Stock Assessment Models for Data Poor Scenarios A Comparison of Spict Stochastic Surplus Production Model in Continuous Time and CMSY CatchMSY Two EBFM research projects FishKOSM and ID: 919720
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Slide1
Paul Bouch and Dave Reid
Stock Assessment Models for Data Poor Scenarios: A Comparison of Spict (Stochastic Surplus Production Model in Continuous Time) and CMSY (Catch-MSY)
Slide2Two EBFM research projects -
FishKOSM and ProByFish
Multi species interactions
Mixed fisheries
MSY management targetsData poor stocksEcosystem/food web models
FishKOSM
-
Fisheries Knowledge for Optimal Sustainable Management
ProByFish
- Protecting bycaught species in mixed fisheries
Slide3Populating the Models
Food Webs
Establish Biomass and fishing pressures
Fisheries and Biomass reference points and ranges
Celtic Sea >200 species, 12 assessed, 13 monitoredNeed MSY proxies for data limited stocks
Slide4Data Limited Stocks
Time series may be limiting in terms of quality and quantity
No age or size data
One option - Surplus production models
Require time series of catch data and maybe an estimate of biomassUse abundance indices IF available?
Slide5Surplus Production Models - Schaefer
Surplus production is the production from a stock that is greater than the losses due to natural mortality
.
The surplus production can be fished without affecting the stock size, and there is a biomass that maximises this yield (MSY)
Bt
= current biomass
r = intrinsic growth rate
k = unexploited stock size
Ct = catch
CatchMSY
CatchMSY
Data Requirements
Uses catch time series
Schaefer model – symmetrical surplus production curve
Prior
Information Ranges
Requires estimate of species resilience to create a prior for r
Uses stock biomass trend.
Expert
opinion or
default
rules based on when in the
max and min catches in a time
series
Estimate
for unexploited stock size (k). Can be expert defined or
default
rules based on max catches and the species resilience
FISH and FISHERIES, 2013,
14
, 504-514
Slide7CatchMSY Method
Monte Carlo Method
Choose pairs of values within the predetermined ranges for r and k, and a starting biomass
An r-k pair is viable if the simulation does not lead to stock collapse and the biomass falls within the predicted biomass ranges.
From the viable pairs, the most likely r-k values are calculatedCalculate FMSY, fisheries reference points and current fishing mortality and stock biomass
Slide8BSM – Bayesian Schaefer Model
An
elaboration
to CMSY
As well as catch time series it also requires an abundance index (CPUE or Survey Index)Uses the prior ranges created with CMSY rulesBayesian state-space implementation of the Schaefer modelUses Schaefer model with the assumption that the surplus production curve is symmetrical
Slide9Stochastic Surplus Production Model in Continuous
Time (SPICT)
Utilised
in ICES 2018 assessments (Plaice 7f-g, Megrim 6b)
Catch data and biomass index (CPUE or Survey Index)Can use multiple abundance indicesShape of the production curve is not fixed although it can beCan accommodate the use of priors for r, k and q
Slide10So how do they compare?
Twenty data rich stocks from Celtic, Irish and North Seas
Using catch and abundance indices from 2017/2018 ICES assessments
Compare ICES
MSY reference points with those generated by CMSY, BSM and SPICTComparing visually and using calculating errorsARE (Absolute Relative Error) and MSE (Mean Squared Error)
Celtic Sea
Irish Sea
Slide11CMSY, BSM and SPICT Comparison - FMSY
Statistically, SPICT and CMSY perform similarly well
-
BSM
weakerCMSY shows poor agreement for FMSY > 0.3SPICT does better for FMSY > 0.3, but more variable than CMSY and wider confidence limits
CI Match
F
MSY
ARE mean
F
MSY
ARE median
F
MSY
MSE mean
F
MSY
MSE median
BSM
14
33.549
30.634
0.014
0.005
CMSY
14
23.787
17.372
0.014
0.002
SPICT
18
27.806
19.503
0.010
0.002
Slide12CMSY, BSM and SPICT Comparison – F/FMSY
CMSY
most accurately matches up with ICES assessment
SPICT often estimates F/FMSY to be lower than 0.05, which would be surprising for a commercially exploited stock.
CI Match
F/F
MSY
ARE mean
F/F
MSY
ARE median
F/F
MSY
MSE mean
F/F
MSY
MSE median
BSM
14
134.285
50.688
4.409
0.359
CMSY
16
32.709
31.801
0.537
0.145
SPICT
14
72.245
94.906
0.671
0.161
Slide13Outcome?
SPICT and CMSY perform equally well in estimating FMSY, but CMSY does far better at estimating current stock biomass and fishing pressure
BSM performs the worst of the three methodologies
CMSY which uses default rules (or expert judgement) to provide ranges performs more robustly
More information, in the form of abundance indices, does not seem to improve the assessmentWhy?
Slide14Effect of using abundance indices – Cod
West of Scotland
6 indices used in the ICES assessment
If SPICT uses them all: FMSY is 0.17
If we use each one individually, FMSY values range from 0.17 to 0.3
ICES
Fmsy
Slide15F/FMSY from multiple indices
F/FMSY ranges from 0.004 to 3.2
Abundance
indices
seem to add variability – especially for current fishing mortality and stock biomassMaybe explain why CMSY outperforms SPICT and BSM?
Slide16Conclusions
CMSY outperforms SPICT and BSM using most metrics
Abundance indices, even when from data rich stocks, appear to not help the assessment
For
these projects, we want to provide provisional reference points as a starting point for ecosystem modelsData will be very limited in quantity and qualityCatchMSY provides the most promising and robust assessment technique for
the projects
Slide17Thanks
The work was funded by the Irish Government’s Department of Agriculture, Food and the Marine’s
Competitive Research Funding Programmes for
FishKOSMAnd: The European Maritime and Fisheries Fund for
ProByFish
Slide18Surplus Production Models – Pella & Tomlinson
N defines the shape of the surplus production curveIf n=2 then it is symmetrical and identical to the top equation (Schaefer)
Modification to the Schaefer Model
Slide19CMSY, BSM and SPICT Comparison – Stock Biomass
The current biomass for each stock is best estimated for by CMSYFor several stocks, SPICT struggles to estimate the biomass with some very large results and huge confidence intervals
CI Match
Biomass
ARE mean
Biomass
ARE median
Biomass
MSE mean
Biomass
MSE median
BSM
5
68.114
74.957
5545899
1129.902
CMSY
6
84.982
58.025
1011252
625.597
SPICT
10
1051.044
67.406
43119634
9204.476
Slide20Building Ecosystem Models
Size based model using the GADGET frameworkEcopath and Ecosim
Calculate MSY within a food web contextTest harvest control rules using MSE