Biological and environmental factors influencing recruitment success of North Sea demersal

and pelagic fish stocks

Alan SinclairFisheries and Oceans Canada

Pacific Biological Station, Nanaimo, BC

Laurence Kell and Georgi DaskalovCEFAS Lowestoft Laboratory, UK

Motivation

North Sea stocks are assessed on a single stock basis• However fishing fleets exploit a range of species

– For example cod are taken by many gears and as a bycatch in various non-target fisheries.

• It is important therefore to look at whether stocks vary together and how environmental factors influence the main commercial fish stocks

• Since this has important implications both for yields to the various fishery sectors and for the management of the North Sea fisheries

The Main Question

Clearly there must be spawners (S) to have recruits (R)– However, inter-annual variability in recruitment far outweighs

variability in spawners

How Do Environmental Conditions Affect Recruitment? • Do environmental conditions determine

– the number of recruits, regardless of spawning stock size? – or the juvenile survival rate (R/S)?

• Is recruitment affected by biological processes such as predation, competition or spawning condition?

• Is recruitment affected by physical processes such as temperature, salinity …?

Main North Sea Commercial Stocks

• Plaice• Sole• Cod• Haddock• Saithe• Whiting• Sandeel• Herring

0.0 0.5 1.0 1.5

0.0

0.5

1.0

1.5

2.0

2.5

cod

Spawning Biomass

Re

cru

itme

nt

North Sea Species Stock/Recruitment

0 1 2 3

02

46

8 haddock

Spawning Biomass

Re

cru

itme

nt

North Sea Species Stock/Recruitment

0.0 0.5 1.0 1.5 2.0 2.5 3.0

0.0

0.5

1.0

1.5

2.0

2.5

herring

Spawning Biomass

Re

cru

itme

nt

North Sea Species Stock/Recruitment

0.0 0.5 1.0 1.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

plaice

0.0 0.5 1.0 1.5 2.00

.00

.51

.01

.52

.02

.5

saithe

0.0 0.5 1.0 1.5 2.0

0.0

0.5

1.0

1.5

2.0

2.5

3.0

sandeel

0.0 0.5 1.0 1.5 2.0

01

23

4

sole

0.0 0.5 1.0 1.5

0.0

0.5

1.0

1.5

2.0 whiting

Species Likelihood Ratiocod 16.93haddock 0.05herring 32.43plaice 1.36saithe 1.66sandeel 0.07sole -0.06whiting 3.78

Stepwise Analysis of Environmental Effects Using Likelihood Ratio Test

Recruitment is density independentWith an Environmental Component

Null: Recruitment varies around a mean

Recruitment is density dependent Following a BH relationship

Recruitment is density dependentWith an Environmental Component

2/2 EeR

2/2

Ee

S

SR

2/2

e

S

SR

2/2 eR

R = recruitment

S = spawning stock biomass

E = environmental covariate

α = maximum recruitment

β = biomass at ½ max recruitment

κ = environmental parameter

σ = residual standard deviation

Biotic Hypotheses

• Competition / Predation – Recruitment of one species is negatively affected by R or S

of another species in the year of spawning.• Juvenile feeding

– Recruitment of a pisciverous juvenile (cod, plaice, saithe, whiting) is positively affected by R of a suitable prey species (herring, sandeel) in the year of spawning.

• Feeding and spawning fitness– Recruitment of cod, haddock, plaice, saithe, sole or whiting

is positively affected by R of any species in the year prior to spawning.

– Recruitment of cod, saithe or whiting is positively affected by S of herring or sandeel in the year prior to spawning.

