487
Pelagic fish abundance in relation to regional environmental
variation in the Gulf of Finland, northern Baltic Sea
Heikki Peltonen, Miska Luoto, Jari-Pekka Pääkkönen, Miina Karjalainen, Antti Tuomaala,
Jukka Pönni, and Markku Viitasalo
Peltonen, H., Luoto, M., Pääkkönen, J.-P., Karjalainen, M., Tuomaala, A., Pönni, J., and Viitasalo, M. 2007. Pelagic fish abundance in relation to
regional environmental variation in the Gulf of Finland, northern Baltic Sea. – ICES Journal of Marine Science, 64: 487 – 495.
This study applies variation partitioning to analyse spatial patterns in hydroacoustic estimates of fish abundance in relation to regional
variation in the hydrography, food resources, and geography of the Gulf of Finland, northern Baltic Sea. Using variation partitioning
based on generalized additive models, daily fluctuations in hydroacoustic abundance estimates were first eliminated, and the remaining
variation in fish abundance was decomposed into independent and joint effects of hydrography, food resources, and geography. The
independent effect of geographic variables (spatial location and water depth) captured the largest fraction of the variation (9.3%) in
the fish-abundance patterns, whereas the independent effects of hydrography (5.8%) and food resources (5.6%) captured slightly less.
However, a considerable portion of the variation in fish-abundance patterns was accounted for by the joint effects of explanatory variables and may therefore be causally related to two or all three groups of variables. The model applied efficiently eliminated the spatial
autocorrelation in the fish abundance between sampling units, especially at distances .2000 m. At smaller scales, the residual autocorrelation may have been due to fish behavioural patterns independent of the explanatory variables in this analysis.
Keywords: Baltic Sea, GAM, geography, herring, hydrography, modelling, prey resources, sprat.
Received 11 May 2006; accepted 6 December 2006; advance access publication 23 January 2007.
H. Peltonen, M. Luoto, and A. Tuomaala: Finnish Environment Institute, PO Box 140, FI-00251 Helsinki, Finland. J.-P. Pääkkönen, M. Karjalainen,
and M. Viitasalo: Finnish Marine Research Institute, PO Box 2, FI-00561 Helsinki, Finland. J. Pönni: Finnish Game and Fisheries Research Institute,
Sapokankatu 2, FI-48100 Kotka, Finland. Correspondence to H. Peltonen: tel: þ 358 20 490123; e-mail: heikki.peltonen@ymparisto.fi
Introduction
Pelagic marine environments are characterized by gradients and
spatial variability in abiotic and biotic factors. Physical boundary
layers such as the thermocline and halocline form efficient boundaries to the distribution of the biota. In the Gulf of Finland (northeastern Baltic Sea), the biotic community consists of a mixture of
marine, freshwater, and brackish-water species. Therefore, distributions of species are connected specifically to hydrographic
changes, such as variations in the inflow of freshwater from rivers
and in saline-water exchange with the Baltic Sea itself. However,
other factors have also been influential in restructuring the biotic
community, especially the cultural eutrophication of the Gulf of
Finland, e.g. the oxygen deficiency in deep water (HELCOM, 2002)
and the increase in cyanobacterial blooms (Raateoja et al., 2005).
Hydrographic changes and eutrophication have strongly influenced the Gulf of Finland as a habitat for pelagic fish. The
environmental changes affect, for example, the prey resources of
fish and the availability of habitats that satisfy the physiological
needs of fish species. As salinity decreased in the Gulf of Finland
during the 1980s and 1990s, the abundance of marine zooplankters decreased, and they were replaced by species preferring low
salinity (Flinkman et al., 1998). Moreover, a sharp increase in
sprat (Sprattus sprattus) abundance in the Gulf of Finland since
the early 1990s (MERI, 2005) has evidently increased predation
on zooplankton, which has contributed to a decline in prey availability for zooplanktivorous fish. Consequently, the nutrition of
# 2007
the dominant fish species, herring (Clupea harengus) and sprat,
deteriorated and this would appear to have been the major factor
contributing to the dramatic decline in herring and sprat growth
since the beginning of 1980s (Flinkman et al., 1998; Rönkkönen
et al., 2004). A shift to the summer dominance of cyanobacteria
may have negative effects on feeding, egg production, and survival
of copepods and mysids (Koski et al., 1999; Engström et al.,
2001), and is a factor that influences fish nourishment. The collapse of benthic food chains through an oxygen deficiency in deep
water (Salemaa et al., 1990; Laine et al., 1997; Kotta et al., 2002) is
yet another factor reducing the prey resources available to fish,
including large herring (Raid and Lankov, 1995).
