AWRI McNair

James N. McNair, Ph.D.

Associate Professor

Grand Valley State University
Annis Water Resources Institute
740 West Shoreline Drive
Muskegon, MI 49441

Office: 133 Lake Michigan Center
Phone: 616-331-3987
Fax: 616-331-3864
E-mail: mcnairja@gvsu.edu

Research Areas

  • Mechanistic models and statistical methods for estimating components of lake metabolism based on free-water dissolved-oxygen dynamics
  • Stochastic models of particle transport (e.g., microorganisms, invertebrates, fine particulate organic matter) in streams
  • Physiologically based models of microbial populations
  • Non-parametric, semi-parametric, and fully parametric methods of statistical time-to-event analysis as applied to data from seed germination experiments
  • Evolutionary responses of invasive aquatic plants to herbicides
  • Spatially explicit watershed models linking landuse/landcover-derived stressors to ecological conditions in streams
  • Applications of artificial neural networks to problems in watershed hydrology and watershed-scale assessment of ecological risk
  • Deterministic and stochastic models of benthic insect distributions in streams
  • Effects of novel trace contaminants on cyanobacteria, algae, rotifers, and cladocerans
  • Density-dependent dynamics of physiologically-structured population models
  • Statistical methods for assessing phenotypic variation within and among populations of invasive aquatic and riparian plants

Teaching

  • BIO 580: Ecological Modeling (Fall semester, 3 credits). Mathematical models are the foundation on which most theories in modern ecology, evolutionary biology, and related disciplines (e.g., fisheries and natural resource management) are based. They are also increasingly being used in various other areas of the biological sciences, such as physiology, molecular biology, epidemiology, and developmental biology. Yet students in the biological sciences often are not required to learn the mathematical and computational skills needed to understand, assess, apply, or develop mathematical models. The main purpose of this course is to partially fill this gap by providing biology students with a set of basic mathematical, computational, and computer graphics skills that will allow them to understand and critically evaluate most deterministic models in the ecological and related literatures, and to develop new models of their own.

    The main types of models covered in this course are difference equations, matrix models, and ordinary differential equations. No prior knowledge of any of these topics is assumed. As background, the course begins by refreshing students' memories of various topics in elementary mathematics, including the basic rules of algebra and various standard mathematical functions (power functions, exponential functions, etc.). The course also provides students with an introduction to the required parts of elementary calculus, tailored to biology students and with no prior knowledge assumed.

    Each modeling technique studied is illustrated by readings from the literature. Applications to various branches of ecology (or other disciplines, depending on student interests) are illustrated with worked examples. Specific applications examined during the course are tailored to interests of the students but may include, for example, animal behavior, growth of organisms, demography, population dynamics, population genetics, community ecology, microbial ecology, fisheries ecology, physiological ecology, and ecotoxicology, as well as statistical topics such as least-squares and maximum-likelihood parameter estimation.

    This is not a mathematics or computer-programming course. Emphasis is placed on how to apply the various techniques to ecological problems rather than on mathematics or programming per se.

General Information

  • Ph.D., Biology (Theoretical Ecology), University of Pennsylvania, 1979
  • B.S., Biology, Davidson College, 1974
  • Curriculum Vitae (PDF)

Research Interests and Approach

Basic research

I have two principal basic research interests at the present time: methods of estimating components of lake metabolism based on free-water dissolved-oxygen (DO) dynamics, and the turbulent transport of suspended particles (e.g., fine particulate organic matter, microorganisms, and invertebrate larvae) in streams.

Exchange of carbon between the biosphere and atmosphere is dominated by rates of photosynthetic CO2 uptake and respiratory CO2 release by aquatic and terrestrial ecosystems worldwide. Obtaining accurate estimates of these rates is therefore important. In lakes, the most common estimation method is based on a model of DO dynamics and a corresponding time series of DO concentrations measured in the well-mixed layer of water at the lake surface. O2 production and consumption are inferred from changes in DO concentration, then converted to estimates of carbon uptake and release using photosynthetic and respiratory quotients. The traditional method of this type uses a simple accounting procedure to estimate daily gross primary production (GPP), total respiration (R), and net production (NP), but it assumes that the time series of measured DO concentrations contains no error, and its estimates of R and GPP hinge on a subjectively assumed value for daytime respiration. In collaboration with Dr. Bopi Biddanda and several of our graduate students, I have developed several new alternative methods for estimating GPP, R, and NP that resolve these problems and facilitate assessment of model adequacy. We are applying these new methods to high-frequency time-series data acquired by AWRI's Muskegon Lake Buoy Observatory and are also working on practical ways to modify the mechanistic model of DO dynamics that underlies the estimating methods to improve their performance.

