Alison Johnston
Research Associate
Expertise
Statistics • Quantitative Ecology • Citizen Science
I develop statistical methods that help us learn more about the natural world. At the Cornell Lab I analyze data from the eBird project, developing analytical approaches that uncover patterns in the data and increase our knowledge about birds. eBird is a rich dataset of bird observations contributed by thousands of people around the world. By analyzing it in new ways, we can discover new knowledge about birds. This knowledge can inform conservation decisions, helping us to develop effective and targeted conservation plans.
Education
Ph.D., University of Cambridge, England
B.S., University of St. Andrews, Scotland
Recent Publications
Péron, G., J. M. Calabrese, O. Duriez, C. H. Fleming, R. García-Jiménez, A. Johnston, S. A. Lambertucci, K. Safi, and E. L. C. Shepard (2020). The challenges of estimating the distribution of flight heights from telemetry or altimetry data. Animal Biotelemetry 8:5.
Johnston, A., T. Auer, D. Fink, M. Strimas‐Mackey, M. Iliff, K. V. Rosenberg, S. Brown, R. Lanctot, A. D. Rodewald, and S. Kelling (2020). Comparing abundance distributions and range maps in spatial conservation planning for migratory species. Ecological Applications 30:e02058.
Johnston, A., N. Moran, A. Musgrove, D. Fink, and S. R. Baillie (2020). Estimating species distributions from spatially biased citizen science data. Ecological Modelling 422:108927.
Fink, D., T. Auer, A. Johnston, V. Ruiz‐Gutierrez, W. M. Hochachka, and S. Kelling (2020). Modeling avian full annual cycle distribution and population trends with citizen science data. Ecological Applications 30:e02056.
Diana, A., E. Matechou, J. Griffin, and A. Johnston (2020). A hierarchical dependent Dirichlet process prior for modelling bird migration patterns in the UK. The Annals of Applied Statistics 14:473–493.
Cooke, S. C., A. Balmford, A. Johnston, D. Massimino, S. E. Newson, and P. F. Donald (2020). [In Press] Road exposure and the detectability of birds in field surveys. Ibis.
Péron, G., J. M. Calabrese, O. Duriez, C. H. Fleming, R. García-Jiménez, A. Johnston, S. Lambertucci, K. Safi, and E. L. C. Shepard (2019). The challenges of estimating the distribution of flight heights from telemetry or altimetry data. bioRxiv. https://doi.org/10.1101/751271
Schuetz, J. G., and A. Johnston (2019). Characterizing the cultural niches of North American birds. Proceedings of the National Academy of Sciences 116:10868–10873.
Wauchope, H., T. Amano, W. Sutherland, and A. Johnston (2019). When can we trust population trends? A method for quantifying the effects of sampling interval and duration. bioRxiv. https://doi.org/10.1101/498170
Johnston, A., W. Hochachka, M. Strimas-Mackey, V. R. Gutierrez, O. Robinson, E. Miller, T. Auer, S. Kelling, and D. Fink (2019). Best practices for making reliable inferences from citizen science data: Case study using eBird to estimate species distributions. bioRxiv. https://doi.org/10.1101/574392
Kelling, S., A. Johnston, A. Bonn, D. Fink, V. Ruiz-Gutierrez, R. Bonney, M. Fernandez, W. M. Hochachka, R. Julliard, R. Kraemer, and R. Guralnick (2019). Using Semistructured Surveys to Improve Citizen Science Data for Monitoring Biodiversity. BioScience 69:170–179.
Wauchope, H. S., T. Amano, W. J. Sutherland, and A. Johnston (2019). When can we trust population trends? A method for quantifying the effects of sampling interval and duration. Methods in Ecology and Evolution 10:2067–2078.
La Sorte, F. A., D. Fink, and A. Johnston (2019). Time of emergence of novel climates for North American migratory bird populations. Ecography 42:1079–1091.
Wessels, P., N. Moran, A. Johnston, and W. Wang (2019). Hybrid expert ensembles for identifying unreliable data in citizen science. Engineering Applications of Artificial Intelligence 81:200–212.
