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A cephalopod shery geographical information system for. Northeast Atlantic waters (CFGIS-NEA) was developed. The system covers the area from 28.0ß W to ...
int. j. geographical information science, 2001, vol. 15, no. 8, 763± 784

Research Article A cephalopod Ž shery GIS for the Northeast Atlantic: development and application GRAHAM J. PIERCE, JIANJUN WANG, XIAOHONG ZHENG, JOSE M. BELLIDO, PETER R. BOYLE Department of Zoology, University of Aberdeen, Tillydrone Avenue, Aberdeen AB24 2TZ, Scotland, UK; e-mail: [email protected]

VENCENT DENIS and JEAN-PAUL ROBIN Laboratoire de Biologie et Biotechnologies Marines, Universite´ de Caen, 14032 Caen Cedex, France (Received 15 January 2001; accepted 8 May 2001) Abstract. Cephalopod stocks are of increasing economic importance. Cephalopod Ž sheries show marked inter-annual  uctuations unrelated to Ž shery landings and eŒort. Their population dynamics, particularly recruitment, are thought to be strongly susceptible to changes in environmental conditions. This arises in part from the short life cycle, resulting in poor buŒering of the population against changing conditions. These characteristics make traditional approaches to stock assessment and Ž shery management inappropriate. GIS oŒers a tool to improve understanding of spatio-temporal trends in abundance and facilitate rational management. A cephalopod Ž shery geographical information system for Northeast Atlantic waters (CFGIS-NEA) was developed. The system covers the area from 28.0ß W to 11.0ß E, and 34.5ß N to 65.5ß N. It was designed for investigating cephalopod resource dynamics in relation to environmental variation. It is based on Unix Arc/Info, and PC ArcView, combined with the statistical software package S-PLUS and supported by a database in Microsoft Access. Environmental data (e.g. sea surface temperature and salinity, sea bottom temperature and salinity, and bathymetric data), cephalopod Ž shery, survey and biological data, from a variety of sources, were integrated in the GIS as coverages, grids, shapeŽ les, and tables. Special functions were developed for data integration, data conversion, query, visualisation, analysis and management. User-friendly interfaces were developed allowing relatively inexperienced users to operate the system. The spatial and temporal distribution patterns of cephalopod abundance by species, the spatial and temporal relationships between cephalopod abundance and environmental factors, and the spatial and temporal patterns of cephalopod Ž shing activity were analysed using a combination of visual (qualitative) and quantitative methods. Predictive empirical models, such as GAMs (generalized additive models), were developed for modelling cephalopod abundance using environmental variables.

1.

Introduction Cephalopod Ž sheries are becoming more important as traditional Ž sheries decline in many parts of the world (Caddy 1983, GLOBEFISH 1994, Caddy and Rodhouse Internationa l Journal of Geographica l Information Science ISSN 1365-881 6 print/ISSN 1362-308 7 online © 2001 Taylor & Francis Ltd http://www.tandf.co.uk/journals DOI: 10.1080/13658810110074500

