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David G. Chapple
Researcher at Monash University, Clayton campus
Publications - 194
Citations - 6405
David G. Chapple is an academic researcher from Monash University, Clayton campus. The author has contributed to research in topics: Skink & Population. The author has an hindex of 33, co-authored 157 publications receiving 5128 citations. Previous affiliations of David G. Chapple include Flinders University & Commonwealth Scientific and Industrial Research Organisation.
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Isolation and characterization of microsatellite loci from the invasive delicate skink (Lampropholis delicata), with cross-amplification in other Australian Eugongylus group species
TL;DR: 12 polymorphic microsatellite loci are isolated and characterize for the delicate skink (Lampropholis delicata), native to eastern Australia, and will be used to investigate the invasion dynamics of L. delicata.
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Genetic Divergence among Regions Containing the Vulnerable Great Desert Skink (Liopholis kintorei) in the Australian Arid Zone.
TL;DR: The genetic structure and differentiation of the great desert skink (Liopholis kintorei), which has a patchy, but widespread distribution in the western region of the Australian arid zone, is characterised and suggests three main regions that should be managed separately, in particular the southeastern locality of Uluru.
Journal ArticleDOI
Pervasive admixture and the spread of a large-lipped form in a cichlid fish radiation
Will Sowersby,Will Sowersby,José Cerca,José Cerca,José Cerca,Bob B. M. Wong,Topi K. Lehtonen,Topi K. Lehtonen,Topi K. Lehtonen,David G. Chapple,Mariana Leal-Cardin,Mariana Leal-Cardin,Marta Barluenga,Mark Ravinet,Mark Ravinet,Mark Ravinet +15 more
TL;DR: In this article, the authors investigated the evolutionary history of the large-lipped form of the Midas cichlid, specifically regarding whether the trait has evolved independently in both lakes from ancestral thin lipped populations, or via dispersal and/or admixture events.
Posted ContentDOI
Automated assessment reveals extinction risk of reptiles is widely underestimated across space and phylogeny
Gabriel Henrique de Oliveira Caetano,David G. Chapple,Richard Grenyer,T. Raz,Jonathan D. Rosenblatt,Reid Tingley,Monika Böhm,Shai Meiri,Uri Roll +8 more
TL;DR: In this article , a machine learning-based automated threat assessment method was proposed to assess 4,369 reptile species that are currently unassessed or classified as Data Deficient by the International Union for Conservation of Nature (IUCN).