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Figure 5 in Under pressure: maternal effects promote drought tolerance in progeny seed of Palmer amaranth (Amoronthus polmeri)

Figure 5. Shifts in distribution of base water potential of progeny seeds from two Amoronthus polmeri populations (California and Kansas) grown under contrasting maternal water conditions (continuous̜ ̜ ̹ water-deficit ̹ vs. well-watered). Seed germination was tested at two temperatures (20 and 30 C) under five water potentials. A hydrotime model, ̜ ̹ θ Equation 3, g w,t¼ Φ w ― H w,σ, was fit to estimate the median base water potentials, w (vertical dashed lines), and their respective standard deviations, σ, g tg bð50Þ wb bð50Þ wb to produce these probability density curves of normal distribution (see Table 1 for parameter estimates). Note that the area under the curve for base water potential values>0 indicates the proportion of seeds that have not germinated (i.e., dormancy level).

Figure 1 in Under pressure: maternal effects promote drought tolerance in progeny seed of Palmer amaranth (Amoronthus polmeri)

Figure 1. Plant height for Amoronthus polmeri populations (California and Kansas) grown under continuous water-deficit or well-watered irrigation conditions. Vertical lines on bars indicate SE.

Figure 4 in Under pressure: maternal effects promote drought tolerance in progeny seed of Palmer amaranth (Amoronthus polmeri)

Figure 4. The effect of maternal water conditions (continuous water-deficit vs. well-watered) on cumulative germination of progeny seeds in two Amoronthus polmeri populations (California and Kansas) tested under various water potentials at 30 C. Lines are fitted values obtained from the hydrotime model, Equation 3, ̜ ̹ ̜ ̜ ̹ ̹ g w,t¼ Φ w ― θH,w,σ, with parameter estimates shown in Table 1. g tg bð50Þ wb

Figure 3 in Under pressure: maternal effects promote drought tolerance in progeny seed of Palmer amaranth (Amoronthus polmeri)

Figure 3. The effect of maternal water conditions (continuous water-deficit vs. well-watered) on cumulative germination of progeny seeds in two Amoronthus polmeri populations (̜ California̜ ̹ and Kansas̹) tested under various water potentials at 20 C. Lines are fitted values obtained from the hydrotime model, Equation ̜ ̹ θ 3, g w,t¼ Φ w ― H, w, σb, with parameter estimates shown in Table 1. g tg bð50Þ w

Figure 2 in Under pressure: maternal effects promote drought tolerance in progeny seed of Palmer amaranth (Amoronthus polmeri)

Figure 2. Differences in 1,000-seed weight, seed surface area, and total germination (dormancy) of progeny seeds from two Amoronthus polmeri populations (California and Kansas) grown under continuous water-deficit (WD) or well-watered (WW) irrigation conditions. Vertical bars on data points indicate SE.

opencc-by-4.0Sep 2024View details →

Data for Stable isotope composition of long and short term carbon pools can screen drought tolerance in cassava

This repository contains data and scripts to reproduce results that are presented in the article: Van Laere, J., Martinez Maya, M.A., Selvaraj M.G., Becerra Lopez-Lavalle, L.A., Guzman, D., Casas, J.A., Merckx, R., Hood-Nowotny, R., Dercon, G. (2024) Stable isotope composition of long and short term carbon pools can screen drought tolerance in cassava.  Field Crops Research. https://doi.org/10.1016/j.fcr.2024.109586

dryad40/100

Testing the chilling: Before drought-tolerance hypothesis in Pooideae grasses

Temperate Pooideae are a large clade of economically important grasses distributed in some of the Earth's coldest and driest terrestrial environments. Previous studies have inferred that Pooideae diversified from their tropical ancestors in a cold montane habitat, suggesting that above-freezing cold (chilling) tolerance evolved early in the subfamily. By contrast, drought tolerance is hypothesized to have evolved multiple times independently in response to global aridification that occurred after the split of Pooideae tribes. To independently test predictions of the chilling before-drought hypothesis in Pooideae, we assessed the conservation of whole plant and gene expression traits in response to chilling versus drought. We demonstrated that both trait responses are more similar across tribes in cold as compared to drought, suggesting that chilling responses evolved before, and drought responses after, tribe diversification. Moreover, we found significantly more overlap between drought and chilling-responsive genes within a species than between drought-responsive genes across species, providing evidence that chilling tolerance genes acted as precursors for the novel acquisition of increased drought tolerance multiple times independently, partially through the cooption of chilling responsive genes.

