The largest of all Nordic countries is Sweden with a size of approximately 447,000 square kilometers. Finland follows in second with a surface area of more than 338,000 square kilometers. The mainland area of Norway amounts to 324,000 square kilometers, but if the islands of Svalbard and Jan Mayen are included, it reaches 625,000 square kilometers. The ice-free area of Greenland, which is an autonomous region under the Kingdom of Denmark, is 410,000 square kilometers, but in total, it has a land area of 2.67 million square kilometers, making it the largest island in the world.

With 450,295 square kilometers, Sweden is the largest Nordic country by area size, followed by Finland and Norway. This makes it the fifth largest country in Europe. Meanwhile, Denmark is the smallest of the five Nordic countries with only 43,094 square kilometers, however, the Danish autonomous region of Greenland is significantly larger than any of the Nordic countries, and is almost double the size of the other five combined.

Population

Sweden is also the Nordic country with the largest population. 10.45 million people live in the country. Denmark, Finland, and Norway all have between five and six million inhabitants, whereas only 370,000 people live in Iceland. Meanwhile, Denmark has the highest population density of the five countries. Greenland is the most sparsely populated permanently-inhabited country in the world, followed by the regions of Svalbard and Jan Mayen.

Geography

The five Nordic countries vary geographically. While Denmark is mostly flat, its highest point only stretching around 170 meters above sea level, Norway's highest peak is nearly 2,500 meters high. Moreover, Finland is known for its many lakes and is often called the land of a thousand lakes, whereas Iceland is famous for its volcanoes.

Land area (sq. km) in Sweden was reported at 407280 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Sweden - Land area (sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

The largest of the three Scandinavian countries is Norway, with a surface of 625,222 square kilometers, when including the arctic islands of Svalbard and Jan Mayen. Without these, Norway's total area size amounts to 385,207 square kilometers. Its neighboring country Sweden has a size of approximately 447,000 square kilometers, whereas Denmark is significantly smaller at around 43,000 square kilometers. Greenland, an autonomous area under the Kingdom of Denmark, has an ice-free surface of 410,450 km2, but its total area amounts to 2.67 million km2, making it the largest island in the world.

Of the total population in Sweden of 10.55 million people, around half resided in the counties Stockholm, Västra Götaland or Skåne. This is also the three counties where the three largest cities in Sweden, Stockholm, Göteborg, and Malmö, are located. In the capital region Stockholm county, there lived nearly 2.5 million inhabitants in 2023. Västra Götaland county had close to 1.8 million inhabitants, while Skåne county, the southernmost region, had roughly 1.4 million inhabitants. The island Gotland had the lowest number of inhabitants with only 60,000.

The highest population density

Stockholm, Skåne and Västra Götaland were also the three counties in Sweden with the highest population density. In 2022, 374.6 inhabitants per square kilometer lived in Stockholm county, while the corresponding figures for Skåne and Västra Götaland were 129 and 73.9, respectively.

The highest rents

Unsurprisingly. Stockholm county is the county in Sweden with the highest rents for rented dwellings, with average prices for one square meter amounting to over 1,400 Swedish kronor in 2022. The lowest average renting prices were in the northwestern region Jämtland, one square meter costing 1,000 Swedish kronor.

Species are classified is based on current threats at different protection levels. Records of the listed species are protected from public view i.e. details like exact location, date, name of observer etc. are hidden. The exact geographical coordinates of the recorded location are obfuscated at a scale corresponding to protection level (5 km, 25 km or 50 km). The only open data is information on the obfuscated positions i.e. grid cells of a gridded map with grid size according to protection level. Classification is updated approximately every 5 years. https://www.artdatabanken.se/var-verksamhet/fynddata/skyddsklassade-arter/

