ANNEX 5
ICES DATA FOR AN HOLISTIC ANALYSIS OF FISH DISEASE PREVALENCE
1
INTRODUCTION
At its 1998 meeting, the ICES Working Group on
Pathology and Diseases of Marine Organisms
(WGPDMO) reviewed the results of a statistical analysis
of the ICES fish disease data; a written report was
presented providing information on location-specific
temporal trends in the prevalence of lymphocystis,
epidermal hyperplasialpapilloma, and skin ulcers in dab
(Limanda limanda) and lymphocystis and skin ulcers in
flounder (Platichthysflesus), as well as a comparison of
trends found at different geographical locations (ICES,
1999).
The results of the analysis revealed marked spatial
differences with respect to both the absolute levels and
the temporal changes of the disease prevalences.
Furthermore, areas characterized either by decreasing,
increasing or stable temporal trends over the past years
were identified. In some areas, temporal changes in the
prevalence of two or more diseases were similar, thereby
indicating the presence of common underlying ecological
factors affecting the disease prevalence. However, the
results of the analysis did not provide any information on
possible causes of the observed trends, since potential
explanatory factors known or suspected to be involved in
disease aetiology and pathogenesis were not included in
the analysis at that time.
WGPDMO emphasized that the integration of the ICES
fish disease data and other types of data (e.g.,
contaminant, oceanographic, and fisheries-related data)
would constitute a subsequent next step in the attempt to
analyse the ICES fish disease data in a more holistic
way, with the aim of obtaining better insight into causeeffect relationships between diseases and environmental
factors. Since ICES has established different data banks
with relevant data, it was recommended that available
data should be assessed with respect to their usefulness
for such a holistic approach.
The results of intersessional activities carried out
according to the above recommendations, prior to the
1999 WGPDMO meeting, are presented below. The first
part of the report describes the outcome of the
assessment of the ICES data banks with respect to the
types and amounts of data available, with particular
emphasis on spatial and temporal data coverage and
overlap. In the second part, preliminary results of a
multivariate statistical analysis of a selected subset of
data are presented. The third part provides conclusions
and perspectives, e.g., by focusing on data limitations
identified and on ways to accomplish a more
comprehensive analysis.
OVERVIEW OF AVAILABLE DATA
Information on the ICES data banks from which data can
be extracted for a holistic analysis and on strategies
applied to obtain an overview of available data is
described below. In addition, some of the data are
presented in order to demonstrate temporal trends.
2.1
ICES Data Banks
ICES data considered relevant for a holistic analysis are
available from the following data banks:
1) ICES Environmental Data Centre;
2) ICES Oceanographic Data Centre;
3) ICES Fishery Data Banks.
ICES provides a detailed overview (partly interactive)
of the data included on its website hltn:Nwww.ICES.dk.
Most of the information on the data banks detailed
below was extracted from this website.
A
ICES Environmental Data Centre
The ICES Environmental Data Centre contains data on:
When reviewing the 1998 WGPDMO report, the ACME
endorsed the view that a more holistic statistical analysis
is desirable and that the various ICES data banks could
serve as a suitable data pool from which the information
required could be extracted. The ACME emphasized that
the first step taken, before any further actions are decided
upon, should be to obtain a detailed overview of the data
availability and compatibility. This should be done in
close collaboration between selected WGPDMO
members and the ICES Secretariat. A second step, a pilot
study, could be subsequently started, using a selected
subset of suitable data extracted from the ICES
databases, in order to assess the practicality and
perspectives of a future holistic data analysis.
1999ACME Report
.
.
.
contaminants in marine invertebrates, fish, birds, and
mammals (approximately 275 000 records);
contaminants in sea water (approximately 280 000
records);
contaminants in sediments (approximately 80 000
records);
biological effects of contaminants: EROD, oyster
embryo bioassay (approximately 4000 records);
fish disease prevalences (approximately 110 000
records);
193
B
supporting data: nutrients, oxygen, temperature,
salinity;
2.1.1
quality assurance (QA) information
Sites
ICES Oceanographic Data Centre
The ICES Oceanographic Data Centre maintains two
data banks in the ICES Secretariat:
the ROSCOP data bank (information on cruise
activities);
the hydrochemical data bank (temperature, salinity,
nutrients, oxygen, etc.).
