1
Supporting information
PLASMA CONCENTRATIONS OF ORGANOHALOGENATED
POLLUTANTS IN PREDATORY BIRD NESTLINGS: ASSOCIATIONS TO
GROWTH RATE AND DIETARY TRACERS
JAN O. BUSTNES†*, BÅRD-J. BÅRDSEN†, DORTE HERZKE‡, TROND V. JOHNSEN†, IGOR
EULAERS§, MANUEL BALLESTEROS†, SVEINN A. HANSSEN†, ADRIAN COVACI||, VEERLE L.B.
JASPERS§, MARCEL EENS§, CHRISTIAN SONNE#, DUNCAN HALLEY††, TRULS MOUM‡‡,
THERESE HAUGDAL NØST‡, KJELL E. ERIKSTAD†, ROLF A. IMS§§
†Norwegian Institute for Nature Research, FRAM – High North Research Centre for Climate and the
Environment, Hjalmar Johansensgate 14, NO-9296 Tromsø, Norway
‡Norwegian Institute for Air Research, FRAM – High North Research Centre for Climate and the
Environment, Hjalmar Johansensgate 14, NO-9296 Tromsø, Norway
§Ethology Research Group, Department of Biology, University of Antwerp, Universiteitsplein 1, BE-2610
Wilrijk, Belgium d. Toxicological Centre, Department of Pharmaceutical Sciences, University of Antwerp,
Universiteitsplein 1, BE-2610 Wilrijk, Belgium
#Department of Bioscience, Faculty of Science and Technology, University of Aarhus, Frederiksborgvej 399, PO
Box 358, DK-4000 Roskilde, Denmark
††Unit for Terrestrial Ecology, Norwegian Institute for Nature Research, Tungasletta 2, NO-7485 Trondheim,
Norway
‡‡Marine Genomics Research Group, Faculty of Biosciences and Aquaculture, University of Nordland, NO-8049
Bodø, Norway
§§Northern Populations and Ecosystems Research Group, Department of Arctic and Marine Biology, University
of Tromsø, Dramsveien 201, 9037 Tromsø, Norway
Corresponding author:
Jan O. Bustnes, Norwegian Institute for Nature Research, FRAM – High North Research Centre for
Climate and the Environment, Hjalmar Johansensgate 14, NO-9296 Tromsø, Norway. E-mail:
jan.o.bustnes@nina.no, Phone: +47 77 75 04 07, Fax +47 77 75 04 01.
2
Included Materials
____________________________________________________________________________________________
S1: Details regarding statistical analyses (Tables S1–S3 and Figures S1 and S2).
S2: Descriptive statistics for the species and compounds (Tables S4 and S5) and estimates
for the best models (Tables S6 and S7).
SI 1: Details regarding statistical analyses
GOSHAWK
Selecting the models used for inference was performed in two different steps in these linear
mixed models (LMEs). First, we defined the ‘full homoscedastic model’ where the most
complex fixed effects structure was applied (i.e. a model that reflected our most complex a
priori biological hypothesis). Second, we used this model, and defined a set of models with
different variance functions in order to assess any heteroscedastic models, i.e. models that
allow for different variance functions, would provide a better fit to our data compared to the
‘full homoscedastic model’ [e.g. Pinheiro and Bates (2000: table 5.1) and Zuur et al. (2009: ch.
4)]. Due to a limited number of observations per nest (see main text) we considered only two
different variance functions: one with different variances per year (where the variance
parameters represent the ratio of the SDs between each year and the first year) and one with no
parameters and where body mass is used as a single variance covariate (see Table S1.1 for
technical details). Third, we used the selected model from the second step, and defined a set of
models with different fixed effects (i.e. models represented different a priori biological
hypotheses; Table S1.2). Following Pinheiro and Bates (2000) we fitted models using
restricted maximum likelihood (method REML) in the step 1-2 (as the fixed effects were
constant), and using maximum likelihood (method ML) in step 2 where we had a set of
candidate models consisting of different fixed effects.
For each of the steps above we rescaled and ranked models relative to the model with the
lowest AIC value (Δi denotes this difference for model i), and we selected the simplest model
with a Δi≤1.5. Additionally, we used standard modelling diagnostics plots in order to assess if
the selected models fulfilled the underlying assumptions for these models (e.g. Zuur et al.
