United States
Environmental Protection
Agency
EPA/600/R-11/058Fb | January 2012 | www.epa.gov
Vulnerability Assessments in Support
of the Climate Ready Estuaries Program:
A Novel Approach Using Expert Judgment
Volume II
Results for the Massachusetts Bays Program
i
United States Environmental Protection Agency
Office of Research and Development, National Center for Environmental Assessment
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EPA/600/R-ll/058Fb
January 2012
Vulnerability Assessments in Support of the Climate Ready
Estuaries Program: A Novel Approach Using Expert
Judgment
Volume II
Results for the Massachusetts Bays Program
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC
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DISCLAIMER
This document has been reviewed in accordance with U.S. Environmental Protection
Agency policy and approved for publication. Mention of trade names or commercial products
does not constitute endorsement or recommendation for use.
ABSTRACT
The Massachusetts Bays Program (MBP) and the Environmental Protection Agency
(EPA) collaborated on an ecological vulnerability assessment, using a novel methodology based
on expert judgment, to inform adaptation planning under EPA's Climate Ready Estuaries
Program. An expert elicitation-type exercise was created to systematically elicit judgments from
experts in a workshop setting regarding climate change effects on two key ecosystem processes
within salt marsh systems: sediment retention and community interactions. Specific workshop
objectives were to assess (1) the relative influences of physical and ecological variables that
regulate each process, (2) their relative sensitivities under current and future climate change
scenarios, (3) the degree of confidence about these relationships, and (4) implications for
management. For each process, an influence diagram was developed identifying key process
variables and their interrelationships (influences). Using a coding scheme, each expert
characterized the type and degree of each influence to indicate its nature and sensitivity under
current and future climate change scenarios. The experts also discussed the relative impact of
certain influences on the endpoints. This report demonstrates how particular pathways in such
diagrams can be linked to management options and examined in the context of planning
documents to identify opportunities for 'mainstreaming' adaptation into strategic planning.
Photo Credits (front cover):
Workshop (Carlton Hunt), Satellite image (Mass GIS), Salt marsh (Stephen Gersh), and Sparrow (Paul Fusco)
Preferred Citation:
U.S. EPA (Environmental Protection Agency). (2012) Vulnerability Assessments in Support of the Climate Ready
Estuaries Program: A Novel Approach Using Expert Judgment, Volume II: Results for the Massachusetts Bays
Program. National Center for Environmental Assessment, Washington, DC; EPA/600/R-1 l/058Fb. Available
online at http://www.epa.gov/ncea.
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CONTENTS
LIST OF TABLES vi
LIST OF FIGURES vii
LIST OF ABBREVIATIONS AND ACRONYMS viii
PREFACE ix
AUTHORS, CONTRIBUTORS AND REVIEWERS xi
ACKNOWLEDGMENTS xii
EXECUTIVE SUMMARY xiii
1. INTRODUCTION 1-1
1.1. BACKGROUND 1-1
1.2. PURPOSE AND SCOPE 1-1
1.2.1. Purpose 1-1
1.2.2. Scope 1-2
1.3. ROADMAP FOR THE REPORT 1-4
2. EXPERT ELICITATION EXERCISE 2-1
2.1. JUSTIFICATION FOR METHOD 2-1
2.1.1. Definition and Uses 2-1
2.1.2. Novel Application 2-1
2.2. WORKSHOP DESIGN AND METHODOLOGY 2-2
2.2.1. Workshop Goals and Objectives 2-2
2.2.2. Approach and Methodology 2-2
2.2.2.1. Influence Diagrams 2-4
2.2.2.2. Climate Scenarios 2-6
2.2.2.3. Expert Facilitation 2-8
2.2.2.4. Coding Scheme and Exercise 2-8
2.2.2.5. Typologies for Understanding Influences and Sensitivities 2-11
2.2.2.6. Understanding Relative Impacts of Influences 2-13
2.2.2.7. Key Questions 2-13
2.3. RESULTS 2-14
2.3.1. Sediment Retention 2-15
2.3.1.1. Group Influence Diagram 2-15
2.3.1.2. Influence Types and Degrees 2-17
2.3.1.3. Influence Sensitivity 2-22
2.3.1.4. Relative Impact 2-25
2.3.1.5. Confidence 2-25
2.3.1.6. Interacting Influences 2-29
2.3.2. Community Interactions 2-31
2.3.2.1. Group Influence Diagram 2-31
2.3.2.2. Influence Types and Degrees 2-34
2.3.2.3. Influence Sensitivity 2-38
2.3.2.4. Relative Impact 2-40
2.3.2.5. Confidence 2-42
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CONTENTS (continued)
2.3.2.6. Interacting Influences 2-42
2.4. DISCUSSION OF ADAPTATION STRATEGIES 2-44
2.4.1. Restoration and Conservation 2-44
2.4.2. Reducing Nonclimate Stressors 2-47
2.4.3. Planning and Monitoring 2-47
3. MAKING THE LINK TO MANAGEMENT 3-1
3.1. USING INFORMATION ON INFLUENCE TYPE and DEGREE,
SENSITIVITY AND RELATIVE IMPACT TO IDENTIFY KEY
MANAGEMENT PATHWAYS 3-1
3.1.1. Crosswalks: Influence Type and Degree, Sensitivity and Relative
Impact 3-1
3.1.1.1. Sediment Retention Crosswalk 3-11
3.1.1.2. Community Interactions Crosswalk 3-12
3.1.1.3. Information Gaps 3-13
3.1.2. Identifying Key Pathways for Management 3-14
3.2. TOP PATHWAYS AND IMPLICATIONS FOR ADAPTATION
PLANNING 3-17
3.2.1. Top Pathways and Associated Adaptation Options 3-17
3.2.1.1. Sediment Retention Top Pathways 3-20
3.2.1.2. Community Interactions Top Pathways 3-24
3.2.1.3. Top Pathway Caveats 3-28
3.2.2. Adaptation Planning 3-29
4. CONCLUSIONS 4-1
4.1. INSIGHTS FROM THE WORKSHOP EXERCISE 4-1
4.1.1. Group Influence Diagrams 4-1
4.1.2. Characterization of Influences 4-3
4.2. Application of Workshop Results 4-5
4.2.1. Top Pathways for Management 4-5
4.2.2. Mainstreaming Adaptation into Planning 4-7
4.3. GENERAL CONCLUSIONS 4-8
4.3.1. Transferability of Results and Method 4-8
4.3.2. Utility of Method for Rapid Vulnerability Assessments 4-9
5. REFERENCES 5-1
APPENDIX A DEVELOPMENTAL PROCES S FOR CRE VULNERABILITY
ASSESSMENT A-l
APPENDIX B. EXPERT ELICITATION WORKSHOP PREPARATION AND
IMPLEMENTATION B-l
IV
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CONTENTS (continued)
APPENDIX C PARTICIPANT HANDOUT ON CLIMATE SCENARIOS C-l
APPENDIX D PARTICIPANT HANDOUT ON CONFIDENCE D-l
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LIST OF TABLES
1-1. Breakout group participants for the expert elicitation workshop 1-4
2-1. Summary of Climate Scenario A ("lower-range" scenario) and Climate Scenario
B ("higher-range" scenario): averages for midcentury 2-7
2-2. Coding scheme used during the workshop exercise to characterize influences 2-9
2-3. Coding scheme used during the workshop exercise to characterize interactive
influences 2-10
2-4. Coding scheme used during the workshop exercise to characterize confidence 2-11
2-5. Sediment Retention variable definitions 2-17
2-6. Sediment Retention group influence judgments 2-18
2-7. Sediment Retention group confidence for influences with agreement 2-28
2-8. Sediment Retention group interactive influences with agreement under current
conditions and Climate Scenarios A and B 2-30
2-9. Community Interactions variable definitions 2-33
2-10. Community Interactions group influence judgments 2-35
2-11. Community Interactions group confidence for influences with agreement 2-43
2-12. Adaptation strategies and associated top pathways for management 2-45
3-1. Sediment Retention group crosswalk for comparison of influence type and degree,
sensitivity and relative impact for current conditions and climate scenarios 3-2
3-2. Community Interactions group crosswalk for comparison of influence type and
degree, sensitivity and relative impact for current conditions and climate scenarios 3-6
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LIST OF FIGURES
1-1. Vulnerability assessment process 1-5
2-1. The Massachusetts Bays 2-3
2-2. Simplified influence diagram for sediment retention 2-5
2-3. Sediment Retention group influence diagram 2-16
2-4. Sediment Retention group summary influence diagram of sensitivities under
current conditions 2-23
2-5. Sediment Retention group summary influence diagrams of sensitivities 2-24
2-6. Sediment Retention influences indicated as having high relative impact under
current conditions 2-26
2-7. Sediment Retention influences indicated as having high relative impact: variance
across current conditions and two climate scenarios 2-27
2-8. Community Interactions group influence diagram 2-32
2-9. Community Interactions group summary influence diagram of sensitivities under
current conditions 2-39
2-10. Community Interactions group summary influence diagrams of sensitivities:
variance across current conditions and two climate scenarios 2-40
2-11. Community Interactions influences indicated as having high relative impact under
current conditions and the climate scenarios 2-41
3-1. Community Interactions example pathway 3-15
3-2. Top pathways for management of the Sediment Deposition/Retention endpoint 3-18
3-3. Top pathways for management of the Saltmarsh Sharp-tailed Sparrow Nesting
Habitat endpoint 3-19
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LIST OF ABBREVIATIONS AND ACRONYMS
CCMP Comprehensive Conservation and Management Plan
CCSP Climate Change Science Program
CRE Climate Ready Estuaries
CSO Combined Sewer Overflow
EPA Environmental Protection Agency
H high
HH high evidence/high agreement
HL high evidence/low agreement
IPCC Intergovernmental Panel on Climate Change
LH low evidence/high agreement
LL low evidence/low agreement
L low
MBP Massachusetts Bays Program
NECIA Northeast Climate Impacts Assessment
OMWM Open Marsh Water Management
ORD U.S. EPA's Office of Research and Development
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PREFACE
This report was prepared by the Global Change Research Program (GCRP) in the National
Center for Environmental Assessment of the Office of Research and Development at the
U.S. Environmental Protection Agency (EPA), in collaboration with the Massachusetts Bays
Program (MBP) of the National Estuary Program (NEP). The report presents the results of a
pilot, targeted, climate change vulnerability assessment for selected ecosystem processes of the
Massachusetts Bays' salt marsh systems, using a new methodology based on expert elicitation
techniques. Both the place-based results and the methodology itself are intended to support not
only the MBP and the larger NEP community, but also other natural resource managers who are
interested in adapting to the impacts of climate change on valued ecosystems.
The genesis of this project came from two sources. The first was a 2008 interagency report
led by the EPA GCRP on behalf of the U.S. Climate Change Science Program, entitled
Adaptation Options for Climate-Sensitive Ecosystems and Resources, which laid out general
principles for understanding vulnerabilities and identifying adaptation approaches and called for
refinement and application of these concepts through place-based activities. In that same year,
EPA's Office of Water and Office of Air and Radiation launched a series of pilot projects under
a new, Climate Ready Estuaries (CRE) program designed to provide targeted assistance to NEPs
to assess climate change vulnerabilities and plan for adaptation. Based on the complementary
nature of both efforts, the EPA GCRP joined forces with the CRE program to support two of its
original pilot projects. These were collaborative vulnerability assessments with the San
Francisco Estuary Partnership (the subject of Volume I of this two-report set) and the
Massachusetts Bays Program (this Volume II report).
The Massachusetts Bays Program is a partnership of citizens, communities, and
government that strives to protect and enhance the coastal health and heritage of Massachusetts
and Cape Cod Bays. It was officially accepted as a U.S. National Estuary Program in 1990,
based on its status as a nationally-significant estuary threatened by pollution, development, or
overuse. As such, the MBP created a comprehensive conservation and management plan to
ensure ecological integrity and protect valued resources such as coastal habitat, shellfish
populations, and clean water. As a CRE pilot partner in 2008, the MBP was provided with
technical support to begin a process to identify climate change vulnerabilities of these resources,
develop adaptation plans and begin to implement selected actions within these plans. This
project is a first step in this process. Starting with a kickoff meeting with local experts and
stakeholders, the MBP/EPA team elected to focus the current vulnerability assessment on
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climate-sensitive salt marsh ecosystems, targeting a narrow subset of key physical and biological
processes essential to salt marsh community health and maintenance.
This report presents the results of this pilot effort. It is intended as a proof of concept for a
new type of assessment exercise rather than a comprehensive vulnerability assessment for the
whole estuary. Thus the scope was designed for a deeper examination of the climate sensitivities
of two selected processes—sediment dynamics and salt marsh sparrow habitat dynamics—that
are integral to functioning salt marshes. Given the multidisciplinary assessment objectives and
the limitations on available data and modeling tools, a new method based on expert elicitation
was developed in order to capture the current understanding of the climate sensitivities of the
system as a starting point for adaptation, which will be an iterative process as new ecosystem
processes are added to the analysis and as our understanding of the climate and management
impacts grows. We hope that this report will be a useful starting point for adaptation action and
a methodological basis for future work on climate change vulnerability assessments for estuarine
systems.
We would like to acknowledge the major contributions of ICF International, Inc.
throughout this project, including conceptualization, methodology development, workshop
support, and report production. Special thanks to Regina Lyons of EPA Region 1 for generous
participation in the form of workshop coordination and venue, and internal technical reviews.
We would also like to express our appreciation to John Wilson, John Whitler and Michael
Craghan (EPA Office of Water), Jeremy Martinich (EPA of Air and Radiation), and the rest of
the CRE team for their leadership, partnership and many useful discussions. We commend Jan
Smith of the Massachusetts Bays Program for initiating this project through the CRE program,
and the participants of the expert elicitation workshop for sharing their knowledge and
judgments. We also appreciate the substantive contributions of our external and internal EPA
reviewers. Finally, we would like to thank Mike Slimak, Anne Grambsch, and all of the EPA
Global Change Research Program staff for their advice and numerous and significant inputs to
this project.
This final document reflects consideration of all comments received during a formal
external peer review and 30 day public review period on an External Review Draft posted
September 8 to October 11, 2011.
Jordan West & Amanda Babson
Global Change Research Program
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Jay Baker
Executive Director
Massachusetts Bays Program
U.S. National Estuary Program
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AUTHORS, CONTRIBUTORS AND REVIEWERS
The National Center for Environmental Assessment within EPA's Office of Research and
Development (ORD) was responsible for the preparation of this report. Portions of this report
were prepared by ICF Incorporated under EPA Contract Nos. GS-10F-0124J and EP-C-09-009.
Jordan M. West served as the EPA Work Assignment Manager, providing overall direction and
coordination of the project, as well as co-author.
AUTHORS
U.S. EPA
Amanda Babson
Jordan M. West
TECHNICAL SUPPORT
ICF Incorporated
Susan Asam
Katy Maher
Elizabeth Strange
Carl ton Hunt
Brock Bernstein
Katharine Hayhoe
CONTRIBUTORS
Massachusetts Bays Program Team
Jason Baker
Carole McCauley
Jan Smith
Christian Krahforst
Expert Workshop Participants
Susan Adamowicz, R. Carson Nat'l Wildlife Ref
Britt Argow, Wellesley College
Walter Berry, U.S. EPA, Atlantic Ecol Div
Robert Buchsbaum, Mass Audubon Society
Dave Burdick, University of New Hampshire
Michele Dionne, Wells Nat'l Estuary Res Reserve
Chris Hein, Boston University
David Johnson, Woods Hole Mar Biol Lab
Gregg Moore, University of New Hampshire
David Ralston, Woods Hole Oceanogr Inst
John Ramsey, Appl Coastal Res and Engin
Peter Rosen, Northeastern University
John Teal, Woods Hole Oceanogr Institution
Cathy Wigand, U.S. EPA, Atlantic Ecol Div
REVIEWERS
The following U.S. EPA reviewers and external peer reviewers provided valuable
comments on earlier drafts of this document:
Diane Gould, EPA Region 1
Regina Lyons, EPA Region 1
Jeremy Martinich, EPA Office of Water
Donna M. Bilkovic, College of William and Mary
Caitlin M. Grain, The Nature Conservancy
Matthew L. Kirwan, University of Virginia
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ACKNOWLEDGMENTS
We would like to acknowledge the support of A. Grambsch and M. Slimak of EPA's
National Center for Environmental Assessment in the Office of Research and Development, and
especially our colleagues in EPA's Global Change Assessment Staff for many helpful
discussions during this project. Special thanks to C. Weaver for technical contributions.
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EXECUTIVE SUMMARY
The Massachusetts Bays estuaries are highly vulnerable to climate-related changes
including changes in precipitation, altered hydrology, increased effects of winds and waves, and
sea level rise. Impacts such as increased inundation of coastal wetlands, changes in water
availability and quality, and altered patterns of sedimentation and erosion are increasingly
interacting with other human stressors such as nutrient loading and land use changes. Thus it is
essential that estuary managers become 'climate-ready' by: assessing the vulnerability of natural
resources to climate change; choosing strategically among adaptation strategies in the near term;
and engaging in longer term planning based on a range of plausible scenarios of future change.
In an era of shrinking budgets coupled with increasingly complex decision-making needs—often
taking place in a context of uncertainty and incomplete information—managing natural resources
in the face of climate change will be challenging. There is a need for assessment methods that
take advantage of existing scientific expertise to help identify robust adaptation strategies, weigh
difficult trade-offs, and justify strong action, all in a timely and efficient manner.
The purpose of this project was to carry out a pilot vulnerability assessment for the
Massachusetts Bays Program's (MBP) natural resources using expert judgment, the results of
which could be linked to adaptation planning. To this aim, EPA's Office of Research and
Development collaborated with MBP on a novel expert elicitation exercise for 'rapid'
vulnerability assessment. A trial exercise was carried out during a two-day workshop in which
two groups of seven experts each focused on two key salt marsh ecosystem processes: sediment
retention and community interactions within salt marsh sharp-tailed sparrow nesting habitat (see
Figure ES-1). The exercise, which was based on formal expert elicitation techniques but tailored
specifically for qualitative analysis of ecosystem processes, was designed to glean expert
judgments on the sensitivities of ecosystem process components under future climate scenarios.
This was followed by group discussions of the implications of the results for management in
light of climate change, as well as feedback on the exercise itself.
Sensitivities and Potential Adaptation Responses
Using the experts'judgments on the sensitivities of key ecosystem process components
to future climate conditions, it is possible to identify 'top pathways'for which there are
available adaptation options. After creating influence diagrams showing the relationships
among key process variables (see Figures ES-2 and ES-3), the experts generated information on
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Salt Marsh
Sediment Retention
The balance between the processes of removal
and deposition of sediment
Community Interactions:
Saltmarsh Sharp-Tailed Sparrow Nesting Habitat
Interactions of native Spartina species and invasive
Phragmites that determine sparrow nesting habitat
Figure ES-1. Selected ecosystem processes for the pilot vulnerability
assessment.
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Coastal and
Nearshore
Erosion
Sediment
Deposition/
Retention
Key
Increasing relative impact
Increasing sensitivity
Threshold
Figure ES-2. Top pathways for management of the Sediment
Deposition/Retention endpoint. Colors are used to distinguish different
pathways. Red symbols highlight potential changes under future climate
conditions.
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Ratio of Native High
Marsh to
Phragmites
Saltmarsh Sharp-
Tailed Sparrow
Nesting Habitat
Figure ES-3. Top pathways for management of the Saltmarsh Sharp-Tailed
Sparrow Nesting Habitat endpoint. Colors are used to distinguish different
pathways. Red symbols highlight potential changes under future climate
conditions.
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which relationships may show, under future climate change (1) increasing relative impact on the
overall process, (2) increasing sensitivity, and (3) abrupt threshold changes. Based on the
amount of expert agreement on each relationship, it is possible to identify 'top pathways' of
interest for management. Three top pathways for each process are described below, with
accompanying discussion of adaptation options for management.
Sediment Retention Purple pathway: In this pathway (see Figure ES-2), the experts
identified the potential for a threshold shift in the effect of marsh edge erosion on sediment
deposition and retention, from a mild inverse effect to a much stronger inverse effect. Marsh
edge erosion occurs when wave energy results in loss of sediment from the seaward edge of the
marsh; under current conditions some sediment is redeposited in the marsh, but some is lost.
Under climate change, increased storm intensity in conjunction with sea-level rise will expose
the marsh edge to greater wave energy for longer periods of the tidal cycle. This will intensify
sediment loss from the system as more sediment is carried out of the marsh, leading to an abrupt
drop in sediment deposition and retention. Management options under this pathway include:
• Establishing "no wake" zones to reduce erosion due to boat wakes
• Protecting barrier beaches (which protect marshes during storms) through dune grass
protection and restoration
• Developing new tools to reduce wave energy before it reaches the marsh edge, such as
methods to establish oyster reefs adjacent to marshes
• Monitoring to detect threshold shifts, to identify areas losing sediment as priorities for
action and to measure effectiveness of interventions
Sediment Retention Green pathway: This pathway (see Figure ES-2) contains a threshold
shift in sediment deposition and retention in response to inundation regime (depth and duration
of marsh flooding). Under current conditions, an increase in inundation leads to increased
transport into, and deposition of sediment onto, the marsh. However, this relationship flips from
a direct to an inverse effect under climate change, when sea level rise increases inundation to
such an extent that increased tidal flow velocities suspend more sediment than is deposited,
leading to a net decrease in deposition and retention. An increasing relative impact of sea level
leads to this threshold through marsh high water level (the transition from marsh to upland
vegetation). Given the significance of tidal restrictions in influencing inundation regime, this
additional branch of the pathway has important implications for management. Options include:
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• In the near term, relieving tidal restrictions to restore upstream hydrology, salinity and
sediment transport, thereby supporting upland migration of marsh high water level
• In the longer term with sea level rise, using tide gates that can be closed prior to storms or
spring tides to avoid peak flooding and associated high flow velocities during inundation
• Modifying ditches to restore more natural hydrology
• Removing barriers to upland migration such as roads and hardened shorelines
• Advancing policies and incentives that limit building of new barriers and encourage
conservation easements and other protections
Sediment Retention Blue pathway: Climate-related changes are expected in three
influences along this pathway (see Figure ES-2). Starting at the sediment deposition and
retention endpoint, an increase in net accretion (net change in elevation) currently decreases
sediment deposition by reducing flow velocities during inundation, such that much sediment
drops out of suspension before making its way very far into the marsh; but with higher sea level
under climate change, a threshold flip will occur where greater water depths during inundation
result in higher flow velocities that carry suspended sediment further into the marsh. Net
accretion is directly affected by below ground biomass, which is itself involved in a second
threshold relationship with nutrient inputs. Nutrients currently have a positive effect on below
ground biomass through stimulation of above ground growth; but under climate change this flips
to a negative effect as excessive nutrients inversely affect below ground productivity and
increase decomposition, with increasing relative impact on the end point. Finally, delivery of
nutrients via freshwater flows is affected by percent impervious cover in the adjacent landscape;
and the sensitivity of flows to impervious cover is expected to increase with climate change as
storms and flashiness of precipitation events intensify. Management options under this pathway
include:
• Improving stormwater management through the use of permeable pavements, rain
catchers, and buffers
• Upgrading sewage treatment plants to include tertiary treatment
• Upgrading combined sewer overflow systems to ensure all sewage passes through
upgraded treatment
• Engaging in public outreach to inform homeowners of the best timing, placement, and
application rates for fertilizers
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Community Interactions Green pathway: The climate-related shift examined in this
pathway (see Figure ES-3) is the effect on marsh elevation of the ratio of native high marsh to
invasive Phragmites (high marsh:Phrag). Marsh elevation is one of only three variables that feed
directly into the nesting habitat endoint, and all of the top pathways converge on this one
relationship. Currently, a decrease in high marsh:Phrag leads to a modest increase in marsh
elevation because Phragmites is more effective at trapping sediment (due to large rhizomes at the
marsh surface). This relationship strengthens under the climate scenarios as a threshold shift to a
stronger inverse relationship. With increasing sea-level rise, Phragmites will be better equipped
to maintain elevation and migrate landward to higher elevations while continuing to more
effectively trap sediment in place, compared to native high marsh that would lose elevation
rapidly. The remainder of the pathway includes the effect on high marsh:Phrag of nitrogen
(which favors Phragmites) and the effect on nitrogen delivery of inundation affected by flows
from residential runoff. Management options under this pathway include:
• Promoting more absorbent land cover (including permeable pavements)
• Upgrading treatment plants and improving stormwater management to reduce nutrient-
rich runoff
• Creating incentives for decreased use of fertilizers on lawns, regular inspections of septic
systems, and rain catchers to further reduce nutrient-rich runoff
• Coupling Phragmites control programs with removal of barriers to migration and
protection of upland areas to allow native high marsh to expand as sea level rises
Community Interactions Purple pathway: Starting at the nesting habitat endpoint and
working backwards, the Purple pathway (see Figure ES-3) corresponds with the Green pathway
in its first two influences; so see above for discussion of the threshold effect of high marsh:Phrag
on marsh elevation. The Purple pathway then diverges to focus on salinity's effect on high
marsh:Phrag. Greater salinity inhibits Phragmites and thus has a direct positive effect on high
marsh:Phrag, with high relative impact on nesting habitat. The high relative impact is due to a
competitive interaction between salinity and nitrogen, where increased salinity has a negative
impact on Phragmites while increased nitrogen has a positive effect. Salinity's high relative
impact will increase more under climate change, as sea level rise leads to increased inundation of
saline water for longer periods, higher into the marsh (placing greater pressure on Phragmites).
Given the effect of freshwater flows (exacerbated by impervious surfaces in residential areas) in
counteracting salinity maintenance, management options under this pathway include:
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• Prioritizing the use of permeable pavements and rain catchers to reduce freshwater
runoff, thereby helping to maintain natural salinity levels
• Controlling the hydrodynamic regime (e.g., through channel creation/ditch modification)
to maintain salinity through unimpeded tidal inundation
• Restoring riparian buffers and upstream freshwater marshes to reduce freshwater flows
and favor local infiltration and storage of rain water
Community Interactions Blue pathway: The Blue pathway (see Figure ES-3) focuses on
marsh elevation from the perspective of sedimentation. Sedimentation directly affects marsh
vertical accretion and is itself directly affected by above ground plant biomass as a source of
organic sediment. Climate-related shifts occur in the way above ground plant biomass is
affected by inundation regime (percent time high marsh is under water April-October).
Currently, inundation regime favors above ground plant biomass since flushing via inundation
prevents soil salinity from reaching levels that inhibit growth. Thus, just as an appropriate
inundation regime is important for maintaining salinity (see Purple pathway above), it is also
important for preventing salinity from becoming too high. Under climate change, however, this
influence shifts from a direct to an inverse effect: as sea level rises, inundation frequency and
duration are expected to reach levels that cause increased hypoxia and marsh die-back, with
increasing relative impact on the endpoint. Management options under this pathway include:
In the near term, restoring tidal connections (e.g., by removing tidal restrictions) to
support appropriate inundation regimes
In the longer term (at some point in the next 30-60 years with sea level rise), utilizing
restrictions (e.g., through use of tide gates) to control flows appropriately
Restoring native high marsh habitat in protected areas where marsh can grow and expand
Prioritizing marsh restoration and protection activities in locations where natural flows
and good sediment supplies are already in place
Based on the nature and timing of the sensitivity, some actions can be taken
immediately while others require monitoring and planning for multiple potential futures. In
the case of relationships that are well understood and for which there are management options
available, the nature of an expected climate-related shift has implications for when managers
may want to take action. In cases where the expected shift is toward increasing relative impact
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(and especially if the relationship is already of high relative impact today), actions can be taken
immediately to implement management options to positively affect those pathways. In the case
of relationships for which a change in sensitivity is possible under future climate scenarios, the
expectation of increasing sensitivity should trigger further study of the relationship in order to
anticipate the degree and timing of the impending sensitivity and prepare best management
responses. Finally, thresholds are a particular challenge, as it is often impossible to predict
exactly when a threshold change will happen. In these cases it will be important to monitor
threshold variables to identify the shift when it occurs; in the meantime managers might act to
keep the system 'below' the threshold as long as possible, while also preparing a plan for what to
do when unavoidable shifts occur. After a shift occurs, managers may decide to manage the
system differently in its new state, or take no action and instead shift priorities to other goals.
Adaptation Planning
Relating top pathways and associated adaptation options to existing management
activities is a path forward for action. The top pathways described above were used to identify
adaptation options that could be applied to sensitive ecosystem process components. Additional
pathways and associated adaptation options can be further explored using the detailed tables of
judgments and strategies provided in this report. The next step toward adaptation planning is to
connect top pathways and adaptation options to existing management activities and plans.
Under its current goals, MBP is already undertaking a variety of activities that can be
related to these adaptation options, as described in its annual, mid-term and long-term planning
documents. These include specific restoration, nutrient management, monitoring and research
projects and strategies. The climate change sensitivities and adaptation strategies identified in
this report can be cross-referenced to activities and objectives in the Strategic and Annual Plans
to identify where existing work can be adjusted to better support adaptation. Some examples of
such cross-referencing are provided as a starting point for more comprehensive adaptation
planning during future planning cycles. The broad goals and objectives articulated in the current
Comprehensive Conservation and Management Plan (CCMP) allow for addition of new mid- and
short-term actions; and the next revision of the CCMP will be an opportunity to incorporate new
higher-level goals and objectives addressing climate impacts beyond sea level rise. The intent is
that the results of this assessment will help inform priority investments in projects that take into
account specific, known climate sensitivities and make use of particular adaptation options that
will be most effective. The assessment results can also assist in priority-setting for long term
research and monitoring investment and for partnership building with other organizations.
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'Mainstreaming' climate change adaptation into ongoing, iterative planning processes
will increase the ability of managers to identify win-win options, weigh multiple trade-offs,
and prepare for long-term changes. For MBP as well as other National Estuary Programs and
organizations with well established planning processes, there are benefits to 'mainstreaming'
(continuously integrating) adaptation into ongoing planning, rather than developing a stand-alone
adaptation plan. The aim is to start with actions that have multiple benefits, i.e., that contribute
to current management goals while also responding to climate change. For example, the same
activities that can protect shellfish resources by using stormwater best management practices for
runoff reduction will also benefit wetlands by favoring native high marsh over invasive
Phragmites under climate change. Since climate change also has the potential to intensify and
even create new trade-offs, mainstreaming adaptation into planning will also be important for
identifying and weighing conflicts among adaptation options within the context of existing (and
emerging) goals.
Given the long-term nature of the climate change challenge, mainstreaming has an
additional advantage over a stand-alone plan in that it helps counteract the tendency to postpone
adaptation actions in the face of more immediate challenges. It often may be possible to adjust
current practices in ways that achieve adaptation while still fulfilling original goals. An example
of this is removal of tidal restrictions; current practices for restoring natural hydrology include
reengineering the size of openings at road crossings to allow full tidal exchange. Tide gates have
been used in other situations to allow the tide to pass in one direction but to restrict flow in the
other. Installing tide gates in places where flooding may become a future problem is one way to
adjust current practices since tide gates can be more actively managed (by opening and closing at
particular times, such as during spring tides) to allow for full tidal exchange in some
circumstances and restricted flow in others.
Finally, thinking ahead as part of planning is essential for anticipating which of today's
best practices may become ineffective and even 'maladaptive' as sensitivities change and
threshold shifts occur under climate change. Once thresholds have been crossed or other
unavoidable changes of significance have occurred, some management goals may have to be
revised. For instance, there may be a point in the future when the currently-beneficial effects of
removing tidal restrictions will start to negatively impact certain habitat goals, necessitating
reevaluation of this technique as a restoration practice.
Evaluation of Expert Judgment Approach
A novel methodology based on expert elicitation was developed and piloted as a tool for
'rapid assessment' of ecological sensitivities to climate change. The aim was to explore
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whether it is possible to synthesize useful information from experts on key climate sensitivities
in the short time frame of a two-day workshop, using expert elicitation techniques. Expert
elicitation is a multidisciplinary process for using expert judgment to inform decision making
when data are incomplete, uncertainties are large, and multiple models can explain available
data. The novel methodology introduced here is a modification of formal (usually quantitative)
expert elicitation that uses qualitative judgments to explore complex ecological questions.
Influence diagrams showing causal relationships among variables were used to capture the
experts' collective understanding of selected ecosystem processes under current conditions and
under two future (midcentury) climate scenarios. A coding scheme was used to record the
judgments, with observational notes and group discussions used to gather additional information.
The result was three categories of information based on the influence diagrams: (1) the
direction and strength of the relationships among variables, (2) the changing sensitivities
(including potential threshold responses) to climate change of some relationships, and (3) the
relationships of highest relative impact on the process as a whole. When this wealth of
information is combined into a 'crosswalk' of all three categories, it is possible to identify top
pathways (see above) comprised of relatively well-under stood relationships that are sensitive to
climate change and for which management options are available. Managers are encouraged to
further 'mine' the tables for other key pathways applicable to their specific sites and to identify
potential research priorities based on information gaps.
The expert elicitation exercise developed for this assessment has the potential to be
useful for other sites, processes, and ecosystems. While an example Great Marsh site was used
as a means to focus the exercise, the variables that ended up in the final influence diagrams are
common enough that most of the results may apply to other Massachusetts Bays marshes as well.
It is likely that the influence diagrams also could be transferred for use with corresponding
ecosystem processes in other northeast estuaries, with minor revisions for place-specific stressors
or other process variables; however, the characterizations of variable relationships, sensitivities
and relative impacts would have to be revised, particular to the location. Where information on
completely different processes is needed, the general methodology should be transferable to
other processes and ecosystems. The strengths of this method include its ability to capture more
recent knowledge than would be available from a literature review and more knowledge of the
type that is closely related to management. It is also effective at integrating across disciplines
and scales, which is particularly important for ecosystem and climate change assessments.
As a proof of concept for a new type of assessment exercise, this method and the pilot
results come with a number of caveats. This was not a comprehensive vulnerability assessment
for the whole estuary, so prioritization based on these results should be considered in the broader
context of other vulnerable processes, ecosystems, and goals. Given the complexity of these
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systems and instances of uneven agreement among experts, actions based on the top pathways
should be taken with care, with each manager considering the applicability of the information to
his or her own specific system. Confidence estimates for individual judgments turned out to be
challenging, so improvements have been suggested for strengthening this aspect in future
assessments. There is also the potential to simplify the coding scheme based on what was
learned in this trial run, to improve efficiency and allow experts more time to fill in data gaps.
