@article{banks_banks_myers_laubmeier_bommarco_2020, title={Lethal and sublethal effects of toxicants on bumble bee populations: a modelling approach}, volume={29}, ISSN={["1573-3017"]}, DOI={10.1007/s10646-020-02162-y}, abstractNote={Abstract}, number={3}, journal={ECOTOXICOLOGY}, author={Banks, J. E. and Banks, H. T. and Myers, N. and Laubmeier, A. N. and Bommarco, R.}, year={2020}, month={Apr}, pages={237–245} }
 @article{stojsavljevic_pinter_lauko_myers_2019, title={PARAMETER IDENTIFICATION AND SENSITIVITY ANALYSIS FOR A PHYTOPLANKTON COMPETITION MODEL}, volume={77}, ISSN={["1552-4485"]}, DOI={10.1090/qam/1514}, abstractNote={Phytoplankton live in a complex environment with two essential resources, light and nutrients, forming various gradients. Light supplied from above is never homogeneously distributed in a body of water due to refraction and absorption from biomass present in the ecosystem and other sources. Nutrients in turn are typically supplied from below mixed-up by diffusion from the benthic region. Here we present a model of two phytoplankton species competing in a deep freshwater lake for light and two nutrients, one of which is assumed to be preferred. The model is comprised of a system of non- linear, non-local partial differential equations with appropriate boundary conditions. The parameter space of the model is analyzed for parameter identifiability - the ability for a parameter’s true value to be recovered through optimization, and for global sensitivity - the influence a parameter has on model response. The results of these analyses are interpreted within their biological context.}, number={1}, journal={QUARTERLY OF APPLIED MATHEMATICS}, author={Stojsavljevic, Thomas and Pinter, Gabriella and Lauko, Istvan and Myers, Nicholas}, year={2019}, month={Mar}, pages={1–18} }
 @article{banks_baraldi_catenacci_myers_2016, title={Parameter Estimation Using Unidentified Individual Data in Individual Based Models}, volume={11}, ISSN={["1760-6101"]}, DOI={10.1051/mmnp/201611602}, abstractNote={In physiological experiments, it is common for measurements to be collected from multiple subjects. Often it is the case that a subject cannot be measured or identified at multiple time points (referred to as unidentified individual data in this work but often referred to as aggregate population data [5, Chapter 5]). Due to a lack of alternative methods, this form of data is typically treated as if it is collected from a single individual. This assumption leads to an overconfidence in model parameter values and model based predictions. We propose a novel method which accounts for inter-individual variability in experiments where only unidentified individual data is available. Both parametric and nonparametric methods for estimating the distribution of parameters which vary among individuals are developed. These methods are illustrated using both simulated data, and data taken from a physiological experiment. Taking the approach outlined in this paper results in more accurate quantification of the uncertainty attributed to inter-individual variability.}, number={6}, journal={MATHEMATICAL MODELLING OF NATURAL PHENOMENA}, author={Banks, H. T. and Baraldi, R. and Catenacci, J. and Myers, N.}, year={2016}, pages={9–27} }