Follow-up ecosystem mapping - Marine ecosystems

Working document Projects

184_1_1 Follow-up ecosystem mapping

Activity

Mapping marine ecosystems

Partners involved

UAB and UMA

Date

30/09/2014

Prepared by:

Raquel Ubach, Ana MarĂ­n and Dania AbdulMalak

Table of Contents 1.

2.

3.

Delimitation of boundaries ................................................................................ 3 1.1 European Sea Regions ............................................................................... 4 1.2 Coastal and marine area by CLC .................................................................. 5 Target classification ......................................................................................... 5 2.1 Marine habitats ......................................................................................... 5 Mapping approach ........................................................................................... 9 3.1 Common framework .................................................................................. 9 3.2 Marine particularities ................................................................................. 9 3.2.1 Seabed ........................................................................................ 9

3.3 3.4 3.5 3.6

4.

3.2.2

Depth ......................................................................................... 14

3.2.3

Light availability........................................................................... 15

3.2.4

Ice ............................................................................................. 17

3.2.5

Water column .............................................................................. 19

Marine rules ............................................................................................20 Datasets .................................................................................................21 Data workflow..........................................................................................25 Methodology ............................................................................................27 3.6.1 Analysis extent ............................................................................ 27 3.6.2

Bathymetry composite .................................................................. 27

3.6.3

Seabed class homogenisation ........................................................ 28

3.6.4

Sea ice data ................................................................................ 29

3.6.5

Marine and coastal ecosystem rules ................................................ 29

Discussion .....................................................................................................30

References ..........................................................................................................32 Annex 1. Rasterize sub regions ..............................................................................33 Annex 2. Extent of analysis script ...........................................................................33 Annex 3. Bathymetry composite script ....................................................................35 Annex 4. Primary seabed data integration ...............................................................38 Annex 5. Secondary seabed data integration............................................................39 Annex 6. Sea ice script ..........................................................................................40 Annex 7. Marine and coastal rules script ..................................................................41

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Table of Figures Figure 1 Conceptual framework including EUNIS and MAES classifications..................... 4 Figure 2 European Marine Sea Regions ..................................................................... 4 Figure 3 EUNIS habitat classification: criteria for marine habitats (A) to level 2 ............. 7 Figure 4 EUSeaMap present data coverage (August 2014) .........................................10 Figure 5 MESHAtlantic present data coverage (August 2014) .....................................10 Figure 6 Available substrate data sources ................................................................11 Figure 7 Global seabed map differentiating hard (greenish, value 2) and soft substrates (brownish, value 1) ..............................................................................................12 Figure 8 Deviance explained by each of the predictors of a tested model .....................13 Figure 9 Bathymetry available datasets for the European Sea Regions (EMODNET coloured, GEBCO in grey) ......................................................................................14 Figure 10 Sea Ice Concentration on the 11th of September 2013 (Hemisphere N) .........18 Figure 11 Sea Ice extent plotted along the year .......................................................19 Figure 12 Data workflow process – Preparing datasets ..............................................25 Figure 13 Data workflow process – Applying marine ecosystems rules .........................26 Figure 14 Example of unclassified pixels (in yellow) due to unmatched boundaries between CLC and sea regions in Svalbard archipelago (left) and Northern Black Sea (right) .................................................................................................................27 Figure 15 Substrate data homogenisation ................................................................28 Figure 16 Distribution of hectares per each ecosystem major type (compared results from 2013 and 2014 methodological approaches) ............................................................30

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1. DELIMITATION OF BOUNDARIES The updated extent of the coastal and marine Pan-European map covers the area from the coastline (as defined by a selection of CLC classes) seawards to the outer limit of the European Sea Regions.

Marine and coastal ecosystems are usually considered together in EEA’s assessments, as both are highly interrelated. The coastal environment is a heterogeneous ecosystem, hosting a wide variety of different habitats associated both to water and land. Coast is defined by the EEA as a mixed area distinguished by the coming together of land and sea, delimited by the strip of land 10 km inland from the coastline plus the first 10 km seaward (EEA, 2006).

For the current assessment, and in accordance to the ecosystems-related tasks within the ETC-SIA framework, the EUNIS classification is the reference for ecosystems definitions and typologies. At the same time, this work is also intended to give support to the general EU Biodiversity strategy 2020 framework (Target 2 - Action 5). For this reason, the proposed ecosystem typology defined by MAES working group has also been considered in the present approach, where the broad marine ecosystem is divided into two environments: coastal and marine. Here, ‘Coastal environment’ considers those terrestrial habitats that always occur along the coast including marshes, sea cliffs, intertidal habitats and coastal dunes; and also some aquatic habitats effectively occurring adjacent to the coast, such as marine inlets and transitional waters. Coastal ecosystems can be defined and spatially delineated using the following EUNIS habitat classes (Figure 1): 

terrestrial coast comprising coastal dunes and sandy shores (B1), coastal shingle (B2), and rock cliffs, ledges and shores (B3), and



aquatic coast including estuaries (X1) and saline and brackish coastal lagoons (X2-X3).

This represents a different approach to the MAES definition of ‘coastal areas’ which refers to coastal, shallow, marine systems that experience significant land-based influences, with diurnal fluctuations in temperature, salinity and turbidity, and also affected by wave disturbance (MAES, 2013). This is why we slightly modified the name to 'coastal littoral', so a clear differentiation is made with the terrestrial stripe of coast, widely used in other assessments, e.g. like the SOER. On the other hand, 'Marine environment' is characterised by marine waters, and composed of habitats directly connected to the oceans below the high tide limit (as defined by EUNIS). Marine ecosystems are a complex of habitats defined by the wide range of physical, chemical, and geological variations that are found in the sea. Habitats range from highly productive near-shore regions to the deep sea floor inhabited only by highly specialised organisms (EEA, 20101).

In this section, there are described the methods and approaches to map the ‘marine wet ecosystems’ as described in Figure 1, which correspond to EUNIS marine habitats (A) and habitat complexes (X01, X02 and X03).

1

EU 2010 biodiversity baseline

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Figure 1 Conceptual framework including EUNIS and MAES classifications

Source: ETC-SIA 1.1 European Sea Regions The European Sea Regions are defined by the MSFD provisional dataset on sea regions and sub-regions - EEA internal version1, Sep. 2013. This draft version of the regions boundaries at sea are defined to be used for the MSFD reporting. Although, this dataset has not yet been approved by Member States, it is a good reference for the mapping boundaries.

Figure 2 European Marine Sea Regions

Source: MSFD provisional dataset on sea regions and sub-regions (EEA)

1

available at: ftps://sdi.eea.europa.eu/data/continental/europe/water/msfd/eea_v_4258_1_mio_msfd-searegions_2013/Regional_seas_extended_version_ETRS89_20130925.shp

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1.2 Coastal and marine area by CLC Considering that the reference dataset for this task is CLC, the coastal and marine area defining the landward boundaries of the extent are defined by several classes of CLC: –

‘Intertidal flats’ (CLC==423) - Generally un-vegetated expenses of mud, sand or rock lying between high and low water mark.

–

‘Coastal lagoons’ (CLC==521) - Stretches of salt or brackish water in coastal areas, which are separated from the sea by a tongue of land or other similar topography.

These water bodies can be connected to the sea at limited points, either permanently or for parts of the year only.

–

‘Estuaries’ (CLC==522) - The mouth of a river, within which the tide ebbs and flows.

–

‘Sea and Ocean’ (CLC==523) - Zones seaward of the lowest tide limit.

2. TARGET CLASSIFICATION The target classification is a combination between the EUNIS classification at level 3 (from http://eunis.eea.europa.eu/habitats-code-browser.jsp?expand=A#level_A1) and a selection of CLC classes at level 3 (Annex 1 - Crosswalks between Marine and Coastal EUNIS habitat types and CLC classes).

The EUNIS is a consolidated common classification scheme in Europe for habitat types with the object to help harmonising existing information on habitats at this wide scale, which started developing in the mid-90s and published its last major revision in 2004 (Evans & Royo-Gelabert, 2013). The EUNIS habitat types are distributed in a hierarchical classification with 10 categories in the highest level 1. Marine habitats are described in 4 levels, while terrestrial and freshwater habitats in 3. However, Marine habitats at level 1 can be considered equivalent to terrestrial and freshwater habitats at level 2 (Davies et al., 2004). Level 2 and 3 divisions are based on physical parameters such as depth related to light penetration, substrate composition and energy; while species composition are used to discriminate divisions at level 4 (Evans & Royo-Gelabert, 2013).

The CLC is a Pan European wide database on land cover that was initiated on 1985 by the EEA. It is a rather terrestrial classification of land use / land cover in Europe, but it will be used to complement EUNIS on the coastal delimitation and mapping.

2.1 Marine habitats EUNIS defined marine habitats as follows: “Marine habitats are directly connected to the oceans, i.e. part of the continuous body of water which covers the greater part of the earth’s surface and which surrounds its land masses. Marine waters may be fully saline, brackish or almost fresh. Marine habitats include those below spring high tide limit (or below mean water level in non-tidal waters) and enclosed coastal saline or brackish waters, without a permanent surface connection to the sea but either with intermittent surface or sub-surface connections (as in lagoons). Rockpools in the supralittoral zone are considered as enclaves of the marine zone. It includes marine littoral habitats which are subject to wet and dry periods on a tidal cycle as well as tidal saltmarshes; marine littoral habitats which are normally water-covered but intermittently exposed due to the action of wind or atmospheric pressure changes; freshly deposited marine strandlines characterised by marine invertebrates. Waterlogged littoral saltmarshes and associated saline or brackish pools above the mean water level in non-tidal waters or above the spring high tide limit in tidal waters are included with marine habitats. It also includes constructed marine saline habitats below water level as defined above (such as in marinas, harbours, etc) which support a semi-natural community of both plants and animals. The marine water column includes bodies of ice.” (Davies et al., 2004).

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At EUNIS level 2, 8 habitat types are described: 

A1 : Littoral rock and other hard substrata Littoral rock includes habitats of bedrock, boulders and cobbles which occur in the intertidal zone (the area of the shore between high and low tides) and the splash zone. The upper limit is marked by the top of the lichen zone and the lower limit by the top of the laminarian kelp zone. There are many physical variables affecting rocky shore communities - wave exposure, salinity, temperature and the diurnal emersion and immersion of the shore.



A2 : Littoral sediment Littoral sediment includes habitats of shingle (mobile cobbles and pebbles), gravel, sand and mud or any combination of these which occur in the intertidal zone. Littoral sediments support communities tolerant to some degree of drainage at low tide and often subject to variation in air temperature and reduced salinity in estuarine situations. Littoral sediments are found across the entire intertidal zone, including the strandline. Sediment biotopes can extend further landwards (dune systems, marshes) and further seawards (sublittoral sediments). Sediment shores are generally found along relatively more sheltered stretches of coast compared to rocky shores. Muddy shores or muddy sand shores occur mainly in very sheltered inlets and along estuaries, where wave exposure is low enough to allow fine sediments to settle. Sandy shores and coarser sediment (gravel, pebbles, cobbles) shores are found in areas subject to higher wave exposures.