Changes in habitat quality inevitably influence the spatial patterns of abundance of pelagic fish. The distribution of pelagic fish
may be related to several interacting environmental factors, which
also correlate with each other (Maravelias and Reid, 1997;
Maravelias et al., 2000). However, identification of the predictor
variables most likely affecting the variation in the response variable, e.g. by traditional regression methods, can be problematic,
particularly if predictor variables are significantly intercorrelated
(Chevan and Sutherland, 1991; MacNally, 2000; Graham, 2003).
Multicollinearity among predictors may result in the exclusion of
ecologically more causal variables from multiple regression models
if other intercorrelated variables explain the variation in response
variable better in statistical terms (MacNally, 2000). There are
currently many approaches available for tackling collinearity
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488
problems. In studies aiming at predictive regression analysis, valuable insights can be developed by methods such as sequential
regression and structural equation modelling (Graham, 2003).
Collinearity can also be addressed by variation-partitioning
(Borcard et al., 1992), which aims to provide an understanding of
the probable causalities and explanatory powers of predictors in
multivariate data sets, not to generate a predictive equation
(Watson and Peterson, 1999). Here, we analyse spatial patterns in
pelagic fish abundance in relation to regional environmental variation in the Gulf of Finland. We focus on variation-partitioning,
which can provide new insights into the relationships between fish
distribution and the environment by decomposing the variation in
response variable(s) into independent components that reflect the
relative importance of the groups of predictors and their joint
effects.
Material and methods
Fish, hydrography, and plankton sampling
The ecosystem structure in the Gulf of Finland was sampled
during survey cruises conducted simultaneously by the research
vessels (RVs) “Aranda” and “Muikku” from 17 to 28 July 2004
(Figure 1). The use of two vessels ensured intensive sampling of
several biological, chemical, and physical variables. Fish abundance
was sampled from the “Muikku” and the environmental factors
from the “Aranda”. An adaptive survey strategy was applied, which
aimed at covering a wide range of environmental variation. For
example, the survey included areas of high abundance of cyanobacteria observed by remote sensing (http://www.ymparisto.fi/
default.asp?contentid=128323&lan=en#a2) and from automated
equipment aboard passenger ferries, other commercial ships, and
coastguard vessels (http://www.fimr.fi/en/itamerikanta/bsds/
1731.html), and it also encompassed areas without surface blooms
of cyanobacteria. The sampling area included the Finnish and
Estonian economic zones in the Gulf of Finland, although some
sampling was also conducted in the easternmost Baltic Main
Basin.
Fish abundance was estimated hydroacoustically aboard
“Muikku”. In addition to fish abundance, the small-scale variation
in water depth [average water depth of an elementary distance
sampling unit (EDSU), which is a specific length of the cruise
track, here 0.1 nautical miles] was estimated from the acoustic
material collected. The acoustic gear comprised a Simrad EY500
echosounder (Simrad, 1995) equipped with a split-beam Simrad
H. Peltonen et al.
ES38– 12 transducer. The echosounder had a transmit frequency
of 38 kHz and a transmit power of 250 W. The beam width of the
transducer was 128 (to 23 dB level from the acoustic axis). Pulse
duration was set to 1 ms and pulse rate to 2 s21. The hydroacoustic equipment was carefully calibrated with a standard copper
sphere on 14 June 2004 using the LOBE software (Simrad, 1995).
During the cruise, the calibration was at times checked with the
copper sphere. Estimates of nautical-area-scattering strength (SA,
m2 nautical mile22; MacLennan et al., 2002) were made with the
Sonar5-pro fisheries-acoustics, post-processing system (Balk and
Lindem, 2004), dividing the cruise track into 0.1 nautical mile
(185.2 m) EDSUs. The data set contained 5803 EDSUs that
included both acoustics and environmental data.
The fish species composition and length distributions were
sampled with a pelagic trawl in the vicinity of 21 environmental
sampling stations. The vertical opening of the trawl was 15 m, its
maximum width 35 m, and the codend mesh had a 5-mm bar
length. Trawling was conducted throughout the 24-h period. The
survey design aimed at simultaneous trawling and environmental
sampling, with minimal influences of the vessels on the fish or the
environmental variables.
