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Making conservation tradeoffs explicit using multi-criteria analysis

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Melinda Agapito

on 2 April 2013

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Transcript of Making conservation tradeoffs explicit using multi-criteria analysis

Values are what we care about. As such, values should be the driving force of our decision-making (Ralph Keeney in Value Focused Thinking, 1992) coral Marine transportation ecological protection
Multiple interests
in planning Issue: We need to balance
these multiple interests Our approach Results So What: Policy Conclusion Wrap-Up
Zoom out Stakeholders' values Evidence-based
information maps share maps judgment discuss maps In MPA planning,
what does it really mean? Optimization MCDM Existing Methods Framework: What matters & how much? Data & data preparation Workshop: test-run In simple terms Criteria details are not lost
Weighting method
AHP - 62%
DW - 38% How important a
criterion over the other?
NL data - Twin Results - Method Questions - Policy - Method MPA Planning Environment Process: Social Methods Social impact
assessment STF-OWA Conclusion So What: Policy & tool development Results Making conservation tradeoffs explicit
using multicriteria analysis Thank you! Goal Tier 3 (Objectives) Maximized Objective A (eg, biodiversity) Evaluate the suitability of planning sites for conservation Indicator1 (eg, richness) Indicator2 (eh, evenness) Indicator1 (eg, rarity) Indicator1 (kg, fish landing) Indicator2 ($, fish landing) Indicator1 (# crew members) Indicator2 (# of fisher groups) Indicator2 (eg, richness) Subjective 1A
(eg, seabird) Subjective 2A
(eg, groundfish) Subjective 1B
(eg, fish landing) Subjective 2B
(eg, employment) maximized objective A
(eg, biodiversity) minimize objective B
(eg, socioeconomic impacts) Evaluate the suitability planning sites for conservation purposes Tier 1 (Attributes) Tier 2 (Sub-objectives) Tier 3 (Objectives) Goal user-input Universal Wt Order Wt 1 Universal Wt Universal Wt Order Wt 3 Order Wt 2 Spatial Tier Framework (STF) STF-OWA Universal
Order weight STF-OWA Can MCDA-OWA help identify these areas? Evaluate MCDA-OWA Approach Methods user-input Evaluate
MCDA-OWA Short questionnaire
Discussion MCE-FLOWA (1) Analytic Hierarchy Process (AHP)

(2) Direct Weighting, individual and group 14 participants Academe ENGOs Government Agencies surface layers ~3000 PUs ~1.2 million sq. km Kernel density Biodiversity Data
are typically
in geographic points Search radius: 15-25 km ArcGIS Density (count)
Biomass (kg)
Rarity (31 sp)
Species status (11 sp) BIOLOGICAL LAYERS Biological indicators Note (e.g. average of 12-year MSS) Average of 31 species per sample set over the 12-yr MSS survey 15 biological layers Sponges
Ground fish
Exploited invertebrates SOCIO-ECONOMIC LAYERS Socio-economic indicators 6 social and economic layers
(A) Distribution of $
(B) Distribution of fishers/licenses OTHER USES/ISSUES LAYERS Other uses/issues: indicators Fish landing Distribution of fishing crew
per boat (employment) How diverse or even (PUs; 21 fishing groups)? Sum:14 types
of fisher groups per PU were recorded over 10-yr period Note: sum/average is based
on 10-year logbook data average: ~ 4 crew
per boat Oil & Gas activities Gear conflict - *effort overlap
between shrimp and crab gears
* boat size LRIT - ship positions recorded on a 6-hour interval (Marine traffic ) Oil & gas - based on well locations and licenses 4 Layers sum of gear severity
per planning unit
(10-year data) Sum of commercial
vessel tracks per km
(1-year data) Sum of event
points per m Gear conflict Surface layers
using kernel density Our questions Results: tradeoff between sub-objectives user-input Universal weight how important
is one criterion over the other? Order weight: how do we want
to trade these criteria off? Universal weight: how important
is one criterion over the other? Order weight: how do we want
to trade these criteria off? B/OU quantitative decisions flexible SE Socioeconomic [ALL] every planning unit is represented with the lowest score; (e.g. scores ranging of 1-7, ALL is 1), compensation capacity is extremely low. Biological, Other uses/issues [Some] some compromise on higher score (e.g. scores ranging 1-7, SOME is 6), compensation capacity is higher. STF usefulness
67% different levels of usefulness;
33 indifferent.
43 % finds the concept
difficult/somewhat difficult
to understand
57% indifferent OWA Policy questions Policy questions How MPA impacts are going to be distributed among these groups? Who will sacrifice more? Who are willing to participate? making the tool more
interactive, flexible
and automated will make it
more useful in planning Fishing several groups possible Policy questions How much fishing dollar can we give up?
As for the industries, how much are they
willing to sacrifice? Species-rich areas
are also rich in social
and economic activities Canada's MPA objective
protect high biodiversity areas Workshop participants puts highest priority on biodiversity some fishing dollar
must be given up some big industries
must share the sacrifice species-rich inshore region came out important sacrificing inshore fishery "backbone of east coast fishery" - NDP fisheries critic How resilient are these fishers to the
possible impact if their employment
will be put at risk? Negotiation Capacity building diverse multi-scaled complex dynamic can never be purely objective tradeoffs and or hard choices, inevitable participative Methods are fitted into the social process interactive Dr. Rodolphe Devillers Dr. Evan Edinger Dr. Ratana Chuenpagdee Dr. Mariano Koen-Alonso DFO EN-CWS CCG-Maritime Security C-NLOPB tradeoff negotiation Our contribution Bottom line:
multiple interests tourism oil & gas Surpervisors
Dr. Evan Edinger
Dr. Rodolphe Devillers Committee members
Dr. Ratana Chuenpagdee
Dr. Mariano Koen-Alonso EN-CWS
DFO CCG-Maritime Security
Workshop participants d
a mixed Expert judgment What's current? How can a GIS-based MCDM approach be implemented in such a way that:

A. quantitative decisions are flexible;
B. criteria details are not totally lost (can be revisited);
C. it will allow greater flexibility in terms
of prioritization of sites;
D. it will be explicit on tradeoffs? Our research questions GIS-based MCDM method STF-OWA will make conservation tradeoffs spatially explicit quantitative decisions
are flexible criteria details are not
totally lost (can be revisited) explicit on criteria tradeoffs Hypothesis it will allow greater flexibility
in terms of prioritization of sites HYPOTHESIS How do we aggregate these criteria? (operationalize) Order weighted averaging (OWA) > Which criterion we value more for protection? A B How do we want criteria to tradeoff? (level of importance between criteria) (degree of protection afforded to each set of criteria) 7 scenarios of protection (ordered weights) Operationalized by WLC Operationalized by logic OR Moderate degree of protection A. Tradeoff (attributes, sub-objectives) B. Tradeoff (objectives) Minimum degree of protection Maximum degree of protection Minimum benefits (eg, biodiversity protection)
Minimum loss on cost (fishing $) Operationalized by logic AND Equal (benefits & costs) Maximum benefits (eg, biodiversity protection)
Maximum loss on cost Alternatives options planning sites Grain of data analysis Source: DFO Distribution of unique vessels Gear impact Gear conflict Marine traffic Tradeoff analysis Corals>groundfish>sponge>exploited invertebrates>seabird Employment>Fish landing $ >
FG-richness> business ownership,
fish landing value Biological>Socio-economic>
Other uses/issues Gear impact>Oil & gas> marine traffic>
Gear conflict Results STF-OWA explicit on tradeoffs STF-OWA allow
flexibility in
prioritizing sites STF-OWA Integrates stakeholder priorities in two ways Criteria details are not lost Explicit tradeoff Site prioritization Flexible quantitative decisions Maps in tiers
can be revisited Provides scored alternatives Tradeoffs/priorities are traceable in each spatial level of aggregation DOES allow *divisible priority sites

*tradeoff between
cost criteria

*stakeholders' priorities
make tradeoffs/hard
choice spatially explicit IS NOT *optimal solution

*objective quantification
of priorities

*target-based (rigorously
quantitative) Inshore - top 20% alternatives 'Easy' tradeoffs, 0.5% of sites (~12,400 sq km) makes tradeoff or
hard choice
spatially explicit Tool needs to be automated
& interactive Classified scores Percentages of alternatives Inshore alternatives decreases from "few" to "most"
South Nfld alternatives increases from "few" to "most" Tradeoffs/hard choice, spatially explicit? Achieving high biodiversity "Most" scenario finds
areas with low
socio-economic impacts Biodiversity vs. employment Tradeoff or hard choice? Biodiversity vs. fishing dollar cannot avoid socio-economic impacts While we want to minimize SE impacts, importance of biodiversity is high. Tradeoff: Most; Univ weight: 0.7 for biodiversity Could 'balancing' avoid loss and gain? Making conservation tradeoffs explicit using multicriteria analysis Evenness Richness Abundance Is it "win-win"? Everyone gets a big part! Worth £10 billion Benefits (gains) are expected ...so are losses Some must sacrifice WHO? "there is no such thing as free lunch" sacrifices ~getting the balance right! Worth £10 billion Benefits How can we integrate these benefits and sacrifices in panning? Can we make them spatially explicit? In MPA planning... the basic question is where? Data & data preparation Existing Methods Biological indicators Socioeconomic indicators ordered weights (Top 20%, alternatives) Biodiversity vs diverse fisher groups Piece the puzzle Marine protected area Marine protected area fishing sponge VS socio-economic protection Competing/conflicting interests In MPA planning, (2) MPA is inherently spatial! the basic question is WHERE Tradeoffs (1) Multiple interests "win-win" "loss & gain" Tradeoffs matters
in locating MPAs "cost" Species A Species B Species C VS. (e.g. forgone fishing benefits) VS. species A species B species C cost A cost B cost C often non-optimal
options can be ordered
target (non-rigorously quantitative) optimal
single indivisible solution
target-based intersection weighted linear combination union (most restrictive) (most inclusive) (equal) C Method evaluation Melinda Agapito | Geography In MPA planning Other issues/uses Note: sum of 10-year
log book data; 1 year LRIT data Fuller et al. 2007 Tradeoff is spatial!
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