The Theoretical Ecology Lab Teas are designed to be informal meetings for members of the research groups of Simon Levin, Steve Pacala, Henry Horn, and Andy Dobson to give talks on their current research and receive feedback from their audience. The talks are usually 30 minutes, including the question and answer sessions, scheduled on Wednesdays at 1:00 PM. Additionally, other members of the Princeton University community and visitors are welcome to attend and to give presentations.
Talk schedules and email lists are
maintained by Adi Livnat and Katie Hampson. Please contact alivnat@princeton.edu
or khampson@princeton.edu
to have your name added to the labtea email list so that you can
receive reminders about upcoming lab teas.
To view previous schedules and
summaries, go to:
Fall
2002 Spring
2003
Fall
2003
Spring
2004
Fall
2004
Spring
2005
Fall
2005
| Wednesday, September 14, at 1:30 PM |
Josh Weitz |
| Wednesday, September 21, at 1:00
PM |
Parviez Hosseini |
| Wednesday, September 28, at 1:00
PM |
Sam
Flaxman - Cornell University |
| Wednesday, October 5, at 1:00
PM |
Akiko Satake |
| Wednesday, October 12, at 1:00
PM |
Dunia
Lopez-Pintado - Columbia University |
| Wednesday, October 19, at 1:00
PM |
Jeremy Lichstein |
| Wednesday, October 26, at 1:00
PM |
Drew Purves |
| Wednesday, November 2, at 1:00
PM |
(Fall break) |
| Wednesday, November 9, at 1:00
PM |
Juliet Pulliam |
| Wednesday, November 16, at 1:00
PM |
Sergey
Kryazhimskiy |
| Wednesday, November 23, at 1:00
PM |
(Thanksgiving break) |
| Wednesday, November 30, at 1:00
PM |
Heather Leslie |
| Wednesday, December 7, at 1:00
PM |
Marissa Baskett |
| Wednesday, December 14, at 1:00
PM |
Yiqi Luo |
Titles and abstracts (posted approximately one
week before the talk):
Sergey Kryazhimskiy
Patterns in the evolution of flu
There is a lot of excitement now around a potential pandemic of a
new
flu strain. It may seem that the currently circulating strains are not
of concern anymore. Unfortunatly, this is not the case. My talk is
about the evolution (also refered to as the 'antigenic drift') of the
H3N2 and H1N1 flu subtypes that are still around in the human
population. I will explain how I am trying to study the drift using
clustering techniques, what potential benefits and pitfalls this
approach has and what rather puzzling observations it allows to make.
Juliet Pulliam Dynamical consequences of repeated
pathogen introduction and implications for control
Host-pathogen systems are well understood from a theoretical
perspective, and the phenomenon of a rapidly-spreading pathogen 'burning
itself out' by quickly depleting the susceptible population after its
initial introduction is well known. Reintroduction of the pathogen into a
population following an initial epidemic will produce dynamics distinct
from the initial introduction into a naïve population, and I will use a
simple SIR model to show that repeated introductions can be considered a
special case of a well-understood type of dynamical behavior. We will see
that reintroduction of a pathogen into a partially immune population
produces epidemics with smaller peaks but extended duration, which may
facilitate spread to surrounding populations. I will explore the
consequences of this change in dynamics between initial and subsequent
introductions by showing that this change may have been pivotal in the
spread of Nipah virus in peninsular Malaysia in 1999. Finally, I will
discuss the implications of these dynamics for designing Nipah virus
prevention and control strategies. Drew Purves Crown shape and competition for
canopy space
The structure of the forest canopy is crucial to the dynamics and
functioning of forests, both because it determines the understory light
environment, which in turn determines population dynamics; and because it
determines the growth rates of canopy trees, which in turn determines
carbon
fluxes and storage. Current models of forest canopies are inadequate,
because they do not capture the most fundamental aspect of tree growth,
namely that trees are plastic in their growth and so compete for, and
fill,
all the available space in the canopy. A couple of years ago I began to
try
to provide a model formulation that would capture this plastic growth.
Ever
since, this apparently esoteric and highly applied project has continued
to
suggest much deeper and broader implications, largely because of the work
of
others in the Pacala lab (including Steve himself). I'll try to explain
what it is about this modeling effort that I've found so exciting: the
fact that the model formulation was worked out by foresters 40 years ago,
but that none of them thought to tell the broader ecological community;
that
it is mathematically
identical to the latest and greatest formulation for below-ground
competition arrived at by plant ecologists only recently; that
(counter-intuitively) its greater biological realism makes it easier to
implement and possible analyze mathematically; that it leads it to a
ridiculously simple mathematical technique to predict the long-term
dynamics
of forest IBMs without modeling any individuals; and the fun (and
heartache)
I've had parameterizing the model for every species in the US, via the
powerful (but occasionally troublesome) hierarchical Bayesian methods that
we heard about from Jeremy last week. It will be interesting to see
whether
any of you come to share any of this enthusiasm, or whether I just have an
unhealthy obsession with virtual trees.