The relationship between fish size and acoustic backscattering
from the fish is needed to transform nautical-area-backscattering
estimates to fish biomass (MacLennan and Simmonds, 1992).
However, within the Baltic Sea, fishery scientists have not yet
agreed on a suitable transformation between herring or sprat
length (L) and backscattering cross-section and the derived quantity target strength (TS). Because of discrepancies in TS– length
dependence, ICES has established a study group for TS estimation
in the Baltic Sea (ICES, 2006). Recently, two studies (Didrikas and
Hansson, 2004; Peltonen and Balk, 2005) established TS– length
models for Baltic Sea clupeids. In both these studies, the TS determined was considerably higher than previously believed but, in
contrast, there was about a 2 –4-dB difference in the TS of a
certain size of a fish when estimated with the equations in those
studies. Because of inconsistencies in TS –length models and small
area differences in the size distribution of the fish, the present
study applied nautical-area-backscattering (SA) estimates as
measures of fish abundance. However, we first evaluated whether
including the catch-composition information in the analyses,
together with the TS-length dependence, would have influenced
the results. We estimated fish biomass employing SA at trawling
stations together with the fish length distribution in trawl catches,
following standard procedures for the Baltic Sea (ICES, 2000).
Figure 1. Location of the environmental sampling stations in the Gulf of Finland.
Pelagic fish abundance in relation to environmental variation in Gulf of Finland
When converting between fish length distributions in the trawl
catches and the acoustic size of the fish, we repeated the calculations with the four different TS –length equations presented in
Didrikas and Hansson (2004) and Peltonen and Balk (2005).
The resulting linear regression analyses between nautical-areabackscattering and fish-biomass estimates encompassing the 21
trawling stations produced coefficients of determination (r 2) of
97%. Such a linear relationship indicated that it was appropriate
to use SA as a relative measure of biomass. For simplicity, the term
fish abundance is applied to denote the relative acoustic biomass.
Temperature, salinity, and oxygen were measured at each
sampling station (Figure 1) from “Aranda” with a CTD profiler
(Sea-Bird Electronics 911plus, WA, USA). Chlorophyll-a was
measured from water samples taken at 1, 3, 7, 18, and 30 m.
Duplicate 100 ml water samples were filtered onto GF/C filters,
10 ml of EtOH was added, and the samples were mixed with a
test-tube mixer and left to extract in the dark for 24 h. Prior to
measurement, the samples were filtered with syringe-operated
GF/C filters and then measured with a fluorometer at 672 nm
(Jasco FP 750, MD, USA).
The zooplankton was sampled vertically at each station with a
200 mm closing WP–2 net with hauls from 0 to 10 m, 10 to 20 m,
20 to 30 m, 30 to 40 m, 40 to 50 m, and 50 m to near the seabed,
and preserved in buffered 4% formaldehyde. Zooplankton subsamples containing at least 500 individuals, extracted by pipette
subsampling (McCallum, 1979), were counted under a binocular
microscope (a Leica MZ12).
Analysis variables and spatial interpolation
The hydroacoustic data consisted of continuous recordings while
cruising, whereas the other explanatory variables were only available from the sampling stations. Therefore, spatial interpolation
was applied to estimate the levels of all environmental variables at
the locations of the hydroacoustic samples (EDSUs). Linear
interpolation and extrapolation were applied to estimate temperature, salinity, oxygen concentration and chlorophyll-a between
and beyond the sampling stations with the DAS system (http://
data.ecology.su.se/models/). A horizontal grid of 1 nautical mile
was employed. These estimates were made in 3D, although
zooplankton abundance was estimated in 2D.
The explanatory variables in the statistical analyses were
grouped under three headings, describing hydrography, food
resources of zooplanktivorous fish, and geography, respectively
(Table 1). Additionally, time of day (UTC: coordinated universal
time) was included to estimate and eliminate the diurnal variation
in acoustic estimates of fish abundance caused by the daily behavioural patterns of fish that might influence hydroacoustic assessment. The hydrographic variables included temperature (surface
and minimum), salinity (surface and minimum), and the
minimum oxygen concentration at each EDSU. The food resource
variables encompassed the estimated abundances of marine copepods (Temora longicornis, Acartia sp., Pseudocalanus elongatus,
Centropages hamatus), Eurytemore affinis, Limnocalanus macrurus,
and cladocerans, and additionally chlorophyll-a to describe the
overall productivity at each location. Finally, the geographic
variables included location (i.e. geographic coordinates), average
water depth at each EDSU, and an estimate of relative depth that
described local variability in water depth as the difference between
the depth estimates from hydroacoustics and from the DAS
system (average depth in a rectangle of 1 nautical mile2).
