Exploring the interaction of ecosystem processes and ecosystem services for effective decision-makingAlistair McVittie & Ioanna Siameti

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Acknowledgements

• Funded by NERC Valuing Nature Network with support from the Scottish Government’s Strategic Research Programme

• Project team:

SRUC: • Alistair McVittie, Klaus Glenk, Ioanna Siameti

James Hutton Institute: • Julia Martin-Ortega, Wendy Kenyon, Matt Aitkenhead, Inge

Alders, Rupert Hough, Helaina Black

Centre for Ecology and Hydrology: • Lisa Norton, Simon Smart, Francois Edwards, Mike Dunbar

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Content

• Motivation for study• Identifying ecosystem interactions• Developing an interdisciplinary model• Scenario results• Next steps• Summary

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Motivation

• Ecosystem services concept is increasingly being used as a framework for science and policy

• A lot has been done to conceptualise the use of ES, but more required to operationalize ES for decision making

• Need for interdisciplinary working• Better understanding of the links between ecosystem

processes, services and benefits

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MEA framework

66

Ecosystem service cascade

Haynes-Young and Potschin, 2009

77

UK NEA framework

88

Identifying ecosystem interactions

• Workshop of researchers and policy makers• Aim was to identify linkages between

– Ecosystem processes;– Management interventions; and– Four desired outcomes (ecosystem services):

• Sustainable crop yield

• Increased biodiversity

• Improved water quality

• Reduced flood damage

99

Identifying ecosystem interactions

1010

Identifying ecosystem interactions

Attercap network analysis – Matt Aitkenhead, James Hutton Inst

1111

Water quality mapping example

Attercap network analysis – Matt Aitkenhead, James Hutton Inst

1212

Developing an interdisciplinary model

• Even a single policy objective resulted in complex set of interactions

• Needed to simplify the model or identify the key components• Needed an approach that was accessible to all team

members• Decided to use Bayesian Belief Networks

1313

What is a BBN?

Runoff

HighMediumLow

22.050.028.0

Flood Management

OnOff

-47.199-68.960

Surface flow

HighMediumLow

20.060.020.0

0 ± 0

Slope

HighLow

0 100

Rainfall

HighLow

100 0

Flood impact

HighMediumLow

27.640.032.4

Welfare impact

Proximity

NearFar

100 0

Flood risk Proximity Welfare impactHigh Near -100High Far -20Medium Near -60Medium Far -10Low Near -20Low Far 0

Surface flowRainfall Slope High Medium Low

High High 100 0 0High Low 20 60 20Low High 20 20 60Low Low 0 0 100

1414

Developing a BBN

ValuesFinal ecosystem services

Aquatic processes

Terrestrial processes

States of nature Management intervention

River flowlowmediumhigh

26.448.824.8

SeasonAutumnWinterSpringSummer

100 0 0 0

Water temperaturelowmediumhigh

20.060.020.0

Infiltration capacitylowmediumhigh

30.054.016.0

Vegetation coveragezerolowmediumhigh density

5.0015.050.030.0

Land covergrasslandarablenatural vegetation

100 0 0

Biological oxygen demand (BOD)lower than four mglfour to six mglsix to nine mglhigher than nine mgl

25.633.328.212.9

Satisfaction

Water qualitybluegreenyellowred

25.633.328.212.9

Flood risklowmediumhigh

26.448.824.8

Water nutrient concentrationlowhigh

61.338.7

Sedimentation loadlowmediumhigh

30.547.821.6

Soil erosion amountlowmediumhigh

31.050.019.0

Soil typesandy lightloamclay heavy

0 0

100

Rainfalllowmediumhigh

10.060.030.0

Overland flowlowmediumhigh

27.246.626.2

Runoff ratelowmediumhigh

28.347.923.8

Riparian vegetation typeGrassNatural vegetationNo riparian management

50.050.0

0

RegionEast EnglandWest England

0 100

Buffer stripsgrasslandnatural vegetationmixedno buffer strip

0 0

59.6200 0

Aquatic vegetation algaevascular plants

30.070.0

Slopelowmediumhigh

0 100

0

1515

Utility values

Flood risk Water quality Utility

Low Blue (high) 100

Low Green (good) 100

Low Yellow (moderate) 75

Low Red (poor) 50

Medium Blue 65

Medium Green 65

Medium Yellow 50

Medium Red 35

High Blue 50

High Green 50

High Yellow 25

High Red 0

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Example scenarios

Scenario Region Land use Soil type Slope

A East England Arable Light free draining (sandy) Low

B West England Grassland Heavy poorly draining (clay) Medium

C West England Grassland Heavy poorly draining (clay) High

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Model results: ‘utility’ valuesScenario No buffer

stripsBuffer strip management

Grass Natural Vegetation

Mixed

A 55.39 56.71 59.37 58.04

B 55.61 58.23 59.91 59.07

C 54.53 57.42 59.25 58.33

A B C0

0.51

1.52

2.53

3.54

4.55

GrassNatural VegetationMixed

Scenario

‘Util

ity’ g

ain

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Changes in outcome probabilities

Scenario A Status quo (%) Grass(%)

Change in pr

Flood riskLow 28.6 35.2 6.6Medium 48.6 46.8 -1.8High 22.8 18.0 -4.8

Water quality

Blue 22.2 25.1 2.9Green 31.6 32.2 0.6Yellow 28.5 27.0 -1.5Red 17.6 15.7 -1.9

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ValuesPreferencesFinal ecosystem

services

Aquatic processes

Terrestrial processes

States of nature Management intervention

Slope

Vegetation cover

Region

Water quality improvement

Rainfall

Water temperature

River flow

Infiltration

Overland flow

Uptake rate

Biological oxygen demand

Runoff rate

Season

Water nutrient concentration

Sedimentation load

Soil erosionSoil type

Aquatic vegetation

Buffer strip type

Buffer strips

Land use

Flood risk mitigation

Peak flow attenuation

Income (water quality)

Water quality index

Site preference index

Available Substitute Sites

Site Amenities

Site Use

Income (flood risk)

Flood risk index

Proximity

Water quality

Flood risk

Nutrient leaching

Expanding the BBN

2020

Combing different models

Runoff model (GWLF)

Nutrient load model

(PLANET)

Algal production

model (PROTECH)

Land coverLand management Weather Lake nutrient

status

Buffer strips

Presence/absence Location Extent Species Vegetation

cover

CostWater quality

Landscape amenity

Wild species diversity

Climate regulation

Carbon storage

2121

Benefits of the approach

• Provides an opportunity to develop joint knowledge and understanding of the system

• Diagrammatic allows easily visualisation of the system• Doesn’t require precise knowledge of biophysical or socio-

economic relationships• Can combine both quantitative and qualitative information• Degree of complexity can be kept to reasonable level

– due to types of data used; and – working back from outcomes of interest

• BBN software is relatively easy to operate

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Issues

• Do ‘utility’ values need to be linked to actual values, or are weights sufficient?

• Probabilistic outcomes may reflect inherent uncertainty in ecosystems, but:

– How do we apportion values across outcomes (e.g. benefit transfer)?

– How do we account for uncertainty in both outcomes and values?

– Are there important thresholds for preferences?

• How, or can, we integrate values across multiple services?

• Statistical measures such as confidence intervals desirable

• Risks becoming a ‘black box’ in decision making

– Need for stakeholder involvement in model building?