Jeremy Lichstein Hierarchical Bayesian analysis of
tree
allometries
Hierarchical statistical models allow us to estimate parameters for
entities
(e.g., species) for which we have little data, or even no data at all,
if
there is a known hierarchical (e.g., phylogenetic) relationship among
the
entities. If closely related species for which we do have lots of
data have
similar parameter values, then we might expect the same to be true for
the
data-poor species. Hierarchical modeling provides a framework for
quantifying the preceding sentence.
I will present analyses of height and leaf mass as a function of
diameter
for all 339 tree species recognized by the US Forest Service inventory
program. Tree height is relatively easy to measure, there are lots of
data
for lots of species, and the analysis works quite nicely. In
contrast, most
forest researchers are not keen on blowing an entire day just to weigh
the
leaves of a single tree, and those who do attempt this foolish
enterprise
are not very good at it. So, the data are few and crappy, and the
analysis
is a mess. This is where the Bayesian part comes in. If you choose
the
right Bayesian priors, you can get some very sweet looking results,
which
admittedly is troubling.
I am hoping to conclude with an open discussion about the pros and
cons of
Bayesian analysis, but this will require that you hang on my every
word as I
try to explain what I did and why.
Dunia Lopez-Pintado Diffusion in Complex Social
Networks
This paper studies the problem of spreading a product (an idea,
cultural fad or technology) among agents in a social network. An
agent obtains the product with a probability that depends on the
spreading rate of the product as well as on the behavior of the
agent's neighbors. This paper shows, using a mean field approach,
that there exists a threshold for the spreading rate that
determines whether the product spreads and becomes persistent or it
does not spread and vanishes. This threshold depends crucially on
the connectivity distribution of the network and on the mechanism
of diffusion.
Akiko Satake An agent-based model for
deforestation
We develop an agent-based model for deforestation to explore how agents
make
decisions associated with deforestation and how these decisions come to
influence macro patterns of land-use over time. We assume that a forest is
composed of many land parcels arranged in a regular square lattice in a
one-dimensional space. Each parcel is in either a forested or a deforested
state. When utilities of forested and deforested states are given,
landowners make a decision to increase the net present value of their
land.
The net present value of the land is the weighting average of the current
utility and the utility to be received in a future. By analyzing
equilibrium
patterns, we show that when landowners largely discount the future
utilities, the deforestation rate is very high and an entire forest
becomes
to be deforested. We give the exact condition representing when
deforestation occurs, and show that the condition is independent of the
landowner's expectation on deforestation rate occurring in a future.
Sam Flaxman
Ideal free
distribution models are commonly employed by both
theoreticians
and empiricists to predict distributions of animals in patchy
environments. These models have undergone nearly constant development and
extension since their original formulation over three decades ago, with
the
result that there are now myriad variations whose predictions have not
been
rigorously tested. I will show how a payoff structure that is common to
many ideal free distribution models causes such models to make predictions
that are paradoxical (and erroneous) for certain empirical
situations. These predictions arise because many of the models lack
sufficient variables to encapsulate natural kinds of variation in
patches. A simple revision of the common payoff structure eliminates the
erroneous predictions, and I provide empirical data that strongly support
the proposed revision.
Parviez Hosseini The potential role of disease in
grassland community structure in California Our project seeks to develop a general, dynamic mathematical
theory of
vector-transmitted pathogen spread in multi-host communities that
explicitly
incorporates the abiotic context. Using barley yellow dwarf virus
(BYDV), we
will build a theory based on a bottom up understanding of how
anthropogenic
environmental change might affect pathogen dynamics and their host
communities, complementing much current research in human and animal
systems. We will independently both parameterize and test
mathematical
models on this pathogen system. Preliminary model predictions suggest
that
BYDV may currently be a key species affecting the community dynamics
of
California grasslands. Thus we plan on elucidating how perennial and
annual
grasses are differentially effect, and are affected by, pathogen
dynamics.
We will also experimentally examine several sets of sub-communities in
the
field that closely match communities of general theoretical interest,
and
also replicate our tests across multiple phylogenetic lineages. By
using
these multiple lineages, we can better understand how these life
history
traits affect pathogen dynamics and community structure. By using
these
sets of sub-communities, we will be able to test how well model
predictions
and experimental results match, strengthening the basis for using
mathematical model to understand how pathogens affect communities. We
will
also examine how vector preference for different hosts, and dependence
on
different hosts, may vary seasonally and effect pathogen dynamics, and
thus
community structure.
Josh Weitz Size and scaling of pedator-prey dynamics
Wednesday,
November 9 @ 1:00 PM
Wednesday,
October 26 @ 1:00 PM
Wednesday,
October 19 @ 1:00 PM
Wednesday,
October 12 @ 1:00 PM
Wednesday,
October 5 @ 1:00 PM
Wednesday,
Septmeber 28 @ 1:00 PM
Wednesday,
Septmeber 21 @ 1:00 PM
Wednesday,
Septmeber 14 @ 1:30 PM
Last updated 01/05/05
pulliam@princeton.edu