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Towards a Marine Environmental Information System (MEnvIS) for the Northwest Atlantic: Experiences and suggestions from a multi-disciplinary GIS conservation research project using public large scale monitoring and research data bases from the internet Falk Huettmann* Atlantic Cooperative Wildlife Ecology Research Network (ACWERN) University of New Brunswick Fredericton NB E6B 6C2 Canada * Current address: Biology and Wildlife Department, Institute of Arctic Biology University of Alaska-Fairbanks Fairbanks AK 99775-7000 USA Abstract: - Large databases present a major investment. Often they are provided by governmental agencies and fulfill a monitoring and descriptive purpose. Here an overview and experiences are presented from a six year research project using a variety of environmental databases, freely available over the internet and from other digital sources. This project makes extensive use of such databases relevant to the Northwest Atlantic environment and for data analysis and predictive modeling for informed decision-making. Findings from this study contribute to a Marine Environmental Information System (MEnvIS) for the study area. Detailed experiences when using internet-databases for research purposes are presented, and suggestions are made how to further improve the delivery of databases to the lay public and to the research community. An outlook is given how the internet could become a central theme for data repository, data delivery and publication towards informed and transparent decision making for the environment. Key-Words: - Large Digital Databases, Seabird Monitoring, public internet/WWW download, Marine Environmental Information System, Northwest Atlantic 1 Introduction Recent and strong advancements in information technology have made free internet/World Wide Web (WWW) data downloads increasingly available to the international community. However, sound applications which use and evaluate such approaches are still rare. Free internet data availabilty allows for the first time for compiling pelagic environmental data sets and seabird and sea mammal inventories into a Geographic Information System (GIS) for the study area of the Northwest Atlantic, Gulf of Maine – Davis Strait - Canadian High Arctic; a Marine Environmental Information System (MEnvIS) can easily be built. This permits fast and quick answers to queries of interest, and it permits to address scientific and management questions related to seabirds and their marine ecosystem. Using this MEnvIS, here I address scientific and conservation management project applications such as descriptive habitat analysis and model predictions. This publication covers results and experiences from a six year research project 1995 – 2000 using the PIROP (Programme Intégré des Recherches sur les Oiseaux Pélagiques) monitoring database for seabirds in concert with free environmental data sets for the marine study area. 2 Project Overview and Objectives The concept of this long-term project is fundamentally based on computing-intense and free environmental data downloads from the internet which then got implemented into MEnvIS. These data sets were selected in order to help explaining the focus data set [see also 1 for using bird data and biodiversity to assess national health questions]. Figure 1: An approach making use of free internet data. data, to create data labels and strata, and to prepare them for the statistical data analysis. For further methods and for selected results using this approach see Fig 1. and Fig. 2 ,or see [11, 12, 13, 14, 15, 16]. Scientific approach, e.g. hypothesis testing and experiment 3 Results: Experiences from Chosen Research Approach and/or Management question Existing data Model building Public data sets from internet/ www and /or governmental agencies 3.1 Locating internet databases of interest Usually, it was not possible to locate most data sets for this project by simply searching the internet for the research data directly. Instead, all data sets were Environmental pointed out to me when visiting conferences or when communicating with experts, e.g. by phone or email. Information I recognized that even contacting agencies directly System (EIS) about their data holding did not necessarily result in locating data sets since often the staff was not aware of latest project data and website updates. Using a communication approach eventually I was able to locate 20 environmental data sets relevant for the Test and evaluate project and study area; most of them were derived model with reserved, from the website of the National Oceanic and randomly selected, Atmospheric Administration [17, 18], see Table 1 for or additional data an overview of data sets used and for additional sets, and/or using details. model residuals Scientific conclusion Prediction Here GIS overlays of data sets were used to investigate how seabird distribution in the Northwest Atlantic is determined by its environment. The huge long-term PIROP monitoring data set from the Canadian Wildlife Service was used for seabird baseline data [2, 3, 4]; other data sets, which were relevant to seabirds and matched temporal and spatial scales, were then selected in a