The Eurasian spruce bark beetle, Ips typographus, is a major pest, capable of killing spruce forests during large population outbreaks. Recorded dispersal distances of individual beetles are typically within hundreds of meters or a few kilometres. However, the connectivity between populations at larger distances and longer time spans and how this is affected by the habitat is less studied, despite its importance for understanding at which distances local outbreaks may spread. Previous population genetic studies in I. typographus typically used low resolution markers. Here, we use genome-wide data to assess population structure and connectivity of I. typographus in Sweden. We used 152 individuals from 19 population samples, distributed over 830 km from Strömsund (63º 46' 8'' N) in the north to Nyteboda (56º 8' 50'' N) in the south, to capture processes at a large regional scale, and a transect sampling design adjacent to a recent outbreak to capture processes at a smaller scale (76 km). Using restriction site-associated DNA sequencing (RADseq) markers capturing 1409-1997 SNPs throughout the genome, we document a weak genetic structure over the large scale, potentially indicative of high connectivity with extensive gene flow. No differentiation was detected at the smaller scale. We find indications of isolation-by-distance both for relative (FST) and absolute divergence (Dxy). The two northernmost populations are most differentiated from the remaining populations, and diverge in parallel to the southern populations for a set of outlier loci. In conclusion, the population structure of I. typographus in Sweden is weak, suggesting a high capacity to disperse and establish outbreak populations in new territories. Methods Material and sampling The I. typographus specimens were captured at local stations that were part of the Swedish Forest Agency and Södra (Sweden’s largest forest owner association) networks. The stations were distributed throughout Sweden, and samples were gathered during the peak activity period in the spring of 2019 (Figure 1A). The large-scale sampling spanned 19 main locations, ranging from Strömsund in the north to Nyteboda in the south (Table S1). These two locations are ca. 830 km apart. As new attacks by spruce bark beetles have typically been suggested to be within 500 meters of previous attacks (Wichmann and Ravn 2001), we additionally designed a transect to resolve population structure at a more local scale. Both transects started at a recent outbreak locality, Nyteboda forest, and stretched towards the north and the south, respectively. The north-south direction was chosen to reflect the main direction in the larger data set. The trap intervals started at 200 m and were doubled for each trap, resulting in a series of distances ranging between 200 m up to 13 km between each pair of traps in the south and 26 km in the north, with a cumulative maximum distance of 76 km between the southernmost and northernmost traps (Figure S1), thus an order of magnitude shorter than the larger scale. Bark beetles were captured using Theysohn pheromone slit traps (Galko et al. 2016) with a ca. 100 m expected sampling range (Schlyter 1992), collected within a week during April 2019, and stored in 96% ethanol and frozen as they arrived to Lund University. Traps from the transect were emptied the day after the trap was set up and only living individuals were preserved at -80 °C. Traps from the larger scale swarming surveillance were emptied within a week, but differences in the time the individuals had been dead prior to being stored in ethanol implied that DNA quality could vary across stations. We sampled 10 individuals per population and transect location, but included fewer specimens in a few populations where the DNA was degraded (Table S1). DNA extraction and library preparation The DNA extraction protocol, the RADtag library protocol, and summaries of the RADtag library preparation have been deposited in a public repository (https://github.com/sjellerstrand/ips_typographus_genomics). DNA was extracted using the Qiagen blood and tissue kit, following the protocol suggested for insect DNA. The whole insect was homogenised with the TissueLyser for 4 min at 30 Hz. The final DNA elution was done in 60 µl buffer EB. We monitored DNA concentration with a NanoDrop, and performed Qubit analyses on a subset of the samples (14 out of 634 extracted samples; 4 of these samples were among the 320 that were sequenced) as nanodrop quantification may inflate the estimated concentration. To further assess DNA-quality, all extractions were run on agarose gels to estimate fragment size. Samples with concentrations below ~13 ng/µl or smeared profiles on the gel, indicative of decomposed DNA, were omitted from further analyses. For the two transects, two single-digest RADtag libraries were created from 160 samples per library. All samples from each transect were pooled in the same library to avoid any cross-library