Parallel polygenic urban adaptation despite high gene flow in a coastal marine invertebrate

Parallel polygenic urban adaptation despite high gene flow in a coastal marine invertebrate | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Parallel polygenic urban adaptation despite high gene flow in a coastal marine invertebrate View ORCID Profile Madison L. Armstrong , Katie L. Erickson , Rebecca Hawthorne , Brenda Cameron , Rachael A. Bay doi: https://doi.org/10.1101/2025.10.03.680100 Madison L. Armstrong 1 University of California, Center for Population Biology , Davis, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Madison L. Armstrong For correspondence: mlarmstrong{at}ucdavis.edu Katie L. Erickson 1 University of California, Center for Population Biology , Davis, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rebecca Hawthorne 1 University of California, Center for Population Biology , Davis, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Brenda Cameron 1 University of California, Center for Population Biology , Davis, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rachael A. Bay 1 University of California, Center for Population Biology , Davis, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Urbanization results in novel environments, offering a unique opportunity to investigate natural selection on small spatiotemporal scales. Using whole genomes from Pacific purple sea urchins ( Strongylocentrotus purpuratus) across three coastal cities spanning >2000km, we investigated genomic signals for adaptation to urban environments. We found genetic variants differentiating urban and nonurban sites within each city region, despite high gene flow and little evidence for differentiation across latitudinal gradients. While these SNP-level candidates for selection were largely non-overlapping, polygenic approaches uncover a distinct parallel signal of urban adaptation across the sampled range. Our results suggest that adaptation over small scale urbanization gradients is possible even in high gene flow systems and the polygenic architecture of adaptation is, at least in part, parallel. More broadly, our work highlights the importance of polygenic methods in ecological genomics in expanding our understanding of how evolutionary forces operate in natural systems. Urbanization fragments habitat, increases temperature, and introduces novel physical substrates and chemical stressors (i.e., pollutants) ( 1 ). Because organisms living in urban environments can be subject to novel conditions not experienced previously in their evolutionary history, urbanization offers an unprecedented opportunity to investigate rapid contemporary evolutionary processes in real time. Simultaneously, urban environments offer replicate testbeds for such adaptation ( 2 – 4 ), although each city can have a uniquely heterogeneous landscape ( 5 , 6 ). So far, the evidence of adaptation to urban environments has been dominated by examples in terrestrial and to a lesser extent aquatic systems, with few examples in marine systems ( 7 , 8 ). Beginning with the seminal studies of industrial melanism in peppered moths ( 9 ), examples of genetic adaptation to urban environmental variation including increased temperature ( 10 – 13 ), pollution/pesticides ( 14 ) and impervious surfaces ( 4 , 15 ) has been documented in a range of terrestrial and aquatic systems. The marine intertidal zone lies at the transition from sea to land, and thus faces a complex mixture of urban stressors, including exposure to runoff, wastewater, and coastline development ( 16 ). Marine species often have larger ranges and experience higher gene flow than their terrestrial counterparts ( 17 – 19 ), potentially limiting adaptation over the small spatial scales at which urbanization gradients are realized. Despite this, a growing body of literature suggests that balanced polymorphisms in marine systems can lead to local adaptation even over small spatial scales ( 20 – 22 ). While we know that stressors associated with urban environments can impact survival, development, reproduction, and dispersal in marine organisms ( 23 – 28 ), we know little about the evolutionary consequences of these effects, with a few exceptions ( 29 – 31 ). Here, we use whole genome sequencing in the broadly distributed Pacific purple sea urchin ( Strongylocentrotus purpuratus ) to investigate genomic signals of selection across three coastal regions associated with cities spanning over 2000 km. Sampling from both urban and nonurban locations in each coastal region near cities, we assess whether signals of urban adaptation exist over small spatial scales and whether those signals are parallel across geographically disparate urban environments. Genomic signals of urban adaptation despite high gene flow We sequenced whole genomes for 209 Pacific purple sea urchins sampled across 19 sites in three cities: Los Angeles, California, San Diego, California, and Victoria, British Columbia ( Fig 1A, B ). The California sites were chosen based on previous classifications of urban development ( 32 , 33 ). 11 sites were sampled across the LA region, with 6 urban (4 intertidal, 2 subtidal) and 5 nonurban (3 intertidal, 2 subtidal) sites. For San Diego 4 sites were sampled, with 1 urban site (subtidal) and 3 nonurban sites (1 intertidal, 3 subtidal). The four sites in Victoria were chosen as near, or far from, a sewage outflow that was untreated until recently and were all collected intertidally ( 34 , 35 ). Urban sites were within 1–8.5km of a wastewater outflow ( 32 , 33 ) and nonurban sites were 17.5–78km from an outflow (see Supplementary Methods). After removing individuals with low coverage, we called single nucleotide polymorphisms (SNPs) from 183 individuals with an average of 7.5X coverage. In total, we identified 219,773 SNPs across the S. purpuratus genome. We used a subset of 19,299 SNPs thinned to reduce linkage equilibrium for analysis of population structure. S. purpuratus has very low linkage disequilibrium ( 36 ), and previous studies have found surprisingly little population structure across the broad geographic range ( 20, 37 – 39 ). Although our study extends much farther north than prior sampling efforts, we still find no evidence of barriers to gene flow or isolation by distance across >2000km ( Fig 1C,D ). Clustering analysis in Tess3r ( 40 ) revealed the most likely scenario is a single ancestral lineage (K=1) and visual inspection of analyses run with K=1-6 clusters shows no geographical signal (Fig S1). Principal Components Analysis (PCA) on the thinned SNP dataset also shows no clustering by city region (Fig S2) or by urbanization ( Fig 1D ). Together with other studies, these findings support one large well-mixed population of S. purpuratus , likely due to their planktonic larval phase ranging from 30–86 days ( 41 ), which allows some offspring to settle extremely far from parents. Download figure Open in new tab Fig. 1: High gene flow in S. purpuratus sampled across three distinct city regions: Victoria, B.C., Los Angeles, CA and San Diego, CA. Both the population structure plot (at K=2) and the inset PCA were created using a thinned dataset of 19,299 SNPs to reduce linkage bias. The structure plot colors refer to the groups observed across the samples, with K=1 being the best fit. To the left of the structure plot are the different regions: Victoria, Los Angeles and San Diego separated by urban and then nonurban samples. In the PCA shapes correspond to the three regions, and color corresponds to nonurban (light blue) and urban (brown). Despite extremely high gene flow across the range, we find genomic signals of selection associated with urban environment in each of the three coastal regions. Using the full set of 219,773 SNPs, we conducted F ST outlier scans across the entire genome to find loci that are highly differentiated between urban and nonurban environments within each city region. Genome-wide