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Brian P. Kinlan1
Collaborators: Dan Reed1, Pete Raimondi2, Libe Washburn1, Brian Gaylord1, Patrick Drake2
1University of California, Santa Barbara2University of California Santa Cruz
The Metapopulation Ecology ofThe Metapopulation Ecology ofGiant Kelp in the Northeast PacificGiant Kelp in the Northeast Pacific
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Photo: K. Lafferty
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I. WHAT IS A METAPOPULATION?I. WHAT IS A METAPOPULATION?
II. CASE STUDY: METAPOPULATION II. CASE STUDY: METAPOPULATION DYNAMICS IN SOUTHERN CA KELP DYNAMICS IN SOUTHERN CA KELP FORESTS?FORESTS?
III. REGIONAL VARIATIONIII. REGIONAL VARIATION
IV. CONCLUSIONSIV. CONCLUSIONS
![Page 4: Brian P. Kinlan 1 Collaborators: Dan Reed 1, Pete Raimondi 2, Libe Washburn 1, Brian Gaylord 1, Patrick Drake 2 1 University of California, Santa Barbara.](https://reader038.fdocuments.us/reader038/viewer/2022103022/56649d385503460f94a118d6/html5/thumbnails/4.jpg)
2. CONNECTIVITY2. CONNECTIVITY
1. PATCHINESS1. PATCHINESS
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3. TURNOVER3. TURNOVER
2. CONNECTIVITY2. CONNECTIVITY
1. PATCHINESS1. PATCHINESS
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Modified from Hanski & Gilpin 1997
Persistence of Most Stable
Patch
Dispersal Distance (Relative to Interpatch Distance)
“Classic”(Levins)
Metapopulation
Patchy Population
Mainland-Island
Non-Equilibrium(headed for extinction)Low (Most patches
have some probability of extinction >> 0)
High (some patches, generally very large,
have virtually no probability of
extinction)Source-sink?Classic, stable
population
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I. WHAT IS A METAPOPULATION?I. WHAT IS A METAPOPULATION?
II. CASE STUDY: METAPOPULATION II. CASE STUDY: METAPOPULATION DYNAMICS IN SOUTHERN CA KELP DYNAMICS IN SOUTHERN CA KELP FORESTS?FORESTS?
III. REGIONAL VARIATIONIII. REGIONAL VARIATION
IV. CONCLUSIONSIV. CONCLUSIONS
![Page 8: Brian P. Kinlan 1 Collaborators: Dan Reed 1, Pete Raimondi 2, Libe Washburn 1, Brian Gaylord 1, Patrick Drake 2 1 University of California, Santa Barbara.](https://reader038.fdocuments.us/reader038/viewer/2022103022/56649d385503460f94a118d6/html5/thumbnails/8.jpg)
32.5ºN
33.6ºN
N
EW
S
Data courtesy of L. Deysher, T. Dean & Southern California Edison
Newport Beach
Pt. Loma
La Jolla
Kelp Bed Dynamics (1967-1999)Kelp Bed Dynamics (1967-1999)
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Reed, Kinlan, Raimondi, Washburn, Gaylord & Drake, In press, Marine Metapopulations (P.F. Sale & J. Kritzer, eds.)
METHODS – Identifying Habitat METHODS – Identifying Habitat Long-term Kelp DistributionLong-term Kelp Distribution
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METHODS – Defining PatchesMETHODS – Defining Patches
>500 m
Bed 28
Bed 27
Patch 17
Patch 18
Patch 16
Patch 19
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– 1000
– 100
– 10
– 0Canopy Biomass(tons/km coast)
36.5°N
35.9°N
35.3°N
34.7°N
34.4°N
34.1°N
33.7°N
33.4°N
32.6°N
32.0°N
31.5°N
30.9°N
30.5°N
29.6°N
LatLocation
Carmel Bay
Pt.Buchon
Pt.Purisima
Coal Oil Pt.
