S.
Lan Smith, PhD
Research Scientist
e-mail: lanimal_at_jamstec.go.jp
(replace "_at_" with "@" in the above address)
Research Scientist
e-mail: lanimal_at_jamstec.go.jp
(replace "_at_" with "@" in the above address)
Marine Ecosystem Modeling Group
Ecosystem Change Research Program
Frontier Research Center for Global Change
Yokohama, Japan
Research Interests
I
study biogeochemical cycles and lower-trophic ecosystems
(plankton)
using modeling and data analysis. I particularly enjoy studying and
developing models of the coupled
cycles of multiple elements. Recently
I have been applying data assimilation to fit models to data
This allows a quantitative comparison of the ability of different
models to
simulate the measurements.
Optimality-based
modeling applied to multi-nutrient uptake kinetics
Inspired
by the success of the
optimality-based
model of Pahlow (Mar. Ecol. Prog. Ser., 287, p. 33-43, 2005) in describing various processes of
phytoplankton growth with fewer parameters than previous models, I
began thinking about whether such an approach might yield simpler,
better models for uptake of multiple nutrients.
The
classic
Michaelis-Menten equation often provides a reasonable description of
uptake.
However, when ratios of ambient nutrient concentrations differ greatly
from the optimal ratio for phytoplankton, straightforward application
of separate Michaelis-Menten equations (one for each nutrient) greatly
overestimates uptake of any nutrient other than the growth-limiting
nutrient. Such extreme nutrient ratios occur because of human
activities (e.g., nitrogen discharge into rivers and coastal waters)
and because of natural processes like nitrogen fixation. Pahlow
(2005) presented a new optimality-based
equation to describe uptake of a single nutrient, and I began by
thinking of how it could be extended to multiple nutrients.
After much thought and many trials (and errors!) I hit upon a strategy that seems to work quite well (based on fits to data from chemostat experiments). I call the new model the SPONGE (Simple Phytoplankton Optimal Nutrient Gathering Equations). For a more detailed description, please see my SPONGE Page.
After much thought and many trials (and errors!) I hit upon a strategy that seems to work quite well (based on fits to data from chemostat experiments). I call the new model the SPONGE (Simple Phytoplankton Optimal Nutrient Gathering Equations). For a more detailed description, please see my SPONGE Page.
A
manuscript
describing and validating this
model has recently been
accepted for publication in
Limnology and Oceanography. You can download a .pdf of
it here, or from the journal's
website (for free), at: http://aslo.org/lo/toc/vol_52/issue_4/1545.pdf.
There is also an important typesetting error in an equation that is central to the model. Please see the correct equation, here.
New: I have prepared a brief Users' Guide to applying Optimal Uptake kinetics in planktonic ecosystem models with fixed-composition (e.g., fixed C:N:P ratios) for phytoplankton.
There is also an important typesetting error in an equation that is central to the model. Please see the correct equation, here.
New: I have prepared a brief Users' Guide to applying Optimal Uptake kinetics in planktonic ecosystem models with fixed-composition (e.g., fixed C:N:P ratios) for phytoplankton.
Comparing Photoacclimation Models
Sometimes
applying a
mathematical algorithm
(data assimilation) to fit models to data can produce unexpected
results. In a study
(recently accepted for
publication in Ecological Modeling),
I compared two
published models of
photoacclimation for phytoplankton. You can download the file
(.pdf) here.
These models describe
the
phytoplankton's acclimation to nutrient and light environment. The
author of a recently published model claimed that it had an inherent
advantage
over previously publshed models, namely in its ability to simulate the
initial lag phase (before rapid growth begins). Authors of a previously
published model had found that their model could not reproduce this lag
phase. By fitting both models to the same data set, I showed that the
older model could indeed simulate the whole data set, including the
initial lag phase, at least as well as the newer model. The reason this
was possible provided insight into the processes responsible for
the initial lag phase. Still, the newer
optimality-based model has advantages (e.g., fewer parameters and
better agreement with the measured Chl:N ratios).
Constraining Flows
Data
assimilation can also constrain material flows (e.g. of
nitrogen
or carbon) for which we rarely (if ever) have direct observations.
(Most
quantitative chemical and biological observations are of
concentrations.) One of my recent papers describes the application of
data
assimilation to constrain flows of nitrogen in an ecosystem model that
includes
the flexible composition of phytoplankton. You can download the file
(.pdf) here.
