Landscape Genetics E-Book

Niko Balkenhol

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 833

Veröffentlichungsjahr: 2015

Beschreibung

LANDSCAPE GENETICS: CONCEPTS, METHODS, APPLICATIONS

LANDSCAPE GENETICS: CONCEPTS, METHODS, APPLICATIONS

Edited by Niko Balkenhol, Samuel A. Cushman, Andrew T. Storfer, Lisette P. Waits

Landscape genetics is an exciting and rapidly growing field, melding methods and theory from landscape ecology and population genetics to address some of the most challenging and urgent ecological and evolutionary topics of our time. Landscape genetic approaches now enable researchers to study in detail how environmental complexity in space and time affect gene flow, genetic drift, and local adaptation. However, learning about the concepts and methods underlying the field remains challenging due to the highly interdisciplinary nature of the field, which relies on topics that have traditionally been treated separately in classes and textbooks.

In this edited volume, some of the leading experts in landscape genetics provide the first comprehensive introduction to underlying concepts, commonly used methods, and current and future applications of landscape genetics. Consistent with the interdisciplinary nature of the field, the book includes textbook-like chapters that synthesize fundamental concepts and methods underlying landscape genetics (Part 1), chapters on advanced topics that deserve a more in-depth treatment (Part 2), and chapters illustrating the use of concepts and methods in empirical applications (Part 3).

Aimed at beginning landscape geneticists and experienced researchers alike, this book will be helpful for all scientists and practitioners interested in learning, teaching, and applying landscape genetics.

Leseprobe

CONTENTS

Cover

Title Page

Copyright

List of Contributors

Website

Acknowledgments

Glossary

Chapter 1: Introduction To Landscape Genetics – Concepts, Methods, Applications

1.1 Introduction

1.2 Defining Landscape Genetics

1.3 The Three Analytical Steps of Landscape Genetics

1.4 The Interdisciplinary Challenge of Landscape Genetics

1.5 Structure of This Book – Concepts, Methods, Applications

References

Part 1: Concepts

Chapter 2: Basics of Landscape Ecology: An Introduction to Landscapes and Population Processes for Landscape Geneticists

2.1 Introduction

2.2 How Landscapes Affect Population Genetic Processes

2.3 Defining the Landscape for Landscape Genetic Research

2.4 Defining Populations and Characterizing Dispersal Processes

2.5 Putting it Together: Combinations of Landscape and Population Models

2.6 Frameworks for Delineating Landscapes and Populations for Landscape Genetics

2.7 Current Challenges and Future Opportunities

References

Chapter 3: Basics of Population Genetics: Quantifying Neutral and Adaptive Genetic Variation for Landscape Genetic Studies

3.1 Introduction

3.2 Overview of Landscape Influences on Genetic Variation

3.3 Overview of DNA Types and Molecular Methods

3.4 Important Population Genetic Models

3.5 Measuring Genetic Diversity

3.6 Evaluating Genetic Structure and Detecting Barriers

3.7 Estimating Gene Flow Using Indirect and Direct Methods

3.8 Conclusion and Future Directions

References

Chapter 4: Basics of Study Design: Sampling Landscape Heterogeneity and Genetic Variation for Landscape Genetic Studies

4.1 Introduction

4.2 Study Design Terminology Used in This Chapter

4.3 General Study Design Considerations

4.4 Considerations for Landscape Genetic Study Design

4.5 Current Knowledge About Study Design Effects in Landscape Genetics

4.6 Recommendations for Optimal Sampling Strategies in Landscape Genetics

4.7 Conclusions and Future Directions

References

Chapter 5: Basics of Spatial Data Analysis: Linking Landscape and Genetic Data for Landscape Genetic Studies

5.1 Introduction

5.2 How to Model Landscape Effects on Genetic Variation

5.3 How to Model Isolation-By-Distance

5.4 Future Directions

Acknowledgments

References

Part 2: Methods

Chapter 6: Simulation Modeling in Landscape Genetics

6.1 Introduction

6.2 A Brief Overview of Models and Simulations

6.3 General Benefits of Simulation Modeling

6.4 Landscape Genetic Simulation Modeling

6.5 Examples of Simulation Modeling in Landscape Genetics

6.6 Designing and Choosing Landscape Genetic Simulation Models

6.7 The Future of Landscape Genetic Simulation Modeling

References

Chapter 7: Clustering and Assignment Methods in Landscape Genetics

7.1 Introduction

7.2 Exploratory Data Analysis and Model-Based Clustering for Population Structure Analysis

7.3 Spatially-Explicit Methods in Landscape Genetics

7.4 Spatial EDA Methods: Spatial PCA and Spatial Factor Analysis

7.5 Spatial MBC Methods

7.6 Habitat and Environmental Heterogeneity Models

7.7 Discussion

References

Chapter 8: Resistance Surface Modeling in Landscape Genetics

8.1 Introduction

8.2 Techniques for Parameterizing Resistance Surfaces

8.3 Estimating Connectivity from Resistance Surfaces

8.4 Statistical Validation of Resistance Surfaces

8.5 The Future of the Resistance Surface in Landscape Genetics

8.6 Conclusions

References

Chapter 9: Genomic Approaches in Landscape Genetics

9.1 Introduction

9.2 Current Landscape Genomics Methods

9.3 General Challenges in Landscape Genomics

9.4 Spatial Autocorrelation

9.5 Applications of Landscape Genomics to Climate Change

References

Chapter 10: Graph Theory and Network Models in Landscape Genetics

10.1 Introduction

10.2 Background on Graph Theory

10.3 Landscape Genetic Applications

10.4 Recommendations for Using Graph Approaches in Landscape Genetics

10.5 Current Research Needs

10.6 Conclusion – Potential for Application of Graphs for Conservation

References

Part 3: Applications

Chapter 11: Landscapes and Plant Population Genetics

11.1 Introduction

11.2 Contemporary Population Genetic Processes

11.3 Historical Population Genetic Processes

11.4 Future Research

References

Chapter 12: Applications of Landscape Genetics to Connectivity Research in Terrestrial Animals

