MINI SYMPOSIUM
ICES CM 1982/GEN:6
VARIATIONS IN FISH STOCKS AND HYPOTHESES
CONCERNING THEIR LINKS WITH CLlHATE
J. G. Shepherd, J. G. Pope and R. D. Cousens
Min1.stry of Agriculture, Fishedes a.ld Food
Dircctorate of Fisheries Research
Fisheries Laboratory
Lowestoft, Suffolk NR33 OHT, England
ABSTRACT
The variations found in time sertes of recruitment to f1sheries are
described.
The extent to which some part of these variations may be
attributed to large scale effects is discussed.
Explanations of the
variability in terms of environmental faetars are reviewed, sud various
hypotheses for the mechanisms linking climatlc factors to recruHment are
classified and critically discussed.
Les variations constataes entre les saries dans le temps concernant
le recrutement dans les peche ries sont
d~crites.
variations peuvent s'attribuer en partie
est
discut~.
Des explications de 1a
m~chanismes
Le degre auquel ces
des effets
variabi1it~
environnementaux sont revues, et diverses
des
~
~
grande echelle
en termes d'elements
hypoth~ses
pout le
d~marrage
tellaot les effects climatiques su recrutement sont
classees et evaluees en profondeur.
I.
INTRODUCTlON
The abundances of fish stocks and their geographical ranges vary
considerably with time, and there is evidence for certain stocks that
major variations have occurred for several centuries or more, lang before
fishing by man could have been a major contributory factor.
Notable
examples are the Scandinavian herring stocks (Cushing and Dickson, 1976),
the Hokkaido herring of the North Pacific (UDA, 1952) and sardines off
California (Soutar & Isaacs, 1974).
These major long-term changes of
abundance and/or geographical range are gene rally atttributed to the
effects of climatic change, and slnce OIe know that most species do have
fair1y wel1-defined geographical ranges, it is clear that climate (or,
strictly, environmental factors) does have a substantial effect.
Furthermore, there 18 a roass of recent evidence showing that
recruitment to fish stocks usually fluctuates substantial1y from year to
year. Such variations are too rapid to be caused by variations in the
abundance of the parent stock, and lead to the ubiquitous scattering of
points on a stoek-reeruitment plot. This seatter is usually attributed
to environmental faetors, but may in part also be due to fluctuations in
the abundances of particular age-classes of predators on the eggs, larvae
and juveniles, of either the parent stock or some other speeies.
The three potential causes of variations in recruitment to fish
stocks are therefore
i.
variations in environmental factors including climate, tidal
conditions, etc.
ii. variations in the abundance of the parent stock as a whole
(the atock-recruitment effect)
iii. varistions in the abundance of predatora on, or competitora
of, the pre-recruit stages (ecological interactions).
The perceived abundance of the stocks will be driven primarily by
these variations of recruitment, compounded by the effects of flshlng and
possible shifts of geographicsl range. The problem of determinlng the
relative importanee of these factors, and the mechanisms by which they
operste, has exercised fisheries biologists since the infancy of their
sclenee. The problem ia fasclnating but intractable, and little progress
has been made.
It is therefore pertinent to ask whether, and for what purpose, we
might wish to determine the effects of the environment on recrultment to
a fish stock. Suppose that one had an excellent predictive relationship
between recruitment and environmental factors: since we cannot at present
predict the weather more than a few days in advance, we should be unable
to predict year-elass strength until the weather that sffecta it has
actually happened. Tbia would therefore give us an advantage of at moat
a year or two on the use of a 0- or l-group survey, and it ia therefore
far from clear that the elucidation of the effects of climate or weather
would be worthwhile simply to ass ist in the setting of short-term management objectives (such as TACs). Unless and until weather is predictable
many years in sdvanee, our medium and long-term advice will also have to
make allowance for substantial unpredictable fluctuations.
There is however, another good reason for attempting to identify and
quantify the effeets of short-term environmental fluctuations. Since
these are a principal eause of obscurity in seeking for possible stockrecruitment relationships, if it were possible to allow for them, one
eould "clean up" the stock-recruitment plot, and clarify whatever
2
•
relatlonshlps may be there. Slnce these relatlonships are of enormous
importance in determining deslrable fishing strategies over long perlods,
thls is a worthwhl1e goal.
The effects of decadal and longer climatie fluetuations are elearly
also worth study, slnee signlfleant predlctive ability on this time seale
would be of vslue to both flshery managers and fishermen, affecting both
management snd investment strategy. Since rllmatie perturbations tend to
pers ist on this sort of tlme-seale, such predictive skill would be of use
even if one eould not predict the elimatle ehanges in advance, and of yet
more use if and when one eould.
We conclude that there almost certalnly are elimatie effects on
recrultment to fish stocks, and that it is worthwhile attempting to
quantify short-term effeets In order to clarlfy possible stockrecruitment relationships, and long-term effects in order to acquire
predictlve abillty in its own right.
2.
PROPOSED RELATIONSHIPS BETWEEN ENVIRONMENT
AND FISH STOCKS
Relatlonships proposed between environment and fish stocks fall
between two extremes: those whlch are essentlally emplrical, and those
whlch are founded more or less flrmly on a speelfie hypothesls about the
mechanism involved. Purely emplrieal studies run a greater risk of findIng spurious correlatlons - If one searehes a large enough set of data
one is likely sooner or later to flnd somethlng that correlates reasonsbly weIl. Most investlgators feel more eomfortable when a falrly speeiflc meehanism is in view; and the logleal procedure (formulate a
hypothesis, predict its eonsequences, and test the prediction against the
data) is certainly more rlgorous. Nevertheless there is no guarantee
that the emplrical approach will not prove suceessful: certalnly it may
generate interesting and surprieing elues, snd st1mulate lateral
thlnking.
In many cases - for example the rise of the W. Greenland cod stock,
associated with the mld-twentleth eentury warming of the cllmate - there
is no elear evidence of a causal relationsh1p between the faetors eorrelated. The evldence is elrcumstant1al, and the judgement of its reallty
depends mainly on the plausibll1ty of the hypothet1eal mechanlsm
involved. It 1s therefore profitable to diseuss our present understanding of how environment ~ affeet fish stocks.
