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112 results for “Scandinavia”
Linked collectors and determiners for: Redescription and taxonomic notes on Cyclops bohater Koźmiński, 1933 and Cyclops lacustris G. O. Sars, 1863 (Arthropoda, Crustacea), with an identification key to the Cyclops species of Fenno-Scandinavia.
Natural history specimen data linked to collectors and determiners held within, "Redescription and taxonomic notes on Cyclops bohater Koźmiński, 1933 and Cyclops lacustris G. O. Sars, 1863 (Arthropoda, Crustacea), with an identification key to the Cyclops species of Fenno-Scandinavia". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/f53f4319-10c7-4617-a52d-75c46e8cc1dc">https://bionomia.net/dataset/f53f4319-10c7-4617-a52d-75c46e8cc1dc</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/f53f4319-10c7-4617-a52d-75c46e8cc1dc">https://gbif.org/dataset/f53f4319-10c7-4617-a52d-75c46e8cc1dc</a>. Formatted as a Frictionless Data package.
Figure 7 in Cranial variation in the European badger Meles meles (Carnivora, Mustelidae) in Scandinavia
Figure 7. Bivariate plots of the multidimensional scaling (MDS) axes (E1 and K1) for three morphological forms of the European badger: 1, 'non–Fennoscandian'; 2, 'south-western Norwegian'; 3, 'main Fennoscandian'; A, males; B, females.
Figure 5 in Cranial variation in the European badger Meles meles (Carnivora, Mustelidae) in Scandinavia
Figure 5. The ranges of the 'south-western Norwegian' and 'main Fennoscandian' badgers, based on posterior classification probabilities: 0–0.3, 'main Fennoscandian' badgers; 0.3–0.6, classification unclear (6.2%); 0.6–1.0, 'south-western Norwegian' badgers.
Figure 3 in Cranial variation in the European badger Meles meles (Carnivora, Mustelidae) in Scandinavia
Figure 3. Bivariate plots of the most important multidimensional scaling (MDS) axes (E1 and K1) for two morphological clusters of the European badger: 1, 'non-Fennoscandian'; 2, 'Fennoscandian'. A, males. B, females.
Figure 2 in Cranial variation in the European badger Meles meles (Carnivora, Mustelidae) in Scandinavia
Figure 2. Measurements taken of the badger skulls: 1, condylobasal length; 2, neurocranium length; 3, viscerocranium length; 4, minimum palatal width; 5, palatal length; 6, maxillary tooth-row length; 7, length of upper carnassial tooth Pm4; 8, greatest length between oral border of the auditory bulla and aboral border of the occipital condyle; 9, length of the auditory bulla; 10, zygomatic width; 11, mastoid width of skull; 12, postorbital width; 13, interorbital width; 14, width of rostrum; 15, greatest palatal width; 16, width of the auditory bulla; 17, width of upper molar M1; 18, cranial height; 19, total length of the mandible; 20, length between the angular process and the infradentale; 21, mandibular tooth-row length; 22, length of lower carnassial tooth M1; 23, height of the vertical mandibular ramus; 24, length of upper molar M1; 25, length of upper canine; 26, width of upper canine; 27, length of lower molar M; 28, width of lower molar M; 29, 2 2 talonid length of lower carnassial tooth M1; 30, length of lower premolar Pm2.
Figure 1 in Cranial variation in the European badger Meles meles (Carnivora, Mustelidae) in Scandinavia
Figure 1. Range of the European badger (Meles meles) (1), and localities of the specimens included in this study (2).
Data for: Wolverine density distribution reflects past persecution and current management in Scandinavia
<p>After centuries of intense persecution, several large carnivore species in Europe and North America have experienced a rebound. Today's spatial configuration of large carnivore populations has likely arisen from the interplay between their ecological traits and current environmental conditions, but also from their history of persecution and protection. Yet, due to the challenge of studying population-level phenomena, we are rarely able to disentangle and quantify the influence of past and present factors driving the spatial distribution and density of these controversial species. Using spatial capture-recapture models and a data set of 742 genetically identified wolverines <em>Gulo gulo</em> collected over ½ million km<sup>2</sup> across their entire range in Norway and Sweden, we identify landscape-level factors explaining the current population density of wolverines in the Scandinavian Peninsula. Distance from the relict range along the Swedish-Norwegian border, where the wolverine population survived a long history of persecution, remains a key determinant of wolverine density today. However, regional differences in management and environmental conditions also played an important role in shaping spatial patterns in present-day wolverine density. Specifically, we found evidence of slower recolonization in areas that had set lower wolverine population goals in terms of the desired number of annual reproductions. Management of transboundary large carnivore populations at biologically relevant scales may be inhibited by administrative fragmentation. Yet, as our study shows, population-level monitoring is an achievable prerequisite for a comprehensive understanding of the distribution and density of large carnivores across an increasingly anthropogenic landscape.</p>
Genetic structure and diversity of the declining orchid Gymnadenia conopsea in Scandinavia: Implications for conservation and management
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Data for: Wolverine density distribution reflects past persecution and current management in Scandinavia
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Proximity-sensors on GPS collars reveal fine-scale predator-prey behavior during a predation event: A case study from Scandinavia
<p>Although the advent of high-resolution GPS tracking technology has helped increase our understanding of individual and multi-species behavior in wildlife systems, detecting and recording direct interactions between free-ranging animals remains difficult. In 2023, we deployed GPS collars equipped with proximity sensors (GPS proximity collars) on brown bears (<em>Ursus</em> <em>arctos</em>) and moose (<em>Alces</em> <em>alces</em>) as part of a multi-species interaction study in central Sweden. On 6 June, 2023, a collar on an adult female moose and a collar on an adult male bear triggered on each other's UHF signal and started collecting fine-scale GPS positioning data. The moose collar collected positions every 2 minutes for 89 minutes and the bear collar collected positions every 1 minute for 41 minutes. On 8 June, field personnel visited the site and found a female neonate moose carcass with clear indications of bear bite marks on the head and neck. During the predation event, the bear remained at the carcass while the moose moved back and forth, moving towards the carcass site about 5 times. The moose was observed via drone with 2 calves on 24 May and with only one remaining calf on 9 June. This case study describes, to the best of our knowledge, the first instance of a predation event between two free-ranging, wild species recorded by GPS proximity collars. Both collars successfully triggered and switched to finer-scaled GPS fix rates when the individuals were in close proximity producing detailed movement data for both predator and prey during and after a predation event. We suggest that, combined with standard field methodology, GPS proximity collars placed on free-ranging animals offer the ability for researchers to observe direct interactions between multiple individuals and species in the wild without the need for direct visual observation.</p>
A long-term study of size variation in Northern Goshawk Accipiter gentilis across Scandinavia, with a focus on Norway
<p><span>Changing climate and growing human impacts are resulting in globally rising temperatures and the widespread loss of habitats. How species will adapt to these changes is not well understood. The Northern Goshawk (<em>Accipiter</em> <em>gentilis</em>) can be found across the Holarctic but is coming under more intense pressure in many places. Studies of recent populations in Finland and Denmark have shown a marked decline in body size of Northern Goshawks over the past century. Here we investigate long-term changes to Norwegian populations of Northern Goshawk by including material from the Middle Ages. We measured 240 skeletons of modern Northern Goshawks from Norway, Sweden, Denmark and Finland, and 89 Medieval Goshawk bones. Our results show that Norwegian and Swedish female Goshawks have decreased in size over the past century, whilst males showed little decline. Medieval female Goshawks were larger than contemporary females. A decline in forest habitats and a concomitant shift towards smaller prey likely drove a shift to smaller body size in Northern Goshawks. Our study shows that significant body size changes in birds can occur over relatively short time spans in response to environmental factors, and that these effects can sometimes differ between sexes. </span></p>
Climate change vulnerability of Arctic char across Scandinavia
<p>Data and code for the paper "Climate change vulnerability of Arctic char across Scandinavia" by Muhlfeld et al. (2024) accepted in the journal <em>Global Change Biology</em>. This an extensive fish community and environmental dataset for 1,762 lakes sampled across Scandinavia (mid-1990s) that were used to model the climate vulnerability of Arctic char (<em>Salvelinus alpinus</em>) under baseline (1990s) and future climate warming scenarios (2050s and 2080s). </p>
Winter moth in Scandinavia: structure file
<p>The frequency and severity of outbreaks by pestiferous insects is increasing globally, likely as a result of human-mediated introductions of non-native organisms. However, it is not always apparent whether an outbreak is the result of a recent introduction of an evolutionarily naïve population, or of recent disturbance acting on an existing population that arrived previously during natural range expansion. Here we use approximate Bayesian computation to infer the colonization history of a pestiferous insect, the winter moth, <i>Operophtera brumata</i> L. (Lepidoptera: Geometridae), which has caused widespread defoliation in northern Fennoscandia. We generated genotypes using a suite of 24 microsatellite loci and find that populations of winter moth in northern Europe can be assigned to five genetically distinct clusters that correspond with 1) Iceland, 2) the British Isles, 3) Central Europe and southern Fennoscandia, 4) Eastern Europe, and 5) northern Fennoscandia. We find that the northern Fennoscandia winter moth cluster is most closely related to a population presently found in the British Isles, and that these populations likely diverged around 2,900 years ago. This result suggests that current outbreaks are not the result of a recent introduction, but rather that recent climate or habitat disturbance is acting on existing populations that may have arrived to northern Fennoscandia via pre-Roman traders from the British Isles, and/or by natural dispersal across the North Sea likely using the Orkney Islands of northern Scotland as a stepping-stone before dispersing up the Norwegian coast.</p>
Duality in the Early Neolithic on Lolland-Falster and in south Scandinavia - supplementary material
<p>Listed and seperated14C-data for the analysis. </p> <p>Cite as: </p> <p>Jensen, T.Z.T. and Sørensen, L.V. 2023. Duality in the Early Neolithic on Lolland-Falster and in south Scandinavia. In: Groß, D. and Rothstein, M.: Changing Identity in a Changing World. Archaeological Studies on Human Interaction in Northern Europe around 4000 cal BC. Leiden: Sidestone, supplementary material.</p>
Winter moth in Scandinavia: structure file
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A long-term study of size variation in Northern Goshawk Accipiter gentilis across Scandinavia, with a focus on Norway
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Proximity-sensors on GPS collars reveal fine-scale predator-prey behavior during a predation event: A case study from Scandinavia
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Data from: Impact of a recolonizing, cross-border carnivore population on ungulate harvest in Scandinavia
<p>Predation from large carnivores and human harvest are the two main mortality factors affecting the dynamics of many ungulate populations. We examined long-term moose (<i>Alces alces</i>) harvest data from two countries that share cross-border populations of wolves (<i>Canis lupus</i>) and their main prey moose. We tested how a spatial gradient of increasing wolf territory density affected moose harvest density and age and sex composition of the harvested animals (n = 549,310), along a latitudinal gradient during 1995-2017. In areas containing average-sized wolf territories, harvest density was on average 37% (Norway) and 51% (Sweden) lower than in areas without wolves. In Sweden, calves made up a higher proportion of the moose harvest than in Norway, and this proportion was reduced with increased wolf territory density, while it increased in Norway. The proportion of females in the adult harvest was more strongly reduced in Sweden than in Norway as a response to increased wolf territory density. Moose management in both countries performed actions aimed to increase productivity in the moose population, in order to compensate for the increased mortality caused by wolves. These management actions are empirical examples of an adaptive management in response to the return of large carnivores.</p>
Specular Meteor Radar Observations of the Semidiurnal Tide in Northern Scandinavia and Northern Germany
<p>The dataset contains specular meteor radar observations of the atmospheric semidiurnal tide in the mesosphere and lower thermosphere. The observations are from specular meteor radars located in Northern Scandinavia (Andenes, Kiruna, and Tromso) and Northern Germany (Collm and Juliusruh). The radar observations in Northern Scandinavia cover the years 1999, 2000, 2001, 2002, 2010, 2012, 2013, 2015, and 2019. Observations in Northern Germany cover the years 2010, 2012, 2013, 2015, and 2019. These data are in support of the publication "Migrating Semidiurnal Tide during the September Equinox Transition in the Northern Hemisphere."</p>
Subspecies and Distribution. V. v. vulpes Linnaeus, 1758 — N Europe (Scandinavia). V. v. abietorum Merriam, 1900 — SW Canada (Alberta & British Columbia). V. v. aegyptiacus Sonnini, 1816 — Egypt, Israel, and Lybia. V. v. alascensis Merriam, 1900 — Alaska and NW Canada (NW Territories & Yukon). V. v. alpheraky: Satunin, 1906 — Kazakhstan. V. v. anatolica Thomas, 1920 — Turkey. V. v. arabica Thomas, 1902 — Arabian peninsula. V. v. atlantica Wagner, 1841 — Algeria (forested Atlas Mts). V. v. bangsi Merriam, 1900 — NE Canada (Labrador). V. v. barbara Shaw, 1800 — NW Africa (Barbary Coast). V. v. beringiana Middendorff, 1875 — NE Siberia (shore of Bering Strait). V. v. cascadensis Merriam, 1900 — NW USA (Cascade Mountains, Oregon & Washington). V. v. caucasica Dinnik, 1914 — SW Russia (Caucasus). V. v. crucigera Bechstein, 1789 — Europe through N & C Russia. V. v. daurica Ognev, 1931 — E Russia (Amur, Siberia & Transbaikalia). V.v. deletrix Bangs, 1898 — NE Canada (Newfoundland). V. v. dolichocrania Ognev, 1926 — SE Siberia (S Ussuri). V. v. flavescens Gray, 1843 — N Iran. V. v. fulva Desmarest, 1820 — E USA. V. v. griffith: Blyth, 1854 — Afghanistan and N Pakistan. V.v. harrimani Merriam, 1900 — Alaska (Kodiak I). V. v. hoole Swinhoe, 1870 — S China (Fujian to Sichuan). V. v. ichnusae G. S. Miller, 1907 — Corsica and Sardinia. V. v. induta G. S. Miller, 1907 — Cyprus. V. v. jakutensis