isotracer

isotracer implements Bayesian hidden Markov models to analyze isotope tracer addition experiment data and estimate nutrient fluxes and turnover rates with quantified uncertainty.

Key Features:

• Bayesian hidden Markov models (HMMs): Uses Bayesian HMMs as the central statistical approach for modeling tracer dynamics over time.
• Formal statistical framework: Provides a formal framework for fitting tracer models and quantifying estimation uncertainty and parameter interdependencies.
• Flux and turnover estimation: Estimates nutrient fluxes between compartments and turnover rates within compartments with associated uncertainty.
• Uncertainty quantification: Calculates estimation errors around model fits and derived parameters.
• Model comparison: Evaluates alternative food web and compartmental models for comparative analysis.
• Statistical comparison across conditions: Supports statistical comparisons of model parameters and derived quantities across sites or treatments.
• Complex experimental designs: Handles tracer datasets arising from experimental manipulations, including environmental treatments such as light manipulation.

Scientific Applications:

• Isotope tracer addition experiments: Analysis and interpretation of isotope tracer addition data to reconstruct nutrient movement through biological and ecological systems.
• Nitrogen cycling studies: Estimation of nitrogen fluxes and turnover rates, including analyses involving 15N-NH4+ tracer additions.
• Food web structure comparison: Comparative evaluation of alternative food web models and their implications for material flows.
• Consumer diet and compartment interdependencies: Inferring consumer diet contributions and interdependencies among ecosystem compartments.
• Case study application: Applied to 15N-NH4+ tracer addition experiments in Trinidadian streams with experimental light manipulation.

Methodology:

Implements Bayesian hidden Markov models within a formal statistical framework to fit tracer time series, estimate nutrient fluxes and turnover rates, calculate estimation errors and uncertainties, evaluate alternative models, and perform statistical comparisons across sites or treatments.