1
Productivity responses of a wide-spread marine piscivore, Gadus morhua, to oceanic thermal
2
extremes and trends
3
4
Running title: Thermal effects on cod recruitment
5
6
Irene Mantzouni1* and Brian R. MacKenzie1, 2, 3
7
8
Supplementary Material
9
10
1
11
Technical University of Denmark (DTU-Aqua)
12
Jægersborg Allé 1
13
Charlottenlund Castle
14
DK-2920 Charlottenlund
15
Denmark
National Institute for Aquatic Resources
16
17
2
18
c/o DTU-Aqua,
19
Jægersborg Allé 1
20
Charlottenlund Castle
21
DK-2920 Charlottenlund
22
Denmark
Department of Marine Ecology, University of Aarhus,
23
24
3
25
Department of Biology
Center for Macroecology, Evolution and Climate
1
26
University of Copenhagen
27
Universitetsparken 15
28
DK-2100 Copenhagen
29
Denmark
30
31
*
Corresponding author (IM): Tel: +45 33963422; ima@aqua.dtu.dk
32
2
33
Datasets
34
35
Cod spawner stock and recruitment data
36
We compiled a database including population time-series for the 21 major cod stocks in the
37
N. Atlantic (Supplementary Table S1, Supplementary Figure S1). Population data include
38
time-series of spawner stock biomass (SSB; in 000’s tons) and recruitment (REC; in 000’s
39
individuals). These numbers are estimated from sequential population analysis (SPA)
40
standardized, in most cases, with fisheries independent (such as research trawl survey) data
41
and were extracted from published stock assessment reports (Supplementary Table S1).
42
43
Temperature
44
Since recruitment success in many stocks and years is determined during the egg-larval-
45
pelagic juvenile phase (Brander 2003), spring temperature time-series averaged over the area
46
occupied by each stock at the surface (0-100 m) layer were used. Temperature was estimated
47
across the entire fisheries statistical areas of the stocks, rather than within the spawning
48
locations, except for the cases mentioned below. The main reason for this choice is that we
49
are aiming at studying thermal effects on the survival of the early stages, rather than only
50
eggs, operating through a broader range of mechanisms. The exact distribution of these stages
51
is only partly known, and can vary inter-annually, depending on oceanographic processes
52
rather than active habitat selection. Also, apart from the ambient conditions altering the
53
physiological rates, temperature can affect cod also indirectly, e.g., by influencing the trophic
54
interactions with prey and predators (Houde 2008; Rijnsdorp et al. 2009). Moreover,
55
substantial spatial correlation has been found among sub-areas comprising large statistical
56
regions (Planque & Frédou 1999; MacKenzie & Schiedek 2007), and thus no considerable
57
bias is expected due to this choice in most cases.
3
58
Some special considerations apply to certain NE and NW areas. For eastern Baltic cod stock
59
(ICES Subdivisions 25-32) we used temperature estimates in Subdivisions 25-29, since
60
Subdivisions 30-32 are unfavorable for cod reproduction due to low salinity (Nissling &
61
Westin 1991). For Barents Sea cod, given that stock distribution can be limited by cold waters
62
(Ottersen et al. 1998), we estimated temperature in the area south of 78oN. Low water
63
temperature can also limit the distribution of Icelandic cod to the southern part (ICES 2005).
64
Therefore we used temperature estimates applying to the region south of 62 oN in ICES
65
subdivision Va. We also applied spatial restrictions in the 4 NW Atlantic areas (Flemish Cap-
66
3M, Grand Bank-3NO, W and E Scotian Shelf- 4VsW and 4X) mostly affected by the Gulf
67
Stream, by excluding temperature observations southern that 42o. For the 3M area, only
68
temperature within the Flemish Cap was used.
69
The temperature time-series for the NE Atlantic stocks were provided by the ICES
70
(International
71
(http://www.ices.dk/datacentre/). For the NW Atlantic, the datasets were extracted from the
72
DFO (Fisheries and Oceans Canada) Hydrographic Climate database (Gregory 2004). The
73
temperature datasets were explored in terms of consistency in spatial coverage throughout
74
years and results were satisfactory for most areas.
Council
for
the
Exploration
of
the
Sea)
data
centre
75
76
Methods
77
78
Effect Sizes
79
80
1. Risk Ratio (RR)
81
The Risk Ratio was employed to test the null hypothesis that the probabilities (risks) of
82
“successful” year-classes were equal during extremely warm (“Exposed” or “Hot” group; T >
4
83
T75th%ile) and cold (“Control” or “Cool” group; T < T25th%ile) seasons. “Successful” year-classes
84
are considered those exceeding the corresponding time-series mean, and vice versa for the
85
“failed” ones (for Ricker residuals, successful and failed events correspond to the positive and
86
negative values, respectively).