opencc-zeroDec 2022View details →

dryad40/100

Testing the chilling: Before drought-tolerance hypothesis in Pooideae grasses

Open the record for dataset details and reuse information.

publicDec 2022View details →

dryad40/100

Physiological tolerance to frost and drought explains range limits of 35 European tree species

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publicNov 2024View details →

edi40/100

Temperate woody angiosperm (Acer, Ilex, Magnolia) drought tolerance (TLP, xylem embolism) data, Arnold Arboretum and Scott Arboretum, MA/PA, USA, 2019-22

Understanding the capacity of temperate trees to acclimate to limited soil water has become essential in the face of increasing drought risk due to climate change. We documented seasonal – or phenological – patterns in acclimation to water deficit stress in stems and leaves of tree species spanning the angiosperm phylogeny. Over three years of field observations carried out in two U.S. arboreta, we measured stem vulnerability to embolism (36 individuals of 7 Species) and turgor loss point (119 individuals of 27 species) over the growing season. We also conducted a growth chamber experiment on 20 individuals of one species to assess the mechanistic relationship between soil water restriction and acclimation. In three quarters of species measured, plants became less vulnerable to embolism and/or loss of turgor over the growing season. We were able to stimulate this acclimatory effect by withholding water in the growth chamber experiment. Temperate angiosperms are capable of acclimation to soil water deficit stress, showing maximum vulnerability to soil water deficits following budbreak and becoming more resilient to damage over the course of the growing season or in response to simulated drought. The species-specific tempo and extent of this acclimatory potential constitutes preadaptive climate change resilience.

openCC (other)Feb 2024View details →

zenodo36/100

Applying RGB- and Thermal-Based Vegetation Indices from UAVs for High-Throughput Field Phenotyping of Drought Tolerance in Forage Grasses

Basic data from publication <a href="https://doi.org/10.3390/rs13010147">https://doi.org/10.3390/rs13010147</a> All_TDRdata.csv contains the data from 48 TDR sensors (30 cm) installed in the three rainout shelters. <ul> <li>Sensors 1 - 18 were installed vertically to obtain soil moisture content averaged over the 10 - 40 cm profile, on 6 locations per shelter</li> <li>Sensors 19-21 were installed diagonally to obtain soil moisture content averaged over the 20 - 40 cm profile on one location per shelter</li> <li>Sensors 22-24 were installed diagonally to obtain soil moisture content averaged over the 40 - 60 cm profile on one location per shelter</li> <li>Sensors 25-27 were installed horizontally to obtain soil moisture content at 10 cm depth on one location per shelter</li> <li>Sensors 28-30 were installed horizontally to obtain soil moisture content at 20 cm depth on one location per shelter</li> <li>Sensors 31-33 were installed horizontally to obtain soil moisture content at 30 cm depth on one location per shelter</li> <li>Sensors 34-36 were installed horizontally to obtain soil moisture content at 40 cm depth on one location per shelter</li> <li>Sensors 37-39 were installed horizontally to obtain soil moisture content at 50 cm depth on one location per shelter</li> <li>Sensors 40-42 were installed horizontally to obtain soil moisture content at 60 cm depth on one location per shelter</li> <li>Sensors 43-45 were installed horizontally to obtain soil moisture content at 70 cm depth on one location per shelter</li> <li>Sensors 46-48 were installed horizontally to obtain soil moisture content at 80 cm depth on one location per shelter</li> </ul> Climate.txt contains the daily averaged microclimatic data PhenotypingData.csv contains the phenotypic data from the UAV flights and the breeder scores

opencc-by-4.0Jan 2021View details →

opencc-zeroNov 2019View details →

Data from: Leaf drought tolerance cannot be inferred from classic leaf traits in a tropical rainforest

