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These authors contributed equally: Claire R. Peart, Sergio Tusso, Saurabh D. Pophaly.Science of Life Laboratories and Department of Evolutionary Biology, Uppsala University, Uppsala, SwedenClaire R. Peart, Sergio Tusso, Chi-Chih Wu, Aaron B. A. Shafer & Jochen B. W. WolfDivision of Evolutionary Biology, Faculty of Biology, LMU Munich, Planegg-Martinsried, GermanyClaire R. Peart, Sergio Tusso, Saurabh D. Pophaly, Fidel Botero-Castro & Jochen B. W. WolfMax Planck Institute for Plant Breeding Research, Cologne, GermanySaurabh D. PophalyLaboratorio de Ecología de Pinnípedos ‘Burney J. Le Boeuf’, Centro Interdisciplinario de Ciencias Marinas, Instituto Politécnico Nacional, Baja California Sur, MéxicoDavid Aurioles-GamboaDepartment of Natural Sciences, University of Houston-Downtown, Houston, TX, USAAmy B. BairdDepartment of Ecology and Conservation Biology, Texas A&M University, College Station, TX, USAJohn W. BickhamBritish Antarctic Survey, Natural Environment Research Council, Cambridge, UKJaume Forcada & Joseph I. HoffmanElephant Seal Research Group, Sea Lion Island, Falkland IslandsFilippo Galimberti & Simona SanvitoDepartment of Anatomy, University of Otago, Dunedin, New ZealandNeil J. GemmellDepartment of Animal Behaviour, Bielefeld University, Bielefeld, GermanyJoseph I. HoffmanNorwegian Polar Institute, Fram Centre, Tromsø, NorwayKit M. Kovacs & Christian LydersenDepartment of Environmental and Biological Sciences, University of Eastern Finland, Joensuu, FinlandMervi Kunnasranta & Tommi NymanNatural Resources Institute Finland (Luke), Joensuu, FinlandMervi KunnasrantaDepartment of Ecosystems in the Barents Region, Norwegian Institute of Bioeconomy Research, Svanhovd Research Station, Svanvik, NorwayTommi NymanLaboratory of Mammal Ecology, Universidade do Vale do Rio dos Sinos, São Leopoldo, BrazilLarissa Rosa de OliveiraNational Oceanic and Atmospheric Administration, National Marine Fisheries Service, Alaska Fisheries Science Center, Marine Mammal Laboratory, Seattle, WA, USAAnthony J. OrrThe Saimaa Ringed Seal Genome Project, Institute of Biotechnology, University of Helsinki, Helsinki, FinlandMia ValtonenForensic Science & Environmental Life Sciences, Trent University, Peterborough, Ontario, CanadaAaron B. A. Shafer More

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Case dataMedically attended influenza B cases in New Zealand were identified from samples taken from patients with influenza-like illness (ILI) attended by a network of general practitioners recruited for surveillance (2430 cases with an identified influenza B lineage) and from non-surveillance hospital samples (1606 cases with an identified lineage) analyzed by regional diagnostic laboratories and by the World Health Organization (WHO) National Influenza Centre at the Institute for Environmental Science and Research (ESR). Briefly, general practice surveillance operates from May to September, with participating practices collecting nasopharyngeal or throat swabs from the first ILI patient examined on each Monday, Tuesday, and Wednesday. ILI is defined as an “acute respiratory tract infection characterized by an abrupt onset of at least two of the following: fever, chills, headache, and myalgia”38. A subset of the New Zealand data (cases from 2002 to 2013) was previously compiled by Vijaykrishna et al.28 along with cases from Australia reported to the WHO Collaborating Centre for Reference and Research on Influenza in Melbourne.Statistical model of influenza B susceptibility based on infection historyFor lineage V (B/Victoria), we modeled the number of cases in people born in birth year b observed in season y as a multinomial draw with probabilities given by:$${theta }_{V}(b,y)=D(b,y)beta (b,y){Z}_{V}(b,y)rho (b,y)$$
(1)
with an analogous equation defining the multinomial distribution θY(b, y) for lineage Y (B/Yamagata). D(b, y) is the fraction of the population that was born in year b as of observation season y. Z(b, y) is the susceptibility to lineage V during season y of a person born in year b relative to that of an unexposed person. β(b, y) is a baseline probability of infection with influenza B that captures differences in transmission associated with age (thus depending on b and y) and is equal to β1 if people born in year b are in preschool during season y (0–5 years old), β2 if they are school-age children or teenagers (6–17 years old), or β3 if they are 18 or older. ρ(b,y) is an age-specific factor equal to a parameter ρ if people are  0. Letting α1 and α2 be the instantaneous attack rates for preschoolers and school-age children:$${P}_{mathrm{N}}(A)=left{begin{array}{ll}{e}^{-{alpha }_{1}(A-m)}frac{(1-{e}^{-{alpha }_{1}})}{{alpha }_{1}},hfill&,{text{if}},A; le; {A}_{mathrm{s}}\ {e}^{-{alpha }_{1}({A}_{mathrm{s}}-m)}{e}^{-{alpha }_{2}(A-{A}_{mathrm{s}})}frac{(1-{e}^{-{alpha }_{2}})}{{alpha }_{2}},&,{text{if}},A; > ; {A}_{mathrm{s}}end{array}right.$$
(26)
