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Iterative near-term ecological forecasting: Needs, opportunities, and challenges

Overview of attention for article published in Proceedings of the National Academy of Sciences of the United States of America, January 2018
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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 (98th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

news
6 news outlets
blogs
1 blog
twitter
142 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
66 Dimensions

Readers on

mendeley
330 Mendeley
citeulike
1 CiteULike
Title
Iterative near-term ecological forecasting: Needs, opportunities, and challenges
Published in
Proceedings of the National Academy of Sciences of the United States of America, January 2018
DOI 10.1073/pnas.1710231115
Pubmed ID
Authors

Michael C. Dietze, Andrew Fox, Lindsay M. Beck-Johnson, Julio L. Betancourt, Mevin B. Hooten, Catherine S. Jarnevich, Timothy H. Keitt, Melissa A. Kenney, Christine M. Laney, Laurel G. Larsen, Henry W. Loescher, Claire K. Lunch, Bryan C. Pijanowski, James T. Randerson, Emily K. Read, Andrew T. Tredennick, Rodrigo Vargas, Kathleen C. Weathers, Ethan P. White

Abstract

Two foundational questions about sustainability are "How are ecosystems and the services they provide going to change in the future?" and "How do human decisions affect these trajectories?" Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

Twitter Demographics

The data shown below were collected from the profiles of 142 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 330 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 330 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 95 29%
Student > Ph. D. Student 82 25%
Student > Master 33 10%
Professor 21 6%
Student > Bachelor 21 6%
Other 49 15%
Unknown 29 9%
Readers by discipline Count As %
Environmental Science 129 39%
Agricultural and Biological Sciences 93 28%
Earth and Planetary Sciences 22 7%
Engineering 9 3%
Biochemistry, Genetics and Molecular Biology 3 <1%
Other 13 4%
Unknown 61 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 140. 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 17 September 2019.
All research outputs
#110,374
of 13,906,046 outputs
Outputs from Proceedings of the National Academy of Sciences of the United States of America
#2,638
of 81,261 outputs
Outputs of similar age
#5,170
of 358,365 outputs
Outputs of similar age from Proceedings of the National Academy of Sciences of the United States of America
#93
of 986 outputs
Altmetric has tracked 13,906,046 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 81,261 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 24.8. This one has done particularly well, scoring higher than 96% 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 358,365 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 98% of its contemporaries.
We're also able to compare this research output to 986 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.