Login / Signup

Use of Fish Telemetry in Rehabilitation Planning, Management, and Monitoring in Areas of Concern in the Laurentian Great Lakes.

Jill L BrooksC BostonS DokaD GorskyK GustavsonD HondorpD IsermannJ D MidwoodT C PrattA M RousJ L WithersC C KruegerS J Cooke
Published in: Environmental management (2017)
Freshwater ecosystems provide many ecosystem services; however, they are often degraded as a result of human activity. To address ecosystem degradation in the Laurentian Great Lakes, Canada and the United States of America established the Great Lakes Water Quality Agreement (GLWQA). In 1987, 43 highly polluted and impacted areas were identified under the GLWQA as having one or more of 14 Beneficial Use Impairments (BUIs) to the physical and chemical habitat for fish, wildlife and humans, and were designated as Areas of Concern (AOC). Subnational jurisdictions combined with local stakeholders, with support from federal governments, developed plans to remediate and restore these sites. Biotelemetry (the tracking of animals using electronic tags) provides information on the spatial ecology of fish in the wild relevant to habitat management and stock assessment. Here, seven case studies are presented where biotelemetry data were directly incorporated within the AOC Remedial Action Plan (RAP) process. Specific applications include determining seasonal fish-habitat associations to inform habitat restoration plans, identifying the distribution of pollutant-indicator species to identify exposure risk to contamination sources, informing the development of fish passage facilities to enable fish to access fragmented upstream habitats, and assessing fish use of created or restored habitats. With growing capacity for fish biotelemetry research in the Great Lakes, we discuss the strengths and weaknesses of incorporating biotelemetry into AOC RAP processes to improve the science and practice of restoration and to facilitate the delisting of AOCs.
Keyphrases
  • climate change
  • healthcare
  • primary care
  • mental health
  • drinking water
  • risk assessment
  • human health
  • endothelial cells
  • heavy metals
  • public health
  • machine learning
  • water quality
  • health information
  • quality improvement