID),
+ ## adding autocorrelation structure
+ # treatment and days_scaled interaction as fixed effects
+ # auto-regressive term by tree ID to account for temporal autocorrelation within plants
+ # random intercept-factor ID to account for non-independence of repeated measurements
+ family = cumulative(),
+ data = time_data_scaled,
+ chains = 4,
+ iter = 6000,
+ warmup = 3000,
+ seed = 2025
+)
+
+# check model summary statistics and plot posterior distribution
+summary(bay_model_ar)
+rhat(bay_model_ar)
+fixef(bay_model_ar)
+ranef(bay_model_ar)
+bayes_R2(bay_model_ar)
+plot(bay_model_ar)
+plot(conditional_effects(bay_model_ar))
+loo(bay_model_ar)
+# autoregressive term causes overfitting
+
+# increase complexity by adding a random slope (Treatment * days_scaled + autoregressive term + random intercept-slope factor ID)
+bay_model_ar_slope <- brm(
+ Stages_Reached ~
+ Treatment *
+ days_scaled +
+ ar(time = days_scaled, gr = ID) +
+ (1 + days_scaled | ID),
+ ## allowing individual plants to have different growth rates
+ # treatment and days_scaled interaction as fixed effects
+ # autoregressive term by tree ID to account for temporal autocorrelation within plants
+ # random intercept-factor ID to account for non-independence of repeated measurements
+ # random slope-factor ID to account for heterogeneity in growth rates between plants
+ family = cumulative(),
+ data = time_data_scaled,
+ chains = 4,
+ iter = 6000,
+ warmup = 3000,
+ seed = 2025
+)
+
+# check model summary statistics and plot posterior distribution
+summary(bay_model_ar_slope)
+rhat(bay_model_ar_slope)
+fixef(bay_model_ar_slope)
+ranef(bay_model_ar_slope)
+bayes_R2(bay_model_ar_slope)
+plot(bay_model_ar_slope)
+plot(conditional_effects(bay_model_ar_slope))
+loo(bay_model_ar_slope)
+# auto-regressive term causes overfitting
+
+# change auto-regressive term causing overfitting to spline function (Treatment + spline term + random intercept factor ID)
+bay_model_s <- brm(
+ Stages_Reached ~ Treatment + s(days_scaled, by = Treatment) + (1 | ID),
+ ## modeling non-linear growth curves by treatment
+ # treatment as fixed effect (no days_scaled interaction because treatment specific splines already capture different growth rates over time)
+ # spline/smoothing term days_scaled to allow for different non-linear growth patterns between treatments
+ # random intercept-factor ID to account for non-independence of repeated measurements
+ family = cumulative(),
+ data = time_data_scaled,
+ chains = 4,
+ iter = 6000,
+ warmup = 3000,
+ seed = 2025
+)
+
+# check model summary statistics and plot posterior distribution
+summary(bay_model_s)
+rhat(bay_model_s)
+fixef(bay_model_s)
+ranef(bay_model_s)
+bayes_R2(bay_model_s)
+plot(bay_model_s)
+plot(conditional_effects(bay_model_s))
+loo(bay_model_s)
+
+# increase complexity by adding a random slope (Treatment + spline term + random intercept-slope factor ID)
+bay_model_s_slope <- brm(
+ Stages_Reached ~
+ Treatment + s(days_scaled, by = Treatment) + (1 + days_scaled | ID),
+ ## modeling non-linear growth curves by treatment
+ # treatment as fixed effect (no days_scaled interaction because treatment specific splines already capture different growth rates over time)
+ # spline/smoothing term days_scaled to allow for different non-linear growth patterns between treatments
+ # random intercept-factor ID to account for non-independence of repeated measurements
+ # random slope-factor ID to account for heterogeneity in growth rates between plants
+ family = cumulative(),
+ data = time_data_scaled,
+ chains = 4,
+ iter = 6000,
+ warmup = 3000,
+ seed = 2025
+)
+
+# check model summary statistics and plot posterior distribution
+summary(bay_model_s_slope)
+rhat(bay_model_s_slope)
+fixef(bay_model_s_slope)
+ranef(bay_model_s_slope)
+bayes_R2(bay_model_s_slope)
+plot(bay_model_s_slope)
+plot(conditional_effects(bay_model_s_slope))
+loo(bay_model_s_slope)
+
+
+### COMPARE MODEL RHAT AND ESS VALUES ###
+
+# extract Rhat values for each model
+rhat_model <- rhat(bay_model)
+rhat_model_slope <- rhat(bay_model_slope)
+rhat_model_ar <- rhat(bay_model_ar)
+rhat_model_ar_slope <- rhat(bay_model_ar_slope)
+rhat_model_s <- rhat(bay_model_s)
+rhat_model_s_slope <- rhat(bay_model_s_slope)
+
+# calculate maximum rhat deviation from 1 for each model
+max_dev_base <- max(abs(rhat_model - 1), na.rm = TRUE)
+max_dev_base_slope <- max(abs(rhat_model_slope - 1), na.rm = TRUE)
+max_dev_ar <- max(abs(rhat_model_ar - 1), na.rm = TRUE)
+max_dev_ar_slope <- max(abs(rhat_model_ar_slope - 1), na.rm = TRUE)
+max_dev_s <- max(abs(rhat_model_s - 1), na.rm = TRUE)