A3 : Infralittoral rock and other hard substrata Infralittoral rock includes habitats of bedrock, boulders and cobbles which occur in the shallow subtidal zone and typically support seaweed communities. The upper limit is marked by the top of the kelp zone whilst the lower limit is marked by the lower limit of kelp growth or the lower limit of dense seaweed growth.



A4 : Circalittoral rock and other hard substrata Circalittoral rock is characterised by animal dominated communities (a departure from the algae dominated communities in the infralittoral zone). The circalittoral zone can itself be split into two sub-zones; upper circalittoral (foliose red algae present but not dominant) and lower circalittoral (foliose red algae absent). The depth at which the circalittoral zone begins is directly dependent on the intensity of light reaching the seabed; in highly turbid conditions, the circalittoral zone may begin just below water level at mean low water springs (MLWS).



A5 : Sublittoral sediment Sediment habitats in the sublittoral near shore zone (i.e. covering the infralittoral and circalittoral zones), typically extending from the extreme lower shore down to the edge of the bathyal zone (200 m). Sediment ranges from boulders and cobbles, through pebbles and shingle, coarse sands, sands, fine sands, muds, and mixed sediments. Those communities found in or on sediment are described within this broad habitat type.



A6 : Deep-sea bed The sea bed beyond the continental shelf break. The shelf break occurs at variable depth, but is generally over 200 m. The upper limit of the deep-sea zone is marked by the edge of the shelf. Includes areas of the Mediterranean Sea which are deeper than 200 m but not of the Baltic Sea which is a shelf sea. Excludes caves in the deep sea which are classified in A4.71 irrespective of depth.



A7 : Pelagic water column The water column of shallow or deep sea, or enclosed coastal waters.



A8 : Ice-associated marine habitats Sea ice, icebergs and other ice-associated marine habitats.

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Figure 3 EUNIS habitat classification: criteria for marine habitats (A) to level 2

Source: EUNIS habitat classification revised 2004 (Davies et al., 2004) Several criteria are used to discriminate the different habitat types. First division criterion is altitude: o o o o o

Littoral – periodically inundated shores of marine water Infralittoral – shallow subtidal Circalittoral – moderately deep subtidal Offshore circalittoral – offshore water, depth <200m Bathyal – depth >200m

Depth zones, more detailed than altitude zones, are divided as follows: o o o o o o o o o o

Upper shore Mid-shore Low shore 0 - 5m 5 -10m 10 - 20m 20 - 30m 30 - 50m 50 - 200m >200m

Substrate can be: o o

Mobile Non-mobile

Salinity values are comprised between: o o o o

Fully saline – 30-40 ppt Variable salinity – 18-40 ppt fluctuating on a regular basis Reduced salinity – 18-30 ppt Low salinity - <18 ppt

According to wetness/dryness criteria: o o

Aquatic – open or free-standing fresh or saline water Frequently submerged – predominantly aquatic (saline or brackish) but subject to occasional but regular emersion

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Considering the description of habitat types and the different discriminating parameters, the marine typology is summarised in Table1, where the main parameters used to define the ecosystem mapping are highlighted.

Table 1 Description parameters for EUNIS level 2 marine habitats

Parameter

Value

A1

A2

A3

A4

A5

A6

Bathyal

Circalittoral

x

Infralittoral

Depth zones

x

x

x

x

x

x

x

Littoral

x

x

?

Upper shore

x

x

?

Mid-shore

x

x

?

Low shore

x

x

?

0 - 5m

x

x

x

5 -10m

x

x

x

x

10 - 20m

x

x

x

x

20 - 30m

x

x

x

x

x

x

x

x

x

30 - 50m 50 - 200m >200m

x

Mobile Substrate

Non-mobile

x x

x x

x

x

x x

Water

x

Ice

Salinity levels

Wetness/dryness

A8

x

Offshore circalittoral Altitude zones

A7

x

Fully saline

x

x

x

x

x

x

x

Reduced salinity

x

x

x

x

x

x

x

x

x

x

x

x

x

x

Low salinity Variable salinity

x

x

x

x

x

Aquatic

x

x

x

x

x

Frequently submerged

x

x

x

x

Source: Adapted from EUNIS habitat description.

The altitude and the depth zones provide similar information to define the different marine habitats. The altitude being rather descriptive and the depth zones are defined based on quantitative measures. For this task, depth zones can be used to discriminate different groups of typologies (A1 and A2; A3, A4 and A5; A6). The substrate can

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discriminate those ecosystems characterised by mobile sediments from those of rocky and hard substrates.

Salinity is not useful to separate habitats at this classification level. Finally, wetness provides the same guidance than depth, but only for A1 and A2, and therefore its use is facultative.

Based on this analysis, the discrimination of the marine habitat typology needs to be based on a multi-criteria approach including depth, substrate and light penetration. The rationale of this approach is this physical variables summary in Table1 together with the wider description of the different habitat types presented at the beginning of this chapter.

3. MAPPING APPROACH 3.1 Common framework As part of a wider work, a common framework is set to provide a pan-European map of terrestrial, coastal, and marine ecosystems. The main rule is that one pixel corresponds uniquely to 1 ecosystem type (1 pixel = 1 ecosystem type).

As marine and terrestrial ecosystems are approached separately in the computing process, the final wall to wall map that will join all ecosystems together needs to consider the spatial continuity of the ecosystems, providing a solution for any spatial gaps that may be encountered in the transitional areas between one ecosystem and the other. In addition the coastal line used is not adjusted to the CLC layer and therefore these boundaries between coastal and terrestrial delimitations need to be consolidates.

While joining the datasets together, the existent gaps should be considered and treated between terrestrial, coastal and marine ecosystems. This will be especially important in the terrestrial-coastal/marine interface. This issue must be solved by setting the inner marine boundary by using CLC layer.

3.2 Marine particularities 5 components are set to help to define the marine habitats particularities: 

Seabed substrate



Depth



Light penetration



Water column



Ice

In EUSeaMap project, energy at both wave and at seabed level is used to classify EUNIS level 3 habitats discriminating infralittoral and circalittoral rock habitats into high, moderate and low energy environments (McBreen et al., 2011a). The present analysis will discriminate EUNIS level 2 classes for the marine environment, consequently energy parameters (wave and currents at seafloor) will not be considered in this study.

3.2.1 Seabed The physical nature of the seabed substratum influences the community types that develop above (McBreen et al., 2011 b). For this reason, it is extremely important to acquire reliable and accurate data on it. In the last decade, many projects have addressed this issue like Balance, MESH, HabMap, Infomar, amongst others. The most relevant results for the current analysis are further described in this section.

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EUSeaMap After the experience and results from previous projects, EUSeaMap produced broad-scale modelled habitat maps for the Baltic, Celtic, North and western Mediterranean seas following the EUNIS classification with some slight modifications (see figure 4). Right now there are available more than 2 million square kilometres of European seabed, unfortunately this dataset does not cover the whole extent of the present analysis. By now, where it is available it will be used as a primary data source for seabed characterisation.

Figure 4 EUSeaMap present data coverage (August 2014)

Source: EUSeaMap project (Mapping European seabed habitats, 2013).

MESHAtlantic Complementarily, there is another project that is mapping some areas of the Atlantic seabed. MESH Atlantic continues to gather existing maps and conduct new mapping survey and will produce (expected by the end of 2014) a broad-scale modelled map to continue the modelling work started by MESH and continued by the EUSeaMap project (as part of the European Marine Observation Data Network, EMODnet).

Figure 5 MESHAtlantic present data coverage (August 2014)

Source: Mapping European Seabed Habitats web portal (http://www.searchmesh.net/)

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EUROSION The geology map from EUROSION shows the geological patterns of the European coast, classifying the coastline into several classes (rocks and hard cliffs, small beaches, muddy sediments, embankments, vegetated strands, soft strands with ‘rocky platforms’, etc.).

Romania, Bulgaria, Cyprus and ultra-peripheral regions are only partially covered.

However, this dataset can be used to discriminate hard from soft substrates where no primary seabed substrate is available.

Figure 6 Available substrate data sources

Source: EUSEAMAP, MESHAtlantic and EUROSION datasets MEDINA MEDINA aims at enhancing monitoring capacity of coastal and marine ecosystems in the Mediterranean Northern African Countries (Morocco, Algeria, Tunisia, Libya and Egypt).

The project contributes to the assessment and implementation of 80 indicators (37 DPSIR indicators and Ecological objectives, 10 indicators of Earth Observation and 33 indicators from Modelling), accessible throught the project geoportal 1. One of the project products is the coastal typology describing the morpho-sedimentological typology of Mediterranean North African (NA) coastline. The division of the NA coastline is a succession of contiguous segments according to main typologies derived by visual discrimination using satellite imagery, which makes possible to distinguish between 5 principal classes: Rocky coast, Beaches, Interdial wetlands, Mouths, and Artificial.

Global map of human impacts to marine ecosystems The NCEAS (National Center for Ecologial Analysis and Synthesis, Santa Barbara – USA) published a global map of human impacts to marine ecosystems (Halpern et al., 2008).

To undertake this analysis several maps were created to identify distinctive ecosystem types, including global maps on hard and soft bottoms. These maps are extracted from the dbSEABED2 project, this database compiles benthic substrate point samples data on

1 2

http://medinageoportal.eu http://instaar.colorado.edu/~jenkinsc/dbseabed

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the rock composition of particular locations around the world. The dbSEABED data has been compiled by the Institute of Arctic & Alpine Research in the University of Colorado at Boulder. Although the distribution of sampling is geographically uneven, areas around developed nations like in Europe, North America or Australia, present major number of samples and so have less errors derived from statistical interpolation (kriging). Moreover, it has to be noted that in general shallow and shelf areas are better sampled than the continental slope and the deep seafloor (Halpern et al., 2008).

Data from the database was extracted to generate binary maps1, where each cell was assigned a value of hard or soft substrate depending on the presence of hard substrate in each sample (samples with greater than 50% hard substrate were counted as hard; and all others were counted as soft). Grid cells were sampled at 2 arc-minutes (~3.7 km, or 13.69 km2 per cell, depending on latitude) and assigned an ecosystem type depending on substrate (hard or soft) and bathymetry (shallow, shelf, slope and deep). A combination of these maps can produce a unique map with the dominant substrate in each cell. The purpose of the use of this datasets is to discriminate between hard and soft seabeds, for this reason the the datasets used to create the combination are: -

Hard shelf Hard slope Deep hard bottom Intertidal mud Rocky intertidal Subtidal soft bottom Soft shelf Soft slope Deep soft benthic

All these maps have the same value (1). In order to mosaic all them in on single dataset, the hard substrate maps have been reclassified with value 2. Afterwards, all the maps have been put together in one sigle dataset by mosaicing where value 1 corresponds to soft bottoms and 2 for hard substrates.

