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Abiotic drivers of coastal fish community change during four
decades in the Baltic Sea
Jens Olsson*, Lena Bergstro¨m, and Anna Ga
˚rdmark
1
Swedish University of Agricultural Sciences, Department of Aquatic Resources, Institute of Coastal Research, Skolgatan 6, 742 22 O
¨regrund, Sweden
*Corresponding Author: tel: +46 10 478 41 44; fax: +46 10 478 41 57; e-mail: Jens.Olsson@slu.se
Olsson, J., Bergstro¨m, L., and Ga
˚rdmark, A. 2012. Abiotic drivers of coastal fish community change during four decades in the Baltic Sea – ICES
Journal of Marine Science, 69: 961–970.
Received 7 October 2011; accepted 2 April 2012; advance access publication 14 May 2012.
Evidence for long-term change of marine ecosystems is increasing worldwide. Coastal areas harbour the socio-economically and eco-
logically most vital aquatic ecosystems, but are under increasing anthropogenic pressure. Little is known, however, about how envir-
onmental perturbations affect the development of coastal systems. In this paper, datasets of coastal fish communities covering almost
four decades (early/mid 1970s to 2008) in three different basins of the Baltic Sea were analysed. There were clear changes in species
composition over time in all but one dataset and coherence among basins in the timing of change. Changes were mainly associated
with variables related to climate (water temperature, salinity, and North Atlantic Oscillation index), but less so with those reflecting
nutrient status (nutrient concentrations and loading). Despite the importance of local water temperature, regional climatic variables
were more important for the temporal development of communities. The results indicate that Baltic coastal fish communities have
undergone large structural changes governed by processes acting on both local and regional scales. The findings suggest that ecological
targets should be set accounting for long-term changes in community structure and that a common management of coastal and
offshore ecosystems would be beneficial.
Keywords: climate change, community structure, eutrophication, geographical scale, species composition, Kattegat, Baltic Proper,
Bothnian Sea.
Introduction
Aquatic ecosystems worldwide have gone through substantial
structural changes during recent decades (Hare and Mantua,
2000;Beugrand, 2004;Weijerman et al., 2005;Mo
¨llman et al.,
2009;Conversi et al., 2010). A reorganization of a single trophic
level, such as a decreased abundance of predators, might result
in a lack of top– down control on consumers and alter the dynam-
ics of the entire foodweb (Carpenter and Kitchell, 1993;Myers
et al., 2007), as shown for example in the Baltic Sea pelagic ecosys-
tem (Casini et al., 2008). Changes in ecosystem structure might
thus influence the function of the system and result in profound
loss of economical and ecological values. Structural changes in
marine ecosystems have been associated with climatic forcing
(e.g. Beamish et al., 2004;Mo
¨llman et al., 2009;Conversi et al.,
2010), introduction of alien species (Daskalov et al., 2007), as
well as eutrophication and overexploitation of key species
(O
¨sterblom et al., 2007;Casini et al., 2009;Mo
¨llman et al.,
2009). An integrated knowledge of different causes of ecosystem
changes and their scales of action is required for implementing
an ecosystem-based management, although research in this area
is still in its infancy (Andersen et al., 2009).
Coastal ecosystems (here defined as inshore areas in the prox-
imity of land), which are among the most productive and econom-
ically vital aquatic systems worldwide, are under increasing
anthropogenic pressure. In contrast to ecosystems in the open
sea, where mainly exploitation and large-scale climate forcing
are influential, potential impacts on coastal ecosystems are more
numerous, including also eutrophication, pollution, habitat deg-
radation and invasive species (Collie et al., 2008). Another
obvious difference is that coastal communities to a larger extent
are locally structured, determined by the availability of suitable
habitat and intra- or interspecific community processes
(Wootton, 1998). To date, few studies have addressed the relative
impacts of drivers on structural change of coastal communities
(but see Lekve et al., 2003;Collie et al., 2008). In particular, knowl-
edge of the relative importance of variables acting on different geo-
graphical scales and the generality of findings across areas and
systems is lacking.
The Baltic Sea is the largest body of brackish water in the world,
and is constituted of several basins with well-differentiated hydro-
logical conditions. There is a strong gradient in salinity, which
ranges from 1 to 2 psu in the innermost parts to 25 psu at the
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ICES Journal of
Marine Science
ICES Journal of Marine Science (2012), 69(6), 961–970. doi:10.1093/icesjms/fss072
at Sveriges Lantbruksuniversitet on June 26, 2012http://icesjms.oxfordjournals.org/Downloaded from
entrance to the North Sea (HELCOM, 1996). This variation in sal-
inity has a strong impact on the species composition and diversity.
Communities in the western part of the Baltic Sea (Kattegat) are
mainly composed of marine species and have higher species diver-
sity, whereas communities in the innermost parts have lower
species diversity and contain a mixture of freshwater and marine
species. Thus, the Baltic Sea offers an opportunity for studying
variation in, and the generality of, drivers of ecosystem change
in areas along this gradient in environmental condition, species
composition, and diversity.
Offshore ecosystems in the Baltic Sea have gone through pro-
found changes during recent decades (Casini et al., 2008;
Mo
¨llman et al., 2008;Diekmann and Mo
¨llmann, 2010)asa
result of large-scale climatic forcing and overexploitation of top-
predators (O
¨sterblom et al., 2007;Mo
¨llman et al., 2009). Coastal
and offshore areas are often managed separately, but an issue of
strong relevance for marine management is whether changes in
coastal areas are driven by the same processes as in offshore
areas, or not (Estes et al., 1998).
In this study, datasets of coastal fish communities from three
different basins of the Baltic Sea (Kattegat, Baltic Proper, and
Bothnian Sea) covering almost four decades were analysed to
assess causes of temporal changes in coastal fish communities
from the aspects of generality and scale. The following questions
were addressed. (i) Are there significant changes in species com-
position of coastal fish communities over time? (ii) Are there simi-
larities in the timing of change across basins? (iii) What
environmental variables are mainly associated with the temporal
changes observed? (iv) At what geographical scale are the most im-
portant variables related to change operating; local, regional, or
globally over all areas assessed?
Material and methods
Study areas
Species abundance data from monitoring areas in three different
basins in the Baltic Sea: Kattegat (K), Baltic Proper (BP) and
Bothnian Sea (BoS), were used (Figure 1). The monitoring area
in Kattegat (Vendelso
¨) is located at a shallow, open coastline
with a maximum depth of 10 m. Nearby areas are, however, as
deep as 30 m. The average salinity is 17 –20 psu, the bottom sub-
strate is dominated by rock, with segments of sand, and the mon-
itoring area covers 0.1 km
2
. In the Baltic Proper, the monitoring
area Kva
¨do
¨fja
¨rden has an average salinity of 6–8 psu. The inner
parts of the area are sheltered, with an average depth of 5m,
whereas the outer parts are more wave exposed and can be as
deep as 25 m; the area covers 7.3 km
2
. The monitoring area in
the Bothnian Sea (Forsmark) has an average salinity of 3– 4 psu.
About 40% of the total area is shallower than 3 m, although
some parts are as deep as 30 m. The area covers 21.0 km
2
.
Small islands and scerries are common, but the area is more
wave exposed than Kva
¨do
¨fja
¨rden. In all areas, human population
density is low, and the level of local anthropogenic impact stem-
ming from land use is therefore low.
Both the Forsmark and Vendelso
¨monitoring areas are used as
reference areas for the surveillance programme of the nuclear
power plants in Forsmark and Ringhals, respectively, monitoring
the effects of cooling water discharges from these. Although
located close to the power plants, both areas are not affected by
the discharge of cooling water (Swedish Board of Fisheries, 2009,
2010). Kva
¨do
¨fja
¨rden serves as a reference area within the
Swedish national environmental monitoring programme
(HELCOM, 1996).
Fish data
Each area was represented by two fish community datasets, one
representing the colder and one the warmer season, so that in
total six time-series were used, starting between 1971 and 1976,
and ending in 2002 or 2008 (Table 1). The rationale for using
data from different seasons was that the species composition of
coastal fish communities in the Baltic Sea generally differs
throughout the year, as a result of species-specific seasonal differ-
ences in activity and migration behaviour (Thoresson, 1996;
Neuman and Piriz, 2000). Species that have a temperature
optimum .208C are generally classified as ‘warm-water species’,
whereas those having a temperature preference below 158C are
recognized as ‘cold-water species’ (Neuman, 1974). In the Baltic
Proper and Bothnian Sea, species with a freshwater origin, e.g.
perch (Perca fluvatilis) and roach (Rutilus rutilus), are generally
more abundant in the warmer season and are with few exceptions
recognized as ‘warm-water species’. Marine species such as Baltic
herring (Clupea harengus membrans), eelpout (Zoarces viviparous),
and cod (Gadus morhua), but also some freshwater species, such as
whitefish (Coregonus maraena), fourhorned sculpin (Triglopsis
quadrocornis), and smelt (Osmerus eperlanus), which have a
Figure 1. Location of areas for fish sampling in the Kattegat
(K; Vendelso¨), Baltic Proper (BP; Kva¨do¨fja¨rden), and the Bothnian
Sea (BoS; Forsmark).
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lower temperature preference, are recognized as ‘cold-water
species’, and are therefore more abundant in the cold season. In
the Kattegat, almost all species are of a marine origin with a
lower temperature preference, but during the warmer season,
species preferring higher water temperatures, such as eel
(Anguilla anguilla), gobids (Gobiidae), and wrasses (Labridae),
dominate. In the colder season, species preferring lower water
temperatures such as cod and eelpout (Zoarces vivparous) are
more abundant. In all three basins, comparing the two
types of time-series, species richness was generally higher in
datasets representing the colder season (Mann–Whitney U-test,
Z¼–2.2, p¼0.028).
Datasets representing the warmer season were sampled in
August in all areas (temperature range: 14 – 238C). Datasets repre-
senting the colder season were sampled in April in the Kattegat
and in October in the Baltic Proper and Bothnian Sea. No mon-
itoring data were available for October in the Kattegat.
Temperatures at fish sampling in the colder season were,
however, similar in all basins (4– 128C). Sampling was performed
using fykenets (K), net series (BP), or coastal survey nets (BoS).
