Ecological coherence of marine protected area networks: a spatial assessment using species...

Ecological coherence of marine protected area

networks: a spatial assessment using species

distribution models

Goran Sundblad1,2*, Ulf Bergstrom1 and Alfred Sandstrom3

1Swedish Board of Fisheries, Institute of Coastal Research, Skolgatan 6, SE-74242 Oregrund, Sweden; 2Department

of Limnology, Evolutionary Biology Centre (EBC), Uppsala University, Norbyvagen 18D, SE-75236 Uppsala, Sweden;

and 3Swedish Board of Fisheries, Institute of Freshwater Research, Stangholmsvagen 2, SE-17893 Drottningholm,

Sweden

Summary

1. The juvenile stages of fish are often dependent on specific habitat types for their survival. Protect-

ing these habitats may be crucial for maintaining strong adult stocks. The Natura 2000 network of

the European Union offers protection of marine habitats that are essential for the recruitment of

many fish species. By protecting these critical habitats the network may be important for maintain-

ing the stocks of these fish species.

2. We present a spatially explicit, GIS-based, assessment of two important components of the eco-

logical coherence of Marine Protected Area (MPA) networks: representativity and connectivity.

Representativity can be measured as the proportion of each conservation feature that is protected,

whereas connectivity assesses the spatial configuration of the network. We apply these analyses to

study the ecological coherence of theNatura 2000 network in a 30 000-km2 archipelago in the Baltic

Sea, with respect to a coastal fish assemblage and associated habitats. The analyses are based on fish

distributionmaps that have been constructed by statistically relating life stage specific occurrence to

environmental variables, and thereafter making spatial predictions based on maps of the environ-

mental variables.

3. The map-based analyses show that both the representativity and the connectivity of the network

are poor with respect to the studied fish species. In total, 3Æ5% (11 km2) of the assemblage recruit-

ment habitat was protected and 48% of the potentially connected habitats were included in the

MPA network.

4. The assessment explicitly identified geographical areas, visually communicated using maps,

where the network should be improved to ensure ecological coherence.

5. Synthesis and applications. Many MPA networks around the world, such as the Natura

2000 network in Europe, have recently come into effect. Establishment of the networks has

often been governed by opportunity rather than by strict ecological analyses, primarily

because distribution maps of species and habitats have been unavailable. Map-based assess-

ments of the strengths and weaknesses of evolving MPA networks, such as the one presented

here, are needed in adaptive management. They can provide an efficient tool for visualising

and communicating the results to stakeholders and policy makers in the process of working

towards ecological coherence.

Key-words: Baltic Sea, connectivity, essential fish habitats, habitat modelling, habitats direc-

tive, marine reserves, Natura 2000

Introduction

The overexploitation of many fish species and their habitats is

one of the key reasons for the increased world-wide interest in*Correspondence author. E-mail: [email protected]

Journal of Applied Ecology 2011, 48, 112–120 doi: 10.1111/j.1365-2664.2010.01892.x

� 2010 The Authors. Journal of Applied Ecology � 2010 British Ecological Society

Marine Protected Areas (MPAs) as a management tool (Pauly

et al. 2002; Airoldi & Beck 2007). In many regions, fisheries

management and nature conservation have traditionally been

governed separately (Sissenwine & Symes 2007). However,

implementation of an ecosystem-based approach to manage-

ment means that conservation and fisheries issues need to be

integrated (e.g. Leathwick et al. 2008). Fish play a central role

in ecosystem functioning (Eriksson et al. 2009), and are impor-

tant in maintaining viable systems withinMPAs. As many fish

species depend on specific habitats to complete their life cycle

(Mumby et al. 2004), MPAs that protect habitats may also

benefit fish and fisheries (Roberts et al. 2001). The early larval

and juvenile life stages are often especially sensitive and even

small changes in the environment may result in large variation

in recruitment success (Juanes 2007). These recruitment habi-

tats are often situated in shallow, coastal areas (Karas 1996;

Snickars et al. 2009) under pressure from human activities

(Eriksson et al. 2004; Airoldi & Beck 2007). This suggests that

adequate protection is essential to manage important fish

stocks andmaintain ecosystem functioning.

In northern Europe, member states of the Helsinki and

Oslo-Paris commissions (HELCOMandOSPAR) have agreed

to establish a coherent network of MPAs by 2010 covering the

north-east Atlantic and the Baltic Sea (JMM 2003), based

upon areas within the Natura 2000 network. According to the

European Union Habitats Directive, favourable conservation

status of the selected habitat types and species should be main-

tained or restored within this network (92 ⁄43 ⁄EEC). However,

the Natura 2000 network in marine areas has been developed

with relatively scarce data and without explicit consideration

of the habitat needs of most fish species, although some rare

species are included in Annex 2 of the Habitats Directive.

Nevertheless, it may still serve to protect species indirectly as

many of the habitats listed inAnnex 1 of theHabitatsDirective

are important spawning and nursery areas for a variety of spe-

cies (Bergstrom et al. 2007; Pedersen et al. 2009). For example,

many coastal fish species in the Baltic Sea are dependent on

shallow vegetated areas for their recruitment (Sandstrom et al.

2005), i.e. habitats that are explicitly protected by the Direc-

tive. Thus, by including habitats important for fish spawning

and feeding, Natura 2000 sites may support the productivity of

not only the rare species included in the Directive, but also

many of the common species that are central to the ecological

functioning of the area (Eriksson et al. 2009).