+max_dev_s_slope <- max(abs(rhat_model_s_slope - 1), na.rm = TRUE)
+
+# create summary dataframe
+rhat_summary <- data.frame(
+ Model = c("Base", "Base_Slope", "AR", "AR_Slope", "S", "S_Slope"),
+ Max_Deviation = c(
+ max_dev_base,
+ max_dev_base_slope,
+ max_dev_ar,
+ max_dev_ar_slope,
+ max_dev_s,
+ max_dev_s_slope
+ ),
+ Parameters = c(
+ length(rhat_model),
+ length(rhat_model_slope),
+ length(rhat_model_ar),
+ length(rhat_model_ar_slope),
+ length(rhat_model_s),
+ length(rhat_model_s_slope)
+ )
+)
+
+# sort by maximum deviation
+rhat_summary <- rhat_summary[order(rhat_summary$Max_Deviation), ]
+
+# print results
+print(rhat_summary)
+
+# extract ESS values for each model
+ess_model <- ess_bulk(bay_model)
+ess_model_slope <- ess_bulk(bay_model_slope)
+ess_model_ar <- ess_bulk(bay_model_ar)
+ess_model_ar_slope <- ess_bulk(bay_model_ar_slope)
+ess_model_s <- ess_bulk(bay_model_s)
+ess_model_s_slope <- ess_bulk(bay_model_s_slope)
+
+# create dataframe of ESS values
+ess_df <- data.frame(
+ Parameter = names(ess_model),
+ Model_Base = ess_model,
+ Model_Base_Slope = ess_model_slope,
+ Model_AR = ess_model_ar,
+ Model_AR_Slope = ess_model_ar_slope,
+ Model_S = ess_model_s,
+ Model_S_Slope = ess_model_s_slope,
+)
+
+# find minimum ESS for each model
+ess_summary <- data.frame(
+ Model = c("Base", "Base_Slope", "AR", "AR_Slope", "S", "S_Slope"),
+ Min_ESS = c(
+ min(ess_model),
+ min(ess_model_slope),
+ min(ess_model_ar),
+ min(ess_model_ar_slope),
+ min(ess_model_s),
+ min(ess_model_s_slope)
+ )
+)
+
+# sort by minimum ESS
+ess_summary <- ess_summary[order(ess_summary$Min_ESS), ]
+
+# print results
+print(ess_summary)
+
+
+### PLOT PREDICTIONS ###
+
+# extract conditional effects data and convert to dataframe
+ce_data <- conditional_effects(
+ bay_model_s_slope,
+ effects = "days_scaled:Treatment"
+)
+ce_df <- as.data.frame(ce_data$`days_scaled:Treatment`)
+
+# calculate scaling parameters for scaled days and scale back to original
+days_mean <- mean(time_data_final$Days)
+days_sd <- sd(time_data_final$Days)
+ce_df$days_original <- ce_df$days_scaled * days_sd + days_mean
+
+# reorder factor levels
+ce_df$Treatment <- factor(
+ ce_df$Treatment,
+ levels = c("Control", "Ceno", "Pilo", "Co_Inoc")
+)
+
+# create growth model plot
+model_plot <- ggplot(
+ ce_df,
+ aes(x = days_original, y = estimate__, color = Treatment)
+) +
+ geom_line(size = 1) +
+ geom_ribbon(
+ aes(ymin = lower__, ymax = upper__, fill = Treatment),
+ alpha = 0.5,
+ color = NA
+ ) +
+ geom_line(aes(y = lower__), linetype = "solid", size = 0.5) +
+ geom_line(aes(y = upper__), linetype = "solid", size = 0.5) +
+ scale_color_manual(
+ values = custom_colors,
+ labels = treatment_labels,
+ name = "Treatment"
+ ) + # apply custom color scheme
+ scale_fill_manual(
+ values = custom_colors,
+ labels = treatment_labels,
+ name = "Treatment"
+ ) +
+ scale_x_continuous(expand = c(0, 0), limits = c(0, 56)) +
+ scale_y_continuous() +
+ labs(
+ title = "c",
+ y = "N Stages Reached",
+ x = "N Days"
+ ) +
+ theme_pubr() +
+ theme(
+ axis.text.x = element_text(size = 12),
+ axis.text.y = element_text(size = 12),
+ axis.title = element_text(size = 14),
+ legend.position = "none",
+ plot.title = element_text(
+ face = "bold",
+ size = 20,
+ hjust = -0.0525,
+ vjust = 2
+ ),
+ panel.grid.major.y = element_line(color = "gray90"),
+ panel.grid.minor.y = element_line(color = "gray95")
+ )
+
+# show plot
+model_plot
+
+
+### GET PREDICTIONS AT LAST DAY OF EXPERIMENT (56 DAYS) ###
+
+# scale day 56
+day_56_scaled <- (56 - days_mean) / days_sd
+
+# create prediction data frame
+newdata_56 <- data.frame(
+ days_scaled = day_56_scaled,
+ Treatment = c("Control", "Ceno", "Pilo", "Co_Inoc")
+)
+
+# get predictions
+pred_day56 <- fitted(
+ bay_model_s_slope,
+ newdata = newdata_56,
+ re_formula = NA, # Population-level estimates
+ summary = FALSE # Get full posterior
+)
+
+# calculate expected stage for each posterior sample
+n_categories <- dim(pred_day56)[3]
+stage_indices <- 1:n_categories
+expected_stages <- matrix(0, nrow = dim(pred_day56)[1], ncol = nrow(newdata_56))
+
+for (i in 1:dim(pred_day56)[1]) {
+ for (j in 1:nrow(newdata_56)) {
+ expected_stages[i, j] <- sum(stage_indices * pred_day56[i, j, ])
+ }
+}
+
+# summarize results
+results_day56 <- data.frame(
+ Treatment = newdata_56$Treatment,
+ Expected_Stage = colMeans(expected_stages),
+ Lower_CI = apply(expected_stages, 2, quantile, probs = 0.025),
+ Upper_CI = apply(expected_stages, 2, quantile, probs = 0.975)
+)
+
+# calculate standard error based on 95% credible intervals
+results_day56$SE <- (results_day56$Upper_CI - results_day56$Lower_CI) /
