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