3
Environmental changes are likely to affect fish stocks via four
principal processes:
1.
Direct physiologiesl effeets.
Metabolie processes are likely to
exhibit some relationship with variables such as temperature and
salinity.
This has been invoked as the cause of strong year classes in
some North Sea stocks in cold years; the species are towards their
southern limits and in all but the eoldest years are below their physiologieal optimum.
Presumably sub-optimal temperatures either direetly
eause mortality or by reducing performance inerease the later probability
of mortality by predation or starvation. Doe would expect some form of
bell-shaped relationship between performance and the environmental vari\
able and if these types of process are important we would perhaps not
expect good fit of monotonie functions to reeruitment.
2.
~.
Certain eombinations of abiotie faetors may be more eon-
ducive to disease. These may trigger outbreaks of a particular disesse
or simply inerease the overall level of infeetion of diseases already
present throughout a stock.
lt has been suggested that warm. eloudy
springs inerease egg mortality as a result of greater fungal infeetions
(Lindquist. 1978).
3.
Feeding. Sueeessful feeding may be affected in a number of ways.
ineluding:a. Food abundance. Changes in the overall abundanee of plankton
from one year to the next may oeeur as a direct response to ehanges in
physieal processes such as upwelling or stratification.
b.
Food quality.
Certain conditions may favour Some food species
over others; for example, abundanee of Ceratium spp. may be correlated
with salioity, whereas other speeies are not (Reid & Budd. 1919). Henee.
although food abundance may be higher in one year than another. its
nutritive value or desirability may be reduced (Lasker. 1915).
c. Temporal match/mismatch of food production and feeding. Cyeles
of food production and of egg production and egg and larval development
rates may be keyed to different environmental processes.
As a result
same eombinations of environmental eircumstances may eause first-feeding
larvae to miss the food production peak. Hence years in whleh overall
food produetion is the same may result in very different amounts of food
aetually available to larvae (Cushing, e.g. 1978).
d. Spatial distribution of food relative to fish. Partieular elimatie events may eause geographie shifts in the positions of high or low
food eoneentrations. which may then not coincide with the distribution of
4
•
fish. The positions of productive fronts may change from year to year,
resulting in a need for longer migrations of adult fish (Beverton and
Lee, 1965). Similsrly, changes in eurrent strengths or directions, or
changes affecting the length of time spent in the pelagie phase, may move
fish away from the best feeding areas (e.g, K1s1yakov, 1961; Reverton &
Lee, 1965).
e. Local eoncentration of food. Food Is not evenly distrlbuted
within the water column but may be eoneentrated at a particular depth and
•
may be patchy. Young fish msy require food st a eoneentration only found
in such strata/patches. Any factor which disturbs the 10eal eoncentrations, such as wind, r~duces the ability of fish to feed suffieiently
since it deereases the chance of a fish finding an area of high enough
eoncentration (Lasker, 1975).
f. Competition.
The rate at which the available food is depleted
will depend on the speeies whieh are feeding, their abundanee and their
food demand (in turn dependent on metabolie rate and size of fish). An
increase in temperature, for example, may inerease metabolie rate,
thereby inereasing food demand, and inerease the rate st which available
food ia depleted. The effect of this may weIl be to reduee the condition
of fish even if it does not direetly result in density-dependent
mortality.
4.
Predation. Like eompetition, predation will depend on the species
involved, their abundances and their food demands. It will also depend
on the species of prey, the size of prey in relation to size of predator,
and the spatial distribution of predator in reist ion to prey. Predation
is usually size-speeifie, so if eonditions are good for the feeding of
the prey and they grow quiekly, they may soon reach a size such that
predation is reduced.
Any
direetly
the ease
the same
of the four principal processes outlined above may result either
in mortality or in a sub-lethal reduetion in performance. In
of a sub-lethal effect, this eould then result in mortality by
mechanism at a later point in the fishes life his tory or by
another mechanism at the same or at a later point in its life his tory.
Many studies have indleated that year elass strength is set by a very
early age, perhaps during some eritieal larval phsse (Hjort, 1914) often
suggested as the time st whieh the egg sac becomes depleted.
The
mechanisms likely to be the principal determinants of year elass strength
are those haviog a major effect at this very early age.
5
Each species of fish has its own unlque set of relationships with
environmental variables; a simple change in one environmental factor may
produce a complex change in the web of competltion and predation interactions between species that is extremely dlfficult to predict. There Is
a multiplicity of ways in which the principal processes ~ be affected
by environmental changes and many of these may act on different specles
in very different ways. In any one year any number of the example
mechanisms outlined above may be affected by a change in the abiotic
environment and it will be difflcult, lf not impossible. to decide which
mechanlsm had the greatest effect on year class strength. Indeed. slnce
there ls a complex web of possible sub-lethal effects, it is very
~
possible that although one factor may not be the immediate cause of mortallty, it may have an important effect through Some sub-lethal effect
earlier 1n the life his tory.
The 1ndividual mechanlsms are unllkely to act independently with
respeet to a single environmental varlable. To give a hypothetlcal
example, a decrease in temperature mlght decrease the hunger of both predator, prey and competltors, decrease overall food abundance, cause
matching of food and development cycles, change the settling area of larvae due to increased time spent ln the pelagic phase; all may on their
own, or only together, cause ehanges ln mortallty.
It may, indeed, be
that In one year one mechanism will be the most 1mportant to recrultment,
but there ls ~ way of telllng what 1t Is from an empirieal correlation.
Looking for a single responslble mechanism for hlgh and low recruitment,
as has often been done, in a partlcular year ls thus llkely to be mlsleadlng and unrewardlng.
3.
OATA ON FISH STOCKS AND METHODS OF ANALYSIS
The dsta available on the abundance of flsh stocks is of three maln
tn es
a.
Oata on eatch welghts, whlcn are usually the only information avallable for seversl or many decades.
There are many advantages in having a
long time series, but the data are often of varIable quallty, and do not
provlde an unamblguous index of stock slze, as they also depend on the
level of flshlng effort, whlch 1s usually unknown.
b.
