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Implemenation of ecosystem service indicators in Germany
Karsten Grunewald,
Ralf-Uwe Syrbe, Benjamin Richter
IOER Dresden, Germany
MAES Conference in Sofia, 6./7. Febr. 2017
Session: Mapping of ES and general
assessment frameworks
1. Background
2. Framework
3. Classification
4. Template
5. Example wood provision
6. Conclusion/Experiences
Implementation of Action 5 of the EU Biodiversity
Strategy. Development and implementation of a
methodology for capturing and assessing
ecosystem services (ES) at the federal level in
the context of the implementation of Target 2 and
Action 5 of the EU Biodiversity Strategy for 2020
„Project“ (BfN/BMUB)
potenzielle Auenretention (2013)
Verfügbarkeit der Flussauen (78. größten Ströme Deutschlands) für Hochwasserretention
Datengrundlagen: Flussauen in Deutschland ©Bundesamt für Naturschutz (2009)ATKIS Basis-DLM ©GeoBasis-DE / Bundesamt für Kartographie und Geodäsie (2014)Gebietsstand: Rastergrundgeometrien (INSPIRE Grid 1 km); Karte: B.Richter, U.Walz, IÖR (2015)
0 - 33
>33 - 66
>66 - 100
Nationwide ES-statements of
relevance in space and time
Framework – cascade, EPPS, IPBES…
Evaluation schedule
Mapping of ecosystems
Assessment of ecosystem conditions
Assessment of ecosystem services
Integrated ecosystem assessment
MAES (What to map?)
MAES (2013) 6 dimensions of biodiversity
Framework for ES indicator selection
Syrbe et al. 2017
Classification of Ecosystem services
CICES V4.3 (2013): 48 ecosystem services classes
44 ES relevant for Germany (Marzelli et al. 2014)
Prioritization regarding
Territorial/spatial importance
Importance of the ES for people
Position of the ES in the scheme
Communicability of the ES-concept
Data regularly available (for monitoring!)
21 ES-classes to be processed in Germany
Principles of the description of indicandum and indicator
Parameters/factors which determine the ES
• Supply/capacity/potential (in the sense of the performance of nature)
• Demand (in the sense of the intensity of demand)
• Stock/flow (actual use – this is the ES in the original sense). This parameter can
be roughly estimated by a superimposition of supply / demand
Indicator(s)
• Name
• Calculation and analysis steps
• Results (values, maps) and interpretation
• Relationship to other sustainability and
biodiversity indicators
Grunewald et al. 2016 (Ecol. Indic.)
Proposal
Coordination
Implementation
Template (includ.
Target setting,
Biodiversity etc.)
ES Class Main indicator Subindicators Structure
1 Crop production Crops and crop products Soil fertility Farmland Counties
2 Meat production Livestock Grassland share Livestock load Counties
3 Water
availability Freshwater provision Water use balance Nitrate content Counties
4 Timber
production
Vegetal and animal row
products from forests Annual wood accrual (6 sub-ind.) Federal states
5 Bioenergy Bioenergy plants Energy crop area Energetically used
biomass
6 Grassland Vegetal and animal row
products from agricult. Grassland grow Share of grassland
Provisioning Ecosystem services
Main indicator:
Annual wood accrual
average 2002-2012 in m3 ha-1 a-1
Example: Germany Raw wood provision
Grunewald et al. 2016 (Ecol. Indic.)
idea of the magnitude of sustainably extractable raw wood (9 to 13 m3 ha-1 a-1 ) in frame of the Biodiversity Strategy the developed indicator should not be communicated alone, because a causal relationship between the indicator and the naturalness of forests cannot be established
Supplement-indicators:
S1 Forest area (in ha at the state, county, or community level)
S2 Wood stock 2012 referred to the forest area (in m³ ha-1)
S3 Development of the annual logging (in m³ timber)
S4 Change in wood stock (2012-2002 in %)
S5 Proportion of near-natural forest areas (in %)
S6 Percentage of unfragmented forests > 50 km² to reference
area (in %)
Grunewald et al. 2016 (Ecol. Indic.)
Tab. Selected results to describe the ES wood provision in Germany
Source: Grunewald et al. 2016, Ecol. Indic.
Parameter Results
Wood accrual in the
forests as quantity
of potentially
usable raw wood
(Indicator M1)
Wood accrual is at a high level in Germany with 11.2 m3 ha-1 a-1 or 121.6 million m³ a-1
(only describes the status quo; a certain wood accrual could be realized at different stock
levels, e.g. by changing the tree species and age structures = “managed potential”). Of
the widespread tree species, the spruce grows most quickly, followed by the beech.
Douglas trees and firs have the greatest accrual, but they only account for 4% of the
forest area together.
