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Overview of attention for article published in Ecology, June 2006
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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 (90th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

1 blog
1 policy source
3 tweeters


648 Dimensions

Readers on

478 Mendeley
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Published in
Ecology, June 2006
DOI 10.1890/0012-9658(2006)87[1445:soefdd]2.0.co;2
Pubmed ID

Barry W. Brook, Corey J. A. Bradshaw


Population limitation is a fundamental tenet of ecology, but the relative roles of exogenous and endogenous mechanisms remain unquantified for most species. Here we used multi-model inference (MMI), a form of model averaging, based on information theory (Akaike's Information Criterion) to evaluate the relative strength of evidence for density-dependent and density-independent population dynamical models in long-term abundance time series of 1198 species. We also compared the MMI results to more classic methods for detecting density dependence: Neyman-Pearson hypothesis-testing and best-model selection using the Bayesian Information Criterion or cross-validation. Using MMI on our large database, we show that density dependence is a pervasive feature of population dynamics (median MMI support for density dependence = 74.7-92.2%), and that this holds across widely different taxa. The weight of evidence for density dependence varied among species but increased consistently, with the number of generations monitored. Best-model selection methods yielded similar results to MMI (a density-dependent model was favored in 66.2-93.9% of species time series), while the hypothesis-testing methods detected density dependence less frequently (32.6-49.8%). There were no obvious differences in the prevalence of density dependence across major taxonomic groups under any of the statistical methods used. These results underscore the value of using multiple modes of analysis to quantify the relative empirical support for a set of working hypotheses that encompass a range of realistic population dynamical behaviors.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 478 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 18 4%
Brazil 12 3%
United Kingdom 6 1%
Argentina 3 <1%
Italy 2 <1%
Switzerland 2 <1%
Spain 2 <1%
Germany 1 <1%
Sweden 1 <1%
Other 11 2%
Unknown 420 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 119 25%
Researcher 116 24%
Student > Master 73 15%
Student > Bachelor 30 6%
Professor 29 6%
Other 82 17%
Unknown 29 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 289 60%
Environmental Science 106 22%
Earth and Planetary Sciences 10 2%
Biochemistry, Genetics and Molecular Biology 8 2%
Physics and Astronomy 5 1%
Other 19 4%
Unknown 41 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 26 July 2021.
All research outputs
of 19,198,440 outputs
Outputs from Ecology
of 6,173 outputs
Outputs of similar age
of 198,616 outputs
Outputs of similar age from Ecology
of 80 outputs
Altmetric has tracked 19,198,440 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,173 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.4. This one has done well, scoring higher than 84% 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 198,616 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 90% of its contemporaries.
We're also able to compare this research output to 80 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 91% of its contemporaries.