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  4. Big-data approaches lead to an increased understanding of the ecology of animal movement
 
Verlagslink DOI
10.1126/science.abg1780

Big-data approaches lead to an increased understanding of the ecology of animal movement

Veröffentlichungsdatum
2022-02-18
Autoren
Nathan, Ran  
Monk, Christopher  
Arlinghaus, Robert  
Adam, Timo  
Alós, Josep  
Assaf, Michael  
Baktoft, Henrik  
Beardsworth, Christine  
Bertram, Michael  
Bijleveld, Allert I.  
Brodin, Tomas  
Brooks, Jill  
Campos-Candela, Andrea  
Cooke, Steven  
Gjelland, Karl Øystein  
Gupte, Pratik  
Harel, Roi  
Hellström, Gustav  
Jeltsch, Florian  
Killen, Shaun  
Klefoth, Thomas  
Langrock, Roland  
Lennox, Robert  
Lourie, Emmanuel  
Madden, Joah  
Orchan, Yotam  
Pauwels, Ine  
Říha, Milan  
Roeleke, Manuel  
Schlägel, Ulrike  
Shohami, David  
Signer, Johannes  
Toledo, Sivan  
Vilk, Ohad  
Westrelin, Samuel  
Whiteside, Mark  
Jarić, Ivan  
Zusammenfassung
Understanding animal movement is essential to elucidate how animals interact, survive, and thrive in a changing world. Recent technological advances in data collection and management have transformed our understanding of animal "movement ecology" (the integrated study of organismal movement), creating a big-data discipline that benefits from rapid, cost-effective generation of large amounts of data on movements of animals in the wild. These high-throughput wildlife tracking systems now allow more thorough investigation of variation among individuals and species across space and time, the nature of biological interactions, and behavioral responses to the environment. Movement ecology is rapidly expanding scientific frontiers through large interdisciplinary and collaborative frameworks, providing improved opportunities for conservation and insights into the movements of wild animals, and their causes and consequences.
Verlag
AAAS
Institution
Hochschule Bremen  
Fachbereich
Hochschule Bremen - Fakultät 5: Natur und Technik  
Dokumenttyp
Artikel/Aufsatz
Zeitschrift/Sammelwerk
Science  
Band
375
Heft
6582
Startseite
eabg1780
Sprache
Englisch

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