Figure 7 Global seabed map differentiating hard (greenish, value 2) and soft substrates (brownish, value 1)

Source: derived from dbSEABED

1

http://www.nceas.ucsb.edu/globalmarine/ecosystems

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Highlights In comparison to previous ecosystem typology map methodology developed last year (2013), the inclusion of more accurate thematic datasets like the outcomes of EUSeaMap and MESH projects in a first instance, and from MEDINA and EUROSION projects based on visual digitalisation of coastal substrate, has notably increased the overall quality of resulting ecosystems map. Nevertheless, this improvement has not been scaled.

Shortcomings This dataset can be used as a secondary data source for seabed substrate filling the gaps where no other datasets are available, however it is not useful along the 1st km seaward from the coast as it considers that rocky intertidal, beach, intertidal mud, suspensionfeeding reefs, and salt marsh ecosystems exist in all cells within the 1st km of shore, not discriminating their presence. As a result, the presence of areas with no substrate data have been reduced but not completely eliminated. Therefore, still unclassified pixels are present in the present version of marine ecosystems map.

Next actions EUSeaMap phase II work is still in progress, and expected to be published under the EMODNET project by the end of 2014 (Evans & Royo-Gelabert, 2013). The EUSeaMap phase II presents some improvements:   

extend the coverage to Canary Islands, the remaining Mediterranean areas (Adriatic, Ionian and Aegean Seas, and the Black Sea) increase thematic reliability of resulting maps by the improvement of intermediate data (hydrodynamics models, seabed substrate layers, bathymetry, etc.) refine working scale to 100 m pixel size in pilot areas

On the other hand, in the areas where no substrate data is available (in particular, in the 1st km seaward) an alternative approach to predict the sediment nature of shores must be done. A proposal could be based on a combination of different spatial variables to predict rocky shore communities. A model should be based on predictor variables easily available at the scale of analysis. A proposal that should be further developed could use depth, slope, terrain curvature, and a measure of coastal exposure (Burrows et al., 2008). The combination of these variables performed well with a high degree of certainty (ROC-values > 0.8) in a model used to predict rocky shores in Norway (Bekkby et al., 2009).

Figure 8 Deviance explained by each of the predictors of a tested model

Source: Bekkby et al., 2009

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3.2.2

Depth

The bathymetry can be used to discriminate the major divisions of coastal, shelf and open ocean. The shelf break occurs at variable depth; however a general rule can be applied considering 200 m the average lower limit for the edge of the shelf (Davies et al., 2004).

The EMODnet Bathymetry data products are Digital Terrain Models (DTM) for selected maritime basins in Europe that have been produced from collated bathymetric data sets and that are integrated into a central DTM. For each region bathymetric survey data and aggregated bathymetry data sets are collated from public and private organizations.

These are processed and quality controlled. A further refinement is underway, also by gathering additional survey data sets. The DTMâ&#x20AC;&#x2122;s have been based, where possible and available, upon high resolution survey data sets, presenting a final resolution of 1/4 arcminutes (15 arc-seconds ~ roughly 500 m).

Figure 9 Bathymetry available datasets (EMODNET coloured, GEBCO in grey)

for

the

European

Sea

Regions

Source: EMODNET and GEBCO For those areas where EMODNET bathymetry data is not available, GEBCO1 (General Bathymetric Chart of the Oceans), which provides global bathymetry data sets for the world's oceans, can be used. The GEBCO 08 Grid is a global 30 arc-second grid largely generated by combining quality-controlled ship depth soundings with interpolation between sounding points guided by satellite-derived gravity data. However, in areas where they improve on the existing grid, data sets generated by other methods have been included. Land data are largely based on the Shuttle Radar Topography Mission (SRTM30) gridded digital elevation model.

The definition of depth ranges must be done according the ecosystem type definitions as summarised in Table 1.

1

http://www.gebco.net/data_and_products/gridded_bathymetry_data/

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Highlights Similarly to the case of seabed, the inclusion of more accurate datasets by means of EMODNET bathymetry data has notably increased the overall quality of the resulting ecosystems typology map. Again, due to short resources available, this improvement has not been calculated.

Next actions A new release of EMODNET bathymetry is expected by mid-December 2014, which will be enlarged incorporating: 

Baltic Sea



Black Sea



Norwegian and Icelandic Seas



Canary Islands

This new release will double the resolution to 1/8 arc-minutes (7’5 arc-seconds ~ roughly 250 m). Therefore, when updating the ecosystems typology map, this new dataset with notably increased resolution should be incorporated and the methodology adapted consequently.

3.2.3

Light availability

The euphotic zone provides a measure of the ocean depth below which light available is insufficient to support significant photosynthetic activity. It is the upper part of the water column, where most of the primary production occurs. The euphotic layer is the depth at which the visible light (400 – 700 nm range) reduces to 1% of the light incident at the ocean surface. It is a measure of water quality, as well as an important variable to estimate water column primary production.

Light availability in the water column and the seabed is affected by depth and proximity to the coast, and by latitude and climate (EUSeaMap, 2012). Light intensity decreases with depth due to the attenuating effects of scattering and absorption in the water column. This attenuation produced by water molecules, suspended particulate matter, phytoplankton and coloured dissolved organic matter, tends to be higher in coastal waters, due to suspended and dissolved matter being washed down rivers, higher phytoplankton concentrations and suspension of sediment caused by wave action in shallow waters. So the proportion of surface light reaching the seabed can be derived by the diffuse attenuation coefficient: Kd(λ,E%)1.

Light attenuation is used to define the infralittoral zone, as below a certain fraction of surface light macrophytes (like kelp, seaweeds, or seagrass) will struggle to grow. It is accepted by convention that the bottom of the euphotic zone, Zeu, is 1% of the proportion of light at the subsurface of water (Ruther, 1956; Morel et al., 2007). Below the infralittoral, the circalittoral zone extends to the maximum depth where multicellular photosynthetic forms can exist, characterised by the predominant presence of sciaphilic algal communities (EUSeaMap, 2012). The circalittoral range is estimated between 1% and 0,01% of the surface light. Despite of this general rule, some regional adjustments can be considered, therefore EUSeaMap presented some regional corrections, as summarised in the following table (Table 2).

1

Kd(λ,E%): Spectral diffuse attenuation coefficient for downwelling irradiance between Ed(λ,0) and % of Ed(λ,0)

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Table 2 Regional corrections for thresholds to define the different biological zones

Regional Seas Zones

Celtic Seas

and

North

Infralittoral

0m - 1.6 depth:Secchi oligohaline OR 0m - 1% light depth:Secchi reaches the seabed mesohaline

Wave base - 200m

Upper slope

200m - 750m

Upper bathyal

750m - 1,100m

Mid bathyal

1,100m - 1,800m

Position of deep halocline 0.01% light reaches the and deeper for mesohaline seabed - Shelf edge (deepest zone) (manual delineation)

Shelf edge (manual delineation) - Slope change (manual interpretation)

Bathyal

Abyssal

ratio of depth for 2.5 ratio of depth for 0m - 1% light reaches the seabed

1% light reaches the seabed - Wave base

Deep circalittoral

Lower bathyal

Western Mediterranean

1.6 ratio of depth:Secchi depth and deeper for oligohaline (deepest zone) OR 2.5 ratio of depth:Secchi depth - 1% light reaches the Position of deep halocline seabed - 0.01% light for mesohaline reaches the seabed

Upper circalittoral Circalittoral

Baltic Sea

1,800m - 2,700m 2,700m and deeper

Slope change (manual interpretation)

Source: EUSeaMap, 2012.

Satellite observations are effective for producing maps of light attenuation across very large areas at relatively high spatial resolution (McBreen et al., 2011b). Different algorithms are generally used to derive the diffuse attenuation coefficient of the downwelling spectral irradiance at wavelength 490nm (K d490) from ocean colour satellite sensors such as the Medium Resolution Imaging Spectrometer instrument (MERIS), the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), and the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. Most of these existing models have been calibrated on open ocean waters and provide good results in these areas, but tend to underestimate the attenuation of light in turbid coastal waters (Frost et al., 2010).

UKSeaMap 2010 used 4km resolution light data (K d490 values) from the MODIS instrument on NASAâ&#x20AC;&#x;s Aqua satellite, together with the UKSeaMap 2010 bathymetry to calculate values for the fraction of surface light reaching the seabed. In 2007, Morel and colleagues produced a global Zeu map using SeaWiFs composite processed data (Morel et

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al., 2007). And more recently, satellite derived kdPAR and Zeu were calculated for European waters using high resolution data at 250m with a wide temporal range from 2005 to 2009 under the framework of the EuSeaMap project (Sauquin et al., 2013).

However, these datasets are still not publicly available.

Highlights Nevertheless, the MERIS Monthly mean Surface productive layer (Euphotic Depth) is already available at the Environmental Marine Information System (EMIS), which provides information on marine ecosystems and coastal state, using biological and physical variables generated by satellite remote sensing. The monthly mean euphotic depth (in meter) derived from the ocean colour MERIS (Medium Resolution Imaging Spectrometer) sensor is available at a low resolution of 4km, covering the time period between May 2002 and September 2011. The product is calculated according to a QuasiAnalytical Algorithm (Lee et al., 2007) in which vertical attenuation coefficient of the subsurface light is modelled by the inherent optical properties of the water. Besides, it has to be accounted that using field measurements in different part of the world’s ocean, the average percentage error in the retrieval of the 1% light depth-level was calculated as ca. 14% (Lee et al., 2007).

Shortcomings This dataset can already be used, though it should be analysed the time period to be considered. A similar approach has been applied to derive the water transparency to define the condition of marine ecosystems using another dataset from EMIS/JRC. The tools developed for this subtask could be directly used, but the methodology should define the time frame (months and years) used to compute the average values. Due to a lack of resources this could not be done in this year task.

Next Actions In the coming future, when considering the update of marine ecosystems mapping, this dataset should be included in the methodological process allowing the discrimination between infra and circa littoral ecosystems (A3 and A4 classes).

3.2.4

Ice

Ice cover affects species distribution in coastal or shallow waters, but it has less influence than the physical parameters previously described (seabed sediment, depth, light penetration) when considering the broad extent of analysis (Cameron & Askew, 2011).

