Idotea balthica, using an array of 33,774 genome‐wide SNP markers derived from 2b‐RAD sequencing. In addition, we generate a biophysical connectivity matrix for I. balthica from a combination of oceanographic current models and estimated life history traits. We find population structure on scales of hundreds of kilometers across the Baltic Sea, where genomic patterns in most cases closely match biophysical connectivity, indicating passive transport with oceanographic currents as an important mean of dispersal in this species. We also find a reduced genetic diversity in terms of heterozygosity along the main salinity gradient of the Baltic Sea, suggesting periods of low population size. Our results provide crucial information for the management of a key ecosystem species under expected changes in temperature and salinity following global climate change in a marine coastal area.Incorporating species' eco‐evolutionary responses to human‐caused disturbances remains a challenge in marine management efforts. A prerequisite is knowledge of geographic structure and scale of genetic diversity and connectivity—the so‐called seascape genetic patterns. The Baltic Sea is an excellent model system for studies linking seascape genetics with effects of anthropogenic stress. However, seascape genetic patterns in this area are only described for a few species and are completely unknown for invertebrate herbivores, which constitute a critical part of the ecosystem. This information is crucial for sustainable management, particularly under future scenarios of rapid environmental change. Here, we investigate the population genetic structure among 31 locations throughout the Baltic Sea, of which 45% were located in marine protected areas, in one of the most important herbivores of this region, the isopod crustacean  If the current marine MPA network within the study area has been optimally designed in this respect, we would expect samples from MPAs to contain higher genetic diversity than non‐MPA ones. Further, we hypothesize that the I. balthica population in the Baltic Sea has a reduced genetic diversity compared to outside locations, due to periodically low population sizes either before, during, or after the colonization of the Baltic, as has been observed in other species , with almost half of the sites being MPA sites. Using 2b‐RAD sequencing  by snorkeling and picking isopods from Fucus from August 8 to October 2, 2014 , with 14 locations being within HELCOM MPAs . All pools were sequenced in an Illumina HiSeq 2,500 machine, 50bp single‐end, at the Swedish National Genomics Infrastructure's SNP & SEQ platform at Uppsala University.2b‐RAD libraries . All commands used in the analyses can be found here: https://github.com/DeWitP/BONUS_BAMBI_IDOTEA. An unpublished draft genome assembly for I. balthica was used as a reference for mapping the 2b‐RAD data, using bowtie2 . The standard GATK pipeline  approach, which summarizes the genetic distances in a multidimensional plane with as few dimensions as possible, after which the first two dimensions were plotted using R. Genetic diversity indices (FIS and expected heterozygosity HE) were calculated using Arlequin v.3.5.2.2  distance (1—IBS) matrix was calculated on an individual basis from the full 33,774 SNP dataset, using plink 1.9 . Cross‐validation scores were examined for each K to infer the most probable number of population clusters.Genotype likelihoods for all SNP sites were extracted from the vcf files using bcftools, which were input to NGSadmix . conStruct uses a Bayesian MCMC algorithm to estimate the posterior distribution of admixture proportions. The input for conStruct was sample allele frequency for each SNP, and the MCMC analysis was run with two chains, 50 000 iterations, in the nonspatial mode of the software. In order to extrapolate NGSadmix‐estimated ancestry coefficients (proportion of the genome belonging to the different clusters) on geographic scales, the R package TESS . A map of the Baltic Sea was downloaded from https://maps.ngdc.noaa.gov/viewers/wcs-client/, from which a grid was generated with a constraint on elevation from 0 m to −30 m . The constraints were chosen to account for variability in depth within each grid cell, as well as for plotting reasons. Ancestry coefficients for the 12 different clusters were then geographically imputed and plotted on the map in different color scales.The software “conStruct” . A full‐rank distance matrix was generated by imputing missing genotype values with observed mean genotype at each SNP site, as implemented by the “bed2diffs_v2” tool distributed with the EEMS software. Geographic coordinates from 86 vertices specifying a polygon enclosing the Baltic Sea were extracted from Google Earth (earth.google.com/web/), after which the EEMS software was run in three separate mcmc chains, each with default lengths , using 300 demes. The output was examined for convergence and plotted on a map of Europe using the rEEMSplots R package (distributed with EEMS).Finally, variability in genetic diversity was modeled and extrapolated geographically using the software package EEMS , and the total duration of the dispersive phase was here approximated to be ca. 5 days. Dispersal of Idotea spp. is not well known but is believed to be short  and multigenerational connectivity estimated from the biophysical model was tested with Mantel tests ) connectivity matrices (64 generations) were tested. Because pairwise FST data are symmetric, we extracted symmetric connectivity matrices by using either the minimum connectivity (minimum of i to j and j to i), the maximum connectivity, or the mean connectivity. In addition, distributions of pairwise FST values within each region and between regions were plotted as boxplots in R, and a regression analysis was performed to examine potential isolation‐by‐distance patterns between mean connectivity and pairwise FST.The correlation between genetic differentiation  were µ = 20.23% ± SD 2.26% (Table iT/vT) to be 1.348. By examining different truth‐sensitivity tranches, we could observe a sharp drop in iT/vTat above 95% truth sensitivity, so we used 95% as the cutoff tranche for SNP filtering. After filtering for highly heterozygous  loci, and thinning the dataset to one SNP/ RAD fragment, we were left with 57,641 SNPs, and after filtering out poorly genotyped sites, the final genotype dataset consisted of 33,774 SNP sites, genotyped at a minimum of 80% of 575 individuals   Figure FST . In this analysis, the Swedish Bothnian Sea samples (SBO‐1 and SBO‐2) in particular diverge strongly from all others along both primary axes. There was significant genetic differentiation  among most samples , primarily due to a major drop at the North Sea–Baltic Sea transition zone . However, there was no significant effect of mean summer (p = .28) or winter (p = .09) temperature, or with geographic coastline distance from the North Sea (p = .56). Heterozygosity was also not significantly different between HELCOM MPA and non‐MPA locations (p = .26) (Mean expected heterozygosity per sample  was strop = .26) .3.3FST outliers across all samples  was based on an IBS distance matrix calculated for all individuals in all samples. BayeScan identified 487 s Figure a, and Ous Figure b‐d reveap = .001; The combined CAP analysis clearly separated samples, or groups of samples, from each other, matching the geographic distribution of the collecting locations almost perfectly Figure . Indeed,K = 2–32 , although the software did identify more fine‐scale population structure also at higher K values. Thus, we chose to focus on K = 3 and 12. At K = 3, western samples, North Baltic/Bothnian Sea ones, and Estonian ones (excluding the Estonian GFE‐3 sample) formed three main clusters   based on the biophysical particle model Figure  showed tThe barrier analysis, which is a way to project the multigeneration connectivity matrix in a geographic dimension, also identified regions with high internal connectivity within the Baltic Sea separated by barriers with high resistance to dispersal Figure . The numFST) . However, the regression analysis identified two separate isolation‐by‐distance (IBD) correlations in the dataset: one involving pairwise comparisons including one of the two SBO samples (R2 = 0.76) and one with all remaining pairwise comparisons (R2 = 0.44)  was strongly correlated with pairwise genetic distance  Figure .44.1Idotea balthica within the Baltic Sea. In some of the regions, there are significant population differences on scales of less than 100 km. This contrasts to patterns in some fish species such as sticklebacks  need to be used to examine patterns arising from historical demographic events.The population genomic data produced here shows the presence of genetically differentiated populations of s Figure . The bars Figure . Some dis Figure , local as Figure , and/or s Figure , in shapFST estimates. They are also found to be significantly more distant from other samples in the isolation‐by‐distance regression analysis. This is partly at odds with the barrier analysis, which indicates that dispersal along the Swedish coast include these localities, except in the most stringent setting of the barrier analysis , and this area is shallow and may provide good stepping‐stone dispersal for shallow‐water organisms, so even species with limited dispersal ability should be able to cross over to Sweden from the Finnish side. However, isopods are not found on the Swedish side  north of this site, although the distribution of Fucus spp. becomes increasingly patchy. Niche modeling studies  show low genetic diversity and are genetically isolated from all other samples with high pairwise s Figure c. Howeves Figure , the bio,2 show l4.2Idotea balthica along the Finnish Bothnian Sea coast is one large continuous population in the northern part . As prevailing currents are northward bound along the Finnish coast, it is likely that gene flow is mainly in the south–north direction . Possibly, the northernmost Finnish population might be a sink population with low reproductive output and few dispersive juveniles, which would add to the explanation of lack of dispersal to the Swedish side. The strong genetic separation of the Swedish and Finnish Bothnian Sea coasts can be compared to the genetic patterns observed in Fucus. While F. radicans show weak structure throughout the region due to dominance of a few large clones, F. vesiculosus is differentiated both between Sweden and Finland and also among sampling sites within both countries  as stepping stones for dispersal. The Polish, Latvian, and Lithuanian coasts have historically supported extensive seagrass beds, although almost all of those are gone today , samples show a strong step‐wise isolation‐by‐distance divergence going in to the Baltic. Such a pattern is expected following a colonization front moving into the Baltic Sea along the coast, with founder effects leading to increasing levels of differentiation . Interestingly, this corresponds very well to the average drop in nuclear genetic diversity of Baltic Sea populations of various