Evaluating eutrophication management scenarios in

the Baltic Sea using species distribution modelling

Ulf Bergstr€om1*, G€oran Sundblad2, Anna-Leena Downie3, Martin Snickars4,

Christoffer Bostr€om4 and Mats Lindegarth5

1Swedish University of Agricultural Sciences, Department of Aquatic Resources, Institute of Coastal Research,

Skolgatan 6, 74242 €Oregrund, Sweden; 2AquaBiota Water Research, L€ojtnantsgatan 25, 11550 Stockholm, Sweden;3Finnish Environment Institute, Marine Research Centre, PO Box 140, 00251 Helsinki, Finland; 4Department of

Biosciences, Environmental and Marine Biology, �Abo Akademi University, Artillerigatan 6, 20520 �Abo, Finland; and5Department of Biological and Environmental Sciences, University of Gothenburg, Tj€arn€o, 45296 Str€omstad, Sweden

Summary

1. Eutrophication is severely affecting species distributions and ecosystem functioning in

coastal areas. Targets for eutrophication reduction have been set in the Baltic Sea Action

Plan (BSAP) using Secchi depth, a measure of water transparency, as the main status indica-

tor. Despite the high economic costs involved, the potential effects of this political decision

on key species and habitats have not been assessed.

2. In a case study including species central to coastal ecosystem functioning, we modelled the

effects of changing Secchi depth on the distribution of bladderwrack Fucus vesiculosus and

eelgrass Zostera marina vegetation as well as recruitment areas of the main predatory fish spe-

cies, perch Perca fluviatilis and pikeperch Sander lucioperca. Specifically, we explored the

effects of changing Secchi depth on species distributions under a set of scenarios based on the

BSAP, using three fundamentally different modelling techniques: maximum entropy, general-

ized additive and random forest modelling.

3. Improved Secchi depth (reduced eutrophication) was predicted to cause a substantial

increase in the distribution of bladderwrack, while the distribution of eelgrass remained lar-

gely unaffected. For the fish, a large increase in perch recruitment areas was predicted and a

concurrent decrease in recruitment areas of pikeperch. These changes are likely to have effects

on biodiversity and ecosystem services.

4. The three modelling methods exposed differences in the quantitative predictions for species

with a weaker coupling to Secchi depth. Qualitatively, however, the results were consistent

for all species.

5. Synthesis and applications. We show how ecological effects of environmental policies can

be evaluated in an explicit spatial context using species distribution modelling. The model-

specific responses to changes in eutrophication status emphasize the importance of using

ensemble modelling for exploring how species distributions may respond to alternative

management regimes. A pronounced difference in response between species suggests that

eutrophication mitigation will have consequences for ecosystem functioning, and thus eco-

system goods and services, by inducing changes in the simple food webs of the Baltic Sea.

These model predictions form a basis for spatially explicit cost-benefit estimates under dif-

ferent scenarios, providing valuable information for both decision-makers and the wider

society.

Key-words: Baltic Sea Action Plan, ensemble modelling, GAM, Maxent, Random Forest,

scenario analysis, Secchi depth, species distribution models

*Correspondence author. E-mail: ulf.bergstrom@slu.se.

© 2013 The Authors. Journal of Applied Ecology © 2013 British Ecological Society

Journal of Applied Ecology 2013 doi: 10.1111/1365-2664.12083

Introduction

As a result of growing human populations and an associ-

ated increase in competition for space, loss of habitat is

now one of the major threats to biodiversity and the ser-

vices provided by ecosystems (Sih, Jonsson & Luikart

2000). Pressures on habitats are particularly severe in

coastal areas, as a consequence of the high concentration

of human activities (Airoldi & Beck 2007). In order to

better manage conflicts of interest, minimize negative

impacts and promote sustainable use of coastal environ-

ments, tools and information systems for marine spatial

planning are currently the focus of extensive efforts. One

area of particular interest for efficient planning is the

development of methods for coupling human pressures

and ecological effects, that is, for predicting the ecological

consequences of alternative policy and management

scenarios.

The complexity of ecosystems, however, makes predic-

tion of ecological responses to environmental change

extremely challenging, and scenario-based assessments are

still in their infancy (Coreau et al. 2009; Elith & Leath-

wick 2009). Scenarios are sets of expectations about plau-

sible futures, which aim to explore the range of potential

outcomes starting from a known situation, to aid in man-

agement planning (Bennett et al. 2003). Scenario analysis

is often perceived only as a qualitative description of pos-

sible futures, given a set of assumptions of driving forces

or extrinsic stressors. However, using scenarios in combi-

nation with predictive ecological models may be a way of

producing quantitative estimates of the span of potential

future outcomes, encompassing different sources of uncer-

tainty (Coreau et al. 2009).

For ecology to grow into a fully integrated part of deci-

sion-making in society, taking these difficult steps towards

a quantitative and predictive discipline is crucial (Pereira

et al. 2010). One branch that has emerged within predic-

tive ecology is species distribution modelling, in which

species–environment relationships are described statisti-

cally and used to make spatial predictions of species dis-

tributions across space and time (Elith & Leathwick

2009). Species distribution models (SDMs) have the

potential to be used as tools for exploring management

scenarios relating to the conservation of species and habi-

tats (Robinson et al. 2011). So far, SDMs have primarily

been used in large-scale studies of climate change effects

(e.g. Keith et al. 2008; Ara�ujo et al. 2011), while their

potential for exploring management options relating to

human pressures that can be managed at a regional scale

is largely unknown. The basis of the method is that by

including a measure of the human pressure(s) of interest

as predictor variable(s) in the model, the effects of

changes in the pressure may be explored. Adding the

spatial component of the SDM into a scenario modelling

approach greatly enhances the information that can be

gained from relating a change in a pressure to changes in

the species of interest, when compared to a purely correlative

approach. The ability to define spatially explicit changes

allows for a more locally relevant and quantitative assess-

ment of expected change, providing answers to the ques-

tions of how much change and where. For this approach

to be successful, the pressure variable should have a

direct, mechanistic effect on the modelled response vari-

able. Further, the future level and distribution of the pres-

sure variable expected under the scenario being evaluated

is needed in order to make spatial predictions of the

modelled species.

In the current study, we set out to test the utility of

SDMs for studying scenarios relating to eutrophication

mitigation measures in the Baltic Sea. Eutrophication, due

to excessive phosphorus and nitrogen loads, is one of the

most serious threats to coastal ecosystems worldwide

(Cloern 2001), including the semi-enclosed Baltic Sea

(Korpinen et al. 2012). The high nutrient concentrations

give rise to excessive growth of planktonic and filamen-

tous algae, which leads to shading, smothering and

reduced recruitment success of perennial macrophytes in

the shallow sublittoral (Berger et al. 2004; Krause-Jensen

et al. 2008), as well as oxygen deficiency and habitat deg-

radation with severe negative effects on ecosystem func-

tioning and the goods and services provided by the sea

(R€onnberg & Bonsdorff 2004; Diaz & Rosenberg 2008).

These problems, in combination with the requirements of

the EU Marine Strategy Framework Directive and the

Water Framework Directive, have led the member states

of Helsinki Commission (HELCOM, the executive body

of the Convention on the Protection of the Marine Envi-

ronment of the Baltic Sea Area) to take action against

eutrophication in the Baltic Sea Action Plan (BSAP). This

action plan was adopted in 2007 by all countries sur-

rounding the Baltic Sea (HELCOM 2007a). It has set up

a number of environmental objectives for 2021, relating

to eutrophication, hazardous substances, maritime activi-

ties and biodiversity conservation (Backer et al. 2010).