+ (2 * 1.96)
+
+# print results
+print(results_day56)
+
+
+##### OVERALL TREATMENT EFFECTS - FIGURE 2D #####
+
+### GET TREATMENT EFFECTS ###
+
+# extract treatment effects
+treatment_effects <- conditional_effects(
+ bay_model_s_slope,
+ effects = "Treatment"
+)
+treatment_effect_data <- as.data.frame(treatment_effects$Treatment)
+
+# reorder factor levels
+treatment_effect_data$Treatment <- factor(
+ treatment_effect_data$Treatment,
+ levels = c("Control", "Ceno", "Pilo", "Co_Inoc")
+)
+
+# calculate grand mean
+avg_data <- aggregate(
+ estimate__ ~ days_scaled,
+ data = treatment_effect_data,
+ FUN = mean
+)
+names(avg_data)[2] <- "avg_estimate"
+
+# merge average estimate with main data
+treatment_effect_data <- merge(
+ treatment_effect_data,
+ avg_data,
+ by = "days_scaled"
+)
+
+# calculate estimate, upper and lower relative to grand mean
+treatment_effect_data$estimate__rel <- treatment_effect_data$estimate__ -
+ treatment_effect_data$avg_estimate
+treatment_effect_data$lower__rel <- treatment_effect_data$lower__ -
+ treatment_effect_data$avg_estimate
+treatment_effect_data$upper__rel <- treatment_effect_data$upper__ -
+ treatment_effect_data$avg_estimate
+
+# remove temporary column
+treatment_effect_data$avg_estimate <- NULL
+
+# create treatment order
+treatment_effect_data$Treatment <- factor(
+ treatment_effect_data$Treatment,
+ levels = treatment_order
+)
+
+# get significance
+treatment_effect_data$Significant <- (treatment_effect_data$lower__rel > 0 |
+ treatment_effect_data$upper__rel < 0)
+
+
+### PLOT TREATMENT EFFECTS AND SAVE RESULTS ###
+
+# create plot for treatment effects on stage development (mean and 95% credible interval)
+overall_treatment_plot <- ggplot(
+ treatment_effect_data,
+ aes(x = Treatment, y = estimate__rel, color = Treatment)
+) +
+ geom_point(aes(shape = Significant), size = 3) +
+ geom_linerange(
+ aes(color = Treatment, ymin = lower__rel, ymax = upper__rel),
+ size = 1.2
+ ) +
+ scale_color_manual(
+ values = custom_colors,
+ labels = treatment_labels,
+ guide = "none" # Remove the color legend
+ ) +
+ scale_x_discrete(labels = treatment_labels) +
+ geom_hline(yintercept = 0, size = 0.2, col = "black") +
+ labs(
+ title = "d",
+ y = "Relative Response",
+ x = "Treatment"
+ ) +
+ theme_pubr() +
+ theme(
+ axis.text.x = element_text(angle = 25, hjust = 1, size = 12),
+ axis.text.y = element_text(size = 12),
+ axis.title = element_text(size = 14),
+ legend.position = "right",
+ legend.title = element_text(size = 14),
+ legend.text = element_text(size = 12),
+ plot.title = element_text(
+ face = "bold",
+ size = 20,
+ hjust = -0.07,
+ vjust = 2
+ ),
+ panel.grid.major.y = element_line(color = "gray90"),
+ panel.grid.minor.y = element_line(color = "gray95")
+ )
+
+# show plot
+overall_treatment_plot
+
+# save results data
+overall_effects <- subset(
+ treatment_effect_data,
+ select = -c(Stages_Reached, ID, cond__, effect1__, days_scaled)
+)
+overall_effects <- overall_effects %>% rename(se = se__)
+overall_effects <- overall_effects %>% rename(estimate_absolute = estimate__)
+overall_effects <- overall_effects %>% rename(lower_absolute = lower__)
+overall_effects <- overall_effects %>% rename(upper_absolute = upper__)
+overall_effects <- overall_effects %>% rename(estimate_relative = estimate__rel)
+overall_effects <- overall_effects %>% rename(lower_relative = lower__rel)
+overall_effects <- overall_effects %>% rename(upper_relative = upper__rel)
+write.csv(
+ overall_effects,
+ "runs/2_timeseries_data_figure2/overall_treatment_effects.csv",
+ row.names = FALSE
+)
+
+
+##### IDENTIFY DAYS OF SIGNIFICANT DIFFERENCE - FIGURE 2E #####
+
+### EXTRACT DAYS OF SIGNIFICANT DIFFERENCE ###
+
+# extract unique treatments from original data
+treatments <- unique(bay_model_s_slope$data$Treatment)
+
+# identify treatment pairs
+treatment_pairs <- combn(treatments, 2, simplify = FALSE)
+
+# create data frame for significantly different days
+significance_days <- data.frame(
+ Treatment1 = character(),
+ Treatment2 = character(),
+ First_Significant_Day = numeric(),
+ stringsAsFactors = FALSE
+)
+
+# extract data
+for (pair in treatment_pairs) {
+ t1 <- pair[1]
+ t2 <- pair[2]
+ first_sig_day <- NA
+ # check each day
+ for (day in 1:56) {
+ # revert scaling of days
+ if (is.null(days_mean) || is.null(days_sd)) {
+ day_scaled <- day
+ } else {
+ day_scaled <- (day - days_mean) / days_sd
+ }
+ # create prediction data for treatment pairs
+ pred_data <- data.frame(
+ Treatment = c(t1, t2),
+ days_scaled = rep(day_scaled, 2),
+ ID = rep("new_subject", 2)
+ )
