scholarly journals Integrating demography and population genetics through landscape genetics

Ecosistemas ◽  
2019 ◽  
Vol 28 (1) ◽  
pp. 75-90
Author(s):  
Cristina García
Keyword(s):  
Population Genetics ◽  
Landscape Genetics

scholarly journals Union of phylogeography and landscape genetics

2016 ◽  
Vol 113 (29) ◽  
pp. 8079-8086 ◽  
Author(s):  
Leslie J. Rissler
Keyword(s):  
Population Genetics ◽  
Landscape Genetics ◽  
Mojave Desert ◽  
Spatially Explicit ◽  
Novel Studies ◽  
Biology I ◽  
Genomic Scale ◽  
Genetics And Genomics ◽  
Lineage Divergence ◽  
Scale Data

Phylogeography and landscape genetics have arisen within the past 30 y. Phylogeography is said to be the bridge between population genetics and systematics, and landscape genetics the bridge between landscape ecology and population genetics. Both fields can be considered as simply the amalgamation of classic biogeography with genetics and genomics; however, they differ in the temporal, spatial, and organismal scales addressed and the methodology used. I begin by briefly summarizing the history and purview of each field and suggest that, even though landscape genetics is a younger field (coined in 2003) than phylogeography (coined in 1987), early studies by Dobzhansky on the “microgeographic races” of Linanthus parryae in the Mojave Desert of California and Drosophila pseudoobscura across the western United States presaged the fields by over 40 y. Recent advances in theory, models, and methods have allowed researchers to better synthesize ecological and evolutionary processes in their quest to answer some of the most basic questions in biology. I highlight a few of these novel studies and emphasize three major areas ripe for investigation using spatially explicit genomic-scale data: the biogeography of speciation, lineage divergence and species delimitation, and understanding adaptation through time and space. Examples of areas in need of study are highlighted, and I end by advocating a union of phylogeography and landscape genetics under the more general field: biogeography.

scholarly journals Open computational landscape genetics

2016 ◽  
Author(s):  
Stéphane Joost ◽  
Solange Duruz ◽  
Estelle Rochat ◽  
Ivo Widmer
Keyword(s):  
Population Genetics ◽  
Landscape Genetics ◽  
Evolutionary Biology ◽  
Geographical Information Systems ◽  
Information Science ◽  
Molecular Data ◽  
Geographic Information ◽  
Research Field ◽  
Environmental Data ◽  
Geographical Information

Geographical Information Systems (GIS) are considered to be applications-led technology. Consequently, geographic information scientists commonly find themselves as guest in host disciplines in order to best exploit spatial analysis tools and methods, appropriately guided by experts in the field. An example is population genetics in evolutionary biology. Genetic information being linked to living organisms can be partially characterized by geographic coordinates. A research field named landscape genetics emerged at the intersection of genetics, environmental and geographic information science. Geocomputation and programming efforts carried out with the help of open sources technologies and dedicated to the analysis of genetic data gather together a key scientific community whose goal is to extract new knowledge from the present data tsunami caused by the advent of high throughput molecular data and of new sources of high resolution environmental data. While the level of sophistication of the population genetics functions included in the analytical frameworks developed until now are cutting-edge, advanced geo-competences are also required to reinforce the spatial side of this discipline. They will be particularly useful in conservation programmes for wildlife preservation, but also in farm animal genetic resources conservation.

scholarly journals Open computational landscape genetics

2016 ◽  
Author(s):  
Stéphane Joost ◽  
Solange Duruz ◽  
Estelle Rochat ◽  
Ivo Widmer
Keyword(s):  
Population Genetics ◽  
Landscape Genetics ◽  
Evolutionary Biology ◽  
Geographical Information Systems ◽  
Information Science ◽  
Molecular Data ◽  
Geographic Information ◽  
Research Field ◽  
Environmental Data ◽  
Geographical Information

Geographical Information Systems (GIS) are considered to be applications-led technology. Consequently, geographic information scientists commonly find themselves as guest in host disciplines in order to best exploit spatial analysis tools and methods, appropriately guided by experts in the field. An example is population genetics in evolutionary biology. Genetic information being linked to living organisms can be partially characterized by geographic coordinates. A research field named landscape genetics emerged at the intersection of genetics, environmental and geographic information science. Geocomputation and programming efforts carried out with the help of open sources technologies and dedicated to the analysis of genetic data gather together a key scientific community whose goal is to extract new knowledge from the present data tsunami caused by the advent of high throughput molecular data and of new sources of high resolution environmental data. While the level of sophistication of the population genetics functions included in the analytical frameworks developed until now are cutting-edge, advanced geo-competences are also required to reinforce the spatial side of this discipline. They will be particularly useful in conservation programmes for wildlife preservation, but also in farm animal genetic resources conservation.

