James N. McNair, Ph.D.
Associate Professor
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
Academic Background
- Ph.D., Biology (Theoretical Ecology), University of Pennsylvania, 1979
- B.S., Biology, Davidson College, 1974
- Curriculum Vitae (PDF)
Current Research Areas
- Stochastic models of disturbance-mediated succession in aquatic and terrestrial communities
- Mechanistic models and statistical methods for estimating components of stream metabolism based on free-water dissolved-oxygen dynamics
- Performance assessment of qPCR-based decision thresholds for protecting human health in recreational waters subject to bacterial contamination
- Statistical methods for environmental studies involving qPCR and ddPCR data
- Statistical methods for environmental studies involving censored concentration data
- Non-parametric, semi-parametric, and fully parametric methods of statistical time-to-event analysis as applied to data from seed germination experiments
- Statistical methods for estimating abundance of stream fish
Research Approach
My main research interests at the present time are developing and analyzing stochastic models of disturbance-mediated succession in aquatic and terrestrial communities, methods of modeling and estimating components of stream metabolism based on free-water dissolved-oxygen (DO) dynamics, statistical methods for environmental studies involving quantitative PCR data (qPCR, ddPCR) and/or censored concentration data, and nonparametric statistical methods for studies of invasive plants.
My work on models of stochastic community dynamics is a collaborative effort with Dr. Dean DeNicola (Slippery Rock University) and Dr. Jiyeon Suh (Mathematics Department, Grand Valley State University). The original idea for this project was Dean's and stemmed from work he did as a PhD student at Oregon State University, dealing with effects of flood events on stream epilithic algal assemblages. I developed a stochastic simulation model based on data Dean had assembled and on his statistical analyses of those data, and Jiyeon and I are now extending this work by developing a novel stochastic process model that applies to a variety of aquatic and terrestrial community types and can be studied analytically.
My work on stream metabolism deals with the production and consumption of DO by stream communities, how these processes are related to overall community metabolism, how best to account for and isolate the simultaneous contributions of community metabolism, stream transport processes, and exchange with the atmosphere to measured DO concentrations in streams, and how best to estimate the various rates statistically. The traditional estimation method, first used systematically by H. T. Odum in 1956 (but derived from earlier engineering studies of severe DO deficits in rivers and estuaries caused by intense organic pollution), uses a simple accounting procedure to estimate daily gross primary production (GPP), total respiration (R), and net production (NP). However, 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 1975, Hornberger and Kelly published a prediction-based statistical method for stream DO time series that resolves these problems. I have developed several alternative extensions of the prediction-based method for estimating GPP, R, and NP in streams. These methods allow one to estimate daytime and nighttime respiration rates independently, acknowledge statistical error in the measured time series, and facilitate assessment of model adequacy. Together with my graduate students and summer interns, I am applying these new methods to high-frequency time-series data acquired with multisensor sondes in streams of West Michigan.
My work on modern statistical methods for environmental studies involving quantitative PCR data and/or censored concentration data stems from several applied projects dealing with estimating concentrations of fecal indicator bacteria, wastewater epidemiology of the SARS-CoV-2 virus, and various stream and river studies in which environmental chemistry data are employed in assessing water quality and potential anthropogenic impacts. Of particular interest to me are statistical methods derived from modern time-to-event analysis, with concentration taking the place of time as the domain for the various probability distributions and intensity functions that lie at the heart of these methods. This work is being conducted in collaboration with Dr. Daniel Frobish (Statistics Department, Grand Valley State University).
Finally, my current work on nonparametric statistical methods for studies of invasive plants stems from various adaptive management projects with invasive plants over the years, including Japanese knotweed, Norway maple, Eurasian and northern watermilfoil (and hybrids), and invasive perennial baby's breath. Some of the practical issues that statistical methods must address in such studies are the need to analyze binary data (e.g., plants that either killed or not killed), the need to assess outcomes of large-scale field experiments that are also key parts of restoration efforts and do not fit well in traditional experimental designs, the need to deal with data that do not satisfy normality assumptions of many classical statistical methods (even with transformations), and the need to assess and minimize potential violations of independence assumptions due to spatial autocorrelation.
