Large aquatic ecosystems around the world have been impacted by the human populations within their watersheds, and many of these impacts have directly or indirectly affected the fish communities. Increased siltation covers rocky habitats, negatively impacting fish spawning locations; contaminants from industrial pollution, agricultural runoff, and atmospheric deposition impairs water quality and stresses fish populations; and excess nutrients from wastewater, agricultural runoff, and urban stormwater runoff have caused eutrophication leading to degraded food webs, water quality, and habitat. Climate change is predicted to further impact these lakes by increasing water temperature, decreasing dissolved oxygen levels, increasing contaminant toxicity, altering lake mixing regimes that influence fish habitat availability; and changing the magnitude and seasonality of runoff.
These types of significant ecoloigcal changes require careful management to preserve and sustain freshwater resources, and restore them where feasible. Fisheries managers have long promoted timely data collation, storage, analysis, synthesis and modeling to guide their management decisions. Accurate and reliable information on fish stocks and their ecology is an essential prerequisite for effective management. Insufficient data accessibility, inadequate modeling tools, poor dissemination of information, and disparate historical datasets all currently inhibit informed decision making. Solving these issues is the key driver of the Global Great Lakes project and its development of a platform and data services that enable the assessment of ecosystem health of the world’s great lakes.
Research and monitoring programs are costly endeavours so the data they gather should be available for more than a single purpose and readily linked with other related data. Long term data storage is also necessary to provide historical information, important for examining patterns of change in large lake ecosystems. Successfully integrating multiple data sources enables new, more robust insights, and supports analyses that are critical to effectively managing ecosystem health. However, capacity to collate, store, and synthesize these comparative data and develop models is generally inadequate. Synthesis, visualization, and modeling tools are needed to translate the data into information for decision makers.
The Global Great Lakes: Integrating Yesterday, Today and Tomorrow and Transforming Environmental Data into Anticipatory Ecosystem Management was funded in July 2009 with support from the University of Minnesota’s Institute on Environment. The primary objective of this project is to establish a web-based system of data acquisition, database management, decision support modeling and informative visualization to enable anticipatory management of the world’s great lakes. Lake Superior has been selected as the prototype for this project, and techniques and tools are already in development. Prototype tools and services are currently available using sample data.