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1
Adaptive Management: participatory collaboration to
integrate research, policy and practice
Jan Sendzimir, Ph.D.International Institute for Applied Systems Analysis
2
Illustration courtesy of WWF Hungary
Hungarian Tisza River Floodplain Pre- and Post- Engineering under the original Vasarhelyi Plan (1870)
The Tisza’s length was lowered by more than 400 kilometers
by cutting meanders to straighten the river to aid transport.
Floodplain area was lowered by squeezing channel between dikes
from 38500 km2 to 1800 km2 (in whole basin)
a.
b.
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Multiple Crises in the Tisza River
Valley Ecology– Loss of biodiversity, habitat, beauty– Rising intensity and frequency of floods
Economic– Farms and related businesses disappearing– Loss of fishery, fruit, nut and timber industry
Socio-political– Disappearance of schools, communities– Children uninterested in history and culture
4
“Wicked” Problems:can recognize but not define
them.Malevolent “Policy Resistance”
Mix of Economical, Ecological, Political, Social factors
Cannot focus only one goal–No single objective function to maximize
Many players, actors, stakeholders–work at different levels (scales)–Use different values that are not commensurate - You can’t add them up
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Wicked Problems*: Complex all the way
down. Can’t decompose any one level
into units that can be added back up to the whole picture.– EU, National, Provincial, County
Things are entangled within levels and across levels (up and down).
*Rittel, H., Webber, M. (1973). "Dilemmas in a General Theory of Planning." Policy Sciences 4:pp. 155-159.
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Policy Resistance
Policies often create initial success, but in the long term the system evolves, re-configures, and creates surprises that completely defeat the initial policy.
Example from the USA– Policy: more appliances to reduce work and
create more leisure time,– Surprise: people have less leisure time now
than 1970 when they had fewer appliances.
7
Outline
Sources of Uncertainty – Nature – non-linear dynamics, hierarchical
structure– Society - management
Adaptive Management (AM)– Framework to integrate research and policy– Example applied to river renaturalization
AM applied for sustainability of river basins– SD Indicators - Oder River basin AM – Renaturalizing the Tisza river basin
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Sudden Collapse of the Oldest, Richest Fishery on
Earth
Northwest Atlantic Cod Harvest (1895 – 1993)
AnnualCatchOf Cod(1000 tons)
1900 tons
90 tons25 yearsMore than 400 years
2003 – after 10 years, no sign of recovery
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Catastrophic Examples ofSudden Shifts and Flips
Catastrophic Examples ofSudden Shifts and Flips
Coral Reefscoral vs. algae
Arid Landscapesshrubland vs. grassland
Shallow Lakeseutrophic vs. clear
North Florida Forest– longleaf pine savanna & fire vs.
hardwood forest without fire
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Adaptive System Dynamics
(after Peterson 2001 Using ecological dynamics to move toward an adaptive architecture IN Kibert,C., Sendzimir, J., Guy, B. (2001) Eds. Construction Ecology: Nature as a Basis for Green Building. Spon, Ltd., London )
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HierarchyAsymmetrical Interactions between
levels
Constraint
Constraint
Noise
Noise
Scale
Macro-
Meso-
Micro-
Example
Tree
Stand
Forest
Person
Village
County
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Constraint – higher to lower level– Global CO2 Plant productivity
Reorganization – higher to lower level– Wind Tree fall Light Shade tolerant plants
Disturbance – lower to higher level– Cigarette grass fire Tree fire Forest fire
Reorganization – small scale and surrounding areas– Mangrove forest recovery after hurricanes
Hierarchy – 4 Kinds of Relations
Asymmetrical Interactions between levels
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Vegetative & Atmospheric Scales
Vegetative & Atmospheric Scales
Atmospheric processes occur faster than vegetative processes occurring at the same spatial scale.
LOG SPACE- km
-1
0
1
2
3
4
century
year
month
decade
420- 2- 4- 6
-3
-2
-4
1 000 yrs
day
hour
1cm
1000km
1km
10km
100m
1m
standpatch
crown
needle
forest
region
El Niño
front
s
long waves
thunderstorms
climate change
LOG TIME - years
Vegetative Structures
Atmospheric Processes
10 000 yrs
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What Processes Produce the Forest Mosaic of Bialowieza?