Oata on year elass strengths, whlch are rarely avallable for more
than a few decades. The more re cent data are often obtalned from VPA,
provlding the most reliable estimates, with older data (if any) based on
catch-per-unit effort of partieular (young) ages in elther eommereial or
6
~
research catches.
•
•
Some sampllng for age and/or length is aprerequisite
for avaI1ability of this form of data.
c.
Data from research vessel surveys of eggs, larvae, or pre-recrult
young fish. Such data ls rarely available for more than a few years. and
tIme serles are rarely complete. It provldes the ooly possibllity for
testing hypotheses about particular mechanisms lin~ing pre-recruit mortality to other (environmental) factors. Even so such testing may be
difficult.
Catch data alone may provide a satisfactory index of abundance
averaged over, say, five years to a decade, and for the study of 10ngterm variatIons, will have to be used as such. This approach has been
used by maoy authors (e.g. Wyatt, this symposIum). However, the most
unamblguous indicators of abuodance are tIme se ries of recruitment, and
we shall focus attention on these.
Examlnation of such data (Garrod. in press; Hennemuth~!!•• 1980)
shows that the frequency dIstribution is roughly log-normal. as might be
expected from a sequence of successive periods with somewhat varIable
mortatlty. If plotted untransformed, the eye is oaturally drswn to
outstandingly large year classes. which sre also those of greatest
interest to fishermen. However, from ao investlgative point of view the
lowest year classes are of equal interest. Furthermore, slnce a large
fractlon of the mean recruitment and Its variance may be contributed by
one or two outstanding year classes. any environmentsl factor whlch also
takes on extreme value in those years Is l1kely to lead to a correlatlon
whose slgnificance level Is greatly overestimated - thls situation ls
11lustrated in Flgure 1.
CertaInly the extreme values (both high and low) are of Interest.
and environmental variables also taking extreme values in those yeara may
provlde valuable clue. about reiationships and mechani.m. which warrant
further exploration. However. for a judiclou. approach to the problem It
i. deslrable to attempt to explain much of the normal variabillty of
recruitment as weIl as Its extrema, and for this purpose a logarlthmic
transformation of recrultment data before further analyais i. de.lrable,
especlally If statistical technlques relying much on the normality of
data are to be used. The varlance of the 10g-traosformed data is also a
useful measure of the variabllity whlch one in trylng to explaln. A
particular level of log-varlance Is related to a general level of varlabIlity obtained by multlplylng the (geometric) mean by a particular
factor, as Indlcated In the text-table below.
7
log-variance
0.1
0.2
0.5
1
2
multlply/divide factor
for 1 C.L.'s
1.37
1.56
2.03
2.72
4.11
Secondly, it is useful to note that both environmental factors and
inter-species interactions are likely to produce coherent effects on
several stocks within a particular area where they co-exist, whilst only
environmental factors are likely to produce coherent effects on stocks
from different areas.
Some analysis of the extent of correlated varia-
tions within snd between species and aress is therefore a promising
approach (see e.g. Garrod & Colebrook, 1978).
One particular approach, principle component analysis, seems particularly well-suited, since it permits this question to be studied, it
slso enables the extrsction of principle components which, ss best estimates of the nature of coherent variations, effectively average over the
"nolse" in the signals from seversl stocks. Such princlple components
may show a clearer link with the environment than any individual stock.
It is also elear that if fish stocks are more or less adapted to
particular environmental conditions, a change in either direction is
likely to be detrimental.
The response to environmental factors is
therefore unlikely to be monotonie (let alone linear) except for species
at the extremes of their range - those which are well-adapted may easily
show a domed response curve (see Cousens, 1982), and routine methods of
statistical analysis (which are mostly founded on the fitting of linear
models) may easily fail to detect such effects. For this reason, and
because they are in any case likely to be more sensitive to environmental
change, stocks at the limit of their range are probably the best
candidates for the detection of environmental effects.
In the same class of difficuities of analysis, one must recognise
that the relationships between year elass strengths of interacting
species may be lsgged one or more yesrs in time. Regrettably this magnifies the problems considerably. In order to sllow for environmental factors, interspecies effects, and parent stock effects, one may need to
consider simultaneously many years data for many stocks and allow for
many years lag in the relationships - and possibly several areas.
Furthermore, the match-mismatch type of meehanism may operate at several
critical stages of life-history, snd thus several environmental factors
8
•
are likely to be potentially important, and some form of multi-variate
analysis seems to be indicated. The number of possible correlation
coefficients which may be computed (and for which the data should ictp.slly
be examined) can all too easily become rather largel
When lagged correlations, or correlations which are valid only on
certain timescales, are po•• ible, methods of analysis which take account
of the sequential nature of the data (unlike ord1nary correlation analysis) may be indicated.
•
The two principle methods of time series analysis
(see Jenkins & Watts, 1968) are spectral analysis, and ARlMA analysis.
The first essentially computes an alternative representation of the data
in terms of deterministic cycles, the latter (commonly known as
Box~
Jenkins analysis) in terms of auto-regressive and moving average
processes.
Both can handle multi-variate ser1es, but it is very
difficult to get meaningful results with few (less than a hundred, say)
data points in a sequence.
Most Investigatlons have relied on multiple
correlation analysis, and have already found the task difficult enough
without consldering the temporal aspect. However, many recruitment
series seem to be serially correlated, so that time se ries aspects are
important, and may confound the re.ults of conventional analyses; We
have recently found tentative evidence of signala clu.tering near certain
periods in some groups of stocks and areas (e.g. near 6 yr in cod stocks,
and a 2-year alternation in the lrish and Celtic Seas). Such findings
may provide valuable clues but require the development of methods for
cross-spectral analysis on short data runS for further study. Also, in
the same way that principle component analysis may be used to extract a
possible signal common to several stocks from the noise, so may time
se ries methods be used to extract correlations which only operate on a
re.tricted range of time scales (e.g. perhaps the atock and recruitment
•
relationship), partlcularly if they are neither exactly in or out of
phase, but lagged by Some arbitrary amount. This may occur when a stock,
by virtue of its own density-dependent regulatory mechanisms, is
particularly sensitive to environmental forcing at certain frequencies
(see Shepherd & Horwood, 1979).