Regardless, the expert elicitation method developed for this study was well suited for achieving
the goals of this assessment, and in a time frame much shorter than would be required for more
traditional, detailed quantitative modeling. Having a well-supported and timely study to
substantiate new and existing ideas on adaptation can position managers to justify the most
appropriate management priorities. It also can validate research priorities by highlighting known
research gaps. Overall, the method offers opportunities to capture and integrate the existing
collective knowledge of local experts, while pushing the boundaries to develop a new
understanding of the system and identify robust adaptation options in the face of climate change.
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1. INTRODUCTION
1.1. BACKGROUND
The estuaries of the Massachusetts Bays are highly vulnerable to the impacts of climate
change. Sea level rise, increased temperatures, changes in precipitation, and changes in storm
climatology are already causing increased inundation of coastal wetlands and marshes, changes
in water availability and quality, and altered patterns of sedimentation and erosion (Scavia et al.,
2002; Fitzgerald et al., 2008). These impacts are interacting with other anthropogenic stressors
such as tidal restrictions and increased impervious land cover to make management of estuarine
ecosystems more challenging than ever. While there are many uncertainties regarding the nature
of future climate changes and the response of ecosystems to those changes, estuary managers can
'ready' themselves by assessing the vulnerability of natural resources to climate change, making
strategic choices about how to implement adaptation strategies1 in the near term, and planning
for longer term management under a range of plausible scenarios of future change. It is the aim
of U.S. Environmental Protection Agency's (EPA's) Climate Ready Estuaries (CRE) Program to
assist National Estuary Programs in meeting such information and planning needs.
As part of the CRE Program, the Massachusetts Bays Program (MBP) and EPA's Office
of Research and Development (ORD) collaborated on the design and trial of a novel
methodology for conducting vulnerability assessments for sensitive ecosystems of the
Massachusetts Bays. The aim was to develop assessment capabilities using expert judgment to
synthesize place-based information on the potential implications of climate change for key
ecosystem processes, in a form that would enable managers to link the resulting information to
adaptation planning.
1.2. PURPOSE AND SCOPE
1.2.1. Purpose
The purpose of this project was two-fold: to conduct a vulnerability assessment using a
novel, expert judgment approach based on expert elicitation methods, and to analyze the
implications for adaptation planning. This was not a comprehensive vulnerability assessment for
the whole estuary but rather a proof of concept for a new type of assessment exercise, using
two key ecosystem processes of salt marsh ecosystems as demonstration studies. This was
accomplished through a series of steps to: (1) identify key management goals and ecosystem
throughout this report, "adaptation" refers to management adaptation rather than evolutionary adaptation.
Management adaptation refers to strategies for the management of ecosystems in the context of climate variability
and change (CCSP, 2008a).
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processes essential to meeting those goals; (2) create conceptual models of selected ecosystem
processes; (3) assess ecosystem process sensitivities to climate change; (4) consider resulting
vulnerabilities with respect to management goals; and (5) explore implications for adaptation
planning. Steps 1-2 were used to define the scope of the assessment, while steps 3-5 comprise
the vulnerability assessment itself.
1.2.2. Scope
The scoping process began with a review of the MBP Comprehensive Conservation and
Management Plan in order to select key management goals upon which to focus the assessment.
The key ecosystem-related goals selected by MBP in consultation with EPA ORD were to:
• Protect and manage existing wetlands
• Restore and enhance the habitat diversity and living resources of wetlands
• Protect submerged aquatic vegetation
• Prevent the spread of marine invasive species in order to maintain biodiversity
After an information-sharing meeting with local experts to discuss the project and learn
about climate change impacts and adaptation work in the region, salt marshes were selected as
the focal ecosystem for study. Salt marshes were identified as highly relevant to MBP's
management goals due to their ecological productivity, their habitat values for vulnerable
species, their susceptibility to ongoing encroachment by invasive species, and their sensitivity to
changes in climate-related variables such as sea level rise and altered hydrology. For more
detailed information on goal and ecosystem selection processes, please see Appendix A.
The second step in the scoping process was the development of a conceptual model to
understand the primary drivers and processes of salt marshes. The conceptual model was used to
explore the linkages among key ecosystem processes within the ecosystem, major stressors of
concern, and climate drivers causing altered or new stressor interactions. The model was refined
to a set of six key ecosystem processes that are essential to the maintenance of salt marsh
systems, as identified through literature review of salt marsh conceptual models and climate
change impacts. Based on this general conceptual model, two specific processes of concern were
selected for further analysis. The purpose was to select good processes for piloting the method,
but the choice does not imply that these are necessarily the most important or the most
vulnerable processes. The processes were selected in consultation with MBP staff, based on the
criteria of being integral to MBP's management goals, increasingly sensitive to climate change,
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and sufficiently well-studied by the scientific community to provide the basis for a more in-depth
assessment.
The two processes selected for further analysis were sediment retention and community
interactions. Sediment retention, which refers to the balance between the processes of removal
and deposition of sediment onto a salt marsh, was selected because of its importance for marsh
development and growth. The topic of community interactions was narrowed to a tractable
"storyline" involving several key species; it was selected based on discussions with local experts
on the MBP staff. The storyline selected was the relationship of marsh vegetation zonation
(between native Spartina and invasive Phragmites grasses) and the availability of nesting habitat
for the Saltmarsh Sharp-Tailed Sparrow (see The IUCN Red List of Threatened Species;
http://www.iucnredlist.org/). Expanded submodels were developed for each of the two processes
and served as the basis for designing the sensitivity analyses of the subsequent expert elicitation
exercise. For more detailed information on process selection and conceptual model
development, please see Appendix A.
The remaining steps of the assessment—the sensitivity analysis, vulnerability assessment,
and analysis of management implications—were accomplished through an expert
elicitation-style workshop, the results of which make up the core of this report. Expert elicitation
is a multidisciplinary process using expert judgment to inform decision making when empirical
data are incomplete, uncertainties are large, more than one conceptual model can explain
available data, and technical judgments are required to assess assumptions. It takes advantage of
the vast amount of local knowledge that is available via regional experts who are familiar with
the state of the science for the system of interest. During a two-day workshop, a novel
application of the expert elicitation method was tested using two groups of seven expert
participants each. A list of the expert participants for each breakout group is provided in
Table 1-1. The experts were selected based on criteria that ensured extensive expertise in the
local system, broad coverage of multiple scientific disciplines, experience in both science and
management, and knowledge of both empirical and theoretical research (for additional
information on participant selection criteria and credentials, please see Appendix B). The
participants assessed the sensitivities of salt marsh sediment retention and community
interactions to climate- and nonclimate stressor interactions, with an eye toward informing
adaptation strategies. The methodology and results of this expert elicitation exercise are
described in the sections that follow.
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Table 1-1. Breakout group participants for the expert elicitation workshop
(see Appendix A for further details on selection criteria and credentials)
Sediment retention group
Susan Adamowicz
Rachel Carson National Wildlife Refuge
Britt Argow
Wellesley College
Chris Hein
Boston University
David Ralston
Woods Hole Oceanographic Institution
John Ramsey
Applied Coastal Research and Engineering
Peter Rosen
Northeastern University
John Teal
Woods Hole Oceanographic Institute
Community interactions group
Walter Berry
U.S. EPA Atlantic Ecology Division
Robert Buchsbaum
Massachusetts Audubon Society
Dave Burdick
University of New Hampshire
Michelle Dionne
Wells National Estuarine Research Reserve
David Johnson
Woods Hole Marine Biological Laboratory
Gregg Moore
University of New Hampshire
Cathy Wigand
U.S. EPA Atlantic Ecology Division
1.3. ROADMAP FOR THE REPORT
This report presents a summary of the entire project, including CCMP goal selection and
conceptual modeling, the expert elicitation methodology, the results of the workshop, and
implications for management. Figure 1-1 provides a flow chart of the assessment process and
report structure.
Section 2 describes the expert elicitation exercise, including the approach, the exercise,
and the results. Section 3 provides an analysis of the results with respect to how they may be
used by estuary managers to understand ecosystem responses to climate change and engage in
adaptation planning. Section 4 provides key conclusions of the assessment. The appendices
provide additional detailed information on the activities conducted prior to and following the
workshop. Appendix A summarizes the goal selection and conceptual modeling processes used
for scoping the vulnerability assessment. Appendix B provides details on the expert elicitation
preworkshop preparations and postworkshop follow-up, including expert selection criteria,
preworkshop preparations by participants, and expert feedback. Appendix C and Appendix D
contain detailed information that was provided to the participants on the development of climate
scenarios and the methodology for estimating confidence.
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Analysis
s,
o.
Selection of Key
Management Goals
Selection of Focal
Ecosystems
Selection of Ecosystem
Processes
Development of
Conceptual Models
'•3 J" Development of
g_ I Climate Scenarios
o.
rj J~ Development of Expert
^ L Elicitation Exercise
co
-
Pre-Workshop
t Selection of Workshop
Participants
Development of "Straw
Man" Influence
Diagrams
Pre-Workshop Briefing
and Homework
Assignment
Development of
Consolidated Influence
Diagrams
co
-
•3
Workshop
Development of Group
Influence Diagrams
Expert Elicitation
Exercise and
Discussions
Discussion of Climate
Scenarios
Management
Discussion
{Diagrams,
Agreement, Relative
Impact, Confidence
•{_ Adaptation Strategies
^ j" Linking Information
^ I to Management
^ r Top Pathways for
fit. Management
^H J" Insights from the
^ L Workshop
<^. S Application of Results
^ I
General Conclusions
*A separate "lessons learned" report will compare the results of this assessment with a parallel effort by the San Francisco Estuary
Partnership, explore synthetic conclusions, and analyze potentialimprovements to the methodology.
Lessons Learned*
Figure 1-1. Vulnerability assessment process.
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2. EXPERT ELICITATION EXERCISE
2.1. JUSTIFICATION FOR METHOD
2.1.1. Definition and Uses
Expert elicitation is a multidisciplinary process for obtaining the judgments of experts to
characterize uncertainty and fill data gaps where traditional scientific research is not feasible or
adequate data are not yet available. The goal of expert elicitation is to characterize each expert's
beliefs about relationships, quantities, events, or parameters of interest. The expert elicitation
process uses expert knowledge, synthesized with experiences and judgments, to produce
conclusions about the nature of, and confidence in, that knowledge. Experts derive judgments
from the available body of evidence, including a wide range of data and information ranging
from direct empirical evidence to theoretical insights.
Because EPA and other federal regulatory agencies are often required to make important
national decisions in the presence of uncertainty, EPA's Science Policy Council formed an
Expert Elicitation Task Force in April of 2005 to investigate how to conduct and use this method
to support EPA regulatory and nonregulatory analyses and decision-making. The result was an
Expert Elicitation Task Force White Paper that affirms the utility of using expert elicitation and
provides recommendations for expert elicitation "best practices" based on a review of the
literature and actual experience within EPA. The draft paper (see
http://www.epa.gov/spc/expertelicitation/index.htm) is currently under external peer review
through EPA's Science Advisory Board. The best practices outlined in the draft White Paper
formed the basis for the design of this project's expert elicitation-style workshop.
2.1.2. Novel Application
The specific elicitation exercise used in this assessment was custom-designed by Dr. Max
Henrion of Lumina Decision Systems, Inc. Dr. Henrion is a nationally-recognized authority on
decision analysis methods and tools, dealing with uncertainty in environmental risk assessment,
and expert elicitation (e.g., Morgan and Henrion, 1990; Henrion et al., 1991; Pradhan et al.,
1996). As a member of EPA's Expert Elicitation Task Force, he was uniquely qualified to assist
in designing a novel application of expert elicitation methods for use in a two-day workshop
format. Specifically, Dr. Henrion developed a qualitative coding scheme for expert judgments
about the sensitivity of ecosystem processes to physical and ecological variables, using
"influence diagrams" to depict the relationships among ecosystem process variables and external
drivers such as climate change. This new methodology, described in detail below, explores the
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utility of expert elicitation for conducting "rapid vulnerability assessments" for ecological
systems.
2.2. WORKSHOP DESIGN AND METHODOLOGY
2.2.1. Workshop Goals and Objectives
The overarching goals of the workshop were to: (1) improve the understanding of the
sensitivity of salt marshes to the projected impacts of climate change; (2) improve the ability to
identify adaptation management strategies that mitigate the impact of climate change in salt
marshes, given the uncertainties; and (3) demonstrate the applicability of an expert elicitation
approach to this type of analysis.
The workshop was held April 27-28, 2010, in Boston, MA, at the EPA Region 1 office.
During the workshop, experts were divided into two breakout groups to consider each ecosystem
process separately. The seven participants in each breakout group (see Table 1-1) were asked to
provide judgments about the ecosystem process under consideration by their group. For each
ecosystem process, the specific workshop objectives were to: (1) characterize the relative
influences of physical and ecological variables that regulate the process; (2) assess the relative
sensitivity of the ecosystem process to key stressors under current conditions and future climate
scenarios; (3) assess the degree of confidence in judgments about these relationships; and
(4) relate the results of the exercise to adaptation planning through group discussions. Given the
range of habitats and issues in the entire Massachusetts Bays area, the participants were asked to
consider Jeffrey's Neck Marsh (Great Marsh System; see Figure 2-1) when a more specific
spatial scope would be useful during the workshop exercise, as well as when considering
management implications. However, issues and options that were not specific to Jeffrey's Neck
Marsh were also considered during group discussions.
For further details on workshop preparation and implementation, including selection
criteria for participants and details on Jeffrey's Neck Marsh, please see Appendix B.
2.2.2. Approach and Methodology
According to protocols put forth in EPA's Expert Elicitation Task Force White Paper,
there are a variety of options for gathering and processing expert judgments. The specific
elicitation approach used in this workshop was one that asked experts to give their individual
judgments independently. This was done to reduce the tendency towards "group-think," i.e., the
tendency for many people to go along with the most vocal participant, even if s/he is not the
most knowledgeable. Participants had an opportunity to make adjustments to their judgments at
any time during or after group discussions; however, consensus was not the goal of the exercise.
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to
Upper Worm Snore
Great Marsh
Sa/em Sound
Metro Boston
"•? "^ \
'—. __r~-*-*"~
«1 Jeffreys Neck «
•J-^L • * _
South Shore
N US
.J L T\ r, K_ / .
Figure 2-1. The Massachusetts Bays, (a) The five regions of the Massachusetts Bays Program planning area; (b)
The Great Marsh Area of Critical Environmental Concern; and (c) Jeffreys Neck salt marsh.
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Rather, the aim was to look at the expert judgments in aggregate, while also retaining
information on variance in judgments. This approach is well-suited to the type of qualitative
judgments participants were asked to make at the workshop.
2.2.2.1. Influence Diagrams
Each breakout group participated in the development of an influence diagram of the
ecosystem process under consideration by their group. Decision analysts use influence diagrams
as a way to define the qualitative structure of causal relationships among variables that experts
believe are of greatest importance for understanding the problem being evaluated. Influence
diagrams typically represent a subset of a larger, more detailed model such as the conceptual
models developed previously (see Appendix A).
A simplified influence diagram for sediment retention is provided in Figure 2-2. By
convention, the variables in an influence diagram are represented by rectangles (labeled boxes)
while arrows between the variables represent causal relationships, or "influences" (labeled with
letters). Sequences of arrows form pathways, all of which ultimately lead to the final variable, or
endpoint, of concern. In Figure 2-2, the endpoint that is being evaluated is sediment
deposition/retention. Interactive effects of multiple variables on each other, or on the endpoint,
can occur where two "causal" variables both influence (have arrows into) a common "response"
variable. In Figure 2-2, an example interaction is indicated by arrows C and D, where freshwater
flow and coastal and nearshore erosion together could have an interactive effect on sediment
supply.
In the case of community interactions, the influence diagram was constrained to a
tractable number of species of interest for study. It focused on the relationship of marsh
vegetation (native Spartina and invasive Phragmites grasses) and the resulting availability of
nesting habitat for the Saltmarsh Sharp-Tailed Sparrow. The Saltmarsh Sharp-Tailed Sparrow
prefers the native, upper marsh species Spartina patens for nesting habitat. This habitat is being
infringed upon by invasive Phragmites from the landward side, and by lower marsh Spartina
alterniflora (which is migrating upland with sea level rise) from the seaward side. Please see
Appendix A for a more detailed explanation of this storyline.
While influence diagrams are widely used and relatively well understood, our proposed
use of qualitative degrees of influence is an innovation in expert elicitation. Typically, an expert
elicitation seeks to obtain expert judgments about uncertain quantities in the form of numerical
probability distributions. For the ecosystem processes considered during this workshop, there
were information, data, and time limitations that made quantifying the influences as probability
distributions unrealistic. Instead, judgments were based on qualitative types (is the relationship
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Altered Flows:
Tidal
Restrictions
A
Land Cover:
% Impervious
Cover
^^\B
Freshwater
Flow
j
f Sediment ] Coastal and
- * Supply < Nearshore
V ) Erosion
Sediment
Deposition/
Retention
Figure 2-2. Simplified influence diagram for sediment retention.
direct or inverse?) and degrees (is the response small or large?) of influences. The use of
qualitative degrees of influence provides much more detail than simply specifying causal
influences with arrows alone, but less specificity than required for quantified probabilities.
Prior to the workshop, the participants attended briefing calls in which they: learned
about the project plan; discussed background reading materials; and were presented with "straw
man" diagrams (see Appendix B) developed from the original conceptual models. They were
asked to review the diagrams and submit their own revised versions the week before the
workshop. Diagram submissions were combined into one consolidated draft diagram for each
group that served as the starting point for discussion at the workshop. The workshop itself began
with each group working together to refine their diagram into a "group diagram". The group
influence diagram was meant to distill the system to a tractable set of key variables and
influences, and as such it was not comprehensive. The groups were given complete freedom to
alter any part of the diagram, with the exception of the ecosystem process endpoint, as long as
they constrained the diagram to a total of no more than 15 boxes. At the same time, participants
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were reminded to keep some of the top row stressor or management boxes, since these would
serve as key linkages back to management options. Participants were also encouraged to
minimize the total number of arrows in the diagram to include only the most key influences. The
purpose was to capture the key components and relationships of each ecosystem process in a
concise form that could be rapidly assessed in a workshop setting. Once the group diagrams
were finalized, all of the participants made their judgments using the same diagram throughout
the remainder of the workshop.
2.2.2.2. Climate Scenarios
Dr. Katharine Hayhoe of Texas Tech University, an experienced climate scientist with an
extensive background in regional climate assessments, developed two climate change scenarios
for use in the expert elicitation exercise (see Appendix C for more detailed information on the
climate change scenarios). The scenarios represented two distinct but scientifically credible
climate futures for a mid-century (2040-2069) time period. (The mid-century time frame was
selected by the MBP partners because of its suitability for adaptation planning.) The projections
were based on six leading climate models, using a lower emissions scenario (Climate
Scenario A) and a higher emissions scenario (Climate Scenario B) to generate values for climate
variables for use by the experts in making their judgments (see Table 2-1).
Under both climate change scenarios, Massachusetts will experience a significantly
warmer climate, accompanied by increases in annual precipitation and higher sea levels. By
mid-century, the "higher-range" Climate Scenario B (which includes higher emissions and a
more sensitive climate) is projected to experience a warmer and somewhat wetter climate
compared to the "lower-range" Climate Scenario A (with lower emissions and a lesser impact on
Massachusetts climate).
At the workshop, Dr. Hayhoe provided the participants with an overview of major
climate drivers and regional trends for Massachusetts. She discussed five main sources of
uncertainty with climate projections, including: (1) the amount of future emissions; (2) the
degree to which the influence of global climate change on local climate is modified by local
factors; (3) the sensitivity of the climate system (as feedbacks are not well understood); (4) the
ability of climate models to simulate climate both globally and locally; and (5) the natural
variability of the climate system. Because of these factors, exact predictions of climate change
are not possible. However, uncertainty can be dealt with by using multiple scenarios to bracket a
range of plausible climate futures and identify key vulnerabilities in the system. In order to
consistently "bound" the consideration of future climate changes in the workshop exercise, the
participants were instructed to use the values provided under Climate Scenarios A and B (see
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Table 2-1. Summary of Climate Scenario A ("lower-range" scenario) and
Climate Scenario B ("higher-range" scenario): averages for midcentury
Temperature
Precipitation
Sea level
Annual average
Geographically
Days>90°Fa
Coldest day of year
Growing season
Winter change
Summer change
Spring change
Fall change
Heavy events
Yearly snow depth
Total increase
Storms/wind
Ice-out
Spring peak flow period
Summer low flow period
Drought0 frequency
Winter flooding events
"Lower-range" scenario
(3-model average of Bl)*
+3.6°F
Boston "moves" to Philadelphia, PA
20 days
+4.3°F
+3 weeks
+10.6%
+7.9%
+15.0%
+1.9%
~8% increase in the max amount of
precipitation to fall within a 5 -day period
-9cm
17 cm [Sea Level Affecting Marshes
Model (SLAMM) model A1B scenario]
"Higher-range" scenario
(3-model average of AlFi)*
+5.6°F
Boston "moves" to Washington, DC
34 days
+6.5°F
+4 weeks
+15.1%
+11.2%
+14.1%
-2.2%
~12.5% increase in the max amount of
precipitation to fall within a 5 -day period
-11 cm
4 1 cm (SLAMM mid-century model
estimate using 1.5m scenario by end of
century)
Northeast Climate Impacts Assessment (NECIA, 2006) suggests little change in the
frequency of winter -time storms for the East Coast. However, under the "higher
range" scenario, between 5 and 15% of these storms (an additional one storm per
year) will move northward during late winter (Jan, Feb, March), affecting the
Northeast. (No change for the "lower range" scenario.) In addition, the impact of a
higher sea level will increase the likelihood of storm damage to coastal locations.
For hurricanes, the most current understanding is that rising sea surface temperatures
will increase evaporation, increasing the amount of rainfall associated with any given
hurricane, but there is too much uncertainty in projections of hurricane frequency and
wind intensity to say much about future trends.
2 weeks earlier
7 days earlier
1 week longer
4 weeks earlier
10 days earlier
2 weeks longer
2 every three years (compared to 1 every two years today)
two-fold increase in number of events
General increases in salinity of estuarine waters, freshwater tributaries, and coastal aquifers during summer
*Please refer to Appendix C for more information on the development of the climate scenarios.
aCompared to the 1960—1990 annual average of 9 days with temperatures above 90°F.
bThe total difference in range between mean and spring tides of 1.3 ft (39.6 cm) is very close to the higher emission
scenario rise of 41 cm. Based on data for Plum Island Sound (south entrance), the spring high tide is generally 0.65 ft
(19.8 cm) higher than the mean high tide. http://tidesandcurrents.noaa.gov/tideslO/tab2eclb.htmW8.
°Defined as the monthly soil moisture is more than 10% below the long-term mean (relative to historic simulations).
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Table 2-1) to contextualize their judgments about future effects on the ecosystem processes
under consideration. For additional details on the climate scenarios, including data sources,
please see Appendix C.
2.2.2.3. Expert Facilitation
Due to the highly technical nature of the exercise, the complexity of the novel
methodology that was being used, and the ambitious time line for accomplishing multiple
outputs, it was essential that the workshop be run by skilled expert facilitators. The expert
facilitators selected were Brock Bernstein, Independent Consultant and President, National
Fisheries Conservation Center and Carlton Hunt, Research Leader with Battelle in Duxbury,
Massachusetts. They were chosen based on a number of criteria including: proven expertise in
facilitating science-based workshops; general knowledge of science behind estuary management
(particularly wetlands ecology); and experience working on national coastal issues and/or issues
in the Massachusetts Bays region. Dr. Hunt is an experienced and trained facilitator who has
been working in Massachusetts Bay for several decades, and has served as the project manager
and technical lead on Battelle's Massachusetts Water Resources Authority program. Dr.
Bernstein is a marine ecologist with research experience in a range of coastal and oceanic
environments and has worked on a wide variety of management and policy issues. Dr. Hunt
served as the facilitator for the Sediment Retention group, while Dr. Bernstein served as the
facilitator for the Community Interactions group.
Prior to the workshop, both facilitators attended training calls in which they were fully
briefed on the project background and conceptual models, the workshop goals and objectives,
and the expert elicitation exercise. Working together and with the MBP/EPA team, the
facilitators contributed to the refinement of the workshop agenda and improvements to the
workshop process.
2.2.2.4. Coding Scheme and Exercise
Participants were asked to characterize each influence in their influence diagram
according to the coding scheme presented in Table 2-2. Influences were characterized first under
current conditions, and then under Climate Scenario A and Climate Scenario B. The extent to
which participants agreed in their judgments was variable across the different influences. The
rule that was adopted for determining agreement for each influence was that a majority (four or
more participants) had to have selected the same code. As this was not a consensus process, and
the small group size limited statistics that could be done, majority was chosen as the most simple
rule as a basis for agreement. A case could be made for a more restrictive rule on what
2-8
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Table 2-2. Coding scheme used during the workshop exercise to characterize
influences. "Small" and "large" changes in variables are defined relative to the
current range of variation for each variable, with "small" indicating that the
variable is within its current range of variation and "large" indicating that the
variable has moved outside its current range of variation
Option
Type and degree of influence definition
0
No influence: we know that changes in X have no effect on changes in Y, holding all other variables
constant.
Unknown influence: we don't know whether an increase in X will increase, decrease, or have no effect on
Y.
Proportional increase: a large increase in X is likely to cause a large increase in Y. A small increase is
likely to cause a small increase.
Proportional decrease: a large decrease in X is likely to cause a large decrease in Y. A small decrease is
likely to cause a small decrease.
Inverse decrease: a small increase in X is likely to cause a small decrease in Y. A large increase in X is
likely to cause a large decrease in Y.
Inverse increase: a small decrease in X is likely to cause a small increase in Y. A large decrease in X is
likely to cause a large increase in Y.
A small increase in X is likely to cause a large increase in Y.
A small increase in X is likely to cause a large decrease in Y.
A large increase in X is likely to cause a small increase in Y.
A large increase in X is likely to cause a small decrease in Y.
10
A small decrease in X is likely to cause a large increase in Y.
11
A small decrease in X is likely to cause a large decrease in Y.
12
A large decrease in X is likely to cause a small increase in Y.
13
A large decrease in X is likely to cause a small decrease in Y.
constitutes agreement, but that would obscure the understanding of many of the influences.
Agreement among four or more participants was considered to indicate substantial agreement
across the group.
Participants were also asked to characterize interactive influences of their choosing (i.e.,
those they deemed important), under current conditions and under the climate change scenarios,
according to the coding scheme presented in Table 2-3. Since participants were given the option
to choose which interactive influences they considered significant and to provide judgments only
2-9
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Table 2-3. Coding scheme used during the workshop exercise to characterize
interactive influences
Interactive influence
Independence
Synergy
AND Gate
NOR Gate
Competition
Definition
The effect of X on Y is independent of Z (default situation)
The effect of X on Y increases with increase in Z
The effect of X on Y happens only with large Z
The effect of X on Y happens only with small Z
The effect of X on Y decreases with increase in Z
for those influences, and were limited by time, there were often interactions where only one or
two participants provided judgments. Only interactions scored by three or more participants
were examined in order to focus on interactions judged by several participants to be significant.
Three or more corresponding judgments were used to define agreement for interactive
influences.
Finally, the participants were asked to assess their current level of scientific confidence in
their judgments for each influence or interactive influence using the confidence coding scheme
presented in Table 2-4. For each influence, each participant was asked to rate his confidence in
his judgment based on: (1) the amount of scientific evidence that is available in the scientific
community at large to support the judgment; and (2) the level of agreement/consensus in the
scientific community at large regarding the different lines of evidence that would support the
judgment. The coding options for "amount of evidence" were high (H) or low (L), based on
whether available information is abundant and well-studied and understood, versus sparse and
mostly experimental/theoretical. The coding options for "level of agreement" were H or L,
based on whether data, reports, and experience across the scientific community reflect a high or
low level of agreement about the influence. Thus it was possible to have four combinations of
evidence and agreement when assessing confidence: high evidence, high agreement (FtH), high
evidence, low agreement (FIL), low evidence, high agreement (LH), and low evidence, low
agreement (LL). The rule for determining agreement in confidence was the same as described
above for influences: agreement was defined as a majority (four or more) of the same
categorization of confidence level. Similarly using the same rule as above for interactive
influences, agreement on confidence for interactive influences was defined as three or more of
the same categorization of confidence. For additional details on the method used to assess
confidence, please see Appendix D.
2-10
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Table 2-4. Coding scheme used during the workshop exercise to characterize
confidence
Confidence
LH
LL
HH
HL
Definition
Low evidence, high agreement =
Low evidence, low agreement =
established but incomplete
speculative
High evidence, high agreement = well established
High evidence, low agreement =
competing explanations
2.2.2.5. Typologies for Understanding Influences and Sensitivities
2.2.2.5.1. Type and degree of influence
The group's level of understanding of the different influences (arrows) in the influence
diagram can be gauged by the amount of agreement in participants' selection of influence codes.
Sometimes participants agreed on the type of influence, but not necessarily the degree (strength)
of the influence. Codes 2-13 (see Table 2-2) represent different combinations of types and
degrees of influences that can be grouped according to the following typology:
Types:
Direct relationship (when X increases, Y increases) = Codes 2, 3, 6, 8, 11, 13
Inverse relationship (when X increases, Y decreases) = Codes 4, 5, 7, 9, 10, 12
Degrees:
Proportional response of Y to X = Codes 2-5
Disproportional response of Y to X = Codes 6-13
Codes can also be paired according to the same type and degree of influence, with the
only distinction being whether one is considering "X" to be increasing or decreasing. For
example 2/3 is a direct proportional influence, with 2 indicating when "X" increases, and 3
indicating when "X" decreases, but in both cases "Y" is responding in a directly proportional
way. Six combinations of pairings are possible:
2-11
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Pairings by type and degree of influence (where "X" can go up or down):
Direct proportional = 2/3
Inverse proportional = 4/5
Direct disproportional, strong response (xY) = 6/11
Direct disproportional, weak response (Xy) = 8/13
Inverse disproportional, strong response (xY) = 7/10
Inverse disproportional, weak response (Xy) = 9/12
In some cases, participants selected the same exact code, indicating that they had the
same understanding of the influence in terms of both type and degree. Or, sometimes
participants chose pairings such as 2/3 while their colleagues may only have noted a 2 or a 3; we
consider these cases to also indicate a correspondence in understanding of type and degree of
influence, since the only distinction was whether a participant was thinking of "X" as going up
or down (or both).
In another group of cases, there was agreement on the type of influence (i.e., whether X
affects Y directly or inversely), although there was lack of agreement on the degree of that
influence. These latter cases amount to an understanding of how X affects Y, just not the
magnitude. It may still be useful for management to know for which influences we at least have
some understanding of the type of response, even if we are not sure of the magnitude.
Finally, there were cases in which there was such a mixture of codes selected as to
indicate no agreement in either type or degree of influence. This indicated that, among this
group of experts, the influence was poorly understood or poorly defined.
2.2.2.5.2. Sensitivity
It is also possible to establish a typology for assessing the sensitivity of each influence
(i.e., how sensitive variable Y is to changes in X), especially with regard to how those may
change under the climate scenarios. Several codes can indicate the same level of sensitivity, so
the following groupings were used to indicate three levels of sensitivity:
Low sensitivity = Codes 8-9 and 12-13
Intermediate sensitivity = Codes 2-5
High sensitivity = Codes 6-7 and 10-11
2-12
-------
This typology was used to document all judgments, along with the following additional
categories of judgments:
No Influence = Code 0
Unknown influence = Code 1
None given = No judgment provided
Other = Response provided that does not fit into the coding scheme
2.2.2.6. Understanding Relative Impacts of Influences
While the coding scheme described above captures the nature of individual influences, it
is also of interest to identify which influences and interactions the participants perceived to have
the greatest relative impact on the ecosystem process endpoint. Here we define relative impact
as the combination of not only sensitivity but also how greatly the variable is changing relative to
other variables. Because relative impact is an emergent property that results from considering all
influences in the diagram together, there was no coding for this in the workshop exercise; rather,
this concept was explored through group discussions that looked at the influence diagram as a
whole and identified influences of greatest relative impact in the context of the entire web of
influences. During group discussions that spanned both days of the workshop, information was
gleaned as to which influences participants perceived to have comparatively greater effects on
the ecosystem process endpoints, and whether this varied under the climate scenarios. These
discussions were captured in the workshop notes as well as in the influence diagrams, in which
the participants identified influences and interactions of highest relative impact (see
Sections 2.3.1.4 and 2.3.2.4).
2.2.2.7. Key Questions
As described above, there are three categories of information that together comprise the
collective understanding of each ecosystem process as represented by its influence diagram:
(1) the type and degree of influences between variables, (2) the sensitivity of "response"
variables to changes in "affecting" variables, and (3) the relative impact of each variable on the
ecosystem process endpoint. For each of the three categories of information, the following key
questions are addressed.
2-13
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Types and Degrees of Influences:
• For which influences and interactions was there agreement in participants' judgments
(codes), and what were those codes?
• How did agreement on influences and interactions vary from current conditions to
Climate Scenario A and Climate Scenario B?
• For influences and interactions for which there was agreement in judgments, how did
confidence levels across the participants vary? Did this change under the climate
scenarios?
Sensitivity of Influences:
• For which influences and interactions is there greatest sensitivity and least sensitivity
in the response variable to changes in the "affecting" variable?
• Were there any influences or interactions where agreement on sensitivity across
participants increased or decreased under the climate scenarios?
Relative Impact of Influences:
• Which influences and interactions did the participants indicate have the greatest
relative impact on the ecosystem process endpoints?
• Were there any influences or interactions for which relative impact changed under the
climate scenarios?
Using the data from the coding exercise as well as information that emerged during group
discussions, these questions are explored in the results sections that follow.
2.3. RESULTS
Major outputs of the expert elicitation exercise included the group influence diagrams,
the judgments on influences (including interactive influences) along with their confidence
estimates, information on sensitivities (including thresholds), and characterizations of relative
impacts. For the purpose of this study, a threshold is defined (as per Groffman et al., 2006) as a
point at which there is an abrupt change in an ecosystem property (such as a flip in influence
type from direct to inverse), or where a small additional change in a driver produces a large
response (such as a shift from a proportionate to a disproportionately strong response of variable
Y to a change in variable X).
2-14
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2.3.1. Sediment Retention
2.3.1.1. Group Influence Diagram
Figure 2-3 shows the group diagram developed by the Sediment Retention group.
