Galeocerdo cuvier, is an apex predator known to play important direct and indirect roles in tropical and subtropical marine ecosystems. While the global and Indo‐West Pacific population genetic structure of this species has recently been investigated, questions remain over population structure and demographic history within the western Indian (WIO) and within the western Pacific Oceans (WPO). To address the knowledge gap in tiger shark regional population structures, the genetic diversity of 286 individuals sampled in seven localities was investigated using 27 microsatellite loci and three mitochondrial genes . A weak genetic differentiation was observed between the WIO and the WPO, suggesting high genetic connectivity. This result agrees with previous studies and highlights the importance of the pelagic behavior of this species to ensure gene flow. Using approximate Bayesian computation to couple information from both nuclear and mitochondrial markers, evidence of a recent bottleneck in the Holocene  was found, which is the most probable cause for the low genetic diversity observed. A contemporary effective population size as low as 111  was estimated during the bottleneck. Together, these results indicate low genetic diversity that may reflect a vulnerable population sensitive to regional pressures. Conservation measures are thus needed to protect a species that is classified as Near Threatened.Population genetics has been increasingly applied to study large sharks over the last decade. Whilst large shark species are often difficult to study with direct methods, improved knowledge is needed for both population management and conservation, especially for species vulnerable to anthropogenic and climatic impacts. The tiger shark,  Consequently, data concerning basic aspects of their biology, such as migration patterns and population structure, are limited  Carcharhinid  gene  and the cytochrome oxidase c subunit I (COI). Bernard et al.  to further investigate population structure and demographic parameters in the tiger shark. As we used samples in common with Holmes et al. , the eastern Indian Ocean , and the western Pacific , and 27 microsatellite loci and three mitochondrial genes , and in the eastern Indian Ocean, along the Australian west coast (AUS1: n = 9). Samples were collected in the western Pacific from the northeast coast of Australia  and in New Caledonia  following manufacturer instructions.Carcharhinus leucas  using an internal size standard . Of the nine loci developed by Bernard et al.  we also used the microsatellite genotypes generated by Bernard et al. , retainin = 275), (b) 8‐msat dataset , (c) Holmes 8‐msat dataset , and (4) Bernard 8‐msat dataset .In summary, four microsatellite datasets were used Table : (a) 27‐s et al.  only; n s et al.  only; n d et al.  only; n CR) was amplified using the set of primers Gc‐CR‐F/Gc‐CR‐R  using the primer cocktails C_FishF1t1/C_FishR1t1  using GluDG/C61121H , 0.3 μM of forward and reverse primers/cocktails, and 1.6 ng/μL of genomic DNA. The thermocycling programme for CR is described in Pirog et al.  on a capillary sequencer ABI 3730XL (Applied Biosystems).The mitochondrial DNA control region  were added to our dataset , adding samples from 10 locations for CR analyses  and inbreeding coefficient FIS  in each location.Null alleles and allelic drop‐out occurrence and frequencies were assessed using  Goudet, . Departu Goudet, . The meageneious v.8.1.2  and nucleotide (π) diversities) were calculated for each region separately and for the concatenated dataset , using dnaSP v.5.10.1  analyses were performed assuming a HKY85 model of substitution as the latter was shown to best fit the data (see Section 3). Rate of variation among sites was modeled with a discrete gamma distribution with four rate categories. We assumed an uncorrelated lognormal relaxed clock to account for rate variation among lineages. To minimize prior assumptions about demographic history, we adopted an extended Bayesian skyline plot (EBSP) approach in order to integrate data over different coalescent histories. Evolutionary model parameters were then estimated, with samples drawn from the posterior every 105 MCMC steps over a total of 108 steps from five independent runs. The first 107 steps were discarded as burn‐in. Good mixing and convergence were assessed using tracer v.1.6 . To further evaluate phylogenetic relations among haplotypes, TCS statistical parsimony networks  between 1 and 10, individual assignment probabilities to each cluster were determined so as to minimize departures from HWE within clusters and maximize LD among clusters. Two analyses were performed, with and without the LOCPRIOR model, which uses prior sampling location information in the Bayesian clustering, to allow detection of weaker genetic population structure  values for increasing K using the find.clusters function. We then used the dapc function for values of K ranging from 1 to 10, retaining a number of principal components sufficient to explain ≥90% of total variance of the data , Pritchard et al. K method, Evanno, Regnaut, & Goudet, K‐values and different numbers of clusters in structure and DAPC, and chose to consider the number of clusters and the individual assignments that were retrieved by both types of analyses and that seemed biologically meaningful.Two complementary clustering methods were used to investigate population structure in the tiger shark. First, Bayesian clustering analyses were performed using Structure v.2.3.4 , (Scenario 2) a more ancient (between 103 and 5 × 105 generations in the past) decrease, (Scenario 3) a recent (less than 500 generations) expansion (N0 > N1), (Scenario 4) a more ancient (between 103 and 5 × 105 generations in the past) expansion, (Scenario 5) an expansion followed by a decrease , (Scenario 6) a bottleneck , and (Scenario 7) a constant effective population size (N0 = N1). The parameters t1  and t2 (t2 > 500 generations) were chosen to reflect relatively recent events that may be linked to anthropogenic factors, or more ancient events, such as glacial/interglacial transitions. For Scenarios 5 and 6, the end of the expansion/bottleneck was set at five generations in the past, which approximately corresponds to the ban on commercial exploitation of the tiger shark in Reunion Island (in 1999). Generation time was supposed to be around 7–10 years  using n = 23), to consolidate the results.To run this analysis, we considered all the individuals from Reunion Island, as it was the location with the highest number of individuals and may be the most representative of the genetic diversity of the whole population. It has indeed been shown that pooling individuals from different sampling locations, even with nonsignificant pairwise differentiation values may bias results  only, and all loci were thus considered independent. The mean rarified allelic richness (±standard error [SE]) was relatively constant among locations, varying from 2.86 ± 0.30 in AUS1  to 3.05 ± 0.28 in New Caledonia while HE varied from 0.52 ± 0.06 in Reunion Island to 0.60 ± 0.05 in Zanzibar and HO from 0.49 ± 0.05 in South Africa to 0.60 ± 0.06 in Zanzibar  was lower for COI (0.09 ± 00) than for cytb (0.47 ± 0.00) and CR (0.48 ± 0.00). Similar results were observed for the total nucleotide diversity (π), varying from 0.00014 ± 0.00000 for COI to 0.00063 ± 0.00001 and 0.00068 ± 0.00000 for cytb and CR, respectively. Overall, variations of haplotype and nucleotide diversities were not constant