Search results

  • Full text

  • Document type

    • Publication year

  • Organisation

Results: 220
Number of items: 220
  • Open Access
    Chittar, C. R., Jang, H., Samuni, L., Lewis, J., Honing, H., van Loon, E. E., & Janmaat, K. R. L. (2023). Music production and its role in coalition signaling during foraging contexts in a hunter-gatherer society. Frontiers in Psychology, 14, Article 1218394. https://doi.org/10.3389/fpsyg.2023.1218394
  • Open Access
    Schaller, C., Ginzler, C., van Loon, E., Moos, C., Seijmonsbergen, A. C., & Dorren, L. (2023). Improving country-wide individual tree detection using local maxima methods based on statistically modeled forest structure information. International Journal of Applied Earth Observation and Geoinformation, 123, Article 103480. https://doi.org/10.1016/j.jag.2023.103480
  • Open Access
    Linssen, H., van Loon, E. E., Shamoun-Baranes, J. Z., Nuijten, R. J. M., & Nolet, B. A. (2023). Migratory swans individually adjust their autumn migration and winter range to a warming climate. Global Change Biology, 29(24), 6888-6899. https://doi.org/10.1111/gcb.16953
  • Open Access
    van Erp, J., Sage, E., Bouten, W., van Loon, E., Camphuysen, K. C. J., & Shamoun-Baranes, J. (2023). Thermal soaring over the North Sea and implications for wind farm interactions. Marine Ecology Progress Series, 723, 185-200. https://doi.org/10.3354/meps14315
  • Open Access
    Li, Q., Steenberg Larsen, K., Kopittke, G., van Loon, E., & Tietema, A. (2023). Long-term temporal patterns in ecosystem carbon flux components and overall balance in a heathland ecosystem. Science of the Total Environment, 875, Article 162658. https://doi.org/10.1016/j.scitotenv.2023.162658
  • Lippert, F., Kranstauber, B., Forré, P., & van Loon, E. E. (2022). Data from: Learning to predict spatio-temporal movement dynamics from weather radar networks [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6874789
  • Lippert, F., Kranstauber, B., Forré, P., & van Loon, E. E. (2022). Data from: Learning to predict spatio-temporal movement dynamics from static sensor networks [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6364941
  • Open Access
    Lippert, F., Kranstauber, B., Forré, P. D., & van Loon, E. E. (2022). Learning to predict spatiotemporal movement dynamics from weather radar networks. Methods in Ecology and Evolution, 13(12), 2811-2826. https://doi.org/10.1111/2041-210X.14007
  • Open Access
    Salvatori, M., De Groeve, J., van Loon, E., De Baets, B., Morellet, N., Focardi, S., Bonnot, N. C., Gehr, B., Griggio, M., Heurich, M., Kroeschel, M., Licoppe, A., Moorcroft, P., Pedrotti, L., Signer, J., Van de Weghe, N., & Cagnacci, F. (2022). Day versus night use of forest by red and roe deer as determined by Corine Land Cover and Copernicus Tree Cover Density: Assessing use of geographic layers in movement ecology. Landscape Ecology, 37(5), 1453–1468. https://doi.org/10.1007/s10980-022-01416-w
  • Open Access
    Lippert, F., Kranstauber, B., van Loon, E. E., & Forré, P. (2022). Physics-informed inference of aerial animal movements from weather radar data. Paper presented at Workshop AI for Science: Progress and Promises, New Orleans, Louisiana, United States. https://doi.org/10.48550/arXiv.2211.04539
Page 5 of 22