Some nonlinear problems in plankton ecology
Citable URI
http://hdl.handle.net/1912/5631DOI
10.1575/1912/5631Keyword
Plankton; Marine ecologyAbstract
In marine ecology, the variability of the physical environment is often considered a main
determinant of biological pattern. A common approach to identifying key environmental
forcings is to match scales of variability: fluctuations of a biological variable at a particular
frequency are attributed to forcing by the physical environment at a similar frequency.
In nonlinear systems, however, different scales of variability interact and forcing at one
frequency can produce variability at a different frequency.
The general theme of this dissertation regards the interplay of scales in nonlinear ecological
systems, with an emphasis on the mismatch of scales between biological variables and
environmental forcings in the plankton. The approach is theoretical: I use simple models
to identify conditions leading to such a mismatch. The models are motivated by planktonic
systems and focus on one ubiquitous nonlinear ecological interaction, that between a
consumer and a resource.
This work is organized in three main parts as follows. In the first part, I consider the
interaction between a phytoplankton population and a limiting nutrient resource. Most
models for this interaction consider all cells as equal and group them under a single variable,
the total biomass or cell density. They do not take into account any population
heterogeneity resulting from the life histories of individual cells. However, single cells do
have life histories: each cell progresses through a determinate sequence of events preceding
cell division and the population is distributed in stages of the cell cycle. I incorporate this
distribution (i.e. population structure) , as well as observations on resource control of cell
cycle progression, into chemostat models for the phytoplanktonnutrient interaction. Simulation
results demonstrate that the· population structure can generate oscillatory dynamics
under a constant nutrient supply, and that such oscillations are important to population
dynamics under a variable nutrient supply. Specifically, for a periodic resource supply, the
population displays an aperiodic response with frequencies different from that of the forcing.
I then show that a chemostat model without population structure (the Droop equations)
does not exhibit this transfer of variability: a periodic nutrient supply produces a periodic
population response of exactly the same frequency.
In the second part, I consider a predator and a prey that interact and diffuse along an
environmental gradient. The model is a reactiondiffusion equation, a type of model used in
biological oceanography for planktonic interactions in turbulent flows. I demonstrate that weak diffusion along a spatial gradient may drive an otherwise periodic system into complex
temporal dynamics that include chaotic behavior. I provide evidence for a quasiperiodic
route to chaos as the diffusion rate decreases. Then, I focus on the spatial properties of
the gradient and their consequences for the spatiatemporal dynamics of the system. In
particular, I ask: how do the spatial patterns of the populations compare to the underlying
environmental gradient in the different dynamic regimes (periodicity, quasiperiodicity, and
chaos)? I show that the spatial patterns of predator and prey can differ strongly from the
environmental gradient. In the route to chaos, as diffusion becomes weaker, this difference
is magnified and the populations display smaller spatial scales.
In the work summarized so far, nonlinearity leads to variability in biological variables
at scales not present in the environmental forcings. In the third part of this work, I consider
another consequence of the transfer of variability in nonlinear systems: the lack of a
dominant scale. Patterns that lack a dominant scale but exhibit scale similarity are known
as fractals. The characterization of numerical quantities that vary intermittently has motivated
a generalization of fractals known as multifractals. Here, I give a first application
of multifractals to biological oceanography. I analyze an acoustic data set on zooplankton
biomass to describe the distribution in time of the total variability in the data. This
distribution is highly intermittent: extreme localized contributions account for a large proportion
of total variability. I show that multifractals provide a good characterization of
such variability.
Description
Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Woods Hole Oceanographic Institution and the Massachusetts Institute of Technology June 1995
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