Teaching
BIO/NRM 480/580: Techniques for Modeling Biological Systems (Fall semester in even-numbered years, 3 credits). Theories based on mathematical models have long been of fundamental importance in subdisciplines of the biological sciences. In population biology, this importance dates at least as far as the 1700s with, for example, Euler's work in mathematical demography and Malthus's work on population regulation. In the early to mid 20th century, there was a great flowering of mathematical approaches in many areas of biology, including ecology, population genetics, fisheries and wildlife management, biophysics, epidemiology, physiology, and biochemistry. More recently, mathematical models have become important in new areas, such as developmental biology, bioinformatics, and systems biology.
Despite the rapidly increasing importance of mathematical theories in the biological sciences, biology students 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 several of the most common types of models in the biological literature, 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 modern biology are illustrated with worked examples. Specific applications examined during the course each year will be selected based on interests of the students but may include, for example, topics in animal behavior, population and community ecology, population genetics, fisheries and wildlife management, ecotoxicology, epidemiology, cell and organism physiology, biochemistry, regulation of genes and metabolism, and statistical topics such as least-squares parameter estimation.
This is not a mathematics or computer programming course. Emphasis is placed on how to apply the various techniques to biological problems rather than on mathematics or programming per se.
BIO 480/580: Advanced Population Ecology (Winter semester in odd-numbered years, 3 credits). This course surveys the main phenomena of population ecology and the main theories that have been proposed to explain them. Emphasis is placed on classic field and laboratory studies that stimulated development of modern theories of population dynamics, and on key population models that were developed to explain results of these studies. All useful theories in population ecology are expressed as mathematical models that predict population dynamics, so discussion of these models and the ideas they represent is an essential part of the subject. But because there are no mathematics or computer programming requirements for this course, the predicted forms of population dynamics---the main basis for assessing and empirically testing the models---are demonstrated primarily through computer programs written in the R programming language. These programs are provided to students so they can run their own computer experiments to explore the types of dynamics that different population models are capable of exhibiting and to determine the roles that different model parameters play.
Selected Publications
McNair, J. N., Frobish, D., Rice, E. K., and Thum, R. A. 2024. Alternative study designs and nonparametric statistical methods for adaptive management studies of invasive plants. (In review.)
Hart, J. J., Jamison, M. N., Porter, A. M., McNair, J. N., Szlag, D. C., and Rediske, R. R. 2023. Fecal impairment framework, a new conceptual framework for assessing fecal contamination in recreational waters. Environmental Management 73(2): 443-456. https://doi.org/10.1007/s00267-023-01878-x
Hart, J. J., Jamison, M. N., McNair, J. N., and Szlag, D. C. 2023. Frequency and degradation of SARS-CoV-2 markers N1, N2, and E in sewage. Journal of Water and Health 21(4): 514–524. DOI: 10.2166/wh.2023.314
Hart, J.J., Jamison, M.N., McNair, J.N., Woznicki, S.A., Jordan, B., Rediske, R.R. 2023. Using watershed characteristics to enhance fecal source identification. Journal of Environmental Management 336:117642. https://doi.org/10.1016/j.jenvman.2023.117642
McNair, J. N., Lane, M. J., Hart, J. J., Porter, A. M., Briggs, S., Southwell, B., Sivy, T., Szlag, D. C., Scull, B. T., Pike, S., Dreeling, E., Vernier, C., Carter, B., Sharp, J., Nowlin, P., and Rediske, R. R. 2022. Validity assessment of Michigan’s proposed qPCR threshold value for rapid water-quality monitoring of E. coli contamination. Water Research 226:119235. https://doi.org/10.1016/j.watres.2022.119235
DeNicola, D., McNair, J. N., and Suh, J. 2021. A stochastic model of epilithic algal succession and patch dynamics in streams. Ecosphere 12(7):e03566.10.1002/ecs2.3566. https://doi.org/10.1002/ecs2.356610.1002/ecs2.3566.
