gallo 2007 dissertation ucsb geography ecpm uncertainty final
TRANSCRIPT
UNIVERSITY OF CALIFORNIA
Santa Barbara
Engaged Conservation Planning and uncertainty mapping
as means towards effective implementation and monitoring
A Dissertation submitted in partial satisfaction of the
requirements for the degree of
Doctor of Philosophy
in Geography
by John A. Gallo
Committee in charge:
Professor Michael F. Goodchild, Chair
Professor Frank W. Davis
Professor Helen Couclelis
Professor Richard L. Church
Dr. Luis Bojorquez-Tapia
March 2007
The dissertation of John A. Gallo is approved.
____________________________________________ Michael F. Goodchild, Committee Chair
____________________________________________ Frank W. Davis ____________________________________________ Helen Couclelis
____________________________________________ Richard L. Church
____________________________________________ Luis Bojorquez-Tapia
March 2007
ii
Engaged Conservation Planning and uncertainty mapping
as means towards effective implementation and monitoring
Copyright © 2007
by
John A. Gallo
iii
Acknowledgements
This research was supported in countless ways, by too many people to thank.
Regardless, I’d like to give some special thanks…
Thanks to a great mentor, advisor, and role model, Mike Goodchild. Special
thanks to the committee, Helen Couclelis, Frank Davis, Rick Church, and Luis
Bojorquez-Tapia for a wealth of constructive criticism that catalyzed my growth to
the next level of scholarship.
Thanks to the sponsors: the U.C. Regents, Philipp Aida Siff Foundation, the
UCSB Department of Geography, and the Jack Dangermond Fellowship.
Thanks to Conception Coast Project, all of its staff and volunteers, and its
underwriters, namely the Foundation for Deep Ecology, the Money-Arenz
Foundation, Lawson-Valentine Foundation, Patagonia, and the Wendy P. McCaw
Foundation. Thanks to my collaborators on the Regional Conservation Guide: Elia
Machado, Greg Helms, James Studarus, and David Stoms. And thanks to all the
members if the focus groups, especially those that have provided extra effort along
the way: Rachel Couch, Sharyn Main, Ralph Philbrick, Paul Jenkin, Liz Chattin,
Paul Collins, and Cory Gallipeau.
Thanks to helpful colleagues and mentors: Rod Nash, Mike McGinnis, Chris
Bacon, Jenn Bernstein, Evan Girvetz, and Matt Rice.
Thanks to my wonderful family: Mom, Dad, Sabre, Annie, Novia, and Diva
And thanks to Wendy, for all of her love and support.
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Vita of John A. Gallo
National Center for Geographic Information and Analysis Department of Geography
University of California, Santa Barbara Santa Barbara, CA 93106
Education
Ph.D. (Candidate) Department of Geography, University of California, Santa Barbara (UCSB). “Engaged Conservation Planning and uncertainty mapping as a means towards effective implementation and monitoring” Advisor: Professor Michael Goodchild. Completion Date: March 2007
Bachelor of Science Biological Sciences, Ecology and Evolution Emphasis. UCSB 1991-1995
Bachelor of Arts Environmental Studies, Natural Science Emphasis. UCSB 1991-95
Research Positions
Conservation Scientist, John Gallo, Conservation Services and Department of Geography: Santa Barbara, Ca., 2000 to 2006. Used participatory action research to develop Conception Coast Project’s Regional Conservation Guide (see below professional position). Utilized local expert knowledge and optimization modeling to create this public reference that maps and communicates the landscape requirements for maintaining ecological integrity. Assisted with outreach efforts of the guide. Advised several other conservation planning analyses.
Museum Associate, Cheadle Center for Biodiversity and Ecological Restoration: U.C. Santa Barbara, Ca.,1995 to 2002. Studied the ecology of local bird populations. Initiated long-term ecological monitoring program. Managed interns in bird database development and breeding season analysis. Print production.
Teaching Positions
Teaching Assistant, Geography 7: Oil and Water. For Dr. Catherine Gautier Winter 2006. Taught all labs, most of which entailed use of GIS for data viewing, mapping, and cursory analyses. Performed standard TA grading and individual assistance duties.
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Undergraduate Research Facilitator, Geography 199: Group Studies. Spring 2003. Mentored six students from the 2003 Geog. 185a class (below) in enhancing the group project into a prototype of a “living” and interactive web-site for developing and implementing a community-based vision for sustainability. www.geog.ucsb.edu/~gallo/vision
Teaching Assistant, Geography 185a: Planning Issues. For Dr. Helen Couclelis. Winter 2003. Designed and administered the term project: students simulated teams of local experts on different planning issues, and created an integrated vision for sustainability on the web. www.geog.ucsb.edu/~gallo/185a/Vision Performed standard TA duties for this upper division class.
Teaching Assistant, Geography 185a: Planning Issues. For Dr. Helen Couclelis. Winter 2002. Taught bioregionalism, conservation planning and ‘smart growth’ in section. Developed term project: solicited local professionals in advance to identify their “real world” research needs; the nine community members then mentored students, provided information, guided the research, and in turn utilized the research findings. Performed normal TA duties for this upper division class.
Teaching Assistant, Geography 167: Biogeography- The Study of Plant and Animal Distribution. For Doug Fischer. Fall 2001. Performed normal TA duties and co-directed field trips for this upper division class.
Teaching Assistant, Geography 185a: Planning Issues. For Dr. Helen Couclelis. Winter 2001. Taught bioregionalism, conservation planning and ‘smart growth’ in section. Directed term research project: applied or case study research within one of these topics. Performed normal TA duties for this upper division class.
Invited Lecturer and Keynote Speaker, See below section for details.
Professional Positions
Wildlife Biologist, John Gallo, Conservation Services: Santa Barbara, Ca., 1997 to 2007. Performed general avian surveys, point counts, wildlife surveys; and endangered species protocol surveys of southwestern willow flycatcher, least Bell’s vireo, and Belding’s savannah sparrow. Clients include environmental consulting firms and U.S. Forest Service. Example: www.geog.ucsb.edu/~gallo/Gallo_2007_HVP_Bird_Survey.pdf
Project Director, Conception Coast Project: Santa Barbara, Ca., 1996 to 2000. Founded a non-profit organization dedicated to protecting ecological integrity of region through science, community involvement, and long-term planning. Developed project strategy; recruited and managed staff, raised funds, educated public, built collaborative relationships. (www.conceptioncoast.org)
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Wildlife Biologist, U.S. Forest Service, Nez Perce National Forest, Red River District, Idaho, 1993 and 1994. Surveyed for timber wolf and northern goshawk.
Publications
Gallo, J. 2005. Mapping Uncertainty to Ease the Tension between Public Participation GIS and Conservation Planning. In Proceedings of the 4th Annual Public Participation GIS Conference. Urban and Regional Information Systems Association (URISA) July 31 - August 2. Cleveland State University. Cleveland, Ohio
Gallo, J., J. Studarus, G. Helms, and E. Machado. 2005. Regional Conservation Guide. Conception Coast Project. www.conceptioncoast.org/projects_rcg_report.html Santa Barbara, CA.
Gallo, J., and J. Smart. 2003. Who Wants to Help Build a Stronger Sustainability Movement? Hopedance: Pathways to Sustainable Living and Positive Solutions. Issue 36. January-February.
Pyke, C., P. Alagona, N. Goldstein, B. Bierwagen, J. Merrick, H. Rosenberg, and J. Gallo. 1999. A Plan for Outreach: Defining the Scope of Conservation Education. Conservation Biology 13:1238
Gallo, J., J. Scheeter, M. Holmgren, and S. Rothstein. 1999. Initiation of a Long term Ecological Monitoring Project: Avian Point Counts and Habitat Assessments in Riparian Communities at Vandenberg Air Force Base, California. University of California, Santa Barbara Museum of Systematics and Ecology, Environmental Report No. 13
Gallo, J. 1999. Species Account for the Bell’s Sage Sparrow In Holmgren, M. and Collins, P. (eds.) Distribution and Habitat associations of Six Special Concern Bird Species at Vandenberg Air Force Base, California. University of California, Santa Barbara, Museum of Systematics and Ecology, Environmental Report No. 7
Ferren, W., C. Gillespie, and J. Gallo. 1999. Habitat Classifications for Wetlands and Uplands of VAFB In above publication.
Gallo, J. 1996. Quantitative Analysis of the Habitat Requirements for the Bell’s Sage Sparrow, Amphispiza belli belli, at Vandenberg Air Force Base, California. Discovery, UCSB Journal of Undergraduate Research. Santa Barbara, CA www.geog.ucsb.edu/~gallo/sage_sparrow.pdf
Conference Presentations
Gallo, J. 2006. Honest Mapping: Communicating the Uncertainty Inherent to Conservation Planning as a Means Towards Implementation. Society for Conservation GIS. San Jose, CA. June 27.
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Gallo, J. 2006. Reconnecting Society and Nature: Bioregionalism for a New Millennium. Annual meeting the Association of American Geographers. Chicago, IL. March 8.
Gallo, J. 2005. Presenting Conservation Plans: the Role of Imperfection. Association of Pacific Coast Geographers. Phoenix, AZ. October 21.
Gallo, J. 2005. Mapping Uncertainty to Ease the Tension in Public Participation GIS and Conservation Planning. Annual Conference of Public Participation GIS sponsored by URISA. Cleveland, OH. July
Gallo, J. and M. Goodchild. 2005. Can the mapping of uncertainty ease the tension between PPGIS and Conservation Planning? Annual meeting the Association of American Geographers. Denver, CO. April
Gallo, J. and C. Gallipeau. 2003. Modeling landscape Connectivity using a Least-Cost Path Function for Puma (Puma concolor) Dispersal. Agricultural Geography and Biogeography Poster Session. Annual meeting of the Association of American Geographers. New Orleans, LA. March 5.
Gallo, J. 2002. Place-based Conservation Planning. Environmental Sustainability and Policy Session. Annual meeting of the Association of American Geographers. Los Angeles, CA. March 23.
Gallo, J. 2001. Place-based Conservation Planning: A Case Study. Association of Pacific Coast Geographers. Santa Barbara, CA. September 15.
Gallo, J. 2000. Perspectives on Stakeholder Involvement, and a Model Metadata Standard: Summary of the Human-Environment Workgroup. 4th International Conference on Integrating Geographic Information Systems (GIS) and Environmental Modeling. Banff, Alberta. September 8.
Invited Lectures and Keynotes
2006. Conservation GIS in the Santa Barbara Region. Geography 176A: Introduction to Geographic Information Systems. October 26 [Link to 86 mb .ppt]
2005. Landscape Connectivity and Multi-Criteria Conservation Planning. Environmental Studies 100: Environmental Ecology. November 18
2005. Gated Least-Cost-Path Modeling and Landscape Connectivity. Geography 176B: Intermediate Geographic Information Systems. March 3
2005. Landscape Connectivity and Wildlife Corridors. UCSB Museum of Systematics and Ecology, Habitat Restoration Club. Feb. 28
2004. Conservation Planning and GIS. Geography 176C: Advanced Geographic Information Systems. May 27
2003. Habitat Connectivity For Large Mammals. Environmental Studies 20: Watershed Issues, Policy, and Research. November 7
2003. The Web of Sustainable Progress: A Vehicle for Social Change? Antioch University: Community Psychology and Social Change. September 27
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2002. The Wildlands Project and Conception Coast Project: Normative Conservation Planning. Environmental Studies 190: Current Topics in Environmental Studies. April 29
2002. Developing “Common Ground” for the Gaviota Coast. Ecology and Evolution 192B: Shoreline Preservation Research.. March 8
2001. The Fourth Wave of Environmentalism: A Ride Towards Sustainability? Environmental Studies 1: Introduction to Environmental Studies. November 29.
2001. Bioregionalism: The Fourth Wave of Environmentalism. UCSB Regional Experiences Program. November 26
2001. Conservation Planning Case Study. Geography 167: Biogeography. November
2001. The Movement’s Two Front Strategy: Damage Control and the Paradigm Shift. Keynote Address at the Annual Banquet of the Shoreline Preservation Fund, Santa Barbara. May 29.
2000. Conception Coast Project: Bridging Academia, Business, and Government towards a Community-Based Vision. UCSB Arts and Lectures at Campbell Hall. Presented after Dave Forman presented The Wildlands Project. May 3
2000. Biodiversity Conservation and “Reserve Design” within a Planning Context. Geography 185A: Planning Issues. Winter
1999. Biodiversity Conservation within a Planning Context. Geography 185A: Planning Issues. Winter
Honors and Awards
Jack Dangermond Award – 2006. “promise in Geographic Information Science”
Regents Special Fellowship- 2000-2005. University of California The Philip and Aida Siff Educational Foundation Fellowship- 2000-2001. Highest College Honors- 1995. Top 2% of graduating class university-wide Excellence in Environmental Studies- 1995. Outstanding undergraduate
achievement Dean’s Scholar- 1991-1995. Excellent academic performance Regent’s Fellowship- 1991-1995. University of California Mensa Society Award- 1991. Outstanding intellectual promise. Elks Club Fellowship- 1991. Most Valuable Student award. Rotary International Award- 1991. proven and continued “service above self” Valedictorian- 1991. The most outstanding student of high school graduating
class
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Memberships and Societies
Association of American Geographers Society for Conservation Biology Defenders of Wildlife Society for Conservation GIS Los Padres Forest Watch The Wildlands Project Mountain Lion Foundation Union of Concerned Scientists National Geographic Society Californians for Electoral Reform World Wildlife Fund
Relevant Volunteer Service
Peer-reviewer for Annals of the Association of American Geographers. Methods, Models, and GIS section. 2002
Graduate Student Representative, Earth-System Processes Faculty Search Committee 2001.
Stakeholder Representative, Gaviota Coast “Common Ground” Steering Committee: 2000 to 2002. Negotiated the terms and make-up of an eventual stakeholder group. At times was the only “environmentalist” among 20 ranchers and developers. Relevant editorial: www.geog.ucsb.edu/~gallo/commonground.jpg
Undergraduate Representative, Environmental Studies Curriculum Revision Committee: 1994 to 1995.
References
Dr. Michael Goodchild, Professor, Department of Geography, University of California, Santa Barbara. CA 93106 (805) 893-8049; (805) 455-6529 [email protected]
Dr. Helen Couclelis, Professor, Department of Geography, University of California, Santa Barbara. CA 93106 (805) 893-2196 [email protected]
Dr. Rod Nash, Professor Emeritus, Environmental Studies Department, University of California, Santa Barbara. 4731 Calle Reina Santa Barbara, CA 93110 (805) 964-7311 (h) (805) 455-1945 (c) [email protected]
Dr. Frank Davis, Professor, Bren School of Environmental Science and Management, University of California, Santa Barbara. CA 93106 (805) 893-3438 [email protected]
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Engaged Conservation Planning and uncertainty mapping
as means towards effective implementation and monitoring
by
John A. Gallo
ABSTRACT
Conservation planning attempts to ascertain and communicate the spatial needs
of biodiversity in an effort to improve land-use decision-making. Unfortunately,
these communications are largely being ignored, in what has been termed the
‘implementation crisis’ of conservation planning. The purpose of this research is to
help improve systematic conservation planning to better facilitate actual
implementation of conservation action. A participatory action research (PAR)
approach was used, requiring that the researcher was actively involved in a
conservation planning case study. After preliminary scoping, it became apparent
that a critical problem was that the traditional conservation planning maps were
controversial, so they were either creating conflict, or being held back, resulting in a
lack of knowledge-sharing. A design principle and corollary were derived and
tested—if the uncertainty involved with implementation of conservation planning
were quantified and mapped, it would decrease the volatility of the maps and
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increase their influence on implementation. A conservation assessment was
performed and a method for quantifying and mapping this ‘implementation
uncertainty’ was developed and applied. Three advisory groups evaluated two sets
of final products—those with and those without the uncertainty communicated.
Some of the uncertainty products were deemed unnecessary, but the remaining
products were considered superior to the traditional products in their expected ability
to facilitate implementation. But the certainty of the finding was hindered by several
other flaws in the assessment. The PAR experience highlighted the need for a
conservation planning framework that not only 1) identifies the spatial priorities of
biodiversity conservation and management, but that also 2) facilitates and monitors
the implementation of these priorities, and 3) fosters understanding and actions to
support biodiversity. Engaged conservation planning and management (ECPM) was
derived, which dramatically increases participation by utilizing a novel blend of
geospatial browsers, conservation assessment, and the emerging culture of Web 2.0.
Scientists, stakeholders, and landscape observers (i.e. citizen scientists) are able to
engage in two-way knowledge-sharing network that builds the capacity of the
regional institutions to achieve socio-ecological resilience. Details and further
research directions of ECPM are provided.
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Table of Contents
Preface.......................................................................................................................... 1
References: .................................................................................................. 2
Chapter 1: Introduction ................................................................................................. 3
Overview ..................................................................................................... 3
The implementation crisis in systematic conservation planning ................ 7
Developing the ecological perspective as a response to the
implementation crisis ........................................................................... 12
References ................................................................................................. 20
Chapter 2: Engaged Conservation Planning and Management: a team approach to
science and implementation................................................................................. 26
Introduction ............................................................................................... 27
Background to ECPM ............................................................................... 30
Emerging approaches for addressing implementation ........................ 31
Additional theory and practice to be selected from in addressing
implementation............................................................................... 32
Costs to be minimized......................................................................... 34
Conceptual framework: overview of Engaged Conservation Planning and
Management ......................................................................................... 36
Stakeholder Collaboration Network.................................................... 40
Landscape Knowledge Network overview ......................................... 43
Conservation planning refinements..................................................... 44
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An Initial operational model for The Landscape Knowledge Network.... 46
Suggestions for effective practice of the Landscape Knowledge
Network .......................................................................................... 49
Discussion: the expected dimensions and benefits of increased
engagement........................................................................................... 55
The people engaged............................................................................. 55
Some expected benefits of this engagement ....................................... 59
Conclusion: ............................................................................................... 61
References: ................................................................................................ 65
Chapter 3: Communicating the implementation uncertainty of spatial decision
support systems to end-users ............................................................................. 100
Introduction ............................................................................................. 101
Background for examining implementation upncertainty....................... 104
The problem of implementation uncertainty..................................... 104
The decision support hierarchy ......................................................... 108
The issue of implementation uncertainty in a conservation planning
SDSS ............................................................................................ 109
Methodology ........................................................................................... 114
Approach and Overview ................................................................... 114
Methodology of Phase IA: Project Scoping...................................... 115
Methodology of Phase IB: The Marginal Value Resource Allocation
Model............................................................................................ 116
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Methodology of Phase IBi: Landscape Connectivity for the Marginal
Value Model ................................................................................. 124
Methodology of Phase II: Products for Communicating
Implementation Uncertainty......................................................... 132
Methods of Phase III: Focus Groups................................................. 140
Results ..................................................................................................... 141
Results of Phase I and II: Conservation Planning Analysis and the
Products for Communicating Implementation Uncertainty ......... 141
Results of Phase III: Focus Groups and Questionnaires ................... 142
Discussion ............................................................................................... 150
Improvements to the approach via visualization............................... 151
Improving the uncertainty analysis and evaluation........................... 154
Improvements to the approach by prioritizing efforts....................... 156
Conclusion............................................................................................... 157
References ............................................................................................... 158
Chapter 4: Mapping the uncertainty of conservation planning as a means towards
successful implementation ................................................................................. 202
The challenge of knowledge transfer in conservation planning ............. 203
Regional context and the proposed design principle .............................. 206
Uncertainty in conservation planning and the proposed corrolary ......... 209
Case Study: the Regional Conservation Guide ....................................... 213
Discussion ............................................................................................... 217
Key lessons learned........................................................................... 217
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Additional benefits to be explored .................................................... 220
Conclusion............................................................................................... 222
References ............................................................................................... 223
Chapter 5: Conclusion............................................................................................... 234
A framework and operational model designed to improve the
implemenation phase of conservation planning ................................. 234
The potential of uncertainty mapping as a means to improve
implementation in ECPM................................................................... 237
Future Research....................................................................................... 240
References ............................................................................................... 247
Appendix A: Additional material referred to by Chapter 3 ..................................... 252
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List of Figures
Figure 1: Conservation Planning “knowing- doing” gaps.. .......................................... 9
Figure 2a: The First Context of conservation assessment........................................... 13
Figure 2b: The Second Context of conservation assessment ...................................... 14
Figure 2c: The Third Context of conservation assessment ......................................... 15
Figure 3: All three contexts of conservation assessment, with the drivers for action
portrayed ............................................................................................................. 19
Figure 4: Engaged Conservation Planning and Management conceptual framework
diagram A; portraying iterative, two-way knowledge sharing to reduce the
“knowing-doing” gaps. ....................................................................................... 37
Figure 5: Engaged Conservation Planning and Management conceptual framework
diagram B ............................................................................................................ 38
Figure 6: The Stakeholder Collaboration Network is the two-way communication
between and among scientists and stakeholders. . .............................................. 41
Figure 7: The Landscape Knowledge Network links the scientists and landscape
observers (e.g. citizen scientists), and also provides information for the
Stakeholder Collaboration Network (i.e. Fig 5).................................................. 44
Figure 8a: The estimated stakeholder cube for traditional conservation planning. ... 56
Figure 8b: The postulated stakeholder cube for initial Engaged Conservation
Planning and Management.................................................................................. 58
Figure 8c: The postulated shift in peoples’ stakeholder status resulting from Engaged
Conservation Planning and Management............................................................ 59
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Figure 9: The preliminary operational model of ECPM.. ........................................... 62
Figure 10: The Standard Map showing the traditional resource allocation model
output................................................................................................................. 106
Figure 11: Simplified portion of the Implementation-Uncertainty Map................... 142
Figure 12: Grouped semiotic triangles of the Standard Map (top) and the
Implementation-Uncertainty Map and animations (bottom). ........................... 149
Figure 13: An example conservation assessment map.............................................. 205
Figure 14: Simplified portion of the Implementation-Uncertainty Map................... 214
Figure 15: The Web of Resilience can help the self-organization and cooperation
among the different efforts working towards sustainability. ............................ 242
Figure 16: Development and maintenance of Ecological Perspectives at various
scales worldwide has the potential of providing a balance to the Economic
Engine. .............................................................................................................. 245
Figure 17: The Role of Effective Presentation of Imperfect Information in Reducing
Imperfect Knowledge and Improving Group Understanding ........................... 274
Figure18: Normative Comparisons of SDSS Semiotic Triangles............................. 276
Figure 19: Normative Comparisons of SDSS Semiotic Triangles............................ 277
Figure 20: Factors Affecting the wise use of an SDSS............................................. 279
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List of Tables
Table 1: Summary of focus group evaluations ......................................................... 144
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Preface
Human society is growing while life on earth is experiencing its sixth mass
extinction (Wilson 1992; Leakey and Lewin 1996). Unlike the destruction of the
dinosaurs and the four other events, this one is caused by one of the species
themselves, namely, humans (Pimm et al. 1995). Aside from the ethical and
aesthetic implications of this mass extinction, it is also a threat to human quality of
life and basic “ecosystem services” such as clean air and water (Balmford and Bond
2005). Further, this problem is joined by a host of inter-related problems, such as
global climate change, global fisheries collapse, and deforestation. Despite all of
this momentum in such a bleak direction, there is hope, as many aspects of humanity
are still flourishing, and the wonders of the human spirit and life itself still abound.
Conservation science seeks to understand nature, humanity, and nature-humanity
interactions in an effort to slow, and eventually reverse this destruction of life.
Geography has much to offer through its rich tradition of examining what is where
and why it interacts how it does. Further, the relevancy debate in the 1970’s and
80’s about the role of geography legitimized the use normative research (Johnston
and Sidaway 2004). Normative research is to not only to gather facts but also to
point out in which respects the object of study can be improved. The purpose of this
research is to utilize the geographic perspective in furthering conservation science.
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REFERENCES:
Balmford, A. and W. Bond (2005). "Trends in the state of nature and their
implications for human well-being." Ecology Letters 8(11): 1218-1234.
Johnston, R. J. and J. D. Sidaway (2004). Geography and geographers : Anglo-
American human geography since 1945. London ; New York, Arnold.
Leakey, R. E. and R. Lewin (1996). The sixth extinction : patterns of life and the
future of humankind. New York, Anchor Books.
Pimm, S. L., G. J. Russell, J. L. Gittleman and T. M. Brooks (1995). "The future of
biodiversity." Frontiers in Biology: Ecology (Cover Story) v269(n5222):
p347(4).
Wilson, E. O. (1992). The diversity of life. Cambridge, Mass., Belknap Press of
Harvard University Press.
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Chapter 1: Introduction
OVERVIEW
The area-based strategy of conservation is to create reserves or special
management areas in an effort to help biodiversity. Systematic conservation
planning is the scientific approach to prioritizing these areas, implementing their
conservation, and monitoring their contribution towards ecological goals (Margules
and Pressey 2000). But research in this field has focused primarily on the objective
of prioritization, and much less effort has been given to the objectives of
implementation or monitoring (Newburn et al. 2005; Knight et al. In Prep). For this
and other reasons, the implementation of conservation priority areas is occurring in a
piecemeal and ineffective manner (Meir et al. 2004; Pyke et al. In review). This
disconnect between the emphasis of research and the end goal is being recognized as
the implementation crisis of systematic conservation planning (Knight et al. 2006a;
Knight et al. 2006b). It begs the question: how can systematic conservation
planning be improved to facilitate actual implementation of conservation priority
areas?
The first goal of this research is to improve the operational framework of
systematic conservation planning. This is comprised of two objectives, one is to
develop a new conceptual framework and the second objective is to start the
development of an associated operational model [a more context-specific
consideration with an emphasis on methodologies (Knight et al. 2006a)]. The
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second goal is to design and test an approach for hurdling one of the specific barriers
to implementation. The barrier is in the communication of conservation planning
products to individual landowners and residents. Specifically, when conservation
planning maps are publicly released they can be quite controversial and
inflammatory to the stakeholders involved, thereby jeopardizing implementation.
The first objective of this goal is to develop a methodology for communicating a
unique type of uncertainty that arises in conservation implementation. The second
objective is to evaluate if such communication is likely to ease the tension in
publicly releasing the maps.
As is apparent, this research blends reductionism and holism in addressing the
implementation crisis. Reductionism is based on the opinion of Descartes that
everything can be understood by reducing it to its smallest parts, understanding
them, and then putting all of the pieces together. Holism is based on Aristotle’s view
that the whole is often more than the sum of the parts, so it is important to examine
the big picture when searching for understanding. An excellent integration of these
philosophies is General Systems Theory, developed by Ludwig von Bertalanffy,
where a system is a configuration of parts connected and joined together by a web of
relationships. Systems Inquiry includes not only identifying and characterizing the
problem and context, but also the type of system that the problem is embedded.
A participatory action research (PAR) method was utilized. This is a form of
normative case study, which entails starting with a body of theory that is to be
improved, and applying it to an existing process or subject that is also to be
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improved. This design allows novel improvements to both the theory and the
activity. PAR is growing in popularity among interdisciplinary researchers and
entails that they are actively involved in the case study in question rather than
studying it as outsiders (Weisenfeld et al. 2003). The novel improvements occur
because PAR allows researchers to incorporate unexpected issues and concerns into
their methods in a way that effectively bridges the gap between theoretical construct
and practical application (Yin 1993; Smith et al. 1997; Gillham 2000). PAR also
provides an opportunity for an interface between academia and the social entities
participating (Castellanet and Jordan 2002; Fagerstrom et al. 2003; Natori et al.
2005).
The organization that provided the vehicle for the case study was founded in
1995 and is called Conception Coast Project (CCP). This non-profit organization in
the south-central coast of California was developed to protect and restore the natural
heritage of the region. After the organization attained legal status and adopted a
long-term strategic plan, the researcher resigned from his role as founding director
and entered into the Department of Geography in 2000. PAR was performed from
this position, with the activity of focus being one of CCP’s objectives: development
of the Regional Conservation Guide (RCG). The purpose of the RCG was to
communicate the landscape requirements for long-term biodiversity conservation in
an effort to help guide community action. These landscape requirements would be
derived from a qualitative analysis of the region’s biogeography, and would be
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communicated in text and in series of maps. The RCG has since been released to the
public (Gallo et al 2005). This dissertation is the culmination of the PAR.
The remainder of this chapter provides some background documenting the
implementation crisis, identifying the three major contexts in which implementation
occurs, and selecting one to be the focus of the dissertation. Chapter two provides a
new operational framework for systematic conservation planning. It was written at
the conclusion of the PAR cycle, and comes to findings by combining the initial
theoretical background, the “on the ground” experiences, and the current literature.