multiple regression scenario for different seasons (summer for breeding, wintering, fall and spring for migration) in order to describe, model and predict the distribution of specific seabird species for the study area. I used Visual FoxPro 6 for SQL queries of data bases, SPLUS 2000 [5] for multivariate statistics and SPANS-GIS for plotting, overlaying and surface interpolation. For multivariate statistics techniques clustering, multiple logistic regression [6, 7], CART [8, 1] and Neural Networks [9] were used; see also [10] for evaluation of prediction techniques. All data sets discussed were imported as ASCII import formats, and if necessary processed and modified with SQL (Standard Query Language) code to filter 3.2 Experiences when using Environmental Data for the study area Data suppliers who offer data sets via the internet are often specific public agencies which have spent for decades time, money and effort on environmental monitoring. Clearly this investment in field work and data processing pays back now for the research community, and usually it relates into a huge data availability for the public - and if the internet is applied - for the global community. Most scientific data for the study area used in this study were collected with public tax money, or by governmental order. Due to the North American Freedom of Information Act, all collected data from North American governmental agencies need to be shared, published and distributed. A strong pressure exists nowadays to release such data, otherwise there is a danger they might get forgotten, become unusable or even get lost. Due to the recent strong advances in hardware, data base software, GIS and spatial statistics (particularly surface interpolation and predictive modeling techniques), there is an increasing availability of, but also an increasing demand for, spatial information. This demand stems from the public, as well as from scientific and management interest. Table 1: List and details for data sets used. Data Subject Seabirds Number and Name of Data Set 1. PIROP Atmosphere 2. Atmospheric Temperature at 880 mbar 3. STD Temperature Atmospheric at 880 mbar 4. Wind Speed 5. Air Pressure at Sea Level 6. Air Temperature Geology Water Additional Data Source Data Type Units Environment Canada, Canadian Wildlife Service, Manomet Bird Observatory U.S. Pathfinder Satellite (NOAA) Continuous Pathfinder Setallite (NOAA) Continuous Standard Deviation COADS (NOAA) COADS (NOAA) Continuous Continuous Meter/sec Mbar April 1987 December 1988 April 1987 December 1988 1854 - 1993 1854 - 1993 COADS (NOAA) Continuous Kelvin 1854 - 1993 7. Wind when seabird observation was done 8. Sea Depth PIROP Discrete Classified 1966 - 1992 ETOPO5 Continuous Meters 1988 9. Slope of Sea Floor 10. Aspect of Sea Floor 11. Distance from Coast 12. Distance from a Seamount 13. Distance from Shelf Edge 14. Sea Surface Temperature 15. Water Temperature 30 m below sea surface 16. Sea Surface Salinity 17. Water Salinity 30 m below sea surface 18. Salinity Difference Surface 30 m depth 19. Temperature Difference Surface 30 m depth 20. Sea State when seabird observations were done 21. Seabird Colonies SPANS-GIS from ETOPO5 SPANS-GIS from ETOPO5 SPANS-GIS Continuous Degrees 1988 Discrete Classified 1988 Discrete 1998 SPANS-GIS from ETOPO5 SPANS-GIS from ETOPO5 WOA (NOAA) Discrete WOA (NOAA) Continuous Distance bands Distance bands Distance bands Degrees Celsius Degrees Celsius WOA (NOAA) Continuous WOA (NOAA) Continuous SPANS-GIS based on WOA (NOAA) Continuous Discrete Continuous Seabirds within a 10minute block Kelvin Time cover 1966-1992 1996 1996 1948 - 1988 1948 - 1988 Parts per million Parts per million 1948 - 1988 Continuous Parts per million 1948 - 1988 SPANS-GIS based on WOA (NOAA) Continuous Degrees Celsius 1948 - 1988 PIROP Discrete Classified 1966 - 1992 e.g. [3] Continuous Breeding Pairs 1948 - 1988 1989, 1994 Abbreviations: COADS = Climate and Ocean Atlas Data Set, ETOPO5 = Earth Topographic Information for 5 minute grids, WOA = World Ocean Atlas. Other abbreviations and details are explained in the text. Figure 2: A research approach predicting Northern Fulmar (Fulmarus glacialis) distribution in the arctic section of the study area (Greenland, Davis Strait, Canada), based on distribution data from the PIROP data base (a), overlaid with 20 Environmental Data sets (see Table 1), and analyzed (b) and predicted (c) with a Classification and Regression Tree (Cart); see [11] for an approach using Artificial Neural Networks. a b c Data collection protocols for large-scale and longterm data bases can be inconsistent and these data sets often have temporal and spatial gaps. Depending on the data history, study area and data collection infrastructure, it is a common situation that some data were collected on a very fine scale for a certain location, perhaps even throughout years. On the other hand, other locations may have never been sampled at all, or may have been surveyed with a different data collection scheme and with varying effort throughout years. However, due to strong data demands and interests, individual data sets then were often merged, pooled together and homogenized since this would be the only way to obtain information and data