This is particularly likely when the
stock is on the verge ~
f"collapse (this may be what happened to the Malabar Sardine, see Cushing &
Dickson, 1976) but is always likely if there is forcing at periods
close to the mean age of first maturity.
We do not suggest that one should bllndly apply the methods of
multlvariate time series analysis to masses of recruitment and
9
environmental data, in the hope that something will come to light.
Ra~her we believe that investigators should be dware of these
~omplications,
necessary.
and have the appropriate technlques available for use when
One needs also to be aware that as one uses more and more
degrees of freedom to fit a model to a set of data, one's hindcasting
skill (i.e. the ability to account for the variance in the data-set
fitted) becomes artificially high, but that one's forecasting skill (when
tested on data not fitted) can actua11y be decreased.
This has been
studied in another context by Oavis (1977), but Seems to be a resu1t of
quite general app11cability.
This may 1ndeed be one reason for the
commen observation that a relationship is found which seemS to explain
the da ta weIL, but 1s subspquently found to have little or no predictive
ability (the Carruthers (1938) correlation of wind and various year class
strengths in the North Sea is a classic example).
We conc1ude that one
shou11 not try to exp1ain the variance by too many variables, probably
se>tling for the (usua11y large) fraction obtained with the two or three
most powerfu1 factors.
It is only too easy (as we know from personal
experience) for the investigator to believe that he has found the
philosphers stone.
Such delusions can and shou1d be regularly quenched
by simply selecting and analysing random numbers instead of data (a
technique applied with some effect by Gulland 1953, for example).
It is also necessary to keep a sense of proportion regarding tests
of statistical significance.
It is not of course surprising, if one has
computed 100 correlation coefficients, to find one that 1s significant at
the 1% level.
Since correlations that do not work often find their way
into the wastepaper basket, however, one can often lose track of this
fact.
A good remedy is to apply an overall test of significance to the
ensemble of results obtained: the method of F1sher (1970. section 21.1)
may be a possible approach.
4.
RELATIONSHIPS BETWEEN RECRUITMENT SEKIES
ANO ENVIRONMENTAL FACTORS
Over the years many relationships have been -found" between recruit-
ment data and various enviroomental factors.
Same of these are expressed
as correlations whereas others are more qualitative.
Examples of
~of
the environmental factors re1ated in the literature are presented in
Iables 1-5; Iable 1 gives references to temperature, Table 2 gives those
to sallnlty, Table 3 to wind, pressure gradients and upwelling, Table 4
to miscelIaneous factors and combinations of two or more factors.
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Table 5 gives examples of the percentages of variation explained by
regressions given withln these references. These tables are of value
since their contents constitute hypotheses which may be checked against
da ta collected subsequently.
It has often been suggested that good year classes occur in the same
year in widely separate areaa. If true, this of itself, must be evidence
of climatic variation since no other cause could act over oc~an basins.
arrod and Colebrook (1978) appesr to find such effects. We note however
that Barlett's Sphericity test (Cooley and Lohnes, 1971) spplied to their
•
correlation matrix does not reject the null hypothesis that all series
are orthogonsi. To investigste this effect further therefore we first
consider correlstions between logged recruitments of 18 N.E. Atlsntic
fish stocks from 1962 to 1976. These were North Ses sole, plaice, cod,
whiting, saithe, herring and haddock, West of Scotland whiting and
saithe, and cod, hsddock and saithe from Faroe, Iceland and the N.E.
Arctic. As indicated in Table 6 in all 15 correlation coefficients with
modulus grester thsn .5139 (P •• 05 on 13 d.f.) were found. Of these 8
were higher than the P • 0.01 level snd 5 of these were higher than the
P - 0.001 level. Since in sll 153 correlstions were considered it would
seem unlikely that more thsn s few of these occurred by chance. These
significsnt correlstions however seems to be largely within geographical
sreas. The text table indicstes numbers of correlstions significant at
the 5% level within sreas and between areas for the various regions
(North Sea and West of Scotlsnd being regarded as one region for this
purpose).
Numbers of significsnt correlations snd numbers of compsrison
withln and between major N.E. Atlantic areas relative to numbers of
comparlsons made
•
Area
Omlttlng all Saithe Stocks
All stocks
Withln Ares
B~tween
North Sea +
West Scotland
6/36
Fsroe
Iceland
Within Area
Between Areas
4/81
5/21
0/42
1/3
5/45
I/I
1/22
1/3
0/45
I/I
0/22
N.!. ArcUc
1/3
3/45
1/1
1/22
TOTAL
9/45
6/108
8/24
I/54
11
Areas
lt is clear from this that the preponderance of significant correlations
occur within areas and that between area correlstions are scarcely more
than could be expected by chance.
Moreover the salthe stocks contributes
to many of the between area varisnces.
Slnce the saithe may posslbly
migrate between areas (P W Jones, pers. comm.) these correlations may
have less force as indicated between ares correlations due to climate
than would correlations between stocks of othe: speciea.
If all saithe
stocks are removed from the analysis the between area correlations reduce
to one significant result in 54 comparisons.
There is thus little
evidence for extensive betweea area climatic effects in this data aet.
Since within area correlations do occur far more frequently thaa
might be expected by chance we may further search for between area
effects usiag combiaation of stocks within area rather than single stocks
in the hope that this may improve the signal to noise ratio of any
environmentsl effect.
•
The most obvious way to combine recruitments for
the stocks in the vsrious sreas is by Principal Component Analysis.
Table 7 shows the eigenvectors of the first snd second principal components from each sres with the percentage of variance explained and the
value of x2 for the Bartlett Sphericity Test. This indlcstes that only
in the North Ses/West of Scotland area snd the Icelsnd srea can the null
hypothesis of independence of recruitment se ries be rejected.
In the Faroe, Iceland and N.E. Arctic areas the first two components
contain most of the varisnce wlth main eontributions almost equally from
cod and haddock in the first eigenvector and slmost entirely from salthe
in the second eigenvector.