The main indicator for eutrophication is water transpar-

ency measured as mean summer Secchi disc depth

(HELCOM 2007a). The Secchi disc method (Preisendorfer

1986) is a simple measure of the amount of phytoplank-

ton in the water column, and long time-series are avail-

able. While phytoplankton only accounts for 17–40% of

light attenuation in the study area, there is a strong rela-

tionship between light attenuation and the chlorophyll a

concentration in the water column, indicating that Secchi

depth is a good indicator of eutrophication status

(Fleming-Lehtinen & Laamanen 2012). Specific target and

reference levels for water transparency have been set for

the different basins of the Baltic Sea. The reference levels

are based on historical data, while the target levels are set

to 25% lower transparency than the reference level.

For the Baltic Sea region, the countrywise nutrient

reductions required to reach these goals, as well as the

necessary measures, have been defined (HELCOM 2007b;

Wulff et al. 2007). Costs associated with eutrophication

mitigation are high, and therefore, it is important not

© 2013 The Authors. Journal of Applied Ecology © 2013 British Ecological Society, Journal of Applied Ecology

2 U. Bergstr€om et al.

only to consider the cost-efficiency of different actions

(Elofsson 2010), but also to study more in detail how eco-

system components and functions may respond to a

decrease in eutrophication. So far, however, the potential

effects of the agreed reduction in nutrient loads on the

ecosystems and associated goods and services have only

been afforded a cursory evaluation. Qualitative studies

indicate that most provisioning, regulating and cultural

services of coastal habitats would increase with an

improved eutrophication situation (R€onnb€ack et al. 2007;

BalticSTERN 2013; Ahtiainen & Vanhatalo 2012). How-

ever, detailed studies, entailing quantitative measures of

how species distributions and ecosystem services may

change with a decrease in eutrophication, are lacking.

In this paper, we show how SDMs can be used for

exploring the effects of alternative management measures

on the distribution of habitats. We utilize a simple sce-

nario approach, where we account for different sources of

uncertainty. Specifically, we use an ensemble of species

distribution models for studying the consequences of

eutrophication mitigation on the distribution of key

coastal macrophyte and fish species in a 40 000-km2

archipelago area in the northern Baltic Sea. The angio-

sperm eelgrass Zostera marina L. and the brown alga

bladderwrack Fucus vesiculosus L. are both considered to

be sensitive to eutrophication and changes in water trans-

parency (e.g. Berger et al. 2004; Krause-Jensen et al.

2008), and as such, they are used as important indicators

for monitoring environmental status in most Baltic coun-

tries (B€ack et al. 2006; Schories, Pehlke & Selig 2009).

The fish species perch Perca fluviatilis L. and pikeperch

Sander lucioperca L., differ in their preferences for recruit-

ment areas with respect to water transparency, with perch

preferring clear water and pikeperch turbid waters

(Sandstr€om & Kar�as 2002; Ljunggren & Sandstr€om 2007;

Veneranta et al. 2011). Based on this knowledge of eutro-

phication responses of the study species, we hypothesized

that the distribution of eelgrass and bladderwrack and

recruitment areas of perch would increase with a higher

water transparency, while recruitment areas of pikeperch

would decrease.

Materials and methods

STUDY AREA AND SPECIES

The study area is located in the northern Baltic Sea, covering

the extensive archipelago between Sweden and Finland (Fig. 1).

The 40 000-km2 area is characterized by high topographic com-

plexity, this being the world’s largest archipelago by the number

of islands. The convoluted shoreline and mosaic of islands create

strong small-scale environmental gradients and patchy habitat

distributions. The salinity range is 4–7 psu, and the area encom-

passes a wide range of wave exposure conditions, from enclosed

bays in the innermost parts of the archipelagos to open sea.

The Baltic Sea is a young brackish-water sea (approximately

4000 years in its current state) characterized by low diversity

and a mix of marine and freshwater organisms, with a few dom-

inant species found in high abundances (Bonsdorff & Blomqvist

1993).

The main canopy-forming alga on hard substrates in the north-

ern Baltic Sea is bladderwrack, while soft substrates are inhabited

by numerous species of angiosperms of freshwater origin and a

few marine ones, most importantly eelgrass. Bladderwrack and

eelgrass are both key habitat-forming species in the area; by pro-

viding complex structures to their respective habitats, they consti-

tute the basis for diverse and highly productive coastal biotopes

(Bostr€om et al. 2006; Wikstr€om & Kautsky 2007). The demersal

fish community of the area is dominated by freshwater percids

and cyprinids together with whitefish and marine flatfishes. Two

of the dominating fish species are the large predators perch and

pikeperch, which are also among the most important species for

both recreational and commercial fisheries (Lehtonen, Hansson &

Winkler 1996; �Adjers et al. 2006). Both species have specific

habitat requirements for their recruitment, selecting shallow, shel-

tered bays that warm up early in spring (Lehtonen, Hansson &

Winkler 1996; Snickars et al. 2010). One important aspect of the

ecology of these two species is that they are both sensitive to the

Fig. 1. Map of the study area in the

northern Baltic Sea. Sampling stations for

bladderwrack, eelgrass, perch and pike-

perch are shown on a background of cur-

rent mean summer Secchi depth.

© 2013 The Authors. Journal of Applied Ecology © 2013 British Ecological Society, Journal of Applied Ecology

Evaluating eutrophication management scenarios 3

level of water transparency, perch preferring clear waters and

pikeperch turbid waters (see Introduction).

DATA SETS

Field data on the occurrence, that is, presence and absence records,

of the study species (Fig. 1) were collated from a number of sources

(see Appendix S1, Supporting information). Bladderwrack (1139

records, including 333 presences) and eelgrass (1340 records,

including 151 presences) were compiled from drop-video, ROV,

snorkelling and scuba-dive surveys performed in 2004–2009. The

data sets on fish recruitment areas came from studies performed

in 2003–2009, using visual transect survey by snorkelling for

perch egg strands (4039 records, including 302 presences) and

small underwater detonations for young-of-the-year pikeperch

(570 records, including 33 presences).

Three environmental variables with effects on species distribu-

tions were used as predictors in the modelling – water depth,

wave exposure and Secchi depth (Eriksson & Bergstr€om 2005;

Sundblad, Bergstr€om & Sandstr€om 2011). All environmental vari-

able rasters had a grid cell size of 25 m. The water depth raster

was interpolated using depth data available from official sea

charts. The wave exposure raster, log-transformed to normalize

distributions, was calculated with the Wave Impact software

(Isæus 2004), which combines fetch calculations with wind condi-

tions and also accounts for refraction and diffraction effects. A

raster of Secchi depth, the pressure variable in the scenarios, was

produced using a generalized additive model (GAM) based on

monitoring and inventory data from 2000–2008 (see Appendix

S2, Supporting information). The resulting raster showed mean

summer (months 5–9) water transparency, as this time period is

used as an indicator for eutrophication in the BSAP. The model

explained 85% of the variation and evaluation against withheld

data resulted in an r2 of 0�79 and a root mean square error of

0�69 m. Secchi depth rasters for each scenario were produced by

recalculating the current Secchi depth values, multiplying each

grid cell in the raster according to the percentage change expected

under the scenarios described below.

SCENARIOS

The effects of changes in water transparency on the predicted

spatial distribution of the study species were explored by adjust-

ing the Secchi depth according to a set of scenarios. We specified

three scenarios relating to changes in water transparency, based

on the BSAP target and reference levels as well as the current

trend of Secchi depth in the study area and surrounding waters.

