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Variability in environmental factors affecting the
recruitment of fish species in the North East Atlantic
Variability in environmental factors affecting the
recruitment of fish species in the North East Atlantic
Ralf van Hal Wagenigen IMARES
Catherine L. Scott University of Liverpool
Christine Röckmann Wageningen IMARES
INEXFISH
Incorporating extrinsic drivers into
fisheries managementIN EX FISH
Incorporating extrinsic drivers into
fisheries managementIN EX FISH
Wageningen IMARES
Approach
Recruitment at age 0 or 1
Spatially explicit drivers
General additive modelling (GAM)Model selection by AIC
Comparing with SR-relationshipsRicker and Beverton & Holt
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Dataset used in the analysis
Recruitment and SSB from ICES Stock assessments
NAO winter index (CGD, Hurrell)
North Sea:Bottom temperature and salinity
ECOSMO model (Schrum & Alekseeva, RECLAIM)
ICES area VIa:SST satellite images (ICOADS)Plankton data from CPR (SAHFOS)
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RECRUITMENT
Feeding areaSpawning area
Nursery area
SSB
Condition of the spawner Survival eggs
Survival larvae, juveniles
Recruitment hypotheses
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Species and areas
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Data used in the analysis, plaice example
Time
NAO_1 Feeding
period
NAO Spawning period
Nursery period
Recruitment
SSB
Year -1 Year Year +1
winter May-Sept. winter
Jan. –Feb. Jan.April-
Dec.
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Stock vs. Recruitment of plaice
year1950 1960 1970 1980 1990 2000 2010
Rec
ruitm
ent (
X10
00)
0
1000
2000
3000
4000
5000
SSB (ton)0 100 200 300 400 500
Rec
ruitm
ent (
x100
0)
0
1000
2000
3000
4000
5000
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Results for North Sea plaiceFinal model:
NAO_1 Min_temp_spawning
Log(Recruitment)~ s(NAO_1) +s(min_temp_spawning) + temp_feeding(neg.)
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Comparison with SR-relationships for North Sea plaice
R2
Ricker+drivers 0.19Bev & Holt+drivers 0.32GAM 0.55
year
1950 1960 1970 1980 1990 2000 2010
Rec
ruitm
ent (
X10
00)
0
1000
2000
3000
4000
5000Recruitment GAM Ricker Bev & Holt
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Results for North Sea Herring
Final model:
SSB 100kt Salinity spawning area
Recruitment ~ s(SSB) + s(salinity spawning area)
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Results for cod in area VIa
Final model:
SST in April
Ln(Recruitment) ~ SSB (pos.) +C. finmarchicus (neg.) + s(SST in April)
Wageningen IMARESyear
1975 1980 1985 1990 1995 2000 2005 2010
Rec
ruitm
ent
0
10
20
30
40
50
60
Recruitment GAM LinearBev & holt
year
1950 1960 1970 1980 1990 2000 2010
Rec
ruitm
ent (
Billi
on)
0
20
40
60
80
100
120Recruitment GAM Ricker Bev & holt
Comparison with SR-relationships
R2
Ricker+drivers 0.27Bev & Holt +drivers 0.24GAM 0.55
R2
Linear 0.36Bev & Holt +drivers 0.38GAM 0.40
North Sea herring Area VIa cod
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Discussion on the use of GAM
GAM only useful for predictions within the boundaries of the data
GAM will not obey by definition the assumptions of some SR-curves
The curve should pass through the origin.The curve should not cross the zero-recruitment axis at non-zero stock size.Recruitment must exceed parental stock over some part of the range of the stock valuesRecruitment should vary smoothly with stock size.
No Relationship with SSBStocks with a limited range of SSB
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Conclusions
GAM models could improve the prediction of recruitment
Incorporation of extrinsic drivers improves predictions
Spatially and temporally resolved data are necessary to find biological meaningful relationships
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Next steps
Validation of the models with new data
Incorporation of the results into a population dynamics model
Evaluate this model with various fishing scenarios and climate scenarios
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Acknowledgements
INEXFISH partners,Chris Frid (Uni of Liverpool), Gerjan Piet (IMARES), Sture Hanson (Uni of Stockholm), Mikael van Deurs, Per Dolmer (DTU-aqua), Piotr Margonski, Ryszard Grzebielec (MIR), Fatima Borges, Hugo Mendes (IPIMAR), Gudmundur J. Oskarsson, Stefan A. Ragnarsson (MRI), Chris Birdges (Uni. of Dusseldorf) , Michele de Florio (Uni of Bari)
RECLAIM, especially Corinna Schrum
SAHFOS