+ # get posterior predictions
+ preds <- posterior_epred(
+ bay_model,
+ newdata = pred_data,
+ allow_new_levels = TRUE
+ )
+ # compute expected stages for each treatment
+ expected_stages <- array(0, dim = c(dim(preds)[1], 2))
+ for (i in 1:dim(preds)[3]) {
+ # for each outcome category/stage
+ expected_stages[, 1] <- expected_stages[, 1] + i * preds[, 1, i]
+ expected_stages[, 2] <- expected_stages[, 2] + i * preds[, 2, i]
+ }
+ # compute differences and check significance
+ stage_diffs <- expected_stages[, 1] - expected_stages[, 2]
+ # check if 95% CI excludes zero
+ ci_lower <- quantile(stage_diffs, 0.025)
+ ci_upper <- quantile(stage_diffs, 0.975)
+ is_significant <- (ci_lower > 0) | (ci_upper < 0)
+ # record first significant day
+ if (is_significant && is.na(first_sig_day)) {
+ first_sig_day <- day
+ break
+ }
+ }
+ # add results to data frame
+ significance_days <- rbind(
+ significance_days,
+ data.frame(
+ Treatment1 = t1,
+ Treatment2 = t2,
+ First_Significant_Day = first_sig_day
+ )
+ )
+}
+
+# check results
+print(significance_days)
+
+# create data frame to store results
+diff_over_time <- data.frame(
+ Treatment1 = character(),
+ Treatment2 = character(),
+ Day = numeric(),
+ Mean_Diff = numeric(),
+ Lower_CI = numeric(),
+ Upper_CI = numeric(),
+ Significant = logical(),
+ stringsAsFactors = FALSE
+)
+
+# for each pair, calculate differences over time
+for (pair in treatment_pairs) {
+ t1 <- pair[1]
+ t2 <- pair[2]
+ for (day in 1:56) {
+ # scale day parameter
+ if (is.null(days_mean) || is.null(days_sd)) {
+ day_scaled <- day
+ } else {
+ day_scaled <- (day - days_mean) / days_sd
+ }
+ # create prediction data for treatment paris
+ pred_data <- data.frame(
+ Treatment = c(t1, t2),
+ days_scaled = rep(day_scaled, 2),
+ ID = rep("new_subject", 2)
+ )
+ # get posterior predictions
+ preds <- posterior_epred(
+ bay_model,
+ newdata = pred_data,
+ allow_new_levels = TRUE
+ )
+ # compute expected stages for each treatment
+ expected_stages <- array(0, dim = c(dim(preds)[1], 2))
+ for (i in 1:dim(preds)[3]) {
+ # for each outcome category/stage
+ expected_stages[, 1] <- expected_stages[, 1] + i * preds[, 1, i]
+ expected_stages[, 2] <- expected_stages[, 2] + i * preds[, 2, i]
+ }
+ # compute differences and check significance
+ stage_diffs <- expected_stages[, 1] - expected_stages[, 2]
+ # calculate mean and CI
+ mean_diff <- mean(stage_diffs)
+ ci_lower <- quantile(stage_diffs, 0.025)
+ ci_upper <- quantile(stage_diffs, 0.975)
+ is_significant <- (ci_lower > 0) | (ci_upper < 0)
+ # add to results data frame
+ diff_over_time <- rbind(
+ diff_over_time,
+ data.frame(
+ Treatment1 = t1,
+ Treatment2 = t2,
+ Day = day,
+ Mean_Diff = mean_diff,
+ Lower_CI = ci_lower,
+ Upper_CI = ci_upper,
+ Significant = is_significant
+ )
+ )
+ }
+}
+
+# define significant treatment pairs for final plot
+treatment_pairs_final <- list(
+ factor(
+ c(2, 4),
+ levels = 1:4,
+ labels = c("Control", "Ceno", "Co_Inoc", "Pilo")
+ ),
+ factor(
+ c(2, 3),
+ levels = 1:4,
+ labels = c("Control", "Ceno", "Co_Inoc", "Pilo")
+ ),
+ factor(
+ c(2, 1),
+ levels = 1:4,
+ labels = c("Control", "Ceno", "Co_Inoc", "Pilo")
+ ),
+ factor(
+ c(3, 1),
+ levels = 1:4,
+ labels = c("Control", "Ceno", "Co_Inoc", "Pilo")
+ ),
+ factor(
+ c(3, 4),
+ levels = 1:4,
+ labels = c("Control", "Ceno", "Co_Inoc", "Pilo")
+ ),
+ factor(
+ c(1, 4),
+ levels = 1:4,
+ labels = c("Control", "Ceno", "Co_Inoc", "Pilo")
+ )
+)
+
+# create data frame to store results for final plot
+diff_over_time_final <- data.frame(
+ Treatment1 = character(),
+ Treatment2 = character(),
+ Day = numeric(),
+ Mean_Diff = numeric(),
+ Lower_CI = numeric(),
+ Upper_CI = numeric(),
+ Significant = logical(),
+ stringsAsFactors = FALSE
+)
+
+# for each final pair, calculate differences over time
+for (pair in treatment_pairs_final) {
+ t1 <- pair[1]
+ t2 <- pair[2]
+ for (day in 1:56) {
+ # scale day parameter
+ if (is.null(days_mean) || is.null(days_sd)) {
+ day_scaled <- day
+ } else {
+ day_scaled <- (day - days_mean) / days_sd
+ }
+ # create prediction data for treatment paris
+ pred_data <- data.frame(
+ Treatment = c(t1, t2),
+ days_scaled = rep(day_scaled, 2),
+ ID = rep("new_subject", 2)
+ )
+ # get posterior predictions
+ preds <- posterior_epred(
+ bay_model,
+ newdata = pred_data,
+ allow_new_levels = TRUE
+ )
+ # compute expected stages for each treatment
+ expected_stages <- array(0, dim = c(dim(preds)[1], 2))
+ for (i in 1:dim(preds)[3]) {
+ # for each outcome category/stage
+ expected_stages[, 1] <- expected_stages[, 1] + i * preds[, 1, i]
+ expected_stages[, 2] <- expected_stages[, 2] + i * preds[, 2, i]