489
Table 1. Means and variation ranges of fish abundance and the
explanatory variables.
Variable
Grouping
Mean Minimum Maximum
Relative acoustic
Response
457
0
20 697
biomass (SA, m2
nautical mile – 2)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .
North coordinate
Geography
59.9
59.3
60.5
(8N)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .
East
coordinate (8E)
Geography
25.1
22.9
27.3
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .
Average water depth
Geography
56.6
5.0
100.9
(m)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .
Relative water depth
Geography
7.0 256.0
90.5
(m)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .
Surface
salinity
Hydrography
5.4
3.5
6.6
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .
Maximum
salinity
Hydrography
6.4
3.5
9.1
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .
Surface temperature
Hydrography 17.9
16.6
20.9
(8C)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .
Minimum
Hydrography
6.9
1.8
19.0
temperature
(8C)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .
Hydrography
5.0
0.4
6.8
Minimum oxygen
(ml l21)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .
Chlorophyll-a
Resources
6.8
4.0
12.9
21
(mg
l
)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .
Resources
4.9
0.0
26.0
Cladocerans
(number m22 105)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .
Eurytemora (number
Resources
78.8
17.4
200.3
m22 104)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .
Resources
3.3
0.0
18.2
Limnocalanus
22
3
10
)
(number
m
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .
Resources
45.2
0.6
422.9
Marine copepods
(number m22 104)
The variables are grouped under response, geography, hydrography, and
food resources.
Numerical analyses
Generalized additive models (GAMs) are semi-parametric extensions of generalized linear models (GLMs) (Hastie and Tibshirani,
1990). They are designed to capitalize on the strengths of GLMs
without requiring the problematic steps of postulating a response
curve shape or specific parametric response function. The only
underlying assumptions are that the functions are additive and
that the components are smooth (Hastie and Tibshirani, 1990;
Wood and Augustin, 2002). Whereas GLMs fit functions linear in
their parameters, allowing for linear and polynomial response
shapes, GAMs are more flexible, permitting both linear and
complex additive response shapes, as well as a combination of the
two within the same model (Wood and Augustin, 2002). A GAM,
like a GLM, uses a link function to establish a relationship
between the mean of the response variable and a “smoothed”
function of the explanatory variables. The response curve is hence
more data- than model-driven (Yee and Mitchell, 1991). This is
because the data determine the nature of the relationship between
the response and the set of explanatory variables, rather than
assuming some form of parametric relationship (Hastie and
Tibshirani, 1990).
Variation partitioning was applied to decompose the variation
in pelagic fish abundance among the three groups of predictors:
hydrography, food resources, and geography. The significant
adjusting “time” variable was added into the models first, before
490
considering the effects of the environmental variables of primary
interest. Variation in the abundance data was partitioned using a
series of (partial) GAMs, as implemented in the mgcv library of
the statistical package R (Wood and Augustin, 2002). GAMs are
represented in mgcv as penalized GLMs, each smooth term of a
GAM being represented using an appropriate set of basis functions (Wood and Augustin, 2002). The GAM model-building procedures followed the guidelines of Wood (2001), using penalized
regression splines with integrated backward model selection via
generalized cross-validation scores, where mgcv-optimization
selects the degrees of freedom for each term automatically (Wood,
2001; Wood and Augustin, 2002). Logarithmic transformation of
fish abundance [log(fish abundance þ 1)] was modelled using an
identity link and a Gaussian error term (Borcard et al., 1992;
Heikkinen et al., 2004). In model building, we used a strict criterion (p , 0.001) for variable exclusion. This very low value of p
was chosen because of the relatively large sample size (5803),
which tends to decrease p-values compared with smaller sample
sizes (McBride et al., 1993). In addition, both such a severe criterion and the large size of the data set contribute to limit inference problems potentially caused by autocorrelation in our data
set (which usually contributes to lower the effective degrees of
freedom). The goodness-of-fit for each added variable was
measured by deviance statistics (Venables and Ripley, 2002).