<ol> <li>Plants are enormously diverse in their traits and ecological adaptation, even within given ecosystems, such as tropical rainforests. Accounting for this diversity in vegetation models poses serious challenges. Global plant functional trait databases have highlighted general trait correlations across species that have considerably advanced this research program. However, it remains unclear whether trait correlations found globally hold within communities, and whether they extend to drought tolerance traits.</li> <li>For 134 individual plants spanning a range of sizes and life forms (tree, liana, understory species) within an Amazonian forest, we measured leaf drought tolerance (leaf water potential at turgor loss point, πtlp), together with 17 leaf traits related to various functions, including leaf economics traits and nutrient composition (leaf mass per area, LMA; and concentrations of C, N, P, K, Ca, and Mg per leaf mass and area), leaf area, water use efficiency (carbon isotope ratio), and time-integrated stomatal conductance and carbon assimilation rate per leaf mass and area. We tested trait coordination and the ability to estimate πtlp from the other traits through model selection. Performance and transferability of the best predictive model were assessed through cross-validation.</li> <li>πtlp was positively correlated with leaf area, and with N, P and K concentrations per leaf mass, but not with LMA or any other studied trait. Five axes were needed to account for >80% of trait variation, but only three of them explained more variance than expected at random. The best model explained only 30% of the variation in πtlp, and out-sample predictive performance was variable across life forms or canopy strata, suggesting a limited transferability of the model.</li> <li> Synthesis. We found a weak correlation among leaf drought tolerance and other leaf traits within a forest community. We conclude that higher trait dimensionality than assumed under the leaf economics spectrum may operate among leaves within plant communities, with important implications for species coexistence and responses to changing environmental conditions, and also for the representation of community diversity in vegetation models.</li> </ol>

opencc-zeroNov 2023View details →

Drought tolerant grassland species are generally more resistant to competition

<ol> <li>Plant populations are limited by resource availability and exhibit physiological trade-offs in resource acquisition strategies. These trade-offs may constrain the ability of populations to exhibit fast growth rates under water limitation and high cover of neighbors. However, traits that confer drought tolerance may also confer resistance to competition. It remains unclear how fitness responses to these abiotic conditions and biotic interactions combine to structure grassland communities and how this relationship may change along a gradient of water availability.</li> <li>To address these knowledge gaps, we estimated the low-density growth rates of populations in drought conditions with low neighbor cover and in ambient conditions with average neighbor cover for 82 species in six grassland communities across the Central Plains and Southwestern United States. We assessed the relationship between population tolerance to drought and resistance to competition and determined if this relationship was consistent across a precipitation gradient. We also tested whether population growth rates could be predicted using plant functional traits.</li> <li>Across six sites, we observed a positive correlation between low-density population growth rates in drought and in the presence of interspecific neighbors. This positive relationship was particularly strong in grasslands of the northern Great Plains but weak in the most xeric grasslands. High leaf dry matter content and low (more negative) leaf turgor loss point were associated with high population growth rates in drought and with neighbors in most grassland communities.</li> <li> Synthesis: A better understanding of how both biotic and abiotic factors impact population fitness provides valuable insights into how grasslands will respond to extreme drought. Our results advance plant strategy theory by suggesting that drought tolerance increases population resistance to interspecific competition in grassland communities. However, this relationship is not evident in the driest grasslands where aboveground competition is likely less important. Leaf dry matter content and turgor loss point may help predict which populations will establish and persist based on local water availability and neighbor cover, and these predictions can be used to guide the conservation and restoration of biodiversity in grasslands.</li> </ol>

opencc-zeroFeb 2024View details →

Recent tree diversity increase in NE Iberian forests following intense management release: a task for animal-dispersed and drought tolerant species