where As is the age at which children start going to school (4 years old in the Netherlands69). It is noteworthy that for school-age children (the equation for A  > As on the bottom), the correction term for uncertainty in sampling is not necessary for the time spent in preschool (assumed to be exactly As years), only for the time after preschool (A − As).Handling cases with missing lineage informationWe assumed cases with missing lineage information in 2002 (n = 61), 2011 (n = 312), and 2019 (n = 206) belonged to B/Victoria, as 99% or more of identified cases in those seasons were B/Victoria (86/87 cases in 2002, 276/280 cases in 2011, and 552/552 cases in 2019) as were 94%, 92%, and 92%, respectively, of isolates from sequence databases (for Australia and New Zealand combined). We assumed cases with missing lineage information belonged to B/Yamagata in 2013 (n = 37), 2014 (n = 77), and 2017 (n = 87), when the majority of identified cases were B/Yamagata (268/272, 131/138, and 473/489, respectively), as were 99%, 94%, and 84%, respectively, of isolates in sequence databases. Unidentified cases in other seasons were disregarded, because both lineages were present at higher frequencies among identified cases. Removing unidentified cases altogether in all seasons led to similar parameter estimates.Sequence divergence analysisTo estimate the amount of evolution within and between lineages, we analyzed all complete HA and NA sequences from human influenza B isolates available on GISAID in July 2019. The set of isolates used in this analysis differs from the set used to estimate lineage frequencies, because we required isolates to have complete sequences (although not all sequences listed as complete on GISAID were in fact complete). Two isolates collected in 2000 (B/Hong Kong/548/2000 and B/Victoria/504/2000) were deposited as B/Victoria but our BLAST assignment indicated they were in fact B/Yamagata (their low divergence from B/Yamagata strains was a clear outlier). NA sequences from isolates B/Kanagawa/73 and B/Ann Arbor/1994 were only small fragments (99 and 100 amino acids long) poorly aligned with other sequences and were thus excluded. We also excluded NA sequences from B/Yamagata isolates B/Catalonia/NSVH100773835/2018 and B/Catalonia/NSVH100750997/2018, because they were extremely diverged (60% and 38%) from the reference strain B/Yamagata/16/88 and aligned poorly with other sequences.To compare sequence diversity within and between lineages over time, we aligned sequences using MAFFT v. 7.31070 and calculated percent amino acid differences in pairs of sequences from the same lineage and in pairs with one sequence from each. For each year, we sampled 100 sequences from each lineage (or used all sequences if 100 or fewer were available) to limit the number of pairwise calculations. To estimate how much B/Yamagata and B/Victoria evolved since the late 1980s, we calculated percent amino acid differences between each B/Yamagata and B/Victoria sequence, and the corresponding HA and NA sequences of reference strains B/Yamagata/16/88 and B/Victoria/2/87. Unlike in the analysis of pairwise divergence within each time point, we used all sequences from each lineage in each year. We excluded sites in which one or both sequences had gaps or ambiguous amino acids.To compare HA and NA divergence between influenza B lineages with divergence between influenza A subtypes, we downloaded complete HA and NA sequences from H3N2 and H1N1 isolated since 1977 and available on GISAID in August 2019. Homologous sites in the HA of H3N2 and H1N1 are difficult to identify by conventional sequence alignment, and instead we used the algorithm by Burke and Smith71 implemented on the Influenza Research Database website72. Both H3N2 and H1N1 sequences were aligned with the reference H3N2 sequence A/Aichi/2/68. We verified that this method matched sites on the stalk and head of the H1N1 HA with sites on the stalk and head of H3N2 HA by comparing the resulting alignment with the alignment in Supplementary Fig. S2 of Kirkpatrick et al.73. To limit the total number of influenza A sequences analyzed, we randomly selected 100 H3N2 and 100 H1N1 sequences for years in which more than 100 sequences were available, and used all available sequences for the remaining years. Isolates A/Canterbury/58/2000, A/Canterbury/87/2000, and A/Canterbury/55/2000 were excluded, because both H1N1-like and H3N2-like sequences were available under the same isolate name on GISAID.