Several data sources have been identified so far as:  Myocean1  MODIS2  NASA products:National Snow and Ice Data Centre 3

1

http://www.myocean.eu/web/69-myocean-interactivecatalogue.php?option=com_csw&task=results&page_int=1&page_ext=3&scope=ext&referenced_area[]=all&oc ean_variable[]=cf-standard-name%23sea-ice&product_type[]=temporal-scale%23multiyear&product_type[]=temporal-scale%23invariant&text[]=&text[]=&records_per_page=5 2

http://modis-snow-ice.gsfc.nasa.gov/?c=sea

3

http://nsidc.org/data/seaice/visible.html

http://n4eil01u.ecs.nasa.gov:22000/WebAccess/drill?attrib=home&nextKey=group

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ď&#x192;° Reverb Echo1 ď&#x192;° British Atmospheric Data Centre2 Highlights It is to note that so far EUSeaMap project has been considered as a main reference in our work, and that ice was not considered in this study (Cameron & Askew, 2011). However, a test has been undertaken as a proof of concept. To do so, data from MODIS has been used, as it provides best available spatial (1km) and temporal resolution (from 2000 to present). The sea ice map produced is based on the sea ice by reflectance identifies pixels as sea ice, ocean, land, inland water, cloud or other condition. Here, only sea ice has been considered ignoring the rest of the values.

As sea ice can be considered as a mobile substrate, allowing the development of other ecosystem types beneath, a mixed class is proposed combining sea ice (EUNIS A8) with the other ecosystem types (e.g. A18 = A1 + A8).

Shortcomings Due to resources constrains, data from a single day has been downloaded (corresponding to 2013-09-11); though mean values covering a wider temporal range would increase the accuracy of results. The selection of the day has been done on a conservative approach considering the time of the year with lowest sea ice concentration. The 11th of September 2013 the sea ice covered 5131 million km2. In this way, selected pixels are more likely to be part of a sea-ice associated ecosystem most of the year.

Figure 10 Sea Ice Concentration on the 11th of September 2013 (Hemisphere N)

Global image, Arctic centred. In light grey, land area not considered in the analysis.

Source: National Snow and Ice Data Center (NSIDC)

1

http://reverb.echo.nasa.gov/reverb/#utf8=%E2%9C%93&spatial_map=satellite&spatial_type=rectangle&key words=ice%20surface 2

This dataset contains Sea Surface Temperature climatologies (HadISST SST, Version 1.1) and Sea Ice coverage (HadISST ICE, Version 1.1). http://badc.nerc.ac.uk/view/badc.nerc.ac.uk__ATOM__dataent_hadisst

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It has to be noted that, there is trend decreasing mean values of sea ice coverage due to climate change; this is already captured by the interactive graph (Figure 11) showing a shift towards lowest values (see from 2000 onwards, where in general values are found below average).

Figure 11 Sea Ice extent plotted along the year

Average values for the period 1981-2010 is computed (black thick line, 2 standard deviations shaded grey area), however 2013 values are also highlighted (brownish thick line).

Source: National Snow and Ice Data Center (NSIDC), interactive graph at http://nsidc.org/arcticseaicenews/charctic-interactive-sea-ice-graph/

3.2.5 Water column Note the strong temporal character of the pelagic environment, for this reason the water column can be classified differently at different periods of the year (Davies et al., 2004).

Additionally, the water column presents a different spatial dimension, the depth.

Consequently, a range of different ecosystem types can exist at different depths of the water column. This quality mismatches the approach proposed (1 pixel = 1 ecosystem type). For these reasons and considering the resources available, the water column ecosystem types are not included in the present study. However, for a future approach it is proposed to consider the possibility to combine ecosystem types creating new classes (e.g. combined ecosystem 1: water column over sublittoral sediment - A1 and A7). But this proposal has to be further explored and developed.

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3.3 Marine rules According to all the described particularities and the data availability, the rules to define marine ecosystems are based on depth, substrate and presence of sea ice. Additionally, euphotic zone could discriminate infra and circa littoral ecosystems (A3 and A4).

Table 3 Summary of marine ecosystems rules

Source: ETC-SIA Right now, the discrimination between EUNIS classes A3 and A4 is not possible with the available datasets, as to do so it is needed the depth zone. Accordingly, it is proposed a mixed class composed by A3 and A4 (A34: code 134).

As it has been commented in previous section (3.2.4), an approach is proposed to test the 3D ecosystems associated to sea ice cover. The rationale considers where ice ecosystems are present other ecosystem types may develop below the ice substrate.

Accordingly, two different ecosystems do develop in one single place (or pixel). This issue can be solved by a specific coding which can describe a mixed class composed by sea ice class (A8) and any other one from A1 to A6. Following the same example, where there is presence of sea ice and below it is developed a littoral rock ecosystem (A1), the final code for that ecosystem would be A18 as it combines A1 and A8.

A99 corresponds to those pixels where substrate is none of the analysis selected classes, and thus it is unclassified. In practise, this is occurs in coastal areas where the coastline of the different input layers do not match.

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Table 4 Grid labels, codes and short description of ecosystem types

Source: ETC-SIA 3.4 Datasets A summary of datasets used in the current analysis is presented in the following table.

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Table 5 Datasets used as input data to define the marine ecosystems

Source

EEA

EEA

EEA

Spatial res

Dataset

Short description

CLC 00 v17

The Corine Land Cover (CLC) is an European programme, coordinated by the European Environment Agency (EEA), providing consistent information on land cover and land cover changes across Europe. CLC products are based on the photointerpretation of satellite images by the national teams of the participating countries - the EEA member or cooperating countries. The resulting national land cover inventories are further integrated into a seamless land cover map of Europe based on standard methodology and nomenclature.

CLC 06 v17

The Corine Land Cover (CLC) is an European programme, coordinated by the European Environment Agency (EEA), providing consistent information on land cover and land cover changes across Europe. CLC products are based on the photointerpretation of satellite images by the national teams of the participating countries - the EEA member or cooperating countries. The resulting national land cover inventories are further integrated into a seamless land cover map of Europe based on standard methodology and nomenclature.

European Sea Regions

MSFD provisional dataset on sea regions and sub-regions. Draft version of the regions boundaries at sea to be used for the MSFD reporting. This dataset has not been approved by Member States. EEA internal version, Sep. 2013

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100 m

100 m

Temp res

Link

2000

/data/continental/e urope/natural_area s/corine_land_cove r/land_cover/eea_r _3035_100_m_clc_ 2000_rev17/

2006

/data/continental/e urope/natural_area s/corine_land_cove r/land_cover/eea_r _3035_100_m_clc_ 2000_rev17/ ftps://sdi.eea.europ a.eu/data/continen tal/europe/water/ msfd/eea_v_4258_ 1_mio_msfd-searegions_2013/Regio nal_seas_extended _version_ETRS89_2 0130925.shp

Comments

European Topic Centre Spatial Information and Analysis

EUSeaMap

Predicted habitats

These layes iare predictive EUNIS seabed habitat maps for the analysed seas. These maps follow the EUNIS 2007-11 classification system. They do not include the intertidal zone.

This layer is a predictive EUNIS seabed habitat map for the Atlantic area. The layer has been created using three pre-processed input datasets: substrate, biological zone and energy.

http://www.emodn etseabedhabitats.eu

The seabed substrate type layer is a compendium of historical maps. The biological zones layer was modeled thanks to layers of bathymetry, light attenuation, and wave wavelength. The layer of energy was prepared thanks to archived results of numerical models of waves and currents.

MESH Atlantic

EUROSION

MEDINA

Broad-scale EUNIS habitat maps

The map follows the EUNIS 2007-11 classification system supplemented by additional categories in deep sea areas (Howell et al., 2010). The map does not include the intertidal zone.

1:1,000,000

2013

Coastal classification map

Baseline information on the different factors influencing coastal erosion processes and the value of assets at risk. The geology map from EUROSION shows the geological patterns of the European coast, classifying the coastline into several classes (rocks and hard cliffs, small beaches, muddy sediments, embankments, vegetated strands, soft strands with â&#x20AC;&#x2DC;rocky platformsâ&#x20AC;&#x2122;, etc.)

1:100,000

2004 n.org/database/

Coastal typology

Morpho-sedimentological typology of Mediterranean North African (NA) coastline.

The division of the NA coastline is a succession of contiguous segments according to main typologies derived by visual discrimination using satellite imagery, which makes possible to distinguish between 5 principal classes: Rocky coast, Beaches, Interdial wetlands, Mouths, and Artificial.

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http://www.emodn etseabedhabitats.eu

http://www.eurosio

2013 http://medinageoportal.eu

European Topic Centre Spatial Information and Analysis

dbSEABED

Several maps were created to identify distinctive ecosystem types, including global maps on hard and soft bottoms. Data from the database was extracted to generate binary maps , where each cell was assigned a value of hard or soft substrate depending on the presence of hard substrate in each sample (samples with greater than 50% hard substrate were counted as hard; and all others were counted as soft).

Bathymetry

A harmonised EMODnet Digital Terrain Model (DTM) is generated for European sea regions from selected bathymetric survey data sets and composite DTMs, while gaps with no data coverage are completed by integrating the GEBCO Digital Bathymetry.

http://portal.emod net-bathymetry.eu

GEBCO

GEBCO 08 Grid

GEBCO (General Bathymetric Chart of the Oceans) provides global bathymetry data sets for the world's oceans. The GEBCO 08 Grid is a global 30 arc-second grid largely generated by combining quality-controlled ship depth soundings with interpolation between sounding points guided by satellite-derived gravity data.

http://www.gebco. net/data_and_prod ucts/gridded_bathy metry_data/

NASA

The sea ice map produced based on the sea ice by reflectance in the algorithm is stored as coded integers in the Sea_Ice_by_Reflectance SDS. The sea ice algorithm MODIS_MOD29 identifies pixels as sea ice, ocean, land, inland water, cloud or other condition.

NCEAS

EMODNET

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http://www.nceas. ucsb.edu/GlobalMa rine

2 arcminutes

30 arcsecond

1km

2000presen t

http://nsidc.org/dat a/modis/order_dat a.html

More information on dbSEABED: http://instaa r.colorado.e du/~jenkinsc /dbseabed/

3.5 Data workflow The data workflow is summarised in the following schemas.

Figure 12 Data workflow process â&#x20AC;&#x201C; Preparing datasets

Source: ETC-SIA

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Figure 13 Data workflow process â&#x20AC;&#x201C; Applying marine ecosystems rules

Source: ETC-SIA

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The workflow can be divided into three different steps described in python scripts allowing future reproducibility (see Annexes for further details): 

Analysis extent – to define the extent of analysis



Pre-processing thematic data – those processes to prepare data for the analysis



Marine rules – algorithms matching the criteria to define the described ecosystem types

3.6 Methodology 3.6.1 Analysis extent The final extent of analysis must be enlarged (compared to first version map of ecosystem types) to include all the European sea regions together with wet coastal and marine CLC classes (see ch. 1 Delimitation of boundaries). It has to be noted that CLC 00 has been used to fill the gaps present at CLC 06 (e.g. Greece). First of all, a rasterization of sea regions must be performed (Annex 1. Rasterize sub regions).

