2022
[1] E. Baker, P. Barbillon, A. Fadikar, et al. “Analyzing Stochastic Computer Models: A Review with Opportunities”. In: Statistical Science 37.1 (2022), pp. 64 - 89. DOI: 10.1214/21-STS822.
[2] O. C. Villena, S. J. Ryan, C. C. Murdock, et al. “Temperature impacts the transmission of malaria parasites by Anopheles gambiae and Anopheles stephensi mosquitoes”. In: Ecology (In Press) (2022). <URL: https://www.biorxiv.org/content/10.1101/2020.07.08.194472v2.abstract>.
2021
[1] F. El Moustaid, Z. Thornton, H. Slamani, et al. “Predicting temperature-dependent transmission suitability of bluetongue virus in livestock”. In: Parasites & Vectors 14.1 (2021), pp. 1-14.
[2] Z. Gajewski, L. A. Stevenson, D. A. Pike, et al. “Predicting the growth of the amphibian chytrid fungus in varying temperature environments”. In: Ecology and Evolution 11 (2021), pp. 17920-17931.
[3] S. J. Ryan, C. J. Carlson, B. Tesla, et al. “Warming temperatures could expose more than 1.3 billion new people to Zika virus risk by 2050”. In: Global Change Biology 27.1 (2021), pp. 84-93.
[4] B. Zhang, R. B. Gramacy, L. Johnson, et al. “Batch-sequential design and heteroskedastic surrogate modeling for delta smelt conservation”. In: Annals of Applied Statistics (Accepted) (2021). <URL: https://arxiv.org/abs/2010.06515>.
2020
[1] L. J. Cator, L. R. Johnson, E. A. Mordecai, et al. “The role of vector trait variation in vector-borne disease dynamics”. In: Frontiers in Ecology and Evolution 8 (2020), p. 189.
[2] K. Miazgowicz, M. Shocket, S. Ryan, et al. “Age influences the thermal suitability of Plasmodium falciparum transmission in the Asian malaria vector Anopheles stephensi”. In: Proceedings of the Royal Society B 287.1931 (2020), p. 20201093.
[3] M. S. Shocket, A. B. Verwillow, M. G. Numazu, et al. “Transmission of West Nile and five other temperate mosquito-borne viruses peaks at temperatures between 23 C and 26 C”. In: Elife 9 (2020), p. e58511.
2019
[1] S. R. Adapa, R. A. Taylor, C. Wang, et al. “Plasmodium vivax readiness to transmit: implication for malaria eradication”. In: BMC Systems Biology 13.1 (2019), p. 5.
[2] P. H. Boersch-Supan and L. R. Johnson. “Two case studies detailing Bayesian parameter inference for dynamic energy budget models”. In: Journal of Sea Research 143 (2019), pp. 57-69.
[3] S. C. Burgan, S. S. Gervasi, L. R. Johnson, et al. “How individual variation in host tolerance affects competence to transmit parasites”. In: Physiological and Biochemical Zoology 92.1 (2019), pp. 49-57.
[4] L. M. Childs, F. El Moustaid, Z. Gajewski, et al. “Linked within-host and between-host models and data for infectious diseases: a systematic review”. In: PeerJ 7 (2019), p. e7057.
[5] F. El Moustaid and L. R. Johnson. “Modeling Temperature Effects on Population Density of the Dengue Mosquito Aedes aegypti”. In: Insects 10.11 (2019), p. 393.
[6] F. El Moustaid, S. J. Lane, I. T. Moore, et al. “A Mathematical Modeling Approach to The Cort-Fitness Hypothesis”. In: Integrative Organismal Biology 1.1 (2019), p. obz019.
[7] M. A. Johansson, K. M. Apfeldorf, S. Dobson, et al. “An open challenge to advance probabilistic forecasting for dengue epidemics”. In: Proceedings of the National Academy of Sciences 116.48 (2019), pp. 24268-24274.
[8] E. A. Mordecai, J. M. Caldwell, M. K. Grossman, et al. “Thermal biology of mosquito-borne disease”. In: Ecology Letters 22.10 (2019), pp. 1690-1708.
[9] S. J. Ryan, C. J. Carlson, E. A. Mordecai, et al. “Global expansion and redistribution of Aedes-borne virus transmission risk with climate change”. In: PLoS Neglected Tropical Diseases 13.3 (2019), p. e0007213.
[10] R. A. Taylor, S. J. Ryan, C. A. Lippi, et al. “Predicting the fundamental thermal niche of crop pests and diseases in a changing world: a case study on citrus greening”. In: Journal of Applied Ecology 56.8 (2019), pp. 2057-2068.
2018
[1] P. H. Boersch-Supan, L. R. Johnson, R. A. Phillips, et al. “Surface temperatures of albatross eggs and nests”. In: Emu-Austral Ornithology 118.2 (2018), pp. 224-229.
[2] D. J. Civitello, H. Fatima, L. R. Johnson, et al. “Bioenergetic theory predicts infection dynamics of human schistosomes in intermediate host snails across ecological gradients”. In: Ecology Letters 21.5 (2018), pp. 692-701.
[3] L. R. Johnson, R. B. Gramacy, J. Cohen, et al. “Phenomenological forecasting of disease incidence using heteroskedastic Gaussian processes: a dengue case study”. In: The Annals of Applied Statistics 12.1 (2018), pp. 27-66.
