Edited by: John A. Cigliano, Cedar Crest College, United States
Reviewed by: Carolyn J. Lundquist, National Institute of Water and Atmospheric Research (NIWA), New Zealand; Jose M. Fariñas-Franco, National University of Ireland Galway, Ireland
This article was submitted to Marine Conservation and Sustainability, a section of the journal Frontiers in Marine Science
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Marine Protected Areas (MPAs) are essential for safeguarding marine biodiversity. Various international and regional agreements require that nations designate sufficient marine areas under protection. Assessing the functionality and coherence of MPA networks is challenging, unless extensive data on species and habitats is available. We evaluated the efficiency of the Finnish MPA network by utilizing a unique dataset of ∼140,000 samples, recently collected by the Finnish Inventory Programme for the Underwater Marine Environment, VELMU. Using the quantitative conservation planning and the spatial prioritization method Zonation, we identified sites of high biodiversity and developed a balanced ranking of marine conservation values. Only 27% of the ecologically most valuable features were covered by the current MPA network. Based on the analyses, a set of expansion sites were identified that efficiently complement the ecological and geographical gaps in the current MPA network. Increasing protected sea area by just one percent point, would double the mean conservation cover, and specifically increase the protection levels of habitat types based on IUCN Red List of Ecosystems, key species, threatened species and fish reproduction areas. We also discovered that a large part of ecologically valuable species, such as many brown and red algae, blue mussels and eelgrass, exist in the underwater parts of rocky islands and sandy shores. These areas do not belong to the present (Finnish) interpretation of the habitats (e.g., reefs and underwater sandbanks) listed in the EU Habitats Directive. Neglecting these environments may lead to lack of protection of functionally important biodiversity. We emphasize that, in addition to establishing MPAs, also ecosystem-based marine spatial planning is needed to safeguard the integrity of marine biodiversity in the northern Baltic Sea. The spatial prioritization maps produced in this study are essentially environmental value maps which can also be used in impact avoidance, such as siting of wind energy and aquaculture, or in avoiding overfishing in the most valuable fish areas. Our approach and analytical procedure can be replicated in the Baltic Sea or elsewhere provided that sufficient data exist.
Marine ecosystems are facing unprecedented loss of biodiversity due to habitat destruction, a changing marine environment and increasing resource extraction (
International agreements require nations to establish ecologically coherent MPA networks to support and maintain marine processes and functions (
In Europe, the cornerstone of conservation has been the Habitats Directive (CD 92/43/EEC) (
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
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 (
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) (
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
Methodologically, our work relies on the Zonation method and software designed for ecologically based land use planning (
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.
Our analysis area consists of Finnish territorial waters and exclusive economic zone (EEZ), covering 21% (81,500 km2) of the Baltic Sea. Steep environmental gradients of salinity, turbidity, exposure and geomorphology characterize the Finnish marine areas, forming harsh marine habitats and conditions, where adaptation and specialization is necessary for survival. To the north, Bothnian Bay is shallow and low-saline, with exposed shores and monotonic geomorphology. The Quark, located in the middle of the Gulf of Bothnia, acts as a dividing biogeographical line between the north and south, beyond which survival of many marine species becomes impossible. Continuing south from the Quark, the Archipelago Sea with its 52,500 islands constitutes one of the most complex archipelago systems in the world (
The Finnish Inventory Programme for the Underwater Marine Environment (VELMU) has gathered information on species, communities and habitats during 2004–2016 from ∼140.000 locations. Video observations form the bulk of the data together with a reputable ∼28,000 diving sites (Figure
Dive (gray) and video (blue) points collected during the VELMU project 2004–2016 with zoomed-in example areas from the Bothnian Bay (I) and the Archipelago Sea (II).
The mean density of observation sites for videos is ∼4/km2, and for dive sites ∼3/km2 above 30 m depth, if considering areas where VELMU inventories are targeted. Most of the data represent rather shallow waters, where macrophytes dominate. In addition to the VELMU project, data from areas suitable for bottom fauna exist from other projects and national monitoring programs (see the section “Modeling of Species Distributions”). Observations are distributed in the marine space mostly through random stratified sampling; representing different environmental conditions, ranging from saline, exposed marine areas to enclosed, low-saline shallow bays. During the study years, additional targeted sampling has been conducted based on certain specific criteria, such as endangered species, certain marine environments and specific vulnerable habitats. Overall, these data provide an exceptionally good basis for ecosystem-based marine spatial planning and for analyses on marine biodiversity.