12.1 Introduction

12.2 General Overview of Terrestrial Animal Study Systems and Research Challenges

12.3 Detecting Barriers and Defining Corridors

12.4 Evaluating Population Dynamics

12.5 Detecting and Predicting the Response to Landscape Change

12.6 Common Limitations of Landscape Genetic Studies Involving Terrestrial Animals

12.7 Testing Ecological Hypotheses about Gene Flow in Heterogeneous Landscapes

12.8 Knowledge Gaps and Future Directions

References

Chapter 13: Waterscape Genetics – Applications of Landscape Genetics to Rivers, Lakes, and Seas

13.1 Introduction

13.2 Understanding Marine and Freshwater Environments

13.3 Typical Research Questions and Approaches

13.4 Applications of Landscape Genetic Approaches

13.5 Future Directions: Knowledge Gaps, Research Challenges, and Limitations

Acknowledgments

References

Chapter 14: Current Status, Future Opportunities, and Remaining Challenges in Landscape Genetics

14.1 Introduction

14.2 Conclusion 1: Issues of Scale Need to be Considered

14.3 Conclusion 2: Sampling Needs to Specifically Target Landscape Genetic Questions

14.4 Conclusion 3: Choice of Appropriate Statistical Methods Remains Challenging

14.5 Conclusion 4: Simulations Play a Key Role in Landscape Genetics

14.6 Conclusion 5: Measures of Genetic Variation are Rarely Developed Specifically for Landscape Genetics

14.7 Conclusion 6: Landscape Resistance is Just One of the Possible Landscape–Genetic Relationships

14.8 Conclusion 7: Genomics Provides Novel Opportunities, But Also Creates New Challenges

14.9 Conclusion 8: The Scope of Landscape Genetics Needs to Expand

14.10 Conclusion 9: Specific Hypotheses are Rarely Stated in Current Landscape Genetic Studies

14.11 Conclusion 10: A Comprehensive Theory for Landscape Genetics is Currently Missing

14.12 The Future of Landscape Genetics

References

Index

End User License Agreement

List of Tables

Table 1.1

Table 2.1

Table 3.1

Table 3.2

Table 3.3

Table 3.4

Table 3.5

Table 4.1

Table 4.2

Table 6.1

Table 6.2

Table 7.1

Table 9.1

Table 10.1

Table 10.2

Table 10.3

Table 10.4

Table 12.1

Table 12.2

Table 12.3

Table 12.4

List of Illustrations

Fig. 2.1

Fig. 2.2

Fig. 2.3

Fig. 2.4

Fig. 2.5

Fig. 2.6

Fig. 2.7

Fig. 2.8

Fig. 2.9

Fig. 2.10

Fig. 2.11

Fig. 3.1

Fig. 3.2

Fig. 3.3

Fig. 3.4

Fig. 4.1

Fig. 4.2

Fig. 4.3

Fig. 4.4

Fig. 5.1

Fig. 5.2

Fig. 5.3

Fig. 6.1

Fig. 7.1

Fig. 7.2

Fig. 8.1

Fig. 8.2

Fig. 8.3

Fig. 8.4

Fig. 8.5

Fig. 10.1

Fig. 10.2

Fig. 10.3

Fig. 13.1

Fig. 13.2

Fig. 13.3

Fig. 13.4

Fig. 13.5

Fig. 13.6

Fig. 14.1

Guide

Cover

Table of Contents

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Landscape Genetics

Concepts, Methods, Applications

Edited by

Niko Balkenhol

Department of Wildlife Sciences, University of Göttingen, Germany

Samuel A. Cushman

Forest and Woodlands Ecosystems Program, Rocky Mountain Research Station, United States Forest Service, USA

Andrew T. Storfer

School of Biological Sciences, Washington State University, USA

Lisette P. Waits

Fish and Wildlife Sciences, University of Idaho, USA

This edition first published 2016 © 2016 by John Wiley & Sons Ltd

Registered office: John Wiley & Sons, Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK

Editorial offices: 9600 Garsington Road, Oxford, OX4 2DQ, UK

The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK

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All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of the publisher.

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Limit of Liability/Disclaimer of Warranty: While the publisher and author(s) have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. It is sold on the understanding that the publisher is not engaged in rendering professional services and neither the publisher nor the author shall be liable for damages arising herefrom. If professional advice or other expert assistance is required, the services of a competent professional should be sought.

Library of Congress Cataloging-in-Publication Data

Landscape genetics : concepts, methods, applications / edited by Niko Balkenhol, Samuel A. Cushman, Andrew T. Storfer, and Lisette P. Waits.

pages cm

Includes bibliographical references and index.

ISBN 978-1-118-52528-9 (cloth) – ISBN 978-1-118-52529-6 (pbk.)

1. Ecological genetics. 2. Landscape ecology. 3. Population genetics. I. Balkenhol, Niko.

QH456.L36 2015

576.5'8–dc23

2015015467

A catalogue record for this book is available from the British Library.

Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic books.

List of Contributors

Michael F. Antolin

Department of Biology, Colorado State University, USA

Niko Balkenhol

Department of Wildlife Sciences, University of Göttingen, Germany

Samuel A. Cushman

Forest and Woodlands Ecosystems Program, Rocky Mountain Research Station, United States Forest Service, USA

Rodney J. Dyer

Department of Biology, Virginia Commonwealth University, USA

Bryan K. Epperson

Department of Forestry, Michigan State University, USA

Marie-Josée Fortin

Department of Ecology and Evolutionary Biology, University of Toronto, Canada

Olivier François

Grenoble INP, Université Grenoble-Alpes, France

Heather M. Galindo

University of Washington Bothell, USA

Erin Landguth

Division of Biological Sciences, University of Montana, USA

Stéphanie Manel

Centre d'Ecologie Fonctionnelle et Evolutive (CEFE), France

Kevin McGarigal

Department of Natural Resources Conservation, University of Massachussetts, USA

Brad H. McRae

The Nature Conservancy, North America Region

Melanie Murphy

Department of Ecosystem Science and Management, Program in Ecology, University of Wyoming, USA

Kim T. Scribner

Department of Fisheries and Wildlife & Department of Zoology, Michigan State University, USA

Kimberly A. Selkoe

National Center for Ecological Analysis and Synthesis (NCEAS), University of California Santa Barbara, USA & Hawaii Institute of Marine Biology, University of Hawaii, USA

Stephen F. Spear

The Orianne Society, USA

Andrew Storfer

School of Biological Sciences, Washington State University, USA

Helene H. Wagner

Department of Ecology and Evolutionary Biology, University of Toronto, Canada

Lisette P. Waits

Fish and Wildlife Sciences, University of Idaho, USA

Website

Please visit the website accompanying this book to learn about the newest developments in landscape genetics:

www.landscapegenetics.info

The website lists landscape genetic papers, provides links to analytical tools and research labs, and announces jobs, conferences, and training opportunities in landscape genetics.

