A New Method to Reconstruct Quantitative Food Webs and Nutrient Flows from Isotope Tracer Addition Experiments

Area of Science:

• Ecosystem ecology
• Nutrient cycling
• Food web dynamics

Background:

• Nutrient flow analysis is crucial for understanding ecosystem ecology.
• Isotope tracer experiments track nutrient movement but lack a formal statistical framework for analysis.
• Existing methods limit the calculation of estimation errors and uncertainty in food web parameters.

Purpose of the Study:

• To develop a statistical framework for analyzing isotope tracer experiments.
• To estimate nutrient fluxes, turnover rates, and associated uncertainties in ecosystem compartments.
• To enable statistical comparisons between different ecosystems or experimental treatments.

Main Methods:

• Development of a Bayesian hidden Markov model approach.
• Application to nitrogen (N) tracer additions in Trinidadian streams.
• Experimental manipulation of light conditions to assess ecosystem responses.

Main Results:

• The Bayesian method successfully estimates N fluxes and turnover rates between ecosystem compartments.
• Quantification of uncertainty associated with derived food web parameters.
• Demonstration of the method's utility in comparing alternative food web models and experimental sites.

Conclusions:

• The developed Bayesian hidden Markov model provides a robust statistical framework for isotope tracer studies.
• This method enhances the accuracy of nutrient flow analysis and uncertainty estimation in food webs.
• Facilitates rigorous statistical comparisons in ecological research, advancing ecosystem ecology.