The framework is designed to dramatically increase the amount of public
participation in systematic conservation planning without jeopardizing the scientific
process. The costs of such an endeavor are minimized through the appropriate use of
recent innovations in geographic information systems (GIS), and information and
communications technology (ICT). The expected benefits of such an approach are
detailed, as well as specific procedural and practical suggestions.
Chapter three addresses the second goal (the communication barrier) by
developing an approach for communicating implementation uncertainty. This
uncertainty arises when a conservation assessment map is used as decision-support
after the starting conditions have changed, making the certainty of the
recommendations for priority areas unknown. To address this uncertainty, a
stochastic simulation is used to quantify and map the areas that are likely to become
priorities after any future perturbations to the plan occur. Focus groups are used to
examine how well the issue was conceptualized and communicated. Chapter four
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examines the supposition that communicating this implementation will decrease
volatility, and thus increase conservation area implementation. Focus groups are
used to examine this issue as well as to determine if the product is suitable for public
release. Chapter five concludes the dissertation, providing a summary of the major
points and indicating some future research agendas. Appendix A is a compilation of
material and focus group quotes that were deemed too bulky to provide in the body.
An important disclaimer is that the terms used for the new concepts (e.g.
implementation uncertainty) and methods are placeholders at the moment, and will
be critically evaluated before broader dissemination.
THE IMPLEMENTATION CRISIS IN SYSTEMATIC CONSERVATION PLANNING
Conservation biology was born “to provide principles and tools for preserving
biological diversity” (Soule 1985). Over the past two decades the discipline has
largely centered around the complex and laudable question of what biodiversity
needs. In addition to the area-based approach mentioned before, the science also
supports the species conservation approach, the ecosystem-based management
approach. It is only recently that conservation biologists started critically looking at
human-environment relations in an effort to actually meet these needs of biodiversity
(Mascia et al. 2003). As alluded before, systematic conservation planning is
especially ripe for evolution.
The suite of systematic conservation planning approaches for prioritizing areas
for conservation is collectively called conservation assessment. Conservation
assessments efforts consider a variety of principles. Early principles included
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making sure that certain biodiversity indicators were well represented in a reserve
system (Scott et al. 1996) and that the reserve system has an effective spatial
configuration regarding size, connectivity, and adjacency of reserves (Noss and
Harris 1986; Margules et al. 1988; Noss and Cooperrider 1994). To be pragmatic,
efforts also attempt to minimize the cost (in area, or estimated monetary value)
required to attain these thresholds of biodiversity protection (Margules et al. 1988).
Ideally, efforts also seek to conserve ecological processes (Rouget et al. 2006). Two
criteria being increasingly included are vulnerability (i.e. threat of destruction) and
irreplaceability (i.e. importance of a particular site to achieving all conservation
criteria) (Margules and Pressey 2000; Cowling et al. 2003; Davis et al. 2006). New
efforts at addressing feasibility via opportunity costs are also being explored (Stoms
et al. 2004). In short, the discipline has developed an impressive practice for
assessing the spatial conservation priorities of a landscape. This overall task is
considered conservation assessment, and is only one component of conservation
planning.
Much less attention has been given to ensuring implementation of these
priorities. There is a “knowing-doing gap” (Pfeffer and Sutton 1999) between
assessment and planning and then again between planning and action (Fig. 1)(Knight
et al. 2006b). A consequence of this result, most conservation assessments have had
only marginal impact towards biodiversity conservation (Cabeza and Moilanen
2001; Meir et al. 2004; Knight et al. 2006a). Further, while the science of
conservation
8
Figure 1: Conservation Planning “knowing- doing” gaps. These problematic gaps
occur in conservation planning when the end goal is conservation assessment rather
than implementation (Knight et al. 2006a).
assessment is being developed and improved in an impressive rate, it is hardly being
translated into practice by practitioners (Prendergast et al. 1999; Pyke et al. In
review). This overall problem is being termed the implementation crisis in
conservation planning (Knight et al. 2006a).
It is difficult to determine how pervasive the problem is because there is a
tendency to publish successes, not failures (Knight 2006). Further, with limited
9
budgets and short funding cycles, the monitoring of the success of plans is often
limited or nonexistent. However, initial efforts are underway to quantify the
dimensions of the crisis. Knight et al. (In Prep) sent a questionnaire to 159 lead
authors of peer reviewed articles that involved conservation assessment and were
from the period 1998-2002. More then two thirds of the assessments did not deliver
action. Of the ones that did, only 13.0% of the actions were considered to be “highly
effective”. The majority of implemented actions – 58.3% – were considered only
“fairly effective”. Almost one fifth, 19.4%, were reported as “poorly effective” and
“ineffective.”
Looking at the issue from another angle, researchers at the US EPA and ICF
International investigated the 406 voter approved biodiversity protection programs in
the US. In these programs, the government creates a large allocation of money, be it
through taxes or bonds, which is strategically used in conserving private property
from willing sellers. Individual landowners would submit an application to have
their land conserved for compensation, and the application would be reviewed by a
program board or decision-maker. The researchers parsed this sample of 406
programs down to 19 that had sufficient information available for in-depth analysis.
They found that 44% of the programs simply reviewed applications on a case-by
case basis using the characteristics of the property, 42% of them used a hybrid
approach that considered the property characteristics and if it was within one of the
general priority areas, and only 4% used exact plans (Pyke et al. In review). Also,
most of the programs used a much larger variety of criteria than are found in the
10
conservation planning literature, such as compatibility with zoning, aesthetics,
accessibility, recreational opportunities, and the availability of partnerships.
This ad hoc decision making will almost certainty increase with the “cooperative
conservation” approach being promoted by the Department of the Interior through
adjustments to existing programs and proposed legislation. As Assistant Secretary
Lynn Scarlett states, the program is “rooted in bottom-up decision-making, respect
for private property, and cooperation rather than conflict” (Christensen 2005).
It is clear that the conservation assessments alone are not enough to inspire
action, and having a plan still does not guarantee effective action (Knight et al. In
Prep). Further, practitioners are often not utilizing the science of conservation
assessment, and that science is not providing tools that meet the needs of
practitioners (Pyke et al. In review). My personal experience corroborates these
findings. During the late 1990’s many of the Conservation Area Designs
(conservation plans) of the Wildlands Project were being completed. As project
director of a regional affiliate, I attended several workshops and rendezvous
meetings. An emerging consensus was that assessment was easy compared to
implementation, and that the implementation strategy should be considered when
designing the assessment. Meanwhile, in the local county of practice, planners and
land trusts are not using systematic conservation planning assessment. The science
of conservation planning is especially ripe for critically looking at human-
environment relations in an effort to actually meet the needs of biodiversity.
11
DEVELOPING THE ECOLOGICAL PERSPECTIVE AS A RESPONSE TO THE IMPLEMENTATION CRISIS
Systematic conservation assessments are used to affect implementation in a
variety of contexts. Three of the general contexts that are in use today are outlined
here. The First Context is when the conservation assessment is merged with the
socio-political culturescape (i.e. society, culture, government, and politics) to create
formal land use plans and policies. These plans are then used to affect conservation
action. Examples of this context are habitat conservation plans, or the Placer County
Legacy (Fig 2a)(Duerksen and Snyder 2005). Most instances of conservation on
public lands also fall into this context, such as the U.S. Forest Service Management
Plan Updates, and the designation of new Wilderness areas. The Second Context for
conservation assessment operates at the parcel scale, and is in implementing
conservation policies that are not spatially explicit. Examples of this include the
government conservation programs outlined above, or similar programs that are
privately fund and operate under general land-use policies (e.g. conservation
easements)(Fig 2b). The Nature Conservancy will often do a Phase one analysis to
identify portfolio areas, which are then approached on a parcel by parcel basis to
explore implementation options. Thus, they use conservation planning in the First
and Second Context.
The Third Context results in products that are not legally binding. Here, the
conservation assessments are used to communicate the ecological principles for
biodiversity conservation in a spatially explicit manner, hereby called the eco-spatial
perspective. This is combined with non-spatially explicit ecological principles to
12
Figure 2a: The First Context of conservation assessment
create the ecological perspective (Fig 2c). This perspective can then be
integrated with community values and goals in the socio-political culturescape, to
create conservation vision and/or implementation strategy that has no legal standing
initially. These ecological perspectives can be used to guide individual action
irrespective of formal laws, or they can be navigated through the socio-political
13
Figure 2b: The Second Context of conservation assessment
culturescape to either instigate Context One or Two conservation planning, or to
influence policy and land-use planning directly. Examples of this approach include
The Wildlands Project, and the Green Vision program of the Environmental
Protection Agency that was especially active in the late 1990’s (Foreman 2000;
Foreman et al. 2000a; EPA 2006).
14
Figure 2c: The Third Context of conservation assessment
In addressing the implementation crisis in conservation planning, it is important
to specify which context is being addressed. Research is needed in all three
15
contexts. Nearly all research in conservation planning is directed at the first two
contexts. Practitioners in the Third Context choose from the tools, techniques, and
practices developed in the other two contexts and then apply them. Granted, it is
through the first two that the most secure conservation actions occur, but what about
the power of democracy, and of bottom-up pressure for policy change? What role
does the Third Context play in instigating conservation action?
Conservation action is an interesting phrase, as it is often not action at all, but
really development inaction. Conservation action can be defined as a commitment to
keep the degree of human impact on an area of land the same as it is, or to go a step
further and decrease current human impact. There are varying degrees of
commitment, such as the designation of a land as United States Wilderness Area,
which will have a high likelihood of being in existence in 50 years, a Habitat
Conservation Plan is an agreement that landowners can develop some lands with “no
surprises”, but in turn cannot develop others until the plan expires (usually about 50
years), enrollment in Williamson Act (A U.S. law that provides a tax incentive to
landowners that promise not to develop their land in 10 years, and penalizes them for
developing sooner), or enrollment in a conservation easement which is a
commitment not to develop the land until the law is changed. An often overlooked
form of commitment is stewardship. This is the commitment of the landowner not
to develop their land, regardless of monetary incentives. The degree of commitment
depends on the individual landowner, and changes over time, especially generations.
16
What are these different factors that affect environmental commitment besides
money? Paul Stern (2000)of the National Resources Council provided a seminal
paper toward a coherent theory of environmentally significant behavior. In it, he
cites his earlier findings which are very significant to this discussion:
“Many approaches toward changing individuals’ environmentally significant
behavior have been tried. Gardner and Stern (1996) reviewed the evidence on
four major types of intervention: religious and moral approaches that appeal
to values and aim to change broad worldviews and beliefs; education to
change attitudes and provide information; efforts to change the material
incentive structure of behavior by providing monetary and other types of
rewards or penalties; and community management, involving the
establishment of shared rules and expectations. They found that each of these
intervention types, if carefully executed, can change behavior. However,
moral and educational approaches have generally disappointing track
records, and even incentive- and community-based approaches rarely
produce much change on their own. By far, the most effective behavior
change programs involve combinations of intervention types.” (Stern 2000,
emphasis added)
This finding is common sense, but it has profound implications regarding the
implementation crisis. Formal land use plans and policies almost exclusively use
just one of these four interventions (monetary incentives/disincentives). It is in the
socio-cultural landscape that all four interventions occur to affect conservation
17
action. Examples of moral/religions intervention are the appeal to the intrinsic value
of nature, or the latest headlines of evangelical policy director Reverend Richard
Cizik talking about “creation care.” Educational approaches include taking people
on hikes, or nature shows on television. Community-based interventions are
multiple-generation neighbors vowing to work together and to not sell their ranches
for development like the folks in the next valley over. Example economic
approaches that are not part of formal land-use law are nature-based tourism on
private game reserves or ranches, and the concept of “predator friendly beef” sold at
a premium price.
It is only the Third Context (i.e. creating ecological perspectives, Fig 2c) that
systematic conservation assessment is used to affect implementation via moral,
educational, community-based, and economic approaches (Fig 3). There is an
untapped wealth of opportunity for conservation action that is feasible through Third
Context conservation assessment, especially if strategic approach and long-term
timeframe are adopted (20-50 years). It is for this reason coupled with the paucity
of research in this arena that the rest of this dissertation is focused on improving
Third Context conservation assessment in order to better facilitate implementation.
18
Figure 3: All three contexts of conservation assessment, with the drivers for action
portrayed
19
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25
Chapter 2: Engaged Conservation Planning and
Management: a team approach to science and
implementation
Abstract. Conservation scientists are creating increasingly sophisticated
tools and algorithms for determining the spatially explicit needs of
biodiversity. But once determined, these needs are not actually being met by
the institutions responsible for implementation. It is increasingly clear that
the complex social, political, and economic dimensions of human-
environment relations, no matter how daunting, need to also be addressed.
This paper synthesizes several literatures into a framework termed engaged
conservation planning and management (ECPM). This framework combines
conservation assessment and ecosystem-based management using the
increased knowledge sharing capabilities of Web 2.0 and geospatial
technologies. Citizens can engage in two primary ways: gathering
biophysical information through sound citizen science, and/or helping the
relevance of the scientific analyses and implementation strategies through a
web-enabled collaborative environment. If effectively implemented, ECPM
has the potential to affect immediate conservation action as well as a long-
term shift in values. Conservation scientists have a window of opportunity to
26
engage this emerging culture and technology to effectively benefit
biodiversity.
Keywords: systematic conservation planning, public participation GIS, PPGIS,
citizen science, Web 2.0, community-based natural resource management, CBNRM,
resilience
INTRODUCTION
Despite much progress in the science of assessment, the principles of island
biogeography and systematic conservation planning are being poorly implemented.
One reason for this is the imbalance of research—most is focusing on modeling the
spatial needs of biodiversity and how to prioritize them amidst human land-use
change, while little focuses on the process of implementation (Newburn et al. 2005).
This is being referred to as the implementation crisis in conservation planning
(Knight et al. 2006a). In starting to look at the implementation process, it is useful to
consider the three general contexts in which conservation assessment leads to
conservation action—(1) integrating with formal land use planning, (2)
implementing land-use zoning and policies in an efficient manner, and (3)
integrating with society irrespective of formal plans or policies (Chapter 1). This
Third Context of conservation assessment results in the creation of long-term visions
and guidance that provide the ecological perspective to the different people in
society that affect conservation action. To be effective, this ecological perspective
should be accessible to a large number of people. Fortunately, new advances in GIS
27
and information and communication technology (ICT) provide opportunities for this
increased engagement, but also pose new constraints and challenges. This leads to
the guiding questions of this essay. How can the engagement of society in
systematic conservation planning be increased and managed effectively? Why is this
worth the cost and hassle?
The goal of this paper is to provide a starting point for this line of research and
development hereby termed engaged conservation planning and management
(ECPM). It has four objectives:
(1) To provide a conceptual framework for ECPM that defines roles and
communication channels for scientists and stakeholders, where
stakeholders are defined as anyone with an interest in an issue.
(2) To propose some methodologies for the specific regional context in
which many of the stakeholders have computers and/or broadband
internet access.
(3) To overview the expected benefits of ECPM.
(4) To briefly point to a variety of references and resources that conservation
planners committed to implementation might find helpful.
ECPM can be loosely defined as the scientific and team-based approach to
priority area assessment, conservation action, and monitoring. An underlying
assumption of this line of research is that engaging more people in at least one stage
of the process will lead to a more thorough outreach of the eventual products. An
initial benchmark of ECPM is to increase the number of people engaged in some
28
stage of any particular conservation planning process by two to three orders or
magnitude. So, instead of the dozens to hundreds of people that are involved in a
typical process, there will be thousands to tens of thousands in an ECPM process.
While ECPM was tailored for the Third Context of conservation planning, its
principles and practices can be applied to conventional Context One or Two
processes in which public participation is beneficial.
This essay begins with some background about emerging approaches to
addressing the implementation crisis. Additional bodies of theory and practice that
influence ECPM are overviewed, and include public participation GIS (PPGIS),
socio-ecological resilience. The many costs and constraints facing ECPM are also
summarized, with the implicit goal of the essay being to find ways of minimizing
them while achieving the engagement benchmark. The second section provides the
conceptual framework of ECPM, introducing two networks of communication, the
stakeholder collaboration network and the landscape knowledge network. The third
section provides key methodological strategies and references for an operational
model, with a focus on the landscape knowledge network. (A conceptual framework
is a general approach and understanding, and an operational model provides methods
suitable for particular conditions (Knight et al. 2006a). In practice, a conservation
planner should move between these two constructs in an action research cycle.) The
discussion lists the expected benefits of the different types of engagement, and points
to studies and essays that further detail each type of benefit. A few lines of further
research are provided in the conclusion, although most further research is discussed
29
at the conclusion of the dissertation (Chapter 5). This essay is novel in several ways,
perhaps most generally in that a synthesis of systematic conservation planning with
PPGIS and ICT in order to improve the practice has not previously been executed as
far as the author is aware.
BACKGROUND TO ECPM
Balmford and Cowling (2006) reflect on the past 20 years of conservation
biology and report that while the discipline has won a few battles, we are losing the
war. The disconnect between people and nature is growing, caused in a large part by
the decreased direct contact people have with nature (Balmford and Cowling 2006).
“Reversing [the trends of this disconnect] and encouraging people to care is an
enormous but in our view, inescapable challenge.” Similarly, the past presidents of
the Society for Conservation Biology state that “success [of the discipline] will be
measured by the degree to which we can integrate scientific understanding into our
community life, by the effectiveness of our approaches to sustaining the diversity of
life and the health of ecosystems, and by the respect for the living world we are able
to foster within our varied cultures and within the human heart.” (Meine et al. 2006)
Despite the emphasis within the discipline on scientific inquiry of biodiversity needs,
these quotes and others indicate the growing mandate to include human values and
institutional processes in the scope of inquiry (Mascia et al. 2003).
30
Emerging approaches for addressing implementation
Fortunately, there is a growing emphasis on examining and addressing
implementation strategies while performing conservation planning (Foreman et al.
2000b; Song and M'Gonigle 2001; Angelstam et al. 2003; Fagerstrom et al. 2003;
Younge and Fowkes 2003; Loucks et al. 2004; Natori et al. 2005; Pierce et al. 2005,;
Davis et al. 2006). Incorporating implementation into the purview of conservation
planning requires a transdisciplinary approach to include both the natural and social
sciences (Angelstam et al. 2003). Most efforts do this informally. Angelstam et. al.
(2003) suggest a more formal Two-dimensional Gap Analysis in which the
horizontal dimension is the traditional conservation planning approach, and the
vertical analysis is a critical evaluation of the institutions and other societal issues
relevant to implementation. Application of this tool improves the implementation
success of the conservation planning activity.
Conservation planning efforts that formally or informally apply this technique
are finding that it is essential to engage the “institutions” that will be involved in
implementation (Angelstam et al. 2003; Fagerstrom et al. 2003; Younge and Fowkes
2003; Loucks et al. 2004; Natori et al. 2005; Pierce et al. 2005). “Institutions
include, but are not limited to beliefs, norms, relationships, property rights, and
agencies” (Angelstam et al. 2003).
A common element in all of these efforts is the inclusion of stakeholders in the
process. Knight et al. (2006a) suggest four other interrelated hallmarks in addition to
stakeholder collaboration: links to a conceptual framework, attention to social
31
learning and action research, development of an implementation strategy, and links
with land-use planning.
This essay seconds the need of these five hallmarks, and focuses on the issue of
social learning institutions—the processes and structures for facilitating two-way
knowledge sharing among scientists, planners, and decision-makers to explore
problems and their solutions (Daniels and Walker 1996; Maarleveld and
Dabgbegnon 1999; Keen et al. 2005; Knight et al. 2006b). ECPM takes this concept
and raises the bar to include interested community members as well.
Additional theory and practice to be selected from in addressing
implementation
Geographic information science and technology (GIScience) provides a key to
dilemmas of environmental management such as the questions driving ECPM
(Goodchild 2003). Of direct relevance is the sub-field of GIScience termed public
participation GIS (PPGIS) (Weiner et al. 2002), or simply participatory GIS (PGIS)
(Rambaldi et al. 2006). It “provides a unique approach for engaging the public in
decision making through its goal to incorporate local knowledge, integrate and
contextualize complex spatial information, allow participants to dynamically interact
with input, analyze alternatives, and to empower individuals and groups” (Siebor
2006). The world wide web (web) has become a central component of PPGIS
communication because of the interactivity and connectivity provided (Goodchild
2000a; Weiner et al. 2002; Liu et al. 2006). The web also harbors a host of
32
opportunities for conservation in general, if the associated threats are minimized
(Levitt 2002; Scharl 2004).
The concept of socio-ecological resilience is especially relevant to social
learning institutions, and hence ECPM. Many leading ecologists, such as Levin
(1999) and Holling (2001), see ecosystems as complex adaptive systems
characterized by historical dependency, complex dynamics, inherent uncertainty,
multiple scales, and multiple equilibria (Berkes 2004). Socio-ecological resilience is
the capability for society to self-organize, learn and adapt within this complex
system (Berkes et al. 2003). The resulting research agenda, embodied by the
resilience alliance, is one of the most exciting in conservation (Ostrom 2006).
Adaptive co-management systems are a central tenet to socio-ecological resilience.
These are flexible, community-based systems of resource management tailored to
specific places and situations and supported by, and working with, various
organizations at different levels (Olsson et al. 2004). Several factors need to be
pursued for such a system including building vision, leadership, and trust; enabling
legislation to create political opportunities; monitoring the environment; combining
different kinds of knowledge; and supporting collaborative learning (Olsson et al.
2004). The challenge lies in applying the principles to conservation planning in
which the lan-use decision of development is more permanent the most adaptive
management treatments.
Engaged conservation planning and management also draws from the theory,
successes and lessons of community-based or integrated natural resource
33
management (Sayer and Campbell 2001; Weber 2003; cbnrm.net 2006), integrated
conservation and development projects (Alpert 1996), community-based
conservation (Western et al. 1994), and bioregionalism (McGinnis 1999). ECPM is
designed to also answer the call for a more public ecology (Robertson and Hull
2001), collaborative learning, (Daniels and Walker 1996) and a social learning
approach to environmental management (Keen et al. 2005). Finally, conservation
psychology provides guidance on how ECPM can best interface with society to
bolster the non-formal implementation drivers discussed in Chapter 1 (Saunders
2003; Winter et al. 2004; Saunders et al. 2006).
Costs to be minimized
There are many direct and indirect costs associated with a participatory approach
such as ECPM. The obvious cost is that increased participation is resource intensive,
requiring additional time, money and staff (Brechin 2003; Dalton 2005). Systematic
conservation planning can occur at many scales, from the landscape scale
(kilometers-wide)(Forman 1995) to the global scale. If the goal of an effort is to
adequately address the theory of island biogeography, and issues such as large
carnivore landscape connectivity, it should be done at the regional scale (hundreds of
kilometers wide) or coarser. Doing this dramatically increases the number of people
that might want to engage and be heard. Because building the ecological perspective
is not situated in the direct path of the economic engine (Chapter 1), then cost is an
especially critical consideration. In other words, there is much less research funding
and institutional support for systematic conservation assessment and PPGIS that is
34
not within the context of formal land-use planning and policies. So money has to be
spent wisely.
Secondly, the spatially explicit results of conservation assessments (e.g. maps of
conservation priorities) can be used to incite fears or anger among landowners about
“loss of liberty” to manage their property as they know is best
(Environmental_Perspectives 2005). This fear and anger leads to resistance,
confrontations, land degradation, and price gouging for conservation on private land
(Foreman 1999; Cohen 2001; Stoneham et al. 2003; Weiss 2003;
Environmental_Perspectives 2005). Due to the coarse scale of the process, the scale
of conflict can be very large and will need to be managed effectively.
Conversely, making the systematic conservation planning process open to local
knowledge and values is threatening to some scientists. The fear is that the
incompetence and irrationality of citizens will be problematic unless they are
properly informed (as reported by Irwin 1995). In other words, the fear is that
community-based projects will benefit local interests at the expense of ecological
objectives (McClosky 1999).
Having the raw data available as part of ECPM can also be quite problematic.
There are privacy issues to reconcile, such as “is it fair for anyone to know what
species or habitats are on a persons property?” There are also concerns of data
custodians, such as “loss of competitiveness, publication by others, copyright and
public acceptability of interpretations” (Froese et al. 2004). Finally some raw data
are sensitive, and public release can be problematic to the processes they represent
35
(Gallo unpublished). For instance, the world’s oldest tree (about 4700 years old) is
not represented on any public map because of the potential for vandalism, or worse
(pers. com. Church).
CONCEPTUAL FRAMEWORK: OVERVIEW OF ENGAGED CONSERVATION PLANNING AND MANAGEMENT
ECPM utilizes the web to increase the level of participation in both stakeholder
collaboration and in the gathering of knowledge, and then uses this information in
performing iterative conservation assessments and developing implementation
strategies (Fig 4). The primary communication channels are through the stakeholder
collaboration network (SCN) and the landscape knowledge network (LKN); both of
which utilize the same knowledge base, with the protocols for accessing and entering
information and human values different for different groups (Fig 5). The structure
for this process can be borrowed from comprehensive planning (Levy 2000) and/or
from framework with roots in planning that was designed originally for landscape
architects but has proven to be especially robust for conservation planning (Steinitz
1990, 1997). Decades of trial-and-error that have gone into developing the practice
of comprehensive planning, which has evolved from having a small group of experts
provide a final product to being centered around participatory planning (Levy 2000).
The first of five phases is the research phase in which data and information are
gathered to examine the current context and past trends. Next, the goals of the
affected community are formulated based on a clear-eyed view of the facts,
constraints, and options. Formulation of the plan follows, usually by developing and
36
Figure 4: Engaged Conservation Planning and Management conceptual framework
diagram A; portraying iterative, two-way knowledge sharing to reduce the
“knowing-doing” gaps.
evaluating alternative courses of action. The fourth phase is implementing the
plan by using all of the mechanisms available. The fifth and crucial element is the
review and updating of the plan, because deviations and surprises are inevitable.
Steinitz (1990) provides an approach for conservation planning that parses the
problem into several model: representation, process, evaluation, change, impact, and
decision. In the scoping phase, the models are created by moving through them in
the reverse order. Then the data needed for each is determined by looking at them in
sequence, then the analysis is performed. After the analysis, the results are
37
Figure 5: Engaged Conservation Planning and Management conceptual framework
diagram B
monitored and the process is either repeated or performed for a different scale or
location.
A key element of ECPM is Web 2.0—an emerging culture and set of tools that
allow asynchronous, distributed, two-way interaction that is stimulating and does not
require an intermediary (O'Reilly 2005; Rogers 2006). The culture emphasizes
online collaboration and sharing. Example websites that embody Web 2.0 principles
38
include Wikipedia (an encyclopedia), Google Earth Community (geographic layers
of information), and YouTube (videos). Users can even create information content
automatically, such as the tallies of the number of visits to a particular item on a site
(e.g. Amazon.com). These developments are more than just a fringe curiosity, but
are instead a central component of the shift to the global knowledge economy and
network society (Corey and Wilson 2006; Kriegman 2006). Scientists, planners, and
stakeholders will all be better served by participating in and helping direct this shift
(Butler 2005; Corey and Wilson 2006; Vinge 2006). With the adoption of Web 2.0
as an integral part of ECPM, communication can occur as never before between and
among these groups, be they within a region, among regions, or between scales (i.e.
from neighborhood to global contexts) (e.g. Stonich 2002).
Adoption of the technological approach to participation is not a panacea by any
means. It requires a careful and critical understanding of the shortfalls of such an
approach. The digital divide between people with computers and those without is
well known, but inadequate in its binary simplicity. Rather, there is a gradation, and
the effectiveness of an endeavor is as much about hardware and broadband access as
it is about social inclusion and context (Warschauer 2003). This is especially true
now that a laptop computer that will cost only $150 is set to be released in mid-2007
(Gardner 2006). Social informatics provides a helpful tradition of examining the
relationship between ICT and society (Kling 2000). Viewing ICT as a socio-
technical network rather than a tool encourages a more nuanced, sustainable
approach to its development. Such development includes combining an ecological,
39
holistic view of social interaction with the conventional business model (Kling
2000). This view influences ECPM in several ways: 1) some regions lend
themselves to ECPM more than others, 2) sociotechnical scoping should be used to
determine an appropriate conservation planning operational model, and 3) in all
ECPM applications, opportunities for meaningful engagement should also be
available to people without computers or fast internet connections. Meaningful
engagement can be through traditional communication channels, or through
accessing ICT via libraries, other public places, internet café sponsors, and ECPM
ambassadors.