across locations and across genes, with lowest values estimated for Reunion Island (h = 0.34 ± 0.01 and π = 0.00047 ± 0.00005) and Zanzibar (h = 0.00 ± 0.00 and π = 0.00000 ± 0.00000) and highest values for AUS1  for CR and New Caledonia (h = 0.61 ± 0.03 and π = 0.00099 ± 0.00018) for cytb. For COI, nearly all locations showed null haplotype and nucleotide diversities, except for South Africa (h = 0.07 ± 0.01 and π = 0.00010 ± 0.00004), New Caledonia (h = 0.09 ± 0.02 and π = 0.00013 ± 0.00006), and Reunion Island  resolved 22 haplotypes and 22 polymorphic sites, with an overall haplotype diversity of 0.78 ± 0.00 and a nucleotide diversity of 0.00053 ± 0.00000  was retrieved for K = 3. Individuals from the western Indian Ocean  belonged to one genetic cluster, those from the eastern Indian Ocean (AUS1), the western , and Central (HAW) Pacific to a second one, and those from the western Atlantic (BRA) to a third one , from K = 2 each new cluster was mostly uninformative, with individual membership proportions in new clusters extremely low .Using the LOCPRIOR model on these both microsatellite datasets, a single mode regrouping the five runs was retrieved for each K = 5), the first axis explained 19.58% of total inertia and the second axis 18.01% and ellipses for each location overlapped, with no clear clusters identified , individuals seemed to cluster into three groups, not related to their geographical origins . With the Bernard 8‐msat dataset, individuals were clearly clustered into two groups, one from the western Atlantic and one from the Indo‐Pacific , except Zanzibar (Dest = 0.026NS). AUS2  was also significantly different from the Seychelles, South Africa and Reunion Island  and a low but significant value was also retrieved between Reunion Island and South Africa ; and between South Africa and Reunion Island, AUS1 , AUS4  and AUS3  . Significant Dest values were also retrieved between: Brazil and all other locations ; between Reunion Island and South Africa, AUS1  and AUS3  ; between Reunion Island and AUS1, AUS4, and AUS3 ; and between AUS1 and AUS3 ; between AUS2  and New Caledonia; as well as between South Africa and AUS1 , AUS2 , and New Caledonia  and locations from the Indian and Pacific Oceans . Within the Indo‐Pacific and the western Atlantic, some locations were significantly differentiated from each other, but this was not related to geographical distance or separation by coastline , suggesting a population expansion. Furthermore, all Fu's FS estimates were significantly negative, either considering all locations separately  or pooling all locations  was equal to 111 , the ancestral effective population sizes (N1) equal to 5,150  and the time at which the bottleneck began (t) estimated to 319  generations, corresponding to approximately 2,000 – 3,000 years (RMSE = 1.347). However, the actual effective population size N0 was less precisely inferred  and mitochondrial haplotype network clustered individuals from Brazil  separatd et al.  and Holmd et al. . Furtherd et al.  with the4.2structure with the LOCPRIOR model identified two genetic clusters, one grouping individuals from the western Indian Ocean  and the other, locations from the eastern Indian Ocean  and from the Pacific . However, structure without the LOCPRIOR model and DAPC did not partition the individuals into distinct clusters. Considering the 27‐msat dataset, none of the analyses performed identified distinct clusters within the Indo‐Pacific. Increasing the number of markers has improved the resolution, highlighting the need of sufficient number of microsatellite loci for population genetics analyses  with the 8‐msat dataset does not reflect a biological pattern, but instead the microsatellite characteristics when used in low number.Patterns of genetic differentiation within the Indo‐Pacific were more difficult to interpret. First, considering the 8‐mast dataset, FST or Dest) and were not related to whether locations were connected by coastlines or separated by large oceanic expanses. If we only take into account differentiation values significantly different from zero for both indices (FST and Dest), genetic differentiation was only highlighted for the 8‐msat dataset and not particularly between very distant locations: South Africa and Reunion Island , South Africa and Western Australia (AUS1) or South Africa and the northeastern Australia (AUS3). When using the 27‐msat dataset, none of the population pairs were significantly differentiated, supporting the hypothesis of one genetic cluster in the Indo‐Pacific. The same pattern was established by Bernard et al.  were nevertheless significantly different from zero, which would point to some level of genetic differentiation. Notably, it concerned individuals from Reunion Island and South Africa, for which ΦST estimates were significantly different from individuals from the Seychelles, Australia, and New Caledonia. No significant estimates were calculated between the individuals from Seychelles and those from Australia or New Caledonia, locations separated by almost same distance between South Africa and Australia, which make these results difficult to interpret. It is here useful to remember that ΦST are based on haplotype frequencies and are highly dependent on sampling.The TCS haplotype networks constructed in this study and the one of Bernard et al. , using eThus, altogether, weak genetic differentiation was highlighted between locations in the Indo‐Pacific, both with microsatellite and mitochondrial markers. Tiger sharks are known to cross large oceanic expanses  is lower than the minimal estimate of 500 thought to be required for a population to be able to adapt to environmental changes or pressures . Approximate Bayesian computation has been proven to be one of the most accurate methods to detect recent demographic changes, as evidenced by the study of a large marine mammal for which historic fisheries data were available, the Antarctic fur seal 5The study presented here confirms the genetic connectivity of tiger sharks within the western Indo‐Pacific, using both microsatellites and mitochondrial markers. While mitochondrial differentiation estimates were slightly higher than those of microsatellite, further analyses are needed to confirm whether differentiation reflects female philopatry to specific nurseries. Individuals from the Indo‐Pacific form a discrete population. Management and conservation programmes thus need to be designed at such scales in order to maximize potential efficacy. Meanwhile, localized intensive fishing may yet stand to impact the whole population. Furthermore, the detection of low genetic diversity as well as a recent bottleneck (in the Holocene), during which the effective population size of tiger sharks in this region may have dropped as low as 111 individuals, points to a potentially resultant vulnerable population. Further assessments of the health status of this population as well as conservation plans are thus particularly needed to conserve the tiger shark within the western Indo‐Pacific. Its conservation status might need to be revised toward a higher vulnerability level, as the ability of tiger sharks to withstand high levels of fishing pressure might be lower than previously thought.None declared.GC, EC, BJH, NEH, JEGN, AJT, PB, LV, and SJ provided samples. AP and HM did lab work. AP, HM, and VR analyzed the data. All authors contributed to the writing of the manuscript.HM and SJ designed research. Click here for additional data file.