The fykenets (K) were set perpendicular to the shoreline at 2 –
5 m depth. During each season, six fixed stations were fished
during nine and 12 consecutive nights per year, with two fykenets
at a time (Thoresson, 1996;HELCOM, 2008). The fykenets repre-
sentatively catch fish down to 9 cm length. For the net series
(BP), 18 gillnets of seven different mesh sizes (21– 60 mm) were
set one at a time at one of four fixed stations, at the bottom at a
15–20 m depth (Thoresson, 1992). The coastal survey nets
(BoS) were composed of two linked multimesh gillnets (with
five different mesh sizes, 17 –33 mm) at each station and were
set at the bottom at 2 –5 m depth in the warmer season, and at
15–20 m in the colder season. In each season, three fixed stations
were fished repeatedly during three nights each year (A
˚djers et al.,
2006; HELCOM, 2008). To achieve a general estimate of the
studied communities, an average per species, season, and year
across all stations for each monitoring area was used as the
input data for further analysis. As such, each dataset comprised
a matrix of one observation per year and species. Both types of
gillnet catch fish down to 14 cm length representatively. For
all methods, data were sampled in the same way throughout the
whole period assessed, by the same institute (Institute of Coastal
Research).
In total, the datasets included 45 species (30 in K, 22 in BP, and
21 in BoS; Supplementary material, Table S1). These figures prob-
ably underestimate the true number of species in each area, since
the gears do not sample all species present. The majority of the
species were, however, sampled, and, in the text to follow, the
term ‘community’ will therefore refer to the sampled part of the
fish community. Since different sampling methods were used in
the different basins, catch levels and community compositions
could not be directly compared. The analyses were therefore
restricted to assess relative changes in species compositions
over time.
Environmental data
Data on environmental variables potentially influencing the tem-
poral development of coastal fish community structure were
defined as representing three geographical scales: local, regional,
and global. Local variables representing temperature (Tl) and
water transparency (Trl ) were collected at the exact stations of
fish sampling, whereas data on local nutrient loading (TotN)
were based on calculated discharges of nitrogen from land
within the county of each fish-monitoring area (see Table 1for
details ). Ideally, we would have data for nutrient loading at the
very same scale as for Tl and TrL, but appropriate data were
only available for K and BP, which showed good coherence with
the data at the county level used here (r
2
¼0.7–0.8). The regional
scale was represented by data from the national open-sea monitor-
ing programme in Sweden using monthly averages from all avail-
able monitoring stations in each of the basins (see Table 1for
further details). Variables related to climate on the regional scale
were open-sea summer surface temperature (T), as well as
Table 1 Environmental variables included in the analysis and output from the DISTLM models.
Basin Season Dataset Years
Local Regional
d
Global % Variation
Tl
a
Trl
b
TotN
c
T
e
S
f
DIN
g
DIP
g
pH
f
NAO
Kattegat Cold K Cold 1976–2008 12.3 * 12.3
Warm K Warm 1976–2008 13.9 * 13.0 26.9
Baltic Proper Cold BP Cold 1971 – 2008 6.9 23.9 9.7 40.5
Warm BP Warm 1971 – 2008 13.7 21.3 35.0
Bothnian Sea Cold BoS Cold 1975–2002 32.2 10.5 42.7
Warm BoS Warm 1975–2008 11.0 29.6 40.6
Tl, local temperature measured during fish sampling; Trl, local transparency measured during fish sampling; TotN, local yearly nutrient load.
Regional variables were T, offshore surface summer temperature; S, offshore surface salinity; DIN, winter concentrations of dissolved inorganic nitrogen; DIP,
winter concentrations of dissolved inorganic phosphorus; pH, offshore surface pH. As a global variable we used the winter PC-based index of NAO. Values in
each row show the percentage variation in fish community structure explained by each variable according to the DISTLM analyses. Underlined figures denote
the main contributing variable of each dataset. Variables excluded from analysis due to collinearity (VIF-value .3) are indicated by *. Total variation
explained is shown in the last column (% Variation).
a
Bottom temperature, average over all stations and days sampled, Swedish Board of Fisheries (SBF).
b
Measured as Secchi depth, average over all days sampled, SBF.
c
Based on monthly averages of concentration and flow per county: for K Halland, for BP O
¨stergo¨tland, and for BoS Ga¨vleborg, Swedish University of
Agricultural Sciences.
d
The regional data for Kattegat were based on six stations (Fladen, Anholt E, L:a Middelgrund, S:t Middelgrund, A
˚lborg bugt, and Kattegat SW), for Baltic
Proper 12 stations (BY10, BY15, BY20, BY29, BY31, BY32, BY38, BY1, BY2, BY4, BY5, and BCS III-10), and for Bothnian Sea six stations (SR5, MS4, C3, US3,
US5B, and F26).
e
Average at 0– 10 m depth in July– September for BP and June –September for BoS and K, Swedish Meteorological and Hydrological Institute (SMHI).
f
Annual average at 0 –10 m depth, SMHI.
g
Average at 0– 10 m depth in January– February for BoS and K, January– March for BP, SMHI.
Drivers of coastal fish community change 963
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surface salinity (S), and pH, which were included as averages over
the year. Salinity levels in the Baltic are tightly linked to climate in
that the inflow of saline water in the area is influenced by the dir-
ection and strength of prevailing winds. The link between climate
and pH is that increased levels of atmospheric carbon dioxide
(CO
2
) have resulted in increased uptake of CO
2
by the oceans
and thus a decrease in pH levels (Feely et al., 2004). Variables
related to nutrient status were open-sea concentrations of dis-
solved inorganic nitrogen (DIN) and phosphorus (DIP), repre-
sented by winter values. The same regional data were used for
both fish datasets within a basin. There was no spatial overlap
between the fish-monitoring areas and the areas where the regional
variables were sampled. The regional variables were, however,
anticipated potentially to affect coastal fish communities either
directly (via fish migration) or indirectly (affecting prey availabil-
ity or local abiotic conditions). At the global scale, defined as in-
fluencing all basins, large-scale climate forcing was represented
by the winter PC-based North Atlantic Oscillation (NAO) index
(Hurell, 1995). This variable was anticipated potentially to influ-
ence coastal communities indirectly, through influence on the
local abiotic environment (Lekve et al., 2003).
