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Cycles and trends in the Iberian sardine (S. pilchardus) stock and catch series and their relationship with the environment
M.B. Santos, G.J. Pierce, I. Riveiro, J.M.Cabanas, R. González-Quirós & C. Porteiro
Sardine and climate
CANTABRIAN SEABAY OF BISCAY
GULF OF CADIZ
IXa
VIIIcVIIIb
VIIIc-EastVIIIc-West
IXa-North
IXa-Central North
IXa-Central South
IXa-South Portugal IXa-South
Cadiz
Sardine and climate
• Single stock, delimited by Spanish-French border and Strait of Gibraltar
• Supports important fishery in Spain and Portugal
• Sardine has rapid growth rate, short generation time, long spawning season; females produce high number of eggs
Iberian sardine
Sardine and climate
Iberian sardine
Year
SS
B, '
00
0 to
nn
es
1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008
02
00
40
06
00
Year
F, y
ea
r-1
1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008
0.0
0.1
0.2
0.3
0.4
Year
Re
cru
itme
nt,
mill
ion
s
1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008
05
00
01
00
00
15
00
02
00
00
spaly10
WG2009
WG2008
• High importance of recruitment in overall population dynamics
• Periods of consecutive low recruitments (+ high F) have led to “crises” in the fishery
SSB
F
R
• Single stock, delimited by Spanish-French border and Strait of Gibraltar
• Supports important fishery in Spain and Portugal
• Sardine has rapid growth rate, short generation time, long spawning season; females produce high number of eggs
Sardine and climate
López-Jamar et al, 1995Negative correlation: R v
upwelling
Dickson et al, 1988Galician upwelling v
catches
Roy et al., 1995Wind strength v R
Previous studiesMany studies highlight apparent environmental relationships
Sardine and climate
Stock, catch + environmental variables
R, SSB series: 1978-2009 (32 y)
Landings in each area 1948-2009 (62 y)
Sun spots
Upwelling , IPC indexes, SST, Wind, AT, CLO, etc
NAO, NAO winter, AMO, GULF, EA
and a series of global, regional and local environmental variables
Posiciones variables
VIIIc
IXa
VIIIb
40
41
42
43
44
10 5 0
10 5 0
40
41
42
43
44
España
Po
rtug
al
Golfo de Vizcaya
6
Sardine and climate
Selection of explanatory variables
Dynamic Factor Analysis (a dimension-reduction technique) to try to identify
common trends in the EVs.
jan
feb
mar
apr
may
jun
jul
aug
sep
oct
nov
dec
Time
20 40 60
Fitt
ed v
alue
s
50
100
150
Monthly. Best model: 1 common trend. Average value used for analysis.
Exploration of collinearity in EVs + variable selection
Relationships between EVs: are they correlated?
Time series = trend + cycle and/or AC + residual
Sardine and climate
Modelling approach
• Model each RV as function of EVs: select best EVs (GAM)
• Quantify AC. If AC persists in model residuals, use GAMM
• Decompose RVs, EVs into simple trends and residuals (GLM v time)
• Compare RV trends and residuals with EV trends and residuals (which components appear to be driving the relationship?)
Trends in Recruitment + SSB
Sardine and climate
Exploration of time series
Possibility of linear + simple polynomial relationships with time - GLMs
0
5000000
10000000
15000000
20000000
25000000
Linear trend in R: decreasing from high values in the 80s to low values in the 90s and now
No trend in SSB
0
100000
200000
300000
400000
500000
600000
700000
800000
SSBR
Cycles in Recruitment + SSB
Sardine and climate
Exploration of time series
0.25 cycle / y = 1 cycle every 4 years
Spectral analysis (note: we are also detrending the data because we want to concentrate on the cycles)
0.1 cycle / y = 1 cycle every 10 years (short time series, only 32 years)
SSBR
(Partial) AC in Recruitment + SSB
Sardine and climate
Exploration of time series
Recruitment:PAC significant at time lag 1
Partial autocorrelograms
SSB:PAC very significant at time lag 1
All response variables
Sardine and climate
Exploration of time series
Variable Cycle(years)
Trend PAC (lag, years)
R* 4 Linear ↓ 1
SSB 10 None - 1
L_Total* 20? Cubic ∩ 1
L_VIIIcW* ≥5 Cubic ↘ 1
L_IXaN 20? Quadratic ∩ 1, 4
L_IXaCN* 20? Cubic ↘ 1 (3)
L_IXa 20? Quadratic ∩ 1
L_VIIIc 20? Cubic ∩ 1, 2, 16
* Log transformed
Explanatory variables
Sardine and climate
Exploration of time series
Variable Cycle(years)
Trend PAC (lag, years)
Sunspots 10 Linear ↓ 1, 2, 3
NAO 4 None - No AC
AMO 11 Cubic ↗ 1, 9
EA 4 Linear ↑ 1, 2
Upwelling Unclear None - 1, 2, 3
IPC 15 Linear ↓ No AC
W40350_W 3 Linear ↑ 1, 2, 3
SST40350_W Unclear None - 1
AT40350_W Unclear Quadratic ∩ 1
CLU40350_W 20 Cubic ↘ 1
13
Sardine and climate
Results: L_IXaCN
Explanatory Variable %DE in singleEV model
P (final model) Df (final model)
SST42350_W W42350_Wavspots
10.1%21.2%16.1%
0.00100.03990.0023
2.21
2.1
• Landings are the most complex series to model as they will a priori contain stock, fishing and environment effects
• AC persists in model residuals• GAMM with AR1 variance structure is an “improvement”• …but AC persists and all environmental effects then become non-
significant
Final GAM for L_IXaCN (%DE=45.6%, AIC = 20.47)
GAM/GAMM : which EVs best explain Landings?