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1998 ). In European waters, cephalopod Ž sheries are largely unregulated and the exploited populations show marked inter-annual  uctuations unrelated to Ž shery landings and eŒort. The population dynamics, particularly recruitment, are thought to be strongly susceptible to variation in environmental conditions. This arises in part through the short life cycle, which is annual in many species, resulting in poor buŒering of the population against changing conditions (Caddy 1983, 1995, Coelho 1985, Rodhouse and HatŽ eld 1990, Rodhouse et al. 1992, Boyle and Pierce 1994, Waluda and Pierce 1998 ). Approaches to population estimation and Ž shery management developed for traditional Ž nŽ sh Ž sheries are generally inappropriate due to the short life-span of these species (Boyle 1990 ). Therefore, it is necessary to develop a suitable technique for cephalopod Ž shery resource monitoring and management. Another feature of cephalopod populations is a very patchy distribution. For example, although squid L oligo forbesi is caught throughout British waters (Waluda and Pierce 1998), at any one time, the distribution is patchy, with areas of high abundance separated by areas in which squid are eŒectively absent (Pierce et al. 1998a). Since GIS has powerful and specially designed functions to integrate, manage, and visualize spatially and temporally referenced data, marine geographical information systems have become an important tool in Ž shery resources management (Mehic et al. 1996, Meaden and Do Chi 1996, Meaden 1996, Kemp and Meaden 1998, Valavanis et al. 1998, Waluda and Pierce 1998, Meaden 2000). The power of GIS lies in its ability to visualize and relate various diŒerent types of data, allowing users to Ž nd the hidden patterns and connections between them. Access to insight and information may allow users to answer the key questions and address key issues. The combination of GIS with statistical analysis methods has become an important and powerful approach for spatio-tempora l analysis, understanding , prediction, and visualization of Ž shery resources in relation to environmental variation in spatial and temporal dimensions. In a project funded by the European Commission’s Fisheries and Agro-Industrial Research programme (FAIR CT 1520, 1997–2000), a cephalopod Ž shery information system for Northeast Atlantic waters (CFGIS-NEA) was developed. It utilizes Unix Arc/Info, and PC ArcView, combined with the statistical software package S-PLUS and the Microsoft (MS) Access database system. Spatial coverage for CFGIS-NEA is the area from 28.0ß W to 11.0ß E, and 34.5ß N to 65.5ß N (Ž gure 1). Functions were developed for data integration, queries, analysis, modelling, visualization, and management. User-friendly interfaces were developed for the GIS based on both UNIX Arc/Info and PC ArcView, and the MS Access database, allowing relatively inexperienced users to operate the system. Within the study area, there are several important directed and by-catch Ž sheries for cephalopods. CuttleŽ sh Sepia oYcinalis, and long-Ž nned squids L oligo forbesi and L oligo vulgaris are the most important resources, but several other species are landed. The CFGIS-NEA is used to integrate and manage data on and related to the cephalopod Ž sheries, including information on Ž shery landings and Ž shing eŒort, trawling surveys, biology, and environmental factors, and to analyse and model the dynamics of cephalopod abundance in relation to environmental variation. In the present paper we describe the development of the CFGIS-NEA, and the primary applications in analysing and modelling the dynamics of Ž shery resources in relation to environmental factors, and Ž shery management. The cephalopod categories that have been included are the Ž shed species of long-Ž nned squid (L oligo vulgaris, L oligo

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Figure 1. The Northeast Atlantic cephalopod Ž shery GIS (CFGIS-NEA) covers the area from 28.0ß W to 11.0ß E, and 34.5ß N to 65.5ß N (the area covered by the rectangle). Also shown is the 200 m isobath.

forbesi ), short-Ž nned squid (Illex coindetii, T odaropsis eblanae), cuttleŽ sh (Sepia oYcinalis), and octopus (Octopus vulgaris and Eledone cirrhosa). In the UK, these are mainly by-catch species but there are targeted Ž sheries in the English Channel and further south. 2. Development of CFGIS-NEA 2.1. Hardware and software The CFGIS-NEA is based on both UNIX workstation and PC Window platforms and uses the ESRI (Environmental System Research Institute, Inc) GIS software packages UNIX Arc/Info, and ArcView. A MS Access database was also developed to support ArcView, and for the utility of its  exible and powerful data integration

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and query functions. MathSoft Inc.’s statistical software package S-PLUS, including S-PLUS 2000, S-PLUS for ArcView GIS, and S1 SpatialStats , was used for most statistical analysis and modelling. Figure 2 shows the structure of the CFGIS-NEA. The use of UNIX Arc/Info enables the system to use the sophisticated and powerful GRID module. However, since a PC is more portable than a UNIX workstation, a desktop computer supports commonly used database software such as MS Access, dBase, and MS Excel, which support all data collected in previous projects, and the data can be directly imported into ArcView using SQL. Moreover, compared with the UNIX system and UNIX Arc/Info, a PC and PC ArcView are

Figure 2.

A schematic diagram of the CFGIS-NEA.