The EU Habitats Directive aims to protect Annex I Habitats (from here on referred as marine habitats). Of the listed habitat types, 69 occur in Finland, of which eight are associated with marine environments: (1) Baltic esker islands (1610), (2) Boreal Baltic islets (1620), (3) Boreal Baltic narrow inlets (1650), (4) Coastal lagoons (1150), (5) Estuaries (1130), (6) Large shallow inlets and bays (1160), (7) Sand banks (1110), and (8) Reefs (1170). Here, we utilized the existing models for (1), (2), (7), and (8) (
As Finland hosts extensive environmental gradients (see the section “Study Area”), we developed layers describing the varying nature of marine environments; seabed topography, hydrographical parameters, light conditions and eutrophication (Table
Environmental predictor variables developed and compiled for the statistical distribution modeling of the species, communities and IUCN Red List of ecosystems, and marine pressures utilized in the prioritization.
Predictor variable | Unit | Explanation | Methods |
---|---|---|---|
Bathymetry | m | Bathymetry model | Triangular irregular network tool in ArcGIS |
Bathymetric Position Index (BPI) with varying search radii | Index | An estimate of a higher topographic features than the surrounding environment, search radius 0.1, 0.2, 0.4, 0.8, 2, 4, 10, 20 km | Benthic terrain modeler tool in ArcGIS |
Bottom temperature | °C | Temperature (average, min, max) near the seabed (1 m) and temperature difference during the growing season | Random forests |
Bottom and surface salinity | PSU | Salinity near the seabed (1 m) and in the surface (1 m), corrected with the effects of rivers | Random forests |
Colored Dissolved Organic Matter (CDOM) | m-1 | Yellow substance; optically measurable component of the dissolved organic matter in the water | Kriging tool in ArcGIS |
Depth Attenuated Exposure (SWM(d)) | Index | Fetch + average wind speed + depth | Raster calculator in ArcGIS ( |
Distance to sandy shores | m | Closest distance to sandy shore | Cost distance in ArcGIS |
Effect of rivers | Index | The distance of fresh water traveled from the river discharge station, multiplied with the average riverine flow | Python in ArcGIS |
Euphotic, optical and Secchi depth | m | 1% of the radiation level, euphotic depth and Secchi depth | Envisat-MERIS satellite sensor products |
Oxygen variability | mg/l | Continuous oxygen (average, min) content | Boosted regression trees |
Rocky bottoms | % | The proportion of rocky bottom substrates (boulders and stones, 0.1–3 m) | Random forests |
Rock bottoms | % | Proportion of rock substrate | Random forests |
Sandy bottoms | % | Proportion of sandy substrates | Random forests |
Share of sea proportional to land area | % | Proxy for the complexity of archipelago; search radius 1, 5, and 10 km | Focal Statistics in ArcGIS |
Slope | ° | Slope of the seabed | Slope in ArcGIS |
Topographical shelter (TSI) | Index | Sheltering effect of topography | Hillshade in ArcGIS |
Total nitrogen and phosphorous | mg/l | Total nitrogen and phosphorous content in the water near bottom | Spline with barriers in ArcGIS |
Turbidity | FNU | Turbidity | MODIS-Aqua satellite product |
Unstable seafloors | % | Proportion of soft bottom substrates (gravel, sand, silt, mud, clay; <60 mm), unstable growing foundations | Random forests |
Coastal construction | Index | Distance and density calculations | Cost distance + focal statistics ArcGIS |
Frequent hypoxia | % | Probability of frequent hypoxia with ≤2 and ≤4.6 mg/l threshold value | Boosted regression trees |
Habitats lost | Index | Distance and density calculations to pressures: dredging (≥500 m3), harbors, dredging of shipping lanes, dumping of material, resource extraction, landfill | Euclidean distance + focal statistics in ArcGIS |
Reed belts | 1/0 | Calculation of the extent of reeds from Sentinel 2 instrument | Normalized Difference Vegetation Index in Erdas Imagine |
Habitat loss is the greatest threat to biodiversity (
Species distribution models (SDMs) are commonly used to inform a variety of ecological questions regarding, e.g., conservation planning, changing climate and biogeographical patterns (
Boosted regression trees (BRT), an ensemble method from statistical and machine learning traditions (
Random subsets (bag fraction) of data (50–80%) were used in the BRT modeling. The contribution of each tree to the next model (learning rate) was controlled by the cross-validated change in model deviance. Tuning of model parameters in general was dependent on sample size and the prevalence of the response variable, affecting the choice of learning rate. Higher tree complexities required slower learning rates (e.g., rare species), and vice versa.
