Overview of data

This dataset is for our research examining the ecological and genomic variation of traditional crop species- black pea, and barley, cultivated across Indian Trans-Himalaya. The field study was conducted in March-September 2023 to evaluate the growth performance of three crop species: green peas, black peas, and barley. The field experiment was carried at three sites characterized: low (4000m), mid (4200m), and high (4500m) elevations. The experiment was laid out in a randomized block design with a factorial arrangement, incorporating three factors: crop species, water treatments, and elevation levels. Each treatment combination was replicated three times, and thus a total of 81 plots were monitored. The seeds were sown uniformly across all plots and spacing was determined by interacting with farmers. The sowing density for green pea was lower than black pea seeds since black pea seeds are relatively smaller. The farmers also advised on sowing depth and bed preparation to ensure optimal growth conditions. The IRB eprotocol number is 71749.

We set up plots for green, black pea, and barley (1.5 by 1 meter) along a climate gradient in three villages (at elevations ~4000m, ~4200m, ~4500m) with three water treatments. We monitored the plants for photosynthetic, non-reproductive and reproductive traits such as, germination rate, flowering time, height at maturity, leaf coloration (disease infestation), and flower/pod production. Data on growth parameters (plant height, leaf area, and biomass) were collected at regular intervals (every 7-10 days). Yield components were assessed at the time of harvest. Data were analyzed using regression analysis to determine the effects of the crop type, water treatment, elevation, and their interactions. Post hoc tests were performed to locate differences between treatment means at a significance level of p<0.05. All statistical analyses were carried out using R software.

Additionally, black pea genotypes were analyzed using whole-genome sequencing and compared to published Pisum genomic datasets to understand population structure and diversity.

Files Included

2023_crop_data.csv

peapca_supplement_genome.csv

Pea_n18_NC_066579.covar- Covariance matrix for black pea chromosome NC_066579 (18x18).

Pea_n18_NC_066580.covar- Covariance matrix for black pea chromosome NC_066580 (18x18).

Pea_n18_NC_066581.covar- Covariance matrix for black pea chromosome NC_066581 (18x18).

Pea_n18_NC_066582.covar- Covariance matrix for black pea chromosome NC_066582 (18x18).

Pea_n18_NC_066583.covar- Covariance matrix for black pea chromosome NC_066583 (18x18).

Pea_n18_NC_066584.covar- Covariance matrix for black pea chromosome NC_066584 (18x18).

Pea_n18_NC_066585.covar- Covariance matrix for black pea chromosome NC_066585 (18x18).

biocultural_code_for_manuscript.R- R script for PCA and clustering on covariance matrices. Contains code to generate all figures in the manuscript.

Description of data files

File: 2023_crop_data.csv

Tabulates ecological data on plant growth and phenology across crop types and treatments as well as across the 3 sites for green pea, barley, and black pea. Each row corresponds to a crop plot defined by crop type, elevation (site), and replication. The variable names include:

Sno | Serial number for each Site.

Site | Village where the plot was located. There are four sites: Kibber, Kiamo, Tashigang, Thinam; each at a different elevation.

Helper_ID | The column is a copy of the Site column with numbers to coding and analysis ease.

Field no | Field identifier within the site.

Plot no | Plot identifier.

Crop | Crop species. There are three crops: green pea, black pea, or barley.

Water_treatment | There were three water treatments S1, S2, S3 with three replicates each. S1 corresponds to business as usual irrigation scheme as advised by the farmers. S2 refers to reduced irrigation and S3 corresponds to drought like conditions with least irrigation scheme for the crops. However, our water treatments are not used as a predictor variable since there were more rains in the summer season. The treatments were used as replicates and averaged.

Irrigation_times | Denotes the number of times the fields were watered throughout the growing season. The irrigation times and days were advised by the farmers. This ranges from 2 times for Black pea in Kiamo to 9 times for green pea in many sites depending on the water treatment as noted below.

Average leaves [X] | Mean number of leaves per plant at X days since sowing the seeds for respective crops.

Average height [X] (units cm) | Mean stem height in cm at X days after sowing.

Flowering plants [X] | Proportion of plants that had flowered by day X for that crop.

Plants pods [X] | Proportion of plants bearing pods by day X.

Survival | Proportion of seeds that produced sapling 30 days after sowing.

Average leaf length [X] days (units cm) | Average length of leaves X days after sowing.

Average leaf breadth [X] days (units cm) | Average breadth of leaves X days after sowing.

Average stem thickness [X] days (units cm) | Average stem thickness X days after sowing.

Average no of pods per plant [X] | Average no of pods X days after sowing.

Average pod height [X] (units cm) | Average pod height X days after sowing.

Average no of seeds per pod [X] | Average no of seeds per pod per plant X days after sowing.

Average weight of pod [X] (units grams) | Average weight of pod X days after sowing.

Average fresh weight above ground [X] days (units grams) | Average weight of plant above ground X days after sowing.

Average dry weight above ground [X] days (units grams) | Average dry weight of plant above ground X days after sowing.

Average dry weight below ground [X] days (units grams) | Average dry weight of plant below ground X days after sowing.

Total weight of pods [X] days (units grams) | Weight of pods X days after sowing.

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Please note that there are empty cells for some columns in the .csv file. For the leaves, flowering and pods columns, the empty cells represent there were no leaves, flowers or pods in the initial months. The cells become non-empty as these traits begin to show up. There are also empty cells for the Barley crop column, since Barley is a grass and we did not count number of leaves for a grass species.

File: peapca_supplement_genome.csv

This file contains metadata on comparative genome accessions and clustering assignments.

Run No. | Identifier for sequencing run

Sample ID | Identifier for each accession

Accession No. | NCBI accession number

Original taxa | Reported taxonomic name

Country | Country of sample origin

Type | Domesticated or wild pea sample

ADMIXTURE Group (SNPs) | Cluster assignment (q > 0.6) from SNP-based ADMIXTURE at K=5

ADMIXTURE Group (SVs) | Cluster assignment from SV-based ADMIXTURE at K=5

Description of .covar Files

The .covar files are numeric covariance matrices of dimension 18 × 18, generated from genomic variation among black pea accessions. Each file corresponds to one of the seven chromosomes: Pea_n18_NC_066579.covar -> Chromosome NC_066579, Pea_n18_NC_066580.covar -> Chromosome NC_066580... and so on. These files can be opened using:

- Text editors (e.g., Notepad)

- Spreadsheet programs (e.g., Microsoft Excel)

- R or Python (e.g., in R: as.matrix(read.table("filename.covar", header = FALSE)))

Each row and column represents one of the 18 accessions, and the values indicate pairwise covariance.

Data Sources

Comparative genomic data were obtained from:

Yang, T. et al. (2022). "Improved pea reference genome and pan-genome highlight genomic features and evolutionary characteristics." Nature Genetics, 54(10), 1553–1563. https://doi.org/10.1038/s41588-022-01178-6

The newly sequenced black pea sample from this study has been deposited at NCBI under: BioSample accession: SAMN44380099

Software and Code

All code for PCA and clustering analyses have been shared in the script.

To run the script, you will need:

- R version 4.2.0 or higher

- R packages: RColorBrewer, tidyverse, reshape2, magick, ggbiplot, factoextra, ggpubr, ggfortify, cowplot, corrplot, ggpmisc, patchwork, hrbrthemes, Hmisc, RSpectra, plot.matrix, FactoMineR, scatterplot3d, kernlab

The script reads the .covar files, performs PCA, and runs k-means, hierarchical, and spectral clustering. Outputs include 2D and 3D PCA plots and cluster visualizations.