Estimating trophic levels and trophic magnification factors using Bayesian inference.

Area of Science:

• Environmental Chemistry
• Ecotoxicology
• Ecological Modeling

Background:

• Food web biomagnification is commonly assessed using trophic magnification factors (TMFs), derived from regressions of contaminant concentrations against trophic levels.
• Traditional TMF regressions often overlook variability and uncertainty associated with trophic level estimations, particularly those based on nitrogen stable isotopes (δ(15)N).

Purpose of the Study:

• To develop a more accurate method for estimating TMFs by accounting for uncertainties in trophic level determination.
• To improve the quantification of bioaccumulation measures in food webs.

Main Methods:

• Utilized a Markov Chain Monte Carlo (MCMC) method to model food web structure and contaminant dynamics.
• Incorporated measurement errors in stable isotopes of nitrogen (δ(15)N), reference baseline trophic levels, and nitrogen enrichment factors (ΔN) into the TMF calculations.
• Employed knowledge of food web structure within the MCMC framework.

Main Results:

• Achieved a significant increase in the precision of TMF estimates compared to traditional methods.
• Enabled the quantification of uncertainty in bioaccumulation measures, moving beyond simple point estimates.
• Demonstrated the ability to assign probabilities to biomagnification (e.g., TMF > 1).

Conclusions:

• The MCMC approach provides a more robust framework for assessing food web biomagnification by addressing trophic level uncertainties.
• This refined methodology leads to a better understanding of the reliability and variability of bioaccumulation assessments.
• Quantifying TMF uncertainty is crucial for accurate ecological risk assessment of contaminants.