Selection criteria
Three North Sea areas (extended German Bight, Dogger
Bank, Firth of Forth) were selected for which a
considerable amount of disease data are available and
which differ both in the absolute prevalence levels and
the temporal trends recorded over the past years. As
shown in Figure A5.1, the areas were relatively large and
consisted of four to nine ICES Statistical Rectangles in
order to obtain sufficient data for the subsequent
statistical analysis.
Time span
In addition to these holdings, there is access to a number
of project data sets (including oceanographic data from
the International Young Fish/Bottom Trawl Surveys).
C
Since the fish disease data date back to 1981, this was
the year used as the starting point for the temporal
overview.
ICES Fishery Data Banks
Parameters
The ICES Fishery Data Banks include five fisheriesrelated data banks:
STATLANT 27A (official statistics on nominal
catches of fish and shellfish);
ICES Fisheries Assessment Package (for use by
approximately twenty working groups for ICES stock
assessments-includes
catches in tonnes, fishing
effort, catch in number at age, and relevant biological
data);
International Bottom Trawl Survey (IBTS) (results
from an international survey conducted each year in
the North Sea since 1970 which provides an annual
index of abundance by ICES Statistical Rectangle;
additional data on temperature, salinity, nutrients);
North Sea data bank (contains details of catches and
fishing effort originally set up by the EC);
North Sea multispecies data bank (contains stomach
content data for each of the main predatory fish
species in the North Sea-for use in multispecies
models).
2.2
Strategies Applied to Obtain an Overview
of ICES Data
Since the ICES data that are available and potentially
relevant for a holistic analysis are overwhelming in terms
of parameters measured and results of measurements, it
was considered impossible to present a full overview on
their spatial and temporal distribution patterns. It was,
therefore, decided to extract some of the data by using a
priori selection criteria that were mainly based on the
availability of ICES disease prevalence data for common
dab (Limanda limanda).
From the ICES Fishery Data Banks, data on catch per
unit effort (CPUE) for dab (all specimens, independently
of size) derived from the International Young
Fish/Bottom Trawl Survey (IYFSLlBTS) were selected.
Originally, it was also planned to incorporate fishing
effort data (STECF data, EU Scientific Technical and
Economic Committee for Fisheries). However, since
they are not structured in an easily accessible way (e.g.,
effort data are only available for a short time period and
separately for sixty different fishing fleets), they were
excluded. From the ICES Oceanographic Data Centre,
information on water temperature, salinity, dissolved
oxygen, total phosphorus, phosphate, ammonium, nitrite,
nitrate, silicate, and chlorophyll were considered, partly
derived from the International Young FishBottom Trawl
Survey. From the ICES Environmental Data Centre, data
on contaminants (Pb, Hg, Cd, HCH, HCB, CB118,
CB153, o,p DDT) in water, sediments, dab muscle and
liver, and blue mussel (Mytilus edulis) were selected.
After the selections were made, the ICES Secretariat was
contacted and the data were requested in electronic form.
According to the ICES data policy, raw data are not
available as this would require permission from the data
originators. Instead, aggregated data, consisting of, e.g.,
calculated mean values, were provided by the Secretariat.
2.3
Brief Overview
Figures A5.2-A5.4 provide an overview of the data
available according to the selection criteria described
above. In addition to the parameters mentioned, ICES
disease prevalence data for female dab, size group 2 G 2 4
cm, are also included. The figures clearly show that most
data are available for Area 1 (extended German Bight),
followed by Area 2 (extended Dogger Bank) and, finally,
Area 3 (extended Firth of Forth). Apart from the disease
data, there is relatively good temporal coverage of data
on CPUE, water temperature, salinity, and nutrients for
1999ACME Report
all three areas for almost the whole period, but only few
data are available on contaminants in water (with the
exception of Area 1, where data are available from 1985
onwards), sediments (with the exception of data from
1990-1992), and dab (with the exception of Areas 1 and
2 from 1990-1996).
