Methods to quantify human effects on marine ecosystems

Samuli KorpinenOSPAR 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, impacts2. Spatial resolution and flexibility3. Flexibility in data formats4. Transparency5. Clarity6. Temporal aspect7. Flexibility for different purposes8. Efficacy of the method

• Analyzing differences• Finding similarities• Potential to merge the methods

1. The HARMONY method

Halpern et al. 2008

, 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)

Sanderson E W et al. BioScience 2002;52:891-904

© 2002 American Institute of Biological Sciences

Baltic Sea Impact Index + North Sea Impact Index

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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

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 (%).

6DeVries et al. 2011

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.

HARMONY ODEMMCUMULEO

Pressure GIS data(Habitat

loss, km2)

Ecosystem GIS data (species

diversity/group)

Pressure GIS data

(intensity 0-1)

Ecosystem GIS data

(presence 0-1)

Pressure GIS data

(presence 0/1)

Ecosystem GIS data

(presence 0/1)

Comparison of the methods

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.

ECOSYSTEM DATA-Components defined.-Spatial data covers the entire assessment area.-Presence/absence

Differences:-HARMONY: probability of occurrence;-CUMULEO: weighting of data.

HARMONY ODEMMCUMULEO

Pressure GIS data(Habitat

loss, km2)

Ecosystem GIS data (species

diversity/group)

Pressure GIS data

(intensity 0-1)

Ecosystem GIS data

(presence 0-1)

Pressure GIS data

(presence 0/1)

Ecosystem GIS data

(presence 0/1)

Impact score0-1: consisting of

impact level, impact extent, recovery time

Impact score0-1: consisting of studied impact on

habitat use

Degree of impact:

low, chronic, acute

Recovery time: low, moderate,

high, severe

Comparison of the methods

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 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 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.

Crain et al. 2008

Cumulative effectsSY

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Linkage with the state of the sea?

Andersson et al. 2015

Halpern et al. 2008Percent degradation