Stakeholder Collaboration Network
One opportunity for public participation in ECPM is through the stakeholder
collaboration network (SCN), a web-enabled collaboration environment in which
stakeholder values and visions for the region are gathered and synthesized for
incorporation into the scientific analyses and the creation of implementation
strategies (Fig 6). In ECPM, the stakeholders can interact online from their own
home, on their own time schedule. This can be in something as simple as an e-mail
list-serve, or more structured such as the use of a web-portal linking to functions
such as agenda management, concerns-values organization, alternatives generation,
choice modeling, and reflective review (Dragicevic and Balram 2004; WebLab
2005; Nyerges et al. 2006a). It is also possible to use interactive television (Squire
and Johnson 2000; Pagani 2003; Steinmann and Krek 2006) for some or all of these
objectives. For instance, users can watch a documentary or actors simulating a
40
Figure 6: The Stakeholder Collaboration Network is the two-way communication
between and among scientists and stakeholders. Clouds represent internet
environments, rounded boxes are actions, boxes are people, and arrows show the
predominate direction of information flow. Some information is transferred via
hardcopy, but not depicted on these diagrams.
debate, and then the user can tally their vote and comments on the issue. Soon, these
shows can also be available on the web. This web and/or television interaction
environment can be termed the Web of Values and Teamwork. Mail-in and in-
41
person interactions (e.g. meetings, surveys, and workshops) are also necessary.
Lynam et. al (2007) provide an excellent review of several tools available.
Summaries of mail-in surveys and online interactions can be used in the workshops,
and the workshop dialogue and results can in turn be added to the Web.
The conservation scientists can use this information as it is developed in shaping
and performing the conservation assessments, which are then linked back to the
community for review, evaluation, and use. Thus, there is a robust communication
channel from the people formulating and using the implementing strategies to the
people performing the scientific process (i.e. Figs 4,5) (Irwin 1995; Carver 2003;
Dalton 2005). Other important characteristics of effective stakeholder participation
that should be included are fair decision making, efficient administration, and
positive participant interactions (Haklay and Tobon 2003; Dalton 2005).
The conservation planning literature is often vague about the term ‘stakeholder.’
In PGIS, there is much discussion about stakeholders (e.g. Schlossberg and Shuford
2005), which stemmed from one of the initial concerns of the GIS and Society
debate: the potential for continued marginalization of underprivileged people
(Pickles 1995; Weiner et al. 2002). A useful conception is that everyone in a region
is a stakeholder, but to varying degrees for various issues (Nyerges et al. 2006b).
Determining which of these stakeholders get to participate in a process is delicate.
Experienced practitioners in natural resource management are finding that the best
strategy in the long run is stakeholder self-selection (Jackson 2001). Inviting
everyone from the start to participate protects against individuals or groups derailing
42
the process near completion because they were not included (Jackson 2001). But this
luxury is rarely an option for traditional conservation planning efforts due to
logistical challenges (meeting spaces, outreach, etc.), and even when it is, there can
be factors (such as a large time commitment for midday meetings) that marginalize
the participation of some groups of people. The use of the internet in ECPM
minimizes these logistical and marginalizing factors.
Landscape Knowledge Network overview
Another opportunity for engagement is through the Landscape Knowledge
Network (LKN). The LKN is the portion of ECPM in which data, information, and
knowledge about the regional landscape are gathered, utilized, evaluated, and revised
(Fig 7). The direct actors in the LKN are the landscape observers and the
conservation scientists. The landscape observers collect and review useful data,
information, and knowledge about the region. (Landscape observers include citizen
scientists. The definitions and distinction between the two will be made in the LKN
section of the essay.) The conservation scientists perform two major responsibilities:
1) they implement the scientific analyses (i.e. conservation assessments, land-use
modeling, etc.), and 2) they facilitate all of the processes within the LKN and the
SCN. All of the data, information, and knowledge generated by the landscape
observers and the conservation scientists are organized and communicated to each
other and the stakeholders via the internet and available upon request in hardcopy
materials.
43
Figure 7: The Landscape Knowledge Network links the scientists and landscape
observers (e.g. citizen scientists), and also provides information for the Stakeholder
Collaboration Network (i.e. Fig 5).
Conservation planning refinements
In theory, any of the existing conservation assessment approaches in the
conservation biology literature can be used in the scientific assessment stage of
ECPM. However, it is recommended that the approach should lend itself to iterative
updates using new data and knowledge due to the following logic. Traditional
conservation planning uses complex algorithms to identify the comprehensive design
of a large network of sites that minimizes cost and meets a set of biodiversity
44
thresholds. But a problem arises because the financial and political resources
necessary to conserve such a network are usually enormous, so implementation
occurs on a piecemeal basis (Faith et al. 2003; Meir et al. 2004; Chomitz et al. 2006).
In the meantime, many of the priority areas get degraded or developed, other
conditions change, new data are obtained, cultural values change, and the
understanding of ecological requirements changes. In short, the original set of
conservation priorities becomes outdated and obsolete—it is a moving target. This
issue is especially acute in areas where bottom-up conservation (locations identified
by grant programs, conservancies, or nomination by individuals) is occurring. In the
U.S., such bottom-up conservation is significantly more prevalent than top-down
(Pyke 2006; Pyke et al. in prep.), and this prevalence is likely to grow with the
“cooperative conservation” approach being promoted by the Department of the
Interior (Christensen 2005). (And yet, most of the conservation planning algorithms
favor the far more uncommon top-down paradigm (Pyke 2006; Pyke et al. in prep.).)i
One approach to this problem of the “moving target” that is exacerbated by bottom-
up conservation has been to increase the complexity of the original analysis by
including predictions of how this dynamic process is expected to unfold (Costello
and Polasky 2004; Haight et al. 2005). The most straightforward approach is to
simply re-run the conservation assessment after a period of time, and generate an
new network design based on current data. The cost of this reiteration has
traditionally been prohibitive (Meir et al. 2004). However, recent advances of GIS
software such as ESRI’s ModelBuilder, allow scientists to use drag-and-drop menu
45
interfaces to generate coded scripts of all of their analyses, greatly decreasing costs
of reiteration. For example, a newly minted base layer of data (e.g. land-use) could
be substituted for the old one and the complete analysis, which took weeks to
perform the first time, could be performed automatically overnight. It is also
possible to turn the traditional optimization approach around, by identifying a small
set of sites, given a short-term and realistic budget, that work best towards an
eventual and unknown comprehensive network (Davis et al. 2006). In summary, the
iterative approach is now a viable option for conservation planning, and should
provide the most useful information to society in the long run.
The second suggestion for conservation assessment comes from the oft-
overlooked recommendation of Margules and Pressey’s (2000) seminal paper on
systematic conservation planning: “the realization of conservation goals requires
strategies for managing whole landscapes including areas allocated to both
production and protection”. This is because many biodiversity elements can be
conserved on landscapes that are also managed for human use as well (Binning
1997; Theobald 2004), and doing so is much more feasible then relying on the
reserve-only strategy (Pence et al. 2003). As a result, the conservation assessments
of ECPM should include algorithms that can also select off-reserve conservation
areas (Possingham et al. 2001), and that quantify the relative value of these areas in
contributing to biodiversity goals (e.g. Davis et al. 2006).
AN INITIAL OPERATIONAL MODEL FOR THE LANDSCAPE KNOWLEDGE NETWORK
46
Because of all of the excellent and overlapping threads of research surrounding
the concept of the SCN, and because of limited space here, I will focus on the details
of the LKN in starting to populate an operational model for ECPM.
The data, information, and knowledge generated or used by the landscape
observers and the conservation scientists is distributed throughout the internet, and
collectively called the Web of Landscape Knowledge. This web includes geospatial
knowledge (the geospatial web) as well as aspatial landscape knowledge. At the
regional level, this challenge can be organized through a web-portal collaboration
environment (e.g. Workman 2003; Sakai 2006) which can link to internet sites that
provide spatial and aspatial information. The aspatial sites can have help documents
and tutorials that are linked to a global glossary (EOEARTH 2006), a user designed
encyclopedia (a local wikipedia) and complemented by videos that showcase the
portal, its tools, and how they are used. Videos that describe the project principles
and conservation actions such as ‘best management practices’ will also be linked
from the web-portal (e.g. Workman 2002; Grimm 2006). Locals can also be
empowered to generate videos, images, and text for the portal (Nakashima 2005;
UNESCO 2006). It is important to also distribute the videos and information via
CD-ROMS, VHS, and print-outs for those that don’t have internet, or even
computers.
Google Earth is an emerging example how the spatial information of a region can
be communicated. This free desktop client is very popular, with 100 million user-
downloads by June 2006, the end of its first year. Users can now zoom into any
47
place on earth, change the viewing angle, view the high resolution aerial photograph,
turn on a roads layer, turn on any other layers of information that they have accessed
through their queries, and create their own layers to share, all for free. They can
even access informative, 3-D flyby’s through a region with the click of a mouse (e.g.
Moore 2005). Conservation scientists can make their GIS data viewable by Google
Earth or any web-GIS client.
An excellent summary article in Nature on the impact of this technology includes
the claim: “just as the PC democratized computing, so systems like Google Earth
will democratize GIS” (Goodchild in Butler 2006). ESRI’s new ArcExplorer is
similar to Google Earth and has some additional analytical functions (but does not
have the change in viewing angle). Further, users can query a catalogue (e.g.
Conservation_Geoportal 2006) to find and import methodologies saved using the
aforementioned Modelbuilder. The end-user can then run the method with a click of
the mouse, explore the results of the model, change weights and assumptions, and
then post suggested changes on the communication network.ii World Wind (Kim
2006; Zimmerman 2006) is similar to Google Earth, but is open source and has the
additional capability of viewing data saved locally. A DVD or set of CD’s with
World Wind on it and all of the data for the region (and some pre-programmed 3-D
flybys) could be distributed to and usable by end-users with limited or no internet
connection.
Landscape observers can contribute content to the Web of Landscape Knowledge
by using these GIS clients or through hard-copy entry forms. These GIS clients
48
allow the rapid development of point data layers, with at least one attribute field per
point. Attributed polygon’s can also be created (e.g. wikimapia.com). But both of
these approaches have little structure. Some sites provide more structure, requiring
several attribute fields of data. An example is Worldbirds (2006) which allows users
in many parts of the world to log in and enter the birds they observed during a
survey. The Global Biodiversity Informatics Facility (GBIF 2006a) has a similar
interface, is for all organisms, has protocols on data collection and entry, and links
directly with Google Earth. To make issues even more convenient, it is now possible
to use cell phone text messages while out in the field to enter location data directly
onto the internet (Glennon 2006).
Suggestions for effective practice of the Landscape Knowledge Network
But with all of these opportunities come a host of interrelated challenges in
developing an effective LKN. I will focus on four challenges that directly overlap
with the ECPM goal of engaging significantly more people and still having the
process scientifically sound. The first challenge of the LKN is that the data,
information, and knowledge provided by landscape observers have varying and often
unknown levels of certainty and rigor. How do we know that “Joe Naturalist” really
saw a mountain lion track at the headwaters of his home watershed? Secondly,
traditional ecological knowledge about the landscape is usually structured very
differently than scientific knowledge, and also has associated cultural values that are
embedded and hard to map and analyze (Huntington 2000; Rambaldi 2006).
Thirdly, it is often difficult to motivate people to participate and to garner their trust
49
that the content they provide will be used responsibly. And lastly, the amount of
information available is already overwhelming, gathering more and synthesizing it
all in an ever-changing world requires new means of organization and
communication.
The effective development and use of geoportals is a key to meeting the
challenges of knowledge organization and uncertainty. A geoportal (e.g.
Conservation_Geoportal 2006; geodata.gov 2006; GNO 2006; INSPIRE 2007) is a
web gateway that organizes and communicates geographic content and services such
as directories, search tools, community information, support resources, data, and
applications (Maguire and Longley 2005; Tait 2005). Metadata provides the
organizing structure of geoportals, and is loosely defined as the information
describing the data, content or service. If all the spatial information in the Web of
Landscape Knowledge has some similar metadata categories, then any end-user, be
they a conservation scientist doing analysis or a landscape observer seeing what is
already known for an area they are about to survey, can quickly access the
information that meets their needs and standards. There is a long history of
developing metadata standards within geography (Maguire and Longley 2005), and
the US National Spatial Data Infrastructure has perhaps the most comprehensive
metadata standard. But in many cases the effort required to populate such
comprehensive metadata standards is daunting to the average information provider.
One of the current opportunities for conservation biologists is to prioritize the most
50
important minimum metadata standards for these geoportals such that ECPM is
facilitated.
I suggest that the effective implementation of the LKN requires that the certainty
metadata of content (i.e. an observation of a rare species) is documented. This
allows for the participation to occur in a scientifically sound manner because
everyone could contribute to the knowledge web, and the scientists could easily
extract and use only the information that meets a minimum certainty threshold. The
threshold would depend on the analysis being performed, and would also effect the
certainty of the knowledge that results from the analysis.
But requiring every landscape observer to laboriously document the certainty
metadata (i.e. with a GPS or not, photograph available, etc.) of their content is likely
to discourage new people from getting engaged. Motivation to participate is
challenging enough. For this reason, there are at least three ranks of landscape
observer that correspond to increasing levels of certainty and rigor— amateur
geographers, citizen scientists, and professional scientists. Amateur geographers are
not required to enter in any metadata information that would indicate the certainty of
their observation. Further, they would not be required to follow any survey protocol,
or scientific design (Noss 2001). Their data would be treated accordingly in
subsequent analyses. Such a framework would provide an easy and fun opportunity
for “Joe Naturalist” or even “Jane Stakeholder” to get involved in observing and
learning about their home region as a amateur geographer. Once “hooked,” they will
have the incentive to improve their skills and methods to become a citizen scientist if
51
they want their data to be more useful. The term “amateur geographer” is used
instead of “amateur naturalist” (Noss 2001) because it is has a broader scope and
also invites people that do not consider themselves naturalists.
Citizen science is generally defined as the participation of non-scientists in data
collection for scientific investigations (Trumbull et al. 2000; Lee et al. 2006). It is
rapidly emerging worldwide as a very useful and viable data collection and
engagement strategy (Irwin 1995; Mayfield et al. 2001; Fitzpatrick and Gill 2002;
Kelley and Tuxen 2003; McCaffrey 2005). But the conservation biology community
has been relatively silent, especially in the U.S., in providing guidance in the
development of citizen science. The community has an opportunity to help guide an
emerging culture such that it is most useful to science and biodiversity conservation.
I suggest, for discussion, a minimum of two such protocols. The first is a more
robust approach to documenting survey effort. Presence/absence data can be
extremely effective in conservation analyses and is often more cost effective then
demographic studies (Joseph et al. 2006). This is especially true if legions of
volunteers are employed. But there needs to be a requirement for documenting
surveys that resulted in no observations for the species of note.
The second suggested minimum protocol is an observer certification process of
some sort. This would greatly increase the utility of citizen science for conservation
assessments and other scientific analyses. If every citizen scientist had a rank of
qualification (which could be taxa specific) then any information they entered into
the database would be associated with the observer’s rank. Certainty of any
52
information would then be a function of the person’s qualification and their certainty
of the particular observation (which could be associated with digital photographs).
Scientists could then easily query and use data that met a required threshold of
certainty. The certification process could be based on a combination of written and
field exams (e.g. FGASA 2006), and include random ground-truth validation by
professionals (Fleming and Henkel 2001). These improved protocols should
encourage professional scientists, that have generally been reluctant, to finally start
using such data and encouraging its collection.
Citizen science can be used to fill in gaps of existing data, ground truthing
remote sensing and observational databases, monitoring of conservation actions, and
for user-selected surveys of the region. Obviously, it is most effective if it has a
purpose and a design (Hunsberger 2004). Focusing on monitoring might be the best
way of managing citizen science in the initial stages of a citizen science program.
Monitoring is an imperative component of the adaptive management cycle, and
hence ECPM. Further, it is in monitoring applications that citizen science efforts
seem to be the most developed (Lee 1994; Fleming and Henkel 2001; Mayfield et al.
2001; Hunsberger 2004; Biodiversity_and_Conservation_Journal 2005; Danielsen et
al. 2005).
On a related note, the sharing of sensitive biological data can be problematic for
many reasons (Froese et al. 2004), so it is important to recognize these and address
them accordingly (Froese et al. 2004; GBIF 2006b). This is a research agenda in
itself that is not addressed here.
53
These efforts are aimed primarily at data collection and communication. What
about knowledge, and especially the traditional ecological knowledge and expert
knowledge of local people (TEK)? TEK can be mapped and stored in a GIS format,
and subsequently linked to the Web of Landscape Knowledge (Brodnig and Mayer-
Schönberger 2000; Balram et al. 2004). The ongoing challenge is to document the
values, culture, and ways of knowing that is attached to such information (Tripathi
and Bhattarya 2004; Cundill et al. 2005). In many cases, the people that have
walked the land for their whole life, and learned from generations before them, know
the biodiversity hotspots of a region as well as or better than a multi-criteria analysis
based on necessarily incomplete data (ICSU and UNESCO 2002; UNESCO 2006).
In my experience, the best programs combine both ways of knowing to identify the
areas of corroboration and contention, and then looking in more detail at the sources
of uncertainty in the areas of contention to better determine the situation (e.g.
EDAW 2002). The knowledge that emerges from such deliberation is arguably of
higher quality then the knowledge from either approach individually (Irwin 1995,
2001; Hunsberger 2004; Moller et al. 2004; Drew and A. P. Henne 2006).
People will need some motivational drivers to participate in the LKN. Most
efforts will have a limited budget, so creativity is a premium for this issue.iii People
pursue activities that give them intrinsic satisfaction (De Young 2000). It is
empirically proven that people find intrinsic satisfaction from participating in a
community as well as gaining a sense of competence through an action (De Young
2000; Kaplan 2000). Further, it makes people feel good to be needed, and people
54
avoid situations that make them confused, or helpless (Kaplan 1990; Kaplan 2000).
Development of the LKN should occur such that these motivational drivers are in
place. There can be a sense of community through wikis, web-blogs and e-mail list-
serves allowing for dialogue and updates. In person community events should also
be arranged, such as the annual Christmas bird count (Dunn et al. 2005) that usually
culminates in a potluck dinner in each region. When people move up the ranks of
citizen scientist, they can have different rankings such as the “green belt” and “black
belt” ratings of the revolutionary six sigma business management approach (Harry
and Schroeder 2000; Hoerl 2001). Rather then metaphorical belts, actual lapel pins,
or earth patches can be distributed. In short, a key motivational approach is to build
a community of practice (Brown and Duguid 1991; Wenger and Snyder 2000;
Wenger et al. 2002) around the LKN. One strategy for recruitment is to target
already developed virtual communities (Bragge et al. 2005), such as
geocaching.com, tribe.net, and craigslist.org. Special efforts should also be made to
expose key stakeholders to nature, such as public opinion leader trails (Muir 1999)
with a landscape observer component.
DISCUSSION: THE EXPECTED DIMENSIONS AND BENEFITS OF INCREASED ENGAGEMENT
The people engaged
A three dimensional cube can illustrate the participation in traditional
conservation planning versus ECPM. Stakeholders can be classified according to
their degree of urgency, legitimacy, and power regarding land-use decisions
55
(Mitchell et al. 1997). These metrics are the axes of a cube that represents a regional
community (Fig 8a). These characteristics are socially defined, change over time,
Figure 8a: The estimated stakeholder cube for traditional conservation planning.
Each grey dot represents a person that has a certain degree of power, legitimacy,
and urgency regarding the sustainability of the region. The red dots represent the
people participating in the conservation planning process.
and are sometimes not even consciously acknowledged (Mitchell et al. 1997). The
cube can be populated using empirical data, or, in this case, used metaphorically.
Government employees are part of the cube in this case, but a more nuanced
56
conception may eventually be necessary.iv In traditional conservation planning that
has a stakeholder component (usually as workshops or meetings) the stakeholders
come from various interest groups but is usually a small group (Brown 2003) with
have high degrees of these three metrics (Fig 8a). This is especially true if land-
ownership is one of the determinants of power and legitimacy. ECPM is expected to
change this cultural landscape in two ways. First, people with lower levels of
urgency, legitimacy, or power will be able to easily participate and know that the
information and values they share will be used (Fig 8b). Secondly, this change is
expected to in turn lure some of the latent stakeholders (people with a “low”
classification) into action, thereby shifting their position from low to medium (Fig
8c). These two factors are expected to increase the number of people engaged by at
least one order of magnitude, and hopefully two.
57
Figure 8b: The postulated stakeholder cube for initial Engaged Conservation
Planning and Management
58
Figure 8c: The postulated shift in peoples’ stakeholder status resulting from
Engaged Conservation Planning and Management
Some expected benefits of this engagement
There are many implications to this increased engagement. The costs were
outlined in the end of the introduction, and many of the expected benefits follow.
Some of these benefits have more empirical proof than others, and one of the ECPM
59
research agendas is to further evaluate these claims of conservation psychology and
political ecology. To start with, involvement in the process makes people more
likely to use the final products of a conservation assessment because they have
partial ownership and “buy-in,” and the products are more likely to suit their needs
(Moller et al. 2004; Ostrom and Nagendra 2006). Public participation in decision-
making processes also helps in consensus building and reducing conflicts (Couclelis
and Monmonier 1995; Joerin et al. 2001). It builds the trust between and among
local experts, stakeholders and the scientists that is vital for successful teamwork
(Forester 1999; Jackson 2001; Weber 2003; Stringer et al. 2006). Having more
participants also helps navigate the science through the socio-political maze of
implementation (Brooks et al. 2006)(Holling 1998; Brosius and Russell 2003; Johns
2003; Danielsen et al. 2005; Schwartz 2006; Stringer et al. 2006), especially because
local people are often more effective at advocacy then the scientists (Brewer 2002;
Sheil and Lawrence 2004).
Having more people involved also helps in society’s understanding of
conservation science (Trumbull et al. 2000; Main 2004). The Society for
Conservation Biology has an oft-overlooked goal of public education (SCB 2005a;
Bride 2006). Education is a known driver of a change of values, and hence behavior
(De Young 2000; Stern 2000); and may be one of the cheapest long-term strategies
for biodiversity protection (Yaffee 2006). With a shift towards ecological values, all
sorts of conservation efforts are bolstered—such as ecological economics and energy
conservation—not just the implementation of conservation plans.
60
Public participation in conservation planning and management has an element
not available to public participation in other conservation efforts—fieldwork in
nature. Giving people a purpose to be in nature leads to a connectedness to nature
which enables attitude change—a concern for nature, and stewardship (Leopold
1949; Carr 2004; Mayer and Frantz 2004; Miller 2005; Balmford and Cowling
2006). The same mechanism provides scientists, students, conservationists and
planners with the much needed opportunity to get out and see firsthand the landscape
they are conceptualizing and/or trying to save (Warburton and Higgitt 1997; Noss
2001; Fuller et al. 2003; Fuller 2004). It is clear that far better data are needed in the
“quantity and quality of populations, habitats, and the benefits they confer on
society” (see also Armsworth et al. 2006; Balmford and Cowling 2006). This
participation utilizes the economies of scale in attaining this goal(Fleming and
Henkel 2001; Carr 2004; Sheil and Lawrence 2004).
All of these benefits are context specific, and arise or are absent for unexpected
reasons (Brossard et al. 2005). A critical approach is needed in evaluating these
propositions and determining specific forms of best practice (Brossard et al. 2005).
CONCLUSION:
Engaged conservation planning and management is a web-enabled approach for
increasing the collaboration, knowledge, and sharing of values among and between
stakeholders, conservation scientists, and landscape observers (Fig 9). The approach
could go by many names including a type of community-based natural resource
management that incorporates conservation planning and uses the internet. The
61
Figure 9: The preliminary operational model of ECPM. It can start with a small
number of participants and expand over time.
point is that conservation planning does not occur in a bubble, nor does the
implementation of the conservation plans. These are both interconnected with the
shifting currents of the dominant social paradigm. With some more fortitude and
vision, conservation planning can merge with ecosystem management efforts to have
62
three interconnected goals: identifying the spatially explicit and management needs
of nature; meeting those needs; and positively influencing the social paradigm by
rejuvenating the values of ecological respect and action.
A conservation think-tank is analyzing years of extensive survey data regarding
social values and providing advice on how conservation professionals can help
achieve a more ecological society (American_Environics 2006). The findings
relevant to conservation scientists are that we need to: 1) develop strategies that
more deeply engage fulfillment-oriented youth who don’t consider themselves
environmentalists 2) inspire optimism among survival oriented citizens and 3) create
Strategic Initiatives that activate values held more strongly than Ecological Concern
— and that create new non-environmentalist ecological identities. This builds upon
the strategy of building the social capital among communities (Putnam 1995; Putnam
2000; Hutchinson and Vidal 2004) in an effort to further conservation(Van Rijsoort
and Zhang 2005; Schwartz 2006). ECPM not only helps with the proximate issue of
conservation plan implementation, but it also fulfills these broader strategic goals. It
provides enticing opportunities for non-environmentalist fulfillments, such as
belonging, status, personal expression, learning, and civic engagement. It also builds
the resilience of the region to withstand and adapt effectively to the uncertainties of
the future. This builds the hope and security that are vital in the adoption of
ecological values. These opportunities and sense of optimism will provide a lifeline
to halt the very disturbing trend away from fulfillment values and towards survivalist
values (American_Environics 2006). Indeed, the “regional citizen” that emerges
63
might be the new non-environmentalist ecological identity that is needed.v Of
course, the components of ECPM that give people a purpose to be in nature are also
likely to bolster the Ecological Base, the importance of which cannot be
underestimated.
ECPM is not just a social endeavor. It is also designed to significantly bolster
the conservation planning process. The wealth of observation data that meets high
quality standards can be used directly in analyses, or simply to help evaluate the
uncertainty of the predictive models (such as wildlife habitat prediction) used in the
conservation assessments. The local knowledge of the region also provides a
counterpoint to the quantitative approach that is has its own set of uncertainties and
weaknesses. Conservation scientists can learn from these perspectives and be able to
better prioritize the improvements needed in their models and algorithms. By
weaving the two perspectives together, a more complete understanding can be gained
regarding the region, its needs, and how the people of the region will meet those
needs.
ECPM shows great promise and has a rich research and application agenda. It is
also quite comprehensive, and probably beyond the scope of any individual effort.
By designing research carefully (Ferraro and Pattanayak 2006) we can work on
pieces of the ECPM vision for eventual synthesis. Further, ECPM is built around
trust and ethics, and is decidedly technocentric, so it is not appropriate everywhere.
A careful scoping of the study region is important to determine if and how ECPM or
its components should be applied and/or researched. By doing this while adhering to
64
relevant ethical guidelines (Clarke et al. 2002; SCB 2005b; Rambaldi et al. 2006) we
can progress effectively towards this exciting vision of teamwork and biodiversity
conservation.
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i Researchers at the US EPA and ICF International investigated the decision-
making systems used by US state, county, and municipal land protection programs
and found that only 4% of the programs were top-down (when an agency or
organization identifies all of the locations to be protected), while 44% were bottom-
95
up (locations identified by grant programs or nomination by individuals) and 42%
used a hybrid approach (nominations within a set identified by a program)(Pyke
2006; Pyke et al. in prep.).
Regarding the cooperative conservation program, Assistant Secretary Lynn
Scarlett states that it is “rooted in bottom-up decision-making, respect for private
property, and cooperation rather than conflict” (Christensen 2005).
The decision-support needs of a bottom-up institutional structure are diverse and
distributed. Policy-makers design the incentive mechanisms for conservation and
the penalties for illegal degredation. Most bottom-up conservation actions are then
initiated by the landowners themselves. They find out about the incentive, they
assess their land, personal and family values, risks, etc. and then apply to the NGO
or agency with the ability to administer the program. Negotiations then ensue about
eligibility and amount of incentive available, and management guidelines. Key
decision-support needs are: what incentives are available, and what opportunities for
resource-use would still be available? Further, what is the relative ecological value
of the particular parcel? Why? (i.e. what are the ecological characteristics of
significant value?)
Most systematic conservation assessments only provide a partial answer to these
questions. They determine if the parcel is part of the optimal set of sites for
conserving the region’s biodiversity with the least amount of cost, but most end their
usefulness there. These products are useful in designing the initial set of target areas
96
in the hybrid approach, but even then they are often problematic because they are not
at the parcel scale or do not account for other needs of the planners (Knight et al.