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less complex and easier to learn and use, and we therefore developed the system based on both UNIX Arc/Info and PC ArcView. 2.2. Data pre-processing and integration The data integrated include environmental data, Ž shery data, trawling survey data, biological data, genetic data, and results from statistical analyses and modelling, on appropriate temporal and spatial scales, from a variety of sources. Fishery data include the data on UK registered vessels landing at ports in Scotland (1970 –1997), England and Wales (1980–1997), Northern Ireland (1980 – 1997), and French vessels landing at French ports (1989 –1997). Data were recorded by ICES (International Council for the Exploration of the Sea) statistical rectangles (i.e. 1ß longitude by 0.5ß latitude), and include the records of Ž shing hours by boats using diŒerent Ž shing gears, and landings (kg) of diŒerent species. Since little squid is discarded, landings are thought to be a good indication of catches. UK data were supplied by the Fisheries Research Services (FRS) and the Centre for Environment, Fisheries and Aquaculture Science (CEFAS). French data were provided by Fishery Centre Administratif des AŒaires Maritimes (FCAAM). Spanish and Portuguese Ž shery data were also integrated into the system, but these data cover only certain local areas. Trawling survey data (1980–1997) were provided by FRS, and include total catch and hours Ž shing on a haul-by-hau l basis. Environmental data include sea surface temperature (SST), sea bottom temperature (SBT), sea surface salinity (SSS), sea bottom salinity (SBS), bathymetry, monthly composites of ocean chlorophyll (pigment) concentration derived from Coastal Zone Colour Scanner (CZCS) data, and Sea-Viewing Wide Field-of-view Sensor (SeaWiFs) data. SST data were downloaded from the web site of the National Center for Atmospheric Researches (NCAR). SST data are global monthly data with 1ß Ö 1ß resolution, and are the output of a model, that uses marine surface observations, the NOAA Advanced Very High Resolution Radiometer (AVHRR) data and the presence of sea ice (Reynolds and Marsico 1993 ). ICES provided SBT, SSS, and SBS data from January 1980 to December 1997, which were expressed as monthly averages by ICES statistical rectangles, but do not cover every ICES statistical square nor every month, re ecting the opportunisti c nature of data collection. Bathymetric data with 5¾ Ö 5¾ resolution were downloaded from the National Oceanic and Atmospheric Administration (NOAA) National Geophysical Data Center website. Coastline data were extracted from General Bathymetric Chart of the Oceans (GEBCO) CD-ROM. CZCS and SeaWiFs data were downloaded from Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA). Additional SST, SSS, SBT, SBS data for the area and time period covered by the Ž shery data were obtained from trawling surveys (CTD data). Biological data (on maturity, recruitment and length-frequencies for squid L oligo forbesi ), trawling survey data (e.g. catch per unit eŒort) and genetic data (e.g. allele frequency data providing evidence of stock identity) from previous EU-funded projects were also integrated into the system. Data were integrated in Arc/Info as coverages, grids, and INFO tables, into ArcView as shapeŽ les, and in MS Access as tables. 2.3. User-f riendly interface The interfaces were developed for Arc/Info and ArcView, along with development of programmes and functions to handle new data and new items. The user-interface

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is an Arc Macro Language (AML) customization of an Arc/Info environment, using hierarchically organized menus to call AML scripts for data handling, selection, query, statistical analysis, integration, modelling and visualization. The ArcView interface was developed using the ArcView Avenue Language. Analytical and integration routines were developed to address speciŽ c research questions about cephalopod resource dynamics regarding, e.g. Ž shing areas and geo-distribution of abundance. Figure 3 is an example of using the UNIX Arc/Info interface, which includes hierarchically arranged toolbars. In the example, choices were made about species (squid L oligo), time-scale (single month–December 1990 ), type of data ( landings and Ž shery eŒort), environmental data to overlay (SST, same month) and display formats. Figure 4 is an example of using the PC ArcView interface. The Ž gure displays squid landings per unit eŒort (LPUE, kg hrÕ 1 ) by  eets of diŒerent countries for each ICES rectangle (represented by diŒerent marker with diŒerent size) in January 1990, with a background of monthly mean SST (as lines) in January 1990, and long-term monthly average SST (as shaded background ). Since all squid caught are generally landed, LPUE is essentially equivalent to catch per eŒort (CPUE). 3.

Application Although CFGIS-NEA has also been used in visualization and analysis of biological and genetic data on cephalopods, this paper, as mentioned above, focuses on the application to the analysis and modelling of spatial and temporal patterns of

Figure 3. One page of the user-friendly interface developed for the CFGIS-NEA based on UNIX Arc/Info. The user-interface is an Arc Macro Language (AML) customization of an Arc/Info environment, using hierarchically organized menus to call AML script routines for data handling selection and query, statistical analysis, integration, modelling and visualization.

