population genetics kellet’s whelk kelletia kelletii mtdna coi & 11 microsatellite markers 28...
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Population Genetics
Kellet’s whelk Kelletia kelletii
mtDNA COI & 11 microsatellite markers
28 sampling sites across entire range
1000+ larvae in each capsule
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P = 0.1
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Prominent barriers to gene flow
Point Conception
Punta Eugenia
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Point Conception
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100 km
Point Conception
Punta Eugenia
Expanded range
High genetic diversity
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100 km
Genetic isolation by geographic distanceEstimated mean dispersal distance in 10s of km
100 km
Geographic distance
Point Conception
Punta Eugenia
Gen
etic
d
iffe
ren
ceExpanded range
High genetic diversity
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100 km
Genetic isolation by geographic distanceEstimated mean dispersal distance in 10s of km
100 km
Geographic distance
Point Conception
Punta Eugenia
Gen
etic
d
iffe
ren
ceExpanded range
High genetic diversity
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Geographic distance
Gen
etic
diff
eren
ce
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Oceanographic connectivity
Gen
etic
diff
eren
ce
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Lagrangian Particle Trajectories
Velocity fields from Oey et al. [2003] data assimilation
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P = 0.006 (Mantel test)
1995
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Negative ENSO (i.e., la Nina) oceanographic conditions correlate most strongly with predicted pattern of gene flow
P = 0.009 (all years)
P = 0.078 (remove 1999)
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Negative ENSO (i.e., la Nina) oceanographic conditions correlate most strongly with predicted pattern of gene flow
All P-values calculated via Mantel Test (10,000 permutations)
P = 0.3 0.15 0.006 0.024 0.071 0.027 0.039
P = 0.009 (all years)
P = 0.078 (remove 1999)
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P = 0.009 (all years)
P = 0.078 (remove 1999)
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Vorticity
“Effective dispersal” may predominantly occur during la Nina conditions
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Are reserves good for fisheries?
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Oikos 2007
Spatially-implicit difference equations:
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Reserves enhance fishery yield
(White and Kendall 2007 Oikos)
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Fishing costs money
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Reserves still enhance fishery profit
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POPULATION REGULATION
Density dependent larval recruitment
Inter-cohort: Adults compete with larvae for space and food as they grow older
Intra-cohort: Larvae compete amongst themselves for space and food
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Spatially and Temporally Explicit Integrodifference Model
Settlers at x =
R = proportion of settlers that successfully recruit into the local population
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Spatially and Temporally Explicit Integrodifference Model
Ricker:
Density dependence: Inter-cohort Intra-cohort
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Spatially and Temporally Explicit Integrodifference Model
Beverton-Holt:
Density dependence: Inter-cohort Intra-cohort
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Hastings & Botsford 1999
Gaylord et al. 2005 White & Kendall 2007White et al.
In Review Ecol Lett
Increasing cost of fishing
Density dependence
Inter-cohort
Intra-cohort
Reserves do not necessarily enhance fishery profit
Gray horizontal plane represents
equivalence
Costello & Ward In Prep.
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Optimal management
Impractical to implement / regulate
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For reserves to work, policy must have a single %MPA regulated across the entire fishing community (“Community MPA”).
Note: escapement can still be species-specific
Question: Given a community of fishery species in a region characterized by a set of D- and θ-values, does “Community MPA” management enhance profit compared to conventional management?
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Cod
Wrasse
Cabezon
Marine bass
Rockfish Scallop
Lobster
Urchin & damselfish
KelpCoral
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Example community distribution
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Example community distribution
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Sub-sampling of evaluated β distributions
All distributions with peak(D) = 0.2
All distributions with peak(θ) = 10
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Sub-sampling of evaluated β distributions
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Evaluated β distributions
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Sub-sampling of evaluated β distributions
All distributions with peak(D) = 0.2
All distributions with peak(θ) = 10
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Each point represents the mean of all species assemblage distributions with same peak
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Policy: Community % MPA and flexible escapement
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Policy: Community % MPA and flexible escapement
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Policy: Community % MPA and flexible escapement
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Policy: Community % MPA and flexible escapement
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Policy: Community % MPA and flexible escapement
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Policy: Community % MPA and flexible escapement
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Policy: Community % MPA and flexible escapement
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Policy: Community % MPA and flexible escapement
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Policy: Community % MPA and flexible escapement
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Policy: Community % MPA and flexible escapement
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Large % MPA is a poor policy, unless confident θ < 10 and D > 0.6
Consequences of miscalculation are severe
Policy: Community % MPA and flexible escapement
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Moderate % MPA is a decent compromise policy, Consequences of miscalculation are not severe
Max 10% loss
Policy: Community % MPA and flexible escapement
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Conclusions (part I): Choosing a policy Optimal management is impractical because depends on species-specific %MPAs
Optimal Community %MPA depends on θ and D
Cheap, inter-cohort species dominate fishery → Reserves good
Expensive, intra-cohort species dominate fishery → Reserves bad
Given zero knowledge of θ or D, conventional management is least risky option
Common scenario: 20% MPA
Okay compromise. Worst-case negative consequences generate 90% profits compared to optimal conventional management
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Question: Given a Community %MPA policy what is optimal escapement?
Note: includes %MPA = 0
Are there rules of thumb?