An ecologically coherent network of MPAs should (i) inter-

act with and support the wider environment, (ii) maintain the

processes, functions and structures of the protected features

across their natural range, (iii) function synergistically as a

whole, such that individual sites benefit from the wider net-

work and (iv) be resilient to changing conditions (Ardron

2008a; Piekainen & Korpinen 2008). There are four major cri-

teria that can be quantified when assessing the ecological

coherence of a network, i.e. adequacy, representativity, replica-

tion and connectivity (Ardron 2008a). Ensuring adequacy

involves determining an appropriate size, shape and location

of the protected areas tomaintain ecosystem services and func-

tioning. The level of representativity can be determined by the

proportion of each conservation feature that is protected,

whereas replication is measured by the number of each conser-

vation feature protected. Finally, connectivity reflects the spa-

tial configuration of the network and the potential for

organisms to move between the individual MPAs. Connectiv-

ity can be measured at different levels, from genetic connectiv-

ity to species, assemblage and seascape habitat components.

Despite the existence of advanced tools for zoning and MPA

designation (e.g. Mumby 2006; Moilanen, Leathwick & Elith

2008; Watts et al. 2009) and the fact that ecological coherence

is stated as the main objective for the majority of MPA net-

works world-wide, there have been few attempts to assess the

level of network coherence (Ardron 2008b; Johnson et al.

2008; HELCOM 2009). This is probably due to the scarcity of

detailed and reliable maps of marine habitats and the lack of

suitable evaluationmethods.

Recent advances in marine habitat mapping techniques and

the rapid development of GIS-based tools for predicting habi-

tat and species distributions (Elith& Leathwick 2009) facilitate

more detailed and accurate assessment of the ecological coher-

ence of MPA networks (Ardron 2008b). We present two

straightforward GIS-based analyses that allow quantitative

assessments to be made. The basis for these analyses is spatial

predictive modelling to identify essential habitats for the

species ⁄assemblage under study. We constructed maps of

recruitment habitats for an assemblage of coastal fish species

in a 30 000-km2 archipelago in the northern Baltic Sea. An

assessment of the ecological coherence of the Natura 2000

network was conducted by combining the habitat maps with

knowledge of the migratory behaviour of the species in the

assemblage. We show how two major criteria of ecological

coherence, representativity and connectivity, can be evaluated

in a spatially explicit context. The resultingmaps of representa-

tivity and connectivity are used to identify weak spots in the

network of protected areas and target areas for future MPA

establishment.

Materials and methods

STUDY AREA AND SPECIES

The field study was conducted in the northern Baltic Sea, in the exten-

sive, topographically complex archipelago between Sweden and Fin-

land. The northern Baltic Sea is a non-tidal, brackish water basin

with a near shore salinity range of 3–7 ppt. The Swedish–Finnish

archipelago is rich in islands and small islets and is substantially influ-

enced by a land-uplift of 4–6 mm per year. The shallow coastal areas

are normally characterized by soft sediment bottoms with a macro-

phyte community mainly consisting of submerged species including

pondweedsPotamogeton spp., stonewortsChara spp., milfoilsMyrio-

phyllum spp. and reed Phragmites australis L., as well as bladder-

wrack Fucus vesiculosus L., on harder substrates (Appelgren &

Mattila 2005).

To map the distribution of recruitment habitats of an assemblage

of common fish species, we constructed spatial predictive models for

four species: Eurasian perch Perca fluviatilis L., northern pike Esox

lucius L., pikeperch Sander lucioperca L. and roach Rutilus rutilus L.

These are central species in the coastal system of the Baltic Sea and,

Assessing eco-coherence of MPA networks 113

� 2010 The Authors. Journal of Applied Ecology � 2010 British Ecological Society, Journal of Applied Ecology, 48, 112–120

all except roach, are also top predators and among the most impor-

tant fish species for both the commercial and recreational fishery.

Despite some dissimilarity in their life-history, all four species utilize

specific near-shore habitats during their early life stages (Karas

1996; Sandstrom et al. 2005; Snickars et al. 2009, 2010; Sundblad

et al. 2009). By predicting the distribution of early life stages of these

species, we also predict the distribution of a diverse set of other ani-

mals and plants associated with similar habitats (Snickars et al.

2009).

Perch spawn in shallow water where they normally attach their sin-

gle egg strand to structurally complex objects such as submerged veg-

etation. An area of 66 000 m2 was surveyed for perch egg strands

three times during a period from late April to mid June 2003, with

intervals of 14–20 days (see Snickars et al. 2010). Egg strands were

mapped by snorkelling along parallel transects (length 20–480 m, 4–8

lines per site) c. 50–100 m apart. All visible egg strands within 1 m on

both sides of the transect lines were registered.

Sampling of fish was conducted in August–early September 2005–

2006, a period when the majority of young-of-the-year (YOY) of

spring-spawning fish have undergone metamorphosis and aggregate

in shallow, vegetated and near-shore areas. The field sampling sites

were randomized in different wave exposure and depth strata, within

a depth range of 0–6 m. Sampling sites were dispersed over the

entire archipelago gradient and covered the full distribution range of

the studied species. All study sites were in areas with a limited degree

of anthropogenic disturbance. Fish were surveyed by point abun-

dance sampling with small underwater detonations (described by

Snickars et al. 2007), with the addition of 10 g Nobel Prime explo-

sives to increase sampling area. The underwater detonations stun

and kill small fish (14–150 mm) within an area of c. 80 m2 and

allows quantitative sampling of fish with swim bladders in all shal-

low habitats, including dense vegetation, without severely affecting

the habitat (Snickars et al. 2007 and references therein). All stunned

and killed individuals (floating and sinking) were collected via snor-

kelling for determination of species, abundances and individual

lengths.