NAO is a climatic phenomenon reflecting differences in atmos-
pheric pressure at the sea surface between the Arctic and the sub-
tropical Atlantic (Hurell and Deser, 2009). It often has an impact
on water surface and air temperature, precipitation, salinity, and
wind directions over its area of influence (the North Atlantic
Ocean and adjacent continents). The NAO index is commonly
used as integrated measure of large-scale climatic events, potential-
ly affecting both the growth and reproduction of aquatic organ-
isms (Lekve et al., 2003), as well as the structure and functioning
of marine ecosystems (Hurell and Deser, 2009). In the Baltic
Sea, positive values of the NAO index generally coincide with ele-
vated sea-surface temperatures and westerly winds, whereas nega-
tive values conform to the opposite (Kalnay et al., 1996). A
correlation between NAO and other environmental variables
included in the current study was seen for some datasets, but
the relationship was relatively weak and showed inconsistent direc-
tion among datasets, suggesting that the variables to some extent
reflected different aspects of environmental variation, with poten-
tially different impacts on the communities assessed. The strongest
correlation with NAO was seen for local temperature, regional nu-
trient levels (DIN and DIP), and pH (Supplementary material,
Table S2). The strength and direction of these relationships did
vary to some extent, but was generally positive with respect to
local temperature and regional nutrient levels, and negative
for regional pH.
Analyses
To identify significant changes in species composition over time,
chronological clustering analysis (Legendre and Legendre, 1998),
as implemented in Brodgar 2.5.7 (www.brodgar.com), was used.
The analyses were based on the Bray– Curtis similarity index,
which gives balanced weight to rare and abundant species (Zuur
et al., 2007), and by which joint absences do not contribute to
similarity between samples. The analyses were applied using a
level of connectedness of 0.5; varying this level did not, however,
affect the general results. A temporal change was interpreted as
statistically significant from one year to the next at a¼0.01, to
include only the strongest patterns in each dataset (Zuur et al.,
2007). Given the differences in species composition and diversity
among basins and seasons, the timing of change was considered
similar across datasets if changes occurred within the same 2 – 3
years. In order to down-weight the influence of observations
with high values, all analyses were performed on ln(x+
1)-transformed data as suggested by Clarke and Warwick (2001).
Species with a frequency of occurrence .5% were excluded
from further analyses.
The temporal development of the fish communities was further
assessed by metric multidimensional scaling, using principal co-
ordinate analysis (PCO; Zuur et al., 2007), as implemented in
PERMANOVA+of PRIMER v6 (Anderson et al., 2008). For con-
sistency between analyses, the Bray–Curtis similarity index was
used. Species with a multiple metric correlation .0.2 with any
of the first two PCO axes were considered as contributing signifi-
cantly to the temporal development of each dataset (Anderson
et al., 2008). The temporal development of these species was also
presented as anomaly graphs.
To test the relationship between community composition
and environmental variables, distance-based linear modelling
(DISTLM) was used. This is a multivariate multiple regression
method where the ordination axes from a resemblance matrix of
the response dataset is regressed against a matrix of explanatory
variables (software PERMANOVA+of PRIMER v6; Anderson
et al., 2008). Prior to analysis, the datasets on environmental vari-
ables were checked for skewness using draftman plots (pairwise
plots of all environmental variables; Clarke and Gorley, 2006)
and for collinearity by analysis of variation inflation factors
(VIFs), setting the limit for inclusion in analyses at VIF ,3
(Table 1;Zuur et al., 2010). For each species dataset, the set of en-
vironmental variables was regressed on a similarity matrix based
on the Bray–Curtis similarity index, using years as samples.
Final models were selected using the BEST selection procedure
in PRIMER v6 (Anderson et al., 2008), based on two selection cri-
teria, the corrected Akaike information criterion (AICc; Burnham
and Anderson, 2002) and Bayes information criterion (BIC;
Schwarz, 1978). As models with AICc criteria within two units
of the best model indicate some redundancy among models
(Burnham and Anderson, 2002), we performed the model selec-
tion procedure in four separate steps. First, by minimizing the
mean value of the two criteria (AICc and BIC) for all possible
models following Anderson et al. (2008). The model with the
smallest mean value for the two criteria was considered as superior.
Second, we maximized the log-likelihood value for the AICc criter-
ion for the models appearing within two units of the best model
(Burnham and Anderson, 2002). This was done to assess
whether redundancy among models was attributable to the inclu-
sion of the penalty term for including additional variables
(Burnham and Anderson, 2002). The model having a substantially
higher log-likelihood value compared with the other models
(commonly more than two units higher) was considered as super-
ior. Third, we assessed the individual weights (i.e. the number of
models in which a given variable occurs) of the variables included
in all models appearing within two units of the best model accord-
ing to the AICc selection criterion (Burnham and Anderson,
2002). The variables having the highest individual weights were
considered as superior in explaining the observed changes in fish
community response data. Fourth, by assessing the significance
at a¼0.05 of variables in marginal F-tests as offered by the
DISTLM routine. Variables exhibiting a significant relationship
with the response dataset were considered superior. In all, the
finally selected models were those fulfilling all of the four above-
listed criteria. The partitioning of variation between the
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environmental variables included in the final model was then
assessed using the sequential selection procedure (Anderson
et al., 2008), and their temporal development was illustrated by
anomaly graphs.