Variable definitions that were developed by the participants during the construction of the
diagram are found in Table 2-5. The diagram highlights the balance of erosion and accretion
processes in determining the Sediment Deposition/Retention endpoint. On the erosion side,
Marsh Edge Erosion directly impacts the endpoint, while Coastal and Nearshore Erosion include
impacts of erosion outside the marsh. Erosion from external sites can serve as a sediment source
to the marsh and so acts through Sediment Supply, as well as impacting Tidal Exchange as
erosion changes basin bathymetry and the resulting hydrodynamics. Both erosion variables are
impacted by Storms, while Coastal and Nearshore Erosion is also influenced by Marsh High
Water Level. Marsh High Water Level integrates sea level, topography and vegetation in that it
is the transition between marsh and upland vegetation that is responsive to sea level, which is
dependent on topography through slope.
On the accretion side, Net Accretion accounts for the accretion component directly, and
is a two-way influence on the endpoint. Below Ground Biomass and Surface Roughness also
influence accretion related processes, the former accounting for below-ground accretion, the
latter for above ground accretion. Surface Roughness is another integrative variable. The
characteristics of different grass species have differing impacts as water flows through them,
which influences the deposition and retention of sediment. Inundation Regime is another
two-way influence on the endpoint, one that can contribute to either accretion or erosion on the
marsh surface. The diagram shows a high degree of interconnectivity between variables,
especially among these accretion-related variables.
In addition, there are several feedback loops with the endpoint, including through Net
Accretion and Inundation Regime. Inundation regime is itself influenced by multiple other
variables, including Marsh High Water Level and Storms, as well as Tidal Exchange and
Freshwater Flow. The management related variables at the top of the diagram include Nutrient
Inputs, Altered Flows: Tidal Restrictions, and Land Cover: Percent Impervious Cover. Storms
and Marsh High Water Level are additional stressor variables that are less clearly connected to
management-related variables. The management options for Marsh High Water Level are one
step removed and related to maintaining transitional uplands for upslope migration. These top
level variables influence middle level ones which are primarily physical and hydrologic in
nature. These include Tidal Exchange, Freshwater Flow, Sediment Supply, Coastal and
Nearshore Erosion and Marsh Edge Erosion. Freshwater Flow and Inundation Regime both
influence Sediment Deposition/Retention through Surface Roughness.
2-15
-------
Land Cover:
% Impervious
Cover
Sediment
Deposition /
Retention
Figure 2-3. Sediment Retention group influence diagram.
2-16
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Table 2-5. Sediment Retention variable definitions
Variable
Nutrient Inputs
Altered Flows: Tidal Restrictions
Land Cover: Percent Impervious Cover
Marsh High Water Level
Storms
Tidal Exchange
Freshwater Flow
Sediment Supply
Coastal and Nearshore Erosion
Surface Roughness
Marsh Edge Erosion
Inundation Regime
Below Ground Biomass
Net Accretion
Sediment Deposition / Retention
Definition agreed upon by group
Annual loading rate (of Nitrogen and Phosphorous)
Percent reduction compared to unrestricted flow
Percent impervious cover
High tide limit, measured by where marsh vegetation changes to upland
vegetation — includes integrated sea level
Frequency and intensity of (severe) storms
Tidal prism
Rate of freshwater inflow to the estuary from the watershed
External sources (terrestrial and marine) of inorganic material feeding
the marsh, as measured by mass flux
Net volume of eroded sediment from coastal zone
The interaction of stem density, height and diameter (based on plant
species characteristics) with hydrodynamic regime
Volume of peat calved off marsh edges
Frequency, depth, and duration of marsh flooding
Below-ground biomass accumulation rate
Net elevation change
Amount per year (e.g., mm/yr)
2.3.1.2. Influence Types and Degrees
2.3.1.2.1. Agreement
The influences upon which participants agreed with respect to type and degree help to
establish the nature of those relationships and indicate which are best understood. Table 2-6
presents these results for the Sediment Retention group.
In some cases, participants gave multiple codes for an arrow. When the multiple codes
represented one of the pairing types described above in Section 2.2.2.4 (e.g., 2/3), both codes are
shown, separated by a "/".
If multiple codes that do not fall into a pairing were given, both codes are shown,
separated by a symbol indicating the nature of the combination. In the first type of combination,
multiple codes with "X" going in the same direction (e.g., X is increasing in both codes) are
separated by a "A" symbol; and where these codes conflict and would make a difference in
determining agreement, those cells were not counted. In the second type of combination, codes
with "X" going in different directions (e.g., X is increasing in one code and decreasing in the
2-17
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Table 2-6. Sediment Retention group influence judgments. Columns A-Z
represent individual influences (arrows) in the influence diagram and rows
represent individual respondents: dark green = agreement on influence type and
degree, light green = agreement on type but not degree, gray = no agreement;
within columns, green numbers = same (majority) grouping of type (though
degree may be different), pink numbers = disagreement about type,
red outline = threshold response
Current
Resp. 1
Resp. 2
Resp. 3
Resp. 4
Resp. 5
Resp. 6
Resp. 7
Climate A
Resp. 1
Resp. 2
Resp. 3
Resp. 4
Resp. 5
Resp. 6
Resp. 7
Climate B
Resp. 1
Resp. 2
Resp. 3
Resp. 4
Resp. 5
Resp. 6
Resp. 7
A
2A6
2/3
2/3
6
2
2
A
2
2/3
2/3
0
6
2
2
A
2A6
2/3
2/3
6
2
2
B
2
6|3
2/3
2
8
2
2
B
2
6
2/3
2
6
2
2
B
2
2
2/3
2
6
2
2A6
C
2
8A<
2/3
2
6
2
2
C
2
2
2/3
2
6
2
2
C
2A6
8
2/3
2
6
2
2A6
D
9
1
4/5
4
9
8
5
D
2A9
0A4
1
4
9
8
5
D
2A9
0A4
1
4
9
8
5
E
2
7
2/3
7
6
2
4
E
9
7
2/3
7
4
2
4
E
9
7
2/3
7
4
2
4
F
2A6
2/3
4/5
4
7
2
4
F
2A6
4
4/5
4
7
2
2
F
2A6
4
4/5
4
7
2
2
G
0
0
0
0
0A
G
0
0
-t
0
0
G
0
0
0
0
H
2A6
2/3
2/3
2/3
6
8
2
H
2A6
6
2/3
2/3
6
8
2
H
6
6
2/3
2/3
6
8
2A6
I
2A6
2/3
2/3
2
6
2
2
I
2A6
6
2/3
2
6
2
2
I
6
6
2/3
2
6
2
2A6
J
6
6|3
2/3
6
6
2
2
J
6
6
2/3
6
6
2
2
J
6
6
2/3
6
6
2
2A6
K
6
2
2/3
2
6
2
2
K
6
6
2/3
2
6
2
2
K
6
6
2/3
2
6
2
2A6
L
6
2/3
2/3
2
LA6
8
2
L
6
2
2/3
2
6
8
2
L
6
2
2/3
2
6
2
2A6
M
2
8/13
8/13
2
8
9
0A2
M
2
8
8/13
1
0
9
0A2
M
2
8
8/13
0
2
0A2A6
N
12
4/5
1
9
6
9
4A6
N
5
N
5
1
O
6
2/3
2/3
2
2
2
O
6
2
2/3
2A
8
2
2
O
6
2
2/3
2A
8
2
2
P
2A8
2/3
2/3
2
2
8
2
P
2A8
8
6/11
2
8
8
2
P
2A8
8
6/11
2
8
8
2
2-18
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Table 2-6. Sediment Retention group influence judgments. Columns A-Z
represent individual influences (arrows) in the influence diagram and rows
represent individual respondents: dark green = agreement on influence type and
degree, light green = agreement on type but not degree, gray = no agreement;
within columns, green numbers = same (majority) grouping of type (though
degree may be different), pink numbers = disagreement about type, red
outline = threshold response (continued)
Current
Resp. 1
Resp. 2
Resp. 3
Resp. 4
Resp. 5
Resp. 6
Resp. 7
Climate A
Resp. 1
Resp. 2
Resp. 3
Resp. 4
Resp. 5
Resp. 6
Resp. 7
Climate B
Resp. 1
Resp. 2
Resp. 3
Resp. 4
Resp. 5
Resp. 6
Resp. 7
Q
2A8
2
2/3
2
8
2
2
Q
2A8
2/3
2
8
2
2
Q
2A8
1
2/3
2
8
2
2A6
R
2
6
2/3
2
2
2
2
R
2
6
2/3
2
8
2
2
R
2
6
2/3
2
8
2A4
2A6
S
2A6A
S
2A7
S
2A7
4
T
0A2
2/3
8/13
2
9
8
1
T
0
2
8/13
8
6
8
1
T
0
2
8/13
8
6
8
1
U
0A2
1
U
2A4
U
2A4
1
V
2
2/3
2/3
2
6
8
2
V
2A4
2
6/11
2
6
2
1A2
V
2A4
2
6/11
2
6
2
1A2
W
2A6
tA6
2/3
2
2
2
2
W
2A6
2/3
2/3
2
2
2
2/3
W
6
2/3
2/3
2
2
2
2/3
X
2A6
0
2/3
2
0
1
2
X
0
I
X
0
3
Y
4
4/5
4/5
4
9
5
Y
4
4/5
4
o
5
Y
4
4/5
4
5
Z
2A4
2A4
2/3
2
2
2A4
2A4
Z
2A4
2A4
2/3
2
2
2A4
2A4
Z
2A4
2A4
2/3
2
2
2A4
2A4
AA
2A4
AA
4
4
4/5
4
2A4
AA
4
4
4/5
4
2A4
BB
2A4
9
2
2
2A4
BB
2A4
2
9
3
2A4
BB
2A4
4
-|
9
3
2A4
CC
2
6/11
2/3
2
2
CC
8
8
2/3
2
1
2A4
CC
8
8
2/3
2
1
2
DD
2
2/3
2/3
2
3
2
2A
DD
2
2/3
2/3
2
2
2
2A4
DD
2
2/3
2/3
2
2
2
2
EE
4
4/5
5
4
4
EE
4
4/5
5
2
4
EE
4
2/3
2
2
2
FF
2
2/3
2/3
3
2
2
2
FF
2
8
2/3
3
2
2
FF
2
8
2/3
3
2
2
2-19
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other) are separated by a " ". Since the response to X can indeed be different depending on
whether X is increasing or decreasing, these cells do not represent a conflict but rather the
opportunity to consider agreement in both the "X-up" and "X-down" direction. In these cases it
was possible to have agreement in one direction but not the other.
The columns in Table 2-6 represent individual influences (arrows) in the group influence
diagram, and rows represent individual respondents. Dark green shaded columns indicate
agreement on both type and degree of influence; light green shaded columns indicate agreement
on type but not degree; gray shaded columns indicate no agreement. Within columns, numbers
in green are those that fall into the same (majority) grouping in terms of type of influence (even
though degree is different), while codes in pink indicate disagreement about type. Columns
outlined in red indicate threshold influences where there was either: (1) a change in type of
influence in the climate scenarios compared to current conditions (e.g., from a direct to an
inverse relationship), (2) a change in sensitivity (e.g., a change from a proportional to
disproportional response, or (3) an indication by multiple participants in their notes or in the
group discussions that the influence was likely a threshold relationship (although they did not
always know exactly which scenario in which this would occur). In these cases the type and/or
degree of influence for the relationship would depend on a threshold, the exact location of which
is uncertain.
There were 32 influences in total. Under current conditions, there was agreement on both
type and degree of influence for 62% of the influences, agreement on type but not degree for
22% and no agreement for 16%. Under Climate Scenario A, this shifted to 53% of influences
with agreement on both type and degree, 28% with agreement on type but not degree and 19%
with no agreement. Under Climate Scenario B, influences with agreement continued to decline,
with 40.5% for which there was agreement on both type and degree, 40.5% with agreement on
type but not degree and 19% with no agreement.
2.3.1.2.2. Thresholds
Relationship E (Nutrient Inputs on Below Ground Biomass) and Relationship EE (Net
Accretion on Sediment Deposition/Retention) were identified to be threshold relationships under
the climate scenarios. In both of these cases the type of influence changed across the scenarios,
with Relationship E changing from direct to inverse under Climate Scenario A, and Relationship
EE changing from inverse proportional to direct proportional under Climate Scenario B. The
sensitivity for both of these influences did not change across the scenarios.
The threshold of Relationship E is related to the vegetative response to nutrient inputs.
An increase in nutrients can increase net below ground peat because it spurs above ground
productivity, a portion of which adds to below ground peat. At the same time, nutrients decrease
2-20
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below ground production and increase decomposition. In the long term, the below ground
effects of nutrients could outweigh the above ground ones and cause the relationship to change
from direct to inverse.
The threshold of Relationship EE is related to the response of sediment deposition and
retention to net accretion. A threshold could occur where a given location is under a different
inundation regime due to sea level rise, and thus exposed to different tidal velocities and a
different deposition regime. Where the marsh is shallow enough, an increase in accretion would
decrease net sediment deposition because the water would have already been slowed during
inundation and dropped its sediment load. However with a sufficient increase in sea level under
climate change, the marsh could now be at a depth where the water would arrive at higher
velocities during inundation, still carrying a high sediment load, such that now an increase in
accretion would cause water to slow and increase deposition.
Relationship Z (Inundation Regime on Sediment Deposition/Retention) and Relationship
BB (Inundation Regime on Below Ground Biomass) were identified to be threshold relationships
under current conditions and the climate scenarios. Here the type and sensitivity of the
influences did not change across the scenarios; there was no agreement on type or degree of
influence in both cases, but this was because the codes were a mixture of direct proportional and
inverse proportional, with some participants indicating both codes at once. It emerged through
participant discussions that these are threshold relationships for which it is unclear exactly when
the tipping points would occur (hence the inability to identify them as a change across scenarios).
For both of these influences, the threshold was indicated to be where too much inundation leads
from a direct (positive) relationship to a tipping point (inverse relationship) with the response
variable. In the case of Relationship Z, an increase in inundation would initially increase
transport and deposition of sediment, but at some point too great an increase in inundation could
lead to such an increase in erosion as to cause a net decrease in deposition and retention.
Similarly for Relationship BB, while increased inundation initially supports productivity of
below ground biomass, too great an increase in inundation would lead to low levels of oxygen
and "smothering" of below ground biomass.
Relationship AA (Marsh Edge Erosion on Sediment Deposition/Retention) was identified
as a threshold relationship under the climate scenarios in discussions. The type and sensitivity of
the influence did not change across the scenarios (the relationship had no agreement under
current conditions, and was identified as inverse proportional under the climate scenarios).
However, it was identified in the later group discussion as an important potential threshold due to
the sensitivity of marsh edge erosion to future increases in storm intensity (with a strong seasonal
component), especially given sea level rise. The greater influence of storms under the climate
scenarios would lead to increasing marsh edge erosion. The resulting effect on sediment
2-21
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deposition and retention would depend on where the sediment was transported—it could either
be carried onto the marsh for potential redeposition or lost from the system. The majority of the
participants judged that under the climate scenarios, the sediment is more likely to be lost from
the system due to the combined effects of sea level rise and changes in inundation and flow
regimes. This will serve to greatly increase the inverse effect of marsh edge erosion on sediment
deposition and retention. A threshold will occur when erosion losses from the marsh edge
exceed the ability of the marsh to capture and retain enough sediment such that accretion no
longer sufficiently counteracts losses and the marsh eventually collapses.
One possible reason why some thresholds identified in discussions did not show up in the
coding as changes in sensitivity is because participants did not know where the threshold would
occur, so they did not want to attach the shift to a particular climate scenario. Alternatively, it
may be that there is a threshold that represents a state change that falls within the range of natural
variability, so this method was not sensitive enough to identify the threshold.
2.3.1.3. Influence Sensitivity
Figure 2-4 shows the sensitivity results using the influence diagram, indicating where
there is agreement under current conditions. The typology described in Section 2.2.2.5 was used
to code sensitivity, with an additional differentiation within the "no agreement" category. In all
"no agreement" cases, there was a mixture of codes for intermediate sensitivity along with low
and/or high sensitivity; if at least four participants provided judgments, and there were more high
sensitivity judgments than low sensitivity judgments, then the dashed arrow was colored orange
to indicate intermediate-to-high sensitivity. Under current conditions, 23 influences with
agreement were categorized as intermediate sensitivity. Relationship M (Coastal and Nearshore
Erosion on Tidal Exchange) was the only influence categorized as "low sensitivity". For
Relationship J (Marsh High Water Level on Coastal and Nearshore Erosion), there was
agreement that there is high sensitivity when marsh high water level is increasing and
intermediate sensitivity when marsh high water level is decreasing. There was no agreement on
sensitivity for six influences. Relationship G (Altered Flows: Tidal Restrictions on Freshwater
Flow) was categorized as having no influence.
Figure 2-5 compares the sensitivities as in Figure 2-4, across the three scenarios. Under
Climate Scenario A, Relationship J (Marsh High Water Level on Coastal and Nearshore Erosion)
continued to show agreement on high sensitivity when marsh high water level is increasing,
while agreement on sensitivity was lost when marsh high water level is decreasing.
Relationship H Land Cover: Percent Impervious Cover on Freshwater Flow) showed a trend
2-22
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Land Cover:
% Impervious Cover
Altered Flows:
Tidal
Restrictions
Marsh High
Water Level
Freshwater Row
SedimentSupply
Coastal and
Nearshore
Erosion
Marsh Edge
Erosion
Surface
Roughness
Sediment
Deposition /
Retention
Inundation
Regime
Below Ground
Biomass
Low sensitivity
Intermediate sensitivity
High sensitivity
Noagreement
Noinfluence
Figure 2-4. Sediment Retention group summary influence diagram of
sensitivities under current conditions.
2-23
-------
Current
Scenario A
Scenario B
Key
Low sensitivity
Intermediate sensitivity
Highsensitivity
Intermediate-to-high trend
No agreement
Figure 2-5. Sediment Retention group summary influence diagrams of
sensitivities: variance across current conditions and two climate scenarios.
from intermediate (to intermediate-to-high sensitivity (orange arrow). Most of the other
influences that were previously characterized as intermediate sensitivity remained the same, with
the exception of: Relationship P (Freshwater Flow on Sediment Supply) and Relationship CC
(Below Ground Biomass on Sediment Deposition/Retention), for which there no longer was
agreement. There was no agreement on sensitivity under the climate scenarios for
Relationship Q (Coastal and Nearshore Erosion on Sediment Supply), which had low sensitivity
under current conditions.
Under Climate Scenario B, four additional intermediate sensitivity influences dropped
below the standard of agreement: Relationships C, I, and K (Storms on Inundation Regime, on
Freshwater Flow, and on Coastal and Nearshore Erosion), and Relationship Q (Coastal and
Nearshore Erosion on Sediment Supply). However, in the case of Relationships C, I, and K, the
lack of agreement was due to a subset of participants indicating a change toward increasing
sensitivity (orange arrows). Thus, these influences (along with Relationship J, which remained
the same as in Climate Scenario A) are considered intermediate-to-high in sensitivity.
2-24
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One reason for lack of agreement on changes in sensitivity across scenarios, as well as
lack of agreement within scenarios, may have been the degree of variability among participants
in their judgements. Overall, there was more variability among participants than across
scenarios for any given participant. There were no patterns across participants, such as
characterizing only increasing sensitivity. Further description, as well as figures depicting
variability in judgments across participants, can be found in Appendix B.
2.3.1.4. Relative Impact
Figure 2-6 presents the characterization of relative impacts for current conditions while
Figure 2-7 compares the relative impacts across all three scenarios. Under current conditions, a
total of 24 influences were identified as having high relative impact. The Sediment Retention
group distinguished relative impact of the influences by indicating primary and secondary
degrees of impact. Primary impact was indicated for 14 influences, while secondary impact was
indicated for 10 influences. Influences of primary impact at the top of the diagram (which are
associated with management options) include Relationships B and J (Marsh High Water Level
on Inundation Regime and on Coastal and Nearshore Erosion), Relationship E (Nutrient Inputs
on Below Ground Biomass), and Relationship F (Altered Flows: Tidal Restrictions on Tidal
Exchange).
Under both Climate Scenarios, the influence of Relationship B (Marsh High Water Level
on Inundation Regime) was identified as having increasing impact. Relationship E (Nutrient
Inputs on Below Ground Biomass) and Relationship V (Surface Roughness on Sediment
Deposition/Retention) were identified as having increasing impact under Climate Scenario B.
Relationship CC (Below Ground Biomass on Sediment Deposition/Retention) increased from
secondary impact under current conditions to primary impact under Climate Scenario A, yet
decreased back to secondary impact under Climate Scenario B.
2.3.1.5. Confidence
The confidence results shown in Table 2-7 are provided for the Sediment Retention
influences for which there was agreement on type. The lack of agreement on confidence for
almost half of the judgments is a significant gap, limiting our ability to prioritize around
confidence judgments. All of the 12 influences for which there was agreement on confidence
across all three scenarios were scored as high evidence and high agreement (HH). Relationship
G (Altered Flows: Tidal Restrictions on Freshwater Flow), Relationship Y (Sediment
2-25
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Land Cover:
% Impervious Cover
Sediment
Deposition /
Retention
i Primaryimpact
Secondaryimpact
. Very little impact
Figure 2-6. Sediment Retention influences indicated as having high relative
impact under current conditions.
2-26
-------
Current
Scenario A
Scenario B
Increased impact under climate scenarios
Primaryimpact
Secondaryimpact
_- Very little impact
Figure 2-7. Sediment Retention influences indicated as having high relative
impact: variance across current conditions and two climate scenarios.
2-27
-------
to
to
oo
Table 2-7. Sediment Retention group confidence for influences with agreement
Current
Scenario A
Scenario B
A
HH
HH
HH
B
HH
HH
HH
C
HH
HH
HH
D
NA
NA
NA
E
NA
NA
NA
F
HH
HH
HH
G
HH
NA
NA
H
HH
HH
HH
I
HH
HH
HH
J
NA
HH
HH
K
HH
HH
HH
L
NA
HH
HH
M
NA
NA
NA
N
NA
NA
NA
0
HH
HH
NA
P
HH
HH
HH
Q
NA
NA
NA
R
HH
HH
HH
T
NA
NA
NA
V
HH
HH
HH
W
HH
HH
HH
X
NA
NA
NA
Y
HH
NA
NA
AA
NA
NA
NA
CC
NA
NA
NA
DD
HH
HH
HH
EE
NA
NA
NA
FF
HH
NA
NA
NA = no agreement; HH = high evidence, high agreement; HL = high evidence, low agreement; LH = low evidence, high agreement; LL = low evidence, low
agreement.
-------
Deposition/Retention on Inundation Regime), and Relationship FF (Below Ground Biomass on
Net Accretion), which were categorized as HH under current conditions, showed declining
agreement on confidence under the climate scenarios, with no agreement under Climate
Scenarios A and B. Relationship J (Marsh High Water Level on Coastal and Nearshore
Erosion), as well as Relationship L (Storms on Marsh Edge Erosion), for which there was no
agreement under current conditions, showed increasing agreement under the climate scenarios,
with a score of HH under Climate Scenario A and Climate Scenario B. An overall decrease in
the total number of HH judgments from current conditions to the climate scenarios and a
corresponding increase in the total number of LL judgments show that influences become less
well-understood due to less information being available about future climate conditions.
2.3.1.6. Interacting Influences
Table 2-8 presents the interactive influences upon which there was agreement for the
Sediment Retention group. The interactive influence columns indicate the type of interactive
influence and associated number of participants that chose that particular interactive influence
type. The confidence columns indicate the confidence judgment and associated number of
participants that chose that particular confidence score.
Under current conditions, there were two interactive influences for which there was
agreement among participants in the Sediment Retention group. For both of these interactive
influences, Synergy was the type of influence chosen. These interactions included
Relationship B with C (Marsh High Water Level on Inundation Regime with Storms), and
Relationship V with W (Surface Roughness on Sediment Deposition/Retention with Sediment
Supply). There was only agreement on the confidence for the interactive influence of
Relationship B with C, which was scored HH.
Under both Climate Scenario A and Climate Scenario B, there was one of the previous
two synergistic interactive influences for which there was agreement on synergy as the type of
interactive influence (Relationship B with C). This interactive influence remained as HH under
the climate scenarios. There were two new interactive influences for which there was agreement
under the climate scenarios, both of which were scored as Synergy. These interactions included
Relationship H with I (Land Cover: Percent Impervious Cover on Freshwater Flow with Storms),
and Relationship W with V (Sediment Supply on Sediment Deposition/Retention with Surface
Roughness). Confidence on both of these interactive influences was scored as HH under Climate
Scenario A, though there was no agreement on confidence under Climate Scenario B for
Relationship H with I.
2-29
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Table 2-8. Sediment Retention group interactive influences with agreement under current conditions and
Climate Scenarios A and B
Interaction
B+C
H+I
v+w
w+v
Variable X
Marsh High
Water Level
Land Cover:
Percent
Impervious
Cover
Surface
Roughness
Sediment
Supply
on
on
on
on
on
Variable Y
Inundation
Regime
Freshwater
Flow
Sediment
Deposition/
Retention
Sediment
Deposition/
Retention
with
with
with
with
with
Variable Z
Storms
Storms
Sediment
Supply
Surface
Roughness
Current
Interactive
influence
Synergy (4)
NA
Synergy (3)
NA
Confidence
HH
NA
NA
NA
Climate A
Interactive
influence
Synergy (6)
Synergy (3)
NA
Synergy (3)
Confidence
HH
HH
NA
HH
Climate B
Interactive
influence
Synergy (6)
Synergy (3)
NA
Synergy (3)
Confidence
HH
NA
NA
HH
to
OJ
o
NA = no agreement; HH = high evidence, high agreement; HL = high evidence, low agreement; LH = low evidence, high agreement; LL = low
evidence, low agreement; () = number of respondents.
-------
There was no agreement on type of interactive influence under the climate scenarios for
the interaction of Relationship V with W (Surface Roughness on Sediment Deposition/Retention
with Sediment Supply), which was identified as Synergy under current conditions. Meanwhile,
the Relationship W with V (Sediment Supply on Sediment Deposition/Retention with Surface
Roughness) was identified as Synergy under the climate scenarios. The change from "V with
W" to "W with V" distinguishes between the effect of Surface Roughness on the endpoint
increasing with an increase in Sediment Supply, and the effect of Sediment Supply on the
endpoint increasing with an increase in Surface Roughness. It is unclear whether participants
intended to highlight this difference, or if there was confusion about the definition during the
exercise. Both interactions may be important, but there may not have been time to explore
interacting influence pairs separately across scenarios.
One additional interaction, Relationship J with K (Marsh High Water Level on Coastal
and Nearshore Erosion with Storms), was only identified by two participants (as a Synergy) in
the coding. However, this same interplay was brought up during group discussions as an
interaction of potential importance under climate change, implying that further investigation into
the relationship of these influences may be warranted.
The limited number of interacting influences for which there was agreement was
primarily due to not having many influences with enough participants characterizing the same
interacting influences. Of the 48 combinations of influences with interactions characterized by
participants, only nine could be considered for agreement with at least three participants making
a judgment; less than half of those had three participants in agreement.
2.3.2. Community Interactions
2.3.2.1. Group Influence Diagram
Figure 2-8 shows the group diagram developed by the Community Interactions group.
Variable definitions that were developed by the participants during the construction of the
diagram are found in Table 2-9. Three variables directly influence the endpoint of Saltmarsh
Sharp-Tailed Sparrow Nesting Habitat: the Ratio of Low Marsh (Spartina alterniflora) to High
Marsh (Spartina patens) species, Inundation Regime, and Marsh Elevation. The Ratio of Native
High Marsh to invasive Phragmites is a key factor influencing the endpoint through the Ratio of
Low Marsh to High Marsh as well as Marsh Elevation. The middle level in the diagram includes
Salinity, Sedimentation, Nitrogen, Above Ground Plant Biomass, and Below Ground Plant
Biomass. These variables and the ones that directly influence the endpoint are all highly
interconnected, with Inundation Regime, Above Ground Biomass and both ratio variables
serving as hubs.
2-31
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Land Use / Land Cover:
Tidal
Restrictions
Freshwater Flow
Residential
Development
Soil
Temperature
Inundation
Regime
Above Ground
Plant Biomass
Below Ground
Plant Biomass
Ratio of Native High
Marsh to Phragmites
Ratio Low Marsh to
High Marsh
Saltmarsh Sharp
Tailed Sparrow
Nesting Habitat
Figure 2-8. Community Interactions group influence diagram.
2-32
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Table 2-9. Community Interactions variable definitions
Variable
Open Marsh Water Management
(OMWM)
Sea Level
Freshwater Flow
Land Use / Land Cover:
Residential Development
Soil Temperature
Tidal Restrictions
Inundation Regime
Sedimentation
Nitrogen
Above Ground Plant Biomass
Salinity
Below Ground Plant Biomass
Ratio of Native High Marsh to
Phragmites
Marsh Elevation
Ratio Low Marsh to High Marsh
Saltmarsh Sharp-Tailed Sparrow
Nesting Habitat
Definition agreed upon by group
Acreage of projects creating and connecting ponds and pools
Water height (mm) at mean lower low water
[1] cfs at gauging stations on Ipswich and Parker Rivers, trends over time
[EPA] Rate of freshwater inflow to the estuary from the watershed
[1] (relative area of upland cleared *0.5) + (relative area of impervious surface)
[2] Percent border developed and proximity (km) from sensitive habitats (i.e.,
marsh)
[3] Percent watershed developed (all human made structures and landscapes)
[4] Percent residential (among others)
[5] Lawn/asphalt in shoreland zone
Soil temperature in °C or °F
Any restriction to tidal inundation into the marshes (e.g., road crossings or any
other barrier to inflow)
Percent time high marsh under water during April-October
Average concentration of suspended sediment in the water column (mg/L)
[1] Unit N/unit area/year (g N/m2/yr)
[2] Total inorganic Nitrogen inputs from uplands
[3] kg/ha/yr to Plum Island Sound measured from permanent Long Term
Ecological Research Network sampling stations
[1] Biomass accumulation rate
[EPA] Total mass of plant material
Soil salinity (ppt)
Percent organic matter
Percent extent (m) of high marsh vegetation to Phragmites cover
Height above mean lower low water
[1] Percent extent (m) of low marsh vegetation to high marsh vegetation
[2] Percent cover, species composition/abundance
Percent extent of habitat as proportion of total marsh extent, or total area (m2)
available as habitat
The management and stressor variables include: Tidal Restrictions, Open Marsh Water
Management (OMWM), Freshwater Flow, and Land Use/Land Cover: Residential Development.
OMWM is a mosquito control technique that involves ponding and ditching marshes in order to
restore hydrologic conditions to improve fish habitat and thus increase mosquito predation.
Removing tidal restrictions, by increasing the size or lowering the opening in the crossing has
been one of MBP's management options that restores the inundation regime of the upstream
2-33
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marsh and improves freshwater flow through the restriction to the benefit of the downstream
marsh. Soil Temperature and Sea Level are intermediate type variables that could be considered
both stressor variables and system variables and are less clearly connected to management-
related variables.
2.3.2.2. Influence Types and Degrees
2.3.2.2.1. Agreement
Table 2-10 presents the results for the Community Interactions group. As in Table 2-6,
the columns in Table 2-10 represent individual influences (arrows) in the group influence
diagram, and rows represent individual respondents. Dark green shaded columns indicate
agreement on both type and degree of influence; light green shaded columns indicate agreement
on type but not degree; gray shaded columns indicate no agreement. Within columns, numbers
in green are those that fall into the same (majority) grouping in terms of type of influence (even
though degree is different), while codes in pink indicate disagreement about type. For further
explanation of table details, see Section 2.3.1.2.
There were 32 influences in total. The participants agreed on the type and degree of
influence for slightly fewer of the total number of influences than the Sediment Retention group
did. Under current conditions, there was agreement on both type and degree for 56% of the
influences, agreement on type but not degree for 25% and no agreement for 19%. Under Climate
Scenario A, the number of influences with agreement on both type and degree dropped 47%, the
number with agreement on type but not degree remained at 25% and the number with no
agreement rose to 28%. Under Climate Scenario B, the number of influences for which there
was agreement on both type and degree was 41%, those with agreement on type but not degree
was 31% and those with no agreement was 28%.
Compared to the results for the Sediment Retention group, the larger number of
influences for which there was no agreement under all scenarios leaves more of a gap in
understanding of the type or degree of influence for these relationships. It is difficult to
differentiate between lack of response due to insufficient time and disinclination to answer due to
lack of knowledge about the influence, however occasionally participants noted if a particular
influence was not within their realm of expertise.
