Marine biological valuation mapping of the Basque continental shelf (Bay of Biscay), within the context of marine spatial planning

Estuarine, Coastal and Shelf Science 95 (2011) 186e198

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Marine biological valuation mapping of the Basque continental shelf (Bay of Biscay), within the context of marine spatial planning Marta Pascuala, *, Angel Borjaa, *, Sarah Vanden Eedeb, Klaas Deneudtc, Magda Vincxb, Ibon Galparsoroa, Irati Legorburua a b c

AZTI-Tecnalia, Marine Research Division, Herrera Kaia, Portualdea s/n, 20110 Pasaia, Spain Marine Biology Section, Biology Department, University of Ghent, Krijgslaan 281, 9000 Ghent, Belgium Flanders Marine Institute, Wandelaarkaai 7, 8400 Oostende, Belgium

a r t i c l e i n f o

a b s t r a c t

Article history: Received 10 March 2011 Accepted 23 August 2011 Available online 27 August 2011

Marine Biological Valuation (BV) has increased in importance in recent years, due to the need to establish accurate maps of biodiversity value. However, there have been few exercises undertaken in Southern Europe, in putting a value on marine biodiversity whilst at the same time looking at several biological components. This paper presents the complete Biological Valuation Map (BVM) of the Basque continental shelf and estuaries, using the methodology developed for the Belgian Continental Shelf. It includes all available biological data (zooplankton, macroalgae, macrobenthos, demersal fish, seabirds and cetaceans), from 2003 to 2010. BVMs aim to compile all available biological and ecological information for a selected study area, allocating an integrated intrinsic biological value to the subzones within the study area. Here, the results highlight specific areas (such as Jaizkibel or Cap Breton Canyon), as having high or very high integrated BV, using all of the components. Furthermore, some biodiversity ‘hotspots’ have been identified, according to a specific ecosystem component (e.g. mid-parts of the Oka estuary, for macroalgae, and the Cap Breton Canyon, for cetaceans). Comparison with the results obtained from other European countries, and with previously high-importance delimited zones within the study area, showed similar spatial trends and patterns. Therefore, the objectives of this contribution are: (i) to analyse and establish a spatial ecological value map of the continental shelf of the Basque Country (southern Bay of Biscay), using present BV methods; (ii) to compare the results obtained to other European countries, and (iii) to explore the application of these results to the Marine Spatial Planning (MSP) and the European Marine Strategy Framework Directive (MSFD) requirements. This map can serve as a baseline for future MSP and can also be used for the determination of the environmental status, within the MSFD, for the qualitative descriptor 1 (biodiversity). Ó 2011 Elsevier Ltd. All rights reserved.

Keywords: biodiversity biological valuation goods and services marine spatial planning marine strategy framework directive Basque Country

1. Introduction Increased anthropogenic pressures on the marine environment, together with the potential for multiple use conflicts, have led to an increased interest in sea-use planning with particular emphasis placed upon Marine Spatial Planning (MSP) (Douvere and Ehler, 2009). Several European countries, such as Belgium, the Netherlands, Germany and the United Kingdom have already completed preliminary sea-use plans and zoning proposals for marine areas, within their national jurisdictions (Douvere et al., 2007; Douvere and Ehler, 2009). Some of these MSPs are related

* Corresponding authors. E-mail addresses: [email protected] (M. Pascual), [email protected] (A. Borja). 0272-7714/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecss.2011.08.031

to European legislation, such as the Marine Strategy Framework Directive (MSFD, 2008/56/EC), the integrated Maritime Policy (COM(2007) 575 final), or the Recommendation 2002/413/EC of the European Parliament and of the Council, concerning the implementation of Integrated Coastal Zone Management in Europe. However, this MSP approach is being introduced increasingly worldwide, related to the ecosystem-based management in marine waters (Borja et al., 2008a). Ecosystem-based management is defined by ICES (2003) as the comprehensive integrated management of human activities based upon the best available scientific knowledge about the ecosystem and its dynamics, in order to identify and take action on influences which are critical to the health of marine ecosystems, thereby achieving sustainable use of goods and services and maintenance of ecosystem integrity. The MSP and ecosystem-based management

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are concerned, amongst other objectives, with: (i) the integrated management and sustainable development of marine waters; (ii) marine environmental protection, and (iii) sustainable use and conservation of marine living resources and biodiversity (Douvere and Ehler, 2009). Biodiversity is a complex, abstract concept which includes all organizational levels of the ecosystems and can be associated with a range of benefits to human society (Nunes and van den Bergh, 2001). However, goods and services accruing from living organisms are used sometimes as a proxy for those accruing from biodiversity (Beaumont et al., 2007). The value of biodiversity can be assessed in terms of its impact on the provision of inputs to production processes, as well as in terms of its direct impact on the regulation of the nature-ecosystem-ecological functions relationships. In addition, biodiversity is one of the 11 qualitative descriptors which must be investigated within the MSFD (Cochrane et al., 2010), to assess the environmental status of European seas (Borja et al., 2010). Oceans and coastal ecosystems have long been recognized as one of the most important natural resources (Costanza, 1999), as they provide an array of ecosystem services that directly or indirectly translate into economic services and values to humans (Hanley et al., 2003; MEA, 2003; Eggert and Olsson, 2009; Remoundou et al., 2009 and Granek et al., 2010). The provision of some goods and services is linked to biodiversity, although the exact mechanisms and quantifications of this linkage are not yet well known (Nunes and van den Bergh, 2001; Balvanera et al., 2006; Beaumont et al., 2006 and Worm et al., 2006). The utilisation of this goods and services approach has the capacity to play a fundamental role in the ecosystem-approach (Beaumont et al., 2007), as the ecosystem-based management of marine ecosystems requires integrating the pressures and demands of society, economy and environment (Granek et al., 2010). Biodiversity issues are playing an increasingly significant role in marine environmental policy (DEFRA, 2002, 2006; Sheppard, 2006) and need to be evaluated within the framework of the MSP. Different approaches for BV have been developed recent in times (Costanza et al., 1997; de Groot et al., 2002; Balvanera et al., 2006; Beaumont