Understandably the two North Sea componenta
based on 9 stocks explain rather less of the total variance.
The firat
eigenvector is composed of major positive contributions from eod, sole
and plaice and major negative contributions from haddock and the two
whiting stocks, while the major contributors to the second eigen vector
were the saithe stocks and herring.
Since these four principal component
analyses have combined stocks in a rather coherent fashion it seems
worthwhile to consider correlations between the components of the four
areas to see whether these condensed results would indlcate any between
area correlations undetected with the more noisy stock data.
shows the result of the 28 correlations.
Table 8
No significant correlations
appeared between first principal components or between first and second
principal componenta.
One very aignificant correlatlon occured between
the seeond components of the North Sea and Faroe whlch is of course a
conaequence of the strong correlations between Faroe salthe on the one
12
•
hand and North Sea herring and salthe on the other hand.
This eorrela-
tion is thus the only evidence for slmilar variations across areas whieh
exists in the prineipal component analysis results.
Since it exiats
between two adjacent areas due to a stoek which may migrate it would not
appear a partieularly strong item of evidence for climate induced variation.
More gene rally we would eonclude that there is little evidence for
a general North East Atlantic climatic effeet in our data series since
apart from the North Sea/Faroe result the R2 values are on average no
•
higher than their expected value.
It seems therefore that we must look for climatie effects within
areas rather than between areas.
The strong eorrelations between a num-
ber of North Sea and west of Scotland fish stocks suggest that this would
be a good place to look for associations with climate. Indeed the apparent negative eorrelation between a northern speeies sueh as North Sea
haddoek and a southern speeies such as sole mlght be taken as some evidence for a elimatic effeet in itself.
The approach taken however was to
assume that the first prineipal eomponent for the North Sea descrlbed
above represented a signal common to the six main compooent fish stocks
and that speeies speclflc effeets and noise might be redueed relative to
this signal by the averaging proeess inherent In a Princ1ple Component
Analysis.
Our first investigation was to relate thia to the average
March temperature from 1962-73 for four central North Sea rectangles
(leES, 19
-1980) (Figure 2 shows the positIon of the rectangles).
This area was chosen as belng the area where Diekson et al 1974
found strong negatIve eorrelations wlth North Sea cod in an earlier tIme
series.
This series gave a remarkably strong negative eorrelation
(r • -.85 10 df) with the 1st principal eomponent and also gave s1gniflcant correlatlons with four of the six main individual species in the
•
first eomponent and also wlth North Sea saithe.
The text table shows
eorrelation coeffieients, percentage of varianee explained and slgnifieanee levels for eaeh of the 9 flsh stocks involved.
13
Text table
Correlation coefficients, percentage of variance explained and
significance levels for log recruits of some North Sea/West of
Scotland Fish stocks and average March temperatures of 4 eentral
North Sea temperature rectangles from 1962-73
R
10OxR2
P
Major eomponents:
1st Principal Components
Cod
Haddoek
Sole
Plaiee
Whit1ng
IVa Whiting
-0.85
-0.60
0.80
-0.61
-0.42
0.48
0.69
73%
36%
64%
37%
18%
23%
48%
(.001
(.05
(.01
(.05
N.S.
N.S.
<'02
M1nor components:
Saithe
IVa Saithe
Herring
0.59
0.07
-0.29
35%
01%
08%
(.05
N.S.
N.S.
-------
- - - - - - - - -- -
It may be seen from the table that of the 9 stocks eonsidered 5 were
eorrelated with the temperature series of at least the 5% level, and of
the 6 major components 4 were eorrelated at the 5% level.
Tbe results
thus suggest that the first principal components is a elimatie signal
whieh is linked with temperature.
Moreover it indieates that the averag-
ing proeess of a Prineipal Component Analysis may serve to improve the
signal to noise ratio of such effeets.
We note that this signal explains 37% of the normalised log recruitment variance for the 9 North Sea/West of Seotland fish stock and that
perhaps 73% of this may be related to temperature.' Henee we may think of
an average of 27% of normalised log reeruitment variation of the nine
stocks being assoeiated with a elimatic effect.
Tbe problem with eorrelations of recruitment with elimatie variables
is that there is no limit to the number of eomparisons a eurious investigator will want to make.
Suceumbing to this euriousity however inevit-
ably leads to a range of eorre1ation coeffieients which make it is very
diffieult to decide whether or not the beat reaults might have oeeurred
by chance. We decided to allow ourself rather limited euriousity by
examining a few other temperature series.
14
Aseries of tempersture data
•
was available for the entire data series 1962-76 from the Smith Knoll
lightvessel. A correlation with thia aeries gave a lower though still
significant correlation coefficient for the 1st principal component and
generally lower and non-significant correlation coefficients for the,
various species. The exception to this was North Sea plaice which was
significantly correlated with this series., The text tabte shows these
results.
•
Correlation coefficients, percentage of varlance explalned and
signlficance levels for log recrults of some North Ses/West of
Scotland flsh stocka with March temperatures a the Smlth's Knoll
Light Vessel. 1962-76
1st Prlnclpal Components
Cod
Haddock
Sole
Plaice
WhHlng
lVa Whitlng
R
10OxR2
P
-0.64
-0.34
0.41
-0.41
-0.64
0.32
0.50
41%
12%
17%
17%
41%
10%
25%
<.02
N.S.
N.S
N.S
<.02
N.S.
N.S.
0.40
-0.19
-0.48
16%
4%
23%
N.S.
N.S.
<.10
------- - - - - ------- - Saithe
lVa SaUhe
Herrlng
Simllar regresslons on the Aprll temperatures at Smiths Knoll L.V.
lead to generally smaller correlation coefflcients wlth only the 1st
Principal Component showlng a signiflcant correlatlon coefficient.
Clearly an investigatlon of the correlatlon of log recruitment with
temperature at various places in the North Sea would be Interesting but
•
carrles the rlsk of the proliferstion of correlation coefflclents. To
avoid this temperature series were chosen from four areas (see Figure 2).
The 4 central North Sea sea rectang1es used previously, the Smiths Knoll
light vessel da ta used previously and single rectang1e data from the
rectangle of Falr laIe and the rectangle north west of Frlesian lslands.
Temperature serles were obtained for these four areas for ~ebruary, March
and Aprll of each year from 1962-1973 and a Principal Component Analysis
conducted on these twelve time series.
The first three components of this cover 84% of the total variance
of these temperature serles and as can be seen from Table 9 the first two
have a clear elgen vector structure. The first ls clearly a meao
temperature effeet, The seeond ls elearly concerned with the rate of
rlse from February to Aprll slnce it wlll be hlgh in years wlth a cold
February and a warm Aprll. The thlrd component is less e1ear but wou1d
15
mostly seem to re la te to changing balances between the northern and
southern stations.
Correlations of these three features of the tempera-
ture series (calied Tl' T2 , T3 ) with the recruitment series are shown in
text table 10.
The table shows that the 1st Principal component of the twelve
temperature se ries (Tl) has a very significant correlation with the first
principal component of the log recruitment of the 9 North Sea/West of
Scotland fish stocks.
Moreover 4 of the individual fish stock have
significant correlations with this signal at the 5% level and a further 2
at the 10% level.
All in all these results seem rather compelling but we note that
fisheries scienee is littered with impressive correlations which didn't
pass the test of time and the only firm conelusion we draw from this is
that predictions from these resu1ts should be made which may be tested
against fortheoming results for years from 1974 to 1982.
Predietions of
·expeeted North Sea temperatures for these years may be an unusual but
exeiting way of testing the strength of these results and we may thus be
able to show that a study of .fish populations serves to illuminate the
broadscale temperature processes occurring in the North Sea.
5.
CONCLUSIONS
We have undertaken especially for this symposium, and without much
prior involvement in the subject, a sceptieal review of the evidence for
a link between climate and the abundanee of fish stocks.
We conclude
that there is strong evidence, from the long history of fluctuations of
abundance and the existence of fairly well-defined geographlcal ranges,
that an important relationship must exist between reeruitment and
climate.
Determlnlng the nature and magnitude of the relationship, and
elucidating the mechanisms involved, is however a most difficult problem.
The study of extreme events (outliers) has provided valuable clues
about the environmental
f~ctors
which may be significant, but undue
emphasis on them may too easily lead to spurious eorrelations.
There is
much data available, though relatively little is of the extent (particularly in time) or the exact nature that one would have wished.
With
relatively short sequences of data, and few degrees of freedom, there is
a perennial danger of over-estimating the statistieal and practieal
significance of any relationships found, and their predictive power.
Regular testing on random number sequences may be a remedy, but there is
an unquantifiable element involved in the selection of data (which
16
•
month's run-off figures, temperatures from whieh rectangles are to be
used?). Great eare, integrity and a self-eritieal attitude on the part
of the investigator are required, but the acid test must always be the
ability to forecast.
It is very desirable that the seleetion of environmental variables
should be based on elearly stated prior hypotheses eoneerning the
meehanisms involved, and not on posterior seleetion simply on the basis
of high eorrelation. This ideal is regrettably unlikely to be attainable
in practice.
Investigators should beware of the effeets of non-normality in their
•
data, non-linearity in the relationships sought, the effeet of time-lags
on eorrelation analysis, and the likely over-estimation of forecasting
skili.
!bey should try to remember the 99 non-signifieant eorrelations
they threw away, for every one they kept.
The final test should always
be of forecasting skill on data not available when the analysis was made.
There do seem to be signs of positive progress in the field over the
last eentury, though the rate of advanee remains depressingly slow.
The eorrelation between North Sea stocks and temperature described in
Seetion 4 is interesting and warrants further investigation:
mechanism(s) involved need to be explored.
the
In addition the relationship
between various Pacific stocks and an index of upwelling on the
California eoast based on a larval transport mechanism (Bakun & Parrish,
~~):
that between several NW Atlantie stocks on run-off in the St
Lawrenee river, based also on a larval drift meehanism (Suteliffe
~. ~);
~
a1,
and the relation between the Northern anehovy and the incidence
of suitable food, and maintenanee of stable stratifieation, based on a
larval starvation meehanism (Lasker
~. ~):
these probably are the most
hopeful indications that positive progress is being and ean be made •
•
17
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~:
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~
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•
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22
~
•
Table 2
Sal1nity
Species
Reference
Proposed meehanIsm
Hypothesised ult!mate
cause
PIa!ee
Johansen (1927)
Ova/larva v!abI1Ity (phys!ologleal);
reduet!on of food supply; predation
Ba1tie outflow
Hart (1974)
Reduetlon in either food or predators
ll.
Ses Sandeel
Penetratlon of Atlant!e
water
Paeif1e sardine
Walford (1946)
Areto Norweglan eod
Cushing (1972)
Upwelllng/w!nds
Mateh/mismateh
WInd strength/direetion
Tah1e 1
Tellperature
•
Spedes
Referenee
Pldee
Banniater ll.!!. (1976)
Proposed !Dechani,a.
Hypothet lea1 ult11118te
cause
Reduction of predator I. Ol' wht'ceabouts.
feod requirt'Qll;:lltil of pcedat"r8
01'
Atlanto-Scandian herrlng
Benko and Seliverston (1971)
North Sea Plaice
Beverton and Lee (1965)
herring
Bowera and Brand (1973)
MaRX
Herring • East Angl1an
autumn fiahery
Carrutheu and Hodgson (1937)
Areto Norwegian cod
Cushing (1972)
North Sea Cod
Dickson!!.!l. (1974)
Duntion of pelagie phase + drift
Change. in wind strellgth
and dlrection
Match/mismatch
Phyaiologie.l' effect of temperature on
larval 8tze, density dependence; ability
larvae to eOrlauae food.
West Greenland eod
El1zarov (1965)
South tiewfoundland and
ledend eod
Gnrod and Colebrook (1978)
!!.!.!..