Results
Temporal changes of fish communities
The first two ordination axes of the PCO analyses explained
between 59.9% and 81.4% of the total variation in the different
datasets (Figure 2). For the datasets representing the cold season,
assessed communities have gone through substantial change. In
the Kattegat colder season, a first period in the 1980s was charac-
terized by species preferring lower water temperatures (shorthorn
sculpin, Myoxocephalus scorpius, and eelpout, Figure 2a), followed
by a period with high abundance of four-beard rockling
(Enchelyopus cimbrius) and goldsinny wrasse (Ctenolabrus rupes-
tris), both preferring warmer waters. From the mid 1990s, the
abundance of black goby and flounder was high, whereas the
abundances of species preferring low water temperatures were
low (Figure 2a). In the Baltic Proper and the Bothnian Sea, fish
communities in the colder season generally exhibited a decrease
in marine species, and an increase in freshwater species over
time (Figure 2c and e; Supplementary material, Figure S1). In
both basins, the abundance of cod and herring was high until
the late 1980s, after which it decreased strongly (correlation of
herring with ordination axes in BP Cold; first ¼– 0.18,
second ¼–0.17, and cod in BoS Cold; first ¼0.17, second ¼
–0.17). The following years until the mid 1990s represented a tran-
sitional period in the Bothnian Sea and Baltic Proper, with rela-
tively high abundances of freshwater species preferring lower
water temperatures, such as fourhorned sculpin, smelt (BoS),
and whitefish (BoS; Figure 2c and e). From the mid 1990s, fresh-
water species preferring warmer waters, such as perch and roach
(both basins), white bream (Abramis bjoerkna; BP), and ruffe
(Gymnocephalus cernus; BoS), increased. However, the latest
years in the Baltic Proper dataset inclined towards similarity to
the fish community in the late 1970s, as explained by an
increased abundance of flounder (Platichthys flesus) and roach
(Supplementary material, Figure S1).
Changes in community composition during the warmer season
showed a general coherence with the cold seasons in the Kattegat.
Species favoured by lower water temperatures (such as cod,
eelpout, and shorthorn sculpin) were abundant during the early
1980s, after which they decreased (Figure 2b). Between the late
1980s and early 1990s, the fish community of the warmer season
in Kattegatt was characterized by high abundances of goldsinny
wrasse, flounder, and yellow eel, all preferring higher water tem-
peratures. During the most recent years, the fish community was
dominated by another species favoured by warmer waters, the
corkwing wrasse (Symphodus melops). In both the Baltic Proper
and Bothnian Sea warmer season datasets, increases in freshwater
species preferring warmer water temperatures were seen, such as
perch and pikeperch (both basins) and roach and white bream
(BoS; Figure 2d and f; Supplementary material, Figure S1).
There was also a recent increase in smelt in the BP
(Supplementary material, Figure S1).
Timing of coastal fish community change
Significant changes in species composition over time were
observed in all but one of the six datasets assessed and were
distributed between 1976 and 2002 (Table 2, Figure 3). In
general, changes occurred between two and four times in each
dataset, with the highest intensity in the BP Cold and in both
Kattegat datasets (Table 2). Parts of the changes occurred at the in-
dividual dataset level, probably as a result of differences in species
composition, diversity, and to some extent also time-series length
across datasets. Two of the changes observed were unique for the
BP Cold dataset (1976/1977 and 2002/2003; Table 2). Of these,
the earlier was mainly related to an increase in cod, and the later
change by altered abundances of several species, such as white
bream, four-horned sculpin, flounder, and roach (Supplementary
material, Figure S1).
Changes occurred in all basins during the early 1980s, the late
1980s, and the mid 1990s (Table 2; Figure 3). In the early 1980s,
four changes occurred (K Cold, 1979/1980; K Warm, 1982/
1983; BP Cold, 1983/1984; and BoS, 1981/1982), and during
the late 1980s changes were seen in the colder season in all
basins and also in the Kattegat warmer season. In all these cases,
the changes occurred in 1988/1989 (Table 2). In the mid 1990s
(1992–1995) changes occurred in both seasons in the Kattegat
and the Bothnian Sea, but no change was observed in the Baltic
Proper (Table 2).
Association with environmental variables
The final models according to the DISTLM analyses explained
between 20.2% and 51.8% of the total variation in the datasets
(Table 1). Water temperature (Tl) was the only local variable
included in the final models. It was significant in all but the BoS
Cold dataset, but never explained .13.9% of the total variation,
and was only identified as the variable explaining most of the vari-
ation in the two Kattegat datasets (Table 1). The only regional vari-
able included in the final models was salinity (S), found in four of
the datasets. It explained in total almost twice as much of the vari-
ation as that captured by the local variable water temperature
(Table 1). Salinity was the main contributing variable in the
Baltic Proper and Bothnian Sea datasets, where on its own it
always explained .20% of the total variation. In addition, the
NAO index was included in three of the selected models (K
Warm, BP Cold, and BoS Cold), explaining between 9.7% and
13% of the variation, but never identified as the strongest contrib-
uting variable (Table 1).
