new publication on interdisciplinary training of remote sensing and movement

Some colleagues also partly related to the ongoing AniMove activities published a great article on “Bridging disciplines with training in remote sensing for animal movement: an attendee perspective”. From the abstract: Remote sensing and animal movement datasets are increasingly used to answer key questions in ecology and conservation. Collecting and accessing this data is becoming ever cheaper and easier, but limited analytical expertise limits its wider use. Working at the interface between these two disciplines is challenging as there are no standard techniques for handling the complex spatial data, so specific and in-depth training is required. Higher education programs rarely cover remote sensing for animal movement, so external courses play a major role in training newcomers and creating a more unified global community. We conducted an online survey to investigate the views of previous attendees of four training courses that involve remote sensing and animal location data. These courses provided subject-specific knowledge, practical and coding skills, networking, collaboration opportunities, insightful discussions and transferable research skills. Our survey highlighted the importance of real-world examples, practical sessions, time for participants to work with their own data, preparatory material and open source software. Despite the value of interdisciplinary training in remote sensing and animal movement, it reaches few ecology and conservation practitioners outside of academia. We advocate more funding for underrepresented participants to attend existing course and the development of new courses.

Clark, B. L., Bevanda, M., Aspillaga, E. and Jørgensen, N. H. (2016), Bridging disciplines with training in remote sensing for animal movement: an attendee perspective. Remote Sens Ecol Conserv. doi:10.1002/rse2.22

preview of the content of the book “Analysing and Mapping of Animal Movement in R”

the first outline of the content of the book  “Analysing and Mapping of Animal Movement in R” by Kamran Safi and Bart Kranstauber can be checked below. The book will provide theoretical background to animal movement analysis as well as practical hands-on exercises how to analyse the data using R.


  1. Introduction to R
  2.  Methods of data collection and implications for analysis
    1.  Tracking technologies
    2.  Movement in space
    3. Loading movement data
  3. Initial exploration of movement data
    1. mapping
    2. temporal organization of the trajectories
    3. spatial organization of the track
    4. unused locations
  4. Trajectory centered analysis
    1. within track analysis
    2. computer-intense models: simulating walks
    3. Auto-correlation structure of trajectories
    4. Segmentation of trajectories
    5. Track analysis
    6. Consensus paths
  5. Area centered analysis
    1. MCP, kernel, Brownian Bridges: From trajectories to utilization distributions
    2. methods of calculating UD overlap
    3. Statistical approaches in area based analysis
  6. Movement in context
    1. geoinformation (remote sensing, raster and vector data)
    2. contextual annotation of trajectories
    3. raster resolution and UD calculation
    4. compositional analysis
    5. species distribution models in movement analysis
    6. step selection functions
  7. Visualisation of animal movement
    1. general mapping of spatio-temporal data
    2. time-space cubes
    3. plotting of additional information
    4. animations
    5. web applications using shiny
    6. more advances options
  8. The future of animal movement analysis

new book on animal movement

a new interesting book related to AniMove topics just got published by Arild Gautestad.

from the webpage: How to make sense of animal movement and population dynamics, which typically is influenced by effects of spatial memory and multi-scaled space use? Whether you are studying GPS relocations or estimating population abundance, a realistic model depends on realistic assumptions. In this book you are introduced to a biophysical perspective on animal space use. The presentations include more than 100 illustrations, some basic concepts of statistical mechanics and a range of thought-provoking ideas. Step-by-step the book leads you towards a broadened theoretical toolbox for ecological inference. More details here:

special issue on movement ecology

a very interesting special issue compiled by Luca Börger in Animal Ecology is available at

Article covers topics such as:

and more can be accessed in this special issue, some of them free of charge.

article on the GPS craze: six questions to address before deciding to deploy GPS technology on wildlife

Interesting article on GPS techologies for tracking animals, what has to be thought of prior any use of tagging devices. GPS and satellite technology for studies on wildlife have improved substantially over the past decade. It is now possible to collect fine-scale location data from migratory animals, animals that have previously been too small to deploy GPS devices on, and other difficult-to-study species. Often researchers and managers have formatted well-defined ecological or conservation questions prior to deploying GPS on animals, whereas other times it is arguably done simply because the technology is now available to do so. We review and discuss six important interrelated questions that should be addressed when planning a study requiring location data. Answers will clarify whether GPS technology is required and whether its use would increase efficiency of data collection and learning from location data. Specifically, what are the required: (1) ecological question(s); (2) frequency and duration of data collection; (3) sample size; (4) hardware (VHF or GPS or satellite) and accessories; (5) environmental data; and (6) data-management and analysis procedures? This approach increases the chance that the appropriate technology will be deployed, budgets will be realistic, and data will be sufficient (but not excessive) to answer the ecological questions of interest. The expected results are important advances in ecological science and evidence-based management decisions.

