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Identifying cryptic diversity with predictive phylogeography

Overview of attention for article published in Proceedings of the Royal Society B: Biological Sciences, October 2016
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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 (88th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (56th percentile)

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22 X users
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1 Redditor
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1 Bluesky user

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191 Mendeley
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Article details
Title
Identifying cryptic diversity with predictive phylogeography
Published in
Proceedings of the Royal Society B: Biological Sciences, October 2016
DOI 10.1098/rspb.2016.1529
Pubmed ID
Authors
Abstract

Identifying units of biological diversity is a major goal of organismal biology. An increasing literature has focused on the importance of cryptic diversity, defined as the presence of deeply diverged lineages within a single species. While most discoveries of cryptic lineages proceed on a taxon-by-taxon basis, rapid assessments of biodiversity are needed to inform conservation policy and decision-making. Here, we introduce a predictive framework for phylogeography that allows rapidly identifying cryptic diversity. Our approach proceeds by collecting environmental, taxonomic and genetic data from codistributed taxa with known phylogeographic histories. We define these taxa as a reference set, and categorize them as either harbouring or lacking cryptic diversity. We then build a random forest classifier that allows us to predict which other taxa endemic to the same biome are likely to contain cryptic diversity. We apply this framework to data from two sets of disjunct ecosystems known to harbour taxa with cryptic diversity: the mesic temperate forests of the Pacific Northwest of North America and the arid lands of Southwestern North America. The predictive approach presented here is accurate, with prediction accuracies placed between 65% and 98.79% depending of the ecosystem. This seems to indicate that our method can be successfully used to address ecosystem-level questions about cryptic diversity. Further, our application for the prediction of the cryptic/non-cryptic nature of unknown species is easily applicable and provides results that agree with recent discoveries from those systems. Our results demonstrate that the transition of phylogeography from a descriptive to a predictive discipline is possible and effective.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 22 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 191 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Geographical breakdown
Country Count As %
United States 3 2%
Brazil 3 2%
France 1 <1%
Unknown 184 96%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 34 18%
Student > Ph. D. Student 31 16%
Student > Master 22 12%
Student > Bachelor 19 10%
Student > Doctoral Student 13 7%
Other 32 17%
Unknown 40 21%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 94 49%
Biochemistry, Genetics and Molecular Biology 21 11%
Environmental Science 17 9%
Business, Management and Accounting 2 1%
Medicine and Dentistry 2 1%
Other 4 2%
Unknown 51 27%
Attention Score in Context

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 16 June 2025.
All research outputs
#3,031,107
of 32,322,253 outputs
Outputs from Proceedings of the Royal Society B: Biological Sciences
#4,866
of 12,063 outputs
Outputs of similar age
#39,327
of 329,526 outputs
Outputs of similar age from Proceedings of the Royal Society B: Biological Sciences
#54
of 125 outputs
Altmetric has tracked 32,322,253 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 12,063 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 42.6. This one has gotten more attention than average, scoring higher than 59% 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 329,526 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 88% of its contemporaries.
We're also able to compare this research output to 125 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 56% of its contemporaries.