Age

Accessing and developing the required biophysical datasets and data layers for Marine Protected Areas network planning and wider

marine spatial planning purposes

Report No 21: Task 2H. Benthic Productivity Data Layer Development - North Sea Pilot Study

Version (Final)

May 2010

© Crown copyright

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Project Title: Accessing and developing the required biophysical datasets and data layers for Marine Protected Areas network planning and wider marine spatial planning purposes Report No 21: Task 2H. Benthic Productivity Data Layer Development - North Sea Pilot Study Project Code: MB0102 Marine Biodiversity R&D Programme Defra Contract Manager: Jo Myers Funded by: Department for Environment Food and Rural Affairs (Defra) Marine and Fisheries Science Unit Marine Directorate Nobel House 17 Smith Square London SW1P 3JR Joint Nature Conservation Committee (JNCC) Monkstone House City Road Peterborough PE1 1JY Countryside Council for Wales (CCW) Maes y Ffynnon Penrhosgarnedd Bangor LL57 2DW Natural England (NE) North Minister House Peterborough PE1 1UA Scottish Government (SG) Marine Nature Conservation and Biodiversity Marine Strategy Division Room GH-93 Victoria Quay Edinburgh EH6 6QQ Department of Environment Northern Ireland (DOENI) Room 1306 River House 48 High Street Belfast BT1 2AW

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Isle of Man Government (IOM) Department of Agriculture Fisheries and Forestry Rose House 51-59 Circular Road Douglas Isle of Man IM1 1AZ Authorship: Heidi Tillin ABP Marine Environmental Research Ltd htillin@abpmer.co.uk Stefan Bolam CEFAS stefan.bolam@cefas.co.uk Jan Hiddink School of Ocean Sciences: Bangor University j.hiddink@bangor.ac.uk ABP Marine Environmental Research Ltd Suite B, Waterside House Town Quay Southampton Hampshire SO14 2AQ www.abpmer.co.uk Centre for Environment, Fisheries & Aquaculture Science (Cefas) Lowestoft Laboratory Pakefield Road Lowestoft Suffolk NR33 0HT www.cefas.co.uk School of Ocean Sciences Bangor University Bangor Gwynedd LL57 2DG www.bangor.ac.uk Disclaimer: The content of this report does not necessarily reflect the views of Defra, nor is Defra liable for the accuracy of the information provided, nor is Defra responsible for any use of the reports content. Acknowledgements: To Andrew Pearson and Nigel West of ABPmer for the front cover images.

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Executive Summary The UK is committed to the establishment of a network of marine protected areas (MPAs) to help conserve marine ecosystems and marine biodiversity. MPAs can be a valuable tool to protect species and habitats and can also be used to aid implementation of the ecosystem approach to management, which aims to maintain the ‘goods and services’ produced by the healthy functioning of the marine ecosystem that are relied on by humans. A consortium1 led by ABPmer have been commissioned (Contract Reference: MB0102) to develop a series of biophysical data layers to aid the selection of Marine Conservation Zones (MCZs) in England and Wales under the Marine and Coastal Access Act and the equivalent MPA measures in Scotland. Such data layers would also be of use in taking forward marine planning in UK waters. The overall aim of the project is to ensure that the best available information is used for the selection of MPAs in UK waters, and that these data layers can be easily accessed and utilised by those who would have responsibility for selecting sites. The Marine and Coastal Access Act allows for the designation of MCZs for geological and geomorphological features of interest, species and habitats, and the benthic productivity. As such there has been a need to identify those features of interest through the delivery of datalayers and in order to deliver this requirement, the project has been divided into a number of discrete tasks, one of which is to review and assess methodologies for the development of a ‘benthic productivity’ datalayer. Prior to the study outlined in this report a review of the scientific literature on approaches to assessing production/productivity, was undertaken (Tillin et al, 2009) and the conclusions assessed by external, independent experts at a workshop. This process identified two approaches as potentially suitable to develop a UK wide seabed production datalayer without requiring biological data (which is expensive and time consuming to collect). These were; 1) the Duplisea et al. (2002) model modified by Hiddink (2006), referred to in this study as the Duplisea/Hiddink model, that predicts production from environmental variables and other drivers of production and; 2) models parameterised using environmental variables to predict production (so that environmental variables are used as proxy indicators of production). To assess the feasibility of these two approaches the pilot study outlined in this report was carried out to test and compare the performance of these approaches. A region of the North Sea seabed that had been previously sampled by Cefas as part of a Defra funded programme (project no.ME3112) was selected as the pilot study area. The earlier sampling programme provided environmental and biological information and estimates of benthic macroinvertebrate production for this area based on empirical modelling using the Brey model developed by Thomas Brey. This model has been judged in peer-reviewed papers to be the most accurate approach available to assess production. The estimates from the Brey model formed a baseline to compare the estimates of the Duplisea/Hiddink approach and underpinned the development of models using proxy indicators of production. These approaches were

1 ABPmer, MarLIN, Cefas, EMU Limited, Proudman Oceanographic Laboratory (POL) and Bangor University.

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applied to the same area to determine if the more cost-effective, proxy indicator approach, could be justified rather than the more data intensive models. The review and this pilot study concentrated on seabed soft-sediment (sedimentary rather that rock substrates) productivity of benthic macroinfauna, as approaches in this area were considered the most developed and tested. Sediments are the most wide-spread type of marine benthic habitat in UK waters so that development of a methodology to map production/productivity for these habitats ensures that the outputs of the study are applicable across the majority of the UK Continental Shelf. Approaches for assessing secondary production/productivity of hard substrate assemblages are in development and were not available. The results from the Duplisea/Hiddink model was not found to correlate with the Brey model. It was therefore not possible to demonstrate that the Duplisea/Hiddink model could be used for datalayer development instead of the more direct estimation approach of Brey (2001) that relies on sampling and processing of biological data and published literature values. The pilot study also found that it was not possible to identify robust proxy indicators of soft-sediment benthic production applicable to the UKCS. Taking the results of this pilot study into account and data availability, it is suggested that the best practical option available to produce a UK benthic, secondary production data layer would be to seek to use the productivity values (estimated using the Brey model) that were developed by Cefas (gathered by the Defra funded project ME3112). Currently, this is judged to be the approach with the greatest associated confidence (as the Brey model is widely supported) and coverage (the point data is available for UK seas).

