Transcript How changing human lifestyles are shaping Europe`s regional seas

How changing human
lifestyles are shaping Europe’s
regional seas
Laurence Mee,
Coordinator
European Lifestyles and Marine Ecosystems
The European Union at 50
Four seas, one economic and social framework
• Half a billion people
• Unprecedented
mobility
• Increasing affluence
• Growing evidence of
deterioration of our
seas
• How to sustainably
manage our marine
natural capital?
European Lifestyles and Marine Ecosystems
ELME Objective
Through improved
understanding of the
relationship between
European lifestyles and the
state of marine ecosystems,
ELME will model the
consequences of alternative
scenarios for human
development in postaccession Europe on the
marine environment.
European Lifestyles and Marine Ecosystems
Who are we?
• 28 institutions from 15 countries
across Europe in a single
consortium
• Institutions in every European
regional sea
• Leading environmental and
social scientists, together with
policy specialists gathered in
five task teams
• €2.5 millions core funding from
the European Research Area
European Lifestyles and Marine Ecosystems
Ambitions
• An assessment of the consequences of current human
lifestyles on Europe’s regional seas.
• A region-by-region predictive model of key problems during
different European development scenarios.
• Options for dealing with predicted future change. Will the
action plans and conventions be sufficient to prevent further
degradation?
Methodology
Why is it difficult to model socio-ecological
systems?
S-E systems demonstrate:
• Non-matching scales
• Surprises (non-linearities)
• Interconnection with other
systems
• Memory effects
• Choke points
Difficult to find a common currency between the social and natural
sciences. Bayesian belief networks are probabilistic models that
use probability density functions as a common currency.
Methodology
Spatial scale
• Regional Seas scale analysis
• Requires understanding of subsystems
Methodology
Surprises (non-linearities)
Systems undergo
sudden and often
unpredictable
changes
Regime shift
Methodology
Systems are Interconnected
Exporting our global footprint
Methodology
Systems have choke points
The web of our life is of a mingled yarn, good and ill together.
SHAKESPEARE
Methodology
The D-P-S-I-R model
Human Society
Driver
Response
Impact
Pressure
State
Change
Natural System
ELME focused on the D-P-S relationship
Methodology
Why lifestyles?
Individual values
Lifestyle
Consumption
choices
Consumption
activities
Political choices
Production
activities
Policy
Socio-economic drivers
Environmental change
lead(s) to/cause(s)
is associated with
influences
Methodology
Focus and initial priorities
Environmental focus:
• destruction of habitats
and species;
• eutrophication;
• chemical pollution; and
• the unsustainable
extraction of living
resources.
Habitat
loss
Eutrophic Pollution
ation
Unsustainable
Fisheries
NE Atlantic
3
3
5
5
Baltic
2
5
4
4
Black Sea
4
5
2
4
Mediterranean
5
2
2
4
Priority: 5 = highest, 1 = lowest
Selected for module 1
Methodology
Modelling methods and requirements
e.g. Bayesian networks
PARENT NODE
N
input (N input =
f (error)
marginal
probability
distribution
N
input
flow
ALGAL
DENSITY
ALGAL
DENSITY
DEPENDENT NODE
(algal density = f (N input, error)
conditional
probability
distribution
temp
Adapted from Borsuk et al., 2004)
• Cross disciplinary systems best modelled stochastically
rather than deterministically
• Probability density functions provide a common currency
between social and natural sciences
Examples of Issues
Seagrass loss in the Mediterranean
Metadata
Seagrass loss in the Mediterranean
Time series data
Sea grass loss in the Mediterranean
Initial conceptual model
Shipping and navigation:
Land management
Construction of port and marinas
Dredging spoil disposal
Urbanisation:
Input of
sewage
WP5: Fishing
InshoreTrawling
Aquaculture
Finfish
cages
Coastal
development
Land claim
Removal of
biological
resources
WP3 Nutrients
Anthropogenic
structures
Mechanical
disturbance: Intensity 
Sedimentation
Physical
oceanographic
change
% organic matter
Rate of deposition
Turbidity
MD: Frequency
Over-stimulation of
biota:nutrients 
MD: Extent
[Nutrients]
Area lost 
Depth limit 
Driver
Pressure
Proportion of
opportunistic
species* 
Degradation* 
State Change
Sea grass loss in the Mediterranean
Bayesian Belief Network Model
Ecosystem degradation in the Black Sea
System loss and recovery
800000
Phyllophora system
700000
Threshold 1
600000
Tonnes
Recovery?
Mytilus
system
Recovery?
Depleted
benthic
system
500000
400000
300000
200000
100000
99
96
02
20
19
90
87
84
81
78
75
72
69
93
19
19
19
19
19
19
19
19
19
63
66
19
19
19
60
0
19
Ecosystem complexity
Threshold 2
Human pressure
Total fish production
Black Sea catches
Anchovy catches
Aquaculture
Fisheries modelling
Basis of BBN model for Spanish hake
fisheries
Scenario modelling methodology
Information gathering for drivers
KEY
INPUTS
PROCESS
WP survey
Identification of
drivers
OUTPUTS
Driver Indicator
Table
Databases:
FAO, WDI,
IEA, Eurostat
The Third
Assessment
(EEA)
UKCIP, EEA,
ACACIA,
Millennium
Assessment,
AFMEC
Selection of socioeconomic indicators
Analysis of
economic trends
Analysis of
policy/legal status
Trend plots &
“forecasts”
Sectoral Issues
Papers
Definition of
scenarios
Scenario
Descriptions
Characterisation of
outcomes
Driver
Outcomes
Scenario modelling methodology
Scenarios for future change
Autonomy
National Enterprise
Local Responsibility
“Pull up the drawbridge!”
