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When to be discrete: the importance of time formulation in understanding animal movement

Overview of attention for article published in Movement Ecology, October 2014
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Title
When to be discrete: the importance of time formulation in understanding animal movement
Published in
Movement Ecology, October 2014
DOI 10.1186/s40462-014-0021-6
Pubmed ID
Authors

Brett T McClintock, Devin S Johnson, Mevin B Hooten, Jay M Ver Hoef, Juan M Morales

Abstract

Animal movement is essential to our understanding of population dynamics, animal behavior, and the impacts of global change. Coupled with high-resolution biotelemetry data, exciting new inferences about animal movement have been facilitated by various specifications of contemporary models. These approaches differ, but most share common themes. One key distinction is whether the underlying movement process is conceptualized in discrete or continuous time. This is perhaps the greatest source of confusion among practitioners, both in terms of implementation and biological interpretation. In general, animal movement occurs in continuous time but we observe it at fixed discrete-time intervals. Thus, continuous time is conceptually and theoretically appealing, but in practice it is perhaps more intuitive to interpret movement in discrete intervals. With an emphasis on state-space models, we explore the differences and similarities between continuous and discrete versions of mechanistic movement models, establish some common terminology, and indicate under which circumstances one form might be preferred over another. Counter to the overly simplistic view that discrete- and continuous-time conceptualizations are merely different means to the same end, we present novel mathematical results revealing hitherto unappreciated consequences of model formulation on inferences about animal movement. Notably, the speed and direction of movement are intrinsically linked in current continuous-time random walk formulations, and this can have important implications when interpreting animal behavior. We illustrate these concepts in the context of state-space models with multiple movement behavior states using northern fur seal (Callorhinus ursinus) biotelemetry data.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 250 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 4 2%
Australia 1 <1%
Brazil 1 <1%
South Africa 1 <1%
Germany 1 <1%
Finland 1 <1%
Argentina 1 <1%
Jersey 1 <1%
United States 1 <1%
Other 0 0%
Unknown 238 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 60 24%
Student > Master 43 17%
Researcher 42 17%
Student > Doctoral Student 13 5%
Student > Bachelor 11 4%
Other 36 14%
Unknown 45 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 129 52%
Environmental Science 44 18%
Mathematics 9 4%
Engineering 8 3%
Earth and Planetary Sciences 4 2%
Other 9 4%
Unknown 47 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 July 2020.
All research outputs
#15,403,045
of 24,417,958 outputs
Outputs from Movement Ecology
#274
of 356 outputs
Outputs of similar age
#137,749
of 260,724 outputs
Outputs of similar age from Movement Ecology
#5
of 6 outputs
Altmetric has tracked 24,417,958 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 356 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 22.3. This one is in the 19th percentile – i.e., 19% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 260,724 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one.