2-34
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Table 2-10. Community Interactions group influence judgments. Columns
A-FF represent individual influences (arrows) in the influence diagram and rows
represent individual respondents: dark green = agreement on influence type and
degree, light green = agreement on type but not degree, gray = no agreement;
within columns, green numbers = same (majority) grouping of type (though
degree may be different), pink numbers = disagreement about type, red
outline = threshold response
Current
Resp. 1
Resp. 2
Resp. 3
Resp. 4
Resp. 5
Resp. 6
Resp. 7
Climate A
Resp. 1
Resp. 2
Resp. 3
Resp. 4
Resp. 5
Resp. 6
Resp. 7
Climate B
Resp. 1
Resp. 2
Resp. 3
Resp. 4
Resp. 5
Resp. 6
Resp. 7
A
6
2
2|5
2
2/3
2
A
6
2A6
9
2/3
2
A
6
8
9
2/3
8
B
2/3
2
2
2
2
2
B
2/3
6
2
2A6
2
2
7(11
B
2/3
8
2
2A6
2
2
7|11
C
4
9
9
4
4/5
4
4/5
C
4
9
9
4
4/5
4
4/5
C
4
9
9
4
4/5
4
4/5
D
2/3
8
8
8
2
8|
D
2/3
8
8
A8
2
8|
D
2/3
8
8
8
2
8|
E
2/3
2
2
2
2/3
2
E
2/3
2
2
2
2/3
4
2
E
2/3
2
2
2A6
2/3
2
F
6
8
2
8
1
2/3
F
6
8
6
8
1
2/3
F
6
8
6
8
1
2/3
G
6
9
4
8
4/5
4
2/3
G
6
9
7
8
4/5
4
2/3
G
6
9
7
2
4/5
4
2/3
H
6/11
>j
9
4/5
H
6/11
4/5
H
7
4/5
I
7|3
7
4
1
1
4/5
I
3|7
4/5
I
7
4/5
J
J
2/3
^^^^
J
6A9
4
7
7
4
4
K
2/3
2
2
2
2/3
K
2/3
2
2
2
2A4
2/3
K
2/3
L
2/3
2
2
2
2/3
2/3
L
2/3
2
2
2
2
2/3
L
2/3
2
2A8
2A8
2/3
M
2/3
6
2
2
2/3
2
2/3
M
2/3
6
6
2
2/3
2
2/3
M
2/3
6
6
2
2/3
2
2/3
N
Q
8A9
2
2A4
-j /'i
2/3
N
N
9
O
2/3
8
2
2
2/3
2
2/3
O
2/3
2
2
2
2/3
2
2/3
O
2/3
2
6
2A
2/3
2
2/3
P
7
9
4
7
4/5
4
7
P
7
4
4
7
4/5
4
7
P
7
4
4
7
4/5
4
7
2-35
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Table 2-10. Community Interactions group influence judgments. Columns
A-FF represent individual influences (arrows) in the influence diagram and rows
represent individual respondents: dark green = agreement on influence type and
degree, light green = agreement on type but not degree, gray = no agreement;
within columns, green numbers = same (majority) grouping of type (though
degree may be different), pink numbers = disagreement about type, red
outline = threshold response (continued)
Current
Resp. 1
Resp. 2
Resp. 3
Resp. 4
Resp. 5
Resp. 6
Resp. 7
Climate A
Resp. 1
Resp. 2
Resp. 3
Resp. 4
Resp. 5
Resp. 6
Resp. 7
Climate B
Resp. 1
Resp. 2
Resp. 3
Resp. 4
Resp. 5
Resp. 6
Resp. 7
Q
2
2
2
2
2/3
2
2/3
Q
2
2
8
2
2/3
2
2/3
Q
2
2
8
2
2/3
2|5
2/3
R
2/3
2
2
2/3
R
2/3
2
8
2
2/3
R
2/3
2
2
2/3
S
4/5
4
4
4/5
4
S
4/5
4
4
4A9
4/5
4
S
4/5
4
7
4A9
4/5
4
2/3
T
7
8
2A4
4
4/5
4/5
T
7
2A4
9
4/5
4/5
T
7
8
2A4
9
4/5
4/5
U
2
2
2
8
2/3
2
2/3
U
2
2
2
8
2/3
2
2/3
U
2
2A8
2/3
2
2/3
V
2
2
3
2/3
2
2/3
V
2
2J12
8
3
2/3
2
2/3
V
6
1
8
3A11
2/3
2
2/3
W
0
2|12
0
0A4
W
1
W
0
1
X
0
X
0
X
0
Y
10
4
4
4
5
Y
10
4
4
5
Y
10
4
4
5
Z
6/11
8
2
8
8
2/3
Z
6/11
8
2
0A8
8
2/3
Z
6/11
8
8
1
4
2/3
AA
11
3
2
8
2/3
6
7|11
AA
11
2/3
8
2/3
6
7|11
AA
11
2/3
8
2/3
2
11
BB
2/3
3
4
7
4/5
12
4/5
BB
1/3
BB
2/3
4/5
CC
2/3
8
2/3
2/3
2
2/3
CC
2/3
8
8
8
2/3
2
2/3
CC
2/3
8
8
8
2/3
2
2/3
DD
5
2|5
?AJ
4/5
2|5
7
DD
2|5
DD
EE
4
12
12
12
12
5
EE
4
12
12
2/3
4
4
EE
4
12
12
2/3
4
4
FF
FF
4
4/5
FF
4
4/5
2-36
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2.3.2.2.2. Thresholds
Two relationships were identified as threshold relationships under the climate scenarios,
based on the coding scheme, notes and discussions. These were: Relationship J (Inundation
Regime on Above Ground Plant Biomass) and Relationship EE (Ratio of Native High Marsh to
Phragmites on Marsh Elevation). There was no agreement on type or degree of influence for
Relationship J under current conditions and Climate Scenario A; however, this was due to the
participants recording a mixture of direct and inverse codes and accompanying notes indicating
agreement that a threshold response would be expected at some point that is not currently
possible to pinpoint. There was agreement on type (inverse) under Climate Scenario B, as a
majority of participants agreed that by now the threshold would have likely been passed. The
nature of the threshold relationship involves a tipping point in which inundation regime (percent
time that high marsh is under water during April-October) at first has a positive effect on above
ground plant biomass, but with a sufficient increase would trigger an abrupt decrease in above
ground biomass. According to Morris et al. (2002), inundation of sufficient duration is
beneficial in that it prevents soil salinity from reaching levels that inhibit growth. However, with
sea level rise, inundation frequency and duration is expected to reach levels that cause increased
hypoxia and result in marsh die-back (i.e., marsh drowning).
Relationship EE was identified as a threshold relationship because of changes in
sensitivity under the climate scenarios. The type of sensitivity for this influence changed from
low sensitivity under current conditions to intermediate sensitivity under the climate scenarios.
Under current conditions, Relationship EE was identified as an inverse disproportional weak
influence; a decrease in the Ratio of Native High Marsh to Phragmites would lead to a modest
increase in marsh elevation because Phragmites is more effective at trapping sediment. Under
the climate scenarios, Relationship EE was identified as an inverse influence, with no agreement
on degree (due to a mixture of codes moving from an inverse weak relationship toward a more
proportional one); here, rising sea levels were identified as the cause of the increasing sensitivity,
as Phragmites would be better equipped to migrate landward to higher elevations while
continuing to more effectively trap sediment in place.
One additional influence, Relationship P (Inundation Regime on Saltmarsh Sharp-Tailed
Sparrow Nesting Habitat) was not coded in the exercise as a threshold occurring across the
climate scenarios, but was discussed as a unique category of threshold that operates on a shorter
time scale. This is an influence that can change dramatically with only slight changes in
conditions. Availability of Saltmarsh Sharp-Tailed Sparrow nesting habitat is highly dependent
on the timing and amount of inundation, even under current conditions, where nesting habitat can
be abruptly flooded out if an even slightly amplified inundation event coincides with the critical
2-37
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nesting period. This phenomenon will become increasingly important as increases in sea level
and other factors lead to increased frequency of such flooding events in the future.
2.3.2.3. Influence Sensitivity
Figure 2-9 shows the sensitivity results using the influence diagram, indicating where
there is agreement under current conditions. The typology described in Section 2.2.2.5 was used
to code sensitivity, with an additional differentiation within the "no agreement" category. In all
"no agreement" cases, there was a mixture of codes for intermediate sensitivity along with low
and/or high sensitivity; if at least four participants provided judgments, and there were more high
sensitivity judgments than low sensitivity judgments, then the dashed arrow was colored orange
to indicate intermediate-to-high sensitivity. Under current conditions, 21 influences with
agreement were categorized as intermediate sensitivity. Two influences were categorized as low
sensitivity: Relationship D (Freshwater Flow on Inundation Regime), and Relationship EE (Ratio
of Native High Marsh to Phragmites on Marsh Elevation). There was no agreement on
sensitivity for nine influences; however, five of these influences are indicated in orange due to a
combination of intermediate and high sensitivity codes. There were no instances of agreement
on influences with high sensitivity.
Figure 2-10 compares the sensitivities as in Figure 2-9, across the three scenarios. Under
Climate Scenario A, 20 influences with agreement were categorized as intermediate sensitivity.
One influence changed from low sensitivity under current conditions to intermediate sensitivity
under the climate scenarios (Relationship EE). Relationship D was the only influence
categorized as low sensitivity. No influences were categorized as high sensitivity. The number
of influences with no agreement increased to 11; however, three of these are indicated in orange
due to a combination of intermediate and high categorizations of sensitivity. Such decreases in
agreement highlight a trend of increasing sensitivity for some participants, but not enough to
shift to agreement on a new category. It could be indicative of either disagreement about at what
point such a shift would occur or of differing assumptions about what falls outside the current
range of variability, which was left up to each participant to decide based on their own
knowledge and intuition.
Under Climate Scenario B, 17 influences with agreement were categorized as
intermediate sensitivity. As with Climate Scenario A, only one influence was categorized as low
sensitivity. The number of influences with no agreement increased further, to 14; however, four
of those are indicated as orange due to a combination of intermediate and high sensitivity. No
influences were categorized as high sensitivity.
2-38
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OMWM
Tidal
Restrictions
_•
DD
^
C
r X**^
Freshwater Flow
E
Land Use /Land Cover:
Residential
Development
^/D
/ J
G
Soil
Temperature
Inundation
Regime
Above Ground
Plant Biomass
Below Ground
Plant Biomass
Ratio of Native High
Marsh to Phragmit
Marsh
Elevation
Ratio Low Marsh to
High Marsh
SaltmarshSharp-
Tailed Sparrow
Nesting Habitat
Key
Low sensitivity
Intermediate sensitivity
High sensitivity
Intermediate-to-high trend
No agreement
Figure 2-9. Community Interactions group summary influence diagram of
sensitivities under current conditions.
2-39
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Current
Scenario A
Scenario B
Low sensitivity
Intermediate sensitivity
High sensitivity
Intermediate-to-high trend
No agreement
Figure 2-10. Community Interactions group summary influence diagrams of
sensitivities: variance across current conditions and two climate scenarios.
One reason for lack of agreement on changes in sensitivity across scenarios, as well as
lack of agreement within scenarios, may have been the degree of variability among participants
in their judgements. Overall, there was more variability among participants than across
scenarios for any given participant. The majority of changes in sensitivity type across the
climate scenarios are of increasing sensitivity. Further description, as well as figures depicting
variability in judgments across participants, can be found in Appendix B.
2.3.2.4. Relative Impact
Figure 2-11 presents the characterization of relative impact between current and future
climate scenarios (the group's discussion did not differentiate between the two future climate
scenarios). This group distinguished among the influences by indicating primary impact,
interactive influences with high relative impact, and influences that had some agreement but no
consensus on relative impact. Under current conditions, nine influences were indicated as
having high relative impact, based on the discussion. Influences of primary impact at the top of
the diagram (which are associated with management options) include Relationship A (OMWM
2-40
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Current
Future
Key
Primary impact
Interactive influence of high impact
Some agreement on impact, but no consensus
Interactive influence impact increased under climate scenarios
Influence impact increased under climate scenarios
Figure 2-11. Community Interactions influences indicated as having high
relative impact under current conditions and the climate scenarios.
on Inundation Regime), Relationship C (Freshwater Flow on Salinity), Relationship E (Land
Use/Land Cover: Residential Development on Freshwater Flow), and Relationship DD (Tidal
Restrictions on Inundation Regime). Two influences were indicated as having some agreement
on high relative impact: Relationship X (the Ratio of Native High Marsh to Phragmites on the
Ratio of Low Marsh to High Marsh), as well as Relationship BB (the Ratio of Low Marsh to
High Marsh on Saltmarsh Sharp-Tailed Sparrow Nesting Habitat). Two pairs of interactive
influences were indicated as having high relative impact: Relationship O with R (Inundation
Regime on the Ratio of Low Marsh to High Marsh with Nitrogen), as well as Relationship S with
V (Nitrogen on the Ratio of Native High Marsh to Phragmites with Salinity).
Under the climate scenarios (see Figure 2-11) it is assumed that the same relationships
are still of high impact, and only additions or changes in relative impact are shown in the second
panel. Three influences were indicated as increasing in relative impact under climate change
conditions for the Community Interactions group: Relationship L (Inundation Regime on
2-41
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Sedimentation), Relationship O (Inundation Regime on the Ratio of Low Marsh to High Marsh),
and Relationship V (Salinity on the Ratio of Native High Marsh to Phragmites). The interactive
influence of Relationship H with J (Soil Temperature on Above Ground Plant Biomass with
Inundation Regime) was indicated as having increasing relative impact under the climate
scenarios.
2.3.2.5. Confidence
The confidence results shown in Table 2-11 are provided for the Community Interactions
influences for which there was agreement on type. The lack of agreement on confidence for
almost half of the judgments is a major gap, limiting our ability to prioritize around confidence
judgments. Three of the four influences for which there was agreement on confidence across all
scenarios were scored as HH. Relationship AA (Marsh Elevation on Saltmarsh Sharp-Tailed
Sparrow Nesting Habitat) was scored as LH across all scenarios. The HH type of confidence
was the most common judgment. The dominant pattern on confidence across the climate
scenarios was a decrease in the number of influences on which there was agreement. An overall
decrease in the total number of HH judgments from current conditions to the climate scenarios
and a corresponding increase in the total number of LL judgments show that influences become
less well-understood due to less information being available about future climate conditions.
2.3.2.6. Interacting Influences
Under all scenarios, the interaction of Relationship A with B (OMWM on Inundation
Regime with Sea Level) was the only interactive influence with agreement among participants.
Synergy was the type of influence chosen; this means that the effect of open marsh water
management (which creates and connects ponds and pools) on inundation regime is intensified
with sea level rise. There was no agreement on the confidence for this interactive influence.
The lack of agreement on any other potential interacting influences was primarily due to
not having many instances of enough participants characterizing the same interactions. Of the
25 combinations of influences with interactions characterized, only two could be considered for
agreement with at least three participants making a judgment; only one of those had three
participants in agreement. One of the interactions that was only identified by one participant in
the coding, Relationship H with J (Soil Temperature with Inundation Regime on Above Ground
Plant Biomass, Competition) was brought up in the group discussion as an interplay of
increasing relative impact under climate change (see Figure 2-9), indicating that further
investigation of this interaction may be desirable.
2-42
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Table 2-11. Community Interactions group confidence for influences with agreement
Current
Scenario A
Scenario B
A
NA
NA
NA
B
HH
NA
NA
C
HH
HH
HH
D
NA
NA
NA
E
HH
HH
HH
F
NA
NA
NA
G
NA
NA
NA
I
NA
NA
NA
J
NA
NA
NA
K
HH
NA
NA
L
HH
HH
NA
M
HH
HH
HH
O
HH
NA
LH
P
HH
NA
NA
0
HH
HH
NA
R
NA
NA
NA
S
NA
NA
NA
T
NA
NA
NA
U
HH
HH
NA
V
HH
NA
NA
Y
HH
NA
NA
Z
NA
NA
NA
AA
LH
LH
LH
BB
LH
NA
NA
CC
HH
NA
NA
DD
HH
NA
NA
EE
HH
NA
NA
FF
HH
NA
NA
NA = no agreement; HH = high evidence, high agreement; HL = high evidence, low agreement; LH = low evidence, high agreement; LL = low evidence, low
agreement.
to
-------
Finally, two additional interactions that were not coded by individual participants were
identified by the group as interactions of high relative impact under current conditions (see
Figure 2-9). These were the effects of: (1) nitrogen with inundation regime on ratio of low
marsh to high marsh; and (2) nitrogen with salinity on ratio of high marsh to Phragmites. The
first interaction is a synergy between inundation regime and nitrogen. Low marsh plants are
better at dealing with inundation than high marsh plants due to their greater tolerance of high
salinity and low soil oxygen content; and the high marsh species dominate the upper zone due to
their superior competitive ability in obtaining below-ground nutrients (Bertness and Pennings,
2002). Under climate change, nitrogen may no longer be limiting due to greater nutrient runoff,
while greater inundation of saline water is also expected; together these factors will
synergistically favor low marsh species. The second interaction—the effect of nitrogen with
salinity on ratio of high marsh to Phragmites—is a competition, where increased nitrogen has a
positive impact on Phragmites while increased salinity has a negative effect.
2.4. DISCUSSION OF ADAPTATION STRATEGIES
With a structure for considering management priorities provided by MBP, the workshop
participants discussed the implications of the exercise results for management. Workshop
observers also participated in the discussion. Table 2-12 lists adaptation strategies that emerged
during the group discussions. The experts discussed a variety of general adaptation strategies as
well as some specific adaptation activities that would be responsive to key potential
climate-related changes identified through their judgments. The strategies fall into several broad
categories including Restoration and Conservation, Reducing Nonclimate Stressors, and
Monitoring and Planning. While some of the strategies were specifically generated for
management of one or the other ecosystem process, many are applicable to both Sediment
Retention and Community Interactions and to salt marsh ecosystem processes not included in the
workshop.
2.4.1. Restoration and Conservation
Restoration and conservation together make a powerful adaptation strategy because they
contribute to increased resilience of the overall system. General restoration guidelines that were
discussed include restoring the "habitat mosaic" in order to provide a connected landscape that
maintains biodiversity in case of disturbance of individual pieces of the mosaic. Conservation is
2-44
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Table 2-12. Adaptation strategies and associated top pathways for
management (see Figures 3-3 and 3-4 for pathways)
Adaptation strategies
Conduct "multihabitat restoration" (i.e., restore the "habitat mosaic") with a priority on habitats with
the highest values
Recognize and take advantage of the ability of marshes to "restore" themselves under the right
conditions
Monitor the composition of the inorganic sediments in the marsh, as well as the structure of the peat
Measure local maximum growth rates to determine the degree of sea level rise that vegetation can
withstand, and manage around that threshold/target level
Monitor the line between high and low marsh areas to determine how the marshes are holding up
against sea level rise
Identify, acquire and/or protect potential areas where marsh can grow and expand, and remove
barriers to marsh migration
Upgrade sewage treatment plants (e.g., tertiary treatment) and combined sewer overflow systems to
reduce the flow of excess nutrients into the marsh
Improve stormwater management to reduce nonpoint source nutrient inputs into the marsh
Promote more absorbent land cover and "rain catchers" to prevent additional runoff
Control the hydrodynamic regime (including through channel creation/ditch modification) to favor
certain vegetation types
Restore tidal connections (e.g., remove tidal restrictions) in the near term, with awareness that
negative effects could arise under climate change
Control invasive species (e.g., Phragmites)
Conduct activities to control erosion, (e.g., create "no wake zones" to reduce marsh edge erosion
from boat wakes)
Establish oyster reefs for habitat, filtering of pollutants and erosion control.
Work with programs responsible for protecting coastal infrastructure to ensure that marsh protection
is included in management plans (i.e., take advantage of capacity of marshes to buffer infrastructure
against coastal storms and sea level rise)
Conduct education and outreach to promote good practices for marsh management
Avoid potential maladaptations (e.g., placement of dikes that result in an unintentional magnification
of erosion effects on adjacent salt marshes)
Where change is unavoidable, manage and sustain new habitats that are created when others are
wiped out (e.g., when mudflats replace low marsh areas)
Pathways
CG
SG, CG, CB,
CP
SB, SP
CG, CB, SB
SG, CG, CP
SG, CG, CB
SB, CG
SB, CG
SB, CG
CG, CP
SG, CB
CG
SP
SP, CG
SP, CG
SB, SP, CG
SP
SG, SP, CG
SG = Sediment Retention Green pathway; SB = Sediment Retention Blue pathway; SP = Sediment Retention Purple
pathway; CG = Community Interactions Green pathway; CB = Community Interactions Blue pathway;
CP = Community Interactions Purple pathway.
2-45
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implicit in consideration of where to prioritize restoring habitats within the existing landscape
continuum of healthy to degraded habitats, with a need to conserve the habitats that are adjacent
to or otherwise complementary to ones which are restored. Conservation strategies include
acquiring and protecting areas where existing marsh can expand. Adjusting development
practices that will interfere with upland marsh expansion or future restoration opportunities is
another important strategy. Conservation policy options include incentives to remove barriers to
marsh migration and regulations that support development practices that protect sensitive
resource areas where there is potential for adjacent restoration. This may include identifying
areas for restoration adjacent to current healthy marshes and protecting those healthy marshes,
especially where the adjacent uplands currently include complementary habitats that would
contribute to a diverse landscape.
Sustaining new habitats that are created when current ones become unviable under future
conditions will be an emerging management challenge. Choices will have to be made between
enabling a transition versus aggressive restoration in the face of unsuitable conditions. Also
important for restoration is creating conditions conducive to marshes being able to "restore
themselves", e.g., through ditch modification to increase hydrologic connectivity or by working
to reduce localized nonclimate stressors that can be controlled (see Section 2.4.2).
A more specific restoration project identified is removing tidal restrictions. Restoring
tidal connections and removing or reengineering restrictions (e.g., culverts, road crossings, and
tide gates) are already important management tools. Tidal restrictions—including levees, dikes,
dams, and filling and channeling activities—change the natural flow of freshwater and sediment
into the marsh. Restoring tidal connections enables sediment and tidal flows to distribute along
natural gradients throughout the marsh, which may help the marsh to respond to changes in
climate and keep pace with sea level rise. Since changing conditions may impact the amount and
timing of freshwater flow, it will be important to use up-to-date precipitation and flow data and
consider potential future climate scenarios when making assessments regarding reengineering
designs. Once tidal restrictions have been removed, the most efficient way to achieve upstream
restoration may be to facilitate favorable conditions for the marsh to "restore" itself. For
instance, as the salinity regime adjusts to the restored flows, invasive Phragmites will die back,
and the key will be to manage the transition so that native high marsh can return to fill that space.
Controlling invasive species was another specific restoration project discussed. Since one
characteristic of invasive species is the ability to thrive after disturbance, reducing the prevalence
of invasive species aids adaptation by restricting competition while native species recover after
future climate-related disturbances. Phragmites was the invasive species discussed at the
workshop and is one of MBP's current invasive removal priorities, but MBP also currently
2-46
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controls for other marsh species such as Pepperweed and is monitoring emerging threats such as
invasive tunicates, algae and crabs.
2.4.2. Reducing Nonclimate Stressors
Reducing nonclimate anthropogenic Stressors of concern is another category of
recommended adaptation strategies, one that especially needs to be considered in conjunction
with conservation and restoration efforts. Healthy habitats will be better able to survive climate
related Stressors if they are not also struggling under the pressure of nonclimate Stressors. This
applies to both maintaining healthy priority conservation sites and ensuring that restoration
projects are successful and able to become established.
Some of the management strategies discussed for reducing nonclimate Stressors include
nutrient management and methods to limit erosion. Excess nutrients favor invasive Phragmites
over native marsh species. For reducing excess nutrient inputs, both point and nonpoint nutrient
sources are of concern. Tools include upgraded wastewater treatment, upgraded combined sewer
overflow systems and stormwater best management practices that slow the flow of stormwater
(e.g., swales or buffers) and provide opportunities for nutrient filtration before it reaches the
marsh. These practices also include land use policies that promote more absorbent land cover
(e.g., through landscaping best management practices and policies that reduce impervious cover)
and "rain catchers". Erosion control options discussed include creating "no wake zones" in areas
where wave energy from boat wakes is contributing to marsh edge erosion. Erosion control
structures have the potential to be maladaptive (i.e., when structures designed to protect
infrastructure redirect wave energy or interrupt sediment supply to the adjacent marsh). This risk
that can be minimized through planning processes for protecting coastal infrastructure that are
required to demonstrate that they will not magnify erosion effects on adjacent marshes. There is
also the opportunity to highlight the buffering capacity of healthy marshes when planning efforts
highlight potential trade-offs between protecting both infrastructure and marsh, in order to build
support for marsh conservation and restoration efforts.
2.4.3. Planning and Monitoring
The last category of adaptation strategies discussed at the workshop addresses planning
and monitoring, and the above categories each have planning and monitoring aspects to them.
Many of the recommendations in Table 2-12 are based on planning, including prioritizing.
Information needs were the basis of much of the discussion, including an exploration of a
number of potential indicators of ecosystem responses to climate change. These indicators can
help managers articulate some of the characteristics of the marsh that need to be examined first,
2-47
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in order to decide where to most effectively focus. Planning for restoration was discussed only
so far as to recommend prioritizing restoring highest value habitats, leaving the question of how
to determine which habitats have the highest value up to MBP. A related planning aspect related
to conservation is how to determine where change is unavoidable, in order to manage the
transition to a new habitat and the values that new habitat will provide. There is a need for
management to have two plans: one to follow as long as maintaining current conditions is still
possible, and another plan to follow once an unavoidable threshold is reached.
Monitoring priorities was another major aspect of the discussion. These included
variables such as composition and structure of sediments, the position of the transition between
high and low marsh and maximum growth rates. The ability to determine what the maximum
level of vegetation growth is relative to sea level rise, and then to monitor changes in rates of
growth and sea level rise, will be important for anticipating the threshold after which accretion
will no longer keep pace vertically with sea level rise. Additional understanding of sediment fate
and transport is needed. Storms and sea level rise will require attention in terms of how they
impact sediment supply and erosion. Prioritization among management options for the
two different types of erosion for Sediment Retention will depend on how storms and sea level
differentially affect nearshore and coastal erosion versus marsh edge erosion.
The discussion of adaptation strategies described above was broad and free-ranging. The
next section will combine the analysis of the exercise results with the ideas in Table 2-12 to
discuss top pathways for management given climate change and to identify specific adaptation
options in response.
2-48
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3. MAKING THE LINK TO MANAGEMENT
As detailed above, the workshop resulted in a large volume of information on the
sensitivities of the sediment retention and community interactions processes to stressor
interactions under current conditions and future climate scenarios. The next step lies in
organizing this information into a form that managers can use to identify influences of particular
importance upon which to focus management interventions and adaptation planning.
3.1. USING INFORMATION ON INFLUENCE TYPE AND DEGREE, SENSITIVITY
AND RELATIVE IMPACT TO IDENTIFY KEY MANAGEMENT PATHWAYS
In the workshop exercise and group discussions, the experts generated three categories of
information about the relationships in the influence diagrams: (1) the type and degree of each
influence; (2) the sensitivity of each influence (including thresholds); and (3) the high relative
impact of certain influences on the endpoints. All three categories of information should be
considered in concert when interpreting management implications. This can be done by
performing a "crosswalk" of all three categories of information in order to identify pathways of
particular interest that connect each endpoint (Sediment Deposition/Retention or Saltmarsh
Sharp-Tailed Sparrow Nesting Habitat) to stressors or drivers that can be addressed through
particular management activities. The crosswalks as well as example pathways are presented
below.
3.1.1. Crosswalks: Influence Type and Degree, Sensitivity and Relative Impact
The crosswalks for Sediment Retention and Community Interactions are presented in
Tables 3-1 and 3-2. For each influence, information on type and degree, sensitivity, and relative
impact is listed side-by-side, first for current conditions, followed by Climate Scenarios A and B.
This allows for easy comparison of all three categories of information, across all three scenarios.
The influences have also been rank-ordered based on the amount of information available for
each in terms of agreement on influence type, degree, sensitivity, relative impact and threshold
potential.