Predictor selection is an automated process in BRT, as the algorithm ignores irrelevant variables in the model building. Predictor selection was performed only for the small datasets (i.e., rarely occurring species), where excess predictors increase the model variance. For modeling rare and threatened species with few occurrences, we applied the methodology of Ensemble of Small Models (ESM) for BRT (
Species records above a certain presence threshold were used in the BRT model iteration, depending on the model in question. In general, a threshold value of 0.1% was used for species distribution and abundance models and 10% for IUCN Red List of Ecosystems (Supplementary Table
Models were fitted in R 3.1.2. (
Zonation is an approach and software for ecologically based spatial prioritization, for the purposes of conservation planning, zoning, spatial impact avoidance, and other similar applications (
Zonation starts from the full landscape (seascape) and produces a spatial priority map by iterative ranking and removal of those grid cells that can be lost with smallest aggregate loss for biodiversity. This implies that areas to receive lowest ranks include hypoxic bottoms and areas where strong pressures have degraded water quality and habitats. Areas receiving highest ranks are the ones that host many species, ecosystems and habitats, including rare and highly weighted ones. A very important Zonation method is a form of analysis specifically developed for answering questions about PA network expansion (often called hierarchic analysis), in which the priority ranking is developed in two (or more) steps constrained by land use, land ownership or some other similar factor. In the present case, the hierarchic ranking was constrained by the present MPA network [see e.g.,
It should be noted that Zonation is not based on direct summing of layers. During iteration, it tracks what is remaining for each feature, and if a feature suffers loss (as is inevitable), the importance of its remaining occurrences goes up relative to features that do not suffer a loss at that particular ranking iteration (
An overview of the present prioritization analyses is shown in Figure
Schematic of work flow for spatial prioritization. Spatial prioritization starts with the data acquisition process. In this case data on species, environments, habitats, and human activities was collated, and pre-processed, for spatial prioritization by statistical modeling and extrapolation over the seascape at a high resolution of 20 m.
An integral part of Zonation analyses is the setting of weights. As a starting point, all features are equally weighted. Then again, there are numerous reasons why the weight of a feature might be modified, including Red List status, phylogenetic uniqueness, functional position, economic importance, or relative uncertainty of information (
Criteria used to moderate feature weights in the Zonation analyses.
Aggregate weight | Feature type | Features count | Sub-weighting criteria (effect) |
---|---|---|---|
1000 | EU Habitats Directive Annex I Habitats | 8 | Red List (1.5–2x) |
1000 | IUCN Red List of Ecosystems | 19 | National evaluation 2018 (2–4x) |
210 | Fish reproduction areas | 3 | Proxy for habitats of small-sized fish (equal sub-weights) |
3000 | Species (alga, bryophytes, invertebrates, vascular plants) | 125 | Key species (3x), higher taxonomy (2x), Red List (1.5x), non-indigenous species (-1x), harmful bacteria (-1x) |
1000 | Marine pressures | 11 | Negative effects (-1x), loss of habitats (-2x) |
Additional sub-weighting criteria were used inside relative weights for major data blocks (Table
Connectivity is an integral component of spatial analyses, and becomes relevant at a high resolution (as in this study), because individual small areas are linked to their neighborhood. Connectivity was induced into solutions using two very basic methods, with the primary objective of accomplishing such aggregation that would facilitate the logistics of decision making. These techniques are called matrix connectivity and edge removal. Matrix connectivity identifies and enables connectivity of similar habitats and on the other hand of habitats close to each other (
Zonation requires decision about certain analysis settings that influence the prioritization. Zonation includes several ways of aggregating conservation value across many biodiversity features. From alternatives available, we used one called the additive benefit function (ABF), which tracks feature performance along individual species-area curves, aiming at minimization of aggregate expected extinction risk (
The dataset used has a large spatial dimension, 205 million effective grid cells at the spatial resolution of 20 m. In order to accelerate computational times, we aggregated (summed) data into 40 m grid cells. Planning units of this size are sufficient for marine spatial planning and conservation management purposes. An acceleration factor (called warp factor) of 5000 was used, implying that each algorithm iteration the 5000 grid cells leading to lowest loss in conservation value were ranked and removed from the remaining seascape. Despite the acceleration, the resolution of the x-axis in the performance curves (see Results) is higher than 0.01%, which is sufficient for all practical applications.
