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Phylogenetic signal and potential for invasiveness

Published on Aug 1, 2016in Agricultural and Forest Entomology1.81
· DOI :10.1111/afe.12158
Kapil R. Raje4
Estimated H-index: 4
(Purdue University),
V. R. Ferris14
Estimated H-index: 14
(Purdue University),
Jeffrey D. Holland10
Estimated H-index: 10
(Purdue University)
Abstract
We hypothesized that the evolutionary stability of insect traits relevant to invasion risk can be used to characterize this risk and the confidence in assigning potential invasiveness. Different longhorned beetle species have biological trait combinations (diet breadth, host tree condition and adult feeding) that predispose them to different degrees of potential invasiveness if introduced outside their native range. Relevant biological information is often not available for newly introduced species; however, the approach described in the present study could rapidly allow risk to be assigned to such discoveries. To test this approach, we used a molecular phylogeny for 56 species of Cerambycidae from five subfamilies to measure phylogenetic signal associated with invasive traits. The biological traits were used to cluster species into seven hypothesized risk groups. Mapping these risk designations back onto the phylogeny allowed us to examine the risk within clades at different taxonomic levels and the stability of the risk designation. We used additional species known to be non-native invasive problems to test our framework. Our method would have correctly predicted the risk of Anoplophora glabripennis (Motschulsky) but not Callidiellum rufipenne (Motschulsky). There was significant phylogenetic signal in both the biological traits and the risk category membership. Our approach can be modified for other groups of concern.
  • References (39)
  • Citations (4)
References39
Newest
#1Martin MaechlerH-Index: 2
#2Peter J. RousseeuwH-Index: 57
Last.Mia HubertH-Index: 37
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#1Alexei J. Drummond (University of Auckland)H-Index: 58
#2Marc A. Suchard (UCLA: University of California, Los Angeles)H-Index: 64
Last.Andrew Rambaut (Edin.: University of Edinburgh)H-Index: 93
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#1Tamara Münkemüller (CNRS: Centre national de la recherche scientifique)H-Index: 23
#2Sébastien Lavergne (CNRS: Centre national de la recherche scientifique)H-Index: 38
Last.Wilfried Thuiller (CNRS: Centre national de la recherche scientifique)H-Index: 99
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#1Koichiro Tamura (ASU: Arizona State University)H-Index: 29
#2Daniel S. Peterson (ASU: Arizona State University)H-Index: 14
Last.Sudhir Kumar (ASU: Arizona State University)H-Index: 65
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#1James D. Barbour (UCR: University of California, Riverside)H-Index: 19
#2Jocelyn G. Millar (UCR: University of California, Riverside)H-Index: 40
Last.Lawrence M. Hanks (UIUC: University of Illinois at Urbana–Champaign)H-Index: 41
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Cited By4
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#1Jun Wang (ZJNU: Zhejiang Normal University)H-Index: 1
#2Xin-Yi Dai (ZJNU: Zhejiang Normal University)H-Index: 1
Last.Jia-Yong Zhang (ZJNU: Zhejiang Normal University)H-Index: 8
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#1Stephanie Haddad (U of M: University of Memphis)H-Index: 4
#2Seunggwan Shin (U of M: University of Memphis)H-Index: 7
Last.Duane D. McKenna (U of M: University of Memphis)H-Index: 17
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#1Jordan Lewis Burke (UBC: University of British Columbia)H-Index: 3
#2Joerg Bohlmann (UBC: University of British Columbia)H-Index: 29
Last.Allan L. Carroll (UBC: University of British Columbia)H-Index: 29
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