Evaluating and improving analytical approaches in landscape genetics through simulations and wildlife case studies /by Niko Balkenhol.

• Main Author: Balkenhol, Niko.
• Other Authors: Lisette P. Waits.
• Format: Other/Unknown Material
• Language: unknown
• Published: 2009
• Subjects: Biodiversity > Computer simulation.; Wolverine > Idaho > Genetics.; Wolverine > Montana > Genetics.; Wolverine > Wyoming > Genetics.; Nature > Effect of roads on; Gulo gulo; wolverine
• Online Access: http://proquest.umi.com/pqdweb?did=1830973381&Fmt=7&clientId=58634&RQT=309&VName=PQD; http://digital.lib.uidaho.edu/cdm/ref/collection/etd/id/390

Landscape genetics is an emerging scientific field that combines population genetics, landscape ecology, and spatial statistics. The goal of landscape genetics is to detect and explain landscape and environmental impacts on genetic diversity and structure in plant and animal populations. In the fast few years, a plethora of statistical methods for analyzing landscape genetic data has been proposed. However, the reliability and comparability of these methods remains largely untested. In this dissertation, simulated and empirical data are used to investigate the advantages and limitations of different analytical approaches, and to improve current approaches for landscape genetic data analysis.;In chapter 1, simulated data were used to assess the impacts of spatial sampling design and sampling intensity on Bayesian clustering methods. Results suggested that sampling intensity has a much stronger impact on all clustering methods than spatial sampling design. Furthermore, the study demonstrated that drawing inferences from genetic clustering methods can be challenging, and suggestions for improved interpretation and reporting of results are given.;Chapter 2 evaluated statistical methods for linking landscape and genetic data. Results showed that error rates for certain methods are high, and that analyzing a data set with only one method can lead to method-dependent and potentially erroneous conclusions. Based on these findings, guidelines for choosing an optimal combination of statistical methods were developed.;In chapter 3, a landscape genetic approach was used to assess environmental influences on genetic connectivity of wolverines (Gulo gulo) in Idaho, Montana, and Wyoming. The analyses suggest that wolverine gene flow in the study area was influenced by a combination of snow depth, terrain ruggedness, and housing density, and that the relative importance of these different variables was scale-dependent.;Finally, in chapter 4, a new field, molecular road ecology, is defined. Molecular road ecology uses genetic ...