↓ Skip to main content

Quantifying temporal change in biodiversity: challenges and opportunities

Overview of attention for article published in Proceedings of the Royal Society B: Biological Sciences, January 2013
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (54th percentile)

Mentioned by

twitter
15 X users
patent
1 patent
f1000
1 research highlight platform

Citations

dimensions_citation
197 Dimensions

Readers on

mendeley
893 Mendeley
citeulike
6 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Quantifying temporal change in biodiversity: challenges and opportunities
Published in
Proceedings of the Royal Society B: Biological Sciences, January 2013
DOI 10.1098/rspb.2012.1931
Pubmed ID
Authors

Maria Dornelas, Anne E. Magurran, Stephen T. Buckland, Anne Chao, Robin L. Chazdon, Robert K. Colwell, Tom Curtis, Kevin J. Gaston, Nicholas J. Gotelli, Matthew A. Kosnik, Brian McGill, Jenny L. McCune, Hélène Morlon, Peter J. Mumby, Lise Øvreås, Angelika Studeny, Mark Vellend

Abstract

Growing concern about biodiversity loss underscores the need to quantify and understand temporal change. Here, we review the opportunities presented by biodiversity time series, and address three related issues: (i) recognizing the characteristics of temporal data; (ii) selecting appropriate statistical procedures for analysing temporal data; and (iii) inferring and forecasting biodiversity change. With regard to the first issue, we draw attention to defining characteristics of biodiversity time series--lack of physical boundaries, uni-dimensionality, autocorrelation and directionality--that inform the choice of analytic methods. Second, we explore methods of quantifying change in biodiversity at different timescales, noting that autocorrelation can be viewed as a feature that sheds light on the underlying structure of temporal change. Finally, we address the transition from inferring to forecasting biodiversity change, highlighting potential pitfalls associated with phase-shifts and novel conditions.

X Demographics

X Demographics

The data shown below were collected from the profiles of 15 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 893 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 17 2%
United States 15 2%
United Kingdom 9 1%
Canada 4 <1%
Mexico 4 <1%
Spain 3 <1%
Switzerland 3 <1%
Germany 3 <1%
Sweden 2 <1%
Other 26 3%
Unknown 807 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 221 25%
Student > Ph. D. Student 209 23%
Student > Master 119 13%
Student > Bachelor 74 8%
Professor > Associate Professor 43 5%
Other 140 16%
Unknown 87 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 435 49%
Environmental Science 219 25%
Earth and Planetary Sciences 37 4%
Biochemistry, Genetics and Molecular Biology 30 3%
Social Sciences 7 <1%
Other 39 4%
Unknown 126 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 09 February 2023.
All research outputs
#3,113,371
of 25,576,801 outputs
Outputs from Proceedings of the Royal Society B: Biological Sciences
#5,276
of 11,384 outputs
Outputs of similar age
#29,901
of 290,496 outputs
Outputs of similar age from Proceedings of the Royal Society B: Biological Sciences
#30
of 66 outputs
Altmetric has tracked 25,576,801 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,384 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 40.6. This one has gotten more attention than average, scoring higher than 53% 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 290,496 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 66 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.