At the beginning of the 19th century, the population of the present-day area of Norway was estimated to be just under one million people. Norway's population growth rate would fluctuate throughout the first half of the century, as repercussions from the Napoleonic Wars would see several economic crises hit the country. The rate of growth would increase somewhat between the 1850s and 1880s, as a large expansion of the Norwegian shipping industry would bring economic growth to the country, and access to new crops, such as potatoes, and improved standards of living would see mortality fall. As a result, by the time of Norway's independence from Sweden in 1905, Norway would have a population of over two million.

Norway would see significant growth in the years following its independence, however, as a series of social reforms and renewed economic growth led to further improvements in standards of living. Growth would largely be unaffected by the World Wars of the early 20th century, as a policy of neutrality in the first and a somewhat stable continuation of economic and social welfare programs under German occupation would allow Norway to escape many of the more dire impacts of the conflict. As a result, by the end of the Second World War in 1945, Norway was estimated to have a population of just over three million.

Population growth would continue steadily for Norway in the post-war years, as the discovery of off-shore oil allowed for a significant expansion of health and social programs in the country, but would largely stagnate in the 1980s as the country would experience an economic crisis, forcing many public programs to be cut back. However, population growth would resume once more, as immigration rose in the 2000s, following the country’s inclusion into the Schengen Area in 2001. Today, Norway is estimated to have a population of over five million people in 2020, and is one of the wealthiest and most developed nations in the world.

In Norway, the crude birth rate in 1800 was thirty live births per thousand people, meaning that three percent of the population had been born in that year. In the nineteenth century, Norway's crude birth rate generally fluctuated between 27 and 34 births per thousand people, during a time of war, independence and industrialization. From the turn of the twentieth century until 1935, the crude birth rate dropped from just under thirty in 1900, to 15.2 in 1935. During and after the Second World War, Norway experienced a baby boom, where the rate increased to over twenty children per thousand people in the late 1940s, and it did not fall back to it's pre-war level until the late 1970s. From 1980 onwards, the crude birth rate of Norway remained between eleven and fourteen, and in 2020 it is expected to fall to it's lowest level of 11.1 births per thousand people.

This dataset includes files and scripts used for the paper "Genomic data provides new insights on the demographic history and the extent of recent material transfers in Norway spruce".