scholarly journals Probabilistic graph models for landscape genetics

2017 ◽  
Author(s):  
Brook G. Milligan
Keyword(s):  
Population Genetics ◽  
Landscape Genetics ◽  
Genetic Factors ◽  
Spatial Interaction ◽  
Genomic Variation ◽  
Graph Models ◽  
Conceptual Foundation ◽  
Probabilistic Graph ◽  
Model Structures ◽  
Flexible Models

Landscape genetics combines population genetics, landscape ecology, and spatial analysis to identify landscape and genetic factors that influence genetic and genomic variation. Progress in the field depends on a strong conceptual foundation and the means of identifying mechanistic connnections between environmental factors, landscape features, and genetic or genomic variation. Many existing approaches and much of the software commonly in use was developed for population genetics or statistics and is not entirely appropriate for landscape genetics. Probabilistic graph models provide a statistically rigorous and flexible means of constructing models directly applicable to landscape genetics. Probabilistic graph models also allow construction of mechanistic models, which are crucial elements in testing hypotheses. Sophisticated software exists for the analysis of graph models; however, much of it does not handle the types of data used for landscape genetics, model structures involving autoregressive spatial interaction between variables, or the scale of landscape genetics problems. Thus, an important priority for the field is to develop suitably flexible software tools for graph models that overcome these problems and allow landscape geneticists to explore meaningfully mechanistic and flexible models. We are developing such a library and applying it to examples in landscape genetics.

scholarly journals Probabilistic graph models for landscape genetics

2016 ◽  
Author(s):  
Brook G Milligan
Keyword(s):  
Population Genetics ◽  
Environmental Factors ◽  
Landscape Genetics ◽  
Spatial Interaction ◽  
Genomic Variation ◽  
Graph Models ◽  
Conceptual Foundation ◽  
Probabilistic Graph ◽  
Model Structures ◽  
Flexible Models

Progress in landscape genetics depends on a strong conceptual foundation and the means of identifying mechanistic connections between environmental factors, landscape features, and genetic or genomic variation. Many existing approaches and much of the software commonly in use was developed for population genetics or statistics and is not entirely appropriate for landscape genetics. Probabilistic graph models provide a statistically rigorous and flexible means of constructing models directly applicable to landscape genetics. Sophisticated software also exists for the analysis of graph models. However, much of that software does not handle the types of data used for landscape genetics, model structures involving autoregressive spatial interaction between variables, or the scale of landscape genetics problems. Thus, an important priority for the field is to develop suitably flexible software tools for graph models that overcome these problems and allow landscape geneticists to explore meaningfully mechanistic and flexible models. We are developing such a library and applying it to examples in landscape genetics.

Landscape genetics: combining landscape ecology and population genetics

2003 ◽  
Vol 18 (4) ◽  
pp. 189-197 ◽  
Author(s):  
Stéphanie Manel ◽  
Michael K. Schwartz ◽  
Gordon Luikart ◽  
Pierre Taberlet
Keyword(s):  
Population Genetics ◽  
Landscape Ecology ◽  
Landscape Genetics

scholarly journals Is there such a thing as landscape genetics?

2015 ◽  
Author(s):  
Rodney J. Dyer
Keyword(s):  
Population Genetics ◽  
Natural Language Processing ◽  
Language Processing ◽  
Landscape Genetics ◽  
Population Based ◽  
Scientific Discipline ◽  
Genetic Studies ◽  
Analytical Work ◽  
Statistical Approaches ◽  
Landscape Genetic

AbstractFor a scientific discipline to be interdisciplinary it must satisfy two conditions; it must consist of contributions from at least two existing disciplines and it must be able to provide insights, through this interaction, that neither progenitor discipline could address. In this paper, I examine the complete body of peer-reviewed literature self-identified as landscape genetics using the statistical approaches of text mining and natural language processing. The goal here is to quantify the kinds of questions being addressed in landscape genetic studies, the ways in which questions are evaluated mechanistically, and how they are differentiated from the progenitor disciplines of landscape ecology and population genetics. I then circumscribe the main factions within published landscape genetic papers examining the extent to which emergent questions are being addressed and highlighting a deep bifurcation between existing individual- and population-based approaches. I close by providing some suggestions on where theoretical and analytical work is needed if landscape genetics is to serve as a real bridge connecting evolution and ecology sensu lato.