batch effects. Of the 320 samples, 13 constituted individual duplicates originating from the same extract, and were split into two different tubes and treated as separate samples before library preparation to enable us to assess the repeatability of the method. From each sample, 500 ng DNA was used for restriction digest with the restriction enzyme SbfI from New England Biolabs. The DNA was sheared to allow for identification and removal of PCR duplicates. Individual barcoding was performed with 40 P1 inline barcodes of 8 bp differing by at least three sequence positions, combined with four P2 index barcodes of 8 bp differing by at least six sequence positions. Both libraries were amplified with a 16 cycle PCR. We performed size-selection using SPRI beads throughout the library preparation, with a final size selection following the PCR step. The bulk of the final library consisted of fragments in the size range of 380-730 bp. The RADtag libraries were sequenced on separate lanes on an SP flow cell as paired-end 150 bp reads on an Illumina NovaSeq 6000 platform by National Genomics Infrastructure – Stockholm, Sweden (NGI Stockholm, https://www.scilifelab.se/facilities/ngi-stockholm/ http://ugc.igp.uu.se/our-services/ngs-technologies/). RADseq data filtering and variant calling All the code for RADseq data filtering, variant calling, and population genomic analyses have been deposited in a public repository (https://github.com/sjellerstrand/ips_typographus_genomics). The RADseq data were demultiplexed by the NGI sequencing facility to sublibraries based on the index barcodes on the P2 adapter. These libraries were then demultiplexed with the bioinformatics pipeline Stacks 2.53 (Catchen et al. 2013; Catchen et al. 2011; Rochette et al. 2019), rescuing barcodes with a maximum of one mismatch, and filtering adapter sequences allowing for two mismatches. PCR duplicates were then removed based on the randomly sheared paired-end reads, and adapters were removed using Trimmomatic 0.36 (Bolger et al. 2014), by providing custom adapter lists. Read quality was evaluated with FastQC 0.11.8 (https://www.bioinformatics.babraham.ac.uk/projects/fastqc). The reads were aligned to a concatenated reference genome consisting of the nuclear genome assembled by Powell et al. (2021) and the mitochondrion assembled by Lv et al. (2017) using BWA-MEM 0.7.17 (H. Li and Durbin 2009). After alignment, the bam files were sorted by coordinates with SAMtools 1.10 (H. Li et al. 2009) and only the single-end reads were extracted for variant calling. Variants were called with HaplotypeCaller from GATK 4.1.4.1 (McKenna et al. 2010) using a minimum base quality of 20 from non-soft clipped bases. To correct for low levels of sequencing errors, batch effects and cross-contamination, we filtered out minor alleles representing less than 10% of the reads per individual and site. The resulting gVCF files were combined with CombineGVCFs and genotypes were called using GenotypeGVCFs on properly paired reads with a minimum phred-scaled confidence of 20, and then SNPs were extracted. The assembled reference genome contains a large portion of repetitive sequences and low complexity regions, which were hard masked in the assembly (Powell et al. 2021). VCFtools 0.1.16 (Danecek et al. 2011) was used to filter out the hard masked regions. Briefly, we filtered SNPs according to GATK hard filtering practice, and for a minimum quality of 30, minimum read depth of 5, as well as additional filters following O’Leary et al. (2018), which are specified in Table S3. Samples with low coverage, high missingness, or evidence of severe cross-contamination were removed from the dataset as follows: genotypes with a depth below 30, sites with a mean depth less than 48, a mean depth higher than 52, sites present in less than 80% of the individuals were filtered out. The error rate of the dataset was evaluated for seven duplicates samples using BCFtools gtcheck with genotype likelihoods and used to examine the error rate, and we determined filtration criteria based on this rate. Duplicate samples were then removed from the dataset. To obtain balanced sample sizes, we sampled eight individuals per sampling location, with only seven individuals in two of the transect locations, as we did not have eight individuals for these. Variants present in less than five individuals in any sampling location were removed, as well as sites present in less than 90% of the remaining individuals. We divided the samples into one dataset containing all the regional locations and two of the transect locations, and a second data set containing the transect locations only. We kept only alleles still present at least 3 times in each new dataset. Plink 1.90b4.9 (Purcell et al. 2007) was used to linkage prune the data using 50 kb windows, step sizes of 10 kb and a cut-off at a r-squared value higher than 0.1 (see Table S3 for number of SNPs following each filter step). Due to the absence of