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Optimal escapement, given…
Harvest all fish
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Optimal escapement, given…
Minimal variance across D-values
Harvest all fish
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Optimal escapement, given…
Harvest all fish
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Optimal escapement, given…
Harvest all fish
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Optimal escapement, given…
Harvest all fish
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Optimal escapement, given…
Harvest all fish
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Optimal escapement, given…
Harvest all fish
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Dependent variable: Optimal escapement
Independent variable R square
D < 0.005θ 0.70%MPA 0.23θ & %MPA 0.94
Multiple Linear Regression
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Dependent variable: Optimal escapement
Independent variable R square
Model (BH or Ricker) 2e-5P (fecundity) 0.05m (mortality) 0.01D < 0.01θ 0.62%MPA 0.19θ & %MPA 0.81
Multiple Linear Regression
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Escapement = 0.014* θ – 0.30*(%MPA) + 0.18
R square = 0.81
P < 1e-10
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Optimal management
Ricker P = 1 m = 0.1
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Policy: P1 = Optimal management
When reserves are part of
optimal solution
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Conclusions (part II): Policy regulation Optimal escapement decreases as % MPA increases
Fish harder to make up for displacement by reserves
Given community % MPA policy
Escapement decreases with decreasing cost of fishing (θ → 0)
The cheaper the fishing, the harder you should fish
Escapement minimally influenced by D, m, P or model form
Once a policy is implemented (whether conventional or with reserves):
Avoid spending time/money/effort estimating demographic profiles for each fishery species
Focus on developing efficient method for regulating escapement in relation to θ (e.g., via a tax)
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Fishing costs money
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Negative ENSO (i.e., la Nina) oceanographic conditions correlate most strongly with predicted pattern of gene flow
All correlations significant (P < 0.05; Mantel) except 1997 (strongest el Nino year in record)
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Pre-harvest
Cost
Post-harvest
θ
Population density=
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M = 0.05 (dash) M = 0.1 (solid) P = 1, 2, 3
(White et al. In Review Ecol Lett)
Increasing cost of fishing
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SOUTHERN CALIFORNIA BIGHT
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Reserve
Radius of larval export from reserve
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FISHERY PROFIT UNDER OPTIMAL RESERVE VS. CONVENTIONAL MANAGEMENT
Ricker P = 1 m = 0.1
Increasing cost of fishing
Inter-cohort
Intra-cohort
Density dependence
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sy'all
txyx
ty
ty
tx
tx
tx
tx
1tx R)FLKH(A)HM(AHAA
An integro-difference model describing coastal fish population dynamics:
Adult abundance at location x during time-step t+1
Number of adults
harvested
Natural mortality of adults that
escaped being harvested
Fecundity
Larval survival
Larval dispersal (Gaussian)(Siegel et al. 2003)
Larval recruitment at x
Number of larvae that successfully recruit to location x
(White et al. In Review Ecol Lett)
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Major questions in marine ecology and fisheries management
What is the optimal management strategy for coastal fisheries?
Are reserves a part of the optimal strategy?
How are populations regulated (i.e., where/how does density dependence occur)?
What are the management consequences to different forms of demographic regulation?
How connected are populations?
What drives connectivity, and how variable are patterns of connectivity over time?
What are the management consequences of population connectivity?
THESE QUESTIONS APPLY TO ALL RENEWABLE NATURAL RESOURCE MANAGEMENT SCENARIOS
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Major questions in marine ecology and fisheries management
What is the optimal management strategy for coastal fisheries?
Are reserves a part of the optimal strategy?
How are populations regulated (i.e., where/how does density dependence occur)?
What are the management consequences to different forms of demographic regulation?
How connected are populations?
What drives connectivity, and how variable are patterns of connectivity over time?
What are the management consequences of population connectivity?
THESE QUESTIONS APPLY TO ALL RENEWABLE NATURAL RESOURCE MANAGEMENT SCENARIOS
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“Garibaldi”
California Department of Fish and Game
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Genetic isolation by geographic distanceEstimated mean dispersal distance in 10s of km
100 km
Geographic distance
Gen
etic
d
iffe
ren
ce
Point Conception
Punta Eugenia
100 km
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100 km
Genetic isolation by geographic distanceEstimated mean dispersal distance in 10s of km
100 km
Geographic distance
Point Conception
Punta Eugenia
Gen
etic
d
iffe
ren
ce
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Point Conception
Punta Eugenia
Expanded range
High genetic diversity
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Point Conception
Punta Eugenia
Expanded range
High genetic diversity
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(Halp
ern
20
03
, Palu
mbi 2
00
3)
Reserves are good for marine life:
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Focus of developing fishery
Sold to US domestic Asian market (mostly in LA)
Mean price = $1.43/kg = ~$0.15/whelk
Ase
ltin
e-N
eils
on
et
al.
200
6
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Population and fishery dynamics outside reserves:
Population dynamics inside reserves:
Questions:
What is the optimal value for c?
If c* > 0, how much better are reserves compared to conventional management?
(White and Kendall 2007 Oikos)
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Optimal Reserves(30 day larval dispersal period)
-121.5 -121 -120.5 -120 -119.5 -119 -118.5
33.5
34
34.5
35
35.5
36
Base Case PLD
Reserve
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PROFIT =
Pre-harvest
Fishery yield at location x during time
step t
Revenue - Cost
Post-harvest
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Conventional Isolation-by-Distance