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Time and Space Scales of Processes Structuring the
Bialowieza ForestNo. Process Time
(Cycle Time)
Space(Window Edge)
1 Competition (Light) Seconds Centimeters
2 Senescence/Wind Throw
Days to Months Cm – 5 meters
3 Browsing by Megafauna
Months to Years Cm – 5 meters
4 Contagious Agents (Moths, Fire)
Year to 10 Years 50 m - kilometer
5 Agriculture, tree harvest
Year to 80 years 100 m - kilometers
6 Drought 10 to 100 years 100 kilometers
7 Geomorphology (Glaciers)
1000 to 10 000 yrs
1000 kilometers
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Conceptual Basis for Engineering
Physical Systems1. Static, Stable2. Monotonous,
Homogenous3. Ahistoric4. Rules apply
over a range of scales
Ecological Systems1. Dynamic, Evolving2. Diverse,
heterogeneous3. Historic (memory)4. Different rules
apply at different scales
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Engineering DangersPhysics-based Ecology-
based Rules, laws
– Apply over many scales because one set of physical processes works at more than one scale
– Cross-scale interactions are predictable
Negative externality– Failure=local, abrupt,
catastrophic– Large load bridge
collapse
Rules, laws– Apply at one scale because
one set of ecological processes works at only one scale
– Cross-scale interactions are hard to predict
Negative externality– Failure= Delayed, diffused,
spread over large area, – Irrigation canal blindness
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Engineering DangersNatural Systems Human
Systems Backward looking
– Memory of landscape
• Storages of seeds, nutrients and water
• Genetic legacy• Landscape mosaic
– History of self-organization
• Patterns created by mutually reinforcing processes
Backward looking– Path dependency
• Built environment• Political & Cultural
history
Forward looking– Study, anticipate and
plan for the future– Gather information
and resources, organize and schedule work.
19
Outline
Sources of Uncertainty – Nature – non-linear dynamics, hierarchical
structure– Society - management
Adaptive Management (AM)– Framework to integrate research and policy– Example applied to river renaturalization
AM applied for sustainability of river basins– SD Indicators - Oder River basin AM – Renaturalizing the Tisza river basin
20
Conventional Response to Crisis: Reliving Mistakes
Policy asPolicy asSolutionSolution
CrisisCrisis
ManagementManagementAction as FixAction as Fix
AssessmentAssessment
Report StoredReport StoredIn LibraryIn Library
NextNextCrisisCrisis
Forgetting
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A Failed Management Paradigm Reduce Nature’s Variability = An Economic
Engine Examples of Emergent Problems
following initial success at reducing variability Variability Reduction
Tool1. Nurseries, Larger Catch
capacity
2. Pesticides reduce crop yield variation due to pests
3. CFCs sustain cool temperatures
4. Dikes and channelization contain river level fluctuations
Emergent Problem1. Lost salmon wild
stocks
2. New species become pests, new capacities in old species
3. Ozone hole
4. Rising floodplain, lower capacity to absorb floods
22
New challenges for toxicologyFrom Endocrine Disruptor Chemicals
1. EDC’s have many counter-intuitive properties:• threshold assumption and non-monotonic effects
dosage
effect
Toxic
Non-Toxic
ConventionalAssumption
ObservedDose-Effect Relations
• Synergy At a certain dosage, Chemical A is safe by itself but toxic in combination with Chemical B
Toxic
Non-Toxic
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Systems evolve – animals respond to global warmingby giving birth earlier (2 weeks earlier in Europe).
Actions may yield irreversible resultsEcosystems change “permanently”, afterthousands years they do not change back.
Oak Forests in Scotland 3000 B.C.
Pre-historicForestClearance
SoilChemistryChanges
Bogs in Scotland – Present Day
Blocked
Functioning of Dynamic ComplexityFunctioning of Dynamic Complexity
Because we cannot go back, it is dangerous to compare past with present.