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Table of Contents Executive Summary 4

1. Introduction 8 1.1 Project Background 8 1.2 Benthic Production and Productivity 9 1.2.1 Assessing Benthic Production and Productivity 9 1.3 Aims and Objectives 11 1.4 Format of Report 11

2. Pilot Area 12 2.1 Survey and Sampling Design 12 2.2 Variables and Sampling Methodology 12 2.3 Sampling Processing 12 2.4 Environmental Variables 13

3. Brey Model 16 3.1 Data Acquisition 16 3.2 Estimates of Productivity and Production 16 3.3 Results 17

4. Duplisea/Hiddink Model 19 4.1 Description of Model 19 4.2 Model Run 20 4.3 Results 20 4.4 Comparison of Brey and Hiddink Models 21

5. Proxy Indicators of Productivity 23 5.1 Answer Tree 23 5.2 Linear Regression 24 5.3 Multiple Regression 24 5.3.1 Multi-colinearity- When Variables Are Correlated 25

6. Issues and Considerations 27 6.1 Correlation Between Brey and Hiddink/Duplisea Model 27 6.2 Spatial Variability of Production 28 6.3 Options 28 Abbreviations 32 Units 32 References 32 Annex Annex A. Results from Duplisea/Hiddink Model Approach 35

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List of Tables Table 1: Environmental variable information and summary descriptive statistics from the pilot area 14 Table 2: Predicted biomass and production of benthic invertebrates for selected stations in the southern North Sea 35 Table 3: Explanatory models developed using linear and backward stepwise regression on environmental variables and predictive ability (to estimate productivity). Variable significance values in parentheses Environmental variable information and summary descriptive statistics from the pilot area 26 List of Figures Figure 1: Location of Pilot Study Area and Sampling Stations 15 Figure 2: Productivity Estimates from the Brey (2001) Model 18 Figure 3: Productivity Estimates from the Duplisea/Hiddink Model 22 Figure 4: Location of UK Wide Sampling Stations (Defra funded project ME3112) 31

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1. Introduction 1.1 Project Background 1.1 The UK is committed to the establishment of a network of Marine Protected

Areas (MPAs) to conserve marine ecosystems and marine biodiversity. MPAs can be a valuable tool to protect species and habitats. They can also be used to aid implementation of the ecosystem approach to management, which aims to maintain the ‘goods and services’ produced by the normal functioning of the marine ecosystem that are relied on by humans.

1.2 As a signatory of OSPAR the UK is committed to establish an ecologically

coherent network of well-managed MPAs. The UK is already in the process of completing a network consisting of Special Areas of Conservation (SACs) and Special Areas of Protection (SPAs), collectively known as Natura 2000 sites to fullfill its obligations under the EC Habitats Directive (92/43/EEC). Through provisions in the Marine and Coastal Access Act a network of Marine Conservation Zones (MCZs) will be designated in English and Welsh territorial waters and UK offshore waters. The Scottish Government is also considering equivalent Marine Protected Areas (MPAs) in Scotland. These sites are intended to help to protect areas where habitats and species are threatened, and to also protect areas of representative habitats. For further information on the purpose of MCZs and the design principles to be employed see [http://www.defra.gov.uk/marine/biodiversity/marine-bill/guidance.htm, Defra, 2009].

1.3 Selection of MPAs should be based on the best available data and will come

from a range of sources including biological, physical and oceanographic characteristics and socio-economic data such as the location of current activities. To ensure such data are easily available to those who will have responsibility for selecting sites, Defra and its partners commissioned a consortium led by ABPmer Ltd and partners to take forward a package of work. New Geographical Information System (GIS) data layers to be developed included: Geological and geomorphological features; Listed habitats; Fetch and wave exposure; Marine diversity layer; Benthic productivity; and Residual current flow.

1.4 This report provides a pilot trial of three approaches to estimating benthic productivity. The need for this trial was identified through the review of approaches to measuring benthic productivity produced by ABPmer (Tillin et al, 2009).

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1.2 Benthic Production and Productivity 1.5 Production (of organic material) is a key ecosystem process that underpins

ecosystem function. Somatic or secondary production is the change in biomass with time (Brey 2001). Measures of productivity (production of organic material per unit area and time) indicate the amount of matter/energy that is potentially available as reproduction stages or food (Brey, 2001).

1.6 Benthic macroinvertebrates are an important part of the energy flow occurring

in marine ecosystems, they link primary producers (which are an important food source for the benthos), and higher trophic (feeding) levels, (as prey items for fish). They can therefore be understood to be an important part of productivity of the seas, in terms of both ecosystem function (through organic matter cycling) and economic value (underpinning the production of commercial fish). The creation of a benthic production/productivity datalayer based on the secondary production of macroinvertebrates may be a useful tool to discriminate between areas where productivity rates from benthic invertebrate communities differ. Given the importance of benthic invertebrate production of organic material to the ecosystem, this could aid the identification of areas that could be considered for protection as part of the MPA selection process.

1.2.1 Assessing Benthic Production and Productivity 1.7 Of necessity the review of methods for assessing benthic

productivity/production, and this subsequent pilot study, concentrate on the estimation of soft-sediment (sedimentary rather than rock substrate) habitat productivity of macroinfauna, as approaches for these habitat types and organisms are most developed and tested. Soft-sediments are the most wide-spread type of marine benthic habitat in UK waters so that development of a methodology to map production/productivity for these habitats would be valuable for covering a broad extent of the seabed. Methods of assessing productivity of epifauna, particularly those on hard substrates are poorly developed: currently work is being undertaken to refine models and develop measures of productivity for hard substrates but these will not report in the time-scale of this project (before 2012). The focus on soft-sediment productivity ensures, however, that the outputs of the study will be applicable across the majority of the UKCS.

1.8 The review that preceded this pilot study, and the subsequent workshop,

identified that there was uncertainty over the most suitable methods to employ for data layer development. The most accurate estimates of production/productivity are those that rely on direct measurement of macroinvertebrate populations over time (Tillin et al, 2009), however these methods are too data intensive and time consuming to support datalayer development. Other approaches to estimating production use biological sample data with information on biomass (either measured or estimated from species abundances using available conversion information from the literature). While the value of this approach was recognised, concerns over data scarcity, particularly for offshore areas, meant that the workshop

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attendees found it desirable that other methods of developing broadscale production/productivity estimates were trialled. Specifically there was interest in testing whether production could be predicted using environmental variables that are already available or are subject to ongoing monitoring and collection. Two approaches were identified at the workshop that have the potential to be used in this way. These form the basis of this pilot study and are described in further detail below (Section 4 Duplisea/Hiddink model and Section 5 Proxy Indicators).

1.9 Predicted results from these methods need to be validated (ground-truthed)

against productivity estimates, derived from a method, which stakeholders and managers can be confident provides an accurate measure of productivity. Direct measures of assessing productivity (cohort and size-class methods) would be most desirable as the validation data, but the cost and time scale of producing these is prohibitive.

1.10 Therefore, for this purpose we used estimated productivity values from an

empirical modelling approach developed by Thomas Brey (2001) and referred to in this report as the ‘Brey approach’. This approach to assessing productivity uses a multivariable regression model parameterised using environmental variables and biomass energy values from the literature to estimate the productivity of benthic macroinvertebrate assemblages (using sampled biomass data). A number of studies have supported the use of this model to estimate production (Cusson & Bourget, 2005, Dolbeth, et al. 2005). In a comparative study of empirical models Dolbeth et al (2005) found that the modelled estimates of production from the empirical Brey (2001) model most closely matched the production estimates from the more accurate cohort based model.

1.11 While the ultimate aim would be to produce a UKCS wide datalayer, for the

purposes of the pilot a more restricted, suitable test location was required to assess the different approaches. The pilot trial uses data collected from sampling stations in the southern North Sea region, as this area allows a robust evaluation of the three methodologies to be undertaken. The most extensive broad scale marine habitats in the UKCS area are soft-sediment sand and gravel habitats. The test area generally consists of sandy sediments and therefore provides a test of the methodologies in an environment that is representative of large parts of the UKCS.

