Abstract
Oceanographic field programs often use δ15N biogeochemical measurements and in situ rate measurements to investigate nitrogen cycling and planktonic ecosystem structure. However, integrative modeling approaches capable of synthesizing these distinct measurement types are lacking. We develop a novel approach for incorporating δ15N isotopic data into existing Markov Chain Monte Carlo (MCMC) random walk methods for solving linear inverse ecosystem models. We test the ability of this approach to recover food web indices (nitrate uptake, nitrogen fixation, zooplankton trophic level, and secondary production) derived from forward models simulating the planktonic ecosystems of the California Current and Amazon River Plume. We show that the MCMC with δ15N approach typically does a better job of recovering ecosystem structure than the standard MCMC or L2 minimum norm (L2MN) approaches, and also outperforms an L2MN with δ15N approach. Furthermore, we find that the MCMC with δ15N approach is robust to the removal of input equations and hence is well suited to typical pelagic ecosystem studies for which the system is usually vastly under-constrained. Our approach is easily extendable for use with δ13C isotopic measurements or variable carbon:nitrogen stoichiometry.
Highlights
Reconstructing ecosystem structure and trophic flows through planktonic ecosystems is crucial for understanding fisheries production, pelagic biogeochemistry, and the response of each of these to a changing climate
Studies incorporating nitrogen or carbon isotopic information from benthic ecosystems into the Markov Chain Monte Carlo (MCMC) approach have either assumed that the isotopic signature of all compartments within an ecosystem are known [31] or used enriched stable isotope tracer additions to trace mass flow within the ecosystem [32, 33]. Since neither of these approaches is feasible for pelagic systems in which methodological considerations make it impossible to determine the isotopic signatures of many ecosystem compartments, we develop a new approach for incorporating isotopic information into the existing MCMC Linear Inverse Model (LIM) framework
We evaluate the ability of these LIM approaches to recover key ecosystem parameters related to nitrogen biogeochemistry and plankton trophic dynamics
Summary
Reconstructing ecosystem structure and trophic flows through planktonic ecosystems is crucial for understanding fisheries production, pelagic biogeochemistry, and the response of each of these to a changing climate. Quantitative investigation of energy transfer between plankton functional groups is hampered by methodological limitations in separating ecological groups and in making rate measurements on specific trophic levels. Oceanographers often rely on mass-balance ecosystem models such as EcoPath [1] or Linear Inverse Ecosystem Models (LIM [2, 3]) to reconstruct trophic structure from sparse measurements. The paucity and poor taxonomic resolution of common marine ecological measurements leaves such modeling approaches vastly under-constrained
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