as good as possible. However, accuracy assessments, reliability and consistency could have been seriously sacrified for such data sets. A typical list of applications for environmental data sets from the internet cover (i) general data queries, (ii) trend information on long-term ecological data, e.g. global change, abundance and resource overexploitation, (iii) GIS applications and (iv) using data for scientific multiple regression scenarios, spatial statistics, modeling and predictions. Such demanding and advanced research questions cause often the problem of using data sets in ways for which they were originally not created for. Sound multipurpose data set collection protocols are still rare. Instead, single purpose data sets are often brought into a very different context than they were originally designed for, e.g. from originally addressing general monitoring questions towards being used to answer scientific questions on longterm and spatial quantitative trends with a high statistical accuracy. 3.3 Experiences when using the internet as a research tool The development of the internet, including free use of email, make data widely available to the public for a relatively cheap price to the data provider (provision costs: formatting data set for the internet server, website, maintenance and updates) as well as for the receiver (user costs: computing and internet infrastructure). Since the internet can be accessed and used nationally and internationally, the international community can benefit greatly from national approaches for no costs, as obvious with the North American Freedom of Information Act. However, such advantages could also turn into disadvantages, e.g. when the user is faced with communication problems between North American federal vs. provincial/state agencies. The national legal set-up is still crucial for free internet data, and it can affect for instance data availability for internationally owned marine offshore waters, their management and conservation. 3.4.Experiences when using data from the internet 3.4.1.Database websites and data base support All web publications and data sets used for this project were provided in the English language, allowing for a truly international usage. However, most websites suffered from information overload and a poor graphical design; they failed to indicate what they really had to offer. Abbreviations were rarely explained. Normally, a support staff was necessary to guide me via phone through the website and its design, and explain data available and data policy provided at the specific URL. Therefore, directly connecting email addresses and telephone numbers were found of major importance to request for help and guidance with data access and downloads, but they were not always provided. Twenty-four hour support was not encountered, but could easily be justified when dealing with the international community and customers across the continent. In terms of database support I experienced cases where staff was not trained, and the user got simply referred to URLs without getting the specific questions answered. In similar cases staff was not able to answer basic questions on the presented data, e.g. correct data set citations for publications, data collection schemes, error fixes, data upgrades, data set versions, temporal and spatial scale of data, accuracy assessments or processing algorithms used. Mailing of publications and data set documentations, instead of downloading PDF (Portable Document Format) files, was found very useful. 3.4.2. The data sets Although the legal copyright situation is relatively clear and often stated online for such data, the international downloading situation across countries and legal spheres still can create confusion about ownerships and copyrights once data are in hand of the user. Information on database software and operating systems in which the data were stored by the agency was never provided ad-hoc. Since data sets can change hands and ownership, confusing situations can occur with names, labels, data quality and data subsets. A typical issue for North American data sets can arise from readily available raw data sets released by federal agencies to the public for free via internet, which then get re-interpreted and extended by provincial/state agencies or consultants, e. g. commonly the case for road networks, Digital Elevation Models (DEM) or remote sensing information about land use. To improve the documentation, it was found very useful when data sets were already published under a peer-review system, e.g. as a special journal edition or as an Atlas [3,19]. In general, I did not find a meta-data standard for all data sets I used. Although meta-data standards exist, particularly for data hosted in the U.S., I never came across any standardized ways of presenting them, nor were they really made available. Descriptions of data are crucial to the user but sometimes lacking or inaccurate, e.g. for logs of error fixes, names and versions of data sets, collection schemes, and their compatibility. With large data sets I found sometimes that several data sets were merged together using different collection schemes, which are often not fully consistent. Exact protocols about