average F ST between urban and nonurban sites were extremely low in all three coastal city regions (Victoria: 0.0012, Los Angeles: 0.0006, San Diego: 0.0008), but individual loci often had much higher differentiation (max F ST for Victoria: 0.379, Los Angeles: 0.201, San Diego: 0.532). Analyzing all three regions jointly, we identified 165 SNP outliers associated with urbanization (Figure S3). When we separated by region to assess outliers associated with urbanization we found 17 outliers in Victoria, 741 outliers in San Diego and 2271 outliers in Los Angeles ( Fig. 2 ). There was little outlier overlap between regions; seven outliers were shared between Los Angeles and San Diego, and one was shared between Los Angeles and Victoria ( Table 1 ). Of these eight overlapping SNPs, two were associated with oxidative stress which is a generalized stress response with known ties to temperature and other stressors ( 42 , 43 ), including those associated with urbanization ( 44 – 48 ). View this table: View inline View popup Download powerpoint Table 1: Shared outlier genes identified between city regions. Outlier genes were identified via OUTFlank (v. 0.2) and shared genes between regions are reported below with function and gene type identified using gene ontology (GO). Seven outliers were found to be shared between LA and San Diego, with one having no GO function identified, and one outlier was shared between LA and Victoria. Download figure Open in new tab Fig. 2: Genomic signals of selection associated with urbanization within three city regions. Using OUTFlank (v. 0.2), we created a Manhattan plot for each city region of interest, identifying SNP outliers associated with urbanization (above the red line, p<0.05). The x axis of the Manhattan plots are the scaffolds in the S. purpuratus genome. There are 17 outliers in Victoria, 670 outliers in San Diego and 2271 outliers in LA associated with urbanization. The stars on each Manhattan plot indicate the shared outliers between the regions. Los Angeles and Victoria share 1 outlier and Los Angeles and San Diego share 7 outliers (Details on shared outliers in Table 1 ). Genome scans, especially at the moderate sample sizes achievable in natural systems, are likely to miss loci of small to medium effect size (Regoli et al., 2006). One approach that can increase power is to integrate effects across multiple neighboring SNPs using sliding windows ( 50 , 51 ). We therefore combined local PCA ( 52 ) and linear models into a novel framework for detecting signals of selection in windows of 100 SNPs at a time (Supplementary Methods). For each of 2161 total windows, we tested for signals of selection associated with urbanization or city region. We also tested for an interaction between urbanization and city region, which would suggest that different cities may be adapted to urban environments using different genetic mechanisms. Of the 2090 usable windows, we found 53 outlier windows comparing urban vs. nonurban locations, 47 outlier windows associated with city region and 56 outlier windows with significant interaction between the urban and region variables ( Figure 3A ). Although the number of genomic windows with signals of selection associated with urbanization and city region were comparable, we did find that the effect size (F statistic) was larger for urbanization outliers (F-stat= 8.748) than for comparisons among city regions (F-stat=5.837) or the interaction term (F-stat=6.038) ( Fig 3B-C ), supporting our SNP-level findings differentiating urban and nonurban sites. However, the window-based analysis uncovered signals of selection absent from the SNP-based analysis; only five outlier SNPs from our combined analysis fell in significant windows (Fisher’s exact test p=0.603), suggesting that our significant windows are made up of likely linked, small effect SNPs associated with urbanization. The genomic windows significantly associated with urbanization may reflect parallel signals that were not apparent in our SNP-level analysis. Because window-based analyses integrate across multiple SNPs in a genomic region, they have increased power to pick up smaller effect size signals ( 49 , 50 ). Download figure Open in new tab Fig. 3: Sliding window analysis identifies signals of selection associated with urbanization and city region. Colors represent different terms: urban (brown), city region (pink) and the interaction between the two (green). Windows of 100 SNPs were used to test significant associations with urban, city region or the interaction. (A) Manhattan plots of each term of interest and the SNP window outliers associated. The points below the threshold line on each plot are nonsignificant SNP windows while points above the line represent significant SNP windows. P-value thresholds are determined by randomization. For urbanization (brown), the p-threshold was at 0.0098 and 53 outliers were identified. For city region (pink), the p-threshold was at 0.0098 and 47 outliers were identified. Finally for the interaction (green), the p-threshold was at 0.0099 and 56 outliers were identified. (B-C) F-statistic distributions of P-value outliers for each group for significant (B) and nonsignificant (C) SNP windows. (B) Mean effect sizes were larger for urban significant SNP windows compared to city region or the interaction significant SNP windows. (C) This effect is not seen in the null distribution. Mean effect sizes of non-significant SNP windows did not differ across region, urban or the interaction, thus this is unlikely to be an artifact of degrees of freedom differences between our terms (region=3 while urban=2). Parallel polygenic signatures of urban adaptation Although there was little overlap in SNP-level F ST outliers among regions, we find evidence for parallelism with polygenic methods. Polygenic approaches are quickly emerging as an effective alternative to genome scans, because for complex traits they can more accurately model signals of selection and phenotypic association ( 53 , 54 ). Using multiple polygenic approaches, we find differentiation between urban and nonurban environments across the three regions ( Fig. 4 ). We conducted a Redundancy Analysis (RDA) using latitude, urbanization and tidal zone as predictors. Using backwards model selection we found that urbanization (F 1,238 = 1.046 p=0.023) and tidal zone (F 1,241 = 1.062 p=0.005) were significant, but latitude was not (F 1,230 = 1.012 p=0.124) (Table S1; Fig. 4D ). Principal component analysis (PCA) using pcadapt on the full SNP set showed differentiation by urbanization along PC3 (Fig S4; t-value=2.941, p=0.0037), PC5 ( Fig 4A , t-value= 4.997, p<0.001), and PC6 ( Fig 4A , t-value= -4.586, p<0.001). This is counter to the PCA results from the linkage-thinned SNP set, suggesting that signals of selection across urban gradients may be clustered within linked genomic regions. Differentiation between urban and nonurban environments on pcadapt PCs were not driven by a single city region; analysis of PC loadings showed that on these PCs urban and nonurban environments were differentiated across either two ( Fig 4B ) or all three city regions ( Fig 4C ). Download figure Open in new tab Fig. 4: Parallel polygenic signals of urban adaptation. Colors distinguish urban (brown) and nonurban (light blue) samples, while shapes in (D) denote city region (Los Angeles, San Diego and Victoria). (A) Principle components (PCs) were identified using pcadapt (v. 4.4.0). PC5 and PC6 showed significant variation between urban and nonurban samples. (B-C) This differentiation was not driven by one urban region, with urban and nonurban samples separating in Los Angeles (LA), San Diego (SD) and Victoria (Vic) as well. (D) In our Redundancy Analysis (RDA), only urbanization and tidal zone were significant for model prediction. RDA1 showed the spread of data across the intertidal to subtidal gradient, while RDA2 showed the separation of urban and nonurban samples. (E) To