PREDICTIVE SPECIES DISTRIBUTION MODELS

Three environmental variables were used as predictors in the models:

depth, wave exposure and water clarity (measured as Secchi depth;

Preisendorfer 1986). These variables have been shown to influence

juvenile fish and perch egg strand distribution and were available as

high resolution (25 m) continuous maps for the entire study area

(Sandstrom et al. 2005; Florin, Sundblad&Bergstrom 2009; Snickars

et al. 2009, 2010). Depth was sampled simultaneously in both the sur-

veys of juvenile fish and perch egg strands, and bathymetrymapswere

acquired from sea charts and limited tomaximum6 mdepth in accor-

dance with field sampling. Wave exposure estimates, log10-trans-

formed, were derived from the software WaveImpact (Isæus 2004),

which uses fetch and algorithms incorporating refraction and diffrac-

tion in a step-wise procedure. Wave exposure has previously been

used to model the distribution of both Natura 2000 habitats (Bekkby

& Isæus 2008), and larval and YOY fish habitat characteristics (Flo-

rin, Sundblad & Bergstrom 2009; Sundblad et al. 2009). In addition,

wave exposure can also have an indirect effect on YOY fish distribu-

tion via its relationships with water exchange, temperature and vege-

tation (Bekkby et al. 2008). A proxy variable for water clarity was

produced in a GIS using water distance from the baseline of the

territorial sea (connecting the outermost islands and thereby defining

the archipelago zone) and the wave exposure estimates using the

equation;

Proxy for Secchi depth ¼ log10ðwave exposureÞffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffilog10ðdistanceÞ

p:

eqn 1

High proxy values indicate clear water. Evaluation of the index

was made against 293 in situ measurements of Secchi depth (range

0Æ3–10 m), from different years but the same season, from both the

Swedish and Finnish parts of the study area. There was a significant

positive correlation between the index and field measurements of Sec-

chi depth (P = 0Æ54, P < 0Æ001). Although the level of unexplained

variation was high the precision was deemed adequate (root mean

square error 1Æ87), as the index acceptably described both the large

scale gradients in water clarity that are found from near to offshore

areas and the smaller scale gradients that are found from open bays

to small sheltered inlets, which are important for the distribution of

juvenile fish (e.g. Sandstrom&Karas 2002).

Separate models with presence ⁄ absence of YOY of northern pike,

pikeperch and roach, as well as spawning of perch as response

variables were fitted using generalized additive models and the three

predictor variables (above). Models and look-up tables, for produc-

ing the spatial predictions in a GIS, were based on the generalized

regression and spatial prediction work package (Lehmann, Overton

& Leathwick 2002) using S-PLUS version 6Æ0 [Insightful Corp., Seat-tle, WA, USA]. Binomial error distributions and smoother spline

functions with three degrees of freedomwere used to avoid overfitting

the models (Sandman et al. 2008). Model performance was evaluated

by the area under the curve values of receiver-operating-characteristic

(ROC) plots and five-fold cross-validated ROC (cvROC)-plots

(Fielding & Bell 1997). ROC gives an estimate of model fit and

cvROC is an indication of model stability in new situations (Maggini

et al. 2006; Sandman et al. 2008). The area under the curve value of

ROC-plots ranges between 0Æ5 and 1Æ0, and following the classifica-

tion of Maggini et al. (2006) values below 0Æ7 were regarded as poor,

0Æ7–0Æ9 as reasonable and above 0Æ9 as having a very good discrimina-

tory ability. The resulting maps, showing probability of presence,

were dichotomized into maps of suitable and unsuitable habitat for

each response variable using the true-skill statistic (Allouche, Tsoar &

Kadmon 2006), where the sum of specificity and sensitivity, i.e.

correctly predicted absences and presences, is maximized (Fielding &

Bell 1997). In addition, the predicted distribution of suitable habitats

was externally validated using independent data sets of juvenile fish

distribution collected between 2001 and 2003 that covered the entire

western (Swedish) part of the study area (these data are reported in

Sandstrom et al. 2005).

SPATIAL ANALYSES

The coherence analyses weremade using aGIS [ArcGIS 9Æ1; Environ-mental Systems Research Institute, Redlands, CA, USA]. To assess

representativity and connectivity, a habitat map for the species assem-

blage was produced by combining the habitat maps of each species

and classifying areas where the habitat of at least three species

occurred as an assemblage habitat. Representativity was measured

on two local scales, i.e. within 10 · 10 and 20 · 20 km squares, and

calculated as the amount of the predicted assemblage habitat that was

protected by the Natura 2000 network in each square. Square size

should be ecologically relevant, yet can be considered a trade-off

between level of detail and interpretability and should also be chosen

to aid communication. Alternative grid sizes, which are easily pro-

duced in a GIS, could therefore be appropriate depending on the pur-

pose of the analysis. The 20 km scale represented the longest typical

migration distance of the studied assemblage (see below), while the

114 G. Sundblad, U. Bergstr€om & A. Sandstr€om


10 km scale provided amore detailed picture of local representativity,

at the same time providing a measure of sensitivity to grid resolution.

In an attempt to meet the adequacy criterion, only predicted assem-

blage habitat patches larger than 1 ha were included in the analyses.