marine species (11%–12%)  beds, so it cannot be excluded that there are additional genetic clusters present there. Further sampling efforts will be necessary in order to resolve this issue.The reduced within‐species genetic diversity, and the need for adaptations to the rapidly changing Baltic Sea, call for management and conservation actions  while considering the overall salinity effect can be found on the eastern side of the Estonian islands of Hiiumaa and Saaremaa (samples GFE‐5 and 6), where oceanographic currents from both the Gulf of Finland, the Bay of Riga, and the Baltic Proper converge, and also in the Västervik area on the Swedish southeast coast (sample SBA‐4) , especially along the Swedish Bothnian Sea and northern Baltic Sea coasts, as it may otherwise be difficult for low‐salinity adapted genotypes to track the receding salinity gradient (Kotta et al., Fucus spp., an overview of which seems motivated to maintain stepping‐stone possibilities for north to south gene flow in I. balthica. It might also be considered to physically translocate northern populations to more southerly areas. While this type of manipulation is controversial, it might prove necessary under rapidly decreasing salinities in order to sustain more southern Baltic Sea populations of isopods. Surface salinity has been decreasing since 1980 and may reach 3 psu in large parts of the Baltic Proper before the end of the century (Meier, Andersson, et al., Fucus spp. and I. balthica.Finally, as significant warming and decrease in salinity are expected in the Baltic Sea before the end of this century, local extinctions of isopod populations throughout the area, especially near current range margins, are to be expected (Kotta et al.,  Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Galaxias species with different life-history traits to investigate how these biological differences impacted their evolution in the Valdivia River basin, Southern Chile. We analysed mitochondrial DNA (mtDNA) sequences from 225 individuals of Galaxias maculatus and 136 of G. platei to compare patterns of genetic diversity, structure and demographic growth across the basin. Galaxias maculatus presented higher genetic diversity and higher genetic structure than G. platei. Demographic analyses showed G. maculatus kept a higher population size over time, with a signal of demographic expansion in the last 250 kyr. Whereas Galaxias platei, exhibited lower, but constant population size over time. Furthermore, haplotype networks revealed higher lineage diversity in G. maculatus with a tendency to occupy different areas of the basin. Coalescent simulations ruled out that genetic differences between species could be explained by stochastic processes (genetic drift), suggesting species-specific biological differences as responsible for the observed genetic differences. We discuss how differences in life-history traits and past glaciations interact to shape the evolutionary history of the two Galaxias species.Life-history traits are among the most important factors affecting population abundance and genetic diversity of species. Here, we analysed the genetic patterns of two  It is not yet clear though how strong or consistent these patterns are across taxa, as freshwater phylogeography in Patagonian systems is still in an early stage. Moreover, comparative studies of co-distributed species are still rare. Some of these studies, such as Ruzzante et al. [Phylogeographic studies in freshwater fish have recently increased our understanding on the relevant processes shaping biodiversity in southern South America ,2. Most et al.  found cle et al. , have emet al. [Galaxias platei, which currently inhabits areas [Percichthys trucha was able to maintain more stable populations across the last two glacial periods, possibly due to their ability to survive in refugia away from areas of high glacial impact. At a much smaller geographical scale (i.e. within a single river basin), but studying a larger number of species, Victoriano et al. [et al. [Galaxias maculatus with G. platei and Aplochiton taeniatus with A. zebra). Nevertheless, G. maculatus and G. platei, both being widespread and abundant within the Valdivia River basin, could represent an excellent opportunity to study the effects of biology and history on demographic species’ responses in greater detail.Although few, comparative phylogeographic studies on Patagonian freshwater fish have suggested that species can be differently impacted by similar historical events at both large  and smalet al.  found thts areas ,5 that wo et al.  found a o et al. . Indeed, [et al. , only a Galaxias encompasses several cool-temperate adapted species distributed across the former regions of Gondwana [Galaxias maculatus (Jenyns 1842) and G. platei (Steindachner 1898) are freshwater fish that coexist across the Valdivia River basin [Galaxias maculatus is a facultative amphidromous species [Galaxias platei, on the other hand, is restricted to South America and Falkland Islands, where it shows a distribution pattern similar to G. maculatus, although more southernward, starting at about 38°S in Chile and 41°S in Argentina down to Tierra del Fuego. In contrast to G. maculatus, G. platei is a strictly freshwater species and reaches much larger body sizes (350 versus 150 mm in G. maculatus) [G. platei appears to have persisted throughout glacial maxima in local refugia at high elevation [G. maculatus retains numerous populations living in potamal, estuarine and coastal areas that were less affected by glaciations [The genus Gondwana . Galaxiaer basin –16. Desp species  which ca species –19. This species ,20, and  species . The disculatus) . It can culatus) ,23, whiclevation ,25. By cciations .Galaxias [G. maculatus have provided regional-level genetic information to understand broad-scale evolutionary processes. For instance, Zemlak et al. [G. maculatus finding that the current distribution of this species most likely reflects a west–east colonization process, followed by localized dispersal events across the Andes via drainage reversal during glacial–interglacial shifts. González-Wevar et al. [G. maculatus. Furthermore, this study also showed the Valdivia basin harbouring an endemic lineage, suggesting the strong isolation of its populations. Unfortunately, the study only included estuarine samples. Recently, Delgado et al. [G. maculatus, including populations of the Valdivia River basin. Regarding G. platei, much lower genetic structure and diversity have been reported in the Valdivia basin [In the last few years, a number of phylogeographic studies have contributed to the knowledge of the evolutionary history of southern South American Galaxias –10,25. Gk et al.  analysedr et al.  conducteo et al.  have revia basin  when usiia basin .Galaxias species are due to their biological differences or random variations due to coalescent stochasticity is a question that remains untested, yet approachable with appropriate, model-based methods. In this study, we analyse a large number of populations and individuals using mtDNA sequences of both G. maculatus and G. platei with the aim to understand in greater detail their patterns of genetic diversity across the Valdivia River basin. Our specific goals are (i) to thoroughly document the genetic diversity and structure across the Valdivia River basin for G. maculatus and G. platei, (ii) to infer their demographic histories in relation to past glacial events, and (iii) to evaluate whether differences in the genetic patterns observed between species (if any) are greater than expected under a null model of random variation due to genetic drift.However, differences in observed patterns of genetic diversity between co-distributed species can be due not only to differences in their biological traits but also to random processes like coalescent stochasticity. Thus, a strong statement about the role of biological differences in the generation of disparate genetic patterns should minimally account for random genetic processes like genetic drift . Whether2.2.1.The Valdivia River basin (40° S) is a complex Andean basin with two headwaters, located in east  and west  Andes . Both heG. maculatus individuals and 136 G. platei individuals were collected from 39 locations in four zones across the Valdivia River basin . These four adjacent zones encompassing roughly similar areas differ in particular characteristics such as the type of environment , the orientation of the subwatersheds, and the type of river stretch (hypo-rhithron/potamon). These zones correspond to: Zone 1 (Z1), lentic bodies and watercourses draining north–south and west–east, from Lake Calafqén to Lake Panguipulli; Zone 2 (Z2), watercourses and lakes draining west–east, from the eastern edge of Lake Riñihue onwards, including its tributaries and outlet ; Zone 3 (Z3), hyporitral type watercourses, spanning from the mouth of Lake Riñihue to the confluence of the Rivers San Pedro and Quinchilca ; and Zone 4 (Z4), composed mostly of potamal stretches of river, from Quinchilca to the Cutipay bridge on the Calle Calle River   . All ind2.2.® where each reaction was conducted in a total volume of 30 µl, containing 1X PCR buffer, 2.5 mM MgCl2, 0.5 mM of primer L19 [−1 GoTaq DNA Polymerase® (Promega®). PCR amplification was performed according to the following programme: a cycle of 95°C for 5 min, 60°C for 30 s and 72°C for 2 min, followed by 40 cycles of 95°C for 30 s, 54°C for 45 s and 72°C for 2 min, and a final elongation cycle of 72°C for 5 min. Each PCR product was purified using Millipore filter plates and sequenced in the DNA Sequencing Center of the Department of Integrative Biology at Brigham Young University (EEUU), and Macrogen® sequencing service. All sequences were visually inspected, reviewed, edited and aligned using the program CodonCode Aligner . Sequences were deposited in GenBank database under the accession numbers MT339714–MT340074.Total DNA was extracted from muscle tissue, using the QIAGEN DNeasy Tissue DNA extraction kit following the manufacturer's instructions. The control region fragment was amplified using the kit GoTaq Green Mastermix Promegaimer L19 , 0.5 mM imer L19 , 0.02 mM2.3.FST and hierarchical analyses of molecular variance (AMOVA) in ARLEQUIN v. 3.5 [FST), among sites within groups (FSC) and among groups (FCT). Significance of F-statistics was achieved using 10 000 permutations. We tested with AMOVA different combination of zones as hypotheses (i.e. (Z1)(Z2)(Z3)(Z4); (Z1)(Z2 + Z3 + Z4); (Z1 + Z2)(Z3 + Z4); (Z1 + Z2 + Z3)(Z4)).For each species, we estimated the haplotype number (K), haplotypic diversity (Hd), number of segregating sites (S) and nucleotide diversity (π) as indexes of genetic diversity. These indexes were estimated with DNASP v. 5.0 software . Levels N v. 3.5  for each2.4.Ne) through time [G. platei and G. maculatus (BF > 2). Finally, we ran 10 independent analyses of MCMC with a length chain of 10 000 000, sampled each 500th iteration, and we discarded 10% of initial samples given its low estimated likelihood in the first iterations. Frequencies of each base