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Figure 4. One page of the user-friendly interface developed for CFGIS-NEA based on PC ArcView. The ArcView interface was developed using ArcView Avenue Language. The Ž gure displays squid LPUE by  eets of diŒerent countries for each ICES rectangle (represented by circles in diŒerent colour and size) in January 1990, with background of monthly mean SST (in contour line) in January 1990, and long-term monthly average SST (in colour shade).

cephalopod abundance, and the spatial and temporal relationships between cephalopod abundance and environmental factors. Primary applications were made for visual analyses, statistical analyses and modelling, concerning: Spatial and temporal patterns of Ž shing Spatial and temporal patterns of cephalopod abundance Spatial and temporal relationships between cephalopod abundance and environmental factors Spatial and temporal modelling of cephalopod abundance 3.1. T he data used in analysis and modelling UK and French Ž shery data were used in analysis and modelling. LPUE by ICES statistical rectangles (1ß longitude by 0.5ß latitude) was used as an abundance index in analysis and modelling as in previous studies (Pierce et al. 1994 ). Four kinds of LPUE index with diŒerent temporal resolutions were calculated. The Ž rst is overall LPUE for a single month, which is calculated by summing the total landings (kg) from Scottish, Northern Irish, English and Welsh, and French  eets and dividing by the total eŒort (hours) from all these  eets. The second is LPUE for a single month and ICES statistical rectangle by  eets of individual nations, which is obtained by summing the total landings from the  eets of individual nations

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and dividing by the total hours from the  eets. The third is long-term monthly average LPUE, which is calculated by averaging the 18 single monthly overall LPUE values for each ICES rectangle from 1980–1997. These LPUE indices are crude, in that they combine data from  eets using diŒerent kinds of Ž shing gear. However, indices for individual gear-types are not easily comparable between nations, due to diŒering classiŽ cations of Ž shing activity. The fourth type of index is based on data from individual nations, from boats using particular kinds of Ž shing gear. The environmental data used are SST, SBT, SSS, SBS and bathymetry. SST data were resampled to the spatial resolution of 0.5ß latitude Ö 1ß longitude. Bathymetric data were also resampled to the rectangles of 0.5ß latitude Ö 1ß longitude with mean, maximum and minimum depths for analysis and modelling. Abundance indices from Ž shery landings data and survey data were compared. Monthly CPUE (catch per unit of eŒort, kg hrÕ 1 ) for single ICES rectangles was calculated by summing landings and dividing by the sum of hours from the trawling survey data in each month for a single ICES rectangle. It was found that both data sets show similar annual patterns and there is only a small diŒerence between them. Therefore, the Ž shery data were considered to be reliable for use in the analysis and modelling. The comparison was also made between the SST data from NCAR and the SST data from surveys. As expected, the diŒerence between these two data sets was very small and can be ignored. 3.2. V isual analysis Visual analysis was carried out to reveal patterns in the spatio-tempora l distribution of abundance and Ž shing activity, and the relationships between abundance and environmental factors and anomalies. This work involved overlaying and displaying diŒerent sets of Ž shery and environmental data in a selected visualization option, including: diŒerent time-periods and temporal resolutions, diŒerent areas and spatial resolutions, diŒerent species and gear combinations, diŒerent display formats and symbols, and diŒerent map projections. Programs developed in AML and Avenue, in conjunction with GIS built-in functions, were used to select and query data sets, to process the data into the selected spatial and temporal resolution, to transform the data into the selected display format such as grids and polygons, to display the data in the selected symbols, colours, and map projections, and, then, to export the visualization as a map to a printer or to save it as system Ž le. DiŒerent visualization options are listed as menus in the user-friendly interfaces of the GIS. Visual analysis was based on GIS visualization results and carried out in two ways: na¨ve-visual analysis and GIS-aided analysis. In the former, the relationship between diŒerent data sets was visually investigated, that is spatial patterns were detected within single maps. Spatio-tempora l dynamics were analysed using timeseries maps. However, during GIS-aided analysis, queries and data processing such as buŒering were carried out with particular spatio-tempora l and quantitative criteria, and further selecting and highlighting procedures were applied to the display. Statistical charts, and quantitative description forms were displayed together with visualized results. 3.2.1. Fishing patterns Visual analysis of commercial Ž shing activities were used to reveal the spatio-tempora l patterns of Ž shing, including seasonal Ž shing areas, hours, and

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landings by diŒerent gears and vessels from diŒerent countries. Figure 5 shows the spatio-tempora l distribution of total Ž shing hours from UK and French boats in 1991. Most Ž shing activities were located within the shelf area at sea depths < 200 m, and along the shelf edge on the west coasts of the UK and France. The

Figure 5. The spatio-temporal distribution of total Ž shing hours from UK and French boats in 1991. Most Ž shing activities were located within shelf area with sea depth < 200 m, and along the shelf edge of the UK west coast and French west coast. Each map shows one month.