This threshold was applied to ensure that only patches with a signifi-

cant production of juveniles were included. Determining an appropri-

ate level of connectivity is dependent on the aim of the network of

protected areas, as well as the target organisms within it. Fish are

mobile organisms with large differences in dispersal capability. The

species in this study are relatively sedentary also as larvae (Swedish

Board of Fisheries, unpublished data) and the assessment of connec-

tivity was based on the longest typical migration distance of the most

mobile of them, i.e. pikeperch. Marking studies from the region have

shown a 75% recapture rate of pikeperch within 16 km of the tagging

location (reviewed in Saulamo &Neuman 2002) so the migration dis-

tance was set to 20 km to allow for a more conservative estimate of

connectivity between the reserves. The connectivity assessment was

made in two steps. First, to assess the level of connectivity between all

habitat patches, regardless of the level of protection, the water dis-

tance between separate patches in the assemblage habitat map was

calculated using the cost distance procedure in the GIS software, thus

simulating fish movement by moving around islands. Secondly, to

assess the level of connectivity between protected habitats, we calcu-

lated the water distance between habitat patches currently within Na-

tura 2000 areas. Both distance data sets were cut at 20 km, thus

assessing potential connectivity within the set migration distance. To

evaluate connectivity between currently protected habitats, the sec-

ond distance data set was cut also at 10 km. Thus, when two 10 km

zones meet, the protected habitats are directly connected by fish

migrating 20 km. For all analyses only Natura 2000 areas as defined

by the EU Habitats Directive, i.e. Special Areas of Conservation

(SAC), were included in the analyses, whereas Special Protection

Areas (EU Birds Directive 79 ⁄ 409 ⁄EEC) that aim to support wild

birds and their habitats, were excluded.

Results

PREDICTIVE SPECIES DISTRIBUTION MODELS

The performance of the distribution models was highest for

the pikeperch model, intermediate for the roach and perch

spawning models and lowest for the pike model. The ROC-

values were relatively high, indicating reasonable to very good

model fit for all models. The cross-validated ROC-values were

in the same range as the ROC-values and indicated reasonable

model stability, except for pike (Table 1). An external vali-

dation of the predicted distribution of pike was therefore

performed, using an independent data set from previous

studies (data reported in Sandstrom et al. 2005). As a smaller

sampling unit size had been used when collecting the indepen-

dent data set, the degree of uncertainty in observed absences

was inflated and specificity (correct classification of absences)

could not be calculated (Lobo 2008). However, the external

validation of model sensitivity resulted in 88% correct classifi-

cation of predicted presences (n = 91), which can be consi-

dered high since the predicted suitable area for pike only

covered 593 km2 (of the total predicted area 0–6 m deep,

3042 km2). Based on the external validation, the pike model

was judged to be useful for the coherence analyses.

The partial response curves for the generalized additive

models illustrate how each explanatory variable affects the dis-

tributions of the species and life stages modelled (Fig. 1). In

general there was a negative effect of high wave exposure for

all species and life stages, while the responses to depth and

water visibility were species-specific, reflecting differences in

life-history characteristics (e.g. Lehtonen, Hansson &Winkler

1996; Sandstrom & Karas 2002; Snickars et al. 2009, 2010).

The predicted recruitment areas of the separate species consti-

tuted only a small proportion of the total water area (Table 1).

Therewas a strong overlap between the recruitment areas, con-

firming earlier results (Snickars et al. 2009) showing that these

species to a large extent utilize a common nursery habitat

(Table 1).

COHERENCE ANALYSES

The coherence of the network was assessed with respect to rep-

resentativity and connectivity using predicted assemblage habi-

tats of adequate size. The representativity analysis illustrated

the level of protection of predicted recruitment habitats for

the studied fish assemblage at local scales (10 · 10 and

20 · 20 km squares). Both analyses identified similar areas

with a representativity of over 10%. The main difference

between the two scales was in the level of detail for specific

areas, see for example the inner part of the Finnish archipelago

(Fig. 2). In total, only 3Æ5% (11 km2) of the assemblage

recruitment habitat was protected by the Natura 2000 net-

work.

Connectivity between protected habitats should support

natural dispersal and ensure a network resilient to changes.

The connectivity between all predicted habitats, both protected

Table 1. Sample size (N), prevalence, model performance (ROC and cvROC) for the species distribution models, area coverage of the predicted

distribution and proportion of predicted distribution in the assemblage recruitment habitat. The total area of the assemblage recruitment habitat

was 322 km2 and the total water area was 28 761 km2

Species ⁄ statistic N

Prevalence

(% occurrence) ROC cvROC

Predicted distribution

area (km2)

Proportion in

assemblage habitat (%)

Perch spawning 3242 8 0Æ78 0Æ77 126 93

Pike YOY 296 8 0Æ77 0Æ66 593 52

Pikeperch YOY 296 9 0Æ91 0Æ87 628 41

Roach YOY 296 13 0Æ80 0Æ71 782 41

ROC, receiver-operating-characteristic; cvROC, cross-validated ROC; YOY, young-of-the-year.



and unprotected, revealed two distinct regions divided by the

deep waters between Sweden and the Aland islands in the cen-

tral part of the study area. This can be considered the potential

level of connectivity, where habitats within each of the two

regions should be interconnected. However, when assessing

the connectivity of protected recruitment habitats, large gaps

could be identified and only 48% of the potentially connected

habitats were included in theMPAnetwork (Fig. 3).

