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Demographic Model: Structure. Mary C. Christman (UMD/UFL) Danny Lewis (UMD) Jon Volstad (Versar). Overview. Yearly time step starting in October each year Parameters and structure are modified according to alternative under consideration

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demographic model structure

Demographic Model:Structure

Mary C. Christman (UMD/UFL)

Danny Lewis (UMD)

Jon Volstad (Versar)

overview
Overview
  • Yearly time step starting in October each year
  • Parameters and structure are modified according to alternative under consideration
  • Done on a per bar basis and aggregated to desired spatial scales
spat fall
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)
parameterization of demographic model based on currently available data

Parameterization ofdemographic model based on currently available data

Jon H. Vølstad, Jodi Dew and Ed Weber

Versar, Inc., Columbia, MD

and

Mary Christman, UFL

data sources for estimating growth parameters c va
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 Paynter (unpublished)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
VBGC growth

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

growth equation for oysters at 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.
vb growth parameters
VB Growth Parameters

K

L-infinity (mm)

(1)

corr(L_inf, K) = -0.6815

growth of diploid crassostrea ariakensis
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
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
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
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
mortality small and market sized crassostrea virginica
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)
msx events for crassostrea virginica
MSX events forCrassostrea virginica
  • MSX event assigned when two or more dry years occur in a row
    • estimated from Maryland DNR historic disease data
increased mortality due to msx events crassostrea virginica
Increased mortality due to MSX events Crassostrea virginica
  • Increased baseline mortality by 10% -points for bars with high MSX events
density dependent mortality
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
Natural Mortality of Disease TolerantCrassostrea virginica

“Standard” =Tier 3

From Calvo et al. (2003)

natural mortality of disease tolerant crassostrea virginica1
Natural Mortality of Disease TolerantCrassostrea virginica

From Calvo et al. (2003)

“Standard” =Tier 3

natural mortality of disease tolerant crassostrea virginica limitations
Natural mortality of disease tolerantCrassostrea 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 introduced c ariakensis
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
Harvest Mortality
  • Exploitation rates by spatial area and year for each alternative
    • Provided by DNR
estimating recruitment for crassostrea ariakensis
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)
estimating recruitment for crassostrea ariakensis1
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
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
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
slide36

Linked

Modeling Strategy

North et al.

Juvenile/adult

demographic model

Circulation models

Predictions

river flow

mean

abundance

high

Larval transport

model

low

Settlement at

each oyster bar

time

slide37

Outside suitable habitat:

continue swimming

Larval Transport Settlement Model

Dead

Incorporates habitat data from MD DNR’s Bay Bottom Survey

Inside: settle

Oyster bars in 1980s

Present day oyster bars

Choptank River

(Smith et al. in press)

slide38

Distribution of spat

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)

Blue line is QUODDY model boundaries

Black shapes are oyster habitat polygons

slide39

C. virginica

C. virginica

C. virginica

Larval

Transport

Model

Strategy

Step 1: Release particles from each oyster polygon

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

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

slide40

C. virginica

C. virginica

C. virginica

Larval

Transport

Model

Strategy

Step 1: Release particles from each oyster polygon

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

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

slide41

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

1995 1996 1997 1998 1999

dry

wet

ave

dry

wet

slide42

Forward projections in demographic model replicate past weather patterns by simulations

Scenario 1: bootstrap, 5 year blocks from 1935-2005

Scenario 2: random selection from recent 10 years

model runs
Model Runs
  • Start with baseline run for c. virginica
    • Scientific review
  • Additional runs for alternatives with C.ariakensis after review
model output
Model output
  • Number and biomass of oysters by size class;
    • By habitat polygon
    • By NOAA code/Chesapeake Bay segment
    • By State
acknowledgements
Acknowledgements
  • Tom O’Connel and Phil Jones, DNR, for technical support and project management
acknowledgements1
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
independent oyster advisory panel
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; and
  • Recommend additional research, and associated timeline, that could be obtained to reduce the level of risk and uncertainty.

Membership

  • Brian Rothchild
  • Jim Anderson
  • Mark Berrigan
  • Maurice Heral
  • Roger Mann
  • Eric Powell
  • Mike Roman
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