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month-to-mont h change in the spatial pattern of Ž shing activities is very limited. Thus, the locations with high Ž shing hours remain within broadly the same areas, with only limited monthly shifts, and indeed reported total Ž shing eŒort is generally quite consistent from month to month. Figure 6 shows the spatial distribution of reported Ž shing hours by the boats from diŒerent countries in January 1991. Most Ž shing activities by French, Scottish,

Figure 6.

The spatial distribution of Ž shing hours by boats from diŒerent countries in January 1991. Most Ž shing activities were located close to home ports.

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English (including Welsh), and Northern Irish  eets were located close to home ports. French boats mainly Ž sh along the French west coast and in the English Channel. Boats from England and Wales Ž sh mainly in the English Channel and the southern North Sea. Boats from Scotland Ž sh mainly in the northern North Sea and the west coast of Scotland. Boats from Northern Ireland Ž sh mainly in the east coast of Northern Ireland in the Irish Sea. However, compared with other  eets, boats from France have a larger Ž shing activity area, which covers the French west coast, English Channel, Celtic Sea, and West Coast of Scotland and extends to the northern North Sea. It should be noted that we have been unable to display Ž shery activity by other nations in this area, notably Ireland, since they do not record Ž shery activity on a monthly-by-ICES rectangle basis. 3.2.2. Spatio-tempora l distribution of cephalopod abundance The spatio-tempora l distribution of cephalopod abundance was visualized by mapping long-term total landings, long-term average landings and LPUE, and individual yearly and monthly total landings and LPUE. Figure 7 is an example map to show the amount and distribution of total monthly cephalopod landings by French and UK  eets in 1991. Compared with Ž gure 5, the spatial distribution of landings broadly re ects the distribution of Ž shing activity, although very little of this activity is directly targeted at cephalopods. However, Ž shing eŒort is generally quite consistent from month to month, as also seen in Ž gure 5. The seasonal shifts in landings distribution are largely due to changes in cephalopod distribution and abundance. Thus shifts in catch distribution can help to track the recruitment, migration and spawning patterns of the species (Caddy 1983, Pierce et al. 1998b). Figure 8 shows the distribution of total cephalopod landings by category in 1991. The landings of squid, cuttleŽ sh, and octopus are displayed. The total landings per ICES rectangle range from 1 kg to over 1000 tonnes, represented by diŒerent sizes of circles. It is seen, by comparing time-series maps of the spatial distribution of LPUE, total landings and landings by category (Ž gures 7 and 8), that the French West Coast and English Channel are the major cephalopod catching areas. CuttleŽ sh is mainly distributed along the French west coast, English Channel and adjacent waters, and represents the major cephalopod Ž shery resource in these areas. Squid is Ž shed over the whole area, but the abundance  uctuates annually, and higher catches are located on the French west coast, English Channel, the west coast of Scotland and at Rockall (the oŒshore bank at appox. 60ß N, 14ß W). 3.2.3. Cephalopod abundance in relation to environmental factors To investigate the relationship between cephalopod abundance and environmental factors, LPUE was displayed against a background of selected environmental factors, such as SST, SBT, SSS, SBS, and bathymetry. Visualizations were also made to display and analyse the  uctuation of LPUE in an individual year or month in relation to the  uctuation of selected environmental variables in the same time period or a previous period, i.e. to investigate time-lagged eŒects. Since Ž shery resources and most marine environmental factors are mobile, animation displays of time-series of Ž shery abundance with a background of environmental variables were made to discover possible spatio-tempora l relationships between abundance and environmental variables. Figures 9 and 10 are two time-series maps for visualizing the  uctuation of squid

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Figure 7. The amount and distribution of monthly cephalopod landings in 1991. Also shown is the 200 m isobath. Total cephalopod landings were calculated by summing UK and French landings data on a monthly and ICES rectangle basis.

abundance in relation to the  uctuation of SST in January and August 1990. In these two maps, the LPUE ‘diŒerence’ is calculated by subtracting long-term average LPUE from the LPUE in January (Ž gure 9) or August (Ž gure 10), represented by diŒerent sizes of circles. The SST diŒerence is calculated by subtracting long-term average SST from the SST in January (Ž gure 9) or August (Ž gure 10), interpreted as

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Figure 8. The distribution of total cephalopod landings by species in 1991. Also shown is the 200 m isobath. The total cephalopod landings by species was calculated by summing UK and French landings data on monthly and ICES rectangle basis.

