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Making ecological models adequate

Overview of attention for article published in Ecology Letters, December 2017
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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 (96th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

Mentioned by

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112 X users
facebook
1 Facebook page
googleplus
1 Google+ user

Citations

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116 Dimensions

Readers on

mendeley
339 Mendeley
citeulike
1 CiteULike
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Title
Making ecological models adequate
Published in
Ecology Letters, December 2017
DOI 10.1111/ele.12893
Pubmed ID
Authors

Wayne M. Getz, Charles R. Marshall, Colin J. Carlson, Luca Giuggioli, Sadie J. Ryan, Stephanie S. Romañach, Carl Boettiger, Samuel D. Chamberlain, Laurel Larsen, Paolo D’Odorico, David O’Sullivan

Abstract

Critical evaluation of the adequacy of ecological models is urgently needed to enhance their utility in developing theory and enabling environmental managers and policymakers to make informed decisions. Poorly supported management can have detrimental, costly or irreversible impacts on the environment and society. Here, we examine common issues in ecological modelling and suggest criteria for improving modelling frameworks. An appropriate level of process description is crucial to constructing the best possible model, given the available data and understanding of ecological structures. Model details unsupported by data typically lead to over parameterisation and poor model performance. Conversely, a lack of mechanistic details may limit a model's ability to predict ecological systems' responses to management. Ecological studies that employ models should follow a set of model adequacy assessment protocols that include: asking a series of critical questions regarding state and control variable selection, the determinacy of data, and the sensitivity and validity of analyses. We also need to improve model elaboration, refinement and coarse graining procedures to better understand the relevancy and adequacy of our models and the role they play in advancing theory, improving hind and forecasting, and enabling problem solving and management.

X Demographics

X Demographics

The data shown below were collected from the profiles of 112 X users who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 339 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 339 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 88 26%
Student > Ph. D. Student 83 24%
Student > Master 38 11%
Professor 16 5%
Student > Bachelor 13 4%
Other 48 14%
Unknown 53 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 133 39%
Environmental Science 72 21%
Earth and Planetary Sciences 12 4%
Biochemistry, Genetics and Molecular Biology 8 2%
Computer Science 7 2%
Other 30 9%
Unknown 77 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 62. 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 07 December 2020.
All research outputs
#725,756
of 26,233,885 outputs
Outputs from Ecology Letters
#355
of 3,203 outputs
Outputs of similar age
#16,100
of 454,571 outputs
Outputs of similar age from Ecology Letters
#10
of 42 outputs
Altmetric has tracked 26,233,885 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,203 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 29.3. This one has done well, scoring higher than 88% 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 454,571 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 96% of its contemporaries.
We're also able to compare this research output to 42 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.