Hatch/mismatch
St. Lawrenee dtseharge
Weat Greenland eod
Hermann
Not"th Se. eod
Holde. (1970)
PI8iee
Johansen (1927)
Physiologteal effect of survival;
reduction of food 8upply. Predation
Larval drift and deve10pment tll11e
(1965)
Norwegian and Barent sea eod
Kislyalt.ov (1961)
FlemLsh Cap eod
Konstantinov (1975/77/80/81)
PacHie aardine
Harr (1960)
Phyaiologlcal dfeet on 'lllllturation.
Match/miauten. Competition with anchovy.
Southern leNAl eod
MarHn aod Kohlet' (1965)
Larval transport
Down. and Dogger herring
Postuma (1971)
Phyll1ological effect on egg IlOrtality
Horth Sea her ring
P08tuaa and Zijlstra (1974)
Horth-esst Baltle herring
Rannall (1971)
Baltle spring herring
Rannak (1973)
Phyliological effeet on aortality
ß.altie run-off
Upwell1ng/advectioD
lnflow of Horth Seil water
Wind.
P.dUe Sardines
Sett. (1958/59)
'lellowtall flounder.
Southern New England
S18senwine (1974)
Sandeel (A. lIar1nus).
North
Hart (1974)
Rcduct10n in eiCher food 01' predatlon
Atlantie penetration 1nto
North Sea
General. off Plymouth
Russell (J973)
Incu.ased su['v! val. due to more
productive water
Water flov up channe1
Se. ---
Overall varmlng in 40'.
----------------
Iable 3
Wind, preS8ure gradient 11 and upwel1ing
Species
...
~
I
Related to
Reference
North anchovy
Bakun and Parrlsh (1960)
Vpwelling
Offshore transport
Proposed mechnisll
Hypothesised ultimate
eause
Pressure over Europe
Pacif1e hake
Bakun end Parrish (1980)
Pacif!c bonito
Bakun end Pardsh (1980)
Upwelling
Pac1f1c mackere!
Bak.un and Parr1sh (1980)
Wind stress curl, temperature,
upwelling
Atlantic menhaden
Nelson
Pacifie S8Tdlne
Bakun snd Pardsh (1980)
Upwelling, wind curl
Dover sole
Bakun snd Parr1sh (1980)
Upwell1ng, winds
North Ses herring, haddoek
snd cod
Carruthera (938)
Carrul:hers snd Hodgson (1937)
Carrutbers !E..!!. (1951)
Pres8ure gradlenU
Nortb Sea plalee and ArcUc
eod
Garrod snd Colebrook (1978)
'Wind
Turbulence
Bear bland tod
Hill end Lee (1958)
'Wind
Water transport
Sardines (Paelf1e)
Sette (1958/9)
Wind
North Sea Haddock
Rae (1957)
Wind
ll!!..
(1977)
Ekman taDsport
Movement of foad
•
•
Table 4
VarlaUB
Speclu
Referenee
Related co
Proposed meehanhlll
Proposed ultimat_
cause
PIdel!! (Horch Sea, Kattegat)
Johansen (1927)
Day, 1ce in Danleh watere
Physiologieal effeet of larval 8urvival.
ReducUon of food. Predation
Preshwater outllow frolll
Norwegian aod Sotthh herring
Beverton and Lee (l965)
lee cover Horch of lee land
Temperature affecU durst ion of developmeßt and pe1aglc phase, henee affeCtl
dhtance dritted and thereby how elose
they get Co Ioeation of polar front.
Change in water telllpera-
Heavy spring floods drive larvae out to
Snowfall
Cod (Norveghn)
Sund (1924)
Tree ring wtdth
Baltte
tute
aea, henee paar settlement
Atlant!e halibut
Sutcl1ffe (1972/73)
Sc Lawrence dis charge
Eng118h Channe1 herring
Cu.hing (1961)
PO lf winter maximum
Affectsjresultl froll balance of campet! ...
tion with pl1chard
Horchern anchovy
L••k.r (1975/81)
Stabiltcy of water.
Stable water allows loeal eoncl.
of foad to bul1d up, these enable
bettet hed!ng for larV8l!
Labrador cod
Borovkov (1980)
Index of meridional atmo•• eire.
DlItance dritted by larvae
Haddock (Quebec landings)
Sutel1ffe (1972)
Sc Lawrence
Mackere1 and herring,
Paclf1c
Skud (1982)
TelDperature and competltloQ
Compet1tlon
Temperatur4! change.
Sardines, PacHle
Skud (1982)
Temperature and sal1nity and
COlllpetition
UpweUing
rUß
Storm'
off
compet1tlon
!!.!l.
Wind aod ses level
4 WX herring
51nclair
Skagerrak and Kattegat sput
Lindqu1st (1978)
Ratio of temperature and wind
force
Georges: Bank haddock
Cha•• (1955)
Temperature anel wind strength
(1980)
Barometrie pre'8ure
Fungal infection of egga
Kxamples of % varlanee explalned by regressions relating reerultment
to environment variables. Where 8 paper presents more than one correlation
only the highest r 2 is given
'faDIe ~
Referencoi!
-_.~
e
_.-------------
Konstantlnov (1977)
Hermann et al., (1965)
Martln &-Kohler (1965)
Johansen (1927)
Benko & Selivertov (1971)
Postuma (1971)
P"Sl<1ma (1971)
Di(.\<sOtl et al •• ( 1974)
Rann,," (1973)
ßowers & BranJ (1973)
Slnclair et al., (1980)
Johanscn (19 V)
Johs"."n (1927)
Nelson et al., (1971)
..-
100 r 2
n
Species
Variable
68.9
75.7
55.7
10.9
59.3
59.6
28.0
56.3
51.2
24.0
86.0
7.3
32.5
84.5
8
10
8
19
18
9
12
15
Flemish Cap Cod
W Greenland Cod
4X Cod
Kattegat & Belt Sea Plalee
At lan to ··Seand ian Herring
Dogger Herring
Downs Hening
North Sea Cod
Baltie Hening
Hanlt Herring
4WX Herring
North Sea Plalee
North Sea Plaiee
Atlantie Henhaden
Temperature
?
