Abstract Urban habitats are quickly becoming exceptional models to address adaptation under rapid environmental change, given the expansive temporal and spatial scales with which anthropogenic landscape conversion occurs. Urban ecologists in the last 10–15 years have done an extraordinary job of highlighting phenotypic patterns that correspond with urban living, as well as delineating urban population structure using traditional genetic markers. The underpinning genetic mechanisms that govern those phenotypic patterns, however, are less well established. Moreover, the power of traditional molecular studies is constrained by the number of markers being evaluated, which limits the potential to assess fine-scale population structure potentially common in urban areas. With the recent proliferation of low-cost, high-throughput sequencing methods, we can begin to address an emerging question in urban ecology: are species adapted to local optima within cities or are they expressing latent phenotypic plasticity? Here, I provide a comprehensive review of previous urban ecological studies, with special focus on the molecular ecology and phenotypic adjustments documented in urban terrestrial and amphibious fauna. I subsequently pinpoint areas in the literature that could benefit from a genomic investigation and briefly discuss the suitability of specific techniques in addressing eco-evolutionary questions within urban ecology. Though many challenges exist with implementing genomics into urban ecology, such studies provide an exceptional opportunity to advance our understanding of eco-evolutionary processes in metropolitan areas. Genomics and gene mapping, Molecular adaptation and selection, anthropogenic drivers, endocrine function, local adaptation, personality, population genomics, urbanization In cities, the interplay between humans and the environment profoundly shapes eco-evolutionary dynamics that generate wholly novel selective constraints on organisms unlike any experienced in rural or natural environments (Alberti 2015; Donihue and Lambert 2015; McDonnell and Hahs 2015; Johnson and Munshi-South 2017). For instance, heightened road densities in metropolitan areas compel organisms to develop road-crossing strategies that decrease the likelihood of vehicular mortality (Balkenhol and Waits 2009; Riley et al. 2014a), such as changes in sensory mechanisms that allow individuals to accurately assess vehicular speed (Lima et al. 2015). Acoustic pollution caused by ambient road noise or construction degrades the acoustic environment (Tuomainen and Candolin 2011), which may require species to alter predator alarm calls or song structure to attract mates (Templeton et al. 2016). Case in point, birdsong diversity often decreases (Proppe et al. 2013) and song frequency shifts upward under noisy urban conditions (Slabbekoorn 2013). Human-driven environmental changes can also have community-level effects. For example, food subsidies in cities are associated with reduced species richness and large-scale community restructuring in which invasive species frequently outcompete natives (Galbraith et al. 2015; Plummer et al. 2015). The timescale with which these examples occur happen on the order of years rather than centuries (McDonnell and Hahs 2015). Consequently, urban environments are quickly being considered natural laboratories to address human-induced, rapid adaptive change (Pickett et al. 2016; Alberti et al. 2017a). Prior empirical work in urban systems has done an exceptional job detailing the phenotypic consequences of urbanization in a variety of taxa (Table 1). These efforts have enabled researchers to begin describing phenotypic similarities and dissimilarities that exist across urban environments (Alberti 2015; Alberti et al. 2017b). Now given a critical mass of information, we have the opportunity to consider if urban areas across the globe share synonymous ecological dynamics that facilitate instances of parallel evolution (Magle et al. 2012; Johnson and Munshi-South 2017). Much of the work to date, however, is insufficient in completely addressing urban adaptation. This is particularly because assessment of phenotypic patterns alone makes it difficult to ascertain whether expressed traits are simply within the natural range of plasticity or genetic signals of adaptation to local optimum within cities (Miranda et al. 2013; Miranda 2017). Hence, by determining the genetic basis of adaptive change in urban areas we can begin to examine whether previously observed phenotypic changes are locally adapted to metropolitan environments. The use of traditional genetic markers (i.e., microsatellites and short fragments of mitochondrial DNA) have partially begun to address urban population divergence. For instance, several studies have examined patterns of gene flow and kinship as a function of road ecology and demography (Riley et al. 2003; Balkenhol and Waits 2009; Delaney et al. 2010; Munshi-South and Kharchenko 2010; Munshi-South 2012). A few recent studies have also used a candidate gene approach to observe genotype–phenotype associations correlated with the degree of urbanization (Orsini et al. 2012; Mueller et al. 2013; van Dongen et al. 2015). However, these approaches have several methodological restrictions that limit the types of inferences that can be drawn. Traditional genetic approaches are not as efficient in addressing rates of gene flow specific to adaptive loci or founder-specific inbreeding coefficients because the number of loci surveyed is significantly smaller compared with genomic studies (Allendorf et al. 2010). Traditional methods also provide insufficient coverage of the genome, which may potentially lead to a false assessment of null divergence when populations are separated by small spatiotemporal scales (Ekblom and Galindo 2011; Richardson et al. 2014). Further, if a single identified candidate gene is not associated with the observed trait or that trait is polygenic (i.e., multiple genes for one trait), the potential to accurately describe adaptive genetic variation is reduced (Storz 2005; Flint and Munafò 2013; Shafer et al. 2015). Various advancements in next-generation sequencing (NGS) methods can overcome limitations inherent in traditional molecular practices by assessing the genome on orders of magnitude greater than previously attainable (Andrews et al. 2016). The number of single nucleotide polymorphisms (SNPs) that can be examined (e.g., 103–105) dwarf previous benchmarks, which increases the likelihood of detecting loci under positive directional selection (Allendorf et al. 2010; Oyler-McCance et al. 2016). In addition, the reduction in SNP library preparation and sequencing costs makes implementing a genomic framework to ecological studies more tenable (Ekblom and Galindo 2011; Andrews et al. 2016). Growth in the number of reference genomes being described greatly facilitates future genomic studies for ecologists working on closely related nonmodel organisms (Hoban et al. 2016), and even de novo assembly of genomes is an option when a reference genome is unavailable for use (Kajitani et al. 2014; Mastretta-Yanes et al. 2015). Moreover, the ability to sequence the transcriptome facilitates the development of novel studies examining adaptive and nonadaptive gene expression (Alvarez et al. 2015; MacManes 2016). The parallel surges in both population genomics and urban ecology have built a strong foundation to quantify the genetic consequences of human agency on urban eco-evolutionary processes. This comprehensive review is partitioned into three overarching sections. First, I survey the literature to assess publication trends in urban molecular studies from 2000 to 2017. Second, I provide a targeted review of studies that intentionally compare urban and rural phenotypes, highlighting the adaptive significance of such studies. Finally, I pinpoint areas that could benefit from a genomics approach by distilling 3 broad questions from previous literature that potentially encapsulate the most impactful areas of study within urban evolutionary ecology over the coming decades (Magle et al. 2012; Alberti 2015; McDonnell and Hahs 2015; McPhearson et al. 2016; Pickett et al. 2016; Johnson and Munshi-South 2017). Those 3 questions are: What is the finest spatial scale at which anthropogenic structures can reduce gene flow in cities? How is standing genetic variation related to historical human development in cities? Are gene–environment associations and expression patterns the product of plasticity or local adaptation? I close this review by providing considerations and challenges to implementing a genomics approach for urban ecological studies. Throughout the review, I specifically focus on terrestrial and amphibious fauna because they represent the most well-studied taxonomic groups in urban areas (Pickett et al. 2008). This review does not discuss the new and upcoming technologies used to generate and sort genomic data (Storz 2005; Allendorf et al. 2010; Cammen et al. 2016; Hoban et al. 2016; MacManes 2016; Oyler-McCance et al. 2016). Recent Trends: Urban Molecular Ecology I surveyed peer-reviewed literature to 1) evaluate trends in publication rates from 2000 through 2017, and 2) map studies by taxon and metropolitan location to visually examine urban research hotspots. Specifically, I performed dual searches of the Web of Science and Google Scholar databases using the terms “urban ecology,” “population genetics,” and several molecular and NGS terms (e.g., “microsatellite,” “mitochondria,” “RADseq,” “SNPs,” “transcriptome,” “PCR,” “next-generation sequencing,” “Illumina”), and paired them with each one of the following terms indicating study taxa—“mammals,” “birds,” “reptiles,” “amphibians,” and “invertebrates.” Any studies not directly related to the genetics or genomics of terrestrial or amphibious species were excluded, as well as those in which populations were not explicitly sampled within the limits of a designated metropolitan area. Thus, studies that were solely conducted in agricultural or disturbed natural areas were not considered. I also excluded any studies on aquatic and plant species. The remaining 120 articles that were relevant to urbanized terrestrial or amphibious species were further binned by organismal class (i.e., mammals, birds, reptiles, amphibians, and insects), primary methodology (i.e., traditional markers vs. NGS), and metropolitan location. The fully annotated reference list of these articles is described in Supplementary Table S1. From 2000 through 2017, there was a noticeable increase in the overall number of molecular studies performed in urban environments (Figure 1). This growing trend coincides with previously documented increases in publication volume on urban wildlife overall (Magle et al. 2012). Not surprisingly, most studies employed traditional molecular approaches (n = 113, ~94%; Figure 1). Of the 7 studies that did employ an NGS approach, 5 focused on Rodentia species (i.e., white-footed mice, Peromyscus leucopus, Harris et al. 2013, 2015; Harris and Munshi-South 2016; Munshi-South et al. 2016; the brown rat, Rattus norvegicus, Puckett et al. 2016), one on great tits (Parus major, Watson et al. 2017), and one on African mosquitos (Anopheles gambiae and Anopheles coluzzii, Cassone et al. 2014). Most publications focused on mammalian species (n = 52, 43.7%), with both birds and insects tied for second-most represented in the literature (n = 26 per clade, 21.8% each). Of all studies surveyed in this analysis, only 33 studies of the 120 explicitly compared urban populations to a natural, forested, or rural counterpart. Further, existent urban molecular studies span a wide geographic distribution, but most studies are in temperate locations (Figure 2). Figure 1. View largeDownload slide Number of publications on the molecular ecology of terrestrial and amphibious fauna in urban environments, as well as the proportion of studies (pie chart) that used traditional molecular techniques (i.e., microsatellite and/or mitochondrial analyses) versus next-generation sequencing (i.e., genomic approaches), from 2000 to 2017. See online version for full colors. Figure 1. View largeDownload slide Number of publications on the