Demographic Model:Structure

Mary C. Christman (UMD/UFL)

Danny Lewis (UMD)

Jon Volstad (Versar)

Overview Spat Fall Seeding

Growth

Harvest Mortality?

Current Population at time t

Disease Event?

Freshet Event?

Natural Mortality

Density Dependent

Effects

Current Population at time t – 1

1) Yearly time step starting in October each year

2) Parameters and structure are modified according to alternative under consideration

3) Done on a per bar basis and aggregated to desired spatial scales

Spat Fall

Predictions based on:1: Projected number of spat surviving to 6-40 mm (mode

30 mm) 2: Spatial distribution based on larvae settlement

projected from hydrodynamic modeling (Dr. Elizabeth North, HPL)

Part 1: predicted # spat per spawner (standardized fecundity to 77 mm oyster) based on stock-recruit regressions:Use the DNR spat fall survey data (1991-2003)Estimate regression parameters w/ standard deviation

by regions and type of weather year (dry, average, wet)

Spat Fall Seeding

Current Population at time t

Current Population at time t – 1

Parameterization ofdemographic model based on currently available data

Jon H. Vølstad, Jodi Dew and Ed Weber

Versar, Inc., Columbia, MDand

Mary Christman, UFL

Data sources for estimating growth parameters (C. Va) Dr. Paynter (unpublished)

Individual growth data from 25 MD sites with sufficient sample sizes

Coakley (2004) Growth parameters based on cohort

analysis (29 MD sites) Virginia growth data from James

River

VBGC growth

0

50

100

150

200

Leng

th

-2 0 2 4 6 8 10

Time

Fit Each Value

Bivariate Fit of Length By Time

Assume growth (length) during a time step is a function of size not age

Growth equation for oysters at size (not age)

VB function

L1 is the size class in the current year

L2 is the mean size class after growth in a single time step.

KeLLLL 1112

VB Growth Parameters

50

100

150

200

250

300

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

L-infinity (mm) K

corr(L_inf, K) = -0.6815 KeLLLL 1112

(1)

Growth of diploid Crassostrea ariakensis

apply the growth rate for C. virginica, but with an extended growing season through the winter months

Data for estimating Mortality Maryland fall survey, 1991+ Used ‘recent’ and total box counts The category of “box” includes dead

oysters with shells still articulated “recent” include gapers, in which tissue is

still found within the shell, as well as boxes with no fouling or sedimentation on the inner valve surfaces

“old” (boxes in which fouling and/or sedimentation is found on the inner valve surfaces and no tissue remains).

Assumptions when using box counts for estimating mortality

‘Old boxes’ -- assumed to represent mortality within

the last year prior to October survey; ‘Recent boxes’ --

assumed to represent mortality for ~2 weeks period prior to October survey;

Yearly mortality mostly occur from May to October (20 weeks)

Limitations of using box counts or size-class cohort analysis:

Older boxes -- may represent mortality over 1+ years; Transition between size classes due to growth not

accounted for; Less separation between disease tiers

Recent boxes – Time since death can only be defined approximately

Cohort analysis Lack info on age; cohorts overlap

Classified years by disease level

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

Tier2

Tier1

Tier2

Tier2

Tier3

Tier2

Tier3

Tier3

Tier3

Tier1

Tier2

Tier1

Tier1

Tier3

Tier3

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Disease Tier and Year

Mea

n D

isea

se In

ten

sity

Mortality: Small and market sizedCrassostrea virginica

Empirical estimates of mortality by salinity (ppt) and disease tier Salinity classes: high ( 15 +), medium

(11-15), low (<=11) Disease intensity: Tiers 1-3 Likelihood of disease Tier determined

by type of year (Wet, Average, Dry)

Mortality of small oysters Crassostrea virginica

Sm all-sized Oysters

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

tier 1 tier 2 tier 3 tier 1 tier 2 tier 3 tier 1 tier 2 tier 3

high high high med med med low low low

Disease Tier and Salinity Class

Pro

po

rtio

nal

An

nu

al

Mo

rtal

ity

Recent Boxes All Boxes

Mortality of market sized oysters Crassostrea virginica

Market-sized Oysters

0.00

0.100.20

0.300.40

0.50

0.600.70

0.80

tier 1 tier 2 tier 3 tier 1 tier 2 tier 3 tier 1 tier 2 tier 3

high high high med med med low low low

Disease Tier and Salinity Class

Pro

po

rtio

nal

An

nu

al

Mo

rtal

ity

Recent Boxes All Boxes

Mortality by year for MD Crassostrea virginica (across salinity zones)