Secchi depth monitoring data show that in offshore areas of the

Baltic Proper and the Bothnian Sea, water transparency contin-

ues to decrease (HELCOM 2009). Long-term data from the study

area collated in this study and in HELCOM (2012) show

decreases or no trends, while in areas close to local eutrophica-

tion sources such as Stockholm even increases in Secchi depth

have been observed (Karlsson et al. 2010).

The scenarios were defined based on the difference between the

current level of water transparency and the BSAP target and refer-

ence levels (HELCOM 2007a), according to the following: (1)

Return to BSAP reference level: according to the BSAP, the refer-

ence level for water transparency is 9�3 m and the current level is

6�3 m in the Baltic Proper. A return to the reference level corre-

sponds to a 48% increase in Secchi depth. (2) Return to BSAP

target level: for the Baltic Proper, a return to the BSAP target is

defined as a 25% deviation from the reference level. This corre-

sponds to an 11% increase in water transparency compared to the

current situation. (3) Business-as-usual: although not fully consis-

tent, the general trend in available data suggests that the Secchi

depth is still decreasing at a rate that corresponds to approximately

a 10% decrease until 2021, for which the BSAP targets are set. The

business-as-usual scenario is therefore set to �10%.

The three scenarios of this study only involve changes in Secchi

depth, all other variables being constant. Thus, potential changes

in other pressure variables relating to, for example, climate

change and physical habitat loss are not considered in the study.

To be able to make predictions for the different scenarios, the

Secchi depth map was changed according to the levels specified

above. To account for the uncertain effects of the ongoing eutro-

phication mitigation actions and to increase interpretability of the

results, predictions for three more levels of change in Secchi

depth were calculated: increases by 20, 30 and 40%. Making pre-

dictions for a total of seven different levels of Secchi depth

enabled us to produce curves that are helpful in taking the uncer-

tainty of future water transparency into account, by displaying

patterns of species distributions in relation to Secchi depth.

MODELLING AND SPATIAL PREDICTIONS

Species–environment relationships were modelled using three con-

ceptually different techniques, in order to minimize methodologi-

cal errors (Ara�ujo & New 2007). The methods employed were

maximum entropy (Maxent), GAM and random forest (RF)

modelling. These three methods have been shown to perform

favourably compared to other novel methods for spatial predic-

tion (Elith et al. 2006; Knudby, Brenning & LeDrew 2010). Max-

ent is a machine learning method for presence-only data. Instead

of using absence data, Maxent uses the principle of maximum

entropy to contrast presence records to the environmental back-

ground represented by the predictor grids (Phillips, Anderson &

Schapire 2006; Elith et al. 2011). The models were run using the

MAXENT software (version 3.3.3a; available at: http://www.cs.

princeton.edu/~schapire/maxent/). All background environmental

layers were limited to the maximum sampling depth to avoid a

spatial bias in the random background data drawn from the grids

(Phillips et al. 2009). This restriction was set at a depth of 6 m

for the fish recruitment models and 10 m for the vegetation mod-

els. The regularization parameter was set to 2 in all models to

make the responses smooth and ecologically interpretable.

Generalized additive models are semi-parametric extensions of

generalized linear models, useful for fitting nonlinear relation-

ships without prior assumptions on the shape of the response.

GAMs with binomial error distribution were run on presence/

absence data using the ‘mgcv’ package for R (Wood 2006).

Model selection was based on penalized regression splines with

gamma values of 1�4 and a maximum of two degrees of freedom

for the predictor variables in order to maintain ecologically inter-

pretable models (Wood & Augustin 2002; Sandman et al. 2008).

Relative variable importance was measured based on chi-square

values from the summary command.

Random forest is an ensemble method where a large number

of decision trees are built and responses are predicted based on

majority rule (for presence/absence data) from all trees (Breiman

2001; Cutler et al. 2007). In comparison with traditional classifi-

cation trees, the main advantages are that RF produces more

© 2013 The Authors. Journal of Applied Ecology © 2013 British Ecological Society, Journal of Applied Ecology

4 U. Bergstr€om et al.

accurate predictions and is easier to use as it requires no pruning.

For modelling and predicting presence/absence, we used the

package ‘randomForest’ for R (Breiman 2001). We developed

models with 1000 classification trees each. Exploratory graphs

indicated that error rates became stable well before this number

of trees was developed. Variable importance was measured using

the mean decrease in accuracy when permuting the predictor’s

values over the data set (%IncMSE). To ease interpretation of

the partial response curves, we applied a smooth spline function

with 5 degrees of freedom on the predicted values.

The GAM and Maxent model were validated using 10-fold

cross-validation and the area-under-curve value (AUC) of recei-

ver-operating characteristic plots (Fielding & Bell 1997). For each

fold in a cross-validation procedure, a subset of the data is with-

held during model building and used as a test set. This has the

advantage of utilizing all available data for both model building

and validation. AUC values stretch between 0�5 and 1, where mod-

els with values above 0�7 have a reasonable discriminatory ability

and models with AUC values above 0�9 are considered excellent

(Maggini et al. 2006). For RF, models were validated using the

bootstrapped out-of-bag error estimate (OOB), which is estimated

internally in the model runs. OOB is conceptually similar to cross-

validation as the sample is split into a training set and a validation

set in the construction of each tree, although OOB values are given

as percentage error rates.

Extrapolation of models outside the range of observations used

for model specification was necessary to some extent for the sce-

nario predictions. The assumptions made on the behaviour of the

species–environment relationship outside the bounds set by the

observations may potentially have a large influence on the predic-

tions (Sundblad et al. 2009; Elith, Kearney & Phillips 2010). To

minimize the risk of unrealistic extrapolation, we adopted a con-

servative approach of extrapolation, where the response to each

environmental variable was held constant outside the range of

the training data.

For the scenarios, we predicted the distributions in geographi-

cal space for the four species and three modelling methods at

the seven levels of Secchi depth. Each model prediction resulted

in a species-specific map with a probability of occurrence for

each raster cell. The predictions were dichotomized into suitable

and unsuitable habitats, by cutting at the probability that maxi-

mized both sensitivity and specificity (Jim�enez-Valverde & Lobo

2007). The map predictions were restricted to the maximum

water depth of potential occurrence of the species, which was

6 m for the fish and 10 m for vegetation. The total areal cover

of each species (measured by the number of cells in the raster

predicted as presences) was computed for each combination of

modelling method and Secchi depth level. To combine the fore-

casts of the three methods, we calculated the mean of the

predicted areal extents, as this is a robust consensus method

(Marmion et al. 2009).

Results

The resulting distribution models for bladderwrack, eel-

grass, perch and pikeperch had a moderate to high level

of precision and stability (Table 1), implying that they

had an adequate discriminatory ability and predictive per-

formance (Maggini et al. 2006). The models for perch and

pikeperch performed better than those for eelgrass and

bladderwrack. Bladderwrack showed a positive response

to Secchi depth, while the partial response of eelgrass

differed from negative to positive, depending on model-

ling method. Perch had a positive response to Secchi

depth, while pikeperch responded negatively (Fig. 2).

Thus, all species except eelgrass responded as hypothe-

sized. A comparison of the contribution of the three pre-

dictor variables to the explained variation of the models

indicated that the influence of the three predictor vari-

ables varied between species and models (Table 2). For

pikeperch, Secchi depth consistently was the most impor-

tant predictor among methods. For bladderwrack and

perch, Secchi depth was on average the second most

important variable, while for eelgrass this predictor vari-

able contributed least to the explained variation.