87
In other words, the aim is to investigate whether the chances of higher recruitment survival
88
[log(REC/SSB) or Ricker model residuals] differ between colder and warmer seasons.
89
Therefore, for each stock, we count the number of observations falling in each cell of the
90
following fourfold table:
Groups of
Observations
91
92
Frequencies of Events
Successful
Failed
Exposed-“Hot” years
[T > T75 %ile ]
Control-“Cool” years
[T < T25 %ile ]
G
g
C
c
93
94
From these frequencies we estimate the RRi for each stock i:
95
RR i 
96
where RISK i ,hot  Gi /( gi  Gi ) and
97
there is higher probability (risk) of “successful” events during ”Hot” seasons, and vice versa
98
if RR < 1.
99
For the statistical analysis the natural logarithm of RRi is used, because of its better statistical
RISK i ,hot
RISK i ,cool
RISK i ,cool  Ci /(ci  Ci ) . Thus, if RR is above 1,
100
properties (Cooper & Hedges 1994: 248). The associated sampling variance is:
101
vlog(RR i ) 
1
1
1
1

 
Gi gi  Gi Ci ci  Ci
102
103
2. Hedges’g (HG)
5
104
HG belongs to a broader category of t-test related metrics, used to quantify the difference
105
between the means of two groups, subjected to different treatments (Hedges & Olkin 1985).
106
Hence, HG is used to compare average recruitment survival between “Cool” and ”Hot”
107
seasons, testing the null hypothesis that there is no substantial difference. The stock- specific
108
HGi estimator is:
109
HG i 
110
where X i , E ( N i , E ) and X i ,C ( N i ,C ) is the mean (sample size) in the warmer and colder of
111
seasons, respectively. J =1-3/(4( N i ,C  N i ,E )-9) is the correction for small sample size and
112
approaches 1 as the number of observations increases (Hedges & Olkin 1985). The s pi is the
113
pooled standard deviation between the two groups of observations:
X i , E  X i ,C
s pi
Ji
( Ni , E  1) si2, E  ( Ni ,C  1) si2,C
114
s pi 
115
where s E and sC is the standard deviation of the observations during the warmer and colder
116
seasons, respectively. The variance of HGi is given by:
117
vHGi 
118
A negative estimate of HG implies that mean recruitment survival is lower during relatively
119
warm seasons, and vice versa for HG > 0.The HG can be transformed into Cohen’s d (CD), an
120
effect-size based also on the standardized difference between the means (Cohen 1988, Cooper
121
& Hedges 1994: 239). The CD has useful interpretations, such as the percent of non-overlap
122
between the distributions of the recruitment survival observations during the warmer and
123
colder season, respectively (Cohen 1988).
N i ,C  N i , E  2
N i ,C  N i , E
N i ,C N i , E

HG i 2
2( N i ,C  N i , E )
124
125
3. Fisher’s z correlation coefficient (FZ)
6
126
FZ is simply the Fisher’s z-transform of the correlation (ri) between recruitment survival and
127
temperature. T his transformation is usually preferred, since it is nearly normally distributed
128
(Cooper & Hedges 1994:240):
129
1  ri
FZi  0.5log(
)
1  ri
130
The effective sample size ni , corrected for autocorrelation at lag 1 within the stock and
131
temperature time-series, was estimated using the “modified Chelton” method (Pyper &
132
Peterman 1998):
133
1 1
2
  wi , P wi ,T ,
ni n i ni
134
where ni is the number of observations and wi,P, wi,T the autocorrelation at lag 1 in the stock
135
and temperature time-series, respectively. The following formula was used to estimate the
136
variance, in order to avoid inflation due to low sample size (Stuart & Ord 1987: 533):
137
vFZi
4  ri 2
1


2
ni  1 2( ni  1)
138
139
Random-Effects Meta-Analyses
140
The above metrics (Risk Ratio-RR, Hedges’ g-HG, Fisher’s z correlation coefficient- FZ)
141
were analyzed separately for the Cold and Warm stocks using random-effects meta-analyses.