A standard technique of contrasting analysis variants computed under different assumptions was used also here to gain useful information. (
Zonation post-processing analyses allow access to feature-level information about individual areas or area networks. Here, we used an analysis that allows getting information for pre-specified areas (groups of grid cell) that are identified to Zonation by inputting an additional mask file (landscape mask; LSM analysis) in which each area of interest is identified by a unique integer code (details in
The same operation was carried out for habitats in order to evaluate the quality of each habitat type and to identify good-quality habitat patches outside the existing MPA network.
The last part of the work was to identify potential MPA network expansion areas, which was primarily based on information gained from the hierarchical prioritization that accounts for the present MPA network. Expansion candidate areas were identified taking the highest ranked 3% of areas outside the present MPA network, which were filtered according to size (>1 km2), leading to a net 1% expansion of protected sea area. The limit of 1% was chosen for illustrative purposes – we expect that decision makers might well appreciate how much can be achieved starting from a modest 1% expansion consisting of comparatively large areas. Establishment of new MPAs carries an administrative burden, and very small MPAs would likely not be favored. Additionally, conservation value hotspots were identified. This was done by combining the priority rank map and the weighted range-size rarity map (i.e., weighted range-size corrected richness map), another standard output from Zonation analyses. The first of these describes a relative ranking that is balanced across features and the latter is a weighted sum that emphasizes locations having many features in them. The combination of the two has increased emphasis on species richness and ecosystem function compared to the priority rank map. Conservation value hotspots were identified from a 500 m moving window calculation applied on the product map.
Species distribution models were produced for 19 IUCN Red List of Ecosystems and for over 100 taxa representing algae, invertebrates and vascular plants, summarized in Figure
Statistical distribution models and their performance reported as deviance explained (%)
Based on the SDMs, habitats and fish reproduction area data, two priority rankings were produced using Zonation (i) an unconstrained “clean slate” solution and (ii) a hierarchical solution constrained by the present MPA network. Figures
Zonation priority rank maps across the Finnish seascape.
Second standard output of a Zonation analysis, performance curves.
The unconstrained priority rank map (Figure
The priority rank maps (Figures
The quantification of performance shown above (Figures
Figure
We evaluated the quality of individual MPAs based on the unconstrained prioritization solution and using the LSM analysis. Generally, the existing MPAs protected marine biodiversity fairly well, as median ranks were above 78% (Figure
Evaluation of the existing MPAs based on data from Landscape Mask (LSM) analysis (see details in section “Analysis Variants and Post-processing”). MPA categories: (1) HELCOM MPAs, (2) Natura 2000 sites, (3) National parks, (4) Nature reserves, (5) Private MPAs, and (6) Ramsar sites.
Based on top-priority areas from the constrained analysis, we show in Figure
Proposed top 3% MPA expansion areas. Suggestions are based on the product of priority ranks and weighted range-size rarity (see details in section “Analysis Variants and Post-processing”). Zoomed-in example images (I and II) are shown from the northern and southern parts of Finnish marine areas. The color ramp in these areas is different from the previous figures; here the ramp shows internal variation inside the top 3% area fraction. Areas of high conservation value inside the present MPAs are not shown.
Characterization of high-quality potential MPA expansion areas, based on mean rank, feature density and area, shown at a random order (full table in
Name | Area (km2) | Mean rank | Distribution (10/1/0.1%) | Feature density | A brief characterization of the area |
---|---|---|---|---|---|
Västerön archipelago | 52.8 | 88.3 | 4/17/53 | 18.9 | A variety of IUCN Red List of Ecosystems, marine algal species, key species |
Herakari | 17.4 | 84.6 | 4/22/46 | 59.4 | IUCN Red List of Ecosystems, various threatened species, water mosses |
Korpskär/Kobbfjärden | 86.7 | 70 | 1/17/68 | 5.4 | IUCN Red List of Ecosystems, key species, marine habitats, various alga, vascular plants |
Skalofjärden | 48.7 | 88.8 | 2/19/50 | 10.8 | High occurrence rate of charophytes, IUCN Red List of Ecosystems, threatened species |
Bay of Ravijoki | 14.9 | 89.7 | 1/7/22 | 81 | Various threatened species, IUCN Red List of Ecosystems |
Estuary of Tornio | 7.2 | 88 | 1/15/38 | 240 | Fish reproduction area, threatened species, a variety of brackish water species |
Kökar archipelago | 171 | 77.8 | 0/22/76 | 2.7 | IUCN Red Listed Ecosystems |
Måderviken | 29.5 | 88.6 | 0/5/32 | 13.5 | Threatened species, vascular plants |
Brändö | 95.8 | 78.9 | 0/9/49 | 18.9 | IUCN Red List of Ecosystems, key species, marine algal species |
Etukari archipelago | 18.2 | 78.8 | 1/16/22 | 8.1 | Occurrences of water mosses, IUCN Red List of Ecosystems, IUCN Red Listed species |
Saltviksfjärden | 11.03 | 89.9 | 1/4/18 | 35.1 | Coastal lagoon of good quality, important occurrence site for charophytes |
Surrogacy of species and marine habitats.