2009). Random intercepts fitted per nest were selected in all analyses, but both homoscedastic
3
and hetereoscedastic models were selected in the different analyses: a homoscedastic models
was selected in the analysis of PCB-153 whereas heteroscedastic models were preferred for the
rest of the models (Table S1.1). Different fixed effects were also selected in the different
analyses (Table S1.2), and details regarding the selected models are presented in the Appendix
S2. All models were fitted to the exact same dataset: rows containing missing values for all
predictors in the ‘full model’ were thus removed to the data fitted to all nested models. If the
model used for inference did not include all predictors we did, however, fit this model to a
dataset where rows containing missing values for the included predictors were removed. This
means that the sample size (n) can be larger for the model used for inference (Appendix S2:
Table S2.1 vs. Table S1.1) compared to the models showed in the model selection tables (Table
S1.1-2). Confounding was assessed by checking for correlations between the different
predictors, and a rather strong correlation between daily growth rate and body mass means that
these effects could be confounded (Figure S1.1). We did, however, keep both as potential
predictors as we had strong a priori expectations to both effects (the rationale behind this is
outlined in the Introduction). Additionally, we tested whether the removal of either body mass
or daily growth rate affected the direction or statistical significance of the remaining effect.
The statistical significance did not change for either effect as: (1) the effect of body mass did
not change from non-significant to significant and; (2) the effect of daily growth rate did not
change from significant to non-significant (results not shown). Moreover, the sign of the effect
of daily growth, i.e. the only significant effect in the original analyses (Appendix S2: Table
S2.3 c-d), did not change (results not shown). In conclusion, a potential confounding between
daily growth rate and body mass was indicated to be minor.
WHITE-TAILED EAGLE
Models selection was similar to what we did for goshawk, but as we applied linear models
(LMs) instead of LMEs in these analyses we skipped step 1-2. The rationale for choosing LMs
over LMEs is provided in the main text, but the rationale for not testing for issues related to
heteroscedasticity was that none of these models seemed to violate any of the underlying
assumptions as assessed through regular regression diagnostics. As in the goshawk analyses
different fixed effects were also selected in the different analyses for this species (Table S1.3:
see Appendix S2 for details regarding the selected models). As in the analysis above the
sample size can differ between the model selected and used for inference and the candidate
models shown in the model selection table (Appendix S2: Table S2.2 vs. Table S1.3).
Confounding was also assessed in these analyses by checking for correlations between
4
different potential predictors, but as no high correlation were present we concluded that
confounding between the included variables was unlikely to cause any large problems (Figure
S1.2).
REFERENCES
Bolker, B. M., M. E. Brooks, C. J. Clark, S. W. Geange, J. R. Poulsen, M. H. H. Stevens, and J.
S. S. White. 2009. Generalized linear mixed models: a practical guide for ecology and
evolution. Trends in Ecology & Evolution 24:127-135.
Pinheiro, J. C. and D. M. Bates. 2000. Mixed effect models in S and S-PLUS. Springer, New
York, USA.
Zuur, A. F., E. N. Ieno, and C. S. Elphick. 2010. A protocol for data exploration to avoid
common statistical problems. Methods in Ecology and Evolution 1:3-14.
Zuur, A. F., E. N. Ieno, N. J. Walker, A. Saveliev, A., and G. M. Smith. 2009. Mixed effects
models and extensions in ecology with R. Springer, New York, USA.
5
Table S1. The relative evidence for each candidate model (i), in the assessment of homoscedastic vs.
hetereoscedastic correlations structures, based on differences in AIC values (Δi) in the analysis of
goshawk. The model in underlined in bold was selected and used for inference. Following Pinheiro
and Bates (2000), these models were fitted with a restricted maximum likelihood (REML). The ‘full
model’ with respect to the fixed effects is similar to model 1 in Table S1.2.
Nest within Yeara
Yeara
Nesta
Nest within Yeara
Yeara
Nesta
Nest within Yeara
Yeara
varFixed (Body mass)b
Nesta
varIdent (Year)b
Response
Homoscedastic model
PCB-153
p,p’ -DDE
HCB
PFOS
0
2.110
10.949
55.805
2.000
4.110
12.949
57.805
32.413
26.291
57.261
58.377
3.539
4.130
0
0
5.539
6.130
2.000
2.000
28.735
28.794
50.390
26.437
18.811
0
16.081
65.411
20.811
2.000
18.081
67.411
86.523
40.28
77.770
66.232
dfc
9
10
9
11
12
11
9
10
9
aThe
syntax for the random effects (for models fitted via a call to the lme function) were as follows: (1) random = ~1|Nest
for the ‘Nest’ model; (2) random = ~1|Year/Nest for the ‘Nest within Year’ model; and (3) random = ~1|Year for the
‘Year’ model.
bThe
syntax for the different variance functions (for the hetereoscedastic models fitted via a call to the lme function)
were as follows: (1) weights = varIdent(form = ~1|Year) for the ‘varIdent (Year)’ model; and (2) weights = varFixed(form = ~
Body mass) for the ‘varFixed (Body mass)’ model.
cdf
denotes the number of parameters, whereas the number of observations (n) was 58 in all analysis except for PFOS
where n = 53.
6
Table S2. The relative evidence for each candidate model (i), in the assessment of different fixed effects, a based on differences in AIC values (Δi) in the
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
aThis
bdf
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
p,p’ -DDE
HCB
PFOS
Year
δ15N
PCB-153
δ13C
Daily growth rate
i
Body massa
analyses of goshawk. The model in underlined in bold was selected and used for inference. Following Pinheiro and Bates (2000), these models were fitted
with a maximum likelihood (ML), whereas the final model was re-fitted with a REML.
x
x
x
x
x
x
dfb
Δi
dfb
Δi
dfb
Δi
dfb
Δi
9
7
6
5
4
6
5
5
6
7
8
6
7
7
8
8
2.782
0.051
5.340
8.765
9.413
11.936
3.591
5.239
3.123
11.423
2.668
0
4.863
8.517
8.720
4.514
9
7
6
5
4
6
5
5
6
7
8
6
7
7
8
8
1.658
2.337
7.074
18.423
16.625
15.028
11.119
5.505
13.119
16.270
0
0.393
8.106
6.175
7.079
9.958
11
9
8
7
6
8
7
7
8
9
10
8
9
9
10
10
2.754
2.914
1.874
0.419
6.128
7.014
6.250
7.968
1.758
0.000
8.843
7.801
7.199
8.921
1.552
1.464
11
9
8
7
6
8
7
7
8
9
10
8
9
9
10
10
2.883
1.564
0.140
0
2.170
3.203
2.827
1.865
1.311
1.317
3.801
2.634
4.044
2.848
1.369
2.710
predictor was kept in all models based on our a priori expectations.
denotes the number of parameters, whereas the number of observations (n) was 58 in all analysis except for PFOS where n = 53.
7
Table S3. The relative evidence for each candidate model (i), in the assessment of different fixed effects, a based on differences in AIC values (Δi) in the
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
aThis
bdf
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
p,p’ -DDE
HCB
PFOS
Year
δ15N
PCB-153
δ13C
Daily growth rate
i
Body massa
analyses of white-tailed eagle. The model in underlined in bold was selected and used for inference.
x
x
x
x
x
x
dfb
Δi
dfb
Δi
dfb
Δi
dfb
Δi
8
6
5
4
3
5
4
4
5
6
7
5
6
6
7
7
1.545
1.735
1.062
3.740
5.708
8.740
7.584
1.262
5.685
6.353
0
2.646
10.732
1.493
3.119
8.022
8
6
5
4
3
5
4
4
5
6
7
5
6
6
7
7
0.014
2.772
4.908
7.027
9.642
11.063
11.558
5.702
7.994
7.394
0
5.341
12.461
6.053
6.636
6.506
8
6
5
4
3
5
4
4
5
6
7
5
6
6
7
7
4.065
1.919
5.305
15.365
13.453
5.942
15.250
3.825
17.046
5.751
2.306
0
7.743
3.738
5.544
6.839
8
6
5
4
3
5
4
4
5
6
7
5
6
6
7
7
5.291
3.673
1.734
0
4.469
4.639
5.952
6.407
1.999
1.863
5.447
7.947
6.149
3.504
3.354
3.855
predictor was kept in all models based on our a priori expectations.
denotes the number of parameters, whereas the number of observations (n) was 30 in all analysis except for PFOS where n = 29.
8
Goshawk
200
0
-200
-1.0
400
-0.5
0.0
0.5
1.0
1.5
10
30
-400
200
-30
-10
growth rate
Daily
MassDevDay.sc
-0.548
r
0
mass.sc
mass
Body
57
df
2
3
-400
p <0.001
p
0.091
p
0.161
1.0
r
-0.036
r
0.127
0.0
df 57
p 0.789
1
0
δ15N
dN15.sc
57
-3 -2 -1
df
57
df
-0.185
r
0.222
r
r
0.123
dC13.sc
δ13C
df 57
p 0.353
-1.0
d f 57
p 0.336
-30
-20
-10
0
10
20
30
-3
-2
-1
0
1
2
3
Figure S1. Pearson’s product-moment correlation (r) with associated degrees of freedom (df) and P-values (p) between continuous predictors in the analyses
of goshawk. All variables were centered (i.e. subtracting the average). Red line shows linear relationship whereas the blue dotted line shows the same using a
LOWESS smoother.
9
White-tailed sea eagle
1000
0
-2
2000
-1
0
1
-20 0 20
60
-1000
1000 2000
-60
MassDevDay.sc
growth rate
Daily
-0.227
r
0
p
0.229
r
0.305
1
-1000
mass.sc
mass
Body
28
df
0.101
p
0.608
r
0.251
r
0.086
1
-3
p
-1
dN15.sc
δ15N
28
-2
df
0
-0.098
r
28
df
0.399
dC13.sc
δ13C
0
r
-2
-1
df 28
p 0.029
d f 28
p 0.650
df 28
p 0.181
-60
-40
-20
0
20
40
60
-3
-2
-1
0
1
Figure S2. Pearson’s product-moment correlation (r) with associated degrees of freedom (df) and P-values (p) between continuous predictors in the analyses
of eagle. All variables were centered (i.e. subtracting the average). Red line shows linear relationship whereas the blue dotted line shows the same using a
LOWESS smoother.
10
S2: Descriptive statistics for the species and compounds and results from model selections, including estimates
Table S4. Descriptive statistics for the predictors included in the model selected and used for inference in the goshawk analyses (removing missing values
similar as in the analysis presented in the main text).
Predictor
Average
SD
Median
25%
quantile
75%
quantile
n
PCB-153
Daily growth rate
δ13C
δ15N
Body mass
-21.496
7.106
580.259
0.646
1.663
209.399
-21.527
7.478
547.500
-22.095
5.924
432.500
-21.039
8.300
737.500
58
58
58
p,p’ -DDE
Daily growth rate
δ13C
δ15N
Body mass
-21.496
7.106
580.259
0.646
1.663
209.399
-21.527
7.478
547.500
-22.095
5.924
432.500
-21.039
8.300
737.500
58
58
58
25.086
13.611
25.536
17.095
31.423
58
HCB
Daily growth rate
δ13C
δ15N
Body mass
580.259
209.399
547.500
432.500
737.500
58
24.609
13.849
25.000
17.000
29.688
53
576.226
220.617
550.000
430.000
730.000
53
Response
PFOS
Daily growth rate
δ13C
δ15N
Body mass
11
Table S5. Descriptive statistics for the predictors included in the model selected and used for inference in the white-tailed eagle analyses (removing missing values similar as
in the analysis presented in the main text).
Response
Predictor
Average
SD
Median
25%
quantile
75%
quantile
n
Daily growth rate
δ13C
δ15N
Body mass
-16.454
1.085
-16.506
-17.162
-15.723
32
2627.031
1021.335
2645.000
2068.750
3362.500
32
p,p’ -DDE
Daily growth rate
δ13C
δ15N
Body mass
-16.454
14.239
2627.031
1.085
0.924
1021.335
-16.506
14.351
2645.000
-17.162
13.860
2068.750
-15.723
14.856
3362.500
32
32
32
HCB
Daily growth rate
δ13C
δ15N
Body mass
-16.454
14.239
2627.031
1.085
0.924
1021.335
-16.506
14.351
2645.000
-17.162
13.860
2068.750
-15.723
14.856
3362.500
32
32
32
83.752
34.257
86.667
57.511
103.590
31
PFOS
Daily growth rate
δ13C
δ15N
Body mass
2755.161
942.734
2700.000
2175.000
3375.000
31
PCB-153
12
Table S6. Estimates from linear mixed-effect models relating different POPs (PCB-153, p,p’-DDE,
HCB and PFOS) in the blood plasma of goshawk nestlings to various predictors. All predictors were
centred (i.e. subtracting the average) meaning that the intercept represents the predicted level of each
pollutant at the average level for the predictor(s) included in each analysis respectively. The models
presented here were re-fitted using all available data for the predictors included in the model used for
inference (see Supplement S1 for details).
Parameter
(a) log e(PCB153)
Fixed effects
Intercept
δ13C
δ15N
Body mass
Random effects
Among-nest SD
Within-nest SE (residuals)
(b) log e(p,p’ -DDE)
Fixed effects
Intercept
δ13C
δ15N
Body mass
Random effects
Among-nest SD
Within-nest SE (residuals)
Variance function (varFixed)
Proportional to body mass
Estimate
SE
df
7.859
-0.372
0.215
6.97×10-5
0.199
0.156
0.080
0.001
31
31
31
31
<0.001
0.023
0.012
0.889
31
31
31
31
<0.001
0.001
0.012
0.009
0.916
0.476
8.238
-0.341
0.132
0.001
0.629
0.025
t
39.576
-2.384
2.686
0.128
R 2 lme = 0.889
n Observation = 58
n Groups = 24
0.136
0.093
0.049
3.63×10-4
60.795
-3.655
2.678
2.798
R 2 lme = 0.886
n Observation = 58
n Groups = 24
(c) log e(HCB)
Fixed effects
Intercept
6.176
0.159
32
38.834
Daily growth rate
0.015
0.005
32
3.119
Body mass
32
0.603
1.37×10-4 2.28×10-4
Random effects
R 2 lme = 0.930
Among-nest SD
0.749
n Observation = 58
Within-nest SE (residuals)
0.277
n Groups = 24
Variance function (Different SD per year; 2008 as baseline)
2009
1.606
2010
0.445
(d) log e(PFOS)
Fixed effects
Intercept
2.804
0.235
28
11.941
Daily growth rate
-0.009
0.004
28
-2.189
Body mass
28
0.469
8.84×10-5 1.88×10-4
Random effects
R 2 lme = 0.529
Among-nest SD
0.990
n Observation = 53
Within-nest SE (residuals)
0.136
n Groups = 23
Variance function (Different SD per year; 2008 as baseline)
2009
14.919
2010
0.770
P
<0.001
0.004
0.551
<0.001
0.037
0.642
Note: R2lme denotes the R2 for the relationship between the fitted values (i.e. model predictions) and the
response variable. Consequently, it is a measure of the amount of variance in the response variable
being explained by the model predictions. Even though this can be interpreted as a measure of model
performance it cannot be used or interpreted equivalent to the R2 values presented in Table S2.4.
13
Table S7. Estimates from linear models relating different POPs (PCB-153, p,p’-DDE, HCB and PFOS)
in the blood plasma of white-tailed eagle nestlings to various predictors. All predictors were centred
(i.e. subtracting the average) meaning that the intercept represents the predicted level of each pollutant
at the average level for the predictor(s) included in each analysis respectively. The models presented
here were re-fitted using all available data for the predictors included in the model used for inference
(see Supplement S1 for details).
Parameter
Estimate
SE
t
P
(a) loge(PCB153)
Intercept
9.190
0.168
Body mass
-1.4×10-4 1.7×10-4
-0.406
0.157
δ13C
(F = 3.77; df = 2,29; P = 0.04; R 2 = 0.21)
54.714
-0.865
-2.586
<0.001
0.394
0.015
(b) loge(p,p’ -DDE)
Intercept
9.228
0.479
-0.840
0.220
δ13C
0.569
0.209
δ15N
Body mass
-1.1×10-4 1.6×10-4
Year [2010]
0.436
0.529
Year [2011]
-0.823
0.592
(F = 3.80; df = 5,26; P = 0.01; R 2 = 0.42)
19.277
-3.820
2.719
-0.661
0.823
-1.391
<0.001
0.001
0.012
0.515
0.418
0.176
(c) loge(HCB)
Intercept
7.259
0.106
-0.511
0.111
δ13C
0.318
0.131
δ15N
Body mass
-1.9×10-5 1.1×10-4
(F = 2.28; df = 3,28; P < 0.01; R 2 = 0.44)
68.441
-4.607
2.420
-0.187
<0.001
<0.001
0.022
0.853
(d) loge(PFOS)
Intercept
3.469
0.104
Daily growth rate
-0.009
0.003
Body mass
-6.4×10-5 1.1×10-4
(F = 3.81; df = 2,28; P = 0.03; R 2 = 0.21)
33.424
-2.761
-0.563
<0.001
0.010
0.578