were estimated empirically, we set the HKY + G model, and a substitution rate (µ) of 0.01876 substitutions/site/Myr [G. platei and G. maculatus.Demographic analyses were carried out using three approaches: (i) the neutrality tests Tajima's D  and Fu'sugh time  for eachugh time . Each anugh time . Before ugh time . The besugh time ,36,38. Wugh time . The besugh time  for bothsite/Myr ,40. The 2.5.G. platei, sampling 72 mtDNA sequences of 786 bp, and assuming a population size of 10 000 individuals. We varied the mutation rate randomly, selecting values between 0.00000001 and 0.000001 from a uniform distribution. For each of the simulations, we calculated the number of segregating sites (S) with a command line version of the software ARLEQUIN. We then used this simulated data to select a mutation rate that was able to produce the empirical S value of G. platei (S = 34). Instead of using the value suggested by the linear regression between mutation rate and S, we used a more conservative approach by using the 5% lower quantile regression to select the highest possible mutation rate value that would still produce at least 5% of simulated S values equal or higher than 34 . We then used this mutation rate to simulate 10 000 replicates of a population of G. maculatus, using the same population size as for G. platei, but sampling 217 sequences of 505 bp . We calculated for every simulated replicate the number of segregating sites (S), the number of haplotypes (K), and the average number of nucleotide differences (π) using ARLEQUIN. Finally, we compared the distribution of these simulated summary statistics with the empirical values obtained for G. maculatus to estimate the proportion of simulations that are equal to or higher than the empirical values. Failing to reproduce the observed patterns of G. maculatus in at least 5% of the simulations would be strong evidence that differences between these two species are not due to coalescent stochasticity.We conducted genetic simulations to test whether any dissimilarity found between the genetic patterns of the two studied species are likely under a purely coalescent stochastic process, rather than other biological or demographic explanations. For this, we used the empirical data from one species to parametrize a model of a single population, and then we used that model to see whether the genetic patterns of the other species can be reproduced at some degree. If patterns are reproducible in at least 5% of the simulations, we then would infer that the genetic patterns between species are not statistically different. All genetic simulations were conducted with SIMCOAL 2.0 . We firs3.3.1.G. platei than in the diadromous G. maculatus. The genetic diversity indexes for G. platei showed a total of 44 haplotypes (K), 56 segregating sites, a haplotypic diversity (Hd) of 0.96 and a value of nucleotide diversity (π) of 0.0033 . Conversely, for G. maculatus, all pairwise FST values showed significant differences, with FST values ranging from 0.031 to 0.567 (G. platei showed significant differences among groups only for the comparison (Z1 + Z2) (Z3 + Z4), with 7.42% of variance explained  and a positive value for G. maculatus (D = 0.886), with both estimations being non-significant (p > 0.05). Conversely, for Fu's Fs, estimations for both species were negative and significant . Mismatch analyses showed a unimodal pattern for G. platei, consistent with scenarios of demographic instability and recent bottlenecks, whereas G. maculatus showed a strongly multimodal pattern, frequently associated with a more stable demographic history .Demographic analyses based on the neutrality test Tajima's D showed a negative value for G. platei showed some relatively abundant and geographically widespread haplotypes connected with several singletons separated by few mutational steps (a). For G. maculatus, on the contrary, three highly divergent haplogroups were identified (b). The haplogroup I shows a star-like network, mainly represented by individuals from zone 3 and zone 4 and a few individuals from zone 1, and it is separated by 25 mutational steps from the haplogroup II (b). The haplogroup II shows one main haplotype shared mainly by zones 2 and 3, with a few individuals from zone 1. This haplogroup is separated by 23 mutational steps from the haplogroup III (a). The haplogroup III shows many low-frequency haplotypes from all zones, although zones 1 and 2 were represented with the highest frequency. Only a few haplotypes were shared between two or more zones probably because of the low frequency of haplotypes (b).Regarding haplotype networks, species produced highly contrasting patterns. Genealogical relationships for al steps a. For G.entified b. The hagroup II b. The haroup III a. The haplotypes b.FigureG. maculatus showed an increase in Ne that took place about 300 000 years ago (a). Conversely, for G. platei, Ne showed a constant population size over time and much lower Ne values compared with G. maculatus (b).The demographic reconstruction via the skyline plot analysis for ears ago a. Converaculatus b.Figure3.3.G. platei and G. maculatus were likely to arise due to coalescent stochasticity as the sole explanation. Failure to reject the null hypothesis would preclude the need for invoking deterministic explanations such as differences in life-history traits . Our results show that the difference in genetic diversity patterns observed in G. platei and G. maculatus are greater than those expected under coalescent stochasticity only (a–c). The number of segregating sites simulated for the hypothetical species with G. platei characteristics ranged from 17 to 102 with a mean of 44.76, while the empirical value for G. maculatus was 116 (a). In the case of the number of haplotypes, the simulated values ranged from 13 to 46 with a mean of 26.8, whereas the observed value for G. maculatus was 152, a value three times larger than the mean of the simulated values (b). Finally, for the average number of nucleotide differences, the simulated values ranged from 0.6 to 39.9, whereas the empirical value for G. maculatus was 25.1 (c). Here, only 20 out of 10 000 values were larger than the empirical value for G. maculatus (25.1), indicating a very low probability of obtaining values equal to or larger than the empirical value for G. maculatus . These results indicate that the strong genetic differences observed between species are not due to coalescent stochasticity.We conducted genetic simulations to test whether the genetic patterns found for both ity only a–c. The  was 116 a. In thed values b. Finallwas 25.1 c. Here, 4.Galaxias maculatus and Galaxias platei in the Valdivia River basin. G. maculatus was characterized by populations displaying generally high genetic diversity (i.e. K and Hd), higher genetic structure, and increase in Ne throughout time. On the other hand, G. platei appears to have not being seriously affected by environmental changes of the past, presenting lower but constant population size. Variation in genetic patterns across co-distributed species could be due to differences in biological traits  or to stochastic processes such as mutational or coalescent stochasticity [G. maculatus and G. platei.We found strong differences in the patterns of genetic diversity and structure between asticity . Althoug4.1.G. maculatus and its tolerance to the marine environment could have provided the species with a good chance to survive glacial periods in lower freshwater reaches of the river. By contrast, G. platei may have had to face a harsher environment, with more difficulties to maintain stable populations during colder periods due to, perhaps, its lack of active migratory behaviour [G. maculatus might allow dispersal across larger distributional areas, connecting more distant populations, escaping local extinctions and maintaining larger population sizes , reducing the risk of local extinction, albeit with lower genetic diversity. Alternatively, differences in genetic diversity could also be influenced, at least in part, by the different placement of the Valdivia basin within the overall distribution of both species. For G. maculatus, the Valdivia basin is placed in a more intermediate position of its distribution, giving the chance for a greater genetic interchange with neighbouring basins from north and south. By contrast, the G. platei population from the Valdivia basin is at the north margin of the species distribution, allowing genetic interchange only with southern populations or, via river reversals, from Atlantic basins [Idiosyncratic responses in terms of genetic diversity have been observed in several other studies, highlighting the relevance of biological attributes of each taxon in predicting microevolutionary responses ,43,44. Tehaviour . These bpulation ). Conseqronments ,23 may hc basins .Ne and its low value found for G. platei, based on the Bayesian skyline plot results, is consistent with the idea of a persistent and strong drift effect driven by survival in less suitable environments. It is not clear if the current populations of G. platei from the glaciated range have an origin in downstream refuge areas either in the Pacific or Atlantic basins, or could represent remnants from populations that persisted within the glacier range. Although evidence of the latter have been found for some taxa like crustaceans [et al. [G. platei. For G. maculatus, the finding of significant genetic structure and the presence of more than one lineage may violate the panmixia assumption of the Bayesian skyline plot analysis. Hence, Bayesian skyline plot results for G. maculatus should be interpreted with caution.The pattern of little change in staceans , amphibistaceans , fish [4staceans , beetlesstaceans   barriers. One possibility could be that genetic structure is being driven by population differences in life-history traits (e.g. diadromous versus resident populations). Campos [G. maculatus from the lower Valdivia River basin, but little had been discussed of populations with other strategies in the basin. However, Górski et al. [G. maculatus across several basins including the Valdivia River basin, showed evidence of the existence of upstream populations completing their life cycle entirely in the freshwater environment [a). This spatial variation in life-cycle strategy has been observed in G. maculatus from other areas (like in some Argentinean basins), where the species can exhibit landlocked populations in interior lakes [In addition to genetic diversity, . Campos  highlighi et al. , who anaironment  and suppironment . Similarironment . Differior lakes ,53,54.Galaxias species have been documented in New Zealand [Molecular structure driven by variation in migratory strategies have been documented in other fish species, suggesting this process can play a key role in driving genetic structure and ecological speciation –58. In a Zealand  as well  Zealand .G. platei [G. maculatus [Alternatively, deep divergences between currently sympatric haplogroups could also be explained by long-term isolation in the past followed by a relatively recent