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Marine ecosystems are facing unprecedented loss of biodiversity due to habitat destruction, a changing marine environment and increasing resource extraction (Worm et al., 2006; Halpern et al., 2008, 2015). A key aspect in safeguarding marine biodiversity is the design of ecologically effective networks of Marine Protected Areas (MPAs). Especially no-take reserves have been shown to support high biodiversity and food web complexity (Halpern and Warner, 2002; Lester et al., 2009; Halpern, 2014). Increased emphasis is required on an ecologically efficient MPA design and sustainable management, to ensure that MPAs achieve global conservation objectives (Edgar et al., 2014).

International agreements require nations to establish ecologically coherent MPA networks to support and maintain marine processes and functions (CBD, 2004; Directive, 2008). In 2010, the Convention on Biological Diversity (CBD) agreed on the Aichi biodiversity target 11, stating that by 2020 at least 10% of coastal and marine areas should be conserved and effectively managed. The areas under protection need to fulfill four criteria: adequacy, representativity, replication and connectivity, in order to be ecologically efficient (CBD, 2010; HELCOM, 2010). In addition, in 2014, the World Parks Congress urged that at least 30% of each marine habitat should be under strict protection thus increasing the previous recommendation of at least 20% protected (Wenzel et al., 2016). This ambitious recommendation known as the Promise of Sydney highlights progress in marine protection in recent years.

In Europe, the cornerstone of conservation has been the Habitats Directive (CD 92/43/EEC) (EC, 1992). It aims to maintain an adequate conservation status for habitats and species, listed in Annexes I and II. Over 200 habitats are protected by the Directive, identified as either biogeographically important or being in danger of disappearing. It is the responsibility of the Member States to ensure that adequate conservation status is achieved by designating areas for protection, with habitats listed in Annex I of the Directive as the key selection criteria for Natura 2000 sites (Evans, 2012).

The challenge is to establish MPAs in areas where they provide the highest conservation benefits for the marine environment. Designation of MPAs has in many areas mostly relied on ad hoc decisions and not necessarily on knowledge on marine habitats and species (Agardy et al., 2011). Consequently, it has been concluded that the majority of MPAs fail to meet their management objectives (Jameson et al., 2002; Edgar et al., 2014). Furthermore, as MPAs often focus on conserving habitats or individual species, but not on the functionality of the marine ecosystem, the true ecological efficiency of the networks has mostly remained obscure. Studies about the effectiveness and performance of MPAs have, for example, evaluated effects on coastal fish (Sundblad et al., 2011; Olsen et al., 2013; Gill et al., 2017), broad-scale habitats (Foster et al., 2017), or shellfish (Fariñas-Franco et al., 2018). Especially if MPA designation allows significant resource use, as is the case for most MPAs globally, one may be skeptical about the effectiveness of MPA status (Sala et al., 2018). Unfortunately, data needed for the assessment of the functioning of MPA networks is often missing.

The brackish and semi-enclosed Baltic Sea is ecologically unique, as it possesses steep horizontal and vertical environmental gradients in salinity and temperature, and hosts a mixture of marine and freshwater species. Due to the low salinity (0–7 PSU) in the northern Baltic Sea, diversity of benthic species, especially benthic animals and marine algae, is low. In contrast, the low salinity enables a variety of vascular plants to grow along the shallow water areas of the Baltic Sea (Bonsdorff, 2006; Ojaveer et al., 2010; HELCOM, 2012; Zettler et al., 2014). Long scientific tradition, experience in cross-border environmental management, and a long-term struggle against multiple pressures, such as chemical pollution, eutrophication, non-indigenous species and habitat destruction, makes the Baltic Sea an ideal test case for other coastal and marine systems world-wide tackling with similar problems (Reusch et al., 2018). The Baltic Sea was also one of the first regional seas in the world to reach the Aichi target 11 (EEA, 2015). Qualified MPAs in the Baltic Sea consist of: HELCOM MPAs – aiming to protect Baltic biodiversity, EU marine Natura 2000 sites – protecting habitats and species, Ramsar sites – protecting important wetlands, and national parks and nature reserves.