finally validate that the separation of urban and nonurban samples was nonrandom, we implemented a polygenic score test. After 100 runs, we found that 90/100 runs identified significant differences between urban and nonurban samples (p<0.05). In our random model, we found no differences between groups (Fig S5). Complementary to the polygenic approach using RDA, we created a polygenic score by summing weighted effects across all SNPs ( 55 ). This approach has previously been used to predict polygenic phenotypes in medical and agricultural applications ( 56 – 58 ) and is gaining traction in ecological settings ( 53 , 59 ). We randomly selected training sets consisting of 60% of individuals and validated the model on the remaining 40%, running the analysis 100 times and calculating the mean for each city region/urbanization group. Across the 100 runs, 90 yielded significantly different predicted scores between urban and nonurban samples in the validation group ( Fig 3E ). When urban status was randomly assigned, we saw no statistical difference between groups (Fig S5). To further interrogate parallel signatures using this approach, we conducted a separate analysis using a single city region as the training set and the remaining two as the validation sets for three total comparisons (Fig S6). Here we found differences in the predictive ability depending on the population used for training. A model trained on San Diego populations differentiated between urban and nonurban sites (two-way ANOVA, F 1,1 = 6.845 p=0.010) but more strongly in Victoria (two-way ANOVA interaction, F 1,1 = 5.493, p=0.020) (Fig S6 B). Reciprocally, the model trained on Victoria populations accurately predicted urbanization in San Diego, but not in Los Angeles (Fig S6 C, two-way ANOVA interaction, F 1,1 = 9.699, p=0.002). The model trained on Los Angeles did not perform well, with no differences between predicted scores for urban and nonurban individuals in either Victoria or San Diego (two-way ANOVA, F 1,1 = 0.341, p=0.561). These results suggest genetic architecture of urban adaptation in San Diego and Victoria appears to be more similar to one another while Los Angeles is distinct. Intuitively, we would have expected Los Angeles and San Deigo to have stronger parallel signatures as they are more likely to share standing genetic variation due to geographic proximity ( 60 ). Our result could be explained by differences in the urban environment itself. For example, Los Angeles is a much bigger city, with a population of 3.8 million while San Diego and Victoria are at 1.4 million and 398,000 respectively. Other differences between these cities such as wastewater treatment methods, shipping port activity and more may be driving genomic differences in nonurban and urban samples, as heterogeneity across cities has been highlighted in several studies ( 5 , 6 ). Urban-associated F ST outliers were smaller in magnitude in Los Angeles than the other two cities (max F ST for Victoria: 0.379, Los Angeles: 0.201, San Diego: 0.532), perhaps reflecting a more complex landscape of selection than our binary urban/nonurban classification captures. Despite this, our analysis of the full dataset did find a polygenic model that captured genetic differentiation in response to urban environments across all three cities. Because most complex traits are likely to be polygenic, approaches that more closely model this genetic architecture are increasing in use for understanding adaptation and trait evolution in natural populations ( 61 , 62 ). Genome scans including genome wide association studies (GWAS) and genotype environment associations (GEA) have, in many cases, uncovered large effect loci or even genomic structural variation underlying adaptation and the expression of ecologically important traits ( 63 – 65 ). However, these methods are underpowered for quantitative traits, likely leading to a high rate of false negatives in natural systems ( 49 ). In many cases, polygenic approaches can yield insight where genome scans fail. For example, Fuller et al. (2020) found no SNPs significantly associated with bleaching phenotype, but polygenic scores increased the power to predict bleaching. Similarly, Laporte et al. (2016) used polygenic approaches to model urban adaptation in American and European eels, uncovering signals of adaptation in sterol regulation pathways in response to pollution. Like in our study, because polygenic approaches are explicitly predictive, they can easily be used to test for parallelism even when there no overlap in SNP-level candidates for selection. In a study of eelgrass in the eastern Pacific, the authors found no shared SNPs responding to independent temperature gradients, but polygenic scores were able to predict temperature across both populations ( 66 ). With increasing studies of genomic data in natural systems, polygenic adaptation seems to be common across the tree of life and approaches that appropriately model polygenic selection are likely to yield novel insights ( 67 – 73 ). Our findings alone cannot disentangle the many potential mechanisms of selection to marine urban environments. Urban impacts on marine environments are varied, including stormwater and sewage discharge, hardened shorelines, and increased human use (Alter et al. 2021). While our study doesn’t focus on a specific urban stressor, some chemicals have been shown to be in much higher concentrations in urban wastewater, including alkylphenols which were identified by Mussel Watch to be in higher concentrations in urban areas along the California coastline ( 32 , 33 ). Our urban sites were over on average 4x closer to wastewater sites compared to nonurban sites, with one site colloquially named “garbage point” by locals (Clover Point in Victoria B.C.). Adaptation to these novel chemicals can be rapid. For example, in response to polychlorinated biphenyl (PCB) exposure, which is highly correlated with Superfund sites, Atlantic killifish have been shown to rapidly adapt to these conditions with little phenotypic abnormalities but large adaptive shifts in their AHR pathways to mitigate stress responses ( 31 ). Another potential stressor associated with urban sites is harvesting pressure. Many of our nonurban sites were in marine protected areas (MPAs) where collection is prohibited, while urchins can be harvested with a fishing permit at many of the urban sites (sites labeled in Table S1). Fishing pressure has been shown to result in decreased genomic variation and result in selection in a variety of species ( 74 – 78 ). Experiments exposing urban and nonurban populations to treatments associated with urban environments will be required to disentangle the mechanisms of adaptation. Strong adaptation over small, but not large, spatial scales In contrast to signals of selection across urbanization gradients, which span small spatial scales, we see little evidence of selection across latitude. Our samples span 2000km, 16 degrees of latitude, 9 degrees Celsius of mean temperature, and a plethora of other environmental differences. Despite this, latitude was not significant in the RDA model (F 1,230 = 1.012, p=0.124), we found no outliers in SNP-level genome scans comparing city regions, F ST among regions was lower than within region comparisons (mean=0.0001, max=0.131) and PC axes from pcadapt were not explained by differences among regions (Figs S6, S7). In our sliding window analysis, some windows were significantly different among city regions, but the effect size was lower than that comparing urban to nonurban sites. Previous studies have found evidence of selection associated with pH in populations spanning Oregon to San Diego ( 37 – 39 ) and our previous study found weak but significant evidence of selection across the range of sea surface temperatures in California ( 20 ). Still, our results suggest that long-term selection to broad-scale climate gradients is perhaps not the predominant driver of