DroughtDrought
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Dynamics in SocietyWomen’s Rights in the 20th
Century 1920 – Vote 1940 – Cannot own business without
husband’s permission (Germany). 1970 – Cannot file tax return alone,
husband must co-sign (Austria). 1990 – Earn less money than men for
the same work. 2020 – Genetic engineering
– Design your children – what color eyes do you want?
25
Summary of ChallengesComplexity forbids us to rely
on: Standards
– Official levels for toxicity, biodiversity– Physical laws apply at all scales
Rules, Strategies, Legal Codes– Management actions often succeed
initially but fail when no learning or adapting occur
Understanding, Theory– System structure and functions change
26
Outline
Sources of Uncertainty – Nature – non-linear dynamics, hierarchical
structure– Society - management
Adaptive Management (AM)– Framework to integrate research and policy– Example applied to river renaturalization
AM applied for sustainability of river basins– SD Indicators - Oder River basin AM – Renaturalizing the Tisza river basin
27
Ways of explaining realityLife is a series of events
12
3
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Management Pathology created by Events World
ViewHealth
Crisis
Chernobyl
Research Report
Myth: every event has one cause and one effect
GAP
Library
Policy
After 5Years thePublic Forgets
maybe
Eventual feedback loop – unplanned, unexpected, often wasted
Challenge: to improve learning and avoid crises
29
Ways of explaining reality
Events
Patterns, Trends
Systemic Structures
Mental Models
What just happened?
What’s been happening?Have we been here or some place similar before?
What are the forces at play contributing to these patterns?
What about our thinking allows this situation to persist?
30
Learning That Persistently Adapts
Truth is not constant but evolutionarySocial and natural systems continue to change
Initial responses to crises were not as important as the sustained capability to learn and respond accordingly.
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Research and Management Linked in a
Cycle of Integrated Learning
Research and Management Linked in a
Cycle of Integrated LearningPolicy asPolicy asHypothesisHypothesis
EvaluationEvaluation
ManagementManagementActions asActions asTestsTests
AssessmentAssessment
32
Outline
Sources of Uncertainty – Nature – non-linear dynamics, hierarchical
structure– Society - management
Adaptive Management (AM)– Framework to integrate research and policy– Example applied to river renaturalization
AM applied for sustainability of river basins– SD Indicators - Oder River basin AM – Renaturalizing the Tisza river basin
33
StraighteningThe RiverKissimmee
1950 - 70
DECLINE or LOSS1. 75% of Floodplain Area2. 90% of Number of Birds3. 50% of River Length4. Hydrological link
between floodplain and river
5. Massive algal blooms on Lake Okeechobee
34
Restoring theKissimmee River Basin 1975 - 90Results – Project
restored1. River curves 2. Floodplain Area (11000
ha)3. Biodiversity 4. Hydrological link
between floodplain and river
5. Lake Okeechobee clear of algal blooms
6. Ecological functions of river and floodplain
35
Kissimmee River Basin Restoration
Elements of Success
Focus on large, slow driving variables– Raise big questions that inspire learning
• Don’t become lost in putting out fires
– Evolution of goals over 100 years• Survival Conquest Water Quantity
Control (Flood) Water Quality Control Provide Recreational and Environmental Values Provide Ecological Services Build Ecological Integrity Build Resilience
36
Kissimmee River Basin Restoration
Elements of Success
Plan to learn, to change goals, adapt– We learn how to get there along the way
• We never know how at the beginning
– Demonstration and Pilot Projects• Explore technical issues related to restoring
ecological resilience• Research into public values and opinions
– Program to Monitor and Re-Evaluate• Hyrological performance and uncertainties
37
The Uncertainty ofUn-straightening a
River Stability of Back-filled Soils– 5 year monitored demonstration project
Sediment and River Mechanics– 3 year physical and math modeling
project Pre-channelization hydrology?
