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651 results for “netherlands”
Figure 1 from: Milotic T, Desmet P, Anselin A, De Bruyn L, De Regge N, Janssens K, Klaassen R, Koks B, Schaub T, Schlaich A, Spanoghe G, T'jollyn F, Vanoverbeke J, Bouten W (2020) GPS tracking data of Western marsh harriers breeding in Belgium and the Netherlands. ZooKeys 947: 143-155. https://doi.org/10.3897/zookeys.947.52570
Figure 1 INBO researcher Anny Anselin holding Peter (animal ID L143457), one of the tagged Western marsh harriers in the MH_WATERLAND dataset (tag ID 623).
Figure 2 from: Milotic T, Desmet P, Anselin A, De Bruyn L, De Regge N, Janssens K, Klaassen R, Koks B, Schaub T, Schlaich A, Spanoghe G, T'jollyn F, Vanoverbeke J, Bouten W (2020) GPS tracking data of Western marsh harriers breeding in Belgium and the Netherlands. ZooKeys 947: 143-155. https://doi.org/10.3897/zookeys.947.52570
Figure 2 Left: Map giving an overview of the extent of the three datasets including the winter migration tracks; top right: summering data in H_GRONINGEN; middle right: summering data of MH_WATERLAND; and bottom right: summering data in MH_ANTWERPEN.
Figure 2 from: Haelewaters D, De Kesel A (2020) Checklist of thallus-forming Laboulbeniomycetes from Belgium and the Netherlands, including Hesperomyces halyziae and Laboulbenia quarantenae spp. nov. MycoKeys 71: 23-86. https://doi.org/10.3897/mycokeys.71.53421
Figure 2 Maximum clade creditability tree of Laboulbenia isolates reconstructed from an LSU dataset, with L. bruchii as outgroup. The topology is the result of Bayesian inference performed with BEAST. For each node, ML BS (≥ 65) and Bayesian pp (≥ 0.7) are presented above/below the branch leading to that node. Isolates are color-coded by host; L. quarantenae sp. nov. is highlighted with gray shading.
Figure 1 from: Haelewaters D, De Kesel A (2020) Checklist of thallus-forming Laboulbeniomycetes from Belgium and the Netherlands, including Hesperomyces halyziae and Laboulbenia quarantenae spp. nov. MycoKeys 71: 23-86. https://doi.org/10.3897/mycokeys.71.53421
Figure 1 Maximum clade creditability tree of Hesperomyces isolates reconstructed from an ITS dataset, with H. coleomegillae as outgroup. The topology is the result of Bayesian inference performed with BEAST. For each node, ML BS (≥ 65) and Bayesian pp (≥ 0.7) are presented above/below the branch leading to that node. Hesperomyces virescens sensu lato is highlighted with light gray shading, isolates are color-coded by host; H. virescens sensu stricto and H. halyziae sp. nov. are highlighted with dark gray shading.
Figure 3 from: Haelewaters D, De Kesel A (2020) Checklist of thallus-forming Laboulbeniomycetes from Belgium and the Netherlands, including Hesperomyces halyziae and Laboulbenia quarantenae spp. nov. MycoKeys 71: 23-86. https://doi.org/10.3897/mycokeys.71.53421
Figure 3 Hesperomyces halyziae Haelew. & De Kesel from Halyzia sedecimguttataA mature thallus from slide D. Haelew. 955a, holotype B mature thallus from slide BR5020212156406V. Scale bar: 100 µm.
Figure 4 from: Haelewaters D, De Kesel A (2020) Checklist of thallus-forming Laboulbeniomycetes from Belgium and the Netherlands, including Hesperomyces halyziae and Laboulbenia quarantenae spp. nov. MycoKeys 71: 23-86. https://doi.org/10.3897/mycokeys.71.53421
Figure 4 A–ILaboulbenia quarantena De Kesel & Haelew. from Bembidion biguttatum, specimen ADK6448: A mature thallus from prothorax, slide BR5020212163329V, holotype B mature thallus from prothorax with less pigmented perithecium C mature thallus from the right mesofemur D–F mature thalli from the right protibia G immature thallus from the prothorax H mature thallus from the right mesofemur I ascospores J–K laboulbenia vulgaris Peyr: J mature thallus from prothorax of Bembidion tetracolum, specimen ADK5557 K mature thallus from mesothorax of Ocys harpaloides, specimen ADK6353. One of the diagnostic characteristics of the new species–the positioning of the insertion cell–is shown in a mature thallus of L. quarantenae (E) and one of L. vulgaris (J). Scale bar: 100 µm.
Data from: Assessing species richness trends: declines of bees and bumblebees in the Netherlands since 1945.