In the scenario analyses, the predicted responses to

changes in eutrophication were quite different among the

study species. Based on the ensemble of SDMs, the distri-

bution area of bladderwrack and perch increased with Sec-

chi depth, while eelgrass did not show a marked response to

changes in water transparency. The distribution area of

pikeperch was predicted to decrease (Fig. 3). For pike-

perch, the different modelling methods gave highly consis-

tent results, while for the other species the estimated change

in distributions varied between methods.

The business-as-usual scenario, involving a 10% decrease

in Secchi depth, predicted changes in species distributions

ranging from a 16 � 1�5% (mean � SE) increase for pike-

perch to a 44 � 13% decrease for perch, with decreases

also for bladderwrack and eelgrass. An increase in Secchi

depth to the BSAP target level (+11%) again predicted

stronger average responses in the fish, with a decrease in

pikeperch recruitment areas by 15 � 1�0% and an increase

in perch recruitment areas by 46 � 20%. The distribution

area of bladderwrack increased by 14 � 6�0% in the BSAP

target scenario, while for eelgrass there was a large discrep-

ancy between the methods and no clear trend. The

responses in species distributions showed only weak signs

of levelling off towards the BSAP reference-level scenario,

indicating that larger increases in water transparency than

the BSAP target would continue to change species distribu-

tions more or less linearly. For the two fish species and

bladderwrack, reaching the BSAP reference level of Secchi

depth, corresponding to an increase by 48%, is predicted to

change distribution areas by 52–215%.

Table 1. Performance of bladderwrack, eelgrass, perch and pike-

perch distribution models. For maximum entropy models (Max-

ent) and generalized additive models (GAM), performance is

measured as cross-validated area-under-curve (cvAUC�SE) and

for random forest models (RF) as out-of-bag error rate (OOB)

Maxent

(cvAUC)

GAM

(cvAUC)

RF

(OOB; %)

Bladderwrack 0�84 � 0�01 0�86 � 0�01 25

Eelgrass 0�85 � 0�02 0�74 � 0�03 12

Perch 0�98 � 0�01 0�85 � 0�01 2�8Pikeperch 0�96 � 0�01 0�94 � 0�01 6�1

© 2013 The Authors. Journal of Applied Ecology © 2013 British Ecological Society, Journal of Applied Ecology

Evaluating eutrophication management scenarios 5

Discussion

By utilizing water transparency as a predictor in the

models, we show how SDM can be used to predict

changes in the distribution of key species in the Baltic

Sea as a response to changes in eutrophication status.

The analyses suggest that we can expect notable effects

on the distribution of species if the Baltic Sea Action

Plan targets are reached, and that there will be large

differences in response among species. A lowered nutrient

load, accompanied by an increasing water transparency,

is predicted to cause an increase in the areal cover of

bladderwrack, while eelgrass would remain largely unaf-

fected. For fish, recruitment areas of perch are predicted

to increase strongly, while recruitment areas of pikeperch

would likely decrease. It is important to note that a

change in the availability of recruitment areas for fish is

not necessarily manifested in a change in stock sizes, if

2·5 3·0 3·5 4·0 4·5 5·0 5·5 6·00 2 4 6 8 100

0·2

0·4

0·6

0·8

1

0 2 4 6 8 10

Prob

abili

ty o

f bla

dder

wra

ckpr

esen

cePr

obab

ility

of e

elgr

ass

pres

ence

Prob

abili

ty o

f per

ch p

rese

nce

Prob

abili

ty o

f pik

eper

ch p

rese

nce

MaxentGAMRF

2·5 3·0 3·5 4·0 4·5 5·0 5·5 6·00 2 4 6 8 100

0·2

0·4

0·6

0·8

1

0 2 4 6 8 10

2·5 3·0 3·5 4·0 4·5 5·0 5·5 6·00 2 4 60

0·2

0·4

0·6

0·8

1

0 2 4 6 8 10

2·5 3·0 3·5 4·0 4·5 5·0 5·5 6·0

Wave exposure log (m2s–1)

0 2 4 6Depth (m)

0

0·2

0·4

0·6

0·8

1

0 2 4 6 8 10Secchi depth (m)

Fig. 2. Partial responses to Secchi depth, water depth and wave exposure for the three different modelling methods for bladderwrack,

eelgrass, perch and pikeperch.

© 2013 The Authors. Journal of Applied Ecology © 2013 British Ecological Society, Journal of Applied Ecology

6 U. Bergstr€om et al.

recruitment areas are not limiting population growth

(Levin & Stunz 2005). For these two species, however, there

is a strong positive relationship between the availability of

recruitment areas and the size of the adult stocks (G. Sund-

blad , U. Bergstr€om, A. Sandstr€om , P. Ekl€ov, unpublished

data), suggesting that the predicted changes would have sig-

nificant effects on population sizes.

All four of the species studied are vital for the function-

ing of the coastal ecosystem of the northern Baltic Sea,

providing important provisioning, regulating and cultural

services. Canopy-forming vegetation provides the basis of

diverse habitats important for zoobenthic and fish pro-

duction, wave energy absorption and aesthetic values

(Bostr€om, Baden & Krause-Jensen 2003; R€onnb€ack et al.

2007), and changes in their distribution may widely affect

the services provided by the system. Perch and pikeperch

generate a suite of goods and services, for example food,

recreation and trophic control, and are among the most

valued species for both recreational and commercial fish-

eries in the area (FGFRI 2009; Th€ornqvist 2009; ICES

catch statistics). Being the most abundant large predatory

fish at the coast, they are also important regulators of

mesopredatory fish, with cascading effects in both algal

and eelgrass habitats (Eriksson et al. 2009; Baden et al.

2010). The scenarios predict extensive changes in the dis-

tribution of all these species, except eelgrass, as a response

to an improved eutrophication situation, suggesting that

there may be large modifications in ecosystem services

provided by the coastal system should the politically

agreed nutrient reductions in the BSAP come into effect.

Notably, the changes in species distributions are both

positive and negative depending on species, indicating that

the effects on ecosystem services are multifaceted and

require detailed analyses to be quantified. To this end,

SDMs in combination with scenario analysis as applied

here can constitute a good basis for evaluation of the

effects of management measures on ecosystem services.

The reliability of such scenario analyses depends upon

a number of assumptions that have the potential to

change not only the quantitative estimates of the pre-

dicted changes, but in severe cases also the qualitative

predictions. To validate the scenario predictions qualita-

tively, they may be compared to mechanistic studies and,

in cases where management actions are expected to reduce

the levels of a human pressure, also to historical data

(Elith & Leathwick 2009). As eutrophication in the Baltic

Sea has increased steadily during the last decades

(HELCOM 2009), there are, to some extent, old data

available that correspond to the scenarios of eutrophica-

tion mitigation that have been modelled in this study. For

the macrophytes, mechanistic studies suggest that both

bladderwrack and eelgrass may gain from increases in

water transparency (Berger et al. 2004; Krause-Jensen

et al. 2008), which agrees with the model predictions for

bladderwrack, while for eelgrass the models predict no

change in distribution. Historical data from the study area

show that bladderwrack declined substantially between

Table 2. Contribution (%) of each predictor variable in bladderwrack, eelgrass, perch and pikeperch distribution models

Species

Secchi depth Depth Wave exposure

GAM Maxent RF GAM Maxent RF GAM Maxent RF

Bladderwrack 49 11 32 24 88 34 27 2 35

Eelgrass 0 1 30 7 40 29 93 58 41

Perch 50 8 34 32 22 34 18 71 32

Pikeperch 67 62 57 24 20 21 10 18 22

–50

0

50

100

% C

hang

e in

dis

tribu

tion

area

Eelgrass±SEBladderwrack±SE

–100

–50

0

50

100

150

200

250

300

–10 Current 11 20 30 40 48

% C

hang

e in

dis

tribu

tion

area

% Change in water transparency

Pikeperch±SEPerch±SE

BSAP ref.BSAP targetBAU

Fig. 3. Predicted effects on the distribution of bladderwrack,

eelgrass and recruitment areas of perch and pikeperch as a response

to changes in water transparency according to a set of eutrophica-

tion scenarios. Curves show percentage change in areal cover with

changes in Secchi depth, where numbers on x-axis denote % devia-

tion from current Secchi depth level. Dotted lines show standard

errors of predictions from three separate modelling methods. The

arrows indicate, from left to right, the Secchi depth changes

according to the scenarios business-as-usual (BAU), Baltic Sea

Action Plan (BSAP) target and reference levels.