My work on turbulent transport of suspended particles centers on a stochastic diffusion model called the Local Exchange Model, which I developed to describe the random-like dynamics of individual particles suspended in a turbulent fluid. The model can be applied to the transport of molecules, seston, microorganisms, and invertebrates in aquatic systems characterized by turbulent flowing water (e.g., streams, estuaries, and marine systems), and to a variety of other turbulent transport problems, as well.

A complete theory of particle transport in turbulent aquatic systems can be decomposed into at least four problems: (1) The entrainment problem—how does a particle on the bottom (or other solid surface) become entrained into the water column? (2) The travel-time problem—how long does a suspended particle take to hit the bottom for the first time, following release from a given initial elevation? (3) The travel-distance problem—how far does a suspended particle travel before hitting the bottom for the first time, following release from a given initial elevation? And (4) the settlement problem—what determines whether a particle settles on the bottom when it hits, rather than bouncing off and immediately returning to the water column? Thus far, I have derived equations governing the probability distribution and moments of the hitting time, hitting distance, settling time, and settling distance (the hitting time and distance are the travel time and distance at which a suspended particle hits the bottom for the first time; the settling time and distance are the travel time and distance at which a suspended particle settles for the first time). I have applied these theoretical results to empirical settling-distance distributions for 14C-labeled natural FPOM in streams, and I am currently appling them to data for additional types and sizes of particles in streams and flumes.

Applied Research

My principal applied research interests at the present time are catchment-scale modeling of effects of landcover- and landuse-derived stressors on stream ecological integrity, and the effects of novel chemical stressors (e.g., antibiotics, engineered nanomaterials) and their mixtures on individuals, populations, and communities of aquatic organisms. A guiding belief underlying this work is that applied research can and should be conducted in essentially the same way as basic research; i.e, using the same methods and the same level of rigor, and structured around a theory of how the system under study works.

My work on landcover- and landuse-derived stressors on stream ecological integrity is related to my interest in predicting effects of chemical stressors. Several colleagues and I are developing alternative modeling approaches for routing stressors such as sediment and nutrients from terrestrial source areas to streams and then to drainage-basin outlets. Stressor loads or concentrations can be predicted at any location in a stream network, and stressor-response modeling techniques can be used to predict impacts on components of stream ecological integrity using various indexes based on periphyton, macroinvertebrate, and fish assemblages.

My research on chemically stressed populations employs both theoretical and experimental approaches and is a natural extension of my basic research in the area of physiologically structured population models. Key components of this research include the relationship between individual-level, population-level, and community-level effects of stressors, and methods for quantifying and predicting effects of chemical mixtures from known effects of constituent compounds.

Selected Publications

McNair, J. N., Gereaux, L. C., Weinke, A. D., Sesselmann, M. R., Kendall, S. T., and Biddanda, B. A. 2012. New methods for estimating components of lake metabolism based on free-water dissolved-oxygen dynamics. (In review)

Sisson, A.J., Wampler, P.J., Rediske, R.R., McNair, J.N., and Frobish, D. 2012. Long-term field performance of the Biosand Filter in the Artibonite Valley, Haiti. (In review)

Homola, J.J., Scribner, K.T., Elliott, R.F., Donofrio, M.C., Kanefsky, J., Smith, K.M., and McNair, J.N. 2012. Genetically-derived estimates of contemporary natural straying rates and historical gene flow among Lake Michigan lake sturgeon populations. Transactions of the American Fisheries Society 141: 1374–1388.

McNair, J.N., and Newbold, J.D. 2012. Turbulent particle transport in streams: Can exponential settling be reconciled with fluid mechanics? Journal of Theoretical Biology 300: 62–80.

McNair, J.N., Sunkara, A., and Frobish, D. 2012. How to analyze seed germination data using statistical time-to-event analysis: nonparametric and semiparametric methods. Seed Science Research 22: 77–95.

