landgenreport : a new r function to simplify landscape genetic analysis using resistance surface layers

2015 ◽  
Vol 15 (5) ◽  
pp. 1172-1178 ◽  
Author(s):  
Bernd Gruber ◽  
Aaron T. Adamack
Keyword(s):  
Genetic Analysis ◽  
Surface Layers ◽  
Resistance Surface ◽  
Landscape Genetic

Comparative landscape genetic analysis of three Pacific salmon species from subarctic North America

2010 ◽  
Vol 12 (1) ◽  
pp. 223-241 ◽  
Author(s):  
Jeffrey B. Olsen ◽  
Penelope A. Crane ◽  
Blair G. Flannery ◽  
Karen Dunmall ◽  
William D. Templin ◽  
...  
Keyword(s):  
North America ◽  
Genetic Analysis ◽  
Pacific Salmon ◽  
Landscape Genetic

scholarly journals Landscape-Genetic Analysis of Population Structure in the Texas Gray Fox Oral Rabies Vaccination Zone

2009 ◽  
Vol 73 (8) ◽  
pp. 1292-1299 ◽  
Author(s):  
Randy W. Deyoung ◽  
Angeline Zamorano ◽  
Brian T. Mesenbrink ◽  
Tyler A. Campbell ◽  
Bruce R. Leland ◽  
...  
Keyword(s):  
Population Structure ◽  
Genetic Analysis ◽  
Gray Fox ◽  
Landscape Genetic ◽  
Rabies Vaccination

scholarly journals ResistanceGA: An R package for the optimization of resistance surfaces using genetic algorithms

2014 ◽  
Author(s):  
William E Peterman
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
R Package ◽  
Genetic Distances ◽  
Simultaneous Optimization ◽  
Multiple Resistance ◽  
Resistance Surface ◽  
Considerable Controversy ◽  
Cost Paths ◽  
Landscape Genetic

1. Understanding how landscape features affect functional connectivity among populations is a cornerstone of landscape genetic analyses. However, parameterization of resistance surfaces that best describe connectivity is largely a subjective process that explores a limited parameter space. 2. ResistanceGA is a new R package that utilizes a genetic algorithm to optimize resistance surfaces based on pairwise genetic distances and either CIRCUITSCAPE resistance distances or cost distances calculated along least cost paths. Functions in this package allow for the optimization of both categorical and continuous resistance surfaces, as well as the simultaneous optimization of multiple resistance surfaces. 3. There is considerable controversy concerning the use of Mantel tests to accurately relate pairwise genetic distances with resistance distances. Optimization in ResistanceGA uses linear mixed effects models with the maximum likelihood population effects parameterization to determine AICc, which is the fitness function for the genetic algorithm. 4. ResistanceGA fills a void in the landscape genetic toolbox, allowing for unbiased optimization of resistance surfaces and for the simultaneous optimization of multiple resistance surfaces to create a novel composite resistance surface.

scholarly journals A “seascape genetic” snapshot of Sebastes marinus calls for further investigation across the North Atlantic

2009 ◽  
Vol 66 (10) ◽  
pp. 2219-2222 ◽  
Author(s):  
Christophe Pampoulie ◽  
David Gíslason ◽  
Anna Kristin Daníelsdóttir
Keyword(s):  
Genetic Structure ◽  
Genetic Analysis ◽  
North Atlantic ◽  
Microsatellite Loci ◽  
The North ◽  
Preliminary Information ◽  
The Subject ◽  
The North Atlantic ◽  
Landscape Genetic ◽  
Genetic Pools

Abstract Pampoulie, C., Gíslason, D., and Daníelsdóttir, A. K. 2009. A “seascape genetic” snapshot of Sebastes marinus calls for further investigation across the North Atlantic. – ICES Journal of Marine Science, 66: 2219–2222. A collection of 376 golden redfish (Sebastes marinus) from several fishing grounds in the North Atlantic in late 2001 was genotyped at nine microsatellite loci to provide preliminary information on the possible genetic structure in this species. Landscape genetic analysis revealed the presence of two distinct genetic pools within the North Atlantic, suggesting that S. marinus might be structured within the North Atlantic and should be the subject of more investigation.

Detection of Altered Extracellular Matrix in Surface Layers of Unstable Carotid Plaque: An Optical Spectroscopy, Birefringence and Microarray Genetic Analysis

2011 ◽  
Vol 87 (5) ◽  
pp. 1164-1172 ◽  
Author(s):  
Renee M. Korol ◽  
Peter B. Canham ◽  
Li Liu ◽  
Kasinath Viswanathan ◽  
Gary G. Ferguson ◽  
...  
Keyword(s):  
Extracellular Matrix ◽  
Genetic Analysis ◽  
Optical Spectroscopy ◽  
Carotid Plaque ◽  
Surface Layers

scholarly journals A landscape genetic analysis of important agricultural pest species in Tunisia: The whitefly Bemisia tabaci

PLoS ONE ◽  
2017 ◽  
Vol 12 (10) ◽  
pp. e0185724 ◽  
Author(s):  
Ahmed Ben Abdelkrim ◽  
Tarek Hattab ◽  
Hatem Fakhfakh ◽  
Mohamed Sadok Belkadhi ◽  
Faten Gorsane
Keyword(s):  
Genetic Analysis ◽  
Bemisia Tabaci ◽  
Pest Species ◽  
Agricultural Pest ◽  
Landscape Genetic

Spatial scale affects landscape genetic analysis of a wetland grasshopper

2013 ◽  
Vol 22 (9) ◽  
pp. 2467-2482 ◽  
Author(s):  
Daniela Keller ◽  
Rolf Holderegger ◽  
Maarten J. van Strien
Keyword(s):  
Genetic Analysis ◽  
Spatial Scale ◽  
Landscape Genetic

Thickness and doping effects observed in impure vacuum deposited a-Si films

1984 ◽  
Vol 62 (9) ◽  
pp. 848-852 ◽  
Author(s):  
G. Talukder ◽  
J. A. Cowan ◽  
D. E. Brodie ◽  
J. D. Leslie
Keyword(s):  
Fermi Level ◽  
Band Gap ◽  
High Resistance ◽  
Bulk Material ◽  
Film Deposition ◽  
Surface Layers ◽  
Resistance Surface ◽  
High Concentrations ◽  
Dependent Sample ◽  
Si Films

Films of a-Si have been prepared by vacuum deposition from crucibles of BeO and BN in a hydrogen ambient. The electrical conductivity of these impure films was modified by the addition of aluminum that was coevaporated during the film deposition. The changes observed are consistent with the assumption that the aluminum doping results in a p-type film for low conconcentrations (<2.3 at.%) whereas high concentrations introduce band structure changes as well. Thickness dependent sample resistivities are observed. This result is interpreted using a layer model to approximate two surface layers with resistivities ρs = 108 Ω∙cm and a bulk layer with ρB = 8 × 103 Ω∙cm. Each high-resistance layer, which is approximately 0.12 μm thick, seems to be caused by the increased hydrogen concentration at both surfaces, which is observed in the hydrogen profiling study. It is suggested that these high-resistance surface layers are due to a wider band gap material with the Fermi level near midgap. The bulk material has a smaller band gap with the Fermi level closer to one of the band edges.

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