At the end of this page, you can find the full list of publications and patents.
Which R package should I use for species distribution modeling? The answer is many of them, together. We assessed the wide variety of R packages for species distribution models and highlighted how they can work together to improve methodological standards and diversify analyses. We also introduce the new R package ‘sdmverse’ to catalog metadata for packages and visualize their relationships.
Kass, J. M., Smith, A. B., Warren, D. L., Vignali, S., Schmitt, S., Aiello-Lammens, M. E., Arlé, E., Márcia Barbosa, A., Broennimann, O., Cobos, M. E., Guéguen, M., Guisan, A., Merow, C., Naimi, B., Nobis, M. P., Ondo, I., Osorio-Olvera, L., Owens, H. L., Pinilla-Buitrago, G. E., Sánchez-Tapia, A., Thuiller, W., Valavi, R., Velazco, S. J. E., Zizka, A., Zurell, D.
Press Releases: Tohoku University, Tohoku University Graduate School of Life Sciences
We explain how the latest advancements in biodiversity modeling can improve predictions related to nature’s contributions to people. Includes examples of potential implementations and discusses implications for conservation and policy.
Kass, J. M., Fukaya, K., Thuiller, W., Mori, A. S.
Trends in Ecology and Evolution, 39(4), 338–348 (2024)
Press Releases: Tohoku University, Tohoku University Graduate School of Life Sciences
Using two years of biweekly ant community surveys for 24 sites spanning an historical land-use gradient on Okinawa Island, we found that the seasonal activity patterns of ant communities were increasingly diminished as forest shifted to urban and agricultural land-use.
Kass, J. M., Yoshimura, M., Ogasawara, M., Suwabe, M., Hita Garcia, F., Fischer, G., Dudley, K. L., Donohue, I., & Economo, E. P.
Proceedings of the Royal Society B: Biological Sciences, 290, 20231185
Press Release: OIST
We employed an extensive ant occurrence database and species distribution modeling to map global patterns of ant biodiversity at relatively high resolution. We also estimated areas of unknown diversity using machine learning that highlight regions for future sampling to discover new species.
Kass, J. M., Guénard, B., Dudley, K. L., Jenkins, C. N., Azuma, F., Fisher, B. L., Parr, C. L., Gibb, H., Longino, J. T., Ward, P. S., Chao, A., Lubertazzi, D., Weiser, M., Jetz, W., Guralnick, R., Blatrix, R., Lauriers, J. D., Donoso, D. A., Georgiadis, C., Gomez, K., Hawkes, P. G., Johnson, R. A., Lattke, J. E., MacGown, J. A., MacKay, W., Robson, S., Sanders, N. J., Dunn, R. R., & Economo, E. P.
Press Release: OIST