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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Study systemWe studied a D. magna population (OHZ) from a small, shallow man-made pond in Oud-Heverlee, Belgium (50°50´ N– 4°39′ E). This pond was constructed for pisciculture in 1970 and has a detailed record of fish-stocking densities for 16 years (Fig. 1a). Dormant stages of D. magna were sampled from three depths of a sediment core, corresponding to three time periods that varied in the level of fish-predation pressure: (1) the pre-fish period (1970–1972), during which no fish were stocked in the pond; (2) the high-fish period (1976–1979), a period with high fish-predation pressure due to intensive fish stocking; (3) the reduced-fish period (1988–1990), with relaxed fish predation pressure due to a reduction in fish stocking (Fig. 1a)9,10,37. This archive was previously sampled using a standard Plexiglas corer with inner diameter of 5.2 cm10. Dating of the sedimentary archive could not be completed with traditional radioisotope analysis, but was based on dry weight and organic matter content under the assumption of a constant sedimentation rate since the establishment of the pond10. The cores contained the full sediment archive, including the transition to the mineral sediment. Sediment cores were aligned using the patterns of Daphnia dormant egg abundance and changes in size of the dormant egg cases as described in Cousyn et al.10. The dormant stages were hatched in the laboratory and taking advantage of the parthenogenetic reproduction mode of D. magna as long as conditions are favorable, we started up clonal lines. The resulting clonal lines are each genetically unique, as dormant stages in D. magna are the result of sexual reproduction. Our approach was thus to sequence the full genome of a random sample of 12 individuals from each of three depth layers of a sediment core representing populations that occurred in three periods with distinct fish-predation pressure.In addition to the reconstruction of temporal genome dynamics, we used twelve regional populations of D. magna distributed along strong environmental gradients of fish-predation pressure in the region. Six populations (DANA, U2, TER1, MO, KNO15, and TER2) were sampled from fishless ponds, while six populations (ZW4, LRV, ZW3, OHN, OM2, and OM3) were sampled from ponds that harbored fish (Supplementary Table 1). These genotypes were hatched from dormant eggs isolated from the upper 2–3 cm of sediment of the study ponds.Whole-genome sequencingTo reconstruct the genomic history, we resequenced the 36 D. magna lines resurrected from the OHZ pond and validated it with additional whole-genome resequencing of 144 D. magna genotypes spread across twelve spatial populations along a fish gradient in the region (Supplementary Table 1). Twelve individuals from each temporal subpopulation of the sediment core and 8–17 individuals per population in the spatial survey were used for genomic DNA extraction using the Nucleo Spin Tissue extraction kit (Macherey-Nagel, Germany), with overnight incubation at 56 °C and following the manufacturer’s instructions. We quantified DNA using PicoGreen reagent (Life Technologies) on a DTX 880 spectrofluorometer (Beckman Coulter). For each sample, 1 µg of gDNA was normalized in a final volume of 50 µl of Tris Buffer, pH 8.5, and sheared using an E220 Focused Ultrasonicator in conjunction with a microTube plate (Covaris) in accordance with the manufacturer’s recommendations. Sheared genomic DNA was assayed on a 2200 TapeStation (Agilent) with High Sensitivity DNA Screentapes to determine the distribution of sheared fragments. The sheared genomic DNA was then prepared into Illumina compatible DNA Sequencing (DNASeq) 100-bp paired-end libraries utilizing NextFlex chemistry (Bio Scientific). Following library construction, libraries were assayed on a 2200 TapeStation (Agilent) with High Sensitivity DNA Screentapes to determine the final library size. Libraries were quantified using the Illumina Library Quantification Kit (Kapa Biosystems) and normalized to an average concentration of 2 nM prior to pooling. Genomic DNA quantification and normalization, shearing setup, library construction, library quantification, library normalization, and library pooling were performed utilizing a Biomek FXP dual-hybrid automated liquid handler (Beckman Coulter). C-Bot (TruSeq PE Cluster Kit v3, Illumina) was used for cluster generation and the Illumina HiSeq2500 platform (TruSeq SBS Kit v3 reagent kit) for paired-end sequencing with 100-bp read length following the manufacturer’s instructions.Short-read mapping and variant callingThe paired end reads (100 × 2) of each individual were first analyzed using FASTQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) for quality checks. Subsequently, low-quality base trimming and adapter cleaning was performed using the Trimmomatic software38. Here, parameter values to remove adapter sequences were chosen for seedMismatches (2), palindromeClipThreshold (30), and simpleClipThreshold (10). The minimum phred quality required to keep a base was set to 28, and the minimum read length to 50 bp. Furthermore, the cleaned reads were mapped to the D. magna genome version 2.439 using Bowtie240 software with very-sensitive parameter settings (-D 20 -R 3 -N 0 -L 20 -i S,1,0.50) and insert size between 200 and 700 bp. The mapped reads were then marked for duplicates using the MarkDuplicates feature of Picard tools (http://broadinstitute.github.io/picard/) to avoid PCR duplicates. The resulting sorted BAM files were then