GRIDs, and displayed in diŒerent colour shades. It is found, by analysing time-series of such maps, that squid LPUE tended to be positively correlated with winter SST and negatively correlated with summer SST. This is consistent with results of previous work by Waluda and Pierce (1998 ): squid abundance in the North Sea in winter was also found to be spatially correlated with SST, but weak or negative correlations were found in summer. The clear spatial pattern in the relationship between LPUE

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Figure 9. A map showing the  uctuation of squid abundance in relation to the  uctuation of sea surface temperature. The  uctuation of LPUE is expressed as circles with diŒerent sizes representing the diŒerence between LPUE in January 1990 and longterm average LPUE, and with purple colour representing negative  uctuation and dark blue colour representing positive  uctuation. Similarly, the  uctuation of SST was calculated by subtracting long-term average SST from SST in January 1990. The  uctuation of SST is represented by diŒerent background colours.

and SST oŒSW Britain (positive correlations in the central (Celtic Sea) area and negative correlations on the shelf edge around this area) is interesting and could indicate that squid stay in deeper water in cold winters. It is also clear that the spatio-tempora l patterns of abundance and the patterns of the relationship between abundance and environmental factors, e.g. SST, are not homogenous over the study area. Thus, statistical methods were used to classify the study area into sub-areas

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Figure 10. Similar to Ž gure 9, this Ž gure shows the  uctuation of squid abundance in relation to the  uctuation of sea surface temperature in August 1990.

according to the spatio-tempora l pattern of abundance, and then further analysis and modelling were carried out for the whole area and each classiŽ ed sub-area. 3.3. Statistical analysis and modelling Statistical and geo-statistical analysis and modelling were carried out to investigate and model the relationship between cephalopod abundance and environmental factors in the spatial and temporal dimensions. Auto-correlation functions (ACF), correlation, and variograms were used to investigate the spatial and temporal correlation of cephalopod abundance. Statistical models, such as generalized linear models (GLM), generalized additive models (GAM), autoregressive integrated movingaverage models (ARIMA), and tree-based models, were developed to Ž t and predict the abundance of Ž shery resources. Most statistical analyses and modelling were carried out with S-PLUS statistical software, which combines GIS with statistical and graphical tools for visualization, exploration, and analyses. In the ArcView environment, S-PLUS is an extension. As shown in Ž gure 4, once the S-PLUS extension is available, S-PLUS and Spatial Statistics are listed as menus in ArcView.

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Statistical analyses and modelling can be carried out in the ArcView environment, or in the S-PLUS environment separately. Statistical analysis and modelling results at each step were visualized in order to detect outliers, reŽ ne the next analysis and modelling step, and achieve the optimum results. In this paper, we present some examples of statistical analysis and modelling, but do not discuss the technical details of these statistical methodologies. 3.3.1. Spatial classiŽ cation of squid abundance As demonstrate d by visualization, squid landings are distributed throughout the Ž shing area and the spatio-tempora l pattern of abundance is diŒerent from place to place, thus the spatial classiŽ cation of squid abundance was carried out according to the seasonal pattern and level of LPUE in each ICES rectangle. Long-term monthly average LPUE values were calculated from overall single month LPUE (January 1980 to December 1997) for each ICES statistical rectangle, and used in the classiŽ cation. Principal components analysis (PCA) was used to reduce the complexity of the data, and to remove the correlation of the 12 monthly long-term averaged LPUE variables by transforming them into the principal components. The principal components were ordered according to the amount of variance explained (from most to least). The lack of correlation between principal components is useful because they measure diŒerent ‘dimensions’ of the data. The number of principal components is the same as the number of variables in the original data. The Ž rst principal component has the largest variance among all principle components. The second has the second most variance not described by the Ž rst, and so forth. Usually, the Ž rst 4 or 5 principal components can describe over 95% of the variance. The remaining principal components can be dropped, and multivariate procedures can be performed on these few principal components, instead of all. This makes computation faster and accuracy is maintained (Manly 1986). Cluster analysis was used to deŽ ne areas with similar spatio-tempora l patterns of LPUE based on the chosen principal components. Cluster analysis identiŽ es groups (clusters) in the data, in such a way that objects belongings to the same cluster resemble each other, whereas objects in diŒerent clusters are dissimilar (Hartigan 1975, Gordon 1981). The Ž rst 5 PCAs, representing over 92% of the variance, were selected and used in the classiŽ cation. The spatial distribution of squid L oligo abundance was classiŽ ed into Ž ve classes from the Ž rst 5 principal components by use of cluster analysis. The areas were characterized by their levels of LPUE and are henceforth referred to as areas 1–5. The Ž rst area contains 57 ICES statistical rectangles, the second 118, the third 56, the fourth 89, and the Ž fth 65 ICES statistical rectangles. The classiŽ cation results were integrated into the GIS and displayed with a background of bathymetri c data (Ž gure 11). Figure 11 shows that each classiŽ ed area is well centralized within a limited geographical region, though there are some dispersed data points. Area 1, which is mainly located in the southern part of the North Sea, has a very low LPUE which shows relatively little seasonal variation. It was not used in further analysis. Area 2 is mainly located in the northern North Sea, oŒthe south coast of Ireland and on the west coast of Scotland. Area 3 is along the southeast coast of Ireland and the north coast of Scotland. Area 4 is mainly along the west coast of Ireland, and oŒwestern Scotland. Area 5 is along the French west coast and in the English Channel. The classiŽ cations are not coincident with