Finland has an important role in the conservation efforts of the Baltic Sea, not only because of the long coastline (48,000 km) and large number of islands (∼100,000) (Viitasalo et al., 2017), but also because Finland has since 1930 systematically developed its protected area network. Presently 10% of the Finnish sea areas (including EEZ) are under protection. The Finnish MPAs consist of Natura 2000 sites (8.5%), HELCOM MPAs (7.7%), Ramsar sites (2.2%), National Parks (1.9%), private MPAs (1.8%) and Nature Reserves (0.7%) and (MPAtlas, 2018) (one MPA can belong to more than one MPA class.) While they cover extensive sea areas, MPAs in Finland and elsewhere in the Baltic Sea have often been established to protect habitats, bird areas, seals, or terrestrial environment on islands and skerries, without prior knowledge of underwater species or nature values in general. It is also notable that while many human activities are restricted within MPAs, no-take zones for fishing are rare amongst the Finnish MPA network. This leaves an important part of the ecosystem without any protection.

A proper evaluation of the ecological coherence of MPAs requires extensive data and suitable analytical tools. A major project for the improvement of ecological knowledge of marine nature started in 2004, known as the Finnish Inventory Programme for the Underwater Marine Environment VELMU. The project has produced the most extensive dataset on marine biodiversity to date in the Baltic Sea, with ∼140,000 standardized sampling sites. The data collected, along with data on environmental parameters and human activities, is viewable at the VELMU Map Service paikkatieto.ymparisto.fi/VELMU_mapservice/. Here, we take advantage of this new data by building new models that describe distributions of species and utilize other existing data on habitats as well as fish reproduction areas. In addition, we develop estimates for marine pressures that contribute to the decline and loss of important marine habitats.

Methodologically, our work relies on the Zonation method and software designed for ecologically based land use planning (Moilanen et al., 2005, 2011a; Lehtomaki and Moilanen, 2013). Zonation produces a hierarchical prioritization across the landscape, balanced across many factors such as species, habitats, ecosystem services and connectivity, and accounting for costs and/or threats if sufficient data exists. Most applications of Zonation have been terrestrial, but there have been some marine applications as well. For example, Leathwick et al. (2008) used Zonation to evaluate a proposed MPA network in New Zealand, with the conclusion that the proposal was of fractional quality compared to what could have been achieved using Zonation, and with zero cost to fishermen. Analytically, it makes no difference whether data grids represent terrestrial or marine biodiversity.

The new extensive data and novel models combined with spatial prioritization allow the first comprehensive assessment in the Baltic Sea of how well the present MPA network protects marine features of highest ecological value. Going further, our analysis identifies new MPA candidates that would improve the conservation coverage achieved by the MPA network efficiently. With the analysis we are also able to assess if the habitats protected by the EU Habitats Directive Annex I can be used as a proxy for safeguarding biodiversity, which allows an evaluation of the fundamental conservation principles implemented in the EU. We also assess the state and quality of marine habitats and identify areas where additional habitat protection is needed. This is the first time that any country in the Baltic Sea has data available at a spatial scale and resolution that enables identifying where the biologically most valuable marine areas are located. The analysis implemented here can serve as a template and a recipe for similar analyses in the Baltic Sea and also in other sea areas elsewhere in the world.

The Finnish Underwater Inventory Programme, VELMU, has taken an unprecedented step forward in the amount and quality of marine data available, even in the global context. Our evaluation of the Finnish MPA network and its expansion was based on a substantial amount of biodiversity data (∼140,000 samples), high-resolution environmental data, and a comprehensive set of analyses, using scientifically established techniques of spatial conservation prioritization (Zonation). Our study revealed that the ecological efficiency of the present Finnish MPA network is mediocre, and it is unbalanced in its coverage of marine biodiversity. This is not surprising, as there was scarce ecological data on marine species and habitats available at the time of the establishment of most of the Finnish MPAs. Protection was based on information on other species, such as seabirds and marine mammals (seals), as well as terrestrial plant species on the islands and skerries. Our approach allowed us to assess the patterns of marine biodiversity features in the Finnish sea area and to evaluate the validity of certain fundamental principles of marine protection, such as usefulness of habitats as surrogates for species.

According to our analyses, marine biodiversity is highly concentrated in the Finnish waters: a smallish fraction (∼22%) of the overall seascape includes more than 91% of the feature coverage. Most of these features occur in relatively shallow and well-lit waters. In contrast, the lowest priority areas, which support little biodiversity, are in the present analysis the deep, dark soft bottom areas, or hypoxic seafloors, and areas with habitat constraints, e.g., harbors. This characterization also applies to the Baltic Sea as a whole. One third of the seafloor is sediment accumulation area in which hypoxia occurs, forming dead zones with little value for biodiversity (Conley et al., 2009; Kaskela et al., 2012; Reusch et al., 2018).

