Incorporation of diet information derived from Bayesian stable isotope mixing models into mass-balanced marine ecosystem models: A case study from the Marennes-Oléron Estuary, France

• Published in: Ecological modelling 2013-10, Vol.267, p.127-137
• Main Authors: Pacella, Stephen R., Lebreton, Benoit, Richard, Pierre, Phillips, Donald, DeWitt, Theodore H., Niquil, Nathalie
• Format: Article
• Language: English
• Subjects: Animal and plant ecology; Animal, plant and microbial ecology; Autoecology; Biological and medical sciences; carbon; case studies; diet; Ecological model; Environmental Sciences; estuaries; Food web; food webs; Fundamental and applied biological sciences. Psychology; General aspects; General aspects. Techniques; Inverse analysis; marine ecosystems; Methods and techniques (sampling, tagging, trapping, modelling...); mixing; nutrition information; Sea water ecosystems; Seagrass; seagrasses; Stable isotope; stable isotopes; statistical models; Synecology; uncertainty
• ISSN: 0304-3800; 1872-7026
• DOI: 10.1016/j.ecolmodel.2013.07.018

•We modeled an intertidal seagrass food web of France using linear inverse analysis.•Bayesian isotope mixing model output was coupled with the food web model.•δ13C and δ15N isotope data significantly constrained the food web model.•Coupling isotope mixing models with ecosystem models reduces model uncertainty. We investigated the use of output from Bayesian stable isotope mixing models as constraints for a linear inverse food web model of a temperate intertidal seagrass system in the Marennes-Oléron Bay, France. Linear inverse modeling (LIM) is a technique that estimates a complete network of flows in an under-determined system using a combination of site-specific data and relevant literature data. This estimation of complete flow networks of food webs in marine ecosystems is becoming more recognized for its utility in understanding ecosystem functioning. However, diets and consumption rates of organisms are often difficult or impossible to accurately and reliably measure in the field, resulting in a large amount of uncertainty in the magnitude of consumption flows and resource partitioning in ecosystems. In order to address this issue, this study utilized stable isotope data to help aid in estimating these unknown flows. δ13C and δ15N isotope data of consumers and producers in the Marennes-Oléron seagrass system was used in Bayesian mixing models. The output of these mixing models was then translated as inequality constraints (minimum and maximum of relative diet contributions) into an inverse analysis model of the seagrass ecosystem. The objective of this study was to investigate how the addition of diet information gained from the stable isotope mixing models would help constrain a linear inverse food web model. In order to investigate this, two inverse food web models were built to track the flow of carbon through the seagrass food web on an annual basis, with units of mgCm−2d−1. The first model (Traditional LIM) included all available data, with the exception of the diet constraints formed from the stable isotope mixing models. The second model (Isotope LIM) was identical to the Traditional LIM, but included the Bayesian mixing model diet constraints. Both models were identical in structure, and intended to model the same Marennes-Oléron intertidal seagrass bed. Each model consisted of 27 compartments (24 living and 3 detrital) and 175 flows. Comparisons between the outputs of the models showed the addition of the Bayesian mixing model-derived isotopi