Discussion

We show that it is possible to assess two vital aspects of ecolog-

ical coherence, i.e. connectivity and representativity, of net-

works of protected areas using species distribution models.

The level of representativitymeasures the quantity of protected

habitats while the level of connectivity measures the linkage

between local populations within a region. To guarantee suffi-

cient protection both aspects need to be taken into account

in parallel in the design and spacing of protected areas. Our

analyses showed low overall representativity and large gaps in

connectivity of the MPA network, with respect to the studied

coastal fish assemblage. In total, 3Æ5% of the predicted juvenile

fish habitats in the study area were protected and 48% of the

potentially connected habitats were supported by the network,

which is well below the suggested goal of 10–50% for represen-

tativity and 100% for connectivity (Ardron 2008a). Interest-

ingly, the level of representativity was slightly less than would

be expected by a random allocation of protected areas, as

3Æ8% of the study area was designated as SACs. In compari-

son, in a Baltic wide assessment of the coherence of the Baltic

(a)

(b)

(c)

(d)

Fig. 1.Model responses to environmental predictor variables for (a) spawning of perch and young-of-the-year of (b) roach, (c) pike and (d) pike-

perch. For each species and life stage high values on the Y-axis indicate higher contribution within the model. Circles indicate observed presence

(top) and absence (bottom) of the response variable along the continuous predictor variables. Dashed lines indicate twice standard error.



Sea Protected Area network, which largely overlaps with

Natura 2000, the lowest level of representativity was found for

offshore areas and large gaps in connectivity were shown for

short and mid-distance dispersers (HELCOM 2009). Our

regional analysis, which is based on high-resolution habitat

maps and estimates of the mobility of the species, adds detail

to this picture for the largest archipelago region of the Baltic

Sea, as called for by HELCOM (2009). Our results show that

the MPA network is not coherent in coastal areas for short-

distance dispersers such as the studied fish assemblage.

The primary aim of the Natura 2000 network was not

to protect fish, so our results are perhaps not surprising.

However, we expected the network to provide a high degree of

protection for fish since many of the habitat types included in

the network, e.g. estuaries, lagoons and large inlets and bays,

are important nursery areas for the studied fish assemblage

22°E21°E20°E19°E18°E17°E

60°N

59°N

22°E21°E20°E19°E18°E17°E

60°N

59°N

Sweden

Finland

0 20 40 60 80 10010km

Natura2000 (SAC)

Representativity (%)0 – <10

10 – <50

50 – 100

Fig. 2. Representativity assessment of recruitment habitats of the coastal fish species assemblage on two scales. Rectangles show the amount (%)

of predicted recruitment habitats protected by the Natura 2000 network in cells of a 10 · 10 km grid (top) and a 20 · 20 km grid (below). Grid

cell size is chosenwith regard to the ecology of the study organisms and is a trade-off between level of detail and interpretability.



(e.g. Karas 1996; Eriksson et al. 2004; Sandstrom et al. 2005;

Snickars et al. 2010). These habitat types, such as shallow

sheltered bays, are popular for boating and recreational

housing (Sandstrom et al. 2005; HELCOM 2007). To

minimize conflicts between conservation and development

interests, protected areas have been designated in areas where

there is low exploitation pressure (Margules & Pressey 2000),

primarily in the outer archipelago regions. However, the

exploitation of shallow, near-shore areas is increasing

(Kindstrom & Aneer 2007), and this development has a

negative effect on fish recruitment (Sandstrom et al. 2005). The

maintenance of viable fish populations in the long term will

depend on the protection of sufficient recruitment habitat even

where there is conflict with human development interests.

The strong environmental gradients at multiple scales found

in the archipelago regions of the Baltic Sea provide suitable

conditions for predictive distribution modelling of the juvenile

stages of fish since they are often highly dependent on specific

environmental conditions (Florin, Sundblad & Bergstrom

2009; Snickars et al. 2009, 2010). The models were relatively

robust, especially given the high resolution in relation to the

large extent of the study area. The accuracy of the predictions

might be improved further by increasing the availability and

precision of the maps of potential predictor variables, prefera-

bly using variables with direct mechanistic effects, for example

temperature and ⁄or vegetation cover (Austin 2002; Sundblad

et al. 2009). In the meantime, we believe that the analyses

presented here identify high-priority areas for future MPA

designation and improvement to theNatura 2000 network.

One of the main factors driving the design of a network of

protected areas should be the level of connectivity among local

populations of the target species, a feature of marine popula-

tions about which we know relatively little (Mumby 2006). In

our analyses, we used the longest migration distance of those

species included in the assemblage, thus potentially overesti-

mating connectivity for other species with shorter dispersal dis-

tances (reviewed in Saulamo & Neuman 2002). Although

different dispersal capabilities can have substantial effects on

the level of connectivity (Johnson et al. 2008), testing the

effects of different dispersal distances on network connectivity

can be performed without much difficulty using a GIS. Includ-

ing species or life stage specific cost layers for dispersal in the

connectivity analyses could allow a more precise estimate of

the coherence between habitats (Agnes et al. 2009), and should

be explored in the future. Currently, it is not the tools that are

lacking; rather it is information on species or life stage specific

dispersal capabilities (Palumbi 2004). However, determining

an appropriate level of connectivity should not only be based

on determining the length of ‘blue corridors’ to connect popu-

lations, but should also address the level of genetic connectivity

(Galarza et al. 2009; Raeymaekers et al. 2009). Migration dis-

tance and gene flow are not necessarily the same thing, as

migrating individuals also need to actually spawn away from

their original site to have an effect on gene flow and thus

22°E21°E20°E19°E18°E17°E

60°N

59°N

Sweden

Finland

0 20 40 60 80 10010km

Predicted habitats

10km protected habitats

20km protected habitats

20km all habitats 0 5 102·5km

Fig. 3. Connectivity assessment based on distances from recruitment habitats of the coastal fish species assemblage. Predicted recruitment habi-