such a policy were not available to the public; other data processing work was simply given to contractors which could not be contacted anymore once the contract was completed. In addition, the applied types of classification schemes for data sets can be subjective and intransparent, and accuracy assessments are often lacking. A very important issue for georeferenced data sets is their spatial accuracy [see 20, 21]. However, spatial accuracy questions were not discussed or mentioned in most of the data sets I used. This presents for instance a particular uncertainty for offshore bathymetrical information where ground-truthing and data evaluation can hardly be done; in such cases the user relies entirely on the data provider. For large data bases the same could be said in regards to errors, such as caused by typing and data handling (rounding, data transfers and imports). 3.4.3. Online data queries Speed of server and internet connection was found to be of major importance for a convenient data base query and for the success of a website per se. Slow server and internet connections, including loading and transfer errors, lead to query problems, slow data transfers, or even data loss for the user. Zooming, pre-mapping and data selection features were found to be very important to investigate and to pre-view data of interest before downloading. 3.4. 4. Data download and data compression All downloads of data used were free of charge, and were usually done directly onto the hard drive of the user machine. Data download formats found most useful were ASCII, DBF and EXCEL; other specific formats like ArcInfo Export files (E00) or HDF (Hierarchical Data Format) exist but were not used. In most cases ftp (File Transfer Protocol) was used for file transfer, where occasionally a confusion occurred about how to log-in, where the data were stored, and if binary or ASCII transfer options was to be used. Email attachment was not offered by data providers as a data transfer, but could also be useful when transfering smaller data sets. Due to the large size of requested data, data sets were normally compressed using WinZip or Tar. However, these techniques can already constrain the free availability of data because user skills, basic though, are required to make data useable. So far, I found that most large data sets on the internet were provided, queried and processed on UNIX servers. Although using ftp, problems can still occur when transferring data sets and query results across operating systems, e.g. Windows vs. UNIX and LINUX. CD-ROM is a good option to deliver data, but this option creates costs and delays, which mostly are covered by the user. I found it inconvenient and confusing when queries from data requests were split up into smaller data packages or subsets (mostly due to file size problems). Sometimes these subsets were given intransparent file names; after the data transfer the user has to compile these data sets into one again. This issue particularly occurs when working with GIS map sheets (for a discussion see 3.4.5 below). However, most requested data queries were ready for ftp pick-up within seconds, or within a day, which met the project time frame very well. Some data sets require a user registration, which can be time consuming. A particular problem with free data sets occurred when the user has to pay for, or to obtain, a compression software, or any other technical tool, beforehand. Such approaches still present a bottleneck in the whole concept of free data availability. Any data provided free of charge through the internet to the public in any directly non-readable format are technically not useable to all users. A similar problem occurred with formats such as HDF, or data that require programming languages (e.g. FORTRAN), where the user had to know the compression software and the algorithms, and had to learn the specifics about particular formats. 3.4.5. GIS and Remote Sensing GIS applications demand their own standards; formatting compatibilities occurred when using different GIS software other than the offered ones. Data can be downloaded in GIS formats, or better, as general point and raw data for specific or further processing. Projection free coordinate systems, such as latitude and longitude, were preferred. However, due to file size, and lack of compatibility among GIS software and across operating systems, the latter approach still proved to be problematic. Most GIS download applications provided DXF (Data Exchange Format) and/or E00 ArcInfo export files; although other GIS formats were also found, e.g. Spatial ORACLE data bases or specific local and agency GIS formats. Still, a full and true compatibility hardly exist among GIS formats, and even among compatible formats information still might get lost during the transfer process, e.g. specific formatting and column attributes. A major issue in GIS data downloads can be the separation of the data for a requested area into smaller GIS map sheets. Often, such situations offer no guidance or contact addresses about how to merge map sheets into one after the data were downloaded. I experienced that data from remote sensing applications were usually not provided in GIS formats but as a matrix. No help was provided to import these data into a GIS. Instead, a viewing and de-compression software was necessary to look at the data. If raw data for satellite images were provided, most of these data consisted of georeferenced raster pixels with a bit value (x,y,z format) in ASCII format. This approach proved convenient, but could present a problem if raster grids and data sequence were not accurately documented. 