secondary contact. Given the glacial history of Southern Chile, this could be possible if different populations were isolated in different refugia during glaciations, which would later join the Valdivia River basin from these different origins. However, this explanation would require some level of current reproductive isolation between populations in order to preserve the integrity of different haplogroups within a single basin; otherwise, as a panmictic population, its lineages would be progressively sorted by genetic drift over long periods of time. Furthermore, the relatively high genetic structure observed between high and low areas of the basin supports the idea that these populations possess ecological differences that limit gene flow. Nevertheless, the historical isolation and secondary contact counts with evidence in previous studies. Geomorphological work  have evi. platei  and G. maculatus  and suggG. platei from other basins [et al. [G. maculatus like the one studied by González-Wevar et al. [G. maculatus is far higher and warranting a more comprehensive study.The potential confluence of individuals from distinct lineages and geographical origins to reproduce in a single basin (philopatry of multiple lineages) could also explain the presence of multiple lineages in the Valdivia River basin, as it has been shown for r basins . Delgado [et al.  have recr et al.  from offr et al.  to our sG. maculatus and G. platei are consistent with their biological differences. The ability of G. maculatus to evolve different migratory strategies  may have provided the ability to escape glacial impacts in refugia near the coast, allowing the species to maintain larger effective population size, which in turn, preserved high levels of genetic diversity avoiding the eroding effect of strong genetic drift. However, whether these migratory strategies have the potential for promoting and maintaining multiple lineages within the Valdivia River basin is an aspect that remains to be further investigated and is beyond the scope of the present study. The absence of an active migratory behaviour in G. platei, could explain its lower population size and thus, lower genetic diversity, and its adaptations to cold environments could additionally explain the persistence of the species in a highly impacted basin during glaciations. Our work shows how biological differences between species cannot only impact on their current patterns of genetic diversity but also influence different evolutionary responses to historical events.In summary, the microevolutionary responses that can be displayed by fish when faced with environmental and historical factors are species specific, given the biological singularity of each taxon . We show


Many transport planners consider urban population density to be a significant determinant of travel behaviour. Much of the evidence for this comes from research in low-density, high-income settings. The 2011 Census of India reported mode of travel to work and distance for the first time. We have used these data to investigate the effect of urban density on commute travel patterns at city-level for Indian cities. In addition, we investigated the relationship between travel behaviour and other city-level variables. Using regression, we found almost no independent effect of density on the mode share of walk, cycle, motorised two-wheelers, cars and public transport, after controlling for population and income levels for the cities. Further, it appears that once density levels are greater than ~80 persons per hectare (pph), other factors become more important in determining travel patterns in cities. This evidence has significant implications for urban planning and transport policy in Indian cities and for many other low- and middle-income cities where average density tends to be higher than ~80 pph. For these cities, growth in the use of sustainable transport may not depend on further densification of already dense cities, but on details of how neighbourhoods and streets are designed. •Census in India reported travel mode and distance of work trips for the first time.•Most Indian cities have higher density than the densest cities in the high-income countries.•Large changes in population density have only negligent effect on travel patterns.•Population size and city-level income are more important determinants of travel behavior. In the past 20 years, the area of urban cover in the top 100 Indian cities has increased about 2.5 times  of metropolitan cities of India to enable densification. Interestingly, the recommendation was made solely on the basis of differences in the FAR policy of Indian cities and that of various western counterparts, while no attempt was made to compare the density expressed as persons per unit area . GovernmCriticism of such policies has revolved around the issue of whether to interpret density as a correlating variable or a causal factor affecting travel behaviour . It is hStudies have reported many other factors that impact travel behaviour. Metropolitan size (in terms of population) has been found to be associated positively with commuting distance. As cities become larger, there are many more varieties of specialised services and goods, and therefore, individuals are likely to travel longer in order to access jobs suited to their abilities. As cities grow in population they also become prosperous. With more prosperity, individuals are more likely to afford expensive modes of transport, and to live farther from their work location in order to live in larger homes .Most studies regarding travel behaviour and its linkages with the built environment have been based in the high-income countries, particularly in the USA . Most ofMoreover, current car ownership in India is 15 per 1000 persons , while tIn India, such research has been difficult due to the lack of publicly available data, such as national-level or city-level household travel surveys or census-based travel to work data. Travel surveys have been conducted in many cities in India to prepare urban mobility plans, . Howevera)To develop a database of Indian cities with harmonised set of variables on built environment, demographic and socioeconomic characteristics, and travel behaviourb)To study the association of travel behaviour and population density at the city level, and to investigate the mediating effect of other city-level correlatesc)To put the relationship between density and travel behaviour in Indian cities within an international contextIn this paper we aim to achieve the following objectives:22.1The 2011 Census of India reported mode of travel to work and travel distance . These dIn this paper, our objective is to understand travel to work and its various correlates at the city level. In the context of the urban travel patterns, it is important to analyse data at the city level since a city is largely a contiguous land mass, while a district on the other hand consists of fragmented urban areas. As a result, parameters such as the population of a district or its size are not relatable to a small town within it. To circumvent this problem, we made some assumptions. To approximate city-level data from that of the whole of district, we selected those UA that contribute at least 75% of urban population to their corresponding districts. This ensures that the urban data at district level is representative of the selected cities. Some UA include cities of more than one district. For those cases, we used the combination of all the constituting districts to calculate that data of district corresponding to those UA. A total of 72 UA were selected out of which 62 contributed >80% of the population to their corresponding district. In the subsequent text, the selected UA will be referred to as cities.2.22). For our analysis, we assumed that the density of urban area estimated at the district level represents density at the city level. As a result, for some of the cities, density may be underestimated since it includes all the urban areas within the district.We estimated population density of the cities using district-level urban built-up area reported by the National Remote Sensing Centre through their web portal, Bhuvan . The NRS2, using district-level urban population density and city-level population. For GDP, we used district-specific data reported for year 2004–05 from the web portal of Open Government Data Platform India  walk, (2) cycle, (3) moped/scooter/motorcycle, (4) car, (5) tempo/autorickshaw/taxi, (6) bus, (7) train, (8) water transport, and (9) any other. We refer to Category 3 as motorised two wheelers (MTW), and category 5, which consists of intermediate public transport, or para-transit modes, common in most Indian cities  have density higher than 150 pph, 27 (38%) between 100 and 150 pph, 17 (24%) between 70 and 100 pph, 7 between 50 and 70 pph and only 2 below 50 pph. All the 45 cities with population >1 million have density >90 pph with an exception of 3 cities, and the average density of the 10 largest cities is 200 pph. Thus, almost all Indian cities have higher density than the densest cities in the west. For instance, in year 2000, London (population: 6.1 million) had a density of 54 pph, Paris (9.6 million) had 65 pph, and New York (19 million) had 26 pph .On an average, less than one-tenth of all the households own at least one car, <30% own an MTW, and >40% own a cycle. In terms of the commute patterns, half of all the trips are either walked or cycled, one-fifth of all the trips use some form of public transport—IPT, bus, or train, a quarter of all the trips use MTW, and 5% trips use cars. >60% of all the trips are shorter than 5 km, and less than one-third of all trips are longer than 10 km. Among the western settings, 86% of the work trips in the US 2009; , 64% in 3.2Given that there are multiple variables corresponding to each city, it is important to understand the interrelationships between the variables. For this, we calculated the Pearson correlation between different variables, as presented in GDP per capita also has a positive correlation with all the variables population is correlated with. More importantly, GDP per capita shows high correlation with the car ownership (0.78) and its mode share (0.81), mode share of bus, and with distance categories 11–20 km and 30–50 km. Higher GDP is also associated with lower share of walking and cycling in commuting. Density has a positive correlation with car ownership, mode share of PT, and trips longer than 10 km. More significantly, density is negatively correlated with the ownership of cycle and its mode share, mode share of MTW, and distance category 2–5 km. The positive association between longer distance commute and population density is counterintuitive, however, this is probably because higher density is linked with larger city size.There is correlation between mode shares of different vehicle types with the specific distance categories. Walking has the highest positive correlation (0.85) with distance <1 km, cycling and MTW with distance of 2–5 km , car and bus with distance category of 11–20 km , and