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

Dudaniec, Rachael LU ; Rhodes, Jonathan R. ; Worthington-Wilmer, Jessica ; Lyons, Mitchell ; Lee, Kristen E. ; McAlpine, Clive A. and Carrick, Frank N. (2013) In Molecular Ecology
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... (More)
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 additional land-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. (Less)
Please use this url to cite or link to this publication:
author
publishing date
type
Contribution to journal
publication status
epub
subject
keywords
habitat fragmentation, landscape genetics, mammal dispersal, multilevel model, spatial scale, wildlife management
in
Molecular Ecology
publisher
Wiley-Blackwell
external identifiers
  • scopus:84880149808
ISSN
0962-1083
DOI
10.1111/mec.12359
language
English
LU publication?
no
id
1de68fc6-69b9-4958-bbda-4622b26d6a17 (old id 3738443)
date added to LUP
2016-04-01 09:48:53
date last changed
2020-01-22 01:02:00
@article{1de68fc6-69b9-4958-bbda-4622b26d6a17,
  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<br/><br>
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<br/><br>
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<br/><br>
gene flow (i.e. low resistance) until it falls below approximately 30%. Out of six additional land-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<br/><br>
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.<br/><br>
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<br/><br>
to identify appropriate scale-dependent management actions.},
  author       = {Dudaniec, Rachael and Rhodes, Jonathan R. and Worthington-Wilmer, Jessica and Lyons, Mitchell and Lee, Kristen E. and McAlpine, Clive A. and Carrick, Frank N.},
  issn         = {0962-1083},
  language     = {eng},
  month        = {06},
  publisher    = {Wiley-Blackwell},
  series       = {Molecular Ecology},
  title        = {Using multilevel models to identify drivers of landscape genetic structure among management areas},
  url          = {http://dx.doi.org/10.1111/mec.12359},
  doi          = {10.1111/mec.12359},
  year         = {2013},
}