Using multilevel models to identify drivers of landscape genetic structure among management areas
(2013) In Molecular Ecology 22(14). p.3752-3765- 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:
https://lup.lub.lu.se/record/3738443
- author
- Dudaniec, Rachael LU ; Rhodes, Jonathan R. ; Worthington-Wilmer, Jessica ; Lyons, Mitchell ; Lee, Kristen E. ; McAlpine, Clive A. and Carrick, Frank N.
- publishing date
- 2013-07
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- habitat fragmentation, landscape genetics, mammal dispersal, multilevel model, spatial scale, wildlife management
- in
- Molecular Ecology
- volume
- 22
- issue
- 14
- pages
- 14 pages
- 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
- 2022-04-19 19:45:53
@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}},
keywords = {{habitat fragmentation; landscape genetics; mammal dispersal; multilevel model; spatial scale; wildlife management}},
language = {{eng}},
number = {{14}},
pages = {{3752--3765}},
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}},
volume = {{22}},
year = {{2013}},
}