Our expansion suggestions would increase the conservation level of IUCN Red List of Ecosystems, Red Listed species, threatened species, ecosystem engineers supporting other marine life, and fish reproduction areas. In addition, increases would be achieved in the conservation status of Habitats Directive Annex I habitats (following the guidelines of the Promise of Sydney), and for marine habitats not represented by the Habitats Directive. Table
Within the EU, habitats are an accepted basis for MPA design, as much of the knowledge of marine nature in most of the countries relies on information about the locations of marine habitats. We evaluated how well species would act as surrogates for these habitats and how habitats perform as a proxy for species. Figure
The mismatch in species surrogacy of habitats can be seen clearly in an example image from the Archipelago Sea (Figure
Surrogacy analysis priority rank maps in an example area in the Archipelago Sea.
The habitats listed in the EU Habitats Directive Annex I cover 6% of the Finnish seascape, and is composed of: 3.2% in Reefs, 0.7% in Boreal Baltic islets, 0.8% in Coastal lagoons, 0.6% in Large shallow inlets and bays, 0.5% in Boreal Baltic narrow inlets, 0.4% in Sand banks, 0.08% in Baltic esker islands, and 0.9% in Estuaries. The existing MPA network protects 24% of Reefs, 32% of Boreal Baltic islets, 18% of Coastal lagoons, 34% of Large shallow inlets and bays, 40% of Boreal Baltic narrow inlets, 49% of Sand banks, 53% of Baltic esker islands, and 21% of Estuaries. Although these habitats are quite well covered by the MPA network, our analysis shows that they miss a large part of functionally important species occurring on rocky and sandy shores, such as major concentrations of brown and red algae, blue mussels and eelgrass. Using the LSM analysis, we assessed how much of the marine biodiversity features each of the habitat types maintain, and how individual habitat patches are ranked in the Zonation constrained solution. Utilizing this information, we were able to identify highly valuable habitat patches outside the current MPA network, and on the other hand evaluate the quality of habitat patches already protected.
Figure
Quality of patches of marine habitats based on Zonation LSM-analysis (see details in section “Analysis Variants and Post-processing”). Marine habitats: (1) Baltic esker islands, (2) Boreal Baltic islets, (3) Boreal Baltic narrow inlets, (4) Coastal lagoons, (5) Estuaries, (6) Large shallow inlets and bays, (7) Sand banks, and (8) Reefs.
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 (
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
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 (
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 (
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
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 (
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 (
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 (
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 (
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 (
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.
We acknowledge the support from the Academy of Finland Strategic Research Council (project SmartSea, Grant Nos. 292985 and 314225), and the Finnish Inventory Programme for the Underwater Marine Environment (VELMU) and the Finnish Ecological Decision Analysis project (MetZo), both funded by the Ministry of the Environment. We wish to thank the dedicated field staff from several institutes, especially Parks & Wildlife Finland. We wish to acknowledge CSC – IT Center for Science, Finland, for generous computational resources. We thank Tytti Kontula for providing the threat status 2018 report on IUCN Red List Ecosystems, Meri Kallasvuo for providing the fish reproduction area data, Anu Kaskela, Henna Rinne, Matti Sahla, Lasse Kurvinen, and Ville Karvinen for the habitat data, Marco Nurmi for the compilation of human activity data, Kari Kallio and Sofia Junttila for Envisat-MERIS satellite sensor products, and Meri Koskelainen for providing the reed belt data. We thank the two reviewers for insightful comments that significantly improved the manuscript. We thank Husö biological station of Åbo Akademi University for providing Åland islands biological data.
The Supplementary Material for this article can be found online at:
Coverage of distributions of different subgroups under different priority solutions. Red List of Ecosystems and Habitats, based on threatened status (NT, VU), key species and fish reproduction areas. A is the unconstrained solution (Figure
Species observed in the VELMU programme 2004-2016 from dives and videos, observation thresholds of species included in the model iterations, and a weighting group where each species belongs to (highest weighting criteria reported. If species belongs to another weighting group, the weights have been balanced accordingly).
MPA expansion suggestions reported at a random order, based on mean rank, feature density and size.