Simmons, B. I., A. Balmford, A. J. Bladon, A. P. Christie, A. De Palma, L. V. Dicks, J. Gallego‐Zamorano, A. Johnston, P. A. Martin, A. Purvis, R. Rocha, et al. (2019). Worldwide insect declines: An important message, but interpret with caution. Ecology and Evolution 9:3678–3680.
Fink, D., T. Auer, A. Johnston, V. Ruiz-Gutierrez, W. M. Hochachka, and S. Kelling (2019). Modeling avian full annual cycle distribution and population trends with citizen-science data. bioRxiv:251868.
Johnston, A., T. Auer, D. Ardia, M. E. Strimas-Mackey, M. J. Iliff, K. V. Rosenberg, S. Brown, R. Lanctot, A. D. Rodewald, and S. Kelling (2019). [In Press] Performance of abundance distributions and range maps in spatial conservation planning for migratory species. Ecological Applications.
Border, J. A., A. Johnston, and S. Gillings (2018). Can climate matching predict the current and future climatic suitability of the UK for the establishment of non-native birds? Bird Study:1–12.
Hewson, C. M., M. Miller, A. Johnston, G. J. Conway, R. Saunders, J. H. Marchant, and R. J. Fuller (2018). Estimating national population sizes: Methodological challenges and applications illustrated in the common nightingale, a declining songbird in the UK. Journal of Applied Ecology. https://doi.org/10.1111/1365-2664.13120
Eldridge, A., P. Guyot, P. Moscoso, A. Johnston, Y. Eyre-Walker, and M. Peck (2018). Sounding out ecoacoustic metrics: Avian species richness is predicted by acoustic indices in temperate but not tropical habitats. Ecological Indicators 95:939–952.
La Sorte, F. A., D. Fink, and A. Johnston (2018). Seasonal associations with novel climates for North American migratory bird populations. Ecology Letters 21:845–856.
Johnston, A., D. Fink, W. M. Hochachka, and S. Kelling (2018). Estimates of observer expertise improve species distributions from citizen-science data. Methods in Ecology and Evolution 9:88–97.
Hinsley, A., W. J. Sutherland, and A. Johnston (2017). Men ask more questions than women at a scientific conference. PLOS ONE 12:e0185534.
Massimino, D., A. Johnston, S. Gillings, F. Jiguet, and J. W. Pearce-Higgins (2017). Projected reductions in climatic suitability for vulnerable British birds. Climatic Change 145:117–130.
Buckland, S. T., and A. Johnston (2017). Monitoring the biodiversity of regions: Key principles and possible pitfalls. Biological Conservation 214:23–34.
Reynolds, M. D., B. L. Sullivan, E. Hallstein, S. Matsumoto, S. Kelling, M. Merrifield, D. Fink, A. Johnston, W. M. Hochachka, N. E. Bruns, M. E. Reiter, et al. (2017). Dynamic conservation for migratory species. Science Advances 3:e1700707.
Kelling, S., D. Fink, F. A. La Sorte, A. Johnston, N. E. Bruns, and W. M. Hochachka (2015). Taking a “Big Data” approach to data quality in a citizen science project. Ambio 44 Suppl 4:601–611.
Kelling, S., A. Johnston, W. M. Hochachka, M. Iliff, D. Fink, J. Gerbracht, C. Lagoze, F. A. La Sorte, T. Moore, A. Wiggins, W.-K. Wong, et al. (2015). Can observation skills of citizen scientists be estimated using species accumulation curves? PLOS ONE 10:e0139600.
Johnston, A., D. Fink, M. D. Reynolds, W. M. Hochachka, B. L. Sullivan, N. E. Bruns, E. Hallstein, M. S. Merrifield, S. Matsumoto, and S. Kelling (2015). Abundance models improve spatial and temporal prioritization of conservation resources. Ecological Applications 25:1749–1756.
Center
Avian Population Studies, Information Science
Projects
eBird
Email aj327@cornell.edu