päivi haapasaari samu mäntyniemi sakari kuikka fisheries and environmental management group
DESCRIPTION
PARTICIPATORY MODELLING TO ENHANCE UNDERSTANDING AND CONSENSUS WITHIN FISHERIES MANAGEMENT: THE BALTIC HERRING CASE. Päivi Haapasaari Samu Mäntyniemi Sakari Kuikka Fisheries and Environmental Management Group (FEM) University of Helsinki. O:13. JAKFISH ( eu 7th programme ). - PowerPoint PPT PresentationTRANSCRIPT
PARTICIPATORY MODELLING TO ENHANCE
UNDERSTANDING AND CONSENSUS WITHIN
FISHERIES MANAGEMENT: THE BALTIC HERRING
CASEPäivi Haapasaari
Samu Mäntyniemi
Sakari Kuikka
Fisheries and Environmental Management Group
(FEM)
University of Helsinki
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O:13
JAKFISH (EU 7TH PROGRAMME)
Aim: examine and develop institutions, practices and tools that allow complexity, uncertainty and ambiquity to be dealt with effectively within participatory decision making processes
Develop participatory facilitation tools, like participatory modeling
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CASE :PARTICIPATORY MODELLING OF BALTIC MAIN BASIN HERRING
Focus: Factors behind the negative biomass trend and poor growth rates of Baltic Main Basin herring stock
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PARTICIPATORY MODELLING OF BALTIC HERRING : TWOFOLD FOCUSES AND AIMS
Influencing factorsHypotheses →
modelsBuild a meta-
model?Embed parameters
provided by scientific research?
Examine, develop methodology
Validity and reliability of models?
Benefit knowledge base and management?
Understand herring fishery Participatory modelling
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TWO PARTS OF MODELLING
1. Five most important factors that influence
Survival of eggs Growth Mortality
2. + or - effect?3. Strengths of effects4. Uncertainty of
assessments?
1. Which variables?2. Objectives?3. Management
measures?
→ no quantitative information
Biological system model Boundaries for herring fishery management
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MODEL TYPE : BAYESIAN NETWORKS
Qualitative part (graphical model of variables and their relationships)
Model structure based on subjective conceptualisation of problem→ Structure complex systems in understandable
way→ Focus for discussion
Quantitative part: (probability distributions)→ Uncertainty explicit→ Knowledge from different sources and
accuracies6
PARTICIPATION MODE
6 selected stakeholders Researcher Manager Fisherman organisation Commercial fisherman Environmental NGO
Individual stakeholders separately → 6 different models
4-6 hours Stakeholder
(modeling decisions) Modeling expert
(facilitator) Social scientist
(observer) Documentation:
record modelling, discussions, enquiry
Stekeholders and models Modelling sessions
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STAKEHOLDER MODELS
Stakeholders had quite a similar understanding on factors influencing growth, recruitment and natural mortality (total sum of different factors not high) but more differences emerged in assessing strengths of the links → most difficult task!
Defining boundaries and components for herring fishery system easier, but different perspectives brought much variability
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Growth
NaturalMort.
Survival ofeggs
Size distribution
Biomass
Number of fish
Catch inweight
Catch innumbers
Size dist.In catch
Decision Uncertain UtilityBaltic Main Basin herring: framing the problem
Time frame: Annual dynamics
Average salary for fiehermen, stable over years. Nation specificForever, uncertain value
Fishing capacity
Number of fishermen
Price of fish
Fishing effort
Fishing cost
TAC
Dist. Of TAC
Feedback!
Marketsituation
State of economy
Type of processing
Price of fuel
Fishing taxPort costs
Gear cost
Gear regulationsClosed areas/seasons
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Baltic Main Basin herring: framing the problem
Time frame: Annual dynamics
Decision
Growth
NaturalMort.
Survival ofeggs
Size distribution
Biomass
Number of fish
Catch inweight
Catch innumbers
Size dist.In catch
Uncertain
Keep herring pop..on certain level
Fish.mort
SSB not affecting Recr. (H-stick)Seas.&spatial closure
on Sp. groundsTAC
ObserverScientific surv.
Stomach sampling
Cod
Manag. Measures for cod
Sprat
Manag. Measures for sprat
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STAKEHOLDER FEEDBACK
Complexity and high uncertainty of herring fishery Epistemic
uncertainty: general lack of knowledge
Variability uncertainty: system in constant change
Improve understandin Raise awareness Share questions Demonstrate views Combine viewpoints to
improve consensus Improve communi-
cation and cooperation Bring decision-making
closer to grass root level
Difficult Positive
Mee
t eac
h ot
her: G
o th
roug
h th
e st
eps to
gethe
r!
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RESEARCH CONTINUES… Analyse, compare individual models and
build meta-model with the BN tool Present the model to stakeholders, ask
Whether they can adopt the information? Problems?
Assess how well meta-model covers important variables?
Discuss major areas of uncertainty Analyse differences between views →update
model Consider management actions Analyse the process!
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SUMMARY: INCLUDING ECONOMIC AND SOCIAL INFORMATION TO FISHERIES ANALYSIS AND ADVICE: WHY, HOW AND BY WHOM?
Why? Improve understanding of a complex system and its uncertainties.
How? Through synthesising relevant knowledge from different stakeholders and sources through participatory modeling using BNs
By whom? Individuals from different stakeholder groups + scientific expertise of statisticians, fishery scientists, social scientists (etc.)
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