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Nick Strigul Laboratory Mathematical Ecology and Environmental Modeling



Thank you for your interest in my laboratory!


I conduct an interdisciplinary research across traditional disciplinary boundaries, in particular my research lies at the interface of applied mathematics, statistics and biology. I am interested in projects where mathematical methods in concert with field and experimental studies can lead to better understanding of multiple scale biological phenomena. I am also interested in remote sensing of environment, in particular, I lead a research project on 3D modeling of complex objects that includes a robotics engineering component. My research has focused on environmental problems, and has included research in mathematical biology, as well as a variety of modeling, experimental and field studies in different areas such as ecotoxicology, soil, microbial and avian ecology. My current primary research project is focused on forest modeling. One of the major directions of this project is to investigate effects of environmental stressors and different land-use practices on forest dynamics. My research approach is to consider forested ecological systems as complex adaptive systems, which result from self-organization on multiple levels. I also conduct research on ecotoxicology, in particular tungsten ecotoxiology, in order to provide a scientific basis for pollution regulation and environmental risk-assessment.

Undergraduate students who would like to be involved in research are very welcome in my laboratory. I have supervised numerous undergraduate research projects, and many of them resulted in research publications. In recent years research projects conducted by undergraduate students were funded by the Landscape Ecology and Ecosystem Dynamics REU project (please check the REU website for deadlines and application instructions), by the UBM UI-WSU Program in Undergraduate Mathematics and Biology and by grants awarded by USDA Forest Service.

Prospective graduate students are encouraged to contact me directly. I can supervise students pursuing M.S. and Ph.D. degrees in mathematics and statistics as well as M.S. and Ph.D. in environmental sciences. WSU also offers an individual interdisciplinary doctoral degree program (IIDP) that is especially suitable for students with unique research interests across disciplines, in particular who would like to work at the interface of mathematics, computer sciences and biology.

Prospective postdoctoral researchers can also be contact me directly though such openings are occasional, depending on grant availability.


Research approach: complex adaptive systems

My research approach is to consider biological populations and communities as complex adaptive systems, which result from self-organization on multiple levels. These levels, or scales, include: a) genomic and cellular levels, b) organs, c) individual organisms, and d) populations and ecosystem level. In experimental biology it is often necessary to concentrate on one focal level of organization, while ignoring processes at the other scales. However, the theoretical framework of complex adaptive systems allows us to study organizational patterns and processes across multiple scales. The mathematics include three major components: 1) the use of individual-based models, as they are among the most suitable and promising tools for simulating complex-adaptive systems and interactions on multiple scales, 2) the development of different scaling methods that approximate individual-based processes, and 3) the investigation of various inverse problems to connect models with empirical data. The first component involves mostly computer simulations of what are, in general, analytically intractable stochastic processes. Scaling methods allow models to be reduced to analytically tractable objects–such as different stochastic and deterministic dynamical systems–which are both more valuable for experimental scientists and, also, computationally simpler. Usually the same scaling method can be presented in several alternative mathematical forms depending on the assumptions concerning time, space and underlying processes. I work with scaling methods that are non-linear partial differential or integral equations in case of continuous models, and non-linear recursive and difference equations in case of discrete models. The mathematical problems that emerge at this stage are quite challenging including analysis of the transient dynamics, stationary states and their stability for non-linear discrete or continuous models. The third component belongs to applied-statistics, and my research involves the study of and the application of regression models, Bayesian methods, and optimal experimental designs.

Forest Modeling

My current primary research project is focused on forest modeling. I am interested in both basic and applied problems such as forest biocomplexity, effects of environmental stressors and different land use practices on forests and sustainable management of forested ecosystems. I have recently developed a model, called Matreshka (after the Russian nesting doll), for scaling of vegetation dynamics from individual-level to the landscape level through the ecosystem hierarchical structure (Strigul 2012). The Matreshka model employs the Perfect Plasticity Approximation (PPA) model (Strigul et al. 2008) as an intermediate step of scaling from the individual level to the forest stand-level (or patch-level).  I have also developed a spatially-explicit individual-based model, called LES (after the Russian word for forest). This model includes tree phenotypic plasticity and simultaneous below (root) and above (crown) ground competition for light and water, respectively (Strigul 2012, Lienard and Strigul 2016). A Markov Chain modeling framework for forest stand dynamics is yet another recent original development resulted in several research papers (Strigul et al. 2012, Lienard, Gravel and Strigul 2015, Lienard and Strigul 2016). Finally, another my original idea is the tolerance-based approach for forest modeling. This is currently one if the major research directions in my laboratory. In particular, we have investigated macroscopic patterns of shade-tolerance in North-American forests, which are linked to the mechanisms of forest succession (Lienard, Florescu and Strigul 2015, Lienard and Strigul 2015). We have recently developed the Tolerance Distribution Model (TDM), which allows better understanding and predicting changes of forest distribution in response to environmental stressors such as drought (Lienard, Harrison and Strigul, 2016).