train with distance category 30–50 km (0.68). This highlights a strong link between type of travel modes and travel distance, with walking catering to the shortest trips, cycling and MTW to the mid-length trips, car and PT to the long trips, and train to the longest of the trips. This is also the reason that the bus mode share is negatively correlated with that of the cycle (−0.76), MTW (−0.44) and IPT share (−0.37), and positively related to car mode share (0.25). This indicates that as cities become larger in population, or have longer commute distance, the two are highly correlated as discussed above, travel modes catering to the longer travel distance attain a higher share. Among public modes, the share of IPT reduces, and that of the bus increases, and among private modes, share of cycle and MTW reduces and that of the car increases.3.33.43.52), the share of short trips remains almost constant, though there are a few much smaller cities with share of short trips similar to that of the large cities.3.62 values for a linear relationship. What is noteworthy is the very wide scatter of mode shares at all population density levels except for the cities above 250–300 pph, where the number of cities itself is limited. For instance, for the density of 120–125 pph, walk share varies from 16% to 44%, cycle from 9% to 21%, PT from 9% to 33%, MTW from 14% to 40%, and car from 1% to 15%. In case of walk and car, almost no variance is explained by the density.The associations of the shares of five travel modes with density are also shown as scatter plots in 2 for the set of cities excluding the outliers, referred to as <350 pph in the plots. The resulting linear plots and corresponding R2 values have been shown in the same plots for all the five travel modes  of walk, cycle, PT, MTW, and car. In order to assess an independent effect of the density, we included four explanatory variables— population of the city , and ownerships of cycle, MTW, and car. The correlation of GDP per capita is much higher with car ownership than with 2 W ownership , while PT with longer travel distance see . Thus ciThe coefficient of population density for MTW share is an order of magnitude higher than the coefficient for car share. To understand the difference in the usage of two modes, we present the scatter plots of their mode shares with respect to their corresponding ownership levels . The sta−4 or less), while the statistical significance changed for some , the travel distance increases, and the trips travelled by walk, cycle and MTW reduce and those by car, bus, and train increase. We found that each travel mode is associated with a specific range of travel distance— car, bus and train with long trips (>10 km), and walk, cycle and MTW with short trips (<10 km). Therefore, an increase in overall PT share as cities become larger (or more populated) is because of a need to travel in motorised modes as trips become longer. Since population and density are positively correlated, increase in PT share is also positively correlated with density. Therefore, density in this case seems a facilitator for a feasible public transport system, rather than the cause for higher PT usage.A major part of the discourse relating density and travel behaviour has been contributed through an international perspective rather than within-country differences among the cities, and the former has been pioneered by Similar to 2 values are much lower (<0.3) than those reported by 2 of 0.22, using the best fit for the data of Australian, American, and Canadian cities reported by 2 of best fit increases to 0.85  can also be used to explain the differences among countries. One of the major problems with the power function presented in 5We found no evidence to suggest that an increase in overall urban density has any effect on travel behaviour in cities in India, after accounting for population and income level. It appears that once density levels are greater than ~80 persons per hectare, car use is likely to be low and other factors determine travel mode choices than density. Our findings have significant implications in terms of land-use and transport policies in most cities around the world. The further densification of cities with >80 pph is not likely to result in better usage of public transportation modes. Given the large variation of travel behaviour for a given level of population density, future research should focus on understanding details of street and urban layout that result in large variations in travel behaviour. More detailed analysis will be feasible as Census publishes data at city or ward level in the near future. A major limitation of this study is that the conclusions are based on city-level aggregate variables, such as overall density as well as commute travel pattern.


G) among damselfly populations situated along a latitudinal gradient in Europe. Damselflies were reared at photoperiod treatments that simulated the seasonal time constraints experienced at northern (strong constraints) and southern (relaxed constraints) latitudes. This allowed us to partition the effects of (1) latitude, (2) photoperiod, and (3) environmental novelty on G and its putative alignment with adaptive plasticity and divergence. Environmental novelty and latitude did not affect G, but photoperiod did. Photoperiod increased evolvability in the direction of observed adaptive divergence and developmental plasticity when G was assessed under strong seasonal time constraints at northern (relative to southern) photoperiod. Because selection and adaptation under time constraints is well understood in Lestes damselflies, our results suggest that natural selection can shape the alignment between divergence, plasticity, and evolvability.Phenotypic plasticity can either hinder or promote adaptation to novel environments. Recent studies that have quantified alignments between plasticity, genetic variation, and divergence propose that such alignments may reflect constraints that bias future evolutionary trajectories. Here, we emphasize that such alignments may themselves be a result of natural selection and do not necessarily indicate constraints on adaptation. We estimated developmental plasticity and broad sense genetic covariance matrices ( Whether phenotypic plasticity hinders or facilitates adaptation in new environments has been debated, and recently interest in this issue has intensified due to the increasing need to understand species’ responses to global environmental change Whitlock . One argAgainst this background, a prominent debate has surfaced about causation in the evolutionary process and why such an alignment might be expected in the first place variances are aligned. Making this type of analysis among multiple populations, locally adapted to the alternative environments for which multivariate selection is understood, offers the opportunity to test the prediction that the alignment between divergence, plasticity, and G is related to the differences in natural selection imposed by the respective environments. Here, we applied this approach in the damselfly Lestes sponsa (Odonata: Zygoptera), along a latitudinal gradient in Europe, to explore how multivariate selection mediated via seasonal time constraints affects the alignment between developmental plasticity, G, and divergence.Correlated genetic and plastic responses to selection need to be studied using a multivariate approach, which can be achieved by examining the additive genetic variance and covariance between traits as summarized in a G‐matrix  follow these general patterns and are likely adaptive, with evidence for local adaptation in the use of photoperiodic cues  phenotypic plasticity in response to photoperiod, (2) population divergence in mean trait values, and (3) the broad‐sense genetic covariance matrix (G) measured at both photoperiods. This experimental design allowed us to assess effects of latitude (southern vs. northern origin) and photoperiod (northern photoperiod that induces strong time constraints vs. a more southern photoperiod with its more relaxed time constraints) on G and its potential alignment with developmental plasticity and adaptive divergence. We performed three main tests aimed at determining evolutionary causality:In the damselfly G, plasticity, and divergence, which would suggest a putative role for developmental bias and constraints in our study system. However, evidence against such a constraint includes previous work showing that plasticity in response to photoperiod and divergence along latitude in L. sponsa both reflect adaptive responses to seasonal time constraints  affected the alignment between plasticity and G. As multivariate selection on the measured life history phenotypes is known to be induced by seasonal time constraints, an effect of photoperiod on the alignment would thus suggest natural selection as its causal driver. An effect of photoperiod on the alignment between G, plasticity, and divergence is, however, not informative as to whether natural selection shaped the alignment over long periods of time during the evolutionary history of L. sponsa, or whether the alignment has been shaped over a more recent and shorter time scale.Because an alignment between plasticity and Our third test was therefore to examine whether differences in contemporary selection among our northern and southern populations had shaped the alignment. We did this by exploring if the effect of photoperiod on the alignment differed between the two types of populations. Because northern and southern populations differ in their recent evolutionary history, signified by stronger seasonal time constraints and observed concomitant responses to photoperiod in northern populations (Sniegula and Johansson. Lestes sponsa (Hansemann 1823). In Europe, L. sponsa’s latitudinal distribution extends from northern Spain , central Sweden , and north‐western Poland  on 1 August, 23‐27 July and 8‐14 August, and 29‐30 July, respectively. Although Poland is not in southern Europe, we refer to these populations as north, central, and south  for simplicity. We have no data about the level of genetic divergence among these populations, but development time, growth rate, and additive genetic variance for growth rate and egg development time differ significantly between populations of L. sponsa that span the latitudinal range used in our study  were reared individually  until emergence, after which individuals were weighed and preserved in 95% ethanol. We measured eight traits for three to six larvae from each clutch: larval development time between hatching and emergence, adult body mass at emergence, head width, thorax length, thorax width, abdomen length, tibia length (third leg on the right side), and wing length (posterior right wing). Exact landmarks, used to characterize the phenotype (see Supplementary Information 3), were measured from the first larvae that emerged from each clutch, with one clutch of eggs typically consisting of 50‐100 eggs.In 2018, we collected eggs from L. sponsa may be half‐sibs  metric size, (2) (linearized) body mass1/3, (3) wing