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Identification of landscape features influencing gene flow: How useful are habitat selection models?

Overview of attention for article published in Evolutionary Applications, June 2016
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Title
Identification of landscape features influencing gene flow: How useful are habitat selection models?
Published in
Evolutionary Applications, June 2016
DOI 10.1111/eva.12389
Pubmed ID
Authors

Gretchen H. Roffler, Michael K. Schwartz, Kristy L. Pilgrim, Sandra L. Talbot, George K. Sage, Layne G. Adams, Gordon Luikart

Abstract

Understanding how dispersal patterns are influenced by landscape heterogeneity is critical for modeling species connectivity. Resource selection function (RSF) models are increasingly used in landscape genetics approaches. However, because the ecological factors that drive habitat selection may be different from those influencing dispersal and gene flow, it is important to consider explicit assumptions and spatial scales of measurement. We calculated pairwise genetic distance among 301 Dall's sheep (Ovis dalli dalli) in southcentral Alaska using an intensive noninvasive sampling effort and 15 microsatellite loci. We used multiple regression of distance matrices to assess the correlation of pairwise genetic distance and landscape resistance derived from an RSF, and combinations of landscape features hypothesized to influence dispersal. Dall's sheep gene flow was positively correlated with steep slopes, moderate peak normalized difference vegetation indices (NDVI), and open land cover. Whereas RSF covariates were significant in predicting genetic distance, the RSF model itself was not significantly correlated with Dall's sheep gene flow, suggesting that certain habitat features important during summer (rugged terrain, mid-range elevation) were not influential to effective dispersal. This work underscores that consideration of both habitat selection and landscape genetics models may be useful in developing management strategies to both meet the immediate survival of a species and allow for long-term genetic connectivity.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 146 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 1%
Spain 2 1%
Germany 1 <1%
Uruguay 1 <1%
Canada 1 <1%
Portugal 1 <1%
New Zealand 1 <1%
United States 1 <1%
Unknown 136 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 35 24%
Student > Ph. D. Student 30 21%
Student > Master 30 21%
Student > Bachelor 10 7%
Student > Doctoral Student 8 5%
Other 19 13%
Unknown 14 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 80 55%
Environmental Science 31 21%
Biochemistry, Genetics and Molecular Biology 12 8%
Business, Management and Accounting 3 2%
Computer Science 2 1%
Other 3 2%
Unknown 15 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 June 2016.
All research outputs
#19,942,887
of 25,373,627 outputs
Outputs from Evolutionary Applications
#1,360
of 1,578 outputs
Outputs of similar age
#255,023
of 354,235 outputs
Outputs of similar age from Evolutionary Applications
#20
of 23 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
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