molecular ecology of terrestrial and amphibious fauna in urban environments, as well as the proportion of studies (pie chart) that used traditional molecular techniques (i.e., microsatellite and/or mitochondrial analyses) versus next-generation sequencing (i.e., genomic approaches), from 2000 to 2017. See online version for full colors. Figure 2. View large Download slide Map of study locations on the molecular ecology of mammals (brown), birds (blue), reptiles (green), amphibians (purple), and insects (orange) in cities across the globe. Citations can be found in Supplementary Table S1. See online version for full colors. Figure 2. View large Download slide Map of study locations on the molecular ecology of mammals (brown), birds (blue), reptiles (green), amphibians (purple), and insects (orange) in cities across the globe. Citations can be found in Supplementary Table S1. See online version for full colors. These general findings highlight the depth and breadth of accomplished work performed in urban habitats worldwide, but similarly emphasize areas of future research need. First, mammals are overrepresented in the number of urban molecular studies performed (Figure 1). Second, only 26.9% of previous studies intentionally assess genetic structure of both urban and rural populations. While within-urban molecular studies are valuable for assessing genetic structure as a function of extreme anthropogenic habitat fragmentation, a suitable control population is necessary to determine if local adaptation is occurring in urban environments (Richardson et al. 2014; Hoban et al. 2016). Third, most studies reviewed were conducted in North America and Europe, with temperate zones and wealthier countries being overrepresented in the literature (Figure 2). Cities in tropical and desert biomes are undoubtedly going to have varying climatic conditions that may interact with anthropogenic features much differently than conditions in temperate cities. Hence, there is tremendous opportunity to expand urban molecular work into climatic regions and biomes not well represented here. Finally, there is a lack of species diversity throughout the NGS publications that currently exist (Figure 1). However, that is expected to change rapidly in the next few years with the increasingly low-cost of high-throughput sequencing and parallel growth in the number of reference genomes for non-model organisms (Cammen et al. 2016; Oyler-McCance et al. 2016). Human-Driven Phenotypes: Urban Versus Rural Comparisons Within the last few years, urban ecologists, designers, and environmental planning professionals have worked together to articulate the links among specific anthropogenic features and individual phenotypic outcomes (Clucas and Marzluff 2011; Alberti 2015; Donihue and Lambert 2015; McDonnell and Hahs 2015; Pickett et al. 2016). In so doing, a concerted effort to describe the adaptive significance of human-driven changes to functional traits has been made (Sol et al. 2013; Alberti et al. 2017b). Here, I review and synthesize recent work across terrestrial and amphibious fauna that examine phenotypic modifications (e.g., behavior, physiology, and/or morphology) as a function of urban living. I specifically focus on studies that explicitly compare urban versus nonurban populations for several reasons. Landscape-level approaches are certainly invaluable in describing correlational patterns among anthropogenic features and observed traits, as seen in a broad range of previous urban studies (Tigas et al. 2002; Prange et al. 2004; Gehrt et al. 2013; Riley et al. 2014b; Poessel et al. 2017). However, such studies by themselves are insufficient to examine the mechanistic basis underlying observed phenotypic patterns (Shochat et al. 2006; Rees et al. 2009). Comparison of urban populations to an adequate control provides an experimental design better equipped to determine the fitness benefits of an urban phenotype and identify the drivers that induce such adaptations (Donihue and Lambert 2015). Several common garden studies comparing offspring from urban and rural habitats have provided supporting information for this claim (Table 1, Supplementary Table S2). Further, comparison of geographically and ecologically distinct habitats in follow-up gene-phenotype analyses is less likely to encounter problems of spatial autocorrelation that would significantly limit a study’s power to interpret local adaptation in urban systems (Hoban et al. 2016). By no means is this review complete: previous reviews have comprehensively described phenotypic adaptations exhibited toward human drivers (Tuomainen and Candolin 2011; Bateman and Fleming 2012; Sol et al. 2013; Donihue and Lambert 2015; Šálek et al. 2015; McDonnell and Hahs 2015; Alberti et al. 2017a). Whereas prior reviews have pooled studies conducted in urban and natural environments (i.e., anthropogenic fragmentation of natural green space), and included landscape-level analyses within urban areas, this review is unique in that it is solely focused on urban versus rural comparisons (Table 1). Behavioral Adjustments Initial adaptive responses toward anthropogenic pressures are often behavioral (Ditchkoff et al. 2006; Tuomainen and Candolin 2011; Lowry et al. 2013). Proper behavioral modifications to urban contexts by founder individuals in the short term are expected to provide fitness benefits that ultimately have cascading influences on future population dynamics and distribution (Tuomainen and Candolin 2011). Over the relative long-term, if we assume that such behavioral traits covary with underlying genotypes, then we may expect such heritable behaviors to proliferate and result in phenotypic differentiation among urban and nonurban populations (Lowry et al. 2013; Sol et al. 2013; McDonnell and Hahs 2015). There are several examples in the literature that support this hypothesis, particularly in avian systems (Table 1; fully annotated information in Supplementary Table S2). Flight initiation distance (FID: a measure of distance at which an organism flees from an encroaching human) is most frequently used to assess human-associated boldness in urban taxa (Sol et al. 2013). However, it is by no means the only observed behavioral modification, with several studies demonstrating reduced alarm calling (Møller and Ibáñez-Álamo 2012), reduced neophobia (Audet et al. 2016; Greggor et al. 2016; Ducatez et al. 2017; Riyahi et al. 2017), and greater problem-solving performance (Papp et al. 2015; Audet et al. 2016; Preiszner et al. 2017) in urban organisms. The unifying theme amongst most of these studies is the central role food subsidies in cities play in driving selection for reduced proximity to humans. Individuals that maintain close proximity to humans are consequently able to withstand adverse environmental conditions by exploiting abundant food resources provided by humans (Møller et al. 2013). This, paired with an often-dramatic reduction in mortality from relaxed predation pressure, disadvantages individuals that are either less plastic or more timid in human-contexts (Møller and Ibáñez-Álamo 2012; Lowry et al. 2013). Table 1. Empirical studies documenting phenotypic differences between urban and rural populations of terrestrial and amphibious fauna Common name Scientific name Region Trait category Proposed driver(s) Citations Birds House sparrows Passer domesticus EU B Population-level habitat differences Bókony et al. (2012) EU B Habituation Vincze et al. (2016) EU M Increased predator–prey dynamics Dulisz et al. (2016) EU P, M Food supplementation Meillère et al. (2015) Tree sparrows Passer montanus AS P Population-level habitat differences Zhang et al. (2011) Song sparrows Melospiza melodia NA P Social networks and anthropogenic resources Sewall and Davies (2017) NA B Food supplementation Foltz et al. (2015) Dark-eyed junco Junco hyemalis NA B, P Genetic Atwell et al. (2012) Bullfinch Loxigilla barbadensis CA B, P Enhanced immunocompetence Audet et al. (2016) European blackbird Turdus merula EU M Latitudinal gradient (Seebohm’s rule) Evans et al. (2009) EU P Genetic Partecke et al. (2006) EU B Genetic Miranda et al. (2013) EU M Genetic Costantini et al. (2014) EU P, M Genetic Partecke et al. (2004) Common mynas Acridotheres tristis AU B Increased food variety in cities Federspiel et al. (2017) Eurasian coot Fulica atra AU B Genetic van Dongen et al. (2015) Great tits Parus major EU B Food supplementation Preiszner et al. (2017) EU B Phenotypic plasticity Riyahi et al. (2017) EU M Density of low-carotenoid food items Biard et al. (2017) Burrowing owls Athene cunicularias SA B Genetic Carrete and Tella (2013) SA B, P Genetic Rebolo-Ifrán et al. (2015) SA B Predator-release in urban environments Carrete and Tella (2017) Curve-billed thrashers Toxostoma curvirostre NA B, P Water availability Fokidis and Deviche (2012) Mammals Eurasian red squirrel Sciurus vulgaris AS B Phenotypic plasticity Uchida et al. (2016) Fox squirrels Sciurus niger NA B Habituation Mccleery (2009) Eastern chipmunk Tamias striatus NA B, P, M Food supplementation Lyons et al. (2017) Red fox Vulpes vulpes EU P Urban pollution Dip et al. (2001) Coyotes Canis latrans NA B Food supplementation Murray et al. (2015) Wild boar Sus scrofa EU B Food supplementation Stillfried et al. (2017) Raccoons Procyon lotor NA B Stable food subsidies Prange et al. (2004) Reptiles Puerto Rican crested anole Anolis cristatellus NA M Percentage of artificial surfaces, genetic Winchell et al. (2016) Australian freshwater turtle Chelodina longicollis AU B Artificial manipulation of suburban estuaries Rees et al. (2009) Brown anole Anolis sagrei NA B Foraging niche Lapiedra et al. (2017) Ornate tree lizard Urosaurus ornatus NA P Down-regulation of hypothalamic- pituitary-adrenal axis French et al. (2008) European wall lizard Podarcis muralis EU M Parasite densities Lazić et al. (2016) Amphibians Marsh frog Rana ridibunda EU P Industrial contamination of downstream waters Falfushinska et al. (2008) Insects Common field grasshopper Chorthippus brunneus EU M Urban heat-island effect and climate change San Martin y Gomez and Van Dyck (2012) Blowfly Calliphora vicina EU M Urban heat-island effect Hwang and Turner (2009) Damselfly Coenagrion puella EU M Urban heat-island effect Tüzün et al. (2017a) EU B, M Sexual selection Tüzün et al. (2017b) Leaf-cutter ants Atta sexdens rubropilosa EU B, M Urban heat-island effect Angilletta et al. (2007) Common name Scientific name Region Trait category Proposed driver(s) Citations Birds House sparrows Passer domesticus EU B Population-level habitat differences Bókony et al. (2012) EU B Habituation Vincze et al. (2016) EU M Increased predator–prey dynamics Dulisz et al. (2016) EU P, M Food supplementation Meillère et al. (2015) Tree sparrows Passer montanus AS P Population-level habitat differences Zhang et al. (2011) Song sparrows Melospiza melodia NA P Social networks and anthropogenic resources Sewall and Davies (2017) NA B Food supplementation Foltz et al. (2015) Dark-eyed junco Junco hyemalis NA B, P Genetic Atwell et al. (2012) Bullfinch Loxigilla barbadensis CA B, P Enhanced immunocompetence Audet et al. (2016) European blackbird Turdus merula EU M Latitudinal gradient (Seebohm’s rule) Evans et al. (2009) EU P Genetic Partecke et al. (2006) EU B Genetic Miranda et al. (2013) EU M Genetic Costantini et al. (2014) EU P, M Genetic Partecke et al. (2004) Common mynas Acridotheres tristis AU B Increased food variety in cities Federspiel et al. (2017) Eurasian coot Fulica atra AU B Genetic van Dongen et al. (2015) Great tits Parus major EU B Food supplementation Preiszner et al. (2017) EU B Phenotypic plasticity Riyahi et al. (2017) EU M Density of low-carotenoid food items Biard et al. (2017) Burrowing owls Athene cunicularias SA B Genetic Carrete and Tella (2013) SA B, P Genetic Rebolo-Ifrán et al. (2015) SA B Predator-release in urban environments Carrete and Tella (2017) Curve-billed thrashers Toxostoma curvirostre NA B, P Water availability Fokidis and Deviche (2012) Mammals Eurasian red squirrel Sciurus vulgaris AS B Phenotypic plasticity Uchida et al. (2016) Fox squirrels Sciurus niger NA B Habituation Mccleery (2009) Eastern chipmunk Tamias striatus NA B, P, M Food supplementation