SUPPLEMENT Paranaguá Rattus norvegicus microsatellite data

Using whole genome shotgun sequences from 92 white-tailed eagles (Haliaeetus albicilla) sampled from Greenland, Iceland, Norway, Denmark, Estonia, and Turkey between 1885–1950 and after 1990, we investigate the genomic variation within countries over time, and between countries. Clear signatures of ancient biogeographic substructure across Europe and the North‐East Atlantic are observed. The greatest genomic differentiation was observed between island (Greenland and Iceland) and mainland (Denmark, Norway and Estonia) populations. The two island populations share a common ancestry from a single mainland population, distinct from the other sampled mainland populations, and despite the potential for high connectivity between Iceland and Greenland they are well separated from each other and are characterized by inbreeding and little variation. Temporal differences also highlight a pattern of regional populations persisting despite the potential for admixture. All sampled populations generally showed a decline in effective population size over time, which may have been shaped by four historical events: I) isolation of refugia during the last glacial period 110‐115,000 years ago, II) population divergence following the colonization of the deglaciated areas ~10,000 years ago, III) human population expansion, which led to the settlement in Iceland ~1,100 years ago, and IV) human persecution and exposure to toxic pollutants during the last two centuries. Methods Tissue was obtained from 92 specimens: 63 contemporary and 29 historic, from six different countries. These included 12 contemporary and eight historic individuals from Greenland, 25 contemporary and two historic individuals from Iceland, 12 contemporary and 13 historic individuals from Norway, 11 contemporary and five historic individuals from Denmark, three contemporary individuals from Estonia, and one historic individual from Turkey (Figure 1). The historic specimens were sampled between 1885 and 1950 (all but the two Icelandic individuals were sampled prior to 1937), while all contemporary individuals were sampled post-1990 (full individual information is presented in Table 1). Muscle tissue and whole blood from contemporary samples from Estonia, Denmark, and Greenland (Table 1) were stored at -20 °C until DNA extraction and were provided by the Department of Ecoscience, Arctic Research Centre, AU, Roskilde, Denmark (Estonian, Danish, and Greenland samples), Natural History Museum of Denmark, University of Copenhagen, Copenhagen, Denmark (Danish samples) and the Greenland Institute of Natural Resources, Nuuk, Greenland (Greenland samples). Whole blood samples from contemporary samples from Iceland were collected in an ongoing monitoring project of the white-tailed eagle in Iceland (led by the Icelandic Institute of Natural History) and stored in EDTA at -20 °C until DNA extraction. Whole genome shotgun DNA sequences from twelve Norwegian individuals were provided by the Department of Natural History, University Museum, Norwegian University of Science and Technology (NTNU), Trondheim, Norway. DNA extraction, library building, and sequencing of all contemporary samples are described in Hansen et al (2021, in review). Historic samples consisting of toepad clippings, taken with disposable sterile scalpel blades, from museum samples provided by The Natural History Museum of Denmark, University of Copenhagen, Denmark; Icelandic Institute of Natural History, Reykjavik, Iceland, and Department of Natural History, NTNU University Museum, Norwegian University of Science and Technology (NTNU), Trondheim, Norway. Historic samples from Greenland, Iceland, Denmark, Turkey, and five of the thirteen Norwegian specimens were processed at the clean laboratory facilities at the Globe Institute at the University of Copenhagen. Firstly, to prevent cross-contamination from other museum specimens, the samples were cleaned with a dilute bleach solution (ca. 5% commercial strength), then rinsed with 70% ethanol followed by molecular biology grade water performed using a proteinase-based lysis-buffer according to Gilbert et al. (2008). Each sample was added 300 µL lysing buffer including 20 µL proteinase K and incubated for 3 hours. The supernatant was purified by combining 720 µL binding buffer modified as in Allentoft et al. (2015), with 80 µL sample lysate, vortexed and centrifuged through a Monarch® DNA Cleanup Column (New England Biolabs Inc., Beverly, Massachusetts, USA). The binding step was repeated 3 times after which the column was washed with 800 µL PE buffer, from where the DNA eluded into 21.5 µL EBT buffer. Throughout the entire process, only LoBind Eppendorf tubes were used. The remaining eight Norwegian historic specimens were processed at the Norwegian University of Science and Technology (NTNU) University Museum’s dedicated palaeo-genomics laboratory. For these, the genomic DNA extractions were performed with a Qiagen DNeasy Blood & Tissue kit. The manufacturer’s protocol was used except that the amount of proteinase K was doubled, and the lysis step incubation at 56°C was extended to 15 hours. The DNA solutions were incubated at 37°C for 10 minutes prior to elution. For all historic samples, blunt-end Illumina shotgun sequencing libraries were prepared using the BEST protocol (Carøe et al. 2018). In both of the aDNA laboratories, extraction and library blanks were also included to monitor for contamination. Indexed libraries from historic samples from Greenland, Iceland, Denmark, Turkey and five Norwegian specimens processed at the University of Copenhagen were paired-end sequenced on four flow cells with 2x150 bp read length at deCODE Genetics in Iceland using an Illumina NovaSeq 6000. The purified and indexed libraries for the eight Norwegian specimens processed at NTNU were pooled and paired-end sequenced over two runs on the Illumina HiSeq 4000 platform at the NTNU Genomics Core Facility, and over one run on an Illumina NovaSeq 6000 at the University of Oslo Norwegian National Sequencing Centre. Fastq file quality of all samples was checked using FastQC (Babraham Bioinformatics 2010), then run through AdapterRemoval v2 using standard-setting, but providing adapter sequences for samples, and using the arguments --collapse and –trimns (Schubert et al. 2016). The fastq files were mapped to the golden eagle (Aquila chrysaetos) genome (GCA_900496995.3) using bwa aln, samse, and sampe, with the flags -q 15 and -k 1 (Li and Durbin 2009). Although a white-tailed eagle genome is available, the golden eagle was deliberately chosen as the reference to minimize the potential of mapping biases derived from the fact that the available white-tailed eagle genome is not equally related to all populations studied here (the published white-tailed eagle genomes come from Greenland, UK, and Germany), thus might introduce errors in the analyses (Gopalakrishnan et al. 2017). A further benefit of aligning to the golden eagle genome is that it has been assembled to chromosome level completeness and annotated, thus enabling us to both identify and exclude sex chromosomes as needed in some of the downstream analyses, and identify the genes present in regions under selection. Picard (Broad Institute 2020) was used to remove duplicate reads. To identify likely damaged bases the base quality score was rescaled with mapDamage 2.0 (Jónsson et al. 2013). Genotypes were called using GraphTyper2 (Eggertsson et al. 2019) with standard settings. The VCF file for the 92 individuals was filtered using VCFtools, BCFtools, and VCF-annotate; SNPs had to have a minor allele count of one.