© 2013 The Authors. Journal of Applied Ecology © 2013 British Ecological Society, Journal of Applied Ecology

Evaluating eutrophication management scenarios 7

the 1940s and the 1980s (Kangas et al. 1982; Kautsky

et al. 1986). For eelgrass, available long-term studies from

the northern Baltic Sea suggest no declines in the distribu-

tion in Finland (Bostr€om et al. 2002) and Estonia (M€oller

& Martin 2007). In the study region, eelgrass distribution

appears to be influenced by a trade-off between light

availability and physical exposure. In contrast to marine

coastal regions, eelgrass in our study area thrives on

exposed sandy sea floor in the outer archipelago areas

with relatively good water transparency (Bostr€om et al.

2006). However, with increasing Secchi depth, exposure

values peak beyond the suitable range for eelgrass, and

the subsequent response in areal distribution remains

weak. The lack of response in eelgrass can also be

explained by other factors, such as stochastic events, com-

petition with freshwater plants in sheltered areas and

availability of suitable sandy substrates, may be more

important in the northern Baltic Sea (Krause-Jensen et al.

2008). Thus, for bladderwrack, the results of our scenario

analyses agree well with current knowledge, while for

eelgrass mechanistic studies and historical data from the

study area are somewhat contradictory. These results

illustrate the potential utility of SDMs for exploring how

the distribution of species suggested as indicators within

the Water Framework Directive and the Marine Strategy

Framework Directive responds to changes in specific

pressures.

The responses of perch and pikeperch are consistent

with knowledge about water transparency preferences for

early life stages of the two species. Perch recruits are

known to gain from a high water transparency, while

young pikeperch is adapted to turbid waters (Sandstr€om

& Kar�as 2002; Ljunggren & Sandstr€om 2007). When com-

paring to historical data, based on commercial catch sta-

tistics and coastal fish monitoring, the picture becomes

more complex. In the commercial fishery, pikeperch

catches increased dramatically from the 1950s to the

1980s, that is, at the onset of eutrophication, while perch

catches decreased several-fold during the same period in

the northern Baltic Sea. These changes probably reflect

the increase in eutrophication status and turbidity (Lehto-

nen 1985; Hansson & Rudstam 1990; Swedish official

catch statistics), in line with our predictions. On the other

hand, fish monitoring data, which are available from the

1980s onwards, point at increases in perch populations in

the study area during the last decades, while pikeperch

populations have been stable or even decreasing. The

development in the last decades is probably the result of

interacting effects of climate change, eutrophication and

fishing (HELCOM 2012; Olsson, Bergstr€om & G�ardmark

2012, N. Mustam€aki , K. �Adjers , U. Bergstr€om, J. Mattila,

unpublished). These dynamics illustrate the complex

nature of population regulation in fish stocks and show

that SDMs incorporating only one pressure variable may

not be successful in predicting the dynamics of popula-

tions. In this study, however, we wanted to test the

hypothesized effects of the politically agreed eutrophica-

tion targets in isolation from other potential changes in

the environment, and as such, the scenario analyses are

still valuable.

Species distribution modelling, and predictions of future

distributions in particular, may be affected by uncertainty

stemming from both the modelling process and the specifi-

cation of scenarios (Coreau et al. 2009; Elith & Leathwick

2009). Quantifying and communicating these uncertainties

is a central part in scenario analysis, as this information is

vital for politicians or managers using the scenarios in deci-

sion-making. For the Baltic Sea, it is difficult to predict

how soon nutrient reduction measures will translate into

alleviated eutrophication symptoms of the Baltic Sea,

including changes in water transparency, and how much

change we can expect, especially as climate-induced changes

will likely counteract these measures (Meier, Eilola &

Almroth 2011). To accommodate for this source of uncer-

tainty, we made predictions of species distributions across a

range of Secchi depths, not only those specified by the sce-

narios. Thereby, we could graphically illustrate how species

respond to Secchi depth. This approach is helpful in the

interpretation of results in cases where the magnitude of

change in the pressure variable is uncertain. These graphs

show that the response to water transparency changes is

predicted to be fairly linear, not levelling off until Secchi

depth approaches the BSAP reference level. This is true also

for a situation with no action taken. If no nutrient reduc-

tions are achieved and Secchi depth continues to decrease,

then a close to linear negative effect can be anticipated in

bladderwrack and perch, and a positive one in pikeperch.

The lack of thresholds and other complex responses sug-

gests that the qualitative conclusions from the analyses are

robust and that the quantitative effects on habitat distribu-

tions will be nearly linearly related to how large improve-

ments in Secchi depth can be reached for these key species

of the northern Baltic Sea. Nevertheless, it is important to

bear in mind that changes in coastal ecosystems as a result

of nutrient reduction have been observed to be nonlinear

with respect to nutrient loads, water transparency and time.

These complex trajectories, which can probably be

explained by concurrent changes in several human pres-

sures (Duarte et al. 2009), are not captured in simplified

models taking into account only one of these pressures.

Apart from the specification of scenarios, uncertainty in

predictions may also arise from data deficiencies and from

the actual modelling process (Elith & Leathwick 2009).

Quantifying these methodological errors inherent in the

sampling and modelling procedure is crucial for measur-

ing and communicating the precision of the predictions to

stakeholders and policy makers. To increase the quantita-

tive predictive ability of the models, it is important to

minimize these sources of error, while to test the stability

of the predictions, sensitivity analyses need to be per-

formed. Sampling uncertainty, which comes from deficient

data sets due to, for example, small sample sizes or unrep-

resentative sampling, was estimated by cross-validation.

The models had a moderate to high level of precision and

© 2013 The Authors. Journal of Applied Ecology © 2013 British Ecological Society, Journal of Applied Ecology

8 U. Bergstr€om et al.

stability (cvAUC 0�74–0�98, OOB 2�8–25%), indicating

that the samples of species occurrence and the predictor

variables used for calibrating the models captured the

major patterns of species–environment relationships of the

complex coastal region. Model uncertainty, that is,

method-specific errors in the description of species–

environment relationships, was assessed by comparing

predictions of three conceptually different modelling tech-

niques. The performance of the methods was comparable,

all three producing useful models for the species. For all

species except pikeperch, there were, however, relatively

large differences in predicted changes in habitat cover

between the methods. In the perch, bladderwrack and eel-

grass models, the partial response to Secchi depth had a

similar shape but varied in importance between models.

Consequently, the span in the predictions between the

methods was large for these species when applied in geo-

graphical space. This can be contrasted to pikeperch, where

both response curve shape and variable contribution of

Secchi depth were comparable between methods, and the

three models produced very consistent estimates of habitat

distributions for the different scenarios. Our experience

thus suggests that relatively small differences in the shape

of the partial response curves, as well as their relative

importance (within method), may increase when they are

applied in a geographical, predictive, context. In line with

previous studies (Ara�ujo & New 2007; Marmion et al.

