DOI: 10.5061/dryad.z34tmpgpw
title: Data from: Nitrogen content of herbarium specimens from arable fields and mesic meadows reflect the intensifying agricultural management during the 20th century

description: Arable fields and mesic meadows have been affected by intensifying
 agricultural management and nutrient input during the 20th century, but
 direct evidence for the long-term impact of intensification on plant
 nutrient contents remains scarce. Non-destructive novel spectroscopic
 methods can produce such data from herbarium specimens, making it possible
 to investigate how contents of leaf nutrient traits, especially nitrogen
 and phosphorus, changed over the last century, and what role habitat type
 and management practices play. We carried out a resurvey study of
 functional traits in arable field and mesic meadow communities. We used
 specimens from two German herbaria with a high coverage of their local
 floras: the Senckenberg Herbarium Görlitz and the Herbarium Haussknecht in
 Jena. Following specimen information, the same plant species were
 resampled in the field in 2022 at the same locations. We employed
 Near-Infrared Spectroscopy to predict leaf nitrogen, phosphorus, and
 carbon content of herbarium and field specimens. Nutrient content changes
 over time were compared with publicly available records of P and N
 fertilization. Overall, 1270 specimens of 76 species from both herbarium
 and field were studied, the oldest from the 19th century. Leaf nitrogen
 increased significantly through time, with a corresponding increase in
 leaf nitrogen:phosphorus ratio in both habitats. Leaf phosphorus and
 carbon content decreased significantly over time, with the latter
 decreasing significantly stronger in mesic meadows compared to arable
 fields. The total amount of nitrogen or phosphorus fertilizer applied per
 year on a regional scale was found to be significantly correlated with the
 respective leaf nutrient content levels. Mesic meadow species showed a
 stronger response in leaf nitrogen and leaf phosphorus content over time.
 Synthesis: Our study shows a long-term increase of leaf nitrogen in the
 studied habitats, running in parallel to increased chemical fertilizer
 application in Germany. Our data indicates a shift from predominantly
 N-limited towards more P-limited growth conditions. The stronger response
 of species from mesic meadows compared to species from arable fields could
 indicate a faster adjustment to environmental pressures. This study thus
 also serves to showcase the potential of the combination of herbarium
 collections and NIR spectroscopy. 
description: The dataset consists of metadata, functional trait data, and
 fertilization data for herbarium specimens and plant specimens sampled in
 2022.The herbarium specimens are from the collections of the Herbarium
 Haussknecht in Jena, Germany (JE), and the Senckenberg Herbarium Görlitz
 in Görlitz, Germany (GLM). The field specimens were gathered from May to
 August of 2022, and were press-dried in the same way as the herbarium
 specimens. Near-Infrared Spectroscopy (NIRS) measurements were taken from
 both specimen types using an ASD FieldSpec 4 and a contact probe. A subset
 of the field specimens was selected to create a NIRS calibration dataset,
 which involved conventional wet lab analyses of the leaf nutrient contents
 of leaf nitrogen, leaf carbon and leaf phosphorus contents for these
 specimens. These wet lab measurements were used in conjunction with the
 NIRS measurements to build partial least squares regression (PLSR) models
 for each trait. The PLSR models were in turn used to predict the
 respective trait values from NIRS measurements alone, allowing for the
 non-destructive functional trait measurements of the other field specimens
 and the historical herbarium specimens. Because some of the wet lab
 measurements were log transformed to improve the calibration model
 accuracy, some of the predicted trait values are log transformed as well.
 Further details on the calibration workflow and the use of NIRS in
 herbaria can be found in Proß et al. 2023 (doi.org/10.1111/oik.10255) and
 Kühn et al. 2024 (doi.org/10.1186/s13007-024-01146-x).