Quercus rugosa in Mexico. Using genotyping by sequencing, we identified 5,354 single nucleotide polymorphisms (SNPs) genotyped from 103 individuals across 17 sites in the Trans‐Mexican Volcanic Belt, and, after controlling for neutral genetic structure, we detected 74 FST outlier SNPs and 97 SNPs associated with climate variation. Then, we deployed a nonlinear multivariate model, Gradient Forests, to map turnover in allele frequencies along environmental gradients and predict areas most sensitive to climate change. We found that spatial patterns of genetic variation were most strongly associated with precipitation seasonality and geographic distance. We identified regions of contemporary genetic and climatic similarities and predicted regions where future populations of Q. rugosa might be at risk due to high expected rate of climate change. Our findings provide preliminary details for future management strategies of Q. rugosa in Mexico and also illustrate how a landscape genomic approach can provide a useful tool for conservation and resource management strategies.Local adaptation is a critical evolutionary process that allows plants to grow better in their local compared to non‐native habitat and results in species‐wide geographic patterns of adaptive genetic variation. For forest tree species with a long generation time, this spatial genetic heterogeneity can shape the ability of trees to respond to rapid climate change. Here, we identify genomic variation that may confer local environmental adaptations and then predict the extent of adaptive mismatch under future climate as a tool for forest restoration or management of the widely distributed high‐elevation oak species  In addiPopulus balsamifera sampled in a wide geographic region as a case study, they have demonstrated that community‐level modeling frameworks  and develop first‐draft management guidelines for populations facing rapid climate change. Climate change projections for Mexico indicate trends that would involve temperature increase, an overall precipitation decrease, and a change in the temporal distribution of precipitation  than neutral expectations may be indicative of local adaptation. These loci with significantly high FST, however, do not point to which environmental factors might be the cause of selection  with a wide geographic distribution, from Honduras and Guatemala in Central America to Arizona, New Mexico, and western Texas in the United States. In Mexico, it can be found from the subtropics in the highlands of Los Altos de Chiapas to the temperate zones of the Sierra Tarahumara in the State of Chihuahua at altitudes ranging from 1,700 m to 3,550 m  in southeastern Mexico Figure . Initial2.2ApeKI) and adapter concentration (0.036 ng/μl of each adapter). However, we pooled 48 samples per preparation instead of 96, we added adapters during the ligation step not before the restriction digest, and we added AMPure XP bead‐based size selection/purification steps after the ligation step and again after the PCR step to ensure a consistent distribution of fragment sizes between 200 and 500 bp (including adapters) among all preps. We also reduced the number of PCR cycles from 18 to 16. Final libraries were checked for the proper size distribution on an Agilent BioAnalyzer with the High Sensitivity DNA assay and quantified using a Qubit fluorometer. Samples were sent to the UCLA Broad Stem Cell Research Center for single‐end, 100‐bp sequencing on an Illumina HiSeq2000 v3.Total genomic DNA was extracted from the leaves using the DNeasy Plant Mini Kit  according to the manufacturer's instructions. For samples that produced final products with coloration, presumably due to unremoved secondary compounds, we repeated the extractions applying a prewash protocol  and extracted values for 17 Q. rugosa point locations. This procedure was performed in R 3.2.0  resulting in the following set of climate variables: temperature seasonality (BIO4), minimum temperature of coldest month (BIO6), precipitation seasonality (BIO15), precipitation of wettest quarter (BIO16)  between population pairs to the log of pairwise spatial distances between populations as proposed by Rousset  and FIS per population in genepop, according to Weir & Cockerham . Simulation studies have shown that BayeScan has the best performance under departure from the island model compared to other population differentiation methods  is included in the model as a covariate to control for demographic history and environmental gradients not included in the study  were calculated using the genomic inflation factor (λ) procedure described in Devlin and Roeder . The SNP data were converted into minor allele frequencies per population. To ensure robust regressions, we set a filter to remove SNPs that were polymorphic in three or less than 17 populations, but only one locus of the complete SNP set was removed with this filter. For each model, we used the four climatic variables chosen for LFMM analyses as environmental predictors. As GF does not directly incorporate geographic distances, the effects of spatial processes and unmeasured environmental variation were included in the models using Moran's eigenvector map (MEM) variables as spatial predictors. MEM variables are spatial eigenfunctions calculated from the geographic coordinates of the sampling locations. This approach, which was initially named principal coordinates of neighbor matrices, was proposed by Borcard and Legendre (2(0.368n)/2 and a variable correlation threshold of 0.5 to calculate conditional variable importance values as recommended  into multivariate synthetic variables using PCA. The PCA was centered but not scaled to preserve the differences between genetic importance values among the environmental variables. For each of the five GF models, the first three PCs were assigned to a red‐green‐blue color palette, respectively, and visualized in geographic space. In our maps, color similarity corresponds to the similarity of expected patterns of genetic composition. We then performed a Procrustes superimposition Gower,  on the PTo estimate vulnerability to climate change, we transformed future climate scenarios for 2080 into genetic importance values using the previous GF functions calculated for current climate. For each data point, we averaged future climate data corresponding to the representative concentration pathway 6.0 (RCP 6) scenario of greenhouse gas concentration trajectories  and the highest mean pairwise FST (0.095) , and the range was from 0.165 to 0.314. We did not detect significantly low outlier FST values that would be indicative of balancing or purifying selection.BayeScan identified 74 SNPs  with elevated n Figure . Mean F3.3p‐values were uniformly distributed and thus indicated that K = 2 adequately controlled for the potentially confounding effects of population structure  for these SNPs in case additional EAs are due to correlation among climate variables  and 52 with minimum temperature of the coldest month (mean ǀzǀ = 4.02). Only six SNPs were associated with precipitation, but mean ǀzǀ‐scores were usually higher than in temperature variables. Four of these SNPs were associated with precipitation seasonality (mean ǀzǀ = 5.39) and two with precipitation of the wettest quarter (ǀzǀ = 4.50).Histograms of adjusted n = 97) had mean FST = 