2009; Mateo et al. 2012), our results highlight the value of

applying an ensemble approach for minimizing model-

specific errors in predictions of species distributions.

Our results provide a good basis for more in-depth analyses

of the potential consequences of eutrophication mitigation

measures taken around the Baltic Sea. By coupling the

effects on the distribution of key species to ecosystem ser-

vices, it may be possible to generate spatially explicit cost-

benefit estimates (Sanchirico & Mumby 2009), which could

add detail to the coarse-scale analyses of the BSAP done so

far (BalticSTERN 2013). SDMs coupled with scenario

analysis thus have a large potential for use as decision

support tools in conservation planning, as they provide a

systematic way of comparing the effects of alternative

policy options and management measures on species and

habitats. This avenue could be highly attractive for manage-

ment, to help bridging the current gap between economy and

ecology in decision-making (R€onnb€ack et al. 2007; Carpen-

ter et al. 2009). Another application of SDMs and prediction

of future species distributions is in the design of marine pro-

tected area networks. By taking into account future changes

in species distributions as a response to an altered environ-

ment, networks may be designed to be resilient to environ-

mental change (Ara�ujo et al. 2004; Mumby et al. 2011). The

MPA network of the northern Baltic Sea is still deficient

(Sundblad, Bergstr€om & Sandstr€om 2011), and in the work

towards making it ecologically coherent, it would be benefi-

cial to incorporate forecasts of species distributions.

In conclusion, we expect that the effects of eutrophica-

tion mitigation on the Baltic Sea coastal ecosystem will be

pronounced, with species-specific responses to improve-

ments in water transparency leading to changes in ecosys-

tem functioning. The politically agreed environmental

targets of the BSAP will require large-scale and costly

actions in the near future, and taxpayers have the right to

know how they might be affected by the measures, as well

as how the alternative of not taking action would affect

them. The most direct experience of changes in the marine

ecosystem will relate to the highly populated coastal zone.

This study provides a step towards analysing the ecologi-

cal and economic consequences of the BSAP eutrophica-

tion objectives for the coastal ecosystem by providing

quantitative estimates of changes in species distributions

in relation to water transparency.

Acknowledgements

The study was performed within the project PREHAB (Spatial prediction

of benthic habitats in the Baltic Sea), financially supported from the Euro-

pean Community’s Seventh Framework Programme (FP/2007-2013) under

grant agreement n° 217246 made with the joint Baltic Sea research and

development programme. Finnish Environment Institute and Svealandsku-

stens kustvattenv�ardsf€orbund are acknowledged for providing water trans-

parency data, and the Nannut project, Mikael von Numers and Suvi

Kivilahti for species distribution data.

References

�Adjers, K., Appelberg, M., Eschbaum, R., Lappalainen, A., Minde, A.,

Repecka, R. & Thoresson, G. (2006) Trends in coastal fish stocks of the

Baltic Sea. Boreal Environment Research, 11, 13–25.Ahtiainen, H. & Vanhatalo, J. (2012) The value of reducing eutrophication

in European marine areas — a Bayesian meta-analysis. Ecological Eco-

nomics, 83, 1–10.Airoldi, L. & Beck, M.W. (2007) Loss, status and trends for coastal mar-

ine habitats of Europe. Oceanography and Marine Biology: An Annual

Review, 45, 345–405.Ara�ujo, M.B. & New, M. (2007) Ensemble forecasting of species distribu-

tions. Trends in Ecology & Evolution, 22, 42–47.Ara�ujo, M.B., Cabeza, M., Thuiller, W., Hannah, L. & Williams, P.H.

(2004) Would climate change drive species out of reserves? An assess-

ment of existing reserve-selection methods. Global Change Biology, 10,

1618–1626.Ara�ujo, M.B., Alagador, D., Cabeza, M., Nogu�es-Bravo, D. & Thuiller,

W. (2011) Climate change threatens European conservation areas. Ecol-

ogy Letters, 14, 484–492.B€ack, S., Kauppila, P., Kangas, P., Ruuskanen, A., Westberg, V., Perus,

J. & R€aike, A. (2006) A biological monitoring programme for the

coastal waters of Finland according to the EU Water Framework

Directive. Environmental Research, Engineering and Management, 4,

6–11.Backer, H., Lepp€anen, J.-M., Brusendorff, A.C., Forsius, K.,

Stankiewicz, M., Mehtonen, J., Pyh€al€a, M., Laamanen, M.,

Paulom€aki, H., Vlasov, N. & Haaranen, T. (2010) HELCOM Baltic

Sea Action Plan – a regional programme of measures for the marine

environment based on the Ecosystem Approach. Marine Pollution

Bulletin, 60, 642–649.BaltiSTERN 2013. The Baltic Sea – Our Common Treasure. Economics of

Saving the Sea. SwAM Report 2013:4, pp. 1–140. Swedish Agency for

Marine and Water Management, Gothenburg, Sweden.

Baden, S., Bostr€om, C., Tobiasson, S., Arponen, H. & Moksnes, P.-O.

(2010) Relative importance of trophic interactions and nutrient

enrichment in seagrass ecosystems: a broad-scale field experiment in

the Baltic-Skagerrak area. Limnology and Oceanography, 55,

1435–1448.Bennett, E.M., Carpenter, S.R., Peterson, G.D., Cumming, G.S., Zurek,

M. & Pingali, P. (2003) Why global scenarios need ecology. Frontiers in

Ecology and the Environment, 1, 322–329.

© 2013 The Authors. Journal of Applied Ecology © 2013 British Ecological Society, Journal of Applied Ecology

Evaluating eutrophication management scenarios 9

Berger, R., Bergstr€om, L., Gran�eli, E. & Kautsky, L. (2004) How does

eutrophication affect different life stages of Fucus vesiculosus in the Bal-

tic Sea? – a conceptual model. Hydrobiologia, 514, 243–248.Bonsdorff, E. & Blomqvist, E.M. (1993) Biotic couplings on shallow water

soft bottoms - examples from the northern Baltic Sea.. Oceanography

and Marine Biology: An Annual Review, 31, 153–176.Bostr€om, C., Baden, S. & Krause-Jensen, D. (2003) The seagrasses of

Scandinavia and the Baltic Sea. World Atlas of Seagrasses: Present Sta-

tus and Future Conservation (eds E.P. Green & F.T. Short), pp. 27–35.University of California Press, Berkeley.

Bostr€om, C., Bonsdorff, E., Kangas, P. & Norkko, A. (2002) Long-term

changes of a brackish-water eelgrass (Zostera marina L.) community

indicate effects of coastal eutrophication. Estuarine, Coastal and Shelf

Science, 55, 795–804.Bostr€om, C., O’Brien, K., Roos, C. & Ekebom, J. (2006) Environmental

variables explaining structural and functional diversity of seagrass mac-

rofauna in an archipelago landscape. Journal of Experimental Marine

Biology and Ecology, 335, 52–73.Breiman, L. (2001) Random forests. Machine Learning, 45, 5–32.Carpenter, S.R., Mooney, H.A., Agard, J., Capistrano, D., DeFries, R.S.,

D�ıaz, S., Dietz, T., Duraiappah, A.K., Oteng-Yeboah, A., Pereira,

H.M., Perrings, C., Reid, W.V., Sarukhan, J., Scholes, R.J. & Whyte,

A. (2009) Science for managing ecosystem services: Beyond the Millen-

nium Ecosystem Assessment. Proceedings of the National Academy of

Sciences, 106, 1305–1312.Cloern, J.E. (2001) Our evolving conceptual model of the coastal eutrophi-

cation problem. Marine Ecology Progress Series, 210, 223–253.Coreau, A., Pinay, G., Thompson, J.D., Cheptou, P.-O. & Mermet, L.