In order to provide an overview of temporal trends,
Figures A5.5a-A5.5f show the data for selected
parameters measured in Area 1, the extended German
Bight. Both the observed values and the values derived
from interpolation in preparation of the subsequent
analysis (see below) are shown, with their corresponding
confidence intervals. For contaminants in dab, only data
for female dab, size group 2 20 cm, are included since
they correspond to the standard disease prevalence data
used in previous statistical analyses (females, 20-24 cm;
see ICES, 1999). In the ICES Environmental Data
Centre, contaminant and disease data for other size
groups and for males are also available.
3
CASE STUDY (STATISTICAL
ANALYSIS)
As requested by ACME, a case study was performed for
which only the data set from Area I (extended German
Bight) was used since it constituted the largest set with
the best temporal coverage of parameters.
values. A multivariate analysis comprising all parameters
simultaneously was considered inappropriate at this stage
as it would have required either massive extrapolations
or would have been restricted to a very narrow time span.
3.2
Results and Discussion
The results of the univariate analysis are given in Table
A5.1. A comparison of the ratio of the number of
observations (no) and the number of interpolated data
points (n;) used in the analysis for each parameter
provides not only an overview of the data availability but
also a rough indicator of the reliability of the results of
the analysis; if the balance is towards the number of
observations, interpolated data can be considered more
valid than if the balance is towards the interpolated
values.
For each parameter and disease tested, the table includes
information on the direction of the relationship and the
significance levels. In total, a significant relationship was
identified in 3 1 of 66 cases, which could be taken as an
indicator of the multifactorial aetiology of the diseases
considered. Water temperature was the parameter with
the strongest impact and the prevalences of all three
diseases were positively and highly significant when
related to temperature. CPUE was also significantly
correlated to the prevalence of the diseases. However, a
positive relationship was only found for lymphocystis
and epidermal hypirplasidp~pilloma,while a negative
relationship was found for acutethealing skin ulcerations.
Information on the significance of other parameters can
be taken directly from the table.
~~
3.1
Material and Methods
Figures A5.2-A5.4 reveal that not all data to be
compared were recorded at the same time and, therefore,
somk values had to be interpolated prior to analysis.
Since the intention was to relate the data to the disease
prevalence data, interpolations were calculated for those
time points (days) for which disease data were available.
A kernel smoother with Gaussian kernel was used to
interpolate and to remove random fluctuation. The
smoother bandwidth was selected by generalized crossvalidation. No extrapolation outside the time range
covered by real observations was done. Pointwise
confidence intervals (upper and lower lines in Figures
A5.5a-A5.50 for interpolated values were derived as
two standard deviations around smoothed values.
As a first approach to identify relationships between the
disease prevalence and the parameters listed in Section
2.2, univariate logistic models involving observed and
interpolated values were fitted (see Table A5.1).
As a second approach, a multivariate logistic model was
fitted for each of three scenarios comprising different
sets of parameters (see Table A5.2). In the long-term
model, parameters with observations for nearly the entire
range from 1981-1997 were included. The medium and
short-term models contained additional parameters that
covered approximately one half and one third,
respectively, of this range. A stepwise selection was used
to identify parameters with the highest explanatory
1999 ACME Report
~
Table A5.2 provides information on the parameters
found significantly correlated to disease prevalence by
using a stepwise multivariate analysis. Assuming an
'ideal', e.g., consistent, relationship between disease
prevalence and certain parameters, one would have
expected that terms with a highly significant impact in
the long-term model would also appear in the mediumterm and short-term models for the respective diseases.
However, this was only the case for water nitrate and
epidermal hyperplasidpapilloma. Other significant terms
occurring at least twice were salinity and cadmium in M.
edulis (Ivmohocvstis).
.. .. CB153 in M. edulis and cadmium
in unfiltered sea water (epidermal hyperplasidpapilloma), and cadmium in unfiltered sea water and
CPUE (acutehealing skin ulcerations).
.
The inconsistency between parts of the univariate and the
multivariate analyses with regard to the parameters
identified as significant (e.g., for water temperature) is at
least partially due to the fact that these analyses referred
to different time ranges. Furthermore, some of the
parameters were found to be highly correlated (e.g.,
water temperature with CPUE, nitrate, and phosphate),
which means that, in the multivariate models, certain
parameters might replace others in order to explain
variation in prevalence.