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et al., 2006; Derous et al., 2007; Nijkamp et al., 2008; and JonesWalters and Mulder, 2009). The valuation of biodiversity requires the use of special valuation tools (Nunes and van den Bergh, 2001); these respond to the constant requests of policy-makers and marine managers, for reliable and meaningful biological baseline maps, to be able to make well-deliberated choices concerning sustainable use and conservation within the marine environments. BVM aims at compiling all available biological and ecological information for a selected study area, allocating an integrated intrinsic biological value to the subzones within the study area (Derous et al., 2007). Therefore, they can be used as baseline maps for future MSP, marine policy and management approaches. Within this context, in the Basque Country (northern Spain, Fig. 1), substantial information is available for a complete MSP of this area, within the Bay of Biscay. Hence, geomorphological mapping (Galparsoro et al., 2010), habitat mapping (Chust et al., 2007, 2008), and economically-relevant species habitat suitability (Galparsoro et al., 2009) have been investigated. Such information allows for an improved knowledge of habitats, uses, goods and services, and the ecosystem-based management of marine waters (Borja et al., 2008a; Douvere and Ehler, 2009). Finally, it was applied in the assessment of the environmental status, within the framework of the MSFD (Borja et al., 2011). Therefore, the objectives of this contribution are: (i) to establish and analyse a spatial ecological valuation map of the continental shelf of the Basque Country (southern Bay of Biscay), using available BV methods; (ii) to compare the results obtained, to other European countries, and (iii) to explore the application of these results to the MSP and MSFD requirements. 2. Material and methods 2.1. Study area In the Basque Country, studies have been undertaken over the past 25e30 years, monitoring estuarine, coastal and offshore marine waters, within the framework of European, national and regional projects. Borja and Collins (2004) give a synthesis of the

Fig. 1. Study area of the Basque continental shelf and estuaries (Bay of Biscay), including all geographical locations cited in the text. The Grid 1 study area extends up to 200 m water depth, including subzones of 0.06 km2; Grid 2 study area doubles this extension with subzones of 9 km2.

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methods used and the ecosystem components sampled (which included zooplankton, macroalgae, macrobenthos, fishes, mammals and seabirds). A 2301.5 km2 study area (Fig. 1), containing the 12 Basque estuaries and the coastal/continental shelf area up to a water depth of 200 m, was divided into 250
250 m cells grid (see Grid 1, Fig. 1), for the valuation of zooplankton (on the basis of limited sampling points), macrobenthos and macroalgae (sessile ecosystem components). This area was extended up to 5042.6 km2, divided into 3
3 km resolution grid (see Grid 2, Fig. 1) for the valuation of cetaceans and demersal fish data (highly mobile ecosystem components). The selection of the size of the subzones differs for the ecosystem components under consideration, responding to ecologicallymeaningful reasoning. However, such a selection concerns compatible (nested) grids, meaning that the corner points of the 3
3 cells grid correspond with the 250
250 cell grids corner points. No subdivision was applied to the valuation of seabirds, as non-point data were used in its valuation.

2.2. Databases All marine ecosystem components, for which detailed spatial distribution data were available, were included and integrated into a database, in order to obtain a BVM. Although data for some components were available since 1944, due to uneven data availability and sampling effort throughout the total sampling period (Fig. 2a,b), it was decided to study the BVM for the period 2003e2010. This decision permits the comparison of similar sampling effort and avoids shifts produced by global factors, such as climate change, which is affecting the area (Chust et al., 2011). In such a way, following this decision, the maximum information available over the whole of the ecosystems components coverage was assured. The zooplankton database covered two sampling years (2003 and 2004), which accounted for a total of 27 samples, distributed over a regular grid on specific transects. The macroalgae database holds information on percentage of spatial cover. Due to the close

Fig. 2. (a) Availability of data from sampled sites; (b) sampling effort, per ecosystem component, for the period 1982e2010.

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relationship between the physical characteristics of the substrata and the macroalgae communities, separating the valuation of softbottom (mud, sand and gravel) and hard-bottom (rock) macroalgae data was decided upon, as sediment structure is important in the distribution of different species (Gray, 2002) and in the characterisation of different habitats. The soft-bottom macroalgae data period ranged from 2003 to 2008 (187 samples); in comparison, the hard-bottom macroalgae data ranged from 2003 to 2009 (683 samples). Macrobenthos was sampled intensively and studied during the period 2003e2009 (891 soft-bottom and 386 hardbottom samples). The database consisted of a set of sample sites where abundances (per sampled surface area) were known. Fish abundance data, from 2003 to 2010, were obtained from trawling capture and discards surveys, in coastal waters, together with beam-trawl surveys undertaken in estuaries (Uriarte and Borja, 2009). A total of 265 samples were used: the data were standardized to a comparable sampling area, based upon the trawled distance and the width of the trawl net. Trawl data covering multiple grid cells were modified, such that every grid cell passed by the trawl was ascribed the density value of the entire trawl. Due to the lack of spatial data on seabird presence, information on patterns of breeding and foraging was gathered following an intensive bibliographical search, for 1996e2009 (Galarza, 1997; Franco et al., 2004; Boyd et al., 2006; Ocio and Astigarraga, 2007 and SEO/BirdLife, 2009). The cetacean database contains ‘opportunistic’ observational spatial point data between 2003 and 2007 (91 observations), with a total of 11 different species of sea mammals accounted for. As implied by the term ‘opportunistic’, these observations were not obtained in surveys associated with specific sea mammal abundance. Therefore, observer effort and error was not determined; as such, their BV should be treated with caution. Finally, general data quality control was undertaken (geographical coordinates, dates, time and taxonomy). Taxonomy was checked against the ERMS (European Register of Marine Species), in order to avoid the use of synonymous taxa that could led to the overestimation of the number of species. 2.3. Biological valuation method 2.3.1. Method application The BV method used here is based upon the Derous et al. (2007) approach. This method has been applied to the Belgian part of the North Sea (Belgium; Derous et al., 2007), the Pico-Faial Channel (Portugal; Rego, 2007), Puscz Bay (Poland; Weslawski et al., 2009), Isles of Scilly (UK; Vanden Eede, 2007), Flamborough Head (UK), and Sylt-Rømø (Denmark; Forero, 2007). This methodology aims to provide an integrated view on nature’s intrinsic non-anthropogenic value of the subzones (relative to each other), within a study area (Derous, 2007). By querying a set of assessment questions (Table 1), within the database and