Forest area
(Indicator S1)
Area used for forestry currently amounts to 11.4 million ha (about 31% of the land area of Germany) and is relatively constant. From 2002 to 2012 a slight increase by 0.4% (50,000 ha) was recorded (note: forest area increased in rural and peripheral areas, mostly at the expense of valuable, extensively used agricultural land, tended to decrease in conurbations).
Wood stock
(Indicator S2) Wood stock in German forests: 3.7 billion m³, or 336 m³ ha-1.
Change in wood
stock
(Indicator S4)
Wood stock increased by 7% from 2002 to 2012. The increase in stock (accrual
minus use and mortality) is specified at currently 11.23 m³ per hectare and year.
Use values
The production value of raw wood production in the German forests amounted to about
3.5 billion € in 2011, predominantly from softwood. More than 1.1 million people are
employed in the cluster forest and wood in Germany. In practice, every inhabitant is an
“end user” of raw wood production.
Indicator-based approach
Measures and sums up ES in their spatial expression and temporal
change (trend) and compares them with objectives
Data can directly be taken from governmental records (National Forest
Inventory)
Result only provides information about a part of sustainability (Trees and
timber are only a part of the forest ecosystem and wood provision is only
one of the multiple services of woodland)
Conclusion wood provision
Regulating Ecosystem services
ES Class Main indicator
1 Ground water
protection
Filtering, fixation, and accumulation by
ecosystems Proportion of well-protected areas
2 Purification of flowing
waters Dilution by water High quality structure streams
3 Prevention against
soil erosion Stabillizing of soil Avoided soil loss
4 Flood protection River flood protection Area for flood retention
5 Climate regualtion Air exchange and transpiration Minimization of urban heat island
6 Pollination Pollination and seeding Density of small structure
landscape elements
7 Genetic materials Protection of populations
8 Pest control Pest control Density of small structure
elements in arable land
9 Water quality Freshwater quality Proportion of water quality areas
10 Carbon sequestration Global climate protection Proportion of C-storing areas
11 Climate regulation Micro, lokal and regional climate Access to urban green space
(Revised) Universal soil loss
equation (R)USLE:
R rain erosivity: summer
precipitation (DWD 1981-2010)
K soil resistance: BÜK1000Ob
S slopeness: ATKIS-DHM25
C land use: LBM-DE 2009 (2012)
+ DESTATIS agricultural data
L slope length
(field + small elements)
No data: P plant cultivation factor
Modelling Water Erosion
Main indicator
Avoided water erosion
Supplement indicators
Actual water erosion
Share of organic cultivation
Effect of small
structures
Avoided wind
erosion
Results: Indicators of the ES water erosion
ES Class Main indicator
1 Recreation in
landscapes Experiences of landscape, animals, plants
Landscape potential for after-
work/daily/weekend
recreation
2 Recreation in cities Utilization of green infrastructure Accessibility of green spaces
3 Esthetics Experiences of landscape, animals, plants Esthetic value of landscapes
4 Potential for
biodiversity Existence values Landscape diversity
Cultural Ecosystem services Ecosystem Service Capacity
“after work recreation” Ecosystem Service Demand
Green areas (>1 ha) Population density
Ecosystem Service Flow
f
Access to urban green for “after work recreation”
Access to urban green?
Yes No
Population density
High
Low
Proportion of cities / dwellers with access to urban
green space near to residence (indicator M)
In total, green spaces
are accessible for daily
recreation for 74.3 % of
the inhabitants in
German cities, which
means that
underprovision affects
8.1 million city dwellers.
Summary
Publication and implementation
Description by template (10 p.) and brief profile (1 p.)
Agreement and discussion with technical experts
Revision by Federal Agency and Ministry
Publication as papers (German and English)
If applicable: integration into national reporting
Further maintenance
Periodical calculation and reporting (triannual)
Reporting by the IOER monitor http://www.ioer-monitor.de/
Indicator
Spatial approach*
Timesheet First Trend DE**
Assessment of service provision IN DE BL KR GE RA
Annual wood accrual x x x 2002-2012 (2022) 3
Area for flood retention x x x x x x 2010-2015 (2020) 2-3
Avoided water erosion x x x x x 2009-2012 (2015) 2
Accessibility of green spaces
x x x x 2008-2013-2015
(2018) 2-3
* IN-international, DE-Deutschland, BL-Bundesland, KR-Kreis, GE-Gemeinde, RA-Raster
** Einschätzungsskala: 0-keine Angabe möglich, 1-sinkend, 2-schwach abnehmend, 3-gleichbleibend, 4-schwach steigend, 5- stark steigend
Synthesis of implemented main indicators
Experiences:
MAES – dynamic field (What indicate the results for whom?)
Expections (practice) – high (at least in Germany)
Maps/quantifications – new insights, target values?
High aggregation values, basis for monitoring
Additional arguments for nature protection?
Negotiation with experts from different sectors (acceptance?)
Outlook: MAES Germany (action 5 indicators):
Work in progress… - 4 of 21 ESS-classes finished
- ES-condition in preparation
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