Publications (with peer review)
Mohan P. Niraula, Beatriz E. Casareto, S. Lan Smith, Takayuki Hanai, and Yoshimi Suzuki.
Examining the effects of nutrients on the composition and size of phytoplankton with a new approach: using incubations of unaltered mesopelagic seawater. J. Expt. Mar. Biol. Ecol., In Press, February, 2007
S. Lan Smith and Yasuhiro Yamanaka.
Optimization-based model of multi-nutrient uptake kinetics. Limnology and Oceanography. 52: 1545-1558, 2007.
S. Lan Smith and Yasuhiro Yamanaka.
Quantitative comparison of photoacclimation models for marine phytoplankton. Ecological Modeling, DOI: 10.1016/j.ecolmodel.2006.09.016, 2007
S. Lan Smith, B. E. Casareto, M. P. Niraula, Y. Suzuki, J. C. Hargreaves, J. D. Annan, and Y. Yamanaka.
Examining the regeneration of nitrogen by assimilating data from incubations into a multi-element ecosystem model. J. Marine Systems, vol. 64, p. 135-152, 2007
Kishi, M. J., D. L. Eslinger, M. Kashiwai, B. A. Megrey, D. M. Ware, F. E. Werner, M. Aita-Noguchi, T. Azumaya, M. Fujii, S. Hashimoto, H. Iizumi, Y. Ishida, S. Kang. G. A. Kantakov, H. Kim, K. Komatsu, V. V. Navrotsky, S. L. Smith, K. Tadokoro, A. Tsuda, O. Yamamura, Y. Yamanaka, K. Yokouchi, N. Yoshie, J. Zhang, Y. I. Zuenko, V. I. Zvalinsky. NEMURO: Introduction to a lower trophic level model for the North Pacific marine ecosystem. Ecological Modeling, DOI: 10.1016/j.ecolmodel.2006.08.021, 2007
Megrey, B. A., K. A. Rose, R. Klumb, D. Hay, F. E. Werner, D. L. Eslinger, and S. L. Smith.
A biogenic population dynamics model of Pacific Herring (Clupea, pallasii) coupled to NEMURO: Dynamics, description, validation and sensitivity analysis. Ecological Modeling, DOI: 10.1016/j.ecolmodel.2006.08.020, 2007
Mohan P. Niraula, Beatriz E. Casareto, Takayuki Hanai, S. Lan Smith and Yoshimi Suzuki.
Developing phytoplankton assemblages using incubations of unaltered mesopelagic water; investigating nutrient-size relationships, Journal of Eco-Engineering, vol.17, no.2, p. 121-131, 2005
S. Lan Smith, Yasuhiro Yamanaka and Michio J. Kishi.
Attempting consistent simulations of Stn. ALOHA with a multi-element ecosystem model, Journal of Oceanography, vol. 61, no. 1, p. 1-23, 2005
P. R. Jaffe, S. Wang, P. L. Kallin and S. L. Smith.
The Dynamics of Arsenic in Saturated Porous Media: Fate and Transport Modeling for Deep-Water Sediments, Wetland Sediments and Groundwater Environments, Water Rock Interactions, Ore deposits, and Environmental Geochemistry: A Tribute to David Crerar. R. Hellman and S.A. Wood., Eds. The Geochemical Society, Special Publication No 7., 2001
S. Lan Smith and Peter R. Jaffe.
Modeling the Transport and Reaction of Trace Metals in Water Saturated Soils and Sediments, Water Resources Research, vol. 34, no. 11, p. 3135-3147, 1998
Publications without peer review
S. Lan Smith, Yasuhiro Yamanaka and Michio J. Kishi
Simulating the Cycling of Carbon with a Nitrogen-based Oceanic Ecosystem Model, PICES (North Pacific Marine Science Organization) Scientific Report No. 20, pp. 6-7, 2002
PhD Thesis
S. Lan Smith
Modeling the Transport of Trace Metals in Water Saturated Soils and Sediments as Affected by the Biodegradation of Organic Substrates via Various Terminal Electron Acceptors, Ph.D. Thesis, Princeton University, Dept. of Civil and Environmental Engineering, Environmental Engineering and Water Resources Program, June, 1998
Education
Ph.D., Environmental Engineering and Water Resources Program June, 1998
Dept. of Civil Engineering Princeton University, Princeton, NJ (attended Sept., 1992 to May, 1998)
Bachelor of Science, Chemical Engineering June, 1992
California Institute of Technology, Pasadena, CA (attended Sept., 1988 to May, 1992)