genetic variation in this system. This counterintuitive result of signals for selection at small, but not large spatial scales was also found in our previous work showing stronger support for selection across tidal zone than across climate gradients ( 20 ). Here, our RDA on independent samples also support this finding, with tidal zone (F 1,241 = 1.062 p=0.005), significantly contributing to the distribution of genetic variation ( Fig 3D ) and pcadapt PCs diverging across tidal zone (Fig S8). Selection over the small spatial scales are likely maintained through post-settlement mortality each generation ( 38 , 79 , 80 ). This phenomenon been observed in vertical zonation of barnacles along the intertidal zone ( 81 ) and in mussels across an estuarine gradient ( 82 ). Because gene flow is so high in the S. purpuratus system, perhaps selection at smaller spatial scales is no less likely than at larger spatial scales, as both are derived from standing genetic variation in a single large population. In this case, balanced polymorphisms are maintained across the species range. This could also increase the probability of parallelism ( 60 ) if very high gene flow allows populations within each city region access to the same putatively adaptive alleles. Together, our results support parallel signals of selection across urbanization gradients in S. purpuratus , a broadly distributed coastal invertebrate. This is despite extremely high gene flow and a lack of latitudinal signatures of selection. We add to a growing literature suggesting that novel urban environments can result in rapid selection on contemporary timescales ( 4 , 31 ). We also show that a lack of parallelism at large-effect loci does not necessarily mean that genetic mechanisms of adaptation are independent; polygenic models find range-wide signals of urban adaptation. Increased use of polygenic approaches may uncover parallelism that is overlooked at the level of SNPs. General Thank you to all of fieldwork assistants who collected S. purpuratus spine tissue samples: Rob Dellinger, Mackenzie Kawahara, Kathryn Sutherland, Benjamin Lee, Camille Rumberger, Ed Parnell, Jason Toy, Zoe Scholtz and Adam and Jenesa Wall. Thank you to K. Lee for the color theme used throughout this paper. Thank you to Andrew Whitehead, Eric Sanford and all anonymous reviewers for providing feedback on this manuscript. Funding National Science Foundation Graduate Research Fellowship to MLA, University of California Davis, Center for Population Biology Research Fellowship to MLA, Packard Grant to RAB Author contributions Conceptualization: MLA, RAB. Methodology: MLA, RH, KLE. Investigation: MLA, RAB, KLE. Visualization: MLA, RAB. Funding acquisition: MLA, RAB. Project administration: MLA. Supervision: RAB. Writing – original draft: MLA. Writing – review & editing: MLA, RH, RAB, KLE Competing Interests The authors declare no competing interests. Data and Materials Availability Raw reads are available on NCBI accession: PRJNA1317549, vcf file on dryad and all other scripts and data on github: https://github.com/mlarmstrong/urbanurchins Supplementary Materials Methods Collections & Sample Processing A paired urban/nonurban site design was implemented across three Eastern Pacific coastal cities: Victoria B.C., Los Angeles, CA and San Diego, CA ( Fig. 1 map). These three cities are all within the range of S. purpuratus and have distinct outflow of wastewater output in areas where urchin populations have been identified. Urban sites were on average 4.3km (range: 1.04km to 8.48km) from an urban outflow site, while nonurban sites were over double this distance, with sample sites an average of 29km (range: 17.48km to 78.77km) away from an outflow site. The Mussel Watch program ( 32 , 33 )has classified 87 intertidal sites based on surrounding land use and found drastic differences in pollutant concentrations between low, mixed and urban development sites. Victoria, B.C. provides a geographically distinct city in order to disentangle other environmental variables such as temperature, salinity and pH ( 83 ). We collected tissue samples from 209 urchins across 19 sites in Los Angeles, San Diego and Victoria, B.C.. Additionally subtidal sites were also collected for Los Angeles and San Diego by collaborators (Ed Parnell, Jason Toy, Zoe Scholtz and Adam and Jenesa Wall). From each site, we collected spine tissue samples from 10-15 urchins and stored them in ethanol for DNA extractions using a Qiagen blood and tissue DNA extraction kit. Permits were obtained through the California Department of Fish and Wildlife (CDFW). Sequencing & Bioinformatics Methods Whole Genome Sequencing libraries were made similarly to ( 84 ), following the Nextera Lite protocol with modifications ( 85 ). Samples were first normalized, then tagmentation and PCR were done before pooling samples and finally using bead to size select for large enough fragments. Purified samples were run on a Bioanalyzer chip and sent to BGI Genomics for whole-genome sequencing on a NovaSeq machine with 150bp paired end reads. Samples were sequenced at an average of 7.5x coverage. SnpArcher (v. 0.1), an automated snakemake pipeline( 86 ), was used to process the data from fastq format to a single vcf ( 87 ). Briefly, fastqc was done to quality check samples, bwa was used to align samples to the reference genome, GATK was used to call haplotypes and genotype samples in this pipeline and vcftools (v. 0.1.16) was used to compile all samples. We conducted additional downstream filtering using vcftools to discard SNPs with minor allele frequency less than 0.05, SNPs with <70% of individuals genotyped, and genotype quality <10. Samples with low coverage were filtered out, leaving 183 samples total that were spread across the three regions and urban/nonurban sampling sites. Population Structure For identifying population structure, we used vcftools (v. 0.1.16) to apply additional filters based off of preliminary analyses to avoid any samples that had coverage issues or linkage bias (min-meanDB > 8, max-meanDB < 20). Our post-thinned dataset was 19,299 SNPs. We used hierarchical clustering implemented in Tess3r (v. 1.1.0) to identify population structure in our dataset. Pophelper (v. 2.3.1) was used for visualization of Tess3r results. We also conducted a principal components analysis (PCA) using SNPRelate (v. 1.38.1) ( 88 ). Outlier Analyses Using the full dataset of 219,773 SNPs, genome scans were used to identify genomic signals of adaptation. We used these methods to compare urban and nonurban sites within regions and also to compare across the three regions, investigating broad-scale signals of adaptation likely associated with latitude. Outlier SNPs were identified by using OUTFlank (v. 0.2)( 89 ). OUTFlank is an F ST based analysis that identifies loci that are highly differentiated between groups compared to the null distribution defined by the genome-wide distribution and groups are specified a priori . Because we are particularly interested in directional selection, not balancing selection, candidates for selection were right tail outliers. SNPs were considered significant if fdr-corrected p-value (i.e. q-value) was less than 0.01. OUTFlank was used on the full dataset and to contrast urban and nonurban sites in each region separately. We used a slightly less strict q-threshold of 0.1 for comparing the three regions to identify shared outlier SNPs. We combined local PCA ( 52 ) and linear models to create a framework to identify windows of SNPs differentiating urban and non-urban regions. First, to find an appropriate windows size, we ran 8 trials from 30 to 100 SNPs and ran a local PCA for each window. We found that smaller windows were prone to producing NAs rather than PC coordinates due to missing data, so we moved forward with a window size of 100 SNPs since it had the lowest proportion of windows that produced NA PC coordinates (0.048%). We then ran ANOVA tests to find