Acknowledgments

This book is the result of a long and ongoing journey that started with the seemingly simple idea of teaching landscape genetics for graduate students and professionals. Since the first landscape genetics workshop and symposium were conducted at the 2007 International Association for Landscape Ecology (IALE) in Wageningen, Netherlands, we and several of the chapter authors have collaborated to teach multiple landscape genetics short courses, classes and workshops. We are grateful to the various funding agencies that allowed us to organize these training opportunities, including the Vokswagen Foundation (Germany), the National Center for Ecological Analysis and Synthesis (NCEAS, USA), the University of Idaho, American Genetics Association, and the Canadian Institute of Ecology and Evolution. In 2008, funding from NCEAS allowed Lisette Waits and Helene Wagner to initiate a distributed graduate seminar that has been taught to over 400 participants across the globe three times in the last six years by a community of landscape geneticists. We benefitted substantially from our experiences during these various courses while interacting with colleagues and students from many different countries, cultures, and disciplines. We thank you all for all your valuable feedback and encouragement over the last years and hope that the positive vibe and energizing momentum of landscape genetics is also reflected in this book.

We are also deeply grateful to the many reviewers of the chapters in this book whose comments greatly improved its quality.

The Editors

Göttingen, Flagstaff, Pullman, Moscow

Spring 2015

Glossary

Adaptive locus

See non-neutral locus.

Adfluvial

Species that migrate between freshwater lakes and streams or rivers.

Advection

Horizontal movement (e.g., of water), usually due to transport by currents.

Agent-based models (ABMs)

A class of computational models for simulating the actions and interactions of autonomous agents (both individual or collective individuals grouped into populations). Synonymous to individual-based models, which is the term more commonly used in ecology.

Allele

A unique genetic variant observed at a particular locus.

Anadromous

Aquatic species that migrate from salt water into fresh water to reproduce.

Ancestry distribution models (ADM)

Correlative models relating ancestry coefficients to environmental predictors.

Anisotropy

A situation where the autocorrelation in the data depends on direction as in directional asymmetry (e.g., when movement between two locations is more frequent or more probable in one direction than the opposite direction, e.g., due to the main wind direction across the study area).

Assignment methods/assignment tests

Methods that use genotypic information to evaluate population membership of sampled individuals.

Autocorrelation

A measure of the average similarity of any two observations depending on their spatial, temporal, or phylogenetic lag (i.e., distance). May be positive (nearby observations are more similar than distant ones), zero (independence, absence of significant autocorrelation), or negative (nearby observations are less similar than distant ones).

Backward simulator

Genetic signature is reconstructed typically using coalescent theory from time 0 to time

n

where

n

< 0.

Bandwidth

Area associated with a graph edge (edge “width”).

Betweenness (Node/Edge)

The number of times this element is traversed between all “shortest paths” in the graph.

Biophysical model

In the study of ocean and lake connectivity, biophysical models are simulations of larval dispersal and/or population dynamics based on representation of the geographic distribution of habitat, current flow, and life history parameters of the taxa of interest. More complex models incorporate the effects of seasonality, temperature, productivity, tides, and other environmental characteristics that impact demographic rates.

Bottlenecks

When a population goes through a period where its effective population size is extremely small, resulting in an increase in the effects of genetic drift and a consequent loss of genetic variation.

Catadromous

Aquatic species that migrate from fresh water in salt water to reproduce.

Centrality (Node/Edge)

A general term for measures of the relative position of a node or an edge in terms of direct or indirect connectivity or facilitation of flow through a network. There are four major types of centrality: degree, betweenness, closeness, and eigenvector.

Chloroplast DNA (cpDNA)

A circular DNA molecule found in the chloroplast of plants.

Clique (Node)

In general, a group of nodes more connected than expected by chance.

Coalescent

A body of theory that investigates time of divergence from a common ancestor. In population genetics, this theory can be applied to understanding differences in allele frequencies among populations.

Conditional genetic distance (cGD)

A measure of genetic dissimilarity based upon conditional genetic covariance. This measure differs from genetic distance measures in that it is not a pair-wise measure but the distance through a Population Graph constructed based on all of the data.

Cycles (Edge)

A sequence of paths that forms a closed loop.

Degree

The number of connections a node has to other nodes.

Deme

A deme is a group of individuals that is sufficiently genetically isolated from other groups of individuals and can also be considered a population.

Dendritic

Description of the geometric pattern of branching, consisting of a main stem and branches that decrease in size and increase in number hierarchically from downstream to upstream reaches of the network.

Diameter (Graph)

The shortest path through the graph with the longest length (e.g., the major axis of a graph).

Dispersal kernel

Probability distribution of the distance travelled by individuals or their propagules (e.g., larvae, seeds, pollen).

Eddy

A circular movement of water, counter to a main current, causing a small whirlpool.

Edge (Graph)

Connection between locations.

Effective population size (Ne)

The number of breeding individuals in an idealized population that would show the same amount of change in allele frequencies under random genetic drift or the same amount of inbreeding as the population under consideration.

Essential parameter(s)

Variables used to control the essential processes. In most simulations, some of these essential parameters are held constant, while others are varied, so that quasi-experimental studies can be conducted.

Essential process(es)

Processes that are included in a model because they are assumed to be vital for the functioning of the modelled system. They are often also the processes that are of interest to the study. In landscape genetic simulations, essential processes could, for example, be movement, mating, and reproductive fitness of individuals.

