new article: habitat use and animal movement

new article: habitat use and animal movement

October 24, 2016

vantoor_2016_ecs21498-fig-0001The 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.

van Toor, M. L., S. H. Newman, J. Y. Takekawa, M. Wegmann, and K. Safi. 2016. Temporal segmentation of animal trajectories informed by habitat use. Ecosphere 7(10):e01498. 10.1002/ecs2.1498

follow us and share it on:

you may also like:

A First Get-Together with the New EAGLE Generation 🦅

A First Get-Together with the New EAGLE Generation 🦅

A new EAGLE generation means new faces, new ideas, and the start of another journey in Earth Observation. And what better way to kick things off than with a relaxed evening at the pub? Our first social event brought together the new students, older EAGLE generations,...

An Evening with Professor Jarugu Narasimha Moorthy

An Evening with Professor Jarugu Narasimha Moorthy

It was a pleasure to have dinner with Professor Jarugu Narasimha Moorthy, who received an honorary doctorate from the Faculty of Chemistry and Pharmacy in recognition of his achievements. The ceremony took place on 29 September 2026 in the Chemistry Central Building....

Ecology or Sampling Bias? A Roadside Perspective on Invasion Risk

Ecology or Sampling Bias? A Roadside Perspective on Invasion Risk

At the 2026 annual meeting of the AK Fernerkundung (German Working Group on Remote Sensing), which was hosted by the Thünen Institute in Braunschweig, our EORC team member Dr. Insa Otte presented research results on sampling bias in species distribution models in...

Space Days in Würzburg: EORC on site

Space Days in Würzburg: EORC on site

This Saturday turned out busier than we expected. At the Space Days at the University of Würzburg, Sarah Schoenbrodt-Stitt and Mirjana Bevanda represented EORC with a small booth, and the visitors were quite numerous. Students and colleagues stopped by, as expected....

bzgl. Art. 50 Verordnung (EU) 2024/1689 (AI Act):

Überwiegend werden eigene originäre Texte und Bilder des EORC genutzt, jedoch sind einige Inhalte von blog post Texten teilweise mit Hilfe von KI überarbeitet worden und einige Bilder sind ganz mit KI erstellt, die jedoch deutlich keine photorealistische Darstellungen abbilden. Aussnahmen sind KI generierte fernerkundliche Datensätze, die explizit für Forschungszwecke mit KI erstellt wurden, hier wird aber durch den assoziierten Text der wissenschaftliche Grund der KI generierten Bilder erläutert.

Share This