3-1
-------
Table 3-1. Sediment Retention group crosswalk for comparison of influence type and degree, sensitivity and
relative impact for current conditions and climate scenarios. NA = no agreement; prop = proportional;
disprop = disproportional; L = low sensitivity; I = intermediate sensitivity; H = high sensitivity; H-trend = no
agreement but trending toward high sensitivity; t = increasing relative impact from current; () = number of
respondents; ranking column orders the influences according to completeness of information
Influence
L
J
0
W
Y
DD
FF
B
Variable X
Storms
Marsh High
Water Level
Freshwater
Flow
Sediment
Supply
Sediment
Deposition/R
etention
Sediment
Deposition/R
etention
Below
Ground
Biomass
Marsh High
Water Level
on
on
on
on
on
on
on
on
on
Variable Y
Marsh
Edge
Erosion
Coastal and
Nearshore
Erosion
Nutrient
Inputs
Sediment
Deposition/
Retention
Inundation
Regime
Net
Accretion
Net
Accretion
Inundation
Regime
Current
Influence
Direct prop
(4)
Direct
disprop
strong (4)
Direct prop
(5)
Direct prop
(6)
Inverse
prop (5)
Direct prop
(6)
Direct prop
(7)
Direct prop
(6)
Sensitivity
1(4)
I(4)/H(4)
1(5)
1(5)
1(5)
1(7)
1(7)
1(5)
Relative
Impact
Secondary
Primary
Secondary
Primary
Secondary
Primary
Primary
Primary
Climate A
Influence
Direct prop
(4)
Direct
disprop
strong (4)
Direct prop
(4)
Direct prop
(7)
Inverse
prop (4)
Direct prop
(6)
Direct prop
(5)
Direct prop
(5)
Sensitivity
1(4)
H(4)
1(4)
1(6)
1(4)
1(7)
1(5)
1(5)
Relative
Impact
Secondary
Primary
Secondary
Primary
Secondary
Primary
Primary
1
Climate B
Influence
Direct prop
(5)
Direct
disprop
strong (5)
Direct prop
(4)
DDirect
prop (6)
Inverse
prop (4)
Direct prop
(7)
Direct prop
(5)
DDirect
prop (6)
Sensitivity
1(4)
H(4)
1(4)
1(6)
1(4)
1(7)
1(5)
1(5)
Relative
Impact
Secondary
Primary
Secondary
Primary
Secondary
Primary
Primary
t
Ranking
1
1
1
1
1
1
1
1
oo
to
-------
Table 3-1. Sediment Retention group crosswalk for comparison of influence type and degree, sensitivity and
relative impact for current conditions and climate scenarios. NA = no agreement; prop = proportional;
disprop = disproportional; L = low sensitivity; I = intermediate sensitivity; H = high sensitivity; H-trend = no
agreement but trending toward high sensitivity; t = increasing relative impact from current; () = number of
respondents; ranking column orders the influences according to completeness of information (continued)
Influence
C
R
AA
E
I
K
Q
V
Variable X
Storms
Tidal
Exchange
Marsh Edge
Erosion
Nutrient
Inputs
Storms
Storms
Coastal and
Nearshore
Erosion
Surface
Roughness
on
on
on
on
on
on
on
on
on
Variable Y
Inundation
Regime
Inundation
Regime
Sediment
Deposition
/Retention
Below
Ground
Biomass
Freshwater
Flow
Coastal and
Nearshore
Erosion
Sediment
Supply
Sediment
Deposition
/Retention
CURRENT
Influence
Direct prop
(5)
Direct prop
(6)
NA
Direct (4)
Direct prop
(6)
Direct prop
(5)
Direct prop
(6)
Direct prop
(5)
Sensitivity
1(5)
1(6)
1(5)
1(4)
1(5)
1(5)
1(4)
1(5)
Relative
Impact
Primary
Primary
Secondary
Primary
Secondary
Primary
Primary
Primary
CLIMATE A
Influence
Direct prop
(6)
Direct prop
(5)
Inverse
prop (4)
Inverse (5)
Direct prop
(5)
Direct prop
(4)
Direct prop
(5)
Direct (6)
Sensitivity
1(6)
1(5)
1(5)
1(4)
1(4)
1(4)
1(4)
1(4)
Relative
Impact
Primary
Primary
Secondary
[threshold]
Primary
[threshold]
Secondary
Primary
Primary
Primary
CLIMATE B
Influence
Direct prop
(5)
Direct (6)
Inverse
prop (4)
Inverse (5)
Direct (7)
Direct (7)
Direct (6)
Direct (5)
Sensitivity
NA
1(4)
1(5)
1(4)
NA
NA
NA
1(4)
Relative
Impact
Primary
Primary
Secondary
[threshold]
t
[threshold]
Secondary
Primary
Primary
t
Ranking
2
2
2
3
3
3
3
3
-------
Table 3-1. Sediment Retention group crosswalk for comparison of influence type and degree, sensitivity and
relative impact for current conditions and climate scenarios. NA = no agreement; prop = proportional;
disprop = disproportional; L = low sensitivity; I = intermediate sensitivity; H = high sensitivity; H-trend = no
agreement but trending toward high sensitivity; t = increasing relative impact from current; () = number of
respondents; ranking column orders the influences according to completeness of information (continued)
Influence
EE
A
F
H
P
CC
M
Z
Variable X
Net
Accretion
Land Cover:
Percent
Impervious
Cover
Altered
Flows: Tidal
Restrictions
Land Cover:
Percent
Impervious
Cover
Freshwater
Flow
Below
Ground
Biomass
Coastal and
Nearshore
Erosion
Inundation
Regime
on
on
on
on
on
on
on
on
on
Variable Y
Sediment
Deposition
/Retention
Nutrient
Inputs
Tidal
Exchange
Freshwater
Flow
Sediment
Supply
Sediment
Deposition
/Retention
Tidal
Exchange
Sediment
Deposition
/Retention
CURRENT
Influence
Inverse
prop (5)
Direct prop
(5)
Inverse (4)
Direct prop
(4)
Direct prop
(6)
Direct prop
(4)
Direct (6)
NA
Sensitivity
1(6)
1(4)
1(5)
1(5)
1(5)
1(5)
L(4)
1(6)
Relative
Impact
Primary
Secondary
Secondary
Secondary
Very little
impact
Primary
[threshold
]
CLIMATE A
Influence
Inverse
prop (4)
Direct prop
(5)
Inverse (4)
Direct (7)
Direct (7)
Direct (4)
Direct (4)
NA
Sensitivity
1(7)
1(5)
1(5)
NA
NA
NA
NA
1(7)
Relative
Impact
Primary
Secondary
Secondary
t
Primary
Very little
impact
Primary
[threshold]
CLIMATE B
Influence
Direct prop
(4)
Direct prop
(5)
Inverse (4)
Direct (7)
Direct (7)
Direct (5)
Direct (5)
NA
Sensitivity
1(7)
1(4)
1(5)
NA
NA
NA
NA
1(7)
Relative
Impact
[threshold]
Primary
Secondary
Secondary
1
Secondary
Very little
impact
Primary
[threshold]
Ranking
3
4
4
5
5
5
6
6
-------
Table 3-1. Sediment Retention group crosswalk for comparison of influence type and degree, sensitivity and
relative impact for current conditions and climate scenarios. NA = no agreement; prop = proportional;
disprop = disproportional; L = low sensitivity; I = intermediate sensitivity; H = high sensitivity; H-trend = no
agreement but trending toward high sensitivity; t = increasing relative impact from current; () = number of
respondents; ranking column orders the influences according to completeness of information (continued)
Influence
D
BB
G
S
T
U
N
X
Variable X
Nutrient
Inputs
Inundation
Regime
Altered
Flows: Tidal
Restrictions
Inundation
Regime
Freshwater
Flow
Freshwater
Flow
Tidal
Exchange
Inundation
Regime
on
on
on
on
on
on
on
on
on
Variable Y
Net
Accretion
Below
Ground
Biomass
Freshwater
Flow
Surface
Roughness
Inundation
Regime
Surface
Roughness
Nutrient
Inputs
Sediment
Supply
CURRENT
Influence
Inverse (5)
NA
No
Influence
(4)
NA
Direct (5)
NA
Inverse (4)
Direct (4)
Sensitivity
NA
1(4)
No
Influence
(4)
NA
NA
NA
NA
NA
Relative
Impact
Secondary
[threshold
]
Primary
Secondary
Uncertain
impact
CLIMATE A
Influence
Inverse (4)
NA
No
Influence
(4)
NA
Direct (5)
NA
NA
NA
Sensitivity
NA
1(4)
No
Influence
(4)
NA
NA
NA
NA
NA
Relative
Impact
Secondary
[threshold]
Primary
Secondary
Uncertain
impact
CLIMATE B
Influence
Inverse (4)
NA
No
Influence
(4)
NA
Direct (5)
NA
NA
NA
Sensitivity
NA
1(4)
No
Influence
(4)
NA
NA
NA
NA
NA
Relative
Impact
Secondary
[threshold]
Primary
Secondary
Uncertain
impact
Ranking
7
8
9
9
9
9
10
10
-------
Table 3-2. Community Interactions group crosswalk for comparison of influence type and degree, sensitivity
and relative impact for current conditions and climate scenarios. NA = no agreement; prop = proportional;
disprop = disproportional; L = low sensitivity; I = intermediate sensitivity; H = high sensitivity; H-trend = no
agreement but trending toward high sensitivity; t = increasing relative impact from current; () = number of
respondents; ranking column orders the influences according to completeness of information
Influence
B
C
E
M
O
R
Variable X
Sea Level
Freshwater
Flow
Land
Use/Land
Cover:
Residential
Development
Nitrogen
Inundation
Regime
Nitrogen
on
on
on
on
on
on
on
Variable Y
Inundation
Regime
Salinity
Freshwater
Flow
Above Ground
Plant Biomass
Ratio Low
Marsh to High
Marsh
Ratio Low
Marsh to High
Marsh
CURRENT
Influence
Direct
prop (6)
Inverse
prop (5)
Direct
prop (6)
Direct
prop (6)
Direct
prop (6)
Direct
prop (4)
Sensitivity
1(6)
1(5)
1(6)
1(6)
1(6)
1(4)
Relative
Impact
Primary
Primary
Primary
Primary
Interactive
withR
Interactive
withO
CLIMATE A
Influence
Direct
prop (5)
Inverse
prop (5)
Direct
prop (6)
Direct
prop (5)
Direct
prop (7)
Direct
prop (4)
Sensitivity
1(4)
1(5)
1(7)
1(5)
1(7)
1(4)
Relative
Impact
t
CLIMATE B
Influence
Direct
prop (5)
Inverse
prop (5)
Direct
prop (6)
Direct
prop (5)
Direct
prop (5)
Direct
prop (4)
Sensitivity
1(4)
1(5)
1(6)
1(5)
1(5)
1(4)
Relative
Impact
1
Ranking
1
1
1
1
1
1
-------
Table 3-2. Community Interactions group crosswalk for comparison of influence type and degree, sensitivity
and relative impact for current conditions and climate scenarios. NA = no agreement; prop = proportional;
disprop = disproportional; L = low sensitivity; I = intermediate sensitivity; H = high sensitivity; H-trend = no
agreement but trending toward high sensitivity; t = increasing relative impact from current; () = number of
respondents; ranking column orders the influences according to completeness of information (continued)
Influence
S
D
L
P
Q
U
Variable X
Nitrogen
Freshwater
Flow
Inundation
Regime
Inundation
Regime
Sedimentation
Above Ground
Plant Biomass
on
on
on
on
on
on
on
Variable Y
Ratio of
Native High
Marsh to
Phragmites
Inundation
Regime
Sedimentation
Saltmarsh
Sharp-Tailed
Sparrow
Nesting
Habitat
Marsh
Elevation
Sedimentation
CURRENT
Influence
Inverse
prop (5)
Direct
disprop
weak (4)
Direct
prop (6)
Inverse (7)
Direct
prop (7)
Direct
prop (6)
Sensitivity
1(7)
L(4)
1(6)
NA
1(7)
1(6)
Relative
Impact
Interactive
withV
Primary
CLIMATE A
Influence
Inverse
prop (6)
Direct
disprop
weak (4)
Direct
prop (6)
Inverse
prop (4)
Direct
prop (6)
Direct
prop (6)
Sensitivity
1(6)
L(4)
1(6)
1(4)
1(6)
1(6)
Relative
Impact
t
CLIMATE B
Influenc
e
Inverse
prop (5)
Direct
disprop
weak (4)
Direct
prop (5)
Inverse
prop (4)
Direct
prop (6)
Direct
prop (5)
Sensitivity
1(5)
L(4)
NA
1(4)
1(6)
1(4)
Relative
Impact
t
Ranking
1
2
2
2
2
2
-------
Table 3-2. Community Interactions group crosswalk for comparison of influence type and degree, sensitivity
and relative impact for current conditions and climate scenarios. NA = no agreement; prop = proportional;
disprop = disproportional; L = low sensitivity; I = intermediate sensitivity; H = high sensitivity; H-trend = no
agreement but trending toward high sensitivity; t = increasing relative impact from current; () = number of
respondents; ranking column orders the influences according to completeness of information (continued)
Influence
V
CC
EE
A
DD
K
G
N
Variable X
Salinity
Below Ground
Plant Biomass
Ratio of
Native High
Marsh to
Phragmites
OMWM
Tidal
Restrictions
Inundation
Regime
Land
Use/Land
Cover:
Residential
Development
Inundation
Regime
on
on
on
on
on
on
on
on
on
Variable Y
Ratio of
Native High
Marsh to
Phragmites
Marsh
Elevation
Marsh
Elevation
Inundation
Regime
Inundation
Regime
Nitrogen
Ratio of
Native High
Marsh to
Phragmites
Salinity
CURRENT
Influence
Direct
prop (6)
Direct
prop (5)
Inverse
disprop
weak (4)
Direct
prop (5)
Inverse
prop (4)
Direct
prop (5)
Inverse (4)
NA
Sensitivity
1(6)
1(5)
L(4)
1(5)
1(5)
1(5)
1(4)
1(4)
Relative
Impact
Interactive
withS
Primary
Primary
Primary
CLIMATE A
Influence
Direct
prop (6)
Direct
prop (4)
Inverse
(5)
Direct (4)
NA
Direct
prop (5)
Inverse
(4)
NA
Sensitivity
1(5)
1(4)
1(4)
NA
1(4)
1(6)
NA
1(4)
Relative
Impact
t
[threshold]
CLIMATE B
Influenc
e
Direct
(6)
Direct
prop (4)
Inverse
(5)
Direct
(4)
NA
NA
Inverse
(4)
NA
Sensitivity
NA
1(4)
1(4)
NA
1(4)
NA
1(4)
1(4)
Relative
Impact
t
[threshold]
Ranking
2
2
3
4
4
5
6
6
oo
oo
-------
Table 3-2. Community Interactions group crosswalk for comparison of influence type and degree, sensitivity
and relative impact for current conditions and climate scenarios. NA = no agreement; prop = proportional;
disprop = disproportional; L = low sensitivity; I = intermediate sensitivity; H = high sensitivity; H-trend = no
agreement but trending toward high sensitivity; t = increasing relative impact from current; () = number of
respondents; ranking column orders the influences according to completeness of information (continued)
Influence
Y
AA
BB
J
T
F
Variable X
Marsh
Elevation
Marsh
Elevation
Ratio Low
Marsh to High
Marsh
Inundation
Regime
Nitrogen
Land
Use/Land
Cover:
Residential
Development
on
on
on
on
on
on
on
Variable Y
Ratio Low
Marsh to High
Marsh
Saltmarsh
Sharp-Tailed
Sparrow
Nesting
Habitat
Saltmarsh
Sharp-Tailed
Sparrow
Nesting
Habitat
Above Ground
Plant Biomass
Below Ground
Plant Biomass
Ratio Low
Marsh to High
Marsh
CURRENT
Influence
Inverse
prop (4)
Direct (7)
Inverse (5)
NA
Inverse (4)
Direct (5)
Sensitivity
1(4)
NA
1(5)
NA
1(4)
NA
Relative
Impact
Primary
Some
CLIMATE A
Influence
Inverse
(4)
Direct (6)
NA
NA
Inverse
(4)
Direct (5)
Sensitivity
NA
NA
1(5)
1(4)
NA
NA
Relative
Impact
t
Interactive
withH
CLIMATE B
Influenc
e
Inverse
(4)
Direct
(6)
NA
Inverse
(5)
Inverse
(4)
Direct
(5)
Sensitivity
NA
NA
1(5)
NA
NA
NA
Relative
Impact
t
Interactive
withH
[threshold]
Ranking
6
6
6
7
7
8
-------
Table 3-2. Community Interactions group crosswalk for comparison of influence type and degree, sensitivity
and relative impact for current conditions and climate scenarios. NA = no agreement; prop = proportional;
disprop = disproportional; L = low sensitivity; I = intermediate sensitivity; H = high sensitivity; H-trend = no
agreement but trending toward high sensitivity; t = increasing relative impact from current; () = number of
respondents; ranking column orders the influences according to completeness of information (continued)
Influence
Z
FF
X
H
I
W
Variable X
Ratio Low
Marsh to High
Marsh
Ratio of
Native High
Marsh to
Phragmites
Ratio of
Native High
Marsh to
Phragmites
Soil
Temperature
Soil
Temperature
Salinity
on
on
on
on
on
on
on
Variable Y
Above Ground
Plant Biomass
Above Ground
Plant Biomass
Ratio Low
Marsh to High
Marsh
Above Ground
Plant Biomass
Below Ground
Plant Biomass
Ratio Low
Marsh to High
Marsh
CURRENT
Influence
Direct (6)
NA
NA
NA
Inverse (4)
NA
Sensitivity
NA
1(4)
NA
NA
NA
NA
Relative
Impact
Some
CLIMATE A
Influence
Direct (6)
NA
NA
NA
NA
NA
Sensitivity
NA
1(5)
NA
NA
NA
NA
Relative
Impact
t
Interactive
withJ
CLIMATE B
Influenc
e
Direct
(4)
NA
NA
NA
NA
NA
Sensitivity
NA
1(5)
NA
NA
NA
NA
Relative
Impact
t
Interactive
with J
Ranking
8
8
9
10
10
11
-------
3.1.1.1. Sediment Retention Crosswalk
For Sediment Retention (see Table 3-1), there was agreement on type/degree and
sensitivity across all three scenarios for over one third of the influences. Especially when
coupled with a designation of high relative impact, these influences are of special interest for
management because we have a good understanding of the nature of the relationships, their
sensitivity to changes now and in the future, and their high relative impact on the endpoint of
Sediment Deposition/Retention. Therefore these are influences for which management
interventions are most likely to have the intended effects. Influences ranked number one in
Table 3-1 fall into this category. Influences of ranking two are of almost equal status, as each
has only one instance of lack of agreement in sensitivity or type/degree under only one scenario.
Influences of ranking three and four were almost all identified as having high relative
impact and quite a bit of agreement on type/degree and sensitivity, as well. However, these
influences each had more than one instance of lack of agreement, i.e., gaps across multiple
information categories and/or across multiple climate scenarios.
The remaining rankings (5 through 10) continue the pattern of gradual loss of
information. Some influences had high relative impact but lacked agreement on many (or even
all) other categories of information. For these influences the implication is that, while each is
believed to have significant potential to impact the end point, there is little concurrence on the
actual mechanics of the relationship. However, it should be noted that Relationship BB
(Inundation Regime on Below Ground Biomass) was tagged as a likely threshold relationship by
the participants, where an inability to explain where the threshold might occur contributed to the
lack of agreement (due to a mixture of codes) for this influence (see threshold discussion in
Section 2.3.1.2 for further details).
In general, lack of agreement on one or more of the type/degree and sensitivity categories
may be an indication that more information is needed to understand the particular influence. It
does not imply that the relationship is not potentially important, but rather that there was not
sufficient concurrence by this specific group of experts for managers to be confident about the
response to either climate change or to associated management interventions. Relationship S
(Inundation Regime on Surface Roughness) and Relationship U (Freshwater Flow on Surface
Roughness) are interesting cases in that there was no agreement on influence type/degree or
sensitivity, but there was agreement on high relative impact across all scenarios. In the case of
these influences as well as others with multiple gaps in agreement, priorities for further
investigation (through literature reviews and further basic research where needed) could be based
in part on which of these influences are most critical to understand since they have a high
relative impact or have links to other influences of special importance to the endpoint.
3-11
-------
3.1.1.2. Community Interactions Crosswalk
For Community Interactions (see Table 3-2), there was agreement on both type/degree
and sensitivity across all three scenarios for just over one third of the influences. Those coupled
with a designation of high (or increasing) relative impact across the scenarios may be of special
interest for management since they are well understood in terms of the nature of each
relationship, its sensitivity to changes now and in the future, and its high relative impact on the
Saltmarsh Sharp-Tailed Sparrow Nesting Habitat endpoint. These are the influences for which
management interventions are most likely to have the intended effects. All of the influences of
ranking one in Table 3-2 fall into this category. Within ranking two, there are two additional
influences [Relationship L (Inundation Regime on Sedimentation) and Relationship V (Salinity
on Ratio of Native High Marsh to Phragmites)] that are of nearly equal status in that they have
high relative impact and nearly full agreement on type/degree and sensitivity across all scenarios,
with the single exception of losing agreement on sensitivity under Scenario B.
Even though not designated as highest relative impact, the remaining influences of
ranking two are equally important to consider. These are influences for which there was
agreement on type/degree and sensitivity across all scenarios. While not of highest relative
impact, these relationships are well understood and sensitive to change, and may be linked with
other influences for important cumulative effects on the endpoint.
The rest of the influences ranked three to 11 follow a pattern of gradually increasing lack
of agreement across multiple scenarios on type/degree and/or sensitivity. Again, lack of
agreement on one or more of the type/degree and sensitivity categories indicates that more
information is needed on the particular influence. It does not imply that the relationship is not
potentially important, but rather that there was not sufficient concurrence by this specific group
of experts, and more information is needed. In the case of Relationship H (Soil Temperature on
Above Ground Plant Biomass) and Relationship J (Inundation Regime on Above Ground Plant
Biomass), there was very low agreement for each on type/degree and sensitivity across the
scenarios, yet there was agreement on high relative impact of the two relationships working
together as an interactive influence under the climate scenarios. In the case of these influences
as well as others with multiple gaps in agreement, priorities for research could be based in part
on which of these influences are most critical to understand since they have a high relative
impact or have links to other influences of special importance to the endpoint.
3-12
-------
3.1.1.3. Information Gaps
3.1.1.3.1. Crosswalks
Patterns of information gaps in the crosswalk tables were similar for Sediment Retention
(see Table 3-1) and Community Interactions (see Table 3-2). Over one third of the influences for
both groups were well understood across type/degree and sensitivity categories of information
across all scenarios. In quite a few additional cases, there was agreement on type although not
on degree. Another common pattern for both groups is that influences of progressively lower
rank tend to show lack of agreement under the climate scenarios first, while agreement under
current conditions is often better. This drop in agreement across the scenarios is consistent with
greater uncertainty about future conditions and ecological responses compared to current
conditions. With such a variety of information gaps, it will be necessary to prioritize targeted
literature reviews and/or basic scientific research to focus on key process components of interest.
A starting point would be to establish a basic understanding of type and degree under current
conditions for influences within otherwise well-understood pathways that link to rich
opportunities for management. From there, the next step would be improving understanding of
type/degree and sensitivity under potential future climate conditions, which will be less likely to
be fully supported in the existing literature and may require theoretical approaches. Another
method for sorting through and prioritizing "nonagreement" influences for further study might be
to start from the perspective of management opportunities. Managers could look at their most
tractable and effective management strategies currently available, and trace pathways from the
associated management-related variables down to the endpoint of interest, as a means of
identifying and selecting priority influences for research. Examples of promising pathways are
presented in Section 3.1.2 below.
3.1.1.3.2. Confidence
Confidence estimates were not included in the crosswalk tables or used as a means of
identifying management pathways because of extensive information gaps in the form of missing
estimates. It is possible that this was partly due to time limitations as participants prioritized
characterizing the influences before marking confidence. This may have been exacerbated by
lack of familiarity with the coding scheme and the limited time that was available to discuss the
definitions of high and low evidence and agreement.
Another problem that may have led to gaps was that the confidence exercise did not take
into account specialty areas of participant knowledge. Due to the complex and interdisciplinary
nature of the influence diagrams and the individual specialties of the participants, some
participants may have been asked to make judgments on influences for which they felt they had
3-13
-------
insufficient expertise. In some cases they may have elected to leave those cells blank rather than
indicate low confidence, as that would have incorrectly indicated that the participant knew that
the scientific literature is lacking evidence or agreement on the influence, when really it was a
case of lacking familiarity with the literature.
Thus the large number of missing cells for confidence could have been due to one or
more of the following: (1) lack of time; and (2) confusion about the confidence definitions and
coding scheme; and (3) inability to judge confidence in certain influences due to lack of
expertise. These problems could be corrected in subsequent workshops through preworkshop
trainings to increase familiarity with using the coding scheme; provision of a code to allow
participants to indicate lack of expertise as a reason for leaving a cell blank; and additional time
to complete the exercise.
3.1.2. Identifying Key Pathways for Management
Using the crosswalk tables (see Tables 3-1 and 3-2), it is possible to identify influences
that are well understood, become more sensitive, and have a greater relative impact under future
climate scenarios. By combining a series of such influences into a pathway to the endpoint, we
can begin to identify key responses and changes in variables of interest to management. A
"pathway" is defined as a series of connected variables and their influences, beginning with a
driver or stressor variable and ending at the endpoint. The purpose is to be able to apply
management interventions in order to impact the endpoint. "Management levers" are those
variables for which it is possible to intervene with management options; the clearest connections
to management options are for the top-level variables that are drivers or stressors. When
multiple management levers are available for a pathway, the one that was more completely
characterized or that had potential changes under the climate scenarios identified was selected.
An example pathway from the Community Interactions process (see Figure 3-1) is described
here, to show the process by which these types of pathways can be analyzed. This will be
followed in the next section by summary diagrams showing the top three pathways of interest for
each process, along with discussion of specific management options.
The Community Interactions example pathway (see Figure 3-1) begins with the
management lever is Land Use/Land Cover: Residential Development, which affects Freshwater
Flow (Relationship E). The pathway then goes to Inundation Regime (Relationship D) to
Nitrogen (Relationship K), to the Ratio of Native High Marsh to Phragmites (Relationship S), to
Marsh Elevation (Relationship EE), to the Saltmarsh Sharp-Tailed Sparrow Nesting Habitat
endpoint (Relationship AA). During discussions, several participants noted that the diagram
3-14
-------
Influence Type
Current
Future
Sensitivity
Freshwater
Flow
g E
Residential Development
Relative Impact
Inundation
Regime
Marsh
Elevation
AA
Freshwater
Flow
D \f,''- K
Inundation
Regime
Marsh
Elevation
AA
Nitrogen
|S
Ratioof Native High
Marsh to Phragmites
•7^
X^Saltmarsh Sharp-^*V
( Tailed Sparrow J
\. Nesting Habitat^^X
E Residential Development
Nitrogen
I'
Ratioof Native High
Marsh to Phragmites
,'
tf' EE
X^SaltmarshSharp-^N.
™f Tailed Sparrow 1
\. Nesting Habitat^/
Inundation
Regime
Marsh
Elevation
AA
Freshwater
Flow
° ~ K
Inundation
Regime
Marsh
Elevation
AA
Nitrogen Regj
1s
Ratioof Native High
Marsh to Phragmites
EE l\
.-— ' ---. El<
Xbaltmarsh Sharp-^^V
f Tailed Sparrow j
X^Nesting Habitat^^/
E Residential Development Freshw
Flo
D
Nitrogen lnund;
. Regi
1
Ratioof Native High
Marsh to Phragmites
^^^*
(Threshold) ^
X^Saltmarsh Sharp- ^v
*f Tailed Sparrow J
>w^ Nesting Habitat ./
me Nitrogen
Is
Ratioof Native High
Marsh to Phragmites
'
/larsh **
ovation ^^, ^^^
"^^^^ f SaltmarshSharp-
AA ^^rf Tailed Sparrow
\^Nesting Habitat
ater . E Residential Develop
^ K ^%
tion
me Nitrogen
Is
Ratioof Native High
Marsh to Phragmites
/larsh **
swatinn ^
^^^^^ X^SaltmarshSharp-
^H Tailed Sparrow
\^ Nesting Habitat
Inverse
Direct, no agreement on degree
Inverse, no agreement on degree
Noagreement
Thickness denotes degree: all are proportional or disproportional weak
Intermediate sensitivity
Low sensitivity
Intermediate-to-high trend
Noagreement
Primaryimpact
Interactive impact (with Salinity)
Not identified as high relative impact
Figure 3-1. Community Interactions example pathway. Future = Climate Scenario B.
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lacked an arrow to show the direct influence of Residential Development on Nitrogen; however
the diagram did capture the indirect linkages of Residential Development with Nitrogen through
Freshwater Flow via Inundation Regime. Nitrogen is delivered to the marsh through a number of
pathways such as marine and freshwater sources along with stormwater runoff. Both direct
disturbance from development and changes in nitrogen in the marsh will impact marsh
vegetation, differentially affecting native high marsh and Phragmites growth.
Relationship E was characterized as a direct proportional influence of intermediate
sensitivity under all scenarios. It was also identified as of primary relative impact.
Relationship D was characterized as disproportionately weak direct influence under all scenarios.
It was characterized as a low sensitivity influence under all scenarios and not identified as having
high relative impact. Relationship K was characterized under current conditions as a direct
proportional influence with intermediate sensitivity. Under the climate scenarios Relationship K
was less understood, with no agreement on type or degree of influence or sensitivity under either
climate scenario. Relationship K was not identified as having high relative impact under any of
the scenarios.
Relationship S had more agreement, as it was characterized as an inverse proportional
influence with intermediate sensitivity under all scenarios. Relationship S was identified as
having a high interactive impact with Relationship V (Nitrogen on the Ratio of Native High
Marsh to Phragmites with Salinity), and this relative impact remained the same under the climate
scenarios.
Relationship EE was characterized as an inverse, disproportionately weak influence
under current conditions, and an inverse influence with no agreement on degree under the
climate scenarios. For sensitivity, Relationship EE was characterized as having low sensitivity
under current conditions and intermediate sensitivity under the climate scenarios, a change that
indicates a threshold; as sea level rises, marsh elevation will become increasingly sensitive to the
ratio of native high marsh to Phragmites. This is because Phragmites is more effective at
trapping sediment (due to its large rhizomes located right at the surface) and thus better equipped
to build elevation in place. More importantly, if the shrinking of native high marsh accelerates
due to the combined effects of Phragmites takeover and sea level rise, there could be an abrupt
shift in the relative contribution of Phragmites to the ratio, leading to a change in the relationship
between the ratio and marsh elevation. Relationship EE was not characterized as an influence
with high relative impact under any of the scenarios.
Relationship AA was characterized as a direct influence under all scenarios, with no
agreement on the degree of influence. The coding for sensitivity indicated a trend from
intermediate-to-high sensitivity under all scenarios. Under current conditions, Relationship AA
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was identified as having primary relative impact, and this remained the same under the climate
scenarios.
While there are aspects of this pathway that are not fully understood, it is a pathway that
may be responsive to a variety of management options when planning for climate change. The
management lever of Land Use/Land Cover: Residential Development is an ongoing concern for
salt marsh habitats. Nutrient inputs, especially nitrogen, favor Phragmites over native species
such as Spartinapatens. These inputs can come from point sources such as sewage plants or
nonpoint sources in the form of runoff that is exacerbated by residential development and
associated increases in impervious surface cover. New or expanded residential development can
also cause disturbance when adjacent to the marsh, which favors invasive species such as
Phragmites. Thus this pathway emphasizes the priority importance of focusing on management
options that prevent or mitigate disturbance of adjacent marshes during residential development,
improve sewage treatment practices, and promote use of buffers, rain catchers and absorbent
surfaces to reduce runoff.
3.2. TOP PATHWAYS AND IMPLICATIONS FOR ADAPTATION PLANNING
Section 3.1.2 above has used an example to demonstrate how the results of the expert
elicitation exercise can be used to help identify key pathways for management. This method of
identifying well-understood pathways that can be traced from endpoints of concern to
management levers is a useful way to explore the implications of the workshop results for
adaptation planning. In some cases it may be possible to identify management actions for
immediate implementation, i.e., where there is sufficient understanding of the relationships
among the variables as well as their sensitivities to act with relative confidence in the effects of
management interventions. Additional pathways of interest can be identified through further
examination of the crosswalk tables (see Table 3-1 and Table 3-2), using amount of information
with agreement (to identify current best-understood influences) as well instances of climate
thresholds (indicating potential climate-induced shifts) to identify "top pathways" of interest for
management. This section describes three "top pathways" for the Sediment Retention and
Community Interactions processes, as well as potential adaptation responses. This is followed
by a brief review of MBP planning documents and discussion of where adaptation activities
could be linked into these existing plans and strategies.
3.2.1. Top Pathways and Associated Adaptation Options
Three top pathways for each process are presented in Figure 3-2 (Sediment Retention)
and Figure 3-3 (Community Interactions). For ease of viewing, each pathway is highlighted by a
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Sediment
Deposition/
Retention
Direct > Inverse Effect
(Threshold), 1°
Inverse > Direct Effect
(Threshold)
Figure 3-2. Top pathways for management of the Sediment
Deposition/Retention endpoint. Green, blue and purple colors are used to
distinguish different pathways. Red boxes highlight changes under future climate
conditions. A indicates increasing relative impact under future conditions. 1°
indicates primary relative impact under current conditions. 2°Dindicates
secondary relative impact under current conditions. A direct to inverse threshold
occurs where there is a direct effect under current conditions that may shift to an
inverse effect under future climate conditions. Dashed lines indicate inconsistent
agreement across scenarios of current and future conditions.
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Freshwater
Flow
Residential
Development
Temperature
Weak Direct
Effect
Direct > Inverse Effect
(Threshold) A
Inundation
Regime
Above Ground
Plant Biomass
BelowGround
Plant Biomass
Ratio of Native
High Marsh to
Phragmites
Weak inverse >
Inverse Effect
(Threshold)
Ratio Low Marsh to
High Marsh
altmarsh Sharp
Tailed Sparrow
Nesting Habitat
Figure 3-3. Top pathways for management of the Saltmarsh Sharp-tailed
Sparrow Nesting Habitat endpoint. Purple, blue and green colors are used to
distinguish different pathways. Red boxes highlight changes under future climate
conditions. A indicates increasing relative impact under future conditions. An
inverse to strong inverse threshold occurs where there is an inverse effect under
current conditions that may shift to a very strong inverse effect under future
climate conditions. Dashed lines indicate inconsistent agreement across scenarios
of current and future conditions.
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color (green, purple or blue), and influences that undergo changes under the climate scenarios are
highlighted with red boxes indicating the nature of the change. Dashed lines indicate
inconsistent agreement among participants in at least one scenario. The order in which the
pathways are presented below is not an indication of order of importance. These are all
management pathways with notable potential for addressing the climate sensitivities identified.
3.2.1.1. Sediment Retention Top Pathways
3.2.1.1.1. Purple pathway
In this pathway (see Figure 3-2), starting with the Sediment Deposition/Retention
endpoint and working "up" the diagram, marsh edge erosion represents one major component of
how sediment can be lost to the marsh. The relationship is an inverse effect where increased
erosion leads to decreased sediment retention, and this was identified by the workshop experts as
an effect of high relative impact (secondary category). Marsh edge erosion occurs when wave
energy from wind-driven waves (especially during storms), boat wakes, and ice scour (removal
of vegetation and underlying peat by tidal movement of overlying ice) lead to loss of sediment
from the seaward edge of the marsh. The proportion of the eroded material that the marsh is able
to retain through sediment trapping by marsh vegetation plays a major role in whether the marsh
is able to rebuild along the eroded edge and to accrete vertically to keep pace with sea level rise
within the interior. A threshold in sensitivity of the influence of erosion on sediment retention is
explained below.
At the next level up the pathway, storms are a major contributor to marsh edge erosion;
they have a direct effect which was characterized as having high relative impact (secondary
category) on the endpoint. Marsh edge erosion is considered a threshold variable that is sensitive
to future increases in storm intensity (with a strong seasonal component), especially given sea
level rise. At a higher sea level, the marsh edge is exposed to storm surge and wave energy from
storms for longer periods of the tidal cycle. Also, under Climate Scenario B, between 5 and 15%
of East Coast storms (an additional one storm per year) are expected to move northward during
late winter (Jan, Feb, March), further increasing storm energy effects in the Northeast. The
greater influence of storms under the climate scenarios will intensify marsh edge erosion, and
sediment eroded during storms will be more likely to be transported outside of the local marsh.
The combined effects of sea level rise and changes in storms are likely to cause a threshold shift,
where much of the sediment eroded from the marsh edge will no longer be available for
accretion within the system, leading to an abrupt drop in sediment deposition and retention.
The management implications for adaptation under this pathway begin with a need to
apply current erosion control tools, such as "no wake" zones to reduce erosion due to boat
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wakes. Barrier beaches that protect marshes from storms can be conserved through dune grass
protection and restoration. Next, new tools for reducing wave action on the front edge of
marshes need to be developed. These could include methods to establish oyster reefs adjacent to
marshes exposed to storms or alternative protective barriers that reduce wave energy before it
reaches the marsh edge. For these options to be successful, an improved understanding of the
specifics of the local sediment budget, including coastal and nearshore sediment sources, will be
needed for determining how, when and where to protect marshes against erosion due to storms.
Monitoring of erosion and sediment transport at both the marsh edge and along the coastline will
be increasingly important as the climate changes in order to detect threshold shifts, to identify
areas losing sediment as priority sites for management intervention, and to measure effectiveness
of such interventions. Another research area that would help prioritize most vulnerable areas for
protection is monitoring the structure of the peat along the marsh edge. The age and structure of
the marsh peat may put some areas at risk of passing the threshold earlier than others,
necessitating their placement as highest priority for protection.
3.2.1.1.2. Green pathway
Starting with the Sediment Deposition/Retention endpoint and working along the Green
top pathway (see Figure 3-2), Inundation Regime (frequency, depth and duration of marsh
flooding) plays an important role in the delivery of sediment and the conditions necessary for its
retention within the marsh. The relationship is considered a direct effect of high relative impact
(primary category) under current conditions, where an increase in inundation leads to increased
transport into, and deposition of sediment onto, the marsh. Under climate change, a threshold
flip from a direct to an inverse relationship is expected when too much inundation increases tidal
flow velocities and suspends more sediment than is deposited, leading to a net decrease in
deposition and retention.
At the next level up the pathway, inundation regime is directly affected by marsh high
water level, which is an indicator of sea level marked by the transition from marsh to upland
vegetation. The ability of this transition zone to migrate upland with sea level rise will
determine the extent (and even the existence) of the future marsh, and inundation regime will
change accordingly. So with climate change, as sea levels rise and cause increasing pressure on
the transition zone of marsh high water level, this in turn will have a greater impact on
inundation regime and, ultimately, on sediment deposition/retention; therefore this is an
influence of increasing relative impact under climate change. Marshes with barriers to migration
will be limited to responding to sea level rise through vertical accretion only, as they will be
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unable to move upland (i.e., by adjusting the location of the marsh high water level/transition
zone).
Meanwhile, another important determinant of inundation regime is tidal exchange, and
this relationship was identified by the experts as having a high relative impact (primary category)
on the endpoint. Tidal Exchange was defined by the participants as tidal prism, which is the
difference between the volume of water at mean low tide and mean high tide. Tidal Exchange is
in turn inversely affected by tidal restrictions, again with high relative impact (primary category).
Tidal restrictions occur where infrastructure (e.g., roads, bridges, railroads, causeways and
footpaths) cross wetlands such that insufficiently large openings (such as culverts and pipes) at
tidal creeks alter the hydrology and salinity of the upstream marsh. The smaller the opening is
relative to the volume of flow that needs to pass through in a tidal cycle, the less the tidal
exchange. Tidal restrictions also affect the flow of freshwater downstream of the restriction.
Management options for this pathway are to remove or reengineer tidal restrictions and to
remove barriers to upland migration of marsh high water level. In the near term, management
options should continue efforts to relieve tidal restrictions in order to restore upstream
hydrology, salinity and sediment transport across the restrictions. However, as sea level rise
continues, it is possible that the inundation regime could reach a tipping point at which too much
inundation could now have a negative effect on sediment deposition and retention. Tidal
restrictions in the future could be managed to minimize excess inundation. Thus, this
management lever will have to be used with care to avoid the unintended consequence of today's
adaptation becoming tomorrow's 'maladaptation'.
One option to consider when reengineering tidal restrictions is the addition of tide gates
so that the hydrology of the upstream marsh can be managed more precisely under the greater
range of conditions expected in the future. Tide gates can be closed prior to storms or spring
tides to avoid peak flooding, but reopened for normal tidal exchange. Other means to control
hydrodynamic regime are through channel and ditch modification. Reduced flows have
gradually led to wider and shallower channels; thus one way to restore hydrology is by cutting
narrower, deeper channels within these altered channels. This would be especially effective in
areas that have been diked or when done in conjunction with tidal restriction removals.
Meanwhile, ditching has been used historically to increase drainage for mosquito control, and
some ditch maintenance for that purpose continues today. There is an opportunity to work with
the State Reclamation and Mosquito Control Board, which is responsible for ditch maintenance,
to see where ditches have been maintained and compare their impacts on drainage and
inundation regime to where they have been filled in or become revegetated. A long-term
monitoring plan that includes sediment transport as well as inundation regime at different
vegetation transition zones (including the marsh high water level) would allow for conditions to
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be consistently measured and may assist in understanding the level at which inundation causes a
change in sediment deposition and retention in marshes.
Looking at marsh high water level together with tidal restrictions is important because
tidal restrictions alter the marsh high water level upstream of the restriction. When prioritizing
areas for removal or reengineering of tidal restrictions to restore upstream hydrology, there needs
to be consideration of what the marsh high water level will adjust to once the vegetation adjusts
to the restored hydrodynamic regime. Ideally, this can be done in such a way as to take
advantage of the marsh's ability to 'restore' itself under the right conditions. Restoration
prioritization should go to places where there is room for the restored marsh high water level to
further migrate upland with sea level rise. Whether or not there is a tidal restriction,
management options for marsh high water level are to remove barriers to migration such as roads
and hardened shorelines. In areas without barriers, where the adjacent slope, soil and vegetation
are suitable to marsh migration, there is a need for policies and incentives that discourage new
barriers from being built and encourage conservation easements or other protections.