14
Sardine and climate
Results: SSB
Explanatory Variable %DE in singleEV model
P (final model) Df (final model)
CLU40350_W AT40350_WAMO
26.0%30.0%33.9%
0.00100.03990.0023
12.12.8
No autocorrelation (confirmed by comparing AIC of best model with/without an AR1 variance structure)
Final GAM for SSB (%DE=68.3%, AIC = 819.18)
GAM/GAMM : which EVs best explain SSB?
15
Sardine and climate
Results: Recruitment
Explanatory Variable %DE in singleEV model
P (final model) Df (final model)
W40350_W SST40350_WavspotsNAO
20.2%35.3%25.2%3.2%
0.02600.00020.02730.1451
111
2.84
No autocorrelation (confirmed by comparing AIC of best model with/without an AR1 variance structure)
Final GAM for LogR (%DE=64.6%, AIC = -17.44)
GAM/GAMM: which EVs best explain R?
Variable Trend Cycle
LogR Linear ↓ 4 y
W40350_W SST40350_WavspotsNAO
Linear ↑None -Linear ↓None -
3 yUnclear
10 y4 y
16
1. GLM Extract trend and residuals (noise) from both LogR and Wind strength2. GAM LogR as a function of W, W trend and W noise
3. GAM LogR noise as function of W noise
LogR v trend + noise in winter wind strength (W40350_W)
Sardine and climate
Results: effect of wind strength
Log R v W(%DE=20.2P=0.0143)
Log R v W trend(%DE=39.2P=0.0001)
Log R v W noise(%DE=0.1P=0.870)
Log R noise v W noise(%DE=0.1P=0.837)
17
1. GLM trend and residuals (noise) from LogR (no significant trend in SST40350_W)2. GAM LogR as function of SST
3. GAM LogR trend and LogR noise as function of SST
LogR v trend + noise in winter SST (SST40350_W)
Sardine and climate
Results: effect of SST
Log R v SST(%DE=35.3P=0.0115)
Log R noise v SST(%DE=16.3P=0.0219)
Log R trend v SST(%DE=13.0P=0.289)
18
1. GLM Extract trend and residuals (noise) from both LogR and Sun2. GAM LogR as a function of Sun, Sun trend and Sun noise
3. GAM LogR noise as function of Sun noise
LogR v trend + noise in average number of sunspots (avspots)
Sardine and climate
Results: effect of sunspots
LogR v Sun(%DE=25.2P=0.0034)
LogR v Sun trend(%DE=39.2P=0.0001)
LogR v Sun noise(%DE=4.1P=0.267)
LogR noise v Sun noise(%DE=8.0P=0.116)
19
• In short-lived fish, environmental relationships can be an important component of stock and fishery dynamics
• Iberian sardine R, SSB, catch series all show “environmental” effects:
- wind strength, SST, AT, NAO, AMO, sunspots,
• GAMM sometimes permits removal of AC (and may not be needed)
• Need to investigate the nature of the relationships to understand mechanisms; separating trends and noise is useful guide
• Short time series remain a limitation (e.g. to detect cycles)
• Relationships for R:
- wind, sunspots: effects due to opposite/similar linear trends
- SST effect relates more to short-term variation around trend
Sardine and climate
Conclusions
We would like to thank all our Portuguese and Spanish colleagues working on sardine, all the crew and scientists in the acoustic surveys, everyone who collected the landings data, and Alain Zuur (Highland Statistics) for statistical advice
Xunta de Galicia, Programa de Recursos Humanos
Plan Nacional de I + D + I, Proyecto CTM 2010- 16053 (LOng-Term variability OF small-PELagic fishes at the North Iberian shelf ecosystem)
Sardine and climate
Acknowledgements