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Whose priorities?Providing decision-support for Finnish forest conservation decision-making
Joona LehtomäkiHelsingin yliopisto, Finnish Environment InstituteEEB seminar 2015-01-28
@jlehtoma
1. Suitability of commonly available forest inventory data for informative spatial conservation prioritization in Finnish forests
2. Effects of scale and connectivity at regional and national extents
3. Develop, demonstrate, and implement a practical workflow using Zonation
Dissertation
Objectives
• Whose priorities?
• Have the results provided support for decision
making?•
• Additional considerations
• Science-policy interfaces
• Knowledge systems
• Policy X (research, relevance etc.)
This talk
Objectives
Emphasize here what the talk is about and what are they key themes against which the
listener can compare things.
Input data
GIS
Experts
Ecologicalknowledge
Features
Weights
Costs
Connectivity
Higher/lowerpriority areas
for conservation
Performance/potential for
protection
Data collection
Data preparation
Data analysis
Inference/ Decision
Extending the PA network on public land
Typical Zonation workflow
~10 000 ha on public land
~100 ha / location
1 view
Lehtomäki et al. 2009
Extending the PA network on public land
Objectives
Lehtomäki et al. 2009
Extending the PA network on public land
Decision-support
~20 000 ha on public land
~100 ha / location
1 view
Input data
GIS
Experts
Ecologicalknowledge
Features
Weights
Costs
Connectivity
Higher/lowerpriority areas
for conservation
Performance/potential for
protection
Data collection
Data preparation
Data analysis
Inference/ Decision
Extending the PA network on public land
Typical Zonation workflow
Data
Scientists
Policymakers
Stakeholders and the public
Knowledge
Decisions
Models of science-policy interaction
The deficit-linear model of science-policy interaction
Soranno et al. 2014; Young et al. 2014
Basic science
Applied science
Societal need
”The Pure Scientist”
”The Science Arbiter”
Basic science
Applied science
Societal need
Societal need Science
Policy A
Policy B
”The Issue Advocate”
”The Honest Broker”
Societal need
Science
Policy A
Policy B
Policy C
Pielke 2008; Calow 2014
”The Science Arbiter”
Basic science
Applied science
Societal need
Societal need
SciencePolicy A
Policy B
”The Issue Advocate”
Stealth advocacy
Pielke 2008; Calow 2014
Private
Reserves
State
ForestryPrivate
State
Lehtomäki et al. in prep
Extending the PA network on private land
Objectives
Kainuu
Pohjois-Pohjanmaa
Etelä-Savo
Keski-Suomi
Pohjois-Savo
Pirkanmaa
Lapin METSO-alue
Pohjois-Karjala
Etelä- ja Keski-Pohjanmaa ja Ra
Lounais-Suomi ja Rannikko
Häme-Uusimaa ja Rannikko
Kaakkois-Suomi
Kuva: Joona Lehtomäki, CC-BY-3.0
CredibilityThe scientific adequacy of the technical evidence and arguments.
SalienceThe relevance of the assessment to the needs of decision makers.
LegitimacyThe perception that the production of information and technology has been respectful of stakeholders’ divergent values and beliefs, unbiased in its conduct, and fair in its treatment of opposing views and interests.
Cash et al. 2003
Models of science-policy interaction
Attributes of science-policy interface
• Data• Knowledge• Decisions
Scientis
ts
Policym
akersPublic
Stakeholders
Models of science-policy interaction
The roundtable model of science-policy interaction
Soranno et al. 2014; Lynman et al. 2007
CredibilityIncreased by bringing multiple types of expertise to the table.
SalienceIncreased by engaging end-users early in defining data needs.
LegitimacyIncreased by providing multiple stakeholders with more, and more transparent, access to the information production process.
Cash et al. 2003
Models of science-policy interaction
Attributes of science-policy interface
Credibility
Quality assessment
Communication of uncertainties
Supply-driven
Salience
Timely
Simple
Demand-driven
Legitimacy
Consensus
Wide participation
Range of views
Sarkki et al. 2013
Models of science-policy interaction
Trade-offs
Providing support for conservation decision making
Whose priorities?
• Credibility• Results genuinely useful for tackling complex issues• Objective vs. subjective (careful here…)
• Salience• Types of information is actually needed• Types of data actually available• Values and preferences
• Legitimacy• Involvement can mean acceptance can understanding• Explicit path of choices for decision-making
Providing support for conservation decision making
Alternative/complementary models
Dicks et al. 2014
Providing support for conservation decision making
Scientists’ role
The Science Arbiter / The honest broker
Whistleblower
ReferencesCash, D.W. et al. (2003) Knowledge systems for sustainable development. Proceedings of the National Academy of Sciences of the United States of
America 100, 8086–91Dicks, L. V et al. (2014) Organising evidence for environmental management decisions: a “4S” hierarchy. Trends in Ecology & Evolution 29, 607–613Lehtomäki, J. (2014) , Spatial conservation prioritization for Finnish forest conservation management. , University of HelsinkiLehtomäki, J. et al. (2009) Applying spatial conservation prioritization software and high-resolution GIS data to a national-scale study in forest
conservation. Forest Ecology and Management 258, 2439–2449Lynam, T. et al. (2007) A Review of Tools for Incorporating Community Knowledge , Preferences , and Values into Decision Making in Natural
Resources Management. Ecology And Society 12, 5Sarkki, S. et al. (2013) Balancing credibility, relevance and legitimacy: A critical assessment of trade-offs in science-policy interfaces. Science and Public
PolicySoranno, P.A. et al. (2015) It’s good to share: Why environmental scientists' ethics are out of date. BioScience 65, 69–73Pielke Jr, R.A. (2007) The Honest Broker: Making Sense of Science in Policy and Politics, Cambridge University Press.Young, J.C. et al. (2014) Improving the science-policy dialogue to meet the challenges of biodiversity conservation: Having conversations rather than
talking at one-another. Biodiversity and Conservation 23, 387–404
References – conservation biologyCook, C.N. et al. (2013) Achieving conservation science that bridges the knowledge-action boundary. Conservation Biology 27, 669–678Opdam, P. (2010) Learning science from practice. Landscape Ecology 25, 821–823Reyers, B. et al. (2010) Conservation Planning as a Transdisciplinary Process. Conservation biology 24, 957–65