length, and (4) development time. As all traits were approximately normally distributed, we assumed Gaussian response variables in all analyses.All traits were first mean‐standardized  estimation and the Kenward‐Rodgers correction for the denominator degrees of freedom using lme4  significant genetic differentiation in the four traits, (2) an effect of photoperiod treatment on trait expression , and (3) significant genetic differentiation in the use and interpretation of photoperiod to cue development . We used mixed models for each trait excluding the two populations from central latitude (that were reared only at their own native photoperiod). The data from the central latitude populations were thus used only to examine if there was a latitudinal cline in expressed life history traits: populations from two latitudes will  always differ in one way or another. These models thus compared the populations from northern and southern latitude that had been reared in the common garden experiment including both the northern (long) and southern (short) photoperiods, and the changes in temperature. Latitude, photoperiod treatment, and sex were included as fully crossed fixed effects, and population crossed with photoperiod treatment was included as a random effect to ensure the correct level of replication for the effects of latitude and the latitude:photoperiod interaction. The family identity crossed with offspring sex was included as an additional random effect (model specification and output in Supplementary Information 5).G, for each combination of latitude (northern and southern latitude) and photoperiod treatment (northern and southern day length), we fitted family identity as a latitude‐ and photoperiod‐specific random effect, with broad sense genetic (co)variances approximated as twice the (co)variance among full‐sibs. To retrieve corresponding trait heritabilities, the residual covariance matrix was fitted for latitude and photoperiod treatment. There was little indication of sex‐specific genetic variance in any of our single‐trait analyses (Supplementary Information 5). Likewise, there was no statistical support for population‐specific genetic covariance structure between populations within each latitude (results not shown). Hence, we estimated the four G‐matrices by averaging across sexes and populations within each latitude. We used weak and unbiased priors for the covariance matrices as recommended by Hadfield  and Bayesian 95% credible intervals for all matrix parameters as well as to calculate P‐values for all matrix comparisons (see below).To compare G‐matrices, we used multivariate response models including the four traits in a Bayesian mixed modeling framework incorporating Markov Chain Monte Carlo simulations using the MCMCglmm package Hadfield  in R (moHadfield  (SupplemG). We then inspected the orientation of G by performing eigen‐analysis of G and its corresponding genetic correlation matrix, R . We explored how the first eigen‐vector, explaining most of the variation in G and R (maxG and maxR from here on), loaded on the four original traits, giving a representation of the orientation of the major axis of genetic variation and the genetic correlation structure across the four latitude:photoperiod combinations. We also explored how much of the total variance in G and R was explained by maxG and maxR, giving a representation of matrix dimensionality. We quantified (dis)similarity in the orientation of maxG and maxR, respectively, across the four groups by calculating pairwise vector correlations:g1 and g2 are maxG or maxR for group 1 and group 2, |g1| and |g2| are the corresponding vector norms, and rg1,g2 is the correlation between the vectors, ranging from 0  to 1 .First we compared trait‐specific genetic variation and heritability across the four latitude:photoperiod combinations. We then compared overall genetic variation by summing the variance across all four traits  to 0 . Second, following Hansen and Houle (e) of G along the vector of multivariate developmental plasticityDP’ is the transpose of DP. DPe thus gives the expected response to selection along the plasticity vector for a unit strength of selection acting in the same multivariate direction as plasticity. We calculated posterior modes and 95% credible limits for all these metrics based on the Bayesian posterior estimates. We performed two‐sided hypotheses tests for the effect of latitude . As the alignment between maxG and the vector of developmental plasticity essentially is a fraction  for each stored iteration. We then created a random unstructured G‐matrix by sampling the same number of full‐sib families as in our original data  from a multivariate normal distribution with the same amount of total genetic variance as for the estimated matrix, but assuming homogeneous trait variances and covariances = 0. We then calculated pairwise evolvabilities for the empirical matrix and the resulting unstructured random matrix for each of the 1000 stored iterations , with southern populations generally entering metamorphosis at an older age and larger size , with southern populations being larger. All four traits showed strong plastic responses to photoperiod treatment, with a northern photoperiod generally rendering the faster development and smaller body size that is expected under perceived time constraints . There was a significant latitude‐by‐photoperiod interaction for development time  and a marginally nonsignificant interaction for body mass , signifying genetic divergence along the direction of multivariate plasticity and local adaptation in the use of photoperiodic cues to program development (Supplementary Information 5). Consistent with the putative effect of strong time constraints, the northern populations, evolving under stronger seasonal time constraints at northern latitude, showed a stronger response to photoperiod  and photoperiod treatments (northern vs. southern day length), there was a tendency for an increase in broad sense genetic variance and heritability for development time in southern populations raised at the novel northern photoperiod. However, there was no concomitant increase in the northern populations when they were reared at a southern photoperiod , suggesting little evidence for revealed “cryptic” genetic variation in a novel photoperiod (summary in Table When comparing the variance‐standardized broad sense genetic (co)variance matrix, iod Fig. . MoreoveG and R, we found pronounced effects of photoperiod treatment on the trait loadings of maxG and maxR . However, the photoperiod treatment had a large influence on the alignment between maxG and plasticity; development in the northern photoperiod (increased time constraints) caused genetic covariances to align with the direction of multivariate developmental plasticity, and this increase in alignment (relative to southern photoperiod and relaxed time constraints) was significant , but not for the southern photoperiod  , although we note that the power of our analysis would only pick up very pronounced differences as being statistically significant. In summary, developmental plasticity and evolvability were aligned only when G was measured at the photoperiod that signals strong multivariate selection via seasonal time constraints. However, as the alignment in northern and southern populations showed a similar response to photoperiod, we did not find evidence to suggest that any relatively recent change in selection at the different latitudes has affected the alignment.Latitudinal origin had no significant effect on alignment or evolvability (27) Fig. . We founL. sponsa uncovered that G was aligned with the direction of (multivariate) adaptive developmental plasticity and divergence in response to seasonal time constraints, only when G was estimated in the photoperiodic conditions that signal seasonal time stress. This suggests that the observed alignment is not a result of pure constraints, but rather implies that changes in natural selection imposed by seasonality have been instrumental in shaping the observed alignment between G and phenotypic plasticity. Indeed, insects that experience seasonal time constraints pay large fitness costs of suboptimal growth strategies due to intensified costs of postponing age at maturity and are therefore expected to show (i) fixed growth maximization strategies  expressed phenotypes are moved closer toward the new trait optima, and (2) there is abundant genetic variation for the phenotypes in the direction of increased fitness, leaving opportunity for subsequent genetic assimilation ies Fig. , as showon Lande . Howeveron Lande  even if G, plasticity, and divergence. We note, however, that statistical power to identify more nuanced differences in the alignment was limited in our study, as often is the case for quantitative genetic designs. The result suggests that past selection, rather than contemporary selection following the evolutionary split of our northern and southern populations during the last ice age, has caused the observed alignment. Our study thus implies that the observed plasticity in trait means and in G itself is ancestral, but leaves open the question of how much developmental constraints may bias future adaptive potential to seasonality under climate change. Even though we did find that the northern populations have evolved a stronger plastic response in trait means to photoperiod cues than southern populations, southern populations also show strong responses to photoperiod  from those stemming from photoperiod per se. However, we found no general support for a release of cryptic variation in the studied life history traits, nor an effect of environmental novelty on G and its alignment with plasticity. In a meta‐analysis by Wood and Brodie  by a change in the environment. Wood and Brodie , even though the applied experimental day lengths are not experienced currently in the natural populations.A population that enters a novel environment may exhibit an increase in genetic variation via release of “cryptic” genetic variation  genetic divergence in the studied phenotypes.The evolutionary causality of alignments between phenotypic plasticity and additive genetic variance is of fundamental importance for realized adaptive potentials in changing environments. Here, we have shown that the alignment between FJ, PCW, and DB designed the study. FJ and SS collected the data. DB analyzed the data. FJ, PCW, SS, and DB wrote this article.The authors declare no conflict of interest.Associate Editor: M. R. WalshHandling Editor: D. W. HallTable S1: Photoperiod and temperature used for each latitude treatment in the experiment simulating the progress of the season at each latitude.Figure S1: Morphological measurements on Lestes sponsa: (a) head width, (b) tibia length, (c) wing length, (d) abdomen length, (e) thorax width, (f) thorax width.Table S7a: Bayesian posterior estimates of mean‐standardized broad sense genetic variance and heritability for each of the four traits.Table S7b: Best Bayesian posterior estimates of Gmax and Rmax.Click here for additional data file.