195
For interpretation of the results of the analyses, it has to
be taken into account that a considerable part of the data
used was derived from data interpolation, which is
always problematic since one does not know how the
parameters have behaved in between two sampling dates.
With increasing distance between sampling dates, this
problem becomes more serious and it is self-evident that
the reliability of results from any statistical analysis
suffers from this. Furthermore, automatic interpolation
without adaptation to the type of data used might lead to
biased data, e.g., if data series characterized by
pronounced seasonal variation are not consistent in terms
of temporal coverage of data points. An example from
the existing ICES data set is the measurement of water
temperature (Figure A5.5a). For the period 1981-1991,
only winter data are available and interpolated values
used for the analysis were generally low. From 1991,
however, springlsummer values are also available and,
therefore, interpolated values were considerably higher
although winter values remained at about the same level
as in previous years. This example clearly demonstrates
that, for further analyses, it will be more appropriate to
employ specific parameter-oriented interpolation
techniques.
4
CONCLUSIONS AND PERSPECTNES
First of all, with regard to the activities of the WGPDMO
during previous years, the data overview reveals that the
fish disease data constitute one of the most
comprehensive and consistent data sets in the ICES
Environmental Data Centre, in terms of both spatial and
temporal coverage.
In contrast, there is a striking lack of other data, in
particular for contaminants in biota and sediments,
creating major problems in the initiated holistic data
analysis. However, there are undoubtedly more data
around held by national data banks. Therefore, ICES
Member Countries should be encouraged to submit these
data to the ICES Environmental Data Centre by using
standard procedures already developed by ICES and,
therefore, applicable without major effort.
The availability of additional data would improve the
spatial and temporal data coverage and would, therefore,
possibly facilitate an analysis based on smaller
geographical areas than those used in the present case
study. Large areas create problems since conditions
normally are not the same over the entire area. For
instance, Area 1 (extended German Bight) includes
coastal, estuarine, and offshore areas and a comparison
of, e.g., contaminant levels in mussels from estuarine
areas with disease prevalences of dab collected offshore
can only be regarded as a rough approach to identify
possible relationships. Furthermore, additional data could
minimize the need for temporal interpolation and related
problems (see above).
Despite the shortcomings identified, the results of the
analysis seem to be promising since, for a number of
parameters included, a close relationship with variation
in disease prevalence could he identified. It is, therefore,
concluded that further activities with respect to
enhancing the data basis and improving the models
applied and the methods for statistical analysis are
desirable and should be initiated.
5
ACKNOWLEDGEMENT
This report was prepared by Dr T. Lang,
Bundesforschungsanstalt fiir Fischerei, Institut fiir
Fischereiokologie, Cuxhaven, Germany, and W.
Wosniok, Universitat Bremen, Institut fur Statistik,
Bremen, Germany.
6
REFERENCES
ICES. 1999. Statistical analysis of fish disease
prevalence data from the ICES Environmental Data
Centre. In Report of the ICES Advisory Committee
on the Marine Environment, 1998. ICES Cooperative
Research Report, 233: 297-327.
1999 ACME Report
Abbreviations used in the figures and tables, if not further specified
1999 ACME Report
CPUE
catch per unit eifort (number of fish per how of trawling)
DIS OXY
dissolved oxygen
F1
females, size group 10-14 cm
F2
females, size group 15-19 cm
F3
females, size group 2@24 cm
F4
females, size group 2 25 cm
F20
fraction i20 pm
F63
fraction < 63 prn
LI
liver tissue
LIMA
Limanda limanda
M1
males, size group 10-14 cm
M2
males, size group 15-19 cm
M3
males, size group 2c-24 cm
M4
males, size group 2 25 cm
MU
muscle tissue
MYTI
Mytilus edulis
P TOTAL
total phosphorus
SB
soft body tissue
SED
sediment
uoo
unfractionated
WAT
water
WAT A
filtered sea water
WAT B
unfiltered sea water
Table A5.1. Area 1 (extended German Bight): case study on the relationship between the prevalence of dab (L. limanda) diseases
and parameters available from the ICES data banks, results of a univariate analysis (if p < 0.05, a significant relationship existed). no:
number of observations; nj: number of interpolations; dir: direction of relationship; p: significance level.