1.12 The North Sea region has been extensively studied through international

research efforts. The study area was sampled for both the 1986 North Sea Benthos Survey and the 2000 North Sea Benthos Project, which studied biological communities and environmental conditions in the North Sea. The abundance and distribution of sampling stations in the trial area means that the three approaches can be thoroughly tested. The station locations in this area were selected based on a grid sampling design, survey coverage of the area is distributed evenly and, as such, should be independent of sampling bias. The level of survey effort in this area was higher than other regions and data from 39 stations are used in this study. The effects of fishing on benthic communities in the North Sea are also relatively well understood and reliable

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fishing effort data is available for this area to parameterise the model that has been trialled by Hiddink (University of Wales).

1.13 The study of benthic secondary production undertaken by Cefas (Defra funded

project ME3112), found that benthic productivity in the area is spatially variable. This heterogeneity between sampling stations allows the discriminatory ability of the three methodologies to be evaluated and compared for a range of benthic production states.

1.3 Aims and Objectives 1.14 The aim of this pilot study was to compare the performance of three different

approaches to estimating benthic productivity, two of which were identified as having potential for the development of a production datalayer for the UKCS (the Duplisea/Hiddink approach and proxy indicators). This pilot study will identify whether, 1) model approaches perform sufficiently well (assessed using the Brey 2001 model) to be adopted, or if, 2) quantitative empirical models are required that use sample data (biomass or abundance), although data requirements may mean these are too costly to employ. Hence, the pilot study was required to provide information on the feasibility of constructing a UK wide datalayer, the approaches that could be utilised and the potential data requirements/cost.

1.4 Format of Report 1.15 The report has been divided into 6 sections, comprising 1) an introduction

which supplies the background information to this pilot study, 2) a description of the pilot study area, sampling and environmental variables, descriptions of the approaches and the results of these (sections 3, 4 and 5) and a final discussion and recommendations (section 6)

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2. Pilot Area 2.1 Survey and Sampling Design 2.1 The pilot study utilises data collected by Cefas through the Defra funded

project ME3112. This was a four-year study that sampled 155 offshore stations from the North Sea, English Channel, Irish Sea and waters to the west coast of Scotland covering the majority of the continental shelf of England, Wales, Northern Ireland and the west coast of Scotland. The majority of the sampling stations were positioned using a grid design, with stations at intervals of 0.25° latitude and 0.5° longitude, resulting in neighbouring stations being 24 nm apart. Some stations were positioned outside this regular design to provide coverage where land would have otherwise prevented sampling (e.g., around the Isle of Wight, English Channel).

2.2 Using a systematic design at such a large spatial scale means that sites were

selected without bias towards environmental factors or the nature of the seabed environment at each station. This means that the survey design is, in effect, a random sampling strategy (Bolam et al., 2008).

2.2 Variables and Sampling Methodology 2.3 Samples of sediment for benthic macrofauna and sediment particle size

distribution analyses were collected from each sampling station. Most of the sampling was conducted aboard the RV Cefas Endeavour during May and June of 2005-07 using methods that were compatible with the sedimentary habitat being targeted. For the soft sediments (sand and silt/clay) distributed in the North Sea, a 0.1m² Day grab was deployed.

2.4 Stations were located using a differential Global Positioning System and the

ship’s software that logs the position at the exact time of sampling. At each station, three replicate samples were collected within close proximity of each other (50 m radius range ring). At a small number of stations, the presence of cobbles hampered successful operation of the grab resulting in fewer replicates being taken. Once on deck, the total volume of each grab sample was measured and a sub-sample of sediment was removed for particle size distribution analysis. The remaining sample was washed over 1 mm mesh sieves and the retained residue containing the benthic macrofauna was fixed in a 4% formaldehyde solution for later processing in the laboratory.

2.3 Sampling Processing 2.5 Macrofauna were extracted from the samples by viewing under light

magnification and preserved in 70% Industrial Methylated Spirit. Animals were identified to the highest possible taxonomic separation (e.g. species where possible) and each taxon counted and weighed, after blotting, to the nearest 0.0001 g. Data were entered into UNICORN© – a Microsoft Access database developed by Unicomarine Ltd – following standard quality assurance protocols. This database aided standardisation of the outputted abundance

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and biomass data (and at the appropriate taxonomic level for numerical analyses), across the whole of the sampling area.

2.6 The sediment sub-samples removed for analysis of particle size distribution

were initially wet sieved on a 500 µm stainless steel test sieve using a sieve shaker. The < 500 μm fraction was then freeze-dried, weighed and a sub-sample analysed using a Coulter LS 130 Laser- Sizer. The > 500µm fraction was oven dried at 80oC for 12 h and then sieved over a range of test sieves down to 500 µm at 0.5 phi intervals. The sediment retained on each sieve was weighed to the nearest 0.01 g and the results recorded. The results from these analyses were combined to give a full particle size distribution. The mean particle size and sorting values were also calculated. Data from the three replicates were averaged for each station.

2.4 Environmental Variables 2.7 Environmental variables quantified for each station include depth, a number of

derived granulometric parameters (e.g., % silt, % gravel, sorting coefficient, skewness, kurtosis), together with modelled parameters such as bed tidal stress, wave stress, a stratification index and satellite-derived surface chlorophyll a (mean values for 2000-07 used). The variables and associated summary statistics are shown in Table 1). The variables were calculated for each station and the standard deviations indicate that there is a high degree of variation between stations for many variables. Environmental variables were used in the Brey model as described below (Section 3) and the feasibility of using these as proxy indicators of production was explored (as described in Section 5).

2.8 Tidal parameters were generated using a 3D hydrodynamic model (Davies

and Aldridge, 1993), run in depth-integrated form on an approximately 3.5 km resolution grid covering the European continental shelf. Average and peak wave stress were calculated from a one-year model run covering the period September 1999 to September 2000, on an approximately 12km grid, using the WAM spectral wave model run at the Proudman Oceanographic Laboratory (Osuna & Wolf, 2004). For further details regarding the derivation of these modelled values see Bolam et al. (2008). The stratification parameter ‘S’ was derived from the formulation presented in Pingree & Griffiths (1979), using modelled M2 tidal velocities and measured depths at the benthic stations.

2.9 Surface chlorophyll a concentration images, derived from MODIS-Aqua

satellite ocean colour data using a modified version of the case-II water algorithm ‘OC5’ (Gohin et al., 2002), were used to derive mean surface chlorophyll a concentrations between 2000-07. This regional algorithm differs from generic, global satellite chlorophyll products in that additional sensor bands are used to correct for the presence of suspended sediments and the chromophoric (coloured) fraction of dissolved organic matter (cDOM). The surface chlorophyll maps cover the north-west Atlantic shelf at a resolution of 1 km2. Maps in the archive were queried with the ‘Zonal Statistics’ function of ArcMap version 9.2 in order to extract discrete chlorophyll a values for the

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surface waters of the sampling stations. The review and attendees at the workshop held prior to this pilot study had particularly indicated that assessing whether chlorophyll concentrations, as an indicator of food supply, could be used as a proxy indicator of productivity was of particular interest.

