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A Methodology for Adaptable and Robust Ecosystem Services Assessment

Overview of attention for article published in PLOS ONE, March 2014
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

news
2 news outlets
policy
1 policy source
twitter
11 tweeters
facebook
1 Facebook page
wikipedia
4 Wikipedia pages

Citations

dimensions_citation
250 Dimensions

Readers on

mendeley
719 Mendeley
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Title
A Methodology for Adaptable and Robust Ecosystem Services Assessment
Published in
PLOS ONE, March 2014
DOI 10.1371/journal.pone.0091001
Pubmed ID
Authors

Ferdinando Villa, Kenneth J. Bagstad, Brian Voigt, Gary W. Johnson, Rosimeiry Portela, Miroslav Honzák, David Batker

Abstract

Ecosystem Services (ES) are an established conceptual framework for attributing value to the benefits that nature provides to humans. As the promise of robust ES-driven management is put to the test, shortcomings in our ability to accurately measure, map, and value ES have surfaced. On the research side, mainstream methods for ES assessment still fall short of addressing the complex, multi-scale biophysical and socioeconomic dynamics inherent in ES provision, flow, and use. On the practitioner side, application of methods remains onerous due to data and model parameterization requirements. Further, it is increasingly clear that the dominant "one model fits all" paradigm is often ill-suited to address the diversity of real-world management situations that exist across the broad spectrum of coupled human-natural systems. This article introduces an integrated ES modeling methodology, named ARIES (ARtificial Intelligence for Ecosystem Services), which aims to introduce improvements on these fronts. To improve conceptual detail and representation of ES dynamics, it adopts a uniform conceptualization of ES that gives equal emphasis to their production, flow and use by society, while keeping model complexity low enough to enable rapid and inexpensive assessment in many contexts and for multiple services. To improve fit to diverse application contexts, the methodology is assisted by model integration technologies that allow assembly of customized models from a growing model base. By using computer learning and reasoning, model structure may be specialized for each application context without requiring costly expertise. In this article we discuss the founding principles of ARIES--both its innovative aspects for ES science and as an example of a new strategy to support more accurate decision making in diverse application contexts.

Twitter Demographics

The data shown below were collected from the profiles of 11 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 719 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 <1%
Portugal 3 <1%
Canada 3 <1%
Sweden 3 <1%
France 2 <1%
Norway 2 <1%
Netherlands 1 <1%
Nepal 1 <1%
Austria 1 <1%
Other 12 2%
Unknown 685 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 167 23%
Researcher 151 21%
Student > Master 109 15%
Student > Doctoral Student 35 5%
Student > Bachelor 34 5%
Other 124 17%
Unknown 99 14%
Readers by discipline Count As %
Environmental Science 288 40%
Agricultural and Biological Sciences 126 18%
Earth and Planetary Sciences 39 5%
Engineering 25 3%
Economics, Econometrics and Finance 24 3%
Other 74 10%
Unknown 143 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 29. 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 12 May 2022.
All research outputs
#1,066,404
of 21,775,893 outputs
Outputs from PLOS ONE
#14,600
of 186,108 outputs
Outputs of similar age
#11,288
of 201,608 outputs
Outputs of similar age from PLOS ONE
#457
of 4,979 outputs
Altmetric has tracked 21,775,893 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 186,108 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.7. This one has done particularly well, scoring higher than 92% of its peers.
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 201,608 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 4,979 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.