Due to the different delineation of both datasets, an overlapping of boundaries produces the selection of areas that should not be included in the analysis; that is some areas where CLC is anything different from wet coastal or marine classes but it is artificially included in the analysis because it is contained in a sea region. In these cases, a mask made by CLC can be applied using the map algebra. However, this is not useful for those areas where CLC is not available (e.g. Svalbard archipelago).

Figure 14 Example of unclassified pixels (in yellow) due to unmatched boundaries between CLC and sea regions in Svalbard archipelago (left) and Northern Black Sea (right)

Source: Marine Ecosystem Map (2014) For further technical details, check the Annex 2. Extent of analysis script.

3.6.2 Bathymetry composite Similarly to the case of CLC, a gap filling of high resolution EMODNET bathymetry is proposed with GEBCO data.

For further technical details, check the Annex 3. Bathymetry composite script.

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3.6.3 Seabed class homogenisation The different seabed datasets need to be reclassified according the EUNIS coding scheme proposed before (see Table 4).

Figure 15 Substrate data homogenisation

Source: ETC-SIA For the Atlantic North, the following table has been used to aggregate the biological zones into the target EUNIS classification.

Table 6 Depth ranges for the Biological zones used by the UK EUSeaMap project

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Source: McBreen et al., 2010 (table 8, page 32) The same occurs with the ancillary substrate datasets that provide information of seabed nature at the coast. These datasets come from EUROSION project (see Figure 6 for the extent coverage) and MEDINA project (for North African coast, from Morocco to Egypt).

Table 7 EUROSION geomorphology classes aggregation into hard/soft substrate

Source: Adapted from EUROSION project (2004) Table 8 MEDINA geomorphology classes aggregation into hard/soft substrate

Source: Adapted from MEDINA project (2014) Once the homogenisation field has been created, the different sources must be combined in a single composite raster one for primary seabed sources (EUSeaMap and and another for secondary sources (EUROSION, MEDINA and Halpern). For further details on the computation process check the scripts at Annex 4. Primary seabed data integration and Annex 5. Secondary seabed data integration.

3.6.4 Sea ice data This dataset was already processed last year; check the script at Annex 6. Sea ice script.

3.6.5 Marine and coastal ecosystem rules Finally, the execution of ecosystem rules can be applied according to defined workflow (ch. 3.5 Data workflow). For further technical details, check Annex 7. Marine and coastal rules script.

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4. DISCUSSION First remark goes to the increase in the extent of analysis. An amount of 334.788.466 ha have been added in the new version (year 2014) of the ecosystems typology map, enlarging the extent to more than 1.000 million ha of marine ecosystems (1.128.129.219 ha). This enlargement has caused a notable increase in the number of unclassified pixels, resulting from a lack of data for at least one of the defining variables.

Figure 16 Distribution of hectares per each ecosystem major type (compared results from 2013 and 2014 methodological approaches)

Source: ETCSIA On the other side, the prioritised used of more accurate data has reduced the thematic uncertainty. This is derived by the rule of using 1) EUSeaMap and MESH data where it is available; 2) EUROSION and MEDINA substrate data for 1 st km seawards; and 3) NCEAS elsewhere. Though the improvement achieved by the inclusion of these datasets has not been directly computed, it can be particularly observed by the distribution of hectares in the littoral ecosystems. In the version of 2014, there is a clear dominance of littoral sediment ecosystems (a2) over littoral rock ones (a1).

In this line, further room for improvement is available and expected to be achievable as more accurate datasets will soon be released. Another improvement comes from the enlargement of extent, as more areas will be covered. This is the case for the EUSeaMap phase II datasets as hydrodynamics models, seabed substrate layers, bathymetry, etc., are expected to be publicly available by the end of 2014. In general, the data sources used so far are of a low level of detail (broad-scale), but as source layers with higher resolution will be available, enhanced modelled habitat maps will be produced and released; consequently, the resulting maps will be highly enhanced.

It is important to highlight that the EUSeaMap project does not include the intertidal zone. Therefore, there is an important gap in seabed data for this ecological range; consequently, the proposed model is a good approach, with a resulting map available and comparable at pan-European level for the littoral ecosystems.

Nevertheless, this map presents some shortcomings due to the lack of spatially explicit seabed data at this ecological zone. Possible improvements in this area should consider the modelling of substrate character (hard/soft) including slope, depth, curvature, and wave exposure.

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In the proposed model, it is still missing a general discrimination between infra and circa littoral ecosystems. A proposed approach using the Euphotic Depth layer from EMIS/JRC has been presented in this report (see 3.2.3 Light availability); it should be considered and incorporated in any future update of the model.

Finally, it has also to bear in mind the update of EUNIS classification. In 2014–2015 the European Topic Centre on Biological Diversity plans to update the marine section of the EUNIS habitat classification. As the present methodology is primarily based on the EUNIS classification, any update on this reference will imply a modification of the proposed model.

5. PROPOSED ACTIONS FOR FUTURE A summary of next actions already described in the different sections of the present report is proposed as an ending note: 

Use enhanced EUSeaMap phase II datasets available by mid-December 2014: o

extend the coverage to Canary Islands, the remaining Mediterranean areas (Adriatic, Ionian and Aegean Seas, and the Black Sea)

o

increase thematic reliability of resulting maps by the improvement of intermediate data (hydrodynamics models, seabed

substrate layers,

bathymetry, etc.). 

Propose a model to define seabed substrate at 1 st km from the coast using a combination of depth, slope, terrain curvature, and a measure of coastal.



Incorporate Euphotic Depth layer from EMIS/JRC.



Analyse the changes derived from new EUNIS habitat classification.

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REFERENCES Bekkby, T., Moy, F.E., Kroglund, T., Gitmark, J.K., Walday, M., Rinde, E.R. and Norderhaug, K.M. 2009. Identifying rocky seabed using GIS-modeled predictor variables.

Marine Geodesy, 32: pp. 379-390, DOI: 10.1080/01490410903297816 Burrows, M.T., Harvey, R., Robb, L. 2008. Wave exposure indices from digital coastlines and the prediction of rocky shore community structure. Mar Ecol Prog Ser, 353:1-12, DOI: 10.3354/meps07284 Cameron, A. and Askew, N. (eds.). 2011. EUSeaMap - Preparatory Action for development and assessment of a European broad-scale seabed habitat map final report.

Available at http://jncc.gov.uk/euseamap (Accessed on 23/04/2013) Davies, C.E., Moss, D. & Hill, M.O. 2004. EUNIS habitat classification revised 2004.

European Topic Centre on Nature Protection and Biodiversity, Paris.

EUSeaMap. 2012. Light data and thresholds – Technical appendix N.1. Available at https://webgate.ec.europa.eu/maritimeforum/system/files/TechnicalAppendix_Light_EUS eaMap_20121010_FINAL.pdf (Accessed on 23/07/2013) Evans, D. & Royo-Gelabert, E. 2013. Crosswalks between European marine habitat typologies: A contribution to the MAES marine pilot. ETCBD report for the EEA. Frost, N. J., & Swift, R.H. 2010. Accessing and developing the required biophysical datasets and datalayers for Marine Protected Areas network planning and wider marine spatial planning purposes: Report No.11: Task 1C. Assessing the confidence of broad scale classification maps.

Halpern, B., Walbridge, S., Selkoe, K., Kappel, C., Michelli, F., D’Agrosa, C., Bruno, F., Casey, K., Ebert, C., Fox, H., Fujita, R., Heinemann, D., Leninah, H., Madin, E., Perry, M., Selig, E., Spalding, M., Steneck, R. & Watson, R. 2008.A global map of human impact on marine ecosystems. Science 319 (5865): 948-952 Lee, Z. et al. 2007. Euphotic zone depth: Its derivation and implication to ocean-color remote sensing. J. Geophys. Res. 112, C03009, DOI: 10.1029/2006JC003802 McBreen, F., Askew, N. & Cameron, A. 2011 a. UKSeaMap 2010: Technical Report 4 – Energy. JNCC Report McBreen, F., Askew, N., Cameron, A., Connor, D., Ellwood, H. & Carter, A. 2011 b.

UKSeaMap 2010: Predictive mapping of seabed habitats in UK waters. JNCC Report , No. 446.

Morel, A., Huot, Y., Gentili, B., Werdell, P.J., Hooker, S.B., Franz, B.A. 2007. Examining the consistency of products derived from various ocean color sensors in open ocean (Case 1) waters in the perspective of a multi-sensor approach. Remote Sensing of Environment, 111, pp. 69–88 Ryther, J.H.. 1956. Photosynthesis in the ocean as a function of light intensity. Limnology and Oceanography, 1, pp. 61–70 Sauquin, B., Hamdi, A., Gohin, F., Populus, J., Manguin, A., Fantond’Andon, O. 2013.Estimation of the diffuse attenuation coefficient K dPAR using MERIS and application to seabed habitat mapping. Remote Sensing of Environment, 128 (2013), pp. 224-233 VLIZ.2012. Maritime Boundaries Geodatabase, version http://www.marineregions.org/.Consulted on 2013-06-27.