tats are shown in black. Crosshatched areas symbolize connectivity of all recruitment habitats within a 20-kmmigration distance. Dark and light

blue colours represent areas within 10 and 20 km, respectively, from recruitment habitats that are currently protected. To improve network con-

nectivity and support natural population exchange, future protection efforts should be concentrated to light blue areas. In a fully coherent net-

work all crosshatched areas should be covered by the dark blue colour, i.e. 100% connectivity.



genetic connectivity (Laikre, Palm & Ryman 2005). Studies

of the genetic structure of our target species show that genetic

differentiation exists over relatively small spatial scales

(1–100 km), even in environments without apparent migration

barriers that may limit the exchange between populations

(Laikre et al. 2005; Bergek, Sundblad & Bjorklund 2010). If

connectivity is to be maintained within a network, it follows

that species with local populations or short dispersal distances

will require more closely spaced protected areas than more

migratory species (Johnson et al. 2008). Yet, determining

dispersal distances is not an easy task. However, as new

information on genetic structure and dispersal capabilities of

organisms with different life histories becomes available,

re-assessment of the ecological coherence of the network can

be carried out using the approach presented in this study.

We believe that a better knowledge of the distribution of

essential habitats of ecologically important species is crucial

to the effectiveness of MPA networks, and that a key tool for

efficient mapping of conservation values is predictive species

distribution modelling. As shown in this study, species distri-

bution maps based on modelling may also serve as the basis

for spatially explicit assessments of ecological coherence of

MPA networks, aiding the identification of areas in need of

further protection.

Conclusions

There is a strong political will in Europe to preserve and restore

the structure and functioning of the marine environment by

establishing protected areas in ecologically coherent networks

(e.g. HELCOM, OSPAR conventions). A crucial step for the

long term success of these networks is the mapping of key fea-

tures, such as habitat types required by ecologically important

species. Detailed maps of habitat distribution are central to

marine spatial planning, e.g. to minimize disturbance of sensi-

tive marine habitats. In this study, we have shown that only

small amounts (3Æ5%) of important juvenile fish habitat are

protected by the current designations and that about half of

the potentially connected habitats were included in the MPA

network. The spatially explicit analyses identified geographical

areas where the network needs to be expanded to improve its

ecological coherence. The low level of existing protection indi-

cates that the traditional way of managing fish and fisheries

separately from nature conservation has not been successful

and calls for an integrated approach in the future development

of the Natura 2000 network. We advocate this kind of map-

based analysis of ecological coherence as an efficient tool in

adaptive management, both for assessing the relative strengths

and weaknesses of evolving MPA networks, and for visualiz-

ing and communicating the results to stakeholders and policy

makers.

Acknowledgements

This study was in part funded by BSR INTERREG IIIB Neighbourhood

Programme project BALANCE and by the PREHAB project funded under

BONUS+ call. We are grateful to the BALANCE partners for stimulating

discussions on this work, all the people involved in collecting data from the field

and to Dr Stefan Palm, five anonymous referees and the Editor for valuable

comments on themanuscript.

References

Agnes, J., Jean-Paul, L., Nicolas, R., Christophe, D., Pascal, A. & Pierre, J.

(2009) Assessing landscape connectivity with calibrated cost-distance model-

ling: predicting toad distribution in a context of spreading agriculture. Jour-

nal of Applied Ecology, 46, 833–841.

Airoldi, L. &Beck,M.W. (2007) Loss, status and trends for coastalmarine hab-

itats of Europe. Oceanography and Marine Biology: An Annual Review, 45,

345–405.

Allouche, O., Tsoar, A. &Kadmon, R. (2006) Assessing the accuracy of species

distribution models: prevalence, kappa and the true skill statistic (TSS).

Journal of Applied Ecology, 43, 1223–1232.

Appelgren, K. &Mattila, J. (2005) Variation in vegetation communities in shal-

low bays of the northern Baltic Sea.Aquatic Botany, 83, 1–13.

Ardron, J.A. (2008a) Three initial OSPAR tests of ecological coherence: heuris-

tics in a data-limited situation. ICES Journal of Marine Science, 65, 1527–

1533.

Ardron, J.A. (2008b) The challenge of assessing whether the OSPAR network

ofmarine protected areas is ecologically coherent.Hydrobiologia, 606, 45–53.

Austin, M.P. (2002) Spatial prediction of species distribution: an interface

between ecological theory and statistical modelling. Ecological Modelling,

157, 101–118.

Bekkby, T. & Isæus, M. (2008) Mapping large, shallow inlets and bays: model-

ling a Natura 2000 habitat with digital terrain and wave-exposure models.

ICES Journal ofMarine Science, 65, 238–241.

Bekkby, T., Rinde, E., Erikstad, L., Bakkestuen, V., Longva, O., Christensen,

O., Isæus, M. & Isachsen, P.E. (2008) Spatial probability modelling of

eelgrass (Zostera marina) distribution on the west coast of Norway. ICES

Journal ofMarine Science, 65, 1093–1101.