description: # Nitrogen content of herbarium specimens from arable fields and mesic
 meadows reflect the intensifying agricultural management during the 20th
 century. [https://doi.org/10.5061/dryad.z34tmpgpw
 ](https://doi.org/10.5061/dryad.z34tmpgpw) ## Description of the data and
 file structure “`Resurvey_analysis_code.R`” is a commentated script for
 RStudio, which allows the user to load the data, calculate statistics, and
 create plots. "`01_resurvey_combined_table_cleaned.csv`" is a
 data table containing data for 1270 herbarium and field specimens of 49
 plant species. For each specimen, data on the species and sampling date,
 the locality description, GPS coordinates (if available) and the
 collection it is stored in are included. Leaf functional trait values are
 also included: leaf nitrogen content, leaf phosphorus content, leaf carbon
 content, and the leaf carbon:nitrogen ratio and the leaf
 nitrogen:phosphorus ratio were all predicted from NIR spectra using PLSR
 calibration models. "`02_fertilizer_values_historical.csv`"
 consists of anorganic nitrogen and phosphorus fertilization, tracked in N
 or respectively P kg ha-1 a-1. The data is derived from statistical
 yearbooks of the German Reich, the German Democratic Republik, and the
 Federal Republic of Germany, and is collected either on a national or
 federal state level. "`03_resurvey_NIRS`" is a folder containing
 the calibration datasets (spectral and functional trait data) and the R
 code needed to create PLSR calibration models as described by Kühn et al.
 2024 (doi.org/10.1186/s13007-024-01146-x ) .  In all files, "NA"
 is used to denote missing values. ###
 "Resurvey\_analysis\_code.R" A commentated script for RStudio,
 which allows the user to load the data, calculate statistics, and create
 plots. ### "01\_resurvey\_combined\_table\_cleaned.csv" A
 comma-seperated table with 1271 rows, including the header. The 32 columns
 are as follows: * sampleID: Unique identifier of each sample * Area:
 Defines if the sample came from the region of Jena or Görlitz * Loc:
 Specifies if the sample came from the herbaria JE or GLM, or the field *
 speciesID: six-letter abbreviation of plant species name * Species: taxon
 name with author abbreviation * Catalogue: catalogue number for GLM
 specimens * sampleno: ID number, unique within species and loc_abbrv *
 Location: Description of sampling locality in German * Habitat_type: Mesic
 meadow or arable field * Latitude: Latitude in decimal degrees (WGS84) *
 Longitude: Longitude in decimal degrees (WGS84) * Year: Sampling year *
 Field_visit: Was the locality from which the specimen is from visited in
 2022, Y or N? * Found_in_field: Was a herbarium specimen precisely
 relocated in 2022, sampling the same species from the same location? *
 spectra_file: Filename for NIRS spectra file * Loc_abbrv: 4 letter
 abbreviation for each sampling locality, including herbaria *
 Predicted_leafN: Predicted leaf nitrogen content in % (transformed from
 logleafN_pred) * Predicted_leafP: Predicted leaf phosphorus content in
 mg/g (transformed from logleafP_pred) * leafC_Pred: Predicted leaf carbon
 content in % * logleafCN_pred: Predicted leaf carbon:nitrogen ratio *
 logleafN_pred: Predicted logarithmic leaf nitrogen content in % *
 logleafP_pred: Predicted logarithmic leaf phosphorus content in % * NP:
 Predicted leaf nitrogen : phosphorus ratio * Prediction_present: Are
 predictions/ .dpt spectral files present for the sample? * GDR_district:
 GDR administrative district ("Bezirk") in which sample would be
 located in, irrespective of sampling year * Fiscal.Year: Fiscal year from
 which fertilization values are taken for this sample * N_kg_ha_a: Amount
 of nitrogen in kg per hectare per year applied in the GDR district/FRG
 federal state in sampling year * P_kg_ha_a: Amount of phosphorus in kg per
 hectare per year applied in the GDR district/FRG federal state in sampling
 year * sampling_period: Past (historical herbarium specimens) or present
 (2022 herbarium specimens) * Flowering status: Only for field specimens.
 What was the latest phenological stage observed at the time of sampling? *
 Height: Only for field specimens. What was the maximum vegetative height
 observed at the time of sampling? * Functional group: Based on taxonomy,
 is the species a forb, grass or legume? ###
 **"02\_fertilizer\_values\_historical.csv"** A comma-seperated
 table with 785 rows, including the header. The 5 columns are as follows: *
 Fiscal year: the starting year of the fiscal year in which the values were
 recorded * GDR_district: The district for which the values were recorded.