Less than half (five out of 12) of the variables included in
the selected models exhibited a significant linear trend over
time, whereas the others showed a more variable pattern
(Supplementary material, Figure S1). Among the climate-related
variables, local and regional temperature (Tl and T) showed a sig-
nificant long-term increase in all three basins in the warmer
season, but not in the cold season. Salinity showed a significant de-
crease in both the Baltic Proper and Bothnian Sea, but not in the
Kattegat. The NAO index showed on average negative values in the
1970s, positive in the 1980s, increasing in the first half of 1990s,
and decreasing again in the most recent years studied
(Supplementary material, Figure S1). Among the nutrient-related
variables, local water transparency increased in the Kattegat,
whereas it decreased in the Baltic Proper.
Discussion
Despite their strong socio-economic and ecological importance
(Harley et al., 2006), few studies have hitherto addressed causes
of long-term structural change in coastal ecosystems (but see
Jackson et al., 2001;Lekve et al., 2003;Collie et al., 2008). In
Drivers of coastal fish community change 965
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this study, the temporal development of coastal fish communities
in three different basins of the Baltic Sea during the last four
decades was assessed in relation to environmental factors on
local, regional, and global scales. Climate-related variables
appeared to be more important than those related to nutrient
levels, as observed at the local (temperature) as well as the global
(NAO) scale in all three basins. In the Baltic Proper and
Bothnian Sea communities, however, the environmental variable
mainly associated with community changes was regional salinity.
The results corroborate previous findings suggesting that coastal
communities in the area to a large extent are local in their appear-
ance (Saulamo and Neumann, 2002; Laikre et al., 2005;Olsson
Figure 2. PCO ordinations based on the Bray –Curtis similarity index for each fish community dataset. Projected vectors show changes in the
abundance of species with a correlation .0.2 with any of the two first ordination axes. Periods with similar species composition according to
the chronological clustering analyses (Table 2) are indicated by the same symbols. The line indicates the temporal trajectory. K, Kattegat; BP,
Baltic Proper; BoS, Bothnian Sea. G wrasse, goldsinny wrasse; Fb rockling, four-beard rockling; Sh sculpin, shorthorn sculpin; C wrasse, corkwing
wrasse; L forkbeard, lesser forkbeard; W bream, white bream; Fh sculpin, fourhorned sculpin.
966 J. Olsson et al.
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et al., 2011,2012) and influenced by local environmental factors
(Wottoon, 1998). However, they additionally suggest that variables
acting on a regional (mainly salinity) and global (NAO) scale may
be at least equally important for the temporal development of
coastal fish communities.
Association with environmental variables
Gradients in abiotic conditions, species composition, and diversity
are pronounced over the Baltic Sea area (HELCOM, 1996). Our
study is, to the best of our knowledge, one of the first attempts
to assess factors driving temporal changes at the community
level along such a gradient. As expected, there were some unique
basin- and season-specific responses to changes in environmental
conditions, but several common patterns are also discernible.
Generally, over the studied period, increasing sea surface water
temperatures and decreasing salinity were observed, coinciding
with a decrease in marine species and species preferring lower
water temperatures of all fish communities in all three basins
studied. At the same time, there was an increase in freshwater
species, especially those favoured by higher water temperatures.
The variables most significant in relation to the observed
changes in fish community composition were local temperature
and regional salinity, both related to climate change. Regional sal-
inity was included in the final models in both the Baltic Proper and
the Bothnian Sea datasets, but not in the more marine Kattegat
communities. This may be expected, as salinity is a limiting
factor for the distribution of many marine and freshwater
species in the Baltic Sea (Voipio, 1981), but less so in the
Kattegat. The strong relationship with regional salinity observed
in the colder season was rather expected, since coastal fish commu-
nity composition in the Baltic Sea datasets at this time of the year is
influenced by marine species immigrating from offshore areas. The
association between salinity and fish community composition in
the warmer season was more surprising, since the species domin-
ating coastal fish communities in this season are typically of fresh-
water origin and have more local population structure (Saulamo
and Neumann, 2002; Laikre et al., 2005;Olsson et al., 2011,
2012). As such, it is evident that the development of these types
of coastal fish communities may also be influenced by large-scale
environmental change. In all, these findings indicate the import-
ance of large-scale climatic conditions for coastal fish communities
across geographically separated basins, and also that open-sea and
coastal ecosystems might be linked through pathways other than
migrating fish species.
The weakest relationship with the studied environmental vari-
ables was seen for the Kattegat datasets, but these communities
showed the strongest link to the increase in water temperatures
experienced during the last four decades. In all communities,
however, there were community responses to changes in local
ambient temperatures. This is also manifested as an increase in
species favoured by warmer waters in all communities assessed.
In addition, the effect of local temperature might at least to
some extent be a result of species-specific responses to temperature
of catchability (Thoresson, 1996). For the datasets representing the
warmer season, we considered a response at the community level
more likely than catchability effects, since only a minority of the
individual species exhibited a significant correlation between
abundance and local temperature (JO, unpublished data).
Moreover, local temperature at sampling in the warmer season
either showed a significant linear increase over time or was strong-
ly correlated with summer temperatures. Also in the colder season
datasets, the relationship between species abundance and local
temperature was only significant for a fraction of the species, but
there was no or weak correlation between local temperature at
fish sampling and summer temperature. As a result, changes in
coastal community composition during the cold season might to
some extent also partly reflect changes in migratory behaviour of
the species due to ambient local temperatures (up to 6.9 – 2.3%
of the total variation in two of the datasets).
