Department of Earth, Ocean, and Atmospheric Science, Florida State University, Tallahassee, FL, United States of America.
Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, FL, United States of America.
PLoS One. 2018 Jun 18;13(6):e0199123. doi: 10.1371/journal.pone.0199123. eCollection 2018.
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.
海洋学实地考察项目通常使用 δ15N 生物地球化学测量和原位速率测量来研究氮循环和浮游生态系统结构。然而，缺乏能够综合这些不同测量类型的综合建模方法。我们开发了一种将 δ15N 同位素数据纳入现有马尔可夫链蒙特卡罗（MCMC）随机游走方法的新方法，用于解决线性逆生态系统模型。我们测试了这种方法从模拟加利福尼亚洋流和亚马逊河羽流浮游生态系统的正向模型中恢复食物网指数（硝酸盐吸收、氮固定、浮游动物营养级和二次生产力）的能力。我们表明，与标准 MCMC 或 L2 最小范数（L2MN）方法相比，带有 δ15N 的 MCMC 方法通常更擅长恢复生态系统结构，并且也优于带有 δ15N 的 L2MN 方法。此外，我们发现带有 δ15N 的 MCMC 方法对输入方程的删除具有鲁棒性，因此非常适合通常严重欠约束的典型浮游生态系统研究。我们的方法易于扩展，可用于 δ13C 同位素测量或可变碳：氮化学计量比。
