David Millie

David Millie


Environmental Scientist, Michigan Tech Research Institute (MTRI)

  • PhD, Botany, Aquatic Plant Biology, Iowa State University, 1984
  • MS, Phycology, Limnology, Wetland Ecology, Bowling Green State University, 1979
  • BSc, Biology, Chemistry, Bowling Green State University, 1976
  • AA, Bowling Green State University, 1974

Research Description

25+ years experience in Algal Quantitative & Physiological Ecology and Organismal/Cell Biochemical Regulation, encompassing a spectrum of marine, estuarine/wetland, and freshwater systems. Recent work addresses the linkages among anthropogenically-derived stressors, water quality, and the regulation of harmful algal blooms. Currently implementing cutting-edge statistical methodologies (e.g. artificial intelligence, Bayesian networks, decision/regression trees, non-parametric analyses) to model the relationships among ecological quantifiers and biota. Expertise in photopigment-based characterization of algal populations and associated physiological rate processes.

Research Interests

  • Phycology - Physiological and Quantitative Ecology
  • Coastal Marine, Estuarine/Wetland Biology and Limnology
  • Univariate and Multivariate Statistics, Experimental Design
  • Environmental Modeling

Recent Publications

  • Millie, D. F., Weckman, G. R., Young II, W. A., Ivey, J. E., Fries, D. P., Ardjmand, E., & Fahnenstiel, G. L. 2013. Coastal ‘big data’ and nature-inspired computation: prediction potentials, uncertainties, and knowledge derivation of neural networks for an algal metric. Estuarine Coastal & Shelf Science 125:57-67.
  • Millie, D. F., Weckman, G. R., Young, W. A., Ivey, J. E. & Fahnenstiel, G. L. 2012. Modeling algal abundance with artificial neural networks: demonstration of a heuristic, ‘Grey-Box’ technique to deconvolve and quantify environmental influences. Environmental Modelling & Software. 38: 27-39.
  • Millie, D. F., Fahnenstiel, G. L., Weckman, G. R., Klarer, D. M., Dyble Bressie, J., Vanderploeg, H. A., & Fishman, D. 2011. An ‘enviro-informatic’ assessment of Saginaw Bay (Lake Huron USA) phytoplankton: characterization and modeling of Microcystis (Cyanophyta). Journal of Phycology 47: 714-730.
  • Pinckney, J. L., Millie, D. F., & Van Heukelem, L. 2011. Appendix 1; update on filtration, storage, and extraction solvents. In: Roy, S., Llewellyn, C. A., Egeland, E. S., and Johnsen, G. [Eds.]. Phytoplankton Pigments: Characterization, Chemotaxonomy and Applications in Oceanography. Cambridge University Press. Cambridge. pp. 627-635.
  • Young, W. A. II, Millie, D. F., Weckman, G. R., Anderson, J., Klarer, D. M., & Fahnenstiel, G. L. 2011. Modeling net ecosystem metabolism with an artificial neural network and a bayesian belief network. Environmental Modelling & Software. 26: 1199-1210.
  • Millie, D. F., Pigg, R. L., Fahnenstiel, G. L., & Carrick, H. J. 2010. Algal chlorophylls: a synopsis of analytical methodologies. In: American Water Works Association, Manual M57, Algae. American Water Works Association, Denver, Colorado USA. pp. 93-122.
  • Stivaletta, N., López-García, P., Boihem, L., Millie, D. & Barbieri, R. 2010. Biomarkers of endolithic communities within gypsum crusts (Southern Tunisia). Geomicrobiology Journal 27: 101–110.
  • Millie, D. F., Fahnenstiel, G. L., Dyble, J., Pigg, R., Rediske, R., Litaker, R. W, & Tester, P. A. 2009. Late-summer phytoplankton in western Lake Erie (Laurentian Great Lakes): bloom distributions, environmental influences, and toxicity. Aquatic Ecology 43: 915–934.