<ol> <li>Under increasing human-related threats to forests, many studies suggest that increasing tree species diversity may boost forest resilience by enhancing the range of species' responses to disturbances. However, it remains unclear whether passive or active forest management strategies should be applied to increase tree diversity. This issue would benefit from investigating which management and environmental factors, together with species' functional traits influence temporal changes in tree species diversity.</li> <li>We explored the influence of the bioclimatic region, land-use history, forest cover, protection, management, forest structure and changes in temperature and precipitation, to explain tree species diversity changes in NE Iberian forests, by comparing 3141 plots from the Spanish National Forest Inventory sampled between 1989 and 2016. Moreover, we assessed which species' functional traits (dispersal habit, drought and shade tolerance) were most relevant for diversity changes.</li> <li>After 27 years, tree species richness and diversity moderately increased in the tree and regeneration layers. This trend occurred mostly in long-established, non-recently managed forests and in those with a lower initial basal area. Increasing temperature had negative effects for diversity increase in the tree layer but positive for the regeneration compartment, while decreasing precipitation showed the opposite effects.</li> <li>Tree species with higher drought tolerance, and especially those animal-dispersed ones arriving from the regional pool, mostly contributed to the local diversity increase. This pattern occurred in all forest types, although the taxonomic array of species varied.</li> <li> Synthesis and applications. The main drivers influencing the passive increase in tree species diversity suggest a primary role of diminishing forest exploitation in this recovery process, fine-tuned by climatic changes. This ecological scenario has particularly favored animal-dispersed tree species with higher drought tolerance, which mostly led the diversity increase. A higher presence of such highly mobile and drought-tolerant species can be crucial to increase functional diversity and, ultimately, increase forest resilience under future scenarios of greater aridity. In light of these results, management strategies should continue fostering the restoration of diversity in once intensively exploited forests while ensuring the maintenance of the already gained tree species diversity.</li> </ol>

opencc-zeroJun 2022View details →

Functional traits and their plasticity shift from tolerant to avoidant under extreme drought

Under climate change, extreme droughts will limit water availability for plants. However, the species-specific responses make it difficult to draw general conclusions. We hypothesized that changes in species' abundance in response to extreme drought can be best explained by a set of water economic traits under ambient conditions in combination with the ability to adjust these traits towards higher drought resistance. We conducted a four-year field experiment in temperate grasslands using rainout shelters with 30% and 50% rainfall reduction. We quantified the response as the change in species abundance between ambient conditions and the rainfall reduction. Abundance response to extreme drought was best explained by a combination of traits in ambient conditions and their functional adjustment, most likely reflecting plasticity. Smaller leaved species decreased less in abundance under drought. With increasing drought intensity, we observed a shift from drought tolerance, i.e. an increase leaf dry matter content, to avoidance, i.e. a less negative turgor loss point (TLP) in ambient conditions and a constancy in TLP under drought. We stress the importance of using a multidimensional approach of variation in multiple traits and the importance of considering a range of drought intensities to improve predictions of species' response to climate change.

opencc-zeroJun 2024View details →

Exposure to teflubenzuron reduces drought tolerance of collembolans

This is the raw data from an experiment investigating the effects of sequential exposure to a pesticide (teflubenzuron) and three abiotic stressors (heat, cold, and drought) on soil arthropods. We exposed adult collembolans (Folsomia candida) to teflubenzuron through food for two weeks. Then survivors were immediately divided into three groups for acute heat, cold, and drought exposure. After acute exposure to these natural stressors, the collembolans were moved to optimal conditions for a one-week recovery period during which their survival, time to regain reproduction, and egg production were examined.

zenodo36/100

Untargeted mutagenesis of brassinosteroid receptor SbBRI1 confers drought tolerance by altering phenylpropanoid metabolism in Sorghum bicolor

