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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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1 tweeter

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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.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 188 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 5 3%
Germany 2 1%
Finland 1 <1%
South Africa 1 <1%
Brazil 1 <1%
Australia 1 <1%
Argentina 1 <1%
Jersey 1 <1%
United States 1 <1%
Other 0 0%
Unknown 174 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 47 25%
Student > Master 38 20%
Researcher 32 17%
Student > Postgraduate 11 6%
Student > Bachelor 10 5%
Other 32 17%
Unknown 18 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 107 57%
Environmental Science 33 18%
Mathematics 10 5%
Engineering 4 2%
Social Sciences 4 2%
Other 8 4%
Unknown 22 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 06 March 2015.
All research outputs
#9,958,772
of 12,440,396 outputs
Outputs from Movement Ecology
#123
of 137 outputs
Outputs of similar age
#152,139
of 219,051 outputs
Outputs of similar age from Movement Ecology
#2
of 2 outputs
Altmetric has tracked 12,440,396 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 137 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.6. This one is in the 4th percentile – i.e., 4% of its peers scored the same or lower than it.
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