Using multilevel models to identify drivers of landscape-genetic structure among management areas

Dudaniec, Rachael Y., Rhodes, Jonathan R., Wilmer, Jessica Worthington, Lyons, Mitchell, Lee, Kristen E., McAlpine, Clive A. and Carrick, Frank N. (2013) Using multilevel models to identify drivers of landscape-genetic structure among management areas. Molecular Ecology, 22 14: 3752-3765. doi:10.1111/mec.12359


 
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Author Dudaniec, Rachael Y.
Rhodes, Jonathan R.
Wilmer, Jessica Worthington
Lyons, Mitchell
Lee, Kristen E.
McAlpine, Clive A.
Carrick, Frank N.
Title Using multilevel models to identify drivers of landscape-genetic structure among management areas
Journal name Molecular Ecology   Check publisher's open access policy
ISSN 0962-1083
1365-294X
Publication date 2013-07
Year available 2013
Sub-type Article (original research)
DOI 10.1111/mec.12359
Volume 22
Issue 14
Start page 3752
End page 3765
Total pages 14
Place of publication Oxford, United Kingdom
Publisher Wiley-Blackwell Publishing
Collection year 2014
Language eng
Abstract Landscape genetics offers a powerful approach to understanding species’ dispersal patterns. However, a central obstacle is to account for ecological processes operating at multiple spatial scales, while keeping research outcomes applicable to conservation management. We address this challenge by applying a novel multilevel regression approach to model landscape drivers of genetic structure at both the resolution of individuals and at a spatial resolution relevant to management (i.e. local government management areas: LGAs) for the koala (Phascolartos cinereus) in Australia. Our approach allows for the simultaneous incorporation of drivers of landscape-genetic relationships operating at multiple spatial resolutions. Using microsatellite data for 1106 koalas, we show that, at the individual resolution, foliage projective cover (FPC) facilitates high gene flow (i.e. low resistance) until it falls below approximately 30%. Out of six additionalland-cover variables, only highways and freeways further explained genetic distance after accounting for the effect of FPC. At the LGA resolution, there was significant variation in isolation-by-resistance (IBR) relationships in terms of their slopes and intercepts. This was predominantly explained by the average resistance distance among LGAs, with a weaker effect of historical forest cover. Rates of recent landscape change did not further explain variation in IBR relationships among LGAs.By using a novel multilevel model, we disentangle the effect of landscape resistance on gene flow at the fine resolution (i.e. among individuals) from effects occurring at coarser resolutions (i.e. among LGAs). This has important implications for our ability to identify appropriate scale-dependent management actions.
Keyword Habitat fragmentation
Landscape genetics
Mammal dispersal
Multilevel model
Spatial Scale
Wildlife management
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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