2006a). And after the target areas are set, implementation has the same problem.
Further, deviations from the plan are inevitable(Meir et al. 2004), thereby making it
increasingly obsolete. Really, these assessments are best suited for top down
institutional frameworks, but these are becoming increasingly rare in this world. It is
clear that conservation planners are not addressing the full range of issues facing
land protection practitioners and should attempt to conduct more directly relevant
research(Pyke 2006; Pyke et al. in prep.). I maintain that this disconnect is one of
the reasons for the implementation crisis.
ii This process of finding models will be facilitated by the use of model metadata
(Crosier et al. 2003).
iii In many places, engaged ecotourism can help finance the LKN. Policy change
can also be useful, such a small (e.g. .25%) increase or reallocation of property taxes
or mitigation funds into a community organized cooperative designed to manage the
LKN. Of course, a huge majority of the costs are absorbed by the volunteers.
iv In determining these values, it is also important to ask what is at stake
(Mitchell et al. 1997). In the case of ECPM, the sustainability of the region is at
stake. A draft approach of defining the relative values on each axis is as follows.
97
The average citizen has a low degree of power. Someone with medium power can
strongly influence policy creation or implementation. Someone with high degree of
power has both of these characteristics, or is a direct policymaker. A person with
low urgency does not care about the issue, or participate. Someone with medium
degree of urgency cares about the issue, but does not typically participate in public
hearings or submit written comments on an issue because the opportunity costs
outweigh the perceived benefits of such participation. Someone with high a degree
of urgency actively participates in the issue because the time –sensitivity or
“criticality” (Mitchell et al. 1997) is enough to make them overcome any
discouraging obstacles. (It is important to note that this introduction of discouraging
obstacles is really making the urgency axis have two variables. It may be better to
define time-availability as a part of the definition of the power axis, and also to
define “perceived power” as part of the power axis.) A person with a high degree of
legitimacy is a rural-land or reserve owner or manager (including public land
managers). A person with a medium legitimacy is rural renter, or an urban resident
of the region that influences the region’s sustainability more then the average urban
resident. A person with low legitimacy only has one of these two characteristics
(urban resident or influencing sustainability).
v Another way of looking at this whole issue is as follows: How do we foster this
value of ecological concern? There are four major types of intervention that have
98
been tried: education, incentives, moral drivers, and community-based drivers (Stern
2000). Education and moral drivers have disappointingly low track records. By far,
the most effective approaches utilize a combination of these ways. Incentives are
commonly monetary, but here is an incentive that is often overlooked and yet has the
potential of making durable change in values and behaviors—appealing to people’s
drive for intrinsic satisfaction (De Young 2000). A lot of people have a strong need,
and derive much satisfaction, from a sense of competence (De Young 2000). If
conservation planning could give people a sense of competence then they would
have an incentive to be a part of the process. Being a part of the process could entail
contributing information/opinion, or implementing. In fact participation itself is
another source of intrinsic satisfaction, further adding incentive to engage. People
also derive intrinsic satisfaction from being engaged in a process; this satisfaction is
another behavior-change driver (De Young 2000; Main 2004; Evans et al. 2005).
This line of argument points the extreme importance of having options for
participation that are extremely easy and can show a sense of accomplishment.
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Chapter 3: Communicating the implementation uncertainty
of spatial decision support systems to end-users
Abstract. We developed and assessed a method for communicating an
important type of uncertainty that has not been explicitly examined. This
‘implementation uncertainty’ occurs when an optimal or near-optimal set is
pursued incrementally, but the end-user cannot re-iterate the model and get a
new set every time conditions change. A conservation planning case study
was performed. Conventional maps lacking uncertainty information were
compared to products that communicated implementation uncertainty using
animations and maps derived from a stochastic approach. All products were
evaluated by three pools of conservation end-users through focus groups.
The end-users were unaware of the uncertainty before it was presented, then
they developed a more complete understanding of the model’s limitations.
The uncertainty products were preferred, as they provided guidance and
flexibility for the end-users’ dynamic implementation needs. These findings
indicate an opportunity for improving the utility of many spatial decision
support systems. More work is needed in examining this new type of
uncertainty.
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INTRODUCTION
Spatial decision support systems (SDSS) are designed to provide end-users with
helpful information relevant to a decision (Densham 1991; Ehler et al. 1995). An
SDSS usually addresses complex and challenging spatial problems that are ill-
structured and poorly defined (Yeh 1999). It usually consists of a decision
framework along with a GIS and several operational techniques, such as multi-
criteria decision making, uncertainty assessment, and visualization techniques (Aerts
2002). As GIS technology advances, the distinction between an SDSS and a GIS is
becoming increasingly blurred (Yeh 1999). Regardless of the semantics, the
challenges facing SDSS also face the emerging GIS technologies that are filling the
traditional role of SDSS.
One of the foremost challenges in SDSS development is in the treatment of
uncertainty. One significant source of uncertainty is in the data itself. It could be
uncertainty in the exact location and boundaries of the data, or in the classification of
the data (Goodchild and Case 2001; Zhang and Goodchild 2002). There is also
uncertainty in how well the models in the SDSS represent what they are modeling
(Goodchild and Case 2001; Sklar and Hunsaker 2001). These data and model
uncertainties often compound and amplify within the SDSS (Heuvelink 1999).
There is extensive research focused on defining, describing, and modeling these
uncertainties, their impacts on the results of an analysis, and their visualization and
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communication (MacEachren 1992; Buttenfield and Beard 1994; Goodchild et al.
1994; Van der Wel et al. 1994; UCGIS 1996; Davis and Keller 1997; Ehlschlaeger et
al. 1997; Flather et al. 1997; Fisher 1999; Goodchild 2000b; Stine and Hunsaker
2001; Aerts 2002; Gahegan and Brodaric 2002; Zhang and Goodchild 2002; Aerts et
al. 2003b; MacEachren et al. 2005).
This paper is about another kind of uncertainty. MacEachren et al. (2005)
emphasize that in addition to the widely researched forms of uncertainty there are
other forms that have profound implications and deserve attention. The uncertainty
examined here is termed implementation uncertainty. An SDSS is often used to
create a static map that is then used to support decisions that must be implemented
incrementally. Implementation uncertainty arises when the outcomes of each stage
in the decision sequence cannot be fully anticipated, and the static map cannot be
updated once the outcomes occur. Once a decision occurs that is outside of the
assumptions of the SDSS, then the decision support provided by the static map
becomes outdated. The problem is further described and illustrated in the overview
below.
How should the plan be portrayed and communicated when implementation
uncertainty is expected? Traditionally it has taken the form of an ideal plan, that
may or may not materialize depending on the actual acquisitions. Such ideal plans
can be misleading to decision-makers and counterproductive. The goal of this paper
is to explore ideas and an approach for addressing implementation uncertainty such
that the goal of decision support is furthered. This goal is comprised of two
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objectives: 1) to communicate the issue of implementation uncertainty, and 2) to
estimate the implementation uncertainty of a resource allocation model.
The paper uses the field of systematic conservation planning (i.e. conservation
planning) to provide structure in pursuing this goal. The following section illustrates
the problem by presenting an example conservation planning SDSS application.
Then, some context is provided regarding uncertainty analysis in conservation
planning, resource allocation modeling, and in the communication of uncertainty to
improve knowledge. A technique is then devised for modeling and communicating
implementation uncertainty, and is implemented in a real-world conservation
planning application. The implementation-uncertainty products are assessed via
focus groups. An unexpected result of the participatory action research process
(PAR) is the drafting of a potential theoretical framework for SDSS research and
development. The results of the assessment are provided and discussed.
This foray into the communication of uncertainty for end-users addresses several
topics in GIScience: geovisualization, public participation GIS (PPGIS), and of
course, uncertainty. This paper should also be of particular interest to SDSS
developers in all application fields, and especially within conservation planning.
The unexpected framework that results might also appeal to geographers making a
case for increased geographic education in general.
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BACKGROUND FOR EXAMINING IMPLEMENTATION UPNCERTAINTY
The problem of implementation uncertainty
In the common vernacular, the term “uncertainty” can mean an instance of doubt
or a state of being doubtful. This chapter uses the statistical definition: estimated
amount or percentage by which an observed or calculated value may differ from the
true value (uncertainty n.d.). For instance, spatial and attribute uncertainty can be
formally defined using the fundamental unit of measurement to study the geographic
perspective, the tuple (X,G). X refers to a location in time and space, and G stands
for one or more properties, attributes or things. Thus, if (X’,G’) is the statement (i.e.
map) of the true real world tuple (X,G), then the differences X-X’ and G-G’ are the
uncertainties (Zhang and Goodchild 2002). In other words, a map is a statement
about the world, and if it is exactly replicates the world, there is no uncertainty. If it
is an estimate, then the information about how the estimate falls short is the
uncertainty.
Resource allocation models are a type of SDSS that combine geographic data to
identify a set of sites or resources across a landscape designed to maximize or
minimize some combination of costs or benefits. The goal chosen is known as the
objective function, and could include maximizing biodiversity value, profits, or
people served; or minimizing cost. There is also a set of constraints that the solution
must meet, such as “any solution must have a set of sites that as a set, includes X, Y,
and Z characteristics.” Formal optimization models can be used to identify the
solution sets, or heuristics that are faster to compute but only result in a near-optimal
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solution can be used. In short, an impressive wealth of data can be included in the
analysis, far outperforming any human capacities.
The concept of implementation uncertainty can be clarified through an
illustration. It can arise when the SDSS output is based on a resource allocation
model. For instance, the allocation model could be used to inform the director of a
land trust of the best sites for conservation. The model could incorporate variables
such as species locations, habitat types, spatial configurations of habitat, purchase
price, and management costs in providing its solution. The output could be a map of
the set of 13 parcels in a landscape that, in total, would have the highest estimated
benefit for biodiversity. This optimal or near-optimal set of sites is hereafter termed
the standard set, and the resultant map the standard map. (For a simplified example
of the standard map, see Fig 10).
Implementation uncertainty arises when the SDSS output is implemented
piecemeal over time rather than all at once. Using the standard map, the director
starts trying to buy the standard-set sites or at least to buy their development rights
so they are conserved. However, as time goes by, some of the sites get developed
before they can be conserved. Conversely, some easy opportunities arise to conserve
some of the non-standard-set sites. The director often conserves these bargain
opportunities, and then keeps working to conserve any of the 13 standard-set sites
that are still available.
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Figure 10: The Standard Map showing the traditional resource allocation model
output
(In this case grid cells were used as potential sites rather than parcels.)
But there is a problem. As soon as either of these deviations occur, then the
implicit assumptions of the resource allocation problem are no longer met. These
assumptions are that all undeveloped sites will be available for conservation, and that
un-conserved sites will remain un-conserved unless identified in the standard set
(Church et al. 1996). Losing some of the standard-set sites to development may
make some of the non-standard-set sites very important. Conversely, a newly
conserved, non-standard-set site may have very similar characteristics to one of the
standard-set sites still targeted. This standard-set site has become redundant and is
no longer useful in achieving the objective function under the given set of
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constraints. So, the wisdom of conserving a particular site will change over time,
and it cannot be known in advance how much it will change, or in which direction,
or at what rate. If the director continues to diligently pursue the original standard-set
sites despite these issues, then a very poor allocation of resources could result,
thereby undermining the entire premise of resource allocation modeling and the
SDSS.
This problem is characteristic of other resource allocation applications, not just
conservation planning. For instance, it could apply to Wal-Mart executives trying to
locate 20 new stores in the western United States. The model could incorporate
variables such as the proximity of competitors, population density, income class,
land price, development policies of counties and cities, and so on. The output would
be a map of the 20 best sites for new stores. The uncertainty would arise after one of
the cities denies the request to site a store, or a new store is located in a city that was
not on the list. These changes are quite feasible due to external changes such as
policy changes in the target cities, land market fluctuations, and other cities offering
new bargains.
In summary, implementation uncertainty arises when three general conditions are
met. The primary condition is that the resource allocation model cannot be re-
executed for the end-user after every change in initial conditions. This condition is
especially common in conservation planning because the budget for planning is
usually very small compared to the budget needed for land acquisition (Meir et al.
2004). Secondly, external conditions that affect the cost and benefit of each site
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change over time. Finally, in implementing the plan, the end-user is allowed to
select sites that are outside of the standard set. This problem is especially acute
when the end-users include non-experts, as they do not have easy access to and
understanding of the data and method driving the analysis. The implementation
uncertainty of a tuple is the quantitative estimate of how likely the tuple will retain
its attribute value after future perturbations to the plan. The inverse is used, such
that a point which is likely to retain its value has a low implementation uncertainty.
The decision support hierarchy
In exploring approaches to address implementation uncertainty, it is important to
first look at the broader context in which the problem resides. Many SDSS couple
GIS technology with specific analytical modeling approaches with an emphasis on
information display (Xuan Zhu et al. 1998). These models must then be couple with
human expertise in making a decision (Xuan Zhu et al. 1998). Because the goal of
SDSS development is to improve decisions, it is important to consider the human
element.
A decision support hierarchy provides a useful starting point in studying how to
enhance the beneficial influence of an SDSS (Roots 1992; Longley et al. 2005). An
SDSS combines bits of geographic data (numbers, text, or symbols) in useful ways
to form information. This information is then presented to end-users. It is
interpreted by each end-user to enhance their knowledge. “Knowledge can be
considered as information to which value has been added by interpretation cased on
a particular context, experience, and purpose (Longley et al. 2005).” While
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information can exist independently, knowledge entails a knower, and is classified in
two types: codified (i.e. can be written down and transferred easily) or tacit (i.e.
slowly acquired personal knowledge that is hard to transfer). A person’s knowledge
of the issue is combined with their innate and intuitive understandings to form
wisdom. A personal decision relies on the person’s wisdom, while a group decision
relies on the collective wisdom of the group. The implicit goal of an SDSS is to
facilitate wise decisions by organizing and communicating data and information to
expand knowledge. This support hierarchy will be revisited. For now the important
message is that improving an SDSS can occur by improving the quality of the
information, and/or by improving the quality of the communication of that
information. In straightforward types of uncertainty, this issue is not as important as
for more abstract forms such as implementation uncertainty.
The issue of implementation uncertainty in a conservation planning SDSS
Systematic conservation planning is a common application of SDSS. It is the
science behind the effort to create reserves or special management areas in an effort
to help conserve biodiversity. One of the early mainstays of conservation planning
was the Gap Analysis Project (GAP). In general, GAP identifies biodiversity
elements that are under represented (under protected) in reserve systems (Scott et al.
1993). Complementing this representation approach was the study of “reserve
design” which utilizes an ecosystem approach and spatial relationships to identify a
set of new reserve sites that would combine with current reserves to adequately
protect biodiversity for a region (Noss and Harris 1986; Margules et al. 1988; Noss
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and Cooperrider 1994). To be pragmatic, this set of reserves should have the lowest
cost to implement. Thus, the field jumped into optimization (Margules et al. 1988),
minimizing the cost (usually total area) required to attain a set of sites that, in total,
met a set of biodiversity criteria (e.g. “20% of each habitat type”). There are various
mathematical approaches for pursuing optimality, with some such as integer linear
programming being more computer intensive but more accurate and able to define
how far the solution is from true optimality (Church et al. 1996). For a more
thorough overview of systematic conservation planning, see Chapter 1.
Mapping the uncertainties involved in conservation science was identified as one
of the research priorities of the current decade (Possingham et al. 2001).
Examinations of many types of uncertainties in conservation planning have occurred,
including those regarding spatial uncertainty of species distribution data (Todd and
Burgman 1998; Regan and Colyvan 2000; Regan et al. 2000; Robertson et al. 2004)
linguistic uncertainty confounded by parameter uncertainty in determining the
conservation status of species (Burgman et al. 1999; Akcakaya et al. 2000); and
model uncertainty in wildlife habitat models (Stoms et al. 1992; Loiselle et al. 2003;
Johnson and Gillingham 2004) and in the biogeographic assumptions of conservation
assessments (Flather et al. 1997; Whittaker et al. 2005; Grenyer et al. 2006). It is
another layer of complexity to examine how all these uncertainties propagate and
interact in affecting the final uncertainty of the assessment. With standard project
budgets, it is extremely difficult if not impossible to model and communicate.
Moilanen et al. (2006) provide a promising approach for addressing this complexity
110
based on info-gap theory (Ben-Haim and Ben-Haim 2006). An info-gap is “the
disparity between what is known and what needs to be known in order to make a
well founded decision (Ben-Haim and Ben-Haim 2006).”
Meir, Andelman, and Possingham (2004) address the issue of implementation
uncertainty, although the term was not used. They point out that conventional
methods of conservation assessment rely on a snapshot in time to identify the lands
necessary for conservation, and assume that these lands can be conserved
immediately. But in practice, this implementation occurs over decades. During
these decades, some biodiversity is lost and the human dominated and natural
landscapes change, thereby changing the priorities. They show that the ramification
of this are such that optimal or near optimal resource allocation models do not
perform any better that simple rules-of-thumb such as choosing the site of highest
value at any given time.
One response to this problem is to focus on improving the quality of the
implementation uncertainty information in the SDSS (Costello and Polasky 2004;
Haight et al. 2005; Armsworth et al. 2006; Newburn et al. 2006). Armsworth et al.
(Armsworth et al. 2006) examine how land market feedbacks affect conservation by
using a macro-economic model of supply and demand. Conserving lands has two
impacts to the land markets, and hence, future conservation. First of all, it often
increases the amenity value of the nearby un-conserved lands, thereby driving up
their cost. Secondly, conserving lands in one area often displaces development
pressure to another area. This can be in an area that was originally unthreatened and
111
had a higher biodiversity value than the original area conserved. Thus, conservation
has the potential of doing more harm than good. The study corroborates the need to
consider implementation uncertainty, and calls for the inclusion of land market
feedbacks in conservation planning SDSS.
Costello et al. (2004) examine the issue of how timing in development threat as
well as the generation of conservation funds are critical elements of implementation
uncertainty. Conservation priority areas with a higher development threat have a
higher implementation uncertainty, because it is a higher likelihood that in the next
time step they won’t be available for conservation. If conservation funds are limited
and trickle in over time, it is important to focus efforts on these areas of high
implementation uncertainty. But doing so then increases the threat in other areas.
Prioritizing the most important areas for conservation given these dynamic issues is
quite complex, yet they tackle it anyway. They use a dynamic linear programming
to identify an optimal solution given a sample set of data for three sites. They then
look at heuristics that are sub-optimal, but less computationally intensive, to provide
decision support based on the dynamic model. The results are promising, but need to
be tested with large datasets over longer periods of time, with added modules of
realism, such as land market feedbacks.
Haight, Snyder et al. (2005) incorporate implementation uncertainty into the
SDSS through probabilistic scenarios of site availability for two time steps, and
utilize these and other data in a dynamic optimization model. By treating the issue
as dynamic rather then static, the model can provide current site selection
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recommendations for a given budget and a given scenario of site availability. For
instance, the model outputs can recommend immediate conservation of all the
optimal sites that the total budget will allow, or conservation of only a few of the
sites and saving money in the hopes that some of the highest-quality sites will
become available in the next time period. It assumes the ability to immediately
conserve all of the optimal sites exists if the budget is available.
Newburn et al. (2006) illustrate the high importance of considering the
probability of land conversion in identifying conservation priorities. An approach is
developed that includes this as well as cost for conversion in addition to the standard
issue of biodiversity value. A similar approach is developed by Davis et al. (2006)
in the respect that cost of conservation and the threat of development are explicitly
addressed. A major difference though, is that it does not look at probability of
development, rather, it looks at the different types of development, and identifies the
change in ecological impact that is expected to occur at a site for a given time period.
The primary strategy of all of these efforts is to increase the usefulness of the
SDSS by improving the quality of the information provided. This focuses the
limited resources of the SDSS development team on minimizing the uncertainty of
the model. But when the uncertainty is inherent to the problem, this is often a path
of rapidly diminishing returns (Dovers et al. 1996; Dovers et al. 2001). There is a
complementary response that may be as effective in the long-run: to acknowledge
that uncertainty is inevitable and in many cases, irresolvable (Couclelis 2003). In
such a response, a reasonable effort is made to reduce the uncertainty, while a
113
comparable effort is made in trying to communicate the uncertainty issue and
repercussions to the end-users (Clarke et al. 2002). As discussed earlier, improving
an SDSS is a function of both improving the information provided and the way that
it is communicated. This is significant, as the end-users of the SDSS often do not
understand the effects of uncertainty, or that it even exists (Keuper 2004). Of
course, a majority of research and development should be in improving the
information available, just not all of it. For instance, if $1 million were available for
improving the treatment of implementation uncertainty within SDSS, it may be
prudent to spend $20,000, at a minimum, on a study that provides a cursory approach
to quantifying implementation uncertainty, but emphasizes the communication of the
issue itself. These considerations lead to the objectives of the paper paraphrased in
the introduction: to devise and assess a method for estimating and communicating
implementation uncertainty.
METHODOLOGY
Approach and Overview
To meet the research objectives, a participatory action research (PAR) case study
approach was utilized. PAR is growing in popularity among interdisciplinary
researchers and entails that they are actively involved in the case study in question
rather than studying it as outsiders (Weisenfeld et al. 2003). PAR allows researchers
to incorporate real-world issues and concerns into their methods in a way that
effectively bridges the gap between theoretical construct and practical application
(Yin 1993; Smith et al. 1997; Gillham 2000). Research about the relationship
114
between GIS and society requires an interface between academia and the social
entities participating. PAR provides an opportunity for such an interface
(Castellanet and Jordan 2002; Fagerstrom et al. 2003; Natori et al. 2005).
In this study, we performed a conservation planning resource allocation analysis,
then developed a set of maps and explanatory animations to communicate
implementation uncertainty to SDSS end-users. These new products were assessed,
along with the map of the standard set, using focus groups. A preliminary draft of
this research was presented by Gallo (2005).
Methodology of Phase IA: Project Scoping
The analysis was performed for a non-profit organization, Conception Coast
Project (CCP), dedicated to protecting and restoring the natural heritage of the region
through science, community involvement, and long-term planning (CCP 2006). The
product was to be released publicly to show the landscape requirements for long-
term ecological sustainability, and to help guide community action towards
achieving them (Gallo et al. 2005). (The Regional Conservation Guide can be
viewed at http://conceptioncoast.org/Regional_Conservation_Guide.pdf .)
Two advisory groups were assembled to assist in the process, and, along with the
CCP personnel, provided the three pools of people for the focus groups used in
Phase III (evaluation). The Ecological Expert group was comprised of 12 biologists
with a variety of taxonomic specialties and professional occupations. The Land and
Resource Management group of 15 people was comprised of county planners, land-
115
trust directors, and resource-agency representatives (For a listing of individuals, see
Gallo et al. 2005).
Initial scoping meetings were held to determine the general guidelines of the
final product. Some of the questions asked during the scoping sessions include the
following: Should the final product be a hardcopy map? What scale should the
map(s) be at? What is the timeframe for implementation, and similarly, about how
many acres should be targeted as conservation priorities? The working meetings
held to parameterize the model involved identifying any major gaps in the model that
could be filled, identifying the relative weights among the five biodiversity
measures, determining the ecological impact of various human land-uses, and
determining the relative weights among the multiple criteria leading to the cost layer.
Methodology of Phase IB: The Marginal Value Resource Allocation
Model
A conservation planning process was performed for a 14,000 km2 region of the
south-central coast of California (Figure 1 of Gallo et al. 2005). It was based on an
resource allocation modeling approach that integrates the threat of habitat
degradation, cost of conservation, and six ecological criteria (Davis et al. 2003;
Davis et al. 2006). The scoping meetings resulted in the call for the identification of
100,000 acres (approx. 400 km2) of conservation priority areas. Thus, the model was
used to identify the standard set of 180 sites (each 2.25 km2) estimated to have the
highest combined conservation value.
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The resource allocation framework was created by the Biogeography Lab of
U.C. Santa Barbara and the National Center for Ecological Analysis and Synthesis as
part of their work with the State of California’s Legacy Project (Davis et al. 2003;
Davis et al. 2006). This framework divides a study region into thousands of
candidate sites, and estimates the marginal conservation value of each site by
examining the threat of habitat degradation, cost of conservation, and six
conservation objectives (which can be weighted differently) for all of the cells within
each site. This “multiple track” approach follows from Noss (2000), and includes: 1)
hotspots of rare threatened and endangered species, 2) areas supporting vulnerable
habitat types, 3) wildlands for area-dependent species (i.e. complete food webs), and
4) areas adjacent to small reserves. A fifth objective was added: coarse-scale habitat
connectivity. The relative cost of conservation of each site was modeled based on
parameters that affect purchase price.
The framework used a resource allocation model based on a greedy heuristic (a
sub-optimal solution) to identify a set of sites that, as a whole, tried to maximize the
total conservation value given a defined budget of money or area. The number of
sites in the set is defined by the user. Each site has a subset of cells that are
aggregated in getting a score for the site. The heuristic operates by assessing the
marginal conservation value of all sites, selecting the one with the highest marginal
value (a benefit/cost ratio), recalculating the values of all the other sites given that it
will be conserved, selecting the next site with the highest value, and so on.
117
This model was chosen over conventional resource allocation models like SITES
(Andelman et al. 1999) for several reasons. A benefit of SITES is that it uses a
simulated annealing algorithm which is more accurate than the greedy heuristic for
most problems. However, it is a target based model, which is problematic compared
to the marginal value approach.. For example, it is programmed to conserve, say,
20% of the remaining pine forest of a region, along with other similar targets, all at a
minimum cost. After the target is met, there is no benefit of protecting the 21st
percent. The marginal value model has a decreasing value function which
recognizes the need for targets to be fuzzy. Thus, it considers it valuable to some
degree if a site can be added to the portfolio and the total protection increases from
20 to 21% (Fig. 1 in Davis et al. 2003). The exciting ramification of this is that the
model provides valuable decision support for an iterative, adaptive management
approach towards conservation (Termed ECPM in Chapter 1 and 2). In essence, it
can provide a small solution set of sites, or even just one, that provides the highest
benefit/cost ratio towards a conservation portfolio. (And benefit is also a function of
threat). Target based models cannot do this, they need to identify the entire set.
The marginal value model was also chosen because of some other appealing
functions. It incorporates the variance of human impact throughout the landscape,
while SITES uses the binary approach: either a cell is good habitat or it is not. Also,
the marginal value approach does a good job at considering “threat” and balancing
this with cost. It focuses conservation priorities in areas that are predicted to have
increased human impact in the future if they are not protected. Further, the absolute
118
amount of change is considered (i.e. the expected change from rangeland use to
urban being incorporated as a higher impact than from rangeland to low intensity
agriculture).
The mathematical functions of the marginal value analysis are as follows, copied
almost verbatim from the a working draft of Davis et al. (2003). If repeating the
methodology, it is advised to refer to Davis et al. (2003) for the functions, and the
RCG (Gallo et al. 2005) for the parameters. The model was written in VBA script
within ArcGis 8.3 and adapted to version 9.0.
Objective Function:
∑ =
N
i ii XVMax1
over sites i = 1,2 …N
subject to the constraint
BNi CiX i ≤∑ =1
where
Xi = 1 if site is selected for conservation, 0 otherwise;
Vi = the conservation value of Site i;
Ci = the conservation cost of site i;
B = funding available for conservation during the planning period.
and where
∑ ∑=
=
= J
j N
i ij
ijji M
MwV 11
)(
119
Mij = marginal value of conserving site i for conservation objective j;
wj = weight associated with objective j
And where M1 relates to Rare Species
AuM
i
iE
ei Ni Qei
Qei*1
*11
∑ == ∑ =
M1 = Marginal value based on rare species1
Qej = the quality of occurrence of species e in planning unit i (0 Q 1). 2
And
∑∈
=io
ooi saA
A = condition-weighted area of planning unit i
o = observation area (100 m cell)
a = area
s = ecological condition indicated by human impact3
1 See Table A3 in the appendix of the Regional Conservation Guide (Appendix B
of the dissertation)
2 Quality is a function of the spatial accuracy of the observation. If the species
occurred in an 80 m diameter circle then the underlying cell had a higher Q value
then if it was seen somewhere in a larger circle. The largest circles in the data were
8000 m in diameter. The value is a linear function of the area of the circle.