Table 1 Set of assessment questions criteria, related to the different structure and processes of biodiversity (Derous et al., 2007). Q1: Is the subzone characterized by high counts/coveragea of many species? Q2: Is the abundance/coveragea of certain species very high in the subzone? Q3: Is the subzone characterized by the presence of many rare species? Q4: Is the abundance/coveragea of rare species high in the subzone? Q5: Is the abundance/coveragea of habitat-forming species high in the subzone? Q6: Is the abundance/coveragea of ecologically-significant species high in the subzone? Q7: Is the species richness in the subzone high? a When only coverage data and not abundance data was available, as occurring with the macroalgae ecosystem component data.

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through mathematical algorithms, it is possible to visualize all the biodiversity aspects linked to the biological and ecological valuation. These questions were determined within the context of an European Workshop, following expert judgement and a focus upon their criteria on rarity and aggregation-fitness consequences (Derous et al., 2007). An overall summary of the BV methodology, applied to the Basque Country, is shown in Fig. 3. Derous et al. (2007) determined the criteria on rare species, by their percentage of occurrence in the samples: rare species were defined as those appearing in less than 5% of the studied subzones. However, since there is no consensus on how to define rareness or commonness, Josefson’s (2009) definition of rareness was selected for this valuation process and it comprises the percentage of total abundance criteria. This latter author, following the definition used by Gering et al. (2003), defined rare species as those that comprised <0.05% of the total number of individuals while common species were defined as those >0.5% of total number of individuals. The assessment questions used in this study, for scoring the biological value of a subzone, are listed in Table 1. Questions 5 and 6 were considered particularly important for the valuation procedure; thus, considerable effort was put into distinguishing between Habitat-Forming (HF) and EcologicallySignificant (ES) species.The selection of the species regarded within each of these categories is based upon intensive bibliographical information gathering and local expert judgement, for each component of the ecosystem. The criteria to determine zooplankton ES species were obtained from Gannon and Stemberger (1978) and Franks et al. (2001); these are used as a measure of the trophic conditions of an area, together with the taxonomic group’s top-down forces and structure in food webs (Sommer et al., 2000; Sommer, 2000; and Sommer and Stibor, 2002). In such a way, copepods (mostly calanoids and cyclopoids), cladocerans and appendicularians were identified as the most ecologically-important zooplankton taxonomic groups. A step forward was taken also in determining the importance of those groups internally, providing, as a result, their classification as having high or very high biological value (see Table 1, in Supplementary Material). Selection of macroalgae HF and ES species followed the description of the Basque benthic communities as outlined by Borja et al. (2004), taking into account the structuring species, together with a list of underlying associated species. This classification, together with further EUNIS (European Nature Information System) habitat classification results and expert judgement, led to the final HF and ES species selection. To select out of 1597 macroinvertebrate species, those classified as HF and ES, it was decided to identify as HF species those considered in Borja et al. (2004) as structural, habitat modifiers or ‘engineers’ species. In such a way, on the basis of ecological considerations (Bouma et al., 2009), habitat-forming species were further intra-classified into Autogenic (Au) and Allogenic (All) species: firstly being those which provide, or modify, a habitat through their presence or physical structure (creating what are known commonly as ‘community of’); and secondly, those which alter the environment through their activities (burrowers, dam creators, etc.). According to Bouma et al. (2009), ecosystem engineers may affect biodiversity in coastal sediments with: an increase in habitat complexity and diversity, provided by autogenic ecosystem engineers; and a decrease in biodiversity and simplification of structures complexity, provided by allogenic species (Crooks, 2002). ES species determination followed a functional guild approach, as characterised by Gaudencio and Cabral (2007). Their (7) distinct trophic groups (carnivores, herbivores, filter feeders, surface deposit feeders, sub-surface deposit feeders, filter feeders/detritivores, carnivores/detritivores) was reduced (to 5), by

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Fig. 3. Protocol for the application of the marine biological valuation to the Basque Country study area, including six hypothetical subzones. The values and reliability labels are also hypothetical and are used to illustrate the protocol (modified from Derous et al., 2007).

grouping surface deposit feeders and sub-surface deposit feeders into a single group (named as deposit/detritus feeders) and eliminating the groups of filter feeders/detritivores and carnivores/ detritivores. The determination of the most appropriate group for each of the species followed bibliographical information gathering (Fauchald and Jumars, 1979; Hily, 1984; and Antoniadou and Chintiroglou, 2006). Fish-richness maximum values, as well as the characterisation of ES species, were determined following the indicator boundaries assigned by Uriarte and Borja (2009). These authors state a maximum value for estuarine demersal fish-richness, at those higher than 9; they lists also ES species according to their trophic composition, as omnivores or piscivores. Differently valued buffered ring areas were determined around each of the seabird breeding areas, depending upon the total number of breeding pairs that were gathered at those. GIS-aided spatial tools were used further to merge the information, to obtain a final mapping of the BV of seabirds. It should be noted that subjectivity cannot totally be excluded in this BV method and, as such, this selection should be regarded as being based upon expert judgment assessment. A detailed classification of the selected species, per ecosystem component and for each category, is shown in Table 1 of the Supplementary Material. Due to the absence of subzone specific data, quantitative scoring is often impossible and the subzones are weighted qualitatively, scored against each other; or semi-quantitatively, ranking subzones in categories of high, medium or low values (Derous et al., 2007). In the present study, each of the ecosystem components was evaluated separately by averaging the scores of the assessment questions used, where each assessment question had an equal weight over the total score. The integrated biological value of each of the subzones was determined then by averaging the values obtained for the different ecosystem components (when such values were available). Five value classes were used in the proposed scoring system (very low, low, medium, high and very high biological

