Download Dispersal of marine organisms in the Baltic Sea

Dispersal of marine
organisms in the
Baltic Sea
Per Jonsson
Dept of Marine Ecology
Tjärnö Marine Biological Laboratory
University of Gothenburg
“The marine ecosystem in changing climate”, SMHI
October 16th, 2009
Dispersal of marine
organisms
•The
Challenge:
•How
?
are marine
populations
connected
through
dispersal?
?
?
?
ecological
evolutionary
time
time
Dispersal &
connectivity
affect:
Fluctuations in
population size
Local extinction
Invasion of non-native
Evolution of local
species
adaptations
Evolutionary rate &
Management of exploited
speciation
species
Design of Marine Protected
Areas (MPA)
Why is connectivity
important when
designing
local
recruitment ofMPAs?
isolated
MPAs
reduce risk
of local
extinction in networks
di
sp
er
s
al
Many sedentary (or
sessile) adults
photo: Pia Pettersson
photo: Nils Kautsky
photo: Lars-Ove Loo
Typical life cycle
sessile
sessile adult
adult
gametes
gametes
juvenile
juvenile
zygot
zygot
embryo
embryo
free-swimming
free-swimming
larva
larva
Marine dispersal is
difficult
to
study
•Most marine propagules (spores
&
larvae) are numerous, sub-mm, and
drift with ocean circulation
•Duration
of planktonic dispersal:
hours to weeks
Marine dispersal is
poorly understood
Mean dispersal distance?
Direction?
Temporal and spatial
variation in dispersal?
How can we study
connectivity?
Our approach:
Models based on oceanographic
circulation
Oceanographic model
Multiple
trajectories from
each model grid
Biological models
Vertical migration &
settling
0
-2
Depth (m)
-4
-6
-8
-10
-12
-14
-16
-18
Poorly
Poorly known
known indeed!
indeed!
23
21
19
15
17
Time (h)
13
11
9
7
5
3
1
-20
Model domain
Mytilus edulis
(trossulus)
areas ≤12 m
depth
Sub-populations and
the connectivity
matrix
From
1 2 3 4 5
To
To
1 s e21 e31 e41 e51
sub
-population
sub-population
1
2
3
4
2 e12 s e32 e42 e52
3 e13 e23 s e43 e53
4 e14 e24 e34 s e54
5 e15 e25 e35 e45 s
5
The connectivity
matrix of the
shallow
Baltic
Sea
27 million elements
Larval dispersal
Longest dispersal:
380 km
6.E+07
6.E+07
21 days
of
dispers
al
5.E+07
5.E+07
4.E+07
4.E+07
3.E+07
3.E+07
2.E+07
2.E+07
1.E+07
1.E+07
0.E+00
0.E+00
00
15
15 30
30 45
45 60
60 75
75 90
90 105
105 120
120 135
135 150
150
Dispersal
Dispersal distance
distance (km)
(km)
Can we use
connectivity
information to improve
MPA
design?
Identify
dispersal
barriers
Select networks of MPA that
are internally connected
Assess the connectivity of
existing MPAs
We suggest a new
framework for analysis
of connectivity
Based on eigenvectors of the
connectivity matrix
1
2
3
4
5
6
0.
0
3
0.
0
3
0. 0.
3 5
7
1
1
0
0
0
0
2
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
4
5
6
0.
5
0.
5
0
0
1
0
0
0
4
0
0
0. 0.
5 5
0.
0 5
7 0 0 0 0 0
define
sources &
sinks
identify well-linked
regions
5
2sink
3
7 source
6
Internally linked
regions
May indicate
gene flow
May indicate
local
populations
Stratification
criterion for
MPAs
Finding an
‘optimum’ MPA
We propose that the optimum network
network
consists of the subset of areas
having the largest product of
dominant right- and left
We
define the optimum network as the
eigenvectors
subset of areas having the largest
metapopulation size based on a
simplistic population projection
model
Optimum network
subset of
5000 km2
Optimum network:
performance
10
Network population
size
optimum network
1
0.1
0.01
10 random networks
0.001
0.0001
0.00001
0
0
2500
200
5000 7500 10000 12500 15000
400
600
800
1000
MPA network size
2)
MPA
number
(km
1200
Optimum network vs Natura
2000
populatio
n size
18% of
optimum
network
all areas
optimum
all areas
N 2000
Will connectivity
change with the
•increasedclimate?
precipitation
•increased
stratification
•changing wind
fields
ocity fields for IPCC scenarios up to year 2
Predicted change in
salinity
Present
2070
RCAO-ECHAM4/A2
5 psu
from Meier 2008, SMHI
Conclusions
Dispersal models based on
ocean circulation models is a
way forward
Connectivity is presently not
used when selecting MPAs
Connectivity may significantly
affect MPA performance
Eigenvector analysis may be an
easy way to link connectivity
to MPA design
Ongoing Formas project:
“Larval dispersal and the design of
marine reserve networks in Sweden”
• Assess the importance of
connectivity for MPA design
• Include Skagerrak (+North
Sea?)
Vattenkikaren
• Develop a tool to include
connectivity as a criterion in
design of MPA and no-take
areas
Problems &
Challenges
dispersal distance
distance (km)
(km)
dispersal
Larval
Larval behaviour
behaviour -- not
not only
only passive
passive
‘particles’
‘particles’
swim depth:
larval
larval duration
duration (days)
(days)
Spread of invasive species
Kattegatt
Öresund
Roslagen
Gävle
Åland
Gulf of Finland
Hanö
Kvarken, SE
Gulf of Riga
Öland
Kvarken, Fin
Poland
Thank you !
Collaborators:
Kristofer Döös, Stockholm University
Hanna Corell, Stockholm University
Per Moksnes, University of Gothenburg
Per Nilsson, University of Gothenburg
David Kleinhans, University of
Gothenburg
Martin Nilsson Jacobi, Chalmers