Abiotic Hypotheses

• North Atlantic Oscillation (NAO)– A single annual mean NAO index was used– Positive or negative effect of NAO on recruitment

• Temperature– May act on different part of life history, therefore

temperature variables were created from monthly time series and from North Sea sea surface temperature (SST)

– Effect of temperature variables may be positive or negative

Temperature Variables

• SSTY– Sea surface temperature annual mean anomaly

• Q1Y, Q2Y, Q3Y, Q4Y– Quarterly mean anomalies in year of spawning

• Q1Y-1, Q2Y-1, Q3Y-1, Q4Y-1– Quarterly mean anomaly in the year prior to spawning

• SSTDJF: – Mean winter anomaly, Dec Y-1, Jan Y, Feb Y;

• SSTFJ – Mean anomaly Feb – July

• PC1, PC2, PC3– First 3 principle components

PCA of Monthly Sea Surface Temperature

• PC1 ~ 54% of Variance– annual signal

• PC2 ~ 16% of Variance– contrast between

first and second half of year

• PC3 ~ 10% of Variance– contrast between

summer and winter temperature

-0.75

-0.50

-0.25

0.00

0.25

0.50

0.75

Eig

enve

ctor

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep Oct

Nov

Dec

Month

First 3 Components

Y

PC1

PC2

PC3

PCA Monthly Sea Surface Temperature

Results

A preliminary first cut at an analysis of this type

A broad look at how the North Sea commercial fish species vary together and the possible mechanisms

Cod 1963-2002Model Sigma Alpha Beta Term Estimate Chi pBase 0.668 1.012

BH 0.541 2.929 1.765 16.926 0.0000

BH + PC1 0.436 1.053 0.142 PC1 -0.170 17.135 0.0000

• Recruitment density dependent• Negative effect of SSTemp (PC1) on recruitment

– this has been noted by Planque and Frédou 1999 among others

α = maximum recruitment

β = biomass at ½ max recruitment

Cod 1983-2002Model Sigma Alpha Beta Term Estimate Chi pBase 0.610 0.674

BH 0.535 4.636 3.325 5.266 0.0218

BH + R1San 0.356 3.066 4.421 R1San 0.582 16.277 0.0001

BH + PC1 0.450 0.867 0.018 PC1 -0.188 6.862 0.0088

BH + R1San + PC1 0.335 0.873 0.586 R1San 0.497 11.869 0.0006PC1 -0.086 2.453 0.1173

• Positive effect of Sandeel Recruitment (R1San) and negative effect of SST (PC1)

• The PC1 term not significant in model with both terms• The stock/recruitment parameters are very sensitive to the

environmental effect• Resolving which environmental effect is operating is important

for interpreting stock/recruitment dynamics

α = maximum recruitment

β = biomass at ½ max recruitment

Haddock 1963-2002Model Sigma Alpha Term Estimate Chi pBase 1.079 0.981

R1Sol 0.953 1.592 R1Sol -0.617 9.925 0.0016

S1Her 0.978 1.600 S1Her -0.646 7.826 0.0052

R1Sol + S1Her 0.910 1.979 R1Sol -0.474 5.785 0.0162S1Her -0.435 3.686 0.0549

• Cannot reject density independent recruitment hypothesis (i.e. no evidence of a significant stock recruitment relationship)

• Negative effect of sole recruitment (R1Sol) and herring spawning biomass (S1Her)

• The S1Her term not significant in model with both terms• High residual standard deviation (Sigma) regardless of model

Sole 1957-2002

Model Sigma Alpha Term Estimate Chi pBase 0.770 1.001

PC2 0.681 0.954 PC2 0.276 11.254 0.0008

R1Pla 0.694 0.483 R1Pla 0.673 9.596 0.0020

PC2 + R1Pla 0.634 0.549 PC2 0.224 8.207 0.0042R1Pla 0.518 6.549 0.0105

• Cannot reject density independent recruitment hypothesis Positive effect of contrast in SST between winter and summer (PC2)

• Positive effect of plaice recruitment (R1Pla)• Both effects significant in 2-parameter model