120
usau oio
ooi ∑∈
=
ui = condition weighted area of unprotected land in the site
uo = protection status of the cell, protected (0) or unprotected (1)
And where M2 relates to Habitat Representation
The amount of the habitat type k (k = 1,…K) in the planning region R at future
time t is a function of the area (a) and condition (s) of all observations (o = 1,…O) of
type k in the planning region:
∑ == O
o
t
ok
t
oktRk saA 1
The current value of site i for protecting habitat type k (vik) depends on the
difference at the end of the planning period between the condition-weighted area of
habitat type k in site i assuming conservation actions are taken ( Aik ) and the amount
in future time t assuming that no additional conservation actions are taken in site i
( Atik ). That is:
AAv tikikik −=
3 For a list of human impact values, see Table A1 from appendix A of the
Regional conservation Guide, which is Appendix B of the dissertation. If a cell has
several land-use categories, the one with the highest impact was used. Higher impact
is a lower value of s.
121
∑ =
+−= K
k ikk
iktRk
i vGvAM 12 *]
)*5.0(1[
Gk = Conservation goal for the particular habitat. (i.e. G of Fig. 1 in Davis et al.
(2006))
And where M4 relates to Wildlands
If the goal is to maintain blocks above a minimum area Bl, and assuming there is
diminishing return on additional conservation beyond that threshold up to a target
area of BM (beyond which additional conservation has negligible marginal value), a
simple measure of wildland conservation value can be formulated similar to that for
landscape-based conservation. Let the difference in condition-weighted area of that
part of wildland block W (W = 1,…n blocks in the region) in planning unit i
between today and at the end of the planning period (time t, assuming no
conservation) be:
∑∈∈
−=Woio
t
o
t
oooiW sasav,
)(
If the condition-weighted area of block W in time t is calculated as
∑∈
=Bo
t
o
t
otW saA
then the approximate marginal value of planning unit i for conserving wildlands
is
122
vBvAM
MBAvMBA
iWu
iWtWn
Wi
iUtW
n
W iwiLtW
elseif
elseif
*)]*5.0[
1(
0,
,
14
4
14
+−=
=≥
=≤
∑
∑
=
=
And where M5 relates to Adjacency to Reserves:
reserve or protected area pa (apa) in hectares increases.
zpapa a* – c D 1=
For this application we set c equal to 0.0001845 [such that an area twice as large
as the largest reserve in the region (1080325880 meters squared) would receive a
demand of 0] and z equal to 0.4. Although z is expected to be lower for mainland
terrestrial environments, we use a higher value such as would be expected for insular
habitats. We assume that the ability of a cell to meet demand for buffering decays
with its distance ( 1 km) from its nearest reserve. The supply term for each
available cell is calculated as:
paod ,
paopao d* D Supply ,/1=
where the protected area pa is the one nearest the cell, and thus serves as that
cell’s reference region for this objective. In this formulation, a cell only gets credit
for buffering one protected area even though it may be located within the threshold
distance of more than one protected area.
Proximity to a small protected area is not sufficient to give a high conservation
value to a site, however. As with the other objectives, the conservation value is
123
modified by the expected future condition in relation to present condition. The
marginal value for site i is then calculated as:
∑∈
−=io
t
oooo ssM SupplySupplyi )(5
And where M6 relates to landscape connectivity to Reserves. M6 is described
below.
Methodology of Phase IBi: Landscape Connectivity for the Marginal
Value Model
As mentioned, a connectivity analysis was not in Davis et al. (2003), and was
added to the resource allocation model. As per key portions of Gallo et al. (2005):
connectivity is the concept that if two or more large areas of quality habitat are
connected by a narrower area of habitat that facilitates animal movement, then the
overall biodiversity value of the region is increased (Soule and Terborgh 1999). A
connectivity analysis is ideally performed at multiple scales, but if only one scale is
feasible, it is best to use a coarse scale approach to ensure the core wild lands of a
region are interconnected. Unless a protected area is millions of acres in size,
individual core protected areas will not be able to function independently as whole
ecosystems, in the sense of maintaining viable populations of animals and ecological
and evolutionary processes (Noss and Harris 1986). The mountain lion was selected
as the connectivity focal species because it operates at this coarse scale, with males
having a home range of nearly 400 square kilometers (Dickson 2001). The mountain
lion is also a keystone species since it maintains the integrity of an ecosystem by
controlling the population of large herbivores and “meso-predators” (medium sized
124
predators such as skunk and opossum). The loss of such keystone species are more
profound and far-reaching than others, because their elimination from an ecosystem
often triggers cascades of direct and indirect changes, leading eventually to loss of
habitat and extirpation of other species in the food web (Noss and Soule 1999).
A “gated” least cost path analysis was utilized that indicates the path between
two habitat areas with the lowest level difficulty of travel (i.e. “movement cost”) for
a mountain lion (Lombard and Church 1993; Singleton et al. 2001). A movement
cost GIS layer is created such that the value of every location has a measure of how
difficult or dangerous it is for a mountain lion to move across it. For example, a path
across highway 101 will have a very high cost, whereas the path in the wilderness
forest will have a very low cost. The “gated” variety of least cost path analysis
provides a value for each cell on the landscape that is equal to the total cost of the
least cost path that must pass through the particular cell. Thus, the output has a
width rather than a very fine line of habitat connecting two wild lands. It is also a
fuzzy output. This type of analysis lends itself to projects in which a fast method is
needed for identifying general corridors that are to be communicated to the public
(i.e. ECPM of Chapters 1 and 2).
In parameterizing this analysis, the following operating assumptions were used.
(1) It is assumed that it is important to maintain the connectivity between all pairs of
wild lands, not just the ones that have high quality linkages already in place. This is
based on the principle that all populations of mountain lion need to be connected to
other populations. However, it is also important to give a higher value to these
125
higher quality linkages as a matter of pragmatism. (2) If two linkages are equal in all
respects except for the distance they span between core wild lands, then they will be
valued as equals (i.e. standardize by distance). (3) To account for the “stepping stone
effect,” it is assumed that all cells along a linkage are not considered equal value.
The connectivity value of a cell is a function of the habitat suitability and human
impact value of that cell, as well as the quality of the overall linkage that the cell lies
in. and (4) “Landscape Connectivity” addresses coarse scale connectivity for large,
wide ranging species; not the equally important fine scale connectivity for smaller
species.
Identify Core and “Destination” Zones
The first step in the connectivity analysis is to identify the pieces of land that will
be connected. To be consistent with the rest of the RCG, these lands will be the
Wild Lands identified in objective M4. Due to the time consuming nature of
analyzing pairs of wild lands, the large wild lands in the center of the region that are
nearly touching were combined together into one wild land. All the other wild lands
are considered “destination” wild lands. These are often called cores and sinks, but
because a meta-population analysis has not been performed, it is not known if the
smaller, peripheral zones are indeed classic sinks, thus the term “destination” is used.
126
Create Movement “Cost” Surface
In this analysis, movement cost is a function of mountain lion habitat suitability,
human impact value in general, roadedness specifically, and a constant.
The California Wildlife Habitat Relationships (CWHR) model was used in
conjunction with the Multi-Source Land Cover Data (See Table A2). This model
predicts the suitability of a habitat for mountain lions, based on expert knowledge
and literature review. Of the three categories of habitat suitability (cover, feeding,
and reproduction), cover was the factor used because the model is looking at
mountain lion dispersal. [See Figure 13 of Gallo et al. 2005: Habitat Quality for
Mountain Lion Dispersal.]
The human impact value layer developed earlier was used.
The roadedness layer that went into the human impact layer was used on its own
as well. This is because roads are the primary source of death to mountain lions in
southern California (Beier 1995; Beier et al. 1995).
Solving the “bleeding” problem inherent to gated least cost path analyses.
After initial runs of the model it was realized that the gated least cost path
algorithm is problematic for cells on the lee side of core/destination areas (M5
areas). These cells get a high value, even though they are not between the two areas.
The paths they were using passed through the core or destination areas went out into
the cost surface and through the gate cell, and then back to the core/destination area.
Thus there was a “bleeding” effect that happened, making the output slightly
problematic.
127
To address this, it was assumed that even ideal habitat has a small cost for
movement. Otherwise lions would be able to move an infinite amount of distance
through ideal habitat. Thus, a mathematical constant was added to the movement
cost equation. This constant simulates the energetic cost of movement, and that
dispersal through perfect habitat also has a cost because it is likely through a hostile
male’s territory. Several values were evaluated (0.03, 0.05, 0.07, 0.1,and 0.15), and
the one (0.05) that balanced the benefits and detriments of using such a factor was
used. (The highest cost of movement was 1.0.)
The other three factors were combined with even weight for a contiguous 90 m
cell resolution “movement cost” layer =
[ ]( ) [ ] [( )( )] 05.3
__Im__1 +++− ValueRoadednessValuepactHumanySuitabilitHabitat
While this constant helped the “bleeding” problem, it still was apparent. To
account for this flaw, the movement cost surface and core and destination zones were
modified. The two outer, boundary cells of each core and destination zone was
given a very high value. The new core zone was then drawn inside this buffer, or
“moat” of cost. This way, every gated least cost path enters each zone just once
rather than multiple times, which caused the flaw. This solved the bleeding problem.
The aforementioned approach of adding movement cost to all cells was not needed.
128
Perform Gated Least Cost Path Analysis
For each core/destination wild land pair the following analysis was performed.
(It was also performed between two “destination” wild lands: the Santa Monica
Mountains and the San Gabriel Mountains.) The enhanced movement cost layer was
used to create a cost distance surface to each wild land. The pair of cost distance
surfaces were then used for the “corridor” analysis using ESRI ArcGIS 9.0 software.
The cost of traveling through the “moats” was then subtracted. Next the layer was
divided by the Euclidian distance between the two wild lands so that the analysis did
not bias against linkages that had to span a large distance.
At this point the layer had a linkage value for every cell on the landscape, even
the cells in the middle of cities. In order to highlight the feasible wildlife linkages, a
new layer was created that selected just the good (low) values. After evaluating
several threshold values and comparing them to knowledge of the landscape, all
values 1.04 times the minimum value were selected. (In this analysis, lower cost is a
better linkage). This selected about a quarter to a half of the landscape, depending
on the pair of wild lands analyzed. This layer for each wild land pair was called a
paired raw linkage layer. All of the paired raw linkage layers were combined, and
where values from two linkages overlapped, the minimum value was chosen. The
combined raw linkage layer had a wide variance in values between linkages, and a
narrow variance within a linkage. For instance, one linkage had values ranging from
606-630 cost units, while another had values ranging from 100-104.
129
To address this variance, the paired raw linkage layers were also classified into 5
categories of equal classes (high, medium-high, medium, medium-low, and low
linkage value) to create the paired relative linkage value layers. The paired relative
linkage layers were combined in a similar fashion to create the relative linkage
value.
It was decided that rather than use one or the other technique, a combination
would be used, with a higher emphasis on the relative linkage layer (see Theoretical
Overview and Assumptions for justification). There are many mathematical
approaches to combining these, but because the variance of the raw linkage layer
needed to be decreased, the square root was used, along with multiplication.
valuecorridorrawvaluecorridorrelativeLayerCorridor _____ ×=
It was noticed after the analysis that seven important but short linkages had not
been mapped because the corresponding pair of wild lands had been grouped
together as the central core zone or had not been analyzed. These short linkages
were digitized by hand using the movement cost layer as a guide. These are
classified as “estimated linkages” and given a value of the mean plus one standard
deviation of the linkage layer and added to that layer. Finally, the linkage layer was
inverted and standardized, such that the best linkage value is 1 and the worst is 0.
[See Figure 14 of Gallo et al. 2005: Large Wildlife Linkages within the Conception
Coast Region].
130
Combine results with Habitat Suitability and Human Impact Layers
The “connectivity value” of a cell is a function of the linkage value as well as the
habitat quality value of the cell and the human impact score of that cell (See
Theoretical Overview and Assumptions). A variety of different weighted
combinations were evaluated, and the one chosen had a good balance between
maintaining the integrity of linkages, but also allowing for the site specific
importance to be accounted for, with a slight bias toward habitat suitability as
opposed to human impact. Thus the value for a cell within the connectivity layer, L
is as follows.
( )11
Im_21(_3_6_ pactHumanySuitabilitHabitatLayerCorridorLayertyConnectivi ×−+×+×=
Thus, if a cell has the highest possible linkage score, the highest possible habitat
suitability score, and the lowest possible human impact score, then it will receive a
value of 1. All other cells will receive scores less than 1.
Assign Connectivity Value to each Site i:
∑∈
−=io
t
oooo ssM LLi )(6
Again,
o = observation area (100 m cell)
a = area
s = ecological condition indicated by human impact
131
t = the time analysis used to predict future conditions (in this case, the year 2050)
[See Figure 15 of Gallo et al. 2005: Connectivity Value within the Conception Coast
Region].
Cost:
Cost was a weighted summation of slope (less is more expensive), distance to
urban areas (less is more expensive), zoning (less protection is more expensive),
viewshed of the ocean (more is more expensive), proximity to ocean (closer is more
expensive), and viewshed of the mountains (view of more peaks is more expensive).
Each cell was given a value for each of these criteria, the weighted sum was found
for that cell, and then all of the cells were summed to get the value for the site. The
tables of the exact thresholds and corresponding values for each of these criteria are
available by request from the author.
Methodology of Phase II: Products for Communicating Implementation
Uncertainty
The standard map is a static presentation of the original standard set, and ignores
the strong likelihood that the importance of these sites will change over time. Four
products were designed to communicate implementation uncertainty and its
implications. These four products are detailed below, and are as follows: 1) an
introductory animation that used a small sample grid to illustrate the concept of
maximizing resource allocation, 2) a second introductory animation that illustrated
implementation uncertainty, 3) an animation illustrating the methodology used to
132
estimate this uncertainty and 4) maps designed to visualize the uncertainty. (Product
Four is described below before Product Three for clarity in this paper, but during the
focus groups, Product Three was shown first.)
Animation of the Concept of Maximizing Resource Allocation
The two minute introductory animation was created because multi-criteria
conservation planning is a complex concept, and combining it with a resource
allocation site selection algorithm makes it even more complex. The animation
illustrated that an resource allocation model considers how all the sites combine with
each other, rather then just looking at each site in isolation. A square grid of 36 sites
was draped over a sample distribution of four species, shown by icons. The
conservation objective was to conserve two individuals of each species in a set of
reserves using the least amount of land. An animation showed the sequential
conservation of sites in which the next site selected was the one with the highest
species diversity. The animation also showed the running tally of the total number
of individuals of each species conserved, along with the total number of sites. It
stopped once the objective was met. The resource allocation approach was then
described, and the solution was mapped in place of the species diversity solution.
The resource allocation approach only needed three sites to meet the objective, while
the species diversity approach needed five. [More detail about the introductory
modules are in Appendix A.]
133
Animation of the Significance of Implementation Uncertainty
A second animation illustrated the significance of implementation uncertainty by
using a real-world scenario of land ownership and development. Sites could only be
conserved one at a time, and while one was being conserved, another could be
getting developed. The species distribution and the standard-set solution from the
previous animation were overlaid with land-ownership boundaries. The animation
showed the selection of sites and tallying as per the first animation. During the first
step of the scenario one site was being conserved while one of the other landowners
identified by the original standard-set solution decided to develop their site. Given
this development, it became apparent that if two of the previously ignored sites were
conserved, they would combine with the first site conserved to meet the objective.
These landowners were approached and agreed to enter into conservation easements.
Thus, due to the dynamic nature of the socio-political landscape (implementation
uncertainty) the best actual solution can have a very different spatial configuration
compared to the original standard-set solution. Further, the example showed that the
actual solution (only three sites needed, yielding a total of two individuals of each
species) can be nearly as efficient in resource conservation as the original standard-
set solution (three sites, with a total of two individuals of three species, and three
individuals of the fourth species). Such an effective solution would not have been
possible if the conservation implementation was not so flexible and adaptive.
134
Estimating and mapping implementation uncertainty
There are a number of methods for estimating geographic uncertainty (Heuvelink
1999; Crosetto and Tarantola 2001; Zhang and Goodchild 2002; Aerts et al. 2003a).
A common method is based on a stochastic approach, such as a Monte Carlo
analysis. This approach combines a large number of model runs, each time with
slightly different input parameters that are varied randomly within some pre-defined
limitations (Davis and Keller 1997; Heuvelink 1999; Crosetto and Tarantola 2001;
Aerts et al. 2003a). A Taylor series method is an alternative, polynomial based
approach that can be used in estimating the uncertainty of non-linear GIS operations
by using an estimate of what the linear GIS operation would have been (Heuvelink
1999). A benefit of the Taylor series is that it does not require all of the model runs
of a Monte Carlo analysis. But its approach is less intuitive, and the estimate cannot
be improved, whereas the uncertainty estimate of the Monte Carlo approach can be
improved by doing more model runs (Heuvelink 1999). In the other direction, there
are simple approaches that can be employed to communicate uncertainty. For
instance, the resource allocation model could be programmed to identify twice as
many sites as is the target. It could then be communicated that only about half of the
sites mapped are conservation priorities, but it is difficult to know which ones due to
implementation uncertainty. While the usefulness of such an approach is suspect, a
similar version is revisited in the discussion.
Due to these considerations, the Monte Carlo stochastic simulation was chosen to
estimate and visualize implementation uncertainty. To illustrate the Monte Carlo
135
analysis, consider a resource allocation problem that requires three themes of input
data to select the standard set. One theme uses a data layer in which only 10% of the
point locations are within 1 meter of the true locations. The rest of the data in this
layer represent points that are within 100 meters of the actual location. For each
point, a probability density function can be derived that indicates the range of
potential values and their likelihood of being the actual value. Depending on the
amount of ground-truth data collected to examine this uncertainty, these distribution
curves can be programmed to vary for different areas of the study region or to be
spatially uniform. Using this information, a large number of alternative point
location data layers can be generated by randomly selecting a value for each point
based on its distribution curve. Each one of these input layers has the same
likelihood of being the truth. The resource allocation model is performed using the
two other themes of data and one of these alternative input layers to create an output,
or realization. The power lies in running the model for every alternative input layer,
and synthesizing all of these realizations. This synthesis can be a map representing
the number of times in which a site was part of the realization’s standard set.
It was assumed that a site may or may not be available for conservation by the
time it is actually considered for conservation. One way to model this is to simulate
what the new standard-set would look like after some portion of the sites become
developed. The input parameter that was perturbed was the sites available for
selection. The resource allocation model was performed given this new constraint to
provide a realization. Thus, if one of the original, standard-set sites was delineated
136
as unavailable for conservation, then the resource allocation model would identify an
alternative site. The biodiversity composition of this alternative site might make
other standard-set sites redundant, so other new sites would be chosen. The more
times the process is repeated the more robust the results. In this case, the process
was repeated 120 times.
For the perturbation, the set of sites chosen as unavailable for conservation
totaled 50% of the total sites. The most robust Monte Carlo approach would be for
these sites to be selected based on their likelihood of development. A less robust
approach would be to randomly select those sites and have the allocation model itself
address development likelihood through its use of cost and human impact in creating
the realization. There are potential data distributions such that the more robust
approach would develop a different, and more accurate answer. But probability data
were not available for all five human impacts (predicted urban expansion, suburban
expansion, grazing expansion, agricultural expansion, oil extraction expansion).
Further, generating them would have lessened the time available for the project
objective of clearly elucidating the issue of implementation uncertainty to decision-
makers so they can use the SDSS more responsibly. Because this research is just a
first step in improving the treatment of implementation uncertainty in SDSS, it was
decided to keep both objectives and use the random approach. [For the method of
how the sites were randomly selected see Appendix A: Selecting 50% of the sites].
The 120 realizations were combined to create a synthesis layer, such that each
site had a single value (Equation 1). This value was correlated to how many
137
realizations had the site selected as a conservation priority. With the particular
resource allocation algorithm used for this model (a greedy heuristic), there was
additional information useful in scoring each site. The heuristic selects sites
sequentially in deriving its solution set, and the sites selected first have a higher
initial conservation value then the sites selected last. This ranking influence was
tempered though, as it assumes that all sites are available for conservation and thus
biases against sites that are similar to the top ranked sites. The last issue addressed
was that counting the frequency and/or rank of site selection ignores the influence of
random selection (i.e. some sites had more or less than 60 opportunities to be
selected as a conservation priority) [See Appendix A: Monte Carlo Synthesis]. The
synthesis layer formula was as follows:
(1) ∑
=
−+
⋅= R
rir
ii
GT
ATU
1
3
3
1
Ui = Implementation-uncertainty value of site i
T = Number of standard sites selected in each realization
A = Total number of realizations that site i was designated in the input layer as
available for conservation
R = Number of realizations
G = The ranking of the site in the greedy analysis
The implementation-uncertainty map was created based on this layer. There are
several different cartographic approaches that could be used to map composite
138
results of uncertainty. Bertin (1983) identified six visual variables: shape, pattern,
hue, orientation, size, and gray-tone value. Other promising variables are
“abstraction” (Van der Wel et al. 1994) as well as “focus,” with its manipulations
available in contour crispness, fill clarity, fog, and resolution (MacEachren 1992).
Experimental results indicate that the variables of texture and saturation may be best
utilized in expressing the issues of uncertainty (Leitner and Buttenfield 2000).
Saturation was used for this study. The standard-set sites were shown uniformly in
highest saturation, and the alternative sites identified by the implementation-
uncertainty analysis were shown also, in decreasing levels of saturation proportional
to their certainty value. This value indicates the relative likelihood that, at some
time in the future, the site would be part of the new standard-set if the resource
allocation model where performed at that time. The decision to have the standard-set
sites with a uniform saturation rather than varied based on their certainty value was
due to preliminary concerns from end-users about the complexity of the overall
approach.
Description of the Monte Carlo Animation
A third animation was created to illustrate the Monte Carlo methodology. It was
realized that understanding the implementation-uncertainty maps might hinge not
only on understanding the problem, but also on understanding the Monte Carlo
approach itself. A grid of the entire region was shown, and the solution of the
standard run was shown. A reminder screen of the second animation in which some
landowners are not willing sellers was provided. Then a random selection of 50% of
139
the sites were mapped and classified as eventually having unwilling sellers. Given
this constraint, the new resource allocation solution was determined and shown (a
realization). Then several other realizations were shown in increasingly rapid
animated succession, and it was explained that they would be layered on top of each
other to create the composite, implementation-uncertainty map.
Methods of Phase III: Focus Groups
Focus groups were used to explore and evaluate the research questions (Gibbs
1997; Litosseliti 2003). To be clear, findings from the focus groups may not
generalize to the entire population of possible end-users. Such generalizations were
beyond the scope of this research, and are likely not even possible due to factors that
are specific to each context. Instead, focus groups are used to identify
interpretations, to suggest potential general findings that can be explored elsewhere,
to develop theory, and to develop other hypotheses (Gibbs 1997; Litosseliti 2003).
The primary objective of the focus groups was to assess the method for
communicating uncertainty. The agenda was designed to include assessment of the
following questions:
1) How well do the three animations communicate the implementation-
uncertainty issue?
2) What is the perceived message and utility of the implementation-uncertainty
map compared to the standard map?
All members of the three advisory groups of Phase I were invited to a respective
focus group meeting. The products of Phase II were presented to each group for
140
discussion. One of the researchers was the focus group moderator, and followed a
topic guide of issues to be explored during the session, with key words or questions
(Litosseliti 2003). The topic guide questions were 1) clearly formulated and easily
understood, 2) neutral so that they did not influence the answer, 3) carefully
sequenced with easier, general questions preceding more difficult ones, 4) ordered so
that less intimate topics preceded the more personal questions, and 5) complemented
with a similar question in case the original question did not invoke discussion
(Proctor 1998a; Langford and McDonagh 2003; Litosseliti 2003; pers. com. A.
Goodchild 2004). Abridged coded transcripts with analytical categories were
created from the video recordings (Litosseliti 2003). Voluntary questionnaires and
comment pages were provided to augment the focus groups, but not enough were
completed to be useful. See Gallo (2006) for more information about the focus
group methodology. [Appendix A insert available: Additional Focus Group
Methodology]
RESULTS
Results of Phase I and II: Conservation Planning Analysis and the
Products for Communicating Implementation Uncertainty
The result of Phase I pertinent to this study was the standard map. A simplified
portion of the standard map was provided earlier as Fig 10. The actual map
portrayed the standard sites for the entire region, and had several base layers for
reference such as roads, water bodies, cities, and labels. Phase II entailed
development of the introductory animations and the implementation-uncertainty
141
maps. Draft versions of the three introductory animations were created in
PowerPoint, with an associated soundtrack. A simplified portion of the
implementation-uncertainty map is presented in Fig 11. [Appendix A insert
available: Additional Details for the Results of Phase I and II]
Figure 11: Simplified portion of the Implementation-Uncertainty Map
Results of Phase III: Focus Groups and Questionnaires
Summary
The focus group results are summarized here in Table 1, and described further
along with a gleaning of relevant quotes. The resource allocation model and
implementation-uncertainty animations were considered quite helpful. Most users
142
were unaware the issue of implementation uncertainty until it was explained. At that
point they began to understand the implications. Further, the consensus of each
group was that the implementation-uncertainty map was a significant improvement
to the standard map.
The prevailing interpretation of the implementation-uncertainty map was that
while there is an original set of the best sites to conserve at the moment, there is also
a set of alternatives that might actually be the best sites in the future, depending on
how things go. Consequently, these alternative sites are worth considering for
conservation, especially if some of the original standard-set sites turn out to not be
available. This understanding was much more consistent with the reality of the
situation. So, in essence, presenting the uncertainty caused people with varying level
of expertise to get a similar and fuller understanding of the information. They also
shared the knowledge that the information is less precise than originally presented.
143
Table 1: Summary of focus group evaluations
Product CCP Group Ecological
Group
Land-use
Group
Animation of Resource
Allocation
Helpful. Limits to
metaphor not clear. Helpful Helpful
Animation of Implementation
Uncertainty Helpful
Helpful. Limits to
metaphor not clear. Helpful
Animation of Monte Carlo
Method
Complex,
unnecessary
Complex,
unnecessary Not Evaluated
Implementation-Uncertainty
Map compared to Standard Map
Substantial
Improvement
Substantial
Improvement
Substantial
Improvement
The Three Animations
The three animations had mixed reviews. Overall, the resource allocation
animation was clearly understood. However, there was a question from a member of
the CCP group requesting clarification about a minor detail. Specifically, icons of
oak trees and pine trees were used in the animation to symbolize rare species to be
conserved. But the actual model does not consider individual locations of common
trees such as pines and oaks, only rare plants. The end-user was originally aware of
this distinction, but then doubted their knowledge when the animation was shown.
This mild confusion was not about the message being conveyed by the animation,
144
but by how far the metaphor of the animation extended to the detailed methodology
of the actual model.
The concept of the uncertainty animation was understood by all three groups, but
a member of the land-use group had some constructive criticism. The animation
simulated all development, both new housing and oil fields, with blacked out
squares. This implied that when development occurs, all biodiversity value of the
site is lost, which is not true. Meanwhile, the actual model addresses this nuance,
and calculates the ecological impact of various development types (Davis et al.
2006). As with the previous issue, the participant erroneously assumed that the
animation was acting as a complete metaphor for all of the detail of the model: “I'm
not sure that oil field development is going to black out every species, whereas strip mall
development will.” More careful consideration of this metaphor issue should be
employed in future iterations. Regarding the message of the animation itself, people
felt it was useful in illustrating a problem they had not thought about before.
The CCP group and the ecological group both rejected the Monte Carlo
animation for a variety of reasons. It provided too much information, was too
complex, and was difficult to understand. It was clear that people were confused,
especially by the notion that random selection in part of the process could still lead
to a prioritization of sites. “I don't grasp it though, it seems to me that if you were to do
random development that you would eliminate all of those sites eventually.” Most
importantly, it was felt to be unnecessary. The CCP focus group suggested that the
problem was illustrated in the second animation, and the methodology could simply
145
be summarized by a bulleted slide and few sentences instead of its own animation.
The Ecological Advisors agreed with this suggestion. In an effort to allow for more
time for discussion of the other focus group objectives and for applied issues, this
animation was not shown in the land-use group.
Implementation-uncertainty Map
The three groups evaluated the implementation-uncertainty map in comparison to
the standard map. The CCP focus group strongly preferred the implementation-
uncertainty map over the standard map. There was excitement that the
implementation-uncertainty map showed opportunities for conservation if some of
the standard-set sites were to be developed before they could be conserved.
Similarly, the larger set of opportunities was preferred because it showed more
opportunities: “When you have a [black] site surrounded by a bunch of [grey] sites, it suggests
that we can be a little choosy, instead of saying ‘we have to have this ranch.’”