Exploratory data analysis (EDA)

A set of descriptive methods for summarizing data using visual representations from computer packages without statistical models.

Extent

The area within the landscape boundary and defines the population for the analysis.

Factor analysis

A statistical model that attempts to explain a set of observed variables in terms of combinations of unobserved variables called factors.

Fitness surface

In analysis of adaptive evolution in a spatially explicit context, a fitness surface represents the local fitness of a particular genotype at each location in the landscape and is used to model selection for different genotypes as a function of landscape conditions.

Forward simulator

Genetic divergence is simulated from time 0 to time

n

where

n

> 0.

Functional connectivity

The degree to which landscape composition facilitates or impedes movement.

Genetic clines

Large-scale continuous variation of allele frequencies in geographic space.

Genotype

The combination of alleles observed at a particular locus for a specific individual.

Grain

In the context of landscape definition, grain refers to the dimension of the smallest resolved element in the landscape, and is typically corresponds to pixel size in a raster landscape and to minimum mapped patch size in a vector representation of a landscape.

Graph

Collection of nodes connected by edges representing similarity or connections. A graph representing flow (e.g., movement of individuals, information, genes) is often referred to as a network.

Graph topology

Overall structure of a graph.

Gravity model

Network flow model based on Newton's gravitational interactions formula.

Groundwater

Water located beneath the ground's surface in soil and in fractures of bedrock.

Haploid

An organism or a cell that contains only one set of chromosomes in its genome.

Haplotype

An allele or combination of alleles passed on from a single parent.

Headwater

Source of a river or stream.

Heterozygote

In a diploid organism, an individual that has two different alleles at each of its homologous chromosomes.

Homozygote

In a diploid organism, an individual that has two copies of the same allele on homologous chromosomes.

Hydrodynamic connectivity

Transfer of water, energy, matter, or organisms caused by the motion of fluids, commonly in reference to ocean currents.

Hydrographic regime

Aquatic setting defined by dominant physical features such as waves, tides, and current pattern, and associated depth profile (e.g., sandbars and channels).

Hydrologic connectivity

Transfer of water, energy, matter, or organisms within or between elements of the hydrologic cycle.

Hyphoretic zone

A

region mixing of groundwater and surface water beneath and alongside a stream bottom.

Inbreeding

Mating between closely related individuals.

Individual-based models (IBMs)

a class of computational models for simulating the actions and interactions of autonomous agents (both individual or collective individuals grouped into populations). Equivalent to ABMs, but the term individual-based is more commonly used in ecology.

Induced spatial dependence

Spatial autocorrelation created by response to spatially structured landscape factors. If these factors are correctly included in the model, the autocorrelation will be removed from the residuals, but if some factors are missing or incorrectly specified, the residuals may show spatial autocorrelation.

Influential points

Unusual observations that may have a large influence on significance tests and parameter estimates. Such points may be difficult to detect in multivariate regression and in statistical methods based on distance matrices (Mantel tests, multiple regression of distance matrices (MRMs)).

Inherent spatial autocorrelation

Autocorrelation created by a biological process affecting the response (e.g., allele frequencies) directly, such as spatially restricted mating and dispersal.

Isotropy

When a spatial process, such as dispersal in a species, has the same intensity in all compass directions.

Jet

A stream of water moving in a rapid and organized fashion, relative to a main current.

Landscape

A landscape is a system of interacting ecological patterns and processes at any scale and can be considered to be an area that is spatially heterogeneous in at least one factor of interest at a scale relevant to the pattern-process relationships related to that factor of interest.

Landscape definition

The spatial definition of the landscape data utilized for analysis, including the thematic content, thematic resolution, grain and extent.

Landscape genetics

Research that combines population genetics, landscape ecology, and spatial analytical techniques to explicitly quantify the effects of landscape composition, configuration, and matrix quality on microevolutionary processes, such as gene flow, drift, and selection, using neutral and adaptive genetic data.

Lentic

Standing or still water; lentic aquatic systems include ponds, lakes, and wetlands.

Likelihood

A quantity that describes the probability of the data in a particular statistical model conditional on the model parameters.

Linkage disequilibrium

Non-random association of alleles at two or more loci; these alleles tend to be inherited together significantly more often than expected by random chance.

Locus (Loci)

A locus is a particular location in the genome of an organism. The term loci is the plural form of locus.

Lotic

Flowing water, such as rivers, springs, and streams.

Mitochondrial DNA (mtDNA)

A circular DNA molecule of the mitochondrion which is haploid and generally passed on only from mother to offspring.

Model-based clustering (MBC)

A set of approaches where the data are clustered using statistical mixture models.

Model evaluation

The process of evaluating whether a certain model structure is useful for addressing a specific research question.

Moran eigenvector maps (MEM)

A method for extracting spatial eigenvectors (see definition below) at multiple scales whose associated eigenvalues are proportional to their Moran's

I

.

Moran's

I

A measure of spatial autocorrelation. Varies mostly between 1 (perfect positive autocorrelation) and –1 (perfect negative autocorrelation).

Multivariate regression

Linear model relating the variation in a matrix Y of multiple response variables (e.g., table of allele frequencies) to one (simple regression) or more than one (multiple regression) predictors X.

Mutation

A change in the DNA sequence of an organism. Mutations that occur in the DNA of germ cells can be passed on to the next generation.

Neutral locus

A locus where the frequency of alleles is not affected by selection.

Node

A point location (e.g., in a graph). In addition to location (a), nodes can have size (area), shape (polygon), and characteristics (e.g., habitat quality).

Non-neutral or adaptive locus

A locus where the frequency of alleles is affected by selection.

Non-stationarity

Refers to violations of the assumption of stationarity (i.e., that the process that generated the pattern has the same intensity and variability over the entire study area) and can be detected when either the correlation between genetic data Y and landscape predictors X, or the mean, variance, and spatial autocorrelation in the residuals U are not constant across the study area.

Non-synymous substitutions

Mutations in coding DNA that result in changes in amino acid sequence.

Nuclear DNA (nDNA)

The DNA of the chromosomes found in the nucleus of a cell.

