Beyond simple linear mixing models: process-based isotope partitioning of ecological processes.

Area of Science:

• Ecological modeling
• Isotope geochemistry
• Environmental science

Background:

• Stable isotopes are crucial for understanding ecological processes like diet, resource use, and fluxes.
• Simple linear mixing (SLM) models are common but limited, lacking mechanistic understanding and predictive power.
• SLM models struggle with complex data and identifying multiple sources.

Purpose of the Study:

• Introduce a novel process-based mixing (PBM) model framework.
• Integrate stable isotope data with process models and other data sources.
• Enhance the partitioning of ecological process components and improve predictive ability.

Main Methods:

• Utilize a hierarchical Bayesian framework for uncertainty quantification.
• Incorporate process models and prior information to constrain source contributions.
• Apply the PBM framework to diverse ecological examples: streamflow, plant water uptake, and animal diets.

Main Results:

• The PBM approach successfully partitions sources in complex ecological scenarios.
• It overcomes identifiability issues common in SLM models with multiple sources.
• Demonstrates improved integration of ecological theory and diverse data.

Conclusions:

• The PBM model offers a more robust and predictive alternative to SLM for stable isotope data analysis.
• This framework facilitates a deeper mechanistic understanding of ecological processes.
• Enables accurate partitioning of process components across temporal and spatial scales.