Estimating and predicting temporal trends in species richness is of general importance, but notably difficult because detection probabilities of species are imperfect and many datasets were collected in an opportunistic manner. We need to improve our capabilities to assess richness trends using datasets collected in unstandardized procedures with potential collection bias. Two methods are proposed and applied to estimate richness change, which both incorporate models for sampling effects and detection probability: (1) non-linear species accumulation curves with an error variance model and (2) Pradel capture-recapture models. The methods are used to assess nationwide temporal trends (1945-2018) in the species richness of wild bees in the Netherlands. Previously, a decelerating decline in wild bee species richness was inferred for part of this dataset. Among the species accumulation curves, those with non-constant changes in species richness are preferred. However, when analysing data subsets, constant changes became selected for non-Bombus bees (for samples in collections) and bumblebees (for spatial grid cells sampled in three periods). Smaller richness declines are predicted for non-Bombus bees than bumblebees. However, when relative losses are calculated from confidence intervals limits, they overlap and touch zero loss. Capture-recapture analysis applied to species encounter histories infers a constant colonization rate per year and constant local species survival for bumblebees and other bees. This approach predicts a 6% reduction in non-Bombus species richness from 1945 to 2018 and a significant 19% reduction for bumblebees. Statistical modelling to detect species richness time trends should be systematically complemented with model checking and simulations to interpret the results. Data inspection, assessing model selection bias and comparisons of trends in data subsets were essential model checking strategies in this analysis. Opportunistic data will not satisfy the assumptions of most models and this should be kept in mind throughout.
Fragmentation and translocation distort the genetic landscape of ungulates: red deer in the Netherlands
<p>Many ungulate populations have a complex history of isolation and translocation. Consequently, ungulate populations may have experienced substantial reductions in the level of overall gene flow, yet simultaneously have augmented levels of long distance gene flow. To investigate the effect of this dual anthropogenic effect on the genetic landscape of ungulates, we genotyped 35K SNPs in 47 red deer (Cervus elaphus) of Netherlands, including putative autochthonous relic populations as well as allochthonous populations established in private estates and rewilding areas. We applied FST and ordination analyses to determine the meta-population genetic structure and thereby the occurrence of hybridization. At population level, we investigated levels of inbreeding through individual-based diversity measures, including Runs of Homozygosity. We documented that both spatial genetic structure and within-population genetic variation differed markedly from patterns assumed from present-day abundance and distribution. Notwithstanding the small spatial scale, red deer populations formed distinct genetic clusters, and some had higher genetic similarity to distant than to nearby populations. Moreover, the putative autochthonous relic deer populations had much reduced levels of polymorphism and multi-locus heterozygosity, despite relatively large current population sizes. Accordingly, genomes of these deer contained a high proportion of long (>5 Mb) Runs of Homozygosity. Whereas the observed high levels of inbreeding warrant defragmentation measures, the presence of adjacent autochthonous and allochthonous genetic stocks imply that facilitation of gene flow would cause genetic homogenization. Such distortions of the genetic landscape of ungulates creates management dilemmas that cannot be properly anticipated without baseline genetic monitoring.</p>
Figure 5 from: Edgecombe GD, Akkari N, Netherlands EC, Du Preez G (2020) A troglobitic species of the centipede Cryptops (Chilopoda, Scolopendromorpha) from northwestern Botswana. ZooKeys 977: 25-40. https://doi.org/10.3897/zookeys.977.57088
Figure 5 Cryptops (Cryptops) legagus sp. nov., paratype NHMW 10151 A habitus, dorsal view B, C head and segment 1, dorsal and ventral views D detail of head (clypeus, first maxilla and forcipule), ventral view E leg-bearing segments 1 and 2, dorsal view F cruciform sulci on sternites.
Figure 7 from: Edgecombe GD, Akkari N, Netherlands EC, Du Preez G (2020) A troglobitic species of the centipede Cryptops (Chilopoda, Scolopendromorpha) from northwestern Botswana. ZooKeys 977: 25-40. https://doi.org/10.3897/zookeys.977.57088
Figure 7 Bayesian tree for blind scolopendromorphs based on partitioned concatenated datasets of four molecular loci 18S rRNA, 28S rRNA, 16S rRNA and cytochrome c oxidase subunit I. Numbers at nodes are posterior probabilities. The scale bar represents 0.05 nucleotide substitutions per site.
Figure 1 from: Edgecombe GD, Akkari N, Netherlands EC, Du Preez G (2020) A troglobitic species of the centipede Cryptops (Chilopoda, Scolopendromorpha) from northwestern Botswana. ZooKeys 977: 25-40. https://doi.org/10.3897/zookeys.977.57088
Figure 1 Cryptops (Cryptops) legagus sp. nov., holotype (NHMW 10149) A habitus, dorsal view B head and T1, dorsal view C head and segment 1, ventral view D detail of head, ventral view E segments 2–4, lateral view, showing spiracle on segment 3 F legs 9–10, lateral view.
Figure 6 from: Edgecombe GD, Akkari N, Netherlands EC, Du Preez G (2020) A troglobitic species of the centipede Cryptops (Chilopoda, Scolopendromorpha) from northwestern Botswana. ZooKeys 977: 25-40. https://doi.org/10.3897/zookeys.977.57088
Figure 6 Cryptops (Cryptops) legagus sp. nov., paratype NHMW 10151 A segments 20–21, dorsal view B segments 18–21, ventrolateral view C, D distal articles of ultimate leg and detail of tibia, tarsus and pretarsus, ventral views, showing saw teeth.
Figure 3 from: Edgecombe GD, Akkari N, Netherlands EC, Du Preez G (2020) A troglobitic species of the centipede Cryptops (Chilopoda, Scolopendromorpha) from northwestern Botswana. ZooKeys 977: 25-40. https://doi.org/10.3897/zookeys.977.57088
Figure 3 Cryptops (Cryptops) legagus sp. nov., paratype NHMW 10152 A head and segment 1, dorsal view B ultimate leg-bearing segment, posterolateral view, showing coxopleural pore field C distal articles of ultimate leg, showing femoral, tibial and tarsal saw teeth.
Figure 4 from: Edgecombe GD, Akkari N, Netherlands EC, Du Preez G (2020) A troglobitic species of the centipede Cryptops (Chilopoda, Scolopendromorpha) from northwestern Botswana. ZooKeys 977: 25-40. https://doi.org/10.3897/zookeys.977.57088
Figure 4 Cryptops (Cryptops) legagus sp. nov., paratype NHMW 10150 A, B head and segment 1, dorsal and ventral views C forcipular coxosternal margin, ventral view D segments 19–21, ventral view E distal articles of ultimate leg, showing femoral, tibial and tarsal saw teeth.
Figure 2 from: Edgecombe GD, Akkari N, Netherlands EC, Du Preez G (2020) A troglobitic species of the centipede Cryptops (Chilopoda, Scolopendromorpha) from northwestern Botswana. ZooKeys 977: 25-40. https://doi.org/10.3897/zookeys.977.57088
Figure 2 Cryptops (Cryptops) legagus sp. nov., holotype (NHMW 10149). A–C segments 19–21, dorsal, ventral and posterolateral views, respectively D ultimate leg-bearing segment, ventrolateral view.
The roles of data stewards in the data stewardship landscape identified in Denmark and the Netherlands
<p>The roles and stakeholders in the data stewardship landscape that were identified in Denmark and the Netherlands align very well as is shown in the Figure. </p> <p>References:<br> Danish project report: <strong> </strong>Wildgaard, L., Vlachos, E., Nondal, L., Larsen, A. V., & Svendsen, M. (2020, January 31). National Coordination of Data Steward Education in Denmark: Final report to the National Forum for Research Data Management (DM Forum) (Version 1). Zenodo. <a href="http://doi.org/10.5281/zenodo.3609516">http://doi.org/10.5281/zenodo.3609516</a><br> Dutch project report: Scholtens, S., Jetten, M., Böhmer, J., Staiger, Ch., Slouwerhof, I., Van der Geest, M. & Van Gelder, C.W.G.. (2019, October 3). Final report: Towards FAIR data steward as profession for the lifesciences. Report of a ZonMw funded collaborative approach built on existing expertise. Zenodo. <a href="http://doi.org/10.5281/zenodo.3474789">http://doi.org/10.5281/zenodo.3474789</a> <br> Dutch project report: Jetten, M. et al. Professionalising data stewardship in the Netherlands: competences, training and education - Dutch roadmap towards national implementation of FAIR data stewardship (to be published in 2021)</p>
DELTATRACK - Herring gulls (Larus argentatus, Laridae) and lesser black-backed gulls (Larus fuscus, Laridae) breeding at Neeltje Jans (Netherlands)