used for variant calling using FreeBayes41. FreeBayes41 is a haplotype-based variant caller that calls SNPs, indel, and complex variants. Minimum base quality was set to 30 with minimum coverage of four reads. We obtained more than 3 × 106 raw variants (3 441 615) for the OHZ temporal subpopulations and 6 × 106 raw variants for the spatial populations.Only biallelic SNPs supported by at least four reads and sequenced in at least 90% of individuals were retained after filtering. The draft genome of Daphnia magna consists of thousands of scaffolds and contigs. To remove repetitive and paralogous regions in the genome, we used the 293 scaffolds greater than 5 kb that altogether represent 84% of the sequenced genome. Further SNP filtering was performed based on D. magna gene models, such that each polymorphic SNP contained within genic regions could be unambiguously assigned to only one gene locus, thereby removing uncertainties attributed to sequence reads mapping to paralogs and to overlapping genes coding on alternative strands of DNA. Finally, SNPs at frequencies below 5% (pooled subpopulations) were removed retaining a total of 724,321 SNPs (mean coverage 20 reads per SNP/individual; 99.6% SNPs with missing values less than 5%) for the temporal analysis of the OHZ population and 748,511 SNPs for the spatial populations. These SNPs were used for downstream analyses.Population differentiationWe calculated both genome-wide and locus-specific levels of genetic differentiation (FST; Weir & Cockerham 198442) using the diffCalc function of the diveRsity43 package in R44. These calculations were performed for each pair of temporal subpopulations (i.e., pairwise FST) in the temporal setting (OHZ) and for six random pairs of nonfish and fish populations in the spatial survey.To calculate allele frequencies in the temporal analysis, we used vcfglxgt function of vcflib (https://github.com/vcflib/vcflib) to set genotypes that are most likely to be true based on maximum genotype likelihood. We then identified the significant differences in allele frequencies between temporal subpopulations over time (P value < 0.01) using a modified chi-square test developed by RS Waples (Waples 1989)11 and implemented in the TAFT software45 (hereafter referred to as Waples test) that accounts for effective population size (Ne), yielding 30,669 significant SNPs (4.23% of total OHZ SNPs) by comparing the prefish and high-fish temporal subpopulation and yielding 11,257 SNPs (1.55% of total OHZ SNPs) for the high-fish and reduced-fish temporal subpopulation comparison; 1771 SNPs showed significant allele-frequency changes in both transitions, most of them also showing a significant reversal in the second transition. To determine whether the observed number of SNPs that showed a significant reversal in allele frequencies is higher than expected by chance, we estimated the null distribution by randomly permuting the temporal subpopulation labels (i.e., prefish, high fish, and reduced fish) and alleles per locus (724,321 SNPs), and recalculating the number of reversals based on Buffalo and Coop 202012.Estimation of effective population size (N e)Effective population sizes (Ne) were calculated from θ = 4Neµ, across the whole genome and with a mutation rate per generation of 4 × 10−946 and a generation time of one year (Daphnia undergoes 10–15 asexual generations and one sexual generation per year), where Ɵ is Watterson’s diversity index and µ is mutation rate. Watterson Ɵ was calculated using the folded SFS option in ANGSD software47 and found to be stable, i.e., near 0.03 across the three subpopulations (prefish, high fish, and reduced fish). The calculated Ne was found to be ~1.66 million in the prefish temporal subpopulation and ~1.72 million for the high-fish and reduced-fish temporal subpopulations. Similarly, for spatial populations, the value ranges from ~1.06 to 1.45 million (Supplementary Table 2).Detecting genomic islands of differentiationFor each scaffold, and for each pairwise comparison among temporal subpopulations and six independent fish and no-fish replicate pairs in the spatial survey, a hidden Markov model (HMM) was used to distinguish genomic regions of high, moderate, or low differentiation among (sub)populations. We used a similar approach as used earlier by Soria-Carrasco et al. (2014)48. In brief, for each of these three levels of genetic differentiation (i.e., the hidden states), a Gaussian distribution of log10(FST + 1) was assumed with the mean and variance initialized as those of the log10(FST + 1) values within each respective level. We then used the Baum–Welch algorithm49 to refine the Gaussian model for each state and the transition matrix among the states. Direct transition from the low to the high state was not allowed. Hidden states were then estimated from the data and we estimated the parameters by the Viterbi algorithm using the R package HiddenMarkov50. A high differentiating island between genomes is defined to contain at least three consecutive