population genetics kellet’s whelk kelletia kelletii mtdna coi & 11 microsatellite markers 28...

Population Genetics

Kellet’s whelk Kelletia kelletii

mtDNA COI & 11 microsatellite markers

28 sampling sites across entire range

1000+ larvae in each capsule

P = 0.1

Prominent barriers to gene flow

Point Conception

Punta Eugenia

Point Conception

100 km

Point Conception

Punta Eugenia

Expanded range

High genetic diversity

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


Geographic distance

Gen

etic

diff

eren

ce

Oceanographic connectivity

Gen

etic

diff

eren

ce

Lagrangian Particle Trajectories

Velocity fields from Oey et al. [2003] data assimilation

P = 0.006 (Mantel test)

1995

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)


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 = 0.078 (remove 1999)


P = 0.078 (remove 1999)

Vorticity

“Effective dispersal” may predominantly occur during la Nina conditions

Are reserves good for fisheries?

Oikos 2007

Spatially-implicit difference equations:

Reserves enhance fishery yield

(White and Kendall 2007 Oikos)

Fishing costs money

Reserves still enhance fishery profit

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

Spatially and Temporally Explicit Integrodifference Model

Settlers at x =

R = proportion of settlers that successfully recruit into the local population


Ricker:

Density dependence: Inter-cohort Intra-cohort


Beverton-Holt:

Density dependence: Inter-cohort Intra-cohort

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.

Optimal management

Impractical to implement / regulate

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?

Cod

Wrasse

Cabezon

Marine bass

Rockfish Scallop

Lobster

Urchin & damselfish

KelpCoral

http://www.larvalbase.org/

Example community distribution

Sub-sampling of evaluated β distributions

All distributions with peak(D) = 0.2

All distributions with peak(θ) = 10

Evaluated β distributions


All distributions with peak(D) = 0.2

All distributions with peak(θ) = 10

Each point represents the mean of all species assemblage distributions with same peak

Policy: Community % MPA and flexible escapement

Large % MPA is a poor policy, unless confident θ < 10 and D > 0.6

Consequences of miscalculation are severe


Moderate % MPA is a decent compromise policy, Consequences of miscalculation are not severe

Max 10% loss


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

Question: Given a Community %MPA policy what is optimal escapement?

Note: includes %MPA = 0

Are there rules of thumb?

Optimal escapement, given…

Harvest all fish


Minimal variance across D-values

Harvest all fish


Harvest all fish

Dependent variable: Optimal escapement

Independent variable R square

D < 0.005θ 0.70%MPA 0.23θ & %MPA 0.94

Multiple Linear Regression

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

Escapement = 0.014* θ – 0.30*(%MPA) + 0.18

R square = 0.81

P < 1e-10

Optimal management

Ricker P = 1 m = 0.1

Policy: P1 = Optimal management

When reserves are part of

optimal solution

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)

Fishing costs money


All correlations significant (P < 0.05; Mantel) except 1997 (strongest el Nino year in record)

Pre-harvest

Cost

Post-harvest

θ

Population density=

M = 0.05 (dash) M = 0.1 (solid) P = 1, 2, 3

(White et al. In Review Ecol Lett)


SOUTHERN CALIFORNIA BIGHT

Reserve

Radius of larval export from reserve

FISHERY PROFIT UNDER OPTIMAL RESERVE VS. CONVENTIONAL MANAGEMENT

Ricker P = 1 m = 0.1


Inter-cohort

Intra-cohort

Density dependence

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)

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

“Garibaldi”

California Department of Fish and Game


100 km

Geographic distance

Gen

etic

d

iffe

ren

ce

Point Conception

Punta Eugenia

100 km

100 km


100 km

Geographic distance

Point Conception

Punta Eugenia

Gen

etic

d

iffe

ren

ce

Point Conception

Punta Eugenia

Expanded range


(Halp

ern

20

03

, Palu

mbi 2

00

3)

Reserves are good for marine life:

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

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)

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

PROFIT =

Pre-harvest

Fishery yield at location x during time

step t

Revenue - Cost

Post-harvest

Conventional Isolation-by-Distance

population genetics kellet’s whelk kelletia kelletii mtdna coi & 11 microsatellite markers 28...

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