4 Conclusions and Suggestions The internet can offer a borderless research tool to be used across countries. Therefore, the international community can benefit greatly from national approaches at no costs, as currently achieved with the North American Freedom of Information Act for instance. So far, countries which do not have such a legal framework, or which do not share their national data heritage with others, clearly do not contribute fully to this concept. Due to this imbalance, they even put this great concept at risk since not fully and equally supported by the global community as a whole. Free internet data availability is a (research) infrastructure to the international community; but who pays for it to improve, evaluate and maintains it? Data sets for the internationally owned body of the offshore ocean are a good example for such a situation. Proprietory information for these international waters and for which a larger international community has a strong interest (e.g. conservation or fish harvest data) present such a classical problem. In addition, criticism is often expressed that consultants and NGOs could have financial benefits from free internet data, for which these data were originally not meant for. Agencies have the tendency not to publish controversial data, and instead provide only smoothed out information. For the public user such a selective information provision can lead to a wrong perception of reality. Agencies that have the monopoly on data sets clearly can drive assumptions and opinions due to loss of variety and additional evaluation options for the public. Currently, the internet is not making use of its full potential to promote its own research potential and data sets; standardized ways to present free data sets, and data inventory pages are still missing in search engines. During my six years research work I did not experience that a distinction was made between data for the public, or for data for the scientific community. However, most data provided were very specific and technical knowledge was needed; the lay public would not really be able to make use of data and information potentially available to them. Generally, it might be a good approach to present the same data for scientists, and alternatively offer on the same website for the lay public an interpretation of these data and how the conclusions were derived. I do not recommend to pre-screen, filter or smooth-out raw data, nor carrying out any other type of intransparent data interpretation. Suggestions on how to interpret data best might prove useful though. However, if any data smoothing and interpretation has occurred, raw data and algorithms should be provided and must allow for a transparent evaluation process of results for the public. A frequently discussed topic is the problem of data overload ('digital data jungle') resulting from the free internet downloading policy of data agencies. I feel that currently a data overload is not an existing problem for the scientific community. A more relevant problem might be the transparent data presentation, e.g. for the lay public. Because of the increasing availability of data sets through the internet there is a temptation for scientists and others to make use of these data without a proper research design and without prior investigation of data history and data purpose. A sound and well thought-out hypothesis still needs to be the driving force for the science done with these data sets, instead of ‘surfing the internet’ to see which data set might explain trends, perceptions and models best, and then dismissing the ones that do not. Current tendencies for free internet data downloads using increasing beaurocratic or technological thresholds, and also the release of governmental data on a cost recovery basis only, hinders the concept of free data availability through the internet to the global community; a MEnvIS, crucial for effective management and conservation of the study area, cannot possibly be built without free data. Research from this project has shown how internet data free of charge can be used efficiently for management, conservation and research questions in the study area. Only providing free data through the internet made compiling an MEnvIS for the study area possible, which better ensures the management and conservation of the huge offshore ecosystem we know so little about. applications matching this future trend very well, and which strongly benefit from free data availability. The spatial and temporal resolution of GIS and databases are still increasing, and so are its data volumes. This raises several issues, such as data storage, data handling, data management, data presentation and data provisioning to be addressed in an efficient manner. In addition, questions like international copyrights, data accuracy, meta-data standards, implementation of standards, sharing costs for data provision and downloading, and providing a public, well-maintained and scientifically sound internet infrastructure will become of increasing importance. References: [1] O’Connor, R., and Jones, M. T. Using hierarchical models to index the ecological health of the nation. Transactions Number 62 of the American Wildlife and Natural Resources Conference. 