The Arab population in Israel is a minority ethnic group with its own distinct cultural subgroups. Minority populations are known to underutilize genetic tests and counseling services, thereby undermining the effectiveness of these services among such populations. However, the general and culture-specific reasons for this underutilization are not well defined. Moreover, Arab populations and their key cultural-religious subsets  do not reside exclusively in Israel, but are rather found as a minority group in many European and North American countries. Therefore, focusing on the Arab population in Israel allows for the examination of attitudes regarding genetic testing and counseling among this globally important ethnic minority population.We used a qualitative research method, employing individual interviews with 18 women of childbearing age from three religious subgroups  who reside in the Acre district, along with focus group discussions with healthcare providers  working in the same geographical district.A general lack of knowledge regarding the goals and practice of genetic counseling resulting in negative preconceptions of genetic testing was identified amongst all counselees. Counselors’ objective of respecting patient autonomy in decision-making, together with counselees’ misunderstanding of genetic risk data, caused uncertainty, frustration, and distrust. In addition, certain interesting variations were found between the different religious subgroups regarding their attitudes to genetic counseling.The study highlights the miscommunications between HCPs, particularly counselors from the majority ethno-cultural group, and counselees from a minority ethno-cultural group. The need for nuanced understanding of the complex perspectives of minority ethno-cultural groups is also emphasized. Such an understanding may enhance the effectiveness of genetic testing and counseling among the Arab minority group while also genuinely empowering the personal autonomy of counselees from this minority group in Israel and other countries. In the last decade, significant developments in genetic technology have expanded the breadth of potential prenatal diagnoses for numerous genetic conditions and prenatal genetic testing. Consequently, genetic counseling has become increasingly intricate for both genetic counselees and healthcare providers (HCPs), such as genetic counselors. HCPs working in the field of human genetics and specifically genetic counselors are expected to communicate complex genetic information while operating under an obligation for patient autonomy to help patients make informed choices free of the influence of the HCP’s value system –3.This approach acknowledges people’s tendency to make decisions based on their social and personal backgrounds—namely, decisions affected by personal and cultural preferences, not only by medical considerations , 5. At town informed choices about genetic testing. In fact, the emphasis on counselees’ autonomy follows current standard guidelines for genetic counseling as they were formulated by the Genetic Counseling Definition Task Force of the National Society of Genetic Counselors (NSGC) , and all participants signed an informed consent form prior to participating in the study.For the individual counselee interviews, our study participants consisted of 18 women  and expressed interest in the project.The seven genetic counselors who participated in the focus group worked in genetic departments in four hospitals in Northern Israel. The inclusion criteria for this focus group were counselors who worked in the four hospitals’ genetic departments and who had at least 5 years of genetic counseling experience for varied ethno-cultural groups, including the Arab ethnic minority.Interviews lasting 1–1.5 h were conducted at mother and infant clinics in separate rooms to ensure privacy. They were carried out in Hebrew and Arabic and utilized a semi-structured interview strategy. Demographic data were collected via a standard questionnaire prior to the start of each interview. The interview utilized fictional vignettes regarding birth defects, prenatal diagnoses, and termination-of-pregnancy options to elicit the interviewees’ viewpoints regarding genetic counseling (see Construction of the Interview Guide). For the risk communication analysis, two hypothetical scenarios were presented to the participants. First, participants were asked to place a mark anywhere along a horizontal line (from 0 to 100% chance) to indicate their perceived risk regarding amniocentesis . For theCongenital heart defect during pregnancy as a trigger for discussion of risk assessment and prenatal diagnosisFamily history of developmental impairment as a trigger to the question of etiology for genetic counseling and risk assessments for birth defectsFamily history of hereditary disease as another trigger for the issue of prenatal diagnosis and the possibility of termination of pregnancyThe first author, an experienced genetic counselor, drafted several common scenarios revolving around reasons for referral to genetic counseling and relating to risk assessments, possibilities of prenatal diagnosis, and possibilities of the termination of pregnancy. These scenarios were further discussed and enhanced with the last author, who is the head of the genetic counseling unit at one of the participating hospitals. Ultimately, three different scenarios were chosen:Focus groups were conducted, as previously reported, in a group dynamic to stimulate discussion, gain insights, and generate ideas to pursue a topic in greater depth . The priAll interviews, both individual and with the focus groups, were audio-recorded by the first author and transcribed verbatim for subsequent analysis by a professional transcription service (Mao’f Inc.). Grounded theory content analysis was performed on the transcribed text to identify key themes that emerged from the participants’ unique perspectives , 41. TheThe discussions regarding the analysis of the interviews included a 4-step process. (1) The first author conducted an initial coding of the data for either one interview at a time  or three interviews . This coding was done using a table that indicated the themes and subthemes that have emerged and highlighting the relevant text in the interview from which these themes and subthemes were derived. (2) The second author read and made written comments pertaining to instances where she had questions regarding the analysis of the first author. (3) Both authors discussed the questions raised by both authors regarding the analysis. In cases where the questions regarding the analysis turned into disputes about how to interpret the text, both authors went back, simultaneously, to the original transcript and examined the larger context from which the particular disputed fragment was taken until reaching an agreement. (4) At the end of their discussion, the first author updated, as necessary, both the interpretation table and an overall codebook table for the interviews so that they would reflect the most updated and agreed-upon coding.Table “And she asked me—if the fetus has a problem/a defect, will you terminate the pregnancy? I told her, of course not. That's my answer ... And then she gave me a paper to sign.”Another Muslim participant said:“Yes, I heard (about genetic counseling), I went once in the second pregnancy, because there was a problem for my daughter in the heart she had ‘Vias di’ [referring to VSD] … I don't know, I didn't like it, just asked me ... (about) the family, who's sick in the family, who's not sick ... That's how they asked me ...”Some participants did not understand the full extent of the risks for birth defects and genetic diseases. More than two-thirds believed that “normal” screening tests implied a healthy pregnancy, rendering further genetic testing and counseling unnecessary, and they did not appreciate the limited focus of screening or that other genetic risks are involved Currently, potential genetic counselees are identified by a primary care physician in the community and referred to a genetic counseling service. Eight of the 18 participants (predominantly Muslim and Druze) stated that they had never heard of genetic counseling Table , 1a. Mosed Table , 1b. Fores Table , 1c.“… N“Everyone who goes to genetic counseling has a 'black stain'… She went for genetic counseling… [which] means that there is something [wrong] with her son.”Another Muslim woman elaborated on the reason for her stance against GC:“I'm against [genetic counseling] ... Because, if they say everything is fine, to her and to the child, and a perfectly healthy child is born, so, a month later—then he is ill, then to throw him away?“The genetic counselor will convince her to abort .... I don't know ... I'm against ... genetics ... I don't know why, I'm against … I want to tell you, I did not go ... [to genetic counseling], I have family, with deafness—my mother's nephew, but I didn't ... go to receive [genetic counseling].”Nearly one-third (5/18) of the participants regarded genetic testing and counseling as unnecessary, although an ultrasound was perceived to be reliable enough  of the participants described negative attitudes toward the use of genetic services due to family members’ negative views “… Me too, you know, it is not only my decision, I have [a] husband, my husband’s parents … it is not only my decision. However, when I was pregnant, and they told me that he (the fetus) has heart anomaly, my husband told me he does not want (the pregnancy) … He wanted to terminate the pregnancy in case of ‘mongoly’ (derogatory slang for Down syndrome). I said I don’t want to terminate… In the end, I was the one to decide. ...” (Muslim participant)“You know, my mom, … she terminated a previous pregnancy. In my brother's pregnancy she refused and he was born naturally. ... My father ... at that time, [he] told her, do not terminate pregnancy, but she did...” (Christian participant)Only two of the 18 participants stated that the father dictated the decision regarding a pregnancy  because ... [it is] not good to terminate a pregnancy (!) It is wrong to end someone's life … Even in our religion (Muslim), it is unacceptable ... haram ... if it was in the beginning, it is possible ... if it, there is a spirit child, and there are all these … then no ...” (Muslim participant)“Abortion, no. We… because in our religion it is forbidden to us. Oh, it is like you killed a person. According to your religion (Jew), your commandments, ‘not to kill,’ here too, no … even a fetus ...” (Druze participant)Only two out of the six Christian women interviewed stated opposition to the termination of a pregnancy due to either conscience or religion  Druze women and half (3/6) of the Muslim women said they opposed the termination of a pregnancy for cultural or religious reasons Table , 5b, oftmayte al ra’as” meaning “water off the head of the fetus.”We next wanted to focus on patient comprehension and perceptions of genetic risk as communicated during counseling sessions  is not scary, is not so threatening, it is when you come with your husband … it is something like a nurse working at the station they are used to ...” (Druze nurse)As for the explanation of risk, counselors described a variety of technical approaches to explaining the statistical data available about genetic risk to motivate counselees to act , we don't have the time and means [professional training] to do so.”Second, the counselor group made extensive references to the difficulty in communication, with language being one specific barrier, although the counselees did not recognize this as an issue  villages there are language and cultural difficulties ...”Nurses were more attuned to differences among their Muslim, Druze, and Christian patients. Their comments often more closely depicted the general themes expressed during the individual counselee interviews  are already 'anti' … unwilling to hear counseling related to invasive tests and so on. … Consulting has become bland; you say what you have to say. ... They no longer have the ability to listen. … It seems to me they come only to sign an attendance card…”Third, counselors tended to overestimate the influence that spouses had on pregnancy decisions Table , particuws Table . These dOur study addresses and highlights three main important themes related to a more nuanced understanding of cultural competence in the context of genetic testing and counseling for minority ethnic and cultural groups. In what follows, we shall elaborate on each of these themes while referring to relevant existing literature and finally connecting them to the issue of autonomy in genetic testing and counseling for minority ethno-cultural groups.Our study has revealed fundamental miscommunications and gaps in the expectations between counselors and counselees, which engenders distrust and frustration with the counseling process on the part of both parties. These miscommunications and gaps between the two groups pertained to the following three main facets: (a) communication approaches, whereby counselors were more focused on the presentation of objective aspects of genetic risks and verbally expressing them in a comprehensible manner while counselees were more expectant of definitive messages accompanied by emotional reassurance and connection with counselors; (b) comprehension and perception of genetic risk counselees, whereby they overestimated the meaning of the