Variation partitioning with three explanatory matrices has
been described in detail by Liu (1997), Anderson and Gribble
(1998), and Heikkinen et al. (2004). Here, it led to eight fractions:
(i) pure effect of hydrography, (ii) pure effect of food resources,
(iii) pure effect of geography, the combined variation attributable
to the joint effects of (iv) hydrography and food resources, (v)
hydrography and geography, (vi) food resources and geography,
(vii) the three groups of explanatory variables, and finally (viii)
unexplained variation. Several fractions, or groups of fractions,
can be obtained directly by a (partial) GAM run: (i) þ (iv) þ
(v) þ (vii), i.e. fish abundance by hydrography (H); (ii) þ (iv)
þ (vi) þ (vii), i.e. fish abundance by food resources (R); (iii) þ
(v) þ (vi) þ (vii), i.e. fish abundance by geography (G); (i) fish
abundance by hydrography, controlling for food resources and
geography (i.e. fitting food resource and geography variables first
as “covariables” and hydrography variables subsequently to
explain the residual variation); (i) þ (ii) þ (iv), fish abundance by
hydrography and food resources, controlling for geography; etc.
The total explained variation in fish abundance data, i.e. (i) þ
(ii) þ (iii) þ (iv) þ (v) þ (vi) þ (vii) was obtained by regressing
the fish-abundance against selected statistically significant variables of the three groups of explanatory variables together (the
so-called “final model”). Correlograms of Moran’s I were constructed to further assess the degree of spatial autocorrelation in
the fish-abundance data and in the derived residuals of the final
model, using the program ROOKCASE (Sawada, 1999). Ten intersample distance classes were formed using a lag of 1000 m
(Legendre and Fortin, 1989).
H. Peltonen et al.
large local variations in fish abundance, as well as in the explanatory variables in the region studied (Table 1).
Most of the bivariate correlations between our 14 explanatory
variables were statistically significant (p , 0.01), indicating potential collinearity problems in the analyses. The best correlation was
between the north coordinate and maximum salinity (Spearman’s
rank correlation, rs ¼ 20.944), between the east coordinate and
surface water temperature (rs ¼ 0.882), and between water depth
and maximum salinity (rs ¼ 0.863). The adjusting time variable
explained 4.4% of the total deviance of fish abundance (explained
deviance 167.7 out of the total deviance 3792.1, p , 0.0001). The
time variable showed a bimodal response, with maximum fishabundance values between 10 : 00 and 13 : 00 (UTC) and between
20 : 00 and 01 : 00, and lowest values between 03 : 00 and 06 : 00
and between 15 : 00 and 18 : 00 (Figure 2).
Separate testing (lone contribution) of the relationships
between fish abundance and each environmental variable on its
own showed that the environmental variables explained 0.9–
17.9% of the variation in fish abundance (Table 2). The variables
accounting for the greatest change in deviance in the univariate
GAM analysis were the east coordinate (which explained 17.9% of
the variation), surface salinity (14.2%), the north coordinate
(12.6%), and surface temperature (10.0%). Largest increases in
deviance when omitting each environmental variable from the full
fish-abundance model (drop contribution) were associated with
water depth (4.1%), relative water depth (3.8%), the north coordinate (3.3%), and the quantity of marine copepods (2.5%). In
general, explanatory variables often showed non-linear responses
to fish abundance (Figure 3). For example, the smoothing spline
of the GAM showed a clear peak in fish abundance at rather
shallow water depth, 20 m, whereas the optimum minimum
temperature for fish abundance was 58C.
The hydrography variables together [(i) þ (iv) þ (v) þ (vii)]
accounted for 27.7% of the variation in the fish abundance data,
the food resource variables [(ii) þ (iv) þ (vi) þ (vii)] for 27.1%,
Results
The trawl catches during the research cruise indicated that the
studied fish assemblage consisted of small sprat and herring,
which together constituted about 99% of the yield. Sprat made up
83% of the yield, and it was numerically dominant in 19 of the 21
trawl catches. Trawl sampling revealed small differences in fish
size distributions in different parts of the basin, but there were
Figure 2. Response shapes of time (UTC) in the final GAM model
for pelagic fish abundance. The dashed lines are 95% pointwise
confidence intervals, tick marks show the location of observations
along the variable range, the y-axis represents the effects of the
respective variables, and S is a smoother term of the GAM. The two
vertical dashed lines indicate sunrise and sunset.
