``Advanced Fisheries Management Information System (AFMIS)''
Prof. Paul J. Fortier
University of Massachusetts Dartmouth
Electrical and Computer Engineering
North Dartmouth Massachusetts 02747 E-mail: pfortier@umassd.edu
URL: http://www.ece.umassd.edu/ece/faculty/pfortier/pfortier.htm

Ocean systems science seeks a regional and ultimately a global scale understanding of the components, interactions, and evolution of the complete ocean system, including the physical, biological and fish components. The raw data supporting ocean system sciences is rapidly growing per year and shows no signs of abaiting. The intrinsic problem of archiving, searching, and distributing such hugh data sets are compounded by the heterogeneity of the data and the generators of the information. In addition to these issues, users of information come from a variety of scientific disciplines with differing views and requirements for this information. A successful ocean data management system must provide seamless access to arbitrary local and remote data sets, and be compatible with existing and evolving data analysis, data location and data transfer environments such as HOPS, CORBA or the WEB if it is to be available and useful to a diverse group of scientists

Technical and scientific activities such as fishery management within the seas, require knowledge of the multi-dimensional spatial distributions of the oceans and embedded biological properties along with related time dependencies. An understanding of these multi-dimensional fields and their relationships is essential to further knowledge of this domains dynamic nature. Field estimation in the ocean domain is very complex and made even more difficult due to the sparseness of information available. In order to make continuous, accurate, and complete oceanic field estimates requires more complete data sets describing the measured ocean and its inhabitants. To develop these data sets requires the collaboration of experts from a variety of disciplines; oceanography, physics, electrical engineering, computer engineering, computer science, ocean biology, chemistry, and geology, each providing insights on their unique understanding of the ocean environment and technologies applied to ocean research. The primary outcome of such interaction is an integrated information model of the multi-dimensional ocean domain.

The ocean sciences area of interest to our research is the inhabitants of the commercial fisheries. The present situation in fishery management is deemed prehistoric at best. The criticism may not be fully warranted, but the present situation is not adequate, nor time effective for the participants, as well as fishery policy and management agencies. Present fishery data, for end participants, is sparse to non existent, and for fishery management units it is not much better. Typical fishery management units use data relating to single populations of fish, which consist mainly of aggregate, non validated data, collected over some long temporal period, typically an entire year. Due to this, fishery management decisions are generally referable to the regulation of size or age specific fishing mortalty covering long temporal periods. Improvements in data capture, timeliness, assimilation and presentation can greatly improve the performance of present fishery management and the lives of those germainly dependent on sound and timely policy and management directives.

Fishery management and fisherman's commercial operations in the seas depend on the prey they both seek. The populations of fish are interdependent, so it makes sense to manage them in the context of all of the species involved and the environment they exist within. In addition it would be desirable to manage a single population or multiple populations within time scales that are much shorter than the present yearly basis. In fact the most desirable outcome would be to have the ability to model and therefore manage fisheries much like we monitor atmospheric weather today, in near real-time fashion. Beyond the fisheries, another important component within a fisheries management and prediction system is the environment. The environment changes over a spectrum of time scales, and these multiscale environmental interactions are important sources of information that contribute to fish mortality and fish population dynamics. The atmospheric, physical ocean, chemical ocean, geological, biological and fish information must be extracted, assimilated into the database, modeled and mined for knowledge when forecasting fisheries.

There are several problems with present fishery data management as introduced above. The first deals with the inadequacy of an integrated data model describing the multimedia environmental, biological and fisheries information which in turn dictate the usefulness of this data to diverse user groups. A second problem, deals with present environmental and in particular fishery data and data collection processes. The fishery data is based on sparse, temporally sampled information which is not at present highly correlated, nor validated to physical systems data, for example a complete spatial and temporal ocean model. The third problem deals with the lack of a comprehensive data model for integrating numerous, diverse non homogeneous, multimedia data sets of the ocean and fishery environment into one meta data model, useful to a variety of research, development and operational organizations. Data collection for many ocean environmental components is periodic (e.g. satellite scans, in situ sensors), while others are aperiodic, sporadic or ad hoc. Many assessments are even only performed on a yearly or bi-annual basis (e.g. fish catches, surveys etc.). The true environmental and biological population effects are continuous and highly dependent on a variety of conditions, implying the inadequacy of the present scheme. Presently data is collected from a variety of sensors and merged into some existing and proposed experimental systems, for example LOOPS, HOPS, DODS, and ultimately AFMIS. Existing systems provide partial data on the ocean environment and hopefully in the future, more complete biological and pollution information, however they do this in forms useful to a subset of interested parties both commercial and governmental. Many of these efforts are only in initial conceptual phases and are also struggeling with concepts of how to view this multidimensional data within some all encompassing meta data framework. The SQL3 database standard to be released this spring-summer, provides a model (object / relational) amenable to capturing the variety of meta and real information described, as well as means to uniformly manage and operate upon diverse data sets. In addition numerous vendors, are delivering commercial systems implementing some subset of this language, which can form a base to build the prototype research system and ultimately a commercial system upon making this system more widely available.

An integrated synthetic ocean database system which possesses the capability to search out (data mine), collect, integrate and deliver information describing the continuous (or near continuous) nature of the ocean environment and its inhabitants as described above, will provide a platform to develop computer based simulation and modeling tools to aid participants in numerous commercial and policy organizations involved in the oceans. For example fisherman, conservationalist, politicians, fish processors, fish traders, etc. all have an interest in a more complete understanding of the dynamics which impact the environment they participate in or manage. A database system which provides integrated record, abstract data sets, objects, temporal and spatial data sets can be used to develop a variety of ocean and biological forecasting tools much like those available at present for weather forecasting. These tools and data sets, in turn will spur on further research and development of extensions useful to the full spectrum of interested parties. Such tools would aid DOD, NOAA, NMFS, NASA, EPA, State organizations, educators, students, fishing cooperatives, fish processors, regional fishery councils and ultimately fisherman in better understanding and managing this important earth resource. The developed systems could provide a near real-time information service to parties much as the weather services do today to aid in day to day activities of these organizations.

With the basic concept of an ocean information system, we can then move towards development of additional products that use this information bank, much like what occurred based on the advent of the GPS systems numerous related product spinoffs. One initial possability is an informational source available for access in real-time by anyone with a computer and communications capabilities. Users could register for specific information and have it automatically down loaded to their site. For example, maps forecasting the best locations for finding flounder over the next few days, or fishing grounds to be shut down to foster spawning and population recovery.

Present research at UMD and CMAST focuses on the development of a state of the art management information system for fisheries management and to support the implementation of a long-term full-spectrum environmental management system for the southern New England coastal waters. The Advanced Fisheries Management Information System (AFMIS), will be used for the real-time management of marine fisheries and to develop a fishery information service. The basic conceptual structure of the system consist of an integrated simulation and sensor system centered on a sensor, assimilation and simulation structure, all supported by an underlying information management component. Ocean, biological, and fish dynamic models form the foundation of the system. The models are driven by satellite, aircraft, extracted legacy data and in-situ sensor data. This ocean database will be supported and augmented by fishing boat and port sampling data acquired from the National Marine Fisheries Service and other commercial sources. The sensor, assimilation and simulation system structure generates an environmental assimilation, which can be thought of as the computed ocean environment. The computed ocean environment includes; the status of physical ocean properties, chemical ocean properties, geologic properties of the ocean floor, ocean lower trophic biological properties, the status of spatially integrated size-class fish populations and other high trophic levels in the biological food chain.