11
12
19
19
16
-------------------------
__.-----------_._-----_.._--------------
Table 6
Slgnifieant "orrelations bet..een the loge recrultment oE
varlous North East Atlantlc fish stocks from 1952-1976
Signifleance
Fish stocks related
.. ...
~
__
,N"Hth
._-~._._-------_.,-_
S~l
Sole
rhrth Slil<i Sole
Notlh Sea lladdn,·k
."iUL ttl S..aa Haddud<.
•
Wind & Sea Level
lee around Denmark
Salinity
Temperature!
Ekman transport I
river discharge
!lorch Sea Whitlng
i.~urth Saa Satt he
NUllh Sea S.. ilhe
t.orth Sea Herrin&:
~,Hth Sea Herrlng
Faroe Cod
Faroe Haddock
Faroe Haddoek
lee land Cod
NE Aretle Cod
..
level
_--.-_._--------------- ---_._-Norch Sea Plalee
North Sea Haddoek
North Sea Whitlng
West Seotland Whltlng
W~st Seotland Whiting
lo-aroe SaUhe
NE Aretie Salthe
West Seotland Salthe
Faroe Saithe
Faroe Haddoek
NE Aretic Haddoek
NE Aretie SaUhe
leeland lIaddoek
NE AreUe Haddoek
----_._------ ------
P
P
P
P
< .05
< .05
< .05
< .01
P < .001
P< .05
P < .05
P < .001
P < .001
P < .05
P < .01
P < .05
P < .001
P < .01
---_._---------------
2.7
Table 7
North Sea and
West of Seotland
Faroe
-
Sp~eles
lee land
NE Aretie
----------
---------
1st
Eigen
Veetor
2nd
Eigen
Veetor
1st
Eigen
Veetor
2nd
Eigen
Veetor
1st
Eigen
Vector
2nd
Eigen
Veetor
Cod
Haddoek
Salthe
Sole
Pla1ee
Whiting
Herring
lVA Whiting
lVA Saithe
.33
-.48
-.18
.35
.32
-.40
.17
-.47
.01
.16
.14
.38
-.20
-.13
-.13
.59
-.08
.61
.68
.70
-.20
.24
.05
.97
.68
.65
.33
% Variation
explained
37%
24%
53%
33%
65%
Bartlett's spherieity
69.47
test
X2
d.f.
36
P
<.001
Table 8
NI
Fl
11
Al
N
Z
FZ
12
AZ
15.75
3
P<.Ol
Correlation eoeffieients between all first and
seeond prineipal eomponents of log-reeruits from
North Sea/West of Seotland (Nl ,N2), Faroe (Fl,F Z)'
leeland (ll,I Z) and NE Aretie (A l ,A2 )
N2
F2
IZ
NI
Fl
1.00
-.40
.17
.39
.00
.02
.03
-.12
1.00
.34
-.48
-.06
.00
-.38
.20
1.00
-.17
.05
-.11
.00
.16
1.00
-.04
-.07
-.19
.00
1.00
.94
.36
.34
.36
.26
1.00
.06
Al
2nd
Eigen
Veetor
-.12
-.34
.93
.70
.67
-.26
.07
.29
.95
30%
57%
32%
e
4.90
3
<.2
11
1st
Eigen
Veetor
1.00
Az
1.00
'7.57
3
P<.l
Table 9
1st, 2nd and 3rd Eigen Vectors from a Principal
Compolle-nt Analysis of selected temperature sarles
from the North Sea
Eigen Vector
1st
2nd
3rd
.21
.32
.29
-.50
-.28
-.31
-.40
.21
-.43
-.01
.26
.31
.31
.32
.35
.03
.13
-.01
-.28
-.43
.26
.31
Smlths Knoll
.21
.29
.32
.3L
.31
.14
.25
.24
.12
-.39
.21
.29
% Variance explalned
63%
23%
8%
------
February
Fair Isle
Cent ra 1 Rectangles
Fresian
Smiths Knoll
.28
March
Fair Isle
Central Re.ctangles
Freslau
Smiths Knoll
April
Fair Isle
Central Rectangles
Freslan
Table LO
Correlatlon coefficlents between the
fir.t three princlpal components of
North Sen temperature and logged recrult-
ment series for varlous flsh stocks from
1962-1973
North Seal
West of Scotland
Tl
T2
T3
of recruitment
-0.80
-0.33
-0.23
Sole
Plalce
Cod
Haddock
Whiting
VIa Whitlng
Saithe
VIa Saithe
Herring
-0.45
-0.55
-0.65
0.60
0.43
0.65
0.64
-0.06
-0.56
0.35
0.45
-0.26
0.13
0.17
0.07
0.06
-0. L9
-0.14
0.25
-0.26
0.25
-0.26
-0.13
-0.24
0.13
-0.13
-0.26
1st Priucipal component
.07
HGURE CAPTIONS
Figure 1
Cumulative percentage of variance contributed by the highest
in recruitments for various North Sea fish stocks.
Figure 2
Chart showing position of North Sea temperature stations
used in analyses.
•
Plaice
Sole
Haddock
Saithe
Whiting
1!l
~
'C
~
-0
~~
~:~ring//··
95
90
.
QJ
Fc
QJu
....
•••••
80
70
........ ....-
,,'" / "
/.
~ 60
.~ 50
'/
/'
/ /
f"
,,/'
/1./ .f/ /
d!1f /
w,..../ /
/..
...... / / / '
/.••/.' /
.....
".".~ .'
?;...:;.'
"....-;....../.~.
"/'
L..
!§
.'
./' ,
'
,//
.. /
/'/
Percentage of variance
for correlation with
p < -01
404-~~h//~----------_--l--
~
30
:::J
LJ
20
{/
~~----------------~
p<'05
10 L.-.,..--~--r---r-----r~~r-.,..---r---r---r----r-~---,r--r---1
o 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Cumulative number of ranked data points
10·
o·
5·
,r/!.
d
60·
.:
'i:i:
60·
Fairlsle
Temperature
Rectangle
Central North Sea
Tem erature Rectangles
55·
N.W Texel Temperature Rectangle
50·
50·
10·
o·
s·
10·
:'
~ ,
,.
..' ..