Big-data approaches lead to an increased understanding of the ecology of animal movement.
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60077344%3A_____%2F22%3A00555922" target="_blank" >RIV/60077344:_____/22:00555922 - isvavai.cz</a>
Alternative codes found
RIV/60076658:12310/22:43905027
Result on the web
<a href="https://doi.org/10.1126/science.abg1780" target="_blank" >https://doi.org/10.1126/science.abg1780</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1126/science.abg1780" target="_blank" >10.1126/science.abg1780</a>
Alternative languages
Result language
angličtina
Original language name
Big-data approaches lead to an increased understanding of the ecology of animal movement.
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10618 - Ecology
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Science
ISSN
0036-8075
e-ISSN
1095-9203
Volume of the periodical
375
Issue of the periodical within the volume
6582
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
eabg1780
UT code for WoS article
000758142600036
EID of the result in the Scopus database
2-s2.0-85124775275