associations between the first two PC coordinates calculated for each SNP window and the explanatory variables: city region, urban vs nonurban, and the interaction between the two. To determine a p-value that accounts for multiple testing, we ran 100 permutations with randomized city region and urban metadata. In each randomized dataset, we ran an ANOVA to test for associations between each PC in each window and the randomized metadata. For each explanatory variable in our models, we found the 1st percentile p-value for each randomization and set the p-value threshold for each term to be the minimum of those values. The F-statistic threshold was obtained in a similar manner, by calculating the 0.99 quantile per randomization and then taking the maximum of those values. The p-values of each model were plotted across a Manhattan plot ( Figure 4A ). The F-statistic distribution of the outlier windows was highest for the urban outliers vs the region or interaction F-statistics, and this effect was not seen in the null distribution ( Fig 4B,C ). To determine whether outlier SNPs were enriched in the significant windows, we found OUTFlank urban outliers across all city regions (Fig S3). We found that only 5 outlier SNPs associated with urbanization fell in significant SNP windows, while the remaining 158 outlier SNPs did not. On the other hand, 4,972 SNPs fell in significant windows but were not significant OUTFlank outliers and a remaining 200,693 were neither SNP outliers nor located in significant regions. We ran a Fisher’s Exact Test which demonstrated that there was not enrichment of OUTFlank outliers within significant windows (p=0.603). Detection of Polygenic Signals We first used pcadapt (v. 4.4.0) ( 90 ) to identify polygenic signals of adaptation in our dataset. Pcadapt uses principle component (PC) loadings that don’t assume demography ( 88 ) and performs well with populations that have high admixture ( 91 ). However, pcadapt does not explicitly test for selection among a priori groups, it finds the largest sources of variation in the population without regard to experimental design. We additionally conducted a redundancy analysis (RDA) to assess which environmental variables were driving the variation between outlier SNPs in our dataset ( 54 ). We first read in all environmental and sample predictors (sample ID, site ID, latitude, longitude, region, urbanization and tidal zone) and screened those that were highly correlated, retaining latitude, urbanization and tidal zone. We then conducted backwards model selection with ordistep through the R package vegan (v. 2.7.1) similar to Rumberger and Armstrong et al. (2025), starting with a null model and testing against additional models with and without latitude. Only urbanization (p=0.014) and tidal zone (p= 0.003) were significant for model prediction. We also modeled polygenic selection using polygenic scores. These methods sum effect sized across all SNPs to create a single estimate of the response variable and can perform better than genome scans in modeling the genetic basis of phenotypes in natural systems ( 53 , 55 ). First, We split our data into a training (60%) and validation (40%) sets, and for each SNP we assessed the relationship between environment (urban or nonurban) and genotype. Beta coefficients were then calculated using a latent factor mixed model (LFMM). We then used these beta coefficients to predict a polygenic score for each sample in the validation set, and repeated this test 100 times with different splits of the data, ensuring training and validation sets always had samples from each region and urban/nonurban sites. From each run, we used a t-test to determine whether urban and nonurban validation samples were statistically different. For visualization, we calculated the mean for each region/urbanization group and plotted the distributions of those means across 100 runs. We also ran a null model where the urban/nonurban labels were randomized. This null model showed no ability for the polygenic score to differentiate samples (Fig S4). We tested the prediction power of each region by testing with one region (Los Angeles, San Diego or Victoria) and validating with the remaining two regions for differences between urban and nonurban sites, regions and the interaction of the two. Acknowledgements Funder Information Declared University of California, Davis David and Lucile Packard Foundation, https://ror.org/032atxq54 , 2021-72989-0 Footnotes https://github.com/mlarmstrong/urbanurchins References 1. ↵ L. R. Rivkin , J. S. Santangelo , M. Alberti , M. F. J. Aronson , C. W. De Keyzer , S. E. Diamond , M. Fortin , L. J. Frazee , A. J. Gorton , A. P. Hendry , Y. Liu , J. B. Losos , J. S. MacIvor , R. A. Martin , M. J. McDonnell , L. S. Miles , J. Munshi-South , R. W. Ness , A. E. M. Newman , M. R. Stothart , P. Theodorou , K. A. Thompson , B. C. Verrelli , A. Whitehead , K. M. Winchell , M. T. J. Johnson , A roadmap for urban evolutionary ecology . Evolutionary Applications 12 , 384 – 398 ( 2019 ). OpenUrl PubMed 2. ↵ S. E. Diamond , R. A. Martin , Evolution in Cities . Annual Review of Ecology, Evolution, and Systematics 52 ( 2021 ). 3. M. R. Lambert , K. I. Brans , S. Des Roches , C. M. Donihue , S. E. Diamond , Adaptive Evolution in Cities: Progress and Misconceptions . Trends in Ecology & Evolution 36 , 239 – 257 ( 2021 ). OpenUrl PubMed 4. ↵ J. S. Santangelo , R. W. Ness , B. Cohan , C. R. Fitzpatrick , S. G. Innes , S. Koch , L. S. Miles , S. Munim , P. R. Peres-Neto , C. Prashad , A. T. Tong , W. E. Aguirre , P. O. Akinwole , M. Alberti , J. Álvarez , J. T. Anderson , J. J. Anderson , Y. Ando , N. R. Andrew , F. Angeoletto , D. N. Anstett , J. Anstett , F. Aoki-Gonçalves , A. Z. A. Arietta , M. T. K. Arroyo , E. J. Austen , F. Baena-Díaz , C. A. Barker , H. A. Baylis , J. M. Beliz , A. Benitez-Mora , D. Bickford , G. Biedebach , G. S. Blackburn , M. M. A. Boehm , S. P. Bonser , D. Bonte , J. R. Bragger , C. Branquinho , K. I. Brans , J. C. Bresciano , P. D. Brom , A. Bucharova , B. Burt , J. F. Cahill , K. D. Campbell , E. J. Carlen , D. Carmona , M. C. Castellanos , G. Centenaro , I. Chalen , J. A. Chaves , M. Chávez-Pesqueira , X.-Y. Chen , A. M. Chilton , K. M. Chomiak , D. F. Cisneros-Heredia , I. K. Cisse , A. T. Classen , M. S. Comerford , C. C. Fradinger , H. Corney , A. J. Crawford , K. M. Crawford , M. Dahirel , S. David , R. De Haan , N. J. Deacon , C. Dean , E. del-Val , E. K. Deligiannis , D. Denney , M. A. Dettlaff , M. F. DiLeo , Y.-Y. Ding , M. E. Domínguez-López , D. M. Dominoni , S. L. Draud , K. Dyson , J. Ellers , C. I. Espinosa , L. Essi , M. Falahati-Anbaran , J. C. F. Falcão , H. T. Fargo , M. D. E. Fellowes , R. M. Fitzpatrick , L. E. Flaherty , P. J. Flood , M. F. Flores , J. Fornoni , A. G. Foster , C. J. Frost , T. L. Fuentes , J. R. Fulkerson , E. Gagnon , F. Garbsch , C. J. Garroway , A. C. Gerstein , M. M. Giasson , E. B. Girdler , S. Gkelis , W. Godsoe , A. M. Golemiec , M. Golemiec , C. González-Lagos , A. J. Gorton , K. M. Gotanda , G. Granath , S. Greiner , J. S. Griffiths , F. Grilo , P. E. Gundel , B. Hamilton , J. M. Hardin , T. He , S. B. Heard , A. F. Henriques , M. Hernández-Poveda , M. C. Hetherington-Rauth , S. J. Hill , D. F. Hochuli , K. A. Hodgins , G. R. Hood , G. R. Hopkins , K. A. Hovanes , A. R. Howard , S. C. Hubbard , C. N. Ibarra-Cerdeña , C. Iñiguez-Armijos , P. Jara-Arancio , B. J. M. Jarrett , M. Jeannot , V. Jiménez-Lobato , M. Johnson , O. Johnson , P. P. Johnson , R. Johnson , M. P. Josephson , M. C. Jung , M. G. Just , A. Kahilainen , O. S. Kailing , E. Kariñho-Betancourt , R. Karousou , L. A. Kirn , A. Kirschbaum , A.-L. Laine , J. M. LaMontagne , C. Lampei , C. Lara , E. L. Larson , A. Lázaro-Lobo , J. H. Le , D. S. Leandro , C. Lee , Y. Lei , C. A. León , M. E. Lequerica Tamara , D. C. Levesque , W.