cited genetic risks; and (c) estimation of spousal influence, in which counselors expressed an overestimation of the spousal role and influence, especially among Muslim and Druze counselees, compared to counselees’ reference to this issue.Other studies have already shown possible differences in attitudes toward genetic testing between minority groups and the majority group within the same country. For instance, some studies in the USA have noted that Caucasian people and those with Hispanic and Asian origins have more positive views toward genetic tests compared to African-Americans –49. A moHowever, our study is the first to focus on actual gaps between patients  from a minority group and HCPs  from the majority cultural group regarding the process of genetic testing and counseling. One possible reason for this persisting miscommunication is that the genetic counselors were entirely comprised of the Jewish majority group. Consequently, it could be argued that the interaction between counselors and counselees was reflective of the overall majority–minority interaction in the country as a whole, regardless of the fact that the Arab population in the Western Galilee geographic regions are the majority. Indeed, the nurses in our study originated from minority subgroups and, unlike the counselors, spoke Arabic and recognized both the counselor and counselee perspectives on many of the points presented herein—perhaps due to being part of both ethnic worlds. They proved their extreme usefulness as liaisons between the two parties.Our study further stresses the possible negative sentiments among minority ethno-cultural and religious groups in a manner that was not exposed before. For instance, two previous studies in Israel demonstrated the underutilization of genetic counseling services among Arab women compared to the Jewish majority, concluding that the reasons were lower income level, negative attitudes toward genetic counseling, perception of amniocentesis as risky, spousal opposition to the process, and poor accessibility , 28. PreHowever, the current study uncovered aspects of negativity toward genetic counseling among Arab counselees that were not depicted as vividly before. Thus, we observed that even the act of referral to genetic counseling services carries a great risk for ignominy amongst our study populations. In addition, we found a drastic overestimation of risk among the Arab populations regarding amniocentesis as well as the interpretation of relative risk as much higher than objectively specified. As the hypothetical scenarios used in our study closely mimic those that occur in many counseling sessions, a similar lack of communication of essential knowledge regarding genetic risk and the tests involved in genetic counseling is likely common in the community. In fact, previous studies have already described a lack of knowledge and awareness of genetic testing and counseling , 58, 59.At the same time, our study highlights possible complexities in the attitude of counselees from the minority Arab group toward genetic testing and counseling. As noted herein, many of our interviewees exhibited negative attitudes, suspicion, and a lack of understanding regarding genetic counseling and its meaning. Yet they also expressed a desire to be more informed and educated regarding this subject. This tension between the negative attitudes toward genetic counseling and the desire to be more informed about it echoes similar tensions already observed in other studies involving cultural underpinnings in healthcare provision. Therefore, this tension supports the culturally driven lack of communication between genetic counselors and their counselees, as will be further elaborated in the next subsection.We also uncovered important differences between Arab subsets regarding the desire for family involvement and termination of pregnancy—differences largely overlooked by the counselors themselves. These areas of contrast between the varied cultural subsets within the Arab minority group could greatly influence who should be included in genetic counseling discussions and what goals or expectations should be addressed. These issues are currently not being effectively addressed by the counseling services, especially the genetic counselors who appeared to perceive the Arab counselees as one monolithic group. Indeed, other cultural or religious subgroups within populations vary on similar topics . Thus, cown cultural underpinnings and not merely the influence of culture on their patients’ perceptions . Therefore, we argue that, by fostering a more culturally sensitive perspective, HCPs from the majority (Jewish) group, such as the case of the genetic counselors in our study, may find a better balance between mastery and harmony values. Such a balance, in turn, may assist these HCPs in genuinely attaining the goal of genetic testing and counseling through the empowerment of personal autonomy of counselees from minority group(s).In other words, the same value  that is supposed to entail respect for personal autonomy in genetic counseling seems to also underlie the insufficient sensitivity to the cultural complexities underpinning the viewpoint of genetic counselees from the Arab cultural group (or one of its subsets). On the other hand, harmony, the contrasting cultural value, seems to be related to a cultural viewpoint that cherishes cultural awareness as part of efforts to foster As with all qualitative studies, our results are based on interviews with a relatively small number of participants. Thus, generalizability is always a concern. We did not include a non-Arabic interview group, such as the majority Jewish population in Israel, as a control to highlight minority-specific issues. However, other studies referenced in the Discussion section that do compare results with the Jewish majority support our conclusions. Moreover, the counselors and nurses were able to provide some insights into Arab-specific issues due to their experience caring for both populations, and similar results have been found for other minority populations around the world (see the Discussion section).With respect to the method of performing the analysis, we acknowledge that it is often suggested to perform an inter-rater reliability test, with two researchers independently analyzing the data. However, in our research group, only the first author is a professional genetic counselor; thus, we preferred for this author to perform the main analysis while engaging in an ongoing discussion with the second author in order to ensure the trustworthiness of the study. Indeed, in cases where two independent analyses are less feasible, such an approach has been used in other publications and deemed acceptable –71.Finally, a few confounding factors were not addressed, such as differences in levels of education, employment status, occupation, social isolation, and level of religiosity between the Arab subsets, all of which may have influenced the extent to which participants were exposed to health-related information. However, as the current study is qualitative rather than quantitative, it is beyond its scope to explore possible confounding factors. Indeed, future statistically based quantitative studies in this domain may further explore the potential influence of these confounding factors. In addition, the major differences that we observed between groups, particularly regarding family involvement in decision-making and termination-of-pregnancy decisions, were largely based on cultural and religious grounds and were thus unlikely to be dependent on occupation. The degree of religiosity may impact pregnancy decision-making; therefore, comparisons of religious versus non-religious groups among minority and majority populations may reveal impactful results.Acknowledging and better understanding identified barriers to genetic counseling in the Arab subpopulations are likely to help decrease gaps in the utilization of genetic counseling, improve the multicultural competence of counselors and their services in general, and enable more effectively tailored interventions to serve this large ethno-cultural and religious minority group better within Israel and many other countries. Such an understanding may also genuinely empower the personal autonomy of counselees from the Arab minority group and its applicable subsets, thereby potentially better achieving a key goal in the Western approach of non-directive counseling, whether in Israel or in other countries with this minority group.Additional file 1. Communication of genetic risk.


Fusarium graminearum, is one of the most devastating fungal wheat diseases. During the past decades, many efforts have been deployed to dissect FHB resistance, investigating both the wheat responses to infection and, more recently, the fungal determinants of pathogenicity. Although no total resistance has been identified so far, they demonstrated that some plant functions and the expression of specific genes are needed to promote FHB. Associated with the increasing list of F. graminearum effectors able to divert plant molecular processes, this fact strongly argues for a functional link between susceptibility-related factors and the fate of this disease in wheat. In this review, we gather more recent data concerning the involvement of plant and fungal genes and the functions and mechanisms in the development of FHB susceptibility, and we discuss the possibility to use them to diversify the current sources of FHB resistance.Fusarium head blight (FHB), primarily caused by  Fusarium graminearum (Fusarium head blight (FHB) is a cereal fungal disease primarily induced by minearum . In wheaminearum . FHB hasminearum . For examinearum . Such lominearum .Fhb1, the most stable and efficient locus for wheat resistance to FHB  gene to FHB in wheat and directly questions the involvement of susceptibility factors in the disease progress. With this in mind, the purpose of this review is to discuss the growing interest about the determinism of susceptibility to FHB by gathering information from both interacting partners and by emphasizing on its benefits in diversifying the current sources of FHB resistance.Although the combined use of tolerant wheat cultivars, fungicides, and specific management practices  can reduce part of the losses due to the disease , no effie to FHB , it was sistance . Althougpmr6 plant gene that promotes the infection process and supports the pathogen’s growth and development , suggesting the intricate participation of a wealth of potential susceptibility factors , has been clearly defined in 2002 by describing the function of the elopment . Many S nfection . Its mut TaSTP13 , emphasi factors .F. graminearum infection, suggesting the removal of pivotal susceptibility factors along with chromosome fragment deletion. Likewise, Freedom” into the susceptible cv. “Apogee,” have shown that the most resistant line was characterized by the deletion of a chromosome segment of about 19% of the length of the 3DL arm in comparison with the cv. “Apogee”  has not been associated to FHB susceptibility by a direct effect of the plant height but rather through a pleiotropic or linkage effect  are important promoters of wheat susceptibility to FHB (F. graminearum xylanases with a direct impact on cell wall weakening and an indirect role in enhancing hypersensitive-like symptoms in plant tissues (One of the first characterized effectors of ycotoxin . Althougycotoxin . DON actycotoxin . Throughycotoxin . A recenchanisms . Other Fy to FHB . For ins tissues . The FGL tissues , was sho tissues . In addi tissues . Similar tissues . Besides tissues . The ideF. graminearum effectors remains limited, substantial efforts using genomics approaches have provided a large set of new candidates. Using the reference genome sequence, Cladosporium fulvum (in vitro or in planta approaches have also been successful in enlarging the list of candidate effectors (in planta using qRT-PCR, these extended the range of putative function including a number of proteases, esterase, and nucleases. Based on the few structural information available about fungal effectors (in planta dual-proteome approach (Although the catalog of characterized m fulvum . Other sffectors . By evidffectors , severalffectors , b. By dapproach . This stapproach , emphasiThe past decades of researches on FHB in wheat have provided a wealth of information on the genetic and the molecular determinants of the disease progress in spikes, mostly focused on resistance mechanisms. Although still marginally investigated, wheat susceptibility factors to FHB are emerging as key components that determine the fate of the disease, involving a complex molecular dialogue based on the interplay of fungal effectors and their plant targets. Understanding wheat susceptibility still requires many efforts on both partners and needs to fill the gap between wheat and fungal studies. This knowledge will open new strategies in order to control this complex plant/fungus interaction, providing alternative forms of resistance that are potentially more sustainable. While still a challenge, such loss-of-susceptibility forms have already demonstrated their potential to provide efficient and durable sources of disease resistance in crops . They reFF, FR, and TA organized, prepared, and drafted the manuscript. TL and LB designed, reviewed, and finalized the manuscript.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.