A Gap Analysis of the Marine Protected Area network in the eastern English Channel

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European and National Marine Protected Areas in the eastern English Channel

Not all the described MPAs types are present in the eastern English Channel. Some very coastal MPAs may be not presented in the map (Figure 3), such as the RAMSAR site of the bay of Somme. Concerning the EMS, on the UK side a distinction is made between the coastal and the offshore Special Areas of Conservation. The so-called “UK marine SACs or SPAs” on the map were terrestrial SACs or SPAs with a marine component. The “offshore pSAC” is a potential marine SAC with a perimeter that was proposed to the European Commission the 30th of August 2012. It is also important to notice that the presented Natural Marine Park may not be the definitive one as this perimeter is still under discussion. Many MPAs are found to overlap; this matter will be investigated in the Chapter Four as well as the characteristics of these MPAs in term of their representativity of the biodiversity of the English Channel.

Systematic conservation planning definition

Despite the increasing popularity of MPAs as management tools, until recently, decision on the design and locations of a majority of MPAs largely resulted from political or social processes (Halpern, 2003; Margules and Pressey, 2000). In parallel to this expert approach, another process was developed about twenty years ago: “systematic conservation planning”.
Systematic conservation planning consists of using specific protocols to identify networks of PAs to be conserved in order to protect biodiversity (Margules and Sarkar, 2007). The key concept underpinning systematic conservation planning is complementarity (Margules and Sarkar, 2007), which means that the biodiversity of a set of locations is a non-additive property of these locations. In other words, the contribution of a given location to the overall representativeness of a PAs network depends less on the local richness of the location than on the extent to which the biodiversity features complement (are different from) those already protected at other locations (Ferrier, 2009; Justus and Sarkar, 2002; Kirkpatrick, 1983). This principle of complementarity requires a dynamic process for PAs selection, because each time a location is selected or added to the existing PA network, the complementarity has to be recalculated for all the other locations (Margules and Sarkar, 2007; Possingham, 2009; Wilson et al., 2009).
Systematic conservation planning can be described as a nine stages process as described in Margules and Sarkar (2007) (Box 1). The detailed investigation of some of these stages is the focus of this thesis and these particular stages will be more precisely described in the following sections.

The eastern English Channel environment

The eastern English Channel (EEC) is a shallow epicontinental sea that geographically separates France and the United Kingdom. It is delimited by the Cotentin peninsula in the west and the Dover Strait to the east and is about 35 000 km² (Figure 4). Ecologically, it can also be described as a biogeographical transition zone between the Lusitianian (to the south) and boreal (to the north) provinces.
The Channel is a megatidal sea where tidal currents are dominant and structure the sediment distribution (Larsonneur et al., 1982; Reynaud et al., 1993). Tidal range is always greater than 5 m on the French coast but is closer to 2 m on the English side (around the Isle of Wight) (Dauvin and Lozachmeur, 2006). The study of the residual tidal currents highlights marked retention, dispersion and advection areas (figure 5). In the Dover strait, the residual tidal circulation contributes up to 30% to the total flow rate (with an average of 120 m3.s-1) entering the North Sea. The shear bedstress (Figure 6) resulting from these tidal currents creates a sediment succession from gravels and pebbles in areas with strong currents to fine sediments locked in bays and estuaries (Figure 7) (Alridge and Davies, 1993; Dauvin and Lozachmeur, 2006). At its maximum, the EEC is 70 m deep in the trench running through the center of the Channel; it then becomes progressively shallow toward the east with a depth of 40 m in the Dover Strait (Carpentier et al, 2009).

Habitats in the eastern English Channel

Appropriate knowledge on benthic habitats is a prerequisite to an efficient management of marine regions (Coggan and Diesing, 2011). Seabed habitat mapping lags behind terrestrial mapping by several decades, mostly due to the difficulty and cost of sampling (Coggan and Diesing, 2011). To provide a common European framework to produce these habitats maps, the EUNIS (EUropean Nature Information System) now exists (European Environment Agency, 2006). Marine habitats are grouped in the hierarchical EUNIS classification scheme, based on studies led by the JNCC (Connor et al., 2004), OSPAR parts and ICES (International Committee for the Exploitation of the Sea) working groups (Lozach, 2011). In Chapter Three, Four and Six, I will make extensive use of the benthic habitat map produced within the EUNIS framework by Coggan and Diesing (2011) (Figure 8). The EUNIS classification scheme has different levels of precision, in the EEC, rocky habitats are classified up to the third level and soft sediment habitats to the fourth level i.e. more precisely (Coggan and Diesing, 2011). This map was produced based only on environmental data. The introduction of biological descriptors (species composition) could lead to finer levels of description (level 5 and 6) (Lozach, 2011). However, the authors compared their work to the faunal associations described by Holme (1966), and found good matches. In the eastern English Channel, benthic communities closely match the sediment structures (Carpentier et al., 2009).
Other benthic classifications of the area exist. In 1995, San-Vincente añorve produced a map comprising five benthic communities associated with different types of sediments (Carpentier et al., 2009; San-Vicente Añorve, 1995). This map is the one used in the Chapter Five of the thesis because when this study was produced the EUNIS map produced by Coggan and Diesing in 2011 was not yet available. In contrast to the EUNIS map, this benthic habitats map represents benthic species communities. The EU Habitat Directive (1992) also provided a classification of benthic habitats (Glemarec and Bellan-Santini, 2005) and over the nine described marine habitats, five are found in the EEC (Dauvin and Lozachmeur, 2006).