ODD protocol

Guidelines for describing a simulation model that enables other researchers to follow both the general model structure and the specifics of model parameterization and implementation. The three letters stand for Overview, Design concepts, and Details.

Panmixia

Random mating of individuals within a population or geographic area.

Pattern-oriented modeling

Synonymous to pattern-process modeling.

Pattern-process modeling

Evaluates whether an underlying process inferred through empirical induction can produce the patterns observed in the data, and how well it can do so.

Population graph

Graph theoretic representation of genetic relationships among sample locations.

Principal component analysis (PCA)

A method of data reduction that replaces a set of observed variables, such as allele frequencies, that may be correlated among themselves by a set of synthetic variables, the PCA axes, that are orthogonal and perfectly uncorrelated with each other. The first PCA axis is defined to capture a maximum of the variation in the original data set, the second PCA axes a maximum of the remaining variance, and so forth.

Residual analysis methods

A set of exploratory data analysis methods used to detect evidence of violations of regression assumptions.

Resistance surface

A representation of the landscape in which each location is assigned a cost or resistance which affects movement and gene flow through the landscape.

Riparian

At the interface between terrestrial and freshwater habitats, such as the edge of a river.

Robustness analysis

The process of evaluating whether simulation results are robust to changes in the actual model structure (i.e., by changing essential processes).

Sensitivity analysis

A technique used to determine how different values of an independent variable will impact a particular dependent variable under a given set of assumptions. This technique is used within specific boundaries that will depend on one or more input variables, such as the effect that changes in mutation rates will have on a landscape genetic inference.

Simplifying assumption(s)

Decisions about what processes and parameters not to include in the model. These assumptions basically represent hypotheses about how a system works.

Spatial autocorrelation

Measure of the degree to which individuals that are closer together in space are more genetically similar than those further apart.

Spatial eigenvectors

A set of completely uncorrelated, orthogonal synthetic variables that provides a spectral decomposition of the data based on the spatial coordinates of the sampling locations. Each spatial eigenvector, when plotted in space, will show a sine-type pattern with a specific period. On a regular transect, the largest-scale pattern has a period of the entire transect length, whereas the smallest-scale pattern has a period of twice the distance between adjacent sampling locations, so that high and low values alternate. For two-dimensional or irregular sampling designs, the patterns may be more complex.

Spatially-explicit

Spatial locations of individuals or groups of individuals (i.e., populations) are defined or monitored.

Stream reach

Segment of stream or river bounded by the confluence of another stream or river.

Sweepstakes reproductive success

When offspring of a small number of individuals dominate the production of new cohorts, resulting in a strong genetic drift that can enhance genetic differentiation of samples compared across space and time, often in an apparently random or unexpected way. The phenomenon, which has been theorized but rarely shown robustly, requires that individual fecundity is very high (e.g., oysters that spawn millions of larvae at a time) and that the conditions favorable for survival and settlement of larvae are patchy in space and time.

Synonymous substitutions

Mutations in coding DNA that do not result in changes in amino acid sequence. Often called “silent substitutions”.

Tessellation

A pattern of non-overlapping polygons without gaps is used to partition a focal area. In landscape genetics, tessellation is generally used to create polygons around each point where genotypes are collected.

Tidal bore

A breaking wave caused by the leading edge of an incoming tide that may entrain material, such as larvae, that travel with it. The wave may be positioned at depth below the surface layer.

Thematic content

The factors included within the landscape definition, such as landcover categories, abiotic variables, linear features, and topographical landforms. Choice of which factors to include in the landscape definition should be guided by

a priori

hypotheses about how these features will affect the pattern-process relationship of interest, which in landscape genetics may be gene flow, genetic diversity or adaptive evolutionary processes.

Thematic resolution

The resolution or functional form at which the variables included in the landscape definition (thematic content) are represented in the landscape definition. For categorical variables such as land cover this will involve choice of how many map classes to represent. For continuous variables such as elevation or canopy cover thematic resolution will be the functional form of the relationship between the variable and the response, such as linear, quadratic, exponential, Gaussian, and power functions. Choices about thematic resolution should be governed by

a priori

hypotheses or prior knowledge about the relationship between the landscape variable and the pattern-process relationship of interest.

Tributary

A river or stream flowing into a larger river or lake.

Turbulence

Disorganized or chaotic water movement. Turbulence promotes mixing and can transport larvae in unpredictable, circuitous routes.

Uncertainty analysis

A technique used to assess the confidence in model variables or results.

Upwelling/relaxation cycles

Upwelling is the rising of deep water to the surface, often associated with strong wind pushing coastal surface waters offshore. When winds cease, prevailing currents push the same water back to shore during the relaxation phase. Release of coastal marine larvae is sometimes associated with upwelling/relaxing cycles, allowing larvae to complete development offshore where predation is less and then return to their natal habitat with little down coast movement.

Validation

Establishing evidence that provides a high degree of assurance that simulation programs accomplish intended requirements.

Verification

Ensuring that simulation programs are correctly working (i.e., that no programming errors occur).

Waterscape features

Features of an aquatic environment that may structure the genetic variation of populations and individuals by effecting movement through water and the entry and exit of habitat patches. Examples include wind and water current speeds and directions, water clarity, temperature and nutrient gradients, bathymetry and bottom type, and ecological factors controlling variation in population size or migration rates.