A major finding was that the present MPA network covers only ca. 27% of the distributions of marine biodiversity features. Overall, MPAs protect marine biodiversity, but not adequately. Our analysis shows that the feature coverage could be significantly improved by minor expansion of the protected sea area. Increasing the MPA coverage by just 1%, from 10 to 11% of the seascape, would increase the mean coverage of features from 27 to 60% (Figure 6B). This shows that many species worth conserving have very narrow distributions, and that many key areas for such species are missing from the network. Highest ranked areas outside the present MPA network could fix gaps in conservation, both geographically and ecologically (Table 2). The strong concentration of the biodiversity features is both a benefit and a challenge for conservation. The narrow distribution implies that very small additions in the MPA network are useful. On the other hand, if such areas exist on private waters, or in areas claimed for other human uses, such as aquaculture, energy production or extraction of bottom materials, conflicts between conservation and usage of the sea may arise. If the protection has a scientific definition, rather than a legal basis, the conservation aspects of smallish areas may be neglected in the stakeholder process.

We here focused on searching for individual expansion areas, but Zonation’s post-processing analyses could also be used for identifying connected sets of small areas (skerries, reefs, etc.) that jointly form management landscapes (Moilanen et al., 2005). The priority ranking (Figures 4A,B) has at least two further uses. In addition to identifying areas important for biodiversity conservation, the analysis also identifies areas that are ecologically less important. These low-ranked areas could be suitable for ecological impact avoidance (Kareksela et al., 2013, 2018), for example when planning wind power sites or aquaculture (fish farms). Impact avoidance analyses would benefit from additional data concerning the economic benefits expected from investments in different areas. Combining these economic data with biodiversity data would yield an ecosystem-based and cost-effective solution for placement of human activities in the seascape.

One of the basic demands for an efficient MPA network is that it has adequate representativity. The 2003 World Parks Congress stated that MPAs need to cover at least 20–30% of each marine habitat in order to ensure viability of marine ecosystems (IUCN, 2003). We found that this target has already been reached in Finland for several marine habitats (mentioned in the EU Habitats Directive Annex I). The existing MPA network covers 53% of Baltic esker islands, 49% of sand banks, 40% of Boreal Baltic narrow inlets, 34% of large shallow inlets and bays, 32% of Boreal Baltic islets, 24% of reefs, 21% of estuaries, and 18% of coastal lagoons (Figure 5B). On the other hand, the 20% level is a minimum requirement that refers to strictly protected areas. Much higher levels, up to 50%, are often recommended in scientific studies (e.g., Airamé et al., 2003) and even higher percentages may be needed for, e.g., rare or isolated habitats, and for habitats that form bottleneck areas for reproduction (Roberts et al., 2003). It is notable that the Finnish marine protection is more focused on protecting habitats than rare or functionally important species: the coverages of species, IUCN Red Listed ecosystems and habitats were 26, 28, and 39%, respectively (Figure 5B), but many species have low coverage between 0 and 10% (Figure 6A). We conclude that the representativity of the MPA network could still be improved to better achieve the scientifically accepted conservation objectives.

In areas where the data on species is scarce, the MPAs need to be selected based on information on habitats. This practice is the basis of the EU Habitats Directive, and it is especially important in the northern Baltic, where only a handful of marine species are listed as species requiring protection (cf. EU Habitats Directive Annex II). Many types of habitats common in the European Seas, such as estuaries, large shallow inlets and bays, and coastal lagoons, indeed harbor a large number of species. We however found that – in the Finnish sea area – the habitats listed in the Habitats Directive Annex I act as poor surrogates for species. If only habitat data would be used, approximately 60% smaller coverage of species distributions would be achieved, compared to a situation where features are searched based on both habitats and species (Figure 9). This is at least partly caused by the fact that the extensive shallow water areas surrounding thousands of larger islands are not interpreted as “reefs” (habitat no. 1170 in Habitats Directive) in the Finnish interpretation of the Habitats Directive, neither are they considered together with the habitat “boreal Baltic islets and small islands” (1620). These areas, which may consist of various bottom types from rocky shores to mixed sediments are, according to our prioritization, important biodiversity hotspots, harboring a large number of functionally important species, such as brown, green, and red algae and associated flora and fauna. Similarly, the underwater parts of sandy shores and beaches are not considered to belong to the habitats “sandbanks which are slightly covered with sea water all the time” (1110) or “Baltic esker islands with sandy, rocky and shingle beach vegetation and sublittoral vegetation” (1610). Such environments provide a suitable habitat for various vascular plants and harbor some of the most important occurrences of the keystone species eelgrass (Zostera marina) in Finland. Nevertheless, these areas are not necessarily considered as MPA candidates when marine Natura 2000 areas are designated.