3.2.1.1.3. Blue pathway
Starting from the Sediment Deposition/Retention endpoint, the Blue pathway (see
Figure 3-2) begins with a link to net accretion. Net accretion was defined by the group as
referring to net change in elevation, which under current conditions has an inverse effect on
sediment deposition and retention. Increased accretion decreases sediment deposition because
the additional elevation reduces flow velocities during inundation to the point where more
sediment will come out of suspension before it makes its way very far into the marsh. With
higher sea level, a threshold shift could occur, changing this to a direct relationship. The
mechanism behind this threshold is that when the marsh is at a greater depth during inundation,
the water will arrive at higher flow velocities, carrying sediment still in suspension further into
the marsh to be deposited.
The next influence in this pathway is the effect on net accretion of below ground
biomass, which comprises the biological component of net accretion. It has a direct effect and is
of high relative impact (primary category). Nutrient inputs, in turn, have a direct impact on
below ground biomass under current conditions, also of high relative impact (primary category).
This relationship works through nutrient additions being beneficial to above ground productivity,
a portion of which adds to below ground peat. However, a threshold can occur where excess
nutrients will have a negative impact on below ground productivity and increase decomposition.
In the long term under the climate change scenarios, these effects are likely to outweigh the
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benefits of above ground productivity and cause the relationship to change from direct to inverse.
This threshold change would increase the already high relative impact of this influence.
In the next step of the pathway, freshwater flow directly affects nutrient inputs, with high
relative impact (secondary category) on the endpoint. Finally, at the management lever end of
the pathway, freshwater flow is directly influenced by the amount of impervious cover, which
delivers a greater portion of precipitation to rivers and streams, circumventing infiltration. The
relationship is of high relative impact (secondary category). This relationship is likely to
increase in sensitivity under the climate scenarios, as the effects of storms and flashiness of
precipitation events increase.
Management options under this pathway should focus on both reducing nutrient sources
and reducing delivery of nutrients through improved stormwater management. Stormwater
management policies can promote the use of absorbent land cover (e.g., permeable pavements),
rain catchers and buffers. In order to reduce direct nutrient sources, sewage treatment plants can
be upgraded to tertiary treatment, which removes nutrients. Likewise, combined sewer overflow
(CSO) systems can be upgraded to ensure that all sewage passes through upgraded treatment.
CSO upgrades will become a high priority under climate change as larger precipitation events
that trigger overflows are expected to become more frequent. Septic systems should be
appropriately sited, regularly inspected and properly maintained. Education and outreach efforts
can inform homeowners of proper timing (not directly before or after any rainfall event),
placement and application rates for fertilizers.
3.2.1.2. Community Interactions Top Pathways
3.2.1.2.1. Green pathway
The Community Interactions example pathway described in Section 3.1.3 above is
elaborated upon here as the Green top pathway (see Figure 3-3). Starting with the Saltmarsh
Sharp-Tailed Sparrow Nesting Habitat endpoint and working "up" the diagram, nesting habitat is
directly dependant on marsh elevation, as nests must be located high enough to avoid inundation
at maximum tide during the incubation period. Therefore this is a direct relationship of high
relative impact. Because this is one of only three variables that feed directly into the nesting
habitat variable in the influence diagram, and because all of the top pathways converge on this
one relationship, marsh elevation is arguably the most essential feature of this diagram.
At the next level up the pathway, we look at the effect on marsh elevation of the ratio of
native high marsh to Phragmites. Under current conditions, this relationship is considered a
weak inverse influence; a decrease in the ratio of native high marsh to Phragmites would lead to
a modest increase in marsh elevation because Phragmites is more effective at trapping sediment
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(due to its large rhizomes located right at the marsh surface). The relationship strengthens under
the climate scenarios, where there is a mixture of codes moving from weak to intermediate; the
workshop participants identified this as a threshold shift to a stronger inverse relationship. They
cited rising sea levels as the cause of the increasing sensitivity, as Phragmites would be better
equipped to migrate landward to higher elevations while continuing to more effectively trap
sediment in place; thus Phragmites is expected to better maintain marsh elevation compared to
native high marsh, which may lose elevation more rapidly. This leads to a trade-off between
maintenance of marsh elevation/extent in the face of sea level rise (favored by Phragmites) to
preserve filtration and coastal protection functions, versus maintenance of native high marsh
grasses (preferred sparrow nesting habitat) that will more rapidly be overcome by rising seas.
This greater vulnerability of native high marsh underscores how critical it will be for
management of sparrow nesting habitat to include provision of adjacent upland areas to allow
migration of native high marsh in advance of rising sea levels (see management discussion
below).
Next, ratio of native high marsh to Phragmites is inversely affected by nitrogen since
nitrogen favors Phragmites growth over that of native high marsh. This is considered an effect
of high relative impact on the endpoint. Nitrogen is in turn directly affected by inundation
regime, which distributes and pools nitrogen-rich waters over the marsh. Inundation regime was
defined slightly differently by the Community Interactions group compared to the Sediment
Retention group, as the percent time the high marsh is under water during April-October.
Inundation regime can be weakly affected (direct positive effect) by freshwater flow through its
contribution to longer periods of inundation over the marsh. Finally, freshwater flow is directly
affected by residential development, which has a high relative impact because increased
impervious cover leads to greatly increased runoff.
Management options for adaptation based on the relationships in this pathway should
simultaneously address both maintenance of marsh elevation and control of Phragmites. A good
starting point would be intensifying efforts that mitigate the negative effects of residential
development, which are already an ongoing concern for salt marsh habitats. The most direct
options would be to promote more absorbent land cover (including permeable pavements) while
also placing a priority on upgrades to treatment plants (to tertiary treatment) and improved
stormwater management to reduce nutrient-rich runoff to marshes. At the same time, public
programs can continue to raise awareness and create incentives for decreased use of fertilizers on
lawns, regular inspections of septic systems, and use of rain catchers to reduce the volume of
runoff during large rain events.
Meanwhile, management actions to preserve native high marsh while also maintaining
marsh elevation will be essential. Phragmites, while better at maintaining marsh elevation, is
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undesirable as sparrow nesting habitat compared to native high marsh. Phragmites control
programs (e.g., through mechanical harvesting or application of herbicides) should be targeted
for implementation during or immediately after disturbance events from development projects
(since disturbance favors invasions). However, it will be essential to couple this with removal of
any barriers to marsh migration and protection of upland areas for native high marsh to grow and
expand as sea level rises. Identification, acquisition and protection of such areas for marsh
migration should focus on locations where room for marsh expansion is available and nitrogen
sources are currently under best control for water quality maintenance.
3.2.1.2.2. Purple pathway
Starting with the saltmarsh sharp-tailed sparrow nesting habitat endpoint, the Purple
pathway (see Figure 3-3) follows the Green pathway (see above) in its first two influences. As
explained for the Green pathway, marsh elevation directly affects sparrow nesting habitat
through a positive relationship of high relative impact. Marsh elevation is in turn inversely
affected by the ratio of native high marsh to Phragmites (hereafter referred to as native high
marsh: Phragmites); this inverse relationship is expected to intensify in the form of a threshold
under climate change.
At this point the Purple pathway diverges from the Green pathway to focus on the effect
of salinity on native high marsh: Phragmites. Greater salinity levels inhibit Phragmites, so any
increase in salinity has a direct positive effect on native high marsh: Phragmites., and this
influence is considered one of high relative impact on the end point. The designation of high
relative impact for salinity—and also for nitrogen (Green pathway)—under current conditions is
due to a competitive interaction between salinity and nitrogen that was identified by the
workshop experts. Increased salinity has a negative impact on Phragmites while increased
nitrogen has a positive effect. Salinity is expected to have an increasingly high relative impact
under climate change as sea level rise leads to increased inundation of saline water for longer
periods of time, and higher into the marsh (placing greater pressure on Phragmites). This is an
instance in which a climate change effect actually supports the goal of maintaining native high
marsh.
The last influence in this pathway is the effect on salinity of freshwater flows. This is an
inverse effect because freshwater flow counteracts salinity through dilution. This is considered
an influence of high relative impact under current conditions. Since both climate scenarios
project an increase in precipitation in winter, spring, summer and fall (with the single exception
of fall in Climate Scenario B), there is potential for this effect to increase in the future.
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Management implications for adaptation under this pathway include some of the same
actions as those discussed above for the Green pathway, as well as a few additional ones.
Strategies for reducing freshwater runoff are further justified under this pathway since
controlling runoff prevents salinity reductions that would favor Phragmites over the more
salinity-tolerant native high marsh grasses. This places an even higher priority on the use of
permeable pavements and rain catchers to mitigate freshwater runoff, since these options reduce
nitrogen runoff while also helping to maintain salinity.
Other actions to maintain appropriate salinity levels can also be considered. These
include controlling the hydrodynamic regime (including through channel creation/ditch
modification) to maintain salinity through unimpeded inundation. Also advantageous would be
restoration of riparian buffers and upstream freshwater marshes to reduce freshwater flows and
favor local infiltration and storage of rain water.
3.2.1.2.3. Blue pathway
The Blue pathway (see Figure 3-3) shares the same first influence as the previous two
pathways, but then it diverges to explore another set of variables that contribute to marsh
elevation. We have already established that Saltmarsh Sharp-Tailed Sparrow Nesting Habitat is
directly affected by marsh elevation. Working "up" the blue pathway from here, marsh elevation
is directly affected by sedimentation (the average concentration of suspended sediment in the
water column), which contributes positively to marsh vertical accretion. The effect on
sedimentation of above ground plant biomass is also direct and positive, as plant material serves
as a source of organic sediment that contributes to sedimentation.
In the next step of this pathway, inundation regime (percent time the high marsh is under
water during April-October) has an important threshold effect on above ground plant biomass.
Under current conditions the influence is direct: inundation regime favors above ground plant
biomass since sufficient flushing through inundation prevents soil salinity from reaching levels
that inhibit growth. Thus, just as an appropriate inundation regime is important for maintaining
salinity (see Purple pathway above), it is also important for preventing salinity from becoming
too high. Under the climate change scenarios, however, this influence shifts from a direct to an
inverse effect. As sea level (which directly affects inundation regime) rises, inundation
frequency and duration is expected to reach levels that cause increased hypoxia and result in
marsh die-back (i.e., marsh drowning); therefore this influence is expected to have increasing
relative impact on the endpoint.
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Finally, inundation regime is inversely affected (with high relative impact) by tidal
restrictions such as road crossings or other barriers to tidal exchange. This is considered an
influence of high relative impact.
Adaptation options under this pathway center on supporting an appropriate inundation
regime and protecting the ability of above ground biomass and sedimentation to maintain marsh
elevation. Management of tidal restrictions will require care, as plans for both pre- and
postthreshold conditions will be needed, as well as an ability to switch with agility from one
management pan to the other. In the near term (under current conditions), ongoing efforts to
restore tidal connections (e.g., remove tidal restrictions) continue to be advantageous. However
in the longer term (at some point in the next 30-60 years under future climate change), these
same efforts could become disadvantageous due to sea level rise, such that management should
then switch to utilizing restrictions to manage the flows (e.g., through use of tide gates that allow
control of flows).
Meanwhile, regardless of when a potential threshold change may occur in the relationship
of inundation regime to above ground plant biomass, priority can continue to be placed on
management activities that directly support the maintenance of above ground biomass and the
ability of the marsh to accrete both vertically and landward with sea level rise. This includes
actions to (1) identify, acquire and protect areas where marsh can grow and expand; (2) restore
native high marsh habitat (with item #1 being a prerequisite); and (3) remove barriers to marsh
migration. Furthermore all of these should be concomitant as much as possible with locations
where natural flows and good sediment supplies are already in place.
To conclude this discussion of top pathways, it is worth noting that while this exercise
has focused on management adaptations to climate change, there is also the potential for
acclimation on the part of the Saltmarsh Sharp-Tailed Sparrow in the form of beneficial range
shifts. Massachusetts is currently at the high end of the sparrows' range. Under a warming
climate, the MBP region could become the middle of the range, which would be beneficial to the
overall sparrow population in the region. Breeding season and incubation period could actually
decrease with warming, especially if the food supply improves. Currently, the timing of the
nesting cycle is relatively fixed (consistently close to 26 days). If the sparrows could gain an
advantage of needing one day less to nest, this could have a beneficial impact that could
counteract some of the impact of sea level rise on tidal flooding of nests.
3.2.1.3. Top Pathway Caveats
Above we have described three pathways that scored as especially promising for
successful management application in light of the information provided by the particular group
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of experts at this workshop. Given the complexity of these systems and instances of uneven
agreement among participants, actions based on these pathways should be considered with care.
A different set of pathways could be chosen based on additional meaningful criteria that are site
specific and specific to individual managers' expertise. Based on their own knowledge of their
sites, and/or input from different experts, managers are encouraged to examine the potential for
additional top pathways for their own specific systems by examining the crosswalk tables and
applying their unique knowledge.
While top pathways based on the expert knowledge from this workshop are useful, it is
equally important to look at gaps in the crosswalk tables, where some influences did not show
agreement in type, degree, and/or sensitivity. Lack of agreement does not necessarily mean there
is no information available; often the experts did not agree based on competing evidence, or as a
result of limitations of the expert elicitation process. In these cases, further investigation is
needed. Where there are gaps in otherwise-strong pathways for management, further
research—in the form of literature searches, data mining, or original research if needed—could
be highly valuable.
A final consideration is that the influence diagrams do not explicitly represent temporal
variability of stressors. In the Sediment Retention group, the issue of seasonality was raised.
Components of seasonality can include storm frequency, timing and volume of precipitation,
annual temperature range and number of days below freezing. These can affect multiple
variables in the diagram such as storms, freshwater flow, nutrient inputs, sediment supplies, and
biological factors such as below ground biomass. Just as managers need to consider the specifics
of each site when making decisions about managing a particular pathway, they need to also
account for timing considerations, including accounting for seasonality of certain stressors. For
example, managers might focus on reducing boat wakes if marsh edge erosion is occurring in the
summer, versus using protective barriers if marsh edge erosion is more of a problem during
winter storms.
3.2.2. Adaptation Planning
There can be numerous approaches to climate change adaptation planning, including
integrating adaptation into existing plans, or developing a stand-alone adaptation plan. This
report focuses on the planning options for MBP, which as a National Estuary Program has
several key management plans. The MBP management plans discussed here are used to
demonstrate the type of adaptation planning that can be done to address the issues presented
here. Other organizations can use their particular planning documents to apply the same
approach.
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MBP's planning documents include a CCMP, which articulates long-range goals and
objectives, a Strategic Plan for mid-term objectives, and an annual Work Plan that lays out
short-term actions to implement the goals and objectives. Each of these plans addresses climate
change and climate-related effects on some level, so it makes sense to use the results of this
study to continue mainstreaming climate change into each of these planning scales. The
1996 CCMP considers sea level rise, including the context of acceleration due to global
warming, but the accompanying actions are limited due to the associated uncertainty. The
2009-2012 Strategic Plan advocates for managers to "Adapt for projected impacts of climate
change" as an emerging priority action area for implementing the CCMP. The FY11 Annual
Work Plan (MBP, 2010) includes multiple proposed and ongoing projects with strong climate
change connections. In this section we provide some links between MBP's plans and the top
pathways and management options discussed above; this set of examples is not comprehensive,
but rather is meant to illustrate how the results of this study can be used to inform adaptation
planning.
One management strategy outlined in the 1996 CCMP that pertains to the Purple
Sediment Retention pathway (see Figure 3-2) is Action 13.1: "Municipalities should adopt and
implement strict development/redevelopment standards within Federal Emergency Management
Agency A and V flood hazard zones and other areas subject to coastal flooding, erosion, and
relative sea level rise" (MBP, 1996). The relevance of the pathway to this action is that
development activities can impact both coastal/nearshore and marsh edge erosion and these
effects can be exacerbated through increases in water level and storms depicted in this pathway.
Additional management options listed in Table 2-12 relevant to these
development/redevelopment standards include "Identify, acquire and/or protect potential areas
where marsh can grow and expand, and remove barriers to marsh migration" and "Work with
programs responsible for coastal infrastructure to ensure that marsh protection is included in
management plans". This CCMP action is also relevant to the Green Community Interactions
pathway (see Figure 3-3), as development standards affect how residential development impacts
the ratio of native high marsh to Phragmites through disturbance or additional nutrient loading.
Management options from Table 2-12 specific to these issues include "Improve stormwater
management to reduce nonpoint source nutrient inputs into the marsh" and "upgrade sewage
treatment plants (e.g., tertiary treatment) and combined sewer overflow systems to reduce the
flow of excess nutrients into the marsh".
Another strategy that relates to the Green Community Interactions pathway (see
Figure 3-3) is CCMP Action 11.2: "The Regional Planning Agencies, in collaboration with the
Department of Environmental Protection and municipalities, should expand upon current
Massachusetts Bays Program efforts to identify nitrogen-sensitive embayments, determine
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critical loading rates, and recommend actions to manage nitrogen so as to prevent or reduce
excessive nitrogen loading to coastal waters and groundwater" (MBP, 1996). This CCMP action
is also relevant to the Blue Sediment Retention pathway (see Figure 3-2). While this action was
likely designed in reaction to concerns about hypoxia in nitrogen-sensitive embayments, given
that marshes are nitrogen sensitive, there is an opportunity to apply related actions to managing
this pathway. There are multiple ways that residential development can increase nutrient loads.
A starting point for determining where to focus nutrient-reduction management actions is better
information on the relative contributions of point and nonpoint nutrient loadings. Especially
when considering how the inundation regime may change nitrogen inputs to marshes under
climate change, it is important to consistently monitor and manage nutrient loadings in the
marsh. The 2009-2012 Strategic Plan (MBP, 2009) highlights this through the action "Promote
and expand the role of volunteers and local officials in monitoring stormwater and receiving
water quality and identifying sources of nonpoint source pollutants".
Wastewater carries high concentrations of nutrients that can be from either a nonpoint
source such as areas around septic systems, or from a remote point such as sewage treatment
plant outfalls. One applicable management option that several participants discussed would be to
upgrade sewage treatment plants to tertiary treatment in order to reduce the flow of excess
nutrients into the marsh. The 2009-2012 Strategic Plan articulates the means for implementing
actions to manage nitrogen. One such action is to "Provide technical assistance to develop and
implement wastewater management plans, including sewering efforts aimed at managing
contaminant and nutrient loading to local embayments".
Additional nitrogen sources include lawn fertilizer and other landscaping sources. MBP
is a key partner in the Greenscapes Massachusetts Program, which seeks to educate citizens and
professionals about landscaping practices that have less adverse impacts on the environment.
Residential development also affects stormwater management, which is another nonpoint
nutrient source. The suggested management option in Table 2-12 of "Promote more absorbent
land cover and "rain catchers" to prevent additional runoff, along with outreach, technical
assistance and building guidelines, are potential options for reducing nutrient loads from these
nonpoint sources.
Many of the current projects in the 2010-2011 Annual Work Plan are examples of
management options that potentially could be informed by the results of this study. For instance,
many of the projects include restoration activities. For the Green Community Interactions
pathway (see Figure 3-3), one management option cited in Table 2-12 is "Control invasive
species". The "Great Marsh Phragmites Monitoring and Control" project is directly relevant to
this pathway, and work to date has included the development of a Phragmites control
prioritization plan and a proposal for use of aerial photography to prioritize Phragmites control
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efforts throughout the Great Marsh. Factors within this pathway that could be identified from
aerial photography to consider in the prioritization plan would be residential development and
the ratio of native high marsh to Phragmites. Before investing in invasive species control in
areas with residential development, it may be worth first implementing efforts to reduce
disturbance or nutrient inputs.
The "Restore tidal connections" management option (see Table 2-12) is a major focus for
both the Green Sediment Retention and Blue Community Interactions pathways, and for multiple
projects in the Annual Work Plan. Implementation of the Cape Cod Natural Resources
Conservation Service Watershed Action Plan will include restoration of 26 tidally restricted salt
marshes and is an excellent example of how important this management option is to MBP.
These projects could also consider another management option from Table 2-12, "Recognize and
take advantage of the ability of marshes to "restore" themselves under the right conditions", as
removing tidal restrictions can create the "right conditions".
Within each plan are a variety of additional opportunities for incorporating the workshop
results. The examples offered here are intended to demonstrate the links, but are not
comprehensive. In addition to the adaptation of current management projects and strategies, this
study has identified sensitivities that may require the development of entirely new management
options. Planning for future projects should identify opportunities to fill those needs and test
new methods. In some cases it may even be necessary to reexamine and modify goals. Where
impending threshold changes are unavoidable, it would be advantageous to have two plans: one
to follow while species maintenance is still possible, and another plan (and goal) for after a
threshold change has occurred. Thresholds aside, climate change will also raise new issues of
conflicting goals due to trade-offs, and may result in additional situations where previous goals
are no longer attainable. One example of potential conflicting goals in the future is between
managing for sediment retention versus Saltmarsh Sharp-Tailed Sparrow habitat. As native high
marsh is lost to sea level rise, will there come a point when it is advantageous to stop controlling
Phragmites, given its sediment trapping and nutrient filtering capabilities? Even though
Phragmites does not have the same habitat value as the native marsh, it could serve as a fringing
buffer, should mudflat habitat replace the salt marsh. Thus in some cases, trade-offs may
necessitate reevaluation of habitat goals, and even the application of a "triage" approach to
prioritize certain habitats over others in the system.
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4. CONCLUSIONS
This report has described the results of a vulnerability assessment aimed at synthesizing
place-based information on the potential implications of climate change for key ecosystem
processes, with the intent of enabling managers to undertake adaptation planning. The
assessment involved identification of key management goals and ecosystem processes,
conceptual modeling of those processes, a climate change sensitivity analysis in a workshop
setting, and discussions/analysis of the potential applicability of the results for adaptation. The
workshop exercise—an expert elicitation sensitivity analysis combined with management
discussions—tested a novel approach for conducting "rapid vulnerability assessments" for
ecological systems. The sections that follow discuss general observations, insights, and
conclusions that emerged from the workshop exercise, from the analyses of management
implications, and from our assessment of the methodology's utility for potential use in other
locations/ecosystems.
4.1. INSIGHTS FROM THE WORKSHOP EXERCISE
4.1.1. Group Influence Diagrams
The group influence diagrams (see Figure 2-3 and Figure 2-8) were developed by the
workshop participants based on edits to straw man diagrams prior to the workshop, followed by
group discussions and refinement of a final group diagram during the workshop. While the main
purpose of the group influence diagrams was to establish a framework for the subsequent
sensitivity analysis, these diagrams represent key outputs in and of themselves. The construction
of the diagrams proved to be an interesting group exercise in building a highly-constrained
representation of a complex system, with only the most critical elements and interrelationships
included. The iterative process of distillation into basic diagrams by the two interdisciplinary
teams of experts resulted in some interesting differences in the Sediment Retention and
Community Interactions diagrams.
The Sediment Retention group focused on the physical components of sediment
processes as the highest priority factors influencing the balance of salt marsh accretion and
erosion in their diagram, with less focus on biological factors. There appeared to be good
familiarity with each piece of the diagram across all members of the group; this allowed them to
be specific in defining (and hence envisioning the effects of) management-related variables
(levers), which may have contributed to the high amount of agreement in judgments during the
subsequent coding exercise. The participants reported that given the opportunity they would
have added additional variables beyond the 15-variable constraint. Several participants noted
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that seasonality is an important variable that would have been added, especially as this variable
would become even more of an issue under the climate scenarios. Components of seasonality
can include annual temperature range and number of days in growing season (or conversely
number of days below freezing). The participants decided to create a separate diagram showing
the variables that would be affected by seasonality as a "confounding factor". Participants were
asked to consider seasonality and include notations in the "Notes" section as to any effect of
these considerations on their judgments.
The Community Interactions group was also successful in agreeing on an acceptable
influence diagram for the exercise. As with the Sediment Retention group, their diagram was
complex, with a mixture of both physical sediment processes (which maintain marsh elevation)
and biological processes (which determine shorebird nesting habitat and vegetation). The
management levers within the Community Interactions diagram were primarily climate change
stressors (e.g., sea level, soil temperature) versus ongoing human influence stressors (residential
development). Several participants noted a lack of expertise in certain areas of the diagram,
which led to a higher number of blanks in judgments than in the Sediment Retention group.
Despite these factors, the level of agreement for the exercise was relatively consistent across the
two groups.
A direct comparison of the Sediment Retention and Community Interactions diagrams is
instructive in revealing important similarities and differences. There is significant overlap
between the diagrams, which validates a common set of key "management lever" variables (i.e.,
tidal restrictions, impervious cover/residential development, freshwater flow, and nutrient
inputs/nitrogen) that were selected independently by both groups. Some sedimentation-related
variables are embedded in the community diagram (i.e., inundation regime, net accretion/marsh
elevation); which is appropriate since maintenance of marsh elevation through sediment
processes is essential to provision of sparrow nesting habitat. At the same time, the community
diagram shows less detail on sediment supply processes in order to include variables on plant
relationships that determine nesting habitat. The erosion component is the main element of the
sediment diagram that is not explicitly represented in the community diagram. The community
diagram includes both above ground and below ground biomass variables while the sediment
diagram only includes below ground biomass. The one common relationship with somewhat
conflicting results between the two groups is the influence of nutrients/nitrogen on below ground
biomass. The Sediment Retention group identified this as an influence of increasing relative
impact and a potential threshold, but the Community Interactions group did not. Hence this is a
relationship for which further investigation is needed to explain the disparate findings.
In conclusion, while the two groups had different experiences and challenges in building
their influence diagrams, both groups were effective in generating a useful representation of their
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ecosystem process for the sensitivity exercise. Participants reported that the highly constrained
diagram-building procedure was productive in challenging them to focus on the most key
elements of the system while still maintaining a sufficiently realistic model for sensitivity
analysis. Designing the diagrams while considering current conditions, then applying climate
scenarios to the same diagrams during the sensitivity exercise, worked smoothly. The one
exception was the seasonality variable that several participants wanted to add to the Sediment
Retention diagram; this variable was not added to the final diagram in order to allow enough
time to make judgments for all of the existing influences. This and other complications could be
avoided in future workshops by allowing the participants one more "iteration" with the diagrams
after being briefed on the climate change scenarios. This would allow them to account for how
future climate might raise additional variables for priority consideration in the diagrams.
4.1.2. Characterization of Influences
One technique for ensuring the effectiveness of expert elicitation is to break down the
problem (i.e., what are the climate change sensitivities of the selected ecosystem processes?) into
a set of distinct questions that clearly and explicitly define parameters and relationships of
interest (see EPA's white paper at http://www.epa.gov/spc/expertelicitation/index.htm). This
was accomplished by way of a systematized coding exercise—using the influence diagrams as a
framework—in which the experts made a series of judgments about individual components of
the system, in order to ultimately better understand the system as a whole. For each individual
influence arrow in the diagram, each expert was asked to characterize the effect of variable "X"
on the response variable "Y", including their confidence in that judgment. In future applications
of this method, the complexity of the coding scheme could be greatly reduced by condensing the
original 13 codes (see Table 2-2) down to the six typologies described in Section 2.2.2.5. This
would reduce confusion and increase efficiency of the exercise. Nevertheless, using the pilot
coding scheme, the experts were able to provide characterizations of all relationships in each
process, and based on these results, some general observations of interest have emerged.
Participant notes and discussions revealed that for both processes, while there are many
intermediate (and some high) sensitivity relationships among variables that are useful to be
aware of for management, it was difficult to detect changes in sensitivities across the scenarios
based on this method. Under the climate scenarios, one influence for the Sediment Retention
group became highly sensitive while four others showed a trend (but no majority agreement)
toward greater sensitivity; however, most of the sensitivities remained intermediate. For the
Community Interactions group, there were two influences of low sensitivity, five influences with
an intermediate-to-high sensitivity trend, and the majority being intermediate sensitivity under
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current conditions. Under the climate change scenarios there was one influence that decreased in
sensitivity, while the majority of influences remained intermediate in sensitivity, or lost
agreement. There were no influences which increased in sensitivity under the climate scenarios
for the Community Interactions group. It was noted that the climate scenarios may cause
thresholds to be reached in a number of different influences, though it was hard to determine at
what point these thresholds would be reached. Two thresholds (Relationships E and EE, see
Table 2-6) were indicated through coding in the Sediment Retention group, and one threshold
(see Relationship G, Table 2-10) was indicated through coding in the Community Interactions
group.
Yet outside of the coding exercise, there were indications based on participant notes and
discussions that additional potential threshold relationships do exist. Identifying thresholds is
challenging because while there may be general recognition of the potential for certain threshold
effects, it can be very difficult to identify where—and especially when—a threshold may occur.
Multiple potential thresholds were identified in both processes, through one of two ways. In
some cases, participants tried to indicate thresholds with their sensitivity codes, but did so by
including two codes under each of the scenarios to signal uncertainty as to when the threshold
might occur. Others did not indicate the threshold with their codes at all because they were not
sure whether the climate scenarios represented a big enough change to cause a threshold to be
exceeded. In these cases, the thresholds indicated in Table 2-6 and Table 2-10 were ultimately
identified through the participants' notes and discussions as relationships that could change
dramatically at some point which is currently difficult to define.
Another way of identifying relationships of particular interest for management is to
examine the relative impact of certain influences in the context of the whole process. For both
processes, under current conditions the influences identified as having primary impact included
variables spread throughout the diagrams, though there were several originating from the
management levers and several closer to the endpoints (see Figure 2-6 and Figure 2-11). Under
the climate scenarios, several of the management levers and influences going directly to the
endpoint increased in relative impact for the Sediment Retention group (see Figure 2-7). The
Community Interactions group only had a few influences increase in relative impact under the
climate scenarios, none of which were directly linked to the endpoint (see Figure 2-11). This
implies that while some variables related to management levers may become increasingly
important as climate changes, there are a number of these variables that are less understood and
may require additional monitoring and research.
Finally, characterization of interactions and confidence were also included in the
sensitivity exercise, with mixed results. Trying to consider interactive effects of multiple
variables moves the exercise to a much greater level of complexity. The number of possible
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pairwise interactions in the influence diagrams was very large, and the challenge of
understanding combinations of effects could become very complicated. Thus the participants
were not asked to attempt every possible pairwise combination, but rather were asked to indicate
which interactions "jumped out at them" as well understood and important. Of course, even
looking at all pairwise interactions would be a vast oversimplification because variables interact
in greater multiples than just pairs. Nevertheless, while there were only a few pairwise
interactions identified by enough participants to stand out, clearly these are relationships that are
sufficiently well understood to merit consideration in management planning. With regard to
confidence, the exercise made a good start of acknowledging the need to gauge confidence in the
judgments and providing a systematic way for doing so; however, the large number of data gaps
indicate that there were difficulties with this part of the methodology. Potential reasons for these
difficulties, as well as potential improvements, have been discussed in Section 3.1.2.3.2. Both
interactions and confidence are concepts that need further refinement and better estimation
methods before they can be effectively interpreted for management planning.
4.2. APPLICATION OF WORKSHOP RESULTS
4.2.1. Top Pathways for Management
When using the workshop results, it is essential to examine all three types of
information—influence type, sensitivity, and relative impact—when thinking about management
applications. For some questions, one type of information may be useful individually, but
because there are gaps and limitations within each type of information, a more complete
management picture can be built using all three types together. It is helpful to focus on
influences that are well understood, become more sensitive, and have a greater impact under
future climate scenarios. In some cases, it is possible to connect a series of influences that meet
these criteria to identify a path between the endpoint and a management lever. We have
presented what we consider to be three top pathways for management (see Figures 3-2 and 3-3)
for each process based on the information currently available from the workshop results. These
delineate relatively well-understood relationships that are climate sensitive and for which there
are consequent implications for management adaptation.
The climate-related changes of interest in the top pathways are of three main types:
(1) changes in relative impact under climate change; (2) changes in sensitivity under climate
change; and (3) threshold shifts under climate change. In the case of the influences for which
relative impact is likely to increase under one or both future climate scenarios, and especially
where relative impact is already high under current conditions as well, action could be taken
immediately. These are influences for which there is sufficient understanding and opportunity to
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connect to management options that favor desirable outcomes, with increasing relative impact on
the process as a whole as climate change continues. In the case of influences for which an
increase in sensitivity is expected under climate change, there is still time to further study and
anticipate the degree and timing of the sensitivity and to prepare best management responses.
An expectation of increasing sensitivity could be considered a notification to managers to
monitor and plan for when and how management practices should be adjusted to account for the
impending change. Finally, in the case of thresholds, there is often a strong expectation that a
threshold shift is likely, but usually a great deal of uncertainty as to exactly when the threshold
will be crossed. Monitoring of threshold variables is needed so that managers will be alerted
immediately to the shift when it occurs. In the meantime, actions can be taken to attempt to
prevent the shift by keeping the system "below" the threshold as long as possible, while
preparing a plan for what to do if an unavoidable shift occurs. After a shift occurs, managers
should have a plan as to how they will manage the system differently in its new state, or whether
they will take no action and instead shift their priorities to other goals.
It is important to note at this point that each pathway sits in the context of other
influences with which there could be important interactions, so there may be opportunities for
management options beyond those most directly evident from the main pathways. In the case of
other management pathways for which there are currently information gaps based on the
workshop results, it is vital to remember that lack of agreement does not mean zero
understanding of influences or zero degree of sensitivity. Closer inspection can show that the
agreement may be split between intermediate and high sensitivity, so the understanding that the
sensitivity of the influence is important may be obscured by the distinctions between categories.
It is of note that for influences for which there was agreement, the variation among participants
was greater than that between scenarios. This could be due to a number of reasons: a limited
range between the two mid-century climate scenarios; the number of assumptions each
participant was required to make individually for each judgment; and the interdisciplinary and
complex nature of the questions. This is an indication that these types of questions do not lend
themselves to consulting a single expert, but rather require the combined judgments of a group of
experts to complete the full picture. This also highlights the need for caution against relying
solely on combined (agreement) information: the nature of the variation across participants is
also important to consider.
