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Modelling dendritic ecological networks in space: an integrated network perspective

Overview of attention for article published in Ecology Letters, March 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
10 tweeters

Citations

dimensions_citation
92 Dimensions

Readers on

mendeley
329 Mendeley
citeulike
1 CiteULike
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Title
Modelling dendritic ecological networks in space: an integrated network perspective
Published in
Ecology Letters, March 2013
DOI 10.1111/ele.12084
Pubmed ID
Authors

Erin E. Peterson, Jay M. Ver Hoef, Dan J. Isaak, Jeffrey A. Falke, Marie-Josée Fortin, Chris E. Jordan, Kristina McNyset, Pascal Monestiez, Aaron S. Ruesch, Aritra Sengupta, Nicholas Som, E. Ashley Steel, David M. Theobald, Christian E. Torgersen, Seth J. Wenger

Abstract

Dendritic ecological networks (DENs) are a unique form of ecological networks that exhibit a dendritic network topology (e.g. stream and cave networks or plant architecture). DENs have a dual spatial representation; as points within the network and as points in geographical space. Consequently, some analytical methods used to quantify relationships in other types of ecological networks, or in 2-D space, may be inadequate for studying the influence of structure and connectivity on ecological processes within DENs. We propose a conceptual taxonomy of network analysis methods that account for DEN characteristics to varying degrees and provide a synthesis of the different approaches within the context of stream ecology. Within this context, we summarise the key innovations of a new family of spatial statistical models that describe spatial relationships in DENs. Finally, we discuss how different network analyses may be combined to address more complex and novel research questions. While our main focus is streams, the taxonomy of network analyses is also relevant anywhere spatial patterns in both network and 2-D space can be used to explore the influence of multi-scale processes on biota and their habitat (e.g. plant morphology and pest infestation, or preferential migration along stream or road corridors).

Twitter Demographics

The data shown below were collected from the profiles of 10 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 329 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 17 5%
Brazil 5 2%
Spain 4 1%
Portugal 3 <1%
South Africa 2 <1%
United Kingdom 2 <1%
Germany 2 <1%
Canada 2 <1%
Sweden 1 <1%
Other 8 2%
Unknown 283 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 89 27%
Student > Ph. D. Student 76 23%
Student > Master 42 13%
Student > Bachelor 20 6%
Professor 20 6%
Other 82 25%
Readers by discipline Count As %
Agricultural and Biological Sciences 143 43%
Environmental Science 113 34%
Unspecified 28 9%
Earth and Planetary Sciences 25 8%
Engineering 5 2%
Other 15 5%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 14 February 2016.
All research outputs
#2,978,029
of 12,352,699 outputs
Outputs from Ecology Letters
#1,271
of 1,977 outputs
Outputs of similar age
#32,335
of 142,432 outputs
Outputs of similar age from Ecology Letters
#34
of 52 outputs
Altmetric has tracked 12,352,699 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,977 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.3. This one is in the 35th percentile – i.e., 35% 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 142,432 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 52 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.