In the Ecological focus group, there was also much more support for the
implementation-uncertainty map than the standard map. Similar to the CCP group,
support was based in part on the idea that it is useful to show alternatives.
“Opportunities will be based more on whether you have a cooperative land owner, or funding for
a particular property, so yeah, it seems like you would want to have first and second priorities for
alternatives with the idea that some of your second tier selections, because of the timing, or
maybe somebody’s particular interest or whether you can get wide public support for it.”
Further, it is useful to have bigger areas identified: "I like having alternatives like that
defined. So you can get the bigger picture- ‘its that region,’ [or] ‘ its that area.’"
146
The Land-Use focus group also strongly preferred the implementation-
uncertainty map over the standard map. Again, they perceived it as increasing the
utility of the tool by providing alternatives. For instance, “For a very practical
perspective, if you have a situation where . . .you have an option for one of the [grey sites], and
not for one of the [black] ones, you wouldn't know unless you had this map, and if you were to
go to a funder, you could show this map, and say this is a high priority area. Being a pragmatist,
when it comes down to it, it depends on if someone wants to sell their property or not. This gives
us more options.” Again, they liked having bigger areas to target, both due to
economies of scale and for the ecological objective of core areas, “when you are trying
to do conservation, you get to that minimum area issue where doing conservation on small areas
is very costly, you can’t really manage for natural processes, can’t go in and do a whole lot, you
got a lot of invasives coming in because you have so much edge. We like to bias ourselves towards
large areas, and this is more of what we call the landscape scale than the other one.” [A more
complete narrative of quotes is available in Appendix A, and the abridged coded
transcript is available upon request.]
147
Visualization of Results: Grouped-Semiotic Triangles
The results of the focus groups can be visualized by grouped semiotic triangles
(MacEachren and Brewer 2004) (Fig 12). The referent signifies the real world issue
in question. In this case, it is the conservation priorities of a region. The sign-
vehicle is the media representing the referent. In the top group the sign-vehicle is
the standard map, in the bottom group, it is the implementation-uncertainty map and
the animations. The interpretant is the meaning that the end-user derives from the
sign-vehicle and referent relationship. A tight grouping of interpretants indicates a
similar understanding among end-users about the referent. This similar
understanding is especially useful in facilitating effective collaboration (MacEachren
and Brewer 2004).
In the top group of Fig 12, Interpretant 1 could be the GIS modeler who
performed the analysis, understands implementation uncertainty, and also can look at
intermediate data to estimate which sites might be viable alternatives. Interpretant 2
could be the savvy end-user who understands resource allocation modeling and the
concept of implementation uncertainty, but only has the final output and has no idea
which other sites might be viable alternatives. Interpretants 3 and 4 are not aware of
the implementation uncertainty issue and have similar but slightly different
interpretations of the map due to different cognitive abilities. The bottom group
illustrates how the end-users have a more similar and more accurate perception of
the referent. This perception is not as simple and ‘black-and-white’ as it was before
148
for interpretants 3 and 4, but it is cognizant of the nuances of implementation
uncertainty.
Figure 12: Grouped semiotic triangles of the Standard Map (top) and the
Implementation-Uncertainty Map and animations (bottom). (Adapted from
MacEachren and Brewer 2004).
Note: The grid represents the maps, and the video camera symbol represents the animations.
There was one issue in which the communication approach fell short. People did
not seem to fully understand the solution set concept. They made the assumption
that if a particular standard site turns out to be unavailable for purchase in the real
149
world, then the nearby sites identified in the uncertainty analysis would be good
alternatives. While Tobler’s first law of geography indicates that proximity is a
good surrogate for similarity (Tobler 1970), it is not automatically the case. Some of
the nearby sites might be very different from the standard site, and thus, very poor
replacements.
DISCUSSION
The results indicate that the devised method for estimating and communicating
implementation-uncertainty has several apparent benefits. It allows end-users to
better understand the limits of the SDSS and the alternatives to the suggested
decisions (in this case, conservation of a standard site). This understanding should
lead to a more appropriate use of resource allocation outputs as well as facilitate
communication and collaboration among end-users. At stake are millions of dollars
in the example of retail location decisions, or the public good, as in the case of land-
use planning.
The results regarding the animations illustrate the importance of an obvious but
oft-overlooked fact-- any uncertainty analysis must be presented somehow to end-
users. The communication products and process used will have a significant
influence on the end-user’s understanding. This study highlighted the importance of
the animations, and of clearly identifying what part of the modeling process they
illustrate. Improving such products and processes appears to be an under-estimated
opportunity for easily enhancing the real world utility of SDSS. Further, if the scope
150
for designing the uncertainty analysis includes designing the communication
process, then there is a greater likelihood of cohesion between the two.
Reflection upon the focus group experience led to a hypothesized benefit of the
method. Many people are skeptical or mistrusting of models. Modeling
implementation uncertainty appeared to ease these tensions. Perhaps this is because
when scientists acknowledge such uncertainty by mapping it, they concede that their
model is not immune to the unpredictable and dynamic complexity of human-
environment interactions. This concession empowers the validity of the end-user’s
common-sense and implicit knowledge. This empowerment likely leads to
engagement and effective implementation. A related issue is that the
implementation-uncertainty outputs are fuzzier then the traditional outputs.
Preliminary research indicates that both this fuzziness and this concession of
fallibility should calm situations where the release of traditional conservation
priority maps were inflammatory to stakeholders with entrenched positions (Gallo In
Prep).
Improvements to the approach via visualization
The misperception mentioned about proximal substitutability is indicative of
what could be the weakest part of the method. The current method does not attempt
to identify which of the non-standard-set sites are more strongly associated with each
other and with particular standard-set sites. The end-users seemed to view
conservation of a high certainty alternative site as nearly equivalent to conserving a
151
standard-set site, when it could be completely redundant to some of the other sites
already conserved and computationally useless.
This problem could be addressed via better communication. An example
communication approach would be a simple animation illustrating the issue. It could
be subtly reinforced by changing the label of “Alternative Sites” to “sites of the
Alternative Solution Sets.” Regarding modeling, a script could be created such that
when changes to the standard set are made, the only realizations synthesized in a
computer generated implementation-uncertainty map are those with an input layer
matching the changed conditions. A larger set of model runs would be needed for
this to draw from, and the cartographic designation of standard-set sites would need
to be boundary only, not fill.
A simple response to the solution set issue problem mentioned would be to
provide a look-up table of the ecological values of each site, and encourage the end-
user to look for similar matches when a site becomes unavailable. This is
problematic though, because several of the criteria are based on spatial variables
which don’t translate well into look-up tables.
A visualization technique for identifying correlation could be used. The question
would then be how to do it in such a way that the output is not too complex to be
useful. One option briefly overviewed in the body and slightly expanded here,
would be to look at which set of sites would be best to attain if a particular optimal
site becomes unavailable. This could be done by overlaying all of the realizations
that occurred when that site was randomly chosen as ‘not available for conservation.’
152
The standard-set sites, rather then being mapped as black, could be mapped as
having a black and white dashed boundary, with the new overlay score determining
the fill color. This way the end-user could not only see which non-standard sites are
part of the solution when the site in question is not available, but also which of the
standard-set sites are no longer part of the solution. This output could be created for
every optimal site and provided to the client. The problem with this approach is that
it does not negate the problem of implementation uncertainty, it just postpones it one
time step, and minimizes it thereafter.
One technique that might work for several time steps would be as follows. In
such a technique, each one of the realizations would be represented by a unique
combination of hatching angle and hue, with a low saturation used. Sites that were
chosen in two realizations would be represented by two hatching angles, each with
their associated hue. When hatching angles were the same, then the two sets of lines
would overlap as one line. A function could be programmed such that the new line
represented would have an increased saturation and/or increased value (making a
darker hue). Thus, very common sites would again have high saturation, but this
would be due to overlapping of many different hatchings and background colors.
Further, it seems that visually similar groups of sites will emerge, such as a group of
sites that are varying shades of red and orange, with an emphasis on the vertical
hatching. This would indicating to the viewer that if one such site is conserved then
others in that group should probably be targeted. [A few other potential
improvements are provided in Appendix A]
153
Improving the uncertainty analysis and evaluation
Because this is a proof of concept application, this implementation-uncertainty
value was communicated as a relative indicator, and not a quantitative value. This is
because there are several sources of uncertainty in the way it was derived. Similar to
the decision not to show the certainty value of the standard-set sites, this second
order uncertainty was not communicated to the end-users. Practitioners should be
aware of these issued though. As discussed in the methods, the selection of the sites
used in the Monte Carlo analysis should have been based on the inverse of a
modeled likelihood of development, rather than a random sample. This would also
require a larger number of realizations to get a sufficient sample size of runs in
which the sites with high development potential were available for conservation.
Such a model would entail adding probabilities to the current threat model. The
current threat model only looks at if the human land-use at particular 100 m cell is
more likely to change to a higher impact use by the year 2050, or more likely to stay
the same. The highest impact land use that is likely for that cell is the one assigned
for the 2050 human impact layer, and threat to that cell is the difference in human
impact from the year 2000 to the year 2050. The model does not indicate if the
change is 99% likely or only 51% likely, just that it is more than 50% likely. To get
these data, it would be best to re-program the different threat sub-models (urban
outgrowth, sub-urban growth, oil development, agricultural expansion, and grazing
expansion) so that they provide an output indicating probability. As an intermediate
154
alternative, a gross surrogate for threat could be used, such as the cost layer, using
the assumption that areas of high cost are of high value, and areas of high value are
more likely to get developed then areas of low value.
Secondly, it would be best to perform such an analysis using an optimal resource
allocation model that uses integer linear programming, rather then on a resource
allocation model that uses a heuristic. This is because the heuristic usually does not
arrive at the true optimal solution and thus has some degree of uncertainty. This
uncertainty propagates through the Monte Carlo analysis, so the composite layer
represents both the effects of this uncertainty and the effects of the implementation-
uncertainty. If a greedy heuristic needs to be used in an approach that is beyond a
proof-of concept, then a sensitivity analysis of the heuristic should also be performed
and integrated with the implementation-uncertainty analysis. Such an analysis could
be to take the last site chosen in the standard set, and run the greedy algorithm again
but forcing it to choose that site first. This could be repeated for the second to last
site, and then using the last two sites to start off, and so on. All of the results could
then be overlaid to get a composite layer indicating the uncertainty of the greedy
heuristic output.
Finally, the findings themselves have a degree of uncertainty. The focus group
participants knew the project and field. If an end-user had no prior experience at all
in planning, the animations might not have been as understandable. However, it is
assumed that most end-users with significant decision-making abilities would have
some knowledge of land-use planning. Secondly, the focus group participants knew
155
each other and were working together also as advisors. One of the repercussions of
such a scenario was that they may be less likely to ask questions that might be
perceived as “dumb.” This problem was anticipated and addressed: participants
were reminded to ask any question at all, and that asking a basic question is
commendable. Further, initial examples of such questions were praised as helpful
and candid. Thirdly, the findings are region and context specific. Most of the
findings are likely transferable, but the contextual factors determining this
transferability have not yet been clarified or valued.
Improvements to the approach by prioritizing efforts
Another research direction would be in comparing the costs and benefits of
efforts in SDSS development towards communicating uncertainty to those of
reducing it. For instance, if the above two research directions were pursued, it would
be good to tally the costs for improving the visualization versus those of improving
the accuracy of the uncertainty analysis. In the other direction, a related cost and
benefit evaluation could compare the implementation-uncertainty approach
presented here with a simple surrogate for general uncertainty. For instance, most
resource allocation heuristics provide a standard set that is an estimate of the true
optimal set, and they also identify sites that were close to being considered in this
estimated standard set. In the greedy heuristic, these are the next several sites that
would be selected after the target number of sites have been reached. In a simulated
annealing heuristic (Murray and Church 1996), these near misses are the ones
identified in the local optima solutions but not in the standard set. In both of these
156
cases, these alternative sites were not identified based on implementation uncertainty
per se, but they might have similar spatial distributions. The similarity will likely be
lower in cases where there is a high degree of irreplaceability among the standard
set. It would be good to characterize the statistical similarity between these alternate
approaches of displaying uncertainty.
CONCLUSION
This paper explores a type of uncertainty that has not been explicitly addressed
before. It occurs when a SDSS plan is implemented incrementally by end-users
while conditions are changing. End-users adapt to these dynamic conditions, and
deviations from the plan almost certainly occur. Re-iteration of the SDSS is often
not feasible, so it becomes uncertain what the next best steps would be given the
changed conditions. This implementation uncertainty can be ignored or
acknowledged in some way. We devised a method for communicating the issue and
estimating the usefulness of alternative decisions.
True to expectations, end-users were not consciously aware of implementation
uncertainty or its effects on the original model outputs. Presentation of the
uncertainty and its effects through relatively straightforward techniques changed
their understanding. It brought all the end-users to a similar and more complete
knowledge of the issue of implementation uncertainty, and the actual sites that it
affected most. This more accurate understanding facilitates wiser allocation of
resources, the overarching goal of a spatial decision support system.
157
There are opportunities for further research in improving both the method and its
evaluation. Enhancements vary in scope and complexity, including a simple
program that can provide updated outputs based on changing conditions. Evaluation
improvements include increasing the sample size of participants and regions
evaluated, and better comparing the costs and benefits of the different approaches.
Evaluation would be most useful if it is based on the wisdom of decisions made or at
least the improved knowledge of end users rather than simply the accuracy of the
uncertainty analysis. [Some preliminary Discussion about Uncertainty, Knowledge,
and Wisdom is in Appendix A.]
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Chapter 4: Mapping the uncertainty of conservation planning
as a means towards successful implementation
Abstract. Biodiversity conservation is suffering due especially to poor
implementation of scientific findings. This is very prevalent in conservation
planning, where communication of spatially explicit results has been
extremely contentious and often counter-productive. A proposed design
principle is that quantifying the uncertainty of conservation assessment and
visualizing this as part of the final map product is expected to decrease
volatility and facilitate implementation. A corollary is that visualizing the
uncertainty involved in implementation is expected to further bolster
implementation. A case study was performed. Maps with and without
implementation uncertainty were evaluated by focus groups to assess the
proposed design principle and associated corollary. Indications are that the
design principle is true in this case, but confounding factors limited the
certainty of this finding. The groundwork is laid for further verification and
research, including the discovered hypotheses that mapping uncertainty may
help implementation through the building of trust, instigating the desire to
learn, and guiding adaptive management.
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THE CHALLENGE OF KNOWLEDGE TRANSFER IN CONSERVATION PLANNING
Conservation planning has been only marginally effective at conserving
biodiversity because too much emphasis and research is channeled towards
identifying what nature needs (conservation assessments), and not enough on how to
implement these findings in the complex real-world (Prendergast et al. 1999;
Balmford and Cowling 2006; Knight et al. 2006a). As a result of this
implementation crisis there is a growing emphasis on examining and addressing
implementation strategies as part of the conservation planning research agenda (e.g.
Angelstam et al. 2003; Fagerstrom et al. 2003; Younge and Fowkes 2003; Natori et
al. 2005; Pierce et al. 2005,; Davis et al. 2006; Knight et al. 2006a). “Conservation
is primarily not about biology, but about the choices that people make” (Balmford
and Cowling 2006).
Efforts addressing implementation are coming to several conclusions, including
three interrelated findings: (1) It is essential to engage the institutions that will be
involved in implementation from the start and throughout the process (Angelstam et
al. 2003; Fagerstrom et al. 2003; Younge and Fowkes 2003; Natori et al. 2005;
Pierce et al. 2005; Knight et al. 2006a; Knight et al. 2006b). “Institutions include,
but are not limited to beliefs, norms, relationships, property rights, and agencies”
(Angelstam et al. 2003). (2) It may be more important to set up an enduring process
that allows for updated information and adaptive management amidst changing
socio-ecological conditions than it is to identify an optimal solution snapshot
(Salafsky et al. 2001; Angelstam et al. 2003; Meir et al. 2004). (3) Considering both
203
formal reserves and the working landscape (areas that are managed simultaneously
for biodiversity conservation and resource use) is more feasible and cost effective for
society than solely relying on reserves for biodiversity conservation (Knight 1999;
Scott et al. 2001; Pence et al. 2003). Thus, success for many regions throughout the
world is highly dependent upon the establishment of institutions, mechanisms, and
incentives for private participation in conservation (Pence et al. 2003). A
fundamental implication of all three of these findings is the need for effective
knowledge-transfer from the conservation scientists to the various stakeholders in
the implementation process (Theobald et al. 2000).
In practice, however, this ideal of knowledge-transfer can be quite problematic.
This essay examines specifically the public release of conservation assessment maps
(e.g. Fig 13) (see also Fig. 2 in Margules and Pressey 2000). These maps typically
show areas (e.g. squares, hexagons, parcels, etc) of land (both public and private)
designated as conservation priorities. Some private property rights activists are livid
when they see these maps (Cohen 2001; Environmental_Perspectives 2005). They
see a land-grab, a global environmental conspiracy, or at least a huge increase in
environmental restrictions (Cohen 2001; Hurley and Walker 2004). Subsequently,
they respond with fear and suspicion, thereby blocking any knowledge transfer or
subsequent collaborations. There are other problematic responses to mapping
conservation priorities. In some cases there is a fear expressed by local governments
of a loss in property-tax revenue (Cohn and Lerner 2003). Further, government
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Figure 13: An example conservation assessment map
agencies tasked with multiple-use mandates are thrust into a hotbed of controversy
and confronted with jurisdictional conflicts. Finally, when property owners see their
land mapped as high ecological significance, they sometimes rush to develop or
degrade it before any new ecosystem-based policy gets enacted; or they simply
increase the selling price to land-trusts (personal communication with Michael
Feeney 2002; Stoneham et al. 2003). Some entrepreneurs are even buying
inexpensive land with high conservation value and then selling it later to the
government or land trusts for a large profit (Weiss 2003).
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As a consequence of these and similar issues, the lead organizations and agencies
involved are typically forced to keep the results in-house as much as is allowed.
Subsequently, the ideals of institutional engagement, adaptive management, and
working landscape collaborations are difficult to implement. This leads to the
research question: given that the traditional approach to presenting results to the
public jeopardizes the implementation process, in what form can the results be
presented so they have a less negative impact and still maintain their usefulness?
This qualitative study was designed to explore answers this question, not to
empirically prove or disprove a hypothesis. The essay continues with an overview of
the case study, and a proposed design principle for presenting conservation planning
results. Background regarding this general approach is provided, along with a
corollary regarding the specific approach taken. The approach was implemented in
the case study and evaluated through focus groups. The findings point out the
positive aspects of the proposed approach, as well as the shortfalls and lessons
learned. The findings apply not only to conservation planning, but also to other
elements of socio-ecological resilience (Olsson et al. 2004) that involve spatial
decision support. The problem is framed around the culture of the American west,
but the findings should transfer to most other areas where public release of
conservation priorities is volatile.
REGIONAL CONTEXT AND THE PROPOSED DESIGN PRINCIPLE
The case study occurred on a watershed-defined, 14,000 km2 region on the south-
central coast of California [See also Fig. 1 of Gallo et al. 2005: Conception Coast
206
Region and Watersheds]. The drivers of land-use change are especially strong here
due to the appealing climate and proximity to the Los Angeles wealth, culture and
population pressure. As with many areas of the rural and semi-rural American west,
this region is a hotbed of controversy regarding land-use. Attempts to manage the
burgeoning growth in an ecologically and socio-economically responsible manner
have been extremely contentious.
Four recent land-use initiatives illustrate this tension. Conservationists lobbied
the National Park Service to study the feasibility of adding a rural stretch of coast
into the national park system as a public and private National Seashore, Heritage
Area, or some other similar designation. The NPS obliged, but was met with such
vehement opposition by some of the landowners that the conclusion of the study was
that the Gaviota Coast had global significance, but it was entirely up to the local
community to conserve it. Secondly, Santa Barbara County Planning Department
initiated a rural resources program designed to better identify the ecologically
sensitive areas in the county, and to allow for streamlined permitting and regulation
in the other areas. The effort involved public meetings and stakeholder
collaboration, and was derailed when the agricultural block of stakeholders left the
process in protest. Thirdly, property-rights activists organized an initiative to split
the county in half because they felt misrepresented in land-use and business issues.
While the initiative failed, the vote occurred after a pro-development change in the
majority of the Board of Supervisors. Lastly, a stakeholder-based collaborative
207
process designed an oak tree protection compromise, but the new board majority
rolled back the provisions after gaining power.
Amidst this backdrop, a local non-governmental organization was formed in
1998 to help “protect and restore the natural heritage of the region through science,
community involvement, and long-term planning” (Conception_Coast_Project
2004). In 2003, the organization began creating the Regional Conservation Guide
(RCG), the vehicle for this essay’s case study. The RCG was to result from a
conservation assessment of the region, and would publicly release maps estimating
the landscape requirements for long-term ecological integrity in an effort to help
guide community action. One of these maps was to show the locations of
conservation priorities. This is the agenda that generated the aforementioned
research question.
The researcher proposed a design principle: if the uncertainty involved in the
scientific analyses is estimated and then visualized as part of the conservation
priorities map, then the information could be publicly released and should have a
less negative impact then the traditional approach while still maintaining usefulness.
The key argument of the principle regards how the uncertainty results will look.
There are many approaches to visually representing uncertainty, with variations in
color saturation and clarity being particularly suited (MacEachren 1992). Clarity can
be mapped by variations in “crispness” (MacEachren 1995) or “abstraction” (Van
der Wel et al. 1994). Any of these techniques will result in a conservation priorities
map that is fuzzier and/or less precise looking than the traditional conservation
208
assessment map. Sketchy and less polished looking graphics tend to encourage
public participation, as they infer that the proposal is still in the undecided stage and
open for comment (MacEachren 1995; Krygier 2002). It was reasoned that this
would diffuse the threat felt by some stakeholders upon seeing the map. These types
of maps are also less dogmatic. Any particular site would have a relative certainty
of being a priority area, rather than being a priority area or not. This was expected to
be more palatable to the landowner or manager of the site that would normally be
marked as absolutely a priority area.
These expectations join the common motivation for uncertainty visualization—to
allow end-users to understand which results are more reliable, and hence, make a
more informed decision (e.g. Taylor 1995; Flather et al. 1997; Bradshaw and
Borchers 2000). Oftentimes users will be unaware of sources of uncertainty unless
explicitly presented with them (Keuper 2004). In some cases, making decisions
without the uncertainty information is downright irresponsible and leads to
biodiversity loss (e.g. Beissinger and Westphal 1998).
UNCERTAINTY IN CONSERVATION PLANNING AND THE PROPOSED CORROLARY
Several taxonomies exist that can classify the many sources of uncertainty in
conservation planning (e.g. Rejeski 1993; Goodchild and Case 2001; Regan et al.
2002; Brown 2004). Rejeski (1993) offers a comprehensive and straightforward
taxonomy comprised of four general categories: (1) spatial uncertainty includes
locational error, categorical disparities, and boundary issues; (2) linguistic
uncertainty arises due to issues of vagueness in thresholds, ambiguity, and context
209
specificity; (3) model uncertainty results from the inevitable simplification that
occurs when using mathematical metaphor to mimic the enormous complexity of
human, natural, or human-natural systems; and (4) parameter uncertainty arises and
propagates because the uncertainty of each parameter value is often unknown or
unarticulated.
Examinations of these types of uncertainties in conservation planning have
occurred, including those regarding (1) spatial uncertainty of species distribution
data (Todd and Burgman 1998; Regan and Colyvan 2000; Regan et al. 2000;
Robertson et al. 2004) (2) linguistic uncertainty confounded by parameter
uncertainty in determining the conservation status of species (Burgman et al. 1999;
Akcakaya et al. 2000); and (3) model uncertainty in wildlife habitat models (Stoms
et al. 1992; Loiselle et al. 2003; Johnson and Gillingham 2004) and in the
biogeographic assumptions of conservation assessments (Flather et al. 1997;
Whittaker et al. 2005; Grenyer et al. 2006).
Systematic conservation planning involves a multi-criteria hierarchy of
interconnected models (i.e. species presence and connectivity models feeding into
optimization models), so it is another layer of complexity to examine how all these
uncertainties propagate and interact in affecting the final uncertainty of the
assessment. With standard project budgets, it is extremely difficult if not impossible
to model and communicate all of the uncertainties of an effort. Moilanen et al.
(2006) provide one of the most promising approaches to date of addressing this
complexity.
210
An assumption of this paper is that only one or a few uncertainties need to be
modeled in order to communicate the issue and attain most of the desired benefits.
In order to maximize the associated benefits and minimize costs, what kind of
uncertainty(ies) in conservation planning should be estimated and communicated?
The answer is almost certainly context specific and best achieved through
cost/benefit scoping, but an approach that might be consistently useful is
preliminarily evaluated here.
Meir, Andelman, and Possingham (2004) uncover an important type of model
uncertainty related directly to the implementation crisis. They point out that
conventional methods of conservation assessment rely on a snapshot in time to
identify the lands necessary for conservation, and assume that these lands can be
conserved immediately. But in practice, this implementation occurs over decades.
During these decades, some biodiversity is lost and the human dominated and natural
landscapes change, thereby changing the priorities. In other research, the author
examines this “implementation uncertainty” in more detail, devises methods for
visualizing it, and evaluates how effectively the different visualization techniques
communicate the uncertainty (Gallo 2006; Gallo and Goodchild Unpublished)[see
chapter 3]. A similar approach would be visualize the opportunity uncertainty
alluded to in Moilanen et al. (2006). The emphasis here is on the implications of
communicating such uncertainty.
The corollary proposed is that mapping implementation uncertainty gains the
postulated benefits of uncertainty mapping outlined above, and two additional
211
benefits unique to this type of uncertainty. If there is a land-owner backlash to
traditional conservation planning maps, the driving value is usually the fear of the
loss of liberty in general, and private property rights specifically (Hurley and Walker
2004; Environmental_Perspectives 2005). Mapping implementation uncertainty
should calm this fear by implicitly reaffirming landowner control-- it acknowledges
that the scientists do not know which landowners will explore conservation or
development opportunities, and the scientists do not know when or how this will
change. In other words, what the landowners do with their land is in their hands and
cannot be mandated or controlled by the conservation planning process.
The proposed corollary is also designed to address one of the drawbacks of
mapping uncertainty—the strategy used by naysayers of encouraging the status-quo
until the uncertainty is “solved” (Stocking and Holstein 1993; Friedman et al. 1999).
Arguments for the status-quo are often based on the belief values of the person
involved, not the uncertainty (Kinzig et al. 2003). Politicians have to make decision
every day in the face of uncertainty, and move forward regardless (Kinzig et al.
2003). Similarly, people that disagree with scientific findings can focus on
uncertainty as a means of discrediting the science. All of these examples have been
illustrated by the global warming “debate.” In this case the source of
implementation uncertainty is the cherished value of liberty, and the flexibility for
land-owners to determine their future. It would be disingenuous for property-rights
activists to cite model uncertainty as a reason for discrediting or stalling the process,
for they would be pointing to the value of liberty as the cause.
212
CASE STUDY: THE REGIONAL CONSERVATION GUIDE
A conservation assessment was performed based on an optimization modeling
approach that integrates the threat of habitat degradation, cost of conservation, and
five ecological criteria (Davis et al. 2006). A sixth ecological criterion was added:
coarse-scale habitat connectivity (Gallo et al. 2005) (Ch 3). A group of ecological
advisors and a group of advisors with extensive knowledge and experience in the
region’s land-use politics provided guidance through a series of meetings and
workshops. In the initial scoping workshops, it was determined that the final maps
should be hardcopy, be at about 1:500,000 scale (11” X 17” maps), and should
identify conservation priorities for the next twenty years. The experts estimated a
background rate of conservation of about 200 km2 (50,000 acres) conserved per
decade, so the target acreage for the conservation priorities map was set at ~400 km2
(100,000 acres). Working meetings were also held to gather expert ecological
knowledge and to parameterize the model. A simplified version of the standard-run
map that resulted was provided in Fig 13. In the full color version complete with
landmarks and land-use, the conservation priority areas comprised 3% of the region.