McNair, J.N. 2009. Two new methods for predicting effects of landcover-related stressors on stream biotic integrity at the catchment scale. Proceedings of the Academy of Natural Sciences of Philadelphia 158: 61–88.

Sieg, A.E., O'Connor, M.P., McNair, J.N., Grant, B.W., Agosta, S.J., and Dunham, A.E. 2009. Mammalian metabolic allometry: do intraspecific variation, phylogeny, and regression models matter? American Naturalist 174: 720–733.

Araujo, A. and McNair, J.N. 2007. Individual- and population-level effects of antimicrobials on the rotifers, Brachionus calyciflorus and B. plicatilis. Hydrobiologia 593: 185–199.

Johnson, T.E., McNair, J.N., Srivastava, P., and Hart, D.D. 2007. Stream ecosystem responses to spatially variable landcover: a model for developing riparian restoration strategies. Freshwater Biology 52: 680–695.

O’Connor, M.P., Agosta, S.J., Hansen , F., Kemp, S.J., Sieg, A.E., McNair, J.N. and Dunham, A.E. 2007. Phylogeny, regression, and the allometry of physiological traits. American Naturalist 170: 431–442.

O’Connor, M.P., Agosta, S.J., Hansen , F., Kemp, S.J., Sieg, A.E., Wallace, B.P., McNair, J.N. and Dunham, A.E. 2007. Size, selection, and physiology: Reconsidering the mechanistic basis of the metabolic theory of ecology. Oikos 116: 1058–1072.

McNair, J.N. 2006. Probabilistic settling in the Local Exchange Model of turbulent particle transport. Journal of Theoretical Biology 241: 420–437.

Srivastava, P., McNair, J.N., and Johnson, T.E. 2006. Comparison of process-based and artificial neural network approaches for streamflow modeling in an agricultural watershed. Journal of the American Water Resources Association 42: 545–563.

Fingerut, J.T., Hart, D.D. and McNair, J.N. 2006. Silk use enhances benthic invertebrate settlement. Oecologia 150: 202–212.

Bram, M.R. and McNair, J.N. 2004. Seed germinability and its seasonal onset in three populations of Japanese knotweed. Weed Science 52: 759–767.

McNair, J.N., and Newbold, J.D. 2001. Turbulent transport of suspended particles and dispersing benthic organisms: the hitting-distance problem for the Local Exchange Model. Journal of Theoretical Biology 209: 351–369.

McNair, J.N. 2000. Turbulent transport of suspended particles and dispersing benthic organisms: the hitting-time distribution for the Local Exchange Model. Journal of Theoretical Biology 202: 231–246.

Goulden, C.E., Moeller, R.E., McNair, J.N., and Place, A.R. 1999. Lipid dietary dependencies in zooplankton. Pages 91–108 in: Arts, M.T. and Wainman, B.C. (Eds.) Lipids in Freshwater Ecosystems. New York: Springer-Verlag.

McNair, J.N., Boraas, M.E., and Seale, D.B. 1998. Size-structure dynamics of the rotifer chemostat: a simple physiologically structured model. Hydrobiologia 387/388: 469–476.

Boraas, M.E., Seale, D.B., Boxhorn, J.E., and McNair, J.N. 1998. Rotifer size distribution changes during transient phases in open cultures. Hydrobiologia 387/388: 477–482.

McNair, J.N., Newbold, J.D., and Hart, D.D. 1997. Turbulent transport of suspended particles and dispersing benthic organisms: how long to hit bottom? Journal of Theoretical Biology 188: 29–52.

McNair, J.N. 1995. Ontogenetic patterns of density-dependent mortality: contrasting stability effects in populations with adult dominance. Journal of Theoretical Biology 175: 207–230.

McNair, J.N., Goulden, C.E., and Ziegenfuss, M.C. 1995. Is there a place for ecotoxicology? Setac News 15: 18–21.

McNair, J.N. and Goulden, C.E. 1991. The dynamics of age-structured populations with a gestation period: density-independent growth and egg ratio methods for estimating the birth rate. Theoretical Population Biology 39: 1–29.

McNair, J.N. 1989. Stability effects of a juvenile period in age-structured populations. Journal of Theoretical Biology 137: 397–422.

Page last modified January 5, 2013