values) as they permitted an improved detection of value patterns, without losing too many details. In order to avoid possible bias that could occur when the amount of information for each subzone was unequal (Breeze, 2004), ‘reliability and sampling effort’ labels were attached to each of the BV, for a better interpretation of the results. Reliability and sampling effort labels display the quality and amount of the data used, respectively, to assess the biological value. The results of the BV of the study area were then mapped, named as BVM, which integrates all available biological information for the different ecosystem components, where each subzone within the area was assigned a colour corresponding to its value. Reliability/Sampling effort were indicated over the BVM, using different fillings as indications. 2.3.2. Interpolation The biological value for each of the ecosystem components cannot be calculated at all of the cells along the Basque coast, but only at those locations sampled. However, values can then be extrapolated to the sites where no samples are available, using GISaided inter-and extrapolation methods. Interpolation provides values at such points where no measurements are available, by converting point data to surface data. This approach permits the creation of full-coverage BVM, for each of the ecosystem components and for the whole of the coastal/shelf areas of the Basque Country. Different interpolation methods were used for each ecosystem component. Hence, an interpolation following the concept of a predictive Habitat Suitability Model (HSM), which relies upon the close association between the benthic communities and their physical habitat (Degraer et al., 2008), was performed on the macrobenthos and macroalgae data over the specific HSM map developed by Galparsoro et al. (2009). The availability of detailed abiotic habitat information allows for the detection of small-scale patchiness within the macrobenthos (Derous, 2007).

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Water depth and coastal/continental shelf slope have been shown to be two of the major forces that direct cetacean gatherings (Mangion and Gannier, 2002; Cañadas et al., 2002). Due to this observation, the cetacean biological value obtained was extrapolated to the total study surface area covered by calculating the biological value at the seabed steepest slopes (>50%) and applying the values obtained to other areas of similar slope. For the zooplankton and cetaceans databases, the Kriging interpolation method (point linear variogram model) was performed (Surfer Mapping System Version 8.02; Golden Software). Due to the absence of criteria to delimit the seabirds or demersal fish ecosystem components

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movements, no interpolation method was used for these two components. The reliability of the results obtained when fulfilling some kind of interpolation, can be consulted through the reliability mapping for each of the ecosystem components. 3. Results 3.1. Biological valuation of each ecosystem component Some areas showed high or very high BV, according to a specific ecosystem component (see Figs. 1e6, in the Supplementary

Table 2 Summary of mean and standard deviation values for each ecosystem component and the total Basque Country, per estuary and different coastal water depth ranges: (a) biological value; (b) reliability value; and (c) sampling effort value. Zone (a) Barbadun Outer Nervión Inner Nervión Butron Oka Lea Artibai Deba Urola Oria Urumea Oiartzun Bidasoa 0e25 m 25e50 m 50e100 m 100e150 m 150e200 m (b) Barbadun Outer Nervión Inner Nervión Butron Oka Lea Artibai Deba Urola Oria Urumea Oiartzun Bidasoa 0e25 m 25e50 m 50e100 m 100e150 m 150e200 m (c) Barbadun Outer Nervión Inner Nervión Butron Oka Lea Artibai Deba Urola Oria Urumea Oiartzun Bidasoa 0e25 m 25e50 m 50e100 m 100e150 m 150e200 m

Zooplankton

Macroalgae

Macrobenthos

Demersal Fish

Seabirds

Cetaceans

Total

No Data No Data No Data No Data No Data No Data No Data No Data No Data No Data No Data No Data No Data 4.50  0.58 4.50  0.58 4.40  0.55 4.33  0.52 4.50  0.58

3.27   0.49 3.83  0.54 3.01  0.12 3.26  0.60 3.31  0.52 3.16  0.50 3.09  0.46 3.09  0.56 3.10  0.30 3.20  0.56 3.10  0.30 3.22  0.42 3.09  0.29 3.37  0.71 3.36  0.54 No Data No Data No Data

3.33 3.92 3.01 3.50 3.42 3.20 3.13 3.23 3.03 3.29 3.12 3.21 3.02 3.73 3.46 3.42 3.47 3.21

                 

0.47 0.27 0.12 0.51 0.50 0.40 0.34 0.42 0.17 0.47 0.33 0.41 0.16 0.44 0.50 0.49 0.60 0.98

2.00 3.67 3.00 2.00 3.00 2.00 4.00 2.00 2.00 2.00 2.00 2.07 2.00 2.79 3.80 3.71 3.88 4.29

                 

0.00 0.58 0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.38 0.00 0.98 0.45 0.49 0.64 0.76

3.02 3.50 3.00 4.00 3.01 3.23 3.00 3.00 3.00 3.36 3.05 3.11 3.00 3.83 3.71 2.62 1.25 1.00

                 

0.13 0.58 0.00 0.00 0.08 0.42 0.00 0.00 0.00 0.50 0.22 0.42 0.00 0.82 1.07 1.07 0.50 0.00

No Data No Data No Data No Data No Data No Data No Data No Data No Data No Data No Data No Data No Data 1.48  0.88 2.52  1.21 2.14  1.25 2.38  1.02 2.25  0.97

2.10 3.61 3.00 2.95 3.01 2.21 2.98 2.15 2.11 2.36 2.05 2.11 2.15 3.14 2.84 3.18 3.24 2.33

                 