Chapter 1Introduction To Landscape Genetics – Concepts, Methods, Applications

NikoBalkenhol1, Samuel A.Cushman2, AndrewStorfer3 and Lisette P.Waits4

1Department of Wildlife Sciences, University of Göttingen, Germany

2Forest and Woodlands Ecosystems Program, Rocky Mountain Research Station, United States Forest Service, USA

3School of Biological Sciences, Washington State University, USA

4Fish and Wildlife Sciences, University of Idaho, USA

1.1 Introduction

Genetic variation is considered the most basic level of biological diversity and a prerequisite for the variability of species, populations, and ecosystems (Primack 2014). Diversity at the genetic level is also crucial for the fitness and survival of individuals, the viability of populations, and the ability of species to adapt to environmental change (Allendorf et al. 2012; Frankham et al. 2010). Thus, conserving genetic diversity is important in itself, and researchers in many disciplines, including ecology, evolution, and conservation, are interested in understanding the factors that shape patterns of genetic variation in nature. The foundations for understanding genetic diversity were laid more than 100 years ago (e.g., Hardy 1908; Weinberg 1908; Wright 1917), at which time time, laboratory techniques did not yet allow the actual quantification of genes or DNA (deoxyribonucleic acid, see Chapter 3). Consequently, much of the early work of population geneticists was theoretical and conceptual. This changed after the discovery of the structure of DNA in 1953 by Francis Crick, James Watson, and Maurice Wilkins, and even more so after the development of PCR (polymerase chain reaction) by Kary Mullis in 1983. PCR made it possible to obtain large quantities of DNA even from minuscule samples, and the technique revolutionized many research disciplines, including medicine, forensics, genetic engineering, and population genetics.

Due to these technological advancements, genetic data also became more readily available to ecologists and conservationists, who increasingly realized the tremendous impact of human activities on biological diversity. In the 1970s and 1980s, genetic factors were recognized to be of fundamental importance for successful conservation strategies (e.g., Frankel 1970, 1974) and genetic diversity was explicitly considered in two of the earliest books on conservation biology (Soulé & Wilcox 1980; Frankel & Soulé 1981). Furthermore, human-caused loss and fragmentation of habitats were determined to be major drivers (e.g., Wilcove et al. 1986) and the ability to move among remaining habitat patches was identified as a key for the long-term conservation of populations and species in fragmented landscapes (e.g., Levins 1969; Hanski 1998). The consequences of changing environments also became a central topic of landscape ecology, which emerged as a scientific discipline in the 1980s (e.g., Naveh & Lieberman 1984; Forman & Godron 1986). Given these almost simultaneous developments in several research areas, it is not surprising that scientists began to combine concepts and methods from population genetics and landscape ecology to assess the influence of environmental heterogeneity on gene flow and genetic diversity (e.g., Pamilo 1988; Merriam et al. 1989; Manicacci et al. 1992; Gaines et al. 1997). Nevertheless, “landscape genetics” did not exist as a research area until it was formally defined in a seminal paper by Manel et al. (2003). This paper stimulated a tremendous interest in the scientific community, so that many novel methods for analyzing landscape genetic data were introduced (e.g., Guillot et al. 2005; Murphy et al. 2008) and the number of published landscape genetic studies grew quickly (reviewed in Holderegger & Wagner 2006; Storfer et al. 2010). Just ten years after its first formal definition, landscape genetics had already contributed substantially to research in ecology, evolution, and conservation (see Manel & Holderegger 2013). Currently, landscape genetics still presents itself as a highly dynamic and rapidly advancing field. New methods are frequently suggested and novel research questions are identified as a result of both conceptual and technological improvements. The rapid growth of landscape genetics is both exciting and motivating, but it is also accompanied by tremendous challenges.

In this introductory chapter, we highlight some of these challenges and explain the rationale for this book and its particular structure. Before doing so, we provide a definition of what we feel constitutes landscape genetics. Furthermore, we provide a simple conceptual framework for landscape genetic analyses, which can be particularly useful for the novice landscape geneticist.

1.2 Defining Landscape Genetics

Most readers of this book will already know that landscape genetics combines landscape ecology and population genetics. This is certainly correct, but is also not very specific or precise. To better understand landscape genetics, it is worthwhile to define the field more clearly. Three commonly used definitions of landscape genetics are shown in Table 1.1.

Table 1.1 Overview of definitions of landscape genetics.

Reference

Definition of landscape genetics

*

Analytical consequence

Manel et al. (2003), page 189

[Landscape genetics…] aims to provide information about the interaction between landscape features and

microevolutionary processes, such as gene flow, genetic drift and selection.

Need to quantify mircoevolutionary processes

Holderegger and Wagner (2006), page 793

[…] landscape genetics endorses those studies that combine population genetic data, adaptive or neutral, with data on

landscape composition and configuration, including matrix quality.

Need to quantify landscape heterogeneity

Storfer et al. (2007), page 131

[…] research that

explicitly quantifies the effects

of landscape composition, configuration and [or] matrix quality on gene flow and [or] spatial variation.

Need to explicitly test for landscape-genetic relationships

*

Bold emphases are ours.

In the original definition of Manel et al. (2003) the focus was on “microrevolutionary processes”, which can be measured using genetic data. Thus, the emphasis of this definition lies on the population genetic aspects of landscape genetics, but was not very specific about the ‘landscape features’ to be included in the analyses. The definition was extended by Holderegger and Wagner (2006) who clarified that landscape heterogeneity can be measured in terms of landscape composition, configuration and/or matrix quality (see Chapter 2 for explanations of these terms). Holderegger and Wagner (2006) also noted that landscape genetics can be conducted using different types of genetic data and that appropriate analyses and correct inferences depend strongly on whether the data is adaptive (i.e., under selection) or not (i.e., neutral; see also Holderegger et al. 2006). Finally, Storfer et al. (2007) highlighted that landscape genetics needs to quantitatively link landscape and genetic data to explicitly test for landscape-genetic relationships. This aspect is particularly important, because it allows landscape genetics to move beyond descriptive studies that visually assess spatially coinciding patterns in genetic and landscape data, towards quantitative models that make it possible to predict the genetic consequences of environmental change (e.g., Jay et al. 2012, Wasserman et al. 2012).

Putting these three definitions together, we can define landscape genetics as research that combines population genetics, landscape ecology, and spatial analytical techniques to explicitly quantify the effects of landscape composition, configuration, and matrix quality on microevolutionary processes, such as gene flow, drift, and selection, using neutral and adaptive genetic data.

1.3 The Three Analytical Steps of Landscape Genetics

The definitions provided above lead to a simple conceptual framework for landscape genetic data analysis. Specifically, three general steps are necessary to reach the goals of landscape genetics (see last column in Table 1.1). First, we have to measure genetic variation so that we can quantify the miroevolutionary processes we are interested in. This step relies heavily on population genetic approaches and involves the description of the genetic composition of individuals or populations sampled across space – see Chapters 3, 7, and 9 for details.