Myers, D. T., Rediske, R. R., McNair, J. N., Parker, A. D., and Ogilvie, E. W. 2021. Relating environmental variables with aquatic community structure in an agricultural/urban coldwater stream. Ecological Processes 10:37. https://doi.org/10.1186/s13717-021-00312-6
Mock, A. J., Ruetz, C. R., McNair, J. N., Mays, D., and Martell, A. 2020. Evaluating remote site incubators in Michigan streams: implications for Arctic Grayling reintroduction. North American Journal of Fisheries Management. https://doi.org/10.1002/nafm.10534
Lane, M. J., Rediske, R. R., McNair, J. N., Briggs, S., Rhodes, G., Dreelin, E., Sivy, T., Flood, M., Scull, B., Szlag, D., Southwell, B., Isaacs, N. M., and Pike, S. 2020. A comparison of E. coli concentration estimates quantified by the U.S. EPA and a Michigan laboratory network using U.S. EPA Draft Method C. Journal of Microbiological Methods. https://doi.org/10.1016/j.mimet.2020.106086
Myers, D. T., Rediske, R. R., McNair, J. N., and Allen, M. E. 2020. Watershed and streambank erosion modeling in a coldwater stream using the GWLF‐E model: application and evaluation. Modeling Earth Systems and Environment. https://doi.org/10.1007/s40808-020-00882-y
Rice, E. K., Leimbach-Maus, H., Partridge, C., and McNair, J. N. 2020. Assessment of invasive Gypsophila paniculata control methods in the northwest Michigan dunes. Invasive Plant Science and Management. doi: 10.1017/inp.2020.10
Lane, M. J., McNair, J. N., Rediske, R. R., Briggs, S., Sivaganesan, M., and Haugland, R. 2020. Simplified analysis of measurement data from a rapid E. coli qPCR method (EPA Draft Method C) using a standardized Excel workbook. Water 2020, 12, 775; doi: 10.3390/w12030775
Rice, E. K., Martínez-Oquendo, P., and McNair, J. N. 2019. Phenology of seed maturation in Gypsophila paniculata in northwest Michigan, USA, and its relation to glyphosate efficacy. Invasive Plant Science and Management 12: 194-201. DOI: 10.1017/inp.2019.21
Myers, D. T., Rediske, R. R., and McNair, J. N. 2019. Measuring streambank erosion: a comparison of erosion pins, total station, and terrestrial laser scanner. Water 11(9). DOI: 10.3390/w11091846
McNair, J. N., Ruetz III, C. R., Carlson, A., and Suh, J. 2018. Reducing effects of dispersal on the bias of 2-sample mark-recapture estimators of stream fish abundance. PLOS ONE 13(8): e0200733. https://doi.org/10.1371/journal.pone.0200733
Thum, R. A. and McNair, J. N. 2018. Inter- and intraspecifc hybridization affects germination and vegetative growth in Eurasian watermilfoil. Journal of Aquatic Plant Management 56: 24-30.
Taylor, L. L., McNair, J. N., Guastello, P., Pashnick, J., and Thum, R. A. 2017. Heritable variation for vegetative growth rate in ten distinct genotypes of hybrid watermilfoil. Journal of Aquatic Plant Management 55: 51-57.
Thum, R. A., Parks, S. R., McNair, J. N., Tyning, P., Hausler, P., Chadderton, L., Tucker, A., and Monfils, A. 2017. Survival and vegetative regrowth of Eurasian and hybrid watermilfoil following operational treatment with auxinic herbicides in Gun Lake, Michigan, USA. Journal of Aquatic Plant Management 55: 103-107.
Parks, S. R., McNair, J. N., Hausler, P., Tyning, P., and Thum, R. A. 2016. Divergent responses of cryptic invasive watermilfoil to treatment with auxinic herbicides in a large Michigan lake. Lake and Reservoir Management 32: 366-372.
McNair, J. N., Sesselmann, M. R., Gereaux, L. C., Weinke, A. D., Kendall, S. T., and Biddanda, B. A. 2015. Alternative methods for estimating components of lake metabolism using process-based models of dissolved-oxygen dynamics. Fundamental and Applied Limnology 186: 21-44.
Ruetz, C. R. III, Harris, B. S., McNair, J. N., and Homola, J. J. 2014. Removal and mark-recapture methods for estimating abundance: empirical and simulation results for Mottled Sculpin in streams. North American Journal of Fisheries Management 35: 62-74.
McNair, J. N., Gereaux, L. C., Weinke, A. D., Sesselmann, M. R., Kendall, S. T., and Biddanda, B. A. 2013. New methods for estimating components of lake metabolism based on free-water dissolved-oxygen dynamics. Ecological Modelling 263: 251-263.
Sisson, A.J., Wampler, P.J., Rediske, R.R., McNair, J.N., and Frobish, D. 2013. Long-term field performance of the Biosand Filter in the Artibonite Valley, Haiti. American Journal of Tropical Medicine and Hygiene 88: 862-867.
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 trategies. 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.