Bergek, S., Sundblad, G. & Bjorklund, M. (2010) Population differentiation in

Eurasian perch: environmental effects on gene flow? Journal of Fish Biology,

76, 1159–1172.

Bergstrom, L., Korpinen, S., Bergstrom, U. & Andersson, A. (2007) Essential

Fish Habitats and Fish Migration Patterns in the Northern Baltic Sea. BAL-

ANCE Interim Report No. 29, pp. 1–33. Balance, Copenhagen, Denmark.

Available at: http://balance-eu.org/publications/index.html, accessed 28

June 2010.

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.

Eriksson, B.K., Sandstrom, A., Isæus, M., Schreiber, H. & Karas, P. (2004)

Effects of boating activities on aquatic vegetation in the Stockholm archipel-

ago, Baltic Sea.Estuarine, Coastal and Shelf Science, 61, 339–349.

Eriksson, B.K., Ljunggren, L., Sandstrom, A., Johansson, G., Mattila, J.,

Rubach, A., Raberg, S. & Snickars,M. (2009)Declines in predatory fish pro-

mote bloom-formingmacroalgae.Ecological Applications, 19, 1975–1988.

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

prediction errors in conservation presence ⁄ absence models. Environmental

Conservation, 24, 38–49.

Florin, A.-B., Sundblad, G. & Bergstrom, U. (2009) Characterisation of juve-

nile flatfish habitats in the Baltic Sea. Estuarine, Coastal and Shelf Science,

82, 294–300.

Galarza, J.A., Carreras-Carbonell, J., Macpherson, E., Pascual, M., Roques,

S., Turner, G.F. & Rico, C. (2009) The influence of oceanographic fronts

and early-life-history traits on connectivity among littoral fish species. Pro-

ceedings of the National Academy of Sciences of the United States of America,

106, 1473–1478.

HELCOM (2007) HELCOM lists of threatened and ⁄ or declining species and

biotopes ⁄ habitats in the Baltic Sea area. Baltic Sea Environmental Proceed-

ings, No 113 (edsD. Boedeker&H. vonNordheim), pp. 1–18.Helsinki Com-

mission,Helsinki, Finland.

HELCOM (2009) Biodiversity in the Baltic Sea – an integrated thematic assess-

ment on biodiversity and nature conservation in the Baltic Sea. Baltic Sea

Environmental Proceedings, No 116B, pp. 1–188. Helsinki Commission,

Helsinki, Finland.

Isæus, M. (2004) Factors Structuring Fucus Communities at Open and Complex

Coastlines in the Baltic Sea. PhD thesis, Stockholm University, Stockholm,

Sweden.

JMM (2003) Declaration of Joint Ministerial Meeting of the Helsinki and

OSPAR Commissions. Convention of the Protection of the Marine Environ-



ment of the Baltic Sea Area (Helsinki Convention) and OSPAR Convention

for the Protection of the Marine Environment of the North-East Atlantic,

25–26 June 2003, Bremen. JMM2003 ⁄ 3.Available at: http://www.ospar.org,

accessed 25 June 2009.

Johnson, M.P., Crowe, T.P., McAllen, R. & Allcock, A.L. (2008) Characteriz-

ing themarine Natura 2000 network for theAtlantic region.Aquatic Conser-

vation:Marine and Freshwater Ecosystems, 18, 86–97.

Juanes, F. (2007) Role of habitat in mediating mortality during the post-settle-

ment transition phase of temperate marine fishes. Journal of Fish Biology, 70,

661–677.

Karas, P. (1996) Basic abiotic conditions for production of perch (Perca fluvia-

tilis L.) young-of-the-year in the Gulf of Bothnia. Annales Zoologici Fennici,

33, 371–381.

Kindstrom, M. & Aneer, G. (2007)What is Happening to Our Shores? Balance

InterimReport, No. 26, pp. 1–28. Balance, Copenhagen,Denmark. Available

at: http://balance-eu.org/publications/index.html, accessed 28 June 2010.

Laikre, L., Palm, S. &Ryman, N. (2005) Genetic population structure of fishes:

implications for coastal zonemanagement.Ambio, 34, 111–119.

Laikre, L., Miller, L.M., Palme, A., Palm, S., Kapuscinski, A.R., Thoresson,

G. & Ryman, N. (2005) Spatial genetic structure of northern pike (Esox

lucius) in the Baltic Sea.Molecular Ecology, 14, 1955–1964.

Leathwick, J., Moilanen, A., Francis, M., Elith, J., Taylor, P., Julian, K.,

Hastie, T. &Duffy, C. (2008)Novelmethods for the design and evaluation of

marine protected areas in offshorewaters.ConservationLetters, 1, 91–102.

Lehmann, A., Overton, M.J. & Leathwick, J. (2002) GRASP: generalized

regression analysis and spatial prediction. Ecological Modelling, 157, 189–

207.

Lehtonen, H., Hansson, S. & Winkler, H. (1996) Biology and exploitation of

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

ici Fennici, 33, 525–535.

Lobo, J.M. (2008) More complex distribution models or more representative

data?Biodiversity Informatics, 5, 14–19.

Maggini, R., Lehmann, A., Zimmerman, N.E. & Guisan, A. (2006) Improving

generalized regression analysis for the spatial prediction of forest communi-

ties. Journal of Biogeography, 33, 1729–1749.

Margules, C.R. & Pressey, R.L. (2000) Systematic conservation planning.Nat-

ure, 405, 243–253.