142
The method uses the stock-specific estimates to produce a weighted, average metric,
143
representing the across-stocks effect within each group (Cold or Warm). The stock-specific
144
proportional contribution (weight) to the estimation of the mean effect-size depends on both
145
the sampling error (estimated using the above presented formulas) and the random-effects
146
variance. The latter represents true differences among the individual metrics, due to
147
variability in the underlying conditions (Cooper & Hedges 1994: 316). In other words, there is
148
a (normal) distribution of metrics across the stocks, with each stock-specific estimate being
7
149
drawn from that distribution. This approach is of hierarchical nature, since it considers two
150
levels of variability, both within (sampling variance) and across (random-effects variance)
151
stocks (Cooper & Hedges 1994: 366). Thus, this method provides more conservative
152
estimates of significance compared to a fixed-effects model, considering only the former
153
source of variability (Cooper & Hedges 1994: 275). The choice of random-effects meta-
154
analysis is more justified in our study where we are combining estimates obtained for various
155
stocks, distributed across N. Atlantic and thus occupying a number of ecosystems that differ
156
in many biotic and abiotic aspects (Osenberg et al. 1999; Worm & Myers 2003; Lilly et al.
157
2008). The meta-analyses were conducted using the MS Excel add-in tool, MIX 1.7 (Bax et
158
al. 2008).
159
160
161
Auto-correlation (AC)
162
An important aspect that should be considered for the first two sets of meta-analyses is the
163
presence of auto-correlation (AC) in the stock time-series. In order to identify this effect, we
164
plotted the empirical auto-correlation function referring to the entire time-series of each stock.
165
Positive AC at lag 1 was found significant in a number of cases as illustrated in the
166
Supplementary Table S1. The issue is readily dealt with for Fisher’s z, by using the
167
appropriate formulas for the estimation of the effective sample size, as presented above.
168
In order to reduce AC effects within the stock-specific “Cool” and “Hot” groups for the Risk
169
Ratio estimation, when present, a possible approach is to delete the appropriate observations
170
so that consecutive events are eliminated from the groups (e.g., if the recruitment observations
171
in the “Exposed” group of a given stock correspond to years 1990, 1991 and 1992, the 1992
172
observation was omitted, so that there is at least a two-year interval between the events). RRi
8
173
is based on the number of successful (Gi or Ci) and failed (gi or ci) events, and hence the value
174
of the ratio is not sensitive to which observations are excluded.
175
Regarding Hedges’ g, this metric is sensitive to the values of the observations included in the
176
groups under comparison [“Hot” (or “Exposed”) and “Cool” (or “Control”) seasons].
177
Therefore, the method used in order to eliminate possible AC within the groups in RR
178
estimation, could not be applied to this analysis. The Wilcoxon test described below is an
179
alternative approach used in order to accommodate possible AC effects between the
180
observation groups, in this case.
181
182
Wilcoxon signed rank test
183
This nonparametric test is an alternative to the Student’s paired t-test, applicable to cases
184
when means are compared between two correlated groups (Zar 1999). In the present case, it is
185
used as an alternative to the Hedges’ g, presented above, in order to consider also the auto-
186
correlation in the recruitment survival observations (discussed later), resulting in correlated
187
groups of observations. The test is based on a similar metric:
188
log( X i ,E / X i ,C )
is the mean recruitment survival in the ”Hot and “Cool” seasons,
189
where X i , E and X i ,C
190
respectively. The null hypothesis tested is that the median of the estimates distribution is 0,
191
i.e., that there is no difference in recruitment survival under the two temperature regimes. The
192
alternative, one-sided hypotheses were that the median is either negative or positive. For these
193
tests, the exact probability was used, since the number of stocks within either stock group is <
194
25 (Zar 1999).
195
9
196
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Houde, E. D. 2008 Emerging from Hjort’s Shadow. J. Northw. Atl. Fish. Sci., 41, 53–70.
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Hammill, M., Rosing-Asvid, A., Svedäng, H. & Vázquez, A. 2008 Decline and
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GARM), Northeast Fisheries Science Center, Woods Hole, MA, 15–19 August
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Northeast groundfish stocks through 2004. 2005 Groundfish Assessment
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Hole, Massachusetts, 15–19 August 2005, pp. 2.2–2.29. Ed. by R. K. Mayo, and
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M. Terceiro, Northeast Fisheries Science Center Reference Document, 05–13.
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261
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Osenberg, C.W., Sarnelle, O., Cooper S.D. & Holt D.H. 1999 Resolving ecological questions
through meta-analysis: Goals, metrics, and models. Ecology, 80, 1105-1117.
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Ottersen, G., Michalsen, K., & Kakken, O. 1998 Ambient temperature and the distribution of
264
Northeast Arctic cod. ICES Journal of Marine Science, 55, 67–85.
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266
morhua). Canadian Journal of Fisheries and Aquatic Sciences, 56, 2069-2077.
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Power, D., Healey, B. P., Murphy, E. F., Brattey, J., & Dwyer, K. 2005 An assessment of the
268
cod stock in NAFO Divisions 3NO. NAFO Scientific Council Research
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Document, 05/67.
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270
Pyper, B.J. & Peterman, R.M. 1998 Comparison of methods to account for autocorrelation in
271
correlation analyses of fish data. Canadian Journal of Fisheries and Aquatic
272
Sciences, 55, 2127-2140, plus the erratum printed in CJFAS, 55, 2710.
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Rijnsdorp, A., Peck, M.A., Engelhard, G.H., Möllmann, C., & Pinnegar, J.K. 2009 Resolving
274
the effect of climate change on fish populations. ICES Journal of Marine
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277
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280
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Stuart, A., & Ord, J.K. 1987 Kendall’s advanced theory of statistics, Volume 1 Distribution
theory. Oxford University Press, New York, USA, 704 pp.
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NAFO Scientific Council Research Document, 02/58.
Worm, B., & Myers, R.A. 2003 Meta-analysis of cod-shrimp interactions reveals top-down
control in oceanic food webs. Ecology, 84, 162–173.
Zar, J.H., 1999 Biostatistical Analysis, 4th edition. Prentice-Hall, Inc., Upper Saddle River,
NJ, 931 pp.
284
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285
Supplementary Figures
a
b
286
287
Figure S1. Fisheries statistical areas in the western (a) and eastern (b) N Atlantic. The locations of the cod stocks are listed in Supplementary
288
Table S1.The maps are reproduced with the permission of FAO.
14
Warm Stocks
“Hot” seasons
(Exposed)
G/ (G+g)
“Cool” seasons
(Control)
C/(C+c)
cod-347d
codgb
cod-iceg
codviia
cod-7e-k
cod-farp
codvia
2/5
2/5
5/10
2/9
4/9
4/11
3/7
3/5
2/5
8/10
7/9
4/9
6/11
5/7
META-ANALYSIS:
22/56
35/56
0.01
(a)
0.1
1
10
RR (log scale)
Warm Stocks
“Hot” seasons
(Exposed)
G/ (G+g)
“Cool” seasons
(Control)
C/(C+c)
cod-347d
codgb
cod-iceg
codviia
cod-7e-k
cod-farp
codvia
2/5
2/5
5/10
2/9
4/9
2/11
2/7
3/5
3/5
7/10
6/9
4/9
7/11
5/7
META-ANALYSIS:
19/56
35/56
0.01
(b)
0.1
1
RR (log scale)
10
Figure S2. Stock-specific (squares) Risk Ratios (RR; see Table 1 for interpretation) and
the overall (diamond) RR with 95% confidence intervals (CI’s) estimated with random
effects meta-analysis across the Warm cod stocks for (a) the recruitment survival
[log(REC/SSB)] and (b) the Ricker model residuals. The width of the diamond represent
the 95% CI’s of the overall RR. The vertical black line corresponds to the mean overall
RR and is plotted for comparison with the individual estimates. The size of the squares is
15
proportional to the weight of the individual RR in the meta- analysis. The number of
successful events in the “Cool” (C) and “Hot” (G) groups of seasons (see Table 1 and
Supplementary Methods) are also given for each stock. The total number of observations
within groups is denoted as G+g and C+c, respectively.
16
cod-iceg
codviia
cod-7e-k
cod-farp
codvia
Stocks
Stocks
cod-347d
codgb
cod-347d
codgb
cod-iceg
codviia
cod-7e-k
cod-farp
codvia
(a)
0
0.5
1
1.5
2
2.5
(b)
-2
3
-1
RR
cod-347d
codgb
codviia
Stocks
Stocks
cod-iceg
cod-7e-k
cod-farp
codvia
HG
0
1
cod-2224
cod-2532
cod-arct
cod3no
cod3m
cod2j3kl
cod3pn4rs
cod3ps
cod4tvn
(d)
(c)
-1
-0.5
0
0.5
-1
-0.5
FZ
0
FZ
0.5
1
Figure S3. Cumulative meta-analyses of temperature effects on recruitment survival of
the Warm cod stocks using (a) Risk Ratio (RR), (b) Hedges’ g (HG) and on the Ricker
model residuals using Fisher’s z (FZ) for the (c) Warm and (d) Cold stocks. For this
purpose, the meta-analysis is repeated by adding each stock progressively in the analysis.
Thus, every result in each plot is the meta-analysis outcome based on the stock denoted
on the left column and all the above. The point meta-analytic estimates with confidence
intervals (95% for (a)-(c) and 90% for (d)) are plotted with grey symbols and grey
horizontal lines. The vertical black lines correspond to the final meta-analytic result
estimated based on all the stocks of each group, and are plotted for comparison. See
Supplementary Table 1 for stock codes.