Some effects of NAO on community composition were also
discernible. Generally, the conditions over the Baltic were charac-
terized by regular in-flows of saline water from the North Sea and
lower sea surface temperatures between the 1970s and the end of
the 1980s (i.e. on average negative values of NAO; Kalnay et al.,
1996). During the following years, the index values were on
average positive and the Baltic Sea relatively warmer and less
saline. Thus, the effects of NAO on the coastal fish communities
Table 2. Timing of significant changes in community structure for the studied datasets, according to chronological clustering analyses at
a¼0.01.
Basin Data Set Years
Changes
Intensity (no. of changes/year)
1970s 1980s 1990s 2000s
Kattegat K Cold 1976 – 2008 1979/1980 1988/1989 1994/1995 0.09
K Warm 1976–2008 1982/1983; 1988/1989 1995/1996 0.09
Baltic Proper BP Cold 1971 – 2008 1976/1977 1983/1984; 1988/1989 2002/2003 0.11
BP Warm 1971 –2008 0
Bothnian Sea BoS Cold 1975 –2002 1988/1989 1992/1993 X 0.08
BoS Warm 1975– 2008 1981/1982 1993/1994 0.06
Results are presented by decade between 1970 and 2000, X denotes that the dataset did not cover the decade. Intensity shows the number of changes
observed per total number of years studied in each dataset. Abbreviations of dataset names are as in Table 1.
Figure 3. Number of changes in coastal fish community structure
identified per year in all six datasets, according to chronological
clustering analyses at a¼0.01. Bar colour denotes the season (white,
warmer season; black, colder season).
Drivers of coastal fish community change 967
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observed in this study are likely to be similar to those observed for
salinity and local temperatures, but manifested on a larger geo-
graphical scale.
The level of unexplained variation in the final models was often
rather high, probably reflecting that only abiotic variables were
included in the analyses. Other potentially important drivers of
change, such as pollution, habitat degradation, and exploitation
of key species, or biological processes such as competition and pre-
dation were not considered in this study. These variables are
known to impact community structure in fish assemblages
(Wootton, 1998;Lekve et al., 2003;O
¨sterblom et al., 2007;
Mo
¨llman et al., 2009). Despite the fact that the main results of
this study are explicable only in light of the abiotic variables
included, additional aspects, such as those listed above, should
preferably be included in any further studies. Although the major-
ity of species with a freshwater origin in the communities assessed
are not targeted by large-scale fisheries, the decrease in coastal fish-
eries during the last decade might potentially be an important
explanatory factor for the observed changes. Furthermore, incorp-
oration of biotic explanatory variables may be especially important
for the Kattegat datasets, where the weakest relationships with the
abioic factors assessed were observed. For example, the increase in
small-bodied species (wrasses and black goby) was mainly asso-
ciated with increased temperatures in our study, but is probably
also related to predatory release (Pihl, 1982;Pihl and
Ulmestrand, 1993;Jackson et al., 2001;Myers et al., 2007;
Savenkoff et al., 2007) as a result of the overexploitation of cod
(ICES, 2010;Eriksson et al., 2011). Cod has also decreased in
the Baltic Proper and Bothnian Sea during the past decades
(ICES, 2010), concurrent with a cascading effect on lower
trophic levels in coastal foodwebs caused by predatory release
(Eriksson et al., 2011). In this study, a decrease in cod was observed
in the Baltic Proper and Bothnian Sea datasets representing the
colder season. However, most small-bodied species, including
the most influential mesopredator in Baltic Sea coastal ecosystems,
the three-spined sticklebacks (Gasterosteus aculeatus), were not
representatively sampled in these two basins by the gears assessed
in this study. Predatory release from cod on earlier life stages than
those representatively sampled by these gears might however also
have facilitated the increase of the freshwater species in the
Baltic Proper and Bothnian Sea datasets since the early 1990s.
Timing of change
As might be expected from the variability in species composition
across basins and seasons, specific changes at the dataset level
did occur. Given that unique responses to external perturbation
and internal dynamics across local fish communities over such a
strong environmental gradient as in this study might be antici-
pated, we believe that the coherence in timing of change across
basins and seasons is striking. During 1988/1989, changes in com-
munity composition were observed in all basins, but were mainly
seen in datasets representing the colder season. Two other
common periods of change were also identified, the late 1970s/
early 1980s when changes occurred in all basins and seasons,
and the mid 1990s when changes were restricted to the Kattegat
and Bothnian Sea datasets. Dataset-specific timing of changes
was most frequent in the communities assessed in the Baltic
Proper. This may be explained by the higher degree of mixture
of marine and freshwater species, making unique responses to en-
vironmental perturbation within this basin more likely.