Lyons et al. (2017) Red fox Vulpes vulpes EU P Urban pollution Dip et al. (2001) Coyotes Canis latrans NA B Food supplementation Murray et al. (2015) Wild boar Sus scrofa EU B Food supplementation Stillfried et al. (2017) Raccoons Procyon lotor NA B Stable food subsidies Prange et al. (2004) Reptiles Puerto Rican crested anole Anolis cristatellus NA M Percentage of artificial surfaces, genetic Winchell et al. (2016) Australian freshwater turtle Chelodina longicollis AU B Artificial manipulation of suburban estuaries Rees et al. (2009) Brown anole Anolis sagrei NA B Foraging niche Lapiedra et al. (2017) Ornate tree lizard Urosaurus ornatus NA P Down-regulation of hypothalamic- pituitary-adrenal axis French et al. (2008) European wall lizard Podarcis muralis EU M Parasite densities Lazić et al. (2016) Amphibians Marsh frog Rana ridibunda EU P Industrial contamination of downstream waters Falfushinska et al. (2008) Insects Common field grasshopper Chorthippus brunneus EU M Urban heat-island effect and climate change San Martin y Gomez and Van Dyck (2012) Blowfly Calliphora vicina EU M Urban heat-island effect Hwang and Turner (2009) Damselfly Coenagrion puella EU M Urban heat-island effect Tüzün et al. (2017a) EU B, M Sexual selection Tüzün et al. (2017b) Leaf-cutter ants Atta sexdens rubropilosa EU B, M Urban heat-island effect Angilletta et al. (2007) Synoptic examples include common and scientific names, region of study (Africa, AF; Asia, AS; Australia, AU; Caribbean, CA; Europe, EU; North America, NA; South America, SA), trait category studied (behavioral, B; physiological, P; morphological, M), control group, and proposed drivers of phenotypic changes. An expanded list with results, specific cities, specific traits measured, control group, and sample size is included in Supplementary Table S2. View Large Whether these apparent urban–rural differences in behavior are primarily explained by plasticity, locally adapted phenotypes, or both is still uncertain. However, a repeated measures approach across contexts and time may begin to help disentangle environmentally dependent and genetically associated behaviors (Miranda et al. 2013; Miranda 2017). Recent developments in the study of animal personality provide an established rubric for quantifying behavior that is consistent across multiple time points and contexts (Dingemanse and Réale 2005; Dingemanse et al. 2010). In addition, personality is heritable and is frequently associated with underlying genetic components (van Oers et al. 2005; Dochtermann et al. 2014). Hence, animal personality research may be well-situated to determine if behaviors linked to anthropogenic drivers are also genetically linked. Several recent studies have already provided data that successfully integrate personality research and urban ecology (Bókony et al. 2012; Miranda et al. 2013; Carrete and Tella 2013, 2017; Arroyo et al. 2017; Lapiedra et al. 2017). Others have also demonstrated that personality variation is linked with specific gene regions (e.g., DRD4 exon 3) that are selected for in urban avian systems (Mueller et al. 2013; Riyahi et al. 2015; van Dongen et al. 2015), supporting the hypothesis that selection acts on specific personality types in urban environments. Despite the potential benefits of a personality approach, several drawbacks still exist to implementing such methods in wild settings. Quantifying personality necessitates multiple measures from a single individual, which may require a mark-recapture study. If relocating an individual is particularly challenging either due to difficulty in capture, low abundance, or low survivorship, quantifying personality may be improbable. In addition, recapture for all studied individuals may occur in different contexts and at different developmental time points, which may add more undue variance. For these reasons, the ease and feasibility of recapture will determine the potential success of quantifying personality in urban studies. Physiological Adjustments A multitude of human-driven biotic and abiotic stressors in cities pressure organisms to develop effective coping mechanisms that maintain homeostasis (Isaksson 2015). Human disturbances such as light pollution (Dominoni et al. 2014; Swaddle et al. 2015) and artificial elevation of noise levels (Proppe et al. 2013; Slabbekoorn 2013; Templeton et al. 2016) disorient the circadian rhythms and auditory signals of organisms, whereas urban heat-island effects disrupt physiological systems sensitive to temperature (Angilletta et al. 2007; Hwang and Turner 2009; Tüzün et al. 2017a). Fluctuations in resource availability, novel predators, and conspecific densities reorient trophic dynamics that characteristically alter competition regimes within already novel environments (Fischer et al. 2012; Giraudeau et al. 2015; Plummer et al. 2015). In sum, these human-induced stressors present in cities are not represented in other habitats, and should differentially affect endocrine mechanisms of urban and rural individuals (Shochat et al. 2006; Clucas and Marzluff 2011; Alberti et al. 2017a; Lyons et al. 2017). Indeed, recent research focus has sought to examine potential population differences in stress physiology as a function of urbanization (Table 1). Justifiably so, as stress hormones (i.e., glucocorticoids) govern the fight-or-flight response in all organisms (Partecke et al. 2006; Isaksson 2015). Moreover, stress hormones often mediate the expression of behavioral traits (Carere et al. 2010; Taff and Vitousek 2016) and strongly covary with personality variance in a population (Korte et al. 2005; Carere et al. 2010). It makes intuitive sense, then, that baseline stress profiles may be a direct or indirect target of selection in urban environments. For instance, individuals able to quickly return to baseline glucocorticoid concentrations post-stressor may fair better in an anthropogenic environment with constant stressors (i.e., direct target of selection; Bonier 2012). Comparatively, a behavior under positive selection (e.g., human tolerance) may be mediated by a specific glucocorticoid profile (i.e., indirect target of selection). In both instances, initial colonizers of cities may experience novel urban stressors (i.e., vehicles, disease, competition) that alter stress profiles of parents, subsequently preparing the endocrine profiles of their offspring (Watson et al. 2017). This may result in the parental transmission of locally adapted stress phenotypes, suited for urban living, within a small number of generations. Previous work in several taxa has assessed glucocorticoid variance in relation to urbanization, but no uniform patterns emerge across studies (Table 1). A priori predictions suggest that urban organisms should express higher baseline stress because of more frequent experience with anthropogenic stressors (Isaksson 2015). However, several studies observed lower corticosterone concentrations for urban compared to rural individuals (Table 1). The broad differences in results across studies may be explained by the inherently complicated nature of quantifying individual hormonal baselines. Functional or upstream differences in endocrine systems may contribute to the observed variation in serum or fecal concentrations. For instance, to account for greater concentrations of free-floating glucocorticoids, urban individuals may develop endocrine profiles with greater stress hormone receptor densities and sizes (Koolhaas et al. 1999). Urban individuals may also exhibit greater sensitivity to glucocorticoids (Bonier 2012); hence, urban and rural individuals may exhibit synonymous glucocorticoid concentrations, yet show marked differences in behavioral profiles. Functional modifications to the hippocampus due to chronic stress can also occur (Magarinos et al. 1997). Such structural changes may affect the upstream production of, and sensitivity to, hormones (Fokidis and Deviche 2012; Sewall and Davies 2017). Alterations to products of other endocrine systems (e.g., testosterone, thyroxine) can interact with stress physiology to either mask or accentuate urban–rural differences (Mastorakos et al. 2006). Further, several pollutants and chemicals in cities (e.g., organochlorine, DDT, phthalates) disrupt proper endocrine function and negatively impact reproductive health and behavior (Falfushinska et al. 2008; Tuomainen and Candolin 2011); thus, variation in behavior may be explained more by environmental contaminants rather than intrinsic differences in hormonal production. Given these hypotheses, a cautious optimism may be necessary to interpret single-sample concentration differences as a signal of local adaptation. Disentangling the multitude of potential changes to functional endocrine responses (i.e., sensitivity, receptor density, receptor number), and how they correspond to adaptive processes, may be intractable with single concentrations from multiple individuals. Genomics and epigenetic mechanisms may provide a workable solution. It is known that environmental stressors have the potential to methylate DNA, and subsequently affect how gene expression is regulated (Riyahi et al. 2015; Watson et al. 2017). Use of RNAseq techniques would help to disentangle the complex endocrine responses that could occur with urban stressors, and prove valuable to determining how rapid epigenetic changes to endocrine function are related to anthropogenic drivers. Morphological Adjustments Morphological modifications observed in urban habitats are less well documented compared with endocrine or behavioral measures, yet arguably have as much functional significance in persistence throughout the urban matrix. Of the three broad phenotypic categories documented here, we may predict that the distribution of morphological forms is more selectively constrained within urban habitats because small changes in limb or wing structure can significantly alter an organism’s ability to locomote within the urbanized landscape. Moreover, residential or commercial development in cities alter landscape features that fundamentally affect how organisms traverse an urban environment (Hoban et al. 2016; Lowry et al. 2017a). For these reasons, morphological investigations can provide solid inferences of local adaptation in urbanized landscapes. To date, morphology–environment relationships in urban ecological studies have most often been observed in smaller organisms with short life-histories and dispersal distances (Table 1). For instance, several reptile and insect species demonstrate variation in limb length and appendage properties that increase the organism’s ability to traverse smooth manmade structures in cities (San Martin y Gomez and Van Dyck 2012; Donihue 2016; Winchell et al. 2016). Whether similar variation exists in limb morphology of mammals, birds, or amphibians remains unclear. However, prior work has demonstrated morphological differences in cranial capacity, with urban populations of multiple taxa exhibiting larger craniums compared to rural populations (Snell-Rood and Wick 2013). Other studies have focused on body size and condition, with mixed results across taxa (Table 1). Several authors suggest that urban–rural differences in morphology are related to the abundance of anthropogenic food subsidies in cities (Table 1), not unlike studies on increased human tolerance. Hence, there may indeed be a strong likelihood that food subsidies are driving changes to coordinated suites of traits (i.e., behavior, hormones, and morphology), and a genomic investigation may be best equipped to identify such associations. Synthesizing Urban Ecology with Genomics The swell of urban ecology studies in recent decades has certainly provided convincing evidence that urban populations are locally adapted to anthropogenic environments. Such studies have converged on the importance of socio-ecological factors in affecting evolutionary change of urban populations (Alberti 2015; Donihue and Lambert 2015; McDonnell and Hahs 2015; McPhearson et al. 2016). Still, determining whether selection acts on standing variation or novel mutations, describing synonymous genetic solutions across landscapes, or assessing the scale at which microgeographic adaptation occurs requires knowledge of