Metabolomics data from the study: Untargeted mutagenesis of brassinosteroid receptor SbBRI1 confers drought tolerance by altering phenylpropanoid metabolism in Sorghum bicolor. Files are raw chromatograms. Metabolite profiling analysis Metabolite Extraction and Quantification. The whole metabolite extraction and quantification pipeline followed the guidelines as described in Giavalisco et al. (2011) and Salem et al. (2020). Briefly, 10-25 mg of fresh plant tissue was harvested and immediately frozen in N2, and ground using zirconia beads with the help of Tissue Lyser Mixer-Mill (Qiagen) 3 min at 25 Hz. After samples were extracted in 100% cold methanol, they were centrifuged for 10 min at 14000 rpm (Room temperature) and 3:1 chloroform was added. After vortexing thoroughly, 1:1 volume of water was added and the samples were subsequently vortexed and centrifuged. The semi-polar phase was used for analysis of semi-polar secondary and primary metabolites. For secondary semi-polar metabolites, the dried polar aliquots were resuspended in water:methanol (1:1 v/v). 3 Analysis of semi-polar metabolites was performed using a Thermo Q Exactive Focus coupled to a reverse-phase C18 column. The column was maintained at 40°C with a flow rate of 400 μl/min, and the eluent system consisted of water (eluent A) and acetonitrile (eluent B), both containing 0.1% formic acid. Mass spectra were acquired in full scan mode over a range of 100-1500 m/z using data-independent acquisition (DIA) with high-energy collisional dissociation (HCD) energy set at 30 eV in positive mode. 9 For primary metabolite metabolites, the dried polar was derivatized as described in Lisec et al. (2006). Derivatization was carried out at 37°C for 120 min using 40 μl of 20 mg/ml methoxyamine hydrochloride in pyridine, followed by a 30-min treatment at 37°C with 70 μl of trimethylsilyl-N-methyl trifluoroacetamide (MSTFA). The derivatized samples (1 μl) were injected in splitless mode into a gas chromatograph coupled to a time-of-flight mass spectrometer (Pegasus HT TOF-MS). Gas chromatography was performed on a 30-m DB-35 column using helium as the carrier gas. The initial temperature of the oven was 85°C, and it was ramped up at a rate of 15°C/min to a final temperature of 360°C. Mass spectra were recorded in the range of 70-600 m/z at a rate of 20 scans/s. Data Processing and Compound Annotation. LC-MS full scan data were processed using MS Refiner (Expressionist 14.0). Processing of chromatograms, peak detection, and integration were performed using RefinerMS (version 5.3; GeneData). Metabolite identification and annotation were performed using in-house reference compound library, tandem MS (MS/MS) 22 fragmentation, and metabolomics databases (Alseekh et al., 2021). For the annotation of metabolites measured by GC-MS the Golm Metabolome Database was used (Kopka et al., 2005). <div> <div> #heatmap of fold changes  </div> <div> ts <-read.csv("all_log2fc_heat_all.csv", sep=";", fileEncoding='latin1',check.names=F, header=TRUE)  </div> <div> ts1 <- ts  </div> <div> ts1$class <- NULL   </div> <div>   </div> <div> row.names(ts1) <- ts1$compound  </div> <div> ts1$compound <- NULL  </div> <div> m <- as.matrix(ts1)  </div> <div>   </div> <div> ann <- ts[1:2]  </div> <div> row.names(ann) <- ann$compound  </div> <div> ann$compound <- NULL  </div> <div>   </div> <div> install.packages("pheatmap")  </div> <div>   </div> <div> library("pheatmap")  </div> <div>   </div> <div> m1 <- m[rownames(ann), ]  </div> <div>   </div> <div> pheatmap(m1, annotation_row = ann, cluster_rows = F,cluster_cols = F, cellheight = 10,   </div> <div>          cellwidth = 10, gaps_row = cumsum(c(3,105,30,11,21,4,32,5,17)), gaps_col = cumsum(c(2,2,2)), scale = 'none')  </div> <div>   </div> <div> #boxplots  </div> <div> install.packages("ggplot2")  </div> <div> install.packages("readr")  </div> <div>   </div> <div> library(ggplot2)  </div> <div> library(readr)  </div> <div>   </div> <div> install.packages("tidyverse")  </div> <div> library(tidyverse)  </div> <div> grid <- read.csv("sec_exp1_logfc_20.csv", sep=";", header=TRUE)  </div> <div>   </div> <div> grid_g <- grid%>% gather("compound", "value", 6:25)  </div> <div>   </div> <div> d=ggplot(data = grid_g, aes(x=compound, y=value, fill=factor(group)))+  </div> <div>   geom_boxplot(outlier.shape = NA)+  </div> <div>   geom_point(position = position_jitterdodge(0.1))  </div> <div>   </div> <div> d+theme(text = element_text(size = 15))+  </div> <div>   scale_fill_manual(values = c("#ca5826","#e4ab92", "#008080", "#99cccc"))+  </div> <div>   labs(y="log2fc(metabolite content)",fill="gtype")+  </div> <div>   theme(axis.text.x = element_text(angle = 60, hjust = 1, size = 5)) +   </div> <div>   theme_classic() +  </div> <div>   coord_flip() +  </div> <div>   facet_grid(condition ~ .)  </div> <div>   </div> <div> ggsave("logfcsum_exp1_15_bri1.png", width = 10, height = 10)  </div> <div>   </div> <div> #boxplots all data  </div> <div> library(ggplot2)  </div> <div> library(readr)  </div> <div> library(viridis)  </div> <div> library(tidyverse)  </div> <div> library(dplyr)  </div> <div>   </div> <div> sPCA <-read.csv("GCMS_polar.csv", sep=",", fileEncoding='latin1', check.names = F, header=TRUE)  </div> <div>   </div> <div> row.names(sPCA) <- sPCA$Sample  </div> <div> sPCA_D <- sPCA[6:155]  </div> <div> sPCA_Dlog <- log(sPCA_D)  </div> <div> sPCA_Dlog10 <- log10(sPCA_D)  </div> <div>   </div> <div> sPCA_Dlog <- cbind(sPCA_Dlog, group = sPCA$Sample_group)  </div> <div> sPCA_Dlog <- sPCA_Dlog%>% select(group, everything())  </div> <div>   </div> <div> sPCA_Dlog <- cbind(sPCA_Dlog, sample = sPCA$Sample)  </div> <div> sPCA_Dlog <- sPCA_Dlog%>% select(sample, everything())  </div> <div>   </div> <div> write.csv(sPCA_Dlog10, "GCMS_polar_lg.csv")  </div> <div>   </div> <div> sPCA_Dlog_g <- sPCA_Dlog%>% gather("compound", "value", 3:152)  </div> <div>   </div> <div> d <- ggplot(sPCA_Dlog_g, aes(x=sample, y=value, fill=factor(group)))+  </div> <div>   geom_boxplot(outlier.shape = NA)+  </div> <div>   geom_point(position = position_jitterdodge(0.1))  </div> <div>   </div> <div> d + theme(text = element_text(size = 15))+  </div> <div>   scale_fill_manual(values = c("#CC6677","#882255","#44AA99", "#117733"))+  </div> <div>   labs(y="lg(normalized metabolite content)", x= "sample" , fill="group")+  </div> <div>   theme(axis.text.x = element_text(angle = 60, hjust = 1)) +   </div> <div>   theme_classic() +  </div> <div>   geom_hline(yintercept=0, linetype="dashed", color = "black")  </div> <div> #PCA  </div> <div> install.packages("plotly")  </div> <div> install.packages("ggfortify")  </div> <div> library(plotly)  </div> <div> library(ggfortify)  </div> <div>   </div> <div> sPCA <-read.csv("prim.csv", sep=";", header=TRUE)  </div> <div>   </div> <div> sPCA <- subset(sPCA, experiment!