Our EAGLE M.Sc. student Jakob Schwalb-Willmann developed the moveVis package and is now available via CRAN. Beside the animation of movement also the corresponding environmental values can be shown for the whole extent, as cumulative values per individual or across the whole group or just the average value of the last n steps to highlight changes in species-environment relationships. All graphical parameters can be changed by the user via ggplot commands for the map and the statistics graphs. MoveVis will also be covered in the next AniMove course.
Please report any issues to the bug manager at www.movevis.org or post your questions to the AniMove mailing list.
Please also send us (via mail, twitter or FB) your best moveVis animations and we are happy to create a gallery of animations. Please also provide information how to reference the animation (your name, affiliation, email).
The M.Sc. thesis “Can animal movement and remote sensing data help to improve conservation efforts?” by Matthias Biber M.Sc. student within the Global Change Ecology program handed in his thesis. He explored the potential of remote sensing data to explain animal movement patterns and if these linkages can help to improve conservation efforts. He used Zebra as study animal in Southern and Eastern Africa. The second supervisor of his M.Sc. was Prof. Thomas Müller from BIK-F.
Climate and land-use change have a growing influence on the world’s ecosystems, in particular in Africa, and increasingly threaten wildlife. The resulting habitat loss and fragmentation can impede animal movement, which is especially true for migratory species. Ungulate migration has declined in recent years, but its drivers are still unclear. Animal movement and remote sensing data was combined to analyse the influence of various vegetation and water indices on the habitat selection of migratory plains zebras in Botswana’s Ngamiland. The study area experienced a more or less steady state in normalised difference vegetation index (NDVI) over the last 33 years. More than half of the study area was covered by PAs. NDVI increased stronger in PAs compared to areas that were not protected. NDVI was always higher in the Okavango Delta compared to the Makgadikgadi Pans. Although zebras are thought to select for areas with high NDVI, they experienced a lower NDVI in the Makgadikgadi grasslands during wet season. Step selection functions (SSFs) showed that NDVI derived from Landsat as well as NDVI derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) were significant drivers of habitat selection across all individuals. Migration seems to be driven by the high nutritional value of the Makgadikgadi grasslands and not seasonal resource limitation. Landsat imagery was shown to provide different environmental information compared to MODIS data. This highlights not only the importance of NDVI for explaining animal movement, but also the importance of Landsat imagery for monitoring habitat extent and fragmentation. Incorporating the animal’s behavioural state and memory into SSFs will help to improve our ecological understanding of animal movement in the future.
The next AniMove summerschool will be from August 27th to September 9th 2017. This intense 2 weeks course covers how to analyze animal movement data and environmental remote sensing data for ecological applications. We will cover again the remote sensing and spatial data analysis part and how to combine it with movement tracks. Data access and preprocessing will be taught as well as the derivation of ecological relevant remote sensing products. More details can be found on the course page: 2017 AniMove at MPI
Joe Premier submitted his M.Sc. thesis on “The Lynx Effect: Behaviour of Roe Deer in the Presence of Lynx in a European Forest Ecosystem” within the Global Change Ecology M.Sc. program. He was co-supervised by Marco Heurich from the Bavarian Forest Nationalpark. Predation risk is one of the main drivers of prey behaviour. In this study the behavioural responses of roe deer under the predation risk of lynx were investigated using a combination of spatial analysis and statistical analyses. Evidence for the hypothesis that roe deer exhibit avoidance behaviour to lynx locations both spatially and temporally could not be found, however the upper limit of avoidance behaviour was constrained to within 4 hours. It was hypothesised that the activity level of roe deer was driven by proximity to lynx, with activity levels increasing with decreasing separation distance. The activity level of roe deer was in general found not to be strongly driven by the variable distance to lynx. As hypothesised, the activity level of roe was associated with habitat, such that lower activity levels occurred in areas of highest visibility (low cover) and higher activity in lowest visibility (high cover). It was found in general that a LiDAR habitat index was the most important explanatory variable of roe deer activity level. In the specific case of the closest encounters (within 24 hours and 1Km) during the night, lynx’s most active time, activity level of roe deer was found to be elevated compared to less proximate individuals. There is also a suggestion that these roe deer move further than those more distant to lynx. The hypothesis that roe deer select habitats of lower predation risk when close to lynx was partially supported; it was found that roe deer selected lower predation risk areas when closest to lynx (within 24 hours and 1Km) during winter nights and consistently inhabited lower predation risk habitats during summer when compared to winter. Furthermore, it was shown that activity level was lower in high risk habitats as hypothesised. Under the predation risk of an ambush hunter, in this case lynx, it is suggested that roe deer adopt a “business as usual” behaviour, with energy diverted for anti-predator behaviour limited to scenarios of heightened risk. It is believed a near continuous GPS tracking schedule would be required to resolve lethal and non-lethal encounter events and illuminate avoidance behaviour and perception distance further.
The article “Temporal segmentation of animal trajectories informed by habitat use” in Ecosphere is finally out. Most animals live in seasonal environments and experience very different conditions throughout the year. Behavioral strategies like migration, hibernation, and a life cycle adapted to the local seasonality help to cope with fluctuations in environmental conditions. Thus, how an individual utilizes the environment depends both on the current availability of habitat and the behavioral prerequisites of the individual at that time. While the increasing availability and richness of animal movement data has facilitated the development of algorithms that classify behavior by movement geometry, changes in the environmental correlates of animal movement have so far not been exploited for a behavioral annotation. Here, we suggest a method that uses these changes in individual–environment associations to divide animal location data into segments of higher ecological coherence, which we term niche segmentation. We use time series of random forest models to evaluate the transferability of habitat use over time to cluster observational data accordingly. We show that our method is able to identify relevant changes in habitat use corresponding to both changes in the availability of habitat and how it was used using simulated data, and apply our method to a tracking data set of common teal (Anas crecca). The niche segmentation proved to be robust, and segmented habitat suitability outperformed models neglecting the temporal dynamics of habitat use. Overall, we show that it is possible to classify animal trajectories based on changes of habitat use similar to geometric segmentation algorithms. We conclude that such an environmentally informed classification of animal trajectories can provide new insights into an individuals’ behavior and enables us to make sensible predictions of how suitable areas might be connected by movement in space and time.
2016. Temporal segmentation of animal trajectories informed by habitat use. Ecosphere 7(10):e01498. 10.1002/ecs2.1498, , , , and .