the genetic loci underpinning urban phenotypes (Richardson et al. 2014; Tiffin and Ross-Ibarra 2014; Hoban et al. 2016). These questions are central to evolutionary biology, and encompass emerging themes in urban evolutionary ecology. Previous use of fragment-based markers (i.e., microsatellites or mitochondria) to describe genetic differentiation is necessarily limited by the number of loci surveyed, and therefore provides a limited resolution to assess adaptive divergence (Tiffin and Ross-Ibarra 2014). We now have an array of genomic tools to provide even further evidence for microgeographic adaptation to cities. Determining which genomic technique is appropriate for the emerging urban ecological questions of interest, however, is not a trivial endeavor. Here, I revisit the 3 broad questions that will arguably define the field of urban evolutionary ecology within future decades (see Introduction section), and position the role of genomics in facilitating that forward march. In so doing, I lean on recent impactful studies in urban systems that have successfully employed a genomics approach as a proof of concept for other urban systems. Further, I attempt to highlight how specific genomic techniques may be appropriate for the question at hand, with the understanding that several methodological options exist to address a single question. Previous reviews have done an exceptional job of meticulously describing the sequencing and computational minutiae of approaches such as restriction site-associated DNA sequencing or RADseq (Andrews et al. 2016; Benestan et al. 2016; Lowry et al. 2017b; Mckinney et al. 2017), SNP arrays and target sequence capture (Cammen et al. 2016), whole genome sequencing (WGS; Kajitani et al. 2014; vonHoldt et al. 2016), genotyping-by-sequencing (GBS; Narum et al. 2013; He et al. 2014), population-level genome re-sequencing (Ekblom and Galindo 2011; Oyler-McCance et al. 2016), and transcriptomic sequencing (Wang et al. 2009; Alvarez et al. 2015). For a more comprehensive reading of techniques addressed here, interested urban ecologists should delve further into these reviews. What Is the Finest Spatial Scale at Which Anthropogenic Structures Can Reduce Gene Flow in Cities? This question is fundamental to evolutionary biology, and is certainly not restricted to urban systems. Such an inquiry is difficult to generalize across various taxa and habitats. These concerns aside, myriad cities do exhibit similar spatial arrangements of green spaces, roadways, and buildings that establish a densely-populated urban core surrounded by decreasing human densities in suburban space that radiates outward (Johnson and Munshi-South 2017). This repeated pattern among multiple cities suggests that shared socio-ecological processes across urban areas may similarly select for traits of organisms regardless of specific city or taxon (Hendry et al. 2017). Hence, urban ecological properties may converge on a spatial threshold of geographic distance, that by which movement and gene flow is inhibited. Consequently, principles from island biogeography and metapopulation theory may be useful paradigms with which to view genetic connectivity and isolation in urban systems (Magle et al. 2010). This is particularly because the tenets of both theories demonstrate that species persistence, whether across oceanic islands or terrestrial habitat fragments, is directly linked to patch connectivity and size (Losos et al. 1967; Ricklefs et al. 1967; Moilanen and Hanski 2006). These key drivers ultimately have genetic consequences, with larger more connected patches exhibiting greater genetic diversity and admixture (Lowe and Allendorf 2010). Dispersal and gene flow are similarly dictated by an intervening matrix (e.g., roads, buildings, etc.) in urban fragments as they are in oceanic islands or terrestrial habitat fragments. However, as Magle et al. (2010) have previously articulated, the intervening matrix in cities is comprised of multiple elements (i.e., not just open ocean or habitat) that vary over contemporary timescales. These variable elements may strengthen or dampen matrix permeability for dispersing organisms despite geographical distance between urban fragments (Magle et al. 2010). Consequently, anthropogenic elements within the urban matrix may be as equally important as matrix size in genetically isolating populations. Indeed, the molecular studies surveyed here have observed genetic differentiation within cities at geographic distances under 1 km (Dronnet et al. 2005; Noël et al. 2007; Hemme et al. 2010; Mikulíček and Pišút 2012; Jha and Kremen 2013; Lourenço et al. 2017). An emerging question is whether genomic tools are better equipped to detect divergence at small spatial scales in cities. The chosen solution may be determined according to sample size and dispersal capability of the study species. One of the benefits of using, say, a RADseq approach compared with microsatellites or mitochondrial DNA is an expansion in genomic coverage (Oyler-McCance et al. 2016), increasing the likelihood of detecting divergence at fine spatial scales (Figure 3). Certainly, RADseq-generated SNPs have extended inferences made from previous microsatellite explorations in heterozygosity estimates (Hoffman and Nichols 2011) and population structure (Malenfant et al. 2015) of several nonmodel organisms, so it may be reasonable to expect similar resolution improvements in determining the spatial scale of divergence (which is typified in the conceptual diagram, Figure 3). In addition, RADseq, double-digest RADseq (ddRADseq), or other RAD methods can be performed without a reference genome from close relatives (Andrews et al. 2016; Benestan et al. 2016), though any available reference genome greatly improves interpretive power (Mcmahon et al. 2014; Shafer et al. 2015). This effectively opens the door to genomic investigations for urban ecologists working on nonmodel systems. Still, at some zenith SNPs generated from RADseq may be equally able to detect divergence as traditional markers (Figure 3), so the scale of study, the organism’s dispersal ecology, sampling design, and the specific research question should dictate which technique is most appropriate. Figure 3. View largeDownload slide A conceptual model of the relationship between next generation sequencing (NGS; >1000 loci surveyed) and traditional molecular approaches (i.e., 5–50 loci surveyed) in detecting population divergence over varying spatial scales and species’ dispersal ability. In this conceptual figure, whole-genome sequencing (WGS) should provide substantially more coverage of the genome as compared with reduced representation methods (i.e., RADseq). This inherent variation is reflected in the shaded regions, with WGS considered at the zenith of detection. In both cases of heightened or reduce dispersal ability, NGS methods are expected to detect divergence at smaller spatial scales. See online version for full colors. Figure 3. View largeDownload slide A conceptual model of the relationship between next generation sequencing (NGS; >1000 loci surveyed) and traditional molecular approaches (i.e., 5–50 loci surveyed) in detecting population divergence over varying spatial scales and species’ dispersal ability. In this conceptual figure, whole-genome sequencing (WGS) should provide substantially more coverage of the genome as compared with reduced representation methods (i.e., RADseq). This inherent variation is reflected in the shaded regions, with WGS considered at the zenith of detection. In both cases of heightened or reduce dispersal ability, NGS methods are expected to detect divergence at smaller spatial scales. See online version for full colors. SNP array and target sequence capture (TSC) approaches may be viable alternatives to RADseq if a reference genome is made available (Grueber 2015). Both techniques involve the design of custom arrays that target specific regions of the genome (Cammen et al. 2016). The caveat is that arrays contain biased sets of pre-ascertained SNPs (i.e., ascertainment bias) from specific populations that may portray a dramatically different picture of demographic history and natural selection than a whole genome sequencing approach (Helyar et al. 2011; Lachance and Tishkoff 2013). For instance, if founding populations were isolated by historical urban development, and the alleles targeted by SNP arrays had randomly drifted out of the observed population, then SNP arrays may incorrectly suggest strong natural selection. In this instance, a WGS approach would correctly identify historical demographic patterns as the primary explanation behind observed genetic structure (Hoban et al. 2016). Using SNP arrays and WGS in tandem could reduce the effects of ascertainment bias if careful filtering is done based on SNP genomic context before array development (Cammen et al. 2016; Humble et al. 2016). Intentional sampling across the geographic range of the target species can also limit ascertainment bias (Malenfant et al. 2015; Cammen et al. 2016). Though an improvement over traditional molecular approaches, reduced representation libraries may also be too sparse to survey adaptive loci because of the random nature with which SNPs are generated. Short fragments are chosen near restriction cut sites and randomly distributed across (mostly) noncoding regions, at best only covering ~5% of the genome (Ekblom and Galindo 2011). This is before filtering processes and SNP quality checks, so the final volume of available SNPs to use in genetic analyses is often less than 5%. Given these considerations, gathering sufficient knowledge of the species’ dispersal ecology and the relative importance of anthropogenic barriers (e.g., road density, temperature gradients, degree of impervious surface) to the study organism is suggested before making the genomics leap. For reference, a single representative study on 23 populations of white-footed mice (n = 191 individuals) in New York City, NY used a ddRADseq protocol to demonstrate both strong relationships between resistance distance (i.e., the degree of impervious surface) and genetic structuring, as well as an overall negative relationship between urbanization and genomewide variation (Munshi-South et al. 2016). How Is Standing Genetic Variation Related to Historical Human Development in Cities? When graded on geologic or ecological timescales, cities have relatively short yet dramatic developmental histories. Punctuated and often sudden human developments can radically alter or eradicate entire habitats on the order of years or decades (Gardner-Santana et al. 2009; Magle et al. 2010). Thus, historical dispersal or migration patterns across a less-developed urban landscape in 1900s may be fundamentally different from patterns in the 21st century (Munshi-South and Nagy 2014). For instance, anthropogenic development around urban fragments may isolate once highly connected habitat patches. This may have led to genetic bottlenecks with high levels of drift in the past that predict genetic structure in the modern era. Historical development may have also established novel niches or corridors that become increasingly permeable for new invaders, creating novel trophic interactions. Hence, modeling historical urban development in cities is invaluable to understanding observed patterns of genetic diversity and structure in contemporary urban populations (Harris and Munshi-South 2016; Hoban et al. 2016; Lourenço et al. 2017). Yet, demography is often considered a confounding factor (i.e., background noise in a null or neutral model) in identifying signals of selection, so few studies (traditional markers or NGS) focus primarily on historical demographic patterns to understand standing genetic variation (Oyler-McCance et al. 2016; Lowry et al. 2017a). Using an RNAseq protocol in a study population of 191 white-footed mice (P. leucopus), Harris and Munshi-South (2016) demonstrated that urban populations experienced strong bottlenecks with the conversion of 97% of green space into small urban parks over a 400-year period in New York City, NY. Comparatively, Lourenço et al. (2017) used microsatellite markers to demonstrate that declines in effective population sizes of fire salamanders (Salamandra salamandra) were concomitant with increasing urbanization in Oviedo, Spain. Both studies suggest functional connectivity is lost with historical rises in urbanization that have longstanding consequences for contemporary standing genetic variation, a hypothesis supported by other authors (Vandergast et al. 2009; Beninde et al. 2016; Krtinić et al. 2016). However, NGS techniques considerably improve the accuracy of estimating some demographic parameters (Shafer et al. 2015). Moreover, single population deviations may weight more heavily in statistical analyses of microsatellite markers, impeding adequate assessment of the role that long-term isolation and demographic history play in genetic variation (Lourenço et al. 2017). If it is reasonable to assume that focal populations exhibited minimal gene flow, then use of a reduced representation sequencing approach may be sufficient (Funk et al. 2016). Investigations into deeper historical time or in relation to specific urbanization events may require greater genomic resolution, thus whole-genome sequencing and differentiation outlier methods using genome-wide SNPs may be most suitable (Liu and Fu 2015; Hoban et al. 2016). Are Gene–Environment Associations and Expression Patterns the Product of Plasticity or Local Adaptation? Alberti et al. (2017a) recently conducted an elegant meta-analysis demonstrating that global rates of phenotypic change are greater in urban versus in natural and nonurban systems. In addition, the authors were able to link specific urban disturbances to more than 1600 examples of animal and plant phenotype modifications (Alberti et al. 2017a). It may be reasonable to predict that these urban signatures, connected to human-associated selective drivers, are underpinned by specific genetic loci or patterns of expression if we consider these phenotypic modifications adaptive (Figure 4). With overwhelming evidence that anthropogenic pressures do elicit phenotypic changes, the next critical step is to establish the mechanistic basis behind these urban signatures. Keen investigation of DNA methylation processes, for instance, may provide insight into how urban and rural habitat differences are transduced into variant phenotypic outcomes (Isaksson 2015; Riyahi et al. 2015). Methylation can profoundly influence gene expression patterns according to environmental conditions (Watson et al. 2017). Hence, this epigenetic mechanism is important for creating phenotypic variation, and may help answer whether observed urban signatures are a product of plasticity or local adaptation (Isaksson 2015; McDonnell and Hahs 2015). Previous single candidate gene approaches in black swans (Cygnus atratus; Payne et al. 2012; van Dongen et al. 2015) and great tits (P. major; Riyahi et al. 2017) have proved valuable in discovering specific genotypic markers underlying adaptive behavior in urban birds. In these examples, prior knowledge of the link between a specific suite of candidate genes and behavior (Fidler et al. 2007; Korsten et al. 2010) made a single candidate gene approach sufficient. This process necessarily limits study scope to information on previously identified traits and is unable to provide an exploratory perspective that may discover the multitude of traits and loci that contribute to adaptation (Funk et al. 2012). In addition, traditional candidate gene analyses are ill-equipped to account for polygenic traits or pleiotropy (i.e., a single gene affecting multiple traits; Flint and Munafò, 2013). For these reasons, a transcriptomic or whole-genome approach could prove valuable to addressing genotype–phenotype associations. Perhaps the most cost-effective sequencing means of establishing gene-phenotype and environment associations may be SNP arrays. Arrays developed within several candidate genome regions could be designed to compare multiple urban individuals and/or population, assuming ascertainment bias is accounted for. Alternatively, a genotyping-by-sequencing (GBS) approach generates a large number of SNPs for use in genotyping with no reference sequence limits and easy scalability to large populations (Narum et al. 2013; He et al. 2014). SNPs developed from GBS can subsequently be used in genome-wide association studies (GWAS) and quantitative trait loci (QTL) mapping to investigate candidate loci related to phenotypic traits (Narum et al. 2013). This is particularly attractive for traits with highly polygenic architectures (Hoban et al. 2016). Moreover, GBS is substantially less complicated than original RAD methods because there are fewer DNA purification steps, and fragments are not size-selected allowing for the assessment of variable fragment lengths across the genome (He et al. 2014). A third and characteristically different option may be transcriptomic sequencing (i.e., RNAseq), which provides a large number of potentially differentially expressed genes (Wang et al. 2009; Alvarez et al. 2015; Oyler-McCance et al. 2016). For example, urban white-footed mice populations in New York City, NY demonstrate higher levels of expression for candidate genes involved in lipid metabolism, innate immune response, and detoxification (Harris and Munshi-South 2016). The authors posit that expression variation among rural and urban populations is a direct result of variation in individual diets, as urban mice eat more anthropogenic foods rich in fat and carbohydrates. Similarly, compared with rural populations, urban great tits in Malmö, Sweden demonstrated increased expression in genes related to immune and inflammatory responses, detoxification, and genes linked to endocrine stress responses (Watson et al. 2017). The authors suggested that differences in gene expression between urban and rural individuals reflected differential stress-exposure in the 2 contrasting environments (Watson et al. 2017). Both examples strongly underscore the process by which urban–rural differences in the degree of human drivers (i.e., food subsidies, increased disease prevalence in cities) can induce habitat-specific variance in individual expression patterns and phenotypic traits. There may be several aspects to consider before endorsing an RNAseq protocol. First, minimal degradation to tissue samples and RNA can result in severe loss of technical power in sequencing the transcriptome. Samples must therefore be immediately stored in RNAlater (Ambion, Inc., Austin, TX) solution, liquid nitrogen, Trizol, ethanol, or salt-saturated DMSO to avoid transcriptome degradation (Cammen et al. 2016; Harris and Munshi-South 2016; Oyler-McCance et al. 2016). Second, positively selected genes might be missed if coverage is too low, as they are often expressed in a more tissue-specific manner than evolutionarily conserved genes (Ekblom and Galindo 2011). Finally, a single or multilocus quantitative PCR (i.e., qPCR) could provide as much meaningful information with a fraction of the cost (Alvarez et al. 2015). The qPCR procedure is already used to perform quality checks on tissue samples before RNAseq, and subsequently validates expression postsequencing (Wang et al. 2009), so using a qPCR approach may be less costly and intensive than genomewide RNAseq. However, this is only true if the specific candidate genes are known from previous transcriptomic studies on the taxa of interest. Consequently, for taxa lacking any prior information, a comparative transcriptomics approach may be the most beneficial. Conclusions and Considerations Evidence in recent decades has been mounting to suggest microgeographic adaptation is occurring within cities across the globe (Figures 1 and 2). In addition, a number of recent articles have demonstrated convergent phenotypic changes to anthropogenic stressors within cities (Table 1; Supplementary Table S2), underscoring the power of cities in affecting population change (Alberti et al. 2017b; Johnson and Munshi-South 2017). At this moment, we have a unique opportunity as ecologists and evolutionary biologists to decipher the genetic mechanisms that underscore these urban signatures (Figure 4). Genomic tools and inference may provide that additional link to solidifying urban environments and their role as agents of change. In so doing, we can address fundamental questions in both urban ecology and evolutionary biology that seek to understand the processes that facilitate rapid adaptation. Figure 4. View largeDownload slide A conceptual model on the eco-evolutionary feedbacks of human drivers on urban phenotypes and underlying genotypes. Anthropogenic drivers may affect the distribution of labile behavioral traits (e.g., boldness) that are often genetically associated. Resultant phenotypes may either fall within the range of trait plasticity or signify gene–environment associations. Disentangling whether such associations are due to demographic history, genetic drift, or positive selection is now more feasible with high-throughput sequencing methods. See online version for full colors. Figure 4. View largeDownload slide A conceptual model on the eco-evolutionary feedbacks of human drivers on urban phenotypes and underlying genotypes. Anthropogenic drivers may affect the distribution of labile behavioral traits (e.g., boldness) that are often genetically associated. Resultant phenotypes may either fall within the range of trait plasticity or signify gene–environment associations. Disentangling whether such associations are due to demographic history, genetic drift, or positive selection is now more feasible with high-throughput sequencing methods. See online version for full colors. Still, caution is warranted when deciding whether a genomic approach is most appropriate for the study system and question at hand. Careful consideration should be taken in designing a study to maximize statistical power. Moreover, the question should necessarily guide sampling protocol decisions. For instance, if the aim is to determine whether urban and rural populations are genetically divergent, sampling effort uniformly along an urban–rural gradient may not be optimal because admixture among intermediate populations may impede statistical power to detect gene–environment associations (Hoban et al. 2016). However, if the aim is to describe relatedness in relation to the degree of urbanization, uniform sampling along an urban–rural gradient is necessary. Sampling scale is also important, but determining a sufficient sample size is dependent on several complex factors, including the number and type of markers used, the study taxa, habitat structure, and the question of interest (Hoban et al. 2016). Moreover, though not discussed here in great detail, computational requirements, bioinformatics tools, and statistical programs to properly filter and identify SNP variants vary greatly (Helyar et al. 2011; Andrews et al. 2016; Oyler-McCance et al. 2016). A steep learning curve may exist for urban ecologists unfamiliar with the many postsequencing analysis programs that exist (Ekblom and Galindo 2011; Shafer et al. 2015; Andrews et al. 2016; Cammen et al. 2016), which may weigh strongly as a deciding factor in whether implementing genomics is worthwhile. There are no clear-cut techniques that are significantly better than others in addressing the questions posed in this review; thorough consideration of the pros and cons should be properly weighted with constraints of the biological system(s) of interest. Finally, even though having a reference genome helps, repetitive regions or paralogs may be missed in postfiltering and quality control steps because the reference genome is not representative of the study population (Hoban et al. 2016). These considerations aside, the hope is that this perspective will stimulate future research endeavors implementing genomic approaches to addressing relevant eco-evolutionary questions in urban fauna. Urban expansion will undoubtedly increase throughout the 21st century, and genomic tools will be crucial to determining the evolutionary consequences urban wildlife will face in response. Supplementary Material Supplementary data are available at Journal of Heredity online. Funding The author involved in this work was supported by a National Science Foundation Postdoctoral Research Fellowship in Biology (award no. 1523908). Acknowledgments This review article is the outcome of a symposium entitled, Genomics of Adaptation to Human Contexts, organized by Kathryn Turner, Brook Moyers, and C.J.S. Special thanks to Drs Stewart Breck and Lisa Angeloni for their advice and mentorship through various iterations of this manuscript, and to Dr Antoinette Piaggio for in-depth input on concepts and articles of literature that currently define the field. Additional thanks to the symposium participants and plenary speakers for their feedback and comments on ideas and perspectives, and to the administrative staff within the Departments of Biology and Plant Sciences at Colorado State University. References Alberti M. 2015. Eco-evolutionary dynamics in an urbanizing planet. Trends Ecol Evol . 