Community Ecology
My early work on stochastic community models
(McKane
et al 2000)
and metapopulation models
(Alonso
and McKane 2002)
establishes a stochastic mathematical framework to analyze and model complex
ecological interactions ("The
Stochastic Nature of Ecological
Interactions", PhD Thesis, Alonso 2004).
My approach emphasizes the discrete nature of individuals and
their inherent random interactions. Some of my most influential contributions to community ecology have been done in collaboration with
Rampal
Etienne from the
University of Groningen.
Our research emphasizes, on one side, the evolutionary nature of community assembly (Melian etal, submitted, Rosindell etal, submitted), and, on the other, the unavoidable sampling structure of ecological data
(Etienne et al 2007).
For instance, we have shown than typical ecological patterns, such as the distribution of species abundances, can be predicted from an underlying dynamical community model under different sampling assumptions (Alonso et al 2008).
Simple stochastic dynamical models for communities present two advantages. First, they allow to study in detail the consequences of different models assumptions and, second, they can be used to generate testable predictions providing guidance for empirical studies.
Evolutionary Assembly of Complex Networks
At the University of Groningen, I have been involved in the co-supervision with Rampal Etienne of the PhD project of Francisco Encinas. This project seeks to understand the evolutionary assembly of complex mutualistic networks. Mutualistic communities can be represented by a two-layer network, for instance, a community of plants interacting with a community of pollinator insects. Many other ecological
interactions can be represeted by two-layer networks. My VENI project (Alonso, 2007) uses also a two-layer network representation for a host-parasite community. The ultimate goal of both projects is
to understand the evolution of network structure and its role in maintaining diversity in ecological communities.
The Neutral Theory of
Biodiversity
Neutral theory explores the
theoretical consequences of
assuming that communities are
evolutionarily shaped by random speciation
and ecologically assembled by
random dispersal and random
ecological drift
(Hubbell 2001, Thomson
Special
Topics,
Etienne
and
Alonso
2007).
In the context of this theory, we have shown
that samples from any local community can be seen as a result of
a dispersal-limited sampling
from a larger biogeographical
area
(Alonso
and
McKane
2004,
Etienne
and
Alonso
2005,
Alonso
et al
2006). This new
formulation of sampling has applications both to community
ecology and population genetics and provides a
unifying theoretical framework where other factors beyond
neutrality can be easily considered.
Simple models for food web structure
The topology of food webs is a
major determinant of
ecosystems dynamics and
is ultimately responsible
for their responses to human
impacts (Allesina
et al
2008).
Several simple models have
been proposed for the intricate food webs
observed in nature. However,
none of these previous models
is fully compatible with food
web data. Very often, the best way of testing and comparing alternative models is developing and calculating the probability that the model produces certain observed data set. Given inherent complexities, developing likelihood approaches for food web models is particularly challenging. In collaboration
with
Stefano
Allesina
and
Mercedes
Pascual, we
have developed a method to identify
observed trophic links that are
incompatible with a given
model and a likelihood approach to compare different
simple models based on the
full structure of the network.
These results motivated a new
general model that is able to
generate all empirical data
sets and does so with the
highest likelihood. We hope
that the derivation of the
likelihood for simple models
of network structure will
contribute to a better
understanding of the
relationship between
structure and dynamics of
food webs (Allesina
et al
2008).
Dynamics of Infectious Diseases
In
collaboration with
Alan
McKane from the
University of
Manchester
and
Mercedes
Pascual from
the
University
of Michigan,
I have been
working on the effect of demographic stochasticity on the
non-linear dynamics of infectious diseases. Our recent
theoretical work "Stochastic
Amplification in Epidemics"
(Alonso
et al
2007,
reviewed by
Frederic
Guichard
for Faculty
of 1000 Biology)
provides a novel quantitative description of
stochastic fluctuations in epidemics. There is evidence that
childhood diseases are clustered on specific regions of the
parameter space. Due to inherent instabilities
within these parameter domains, these infectious diseases appear
to be prone to produce huge outbreaks. These fluctuations
challenge control strategies. Our work is the first accurate
quantitative characterization of strong coherent oscillations
through endogenous stochastic resonance in real
epidemiological systems. This phenomenon is also relevant to
population oscillations in
ecological systems in general.
For the last years, I have been developing
methods to analyze time
series data for several
infectious diseases. These
historical records can
potentially register the signal of environmental and climate
change. The main goal of this ongoing research is to quantify the
relative importance of stochasticity, seasonal or climatic
forcing on the nonlinear
dynamics of infectious
diseases. Recently, I have visited University of Michigan several times. There, in collaboration with Mercedes Pascual, we have analyzed one of these time series of monthly malaria counts (1970-2003) from a tee plantation hospital in a Kenyan highland (Kericho, Kenya). Our modeling approach shows that slight increases in average temperatures due to climate change may have a strong non-linear effect on the number of observed malaria cases at the limits of mosquito spatial altitudinal distribution (Alonso, Bouma, and Pascual, in prep).
Conservation
and Community Ecology,
University of Groningen
PO Box 14, 9750 AA Haren,
The Netherlands
Phone: (+31) 50 363
2224
Fax: (+31) 50 363 2273
d.alonso@rug.nl
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