0.067 , slightly higher than the background overall population differentiation (FST = 0.056).Combined, BayeScan and LFMM identified 170 candidate SNPs, and one SNP was identified with both methods. This SNP was associated with temperature seasonality. Climate‐associated SNPs  and the SNP data set of 97 LFMM significant climate‐associated loci (mean R2 = 20.4%)  and most of the SNPs with the greatest R2 (10% upper tail of R2 distribution) were not included in other data sets (because they are not candidates of climate association)  Table . In the Q. rugosa distribution, which was not evident in the data set containing all SNPs. Indeed, the difference between the pattern of genetic distribution predicted for this all SNPs set and the patterns of each of the four climate‐associated SNP sets, evaluated through the mapping of Procrustes residuals  and high genetic diversity (HE = 0.364) indicate high rates of historical gene flow, typical of widely distributed outcrossing woody species (Loveless & Hamrick, Q. lobata in California (GST = 0.05; Grivet, Sork, Westfall, & Davis, 4.2FST values ranging from 0.165 to 0.314, which are 2.7‐ to 6‐fold higher than the background FST of 0.056. This method tends to produce fewer false positives than other genetic differentiation methods (De Mita et al., FST provides a credible set of candidate SNPs due to divergent selection pressures and local adaptation across these Q. rugosa populations. The EA analysis, which can be more powerful than genetic differentiation tests (De Mita et al., Our findings provide compelling evidence of divergent selection. First, the BayeScan analysis revealed 74 outlier SNPs with these Q. pubescens and Q. robur, most of the SNPs are associated with precipitation variables (Rellstab et al., Q. petraea (Rellstab et al., Q. lobata (Gugger et al., Studies of other temperate and subtropical tree species have also identified a greater proportion of SNPs associated with temperature than with precipitation (Cox, Vanden Broeck, Van Calster, & Mergeay, r = −0.86, Supporting Information Table Q. rugosa TMVB populations.For a small number of SNPs, precipitation variables were important, and the strength of their associations was generally greater than SNPs associated with temperature. We are concerned that the lower number of significant precipitation‐associated SNPs in our EA tests may be due to covariance of precipitation with longitude (4.3Q. lobata transcriptome and 42 of these have previously identified orthologs in A. thaliana (Cokus et al., Populus trichocarpa. Eckert, Bower, González‐Martínez, Wegrzyn, and Coop (This study identified 170 candidate loci potentially under selection, of which 67 are within functional genes annotated in and Coop  and Eckeand Coop  also fouWe point out that many environmental factors other than climate, such as soil type and mineral composition, as well as numerous biotic factors such as pathogens, herbivores, or plant competition, have not been assessed and these factors could have contributed to population divergence or influenced other SNPs not identified. Moreover, because GBS protocol examines only a small portion of the genome, the goal is not to identify all genes under selection but to identify spatial patterns of adaptive genetic variation. It can be done because some of the SNPs will be located within genes under selection and others will be close to candidate genes given rapid decay of linkage disequilibrium (Neale & Savolainen, FST outlier tests are known to be very efficient in identifying strong instances of divergent selection (Narum & Hess, The low level of congruence between outliers identified through population differentiation and EA tests indicates that the tests are detecting different signatures of selection (Eckert, van Heerwaarden, et al., 4.4Q. rugosa in Mexico (Figure P. balsamifera and Gugger et al. (Acacia koa, respectively. The strong spatial influence explains the similarity among GF models observed in the maps of Procrustes residuals. Nonetheless, small differences between GF models for reference and candidate loci also illustrate that recent climate environment is also shaping contemporary spatial structure.The five GF models indicate that precipitation seasonality represents a strong environmental driver of the turnover in allele frequencies in o Figure . Geograpo Figure  and 5a,bo Figure  found a r et al.  found veQ. rugosa, our predictions for future gene–environment relationships indicate that populations in northeastern portion of Q. rugosa distribution in Mexico are likely to experience significant disruption (warmer colors in Figure In three models of 4.5Traditionally, forest management plans have utilized provenance studies and climate modeling to select acceptable regions as transplant sources. However, the rapid rate of climate change has called for a new approach that combines spatial models of genetic variation generated by new genomic tools with climate prediction modeling to develop management and conservation strategies. For assessing the risks of climate change, both GF and other spatial models (Fitzpatrick & Keller, Q. rugosa might benefit from AGF (Aitken & Whitlock, In this study, given that future climate might change drastically in some parts of the species range, we explore whether forest management of 5Q. rugosa in TMVB exhibit geographic patterns of genetic structure that are likely the outcome of spatially divergent selection, as well as demographic history. Such information provides a first‐round assessment of regional patterns of adaptive genetic variation that will help develop a conservation and/or management plan for the preservation of oak forests in this region. For example, given the current distribution of putatively adaptive variation and future climate change, our analysis indicates certain regions of the species range that may be most at risk with rapid climate change. Analyses such as the ones presented here provide a basis both for additional sampling to create a more fine‐scale picture of the distribution of adaptive genetic variation and also for specific experiments that could assess the sensitivity of seedlings transplanted into current climate regimes in the anticipation that they will be adapted to future climate. These experiments could suggest whether current genetic variation is sufficient to tolerate future climate conditions or whether practices, such as AGF, would effectively enhance the persistence of ecosystems associated with tree species, such as this Mexican oak species. Our study presents compelling evidence that portions of the species range will be at risk under future climate change scenarios because underlying adaptive genetic variation may no longer be optimal for future climates and that conservation or management strategies of Q. rugosa should take this risk into account.This study demonstrates that natural populations of None declared.AG‐R, PFG, and VLS designed the study with input from KO; PFG, JL‐M, and J‐LZ led the field and laboratory research; SF‐G and KM conducted bioinformatics; HR‐C prepared the maps; KM and VLS led data analysis and manuscript preparation with input from PFG, AG‐R, and SF‐G.https://doi.org/10.5061/dryad.b56tm0t.Data available from the Dryad Digital Repository:  Click here for additional data file. Click here for additional data file.