0.30 0.52 0.00 0.40 0.08 0.41 0.14 0.36 0.32 0.50 0.22 0.42 0.36 0.94 0.96 1.04 1.12 1.01

No Data No Data No Data No Data No Data No Data No Data No Data No Data No Data No Data No Data No Data 3.00  0.00 2.67  0.58 2.40  0.89 3.00  0.00 3.00  0.00

2.36  0.94 1.05  0.30 2.30  0.96 1.75  0.97 1.75  0.97 1.85  0.99 1.85  1.00 2.94  0.34 2.94  0.35 2.40  0.91 2.85  0.54 2.67  0.76 2.69  0.72 1.30  0.71 1.67  0.95 No Data No Data No Data

2.18 1.03 2.31 1.84 1.41 1.96 1.89 2.89 2.79 2.43 2.85 2.59 2.64 1.30 1.45 1.65 2.03 3.00

                 

0.99 0.24 0.95 0.99 0.81 1.00 1.00 0.46 0.61 0.94 0.54 0.80 0.78 0.71 0.80 0.93 1.01 0.00

3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 3.00 2.00 2.50 2.50

                 

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.71 0.71

3.00 2.67 1.40 2.23 1.66 2.52 2.00 2.00 2.61 2.70 2.14 1.85 1.61 2.51 2.29 2.29 2.00 1.50

                 

0.00 0.50 0.51 0.43 0.51 0.50 0.00 0.00 0.49 0.48 0.35 0.46 0.49 0.67 0.95 0.95 1.00 0.71

No Data No Data No Data No Data No Data No Data No Data No Data No Data No Data No Data No Data No Data 2.00  0.00 2.00  0.00 2.00  0.00 2.00  0.00 2.00  0.00

2.76 2.05 2.37 2.09 1.78 2.27 1.59 1.98 2.57 2.50 2.20 2.11 2.01 1.36 1.58 2.02 1.84 2.29

                 

0.46 0.70 0.66 0.58 0.51 0.77 0.50 0.16 0.55 0.52 0.40 0.32 0.48 0.70 0.79 0.77 0.81 0.76

No Data No Data No Data No Data No Data No Data No Data No Data No Data No Data No Data No Data No Data 2.07  0.27 2.19  0.75 1.95  0.69 2.75  0.50 2.75  0.50

1.00  0.00 1.00  0.00 1.00  0.00 1.19  0.40 1.50  0.50 1.11  0.32 1.85  0.36 1.00  0.00 1.00  0.00 1.00  0.00 1.00  0.00 1.04  0.20 2.95  0.22 1.63  0.82 1.79  0.68 No Data No Data No Data

1.00  1.58  1.02  1.00  1.00  1.00  1.00  1.00  1.00  1.00  1.00  2.00  1.00  1.31  1.29  1.06  1.14  1.00 

0.00 0.51 0.19 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.54 0.55 0.27 0.53 0.00

3.00 2.67 2.00 3.00 2.00 2.00 2.00 3.00 2.00 2.00 2.00 2.00 2.00 2.13 1.50 1.00 1.17 1.40

                 

0.00 0.58 0.07 0.00 0.00 0.17 0.00 0.00 0.00 0.00 0.00 0.27 0.00 0.74 1.00 0.00 0.41 0.55

3.00  2.67  1.40  2.23  1.66  2.52  2.00  2.00  2.61  2.70  2.14  1.85  1.61  2.51  2.29  2.29  2.00  1.50 

0.00 0.50 0.51 0.43 0.51 0.50 0.00 0.00 0.49 0.48 0.35 0.46 0.49 0.67 0.95 0.95 1.00 0.71

No Data No Data No Data No Data No Data No Data No Data No Data No Data No Data No Data No Data No Data 1.00  0.00 1.00  0.00 1.00  0.00 1.25  0.45 1.50  0.76

2.25 2.85 1.57 1.91 1.49 1.57 1.59 1.49 1.60 1.87 1.20 1.60 1.65 2.72 2.19 1.95 1.87 1.55

                 

0.43 0.43 0.50 0.77 0.50 0.52 0.49 0.50 0.49 0.34 0.40 0.72 0.48 0.55 0.75 0.69 0.71 0.74

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Material). Zooplankton shows an almost even distribution of very high BV along some 59.2% of the Basque continental shelf (Fig. 1a, Supplementary Material). The remaining 40.8% was classified as high BV areas and were scattered along the eastern, central and western parts of the study area. No sampled point data were gathered for the BV characterisation of the estuarine zooplankton. Therefore, the applied interpolation omitted the total of the estuaries (represented as No Data in Table 2). Due to the low sampling effort over the total of the study area and the application of the interpolation methods, whose feasibility cannot be measured, most of the reliability of the Basque continental shelf area was characterised as low (Fig. 1b, Supplementary Material). However, reliability was high in those places where actual samples were collected. Similar results were found when examining the sampling effort characterisation (Fig. 1c, Supplementary Material), with medium or high sampling efforts where actual samples were located. For macroalgae, 47% of the study area has medium BV and 47% high BV (Fig. 2a, Supplementary Material). No macroalgae data were obtained for depths greater than 50 m and the outer part of the Nervión estuary showed a slight increase in its macroalgae BV, if compared with the remainder of the estuaries. However, in terms of the reliability of these results, differences between estuaries and between depths could be identified (Fig. 2b, Supplementary Material, and Table 2b). The Deba, Urola, Urumea, Oiartzun and Bidasoa estuaries showed the highest reliability values in the medium to high boundary, whilst the outer part of the Nervión estuary showed the lowest reliability value. All of the remaining estuaries, as well as shallowest continental shelf depths, were associated with low to medium reliability values. The macroalgae ecosystem component’s sampling effort was mostly low along all the estuaries sampled, with the exception of the Oka and Artibai estuaries, approaching medium sampling efforts, whilst the Bidasoa estuary showed almost high sampling effort. For macrobenthos, some 45.7% of the area had a medium BV, both in most of the estuaries and at various depths, although some high macrobenthos BV were also found for the outer parts of the Nervión estuary, Butron and at water depths less than 25 m, covering a total of 54.2% of the study area (Fig. 3a, Supplementary Material). However, these high BV results are associated with low reliability values (68.8% of the area). The 30% resting areas showed medium or high reliability values (Table 2b). A low sampling effort characterises all the sampled macrobenthos ecosystem component areas, with the most sampled locations being the Oiartzun estuary and the outer part of the Nervión estuary (Table 2c). Demersal fish shows the BV ranging from low to very high (Fig. 4a, Supplementary Material). Almost 5% of the sampled area had a low BV and was located at the most easterly Basque Country estuaries. The rest of the most western estuaries, together with the defined depth ranges (84% of the area), had medium or high BV. Very high BV was found farther offshore, at depths greater than 200 m. Almost totally high reliability values characterise the demersal fish BVM, with medium reliability within the 50e100 m depth range (Fig. 4b, Supplementary Material). The associated sampling effort values ranged from low to high, with a maximum percentage of coverage with low sampling effort values (Fig. 4c, Supplementary Material). Seabirds show their BV ranging from very low to very high (Fig. 5a, Supplementary Material). Nonetheless, most of the surface area studied involves very low and medium BV, whilst high and very high BV are centred in some specific areas, water depths of less than 50 m along the western coast, such as the areas around Cape Villano, Cape Matxitxako and Cape Ogoño, as well as at the eastern coast locations of Cape San Antón and Ulia (see Fig. 1, for location details). Seabirds within the Basque estuaries have medium BV