-J. Liao , M. Ljubotina , H. Locke , M. T. Lockett , T. C. Longo , J. T. Lundholm , T. MacGillavry , C. R. Mackin , A. R. Mahmoud , I. A. Manju , J. Mariën , D. N. Martínez , M. Martínez-Bartolomé , E. K. Meineke , W. Mendoza-Arroyo , T. J. S. Merritt , L. E. L. Merritt , G. Migiani , E. S. Minor , N. Mitchell , M. Mohammadi Bazargani , A. T. Moles , J. D. Monk , C. M. Moore , P. A. Morales-Morales , B. T. Moyers , M. Muñoz-Rojas , J. Munshi-South , S. M. Murphy , M. M. Murúa , M. Neila , O. Nikolaidis , I. Njunjić , P. Nosko , J. Núñez-Farfán , T. Ohgushi , K. M. Olsen , Ø. H. Opedal , C. Ornelas , A. L. Parachnowitsch , A. S. Paratore , A. M. Parody-Merino , J. Paule , O. S. Paulo , J. C. Pena , V. W. Pfeiffer , P. Pinho , A. Piot , I. M. Porth , N. Poulos , A. Puentes , J. Qu , E. Quintero-Vallejo , S. M. Raciti , J. A. M. Raeymaekers , K. M. Raveala , D. J. Rennison , M. C. Ribeiro , J. L. Richardson , G. Rivas-Torres , B. J. Rivera , A. B. Roddy , E. Rodriguez-Muñoz , J. R. Román , L. S. Rossi , J. K. Rowntree , T. J. Ryan , S. Salinas , N. J. Sanders , L. Y. Santiago-Rosario , A. M. Savage , J. F. Scheepens , M. Schilthuizen , A. C. Schneider , T. Scholier , J. L. Scott , S. A. Shaheed , R. P. Shefferson , C. A. Shepard , J. A. Shykoff , G. Silveira , A. D. Smith , L. Solis-Gabriel , A. Soro , K. V. Spellman , K. S. Whitney , I. Starke-Ottich , J. G. Stephan , J. D. Stephens , J. Szulc , M. Szulkin , A. J. M. Tack , í. Tamburrino , T. D. Tate , E. Tergemina , P. Theodorou , K. A. Thompson , C. G. Threlfall , R. M. Tinghitella , L. Toledo-Chelala , X. Tong , L. Uroy , S. Utsumi , M. L. Vandegehuchte , A. VanWallendael , P. M. Vidal , S. M. Wadgymar , A.-Y. Wang , N. Wang , M. L. Warbrick , K. D. Whitney , M. Wiesmeier , J. T. Wiles , J. Wu , Z. A. Xirocostas , Z. Yan , J. Yao , J. B. Yoder , O. Yoshida , J. Zhang , Z. Zhao , C. D. Ziter , M. P. Zuellig , R. A. Zufall , J. E. Zurita , S. E. Zytynska , M. T. J. Johnson , Global urban environmental change drives adaptation in white clover . Science 375 , 1275 – 1281 ( 2022 ). OpenUrl CrossRef PubMed 5. ↵ A. Dant , L. Bishop , K. M. Dlugosch , Applying a classification approach to categorizing urbanized landscapes in California and their invasion by the Maltese starthistle Centaurea melitensis . bioRxiv [Preprint] ( 2025 ). doi: 10.1101/2025.09.07.674560 . OpenUrl Abstract / FREE Full Text 6. ↵ J. S. Santangelo , C. Roux , M. T. J. Johnson , The effects of environmental heterogeneity within a city on the evolution of clines . Journal of Ecology 110 , 2950 – 2959 ( 2022 ). OpenUrl 7. ↵ N. A. Fusco , E. J. Carlen , J. Munshi-South , Urban Landscape Genetics: Are Biologists Keeping Up with the Pace of Urbanization? Curr Landscape Ecol Rep 6 , 35 – 45 ( 2021 ). OpenUrl 8. ↵ P. A. Todd , E. C. Heery , L. H. L. Loke , R. H. Thurstan , D. J. Kotze , C. Swan , Towards an urban marine ecology: characterizing the drivers, patterns and processes of marine ecosystems in coastal cities . Oikos 128 , 1215 – 1242 ( 2019 ). OpenUrl CrossRef 9. ↵ H. B. D. Kettlewell , Selection experiments on industrial melanism in the Lepidoptera . Heredity 9 , 323 – 342 ( 1955 ). OpenUrl CrossRef 10. ↵ S. C. Campbell-Staton , K. M. Winchell , N. C. Rochette , J. Fredette , I. Maayan , R. M. Schweizer , J. Catchen , Parallel selection on thermal physiology facilitates repeated adaptation of city lizards to urban heat islands . Nat Ecol Evol 4 , 652 – 658 ( 2020 ). OpenUrl PubMed 11. S. E. Diamond , L. D. Chick , A. Perez , S. A. Strickler , C. Zhao , Evolution of plasticity in the city: urban acorn ants can better tolerate more rapid increases in environmental temperature . Conservation Physiology 6 ( 2018 ). 12. M. A. McLean , M. J. Angilletta , K. S. Williams , If you can’t stand the heat, stay out of the city: Thermal reaction norms of chitinolytic fungi in an urban heat island . Journal of Thermal Biology 30 , 384 – 391 ( 2005 ). OpenUrl CrossRef 13. ↵ A. R. Yilmaz , S. E. Diamond , R. A. Martin , Evidence for the evolution of thermal tolerance, but not desiccation tolerance, in response to hotter, drier city conditions in a cosmopolitan, terrestrial isopod . Evolutionary Applications 14 , 12 – 23 ( 2021 ). OpenUrl PubMed 14. ↵ K. I. Brans , R. A. Almeida , M. Fajgenblat , Genetic differentiation in pesticide resistance between urban and rural populations of a nontarget freshwater keystone interactor, Daphnia magna . Evolutionary Applications 14 , 2541 – 2552 ( 2021 ). OpenUrl PubMed 15. ↵ D. Murray-Stoker , M. T. J. Johnson , Mosaic of local adaptation between white clover and rhizobia along an urbanization gradient . Journal of Ecology 112 , 1150 – 1163 ( 2024 ). OpenUrl CrossRef 16. ↵ E. S. Alter , L. Tariq , J. K. Creed , E. Megafu , Evolutionary responses of marine organisms to urbanized seascapes . Evolutionary Applications 14 , 210 – 232 ( 2021 ). OpenUrl PubMed 17. ↵ D. Hedgecock , Is gene flow from pelagic larval dispersal important in the adaptation and evolution of marine invertebrates? BULLETIN OF MARINE SCIENCE 39 , 550 – 564 ( 1986 ). OpenUrl 18. M. E. Hellberg , Gene Flow and Isolation among Populations of Marine Animals . Annu. Rev. Ecol. Evol. Syst . 40 , 291 – 310 ( 2009 ). OpenUrl CrossRef 19. ↵ T. Carrier , A. Reitzel , A. Heyland , Eds P. B. Marko , M. W. Hart , “ Genetic analysis of larval dispersal, gene flow and connectivity ” in Evolutionary Ecology of Marine Invertebrate Larvae , T. Carrier , A. Reitzel , A. Heyland , Eds. ( Oxford University PressOxford , ed. 1, 2017 ; https://academic.oup.com/book/26671/chapter/195440242 ), pp. 164 – 189 . 20. ↵ C. Rumberger , M. Armstrong , M. Kim , R. Ponce , J. Melendez , M. DeBiasse , S. Caplins , R. Bay , Selection Over Small and Large Spatial Scales in the Face of High Gene Flow . Molecular Ecology 34 ( 2025 ). 21. E. Sanford , M. W. Kelly , Local Adaptation in Marine Invertebrates . Annual Review of Marine Science 3 , 509 – 535 ( 2011 ). OpenUrl CrossRef PubMed 22. ↵ C. K. Tepolt , E. D. Grosholz , C. E. De Rivera , G. M. Ruiz , Balanced polymorphism fuels rapid selection in an invasive crab despite high gene flow and low genetic diversity . Molecular Ecology 31 , 55 – 69 ( 2022 ). OpenUrl CrossRef 23. ↵ M. Coleman , B. Kelaher , Connectivity among fragmented populations of a habitat-forming alga, Phyllospora comosa (Phaeophyceae, Fucales) on an urbanised coast . Mar. Ecol. Prog. Ser . 381 , 63 – 70 ( 2009 ). OpenUrl 24. J. Park , C. Brown , C. Hess , M. Armstrong , D. M. Rocke , F. Galvez , A. Whitehead , Multiple Stressors in the Anthropocene: Urban Evolutionary History Modifies Sensitivity to the Toxic Effects of Crude Oil Exposure in Killifish . Evolutionary Applications 18 , e70112 ( 2025 ). OpenUrl PubMed 25. J. B. Puritz , R. J. Toonen , Coastal pollution limits pelagic larval dispersal . Nature Communications 2 ( 2011 ). 26. A. Štambuk , M. Šrut , Z. Šatović , M. Tkalec , G. I. V. Klobučar , Gene flow vs. pollution pressure: Genetic diversity of Mytilus galloprovincialis in eastern Adriatic . Aquatic Toxicology 136–137 , 22 – 31 ( 2013 ). OpenUrl 27. N. C. Uibel , “ Effects of Bisphenol-A and Styrene on Fertilization and Development of the Purple Sea Urchin (Strongylocentrotus purpuratus) ,” thesis, California Polytechnic State University, San Luis Obispo, California ( 2016 ). 