491
Pelagic fish abundance in relation to environmental variation in Gulf of Finland
Table 2. The contributions of selected environmental variables in the fish-abundance model after the adjusting time variable was included
in the model: independent (lone contribution) and drop contribution (when the variable was dropped from the final GAM model) based
on generalized additive modelling.
Variable
Variable grouping
d.f.
Contribution
Direction of trend
Statistical significance (p)
Independent
Drop
North
coordinate
Geography
18.9
12.6
3.3
þi
,0.00001
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
East
coordinate
Geography
16.6
17.9
1.3
–
,0.00001
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Water
depth
Geography
18.9
5.7
4.1
þi
,0.00001
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Relative
water
depth
Geography
14.3
5.2
3.8
2i
,0.00001
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Surface
salinity
Hydrography
18.4
14.2
1.0
þi
,0.00001
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Maximum
salinity
Hydrography
15.3
9.9
0.8
þ
0.00008
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Surface
temperature
Hydrography
15.3
10.0
0.5
no
,0.00001
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Minimum temperature
Hydrography
18.0
7.0
0.7
þi
,0.00001
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Minimum
oxygen
Hydrography
18.1
4.3
0.7
þ
,0.00001
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Chlorophyll-a
Resources
16.5
7.7
1.2
þ
0.00014
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Cladocerans
Resources
7.9
5.7
0.7
no
,0.00001
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Eurytemora
Resources
16.2
0.9
0.6
2
,0.00001
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Limnocalanus
Resources
17.9
3.3
1.3
þ
,0.00001
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Marine copepods
Resources
17.7
9.2
2.5
no
0.00027
d.f. refers to the degrees of freedom for the spline smoother in the final model. The direction of the trend is indicated as positive þ, negative 2, minimum
at intermediate values þi, maximum at intermediate values 2i, or no clear trend ¼ no.
and the geography variables [(iii) þ (v) þ (vi) þ (vii)] for 30.8%.
The amount of variation captured by all three groups of variables [(i) þ (ii) þ (iii) þ (iv) þ (v) þ (vi) þ (vii)] was 46.4%. The
largest fraction of the fish abundance was accounted for by
the joint effect of hydrography, food resources, and geography
(vii) (13.3%) variables and pure effects of geography (iii) (9.3%)
(Figure 4). The pure effects of hydrography (i) (5.8%) and food
resources (ii) (5.6%) variables had moderate influence, but were
Figure 3. Response shapes of (a) water depth, (b) minimum water temperature, (c) maximum salinity and (d) relative water depth in the final
GAM model for pelagic fish abundance. The dashed lines are approximate 95% pointwise confidence intervals, tick marks show the location of
observations along the variable range, y-axes represent the effects of the respective variables, and S is a smoother term of the GAM.
492
Figure 4. Results of variation partitioning for pelagic fish
abundance in terms of fractions of the variation explained. Variation
in the abundance data is explained by three groups of explanatory
variables: H (hydrography), R (food resources), and G (geography),
and by their interactions (HR, HG, RG, and HRG). U is the
unexplained variation.
statistically significant. This result indicates that a considerable
amount of variation in the fish-abundance patterns was accounted
for by the joint effects of predictors and may thus be causally
related to two or all three groups of variables.
In pelagic fish abundance data, there was a clear spatial structure, and Moran’s correlograms indicated a positive autocorrelation
for small-distance categories (Figure 5). Spatial autocorrelation in
the residuals was reduced considerably after including the environmental variables in the models of fish abundance. Clearly, the
greatest autocorrelation was in the first distance class (1000 m),
where it was reduced from 0.24 (original response variable) to 0.10
(residuals of the final model). At distance classes of 2000 m or
more, autocorrelation was ,0.05 in the residuals, indicating that
the environmental variables succeeded rather well in eliminating
spatial autocorrelation compared with the autocorrelation and
trend present in the response variable.
Discussion
The methods of variation partitioning applied provided measures
of the independent and joint explanatory capacities of groups of
predictors (MacNally, 2000; Cushman and McGarigal, 2004;
Figure 5. Spatial correlograms for pelagic fish abundance. The filled
symbols present the original data and the open ones indicate the
residuals after environmental variables were included in the model.