Wasserbomben (water bombs). The subsequent collapse of Dutch tomato exports to Germany fueled breeding for fruit flavor, further increasing diversity since the 1990s. The increased diversity in composition of aroma volatiles observed starting from 1990s may reflect the efforts of breeders to improve fruit quality. Specific groups of aroma compounds showed different quantitative trend over the decades studied. Our study provides compelling evidence that breeding has increased the diversity of tomato varieties considerably since the 1970s.It is generally believed that domestication and breeding of plants has led to genetic erosion, including loss of nutritional value and resistances to diseases, especially in tomato. We studied the diversity dynamics of greenhouse tomato varieties in NW Europe, especially The Netherlands, over the last seven decades. According to the used SNP array, the genetic diversity was indeed very low during the 1960s, but is now eight times higher when compared to that dip. The pressure since the 1970s to apply less pesticides led to the introgression of many disease resistances from wild relatives, representing the first boost of genetic diversity. In Europe a second boost ensued, largely driven by German popular media who named poor tasting tomatoes  Nature Biotechnology on de novo domestication of tomato voiced the general belief that “breeding of crops over millennia for yield and productivity has led to reduced genetic diversity. As a result, beneficial traits of wild species, such as disease resistance and stress tolerance, have been lost (…). Despite the increases in yield conferred by domestication, the breeding focus on yield has been accompanied by a loss of genetic diversity and reduced nutritional value and taste” (A recent paper in d taste” .Helminthosporium maydis) outbreak in maize  epidemic in banana , or the in maize . BecauseThe loss of genetic variation in crops due to the modernization of agriculture has been denoted as genetic erosion . During We have studied this for tomato, as particularly in this crop there have been indications of serious genetic erosion . FurtherNinety tomato varieties introduced in the Netherlands for commercial glasshouse fresh fruit production in a time period from 1950 till 2016 were selected by random picking without any prior knowledge about any of genetic or phenotypic parameters analyzed, to have about 12 varieties per each decade Table S1® SolCAP SNP-array  of Within each decade we calculated for each SNP the nucleotide frequencies among the varieties that were commercially introduced in that decade, and used these frequencies for calculating the genetic diversity index , titratable acidity, firmness, and juiciness from 20 to 30 fruit per variety were harvested per a variety at a mature ripe stage. Fruit ripening was judged using intensity of pigmentation and firmness. Also average fruit weight was recorded for every variety analyzed.For soluble solids content and titratable acidity measurements, fruit quarters (for fruits >30 g per fruit) and whole fruits (<30 g per fruit) were homogenized for 15 s in a Vita-prep 3 blender. The soluble solids content was measured directly from the homogenized fruit sample as °Brix by means of a Refracto 30PX digital refractometer (Mettler Toledo). Titratable acidity (mmol H3O+/100 gram fresh weight) was determined by means of potentiometric endpoint titration with 0.1 mol/l NaOH till pH 8.2 by means of a T50 titrator (Mettler Toledo).For texture measurements, from each fruit a 10-mm-diameter disk was excised from the fruit pericarp at the locular region by means of a cork borer. The disks were patted dry by rolling on filter paper and were weighed on an analytical balance (Mettler Toledo XA 204 DeltaRange). Five disks were enclosed between sheets of screening cloth , placed between two sheets of pre-weighed filter paper, having the skin down (Whatman 1003-917) and compressed by means of an Instron 3343 Universal Testing Machine at a speed of 60 mm/min with a flat plate plunger to 900 N. After this compressing, the filter paper was re-weighed. The juiciness (% Juice) was calculated as the weight increase of the filter papers divided by the fresh weight of the disks. Pericarp firmness was defined as the force [Newton (N)] at break of the force/deformation curve of the five simultaneously compressed disks. The data of the basic flavor parameters are present in 2 was immediately added to give a final concentration of 5 M. The closed vials were sonicated for 5 min. A 1-ml aliquot of the pulp was transferred into a 10-ml crimp cap vial (Waters), capped, and used for SPME GC-MS analysis. Volatiles were automatically extracted from the vial headspace and injected into the GC-MS via a Combi PAL autosampler . Headspace volatiles were extracted by exposing a 65-µm polydimethylsiloxane-divenylbenzene SPME fiber (Supelco) to the vial headspace for 20 min under continuous agitation and heating at 50°C. The fiber was desorbed in the GC-MS injection port and compounds were separated on an HP-5 (50 m × 0.32 mm × 1.05 µm) column with helium as carrier gas (37 kPa). Mass spectra in the 35 to 400 m/z range were recorded by an MD800 electron impact MS (Fisons Instruments) at a scanning speed of 2.8 scans/s and an ionization energy of 70 eV. The chromatography and spectral data were evaluated using Xcalibur software (Thermo Scientific). MSClust software (www.nist.gov). Nine mature ripe fruits per variety were pooled to have a representative sample. Fruits were cut, immediately frozen in liquid nitrogen and were ground to fine powder under liquid nitrogen using A11 analytical mill (IKA). Volatile organic compounds (VOCs) were analyzed, identified, and quantified using a Solid Phase Micro Extraction–Gas Chromatography–Mass Spectrometry (SPME-GC-MS) method previously described . Frozen software  was usedhttps://folk.uio.no/ohammer/past/) was used. To discover trends in the diversity of volatile compound composition, a pairwise Euclidean distances matrix was calculated based on the quantitative profiles of 69 annotated volatiles  of Nei  of Nei , also reThis increase in genetic diversity in recent varieties compared to the low diversity in those from the 1950s and 1960s is not restricted to a few loci only, but has occurred across the whole genome 2. ChromAs Before 1970, nearly all tomato varieties were homozygous, but from 1980 onwards, nearly all new commercial varieties are hybrids (The chromosomes differed considerably in their introgression composition (Solanum peruvianum. This introgression carries the tomato mosaic virus (ToMV) resistance gene Tm2 (derived from S. peruvianum PI 126926) or its allele Tm22 (derived from S. peruvianum PI 18650) (Tm22 (This huge change in the composition of Chr. 9 (I 18650) . We re-s) (Tm22 ]. This l) . This introgression has remained very large since its introgression, covering nearly 60% of the chromosome [Another resistance gene on chromosome 6, introgressed during the same period, is the Analysis of the phenotypic traits of the varieties since the 1960s, as described by descriptive variety lists , showed Wasserbomben (German for “water bombs”) to describe poor, watery-tasting Dutch tomatoes  the concentration of sugars in ripe tomato, mainly fructose and glucose, which can be very well approximated by soluble solids content, measured as a refraction index (°Brix); 2) acidity, which is determined by the concentration of organic acids, mainly citrate; 3) firmness of the fruit pericarp; 4) fruit juiciness; and 5) aroma, caused by a complex combination of VOCs. The ratio of the first two components—sugar content and acidity—is one of the main parameters for the perception of sweetness . The sugMost of the aroma active VOCs in tomato fruit can be classified into five distinct groups according to their biosynthetic origins : 1) VOCsTomato fruit flavor is a product of a complex interaction between all the mentioned factors . Our datSome recent studies, such as a work of Tieman and Klee , compariConsidering the existence of other sources of biodiversity, e.g., landraces or heirloom varieties predating the 1950s and wild relatives, there may still be room for improvement of flavor of modern commercial tomatoes by further enrichment with alleles that can further improve fruit flavor . HoweverS. lycopersicum var. lycopersicum, S. lycopersicum var. cerasiforme, and S. pimpinellifolium), and 129 vintage accessions (including landraces and heirlooms). We used the SolCap array data and the classification of genotypes from In our analysis, we looked at varieties from the 1950s till the present time, reflecting the period of modern commercial tomato breeding. In order to put the evolution of diversity in a wider temporal context, we calculated the diversity index among 385 ancestors groups only, so-called private alleles, Blanca et al. found that the processing contemporary types showed a higher frequency of private alleles compared to genotypes for the fresh market. The frequency of private alleles was lowest in vintage types.Earlier work of S. lycopersicum and S. pimpinellifolium accessions  the manuscript.The research was financed by Plant Breeding (WUR) and by the Board for Plant Varieties, The Netherlands. The views expressed in the text are the authors’ own and do not necessarily reflect the opinions of the Dutch Board for Plant Varieties.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Since seminal descriptions of signal transducer and activator of transcription 3 (STAT3) as a signal transducer and transcriptional regulator, which is most usually activated by phosphorylation of a specific tyrosine residue, a staggering wealth of research has delineated the key role of this transcription factor as a mediator of mammary gland postlactational regression (involution), and paradoxically, a pro-survival factor in breast cancer and some breast cancer cell lines. STAT3 is a critical regulator of lysosomal-mediated programmed cell death (LM-PCD) during mammary gland involution, where uptake of milk fat globules, and consequent high levels of free fatty acids, cause permeabilisation of lysosomal vesicle membranes, in turn leading to cathepsin protease leakage and cell death. A recent proteomic screen of STAT3-induced changes in lysosomal membrane protein components has highlighted wide-ranging effects of STAT3, which may coordinate LM-PCD via the stimulation of endocytosis, intracellular trafficking, and lysosome biogenesis. In parallel, STAT3 regulates the acute phase response during the first phase of involution, and it contributes to shaping the pro-tumourigenic ‘wound healing’ signature of the gland during the second phase of this process. STAT3 activation during involution is important across species, although some differences exist in the progression of involution in dairy cows. In breast cancer, a number of upstream regulators can lead to STAT3 activation and the effects of phosphorylation of STAT3 are equally wide-ranging. Recent studies have implicated microRNAs in some regulatory pathways. In this review, we will examine the multifaceted role of STAT3 in mammary gland involution and tumourigenesis, incorporating a review of these