scholarly journals Probabilistic graph models for landscape genetics

2016 ◽  
Author(s):  
Brook G Milligan
Keyword(s):  
Population Genetics ◽  
Environmental Factors ◽  
Landscape Genetics ◽  
Spatial Interaction ◽  
Genomic Variation ◽  
Graph Models ◽  
Conceptual Foundation ◽  
Probabilistic Graph ◽  
Model Structures ◽  
Flexible Models

Progress in landscape genetics depends on a strong conceptual foundation and the means of identifying mechanistic connnections between environmental factors, landscape features, and genetic or genomic variation. Many existing approaches and much of the software commonly in use was developed for population genetics or statistics and is not entirely appropriate for landscape genetics. Probabilistic graph models provide a statistically rigorous and flexible means of constructing models directly applicable to landscape genetics. Sophisticated software also exists for the analysis of graph models. However, much of that software does not handle the types data used by landscape geneticis, model structures involving autoregressive spatial interaction between variables, or the scale of landscape genetics problems. Thus, an important priority for the field is to develop suitably flexible software tools for graph models that overcome these problems and allow landscape geneticists to explore meaningfully mechanistic and flexible models. We are developing such a library and applying it to examples in landscape genetics.

scholarly journals Probabilistic graph models for landscape genetics

2017 ◽  
Author(s):  
Brook G. Milligan
Keyword(s):  
Population Genetics ◽  
Landscape Genetics ◽  
Genetic Factors ◽  
Spatial Interaction ◽  
Genomic Variation ◽  
Graph Models ◽  
Conceptual Foundation ◽  
Probabilistic Graph ◽  
Model Structures ◽  
Flexible Models

Landscape genetics combines population genetics, landscape ecology, and spatial analysis to identify landscape and genetic factors that influence genetic and genomic variation. Progress in the field depends on a strong conceptual foundation and the means of identifying mechanistic connnections between environmental factors, landscape features, and genetic or genomic variation. Many existing approaches and much of the software commonly in use was developed for population genetics or statistics and is not entirely appropriate for landscape genetics. Probabilistic graph models provide a statistically rigorous and flexible means of constructing models directly applicable to landscape genetics. Probabilistic graph models also allow construction of mechanistic models, which are crucial elements in testing hypotheses. Sophisticated software exists for the analysis of graph models; however, much of it does not handle the types of data used for landscape genetics, model structures involving autoregressive spatial interaction between variables, or the scale of landscape genetics problems. Thus, an important priority for the field is to develop suitably flexible software tools for graph models that overcome these problems and allow landscape geneticists to explore meaningfully mechanistic and flexible models. We are developing such a library and applying it to examples in landscape genetics.

scholarly journals Probabilistic graph models for landscape genetics

2017 ◽  
Author(s):  
Brook G. Milligan
Keyword(s):  
Population Genetics ◽  
Landscape Genetics ◽  
Genetic Factors ◽  
Spatial Interaction ◽  
Genomic Variation ◽  
Graph Models ◽  
Conceptual Foundation ◽  
Probabilistic Graph ◽  
Model Structures ◽  
Flexible Models

Landscape genetics combines population genetics, landscape ecology, and spatial analysis to identify landscape and genetic factors that influence genetic and genomic variation. Progress in the field depends on a strong conceptual foundation and the means of identifying mechanistic connnections between environmental factors, landscape features, and genetic or genomic variation. Many existing approaches and much of the software commonly in use was developed for population genetics or statistics and is not entirely appropriate for landscape genetics. Probabilistic graph models provide a statistically rigorous and flexible means of constructing models directly applicable to landscape genetics. Probabilistic graph models also allow construction of mechanistic models, which are crucial elements in testing hypotheses. Sophisticated software exists for the analysis of graph models; however, much of it does not handle the types of data used for landscape genetics, model structures involving autoregressive spatial interaction between variables, or the scale of landscape genetics problems. Thus, an important priority for the field is to develop suitably flexible software tools for graph models that overcome these problems and allow landscape geneticists to explore meaningfully mechanistic and flexible models. We are developing such a library and applying it to examples in landscape genetics.

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