Herring 1967-2002Model Sigma Alpha Beta Term Estimate Chi pBase 0.924 1.077

BH 0.632 2.568 0.962 27.396 0.0000

BH + S1Sai 0.530 3.092 0.323 S1Sai -0.785 12.626 0.0004

• Recruitment density dependent• Negative effect of Saithe spawning biomass (S1Sai)

on recruitment

PlaiceModel Sigma Alpha Term Estimate Chi p1957-2002Base 0.417 0.992

SSTFJ 0.371 1.053 SSTFJ -0.385 10.673 0.0011

1983-2002Base 0.471 1.003

R1San 0.401 0.680 R1San 0.359 6.380 0.0115

SSTFJ 0.370 1.275 SSTFJ -0.617 9.642 0.0019

R1San + SSTFJ 0.336 0.938 R1San 0.240 3.815 0.0508SSTFJ -0.496 7.077 0.0078

• Cannot reject density independent recruitment hypothesis• Negative effect of SSTemp Feb-Jun (SSTFJ) for 1957-2002 and

1983-2002 periods• Positive effect of Sandeel recruitment (R1San)for 1983-2002

period• Sandeel recruitment not significant (barely) in model with both

effects

Summary of Biological EffectsDependent Variable

Hypothesis Covariate cod haddock herring plaice saithe sandeel sole whitingComp/Pred Recruits cod - - - - - - -

haddock - - - - - - -herring - - - - - - -plaice - - - - - - -saithe - - - - - - -sandeel - - - - - - -sole - - - - - -whiting - - - - - - - -

Spawners cod - - - - - - -haddock - - - - - - -herring - - - - - - -plaice - - - - - - -saithe - - - - - - -sandeel - - - - - - -sole - - - - - - -whiting - - - - - - -

Juv Surv feeding Recruits herring + + + + + +sandeel + + + + + +

Spawning Fitness Recruits cod + + + + +year before spawning haddock + + + + +

herring + + + + + +plaice + + + + +saithe + + + + +sandeel + + + + + +sole + + + + +whiting + + + + +

Spawners herring + + +sandeel + + +

A very large number of plausible biological hypotheses were tested involving competition, predation, feeding of juveniles and feeding of spawners. It was surprising how little evidence was found to support any of these.

Summary of Temperature and NAO Effects

Dependent VariableHypothesis Covariate cod haddock herring plaice saithe sandeel sole whitingTemperature pc1 - ± ± ± ± ± ± ±

pc2 ± ± ± ± ± ± + ±Feb-Jun ± ± ± - ± ± ± ±Q1 ± ± ± ± ± ± ± ±Q4 ± ± ± ± ± ± ± ±Q3 ± ± ± ± ± ± ± ±Q4 ± ± ± ± ± ± ± ±Dec-Feb ± ± ± ± ± ± ± ±Annual ± ± ± ± ± ± ± ±Q1 y-1 ± ± ± ± ± ± ± ±Q2 y-1 ± ± ± ± ± ± ± ±Q3 y-1 ± ± ± ± ± ± ± ±Q4 y-1 ± ± ± ± ± ± ± ±

NAO NAO ± ± ± ± ± ± ± ±

Temperature effects may be important for cod, plaice and sole recruitment. For cod and plaice the effects are negative and related to annual or seasonal temperature values. For sole, recruitment was best in years with high seasonal contrast in temperatures.

The NAO did not enter in any of the ‘best’ models. However, there is a strong correlation between the NAO and SST, especially SST in the first quarter. Thus, colinearity may be masking important relationships with NAO.

Summary and Additional Questions

• Why do there appear to be so few significant relationships

• Is there ancillary information to support the findings through diets, laboratory study, earlier publications, etc.?

• Are some of the ‘significant’ relationships obviously spurious or at odds with accepted conditions in the North Sea?

• Are there other mechanisms that should be investigated?

– For example, is it temperature or Sandeel that affects cod recruitment?

• What are the implications of specific ‘environmental’ relationships for management targets and limits