Environmental indicators of condition, integrity, and sustainability in the Great Lakes basin PI: Gerald Niemi, University of Minnesota Duluth Cooperative Agreement with US EPA ORD; MED Co-PIs: 27 individuals from 10 institutions Great Lakes Environ mental Indicators
Goal What indicators can effectively, efficiently, and economically measure and monitor the condition of the Great Lakes coast as well as point to causes of impairment? Objectives 1. Identify potential environmental indicators 2. Examine relationships between stress and response 3. Recommend a suite of hierarchically-structured indicators that are useful for making informed management decisions
Great Lakes environmental indicators experimental design Objective: To obtain an unbiased sample of of sites that will allow us to to develop indicators of of ecological condition and point to to potential causes of of impairment Problem: Massive coastal area with multiple stressors > 6500 km of of coastline > 750 wetlands Great Lakes Environmental Indicators
Coastal units Land-based stressors in in the basin influence coastal ecosystem condition 1. 1. Shoreline segments = Lengths of of shoreline beginning and ending halfway between each 2 nd nd order or or higher stream 2. 2. Segment-sheds = shoreline segments plus drainageshed Great Lakes Environmental Indicators
762 segment-sheds
Knowing the gradients Categories n Variables Agricultural / ag. chemical 21 Atmospheric deposition 11 Land cover 23 Human population / development 14 Point and non-point pollution 79 Shoreline protection 6 Soils 53 207 A total of 207 variables from 19 different data layers were used to define stressor gradients and distribute our sampling effort.
Knowing the gradients Principal components analysis for each disturbance category new variables (PCs) are gradients of stress P rincipalc om ponent Province C ategory 1 2 3 4 Laurentian A griculture 0.72 0.81 0.86 0.90 M ix e d F o re s t A tm. D e p 0.7 6 0.8 6 0.9 3 0.9 9 Land C over 0.23 0.37 0.49 0.57 P o p. D e n s. 0.2 7 0.4 9 0.5 9 0.6 8 Point S ource 0.38 0.47 0.54 0.60 Shoreline M od. 0.33 0.52 0.69 0.85 Soils 0.2 4 0.4 2 0.5 2 0.5 7 Eastern Agriculture 0.41 0.60 0.72 0.82 B roadle a f A tm. D e p 0.6 0 0.8 5 0.9 4 0.9 7 Land C over 0.23 0.37 0.49 0.58 P o p. D e n s. 0.2 9 0.4 3 0.5 5 0.6 5 Point S ource 0.41 0.50 0.57 0.63 Shoreline M od. 0.29 0.50 0.68 0.85 Soils 0.1 7 0.3 1 0.4 4 0.5 2
Ag. / ag. chem. PC1 76% variance Less ag (nutrients) More ag (nutrients) 72% variance Less ag (nutrients) More ag (nutrients)
Atmospheric deposition PC1 60% variance Less total dep. More total dep. 76% variance Less total dep. More total dep.
Human population / development PC1 29% variance Less pop. dens. / dev. More pop. dens. / dev. 27% variance Less pop. dens. / dev. More pop. dens / dev.
Selecting sites Sampling units are coastal ecosystems within segment-sheds Stress profile of segment-shed is related to stress profile of coastal ecosystems within Open-coast wetland Riverine wetland Protected wetland / high energy shoreline Embayment
Great Lakes Environmental Indicators
GLEI components Extensive Field Efforts! Contaminants -49 Sites Diatoms and WQ -237 sites; > 1500 spp. -52,376 ++ individuals Birds and amphibians - >3,000 points; >250 sites; -195 bird spp; -120,909 individual birds -12 spp. amphibians; Wetland vegetation -91 complexes -526 taxa -20,560 observations Fish & Bugs 145 sites - 104 fish spp, 104,476 individuals 33 b 240 334 i di id l
OVERLAPS >4 teams = 25 4 teams = 28 3 teams = 32 2 teams = 75 1 team = 147
Remote sensing overview 1. 1. Land use and land cover change detection 1992-2001; important variables as pressure and state indicators 2. 2. Quantify submerged aquatic vegetation (SAV); critical vegetation zone in in near shore coastal areas 3. 3. Document shoreline morphology critical information on water level fluctuations; -- -- climate change models predict reduced precipitation and higher temperatures for Great Lakes region Great Lakes Environmental Indicators
NLCD-1992 to NLCD-2001 change
QuickBird satellite data SAV test areas
Quickbird satellite data 2.44 m spatial resolution Mapping SAV near Escanaba, Michigan using change detection techniques - Wolter et al. - Int. J. Remote Sensing (in press)
J. Environmental Monitoring and Assessment. 2004. In press.
Conclusions Successfully implemented a large-scale sampling program for the Great Lakes coastal region Multiple stress gradients identified Field data have verified many of these important gradients (e.g., nutrients, exotic spp., and chemical contamination), Very non-trivial exercise in in multivariate statistics and geographic information systems Great Lakes Environmental Indicators
Acknowledgements This research has been supported by a grant from the US Environmental Protection Agency's Science to to Achieve Results (STAR) Estuarine and Great Lakes (EaGLe) Coastal Initiative through funding to to the Great Lakes Environmental Indicators (GLEI) Project, US EPA Agreement EPA/R-8286750 Great Lakes Environmental Indicators