Data supporting the article "Decomposition rates of organic matter in forest and peatland ecosystems of the middle taiga of Western Siberia: assessment using standard and native substrates"

This dataset comprises raw data from a field experiment assessing organic matter decomposition rates in forest and peatland ecosystems of the middle taiga of Western Siberia (Khanty-Mansi Autonomous Okrug – Yugra, Russia). The study employed two complementary approaches: (1) the Tea Bag Index (TBI) method using standard substrates (green tea and rooibos) to estimate decomposition rates and the stabilization factor of organic matter; and (2) the litterbag method with native plant litter from 12 species representing different functional groups (conifers, deciduous trees, shrubs, graminoids, and sphagnum mosses). The dataset includes three tables: tea.csv – decomposition data for standard substrates (green tea and rooibos) across three habitat types (coniferous forest, raised bog lawn, raised bog ryam) at 3, 12, and 24 months (393 observations); native.csv – decomposition data for native plant species across three habitat types at 12, 24, 36, 48, and 60 months (1678 observations); cn.csv – chemical composition data (carbon C, nitrogen N, and C/N ratio) for 8 plant species (41 samples). Each observation includes: sample identifier (occurrenceID), habitat type (habitat), plant species (scientificName), year of experiment establishment (year), decomposition period in months (period), and mass loss percentage (organismQuantity). The cn.csv file additionally includes carbon (C) and nitrogen (N) percentages. The dataset can be used for: comparative assessment of decomposition rates across different ecosystem types; analyzing the relationship between chemical composition (C/N ratio) and decomposition rates; calibration and validation of organic matter decomposition models; studying the effects of habitat conditions on plant litter mineralization processes. Data analysis code is provided as a Jupyter Notebook in Python, utilizing pandas, numpy, scipy, matplotlib, and seaborn libraries. The analysis includes statistical processing, kinetic modeling (single- and two-pool decomposition models), Tea Bag Index calculation, and result visualization.