Data and R code used in: Plant geographic distribution influences chemical defenses in native and introduced Plantago lanceolata populations
Cite this dataset
Medina-van Berkum, Pamela et al. (2024). Data and R code used in: Plant geographic distribution influences chemical defenses in native and introduced Plantago lanceolata populations [Dataset]. Dryad. https://doi.org/10.5061/dryad.5dv41nsd1
Abstract
Plants growing outside their native range may be confronted by new regimes of herbivory, but how this affects plant chemical defense profiles has rarely been studied. Using Plantago lanceolata as a model species, we investigated whether introduced populations show significant differences from native populations in several growth and chemical defense traits. Plantago lanceolata (ribwort plantain) is an herbaceous plant species native to Europe and Western Asia that has been introduced to numerous countries worldwide. We sampled seeds from nine native and ten introduced populations that covered a broad geographic and environmental range and performed a common garden experiment in a greenhouse, in which we infested half of the plants in each population with caterpillars of the generalist herbivore Spodoptera littoralis. We then measured size-related and resource-allocation traits as well as the levels of constitutive and induced chemical defense compounds in roots and shoots of P. lanceolata. When we considered the environmental characteristics of the site of origin, our results revealed that populations from introduced ranges were characterized by an increase of chemical defense compounds without compromising plant biomass. The concentrations of iridoid glycosides and verbascoside, the major anti-herbivore defense compounds of P. lanceolata, were higher in introduced populations than in native populations. In addition, introduced populations exhibited greater rates of herbivore-induced volatile organic compound emission and diversity, and similar chemical diversity based on untargeted analyses of leaf methanol extracts. In general, the geographic origin of the populations had a significant influence on morphological and chemical plant traits, suggesting that P. lanceolata populations are not only adapted to different environments in their native range but also in their introduced range.
README: Data and R code used in: Plant geographic distribution influences chemical defenses in native and introduced Plantago lanceolata populations
https://doi.org/10.5061/dryad.5dv41nsd1
Description of the data and file structure
- 00_ReadMe_DescriptonVariables.csv: A list with the description of variables from each file used.
- 00_Metadata_Coordinates.csv : A dataset that includes the coordinates of each Plantago lanceolata population used.
- 00_Metadata_Climate.csv : A dataset that includes coordinates, bioclimatic parameters, and the results of PCA. The dataset was created based on the script "1_Environmental variables.qmd"
- 00_Metadata_Individuals.csv: A dataset that includes general information about each plant individual. Information about root traits and chemistry is missing in four samples since we lost the samples.
- 01_Datset_PlantTraits.csv: Size-related and resource allocation traits measured of Plantago lanceolata and herbivore damage.
- 02_Dataset_TargetedCompounds.csv: Phytohormones, Iridoid glycosides, Verbascoside and Flavonoids quantification of the leaves and roots of Plantago lanceolata. Data generated from HPLC
- 03_Dataset_Volatiles_Area.csv: Area of identified volatile compounds. Data generated from GC-FID
- 03_Dataset_Volatiles_Compounds.csv: Information on identified volatile compounds. Data generated from GC-MS.
- 04_Dataset_Metabolome_Negative_Metadata.txt: Metadata for files in negative mode
- 04_Dataset_Metabolome_Negative_Intensity.xlsx : File with the intensity of the metabolite features in negative mode. The file was generated from Metaboscape and adapted as required for the Notame package.
- 04_Dataset_Metabolome_Negative_Intensity_filtered.xlsx: File generated after preprocessing of features in negative mode. During the notadame pacakged preprossesing 0 were converted to na
- 04_Dataset_Metabolome_Negative.msmsonly.csv: File with a intensity of the the metabolite features in negative mode with ms/ms data. File generated from Metaboscape.
- 04_Results_Metabolome_Negative_canopus_compound_summary.tsv: Feature classification. Results generated from Sirius software.
- 04_Results_Metabolome_Negative_compound_identifications.tsv: Feature identification. Results generated from Sirius software.
- 05_Dataset_Metabolome_Positive_Metadata.txt: Metadata for files in positive mode
- 05_DatasetMetabolome_Positive_Intensity.xlsx : File with a intensity of the the metabolite features in positive mode. File generated from Metaboscape and adapted as required for the Notame package.