fundamental processes in tandem with a discussion of recent developments in this field. STAT3 was first described as an acute phase response regulator in the liver, where its transcriptional activity was activated by phosphorylation of a single tyrosine residue ,2. In thMammary gland involution comprises the process of glandular postlactational regression that is initiated by weaning. This is widely appreciated to be a dynamic and dramatic process, encompassing two primary waves of cell death and the marked remodelling of the stromal compartment. While most research has been carried out using genetic models in the mouse, such changes have recently been illustrated in the human breast by diffusion tensor magnetic resonance in the first year following the cessation of lactation. As observed in the mouse, the size and fibroglandular fraction of the human breast in the postweaning period decreased significantly when compared to lactation .The process of involution is both exquisitely complicated and highly controlled, with STAT3 being a key player in this process ,5,6. OneExperiments using mice that have a unilaterally sealed inguinal mammary gland teat have demonstrated that removal of the suckling stimulus, and the associated milk stasis, lead to phosphorylation of STAT3 and the commencement of involution . Thus, tTeat sealing experiments have also demonstrated that transforming growth factor beta 3 (TGF-β3) expression is induced by milk accumulation. STAT3 activity is suggested to be regulated by TGF-β3 expression, which thus impacts cell death during the first phase of involution .C/ebpδ) mRNA content is low during pregnancy and lactation, but it also exhibits a dramatic, exceeding 100-fold, increase within 12 h of the onset of postlactational regression, or in teat sealed glands [C/ebpδ is grouped with genes showing their strongest expression on the first day following the induction of involution [C/ebpδ is a STAT3 target gene, it has been inferred to be an important mediator of the ensuing cell death in the mammary epithelial cells [C/ebpδ transcription has been further confirmed by microarray analysis of conditionally induced STAT3 genes in a mammary epithelial cell line (KIM-2), which indicates that STAT3 induces C/ebpδ expression, along with a number of other genes, including the negative feedback regulator suppressor of cytokine signaling 3 (Socs3) [Interestingly, mammary epithelial CCAAT/enhancer binding protein delta  .Stat3. This was achieved through the utilization of the Cre-lox recombination system where a mammary-specific promoter [Wap-Cre) [Blg-Cre) [During the first, reversible and proteinase independent, phase of involution, STAT3 initiates dramatic mammary epithelial cell death ,20,21,22promoter , such asBlg-Cre) , controlBlg-Cre) ,20.Intriguingly, it has been demonstrated that involution is unaffected by the deletion of the executioner caspases 3, 6, or 7 or by the overexpression of a viral apoptosis inhibitor, p35 . This obThe trigger for STAT3-regulated LM-PCD in mouse mammary epithelial cells is the uptake of milk fat globules, which become toxic to the cell as they are delivered to lysosomes. High levels of free fatty acids cause permeabilisation of lysosomal vesicle membranes in mammary epithelial cells in vitro, which in turn leads to cathepsin leakage and cell death . A recenProteinases, including the matrix metalloproteinases (MMPs) MMP2 (gelatinase A), MMP3 (stromelysin 1), and MMP9, together with the serine proteinase urokinase-type plasminogen activator ,27 degraLIF-induced STAT3 activity causes the upregulation of oncostatin M (OSM) and its receptor from approximately 48 h of involution and OSM is considered to be the primary cytokine activating STAT3 during the second phase of involution, with mice that are deficient in the OSM receptor exhibiting a delayed involution phenotype . OSM hasTgfbr2) gene exhibiting clearly detectable alveolar structures containing milk at one week of involution. Interestingly, more abundant activated STAT3 was detected at day 3 of involution in these mice, which the investigators attributed to local alveolar distension by milk [TGF-β has also been suggested to have a role in the regulation of cell death during the second phase of involution, with mice with a mammary epithelial-specific deletion of exon 2 of the TGF-β receptor 2  are expressed in the mammary gland [Stat3, we have demonstrated that STAT3 positively regulates expression of mClca1 and mClca2. Data from KIM-2 cells stimulated with OSM, and from mice deficient in STAT3, indicate that STAT3 negatively regulates mClca5. The significance of the relationship between STAT3 and CLCA family members within the mammary gland requires further interrogation, but it is tempting to speculate that their role may encompass epithelial and/or microenvironmental modulation during this phase of dramatic remodeling [Members of the chloride channel regulators, calcium activated (CLCA) family, of proteins have been suggested to possess diverse roles in control of cellular proliferation, differentiation of epithelial cells, transmembrane trafficking of anions, tumour suppression, and the activation of macrophages ,44,45,46ry gland ,44,47. UMammary epithelial cells are important contributors to the microenvironment of the mammary gland during involution. During murine postlactational regression, CD14 expression by mammary epithelial cells is strikingly upregulated in a STAT3-dependent manner ,27. TakeMammary gland involution in the dairy cow frequently differs from murine models in that cows in dairy production systems are likely to be in the final trimester of pregnancy during the period of mammary gland involution (termed the dry period) , and thuSTAT3 was first described to be constitutively active in invasive, but not benign, breast tumour biopsies . In a suBcl-xL, MCL1, and survivin are STAT3 target genes [STAT3 signalling in breast cancer cells may confer a survival advantage by promoting aberrant expression of oncogenic target genes . For exaet genes  that areet genes . STAT3 het genes .In breast cancer, as in physiological contexts, STAT3 can be activated by cytokine receptors, particularly receptors for interleukin-6 (IL-6) family cytokines, including the OSM receptor, as described in the second phase of involution. However, it is important to note that STAT3 activation in various cancers may also be achieved by a number of other pathways, including downstream of receptor tyrosine kinases , non-receptor tyrosine kinases, some serine kinases, G protein coupled receptors, Rho GTPases, cadherin engagement, and toll-like receptors .STAT3 for downregulation, thus functioning as a tumour suppressor in breast cancer. Consequently, breast cancer tissues with a low miR-519d expression have higher levels of STAT3 protein. miR-519d is often downregulated in breast cancer [Lin-28, resulting in the upregulation of the let-7 target, high-mobility group A protein 2 (HMGA2). Interestingly, HMGA2 promotes epithelial-to-mesenchymal transition that is driven by OSM [MicroRNAs (miRNAs) also fulfil a crucial role in regulating STAT3 signalling . miR-519t cancer . Memberst cancer . Howevern by OSM . An inven by OSM .ErbB2-driven breast cancer, it was found that STAT3 does not have a role to play in tumour initiation, but it has a dramatic effect on metastatic progression with a 12-fold reduction in the number of metastatic lesions in the lungs of animals with STAT3-null/ErbB2 tumours when compared to STAT3-wild-type/ErbB2 tumours. The authors concluded that this was a consequence of a reduction in both angiogenic and inflammatory responses that were mediated by C/ebpδ [-Neu (ErbB2) background, exhibited more invasive mammary tumours with earlier onset than those with wild-type STAT3 alleles [We have previously reviewed the role of STAT3 and the inflammation/acute phase response in involution and breast cancer . More rey C/ebpδ . Similar alleles . More re alleles .STAT3 and STAT5 may be activated in tandem in breast cancer, with 29% of tumours exhibiting this characteristic. Such tumours are described as being more differentiated than those in which STAT3 alone is activated . In breaGiven the preceding discussion regarding the importance of STAT3 signalling in breast cancer, it is perhaps unsurprising that we and others have observed robust levels of expression of pSTAT3 in tumours resulting from implantation, into the mammary fat pad, of the syngeneic murine mammary carcinoma cell line 4T1 ,83. We oUsing the 4T1 model other investigators have demonstrated the importance of myeloid derived suppressor cells (MDSCs). When compared to the mouse mammary carcinoma cell line EMT6, 4T1 cells exhibit higher levels of IL-6 expression in culture, and in a series of experiments using these two cell lines, it has been demonstrated that IL-6 expression positively impacts the recruitment of MDSCs and metastatic potential. The authors attribute the augmentation of STAT3 activity in the tumour cells to the secretion of IL-6 and soluble IL-6Rα by the MDSCs .Interestingly, administration of the natural napthoquinone compound shikonin has been demonstrated to reduce the in vivo growth of tumours that are derived from implantation of 4T1 cells, and this is suggested to be through modulation of STAT3 and Oct3/4 expression . NifuroxWhilst mammary neoplasia is not a disease that is limited to humans, relatively sparse data currently exist regarding the importance of STAT3 activity in mammary tumours in animals, particularly when considering studies correlating pSTAT3 expression directly with clinical outcome. pSTAT3 levels have been shown to be significantly higher in metastatic canine mammary tumours than in non-metastatic examples, and have similarly been demonstrated to be higher in canine mammary carcinoma cell lines that were derived from tumours with metastatic potential ,87. FeliGiven its undisputed status as a tantalizing target for inhibition, it is unsurprising that considerable focus is placed upon the development of STAT3 inhibitors. A full discussion of the development of such inhibitors is beyond the scope of this discussion, and this topic has recently been reviewed elsewhere ,93. It iThe plethora of studies documenting the importance of STAT3 in mammary gland involution and breast cancer underlines the critical importance of this transcription factor in the regulation of physiological cell death during postlactational regression, and paradoxically, in providing a survival advantage for neoplastic cells in breast cancer. In this context, the role of STAT3 in the tumour microenvironment is critically important, and this, coupled with the fact that STAT3 is not normally activated in undifferentiated mammary epithelial cells, may go part of the way to explain this paradox. It is clear that STAT3 also contributes to shaping the immunomodulatory, and pro-tumourigenic, ‘wound healing’ signature of the mammary gland during involution. The critical importance of STAT3 highlights it as a promising target for therapeutic interventions, and although the development of inhibitors of STAT3 is proving to be challenging, it is likely that the field will continue to develop with advances in this area.


