B aye S can has been shown to be more reliable than alternative methods in detecting outliers and in reducing the number of false positives [ 46 ]. In addition, it is relatively robust to confounding demographic processes and hierarchical structure that may inflate F ST estimates [ 47 ]. To identify loci for which genetic differentiation is associated with urban versus rural environments, we used BayeScEnv [ 48 ].
We did so by means of two approaches. In the first approach, we categorized each sampling site as either rural or urban and tested for an association between genetic differentiation and this binary variable.
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In a second, more refined analysis, we tested for an association of genetic differentiation with the percentage of impervious surface within a m buffer zone around each sampling site; impervious surface is a continuous variable which was highly correlated with building cover and road density see electronic supplementary material, table S3.
B aye S c E nv aims to identify genetic markers subjected to selection and associated to population-specific covariates. Prior to the B aye S c E nv analysis, all environmental variables were standardized to a mean of zero and standard deviation of one. Diagnostics of the log-likelihoods and F ST values for the sampled iterations were checked using R [ 34 ].
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To identify candidate genes associated with the detected outlier SNPs belonging to candidate sequences; see results below , contigs overlapping set of reads across individuals that together represent a consensus region in the genome containing outliers were annotated using the B last 2GO pipeline [ 50 ], which conducts B last similarity searches and maps Gene Ontology GO terms to homologous sequences.
Functional annotation steps were performed with B last 2GO default parameters. Genes under selection were then tested for potential enrichment of specific gene functions using GO enrichment analyses. GO enrichment analyses using Fisher's exact tests were performed in B last 2GO under default parameters and with the entire gene list covered by all of our RAD-Seq data as a reference.
Both of these enrichment analyses re-classify child terms to higher terms prior to enrichment tests. Reads were filtered out due to ambiguous MID barcodes After all filtering steps, the catalogue generated by S tacks included a total of 43 RAD tags covering approx. All B. Expected heterozygosity ranged from 0. The inbreeding coefficient F IS was, on average, low and not significantly different from zero mean 0.
There were no differences in expected heterozygosity LMM: The Potsdam rural site had an unexpected and exceptionally high F IS value see electronic supplementary material, table S4 ; we therefore re-ran all analyses described in this paper after excluding it from the dataset, but results did not change qualitatively or in significance.
We therefore retained data from this sampling locality in all analyses presented here. Similar to sNMF, the find. To infer the demographic history of B. Furthermore, the bottleneck growth model gave nearly identical results with the exponential growth model see electronic supplementary material, figure S1c,d. Results did not change qualitatively when we removed outlier loci see below.
In total, we identified outlier SNPs in contigs, of which 45 Figure 2. The total number of outlier SNPs i. The gene annotation pipeline implemented in B last 2GO allowed us to annotate 61 out of candidate loci contigs that mapped to the B. Five of the remaining 61 contigs mapped to the same candidate locus, giving us 56 candidate loci see electronic supplementary material, table S7.
We could not assign GO terms to 15 out of the remaining 56 candidate loci. Of those 41 candidate loci for which we could find a BLAST hit and to which we could assign GO terms, GO functional annotations of the outlier loci yielded 35 GO terms belonging to all the three functional categories: Both of these enrichment analyses of the genes in the candidate regions under divergent selection, as identified by all three outlier methods, highlighted GO terms associated with: Details of all loci containing significant outlier SNPs, along with transcript product names and related GO terms and GO enrichment analyses, are provided in the electronic supplementary material see electronic supplementary material, tables S7, S8 and S9; note that the GO terms of electronic supplementary material, tables S8 and S9 are hierarchically redundant.
Figure 3. The frequency of the outlier SNP within each land use type, urban U, black or rural R, grey , is plotted on the vertical axis.
Each candidate locus is labelled with the contig name on the horizontal axis; see highlighted rows in electronic supplementary material, table S7. Our study represents the first high-density SNP-based genome scan for signatures of selection due to urbanization in an ecologically and commercially important pollinator species, Bombus lapidarius. Using a replicated paired sampling design and a RAD-seq approach, we found low levels of genetic differentiation among B. To this end, our results support the notion that adaptive genetic divergence can occur through urbanization in the face of high gene flow e.
Although several studies on the population genetics of bumblebees have reported relatively high differentiation across populations, most of them were focused on rare or declining species [ 53 , 54 ]. Our analysis of genetic differentiation and IBD in a common and widely distributed bumblebee, B. This is not surprising as bumblebee reproductive castes queens and males have high dispersal abilities.
Bombus lapidarius queens can disperse over distances ranging from 3 to 5 km [ 55 ] and bumblebee males at least from 2. In addition, B. The low levels of genetic structure we found for B. From our demographic inference using genome-wide site frequency spectra, we found clear signals of past population growth. Assuming a mutation rate of 3. Based on these estimates, we date the population growth of B.
