Download 4-2-Ref.4 CEA methods OSPAR ICG-C

Methods to quantify human
effects on marine ecosystems
Samuli Korpinen
OSPAR ICG-C, IJmuiden 26.2.2015
The review of the three methods
• Familiarizing with the methods
• Making criteria for a comparison
1. Scientific credibility: pressures, ecosystem components, impacts
2. Spatial resolution and flexibility
3. Flexibility in data formats
4. Transparency
5. Clarity
6. Temporal aspect
7. Flexibility for different purposes
8. Efficacy of the method
• Analyzing differences
• Finding similarities
• Potential to merge the methods
1. The HARMONY method
, where:
P: a pressure (scoring 0-1),
E: an ecosystem or its component (scoring 0-1),
μ: an impact score for each PxE combination
(scoring 0-4)
Halpern et al. 2008
Sanderson E W et al. BioScience 2002;52:891-904
© 2002 American Institute of Biological Sciences
Regional adaptations:
• A web survey for the effect scores, incl. a
self assessment of own expertise.
• Spatial extent of effect was estimated by the
web survey.
• Habitat presence 0/1 but species presence
by probability (0-1).
• A software (impact mapper) was made:
• Calculates an activity index, pressure
index and impact index;
• Calculates potential impacts for
selected activities or ecosystem
components.
For different ecosystem components
Andersen et al. 2013
HELCOM 2010, Korpinen et al. 2012
Baltic Sea Impact Index + North Sea Impact Index
2. The CUMULEO method
The method briefly:
- The oldest of the three methods  several versions published.
- The only method which uses data-driven estimates for impacts.
- E.g. CUMULEO-RAM where effects are linked to species productivity.
- Van der Wal & Tamis 2014: effect is estimated as habitat loss (%).
DeVries et al. 2011
6
3. The ODEMM method
• Linkage framework: activities – pressures – ecosystem (
impact chain).
• Counts the number of acute impacts.
• Takes account of ’combined impacts’  two chronic impacts in a
same area become an acute one.
Comparison of the methods
CUMULEO
Pressure
GIS data
(Habitat
loss, km2)
Ecosystem GIS
data (species
diversity/group)
HARMONY
Pressure
GIS data
(intensity
0-1)
Ecosystem GIS
data
(presence 0-1)
PRESSURE DATA
- Assessment area divided to units (e.g.
grid cells).
- Pressures defined.
- Spatial data covers the entire
assessment area.
Differences:
- CUMULEO + HARMONY: intensity;
- ODEMM: presence/absence.
ODEMM
Pressure
GIS data
(presence
0/1)
Ecosystem GIS
data
(presence 0/1)
ECOSYSTEM DATA
- Components defined.
- Spatial data covers the entire
assessment area.
- Presence/absence
Differences:
- HARMONY: probability of occurrence;
- CUMULEO: weighting of data.
Comparison of the methods
CUMULEO
Pressure
GIS data
(Habitat
loss, km2)
Ecosystem GIS
data (species
diversity/group)
Impact score
0-1: consisting of
studied impact on
habitat use
CUMULEO IMPACT SCORES
- Impacts from studies
- E.g. how much habitat is
lost? How much fish
stock is caught? Etc.
- Normalized to 0-1 scale.
HARMONY
Pressure
GIS data
(intensity
0-1)
Ecosystem GIS
data
(presence 0-1)
Impact score
0-1: consisting of
impact level, impact
extent, recovery time
HARMONY IMPACT SCORES
- Impacts from expert
judgment, 0-4 scale.
- The score is the mean of
three criteria:
- impact extent,
- recovery time
- impact level
- Also a weighted mean
- Confidence estimate
ODEMM
Pressure
GIS data
(presence
0/1)
Ecosystem GIS
data
(presence 0/1)
Degree of
impact:
low, chronic,
acute
Recovery time:
low, moderate,
high, severe
ODEMM IMPACT SCORES
- Categorical: Low, Chronic,
Acute
- Only acute pressures are
counted.
- If a pressure is caused by
two different activities 
2 (or more) chronic
become acute 
’combined impact’.
Comparison of the methods
The main difference is that HARMONY and CUMULEO
calculate ’cumulative effects’ but ODEMM calculates ’the
number of effecting pressures’.
A sligthly similar approach was used in HELCOM holistic
assessment:
Testing the robustness of the index
9 scenarios: 3 random pressure data sets and 3 random
ecosystem data sets
Testing the robustness of the index
3 impact scores: expert given, random and equal
We also tested how much the impact scores affect if there is
fewer data in the index. The result was clear: the impact scores
have a stronger effect with fewer pressure data sets.
ANTAGONISTIC
SYNERGISTIC
ADDITIVE
Cumulative effects
Crain et al. 2008
Linkage with the state of the sea?
Halpern et al. 2008
Percent degradation
Andersson et al. 2015