More than 100 years after Grigg’s influential analysis of species’ borders, the causes of limits to species’ ranges still represent a puzzle that has never been understood with clarity. The topic has become especially important recently as many scientists have become interested in the potential for species’ ranges to shift in response to climate change—and yet nearly all of those studies fail to recognise or incorporate evolutionary genetics in a way that relates to theoretical developments. I show that range margins can be understood based on just two measurable parameters: (i) the fitness cost of dispersal—a measure of environmental heterogeneity—and (ii) the strength of genetic drift, which reduces genetic diversity. Together, these two parameters define an ‘expansion threshold’: adaptation fails when genetic drift reduces genetic diversity below that required for adaptation to a heterogeneous environment. When the key parameters drop below this expansion threshold locally, a sharp range margin forms. When they drop below this threshold throughout the species’ range, adaptation collapses everywhere, resulting in either extinction or formation of a fragmented metapopulation. Because the effects of dispersal differ fundamentally with dimension, the second parameter—the strength of genetic drift—is qualitatively different compared to a linear habitat. In two-dimensional habitats, genetic drift becomes effectively independent of selection. It decreases with ‘neighbourhood size’—the number of individuals accessible by dispersal within one generation. Moreover, in contrast to earlier predictions, which neglected evolution of genetic variance and/or stochasticity in two dimensions, dispersal into small marginal populations aids adaptation. This is because the reduction of both genetic and demographic stochasticity has a stronger effect than the cost of dispersal through increased maladaptation. The expansion threshold thus provides a novel, theoretically justified, and testable prediction for formation of the range margin and collapse of the species’ range. The flow of genetic diversity across environments has conflicting effects. On the beneficial side, it increases the genetic variation that is necessary for adaptation and counters the loss of genetic diversity due to genetic drift. However, it may also swamp adaptation to local conditions. This interplay is crucial for the expansion of a species’ range. In this work, I develop a theory that shows how range expansion depends on two dimensionless parameters: (i) the fitness cost of dispersal—a measure of environmental heterogeneity—and (ii) the strength of genetic drift—a measure of the reduction of genetic diversity. The more heterogeneous an environment, the more challenging it is to expand into, and the lower the genetic diversity, the more limited is the scope for potential adaptation. Together, these two parameters define an ‘expansion threshold’: adaptation fails when the number of individuals accessible by dispersal within one generation is so small that genetic drift reduces genetic diversity below that required for adaptation to a heterogeneous environment. This threshold provides a novel, theoretically justified, and testable prediction for the formation of a range margin and for the collapse of a species’ range in two-dimensional habitats. Species’ borders are not just determined by the limits of their ecological niche , 2. A spAs Haldane  previousWhen genetic variance can evolve, such a limit no longer exists in infinitely large populations: the population maintains continuous adaptation as the environmental gradient steepens . DetermiI study the problem of intrinsic limits to adaptation in a two-dimensional habitat. Throughout, I assume that the species’ niche is limited by stabilising selection on a composite phenotypic trait. This optimum varies across one dimension of the two-dimensional habitat—such as temperature and humidity with altitude. Demography and evolution are considered together. Selection is ‘hard’: both the rate of density-dependent population growth and the attainable equilibrium density decrease with increasing maladaptation. Both trait mean and genetic variance can freely evolve via change in allele frequencies and the associations among them (linkage disequilibria). The populations are finite, and both genetic and demographic stochasticity are included. The model is first outlined at a population level in terms of coupled stochastic differential equations. While it is not possible to obtain analytical solutions to this model, this formalisation allows us to identify the effective dimensionless parameters that describe the dynamics. Next, individual-based simulations are used to determine the driving relationship between the key parameters and test its robustness. The details are described in the Model section of the Methods.B measures the steepness of the environmental gradient in terms of maladaptation incurred by dispersal across a heterogeneous environment. The second parameter is the neighbourhood size of the population, 𝒩, which can be understood as the number of diploid individuals within one generation’s dispersal range. Originally, neighbourhood size was defined by Wright  (this includes zero). The effective diploid population density Ne hence equals half of the haploid population density N, and 𝒩 = 4πNeσ2 = 2πNσ2. The life cycle is selection → mutation → recombination → birth → migration. Generations are nonoverlapping, and selfing is allowed at no cost. The genome is haploid with unlinked loci (the probability of recombination between any two loci is 1/2). The allelic effects αi of the loci combine in an additive fashion; the allelic effects are uniform throughout this study, αi ≡ α. Mutation is set to μ = 10−6, independently of the number of loci. Migration is diffusive with a Gaussian dispersal kernel. The tails of the dispersal kernel need to be truncated: truncation is set to two standard deviations of the dispersal kernel throughout, and dispersal probabilities and variance are adjusted so that the discretised dispersal kernel sums to 1 , dispersal distance σ, carrying capacity for a well-adapted phenotype K = , width of stabilising selection Vs, the maximum intrinsic rate of increase rm = , and the mutation rate μ, fixed to μ = 10−6. The  interval gives the parameter range used in the 780 randomly sampled runs, with their distributions described in σ along the neutral direction and at least 10 cline (deterministic) widths along the gradient. The number of genes contributing to the adaptation across the species’ range is nl = , with the estimated number of locally polymorphic genes between 1 and 299. Since mutation rate is fixed at μ = 10−6, the genomic mutation rate has a wide range, U = , with median of = 10−4.There are in total 10 parameters in the individual-based model, but only 7 are used to describe the model dynamics in continuous time. These are listed at the bottom of b = 0.18, σ = 0.52, Vs = 0.23, K = 26.7, rm = 1 and α = 0.14, s = 0.04, μ = 10−6, 97 genes. Median genetic variance is at VG = 0.031, deterministic prediction b = 1, σ = 0.4, Vs = 0.4, K = 4, rm= 1.2, and α = 0.1, s = 0.015, μ = 10−6, 874 genes. Median genetic variance within patches is around 0.02, whilst the maximum contribution by a single cline 1/4α2 = 0.0026; in contrast, variance maintained by gene flow under uniform adaptation , Vs = 1, K = 4, s = 0.1, μ = 10−6, 1,000 genes, 1,000 demes along X, 200 demes along Y. These populations evolved for 500 generations.Parameters for σVs=0.45 . In Fig aptation  would beVG , the change in the trait mean σ; the second term describes the effect of the asymmetric flow from areas of higher density. The third term gives the change due to selection, given by the product of genetic variance and gradient in mean fitness , dispersal distance K = , width of stabilising selection Vs = , the maximum intrinsic rate of increase rm = . White: the number of genes . The number of genes contributing to the adaptation across the species' range is nl= , with the number of locally polymorphic genes between 1 and 299. Light blue: composite parameters: the strength of selection, s = α2 /(2Vs) = , with median of s = 0.006. The effect size α = . Dark green: scale-free parameters: the effective environmental gradient B = , with median B = 0.14 the neighbourhood size 𝒩 = , with median 2.8; strength of selection relative to the strength of density dependence, s/r* = , with median s/r* = 0.006. Not pictured is the uniform mutation rate per locus (μ = 10−6), the number of demes along the two spatial directions, which was set to be at least 10 deterministic cline widths wide along the spatial axis with environmental gradient (X), whereas along the neutral spatial axis (Y), the habitat width was 100σ.Grey: all unscaled parameters used in the continuous time and space model. The unscaled parameters and their  ranges are environmental gradient (EPS)Click here for additional data file.


