A Bayesian parameter estimation method applied to a marine ecosystem model for the coastal Gulf of Alaska

J. Fiechter, R. Herbei, W. Leeds, J. Brown, R. Milliff, C. Wikle, A. Moore and T. Powell

Ecological Modelling, 2013, vol. 258, issue C, 122-133

Abstract: The present study describes a state-of-the-art methodology based on an adaptive Metropolis–Hastings algorithm to facilitate efficient Bayesian sampling for realistic lower trophic level (LTL) marine ecosystem models. The main objective is to explore the ability to differentiate between biological parameters that can learn from observations and those that cannot. The Bayesian approach is applied to the northwestern coastal Gulf of Alaska region and uses both synthetic and actual (in situ and remotely sensed) observations. LTL ecosystem dynamics in the Bayesian framework are described by a process model consisting of a 1-dimensional Nutrient–Phytoplankton–Zooplankton–Detritus formulation with iron limitation (NPZDFe) and vertical mixing. The results illustrate the ability to determine parameter posterior distributions for fundamental biological rates, such as maximum phytoplankton growth or zooplankton grazing. By using various observational platforms as data stage inputs, the results also demonstrate the impact of spatial and temporal sampling on parameter posterior distributions, as well as the benefits of having concurrent measurements for two or more state variables of the process model (e.g., chlorophyll and nitrate concentrations). Extending the method to multiple parameters is non-trivial, as posterior distributions become impacted by correlated and/or disproportionate contributions for certain model parameters. Controlled experiments with “near perfect data” were useful to characterize parameter identifiability based on information content in the BHM data stage inputs, as well as to separate uncertainties due to sampling issues vs. uncertain ecosystem process interpretation.

Keywords: Marine ecosystem model; Bayesian sampling; Adaptive Metropolis–Hasting; Parameter estimation; Posterior distributions; Gulf of Alaska (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S030438001300152X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:258:y:2013:i:c:p:122-133

DOI: 10.1016/j.ecolmodel.2013.03.003

Access Statistics for this article

Ecological Modelling is currently edited by Brian D. Fath

More articles in Ecological Modelling from Elsevier
Bibliographic data for series maintained by Catherine Liu ().