characterisation, with some estuaries characterised with high BV, i.e Butron and the outer part of the Nervión estuary. Reliability and sampling effort high values are associates also with those high BV areas, with a noticeable decrease in reliability and sampling effort, moving farther offshore (Fig. 5b,c, Supplementary Material). Cetaceans reveal areas where their BV ranges from very low to very high, with a clear pattern of an increase in BV in areas >200 m water depth (Fig. 6a, Supplementary Material). However, there are also some scattered high BV areas, which could be of great importance as possible corridors, towards the high valuable zones. One of these zones is located in the coastal areas surrounding Jaizkibel (see location, in Fig. 1) where a very high BV ‘spot’ can be found. Most reliability values for cetaceans are medium: sampling effort values are mostly low, with some medium values lying within the 150e200 m depth range (Fig. 6b,c, Supplementary Material).

3.2. Integrated biological valuation The integrated BV of the whole of the Basque Country continental shelf and estuaries was 2.29  0.44, which falls into the low BV characterisation. The average reliability value was 2.08  0.58, falling into the medium reliability value characterisation. Very low and low BV areas covered 6.9% and 36.8% of the surface, respectively; they were located mostly along the Basque continental shelf, associated with mid to high water depths, with lower BV over the western parts of the shelf. In comparison, most of the medium BV areas were found at the eastern and lesser water depth parts of the shelf, covering a total area of 52.8% of the study area. The remaining 3.5% of the area, located around Jaizkibel and some of the deeper parts of the Cap Breton Canyon (Fig. 1), obtained high to very high BV (Fig. 4a). At the same time, 10% of the area covered obtained a low reliability value and was located at greater depths, around the Cap Breton Canyon. Also, 29% of the study area obtained high reliability values and were located within some of the estuaries, as well as at mid to high water depths over the eastern parts of the continental shelf. The remaining area (61%) attained a medium reliability value (Fig. 4b). The sampling effort value, averaged over the entire study area was 1.83  0.56; this falls into the lowmedium sampling effort value characterisation. Hence, 47% of the study area obtained medium sampling effort values. Most of the western and deep continental shelf areas (34%) showed low sampling effort values, whilst 20% of the remaining area, located mostly along the eastern part of the Basque coast, had high sampling effort values (Fig. 4c). In order to analyse the BV status of the different water bodies within the Basque Country, the biological, reliability and sampling effort values, for the 12 estuaries and several coastal water depth ranges, were derived (Table 2a,b,c). Hence, 85% of the Basque continental shelf area lies beneath the 50 m depth isobath; it has an average of 2.81  1.03 low to medium BV. The highest BV over the continental shelf is located in the water depth of around 50e150 m. Overall, estuaries within the Basque Country showed similarly low BV characterisation (Fig. 5a, Table 2a). Slight differences were noticeable also when making comparisons between them: the BV in the Nervión estuary’s outer part was 2.77  0.42, at the boundary of low to medium BV; in the Butron estuary the BV was 2.40  0.50, distinguishing also this estuary from the others. Highest reliability values were found associated with the Barbadun, Urola and Oria estuaries, with medium to high reliability. This observation enhances the confidence level of the BV obtained in these areas. On the other hand, the Oka, Artibai and Deba estuaries were associated with the lowest reliability values, between low to medium reliability characterisation. The remaining (7) estuaries obtained medium reliability values.

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193

Fig. 4. (a) Integrated Biological Valuation Map (BVM) of the Basque continental shelf and estuaries; (b) reliability of the method used, within the area; and (c) sampling effort maps.

