AniMove is a collective of international researchers with extensive experience in the topics of animal movement analysis, remote sensing and/or conservation. AniMove is a two-week intensive training course for studying animal movement in conjunction with environmental parameters derived from remote sensing for conservation application. AniMove is a non-profit training initiative run by volunteers of various organisations such as MPI, JMU, SCBI or BIK-F.
Our story – or the history of AniMove
The idea of merging the teaching and research activities of animal movement analysis, species distribution modelling, and remote sensing for conservation and biodiversity research was discussed often before the launch of AniMove. The actual starting point was a friday evening socialising event at MPI for Ornithologie in 2012 with the director Martin Wikelski, Kamran Safi, Nathalie Pettorelli and Martin Wegmann, where the outline of AniMove was set. Since then several colleagues joint AniMove, such as Peter Leimgruber and Justin Calabrese (Smithsonian Conservation Biology Institute) who organised also the AniMove 2014, Thomas Müller and Chloe Bracis who organised the AniMove 2016 in Frankfurt. Peter, Martin and Ned Horning are also involved in various remote sensing and GIS training in developing countries applying the same philosophy as in AniMove.
During the planning of the 2013 and 2014 AniMove further colleagues joint us with their specific expertise, such as Chris Flemming (SCBI), Ned Horning (AMNH), Björn Reineking (IRSTEA), Georg Wittemyer (Uni. Colorado), Eli Gurarie (Uni. Washington), Bart Kranstauber (MPI-O), Benjamin Leutner (Uni. Würzburg), Mirjana Bevanda (Uni. Bayreuth) and others. See our member page for some more details of our core group.
Moreover various joint MSc and PhD students as well as joint projects and various teaching activities do exist within this collective.
image credits: mongolian gazelle (Thomas Müller) – all other pictures and webpage design: Martin Wegmann – satellite images: visibleearth.nasa.gov
AniMove is aiming at providing theory and practical approaches to use animal movement, modelling and remote sensing for Biodiversity research and Conservation. Only OpenSource software will be used and as far as possible also only OpenAccess data. All members of AniMove have long experiences in teaching these topics and are highly committed to support and develop interdisciplinary collaboration in order to provide valuable insights for conservation application and biodiversity research. Knowledge about applying remote sensing and GIS within Biodiversity research and Conservation application will be taught using OpenSource software only (R, GRASS, QGIS), moreover the basic and advanced skills to handle remote sensing and GIS data plus developing new ecological relevant data sets are covered as well. Spatial modelling techniques (SDMs, Species Distribution Models, using e.g. RandomForest, GLM, GAM or MaxEnt) are covered by AniMove as well, however the main focus will be on analysing animal movement patterns in conjunction with spatial environmental data sets using e.g. step selection function, BCPA, BB. All these technical expertise are embedded in conservation frameworks in order to ensure the real world applicability.
If you are interested in a Summer School in your region, a course at your institute in order to gain valuable information how to get started and going beyond that, please contact us. We have already started to plan related courses and Summer Schools in upcoming years – please check this page again for updates. topics of these courses are:
- theory and practice of AniMove topics
- remote sensing (different sensors, methods (VI, classification)
- GIS analysis (data formats, spatial data handling)
- specific GIS and Remote Sensing training is aimed at by EcoSens.org
- species distribution models (SDMs: GLM, RF, MaxEnt, GAM etc.)
- animal movement analysis (BCPA etc.)
using only OpenSource software such as R and GRASS:
- introduction to R
- introduction to GRASS
- spatial data handling in R
- remote sensing and GIS with GRASS
- landcover classification, fCover, landcover change with R
- spatial statistics and modelling with R
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