+ }
+ # compute differences and check significance
+ stage_diffs <- expected_stages[, 1] - expected_stages[, 2]
+ # calculate mean and CI
+ mean_diff <- mean(stage_diffs)
+ ci_lower <- quantile(stage_diffs, 0.025)
+ ci_upper <- quantile(stage_diffs, 0.975)
+ is_significant <- (ci_lower > 0) | (ci_upper < 0)
+ # add to results data frame
+ diff_over_time_final <- rbind(
+ diff_over_time_final,
+ data.frame(
+ Treatment1 = t1,
+ Treatment2 = t2,
+ Day = day,
+ Mean_Diff = mean_diff,
+ Lower_CI = ci_lower,
+ Upper_CI = ci_upper,
+ Significant = is_significant
+ )
+ )
+ }
+}
+
+
+### PLOT PAIRWISE DIFFERENCES AND SAVE RESULTS ###
+
+# save pairwise timeseries credible intervals
+diff_over_time <- diff_over_time %>%
+ dplyr::select(
+ Treatment1,
+ Treatment2,
+ Day,
+ Lower_CI,
+ Upper_CI,
+ Significant
+ ) %>%
+ arrange(Treatment1, Treatment2, Day)
+write.csv(
+ diff_over_time,
+ "runs/2_timeseries_data_figure2/pairwise_treatment_differences_over_time.csv",
+ row.names = FALSE
+)
+
+# create final pairwise differences plot
+pairwise_plot_final <- ggplot(
+ diff_over_time_final,
+ aes(
+ x = Day,
+ y = Mean_Diff,
+ ymin = Lower_CI,
+ ymax = Upper_CI,
+ color = Significant
+ )
+) +
+ geom_ribbon(aes(fill = Significant), alpha = 0.5, color = NA) +
+ geom_line() +
+ geom_hline(yintercept = 0, linetype = "dashed") +
+ facet_wrap(
+ ~ paste(Treatment1, "vs", Treatment2),
+ labeller = labeller(
+ .default = label_value,
+ `paste(Treatment1, "vs", Treatment2)` = function(x) {
+ x <- gsub(
+ "Ceno vs Co_Inoc",
+ "*Cenococcum geophilum* vs.<br>Co-Inoculation",
+ x
+ )
+ x <- gsub(
+ "Ceno vs Pilo",
+ "*Cenococcum geophilum* vs.<br>*Piloderma croceum*",
+ x
+ )
+ x <- gsub(
+ "Ceno vs Control",
+ "*Cenococcum geophilum* vs. <br>Control",
+ x
+ )
+ x <- gsub("Co_Inoc vs Control", "Co-Inoculation vs.<br>Control", x)
+ x <- gsub(
+ "Co_Inoc vs Pilo",
+ "Co-Inoculation vs.<br>*Piloderma croceum*",
+ x
+ )
+ x <- gsub("Control vs Pilo", "Control vs.<br>*Piloderma croceum*", x)
+ x <- gsub(
+ "Co_Inoc vs Pilo",
+ "Co-Inoculation vs.<br>*Piloderma croceum*",
+ x
+ )
+ }
+ )
+ ) +
+ scale_color_manual(values = c("gray60", "gray10")) +
+ scale_fill_manual(values = c("gray60", "gray10")) +
+ theme_minimal() +
+ labs(
+ title = "e",
+ x = "N Days",
+ y = "Expected Stage (Treatment 1 - Treatment 2)"
+ ) +
+ theme_pubr() +
+ theme(
+ axis.text.x = element_text(size = 12),
+ axis.text.y = element_text(size = 12),
+ axis.title = element_text(size = 14),
+ legend.position = "right",
+ legend.title = element_text(size = 14),
+ legend.text = element_text(size = 12),
+ plot.title = element_text(
+ face = "bold",
+ size = 20,
+ hjust = -0.0675,
+ vjust = 2
+ ),
+ panel.grid.major.y = element_line(color = "gray90"),
+ panel.grid.minor.y = element_line(color = "gray95"),
+ strip.background = element_rect(fill = "white"),
+ strip.text = element_markdown(size = 12)
+ )
+
+# show final plot
+pairwise_plot_final
+
+# save significant day results
+write.csv(
+ significance_days,
+ "runs/2_timeseries_data_figure2/significance_days.csv",
+ row.names = FALSE
+)
+
+
+###### CHECK FOR DIFFERENCES IN STAGE DURATIONS FOR EACH STAGE BETWEEN TREATMENTS - FIGURE 2F #####
+
+### CALCULATE STAGE DURATIONS ###
+
+# write function for stage duration calculation
+calculate_stage_duration <- function(df) {
+ # convert date data
+ df$Date <- as.Date(as.character(df$Date), format = "%Y%m%d")
+ # sort data by ID and Date
+ df <- df[order(df$ID, df$Date), ]
+ # create results dataframe
+ result <- data.frame(
+ ID = integer(),
+ Treatment = character(),
+ Stage = character(),
+ StartDate = as.Date(character()),
+ EndDate = as.Date(character()),
+ DaysInStage = integer(),
+ stringsAsFactors = FALSE
+ )
+ # analyze each ID separately
+ for (id in unique(df$ID)) {
+ id_data <- df[df$ID == id, ]
+ if (nrow(id_data) <= 1) next
+ # track stages
+ current_stage <- NULL
+ stage_start_date <- NULL
+ for (i in 1:nrow(id_data)) {
+ # check if new stage was reached
+ if (is.null(current_stage) || id_data$Stage[i] != current_stage) {
+ # record duration for previous stage
+ if (!is.null(current_stage)) {
+ result <- rbind(
+ result,
+ data.frame(
+ ID = id,
+ Treatment = id_data$Treatment[i - 1],
+ Stage = current_stage,
+ StartDate = stage_start_date,
+ EndDate = id_data$Date[i],
+ DaysInStage = as.integer(id_data$Date[i] - stage_start_date),
+ stringsAsFactors = FALSE
+ )
+ )
+ }
+ # start tracking new stage
+ current_stage <- id_data$Stage[i]
+ stage_start_date <- id_data$Date[i]
+ }
+ }
+ }
+ return(result)
+}
+
+# calculate stage durations
+stage_durations <- calculate_stage_duration(time_data_final)
