Seminar talk on Ecosystem Modeling
Prof. Ferenc Jordan (The Microsoft Research – Univ. of Trento, Centre for
Computational and Systems Biology, Trento, Italy)
Title: Keystone species in Stochastic Dynamical Ecosystem Simulations
Time: 2010 Feb 04, 10:30 AM
Location: 中研院統計所-蔡元培館2F, 208演講廳
Link:
http://www.stat.sinica.edu.tw/statnewsite/?locale=tw&id=1599#show_seminar
Abstract
The development of approaches to estimate the vulnerability of biological
communities and ecosystems to extirpations and reductions of species is a
central challenge of conservation biology. One key aim of this challenge is
to develop quantitative approaches to estimate and rank interaction strengths
and keystoneness of species and functional groups, i.e. to quantify the
relative importance of species. Network analysis can be a powerful tool for
this because certain structural aspects of ecological networks are good
indicators of the mechanisms that maintain co-evolved, biotic interactions. A
static view of ecological networks would lead us to focus research on
highly-central species in food webs (topological key players in ecosystems).
There are a variety of centrality indices, developed for several types of
ecological networks (e.g. for weighted and un-weighted webs). However, truly
understanding extinction and its community-wide effects requires the use of
dynamic models. Deterministic dynamic models are feasible when population
sizes are sufficiently large to minimize noise in the overall system. In
models with small population sizes, stochasticity can be modelled explicitly.
We present a stochastic simulation-based ecosystem model for identification
of “dynamic key species” in situations where stochastic models are
appropriate. To demonstrate this approach, we simulated ecosystem dynamics
and performed sensitivity analysis using data from the Prince William Sound,
Alaska ecosystem model. We then compare these results to those of purely
topological analyses and deterministic dynamic (Ecosim) studies. We present
the relationships between various topological and dynamic indices and discuss
their biological relevance.
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