In marine ecosystems, substantial structural changes (regime
shifts; Andersen et al., 2009) have generally been seen to follow
worldwide shifts in climatic–oceanic conditions during recent
decades (1977, 1989, and 1998; reviewed in Beamish, 2004). In
many North Pacific and North Atlantic ecosystems, including
the offshore ecosystem of the Baltic Sea, particularly strong shifts
have been observed in the late 1980s (e.g. Hare and Mantua,
2000;Link et al., 2002;Beugrand, 2004; Weijermann et al., 2005;
Mo
¨llmann et al., 2009; Diekmann and Mo
¨llmann, 2010). The
change in 1988/1989 together with the relationship between struc-
tural changes and climate-related variables, as derived in our
study, indicate that changes in community structure in Baltic
coastal areas are also linked to events on the global scale. In our
analyses, however, we observed weaker support for a main
change in 1977, as indicated in other marine systems (Beamish,
2004), and no support for a shift in 1998. Similar to the situation
found for the coastal fish communities of the Kattegat and
Bothnian Sea in this study, Diekmann and Mo
¨llmann (2010)
also identified a structural change in offshore areas of the Baltic
Sea in the mid 1990s. This was observed in all but one of six off-
shore systems assessed (Diekmann and Mo
¨llmann, 2010), but has,
to our knowledge, not been reported for areas outside the Baltic
Sea. These findings further support that the open-sea and
coastal ecosystems might be linked through several pathways.
Whether the changes in the late 1970s/early 1980s, as observed
in this study, represent a unique coastal response has not yet
been addressed due to shorter time-series in the offshore datasets
from the Baltic Sea basins (cf. Diekmann and Mo
¨llmann, 2010;
ICES, 2010).
At the species levels, however, coastal and offshore systems
might exhibit different responses. For example, in the Bothnian
Sea there has been a decrease in coastal herring (this study) con-
current with the increase in offshore stocks of herring (ICES,
2011). A likely explanation for this is that there are different
spawning groups of coastal and open-sea herring in the
Bothnian Sea (Ehnholm, 1951), and that the herring stock assessed
in the Bothnian Sea therefore consists of several populations with
potentially different population dynamics (ICES, 2011). The logic
behind the similar development of the offshore and coastal herring
in the Baltic Proper might be that the herring in this basin is less
bound to the coast compared with the Bothnian Sea herring
(Otterlind, 1976). Yet, at the community or foodweb level, our
results suggest that changes in coastal community structures in
the Baltic Sea are linked to events at larger spatial scale also affect-
ing offshore foodwebs.
Since coastal communities are hypothesized as being of local
appearance and the sampling areas used in this study are selected
to represent one coastal habitat type in each basin, it is difficult to
reconcile how representative our findings are on a basin-wide
scale. We are, however, confident that the findings in this study
could be generalized across Baltic Sea coastal areas for several
reasons. First, the datasets extend over almost four decades and
the overall transition from communities dominated by marine
species and those favoured by cold water to a state characterized
by species of a freshwater origin in favour of warmer waters is
also manifested in the few other datasets existing that cover a
similar time span (Swedish University of Agricultural Sciences,
Institute of Coastal Research, unpublished data). Second, consid-
ering a shorter time-scale, there is a much better spatial coverage in
the Baltic Proper and Bothnian Sea for datasets spanning over the
last two decades. In these time-series there are local differences in
968 J. Olsson et al.
at Sveriges Lantbruksuniversitet on June 26, 2012http://icesjms.oxfordjournals.org/Downloaded from
the temporal dynamics of certain species, but they show a general
pattern of increasing abundances of freshwater species and those
favoured by higher water temperatures (HELCOM, in press).
Third, the common results across the three geographically sepa-
rated basins in this study suggest that the findings are general
for coastal systems in the Baltic; there was a coherence in the
timing of community change in all basins assessed, the temporal
development of these communities has followed a similar
pattern, and the same drivers across basins were related to com-
munity development despite the strong environmental gradient
found across these basins (Voipio, 1981).
Conclusion
The results of this study show that changes in coastal fish commu-
nities may have common causes across geographically distant
areas, and that offshore and coastal systems to some extent
might respond to similar large-scale environmental conditions.
These systems may be linked through several pathways, beyond
species migrations, and the findings in this study advocate a
wider geographical perspective in the management of coastal eco-
systems and provide support for a common management of
coastal and open-sea systems (see also Estes et al., 1998;Eriksson
et al., 2011). Examples include the development of common long-
term management plans and integrated monitoring programmes
for open-sea and coastal ecosystems. Moreover, although variables
related to large-scale climatic changes may not be manageable in
the short term, the results in this study show that management
of other human activities must be undertaken accounting for
climate-related effects. The directional changes in coastal commu-
nity structure observed in this study support the need for a flexible
management, where management targets are continuously set in
relation to community processes, such as altered productivity
(ICES, 2008) or changes in species interactions (Collie and
Gislason, 2001). In this context, assessments of changes in
species composition, as outlined here, may complement the use
of univariate indicators (e.g. indicator species; Mo
¨llmann et al.,
2009) and provide a better representation of community
changes that is still decomposable with respect to single species
and interpretable in relation to external drivers.
Supplementary material
The supplementary material is available at ICESJMS online and
cosists of a list of all species and their associated scientific names
included in the analyses (Table S1), a cross-correlation matrix of
the environmental variables used in each dataset (Table S2), and
the development over time for the species contributing to the tem-
poral development of each dataset (Figure S1).
Acknowledgements
We thank Kerstin So
¨derberg, four anonymous reviewers, the editor
Verena Trenkel, and the ICES/HELCOM Working Group on
Integrated Assessments of the Baltic Sea (WGIAB) for help and in-
spiration during the construction of this manuscript. This study is
part of the project Integrated analyses of Baltic Sea ecosystems, fi-
nancially supported by the Swedish Environmental Protection
Agency, the Swedish Board of Fisheries, and the Swedish
University of Agricultural Sciences.
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