In coastal areas, some of the reliability value differences can be identified along an increasing water depth gradient: low reliabilities were found in the 0e25 m range, increasing within the 25e50 m range etc. (Fig. 5a, Table 2b). The sampling effort in most of the estuaries and the greatest depth ranges sampling effort was similar, having low to medium characterisation. However, the Barbadun estuary and the 25e50 m depth range’s sampling effort were the only areas with a medium characterisation. In comparison, the Nervión estuary’s outer part and areas of less than 25 m water depth were characterised as having medium to high sampling effort (Fig. 5b, Table 2c). 4. Discussion 4.1. Biodiversity ‘hotspots’ for ecosystem components The zooplankton BVM does not indicate real ‘hotspots’ of an high zooplankton value, with a relative homogeneous distribution

of high and very high BV over the entire area (Fig. 1, Supplementary Material). However, some authors have described an increase in abundance near the coast and adjacent to the Cap Breton Canyon (Albaina and Irigoien, 2007). The BVM for macroalgae (Fig. 2, Supplementary Material) shows high values over specific parts of the estuaries, such as the middle part of the Oka estuary, together with shallow coastal waters, coinciding with the results of some previous studies (Díez et al., 2003, 2009b). The BV for the macrobenthos was medium for the whole of the Basque continental shelf, with some high BV areas (Fig. 3, Supplementary Material) which are found generally associated with the 25e50 m water depth range, both in soft- and hardbottom sediments. Martínez and Adarraga (2001), investigating the bathymetric distribution of communities along the Basque coast, found maximum abundances and richness in water depths of around 35 m, with secondary maxima at around 100 m and 225 m. Biodiversity ‘hotspots’ for demersal fish are found in the water depths of around 150e200 m, over the most eastern part of the

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Fig. 5. Averaged Biological Values and standard deviations, together with: (a) reliability values; and (b) sampling effort values, for all the Basque estuaries and several coastal fringes. Note: Low, medium and high reliability and sampling effort boundaries are plotted.

Basque continental shelf (Fig. 4, Supplementary Material). This observation agrees with the high richness, diversity and dominance values found during many sampling campaigns undertaken by the Spanish Institute of Oceanography, at the same locations (Sánchez et al., 2002). Some of the lower values outside of this area could be related to the impacts of bottom trawl fisheries (Sánchez and Olaso, 2004; Serrano et al., 2006). Shallow water areas (<30 m) showed higher seabird BV than the remainder, with some ‘hotspot’ locations along the western coast (including Capes Villano, Matxitxako and Ogoño) and in eastern areas around Ulia (Fig. 5, Supplementary Material). The depth boundary is established by the maximum foraging depth at which the European storm-petrel feeds (J. Hidalgo (Ornithological Society Lanius), personal communication), with the above locations being important for seabird breeding (Franco et al., 2004). In fact, these areas have been proposed for conservation by international seabird protection associations (Sociedad Española de Ornitología (SEO/ BirdLife, 2009)). Additionally, new important areas (i.e. Izaro Island, areas around the Urola, Oria, Urumea and Oiartzun estuaries) have been detected using this valuation method. In some cases, they lie close to the breeding areas (Franco et al., 2004) and as such, they may be important for the feeding or resting of seabirds. The cetacean valuation map shows clearly some high and very high BV close to Cap Breton Canyon, as well as in the surroundings

of the Jaizkibel area (Fig. 6, Supplementary Material). Seafloor topography (water depth and bed slope), together with the distance to the 200 m isobath, have been shown to be more relevant determinands than the coastline for many cetacean species (Mangion and Gannier, 2002). Some of the species show affinity for the canyons and the shelf break area, as these are highly productive habitats (Briggs et al., 1987; Schoenherr, 1991; Springer et al., 1996 and Croll et al., 1998). These geographical areas constitute the location of predictable oceanographic features involved in processes determining prey concentration (Joiris, 1991; Joiris et al., 1996; Hunt, 1997; Croll et al., 1998 and Mehlum et al., 1998) and migratory pathways (Uriarte and Lucio, 2001). 4.2. Integrated valuation The integrated BVM (Fig. 4) shows clearly that the most valuable areas, with medium, high or very high values, can be found along the entire coastal zone. However, higher BV are found around the Jaizkibel area, whose biodiversity value and importance as a biological corridor have been identified previously (Hoyos et al., 2008). The outer most part of the Nervión estuary showed also high BV: this result is somewhat contradictory, because the area has been affected historically by different human pressures (Borja et al., 2006, 2008b). Although the area has recovered in recent times,

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from previous degradation (Díez et al., 2009a), the high BV could be the result of the average of two high values for two ecosystem components (demersal fish and seabirds). The Oka estuary, considered as a World Heritage and Biosphere Reserve, does not show a high BV, when integrating all of the components of the ecosystem; this could be related to the various human pressures that the system is supporting (Borja et al., 2006; Cortazar et al., 2008 and Monge-Ganuzas et al., 2008). However, on the basis of the macrobenthos and macroalgae maps, each of the components shows a high BV. A decreasing gradient in the integrated BV can be observed, from the coastal fringe to the offshore waters, where the areas are characterized mostly by a low BV. This pattern changes near Cap Breton Canyon, where the BV increases. In general, submarine canyons have been described as ‘hotspots’ for biodiversity (Allen and Durrieu de Madron, 2009; Cartes et al., 2009 and Louzao et al., 2010). In this particular area, increasing abundance of zooplankton (Albaina and Irigoien, 2007) and cetaceans (Castège and Hémery, 2009) have been described as being associated to the Cap Breton Canyon; this can be seen in Fig. 6 of the Supplementary Material. High reliabilities are located around 50 m water depth, at other of the most offshore areas and within particular estuaries (such as the Nervión and Oka). The remainder of the study area had an overall predominance of medium reliability. The Basque Country continental shelf biodiversity, despite its broad length and complexity, has been studied intensively recently (see Borja and Collins, 2004). However, most of the sampling effort has been concentrated within estuaries and the shallow waters of the continental shelf (0e50 m), with data from deeper waters being more scarce. 4.3. Comparison with other areas and method analysis It is important to note that BV is a relative method, since it compares subzones to each other; therefore, it is not advisable to make comparisons of total or absolute values, for the different exercises. However, when comparing spatial trends and patterns, it was observed that the BV spatial pattern of the Basque coast was similar to that found at the Belgian part of the North Sea (Derous et al., 2007) or the Isles of Scilly (UK) (Vanden Eede, 2007) sites, even though the sampling effort was more intense on the Basque case. In all three cases, the coastal areas were associated with the highest BV, whilst the method located effectively biodiversity ‘hotspots’. The methodology developed by Derous et al. (2007) is flexible, permitting the modification, development and definition of the suggested mathematical algorithms and interrogations of the database, adapting the valuation to each study area. This fact permits the inclusion of local specific knowledge (new ecosystem components, availability of more information for a particular component, etc.), for a more accurate biodiversity valuation. However, this approach could also introduce subjectivity in the protocol, as scientists could apply different criteria that best suit their individual hypotheses (Derous, 2007). The latter author acknowledges also the possibility of tending towards biases, when including ecosystem components with insufficient information. The present study shows that, when information from several ecosystem components (which have progressive insufficient information) is integrated successively, this could lead to a bias in the integrated BVM, with progressively lower values (Fig. 6). This pattern arises because each of the ecosystem residual errors could be added, increasing the final absolute error. Hence, a selective ecosystem component inclusion, based upon the amount of ecologically-significant data or available information, should be undertaken.

