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An Online Database for Informing Ecological Network Models: http://kelpforest.ucsc.edu

Overview of attention for article published in PLOS ONE, October 2014
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Title
An Online Database for Informing Ecological Network Models: http://kelpforest.ucsc.edu
Published in
PLOS ONE, October 2014
DOI 10.1371/journal.pone.0109356
Pubmed ID
Authors

Rodrigo Beas-Luna, Mark Novak, Mark H. Carr, Martin T. Tinker, August Black, Jennifer E. Caselle, Michael Hoban, Dan Malone, Alison Iles

Abstract

Ecological network models and analyses are recognized as valuable tools for understanding the dynamics and resiliency of ecosystems, and for informing ecosystem-based approaches to management. However, few databases exist that can provide the life history, demographic and species interaction information necessary to parameterize ecological network models. Faced with the difficulty of synthesizing the information required to construct models for kelp forest ecosystems along the West Coast of North America, we developed an online database (http://kelpforest.ucsc.edu/) to facilitate the collation and dissemination of such information. Many of the database's attributes are novel yet the structure is applicable and adaptable to other ecosystem modeling efforts. Information for each taxonomic unit includes stage-specific life history, demography, and body-size allometries. Species interactions include trophic, competitive, facilitative, and parasitic forms. Each data entry is temporally and spatially explicit. The online data entry interface allows researchers anywhere to contribute and access information. Quality control is facilitated by attributing each entry to unique contributor identities and source citations. The database has proven useful as an archive of species and ecosystem-specific information in the development of several ecological network models, for informing management actions, and for education purposes (e.g., undergraduate and graduate training). To facilitate adaptation of the database by other researches for other ecosystems, the code and technical details on how to customize this database and apply it to other ecosystems are freely available and located at the following link (https://github.com/kelpforest-cameo/databaseui).

X Demographics

X Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 94 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
Mexico 2 2%
Spain 1 1%
Canada 1 1%
Unknown 87 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 23%
Researcher 19 20%
Student > Master 8 9%
Student > Bachelor 6 6%
Student > Postgraduate 4 4%
Other 11 12%
Unknown 24 26%
Readers by discipline Count As %
Agricultural and Biological Sciences 34 36%
Environmental Science 13 14%
Computer Science 8 9%
Medicine and Dentistry 3 3%
Biochemistry, Genetics and Molecular Biology 2 2%
Other 12 13%
Unknown 22 23%
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 28 October 2014.
All research outputs
#14,788,263
of 22,768,097 outputs
Outputs from PLOS ONE
#123,518
of 194,212 outputs
Outputs of similar age
#144,310
of 260,971 outputs
Outputs of similar age from PLOS ONE
#2,867
of 5,192 outputs
Altmetric has tracked 22,768,097 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 194,212 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.1. This one is in the 32nd percentile – i.e., 32% 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 260,971 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5,192 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.