<p><em>DELTATRACK - Herring gulls (Larus argentatus, Laridae) and lesser black-backed gulls (Larus fuscus, Laridae) breeding at Neeltje Jans (Netherlands)</em> is a bird tracking dataset published by the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a>. It contains animal tracking data for the project/study <strong>DELTATRACK</strong>, using trackers developed by Ornitela (<a href="https://www.ornitela.com">https://www.ornitela.com</a>). The study has been operational since 2020. In total 99 individuals of European herring gull (<em>Larus argentatus</em>) and lesser black-backed gull (<em>Larus fuscus</em>) have been tagged in the breeding colony at Neeltje Jans in the Netherlands, mainly to study their use of offshore waters near the Borssele windpark. Data are automatically synced with Movebank and from there periodically archived on Zenodo (see <a href="https://github.com/inbo/bird-tracking">https://github.com/inbo/bird-tracking</a>).</p> <h2>Files</h2> <p>Data in this package are exported from Movebank study <a href="https://www.movebank.org/cms/webapp?gwt_fragment=page=studies,path=study1258895879">1258895879</a>. Fields in the data follow the <a href="http://vocab.nerc.ac.uk/collection/MVB">Movebank Attribute Dictionary</a> and are described in <code>datapackage.json</code>. Files are structured as a <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Package</a>. You can access all data in R via <code>https://zenodo.org/records/15696782/files/datapackage.json</code> using <a href="https://frictionlessdata.github.io/frictionless-r/">frictionless</a>.</p> <ul> <li><strong>datapackage.json</strong>: technical description of the data files.</li> <li><strong>DELTATRACK-reference-data.csv</strong>: reference data about the animals, tags and deployments.</li> <li><strong>DELTATRACK-gps-yyyy.csv.gz</strong>: GPS data recorded by the tags, grouped by year.</li> </ul> <h2>Acknowledgements</h2> <p>This dataset was collected by the Research Institute for Nature and Forest (INBO), Waardenburg Ecology, Buijs Eco Consult and Deltamilieu Projecten. Funding was provided by the Wozep programme of Rijkswaterstaat on behalf of the Ministry of Economic Affairs of the Netherlands. All datasets from Wozep will be made publicly available, unless stated otherwise.</p>
Variables related to prices for Belgium, France, Germany, Italy, Netherlands, Spain, United Kingdom and United States
<p><strong><span>Variables in data set</span></strong></p> <p><span>These variables have been collected from the Statistical Office of the European Union over a period of 24 years. </span></p> <p><span> </span></p> <p><span>1. Price Level Indices</span></p> <p><span>2. Expenditure adjusted by purchasing power standard</span></p> <p><span>3. Purchasing power parities</span></p> <p><span>4. Inflation rate</span></p> <p><span>5. Harmonised Index of Consumer Prices for the next categories</span></p> <p><span>CP01. FOOD AND NON-ALCOHOLIC BEVERAGES</span></p> <p><span>CP011. Food</span></p> <p><span>CP012. Non-alcoholic beverages</span></p> <p><span>CP02. ALCOHOLIC BEVERAGES AND TOBACCO</span></p> <p><span>CP021. Alcoholic beverages</span></p> <p><span>CP022. Tobacco</span></p> <p><span>CP03. CLOTHING AND FOOTWEAR</span></p> <p><span>CP031. Clothing</span></p> <p><span>CP032. Footwear</span></p> <p><span>CP04. HOUSING, WATER, GAS, ELECTRICITY AND OTHER FUELS</span></p> <p><span>CP041. Actual rentals for housing</span></p> <p><span>CP045. Electricity, gas and other fuels</span></p> <p><span>CP05. FURNISHINGS, HOUSEHOLD EQUIPMENT AND ROUTINE MAINTENANCE OF THE HOUSE</span></p> <p><span>CP06. HEALTH</span></p> <p><span>CP061. Medical products, appliances and equipment</span></p> <p><span>CP062. Outpatient services</span></p> <p><span>CP063. Hospital services</span></p> <p><span>CP07. TRANSPORT</span></p> <p><span>CP073. Transport services</span></p> <p><span>CP08. COMMUNICATIONS</span></p> <p><span>CP09. RECREATION AND CULTURE</span></p> <p><span>CP094 - Recreational and cultural services</span></p> <p><span>CP096 - Package holidays</span></p> <p><span>CP10. EDUCATION</span></p> <p><span>CP11. Restaurants and hotels</span></p> <p><span>CP111. Catering services</span></p> <p><span>CP112. Accommodation services</span></p> <p><span>CP12. MISCELLANEOUS GOODS AND SERVICES</span></p>
Figure 7 in Some new species records of the predatory mite family Phytoseiidae (Acari: Mesostigmata) from The Netherlands
Figure 7. Proprioseiopsis cf. umidus Karg (Female): (A) Idiosoma, dorsal view; (B) Idiosoma, ventral view; (C) Spermatheca; (D) Leg IV.
Thermal Comfort and Preference Data collected for the Brains4Buildings research at the Haagse Hogeschool, Hague, the Netherlands.
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