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Figure 11. The spatial and seasonal pattern of squid abundance in the Northeast Atlantic with sea depth contour lines. The spatial distribution of squid L oligo abundance was classiŽ ed into Ž ve classes. The areas were characterized by their levels of LPUE and are henceforth referred to as areas 1–5. Each classiŽ ed area is well centralized within a limited geographical region, though there are some dispersed data points.

the ICES Ž shery divisions, though there is a dominant classiŽ cation in each ICES division. Unlike squid, cuttleŽ sh landings occur within a relatively limited area (see Ž gure 8), therefore, classiŽ cation was not carried out. Octopus landings are widely distributed, but data collection is poor due to the low commercial value of the main species caught (Eledone cirrhosa). 3.3.2. Cephalopod abundance analysis and modelling Empirical variograms were calculated for investigating the spatial autocorrelation of squid, cuttleŽ sh and octopus abundance. The empirical variogram provides a description of how the data are related (correlated) as a function of distance. Matheron (1963) deŽ ned the semi-variogram , c(h), as half the average squared

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diŒerence between points separated by a distance h, and in the form of: 1 ž (z Õ z )2 (1) i j 2 | N (h) | ( ) Nh where N (h) is the set of all pairwise Euclidean distances iÕ j 5 h, | N(h) | is the number of distinct pairs in N (h), and z and z are data values at spatial locations i and j, i j respectively. c(h) is the semi-variogram , but it is usually referred to as the variogram. Figure 12 shows the directional empirical variograms of squid abundance in four directions for long-term average squid LPUE in January, April, July and October. The four directions are north–south, southwest–northeast, west–east, and southeast–northwest, and represented by the azimuth angles 0ß , 45ß , 90ß , 135ß , respectively. The azimuth tolerance for each direction is Ô 15ß . In January, the diŒerence between abundance values increases with the distance between them, regardless of direction. However, in April, July and October, the variogram in the direction of north-south (azimuth 5 0ß ) is diŒerent from the other directions (i.e. azimuth 5 45ß , 90ß , and 135ß ). The variograms in the directions of azimuths 5 45ß , 90ß and 135ß are basically  at (except the variogram in the direction of 135ß in April), indicating little spatial variation in abundance. The north–south direction yields generally increasing variograms, corresponding roughly to the patterns of SST change in this area, and the orientation of the west coast shelf edge. While these results are not easy to interpret, they indicate that spatial autocorrelation in abundance varies both seasonally and in relation to direction. Statistical models (e.g. ARIMA, GLM, GAM, tree-based models) were developed to Ž t and predict the abundance of Ž shery resources. ARIMAs were used for temporal modelling. Tree-based models were mainly used to provide the criterion for GIS c(h) 5

Figure 12. Four directional empirical variograms for long-term monthly average squid LPUE ( kg hrÕ 1 ) in January, April, July and October. The directions are north–south (the azimuth angle is 0), southwest–northeast (azimuth angle is 45), west–east (the azimuth angle is 90), and southeast–northwest (the azimuth angle is 135). The azimuth tolerance used in the calculation of each directional empirical variogram is Ô 15ß .