Table 1: Environmental variable information and summary descriptive statistics from the pilot area Variable Information/Units Mean1 Standard

Deviation Mean (phi) Measure of mean particle size 2.21 0.97Sorting A measure of the spread of particle sizes

around the median 1.38 0.87

Skewness A measure of the spread of particlesizes around the mean

2.36 1.7

Kurtosis A measure of the spread of particle sizes around the mean

23.3 16.65

% Gravel Percentage 5.18 12.14% Sand Percentage 91.1 14.69% Silt/clay Percentage 3.71 5.03Depth Metres 50.6 19.39Bed Tidal Stress Generated using a 3D Hydrodynamic model. 0.44 0.46Peak wave stress 0.32 0.35Stratification 1.89 0.84Chlorophyll a (mg m-3) Satellite derived, mean values for 2000-2007

used 1.18 0.46

1 Means and standard deviation shown to describe data. The Brey model and proxy indicator model used measured and derived values for each station, not the mean value

0°0'0"8°1'0"W16°2'0"W

56°7

'0"N

Location of Pilot Study Area and Sampling Stations0 100 200 300 40050 km

Sample Station LocationsUK Continental Shelf

Notes:Data Sources:Licence Numbers:Aug 2009, Version 1.0.© Crown Copyright, All rights reserved.

Map Designed and Produced by ABPmer

ScaleProjection

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1:10,000,000WGS1984 UTM31N

Figure 1

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3. Brey Model 3.1 An empirical model was developed by Thomas Brey (Brey, 2001) using

multiple linear regressions with 11 parameters, including mean energy content of the organism (kJ) depth (m), temperature (oC), motility and phyla to estimate production. The mean energy content of the organism is estimated based on sampled biomass information subsequently converted to energy values using published literature values. The latest models (and conversion factors from biomass to energy value) are available on the website maintained by Thomas Brey and are updated regularly2 as new studies become available. A number of studies have supported the use of this model to estimate production (Cusson & Bourget, 2005, Dolbeth, et al. 2005). In a comparative study of empirical models Dolbeth et al (2005) found that the modelled estimates of production from the empirical Brey (2001) model most closely matched the production estimates from the more accurate cohort based model.

3.1 Data Acquisition 3.2 This approach uses the macrofauna data that were collected by the Cefas

survey (Defra funded project, number ME3112). Before analysis, the raw taxon-by-sample data matrix was systematically truncated to the family level to minimise inconsistencies in the data, such as ambiguous identifications, which would otherwise inflate the total taxon count. As colonial taxa (e.g., bryozoans, hydroids) were recorded as present rather than quantified and lacked biomass data, such taxa were not included in production estimates.

3.2 Estimates of Productivity and Production 3.3 Productivity estimates (i.e. P:B ratio (yr-1 and kJ m-2 yr-1) were derived in a

stepwise approach from the raw abundance and biomass data (collected for each station using grab samples). Firstly, standardised biomass records were converted to energy values using the published conversion factors that are available on-line with the Brey model spreadsheets. For taxa with shells, the biomass data were initially converted to shell-free weights to derive estimates for the metabolically active tissue of such taxa. Data were aggregated to the family level of taxonomic resolution because it was at this level that sufficient numbers of published conversions were generally available for each taxon, i.e., taking an average conversion factor from a larger number of studies resulted in increased confidence in the conversions ultimately applied (T. Brey; pers. comm.).

3.4 The energy values derived from the sampled macroinvertebrate biomass were

then converted to production values using the spreadsheet that is freely available on the Internet3 (Brey, 2001). This method unifies all previous habitat-specific approaches into a multiple regression model estimating annual production of macrobenthos.

2 http://www.thomas-brey.de/science/virtualhandbook/navlog/index.html 3 (http://www.awibremerhavende/benthic/ecosystem/foodweb/handbook/)

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3.5 The model is represented below:

LogP:B = 9.947 – 2.294logM – 2409.86(1/T + 273) + 0.168(1/D) + 0.194Dsubt/int

+ 0.180Dinf/epi + 0.277Dmoti/ses + 0.174Dpoly/crust – 0.188Dechin/moll + 0.33Dinsect – 0.062Dlake/riv/mar + 582.851logM (1/T+273) 69

3.6 Where M is average body mass (kJ), T is temperature (°C) and D is depth (m). 3.7 Variables are set to 0 (no) or 1 (yes) for the categories (1) subtidal or intertidal

(Dsubt/int), (2) infauna or epifauna (Dinf/epi), (3) motile or sessile (Dmoti/ses), (4) polychaete or crustacea (Dpoly/crust), (5) echinoderm or mollusc (Dechin/moll), (6) insect, and (7) lacustrine, riverine or marine (Dlake/riv/mar).

3.3 Results 3.8 The productivity estimates for each family level group derived from the Brey

model were summed to provide a productivity estimate for the assemblage sampled at each station. Total production for the entire (UK) study area of 155 stations ranged between 3.1 and 896.6 kJ m2/year. Total production for the 39 stations in the pilot study ranged between 5.18 and 206.7 kJ m2/year. Mean production was 37.4 kJ m2/year and the high inter-station variability in production is indicated by the standard deviation value (43.44). The production values for each station, estimated using the Brey model, are shown in Figure 2 (below).

4°1'0"E0°0'0"

56°1

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52°1

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Productivity Estimates from the Brey (2001) Model0 50 100 150 20025 km

Production KJ/m²/yr5.2 - 2525.1 - 5050.1 - 100100.1 - 150150.1 - 200

200.1 - 250

250.1 - 350UK Continental Shelf

Notes:Data Sources: Stefan Bolam (Cefas) Licence Numbers:Aug 2009, Version 1.0.© Crown Copyright, All rights reserved.

Map Designed and Produced by ABPmer

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1:3,500,000WGS1984 UTM31N

Figure 2

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4. Duplisea/Hiddink Model 4.1 Duplisea et al (2002) developed a size-spectra model to investigate trawling

impacts (which alter size distribution) on benthic macroinvertebrate assemblages. The model uses a number of variables to predict the size-based distribution of benthic macroinvertebrate assemblages and was parameterised using data collected in six replicate dredges from each of seven sites in the Silver Pit area in the North Sea. As productivity is related to size (biomass) the model can also be used to predict the productivity of an assemblage. Jan Hiddink further developed the model to allow for the effects of habitat and validated the model with new data to assess the impacts of trawl fishery on the biomass and production of benthic assemblages in four areas of the North Sea (Hiddink et al. 2006). This modified Duplisea/Hiddink model using was used as the basis to model production in Welsh coastal waters (Hiddink, 2006) and was re-parameterised to account for local environmental conditions and bottom trawling disturbance (Hiddink, 2006). This pilot study represents a further application of the model to assess productivity, the model was re-parameterised where necessary in this study using environmental variables for the pilot study stations.

4.1 Description of Model 4.2 Production values for the North Sea pilot study stations were predicted using

the size-based model of Hiddink et al. (2006). Details of the development and use of the model are given in Duplisea et al. (2002) and Hiddink et al. (2006), in summary, the Duplisea/Hiddink model contains 32 state variables, in two faunal groups (soft and hard bodied macrofauna). Growth of the population biomass in each body mass – organism type compartment was modelled by modifying Lotka–Volterra competition equations to give the population biomass flux for a compartment. The interaction between habitat type and trawling effects was modelled by including relationships between growth and mortality and the environment in the model. Model development considered the effect of sediment type on trawling mortality, the effect of bed shear stress on population growth rate, the effects of chlorophyll a content of the sediment on carrying capacity and the effects of sediment erosion on mortality. Recovery in the model can only take place through growth of the local populations, as the model assumes that there is no migration between adjacent areas. The model has been validated in previous studies by comparing the observed biomass of benthic communities at 33 stations from 4 habitats in the southern North Sea with modelled values (Hiddink et al. 2006).

4.3 The sources of habitat data used above are described by Hiddink et al. (2006).

The study classified sediments with a gravel content of more than 5% were as gravel. For sediments with less than 5% gravel, sediments with a sand:mud ratio of more than 9:1 were classified as sand. Sediments with a sand:mud ratio between 1:1 and 9:1 were classified as muddy sand, and sediments with a sand:mud ratio smaller than 1:1 were classified as mud.

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4.4 Trawling frequency was calculated from European Community Satellite Vessel Monitoring System (VMS) data. From 1 January 2000 onwards, all EC fishing vessels over 24m were required to report their location, via satellite, to monitoring centres in their flag states, at 2-h intervals. The only exception is made for vessels that undertake trips of <24 h or that fish exclusively within 12 nm of the shoreline (Dann et al. 2002). The proportion of fishing vessels <24 m is probably very low in the offshore areas we examined due to the cost and time associated with steaming to and from port. The VMS-data does not indicate whether a vessel is fishing when it transmits positional data, but the speed of a vessel can be derived from two consecutive records. Accordingly, vessels travelling at speeds greater than 8 knots and stationary vessels were eliminated, as these vessels were assumed not to be fishing (for more details see Dinmore et al. 2003). The number of trawl passes per 9 km2 cell per year was calculated from the number of records in a cell in the period from 1 July 2000 to 31 December 2002. For the calculation of trawling frequency (y-1), it was assumed that trawlers fished at a speed of 5 knots, with a total fishing gear width of 24 m (i.e. 2 beam trawls each of 12 m wide or one 24 m wide otter trawl). Therefore, one record represents a trawled area of 0.449 km2, and one record in a 9 km2 cell over the 2½ year period therefore represents a trawling frequency of 0.0198 yr-1. The lower limit to the scale at which trawling effort could be evaluated was defined by the resolution of the VMS records. The 9 km2 scale as used is close to the 1x1 nautical mile scale at which fishing effort becomes random (Rijnsdorp et al. 1998). For the Dutch beam-trawling fleet, VMS records were not available for all vessels. Therefore, effort-distribution as recorded by the VMS-system was corrected to represent total trawling effort as recorded in logbooks by fishers (G.J. Piet, RIVO, unpublished).

4.2 Model Run 4.5 The model has previously been used to examine the effect of bottom trawling

on benthic productivity in the Dutch and British sectors of the North Sea in the area south of 56°N (Hiddink et al. 2006). The areas inside the coastal 12 nm. zone and the plaice box were excluded from the analysis, as vessels fishing in these areas are generally not required to record their position through VMS, possibly causing a strong underestimation of trawling intensity in these areas. As fishing effort for 2000-2002 was used, predictions represent the state in early 2003. Predicted biomass and production was extracted for the 40 stations in this area that were sampled in the study described by Bolam et al. 2008 (section 3).

4.3 Results 4.6 Predicted biomass and production are presented in Table 2 (See Annex A).

The estimated mean production value for the pilot study area was 7,786 mg of ash free dry material (AFDM)/m2

. Predictions between stations were highly variable as indicated by the high standard deviation value (S.D. 4119.79).

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4.7 For ease of comparison between the model outputs the estimated annual production values from the Duplisea/Hiddink model (expressed as mg of ash free dry material (AFDM)) were converted to KJ m2/year estimated by the Brey (2001) model. We used published conversion factors that were collated by Brey (20014) for 42 benthic macroinvertebrate taxonomic groups. The energy content of different macroinvertebrate taxonomic groups was relatively similar, ranging from 19.01 (Ascidiae, phylum Echinodermata) to 24.99 J/mg AFDM/ (phylum Porifera). The mean was 22.67 J/mg AFDM/ and this was used as the conversion factor. The production values for each station are shown in Figure 3.

4.4 Comparison of Brey and Hiddink Models 4.8 Scatterplots were used to visually assess whether there was a relationship

between production and other estimates (e.g. biomass) outputted by the Brey and Duplisea/Hiddink models. These did not appear to show that there was a relationship between production and biomass estimated by the Duplisea/Hiddink model and the estimates of the Brey model.

4.9 This assumption was tested using the non-parametric test of correlation

Spearman’s Rank, which confirmed that there was no correlation between the variables (rho = -0.1 p=0.54.

4.10 From Figures 2 and 3 (above) which show the productivity values from each

model it appears that the major differences between the modelled results are that the Hiddink model estimates were lower for production at the southern North Sea stations (particularly those close to the coast) in comparison with the the Cefas study (Bolam et al. in press) and greater than those in the north east portion of the pilot area. These results, possible explanatory factors and the implications of these are discussed further in Section 6.

4 (http://www.awibremerhavende/benthic/ecosystem/foodweb/handbook/)

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Productivity Estimates from the Duplisea/Hiddink Model0 50 100 150 20025 km

Production KJ/m²/yr5.6 - 2525.1 - 5050.1 - 100100.1 - 150150.1 - 200

200.1 - 250

250.1 - 350UK Continental Shelf

Notes:Data Sources: Jan Hiddink (School of Ocean Sciences, Bangor)Licence Numbers:Sep 2009, Version 1.0.© Crown Copyright, All rights reserved.

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Figure 3

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5. Proxy Indicators of Productivity 5.1 Given the time and cost constraints in collecting benthic samples for

production/productivity analyses it would be desirable, in terms of datalayer development, if productivity could be estimated using environmental variables that are more easily measured. The environmental variables could then be used to predict production/productivity and hence develop a datalayer. The Duplisea/Hiddink model (discussed in Section 4 above) trialled in this study estimates production from environmental and other parameters. The expert workshop and review that preceded this pilot study (reference) also recommended that the development of simpler models was trialled using environmental variables as proxy indicators of productivity. This trial is discussed below.

5.2 The approach to the development of simple models used the productivity

estimates from the Brey model for the pilot study area and the environmental variables collected through the Defra funded project ME3112 (see Bolam et al. in preparation). Model development involved the use of exploratory methods (scatter plots, AnswerTree and linear regression) to identify relationships between environmental variables and productivity. We used the productivity values estimated by Bolam et al. (in press), using the Brey model (2001) in this study as these were based on biological sampling and are therefore a more direct estimation of annual production. This approach used the individual station values for all of the available environmental variables (see Table 1).