1999 ACME Report
Table A5.2. Area 1 (extended German Bight): case study on the relationship between the prevalence of dab (L. limonda) diseases
and parameters available from the ICES data banks, results of a multivariate analysis. ni= number of interpolations (data values)
remaining for the analysis; dir = direction of relationship; p = significance level.
SB CB153, MYTI SB Cd, MYTI SB Hg,
AT B CB153, WAT B HCB, WAT B Cd, WAT B Hg,
63 CB153, SED F63 Hg,
SB CB153, MYTI SB HCB, MYTI SB Cd, MYTI SB Hg,
LI Cd, LIMA MU Hg,
1999ACME Report
Figure AS.l. Location of the three North Sea areas for which an overview of available ICES data is presented (single marks indicate
positions for which prevalence data of dab (L. l i m n & ) diseases are available).
L. limanda sampling locations between 4" W 50' N / 13' E 63' N
1999ACME Report
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1999 ACME Report
Figure AS.4. Area 3 (extended Firth of Forth): overview of ICES data available for a holistic analysis of fish disease data (extract).
Time pattern of observations for stlab = '41E7','42E7','41E8','40E8'
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1999ACME Report
Figure A5.5.a. Area 1 (German Bight, extended): water data from the ICES Oceanographic Data Centre (extract) (filled circles:
empirical data, empty circles: interpolated values).
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smoothing: h =
60.000 by GcV
O' " 1 ""-"
w w / prep3.ror
Water B: Hg
Sampling date
1999 ACME Report
205
Figure A5.5.e. Area 1 (extended German Bight): data on contaminants in sediments (fraction < 63pm) from the ICES Environmental
Data Centre (extract) (filled circles: empirical data; empty circles: interpolated values).
I
-1
I
I
OlJANBl
:rh;s
/
'
-3.0
X
I
I
-
I
I
I
OM87
I
I
I
I
03JAN89
04JAN91
Sampling date
I
I
WAN93
I
OMAN95
I
WAN99
ww/
I
60.000 by
I
02JAN85
I
I
03JAN87
I
I
I
I
WAN89
WANgl
Sampling date
I
I
04JAN93
I
I
05JAN95
I
WAN99
XW/
=
../
60.000 by GCV
picp3.101
Sedi 63p: Hg
x 1.8-6
0.20
.
0.05
-0.10
1
/
I
MJAN81
gerhig
206
pre~3.10
Sedi 63p: CI
h
Sampling date
/ Imoolninq:
1
I
05JAN97
ccv
12.w1
x 1.0-6
preplra
Sedi 63p: HCI
r---
I
I
I
05JAN97
60 000 by GCV
1.0-9
OWAN83
rmootning: h
I
OWAN85
I
MJAN81
/
I
02JAN83
rmoa1h;ns: h =
""1
ierbig
Sedi 6 3 ~ ~
:~1531
x 1.8-9
3~
/ rmoothing: h =
I
I
02JAN83
360.000 by GN
I
02JAN85
I
I
03JAN87
I
I
I
I
WANW
WAN91
Sampling date
I
I
WAN93
I
I
05JAN95
I
I
05JAN97
I
I
WAN99
ww/
prep3.ror
1999ACME Report
Figure A5.5.d. Area 1 (extended German Bight): data on contaminants in Mytilus edulis (soft body tissue) from the ICES
Environmental Data Centre (extract) (filled circles: empirical data; empty circles: interpolated values).
0.901
•
x 1.0-6
0.63
.
.
MYTI EDU: CB153
0.35
0.08
-0.20
MJAN8l
021ANB3
021AN85
03JAN89
W 9 I
Sampling date
03JAN87
.
( i ~ r b i g/ rmootning: n = 930.000by GCV
WAN93
OW95
WAN97
WAN99
WW/
prepisor
MYTl EDU: HCB
Sampling date
gerbig / imontning: n =
50.300 01 GCV
WW/
OIJiN81
gerbig
/
OZJiN83
rmooihing: h =
OC~P~.SOS
MYTl EDU: Cd
x 1.0-6
OZJANffi
WAN89
W
L
9
l
Sampling
. .date
WAN87
50.300 by GCV
Wh93
Wh95
Wk99
WAN97
w w / orep3rar
.