The growth in the direct-to-consumer genetic testing industry poses a number of challenges for healthcare practice, among a number of other areas of concern. Several companies providing this service send their customers reports including information variously referred to as genetic ethnicity, genetic heritage, biogeographic ancestry, and genetic ancestry. In this article, we argue that such information should not be used in healthcare consultations or to assess health risks. Far from representing a move toward personalized medicine, use of this information poses risks both to patients as individuals and to racialized ethnic groups because of the way it misrepresents human genetic diversity. It may seem surprising now to think that not long ago genetic testing was largely confined to clinical and research contexts. The progressive reduction in the cost of sequencing and genotyping, coupled with the limited demand for clinically-driven testing in the early 2000s led to companies marketing and selling their tests directly to consumers to increase sales . Direct-There is a definite mismatch between what consumers think that they are getting from these tests and what the companies themselves state they are giving to consumers of their products. This despite the fact a number of companies have issued public disclaimers, stating that “their services are not meant to be used as medical advice or as a diagnostic tool” . There iOne feature of some DTC genetic testing companies' output is referred to variously as biogeographical ancestry, genetic ethnicity, genetic heritage, or genetic ancestry (hereafter genetic ancestry), suggesting the person's DNA has been separated into sections associated with the places or groups from which the DNA originates. The output normally consists of a list of percentages associated with particular continents and/or currently existing countries and/or ethnic/racial groups, depending on the company. In many cases, a primary motivation for consumers' engagement with DTC genetic testing is to receive this information . AdvertiSome authors have called for genetic ancestry test results to be used in healthcare, suggesting this would constitute “closing a gap” between health-related genetic testing and genetic ancestry testing . Some teGenetic ancestry results, with their percentages by region and often slick presentation, certainly provide an appearance of precision to the consumer but this very appearance is “dangerously seductive and equally misleading” . The valThe idea of using genetic data to categorize people into continental groups to further knowledge about differences in disease risk by race or ethnicity is not new , nor is Despite this, attempts to reinscribe the notion of biological race in humans continue, particularly in biomedical and biotechnology arenas , 35, 36.In order to make the meaning of the above statements clearer, we expand on them here. Barbujani and Pigliucci have pointed out that any two groups of human beings, divided on any basis, however arbitrary , will differ to some extent in average measures of any particular biological trait one chooses, including genetic traits , and this does not mean these groups represent important taxonomic categories (or that ability to digest milk influences choice of plumbing over dentistry as profession) . As Lee “Statement 2: We recognize that individuals of two different geographically defined human populations are more likely to differ at any given site in the genome than are two individuals of the same geographically defined population Research in human genetics has highlighted that there is more genetic variation within than between human groups, where those groups are defined in terms of linguistic, geographic, and cultural boundaries … Statement 7: We discourage the use of race as a proxy for biological similarity and support efforts to minimize the use of the categories of race and ethnicity in clinical medicine, maintaining focus on the individual rather than the group Although a broad range of associations between genetic markers and human traits—including diseases—is emerging, any accompanying correspondence with race or ethnicity is statistical.” .Even Feldman et al. who suggest that identifying “all contributions to a patient's ancestry can be useful in diagnosing and treating diseases with genetic influences,” a point with which we disagree on the basis that all ancestral contributions cannot, in fact, be identified (see below), note that “attempts to classify people into broad genetic groups based on the frequency of specific genes for, say, drug-metabolizing enzymes, are also likely to be poor predictors of medical outcome . As withSince there is no deterministic connection between being part of a group of people, whether referred to as a population, ethnic group, or race, and a particular genotype, genetic ancestry-based approaches to individual care lack an appropriate theoretical basis  and are The notion of ancestry itself deserves some attention. The term has inherent temporal ambiguities which others have highlighted . The ancancestry?One clear example of this comes from the landmark Rosenberg et al. paper on the structure of human populations . In theibiogeographical ; geographical ; geopolitical ; and cultural   . Additiocultural ancestry, as used in biomedical research and clinical contexts, supposed to work given that Via et al. note that culture is categorically different than an individual's genetic background? Given the varied ways in which the term “ancestry” has been used by researchers and clinicians themselves, and that there is no coherent, operationalizable definition upon which clinical and biomedical research has agreed, the use of “ancestry” for anything of empirical import based on DTC products is in serious doubt.One set of complications is methodological: if Via et al. are correct that determinations of ancestry can be varied, which will win out when one or more of these descriptions of ancestry are in conflict? If ancestry can be determined by an observer, how exactly does a clinician or biomedical researcher become one without importing the flaws that the authors find inherent in the use of either “race” or “ethnicity”? The second set of complications is conceptual: What are the agreed-upon classification schemes for biogeographical, geographical, geopolitical, and cultural ancestry? How are they determined given that from an empirical standpoint there is a large overlap between the four aforementioned definitional levels? Is there a hierarchical ordering of the definitional levels, and if so, what ensures that we can make inferences of import for biomedical and clinical outcomes between those levels and individuals? How is the concept Indeed the term ancestry is inherently misleading, no actual ancestral populations have been (or could be) sampled. The methodological assumptions about past population “purity” and “admixture” are just that—non-empirical assumptions that are at odds with the reality that ancestral populations (which DTC companies purport to report on) cannot be sampled with accuracy. The direct-to-consumer tests at best provide limited support for linking a consumer's DNA profile to places in which individuals whose DNA samples have been analyzed and included in the company's datasets lived at the time of sampling. The reason why the information generated by DTC companies provides limited support is due to the underlying methodology of ancestry studies based on populations. These programs are largely modeled after the program STRUCTURE used in Rosenberg et al. , 44, eveOf course, present-day patterns of residence are rarely identical to what existed in the past due to historical events—both social and natural—that have influenced patterns of migration. Humans have migrated to such an extent that many individuals around the world have relatively recent ancestors from far-flung regions . As a reThe analytic techniques used in genetic ancestry tests clearly focus on the small differences between current populations rather than similarities between populations. Studies looking to estimate genetic clustering have tended to choose samples from places both more geographically and socially distinct from one another. When samples have been collected using more even geographical sampling, clustering has been “far less evident” . This isAs distinct from these individual- consumer-focused commercial practices, when inferences are made about ancestry in academic research these are the level of the group and created with measures reflecting their fundamentally probabilistic nature . Indeed,It is worth highlighting the important implications of use of genetic ancestry in terms of the ethical principle of justice in medical practice. In particular, there are considerations specific to racialized ethnic groups. While the concept of “justice” can be construed and understood in quite a narrow way—we wish to emphasize that there are historical and group-level concerns for people who have been marginalized. With respect to medical interventions, the examples of medical injustices that have been perpetrated at the group level include the Tuskegee Syphilis Study  and the Reinscription of the notion of biological race in medical consultation, even inadvertently, validates the idea that race and ethnicity are natural classifications and runs the risk of encouraging racial/ethnic stereotypes and oversimplifications of the complex origins of most disease, leading to both a naïve genetic essentialism and a misunderstanding of human genetic diversity in society , 46. In For their part, according to some reports, white supremacists groups and individuals have seized genetic ancestry results and publicly used these to argue for scientific racism when they interpret the evidence as in favor of their ideology –57, whilGiven the issues that we have highlighted above regarding DTC products for assessing ancestry for biomedical and other purposes, we wish to reiterate the following important points. The first is that “ancestry estimates,” as currently described in the literature, act as a reinscription of biological race. This is because of the inherent confusion that abounds regarding the concept “ancestry”—both for clinicians, biomedical researchers, epidemiologists, population biologists, and especially the general public. Since there are no principled, operationalizable definitions of “ancestry” (nor “population” for that matter), clinicians, biomedical researchers, and the general public frequently project naive racial categories onto their findings.Secondly, it remains important to reiterate that medical practitioners—the intermediaries between consumers and potential medical interventions—have incredible obstacles that prevent them from giving their patients a better assessment and understanding of the “ancestry data” that are gleaned from DTC products. In a recent survey by Baer et al. , the autOur final point is the following: the use of DTC products for assessing the “ancestry” of individuals has a number of ethical issues that are in urgent need of resolution. It would have been better to have thought through and dealt with these issues before the deployment of DTC products rather than after: the ethical issues include potentially providing misleading or inaccurate information that affects the healthcare decisions by practitioners; confusion about differences between ancestry and genealogy for consumers, clinicians, and biomedical researchers; and the reintroduction of racialized concepts under the guise of “genetic ancestry” which has the potential for harming both individuals and marginalized populations. We recommend that the professional bodies of clinical practitioners  and agencies evaluating medical devices  examine the evidence we discuss here and consider issuing specific guidance or warnings about use of genetic ancestry test results in healthcare since these are not suitable for diagnostic purposes and pose risks to individuals and society as we outline above.We have noted that DTC products are of interest to consumers because of how they are marketed and what they promise to potential users: Ancestry.com promises to “Reveal the places you're from”; in reference to their products, 23andme.com offer “Two easy ways to discover you.” The promissory allure of helping people to “find themselves” by giving a swab of their bodily tissue masks the fact that there are conceptual inconsistencies, methodological shortcomings, and ethical issues that influence whether the general public thinks about personalized medicine as racialized medicine.Both authors drafted the manuscript and readied it for journal submission. MB conceived the paper and invited MH to contribute.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.