Remote sensing of vegetation: small UAVs and photogrammetry


Selected Publications

This list is selected recent publications, a full list of publications is available on my CV webpage. Papers in pdf format are available under request, please e-mail me.

* – indicates a student or a postdoc under my direct supervision

Lienard* J., Harrison J. & Strigul N.S. 2016. U.S. Forest Response to Projected Climate-Related Stress: a Tolerance Perspective. Global Change Biology, 22(8): 2875–2886

Lienard* J. & Strigul N. 2016. An individual-based forest model links canopy dynamics and shade tolerances along a soil moisture gradient. Royal Society Open Science 3 (2), 150589

Lienard* J. & Strigul N.S. 2016. Modeling of hardwood forest in Quebec under dynamic disturbance regimes: a time‐inhomogeneous Markov chain approach. Journal of Ecology,

Lienard* J., A Vogs, D Gatziolis & N. Strigul 2016. Embedded, real-time UAV control for improved, image-based 3D scene reconstruction. Measurement 81, 264-269

Talluto M., I. Boulangeat, A. Ameztegui, I. Aubin, D. Berteaux, A. Butler, F. Doyon, C. Drever, M. Fortin, T. Franceschini, J. Lienard*, D. McKenney, K.A. Solarik , N. Strigul, W. Thuiller, & D. Gravel 2016. Cross-scale integration of knowledge for predicting species ranges: a metamodeling framework. Global Ecology and Biogeography, 25(2): 238–249.

Gatziolis D., J.F. Lienard*, A. Vogs & N.S. Strigul 2015. 3D tree dimensionality assessment   using photogrammetry and small unmanned aerial vehicles. PLOS One, 10 (9): e0137765

Lienard* J., Gravel D. & Strigul N. 2015. Data-intensive multidimensional modeling of forest dynamics.  Environmental modelling and software, 67:138-148.

Lienard* J., Florescu I., & Strigul N. 2015. An appraisal of the classic forest succession paradigm with the shade tolerance index. PLOS One, 10(2): e0117138.

Lienard* J.  &  Strigul N. 2015. Linking forest shade tolerance and soil moisture in North America. Ecological Indicators, 58: 332-334.

Strigul N.S. 2012. Individual-based models and scaling methods for ecological forestry: implications of tree phenotypic plasticity. Sustainable Forest Management, Diez, J.J. (Ed.), InTech, 359-384.

Strigul N.S., I. Florescu, A.R. Welden* & F. Michalczewski* 2012. Modeling of forest stand dynamics using Markov chains. Environmental Modeling and Software,  31:64-75.

Vaccari D.A. & Strigul N.S. 2011. Extrapolating phosphorus production to estimate resource reserves. Chemosphere, 84(6): 792-797.

Strigul N.S. 2010. Does speciation matter for tungsten ecotoxicology? Ecotoxicology and Environmental Safety, 73(6): 1099-1113.

Strigul N.S., A. Koutsospyros, and C. Christodoulatos. 2010. Tungsten speciation and toxicity: acute toxicity of mono- and poly- tungstates to fish. Ecotoxicology and Environmental Safety, 73(2):164-171.

Strigul N.S.. 2009. Can Imitation Explain Dialect Origins? Ecological Modelling, 220(20): 2624-2639.

Strigul N.S., H. Dette, and V.B. Melas. 2009. A Practical Guide for Optimal Designs of Experiments in the Monod Model. Environmental Modeling and Software, 24(9):1019-1026.

Strigul N.S., C. Galdun*, L. Vaccari*, T. Ryan*, W.J. Braida, and C. Christodoulatos. 2009. Influence of Speciation on Tungsten Toxicity. Desalination, 248 (1-3): 869-879.

Strigul N.S., A. Koutsospyros, and C. Christodoulatos. 2009. Tungsten in the Former Soviet Union: Review of Environmental Regulations and Related Research. Land Contamination and Reclamation, 17(1):189-216.

Strigul N.S., L. Vaccari*, C. Galdun*, M. Wazne, F. Xi, C. Christodoulatos, and K. Jasinkiewicz. 2009. Acute Toxicity of Boron, Titanium Oxide, and Aluminum Nanoparticles to Daphnia magna and Vibrio fishery. Desalination, 248(1-3):771-782.

Purves D., J. Lichstein, N.S. Strigul, and S.W. Pacala. 2008. Predicting and Understanding Forest Dynamics Using a Simple Tractable Model. Proceedings of the National Academy of Sciences, 105(44):17018-17022.

Strigul N.S., D. Pristinski, D.Purves, J. Dushoff, and S.W. Pacala. 2008. Scaling from Trees to Forests: Tractable Macroscopic Equations for Forest Dynamics. Ecological Monographs, 78 (4): 523-545.

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