Uncovering the genetic and evolutionary basis of local adaptation is a major focus of evolutionary biology. The recent development of cost-effective methods for obtaining high-quality genome-scale data makes it possible to identify some of the loci responsible for adaptive differences among populations. Two basic approaches for identifying putatively locally adaptive loci have been developed and are broadly used: one that identifies loci with unusually high genetic differentiation among populations (differentiation outlier methods) and one that searches for correlations between local population allele frequencies and local environments (genetic-environment association methods). Here, we review the promises and challenges of these genome scan methods, including correcting for the confounding influence of a species’ demographic history, biases caused by missing aspects of the genome, matching scales of environmental data with population structure, and other statistical considerations. In each case, we make suggestions for best practices for maximizing the accuracy and efficiency of genome scans to detect the underlying genetic basis of local adaptation. With attention to their current limitations, genome scan methods can be an important tool in finding the genetic basis of adaptive evolutionary change.

Landscape genetics has emerged as a new research area that integrates population genetics, landscape ecology and spatial statistics. Researchers in this field can combine the high resolution of genetic markers with spatial data and a variety of statistical methods to evaluate the role that landscape variables play in shaping genetic diversity and population structure. While interest in this research area is growing rapidly, our ability to fully utilize landscape data, test explicit hypotheses and truly integrate these diverse disciplines has lagged behind. Part of the current challenge in the development of the field of landscape genetics is bridging the communication and knowledge gap between these highly specific and technical disciplines. The goal of this review is to help bridge this gap by exposing geneticists to terminology, sampling methods and analysis techniques widely used in landscape ecology and spatial statistics but rarely addressed in the genetics literature. We offer a definition for the term "landscape genetics", provide an overview of the landscape genetics literature, give guidelines for appropriate sampling design and useful analysis techniques, and discuss future directions in the field. We hope, this review will stimulate increased dialog and enhance interdisciplinary collaborations advancing this exciting new field.

Landscape genetics has seen rapid growth in number of publications since the term was coined in 2003. An extensive literature search from 1998 to 2008 using keywords associated with landscape genetics yielded 655 articles encompassing a vast array of study organisms, study designs and methodology. These publications were screened to identify 174 studies that explicitly incorporated at least one landscape variable with genetic data. We systematically reviewed this set of papers to assess taxonomic and temporal trends in: (i) geographic regions studied; (ii) types of questions addressed; (iii) molecular markers used; (iv) statistical analyses used; and (v) types and nature of spatial data used. Overall, studies have occurred in geographic regions proximal to developed countries and more commonly in terrestrial vs. aquatic habitats. Questions most often focused on effects of barriers and ⁄ or landscape variables on gene flow. The most commonly used molecular markers were microsatellites and amplified fragment length polymorphism (AFLPs), with AFLPs used more frequently in plants than animals. Analysis methods were dominated by Mantel and assignment tests. We also assessed differences among journals to evaluate the uniformity of reporting and publication standards. Few studies presented an explicit study design or explicit descriptions of spatial extent. While some landscape variables such as topographic relief affected most species studied, effects were not universal, and some species appeared unaffected by the landscape. Effects of habitat fragmentation were mixed, with some species altering movement paths and others unaffected. Taken together, although some generalities emerged regarding effects of specific landscape variables, results varied, thereby reinforcing the need for species-specific work. We conclude by: highlighting gaps in knowledge and methodology, providing guidelines to authors and reviewers of landscape genetics studies, and suggesting promising future directions of inquiry.

A major objective of ecology is to understand how ecological processes limit population connectivity and species' distributions. By spatially quantifying ecological components driving functional connectivity, we can understand why some locally suitable habitats are unoccupied, resulting in observed discontinuities in distribution. However, estimating connectivity may be difficult due to population stochasticity and violations of assumptions of parametric statistics. To address these issues, we present a novel application of Random Forests to landscape genetic data. We address the effects of three key ecological components on Bufo boreas connectivity in Yellowstone National Park: ecological process, scale, and hierarchical organization. Habitat permeability, topographic morphology, and temperature-moisture regime are all significant ecological processes associated with B. boreas connectivity. Connectivity was influenced by growing-season precipitation, 1988 Yellowstone fires, cover, temperature, impervious surfaces (roads and development), and topographic complexity (56% variation explained). We found that habitat permeability generally operates on fine scales, while topographic morphology and temperature-moisture regime operate across multiple scales, thus demonstrating the importance of cross-scale analysis for ecological interpretation. In a hierarchical analysis, we were able to explain more variation within genetic clusters as identified using Structure (a Bayesian algorithm) (74%; dispersal cover, growing-season precipitation, impervious surfaces) as opposed to between genetic clusters (45%; ridgelines, hot, dry slopes, length of hot season, and annual precipitation). Finally, the analytical methods we developed are powerful and can be applied to any species or system with appropriate landscape and genetic data.