Human uses in the eastern English Channel

In 2008, Halpern and collaborators produced a global map of human impacts on ecosystems (Figure 10). The cumulated impact was calculated from land-based anthropogenic drivers such as nutrient inputs and pollution and ocean-based anthropogenic drivers such as fisheries, shipping or invasive species. Four areas arose as very highly impacted and the North Sea region, especially the Channel, was one of them (Figure 10).
The English Channel is a “congested sea” (Buléon and Shurmer-Smith, 2008) with nearly 500 ships of over 300 tons which enter and leave the Channel every day, 90 to 120 daily rotations by ferries between the continent, the British isles and the Channel Islands, more than 2000 fishing vessels which are licensed in the whole Channel, and 350 000 leisure crafts. In the EEC there are also numerous communication and power cables and gas transport installations. Finally, the EEC is also an important area for extracting living resources (fishing) and mineral resources (aggregates extraction). Moreover, numerous projects exist for deploying sea-based energy production farms with a majority of offshore wind farms and potentialities for wave and tidal power being also explored.

The descriptors of the water column

To produce seasonal pelagic typologies of the EEC, different environmental data layers have been gathered from various sources. The choice of these data was based on their known contribution to the structuration of the different biological communities of the study area, and also on their availability over the whole EEC area. Some data were in-situ observations, while others were model-derived. Depth combined bathymetry and hydrodynamic modeled mean sea level to illustrate the average water column thickness at mid-tide for an average tide coefficient. Bathymetric data were derived from SHOM( Service Hydrographique et Océanographique de la Marine) hydrographic charts and the mean sea level was estimated using a hydrodynamic model (Carpentier et al., 2009). Because the EEC is a megatidal area, the strength of tidal currents is an important structuring feature for the water column, and this has been estimated by the shear bedstress which was estimated using a 2-D hydrodynamic model originally developed for the Irish Sea but extended to cover the Northwest European shelf (Alridge and Davies, 1993). Both depth and bedstress were constant physical parameters across seasons. The difference of temperature between the maximum (in August) and the minimum (in February) was added as a third constant parameter because this yearly temperature variation may highlight the “coastal flow” structure which is a particularly structuring feature of this area (Brylinsky and Laguadeuc, 1990; Koubbi et al., 2006). It was calculated with monthly satellite imagery data mean between 1986 to 2006. Four seasonal parameters were used in addition to the three constant parameters detailed above: the surface temperature and KPAR (Photosynthetically Available Radiation indice), which are both derived from satellite imagery data, and bottom salinity and surface current speeds, which both are model outputs. The sea surface temperature (SST) is calculated using the infra-red channels of the AVHRR (Advanced Very High Resolution Radiometer) sensor on-board NOAA (National Oceanic and Atmospheric Administration) satellites platforms (Carpentier et al., 2009). The SST calculation algorithm is described in Walton et al. (1998). The KPAR is the attenuation coefficient of photosynthetically available radiation, and it is correlated to the suspended particulate matter data and Chlorophyll a. Both SST and KPAR were available as monthly averages, over the last 21 years. The bottom salinity came from the freely available ICES datacenter, and it was available as monthly averages between 1971 and 2000 (Berx and Hughes, 2009). The surface current speed data have been extracted from the ECOSMO model (Schrum et al., 2006), and these were also available as monthly averages. These four seasonal parameters were available with a 10 km spatial resolution. All these data layers have been resampled on a regular grid of 10 km representing 610 grid cells.

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Classification methodology

A Gower dissimilarity coefficient was calculated (Gower, 1971; Legendre and Legendre, 1998) on standardized data and a Hierarchical Agglomerative Clustering (HAC) method was then applied to the resulting dissimilarity matrix. The group average method (or unweighted pair-group method) was used; this technique accounts for group structure and it is reasonably space-conserving. In other words, the probability to be associated to a group is not determined by its size. A typology was produced for each season: spring (March, April, May), summer (June, July, August), autumn (September, October, November) and winter (December, January, February), using the seven parameters described above, the seasons were chosen to coincide with the water column classification work developed in the UKseaMap project 2006 (Connor et al., 2006). Depth, shear bed stress and temperature difference were constant. Seasonal means were calculated for the monthly surface temperature, salinity, KPAR and current speed parameters. In addition, an integrated typology was produced to reflect the multi-seasonal variability performing an HAC on 19 parameters: the three constant parameters, and each seasonal value of the four others.
The classification cut level was obtained by combining two criteria. The optimal number of groups to retain was established by using the Calinsky criterion (Calinsky and Harabasz, 1974; Guidi et al., 2009; Milligan and Cooper, 1985; Smith et al., 2008) which compares the sum of squares of the partition between and within groups. The criterion has been applied to all resulting dendrograms. Then, in order to prevent large numbers of undersized groups with small spatial extent, some clusters were regrouped to the upper cut level to have at least 1% of the observations into each group.