Second, we have to quantify landscape heterogeneity so that we can capture the composition, configuration, and/or matrix quality of the study landscape – see Chapters 2 and 8. Third, we have to statistically link landscape heterogeneity and genetic variation, so that we can explicitly and quantitatively test for landscape–genetic relationships (Chapters 5 and 10).

Note that the order of steps one and two is not crucial and could be reversed. For example, in this book the chapter on landscape ecology (Chapter 2) precedes the chapter on population genetics (Chapter 3) because we felt that it is often more sensible to first think about the landscape and its characteristics and next think about the genetic processes occurring in that landscape for a particular study species. In reality, the two steps will ideally be considered simultaneously, as only this will lead to optimal study design and strong inferences (Chapter 4).

Obviously, this three-step framework simplifies actual landscape genetics studies, because many decisions have to be made during all steps, because analytical choices in one step will affect options for another step, and because some methods actually combine multiple steps within a single analysis. Thus, finding optimal combinations of methods for all three steps and for the specific research questions, study landscape and species is not trivial and is unlikely to be covered by a single, cookbook-style recipe. Nevertheless, viewing landscape genetics in terms of the three basic analytical steps can help tremendously when designing a landscape genetic study and when trying to navigate through the thick jungle of landscape genetic methods. Thus, we encourage readers to keep this simple framework in mind when working through the other chapters of this book.

1.4 The Interdisciplinary Challenge of Landscape Genetics

Regardless of what definition of landscape genetics is used, they all highlight the fact that the field combines multiple, usually autonomous disciplines. Consequently, landscape genetics is often described as “interdisciplinary”. However, the simple combination of various research approaches, concepts, and theories does not necessarily constitute true interdisciplinarity. Specifically, the level of integration across the various disciplines involved determines whether a scientific field is multidisciplinary, interdisciplinary, or transdisciplinary (e.g., Morse et al. 2007). A multidisciplinary field involves various disciplines that address a research topic collaboratively, but still rely on their traditional disciplinary approaches and paradigms. Thus, answers to the research question are often found within involved disciplines and overall conclusions are drawn by comparing and combining results obtained from the different research approaches.

In an interdisciplinary field, the research should be much more coordinated among the different disciplines. This coordination involves the standardization of vocabulary, mutually defined research questions and study design, and the synchronization of conceptual frameworks used in each discipline. Addressing a topic through interdisciplinary research should lead to knowledge that impacts all of the involved disciplines. Thus, research that creates new disciplinary knowledge by simply addressing a question through an unusual analytical approach “borrowed” from some other discipline is not really interdisciplinary.

Finally, in a transdisciplinary field, disciplinary boundaries no longer exist, as research approaches from formerly distinct disciplines are fully integrated into a single conceptual framework. This framework involves all aspects of research, from problem definition and study design to actual data analysis and interpretation of results. Importantly, transdisciplinary research should lead to new ways of thinking about a problem and thus to the development of novel theories and research areas.

In our opinion, not all current landscape genetic research is truly interdisciplinary. While we are beginning to develop analytical and conceptual frameworks specifically for landscape genetics (e.g., Wagner & Fortin 2013; Chapter 5), many landscape genetics methods are still borrowed from other disciplines and usually focus on a single analytical step at a time. Various barriers to truly interdisciplinary research exist, with two of the most substantial ones being (a) the difficulties of effectively communicating across different disciplines and (b) the lack of experts that have received enough training across involved disciplines to close communicative gaps and overcome disciplinary boundaries. This is definitely also the case for landscape genetics.

Currently, very few researchers possess the background knowledge and skills to be experts in all of the subjects involved in landscape genetics. Most scientists to date have received disciplinary training and are either experts in landscape ecology, or population genetics, or spatial data analysis, but not in all three areas. This kind of complementary expertise may not even be available within a single university, and very few academic curricula include a comprehensive combination of population genetics, landscape ecology, and spatial quantitative data analysis (Wagner et al. 2012). Thus, for many students, the only options thus far for learning about the different components of landscape genetics is either to attend a landscape genetics seminar (see Wagner et al. 2012), or to read the many, often very brief and rather technical, published papers on landscape genetics. In our experience, the latter is often not very efficient, because of the rapid developments in the field and because papers are usually targeted towards a specific audience for which a certain level of preexisting knowledge on the topic can safely be assumed. For example, it is not necessary to explain the term “genotype” in a genetics journal or to explain the term “spatial grain” in a landscape ecological journal. However, for the beginning landscape geneticist, unknown technical terms, methods, and concepts used in landscape genetic publications often add up to a substantial mass of ambiguity or even confusion. Furthermore, readers without adequate background knowledge might be able to redo the analysis presented in a certain paper, but it will be unlikely that they will be able to critically evaluate the study and make significant contributions to the advancement of the field. Hence, short-term progress in landscape genetics depends on collaborations among disciplinary experts that know enough about all aspects of landscape genetics to effectively communicate with each other, evaluate published studies, and identify existing limitations and possible improvements (Cushman 2014). Similarly, the long-term future of the field depends on providing sufficient training opportunities for the next generation of landscape geneticists.

1.4.1 The Two Scopes of Landscape Genetic Research

In addition to these challenges, which are quite typical for any interdisciplinary field, landscape genetics is also based on approaches that can be used for quite different purposes. Specifically, we currently see at least two major research avenues that are followed in landscape genetics. On the one hand, there are those studies that are interested in understanding how landscape characteristics affect microevolutionary processes. These studies follow the original idea of landscape genetics as defined by Manel et al. (2003) and are interested in genetic variation itself. Researchers following this avenue often have a background in genetics or evolutionary biology and are currently especially interested in using genomic approaches in landscape genetics (see Chapter 9).

On the other hand, there are those studies that are not interested in genetic variation or microevolution in itself, but rather use genetic data to infer underlying ecological processes, such as dispersal or disease transmission. Researchers following this research avenue are usually trained in ecology, and increasingly try to combine landscape genetics with other field methods, such as mark-recapture or telemetry (e.g., Cushman & Lewis 2010).