Understanding how and why populations evolve is of fundamental importance to molecular ecology. Restriction site-associated DNA sequencing (RADseq), a popular reduced representation method, has ushered in a new era of genome-scale research for assessing population structure, hybridization, demographic history, phylogeography and migration. RADseq has also been widely used to conduct genome scans to detect loci involved in adaptive divergence among natural populations. Here, we examine the capacity of those RADseq-based genome scan studies to detect loci involved in local adaptation. To understand what proportion of the genome is missed by RADseq studies, we developed a simple model using different numbers of RAD-tags, genome sizes and extents of linkage disequilibrium (length of haplotype blocks). Under the best-case modelling scenario, we found that RADseq using six- or eight-base pair cutting restriction enzymes would fail to sample many regions of the genome, especially for species with short linkage disequilibrium. We then surveyed recent studies that have used RADseq for genome scans and found that the median density of markers across these studies was 4.08 RAD-tag markers per megabase (one marker per 245 kb). The length of linkage disequilibrium for many species is one to three orders of magnitude less than density of the typical recent RADseq study. Thus, we conclude that genome scans based on RADseq data alone, while useful for studies of neutral genetic variation and genetic population structure, will likely miss many loci under selection in studies of local adaptation.

The field of landscape genetics has great potential to identify habitat features that influence population genetic structure. To identify landscape correlates of genetic differentiation in a quantitative fashion, we developed a novel approach using geographical information systems analysis. We present data on blotched tiger salamanders (Ambystoma tigrinum melanostictum) from 10 sites across the northern range of Yellowstone National Park in Montana and Wyoming, USA. We used eight microsatellite loci to analyse population genetic structure. We tested whether landscape variables, including topographical distance, elevation, wetland likelihood, cover type and number of river and stream crossings, were correlated with genetic subdivision (F(ST)). We then compared five hypothetical dispersal routes with a straight-line distance model using two approaches: (i) partial Mantel tests using Akaike's information criterion scores to evaluate model robustness and (ii) the BIOENV procedure, which uses a Spearman rank correlation to determine the combination of environmental variables that best fits the genetic data. Overall, gene flow appears highly restricted among sites, with a global F(ST) of 0.24. While there is a significant isolation-by-distance pattern, incorporating landscape variables substantially improved the fit of the model (from an r2 of 0.3 to 0.8) explaining genetic differentiation. It appears that gene flow follows a straight-line topographic route, with river crossings and open shrub habitat correlated with lower F(ST) and thus, decreased differentiation, while distance and elevation difference appear to increase differentiation. This study demonstrates a general approach that can be used to determine the influence of landscape variables on population genetic structure.

Although cancer rarely acts as an infectious disease, a recently emerged transmissible cancer in Tasmanian devils (Sarcophilus harrisii) is virtually 100% fatal. Devil facial tumour disease (DFTD) has swept across nearly the entire species' range, resulting in localized declines exceeding 90% and an overall species decline of more than 80% in less than 20 years. Despite epidemiological models that predict extinction, populations in long-diseased sites persist. Here we report rare genomic evidence of a rapid, parallel evolutionary response to strong selection imposed by a wildlife disease. We identify two genomic regions that contain genes related to immune function or cancer risk in humans that exhibit concordant signatures of selection across three populations. DFTD spreads between hosts by suppressing and evading the immune system, and our results suggest that hosts are evolving immune-modulated resistance that could aid in species persistence in the face of this devastating disease.

Understanding how and why populations evolve is of fundamental importance to molecular ecology. Restriction site-associated DNA sequencing (RADseq), a popular reduced representation method, has ushered in a new era of genome-scale research for assessing population structure, hybridization, demographic history, phylogeography and migration. RADseq has also been widely used to conduct genome scans to detect loci involved in adaptive divergence among natural Correspondence: David B. Lowry, Fax: 517-353-1926; dlowry@msu.edu. Correction noteThe original advance online paper contained two errors associated with the calculation of the median density of RAD-seq tags in the survey of recent RAD-seq genome scan studies (Table S1). The first error was in the size of the assembled stickleback genome, which was reported as 0.53 Gbp, but should have been 0.46 Gbp. The second error was an accidental inversion of terms. These two mistakes contributed to an erroneous statement in the original abstract that the median density of RAD-tags across recent studies "was one marker per 3.96 megabases." The statement has been revised to read: "was 4.08 RAD-tag markers per megabase." Following these corrections, changes were made in the abstract and elsewhere in the paper to reflect a modified interpretation of the results, though we note the main arguments in the article are unaffected. Other minor modifications to the paper were made based upon suggestions by the editors of Molecular Ecology Resources. This version of the article, published as an "accepted article" on 12 November 2016 under DOI 10.1111/1755-0998.12635 replaces the original version of the article published on 12 September 2016 under DOI 10.1111/1755-0998.12596.The idea for the manuscript was conceived collectively by all authors during an NSF National Institute for Mathematical and Biological Synthesis (NIMBioS) working group. All authors contributed to the writing of the manuscript.Supporting Information Additional Supporting Information may be found in the online version of this article: Appendix S1 Supplementary R scripts for Breaking RAD. Table S1 Recent (January 2015 to April 2016) genome scan studies, which used RAD-seq for genotyping. Andrews et al. (2016). Generally, RADseq methods produce DNA libraries for high-throughput sequencing using restriction enzymes that cut at specific motifs throughout the genome. RADseq markers come in the form of RAD-tags, which are short-read sequences adjacent to restriction enzyme cut sites. Because many polymorphic markers are produced by RADseq, it has frequently been used successfully for population genetic analyses, including assessment of population structure, hybridization, demographic history, phylogeography and migration (Catchen et al. 2013;Cavender-Bares et al. 2015;Combosch & Vollmer 2015;Qi et al. 2015). Markers generated by RADseq have also been quite useful for constructing linkage maps and identifying quantitative trait loci (QTL;Pfender et al. 2011;Houston et al. 2012;Weber et al. 2013;Laporte et al. 2015...