28. ↵ M. L. Armstrong , S. Bala , R. A. Bay , Developmental and Transcriptomic Responses in Sea Urchin Larvae to an Urban-Associated Pollutant . Ecology and Evolution 15 , e72183 ( 2025 ). OpenUrl 29. ↵ E. M. A. Kern , R. B. Langerhans , Urbanization drives contemporary evolution in stream fish . Global Change Biology 24 , 3791 – 3803 ( 2018 ). OpenUrl CrossRef PubMed 30. N. M. Reid , D. A. Proestou , B. W. Clark , W. C. Warren , J. K. Colbourne , J. R. Shaw , S. I. Karchner , M. E. Hahn , D. Nacci , M. F. Oleksiak , D. L. Crawford , A. Whitehead , The genomic landscape of rapid repeated evolutionary adaptation to toxic pollution in wild fish . Science 354 , 1305 – 1308 ( 2016 ). OpenUrl Abstract / FREE Full Text 31. ↵ A. Whitehead , B. W. Clark , N. M. Reid , M. E. Hahn , D. Nacci , When evolution is the solution to pollution: Key principles, and lessons from rapid repeated adaptation of killifish (Fundulus heteroclitus) populations . Evol Appl 10 , 762 – 783 ( 2017 ). OpenUrl CrossRef PubMed 32. ↵ K. A. Maruya , N. G. Dodder , S. B. Weisberg , D. Gregorio , J. S. Bishop , S. Klosterhaus , D. Alvarez , E. T. Furlong , S. Bricker , K. L. Kimbrough , G. G. Lauenstein , The Mussel Watch California pilot study on contaminants of emerging concern (CECs): Synthesis and next steps . Marine Pollution Bulletin 81 , 355 – 363 ( 2014 ). OpenUrl PubMed 33. ↵ N. G. Dodder , K. A. Maruya , P. Lee Ferguson , R. Grace , S. Klosterhaus , M. J. La Guardia , G. G. Lauenstein , J. Ramirez , Occurrence of contaminants of emerging concern in mussels (Mytilus spp.) along the California coast and the influence of land use, storm water discharge, and treated wastewater effluent . Marine Pollution Bulletin 81 , 340 – 346 ( 2014 ). OpenUrl PubMed 34. ↵ S. M. Mudge , D. G. Lintern , Comparison of Sterol Biomarkers for Sewage with other Measures in Victoria Harbour, B.C., Canada . Estuarine, Coastal and Shelf Science 48 , 27 – 38 ( 1999 ). OpenUrl 35. ↵ L. A. Taylor , P. M. Chapman , R. A. Miller , R. V. Pym , The effects of untreated municipal sewage discharge to the marine environment off Victoria , British Columbia, Canada . 8 ( 1998 ). 36. ↵ C. Petak , L. Frati , R. S. Brennan , M. H. Pespeni , Whole-Genome Sequencing Reveals That Regulatory and Low Pleiotropy Variants Underlie Local Adaptation to Environmental Variability in Purple Sea Urchins . The American Naturalist 202 , 571 – 586 ( 2023 ). OpenUrl PubMed 37. ↵ M. H. Pespeni , D. A. Garfield , M. K. Manier , S. R. Palumbi , Genome-wide polymorphisms show unexpected targets of natural selection . Proceedings of the Royal Society B: Biological Sciences 279 , 1412 – 1420 ( 2012 ). OpenUrl CrossRef PubMed 38. ↵ M. H. Pespeni , S. R. Palumbi , Signals of selection in outlier loci in a widely dispersing species across an environmental mosaic . Mol Ecol 22 , 3580 – 3597 ( 2013 ). OpenUrl CrossRef Web of Science 39. ↵ M. H. Pespeni , F. Chan , B. A. Menge , S. R. Palumbi , Signs of Adaptation to Local pH Conditions across an Environmental Mosaic in the California Current Ecosystem . Integrative and Comparative Biology 53 , 857 – 870 ( 2013 ). OpenUrl CrossRef PubMed 40. ↵ K. Caye , T. M. Deist , H. Martins , O. Michel , O. François , TESS3: fast inference of spatial population structure and genome scans for selection . Molecular Ecology Resources 16 , 540 – 548 ( 2016 ). OpenUrl PubMed 41. ↵ R. Strathmann , Length of Pelagic Period in Echinoderms with Feeding larvae from the Northeast Pacific . Journal of Experimental Marine Biology and Ecology 34 , 23 – 27 ( 1978 ). OpenUrl CrossRef Web of Science 42. ↵ O. Selmoni , G. Lecellier , H. Magalon , L. Vigliola , N. Oury , F. Benzoni , C. Peignon , S. Joost , V. Berteaux-Lecellier , Seascape genomics reveals candidate molecular targets of heat stress adaptation in three coral species . Molecular Ecology 30 , 1892 – 1906 ( 2021 ). OpenUrl CrossRef 43. ↵ C. Granados-Cifuentes , A. J. Bellantuono , T. Ridgway , O. Hoegh-Guldberg , M. Rodriguez-Lanetty , High natural gene expression variation in the reef-building coral Acropora millepora: potential for acclimative and adaptive plasticity . BMC Genomics 14 ( 2013 ). 44. ↵ A. Herrera-Dueñas , J. Pineda , M. T. Antonio , J. I. Aguirre , Oxidative stress of House Sparrow as bioindicator of urban pollution . Ecological Indicators 42 , 6 – 9 ( 2014 ). OpenUrl 45. A. Herrera-Dueñas , J. Pineda-Pampliega , M. T. Antonio-García , J. I. Aguirre , The Influence of Urban Environments on Oxidative Stress Balance: A Case Study on the House Sparrow in the Iberian Peninsula . Front. Ecol. Evol . 5 , 106 ( 2017 ). OpenUrl 46. C. Isaksson , Urbanization, oxidative stress and inflammation: a question of evolving, acclimatizing or coping with urban environmental stress . Functional Ecology 29 , 913 – 923 ( 2015 ). OpenUrl CrossRef 47. C. Isaksson , Urban ecophysiology: beyond costs, stress and biomarkers . Journal of Experimental Biology 223 , jeb203794 ( 2020 ). OpenUrl Abstract / FREE Full Text 48. ↵ F. Regoli , S. Gorbi , D. Fattorini , S. Tedesco , A. Notti , N. Machella , R. Bocchetti , M. Benedetti , F. Piva , Use of the Land Snail Helix aspersa as Sentinel Organism for Monitoring Ecotoxicologic Effects of Urban Pollution: An Integrated Approach . Environ Health Perspect 114 , 63 – 69 ( 2006 ). OpenUrl CrossRef PubMed 49. ↵ S. Hoban , J. L. Kelley , K. E. Lotterhos , M. F. Antolin , G. Bradburd , D. B. Lowry , M. L. Poss , L. K. Reed , A. Storfer , M. C. Whitlock , Finding the Genomic Basis of Local Adaptation: Pitfalls, Practical Solutions, and Future Directions . The American Naturalist 188 , 379 – 397 ( 2016 ). OpenUrl CrossRef PubMed 50. ↵ T. R. Booker , S. Yeaman , J. R. Whiting , M. C. Whitlock , The WZA: A window-based method for characterizing genotype–environment associations . Molecular Ecology Resources 24 , e13768 ( 2024 ). OpenUrl PubMed 51. ↵ S. Manel , C. Perrier , M. Pratlong , L. Abi-Rached , J. Paganini , P. Pontarotti , D. Aurelle , Genomic resources and their influence on the detection of the signal of positive selection in genome scans . Molecular Ecology 25 , 170 – 184 ( 2016 ). OpenUrl CrossRef 52. ↵ H. Li , P. L. Ralph , Local PCA shows how the effect of population structure differs along the genome . Genetics 211 , 289 – 304 ( 2019 ). OpenUrl Abstract / FREE Full Text 53. ↵ Z. L. Fuller , V. J. L. Mocellin , L. A. Morris , N. Cantin , J. Shepherd , L. Sarre , J. Peng , Y. Liao , J. Pickrell , P. Andolfatto , M. Matz , L. K. Bay , M. Przeworski , Population genetics of the coral Acropora millepora: Toward genomic prediction of bleaching . Science 369 , eaba4674 ( 2020 ). OpenUrl Abstract / FREE Full Text 54. ↵ K. E. Lotterhos , The paradox of adaptive trait clines with nonclinal patterns in the underlying genes . Proc. Natl. Acad. Sci. U.S.A . 120 ( 2023 ). 55. ↵ A. Simon , C. Arbiol , E. E. Nielsen , J. Couteau , R. Sussarellu , T. Burgeot , I. Bernard , J. W. P. Coolen , J. Lamy , S. Robert , M. Skazina , P. Strelkov , H. Queiroga , I. Cancio , J. J. Welch , F. Viard , N. Bierne , Replicated anthropogenic hybridisations reveal parallel patterns of admixture in marine mussels . Evolutionary Applications 13 , 575 – 599 ( 2020 ). OpenUrl PubMed 56. ↵ D. Jayasinghe , S. Eshetie , K. Beckmann , B. Benyamin , S. H. Lee , Advancements and limitations in polygenic risk score methods for genomic prediction: a scoping review . Hum. Genet . 143 , 1401 – 1431 ( 2024 ). OpenUrl PubMed 57. I. J. Kullo , C. M. Lewis , M. Inouye , A. R. Martin , S. Ripatti , N. Chatterjee , Polygenic scores in biomedical research . Nat Rev Genet 23 , 524 – 532 ( 2022 ). OpenUrl CrossRef PubMed 58. ↵ Y. Ma , X. Zhou , Genetic prediction of complex traits with polygenic scores: a statistical review . Trends in Genetics 37 , 995 – 1011 ( 2021 ). OpenUrl CrossRef PubMed 59. ↵ C. Babin , P.-A. Gagnaire , S. A. Pavey , L. Bernatchez , RAD-Seq Reveals Patterns of Additive Polygenic Variation Caused by Spatially-Varying Selection in the American Eel (Anguilla rostrata) . Genome Biology and Evolution 9 , 2974 – 2986 ( 2017 ). OpenUrl CrossRef PubMed 60. ↵ D. J. Rennison , K. E. Delmore , K. Samuk , G. L. Owens , S. E. Miller , Shared Patterns of Genome-Wide Differentiation Are More Strongly Predicted by Geography Than by Ecology . The American Naturalist 195 , 192 – 200 ( 2020 ). OpenUrl CrossRef PubMed 61. ↵ P.