– Small scale restoration experiments with weirs in the floodplain established which hydrological regime was needed
38
Kissimmee River Basin Restoration
Elements of Success
Stay close – learn and do things together– Build trust slowly
• Scoping: broad, inclusive initial assessment• Re-evaluation: persistently revisit key
questions
– Negotiated dialogue unites coalition• Technical Experts never too far in front • Public values challenge technical progress
39
Adaptive Management (AM):
Policies as Hypotheses
Adaptive Management (AM):
Policies as Hypotheses Policies• The question set, based on experience, that sets the stage of further action.
• Not magic bullets that address the right mix of objectives to solve a problem, rather they are astute hypotheses about how the world works
Embrace uncertainty by testing the best questions,
Avoid the trap of assuming certainty by rallying around “solutions.”
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Adaptive Grazing Experiments Minnesota
Dairy Farm
Art Thicke, La Crescent, Minnesota
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Adaptive Science and Practice in Minnesota Prairie
Streams
Adaptive Science and Practice in Minnesota Prairie
Streams Effective Collaboration
– Scientists provide theory and supervise fieldwork– Farmers manage cattle according to experimental
design and help monitor results– Local citizens help monitor stream conditions
Mutual Benefit– Stream conditions improve
– Erosion reduced, water quality improved– Diversity of habitats and species increased
– Farmers increase income and keep their farm– Local citizens learn science, ecology and farming and
spread the knowledge informally– Advance ecological theory on disturbances
Cycles of Erosion and Grazing
A.
B.
C.
43
Outline
Sources of Uncertainty – Nature – non-linear dynamics, hierarchical
structure– Society - management
Adaptive Management (AM)– Framework to integrate research and policy– Example applied to river renaturalization
AM applied for sustainability of river basins– SD Indicators - Oder River basin AM – Renaturalizing the Tisza river basin
44
Connecting our Understanding to the
FutureHot and Dry
Unchanged
Cool and WetHypotheses
Understanding
Alternative Futures
45
Oder River Oder River BasinBasin
in Central in Central EuropeEurope
Study Study AreaArea
Oder River
46
PPoolliiccyy aassHHyyppootthheessiiss
((44,,55,,66))
EEvvaalluuaattiioonn((88))
MMaannaaggeemmeennttAAccttiioonnss aass TTeessttss
((77))
AAsssseessssmmeenntt((11,,22,,33))
Adaptive Management
47
Environmentallyfriendly farms
Revenues fromagri-environmental
programs
Brandattractiveness
Other farmsConversion/Abandoning
to env. friendly farm
Profits of env.friendly farms
Profits from greenlocal products
+
+
Region image
EnvironmentalQuality
Turisticattractiveness
+
+
+
R4Revenues from local products
through green image
Environmentalstandards
Social support forenvironmental
standards
+
Profitsfrom turism +
+
B1
Environmental standardsrise costs and lower crops
+
R2
Nature attractsturists
+
Economic Livingstandard in region +
Environmentallyfriendly practices
+
+
+
+
R1
Revenues throughagri-environmental
programs
Revenuesfrom crops
-+
+ R3
Revenues from localproducts sales to tourists
R5
Env. friendlyfarms supportlocal products
-
B2
Rescueenvironment
-
+
+
+
Turisticinfrastructure
+
Attractiveness ofgreen local products
+
Cost of livingin region
Localself-sufficiency
--
Knowledge andExperience in env.friendly farming
+
Institutional Supportfor env. friendly farms+
Support for greenlocal products
+
Regional foodprocessing capacity
+
Local culturalidentity
+
Farmer's willingnessto cooperate
+
Brand promotion
+
48
Environmentallyfriendly farms
Other farmsConversion/Abandoning
to env. friendly farm
Profits of env.friendly farms
EnvironmentalQuality
Environmentalstandards
Economic Livingstandard in region
Environmentallyfriendly practices
+
+
+
+
+
49
Environmentallyfriendly farms
Revenues fromagri-environmental
programs
Other farmsConversion/Abandoning
to env. friendly farm
Profits of env.friendly farms
+
EnvironmentalQuality
Environmentalstandards
Social support forenvironmental
standards
+
B1
Environmental standardsrise costs and lower crops
Economic Livingstandard in region
+
Environmentallyfriendly practices
+
+
+
+
R1
Revenues throughagri-environmental
programs
Revenuesfrom crops
-
+ -
B2
Rescueenvironment
+
+
Step by step (3)
50
Key Variables RegionalEnvironmentalModel (v.20)
51
Rules for Choosing Indicators
Each variable has at least one indicator
Goal: 15 indicators or less– Transparency in all phases of AM
requires a simple, small set of indicators understandable to everyone.