1997 pp. 501- 608. [2] Brown, R.G.B., Nettleship, D.N., Germain, P., Tull, C.E. and Davis T. Atlas of Eastern Canadian Seabirds. Canadian Wildlife Service, Halifax 1975. [3]Lock, A.R., Brown, R.G.B., and Gerriets, S.H. Gazetteer of marine birds in Atlantic Canada. Canadian Wildlife Service. Atlantic Region. 1994. [4] Huettmann, F., and Lock, A.R. A new software system for the PIROP database; data flow and an approach for the seabird-depth analysis. ICES Journal for Marine Science Vol. 54 1997 pp. 518523. [5] Venables, W. N., and Ripley, B. D. Modern Applied Statistics with S-Plus. 2.ed. Statistics and Computing, Springer Verlag, New York. 1994. [6] Brennan, L.A., Block, W.M., and Gutierrez, R.J. The use of multivariate statistics for developing habitat suitability index models. In Verner, J., Morrison, M.L., Ralph, C.J. (ed.). Wildlife 2000: modelling habitat relationships of terrestrial vertebrates. University of Wisconsin Press, Madison. 1986 pp 177-182. 4.1 Future Trends The internet is well established now, and very likely its research use will still be increasing. Therefore, future research trends can be characterized by using data from these sources, and for similar applications world-wide, as described above. Environmental Information Systems and predictive modeling are [7] North, M., and Reynolds, J. H. Microhabitat analysis using radiotelemetry locations and polytomous logistic regression. Journal for Wildlife Management Vol. 60 1996 pp. 639-653. [8] Miller, T., and Ribic, C. (1995). Tree-Structured variable selection methods. Proceedings of the Statistical Computing Section of the American Statistical Association. 1995 pp. 142-147. [9] Oezesmi, S. L. and Oezesmi, U. An artificial neural network approach to spatial habitat modelling with interspecific interaction. Ecological Modelling Vol 116 1999 pp. 15-31. [10] Dettmers, R., Buehler, D.A. and Bartlett, J. G. A test and comparison of wildlife-habitat modeling techniques for predicting occurrence on a regional scale. In Predicting Species Occurrences: Issues of Accuracy and Scale. Scott, J. M. , Heglund, J. P., Samson, F. et al. (ed.). Island Press, Washington DC. in press [11] Huettmann, F. Making use of public large-scale environmental databases from the WWW and a GIS for geo-referenced prediction modelling: An application based on Generalized Linear Models, Classification and Regression Trees and Neural Networks. In Tochtermann, K. and Rieckert, W.-F. (ed.). Proceedings 3. Workshop “Hypermedia im Umweltschutz”. FAW Ulm, Germany. 2000 pp 308 – 312. [12]Huettmann, F., and Diamond, A.W. Characterizing, Modeling and Predicting locations of Seabird Colonies in the Davis Strait: Using the PIROP database, GIS and Environmental Data to Evaluate the Suitability of Marine Breeding Habitats for Arctic Seabirds. In Shaw, R.W., Danks, M.M.E., Miller, S. (ed.). Proceedings of Environmental Prediction Workshop, 1998. Environment Canada, Halifax. 1998 pp 86-94. [13]Huettmann, F., and Diamond, A.W. Seabird migration in the Canadian North Atlantic: moulting locations and movement patterns of immatures. Canadian Journal of Zoology. Vol. 33 1999 pp.1-25. [14]Huettmann F., and Diamond, A.W. Seabird colony locations and environmental determination of seabird distribution: A spatially explicit seabird breeding model in the Northwest Atlantic. Journal for Ecological Modelling Vol. 141 2001 pp.261298. [15]Huettmann, F., and Diamond, A.W. Using PCA Scores to classify species communities: an example using seabird classifications at sea. Journal for Applied Statistics Vol 28 2001 pp. 843-853. [16]Huettmann, F., and Diamond, A.W. Characterizing the marine Environment in the Canadian North Atlantic using GIS, multivariate statistics and environmental large scale data bases. Canadian Journal for Aquatic Sciences and Fisheries. in review. [17]National Oceanic and Atmospheric Administration. Five minute gridded earth topography data. Http:// edcwww.cr.usgs.gov/glis/ hyper/guide/etopo5. 1996. [18]National Oceanic and Atmospheric Administration. Live Access to Climate Data. Http://ferret.wrc.noaa.gov/fbin/climate_server. 1997. [19]Levitus, S. World Ocean Atlas 1994. Washington, D.C., National Oceanic and Atmospheric Administration, National Oceanographical Data Center. 4 volumes 1994. [20]Agumya, A. and Hunter G.J. Translating uncertainty in Geographical Data into risk in decisions. In Shi, W., Goodchild, M.F., Fisher, P. F. (ed.). Proceedings of The International Symposium on Spatial Data Quality ’99. The Hong Kong Polytechnic University Dept. of Land Surveying and Geo-Informatics, Hong Kong. 1999 pp. 574 – 584. [21]Shi, W., Goodchild, M.F., and Fisher, P. F. (ed.). Proceedings of The International Symposium on Spatial Data Quality ’99. Dept. of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University. 1999