+print(stage_durations)
+
+# add stage letter for plotting
+stage_durations <- stage_durations %>%
+ mutate(StageLetter = substr(Stage, nchar(Stage), nchar(Stage)))
+
+
+### COMPARISONS OF CYCLE DURATIONS BETWEEN TREATMENTS ###
+
+# create and run a function calculating growth cycle durations of different plants
+calculate_cycle_durations <- function(data) {
+ # Extract cycle number and stage letter
+ data <- data %>%
+ mutate(
+ CycleNum = as.integer(sub("(\\d+)([A-D])", "\\1", Stage)),
+ StageLetter = sub("(\\d+)([A-D])", "\\2", Stage)
+ )
+ # group by ID
+ result_data <- data %>%
+ group_by(ID) %>%
+ arrange(Date) %>%
+ mutate(
+ NextStage = lead(StageLetter),
+ NextCycleNum = lead(CycleNum),
+ NextDays = lead(Days)
+ ) %>%
+ # find D→A transitions
+ filter(StageLetter == "D" & NextStage == "A") %>%
+ select(
+ ID,
+ Treatment,
+ CurrentCycle = CycleNum,
+ NextCycle = NextCycleNum,
+ CurrentDays = Days,
+ NextDays
+ ) %>%
+ # prepare for calculating cycle durations
+ ungroup()
+ # skip plants that start with stage A
+ start_stages <- data %>%
+ group_by(ID) %>%
+ slice(1) %>%
+ select(ID, FirstStage = StageLetter)
+ result_data <- result_data %>%
+ left_join(start_stages, by = "ID") %>%
+ filter(FirstStage != "A")
+ # calculate cycle durations
+ cycle_durations <- result_data %>%
+ group_by(ID) %>%
+ mutate(
+ NextTransitionDays = lead(NextDays)
+ ) %>%
+ filter(!is.na(NextTransitionDays)) %>%
+ transmute(
+ ID,
+ Treatment,
+ Cycle_ID = NextCycle, # cycle that starts at this transition
+ Days = NextTransitionDays - NextDays # days between this A and next A
+ )
+ return(cycle_durations)
+}
+cycle_durations <- calculate_cycle_durations(time_data_final)
+
+# do wilcoxon test with Benjamini-Hochberg p adjustment for cycle durations
+wilcoxon_results_cycle <- pairwise.wilcox.test(
+ cycle_durations$Days,
+ cycle_durations$Treatment,
+ p.adjust.method = "BH"
+)
+
+
+### PLOT STAGE SPECIFIC DIFFERENCES ###
+
+# create plot
+cycle_plot <- ggplot(
+ cycle_durations,
+ aes(x = Treatment, y = Days, fill = Treatment)
+) +
+ geom_boxplot(
+ outlier.shape = 16,
+ outlier.size = 2,
+ alpha = 0.5,
+ lwd = 0.5,
+ aes(color = Treatment)
+ ) +
+ scale_fill_manual(
+ values = custom_colors,
+ labels = treatment_labels,
+ breaks = treatment_order
+ ) +
+ scale_color_manual(
+ values = custom_colors,
+ labels = treatment_labels,
+ breaks = treatment_order
+ ) +
+ labs(y = "Cycle Duration (Days)", x = "Treatment") +
+ theme_classic() +
+ theme_pubr() +
+ theme(
+ legend.position = "right",
+ axis.text.x = element_text(size = 12),
+ axis.text.y = element_text(size = 12),
+ axis.title = element_text(size = 14),
+ legend.title = element_text(size = 14),
+ legend.text = element_text(size = 12),
+ plot.title = element_text(
+ face = "bold",
+ size = 20,
+ hjust = -0.075,
+ vjust = 2
+ ),
+ panel.grid.major.y = element_line(color = "gray90"),
+ panel.grid.minor.y = element_line(color = "gray95")
+ )
+
+# show plot
+cycle_plot
+
+
+### COMPARISONS BETWEEN TREATMENTS FOR EACH STAGE ###
+
+# subset stage durations for specific stages
+stage_durations_A <- subset(stage_durations, grepl("A$", Stage))
+stage_durations_B <- subset(stage_durations, grepl("B$", Stage))
+stage_durations_C <- subset(stage_durations, grepl("C$", Stage))
+stage_durations_D <- subset(stage_durations, grepl("D$", Stage))
+
+# add stage letters for stat test
+stage_durations_A$stage_letter <- "A"
+stage_durations_B$stage_letter <- "B"
+stage_durations_C$stage_letter <- "C"
+stage_durations_D$stage_letter <- "D"
+
+# do wilcoxon test with Benjamini-Hochberg p adjustment for each stage
+stat_test_A <- stage_durations_A %>%
+ wilcox_test(DaysInStage ~ Treatment) %>%
+ adjust_pvalue(method = "BH") %>%
+ add_significance()
+stat_test_A$stage <- "A"
+stat_test_B <- stage_durations_B %>%
+ wilcox_test(DaysInStage ~ Treatment) %>%
+ adjust_pvalue(method = "BH") %>%
+ add_significance()
+stat_test_B$stage <- "B"
+stat_test_C <- stage_durations_C %>%
+ wilcox_test(DaysInStage ~ Treatment) %>%
+ adjust_pvalue(method = "BH") %>%
+ add_significance()
+stat_test_C$stage <- "C"
+stat_test_D <- stage_durations_D %>%
+ wilcox_test(DaysInStage ~ Treatment) %>%
+ adjust_pvalue(method = "BH") %>%
+ add_significance()
+stat_test_D$stage <- "D"
+
+# combine results
+wilcoxon_results_durations <- rbind(
+ stat_test_A,
+ stat_test_B,
+ stat_test_C,
+ stat_test_D
+)
+print(wilcoxon_results_durations)
+
+
+### PLOT STAGE SPECIFIC SIGNIFICANT DIFFERENCES AND SAVE RESULTS ###
+
+# save_results
+stage_duration_results <- subset(wilcoxon_results_durations, select = -c(.y.))