 Includes both the regular districts and “Country_” for nation-wide values.
 * Fertilization: the type and measurement unit of mineral fertilizer
 tracked: either nitrogen or phosphorus * Value: the amount of fertilizer
 applied per district per fiscal year in kg per hectare per year * State:
 the current-day federal state which the fertilizer value applies to:
 either Federal for nation-wide measurements, Thuringia or Saxony. ###
 **“03\_resurvey\_NIRS”** This folder contains the relevant files for the
 Near-Infrared Spectroscopy analysis. “`Spectroscopy_v_6_8.R`” is a
 commentated R-script which can be used to calculate partial least square
 regression (PLSR) models to calibrate Near-Infrared spectra for leaf
 functional traits, based on sample NIR data and lab measurements. In the
 course of the script, data is cleaned, compiled, models are calculated and
 performance metrics assigned, and models for the prediction of leaf
 functional trait values based on NIR spectra alone are produced. The PLSR
 itself is calculated using the “*plantspec*” package for R. The subfolders
 “`Spectra_for_Calibration`”, “`Spectra_for_Calibration_field`” and
 “`Spectra_to_be_Predicted`” contain spectral files for all samples in this
 study, encoded as “.dpt” files. Each “.dpt” file is a tab-separated table,
 with the wavelength in nanometers (nm) in the first column and the
 reflectance of the sample at that wavelength in the second column. These
 spectral files have already been spliced and averaged as described in the
 “Spectroscopy_v_6_8.R” script. Filenames correspond to the “sampleID”
 column of “01_resurvey_combined_table_cleaned.csv”, consisting of a
 six-letter species code (e.g. “AchMil” for Achillea millefolium), a sample
 number, and a four-letter code denoting the locality. The subfolder
 “`Prediction_models`” contains the three partial least squares regression
 models calculated for the leaf functional traits of leaf carbon content,
 leaf nitrogen content and leaf phosphorus content, encoded as .RData
 objects for easy loading into the R programming environment.
 “`PTrait_reference_mg_g-l.csv`” is a comma-separated table consisting of
 263 rows and 5 columns with wet laboratory analysis data (nitric acid
 digestion and ion chromatograph analysis) for leaf phosphorus contents.
 The columns are as follows: * SampleID: The unique sample identifier *
 P_mg-l: the Phosphorus concentration in mg per liter of solution. *
 Weight_g: The weigh of leaf powder used for the nitric acid digestion of
 each sample. * Sample volume: The volume which the sample was filled up
 and diluted to. * Concentration_mg-g: The concentration of leaf phosphorus
 in the leaf powder in mg per g. "`Reference_Samples.csv`" is a
 comma-separated table consisting of 4045 rows and 5 columns, giving a
 reference for each .asd-formatted spectral data file and the corresponding
 sample ID. The columns are as follows: * FILES_LIST: The spectral data
 files. Three NIRS readings were taken for each sample. - Sample: The
 unique sample identifier. *“*`Trait_reference_field.csv`*”* is a
 comma-separated table consisting of 260 rows (including header) and 4
 columns, giving the wet laboratory analysis data (gas chromatograph
 analysis) for leaf carbon content, leaf nitrogen content and leaf
 carbon:nitrogen ratio for each sample. The columns are as follows: *
 Sample: The unique sample identifier. * N_perc: The leaf nitrogen content
 in percent. * C_perc: The leaf carbon content in percent. * C-N: The leaf
 carbon:nitrogen ratio. ## Code/software The
 "Resurvey_analysis_code.R" for the analyses is written in R. The
 RStudio IDE is also required. The following packages are required for the
 analyses: library(tidyverse) library(magrittr) library(cowplot)
 library(RColorBrewer) library(readxl) library(sf) library(lme4)
 library(mgcv) library(lmerTest) library(performance) library(emmeans)
 library(gratia) library(car) Additionally, for the NIRS calibration script
 "Spectroscopy_v_6_8.R", the following packages are required:
 library(plantspec) library(Hmisc) library(foreach) library(doParallel)