Introducing model-data fusion to graduate students in ecology

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Introducing model-data fusion to graduate
students in ecology

• Topics of discussion
• The impact of NEON on ecology
• What are the desired outcomes from a basic
  curriculum?
• Content of a 1-2 semester course

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data poor data rich
few, isolated effects and interactions
multiple effects, composite forces, contingencies
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Outcomes of a new curriculum
1) The ability to represent ecological processes
as mathematical models.
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Outcomes of a new curriculum
2) A an understanding of the use of process
models, observations, and probability models as
routes to insight.
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Outcomes of a new curriculum
4) Understanding how inferences may be influenced
by temporal and spatial scale.
Fridley, J. D et al. 2007. The invasion paradox
Reconciling pattern and process in species
invasions. Ecology 883-17.
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Outcomes of a new curriculum
3) The ability to represent hidden processes
including all sources of stochasticity.
data model
process model
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Outcomes of a new curriculum
5) Facility in using multiple sources of data to
parameterize and evaluate models.
Data sources Census 15 years Sex / age ratios
22 years Survival 3 years Annual harvest and
culling Annual weather records Literature
estimates of survival, fertility Response to
perturbation
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Outcomes of a new curriculum
6) The ability to collaborate with statisticians
and mathematicians in a way that is mutually
beneficial.
PRIMES
PRogram for Interdisciplinary Mathematics,
Ecology, and Statistics
Plug and play is good news and bad news.
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Outcomes of a new curriculum
7) Quantitative confidence needed to support a
lifetime of self-teaching.
Hobbs, N. T., S. Twombly, and D. S. Schimel.
2006. Deepening ecological insights using
contemporary statistics. Ecological Applications
163-4.
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Resources
Books Clark, J. M. 2007. Models for Ecological
Data. Princeton University Press., Princeton, N.
J. Bolker, B. 2008. Ecological Models and Data
in R. Princeton University Press, Princeton N.
J. Hilborn, R., and M. Mangel. 1997. The
Ecological Detective Confronting Models with
Data. Princeton University Press, Princeton, N.
J. Software R, WinBugs Courses Univeristy of
Washington, Duke, Colorado State University,
University of Florida, Cornell
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Syllabus NR 575, Systems Ecology

• Deterministic models in ecology
• Mathematical basis for dynamic models in discrete
  and continuous time
• A modelers toolbox of useful functions
• Composing models to represent mechanisms
• Basic probability and probability distributions
• Stochastic models and data simulation
• Likelihood
• Support, strength of evidence
• Likelihood ratios
• Likelihood profiles, profile confidence intervals
• Prior information
• Multiple sources of data
• Information theoretics
• Kullback-Leilbler information discrepancy
• AIC and its allies
• Akaike weights
• Multimodal inference
• More sources of stochasticity Process variance,
  observation error, random effects
• Introduction to Bayesian methods

Laboratory Programming in R and WinBugs Examples
from organismal, population, community, ecosystem
ecology
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Statistical Analyses Used in Journals of the
Ecological Society of America
chi-square analysis of variance linear
regression t - test
maximum likelihood model selection Bayesian
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Karieva, P., and M. Anderson. 1988. Spatial
aspects of species interactions the wedding of
models and experiments. Pages 35-50 in A.
Hastings, editor. Community Ecology.
Springer-Verlag, New York.
97 papers 40 issues
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Update of Karieva and Anderson Each point is
take from a paper in Ecology published between
January 2000-December 2006.
229 papers 80 issues