195

Fig. 6. Biological Valuation evolution trend, with the successive adding of each of the studied ecosystem components at each estuary and designated depth ranges. E.g. only macrobenthos; macroalgae: adding macrobenthos and macroalgae; demersal fish: adding macrobenthos, macroalgae and fish; etc.

In this particular case, information on the mobility of the ecosystem component should also be taken into account when defining coverage boundaries and grid cells size selection (Derous, 2007). Such a decision should be ecologically-relevant for the ecosystem component under consideration (larger for mobile components, smaller for sessile components). Some authors (Schelfaut et al., 2007) have suggested the use of marine landscapes, in an attempt to select these relevant areas. It should be noted that misinterpretations of the valuation maps could occur, when the values on the maps are used without consulting the underlying reliability data and the sampling effort maps. This limitation can be seen in the Oka estuary, where the overall BV and sampling effort was medium, whilst the values of some areas are classified as having high reliability, compared with other medium ones. Thus, this overall medium BV should be considered with different reliabilities. Reliability maps provide certainty on the obtained BV, whilst sampling effort maps allow the most and less sampled areas to be pinpointed, where future surveys should be undertaken. It should be noted that increasing reliability and sampling effort will reduce the subjectivity of the integrated result. 4.4. Using biological valuation for marine spatial planning and environmental assessment The incorporation of the ecosystem-based approach, into the MSP, requires that all aspects of the value associated with marine biodiversity are incorporated into the decision-making process (Rees et al., 2010). Hence, as these latter authors have stated, an ecosystem services approach to value marine biodiversity is recognised as a framework by which economic, ecological and social values may be incorporated into the decision-making process. This MSP requires that progressive steps need to be accomplished (Ehler and Douvere, 2009), which include defining and analysing existing conditions, including biodiversity components. Because a key goal of ecosystem-based MSP is to maintain the delivery of ecosystem services, which humans require and need (provided mostly by biodiversity), it must be based upon ecological principles that articulate the scientifically-recognized attributes of healthy, functioning ecosystems (Foley et al., 2010). These authors

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have proposed 4 main ecological principles: maintaining or restoring native species diversity, habitat diversity and heterogeneity; key species, and connectivity. Hence, it is critical to understand the heterogeneity of biological communities and their key components (e.g. most important predators, habitat-forming species), and key processes (e.g. population connectivity, interaction webs, biogeochemistry) that maintain them, as well as human uses (Crowder and Norse, 2008). Some of these concepts have been taken into account in the biodiversity valuation within the Basque Country and, as such, this investigation can be considered as a first step towards the MSP, within this particular area. In addition, the MSFD seeks to assess the marine environmental status, using 11 qualitative descriptors, from which the first of these examines the biological biodiversity. For the assessment, the European Commission (2010) proposes the use of 14 distinct indicators, which include, amongst others: species distributional range; area covered by the species; abundance; composition and relative proportions of ecosystem components; etc. However, the integration of these indicators is usually somewhat challenging (Cochrane et al., 2010). Hence, the BV approach proposed here could serve as an integrative tool, which would permit the gathering of all the information available, for all the biodiversity ecosystem components. Borja et al. (2011) propose an integration of the biodiversity valuation, into a unique value for the whole of the Basque continental shelf, within a range between 0 (bad status) and 1 (high status). Such an approach would be valuable to assess quantitatively the biological biodiversity status of a certain area and establish a management criterion, under the MSFD. 5. Conclusions This contribution has analysed and established a BVM of the continental shelf of the Basque Country, using presently available BV methods. In such a way, the integrated BVM of the Basque continental shelf and estuaries shows an overall low BV, with the most valuable areas located at: the coast; the surroundings of Jaizkibel; outer Nervión estuary, and at the areas surrounding the Cap Breton Canyon. However, some biodiversity ‘hotspots’ have been identified, according to a specific ecosystem component, such as: the mid-parts of the Oka estuary, for macroalgae; coastal water depths around 25e50 m, for macrobenthos; the shallow areas around Capes Villano, Matxitxako and Ogoño, for seabirds, and the Cap Breton Canyon, for cetaceans. The results are consistent with others found at the Belgian part of the North Sea and the Isles of Scilly locations, as well as with locally studied, previously delimited as important, areas. The present BVM can serve as an important baseline integrative tool and approach, which would gather all the biological biodiversity data available for MSP; hence, it can be used in the environmental status assessment, under the MSFD. Acknowledgements Data for this study were obtained from different projects funded by the Department of Environment, Territorial Planning, Agriculture and Fisheries of the Basque Government. M. Pascual and I. Legorburu were supported by a grant from the Technological Centres Foundation of the Basque Country. We wish to thank also Professor Michael Collins (School of Ocean and Earth Science, University of Southampton, UK), Dr Javier Franco, Dr Iñaki Quincoces and Ainhize Uriarte (AZTI-Tecnalia) and Jon Hidalgo (Ornithological Society Lanius) for kindly advising us on some details of this contribution. This is paper number 549 from the Marine Research Division (AZTITecnalia).

Appendix. Supplementary material Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.ecss.2011.08.031.

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