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

Dry

Ave

rage Wet

Wet

Dry

Wet

Ave

rage Wet

Dry

Ave

rage Dry

Dry

Wet

Wet

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Year

Pro

po

rtio

nal

An

nu

al M

ort

alit

y

New Boxes All Boxes

Disease intensity tier of year by type of weather

Disease Intensity

0

0.2

0.4

0.6

0.8

1

1 2 3

Tier

Pro

ba

bil

ity

Dry

Average

Wet

The disease tier for a given year is randomly assigned based on the type of weather regime

  Tier1 Tier2 Tier3

Dry 0.80 0.20 0

Average 0 0.75 0.25

Wet 0 0.17 0.83

MSX events for Crassostrea virginica MSX event assigned when two

or more dry years occur in a row estimated from Maryland DNR

historic disease data

Salinity Prob(MSX occurring)

Low 0.38

Med 0.71

High 1.00

Markets

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

June July August October CummulativeMonthly -Annual

Fall Survey 02 -Annual

Med Salinity,Tier 1 - Annual

Pro

po

rtio

nal

Mo

rtal

ity

Mortality, high MSX intensity

Increased mortality due to MSX events Crassostrea virginica

Increased baseline mortality by 10% -points for bars with high MSX events

Density-dependent mortality Under development, with input from

scientific review panel Currently assume a maximum of

300 oysters per m2 If the density at a bar exceeds this

following spat-fall, the oysters will be assigned a uniform density dependent mortality across all size groups to scale back the density to the threshold.

Natural Mortality of Disease Tolerant Crassostrea virginica

Great W River Mortality Rates for Dz Tolerant C. virginicaMedium Salinity Site (1997-1999)

0

20

40

60

80

100

J-97 S-97 J-98 A-98 J-98 N-98 F-99 M-99 A-99 D-99

Date

Cu

mu

lati

ve M

ort

alit

y R

ate

F4-DEBY F1-TS F1-MB Est. Standard Est. DEBY

From Calvo et al. (2003) “Standard” =Tier 3

Natural Mortality of Disease Tolerant Crassostrea virginica

York River Mortality Rates for Dz Tolerant C. virginicaHigh Salinity Site (1997-1999)

0

20

40

60

80

100

J-97 S-97 J-98 A-98 J-98 N-98 F-99 M-99 A-99 D-99

Date

Cu

mu

lati

ve M

ort

alit

y R

ate

F4-DEBY F1-TS F1-MB Est. Standard Est. DEBY

From Calvo et al. (2003) “Standard” =Tier 3

Mortality of disease tolerant C.va (Calvo et al. 2003) in high salinity

Mortality of Delaware Bay Oysters (DEBY) Grown in Chesapeake Bay

0

10

20

30

40

50

60

70

80

90

100

Small Market

Size Class

Mea

n M

ort

alit

y (%

)

F3-DEBY YR

F4-DEBY GWR

F4-DEBY YR

F4-DEBY BB

Estimated

Estimated = High Salinity, Disease Tier 3

Natural mortality of disease tolerant Crassostrea virginica: limitations

Estimates are based on off-bottom cage experiments Mortality due to predation is not fully

accounted for Is it reasonable to assume that the

disease tolerance is maintained in future generations, after cross-fertilization with standard oysters?

Natural Mortality for aquacultured triploid C. ariakensis (off-bottom)

Triploid C. ariakensis Cumulative Mortality (VIMS VSC)Oysters were 3 months old in Oct 03

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.10

Oct-03 Nov-03 Dec-03 Jan-04 Mar-04 Apr-04 May-04 Jul-04 Aug-04 Sep-04 Nov-04 Dec-04 Jan-05 Mar-05

Date

% M

ort

alit

y

Kinsale Salinity = 9.7 (8-11ppt) Urbanna Salinity = 11.7 (9-15ppt) Burgess Salinity = 12.1 (10-14ppt)

Saxis Salinity = 14.9 (13-18ppt) Hudgins Salinity = 15 (13-17ppt) Yorktown Salinity = 17.7 (13-22ppt)

Accomac Salinity = 29.5 (23-33ppt) Chincoteague Salinity = 31.7 (25-36ppt)