H. Peltonen et al.
Heikkinen et al., 2004), which were useful in summarizing the
relationships between fish distribution and the environment. Such
analyses would not have been possible with traditional regression
approaches, which do not provide separate measures of the variation explained independently and jointly by two or more groups
of variables. Overall, the results indicate a rather high independent
contribution of geographical variables and somewhat lower independent contributions of hydrographic and food resource variables. However, the highest amount of variation in the fish
abundance explained was related to the joint effect of all three
groups of variables. Other studies have also indicated that the
influences of environmental variables on fish abundance interact.
For example, Maravelias et al. (2000) observed that bottom depth
had both a direct main effect and an interactive effect together
with zooplankton biomass on herring abundance in the North
Sea. Also, Maravelias and Reid (1997) observed that herring distribution was related to zooplankton abundance, along with its
interactions with water temperature and thermocline depth.
The analyses indicated that a considerable increase in the
explanatory power of the model can be achieved if the variables
describing location are included in addition to factors describing
hydrography, food resources, and bathymetry. However, the
strong relationship between fish abundance and the bathymetric
variables identified the importance of water depth and bottom
profiles as important factors linked to fish distribution. Fish were
abundant near uneven seabed characterized by steep bottom
slopes or by shallows surrounded or lined with deep areas.
Similarly, in the North Atlantic, the distribution of herring
schools has been linked to bottom topography, and the schools
seemingly prefer a hard seabed (Reid and Maravelias, 2001). In
the present study, the model indicated an abundance of fish at
locations with a water depth of ca. 20 m and a steep decrease at
the shallow end of the depth range. It is possible that the decrease
of fish in shallow water was due to avoidance, i.e. that fish moved
away from the route of the approaching vessel, even though the
vessel was relatively small and had shallow draft (2.2 m). Herring
do avoid RVs, especially in shallow water (Olsen, 1990), but
avoidance trends are not consistent, being influenced by seasonal,
environmental, or geographical factors too (MacLennan and
Simmonds, 1992). However, despite some weaknesses, such as
avoidance and blind zones near the surface and seabed (Ona and
Mitson, 1996), hydroacoustics are the only way of collecting such
a large amount of data on pelagic fish distribution, as in the
present study.
The preference we found for areas of high salinity over areas of
lower salinity has a natural explanation, because the fish community consisted almost totally of sprat and herring. Although
herring are better adapted to low salinity than sprat (Parmanne
et al., 1994; Uusitalo et al., 2005), the areas with lowest salinity
may not be optimal for herring either, because herring growth
rate is slowest and their populations sparsest in areas with lowest
salinity. Although many fish species preferring low salinity are
present in the Gulf of Finland, during the present study they were
scarce, and they did not replace clupeids even in areas with lower
salinities. However, the study did not include areas with the
lowest salinity, such as estuarine regions with a plentiful flow of
freshwater.
The results indicate that the pelagic fish preferred regions with
deepwater temperature of about 58C to areas with warmer
(especially) and with colder deep water. The avoidance of regions
with deepwater temperatures ,48C is in accordance with the
Pelagic fish abundance in relation to environmental variation in Gulf of Finland
findings of Parmanne et al. (1994), who stated that Baltic Sea
sprat prefer temperatures .48C. Correspondingly, the pelagic fish
in the Gulf of Finland in September 2002 avoided cold temperatures ,38C even though oxygen concentration, for example, was
not a limiting factor (Peltonen et al., 2004). Moreover, North Sea
herring avoided areas of cold bottom water in summer
(Maravelias et al., 2000).
There were several prey species available to the planktivorous
fish, and the abundance of fish was linked especially to that of
marine copepods, which involves taxa (especially T. longicornis)
that are the favoured prey of clupeid fish (Flinkman et al., 1992,
1998; Casini et al., 2004). Fish abundance was also linked with the
glacial freshwater relict L. macrurus. The large size of this species
(Flinkman et al., 1992) may compensate for its relatively low
abundance. The links between fish distribution and the other
freshwater copepod E. affinis may be of less importance, although
this species is abundant in the diets of clupeid fish (Flinkman
et al., 1992, Peltonen et al., 2004). For herring, E. affinis may not
be among the most selected species if, for example, large marine
copepods are available (Flinkman et al., 1992), although herring
do sometimes select large female E. affinis (Flinkman et al., 1998).
Our autocorrelation analysis suggests that spatial variability in
fish abundance was mainly the consequence of variability in the
environment. This is a useful finding from an ecological perspective; in addition it can also be an important piece of information
when planning hydroacoustic surveys of fish abundance. In
hydroacoustic surveys, autocorrelation does not necessarily influence estimates of averages, but it may bias variance estimates
(Jolly and Hampton, 1990). This study indicates that environmental factors explained the autocorrelation except at short distances, and even the correlation between nearby EDSUs was
mainly explained by them. Including environmental factors in the
estimation might result in increased accuracy of hydroacoustic
biomass estimates. It is possible that the residual autocorrelation
structure was the result of fish behaviour, specifically the tendency
to form schools or aggregations irrespective of the environmental
variations included in the analysis. On the other hand, it is possible that the spatial resolution in the environmental variables was
not sufficient to reveal small-scale variation. It is noteworthy that
Johansson et al. (1993) found that Baltic Sea coastal zooplankton
samples were spatially correlated between stations ,700 m and in
some cases ,1400 m apart, but not beyond this. Therefore, the
range of significant autocorrelations in zooplankton were comparable with residual autocorrelations in fish abundance, which may
indicate that fish abundance is linked to spatial patterns in
zooplankton abundance or that both fish and zooplankton abundance patterns are dependent on the same environmental factors.
The bimodal daily pattern in acoustic estimates of fish abundance is another factor to consider in ecological research and
sampling design. Diurnal changes in acoustic estimates are frequently observed in hydroacoustic studies, and this is also the case
in studies in the Baltic Sea, whereas bimodality has not often been
reported. Diurnal changes in nautical-area-scattering strength
may arise either if not all fish are detected because they are sometimes in the near-bottom or near-surface blind zones of the echosounder (Ona and Mitson, 1996), or if the target strength or
backscattering cross-section of one fish changes diurnally.
Changes in backscattering properties may change if fish diurnally
change swimming behaviour and tilt angle relative to the vertical
sound beam of a hydroacoustic transducer (Foote, 1985), or if fish
target strength changes as fish move vertically in the water column
493
(Ona, 2003). In our study, the diurnal pattern in SA was somewhat
different from observations in the southern part of the Baltic Sea
in October, where SA was higher by night than by day (Orlowski,
2001). In a study conducted with a stationary upward-facing
echosounder in a coastal bay in the southern Baltic, SA was linked
to light intensity and was higher by day than by night (Axenrot
et al., 2004).
Bimodal variation in hydroacoustic abundance estimates may
be linked to fish activity. Cardinale et al. (2003) observed that
Baltic Sea herring had a bimodal daily feeding pattern, with greatest stomach fullness in the morning and evening. This might
influence changes in the swimbladder volume of a fish, which
would influence the backscattering properties of individual fish
and perhaps result in hydroacoustic biomass estimates being too
high or too low. However, sprat do not have a pattern of bimodal
daily feeding (Cardinale et al., 2003).
The increasing human impact on the sea makes it increasingly
important to understand the relationships between organisms and
their environment. In the Baltic Sea, climatic variability has been
linked to a regime shift in fish stocks, because since the late 1980s,
cod abundance has declined and sprat has attained an exceptionally high biomass (Alheit et al., 2005). In the North Sea, global
changes in climate have been connected to regional shifts in fish
distributions (Perry et al., 2005). The Baltic Sea is a semi-enclosed,
brackish-water ecosystem with few fish species, particularly
vulnerable to climate change in all probability. Environmental
influences on fish stocks should be considered particularly in
managing exploitation of fish stocks, and this is emphasized by
the emergence of ecosystem-based approaches in the management
of marine resources (Browman et al., 2004). In the Baltic Sea, fish
may influence zooplankton abundance (Möllmann et al., 2005),
so having cascade effects down the foodweb. Moreover, fish
catches in the Baltic may play an important part in nutrient cycles
(Hjerne and Hansson, 2002) and in the removal of toxic substances from the ecosystem (MacKenzie et al., 2004). Therefore,
management of fish stocks could also be viewed as a tool for
environmental management, but implementation of this tool can
be possible only with a good understanding of the relationships
between fish and their environment.
Acknowledgements
We thank two anonymous referees for valuable comments on the
draft manuscript, and all scientific staff and crew aboard the RVs
for help. Funding was provided by the Academy of Finland
project grant 202437 and by the Employment and Economic
Developing Centre for Southeastern Finland through EU FIFG
funding tools, project 226049.
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