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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 (95th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

news
2 news outlets
policy
3 policy sources
twitter
11 X users
facebook
1 Facebook page
wikipedia
4 Wikipedia pages

Citations

dimensions_citation
324 Dimensions

Readers on

mendeley
790 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 11 X users 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 790 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 <1%
Canada 3 <1%
Sweden 3 <1%
Portugal 2 <1%
Norway 2 <1%
France 2 <1%
Netherlands 1 <1%
Latvia 1 <1%
Italy 1 <1%
Other 11 1%
Unknown 759 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 180 23%
Researcher 156 20%
Student > Master 113 14%
Student > Doctoral Student 39 5%
Student > Bachelor 39 5%
Other 131 17%
Unknown 132 17%
Readers by discipline Count As %
Environmental Science 306 39%
Agricultural and Biological Sciences 127 16%
Earth and Planetary Sciences 42 5%
Engineering 27 3%
Social Sciences 26 3%
Other 80 10%
Unknown 182 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 36. 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 01 February 2023.
All research outputs
#1,108,313
of 25,083,571 outputs
Outputs from PLOS ONE
#14,272
of 217,636 outputs
Outputs of similar age
#10,659
of 227,370 outputs
Outputs of similar age from PLOS ONE
#435
of 5,756 outputs
Altmetric has tracked 25,083,571 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 217,636 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.7. This one has done particularly well, scoring higher than 93% 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 227,370 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 95% of its contemporaries.
We're also able to compare this research output to 5,756 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 92% of its contemporaries.