In 2023, approximately ******* people lived in Stockholm, making it not only the capital, but also the biggest city in Sweden. The second biggest city, Gothenburg (Göteborg) had about half as many inhabitants, with about ******* people. Move to the citySweden is a country with a very high urbanization rate, the likes of which is usually only seen in countries with large uninhabitable areas, such as Australia, or in nations with very little rural landscape and agrarian structures, like Cuba. So why do so few Swedes live in rural areas, even though based on total area, the country is one of the largest in Europe? The total population figures are the answer to this question, as Sweden has only about 10.3 million inhabitants as of 2018 – that’s only 25 inhabitants per square kilometer. Rural exodus or just par for the course?It is no mystery why most Swedes flock to the cities: Jobs, of course. Over 65 percent of Sweden’s gross domestic product is generated by the services sector, and agriculture only contributes about one percent to the GDP. Employment mirrors this, with 80 percent of the workforce being deployed in services, namely in foreign trade, telecommunications, and manufacturing, among other industries.

The road freight transport volume in Sweden saw no significant changes in 2022 in comparison to the previous year 2021 and remained at around 47870 million ton-kilometers. Still, the transport volume reached its highest value in the observed period in 2022. Road freight transport refers to the transport of cargo by commercial vehicles as part of the logistics chain and can be both national and international in countries connected by road networks. Road freight transport is typically divided between long- and short-haul, which influences the choice of vehicle: Vans are more prevalent in urban areas and across short distances, while medium and heavy trucks, due to their size, are used for long-haul trips.Find more key insights for the road freight transport volume in countries like Denmark, Finland, and Norway.

By mid-1941, Axis forces had already taken control of most of mainland Europe, as well as much of Northern Africa and parts of the Middle East*. At the outbreak of Operation Barbarossa, the major European countries not under Axis control were the officially-neutral states of Ireland, Portugal, Spain, Sweden, Switzerland, and Turkey, while the Soviet Union and United Kingdom were actively at war with the Axis Powers. By mid-1941, Germany and its allies controlled an area of approximately 3.28 million square kilometers in Europe; by comparison, modern Germany covers an area of approximately 357 thousand square kilometers. Axis control of Europe From September 1938, when Germany annexed the Czech Sudetenland, until June 1941, when Germany launched its invasion of the Soviet Union, the Axis Powers spent much of their time and resources consolidating power across Europe. Countries such as Bulgaria, Hungary, Romania, and Finland aligned themselves with Germany, as did Italy, who became one of the chief aggressors around the Mediterranean and in Africa. Germany and the Soviet Union both invaded and partitioned Poland in September 1939, through the Molotov-Ribbentrop Pact of non-aggression to one-another. Germany then took Denmark and most of Norway in April 1940, before pushing into Benelux and France in May. Italy also annexed Albania in April, which was used as a launching point for its failed invasion of Greece later in the year. As Italy had failed to secure the Balkans, a joint Axis offensive then pushed into Yugoslavia and Greece in April 1941, ultimately overwhelming any resistance. However, German intervention in the south then delayed the Axis invasion of the Soviet Union by over a month; Hitler later claimed that this was the reason for Germany's failure to take Moscow before the winter months, although many historians disagree. Launch of Operation Barbarossa Germany broke their pact of non-aggression with the Soviet Union with a surprise invasion, known as Operation Barbarossa, on June 22, 1941. While Axis forces had already extended control over most of Europe by this point, Operation Barbarossa became the largest military invasion or operation in human history. When compared with German capacities in its pre-1938 borders, within a few months of the invasion, the area of land controlled by Germany had grown by a factor of six, the population by a factor of four, and its access to natural resources and energy had also grown several times larger. These increased capacities were essential in allowing Germany to continue its expansion and aggression for the years to come, before the Soviet Union and the Western Allies eventually defeated the Axis forces four years later.