+write.csv(
+ stage_duration_results,
+ "runs/2_timeseries_data_figure2/stage_duration_wilcoxon.csv",
+ row.names = FALSE
+)
+
+# set treatment order
+stage_durations$Treatment <- factor(
+ stage_durations$Treatment,
+ levels = treatment_order
+)
+
+# create plot
+stage_plot <- ggplot(
+ stage_durations,
+ aes(x = StageLetter, y = DaysInStage, fill = Treatment)
+) +
+ geom_boxplot(
+ outlier.shape = 16,
+ outlier.size = 2,
+ alpha = 0.5,
+ lwd = 0.5,
+ aes(color = Treatment)
+ ) +
+ scale_fill_manual(
+ values = custom_colors,
+ labels = treatment_labels,
+ breaks = treatment_order
+ ) +
+ scale_color_manual(
+ values = custom_colors,
+ labels = treatment_labels,
+ breaks = treatment_order
+ ) +
+ theme_classic() +
+ geom_signif(
+ y_position = c(59, 64, 69, 24, 30),
+ xmin = c(0.72, 0.912, 0.912, 2.907, 3.1),
+ xmax = c(1.095, 1.095, 1.27, 3.1, 3.278),
+ annotation = c("0.012", "0.001", "0.044", "0.024", "0.012"),
+ textsize = 4.4
+ ) +
+ labs(
+ title = "f",
+ x = "Stage",
+ y = "N Days in Stage"
+ ) +
+ theme_pubr() +
+ theme(
+ legend.position = "right",
+ axis.text.x = element_text(size = 12),
+ axis.text.y = element_text(size = 12),
+ axis.title = element_text(size = 14),
+ legend.title = element_text(size = 14),
+ legend.text = element_text(size = 12),
+ plot.title = element_text(
+ face = "bold",
+ size = 20,
+ hjust = -0.075,
+ vjust = 2
+ ),
+ panel.grid.major.y = element_line(color = "gray90"),
+ panel.grid.minor.y = element_line(color = "gray95")
+ )
+
+# show plot
+stage_plot
+
+
+##### FINAL VISUALIZATION - FIGURE 2 #####
+
+# create combined plot
+combined_plot <- (plot_spacer() | basic_growth_plot) /
+ (model_plot | overall_treatment_plot) /
+ pairwise_plot_final /
+ stage_plot +
+ plot_layout()
+
+# show plot
+combined_plot
+
+# save combined plot
+ggsave(
+ "runs/2_timeseries_data_figure2/Figure2.pdf",
+ plot = combined_plot,
+ width = 12.5,
+ height = 20
+)
+dev.off()
diff --git a/workflows/3_additional_analyses_SI/SI.R b/workflows/3_additional_analyses_SI/SI.R
new file mode 100644
index 0000000000000000000000000000000000000000..12cba0703b46a2a24084a59d27f7a1768dd1343f
--- /dev/null
+++ b/workflows/3_additional_analyses_SI/SI.R
@@ -0,0 +1,341 @@
+# ------------------------------------------------------------------
+# Script Name: SI.R
+# Purpose: Reproduces SI figures from "An Ectomycorrhizal Fungus Alters Development Stages Within Pedunculate Oak’s Endogenous Growth Rhythm"
+# Author: Felix Zimmermann
+# Date: 2025-05-07
+# Contact: felix.zimmermann@wsl.ch
+# License: CC BY
+# ------------------------------------------------------------------
+
+
+##### BASICS #####
+
+# load packages
+library(tidyverse)
+library(viridis)
+library(psych)
+library(corrplot)
+library(ggpubr)
+library(patchwork)
+library(forcats)
+
+# check and save session info
+sessionInfo() %>%
+ capture.output(file = "runs/3_additional_analyses_SI/SI_session_info.txt")
+
+
+##### DATA LOADING, CLEANING & SCALING #####
+
+# laod data (dead plants are excluded in time_seriess.xlsx file)
+pheno_data <- read_csv("assays/3_destructive_sampling/dataset/pheno_data.csv")
+randomization_data <- read_csv("assays/1_experimental_setup/dataset/randomization.csv")
+
+# exclude contaminated and dead plants
+pheno_data_final <- subset(pheno_data, Exclude == "no")
+
+# create numeric dataframe excluding rows columns only "0" entries and character columns
+pheno_data_num <- pheno_data_final %>%
+ dplyr::select(-c("N_Other", "N_No", "Max_Stages_Reached")) %>%
+ dplyr::select(where(is.numeric))
+
+# load estimated marginal means data for resource allocation
+resource_allocation_data <- read_csv(
+ "runs/1_phenotypic_data_figure1/emm_resource_allocation.csv"
+)
+
+
+##### DEFINE BASIC PLOTTING PARAMETERS #####
+
+# define treatment labels
+treatment_labels <- c(
+ "Ceno" = expression(italic("Cenococcum geophilum")),
+ "Pilo" = expression(italic("Piloderma croceum")),
+ "Co_Inoc" = "Co-Inoculation",
+ "Control" = "Control"
+)
+
+# define legend order
+treatment_order <- c("Control", "Pilo", "Ceno", "Co_Inoc")
+
+# define custom colors
+custom_colors <- c(
+ "Control" = "#4B8C6F",
+ "Ceno" = "#8C5E8C",
+ "Pilo" = "#D6C9A0",
+ "Co_Inoc" = "#5E96A6"
+)
+
+
+##### PLANT RANDOMIZATION PLOT - FIGURE S1A #####
+
+### RANDOMIZATION BY STAGE ###
+
+# define custom colors
+custom_colors_stage <- c(
+ "A" = "#66CCEE",
+ "B" = "#CCBB44",
+ "C" = "#AA3377"
+)
+
+# set plot order
+randomization_data$Treatment <- factor(
+ randomization_data$Treatment,
+ levels = treatment_order
+)
+
+# calculate stage counts
+stage_counts <- randomization_data %>%
+ count(Treatment, Stage) %>%
+ pivot_wider(
+ names_from = Stage,
+ values_from = n,
+ values_fill = 0
+ )
+
+# convert to long format
+stage_counts_long <- stage_counts %>%
+ pivot_longer(
+ cols = -Treatment,
+ names_to = "Stage",
+ values_to = "N"
+ )
+
+# plot stage counts
+stage_plot <- ggplot(stage_counts_long) +
+ geom_bar(aes(x = Treatment, y = N, fill = Stage), stat = "identity") +
+ theme_pubr() +
+ scale_fill_manual(values = custom_colors_stage) +
+ scale_y_continuous(limits = c(0, 24), breaks = seq(0, 24, 1)) +
+ scale_x_discrete(labels = treatment_labels) +
+ labs(
+ title = "a",
+ x = "Treatment",
+ y = "N Plants"
+ ) +
+ theme(
+ legend.position = "right",
+ axis.text.x = element_text(size = 12),
+ axis.text.y = element_text(size = 12),
+ axis.title = element_text(size = 14),
