Transcript Optimizing Tradeoffs in Woodland Ecosystems

TRADE OFFS IN LAND USE PLANNING: A
CASE STUDY FROM THE MURCHISONSEMLIKI LANDSCAPE
Dan Segan
July 24, 2013
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Table of contents
1.
Project overview
2.
Case Study: Murchison Semliki landscape Uganda
3.
Trade-offs between multiple stakeholders
4.
Trade-off between biodiversity and carbon
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Project overview
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Project overview
Overview: In 2012 USAID allocated funds to three NGOs through the Africa biodiversity collaborative
group to explore the application of systematic conservation planning to navigate trade-offs in land use
planning.
Goal: To provide case studies of how to integrate the objectives of climate change mitigation, climate
change adaptation and biodiversity in working landscapes for REDD+ project developers, government
stakeholders and planners
Methods: Use the Marxan spatial optimization tool in three key landscapes to minimize conflict where
possible and illuminate trade-offs in achieving objectives where they exist.
Landscapes
• Imbirikani Group Ranch, Kenya (AWF) – first workshop in September 2013
• Masito-Ugalla, Tanzania (JGI) – first workshop completed
• Murchinson-Semliki (WCS) – both workshops completed
Format: 2 workshops in each landscape
First workshop - Introduce stakeholders to systematic conservation planning and review data
Second workshop – Present results to stakeholders and
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Marxan
What is Marxan?
The Marxan software package is the most widely used software in conservation
planning around the world. It was developed by Ian Ball and Hugh Possingham and
provides decision support for the design of reserve systems
What problem does Marxan solve?
Objective Function:
•
Minimise the overall “cost”
•
Subject to the “constraint” that all biodiversity targets are met (e.g. 30% of
each vegetation type)
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Case Study: Murchison Semliki landscape Uganda
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Murchison Semliki landscape
Landscape profile:
• High biodiversity landscape
• High population growth forecasted
• Active oil exploration activities
Tropical High Forest
Colonizing Forest
Woodland
Bushland
Grassland
Plantation / woodlot
Farmland
Urban or rural built-up area
Open water
Wetland
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Biodiversity data
1.
Species
I.
7 threatened mammals species
II.
4 threatened birds species
III. 2 endemic plant species
IV. 10 threatened plant species
2.
Ecosystems
I.
3.
6 priority ecosystems
Targets
I.
Species - expert based minimal viable populations
II.
Ecosystems - expert based
Photo credits: Julie Larsen Maher © WCS
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Ecosystem service data
•
Three estimates of above ground biomass
Avitable et al.
NASA
WHRC
Higher
Lower
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Opportunity cost -Timber
•
Seven key timber species
Julie Larsen Maher © WCS
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Opportunity cost – Oil
Photo credit: www.chimpreports.com
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Opportunity cost – Human expansion
1.
Calculation:
I.
Population density
II.
Roads
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Single stakeholder results
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Priority areas
Objective: Minimize area
Objective: Minimize
inclusion of oil exploration
areas
Legend
Always selected
Frequently selected
Regularly selected
Seldom selected
Never selected
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Priority areas
Objective: Minimize inclusion Objective: Minimize inclusion
of areas humans are likely to of high value forestry
expand into
Legend
Always selected
Frequently selected
Regularly selected
Seldom selected
Never selected
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Impact of preferences
Area
Oil
Timber
Population density
Scenario evaluation
90%
• Minimum biodiversity
targets met in all
conditions
80%
Included in selected set
70%
60%
• Area selected inside
areas identified as highly
prospective for oil ranged
from >75% to <25% of the
total oil footprint area
50%
40%
30%
• total area required
increased by 15% when
attempting to avoid
populated
20%
10%
0%
Biodiversity
Human expansion
Scenario
Oil
Timber
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Balancing interests
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Priority areas
Scenario Name: Balance oil and
biodiversity
Targets: Base
Minimize: Balance oil and biodiversity
Legend
Always selected
Frequently selected
Regularly selected
Seldom selected
Never selected
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Additive approach
Biodiversity
Oil
Human expansion
Timber
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Additive approach
Aggregate selection:
Agriculture + Biodiversity + Oil + Timber
Equal weighting of all interests
Useful for identifying areas
amenable to all stakeholders
Legend
Always selected
Frequently selected
Regularly selected
Seldom selected
Never selected
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Balancing interests
Analytic hierarchy process
(Saaty, 2008)
• pair-wise comparison
• feedback on logical consistency of
rankings
Workshop 1
Example output:
Example output:
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Core areas
Scenario Name: Balance 1
Targets: Base
Minimize: Cost to multiple stakeholders
Lower variability than additive
approach
Legend
Always selected
Frequently selected
Regularly selected
Seldom selected
Never selected
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Carbon and Biodiversity
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Carbon for conservation
Will conserving for REDD+ conserve biodiversity?
?
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Biodiversity and Carbon
Minimize cost to capture carbon
Maximize carbon for cost
• Carbon captured in biodiversity scenario • 40% more carbon could be captured in
could be captured in 28% of the area
the same area as the biodiversity scenario
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Biodiversity outcomes when maximizing carbon
Biodiversity
250%
251%
Carbon
225%
194%
Percent of landscape target
200%
175%
168%
164%
172%
150%
130%
129%
125%
101%
100%
100%
100%
110%
101%
100%
100%
83%
75%
64%
45%
50%
26%
25%
32%
35%
16%
6%
0%
White backed
Vulture
Hyaena
Lion
Crested Crane
Leopard
Giraffe
Shoebill
Forest
Elephant
Nahan' s
Francolin
Uganda
Mangabey
Chimpanze
Savannah species under represented when focus is only on carbon.
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Biodiversity / Carbon trade-off curve
100%
95%
13%
Biodiversity
90%
7%
85%
80%
75%
70%
65%
70%
75%
80%
85%
90%
95%
100%
Carbon
Significant biodiversity gains for small loss in carbon when moving away
from a carbon maximization investment strategy
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Thank you
Collaborators
•
James Watson
•
Andrew Plumptre
•
Sam Ayebare
•
Grace Nangendo
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Lilian Pintea
•
David Williams
•
Natalie Bailey
Generous support:
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