30: 114– 126. Google Scholar CrossRef Search ADS PubMed Alberti M, Correa C, Marzluff JM, Hendry AP, Palkovacs EP, Gotanda KM, Hunt VM, Apgar TM, Zhou Y. 2017a. Global urban signatures of phenotypic change in animal and plant populations. Proc Natl Acad Sci U S A . 114: 8951– 8956. Google Scholar CrossRef Search ADS Alberti M, Marzluff J, Hunt VM. 2017b. Urban driven phenotypic changes: empirical observations and theoretical implications for eco-evolutionary feedback. Philos Trans R Soc B Biol Sci . 372. doi: 10.1098/rstb.2016.0029 Allendorf FW, Hohenlohe PA, Luikart G. 2010. Genomics and the future of conservation genetics. Nat Rev Genet . 11: 697– 709. Google Scholar CrossRef Search ADS PubMed Alvarez M, Schrey AW, Richards CL. 2015. Ten years of transcriptomics in wild populations: what have we learned about their ecology and evolution? Mol Ecol . 24: 710– 725. Google Scholar CrossRef Search ADS PubMed Andrews KR, Good JM, Miller MR, Luikart G, Hohenlohe PA. 2016. Harnessing the power of RADseq for ecological and evolutionary genomics. Nat Rev Genet . 17: 81– 92. Google Scholar CrossRef Search ADS PubMed Angilletta MJ, Wilson RS, Niehaus AC, Sears MW, Navas CA, Ribeiro PL. 2007. Urban physiology: city ants possess high heat tolerance. PLoS One . 2: e258. Google Scholar CrossRef Search ADS PubMed Arroyo B, Mougeot F, Bretagnolle V. 2017. Individual variation in behavioural responsiveness to humans leads to differences in breeding success and long-term population phenotypic changes. Ecol Lett . 20: 317– 325. Google Scholar CrossRef Search ADS PubMed Atwell JW, Cardoso GC, Whittaker DJ, Campbell-Nelson S, Robertson KW, Ketterson ED. 2012. Boldness behavior and stress physiology in a novel urban environment suggest rapid correlated evolutionary adaptation. Behav Ecol . 23: 960– 969. Google Scholar CrossRef Search ADS PubMed Audet JN, Ducatez S, Lefebvre L. 2016. The town bird and the country bird: problem solving and immunocompetence vary with urbanization. Behav Ecol . 27: 637– 644. Google Scholar CrossRef Search ADS Balkenhol N, Waits LP. 2009. Molecular road ecology: exploring the potential of genetics for investigating transportation impacts on wildlife. Mol Ecol . 18: 4151– 4164. Google Scholar CrossRef Search ADS PubMed Bateman PW, Fleming PA. 2012. Big city life: carnivores in urban environments. J Zool . 287: 1– 23. Google Scholar CrossRef Search ADS Benestan LM, Ferchaud AL, Hohenlohe PA, Garner BA, Naylor GJ, Baums IB, Schwartz MK, Kelley JL, Luikart G. 2016. Conservation genomics of natural and managed populations: building a conceptual and practical framework. Mol Ecol . 25: 2967– 2977. Google Scholar CrossRef Search ADS PubMed Beninde J, Feldmeier S, Werner M, Peroverde D, Schulte U, Hochkirch A, Veith M. 2016. Cityscape genetics: structural vs. functional connectivity of an urban lizard population. Mol Ecol . 25: 4984– 5000. Google Scholar CrossRef Search ADS PubMed Biard C, Brischoux F, Meillère A, Michaud B, Nivière M, Ruault S, Vaugoyeau M, Angelier F. 2017. Growing in cities: an urban penalty for wild birds? A study of phenotypic differences between urban and rural great tit chicks (Parus major). Front Ecol Evol . 5: 1– 14. Google Scholar CrossRef Search ADS Bókony V, Kulcsár A, Tóth Z, Liker A. 2012. Personality traits and behavioral syndromes in differently urbanized populations of house sparrows (Passer domesticus). PLoS One . 7: e36639. Google Scholar CrossRef Search ADS PubMed Bonier F. 2012. Hormones in the city: endocrine ecology of urban birds. Horm Behav . 61: 763– 772. Google Scholar CrossRef Search ADS PubMed Cammen KM, Andrews KR, Carroll EL, Foote AD, Humble E, Khudyakov JI, Louis M, McGowen MR, Olsen MT, Van Cise AM. 2016. Genomic methods take the plunge: recent advances in high-throughput sequencing of marine mammals. J Hered . 107: 481– 495. Google Scholar CrossRef Search ADS PubMed Carere C, Caramaschi D, Fawcett TW. 2010. Covariation between personalities and individual differences in coping with stress : converging evidence and hypotheses. Curr Zool . 56: 728– 741. Carrete M, Tella JL. 2013. High individual consistency in fear of humans throughout the adult lifespan of rural and urban burrowing owls. Sci Rep . 3: 3524. Google Scholar CrossRef Search ADS PubMed Carrete M, Tella JL. 2017. Behavioral correlations associated with fear of humans differ between rural and urban burrowing owls. Front Ecol Evol . 5: 1– 9. Google Scholar CrossRef Search ADS Cassone BJ, Kamdem C, Cheng C, Tan JC, Hahn MW, Costantini C, Besansky NJ. 2014. Gene expression divergence between malaria vector sibling species Anopheles gambiae and An. coluzzii from rural and urban Yaoundé Cameroon. Mol Ecol . 23: 2242– 2259. Google Scholar CrossRef Search ADS PubMed Clucas B, Marzluff JM. 2011. Coupled relationships between humans and other organisms in urban areas. In: Niemela J, editor. Urban ecology: patterns, processes, and applications. Oxford University Press. p. 135– 147. Google Scholar CrossRef Search ADS Costantini D, Greives TJ, Hau M, Partecke J. 2014. Does urban life change blood oxidative status in birds? J Exp Biol . 217: 2994– 2997. Google Scholar CrossRef Search ADS PubMed Delaney KS, Riley SPD, Fisher RN. 2010. A rapid, strong, and convergent genetic response to urban habitat fragmentation in four divergent and widespread vertebrates. PLoS One . 5: 1– 11. Dingemanse NJ, Kazem AJ, Réale D, Wright J. 2010. Behavioural reaction norms: animal personality meets individual plasticity. Trends Ecol Evol . 25: 81– 89. Google Scholar CrossRef Search ADS PubMed Dingemanse NJ, Réale D. 2005. Natural selection and animal personality. Behaviour . 142: 1159– 1184. Google Scholar CrossRef Search ADS Dip R, Stieger C, Deplazes P, Hegglin D, Müller U, Dafflon O, Koch H, Naegeli H. 2001. Comparison of heavy metal concentrations in tissues of red foxes from adjacent urban, suburban, and rural areas. Arch Environ Contam Toxicol . 40: 551– 556. Google Scholar CrossRef Search ADS PubMed Ditchkoff SS, Saalfeld ST, Gibson CJ. 2006. Animal behavior in urban ecosystems: modifications due to human-induced stress. Urban Ecosyst . 9: 5– 12. Google Scholar CrossRef Search ADS Dochtermann NA, Schwab T, Sih A. 2014. The contribution of additive genetic variation to personality variation: heritability of personality. Proc R Soc B Biol Sci . 282: 20142201. Google Scholar CrossRef Search ADS Dominoni DM, Carmona-Wagner EO, Hofmann M, Kranstauber B, Partecke J. 2014. Individual-based measurements of light intensity provide new insights into the effects of artificial light at night on daily rhythms of urban-dwelling songbirds. J Anim Ecol . 83: 681– 692. Google Scholar CrossRef Search ADS PubMed Donihue CM. 2016. Aegean wall lizards switch foraging modes, diet, and morphology in a human-built environment. Ecol Evol . 6: 7433– 7442. Google Scholar CrossRef Search ADS PubMed Donihue CM, Lambert MR. 2015. Adaptive evolution in urban ecosystems. Ambio . 44: 194– 203. Google Scholar CrossRef Search ADS PubMed Dronnet S, Chapuisat M, Vargo EL, Bagnères AG, Lohou C. 2005. Genetic analysis of the breeding system of an invasive subterranean termite, Reticulitermes santonensis, in urban and natural habitats. Mol Ecol . 14: 1311– 1320. Google Scholar CrossRef Search ADS PubMed Ducatez S, Audet JN, Rodriguez JR, Kayello L, Lefebvre L. 2017. Innovativeness and the effects of urbanization on risk-taking behaviors in wild Barbados birds. Anim Cogn . 20: 33– 42. Google Scholar CrossRef Search ADS PubMed Dulisz B, Nowakowski JJ, Górnik J. 2016. Differences in biometry and body condition of the House Sparrow (Passer domesticus) in urban and rural population during breeding season. Urban Ecosyst . 19: 1307– 1324. Google Scholar CrossRef Search ADS Ekblom R, Galindo J. 2011. Applications of next generation sequencing in molecular ecology of non-model organisms. Heredity (Edinb) . 107: 1– 15. Google Scholar CrossRef Search ADS PubMed Evans KL, Gaston KJ, Sharp SP, McGowan A, Hatchwell BJ. 2009. The effect of urbanisation on avian morphology and latitudinal gradients in body size. Oikos . 118: 251– 259. Google Scholar CrossRef Search ADS Falfushinska HI, Romanchuk LD, Stolyar OB. 2008. Different responses of biochemical markers in frogs (Rana ridibunda) from urban and rural wetlands to the effect of carbamate fungicide. Comp Biochem Physiol C Toxicol Pharmacol . 148: 223– 229. Google Scholar CrossRef Search ADS PubMed Federspiel IG, Garland A, Guez D, Bugnyar T, Healy SD, Güntürkün O, Griffin AS. 2017. Adjusting foraging strategies: a comparison of rural and urban common mynas (Acridotheres tristis). Anim Cogn . 20: 65– 74. Google Scholar CrossRef Search ADS PubMed Fidler AE, van Oers K, Drent PJ, Kuhn S, Mueller JC, Kempenaers B. 2007. Drd4 gene polymorphisms are associated with personality variation in a passerine bird. Proc Biol Sci . 274: 1685– 1691. Google Scholar CrossRef Search ADS PubMed Fischer JD, Cleeton SH, Lyons TP, Miller JR. 2012. Urbanization and the predation paradox: the role of trophic dynamics in structuring vertebrate communities. Bioscience . 62: 809– 818. Google Scholar CrossRef Search ADS Flint J, Munafò MR. 2013. Candidate and non-candidate genes in behavior genetics. Curr Opin Neurobiol . 23: 57– 61. Google Scholar CrossRef Search ADS PubMed Fokidis HB, Deviche P. 2012. Brain arginine vasotocin immunoreactivity differs between urban and desert curve-billed thrashers, Toxostoma curvirostre: relationships with territoriality and stress physiology. Brain Behav Evol . 79: 84– 97. Google Scholar CrossRef Search ADS PubMed Foltz SL, Ross AE, Laing BT, Rock RP, Battle KE, Moore IT. 2015. Get off my lawn: increased aggression in urban song sparrows is related to resource availability. Behav Ecol . 26: 1548– 1557. Google Scholar CrossRef Search ADS French SS, Fokidis HB, Moore MC. 2008. Variation in stress and innate immunity in the tree lizard (Urosaurus ornatus) across an urban-rural gradient. J Comp Physiol B Biochem Syst Environ Physiol . 178: 997– 1005. Google Scholar CrossRef Search ADS Funk WC, Lovich RE, Hohenlohe PA, Hofman CA, Morrison SA, Sillett TS, Ghalambor CK, Maldonado JE, Rick TC, Day MDet al. 2016. Adaptive divergence despite strong genetic drift: genomic analysis of the evolutionary mechanisms causing genetic differentiation in the island fox (Urocyon littoralis). Mol Ecol . 25: 2176– 2194. Google Scholar CrossRef Search ADS PubMed Funk WC, McKay JK, Hohenlohe PA, Allendorf FW. 2012. Harnessing genomics for delineating conservation units. Trends Ecol Evol . 27: 489– 496. Google Scholar CrossRef Search ADS PubMed Galbraith JA, Beggs JR, Jones DN, Stanley MC. 2015. Supplementary feeding restructures urban bird communities. Proc Natl Acad Sci U S A . 112: E2648– E2657. Google Scholar CrossRef Search ADS PubMed Gardner-Santana LC, Norris DE, Fornadel CM, Hinson ER, Klein SL, Glass GE. 2009. Commensal ecology, urban landscapes, and their influence on the genetic characteristics of city-dwelling Norway rats (Rattus norvegicus). Mol Ecol . 18: 2766– 2778. Google Scholar CrossRef Search ADS PubMed Gehrt SD, Wilson EC, Brown JL, Anchor C. 2013. Population ecology of free-roaming cats and interference competition by coyotes in urban parks. PLoS One . 8: 1– 11. Google Scholar CrossRef Search ADS Giraudeau M, Chavez A, Toomey MB, McGraw KJ. 2015. Effects of carotenoid supplementation and oxidative challenges on physiological parameters and carotenoid-based coloration in an urbanization context. Behav Ecol Sociobiol . 