“Think local, act local!”
GOVERNANCE
Consumerism
Baseline
VALUES
Community
Current “best guess”
Global Community
World Markets
“We’ve got the whole
world in our hands”
“Growth is good”
Interdependence
Scenario modelling methodology
Scenario development
Scenario modelling methodology
Scenario development
Identification of Alternative Scenarios by reference
to fundamental factors (values & governance)
Narrative description of Baseline and Alternative Scenarios in terms
of:
values and policy
demography
economy
Driver sectors
Categorical representation of direction of change (/0/+):
-Baseline v. present
-Alternative Scenarios v. Baseline
Scenario outcomes in terms of Underlying and
Immediate Driver Indicators
Scenario modelling methodology
Scenario development
Baltic example
Modelling system complexity: Baltic
Baltic example
Baltic memory effects
BALTIC:
Large decrease in
P consumption
for agriculture
40
500
450
Little change
in P in system
400
30
350
25
300
20
250
200
15
150
10
100
5
50
0
1970
0
1975
1980
Total P load
1985
1990
1995
Dissolved inorganic P
2000
DIP Thousand of tons
Total P (thousand of tons/yr)
35
Baltic example
Winners and losers: Baltic
•Winners shown here are
benthic microalgae, coastal
zoobenthos and sprat
•Losers are native eelgrass,
zostera and large predators
such as cod.
•There are habitat-forming
winners and losers, indicating a
fundamental change in the
natural ecosystem
•Winners providing goods to
the human population (fish in
the case of the Baltic) are of
lower economic value than the
losers.
Baltic example
Model outputs:
Baltic simulation
Under most scenarios, the Baltic
will remain eutrophic, partly
because of phosphorus recycling
from the large sediment pool.
European Lifestyles and Marine Ecosystems
North Sea conceptual model
European Lifestyles and Marine Ecosystems
North Sea Winners and Losers
•Winners include phytoplankton
and trophic dead-end species
such as jellyfish
•Winners also include
transitional waters (estuaries)
•Losers comprise seabirds that
depend on sand eels and small
pelagic fish.
•Bottom water (demersal) fish
species such as plaice, cod and
haddock are losers as are the
other animals and plants that
form sea-bed habitats
European Lifestyles and Marine Ecosystems
North Sea
simulations
Simulations
Predicted winners and losers
MED
Sprat stocks
Filamentous algae
Phyllophora
Demersal stocks
Zoobenthos
Pelagic predator stocks
Invasive species
Small pelagic stocks
Apex predators
Posidonia oceanica
Caulerpa taxifolia
Phytoplankton (N. Adriatic)
Sandeel-dependent seabirds
Demersal stocks
Zoobenthos
Phytoplankton
W
Coastal zoobenthos
L
Cod stocks
W
NEA
L
W
Historical trend
m
m
k
k
k
m
m
m
m
k
k
m
m
k
k
m
m
m
k
Agriculture
l
l
l
l
l
l
?
l
l
l
l
Fisheries
Household
l
Baseline
National Enterprise
Local Responsibility
World Markets
Global Community
l
l
l
l
? l
? l
l
l
Tourism & urbanization
l l
l
l
l
? l
l
l
l
?
l
l
? l
l l l
?
+
+
+
–
+
+
+
+
–
+
+
+
–
l
l
?
+ 0 - + - – –
+ + 0 +
+ 0 - + + 0 +
- + 0 0 - +
– 0 + 0 – +
0 0 - + + - + + - 0 0 + – 0 0 -
l
? l
l
Transport
Natural variability
Scenario
outcomes
BLACK
L
Macrophytes
Sectors
BALTIC
L
W
l
+ 0 + + 0 0 + + + +
+ - + –
+ 0 – -
l
+
+
+
European Lifestyles and Marine Ecosystems
Joined-up thinking
• Economic growth is a
primary goal or
countries joining the
EU
• Affluence brings new
lifestyle, including
increased protein
consumption.
• Demand can be met by
increasing farmed land,
intensifying production,
or through imports
• Unless agricultural
production is
decoupled from
nutrient discharge;
eutrophication will
dominate the future
status of the Baltic and
Black Sea
European Lifestyles and Marine Ecosystems
Joined up thinking (2)
• Increased shipping
benefits economic
growth
• Environmental cost is
increased dispersion
of opportunistic alien
species
• And air pollution
including CO2
• Urgent measures
needed to reduce
species transfer
European Lifestyles and Marine Ecosystems
Joined up thinking (3)
• Offshore wind farms will
help to deliver EU policy
goal of 20% renewable
energy
• They may also help create
marine protected areas
• But fishing will be displaced
and may be concentrated
outside the renewables sites
• Future fisheries policy will
need to consider wider
environmental
considerations
European Lifestyles and Marine Ecosystems
Policy context
• Joined up thinking essential for managing
Europe’s Seas
• Our horizon scanning demonstrates useful
simulations of coupled socio-ecological systems
are possible
• and reveals difficult future choices
• Currently, human impact seems to be coupled to
affluence (economic growth) and technology
• Future policy need to find ways to decouple
economic growth from its impact… or to
constrain growth itself.