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Predicting species distributions for conservation decisions

Overview of attention for article published in Ecology Letters, October 2013
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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 (66th percentile)

Mentioned by

blogs
2 blogs
policy
1 policy source
twitter
45 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
455 Dimensions

Readers on

mendeley
1310 Mendeley
citeulike
2 CiteULike
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Title
Predicting species distributions for conservation decisions
Published in
Ecology Letters, October 2013
DOI 10.1111/ele.12189
Pubmed ID
Authors

Antoine Guisan, Reid Tingley, John B. Baumgartner, Ilona Naujokaitis-Lewis, Patricia R. Sutcliffe, Ayesha I. T. Tulloch, Tracey J. Regan, Lluis Brotons, Eve McDonald-Madden, Chrystal Mantyka-Pringle, Tara G. Martin, Jonathan R. Rhodes, Ramona Maggini, Samantha A. Setterfield, Jane Elith, Mark W. Schwartz, Brendan A. Wintle, Olivier Broennimann, Mike Austin, Simon Ferrier, Michael R. Kearney, Hugh P. Possingham, Yvonne M. Buckley

Abstract

Species distribution models (SDMs) are increasingly proposed to support conservation decision making. However, evidence of SDMs supporting solutions for on-ground conservation problems is still scarce in the scientific literature. Here, we show that successful examples exist but are still largely hidden in the grey literature, and thus less accessible for analysis and learning. Furthermore, the decision framework within which SDMs are used is rarely made explicit. Using case studies from biological invasions, identification of critical habitats, reserve selection and translocation of endangered species, we propose that SDMs may be tailored to suit a range of decision-making contexts when used within a structured and transparent decision-making process. To construct appropriate SDMs to more effectively guide conservation actions, modellers need to better understand the decision process, and decision makers need to provide feedback to modellers regarding the actual use of SDMs to support conservation decisions. This could be facilitated by individuals or institutions playing the role of 'translators' between modellers and decision makers. We encourage species distribution modellers to get involved in real decision-making processes that will benefit from their technical input; this strategy has the potential to better bridge theory and practice, and contribute to improve both scientific knowledge and conservation outcomes.

Twitter Demographics

The data shown below were collected from the profiles of 45 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 1,310 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 28 2%
Brazil 19 1%
United Kingdom 14 1%
Spain 11 <1%
Switzerland 10 <1%
Germany 10 <1%
Italy 8 <1%
Australia 8 <1%
Colombia 6 <1%
Other 56 4%
Unknown 1140 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 313 24%
Student > Ph. D. Student 293 22%
Student > Master 238 18%
Student > Bachelor 118 9%
Student > Doctoral Student 70 5%
Other 278 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 701 54%
Environmental Science 402 31%
Unspecified 117 9%
Earth and Planetary Sciences 39 3%
Computer Science 12 <1%
Other 39 3%

Attention Score in Context

This research output has an Altmetric Attention Score of 42. 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 29 November 2018.
All research outputs
#343,011
of 12,359,775 outputs
Outputs from Ecology Letters
#230
of 1,978 outputs
Outputs of similar age
#5,613
of 168,330 outputs
Outputs of similar age from Ecology Letters
#13
of 39 outputs
Altmetric has tracked 12,359,775 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 1,978 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.3. 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 168,330 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 39 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 66% of its contemporaries.