DOI: 10.25829/bexis.31687-10
title: R-script to clean and calculate data and results used in 'Strengths of Fertilizer and Litter effects on seedling emergence and growth of grassland species differ depending on functional groups and seed size'

description: This is the R script to reproduce the analysis of the manuscript Martin and Roscher 2024 'Strengths of fertilizer and litter effects on seedling recruitment and growth of grassland species differ depending on functional groups and seed size'. The data comes as an .R-file written in RStudio.<br>It requires the following datasets 31627, 31678, 31679, 31682 which are also available in the Biodiversity Exploratories Information System (https://www.bexis.uni-jena.de).<br>Further, the R Script cleans the data and calculates mixed effects models and the random structure of the model. It calculates the log-response ratio and the linear regression of the slopes from the mixed models against seed mass.
description: Study design: We used 75 grassland species for the experiment, which represented three functional groups (16 grass species, 51 non-legume forb species, and 8 legume species). The same species were used in the seed addition treatments of the land-use reduction experiment (REX) of the Biodiversity Exploratories. Seeds were obtained commercially from seed suppliers (Rieger-Hofmann GmbH, Blaufelden-Raboldshausen, Germany, and Wildsamen-Insel, Temmen-Ringenwalde, Germany) and represented regional provenances of the three regions of the Biodiversity Exploratories (North-eastern Germany, Central Germany, and South-western Germany). Seeds were stored in a freezer (at -20°C) until they were prepared for the experiment.<br>The experiment had a full factorial design with fertilizer addition (two-factor levels: no fertilization, and fertilization(+F)) and litter addition (two-factor levels: no litter addition, and litter addition(+L)). This resulted in four treatments (C, +F, +L, +F+L) for each of the 75 species which were replicated four times in four experimental blocks resulting in a total of 1200 microcosms. Pots (11 x 11 cm, 12 cm depth) were filled with commercial potting substrate (Fruhstorfer Erde®, Type P70, Industrie-Erdenwerke Archut GmbH, Lauterbach, Germany) which we mixed with 25 vol% mineral sand. The mixture contained 38.41 mg kg⁻¹ plant-available phosphorus, and 255.99 mg kg⁻¹ plant-available potassium. Soil carbon concentration was 62.3 mg g-1, and total soil nitrogen concentration was 1.3 mg g-1. The pH (measured in a CaCl2 solution) was 5.9. <br>The experiment was conducted at the Experimental Field Station of the Helmholtz Centre for Environmental Research (UFZ) in Bad Lauchstädt (Saxony-Anhalt, Germany). It was sown between 1st and 8th November 2021 in the experimental field station in Bad Lauchstädt near Halle (Saale). Each pot was sown with 60 seeds of a single species. When provenances of different regions were available, seed origins were mixed in equal proportions. <br>For the fertilization treatment, the fertilizer was added in two portions, the first directly after sowing, and the second one at the start of the growing season (1st March 2022). Either 20 ml of water or fertilizer solution (3.024 mg N per ml) was added to the pot with a dispenser, resulting in a total nitrogen addition of 100 kg N ha⁻¹ per year. Nitrogen was added as calammonium nitrate (76% NH4NO3, 24% CaCO3) (Triferto Fertilizers, Kalkammonsalpeter N27). <br>For the litter treatment, 3 gdw litter, which resembles 248 gdw m⁻², was added to the respective pots after the first fertilization and it was lifted for the second portion of nitrogen. The litter was manually chopped to pieces of approx. 3 cm in length. The C and N concentration of the litter was 453.4 mg gdw-1 and 10.6 mg gdw-1, respectively. Pots were dug into the ground in beds filled with bark mulch to a depth of approx. 7 cm organized in four experimental blocks, each containing one replicate of each species x treatment combination. Beds were covered with pond nets (15 mm mesh size) to prevent loss of litter through wind or damage by birds. Pots received water through precipitation and were not additionally watered during winter. On 28th February 2022, pots were brought into an open greenhouse with ambient temperatures and a glass roof that closes during rain. The pots were placed on greenhouse tables arranged in four experimental blocks as before. In the greenhouse, all pots were watered two times per week with tap water. |Sampling design: Seedlings were counted every month starting from 1st March until the end of May (four counting dates). A seedling was counted as emerged when the cotyledons were visible. We did not account for seedling death/survival. Numbers represent the cumulative seedling numbers per pot.<br>When seedlings reached the stage of two fully expanded true leaves (or two leaf pairs in the case of species with opposite leaves), two experimental blocks were used to harvest four to five seedlings of each species x treatment combination. Seedlings were cut at ground level and transported in moistened tissue paper into the laboratory. the remaining two experimental blocks were used for a second harvest (= final biomass) cutting again five seedlings at ground level of each species x treatment combinations one week after all treatment combinations of a certain species had been harvested for the two-leaf biomass. Because the temporal development of the seedlings varied among treatments, our first biomass harvest represents a certain developmental stage of the seedlings, while the final biomass accounts for treatment-dependent differences in individual seedling biomass at a certain point in time. |Sample preparation: In the lab, the stretched height of each seedling was measured with a ruler. After that, the leaves were scanned with a flatbed scanner (Epson Perfection V550 Photo, EPSON) at 300 dpi. The scanned leaves and the remaining parts (stem, cotyledons) of the seedlings were separately dried at 70°C for 48 h and then weighed to obtain seedling biomass in the two-leaf stage (= two-leaf-biomass).<br>Final biomass samples were dried at 70°C for 48 h and then weighed. |Sample analysis: The final biomass was then divided by the number of individuals harvested in the respective probe to receive the final biomass per individual. |Equipment: - Epson Perfection V550 Photo, EPSON |Data preparation: The cleaning of the data in the R script includes:<br>- Deletion of species which did not germinate<br>- Deciding for the dispersule mass of two grass species (instead of seed mass) as all other seed masses were measured as dispersule mass<br>- Inserting columns for fertilization and litter encoded with 1 and 0<br>- Joining data sets for correct calculations<br>- Ensuring the correct format of variables for further calculation |Data analysis: - Linear mixed-effects models where factors were added one after another (additive) for each measured parameter/trait.lt;br&gt;- Random effects were tested with the full model, comparing only the random structure of the model.<br>- Generalized linear mixed-effects models (GLMM) for count data (number of emerged seedlings) with Poisson distribution, for the percentage or early germination, a binomial distribution was used with a cbind function.<br>- The slopes of each species' response to litter and fertilizer of the full mixed effects model were taken and linear regressions were built with the seed mass of each species to estimate the influence of seed size on the effect (direction) of the treatment.<br>- Log response ratios (ln RR) were calculated to estimate the effect size of a trait response to litter, fertilizer or both in comparison to the control being able to compare different units.<br>For further details, see methods - data analysis in the corresponding article 'Strengths of Fertilizer and Litter effects on seedling emergence and growth of grassland species differ depending on functional groups and seed size' |Literature references: Schneider, C. A., Rasband, W. S., Eliceiri, K. W. (2012). NIH image to ImageJ: 25 years of image analysis. Nature Methods, 9(7), 671–675. https://doi.org/10.1038/nmeth.2089 [Titel anhand dieser DOI in Citavi-Projekt übernehmen] |Data references: R Core Team (2022): R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ <br>R packages:<br>tidyverse, ggpubr, dplyr, lme4, sciplot, car, carData, rstatix, ggsignif,multcomp, emmeans, ggeffects, lattice, MuMIn, lmerTest, grid, corrr