Thresholds are clearly relevant to management, but usable information on thresholds
remains elusive. Thresholds are considered likely, but can be difficult to identify in terms of
how and when they will occur. A greater understanding of the location of potential
thresholds—and the system's current proximity to reaching those thresholds—will be needed
before managers can benefit from this type of information. Similarly, the data on interacting
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influences and confidence also raise some interesting issues, but should not be relied upon
heavily for management decisions until their methodologies and comprehensiveness can be
improved. Thresholds, interactions, and confidence are all important, but complex issues
surrounding the understanding of ecosystem processes and vulnerability that are not regularly
included in studies. Though they have not been fully integrated into this analysis, the results are
an important step forward in our understanding of the system, and in the development of study
methods.
4.2.2. Mainstreaming Adaptation into Planning
The vulnerability assessment results for the two ecosystem processes presented here are a
big first step in the climate change adaptation planning process. We have given examples of
ways to tie the vulnerability assessment results to potential management options as a starting
point, but incorporating adaptation fully into management planning will require a more
systematic and comprehensive process. Planning is an iterative process, especially for climate
change adaptation, which is still a nascent field. Due to this iterative nature, the planning
recommendations presented here are based on mainstreaming planning into existing planning
mechanisms and documents, rather than developing a comprehensive, stand-alone adaptation
plan. For MBP, nearer-term planning includes a multiyear Strategic Plan and an Annual Work
Plan, both of which provide ways to insert specific management options into projects that are
currently underway. In future plans, new projects that specifically incorporate climate adaptation
priorities can be added. Repeating vulnerability assessments—once management options have
been tested through project implementation—should be part of the iterative process. This is
consistent with adaptive management approaches that emphasize "learning by doing", by way of
concrete steps to test a range of management choices, monitor and evaluate outcomes,
incorporate learning into future decisions and regularly revisit and revise goals (Boesch, 2006).
Finally, this study only covered two ecosystem processes and did not attempt to evaluate relative
vulnerability or resilience across different ecosystem processes. The vulnerabilities of additional
ecosystems, processes and goals will need to be assessed, taking into account what was most
useful in the results of this study for adaptation. It may be useful to bring together a group of
experienced resource managers to discuss the results of this expert elicitation and the resulting
refined conceptual models and discuss how the results could be used to help MBP develop a set
of specific climate change adaptation recommendations.
Thresholds remain a major unknown, and while much can be done to improve our
understanding of factors affecting thresholds, some may only be revealed after they have been
crossed. Thus it would be advisable for monitoring plans to be put into place to track indicators
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of state changes. Contingency plans for management actions once a system has changed states
could be developed, as well as contingency planning for ways to respond to catastrophic events
such as levee failures or earthquakes. Successful implementation of contingency responses will
require that the political and scientific base be put into place now for responding properly
following catastrophes or threshold changes.
In the meantime, when prioritizing implementation of adaptation actions, it is easiest to
start with win-win options that contribute to current management goals and efforts while also
responding to current and future climate change. Looking beyond the win-win options, many
other actions will force managers to confront trade-offs that will require difficult policy
decisions. One example highlighted in Section 3.2.2 is the trade-off between increasing coarse
sediment supply from tributaries, which comes into conflict with current sediment reduction
efforts for species habitats (such as oyster habitat). While a first step is to set up different best
practices for species habitats, beyond that there may come a decision point when it is no longer
possible to meet both goals, so a choice between the two conflicting goals will be necessary. As
climate change progresses, there are likely to be more trade-offs, often between short and long
term goals. Mainstreaming adaptation planning will provide a better chance of foreseeing
conflicts between long- and short-term goals and identifying opportunities to build support for
hard decisions and creative solutions.
4.3. GENERAL CONCLUSIONS
4.3.1. Transferability of Results and Method
The results of this study were developed for two specific ecosystem processes within a
salt marsh ecosystem. Therefore the question arises as to how transferable the results may be.
The sensitivities examined in this study are specific to sediment retention and community
interactions in salt marshes, so the characterizations of influence type, sensitivity and relative
impact cannot be transferred directly to other ecosystems and do not apply to different processes
within these ecosystems. However, an example site was used as a way to focus the exercise and
was chosen as a representative example of intact ecosystems; thus, the results could be
transferable to other Massachusetts Bays locations in which the same ecosystem processes are
present. The variables that ended up in the group influence diagrams are general enough that
most of the results may transfer to the entire Massachusetts Bays system, with only a few
specific enough to only apply to the Jeffrey's Neck Marsh. In addition, it is likely that the
influence diagrams could also be transferred for use with like ecosystems in other estuaries, with
minor revisions for place-specific stressors or other process variables. The characterizations of
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influence type, sensitivity and relative impact would have to be revisited, particular to that
location.
Where the specific results are not transferable, the methodological process is certainly
transferable to other processes, ecosystems and locations. The methodology used for this
assessment—an analysis of key ecosystem processes through expert elicitation—is a useful
framework for understanding the current state of knowledge and research. The experts in this
study were able to share their combined understanding of key processes and how they are
expected to respond to climate change. The expert elicitation process also helped to identify
where key gaps in understanding exist, what type of research is necessary, and how management
should proceed. This methodology is transferrable in that the process used to compile, distill,
and assess key information can be replicated. Expert elicitation is used in many fields of study
and has been demonstrated here to be especially useful in understanding localized climate
change impacts. Experts can think integratively across studies and disciplines and often have
access to more current research and data than is currently available or published. As the climate
change research is constantly evolving, this type of process is useful for synthesizing the most
current information available. However, as climate change research is constantly changing, new
information and research will need to be integrated concurrent with management decisions.
4.3.2. Utility of Method for Rapid Vulnerability Assessments
Given that the method is transferable, the question of utility arises: in what cases is this
method advantageous? This method could be used again as a "rapid" vulnerability assessment,
with opportunity for some of the improvements that have been suggested for some of the
limitations. By "rapid", we mean assessments that can be carried out within 6 months to a year,
as opposed to assessments based on detailed quantitative modeling that can take multiple years.
Another advantage is that this method is able to capture more recent knowledge than would be
available from a literature review. It is also better able to capture more knowledge of the type
that is closely related to management, which is less frequently published than scientific studies.
Finally, the information is more integrated across disciplines and scales and is designed to better
match the scale of adaptation decisions. In some cases new insights about management
effectiveness may arise while in other cases existing understanding may be validated. Having a
well-supported study to substantiate new and existing ideas can position managers to justify the
most appropriate management options and priorities. It also can validate research priorities by
highlighting known research gaps.
The disadvantages are that this method is designed to focus only on a specific piece of the
system, compared to initial assessments that often rely on surveying the system more
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comprehensively (though less deeply), often through literature reviews. The amount of caution
required to properly interpret the results is another disadvantage, given multiple limitations and
caveats. The method is not intended as a consensus exercise, and the large number of influences
without agreement present challenges to either fill those research gaps to improve agreement or
to manage around limited information. In addition, this is only one group of experts, and another
group could reach different conclusions. Group selection is critical to making sure appropriate
areas of expertise and conflicting views on the system are represented. This is another reason
why in addition to looking for areas of agreement, the results of individual judgments should also
be examined. At the same time, since no participant can have complete expertise in every facet
of a system, it is also important that participants have the opportunity to confer amongst
themselves and adjust their judgments based on what they learn from each other.
Overall, the expert elicitation method developed for this study was well suited for
achieving the purpose and goals of the assessment. In addition to achieving the workshop goals,
several unexpected benefits emerged from the workshop. Participants reported that the
combination of the development of the influence diagrams with systematic judgments facilitated
thinking about the system and questions of climate change vulnerability in a different way than
they had previously. Several expressed an intention to explore adapting the method for use in
other workshop or classroom settings. Many participants found that the multidisciplinary
interactions with colleagues were a valuable, personal learning experience, and that the group
together generated new insights about the system and links to management that may not have
been seen by individuals. In short, the method tested in this project offers opportunities to
capture and integrate the existing collective knowledge of local experts, while pushing the
boundaries to develop a new understanding of the system and management options in the face of
insufficient data and deep uncertainty about future climate.
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5. REFERENCES
Bertness, MD; Pennings, SC. (2002) Spatial variation in process and pattern in salt marsh plant communities in
Eastern North America. In: Weinstein, MP; Kreeger, DA; eds. Concepts and controversies in tidal marsh ecology.
New York: Kluwer Academic Publishers; pp. 39-57, DOI: 10.1007/0-306-47534-0_4.
Boesch, DF. (2006) Scientific requirements for ecosystem-based management in the restoration of Chesapeake Bay
and Coastal Louisiana. EcolEng26:6-26.
FitzGerald, DM; Fenster, MS; Argow, BA; et al. (2008) Coastal impacts due to sea-level rise. Ann Rev Earth Planet
Sci 36:601-647. Available online at
https://darchive.mblwho ilibrary.org/bitstream/handle/1912/2273/SEALEV~l.pdf? sequence=l.
Groffman, PM; Baron, JS; Blett, T; et al. (2006). Ecological thresholds: The key to successful environmental
management or an important concept with no practical application? Ecosystems 9 (1):1-13, DOI: 10.1007/sl0021-
003-0142-z
Henrion, M; Breese, JS; Horvitz, EJ. (1991) Decision analysis and expert systems. Al Magazine 12(4): 64-91.
Available online at http://www.aaai.org/ojs/index.php/aimagazine/article/view/919/837.
MBP (Massachusetts Bays Program). (1996) Comprehensive conservation and management plan: an envolving plan
for action. U.S. Environmental Protection Agency, Massachusetts Executive Office of Environmental Affairs,
Boston, MA. Available online at http://www.mass.gov/envir/massbays/ccmp.htm.
MBP (Massachusetts Bays Program). (2009) Massachusetts Bays Program Strategic Plan: April 2009-June 2012.
April 1, 2009 Draft. Massachusetts Executive Office of Environmental Affairs, Boston, MA.
MBP (Massachusetts Bays Program). (2010) Massachusetts Bays Program FY11 Annual Work Plan: July 1, 2010 to
June 30, 2011. Massachusetts Executive Office of Environmental Affairs, Boston, MA
Morris, JT; Sundareshwar, PV; Nietch, CT; et al. (2002) Responses of coastal wetlands to rising sea level. Ecology
83(10):2869-2877. Available online at
http://www.ias.sdsmt.edu/staff/Sundareshwar/Reprints/morris%20et%20al%202002.pdf.
Morgan, MG; Henrion, M. (1990) Uncertainty: A guide to dealing with uncertainty in quantitative risk and policy
analysis. New York: Cambridge University Press.
NECIA (Northeast Climate Impacts Assessment). (2006) Climate change in the U.S. Northeast: a report of the
northeast climate impacts assessments. Union of Concerned Scientists, Cambridge, MA. Available online at
http://www.climatechoices.org/assets/documents/climatechoices/NECIA_climate_report_final.pdf.
Pradhan, M; Henrion, M; Provan, G; et al. (1996) The sensitivity of belief networks to imprecise probabilities: an
experimental investigation. Artificial Intell 85(l-2):363-397.
Scavia, D; Field, JC; Boesch, DF; et al. (2002) Climate change impacts on U.S. coastal and marine ecosystems.
Estuaries 25(2): 14-164.
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APPENDIX A. DEVELOPMENTAL PROCESS FOR
CLIMATE READY ESTUARIES VULNERABILITY ASSESSMENT
A.l. SELECT KEY GOALS, ECOSYSTEMS, AND ECOSYSTEM PROCESSES
The MBP partners participated in several discussions and meetings to outline
management priorities, key resources to consult, and other considerations for selecting key goals
for the assessment. As a starting point, MBP's Comprehensive Conservation and Management
Plan (CCMP) (MBP, 1996; MBP, 2003) was examined and discussed to select four to six key
management goals as a focus of the assessment. These goals would help to further refine the
analysis to specific ecosystems, ecosystem processes, stressors of concern, and indicators for
measuring changes in the ecosystem. Selected management goals included:
• Protect and manage existing wetlands
• Restore and enhance the habitat diversity and living resources of wetlands
• Protect submerged aquatic vegetation
• Prevent the spread of marine invasive species in order to maintain biodiversity
Following an October 2008 kickoff meeting with MBP staff and other local experts to
gather scientific and management background information on the system, salt marshes were
selected as the wetland habitat of focus for the project. These systems were identified as highly
relevant to MBP's management goals due to their diversity, their habitat values for threatened
and endangered species, their vulnerability to invasive species, and their sensitivity to
climate-related variables such as sea level rise and altered hydrology. As a starting point for
exploring linkages among such climate-related variables, their interactions with nonclimate
stressors of concern, and the key ecosystem processes that maintain the system, a general
conceptual model was developed.
A.2. CONCEPTUAL MODELS
The conceptual models were intended to serve as a framework for further analysis in the
vulnerability assessment. The models depicted likely pathways by which climate drivers may
directly or indirectly affect interacting stressors that impact ecosystem processes. The process is
intended as iterative, as we learn from exploring the first two ecosystem processes, next steps
can involve focusing on additional ecosystem processes, or for repeating a similar analysis for
additional habitats. The development of the conceptual model has also served to help with
A-l
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narrowing process; we began with a comprehensive list of ecosystem processes and indicators
and then chose those more important to and best representing healthy salt marsh functioning.
The total number of possible ecosystem processes was narrowed down to five to six key ones for
the ecosystem. The models also included a similar number of variables that may serve as
indicators for the status of these endpoints. Ecosystem processes and indicators were identified
in discussions among MBP and EPA ORD, as well as through examination of the Delta Regional
Ecosystem Restoration Implementation Plan's conceptual models developed by the CALFED
Bay-Delta Program (Schoellhamer et al., 2007; Kneib et al., 2008; San Francisco Estuary
Indicators Team, 2008). To ensure consistency with current research, these ecosystem processes
and indicators were cross-walked with locally-specific literature on climate change impacts
(Ashton et al., 2007; Cavatorta et al., 2003; Frumhoff et al., 2007; Orson et al., 1998), as well as
research on metrics and indicators for the region (Massachusetts Department of Coastal Zone
Management, 2003; USGS-FWS, 2008).
Stressor interactions are stressors that may work independently or together to affect
ecosystem functioning. These included both nonclimate and climate-related influences that
stress salt marsh ecosystems. Preexisting stressors and stressor interactions were identified
during the development of salt marsh conceptual model, and impacts of these stressors of
concern were identified using the MBP Comprehensive Conservation and Management Plan.
Climate drivers are climate variables that may impact ecosystem processes directly (e.g.,
raise water temperature) or indirectly (e.g., cause changes in nutrient inputs). The climate
drivers relevant to salt marshes were identified by first examining climate drivers for estuarine
systems outlined in Synthesis and Assessment Product 4.4: Preliminary review of adaptation
options for climate-sensitive ecosystems and resources (CCSP, 2008), followed by extensive
discussions among the MBP partners. The climate drivers were then mapped to the key
processes of the ecosystem, either directly or through interactions with preexisting stressors.
These pathways provided the basis for the development of the conceptual models. The pathways
included are intended as a heuristic, without distinguishing between the magnitudes between
them. It is not possible to include all possible system components, nor connections between
them. The general salt marsh model is first presented, and then additional detail for individual
ecosystem processes is described in the two submodels.
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A.2.1. General Models
A.2.1.1. Salt Marshes
The general model for salt marshes in presented in Figure A-l. Climate drivers in the salt
marsh conceptual model include: changes in air temperature, changes in precipitation, sea level
rise, and changes in storm climatology and wind. Changes in air temperature refers to the
variation from the climatological mean surface air temperature in a particular region. Changes in
precipitation refers to variation from the climatological mean of the amount, intensity, frequency
and type of rainfall, snowfall and other forms of frozen or liquid water falling from clouds in a
particular region, changes refer to both the form and flow of precipitation. Sea level rise is
defined as "relative sea-level rise," the change in sea level relative to the elevation of the
adjacent land, which can also subside or rise due to natural and human induced factors. Relative
sea-level changes include both global sea-level rise and changes in the vertical elevation of the
land surface. Changes in storm climatology and wind refers to the variation from the
climatological mean of the frequency, intensity and duration of extreme events (such as
hurricanes, heavy precipitation events, drought, heat waves, etc.) and the changes in the direction
and timing of the dominant seasonal winds.
Stressor interactions within the salt marsh conceptual model include: changes in water
temperature, salinity, flooding, sedimentation and erosion, invasive species, pollutants, other
human uses, altered flows, and land use/land use change. Changes in water temperature refers to
variation in the climatological mean surface water temperature in a particular region. Changes in
salinity are measured by variations in salinity concentration, with respect to lateral gradient or
vertical stratification. Flooding is defined as an excess of water that does not recharge ground
water beyond time frames typical for watersheds due to high precipitation events, storm surge, or
infrastructure damage. Sedimentation and erosion includes the transport, deposition, and
removal of soil and rock by weathering, mass wasting, and the action of streams, waves, winds
and underground water. Invasive species are plants, animals or microbes not native to an area
that are able to exploit a niche and disrupt native species, with negative impacts. Pollutants
include any substance introduced into the environment that, because of its chemical composition
or quantity, prevents the functioning of natural processes and produces undesirable
environmental and health effects. Other human uses is a catch-all category based on the CCMP
which includes the use of the marsh and surrounding area for activities such as fishing, shipping
and ports, dredging, transportation projects, sand mining, recreational use, marinas, and
industrial uses that may impact the marsh. Altered Flows refers to tidal restrictions or upstream
A-3
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Climate
Drivers
Changes in Air
Temperature
Changes in
Precipitation
Sea Level Rise
Changes in Storm
Climatology and Wind
Changes in Water
Temperature
Sedimentation
and Erosion
>
Invasive
Species
Community
Interactions
Primary
Productivity
Nutrient
Cycling
Water
Purification
Sediment
Retention
Indicators
Water
Retention
Species
Population Size
Biomass
Freshwater
Inflow
Invertebrate
Index
Water Quality
Standards
Extent of
Aquatic Habitat
Sediment
Quantity
Figure A-l. Salt Marsh Conceptual Model.
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water diversions for agricultural, industrial, transportation, or urban uses that change the natural
flow of freshwater and sediment into the marsh, including leveeing, diking, damming, filling, or
channeling. Land use/land use change is defined as the current use of marsh and human-induced
changes to the marsh or surrounding land, including wetland alteration and expansion of the built
environment.
Ecosystem processes in the salt marsh conceptual model include: community
interactions, primary productivity, sediment retention, water retention, nutrient cycling, and
water purification. Community interactions is defined as the interrelations among species within
the ecosystem. Primary productivity is the production of energy by plants and phytoplankton
within the entire system. Sediment retention is the balance between the processes of removal
and deposition of suspended sediment. Water retention is defined as the capability to buffer
against flooding. Nutrient cycling is the process of transfer of nutrients between organisms and
the water. Water purification is defined as the removal of pollutants and harmful
microorganisms.
Indicators within the salt marsh conceptual model include: species population size, water
quality standards, freshwater inflow, sediment quantity, extent of aquatic habitat, biomass, and
invertebrate index. Species population size is defined as the number of similar organisms
residing in a defined place at a certain time, including threatened and endangered species, native
species, and invasive species. Water quality standards are provisions of State or Federal law
which consist of designated uses for waters of the United States, and water quality criteria for
such waters based upon such uses. Criteria address the values for water quality indicators (e.g.,
water temperature, salinity, water contaminant exposure, biological thresholds for water
contamination, nutrient concentrations, water toxicity) that are required to support designated
uses. Freshwater inflow is the amount of freshwater inflow to the estuary from the watershed.
Sediment quantity is defined as suspended sediment concentration. Extent of aquatic habitat is
defined as the area of all contiguous, vegetated salt and brackish wetland, or mean width of
marsh (may be divided into low or high marsh or by dominant species). Biomass is the presence
and abundance of different species. The invertebrate index is the collection of metrics that are
aggregated into a single score to measure the composition of the invertebrate community.
The salt marsh conceptual model focuses on a limited number of ecosystem processes
that are key to the habitat and region. In some instances, a component of the system may fill
roles at multiple levels, and the model does not represent all possible roles a particular
component may fill. The model does not take the cumulative effects of climate stressors or
tipping points/critical thresholds into account. The model does not include ocean acidification as
a climate driver, as current understanding of salt marshes indicate it as secondary compared to
the other stressors.
A-5
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A.2.2. Submodels
Following the development of the general salt marsh ecosystem model, two ecosystem
processes within the model were chosen for more detailed investigation. The purpose was to
select good processes to start with to test out the method, but the choice does not imply that these
are necessarily the most important, or the most vulnerable, processes. Sediment retention was
identified as a key salt marsh process because of the importance of sediment supply to allow for
marsh development and growth. In the Massachusetts Bays, sediment supply is influenced by a
number of factors, including storms, heavy precipitation events, and human influences such as
tidal restrictions and development. MBP and other regional partners have done extensive work
on examining changes in sediment and how these changes may be influenced by changes in
climate. This provided the basis for the development of the sediment retention submodel.
Community Interactions was chosen as the second ecosystem process of focus. To select
a specific well-constrained "storyline" of interactions between two to four species for this
process, ICF and EPA consulted with MBP and regional experts on key sensitivities for this
process within the Massachusetts Bays system. The storyline focuses on the relationship of
four species (Spartina alterniflora, Spartinapatens, Phragmites australis, Ammodramus
caudacutus). The Saltmarsh Sharp-Tailed Sparrow (Ammodramus caudacutus) prefers the
native species of Spartina patens as habitat over the invasive Phragmites. The lower marsh
Spartina alterniflora is likely to migrate upland with pressure from sea level rise, perhaps
infringing on the upper marsh Spartina patens. Changes in freshwater flow will affect the less
salt tolerant Phragmites with a major question of whether it will expand into the upper marsh
range of Spartina patens. This storyline provided the basis for the development of the
community interactions submodel.
A.2.2.1. Sediment Retention
The sediment retention submodel is presented in Figure A-2. It focuses on the balance
between the processes of deposition and retention of sediment within a salt marsh and the
resultant ability of the marsh to persist in the face of climate change. The accumulation of
sediments and marsh vertical accretion result from interactions among tidal imports, vegetation
dynamics, and deposit!onal processes (Reed, 1995). Freshwater runoff and coastal storms
transport and deposit sediments onto the marsh surface, and the roots and stems of marsh
vegetation retain sediment that would otherwise be carried away from the marsh by wind and
waves (Roman et al., 1997). Over time, the accumulation of dead and dying organic matter
produces peat, and the combination of peat accumulation and sediment deposition gradually
builds up the marsh surface. Ultimately it is the balance between marsh vertical accretion and
A-6
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Climate
Drivers
>
Changes in
Air
Temperature
Changes in
Precipitation
Changes in Storm
Climatology and Wind
Sea Level
Rise
Altered Flows
Upstream water diversions and controls:
•Change in peak (max or min) flow volume,
•Change in flow variability
OtherHuman Uses
Dredging and dredge disposal:
Frequency, location and extent of dredging
Beating/shipping:
Frequency and degree of wake disturbances
Land Use / Land Cover Change
Increase in impervious cover:
rea or % change in impervious cover
•Land conversion
Area or % change in land use classification
Sediment
Retention
Indicators
Metrics
Species Population Size
Abundance of native speciesfe.g.,
Spartinaalterniflora, reported as%of
ground cover for a particular plot)
Abundance of invasive speciesfe.g.,
Phragmites austral is, reported as % of
ground cover for a particularplot)
Freshwater Inflow
Flow volume (daily average cfs)
Salinity (psu)
Sediment Quantity
Total suspended sediment (mg/L)
Rate of erosion (mm/yr)
Extent of Aquatic Habitat
Area (acres or km2) of high or low marsh
Figure A-2. Sediment Retention submodel.
-------
sea level rise that determines whether a tidal marsh at any given location will persist in the face
of rising seas by migrating inland or will convert to tidal flats or open water (Reed, 1995).
A number of key climate variables (air temperature, precipitation, storm climatology and
wind, and sea level rise) and stressors (altered flows, other human uses, land use/land cover
changes) may impact this process directly or indirectly. In New England marshes, altered
hydrology typically includes tidal restrictions, which reduce the regular tidal flooding of marshes
needed for marsh maintenance (Carlisle et al., 2002). At the upland edge, excess runoff from
heavier precipitation events in areas with impervious surfaces may oversaturate marsh soils and
reduce soil salinity. Increases in the frequency and intensity of storms can change the pattern of
sediment transport along the shoreline, carrying more sediment away from the marsh and
increasing erosion at some locations, reducing the sediment available for marsh development
(Nyman et al., 1995).
A.2.2.2. Community Interactions
The community interactions submodel is presented in Figure A-3. This submodel
focuses on the relationship of marsh vegetation zonation and the availability of nesting habitat
for the Saltmarsh Sharp-Tailed Sparrow, Ammodramus caudacutus, a high-priority species for
bird conservation in New England. The Saltmarsh Sharp-Tailed Sparrow nests in the high marsh
zone to avoid nest flooding (DiQuinzio et al., 2002; Gjerdrum et al., 2005). Under undisturbed
conditions, the low marsh is dominated by the tall form of Spartina alterniflora., and the high
marsh zone is characterized by salt marsh hay (Spartinapatens), black rush (Juncus gerardf) and
the short form of S. alterniflora. This pattern of vegetation zonation results from a combination
of plant competition and the physical characteristics of the intertidal zone. The tall form of S.
alterniflora dominates the low marsh because it is able to tolerate the stress of inundation and
low soil oxygen content, whereas high marsh plants are not. In contrast, S. patens, J. gerardi and
the low form of S. alterniflora dominate the high zone to the exclusion of low marsh species
because of the superior competitive ability of these plants in obtaining below-ground nutrients
(Donnelly and Bertness, 2001; Bertness et al., 2002; Bertness and Pennings, 2007).
A number of key climate variables (changes in air temperature, changes in precipitation,
changes in storm climatology and wind, and sea level rise) and stressors (invasive species,
altered flows, pollutants, land use/land cover changes) may impact this process directly or
indirectly. As sea level rises, the dominant vegetation of the low marsh, the tall form of
S. alterniflora traditionally restricted to the low marsh zone by competition, can invade the high
A-8
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Climate
Drivers
Changes in Air
Temp
Changes in
Precipitation
Sea Level Rise
Changes in Storm
Climatology and Wind
Changes in Salinity
Location of mesohaline/oligohaline transition
Frequency or % of timemesohaline
Sedimentation and Erosion
Episodic storm driven erosion
reqin&pcy of flooding of adjacent
freshwarer wetlands
undation from seaward
Invasive Species
oAbundance of Phragmitesaustralis,
as % of ground cover
oAverage plant height
Altered Flows
oPeak storm discharge into marsh
oPeakstorm discharge timing
Pollutants
oMercury exposure
>
Community Interactions
Saltmarshsharp-
tailedsparrow
Land Use/Land Cover Change
Shoreline armoring
'%hardarmoring/distancefrom shore to
armoring
Wetland disturbance
Increase in impervious cover:
oArea or %change in impervious cover
oLand conversion
oAreaor % change in land use classification
Sport/no altemiflora
Spartina patens
Indicators
Metrics
Phragmites australis
Species Population Size
Abundance of each above plant
species, as % of ground cover
Abundance of saltmarsh
sparrow
Biomass
Plant density
Average plant height
Above ground
biomass
Extent of estuarine habitat
Pinching index: Ratioof
width of Spartina
transects
Area (total acres or
hectares) of sparrow
habitat
Figure A-3. Community Interactions submodel.
-------
marsh zone because of its tolerance of inundation and salinity. This is already being observed in
New England salt marshes (Donnelly and Bertness, 2001). At the same time, the high marsh
may be invaded at its landward border by Phragmites australis because of nutrient-enrichment
from adjacent residential development. This is because high nutrient availability may shift
competition away from competition for below-ground nutrients to competition for light. Under
these conditions, Phragmites is favored over native high marsh plants. Increased nutrient
enrichment may also promote invasion of the high marsh at its seaward edge by S. alterniflora as
it is released from competition for below-ground nutrients (Bertness et al., 2002).
These considerations suggest that the combination of increased sea level rise and nutrient
enrichment from residential development may promote invasion of high marsh by S. alterniflora
at its seaward border and Phragmites at its landward border. This could greatly reduce the
availability of the traditional high marsh nesting habitat of the Saltmarsh Sharp-Tailed Sparrow.
A.3. CONCLUSIONS
The analysis of available data for potential indicators and of existing models indicated
that there was insufficient information available on metrics for the indicators to answer the
sensitivity questions of this assessment using quantitative modeling. However, it was also
evident that a vast amount of local knowledge was available through consultation with regional
experts in the processes of interest. This led to the development of the expert elicitation
workshop approach described in Section 2 of this report. The workshop was meant to serve as
an opportunity to supplement current knowledge based on background research and examine
potential changes that may occur due to climate influences. The conceptual diagrams described
above provided the basis for the development of the initial influence diagrams used at the
workshop (as described in Section 2 of this report) as well as context for how these ecosystem
processes of focus fit with the rest of the ecosystem.
A-10
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A.4. REFERENCES
Ashton, AD; Donnelly, JP; Evans, RL. (2007) A Discussion of the potential impacts of climate change on the
shorelines of the Northeastern US A. Mitig Adapt Strat Glob Change 13(7):719-743. DOI10.1007/sl 1027-007-
9124-3. Available online at
http://mit.whoi.edu/science/GG/coastal/publications/pdfs/AshtonDonnellyEvans_MITI2007.pdf.
Bertness, MD; Ewanchuk, PJ; Silliman, BR. (2002) Anthropogenic modification of New England salt marsh
landscapes. Proc Nat Acad Sci 99:1395-1398. Available online at
http://www.pnas.org/content/99/3/1395.full.pdf+html?sid=86aaf2b6-c384-4819-85b7-8ce54fObfe97.
Bertness, MD; Pennings, SC. (2002) Spatial variation in process and pattern in salt marsh plant communities in
Eastern North America. In: Weinstein, MP; Kreeger, DA; eds. Concepts and controversies in tidal marsh ecology.
New York: Kluwer Academic Publishers; pp. 39-57.
Carlisle, BK; Donovan, AM; Hicks, AL; et al. (2002). A volunteer's handbook for monitoring New England salt
marshes. Massachusetts Office of Coastal Zone Management, Boston, MA. Available online at
http://www.mass.gov/czm/volunteermarshmonitoring.htm.
Cavatorta, JR; Morgan, J; Hopkinson, C; et al. (2003) Patterns of sedimentation in a salt marsh-dominated estuary.
Biol. Bull. 205: 239-241. Available online at http://www.biolbull.Org/cgi/reprint/205/2/239.pdf.
CCSP (Climate Change Science Program). (2008) Preliminary review of adaptation options for climate-sensitive
ecosystems and resources. Synthesis and Assessment Product 4.4. A report by the U.S. Climate Change Science
Program and the Subcommittee on Global Change Research. Julius, SH; J.M. West, JM; eds. Baron, JS; Griffith, B;
Joyce, LA; et al (Authors). U.S. Environmental Protection Agency, Washington, DC, USA, 873 pp.
http://downloads.climatescience.gov/sap/sap4-4/sap4-4-fmal-report-all.pdf
DiQuinzio, D; Patona, PWC; Eddlemanac, WR. (2002) Nesting ecology of saltmarsh sharp-tailed sparrows in a
tidally restricted salt marsh. Wetlands 22(1): 179-185.
Donnelly, J; Bertness, M. (2001) Rapid shoreward encroachment of salt marsh cordgrass in response to accelerated
sea-level rise. Proc Natl Acad Sci 98:14218-14223. Available online at
http://www.pnas.org/content/98/25/14218.full.pdf+html?sid=86aaf2b6-c384-4819-85b7-8ce54fObfe97.
Frumhoff, PC; McCarthy, JJ; Melillo, JM; et al. (2007) Confronting Climate Change in the U.S. Northeast: Science,
Impacts, and Solutions. Northeast Climate Impacts Assessment (NECIA). Cambridge, MA: Union of Concerned
Scientists (UCS). Available online at http://www.northeastclimateimpacts.org.
Gjerdrum, C; Elphick, CS; Rubega, M. (2005) Nest site selection and nesting success in saltmarsh breeding
sparrows: the importance of nest habitat, timing, and study site differences. Condor 107:849-862.
Kneib, R; Simenstad, C; Nobriga, M; et al. (2008) Tidal marsh conceptual model. Sacramento (CA): Delta Regional
Ecosystem Restoration Implementation Plan. Available online at
http://nrm.dfg.ca. gov/FileHandler.ashx?DocumentID=10093
Massachusetts Office of Coastal Zone Management. (2003) North Shore Wetland Assessment project report:
transferring a wetland assessment method to the North Coastal and Ipswich River Watersheds. Report released
February 2003. Available online at http://www.mass.gov/czm/ma_czm_wetlandassess_nsreport2003.pdf.
MBP (Massachusetts Bays Program). (1996) Comprehensive conservation and management plan: an envolving plan
for action. U.S. Environmental Protection Agency, Massachusetts Executive Office of Environmental Affairs,
Boston, MA. Available online at http://www.mass.gov/envir/massbays/ccmp.htm
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MBP (Massachusetts Bays Program). (2003) Comprehensive conservation and management plan: an envolving plan
for action. 2003 revisions to the CCMP. U.S. Environmental Protection Agency, Massachusetts Executive Office
of Environmental Affairs, Boston, MA. Available online at
http://www.mass.gov/envir/massbays/pdf/revisedccmp.pdf
MBP (Massachusetts Bays Program). (2009) Massachusetts Bays Program Strategic Plan: April 2009-June 2012.
April 1, 2009 Draft. Massachusetts Executive Office of Environmental Affairs, Boston, MA
Nyman, JA; Crozier, CR; DeLaune, RD. (1995) Roles and patterns of hurricane sedimentation in an estuarine marsh
landscape. Estuar Coastal Shelf Sci 40(6):665-679.
Orson, RA; Warren, RS; Niering, WA. (1998) Interpreting sea level rise and rates of vertical marsh accretion in a
southern New England tidal salt marsh. Estuar Coastal Shelf Sci 47(4):419-429.
Reed, DJ. (1995) The response of coastal marshes to sea-level rise: survival or submergence? Earth Surf Proc
Landforms 20(l):39-48.
Roman, CT; Peck, JA; Allen, JR; et al. (1997) Accretion of a New England (U.S.A.) salt marsh in response to inlet
migration, storms, and sea level rise. Estuar Coastal Shelf Sci 45(6):717-727.
SFEIT (San Francisco Estuary Indicators Team). (2008) Assessment framework as a tool for integrating and
communicating watershed health indicators for the San Francisco estuary. Technical Report #1. Submitted by the
San Francisco Estuary Indicators Team to the California Department of Water Resources, September 30, 2008.
