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Social network models predict movement and connectivity in ecological landscapes

Overview of attention for article published in Proceedings of the National Academy of Sciences of the United States of America, November 2011
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  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
4 tweeters
googleplus
1 Google+ user

Citations

dimensions_citation
49 Dimensions

Readers on

mendeley
365 Mendeley
citeulike
1 CiteULike
Title
Social network models predict movement and connectivity in ecological landscapes
Published in
Proceedings of the National Academy of Sciences of the United States of America, November 2011
DOI 10.1073/pnas.1107549108
Pubmed ID
Authors

R. J. Fletcher, M. A. Acevedo, B. E. Reichert, K. E. Pias, W. M. Kitchens

Abstract

Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters 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 365 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 19 5%
Canada 8 2%
France 5 1%
United Kingdom 5 1%
Switzerland 4 1%
Brazil 4 1%
Sweden 3 <1%
Spain 3 <1%
Germany 2 <1%
Other 17 5%
Unknown 295 81%

Demographic breakdown

Readers by professional status Count As %
Researcher 99 27%
Student > Ph. D. Student 83 23%
Student > Master 53 15%
Professor > Associate Professor 26 7%
Professor 23 6%
Other 81 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 187 51%
Environmental Science 82 22%
Unspecified 27 7%
Computer Science 16 4%
Social Sciences 11 3%
Other 42 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 22 September 2016.
All research outputs
#6,299,575
of 12,365,005 outputs
Outputs from Proceedings of the National Academy of Sciences of the United States of America
#63,802
of 77,326 outputs
Outputs of similar age
#47,722
of 102,773 outputs
Outputs of similar age from Proceedings of the National Academy of Sciences of the United States of America
#479
of 789 outputs
Altmetric has tracked 12,365,005 research outputs across all sources so far. This one is in the 48th percentile – i.e., 48% of other outputs scored the same or lower than it.
So far Altmetric has tracked 77,326 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 22.1. This one is in the 17th percentile – i.e., 17% 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 102,773 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 53% of its contemporaries.
We're also able to compare this research output to 789 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.