Post on 23-Feb-2016
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Some thoughts on ecosystem service based environmental management: Models, Tools, Examples & Applications. Ralf Seppelt, GEO BON Working Group 6 Meeting, 19-21.3. Paris
Martin-Luther UniversitätHalle-Wittenberg
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Helmholtz Association of German Research Centres
Figures 15 National Centers 24,000 Total staff 8,500 Scientists & Engineers 3,250 Doctorial Students
Headquarters of Helmholtz Centres
Regional branchHelmholtz office
List
Helgoland
Bremerhaven GeesthachtHamburg
Greifswald
Braunschweig
Wolfenbüttel-Remlingen
Göttingen
Magdeburg
Potsdam
Berlin
ZeuthenTeltow
Niemegk
Halle
LeipzigBad
LauchstädtKöln
Jülich
Bonn
Darmstadt
Heidelberg
Lampoldshausen
Karlsruhe
Stuttgart
München
GarchingNeuherberg
Oberpfaffenhofen
Energy
Earth & Environment
Human Health
Key-Technologies
Structure of Matter
Infrastracture and Space
Figures• 900 total staff• 200 Doctorial
Studentswww.ufz.de
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Computational Landscape Ecology
Urban land use and Ecosystem services
Biotic Ecosystem Services
Catchment management
Scenario Develop-ment and Analysis
Plant pheno-logy, stress
Agent based modlling
Statistics
Optimization, High perfor-mance computing
Land use change modelling
Remote Sensing
Model based quantification of robust, reliable relationships between land use, structure and the
functions provided by the ecosystems
Fiel
dsM
etho
ds
Fields and Methods
www.ufz.der/cle
Todays menue
Challenges, methods and tools to analyse ecosystems services on the regional scale Modeling & Analysis Global vs. Regional Scale
Project (GLUES) and research programme in Germany Database Synthesis Model-based analysis
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Global vs. regional assessments
Land management is a regional processo direct feedbacks between
ecosystem services and human well being
o Regional case studies are keyo What are the feedbacks
between regions?o How to synthesize regional
results?
Roudsepp-Haerne et al. (2010, BioScience)
Human well-being and ecosystem services show globally different trends. Why? Reject Hypothesis Lack of data? Lack of undersrtanding Wrong Scale?
Global pollination demand
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Lautenbach et al. (in prep)
Example pollination
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Lautenbach et al. (in prep)
Experimental results on pollination
Dorman et al. (subm.)
Summer 2010 Experiment: • Distribution of bee netsts• Yield increase of 20%
bees released
yield of app
les pe
r tree [kg]
no yes
010
2030
40
bees releasedyes
no
App
le y
ield
[kg/
tree]
**
Analysis of feedbacks: artificial landscapes
Generation 1
10 Generations
Generation 800
Non-linear Trade-offs
~lineare Trade-offs
Recreation = f(forest area, shape)Production = f(fiel size, pollination, soil fertity)Pollination = f(forest edge)
Spatial configuration
Pollination
Non-CultivatedLands
SWAT Watershed System
Channel/Flood PlainProcesses
Point Sources
CultivatedFields
Non-CultivatedLands
SWAT Watershed System
Channel/Flood PlainProcesses
Point Sources
CultivatedFields
Modifications
Parthe basin and biofuel production
study trade-offs between bioenergy production, food production, water quality and water quantity
Varyation of the crop rotation schemes
Objective function: 5 percentile discharge Average NO3- concentration Yield food production Yield bioenergy crops
Results scenario analysis
• trade-offs regarding scenario assumptions
• but: how good are the scenario assumptions?
Runoff [mm]
Yie
ld [t
/ha]
8
9
10
11
12
95 100 105 110
Scenario
Biodiesel_100%Biodiesel_30%Biogas_100%Biogas_30%_aBiogas_30%_avgBiogas_30%_bBiogas_30%_cFood Reference
Group
BiodieselBiogasFoodRef
NO3 Nconc. [mg/l]
Yie
ld [t
/ha]
8
9
10
11
12
3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5
Scenario
Biodiesel_100%Biodiesel_30%Biogas_100%Biogas_30%_aBiogas_30%_avgBiogas_30%_bBiogas_30%_cFood Reference
Group
BiodieselBiogasFoodRef
NO3 Nconc. [mg/l]
Run
off [
mm
]
95
100
105
110
3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5
Scenario
Biodiesel_100%Biodiesel_30%Biogas_100%Biogas_30%_aBiogas_30%_avgBiogas_30%_bBiogas_30%_cFood Reference
Group
BiodieselBiogasFoodRef
Strauch 2010, Strauch, Ullrich, Volk 2010
Generation 2, e.g. Monte Carlo Analysis
Use of genetic algorithms for variation of crop rotation, e.g. variation of land use intensity
Generation 2 -> 30
Generation 30 - 100
Generation 100 - 500
Processes Food yield ~ Bioenergy yield-1
NO3 ~ Food yield-1
NO3 ~ Bioenergy yieldUncertainty Hydrology: high (equifinality
pattern) Yield: low
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Mariage of apple and oranges
Middle Spotted Woodpecker
Red-Backed Shrike
Wood Lark
Leipzig, Germany
Holzkämper & Seppelt, 2007
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Habitat improvement and its valuation
Leipzig, Germany
Task: optimise land use patterns for maximum habitat performance while minimizing costs for land use change
Wood LarkRed-Backed shrikeMiddle Spotted Woodpecker
a) b)
0.0
0.2
0.4
0.6
0.8
1.0
0 5000 10000 15000 0 15000 30000
0 5000 10000 15000
0.0
0.2
0.4
0.6
0.8
1.0
test site 68
profit loss in €/ham
ean
habi
tat s
uita
bilit
y0 5000 10000 15000 20000 25000 30000
0.0
0.2
0.4
0.6
0.8
1.0
test site 92
profit loss in €/ha
Pprofit loss [€/ha]
HSIopt
optim
ized
mea
n ha
bita
t sui
atbi
lity
Pprofit loss [€/ha]
Holzkämper & Seppelt, 2007
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Habitat suitability & Economy
Holzkämper & Seppelt, 2007
Leipzig, Germany
Task: optimise land use patterns for maximum habitat performance while minimizing costs for land use change
Conclusions & Next Steps
Conclusion Trade-offs: Separate (landscape) pattern
and process! Bundles of ecosystem functions/services
are determined by patterns (landscape) and processes and are non-linear.
Next Steps• Regional focus demands synthesis of
place-based studies• Blueprint or standardized prototokoll
required• This is a prerequisite for standardized
data-bases • Modelling, requires data based
backgrounds
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Review: Regional Ecosystem Services Studies
Considered uncertainty Data source Ecosystem services in
isolation Modelling approaches Valuation Number of ecosystem
services Scenario-Analysis Specific recommendations Stakeholder involvement
Seppelt et al. (2011, JApplEcol)
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Review: Regional Ecosystem Services Studies
Seppelt et al. (2011, JApplEcol)
Considered uncertainty Data source Ecosystem services in
isolation Modelling approaches Valuation Number of ecosystem
services Scenario-Analysis Specific recommendations Stakeholder involvement
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Review: Regional Ecosystem Services Studies
It is unclear to what degree biophysical realism is required in ecosystem service assessments
Methods to analyze trade offs among ecosystem services and economic goals are not well developed
Consideration of off-site effects is extremely rare
Involvement of stakeholders rarely extends to the implementation phase (‚ownership‘)
1 2 3 4 5 6 7 8 9 10 11 12 16 17 19 20 22
otherno interactioninteraction
#ES considered in each Study
#Stu
dies
05
1015
20
biophysical realism
service trade-offs
off-site effects
?
stakeholder involvement
Seppelt et al. (2011, JApplEcol)
Blueprint for Assessment Studies
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Purpose & Design
Problemscape & Concept
Analysis, Assessment, Valuation & Test
Recommendation & Results
Monitoring
Seppelt et al. (subm)
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Systainable Landmanagement Programme
www.sustainable-landmanagement.net
Objective of RFP methods and tools for
sustainable land management
different regional, hot spot regions
support these regional research project with consistent global land use and climate change data
Place-based studies in the programme
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GLUES Overarching Scientific Support and Synthesis
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Szenario, Models & Synthesis Common Geodata Infrastructure
Communication & Outreach Stakeholder & Products
GLUES: Global Assessment of Land Use Dynamics, Greenhouse Gas Emissions and Ecosystem Services
GLUES’ methodology
1. GLUES GDI (Geodata Infrastructure) – provides a common infrastructure to publish, share and maintain distributed global and regional data sets, scenario data and model results.
2. Mid-Term Projections (2030/50) – incorporate feedbacks of agricultural markets, land use and climate
3. Long-Term Scenarios (2100) delivers land use change scenarios based on global sustainability goals and climate change, to be used to project land management impacts on global climate.
4. Synthesis develops methods and tools for trade-off and off-site effect analysis, valuation of ecosystem services and support instruments development
GLUES GDI
Concept• Global distributed Geodata Infrastructure: Data
ramains with the owner• INSPIRE Conform• Links up GLUES partners• Links up regional projects (in Version 1)• Open to the community (in Version 2)
Content & Function• Holds global data land use, ecosystem services,
scenarios, climate change etc.• Harvests available databses
• (FAO, IUCN based on standardized links)
Product• embeddeable in Google Earth, ArcGIS• Prototype for internal testing available• Version 1 available 1/2012
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GLUES modelling concept
Concept: Global scale Model based generation of Scenarios on land use,
climate change pattern for short and long term scenarios
Concept: Regional Scale Generec ESF/ESS Models of intermediate complexity
relating land use (change/intensity) to ecosystem services
Using knowlege form well tested established system Covering processes in hot spot regions Partly meta-model to cover processes from regional
projects
Analysis Analysis of trade-offs via optimization and Monte-Carlo
Simulation Analysis on off-site effects
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GLUES synthesis
Concept Use of avaliable data on regional
ecosystem service assessments (meta-analysis, accross projects: TEEB, Conservation Internation, etc.)
Use network and statistical analysis
Analysis Relate Environmental and Economic
Conditions and assessed Ecosystem Services
Identification of threasholds, nonlinearites
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Some concluding statements
Diversity of model developments is beneficial for scientific progress but might be contraproductive for ecosystem service assessments
Off-site effects and trade-off analysis are core challenges for regional studies
Biophysical realism of models supporting ecosystem service assesments is support by systematic analysis
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Thank you for your un-prejudice attention!
Questions welcome:ralf.seppelt@ufz.de (I forgot my cards)
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Some Concluding Statements
Various models and tools are helpful for untangling relationships between land use (intensity) and ecosystem services. In which do we belief?
Modeling provides virtual experiments with landscapes. How not to loose realistic constraints?
Regional resources management is embedded in global processes. How to be quantify off-site effects?
Need for research in appropriate development of reliable instruments and tools for supporting the Ecosystem Service Concept