-A. Gagnaire , O. E. Gaggiotti , Detecting polygenic selection in marine populations by combining population genomics and quantitative genetics approaches . Curr Zool 62 , 603 – 616 ( 2016 ). OpenUrl PubMed 62. ↵ K. E. Lotterhos , The Effect of Neutral Recombination Variation on Genome Scans for Selection . G3 Genes|Genomes|Genetics 9 , 1851 – 1867 ( 2019 ). OpenUrl 63. ↵ R. D. H. Barrett , S. Laurent , R. Mallarino , S. P. Pfeifer , C. C. Y. Xu , M. Foll , K. Wakamatsu , J. S. Duke-Cohan , J. D. Jensen , H. E. Hoekstra , Linking a mutation to survival in wild mice . Science 363 , 499 – 504 ( 2019 ). OpenUrl Abstract / FREE Full Text 64. M. F. Oleksiak , O. P. Rajora , Eds K. Johannesson , R. K. Butlin , M. Panova , A. M. Westram , “ Mechanisms of Adaptive Divergence and Speciation in Littorina saxatilis: Integrating Knowledge from Ecology and Genetics with New Data Emerging from Genomic Studies ” in Population Genomics: Marine Organisms , M. F. Oleksiak , O. P. Rajora , Eds. ( Springer International Publishing, Cham , 2017 ; http://link.springer.com/10.1007/13836_2017_6 ) Population Genomics, pp. 277 – 301 . 65. ↵ S. V. Vollmer , J. D. Selwyn , B. A. Despard , C. L. Roesel , Genomic signatures of disease resistance in endangered staghorn corals . Science 381 , 1451 – 1454 ( 2023 ). OpenUrl CrossRef PubMed 66. ↵ L. M. Schiebelhut , A. S. Guillaume , A. Kuhn , R. M. Schweizer , E. E. Armstrong , M. A. Beaumont , M. Byrne , T. Cosart , B. K. Hand , L. Howard , S. M. Mussmann , S. R. Narum , R. Rasteiro , A. G. Rivera-Colón , N. Saarman , A. Sethuraman , H. R. Taylor , G. W. C. Thomas , M. Wellenreuther , G. Luikart , Genomics and conservation: Guidance from training to analyses and applications . Molecular Ecology Resources 24 , e13893 ( 2024 ). OpenUrl PubMed 67. ↵ K. Csilléry , A. Rodríguez-Verdugo , C. Rellstab , F. Guillaume , Detecting the genomic signal of polygenic adaptation and the role of epistasis in evolution . Molecular Ecology 27 , 606 – 612 ( 2018 ). OpenUrl CrossRef 68. S. K. G. Forsberg , D. Melo , S. W. Wolf , J. K. Grenier , M. Tang , L. P. Henry , L. F. Pallares , A. G. Clark , J. F. Ayroles , Longitudinal sequencing reveals polygenic and epistatic nature of genomic response to selection . Proc. Natl. Acad. Sci. U.S.A . 122 , e2410452122 ( 2025 ). OpenUrl PubMed 69. M. Laporte , S. A. Pavey , C. Rougeux , F. Pierron , M. Lauzent , H. Budzinski , P. Labadie , E. Geneste , P. Couture , M. Baudrimont , L. Bernatchez , RAD sequencing reveals within-generation polygenic selection in response to anthropogenic organic and metal contamination in North Atlantic Eels . Mol Ecol 25 , 219 – 237 ( 2016 ). OpenUrl CrossRef PubMed 70. P. A. Moran , T. J. Colgan , K. P. Phillips , J. Coughlan , P. McGinnity , T. E. Reed , Whole- Genome Resequencing Reveals Polygenic Signatures of Directional and Balancing Selection on Alternative Migratory Life Histories . Molecular Ecology 33 , e17538 ( 2024 ). OpenUrl 71. E. Pinseel , E. C. Ruck , T. Nakov , P. R. Jonsson , O. Kourtchenko , A. Kremp , M. I. M. Pinder , W. R. Roberts , C. Sjöqvist , M. Töpel , A. Godhe , M. W. Hahn , A. J. Alverson , Genome- Wide Adaptation to a Complex Environmental Gradient in a Keystone Phytoplankton Species . Molecular Ecology 34 , e17817 ( 2025 ). OpenUrl 72. C. Rey , A. Darnaude , F. Ferraton , B. Guinand , F. Bonhomme , N. Bierne , P.-A. Gagnaire , Within-Generation Polygenic Selection Shapes Fitness-Related Traits across Environments in Juvenile Sea Bream . Genes 11 , 398 ( 2020 ). OpenUrl CrossRef 73. ↵ N. H. Rose , R. A. Bay , M. K. Morikawa , S. R. Palumbi , Polygenic evolution drives species divergence and climate adaptation in corals: polygenic climate adaptation in corals . Evolution 72 , 82 – 94 ( 2018 ). OpenUrl CrossRef PubMed 74. ↵ C. Helmerson , J. M. Durant , S. N. K. Hoff , M. S. O. Brieuc , P. R. Berg , P. E. Jorde , M. F. Maurstad , I. Bradbury , O. S. Kjesbu , J. A. Godiksen , N. Chr. Stenseth , B. Star , K. S. Jakobsen , S. Jentoft , A century of anthropogenic perturbations impact genomic signatures of the iconic migratory Atlantic cod . Sci. Adv . 11 , eadp3342 ( 2025 ). OpenUrl PubMed 75. V. Mendoza-Portillo , F. J. García-De León , S. Von Der Heyden , Responses of population structure and genomic diversity to climate change and fishing pressure in a pelagic fish . Global Change Biology 29 , 4107 – 4125 ( 2023 ). OpenUrl PubMed 76. E. E. Nielsen , J. Hemmer-Hansen , P. F. Larsen , D. Bekkevold , Population genomics of marine fishes: identifying adaptive variation in space and time . Molecular Ecology 18 , 3128 – 3150 ( 2009 ). OpenUrl CrossRef PubMed Web of Science 77. B. N. Reid , B. Star , M. L. Pinsky , Detecting parallel polygenic adaptation to novel evolutionary pressure in wild populations: a case study in Atlantic cod (Gadus morhua ). Phil. Trans. R. Soc. B 378 , 20220190 ( 2023 ). OpenUrl CrossRef PubMed 78. ↵ D. E. Sadler , T. Sävilammi , S. N. Van Dijk , P. C. Watts , S. Uusi-Heikkilä , Size-selective harvesting drives genomic shifts in a harvested population . Journal of Fish Biology 105 , 1562 – 1571 ( 2024 ). OpenUrl PubMed 79. ↵ H. Levene , Genetic Equilibrium When More Than One Ecological Niche is Available. The American Naturalist 87 , 331 – 333 ( 1953 ). OpenUrl CrossRef Web of Science 80. ↵ L. Thomas , N. H. Rose , R. A. Bay , E. H. López , M. K. Morikawa , L. Ruiz-Jones , S. R. Palumbi , Mechanisms of Thermal Tolerance in Reef-Building Corals across a Fine-Grained Environmental Mosaic: Lessons from Ofu, American Samoa . Front. Mar. Sci . 4 , 434 ( 2018 ). OpenUrl 81. ↵ P. S. Schmidt , M. D. Bertness , D. M. Rand , Environmental heterogeneity and balancing selection in the acorn barnacle Semibalanus balanoides . Proc. R. Soc. Lond. B 267 , 379 – 384 ( 2000 ). OpenUrl CrossRef PubMed Web of Science 82. ↵ R. K. Koehn , R. Milkman , J. B. Mitton , Population Genetics of Marine Pelecypods. IV. Selection, Migration and Genetic Differentiation in the Blue Mussel Mytilus edulis . Evolution 30 , 2 – 32 ( 1976 ). OpenUrl CrossRef PubMed Web of Science 83. ↵ J. A. Johnstone , N. J. Mantua , Atmospheric controls on northeast Pacific temperature variability and change, 1900–2012 . Proc. Natl. Acad. Sci. U.S.A . 111 , 14360 – 14365 ( 2014 ). OpenUrl Abstract / FREE Full Text 84. ↵ E. S. Nielsen , S. Walkes , J. L. Sones , P. B. Fenberg , D. A. Paz-García , B. B. Cameron , R. K. Grosberg , E. Sanford , R. A. Bay , Pushed waves, trailing edges, and extreme events: Eco-evolutionary dynamics of a geographic range shift in the owl limpet , Lottia gigantea. Global Change Biology 30 , e17414 ( 2024 ). OpenUrl PubMed 85. ↵ B. A. Rowan , D. Heavens , T. R. Feuerborn , A. J. Tock , I. R. Henderson , D. Weigel , An Ultra High-Density Arabidopsis thaliana Crossover Map That Refines the Influences of Structural Variation and Epigenetic Features . ( 2019 ). 86. ↵ F. Mölder , K. P. Jablonski , B. Letcher , M. B. Hall , C. H. Tomkins-Tinch , V. Sochat , J. Forster , S. Lee , S. O. Twardziok , A. Kanitz , A. Wilm , M. Holtgrewe , S. Rahmann , S. Nahnsen , J. Köster , Sustainable data analysis with Snakemake . F1000Res 10 , 33 ( 2021 ). OpenUrl 87. ↵ C. D. Mirchandani , A. J. Shultz , G. W. C. Thomas , S. J. Smith , M. Baylis , B. Arnold , R. Corbett-Detig , E. Enbody , T. B. Sackton , A Fast , Reproducible, High-throughput Variant Calling Workflow for Population Genomics . Molecular Biology and Evolution 41 ( 2024 ). 88. ↵ X. Zheng , D. Levine , J. Shen , S. M. Gogarten , C. Laurie , B. S. Weir , A high-performance computing toolset for relatedness and principal component analysis of SNP data . Bioinformatics 28 , 3326 – 3328 ( 2012 ). OpenUrl CrossRef PubMed Web of Science 89. ↵ M. C. Whitlock , K. E. Lotterhos , Reliable Detection of Loci Responsible for Local Adaptation: Inference of a Null Model through Trimming the Distribution of F ST . The American Naturalist 186 , S24 – S36 ( 2015 ). OpenUrl CrossRef PubMed 90. ↵ K. Luu , E. Bazin , M. G. B. Blum , pcadapt an R package to perform genome scans for selection based on principal component analysis . Molecular Ecology Resources 17 , 67 – 77 ( 2017 ). OpenUrl PubMed 91. ↵ K. Silliman , Population structure, genetic connectivity, and adaptation in the Olympia oyster (Ostrea lurida) along the west coast of North America . Evol Appl 12 , 923 – 939 ( 2019 ). OpenUrl PubMed View the discussion thread. 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