Initial analyses of change detection capabilities and data redundancies in the LTRMP

Lubinski, K, R. Burkhardt, J. Sauer, D. Soballe, and Y. Yin. 2001. Initial analyses of
change detection capabilities and data redundancies in the Long Term Resource
Monitoring Program. U.S. Geological Survey, Upper Midwest Environmental Sciences
Center, La Crosse, Wisconsin, September 2001. LTRMP 2001-T001. 23 pp. + 
Appendixes  A E (NTIS #PB2002-100123)

ABSTRACT

Evaluations of Long Term Resource Monitoring Program  sampling designs for water quality,
fish, aquatic vegetation, and macroinvertebrates were initiated in 1999 by analyzing data
collected since 1992 in six trend analysis areas. Initial emphasis was placed on evaluating
statistical power to detect change from one year or sampling interval to the next, and on
determining what spatial, methodological, or target variable redundancies existed in the data
sets. Power to detect change was evaluated at halved, present, and doubled levels of effort.
Power to detect change for different variables varied widely and was greatly influenced by
sample size and for species by their frequency of occurrence. Power for detecting annual and
seasonal changes in most water-quality variables seems adequate. A doubling of effort would
provide little increase in power, and some reduction or redistribution of effort may be
possible. For fish, we could detect a 20% change (at à = 0.05 and power of 0.7) in annual
mean catch-per-unit-effort for 41 species in at least one trend analysis area. Doubling effort
would not appreciably enhance power for rare species. Power for detecting change in aquatic
vegetation seemed adequate. However, power for detecting change in macroinvertebrates was
low, especially in Navigation Pool 26, the Open River, and La Grange Pool. Results of these
analyses should provide useful information for evaluating the effects of potential changes to
sampling designs. 

KEYWORDS

fish, Long Term Resource Monitoring Program, macroinvertebrates, Mississippi River,
monitoring, power analysis, sampling design, statistical analysis, vegetation, water quality