2017
[1] L. R. Johnson, P. H. Boersch-Supan, R. A. Phillips, et al. “Changing measurements or changing movements? Sampling scale and movement model identifiability across multiple generations of biologging technology”. In: Ecology and Evolution 7.22 (2017), pp. 9257-9266.
[2] E. A. Mordecai, J. M. Cohen, M. V. Evans, et al. “Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models”. In: PLoS Neglected tTopical Diseases 11.4 (2017), p. e0005568.
[3] J. Voyles, L. R. Johnson, J. Rohr, et al. “Diversity in growth patterns among strains of the lethal fungal pathogen Batrachochytrium dendrobatidis across extended thermal optima”. In: Oecologia 184.2 (2017), pp. 363-373.
2016
[1] P. H. Boersch-Supan, S. J. Ryan, and L. R. Johnson. “deBInfer: Bayesian inference for dynamical models of biological systems in R”. In: Methods in Ecology and Evolution 8.4 (2016), pp. 511-518.
[2] R. A. Taylor, E. Mordecai, C. A. Gilligan, et al. “Mathematical models are a powerful method to understand and control the spread of Huanglongbing”. In: PeerJ 4 (2016), p. e2642.
[3] R. A. Taylor, S. J. Ryan, J. S. Brashares, et al. “Hunting, food subsidies, and mesopredator release: the dynamics of crop-raiding baboons in a managed landscape”. In: Ecology 97.4 (2016), pp. 951-960.
2013 – 2015
[1] L. R. Johnson, T. Ben-Horin, K. D. Lafferty, et al. “Understanding uncertainty in temperature effects on vector-borne disease: a Bayesian approach”. In: Ecology 96.1 (2015), pp. 203-213.
[2] L. R. Johnson, K. D. Lafferty, A. McNally, et al. “Mapping the distribution of malaria: current approaches and future directions”. In: Analyzing and modeling spatial and temporal dynamics of infectious diseases (2014), pp. 189-209.
[3] L. R. Johnson, L. Pecquerie, and R. M. Nisbet. “Bayesian inference for bioenergetic models”. In: Ecology 94.4 (2013), pp. 882-894.
[4] E. A. Mordecai, K. P. Paaijmans, L. R. Johnson, et al. “Optimal temperature for malaria transmission is dramatically lower than previously predicted”. In: Ecology letters 16.1 (2013), pp. 22-30.
[5] S. J. Ryan, T. Ben-Horin, and L. R. Johnson. “Malaria control and senescence: the importance of accounting for the pace and shape of aging in wild mosquitoes”. In: Ecosphere 6.9 (2015), pp. 1-13.
[6] S. J. Ryan, A. McNally, L. Johnson, et al. “Changing physiological suitability limits of malaria transmission in Africa under climate change”. In: Ecol Lett 16 (2014), pp. 22-30.
[7] S. Ryan, A. McNally, L. Johnson, et al. “Mapping Physiological Suitability Limits for Malaria in Africa Under Climate Change”. In: Vector Borne and Zoonotic Diseases 15.12 (2015), pp. 718-725.
[8] J. Voyles, L. R. Johnson, C. J. Briggs, et al. “Experimental evolution alters the rate and temporal pattern of population growth in Batrachochytrium dendrobatidis, a lethal fungal pathogen of amphibians”. In: Ecology and evolution 4.18 (2014), pp. 3633-3641.
2009 – 2012
[1] L. R. Johnson. “Implications of dispersal and life history strategies for the persistence of Linyphiid spider populations”. In: Ecological modelling 221.8 (2010), pp. 1138-1147.
[2] L. R. Johnson. “Microcolony and biofilm formation as a survival strategy for bacteria”. In: Journal of theoretical biology 251.1 (2008), pp. 24-34.
[3] L. R. Johnson and C. J. Briggs. “Parameter inference for an individual based model of chytridiomycosis in frogs”. In: Journal of theoretical biology 277.1 (2011), pp. 90-98.
[4] L. R. Johnson and M. Mangel. “Life histories and the evolution of aging in bacteria and other single-celled organisms”. In: Mechanisms of ageing and development 127.10 (2006), pp. 786-793.
[5] D. Merl, L. R. Johnson, R. B. Gramacy, et al. “A statistical framework for the adaptive management of epidemiological interventions”. In: PloS One 4.6 (2009), p. e5807.
[6] D. Merl, L. R. Johnson, R. B. Gramacy, et al. “amei: an R package for the Adaptive Management of Epidemiological Interventions”. In: Journal of Statistical Software 36.6 (2010), pp. 1-32.
[7] R. M. Nisbet, E. McCauley, and L. R. Johnson. “Dynamic energy budget theory and population ecology: lessons from Daphnia”. In: Philosophical Transactions of the Royal Society B: Biological Sciences 365.1557 (2010), pp. 3541-3552.
[8] L. Pecquerie, L. R. Johnson, S. A. Kooijman, et al. “Analyzing variations in life-history traits of Pacific salmon in the context of Dynamic Energy Budget (DEB) theory”. In: Journal of Sea Research (2011).
[9] J. Voyles, L. R. Johnson, C. J. Briggs, et al. “Temperature alters reproductive life history patterns in Batrachochytrium dendrobatidis, a lethal pathogen associated with the global loss of amphibians”. In: Ecology and evolution 2.9 (2012), pp. 2241-2249.
Pre-Prints and Working Papers
[1] J. W. Smith, L. R. Johnson, and R. Q. Thomas. “Assessing Ecosystem State Space Models: Identifiability and Estimation”. In: arXiv preprint arXiv:2110.08967 (2021).