Natural Mortality for introducedC. ariakensis

Apply tier 3 mortality rates for C.virginica Assume minimal mortality due to

Dermo and MSX Assume similar predation mortality as

for C.viriginca Sensitivity analysis will involve

increased predation mortality due to thinner shells

Harvest Mortality

Exploitation rates by spatial area and year for each alternative Provided by DNR

Empirical Stock-recruitment function forCrassostrea virginica, Maryland

y = 2.5631x

R2 = 0.4018

y = 2.8445x

R2 = 0.4054

y = 2.061x

R2 = 0.8161

0

5000

10000

15000

20000

25000

30000

35000

0 2000 4000 6000 8000 10000 12000

Number of Standardized (to 77mm) Female Oysters

Nu

mb

er o

f S

pat

Average Dry Wet

2004

1998

Estimating Recruitment for Crassostrea ariakensis

The number of eggs produced per oyster by shell height:

Based on data from Taylor Hatchery and Allen and Merritt (2004)

2.634440.843*eggsN H

Estimating Recruitment for Crassostrea ariakensis

Estimate the standardized spawning stock: Divide the total number of eggs for spawning

stock by the average number of eggs produced by a 77mm C. virginica oyster

Apply stock-recruitment function to estimate # spats Assume that cumulative natural mortality from

egg to spat (in October) is the same as for C. va

Spatial distribution of spat The larval transport model (North

et al.) provides estimates of the spatial distribution of spat that survive from eggs released from each bar in the Chesapeake Bay

Starting population of oysters for model projections out to 2015 Survey data from 2004 used to define

population for C.va: MD survey data used for spatial

distribution by size VA survey data by bar

Number of stocked oysters & locations by year as provided by agencies

Settlement at each oyster bar

Larval transportmodel

Juvenile/adultdemographic model

Circulation models

abun

danc

e

time

mean

low

high

river flowPredictions

Linked Modeling Strategy

North et al.

Outside suitable habitat: continue swimming

Inside: settle

Larval Transport Settlement Model Incorporates habitat data

from MD DNR’s Bay Bottom Survey

Oyster bars in 1980s Present day oyster bars

Dead

(Smith et al. in press)

Choptank River

Blue line is QUODDY model boundariesBlack shapes are oyster habitat polygons

Particles will be released from 2,000+ habitat polygons in circulation model boundaries

Modeled particle behaviors will be based on C. virginica and C. ariakensis laboratory experiments

Simulations will be conducted with predictions from two Chesapeake Bay hydrodynamic models (ROMS and QUODDY)

Distribution of spat

C. virginica

Step 1: Release particles from each oyster polygon

C. virginica

C. virginica

Step 2: Track change in location due to currents and larval behavior

Step 3: Determine which particles settle successfully on polygons

Larval Transport

ModelStrategy

Step 4: Determine the number of particles that start and end on each polygon for input to demographic model

C. virginica Larval Transport

ModelStrategy

Step 1: Release particles from each oyster polygon

C. virginica

C. virginica

Step 2: Track change in location due to currents and larval behavior

Step 3: Determine which particles settle successfully on polygons

Step 4: Determine the number of particles that start and end on each polygon for input to demographic model

1995 1996 1997 1998 1999dry wet wet dryave

Larval transport model will be run for 1995 – 1999 to capture years with different physical conditions

Forward projections in demographic model replicate past weather patterns by simulations

Scenario 1: bootstrap, 5 year blocks from 1935-2005Scenario 2: random selection from recent 10 years

Model Runs Start with baseline run for c.

virginica Scientific review

Additional runs for alternatives with C.ariakensis after review

Model output Number and biomass of oysters by

size class; By habitat polygon By NOAA code/Chesapeake Bay

segment By State

Acknowledgements Tom O’Connel and Phil Jones, DNR,

for technical support and project management

Acknowledgements Chris Judy and Mitchell Tarnowski

(Maryland DNR) provided information on available oyster habitat & survey data for estimating mortality and recruitment;

Elizabeth North et al. for info on larval distribution

PIs on MDNR funded research; Kelly Greenhawk, GIS analysis to

delineate habitat

Membership

Brian Rothchild

Jim Anderson

Mark Berrigan

Maurice Heral

Roger Mann

Eric Powell

Mike Roman

Independent Oyster Advisory Panel

Panel’s Charge:Review the adequacy of data and assessments used to identify the ecological, economic, and cultural risks and benefits, and associated uncertainties for each EIS alternative;Provide advice on the degree of risk that would be involved for each EIS alternative if a decision were made in 2005 based on the available data and assessments; andRecommend additional research, and associated timeline, that could be obtained to reduce the level of risk and uncertainty.