These different scopes of research further complicate the interdisciplinary nature of landscape genetics, simply because someone interested in evolutionary questions will emphasize different data types and methods compared to someone investigating ecological questions. Clearly, ecological and evolutionary processes and resulting population dynamics and biodiversity patterns are often strongly intertwined (e.g., Hairston et al. 2005, Palkovas & Hendry 2010), and landscape genetics has tremendous potential for untangling the relative roles of ecology and evolution in shaping biological patterns (e.g., Wang et al. 2013). However, it will be difficult to realize this potential if researchers interested in evolution neglect the data and methods provided by ecologists or if ecologists shy away from using the novel data and tools developed by geneticists. Thus, to realize the potential of landscape genetics for eco-evolutionary research, we need to maintain and strengthen the communication and collaboration among different disciplines, and ideally provide a reference baseline as a starting point for future developments in the field.

Overall, learning about, applying, and improving landscape genetics remains a challenging task because of the multiple, highly diverse disciplines involved in the field and because of the different research foci in the field.

1.5 Structure of This Book – Concepts, Methods, Applications

With this book, we aim to facilitate the first steps of learning about landscape genetics. We envisage the book as a guide for anybody wanting to learn about the field, as a tool for facilitating interdisciplinary communication and collaboration, and as a primer for disciplinary experts wanting to teach classes on landscape genetics at their home institutions. Ultimately, we also hope that the book with serve as a baseline for critical discussions about landscape genetics and become a starting point for future advancements.

To reach these goals, we structured chapters in this book into three interrelated parts, each reflecting a slightly different purpose. Chapters in the first part deal with Concepts in a broad sense. These chapters serve as an introduction to the three major steps involved in landscape genetics research and are intended for readers with little or no experience in landscape genetics. These textbook-style chapters obviously include introductions to landscape ecology (Chapter 2) and population genetics (Chapter 3), as the two most fundamental disciplines involved in landscape genetics. In addition, this first section includes a chapter on the basics of landscape genetic study design (Chapter 4) and on the spatial analytical approaches for statistically linking landscape and genetic data (Chapter 5). While these latter two chapters are again quite fundamental, they include aspects we hope will be interesting and novel even for more experienced landscape geneticists.

Chapters in the second part of the book deal with Methods and are more in-depth treatments of certain topics that we currently deem particularly important. These chapters are intended for the advanced reader who is interested in the technical details of specific analytical approaches. This second part includes a chapter on landscape genetic simulation modeling (Chapter 6), because simulations hold tremendous potential for advancing landscape genetic methods and theory development. Genetic assignment and clustering methods are among the most commonly used approaches for quantifying genetic structure (analytical step 1) and are therefore covered separately in Chapter 7. Similarly, resistance surfaces are often used to quantify landscape heterogeneity in landscape genetic studies (analytical step 2), so they are treated in detail in Chapter 8. Chapter 9 then deals with genomic approaches that are increasingly used to generate large quantities of genetic data. These approaches are highly valuable for (adaptive) landscape genetics, especially because they can separte the patterns and processes of neutral markers genetic data from markers under selection. Chapter 10 introduces graph theory and network approaches, as these are increasingly used in both landscape genetics and genomics.

The third part of the book contains chapters that summarize Applications of landscape genetics in different systems. This section includes a chapter on landscape influences on plant population genetics (Chapter 11), a chapter on landscape genetic applications for understanding connectivity in terrestrial animals (Chapter 12), and a chapter on landscape genetic approaches for aquatic systems (Chapter 13).

Finally, in Chapter 14, we highlight some of the conclusions contained in the preceding chapters, identify emerging challenges and offer suggestions for future research and development needs in landscape genetics. Throughout the book, bold italics indicate terms that are described in the Glossary.

1.5.1 Limitations and Potential of This Book

The structure of the book, with the combination of introductory-level chapters and quite advanced discussions of certain topics, is rather unusual. However, we believe that this structure reflects the actual situation of contemporary landscape genetics. On the one hand, we still lack a single source of information that covers all of the required basics for interested beginners. On the other hand, certain – mostly analytical – aspects of landscape genetics have advanced rapidlly and are already too complex to be dealt with in a basic manner. Closing the void between the basics and advanced applications in a single book is obviously very challenging. We are aware of the limitations of this book and have no doubt that for many disciplinary experts, the basic chapters will not be as detailed or as all-embracing as they could be. For example, much more could be said about landscape ecology and about population genetics, respectively. As pointed out above, the intention of the book is not to replace the excellent textbooks on these disciplines (e.g., Allendorf et al. 2012; Turner et al. 2010) but rather to provide beginners with first introductions to the respective topics. These introductions will obviously not convert readers into experts, but they should enable beginners to (a) gain a sufficient overview of the disciplines involved and provide a starting point for further explorations; (b) better understand and critically evaluate published landscape genetic studies; and (c) more effectively communicate about landscape genetic research with experts from different disciplines.

Similarly, the advanced chapters provide more details on certain methods and are mostly intended for readers interested in actually applying these methods. The goal of these chapters is to provide guidance to identify common pitfalls and the most crucial assumptions, advantages, and limitations of different approaches. Finally, the three application chapters illustrate that landscape genetic methods can be used in a variety of circumstances and for different research questions. However, these three chapters are not intended to provide syntheses or even comprehensive reviews of the current state-of-the-art in landscape genetics. The field is already too diverse to allow for full reviews of all studies falling under the grand umbrella of landscape genetics. At the same time, the field may be too nascent for extensive syntheses. Nevertheless, the three application chapters should generate many ideas and motivate readers to apply or improve landscape genetic approaches for their own research.

Overall, we realize that the book is limited. Readers who desire a single source that covers all of the aspects involved in landscape genetics in a basic, yet detailed and application-relevant manner, will likely be disappointed. We hope that the book will nevertheless provide a useful overview and first starting point for learning about landscape genetics.

How successful this book will be in reaching its goals will largely depend on its readers, on their willingness to move towards truly interdisciplinary research, and on their motivation for advancing landscape genetics beyond its current state. Hence, we look forward to the feedback and discussion this book will generate and its impact on the future development of landscape genetics toward an inter- or even transdisciplinary field.

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