Animal Movement and Earth Observation Training
AniMove is a collective of international researchers with extensive experience in the topics of animal movement analysis, remote sensing and 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 Max Planck Institute of Animal Behavior, University of Würzburg, German Aerospace Center, Smithsonian, INRAe and Senckenberg (BIK-F).
Intense training activity and networking opportunity
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.
AniMove 2024 will be held in Radolfzell, Germany, from the 17th until the 28th of June 2024
We have received a larger number of applications for AniMove 2023 and are now in the reviewing process. Thank you for your patience. We will be in touch in early April.
As part of our eLearning programme, Animove 2022 lectures were recorded and are available to stream free of charge on this website.
Improve your analytical skills
learn new methods, new (R) packages, applications of Earth Observation and Animal Movement
Requirements for application
- Letter of interest including your expectations towards the course
- Your CV
- A description of your data and research questions that you hope to address during Animove
People with advanced to very advanced programming skill level in R.
PhD, PostDoc, Professionals
Anne Scharf is a postdoc in the Animal-Environment Interactions Lab at the Max-Planck Institute of Animal Behavior.
Through the analysis of movement data, she aims to get a better understanding of how animals interact with their environment and are affected by its changes over time. She mostly works with GPS and acceleration data.
Jakob Schwalb-Willmann is a researcher and lecturer at the University of Würzburg with an academic background in Earth observation, spatial data science and Geography. His research focuses on the machine-learning-driven analysis and exploitation of integrated animal movement tracking and remote sensing data for geoanalytical applications. He has extensive experience in using and developing Open Source software tools for the analysis of satellite imagery and geo-spatial data.
Michael Noonan is a quantitative ecologist with more than a decade of research experience across 3 countries and 5 institutions. He leads the Quantitative Ecology Lab at the University of British Columbia, Okanagan, which is focused on the statistically efficient integration of ecological data into evidence-based conservation. The lab’s work is structured around two separate, but complementary, lines of research. The first falls under an umbrella termed ‘Biology or bias’, and is aimed at developing novel statistical methods, understanding when/why different analytical approaches lead to differing conclusions, and how to avoid estimation bias. The over-arching theme of this work is to demonstrate how the use of biased estimators and/or incorrect statistical procedures can generate misinformative results that weaken both ecological theory and evidence-based conservation initiatives. The second focuses on macro-ecology and species conservation by pairing high quality data with cutting edge analytical tools.
Martina Scacco is a postdoc in the Animal-Environment Interactions lab at the Max-Planck Institute of Animal Behavior.
She is particularly interested in how, and to what extent, the environment affects the movement patterns of different species and their cost of transport through the landscape.
Through studying large-scale movements of soaring birds who are dependent on the support of atmospheric uplifts, she is able to compare the interplay of flight behavior, energy expenditure and environment across different species, to evaluate to what extent different morphologies can define their degree of dependence on the landscape and potentially their differential sensitivity to changes in the environment.
Thomas Müller studies theoretical and applied aspects of movement- and wildlife ecology, from the behavioral underpinnings and social interactions to ecosystem functions and macro-ecological patterns. He is particularly interested in understanding the interactions between moving animals and their environment and the exceptional challenges that an increasing human footprint poses for movements of wildlife, which ultimately leads to the question of human-wildlife coexistence.
Chloe Bracis is a postdoc in the TIMC/MAGe group at Université Grenoble Alpes. She works on modeling a diverse range of biological processes from infectious diseases to animal movement. In movement ecology her research has focused on how animals make decisions about where to move and then carry out these movements, including questions related to cognition, migration, and territoriality, using simulation models across a range of scales and contexts. She has also worked on movement path segmentation and analyzing a trajectory for recursion, or revisits to particular areas.
Kamran Safi is a group leader at the Max Planck Institute for Animal Behavior. His research interests lie in understanding the causes and consequences of biological patterns at various scales and from different perspectives, as well as movement ecology, macro-ecology and macro-evolution. In movement ecology. Kami is interested in relating individual animals to the environmental conditions they operate under to learn the causes and consequences of environmental fluctuation on animal movement across scales. Methodologically he combines and fuses data from the wild, using a wide range of sensors deployed on animals, with remote sensing and other sources of information at large spatial and temporal scales.
Inês Silva is a postdoctoral researcher in the Earth System Science group at the Center for Advanced Systems Understanding (CASUS), located in Görlitz,
Germany. She has a background spanning animal movement, road ecology and community ecology projects in Southeast Asia and South America. She is particularly interested in how animal movement is influenced by anthropogenic impacts, such as animal-road interactions and wildlife-vehicle collisions, study design and facilitating the uptake of new methods in movement ecology.
Christen Fleming develops statistical models and software for animal tracking data and is the lead developer of the continuous-time movement modeling (ctmm) R package. He is an Associate Research Scientist with the University of Maryland and a Research Associate with the Smithsonian Conservation Biology Institute. He obtained his doctorate in physics from the University of Maryland and his baccalaureate in physics, mathematics and statistics from the University of South Alabama.
Björn Reineking is a quantitative ecologist interested in how environmental conditions shape ecological communities and their dynamics. In movement ecology, he is working on methods such as step selection functions to infer habitat selection from movement tracks. Björn is a researcher at INRAE and based in Grenoble.
Tal Avgar (he/him) is a quantitative wildlife ecologist with expertise in space-use (movement and habitat selection) ecology, wildlife population biology, animal conservation, consumer-resource interactions, and ecological modeling and biometry. Tal received his PhD from the University of Guelph where he studied the spatial and cognitive ecology of woodland caribou. Tal had since studied a variety of wildlife species in a variety of systems across North America. In September 2022, Tal has left his tenure-track position at Utah State University to join the Wildlife Science Center (Biodiversity Pathways Ltd.) as a senior research scientist, and is now based in Kelowna, BC.
Hannah Williams is a group leader at Konstanz University affiliated with the Centre for the Advanced Study of Collective Behaviour and the Max Planck Institute for Animal Behavior. Her research interests lie in understanding the connection between social information use and movement energetics at sub-second scales where movement decisions are made up to larger scale patterns in space-use. She focuses on soaring flight where this relationship is arguably most pronounced, and uses different study systems from paragliding to the largest of soaring birds, to explore individual and collective sensory perception of environmental energy, decision-making in multi-dimensional landscapes, and the effect on collective dynamics. She is an expert in inertial measurement data and combines bio-logging methods with virtual reality experiments and simulation to quantify the value of social information in movement.
Stefano Mezzini is a PhD student under the supervision of Dr. Michael Noonan at the University of British Columbia Okanagan. His thesis aims to understand how animals respond to stochastic changes in resources, with a focus on how terrestrial mammals’ spatial needs are affected by resource abundance and stochasticity. Stefano has experience in statistical modeling, animal movement, spatial ecology, paleolimnology, and lake ice phenology.