– Business experience supports this.
52
Importance (working group's view)
Measurabilty Compelling (Stakeholder's view)
Sum
No. Variable Indicator Explanation 0 - 5 0 - 3 0 - 41 Environmental Quality Biodiversity - Species number 5 1 3 9
2 Water quality 5 2 4 11
3 Percentage of viable habitat (green area) 5 1 3 9
4 Environmentally friendly farms Ratio EFF/Total in terms of Number 4 3 3 10
5 Ratio EFF/Total in terms of Area 4 3 3 10
6 Conversion rate 4 3 3 10
7 Revenues from agri-env programs Percentage of maximum subsidy 4 2 3 9
8 Percentage of min yearly income 4 2 3 9
9 GLP Production Sales revenues as percent of total sales per firm 3 2 3 8
10 Number of people employed 4 3 3 10
11 Number of firms 4 3 3 10
12 Profits from GLP total zlotych in region 4 2 2 8
13 Average profitability from GLP per firm 5 2 4 11
14 Profits from env friendly crops total zlotych in region 4 2 2 8
15 Average profitability from env friendly crops per farm 5 2 4 11
16 Profits from green tourism total zlotych in region 4 2 2 8
17 Average profitability from green tourism per firm 5 2 4 11
18 Organizational support for env friendly farms Hours of work on projects Government, NGO3 1 1 5
19 Perceived support by farmers Opinion surveys 4 2 3 9
20 Brand attractiveness Number of people who are aware and/or like brand Opinion surveys 5 1 3 9
21 Support for green local products Hours of work on projects Government, NGO 3 1 1 5
22 Perceived support by green local producers Opinion surveys 5 2 3 10
23 Social support for env standards Percentage of population who support Opinion surveys 5 1 3 9
Voting Criteria
Sustainability Indicators
53
Fitting Indicators to Variables
Key Variables Potential Indicators1. Environmental Quality
Biodiversity - Species number
Water quality
Percentage of viable habitat (green area)
2. Environmentally friendly farms
Ratio EFF/Total (Number)
Ratio EFF/Total (Area)
Conversion rate
54
Fitting Indicators to Variables
Key Variables Potential Indicators
3. Revenues from agri-environmental programs
Percentage of maximum subsidy
Percentage of minimum yearly income
4. Green LocalProduct Production
Sales revenues as percent of total sales per firm
Number of people employed
Number of firms
55
Setting Priorities in Choosing Indicators
Importance (Work Group’s Perspective)– Use experience from all exercises in mapping
interactions, key variables, and feedback loops to estimate what is important to achieve our sustainability goals.
Compelling (Stakeholders’ Perspective)– What is simple, clear, understandable, convincing or
communicable?
Measurability– What is accessible (inexpensive) and quantifiable?
56
Adaptive Assessment in Choosing Indicators
Questions or suspicions at any point can lead back to any other point in the process– Uncertainty about an indicator can
cause re-evaluation of:• Suspect indicator or associated indicators• Key Variable• Structure of mental map (loops,
interactions)
57
The modeling process is iterative.
Figure 3-1 Results of any step can yield insights that lead to revisions in any earlier step (indicated by the links in the center of the diagram).
1. Problem Articulation(Boundary Selection)
3. Formulation4. Testing
5. PolicyFormulation& Evaluation
2. DynamicHypothesis
58
Adaptive FrameworkIntegrating Research, Policy, Management & Local
Action
ProblemArticulation
MappingAssumptions
SettingObjectives
FindingIndicators
DesigningPolicies
Implementation
Monitoring,Evaluation
1
2 3
4
5
6
7
59
How to open the door tonovel visions and solutions?
Events
Patterns, Trends
Systemic Structures
Mental Models
What just happened?