69: 957– 970. Google Scholar CrossRef Search ADS Greggor AL, Clayton NS, Fulford AJ, Thornton A. 2016. Street smart: faster approach towards litter in urban areas by highly neophobic corvids and less fearful birds. Anim Behav . 117: 123– 133. Google Scholar CrossRef Search ADS PubMed Grueber CE. 2015. Comparative genomics for biodiversity conservation. Comput Struct Biotechnol J . 13: 370– 375. Google Scholar CrossRef Search ADS PubMed Harris SE, Munshi-South J. 2016. Scans for positive selection reveal candidate genes and local adaptation of Peromyscus leucopus populations to urbanization. bioRxiv . doi: 10.1101/038141 Harris SE, Munshi-South J, Obergfell C, O’Neill R. 2013. Signatures of rapid evolution in urban and rural transcriptomes of white-footed mice (Peromyscus leucopus) in the New York metropolitan area. PLoS One . 8: e74938. Google Scholar CrossRef Search ADS PubMed Harris SE, O’Neill RJ, Munshi-South J. 2015. Transcriptome resources for the white-footed mouse (Peromyscus leucopus): new genomic tools for investigating ecologically divergent urban and rural populations. Mol Ecol Resour . 15: 382– 394. Google Scholar CrossRef Search ADS PubMed He J, Zhao X, Laroche A, Lu ZX, Liu H, Li Z. 2014. Genotyping-by-sequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding. Front Plant Sci . 5: 484. Google Scholar CrossRef Search ADS PubMed Helyar SJ, Hemmer-Hansen J, Bekkevold D, Taylor MI, Ogden R, Limborg MT, Cariani A, Maes GE, Diopere E, Carvalho GRet al. 2011. Application of SNPs for population genetics of nonmodel organisms: new opportunities and challenges. Mol Ecol Resour . 11( Suppl 1): 123– 136. Google Scholar CrossRef Search ADS PubMed Hemme RR, Thomas CL, Chadee DD, Severson DW. 2010. Influence of urban landscapes on population dynamics in a short-distance migrant mosquito: evidence for the dengue vector Aedes aegypti. PLoS Negl Trop Dis . 4: e634. Google Scholar CrossRef Search ADS PubMed Hendry AP, Gotanda KM, Svensson EI. 2017. Human influences on evolution, and the ecological and societal consequences. Philos Trans R Soc B Biol Sci . 372: doi: 10.1098/rstb.2016.0028 Hoban S, Kelley JL, Lotterhos KE, Antolin MF, Bradburd G, Lowry DB, Poss ML, Reed LK, Storfer A, Whitlock MC. 2016. Finding the genomic basis of local adaptation: pitfalls, practical solutions, and future directions. Am Nat . 188: 379– 397. Google Scholar CrossRef Search ADS PubMed Hoffman JI, Nichols HJ. 2011. A novel approach for mining polymorphic microsatellite markers in silico. PLoS One . 6: e23283. Google Scholar CrossRef Search ADS PubMed Humble E, Martinez-Barrio A, Forcada J, Trathan PN, Thorne MA, Hoffmann M, Wolf JB, Hoffman JI. 2016. A draft fur seal genome provides insights into factors affecting SNP validation and how to mitigate them. Mol Ecol Resour . 16: 909– 921. Google Scholar CrossRef Search ADS PubMed Hwang CC, Turner BD. 2009. Small-scaled geographical variation in life-history traits of the blowfly Calliphora vicina between rural and urban populations. Entomol Exp Appl . 132: 218– 224. Google Scholar CrossRef Search ADS Isaksson C. 2015. Urbanization, oxidative stress and inflammation: a question of evolving, acclimatizing or coping with urban environmental stress. Funct Ecol . 29: 913– 923. Google Scholar CrossRef Search ADS Jha S, Kremen C. 2013. Urban land use limits regional bumble bee gene flow. Mol Ecol . 22: 2483– 2495. Google Scholar CrossRef Search ADS PubMed Johnson MTJ, Munshi-South J. 2017. Evolution of life in urban environments. Science . 358. doi: 10.1126/science.aam8327 Kajitani R, Toshimoto K, Noguchi H, Toyoda A, Ogura Y, Okuno M, Yabana M, Harada M, Nagayasu E, Maruyama Het al. 2014. Efficient de novo assembly of highly heterozygous genomes from whole-genome shotgun short reads. Genome Res . 24: 1384– 1395. Google Scholar CrossRef Search ADS PubMed Koolhaas JM, Korte SM, De Boer SF, Van Der Vegt BJ, Van Reenen CG, Hopster H, De Jong IC, Ruis MA, Blokhuis HJ. 1999. Coping styles in animals: current status in behavior and stress-physiology. Neurosci Biobehav Rev . 23: 925– 935. Google Scholar CrossRef Search ADS PubMed Korsten P, Mueller JC, Hermannstädter C, Bouwman KM, Dingemanse NJ, Drent PJ, Liedvogel M, Matthysen E, van Oers K, van Overveld Tet al. 2010. Association between DRD4 gene polymorphism and personality variation in great tits: a test across four wild populations. Mol Ecol . 19: 832– 843. Google Scholar CrossRef Search ADS PubMed Korte SM, Koolhaas JM, Wingfield JC, McEwen BS. 2005. The Darwinian concept of stress: benefits of allostasis and costs of allostatic load and the trade-offs in health and disease. Neurosci Biobehav Rev . 29: 3– 38. Google Scholar CrossRef Search ADS PubMed Krtinić B, Francuski L, Ludoški J, Milankov V. 2016. Integrative approach revealed contrasting pattern of spatial structuring within urban and rural biotypes of Culex pipiens. J Appl Entomol . 140: 757– 774. Google Scholar CrossRef Search ADS Lachance J, Tishkoff SA. 2013. SNP ascertainment bias in population genetic analyses: why it is important, and how to correct it. Bioessays . 35: 780– 786. Google Scholar CrossRef Search ADS PubMed Lapiedra O, Chejanovski Z, Kolbe JJ. 2017. Urbanization and biological invasion shape animal personalities. Glob Chang Biol . 23: 592– 603. Google Scholar CrossRef Search ADS PubMed Lazić MM, Carretero MA, Živković U, Crnobrnja-Isailović J. 2016. City life has fitness costs: lower body condition and increased parasite intensity in urban common wall lizards Podarcis muralis M. Salamandra . 53: 1– 23. Lima SL, Blackwell BF, DeVault TL, Fernández-Juricic E. 2015. Animal reactions to oncoming vehicles: a conceptual review. Biol Rev . 90: 60– 76. Google Scholar CrossRef Search ADS PubMed Liu X, Fu YX. 2015. Exploring population size changes using SNP frequency spectra. Nat Genet . 47: 555– 559. Google Scholar CrossRef Search ADS PubMed Losos JB, Ricklefs RE, MacArthur RH, Wilson EO, MacArthur W. 1967. The theory of island biogeography revisited . Losos JB and Ricklefs RE, editors. Princeton, New Jersey: Princeton University Press. Lourenço A, Álvarez D, Wang IJ, Velo-Antón G. 2017. Trapped within the city: integrating demography, time since isolation and population-specific traits to assess the genetic effects of urbanization. Mol Ecol . 26: 1498– 1514. Google Scholar CrossRef Search ADS PubMed Lowe WH, Allendorf FW. 2010. What can genetics tell us about population connectivity? Mol Ecol . 19: 3038– 3051. Google Scholar CrossRef Search ADS PubMed Lowry DB, Hoban S, Kelley JL, Lotterhos KE, Reed LK, Antolin MF, Storfer A. 2017a. Breaking RAD: an evaluation of the utility of restriction site-associated DNA sequencing for genome scans of adaptation. Mol Ecol Resour . 17: 142– 152. Google Scholar CrossRef Search ADS Lowry DB, Hoban S, Kelley JL, Lotterhos KE, Reed LK, Antolin MF, Storfer A. 2017b. Responsible RAD: striving for best practices in population genomic studies of adaptation. Mol Ecol Resour . 17: 366– 369. Google Scholar CrossRef Search ADS Lowry H, Lill A, Wong BBM. 2013. Behavioural responses of wildlife to urban environments. Biol Rev . 88: 537– 549. Google Scholar CrossRef Search ADS PubMed Lyons J, Mastromonaco G, Edwards DB, Schulte-Hostedde AI. 2017. Fat and happy in the city: eastern chipmunks in urban environments. Behav Ecol . 0: 1– 8. MacManes MD. 2016. Establishing evidenced-based best practice for the de novo assembly and evaluation of transcriptomes from non-model organisms. bioRxiv . doi: 10.1101/03 Magariños AM, Verdugo JM, McEwen BS. 1997. Chronic stress alters synaptic terminal structure in hippocampus. Proc Natl Acad Sci U S A . 94: 14002– 14008. Google Scholar CrossRef Search ADS PubMed Magle SB, Hunt VM, Vernon M, Crooks KR. 2012. Urban wildlife research: past, present, and future. Biol Conserv . 155: 23– 32. Google Scholar CrossRef Search ADS Magle SB, Reyes P, Zhu J, Crooks KR. 2010. Extirpation, colonization, and habitat dynamics of a keystone species along an urban gradient. Biol Conserv . 143: 2146– 2155. Google Scholar CrossRef Search ADS Malenfant RM, Coltman DW, Davis CS. 2015. Design of a 9K illumina BeadChip for polar bears (Ursus maritimus) from RAD and transcriptome sequencing. Mol Ecol Resour . 15: 587– 600. Google Scholar CrossRef Search ADS PubMed Mastorakos G, Pavlatou MG, Mizamtsidi M. 2006. The hypothalamic-pituitary-adrenal and the hypothalamic-pituitary-gonadal axes interplay. Pediatr Endocrinol Rev . 3 Suppl 1: 172– 181. Google Scholar PubMed Mastretta-Yanes A, Arrigo N, Alvarez N, Jorgensen TH, Piñero D, Emerson BC. 2015. Restriction site-associated DNA sequencing, genotyping error estimation and de novo assembly optimization for population genetic inference. Mol Ecol Resour . 15: 28– 41. Google Scholar CrossRef Search ADS PubMed McCleery RA. 2009. Changes in fox squirrel anti-predator behaviors across the urban-rural gradient. Landsc Ecol . 24: 483– 493. Google Scholar CrossRef Search ADS McDonnell MJ, Hahs AK. 2015. Adaptation and adaptedness of organisms to urban environments. Annu Rev Ecol Evol Syst . 46: 261– 280. Google Scholar CrossRef Search ADS McKinney GJ, Larson WA, Seeb LW, Seeb JE. 2017. RADseq provides unprecedented insights into molecular ecology and evolutionary genetics: comment on Breaking RAD by Lowry et al. (2016). Mol Ecol Resour . 17: 356– 361. Google Scholar CrossRef Search ADS PubMed McMahon BJ, Teeling EC, Höglund J. 2014. How and why should we implement genomics into conservation? Evol Appl . 7: 999– 1007. Google Scholar CrossRef Search ADS PubMed McPhearson T, Pickett STA, Grimm NB, Niemelä J, Alberti M, Elmqvist Tet al. 2016. Advancing urban ecology toward a science of cities. Bioscience . 66: 198– 212. Google Scholar CrossRef Search ADS Meillère A, Brischoux F, Parenteau C, Angelier F. 2015. Influence of urbanization on body size, condition, and physiology in an urban exploiter: a multi-component approach. PLoS One . 10: 1– 19. Google Scholar CrossRef Search ADS Mikulíček P, Pišút P. 2012. Genetic structure of the marsh frog (Pelophylax ridibundus) populations in urban landscape. Eur J Wildl Res . 58: 833– 845. Google Scholar CrossRef Search ADS Miranda AC. 2017. Mechanisms of behavioural change in urban animals: the role of microevolution and phenotypic plasticity. In: Murgui E, Hedblom M, editors. Ecology and conservation of birds in urban environments . Cham: Springer International Publishing, p. 113– 132. Google Scholar CrossRef Search ADS Miranda AC, Schielzeth H, Sonntag T, Partecke J. 2013. Urbanization and its effects on personality traits: a result of microevolution or phenotypic plasticity? Glob Chang Biol . 19: 2634– 2644. Google Scholar CrossRef Search ADS PubMed Moilanen A, Hanski I. 2006. Connectivity and metapopulation dynamics in highly fragmented landscapes. In: Crooks KR, Sanjayan M, editors. Connectivity conservation . Cambridge, UK: Cambridge University Press, p. 44– 71. Google Scholar CrossRef Search ADS Møller AP, Grim T, Ibáñez-Álamo JD, Markó G, Tryjanowski P. 2013. Change in flight initiation distance between urban and rural habitats following a cold winter. Behav Ecol . 24: 1211– 1217. Google Scholar CrossRef Search ADS Møller AP, Ibáñez-Álamo JD. 2012. Escape behaviour of birds provides evidence of predation being involved in urbanization. Anim Behav . 84: 341– 348. Google Scholar CrossRef Search ADS Mueller JC, Partecke J, Hatchwell BJ, Gaston KJ, Evans KL. 2013. Candidate gene polymorphisms for behavioural adaptations during urbanization in blackbirds. Mol Ecol . 22: 3629– 3637. Google Scholar CrossRef Search ADS PubMed Munshi-South J. 2012. Urban landscape genetics: canopy cover predicts gene flow between white-footed mouse (Peromyscus leucopus) populations in New York City. Mol Ecol . 21: 1360– 1378. Google Scholar CrossRef Search ADS PubMed Munshi-South J, Kharchenko K. 2010. Rapid, pervasive genetic differentiation of urban white-footed mouse (Peromyscus leucopus) populations in New York City. Mol Ecol . 19: 4242– 4254. Google Scholar CrossRef Search ADS PubMed Munshi-South J, Nagy C. 2014. Urban park characteristics, genetic variation, and historical demography of white-footed mouse (Peromyscus leucopus) populations in New York City. PeerJ . 