The implementation uncertainty of the model output was quantified and
visualized using a stochastic approach (Gallo 2006; Gallo and Goodchild
Unpublished) [see chapter 3]. The implementation-uncertainty map resulted (Fig 14),
213
Figure 14: Simplified portion of the Implementation-Uncertainty Map
along with three animations created to help communicate the concept of optimality,
the issue of implementation uncertainty, and how it was modeled. The solution-
space (the combined area of all the standard-set sites and the uncertainty
alternatives) was approximately 1,300 km2, or about 9% of the region. Advisory
focus groups were used to assess these uncertainty products. Abridged coded
transcripts with analytical categories were created from the video recordings and
evaluated (Gallo 2005) [see also chapter 3]. One of the objectives was to determine
if the conservation priorities map released to the public should be the standard-run
map or the implementation-uncertainty map, and why. Another objective was to
explore how these products would affect conservation implementation.
214
The consensus within all three focus groups was that the implementation-
uncertainty map was more suitable for public release than the conservation priorities
map. Reasons cited were as expected, focusing on the decrease in volatility. The
implementation-uncertainty map “takes the ‘gun’ away from pointing at one
particular spot” and was expected to lead to a “lower panic button” among
landowners fearful of conservation priorities.
However, there was hesitation regarding the release of the map, namely that it
was expected that some landowners would still feel threatened and/or degrade their
land. “We had this with the listing of the tiger salamander, where a lot of ground got
ripped real quick.” The land-use advisors felt that although the implementation-
uncertainty map was better, it was still not suitable for public release given the acrid
socio-political climate. It was felt that the map was still not fuzzy enough, as the
resolution of the sites (1.5 km2) was still too close to the parcel scale.
The groups also wanted to see a much larger solution-space than the resultant
9%. This was not only because of the volatility issue, but also because it was felt
that mapping more gaps then “hot spots would dissuade people that are in the gaps
from conserving their land.” Some people also felt that the map should better reflect
“continuity, contiguity and functioning ecosystems.” It became clear that the map
was trying to fulfill too many agendas, ranging from the needs of land-trusts to
prioritize which parcels to target for purchase, to the need of education organizations
wanting to show a long-term vision of conservation.
215
In addition to the desire of the larger solution-space, the participants wanted the
final results to be free of the error prone cost analysis, and all groups wanted to see
the results both with and without threat. Both threat and cost were embedded deep
within the model, which was implemented one command at a time, and would
require months to rerun. The uncertainty analysis would then need at least another
month of computer processing time. Unfortunately the project timeline was nearing
termination. It became a choice of (a) leaving the model as is and running the
optimization model for a larger target (e.g. 15% instead of 3% of the region) and
then doing the uncertainty analysis, or (b) removing cost and determining the relative
priority of each site both with and without threat incorporated.
As is sometimes the case with action research in participatory GIS, the needs of
the community had suddenly diverged from the needs of the researcher. The ethical
path at such a crossroads is to cater to the needs of the community (Rambaldi et al.
2006). This is what was done, and option b was pursued. It was also decided that
the goal would be to provide a long-term vision for conservation of the region, so the
timeframe would be 50-100 years rather than 20 years. A biodiversity-value map
would show a synthesis of the six ecological layers by depicting the relative
marginal value of each site (i.e. how important the site would be to conservation
goals if it was the next one conserved). The conservation priorities map would
incorporate threat. Further, the participants wanted to see the uncertainty concept in
some incarnation. So it was decided that the maps would indicate that there was
uncertainty in the analyses by simply being blurred uniformly, thereby getting rid of
216
the grid pattern and making a smooth pattern of values. The result was the smoothed
marginal-value map (Fig 25 of Gallo et al. 2005).
DISCUSSION
There are strong indications that in this case, the quantification and visualization
of uncertainty as part of the conservation priorities map would facilitate
implementation. However, there were conflicting objectives, thereby making the
proposed product unacceptable, and thus deeming the principle less conclusive.
While certainty of these results have much to be desired, Knight (2006) encourages
the sharing of pitfalls encountered in order to build the quality of a discipline. This
call is corroborated by the editor’s note on Knight’s article. Further, participatory
research allows for submersion into a topic that can provide new hypotheses and
framings. In this case, an idea about the corollary and several other hypothesized
benefits of mapping uncertainty were discovered and are shared below.
Key lessons learned
First, the research highlighted the repercussions of not having all parties involved
come to a consensus about the objective of the map(s) produced. Cartographic and
representation needs are different for different objectives (Board and Taylor 1977;
MacEachren 1994). For instance, maps can have the objective of exploration,
communication, negotiation, decision-support, or visioning (pers. com. Couclelis,
MacEachren 1994). In this case study there were conflicting objectives, one was to
provide a long term vision of the ecological requirements of the landscape, and the
other was to provide decision support for conserving priorities areas for the next
217
twenty years. While on the surface these seem compatible, the objective of visioning
and decision support ended up clashing. The decision-support map was deemed too
detailed and deterministic to be released as a publicly available vision.4
MacEachren (1994) and DiBiase (1990) provide a framework for considering the
role of maps and how they match with the prescribed use and user. First, it should
be determined if the map is to be used for visual thinking or visual communication.
Visual thinking is usually in the private realm and includes uses such as exploration
and confirmation. These uses can often include higher order cognitive tasks (or data
sensitivity) designed for users of a particular expertise or objective. Visual
communication is more often the directive in dealing with the public realm, and
includes objectives such as synthesis and presentation. Maps are not objective
(Harley 1989; Wood and Fels 1992). By the time the map gets to the synthesis stage,
the “expert makes informed decisions about what to emphasize, what to suppress,
and which relationships to show (MacEachren 1994).” The issue of matching the
objectives(s) of the map with these cartographic and analytical decisions becomes
even more essential at the presentation stage.
4 Some may wonder if the decision-support map that was generated for the focus
groups was used or still exists. Because of the uproar that this could have caused,
and its inherent uncertainty due to the cost analysis and several other parameters, it
was never printed and the GIS file has since become corrupted.
218
The lesson learned is that if there are multiple and conflicting objectives,
especially for a presentation stage map, they should be prioritized. Further, the
limiting factor of the priority objective should be identified, and the threshold of
acceptance should be determined in order to at least partially meet the secondary
objective. In this case the limiting factor of the primary objective (visioning) was
volatility. To meet this objective and also partially meet the usefulness factor of
decision support, then the community’s tolerance for volatility should have been
scoped before the analyses were planned, performed, and mapped. This could have
been done by showing focus groups and/or stakeholders generic map products that
had varying degrees of crispness, resolution, spatial extent, solution space.
This scoping phase can also try to examine the cultural values for uncertainty of
the issue at hand. The influence of values in decision-making is unavoidable, but at
least it should be made transparent. It is possible to separate values from
uncertainties by having people communicate their opinion regarding the
consequences of a type I error (doing something when it is not necessary) versus a
type II error (doing nothing when action is necessary) and comparing these with the
associated probabilities (Kinzig et al. 2003).
Thirdly, it is important to expect the unexpected in participatory mapping. A
suggested guideline is to build in allowances for the community process to take extra
time (Rambaldi et al. 2006). One suggested approach is to negotiate the unlimited
rollover of unspent foundation funds into subsequent years (Rambaldi et al. 2006).
On a related note, if ESRI ArcGIS is being used, any model should be built in the
219
newly available modelbuilder. This drag-and-drop, menu based interface allows the
creation of model scripts which provide massive timesaving gains when the model
needs to be revised or re-parameterized.
Additional benefits to be explored
Several other benefits of mapping uncertainty and of the corollary were revealed
through the focus groups and submersion in the topic. One such benefit is the
apparent building of trust. Many people view scientific models with a level of
mistrust, knowing that the model cannot incorporate their own innate or local
knowledge (Wynne 1992; Gregory and Miller 1998). Acknowledging the
uncertainties of a model improves its honesty (Rejeski 1993), which can build the
trust that is essential in addressing the implementation crisis (Knight 2006). This
trust issue is largely ignored or unknown to scientists (Wynne 1992). When the
constraints are such that a spatially explicit uncertainty analysis is not feasible, then
this study indicates that portraying the presence of uncertainty through a uniform
fuzziness is preferred and more honest than portraying exact boundaries for an
uncertain result.
When confronted with uncertainty, the end-user is required to apply some of
their innate knowledge in making a decision. Consequently, if they have a little or
no knowledge about the issue then they will be motivated to learn more about it if
they are to make an informed decision (Epstein 1992, in Freidman 1999, page 42).
This will increase the demand by decision-makers for conservation science
educational material, driving the goal to mainstream conservation biology. (The
220
alternative response of frustration by the lack of clarity is also possible, and can be
mitigated by also having simpler information resources available.)
Increased emphasis on uncertainty also could help drive the adaptive
management cycle. It identifies the ways that the conservation planning initiative
needs to improve knowledge through better data collection, more monitoring, model
improvements, etc. (Rejeski 1993). It also provides a mechanism for prioritizing
future research based on real world needs (Kinzig et al. 2003). This reflexive
process can help make ecological modeling more useful for management and policy
decisions (Taylor et al. 2000).
With regards to the issue of volatility and the corollary, one of the negative
repercussions of visualizing ecological uncertainties (as opposed to implementation
uncertainty) in the conservation priority map is that there will inevitably be areas of
high certainty. Landowners/managers of these areas will likely feel even more
threatened then if the certainty was not mapped and all priority areas were targeted
equally. This may be the biggest reason for estimating and mapping implementation
uncertainty as at least one of the uncertainties modeled. Only the areas where the
landowners/managers have expressed a desire for conservation will be able to
receive scores of highest certainty. This may be perhaps the most fruitful line of
research to come out of this study. There are similar uncertainties that address
feasibility, such as the opportunity uncertainty alluded to in Moilanen et al. (2006).
It may be quite interesting to use their info-gap approach to modeling opportunity
cost uncertainty and seeing if it has the expected effects on volatility.
221
CONCLUSION
It is a very different perspective viewing the field of conservation planning with
an emphasis on actual implementation rather than on figuring out the spatial needs of
biodiversity. It results in the conservation planner considering how their products
will be communicated, and the implications of these communication choices.
Visualizing some of the uncertainties of the conservation planning analysis has some
subtle yet profound implications. Indications are that the fuzzier map decreases
volatility by being less threatening to individual landowners and by indicating that
there is still room for discussion. The degree to which this is needed and effective is
doubtless context specific. In this particular context, the design principle appears to
be true, but is not empirically proven.
This essay is more about exploring a new mode of practice then it is about
proving a hypothesis. In doing so it lays the groundwork for several directions of
future research. Empirical and comprehensive evaluations of the effectiveness of
mapping uncertainty to improve implementation are needed. Especially important is
considering the need to use implementation uncertainty (i.e. the effects of
uncertainty in landowner willingness) as one of the uncertainties modeled. Not only
could the volatility issue be examined, but so could the trust-building, educational
and priority guiding aspects as well. It would be best to evaluate the counterfactual,
i.e. comparing the release and use of the implementation-uncertainty map by some
stakeholders to the release and use of only the standard map by others (Ferraro and
Pattanayak 2006). A critical issue only touched upon here is determining the direct
222
and indirect costs of communicating implementation uncertainty. Future studies can
also examine how well mapping uncertainty affects other components of successful
implementation, such as the building of trust, instigating the desire to learn, and
guiding adaptive management. It would also be good to better understand the
dimensions of the problem (e.g. volatility, usefulness, land-use paradigm etc.),
leading ideally to a way of rapidly assessing the best approaches for knowledge
sharing in a particular regional context. In closing, society faces quite a challenge
regarding biodiversity conservation and in attaining the full potential of life on Earth.
The approach presented here will hopefully improve the teamwork that is imperative.
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Chapter 5: Conclusion
A FRAMEWORK AND OPERATIONAL MODEL DESIGNED TO IMPROVE THE IMPLEMENATION PHASE OF CONSERVATION PLANNING
There is a large margin for improvement in the influence that systematic
conservation planning has on actual conservation action. There are three phases to
systematic conservation planning (hereafter “conservation planning”) namely
conservation assessment, implementation, and monitoring. A huge majority of the
research emphasis in the discipline has been on assessment, with much less on
implementation, and even less on monitoring.
In looking at implementation, it is helpful to consider that conservation action is
a commitment to conserve, and the commitment can be of varying degrees. This can
be a formal, legal commitment which is almost always achieved through economic
incentives or disincentives. Commitments to conserve can also be on the personal
level, and can occur due to all four mechanisms of behavior change: moral (i.e. the
intrinsic value of “nature”), community-based (i.e. an agreement with neighbors to
preserve a rural livelihood), educational (i.e. understanding the value of ecosystem
services to livelihoods), and incentives (i.e. nature-based tourism on the ranch, or
predator friendly beef sold at a premium). Conservation planning is primarily used
to support the formal form of implementation. This is done through Context One,
developing ecologically sound land-use policies and plans, and/or Context Two,
supporting the wise purchase or tax easement of individual parcels of land. Context
Three is often overlooked by academia, and is the development of conservation plans
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that do not have any legal standing. Nonetheless, these plans can be used to
influence conservation action on all four levels of behavior change, and can also be
generate the momentum needed for more formalized conservation planning.
The focus of the dissertation was on Context Three. A working assumption was
made that if the amount of public participation could be dramatically increased in
Context Three conservation planning, then the strength of the four drivers for
informal conservation action (moral etc.) will also be increased. To do this
effectively, several constraints need to be minimized, such as the increased cost of
public participation, the threats to “sound” science, and the abuse of sensitive data
and knowledge. A conceptual framework was devised that combines systematic
conservation planning with findings from public participation GIS (PPGIS) and
socio-ecological resilience. The placeholder name for this concept is engaged
conservation planning and management (ECPM). ECPM as presented, consists of
two key communication networks. The first is termed the Landscape Knowledge
Network (LKN) between the conservation planners and the landscape observers
(ecologists of varying skills that collect and review useful data, information, and
knowledge about the region). The second communication network is between the
conservation planners and the community members, termed the Community
Collaboration Network (CCN). It is based on a two-way flow of knowledge and
values in an iterative approach to making science-based and pragmatic conservation
plans and implementation strategies.
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An operational model for ECPM was derived for the regional contexts in which a
lot of the interested community members have access to computers, and broad-band
internet access is common among those. It was also written to provide practitioners
and researchers embarking on its development with references, and practical
suggestions. The operational model utilizes benefits of the emerging culture and
software of Web 2.0, specifically the collaborative software and the geospatial
technologies such as Google Earth. The emphasis of the operational model was on
the LKN, as a detailing of the CCN, with its growing body of knowledge, was
beyond the scope of the dissertation. Landscape observers provide the core of the
LKN, and are comprised of at least three groups of increasing rigor: amateur
ecologists, citizen scientists, and professional ecologists. The data and knowledge
provided to the ecospatial web will be ranked based on the qualifications of the
observer and the observer’s self-reported confidence about the particular knowledge
object. The conservation planners also provide data and knowledge. All told, this
provides a framework for utilizing economies of scale to keep the ecospatial
knowledge of a region up-to-date and verified, as well as providing a purpose for
being in nature.
By providing a careful look at Third Context conservation planning, the
dissertation also implies a point that is made explicit here. Conservation planners
that are doing research and development in one of the two other contexts can now
keep this third context in mind. For instance, when choosing between two different
models/algorithms/theories to enhance or develop into software, they will use several
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different evaluation metrics, such as data requirements, processing time, accuracy,
software requirements, etc. Now, they are encouraged to also ask “what is the
engagement potential of the object under consideration?” How to exactly quantify
this needs to be developed, but such a metric includes aspects of portability,
transparency, ease of application, and ease of understanding. Related to the
portability issue, the sharing of modules and scripts is going to become increasingly
important approach at decreasing costs. Soon they will be able to be shared to
people that have free or nearly free versions of the GIS software, such as the ability
to do simple analyses using the new ArcExplorer.
THE POTENTIAL OF UNCERTAINTY MAPPING AS A MEANS TO IMPROVE IMPLEMENTATION IN ECPM
Communicating conservation planning products to the community can be
especially problematic, and is one of the challenging constraints of Third Context
conservation planning. Maps are inherently political objects, so need to be created
responsibly and carefully. Mapping the uncertainty that is inherent to the
conservation planning process shows promise in decreasing the volatility of the map.
Especially promising is the mapping of implementation uncertainty. This is the
quantitative estimate of how likely a site is to retain its attribute value (i.e. not being
a conservation priority) after future perturbations to the plan occur. A case study
was used as a platform for the initial stages of a technique for quantifying and
communicating this type of uncertainty. A resource allocation model was used that
identifies conservation priority areas based on their ecological value, the human
impact, the cost of conservation, and the expected change in human impact (i.e.
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“threat”). (In populating this model, new techniques for modeling habitat
connectivity were derived.) A Monte Carlo approach was used to identify the sites
that acted as good alternatives to the conservation priorities identified in the resource
allocation model. The analysis is a starting point only, as it did not use probabilities
of development potential in driving the Monte Carlo analysis. Nonetheless, the
results provided much more useful information to the end-users. Because
implementation-uncertainty is an abstract form of uncertainty, efforts were made to
illustrate it to the end-users using simple animations. The animations and the
different maps were evaluated and discussed using focus groups. Two of the three
animations proved useful, and when combined with the implementation-uncertainty
map, effectively communicate both the issue and an indication of its quantitative
values.
It was not possible to provide the products to the broad spectrum of stakeholders,
only the ones with experience in conserving lands and/or with ecosystem
management. They were very familiar with the politics and the sentiments of the
region thought. Indications are that the fuzzier products that emerge from
implementation uncertainty mapping act to dissuade fears. They imply that a
particular landowner will not be pressured to conserve their land because there are
alternatives identified. The uncertainty map also portrays the message that the
results are not exact, leading to a hypothesis that can be tested in the conservation
planning arena: mapping uncertainty can build trust between scientists and
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community members. Similarly uncertainty mapping can aid implementation by
providing an incentive for learning, and helping set priorities for future research.
Despite the benefits of the implementation uncertainty map, the product was still
deemed unsuitable for public release. It was still too volatile. A map that portrayed
the conservation value of every site of the region was released instead, and the
concept of uncertainty was portrayed by uniformly blurring all of the values of the
layer. One of the unexpected findings to come out of this research is along the lines
of building trust. An underlying tension in the evaluation of the product was the
uncertainty of the resource allocation model itself did not match the type of output
that it provided. The focus groups did not trust the results. In retrospect it would
have been much more valuable to focus on a multi-criteria model that identified the
conservation value of all sites and that could be updated as new data became
available. The lesson learned was that in community processes, it is probably wisest
to use a resource allocation model only after the end-users are familiar with and
endorse the base data layers and initial analyses.
This was also a learning experience in other ways as well. In hindsight, several
actions could have been done differently. Because the conservation assessment
method was not being explicitly researched, ‘canned’ software could have been used,
rather then an approach requiring manual implementation. Or, this latter approach
could have been used, but should have been performed through the ESRI
Modelbuilder interface, thereby allowing for easy removal of model components as
per land-management advisor request. Secondly, focus groups comprised of
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individuals without any decision making authority should have complemented those
that included community advisors. Similarly, efforts should have been made upon
initiation to prepare the participants and funders for a delayed timetable if the initial
products did not meet expectations.
FUTURE RESEARCH
What is the conceptual framework that integrates all three contexts of
conservation assessment most effectively? How does Context Three conservation
planning lead to policy change? How are the three contexts performed so that the
knowledge bases are linked to reduce duplication of effort? It may be that the
community can emphasize one at a time, with Context Three iterating more
frequently due to its emphasis on resilience. Thus a sequence of Three-One-Two-
Three-Three that is then repeated may be a wise approach at integrating the three
context in a unified approach to improving conservation implementation. Is this
indeed a good sequence, and how that is done? A related research question is in
bridging the disconnect between conservation planning and land-use planning. In
most instances of Context One conservation planning, these two fields are separated
as is illustrated, instead of integrated, as would be more ideal (pers. com. Davis). As
a result, conservation planning practice and products are not geared towards the
needs of land-use planners (Knight et al. 2006a). For instance, 74 conservation
assessments were examined to see how well they supported implementation, and
only two incorporated parcel boundaries in the spatial models (Newburn et al. 2005).
Efforts should be made to work with planners such that their needs are better met
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(Pierce et al. 2005). Further, the converse is also true. There is a wealth expertise in
the planning literature about issues regarding implementation that is hardly tapped
by the conservation planning community (pers. com Couclelis). This research angle
is a ripe opportunity.
ECPM can realize economies of scale and reduce duplication of effort if it
effectively integrates with other sustainability efforts and other online communities.
The more government-based urban planning, regional planning, comprehensive
planning, and transportation planning have more financial resources, and at least in
the U.S., are leaders in participatory GIS approaches. The principles of the LKN can
be applied to the cityscape as well, and it can eventually be the earthscape
knowledge network, or some other name that encompasses complete coverage of the
planet at multiple scales. The Digital Earth movement (ISDE 2006) is founded on a
similar idea, and might be the logical partner with ECPM. Similarly the Web of
Community Values, Visions, and Teamwork can be linked to other bioregionally
focused web domains such as a living database of sustainability oriented events and
project support (i.e. farmer’s markets and roof-top gardens), and a “green business”
directory. This more comprehensive web that includes the earthscape knowledge
network and these other bioregional domains can be called the Web of Resilience
(Fig 15). Such an alliance would likely increase engagement by one or two more
orders of magnitude given the large and growing proportion of people living in
cities. It might also increase the application of reconciliation ecology (Rosenzweig
2003) in urban areas(Miller 2005).
241
Figure 15: The Web of Resilience can help the self-organization and cooperation
among the different efforts working towards sustainability.
242
A good complement to this cross-topic research would be cross-scale research
(Cash et al. 2006), especially regarding the geospatial web. An initial challenge is
streamlining the search for all of the geospatial information at various scales that is
available for a place of study and/or residence. The standard way of sharing data in
Google Earth is by converting GIS layers into .kml layers. The universal Web
Mapping Service (WMS) interface (OGC 2006) allows use of the data in Google
Earth and all other browsers.vi How can all of this already burgeoning information
be catalogued? With focused research in GIScience then we may be able to move
beyond geoportals to a more dynamic and all encompassing approach where users
are able to search all registered WMS data in the world by keywords and/or
bounding box (Egenhofer 2002; Xiujun et al. 2006). A starting point for this goal
could be to have all WMS servers submit a link to a central web page, which was
then serviced by a web-crawler search engine (personal communication Goodchild).
The cognitive and ethical implications of the web-enabled emphasis ECPM
should also be explored. In many respects, it is the faith in technology that is fueling
the biodiversity crisis, and especially the apathy surrounding it. So embracing
information and communications technology as a centerpiece of the solution is
playing with fire, and needs to be recognized as such. One subtle but profound
implication is that when people are recording their outdoor experience they cannot
completely live the moment. Further, people will spend more time in front of a
computer exploring the world. These have the potential of weakening the
connection that people have with nature. In many cases, it is this connection and
243
resulting passion that motivates people towards environmentally responsible
behavior. How can ECPM be implemented How can ECPM be implemented to fuel
rather than extinguish this passion and wonder for creation?
As indicated by Fig 8c (Ch 2), ECPM has the potential of dramatically increasing
the public participation in the welfare of society and nature. A very interesting line
of research will be to examine if this is a key to Rawlsian democracy, and of
Habermasian dialogue. This has special potential in development of vision.
“Missing from most scholarly writings and public debate about the economy and the
environment are workable visions of the future (Harte 1996).” Third Context
conservation planning has the potential of addressing this glaring weakness of
society by providing a platform for development of ecological perspectives at
various scales from the landscape level to the global. These perspectives will
provide a much needed balance to the engine of economic growth that is prevalent in
all three contexts (Fig 16). (Ecology and economy each come from the root word
“home” and are arguably two sides of the same coin. One side is the study of the
home, and one is the management of the home. It doesn’t seem wise to have one
without the other.) This has indications of being a very good approach at shifting the
economic growth engine out of overdrive into drive. As per my bias stated in the
preface, I think this will be a good thing for life on earth, humanity included.
In moving forward with these and other exciting research agendas, lessons
should be learned from one of the big shortcomings of previous research in
conservation planning. A greater emphasis should be placed on developing and
244
Figure 16: Development and maintenance of Ecological Perspectives at various
scales worldwide has the potential of providing a balance to the Economic Engine.
245
testing operational models (Knight et al. 2006a). In doing this, the difficult task of
examining the counterfactual (how effective conservation would have been in the
absence of the treatment) needs to be performed (Ferraro and Pattanayak 2006).
Case-studies will also be more useful if a conservation taxonomy of regions can be
developed. What are the dimensions of the socio-political culturescape? Every
region is unique but has some characteristics that are the same as other regions. A
taxonomy of regions would allow a quick and standard classification of the key
social, ecological, political, and economic characteristics affecting conservation
implementation. The taxonomy could be used to identify regions with the best
enabling conditions for new conservation efforts (Mascia 2006), and it could be used
in the evaluation of an effort. The effects of the conservation planning treatment and
the counterfactual would be associated with the particular taxonomy, and over time,
correlations among regions should emerge. On the other hand, the taxonomy cannot
be too complicated or resource intensive to populate for any given region, because
time and funding for such endeavors is often scarce in applied research. What would
such a taxonomy look like and how could it be populated?
At this stage of the action-reflection cycle the participatory action researcher
asks, “what is next?” (McNiff and Whitehead 2006). There are so many exciting
directions to go that it is difficult to choose one. Further, much of this research is
underway, but with only marginal cohesion. Thus, one of the next steps in this line
246
of inquiry is to communicate with the community of practice (the group of people
bound by shared expertise and passion for a joint enterprise). In this case the joint
enterprise is the engaged approach to conservation planning and management which
includes development of the geospatial web, citizen science, community-based
conservation and natural resource management, collaborative planning, iterative
conservation assessment, geovisualization, adaptive co-management towards
resilience, and monitoring. In this spirit of communication, a staging ground for this
community of practice has been set up at engagedconservation.netcipia.net. It will
link to a more mature collaborative environment. For the moment, it is a wiki in
which members can begin forging ahead. We hope to “see” you there.
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http://www.iisd.org/pdf/2005/networks_dev_connection_africa.pdf#search=
%22Towards%20a%20Sustainable%20Development%20View%20of%22
Xiujun, M., L. Gang, X. Kunqing and S. Meng (2006). A peer-to-peer approach to
Geospatial Web Services discovery. INFOSCALE '06. Proceedings of the
First International Conference on Scalable Information Systems, Hong Kong.
vi Indications are that the geospatial web would be best able to support resilience
if it was comprised of open-source software, data, and standards. For instance,
Google Earth, part of a for profit company, does not allow the manual creation or
transfer of the memory cache, making it impossible to use without internet access,
and very slow with dial-up connectivity. (This is primarily to protect the proprietary
250
interests of the for-profit companies providing high resolution data.) Meanwhile,
broadband is available only to the elite for much of the world. In South Africa,
which is supposed to have embraced ICT technology(Vosloo 2005), broadband is
about 1,000- 2,000% more of the average person’s income than the same service in
the U.S. (MyADSL 2007). This scenario was one of the fears expressed by the
early GIS and Society discourses (Pickles 1995; Harris and Weiner 1998). The
supposedly democratizing function of GIS is actually used to empower the cultural
elite and to propagate the inequality of wealth… The OpenGIS Consortium (OGC
2006) is the hub of open source software for GIS, and World Wind is an open source
alternative to Google Earth that also allows manual creation and transfer of the
memory cache. In considering the merits of the OGC WMS protocol, it is important
to know that it is not just map images that can be shared, but actual vector and raster
datasets.
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Appendix A: Additional material referred to by Chapter 3
The material in this appendix was in the original draft of chapter 3. This material
has been cut and pasted here in case a reader desires to dive completely into the
subject matter. It also will be a reference for the researcher in future years. This
material is pasted in the order of which it is referred to in the body, so the sections do
not flow together, or reflect later terminology changes to the body.
Insert: The Introductory Modules
The resource allocation animation illustrated the concept of optimization and
related issues such as complementarity. Complementarity is a measure of the extent
to which a new area could contribute unrepresented features to the reserve system
(Margules and Pressey 2000). The maximum species diversity used for the non-
optimal approach was a function of the number of species and individuals, as per the
Shannon-Wiener Index (Krebs 1985). An resource allocation approach was
illustrated by the greedy heuristic that selected a site by how well it would contribute
to the objective function. [Back to main body.]
Insert: Selecting 50% of the sites
The number of sites chosen as unavailable needed to reflect a compromise
between two opposing considerations. On the one hand it was important not to set
252
this value too high, as this increases the influence of the random number generator in
selecting the target sites. On the other hand, setting this value too low produces
outputs that are very similar, thereby requiring thousands of runs to get a useful
variance of outputs. The optimization model was computationally intensive,
requiring 12 hours of computer time to run in initial trials. Achieving thousands of
runs would have been a logistical challenge for this particular study. As a result of
these considerations, the value of 50% was chosen, and implemented using
Microsoft Excel’s random number generator linked to the GIS with a look-up table.
