Environmentally stratified sampling design for the development of Great Lakes environmental indicators.
Understanding the relationship between human disturbance and ecological response is essential to the process of indicator development. For large-scale observational studies, sites should be selected across gradients of anthropogenic stress, but such gradients are often unknown for apopulation of sites prior to site selection. Stress data available from public sources can be used in a geographic information system (GIS) to partially characterize environmental conditions for large geographic areas without visiting the sites. We divided the U.S. Great Lakes coastal region into 762 units consisting of a shoreline reach and drainage-shed and then summarized over 200 environmental variables in seven categories for the units using a GIS. Redundancy within the categories of environmental variables was reduced using principal components analysis. Environmental strata were generated from cluster analysis using principal component scores as input. To protect against site selection bias, sites were selected in random order from clusters. The site selection process allowed us to exclude sites that were inaccessible and was shown to successfully distribute sites across the range of environmental variation in our GIS data. This design has broad applicability when the goal is to develop ecological indicators using observational data from large-scale surveys.
Danz
Nicholas P
NP
Center for Water and the Environment, Natural Resources Research Institute, University of Minnesota Duluth, 5013 Miller Trunk Highway, Duluth, Minnesota, USA. ndanz@nrri.umn.edu
Regal
Ronald R
RR
Niemi
Gerald J
GJ
Brady
Valerie J
VJ
Hollenhorst
Tom
T
Johnson
Lucinda B
LB
Host
George E
GE
Hanowski
Joann M
JM
Johnston
Carol A
CA
Brown
Terry
T
Kingston
John
J
Kelly
John R
JR
D000818:Animals; D016000:Cluster Analysis; D017753:Ecosystem; D004784:Environmental Monitoring / Q000379:methods*; D005618:Fresh Water; D040362:Geographic Information Systems; D006114:Great Lakes Region; D006801:Humans; D025341:Principal Component Analysis; D014881:Water Supply