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ANNEX 1. RASTERIZE SUB REGIONS # ----------------------------------------------------------------------------# rasterize_SubRegions.py # Created on: 2014-09-15 # Author: Raquel Ubach (ETCSIA / UAB) # Description: Rasterization of EU sea subregions # -----------------------------------------------------------------------------

# Import arcpy module import arcpy, os from arcpy import env from arcpy.sa import *

# Local variables: extent = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\extent" inpath = 'V:\\Personal\\r_ubach\\2014\\MarineData\\SeaRegions\\SubRegions' env.workspace = inpath featureList = arcpy.ListFeatureClasses()

for feature in featureList: arcpy.AddMessage(feature) short_name = feature [0:4]

outRaster = inpath + "\\" + short_name + "_200" if not os.path.exists (outRaster): # Process: Polygon to Raster env.snapRaster = extent env.cartographicCoordinateSystem = "PROJCS['ETRS_1989_LAEA',GEOGCS['GCS_ETRS_1989',DATUM['D_ETRS_1989',SPHEROID['GRS_1980',6378137.0,298.25722 2101]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Lambert_Azimuthal_Equal_Area'],PARAM ETER['False_Easting',4321000.0],PARAMETER['False_Northing',3210000.0],PARAMETER['central_meridian',10.0],PARAMETER['l atitude_of_origin',52.0],UNIT['Meter',1.0]]"

arcpy.PolygonToRaster_conversion(feature, "FID", outRaster, "MAXIMUM_AREA", "", 200)

ANNEX 2. EXTENT OF ANALYSIS SCRIPT # --------------------------------------------------------------------------# AnalysisExtent.py # Created on: 2014-07-09 # Author: Raquel Ubach (ETCSIA / UAB) # Description: Computation of the extent of analysis in two steps #

1. extraction of clc classes

#

2. integration of European sea regions

# ---------------------------------------------------------------------------

# Import arcpy module import arcpy, os from arcpy import env from arcpy.sa import *

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# Check out any necessary licenses arcpy.CheckOutExtension("spatial")

# Input data: ## CLC v 17 from sdi.eea.europa.eu ##/data/continental/europe/natural_areas/corine_land_cover/land_cover/eea_r_3035_100_m_clc_2000_rev17/ CLC00 = Raster("D:\\Raquel\\Marine_14\\input\\clc00.tif" ) ##/data/continental/europe/natural_areas/corine_land_cover/land_cover/eea_r_3035_100_m_clc_2006_rev17/ CLC06 = Raster("D:\\Raquel\\Marine_14\\input\\clc06.tif") ## Eureopean Sea Regions from sdi.eea.europa.eu ## data/continental/europe/water/msfd/eea_v_4258_1_mio_msfd-searegions_2013/Regional_seas_extended_version_ETRS89_20130925.shp SeaReg_Pol = "D:\\Raquel\\Marine_14\\input\\SeaReg.shp"

def CLC_Extract (CLC00, CLC06): arcpy.AddMessage("Starting CLC extraction") # Process: Gap filling of CLC06 with CLC00 (CLCv17) # This process refills those pixels with "no data" in clc06 with data provided by clc00, mainly to fill gaps in Greece. arcpy.AddMessage("Process: Gap filling of CLC06 with CLC00") clc = "D:\\Raquel\\Marine_14\\Process\\clc" if not os.path.exists(clc): result1 = Con(IsNull (CLC06), CLC00, CLC06) result1.save (clc)

# Process: Select CLC classes related to coastal and marine waters environment (423, 521, 522 and 523; corresponding to raster values 39, 42, 43 and 44) arcpy.AddMessage("Process: Select CLC classes related to marine waters environment") clc_sel = "D:\\Raquel\\Marine_14\\Process\\clc_sel" if not os.path.exists(clc_sel): arcpy.gp.ExtractByAttributes_sa(clc, "\"Value\" = 39 OR \"Value\" = 42 OR \"Value\" = 43 OR \"Value\" = 44", clc_sel)

def SeaRegion_int (SeaReg_Pol): arcpy.AddMessage("Starting SeaRegion integration") clc_sel = "D:\\Raquel\\Marine_14\\Process\\clc_sel" env.snapRaster = clc_sel env.cellSize = 100 # Process: Project EU Sea Regions polygon layer to LAEA arcpy.AddMessage("Process: Project EU Sea Regions polygon layer to LAEA") SeaRegLAEA = "D:\\Raquel\\Marine_14\\Process\\SeaRegLAEA.shp" if not os.path.exists(SeaRegLAEA): arcpy.Project_management(SeaReg_Pol, SeaRegLAEA, "PROJCS['ETRS_1989_LAEA',GEOGCS['GCS_ETRS_1989',DATUM['D_ETRS_1989',SPHEROID['GRS_1980',6378137.0,298.25722 2101]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Lambert_Azimuthal_Equal_Area'],PARAM ETER['False_Easting',4321000.0],PARAMETER['False_Northing',3210000.0],PARAMETER['Central_Meridian',10.0],PARAMETER['L atitude_Of_Origin',52.0],UNIT['Meter',1.0]]", "", "GEOGCS['GCS_ETRS_1989',DATUM['D_ETRS_1989',SPHEROID['GRS_1980',6378137.0,298.257222101]],PRIMEM['Greenwich', 0.0],UNIT['Degree',0.0174532925199433]]")

# Process: Rasterize EU Sea Region SeaReg = "D:\\Raquel\\Marine_14\\Process\\SeaReg" if not os.path.exists(SeaReg):

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# Process: Combine selected clc classes with outer Sea Region marine_ext = "D:\\Raquel\\Marine_14\\Process\\marine_ext" arcpy.AddMessage("Process: Combine selected clc classes with outer Sea Region") if not os.path.exists(marine_ext): result2 = Con(IsNull (clc), SeaReg, clc_sel) result2.save (marine_ext) arcpy.AddMessage("Process: Full extent")

# Process: Reclass final extent arcpy.AddMessage("Process: Reclass final extent") extent = "D:\\Raquel\\Marine_14\\Process\\marine_ext" if not os.path.exists(extent): result3 = Reclassify(marine_ext, "Value", RemapRange([[1, 44, 1]])) result3.save(extent)

# Process: Remove overlapping clc pixels from sea regions boundaries arcpy.AddMessage("Process: Remove overlapping clc pixels from sea regions boundaries") extent = "D:\\Raquel\\Marine_14\\Process\\extent" clc = "D:\\Raquel\\Marine_14\\Process\\clc" if not os.path.exists(extent): result4 = Con(clc, SetNull(clc, marine_ext, ' "VALUE" < 39 OR "VALUE" = 40 OR "VALUE" = 41'), marine_ext) result4.save(extent)

# Execute functions CLC_Extract (CLC00, CLC06) SeaRegion_int (SeaReg_Pol)

ANNEX 3. BATHYMETRY COMPOSITE SCRIPT # --------------------------------------------------------------------------# Bathymetry_v2.py # Created on: 2014-09-11 # Author: Raquel Ubach (ETCSIA / UAB) # Description: Integration of different bathymetry datasets for the whole #

extent of analysis

# ---------------------------------------------------------------------------

# Import arcpy module import arcpy, os from arcpy import env from arcpy.sa import *

# Check out any necessary licenses

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# Input data: ## Emodnet bathymetry ## http://www.emodnet.eu/bathymetry All_Ascii_folder = "D:\\WkSpace\\Marine\\input\\bathymetry\\AllAscii"

## GEBCO_08 grid 30 arc-second grid ## http://www.gebco.net/data_and_products/gridded_bathymetry_data/ gebco = Raster("R:\\Marine_14\\input\\bathymetry") ### GDAL conversion from nc to tif ### gdal_translate -a_srs EPSG:4326 GEBCO_08.nc GEBCO_08.tif ### gdal_translate -co COMPRESS=LZW -a_srs EPSG:4326 GEBCO_08.nc GEBCO_08.tif ### os.system("gdal_translate -of GTiff " + sourcefile + " " + destinationfile)

def EMODNET_Trans (): arcpy.AddMessage("Process: bathymetry from EMODNET - transformation processes ")

# Process: Ascii to raster conversion arcpy.AddMessage("Process: Ascii to raster conversion") for file in os.listdir(All_Ascii_folder): file_nm = file [0:4] outRaster = "D:\\WkSpace\\Marine\\input\\bathymetry\\grids\\" + str(file_nm) arcpy.ASCIIToRaster_conversion(file, outRaster, "INTEGER")

# Process: Create target raster arcpy.AddMessage("Process: Create target raster") TargetRast = "D:\\WkSpace\\Marine\\Process\\bathy0" out_path = "D:\\WkSpace\\Marine\\Process" out_name = "bathy0"

if not os.path.exists (TargetRast): arcpy.CreateRasterDataset_management(out_path, out_name, "", "16_BIT_SIGNED", "", 1)

# Process: Workspace to raster arcpy.AddMessage("Process: Workspace to raster") in_workspace = "D:\\WkSpace\\Marine\\input\\bathymetry\\grids" in_raster_dataset = TargetRast arcpy.WorkspaceToRasterDataset_management (in_workspace, in_raster_dataset, "", "LAST", "MATCH")

# Process: Project bathymetry datasets to LAEA projection arcpy.AddMessage("Process: Project bathymetry datasets to LAEA projection") arcpy.DefineProjection_management(TargetRast, "GCS_WGS_1984") OutRast_pr = "D:\\WkSpace\\Marine\\Process\\bathy\\bathy_pr" arcpy.ProjectRaster_management(TargetRast, "ETRS_1989_to_WGS_1984")

OutRast_pr,

"ETRS_1989_LAEA",

"BILINEAR",

def GEBCO08 (): arcpy.AddMessage("Process: bathymetry from GEBCO08 - transformation processes ")

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# Process: Project extent of analysis to GEBCO_08 projection (GCS_WGS_1984) arcpy.AddMessage("Process: Project extent of analysis to GEBCO_08 projection (GCS_WGS_1984)") in_Rast = Raster("D:\\WkSpace\\Marine\\Process\\extent") out_Rast = "D:\\WkSpace\\Marine\\Process\\extentWGS84_3" out_CRS = Raster('D:\\2014\\184_1_1_EcoMapping\\inputData\\GEBCO_08.tif') ## "GCS_WGS_1984" transf = "ETRS_1989_to_WGS_1984" if not os.path.exists (out_Rast): arcpy.ProjectRaster_management(in_Rast, out_Rast, out_CRS , "", 100, transf)

# Process: Extract by mask arcpy.AddMessage("Process: Extract by mask") in_rast = Raster('D:\\2014\\184_1_1_EcoMapping\\inputData\\GEBCO_08.tif') mask = out_Rast gebco_extr = "D:\\2014\\184_1_1_EcoMapping\\inputData\\gebco_extr" if not os.path.exists (gebco_extr): outExtractByMask = ExtractByMask(in_rast, mask) outExtractByMask.save (gebco_extr) # Process: Project bathymetry datasets to LAEA projection arcpy.AddMessage("Process: Project bathymetry datasets to LAEA projection") gebco08_LAEA = "D:\\2014\\184_1_1_EcoMapping\\Process\\gebco08_3" if not os.path.exists(gebco08_LAEA): env.snapRaster = "D:\\WkSpace\\Marine\\Process\\clc" LAEA_rast = Raster("D:\\WkSpace\\Marine\\Process\\bathy\\bathy_pr") transf= "ETRS_1989_to_WGS_1984" arcpy.ProjectRaster_management(gebco_extr, gebco08_LAEA, LAEA_rast, "BILINEAR", 100, "")

def Bathymetry_composite (): arcpy.AddMessage("Bathymetry composite filling EMODNET gaps with GEBCO data") emodnet = Raster("D:\\WkSpace\\Marine\\Process\\bathy\\bathy_pr") gebco08_LAEA = Raster("D:\\WkSpace\\Marine\\Process\\gebco_ext")