17
Supplementary Tables
Table S1. Summary of cod stocks and temperature data used in the study. The codes,
geographic locations (shown in Supplementary Figure S1), time-series length, mean
spring temperature (T) and data sources for recruitment and SSB are given for each stock.
The %C or %W (in italics) is the index of “coldness” [proportion of T observations in the
lower (T < 4oC)] or “warmness” [the upper range (T > 6.5oC)], respectively; only those
stocks having > 25% of their temperature observations in either interval were included in
analyses. The presence of auto-correlation (AC) at lag 1 (p < 0.1) in (a) SSB, (b)
recruitment, (c) recruitment survival and (d) residuals of the Ricker model is also
indicated.
The data for the NE Atlantic stocks were extracted from the ICES Stock Assessment
Summary Database (ICES DB 2006; http://www.ices.dk/datacentre/StdGraphDB.asp),
unless otherwise stated.
spring T
Reference for stock
mean %C or %W
data
Stock
Areas
AC
Years
Code
cod-2224 W Baltic (IIId -west)
a, b, c, d
1970 - 2005
4.62
0.25
ICES DB 2006
cod-2532 E Baltic (IIId -east)
a, b, c, d
1966 - 2003
4.47
0.32
ICES DB 2006
cod-arct NE Arctic (I, II)
a, b, c, d
1953 - 2003
4.37
0.34
ICES DB 2006
cod-3no Grand bank (3NO)
a, b, c, d
1959 - 2004
3.53
0.63
Power et al. (2005)
Vázquez & Cerviño
cod-3m Flemish Cap (3M)
cod-2j3kl N Newfoundland
a
1972 - 1993
2.71
0.91
(2002)
a, b, c, d
1962 - 1989
1.22
0.96
Bishop et al. (1993)
18
(2J3KL)
N Gulf of St. Lawrence
cod-3pn4rs (3Pn4RS)
cod-3ps S Newfoundland (3Ps)
a, b, c, d
1974 - 2003
0.54
0.97
Fréchet et al. (2005)
a, b, c, d
1977 - 2002
1.24
1
Brattey et al. (2004)
S Gulf of St. Lawrence
cod-4tvn (4TVn)
Chouinard
et
al.
a, b, c, d
1953 - 2004
0.83
1
a
1963 - 2005
6.45
0.45
ICES (2007)
a
1978 - 2004
7.07
0.67
O'Brien et al. (2005)
a, c
1956 - 2003
7.16
0.82
ICES DB 2006
cod-viia Irish Sea (VIIa)
a
1968 - 2005
7.55
0.92
ICES (2006)
cod-7e-k Celtic Sea (VIIe-k)
a
1971 - 2005
10.63
1
ICES DB 2006
cod-farp Faroe Plateau (Vb)
a, b, c, d
1961 - 2004
7.74
1
ICES DB 2006
cod-via W Scotland (VIa)
a
1978 - 2004
8.79
1
ICES (2006)
cod-gom Gulf of Maine (5Y)
a
1982 - 2004
4.86
Mayo & Col (2005)
a
1983 - 2000
4.27
Clark et al. (2002)
cod-4vsw E Scotian Shelf (4VsW)
a
1970 - 2001
5.05
Fanning et al. (2003)
cod-coas Norwegian Coastal (IIa)
a, b, c, d
1984 - 2004
5.93
ICES DB 2006
a, b
1971 - 2004
5.68
ICES DB 2006
cod-347d North Sea (IIIa-IV-VIId)
cod-gb
Georges Bank (5Z)
cod-iceg Iceland (Va)
cod-4x
W Scotian Shelf (4X)
cod-kat Kattegat (IIIa -east)
19
(2006)
Table S2. The number of observations ( N C , N E ), the mean ( X i ,C , X i , E ) and the standard
deviation ( sC , s E ) for the “Cool” (or Control) and “Hot” (or Exposed) groups of seasons
(denoted with C and E subscripts, respectively) used to estimate Hedges’ g (HG) for the
Warm stocks. See Supplementary Table 1 for stock codes.
Hot seasons
Warm Stocks
cod-347d
codgb
cod-iceg
codviia
cod-7e-k
cod-farp
codvia
Cool seasons
NE
X i,E
sE
NC
X i ,C
5
5
10
9
9
11
7
8.0
5.1
6.2
5.9
5.5
5.3
6.3
0.2
0.6
0.6
0.5
0.5
0.9
0.4
5
5
10
9
9
11
7
8.3
5.6
6.9
6.3
5.6
5.4
6.7
20
sc
0.9
0.8
0.6
0.7
1.0
0.7
0.8