(2009) The rise of research on futures in ecology: rebalancing scenarios

and predictions. Ecology Letters, 12, 1277–1286.Cutler, D.R., Edwards, T.C., Beard, K.H., Cutler, A., Hess, K.T., Gibson,

J. & Lawler, J.J. (2007) Random forests for classification in ecology.

Ecology, 88, 2783–2792.Diaz, R.J. & Rosenberg, R. (2008) Spreading dead zones and conse-

quences for marine ecosystems. Science, 321, 926–929.Duarte, C., Conley, D., Carstensen, J. & S�anchez-Camacho, M. (2009)

Return to Neverland: Shifting baselines affect eutrophication restoration

targets. Estuaries and Coasts, 32, 29–36.Elith, J., Kearney, M. & Phillips, S. (2010) The art of modelling range-

shifting species. Methods in Ecology and Evolution, 1, 330–342.Elith, J. & Leathwick, J.R. (2009) Species distribution models: ecological

explanation and prediction across space and time. Annual Review of

Ecology, Evolution, and Systematics, 40, 677–697.Elith, J., H. Graham, C., P. Anderson, R., Dudik, M., Ferrier, S., Guisan,

A., J. Hijmans, R., Huettmann, F., R. Leathwick, J., Lehmann, A., Li,

J., G. Lohmann, L., A. Loiselle, B., Manion, G., Moritz, C., Nakam-

ura, M., Nakazawa, Y., McC. M. Overton, J., Townsend Peterson, A.,

J. Phillips, S., Richardson, K., Scachetti-Pereira, R., E. Schapire, R.,

Soberon, J., Williams, S., S. Wisz, M. & E. Zimmermann, N. (2006)

Novel methods improve prediction of species’ distributions from occur-

rence data. Ecography, 29, 129–151.Elith, J., Phillips, S.J., Hastie, T., Dud�ık, M., Chee, Y.E. & Yates, C.J.

(2011) A statistical explanation of MaxEnt for ecologists. Diversity and

Distributions, 17, 43–57.Elofsson, K. (2010) Cost-effectiveness of the Baltic Sea Action Plan.

Marine Policy, 34, 1043–1050.Eriksson, B.K. & Bergstr€om, L. (2005) Local distribution patterns of mac-

roalgae in relation to environmental variables in the northern Baltic

Proper. Estuarine, Coastal and Shelf Science, 62, 109–117.Eriksson, B.K., Ljunggren, L., Sandstr€om, A., Johansson, G., Mattila, J.,

Rubach, A., R�aberg, S. & Snickars, M. (2009) Declines in predatory fish

promote bloom-forming macroalgae. Ecological Applications, 19,

1975–1988.FGFRI (2009) Recreational fishing 2008. Official Statistics of Finland –

Agriculture, Forestry and Fishery. URL http://rktl.fi/English/statistics.

Fielding, A.H. & Bell, J.F. (1997) A review of methods for the assessment

of prediction errors in conservation presence/absence models. Environ-

mental Conservation, 24, 38–49.Fleming-Lehtinen, V. & Laamanen, M. (2012) Long-term changes in Secchi

depth and the role of phytoplankton in explaining light attenuation in the

Baltic Sea. Estuarine, Coastal and Shelf Science, 102–103, 1–10.Hansson, S. & Rudstam, L.G. (1990) Eutrophication and Baltic fish com-

munities. AMBIO, 19, 123–125.HELCOM (2007a) HELCOM Baltic Sea Action Plan (adopted by the

HELCOM Ministerial meeting, Krakow, Poland 15th November 2007).

HELCOM (2007b) Outcomes from the Expert Meetings of the HELCOM

Baltic Sea Action Plan, 2.1 Eutrophication Segment. An approach to

set country-wise nutrient reduction allocations to reach good ecological

status of the Baltic Sea. HELCOM HOD 22/2007. Helsinki, HELCOM.

HELCOM (2009) Eutrophication in the Baltic Sea – an integrated the-

matic assessment of the effects of nutrient enrichment in the Baltic Sea

region. Baltic Sea Environment Proceedings, 115B. 1–148. Helsinki Com-

mission, Helsinki, Finland.

HELCOM (2012) Indicator based assessment of coastal fish community

status in the Baltic Sea 2005–2009. Baltic Sea Environment Proceedings,

131. 1–148. Helsinki Commission, Helsinki, Finland.

Isæus, M. (2004) Factors structuring Fucus communities at open and com-

plex coastlines in the Baltic Sea. PhD thesis, Stockholm University.

Jim�enez-Valverde, A. & Lobo, J.M. (2007) Threshold criteria for conver-

sion of probability of species presence to either-or presence-absence.

Acta Oecologia, 31, 361–369.Kangas, P., Autio, H., H€allfors, G., Luther, H., Niemi, �A. & Salemaa, H.

(1982) A general model of the decline of Fucus vesiculosus at Tv€armin-

ne, south coast of Finland in 1977–81. Acta Botanica Fennica, 118,

1–27.Karlsson, O., Jonsson, P., Lindgren, D., Malmaeus, J. & Stehn, A. (2010)

Indications of recovery from hypoxia in the Inner Stockholm Archipel-

ago. AMBIO: A Journal of the Human Environment, 39, 486–495.Kautsky, N., Kautsky, H., Kautsky, U. & Waern, M. (1986) Decreased

depth penetration of Fucus vesiculosus (L.) since the 1940’s indicates

eutrophication of the Baltic Sea. Marine Ecology Progress Series, 28,

1–8.Keith, D.A., Akc�akaya, H.R., Thuiller, W., Midgley, G.F., Pearson, R.G.,

Phillips, S.J., Regan, H.M., Ara�ujo, M.B. & Rebelo, T.G. (2008) Pre-

dicting extinction risks under climate change: coupling stochastic popu-

lation models with dynamic bioclimatic habitat models. Biology Letters,

4, 560–563.Knudby, A., Brenning, A. & LeDrew, E. (2010) New approaches to mod-

elling fish–habitat relationships. Ecological Modelling, 221, 503–511.Korpinen, S., Meski, L., Andersen, J.H. & Laamanen, M. (2012) Human

pressures and their potential impact on the Baltic Sea ecosystem.

Ecological Indicators, 15, 105–114.Krause-Jensen, D., Sagert, S., Schubert, H. & Bostr€om, C. (2008) Empiri-

cal relationships linking distribution and abundance of marine vegeta-

tion to eutrophication. Ecological Indicators, 8, 515–529.Lehtonen, H. (1985) Changes in commercially important freshwater fish

stocks in the Gulf of Finland during recent decades. Finnish Fisheries

Research, 6, 61–70.Lehtonen, H., Hansson, S. & Winkler, H. (1996) Biology and exploitation

of pikeperch, Stizostedion lucioperca (L.), in the Baltic Sea area. Annales

Zologici Fennici, 33, 525–535.Levin, P.S. & Stunz, G.W. (2005) Habitat triage for exploited fishes: Can

we identify essential “Essential Fish Habitat?”. Estuarine, Coastal and

Shelf Science, 64, 70–78.Ljunggren, L. & Sandstr€om, A. (2007) Influence of visual conditions on

foraging and growth of juvenile fishes with dissimilar sensory physiol-

ogy. Journal of Fish Biology, 70, 1319–1334.Maggini, R., Lehmann, A., Zimmerman, N.E. & Guisan, A. (2006)

Improving generalized regression analysis for the spatial prediction of

forest communities. Journal of Biogeography, 33, 1729–1749.Marmion, M., Parviainen, M., Luoto, M., Heikkinen, R.K. & Thuiller,

W. (2009) Evaluation of consensus methods in predictive species distri-

bution modelling. Diversity and Distributions, 15, 59–69.Mateo, R.G., Felicisimo, A.M., Pottier, J., Guisan, A. & Munoz, J. (2012)

Do stacked species distribution models reflect altitudinal diversity pat-

terns? PLoS ONE, 7, e32586.