+ plot.title = element_text(face = "bold", size = 20),
+ )
+
+# show plot
+stage_plot
+
+
+### RANDOMIZATION BY SIZE - FIGURE 21B ###
+
+# plot initial stem length
+stem_length_plot <- ggplot(
+ randomization_data %>%
+ mutate(Treatment = factor(Treatment, levels = treatment_order)),
+ aes(x = Treatment, y = Length_Init, fill = Treatment, color = Treatment)
+) +
+ geom_boxplot(outlier.alpha = 0.5, alpha = 0.5) +
+ theme_pubr() +
+ scale_fill_manual(values = custom_colors) +
+ scale_color_manual(values = custom_colors) +
+ scale_x_discrete(labels = treatment_labels) + # Add this line for custom x-axis labels
+ labs(
+ title = "b",
+ x = "Treatment",
+ y = "Stem Length (cm)"
+ ) +
+ theme(
+ legend.position = "none",
+ axis.text.x = element_text(size = 12),
+ axis.text.y = element_text(size = 12),
+ axis.title = element_text(size = 14),
+ plot.title = element_text(face = "bold", size = 20),
+ )
+
+# show plot
+stem_length_plot
+
+
+### RANDOMIZATION BY ROOTING DATE - FIGURE S1C ###
+
+# plot initial stem length
+date_plot <- ggplot(
+ randomization_data %>%
+ mutate(Treatment = factor(Treatment, levels = treatment_order)),
+ aes(
+ x = Treatment,
+ y = Rooting_Date_Divergence,
+ fill = Treatment,
+ color = Treatment
+ )
+) +
+ geom_boxplot(outlier.alpha = 0.5, alpha = 0.5) +
+ theme_pubr() +
+ scale_fill_manual(values = custom_colors) +
+ scale_color_manual(values = custom_colors) +
+ scale_x_discrete(labels = treatment_labels) + # Add this line for custom x-axis labels
+ labs(
+ title = "c",
+ x = "Treatment",
+ y = "N Days Divergence from Mean Rooting Date"
+ ) +
+ theme(
+ legend.position = "none",
+ axis.text.x = element_text(size = 12),
+ axis.text.y = element_text(size = 12),
+ axis.title = element_text(size = 14),
+ plot.title = element_text(face = "bold", size = 20),
+ )
+
+# show plot
+date_plot
+
+
+#### FINAL VISUALIZATION - FIGURE S1 ####
+
+# create combined plot
+combined_plot_randomization <- stage_plot /
+ stem_length_plot /
+ date_plot +
+ plot_layout(widths = c(5), heights = (10)) +
+ plot_annotation(
+ title = "",
+ theme = theme(
+ plot.title = element_text(size = 16, face = "bold", hjust = 0.5)
+ )
+ )
+
+# show plot
+combined_plot_randomization
+
+# save combined plot (has to be combined with experimental setup plot)
+ggsave(
+ "runs/3_additional_analyses_SI/Randomization_Plot.pdf",
+ plot = combined_plot_randomization,
+ width = 12.5,
+ height = 15
+)
+dev.off()
+
+
+##### CORRELATION MATRIX HEATMAP - FIGURE S2 #####
+
+# calculate correlation matrix and p-values
+corr_results <- corr.test(na.omit(pheno_data_num), method = "spearman")
+corr_matrix <- as.matrix(corr_results$r)
+p_matrix <- as.matrix(corr_results$p)
+
+# create pdf to save plot
+pdf("runs/3_additional_analyses_SI/Correlation_Plot.pdf", width = 10, height = 10)
+
+# plot all correlations in upper triangle
+corrplot(
+ corr_matrix,
+ type = "full",
+ method = "circle",
+ tl.col = "black",
+ tl.srt = 90,
+ tl.cex = 1,
+ cl.cex = 1,
+ diag = FALSE,
+ title = "",
+ mar = c(0, 0, 0, 0)
+)
+
+# plot only significant correlations in lower triangle
+corrplot(
+ corr_matrix,
+ add = TRUE,
+ type = "lower",
+ method = "circle",
+ diag = FALSE,
+ tl.pos = "n",
+ cl.pos = "n",
+ p.mat = p_matrix,
+ sig.level = 0.05,
+ insig = "blank"
+)
+
+# save pdf
+dev.off()
+
+# convert correlation matrices to dataframe
+corr_df <- as.data.frame(corr_matrix)
+corr_df <- corr_df %>%
+ rownames_to_column(var = "var")
+p_df <- as.data.frame(p_matrix)
+p_df <- p_df %>%
+ rownames_to_column(var = "var")
+
+# save results
+write_csv(corr_df, "runs/3_additional_analyses_SI/Spearman_R.csv")
+write_csv(p_df, "runs/3_additional_analyses_SI/Spearman_p.csv")
+
+
+##### RESOURCE ALLOCATION PROPORTIONS - FIGURE S3 #####
+
+# define colors
+custom_colors_allocation <- c(
+ "Roots" = "#BBBBBB",
+ "Shoot" = "#228833"
+)
+
+# calculate proportions
+resource_allocation_data <- resource_allocation_data %>%
+ group_by(Treatment) %>%
+ mutate(proportion = response / sum(response)) %>%
+ ungroup()
+
+# convert proportions to percent
+resource_allocation_data <- resource_allocation_data %>%
+ mutate(percentage = proportion * 100)
+
+
+# create reversed factor for plotting shoot over root
+resource_allocation_data <- resource_allocation_data %>%
+ mutate(Part = fct_rev(Part))
+
+# create stacked barplot
+resource_proportions_plot <- ggplot(
+ resource_allocation_data,
+ aes(
+ x = factor(Treatment, levels = treatment_order),
+ y = percentage,
+ fill = Part
+ )
+) +
+ geom_bar(stat = "identity", position = "stack", width = 0.7) +
+ geom_hline(
+ yintercept = 50,
+ linetype = "dashed",
+ color = "black",
+ size = 0.5
+ ) +
+ scale_y_continuous(
+ name = "Proportion (%)",
+ limits = c(0, 100),
+ breaks = seq(0, 100, by = 25)
+ ) +
+ scale_fill_manual(values = custom_colors_allocation) +
+ scale_x_discrete(labels = treatment_labels) +
+ labs(title = "", x = "Treatment") +
+ theme_pubr() +
+ theme(
+ legend.position = "right",
+ axis.text.x = element_text(size = 12),
+ axis.text.y = element_text(size = 12),
+ axis.title = element_text(size = 14),
+ panel.grid.major.y = element_line(color = "gray90"),
+ panel.grid.minor.y = element_line(color = "gray95")
+ )
+
+
+# show plot
+resource_proportions_plot
+
+# save plot
+ggsave(
+ "runs/3_additional_analyses_SI/Resource_Allocation_Proportions.pdf",
+ plot = resource_proportions_plot,
+ width = 12.5,
+ height = 5
+)
+dev.off()