2: e310. Google Scholar CrossRef Search ADS PubMed Munshi-South J, Zolnik CP, Harris SE. 2016. Population genomics of the Anthropocene: urbanization is negatively associated with genome-wide variation in white-footed mouse populations. Evol Appl . 9: 546– 564. Google Scholar CrossRef Search ADS PubMed Murray MH, Cembrowski A, Latham ADM, Lukasik VM, Pruss S, St Clair CC. 2015. Greater consumption of protein-poor anthropogenic food by urban relative to rural coyotes increases diet breadth and potential for human-wildlife conflict. Ecography (Cop) . 38: 1235– 1242. Google Scholar CrossRef Search ADS Narum SR, Buerkle CA, Davey JW, Miller MR, Hohenlohe PA. 2013. Genotyping-by-sequencing in ecological and conservation genomics. Mol Ecol . 22: 2841– 2847. Google Scholar CrossRef Search ADS PubMed Noël S, Ouellet M, Galois P, Lapointe FJ. 2007. Impact of urban fragmentation on the genetic structure of the eastern red-backed salamander. Conserv Genet . 8: 599– 606. Google Scholar CrossRef Search ADS Orsini L, Spanier KI, DE Meester L. 2012. Genomic signature of natural and anthropogenic stress in wild populations of the waterflea Daphnia magna: validation in space, time and experimental evolution. Mol Ecol . 21: 2160– 2175. Google Scholar CrossRef Search ADS PubMed Oyler-McCance SJ, Oh KP, Langin KM, Aldridge CL. 2016. A field ornithologist’s guide to genomics: practical considerations for ecology and conservation. Auk . 133: 626– 648. Google Scholar CrossRef Search ADS Papp S, Vincze E, Preiszner B, Liker A, Bókony V. 2015. A comparison of problem-solving success between urban and rural house sparrows. Behav Ecol Sociobiol . 69: 471– 480. Google Scholar CrossRef Search ADS Partecke J, Schwabl I, Gwinner E. 2006. Stress and the city: urbanization and its effects on the stress physiology in European blackbirds. Ecology . 87: 1945– 1952. Google Scholar CrossRef Search ADS PubMed Partecke J, Van’t Hof T, Gwinner E. 2004. Differences in the timing of reproduction between urban and forest European blackbirds (Turdus merula): result of phenotypic flexibility or genetic differences? Proc Biol Sci . 271: 1995– 2001. Google Scholar CrossRef Search ADS PubMed Payne CJ, Jessop TS, Guay PJ, Johnstone M, Feore M, Mulder RA. 2012. Population, behavioural and physiological responses of an urban population of black swans to an intense annual noise event. PLoS One . 7: e45014. Google Scholar CrossRef Search ADS PubMed Pickett STA, Cadenasso ML, Childers DL, McDonnell MJ, Zhou W. 2016. Evolution and future of urban ecological science: ecology in, of, and for the city. Ecosyst Heal Sustain . 2: e01229. Google Scholar CrossRef Search ADS Pickett STA, Cadenasso ML, Grove JM, Nilon CH, Pouyat RV, Zipperer WC, Costanza R. 2008. Urban ecological systems: linking terrestrial ecological, physical, and socioeconomic components of metropolitan areas. Urban Ecol . 32: 99– 122. Google Scholar CrossRef Search ADS Plummer KE, Siriwardena GM, Conway GJ, Risely K, Toms MP. 2015. Is supplementary feeding in gardens a driver of evolutionary change in a migratory bird species? Glob Chang Biol . 21: 4353– 4363. Google Scholar CrossRef Search ADS PubMed Poessel SA, Mock EC, Breck SW. 2017. Coyote (Canis latrans) diet in an urban environment: variation relative to pet conflicts, housing density, and season. Can J Zool . 95: 287– 297. Google Scholar CrossRef Search ADS Prange S, Gehrt SD, Wiggers EP. 2004. Influences of anthropogenic resources on raccoon (Procyon Lotor) movements and spatial distribution. J Mammal . 85: 483– 490. Google Scholar CrossRef Search ADS Preiszner B, Papp S, Pipoly I, Seress G, Vincze E, Liker A, Bókony V. 2017. Problem-solving performance and reproductive success of great tits in urban and forest habitats. Anim Cogn . 20: 53– 63. Google Scholar CrossRef Search ADS PubMed Proppe DS, Sturdy CB, St Clair CC. 2013. Anthropogenic noise decreases urban songbird diversity and may contribute to homogenization. Glob Chang Biol . 19: 1075– 1084. Google Scholar CrossRef Search ADS PubMed Puckett EE, Park J, Combs M, Blum MJ, Bryant JE, Caccone Aet al. 2016. Global population divergence and admixture of the brown rat (Rattus norvegicus). Proc R Soc B Biol Sci . 283: 20161762. Google Scholar CrossRef Search ADS Rebolo-Ifrán N, Carrete M, Sanz-Aguilar A, Rodríguez-Martínez S, Cabezas S, Marchant TA, Bortolotti GR, Tella JL. 2015. Links between fear of humans, stress and survival support a non-random distribution of birds among urban and rural habitats. Sci Rep . 5: 13723. Google Scholar CrossRef Search ADS PubMed Rees M, Roe JH, Georges A. 2009. Life in the suburbs: behavior and survival of a freshwater turtle in response to drought and urbanization. Biol Conserv . 142: 3172– 3181. Google Scholar CrossRef Search ADS Richardson JL, Urban MC, Bolnick DI, Skelly DK. 2014. Microgeographic adaptation and the spatial scale of evolution. Trends Ecol Evol . 29: 165– 176. Google Scholar CrossRef Search ADS PubMed Riley SPD, Brown JL, Sikich JA, Schoonmaker CM, Boydston EE. 2014a. Wildlife friendly roads: the impacts of roads on wildlife in urban areas and potential remedies. In: McCleery RA, Moorman CE, Peterson MN, editors. Urban wildlife conservation: theory and practice . Boston, MA: Springer US, p. 323– 360. Google Scholar CrossRef Search ADS Riley SPD, Sauvajot RM, Fuller TK, York EC, Kamradt DA, Bromley C, Wayne RK. 2003. Effects of urbanization and habitat fragmentation on bobcats and coyotes in Southern California. Conserv Biol . 17: 566– 576. Google Scholar CrossRef Search ADS Riley SP, Serieys LE, Pollinger JP, Sikich JA, Dalbeck L, Wayne RK, Ernest HB. 2014b. Individual behaviors dominate the dynamics of an urban mountain lion population isolated by roads. Curr Biol . 24: 1989– 1994. Google Scholar CrossRef Search ADS Riyahi S, Björklund M, Mateos-Gonzalez F, Senar JC. 2017. Personality and urbanization: behavioural traits and DRD4 SNP830 polymorphisms in great tits in Barcelona city. J Ethol . 35: 101– 108. Google Scholar CrossRef Search ADS Riyahi S, Sánchez-Delgado M, Calafell F, Monk D, Senar JC. 2015. Combined epigenetic and intraspecific variation of the DRD4 and SERT genes influence novelty seeking behavior in great tit Parus major. Epigenetics . 10: 516– 525. Google Scholar CrossRef Search ADS PubMed Šálek M, Drahníková L, Tkadlec E. 2015. Changes in home range sizes and population densities of carnivore species along the natural to urban habitat gradient. Mamm Rev . 45: 1– 14. San Martin y Gomez G, Van Dyck H. 2012. Ecotypic differentiation between urban and rural populations of the grasshopper Chorthippus brunneus relative to climate and habitat fragmentation. Oecologia . 169: 125– 133. Google Scholar CrossRef Search ADS PubMed Sewall KB, Davies S. 2017. Two neural measures differ between urban and rural song sparrows after conspecific song playback. Front Ecol Evol . 5: 1– 11. Google Scholar CrossRef Search ADS Shafer AB, Wolf JB, Alves PC, Bergström L, Bruford MW, Brännström I, Colling G, Dalén L, De Meester L, Ekblom Ret al. 2015. Genomics and the challenging translation into conservation practice. Trends Ecol Evol . 30: 78– 87. Google Scholar CrossRef Search ADS PubMed Shochat E, Warren PS, Faeth SH, McIntyre NE, Hope D. 2006. From patterns to emerging processes in mechanistic urban ecology. Trends Ecol Evol . 21: 186– 191. Google Scholar CrossRef Search ADS PubMed Slabbekoorn H. 2013. Songs of the city: noise-dependent spectral plasticity in the acoustic phenotype of urban birds. Anim Behav . 85: 1089– 1099. Google Scholar CrossRef Search ADS Snell-Rood EC, Wick N. 2013. Anthropogenic environments exert variable selection on cranial capacity in mammals. Proc R Soc B Biol Sci . 280: 20131384. Google Scholar CrossRef Search ADS Sol D, Lapiedra O, González-Lagos C. 2013. Behavioural adjustments for a life in the city. Anim Behav . 85: 1101– 1112. Google Scholar CrossRef Search ADS Stillfried M, Gras P, Busch M, Börner K, Kramer-Schadt S, Ortmann S. 2017. Wild inside: urban wild boar select natural, not anthropogenic food resources. PLoS One . 12: e0175127. Google Scholar CrossRef Search ADS PubMed Storz JF. 2005. Using genome scans of DNA polymorphism to infer adaptive population divergence. Mol Ecol . 14: 671– 688. Google Scholar CrossRef Search ADS PubMed Swaddle JP, Francis CD, Barber JR, Cooper CB, Kyba CC, Dominoni DM, Shannon G, Aschehoug E, Goodwin SE, Kawahara AYet al. 2015. A framework to assess evolutionary responses to anthropogenic light and sound. Trends Ecol Evol . 30: 550– 560. Google Scholar CrossRef Search ADS PubMed Taff CC, Vitousek MN. 2016. Endocrine flexibility: optimizing phenotypes in a dynamic world? Trends Ecol Evol . 31: 476– 488. Google Scholar CrossRef Search ADS PubMed Templeton CN, Zollinger SA, Brumm H. 2016. Traffic noise drowns out great tit alarm calls. Curr Biol . 26: R1173– R1174. Google Scholar CrossRef Search ADS PubMed Tiffin P, Ross-Ibarra J. 2014. Advances and limits of using population genetics to understand local adaptation. Trends Ecol Evol . 29: 673– 680. Google Scholar CrossRef Search ADS PubMed Tigas LA, Van Vuren DH, Sauvajot RM. 2002. Behavioral responses of bobcats and coyotes to habitat fragmentation and corridors in an urban environment. Biol Conserv . 108: 299– 306. Google Scholar CrossRef Search ADS Tuomainen U, Candolin U. 2011. Behavioural responses to human-induced environmental change. Biol Rev . 86: 640– 657. Google Scholar CrossRef Search ADS PubMed Tüzün N, Op de Beeck L, Brans KI, Janssens L, Stoks R. 2017a. Microgeographic differentiation in thermal performance curves between rural and urban populations of an aquatic insect. Evol Appl . 10:1067–1075. Tüzün N, Op de Beeck L, Stoks R. 2017b. Sexual selection reinforces a higher flight endurance in urban damselflies. Evol Appl . 10: 694– 703. Google Scholar CrossRef Search ADS Uchida K, Suzuki K, Shimamoto T, Yanagawa H, Koizumi I. 2016. Seasonal variation of flight initiation distance in Eurasian red squirrels in urban versus rural habitat. J Zool . 298: 225– 231. Google Scholar CrossRef Search ADS van Dongen WF, Robinson RW, Weston MA, Mulder RA, Guay PJ. 2015. Variation at the DRD4 locus is associated with wariness and local site selection in urban black swans. BMC Evol Biol . 15: 253. Google Scholar CrossRef Search ADS PubMed van Oers K, de Jong G, van Noordwijk A, Kempenaers B, Drent P. 2005. Contribution of genetics to the study of animal personalities: a review of case studies. Behaviour . 142: 1185– 1206. Google Scholar CrossRef Search ADS Vandergast AG, Lewallen EA, Deas J, Bohonak AJ, Weissman DB, Fisher RN. 2009. Loss of genetic connectivity and diversity in urban microreserves in a southern California endemic Jerusalem cricket (Orthoptera: Stenopelmatidae: Stenopelmatus n. sp. ‘santa monica’). J Insect Conserv . 13: 329– 345. Google Scholar CrossRef Search ADS Vincze E, Papp S, Preiszner B, Seress G, Bókony V, Liker A. 2016. Habituation to human disturbance is faster in urban than rural house sparrows. Behav Ecol . 27: 1304– 1313. Google Scholar CrossRef Search ADS vonHoldt BM, Cahill JA, Fan Z, Gronau I, Robinson J, Pollinger JP, Shapiro B, Wall J, Wayne RK. 2016. Whole-genome sequence analysis shows that two endemic species of North American wolf are admixtures of the coyote and gray wolf. Sci Adv . 2: e1501714– e1501714. Google Scholar CrossRef Search ADS Wang Z, Gerstein M, Snyder M. 2009. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet . 10: 57– 63. Google Scholar CrossRef Search ADS PubMed Watson H, Videvall E, Andersson MN, Isaksson C. 2017. Transcriptome analysis of a wild bird reveals physiological responses to the urban environment. Sci Rep . 7: 44180. Google Scholar CrossRef Search ADS PubMed Winchell KM, Reynolds RG, Prado-Irwin SR, Puente-Rolón AR, Revell LJ. 2016. Phenotypic shifts in urban areas in the tropical lizard Anolis cristatellus. Evolution . 70: 1009– 1022. Google Scholar CrossRef Search ADS PubMed Zhang S, Lei F, Liu S, Li D, Chen C, Wang P. 2011. Variation in baseline corticosterone levels of tree sparrow (Passer montanus) populations along an urban gradient in Beijing, China. J Ornithol . 152: 801– 806. Google Scholar CrossRef Search ADS © The American Genetic Association 2018. All rights reserved. For permissions, please e-mail: email@example.com
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