However, such estimates should be interpreted with caution due to their dependence on an unknown factor: Nevertheless, from our demographic analyses, we found that B. Genome scans for SNPs have been widely used to investigate polymorphisms in order to detect and disentangle neutral demography and adaptive selection evolutionary processes [ 17 , 18 , 21 ].
The most popular approach to identify potential targets of selection compares allele frequency differentiation, measured as F ST between populations [ 17 , 18 ]. Environment association EA tests provide fine-scaled analyses of the ecological processes driving selection by identifying loci with allele frequency differentiation that is correlated with environmental factors, for which several EA tools have already been developed [ 48 ].
However, Combining population differentiation and environmental association approaches to detect candidate loci related to environmental adaptation not only reduces false positives, but also maximizes the chances of detecting potential signals of selection [ 63 ]. In our study, we consistently identified 45 putative targets of local selection that showed both high F ST and an association to the environment, including genes associated with metabolism, heat stress and oxidative stress, which may be logically related to urbanization.
In general, organisms living in urban areas face physiological challenges caused by pollution chemical or from light , urban warming, parasites, diet and costly foraging [ 64 ]. Previous studies have documented greater parasite prevalence among bumblebees in urban than rural areas, suggesting that urban habitats may present greater opportunities for parasite transmission [ 23 ].
Pathogens are known to reduce colony and individual pollinator fitness and abundance as well as to alter foraging performance [ 10 ]. Similarly, foraging habitat, pesticide use and parasite stress were found to be associated with the disruption of metabolic functions related to immunity, energetic resources and antioxidant responses in honeybees [ 65 , 66 ]. In our study, we found that SNPs showing footprints of directional selection were located in genes involved in molecular binding and metabolic processes. Such proteins are involved in regulating downstream kinases in response to environmental stress, such as heat stress and oxidative stress.
They also mediate processes, such as apoptosis and the innate immune response, which might be more pronounced in urban than rural environments and might thus be of adaptive relevance for an urban lifestyle. While these associations with environmental parameters and footprints of selection do not necessarily imply causality, they support our interpretation that the signatures of selection we have recovered are likely to be biologically meaningful.
The large number of putative loci under selection that we detected may give cause for concern that some were false positives.
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From an analytical perspective, the F ST -based methods and the EA method used in our study for detecting local adaptation are all correlational; additional lines of evidence e. Additionally, the lack of a reference genome hindered our annotation of RAD tags. Furthermore, the low genome coverage we achieved with our RAD tags combined with the lack of a genome sequence upon which to link loci meant that we were unable to use the powerful approach of detecting genome-wide signatures of selective sweeps, which can provide a complement to F ST -based outlier locus detection and thereby reduce the rate of false positives among candidate loci under selection [ 67 ].
An open question remains over the establishment and maintenance of allele frequency differences at loci under selection in our study system. We have strong evidence for a lack of differentiation across our study sites at putatively neutral loci, probably because of high N e and ongoing gene flow. That we consistently detected outlier loci across nine rural—urban transitions suggests that outliers are under selection for the local urban environment.
We cannot, though, tell if this is due to strong selection each generation single-generation selection acting on a panmictic urban plus rural gene pool or alternatively due to high N e coupled to a low migration rate and recent selection on our identified outlier loci multiple-generation selection. Though B. Rates of urbanization are accelerating globally, with accumulating evidence in support of rapid evolution in response to urbanization [ 2 ]. Using a RAD-seq approach, we identified thousands of SNPs in a widespread bumblebee, a subset of which exhibited patterns of genetic differentiation strongly associated with urbanization, despite high ongoing gene flow.
To this end, our study demonstrates the power of population genomic approaches to uncover evidence for potential adaptation to human-altered environments. Future work should test whether and how allelic variants are of functional relevance for adaptation to life in the city. Demultiplexed, raw RAD-Seq reads are available under accession no. All authors assisted in drafting the manuscript and gave their final approval for publication.
We declare we have no competing interests. FZT Special feature paper: The evolution of City Life. Guest edited by Marc T. Johnson, L. Ruth Rivkin, James S. Electronic supplementary material is available online at https: Large datasets are available through Proceedings B 's partnership with Dryad. Login to your account. Forgot password? Keep me logged in. New User. Change Password. Old Password. New Password.
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Enter your email address below and we will send you your username. You have access. View PDF. Genome-wide single nucleotide polymorphism scan suggests adaptation to urbanization in an important pollinator, the red-tailed bumblebee Bombus lapidarius L. Biological Sciences http: Section Supplemental Material. You have access The evolution of city life.
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Panagiotis Theodorou Panagiotis Theodorou http: Belinda Kahnt Belinda Kahnt http: Antonella Soro Antonella Soro http: Ivo Grosse Ivo Grosse http: Robert J. Paxton Robert J. Paxton http: Abstract Urbanization is considered a global threat to biodiversity; the growth of cities results in an increase in impervious surfaces, soil and air pollution, fragmentation of natural vegetation and invasion of non-native species, along with numerous environmental changes, including the heat island phenomenon.
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