Interpolation and regionalisation of the classification:

The continuous pelagic typologies maps were obtained using indicator krigging of each class on a 1 km² resolution. This interpolation techniques is adapted to nominal variables (Webster and Oliver, 2001) and resulted into a map of occurrence probability of each class at any given location. The maps of the 7 classes were then combined selecting at each location the class with the highest probability of occurrence.

Evaluation of the classification using Principal Component Analysis

In order to verify the representativeness and robustness of the resulting classifications, the results were projected in a Principal Component Analysis (PCA) to verify that each cluster could be clearly distinguished from each other in a reduced ordinated space.

Table of contents :

1. Chapter One. Context
1.1 Marine Protected Areas
1.1.1. Definition and origins of Marine Protected Areas
1.1.2. MPAs benefits
1.2. Marine Conservation policy
1.2.1. Policy at a global level
1.2.2. Policy at the North East Atlantic level
1.2.3. Integration of the international and EU policy in French and English policy
1.2.4. European and National Marine Protected Areas in the eastern English Channel
1.3. Conservation planning
1.3.1. Systematic conservation planning definition
1.3.2. Biodiversity surrogacy
1.3.3. Target setting
1.3.4. Computational tools
1.3.5. Where are we now?
1.4. The eastern English Channel
1.4.1. The eastern English Channel environment
1.4.2. Habitats in the eastern English Channel
1.4.3 Human uses in the eastern English Channel
1.5. Objectives, methods and plan of the thesis
1.5.1. Objectives and plan
1.5.2. Methods
2. Chapter Two . Defining a pelagic typology of the eastern English Channel
2.1. Introduction
2.2. Materials and methods
2.2.1. The study area
2.2.2. The descriptors of the water column
2.2.3. Classification methodology
2.2.4. Interpolation and regionalisation of the classification:
2.2.5 Evaluation of the classification using Principal Component Analysis
2.2.6. Ecological validation of the typology
2.3. Results
2.3.1. The seasonal typologies
2.3.2. The biological validation
2.4. Discussion
3. Chapter Three. Habitat targets using the Species-Area Relationships with different biological datasets and typologies
3.1. Introduction
3.2. Methods
3.2.1. Habitat maps
3.2.2. Species data
3.2.3. Sensitivity analyses
3.2.4. Calculating habitat targets
3.2.5. Determining the effects of using different habitat targets in Marxan analyses
3.3. Results
3.4. Discussion
3.4.1. Effects of source data
3.4.2. A MPA network reflecting which diversity?
3.4.3. Conclusions
4. Chapter Four. A Gap Analysis of the Marine Protected Area network in the eastern English Channel
4.1. Introduction
4.2. Methods
4.2.1. Classifying and mapping the Marine Protected Area network
4.2.2. Ecological data
4.2.3. GIS analysis
4.2.4. Conservation targets
4.3. Results
4.3.1. The MPA network
4.3.2. Habitats coverage by the MPA network
4.3.3. Species preferential habitats coverage by the MPA network
4.4. Discussion
4.4.1. The MPA network
4.4.2. How successful is the MPA network at representing biological diversity of the EEC?
4.4.3. How efficient could be MPA network to conserve biological diversity of the EEC?
4.4.4. Conclusions
5. Chapter Five. Systematic conservation planning in the eastern English Channel: comparing the Marxan and Zonation decision-support tools
5.1. Introduction
5.2. Material and methods
5.2.1. Mapping the physical data
5.2.2. Mapping the biological data
5.2.3. Designing the conservation planning system
5.2.4. Marxan and Zonation analyses
5.3. Results
5.4. Discussion
5.4.1. Comparing software packages
5.4.2. Implications for designing MPA networks
6. Chapter Six. Cost representation in conservation planning
6.1. Introduction
6.2. Materiel and Methods
6.2.1. Parameterization of the cost function
6.2.2. Calculation of the cost metric equation
6.2.3. Marxan analyses
6.3. Results
6.3.1. Producing the cost layers
6.3.2. Marxan results using fishing activities as cost layers
6.3.3. Marxan results using other human activities as cost layers.
6.2.4. The two final cost equations and their integration in Marxan
7. Chapter Seven. General Discussion
7.1 Contributions to marine policy and management
7.1.1 An Ecosystem-based management perspective
7.1.2 Advances in marine ecology and conservation sciences. How can they inform an EBM implementation?
7.2 Limitations and further research
7.2.1 Uncertainty in environmental data and models
7.2.2 Uncertainty in conservation planning
7.2.3 Further research
8. Conclusion


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