# Process: Full extent - Combine both datasets by fulfilling extent with clc and SeaReg env.extent = "D:\\WkSpace\\Marine\\Process\\extent" bathymetry = "D:\\WkSpace\\marine\\Process\\ext\\bathymetry" if not os.path.exists(bathymetry): result1 = Con(IsNull (emodnet), gebco08_LAEA, emodnet) result1.save (bathymetry) arcpy.AddMessage("Process: Full bathymetry")

# Execute functions EMODNET_Trans () GEBCO08 () Bathymetry_composite ()

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ANNEX 4. PRIMARY SEABED DATA INTEGRATION # --------------------------------------------------------------------------# SeabedInt.py # Created on: 2014-09-16 # Author: Raquel Ubach (ETCSIA / UAB) # Description: Integration of different seabed datasets for the whole #

extent of analysis

# ---------------------------------------------------------------------------

# Import arcpy module import arcpy, os from arcpy import env from arcpy.sa import *

# Check out any necessary licenses arcpy.CheckOutExtension("spatial")

def Seabed_composite (): arcpy.AddMessage("Seabed composite") # Input data: extent = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\extent" atl = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\PrimarySeabed\\atl" atlnc = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\PrimarySeabed\\atlnc" baltic = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\PrimarySeabed\\baltic" wmed = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\PrimarySeabed\\wmed"

# Process: Full extent - Combine both datasets by fulfilling extent with clc and SeaReg env.extent = extent pr_seabed = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\PrimarySeabed\\prseabed" if not os.path.exists(pr_seabed): result1 = Con (extent, (Con(IsNull (wmed), atl, wmed))) result2 = Con (extent, (Con(IsNull (result1), atlnc, result1))) result3 = Con (extent, (Con(IsNull (result2), baltic, result2))) result4 = Con (extent, (Con(IsNull (result3), 1, result3))) result5 = Con (extent, (Con(result4==0, 1, result4))) result5.save (pr_seabed) arcpy.AddMessage("Process: Full primary seabed integration")

# Execute function Seabed_composite ()

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ANNEX 5. SECONDARY SEABED DATA INTEGRATION # --------------------------------------------------------------------------# Secondary_seabed.py # Created on: 2014-09-18 # Author: Raquel Ubach (ETCSIA / UAB) # Description: Preparation of secondary seabed datasets # ---------------------------------------------------------------------------

# Import arcpy module import arcpy, os from arcpy import env from arcpy.sa import *

# Check out any necessary licenses arcpy.CheckOutExtension("spatial")

def Secondary_seabed (): arcpy.AddMessage("Process: Preparation of secondary seabed datasets") # Input data: extent = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\extent"

# Process1: Feature to raster (Eurosion) arcpy.AddMessage("Process1: Feature to raster (Eurosion)") eurosion = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\SarySeabed\\eurosion" coast_EU = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\SarySeabed\\euros_rt" if not os.path.exists (coast_EU): arcpy.AddMessage("Process1: Feature to raster") arcpy.env.snapRaster = extent arcpy.env.outputCoordinateSystem = "PROJCS['ETRS_1989_LAEA',GEOGCS['GCS_ETRS_1989',DATUM['D_ETRS_1989',SPHEROID['GRS_1980',6378137.0,298.25722 2101]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Lambert_Azimuthal_Equal_Area'],PARAM ETER['false_easting',4321000.0],PARAMETER['false_northing',3210000.0],PARAMETER['central_meridian',10.0],PARAMETER['la titude_of_origin',52.0],UNIT['Meter',1.0]]" arcpy.FeatureToRaster_conversion(coast, "Subs_cd", coast_EU, "100")

# Process2: Feature to raster (MEDINA) arcpy.AddMessage("Process2: Feature to raster (MEDINA)") medina = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\SarySeabed\\medina" coast_NA = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\SarySeabed\\na_rt" if not os.path.exists (coast_EU): arcpy.AddMessage("Process1: Feature to raster") arcpy.env.snapRaster = extent arcpy.env.outputCoordinateSystem = "PROJCS['ETRS_1989_LAEA',GEOGCS['GCS_ETRS_1989',DATUM['D_ETRS_1989',SPHEROID['GRS_1980',6378137.0,298.25722 2101]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Lambert_Azimuthal_Equal_Area'],PARAM ETER['false_easting',4321000.0],PARAMETER['false_northing',3210000.0],PARAMETER['central_meridian',10.0],PARAMETER['la titude_of_origin',52.0],UNIT['Meter',1.0]]" arcpy.FeatureToRaster_conversion(medina, "Subs_cd", coast_NA, "100")

# Process3: Integrate both euros_medi_pre = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\SarySeabed\\euromedi0"

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# Process5: Integration with Halpern substrate halpern = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\SarySeabed\\halpern" substrate = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\SarySeabed\\ssubstrate" if not os.path.exists (substrate): result1 = Con (IsNull(euros_medi), Con(beach, 0, halpern), euros_medi) result1.save(substrate)

# Execute function Secondary_seabed ()

ANNEX 6. SEA ICE SCRIPT # SeaIce.py # Created on: 2013-09-13 # Author: Raquel Ubach (ETCSIA - UAB) # Description: Script to reclassify all scenes to one single value for the #

presence of sea ice

# ---------------------------------------------------------------------------

# Import arcpy module importarcpy, os fromarcpy import env from arcpy.sa import *

# Set the current workspace env.workspace = "V:\\Personal\\r_ubach\\2013\\MarineData\\SeaIce"

# Local variables: inputTable = "D:\\2013\\222_51_EcosystemMapping\\SEAICETI.dbf" field = "TILE"

# Process: Iterating through the table where reference of sea ice scenes are stored scenes = arcpy.SearchCursor(inputTable) scene = scenes.next () arcpy.AddMessage("Process: Iterating through the table")

while scene: ref = scene.getValue(field) arcpy.AddMessage("Process: scene " + str(ref)) refEnd = "*" + str(ref) + ".hdf" rasterList = arcpy.ListRasters(refEnd, "")

for raster in rasterList: outRast = "V:\\Personal\\r_ubach\\2013\\MarineData\\SeaIce2\\" + str(ref)

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outRecPath = "V:\\Personal\\r_ubach\\2013\\MarineData\\SeaIce_rec\\rec_" + str(ref) if not os.path.exists(outRecPath): # Process: create a mask with sea ice coverage by reclassifying arcpy.AddMessage("Process: create a mask with sea ice coverage by reclassifying") reclassField = "Value" recRange = RemapRange([[ 0, 199, "NODATA"], [200, 1],[201, 254, "NODATA"]]) outRec = Reclassify (outRast, reclassField, recRange, "NODATA") outRecPath = "V:\\Personal\\r_ubach\\2013\\MarineData\\SeaIce_rec\\rec_" + str(ref) arcpy.AddMessage("Process: save the reclassifying output " + str(ref)) outRec.save (outRecPath) scene = scenes.next()

ANNEX 7. MARINE AND COASTAL RULES SCRIPT # -----------------------------------------------------------------------------# MarineRules14.py # Created on: 2014-09-22 # Author: Raquel Ubach (ETCSIA / UAB) # Description: Execution of marine rules to compute major marine ecosystem types # ------------------------------------------------------------------------------

# Import arcpy module import arcpy, os from arcpy import env from arcpy.sa import *

# Check out any necessary licenses arcpy.CheckOutExtension("spatial")

def Marine_rules (): arcpy.AddMessage("Execution of marine rules to compute major marine ecosystem types") # Input data: bathymetry = Raster ("D:\\WkSpace\\Marine\\Process\\ext\\bathymetry") seaice = Raster ("D:\\WkSpace\\Marine\\Process\\ext\\seaice") pr_seabed = Raster ("D:\\WkSpace\\Marine\\Process\\ext\\prseabed") # Primary seabed -> integration of substrate data from EUSeabed and MESH projects s_seabed = Raster ("D:\\WkSpace\\Marine\\Process\\ext\\sseabed") # Secondary seabed -> integration of substrate data from Eurosion, MEDINA and Halpern projects extent = "D:\\WkSpace\\Marine\\Process\\extent2" clc_sel = Raster ("D:\\WkSpace\\Marine\\Process\\ext\\clc_sel") # selection of coastal and marine classes (clc == 39, 42, 43 and 44) marine_ext = "D:\\WkSpace\\Marine\\Process\\ext\\clc3944_sreg" # extent where primary seabed is null and without clc == 42 and 43 (only marine clc ==39 and 44 and outer boundaries of sea regions) trans_wt = Raster ("D:\\WkSpace\\Marine\\Process\\ext\\transwt_1km") # buffer of transitional waters up to 1km clc = Raster ("D:\\WkSpace\\Marine\\Process\\ext\\clc")

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# Setting workspace ws_folder = "D:\\WkSpace\\Marine\\Output\\MarineRules" if not os.path.exists (ws_folder): os.makedirs (ws_folder)

# Marine ecosystem rules where no primary seabed is available env.cellSize = 100 env. mask = marine_ext env.extent = extent

## Ecosystems with no sea ice # Ecosystem type "Littoral" A12 (nodata available to differenciate between hard and soft substrates (mixed A1 and A2)) outRast = ws_folder + "\\a12" if not os.path.exists (outRast): # Littoral mixed (A12) must agree with following conditions: # 1. CLC == 44 or seawards up to marine sea regions boundaries (env. mask = marine_ext) # 2. bathymetry >= 0 # 3. there is no primary seabed data # 4. there is no secondary seabed data # 5. there is no seaice result = Con (bathymetry >= 0, Con (IsNull (s_seabed), Con (IsNull (seaice), 112))) result.save (outRast) arcpy.AddMessage("Process: finished " + str(outRast))

# Ecosystem type "Littoral rock and other hard substrata" A1 outRast = ws_folder + "\\a1" if not os.path.exists (outRast): # Littoral rock and other hard substrata (A1) must agree with following conditions: # 1. CLC == 44 or seawards up to marine sea regions boundaries # 2. bathymetry >= 0 # 3. there is no primary seabed data (pr_seabed == 1) # 4. secondary seabed data == 2 (hard substrate) # 5. there is no seaice result = Con(bathymetry >= 0, Con (s_seabed == 2, Con (IsNull (seaice), 101))) result.save (outRast) arcpy.AddMessage("Process: finished " + str(outRast))

# Ecosystem type "Littoral sediment" A2 outRast = ws_folder + "\\a2" if not os.path.exists (outRast): # Littoral sediment (A2) must agree with following conditions: # 1. CLC == 44 or seawards up to marine sea regions boundaries # 2. bathymetry >= 0 # 3. there is no primary seabed data (pr_seabed == 1) # 4. secondary seabed data == 1 (soft substrate) # 5. there is no seaice result = Con(bathymetry >= 0, Con (s_seabed == 1, Con (IsNull (seaice), 102))) result.save (outRast)