Schoellhamer, D; Wright, S; Drexler; J; et al. (2007) Sedimentation conceptual model. Sacramento, (CA): Delta
Regional Ecosystem Restoration Implementation Plan. Available online at
http://science.calwater.ca. gov/pdf/drerip/DRERIP_sediment_conceptual_model_fmal_ 111307 .pdf
USGS-FWS (U.S. Geological Survey-Fish and Wildlife Services). (2008) Salt marsh assessment study: metrics
identified for testing at national wildlife refuges. U.S. Department of the Interior, Washington, DC.
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APPENDIX B. EXPERT ELICITATION WORKSHOP PREPARATION
AND IMPLEMENTATION
B.I. PREWORKSHOP
B.I.I. Selecting Workshop Participants
The MBP partners developed a list of criteria for selecting highly qualified local experts
who spanned the range of disciplines, science and management continuum, and empirical versus
theoretical research experience needed to collectively characterize the ecosystem processes under
consideration. Criteria for selecting participants included:
Demonstrated understanding of the body of literature with regard to sediment
retention OR community interactions (depending on which breakout group), as
evidenced by academic training, research, and publications
Demonstrated ability to think of uncertainty in qualitative terms
Knowledge of science behind estuary management, as evidenced by academic
training, research, and publications
Knowledge of estuary management issues as evidenced by academic training,
research, and publications
Past work in MBP region
Past work with salt marsh development/sediment retention processes (the balance of
sediment supply vs. loss) OR salt marsh community interactions (interactions of
shorebird nesting habitat and vegetation zonation), depending on the candidate's
proposed breakout group
These criteria were considered in developing a list of 20-24 qualified candidates for each
breakout group. Candidates were then contacted to determine their availability and interest in
testing a new method for vulnerability assessment. From this larger pool, a group of
seven experts was selected for each breakout group. According to EPA's Expert Elicitation Task
Force White Paper (http://www.epa.gov/spc/expertelicitation/index.htm), a review of the
literature indicates that 90% of successful expert elicitations use between 3 and 11 experts, with
a law of diminishing returns in having a group larger than six. For this study, workshop
participants included the following individuals:
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Sediment Retention Breakout Group:
Susan Adamowicz, Rachel Carson National Wildlife Refuge
Britt Argow, Wellesley College
Chris Hein, Boston University
David Ralston, Woods Hole Oceanographic Institution
John Ramsey, Applied Coastal Research and Engineering, Inc.
Peter Rosen, Northeastern University
John Teal, Woods Hole Oceanographic Institution
Community Interactions Breakout Group:
Walter Berry, U.S. EPA Atlantic Ecology Division
Robert Buchsbaum, Massachusetts Audubon Society
Dave Burdick, University of New Hampshire
Michele Dionne, Wells National Estuarine Research Reserve
David Johnson, Woods Hole Marine Biological Laboratory
Gregg Moore, University of New Hampshire
Cathy Wigand, U.S. EPA Atlantic Ecology Division
The expertise of each of the individual participants contributed to the interdisciplinary
complexity of the group. Experts were selected from the management and adaptation research
communities, and represented federal and state government agencies, research and consulting
organizations, nongovernmental organizations and academia. The credentials for each of the
participants, including past and current work and research and areas of expertise, are summarized
for the Sediment Retention group in Table B-l, and for the Community Interactions group in
Table B-2.
B.1.2. "Straw Man" Influence Diagrams
An initial "straw man" influence diagram (see Figure B-l and Figure B-2) for each
breakout was developed by ICF, EPA, and MBP prior to the workshop based on the more
detailed salt marsh conceptual model and sediment retention and community interactions
submodels developed previously (see Appendix A). The "straw man" influence diagrams
differed from the more comprehensive conceptual models in that they focused on only those
elements of the model that participants believe are most critical for understanding responses of
the ecosystem process to the human and climate stressors under consideration. The "straw man"
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Table B-l. Sediment Retention breakout group participants, affiliations, and
qualifications
Name
Affiliation
Qualifications
Susan Adamowicz
Rachel Carson National
Wildlife Refuge
U.S. Fish and Wildlife Service Land
Management Research Demonstration Biologist.
Expertise in salt marsh ecology, habitat
management, restoration, and tipping points.
Britt Argow
Wellesley College
Research on salt marsh and estuarine
sedimentology, geomorphology, and hydrology.
Expertise in geosciences and coastal
sedimentology.
Research on inorganic sediment processes
coastal systems. Expertise in coastal
sedimentology.
Chris Hein
Boston University
in
David Ralston
Woods Hole
Oceanographic
Institution
Research on fluid mechanics and scalar transport
in estuaries and the coastal systems. Expertise in
estuarine physics and sediment transport.
John Ramsey
Applied Coastal
Research and
Engineering Inc.
Serves on Climate Change Adaptation Advisory
Committee for Massachusetts, and has provided
consulting on coastal engineering projects.
Expertise in coastal processes and engineering.
Peter Rosen
Northeastern University
Research on coastal processes, geomorphology
and sedimentology. Developing a model for the
evolution of Boston Harbor Island shorelines in
response to rising sea levels. Expertise in coastal
geology.
John Teal
Woods Hole
Oceanographic
Institution
Research and consulting on coastal wetlands, salt
marsh restoration, submerged aquatic vegetation,
and nutrients. Currently involved with marsh
restoration in fresh, brackish and salt wetlands.
Expertise in wetlands ecology.
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Table B-2. Sediment Retention breakout group participants, affiliations, and
qualifications
Name
Affiliation
Areas of expertise
Walter Berry
U.S. EPA Atlantic
Ecology Division
Research on human disturbance impacts on avian
species. Expertise in salt marsh ecology.
Robert Buchsbaum
Massachusetts
Audubon Society
Directs Massachusetts Audubon's Ecological
Inventory and Monitoring Project. Research on
coastal plant and animal species, nutrients, and
climate change. Expertise in salt marsh ecology.
Dave Burdick
University of New
Hampshire
Research on salt marsh restoration, invasive
species, and tidal restoration. Recent research on
Spartinapatens and Phragmites austrails.
Expertise in restoration ecology.
Michele Dionne
Wells National
Estuarine Research
Reserve
Research on aquatic habitats, marsh-estuarine
food web ecology, and wetland restoration.
Established monitoring protocols for restoration
projects in the New England region. Expertise in
aquatic, coastal, and salt marsh ecology.
David Johnson
Woods Hole Marine
Biological Laboratory
Research on aquatic species, nutrients, and salt
marsh habitat. Recent study on salt marsh infauna
and nutrient enrichment in Plum Island. Expertise
in salt marsh and invertebrate ecology.
Gregg Moore
University of New
Hampshire
Research on aquatic species, restoration ecology,
invasive species, and plant zonation. Recent
project comparing natural vs. tidally restricted
salt marshes in Cape Cod. Expertise in coastal
wetland ecology.
Cathy Wigand
U.S. EPA Atlantic
Ecology Division
Research on plant species, nutrients, and human
disturbance impacts on salt marshes in New
England. Expertise in wetland ecology.
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Altered Flows: Tidal
Restrictions
Land Use/Land Cover:
% Impervious Cover
Sediment
W Deposition/
Erosion
Figure B-l. Sediment Retention "straw man" influence diagram.
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Flooding
Land Use/Land Cover:
Residential
Development
Ratio Low Marsh to
High Marsh
Saltmarsh Sharp-
Tailed Sparrow
Nesting Habitat
Ratio of Native High ^
Marsh to
Phragmites
Figure B-2. Community Interactions "straw man" influence diagram.
B-6
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influence diagrams were used in the preworkshop briefing and homework assignment in order to
further refine the sediment retention and community interactions influence diagrams.
B.1.3. Preworkshop Briefing and Homework Assignment
Participants participated in two preworkshop briefing calls and a homework assignment
that would be used to develop consolidated influence diagrams to be used at the workshop. The
preworkshop briefing calls were held on January 14 and 28, 2010. These calls gave participants
a briefing on the background of the project, work to date, the purpose of the workshop, and an
overview of the homework assignment. The first call covered the larger context of the project as
part of the CRE Program and the purpose for MBP being involved in this study. The
development of the conceptual models (see Appendix A), and how these led to the ecosystem
processes of focus was also covered. Finally, the expert elicitation approach was explained in
the context of how it would be used for the purposes of the workshop. The second call went into
more detail about the exercise, introducing the influence diagrams and example reference site.
Participants were given an opportunity to ask questions regarding these initial diagrams in
preparation for completing the homework assignment.
Part of the background material presented was information on an example site for
participants to consider when more spatial specificity would be useful during the workshop
exercise and to provide context for management discussions. The Jeffrey's Neck or Little Neck
Marsh, in Ipswich, Massachusetts, is located within the Great Marsh, and was chosen because it
includes classic New England salt marsh features and species composition. Its natural features
include the high marsh/low marsh dynamic examined by the Community Interactions group.
There is an extensive system of creeks and channels, as well as large areas of bordering
vegetated edge and upland. The example site is subject to a number of stressors common to
marshes in the region, including: development surrounding the marsh; tidal restrictions (this
particular site has two, one of which that has been restored); a significant amount of invasive
Phragmites; extensive mosquito ditching; and other hydrologic modifications such as road
crossings and barriers to migration.
The homework assignment asked participants to review a number of items: (1) selected
articles relevant to the ecosystem process breakout group to which they were assigned (for the
Sediment Retention breakout group: Cavatorta et al. [2003]; Donnelly and Bertness [2001];
Scavia et al. [2002]; Schmitt et al. [1998]; for the Community Interactions breakout group:
Bertness et al. [2002]; DiQuinzio et al. [2002]; Donnelly and Bertness [2001]; Gjerdrum et al.
[2005]; and Scavia et al. [2002]); (2) conceptual models of the ecosystem and ecosystem process
to which they were assigned; and (3) the draft influence diagram for the ecosystem process to
B-7
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which they were assigned. Participants were asked to review the draft influence diagram and
provide recommendations on what should be added or removed. Participants were asked to add
or subtract variables or relationships until the preliminary influence diagram matched their
understanding of the process. We asked participants to include no more than 10-15 variables in
the diagram in order to keep it focused on the highest priority influences. We also asked
participants to focus on current conditions (including current climate) when reviewing and
commenting on the diagram.
Participants were asked to provide a quantitative definition for each variable, a metric for
measuring the variable, and a range of values for the metric. Participants were also asked to
assign values to the metrics they selected. This could include actual measured values (e.g.,
35 km3 of inflow) as well as a range of values (e.g., 5 to 50 km3 of inflow).
B.I.4. Consolidated Influence Diagrams
The preliminary diagram for each breakout group was revised prior to the workshop
based on the participants' homework responses. The process involved examining the
participants' responses and constructing a tally of the variables used and influences (arrows)
included. Variables and influences that were most frequent across all responses were included in
the consolidated influence diagrams. For the both the Sediment Retention and Community
Interactions groups, all of the participants provided comments on the preliminary influence
diagram. In addition, due to the rescheduling of the initial MBP workshop, two of the original
participants were not able make the new date, but their homework was taken into account when
developing the consolidated diagrams. Based on the responses from the participants,
consolidated influence diagrams were developed for the workshop.
B.2. WORKSHOP
B.2.1. Group Influence Diagrams
Group influence diagrams were developed during the first day of the workshop. Within
their breakout groups, the participants discussed how the consolidated influence diagrams should
be refined for use as a final "group" influence diagram. The participants added, removed, or
redefined variables based on a group discussion. The group diagrams were to become the basis
for the expert elicitation exercise of assigning judgments about influences among variables. The
Sediment Retention and Community Interactions group influence diagrams are provided in
Section 2.
B-8
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B.2.2. Introduction to Climate Scenarios and Confidence
The participants received two handouts designed to orient them to the climate scenarios
and to the methodology for assessing confidence. The first handout contained a summary of
Climate Scenarios A and B, which was used by the participants in assessing the sensitivity of salt
marshes and mudflats across a range of plausible scenarios of climate change. It explained the
development of two climate futures in a mid-century (2040-2069) time frame. Participants used
these scenarios on Day 2 to make new judgments compared to their judgments under "current
conditions" on Day 2. The full climate scenarios handout can be found in Appendix C.
The second handout presented explanatory information and a coding scheme for use by
the participants in assessing their confidence in each of their judgments under both current
conditions and under Climate Scenarios A and B. The full handout may be found in
Appendix D.
B.2.3. Coding Exercise
Following the development of the group influence diagrams, participants were asked to
make their individual judgments on the diagram using the coding scheme. As described in
Section 2, the participants used the coding scheme to make judgments on the following: (1) type
and degree of influence for each relationship included in the influence diagram; (2) the
associated confidence for each influence judgment; (3) type of interactive influences for
relationships of their own choosing; and (4) the associated confidence for each interactive
influence judgment. These judgments were done for current conditions (on the first day of the
workshop), and Climate Scenario A and Climate Scenario B (on the second day of the
workshop). Example handouts that participants used to make their judgments are provided in
Tables B-3, B-4, and B-5.
B.2.4. Variation Across Participants in Sensitivity Judgments
For both the Sediment Retention and Community Interactions groups, variability among
participants in their judgments contributed to lack of agreement on sensitivities for some
influences. Figure B-3 presents the full range of variation among participants of the Sediment
Retention group by showing the same trio of figures as shown in Figure 2-5 but broken out for
each individual participant. Looking across all the participants, there was more variability
between participants than across scenarios for any given participant. There were no patterns
across participants, such as characterizing only increasing sensitivity. The changes across the
scenarios made by Participants 3, 6, and 7 were of only increasing sensitivity, and Participants 1,
2, 4, 5 had both increases and decreases, sometimes across the scenarios for one influence.
B-9
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Table B-3. Example of expert elicitation handout for influences under current conditions (Sediment Retention
group)
Instructions:
Please assess the effect of X on Y by selecting the appropriate "degree of influence" and its associated "confidence".
^^^^^H Current conditions
Relationship A
Relationship B
Relationship C
Relationship D
Relationship £
Relationship F
Relationship G
Variable X
Land Cover: %
Impervious Cover
Marsh High Water
Level
Storms
Nutrient Inputs
Nutrient Inputs
Altered Flows: Tidal
Restrictions
Altered Flows: Tidal
Restrictions
on
on
on
on
on
on
on
Variable Y
Nutrient Inputs
Inundation Regime
Inundation Regime
Net Accretion
Below Ground
Biomass
Tidal Exchange
Freshwater Flow
Degree of influence
(please select 0-13)
Confidence
(LH, LL, HH, HL)
Notes
td
o
-------
Table B-4. Example of expert elicitation handout for influences under climate scenarios (Community
Interactions group)
Instructions: Please assess the effect of X on Yby selecting the appropriate "degree of influence" and its associated "confidence".
Relationship A
Relationship B
Relationship C
Relationship D
Relationship £
Relationship F
Relationship G
Variable X
OMWM
Sea Level
Freshwater Flow
Freshwater Flow
Land Use/Land
Cover:
Residential
Development
Land Use/Land
Cover:
Residential
Development
Land Use/Land
Cover:
Residential
Development
on
on
on
on
on
on
on
Variable Y
Inundation
Regime
Inundation
Regime
Salinity
Inundation
Regime
Freshwater
Flow
Ratio Low
Marsh to High
Marsh
Ratio of Native
High Marsh to
Phragmites
Climate Scenario A
Degree of influence
(please select 0-13)
Confidence
(LH, LL, HH, HL)
Climate Scenario B
Degree of influence
(please select 0-13)
Confidence
(LH, LL, HH, HL)
Notes
td
-------
Table B-5. Example of expert elicitation handout for interactive influences under climate scenarios (Sediment
Retention group)
Instructions: Please assess the effect of X on Y with Z by selecting the appropriate "interactive influence" and its associated
"confidence".
Example 1:
Relationship
B+C
Example 2:
Relationship
G+H
Variable X
Marsh High
Water Level
Altered
Flows:
Tidal
Restrictions
on
on
on
Variable Y
Inundation
Regime
Freshwater
Flow
with
with
with
Variable Z
Storms
Land
Cover:
Percent
Impervious
Cover
Climate Scenario A
Interactive
Influence
Confidence
(LH, LL, HH,
HL)
Climate Scenario B
Interactive
Influence
Confidence
(LH, LL, HH,
HL)
Notes
td
-------
Current
Scenario A
Scenario B
Participant 1
Participant 2
Participant 3
Participant 4
Key
Low sensitivity
Intermediate sensitivity
Highsensitivity
No influence
Unknown
>-
No answer
Figure B-3. Sediment Retention group summary influence diagrams of
sensitivities: variance across participants.
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Current
Scenario A
Scenario B
Participant 5
Participant 6
Participant 7
Key
Low sensitivity
Intermediate sensitivity
High sensitivity
Noinfluence
Unknown
».
Noanswer
Figure B-3. Sediment Retention group summary influence diagrams of
sensitivities: variance across participants, (continued)
For the Community Interactions group, Figure B-4 presents the full range of variation
among participants by showing the same trio of figures as those shown in Figure 2-10, but
broken out for each individual participant. Looking across all the participants, we see that there
is again more variability between participants than across scenarios for any given participant.
The majority of changes in sensitivity type across the climate scenarios are of increasing
sensitivity. The changes across the scenarios made by Participants 1 are of only increasing
B-14
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Current
Scenario A
Scenario B
Participant 1
Participant 2
Participant 3
Participant 4
Figure B-4. Community Interactions group summary influence diagrams of
sensitivities: variance across participants.
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Current
Scenario A
Scenario B
Participant 5
Participant 6
Participant 7
Key
Low sensitivity
No influence
Intermediate sensitivity
Unknown
High sensitivity
Noanswer
Figure B-4. Community Interactions group summary influence diagrams of
sensitivities: variance across participants, (continued)
B-16
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sensitivity; Participants 2, 3, 4, 6, and 7 had both increases and decreases, but more of the
former; Participant 5 had no changes across the scenarios, and only categorized influences as
intermediate sensitivity or provided no answer.
B.2.5. Exercise Discussions and Report-outs
After participants made their individual judgments on the influence diagram using the
coding exercise, the participants reconvened in their breakout groups for a group discussion.
Participants discussed their reactions to the exercise and how it was structured, individual
judgments on type and degree of influence, individual judgments on confidence, key issues and
gaps in understanding. This group discussion often helped to clarify issues that participants may
have had in understanding the coding scheme or understanding influences.
Based on this group discussion, the facilitator helped the participants to identify some key
points that emerged. These key points addressed issues such as key influences, important
pathways, thresholds, significant changes associated with climate change, management
implications, etc. The facilitator from each breakout group presented these key points to the
larger group to summarize the discussion.
B.2.6. Discussion of Management Implications
Following the breakout group discussions and exercise of making individual judgments,
participants gathered in the larger group to discuss management implications. This discussion
would help MBP to examine some of the key issues that emerged from the expert elicitation
exercise and how to translate those issues into action. The facilitator led the discussion by
asking participants to consider how climate stressors might impact the estuary across a range of
management scenarios. The discussion also explored research and data needs, suggestions for
habitat restoration and reducing existing stressors, and fundamental shifts in management that
may be necessary.
B.3. POSTWORKSHOP
B.3.1. Review of Workshop Report
A report was developed subsequent to the workshop documenting key outputs in two
sections: key results and workshop discussions. This report provides a documentation of all of
the participant materials, including: participant guidance documents, participant homework
responses, handouts and other materials used at the workshop, and individual participant
judgments. Key points that emerged during the breakout group and larger group discussions are
summarized, as well as the discussion on management implications. Participants were asked to
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review this report and provide any comments. These comments were incorporated into a final
workshop report, which is available upon request from the authors.
B.3.2. Synthesis of Results
A synthesis of results was developed in order to analyze the participants' individual
judgments made at the workshop. The synthesis reviews the objectives of conducting the expert
elicitation workshop and identifies key questions that the synthesis of judgments seeks to answer.
It reviews the coding schemes used by participants during the workshop and summarizes a
coding typology that was used to group codes to characterize types and degrees of influences and
sensitivities. Finally, it describes the methodology for analyzing the available judgments and
presents key results in the form of tables and figures. The contents of this synthesis comprise
much of the substance of the results sections of this report.
B.3.3. Review of Draft Report
The workshop report and preliminary results reports were used to develop this technical
report to present the synthesis results and place them in the larger context of the implications for
management and MBP's capacity to respond. The report will be subjected to a separate letter
review, which will be done through an EPA external peer-review process. Following this
review, the final report will be developed, which responds to the peer-review comments. An
additional report that focuses on lessons learned across the two assessments for San Francisco
Estuary Partnership and MBP will also be developed.
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B.4. REFERENCES
Bertness, MD; Ewanchuk, PJ; Silliman, BR. (2002) Anthropogenic modification of New England salt marsh
landscapes. Proc Nat Acad Sci 99:1395-1398. Available online at
http://www.pnas.org/content/99/3/1395.full.pdf+html?sid=86aaf2b6-c384-4819-85b7-8ce54fObfe97.
Cavatorta, JR; Morgan, J; Hopkinson, C; et al. (2003) Patterns of sedimentation in a salt marsh-dominated estuary.
Biol. Bull. 205: 239-241. Available online at http://www.biolbull.Org/cgi/reprint/205/2/239.pdf.
DiQuinzio, D; Patona, PWC; Eddlemanac, WR. (2002) Nesting ecology of saltmarsh sharp-tailed sparrows in a
tidally restricted salt marsh. Wetlands 22(1): 179-185.
Donnelly, J; Bertness, M. (2001) Rapid shoreward encroachment of salt marsh cordgrass in response to accelerated
sea-level rise. Proc Natl Acad Sci 98:14218-14223. Available online at
http://www.pnas.org/content/98/25/14218.full.pdf+html?sid=86aaf2b6-c384-4819-85b7-8ce54fObfe97.
Gjerdrum, C; Elphick, CS; Rubega, M. (2005) Nest site selection and nesting success in saltmarsh breeding
sparrows: the importance of nest habitat, timing, and study site differences. Condor 107:849-862.
Scavia, D; Field, JC; Boesch, DF; et al. (2002) Climate change impacts on U.S. coastal and marine ecosystems.
Estuaries 25(2): 14-164.
Schmitt, C; Weston, N; Hopkinson, C. (1998) Preliminary evaluation of sedimentation rates and species distribution
in Plum Island Estuary, Massachusetts. Biol Bull 195(2): 232-233.
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APPENDIX C. PARTICIPANT HANDOUT ON CLIMATE SCENARIOS
MBP Workshop Climate Change Scenarios
This handout is intended to assist participants in assessing the sensitivity of salt marshes across a
range of plausible scenarios of climate change. It provides the details of two distinct but
scientifically credible climate futures for a mid-century (2040-2069) time period. Participants
will use these scenarios in revisiting their assessments of influence completed on the first day.
Two Climate Change Scenarios: "Lower-Range" and "Higher Range"2
Relatively more mild and more severe mid-century climate change scenarios were selected to
bound plausible futures. Overall, both describe a significantly warmer climate accompanied by
increases in annual precipitation and higher sea levels, but the degree of change is much greater
in the "higher range" compared to the "lower range" scenario. In addition, there are differences
in the seasonality of the changes captured in the two futures, particularly as related to
precipitation amount and intensity and streamflow.
Development of the Climate Scenarios
These two bounding scenarios were developed directly from the climate projections used in the
Northeast Climate Impacts Assessment (NECIA).3 Three leading climate models were used to
develop these projections: U.S. NOAA's Geophysical Fluid Dynamics Laboratory (GFDL)
CM2.1; the United Kingdom Meteorological Office's Hadley Centre Climate Model, version 3
(HadCMS); and the National Center for Atmospheric Research's Parallel Climate Model (PCM).
These three models were selected to provide a range of climate sensitivity representative of the
current models used by the IPCC.4 The models were run with both a lower greenhouse gas
emission scenario (Bl SRES) and a higher emission scenario (AlFi SRES) to capture a range of
possible future emissions trajectories. The "lower-range" and "higher-range" temperature and
precipitation scenarios for 2040-2069 compared to 1961-1990 baseline conditions were
developed by averaging the three climate models' results for the lower and higher emissions
futures, respectively, and then statistically downscaling these results to the 1/8-degree grid
representative of the Ipswich, MA area. Sea level rise information was based on two of the
scenarios used in an application of the Sea Level Affecting Marshes Model (SLAMM 5.0) to the
Parker River National Wildlife Refuge5, an area which included the example Jeffrey's Neck
Marsh. The "lower-range" eustatic sea level rise scenario is based on the conservative IPCC
mean A1B SRES, and the "higher-range" eustatic sea level rise scenario is the mid-century rise
for a project 1.5m rise by 2100, consistent with estimates provided by Rahmstorf (2007).6'7
The usage of the terms "lower-range" and "higher-range" refers to the scenarios provided in this handout and are
not intended to reflect the lowest and highest possible futures.
3 As described in NECIA (2006) and at http://www.northeastclimatedata.org/.
4 "Climate sensitivity is the temperature change resulting from a doubling of atmospheric carbon dioxide
concentrations relative to pre-industrial times" (NECIA 2006).
5 Clough, J. and E. Larsen. 2009. Application of the Sea-Level Affecting Marshes Model (SLAMM 5.0) to Parker
River, Monomoy, and Mashpee NWRs. Obtainable from: Dr. Brian Czech, Conservation Biologist, U. S. Fish and
Wildlife Service National Wildlife Refuge System, Division of Natural Resources and Conservation Planning,
Conservation Biology Program, 4401 N. Fairfax Drive - MS 670 Arlington, VA 22203.
6 Rahmstorf (2007) derived a historical semi-empirical relationship between temperature and sea level rise and
applied this relationship to IPCC projected estimates of temperature rise.
C-l
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Summary of Climate Scenarios: Averages for 2040-2069 compared to 1961-1990
Temperature
Precipitation
Sea Level
Storms/Wind
Annual Average
Geographically
Days > 90 °F*
Coldest Day of
Year
Growing Season
Winter Change
Summer Change
Spring Change
Fall Change
Heavy Events
Yearly Snow Depth
Total Increase
"Lower Range" Scenario
(3-model average of Bl)
+3.6 °F
Boston "moves" to
Philadelphia, PA
20 days
+4.3 °F
+3 weeks
+10.6%
+7.9%
+15.0%
+1.9%
~8% increase in the max
amount of precip to fall
within a 5-day period
-9 cm
17 cm (A1B scenario)
"Higher Range" Scenario
(3-model average of AlFi)
+5.6 °F
Boston "moves" to
Washington, DC
34 days
+6.5 °F
+4 weeks
+15.1%
+11.2%
+14.1%
-2.2%
-12.5% increase in the max
amount of precip to fall
within a 5-day period
-11 cm
41 cm (mid-century model
estimate using 1.5m scenario
by end of century) 9
NECIA (2006) suggests little change in the frequency of winter-time storms for
the East Coast. However, under the "higher range" scenario, between 5 and 15%
of these storms (an additional 1 storm per year) will move northward during late
winter (Jan, Feb, March), affecting the Northeast. (No change for the "lower
range" scenario.) In addition, the impact of a higher sea level will increase the
likelihood of storm damage to coastal locations.
For hurricanes, the most current understanding is that rising sea surface
temperatures will increase evaporation, increasing the amount of rainfall
associated with any given hurricane, but there is too much uncertainty in
projections of hurricane frequency and wind intensity to say much about future
trends.
Note that these projections do not account for changes in dynamic sea level rise or changes in land elevation
through subsidence or uplift. For example, by the end of the century, Yin et al. (2009) suggest changes in sea level
rise resulting from ocean circulation could be of the same order in magnitude as the eustatic sea level rise estimates
for the Boston area.
8 Compared to the 1960-1990 annual average of 9 days with temperatures above 90°F.
9 The total difference in range between mean and spring tides of 1.3 ft (39.6 cm) is very close to the higher emission
scenario rise of 41 cm. Based on data for Plum Island Sound (south entrance), the spring high tide is generally 0.65
feet (19.8 cm) higher than the mean high tide. http://tidesandcurrents.noaa.gov/tideslO/tab2eclb.html#8.
C-2
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What else do these changes mean for our system?
Ice-out
Spring peak flow period
Summer low flow period
Drought1" frequency
Winter flooding events
"Lower Range" Scenario
(3-model average of Bl)
2 weeks earlier
7 days earlier
1 week longer
"Higher Range" Scenario
(3-model average of AlFi)
4 weeks earlier
10 days earlier
2 weeks longer
2 every three years (compared to 1 every 2 years today)
2-fold increase in number of events
General increases in salinity of estuarine waters, freshwater tributaries, and coastal aquifers
during summer
Where can I find additional information?
The Northeast Climate Impacts Assessment (NECIA) was conducted in 2006/2007. Statistically
downscaled climate projections results are discussed in the report, Climate change in the U.S.
Northeast. The report and information is available at www.northeastclimateimpacts.org and
www.climatechoices.org/ne. The data presented in the scenarios above is available at the NECIA
website (www.northeastclimatedata.org).
The U.S. Global Change Research Program (USGCRP) developed another set of climate
projections through statistical downscaling of climate models and provides regional summaries
of projected changes in climate and the potential impacts in the publication, Global Climate
Change Impacts in the United States. This data is also available online at: http://gdo-
dcp.ucllnl.org/downscaled cmip3_proiections/dcplnterface.html.
Works Cited
Northeast Climate Impacts Assessment (NECIA). 2006. Climate change in the U.S. Northeast:
A Report of the Northeast Climate Impacts Assessments. Union of Concerned Scientists,
Cambridge, MA.
Rahmstorf, S. 2007. A Semi-Empirical Approach to Projecting Future Sea-Level Rise. Science
315(5810):368-370.
USGCRP. 2009. Global Climate Change Impacts in the United States, Thomas R. Karl, Jerry M.
Melillo, and Thomas C. Peterson, (eds.). Cambridge University Press, 2009.
Yin, J., M. E. Schlesinger and R. J. Stouffer, 2009. Model projections of rapid sea-level rise on
the northeast coast of the United States. Nature Geosci, 2(4), 262-266. doi: 10.1038/ngeo462.
10 Defined as the monthly soil moisture is more than 10% below the long-term mean (relative to historic
simulations).
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APPENDIX D. PARTICIPANT HANDOUT ON CONFIDENCE
Method for Assessing Confidence in Expert Judgments
Characterization of uncertainty is a critical component of assessment science. Thus this
workshop exercise includes a component in which the expert participants will assess their current
level of scientific confidence in each influence for which they are making a judgment. The aim is
to provide information on not only degrees of influence among variables, but also the degree of
uncertainty associated with each judgment, given the current state of knowledge in the scientific
community.
The design of this analysis is derived from general guidance on uncertainty from recent large
assessment efforts such as those of the Intergovernmental Panel on Climate Change (IPCC) and
the U.S. Climate Change Science Program (CCSP) [e.g., see Moss and Schneider, 2000; IPCC,
2004; IPCC, 2005; CCSP, 2008; CCSP, 2009]. One fundamental principle is the distinction
between uncertainty expressed in terms of "likelihood" of an outcome versus "level of
confidence" in the science underlying the finding. Likelihood is relevant when assessing the
chance of defined future occurrence or outcome, and involves assigning numerical probabilities
to qualifiers such as "probable," "possible," "likely," "unlikely" (CCSP 2009). In contrast, level
of confidence refers to the (qualitative) degree of belief within the scientific community that
knowledge, models, and analyses are accurate, based on the available evidence and the degree of
consensus in its interpretation. We are taking this latter approach.
Each expert is asked to rate his/her confidence in each judgment about degree of influence based
on: (1) the amount of scientific evidence that is available to support the judgment; and (2) the
level of agreement/consensus in the expert community regarding the different lines of evidence
that would support the judgment. These confidence attributes are further described below:
High/low amount of evidence: Is the judgment based on information that is well-studied and
understood, or mostly experimental or theoretical and not well-studied? Does your experience in
the field, your analyses of data, and your understanding of the literature indicate that there is a
high or low amount of information on this influence? Sources of evidence - in order of relative
importance - include: 1) peer-reviewed literature; 2) grey literature; 3) data sets; 4) personal
observations and personal communications.
High/low amount of agreement: Do the studies and reports across the scientific community, as
well as your own experience in the field or analyzing data, reflect a high degree of agreement
about the influence, or do they lead to competing interpretations?
Based on the above, levels of confidence in judgments can be sorted into four general categories:
• Well established = high evidence/high agreement (HH);
• Competing explanations = high evidence/low agreement (HL);
• Established but incomplete = low evidence/high agreement (LH);
• Speculative = low evidence/low agreement (LL).
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References
CCSP, 2008: Preliminary review of adaptation options for climate-sensitive ecosystems and
resources. A Report by the U.S. Climate Change Science Program and the Subcommittee on
Global Change Research. [Julius, S.H., J.M. West (eds.), J.S. Baron, B. Griffith, L.A. Joyce, P.
Kareiva, B.D. Keller, M.A. Palmer, C.H. Peterson, and J.M. Scott (Authors)]. U.S.
Environmental Protection Agency, Washington, DC, USA, 873 pp.
CCSP, 2009: Best practice approaches for characterizing, communicating and incorporating
scientific uncertainty in climate decision making. A Report by the U.S. Climate Change Science
Program and the Subcommittee on Global Change Research. [Morgan, M.G., H. Dowlatabadi,
M. Henrion, D. Keith, R. Lempert, S. McBride, M. Small, and T. Wilbanks (Authors)].
Washington, DC, 156 pp.
IPCC, 2004: Workshop on Describing Scientific Uncertainties in Climate Change to Support
Analysis of Risk and of Options. Working Group I Technical Support Unit, Boulder, Colorado
[Manning, M., M. Petit, D. Easterling, J. Murphy, A. Patwardhan, H.-H. Rogner, R. Swart and
G. Yohe (eds.)], May 11-13, 2004, National University of Ireland, Maynooth, Co. Kildare,
Ireland, 146 pp. Available at:
http://ipcc-wgl.ucar.edu/meeting/URW/product/URW_Report_v2.pdf
IPCC, 2005: Guidance Notes for Lead Authors of the IPCC Fourth Assessment Report on
Addressing Uncertainties, 4 pp. Available at: www.ipcc.ch/pdf/assessment-report/ar4/wgl/ar4-
uncertaintyguidancenote.pdf.
Moss, R. and S.H. Schneider, 2000: Uncertainties in the IPCC TAR: Recommendations to lead
authors for more consistent assessment and reporting. In: Guidance Papers on the Cross Cutting
Issues of the Third Assessment Report of the IPCC [Pachauri, R., T. Taniguchi, K. Tanaka
(eds.)], World Meteorological Organisation, Geneva, Switzerland, 33-51.
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