Data and code accompanying the article: "Canopy Stratification Reshapes Monoterpene Biosynthetic Networks in Sequoia sempervirens"

This repository contains the raw dataset and the complete Python analysis workflow accompanying the scientific article "Canopy Stratification Reshapes Monoterpene Biosynthetic Networks in Sequoia sempervirens" (submitted to Molecules). The dataset provides quantification of monoterpene concentrations (mg g⁻¹ DW) and relative percentages in coast redwood foliage[cite: 1]. Samples are categorized by canopy social status (Dominant vs. Suppressed), season (Winter vs. Summer), geographic site, and genetic origin[cite: 1]. The included Jupyter notebook (sequoia_monoterpene_analysis_clean.ipynb) contains the fully documented scripts used to clean the data, compute descriptive statistics, and execute the advanced multivariate modeling reported in the paper[cite: 1, 2]. This includes:- Custom two-way Bray-Curtis PERMANOVA[cite: 1, 2]- Linear Discriminant Analysis (LDA) and standardized coefficient weights[cite: 1, 2]- Random Forest classification and diagnostic power mapping (500 estimators)[cite: 1, 2]- Pearson metabolic correlation networks (|r| ≥ 0.4)[cite: 1, 2] This repository is designed to ensure full transparency and immediate computational reproducibility of every figure, table, and statistical statement presented in the manuscript[cite: 1].