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# Ecosystem type "Infra and circalittoral rock and other hard substrata" A34 (mixed EUNIS classes A3 and A4) outRast = ws_folder + "\\a34" if not os.path.exists (outRast): # Infra and circalittoral rock and other hard substrata (A34) must agree with following conditions: # 1. CLC == 44 or seawards up to marine sea regions boundaries # 2. (bathymetry < 0)& (bathymetry >= -200) # 3. there is no primary seabed data # 4. secondary seabed data == 2 (hard substrate) # 5. there is no seaice result = Con(((bathymetry < 0)& (bathymetry >= -200)), Con (s_seabed == 2, Con (IsNull (seaice), 134))) result.save (outRast) arcpy.AddMessage("Process: finished " + str(outRast))

# Ecosystem type "Sublittoral sediment" A5 outRast = ws_folder + "\\a5" if not os.path.exists (outRast): # Sublittoral sediment (A5) must agree with following conditions: # 1. CLC == 44 or seawards up to marine sea regions boundaries # 2. (bathymetry < 0)& (bathymetry >= -200) # 3. there is no primary seabed data # 4. secondary seabed data == 1 (soft substrate) result = Con(((bathymetry < 0)& (bathymetry >= -200)), Con (s_seabed == 1, Con (IsNull (seaice), 105))) result.save (outRast) arcpy.AddMessage("Process: finished " + str(outRast))

# Ecosystem type "Deep-sea" A6 outRast = ws_folder + "\\a6" if not os.path.exists (outRast): # Deep-sea (A6) must agree with following conditions: # 1. CLC == 44 or seawards up to marine sea regions boundaries # 2. bathymetry < -200 # 3. there is no seaice result = Con(bathymetry < -200, Con (IsNull (seaice), 106)) result.save (outRast) arcpy.AddMessage("Process: finished " + str(outRast))

## Ecosystems with sea ice # Ecosystem type "Littoral and sea ice" A128 (mixed A1 and A2 and A8)) outRast = ws_folder + "\\a128" if not os.path.exists (outRast): # Littoral and sea ice (A128) must agree with following conditions: # 1. CLC == 44 or seawards up to marine sea regions boundaries (env. mask = marine_ext) # 2. bathymetry >= 0 # 3. there is no primary seabed data # 4. there is no secondary seabed data # 5. there is seaice result = Con (seaice == 1, Con (bathymetry >= 0, Con (IsNull (s_seabed), 128)))

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# Ecosystem type "Littoral rock and other hard substrata and sea ice" A18 outRast = ws_folder + "\\a18" if not os.path.exists (outRast): # Littoral rock and other hard substrata (A1) must agree with following conditions: # 1. CLC == 44 or seawards up to marine sea regions boundaries # 2. bathymetry >= 0 # 3. there is no primary seabed data (pr_seabed == 1) # 4. secondary seabed data == 2 (hard substrate) # 5. there is seaice result = Con (seaice == 1, Con (bathymetry >= 0, Con (s_seabed == 2, 181))) result.save (outRast) arcpy.AddMessage("Process: finished " + str(outRast))

# Ecosystem type "Littoral sediment and sea ice" A28 outRast = ws_folder + "\\a28" if not os.path.exists (outRast): # Littoral sediment and ice (A28) must agree with following conditions: # 1. CLC == 44 or seawards up to marine sea regions boundaries # 2. bathymetry >= 0 # 3. there is no primary seabed data (pr_seabed == 1) # 4. secondary seabed data == 1 (soft substrate) # 5. there is seaice result = Con (seaice == 1, Con (bathymetry >= 0, Con (s_seabed == 1, 182))) result.save (outRast) arcpy.AddMessage("Process: finished " + str(outRast))

# Ecosystem type "Infra and circalittoral rock and other hard substrata" A34 (mixed EUNIS classes A3 and A4) outRast = ws_folder + "\\a348" if not os.path.exists (outRast): # Infra and circalittoral rock and other hard substrata (A34) must agree with following conditions: # 1. CLC == 44 or seawards up to marine sea regions boundaries # 2. (bathymetry < 0)& (bathymetry >= -200) # 3. there is no primary seabed data # 4. secondary seabed data == 2 (hard substrate) # 5. there is seaice result = Con (seaice == 1, Con(((bathymetry < 0)& (bathymetry >= -200)), Con (s_seabed == 2, 138))) result.save (outRast) arcpy.AddMessage("Process: finished " + str(outRast))

# Ecosystem type "Sublittoral sediment and sea ice" A58 outRast = ws_folder + "\\a58" if not os.path.exists (outRast): # Sublittoral sediment and sea ice(A58) must agree with following conditions: # 1. CLC == 44 or seawards up to marine sea regions boundaries # 2. (bathymetry < 0)& (bathymetry >= -200) # 3. there is no primary seabed data

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# Ecosystem type "Deep-sea and sea ice" A68 outRast = ws_folder + "\\a68" if not os.path.exists (outRast): # Deep-sea (A6) must agree with following conditions: # 1. CLC == 44 or seawards up to marine sea regions boundaries # 2. bathymetry < -200 # 3. there is seaice result = Con (seaice == 1, Con(bathymetry < -200, 186)) result.save (outRast) arcpy.AddMessage("Process: finished " + str(outRast))

# Ecosystem type "Unclassified" A99 # It includes all those pixels where there is no data for substrate # except for the ones where bathymetry < -200 that is independent of substrate outRast = ws_folder + "\\a99" if not os.path.exists(outRast): result = Con (IsNull(s_seabed), Con((bathymetry < 0)& (bathymetry >= -200), Con(IsNull(trans_wt), 250))) # remove from unclassified those pixels where clc is terrestrial, due to mismatches bw searegion and extent delineations whereClause = '"VALUE" <39 OR "VALUE" = 40 OR "VALUE" = 41' result2 = SetNull(clc, result, whereClause) result2.save (outRast) arcpy.AddMessage("Process: finished " + str(outRast))

def Coastal_rules (): arcpy.AddMessage("Execution of coastal rules to compute major wet coastal ecosystem types") # Input data: extent = "D:\\WkSpace\\Marine\\Process\\extent" clc_sel = Raster ("D:\\WkSpace\\Marine\\Process\\ext\\clc_sel") # selection of coastal and marine classes (clc == 39, 42, 43 and 44) coastal_ext = Raster ("D:\\WkSpace\\Marine\\Process\\ext\\clc4243") # selection of coastal classes (clc == 42 and 43 ) s_seabed = Raster ("D:\\WkSpace\\Marine\\Process\\ext\\sseabed") # Secondary seabed bathymetry = Raster ("D:\\WkSpace\\Marine\\Process\\ext\\bathymetry") trans_wt = Raster ("D:\\WkSpace\\Marine\\Process\\ext\\transwt_1km") # buffer of transitional waters up to 1km

# Setting workspace ws_folder = "D:\\WkSpace\\Marine\\Output\\CoastalRules" if not os.path.exists (ws_folder): os.makedirs (ws_folder)

# Marine ecosystem rules where no primary seabed is available env.cellSize = 100 env.extent = extent

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## Coastal Ecosystems # Ecosystem type "Estuaries" X1 outRast = ws_folder + "\\x1" if not os.path.exists (outRast): # Estuaries (X1) must agree with following conditions: # 1. CLC == 43 # 2. those unclassified pixels matching with transitional waters result = Con (clc_sel == 43, 109, Con (IsNull(s_seabed), Con(bathymetry >= -200, Con(trans_wt, 109)))) result.save (outRast) arcpy.AddMessage("Process: finished " + str(outRast))

# Ecosystem type "Coastal lagoons" X23 (mixed EUNIS classes X2 and X3) outRast = ws_folder + "\\x23" if not os.path.exists (outRast): # Coastal lagoons (X23) must agree with following conditions: # 1. CLC == 42 result = Con (clc_sel == 42, 110) result.save (outRast) arcpy.AddMessage("Process: finished " + str(outRast))

def Integrate_ecosystems (): arcpy.AddMessage("Integration of major marine ecosystem types into one raster dataset") # Input data: pr_seabed = Raster ("D:\\WkSpace\\Marine\\Process\\ext\\prseabed") ws_folder = "D:\\WkSpace\\Marine\\Output\\MarineRules"

# Integrate all rasters from marine rules into one dataset arcpy.AddMessage("Integrate all rasters from marine rules into one dataset") out_path = "D:\\WkSpace\\Marine\\Output\\Out" if not os.path.exists (out_path): os.makedirs (out_path) out_name = "mosaic_ss" mosaic_ss = str(out_path ) +"\\" + str(out_name)

if not os.path.exists (mosaic_ss): arcpy.AddMessage("Create target dataset to integrate ecosystems derived from secondary seabed data") arcpy.CreateRasterDataset_management(out_path, out_name, 100.0, "8_BIT_UNSIGNED", "", 1) arcpy.WorkspaceToRasterDataset_management(ws_folder, mosaic_ss, "", "FIRST")

# Integrate all rasters from coastal rules into one dataset arcpy.AddMessage("Integrate all rasters from coastal rules into one dataset") cs_folder = "D:\\WkSpace\\Marine\\Output\\CoastalRules" out_name = "mosaic_cs" mosaic_cs = str(out_path ) + "\\" + str(out_name) if not os.path.exists (mosaic_cs): arcpy.AddMessage("Create target dataset to integrate ecosystems derived from coastal data") arcpy.CreateRasterDataset_management(out_path, out_name, 100.0, "8_BIT_UNSIGNED", "", 1) arcpy.WorkspaceToRasterDataset_management(cs_folder, mosaic_cs, "", "FIRST")

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# Integrate the primary and secondary seabed derived ecosystem types (marine types) with the coastal ones arcpy.AddMessage("Integrate the primary and secondary seabed derived ecosystem types") # mosaic_sb = "D:\\WkSpace\\Marine\\Output\\mosaic_sb" # if not os.path.exists (mosaic_sb): # result = Con (pr_seabed == 1, Con(IsNull(mosaic_ss), mosaic_cs, mosaic_ss), pr_seabed) # result.save (mosaic_sb) # arcpy.AddMessage("Process: finished " + str(mosaic_sb)) OutRast_name = "cmarine_ec" marine_ec = str(out_path ) + "\\" + str(OutRast_name) if not os.path.exists (marine_ec): #arcpy.CreateRasterDataset_management(out_path, OutRast_name, 100.0, "8_BIT_UNSIGNED", "", 1) input_rasters = "pr_seabed;mosaic_cs;mosaic_ss" arcpy.MosaicToNewRaster_management(input_rasters, "MAXIMUM", "")

out_path,

OutRast_name,

"",

"8_BIT_UNSIGNED",

100,

# Execute functions Marine_rules () Coastal_rules () Integrate_ecosystems ()

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1,