5.1 Answer Tree 5.3 AnswerTree is a program containing different procedures that solve prediction

and classification problems using decision tree analysis. The method was used in this study to select environmental variables that can be used to predict productivity. Decision trees were developed using the Classification and Regression Tree (C&RT) routines for the predictor variable productivity (Total production kJ/m2/year estimated using the Brey model for each station). Tree based models have some advantages over GLM regression and other commonly used methods. They are designed to be able to handle a large number of predictor variables and the tree-models used in this study- classification and regression trees- are non-parametric and can capture relationships that standard linear models do not easily handle such as non-linear relationships and complex interactions. Predictor variables can be re-used within the models at different levels, if they improve predictions, adding to the flexibility of the approach. Tree-based methods are therefore useful to explore datasets and identify key relationships (in this study between environmental variables and productivity).

5.4 Based on the predictor variables decision tree models successively partition a

dataset into sub-groups in order to improve the classification of the dependent variable. The tree is built by repeatedly splitting the data at each node of the tree into two groups that are as homogenous as possible in terms of response. The split at each tree node into two groups in this study was guided by the

24

impurity value (a measure of the variation between data points in each node) and cost-complexity measures. At each node of the tree, the C&RT routine ordered the data into groups by their value on the predictor, calculated the impurity decrease for all possible cut points and then determined the best split.

5.5 The run identified that productivity in the pilot area was best classified

according to the degree of sorting in sediments. Sediments that were well sorted (sorting coefficient ≤ 2.46) had a mean production of 26.81 kJ/m2/year (Standard Deviation 18.24) compared with three stations which were less sorted (sorting > 2.46) where mean annual benthic production was higher (mean 164.93 S.D. 60.15). Stations where sediments were more sorted could be subdivided based on tidal stress into stations with higher annual production where tidal stress was lower (≤ 0.068) mean 68 kJ/m2/year (S.D. 18.07) and stations with higher tidal stress and lower productivity (mean 23.06 kJ/m2/year S.D> 13.01).

5.6 In summary, AnswerTree identified that the degree of sediment sorting and

tidal stress were the best environmental classifiers of benthic production. Stations with the least sorted sediments were the most productive, the least productive stations were those where sediments were more sorted and tidal stress was higher than average (see Table 1 for mean values for environmental variables). The results from Answer tree were used to support co-efficient interpretation and variable selection and interpretation for the multiple regression analysis outlined below, where decisions had to be made as to which correlated variables should be included or dropped from models.

5.2 Linear Regression 5.7 The relationships between all variables and total production were explored

further using simple linear regression. This identified the amount of variability that was explained by each variable. The variables sorting and % sand explained most of the variation in production values, 64% and 60% respectively. As sand explained some variation in production it was not surprising that the interrelated sediment variables % gravel and % sand were also useful in explaining production variance (47 and 36% respectively). Samples with missing variables were excluded from the analyses.

5.3 Multiple Regression 5.8 Variables that do not appear to have a relationship to productivity may be

useful to predict production when taken into account alongside other environmental variables. The relationship between annual production and the set of environmental variables was explored further using backward stepwise regression models. All variables are entered into these and the variables that are least useful in explaining variation are eliminated and the model recalculated. This technique is therefore useful to identify the subset of variables that best explain production.

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5.3.1 Multi-colinearity- When Variables Are Correlated 5.9 Analyses of benthic community structure are frequently affected by multi-

colinearity, where variables are linearly related. This is due to the fact that many environmental variables are interrelated, in this study, for example, sediment size fractions were reported as percentages and these are obviously not independent, as changes in the amount of one will affect the other(s). The proportion of gravel and silt in a sediment sample varies according to the proportion of sand present. Sediment proportions are, of course, also interrelated with other sediment characterisation metrics, e.g. mean particle size and sorting. Other environmental variables were also found to be correlated. Depth and tidal stress were correlated with stratification (as shallower areas and those experiencing greater tidal stress are less likely to be stratified). (Depth and tidal stress were not correlated).

5.10 If the relationship between the two correlated environmental variables is

consistent they may code for the same effect on productivity and are redundant to each other (both have a similar relationship to productivity because they are not independent from each other). Multi-colinearity does not affect the model predictions (when all the variables are entered in the model) but it does prevent identification of the variables which are important predictors in the model. For example, if one variable is highly collinear with another it could be down-weighted (coefficients not significant) in the model compared to the variable with which it is collinear, and ultimately this can lead to rejection of significant variables. This is clearly undesirable for the aims of this study, which were to identify proxy indicators that could be used to estimate productivity. Preferably multi-colinearity is dealt with by increasing the size of the dataset, but as this option was not available we identified variables that could be dropped from model runs as they were redundant (highly correlated with other variables). Depth for example was considered to be a substitute for peak wave disturbance and was used in the model runs as it was considered preferable to use variables that that are directly measured as these can be reapplied in different areas.

5.11 To summarise, in order to deal with multi-colinearity in multiple regression

models we:

Ran models with one sediment variable in a run; Dropped stratification as a variable and included tidal stress and depth; Dropped peak wave stress (as this was correlated with depth and depth

was included); Models run with mean particle size and re-run with sorting to identify best

explanatory variable; and Chlorophyll a was included in all models as this was uncorrelated with all

other variables.

5.12 For each model run the effect on variable selection and fit was recorded. Following these steps the model was validated using the productivity data for all UK stations (Bolam et al. in press).

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5.13 The model runs identified two models that each explained a similar amount of variance in the data from the pilot area (Table 3). When median particle size was excluded from the entered variables then the stepwise exclusion algorithm selected the variables, sorting and depth, as those that best explained the variation in production variables (adjusted R2 = 0.69 p= 0.00). When median particle size was entered into the model to replace sorting, then the final model selected % sand, mean particle size and depth (adjusted R2 = 0.64 p= 0.00) as explanatory variables.

5.14 The two models were tested for their ability to predict production using stations

from the UK wide dataset previously analysed by Bolam et al. (in press). The models applied in this way did not perform well as the amount of variation explained was much lower (between 21 and 22% (see Table 3), and hence, this indicates that the models were not applicable to the UKCS to predict production.

5.15 The poor predictive ability which precludes the use of these models to develop

a UK wide datalayer could be due to overfitting of the data (where the modelled fit based on the dataset used for model development describes random ‘noise’ rather than an underlying relationship). Alternatively these results may indicate that relationships between production and environmental variables are not consistent between different regions of the UK. An alternate explanation may be due to the scale of the study, where some relevant parameters may not show a great deal of spatial variation so that their overall influence on production is masked (although it should be noted that the pilot study area was partly chosen on the basis of spatial environmental variability as demonstrated by the high standard deviations for variables in Table 1). At a broader scale the influence of this factor (as food supply) and others that were not explored in this study, such as temperature, the degree of thermal stability or other variables may become apparent e.g. where inter-regional differences are explored. These issues are discussed further in Section 6 (below).

Table 3: Explanatory models developed using linear and backward stepwise regression on environmental variables and predictive ability (to estimate productivity). Variable significance values in parentheses Environmental variable information and summary descriptive statistics from the pilot area Area Model Adjusted R2 Pilot Study (27 stations) Sorting (0.00) depth (0.02) 0.69Pilot Study % sand (0.00), mean (phi) (0.08), depth (0.05) 0.64UK-wide (71 stations) Sorting (0.00 ) and depth (0.64) 0.21Uk-wide (70 Stations) % sand (0.035), mean (phi) (0.81), depth (0.82) 0.22

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6. Issues and Considerations 6.1 Correlation Between Brey and Hiddink/Duplisea Model 6.1 The tests in this study found that there was no correlation between productivity

(KJ m2/year )as estimated using the Brey model (Brey 2001) and productivity estimated using the Duplisea/Hiddink model (converted to KJ m2/year using a conversion factor based on published literature values). There are a number of hypotheses that could explain this:

Changes in productivity between sampling dates of variables used to

parameterise models; Sampling methods and seasonality of sampling; and Hiddink/Duplisea model requires further paramaterisation.

6.2 The first hypothesis to explain the lack of correlation between the modelled

values from the Duplisea/Hiddink and Brey model, is that the results would have been comparable but that there have been recent changes in the North Sea system that have altered production rates. As well as natural temporal variability other extrinsic drivers could be responsible for changes in production rates. Changes for example in the distribution and intensity of bottom trawling can alter benthic production and productivity rates through effects on the size distribution of benthic invertebrates, as larger organisms are more likely to be removed or killed (Jennings et al. 2001).

6.3 As the models were parameterised from different sampling periods this may

account for the differences in estimated production. The data used for the Duplisea/Hiddink model was collected in 2003 while the Defra funded study (ME3112) sampled stations between 2005-2007. Therefore temporal variability in production in the pilot study area could explain the differences between modelled outputs. The Hiddink model uses carrying capacity estimates for 1986 in the model and changes in primary production and benthic -pelagic coupling could have occurred since this date.

6.4 Biological and sediment samples were taken at stations in the North Sea using

a day grab. Grab sampling does not always effectively sample larger, low density organisms which add variability to production rates. In addition the Brey (2001) model estimates do not include epifaunal colonial organisms such as hydroids and bryozoans (these were not quantified during sampling (Bolam et al. in press). The composition of benthic samples varies seasonally and while the Brey model estimates provide a ‘snapshot’ of production there is a possibility that the Hiddink model may provide a long-term prediction of average state. It was not possible, given the available data, to test this hypothesis.

6.5 Finally there is a possibility that the Duplisea/Hiddink model approach and the

work on proxy indicators of production have not identified important drivers. Temperature is an important variable affecting benthic production rates (Cusson & Bourget 2005) and while depth and stratification may provide

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proxies, it is possible that average temperatures (or temperature range) when included as a parameter may improve predictive power. On small scales such as the southern North Sea however temperatures would not be expected to vary significantly.

6.2 Spatial Variability of Production 6.6 The work undertaken in this pilot study to identify proxy indicators of

production (Section 5), found that two environmental variables, depth and the degree of sediment sorting, best explained the production estimates across the study area. The model developed in the pilot study when extrapolated to the full dataset was not able to predict production. The study on proxy indicators of production therefore suggests that the relationship between the environmental variables and production in the models derived for the case study area are not consistent over the UK Continental Shelf. This could be because some of the factors that are important in governing overall productivity at a UK scale are not identified as being important at regional or local scales. It should be noted that regression techniques do not identify causative factors only relationships. Other variables that account for the patterns, that are correlated with the measured environmental variables may be responsible for the observed patterns in the pilot study area.

6.7 The large variations in productivity between stations where conditions were

similar can confound the search for explanatory environmental variations. High natural variability is a common feature of ecosystems and creates problems when trying to separate signals from ‘noise’. The population structure of many components of marine assemblages have been shown to vary over small scales (<12km) e.g. picoplankton abundance and composition (Martin et al. 2005), sea-cucumber abundance (Eckert 2007) and fish abundances (Auster et al. 1997).

6.8 The spatial variability between adjacent stations (see Figure 2) suggest that

the factors that control benthic productivity can operate on small-scales. Variation in production may be due to density changes in organisms. Population abundances may be controlled on small scales by biological interactions e.g. predation (Eckert 2007), competition or amensalism (Rhoads and Young 1970). Alternately small scale variation in habitats e.g. changes in heterogeneity or sediment may lead to variations in production as they alter larval settlement rates or mediate other biological interactions e.g. predation and competition rates (Auster et al. 1997). Benthic communities may also be structured by past factors such as recruitment history or discrete disturbance events e.g. the passage of a beam trawl that cannot be accounted for in our study

6.3 Options 6.9 This aim of this pilot study was to trial three different approaches to estimating

production. The review that was undertaken by ABPmer prior to the pilot study (Tillin et al, 2009) identified that direct methods of estimating production were too time consuming and costly to be used to develop a UK production

29

datalayer where biological sample data is not available. Another alternate approach, using expert judgement to categorise production was proposed at the workshop meeting but attendees felt that this was likely to be flawed by lack of transparency and lack of information and would be difficult to justify to stakeholders. Changes in ecosystem dynamics could also mean that expert judgement approaches are not useful as these would be based on out-dated knowledge. Therefore the workshop identified that the best potential options to develop a UK Continental Shelf wide datalayer of benthic production were modelled approaches and proxy indicators of production.

6.10 The purpose of this pilot study was therefore to test and compare three

approaches, two empirical modelling approaches (Duplisea/Hiddink and an approach to identify proxy indicators of production. The approach in which there is (relatively) greater confidence in terms of quantifying production was that based on the Brey (2001) model, which is parameterised using biological samples. While this approach provides a useful snapshot of production at a location the caveats described above (seasonality, sampling, natural spatial and temporal variability) apply to the use of this approach.

6.11 The findings from this study concur with the results of the previous study by

Bolam et al. (in press), that no consistent, quantifiable relationships between environmental variables and production rates could be identified at the scale of the UK. However, it is likely that, at a regional or broad habitat type scale (habitats classified on environmental variables, such as depth rather than biological features e.g. EUNIS level 3), models can be developed to quantify patterns in productivity that could be used to assign productivity values to broadscale habitats, although such an approach would require validation. Bolam et al. (in press) for example were able to identify regional variations in benthic production, they also found that between different broadscale habitat types, different environmental variables are identified as influential (Bolam et al. in press).

6.12 Taking into account available data, time scales and cost, it is suggested that

the best option for producing a UKCS datalayer of benthic production would be to use the information gathered during the Defra funded study ME3112 and analysed in Bolam et al. (in press). This information could be used to develop a coarse grid of benthic productivity based on the ‘raw’ production values derived using the Brey (2001) model that would cover the majority of the UKCS (Figure 4 shows the locations of all the sampling stations). This approach could be further supported through the reanalysis of previously collected samples where biomass information is available, or could be derived from abundance data using published conversion values. The productivity of these samples could be estimated by converting the biomass values into energy values and then assemblage productivity estimates using the spreadsheets that are available on-line from Thomas Brey. This provides a method of rapidly estimating productivity. A simpler method of assessing productivity from biomass would be to use published production:biomass ratios or a general conversion factor e.g. the average value of 2 for mixed assemblage production:biomass ratios (Michael Elliot pers comm. and see Elliott & Taylor 1989). However it should be noted that the review and

30

workshop identified concerns with using already collected sample data on quality grounds (including concerns over sampling strategies and identification) and gaps in information (patchiness of records and limited coverage of the UKCS).

6.13 Alternatively, the work described in Section 5 of this study could be extended

to produce a datalayer of production estimates based on relationships between environmental variables and production at regional or broad habitat scales. Both this pilot study and the work by Bolam et al. (in press) have shown that there is some scope for doing this. Given inter-station variability in production estimates at small scales, the confidence associated with this datalayer would be relatively low for predicting production at a point. However, it can be envisaged that the information could be used as additional supporting justification for some MPAs, for example, to support designation in areas where the Brey model estimates indicate production is higher.

0°0'0"8°1'0"W

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Location of UK Wide Sampling Stations (Defra funded project ME3112)

Sample Station LocationsUK Continental Shelf

Notes:Data Sources:Licence Numbers:Sep 2009, Version 1.0.© Crown Copyright, All rights reserved.

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Figure 40 100 200 300 40050

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Abbreviations MCZ Marine Conservation Zone MPA Marine Protected Area UKCS UK Continental Shelf SAC Special Areas of Conservation SPA Special Areas of Protection VMS Vessel Monitoring System AFDM Ash Free Dry Material GLM General Linear Model cDOM Chromophoric fraction of dissolved organic matter Units nm Nautical mile Km Kilometre m Metre mm Millimetre g Gram mg Milligram µm Microgram oC Degrees Celsius h hour phi logarithmic scale derived from particle diameter S.D. Standard Deviation References Auster, P.J., Malatesta, R.J., Donaldson, C.L.S. (1997): Distributional responses to small-scale habitat variability by early juvenile silver hake, Merluccius bilinearis. Environmental Biology of Fishes 50: 195-200. Bolam, S.G., Barrio-Frojan, C.R.S. and Eggleton, J.D. (in press): Macrofaunal production along the UK continental shelf. Bolam, S.G., Eggleton, J., Smith, R., Mason, C., Vanstaen, K. and Rees, H. (2008): Spatial distribution of macrofaunal assemblages along the English Channel. Journal of the Marine Biological Association of the UK, 88, 675-678. Cusson, M. and Bourget, E. (2005): Global patterns of macroinvertebrate production in marine benthic habitats. Marine Ecology progress Series 297, 1-14. Dann, J. and Millner, R. (2002): Alternative uses of data from satellite monitoring of fishing vessel activity in fisheries management: II. Extending cover to areas fished by UK beamers. Report of EC Project 99/002

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Davies, A.M. and Aldridge, J.N. (1993): A numerical model study of parameters influencing tidal currents in the Irish Sea. Journal of Geophysical Research, 98, 7049-7069. Dinmore, T.A. and Duplisea, D. E. (2003): Impact of a large-scale area closure on patterns of fishing disturbance and the consequences for benthic communities. ICES Journal of Marine Science, 60, 371-380. Duplisea, D.E. and Jennings, S. (2002): A size-based model of the impacts of bottom trawling on benthic community structure. Canadian Journal of Fisheries and Aquatic Sciences, 59, 1785-1795. Eckhert, G.L. (2007): Spatial patchiness in the sea cucumber Pachythyone rubrai in the California Channel Islands. Journal of Experimental Marine Biology and Ecology, 348, 121-132. Elliott, M., Taylor, C.J.L. (1989). The production ecology of the subtidal benthos of the Forth Estuary, Scotland. Scientia Marina 53, 531-541 Gohin, F., Druon, J.N. and Lampert, L. (2002): A five channel chlorophyll concentration algorithm applied to Sea WiFS data processed by SeaDAS in coastal waters. International Journal of Remote Sensing, 23, 1639-1661. Hiddink, J,G,, Jennings, S., Kaiser, M.J. et al. (2006). Cumulative impacts of seabed trawl disturbance on benthic biomass, production, and species richness in different habitats. Canadian Journal of Fisheries and Aquatic Sciences 63: 721-736. Hiddink, J.G. (2006) Modelling the state of soft-sediment benthic communities in Welsh coastal waters. CCW Contract Science Report No 773. Jennings, S., Dinmore, T.A., Duplisea, D.E., Warr, K.J., Lancaster, J.E. 2001. Trawling disturbance can modify benthic production processes. Journal of Animal Ecology 70, 459-475. Martin, A.P., Zubkov, M.V., Burkill, P.H. and Holland, R.J. (2005): Extreme spatial variability in marine picoplankton and its consequences for interpreting Eulerian time-series. Biology letters, 22, 366-369. Osuna, P. and Wolf, J. (2004): Results from a one-year run of a wave model for the UK continental shelf. Proudman Oceanographic Laboratory UK. Internal Document no. 170, 2004. Piet, G. and Quirijns, F. (2009): The importance of scale for fishing impact estimations. Canadian Journal of Fisheries and Aquatic Science, 66, 829–835. Pingree, R.D. and Griffiths, K.D. (1978): Tidal fronts on shelf seas around the British Isles. Journal of Geophysical Research, 83, 4615-4622.

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Rhoads, D.C., Young, D.K. (1970): The influence of deposit-feeding organisms on sediment stability and community trophic structure. Journal of Marine Research 28: 150-178. Rijnsdorp, A.D. and Buys, A.M. (1998): Micro-scale distribution of beam trawl effort in the southern North Sea between 1993 and 1996 in relation to the trawling frequency of the sea bed and the impact on benthic organisms. ICES Journal of Marine Science, 55, 403-419.

Tillin, H.M., Frost, N.J. & Hull, S.C., 2009. Accessing and developing the required biophysical datasets and data layers for Marine Protected Areas network planning and wider marine spatial planning purposes. Report No 4: Task 2H. Development of a Benthic Productivity Datalayer: Assessing the Available Approaches. Report to Defra Version (Final) June 2009.

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Annex A. Results from Duplisea/Hiddink Model Approach Table 2: Predicted biomass and production of benthic invertebrates for selected stations in the southern North Sea Station Latitude Longitude Production (mg

AFDW m-2 y-1) NSBP1 55 -1 12544 NSBP10 55 2 8480 NSBP11 55 0 4567 NSBP12 55 2 7280 NSBP13 55 1 8178 NSBP14 54 1 4966 NSBP15 54 2 8360 NSBP17 54 1 7351 NSBP18 54 2 6609 NSBP19 54 1 4530 NSBP2 55 1 14329 NSBP20 54 2 4776 NSBP21 54 2 2434 NSBP23 53 2 978 NSBP25 52 2 3146 NSBP28 52 2 2711 NSBP3 55 2 5345 NSBP37 56 -1 12984 NSBP38 56 1 11722 NSBP39 56 2 10387 NSBP40 56 3 8309 NSBP41 56 -1 13252 NSBP42 56 0 14653 NSBP43 56 1 13663 NSBP44 56 2 10418 NSBP45 55 3 6823 NSBP46 55 3 9553 NSBP47 54 3 8280 NSBP48 54 3 8509 NSBP49 54 1 4039 NSBP5 55 0 10945 NSBP51 53 2 246 NSBP52 53 3 3483 NSBP53 53 3 1986 NSBP54 52 3 3751 NSBP6 55 1 15186 NSBP7 55 2 11175 NSBP8 55 -1 5720 NSBP9 55 1 12007