MYTl EDU: Hg
0.2
t
0.0
Ip-'
-0.2
MJANN
I
I
I
OUAN83
gerbig / smoothing: h = 180.000 bs GCV
1999 ACME Report
I
02JANffi
I
I
03JAN87
I
I
1
I
I
WAN89
WAN9I
Sampling date
I
I
WAN93
I
I
05JAN95
I
I
WAN97
I
I
06JAN99
w w / nrep3sar
Figure A5.5.e. Area I (extended German Right,: data on contaminants in lt\,er and rnutcie oidah (Limanda i i n w d a ) from the ICES
En\~ronmenralDdra Centre rextrdrtl (fi11cd circles: ernp1nc31dda, empty c ~ r s l e ~nterpolated
~:
v~luer).
Lima Lim F3 liver: ~ ~ ~ 1 5 3 1
C1
'+--0.010
I
O1JANBI
I
I
I
02JANB3
I
I
OZJAN85
I
WAN87
I
I
I
I
WAN89
WAN8
Sampling date
I
I
I
WAN93
I
05JAN95
I
I
WAN97
I
WAN59
../
p.ep3.ras
Lima Lirn F3 liver: HCB
-0.003
-
I
I
OIJANLI1
gerbig
/ Imoafhing:
-
"
I
0ZJANB3
x 1.9-6
0.m
I
qerbig
/
I
I
02JAN85
I
I
WAN87
I
I
I
I
I
NAN89
MAN8
Sampling date
I
WAN93
I
05JAN95
I
I
WAN97
I
?I200 l y GCV
0.2001
OIJANBI
I
ww/ prep3.ror
Lima Lim F3 liver: Cd
I
I
0ZJANB3
ImoOlhing: h = '22 302 oy
I
WAN99
I
02JANffi
I
I
WAN87
I
I
I
I
WAN89
WAN8
Sampling date
I
I
MAN93
I
I
I
05JAN95
I
WAN97
I
I
WAN99
x w / arep3ror
2iV
Lima Lim muscle: Hg
\
0.1448
0.1365
01283
0.12w
MJANBI
gerbig
208
/
OZJANB3
rmoaining: h = 630,000by
ccv
02JAN85
WAN87
03JAN89
WAN9
Sampling date
WAN93
WAN95
OFJAN97
WAN99
WW/
prep3.rar
1999ACME Report
Figure A5.5.f. Area 1 (extended German Bight): Limnda limanda, data on catch per unit effort (CPUE) (all dab) from the ICES
Fishery Data Bank and on diseases (females, 2C-24 cm) from the ICES Environmental Data Centre (extract) (filled circles: empirical
data; empty circles: interpolated values).
.
..
.
571W
Catch per unit effo
-1WW
DIJANBl
,rra;9
/
.imoo,n#nQ: n
WAN83
-
sa.000
WAN85
WAN87
WAN89
W S
Sampllng date
l
WAN93
WAN85
05JAN97
MAN89
"*/
ar ccv
Empirical prevalence of lymphocystis in Uma Lim I females 20-24
"A",
arcp3.ro.
I
cm, gerbig
Pram.".
Empirlcal prevaience of epidermal papilloma in Uma Um I females 20-24
I
VK
.
Zdivnea I Ex-: W"N8
cm, gerbig
"'3
0.3
0.2-
.
0.1-
OlJAN8l
02JAN83
WAN85
WAN87
WAN89
WAN91
Sampling dam
WAN93
05JANffi
"I
WAN97
PW..U I
v.r
Zdiunm
MAN99
,
Eu: 2W"W
Empirlcal prevalence of acutelhealing ulcerations in Uma Um I females 20-24 cm, gerbig
0.2
0.1
0.0
GlJANBl
02JAN83
02JAN85
WAN87
W N 8 9
WAN91
Sampling date
WAN93
WAN85
-1
1999ACME Report
Dmm3.w
05JAN97
I Mr: 2I.lUnDgJ
I
WAN89
EXm pUUNB