Meier, H.E.M., Eilola, K. & Almroth, E. (2011) Climate-related changes

in marine ecosystems simulated with a 3-dimensional coupled physi-

cal-biogeochemical model of the Baltic Sea. Climate Research, 48,

31–55.M€oller, T. & Martin, G. (2007) Distribution of the eelgrass Zostera marina

L. in the coastal waters of Estonia, NE Baltic Sea. Proceedings of the

Estonian Academy of Sciences. Biology, Ecology, 56, 270–277.Mumby, P.J., Elliott, I.A., Eakin, C.M., Skirving, W., Paris, C.B.,

Edwards, H.J., Enr�ıquez, S., Iglesias-Prieto, R., Cherubin, L.M. &

Stevens, J.R. (2011) Reserve design for uncertain responses of coral

reefs to climate change. Ecology Letters, 14, 132–140.Olsson, J., Bergstr€om, L. & G�ardmark, A. (2012) Abiotic drivers of

coastal fish community change during four decades in the Baltic Sea.

ICES Journal of Marine Science, 69, 961–970.

© 2013 The Authors. Journal of Applied Ecology © 2013 British Ecological Society, Journal of Applied Ecology

10 U. Bergstr€om et al.

Pereira, H.M., Leadley, P.W., Proenc�a, V., Alkemade, R., Scharlemann,

J.P.W., Fernandez-Manjarr�es, J.F., Ara�ujo, M.B., Balvanera, P., Biggs,

R., Cheung, W.W.L., Chini, L., Cooper, H.D., Gilman, E.L., Gu�enette,

S., Hurtt, G.C., Huntington, H.P., Mace, G.M., Oberdorff, T.,

Revenga, C., Rodrigues, P., Scholes, R.J., Sumaila, U.R. & Walpole,

M. (2010) Scenarios for Global Biodiversity in the 21st Century.

Science, 330, 1496–1501.Phillips, S.J., Anderson, R.P. & Schapire, R.E. (2006) Maximum entropy

modeling of species geographic distributions. Ecological Modelling, 190,

231–259.Phillips, S.J., Dudik, M., Elith, J., Graham, C.H., Lehmann, A., Leath-

wick, J. & Ferrier, S. (2009) Sample selection bias and presence-only

distribution models: implications for background and pseudo-absence

data. Ecological Applications, 19, 181–197.Preisendorfer, R.W. (1986) Secchi disk science: visual optics of natural

waters. Limnology and Oceanography, 31, 909–926.Robinson, L.M., Elith, J., Hobday, A.J., Pearson, R.G., Kendall, B.E.,

Possingham, H.P. & Richardson, A.J. (2011) Pushing the limits in mar-

ine species distribution modelling: lessons from the land present chal-

lenges and opportunities. Global Ecology and Biogeography, 20,

789–802.R€onnb€ack, P., Kautsky, N., Pihl, L., Troell, M., S€oderqvist, T. &

Wennhage, H. (2007) Ecosystem goods and services from swedish

coastal habitats: Identification, valuation, and implications of ecosystem

shifts. Ambio, 36, 534–544.R€onnberg, C. & Bonsdorff, E. (2004) Baltic Sea eutrophication: area-

specific ecological consequences. Hydrobiologia, 514, 227–241.Sanchirico, J. & Mumby, P. (2009) Mapping ecosystem functions to the

valuation of ecosystem services: implications of species–habitat associa-tions for coastal land-use decisions. Theoretical Ecology, 2, 67–77.

Sandman, A., Isæus, M., Bergstr€om, U. & Kautsky, H. (2008) Spatial pre-

dictions of Baltic phytobenthic communities: Measuring robustness of

generalized additive models based on transect data. Journal of Marine

Systems Supplement, 74 (Suppl. 1), S86–S96.Sandstr€om, A. & Kar�as, P. (2002) Effects of eutrophication on young-of-

the-year freshwater fish communities in coastal areas of the Baltic. Envi-

ronmental Biology of Fishes, 63, 89–101.Schories, D., Pehlke, C. & Selig, U. (2009) Depth distributions of Fucus

vesiculosus L. and Zostera marina L. as classification parameters for

implementing the European Water Framework Directive on the German

Baltic coast. Ecological Indicators, 9, 670–680.Sih, A., Jonsson, B.G. & Luikart, G. (2000) Habitat loss: ecological, evo-

lutionary and genetic consequences. Trends in Ecology & Evolution, 15,

132–134.Snickars, M., Sundblad, G., Sandstr€om, A., Ljunggren, L., Bergstr€om,

U., Johansson, G. & Mattila, J. (2010) Habitat selectivity of

substrate-spawning fish: modelling requirements for the Eurasian

perch Perca fluviatilis. Marine Ecology Progress Series, 398, 235–243.Sundblad, G., Bergstr€om, U. & Sandstr€om, A. (2011) Ecological

coherence of marine protected area networks: a spatial assessment

using species distribution models. Journal of Applied Ecology, 48,

112–120.Sundblad, G., H€arm€a, M., Lappalainen, A., Urho, L. & Bergstr€om,

U. (2009) Transferability of predictive fish distribution models in

two coastal systems. Estuarine, Coastal and Shelf Science, 83,

90–96.Th€ornqvist, S. (2009) Fritidsfiskets ut€ovare 2006. Fem studier av fritidsfiske

2002-2007. FINFO 2009:1, pp. 16–62. Swedish Board of Fisheries,

Gothenburg, Sweden.

Veneranta, L., Urho, L., Lappalainen, A. & Kallasvuo, M. (2011) Turbid-

ity characterizes the reproduction areas of pikeperch (Sander lucioperca

(L.)) in the northern Baltic Sea. Estuarine, Coastal and Shelf Science,

95, 199–206.Wikstr€om, S.A. & Kautsky, L. (2007) Structure and diversity of inverte-

brate communities in the presence and absence of canopy-forming Fucus

vesiculosus in the Baltic Sea. Estuarine, Coastal and Shelf Science, 72,

168–176.Wood, S.N. (2006) Generalized Additive Models: An Introduction With R.

Chapman & Hall/CRC. Boca Raton, FL.

Wood, S.N. & Augustin, N.H. (2002) GAMs with integrated model selec-

tion using penalized regression splines and applications to environmen-

tal modelling. Ecological Modelling, 157, 157–177.Wulff, F., Savchuk, O.P., Sokolov, A., Humborg, C. & M€orth, C.-M.

(2007) Management Options and Effects on a Marine Ecosystem:

Assessing the Future of the Baltic. AMBIO: A Journal of the Human

Environment, 36, 243–249.

Received 29 June 2012; accepted 4 March 2013

Handling Editor: David Angeler

Supporting Information

Additional Supporting Information may be found in the online version

of this article.

Appendix S1. Vegetation and fish data sets.

Appendix S2. Construction of a water transparency map as envi-

ronmental background data for the habitat models.

© 2013 The Authors. Journal of Applied Ecology © 2013 British Ecological Society, Journal of Applied Ecology

Evaluating eutrophication management scenarios 11