SNPs categorized as high-state SNPs by HMM, yielding 6111 and 2879 islands of genomic differentiation between the prefish vs high-fish (mean length: 2428 bp) and the high-fish vs reduced-fish comparison (mean length: 1713 bp), respectively. Similarly, for six independent spatial fish vs no-fish comparisons, the number of islands of differentiation ranged between 4136 and 7493 (with range of mean length 1879–3290 bp), depending upon the comparison (Supplementary Table 3).Functional annotation and enrichment analysisWe investigated the function of the outlier SNPs (P values smaller than 0.01) in the comparison among temporal subpopulations (prefish vs high fish and high fish vs reduced fish) and in HMM-based high-differentiation islands in spatial comparisons. Transcriptome-based functional annotation was performed using the Daphnia magna genome version 2.439. The pathway enrichment analysis was performed using the orthologous genes of D. magna in the D. pulex genome51 based on OrthoDB gene families52 and the KEGG pathway database53. Out of ~29,000 annotated genes of D. magna, 17,400 genes have 17,832 orthologs in D. pulex. However, due to the fragmented status of the D. magna genome assembly, manual curation for high-quality gene models resulted in a total of 12,264 D. magna genes used in our study, of which 2402 genes are annotated to KEGG pathways. Ortholog mapping is not unique. A given gene from the source species, here D. magna, can map to a single, multiple, or no ortholog in the target species, here D. pulex. This can bias statistical tests when referencing to D. pulex genomics resources. We used the number of nonunique mappings for each D. magna gene on the KEGG pathways of D. pulex to weight-adjust the confusion matrix for Fisher’s exact test to obtain the correct P values. Significant pathways are defined as those with FDR corrected (Benjamini–Hochberg method) P values smaller than 0.05. The data analysis was performed using Python packages (NumPy v1.17.454, SciPy v1.4.155, statsmodels v0.11.1, and plotly v4.8.1).Rarefaction analysisRarefaction analyses were used to determine the rate at which outlier SNPs accumulate in the temporal subpopulation or in the set of regional populations as a function of sample size, i.e., the number of individuals sampled from a given population or group of populations. These analyses were performed separately for the prefish as well as for the reduced-fish temporal subpopulation, in both cases to assess the number of individuals needed to accumulate a given percentage of the SNPs that were suggested to be important for the evolution in response to fish through an outlier analysis of the prefish to high-fish transition. With the rarefaction analysis on the prefish population, we estimate the minimum number of individuals that are needed to reach sufficient genetic variation to enable the observed level of adaptation to fish in this population. The rarefaction analysis on the reduced-fish temporal subpopulation was to assess whether the level of genetic polymorphism declined or increased following the period of strong selection by fish. We thus aimed to evaluate how much evolutionary potential a certain number of individuals from the oldest (i.e., before the introduction of fish) as well as the youngest temporal subpopulation (i.e., after a wave of selection) represent. In the first set of analyses, we used the 1109 SNPs belonging to the divergent SNPs that showed significant changes in allele frequencies in the prefish to high-fish transition and also a significant reversal during the high-fish to reduced-fish and were potentially under positive selection (i.e. excluding hitchhiked SNPs) in the OHZ population. This group of SNPs represent polymorphisms that are presumably adaptive or at least contribute to adaptive allelic variants and hence contribute to the adaptive potential of the temporal subpopulations. The analyses were performed by rarefying the genotype matrix of all 12 individuals from either the prefish or the reduced-fish population to all possible (i.e., 4095) subsets of samples of 1–12 genotypes. For each of these subsets we then calculated the average proportion of polymorphic SNPs. These values were plotted against sample size to generate rarefaction curves. Similarly, rarefaction analyses were performed for the 1003 SNPs belonging to the outlier SNPs that were potentially under positive selection (i.e., excluding hitchhiked SNPs) in the OHZ population and that were also present as SNPs in the full spatial dataset (90.4% of the total number of 1109 SNPs). In this case, rarefaction curves were plotted by randomly resampling (1000 times) 1–30 individuals from the total of the 111 individuals of the Leuven regional populations in the spatial data set (i.e., the cluster of populations that represents a sample of nearby populations (within a radius of 10 km) to the focal OHZ population).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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