The identification numbers of all the potential sites were exported to Microsoft
Excel and correlated with a number in an integer sequence from 1 to x where x was
the number of sites. Excel’s random number generator identified x/2 integers
between 1 and x. The results were correlated back to the site numbers, and a GIS
layer was made of just those sites. [Back to main body.]
Insert: Monte Carlo Synthesis
Initially, the realizations were overlaid using a binary approach. For each
realization, r, a site i had a conservation priority score, p, as either yes (1) or no (0).
R is the total number of realizations. pir indicates that the conservation priority
score is for a particular realization of a particular site. The initial Monte Carlo score,
S, of a site was simply:
(1) (“Binary Approach”) ∑=
=R
riri pS
1
253
S had a possible score between 0 and 120 for each site, with the higher score
indicating a higher importance. A score of 120 would mean the site was available in
every realization, and was chosen in each Monte Carlo run.
However, due to the random selection of the sites not available for conservation
for all of the Monte Carlo input layers, there was a variance among the sites for the
highest possible value of S, thereby leading to an unfair bias in the final value of S.
Thus, the first synthesis product created was through the refined binary approach, in
which Ai is the total number of times that site i was actually available for
conservation:
(2) i
R
rir
i A
pS
∑== 1 (“Refined Binary Approach”)
In this approach, the possible score ranged from 0 to 1, with a 1 indicating that
the site was chosen every time it was available.
Another way the outputs were combined was with a ranking approach. Because
the greedy heuristic (described in Insert: Conservation Planning Analysis) chooses
the sites with the highest initial marginal conservation value first, the order in which
the priority sites are selected is telling. If a site is consistently selected near the
beginning of the greedy selection process, then it is arguably very important to
conservation, and it could be argued that the end-user should know this. In this
approach, g was the rank in which a site was chosen (e.g. if it was the second site
selected in the greedy heuristic it got a value of 2). T was the number of target sites
being selected (in this case 180):
254
(3) ( )
i
R
rir
i AT
gTS
⋅
−+=
∑=1
1 (“Ranking Approach”)
The possible score again ranges from 0 to 1, with 1 indicating that the site was
not only chosen every time it was available, but that it was chosen first every time as
well.
Because there is a large correlation between rank and the percentage of times a
site will be chosen when it is available, there is a large correlation between the
values of Si generated by equations (2) and (3). However, some discrepancies are
always likely. Equation (2) biases against sites that are more important at the start of
the implementation period. Equation (3) biases against sites that are more important
after deviations from the initial conditions have occurred (i.e. later in the
implementation period). Thus, the fourth approach was a compromise between the
concepts of the second and third approaches. To do this, the variance of (3) was
reduced by using a root transformation. Several transformations were performed on
a simple data set: some with the root taken before the sum, some after the sum, and
some with different powers. The outputs were examined to see how well they
tempered the two opposing forces. The one that seemed to balance the two equally
was used:
(4) i
R
rir
i AT
gTS
⋅
−+=
∑=
31
3 1 (“Compromise Approach”)
255
Again, possible scores range from 0 to 1, with the higher scores indicating the
site was selected quite often when it was available, and that it had a high average
rank during these selections.
Several maps were created for the focus groups in addition to the standard map:
one that showed the Monte Carlo composite using the refined binary approach, one
of the ranking approach, and one of the compromise approach. For these maps, the
standard sites were shown in highest saturation, and the other sites had a decreasing
level of saturation proportional to their composite value. These maps are henceforth
called the implementation-uncertainty maps.
The Three Representations of the Monte Carlo Results
The three different implementation-uncertainty maps and their corresponding
methodologies for quantifying the Monte Carlo results were presented to the CCP
focus group. The participants were able to understand the difference between the
binary approach and the ranking approach, and that the compromise approach
balanced the factors of each. However, they felt that the question of which one is
best was relatively unimportant compared to the other questions at hand. Further,
they felt that it was mathematically cumbersome and had the potential of negatively
side-tracking the subsequent focus groups. They recommended that we should
choose the approach that best met the objectives of the uncertainty quantification.
Accordingly, the compromise-approach map was presented as the one and only
implementation-uncertainty map to the subsequent focus groups.
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[Back to main body.]
Insert A: Additional Focus Group and Questionnaire Methods
The three focus groups in order of presentation were 1) CCP board of directors
and staff focus group, 2) the ecological expert advisors focus group, and 3) the land
and resource management advisors focus group. Ideally, focus groups would have
been comprised of strangers, but there was no funding to reimburse participants.
Participants volunteered to participate in the focus groups because they were joined
with advisory business. The CCP personnel that attended the first focus group were
a Santa Barbara County official, an ocean conservation organization employee, and
two CCP staff. The ecological advisor focus group was attended by four
environmental consultants, two members of local natural history museums, a
watershed recovery program coordinator, and a conservation GIS analyst. The land-
use advisor focus group was attended by three land trust directors, two county
planners, an environmental historian, and a state wildlife agency representative.
Parallel objectives of the focus groups were targeted for another chapter of the
dissertation:
4) to determine if the conservation priorities map released to the public should be
the standard-run map or the implementation-uncertainty map, and why.
5) to explore how these products would affect conservation implementation.
257
The meetings also provided an opportunity for participants to provide feedback
on other aspects of the model and suggestions for model enhancement if a second
iteration of the process were to be performed.
The focus groups met for 3.5 hours each, and had a meal provided. Also, the
topic guide was loose to allow for differences, as needed, in both the questions and
the approach with the different groups (Proctor 1998b). More questions were listed
than were possible to address, , as it was not known which ones would invoke much
discussion and which ones would be flat (Goodchild 2004). For the coded transcript,
a spreadsheet was created, and any comment that might be related to the research
questions was documented. In that row, the time on the tape was noted, and one of
several columns was filled in. The columns were headed by the different themes of
the research.
Participants were also given an opportunity to provide written, anonymous
feedback. This allowed participants that were shy or hesitant to express an opinion
to do so (Litosseliti 2003; Goodchild 2004). The feedback was structured as a
questionnaire, and a prompt for any specific feedback was also provided. Questions
were designed to look at issues on the topic guide and elicit a response, from 1-9,
ranging from “disagree strongly” to “I’m not sure” to “agree strongly.” Because
participants were not paid to attend the groups, it was decided to make the
questionnaires optional.
Questionnaire response was low (N=6) so the results were tallied and only
examined for trends to provide anecdotal information for the revision process. Only
258
two of the nine questions had a strong consensus and strong opinion about the
answer. The statement “As a whole, if decision-makers and landowners had an
improved ecological knowledge, then they would be better able to make “voluntary”
decisions (not forced by environmental regulation) in other topic areas (such as
transportation or housing) that would also benefit biodiversity” was answered with
an agreement level of 7.8 (out of 9) and a standard deviation of 0.43. The only other
statement with a standard deviation less than one was “The environmental regulatory
mechanisms currently in place (i.e. endangered species act, local grading ordinance,
etc) as a whole are sufficient to yield long term conservation of the region’s
biodiversity.” Everyone disagreed with this statement with an average value of 2 and
a standard deviation of 0.707. [Back to main body.]
Insert: Additional Results for Phase 1, 2, and 3
Phase 1 entailed developing the advisory groups, setting the parameters and
context of the model, and performing the standard run. It was determined by the
land-use advisors to have the final product be in the form of a report with hardcopy
maps, and the maps would be no bigger than 11” X 17” and would encompass the
entire region. This worked out to be a scale of a little coarser than 1:500,000. It was
also determined that a medium range timeframe for implementation should be
employed, about 20 years. It was difficult to estimate the amount of land that would
be conserved in that time frame, but a figure of 100,000 acres was set as the target
area for conservation priorities. The detailed ecological and land-use parameter
values of the model are provided in Gallo, Studarus, et al. (2005).
259
The soundtracks of the modules were created so that each focus group received
the same message, and so the modules could eventually be part of a web site
available to people not able to attend live presentations. On a technical note, there
was criticism from the CCP group about the audio playback of the module. It had
some skips in the narration. They felt that rather then fix the soundtrack before
presenting to the other focus groups, it would be better to simply narrate the modules
in person until they were approved by all parties. Once approved, then the final
soundtrack could be made for final release.
[Back to main body.]
A more complete narrative of quotes.
The following material was provided by Gallo (2005) in a previous document.
The focus groups provided a wealth of information, and the goals of the focus groups
were met. Due to the nature of focus groups, this information is not treated as “the
truth” but rather indications of the truth as well as development of new ideas.
The Introductory Modules
The three introductory modules had mixed reviews. In summary, the first
module was appreciated by all three groups. The second module was appreciated by
the CCP group and the Ecological group, but had mixed reviews from the Land Use
group. The CCP group and the Ecological group both felt it was not necessary to
show the third module to the public, and it was not shown to the third group in order
to allow for more time on other discussion. The second module had mixed reviews
from the Land Use group because it inferred that all development had equal impact
260
on biodiversity, i.e. blacking it out. “I'm not sure that oil field development is going to
black out every species, whereas strip mall development will.” They were able to understand
the concept of uncertainty and alternatives being communicated though.
The third module was rejected for a variety of reasons. It was too much
information, too complex, and difficult to understand. Some of the ecological
advisors did not understand how the random approach could lead to a useful
outcome. “I don't grasp it though, it seems to me that if you were to do random development
that you would eliminate all of those sites eventually.” There was a similar negative
reaction in the CCP focus group, and they suggested that the concept is illustrated in
the second module, and module three could simply be summarized by a bulleted
slide and few sentences instead, such as “the model understands the dynamic nature of
land-use patterns. It incorporates that by using dozens of runs or hundreds of runs to account
for the potential to lose priority sites.” The Ecological Advisors agreed with this
suggestion. In an effort to allow for more time for discussion of higher priority
issues, this module was not shown in the Land Use group. Regarding the audio
narration of all three modules, it was felt that when the presentation is in a live
forum, the narration should be live as well, and be more concise by using a script,
rather then having an audio recording.
In summary, the first introductory module that illustrated the concept of
optimality was the only one that met with all positive reviews, and the second one
would be acceptable with slight color modifications.
The Three Representations of the Monte Carlo Results
261
The three different methodologies for quantifying the Monte Carlo results were
presented to the CCP focus group. They were able to understand the difference
between the binary approach versus the ranking approach, and it was felt that that
this level of evaluation was beyond the scope and expertise of the focus groups, and
that it was largely a scientific question. In short, it would have been a good question
for an academic advisor group. The sentiment about complexity corroborated
predictions by an academic advisor that it was most important to choose an approach
that the researcher best feels meets the objectives and simply document it as it as the
method used for synthesizing the uncertainty results. In an effort to allow for more
time for discussion of higher priority issues, this issue was not discussed in the
subsequent discussion groups. Instead, the joint approach that balanced the binary
approach with the ranking approach was presented as the only option for
representing the Monte Carlo analysis.
Model and Map Evaluation—CCP Focus Group
The CCP focus group preferred the uncertainty map over the map solely showing
the optimal sites. There was excitement that the model shows the type of uncertainty
that it does. “Is this a mitigator for someone who lives in a red square? And thus development
there in year 2 does not mess up the whole house of cards. Some respects it is, it is like… fuzzy
lines, that indicate opportunity, and that the loss of a site in year two of your 20 year plan, does
not mess things up. I like it in that respect” Further, they liked how it had a larger spatial
extent of priority areas: “I feel like the other one is more narrow. I like how the uncertainty
262
map shows more spots.” Similarly they liked how it decreased the volatility of the map:
"It is a lower panic button."
The focus group had concerns with the term “uncertainty” though. “To me people
know this is a model, and that it is uncertain. Don't want to tell people that it is an awkward
approximation of reality when what it is a means to give long term guidance and a set of
workable options into the future. So people know it is a model, and that is uncertain, . . . you
don't want to cut off your legs. It is more powerful to do it this way, but by calling it
uncertainty it makes it sound like it is less powerful.” A variety of other term were
identified and the term “alternatives” was recommended.
Model and Map Evaluation—Ecological Advisors Focus Group
Uncertainty: was good. Alternatives. Two quotes. One talking about the
downside of this approach; loss of optimality. (Olson).
In the ecological advisor focus group, there was also more support for the
uncertainty map then the optimal map. Support was based in part on the idea that it
is useful to show alternatives. “Opportunities will be based more on whether you have a
cooperative land owner, or funding for a particular property, so yeah, it seems like you would
want to have first and second priorities for alternatives with the idea that some of your second
tier selections, because of the timing, or maybe somebody’s particular interest or whether you can
get wide public support for it.” Another reason for support is that it has a greater area of
priorities, "I like having alternatives like that defined. So you can get the bigger picture- its that
region, its that area." Another reason the uncertainty map was preferred is that
263
comparatively, it seems to lesson the threat to landowners “it takes the gun away from
pointing at one particular spot.”
However, there was hesitation regarding the release of the map, namely that
landowners would still feel threatened and/or degrade their land. “We had this with the
listing of the tiger salamander, where a lot of ground got ripped real quick.” Suggested
mitigations for this included further increasing the spatial extent of the solution
space. “Why not [run the analysis to] put twice as many boxes on the map and maybe not even
specify which ones are the alternatives.”
The idea of increasing the spatial extent was supported for other reasons as well.
“I see more gaps on this map then hot spots, so it would dissuade people that are in the gaps from
conserving their land.” It was also felt that the output of the model should better reflect
the ecological requirements of the region: “To me when you think about broad scale land
use planning for the long term, you are not thinking about individual little squares you are
thinking about continuity, and contiguity and functioning ecosystems, and to try to think about
all these miscellaneous little squares, it doesn't lead me in that direction.” Similarly, “the power
of the earlier elements of the model is that they show you the entire landscape and how it works
together. I think by the time you present individual pixels, you are specifying too much.” Along
these lines the advisors felt that the model was giving too much weight to the
objective of expanding small reserves, and not giving enough weight to the
connectivity objective.
264
Another theme of the meeting was frustration that the final results had the threat
layer and cost layer embedded within. These layers were viewed as large sources of
uncertainty, and the ecological advisors wished for an additional result that would
show the conservation priorities based solely on the ecological characteristics of the
region. “Maybe you need to show two different model outputs, one with cost factored in and
one without. Let people decide based on the merits of the ecological value without the purchase
price attached to it.” “You need to present, what is of most significance and that is worth
protecting the most in our region. That should be your driving force to start with. You build off
of that, but you don’t let cost be a controlling factor to that initially.” And there was a
question about the assumption that more expensive areas are harder to conserve.
"Cost be damned in some situations." "A lot of us might be more than willing to pound pavement
to raise $2 million, where you might have a red on a $200,000 project that is not nearly as
passionate." There was also some concern about the assumption that certain areas are
not considered threatened, and thereby not an opportunity for conservation, mainly
because threat changes over time and is difficult to predict.
Model and Map Evaluation—Land Use Advisors Focus Group
The land use advisors also preferred the uncertainty map over the optimal map.
They seemed to prefer it for the larger spatial extent of the areas of priority, and also
because it provided alternatives.
“when you are trying to do conservation, you get to that minimum area issue where
doing conservation on small areas is very costly, you can’t really manage for natural
265
processes, can’t go in and do a whole lot, you got a lot of invasives coming in
because you have so much edge. We like to bias ourselves towards large areas, and
this is more of what we call the landscape scale than the other one.”
and
"For a very practical perspective, if you have a situation where, I don't know if it is
the Gaviota coast or wherever, and you have an option for one of the pink guys,
and not for one of the red ones, you wouldn't know unless you had this map, and if
you were to go to a funder, you could show this map, and say this is a high priority
area. Being a pragmatist, when it comes down to it, it depends on if someone wants
to sell their property or not. This gives us more options.”
There was a lot of discussion about the pro’s and con’s of releasing the map as it
stood. One of the con’s was that even the uncertainty map still had a tendency to
identify specific 1.5 km square sites which was still seen as too close to the parcel
scale, and the output was still not blurry enough. “You have a fairly fine scale, showing
areas where there is a concentration of these things (sites). Its better to identify a region, and
share that information, rather then identifying one landowner . . . I think those maps can work
on a broader scale sometimes better, if people can see the dots in a non-dot way. A blurry way.”
Similarly, later in the discussion, the sites were criticized for their grid like
appearance. “. . .I can tell you where the mountain lions run, furthermore, they don't stay in
one place. These squares are like the Jeffersonian land coordinates, dividing the nation up like
that, I hate to see those squares. I know that’s what you have to work with here, but that’s not
266
thinking like a mountain, or a river, and what we've learned from decades of thought is that you
plan around watersheds, and not around squares." So it was felt that the squares did not
represent the ecological web very well, nor did they support the social interactions
towards conservation of that web.
Perhaps most importantly for the research question on hand, there was discussion
about the history of volatility in the region, with special focus on the lessons learned
from the rural resource study, where the goals slowly got changed around to become
“an issue of ‘how to avoid the county.’ Then the farmers walked out, even before the hardest
part of dealing with sensitive species.” This discussion lead to a critique of the
conservation priorities map. “They will be very upset if they see that map, I fear. How do
we use this to promote conservation? What can we put in front of the public that will facilitate
conservation, and not polarize the stakeholders?” Similarly, one of the members stated “If
that went public, I would question our ability to do conservation in this region.”
On the other hand, there was an extensive discussion about the benefits of
providing conservation planning information to the community. Namely, it helps
start the discussion towards consensus:
"By putting the priorities out there, it opens up a discussion as to what our
priorities are in the community, because if we are all coming from six different
places one guy is looking at it form an ORV, one guy is looking at it from an
ecological standpoint purely, and another guy wants a place to ride his horse, you
gotta see how you can bring those people together, just like we had the discussion
267
today, and to say 'wait a minute, why isn't there anything over there or how did
this come about' and I think that can lead to a deeper discussion, and hopefully
some consensus-building of the kind of places that a community really does want to
protect."
Further, it was felt that conservation provides a valuable regional context, and
shared vision to help make collaboration among groups more efficient and to make
fundraising more successful
“You can use the planning effort to build interest put it all into a regional context.
It can also help build a shared vision among public agency groups and private
groups. We all have slightly different missions and different ways of prioritizing
our actions, but through these conservation planning efforts we can rally around a
shared vision and shared commonalities, and that helps to channel dollars. It also
gives you a good picture at what success looks like, so it doesn't look like you are
always asking for money for yet another thing, it shows you what the end-run looks
like, the vision, it what we want to achieve. The challenge is that you'll never get
everyone to buy into one blueprint." "It gets back to the collaborative thing.” “Yeah."
In summary, there was a lot of support for sharing the conservation planning
information, but the feeling was that the iteration on the table was not appropriate.
There was a lot of support for the intermediate maps that showed the ecological
value of every site on the landscape. Further, similar to the ecological focus group,
the land use advisors suggested having additional products that did not have cost and
268
threat embedded within. The discussion about cost centered around the difficulty of
modeling cost, of comparing cost of conserving private land to that of conserving
public land, and of comparing cost of acquisition versus cost of management. Back
to main body.
Insert: Discussion: Visualizing the solution set issue, irreplaceability, and
other issues.
Irreplaceability helps define priorities, and is the extent to which the loss of the
area will compromise regional conservation targets (Margules and Pressey 2000).
For example, if a species is found in only one site, and the optimization criteria
includes representation of that species in at least one site, that site has the highest
irreplaceability score possible. This is an important component in many
conservation planning efforts, such as Pressey and Taffs (2001). One of the
shortcomings of the marginal value model is that it does not measure irreplaceability
explicitely, only total marginal value relative to the total for a region {Davis, 2006
#350}. Thus, a standard-set site could have several different rare species, but in a
degraded area that is not very threatened and have a relatively low marginal value,
and thus a low ranking in the greedy selection process. Meanwhile this site would
have a high irreplaceability value if it were measured. Because of the rarity of these
species, this site will show up in nearly every Monte Carlo run that the site is
available for conservation. If the Monte Carlo results were shown for the standard-
set sites, not just the sub-optimal ones as shown here, then the irreplaceability of the
sites would be indicated indirectly. This could be visualized by having a thick
269
border for optimal sites, no border for suboptimal sites, and the saturation variation
displayed for all sites, not just the sub-optimal ones.
It is important to note that the stochastic variable chosen for this particular Monte
Carlo analysis would not have worked if the greedy heuristic was updating the value
of sites based on their spatial context. Even though connectivity and proximity were
used in determining initial conservation value, the algorithm did not recalculate these
values as sites become conserved. If it did, the Monte Carlo results would have
biased against these objectives compared to the composition objectives, such as
habitat type, due to the salt and pepper nature of the random selection. This issue
could be accounted for by using a clumping algorithm for identifying the sites not
available for conservation. It might be ideal to use a cellular automata process on
seed sites that are selected based on their probabilities, to identify the sites not
available for conservation in each Monte Carlo realization. In short, it is important
to carefully choose the stochastic variable for the Monte Carlo analysis so that it not
only illustrates the effect of the uncertainty present, but also yields outputs with
equal probability of occurring. [Back to main body.]
Insert: Discussion about Uncertainty, Knowledge, and Wisdom
The June 2006 draft of Chapter 3 had a large emphasis on the role of uncertainty
and imperfect information in the development of knowledge, and then wisdom. For
more information, see that draft, available upon request. Here are the key parts:
270
An emerging Semiotic Framework for SDSS development
Meanwhile, imperfection exists at all levels of the hierarchy. For example, an
SDSS is driven by data which are imperfect due to varying degrees of spatial
accuracy. It combines these data using models which are imperfect because they
have to simplify the real-world somehow, thereby ignoring some important
processes. The resulting information output is imperfect due to error propagation
and problem simplification. The knowledge gained from absorbing this imperfect
information must also be imperfect.
There are a variety of taxonomies that define types of imperfect knowledge (Suter
et al. 1987; Dovers et al. 1996; Gershon 1998; Duckham et al. 2001; Brown 2004).
Imperfect knowledge can be caused by 1) closing a problem (act of ignoring) 2)
being unaware of alternative views of the world, or of their utility (ignorance) 3)
accuracy issues (i.e. uncertainty) 4) or not being able to know the actual thing
(indeterminacy) (Brown 2004). An important distinction here is that uncertainty is a
sub-category of imperfect knowledge (Suter et al. 1987; Dovers et al. 1996; Gershon
1998; Duckham et al. 2001; Brown 2004; MacEachren et al. 2005).
All of these studies lack an actual working definition of imperfect knowledge.
For the purpose of this paper, imperfect knowledge can be defined as the discrepancy
between a person’s understanding of something, and the complete truth. Of course,
the discrepancy is often immeasurable due to the fuzziness of the endpoints (the
person’s understanding, and truth), but it exists nonetheless. The key is that the
271
discrepancy not only exists, but it has a relative magnitude for each end-user. The
relative magnitude can vary over time, and also vary among different end-users.
This concept of a magnitude of imperfect knowledge might not be immediately
intuitive. Consider first the concept of intelligence. We know that differences
among individuals exist, and we have developed surrogates, such as I.Q., which use
tests to try to quantify these differences. Similarly, tests have been made to estimate
how accurately people understand the uncertainty (a type imperfect information) of
mapped information (Leitner and Buttenfield 2000; Aerts et al. 2003b). Tests can be
used to understand the degree of correctness, confidence, and time needed among
end users in understanding different communication techniques (Leitner and
Buttenfield 2000). People that correctly interpreted the uncertainty information on
the map had a lower magnitude of imperfect knowledge then those that interpreted it
incorrectly.
A Portion of the Framework Results and Discussion:
In performing the participatory action research it became increasingly apparent
that “imperfect presentation” of Gershun’s (1998) hierarchy has different
implications then the other forms of imperfect knowledge. A figure was created to
illustrate a new set of relationships, and to also include the concepts of imperfect
information magnitude and variance among users (Figure). The bottom of the figure
is the placeholder for the different sources of imperfect information that might be
present in an SDSS. Presentation of this information is performed effectively,
poorly, or not at all. Effective presentation of the imperfect information will lead to
272
a much lower degree of imperfect knowledge among most end-users. Similarly, if
the information is not presented most end-users will have a higher magnitude of
imperfect knowledge. If poor presentation of the information occurs, most people
will be aware that imperfect information exists, but will not fully understand it. Poor
presentation can be caused many factors, including trying to present too much
information, using inappropriate spatial metaphors, or the wrong device (Gershon
1998). Figure 17 also incorporates the gist of the grouped semiotic triangles by
indicating that good presentation of imperfect information decreases the disparity
among end-users.
273
Figure 17: The Role of Effective Presentation of Imperfect Information in Reducing
Imperfect Knowledge and Improving Group Understanding
274
Discussion of the Conceptual Framework
The framework provided illustrates a variety of concepts regarding the treatment
of imperfect knowledge in SDSS. But the framework as laid out is missing an
important concept: that imperfect knowledge can be reduced in two major ways, not
just one. The first is as discussed and is through the effective presentation of the
imperfections of the SDSS. The second is the traditional approach of making the
SDSS more accurately reflect the real world through better data and modeling. One
of the messages of this paper is that this first approach is largely ignored by the
GIScience community. This is not to belittle the importance and necessity of the
second approach.
A potential answer to this need of an expanded framework is proposed here, and
requires further evaluation and refinement. It starts with the metaphor of the “SDSS
semiotic triangle” illustrated in the chapter in the body of the dissertation. The
referent (point A) is the real world issue that is under study (Fig 18). The sign-
vehicle (point B) is the combination of maps, animations, text, and presentations that
are used to present the model outputs. The interpretant (point C) is the end-user’s
understanding of the real world issue. The goal is for the end-user’s understanding
to match the actual issue as close as possible. The distance in conceptual space
between the interpretant and the referent (length AC) represents the imperfect
knowledge of the end-user, with less being better. The length AB represents how
well the model matches the complexity of reality. The length BC represents how
well the products communicate the results and the imperfections of the model.
275
Angle ABC (theta) represents how well the concept of the imperfect information at
hand is communicated. An extremely obtuse angle indicates that the issue is not
even mentioned. Efforts to minimize the imperfect knowledge of the end-user can
be achieved with three approaches: the length of AB can be minimized, theta
minimized, and/or angle ACB should approach 90 degrees (Fig 19). (Note, if this
metaphor is used, then the grouped semiotic triangles figure in the body of the paper
would need to be revised accordingly).
Figure18: Normative Comparisons of SDSS Semiotic Triangles
276
Figure 19: Normative Comparisons of SDSS Semiotic Triangles
The hypothesis emerging from this study is that the most cost-effective approach
to decreasing the amount of imperfect knowledge is to address all three of these
277
approaches in developing the SDSS. This is because the law of diminishing returns
appears to be present for each one. For example, it might take Y amount of
resources to decrease the length of AB by 50%. But the amount of resources to
decrease it by another 50% is usually greater, and may even be 2Y. At some point,
decreasing the angle of theta or the length of BC will be much more cost effective
then continuing to decrease the length of AB. The challenge is to determine these
thresholds within any given context..
A related component of the framework that needs further development is the
treatment of wisdom. By reducing the imperfect knowledge among end users, the
assumption is that the wisdom of the spatial decision making will be improved. But
there are a variety of other factors that are involved in determining the wisdom of
decisions, and thus should be within the purview of SDSS research. (The implicit
goal of an SDSS is to improve the wisdom of decisions, not simply to reduce the
amount of imperfect knowledge among end-users.) These other factors are highly
individualistic and are based on some understanding of the likely consequences
(Longley et al. 2005). Further, they relate to the education, goals, and values of the
end-user (Fig 20). To improve the overall success of the SDSS, it may behoove the
SDSS developer to take a hard look at the social context in which it will be placed.
Both a conceptual framework of this context and an operational model could be
developed and linked (Knight et al. 2006a).
278
Figure 20: Factors Affecting the wise use of an SDSS.
These frameworks suggest that there are a variety of forms of imperfect
information that lead to imperfect knowledge. The imperfect information can be
communicated to end-users or ignored. A high quality presentation will decrease the
magnitude of an individual’s imperfect knowledge (i.e. improve their
understanding), as well as decrease the variance among individuals (i.e. get everyone
“on the same page”). This better understanding will merge with each person’s tacit
knowledge in effecting the wisdom of the final decisions. Progress in our ability to
communicate the concept of imperfect information and visualize its effects is
279
expected to yield significant improvements to the success of SDSS theory and use.
Back to main body.
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