Moilanen, A., Leathwick, J. & Elith, J. (2008) A method for spatial freshwater

conservation prioritization. Freshwater Biology, 53, 577–592.

Mumby, P.J. (2006) Connectivity of reef fish between mangroves and coral

reefs: algorithms for the design of marine reserves at seascape scales.Biologi-

cal Conservation, 128, 215–222.

Mumby, P.J., Edwards, A.J., Arias-Gonzalez, J.E., Lindeman, K.C., Black-

well, P.G., Gall, A., Gorczynska, M.I., Harborne, A.R., Pescod, C.L., Ren-

ken, H., Wabnitz, C.C.C. & Llewellyn, G. (2004) Mangroves enhance the

biomass of coral reef fish communities in the Carribean. Nature, 427, 533–

536.

Palumbi, S.R. (2004) Marine reserves and ocean neighbourhoods: the spatial

scale of marine populations and their management. Annual Review of Envi-

ronment and Resources, 29, 31–68.

Pauly, D., Christensen, V., Guenette, S., Pitcher, T.J., Sumaila, U.R., Walters,

C.J., Watson, R. & Zeller, D. (2002) Towards sustainability in world fisher-

ies.Nature, 418, 689–695.

Pedersen, S.A., Fock, H., Krause, J., Pusch, C., Sell, A.L., Bottcher, U., Rog-

ers, S.I., Skold, M., Skov, H., Podolska, M., Piet, G.J. & Rice, J.C. (2009)

Natura 2000 sites and fisheries in German offshore waters. ICES Journal of

Marine Science, 66, 155–169.

Piekainen, H. & Korpinen, S. (eds) (2008) Towards an Assessment on Ecologi-

cal Coherence of the Marine Protected Areas Network in the Baltic Sea

Region. BALANCE Interim Report No. 25, pp. 1–140. Balance, Copen-

hagen, Denmark. Available at: http://balance-eu.org/publications/index.

html, accessed 15 September 2009.

Preisendorfer, R.W. (1986) Secchi disk science: visual optics of natural waters.

Limnology and Oceanography, 31, 909–926.

Raeymaekers, J.A.M., Raeymaekers, D., Koizumi, I., Geldof, S. & Volckaert,

F.A.M. (2009) Guidelines for restoring connectivity around water mills: a

population genetic approach to the management of riverine fish. Journal of

Applied Ecology, 46, 562–571.

Roberts, C.M., Bohnsack, J.A., Gell, F., Hawkins, J.P. & Goodridge, R.

(2001) Effects of marine reserves on adjacent fisheries. Science, 294,

1920–1923.

Sandman, A., Isæus, M., Bergstrom, U. & Kautsky, H. (2008) Spatial predic-

tions of Baltic phytobenthic communities: measuring robustness of general-

ized additive models based on transect data. Journal of Marine Systems, 74

(Suppl. 1), S86–S96.

Sandstrom, A. & Karas, P. (2002) Effects of eutrophication on young-of-the-

year freshwater fish communities in coastal areas of the Baltic.Environmental

Biology of Fishes, 63, 89–101.

Sandstrom, A., Eriksson, B.K., Karas, P., Isæus, M. & Schreiber, H. (2005)

Boating and navigation activities influence the recruitment of fish in a Baltic

Sea archipelago area.Ambio, 34, 125–130.

Saulamo, K. & Neuman, E. (2002) Local management of Baltic fish stocks –

significance ofmigrationsReport from the Swedish Board of Fisheries, ISSN

1404-8590. Finfo, 9, 1–19.

Sissenwine, M. & Symes, D. (2007)Reflections on the Common Fisheries Policy.

Report to the general directorate for fisheries and maritime affairs of the

European commission, pp. 1–75.

Snickars, M., Sandstrom, A., Lappalainen, A. & Mattila, J. (2007) Evaluation

of low impact pressure waves as a quantitative sampling method for small

fish in shallow water. Journal of Experimental Marine Biology and Ecology,

343, 138–147.

Snickars, M., Sandstrom, A., Lappalainen, A., Mattila, J., Rosqvist, K. &

Urho, L. (2009) Fish assemblages in coastal lagoons in land-uplift succes-

sion: the relative importance of local and regional environmental gradients.

Estuarine, Coastal and Shelf Science, 81, 247–256.

Snickars, M., Sundblad, G., Sandstrom, A., Ljunggren, L., Bergstrom, U., Jo-

hansson, G. & Mattila, J. (2010) Habitat selectivity of substrate spawning

fish: modelling requirements of the Eurasian perch, Perca fluviatilis. Marine

Ecology Progress Series, 398, 235–243.

Sundblad, G., Harma, M., Lappalainen, A., Urho, L. & Bergstrom, U. (2009)

Transferability of predictive fish distribution models in two coastal systems.

Estuarine, Coastal and Shelf Science, 83, 90–96.

Watts, M.E., Ball, I.R., Stewart, R.S., Klein, C.J., Wilson, K., Steinback, C.,

Lourival, R., Kircher, K. & Possingham, H.P. (2009) Marxan with Zones:

software for optimal conservation based land- and sea-use zoning. Environ-

mentalModelling & Software, 24, 1513–1521.

Received 25March 2010; accepted 4 October 2010

Handling Editor: Chris Frid



Ecological coherence of marine protected area networks: a spatial assessment using species...

Documents

Transcript of Ecological coherence of marine protected area networks: a spatial assessment using species...