@article{vanToorNewmanTakekawaetal.2016, author = {van Toor, Mari{\"e}lle L. and Newman, Scott H. and Takekawa, John Y. and Wegmann, Martin and Safi, Kamran}, title = {Temporal segmentation of animal trajectories informed by habitat use}, series = {Ecosphere}, volume = {7}, journal = {Ecosphere}, number = {10}, doi = {10.1002/ecs2.1498}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-164970}, pages = {e01498}, year = {2016}, abstract = {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.}, language = {en} } @article{RemelgadoSafiWegmann2020, author = {Remelgado, Ruben and Safi, Kamran and Wegmann, Martin}, title = {From ecology to remote sensing: using animals to map land cover}, series = {Remote Sensing in Ecology and Conservation}, volume = {6}, journal = {Remote Sensing in Ecology and Conservation}, number = {1}, doi = {10.1002/rse2.126}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-225200}, pages = {93-104}, year = {2020}, abstract = {Land cover is a key variable in monitoring applications and new processing technologies made deriving this information easier. Yet, classification algorithms remain dependent on samples collected on the field and field campaigns are limited by financial, infrastructural and political boundaries. Here, animal tracking data could be an asset. Looking at the land cover dependencies of animal behaviour, we can obtain land cover samples over places that are difficult to access. Following this premise, we evaluated the potential of animal movement data to map land cover. Specifically, we used 13 White Storks (Cicona cicona) individuals of the same population to map agriculture within three test regions distributed along their migratory track. The White Stork has adapted to foraging over agricultural lands, making it an ideal source of samples to map this land use. We applied a presence-absence modelling approach over a Normalized Difference Vegetation Index (NDVI) time series and validated our classifications, with high-resolution land cover information. Our results suggest White Stork movement is useful to map agriculture, however, we identified some limitations. We achieved high accuracies (F1-scores > 0.8) for two test regions, but observed poor results over one region. This can be explained by differences in land management practices. The animals preferred agriculture in every test region, but our data showed a biased distribution of training samples between irrigated and non-irrigated land. When both options occurred, the animals disregarded non-irrigated land leading to its misclassification as non-agriculture. Additionally, we found difference between the GPS observation dates and the harvest times for non-irrigated crops. Given the White Stork takes advantage of managed land to search for prey, the inactivity of these fields was the likely culprit of their underrepresentation. Including more species attracted to agriculture - with other land-use dependencies and observation times - can contribute to better results in similar applications.}, language = {en} } @article{Schwalb‐WillmannRemelgadoSafietal.2020, author = {Schwalb-Willmann, Jakob and Remelgado, Ruben and Safi, Kamran and Wegmann, Martin}, title = {moveVis: Animating movement trajectories in synchronicity with static or temporally dynamic environmental data in R}, series = {Methods in Ecology and Evolution}, volume = {11}, journal = {Methods in Ecology and Evolution}, number = {5}, doi = {10.1111/2041-210X.13374}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-214856}, pages = {664 -- 669}, year = {2020}, abstract = {Visualizing movement data is challenging: While traditional spatial data can be sufficiently displayed as two-dimensional plots or maps, movement trajectories require the representation of time in a third dimension. To address this, we present moveVis, an R package, which provides tools to animate movement trajectories, overlaying simultaneous uni- or multi-temporal raster imagery or vector data. moveVis automates the processing of movement and environmental data to turn such into an animation. This includes (a) the regularization of movement trajectories enforcing uniform time instances and intervals across all trajectories, (b) the frame-wise mapping of movement trajectories onto temporally static or dynamic environmental layers, (c) the addition of customizations, for example, map elements or colour scales and (d) the rendering of frames into an animation encoded as GIF or video file. moveVis is designed to display interactions and concurrencies of animal movement and environmental data. We present examples and use cases, ranging from data exploration to visualizing scientific findings. Static spatial plots of movement data disregard the temporal dimension that distinguishes movement from other spatial data. In contrast, animations allow to display relocation in both time and space. We deem animations a powerful way to visually explore movement data, frame analytical findings and display potential interactions with spatially continuous and temporally dynamic environmental covariates.}, language = {en} } @article{RemelgadoLeutnerSafietal.2018, author = {Remelgado, Ruben and Leutner, Benjamin and Safi, Kamran and Sonnenschein, Ruth and Kuebert, Carina and Wegmann, Martin}, title = {Linking animal movement and remote sensing - mapping resource suitability from a remote sensing perspective}, series = {Remote Sensing in Ecology and Conservation}, volume = {4}, journal = {Remote Sensing in Ecology and Conservation}, number = {3}, doi = {10.1002/rse2.70}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-225199}, pages = {211-224}, year = {2018}, abstract = {Optical remote sensing is an important tool in the study of animal behavior providing ecologists with the means to understand species-environment interactions in combination with animal movement data. However, differences in spatial and temporal resolution between movement and remote sensing data limit their direct assimilation. In this context, we built a data-driven framework to map resource suitability that addresses these differences as well as the limitations of satellite imagery. It combines seasonal composites of multiyear surface reflectances and optimized presence and absence samples acquired with animal movement data within a cross-validation modeling scheme. Moreover, it responds to dynamic, site-specific environmental conditions making it applicable to contrasting landscapes. We tested this framework using five populations of White Storks (Ciconia ciconia) to model resource suitability related to foraging achieving accuracies from 0.40 to 0.94 for presences and 0.66 to 0.93 for absences. These results were influenced by the temporal composition of the seasonal reflectances indicated by the lower accuracies associated with higher day differences in relation to the target dates. Additionally, population differences in resource selection influenced our results marked by the negative relationship between the model accuracies and the variability of the surface reflectances associated with the presence samples. Our modeling approach spatially splits presences between training and validation. As a result, when these represent different and unique resources, we face a negative bias during validation. Despite these inaccuracies, our framework offers an important basis to analyze species-environment interactions. As it standardizes site-dependent behavioral and environmental characteristics, it can be used in the comparison of intra- and interspecies environmental requirements and improves the analysis of resource selection along migratory paths. Moreover, due to its sensitivity to differences in resource selection, our approach can contribute toward a better understanding of species requirements.}, language = {en} }