Palos Verdes
San Onofre
Pt.Loma
Pta.San Jose
Pta.San Carlos
METHODS – Estimating TurnoverMETHODS – Estimating Turnover
Raw data provided by D. Glantz, ISP Alginates, Inc. & Santa Barbara Coastal LTER
Kelp canopy biomass, 34-year monthly time series
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Historical Kelp Forest Dynamics
AUTOCORRELATION MODEL:48% noise Scale < 1 month27% stochastic patchiness Scale ~4 months9% stochastic patchiness Scale ~ 4 years9% stochastic patchiness Scale ~ 12 years3% annual periodicity Period ~ 1 year4% decadal periodicity Period ~ 20 years
b) Time
AUTOCORRELATION MODEL:28% noise Scale < 10 km39% stochastic patchiness Scale ~30 km21% stochastic patchiness Scale ~150 km6.5% mesoscale periodicity Period ~100 km5.5% regional periodicity Period ~330 km
c) Space
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– 1000
– 100
– 10
– 0
Canopy Biomass(tons/km coast)
36.5°N
35.9°N
35.3°N
34.7°N
34.4°N
34.1°N
33.7°N
33.4°N
32.6°N
32.0°N
31.5°N
30.9°N
30.5°N
29.6°N
LatLocation
Carmel Bay
Pt.Buchon
Pt.Purisima
Coal Oil Pt.
Palos Verdes
San Onofre
Pt.Loma
Pta.San Jose
Pta.San Carlos
Interpolated Canopy Biomass Estimates Interpolated Canopy Biomass Estimates
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METHODS - Turnover CriteriaMETHODS - Turnover Criteria
•EXTINCT if biomass = 0 for for previous 6 months or more
In any given month, all patches in an administrative unit are considered …
•OCCUPIED if biomass >0
Prob(Extinction) = P(OccupiedExtinct)
Prob(Colonization) = P(ExtinctOccupied)
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1970 1975 1980 1985 1990 1995 20000
10
20
30
40
50
60
70
80
90
100
Year
Fra
ctio
n o
f pa
tche
s oc
cup
ied
(%)
Patch OccupancyPatch Occupancy
Reed et al., In press (Marine Metapopulations – P.F. Sale, ed.)
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0 0.08 0.16 0.24 0.320
10
20
30
40
50
P(Extinction)
Rel
ativ
e fr
eque
ncy
(%)
0 0.08 0.16 0.24 0.320
10
20
30
40
50
P(Recolonization)
Rel
ativ
e fr
eque
ncy
(%)
Extinction and RecolonizationExtinction and RecolonizationProbabilitiesProbabilities
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0 2 4 6 8 10 12 14 160
10
20
30
40
50
60
Extinction Duration (Years)
Rel
ativ
e fr
eque
ncy
(%)
0 2 4 6 8 10 12 14 160
10
20
30
40
50
60
Persistence Time (Years)R
elat
ive
freq
uenc
y (%
)
Extinction & Persistence TimesExtinction & Persistence Times
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r2 = 0.05, p = 0.06 r2 = 0.15, p < 0.001
PATCH SIZE
Extinction/recolonization dynamics Extinction/recolonization dynamics weakly related to patch sizeweakly related to patch size
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Metapopulation CriteriaMetapopulation Criteria
PATCHINESS
TURNOVER
CONNECTIVITY??
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0 2 4 6 8 10 12 140
10
20
30
40
50
60
70
Nearest-neighbor distance (km)
Rel
ativ
e fr
eque
ncy
(%)
1 10 100 10000
10
20
30
40
50
60
70
Radius (km)
Mea
n nu
mbe
r of
pat
ches
w
ithin
rad
ius
(± 1
SD
)
Spatial arrangement of patchesSpatial arrangement of patches
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Estimated Dispersal Distance (m)
1 10 100 1000
100
80
60
40
20
0
Per
cent
of T
rials
Individual Plants
1 10 100 1000
Kelp bed
100
80
60
40
20
0
Reed et al., In press; D.C. Reed & P.T. Raimondi, unpubl. data
Empirical Dispersal ProfilesEmpirical Dispersal Profiles
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0 10 20 30 40 50 60
Distance from individual plant (m)
0
10
20
30
40
Spo
res
2.5
mm
-2 (
+/-
SE
)
0 50 100 1500
20
40
60
80
100
Distance from edge of kelp bed (m)
Reed et al., In press; D.C. Reed & P.T. Raimondi, unpubl. data
Empirical Settlement CurvesEmpirical Settlement Curves
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15 Jan – 15 Feb 2002
0 0.2 0.40.4 0.20
40
10
20
30
Fre
quen
cy (
%)
Naples
Carpinteria
eastwest
Along-shore current speed (m ● s-1)
1 - 30 June 2002
0
10
20
30
0 0.2 0.40.4 0.2
Fre
quen
cy (
%)
eastwest
40
Reed et al., In press; D.C. Reed, P.T. Raimondi & L. Washburn, unpubl. data
Modeling Connectivity UsingModeling Connectivity Using Real Ocean Current DataReal Ocean Current Data
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Table 1: Mean, minimum and maximum values for currents and significant wave height for the individual plant and kelp bed experiments. Mean currents were calculated over the duration of each experiment
Current velocity(cm / s)
Significant wave height(m)
Individual Kelp bed Individual Kelp bed
Minimum 0.001 0.04 0.323 0.273
Maximum 8.007 1.797 1.172 0.801
Mean 1.075 0.413 0.697 0.501
![Page 25: Brian P. Kinlan 1 Collaborators: Dan Reed 1, Pete Raimondi 2, Libe Washburn 1, Brian Gaylord 1, Patrick Drake 2 1 University of California, Santa Barbara.](https://reader038.fdocuments.us/reader038/viewer/2022103022/56649d385503460f94a118d6/html5/thumbnails/25.jpg)
0.0001 0.001 0.01 0.1 1 10 1000
20
40
60
80
100
Distance (km)
Per
cent
dis
pers
ing
at le
ast
dist
ance
X
0
20
40
60
80
100
Per
cent
of
inte
rpat
ch d
ista
nces
less
tha
n X
Carpinteria - Jan/Feb
Carpinteria - June
Naples - JuneNaples - Jan/Feb
Numerical Model of Spore DispersalNumerical Model of Spore Dispersal
Gaylord et al. 2002 Ecology 83:1239-1251; Gaylord et al. 2004 J. Marine Systems 49:19-39
Currents measured in vicinity of kelp bed:
![Page 26: Brian P. Kinlan 1 Collaborators: Dan Reed 1, Pete Raimondi 2, Libe Washburn 1, Brian Gaylord 1, Patrick Drake 2 1 University of California, Santa Barbara.](https://reader038.fdocuments.us/reader038/viewer/2022103022/56649d385503460f94a118d6/html5/thumbnails/26.jpg)
(Using Currents for Carpinteria, June)
Weak Source:
Strong Source:
Rel
ativ
e F
requ
ency
0%
0 1 2 3 4 5 6
20%
40%
60%
80%
100%x
c = 2.4 km
0%
0 1 2 3 4 5 6
20%
40%
60%
80%
100%x
c = 0.14 km
# of Connected Patches
(50th percentile)
(90th percentile)
Prediction: Connectivity Variable, But PossiblePrediction: Connectivity Variable, But Possible
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Empirical Test of Connectivity: Empirical Test of Connectivity: Isolation IndexIsolation Index
IIjj = isolation of patch = isolation of patch jj
LLii = area of patch = area of patch ii
TT = month = monthDDi,ji,j = distance from patch = distance from patch jj to patch to patch ii
at closest pointat closest point
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r2 = 0.57, p < 0.0001r2 = 0.39, p < 0.0001
ISOLATED CONNECTED ISOLATED CONNECTED
Extinction & Colonization RatesExtinction & Colonization RatesStrongly Influenced by ConnectivityStrongly Influenced by Connectivity
“RESCUE EFFECT”
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I. WHAT IS A METAPOPULATION?I. WHAT IS A METAPOPULATION?
II. CASE STUDY: METAPOPULATION II. CASE STUDY: METAPOPULATION DYNAMICS IN SOUTHERN CA KELP DYNAMICS IN SOUTHERN CA KELP FORESTS?FORESTS?
III. REGIONAL VARIATIONIII. REGIONAL VARIATION
IV. CONCLUSIONSIV. CONCLUSIONS
![Page 30: Brian P. Kinlan 1 Collaborators: Dan Reed 1, Pete Raimondi 2, Libe Washburn 1, Brian Gaylord 1, Patrick Drake 2 1 University of California, Santa Barbara.](https://reader038.fdocuments.us/reader038/viewer/2022103022/56649d385503460f94a118d6/html5/thumbnails/30.jpg)
N
EW
S
San Francisco
U.S.
Mexico
Los Angeles
Pta. Eugenia
CENTRAL
SOUTHERN
BAJA
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Canopy Biomass by RegionCanopy Biomass by Region
Central
Southern
Baja
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1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 20020%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Date
Fra
ctio
n of
Pat
ches
Occ
upie
d (%
)
Patch Occupancy Patch Occupancy Central
Southern
Baja
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ISOLATED CONNECTED
Isolation EffectIsolation Effect
Central
Southern
Baja
E
C
E
C
E
C
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I. WHAT IS A METAPOPULATION?I. WHAT IS A METAPOPULATION?
II. CASE STUDY: METAPOPULATION II. CASE STUDY: METAPOPULATION DYNAMICS IN SOUTHERN CA KELP DYNAMICS IN SOUTHERN CA KELP FORESTS?FORESTS?
III. REGIONAL VARIATIONIII. REGIONAL VARIATION
IV. CONCLUSIONSIV. CONCLUSIONS
![Page 35: Brian P. Kinlan 1 Collaborators: Dan Reed 1, Pete Raimondi 2, Libe Washburn 1, Brian Gaylord 1, Patrick Drake 2 1 University of California, Santa Barbara.](https://reader038.fdocuments.us/reader038/viewer/2022103022/56649d385503460f94a118d6/html5/thumbnails/35.jpg)
Modified from Hanski & Gilpin 1997
Dispersal Distance (Relative to Interpatch Distance)
“Classic”(Levins)
Metapopulation
Patchy Population
Mainland-Island
Non-Equilibrium(headed for extinction)
Source-sink?Classic single population
A: Context dependent, but A: Context dependent, but metapopulation model likely to be metapopulation model likely to be applicable more often than not.applicable more often than not.
Q: Where do Macrocystis populations fall on the spatial population dynamics spectrum?Q: Where do Macrocystis populations fall on the spatial population dynamics spectrum?
Persistence of Most Stable
Patch
![Page 36: Brian P. Kinlan 1 Collaborators: Dan Reed 1, Pete Raimondi 2, Libe Washburn 1, Brian Gaylord 1, Patrick Drake 2 1 University of California, Santa Barbara.](https://reader038.fdocuments.us/reader038/viewer/2022103022/56649d385503460f94a118d6/html5/thumbnails/36.jpg)
NASA Kelp Forest Dynamics StudyNASA Kelp Forest Dynamics Study
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Modeling FrameworkModeling Framework
Desired features:
Spatial
Dynamic
Predictive
Assimilative
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Modeling FrameworkModeling Framework
Grid Patch
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• Biomass dynamic– Production: BT+1 = f(∫Light, ∫Nutrients)– Loss: M=f(Waves[Substrate], Herbivory, Senescence/
Sloughing)
• Demographic– Density = f(Recruitment, Mortality)– Age/Size structure = f(?)– Fecundity = f(?)– Dispersal = f(Currents, Waves)– Recruitment = f(Light, Substrate, Nutrients(?))
Issues to Consider for first-stage (Grid-based) Model
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• Decisions re: Biomass dynamic vs. Demographic aspects of model
• Linking Data/Observations to Model Elements
• Identify data gaps
• Consider scaling issues
Outputs of 6/4 Meeting?
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0 2 4 6 8 10 12 14 16 18 20 22 24 26 280.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
0.8
Lag (years)
(S
emiv
aria
nc
e)ENSO scale = 2-6 yearsENSO scale = 8-24 years
What if the frequency of ENSO changes?What if the frequency of ENSO changes?
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Scenario Analysis
S h o r t D i s p e r s e r L o n g D i s p e r s e r
Rel
ativ
e S
ettl
emen
t F
ract
ion
H
igh
EN
SO
Fre
qu
ency
S e t t l e m e n t u n d e r l o w E N S O f r e q u e n c y
S h o r t D i s p e r s e r L o n g D i s p e r s e r
Rel
ativ
e S
ettl
emen
t F
ract
ion
H
igh
EN
SO
Fre
qu
ency
S e t t l e m e n t u n d e r l o w E N S O f r e q u e n c y
S h o r t D i s p e r s e r L o n g D i s p e r s e r
Rel
ativ
e S
ettl
em
en
t F
rac
tio
n
Hig
h E
NS
O F
req
uen
cy
S e t t l e m e n t u n d e r l o w E N S O f r e q u e n c y
S h o r t D i s p e r s e r L o n g D i s p e r s e r
Rel
ativ
e S
ettl
em
en
t F
rac
tio
n
Hig
h E
NS
O F
req
uen
cy
S e t t l e m e n t u n d e r l o w E N S O f r e q u e n c y