Computational Ecology is an interdisciplinary research field that aims at understanding complex ecological and evolutionary processes. The research is based on, preferably, large amounts of empirical data and relies on advanced computational methods, mathematical and statistical models with the aim to better describe and understand what characterises ecological processes. The projects in our lab are embedded in three areas: "movement ecology", "biodiversity and conservation" and "macroevolution".
This video exemplifies some of the work the CE lab is regularly doing to relate movement to the environmental conditions. The line is a track of a migrating gull that was fitted with a GPS-Satellite tag. Remote sensing information was used to reconstruct the landuse coverage that the gull crossed on its way South, as well as a digital elevation model showing the elevation. Finally, a global wind model was used to reconstruct the wind conditions along flight. Can you see how ground speed and wind direction and wind speed are related?
With ,move‘ we created an R package that contains functions to access movement data stored in movebank.org as well as tools to visualize and statistically analyze animal movement data. Move is addressing movement ecological questions complementing existing efforts such as adeHabitat and other packages.
We are happy to announce that together with Chapman & Hall we have taken on the challenge to write a book called "Analysis and Mapping of Animal Movement in R". We will provide in this book the necessary information for visualisations and analysis of animal movement data in R with a direct link to the massive data base movebank.org. We will also elaborate on the contextualisation, so how we can enrich positional information using remote sensing. The aim is to provide everyone with the means to analyse movement data in a reproducible way.
M. Wegmann, L. Santini, B. Leutner, K. Safi, D. Rocchini, M. Bevanda, H. Latifi, S. Dech, C. Rondinini (2014) Role of African Protected Areas in maintaining connectivity for large mammals. Philosophical Transactions of the Royal Society of London, Series B. 369 20130193; doi:10.1098/rstb.2013.0193
B. Kranstauber, K. Safi & F. Bartumeus (2014) Bivariate Gaussian Bridges: bi-directional factorization of diffusion on Browninan Bridge Models to accommodate correlated random walks. BMC Movement Ecology. 2(5): 1-10.
M. My-Y Lam, D. Martin-Creuzburg, K.O. Rothhaupt, K. Safi, E. Yohannes & I. Salvarina (2013) Tracking diet preferences of bats using stable isotopes and fatty acids signatures of faeces. Plos one. 8(12): e83452. doi:10.1371/journal.pone.0083452