GFÖ 2013 Talk: Connecting dynamic vegetation models to data - an inverse perspective
1. Florian Hartig
Department of Biometry and Environmental System Analysis
Florian Hartig
Department of Biometry and Environmental System Analysis
Connecting dynamic vegetation models to data -
an inverse perspective
Florian Hartig, University of Freiburg
http://florianhartig.wordpress.com/ GFÖ, 2013, Potsdam
Figures by Ernst Haeckel, scans by Kurt Stüber, MPI Köln
2. Florian Hartig
Department of Biometry and Environmental System Analysis
Purves, D. et al. (2013) Ecosystems: Time to model all life on Earth, Nature, 493, 295-297
3. Florian Hartig
Department of Biometry and Environmental System Analysis
For vegetation models, lots of data
available
► On plant traits
► On a large number of
vegetation distributions /
responses (Hartig et al., 2012)
► The real problem seems
to be to bring this data
together with models in
a meaningful way!
Page 3
Hartig et al. (2012) Connecting dynamic vegetation models to data -
an inverse perspective. Journal of Biogeography, 2012.
4. Florian Hartig
Department of Biometry and Environmental System Analysis
Statistical (correlative) approaches to
using vegetation data
► Response: distribution,
growth, …
► Relate response to other
factors (e.g. soil,
climate) with a simple
relationship
► Essentially inter /
extrapolation of
pattern; difficult to
translate between
different data types
Page 4
Thuiller et al. (2011) Consequences of climate change on the tree of
life in Europe Nature.
5. Florian Hartig
Department of Biometry and Environmental System Analysis
Dynamic (process-based)
vegetation models
Pioneer
Intermediate
Climax
FORMIND animation of a model parameterization for a forest in South Ecuador,
1900-2100 asl ; details see Dislich, C. et al., Simulating forest dynamics of a tropical
montane forest in South Ecuador, Erdkunde, 2009, 63, 347-364
6. Florian Hartig
Department of Biometry and Environmental System Analysis
Recent review
Bayes’ Formula
Direct information
on parameters
Inverse information
on parameters based on
data D on model outputs
Posterior
probability distribution
for parameters Q
7. Florian Hartig
Department of Biometry and Environmental System Analysis
Example: inverse calibration to stand data
► „Vague“ prior information
► Parameter estimation with stand
data across Europe
► Result: better parameters, model
comparison, averaged prediction!
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8. Florian Hartig
Department of Biometry and Environmental System Analysis
Example: inverse calibration to
distribution data
► Physiological model
fit to distribution of
22 European tree
species
► Predicts of course
distributions, but
also carbon / N
uptake …
Page 8
9. Florian Hartig
Department of Biometry and Environmental System Analysis
Example: inverse calibration to
distribution data
► Physiological model
fit to distribution of
22 European tree
species
► Predicts of course
distributions, but
also carbon / N
uptake …
Page 9
10. Florian Hartig
Department of Biometry and Environmental System Analysis
Interim summary
Page 10
► Bayes allows us to fit process-based
models with direct and inverse data
in a statistically meaningful way
► Because process-based models
couple to many outputs
► Data-translators!
► Data synthesizers – challenge:
meaningfull likelihood!
11. Florian Hartig
Department of Biometry and Environmental System Analysis
How to define the inverse term in Bayes
formula?
► As in statistical model, define the
probability of deviating from mean
model predictions by some probability
density function
► Fine for simple problems, problematic
for strongly stochastic models and
for fitting to heterogeneous data Hartig et al. (2012) Connecting dynamic vegetation models to data - an inverse
perspective. Journal of Biogeography, in press.
12. Florian Hartig
Department of Biometry and Environmental System Analysis
Generating complicated likelihood functions:
simulation-based likelihood approximation
Pioneer
Intermediate
Climax
FORMIND animation of a model parameterization for a forest in South Ecuador,
1900-2100 asl ; details see Dislich, C.; Günter, S.; Homeier, J.; Schröder, B. & Huth,
A., Simulating forest dynamics of a tropical montane forest in South Ecuador,
Erdkunde, 2009, 63, 347-364
Local biomass results of 1600
model runs
Field data D
13. Florian Hartig
Department of Biometry and Environmental System Analysis
Generating complicated likelihood functions:
simulation-based likelihood approximation
Pioneer
Intermediate
Climax
FORMIND animation of a model parameterization for a forest in South Ecuador,
1900-2100 asl ; details see Dislich, C.; Günter, S.; Homeier, J.; Schröder, B. & Huth,
A., Simulating forest dynamics of a tropical montane forest in South Ecuador,
Erdkunde, 2009, 63, 347-364
Local biomass results of 1600
model runs
Field data D
14. Florian Hartig
Department of Biometry and Environmental System Analysis
A practical example: fit to data from
Reserva Biológica San Francisco, Ecuador
Pioneer
Intermediate
Climax
FORMIND animation of a model parameterization for a forest in South Ecuador,
1900-2100 asl ; details see Dislich, C.; Günter, S.; Homeier, J.; Schröder, B. & Huth,
A., Simulating forest dynamics of a tropical montane forest in South Ecuador,
Erdkunde, 2009, 63, 347-364
Probability distr. for stem
size distributions
Probability distr. for growth
rates per size class
15. Florian Hartig
Department of Biometry and Environmental System Analysis
A practical example: fit to data from
Reserva Biológica San Francisco, Ecuador
Pioneer
Intermediate
Climax
FORMIND animation of a model parameterization for a forest in South Ecuador,
1900-2100 asl ; details see Dislich, C.; Günter, S.; Homeier, J.; Schröder, B. & Huth,
A., Simulating forest dynamics of a tropical montane forest in South Ecuador,
Erdkunde, 2009, 63, 347-364
Probability distr. for stem
size distributions
Probability distr. for growth
rates per size class
Hartig, F.; Dislich, C.; Wiegand, T. & Huth, A. (2013) Technical Note: Approximate
Bayesian parameterization of a complex tropical forest model Biogeosciences
Discuss., 10, 13097-13128
16. Florian Hartig
Department of Biometry and Environmental System Analysis
Conclusions
► Using Bayes allows coupling proces-
based vegetation models to a wide range
of data (on parameters and outputs)
► Option to use simulation-based
approximations; creates statistical model
based on the ecological processes
► Correlations between heterogeneous data
► Complicated error structures
► What this means for ecological research
► Process-based as data translators and data
synthesizers
► Test of our process-understanding with ALL
data instead of isolated hypothesis with
isolated data
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