What’s been happening?Have we been here or some place similar before?
What are the forces at play contributing to these patterns?
What about our thinking allows this situation to persist?
How to create real, decisive policy impacts?
60
Real World
Decisions(Experiments)
InformationFeedback
Strategy, Structure,Decision Rules
Mental Models ofthe World
Management
Single LoopLearning
Assessment
L1
61
Real World
Decisions(Experiments)
InformationFeedback
Strategy, Structure,Decision Rules
Mental Models ofthe World
Management
Single LoopLearning
Assessment
L1
L2
Double-LoopLearning
Reflexive Appraisal
L1,L2
62
Real World
Decisions(Experiments)
InformationFeedback
Strategy, Structure,Decision Rules
Mental Models ofthe World
Management
Single LoopLearning
Assessment
L1
L2
L3
L4
Multi-LoopLearning
Reflexive Appraisal
L1,L2,L3,L4
63
Outline
Sources of Uncertainty – Nature – non-linear dynamics, hierarchical
structure– Society - management
Adaptive Management (AM)– Framework to integrate research and policy– Example applied to river renaturalization
AM applied for sustainability of river basins– SD Indicators - Oder River basin AM – Renaturalizing the Tisza river basin
64Illustration courtesy of WWF Hungary
Hungarian Tisza River Floodplain
The original is unattainable, to restore structure and function we must create and
learn as we manage.
Within an Adaptive Management framework use models of Ecological-economic interactions to explore management options, prioritize research, monitor and evaluate indicators, challenge and change people’s underlying world views.
65
NagykörüNagykörü
Tisza River Basin – Ukraine, Romania, Slovakia, Hungary
66
What ecological and economic functions work on an enlarged
floodplain?
Nagykörü
67
Restore Fish NurseriesReconnect abandoned clay-pits along the
dike
68
RenewAncientOrchardsReintroduce Grazing
Function (small scale disturbance)
Revitalize backwaternurseries in ephemeral ponds
(1)(2)
(3)
Tisza riv
er
Dike
69
Monitoring Ecosystem Structure
– Geomorphology• Floodplain elevation, cross-sectional area,
– Patch structure • Distribution of Habitat sizes (area) and inter-patch
distances,
– Ecotones (pattern, packing) • Pattern – type, length, curvature, perimeter/area ratio• Packing - density of ecotones,
– Habitat connectivity • Degree to which size, proximity and pattern link
habitats,
– Community structure• Animals - Guilds • Plants and Animals - Species composition
Schiemer, F. and G. Janauer (1994). "Monitoring rivers and floodplains". Proc. Monitoring of ecological change in wetlands of middle Europe, Linz, Austria, Botanische Arbeitsgemeinschaft am Oberosterreichischen Landesmusium. Stapfia 31 pp.93-107
70
Monitoring Ecosystem Function
– Patch Dynamics • Distribution of Habitat sizes (area) and inter-patch
distances,
– Processes (P/R, nutrients, sediments), • Production/Respiration• Nutrient and Sediment cycling and movement
– Hydrological connectivity • Floodplain and channel(s)
– Community Dynamics• Animals - Guilds • Plants and Animals - Species composition
Schiemer, F. and G. Janauer (1994). "Monitoring rivers and floodplains". Proc. Monitoring of ecological change in wetlands of middle Europe, Linz, Austria, Botanische Arbeitsgemeinschaft am Oberosterreichischen Landesmusium. Stapfia 31 pp.93-107
71
Institutionscan neutralize the best science and
methods
Society’s institutions (anthropology)– Incentives
• Formal – constitution, law, rulings, ordinances, policies
• Informal – understandings, customs, habits
Institutional barriers in the Tisza basin– Centralization of political and taxing power– Policies to manage water or set markets
Institutional bridge– EU requirement to lower grain production
72
SummarySummary
Durable solutions to wicked problems– require that understanding, policy and innovative action can flex and adapt to changes in nature and society
– require integration of theory, research and practice across disciplines and sectors of society
–We must work simultaneously on the parameters, the feedback loops and the world views.