To sum up, habitat maps that rely solely on abiotic surrogates do not function well in describing patterns of biodiversity. Habitats can be abiotically similar but biologically very different, because communities differ along environmental gradients (e.g., salinity), as also concluded in other studies (Stevens and Connolly, 2004; Arponen et al., 2008; Jackson and Lundquist, 2016). We therefore suggest that, to locate and protect hotspots of biological diversity, the interpretations of many of the marine habitats need to be broadened, and a multifaceted basis of protection needs to be adopted. Our analyses of surrogacy also suggest that, in addition to habitats and rare species, also functionally important species should be included in MPA network planning in the Baltic Sea and elsewhere.

We want to emphasize that establishing a sufficient amount of MPAs does not safeguard the integrity of the marine ecosystem. Each of the MPAs also needs to be efficiently managed. Unfortunately, management plans are missing from a large part of the Baltic Sea MPAs (HELCOM, 2010, 2013b), and key restrictions are missing from many areas: e.g., fishing is not restricted in most of the Finnish MPAs. In the future analyses, it will be important to also study the level of protection in respect of the human pressures in the different MPA types. Such an approach will shed more light on the true ecological efficiency of the MPA network.

As always, data quality is a concern in spatial prioritization. Our data about marine biodiversity was of exceptionally wide taxonomic coverage and of high quality, originating from 140,000 standardized VELMU sampling sites. We are not aware of a similar data set elsewhere in the world, where the entire sea area of a nation is covered. Spatial prioritization becomes increasingly stable the more data is driving the analysis (Kujala et al., 2018), and therefore our confidence in the present analysis is high.

There are data that could potentially be used to refine the analysis. Integration of ecosystem services into the present analysis would be useful when planning the MPA networks, because these inform of benefits gained from the ecosystem that otherwise may remain concealed. Also, more detailed information on human pressures could be used, such as inclusion of human activities aiming at conflict resolution between biodiversity conservation and human activities (Moilanen et al., 2011b). This sort of data are not necessarily easy to get, and human pressures tend to shift in space (Joppa et al., 2016). A third major category of data that would improve the MPA network analysis are opportunity costs for alternative sea uses. Inclusion of costs allows identification of cost effective solutions and fair division of costs and benefits between stakeholders. Again, reliable information about opportunity costs is difficult to obtain. It is also notable that we have not considered temporal dimension in our analysis. Species distribution areas may shift for various reasons, including eutrophication and climate change, and human pressures tend to shift in space and time. Forecasting such changes by modeling methods would enable a precautionary approach to MPA network development.

This work illustrates that spatial prioritization applied on high-resolution marine SDMs can support the evaluation and design of MPA networks, and ecosystem-based marine spatial planning. The pre-requisites of such work include (i) broad biodiversity data that ideally covers both habitats and a large array of species, (ii) environmental data at a resolution that allows realistic ecological modeling, and (iii) an analysis path able to utilize these data, such as Zonation. Our approach and methods are applicable to any sea area where these prerequisites are met.

In summary, our analysis included (1) identification of biodiversity hotspots, (2) evaluation of the quality of marine habitats and MPAs, (3) evaluation of the surrogacy of habitats and species, (4) suggested expansion of the protected area network, and (5) an illustrative proposal for new MPA candidates. Our results indicate that, despite reaching the Aichi target 11 (10% of the sea area protected) and the Sydney promise (20 or 30% of habitats protected) the Finnish MPA network does not secure sufficient protection of important biological features of the marine ecosystem. Our approach can be refined and expanded by including various types of additional data (species, ecosystem services, human pressures, opportunity costs etc.) and expansion in space and time of the present work. Especially relevant would be the expansion of this work to the broader Baltic Sea context. Adequate data are not yet available for all countries, but several Baltic Sea countries are currently implementing or starting inventories of varying depth and breadth, allowing production of SDMs for wider areas. This gives hope that in some years’ time a reliable ecological prioritization like the present one would be possible for the entire Baltic Sea.

EV, AM, and MV designed the study as a whole and designed the spatial prioritization and EV implemented it. EV and JL designed and implemented the distribution modeling. EV wrote the first manuscript, with subsequent contributions from MV, JL, and AM.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.