A. David M. Latham,M. Cecilia Latham, Dean P. Anderson, Jen Cruz, Dan Herries, and Mark Hebblewhite, (2015) The GPS craze: six questions to address before deciding to deploy GPS technology on wildlife. New Zealand Journal of Ecology (2015) 39(1): 143-152

article: Global aerial flyways allow efficient travelling

Kranstauber_Ecol_letters_animove_orgA very interesting article by some of our AniMove lecturer: Birds migrate over vast distances at substantial costs. The highly dynamic nature of the air makes the selection of the best travel route difficult. We investigated to what extent migratory birds may optimise migratory route choice with respect to wind, and if route choice can be subject to natural selection. Following the optimal route, calculated using 21 years of empirical global wind data, reduced median travel time by 26.5% compared to the spatially shortest route. When we used a time-dependent survival model to quantify the adaptive benefit of choosing a fixed wind-optimised route, 84.8% of pairs of locations yielded a route with a higher survival than the shortest route. This suggests that birds, even if incapable of predicting wind individually, could adjust their migratory routes at a population level. As a consequence, this may result in the emergence of low-cost flyways representing a global network of aerial migratory pathways.

Kranstauber, Weinzierl, Wikelski and Safi (2015) Global aerial flyways allow efficient travelling. Ecology Letters, Volume 18, Issue 12, 1338–1345


article on the opportunities of remote sensing in movement

an interesting article published in Movement Ecology covers the opportunities for the application of advanced remotely-sensed data in ecological studies of terrestrial animal movement. Animal movement patterns in space and time are a central aspect of animal ecology. Remotely-sensed environmental indices can play a key role in understanding movement patterns by providing contiguous, relatively fine-scale data that link animal movements to their environment. Still, implementation of newly available remotely-sensed data is often delayed in studies of animal movement, calling for a better flow of information to researchers less familiar with remotely-sensed data applications. Here, we reviewed the application of remotely-sensed environmental indices to infer movement patterns of animals in terrestrial systems in studies published between 2002 and 2013. Next, we introduced newly available remotely-sensed products, and discussed their opportunities for animal movement studies. Studies of coarse-scale movement mostly relied on satellite data representing plant phenology or climate and weather. Studies of small-scale movement frequently used land cover data based on Landsat imagery or aerial photographs. Greater documentation of the type and resolution of remotely-sensed products in ecological movement studies would enhance their usefulness. Recent advancements in remote sensing technology improve assessments of temporal dynamics of landscapes and the three-dimensional structures of habitats, enabling near real-time environmental assessment. Online movement databases that now integrate remotely-sensed data facilitate access to remotely-sensed products for movement ecologists. We recommend that animal movement studies incorporate remotely-sensed products that provide time series of environmental response variables. This would facilitate wildlife management and conservation efforts, as well as the predictive ability of movement analyses. Closer collaboration between ecologists and remote sensing experts could considerably alleviate the implementation gap. Ecologists should not expect that indices derived from remotely-sensed data will be directly analogous to field-collected data and need to critically consider which remotely-sensed product is best suited for a given analysis.

article on home range estimation

Quantifying animals’ home ranges is a key problem in ecology and has important conservation and wildlife management applications. Kernel density estimation (KDE) is a workhorse technique for range delineation problems that is both statistically efficient and nonparametric. KDE assumes that the data are independent and identicallydistributed (IID). However, animal tracking data, which are routinely used as inputs to KDEs,
are inherently autocorrelated and violate this key assumption. As we demonstrate, using realistically autocorrelated data in conventional KDEs results in grossly underestimated home ranges. We further show that the performance of conventional KDEs actually degrades as data quality improves, because autocorrelation strength increases as movement paths become more finely resolved. To remedy these flaws with the traditional KDE method, we derive an autocorrelated KDE (AKDE) from first principles to use autocorrelated data, making it perfectly suited for movement data sets. We illustrate the vastly improved performance of AKDE using analytical arguments, relocation data from Mongolian gazelles, and simulations based upon the gazelle’s observed movement process. By yielding better minimum area estimates for threatened wildlife populations, we believe that future widespread use of AKDE will have significant impact on ecology and conservation biology.

book “Remote Sensing and GIS for Ecologists” soon available

More details how to order and the expected publishing date can be found at and

News and updates about the book will be posted on the books’ news page, as well as updates of related software packages.Remote_Sensing_GIS_Ecology_book_preorderbook_Remote_Sensing_GIS_Ecology_Wegmann_Leutner_Dech_EcoSens_org_01

Publications and R package

Some interesting publications and R packages which might be good to check for your work:

How do we properly estimate the home range of an animal, given that tracking data are necessarily autocorrelated?
C. H. Fleming, W. F. Fagan, T. Mueller, K. A. Olson, P. Leimgruber, J. M. Calabrese, “Rigorous home-range estimation with movement data: A new autocorrelated kernel-density estimator”, Ecology, accepted for publication (2015)

What is the general framework to estimating autocorrelation in animal tracking data?
C. H. Fleming, J. M. Calabrese, T. Mueller, K. A. Olson, P. Leimgruber, W. F. Fagan, “Non-Markovian maximum likelihood estimation of autocorrelated movement processes”, Methods in Ecology and Evolution 5, 462-472 (2014)

What is the best way to visualize the autocorrelation structure in animal tracking data?
C. H. Fleming, J. M. Calabrese, T. Mueller, K. A. Olson, P. Leimgruber, W. F. Fagan, “From fine-scale foraging to home ranges: A semi-variance approach to identifying movement modes across spatiotemporal scales”, The American Naturalist 183, E154-E167 (2013)

more information at: