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469 results for “spruce”
Data for "Age effect on tree structure and biomass allocation in Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies [L.] Karst.)"
<p>VAPU dataset for tree biomass was collected from southern Finland in 1988-1990 by the Finnish Forest Research Institute (Metla, now Natural Resources Institute Finland, Luke) (VAPU data set).</p> <p>Those sample trees (162 Scots pine and 163 Norway spruce) are originated from the whole VAPU data set. The sheet 'Pine' and 'Spruce' data have been matched between 'sample branch measurements' and the 'biomass' information (by cluster X, Y, and plot, tree number).</p> <p>Biomass estimation for foliage and branches has been described here: https://doi.org/10.1016/j.ecolmodel.2004.04.024 and https://doi.org/10.1093/treephys/25.7.803<br> </p>
Differential associations between nucleotide polymorphisms and physiological traits in Norway spruce (Picea abies Karst.) provenances under contrasting water regimes
<p>Three datasets are provided here, yielded by a study on drought-stressed and control (well-watered) seedlings of Norway spruce (Picea abies Karst.), coming from 5 provenances distributed along a steep altitudinal gradient from 550 to 1,280 m a.s.l. in central Slovakia:</p> <p>1. physiological traits</p> <p>2. double-digest restriction-site associated sequencing data (ddRAD)</p> <p>3. nuclear microsatellite (nSSR) genotypes</p>
Difference in effect of pheromone for monitoring the European spruce bark beetle
<p>In recent decades there have been an increasing number of outbreaks of the European spruce bark beetle (<i>Ips typographus</i>) in Europe. A large amount of sanitary felling has taken place, with significant economic and ecological consequences. In order to anticipate such large-scale outbreaks, an effective monitoring system should be set up. One important aspect of monitoring is the decision on which pheromone to use. We suggest a framework for selecting an effective pheromone with few side effects and implemented it on five different pheromones under different disturbance conditions: Pheroprax, IT Ecolure, Ipstyp, Ipsowit and Typosan. We set 50 traps in two areas with sites that were disturbed and undisturbed by wind storms. We collected bark beetles from traps every one to two weeks from the end of March until the end of September in 2019. We investigated the number of bark beetles caught, bark beetle dynamics, amount of bycatch and predators, the taxonomic groups of the bycatch and the overall costs of the monitoring system. We found that Pheroprax, IT Ecolure and Ipsowit caught the most bark beetles and best showed the population dynamics. There was a low amount of bycatch (less than 6% of the total catch) and predators (a few individuals), but some groups seem to prefer certain pheromones. The cost of the pheromones increased with their effectiveness. However, pheromone costs are low relative to the personnel costs involved in setting traps and collecting bark beetles. The framework and the results will help professionals to decide which pheromones to purchase for their bark beetle monitoring system.</p>
High-density genetic linkage mapping in Sitka spruce advances the integration of genomic resources in conifers
<p><span>In species with large and complex genomes such as conifers, dense linkage maps are a useful for supporting genome assembly and laying the genomic groundwork at the structural, populational and functional levels. However, most of the 600+ extant conifer species still lack extensive genotyping resources, which hampers the development of high-density linkage maps. In this study, </span><span><span>we developed a linkage map relying on 21,570 SNP makers in </span></span><span>Sitka spruce (<em>Picea sitchensis</em> [Bong.] Carr.)</span><span><em><span>, </span></em></span><span><span>a long-lived conifer from western North America that is widely planted for productive forestry in the British Isles. </span></span><span>We used a single-step mapping approach to efficiently combine RAD-Seq and genotyping array SNP data for 528 individuals from two full-sib families. As expected for spruce taxa, the saturated map contained 12 linkages groups with a total length of 2,142 cM. The positioning of 5,414 unique gene coding sequences allowed us to compare our map with that of other Pinaceae species, which provided evidence for high levels of synteny and gene order conservation in this family. We then developed an integrated map for <em>P. sitchensis</em> and <em>P. glauca</em> based on 27,052 makers and 11,609 gene sequences. Altogether, these two linkage maps, the accompanying catalog of 286,159 SNPs and the genotyping chip developed herein opens new perspectives for a variety of fundamental and more applied research objectives, such as for the improvement of spruce genome assemblies, or for marker-assisted sustainable management of genetic resources in Sitka spruce and related species.</span></p>
De novo transcriptome assembly and discovery of drought-responsive genes in eastern white spruce (Picea glauca)
<p>Forests face an escalating threat from the increasing frequency of extreme drought events driven by climate change. To address this challenge, it is crucial to understand how widely distributed species of economic or ecological importance may respond to drought stress. Here, we used RNA-sequencing to investigate transcriptome responses at increasing levels of water stress in white spruce (<em>Picea glauca</em> (Moench) Voss), distributed across North America. We began by generating an expanded transcriptome assembly emphasizing short-term drought stress at different developmental stages. We also analyzed differential gene expression at four time points over 22 days in a controlled drought stress experiment involving 2-year-old plants and three genetically unrelated clones. De novo transcriptome assembly and gene expression analysis revealed a total of 33,287 transcripts (18,934 annotated unique genes), with 4,425 unique drought-responsive genes. Many transcripts that had predicted functions associated with photosynthesis, cell wall organization, and water transport were down-regulated under drought conditions, while transcripts linked to abscisic acid response and defense response were up-regulated. Our study highlights a previously uncharacterized effect of drought stress on lipid metabolism genes in conifers and significant changes in the expression of several transcription factors, suggesting a regulatory response potentially linked to drought response or acclimation. Our research represents a fundamental step in unraveling the molecular mechanisms underlying short-term drought responses in white spruce seedlings. In addition, it provides a valuable source of new genetic data that could contribute to genetic selection strategies aimed at enhancing the drought resistance and resilience of white spruce to changing climates.</p>
Digital repository for: Large-scale forest disturbance and associated management shape bird communities in Central European spruce forests
<p>Repository containing R-script and data to reproduce analysis and main figures on the effect of large-scale forest disturbance and associated pre- and post-disturbance management on bird communities in the Harz Mountains, Germany.</p> <p>R-script includes:</p> <ul> <li>indicator species analysis (R package indicspecies; Cáceres & Legendre, 2009)</li> <li>non-metric multidimensional scaling (R package vegan; Oksanen et al., 2016)</li> <li>rarefaction- and extrapolation of Hill numbers (R package iNEXT; Hsieh et al., 2019)</li> <li>multi-species community distance sampling (R package sp Abundance; Doser et al., 2023)</li> </ul> <p>Attached files:</p> <ul> <li><strong>bird_data_Graser_et_al.csv </strong>(row data of bird species point counts per distance category)</li> <li><strong>bird_data_abundance_100_Graser_et_al.csv </strong>(abundance of species per sampling site, summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>siteCovs_Graser_et_al.csv</strong> (environmental variables for each sampling point)</li> <li><strong>A_species_matrix_100_new_Graser_et_al.csv</strong> (species-site matrix of <strong>bark-beetle disturbance, unlogged </strong>sites for rarefaction and extrapolation, species number summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>B_species_matrix_100_new_Graser_et_al.csv </strong>(species-site matrix of <strong>windthrow disturbance, unlogged </strong>sites for rarefaction and extrapolation, species number summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>C_species_matrix_100_new_Graser_et_al.csv </strong>(species-site matrix of <strong>bark-beetle/windthrow disturbance, underplanted, unlogged </strong>sites for rarefaction and extrapolation, species number summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>D_species_matrix_100_new_Graser_et_al.csv </strong>(species-site matrix of <strong>bark-beetle /windthrow disturbance, salvage-unlogged </strong>sites for rarefaction and extrapolation, species number summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>E_species_matrix_100_new_Graser_et_al.csv </strong>(species-site matrix of <strong>bark-beetle /windthrow disturbance, underplanted, salvage-unlogged </strong>sites for rarefaction and extrapolation, summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong> F_species_matrix_100_new_Graser_et_al.cs</strong>v (species-site matrix of <strong>mature spruce plantation </strong>sites for rarefaction and extrapolation, species number summed up over all four sampling repeats only considering detected individuals up to 100 m around the sampling point)</li> <li><strong>msHDS_bird_data_management_model_Graser_et_al.rds</strong> (R-data set for multi-species community distance sampling of the effect of different pre- and post-disturbance management groups)</li> <li><strong>msHDS_bird_data_stand_age_model_Graser_et_al.rds </strong>(R-data set for multi-species community distance sampling of the effect of post-disturbance forest succession)</li> </ul> <p>A more detailed description of the data can be found in the README.txt document.</p> <p><span>References:</span></p> <p><span>Cáceres, M. D., & Legendre, P. (2009). </span><span>Associations between species and groups of sites: Indices and statistical inference. <em>Ecology</em>, <em>90</em>(12), 3566–3574. https://doi.org/10.1890/08-1823.1</span></p> <p><span>Doser, J. W., Finley, A. O., Kéry, M., & Zipkin, E. F. (2023). spAbundance: An R package for single‐species and multi‐species spatially explicit abundance models. <em>Methods in Ecology and Evolution</em>, <em>15</em>(6), 1024–1033. https://doi.org/10.1111/2041-210X.14332</span></p> <p><span>Hsieh, T. C., Ma, K. H., & Chao, A. (2019). <em>iNEXT-package: Interpolation and extrapolation for species diversity</em>. https://cran.r-project.org/web/packages/iNEXT/vignettes/Introduction.html</span></p> <p><span>Oksanen, J., Blanchet, F. G., Kindt, R., Legendre, P., O’hara, R. B., Simpson, G. L., Solymos, P., Stevens, M. H. H., Wagner, H., Minchin, P. R., Gavin, L., & Henry, H. (2016). Vegan: Community ecology package. R package version 1.17-4. <em>Http://CRAN. R-Project. </em></span><em><span>Org/Package=vegan</span></em><span>.</span></p> <p></p> <p></p>
Fig. 3 in Effects Of Leaf-Litter Addition On Carabid Beetles In A Non-Native Norway Spruce Plantation
Fig. 3. Seasonal dynamics of the average number of individuals per trap for the two species (± S. E.)
Fig. 1 in Effects Of Leaf-Litter Addition On Carabid Beetles In A Non-Native Norway Spruce Plantation
Fig. 1. Ordination (NMDS) of the pitfall catches based on the Bray-Curtis similarity index. ¡: Traps of the control plots and l: Traps of the leaf-litter plots
Phenological time lapse images from ground camera MC116 in Kenttärova Spruce stand
<p>This record contains phenological time lapse images from camera Kenttärova Spruce stand. Camera was mounted at ground view level at location 67.987283;24.242983(N;E, WGS84).</p> <p>First set of images were taken between 07.04.2015--31.12.2016 (Version 1). Subsequent Versions extend the record with newer images, and the version number indicates the years covered by the record.<br> Cameras were set to fix white balance, brightness automatically adjusted by camera.Image have equal resolution throughout the time series, time indicated in UTC+2. Images are taken half-hourly during fixed day-time period over the year. Gaps in time series and dark images possibly exist.<br> More details on the camera installations and operation history can be found at doi 10.5281/zenodo.777952<br> The cameras were set up and images collected under EU Life+ (LIFE ENV/FI/000409) Monimet project, http://monimet.fmi.fi.<br> For further information contact mika.aurela@fmi.fi</p>
From common gardens to candidate genes: Exploring local adaptation to climate in red spruce
<p><span>Local adaptation to climate is common in plant species and has been studied in a range of contexts, from improving crop yields to predicting population maladaptation to future conditions. The genomic era has brought new tools to study this process, which was historically explored through common garden experiments. </span></p> <p><span>In this study, we combine genomic methods and common gardens to investigate local adaptation in red spruce and identify environmental gradients and loci involved in climate adaptation. We first use climate transfer functions to estimate the impact of climate change on seedling performance in three common gardens. We then explore the use of multivariate gene-environment association (GEA) methods to identify genes underlying climate adaptation, with particular attention to the implications of conducting genome scans with and without correction for neutral population structure.</span></p> <p><span>This integrative approach uncovered phenotypic evidence of local adaptation to climate and identified a set of putatively adaptive genes, some of which are involved in three main adaptive pathways found in other temperate and boreal coniferous species: drought tolerance, cold hardiness, and phenology. These putatively adaptive genes segregated into two "modules" associated with different environmental gradients.</span></p> <p><span>This study nicely exemplifies the multivariate dimension of adaptation to climate in trees. </span></p>
Fig. 1 in Damp Water Stream Impact For The Germination Of Norway Spruce (Picea Abies (L.) H. Karst.) Seeds
Fig. 1. Sowing scheme of Norway Spruce seeds (K – control sample – chemical treater was used for the seeds; 1s, 2s, 3s, 4s – damp water steam was used for the seeds).
Рис. 1. Географическое поΛожение Норского заповеΑника (А) и картосхема распоΛожения на его территории (Б) учетных пΛощаΑок с фитоценозами (L_1–L_7) на Αвух мониторинговых станциях (I–II). I — МаΛьцевская: L_1 — березняк с участием осины и Λиственницы рябинниковый вейниково-разнотравный; L_2 — осиново-беΛоберезовый рябинниковый вейниково-разнотравный Λес; L_3 — Λиственничник с участием березы пΛоскоΛистной осоково-вейниковый с разнотравьем; L_4 — беΛоберезово-Λиственничный с примесью осины роΑоΑенΑроновый бруснично-осоковый Λес; L_5 — закустаренный, преимущественно тавоΛгой ивоΛистной, разнотравно-вейниковый Λуг. II — Антоновская: L_6 — Λиственничник роΑоΑенΑроново-брусничный; L_7 — Λиственнично-беΛоберезовый с примесью пихты и еΛи закустаренный разнотравно-вейниковый Λес (коΑ типа местообитания соответствуют таковому в табΛ. 1 и 3 и на рис. 2) Fig. 1. Geographical location of the Norsky Nature Reserve (A) and the map (B) of registration sites with phytocenoses (L_1–L_7) at two monitoring stations (I–II). I — Maltsevskaya: L_1 — birch forest with aspen and larch, fieldfare reed-forb; L_2 — aspen-white-birch, fieldfare reed-forb forest; L_3 — larch forest with flat-leaved sedge-reed birch with forbs; L_4 — white-birch-larch with an admixture of aspen rhododendron lingonberry-sedge forest; L_5 — bushy, mostly meadowsweet, forb-reed grass meadow. II — Antonovskaya: L_6 — rhododendron-cowberry larch forest; L_7 — larch-white-birch with fir and spruce, shrubby forb-reed grass forest (the code of the habitat type corresponds to that in Tables 1 and 3 and in Fig. 2) in Structure and dynamics of the taxocenes of shrews in different habitats of the Norsky nature reserve
Рис. 1. Географическое поΛожение Норского заповеΑника (А) и картосхема распоΛожения на его территории (Б) учетных пΛощаΑок с фитоценозами (L_1–L_7) на Αвух мониторинговых станциях (I–II). I — МаΛьцевская: L_1 — березняк с участием осины и Λиственницы рябинниковый вейниково-разнотравный; L_2 — осиново-беΛоберезовый рябинниковый вейниково-разнотравный Λес; L_3 — Λиственничник с участием березы пΛоскоΛистной осоково-вейниковый с разнотравьем; L_4 — беΛоберезово-Λиственничный с примесью осины роΑоΑенΑроновый бруснично-осоковый Λес; L_5 — закустаренный, преимущественно тавоΛгой ивоΛистной, разнотравно-вейниковый Λуг. II — Антоновская: L_6 — Λиственничник роΑоΑенΑроново-брусничный; L_7 — Λиственнично-беΛоберезовый с примесью пихты и еΛи закустаренный разнотравно-вейниковый Λес (коΑ типа местообитания соответствуют таковому в табΛ. 1 и 3 и на рис. 2) Fig. 1. Geographical location of the Norsky Nature Reserve (A) and the map (B) of registration sites with phytocenoses (L_1–L_7) at two monitoring stations (I–II). I — Maltsevskaya: L_1 — birch forest with aspen and larch, fieldfare reed-forb; L_2 — aspen-white-birch, fieldfare reed-forb forest; L_3 — larch forest with flat-leaved sedge-reed birch with forbs; L_4 — white-birch-larch with an admixture of aspen rhododendron lingonberry-sedge forest; L_5 — bushy, mostly meadowsweet, forb-reed grass meadow. II — Antonovskaya: L_6 — rhododendron-cowberry larch forest; L_7 — larch-white-birch with fir and spruce, shrubby forb-reed grass forest (the code of the habitat type corresponds to that in Tables 1 and 3 and in Fig. 2)
An ultra-dense haploid genetic map for evaluating the highly fragmented genome assembly of Norway spruce (Picea abies)
<p>Data files for construction of the haploid genetic map for Norway spruce (<em>Picea abies</em>). Available at <a href="https://doi.org/10.1101/292151">https://doi.org/10.1101/292151</a></p>
Association mapping identified novel candidate loci affecting wood formation in Norway spruce
<p>Data sets associated with the study for the Association mapping and identification of novel candidate loci affecting wood formation in Norway spruce</p>
Linked collectors and determiners for: Beetles from Old Spruce Forests in Southern Norway 2019.
Natural history specimen data linked to collectors and determiners held within, "Beetles from Old Spruce Forests in Southern Norway 2019". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/67780c67-2157-4809-8b51-0b47a7ae8d3b">https://bionomia.net/dataset/67780c67-2157-4809-8b51-0b47a7ae8d3b</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/67780c67-2157-4809-8b51-0b47a7ae8d3b">https://gbif.org/dataset/67780c67-2157-4809-8b51-0b47a7ae8d3b</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Occurrences from a study of a changing Lutz spruce (Picea x Lutzii) hybrid zone on the Kenai Peninsula, Alaska.
Natural history specimen data linked to collectors and determiners held within, "Occurrences from a study of a changing Lutz spruce (Picea x Lutzii) hybrid zone on the Kenai Peninsula, Alaska". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/6b4d1874-734a-4907-b21f-57cf9b99c148">https://bionomia.net/dataset/6b4d1874-734a-4907-b21f-57cf9b99c148</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/6b4d1874-734a-4907-b21f-57cf9b99c148">https://gbif.org/dataset/6b4d1874-734a-4907-b21f-57cf9b99c148</a>. Formatted as a Frictionless Data package.
Phenological time lapse images from canopy camera MC100 in Tammela Spruce stand
<p>This record contains phenological time lapse images from camera Tammela Spruce stand. Camera was mounted at canopy view level at location 60.64598306; 23.80650111(N;E, WGS84).</p> <p>First set of images were taken between 31.03.2014--31.12.2016 (Version 1). Subsequent Versions extend the record with newer images, and the version number indicates the years covered by the record.<br> Cameras were set to fix white balance, brightness automatically adjusted by camera.Image have equal resolution throughout the time series, time indicated in UTC+2. Images are taken half-hourly during fixed day-time period over the year. Gaps in time series and dark images possibly exist.<br> More details on the camera installations and operation history can be found at http://doi.org/10.5281/zenodo.777952 .<br> The cameras were set up and images collected under EU Life+ (LIFE ENV/FI/000409) Monimet project, http://monimet.fmi.fi.<br> For further information contact Mikko Peltoniemi (mikko.peltoniemi@luke.fi)</p>
Phenological time lapse images from ground camera MC101 in Tammela Spruce stand
<p>This record contains phenological time lapse images from camera Tammela Spruce stand. Camera was mounted at ground view level at location 60.64598306; 23.80650111(N;E, WGS84).</p> <p>First set of images were taken between 31.03.2014--31.12.2016 (Version 1). Subsequent Versions extend the record with newer images, and the version number indicates the years covered by the record.<br> Cameras were set to fix white balance, brightness automatically adjusted by camera.Image have equal resolution throughout the time series, time indicated in UTC+2. Images are taken half-hourly during fixed day-time period over the year. Gaps in time series and dark images possibly exist.<br> More details on the camera installations and operation history can be found at 10.5281/zenodo.777952<br> The cameras were set up and images collected under EU Life+ (LIFE ENV/FI/000409) Monimet project, http://monimet.fmi.fi.<br> For further information contact Mikko Peltoniemi (mikko.peltoniemi@luke.fi)</p>
De novo transcriptome assembly and discovery of drought-responsive genes in eastern white spruce (Picea glauca)
Open the record for dataset details and reuse information.
High-density genetic linkage mapping in Sitka spruce advances the integration of genomic resources in conifers
Open the record for dataset details and reuse information.
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Annotated Behaviour and Observability Dataset (ABODe)
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