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Mapping areas of spatial-temporal overlap from wildlife tracking data

Overview of attention for article published in Movement Ecology, November 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (71st percentile)

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5 tweeters


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148 Mendeley
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Mapping areas of spatial-temporal overlap from wildlife tracking data
Published in
Movement Ecology, November 2015
DOI 10.1186/s40462-015-0064-3
Pubmed ID

Jed A. Long, Stephen L. Webb, Trisalyn A. Nelson, Kenneth L. Gee


The study of inter-individual interactions (often termed spatial-temporal interactions, or dynamic interactions) from remote tracking data has focused primarily on identifying the presence of such interactions. New datasets and methods offer opportunity to answer more nuanced questions, such as where on the landscape interactions occur. In this paper, we provide a new approach for mapping areas of spatial-temporal overlap in wildlife from remote tracking data. The method, termed the joint potential path area (jPPA) builds from the time-geographic movement model, originally proposed for studying human movement patterns. The jPPA approach can be used to delineate sub-areas of the home range where inter-individual interaction was possible. Maps of jPPA regions can be integrated with existing geographic data to explore landscape conditions and habitat associated with spatial temporal-interactions in wildlife. We apply the jPPA approach to simulated biased correlated random walks to demonstrate the method under known conditions. The jPPA method is then applied to three dyads, consisting of fine resolution (15 minute sampling interval) GPS tracking data of white-tailed deer (Odocoileus virginianus) collected in Oklahoma, USA. Our results demonstrate the ability of the jPPA to identify and map jPPA sub-areas of the home range. We show how jPPA maps can be used to identify habitat differences (using percent tree canopy cover as a habitat indicator) between areas of spatial-temporal overlap and the overall home range in each of the three deer dyads. The value of the jPPA approach within current wildlife habitat analysis workflows is highlighted along with its simple and straightforward implementation and interpretation. Given the current emphasis on remote tracking in wildlife movement and habitat research, new approaches capable of leveraging both the spatial and temporal information content contained within these data are warranted. We make code (in the statistical software R) for implementing the jPPA approach openly available for other researchers.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 148 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 <1%
France 1 <1%
United Kingdom 1 <1%
Jersey 1 <1%
Spain 1 <1%
Unknown 143 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 37 25%
Researcher 30 20%
Student > Master 27 18%
Other 8 5%
Student > Bachelor 7 5%
Other 14 9%
Unknown 25 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 74 50%
Environmental Science 23 16%
Computer Science 5 3%
Social Sciences 3 2%
Earth and Planetary Sciences 3 2%
Other 9 6%
Unknown 31 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 January 2016.
All research outputs
of 6,912,770 outputs
Outputs from Movement Ecology
of 79 outputs
Outputs of similar age
of 237,827 outputs
Outputs of similar age from Movement Ecology
of 10 outputs
Altmetric has tracked 6,912,770 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 79 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.4. This one is in the 41st percentile – i.e., 41% 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 237,827 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.