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GRID based analyses. GLMs and GAMs were used to Ž t the spatial distribution of Ž shery resources in relation to environmental factors. GLM extend linear models to accommodate both non-normal distributions and transformation to linearity. GAMs were the major models used in the project. They extend the concept of GLM by Ž tting non-parametric functions to estimate the relationships between response and predictors, in the form g(x) 5 a1 S f (x ), where g(x) is the additive predictor, a is a i i constant intercept, f is a non-parametric function of predictors or terms and x is i i a predictor (Hastie and Tibshirani 1990). Many applications to Ž shery data have been reported (Swartzman et al. 1992, Borchers et al. 1997, Augustin et al. 1998 ). As an example, Ž gure 13 is the Ž tted result of a GAM model of cuttleŽ sh abundance displayed using GIS techniques. It predicts the general spatial pattern of cuttleŽ sh abundance at the ICES statistical rectangle scale. In this example, the model performs relatively poorly at Ž ne spatial resolution, despite broad agreement between predicted

Figure 13. The result of a GAM model displayed by GIS techniques. Model outputs of cuttleŽ sh abundance ( blank bars) compared with observed data (solid bars): GAM model with Log-normal family and August climatic parameters.

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and actual abundance on a large scale. In this context, GIS is a useful tool to test and reŽ ne the model. 3.3.3. Correlation analysis between cephalopod abundance and environmental factors The spatial correlation between cephalopod abundance (LPUE) in diŒerent months and environmental factors, such as SST, SBT, SSS, SBS and bathymetry, was calculated to investigate the possible impact of environmental factors on the abundance of Ž shery resources. Calculations were made at diŒerent spatial and temporal scales, (e.g. single month records, long-term monthly average, individual classiŽ ed sub-areas, and ICES subdivisions). The results have been reported in a number of papers (Pierce et al. 1998b, Waluda and Pierce 1998, Robin and Denis 1999 ). The correlations between Ž shery abundance (LPUE) and SST, SBT, SSS, and SBS show that SST, SBT, SSS and SBS are all related to Ž shery abundance in some way. Cephalopod abundance is overwhelmingly positive correlated to sea temperature. However diŒerent species have diŒerent temperature preference or tolerance in diŒerent life stages. The correlation between cephalopod abundance and sea salinity varies between diŒerent species, the same species in diŒerent sub-areas and at diŒerent life-stages. Overall, there is no simple relationship between abundance and any single environmental variable, that entirely accounts for the overall pattern of abundance. 4.

Summary The development of the Northeast Atlantic cephalopod Ž shery information system CFGIS-NEA involved integration of GIS, database, and statistical techniques. CFGIS-NEA has illustrated some of the advantage s of GIS techniques for the study of marine Ž sheries. Its major contribution lies in the ability to integrate and manage geographically referenced data, opportunities for visualization of selected data sets at diŒerent spatial and temporal scales, with designed colours and symbols, for discovering hidden patterns in the spatio-tempora l dynamics of the abundance of Ž shery resources, and the relationships between the abundance of Ž shery resources and environmental variables, which statistical analysis alone cannot reveal. However, GIS software has limitations. Its statistical functions are limited when compared with statistical software packages such as S-PLUS. Also, its functions for data management are not as powerful as database software packages such as ORACLE and MS ACCESS. The integration of GIS, statistical and database software in CFGIS-NEA makes it possible to overcome such limitations, and facilitates analysis, modelling and forecasting of spatial and temporal patterns of Ž shery resource abundance in relation to environmental variables, using both quantitative and qualitative methods. Possible applications to Ž shery management include monitoring spatial and temporal trends in Ž shery eŒort and stock assessment. Acknowledgments This work was funded by EC contract FAIR CT 96 1520. The authors wish to thank the staŒat the Fisheries Research Services Marine Laboratory for providing access to UK Ž shery data, the ‘Direction des Peˆches Maritimes et Cultures Marines’ and Mr M. Martini (Centre Administratif des AŒaires Maritimes St Malo) for the French Ž shery data. The stuŒat ICES provided SBT, SSS and SBS data. The authors also wish to thank two anonymous referees for their comments.

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