- 05_Dataset_Metabolome_Positive_Intensity_filtered: File generated after preprocessing of features in positive mode.During the notadame pacakged preprossesing 0 were converted to na
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Code/Software
1_Environmental vairables.qmd: Rscript to Retrieve bioclimatic variables from http://www.worldclim.org based on the coordinates of each population and then perform a principal components analysis to reduce the axes variation and included the first principal component as an explanatory variable in our model to estimate trait differences between native and introduced populations. Figure 1b and 1d
2_PlantTraits_and_Herbivory: Rscript for statistical anaylsis of size-related traits, resource allocation traits and herbivore damage. Figure 2. It needs to source: Model_1_Fucntion.R, Model_2_Fucntion.R, Plot_Function.R
3_Metabolome: Rscript for statistical anaylsis of Plantago lanceolata metabolome. Figure 3. It needs to source: Metabolome_preprocessing_R, Model_1_Fucntion.R, Model_2_Fucntion.R, Plot_Function.R.
4_TargetedCompounds: Rscript for statistical anaylsis of Plantago lanceolata targeted compounds. Figure 4. It needs to source: Model_1_Fucntion.R, Model_2_Fucntion.R, Plot_Function.R
5_Volatilome: Rscript for statistical anaylsis of Plantago lanceolata metabolome. Figure 5. It needs to source: Model_1_Fucntion.R, Model_2_Fucntion.R, Plot_Function.R
Model_1_Function.R : Function to run statistical models
Model_2_Function.R : Function to run statistical models
Plots_Function.R : Function to run plot graphs
Metabolome_prepocessing.R: Script to preprocess features
Methods
Experimental design: Plantago lanceolata seeds were collected from nine native and ten introduced populations across a wide latitudinal and longitudinal gradient throughout the world. Sterilized seeds were then germinated in pots filled with a mixture of sand and nutrient-poor soil under greenhouse conditions in Jena, Germany. Half of the eight-week-old P. lanceolata plants were exposed to five 3rd instar S. littoralis caterpillars for 48 hours, and the other half of the plants functioned as undamaged controls (n = 5 per population, per treatment). All plants were enclosed in mesh bags that were tightened at the bottom and at the top with cable ties.
The experiment was staggered over five days, and each day a block of 38 plants (with two plants per population) was processed. To determine chemical defense traits, we first collected volatile organic compounds (VOCs) right after the herbivory treatment. After VOC collection, plant tissues (leaves, inflorescences, root crown and roots) were harvested separately and immediately flash-frozen in liquid nitrogen and then stored at -80 °C until further chemical analyses. Leaf and root samples were then lyophilized and weighed. Samples were homogenized to a fine powder using a ball mill. For chemical analysis, leaf and root powder (10 mg) was extracted with 1 mL methanol.
Leaf area and leaf damage measurements: To estimate leaf area and experimental leaf damage by S.littoralis caterpillars, we took pictures of all leaves of each P. lanceolata individual right after harvesting. Damage by herbivory was determined by reconstructing the original leaf area with Adobe Photoshop CS5 to calculate the proportion of total leaf area (cm2) and grams consumed (leaf area lost divided by specific leaf area).
Size-related and resource-acquisition traits: Number of leaves, leaf area (cm2), and plant dry mass (gdw), including leaves, roots, flower stem, and flower biomass, were analyzed as plant size-related traits, taking into account only non-infested plants. For analyses of carbon and nitrogen concentrations, approximately 10 mg homogenized leaf material were weighed into tin capsules and measured with an Elemental Analyzer.
Chemical analysis: Untargeted metabolic profiles for P. lanceolata populations leaves were obtained by ultra-high-performance liquid chromatography coupled via electrospray ionization (ESI) to a qTOF mass spectrometer (UHPLC-ESI-HRMS), using both the positive and negative ionization modes. Targeted analyses for leaf and root tissues of iridoid glycosides, verbascoside, flavonoids, and phytohormones were conducted using an HPLC-MS/MS system. Volatile organic compound (VOC) emissions of P. lanceolata were identified and quantified using a GC-MS and GC-FID.
Funding
Deutsche Forschungsgemeinschaft, Award: FOR5000