Ognev, 1923 — E Siberia (S of Yakutsk). V. v. japonica Gray, 1868 — Japan. V. v. karagan Erxleben, 1777 — Mongolia, Kazakhstan, and Kirgizstan. V. v. kenaiensis Merriam, 1900 — Alaska (Kenai Peninsula). V. v. kurdistanica Satunin, 1906 — Armenia and NE Turkey. V. v. macroura Baird, 1852 — USA (Mountain States). V. v. montana Pearson, 1836 — Himalayas form China (Yunnan) to C Pakistan. V. v. mecator Merriam, 1900 — SW USA (California & Nevada). V_ v. ochroxantha Ognev, 1926 — E Russian Turkestan, Aksai, Kirgizstan, Semirechie. V. v. palaestina Thomas, 1920 —Jordan and Lebanon. V.v. peculiosa Kishida, 1924 — Korea. V. v. pusilla Blyth, 1854 — NW India to Irak. V.v. regalis Merriam, 1900 — N Great Plains of Canada and USA. V. v. rubricosa Bangs, 1898 — E Canada. V.v. schrencki Kishida, 1924 — N Japan (Hokkaido) and NE Russia (Sakhalin). V. v. silacea G. S. Miller, 1907 — Iberian Peninsula. V.v. splendidissima Kishida, 1924 — E Russia (N & C Kurile Is). V. v. stepensis Brauner, 1914 — steppes of S Russia. V. v. tobolica Ognev, 1926 — Russia (lower basin of Ob River) V. v. tschiliensis Matschie, 1907 — NE China. Foxes of European origin were introduced into E USA and Canada in the 17" century, subsequently mixed with local subspecies. Also introduced to Australia in 1800s, and the Falkland Islands (Malvinas). in Canidae
Subspecies and Distribution. V. v. vulpes Linnaeus, 1758 — N Europe (Scandinavia). V. v. abietorum Merriam, 1900 — SW Canada (Alberta & British Columbia). V. v. aegyptiacus Sonnini, 1816 — Egypt, Israel, and Lybia. V. v. alascensis Merriam, 1900 — Alaska and NW Canada (NW Territories & Yukon). V. v. alpheraky: Satunin, 1906 — Kazakhstan. V. v. anatolica Thomas, 1920 — Turkey. V. v. arabica Thomas, 1902 — Arabian peninsula. V. v. atlantica Wagner, 1841 — Algeria (forested Atlas Mts). V. v. bangsi Merriam, 1900 — NE Canada (Labrador). V. v. barbara Shaw, 1800 — NW Africa (Barbary Coast). V. v. beringiana Middendorff, 1875 — NE Siberia (shore of Bering Strait). V. v. cascadensis Merriam, 1900 — NW USA (Cascade Mountains, Oregon & Washington). V. v. caucasica Dinnik, 1914 — SW Russia (Caucasus). V. v. crucigera Bechstein, 1789 — Europe through N & C Russia. V. v. daurica Ognev, 1931 — E Russia (Amur, Siberia & Transbaikalia). V.v. deletrix Bangs, 1898 — NE Canada (Newfoundland). V. v. dolichocrania Ognev, 1926 — SE Siberia (S Ussuri). V. v. flavescens Gray, 1843 — N Iran. V. v. fulva Desmarest, 1820 — E USA. V. v. griffith: Blyth, 1854 — Afghanistan and N Pakistan. V.v. harrimani Merriam, 1900 — Alaska (Kodiak I). V. v. hoole Swinhoe, 1870 — S China (Fujian to Sichuan). V. v. ichnusae G. S. Miller, 1907 — Corsica and Sardinia. V. v. induta G. S. Miller, 1907 — Cyprus. V. v. jakutensis Ognev, 1923 — E Siberia (S of Yakutsk). V. v. japonica Gray, 1868 — Japan. V. v. karagan Erxleben, 1777 — Mongolia, Kazakhstan, and Kirgizstan. V. v. kenaiensis Merriam, 1900 — Alaska (Kenai Peninsula). V. v. kurdistanica Satunin, 1906 — Armenia and NE Turkey. V. v. macroura Baird, 1852 — USA (Mountain States). V. v. montana Pearson, 1836 — Himalayas form China (Yunnan) to C Pakistan. V. v. mecator Merriam, 1900 — SW USA (California & Nevada). V_ v. ochroxantha Ognev, 1926 — E Russian Turkestan, Aksai, Kirgizstan, Semirechie. V. v. palaestina Thomas, 1920 —Jordan and Lebanon. V.v. peculiosa Kishida, 1924 — Korea. V. v. pusilla Blyth, 1854 — NW India to Irak. V.v. regalis Merriam, 1900 — N Great Plains of Canada and USA. V. v. rubricosa Bangs, 1898 — E Canada. V.v. schrencki Kishida, 1924 — N Japan (Hokkaido) and NE Russia (Sakhalin). V. v. silacea G. S. Miller, 1907 — Iberian Peninsula. V.v. splendidissima Kishida, 1924 — E Russia (N & C Kurile Is). V. v. stepensis Brauner, 1914 — steppes of S Russia. V. v. tobolica Ognev, 1926 — Russia (lower basin of Ob River) V. v. tschiliensis Matschie, 1907 — NE China. Foxes of European origin were introduced into E USA and Canada in the 17" century, subsequently mixed with local subspecies. Also introduced to Australia in 1800s, and the Falkland Islands (Malvinas).
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.