Ecology and genetics can influence the fate of individuals and populations in multiple ways. However, to date, few studies consider them when modelling the evolutionary trajectory of populations faced with admixture with non-local populations. For the Atlantic salmon, a model incorporating these elements is urgently needed because many populations are challenged with gene-flow from non-local and domesticated conspecifics. We developed an Individual-Based Salmon Eco-genetic Model (IBSEM) to simulate the demographic and population genetic change of an Atlantic salmon population through its entire life-cycle. Processes such as growth, mortality, and maturation are simulated through stochastic procedures, which take into account environmental variables as well as the genotype of the individuals. IBSEM is based upon detailed empirical data from salmon biology, and parameterized to reproduce the environmental conditions and the characteristics of a wild population inhabiting a Norwegian river. Simulations demonstrated that the model consistently and reliably reproduces the characteristics of the population. Moreover, in absence of farmed escapees, the modelled populations reach an evolutionary equilibrium that is similar to our definition of a ‘wild’ genotype. We assessed the sensitivity of the model in the face of assumptions made on the fitness differences between farm and wild salmon, and evaluated the role of straying as a buffering mechanism against the intrusion of farm genes into wild populations. These results demonstrate that IBSEM is able to capture the evolutionary forces shaping the life history of wild salmon and is therefore able to model the response of populations under environmental and genetic stressors.

Aim Biological invasions at the intracontinental scale are poorly studied, and intracontinental invasions often remain cryptic. Here, we investigate the recent range expansion of scotch broom (Cytisus scoparius) into Norway and clarify whether the genetic patterns support a natural spread or human introduction. Furthermore, we investigate whether plants were moved within the native range and how this influences invasion success. We also infer the level and structuring of genetic diversity within and between the putative native and introduced range. Location Europe Methods We analysed the chloroplast sequence variation in 267 scotch broom samples from its northern expansion front and from its native range across Europe, including herbarium samples dating back to 1835. For 37 populations, we analysed variation in nuclear single-nucleotide polymorphic markers to study gene flow and genetic diversity. Results We identified 20 different haplotypes, which lacked spatial and temporal distribution patterns in the recent expansion range in Norway. These also mostly lacked patterns across the native European range of scotch broom. The genetic diversity of nuclear genomic SNP markers across populations in the introduced range was similar to that of populations in the native range, with limited differentiation among populations. Main conclusions Scotch broom is alien to Norway and was introduced by humans on multiple occasions from diverse origins over a long period of time. High propagule pressure has probably maintained the high genetic diversity in the novel range through a combination of genetically diverse source populations and high gene flow among them. Within the native European range, our results suggest the presence of cryptic intraspecific admixture, most likely mediated by humans moving genotypes among the regions occupied by distinct native genotypes. Intracontinental invasions may easily go unnoticed and revealing these invasions and the factors driving them may be of great importance for the management of alien species.

Population size information is broken down per age groups. The table reports the averages and standard deviations over 100 years, averaged over 10 independent runs of the model. The overall minimum and maximum values over the 100 years and 10 independent runs are also reported. The results are compared with statistical measures of population surveys carried out in the Os river in the period 1992–2008 (Rådgivende Biologer AS, 2012).Scenario 1—Demographics of spawners population.

Statistical measures of smolt densities per m2 at the beginning and end of the smolt sub-phase. Density data are broken down per life phases and age groups. The tables report the averages and standard deviations over 100 years, averaged over 10 independent runs of the model. The overall minimum and maximum values over the 100 years and 10 independent runs are also reported.Scenario 1—Demographics of smolt population.

Population genetic statistics for Ixodes ricinus mtDNA lineages.

Population size information is broken down per age groups. The table reports the averages and standard deviations over 100 years, averaged over 10 independent runs of the model. The overall minimum and maximum values over the 100 years and 10 independent runs are also reported.Scenario 1—Demographics of oceanic salmon population.

Keywords; Search terms: historical time series; historical statistics; histat / HISTAT; life expectancy; mortality rates .

Abstract:

In this study human life expectancy, which since the start of the 18th century has continually increased, is investigated in comparative perspective in Germany, Sweden and Norway.

Topics: Regional as well as national data sets on population structure and the development of mortality.

The following table overview represents a cutout from the study´s archived total stocks. The complete data stock contains not only time-series data. These complete data are available by GESIS Data Archive on request.

Topics of Data-Tables with Time-Series:

I (risk) population by generations II (risk) population by periods III probability of dying by generations IV probability of dying by periods V life expectancy by generations VI life expectancy by periods

Systematics within the tables (Consecutively Numbering)

1. Place: Letter indicating the region: A. Germany (German Reich)/FRG B. Germany (German Reich)/GDR C. governmental district Aurich/Lower Saxony D. governmental district Kassel/Hessen E. governmental district Minden/North Rhine-Westphalia F. governmental district Trier/Saarland H. Herrenberg/South West Germany (Südwestdeuschland) N. Norway S. Sweden

2. Place: Number for the table´s subject (variable)

3. (risk) population (P´ x)

4. Probability of dying (qx)

5. Life expectancy (ex)

6. Place: Letter for the type of table (meaning of the annual details) P. period table G. generation table

Test scenarios, initialisation of demographic parameters.