DOM scaling law development

Knowledge graph centered on DOM scaling law development with 12 nodes and 22 connections. Top connected: Phytoplankton, riparian vegetation, Fourier Transform Ion Cyclotron Resonance Mass Spectrometry, Environmental Molecular Sciences Laboratory, Molecular diversity of dissolved organic matter re.

Method synopsis

Development of power-law scaling relationships between environmental variables and DOM chemodiversity using ordinary least squares regression on log-transformed data.

Synthesized from method descriptions across 1 paper using this protocol.

Procedure as described in the canonical source

Steps below were extracted from the paper that introduces this protocol — Molecular diversity of dissolved organic matter reflects macroecological patterns in river networks (2025), Scientific Reports. Implementations in other papers (listed below) may differ.

1. Principal component analysis
   The important features were assembled into a dense data matrix of size 54×11 with 54 samples characterized by 11 key features. Prior to dimensionality reduction using PCA, data is independently centered and scaled to unit variance using Scikit-learn's StandardScaler class in Python. The algorithm performs a full singular value decomposition (SVD) yielding two principal components.
   Quantities: 54 samples × 11 features, 2 principal components extractedDuration: Not specifiedConditions: Data standardized and scaled to unit variance
   Equipment: Scikit-learn StandardScaler, Python SVD algorithms
2. Scaling relationship fitting
   Power-law scaling function (y = Bx^Z) describing the relationship between local or non-local descriptors and chemodiversity for each compound class was used to develop scaling laws. Statistical tests were performed to compute the p-value and check if the estimated parameters of the power law are significant. Used Ordinary Least Squares (OLS) regression in Python through the statsmodels library.
   Quantities: Power law function y = Bx^Z applied to each compound classDuration: Not specifiedConditions: Log-transformed data to improve normality
   Equipment: Python statsmodels library, SciPy's ordinary least squares function

Typical Equipment

• Python statsmodels library
• SciPy's ordinary least squares function
• Scikit-learn StandardScaler

Output Measurements

• scaling exponents
• scaling coefficients
• R-squared values

Papers Using This Protocol (1)

Species Studied With This Protocol

Places Where Used