="2")  </div> <div>   </div> <div> sPCAtd <- all_d[7:240]  </div> <div> sPCAtdn <- sPCAtd[-c(24), ]  </div> <div> sPCAn <- sPCA[-c(24), ]  </div> <div>   </div> <div> #normal  </div> <div>   </div> <div> sec.pcat <- prcomp(sPCAtd, center = TRUE,scale. = TRUE)  </div> <div>   </div> <div> summary(sec.pcat)  </div> <div>   </div> <div> p <- autoplot(sec.pcat, data = all_d, colour = 'condition', shape = 'Genotype') +   </div> <div>   geom_text(aes(label = Replicate), nudge_y = 0.02) +  </div> <div>   theme_classic() +  </div> <div>   scale_color_manual(values=c("#56B4E9", "#E69F00"))  </div> <div>   </div> <div> ggplotly(p)  </div> <div> #experiment wise fold changes  </div> <div> write.csv(sec_fc_mean, "sec_foldchange_mean.csv")  </div> <div>   </div> <div> sec_fc_exp1 <- sect[1]  </div> <div>   </div> <div> sec_fc_exp1$bri1.drought <- foldchange(sect$X72.bri1.drought, sect$median.WT.c1)  </div> <div> sec_fc_exp1$bri1.drought1 <- foldchange(sect$X72.bri1.drought.1, sect$median.WT.c1)  </div> <div> sec_fc_exp1$bri1.drought2 <- foldchange(sect$X72.bri1.drought.2, sect$median.WT.c1)  </div> <div>   </div> <div> sec_fc_exp1$bri1.control <- foldchange(sect$X72.bri1.control, sect$median.WT.c1)  </div> <div> sec_fc_exp1$bri1.control1 <- foldchange(sect$X72.bri1.control.1, sect$median.WT.c1)  </div> <div> sec_fc_exp1$bri1.control2 <- foldchange(sect$X72.bri1.control.2, sect$median.WT.c1)  </div> <div>   </div> <div> sec_fc_exp1$WT.drought <- foldchange(sect$X72.WT..drought, sect$median.WT.c1)  </div> <div> sec_fc_exp1$WT.drought1 <- foldchange(sect$X72.WT..drought.1, sect$median.WT.c1)  </div> <div> sec_fc_exp1$WT.drought2 <- foldchange(sect$X72.WT..drought.2, sect$median.WT.c1)  </div> <div>   </div> <div> sec_fc_exp1$WT.control <- foldchange(sect$X72.WT..control, sect$median.WT.c1)  </div> <div> sec_fc_exp1$WT.control1 <- foldchange(sect$X72.WT..control.1, sect$median.WT.c1)  </div> <div> sec_fc_exp1$WT.control2 <- foldchange(sect$X72.WT..control.2, sect$median.WT.c1)  </div> <div>   </div> <div>   </div> <div> write.csv(sec_fc_exp1, "sec_fc_exp1.csv")  </div> <div>   </div> <div> #all compounds for RNAsq comarision  </div> <div>   </div> <div> all_R <-read.csv("all_RNAsq.csv", sep=";", fileEncoding='latin1',check.names=F, header=T)  </div> <div>   </div> <div> all_fc_exp2 <- all_R[1]  </div> <div>   </div> <div> all_fc_exp2$drought <- foldchange(all_R$bri1_drought, all_R$WT_drought)  </div> <div>   </div> <div> all_fc_exp2$control <- foldchange(all_R$bri1_control, all_R$WT_control)  </div> <div>   </div> <div> write.csv(all_fc_exp2, "all_foldchange_exp2.csv")  </div> <div>   </div> <div> #students t test  </div> <div>   </div> <div> secfc_2_t <-read.csv("sec_exp1_logfc.csv", sep=";", fileEncoding='latin1',check.names=F, header=T)  </div> <div>   </div> <div> secfc_2_t <- secfc_2_t[-2]  </div> <div> secfc_2_t <- secfc_2_t[-(14:25)]  </div> <div>   </div> <div> st2<- secfc_2_t %>% gather("compound", "value", 6:171)  </div> <div> stat.test1 <- st2 %>%  </div> <div>   group_by(compound) %>%  </div> <div>   t_test(value ~ group)  </div> <div> add_significance()  </div> <div> stat.test1  </div> </div>

opencc-by-4.0Jul 2024View details →

zenodo36/100

Data files and computer code scripts for reproducing the results of a manuscript on the measurement and ranking of cotton drought tolerance capacity

This upload contains the data files and computer code scripts for reproducing the main results, esp. figures,  of the manuscript entitled "Rapid measurement and statistical ranking of leaf drought tolerance capacity in cotton," by X. Dong, D. A. Mott, J. Garg, Q. Zhou, J. Sunoj V. S., and B. M. McKnight. The manuscriptt is currently under peer review.

opencc-by-4.0Aug 2024View details →