GFÖ 2013 Talk: Connecting dynamic vegetation models to data - an inverse perspective

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

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

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.

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.

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

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

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!

Page 7

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

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

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!

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.

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

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

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

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

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

Page 16

Florian Hartig

Department of Biometry and Environmental System Analysis

Thank you!